lars herngren phdthesis final - qutfigure 3.7 the rainfall simulator fully mounted on box trailer 53...

Build-up and wash-off process kinetics of PAHs and

heavy metals on paved surfaces using simulated

rainfall

Lars Herngren MSc Eng. (KTH)

A THESIS SUBMITTED

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF

THE DEGREE OF DOCTOR OF PHILOSOPHY

FACULTY OF BUILT ENVIRONMENT AND ENGINEERING

2005

II

Keywords: Urban water quality, Rainfall simulation, Polycyclic Aromatic

Hydrocarbons, Heavy metals, Process kinetics, Build-up, Wash-off.

III

Abstract

The research described in the thesis details the investigation of build-up and wash-off

process kinetics of Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals in

urban areas. It also discusses the design and development of a rainfall simulator as an

important research tool to ensure homogeneity and reduce the large number of

variables that are usually inherent to urban water quality research. The rainfall

simulator was used to collect runoff samples from three study areas, each with

different land uses. The study areas consisted of sites with typical residential,

industrial and commercial characteristics in the region. Build-up and wash-off

samples were collected at each of the three sites. The collected samples were

analysed for a number of chemical and physico-chemical parameters. In addition to

this, eight heavy metal elements and 16 priority listed PAHs were analysed in five

different particle size fractions of the build-up and wash-off samples. The data

generated from the testing of the samples were evaluated using multivariate analysis,

which reduced the complexity involved in determining the relative importance of a

single parameter in urban water quality. Consequently, variables and processes

influencing loadings and concentrations of PAHs and heavy metals in urban

stormwater runoff from paved surfaces at any given time were identified and

quantified using Principal Component Analysis (PCA). Furthermore, the process

kinetics found were validated using a multivariate modelling approach and Partial

Least Square (PLS) regression, which confirmed the transferability of chemical

processes in urban water quality.

Fine particles were dominant in both the build-up and wash-off samples from the

three sites. This was mirrored in the heavy metal and PAH concentrations at the three

sites, which were significantly higher in particles between 0.45-75μm than in any

other fraction. Thus, the larger surface area and electrostatic charge of fine particles

were favourable in sorbing PAHs and heavy metals. However, factors such as soil

composition, total organic carbon (TOC), the presence of Fe and Mn-oxides and pH

of the stormwater were all found to be important in partitioning of the metals and

PAHs into different fractions. Additionally, PAHs were consistently found in

concentrations above their aqueous solubility, which was attributed to colloidal

IV

organic particles being able to increase the dissolved fraction of PAHs. Hence,

chemical and physico-chemical parameters played a significant role in the

distribution of PAHs and heavy metals in urban stormwater. More importantly, the

research showed the wide range of factors that distribute metals and PAHs in an

urban environment. Furthermore, it indicated the need for monitoring these

parameters in urban areas to ensure that urban stormwater management measures are

effective in improving water quality. The build-up and wash-off process kinetics

identified using PCA at the respective land uses were predicted using PLS and it was

found that the transferability of the governing processes were high even though the

PAHs and metal concentrations and loads were highly influenced by the source

strength at each site. The increased transferability of fundamental concepts in urban

water quality could have significant implications in urban stormwater management.

This is primarily attributed to common urban water quality mitigation strategies

relying on studies based on physical concepts and processes derived from water

quantity studies, which are difficult to transfer between catchments. Hence, a more

holistic approach incorporating chemical processes compared to the current

piecemeal solutions could significantly improve the protection of key environmental

values in a region. Furthermore, urban water quantity mitigation measures are

generally designed to reduce the impacts of high-flow events. This research suggests

that fairly frequent occurring rainfall events, such as 1-year design rainfall events,

could carry significant heavy metal and PAH concentrations in both particulate and

dissolved fractions. Hence, structural measures, designed to decrease quantity and

quality impact on receiving waters during 10 or 20-year Average Recurrence Interval

(ARI) events could be inefficient in removing the majority of PAHs and heavy

metals being washed off during more frequent events.

The understanding of physical and chemical processes in urban stormwater

management could potentially lead to significant improvements in pollutant removal

techniques which in turn could lead to significant socio-economic advantages. This

project can serve as a baseline study for urban water quality investigations in terms

of adopting new methodology and data analysis.

V

List of Publications

Journal Papers

• L. Herngren, A. Goonetilleke, R. Sukpum and D.Y. De Silva (2005) Rainfall

simulation as a tool for urban water quality research. Environmental Engineering

Science, 22 (3), 378-383.

• L. Herngren, A. Goonetilleke and G.A. Ayoko (2005) Understanding heavy

metal and suspended solids relationships in urban stormwater using simulated

rainfall. Journal of Environmental Management, 76 (2), 149-158.

• L. Herngren, A. Goonetilleke and G.A. Ayoko (2004) Multivariate analysis of

heavy metals in road-deposited sediments. Environmetrics. (Under review).

Peer Reviewed International Conference Papers

• L. Herngren, A. Goonetilleke and G.A. Ayoko (2004). Investigation of urban

water quality using artificial rainfall. Watershed 2004, WEF, MWEA, Dearborn,

Michigan, USA, July 2004.

• A.Goonetilleke, E. Thomas, L. Herngren, S. Ginn and D. Gilbert (2004). Urban

water quality: stereotypical solutions may not always be the answer. 2004

International Conference on Water Sensitive Urban Design (WSUD 2004), Cities

as Catchments, IEAust, AWA, Adelaide, Australia, November 2004.

Conference Papers

• L. Herngren, A. Goonetilleke and G.A. Ayoko (2002). Use of rainfall simulation

for urban water quality research. 10th Bi-Annual PIC Postgraduate Conference,

PIC, School of Civil Engineering, QUT, Australia, December 2002.

VI

Table of Contents KEYWORDS II

ABSTRACT III

LIST OF PUBLICATIONS V

ABBREVIATIONS XV

STATEMENT OF ORIGINAL AUTHORSHIP XVII

ACKNOWLEDGEMENTS XVIII

CHAPTER 1 INTRODUCTION 1

1.1 Background 1

1.2 Project Aims and Objectives 2

1.3 Hypotheses 3

1.4 Scope 3

1.5 Justification for the Research 4

1.6 Methodology for the study 5

1.7 Outline of the Thesis 7

CHAPTER 2 URBAN WATER QUALITY 8

2.1 Introduction 8

2.2 Pollutant build-up 9

2.3 Pollutant wash-off 11

2.3.1 First flush phenomenon 12

2.3.2 Influence of rainfall on pollutant wash-off 14

2.4 Common pollutants in an urban environment 18

2.4.1 Pathogens 18

2.4.2 Oxygen demanding wastes 19

2.4.3 Nutrients 20

2.4.4 Suspended solids 21

2.4.5 Heavy metals 24

2.4.6 Polycyclic Aromatic Hydrocarbons 26

2.5 Build-up and wash-off processes of heavy metals 28

2.5.1 Build-up 28

VII

2.5.2 Wash-off 30

2.6 Build-up and wash-off processes of PAHs 34

2.6.1 Build-up 34

2.6.2 Wash-off 35

2.7 Summary 39

CHAPTER 3 DESIGN AND FABRICATION OF A RAINFALL 42

SIMULATOR

3.1 Introduction 42

3.2 Design of a rainfall simulator 43

3.2.1 Re-production of natural rainfall characteristics 44

3.2.2 Structural design 47

3.2.3 Hydraulic System 51

3.2.4 Oscillation Control System 52

3.2.5 Runoff plot and collection 52

3.2.6 Storage and Transport 53

3.3 Performance calibration of the rainfall simulator 53

3.3.1 Nozzle discharge and pattern 54

3.3.2 Rainfall intensities and uniformity of rainfall 56

3.3.3 Calibration of drop size and kinetic energy 62

3.4 Summary 64

CHAPTER 4 STUDY AREAS AND SAMPLING PROCEDURE 66

4.1 Introduction 66

4.2 Study site selection 67

4.3 Project area 67

4.3.1 Residential site 70

4.3.2 Industrial site 71

4.3.3 Commercial site 72

4.4 Vacuum collection system 74

4.4.1 Selection of Vacuum Cleaner 75

4.4.2 Dry collection system efficiency 76

4.4.3 Wet sample collection system 77

4.5 Dry sample collection in the field 81

VIII

4.6 Wet sample collection in the field 82

4.7 Treatment and transport of samples 84

4.8 Summary 84

CHAPTER 5 ANALYTICAL METHODS 86

5.1 Introduction 86

5.2 Sample testing 86

5.2.1 Pre-treatment of samples 86

5.2.2 Particle size distribution 87

5.2.3 Partitioning of samples 89

5.2.4 Chemical and physico-chemical parameters 90

5.3 Summary 97

CHAPTER 6 DISCUSSION OF TEST RESULTS 98

6.1 Introduction 98

6.2 Volume and weight of the collected samples 98

6.3 Particle size distribution 99

6.3.1 Partitioning of build-up and wash-off samples 103

6.4 Chemical parameters 103

6.4.1 pH and EC 103

6.4.2 Organic Carbon 105

6.4.3 Total Suspended Solids 107

6.4.4 Heavy metals 109

6.4.5 Polycyclic Aromatic Hydrocarbons (PAHs) 112

6.5 Summary 117

CHAPTER 7 PATTERN AND PROCESS RECOGNITION 119

USING PCA

7.1 Introduction 119

7.2 Applications of Principal component analysis 119

7.3 Pre-treatment of data 121

7.4 Build-up samples 124



IX


7.5 Wash-off samples 134




7.6 Summary 159

CHAPTER 8 INVESTIGATING PROCESS KINETICS OF PAHs 163

AND HEAVY METALS USING PCA AND PLS

8.1 Introduction 163

8.2 Application of PCA for prediction of heavy metals and PAHs 164

8.3 Introduction to PLS 172

8.4 Predicting heavy metals and PAHs using PLS1 algorithm 174

8.4.1 Heavy metals in urban stormwater runoff 175

8.4.2 PAHs in urban stormwater runoff 181

8.5 Summary 183

CHAPTER 9 GENERAL DISCUSSION 185

9.1 Rainfall simulation in urban water quality 185

9.2 Chemical processes 186

9.3 Calibration of methodology 187

9.4 Implications of chemical processes found 187

CHAPTER 10 CONCLUSIONS AND RECOMMENDATIONS 189

10.1 Conclusions 189

10.2 Recommendations 192

REFERENCES 194

APPENDIX A RAINFALL SIMULATOR CALIBRATION DATA 217

APPENDIX B TEST RESULTS 235

APPENDIX C CHEMOMETRIC ANALYSIS USING PCA 279

APPENDIX D PREDICTION USING PLS 315

X

List of Figures

Figure 2.1 The effects of urbanisation on hydrological processes 8

Figure 2.2 Hypothetical representation of surface pollutant load over time 12

Figure 2.3 Median drop diameters relationship with rainfall intensity 17

Figure 2.4 Relationship between rainfall intensity and impact energy 17

in South-East Queensland

Figure 3.1 Fan spray pattern nozzle 45

Figure 3.2 Sketch of designed rainfall simulator 48

Figure 3.3 Cross section of the nozzle boom unit 49

Figure 3.4 Catch trays running along the nozzle boom 50

Figure 3.5 Oscillation cycle of the nozzle boom 50

Figure 3.6 Arm and lever system oscillating the nozzle boom 51

Figure 3.7 The rainfall simulator fully mounted on box trailer 53

Figure 3.8 Average discharge values for the twelve Veejet 80100 nozzles 54

(41 kPa pressure)

Figure 3.9 Calibration of spray pattern of the nozzles 55

Figure 3.10 Nozzle spray pattern contours for the chosen nozzles 56

(3, 4 and 12 respectively)

Figure 3.11 Intensity measurements using seven containers 56

Figure 3.12 Container grid pattern used for calculating average rainfall 59

intensity produced by the rainfall simulator

Figure 3.13 Spatial variation of the rainfall intensity using Speed 2, delay 2s 60

Figure 3.14 Pellets obtained by flour pellet method 64

Figure 4.1 Map of Gold Coast region 69

Figure 4.2 Residential research site (Millswyn Crescent) 70

Figure 4.3 Industrial research site (Stevens Street) 72

Figure 4.4 Commercial research site (Centro Nerang) 74

Figure 4.5 Delonghi Aqualand water filter system 76

Figure 4.6 Runoff plot frame used when collecting stormwater samples 79

Figure 4.7 Collection trough with handle to open top for easy sampling 80

using the vacuum cleaner

XI

Figure 4.8 Wet sample collection system showing the connection between 80

25L container and the vacuum cleaner

Figure 4.9 Dry sample collection in the field 81

Figure 4.10 Collection of runoff samples in the field 84

Figure 5.1 Malvern Mastersizer S system used in the project 88

Figure 6.1 Cumulative particle size distribution of the build-up samples 100

collected at the three study sites

Figure 7.1 Scree plot for the determination of number of components to 125

use in exploring residential build-up data

Figure 7.2 Loadings of each variable on PC1 and PC2 obtained from 125

PCA on residential build-up data

Figure 7.3 Loadings of each variable on PC1 and PC2 obtained 129

from PCA on industrial build-up data


PCA on commercial build-up data


PCA on data in the dissolved fraction of wash-off

samples from the residential site


PCA on data in particle size class 0.45-75µm of wash-off



PCA on data in particle size class 76-150µm of wash-off



PCA on data in particle size class >150µm of wash-off




samples from the industrial site




XII









samples from the commercial site










Figure 8.1 Scores plot of the objects (175 samples) containing heavy 165

metal data subjected to PCA

Figure 8.2 Scores plot of the objects (140 samples) containing PAH data 170

subjected to PCA

Figure 8.3 Cross-validation error (SEV) for Cu showing the number 177

of latent variables to use (2) based on first minimum in plot

Figure 8.4 Plot of observed versus predicted Al concentrations 178

using three latent variables, with an SEP of 0.26

Figure 8.5 Plot of observed versus predicted Al concentrations when 180

Adding additional samples from the commercial site, SEP of 0.56

XIII

List of Tables

Table 2.1 Fraction of pollutants associated with each particle size range, 24

% by weight

Table 2.2 Sources of heavy metals in an urban environment 25

Table 2.3 Priority PAHs as listed by US EPA 27

Table 3.1 Rainfall quality profile obtained in Brisbane, Australia 47

Table 3.2 Calculated average rainfall intensities using seven containers for 57

different speed and delay settings of the control box

Table 3.3 Uniformity coefficients (Cu) for the intensities investigated 61

Table 3.4 Design rainfall events selected 62

Table 4.1 Description of possible residential research sites 70

Table 4.2 Description of possible industrial research sites 72

Table 4.3 Description of possible commercial research sites 73

Table 4.4 Sampling recovery efficiencies 77

Table 4.5 Runoff collection efficiencies 83

Table 5.1 Test methods adopted in the project 91

Table 6.1 Amount of build-up sample collected at each site and respective 98

dry period

Table 6.2 Mass median diameter (d50) in µm of the particles in the 102

simulated runoff events

Table 6.3 pH and EC concentrations of the build-up sample at each site 104

Table 6.4 pH and EC mean concentrations in the EMC-samples for each 105

runoff event

Table 6.5 Total Organic Carbon (TOC) and Inorganic Carbon (IC) 105

recorded in build-up samples from the three study sites

Table 6.6 Mean TOC concentration in wash-off samples 107

Table 6.7 TSS concentrations in the build-up samples 108

Table 6.8 TSS mean concentrations in the wash-off samples 108

Table 6.9 Heavy metal concentrations in each particle size class 110

at the three sites

Table 6.10 Metal concentration ranges observed in particulate and 112

dissolved fractions of wash-off samples from each site

XIV

Table 6.11 PAH concentration (mg/kg) in the build-up samples from each 114

site

Table 6.12 Detection frequencies (%), mean concentrations (mg/kg) and 116

standard deviation of 16 PAHs in wash-off samples from the

residential, industrial and commercial site

Table 7.1 Parameter abbreviation as used in PCA 123

Table 8.1 Variables and objects associated with negative and positive 168

loadings and scores on PC3 (excluding build-up and dissolved)

Table 8.2 Variables to be predicted (Y) and predictor variables (X) 176

Table 8.3 Calibration and validation matrices for prediction of metals 176

Table 8.4 Number of latent variables used for each predicted variable 178

Table 8.5 r2 and SEP values generated from the predictions (metals) 179

Table 8.6 Variables to be predicted (Y) and predictor variables (X) 181

Table 8.7 Number of latent variables used for each predicted variable 182

Table 8.8 r2 and SEP values generated from the predictions (PAHs) 183

XV

Abbreviations

ACE Acenaphthene

ACY Acenaphthylene

Al Aluminium

ANT Anthracene

ARI Average Recurrence Interval

BaA Benzo[a]Anthracene

BaP Benzo[a]Pyrene

BbF Benzo[b]Flouranthene

BgP Benzo[g,h,i]Perylene

BkF Benzo[k]Flouranthene

BOD Biological Oxygen Demand

Cd Cadmium

CHR Chrysene

COD Chemical Oxygen Demand

Com Commercial site

Cr Chromium

Cu Copper

DbA Dibenzo[a,h]Anthracene

DC Dissolved Carbon

DCM DiChloroMethane

DCM:ACE DiChloroMethane: Acetone

DOC Dissolved Organic Carbon

EC Electrical Conductivity

EMC Event Mean Concentration

Fe Iron

FLA Flouranthene

FLU Flourene

GC-MS Gas Chromatograph – Mass Spectrometer

HNO3 Nitric Acid

IC Inorganic Carbon

XVI

ICP-MS Inductively Coupled Plasma – Mass Spectrometer

Ind Industrial site

IND Indeno[1,2,3-cd]Pyrene

Mn Manganese

N2 Nitrogen

NAP Naphthalene

Ni Nickel

PAH Polycyclic Aromatic Hydrocarbon

Pb Lead

PC Particulate Carbon

PCA Principal Component Analysis

PHE Phenanthrene

POC Particulate Organic Carbon

ppm parts per million

PRESS Predicted Residual Error Sum of Squares

PVP Particle Volume Percentage

PYR Pyrene

Res Residential site

SEP Standard Error of Prediction

SEV Standard Error of Validation

TDS Total Dissolved Solids

TOC Total Organic Carbon

TSS Total Suspended Solids

US EPA United States Environmental Protection Agency

Zn Zinc

XVII

Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a degree or

diploma for any other higher education institution to the best of my knowledge and

belief. The thesis contains no material previously published or written by another

person except where due reference is made.

Signed:

Lars Herngren

Date: / /

XVIII

Acknowledgements

I wish to express my sincere appreciation to my Principal Supervisor A/Prof.

Ashantha Goonetilleke for his guidance, support and professional advice during the

study. Special thanks are also given to my Associate Supervisor Dr. Godwin Ade

Ayoko for his guidance and professional support throughout the research. Special

thanks are extended to Åke and Greta Lissheds Stiftelse, J. Gust. Richerts Stiftelse

and the Faculty of Built Environment and Engineering at QUT for financial support

during my candidature.

Appreciation is extended to all staff in the School of Civil Engineering and special

thanks to Mr. Brian Pelin and Mr. Jim Grandy for manufacturing the rainfall

simulator. Appreciation is also extended to Mr. Arthur Powell for electronic controls

of the rainfall simulator. I would like to thank Dr. Serge Kokot for advice on

analytical procedures. Appreciation is also extended to the professional staff of the

School of Physical and Chemical Sciences for technical support in GC-MS and

extraction and digestion procedures of PAHs and heavy metals.

I would also like to thank Adjunct Prof. Evan Thomas of Gold Coast City Council

and Mr. Don Pegler and Mr. Mark Silburn of the Department of Natural Resources,

Toowoomba for invaluable feedback on the design of a rainfall simulator. Also

thanks to Dr. Rob Loch at Landloch for showing rainfall simulator designs.

I wish to express my gratitude and appreciation to my father (Stig-Olof) and mother

(Ulla-Britt) for their unlimited support and love. I would also like to thank my sister

with family and all of my friends for support. Lastly, in acknowledgement of the

loving support and constant encouragement extended to me, I would like to thank my

loving partner Brianna Casey.

1

Chapter 1 Introduction

1.1 Background

Urbanisation is a common phenomenon witnessed in most parts of the world,

transforming natural and rural environments and often dramatically altering local

hydrological conditions. This is primarily attributed to the increase of impervious

surfaces and the rate of transport of pollutants into waterways, which could lead to

significant degradation of the quality of receiving waters. Therefore, it is imperative

that innovative strategies are adopted to ensure the protection of key environmental

values. Consequently, appropriate management of urban stormwater runoff has

significant positive socio-economic and environmental implications.

Multiple variables and processes are involved in the generation and transport of

pollutants in urban environments. The variables involved are highly dependent on a

number of characteristics, which are often subject to uncertainty. The weighting or

relative importance of the characteristics involved is often hard to measure and is

highly variable within the urban environment. This has led to limitations in the

transferability of previous research. Knowledge of these characteristics and variables

is the base for successful mitigation of urban stormwater quality impacts on receiving

waters. This is particularly important for micro-pollutants such as Polycyclic

Aromatic Hydrocarbons (PAHs) and heavy metals due to the toxic nature of these

compounds. Furthermore, PAH and heavy metal pollution has been identified as

leading causes of the degradation of receiving waters (Sansalone and Buchberger

1997; Estebe et al. 1997).

The availability of more reliable methodology in urban stormwater will prove

invaluable in the development of management strategies to protect or improve the

existing quality of receiving waters. Retrofitting of existing urban developments for

stormwater quality improvement and the analysis of ‘what if’ scenarios in the

evaluation of land development alternatives and catchment management strategies

will be greatly enhanced with the availability of more reliable modelling capabilities.

It is in this regard that research methodologies commonly used in other disciplines,

such as agriculture, should be adapted and implemented. The use of small test plots to

2

ensure homogeneity and tools such as artificial rainfall simulators could help to

reduce the large number of variables which are usually inherent to urban water quality

research. The use of artificial rainfall has been a common approach in agricultural

research to overcome the lack of data in infiltration, runoff and erosion studies.

However, the application of artificial rainfall in urban water quality research is rarely

mentioned, even though it can significantly improve the transferability of water

quality studies. Hence, it is imperative to find an equally efficient tool in urban water

quality studies. It is a primary hypothesis of this research that rainfall simulation is

capable of improving the quantification of the factors influencing the process kinetics

of PAHs and heavy metals in urban stormwater.

1.2 Project Aims and Objectives

The major aims of this study were to evaluate the influence of physical and chemical

parameters in the distribution of PAHs and heavy metals in build-up and wash-off

from paved surfaces. In addition, the project aimed at assessing the use of rainfall

simulation in developing a reliable urban runoff water quality database. In summary,

the aims of the study were:

1. to develop a rainfall simulator for applying artificial rainfall on a paved surface;

and

2. to develop an in-depth understanding of the build-up and wash-off process

kinetics of PAHs and heavy metals using a number of simulated rainfall events

and multivariate chemometrics techniques.

The primary objectives of the study were:

1. to confirm the validity of using rainfall simulation in urban water quality research;

2. to evaluate the build-up and wash-off process kinetics of PAHs and heavy metals

on urban paved surfaces for different land uses; and

3. to determine the influence of particle size of suspended solids on urban water

quality in relation to PAHs and heavy metals.

1.3 Hypotheses

• The use of rainfall simulation significantly improves the collection of reliable

urban stormwater samples for the analysis of chemical processes inherent to urban

water quality.

3

• Parameters such as suspended solids and dissolved organic carbon significantly

influence the build-up and wash-off process kinetics of PAHs and heavy metals.

• Fine particles carry a significant load of PAHs and heavy metals in urban runoff.

• The understanding of chemical processes facilitates the transferability of urban

water quality research.

1.4 Scope

The primary focus of this research was the surface runoff generated by rainfall events

where a significant fraction of built-up pollutants is transported to receiving waters.

The research investigated the processes involved in distributing PAHs and heavy

metals in the build-up and wash-off from three different urban areas. The research

undertaken was confined to the Gold Coast City Council area, but the processes and

relationships found are applicable anywhere. The research was confined to paved

surfaces in industrial, residential and commercial areas. Pavement characteristics were

not considered as influencing parameters in the research but were used merely as a

classification of the pavement condition at the respective research sites. This was

based on the hypothesis that chemical processes are independent of parameters such

as pavement roughness, permeability and slope. Consequently, the build-up and wash-

off process kinetics of PAHs and heavy metals were considered unaffected by

pavement parameters. However, it is important to note that the pollutant load reaching

receiving waters could vary depending on the pavement characteristics. Hence, the

concentrations and loads of PAHs and heavy metals found in wash-off from less

permeable and smooth surfaces could be significantly higher than concentrations in

wash-off from permeable rough surfaces.

A rainfall simulator, which was accurately calibrated to reproduce natural rainfall

characteristics in the study area, was used to generate the runoff from the paved

surfaces. Artificial rainfall was applied to a small homogenous plot area, which

reduced the number of physical factors involved in the wash-off process.

The project specifically focused on the wash-off of PAHs and heavy metals from

paved surfaces using artificial rainfall. Natural rainfall and runoff events were not

investigated due to insufficient time to collect a similarly reliable database.

4

Additionally, the performance of urban stormwater management measures was not

investigated.

Three different land uses were selected as representative sites and twelve different

rainfall events were simulated at each site. Only one site per land use was chosen.

This was based on the hypothesis that chemical processes are independent of the

pollutant load in an area. Consequently, the density of industries and the traffic

volume was not considered to be determining factors in build-up and wash-off process

kinetics other than influencing the total load available on a paved surface. Hence, the

chosen sites represented typical industrial, residential and commercial characteristics

in the study region and the process kinetics found was considered applicable to any

area. A build-up sample was also collected at the time of the field study and analysed

accordingly. There is limited knowledge available on processes governing the

distribution of PAHs and heavy metals into different particle size classes. As a result,

the runoff generated from each event was separated into a dissolved and a particulate

phase. The particulate phase was further partitioned into four different particle size

classes and analysed individually for chemical characteristics to fill the gaps in the

understanding of water quality processes.

1.5 Justification for the Research

Urbanisation can dramatically alter environmental conditions in a catchment,

particularly the generation and transport of pollutants such as PAHs and heavy metals

on impervious surfaces, thereby adversely changing the quality of water. Though

limited research has been undertaken on the wash-off of pollutants from paved

surfaces, these investigations have been significantly constrained. This has primarily

been attributed to the limited understanding of interactions between various influential

parameters and the influence of chemical processes on the build-up and wash-off

process kinetics of PAHs and heavy metals. Consequently, the management of water

quality impacts in urban areas has proven to be a difficult task.

Unfortunately, in the past, urban water quality models have been strongly based on

water quantity research. Hence, the extension of concepts and processes from water

quantity studies has been used to predict the pollutant generation, dispersion and

transmission in an urban environment. This has led to the reliance on physical factors

5

and limited recognition of chemical processes underlying the build-up and wash-off of

PAHs and heavy metals. Chemical processes exert a strong influence on the quality

characteristics of urban stormwater. It is this oversight which can be attributed to the

often contradictory results reported in research studies and the strongly location

specific nature of outcomes. Additionally, in past research, the general focus has been

on the build-up and wash-off of pollutants in relatively large heterogeneous areas.

Very little work has been undertaken using small, homogenous catchment areas in

order to fully understand the process kinetics of PAH and heavy metals in urban

stormwater.

Data analysis using multivariate chemometrics methods can significantly enhance the

outcomes of water quality studies. This is attributed to the large number of variables

involved in the build-up and wash-off process kinetics. Consequently, univariate

statistical analysis such as linear regression is useful when two variables are compared

against each other. However, the processes inherent in the generation and transport of

pollutants are many and often complementary. Hence, tools that take more than one

variable into account when analysing the build-up and wash-off process kinetics of

PAHs and heavy metals are preferable and can significantly enhance the

understanding of influential parameters in urban stormwater. This study was

formulated to create an improved methodology for urban water quality studies and to

increase the knowledge of processes governing the distribution of PAHs and heavy

metals in urban build-up and wash-off.

1.6 Methodology for the study

The objectives of the research project were achieved through the following steps:

• Design and development of a rainfall simulator

Rainfall simulation is not new to water research. However, it has essentially been

confined to the agricultural arena. Rainfall simulation provides maximum control over

when and where data is to be collected, plot conditions at test time and rates and

amounts of rainfall to be applied to the test plot. The major challenges transferring

rainfall simulation techniques used in agricultural research to urban water quality

research are:

1. the collection of runoff from paved surfaces; and

6

2. the portability of the rainfall simulator, not only from plot to plot but from site to

site.

The design and development of the rainfall simulator was the basis for the site

selection and broad-scale field investigations to be undertaken.

• Study site selection and selection of rainfall events

The study sites selected were representative of characteristics for typical urban areas

in the region. One site per land use was chosen as below:

1. Light Industrial

2. Commercial

3. Residential

Twelve different rainfall events were chosen to be simulated at each of the sites.

These were based on actual rainfall intensities and durations for South-East

Queensland and consisted of 1, 2, 5 and 10-year ARI design rainfall events. The

rainfall simulator was precisely calibrated for each event simulated.

• Data collection/compilation

Physical, physico-chemical and chemical parameters were measured for the samples

collected from the simulated rainfall events at each site. The parameters were chosen

based on factors influencing the generation and transport of PAHs and heavy metals

in the urban environment.

• Sample testing

Build-up and wash-off samples were tested for water quality parameters according to

methods set out by relevant authorities in order to ensure accuracy in the results.

Quality control was an important measure in the analysis of water quality samples.

Full procedural blanks and spiked samples were used to verify the absence of matrix

interferences as specified by relevant water quality sampling and testing methods.

7

• Data analysis

Data analysis of the variables influencing the process kinetics of PAHs and heavy

metals was carried out using both univariate and multivariate analysis and quantitative

relationships were identified.

1.7 Outline of the Thesis

The thesis consists of eleven chapters. The first chapter is an introduction to the

research study and contains aims and objectives of the research. The second chapter

introduces the reader to urban water quality and discusses and reviews previous

research in the area. The third chapter deals with the design and development of a

rainfall simulator in order to collect reliable runoff data. The sampling procedures at

the study areas and the analytical methods used are discussed in Chapters 4 and 5

respectively. Chapter 4 also discusses the study areas chosen for the research. Chapter

6 discusses the results from the sample testing, while Chapter 7 identifies the

processes governing the build-up and wash-off of PAHs and heavy metals using

multivariate analysis. The relationships found in Chapter 7 are then quantified and the

variables predicted as discussed in Chapter 8. Results from the research are discussed

in Chapter 9, which provides a discussion on the outcomes of the research and

provides a platform for future research in this area. Conclusions and recommendations

from the research are presented in Chapter 10. Finally, references used throughout the

thesis are listed. There are also four appendices, Appendix A-D, provided at the end

of the thesis. References have been provided throughout the text where appendices are

relevant.

8

Chapter 2 Urban Water Quality

2.1 Introduction

Urbanisation in a catchment results in an increased percentage of impervious surfaces

such as roads and roofs. Consequently, during rainfall, this leads to an increase in both

water quantity and water quality impacts as illustrated in Figure 2.1 adapted from Hall

(1984).

FIGURE 2.1 The effects of urbanisation on hydrological processes (adapted

from Hall 1984)

The increased flood frequency of urban areas due to the increased runoff volume and

decreased time to peak has been confirmed by numerous researchers (McPherson

1974; Corbett et al. 1997). However, hydrologic and water quality models used to

predict pollutant generation and transport in urban environments require input

parameters that are not known with certainty (Sohrabi et al. 2002). The relationships

developed are largely physically based and inadequate for describing the chemical

processes that take place. This relates not only to the outcomes of previous studies but

also to the conducting of research. Hence, a multi disciplinary approach to a typical

Urbanisation

Population density increases

Building density increases

Waterborne waste increases

Water demand rises

Impervious area increases

Water resource problems

Drainage system modified

Urban climate changes

Stormwater quality deteriorates

Groundwater recharge reduces

Runoff volume increases

Flow velocity increases

Receiving water quality deteriorates

Baseflow reduces Peak runoff rate increases

Lag time and time base reduce

Pollution control problems

Flood control problems

9

engineering problem could provide complementary information and facilitate

transferability of the processes found.

Chemical processes exert a strong influence on urban stormwater quality

characteristics. However, the common oversight of these processes has led to

contradictory results being reported in research studies and a strong location specific

nature of outcomes. This has led to inadequate knowledge of the processes

influencing the wash-off of micro-pollutants such as PAHs and heavy metals in an

urban catchment. This is of particular concern due to the toxic nature of these

pollutants, which have also been identified as the leading cause of the degradation of

receiving waters (Estebe et al. 1997; Sansalone and Buchberger 1997). Furthermore,

most research studies generally report on the total amount of PAHs or heavy metals

present without regard to their physical or chemical state, such as whether they are

tied up into complex inorganic or organic compounds. As water quality models are

increasingly used to evaluate management issues in catchments, there is an increasing

need to assess the processes taking place during urban stormwater runoff.

Consequently, the management of water quality impacts in urban areas has proven to

be a difficult task and the effectiveness of commonly adopted management and

structural measures is open to question.

While not consisting of a substantial portion in most urban catchments, except in

highly commercial areas, the effects of paved surfaces are important for

understanding the quality of stormwater. Examples are the traffic on roads, which

results in deposition of vehicular related particulates on road surfaces for subsequent

removal by wash-off processes; and the role of paved surfaces in acting as flow paths

for stormwater runoff. Additionally, many small rainfall events result in runoff

occurring only from these paved surfaces (Corbett et al. 1997; McPherson 1974).

This chapter focuses on the processes governing the build-up and wash-off of

pollutants on paved surfaces. Special attention is given to the processes involving

PAHs and heavy metals, which are discussed in Section 2.5 and 2.6 respectively. This

chapter also discusses the common sources of pollutants, and identifies important

physical and chemical variables which influence build-up and wash-off processes.

10

2.2 Pollutant build-up

Build-up is defined as the accumulation of pollutants on catchment surfaces during

antecedent dry periods. Pollutants are generated by a variety of anthropogenic

activities and natural phenomena in an urban environment. The pollutants introduced

are later washed out by rainfall and the runoff transports the pollutants to receiving

waters. As noted by Sartor and Boyd (1972), build-up of pollutants is a dynamic

process in urban areas. Hence, if there is sufficient antecedent time for build-up to

occur, the pollutant availability on a paved surface is likely to remain largely the same

(Duncan 1995). The natural sources vary significantly within catchments and include

water-transported material from surrounding soils, dry and wet atmospheric

deposition and biological inputs from vegetation (Sutherland and Tolosa 2000;

Muschack 1990; Rogge et al. 1993). Significant quantities of particulate matter can be

attributed to anthropogenic sources such as industrial processes, vehicle emissions

and, tyre and road surface wear (Sartor and Boyd 1972; Rogge et al. 1993). In urban

areas, particulates derived from automobiles and local soils have been identified as the

dominant sources of pollutant accumulation on a paved surface (Tai 1991; Shaheen

1975). However, the relative importance of these sources depends significantly on site

characteristics such as the fraction of impervious surfaces and traffic conditions.

Dirt or soil is tracked onto streets, for example, by vehicles leaving unpaved

construction sites or simply by the wind blowing garden soil particles onto the paved

surfaces. Because streets are usually built for vehicles, particulate automobile exhaust,

lubricating oil residues, tyre wear particles, weathered street surface particles, and

brake lining wear are direct contributors to the road dust. Indirectly, via atmospheric

transport and fallout, practically any anthropogenic or natural source can add to the

street dust accumulation on a road surface.

The contribution of soil to the accumulation of particulates can be significant. Hopke

et al. (1980) found that 76% of the total street dust mass originated from soil

materials. Similarly, Tai (1991) found that most of the street surface particles

originated from the erosion of local soils. However, the contribution of local soils to

the build-up component in urban stormwater is highly variable and depends on a

number of factors such as the amount of topsoil present and the fraction of impervious

11

surfaces. Similarly, the contribution from anthropogenic sources to the build-up

component is also highly variable within urban areas due to the amount of physical

and geographical factors removing and re-depositing material. The build-up

component of urban stormwater on paved surfaces from both natural and

anthropogenic sources is dependent on primary factors such as:

• Climate including rainfall and wind

• Land use

• Population density

• Percentage impervious area

• Traffic characteristics

• Antecedent dry period

• Street cleaning practices

• Soil type

(Sartor and Boyd 1972; Ball et al. 1998; Brezonik and Stadelmann 2002)

Though the build-up component is dependent on a number of factors, the degree of

influence that these factors exert is highly variable, which significantly constrains the

relative importance of a single parameter in the build-up process. Additionally, a large

homogenous data set is needed to create reliable and predictive relationships between

parameters. Consequently, build-up studies involving a large number of physical and

geographical parameters can be very site-specific and the outcomes can be hard to

transfer to other areas. Hence, the reduction of the number of statistically significant

physical factors could significantly facilitate transferability of research.

2.3 Pollutant wash-off

Pollutants are incorporated into stormwater runoff via wash-off processes. Pollutant

wash-off is influenced by the amount of pollutants available, which in turn is

determined by the build-up process. Since the build-up process is in dynamic

equilibrium (as noted in Section 2.2), the pollutant availability on the catchment

surface for wash-off is likely to remain largely the same. Hence, pollutant build-up

and wash-off show a strong interaction.

12

Vaze and Chiew (2002) proposed two possible alternative pollutant wash-off models

as illustrated in Figure 2.2. The outcomes from their field study, conducted on an

impervious surface in Melbourne Australia, indicated a relatively quick pollutant

accumulation rate after a rain event. However, the rate slowed down after several days

and stayed fairly constant until a cleansing event occurred, as illustrated in Figure

2.2(b). The alternative view to the pollutant accumulation process is illustrated in

Figure 2.2(a), where the surface pollutant load builds up from zero over the

antecedent dry days. Most of the available load is then washed off during a storm

event. Hence, the build-up of pollutants is based on measurements of pollutant wash-

off only. For example, Ball (2000) found that events with an average intensity greater

than 7mm/hr could be considered as cleansing events. Unfortunately, this alternative

view has been adopted in water quality models even though studies have shown that a

storm event may typically only remove a small proportion of the overall surface

pollutant load (Chiew et al. 1997; Malmquist 1978).

FIGURE 2.2 Hypothetical representations of surface pollutant load over time

(adapted from Vaze and Chiew 2002)

The wash-off load is dependent on parameters such as rainfall duration, texture depth

of the runoff surface and the particle size distribution of the accumulated material

(Andral et al. 1999). Thus, there are a number of physical and chemical factors

preventing the pollutant load on a paved surface from returning to zero.

Similarly, Duncan (1995) proposed that the wash-off pollutant load varies throughout

the rain event as a function of rainfall intensity or runoff velocity and would not in

general demonstrate an exponential or linear relationship with runoff volume, rainfall

intensity or pollutants remaining. Hence, exponential or linear wash-off functions

cannot simulate an increase in pollutant concentration at any time during the storm.

13

This would be a situation where a higher order storm can lead to enhanced pollutant

detachment rather than a proportionate increase.

2.3.1 First flush phenomenon

Numerous researchers have reported the first flush as an important phenomenon, as

this is the runoff component that is the most contaminated during a storm event (Lee

et al. 2002). It is defined as the initial period of stormwater runoff during which the

concentration of pollutants is substantially higher than during other stages. However,

Lee and Bang (2000) found that the pollutant concentration peak followed the flow

peak in watersheds that only had an impervious area less than 50% of the total area.

They also found that the concentration peak may vary for different pollutants during a

storm event. As Duncan (1995) has postulated, there is a significant possibility that

different processes dominate under different conditions or at different scales.

The significance of the first flush stems from the fact that management practices such

as detention and retention basins are often designed for the initial component of urban

stormwater. Hence, economic implications in relation to management and treatment

of urban stormwater are incorporated with the first flush. Although the occurrence of

first flush has been confirmed in most instances, the observations noted are not

consistent. Numerous researchers have reported widely varying behaviour of different

pollutants. Additionally, numerous other researchers have claimed that the

significance of first flush is overrated and not all storms exhibit the first flush

phenomenon (Hall and Ellis 1985; Sonzogni et al. 1980).

Hoffman et al. (1984) found that suspended solids exhibited proportionate peaks

during a runoff event with three flow peaks. This was mirrored by the total particulate

hydrocarbon concentration. However, individual hydrocarbons showed different

peaks, with different species showing peak concentrations throughout the runoff

event. A number of factors have been attributed to this behaviour, such as solubility,

volatility, susceptibility to degradation and differences in particle size distribution of

the solids. Similarly, particulate bound metals have been found to exhibit a first flush

whilst the major fraction of the dissolved fraction is transported during the middle of

the storm event (Hall and Anderson 1986; Sansalone and Buchberger 1997). Harrison

14

and Wilson (1985) noted that the physico-chemical associations in which pollutants

are present exert a strong influence on the first flush effect of various pollutants.

As suggested by Ellis (1991), the concentration peaks of pollutants occur in the first

flush, however the pollutant load of the first flush contributes on average only 30-35%

of the total pollutant load. Instead, high correlations between maximum pollutant load

and peak flow have been observed, even though the pollutant concentration declines

with time (Ball 2000; Herrmann 1981; Morrison et al. 1984). Similarly, Hoffman et

al. (1985) found pollutant load peaks of hydrocarbons, heavy metals and suspended

solids coincide with the runoff peak.

The contribution of the first flush is also dependant on catchment characteristics, in

addition to rainfall intensity and runoff volume. The strength of the first flush has also

been found to be more pronounced in small rather than large catchments (Lee and

Bang 2000; Lee et al. 2002). However, the analysed catchments had a significantly

different percentage of impervious surfaces (Small: 80% impervious; Large: 50%

impervious) questioning the relationship between catchment size and the significance

of the first flush. This is supported by Bertrand-Krajewski et al. (1998) who suggested

that the influence of catchment size on first flush strength was minor. Additionally,

pollutants in a larger catchment would be more susceptible to processes such as

dispersion and diffusion due to the increased number of pollutant obstructions, which

makes the contaminant plume spread out. Hence, as contamination spreads out as it

moves, it does not arrive all at once at a given location downstream.

As the above discussion highlights, the reported results from various studies are

confusing and constrain the development of rational concepts to describe the first

flush, mostly due to the difference in quantifying the first flush in the studies. This is

mirrored in the uncertainty of key assumptions in urban water quality models.

2.3.2 Influence of rainfall on pollutant wash-off

The development of water quality models has been closely linked to water quantity.

Understanding the relationship between water quality and water quantity is important

for two main reasons.

15

Firstly, in most water quality models, pollutant concentration and pollutant load

cannot be estimated without the estimation of flow. The flow factor in urban runoff

pollution has been identified as the driving force in the mobilisation, transport and

deposition of pollutants due to the superimposed effect of an urbanised catchment on

flow characteristics (Ellis 1985). Secondly, procedures to mitigate water quantity and

water quality problems are often complementary. Conversely, the procedures for the

estimation of water quantity and water quality in an urban area differ largely from a

rural area due to the large proportion of impervious areas in an urban environment

(Zoppou 2001).

Influence of rainfall characteristics on the wash-off of pollutants is important due to

two primary processes. Firstly, as rainfall hits the ground, it initially wets the surface

and begins to dissolve water soluble pollutants. The impacting raindrops and

horizontal sheet flow provide the necessary turbulence for dissolving the soluble

fraction. Secondly, the pollutants detach as a result of rainfall impact and are

transported by surface runoff. By undertaking impact energy tests on rainfall on paved

surfaces, Vaze and Chiew (1997) showed that both the turbulence created by falling

raindrops and the shear stress imparted by runoff were important in loosening the

surface particles and suspending them in water. Depending on the intensity and

duration of the storm, part of the available surface pollutant load becomes

disintegrated and/or dissolved (Vaze et al. 1997).

It has also been found that rainfall itself is a significant source of some pollutants,

especially nitrogen species (Ebbert and Wagner 1987; Drapper et al. 2000). Similarly,

Brezonik and Stadelmann (2002) found that rainfall in Eastern Minnesota contributed

up to half the concentration of nitrate and ammonium found in roadway runoff. Low

levels of PAHs have also been detected in rainfall, especially in precipitation

occurring in industrial areas (Polkowska et al. 2000). However, as Herrmann (1981)

has noted, the concentrations of PAHs in rainfall are low compared to the

concentrations of PAHs in urban stormwater runoff.

Higher loads of pollutants reaching receiving waterways have been associated with

higher amounts of rainfall (Bruwer 1982). For example, Lee and Bang (2000) found

that concentration levels of suspended solids and chemical oxygen demand rose

16

significantly with increasing runoff, independent of land use. On the contrary, in an

extensive study of stormwater runoff data from 68 catchments in areas surrounding

Minnesota USA, it was found that rainfall duration was negatively correlated with

pollutant concentrations and loads (Brezonik and Stadelmann 2002). This suggests

that a dilution effect was occurring during the runoff event. Furthermore, Schiff et al.

(2002) found no relationship between rainfall duration and constituent concentrations.

Consequently, the influence of rainfall duration on the pollutant load and

concentrations in urban stormwater runoff is highly variable. As noted by Brezonik

and Stadelmann (2002), rainfall amount and rainfall intensity were the most important

variables in multiple linear regression relationships to predict runoff loads, but

uncertainty was high in the models developed. Hence, the relative importance of

rainfall duration in water quality has been linked with uncertainty. This is attributed to

the in-homogeneity of the data sets used. Consequently, methods to control or remove

the dependency of physical factors such as rainfall duration could increase the

knowledge of chemical and physico-chemical processes inherent to urban water

quality.

Rainfall intensity appears to have a stronger correlation with pollutant concentration

and loading rather than rainfall volume and duration primarily due to the relationships

with drop-size, as illustrated in Figure 2.3, and kinetic energy, as illustrated in Figure

2.4 (Hudson 1963; Laws and Parsons 1943; Salles et al. 2002). Brezonik and

Stadelmann (2002) found that all constituents investigated, except dissolved

phosphorous and nitrogen, were correlated with rainfall intensity. Similar results were

found by Vaze et al. (1997) and Muliss et al. (1996), who found most nutrients and

heavy metals to be associated with the storm of highest intensity.

17

FIGURE 2.3 Median drop diameters relationship with rainfall intensity

(adapted from Hudson 1963)

FIGURE 2.4 Relationship between rainfall intensity and impact energy in

South-East Queensland (adapted from Rosewell 1986)

Drop-size composition and kinetic energy of raindrops are considered important

parameters in detaching sediments from paved surfaces and increasing the transfer of

chemicals from soil solution to surface runoff (Ahuja 1990). While drop size is

generally known to increase up to a certain rainfall intensity and then decrease, as

shown in Figure 2.3 (Hudson 1963), the kinetic energy of a rain drop is more

complex. Rosewell (1986), investigating the relationship between kinetic energy and

Median drop diameter vs rainfall intensity

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250Rainfall intensity [mm/hr]

Med

ian

drop

dia

met

er [m

m]

18

rainfall intensity, found that there is a general tendency towards a constant value of

kinetic energy at intensities greater than 100 mm/h, as illustrated in Figure 2.4.

However, Assouline and Mualem (1989) found that the kinetic energy reaches a

maximum value before declining for higher rainfall intensities. This is mirrored in the

decrease in rain drop size during high intensities, as found by Hudson (1963).

Similarly, Salles et al. (2002) found the kinetic energy of rainfall to decrease when a

specific intensity had been reached. In spite of this, much of the variation in kinetic

energy can be due to the different techniques used for drop size measurement and,

more importantly, mathematical expressions of the kinetic energy. Although the drop

size and kinetic energy have an impact on the detachment of particles, the degree of

influence declines with rainfall duration as found by Vaze and Chiew (1997). This is

due to the sheet flow occurring on the surface, which decreases the impact energy of

the falling rain drops. Consequently, the energy of falling raindrops is more important

at the start of a rainfall event, but is less dominant as the surface pollutant availability

decreases and sheet flow depth increases during the event.

2.4 Common pollutants in an urban environment

As urban stormwater runoff has been identified as one of the major causes of

pollution, it is of critical importance to trace the sources of these specific types of

pollutants in an urban watershed. Unlike agricultural areas, where pesticides and

nutrients from fertilizers play a dominant role in the runoff, urban areas generate

pollutants such as heavy metals and hydrocarbons related to land use and traffic

volume in the catchment (Estebe et al. 1997). This section provides a brief description

of important pollutants in an urban area.

2.4.1 Pathogens

Pathogens are disease-causing microorganisms that grow and multiply within the host

until an infection spreads as a result of the growth of the organisms. The diseases

caused by pathogens in water can be classified into four groups:

• Waterborne diseases;

• Water-washed diseases;

• Water-based diseases; and

• Water-related diseases

19

(Masters 1997; Pepper et al. 1996).

The two groups of pathogens most commonly associated with water pollution are

bacteria and viruses, which are responsible for a number of waterborne diseases such

as cholera and hepatitis. Viruses are obligate parasites, meaning that they cannot live

or grow outside the host organism. However, they do not need food for survival,

which makes viruses capable of surviving long periods in an environment. Some

viruses, referred to as enteric viruses, have the ability to replicate themselves within

the host. The existence of bacterial pathogens has been known for more than hundred

years and the major species of concern is salmonella.

Sources of pathogens in the environment include sewage treatment systems and solid

waste. The fate and transport of pathogens in the environment is affected by a number

of environmental factors, with temperature being the most important (Pepper et al.

1996). Pathogens will not be discussed further in this thesis due to the focus being on

PAHs and heavy metals.

2.4.2 Oxygen demanding wastes

One of the most important indicators of water quality is the concentration of dissolved

oxygen present. Oxygen is the lifeline for most of the marine life in the ecosystem and

a decrease in oxygen will have significant implications in an aquatic environment. As

oxygen levels fall, undesirable odours, tastes and colours start to be noticed and the

acceptability of the water as a supply source or recreational resource reduces.

Oxygen-demanding wastes are primarily organic materials that are oxidised by

microorganisms in the water (Ellis 1989).

In addition, the oxidation of certain inorganic compounds may also be a contributor to

oxygen depletion in a water body (Masters 1997). There are three common

measurements of oxygen demand used:

• chemical oxygen demand (COD);

• biochemical oxygen demand (BOD); and

• total organic carbon (TOC)

(Zoppou 2001).

20

COD is an indicator of the amount of oxygen that is needed to chemically oxidise the

wastes. BOD indicates the amount of oxygen consumed as a result of microbial

oxidation of the organic material. TOC, like COD, is an indicator of the total amount

of organic material present in a sample. BOD has traditionally been the most

important measure of the organic pollution (Black 1977; Masters 1997). Common

sources of organic matter in urban street dust have been found to include dead plants

and animals as well as vehicle exhaust, tyre wear and soil (Rogge et al. 1993; DeWitt

et al. 1992). Anthropogenic organic matter such as that originating from vehicle

emissions and tyre wear dominate the finer particulate road dust while vegetative

organic matter dominates the organic content of the coarser fraction of road dust

(Rogge et al.1993).

However, organic matter can have a more serious impact than just giving rise to

biological problems. Colloidal size organic matter, commonly referred to as dissolved

organic carbon (DOC), leads to impacts such as increased solubility of PAHs and

heavy metals. Hamilton et al. (1984) found that dissolved organic carbon plays a

major role in the partition of metals between soluble and particulate fractions in

stormwater. Consequently, interaction between DOC and heavy metals can result in

complexation processes that concentrate the metals in the dissolved phase. This would

ultimately lead to a greater amount of bioavailable pollutants in the environment. The

percentage of DOC has been found to increase with temperature (Wust et al. 1994),

most likely as a result of an increased biological activity. The effect of organic carbon

on PAHs and heavy metals is discussed further in Section 2.5 and 2.6.

2.4.3 Nutrients

Nutrients are compounds that are essential for the growth of living organisms. In

terms of water quality, nutrients are considered as pollutants when the concentration is

high enough to cause excessive growth of vegetation such as algae. Algae blooms are

caused by nutrient enrichment. When the algae eventually die and decompose, they

remove oxygen from the water. The nutrients that play the most important role in the

deterioration of water quality are nitrogen and phosphorous (Carpenter et al. 1998;

Laws 1993; Masters 1997).

21

Most of the nitrogen present in polluted waters is in the form of organic nitrogen. As

time progresses, most organic nitrogen is converted to ammonia nitrogen and further

on, if the conditions are appropriate, to the oxidation of ammonia to nitrates or nitrites

(Sawyer et al. 1994). Municipal and industrial wastewater and septic tanks are

amongst the major point sources of nitrogen in urban areas. However, the highest

concentrations of nitrogen are produced by diffuse sources, mainly originating from

forest runoff, agricultural use and rainfall (Ebbert and Wagner 1987; Zoppou 2001).

There is also a significant amount of nutrients originating from residential areas due to

the use of lawn fertilizer. The inputs from these sources can be significant in an urban

area due to the ability of nitrogen to travel long distances in the atmosphere and later

being washed out by rainfall.

2.4.4 Suspended solids

Sediments originate from both impervious and pervious areas in a catchment. Solids

result in clogging of channels and sewers and smothering of bottom dwelling fauna

and flora in water bodies. However, it is the chemical impact on receiving waters that

is of primary interest. In addition to the obvious water quality impairment caused by

sediments such as high turbidity and reduced photosynthesis, their more serious

impact is insidious. Sediments act as mobile substrates for other pollutants such as

heavy metals (Hunter et al. 1979; Sartor and Boyd 1972). In this regard, the smaller

particles are of more serious concern due to their relatively high surface area, which

leads to an increased adsorption of pollutants (Dong et al. 1984; Liebens 2001). It has

also been found that the anthropogenic sources contribute a higher amount of fine

particles than natural sources in urban environments (Fergusson and Ryan 1984).

Consequently, fine particles play an important role as a pollutant transport tool in

runoff from urban areas.

The adsorption of hydrophobic pollutants such as heavy metals to finer particles is of

particular concern since adsorption to sediment surfaces is important for the growth

and survival of many organisms native to the aquatic environment (Schillinger and

Gannon 1985). Hence, the water quality can be impaired significantly by high

sediment loads. Pechacek (1994) reported that adsorption affinity of a solid particle

varies with the size, structure and physico-chemical properties, such as the electrical

conductivity (EC) of the particle. Most of the eroded material from land surfaces and

22

the material deposited on the ground from anthropogenic sources do not necessarily

reach the receiving waterways as they may be transported only a short distance before

being re-deposited on the land surface. The coarser the material, the quicker it is

deposited. Finer material stays in suspension longer or even forever due to its larger

surface area and electrostatic charge, and is therefore transported a greater distance by

urban runoff (Dong et al. 1983). Hence, the particles reaching the receiving waters

tend to be fine textured. Andral et al. (1999) noted this behavior for particles smaller

than 100μm in diameter which remained in suspension, while particles larger than

100μm were easily separated. It was by Andral et al. (1999) concluded that to treat

runoff, particles smaller than 100μm in diameter, which can represent up to 90% of

the weight of the solids remaining in suspension in runoff, should be removed.

As the specific mass of particles decreases with size, simultaneously the percentage of

organic matter increases (Sartor and Boyd 1972). There are two key reasons why this

occurs. Firstly, as Sartor and Boyd (1972) pointed out using volatile solids as a

surrogate, organic matter has low structural strength and is easily ground into fine

particulates. Secondly, finer particulates provide a larger surface area for non-

particulate organic matter to adhere. Evans et al. (1990) found a high organic carbon

content in sediment fractions larger than 2mm. However, similarly high levels of

organic carbon were found in finer particulates. Fragments of leaf and twig were

found in the larger sediment fraction which explains high levels of organic carbon in

this fraction. Several other researchers have reported that fine particles contain higher

organic carbon content than the coarse sediments (Andral et al. 1999; Warren et al.

2003). Hence, the crucial role played by fine particulates in urban stormwater is not

only due to its ability to stay in suspension, but also due to its organic carbon content.

Nevertheless, the total load of sediments should also be taken into consideration. This

is attributed to pollutant abatement being primarily concerned with the total pollutant

load. Some research has shown that high concentrations of pollutants in sediments

smaller than 50μm is not significant as it only represents a very small fraction of the

total mass of solids in stormwater runoff (Marsalek et al. 1997). However, as Andral

et al. (1999) noted, particles smaller than 50μm can be a significant component in

runoff, contributing to as much as three quarters of the weight of solids. Dong et al.

(1983) found the average composition of suspended sediment to consist of 77% clay

23

particles, while urban street dust and dirt consisted of 5% clay particles and 86% sand

particles. Sartor and Boyd (1972) found that fine material (<43μm) consists only 6%

of the total solids in an urban area but accounts for about one-fourth of the oxygen

demand and one-third to one-half of the nutrients. Ball et al. (1998) found a similar

percentage of fine material in total solids on a suburban road in Sydney, Australia.

These contradictory results underlie the need to consider the site-specific nature of

various phenomena associated with urban stormwater pollution. Data provided by the

United States Environmental Protection Agency (US EPA 1975) on road-deposited

sediments from five US cities confirm the highly variable nature of particle size

distribution. These variations have been attributed to differences in land use, soil and

topographic characteristics.

The importance of fine particles in urban water quality studies has been attributed to

their association with hydrophobic pollutants such as heavy metals and hydrocarbons

(Liebens 2001; Evans et al. 1990). The study by Vaze and Chiew (2002) at the central

business district in Melbourne, Australia supported this conclusion. Their results

indicated that although more than half of the accumulated material was coarser than

300µm, less than 15% of the investigated pollutants were attached to particles coarser

than 300μm. The conclusions by Sartor and Boyd (1972) were similar in terms of the

fraction of pollutants associated with particle sizes below 300μm at different locations

in USA, as shown in Table 2.1 below. These findings are of importance since

sediment removal procedures such as mechanical street sweeping (Bender and

Terstriep 1984) have been found to be inadequate for particles smaller than 250μm.

Gromaire et al. (2000) found that street cleaning procedures in Paris, France have

limited impact on the reduction of street runoff pollution, especially heavy metals.

Contradictory results relating to the particle size distribution of sediment in urban

runoff highlights the site-specific nature of urban stormwater pollution. Liebens

(2001), in confirming the variability of particle size distribution of urban sediments,

attributed this to land use and soil characteristics of the catchment. However, Liebens

(2001) also noted that the differences in particle size distribution between different

land uses were very small and statistically insignificant. This has been attributed to

similar soil erosion processes at the sites chosen by the researcher. Consequently,

finer particles play an important role in the transport of pollutants by runoff to

receiving waters. Nevertheless, the particle size distribution is highly site-specific and

24

depends on variables such as soil characteristics and street cleaning efforts. This

highlights the uncertainty associated with pollutant processes in urban stormwater.

TABLE 2.1 Fraction of pollutants associated with each particle size range, %

by weight (adapted from Sartor and Boyd (1972)

Parameter > 2000μm

840-

2000μm

246-

840μm

104-

246μm

43-

104μm <43μm

Total Solids 24.4 7.6 24.6 27.8 9.7 5.9

Volatile Solids 11.0 17.4 12.0 16.1 17.9 25.6

BOD 7.4 20.1 15.7 15.2 17.3 24.3

COD 2.4 4.5 13.0 12.4 45.0 22.7

Kjeldahl Nitrogen 9.9 11.6 20.0 20.2 19.6 18.7

Nitrates 8.6 6.5 7.9 16.7 28.4 31.9

Phosphates 0 0.9 6.9 6.4 29.6 56.2

Total heavy metals 16.3 17.5 14.9 23.5 27.8 (<104µm)

Total pesticides 0 16.0 26.5 25.8 31.7 (<104µm)

2.4.5 Heavy metals

Stormwater runoff from urban catchments contains significant quantities of metal

elements and solids (Buchberger et al. (1997). Heavy metals are of concern due to

their potential toxicity and unlike many other water pollutants, they are non-

degradable in the environment. Furthermore, most of the heavy metals have low

solubility and hydrophobic nature, making them attach to finer particulates. Hence,

most heavy metals are easily transported by urban runoff to receiving waters.

25

Therefore, understanding the processes governing build-up and wash-off of heavy

metals in an urban environment is of critical importance.

Heavy metals have been primarily recognised as traffic-related pollutants (Dong et al.

1984; Sansalone and Buchberger 1997; Wilber and Hunter 1979). However, a large

number of additional sources have also been listed in the literature. Table 2.2 below

lists some of the most recognised heavy metal sources in urban environments.

TABLE 2.2 Sources of heavy metals in an urban environment (Drapper et al.

2000; Sansalone et al. 1996; Vermette et al. 1991; Fergusson and Ryan 1984;

Ellis et al. 1986)

Source Pb Zn Cd Cu Ni Cr Mn Fe Al

Fuel and exhaust

Tires

Brakes

Engine wear

Vehicular component wear

Paint

Soil

Other sources of heavy metals in urban areas also include corrosion of buildings and

their appurtenances, atmospheric deposition and various industrial activities including

intentional and accidental spills (Christensen and Guinn 1979; Davis et al. 2001).

Several other sources such as fungicides and insecticides (Cu and Cd) and asphalt

26

paving (Ni) have also been found to contribute to the build-up of heavy metals in

urban areas (Ball et al. 1998).

Metals in urban stormwater runoff are partitioned between dissolved and particulate-

bound fractions. The dissolved fraction of urban stormwater runoff is defined as

metals of an un-acidified sample that pass through a 0.45μm membrane filter (APHA

1999). However, most of the heavy metals in urban stormwater runoff have been

found to be attached to suspended solids (Bodo, 1989; Dong et al., 1984). Despite the

strong relationship between heavy metals and suspended solids, the evaluation of the

dissolved fraction of the heavy metal load is important as an indicator of

bioavailability. Furthermore, parameters such as dissolved organic carbon and pH can

significantly enhance desorption of heavy metals from solids. Tai (1991) noted that

the ratio of trace metals released into the dissolved phase at pH 6 against pH 8.1 is

about 180 for Zn, 45 for Pb and 25 for Fe. Sansalone and Buchberger (1997) observed

significantly larger metal element dissolved fractions in events where the rainfall pH

was low and where the average pavement residence time were the highest. Similarly,

DOC plays a major role in partitioning of metals between soluble and particulate

fractions in stormwater (Hamilton et al., 1984). The build-up and wash-off process

kinetics of heavy metals and its influence on urban stormwater are discussed in detail

in Section 2.5.

2.4.6 Polycyclic Aromatic Hydrocarbons (PAHs)

Polycyclic aromatic hydrocarbons (PAHs) or Polynuclear aromatic hydrocarbons are

fused compounds built on benzene rings. Aromatic rings are considered fused when a

pair of carbon atoms is shared. Many PAHs possess high stability due to the benzene-

like properties. The interest in PAHs in environmental studies derives from the fact

that they have been found to give rise to acute lethal and sublethal toxic effects in

freshwater organisms at very low aqueous concentrations (Warren et al. 2003).

However, Warren et al. (2003) noted that toxicity studies have generally been carried

out under controlled conditions in laboratories, often in the absence of sediments and

other factors that might control the actual bioavailability of a compound in water. This

has resulted in limited knowledge on the effect of physico-chemical parameters on the

toxicity of PAHs. The environmentally significant PAHs range from Naphthalene

(C10H8) to Coronene (C24H8). In this range there is a large number of PAHs with

27

varying number, position and eventual chemistry of substituents on the basic aromatic

ring system (Manoli and Samara 1999). It has also been found that physical and

chemical properties, such as solubility of PAHs, vary with molecular weight. The

solubility of PAHs in water is generally low due to their hydrophobic nature. Hence,

PAHs associate mainly with particulate matter.

Due to their adverse impact, PAHs are included in the United States Environmental

Protection Agency (US EPA) and in the European Union priority list of pollutants.

The US EPA has identified 16 unsubstituted PAHs as priority pollutants as listed in

Table 2.5 below, some of which are considered to be possible or probable human

carcinogens. Hence, their distribution in the environment and potential risk to human

health has been the focus of much attention (Manoli and Samara 1999).

TABLE 2.3 Priority PAHs as listed by USEPA (adapted from Manoli and

Samara 1999)

PAH compound Benzene rings Solubility [mg/L] Kow*

Naphthalene, NAP 2 32 2300

Acenaphthene, ACE 3 3.4 21000

Acenaphthylene, ACY 3 3.93 12000

Flourene, FLU 3 1.9 15000

Anthracene, ANT 3 0.05-0.07 28000

Phenanthrene, PHE 3 1.0-1.3 29000

Flouranthene, FLA 4 0.26 340000

Benzo[a]anthracene, BaA 4 0.01 4x105

Benzo[b]flouranthene, BbF 5 - 4x106

Benzo[k]flouranthene, BkF 5 - 7x106

Chrysene, CHR 4 0.002 4x105

Pyrene, PYR 4 0.14 2x105

Benzo[a]pyrene, BaP 5 0.0038 106

Dibenzo[a,h]anthracene, DbA 5 0.0005 106

Benzo[ghi]perylene, BgP 6 0.00026 107

Indeno[1,2,3-cd]pyrene, IND 6 - 5x107 *Kow is an octanol/water partition coefficient

28

The most common carcinogenic PAH is Benzo[a]pyrene, BaP, which contains five

fused benzene rings. However, smaller molecular weight PAHs can undergo

decomposition and react with a number of chemicals in the environment to produce

different derivatives, which can be more toxic than the original compounds (Nicolau

et al. 1984).

Hydrophobic contaminants such as PAHs can exist in a variety of forms in water: a

freely dissolved phase, as a colloidal phase or associated with sedimentary material

(Warren et al. 2003). The distribution of PAHs between the various phases is a central

issue in terms of the fate and effects of PAHs in aquatic environments. It strongly

influences concentrations in the dissolved and particulate phase and therefore has a

strong impact on water quality. In order to understand and predict this distribution, it

is crucial to examine sediments and dissolved organic matter and its influence on the

build-up and wash-off process kinetics of PAHs (Warren et al. 2003). Section 2.6

discusses the important processes determining build-up and wash-off of PAHs in an

urban environment in detail.

2.5 Build-up and wash-off processes of heavy metals

2.5.1 Build-up

Numerous sources of metals are present in an urban environment as outlined in

Section 2.4.5. However, their quantification has been proven to be extremely difficult.

Fluctuations in build-up concentrations of heavy metals have been reported

suggesting that the input and relative importance of these sources will vary in the

urban environment (Sartor and Boyd 1972; Davis et al. 2001). These fluctuations

have been attributed to factors such as antecedent dry periods and land use

characteristics. In addition to this, variability does not appear to be affected by a

single factor but by several interacting factors. For example, Vermette et al. (1991)

noted antecedent dry days and previous street cleaning as well as wind direction

during sampling to affect build-up concentrations of heavy metals. Antecedent dry

period is important due to most heavy metals exhibiting low solubility and metal’s

affinity with sediments (Yuan et al. 2001). Nevertheless, it has been found that

despite an increase in sediment loading over the length of a monitoring period, metal

29

concentrations can stay relatively constant (Ellis et al. 1986). It can be concluded that

the antecedent dry period significantly influences the amount of accumulated particles

available for wash-off. However, the relative importance of the antecedent dry period

is highly variable and questionable in relation to the build-up of heavy metals in an

urban area.

Similarly, land use has been suggested as playing an important role in the build-up of

heavy metals. Brezonik and Stadelmann (2002) found that commercial and industrial

land uses in Minnesota USA contributed a higher amount of heavy metals than a

residential site. Similar results were found by Droppo et al. (1998) in a catchment in

Ontario USA, where total concentrations of Pb, Cu, Cd, Zn and Mn exhibited the

highest concentrations within the industrial part of the catchment. This is not

surprising since Cu, Cd, Zn and Mn have been attributed to increased industrial

industries (Vermette et al. 1991; Yun et al. 2000). For example, Robertson et al.

(2003) noted that Zn compounds were used in the manufacture of alloys, glass, dry

cell batteries and electrical equipment. High metal concentrations in commercial areas

are most likely due to an increased traffic density compared to a residential site. This

is attributed to metals such as Pb and Cr, which have been found to be strongly

dependant on site specific traffic density (Yun et al. 2000). Similarly, Shaheen (1975)

showed that close to 100% of Pb entering receiving waters originated from traffic-

related sources. However, due to the increase in the use of unleaded fuel in the last

two decades, this is questionable and should be interpreted carefully.

Liebens (2001) found that street sweepings from residential areas had higher

concentrations of most of the heavy metals compared to street sweepings from

commercial areas in Florida USA. Sartor and Boyd (1972) found industrial and

residential areas to have a higher loading of heavy metals than commercial areas. The

higher concentrations and loads of metals found in industrial and residential areas

could potentially be attributed to by-products from industrial activities, residential

buildings and garden products, which could explain the higher concentrations reported

in residential and industrial areas (Davis 2001; Ball et al. 1998).

Furthermore, one of the most common sources of Cu and Zn has been found to be

brake pad and tyre wear (Drapper et al. 2000). Hence, commercial or residential areas

30

incorporating acceleration or deceleration lanes could have a higher concentration of

Cu and Zn in road dust. Sartor and Boyd (1972) have further attributed the high

pollutant loading rate for industrial areas to reasons such as less frequent street

sweeping, spillage from vehicles and streets being in poor condition. In contrast, the

reason for commercial areas having the lowest pollutant loading rate was attributed to

more frequent street sweeping. In spite of this, a visual inspection by Vermette et al.

(1991) indicated that street cleaning immediately prior to sampling decreased the

mass of road dust, however street cleaning had no effect on heavy metal

concentrations. Though efficiencies have greatly improved over the last two decades,

the removal of fine fractions is still an issue (Sutherland et al., 1998). Hence, smaller

particles tend to remain behind after street sweeping to be incorporated into

stormwater runoff. Although many loading and concentration estimates have been

reported for various land uses, high variability and inconsistencies exist among

reported values. These differences may incorporate variations or differences in

sampling and analytical methods and highlights the need for more reliable

methodology in urban stormwater management. Consequently, an approach where

physical factors can be reduced or controlled to some extent would be favourable in

investigating important processes. Furthermore, the differences in previous studies

reflect the in-homogeneity in the data sets used which questions some of the

modelling approaches used. The use of univariate analysis such as linear regression

can be useful when the processes are highly correlated. However, it is not particularly

suitable for the investigation of the complex processes inherent to urban water quality.

This is where multivariate methods, which have the ability to analyse strongly

collinear data with numerous variables, are preferable. Furthermore, multivariate

approaches can provide inferential quantitative modelling (Wold et al. 2001).

One of the most important factors that control the accumulation of heavy metals in

sediments has been identified as the grain-size (Krein and Schorer 2000). As

discussed in Section 2.4.4, fine particulates are especially important in the build-up of

heavy metals due to their larger surface area and electrostatic charge. Additionally, it

is essential when investigating processes inherent to stormwater runoff to have

information on both the particulate input from different sources and the particle size

distribution, in order to accurately assess pollutant transport dynamics (Vermette et al.

1987). The particle size distribution of the deposited material on paved surfaces plays

31

a critical role in urban stormwater management strategies. Larger particles are

relatively easy to monitor and remove. Fine material has the ability, when

incorporated into urban stormwater runoff, to stay in suspension longer and is

therefore transported a greater distance by urban runoff (Deletic et al. 1996). The

influence of fine particulates in heavy metal transport is discussed in detail in Section

2.5.2.

2.5.2 Wash-off

As noted in Section 2.4.5, metals transported by urban stormwater are partitioned into

a dissolved and a particulate fraction. However, the distribution of heavy metals in

urban stormwater is difficult to predict as it depends on a variety of factors associated

with anthropogenic metal input, such as pollution origin, concentration and

characteristics of suspension, colloidal and dissolved ligands and changes in physico-

chemical parameters of the water (Gueguen and Dominik 2003). It has also been

found that the longer the sediments stay in aqueous suspension, the greater the degree

of adsorption and/or desorption of pollutants, and the biological transformations of

degradable components will be more extensive (Dong et al. 1984). Generally, metal

elements in urban environments partition into dissolved and particulate-bound

fractions of runoff as a function of:

• pH

• Metal element solubility

• Solid concentration

• Origin of heavy metal pollution

• Pavement residence time

• Runoff volume

• Antecedent dry period

• Dissolved organic carbon

• Reducing conditions (mainly for Manganese (Mn) which is a redox sensitive

metal

• Chemical form

(Ellis and Revitt 1982; Hamilton et al. 1984; Sansalone and Buchberger 1997;

Sonzogni et al. 1980; Ujevic et al. 2000; Gueguen and Dominik 2003)

32

These factors underline the uncertainties in the literature in relation to different

dissolved metal concentrations and loadings noted by many researchers. For example,

Morrison et al (1984) found high levels of Cu in stormwater solids, which may be due

to the high percentage of particulate organic material recorded. Bubb and Lester

(1993) found Cu and Zn to be primarily particulate bound while Shinya et al. (2000)

found Ni to be mostly associated with the dissolved phase. Legret and Pagotto (1999)

found that Pb usually appears in the particulate form (91%) whereas the other heavy

metals investigated were usually dissolved (60% for Zn, 56% for Cu and 54% for Cd).

The association with the dissolved fraction by metals such as Cu, Zn and Ni is not

surprising since they have been found to be strongly complexed by organic ligands

(Qu and Kellermann 2001). Consequently, they may interact in solution with

dissolved organic matter, which in turn is concentrated by the adsorption to fine

particulates such as clay minerals. The association of Cd with the dissolved phase is

most likely due to its loose binding through cation exchange and easily reducible

phases. Hence, Cd, Cu and Zn are likely to be transported in the dissolved phase in

stormwater runoff and be directly bioavailable, when reaching receiving waters.

However, most of the heavy metals in urban stormwater runoff are strongly attached

to suspended solids due to their low solubility (Shinya et al. 2000; Ujevic et al. 2000).

Different metal elements will distribute differently in urban runoff. Generally, a

partition coefficient (Kp) is used to describe a metal’s affinity for sediment

interactions. Kp is defined as the ratio of the solid phase concentration (Cs) with the

aqueous phase concentration (Caq). A metal element generally has a specific Kp value

due to its specific solubility. However, a metal’s partition coefficient can vary

significantly due to the highly laboratory based studies used to develop Kp. Hence, a

number of processes in urban environments can influence the partition coefficient.

DeWitt et al. (1992) found Kp values to vary within a factor of ten when organic

matter was present in urban stormwater (DeWitt et al. 1992). This is most likely due

to chemical transformations occurring in the sediment organic matter as it is digested

and metabolized by benthic fauna and microflora. Similar results have been found in

urban stormwater in Australia by Ball (2000), where the ratio of particulate load to

total load was not consistent between three catchments investigated. The use of

partition coefficients to describe sediment interactions in field samples is further

restricted by the difficulty in obtaining aqueous samples with representative solids

33

concentration (Sansalone and Buchberger 1997). Consequently, analytical methods to

measure the concentrations of micro-pollutants are more accurate if sufficient solids

are available. Hence, measurements based on a relatively small amount of solids

could contain gross errors and bias the partition coefficient.

Metal concentrations generally increase with decreasing particle size (Liebens 2001;

Ujevic et al. 2000). This is due to the relatively large surface area of fine sediments

and their higher sorption capacity (Dong et al. 1984). The enrichment of metals on

fine particles is a major environmental problem since no efficient removal technique

for fine particles is known. Coarse particles can easily be removed by settling ponds

and street sweeping. Research has shown that street sweeping is only effective for

particles of 250μm size and larger (Bender and Terstriep 1984; Sutherland et al.

1998). Since most metals have a greater affinity for smaller particle sizes,

conventional cleaning programs would have little effect in reducing toxic runoff

levels, as fine suspended particulates are readily transported in stormwater. However,

high concentrations of heavy metals have occasionally been found in the coarse

fraction of urban street dust suggesting that a broad range of particle sizes should be

investigated for accurate results (Dong et al. 1984). Hamilton et al. (1984) suggested

that the partitioning of heavy metals in particles occur in five fractions:

• The exchangeable fraction;

• Carbonate fraction;

• Fe-Mn Oxide fraction;

• Organic fraction; and

• Residual fraction.

The exchangeable fractions are metals specifically surface bound and subject to

sorption-desorption processes due to changing ionic composition of stormwater. The

carbonate fraction, on the other hand, contains metals that are moderately available

for release to runoff but are highly susceptible to a pH change. Iron and manganese

oxides exist in road sediments as cement between particles or as surface coatings on

particles. Furthermore, metals can be irreversibly trapped in the crystal lattice of

primary and secondary minerals (Charlesworth and Lees 1999). Hamilton (1984)

found the organic fraction to play a major role in partitioning of metals in stormwater.

34

This has also been observed by Droppo et al. (1998), who found Cu to be mostly

bound within the organic fraction independent of the land use. Similarly, Ellis et al.

(1986) found increased metal levels in the organic fraction of the sediments. This is of

concern due to the enhanced toxicity that can occur in metal-organic complexation.

More importantly, the relationship between organic carbon and heavy metals could

lead to an enrichment of bioavailable metals due to microbial degradation of the

organic matter subsequently bringing the metal into solution (Ellis and Revitt 1982).

Additionally, as Charlesworth and Lees (1999) have noted, the highest proportion of

each heavy metal is bound to different particle fractions independent of particle size.

Carbonates were found to dominate in the binding of Cd, Zn and Cu while the organic

fraction dominated in binding Ni and Pb. This means that changes in the

environmental conditions could lead to the release of different heavy metals into the

water column.

2.6 Build-up and wash-off processes of PAHs

2.6.1 Build-up

The development and industrialisation over the past half-century has resulted in

organic compounds such as PAHs becoming ubiquitous in the urban environment.

Hence, their distribution and accumulation is of critical importance in urban

stormwater studies.

PAHs originate from both anthropogenic and natural sources. Some of the natural

sources of PAHs have been identified as volcanic eruptions and forest fires (Manoli

and Samara 1999; Bae et al. 2002). Savinov et al. (2000) also found that PAHs were

formed by direct biosynthesis by bacteria, fungi, higher plants and insect pigments.

However, the input from natural sources is low compared to anthropogenic sources in

urban areas. PAHs in urban environments are mainly the by-products of incomplete

combustion or pyrolysis of organic material (Van Metre et al. 2002; Grynkiewicz et

al. 2002). For example, Phenanthrene (PHE), Flouranthene (FLA) and Pyrene (PYR)

are typical by-products of diesel combustion while higher-molecular weight PAHs

such as Indeno[1,2,3-cd]pyrene (IND) and Benzo[ghi]perylene (BgP) are typical

gasoline engine by-products (Bae et al. 2002). Possible sources of PAHs may also be

35

assessed by the concentration ratios of individual PAH compounds as noted by Zhou

and Maskaoui (2003), who found that a ratio of PHE/Anthracene (ANT) smaller than

10 and a ratio of FLA/PYR larger than 1 indicated that the PAH contamination is

from combustion processes. Several other important sources listed in the literature

include:

• Oil residues;

• Sewage outfalls;

• Industrial wastewater;

• Aluminum production;

• Tyre wear; and

• Road-wear

(McCready et al. 2000; Rogge et al. 1993; Zhou and Maskaoui 2003).

Although a number of processes have been identified as major sources of PAHs in

urban areas, the input from a single source is difficult to quantify. In fact, PAHs are

often found in a wide range of concentrations, indicating that they have many

different sources. Despite their anthropogenic origin in urban areas, PAHs can occur

at relatively high concentrations in rural and remote areas due to their ability to be

transported over long distances as gases or aerosols (Manoli and Samara 1999). This

is further supported by higher molecular-weight PAHs’ apparent resistance to

biodegradation (Warren et al. 2003). Furthermore, biodegradation of lower molecular-

weight PAHs have been found to be limited in urban stormwater due to the

insufficient time for build-up of enzymes to degrade these compounds (Gavens et al.

1982).

A study by Yamane et al. (1997) found that the following factors affect the

degradation of PAHs:

• Organic and inorganic nutrient loads;

• Temperature;

• pH;

• Salinity;

• Previous chemical exposure;

• Microbial adaptations; and

36

• Existence of other PAHs.

However, the factors mentioned above are highly site specific and can differ between

ecosystems. In addition to this, due to its stability as a compound, the impact these

degradation processes have on the quality of urban stormwater is minor. Hence, a

significant amount of PAH build-up can be incorporated in urban runoff and

transported to receiving waters.

2.6.2 Wash-off

PAHs in urban runoff have been associated with particulate matter, with up to 90%

occurring in the particulate phase. Only a small fraction of their total load is generally

in the aqueous phase and considered bioavailable (Hoffman et al. 1985). This is

supported by Marsalek et al. (1997) who reported dissolved PAHs’ concentrations to

be less than 11%. However, PAHs have been detected in the dissolved phase at

concentrations above its aqueous solubility (Smith et al. 2000). It has been postulated

that this contradictory finding could be due to either the presence of colloidal-size

particles capable of sorbing PAHs to an appreciable extent, or the presence of an oil-

and-grease micro emulsion, which enhances the solubility of PAHs. Similarly, Warren

et al. (2003) noted that colloidal particles can sorb non-ionic organic pollutants and

enhance their apparent solubility in water, and thereby facilitate pollutant transport in

environmental systems. They have referred to this phenomenon as the ‘solubility

enhancement’ effect. Furthermore, the distribution of PAHs in the dissolved phase has

also been found to be a function of the type of sources available in the urban

environment (Readman et al. 1987). This is most likely due to petrogenic sources of

PAHs, such as point sources of oil and petrochemicals being dominant, which would

increase the water/sediment ratio of PAHs. PAHs originating from pyrogenic sources

such as incomplete combustion are generally attached to particulates.

PAHs are not very soluble in the water phase (Gonzalez et al. 2000; Makepeace et al.

1995; Marsalek et al. 1997; Wang et al. 2001). The solubility of PAHs in water have

been found to decrease with increasing molecular weight of the PAH (Manoli and

Samara 1999). This supports findings by Zhou and Maskaoui (2003) and McCready et

al. (2000) where the composition pattern of PAHs in sediments was mostly dominated

by high molecular-weight PAHs consisting of three or more benzene rings. Wang et

37

al. (2001) found the dominant compounds in all particle size fractions to be those with

three or more rings, accounting for 79-93% of the total PAH concentrations in each

size fraction. Hence it can be concluded that a significant amount of highly toxic, high

molecular-weight PAHs are transported by fine sediments in urban stormwater.

According to studies by Choi and Chen (1976), Zuofeng (1987) and Evans et al.

(1990), PAHs have been found to have a tendency to be associated with the silt and

clay fractions. However, Readman et al. (1984) found a decrease in PAH content of

estuarine sediment with decreasing particle size from sand to clay (100 to 10μm) and

Hoffman et al. (1984) stated that PAHs attached to particulates in stormwater had a

maxima in the 125 to 150μm range and below 45μm particle sizes. As discussed in

Section 2.4.2, organic carbon can influence the concentration and distribution of

pollutants attached to sediments. Positive correlations have been found between PAHs

and sedimentary organic carbon by several researchers. For example, Wang et al.

(2001) found a relationship between PAHs and sedimentary organic carbon in all

sediment size fractions. Based on their results, they also suggested that sedimentary

organic matter has PAH sorption characteristics. A number of studies have clearly

demonstrated the important role that organic matter plays in the adsorption of PAHs

in sediments (Gustafsson et al. 1997; Karickhoff et al. 1979; Kleineidam et al. 1999).

On the other hand, several researchers have suggested that the type and source of

organic matter is more important in the sorption of micro-pollutants than the amount

(Krein and Schorer 2000; Warren et al. 2003). Similarly, Dewitt et al. (1992) found

that the spatial and temporal variation in the bioavailability and sorption of PAHs in

water samples was more a function of the source of the dissolved organic carbon

rather than the concentration.

Simpson et al. (1998) found high PAH concentrations to be associated with large size

fractions (300-1180μm and >1180μm), which contained high particulate organic

matter. Kim et al. (1999) observed a positive correlation between PAHs and

sedimentary organic carbon. However, Maruya et al. (1996) reported high

concentrations to be associated with silt and clay fractions and suggested sedimentary

organic matter to have less effect on the distribution of PAHs in sediment. Macias-

Zamora et al. (2002) found PAH concentrations in surface marine sediments to be

correlated (r2=0.612) with organic matter content. Evans et al. (1990) suggested that

38

there are two optimal particle sizes with which organic matter becomes associated,

one between 40 and 63μm, and the other in the 500μm to 2mm size range due to

fragmentary plant material. Evans et al. (1990) also found a positive linear

relationship between organic matter and PAHs, which was similar for each sediment

size fraction. Other researchers who studied the distribution of PAHs into particles

have also found a correlation between PAHs and the organic fraction of particles

(Catallo and Gambrell 1987; Choi and Chen 1976; Readman et al. 1982).

Nevertheless, different PAH compounds have different relationships with sedimentary

organic matter due to its water solubility and the number of partitioning coefficients

that exist. As noted by Zhou et al. (1999), particle-water interactions were one of the

most important mechanisms controlling the distribution and movement of

hydrophobic organic chemicals such as PAHs in aquatic environments. To accurately

predict the transport and fate of PAHs in water, the partition coefficient Kp and the

organic carbon normalised partition coefficient Koc are widely used. The importance

of the organic carbon normalised partition coefficient Koc has been attributed to the

fact that laboratory experiments have shown that organic matter is the principal

particulate component responsible for the adsorption of hydrophobic organic

compounds such as PAHs (Zhou et al. 1999). The three most widely used coefficients

in the literature are listed below:

• Kow (octanol/water partition coefficent);

• Kp (linear partition coefficient between particles and water); and

• Koc (partition coefficient on an organic carbon basis).

However, large variations in partition coefficient values have been reported for the

same compound. In general, this variation is thought to be attributed to differences in

the nature of the organic matter that affect the partitioning of the compound. These

differences have been attributed to:

• Differences in the source and the extent of weathering of the organic matter;

• The effect of particle mineral type on the adsorption of organic matter; and

• The effect of particle mineral type on the configuration of the sorbed organic

matter

(Karickhoff et al. 1979; Warren et al. 2003).

39

In general, it would appear that less-polar organic matter gives rise to higher Koc

values than higher-polarity organic matter (Warren et al. 2003). This is most likely

due to the better hydrophobic environment in less-polar organic matter.

Differences in partition coefficient values for the same compound has also been

attributed to soot carbon content of the particulate matter (Zhou et al. 1999;

Gustafsson et al. 1997). This suggests that PAHs associated with particles could in

fact be present in the form of soot and soot-like particles and not subject to particle-

water interactions. In other words, the PAHs on soot-like particles are strongly bound

and not influenced by further partitioning between the particles and water. This would

significantly affect the speciation of PAHs. Similarly, parameters such as pH and

temperature have been found to influence the sorption characteristics and solubility of

specific PAH compounds (Warren et al. 2003). Hence, variations in the partition

coefficients of a specific PAH compound are likely to occur in urban stormwater.

Consequently, while total PAH concentration has a positive correlation with organic

carbon, the relationship between a specific PAH compound and organic carbon may

vary.

The concentrations and types of PAHs found in sediments have been shown to reflect

the source characteristics and related physico-chemical properties of individual PAHs

that influence sediment binding, such as the octanol/water partition coefficient Kow

(Kucklick et al. 1997). Herrmann (1981) concluded that the sorption behaviour of

PAHs played an important role in their transport in aqueous systems and suggested

that PAH compounds were transported by means of adsorption to suspended solids,

bedload and solution in films of mineral oil around those sorbents. Krein and Schorer

(2000) concluded that different particle sizes were transported by different hydraulic

conditions where intensity of rainfall was found to be responsible for the wash load,

and the grain size distribution to be controlled by the amount of rainfall.

2.7 Summary

Urban stormwater has been identified as one of the leading causes of the degradation

of receiving waters. Generally, this has been attributed to the generation of pollutants

by anthropogenic activities in urban areas. This has led to increased research activity

40

recent decades. Pollutant sources and pathways have been clearly identified in the

literature.

The primary water pollutants identified in the literature include:

• suspended solids;

• nutrients;

• organic carbon;

• heavy metals; and

• hydrocarbons.

Heavy metals and PAHs are of major concern in urban runoff due to their toxicity and

stability as compounds. In addition, numerous PAHs have been recognised as

carcinogenic and acutely toxic to aquatic organisms. Both PAHs and heavy metals

have been identified as primarily traffic-related pollutants. However, numerous

sources such as industrial activities and atmospheric deposition can contribute an

appreciable amount of PAHs and heavy metals to urban areas. The significance of

suspended solids and organic carbon stems from the fact that PAHs and heavy metals

are influenced by numerous chemical processes inherent to suspended solids and

organic carbon characteristics. This primarily relates to the adsorption of PAHs and

heavy metals to fine particles, due to their increased organic matter content and

relatively larger surface area compared to coarse particles. This could have serious

implications for urban stormwater management strategies due to the increased

difficulty in removing pollutants attached to fine particles. Furthermore, a number of

processes such as microbial degradation of the organic matter and the presence of

colloidal particles can significantly influence the bioavailability of PAHs and heavy

metals in urban stormwater.

The processes underlying the build-up and wash-off processes of pollutants have been

difficult to determine. This has primarily been attributed to the limited understanding

of the interactions between various influential parameters in urban water quality.

Consequently, relationships that have been developed have been far from satisfactory

and have led to limited transferability of the results. Furthermore, the influence of

chemical processes on the build-up and wash-off of pollutants have generally been

41

neglected in previous research due to research methodologies being largely based on

physical factors. Hence, the lack of control of physical factors to determine the

influence of chemical and physico-chemical processes in urban water quality has

significantly constrained the outcomes. Therefore, a multi disciplinary approach is

preferable and techniques and methodologies commonly used in agricultural and

chemical research could be successfully implemented. This is primarily attributed to

control of rainfall characteristics and the use of appropriate statistical tools. Hence, an

approach to urban water quality research using rainfall simulation could significantly

enhance the research outcomes and facilitate transferability of relationships found due

to the increased control of physical factors. Furthermore, common modelling

approaches used in urban water quality studies have been linear regression or similar

univariate techniques. However, as identified in the literature, the processes inherent

to urban water quality are complex. Hence, multivariate methods which can handle

incomplete and noisy data with many variables and observations are a more powerful

approach for the analysis of complicated problems.

42

Chapter 3 Design and Fabrication of a Rainfall Simulator

3.1 Introduction

Chapter 2 clearly identified the difficulties encountered in the use of natural rainfall

events in urban water quality research. This was primarily attributed to high

variability in rainfall intensity, non-uniformity of rainfall associated with the use of

large heterogenous areas and lack of control of physical factors in urban water quality

studies. Moreover, the complexity of the processes involved in urban stormwater

requires a reliable database. As Ahyerre et al. (1998) noted, the processes governing

build-up and wash-off of pollutants in urban areas are very complex, as they concern

many media, space and time scales. Additionally, the random nature of the occurrence

and characteristics of natural rainfall introduces further variables into a research arena

where so little of the inherent processes are known. Therefore, the use of artificial

rainfall can help to eliminate significant constraints such as dependency on natural

rainfall experienced by researchers undertaking rigorous research into urban

stormwater quality. Furthermore, rainfall simulation can significantly enhance the

transferability of the research, due to the reduction and control of physical variables

usually inherent to urban water quality research.

The use of rainfall simulators to produce artificial rainfall to generate databases for

urban water quality research is rarely adopted. Conversely, rainfall simulators have

been seen as important tools in agricultural research, such as erosion and infiltration

studies on runoff plots (Grierson and Oades 1977; Loch et al. 2001; Meyer and

McCune 1958). It was imperative to find an equally efficient tool for urban water

quality research. This chapter identifies the characteristics needed for successful

simulation of rainfall on paved surfaces. It also describes the design criteria used for

the development of a rainfall simulator suitable for the research project and how it

was applied in urban water quality research.

3.2 Design of a rainfall simulator

The function of a rainfall simulator is highly dependant on the desired outcome of the

individual research. As Meyer (1988) noted, rainfall simulators need to be designed

and calibrated accurately for the conditions in which they are to be used. Therefore,

43

characteristics used in the design of previous rainfall simulators needed to be

interpreted carefully and re-assessed to fit the needs of the individual research.

Previous rainfall simulators have primarily focused on agricultural research such as

erosion studies. Thus, the rainfall simulators used have been designed specifically for

this purpose. However, there are a number of rainfall simulator design characteristics

from agricultural research that are applicable to urban water quality studies. Moore et

al. (1983) noted that the general criteria for rainfall simulation, independent of the

focus of the study should be:

• Rainfall characteristics;

• Plot size; and

• Portability and construction/operation cost.

More detailed criteria were as follows:

• Drop-size distribution near to that of natural rainfall;

• Drop impact velocities near to that of natural rainfall;

• Uniform rainfall intensity and drop size distribution over the entire plot;

• Rainfall application nearly continuous over the entire plot;

• Reproducible storm patterns of durations and intensities of interest;

• No efficiency losses when used under field conditions, such as high temperature

and moderate winds;

• Plot area adequate to represent the treatments and conditions being examined;

• Highly portable, including: (a) disassembled portability for easy movement from

site-to-site; and (b) assembled portability for easy movement from plot-to-plot at a

given location; and

• Low cost in construction and operation

(Bubenzer 1979a; Bubenzer 1979b; Hall 1970; Meyer 1979; Meyer and McCune

1958; Moore et al. 1983).

The majority of the criteria mentioned above were applicable to rainfall simulation on

paved surfaces such as the reproduction of physical characteristics of rainfall and

portability of the rainfall simulator. However, many of the previous rainfall simulator

44

studies were not consistent with the focus of this research. Thus, the rainfall simulator

developed for this research was designed to meet the following primary requirements:

• complete portability, easy assembly and operation;

• drop-size distribution, terminal velocity and kinetic energy similar to natural

rainfall in the region;

• ability to reproduce the chemical characteristics of natural rainfall in the region;

• ability to create rainfall intensities suitable for the proposed research study;

• small homogenous plot area to reduce the number of inherent variables;

• ability to apply rainfall uniformly over the plot area; and

• satisfactory system for runoff collection from paved surfaces.

Considerable design and development were necessary to ensure that the rainfall

simulator met the research requirements outlined above. The different stages involved

in the design and development process are discussed below. The system developed for

the collection of wash-off samples is described in Section 4.4. Only an introduction to

the sample collection system will be discussed here.

3.2.1 Re-production of natural rainfall characteristics

One of the most important criteria for the developed rainfall simulator was the ability

to accurately reproduce natural rainfall characteristics, both physical and chemical.

Hence, both the chemical and physical profile of rainfall in the region had to be

studied.

(A) Physical Characteristics

The rainfall simulator developed in this research used three Veejet 80100 nozzles to

reproduce natural rainfall characteristics. A number of different nozzles were

available commercially with different characteristics and spray patterns. However, as

noted by Meyer and McCune (1958), nozzles used for rainfall simulation should have

either square or fan spray pattern, as illustrated in Figure 3.1, in order to obtain

acceptable intensity, drop-size, drop velocity and distribution characteristics.

45

FIGURE 3.1 Fan spray pattern nozzle (adapted from www.spray.com)

Nozzle orifice diameter and the pressure of the water entering the nozzle determine

the characteristics noted above (Assouline et al. 1997). Generally, an increase in

pressure generates a smaller drop size from the nozzle. A high discharge nozzle such

as the Veejet 80100 was therefore preferable to obtain drop-sizes and velocities near

natural rainfall. Furthermore, the Veejet 80100 nozzle was chosen due to its median

drop size of 2 1/8 mm diameter (Meyer and McCune 1958), which was similar to the

median drop size in natural rainfall at intensities lower than 160mm/hr (Hudson

1963). The kinetic energy has been calculated as 29.49J/m2mm on the basis of

measured drop-size and velocity (Loch et al. 2001). Similar kinetic energy was

reported for natural rain in South-East Queensland at intensities above 40mm/hr

(Rosewell 1986). Hence, the Veejet 80100 nozzle satisfied all the physical rainfall

characteristics set out in the research. However, the Veejet 80100 nozzle may not

completely replicate the characteristics of natural rainfall as noted by Morin et al.

(1967). Though better than other known nozzles, it has been found that the nozzle

only provides 80% of the kinetic energy per unit volume of rain. Moreover, rainfall

characteristics have regional differences and while the kinetic energy and drop-size

distribution from a Veejet 80100 nozzle provided the most reliable simulation of

natural rainfall, it is important to note that natural rainfall characteristics were very

difficult to reproduce completely.

The pressure used in this research was 41 kPa which has been commonly applied to

the Veejet nozzle 80100 in past rainfall simulation research (Bubenzer and Meyer

1965; Loch et al. 2001; Meyer and Harmon 1979). This was due to the drop size

distribution, impact velocity and kinetic energy of the simulated rainfall being similar

to natural rainfall with intensities over 25mm/hr at the specific pressure for the Veejet

80100 nozzle.

46

Due to the Veejet 80100 nozzle being a high-discharge nozzle, producing an intensity

of about 580mm/hr when spraying continuously over a plot (Moore et al. 1983), a

technique to lower the intensity had to be adopted. Intermittent rainfall spraying was

therefore employed, which has been used successfully in numerous rainfall simulator

studies (Floyd 1981; Grierson and Oades 1977; Moore et al. 1983). Pulsed rainfall

application, such as intermittent rainfall, did not affect the characteristics obtained

(Loch et al. 2001). In fact, rainfall is highly intermittent by nature. Furthermore, the

fan spray pattern of the Veejet 80100 nozzle demanded it to be moved perpendicular

to the long dimension of the spray pattern to cover a greater area during rainfall

(Meyer and McCune 1958). Hence, an oscillating nozzle boom sweeping the plot area

had to be introduced. Oscillating nozzles have been used extensively in rainfall

simulation (Bubenzer and Meyer 1965; Floyd 1981; Loch et al. 2001).

Oscillation of nozzles has been carried out primarily by oscillation of a boom driven

by a small motor, with the nozzles being fixed to the boom (Foster et al. 1979; Loch

et al. 2001; Moore et al. 1983). A similar system was used for the rainfall simulator

developed in this research, which is described in the hydraulic and control systems

sections (Sections 3.2.3 and 3.2.4 respectively). Rainfall intensity applied to the

designated plot area was varied by changing the cycle time of the oscillating nozzles

as described by Loch et al. (2001). Overspray troughs and return pans were required

to collect excess nozzle discharge when rainfall was not applied to the plot area, and

to limit the spray on the plot to near vertical, which is described in Section 3.2.2. The

choice of angle of spray was determined by the height of the nozzle boom and the size

of the designated runoff plot as discussed in Sections 3.2.2 and 3.2.5 respectively. The

nozzles sprayed an area larger than the runoff plots to ensure that the raindrop splash

was the same into the runoff plot as it was out of the runoff plot, which is further

discussed in Section 3.2.5.

(B) Chemical profile

In order to re-produce natural rainfall characteristics in the area as closely as possible,

the chemical quality of rainfall in the study region was investigated by collecting

natural rainfall. Rainwater collected in tanks was not considered due to the risk of

unwanted pollutants, particularly if obtained from metal roofs. Consequently, 23

rainwater samples were collected over a period of 30 days and a rainfall quality

47

profile as provided in Table 3.1, was developed based on statistical analysis of the

sample data. Rainwater samples in the region (see Table A8 Appendix A for details)

were tested for pH, EC and DOC using Standard Methods (APHA 1999). These

parameters were chosen due to their ability to alter the physico-chemical

characteristics of pollutants in runoff (Warren et al. 2003). The pH of rainfall

influences the bio-availability of heavy metals (Tai 1991). Similarly, organic carbon

influences the concentration of PAHs present in the dissolved phase (Wang et al.

2001). EC was important due to its ability to enhance the adsorption affinity of solid

particles (Pechacek 1994). The influence of these parameters has been discussed in

Chapter 2.

TABLE 3.1 Rainfall quality profile obtained in Brisbane, Australia

Parameter Mean Standard deviation

pH 6.40 0.52

EC 51.71 44.26

DOC 8.81 4.29

De-ionised water was spiked with sulphuric acid (for pH), common salt (for EC) and

methanol (for DOC) to obtain the required quality profile shown in Table 3.1, and was

used in rainfall simulation at the proposed study region.

3.2.2 Structural design

Due to the portability criteria set out in the design stage of the rainfall simulator, a

lightweight structure was preferable. The structure had to be easily assembled and

disassembled with minimum labour requirements. A structural design based on

rainfall simulators used by Floyd (1981) and Loch et al. (2001) was developed. These

rainfall simulators used an A-frame structure, which was also employed in this

research. The frame for the developed rainfall simulator consisted of four upright legs

and a nozzle boom unit as shown in Figure 3.2 below. The legs (2.3m long) were

constructed from 40mm diameter lightweight aluminium tubing and connected to the

nozzle boom unit with a connecting splice. The bases of the upright legs were

flattened and bent parallel to the ground surface to form feet. Holes drilled in the

flattened section enabled the rainfall simulator to be pegged to the ground, if required.

This provided the option to simulate rainfall on a steep surface if needed.

48

FIGURE 3.2 Sketch of designed rainfall simulator

The whole unit measured 2.6m high with the nozzles at an elevation of 2.4m. For

practical reasons, such as accessibility to the nozzles, and the fact that terminal

velocity of the rain drops is reached at this height when using Veejet 80100 nozzles, it

has been recommended that the elevation of the nozzles above the plot should be

between 2.4m and 3m (Duncan 1972; Loch et al. 2001; Meyer and Harmon 1979;

Meyer and McCune 1958; Moore et al. 1983). Consequently, kinetic energy of natural

rainfall was closely simulated by replicating the terminal velocity of the drops

produced by the nozzle.

The nozzle boom unit consisted of a 2.6m length of stainless steel tubing with a

diameter of 32mm. The frame for the nozzle boom was an A-frame triangular top, of

2.6m length, which connected to the legs of the simulator. The frame was made of

40mm diameter lightweight aluminium tubing. The boom itself rotated in 2 plastic

49

bushes, which also prevented lateral movement of the boom. The three Veejet 80100

nozzles were spaced 1m apart on the nozzle boom. A sketch of the nozzle boom is

shown in Figure 3.3 below.

FIGURE 3.3 Cross section of the nozzle boom unit

The water supply inlet was via a 38mm flexible hose attached to the nozzle boom

opposite to the row of nozzles. A pressure gauge (0-100 kPa) was fitted to the water

supply inlet so that the operating pressure could be monitored at nozzle height. A

water pressure of 41 kPa was always present in the nozzle boom during simulation

and the nozzles were producing continuous water flow throughout operation. Flow not

required as rainfall on the test surface was recycled via catch trays made of stainless

steel sheets running on either side of the 2.6m long nozzle boom as shown in Figure

3.4. During operation, the nozzles oscillated through approximately 95o. Of this

travel, the middle 55o applied raindrops onto the runoff plot, while the remaining 40o

was used at either end of the travel for overlap into the catch trays as shown in Figure

3.5. Adjusting the frequency and speed of nozzle oscillation altered the rainfall

intensity of the simulated rainfall. Calibration and performance details of the rainfall

simulator are provided in Section 3.3. The catch trays were connected to the return

50

water system which is described in further detail in the Hydraulic System Section

(Section 3.2.3) of this chapter.

FIGURE 3.4 Catch trays running along the nozzle boom

FIGURE 3.5 Oscillation cycle of the nozzle boom

The nozzle boom oscillated in sequences with the use of a small automotive

windscreen wiper motor. The windscreen wiper motor was permanently mounted on

the nozzle boom unit and provided the oscillating action via a simple arm and lever

system as shown in Figure 3.6.

51

FIGURE 3.6 Arm and lever system oscillating the nozzle boom

Similar small motor systems have been used successfully in oscillating nozzle booms

on previous rainfall simulators (Floyd 1981, Loch and Foley 1992). Loch et al. (2001)

used a stepper motor for the oscillation of the nozzles. However, the advantages of a

stepper motor compared to a windscreen wiper motor were minimal. The only

advantage was the ability to drive at the same speed in both directions. This would

have minimal impact on simulating natural rainfall characteristics however, since

rainfall is intermittent by nature. Consequently, the oscillation of the nozzle boom

provided by a small windscreen wiper motor was adequate. A control box, which is

further explained in the Oscillation Control System Section (Section 3.2.4), was used

to control the speed and delay of the oscillation.

3.2.3 Hydraulic System

Water was supplied to the rainfall simulator by a general transfer pump (Grundfos

CH2-30) connected to a Honda 12 Ampere petrol generator. The pump transferred

water from a 200L reservoir to the nozzle boom. The pump and reservoir were

connected with a 38mm diameter flexible hose and two 40mm cam-locks at either

end. A mesh filter was fitted between the reservoir and pump which, prevented

clogging in the pump system. The pump-reservoir unit was permanently mounted on a

conventional trailer (1.8x1.2m), which is described in Section 3.2.6. When needed,

the reservoir was topped up from a 1000L reservoir carried to the field.

52

A 40mm cam-lock connected the pump to the water delivery line, which consisted of

a 4m section of 38mm diameter flexible hose. Another 40mm cam-lock connected the

water delivery line to the nozzle boom. The discharge rate was controlled by a gate

valve at the outlet of the pump and measured by the water pressure entering the nozzle

boom, as indicated by the pressure gauge installed at nozzle boom level. Catch trays

on either side of the nozzle boom were used to collect water from the nozzles not

spraying on the runoff plot as shown in Figure 3.4. Water collected in the catch trays

during rainfall simulation was gravity fed via a 50mm flexible hose back to the 200L

reservoir for reuse.

3.2.4 Oscillation Control System

The oscillation control system consisted of a controller box with circuits controlling

the speed and delay of the automotive windscreen wiper motor, which in turn

controlled the oscillation cycle of the nozzle boom. The controller box could be

switched on and off by a toggle switch on the side. By changing the speed and delay

of the nozzles sweeping over the plot, rainfall intensity could be controlled. The speed

was controlled by a variable speed control switch with six speed settings, which

increased and decreased the speed of the windscreen wiper operating the oscillation of

the nozzle boom. Similarly, the delay was controlled by a variable delay switch

increasing and decreasing the time (0-12s) between oscillation cycles. The controller

box was powered by a rechargeable 12V battery. During field operation of the rainfall

simulator, the battery was recharged using the Honda generator providing power to

the general transfer pump. The control system was calibrated as described in Section

3.3, so that specific settings on the speed and delay switches represented specific

rainfall intensities.

3.2.5 Runoff plot and collection

The runoff plot dimensions (2x1.5m) were chosen after the calibration of the

discharge patterns of the nozzles mounted on the nozzle boom as discussed in Section

3.3. The plot was enclosed by a frame sealed to the ground by rubber flaps, tape and

silicone as discussed in Section 4.4. A specially designed collection trough was

attached to the frame at the bottom of the plot, as can be seen in Figure 3.2 and further

evaluated in Section 4.4. The runoff water collected in the trough was vacuumed into

25L drums by a commercially available vacuum cleaner system. The runoff collection

53

system and its calibration is described in Section 4.4 and will not be discussed further

here.

3.2.6 Storage and Transport

A 1.8x1.2m box trailer was used for storing and transporting the rainfall simulator

from site to site. The trailer held a Honda 12 Ampere petrol generator with

rechargeable 12V battery as well as a 200L water reservoir. The reservoir was

connected to the permanently mounted Grundfos general transfer pump on the bed of

the trailer. The legs and the nozzle boom unit of the rainfall simulator were mounted

on trailer racks and secured with elastic straps to the trailer when stored or

transported. The trailer with the rainfall simulator is shown in Figure 3.7 below. An

additional 1000L water reservoir was carried to the field by an additional vehicle,

where it was used to top up the 200L reservoir when needed.

FIGURE 3.7 The rainfall simulator fully mounted on the box trailer

3.3 Performance calibration of the rainfall simulator

The rainfall simulator was carefully designed to reproduce natural rainfall

characteristics as closely as possible. However, the design was based purely on

theory. Hence, it was unknown how well the rainfall simulator would simulate design

rainfall characteristics in the field. Furthermore, a number of control box settings

needed to be calibrated to represent design rainfall intensities in the area prior to the

rainfall simulator being used in field work. Therefore, parameters such as intensity,

54

uniformity of rainfall and drop size distribution needed to be calibrated in order to

accurately replicate the design rainfall characteristics.

3.3.1 Nozzle discharge and pattern

Initially, twelve nozzles of the Veejet 80100 series were obtained for testing for their

suitability for rainfall simulation. However, only three nozzles were to be mounted on

the nozzle boom. The nozzles were therefore chosen based on their performance in

terms of discharge and spray pattern created by an individual nozzle under 41kPa

water pressure. The nozzles with the most similar spray patterns were then chosen.

The discharge rate was measured using a container held under the nozzle for a period

of time, ranging from 10 to 40 seconds. The average discharge in litres per minute

(L/min) was calculated by weighing the collected water. The temperature of the water

used was measured between each run to accurately determine the density to be used in

each discharge rate calculation. The average discharge rates for each nozzle are shown

in Figure 3.8. As can be seen, the nozzles investigated had an average discharge rate

varying between 13.88L/min and 15.11L/min. The mean discharge rate based on all

twelve nozzles was 14.37±0.32L/min. Nozzles 2, 8 and 11 were determined to be

unsuitable to use in rainfall simulation due to their large deviation from the mean

discharge rate.

FIGURE 3.8 Average discharge rates for the twelve Veejet 80100 nozzles (41kPa

pressure)

13.2 13.4 13.6 13.8

14 14.2 14.4 14.6 14.8

15 15.2

Dis

char

ge ra

te [L

/min

]

1 2 3 4 5 6 7 8 9 10 11 12

Nozzle no

Discharge rate for Veejet 80100 nozzles

Mean discharge

55

The spray pattern of the remaining nine nozzles was investigated in order to determine

which nozzles could be incorporated into the rainfall simulator. Thirty two (32)

containers with an opening of 85mm diameter spaced 300mm apart, as illustrated in

Figure 3.9, were placed underneath a nozzle, which was mounted on the nozzle boom

2.4m high. The containers were exposed to nozzle spray for a time period of five

minutes and the collected water was weighed and converted into a volume

measurement.

FIGURE 3.9 Calibration of spray pattern of the nozzles

The contour of each nozzle was then plotted using the spacing of the containers and

the percentage of total water volume collected in each container. Based on the

findings from the spray pattern investigations, nozzles 3, 4 and 12 were chosen to be

used in rainfall simulation. The spray contours of the three chosen nozzles are shown

in Figure 3.10. A higher value indicates a higher percentage of the total rainfall

recorded over the plot.

56

FIGURE 3.10 Nozzle spray pattern contours for the chosen nozzles (3, 4 and 12

respectively)

3.3.2 Rainfall intensities and uniformity of rainfall

Intensity measurements were carried out by using a row of closely placed containers

and measuring the volume accumulated during a known time interval as described by

Lascelles et al. (2000) and Loch et al. (2001). The volume of water collected in the

containers was converted to rainfall depth per hour (mm/hr). In this instance a row of

seven containers was placed underneath the three nozzles, as shown in Figure 3.11,

and was exposed to simulated rainfall for five minutes.

FIGURE 3.11 Intensity measurements using seven containers

57

The oscillation (speed and delay) settings of the control box were varied for each run,

which gave a reasonable replication of the range of rainfall intensities that could be

simulated. Initial intensity measurements served as platforms in choosing a number of

speed and delay settings suitable for rainfall simulation. Three different speed settings

and eight different delay settings were initially investigated as shown in Table 3.2

below.

TABLE 3.2 Calculated average rainfall intensities using seven containers for

different speed and delay settings of the control box

Speed setting number Delay [s] Average rainfall intensity [mm/hr]

4 1 184

4 2 149

4 5 82

4 6 65

4 8 57

4 10 45

3 1 148

3 2 108

3 3 79

3 5 59

3 6 52

3 8 41

3 10 36

3 12 32

2 1 159

2 2 107

2 3 75

2 5 52

2 6 47

2 8 41

2 10 35

2 12 28

58

The calculated rainfall intensities for the different settings were in the range of 28-

184mm/hr. Due to a number of control box settings not being investigated, the

complete range of rainfall intensities produced by the rainfall simulator is unknown.

However it is postulated, from calibration results, that rainfall intensities as low as 10-

15mm/hr and as high as 220mm/hr can be simulated using the rainfall simulator.

Nevertheless, the rainfall intensities obtained from the initial calibration using seven

containers is highly overestimated for the plot area to be investigated due to end

effects. In fact, a much larger area of containers had to be investigated to be able to

find average intensities for each setting. Therefore, six settings were chosen (intensity

range 47-148mm/hr) for further investigation. The settings investigated were:

• Speed setting 2, delay 2s (Initial intensity 107mm/hr);




• Speed setting 3, delay 1.5s (No initial intensity measured); and

• Speed setting 3, delay 5s (Initial intensity 59mm/hr).

The average intensity of the settings was measured by placing 77 containers in a grid

pattern (7x11) under the nozzles, as shown in Figure 3.12. The spacing between each

container was 230mm in both directions across the plot, making up a grid of

containers 1465mm wide and 2385mm long.

59

FIGURE 3.12 Container grid pattern used for calculating average rainfall

intensity produced by the rainfall simulator

The containers were exposed to five minutes of simulated rainfall for each of the six

settings. The volume of water in each container was then recorded (data provided in

Table A1-A6 Appendix A) and average rainfall intensity over the plot was calculated.

It was found that the initial calculation of rainfall intensities for the different settings

was overestimated due to rainfall depth at the ends of the plot being lower compared

to the rainfall depth at the middle of the plot. Hence, a new set of intensities were

calculated for the different settings. These were:

• Speed setting 2, delay 2s (Average intensity 86mm/hr);




• Speed setting 3, delay 1.5s (Average intensity 115mm/hr); and


60

As observed, the rainfall intensities calculated using 77 containers were up to 20%

lower than the average rainfall intensities obtained using only seven containers.

The major component of spatial variability was found to be along the length of the

grid pattern, with peaks under the nozzles. This was minimised by the overlapping of

nozzles. However, there was still consistent spatial variation, which made the initial

rainfall intensity estimate too high. Generally there was less spatial variability across

the plot, with intensity reaching a peak in the centre of the grid pattern, and falling

towards the edges due to the oscillation angle. The other main points of intensity

variation were at the upper and lower ends of the grid, due to loss of overlapping

effects. The spatial variation of the measured rainfall intensity (speed setting 2 with

delay 2s) over the grid is illustrated in Figure 3.13. This observation was consistent

with the different intensities used.

Intensity distribution

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 500 1000 1500 2000 2500Distance along the plot [mm]

Inte

nsity

[mm

/hr]

WidthwiseLengthwise

FIGURE 3.13 Spatial variation of the rainfall intensity using speed 2, delay 2s

To determine a suitable plot area for the rainfall simulator, uniformity of rainfall was

expressed by calculating the Christiansens uniformity coefficient. Christiansens

uniformity coefficient is expressed as a percentage (a higher percentage indicates a

more uniform rainfall) and is presented as follows:

61

)1(100nmx

Cu ×−×= ∑ (Christiansen 1942)

Where: Cu = uniformity coefficient

m = mean value

n = number of observations

x = deviation of individual observation from mean

A uniformity coefficient of above 80% is considered to be sufficient in successful

rainfall simulation (Loch et al. 2001). However, uniformity coefficients up to 95%

have been reported by researchers when end rows of the runoff plot are excluded

(Lascelles et al. 2000).

Uniformity coefficients (Cu) were calculated for the entire grid pattern used,

(1465x2385mm) as well as when the end longitudinal rows were excluded, which

made the dimensions of the plot 1465x1925mm, in order to determine the best

possible plot area. The uniformity coefficients calculated for the different intensities

are shown in Table 3.3.

TABLE 3.3 Uniformity Coefficients (Cu) for the intensities investigated

Cu [%]

Intensity [mm/hr]

Total grid

(1465x2385mm)

End longitudinal rows

excluded (1465x1925mm)

43 82.6 86.3

54 82.6 86.8

65 82.3 86.6

86 83.1 89.1

115 83.0 87.0

133 83.4 87.5

As expected, the uniformity coefficients increased when the end longitudinal rows

were excluded from the calculation due to the decreased rainfall depth recorded in the

62

end rows. Hence, the plot area chosen was 1500x2000mm due to the improved

rainfall uniformity. No significant pattern in Cu could be observed when the intensity

was changed, which suggests that changing the speed and delay settings of the rainfall

simulator had limited effect on the uniformity of rainfall.

The final intensities obtained, as shown in Table 3.3, were matched with a number of

design rainfall (ARI) events to be simulated in the field. Twelve events based on four

different intensities were selected from design rainfall for Brisbane using the

methodology given in Australian Rainfall and Runoff (Institution of Engineers 2001).

Hence, four of the six intensities calibrated were adequate to satisfy the number of

rainfall intensities to be used in the research project. The design rainfall events chosen

are shown in Table 3.4.

TABLE 3.4 Design rainfall events selected

Event Intensity [mm/hr] Duration [min]

1 year ARI 65 20

1 year ARI 86 10

1 year ARI 115 5

2 year ARI 65 35

2 year ARI 86 20

2 year ARI 115 10

2 year ARI 133 7

5 year ARI 133 13

10 year ARI 65 65

10 year ARI 86 40

10 year ARI 115 25

10 year ARI 133 17

3.3.3 Calibration of drop size and kinetic energy

The final calibration step used to determine the characteristics of simulated rainfall

and compare it to natural rainfall characteristics in the area was the measurement of

the median drop size and the approximate kinetic energy of the simulated rainfall.

Due to the water pressure reaching the nozzles being constant, the ARI events listed in

63

Table 3.4 had the same drop size distribution over the plot. Similarly, due to drop size

distribution being constant, the kinetic energy of the twelve ARI events was identical.

The two most widely used methods for the direct measurement of the diameter of

water drops are to cause the individual drops to form stains on an absorbent material

such as blotting paper, or to form pellets when collected in a powder material such as

flour or cement (Hudson 1963; Assouline et al. 1997). The method chosen to

determine the median drop size of the rainfall simulator at a water pressure of 41kPa

was the flour pellet method developed by Hudson (1963). This was primarily due to

technical difficulties of the stain method, such as establishing the relationship between

the size of the drop and size of stain and the variations in this relation due to different

degrees of splashing by large and small drops and variations in absorbency of the

sampling medium. However, it is important to note that the flour pellet method was

influenced by several factors, including the degree of compaction of the flour and the

height of fall. The method developed by Hudson (1963), and later used by Lascelles

(2000), was followed as closely as possible in order to accurately determine the

median drop size of the rainfall simulator.

A tray (diameter 240mm) of uncompacted flour was exposed to simulated rainfall for

a period of two seconds. Following exposure, the flour was dried for 12 hours at 105o

C and the pellets formed were passed through a nest of sieves as shown in Figure

3.14. The sizes of the sieves were:

• 4.75mm;

• 3.35mm;

• 2.36mm;

• 1.18mm;

• 0.6mm; and

• 0.5mm.

The mass of each pellet represented a mass ratio (drop mass/pellet mass) such that

every pellet could be converted to a corresponding rain drop according to the data

provided by Hudson (1963). The calculated median raindrop size (d50) was 2.1mm,

which was similar to values obtained by several previous researchers using 41kPa

64

water pressure in Veejet 80100 nozzles (Bubenzer 1979). Natural rainfall,

independent of the rainfall intensity, generally has a median drop size of 2.0-2.5mm

as shown by Figure 2.3 in Section 2.3.2 of this thesis. The calculated drop size of the

rainfall simulator was therefore considered to be adequate for the purpose of the

research. Complete data on the drop size calculation can be found in Table A7

Appendix A.

FIGURE 3.14 Pellets obtained by flour pellet method

The drop velocity of the median drop size was estimated to be 6m/s, using the data on

terminal drop velocities developed by Laws (1941). From the drop size diameter and

terminal velocity of the 2.1mm median drop, the kinetic energy was calculated to be

25.44J/m2mm. This was similar to the kinetic energy reported for natural rain at

intensities above 40mm/hr in South-East Queensland (Rosewell 1986; Loch et al.

2001). The values thus obtained were a satisfactory replication of natural rainfall

characteristics for the rainfall intensities used in rainfall simulation.

3.4 Summary

Although rainfall simulation has primarily been used in agricultural research, these

techniques can also be applied to urban water quality research. This chapter discussed

the design criteria for a rainfall simulator and described the application of these

criteria in the fabrication of a rainfall simulator appropriate for the envisaged research.

It is important to understand that whilst natural rainfall characteristics were difficult to

replicate completely, the appropriate design of a rainfall simulator provided a very

close replication of natural rainfall. As the chemical characteristics of rainfall can

influence the characteristics and availability of various pollutants, attention was given

65

to reproduce both the physical and chemical quality of rainfall in the study region as

closely as possible.

66

Chapter 4 Study Areas and Sampling Procedure

4.1 Introduction

The build-up and wash-off process kinetics of PAHs and heavy metals were

investigated for three different land uses in the Gold Coast region in Queensland,

Australia. The influence of land use characteristics and the related anthropogenic

activities on pollutant loadings and concentrations have been investigated by

numerous researchers (for example Sartor and Boyd 1972; Liebens 2001; Hoffman et

al. 1984). However, the outcomes reported are sometimes conflicting and have

limited reliability. As Brezonik and Stadelmann (2002) found when developing

multiple linear regression models on stormwater data from 15 different studies

encompassing 68 different catchments, the most accurate predictions were found to be

where sites were grouped according to land use. On the other hand, Lopes et al.

(1995) observed no statistically significant relationship between land use and

pollutant concentrations for a study in Arizona, USA. Hence, the relationships

developed have been site specific due to the heterogeneity of large catchments and the

dependency on numerous physical factors.

The rainfall simulator described in Chapter 3 used a homogenous area in order to

decrease the number of variables that could influence pollutant concentrations. Thus,

more reliable relationships could be created based on chemical processes, instead of

being influenced by physical factors. Furthermore, chemical processes influencing

pollutant build-up and wash-off were considered to be similar at different land uses,

which would increase the transferability of quantitative relationships developed

through this research. Three different land uses, namely residential, industrial and

commercial were chosen as study sites in order to confirm this hypothesis. The study

involved collecting build-up and wash-off samples in the field using the rainfall

simulator and the analysis of the samples collected. This chapter describes the study

sites chosen and the key attributes for choosing these sites. The sampling techniques

developed for dry and wet sampling in the field and the transport and treatment of the

samples collected from the study areas are also discussed.

67

4.2 Study site selection

Pollutant concentrations have been found to vary significantly between commercial,

industrial and residential areas (Sartor and Boyd 1972; Droppo et al. 1998). However,

it is hypothesised that build-up and wash-off process kinetics are relatively unaffected

by the pollutant concentration available. Consequently, the land use characteristics of

each site was not considered a governing factor in the build-up and wash-off process

kinetics of PAHs and heavy metals, but rather the different land uses would determine

the types of pollutants present of paved surfaces. Hence, sites incorporating typical

characteristics for these land uses were chosen to investigate the build-up and wash-

off process kinetics of PAHs and heavy metals in order to develop reliable and

predictive relationships.

Prior to selecting the study sites, criteria were developed in order to identify the most

appropriate sites for investigation. These were:

• Fair to good street surface condition;

• Good accessibility for the use of rainfall simulation;

• Minimal disturbance to local traffic; and

• Sufficient slope for gravity flow of runoff generated from simulated rainfall.

Furthermore, for the purpose of this research, the residential site had to be in a typical

suburban area incorporating detached family houses with small gardens. The

industrial site had to have a wide range of industries and the commercial site, a

reasonably high commercial activity and vehicle movements.

4.3 Project area

The Gold Coast region is located just south of Brisbane, which is the capital city of

Queensland, Australia. The region is a popular tourist destination, with an estimated

12% of the population being visitors. The beaches, in particular, are a major tourist

attraction. Gold Coast is Australia’s sixth largest City Council with a population of

approximately 455 000 people. The Gold Coast region has a sub-tropical climate with

the majority of its precipitation occurring during the summer months December-

February. The region is 5490 km2 in extent and encompasses three major rivers, the

Pimpama, Coomera and Nerang Rivers, all flowing into the Gold Coast Broadwater as

68

shown in Figure 4.1. There are also numerous other waterways in the catchment that

together cover 7% of the total land area. Natural vegetation occupies a large

percentage of the region. However, high density urban areas are located on the east

coast of the region offering water-side living for residents. This intensive urban

development on waterways provides a direct route for urban runoff that can include a

variety of pollutants. The popularity of the area is reflected in its rapid population

growth, which is one of the highest in the state of Queensland.

69

FIGURE 4.1 Map of Gold Coast region (Not to scale)

70

4.3.1 Residential site

A number of possible research sites with residential characteristics were investigated

prior to the selection of a specific site. All of the investigated residential sites were in

the Nerang area (see Figure 4.1). Descriptions of the different sites are listed in Table

4.1 below.

TABLE 4.1 Description of possible residential research sites

Name of site Characteristics

Travis Lane Steep slope, surface in good condition, access road, no

vegetation, small width of road, medium block size

Millswyn Crescent Steep to mild slope, surface in good condition, fair

vegetation, well established area, medium block size

Woolmere Street Fair slope and surface, close to highway, frequent traffic, not

much vegetation, small width of road, large blocks

Piccadilly Lane Steep slope, fair surface, no vegetation, large blocks, high

traffic flows, access poor, space for simulation poor

Lancaster Court Mild slope, surface fair to rough, small blocks, far from

highway, poor access for simulation, noise concerns

The residential site chosen was based on the criteria listed in Section 4.2. The site was

on an access road (Millswyn Crescent shown in Figure 4.2 below) located in a typical

suburban residential area (Residential A) with detached family houses with an

approximate block size of 700 m2.

FIGURE 4.2 Residential research site (Millswyn Crescent)

71

Millswyn Crescent was chosen due to its typical suburban characteristics and the fact

that rainfall simulation could be successfully carried out without disturbing the traffic

operating at the site. The slope of the road surface (2.5% or 25m/km) was sufficient

for gravity flow of the runoff created by the rainfall simulator. Additionally, the width

of the road (approximately 7m) was sufficient to close off one lane while field studies

were undertaken. Hence, the residents of the site were minimally disturbed by the

field work. The road system is primarily used by the residents for access, which was

reflected in the relatively satisfactory condition of the street surface.

The texture depth of the street surface was measured to be 0.27mm (fine texture),

using the sand patch test described by Waters (1993) where a quantity of glass beads

was pre-weighed in the laboratory before application to the road surface. The road

surface was cleaned with a firm bristled brush in order to remove all loose material

before application of the glass beads. The beads were spread using a ruler in a radial

fashion to form a circle. The diameter of the circle was then measured and related

back to the texture depth of the surface. The diameter was measured at four places to

produce a mean diameter. The texture depth of the paved surface was used only as a

classification and identified the potential for the scouring of particles accumulated on

the surface.

An early investigation of the households at Millswyn Crescent suggested that various

chemicals were used as fertilizers or for other uses and therefore could be

incorporated into the wash-off from the area. It was also found that street sweepers

operate in the area every six weeks, which may influence the availability of pollutants

on the road surface at certain times.

4.3.2 Industrial site

Similar to the residential site, a number of possible sites were investigated prior to the

selection of the selected site (Stevens Street). The sites and their characteristics are

listed in Table 4.2.

72

TABLE 4.2 Description of possible industrial research sites

Name of site Characteristics

O’Shea Drive Timber and sheet metal industries, light slope, medium to rough

surface, good access, close to highway, dust from timber

industry high

Lawson Road Not many industries, steep slope, fair surface, bushland close,

highway close, disturbance to traffic with simulation

Palings Court Medium slope, fencing, furnishing, welding, coating, rough

surface, close to highway, traffic problem, vegetation

Stevens Street Painter, furniture, sheet metal, welding, boat builder, steep slope,

rough surface, good access, disturbance minimal

As outlined in Section 4.2, there was a need for a wide range of industries at the site,

which narrowed the search down to two possible sites (Stevens Street and Palings

Court). Stevens Street was chosen due to the minimal disturbance and better

accessibility when compared to the Palings Court site. Additionally, Stevens Street

had a sufficient slope for gravity flow of wash-off from rainfall simulation and a road

width of approximately 10m, which meant that only one lane of the road needed to be

closed during rainfall simulation. Consequently, the disturbance by the presence of the

field equipment was minimal in the area. The Stevens Street site, shown in Figure 4.3,

was on an access road to a light industrial area. A wide range of industries are located

along the road, which had an average slope of 3% (30m/km).

FIGURE 4.3 Industrial research site (Stevens Street)

The street surface condition compared to the residential site was significantly

degraded, which indicated that heavy traffic regularly operate at the site. The road

73

surface had a texture depth of 0.53mm (medium texture). It is postulated that a higher

amount of pollutants could be embedded in the surface voids at the industrial site

compared to the residential site due to the coarse texture. Hence, the rainfall intensity

could be important in loosening particles from the surface. Similar to the residential

site, street sweepers operate in the area every six weeks, which would release particles

embedded in the voids, making them available for wash-off at the next rainfall event.

4.3.3 Commercial site

A shopping centre in the region was selected as a suitable site representing a

commercial land use. This was primarily attributed to the frequent vehicle movement.

Early investigations showed that field work on access roads to shopping centres

would significantly disturb the traffic. Therefore, it was decided that rainfall

simulation would be performed in the parking lot of the shopping centre. The criteria

set out in Section 4.2 still applied to the selection of an appropriate research site. The

commercial sites considered for the study are listed in Table 4.3.

TABLE 4.3 Description of possible commercial research sites

Name of shopping

centre Characteristics

Robina Town Centre Large car park area, hard to get close to shops without

disturbing traffic, mild slope, even distribution of pollutants

questionable, very good access

Centro Nerang Fair slope, rough surface, car park, close to highway, good

access, busy parking lot

Highland Park

Centre

Mild slope, light commercial, fair surface, light traffic, poor

access

Helensvale Shopping

Centre

Poor access, parking lot, light commercial, fair traffic, medium

rough surface, medium to mild slope

The commercial research site, selected on the basis of the criteria outlined in Section

4.2 was the Centro Nerang (shown in Figure 4.4), located in very close proximity to a

major motorway, suggesting that vehicular pollutants from the traffic on the highway

could contribute to the pollutant concentrations at the site. It was noted that Centro

Nerang is located in close proximity to the Nerang River, which could potentially

74

influence the pH and EC of the soil surrounding the site due to the saline nature of the

river. Hence, particles eroded from local soils at the site could have an increased or

decreased adsorption affinity for hydrophobic pollutants. As noted by Tai (1991) a

change in the pH of stormwater could significantly influence the amount of heavy

metals in the dissolved phase.

FIGURE 4.4 Commercial research site (Centro Nerang)

Centro Nerang had 570 parking spaces and is considered to be one of the busiest

shopping centres in the region, with 45 specialty retailers in the complex. The parking

lot spaces were approximately 3x3m and were on a slope to the shopping centre itself.

Average slope of the study area was 4% (40m/km), which was sufficient for runoff

simulation. It was unknown how frequently the parking lot was cleaned using street

sweepers or similar equipment. The condition of the parking lot surface was found to

be good, but with a coarse texture, having a texture depth of 0.92mm. The coarse

texture suggested that large numbers of particles would be embedded within the voids.

Hence, the efficiency of street cleaning efforts could be significantly degraded

compared to the industrial and residential sites.

4.4 Vacuum collection system

In water quality research projects, collection of street deposit material is commonly

carried out using one of two different methods or a combination of the techniques.

These techniques are:

• Brushing/Sweeping; and

• Vacuuming

(Robertson et al. 2003; Bris et al. 1999).

75

Brushing or carefully sweeping the road surface for street deposit material has

generally been carried out when fine particulates are less important for the study.

Robertson et al. (2003) noted that sweeping or brushing the road surface is preferable

when coarser particulate sizes are favoured. Similarly, the technique used by Mahler

et al. (2004), where street deposit material was collected by scraping the surface with

a metal paint scraper, would bias coarser particle collection. As Bris et al. (1999)

noted, due to the uneven distribution between fine and coarse particulate collection

using sweeping and brushing techniques, sampling using a vacuum cleaner is

preferable. This is particularly important in urban stormwater studies since the

majority of micro-pollutants such as PAH and heavy metals are distributed in the fine

fraction of the particulates (Andral et al. 1999; Sartor and Boyd 1972). Consequently,

a vacuum system was preferable for the research undertaken and was used in the

collection of build-up samples at the study sites.

4.4.1 Selection of Vacuum Cleaner

Both industrial and domestic type vacuum cleaners have been used successfully in

previous research. Tai (1991) suggested that domestic vacuum cleaners were more

efficient in capturing fine particulates due to a finer exhaust filter when compared to a

heavy duty industrial type vacuum cleaner. Tai (1991) found that the domestic

vacuum cleaner he used recovered 96.4% of the street dirt smaller than 74μm. On the

other hand, Shaheen (1975) used an industrial type vacuum cleaner for collecting

street dust and dirt and the recovery rate was 95.3% for particles less than 45μm.

These contradictory findings suggest that the type of vacuum cleaner might be less

important than the model of the vacuum cleaner. Hence, the selection of vacuum

cleaner for this research project was based on particle collection and retention

efficiency as well as portability and power requirements of the model.

The vacuum cleaner selected was a Delonghi Aqualand model (for specifications see

Figure A1 Appendix A), which incorporated a water filtration technique as well as a

High Efficiency Particulate Air (HEPA) filter to ensure minimal escape of fine

particles through the exhaust system. The vacuum cleaner could also be powered in

the field by a generator, as described in Chapter 3. In addition, the vacuum cleaner

could be easily stored during transport to and from the study sites, which made it

76

highly portable. The water filter technique used by the vacuum cleaner is illustrated in

Figure 4.5 below.

FIGURE 4.5 Delonghi Aqualand water filter system

The water filter captured the dust, thus preventing it from being released back into the

air. As no dust-bag was used, the appliance always functioned to its maximum suction

power (1500W) which made it reliable in the field. Due to the particles retained being

suspended once vacuumed, the sample had to be separated prior to further analysis,

which is described further in Chapter 5. However, the performance of a water filter

system was deemed superior, compared to a conventional exhaust filter system, due to

its excellent retention efficiency of sub-micron particles (99.9% of particles less than

0.3μm) as per the manufacturer’s technical specifications (refer to Figure A1

Appendix A).

To further enhance particle collection, a brush-head was fitted at the end of the hose

of the vacuum cleaner. This enabled the finer fraction of street dust and dirt to be

removed from the asphalt surface during the vacuum sweeps, which enhanced the

collection of finer particles. Similar techniques have been used successfully in

previous research by Butler et al. (1992) and Bris et al. (1999). As noted by Bris et al.

(1999), the finer fraction of street dust and dirt can be more strongly bound to the

asphalt surface than the coarser fraction. Therefore, a combination of brushing and

vacuuming would introduce less bias in sampling and collect a representative urban

street dust sample.

77

4.4.2 Dry collection efficiency

An efficiency test was performed prior to collecting the samples in the field to

investigate the particle recovery rate of the vacuum cleaner. The recovery test

simulated field conditions and, the efficiency of the complete collection technique

was investigated. Investigating only the recovery by the vacuum cleaner would have

been inadequate in determining how representative the collected sample was

compared to conditions in the field. This is due to possible particle losses in the

vacuum hose and during transfer of collected samples into sample bottles.

A pre-cast bitumen slab (300x150mm, texture depth of 500µm) was used for the

collection recovery test. It was decided that a medium texture bitumen slab of 400-

600µm texture depth (Waters 1993) would provide a more factual indication of the

collection efficiency in the field rather than a concrete slab as used in the study by Tai

(1991) due to the street surface at the three study areas being of different texture

depth. The bitumen slab was cleaned by repeated vacuuming, flushed with water and

then allowed to dry. Street dirt fractions of known particle size and weight were then

scattered on the slab and total sample collection efficiency was estimated by

vacuuming the surface several times. The particle sizes and their respective collection

efficiency are shown in Table 4.4 below. As can be seen, recoveries for larger

particles were slightly above the input values. Similar results were found by Shaheen

(1975), which is postulated to be due to asphalt particles being released from the

bitumen slab. The loss of particles in the 151-600μm range is most likely due to the

size reduction of particles caused by abrasion during vacuuming.

TABLE 4.4 Sampling recovery efficiencies

Particle size Input [g] Retained [g] Efficiency [%]

0.45-75µm 1.0032 0.9518 94.9

76-150µm 1.0032 0.8888 88.6

151-300µm 1.0565 0.8341 78.9

301-600µm 1.1348 0.8817 77.7

601-1180µm 1.0437 0.9209 88.2

1181-2360µm 1.1714 1.1982 102.3

2361-3350µm 1.0700 1.1339 106.0

78

The total sample recovery from the water filter was 91%, which was considered to be

adequate for the research study. However, during the sampling recovery test, it

became clear that a major loss of particles might occur in the vacuum hose. Therefore,

the inside of the vacuum hose was rinsed four times with water to determine the

percentage loss of particles. Around 2% of the input particles was found in the

vacuum hose, which increased the sample collection recovery to 93%. To increase the

sample collection efficiency of dry samples, the vacuum hose had to be rinsed

accordingly.

4.4.3 Wet sample collection system

Compared to discussion of methodology that can be adopted for the collection of

accumulated street deposit material, documentation of techniques used for wet sample

collection on paved surfaces is scarce. Generally, when relatively large heterogenous

areas are investigated, urban stormwater samples are collected using automatic

samplers, or manually from the outfall using containers when the stormwater enters

drainage channels (Smith et al. 2001; Hoffmann et al. 1984). Due to the nature of this

research, urban stormwater samples had to be collected from a small homogeneous

area to reduce the number of physical variables involved. Additionally, runoff from

parts other than the specific runoff plot area had to be excluded from the sampling.

Hence, a frame as described below was attached securely to the paved surface in order

to prevent any runoff from outside the plot area mixing with the runoff generated

inside the plot area.

Very limited studies have been carried out using stormwater runoff collected from

paved areas due to the difficulty in retrieving representative samples. This has been

overcome in agricultural research by excavating trenches into the soil, isolating the

plot area from adjacent soil surfaces in order to prevent leakage of runoff (Grierson

and Oades 1977; Arnaez et al. 2004). Vaze and Chiew (1997) used a similar

technique when collecting runoff from a paved surface. They used a plywood frame to

isolate the study plot with a hole dug at the outlet to place sample bottles for

collecting runoff. However, this is not practical for collecting runoff samples from a

public roadway. Hence, this gave rise to a number of issues as it was not appropriate

to excavate trenches or borders on the paved surface.

79

The rainfall simulator applied artificial rainfall to an approximate area of 4x3m. In

spite of this, the highest uniformity of distribution was within an approximate area of

2x1.5m directly underneath the nozzles as shown in Chapter 3. Hence, the runoff plot

was defined accordingly. Furthermore, it was assumed that any escape of water due to

splash from rain drops hitting the paved surface inside a 2x1.5m runoff plot was equal

to any water entering the runoff plot due to splash outside the designated plot. A PVC

frame of 2x1.5m and 50mm high was chosen as a plot border. Rubber flaps were fixed

to the bottom of the frame to prevent water from entering or escaping the runoff plot.

The runoff plot frame with rubber flaps is shown in Figure 4.6 below.

FIGURE 4.6 Runoff plot frame used when collecting stormwater samples

The frame was attached to a specially designed collection trough from where the

runoff water was sampled. The collection trough was made out of 1mm thick

galvanised sheet metal and was placed at the outlet of the runoff plot. The collection

trough had an opening 1.5m wide so that it fitted perfectly to the width of the plot

frame. The collection trough could hold 30L of runoff water at any given time. Figure

4.6 shows the frame connected to the runoff trough. The top of the collection trough

was equipped with a handle that opened the half of the collection trough enabling the

user to collect runoff as shown in Figure 4.7 below. The runoff entering the collection

trough was collected using the same vacuum cleaner described above.

Rubber flaps

80

FIGURE 4.7 Collection trough with handle to open top for easy sampling using

the vacuum cleaner

However, some modifications had to be made to the wet vacuum system due to the

limited amount of runoff water that could be collected in the vacuum cleaner tank. A

25L high-density container was mounted between the vacuum cleaner and the suction

head. Hence, the vacuum cleaner provided the required suction, whilst the runoff was

collected separately. Thus, the runoff was collected continuously throughout an event.

Additional containers were used for large volume events. The sampling system

fabricated for wash-off is shown in Figure 4.8 below.

FIGURE 4.8 Wet sample collection system showing the connection between the

25L container and the vacuum cleaner

Prior to the collection of wash-off samples at the three sites, the wet collection system

efficiency had to be determined. An initial investigation was undertaken using only

81

the rubber flaps to enclose the runoff plot frame and the collection trough to the

ground. By visual inspection it was found that the runoff collection efficiency was

fairly low due to the amount of water leaking between the frame and the paved

surface. Hence, additional measures were necessary to enhance collection efficiency.

It was found that the corners connecting the runoff plot frame to the collection trough

were critical points of water escaping the plot. Therefore, cloth tape and a small

amount of silicone sealant was applied to the corners and let dry for an hour before

any water was applied to the plot. Cloth tape was also applied between the rubber

flaps and the ground surface all around the runoff plot frame. The combined

application of cloth tape and silicone sealant was found to be effective in preventing

leakage.

4.5 Dry sample collection in the field

Accumulated material on paved surfaces was collected using the vacuum cleaner at

the three study sites described in Section 4.4. Small asphalt surface test plots (2x1.5m)

were chosen at each of the three sites. At the residential and industrial sites, the plots

were located in the centre between the kerb and the middle strip of the road. At the

commercial site, the test plot was placed in the exact middle of a parking space. Prior

to collection, the vacuum cleaner was carefully cleaned using de-ionized water and

physical characteristics such as slope of the paved surface and length of antecedent

dry period or the last street cleaning event prior to sampling were recorded. The test

plots were then vacuumed using the vacuum cleaner as shown in Figure 4.9.

FIGURE 4.9 Dry sample collection in the field

82

The asphalt surface was vacuumed four times and the built-up material was collected

in the water filter. De-ionized water was used in the water filter of the vacuum cleaner

in order to minimise any chemical reactions with the collected particles.

Following vacuuming of the street surfaces, suspended build-up material was

transferred from the water filter and vacuum hose to sample bottles. Care was taken to

ensure that the loss of material during transfer was minimal. According to Standard

Methods for Water and Wastewater (APHA 1999), sample bottles have to be chosen

carefully, depending on the chemical analysis to be undertaken. The guidelines

provided by APHA (1999) were followed and polyethylene and amber glass bottles

were used for sample storage. Glass amber bottles were used for samples to be

investigated for PAHs, while polyethylene bottles were used for all other chemical

and physical analysis of the samples as specified by APHA (1999).

4.6 Wet sample collection in the field

Four different runoff plots were selected at each site using the same procedure as for

the selection of the build-up sampling plots. The slope of each plot was measured

using a theodolite prior to rainfall application. Although past research has shown that

a large fraction of the sediments is located near the kerb (Sartor and Boyd 1972), the

percentage distribution of heavy metals and PAHs in different particle sizes was

assumed to be the same throughout the width of the road. At the commercial site, four

parking spaces were chosen as runoff plots. The spacing between each runoff plot was

kept to a minimum without any interference occurring between the plots. Due to only

one build-up sample being collected at each site, the amount of street-deposited

pollutants was assumed to be the same for all four individual plots at each site. Hence,

the pollutant availability was considered to be identical for the rainfall events

simulated at the study sites.

Collection efficiencies of the wash-off sampling system were calculated whilst

sampling at the three study sites. The amount of rainfall sprayed on the surface was

determined from the pre set intensities and durations discussed in Chapter 3. The

volume of runoff was also recorded for each rainfall event. Hence, the volume

83

collected divided by the volume of rainfall applied to the plot became the collection

efficiency. The collection efficiencies at each site are shown in Table 4.5.

The low collection efficiency for a number of events at the commercial site was due to

a small natural rainfall event wetting the surface at the time. Even though every effort

was made to ensure the paved surface was dry, the moisture on the surface combined

with the coarse texture depth observed at the site, made it difficult to seal the interface

between the collection trough and the road surface. However, as the decrease in

collected runoff would only affect the pollutant load, it was assumed that the pollutant

concentrations would not be significantly influenced by the volume of runoff

collected. It was also assumed that some water was lost due to infiltration and

evaporation. Nevertheless, the impact of these phenomena was considered to be

minimal. As can be seen in Table 4.5, three events at the residential site and one event

at the industrial site could not be simulated due to unforeseen problems.

TABLE 4.5 Runoff collection efficiencies

Event

ARI/duration

Residential site

[%]

Industrial site

[%]

Commercial site

[%]

1yr/5min 76.9 88.0 38.3

1yr/10min 96.5 90.0 93.0

1yr/20min No data 85.4 36.9

2yr/7min 60.7 94.2 96.7

2yr/10min 72.9 87.3 40.0

2yr/20min 97.0 93.8 90.7

2yr/35min No data 85.6 35.1

5yr/13min 62.6 96.8 100

10yr/17min 75.1 97.3 100

10yr/20min 92.9 No data 33.4

10yr/40min 97.0 91.1 84.3

10yr/65min No data 82.5 34.6

Figure 4.10 shows the use of the rainfall simulator and collection of runoff samples in

the field.

84

FIGURE 4.10 Collection of runoff samples in the field

4.7 Treatment and transport of samples

When the samples had been collected from the sites, they were immediately tested for

pH and EC. They were then kept under refrigeration in the field until transport to the

laboratory for further testing. Blank and replicate samples, for quality assurance and

to measure sample contamination, were essential components in the field sampling.

Section 5 in the Australian/New Zealand Standard Water Quality – Sampling Report

no 5667:1 (AS/NZS 1998) was followed to ensure quality control. The samples were

then transported to the laboratory where they were kept under refrigeration until

analysis.

4.8 Summary

Due to the nature of the research project, different land use sites were chosen for field

sampling. The research sites that were chosen represented typical areas in the Gold

Coast region, Queensland, Australia. The residential site was a typical suburban

access road in Nerang, while the industrial and commercial research sites consisted of

fairly high traffic density and a wide range of industries and retailers respectively. The

research sites chosen were based on generic criteria formulated prior to the site

selection process.

The sampling methods used for collecting build-up and wash-off samples were

carefully chosen and were evaluated prior to use in the field. Collection of build-up

85

material on the road surfaces was done by vacuuming a plot 2x1.5m wide. The

vacuum used was highly efficient in collecting and retaining finer particulates and

used a water filter to ensure a minimum amount of particles escaped from the tank.

Wash-off samples were collected using a specially designed runoff collection system

that was connected to a vacuum system suitable for picking up fine particles. Wash-

off was vacuumed continuously throughout the design rainfall event into 25L

containers, which were transported to the laboratory for analysis.

86

Chapter 5 Analytical Methods

5.1 Introduction

Current approaches adopted to mitigate the impacts of urban runoff on receiving

waters include the use of structural and/or regulatory measures such as detention

basins, gross pollutant traps and restrictive zoning. However, for these measures to be

effective, an in-depth understanding of the processes involved in urban runoff is

essential. Therefore it is imperative to understand the relationships between PAHs and

heavy metals and important parameters such as particle size, pH and organic carbon

content. Consequently, adequate sample testing methods had to be selected and

implemented to accurately measure these parameters. This chapter defines the

methodology adopted for testing these parameters. It also discusses the partitioning of

the samples into different particle size ranges and the use of Event Mean

Concentration (EMC) samples in the analysis.

5.2 Sample testing

The development of appropriate methodology for sample testing was crucial to the

research due to the relatively low concentrations of PAHs and heavy metals expected

at the study sites. Furthermore, in order to understand the wash-off process of PAHs

and heavy metals, the analysis of the build-up processes was considered important due

to wash-off being influenced by the quantum and characteristics of pollutants

available on paved surfaces. Hence, adopting a sound testing methodology was

equally important for the build-up samples as well as for the wash-off samples.

5.2.1 Pre-treatment of samples

Prior to any analysis, the weight and volume of the build-up and wash-off samples

had to be determined. This was necessary in order to determine the quantity of

particles available for chemical analysis and to determine the quantity of accumulated

particulates available for wash-off by a simulated rainfall event. Additionally, the

volume of wash-off was used to determine an Event Mean Concentration (EMC) for

each rainfall event simulated at the study sites. As noted by Lee et al. (2002), the use

of EMC is appropriate for evaluating the effects of stormwater runoff on receiving

waters and was therefore adopted for this project. Furthermore, receiving water bodies

87

respond relatively slowly to storm inflows compared to the rate at which constituent

concentrations change during a storm event. Thus, EMC is an important analytical

parameter. The EMC represents a flow weighted average concentration computed on

the total pollutant mass divided by the runoff volume for a rainfall event of specific

duration. Hence, the volume of runoff water in each 25L container collected at the

study sites was recorded and EMC samples of the twelve rainfall events were obtained

prior to any chemical analysis.

As an example, sub-samples were collected at 20, 35, 60 and 65 minutes at the

commercial site during an event with intensity of 65mm/hr. To create an EMC sample

representing a 10 year event with 65mm/hr intensity (see Table 3.4 Chapter 3), the

percentage of the total runoff each sub-sample represented had to be determined. In

this case, it was found that the sub-sample collected at 20 minutes represented 33%,

the sub-sample collected at 35 represented 22%, the sub-sample collected at 60

minutes represented 34% and the sub-sample collected at 65 minutes represented 11%

of the total runoff. Hence, a one litre EMC sample of a 10 year ARI storm with

intensity 65mm/hr and duration 65 minutes would consist of:

• 330mL of sub-sample taken at 20 minutes;

• 220mL of sub-sample taken at 35 minutes;

• 340mL of sub-sample taken at 60 minutes; and

• 110mL of sub-sample taken at 65 minutes.

An EMC sample for each rainfall event at the study sites was created and analysed

accordingly.

5.2.2 Particle size distribution

Due to the importance of fine particles in the transport of pollutants, the particle size

distribution of the EMC samples and the build-up samples from each site were

measured prior to any chemical analysis. A Malvern Mastersizer S was used for this

purpose. The Malvern Mastersizer S uses a laser to record the scatter pattern from a

field of particles in the size range of 0.05-3500µm depending on the lens used. An

analytical procedure was then used to determine the size of particles that created the

scatter pattern. The specific analyser used in this project had a reverse Fourier lens of

300mm diameter and was able to analyse particles in the range of 0.05-900µm. In this

88

range, the manufacturer has specified a reading accuracy of ±2% of the volume

median diameter.

The Malvern Mastersizer S used in this project is shown in Figure 5.1 below. The

components consisted of a sample dispersion unit connected by two flow cells to the

optical unit. The results obtained from the optical unit were analysed using specialised

software supplied by the manufacturer.

FIGURE 5.1 Malvern Mastersizer S system used in the project

To interpret the results obtained from the instrument, there are a number of

fundamental concepts that need to be understood. It is important to note that the

fundamental size distribution derived from a laser diffraction instrument is volume-

based. The software used also allowed the results to be converted to other distribution

forms such as number distribution. However, due to the initial measurement being

volume-based, such conversions are liable to introduce systematic errors. This can be

explained by the example of a sample that consists of only two sizes of particles, 50%

by number with a diameter of 1µm and 50% by number with a diameter of 10µm. The

volume of each of the larger particles is 1000 times the volume of the smaller ones.

Thus, as a volume distribution, the larger particles represent 99.9% of the total

distribution.

89

The second concept used by the Malvern Mastersizer S in interpreting results is that

the particles are considered to be perfect spheres. In practice they are very rarely so.

The method used by the instrument is known as equivalent spheres, which means that

the size of the particle is related to its volume. As the measurement is volume-based,

it will calculate the diameter of an imaginary particle that is equivalent in volume.

Taking these concepts into consideration, particle size distribution was primarily

measured to identify the particle volume distribution of the build-up and wash-off

EMC samples. It also determined the particle sizes that would be important in the

subsequent analysis.

Particle size distribution measurement using the Malvern Mastersizer S was a non-

destructive test. The samples were well mixed prior to analysis by gently rolling the

sample containers. A tap water sample, which was assumed to contain no particles

was used as a background measurement. Analysis of the build-up and wash-off

samples was then undertaken by measuring the scatter of each sample, including the

blank rainwater sample, and comparing it to the background profile generated by the

tap water sample.

For the results of the measurements to be correctly interpreted, the Malvern software

uses different presentations depending on the refractive index of the dispersant and

the absorption value of the sample. Hence, the refractive index and absorption value

have to be determined in order to interpret the results of the analysis. The validity of

the results is depicted by a residual which is an indication of how well the

presentation data fits to the measurement data and is given as a percentage. A final

residual of less than 1% shows a good fit. Consequently, if an incorrect refractive

index or absorption value is chosen for the sample investigated, the residual is likely

to be above 1%. (Malvern Instrument Ltd. 1997).

5.2.3 Partitioning of samples

Based on the particle size distribution results, the build-up and wash-off EMC

samples were separated into a number of different particle size ranges in order to

investigate the affinity for pollutant interaction in different size ranges. Due to both

the build-up samples and wash-off samples being dispersed in water, partitioning of

samples was made using a wet sieving process. A volume of 1L was passed through a

90

selected sieve size and retained particles were transferred to a cellulose nitrate filter.

The filters were then kept isolated at -18oC temperature until analysis. The above

procedure was repeated with different sieve sizes. Prior to chemical analysis, the

retained material on the filters were divided into two equal portions assumed to have

an identical concentration of solid material. The two segments were then extracted or

digested as appropriate for further analysis.

5.2.4 Chemical and physico-chemical parameters

The build-up and wash-off samples were analysed for a range of physico-chemical

parameters, which were chosen based on important variables governing the

distribution of PAHs and heavy metals in urban stormwater as identified in Chapter 2.

Methods adopted for the analysis were either from Standard Methods for Water and

Wastewater (APHA 1999) or methods developed by the United States Environmental

Protection Agency (US EPA 1986, 1991). Table 5.1 summarises the chemical

parameters analysed and the analytical methods adopted.

91

TABLE 5.1 Test methods adopted in the project

Parameter Method no Comments

pH 4500H (APHA 1999) Combined pH/EC-meter was used

Electrical

Conductivity (EC)

2520B (APHA 1999) Combined pH/EC-meter was used

Organic Carbon

(TOC) / Inorganic

Carbon (IC)

5310B (APHA 1999) A Shimadzu TOC-5000A Total

Organic Carbon analyser was used.

Total Suspended

Solids (TSS)

2540D and 2540C

(APHA 1999)

Samples were filtered using a 0.45μm

nitrocellulose filter. Filtrate was used

to measure Total Dissolved Solids

Heavy metals 3030E, 3120B (APHA

1999)

Digestion with nitric acid. Analysis

by Inductively Coupled Plasma –

Mass Spectroscopy (ICP-MS). Eight

elements (Zn, Fe, Al, Cr, Cd, Mn, Cu, Polycyclic

Aromatic

Hydrocarbons

(PAHs)

6440B (APHA. 1999),

EPA 610 (US EPA

1991), EPA 3550 (US

EPA 1986)

16 species. Particulate matter was

extracted using sonication. Analysis

by Gas Chromatograph – Mass

Spectrometer (GC-MS).

(A) pH and EC

The importance of pH and EC stems from the increased adsorption and desorption

that can occur with a change of any of these parameters in stormwater. Pechacek

(1994) noted that an increased EC led to an increase in pollutants being sorbed to

solid particles in urban runoff. Similarly, Tai (1991) found the pH to significantly

influence the sorption characteristics of heavy metals in urban stormwater. He found

that a decrease in pH led to an increase in the bioavailability of certain heavy metals.

The pH and EC of the build-up and wash-off samples were measured in the field

using a combined pH/EC-meter.

92

(B) Organic Carbon

Organic carbon is an important parameter in urban stormwater. Several researchers

have identified the increased sorption of hydrophobic pollutants to the organic

fraction of particles (Roger et al. 1998; Wang et al. 2001). Furthermore, organic

carbon in the dissolved fraction of runoff has been found to bring about a solubility

enhancement effect that decreases the sediment to water ratio of pollutants (Warren et

al. 2003). Consequently, it is important to analyse the different fractions of organic

carbon in urban water quality studies.

Organic carbon content of the build-up and wash-off samples was measured using a

Shimadzu TOC-5000A Total Organic Carbon Analyzer. Generally, there are three

fractions of organic carbon in a sample, which are defined as:

• Inorganic Carbon (IC);

• Particulate Organic Carbon (POC); and

• Dissolved Organic Carbon (DOC)

(APHA 1999).

POC refers to the non-dissolved organic carbon fraction, meaning the fraction of

organic carbon retained by a 0.45µm filter. The DOC is the organic carbon passing

the same filter. Generally, the IC fraction has to be removed prior to analysis of POC

or DOC. However, the TOC analyser used allowed separate measurements of

particulate or dissolved carbon (PC and DC respectively) and IC. The POC was

therefore defined as the difference between PC and IC. Similarly, DOC was defined

as the difference between the DC and IC measurements.

(C) Total Suspended Solids

Total Suspended Solids (TSS) have physical and chemical impacts on the quality of

receiving waters. However, it is the chemical impact that is accentuated in this

research. PAHs and heavy metals are heavily adsorbed by particulates (Andral et al.

1999; Sartor and Boyd 1972; Roger et al. 1998). Hence, measuring the suspended

solids concentrations is imperative in urban water quality studies.

93

TSS was measured by filtering the build-up and wash-off sample through a 0.45µm

filter. The retained particles were then oven-dried to a constant weight at 103oC

overnight. The increase in weight of the filter represented the TSS concentration of

the specific sample. The particles passing the 0.45µm filter was evaporated to dryness

in a pre-weighed dish and oven-dried to constant weight at 180oC temperature. The

concentration of particles below 0.45µm is referred to as the Total Dissolved Solids

(TDS) according to APHA (1999).

(D) Heavy Metals

Heavy metals were one of the primary pollutant groups investigated in this project.

Eight metal elements were chosen to be analysed due to their frequent detection in

urban areas and their potential toxicity as observed by previous researchers

(Makepeace et al. 1995; Marsalek et al. 1997). These were:

• Iron (Fe);

• Aluminium (Al);

• Lead (Pb);

• Zinc (Zn);

• Cadmium (Cd);

• Chromium (Cr);

• Manganese (Mn); and

• Copper (Cu).

Most of the metal elements investigated are strongly bound to particles in urban

stormwater runoff (Shinya et al. 2000; Ujevic et al. 2000). However, trace levels of

the metal elements have also been found in the dissolved phase of the runoff and are

important as an indicator of bioavailability (Sansalone and Buchberger 1997).

Additionally, the land uses chosen as study areas, especially the residential site

chosen, were not expected to generate high concentrations of heavy metals.

Particulate metals were digested using nitric acid (HNO3) according to Standard

Method 3030E. 5mL of concentrated HNO3 (Ajax) were added to a 250mL

Erlenmeyer flask together with the particles retained on half a 0.45µm filter, 50mL of

de-ionized water and a number of glass beads. The mixture was brought to a slow boil

94

and was left to evaporate on a hot plate to the lowest volume possible before

precipitation occurred. HNO3 was then added in 5mL increments as necessary until

digestion was complete, as shown by a light-coloured clear solution. The solution was

filtered if necessary, transferred to an acid-washed sample bottle and refrigerated until

analysis. Dissolved samples were preserved using HNO3 and refrigerated until

analysis.

A Perkin Elmer Elan 6100DRC Inductively Coupled Plasma – Mass Spectrometer

(ICP-MS) was used in the detection of metal concentrations of the digested and

preserved samples. ICP-MS was chosen due to its ability to detect lower

concentrations (detection limit ranged from 0.001 to 0.005mg/L depending on

element) of the metal elements investigated, compared to instruments such as

Standard Flame Absorption Spectrophotometers (Harris 2002; APHA 1999). The

calibration of the ICP-MS was undertaken using high purity standards bought from

Choice Analytical P/L. For quality control, AccuTrace Reference Standard

“Laboratory Performance Check Standard” ICP Multi-element StandardL-PCS-01-1

was used. Duplicate samples and standards used in the analysis had recoveries ranging

from 80-116%. According to the testing laboratory, acceptable recoveries were in the

range of 75-120%. Lower limits of reporting were 0.005mg/L for Al and Fe and

0.001mg/L for Zn, Pb, Cd, Cr, Cu and Mn. Particulate metal concentrations were

converted to mg/kg as described in Standard Methods for Water and Wastewater

Method 3030 (APHA 1999).

(E) Polycyclic Aromatic Hydrocarbons (PAHs)

PAHs were the other primary group of pollutant investigated in the project. The PAHs

tested in the build-up and wash-off samples from each site have been listed by US

EPA as priority pollutants (Manoli and Samara 1999) and are summarised in Table

2.3 in Chapter 2. Similar to heavy metals, PAHs are primarily particulate bound in

urban stormwater (Hoffman et al. 1985; Marsalek et al. 1997). However, parameters

such as pH and DOC have been found to enhance the solubility of PAHs (Smith et al.

2000; Warren et al. 2003). Consequently, to fully understand the processes governing

the distribution of PAHs in urban stormwater, the dissolved and particulate phase of

stormwater has to be investigated separately. Hence, methods for detecting PAHs in

dissolved and particulate samples had to be employed in the analysis. Furthermore,

95

individual PAHs were considered to be present in very low concentrations, which

demanded efficient extraction techniques, pre-concentration and sufficient extraction

clean-up to remove interferences in the analysis.

The dissolved fraction of the PAHs was extracted using the liquid-liquid extraction

method (US EPA method 610) as described by the US EPA (1991). A 100mL sample

was poured into a 250mL separatory funnel. 60mL of analytical reagent grade

Dichloromethane (DCM) (Australian Chemical Reagents) was then added to the

sample. The funnel was shaken for two minutes with periodic venting to release

excess pressure. The organic layer was separated from the water phase by letting the

funnel stand for a minimum of ten minutes. The DCM was then transferred into a

glass beaker. A second 60mL volume of DCM was then added to the separatory

funnel and the extraction procedure was repeated a second time. A third extraction

was then performed in the same manner. The combined extracts (180mL) were then

dried using anhydrous sodium sulphate (Na2SO4) (Chem-Supply), filtered through

glass wool, and concentrated to circa 5mL using rotary evaporation at 35oC

temperature under vacuum conditions. The remaining DCM extract was further

evaporated to 0.5mL under a gentle stream of pure nitrogen (N2) prior to analysis.

The particulate PAHs were extracted using ultrasonication. Ultrasonication was

preferable due to the reduced extraction time compared to methods such as Soxhlet

extraction (Guerin 1999). The technique used is outlined in US EPA method 3550

(US EPA 1986). However, the method described by US EPA (1986) does not specify

the preferable solvent nor the length of time required for extraction. Guerin (1999)

found that using a mixture of DCM and Acetone (ACE) as solvent provided the most

efficient extraction of the 16 priority PAHs listed by US EPA (Manoli and Samara

1999). He also found that an extraction time of 1 hour was sufficient for extracting 2-

and 3-ring PAHs. Following this time period, their recoveries began to decrease,

indicating that they were being lost by volatilisation. Zhou and Maskaoui (2003)

successfully extracted the 16 priority PAHs using two 30 minute ultrasonication

intervals. Similarly, Nielsen (1996) found that separating the extraction to 20 or 30

minute intervals was preferable for the extraction of the 16 priority PAHs. Therefore,

a DCM:ACE (3:1) mixture was used as the solvent and an extraction time of three 20

minute intervals were chosen.

96

12mL of DCM:ACE (3:1) was used in each extraction interval. Following the 20

minute interval, the extract was transferred to a 100mL glass beaker. This procedure

was repeated twice until extraction was complete. The extracted solution was filtrated

into a 50mL glass beaker using a GF/C glass micro fibre filter (Whatman), which was

purified by column chromatography using activated silica gel and sodium sulphate

(Na2SO4), both from Chem-Supply. The silica gel and Na2SO4 was activated in a

furnace at 100oC for 24 hours and were allowed to cool to room temperature in a

vacuum desiccator prior to use. This clean-up process removed any interfering

compounds so as to obtain as accurate results as possible. Following purification, the

PAH extract was concentrated under vacuum to less than 5mL using a rotary

evaporator (35oC temperature) and further concentrated to approximately 0.5mL using

a gentle stream of N2. The samples were then analysed using a HP5972 Gas

Chromatograph – Mass Spectrometer (GC-MS) which was calibrated using a standard

obtained from Chemservice Sigma-Aldrich for the 16 priority PAHs. Deuterated PAH

standard was added prior to extraction to a number of samples in order to determine

extraction recovery for the specific PAHs. The recovery values of specific PAHs

ranged from 52-95% with the recovery of lower molecular weight PAHs being the

most effective. The decreased recovery of higher molecular weight PAHs was due to

short extraction times. However, it is postulated that low-molecular weight PAHs are

detected more frequently in urban areas. This is supported by Smith et al. (2000) who

found 2- and 3-ring PAHs to be detected most frequently in urban stormwater runoff.

Similar results were found by McCready et al. (2000) in particles originating carried

by urban runoff into Sydney Harbour, NSW, Australia. Consequently, the recovery

values were considered acceptable for the research.

Regular field and laboratory blanks were also analysed and found to be satisfactory,

meaning that individual PAH concentrations in all blanks were less than 5% of PAH

concentrations in build-up and wash-off samples from the study sites. The limit of

reporting for the analysis was 0.01mg/L in a 1mL extract. Any PAHs detected below

the limit of reporting were considered to be noise interferences.

97

Particulate PAH concentrations were reported in mg/kg calculated as given below:

PAH concentration mg/kg C

BA×=

Where:

A = concentration of PAH in extracted solution, [mg/L]

B = final volume of extracted solution, [mL]

C = weight of sample, [g]

5.3 Summary

Due to the low concentrations of PAHs and heavy metals expected in build-up and

wash-off samples from the three study areas, appropriate testing methods needed to be

adopted. Consequently, standard methods described by APHA (1999) and various

methods recommended by the US EPA (US EPA 1986, 1991) were applied to the

dissolved and particulate fractions of the collected samples. This ensured that a high

accuracy was always obtained during the testing regime. Consequently, the results

obtained from the analysis undertaken were reliable and sufficient for urban water

quality investigations. Chapter 6 discusses the results from the tests undertaken.

98

Chapter 6 Discussion of Test Results

6.1 Introduction

Investigating the build-up and wash-off process kinetics of PAHs and heavy metals

was the primary focus of the research. Consequently, the results from the chemical

and physico-chemical analysis of the build-up and wash-off samples need to be

discussed. The results from the tests led to a number of important conclusions and

provided guidelines for the application of univariate and multivariate methods. It also

gave an early indication of possible pollutant sources in the study areas and

differences in concentration of important parameters between the three study areas.

Hence, it was possible to identify the pollutant strength at the respective study sites.

Furthermore, it highlighted the complementary information that multivariate analysis

could provide. The results from the tests described in Chapter 5 are discussed in this

Chapter and limitations with univariate analysis are highlighted.

6.2 Volume and weight of the collected samples

The weight of the built-up particles from each site was measured as shown in Table

6.1. Information on the number of antecedent dry days prior to sampling was also

obtained.

TABLE 6.1 Amount of build-up sample collected at each site and respective dry

period

Research site Weight of particles [g] Dry days

Residential 2.449 2

Industrial 6.876 7

Commercial 15.882 1

The largest quantity of particles was collected at the commercial site, which in turn

had the lowest amount of dry days before collection. This indicated a relatively rapid

accumulation of particles following a rainfall event at the site. However, it could also

be attributed to the relatively coarse texture of the paved surface at the site, which

would embed a larger number of particles compared to fine textured surfaces such as

the street surface at the residential site. Furthermore, it is postulated that a large

99

number of particles at the commercial site could be attributed to vehicle and surface

wear due to the amount of accelerating and decelerating vehicles at the site.

Additionally, a high amount of leaf debris was observed at the time of sampling at the

commercial site.

EMC samples were prepared for the twelve rainfall events simulated at the sites using

the volume of wash-off collected and the time of collection as discussed in Section

5.2.1. The volume of wash-off collected can be found in the raw data tables, Table

B1-B3 Appendix B (Residential, Industrial and Commercial respectively).

6.3 Particle size distribution

The importance of particle size distribution in urban water quality studies stems from

the increased adsorption of hydrophobic pollutants to finer particles (Schillinger and

Gannon 1985). Finer material stays in suspension longer and is therefore transported a

greater distance by urban runoff. Similarly, the effectiveness of many urban

stormwater mitigation measures is dependant on the particle size distribution. Hence,

particle size distribution could significantly influence the management of stormwater

in an urban area. The investigation of the particle size distribution of the build-up and

wash-off samples using the Malvern Mastersizer S gave an indication of the

importance of finer particles in runoff. Furthermore, comparing build-up and wash-off

particle size distribution provided information on the influence of rainfall intensity

and duration, in scouring of particles from a paved surface.

As the particles in build-up and wash-off samples were dispersed in water, the

refractive index chosen for the particle size distribution analysis was 1.33 (Malvern

Instrument Ltd. 1997). The refractive index for the particles was chosen accordingly

(0.1) and represented silt and clay particles in water (Malvern Instrument Ltd. 1997).

The absorption value for clay and silt particles was 1.569. Using these values, a

residual below 1% was achieved for the majority of build-up and wash-off samples.

The refractive indices and absorption value chosen showed a good fit for 88% of the

samples, which was considered satisfactory. Hence, the majority of particles displayed

silt and clay characteristics. In order to assess the higher residual achieved in 12% of

the samples, different refractive indices were tested. However, the residuals remained

above 1% independent of the refractive index chosen for the specific samples. It was

100

concluded that the higher residuals were due to formation of bubbles during mixing of

samples, which biased the reading.

For each build-up sample collected, a particle volume distribution curve was plotted

as shown by Figures B1-B3 Appendix B (Residential, Industrial and Commercial

respectively). Furthermore, a cumulative particle volume distribution curve was

plotted as shown in Figure 6.1.

0

20

40

60

80

100

0.1 1 10 100 1000

Particle size [μm]

Cum

ulat

ive

perc

enta

ge [%

]

CommercialIndustrialResidential

FIGURE 6.1 Cumulative particle size distributions of the build-up samples

collected at the three study sites

As seen in Figure 6.1 and Figure B1-B3 Appendix B, the particle volume distributions

at the three study sites displayed a clear pattern. Up to 90% of the particles were

below 150µm independent of land use characteristics. Similar results have been found

by Andral et al. (1999) suggesting that particles below 150µm are important in

developing effective urban stormwater management systems. In addition, particle

removal techniques such as street sweeping have been found to efficiently remove

only relatively large particles (Bender and Terstriep 1984; Sutherland et al. 1998).

The results from the particle size distribution test indicated that pollutants attached to

particles below 150µm could be detrimental to the quality of receiving waters in the

study area, if transported by wash-off.

Additionally, the residential and commercial sites had a higher percentage of particles

below 50µm compared to the industrial site, as can be seen in Figure 6.1. This could

101

be due to a number of reasons. Firstly, Sartor and Boyd (1972) attributed the higher

volume of larger particles at industrial sites to less frequent street cleaning practices.

However, it was found that the industrial and residential streets had an identical street

cleaning cycle, being swept every six weeks. Hence, other processes had to be

responsible for the higher volume of larger particles at the industrial site. It is

postulated that dispersion of smaller particles due to turbulence caused by infrequent

heavy vehicle traffic operating in the area was the primary reason. Nevertheless,

particles originating from industrial processes in the study area were also likely to

contribute to the higher volume of larger particles. Consequently, the residential and

commercial sites have the potential to introduce a higher load of pollutants through

wash-off to receiving waters compared to the industrial site, due to the higher volume

of fine particles available for wash-off.

In spite of this, by comparing the difference in particle size distribution in the wash-

off samples from the three study sites as shown by the distribution and cumulative

percentage curves in Figures B4-B15 Appendix B, a random distribution was found.

This is further supported by calculating the mass median diameter (d50) of the

particles, which is defined as the size of particle at which 50% of the distribution is

smaller and 50% is larger. As can be seen in Table 6.2, the d50 of the wash-off

samples differed significantly from site to site for the same rainfall event. The trend

was the longer duration, the larger d50. However, d50 reached maximum at a point in

time during the rainfall event and decreased after this point if the rainfall continued.

This suggests that finer particles were more likely to have a fairly even distribution

during a rainfall event, while the volume distribution of coarser particles quickly

reached a maximum and decreased as the rainfall continued. This was independent of

rainfall intensity. This implies that the concentrations of hydrophobic pollutants

stayed reasonably constant during a runoff event due to their affinity with finer

particles (Andral et al. 1999).

102

TABLE 6.2 Mass median diameters (d50) in μm of the particles in the simulated

runoff events

Particle (d50) [µm] ARI event

Intensity [mm/hr]/

duration [min] Residential Industrial Commercial

65/20 - 45.89 43.28

65/35 - 64.09 77.89

65/65 - 137.46 69.44

86/10 66.68 36.30 78.35

86/20 115.75 93.77 101.91

86/40 42.63 98.09 94.01

115/5 44.32 63.22 109.59

115/10 67.26 79.98 78.91

115/25 66.80 - 128.15

133/7 26.89 63.92 80.73

133/13 46.32 63.50 83.90

133/17 48.45 55.62 104.50

Rainfall intensity was important in scouring larger particles from the surface due to

the significantly lower d50 of a 20 minute duration EMC sample with rainfall intensity

of 65mm/hr, compared to an identical duration EMC sample with higher rainfall

intensity (86mm/hr). However, the rainfall intensity was less important in determining

the particle size distribution once a specific intensity had been reached, as seen in

Table 6.2. This supports the findings of Rosewell (1986) who found that the kinetic

energy of rainfall in South-East Queensland remained fairly constant at intensities

higher than 100mm/hr. Hence, the scouring of particles instigated by the impact

energy caused by rainfall remained fairly constant, when rainfall intensity reaches

100mm/hr.

By comparison, there was a larger volume of coarser particles in the wash-off samples

than in the build-up samples. This suggests that there is a relatively large percentage

of fine particles still available for wash-off at the study areas following rainfall. It is

postulated that the fine particles were embedded in the voids of the paved surface or

103

were re-deposited during the wash-off event at a point further downstream. Hence, the

wash-off of particles at the study sites was transport-limited instead of source-limited.

Thus, a relatively large pollutant load was available for wash-off independent of

antecedent dry period.

6.3.1 Partitioning of build-up and wash-off samples

The particle size distribution analysis revealed that the largest volume of particles in

the build-up samples were below 150µm, independent of land use characteristics.

More than 50% of the particles by volume were below 150µm in the wash-off

samples as shown in Table 6.2, independent of land use and rainfall characteristics.

Hence, it was decided to partition the build-up and wash-off samples for chemical

analysis based on these findings. This approach was supported by the findings of

Warren et al. (2003), who suggested that measuring ‘whole water’ (unfiltered)

concentrations of pollutants does not give an accurate indication of the processes

underlying the distribution of these pollutants. Furthermore, to effectively implement

stormwater management practices, knowledge of the distribution of pollutants in

different fractions are fundamental (Smith et al. 2000). Consequently, the dispersed

build-up and wash-off samples were separated into five particle size ranges by wet

sieving as described in Section 5.2.3. The five particle size ranges chosen were:

• Particles passing a 0.45µm filter (<0.45µm);

• Particles passing a 75µm sieve but not a 0.45µm filter (0.45-75µm);

• Particles passing a 150µm sieve but not a 75µm sieve (76-150µm);

• Particles passing a 300µm sieve but not a 150µm sieve (151-300µm); and

• Particles retained on a 300µm sieve (>300µm).

6.4 Chemical parameters

6.4.1 pH and EC

The Australian Laboratory Handbook of Soil and Water Chemical Methods (Rayment

and Higginson 1992) has specified methods for the measurement of pH and EC in soil

sediment (method 4A1 and 3A1 respectively). However, the methods described are

based on a 1:5 soil/water suspension and to get accurate results, a dry weight of 10g is

recommended. Therefore, pH and EC could not be measured accurately in the specific

104

particle sizes. Hence, pH and EC were only measured of the total build-up and wash-

off samples.

TABLE 6.3 pH and EC concentrations of the build-up sample at each site

Research site pH EC [μS/cm]

Residential 6.20 21.82

Industrial 6.43 25.10

Commercial 6.23 82.10

As can be seen in Table 6.3, the pH of the build-up samples showed no significant

change between the study sites. This is attributed to the prevalent soil conditions in

the study areas, which were of similar characteristics. This is supported by the small

difference in EC between the residential and industrial site as shown in Table 6.3.

However, the build-up sample from the commercial site had a significantly higher EC

than the other sites. This can be attributed to a number of reasons. Firstly, leakage and

spills of various fluids from parked vehicles will most likely have contributed to a

higher EC. In addition to this, the car park is surrounded by vegetation that it was

postulated, would be treated with fertiliser. Factors such as ocean salt transported by

vehicles and spills from various activities at the shopping centre could also have

contributed to the elevated EC. However, the largest contribution of salts was most

likely from the Nerang River, which flows just behind the shopping centre. The river

is affected by tidal flow at this location. Hence, soils around the river potentially

would have a higher EC than soils at the residential and industrial sites.

Similar to the build-up samples, the pH was relatively constant during the wash-off

events, independent of rainfall or land use characteristics, as shown in Table 6.4. This

is supported by the mean and standard deviation of the values measured in the wash-

off samples. For pH and EC measurements of individual samples from the residential,

industrial and commercial sites, refer to Tables B1-B3 Appendix B respectively.

105

TABLE 6.4 pH and EC mean concentrations in the EMC-samples for each

runoff event

Range Standard deviation Parameter

Res Ind Com Res Ind Com

pH 6.7-7.3 6.5-6.8 6.6-7.7 0.2 0.1 0.3

EC [μS/cm] 102-130 287-665 27-57 10 135 9

Res = Residential site; Ind = Industrial site; Com = Commercial site

The relatively high mean EC recorded in the wash-off samples collected from the

industrial site was attributed to an elevation of EC in the water used in rainfall

simulation at the site. Hence, the importance of EC as a parameter could be measured

more accurately at the industrial site. Accordingly, the EC values of the wash-off from

the site increased significantly.

6.4.2 Organic Carbon

Organic carbon and inorganic carbon was measured for each particle size fraction of

the build-up and wash-off samples. Hence, the organic carbon passing a 300µm sieve,

but retained on a 150µm sieve, was assumed to be the Total Organic Carbon (TOC) of

the particle range 151-300µm. The organic and inorganic carbon found in the

different particle size ranges of the build-up samples from each site are shown in

Table 6.5 below.

TABLE 6.5 Total Organic Carbon (TOC) and inorganic carbon (IC) recorded in

build-up samples from the three study sites

Research site

<0.45μm

[mg/kg]

0.45-75μm

[mg/kg]

76-150μm

[mg/kg]

151-300μm

[mg/kg]

>300μm

[mg/kg]

TOC 4.255 4.003 4.163 <0.001 3.955 Residential

IC 1.835 1.619 <0.001 <0.001 <0.001

TOC 8.370 0.211 0.641 0.344 11.058 Industrial

IC 3.644 0.153 1.023 0.633 4.642

TOC 18.815 16.613 1.122 0.252 23.993 Commercial

IC 6.505 1.880 <0.001 <0.001 <0.001

106

TOC was always high in the largest particle size range (>300µm) independent of the

site. This could be attributed to the presence of twig and leaf fragments at all the sites.

At the commercial site there would also be a significant amount of organic carbon in

this particle size range originating from vehicle exhaust, so called soot particles

(Warren et al. 2003). However, a significant amount of TOC is distributed in particles

below 75µm. Sartor and Boyd (1972) found a similar distribution and hypothesised

that this could be due to the low structural strength of organic matter and that they can

be easily ground into fine particles. Another possible reason for a higher TOC in fine

particles could have been the relatively larger surface area of the particles. Hence,

non-particulate organic matter could adhere to the surface of fine particles as noted by

Warren et al. (2003). This in turn could have implications with respect to the transport

of heavy metals and PAHs to receiving waters in the region, as TOC has been found

to attract hydrophobic pollutants (Roger et al. 1998).

The inorganic carbon content of the build-up samples was very low and only detected

in particles below 75µm, except at the industrial site where IC was frequently detected

in the different particle size ranges. Whilst the presence of IC at the residential site

was postulated to be due to sand and dust-type materials, the presence of IC at the

industrial site was attributed to minerals originating from industrial activities. These

minerals could contain irreversibly trapped metals (Charlesworth and Lees 1999).

Consequently, mineral-like particles could transport a significant amount of metals to

receiving waters if incorporated into runoff. TOC was found primarily in dissolved

form (<0.45µm) in the wash-off samples independent of land use characteristics as

can be seen in Table 6.6, where mean TOC concentrations for each particle size at the

three sites are reported. This is most likely due to the presence of colloidal organic

matter at the sites.

107

TABLE 6.6 Mean TOC concentrations in wash-off samples

Mean concentration [mg/kg] Standard deviation Size range

[μm] Res Ind Com Res Ind Com

<0.45 7.451 7.184 5.544 1.388 0.836 2.523

0.45-75 1.019 0.602 1.354 0.739 0.614 0.865

76-150 0.957 0.737 0.321 1.321 0.629 0.234

151-300 1.394 0.528 0.656 0.652 0.479 0.892

>300 1.052 0.750 1.018 1.110 0.919 1.195


Organic carbon in dissolved form could have serious implications for the efficiency of

urban stormwater management practices. As noted by Warren et al. (2003) and Smith

et al. (2000), dissolved organic carbon (DOC) could lead to a solubility enhancement

effect of micro-pollutants such as PAHs and heavy metals. This could bring heavy

metals and PAHs into solution, which in turn would make the pollutants bioavailable

to aquatic organisms when reaching receiving waters.

The inorganic fraction in the wash-off samples from the three sites was very low,

suggesting that the organic fraction of particles was more easily transported in wash-

off. Interestingly, the presence of IC in the build-up samples from the industrial site

was not consistent with the presence of IC in the wash-off samples from the same site.

Hence, the IC was significantly lower in the wash-off samples. It is postulated that the

mineral-like particles considered to be by products from industrial activities were of

higher density, hence easily redeposited in the plot area during transport. The results

of the individual measurements of IC and TOC of the wash-off samples from the

residential, industrial and commercial sites can be found in Tables B1-B3 Appendix

B, respectively.

6.4.3 Total Suspended Solids

TSS concentrations were measured for each of the five particle sizes obtained from

the partitioning of the build-up and wash-off samples. At all sites, the particle size

class 0.45-75µm had a significant higher TSS concentration in the build-up samples

as seen in Table 6.7.

108

TABLE 6.7 TSS concentrations in the build-up samples

Research site

<0.45μm

[mg/L]

0.45-75μm

[mg/L]

76-150μm

[mg/L]

151-300μm

[mg/L]

>300μm

[mg/L]

Residential 35 237 95 24 17

Industrial 30 901 218 71 30

Commercial 150 1140 611 170 46

This was supported by the particle volume distribution measured in the build-up

sample prior to partitioning of the samples, as discussed in Section 6.3. Consequently,

the impacts on receiving waters could be detrimental due to the large concentration of

fine particles available for wash-off independent of land use characteristics.

Furthermore, the particle size class 0.45-75µm had the highest concentrations of TOC

at the residential and industrial sites. Hence, the solids concentration effect of TOC

and its effect on PAHs, as discussed by Warren et al. (2003) and Wang et al. (2001),

could be an important process at the residential and industrial site. As a result, the

sorption ratio of sediment to water is increased. This could lead to increased

concentrations of PAHs in smaller particles, which in turn could lead to an increased

pollutant load reaching receiving waters, due to the large volume percentage of finer

particles observed at the study sites. Additionally, it can be seen in Table 6.8 that TSS

concentrations dominated in the fine particle size ranges in the wash-off samples,

suggesting that quite a large pollutant load is in fact carried to receiving waters.

Complete results of TSS measurement of the wash-off samples can be found in Table

B1-B3 Appendix B.

TABLE 6.8 TSS mean concentrations in the wash-off samples

Mean concentration [mg/kg] Standard deviation Size range

[μm] Res Ind Com Res Ind Com

<0.45 (TDS) 77.78 174.55 26.67 11.49 74.21 9.85

0.45-75 29.29 35.76 19.14 21.33 24.26 7.36

76-150 8.43 14.87 19.67 7.69 6.23 5.81

151-300 3.74 10.31 23.54 6.04 6.12 11.40

>300 1.31 5.66 24.33 0.95 2.68 10.99


109

However, it can also be seen that a higher concentration of solids was carried by the

dissolved fraction in wash-off, as indicated by the relatively high TDS concentrations

(particles <0.45µm) found in the wash-off samples compared to the build-up samples.

This is most likely due to colloidal organic particles transported in the dissolved

fraction, suggested by the amount of dissolved organic carbon (DOC) found at the

sites. This supports the finding by Sartor and Boyd (1972) who noted the low

structural strength of organic matter, which is easily ground into finer particles.

The TSS concentrations of the different particle size classes of the wash-off samples

collected at the commercial site showed a slightly different distribution to the other

study areas. The TSS concentrations were fairly evenly distributed between the

particle size classes, which was attributed to the coarse texture depth recorded at the

parking lot. Hence, fine particulates could be trapped in the voids of the paved surface

and not incorporated into runoff. However, frequent traffic at the site and street

sweeping has the potential to release a significant amount of these particles from the

voids, which could lead to higher concentrations of fine particles available for wash-

off at the next event. Furthermore, at the time of sampling at the commercial site,

there was a very light natural rainfall event occurring. Consequently, fine particles

could have been transported by the runoff generated from this natural rainfall event,

leaving coarser particles behind that were incorporated into the wash-off from the

simulated rainfall.

6.4.4 Heavy metals

Metal concentrations were measured in each particle size class of the build-up and

wash-off samples. The highest metal concentrations were consistently found in the

0.45-75µm of the build-up samples, followed by the 76-150µm size range as shown in

Table 6.9, which coincided with highest concentrations of TSS. As a result, the

highest heavy metal load at the three study sites was consistently found in fine

particles, which further strengthens the importance of removing fine particles in urban

stormwater quality management.

110

TABLE 6.9 Heavy metal concentrations in each particle size at the three sites

Metal

<0.45μm

[mg/kg]

0.45-75μm

[mg/kg]

76-150μm

[mg/kg]

151-300μm

[mg/kg]

>300μm

[mg/kg]

Fe 0.007 13.997 12.799 3.480 1.480

Zn 0.390 1.800 0.720 0.288 0.136

Al 0.008 8.800 8.800 2.640 1.240

Pb <0.001 0.044 0.034 0.010 0.005

Cu 0.082 0.560 0.480 0.680 0.344

Cd <0.001 0.002 0.002 0.001 0.001

Cr <0.001 0.018 0.014 0.008 0.006

Res

iden

tial

Mn 0.008 0.228 0.200 0.056 0.028

Fe <0.005 43.960 2.160 0.616 1.360

Zn 0.180 2.318 0.112 0.039 0.080

Al 0.009 13.588 0.632 0.240 0.464

Pb 0.002 0.959 0.035 0.010 0.019

Cu 0.007 0.959 0.037 0.040 0.043

Cd <0.001 <0.001 <0.001 <0.001 <0.001

Cr <0.001 0.063 0.003 0.002 0.013

Indu

stria

l

Mn 0.022 0.512 0.022 0.008 0.014

Fe 0.960 16.781 9.594 4.719 3.840

Zn 0.830 0.424 0.296 0.112 0.060

Al 0.069 5.514 3.518 1.760 1.280

Pb 0.014 0.344 0.192 0.088 0.051

Cu 0.120 0.264 0.240 0.528 0.224

Cd 0.003 0.002 0.008 <0.001 <0.001

Cr <0.001 0.026 0.015 0.008 0.006

Com

mer

cial

Mn 0.270 0.224 0.160 0.072 0.057

The least detected heavy metals in the build-up samples were Cd followed by Cr.

These metals have been linked with brakes and tyres of vehicles (Sansalone and

Buchberger 1997). Hence, the larger traffic volume observed at the residential and

commercial site, compared to the infrequent high vehicle traffic observed at the

industrial site, is postulated to have influenced the presence of these metals in the

111

environment. However, this is a typical example where pattern recognition using

multivariate analysis was useful in analysing sources and processes in relation to the

pollutants. PCA helped identifying the sources of these metals at the sites, which is

further discussed in Chapter 7. The most frequently detected metals at each site were

Al and Fe. These metals have been attributed to common minerals present in soils

(Pierson and Brachaczek 1983).

The highest concentration of Zn in the build-up samples occurred in colloidal size

particles (<0.45µm). It is postulated that the presence of Zn is affected by the DOC

available in this particle size class. Similar correlations between Zn and organic

carbon content have been found by several researchers (Ellis et al. 1987; Morrison et

al. 1984), which suggests that metal-organic complexation was occurring in the build-

up samples. Consequently, the increased bioavailability of Zn due to metal-organic

complexation highlights the importance of understanding fundamental processes in

urban stormwater. Furthermore, organic carbon was also found in relatively high

concentrations in the largest particle size class of the build-up samples analysed in the

three sites. In spite of this, the TOC content had a lesser effect on the distribution of

heavy metals in this size class. This could be due to the lesser sorption capacity of

larger particles (Dong et al. 1984; Ujevic et al. 2000). Another reason could be the

difference in the type and nature of sediment, which can be attributed to the source of

the organic matter present (Warren et al. 2003). This in turn could influence the

interaction between heavy metals and particles.

Similar to the build-up samples, the highest metal concentrations were found in the

particle size class 0.45-75µm of the wash-off samples independent of land use and

rainfall characteristics. It was found that the rainfall intensity had little or no effect on

the distribution of heavy metals. However, the metal concentrations in the EMC

samples generally decreased with duration, suggesting that a dilution effect was

occurring. Table 6.10 shows the concentration range of each metal in the wash-off

samples from each site independent of particle size class. Complete results of the

metal analysis can be found in the raw data tables for each site, Table B1-B3

Appendix B (Residential, Industrial and Commercial respectively).

112

TABLE 6.10 Metal concentration ranges observed in particulate and dissolved

fractions of wash-off samples from each site [ppm]

Range [ppm] Standard deviation

Res Ind Com Res Ind Com

Zn <0.001-3.6 <0.001-0.5 <0.001-0.7 0.7 0.1 0.2

Cu <0.001-0.4 <0.001-0.04 <0.001-0.1 0.08 0.007 0.03

Pb <0.001-0.02 <0.001-0.03 <0.001-0.01 0.002 0.005 0.002

Al <0.001-0.6 <0.001-0.4 <0.001-0.3 0.2 0.09 0.05

Fe <0.001-0.7 <0.001-0.9 <0.001-0.7 0.2 0.2 0.1

Cd <0.001-0.3 <0.001-0.001 <0.001 0.05 0.0002 <0.001

Cr <0.001-0.007 <0.001-0.02 <0.001-0.003 0.002 0.003 0.001

Mn <0.001-0.01 <0.001-0.02 <0.001-0.01 0.003 0.004 0.003


Interestingly, the highest concentration of a number of the metals analysed were

observed at the residential site, primarily Cu and Zn. This was attributed to the fact

that most of the particles available on the paved surface were incorporated into wash-

off at the residential site due to the fine texture of the surface. Hence, the coarser

street texture recorded at the industrial and commercial site restrained metals attached

to particles from being incorporated into wash-off. Therefore, it could be more

important to implement efficient urban stormwater management measures in a

residential area. However, this would depend largely on the street surface texture.

Additionally, frequent rainfall events with a lower intensity could significantly

influence the amount of particles scoured from the surface due to lower impact

energy. This issue was not investigated in this research.

The industrial site had the highest concentrations of Pb, Fe, Mn and Cr, which were

postulated to be attributed to the metal-based industries at the site. This is further

discussed in Chapter 7.

6.4.5 Polycyclic Aromatic Hydrocarbons (PAHs)

The initial results of the analysis undertaken indicated that it was only necessary to

investigate the PAH concentrations in four different particle size classes. It was found

113

that PAH concentrations in particles larger than 300µm were low compared to the

remaining particle sizes. Less than 15% of the individual PAH concentrations were

found in particles above 300µm. Consequently, it was decided that the particle size

classes 151-300µm and >300µm could be combined without losing vital information.

The particle size classes that were analysed for PAHs therefore were:

• <0.45µm;

• 0.45-75µm;

• 76-150µm; and

• >150µm.

Similar to the heavy metal concentrations, PAH concentrations dominated in particle

sizes below 150µm, independent of study site as shown in Table 6.11, which reports

PAH concentrations in each particle size class of the build-up samples. This is

attributed to the higher adsorption affinity of finer particles due to their relatively

larger surface area and electrostatic charge (Andral et al.1999; Roger et al. 1998).

114

TABLE 6.11 PAH concentrations [mg/kg] in the build-up samples from each site

Particle size range [μm] NAP ACY ACE FLU PHE ANT FLA PYR BaA CHR BbF* BaP IND DbA BgP

<0.45 0.08 0.02 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.45-75 1.16 0.12 0.17 0.11 0.21 0.13 0.28 0.38 0.27 <0.01 0.15 0.22 <0.01 <0.01 <0.01

76-150 1.26 0.16 0.11 0.14 0.21 0.15 0.25 0.30 0.30 <0.01 <0.01 0.24 <0.01 <0.01 <0.01

Res

iden

tial

>150 0.92 0.06 0.03 <0.01 <0.01 0.03 0.05 0.54 0.21 <0.01 <0.01 0.15 <0.01 <0.01 <0.01

<0.45 0.06 0.02 0.02 <0.01 <0.01 <0.01 0.02 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.45-75 2.50 0.28 0.34 0.17 0.15 0.11 0.27 0.32 0.17 0.31 0.14 0.18 0.05 <0.01 0.03

76-150 2.15 0.35 0.40 0.12 0.13 0.10 0.10 0.15 0.16 0.31 0.11 0.16 <0.01 <0.01 0.02

Indu

stria

l

>150 1.48 0.28 0.10 0.06 0.08 <0.01 0.14 0.17 0.13 0.17 0.11 0.27 <0.01 <0.01 <0.01

<0.45 0.89 0.09 0.05 0.02 0.02 <0.01 0.03 0.01 0.03 <0.01 <0.01 0.12 <0.01 <0.01 <0.01

0.45-75 15.32 1.77 1.45 0.86 1.85 0.21 0.87 0.87 0.76 0.56 0.56 0.90 0.31 0.17 0.06

76-150 4.88 1.24 0.76 0.65 1.53 0.19 0.68 0.56 0.65 0.65 0.37 0.66 0.21 0.05 0.05

Com

mer

cial

>150 2.53 0.65 0.26 0.15 0.17 0.05 0.53 0.45 0.13 0.08 0.21 0.15 <0.01 0.01 <0.01

* Benzo[b]flouranthene (BbF) and Benzo[k]flouranthene (BkF) were measured as one parameter

115

PAH concentrations were always low in colloidal size particles in the build-up

samples. This is attributed to the low solubility of PAHs, which decreases with

increasing molecular weight (Manoli and Samara 1999). It was no surprise that the

most detected PAH in colloidal particles was NAP, which is a two-ring PAH.

PAHs were detected frequently in the build-up samples as shown in Table 6.11.

However, higher molecular weight PAHs such as IND were not detected at the

residential and industrial site. The commercial site had low concentrations of high

molecular weight, which could be attributed to the traffic volume at the site. Bae et

al. (2002) found IND to be a typical by-product of petrol engines, which supports the

higher presence of this PAH at the commercial site. This is further assessed using

multivariate methods in the recognition of sources and processes in Chapter 7.

Among the 16 individual PAHs analysed, the two-ring PAH, NAP was the most

frequently detected element in wash-off samples independent of particle size class

followed by the three-ring PAH, ACY, as shown in Table 6.12. Mean concentrations

of PAHs in each particle size range can be found in Tables B4-B6 Appendix B, for the

residential, industrial and commercial sites respectively. The commercial site had the

highest mean PAH concentrations of the three study sites including the highly

carcinogenic BaP. The frequent detection of high molecular-weight PAHs in wash-off

from the commercial site can be attributed to the higher availability of PAHs observed

in the build-up samples. Similar to the heavy metal concentrations observed at the

residential site, the PAH concentrations in wash-off from the site was frequently

higher than in wash-off from the industrial site. This was attributed to the finer texture

of the paved surface observed at the residential site. However, it could also be

attributed to higher source strength of PAHs in the residential area compared to the

industrial area. It is postulated that the lifestyle in the residential area could generate a

significant amount of PAHs through frequent car washing or application of gardening

chemicals. Furthermore, PAHs at the residential site were found above their aqueous

solubility, which could be due to petrogenic sources such as oil spills and petrol spills.

Hence, oil and grease emulsions could have been formed. This is supported by Smith

et al. (2000) who noted PAH concentrations above aqueous solubility to be due to oil

and grease emulsions passing a 0.45μm filter. Similar to this, PAHs were found above

their aqueous concentrations in wash-off samples from the commercial site, which

116

could be attributed to oil and petrol leakage from parked cars as noted by Readman et

al. (1987). In addition to this, the aqueous concentrations of PAHs observed could

also be due to the presence of organic colloidal particles as indicated by the DOC

reported in the wash-off samples. Consequently, the presence of DOC in the wash-off

samples could enhance the solubility of PAHs, which in turn could increase their

bioavailability (Warren et al. 2003; Smith et al. 2000).

TABLE 6.12 Detection frequencies (%), mean concentration (mg/kg) and

standard deviation of 16 PAHs in wash-off samples from the

residential, industrial and commercial sites

Residential Industrial Commercial

PAH DF Mean SD DF Mean SD DF Mean SD

NAP 97 0.80 0.43 100 1.11 0.81 98 2.01 2.21

ACY 53 0.11 0.07 82 0.14 0.10 85 0.33 0.26

ACE 22 0.06 0.03 77 0.17 0.13 77 0.12 0.12

FLU 14 0.09 0.04 57 0.05 0.04 67 0.16 0.18

PHE 22 0.14 0.07 57 0.08 0.06 60 0.30 0.34

ANT 31 0.12 0.06 43 0.06 0.03 54 0.07 0.05

FLA 44 0.18 0.10 82 0.09 0.07 79 0.19 0.19

PYR 44 0.14 0.09 61 0.12 0.08 69 0.18 0.14

BaA 31 0.13 0.07 70 0.08 0.05 71 0.20 0.16

CHR 6 0.13 0.03 52 0.14 0.09 60 0.13 0.11

BbF* 11 0.11 0.04 52 0.04 0.03 58 0.23 0.18

BaP 42 0.13 0.08 75 0.08 0.05 85 0.14 0.11

IND 0 <0.01 <0.01 20 0.02 0.01 31 0.08 0.05

DbA 0 <0.01 <0.01 0 <0.01 <0.01 19 0.04 0.02

BgP 0 <0.01 <0.01 34 0.02 0.01 33 0.04 0.02

DF = Detection Frequency; SD = Standard deviation from mean;

* Benzo[b]flouranthene (BbF) and Benzo[k]flouranthene (BkF) were measured as one

parameter

However, the vast majority of PAHs were associated with the particulate phase of

runoff independent of land use and rainfall characteristics, as can be seen in Table

117

6.12. This can be attributed to the primary source being pyrogenic sources such as the

incomplete combustion of fossil fuels. Potential sources of the PAHs present at the

study areas and identification of PAH distribution processes were assessed using PCA

as described in Chapter 7.

6.5 Summary

Prior to chemical analysis, particle size distribution of the build-up and wash-off

samples collected at each site were investigated. The results indicate that as much as

90% of the volume of particles in the build-up and wash-off samples was smaller than

150µm. Consequently, in order to develop an in-depth understanding of the

distribution processes occurring in build-up and wash-off from the three study sites,

the samples were fractionated into different particle size classes. Samples investigated

for heavy metal concentrations were partitioned into five particle size classes, while

samples investigated for PAH concentrations were partitioned into four different

particle size classes, due to initial analysis undertaken indicating that the majority of

PAHs were attached to particles less than 150μm.

Heavy metal and PAH concentrations were highest in the 0.45-75µm particle size

class of the build-up and wash-off samples, independent of land use and rainfall

characteristics. This coincided with the highest TSS concentrations reported.

However, individual metals and PAHs were affected by the DOC present, which

increased the concentration of PAHs and metals in the dissolved phase of runoff.

PAHs were often found in concentrations above their aqueous solubility, which was

attributed to source characteristics such as oil and petrol spills, and the presence of

colloidal organic particles introducing a solubility enhancement effect on PAHs. The

transport of PAHs and heavy metals in the dissolved phase of wash-off could have a

significant effect on aquatic organisms in receiving waters, due to their increased

bioavailability. Furthermore, a significant amount of PAHs and heavy metals are

transported by fine particles in wash-off, which could have implications on urban

stormwater measures such as street sweeping and detention basins due to their

inability in removing finer particles.

More importantly, the mean concentrations and standard deviations reported in this

chapter highlighted the need for multivariate methods in analysing complex data

118

matrices. Complimentary information such as source recognition and pattern

distribution could be identified using common chemometrics methods such as

Principal Component Analysis (PCA), which reduces the number of components

involved to a set of principal components. Furthermore, the relationships between

parameters that can be identified can also be validated using a multivariate

chemometrics approach such as Partial Least Square (PLS) regression. This in turn

could lead to an increased transferability of fundamental concepts relating to urban

water quality which is an essential problem in urban stormwater and its management.

Consequently, the advantages with a multivariate approach to urban water quality

studies can be many and are highlighted in Chapter 7 and 8.

119

Chapter 7 Pattern and Process Recognition using PCA

7.1 Introduction

Multivariate analytical methods were found to be preferable to understand the

processes governing the build-up and wash-off of PAH and heavy metals in urban

areas. The relative importance of the variables involved in distribution processes of

these pollutants was difficult to quantify using univariate analysis. Hence,

multivariate chemometrics approaches to urban water quality studies were necessary

to fully understand relationships between parameters and the processes underlying the

distribution of micro-pollutants.

Univariate analysis can only be used to compare two parameters against each other.

However, the heavy metal concentrations were most likely affected by a number of

parameters and the relative importance of each parameter was difficult to investigate

using univariate methods. Similarly, PAH concentrations could be influenced by

different physico-chemical parameters as well as the source characteristics depending

on the PAH (Kucklick et al. 1997). Therefore, multivariate analysis was preferred,

since a large data volume can be processed in order to explore and understand

relationships between variables and objects (Kokot et al. 1998).

This chapter will focus on the results obtained from the Principal Component

Analysis (PCA). PCA was employed on the data set to identify correlations between

parameters and the possible sources of pollutants at the respective study sites. PCA

was also employed to identify processes that were important in the distribution of

PAHs and heavy metals in build-up and wash-off at the three sites.

7.2 Applications of Principal Component Analysis

The most common pattern recognition method used in multivariate analysis is

Principal Component Analysis (PCA). PCA has been successfully applied to a range

of different data sets in previous research, such as process identification in river water

quality data (Petersen et al. 2001) and to identify parameter relationships of lake-

polluted sediments (De Bartolomeo et al. 2004). In spite of this, as noted by Kokot et

al. (1998), though multivariate analysis can provide complementary information and

120

often produce solutions unobtainable by conventional data interpretation, the

experimental possibilities of chemometrics applications are quite poorly understood.

This is primarily attributed to the difficulty with multidimensional thinking and the

different approaches used in multivariate analysis compared to conventional statistical

approaches. However, multivariate analysis can contribute to significant

improvements in the interpretation of urban water quality data, if applied correctly.

This commonly entails significant pre-treatment of data prior to chemometrics

methods being applied. Nevertheless, chemometrics cannot rectify poor sampling or

analytical laboratory techniques.

PCA is one of the most important techniques employed in chemometrics (Kokot et al.

1998). The central idea behind PCA is to reduce a set of data consisting of a large

number of variables to a much smaller number of orthogonal components that retain

as much as possible of the information available in the original data (Massart et al.

1990). The new variables are linear and called principal components and are estimated

from the eigenvectors of the covariance matrix of the original variables. The PCA

method is described mathematically in Appendix C page 279. The number of

principal components to be used is then determined using the scree plot method

described by Cattell (1966), or by setting the number of principal components to the

number of eigenvalues, generated by PCA, greater than 1 (Kaiser 1960). The scree

plot method, which plots the eigenvalue extracted for each principal component, was

implemented in this research. It is usually shaped like a cliff with a scree slope at the

bottom, an initially sharp descending gradient, as the most important factors are

extracted, which is followed by a gentler gradient corresponding to minor factors.

Finally, the graph flattens out completely as the point of diminishing returns is

reached. Further factors contribute little to the analysis, and may be considered to be

artifacts. PCA was especially useful in this research due to the ability to substantially

reduce the dimensionality of the original data which contained a number of physico-

chemical variables as well as eight metal elements and 16 PAHs.

To apply PCA to a data set, the original data must be arranged into a matrix with

selected variables defining the columns and the rows referred to as sample

measurements or objects. PCA can be applied to this ‘raw’ data set, or it can be pre-

treated to reduce data heterogeneity and reduce irrelevant sources of variation or

121

noise. Normal pre-treatment options include log-transformation of the original data

and column standardisation (Kokot et al. 1998; Kettaneh et al. 2005). Column

standardisation of the data means that each cell in a given column is divided by the

standard deviation of that particular column. Hence, each variable is equally weighted

with a standard deviation of one.

The application of PCA to a data matrix generates a loading for each variable and a

score for each object (build-up and wash-off samples in the case of this research) on

the principal components. Consequently, the data can be presented diagrammatically

by plotting the scores of each object on selected principal components (referred to as a

scores plot), or the loading or importance of each variable on the selected principal

components (referred to as a loadings plot). The relationships between objects and

variables can also be displayed using a biplot, which is a combination of the scores

and loadings plots. A vector drawn from the origin to a set of loadings coordinates on

the biplot represents the size of the loadings of the variables on the respective

principal components. Additionally, it indicates the likely objects (samples) with

which the specific variable is particularly associated.

The software used in this research to undertake PCA was MatLab. The PCA

algorithms used in the research were adopted from Kramer (1993) and are

summarised in Appendix C page 280.

7.3 Pre-treatment of data

Prior to analysis, the concentration data reported for each variable in the build-up and

wash-off samples (refer to Chapter 6) was log transformed to remove large variations

without interfering with the variance of the data set. As an example, TSS could vary

from 10-1000mg/L, while TOC concentration could vary between 1-8mg/L in the

build-up samples. Hence, log transforming the data removed the skewness of the data.

Similarly, the data was mean-centred in order to compensate for different units of

measurement or scale. All data below detection limit of the analytical instrument used

for determining the parameters was set to half the detection limit value in the PCA

(Guo et al. 2004). Parameters not detected were excluded from the data set when

necessary. For example, three PAHs (IND, DbA and BgP) were not detected in any

fraction of the build-up and wash-off samples from the residential site. These PAHs

122

could not be included in the analysis of the specific land use and were therefore

excluded from the residential data set prior to PCA application.

PCA was undertaken on each individual particle size class (wash-off samples only) as

well as bulk samples (build-up samples only) in order to determine trends and patterns

that might be dependent or independent of particle size. Consequently, it was

postulated that processes governing the partition of heavy metals and PAHs between a

dissolved and particulate fraction in wash-off samples could be different to

distribution processes of the pollutants in different particle size classes. The

parameters included in the PCA were given an abbreviation as listed in Table 7.1.

Particle volume percentage (PVP) of the build-up and wash-off samples was

considered an important parameter due to the increased load that can be attributed to

any pollutant associated with PVP. As Pitt (1979) noted, the shock pollutant load that

can result from a stormwater runoff event can have a detrimental impact on the

quality of receiving waters. Similarly, increases in total PAH concentrations have

been found to be mirrored by an increase in TSS concentration (Hoffman et al. 1984).

However, Hoffman et al. (1984) found individual PAHs to differ from this behaviour,

which the authors attributed primarily to difference in solubility and particle size

distribution. Hence, it is important to investigate PVP as well as TSS of urban runoff.

This could lead to significant improvements in stormwater management such as the

knowledge of which pollutants and particle sizes to target to efficiently reduce

impacts on receiving waters. Organic carbon content (TOC, DOC dependent of the

fraction) was also included in the analysis due to its ability to enhance the solubility of

PAHs and increase solid binding affinity when in particulate form (Warren et al.

2003). The influence of organic carbon on stormwater has been extensively discussed

in Chapter 2.

123

TABLE 7.1 Parameter abbreviations as used in PCA

Parameter Abbreviation

Naphthalene NAP

Acenaphthene ACE

Acenaphthylene ACY

Flourene FLU

Anthracene ANT

Phenanthrene PHE

Flouranthene FLA

Benzo[a]anthracene BaA

Benzo[b]flouranthene

Benzo[k]flouranthene BbF (measured as one parameter)

Chrysene CHR

Pyrene PYR

Benzo[a]pyrene BaP

Dibenzo[a,h]anthracene DbA

Benzo[ghi]perylene BgP

Indeno[123-cd]pyrene IND

Iron Fe

Zinc Zn

Aluminium Al

Lead Pb

Copper Cu

Cadmium Cd

Chromium Cr

Manganese Mn

Particle Volume Percentage PVP

Total Suspended Solids TSS

Total Organic Carbon TOC

Dissolved Organic Carbon DOC

Total Dissolved Solids TDS

124

7.4 Build-up samples

Only one build-up sample was collected at each site as it was considered adequate to

determine the characteristics of the pollutants available on the paved surface. This was

based on an implicit assumption that the pollutant build-up similar over the paved

surface. This was attributed to the small distance between the build-up and wash-off

plots used for the experiment. The spatial variation was considered minimal in such a

small area. As discussed in Chapter 6, the samples obtained were separated based on

the particle size distribution and pollutants investigated. The large variance observed

in the data set, together with the large number of variables investigated, made it

difficult to draw any conclusions from univariate analysis, even though only five

objects at each site were investigated. PCA was undertaken on build-up data from the

three study areas separately. Consequently, processes that were responsible for the

distribution of PAHs and heavy metals in accumulated particles on the street surfaces

could be identified and compared for each site. It was decided to undertake PCA on

the separate build-up samples due to pollutant concentrations being different at the

three sites, which could skew the PCA. Moreover, the detection of PAHs was very

site-specific in the build-up samples as shown in Chapter 6, which could further bias

the results from PCA.


The scree plot method developed by Cattell (1966) was adopted to determine the

number of principal components to use in describing the PCA results of the residential

data. The number of principal components was determined as two by identifying the

position of the elbow or steep reduction in slope in the scree plot as shown in Figure

7.1. PC1 and PC2 contained 94.1% (81.4% and 12.7% respectively) of the total

variance in the build-up data at the residential site.

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FIGURE 7.1 Scree plot for the determination of number of components to use in

exploring residential build-up data

The loading of each variable on PC1 and PC2 is shown by the loadings plot in Figure

7.2.

FIGURE 7.2 Loadings of each variable on PC1 and PC2 obtained from PCA on

residential build-up data

ELBOW

FLU, TSS, BbF, Zn, PHE

Cd, BaA, Cr, NAP, BaP, Al, Fe

Quadrant 1

Quadrant 2

126

Since variables in general agreement are orientated in the same direction in the

loadings plot (Kokot et al. 1998), it can be seen that a number of metals and PAHs

were inter-related. Furthermore, two quadrants of the loadings plot contained all the

metal and PAH data. These were:

• Quadrant 1 - Variables with a positive loading on both PC1 and PC2; and

• Quadrant 2 - Variables with positive loading on PC1 but negative loading on PC2.

The majority of PAHs were associated with Quadrant 2, while the majority of metals

are associated with Quadrant 1. However, most of the data variance was explained in

PC1 (81.4%). The positive loading of all heavy metals and PAHs on PC1 indicated

that a majority of PAHs and metals originated from a similar source. It is postulated

that the primary input of the metals and PAHs was traffic-related. Similar results

have been found by numerous researchers (Bomboi and Hernandez 1991; Harrison et

al. 2003; Manoli et al. 2000). The PAHs were separated by the loadings on PC2

depending on their molecular weight. As can be seen in Figure 7.2, PAHs with 3- and

4-rings were associated with Quadrant 1 and 4- or higher ring PAHs were associated

with Quadrant 1. This suggests that additional sources were responsible for some of

the PAHs present at the residential site. It is postulated that atmospheric deposition

and the lifestyle of the area could have been responsible for an appreciable amount of

PAHs being deposited on the road. This is supported by Bae et al. (2002) who

suggested that higher molecular weight PAHs were mainly associated with air-borne

particles. Nevertheless, atmospheric particulate bound PAHs are mostly deposited

regionally while PAHs in the gas phase (2- to 3-ring PAHs) can be transported longer

distances (Bucheli et al. 2004). Therefore, the PAHs associated with Quadrant 2 in

Figure 7.2 were most likely introduced by neighbouring industrial activities or

particles originating from vehicle exhaust in the area, while an appreciable amount of

the low molecular-weight NAP (Quadrant 1) could have been transported over a long

range.

Bomboi and Hernandez (1991) found the presence of PAHs at residential and

landscaped areas to be derived from gardening products and higher vascular plants,

especially NAP. Consequently, NAP could have originated from numerous sources,

127

both locally and long-range transported. However, the importance of PAHs in

Quadrant 1 in urban stormwater management is debatable. This is due to the

correlation between PAHs in Quadrant 2 and PVP and TSS. As a result, PAHs in

Quadrant 2 could potentially have a lower concentration than PAHs in Quadrant 1,

but carry a higher load of PAHs to receiving waters due to the affinity with TSS and

PVP. This finding is of significant importance in developing best management

practices for residential areas. Hence, removing the particle size range with the

highest TSS concentration and PVP percentage could significantly reduce the amount

of PAHs being incorporated into runoff. Additionally, PAHs and heavy metals in

Quadrant 2 were associated with particles between 0.45-150µm as can be seen by its

score in the scores plot in Figure C1 Appendix C. This suggests that current street

sweeping measures in the residential area could be inefficient in removing PAHs and

heavy metals from the paved surface due to limitations in street sweeping techniques,

as observed by previous researchers (Bender and Terstriep 1984; Sutherland et al.

1998).

Similar to the PAHs, the heavy metals were separated by the loadings on PC2 into

Quadrant 1 and Quadrant 2. The majority of the metals were associated with Quadrant

1 except Zn which was associated with Quadrant 2. It is postulated that an additional

source to traffic, such as the corrosion of galvanised roofs in the residential area is

responsible for this separation on PC2. Furthermore, it is postulated that an

appreciable amount of metals would originate from the local soils. This is

strengthened by the correlation between Al and Fe in Quadrant 1 in Figure 7.2.

Previous research has shown that soil dust can account for over 60% of the Al and Fe

load (Pierson and Brachaczek 1983), confirming that the deposition of eroded soil

particles is an important pollutant source at the residential site. Pb and Mn are

primarily traffic-related metals, which are shown by their low loading on PC2. Cu is

separated from the other metals in Quadrant 1 suggesting an additional source could

be contributing to the street deposited sediments. Ball et al. (1998) attributed the

presence of Cu in residential areas to by-products from garden refuse. In addition to

this, Makepeace et al. (1995) suggested that Cu is present in a number of insecticides

used in residential areas. Hence, it is postulated that an appreciable amount of Cu

originated from gardens in the area.

128

No correlation was found between TOC and heavy metals and PAHs in the build-up

at the residential site. This is contradictory to previous research findings (Gustafsson

et al. 1997; Hamilton 1984), where organic matter has been found to play a major role

in the distribution of PAHs and heavy metals in sediments. However, TOC has a

negative loading on PC1, suggesting that TOC is mainly derived from natural sources

such as leaf and twig fragments in the residential area. Furthermore, grass clippings

could contribute to the TOC present on the paved surface. This in turn could affect

interactions between particulate organic carbon and micro-pollutants. As Warren et al.

(2003) noted, differences in the nature of the organic matter, such as the source, could

significantly reduce or increase the effect (if any) TOC has on the partitioning of

PAHs and heavy metals. Additionally, Kucklick et al. (1997) observed variations in

PAH concentrations that could be attributed to the proximity of sampling location to

sources. Moreover, TOC content was associated with the smallest size fraction of the

build-up sample at the residential site, as can be seen in the scores plot Figure C1

Appendix C, where the <0.45µm fraction of the build-up samples has a negative score

on both PC1 and PC2. PAH and heavy metal concentrations recorded in this particle

size range were low, which could explain the lack of correlation found with TOC.


Similar to the residential site, most of the data variance in street sediments collected at

the industrial site could be explained by the first two principal components (75.9%

and 16.2% respectively) as shown by the elbow in the scree plot Figure C2 Appendix

C. 14 of the 15 PAHs analysed (BbF and BkF measured as one PAH) were detected

in sediments collected at the industrial site. The only PAH not detected were DbA,

which is a high-molecular weight compound. PAHs not detected were not included in

the PCA.

Identical to the residential site, all the metals and PAHs detected in build-up samples

from the industrial site were associated with two quadrants in the loadings plot, as

seen in Figure 7.3. These were:

• Quadrant 1 - Variables with a positive loading on both PC1 and PC2; and

• Quadrant 2 - Variables with positive loading on PC1 but negative loading on PC2.

129

Most of the PAHs and heavy metals were separated only by the loading on PC2 as

seen in Figure 7.3. Consequently, a positive loading on PC1 indicated common

sources in the industrial environment. This is attributed to vehicle emissions and

industrial activities at the site. Sharma et al. (1997) suggested similar source

contributions in an industrial land use. However, the relative contribution from

industrial activities would depend largely on the industrial processes at the site.


industrial build-up data

Eleven of 14 detected PAHs were associated with Quadrant 2 as shown in Figure 7.3.

PAHs in Quadrant 2 were closely clustered suggesting that many of the PAHs

originated from a source other than vehicle emissions, most likely steel production at

the site (Sharma et al. 1997). IND and BgP were associated with Quadrant 1 in Figure

7.3. This is attributed to heavy vehicle emissions since IND and BgP are typical by-

products of diesel combustion (Bae et al. 2002). Therefore, the primary source of

PAHs at the industrial site was traffic related while other sources responsible for the

different loadings observed on PC2 are suggested to be industrial activities (Quadrant

2, negative loading on PC2) and by-products from diesel engines (PAHs in Quadrant

1, positive loading on PC2).

ACE, ACY, BaP, FLA, FLU, PHE, NAP, PYR, BaA, CHR, BbF

Zn, Pb, Cu, Al, Mn

Quadrant 1

Quadrant 2

130

Pb, Cu and Cr were closely correlated and associated with PC1 at the industrial site

which suggested that vehicular emissions, combined with wear from vehicles and the

street surface were major contributors of these heavy metals to the build-up. This is

supported by Sansalone et al. (1996). Pb, Cu and Cr were also closely correlated to

TSS, which suggests that high loads of these metals were available for wash-off.

Similar findings have been found by several researchers (Drapper et al. 2000;

Vermette et al. 1991). An appreciable amount of Pb and Cu could be attributed to

various industrial processes at the site (Droppo et al. 1998). This is supported by the

positive loading on PC2. Additionally, Zn and Mn were associated with Quadrant 1,

which has been attributed to industrial activities. Yun et al. (2000) found Zn and Mn

to be attributed to the increase in metal industries, which supports this hypothesis. Al

and Fe are postulated to be primarily related to soil conditions at the site (Pierson and

Brachaczek 1983). The low loading of both Al and Fe on PC2 suggests that additional

sources contribute very little compared to the local soil.

Similar to the residential site, TOC showed no relationship to the distribution of PAHs

or heavy metals. This is attributed to the highest concentration of TOC being found in

the particle size <0.45μm (as discussed in Chapter 6), which was poor in PAHs and

heavy metals. The industrial area was also surrounded by vegetation suggesting that

the majority of TOC could be attributed to broken down leaf and twig fragments.

As can be seen in the scores plot, Figure C3 Appendix C, Quadrant 1 is associated

with particles between 0.45 and 75µm while Quadrant 2 is associated with particles

between 76 and 150µm. PAHs and heavy metals at the industrial site were

predominantly attached to particles below 150μm. This is attributed to the larger

surface area and electrostatic charge of fine particles (Evans et al. 1990). However,

PAHs were primarily associated with particles in the range 76-150µm, while metals

were associated with particles in the range 0.45-75µm. This was attributed to PAHs

being tightly bound to particles and unlikely to undergo any post-depositional changes

(Readman et al. 1987; Simpson et al. 1996; McCready et al. 2000). Consequently,

metals could be more susceptible to sorption processes due to the larger surface area

of fine particles than PAHs. However, this would depend largely on the source

characteristics at the site, as metals have been found to be irreversibly trapped in

131

particles as well (Charlesworth and Lees 1999). As noted at the residential site, PAHs

and heavy metals were both associated with 0.45-75µm particles, which were

attributed to an appreciable amount of PAHs being deposited from the atmosphere.

However, the deposited PAHs in fine particles at the industrial site could have been

dispersed by heavy vehicle traffic and deposited elsewhere. Furthermore, it is likely

that by-products from the industrial processes in the area could have generated PAHs

irreversibly bound to particles in the size range 76-150µm due to the strong

correlation between PAHs and the specific particle size.


All PAHs and heavy metals investigated were detected at the commercial site,

suggesting a wide range of sources and inputs. Similar to the residential and industrial

data analysis, the first two principal components contained the majority of the data

variance (73.4% and 18.5% respectively), as seen in the scree plot, Figure C4

Appendix C.


commercial build-up data

As can be seen in the loadings plot, in Figure 7.4, the PAH and heavy metal

distribution was slightly different compared to the industrial and residential areas.

PC1 (73.4%)

PC2

(18.

5%)

PAHs with positive loading on PC1

PAHs with negative loading on PC1

132

However, all PAHs were associated with a positive score on PC1, suggesting that a

similar source was responsible for the presence of PAHs in street-deposited sediments

at the commercial site. This was attributed to the high vehicular traffic observed at the

site. A similar PAH distribution due to high vehicle traffic was suggested by Mahler

et al. (2004) in simulated runoff from parking lots in Austin, USA. Conversely, the

different loadings on PC2 of the PAHs suggest an additional source of PAHs. This

was attributed to petrogenic sources such as accidental spills and atmospheric

deposition, which has been found to contribute significantly to the PAH concentration

in urban areas (Bucheli et al. 2004; Mahler et al. 2004). In addition, the presence of

petrogenic sources such as oil and grease is strengthened by the PAH concentrations

found in particles less than 0.45µm. PAHs were frequently found in concentrations

above their aqueous solubility, which could be attributed to micro-emulsions of oil

and grease increasing the apparent solubility of PAHs (Smith et al. 2000). It is likely

that PAHs with a negative score on PC2 are related to accidental oil and grease spills

in addition to vehicle emissions. Furthermore, PAHs related with TSS (positive score

on PC2) are likely to have an appreciable contribution from atmospheric deposition,

as well as local traffic emissions.

The heavy metals detected in the build-up sample from the commercial site were

widely scattered in the loadings plot as shown in Figure 7.4. Additionally, Zn and Mn

had negative loadings on PC1, which suggests that the primary contribution of these

metals could have been due to sources additional to vehicle emissions. It is postulated

that vehicle component wear and brake linings are an appreciable source of these

metals at the commercial site. This is supported by Vermette et al. (1991) who found

the presence of Zn and Mn could be attributed to brake linings and tyre wear. It is

likely that the frequent acceleration and deceleration of vehicles at the commercial

site significantly influenced the presence of Zn and Mn. Furthermore, Zn and Mn

were found in high concentrations in particles smaller than 0.45µm, as can be seen in

the scores plot, Figure C5 Appendix C, where particles <0.45µm has a negative score

on PC1 and positive score on PC2. Hence, Zn and Mn were associated with dissolved

solids. This is supported by Makepeace et al. (1995) who suggested that these metals

are primarily associated with dissolved solids, especially Zn. The association between

Zn, Mn and dissolved solids could be attributed to two primary factors. Firstly, Zn

and Mn could have been irreversibly trapped in tyre wear particles and broken up into

133

smaller particles by the frequent vehicle traffic at the site. Secondly, Zn and Mn could

have been sorbed by colloidal organic particles at the site. As discussed in Chapter 6,

the highest concentration of TOC was found in particles less than 0.45µm at the

commercial site. This could have led to complexation of Zn and Mn and consequently

enhanced their bioavailability when incorporated into wash-off (Westerhoff and

Annning 2000). More importantly, this could have implications on urban stormwater

management measures at the site. Hence, removing particles could reduce a relatively

high load of metals and PAHs as seen in Figure 7.4 where most of the PAHs and

heavy metals are associated with TSS and PVP.

Similar to the residential and industrial sites, Fe and Al were closely correlated and

had low loadings on PC2, suggesting that one primary source was responsible for the

presence of these metals in road deposited particles. This is attributed to the soil

conditions at the site, which were similar to the residential and industrial sites.

However, it is postulated that an appreciable amount of Fe could be derived from

vehicle wear at the site. Similarly, in reviewing stormwater concentration data from

140 projects, Makepeace et al. (1995) suggested that the corrosion of vehicular bodies

could be a significant source of Fe.

Cd was found in low concentrations at the commercial site as shown in Chapter 6.

Nevertheless, as can be seen in the loadings plot Figure 7.4, Cd is closely correlated

with PVP. Hence, Cd could impose a significant threat to water quality due to the

higher loads that are associated with PVP. In addition to this, Cd is highly toxic and is

associated with dissolved solids or colloidal material in stormwater runoff

(Makepeace et al. 1995). This is supported by the loadings plot, Figure 7.4, and the

scores plot, Figure C5 Appendix C, where Cd and particle size classes <0.45µm and

0.45-75µm have high loadings and scores on PC2 respectively.

TOC at the commercial site, similar to the residential and industrial sites, does not

show any visible correlation with PAHs or heavy metals in the build-up sample as

shown in Figure 7.4. This is attributed to the highest concentrations of TOC being

recorded in particles less than 0.45µm and particles above 150µm as discussed in

Chapter 6. These particle size classes were poor in PAHs and heavy metals with the

exception of Zn and Mn in particles below 0.45µm. However, TOC in colloidal

134

particles could potentially have influenced the wash-off of PAHs and heavy metals

which is discussed in Section 7.3. This is due to the solubility enhancement effect

observed by Warren et al. (2003) and Smith et al. (2000) where organic matter in

particles dissolved into solution and increased the ratio of PAHs and metals in the

dissolved phase.

7.5 Wash-off samples

Twelve different rainfall events were simulated using the rainfall simulator at each

site. However, no runoff was collected for three events at the residential site and one

event at the industrial site due to water shortage occurring at the time of sampling.

Additionally, the particulate fraction was separated into four different particle sizes

and tested for PAH and heavy metal concentrations individually. Consequently, 9 to

12 objects in each particle size were analysed using PCA for PAHs and heavy metals

depending on the site. Refer to Table 7.1 for parameters included in PCA of the wash-

off data.


Nine runoff events were generated by the rainfall simulator at the residential site.

Accordingly, nine data objects for each fraction of the wash-off sample were analysed

using PCA.

Dissolved fraction (Particles <0.45µm)

Only three PAHs (ACY, NAP and BaP) were detected in the dissolved fraction of

runoff at the residential site. This is attributed to the low solubility of PAHs (Manoli

and Samara 1999). Consequently, a large number of PAHs were not included in PCA

of the dissolved data. Three of eight metals analysed (Pb, Cr and Cd) were not found

in the dissolved fraction and were excluded from PCA. The number of principal

components to use in the analysis was again determined using the scree plot test

developed by Cattell (1966). As can be seen in the scree plot, Figure C6 Appendix C,

the elbow or sudden inflexion in the plot occurred at the third principal component.

Hence, the scree plot suggested that three principal components were needed to

visually explore the data. The three principal components contained 36.2%, 30.4%

and 12.7% of the total data variance respectively. Nevertheless, by visually exploring

the two- and three-dimensional loading plots, Figure 7.5 and Figure C7 Appendix C

135

respectively, adding a third principal component did not provide any additional

information to that already visible in the two-dimensional loadings plot (Figure 7.5).

Hence, PC1 and PC2 provided the majority of the useful information relating to the

dissolved PAH and heavy metal data.


data in the dissolved fraction of wash-off samples from the residential site

As seen in Figure 7.5, the metals and PAHs are spread out on both PC1 and PC2. This

suggests that a number of different processes were governing the distribution of PAHs

and metals in the dissolved fraction at the residential site. BaP was only detected in

one of the nine wash-off samples in the dissolved fraction. However, when detected, it

had a concentration above its aqueous solubility. Furthermore, the detection of BaP

coincided with the highest pH recorded of the wash-off samples. Hence, the increase

in pH could potentially have led to desorption of BaP from its strong particulate

binding observed by many researchers (for example Hoffman et al. 1985 and Wang et

al. 2001). Potentially, this could lead to significant degradation of the quality of

receiving waters due to the increased bioavailability and mobility of PAHs in the

dissolved phase (Westerhoff and Anning 2000). Nevertheless, the low detection of

BaP in the dissolved fraction made it difficult to establish a reliable relationship with

136

pH. In addition to this, previous researchers have suggested low molecular weight

PAHs to be more susceptible to a pH change than higher molecular weight PAHs

(Gao et al. 1998; Warren et al. 2003). No substantial conclusion can be made on the

relationship between dissolved BaP and pH as a result of this research.

The two remaining PAHs were frequently detected in the dissolved fraction of runoff

from the residential site. As can be seen in Figure 7.5, they were both correlated with

the particle volume percentage of the dissolved fraction. Hence, NAP and ACY

exhibited peak concentrations in the dissolved fraction when the volume of colloidal

size particles was high. Consequently, the above solubility concentrations of PAHs

were attributed to the technique used in separating the dissolved fraction from the

particulate fraction. However, the formation of oil and grease emulsions could have

contributed to the aqueous solubility observed (Smith et al. 2000).

Cu and Zn exhibited the highest concentrations of metals in the dissolved fraction. As

can be seen in Figure 7.5, they were both correlated with the EC of the stormwater.

This is attributed to the enhanced sorption affinity of dissolved solids occurring when

the EC is changed as observed by Pechacek (1994). This in turn could have led to an

increase of Zn and Cu in the dissolved fraction of runoff. This is strengthened by the

correlation between TDS and EC in Figure 7.5. In spite of this, the relative importance

of TDS concentrations compared to EC on the distribution of metals and PAHs can be

questioned due to the small loading of TDS on both PC1 and PC2. Hence, as Kokot et

al. (1998) noted, low loadings reflect unimportant variables.

None of the detected metals in the dissolved fraction of wash-off were correlated with

pH. However, the PCA loadings plot shown in Figure 7.5 could still provide

information on the importance of a pH change in runoff and its effect on metals. This

is attributed to the fact that non-correlated parameters could potentially have an

inverse relationship with each other if the angle between the variables in the loadings

plot is close to 180o. This suggests that a decrease in pH could potentially increase the

concentration of metals in the dissolved fraction. A similar relationship was proposed

by Tai (1991), who found that the water/sediment ratio of Zn increased with a

decrease in pH. Hence, pH could significantly influence the bioavailability of metals

in runoff from the residential site.

137

Al, Fe and Mn were correlated with the organic carbon available in the dissolved

phase (DOC) as seen in Figure 7.5. These metals were all attributed to the local soil.

Hence, their correlation with the organic fraction of the colloidal particles could be

attributed to a large percentage of the dissolved organic carbon originating from the

soil at the site. However, their relationship with DOC could have serious

consequences for aquatic organisms due to the microbial degradation of organic

carbon. Thus, the metals could be released into the water column and be readily

bioavailable if bacterial levels are high enough. High bacterial levels in street surface

runoff has been found by Charlesworth and Lees (1999) and Ellis and Revitt (1982).

Furthermore, as seen by comparing the score of each rainfall event in Figure C8

Appendix C and the loading of each variable on PC1 and PC2 shown in Figure 7.5,

the majority of metals and PAHs detected in the dissolved fraction had an affinity to

be washed off during 1- and 2-year ARI events. This suggests that fairly frequent

occurring rainfall events, such as 1-year ARIs, could carry significant heavy metal

concentrations in the dissolved phase. Hence, structural measures designed to

decrease the impact on receiving waters during 10- or 20-year ARI events could be

inefficient in removing the majority of PAHs and heavy metals being washed off

during more frequent events.

Particle size class 0.45-75µm

The detected metals and PAHs at the residential site exhibited the highest

concentrations in particles between 0.45µm and 75µm. This coincided with the

highest TSS concentrations recorded. Hence, identifying processes governing the

distribution of PAHs and heavy metals in this particles size class could lead to

fundamental knowledge on the mitigation of urban stormwater impacts due to the

high pollutant load that could be carried by this particle size class to receiving waters.

Most of the variance in the data set could be explained by the first three principal

components as shown by the scree plot in Figure C9 Appendix C. The first three

principal components, PC1, PC2 and PC3 accounted for 39.4%, 22.3% and 10.3% of

the data variance respectively. Nevertheless, the three-dimensional loadings plot,

shown in Figure C10 Appendix C, revealed similar relationships that could be

138

identified in the two-dimensional loadings plot, Figure 7.6. Consequently, the two-

dimensional loadings plot was used for pattern and process recognition and

interpretation.


data in particle size class 0.45-75µm from the residential site

The PAHs and heavy metals were widely scattered in the loadings plot (Figure 7.6).

However, a number of patterns can be identified. Firstly, the majority of the analysed

heavy metals (Cr, Pb, Cu, Zn and Mn) have a positive loading on both PC1 and PC2.

This is mirrored by the organic carbon content (TOC). As noted by Qu and

Kelderman (2001), most heavy metals have a good correlation with TOC. As a result,

metals interact with organic matter leading to chelation or complexation processes,

which concentrates the metals by adsorbing them onto finer particles. More

importantly, high bacterial levels in street surface runoff could lead to subsequent

mobilisation of metals from particles leading to an enrichment of metals in the soluble

phase (Charlesworth and Lees 1999). Consequently, urban stormwater management

measures such as retention basins, where particles are allowed to settle, could be

inefficient in removing heavy metals if bacterial levels are high and metals are bound

PHE, FLU, PVP

ACY, FLA, PYR, BaA, CHR

139

to the organic matter, due to the increased metal concentrations in the dissolved

fraction this would instigate.

Furthermore, none of the metals were correlated with pH, which suggests that an

increased adsorption of metals occurred when the pH of the runoff water decreased. A

similar observation was found for the dissolved fraction at the residential site, which

is supported by Tai (1991). Hence, a decrease in the pH of urban stormwater led to

increased metal interactions independent of the fraction of runoff. Moreover, Cd was

correlated with EC of the wash-off as seen in Figure 7.6, which supports the increased

adsorption affinity of particles attributed to an increase in EC, noted by Pechacek

(1994). However, Cd was only detected in a few of the samples at the residential site,

making it difficult to establish any relationship it had with physico-chemical

parameters. Al and Fe were closely correlated, which is attributed to the wash-off of

particles originating from the soil at the site (Pierson and Brachaczek (1983).

The majority of PAHs found in wash-off particles at the residential site were highly

inter-related and had a negative load on both PC1 and PC2, as seen in Figure 7.6.

Furthermore, a close correlation between IC and PAHs was found. A similar

relationship was found by De Bartolomeo et al. (2004) when investigating particles

deposited by urban stormwater in an Italian lake. Yet, major differences were reported

based on sampling periods. De Bartolomeo et al. (2004) found that PAHs were mainly

associated with IC during the winter months. Although the rainfall simulation at the

residential site was undertaken in wintertime, the differences in climate between

South-East Australia and Italy are immense. Hence, similarities between the studies

would be difficult to determine. It is not clear why IC is closely correlated with PAH

at the residential site. However, petroleum contamination such as oil spills in the

street could increase the levels of both IC and TOC (LaRiviere et al. 2003). Therefore,

particles already deposited on the road surface prior to the oil or petrol spill could

potentially have adsorbed a significant amount of PAH already bound to inorganic

carbon.

More importantly, PHE, FLU and BbF were correlated with both PVP and TSS in

particles 0.45-75µm. This suggests that a significant load of these PAHs could be

transported by finer particles to receiving waters. Hence, it is important to prevent

140

these PAHs reaching receiving waters due to their association with TSS and PVP and

the shock load that could be introduced. Additionally, PHE, FLU and BbF were

associated with more frequent rainfall events than other PAHs, which is attributed to

the positive score of 1- and 2-year ARIs on PC1, as shown in the scores plot in Figure

C11 Appendix C. Similarly, the majority of the heavy metals were associated with

more frequent events.

Particle size class 76-150µm

As can be seen in the scree plot, shown in Figure C12 Appendix C, the number of

principal components potentially containing important information on the processes

governing the distribution of PAHs and heavy metals was five. However, interpreting

the information retained in five principal components was extremely difficult. Hence,

the number of principal components to use had to be reduced. Therefore, the method

developed by Kaiser (1960) was used to reduce the number of principal components

to three by only including components containing more than 10% of the total

variance. PC1, PC2 and PC3 contained 31.2%, 23.2% and 17.7% of the total variance

respectively and were chosen to diagrammatically investigate PAH and heavy metal

wash-off processes. Nevertheless, by visually comparing the three-dimensional

loadings plot (Figure C13 Appendix C) and the PC1 versus PC2 loadings plot (Figure

7.7), it was found that only using PC1 and PC2 were sufficient in determining patterns

and relationships between the variables. Hence, adding a third principal component

(PC3) contributed little to the information that could be extracted from the loadings

plots. However, the relationships between variables observed in the PC1 vs. PC2

loadings plot should be interpreted with care due to PC1 and PC2 only containing

54.4% of the total variance of the data.

As can be seen in Figure 7.7, heavy metals and PAHs are once again spread out in the

loadings plot. This is attributed to the negative loadings of PAHs on PC1 (with

exception of ACY and ACE) while metals had positive loadings on PC1 (with

exception of Cd). Hence, different processes are governing their distribution in the

specific particle size class. Primarily two different processes are separating the metals

and PAHs in the loadings plot (Figure 7.7). Firstly, the majority of PAHs are

correlated with TOC. This can be explained by the increased adsorption affinity of

hydrophobic pollutants onto particles attributed to TOC (Roger et al. 1998). Secondly,

141

the majority of metals are correlated with EC of the urban stormwater. Hence, an

increase in EC of the runoff could potentially have led to an increase in heavy metal

adsorption to particles. Pechacek (1994) observed a similar relationship. Furthermore,

both metals and PAHs had no correlation with pH in the loadings plot suggesting an

inverse relationship between pH and the adsorption of PAHs and heavy metals to

particles. The similar relationship between PAHs and IC found in particles 0.45-75µm

(Figure 7.6) can also be seen in Figure 7.7. This strengthens the importance of oil and

petrol spills in the distribution of PAHs at the residential site as discussed in particles

0.45-75µm.

FIGURE 7.7 Loading of each variable on PC1 and PC2 obtained from PCA on

data in particle size class 76-150µm from the residential site

PAHs and heavy metals exhibited their highest concentrations during 1- and 2-year

ARI events, as seen by the positive score on PC2 observed by these events in the

scores plot, Figure C14 Appendix C. This further strengthens the need for urban

stormwater management measures to be effective for frequent rainfall and runoff

event.

Cd, DUR

142

Particle size class >150µm

Heavy metals and PAHs were less frequently detected in particles above 150µm

compared to finer particle size classes. This has been attributed to the relatively large

surface area of fine particles compared to coarse particles (Andral et al. 1999; Sartor

and Boyd 1972). Only eight of the 16 analysed PAHs were detected in this particle

size. The scree plot (Figure C15 Appendix C) revealed that three principal

components contained most of the data variance. However, adding a third component

to the loadings plot (refer to Figure C16 Appendix C) did not contribute to the

identification of processes responsible for the distribution of PAHs and heavy metals

in washed-off particles above 150µm.

FIGURE 7.8 Loading of each variable on PC1 and PC2 obtained from PCA on

data in particle size class >150µm from the residential site

Similar to the fine particle size classes at the residential site, the PAHs and heavy

metals are separated by negative (PAHs) and positive (heavy metals) loadings on

PC1, as can be seen in Figure 7.8. In spite of this, the processes governing the

distribution of PAHs and heavy metals in particles above 150µm are slightly different

to the finer particles. This is attributed to TOC being correlated with heavy metals in

particles above 150µm. This is contradictory to the results obtained for particles 76-

143

150µm, where the TOC was correlated with most of the PAHs. Hence, the PAHs and

heavy metals differ in sorption behaviour depending on particle size. This can be

attributed to a number of reasons. Firstly, there could be differences in the source of

the organic matter (DeWitt et al. 1992). Secondly, PAHs have been found to be

irreversibly bound to soot carbon content, which could have influenced the

relationship found between PAHs and TOC in particles 76-150µm (Gustafsson et al.

1997). Thirdly, variations in PAH binding to organic carbon have been found to be a

function of pH and temperature (Warren et al. 2003). Furthermore, TOC is correlated

to the EC of the stormwater as can be seen in Figure 7.8, which could adversely

influence the adsorption of PAHs. Warren et al. (2003) noted that less-polar organic

matter gives rise to a better environment for the sorption of PAHs. More importantly,

microbial degradation of the organic matter could lead to the release of metals into the

water column, which in turn could impose a significant threat to aquatic organisms

due to the increased bioavailability.

Similar to finer particles, the adsorption of heavy metals to particles above 150µm at

the residential site is dependent on the EC of the runoff water. Hence, a rise in EC of

the stormwater led to an increased amount of heavy metals adsorbing to particles

independent of its size. Additionally, pH displayed an inverse relationship with the

adsorption of PAHs and heavy metals to particles at the residential site. This is

contradictory to the results obtained by Tai (1991) who found that a rise in pH

favoured the adsorption of metals to particulates. However, the small changes in pH

observed at the residential site due to the fairly constant pH in the rainfall used for

simulating runoff makes it difficult to fully evaluate the influence of pH on the

sorption of pollutants.

The majority of PAHs detected in the specific particle size class were inter-related as

can be seen in Figure 7.8. PAHs were correlated with TSS concentrations suggesting

that they were strongly bound to particles in the specific size class. Consequently, the

PAHs in the specific particle size class were less susceptible to chemical distribution

processes. This is attributed to the characteristics of the sources available (Latimer et

al. 1990; Larkin and Hall 1998). The percentage of natural sources of PAHs could be

higher in residential areas due to the proximity to parklands and forests. More

importantly, urban stormwater management measures based on gravity settling have

144

the potential to remove a large percentage of the PAHs absorbed to particles above

150µm. Conversely, when compared to the concentrations and loads of PAHs that

could be introduced by particles below 150µm, only a small fraction of the total load

could be removed by gravity settling.

The intensity and duration of the rainfall had an insignificant effect on the wash-off of

PAHs and heavy metals adsorbed to particles above 150µm. Similar to fine particles

at the residential site, the more frequent events such as 1- and 2-year ARI events were

more important in the wash-off of PAHs and heavy metals. This is shown by the

positive score of 1- and 2-year events on PC2 as seen in the scores plot, in Figure C17

Appendix C.


Eleven runoff events were generated by the rainfall simulator at the industrial site.

Thus, eleven data objects for each fraction of the wash-off sample were analysed

using PCA.


Similar to the dissolved fraction of runoff at the residential site, PAHs were rarely

detected. Only six of the 16 analysed PAHs were detected. Three (Cr, Cd and Fe) of

the eight metal elements analysed were below detection limit in the dissolved fraction.

As shown by the scree plot, in Figure C18 Appendix C, three principal components

were preferred to interpret the results from the PCA. The three principal components

contained 78.2% of the data variance (46.3%, 21.2% and 10.7% respectively).

However, the three dimensional loadings plot (Figure C19 Appendix C) did not reveal

any additional information to the two-dimensional loadings plot (Figure 7.9). Hence, a

two-dimensional loadings plot of PC1 and PC2 was sufficient to visually explore the

results from the PCA.

145


the dissolved fraction of wash-off from the industrial site

As can be seen in Figure 7.9, the PAHs and heavy metals were associated with

positive loadings on PC1, which suggests that similar processes were governing their

distribution in the dissolved fraction of runoff at the industrial site. These processes

were attributed to:

• Organic complexation;

• Increased adsorption affinity due to an increase in EC; and

• Inverse relationship with pH.

The primary process distributing the PAHs and heavy metals in the dissolved fraction

at the industrial site was the relationship with DOC. As can be seen in Figure 7.9, six

of seven detected PAHs (BaP, BgP, NAP, BaA, ACY and ACE) and two of the five

detected metals (Zn and Pb) in the dissolved fraction were correlated with DOC.

Furthermore, BaP, BgP and BaA were detected above their aqueous solubility during

the rainfall events. This is attributed to their correlation with DOC. Warren et al.

(2003) noted the solubility enhancement effect of PAHs when bound to dissolved

organic carbon. However, as Smith et al. (2000) noted, the excess concentrations of

PAHs in the dissolved phase can also be attributed to the common definition of the

ACE, ACY

BgP, NAP, Pb, Zn, DOC

146

aqueous phase. Consequently, sub-micron (<1μm) particles passing through a 0.45µm

filter could be responsible for the above aqueous PAH concentrations observed. In

spite of this, no correlation with dissolved solids (TDS) was found for the PAHs,

(except FLA) suggesting that the PAHs were in fact available in a ‘freely’ dissolved

phase and readily bioavailable. It is postulated that the colloidal organic matter has in

fact degraded through various biological activities and hence has brought the PAHs

into solution. Ellis and Revitt (1982) confirmed that biological activity could

decompose colloidal organic matter. However, PAHs in the dissolved phase are also

more susceptible to biodegradation processes such as photo oxidation (McGroddy et

al. 1996). Hence, a detention basin could potentially remove PAHs in the dissolved

phase if the stormwater is stored long enough.

A correlation between DOC and Zn has been found by several researchers (Qu and

Kelderman 2001; Sansalone and Buchberger 1997). This was attributed to the

removal of ionic Zn by organic complexation (Ellis et al. 1987). Additionally, the

majority of PAHs and heavy metals in the dissolved phase showed an inverse

relationship with the pH of the runoff. This was similar to the residential site and is

supported by findings by Tai (1991).

No correlation between PAHs, heavy metals and rainfall intensity could be observed

in the dissolved fraction at the industrial site. Independent of the rainfall intensity, the

positive loadings on PC1 coincided with positive scores on PC1 obtained by 1- and 2-

year design rainfall events with short durations as seen in the scores plot, Figure C20

Appendix C. Thus, higher pollutant concentrations were observed in frequent rainfall

events. It is also important to note that the pollutant load could potentially be higher in

a less frequent rainfall event due to the increased volume of runoff. Hence, measures

taken to improve or protect existing water quality should not be influenced by rainfall

characteristics such as rainfall volume, as often is the case when water quantity and

quality measures are combined.


Only one of the 16 priority PAHs was not detected in particle size 0.45-75µm at the

industrial site. The heavy metal concentrations detected in this particle size class were

the highest observed, which corresponded with the highest volume of particles.

147

Consequently, from an urban stormwater management perspective, this was the most

important particle size class at the site.

Most of the data variance could be explained by using two principal components, as

shown by the elbow occurring at PC2 in the scree plot, in Figure C21 Appendix C.

PC1 and PC2 contained 40.2% and 16.6% of the data variance respectively. As can be

seen in the loadings plot (Figure 7.10), Mn, Zn, Pb, Fe, Al and Cu were all correlated

to pH suggesting that an increase of the pH in the stormwater would increase the

metal concentrations. Hence, it is likely that an equivalent decrease in pH of the

stormwater could increase desorption of metals and bring them into solution

(<0.45µm). Similar results were found by Tai (1991). Hence, the relationship found

between pH and heavy metals in the 0.45-75μm particle size range could have serious

implications for the bioavailability of heavy metals reaching receiving waters.

Furthermore, the majority of metals were associated with TSS and PVP of the

particles. Hence, a significant metal load was carried by surface runoff from the

industrial site.


particles 0.45-75µm in wash-off from the industrial site

PC1 (40.2%)

PC2

(16.

6%)

PHE, ACE

Zn, Mn, Fe, Pb

148

The majority of PAHs in the particles were associated with the organic matter. Similar

observations have been made by several researchers (Warren et al. 2003; Roger et al.

1998), indicating an increased adsorption affinity of hydrophobic pollutants in

particulate organic carbon. The strong relationship between PAHs and TOC in the

specific particle size class could also be attributed to the soot carbon content of the

particles (Gustafsson et al. 1997). PAHs on soot-like particles are strongly bound and

not influenced by further partitioning (Zhou et al. 1999). However, the importance of

these PAHs in urban stormwater management is questionable due to their relatively

small load compared to PAHs and heavy metals correlated with TSS and PVP. Hence,

from an urban stormwater management perspective, it could be more viable to

safeguard against metals such as Zn and Cu, which are correlated with TSS and PVP

as shown in Figure 7.10. However, microbial degradation of organic matter could take

place in stormwater runoff, which in turn could release the PAHs which are bound to

particles (Ellis and Revitt 1982). Consequently, PAHs could be readily bioavailable

for uptake by aquatic organisms in receiving waters.

Similar to the residential site, the majority of PAHs and heavy metals were associated

with 1- and 2-year ARI events. This is shown by the positive score on PC1 obtained

by these events in the scores plot, in Figure C22 Appendix C.


As can be seen in the scree plot, Figure C23 Appendix C, three principal components

were preferred in the analysis. Together they accounted for 67.6% of the total

variance (32.6%, 22.1% and 12.9% respectively). The low percentage covered by

three principal components suggests that there was a significant amount of ‘noise’

occurring in the particle size class. Hence, the correlations observed in a two- or

three-dimensional loadings plot will have reduced validity due to the low percentage

of total variance contained in these plots. Nevertheless, a number of governing

processes can be observed in the loadings plots (2D in Figure 7.11 and 3D in Figure

C24 Appendix C). Both plots show a clear separation of PAHs and heavy metals on

PC2.

149


particles 76-150µm in wash-off from the industrial site

Two primary processes can be attributed to the separation of metals and PAHs, as can

be seen in Figure 7.11. These are:

• PAHs bound to particulate organic matter as shown by negative loadings on PC2;

and

• Heavy metals in the particle size class strongly bound to the inorganic carbon.

Consequently, TOC increased the PAH sorption capacity of the particles. A similar

relationship was found for particles 0.45-75µm at the industrial site. Gustafsson et al.

(1997) found a similar relationship between TOC in particles and the increased

sorption capacity generated by the organic matter, especially on hydrophobic

pollutants. However, a similar relationship could not be found in particles from the

residential site. Thus, this behaviour was site-specific. This could be attributed to the

source of the organic matter rather than the amount of organic matter present. Krein

and Schorer (2000) noted that the type and source of organic material available were

responsible for the sorption of hydrophobic pollutants. Hence, the organic matter at

the industrial site had a favourable environment for the sorption of hydrophobic

pollutants. This could be due to less-polar organic matter (Warren et al. 2003), or

PC1 (32.6%)

PC2

(22.

1%)

Al, Fe

150

PAHs being irreversibly trapped in soot-like carbon particles that are not subject to

further interaction (Zhou et al. 1999). In spite of this, it is important to note that a

number of individual PAHs did not show any correlation with TOC. These were

primarily 2- and 3-ring PAHs correlated with PVP. Hence, the total load of 2- and 3-

ring PAHs could potentially be higher in urban stormwater. Nevertheless, the

increased bioavailability of PAHs that can occur if the organic matter is decomposed

can have serious implications for the water quality.

It is hypothesised that minerals used in sheet metal production at the site, containing

metals such as Zn, Pb and Mn, were responsible for the association of metals with IC

in particles between 76-150μm at the industrial site as seen in Figure 7.11. Similar

results were found by Bertin and Bourg (1995) who suggested that metals were

attached to particles with inorganic support. However, this is contradictory to findings

by Ellis et al. (1986) and Droppo et al. (1998) who suggested that the organic fraction

plays a major role in partitioning of metals in stormwater. Furthermore, it is unlikely

that metals associated with sheet metal production should be distributed into a specific

particle size class. It is unclear why the metals were associated with IC in the specific

particle size class.

The heavy metals and PAHs in the loadings plot, in Figure 7.11 were also separated

by rainfall events. As can be seen in the scores plot, Figure C25 Appendix C, a

slightly negative score on PC2 is attributed to high intensity rainfall, which coincides

with the slightly negative load of INT in the loadings plot (Figure 7.11). This is

mirrored in the TSS concentration. A positive loading on PC2 is attributed to frequent

rainfall events such as 1- and 2-year events.


At the industrial site PAHs, were detected more frequently in particles above 150μm

compared to the residential site. Consequently, the reliability of observed

relationships between PAHs and other parameters was higher. Heavy metals were

frequently detected in the particles. As can be seen in the scree plot, in Figure C26

Appendix C, two principal components contained most of the data variance (35.6%

and 17% respectively).

151

The loadings plot (Figure 7.12) revealed several processes involved in distributing the

PAHs and heavy metals. The most prominent was the correlation between pH, EC and

a number of PAHs. Thus, an increase in pH and EC of the runoff increased the

adsorption affinity of particles above 150µm. Pechacek (1994) attributed the increase

in EC to the increase in the adsorption affinity of a particle. More importantly, a

decrease in pH or EC could potentially lead to an increased desorption of PAHs into

the water column. This would lead to an increased bioavailability of PAHs. However,

desorption of PAHs from particles would depend on factors such as the solubility of

the specific PAH and dissolved organic carbon present (Zhou et al. 1999).


particles >150µm in wash-off from the industrial site

Cu and Cr are correlated with the IC of the particles, which is attributed to their

source specific input at the industrial site. Hence, Cu and Cr could be irreversibly

trapped in the crystal lattice of primary and secondary minerals (Charlesworth and

Lees 1999). However, metals such as Al and Fe are correlated with TSS and INT. It is

postulated that an appreciable amount of these metals is from eroded local soils,

which has been found to be a major contributor of Al and Fe in urban areas (Hopke et

al. 1980).

PC1 (35.6%)

PC2

(22.

1%)

EC, pH, ACY, BaA, BaP, CHR

152


At the commercial site, all 12 design rainfall events were simulated. The highest PAH

concentrations were recorded at this site, which was attributed to the land use

characteristics. Consequently, the higher source strength of PAHs at the commercial

site, due to the relatively high traffic volume, significantly influenced the PAH

concentrations.


Fifteen of the 16 PAHs listed by the US EPA (Manoli and Samara 1999) were

detected in the dissolved phase at the commercial site. This was attributed to

petrogenic sources such as oil and petrol spills. Moreover, the concentrations were

often well above the aqueous solubility of the PAHs. This was attributed to the

presence of oil and grease emulsions and colloidal size particles (Smith et al. 2000).

Nevertheless, the detection frequency was low for many PAHs, especially high-

molecular weight PAHs, making it difficult to investigate any PAH distribution

processes in the dissolved phase. Metals were detected in relatively low

concentrations in the dissolved phase. Cd and Cr were not detected in the dissolved

fraction.

As seen by the elbow in the scree plot Figure C28 Appendix C, most of the data

variance was contained in the three first principal components (44.3%, 19% and 9.6%

respectively). However, the third principal component contained less than 10% of the

total data variance. Hence, the use of two principal components was sufficient in the

analysis (Kaiser 1960). Furthermore, the three-dimensional loadings plot (Figure C29

Appendix C) contributed very little to the information that could be extracted from the

two-dimensional loadings plot (Figure 7.13).

153


data in the dissolved fraction wash-off samples from the commercial site

As can be seen in the loadings plot, Figure 7.13, the solubility concentrations

observed for many of the PAHs are attributed to the correlation with DOC and TDS.

Hence, the presence of colloidal organic material enhanced the solubility of PAHs at

the commercial site (Warren et al. 2003). This relationship was strongest at the

commercial site, which corresponded with the highest concentrations of DOC

recorded for all the sites. Consequently, the amount of DOC available increased the

particulate/water interactions of PAHs. In spite of this, as Krein and Schorer (2000)

observed, the association between DOC and PAHs could also have been attributed to

the type and source of the organic matter. Additionally, Warren et al. (2003) noted

that the temperature of water could significantly increase the solubility of PAHs.

However, this was not investigated.

In conclusion, a number of factors were contributing to the elevated concentrations of

PAHs in the dissolved fraction at the commercial site. In addition, elevated

concentrations were associated with 1- and 2-year design rainfall events as seen by the

negative score of these events on PC2 in the scores plot Figure C30 Appendix C.

BaP, ACE, ACY, FLU, PHE, FLA, CHR, DOC, ANT

PYR, BgP, NAP

154

The dissolved metals at the commercial site did not show any correlation with

chemical parameters measured as seen in the loadings plot (Figure 7.13). However, an

inverse relationship with pH could be identified due to large angle between metal

elements and pH in the loadings plot. Due to small variations in pH of the runoff

water, it was difficult to validate the relationship with metals in the dissolved phase at

the commercial site.

The heavy metals in the dissolved fraction were associated with the lowest rainfall

intensity events simulated, as shown by the positive score of these events in the scores

plot, Figure C30 Appendix C. Due to the drop size and terminal velocity of the

different rainfall intensities being identical in the simulations, the sheet flow created

by the lowest rainfall intensity on the coarse textured surface at the commercial site

had to be insufficient to release particles embedded in the voids. Consequently, higher

rainfall intensities had a higher sediment/water concentration ratio of heavy metals.

Hence, dissolved metals were associated with the lowest rainfall intensity, which is

attributed to the coarse asphalt texture at the commercial site.


All 16 priority PAHs were frequently detected in particles at the commercial site.

Furthermore, all metals except Cd were detected in their highest concentrations in the

wash-off. Thus, the importance of safeguarding against this particle size class at the

commercial site is significant. As can be seen by the elbow or inflexion point in the

scree plot, Figure C31 Appendix C, three principal components were preferred to

investigate correlations and patterns. The three first principal components contained

34.7%, 26.0% and 9.2% respectively of the total data variance. As proposed by Kaiser

(1960), a component containing less than 10% of the total data variance contributes

very little to the reliability of the information that can be extracted from PCA. Hence,

it was decided to use two-dimensional plots in PCA interpretation. As seen by the

loadings plot, Figure 7.14, the majority of metals and PAHs have a positive loading

on PC1. However, they are quite clearly separated by PC2, where metals were

associated with positive and PAHs with negative loading. In addition to this, a number

of PAHs had a negative loading on PC1. The primary reason for the negative loading

on PC1 attributed to FLA, PHE and ANT was the correlation with IC. This

strengthens the hypothesis made from the residential data that petrogenic PAHs

155

containing inorganic carbon could have adsorbed to particles due to accidental spills.

Similar findings were noted by LaRiviere et al. (2003).


particles 0.45-75µm in wash-off from the commercial site

The remaining PAHs are correlated with the TOC content of the particles,

strengthening the increased PAH adsorption affinity observed in organic particles by

Gustafsson et al. (1997) and Kleineidam et al. (1999). Consequently, a significant

amount of PAHs were transported by fine particles due to their affinity with TOC.

The metals attached to 0.45-75µm particles were not correlated with any chemical

processes at the commercial site, as seen in Figure 7.14. However, the correlation

between the majority of metals and Fe and Mn observed in Figure 7.14 could indicate

that the metals are bound to Fe and Mn oxides in particles, as found by Hamilton et al.

1984. This in turn would mean that the metals associated with Fe and Mn oxides were

scarcely available for release but could be susceptible to interactions by a pH change

(Charlesworth and Lees 1999).

IC, EC, pH, ANT, PHE

Mn, Zn, Pb, Al

156


As can be seen in the scree plot, in Figure C33 Appendix C, the reduction in the

gradient of the curve occurred at the fourth principal component. Hence, according to

Catell (1966), four principal components should be used to explore the data. However,

Kaiser (1960) recommended the use of the principal components containing more

than 10% of the total data variance for reliable interpretation of PCA. Consequently,

three principal components were used, which contained 31%, 24.7% and 15.8% of the

total variance respectively.

In spite of this, a visual comparison between the two- and three dimensional loadings

plots indicated that the three-dimensional plot (Figure C34 Appendix C) added very

little to the information that could be retained using a two-dimensional loadings plot.

Hence, the first two principal components were used to visually explore the scores

and loadings of the objects and variables respectively. As can be seen in the loadings

plot, Figure 7.15, most of the metals were inter-related. This is attributed to the strong

particulate bound Fe and Mn oxides occurring in the particles. Furthermore, similar

observations were found in particles 0.45-75µm, which suggests that binding to Fe

and Mn oxides is the dominant process amongst metals at the commercial site. It is

likely that the elevated EC found in the build-up sample at the site is partly

responsible for this behaviour due to the increased adsorption affinity attributed to a

rise in the EC (Pechacek 1994). However, sequential extraction of metals is needed to

further establish metals’ affinity with Fe and Mn oxides (Al-Chalabi and Hawker

1996). Similar to the finer particles at the site, the wash-off of metals from the street

surface is highest during the lowest intensity rainfalls investigated, as shown by the

positive score on both PC1 and PC2 obtained by 65 and 86mm/hr rainfall events

(refer to Figure C35 Appendix C). This is again attributed to the coarse surface texture

of the parking lot, which would embed an appreciable amount of fine particles in the

voids.

157


particles 76-150µm in wash-off from the commercial site

PAHs in the specific particle size class showed somewhat different processes to PAHs

in particles 0.45-75μm. Only a few of the PAHs were correlated to TOC (FLA, FLU,

PHE). However, TOC concentrations were very low in the specific particle size and

on several occasions no organic carbon was detected at all, making it difficult to

evaluate the influence of TOC on heavy metals and PAHs. On the other hand, PAH

concentrations were highly affected by the pH and EC of the stormwater as seen in

Figure 7.15. Warren et al. (2003) has attributed this to the increase in electrostatic

charges on particle surfaces caused by an increase in EC and pH. They also noted that

these factors are much less significant than organic carbon content in determining the

distribution of PAHs. Consequently, the lack of organic carbon in the specific particle

size class could have led to the distribution of PAHs into particles occurring due to a

change in pH and EC. This highlights the number of chemical processes able to

govern the distribution of PAHs in urban runoff. Moreover, it highlights the need to

frequently monitor the chemical characteristics of urban stormwater in order to

efficiently mitigate the negative impacts on receiving waters these pollutants could

impose.

BbF, EC, pH, IND, PYR, ACE, ACY, BaA, BgP, BaP

Fe, Mn, Pb, Zn, Cu, Al

158


Similar to the particle size 76-150µm, the inflexion point of the scree plot occurred at

the fourth principal component suggesting the use of four principal components in

interpreting the PCA (Cattell 1966). However, only the three first principal

components contained more than 10% of the total variance. Hence, it was decided that

three principal components were sufficient to explore the PCA. The three first

principal component contained 38.0%, 20.0% and 13.2% of the total data variance

respectively.

The three-dimensional loadings plot can be found in Figure C37 Appendix C. Similar

correlations between variables were found in a two-dimensional loadings plot (Figure

7.16). Hence, the first two principal components contained much of the reliable

information. Similar correlations can be found in particles above 150µm, as was

found in finer particles. Hence, PAHs are correlated with TOC while the majority of

metals are strongly bound to Fe and Mn oxides. Consequently, the PAHs and metals

distributed in different particle sizes at the commercial site show similar distribution

processes independent of particle size.

However, the metals in the specific particle size class were also correlated with the pH

of the stormwater. Charlesworth and Lees (1999) noted that the carbonate fraction of

metals is susceptible to a change in pH. Hence, the metals could also have existed in

the carbonate fraction. This is supported by Al-Chalabi and Hawker (1996) who found

the carbonate fraction of metals to dominate in street dust in Brisbane, Australia. This

was attributed to street dust primarily consisting of soil materials. Consequently, an

appreciable amount of washed-off particles above 150µm could have originated from

the soil surrounding the site.

159


particles >150µm in wash-off from the commercial site

As can be seen in Figure 7.16, INT and DUR were less important in the wash-off of

PAHs and metals in the specific particle size. Instead, more frequent rainfall events

such as 1- and 2-year events were associated with the wash-off of PAHs and heavy

metals. This observation is further strengthened by the positive score on PC1 obtained

by 1- and 2-year design rainfall events as shown in the scores plot, Figure C38

Appendix C.

7.6 Summary

The build-up and wash-off process kinetics of PAHs and heavy metals were

investigated using principal component analysis (PCA). Multivariate methods were

preferred due to the number of variables that could be analysed in order to understand

relationships between variables and objects. PCA was undertaken on the build-up and

wash-off samples separately. Furthermore, each particle size class was investigated to

determine the influence of particle size on chemical processes.

The results of the PCA of the build-up samples suggested a number of possible PAH

and metal sources and processes at the three study sites. It was found that vehicle

DbA, IND CHR, PYR, ACY

Zn, Al

160

traffic was a major contributor to the deposited material at all three sites. However, its

relative contribution was dependent on the site characteristics. Other sources included

gardening products and atmospheric deposition at the residential site, industrial

activities at the industrial site and accidental oil and grease spills at the commercial

site. The distribution of PAHs and heavy metals into particle size classes was similar

at the three sites, with the particle size class 0.45-75µm dominating. This was

consistently mirrored by the highest particle volume percentage (PVP) and total

suspended solids (TSS) concentrations being found in this size class. Consequently,

the PAHs and heavy metals found in the fine particles could have an adverse impact

on the water quality of receiving waters if incorporated into runoff. Therefore, urban

stormwater management measures should safeguard against fine particles independent

of land use characteristics.

No correlation was found between PAHs, metals and TOC in the build-up samples.

TOC was consistently found in relatively high concentrations in particles less than

0.45µm and particles above 150µm. These particle size classes were always low in

PAHs and heavy metals which made it difficult to investigate the influence of TOC on

PAHs and heavy metals in street deposited material. However, PAHs at the

commercial site were consistently found in concentrations above aqueous solubility in

particles below 0.45µm (referred to as the dissolved fraction) which was attributed to

two primary reasons. Firstly, a large number of oil and grease spills were observed

during sampling. Hence, PAHs absorbed to oil and grease emulsions could have led to

increased solubilities. Secondly, organic colloidal particles could have been

responsible for the solubilities recorded due to the increased adsorption affinity

observed in organic particles (Warren et al. 2003).

Mainly three different processes were governing the distribution of PAHs and heavy

metals. These were:

• Increased adsorption due to correlation with TOC;

• The association with IC; and

• Sediment binding on Fe and Mn oxides.

161

At the commercial and industrial sites, PAHs in the specific particle size class were

strongly bound to particles by the organic fraction. Nevertheless, at the residential site

and to some extent the commercial site, PAHs were primarily associated with

particles with an inorganic carbon support. This was attributed to the presence of

petrogenic sources at the residential and commercial sites. The PAHs bound to the

inorganic fraction were less susceptible to microbial degradation compared to the

organic fraction. However, the inorganic fraction was more susceptible to a change in

pH (Charlesworth and Lees 1999). Hence, processes such as acidic rainfall could

release the PAHs into the water column.

Processes governing the presence of metals in particles 0.45-75µm size class were

highly site specific. Factors such as soil composition, the presence of Fe and Mn-

oxides and pH of the stormwater were all important in partitioning the metals. More

importantly, it showed the wide range of factors that could partition metals in an

urban environment. Furthermore, it indicated the need for monitoring these

parameters in an urban area to ensure that urban stormwater management measures

are efficient in improving water quality.

In particles 76-150µm size, from the wash-off at the three sites, TOC and IC were

again dominating the distribution of PAHs and heavy metals. In spite of this, the

degree of influence differed between the sites. At the residential and commercial sites,

PAHs were correlated with TOC and to an extent IC, while at the industrial site PAHs

were strongly bound to the organic fraction of the particles. Similar to finer particles

at the commercial site, the metals were correlated with Fe and Mn, which was

attributed to the presence of Fe and Mn-oxides at the site.

Metals in particles above 150µm at the commercial site showed a similar distribution

to metals in the finer particles. Fe and Mn-oxides were governing the distribution of

metals independently into particle size classes in the wash-off from the commercial

site. At the industrial site, the presence of Cu and Cr in particles above 150μm was

attributed to industrial processes, due to their association with IC. At the residential

site, TOC was correlated with the majority of metals and the relationship was further

strengthened by an increase in pH. PAHs at the three sites were found in relatively

low concentrations in particles above 150µm size, especially at the industrial and

162

residential sites. However, when detected, they were correlated with an increase in the

pH or EC of the stormwater.

At all three sites, 1- and 2-year design rainfall events were the most important in

transporting PAHs and heavy metals. Consequently, relatively frequent events could

transport a significant pollutant load to receiving waters. Hence, combined water

quantity and quality measures could be inefficient in removing a large percentage of

the PAHs and heavy metals in urban runoff. Additionally, this highlights the

limitation of water quality approaches that has been based on water quantity research

undertaken in the past. Thus, the extension of such concepts and processes is not

satisfactory in urban water quality due to the strong reliance on physical factors and

the limited recognition of chemical processes.

The processes found in the respective size fractions in urban stormwater at the three

sites were considered transferable between the sites due to the reduced number of

physical factors involved in the runoff. However, to fully assess the transferability of

the processes and parameter relationships found, an approach needed to be undertaken

where the data set and processes could be calibrated and validated. According to

Wold et al. (2001), the flexibility of a PLS-approach and its ability to handle

incomplete and noisy data with many variables, makes it a simple but powerful

approach for the analysis of complicated problems. Hence, the use of PLS in urban

water quality studies could significantly enhance the outcomes of the research, which

is discussed in Chapter 8.

163

Chapter 8 Investigating Process Kinetics of PAHs and heavy

metals using PCA and PLS

8.1 Introduction

Chapter 7 highlighted a number of processes common for the distribution of micro-

pollutants, which were independent of site characteristics. For example, PAHs were

found to be strongly bound to the organic fraction of particles at the three sites

depending on particle size. Furthermore, changes to the pH and EC of the stormwater

influenced the bioavailability of metals at the three sites. Knowledge of these

processes and the variables involved are the basis for urban stormwater management

for impact mitigation on receiving waters, and could be detrimental in the

development of more efficient strategies. A key issue in urban stormwater has been

the transferability of fundamental urban water quality concepts and relationships to

other areas. In addition to this, chemical processes are often neglected when build-up

and wash-off of PAHs and heavy metals are studied. Consequently, it became

important to validate the transferability of the chemical processes, by excluding

physical factors and land use characteristics. This was made using a predictive

modelling approach of the chemical processes. Multivariate regression techniques

were used to predict PAHs and heavy metals based on variables such as TOC and Fe

and Mn oxides at the three sites. The performance of the predictive relationships then

determined the transferability of relationships found between sites. However, it is

important to realise that the modelling approach was used to validate the parameter

relationships found and their transferability, not specifically model and predict the

parameter concentrations for determining urban water quality impacts.

During the PCA analysis discussed in Chapter 7, it became apparent that the build-up

process kinetics of PAHs and heavy metals was highly site-specific and reflected the

sources available in the urban environment. Due to the number of build-up samples

collected from each site and the highly site-specific processes identified, it was

decided that the prediction of process kinetics in the build-up samples separately was

inadequate. However, similar processes were found to govern the wash-off kinetics of

heavy metals and PAHs from each site. Hence, the wash-off process kinetics of PAH

164

and heavy metals were found to be less dependent on source strength and could be

predicted independent of site characteristics. Furthermore, it was decided that

including build-up samples in developing predictive relationships based on the

chemical and physico-chemical processes influencing PAHs and heavy metals,

despite their strong source dependency, could identify possible processes and

common distribution characteristics previously unidentified in the separate analysis of

the build-up samples.

Consequently, the multivariate analytical methods PCA and PLS was employed to

investigate if chemical processes, physico-chemical processes and parameters could

explain the distribution of PAHs and heavy metals in build-up and wash-off,

independent of land use and particle size class. Similar to Chapter 7, PCA was applied

to the samples collected from the rainfall simulation. However, the PCA described in

Chapter 7 identified processes by particle size and site characteristics. In the PCA

performed in this Chapter, the complete set of data from the three sites was used as

one single data matrix. Hence, no particle sizes or study sites were biased in the

analysis.

8.2 Application of PCA for prediction of heavy metals and PAHs

In order to establish and validate reliable relationships between variables, outliers in

the data had to be identified and removed. Thus, PCA (refer to Appendix C page 280

for algorithm) was applied to the complete set of data incorporating build-up and

wash-off sample results from each site and particle size class. The pollutant data was

separated into matrices as follows:

• Matrix A containing 175 objects (samples) and 15 variables (8 heavy metal

elements and 7 physico-chemical parameters); and

• Matrix B containing 140 objects (samples) and 22 variables (15 PAH compounds

and 7 physico-chemical parameters).

The objects (samples) in each matrix were numbered prior to applying PCA due to

visual aspects of scores and loadings plots. The object lists containing the

abbreviations for each object used in the analysis can be found in Appendix D Table

D1 (Matrix A) and Table D2 (Matrix B) respectively.

165

Matrix A – Heavy metal data

Most of the variance (73.6%) of the 175 objects containing heavy metal data

subjected to PCA was contained in the first three principal components (41.1%,

21.3% and 11.2% respectively), as shown by the elbow at the third principal

component in the scree plot, in Figure D1 Appendix D.

-8 -6 -4 -2 0 2 4 6-4

-2

0

2

4

6

8

001

002

003004

005

006

007008009

010

011012013014

015

016017

018

019

020

021

022023

024025

026027

028029

030

031032033034

035

036037038

039040

041

042043

044045

046047048

049

050

051

052

053

054

055

056057058

059060

061

062063 064

065

066

067

068069

070

071072

073074075

076077

078

079080

081082

083084085

086087

088089

090

091092

093094

095

096097

098

099100

101102

103

104105

106107108

109110

111112

113114115

116

117118119

120

121

122123124

125

126

127128129

130

131132133

134

135

136137138139

140

141142

143144

145

146147148149

150

151152

153154

155

156157158159

160

161162

163164

165

166167

168169

170

171172

173

174

175

PC1 (41.1 %)

PC

2 (2

1.3

%)

FIGURE 8.1 Scores plot of the objects (175 samples) containing heavy metal data

subjected to PCA

When plotting the scores of each object (samples) on the first two principal

components (PC1 and PC2), as illustrated in the scores plot in Figure 8.1, three

distinct clusters could be identified as outlined below:

• Cluster 1, containing heavy metal data in particles less than 0.45μm (dissolved

wash-off samples) from the three sites;

• Cluster 2, containing heavy metal data in the build-up samples independent of site

and particle size class; and

• Cluster 3, containing heavy metal data in particles above 0.45μm in wash-off

samples from the three sites.

CLUSTER 1

CLUSTER 2

CLUSTER 3

166

Cluster 1 (dissolved samples) and Cluster 3 (particulate samples) identified a clear

separation between distribution processes of heavy metals in the particulate and

dissolved phases of street wash-off. Similar results have been found by numerous

researchers (Warren et al. 2003; Gueguen and Dominik 2003). Consequently,

modelling wash-off process kinetics of heavy metals in total water samples

(particulate plus dissolved phase) can be misleading and lead to gross errors in the

prediction of concentrations. Unfortunately, countless studies have investigated wash-

off of heavy metals using bulk samples (Drapper et al. 2000; Gray and Becker 2002;

Hoffman et al. 1984). Hence, the reliability of these studies in terms of best

management practises for urban stormwater is limited and the results from such

studies should be interpreted carefully. Similarly, there was a clear separation between

Cluster 2 containing the build-up samples and Cluster 1 and 3, which contained wash-

off samples. Furthermore, it can be observed in Figure 8.1 that the objects in Cluster 2

were scattered to some extent. This strengthens the strong site characteristic behaviour

intrinsic to the heavy metal compounds found in street dust, as discussed in Chapter 7.

Similar separations can be found when plotting the scores of the metal data objects on

a PC1 vs PC3 scores plot, as can be seen in Figure D2 Appendix D. However,

complementary information to the PC1 vs PC2 scores plot (Figure 8.1) can be found.

This is attributed to the separation of Cluster 3 (refer to Figure 8.1) on PC3.

Approximately half of the data objects in Cluster 3 had a positive score on PC3 while

the other half had a negative score on PC3. Hence, two primary wash-off processes

were separating the metal data in particles on PC3. As can be seen in the loadings plot

in Figure D3 Appendix D (PC1 vs PC3), the negative score was primarily attributed to

the presence of Fe and Mn oxides binding metals such as Al and Pb, while a positive

score indicated an association between Cu, Cd and Cr and the organic fraction of the

particles. Additionally, the loadings plots of PC1 vs PC3 and PC1 vs PC2 (Figures D3

and D4 Appendix D respectively) clearly separated Zn from the other metals. This

was attributed to Zn primarily being found in the dissolved phase, due to its

association with DOC and TDS independent of site characteristics, as discussed in

Chapter 6 and 7. Consequently, it was postulated that Zn could be predicted based on

DOC and TDS using the data from the dissolved samples at each site. Furthermore,

Chapter 7 identified the decrease in pH as a possible process influencing desorption of

167

heavy metals from particles. This was supported by Tai (1991) who found that the

desorption ratio of Zn increased when there was a decrease in pH. Therefore, it was

decided to include pH in the prediction of Zn (refer to Section 8.3) in the dissolved

fraction of runoff.

The remaining metals to be predicted were separated into two groups, based on the

outcome of the PCA. Hence, metals associated with Fe and Mn oxides were predicted

separately from the metals associated with the organic fraction of the particles, based

on the scores and loadings plots of PC1 vs PC3 (Figures D2 and D3 Appendix D

respectively). Additionally, only objects associated with each group and positive score

on PC1 were used in the prediction. Hence, objects with a positive score on PC3 were

used to predict metals associated with Fe and Mn oxides, while objects with a

negative score on PC3 were used to predict metals associated with the organic

fraction. The objects with negative and positive score on PC3 and the variables

associated with them are shown in Table 8.1. Refer to Table D1 Appendix D for

identification of site, runoff event and particle size class of each object.

As can be seen in Table 8.1, metals associated with Fe and Mn oxides were

dominating at the commercial site (objects 111-175), while the metals in the particles

collected at the industrial and residential site were primarily related to the organic

fraction of the particles. This behaviour was independent of particle size at the three

different sites, suggesting that binding to different fractions of particles was a site-

specific phenomenon. However, this does not exclude metals at the commercial site

from binding to the organic fraction of particles. On the contrary, metals at the

commercial site could very well be bound to the organic fraction of the particles.

Nevertheless, as Al-Chalabi and Hawker (1996) noted, the percentage of particles

with organic support could be significantly lower compared to the Fe and Mn oxides,

which in turn would influence a metal’s affinity with particle fraction. To fully assess

the fractionation of metals in washed-off particles, a number of extraction procedures

needs to be undertaken.

168

TABLE 8.1 Variables and objects (samples) associated with negative and

positive loadings and scores on PC3 (excluding build-up and dissolved fraction

data)

Loadings or scores

on PC3 Variables Objects (samples)

Positive Cd, Cu, Cr, TOC 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18,

19, 21, 22, 23, 24, 26, 27, 28, 29, 31,

32, 33, 34, 36, 37, 38, 39, 42, 43, 44,

47, 48, 49, 56, 57, 58, 59, 61, 62, 63,

64, 67, 68, 69, 71, 72, 73, 74, 77, 78,

81, 82, 83, 84, 86, 87, 88, 89, 91, 92,

93, 96, 97, 98, 99, 102, 103, 104,

106, 107, 108, 109, 116

Negative Mn, Pb, Al, Fe, TSS 79, 94, 117, 118, 119, 121, 122, 123,

124, 126, 127, 128, 129, 131, 132,

133, 134, 136, 137, 138, 139, 141,

142, 143, 144, 146, 147, 148, 149,

151, 152, 153, 154, 156, 157, 158,

159, 161, 162, 163, 164, 166, 167,

168, 169, 171, 172, 173

Matrix B – PAH data

The scree plot (Figure D5 Appendix D) from the PCA on the complete set of PAH

data (abbreviations in Table D2 Appendix D), revealed that most of the data variance

of Matrix B could be explained by the first two principal components (51.8% and

11.6% respectively). Hence, PC1 and PC2 were used to interpret the PCA.

As can be seen in the scores plot (Figure 8.2), primarily two different clusters could

be identified. Cluster 1 (circled in Figure 8.2) contains data in the dissolved fraction

of wash-off samples, while the remaining objects were either build-up data or data

from washed-off particles. Consequently, PAH compounds as well as heavy metal

elements showed distinct differences in distribution processes between the particulate

and the dissolved phases. Furthermore, all the PAHs investigated were primarily

169

associated with particles in the wash-off samples as can be seen in the biplot, in

Figure D6 Appendix D. This is not surprising since PAHs have been found to be

primarily associated with the particulate fraction in urban stormwater (Marsalek et al.

1997). However, as identified in Chapter 7, PAHs could be present in the dissolved

fraction of runoff as oil and grease emulsion or attached to colloidal organic particles.

Nevertheless, the solubility of PAHs is low and it was decided to focus on predicting

PAHs in particles, due to the relatively higher PAH load that particles can transport to

receiving waters, compared to PAHs in the dissolved fraction. In addition, the

detection rate of PAHs in the dissolved fraction was low, which could influence the

accuracy of prediction significantly. As a result, it was decided to focus on the PAHs

found in particles.

Figure 8.2 also identified object outliers to have significantly larger scores on PC1

than other objects. These objects were identified as the build-up samples from each

site. Similar to the PCA of Matrix A, the build-up samples did not cluster closely in

the scores plot, which further strengthened the highly site specific nature of the build-

up of heavy metals and PAHs as identified in Chapter 7. Consequently, the presence

of PAHs and heavy metals in road-deposited material at the three sites was highly

source dependent. Similar results have been found by Sartor and Boyd (1972). The

higher presence of traffic-related PAHs in the build-up sample at the commercial site

is shown in Figure 8.2, where for example object number 91 (Commercial build-up

particle size range 0.45-75μm) had a significantly higher score on PC1 than objects 43

and 3, which were the corresponding build-up samples from the industrial and

residential sites.

170

FIGURE 8.2 Scores plot of the objects from the three sites (140 samples)

containing PAH data subjected to PCA

Consequently, it was decided that both the build-up samples and the dissolved fraction

of the wash-off samples were unsuitable for prediction due to their large PAH

concentration differences between the sites in the build-up samples, and infrequent

detection of PAHs in the dissolved fraction of runoff. Due to the exclusion of the

dissolved fraction of the wash-off in prediction efforts, the data for TDS and DOC

were excluded from the prediction matrix.

High-molecular weight PAHs were difficult to detect in urban stormwater. This has

been confirmed by several researchers (Wang et al. 2001, Marsalek et al. 1997).

Hence, predicting these PAHs in an urban environment is difficult and would demand

an even larger data base. This would in turn be resource intensive. Furthermore, it

could be argued that the low detection of such compounds was reflecting their low

priority in urban environments. However, specific industrial environments where

high-molecular weight PAHs are a product of the industrial processes used should be

identified and preferably investigated separately, in order to determine best pollutant

management practises for these industries. The infrequent detection of three high

CLUSTER 1 Dissolved data

Build-up data

171

molecular weight PAHs (detected in less than 75% of the wash-off samples) led to the

removal of DbA, IND and BgP in the prediction step. Following this, 96 data objects

remained from the original Matrix B with data in 15 variables.

Due to the strong correlation between most of the PAHs and the build-up samples

attributed to the less frequent detection in wash-off samples, it was decided to apply

PCA to the wash-off data separately. Hence, PCA was applied to the 96 objects and

15 variables remaining in order to associate PAHs with specific objects and variables.

This resulted in most of the variance being accounted for in the first two principal

components, 41.0% and 11.3% respectively. This is strengthened in the scree plot, in

Figure D7 Appendix D, where the gradient of the curve flattens out at the second

component. The biplot (Figure D8 Appendix D) from the analysis confirmed the

higher strength of PAHs in the industrial and commercial wash-off samples, as seen

by the negative score obtained by the majority of residential wash-off samples on

PC1. Additionally, the objects with a negative score on PC1 represented PAH data in

particles above 150μm. This strengthens the findings in Chapter 7 where PAHs were

primarily associated with fine particles independent of particle size. Furthermore, the

majority of PAHs show no correlation with TOC in Figure D8 Appendix D. This is

contradictory to the analysis of the individual sites where TOC and PAHs were highly

correlated in fine particles. However, the TOC content was relatively high at all the

sites in particles above 150μm. Consequently, the relatively high TOC concentrations

recorded in the coarse particles skewed the loading of TOC on PC1 in the biplot, in

Figure D8 Appendix D. This shows the importance of analysing separate particle sizes

in urban water quality research since relevant information can be lost when analysing

bulk runoff samples. This is supported by findings by Smith et al. (2000) and Warren

et al. (2003), who suggested partitioning of runoff samples was preferable when

investigating urban water quality. As a result, no variables were left out of the

prediction of PAHs.

In addition, the biplot in Figure D8 Appendix D identified co-linearity between the

majority of PAHs. Hence, it became apparent that predicting individual PAHs

separately would produce the same information as using surrogate groups of PAHs in

a predictive relationship. For example, Figure D8 Appendix D identified co-linearity

between ACY, ACE and BbF based on the small angle separating their loadings in the

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biplot (Kokot et al. 1998). Hence, a separate prediction for each of these groups

would produce a similar result, due to their distribution processes being similar.

However, PC1 and PC2 only accounted for 52.3% of the total data variance.

Therefore, the third principal component (9.2%) was considered statistically

significant in this case. By plotting the loadings of the PAH data on PC1 and PC3, as

shown in the loadings plot Figure D9 Appendix D, it was found that similar co-

linearity was occurring on PC3. However, two PAHs were not consistent in their

clustering on the three principal components analysed and showed an association with

different groups of PAHs, depending on the number of principal components used.

These were BbF and ANT, which consequently were excluded from the prediction

step. Based on the loadings on PC1, PC2 and PC3, the PAHs were separated into

three groups. These were:

• Group 1 (FLA and PYR);

• Group 2 (ACE and ACY); and

• Group 3 (FLU, PHE, NAP, BaP, BaA, CHR).

Hence, three different predictive parameter relationships were established based on

similar wash-off process kinetics of the PAHs attached to particles at the three

different study sites.

8.3 Introduction to PLS

Partial least square (PLS) regression is a generalisation of the more common multiple

linear regression (MLR). However, unlike MLR, PLS regression has the ability to

analyse data that is strongly collinear and noisy. PLS regression uses numerous X-

variables (in this research parameters such as pH, EC and TOC) in order to

simultaneously predict one to several response Y-variables (Wold et al. 2001). The

response of the Y-variables are then compared to observed values in a predicted

versus observed plot of the variable. Each Y-variable predicted is iteratively estimated

as the slope of a simple bivariate regression (least squares) between a matrix column,

or row. One way to see PLS regression is that it forms new X-variables as linear

combinations of the old and thereafter predicts a Y-variable based on these new X-

variables (Wold et al. 2001). Only as many new X-variables are formed as are needed.

Consequently, the significance of the prediction is always high. In order to interpret

the PLS prediction, loadings and scores are given to each variable which contain

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information about the objects and their similarities/dissimilarities with respect to the

given problem. Hence, the loadings give information on how the variables combine to

form the quantitative relation between X and Y. Furthermore, they are essential in

understanding which X-variables are important in the prediction. The mathematical

formulation of PLS is given in Appendix D page 315.

In this research, PLS was particularly useful in order to predict a number of PAHs and

heavy metal elements using estimates of latent variables. Hence, chemical and

physico-chemical parameters was used as predictor variables X (such as TDS, pH and

DOC) which were used to predict selected response variables (Y), such as Zn. This

yielded information on the transferability of the research, as well as potential to

significantly influence current urban stormwater management practises.

There are a number of ways to determine the appropriate number of latent variables to

use in PLS regression. The X-scores are generally optimised and represented by the

least amount of orthogonal factors or latent variables, which provide minimal error in

the model. The PLS algorithm uses these scores and develops error predictions based

on calculated reduced eigenvalues (or error of calibration), cross-validation (Standard

Error of Validation (SEV) or predicted residual error sum of squares (PRESS).

PRESS is generally used to predict the number of latent variables to use when a

separate validation set is used. Furthermore, it is important to know something about

specifying errors in the quantification of the relationships. In multivariate analysis,

errors are usually quoted as the Standard Error of Prediction (SEP), which is

calculated from the square root of the sum of squared deviations between the

predicted and the actual concentrations. Hence, SEP should be similar to the standard

deviation for analyte concentrations and was an important parameter in determining

the transferability of the relationships found. Consequently, the lower SEP, the better

prediction.

There are two common forms of PLS typically employed. These are the PLS1 and

PLS2 routines (Wold et al. 2001). PLS1 are commonly used to predict singular Y-

variables, while PLS2 are utilised to predict several Y-variables at the same time.

Both algorithms have been successfully used to predict concentrations of variables

such as PAHs and dissolved organic carbon in water (Ferrer et al. 1998; Marhaba et

174

al. 2003). However, PLS1 was proven more effective than PLS2 in predicting PAHs

due to each compound being treated individually (Ferrer et al. 1998). Furthermore, as

noted by Beltran et al. (1998), the PLS2 algorithm did not improve the results

obtained by the PLS1 algorithm when predicting PAH concentrations. Thus, the

primary advantage with the PLS2 algorithm is the time saved when predicting several

compounds simultaneously.

In this research, a PLS1 algorithm was chosen primarily to predict the processes and

facilitate transferability of the relationships found. Loadings of each variable used in

the prediction could then be determined and the transferability of the parameter

relationships was based on the linear variation (r2) of the predicted variable (Y), and

the SEP. A step-by-step description of the algorithm is provided in Appendix D page

316, together with the functions used in the prediction. The software used was

MatLab and the algorithms cited were adapted from Mathisen (2004).

8.4 Predicting heavy metals and PAHs using PLS1 algorithm

Following the selection of suitable samples and variables for parameter prediction as

outlined in Section 8.2, a PLS1 algorithm was applied to the remaining PAH and

heavy metal data. The PLS1 algorithm was chosen due to the better performance

observed by a number of authors compared to multi-output processes (Ferrer et al.

1998; Dayal and MacGregor 1997). Additionally, as discussed in Section 8.2, the

majority of PAHs was strongly collinear. Hence, a specific PAH had been chosen as a

surrogate for a group of PAHs showing co-linearity. PLS1 is a quantification method

that uses a number of steps for creating a predictive relationship. These are:

1. The training (calibration) step;

2. The estimation (prediction) step; and

3. The validation step.

There are a number of options in the PLS analysis that needs to be discussed and

evaluated prior to regression can be undertaken. These options generally relate to the

validation of the developed relationships. There are primarily three different

validation options available. The first method is selecting half the objects available for

calibration and the other half for validation (Purcell et al. 2004). This method is useful

when a relatively large data set is available. In spite of this, a disadvantage with this

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type of validation is that fewer objects are available for the calibration step, which

could significantly reduce the performance of the prediction. Thus, the model would

not contain as much predictive information as when the complete data matrix is used

for calibration. The second validation method is having a separate validation data set,

which will allow a more beneficial test of the overall efficiency. However, water

quality data available from the region had been collected on a catchment scale prior to

this research. Furthermore, concentrations had been measured in whole water samples

or dissolved and particulate form without further partitioning. Hence, the data

available was unsuitable for validating relationships based on data collected from a

small paved area, due to the amount of physical parameters influencing wash-off from

a mixed land use area. In addition to this, there was simply no time to collect a

sufficient set of data from natural rainfall during the timeline of the project. Hence,

using a separate validation data set with less detail relating to the distribution of heavy

metals and PAHs in particles was not valid.

In an ideal predictive model, as many calibration samples as possible should be

retained while at the same time being able to establish a realistic estimate for the

error. Thus, an error estimate that is truly indicative of the robustness of the

calibration is preferred. This can be achieved by a cross validation procedure, which

possesses many of the advantages of the conventional validation procedure, but does

not require samples to be sacrificed from the calibration set. This advantage is

achieved by leaving one sample out at the time in the validation procedure, which is

repeated until all samples have been left out of the calibration. The sample left out in

each step of the cross validation is used to gain an independent estimate of the error

for that particular sample. On the other hand, the cross-validation technique does not

allow for an estimation of the prediction error. Consequently, a predictive relationship

could be constructed using the cross-validation leave-one-out-method (Purcell et al.

2004). However, in terms of validation, a completely separate validation matrix is

needed. Therefore, it was decided to use half the objects in a cross validation

technique for calibration and use half the objects for validation. Performance of the

predictive relationship was then assessed from the value of the Standard Error of

Prediction (SEP). A similar technique has been successfully implemented by Purcell

et al. (2004).

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8.4.1 Heavy metals in urban stormwater wash-off

The PLS1 algorithm (refer to Appendix D page 313) was used to predict a response

variable Y (heavy metal element) from numerous predictor variables X (TOC, TSS

and remaining metal elements due to Fe and Mn oxides and inter-correlations). The

metals were predicted using the following predictor variables, as outlined in Table

8.2, based on the outcomes of the PCA discussed in Section 8.2.

TABLE 8.2 Variables to be predicted (Y) and predictor variables (X) used

Y-variable X-variables

Pb Fe, Mn, TSS, pH, EC, IC

Cd TOC, pH, EC, IC

Cr TOC, pH, EC, IC

Zn DOC, TDS, pH, EC

Cu TOC, pH, EC, IC

Al TSS, Fe, Mn, pH, EC, IC

The concentrations measured in particles from the simulated runoff events were used

to predict the selected response variable as outlined above. Similar to PCA, the

concentrations of each variable were log transformed to reduce data heterogeneity.

Following this, the transformed data was column-centred and standardised in order to

reduce irrelevant sources of variation or noise. A calibration set was constructed by

selecting every odd numbered object (refer to Table 8.1) of the respective metal

groups. This resulted in three different calibration matrices depending on the metal

predicted. The calibration and validation matrix dimensions for each of the metal

groups (refer to Table 8.1) are outlined in Table 8.3.

TABLE 8.3 Calibration and validation matrices for prediction of metals

Metal elements

Calibration matrix

(objects x variables)

Validation matrix

(objects x variables)

Zn 16x4 16x4

Cu, Cr and Cd 37x4 37x4

Al and Pb 24x6 24x6

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As discussed in Section 8.3, an important issue for any PLS approach is the number of

latent variables to be included. The number of latent variables to use was determined

when the prediction errors were minimised. Hence, the number of latent variables

corresponding to the first minimum in the cross-validation error plot was selected as

the optimum for prediction of the variables. As an example, the number of latent

variables used for the prediction of Cu was two, based on the first minimum in the

cross-validation error plot shown in Figure 8.3 below.

FIGURE 8.3 Cross-validation error (SEV) for Cu showing the number of latent

variables to use (2) based on the first minimum in the plot

The number of latent variables and their variance (proportion of variance obtained by

dividing the sum of squares of X by the corresponding total sum of squares of Y) for

each Y-variable to be predicted could thus be determined, and are shown in Table 8.4

below. Once the number of latent variables was determined, the X-scores were

recalculated to include the necessary information retained in the latent variables. As

can be seen by the percentage variance explained in Table 8.4, the latent variables

chosen contained the most variance in the data, with the majority of the remaining

variables made up of noise. Following this, the Y-variable was predicted and could be

compared to the original value in an observed versus predicted plot. Consequently, the

SEV

First minimum

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higher the r2 value and the lower the SEP value, the better prediction and

performance.

TABLE 8.4 Number of latent variables used for each predicted variable (Y)

Y-variable Latent variables

Percentage variance

explained [%]

Pb 2 75.6

Cr 2 88.5

Zn 2 93.3

Cu 2 86.6

Al 3 79.4

Cd 2 82.8

As can be seen in the observed versus predicted plot Figure 8.4 below, the predicted

values of Al corresponded very well with the observed values of Al in washed-off

particles at all three sites (r2 = 0.90; SEP = 0.26).

FIGURE 8.4 Plot of observed versus predicted Al concentrations (logarithmic)


Observed Al concentrations log[mg/kg]

Pred

icte

d A

l con

cent

ratio

ns lo

g[m

g/kg

]

------ Predicted -- Observed

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Given the nature of the particle data (different land uses and particle sizes), these

results are very encouraging and suggests that reliable predictions of Al can be made

from measuring pH, EC, TSS and Fe and Mn concentrations. Similar results were

found when predicting Pb concentrations (Figure D14 Appendix D, r2 = 0.92; SEP =

0.25). Hence, the predictive relationships established for Al and Fe proved that the

chemical processes governing the distribution of these metals in urban runoff particles

were independent of physical factors such as land use characteristics, particle size and

rainfall intensity. Additionally, the transferability of these predictions is high. The

loading of each variable (X) used to predict Y is shown in Table D3 Appendix D.

The potential now exists to express a water quality parameter such as Pb on the basis

of accurate measurements derived from simulated rainfall from a small homogenous

plot area. This methodology will not only positively affect the understanding of the

wash-off process kinetics of heavy metals but could also have a significant, beneficial

impact on urban stormwater management strategies. This is attributed to the

transferability of the chemical processes found at the three sites. Consequently, a

feasible approach to urban stormwater management would be to develop strategies

incorporating these results and do appropriate modifications when needed. Hence, an

area which specifically generates a significant amount of a specific pollutant should

be monitored closely, depending on the type of pollutant sources available. This is

further strengthened by the somewhat mediocre performance of the relationships used

in predicting Zn, Cu, Cr and Cd compared to the prediction of Al and Pb as can be

seen in Table 8.5 which shows r2 and SEP for each of the predictions.

TABLE 8.5 r2 and SEP values generated from the predictions (metals)

Heavy metal r2 SEP Observed vs Predicted plot

Al 0.90 0.26 Figure 8.4

Pb 0.92 0.25 Figure D14 Appendix D

Cu 0.31 0.82 Figure D10 Appendix D

Cd 0.25 0.84 Figure D11 Appendix D

Cr 0.11 0.94 Figure D12 Appendix D

Zn 0.12 0.92 Figure D13 Appendix D

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Furthermore, as can be seen in Figures D10-D13 Appendix D (predicted versus

observed plots for Cu, Cd, Cr and Zn respectively), the predicted values did not

correspond very well with the observed values for these parameters. In the case of Zn,

which displayed relatively high concentrations in the dissolved fraction at the

residential site, this could be attributed to the presence of colloidal particles corroded

from galvanised roofs in the area. Makepeace et al. (1995) has confirmed corrosion

from galvanised roofs as an important source of Zn in an urban area. Thus, a point

source of a pollutant could significantly skew or bias prediction efforts and should

preferably be monitored separately. The majority of samples used in predicting Al and

Pb were from the commercial site. Hence, including additional samples from the

residential and industrial site could significantly skew the performance of the

prediction. To investigate the performance and further strengthen the transferability of

the established Al and Pb relationships, additional data from the residential and

industrial site were used. The results from the addition of data in predicting Al are

shown in Figure 8.5 below.

FIGURE 8.5 Plot of observed versus predicted Al concentrations when adding

additional samples from the commercial site, with an SEP of 0.56

Observed Al concentrations log[mg/kg]

Pred

icte

d A

l con

cent

ratio

ns lo

g[m

g/kg

]


181

As seen in Figure 8.5, the predictive performance of Al was still high when more

samples from the commercial site were added (r2 = 0.68; SEP = 0.56). Consequently,

the developed relationship based on Fe and Mn oxides in suspended solids proved the

transferability of the processes governing the distribution of Pb and Al between the

sites.

Additionally, as discussed in Chapter 7, the wash-off process kinetics of metals were

showing less trends and patterns between the three sites in less frequent storm events.

Thus, the importance of chemical processes could be more important in frequent

events and physical factors could potentially dominate in less frequent events. Hence,

the performance of the predictive relationships for Cu, Cr, Cd and Zn could have been

decreased due to the inclusion of less frequent events were the chemical processes

have a less dominating role in the distribution of metals. The Y-loading of each

variable is shown in Table D3 Appendix D.

8.4.2 PAHs in urban stormwater wash-off

As discussed in Section 8.2, the PAHs were split into three groups due to their distinct

co-linearity. Consequently, a surrogate PAH in each group was used to represent the

predictive performance of the PAHs belonging to the specific group. A PLS1

algorithm (refer to Appendix D page 313 for details) was used to predict the Y-

variables (PAH groups) based on a number of X-variables (TOC, IC, TSS, pH, EC).

Table 8.6 shows the Y-variables that were predicted and the predictor variables (X)

used.

TABLE 8.6 Variables to be predicted (Y) and predictor variables (X) used

Y-variable X-variables

Group 1 (FLA and PYR) TOC, IC, TSS, pH, EC

Group 2 (ACE and ACY) TOC, IC, TSS, pH, EC

Group 3 (FLU, PHE, NAP, BaP, BaA,

CHR)

TOC, IC, TSS, pH, EC

Similar to the prediction of heavy metals, a calibration and a validation matrix was

used for each PAH group. All three PAH groups to be predicted had calibration and

182

validation matrices consisting of 5 variables and 48 objects, and was chosen similar to

the calibration and validation matrices used in heavy metal prediction (refer to Section

8.4.1). The data was pre-treated prior to PLS application using column centering and

standardisation.

The number of latent variables was determined for each PAH group prior to the

prediction step. Hence, the cross-validation error for each Y-variable was determined

by visually inspecting the error plot and choosing the number of latent variables that

generated the smallest error for the calibration. The number of latent variables used

for each PAH group can be found in Table 8.7 below. It was observed that a different

number of latent variables were suggested for each PAH group. This was attributed to

the relatively low data variance accounted for in the first two principal components, as

discussed in Section 8.2. Hence, the addition of latent variables increased the

reliability and the performance of the prediction of PAH group 1 and 2. However, the

performance of the prediction of PAH group 3 was not affected by the addition of

latent variables. Following the determination of latent variables, new X-scores were

calculated in order to predict the PAH groups and loadings of each variable used to

predict the different PAH groups were calculated (refer to Table D3 Appendix D).

TABLE 8.7 Number of latent variables used for each predicted variable (Y)

Y-variable

Number of latent

variables

Percentage variance

explained [%]

Group 1 (FLA and PYR) 3 84.9

Group 2 (ACE and ACY) 4 76.5

Group 3 (FLU, PHE, NAP,

BaP, BaA, CHR)

2 87.4

As shown by the r2 values and SEP values of each of the predictions, Table 8.8, the

performance were reasonably good as indicated by the SEP.

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TABLE 8.8 r2 and SEP values generated from the predictions (PAHs)

PAH group r2 SEP Predicted vs Observed plot

1 0.29 0.84 Figure D15 Appendix D



However, a number of objects did not facilitate transferability as can be seen in the

observed versus predicted plots of the three PAH groups (Figure D15, D16 and D17

Appendix D respectively) and as indicated by the r2 value in Table 8.8. These were

identified as residential and commercial data in particles above 150μm. Consequently,

a significant difference in wash-off process kinetics of PAHs was identified in coarser

particles at the residential and commercial site. However, this can be attributed to the

relatively low detection rate of PAHs in particles above 150μm at the residential site.

Similar results have been found in previous studies (Maruya et al. 1996; Manoli and

Samara 1999). Hence, the established relationships were not suitable for predicting

PAHs in particles above 150μm at the residential site. Overall, the PAH predictions

perform relatively well and suggests that similar chemical processes were governing

the distribution of PAHs in particles, independent of land use characteristics. A

similar conclusion was made by Zhou and Maskaoui (2003), when investigating the

composition pattern of high molecular weight PAHs in suspended solids.

8.5 Summary

Predictive relationships of PAHs and heavy metals were established using a PLS

approach of data from particulate and dissolved fractions of runoff. PCA was applied

prior to prediction to determine which samples were suitable to predict the different

variables. It was found that the dissolved fraction of runoff and the build-up samples

collected at each site were skewing the results of the PCA due to the relatively large

difference in concentration of PAHs and heavy metals observed. Furthermore, the

majority of metals and PAHs were associated with the particulate phase of runoff,

with the exception of Zn. Hence, most of the metals and PAHs were predicted using a

calibration and validation set obtained from the data found in the particle fraction of

runoff.

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The performance and reliability of some of the predictions strengthened the

hypothesis that chemical processes were transferable between the sites. Hence, by

reducing the number of physical factors in urban water quality studies, such as land

use characteristics and particle size, the transferability of fundamental concepts

become higher. This in turn could indirectly contribute to more effective urban

stormwater management, through the application of a more holistic approach.

However, there are a number of limitations to the established relationships even

though the methodology and processes used have been justified and validated. The

established relationships were aimed at validating the transferability process kinetics

of heavy metals associated with particles less than 300μm and could only predict

concentrations in this specific particle size class. Hence, “total” water concentrations

and heavy metal concentrations in particles above 300μm could not be predicted using

PLS with the established relationships. Only a small percentage (generally 10-15% of

total concentrations) of the investigated heavy metals was found in particles above

300μm. Similar results have been found by Liebens (2001) and Shinya et al. (2001)

indicating that metal concentrations increase with decreasing particle size. This is not

very surprising since finer particles have a higher sorption capacity and a relatively

larger surface area (Dong et al. 1984). More importantly, the majority of particles at

the three study sites were found to be fine particles. As a result, the majority of the

heavy metal pollutant load is carried by particles below 300µm which could have

serious implications for urban stormwater management.

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Chapter 9 General Discussion

9.1 Rainfall simulation in urban water quality

Rainfall simulation was successfully used to simulate a number of design rainfall

events at three study sites in this research. The rainfall simulator developed was

highly portable due to its lightweight construction and easy assembly and provided

the user with maximum control over when, where and how rainfall was applied.

Consequently, it was a time and cost efficient method to create a database for urban

water quality research. However, a number of limitations were observed with rainfall

simulation and the use of a rainfall simulator, in reproducing natural rainfall

characteristics. These were:

• Limited ability to produce natural kinetic energy of rainfall;

• Intermittent rainfall compared to natural rainfall; and

• Limited drop size distribution.

Thus, it was important to realise that the rainfall simulator was not able to reproduce

rainfall characteristics perfectly. Furthermore, the rainfall simulator was specifically

developed to reproduce rainfall characteristics in the South-East Queensland region. It

is important to note that any developed rainfall simulator needs to be calibrated

accurately for the scope of the research.

The limitations noted above could therefore be reduced by accurately calibrating the

rainfall simulator for the conditions in which it will be used. One of the primary

objectives of this research was to reduce the number of physical parameters involved

in urban water quality. Therefore, the use of identical kinetic energy and drop size

distribution of the simulated rainfall events was preferable in this research. A change

in the water pressure entering the nozzles would change the drop size distribution and

hence the kinetic energy. Consequently, it is postulated that by changing water

pressure and oscillation cycles, rainfall events in regions all over the world could be

successfully simulated if the rainfall simulator is adequately calibrated for the

conditions in which it will be used.

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9.2 Chemical processes

One of the primary objectives of this research was to identify the chemical process

inherent in pollutant build-up and wash-off as well as to reduce the number of

physical factors influencing urban water quality research. Hence, the transferability of

the research would be significantly improved compared to previous studies by the

improvement of methodology and reduction of physical factors used in the

investigation. Nevertheless, there were a number of limitations in the findings that are

important to discuss. Firstly, it was found that the type and source of chemical

pollutants, such as TOC, was influencing the distribution of PAHs and heavy metals

at the study sites. Consequently, the chemical processes for a number of individual

PAHs and metals were influenced by land use characteristics. In addition, this

relationship could differ between the particle size classes at an individual site.

However, an overall correlation was found between TOC and PAHs and metals at the

three study sites which suggested that the adsorption affinity of organic particles was

higher than other particulate fractions.

More importantly, DOC was found to influence the presence of PAHs and metals in

the dissolved phase of the wash-off samples. In spite of this, it was difficult to confirm

or predict this relationship, especially with PAHs, due to the low solubility of the

compounds investigated. It is recommended that future research focus specifically on

the relationship between PAHs, heavy metals and DOC and sampling and analytical

procedures suitable for such research are carefully chosen due to the low

concentrations expected in the dissolved fraction.

Thirdly, the influence of the pH and EC of the particles on the distribution of PAHs

and heavy metals was not investigated. This was due to the low amount of particles

obtained in the wash-off samples primarily due to the partitioning of the samples into

several particle size classes and the relatively small plot area. Hence, the methods

available for measuring pH and EC in particles were insufficient to adequately

determine the relative importance of these parameters in the partitioning of PAHs and

heavy metals. It is recommended that future research in urban water quality could

investigate the influence of the pH and EC of the particles on the adsorption of PAHs

and heavy metals.

187

It is also recommended that processes such as microbial degradation due to high

bacterial levels in the stormwater is investigated due to the negative impact it might

have on the efficiency of urban stormwater management. Furthermore, the impact of a

change in temperature on the wash-off process kinetics of PAHs and heavy metals

should be investigated.

9.3 Calibration of methodology

The parameter relationships and processes established in the research have served as a

baseline for the development of new effective urban stormwater management

strategies. The methodology used have significantly reduced the dependency of

natural rainfall and introduced a more generic approach to urban water quality

research. Furthermore, calibration and validation steps have been performed

throughout the research such as in the design of the rainfall simulator and the use of

design rainfall event as discussed in Chapter 3. Additionally, well known multivariate

methods have been used and the performance of the established relationships have

been calibrated and validated using well established techniques described in Chapter 7

and 8. Therefore, in view of the on-going calibration and validation of the research

methodology adopted throughout this research, significant reliance can be placed on

the outcomes derived.

9.4 Implications of chemical processes found

There are a number of important implications on urban stormwater management

measures that needs to be discussed from the findings of this research. At all three

sites, a relationship between heavy metals, PAHs and TOC were found. Even though

it differed with particle size primarily, due to the source of the organic matter and the

source of metal and PAHs, it can have serious implications for both the water quality

of receiving waters and measures implemented to reduce the impact of these

pollutants. This is attributed to a number of reasons such as:

• Microbial degradation of the organic matter could potentially release PAHs and

metals bound to the organic fraction into the water column;

• Increased bioavailability of PAHs and heavy metals which impose a significant

threat to aquatic organisms; and

188

• Increased mobility of PAHs and heavy metals due to organic matter being

correlated with fine particles.

The primary implications of these processes on urban stormwater management are:

• Limited efficiency of end of pipe solutions (retention/detention ponds, wetlands,

specific gross pollutant traps); and

• Limited efficiency of street sweeping due to the association with fine particulates.

Hence, more innovative solutions incorporating the chemical processes inherent to the

wash-off of PAHs and heavy metals are needed. The findings in this research suggest

the investigation of potential urban stormwater management measures for the

reduction of PAHs and heavy metals based on the chemical properties of the particles

and the water. This could be implemented with measures such as:

• More efficient street cleaning, primarily related to decreased release of fine

particles through the exhaust of street sweepers;

• Longer retention times of stormwater which could lead to an increased photo-

oxidation of PAHs;

• Fine solids separation; and

• Chemical treatment of stormwater.

Additionally, 1 and 2 year design rainfall events were carrying significantly higher

concentrations of PAHs and heavy metals at all three sites compared to 5 and 10 year

design rainfall events. This observation was independent of the rainfall intensity of the

design rainfall events. This highlighted the need for effective urban stormwater

management measures. Moreover, it was found during the prediction of PAHs and

heavy metals that the predictive performance decreased when 10 year design rainfall

events were incorporated. Thus, the chemical processes that were governing the wash-

off of PAHs and heavy metals became less significant in less frequent storms. The

physical processes of urban stormwater were more influential in 10 year design

rainfall events. The generic relationships derived are valid for other geographic

locations. However, the influence of rainfall intensities in different locations needs to

be understood prior to adopting the methodology and the derived relationships.

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Chapter 10 Conclusions and Recommendations

10.1 Conclusions

Processes and concepts relating to urban water quality are well known in a qualitative

sense. However, their quantification has proven to be extremely difficult. This has

been a major failure in most research studies. Furthermore, the strong reliance on

physical factors and limited recognition of chemical processes in current urban water

quality studies have led to contradictory results reported and strong location specific

outcomes. Ultimately, this has led to limited knowledge on the process kinetics of

build-up, transmission and dispersion of PAHs and heavy metals. In order to obtain

sustainable urban stormwater management strategies, more scientifically robust

methodology in obtaining and analysing the data is needed. This is primarily

attributed to a multi-disciplinary approach where research methodologies adopted in

other areas should be considered. The main focus of this research project was to

develop a pragmatic approach for investigating the build-up and wash-off process

kinetics of PAHs and heavy metals on paved surfaces. The research aims and

objectives outlined in Chapter 1 were achieved through the development of three

primary processes. These were:

• The use of rainfall simulation in the investigation of chemical processes inherent

to urban water quality

It became apparent that the lack of control of physical factors in previous research

had significantly reduced the transferability of fundamental concepts in urban

water quality. Therefore, in an attempt to reduce or control the number of physical

factors involved, a rainfall simulator was developed and calibrated accurately for

the conditions on which it was used. Both the physical and chemical

characteristics of natural rainfall characteristics in the area were successfully re-

produced. The rainfall simulator, detailed in Chapter 3, provided satisfactory

control over a number of physical factors such as the duration and intensity of

rainfall and had the following characteristics:

o Ability to simulate rainfall intensities from approximately 10mm/hr up to

approximately 220mm/hr;

o Highly portable, not only from site to site but from plot to plot;

190

o Water efficient by re-using the water not sprayed on the research plot;

o Kinetic energy and drop size distribution similar to natural rainfall in

South-East Queensland; and

o Efficient runoff collection system for paved surfaces.

Furthermore, it generated an extensive data base suitable for undertaking urban

water quality research. Artificial rainfall was then applied to paved surfaces in

three different land uses, which were identified through investigations detailed in

Chapter 4. By using a homogenous plot area, the number of physical factors

involved in the wash-off process kinetics of PAHs and heavy metals was reduced.

This allowed for an in-depth investigation of the chemical processes inherent to

urban water quality.

• Identification of important particle sizes in urban stormwater management

The research undertaken strengthened the importance of fine particles in the

generation and transport of PAHs and heavy metals. This was achieved through

detailed laboratory tests on different particle size fraction of the build-up and

wash-off at the respective sites as discussed in Chapter 5 and 6. The methods

adopted were standard methods detailed by relevant authorities. Independent of

the land use, the highest concentrations of PAHs and heavy metals were

consistently found in particles 0.45-75µm. This was mirrored by the particle

volume percentage. Hence, priority should be given to the removal of fine

particles in urban stormwater to efficiently reduce pollutant loads reaching

receiving waters.

• Evaluation of important wash-off and build-up process kinetics using multivariate

analysis

The identification and recognition of important processes inherent to the

distribution of PAHs and heavy metals in the dissolved and particulate fraction of

runoff was resolved using multivariate analysis and appropriate laboratory

methodologies. The methodologies adopted were calibrated and validated step-by-

step as described in Chapter 5-7. Although multivariate methods have been

implemented by chemists to describe pattern recognition, classification and

191

prediction, it is rarely used in the engineering field even though it can provide

important information on relationships between variables which in turn could lead

to an increased knowledge of the processes involved. The use of multivariate

analysis significantly increased the information that could be obtained from the

collected data. This was primarily attributed to the ability to handle noisy and

collinear data. Chapter 5-7 goes through the data analysis in detail. The

investigations conducted were focused on a number of different fractions of the

build-up and wash-off. These studies involved extensive field investigations,

sampling and laboratory testing and detailed data analysis. The major findings

from the univariate and multivariate analysis of the build-up and wash-off samples

were:

o The adsorption and desorption of PAHs and heavy metals was primarily

related to type and amount of TOC present;

o Metals at the industrial site were trapped in mineral-like particles;

o PAHs were consistently found in concentrations above their aqueous

solubility which was attributed to the colloidal organic particles present

able to sorb hydrophobic pollutants;

o Depending on particle size class, the relationship between specific PAHs

and heavy metals with TOC differed;

o pH and EC influenced the desorption of PAHs and heavy metals from

particles;

o TSS was less important than chemical and physico-chemical parameters in

distributing the PAHs and heavy metals; and

o Rainfall intensity and duration was less important compared to chemical

and physico-chemical parameters in wash-off process kinetics of PAHs

and heavy metals.

Primary implications of these findings in urban stormwater management are:

o PAHs and heavy metals adsorbed to fine particles are easily transported in

urban stormwater, and hence impose a significant threat to receiving

waters and questions the use of conventional methods in removing

pollutants;

o PAHs and heavy metals could be released into a freely dissolved phase if

microbial degradation of the organic matter occur; and

192

o The efficiency of current urban management strategies based on gravity

settling could be significantly reduced if ‘hydrophobic friendly’ conditions

exist in fine particles.

• Validation of the transferability of parameter relationships

To facilitate transferability of processes inherent to urban water quality, predictive

relationships were established. This entailed the identification of key parameters

and key objects that were independent of physical characteristics such as land use.

This was achieved through the PLS approach, which was accurately calibrated and

validated as described in Chapter 8. The predictive relationships established

confirmed the importance of chemical processes in urban water quality and

suggested the processes identified could be transferable to different land uses.

Furthermore, the parameter relationships have the potential to be transferred to

different geographic location with slight modifications.

10.2 Recommendations

Although the developed methodology and established relationships has helped to

strengthen the knowledge of build-up and wash-off process kinetics in urban runoff,

there still remain a number of critical areas that have not been addressed through this

research. Therefore, it is recommended that further research is undertaken in the

following areas:

• The collection of more build-up samples from the sites in order to investigate the

importance of chemical processes in each particle size class. Only one build-up

sample was collected for each site in this research due to antecedent conditions

being kept identical for build-up and wash-off samples. It is recommended that the

influence of the antecedent dry period on the distribution of PAHs and heavy

metals in accumulated particles is investigated and any effect it may have on

chemical processes determined;

• Validation of the relationships and processes established in this research using

natural rainfall and runoff data. This primarily relates to establishing a sufficient

detailed data base for the validation of the chemical processes;

193

• Assessment of the role of suspended solids and dissolved organic carbon in urban

water quality. This issue has been addressed to some extent in this research but the

degradation of organic matter and the microbial activity of suspended solids are

important processes in urban water quality due to the increased bioavailability of

metals and PAHs this could cause. Hence, more detailed studies into the

adsorption and desorption of pollutants and its fate and transport once adsorbed or

desorbed could potentially improve the effectiveness of urban stormwater

management;

• The distribution of heavy metals and PAHs into different fractions, primarily

relating to difference in bioavailability. In this research, it was found that Al and

Pb were primarily bound to Fe and Mn oxides due to the correlation found in the

PCA. However, the partitioning of metals can occur in more fractions such as the

carbonate and exchangeable fraction. Furthermore, the percentage of metals

attached to the Fe and Mn oxide fraction was not determined. It is recommended

that future research could focus on determining the percentage of metals attached

to each fraction due to different sorption and desorption rates observed by

previous researchers between the fractions;

• Assessment of the influence of lower intensity rainfall on the wash-off of PAHs

and heavy metals from a paved surface. This is attributed to potential differences

in sorption and desorption of pollutants due to lower kinetic energy of rainfall.

Hence, the adsorption affinity of a suspended solid subjected to different kinetic

energies of rainfall should be investigated due to the potential difference in

mobility and bioavailability of pollutants. This was not investigated in this

research due to kinetic energy being kept constant during the simulated rainfall

events; and

• The influence of pH and EC of sediments on the adsorption of hydrophobic

pollutants. This is primarily attributed to the increased sorption kinetics of

particles due to changes in pH or EC observed by previous researchers.

194

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APPENDIX A

RAINFALL SIMULATOR CALIBRATION DATA

218

TABLE A1 Intensity data for Speed setting 2 and a delay of 2s Cup no: Volume

[mL] Measured intensity [mm/hr]

Duration [min]

1 26 55.0 5 2 30 63.4 5 3 30 63.4 5 4 30 63.4 5 5 28 59.2 5 6 25 52.9 5 7 20 42.3 5 8 33 69.8 5 9 40 84.6 5 10 42 88.8 5 11 44 93.0 5 12 44 93.0 5 13 44 93.0 5 14 27 57.1 5 15 36 76.1 5 16 49 103.6 5 17 50 105.7 5 18 49 103.6 5 19 46 97.3 5 20 42 88.8 5 21 27 57.1 5 22 40 84.6 5 23 52 110.0 5 24 54 114.2 5 25 53 112.1 5 26 50 105.7 5 27 42 88.8 5 28 30 63.4 5 29 36 76.1 5 30 47 99.4 5 31 48 101.5 5 32 47 99.4 5 33 43 90.9 5 34 40 84.6 5 35 29 61.3 5 36 36 76.1 5 37 44 93.0 5 38 48 101.5 5 39 50 105.7 5 40 53 112.1 5 41 50 105.7 5 42 46 97.3 5 43 34 71.9 5 44 40 84.6 5 45 40 84.6 5

219

TABLE A1 cont 46 40 84.6 5 47 41 86.7 5 48 39 82.5 5 49 30 63.4 5 50 38 80.4 5 51 48 101.5 5 52 52 110.0 5 53 53 112.1 5 54 52 110.0 5 55 47 99.4 5 56 35 74.0 5 57 36 76.1 5 58 50 105.7 5 59 57 120.5 5 60 49 103.6 5 61 49 103.6 5 62 42 88.8 5 63 33 69.8 5 64 30 63.4 5 65 42 88.8 5 66 42 88.8 5 67 40 84.6 5 68 48 101.5 5 69 37 78.2 5 70 29 61.3 5 71 32 67.7 5 72 42 88.8 5 73 44 93.0 5 74 36 76.1 5 75 46 97.3 5 76 32 67.7 5 77 30 63.4 5

220



Duration [min]

1 22 46.5 5 2 23 48.6 5 3 24 50.8 5 4 22 46.5 5 5 22 46.5 5 6 20 42.3 5 7 14 29.6 5 8 25 52.9 5 9 32 67.7 5 10 32 67.7 5 11 33 69.8 5 12 32 67.7 5 13 30 63.4 5 14 20 42.3 5 15 28 59.2 5 16 36 76.1 5 17 38 80.4 5 18 35 74.0 5 19 25 52.9 5 20 32 67.7 5 21 21 44.4 5 22 30 63.4 5 23 40 84.6 5 24 40 84.6 5 25 42 88.8 5 26 38 80.4 5 27 34 71.9 5 28 22 46.5 5 29 29 61.3 5 30 35 74.0 5 31 35 74.0 5 32 35 74.0 5 33 32 67.7 5 34 30 63.4 5 35 22 46.5 5 36 26 55.0 5 37 33 69.8 5 38 40 84.6 5 39 38 80.4 5 40 37 78.2 5 41 40 84.6 5 42 36 76.1 5 43 24 50.8 5 44 30 63.4 5 45 32 67.7 5

221

TABLE A2 cont 46 30 63.4 5 47 30 63.4 5 48 30 63.4 5 49 22 46.5 5 50 27 57.1 5 51 38 80.4 5 52 40 84.6 5 53 40 84.6 5 54 40 84.6 5 55 36 76.1 5 56 26 55.0 5 57 29 61.3 5 58 38 80.4 5 59 39 82.5 5 60 38 80.4 5 61 38 80.4 5 62 34 71.9 5 63 26 55.0 5 64 42 88.8 5 65 30 63.4 5 66 32 67.7 5 67 32 67.7 5 68 37 78.2 5 69 27 57.1 5 70 22 46.5 5 71 21 44.4 5 72 32 67.7 5 73 30 63.4 5 74 28 59.2 5 75 32 67.7 5 76 24 50.8 5 77 20 42.3 5

222



Duration [min]

1 14 29.6 5 2 16 33.8 5 3 18 38.1 5 4 17 36.0 5 5 15 31.7 5 6 15 31.7 5 7 11 23.3 5 8 18 38.1 5 9 22 46.5 5 10 22 46.5 5 11 22 46.5 5 12 23 48.6 5 13 22 46.5 5 14 12 25.4 5 15 18 38.1 5 16 24 50.8 5 17 25 52.9 5 18 25 52.9 5 19 25 52.9 5 20 24 50.8 5 21 14 29.6 5 22 20 42.3 5 23 26 55.0 5 24 27 57.1 5 25 26 55.0 5 26 25 52.9 5 27 23 48.6 5 28 15 31.7 5 29 17 36.0 5 30 22 46.5 5 31 24 50.8 5 32 23 48.6 5 33 22 46.5 5 34 20 42.3 5 35 15 31.7 5 36 17 36.0 5 37 22 46.5 5 38 26 55.0 5 39 25 52.9 5 40 27 57.1 5 41 28 59.2 5 42 25 52.9 5 43 17 36.0 5 44 20 42.3 5 45 21 44.4 5

223

TABLE A3 cont 46 20 42.3 5 47 21 44.4 5 48 20 42.3 5 49 17 36.0 5 50 18 38.1 5 51 25 52.9 5 52 27 57.1 5 53 27 57.1 5 54 26 55.0 5 55 24 50.8 5 56 17 36.0 5 57 18 38.1 5 58 24 50.8 5 59 26 55.0 5 60 24 50.8 5 61 22 46.5 5 62 22 46.5 5 63 16 33.8 5 64 14 29.6 5 65 20 42.3 5 66 20 42.3 5 67 20 42.3 5 68 24 50.8 5 69 18 38.1 5 70 14 29.6 5 71 14 29.6 5 72 20 42.3 5 73 20 42.3 5 74 18 38.1 5 75 20 42.3 5 76 16 33.8 5 77 14 29.6 5

224



Duration [min]

1 46 97.3 5 2 52 110.0 5 3 50 105.7 5 4 50 105.7 5 5 45 95.2 5 6 44 93.0 5 7 32 67.7 5 8 55 116.3 5 9 66 139.6 5 10 68 143.8 5 11 66 139.6 5 12 68 143.8 5 13 62 131.1 5 14 40 84.6 5 15 69 145.9 5 16 76 160.7 5 17 74 156.5 5 18 72 152.3 5 19 70 148.0 5 20 64 135.3 5 21 44 93.0 5 22 62 131.1 5 23 80 169.2 5 24 80 169.2 5 25 80 169.2 5 26 76 160.7 5 27 66 139.6 5 28 44 93.0 5 29 56 118.4 5 30 70 148.0 5 31 74 156.5 5 32 70 148.0 5 33 66 139.6 5 34 58 122.7 5 35 42 88.8 5 36 54 114.2 5 37 68 143.8 5 38 82 173.4 5 39 76 160.7 5 40 77 162.8 5 41 78 164.9 5 42 73 154.4 5 43 54 114.2 5 44 62 131.1 5 45 66 139.6 5

225

TABLE A4 cont 46 68 143.8 5 47 64 135.3 5 48 60 126.9 5 49 46 97.3 5 50 60 126.9 5 51 78 164.9 5 52 80 169.2 5 53 80 169.2 5 54 80 169.2 5 55 72 152.3 5 56 52 110.0 5 57 58 122.7 5 58 76 160.7 5 59 80 169.2 5 60 76 160.7 5 61 72 152.3 5 62 56 118.4 5 63 52 110.0 5 64 50 105.7 5 65 64 135.3 5 66 66 139.6 5 67 63 133.2 5 68 76 160.7 5 69 58 122.7 5 70 48 101.5 5 71 44 93.0 5 72 54 114.2 5 73 61 129.0 5 74 58 122.7 5 75 70 148.0 5 76 48 101.5 5 77 41 86.7 5

226

TABLE A5 Intensity data for Speed setting 3 and a delay of 1.5s Cup no: Volume


Duration [min]

1 32 67.7 5 2 44 93.0 5 3 43 90.9 5 4 45 95.2 5 5 40 84.6 5 6 38 80.4 5 7 31 65.6 5 8 36 76.1 5 9 58 122.7 5 10 70 148.0 5 11 64 135.3 5 12 65 137.5 5 13 60 126.9 5 14 50 105.7 5 15 40 84.6 5 16 66 139.6 5 17 72 152.3 5 18 68 143.8 5 19 65 137.5 5 20 61 129.0 5 21 49 103.6 5 22 44 93.0 5 23 66 139.6 5 24 71 150.1 5 25 72 152.3 5 26 67 141.7 5 27 61 129.0 5 28 46 97.3 5 29 42 88.8 5 30 60 126.9 5 31 63 133.2 5 32 63 133.2 5 33 58 122.7 5 34 56 118.4 5 35 42 88.8 5 36 70 148.0 5 37 72 152.3 5 38 72 152.3 5 39 68 143.8 5 40 68 143.8 5 41 61 129.0 5 42 58 122.7 5 43 48 101.5 5 44 57 120.5 5 45 60 126.9 5

227

TABLE A5 cont 46 59 124.8 5 47 54 114.2 5 48 50 105.7 5 49 40 84.6 5 50 53 112.1 5 51 61 129.0 5 52 72 152.3 5 53 72 152.3 5 54 68 143.8 5 55 62 131.1 5 56 45 95.2 5 57 52 110.0 5 58 68 143.8 5 59 70 148.0 5 60 66 139.6 5 61 65 137.5 5 62 60 126.9 5 63 43 90.9 5 64 46 97.3 5 65 60 126.9 5 66 65 137.5 5 67 58 122.7 5 68 59 124.8 5 69 52 110.0 5 70 40 84.6 5 71 48 101.5 5 72 52 110.0 5 73 51 107.9 5 74 50 105.7 5 75 50 105.7 5 76 50 105.7 5 77 38 80.4 5

228



Duration [min]

1 17 36.0 5 2 17 36.0 5 3 20 42.3 5 4 20 42.3 5 5 19 40.2 5 6 18 38.1 5 7 13 27.5 5 8 24 50.8 5 9 28 59.2 5 10 27 57.1 5 11 27 57.1 5 12 28 59.2 5 13 26 55.0 5 14 15 31.7 5 15 24 50.8 5 16 30 63.4 5 17 30 63.4 5 18 30 63.4 5 19 29 61.3 5 20 27 57.1 5 21 18 38.1 5 22 25 52.9 5 23 33 69.8 5 24 33 69.8 5 25 31 65.6 5 26 30 63.4 5 27 29 61.3 5 28 18 38.1 5 29 23 48.6 5 30 29 61.3 5 31 29 61.3 5 32 29 61.3 5 33 28 59.2 5 34 23 48.6 5 35 16 33.8 5 36 21 44.4 5 37 27 57.1 5 38 35 74.0 5 39 31 65.6 5 40 31 65.6 5 41 32 67.7 5 42 27 57.1 5 43 28 59.2 5 44 26 55.0 5 45 27 57.1 5

229

TABLE A6 cont 46 27 57.1 5 47 25 52.9 5 48 24 50.8 5 49 19 40.2 5 50 25 52.9 5 51 36 76.1 5 52 33 69.8 5 53 33 69.8 5 54 32 67.7 5 55 39 82.5 5 56 21 44.4 5 57 24 50.8 5 58 32 67.7 5 59 32 67.7 5 60 31 65.6 5 61 32 67.7 5 62 27 57.1 5 63 20 42.3 5 64 19 40.2 5 65 25 52.9 5 66 27 57.1 5 67 26 55.0 5 68 30 63.4 5 69 23 48.6 5 70 18 38.1 5 71 19 40.2 5 72 27 57.1 5 73 26 55.0 5 74 24 50.8 5 75 22 46.5 5 76 20 42.3 5 77 19 40.2 5

230

TABLE A7 Calibration of median drop size using flour pellet method

Sieve No. of pellets Total Mass of pellets [mg]

Average pellet mass [mg]

Drop mass according to Hudson (1963) [mg]

4.75 mm 9 382.4 42.49 54.17 3.35 mm 74 1443.9 19.51 24.88 2.36 mm 125 1286 10.29 12.86 1.18 mm 807 2058.4 2.55 2.86 0.6 mm 3200 504.3 0.16 0.11 0.5 mm 3389 463.2 0.14 0.09

Sieve Average drop volume

[cm3] Average drop diameter

[cm] Total Volume [cm3] % of total volume

4.75 mm 0.054 0.47 0.49 7.08 3.35 mm 0.025 0.36 1.84 26.74 2.36 mm 0.013 0.29 1.61 23.35 1.18 mm 0.0029 0.18 2.31 33.48 0.6 mm 0.00011 0.058 0.34 4.91 0.5 mm 0.000090 0.056 0.31 4.44

Drop diameter [mm] % of total volume % Cumulative

volume 0 0.55 4.44 4.44 0.59 4.91 9.35 1.76 33.48 42.83 2.91 23.35 66.18 3.62 26.74 92.92 4.69 7.08 100

231

TABLE A8 Rainfall quality analyses in Brisbane Station Date collected Date

measured pH EC

(μS/cm) DOC (mg/L)

Camp Hill 11/03/2003 8am 13/03/2003 5.40 26.30 5.381

Camp Hill 13/2 2003 14/2 2003 5.90 33.30 na

QUT 19/2 2003 11am 19/2 2003 6.75 228.00 na

QUT 19/2 2003 4pm 19/2 2003 6.10 76.30 14.71

QUT 11/03/2003 11am 13/03/2003 7.20 69.20 na

QUT 13/03/03 9.00 am 13/03/2003 6.80 53.30 7.354

Toowong 19/2 2003 7pm 20/2 2003 6.15 19.00 8.713

Camp Hill 19/2 2003 8pm 20/2 2003 6.20 19.70 5.629

Toowong 21/2 2003 7am 21/2 2003 7.10 92.70 11.07

Toowong 12/03/2003 7pm 13/03/2003 5.90 33.20 6.505

QUT 21/2 2003 8am 21/2 2003 6.80 85.50 11.98

232

TABLE A8 cont. Annerley 01/03/03 4.30pm 13/03/2003 5.50 21.60 5.502

Annerley 12/03/037.30am 13/03/2003 6.80 56.10 6.535

Annerley 22/2 2003 3pm 25/2 2003 6.90 39.90 8.189

QUT 24/2 2003 5pm 25/2 2003 6.10 8.46 9.271

Camp Hill 24/2 2003 10.30am

25/2 2003 6.10 2.72 5.725

Toowong 24/2 2003 8pm 25/2 2003 6.70 40.90 9.386

QUT 25/2 2003 8am 25/2 2003 6.90 60.10 16.49

Sunny Bank

12/03/03 7.00am 13/03/2003 6.30 55.30 9.863

ANZ Stadium

7/03/2003 13/03/2003 6.00 29.00 6.949

Toowong 13/3 2003 7pm 18/3 2003 5.90 35.00 4.606

ANZ Stadium

25/02/2003 13/03/2003 6.20 34.00 6.615

Sunny Bank

27/02/03 7.00am 13/03/2003 6.50 56.66 3.507

233

FIGURE A1 Delonghi Aqualand Vacuum cleaner specification

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library

235

APPENDIX B

TEST RESULTS

236

TABLE B1 – Residential wash-off data PART 2 (Data for simulated rainfall events with intensities 86 and 115mm/hr)

1y10m >300 20/08/2003 86 10 EMC 1 year 10 min 42 0.03 0.27 2 7.601y10m 151-300 20/08/2003 86 10 EMC 1 year 10 min 42 0.03 0.27 2 7.60 1y10m 76-150 20/08/2003 86 10 EMC 1 year 10 min 42 0.03 0.27 2 7.60 1y10m 0.45-75 20/08/2003 86 10 EMC 1 year 10 min 42 0.03 0.27 2 7.60 1y10min Dis 20/08/2003 86 10 EMC 1 year 10 min 42 0.03 0.27 2 7.602y20m >300 20/08/2003 86 20 EMC 2 year 20 min 84 0.03 0.27 2 7.60

2y20m 151-300 20/08/2003 86 20 EMC 2 year 20 min 84 0.03 0.27 2 7.602y20m 76-150 20/08/2003 86 20 EMC 2 year 20 min 84 0.03 0.27 2 7.602y20m 0.45-75 20/08/2003 86 20 EMC 2 year 20 min 84 0.03 0.27 2 7.602y20min Dis 20/08/2003 86 20 EMC 2 year 20 min 84 0.03 0.27 2 7.6010y40m >300 20/08/2003 86 40 EMC 10 year 40 min 167 0.03 0.27 2 7.60

10y40m 151-300 20/08/2003 86 40 EMC 10 year 40 min 167 0.03 0.27 2 7.6010y40m 76-150 20/08/2003 86 40 EMC 10 year 40 min 167 0.03 0.27 2 7.6010y40m 0.45-75 20/08/2003 86 40 EMC 10 year 40 min 167 0.03 0.27 2 7.6010y40min Dis 20/08/2003 86 40 EMC 10 year 40 min 167 0.03 0.27 2 7.60

1y5m >300 20/08/2003 115 5 EMC 1 year 5 min 22 0.03 0.27 2 7.601y5m 151-300 20/08/2003 115 5 EMC 1 year 5 min 22 0.03 0.27 2 7.601y5m 76-150 20/08/2003 115 5 EMC 1 year 5 min 22 0.03 0.27 2 7.601y5m 0.45-75 20/08/2003 115 5 EMC 1 year 5 min 22 0.03 0.27 2 7.601y5min Dis 20/08/2003 115 5 EMC 1 year 5 min 22 0.03 0.27 2 7.60

Dry days b f

Last rainfall[mm]

Event Total volume [L]

Slope [m/m]

Texture depth [mm]

Sample name [ARI andparticle size (µm)]

Sample date

Intensity [mm/hr]

Duration [min]

237

TABLE B1 – Residential wash-off data PART 1 (Parameters continued from previous page)

1y10m >300 39 18.4 6.99 124.90 0.457 1.127 0.951y10m 151-300 39 8.38 6.99 124.90 1.199 0.709 1.501y10m 76-150 39 18.65 6.99 124.90 0.827 0.387 5.301y10m 0.45-75 39 53.09 6.99 124.90 1.583 0.003 16.451y10min Dis 39 1.48 6.99 124.90 9.447 3.603 95.00 2y20m >300 39 6.41 7.20 127.60 0.193 2.779 1.05

2y20m 151-300 39 22.17 7.20 127.60 2.041 0.132 2.102y20m 76-150 39 18.39 7.20 127.60 0.225 0.186 9.10

2y20m 0.45-75 39 51.45 7.20 127.60 1.081 0.478 16.702y20min Dis 39 1.58 7.20 127.60 8.009 3.311 75.0010y40m >300 39 10.63 7.30 130.00 0.250 2.935 0.67

10y40m 151-300 39 32.92 7.30 130.00 1.859 0.665 0.8310y40m 76-150 39 28.83 7.30 130.00 0.006 0.688 4.0010y40m 0.45-75 39 27.62 7.30 130.00 0.265 1.989 7.6010y40min Dis 39 <0.01 7.30 130.00 6.412 3.608 80.00

1y5m >300 39 3.79 6.81 122.00 3.701 1.914 3.401y5m 151-300 39 12.18 6.81 122.00 0.509 0.635 19.40 1y5m 76-150 39 17.73 6.81 122.00 4.033 1.282 27.101y5m 0.45-75 39 64.52 6.81 122.00 2.218 0.038 76.301y5min Dis 39 1.78 6.81 122.00 9.402 1.118 70.00

2417.33

2186.39

2789.02

Total solids [mg]

1004.30


Last swept[days]

Particle volumepercentage [%]

TSS [mg/L]

pH EC [uS/cm]

TOC [ppm]

IC [ppm]

238

TABLE B1 - Residential wash-off data PART 1 (Parameters continued from previous page)

1y10m >3001y10m 151-3001y10m 76-150 0.98 <0.01 <0.01 <0.01 <0.01 0.19 0.23 0.26 <0.011y10m 0.45-75 0.62 0.09 0.07 <0.01 0.09 0.09 <0.01 <0.01 0.071y10min Dis 0.04 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 2y20m >300

2y20m 151-3002y20m 76-150 0.77 0.14 0.13 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 2y20m 0.45-75 0.48 0.11 0.10 <0.01 <0.01 0.08 <0.01 <0.01 <0.01

2y20min Dis 0.07 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.0110y40m >300

10y40m 151-30010y40m 76-150 1.05 <0.01 <0.01 <0.01 <0.01 <0.01 0.28 0.30 0.2510y40m 0.45-75 1.08 0.16 <0.01 <0.01 <0.01 <0.01 0.29 0.24 0.2110y40min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

1y5m >3001y5m 151-300 1y5m 76-150 1.03 0.08 <0.01 <0.01 <0.01 0.09 0.08 0.04 <0.011y5m 0.45-75 0.84 0.06 0.02 0.02 0.03 <0.01 <0.01 <0.01 0.021y5min Dis 0.04 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Sample name [ARIand particle size( )

NAP [ppm] ACY [ppm] ACE [ppm] FLU [ppm] PHE [ppm] ANT [ppm]

FLA [ppm] PYR [ppm] BaA [ppm]

0.84 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.81 <0.01 <0.01 <0.01 <0.01 <0.01 0.19 0.21 <0.01

0.93 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.99 0.03 <0.01 <0.01 0.04<0.01 <0.01 0.09 0.05

239


1y10m >300 0.4801y10m 151-300 0.4001y10m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.5601y10m 0.45-75 <0.01 0.128 <0.01 <0.01 <0.01 <0.01 0.6001y10min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.014 2y20m >300 0.108

2y20m 151-300 0.1842y20m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.288

2y20m 0.45-75 <0.01 <0.01 0.060 <0.01 <0.01 <0.01 0.1162y20min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.00510y40m >300 0.344

10y40m 151-300 0.19610y40m 76-150 <0.01 <0.01 0.250 <0.01 <0.01 <0.01 0.27210y40m 0.45-75 <0.01 <0.01 0.224 <0.01 <0.01 <0.01 0.13210y40min Dis <0.01 <0.01 0.011 <0.01 <0.01 <0.01 <0.005

1y5m >300 0.1001y5m 151-300 0.172 1y5m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.4401y5m 0.45-75 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.7201y5min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.026


CHR [ppm]

BbF [ppm] BaP [ppm] IND [ppm] DbA [ppm] BgP [ppm] Fe [ppm]

<0.01 <0.01 <0.01 <0.01 <0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01 <0.01 <0.01

<0.01 <0.01

<0.01 <0.01<0.01 <0.01 <0.01 <0.01

240


1y10m >300 0.035 0.280 0.001 0.015 <0.001 0.004 0.0081y10m 151-300 0.056 0.344 0.001 0.132 <0.001 0.006 0.0081y10m 76-150 0.080 0.520 0.004 0.068 0.000 0.005 0.0081y10m 0.45-75 0.112 0.640 0.003 0.080 0.001 0.006 0.0141y10min Dis 2.600 0.008 <0.001 0.340 <0.001 <0.001 0.006 2y20m >300 0.040 0.104 0.001 0.020 0.108 0.004 0.003

2y20m 151-300 0.029 0.144 0.001 0.120 0.184 0.004 0.0022y20m 76-150 0.037 0.224 0.002 0.064 0.288 0.004 0.003

2y20m 0.45-75 0.056 0.092 0.001 0.029 0.116 0.005 0.0062y20min Dis 3.600 <0.005 <0.001 0.390 <0.001 <0.001 0.00510y40m >300 0.022 0.096 0.001 0.018 0.001 0.004 0.004

10y40m 151-300 0.036 0.092 0.001 0.128 <0.001 0.004 0.00210y40m 76-150 0.084 0.256 0.015 0.080 0.003 0.006 0.00810y40m 0.45-75 0.044 0.080 0.003 0.026 0.001 0.004 0.00610y40min Dis 1.200 <0.005 <0.001 0.200 <0.001 <0.001 0.003

1y5m >300 0.027 0.060 0.001 0.018 <0.001 0.004 0.0021y5m 151-300 0.044 0.128 0.002 0.092 <0.001 0.004 0.004 1y5m 76-150 0.052 0.316 0.002 0.037 <0.001 0.004 0.0081y5m 0.45-75 0.120 0.520 0.004 0.052 0.001 0.006 0.0141y5min Dis 1.100 0.007 <0.001 0.190 <0.001 <0.001 0.006

Cu [ppm]

Cd [ppm]

Cr [ppm]

Mn [ppm]


Zn [ppm] Al [ppm] Pb [ppm]

241

TABLE B1 – Residential Wash-off data PART 2 (Data for simulated rainfall events with intensities 115 and 133mm/hr)

2y10m >300 20/08/2003 115 10 EMC 2 year 10 min 42 0.03 0.27 2 7.60 2y10m 151-300 20/08/2003 115 10 EMC 2 year 10 min 42 0.03 0.27 2 7.60 2y10m 76-150 20/08/2003 115 10 EMC 2 year 10 min 42 0.03 0.27 2 7.60 2y10m 0.45-75 20/08/2003 115 10 EMC 2 year 10 min 42 0.03 0.27 2 7.60 2y10min Dis 20/08/2003 115 10 EMC 2 year 10 min 42 0.03 0.27 2 7.60 10y25m >300 20/08/2003 115 25 EMC 10 year 25 min 134 0.03 0.27 2 7.60

10y25m 151-300 20/08/2003 115 25 EMC 10 year 25 min 134 0.03 0.27 2 7.60 10y25m 76-150 20/08/2003 115 25 EMC 10 year 25 min 134 0.03 0.27 2 7.60 10y25m 0.45-75 20/08/2003 115 25 EMC 10 year 25 min 134 0.03 0.27 2 7.60

10y25min Dis 20/08/2003 115 25 EMC 10 year 25 min 134 0.03 0.27 2 7.60 2y7m >300 20/08/2003 133 7 EMC 2 year 7 min 28 0.02 0.27 2 7.60

2y7m 151-300 20/08/2003 133 7 EMC 2 year 7 min 28 0.02 0.27 2 7.60 2y7m 76-150 20/08/2003 133 7 EMC 2 year 7 min 28 0.02 0.27 2 7.60 2y7m 0.45-75 20/08/2003 133 7 EMC 2 year 7 min 28 0.02 0.27 2 7.60 2y7min Dis 20/08/2003 133 7 EMC 2 year 7 min 28 0.02 0.27 2 7.60 5y13m >300 20/08/2003 133 13 EMC 5 year 13 min 54 0.02 0.27 2 7.60


10y17m 151-300 20/08/2003 133 17 EMC 10 year 17 min 85 0.02 0.27 2 7.60 10y17m 76-150 20/08/2003 133 17 EMC 10 year 17 min 85 0.02 0.27 2 7.60 10y17m 0.45-75 20/08/2003 133 17 EMC 10 year 17 min 85 0.02 0.27 2 7.60 10y17min Dis 20/08/2003 133 17 EMC 10 year 17 min 85 0.02 0.27 2 7.60


Sample date Intensity [mm/hr]

Duration [min]

Dry daysbefore

Last rainfall

Event Total volumeevent [L]

Slope [m/m]

Texture depth

242


2y10m >300 39 0.13 6.93 118.60 1.708 1.246 0.51 2y10m 151-300 39 6.63 6.93 118.60 2.538 0.400 1.71 2y10m 76-150 39 21.43 6.93 118.60 1.714 0.754 11.84 1.01 0.13 2y10m 0.45-75 39 70.56 6.93 118.60 1.093 0.217 34.62 1.01 0.06 2y10min Dis 39 1.25 6.93 118.60 8.398 2.212 65.00 0.07 <0.01 10y25m >300 39 0.01 7.00 112.80 0.627 2.909 0.95

10y25m 151-300 39 10.13 7.00 112.80 1.276 0.236 0.80 10y25m 76-150 39 32.91 7.00 112.80 0.222 0.174 3.55 0.99 <0.01 10y25m 0.45-75 39 55.97 7.00 112.80 1.826 2.489 16.20 1.23 0.13

10y25min Dis 39 0.89 7.00 112.80 6.369 3.270 60.00 0.06 <0.01 2y7m >300 39 0.13 6.65 104.40 0.419 3.042 2.40

2y7m 151-300 39 2.65 6.65 104.40 0.648 0.582 5.30 2y7m 76-150 39 13.06 6.65 104.40 1.489 0.712 8.90 1.15 <0.01 2y7m 0.45-75 39 82.8 6.65 104.40 0.528 0.578 48.20 0.95 0.18 2y7min Dis 39 1.36 6.65 104.40 6.886 3.214 90.00 0.05 0.01

5y13m >300 39 2.8 6.87 102.00 1.280 2.884 1.01 5y13m 151-300 39 7.16 6.87 102.00 1.135 0.830 1.14 5y13m 76-150 39 17.43 6.87 102.00 0.072 0.493 2.78 1.01 0.11 5y13m 0.45-75 39 71.43 6.87 102.00 0.083 0.443 26.71 1.12 0.11 5y13min Dis 39 1.18 6.87 102.00 5.973 3.127 80.00 0.03 <0.01 10y17m >300 39 2.09 7.06 105.10 0.835 1.824 0.86

10y17m 151-300 39 9.82 7.06 105.10 1.344 0.559 0.86 10y17m 76-150 39 18.78 7.06 105.10 0.029 0.290 3.29 1.06 0.15 10y17m 0.45-75 39 68.19 7.06 105.10 0.498 0.397 20.79 1.11 0.19 10y17min Dis 39 1.12 7.06 105.10 6.164 3.336 85.00 0.04 <0.01

1711.72

0.98 <0.01

2190.42

0.84 <0.01

2870.25

0.89 0.17

1833.84

1.10 0.14

TSS [mg/L]

NAP [ppm]

ACY [ppm]

2039.69

0.97 <0.01

EC [uS/cm]

TOC [ppm]

IC [ppm] Total solids [mg]Sample name [ARI andparticle size (µm)]

Last swept[days]


pH

243


2y10m >300 2y10m 151-300 2y10m 76-150 <0.01 <0.01 0.15 0.19 <0.01 <0.01 <0.01 <0.01 <0.01 0.13 2y10m 0.45-75 <0.01 0.04 <0.01 0.05 0.09 0.04 <0.01 <0.01 0.06 0.05 2y10min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y25m >300

10y25m 151-300 10y25m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y25m 0.45-75 0.07 <0.01 <0.01 0.10 0.17 0.09 0.09 <0.01 <0.01 0.09

10y25min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 2y7m >300

2y7m 151-300 2y7m 76-150 <0.01 0.11 0.20 0.13 0.25 0.15 0.19 0.16 <0.01 0.19 2y7m 0.45-75 0.07 0.19 0.30 0.11 0.20 0.21 0.18 <0.01 0.16 0.14 2y7min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 5y13m >300

5y13m 151-300 5y13m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 5y13m 0.45-75 0.06 0.07 0.04 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.04 5y13min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y17m >300

10y17m 151-300 10y17m 76-150 <0.01 <0.01 0.12 0.15 0.15 0.09 0.09 <0.01 <0.01 0.12 10y17m 0.45-75 <0.01 <0.01 0.13 0.13 0.16 0.12 0.17 0.10 <0.01 0.15 10y17min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.12 0.090.17 0.12 <0.01 <0.01<0.01 <0.01 <0.01 <0.01

<0.01 <0.01 <0.01

<0.01 0.07

<0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.18 0.10 0.08 <0.010.07 <0.01 <0.01 <0.01

<0.01 <0.01 0.23

<0.01 <0.01

<0.01 <0.01 <0.01 <0.01 0.20 0.17 <0.01

0.25 0.23 <0.01 <0.01<0.01 <0.01 <0.01 <0.01

CHR [ppm]

BbF [ppm] BaP [ppm]ANT [ppm]

FLA [ppm]

PYR [ppm]

BaA [ppm]Sample name [ARI andparticle size (µm)]

ACE [ppm]

FLU [ppm] PHE [ppm]

244


2y10m >300 0.144 0.056 0.152 0.004 0.021 0.001 0.006 0.003 2y10m 151-300 0.248 0.048 0.160 0.001 0.148 0.001 0.006 0.004 2y10m 76-150 <0.01 <0.01 <0.01 0.336 0.040 0.252 0.002 0.030 0.001 0.004 0.005 2y10m 0.45-75 <0.01 <0.01 <0.01 0.480 0.084 0.372 0.003 0.044 0.001 0.006 0.010 2y10min Dis <0.01 <0.01 <0.01 0.008 1.100 0.005 <0.001 0.160 <0.001 <0.001 0.004 10y25m >300 0.088 0.033 0.064 0.001 0.022 0.001 0.004 0.002

10y25m 151-300 0.152 0.026 0.100 0.001 0.164 <0.001 0.003 0.004 10y25m 76-150 <0.01 <0.01 <0.01 0.196 0.038 0.156 0.002 0.027 <0.001 0.002 0.004 10y25m 0.45-75 <0.01 <0.01 <0.01 0.104 0.035 0.088 0.001 0.025 <0.001 0.002 0.004

10y25min Dis <0.01 <0.01 <0.01 <0.005 0.970 <0.005 <0.001 0.170 <0.001 <0.001 0.002 2y7m >300 0.068 0.023 0.048 <0.001 0.016 <0.001 0.007 0.002

2y7m 151-300 0.108 0.044 0.072 <0.001 0.168 <0.001 0.004 0.002 2y7m 76-150 <0.01 <0.01 <0.01 0.140 0.036 0.084 0.001 0.056 <0.001 0.004 0.003 2y7m 0.45-75 <0.01 <0.01 <0.01 0.560 0.076 0.364 0.002 0.039 <0.001 0.003 0.010 2y7min Dis <0.01 <0.01 <0.01 <0.005 0.800 <0.005 <0.001 0.140 <0.001 <0.001 0.003 5y13m >300 0.096 0.028 0.052 <0.001 0.010 <0.001 0.001 0.002

5y13m 151-300 0.108 0.036 0.120 0.003 0.064 <0.001 0.002 0.002 5y13m 76-150 <0.01 <0.01 <0.01 0.148 0.052 0.124 0.001 0.016 <0.001 0.005 0.002 5y13m 0.45-75 <0.01 <0.01 <0.01 0.392 0.060 0.364 0.002 0.032 <0.001 0.002 0.008 5y13min Dis <0.01 <0.01 <0.01 <0.005 0.610 0.006 <0.001 0.110 <0.001 <0.001 0.004 10y17m >300 0.037 0.040 0.040 <0.001 0.014 <0.001 0.002 0.001

10y17m 151-300 0.076 0.040 0.048 <0.001 0.048 <0.001 0.004 0.001 10y17m 76-150 <0.01 <0.01 <0.01 0.076 0.030 0.064 0.001 0.016 <0.001 0.004 0.002 10y17m 0.45-75 <0.01 <0.01 <0.01 0.124 0.040 0.080 0.001 0.018 <0.001 0.005 0.004 10y17min Dis <0.01 <0.01 <0.01 <0.005 0.620 <0.005 <0.001 0.120 <0.001 <0.001 0.005

<0.01 <0.01

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

Cu [ppm]

Cd [ppm]

Cr [ppm]

Mn [ppm]

Fe [ppm] Zn [ppm] Al [ppm] Pb [ppm]


IND [ppm]

DbA [ppm] BgP [ppm]

245

TABLE B1 – Residential Wash-off data PART 3 (Blanks and replicate samples)

Field blank 20/08/2003 115 BLANKField blank 20/08/2003 115 BLANKField blank 20/08/2003 86 BLANKField blank 20/08/2003 86 BLANKField blank 20/08/2003 133 BLANKField blank 20/08/2003 133 BLANK

Laboratory blank 20/08/2003 - Distilled blankLaboratory blank 20/08/2003 - Distilled blank

Build-up <0.45 Rep 20/08/2003 - Replicate10y25min Dis Rep 20/08/2003 115 115 Replicate

Build-up 76-150 Rep 20/08/2003 - Replicate2y7 0.45-75 Rep 20/08/2003 133 133 Replicate

Sample name Sample date Intensity [mm/hr]

Duration [min]

Dry daysbefore

Last rainfall [ ]


Slope [m/m]

Texture depth [ ]

Field blank 7.07 99.70 6.367 4.013 <0.01 <0.01 <0.01Field blank 7.07 99.70 <0.001 <0.001 0.02 0.04 <0.01Field blank 7.15 119.70 5.256 4.335 <0.01 <0.01 <0.01Field blank 7.15 119.70 <0.001 <0.001 0.01 0.04 <0.01Field blank 7.02 97.60 5.689 2.972 <0.01 <0.01 <0.01Field blank 7.02 97.60 <0.001 <0.001 0.04 0.05 <0.01

Laboratory blank 6.27 8.76 4.124 <0.001 <0.01 <0.01 <0.01Laboratory blank 6.27 8.76 <0.001 <0.001 <0.01 0.02 <0.01

Build-up <0.45 Rep - - 4.135 1.844 35.00 0.08 0.0210y25min Dis Rep - - 6.298 3.274 60.00 0.07 <0.01

Build-up 76-150 Rep - - 4.125 0.000 96.15 1.24 0.162y7 0.45-75 Rep - - 0.519 0.598 47.90 0.95 0.18

EC [uS/cm]

NAP [ppm]Sample name ACY [ppm]TOC [ppm]

IC [ppm]

TSS [mg/L]

Last swept [d ]


pH

246

TABLE B1 – Residential Wash-off data PART 3 (Parameters continued from previous page)

Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Build-up <0.45 Rep <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.0110y25min Dis Rep <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Build-up 76-150 Rep 0.11 0.15 0.21 0.16 0.26 0.30 0.29 <0.01 0.012y7 0.45-75 Rep 0.07 0.19 0.30 0.12 0.20 0.21 0.18 <0.01 0.16

Sample name ACE [ppm] FLU [ppm] PHE [ppm] CHR [ppm] BbF [ppm]ANT [ppm] FLA [ppm] PYR [ppm]

BaA [ppm]

Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.085 <0.005 <0.001 0.014 <0.001 <0.001 0.002Field blank <0.01 <0.01 <0.01 <0.01 0.140 0.060 0.094 0.002 0.009 <0.001 0.010 0.005Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.077 <0.005 <0.001 0.011 <0.001 <0.001 0.005Field blank <0.01 <0.01 <0.01 <0.01 0.330 0.069 0.150 0.006 0.009 <0.001 0.012 0.003Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.091 <0.005 <0.001 0.009 <0.001 <0.001 0.004Field blank <0.01 <0.01 <0.01 <0.01 0.290 0.065 0.160 0.002 0.010 0.003 0.012 0.005

Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.005 0.080 <0.005 <0.001 0.003 <0.001 <0.001 <0.001Laboratory blank <0.01 <0.01 <0.01 <0.01 0.230 0.081 0.230 0.002 0.008 0.004 0.013 0.003

Build-up <0.45 Rep <0.01 <0.01 <0.01 <0.01 0.006 0.384 0.008 <0.001 0.080 <0.001 <0.001 0.00810y25min Dis Rep <0.01 <0.01 <0.01 <0.01 <0.005 0.968 <0.005 <0.001 0.172 <0.001 <0.001 0.003

Build-up 76-150 Rep 0.25 <0.01 <0.01 <0.01 12.634 0.710 8.750 0.035 0.480 0.002 0.013 0.2022y7 0.45-75 Rep 0.14 <0.01 <0.01 <0.01 0.560 0.075 0.364 0.002 0.038 <0.001 0.003 0.010

Cr [ppm] Mn [ppm]Al [ppm] Pb [ppm] Cu [ppm] Cd [ppm]Sample name Fe [ppm] Zn [ppm]BaP [ppm]

IND [ppm] DbA [ppm]

BgP [ppm]

247

TABLE B2 - Industrial wash-off data PART 1 (Data for simulated rainfall events with intensities 65 and 86mm/hr)

1y20min >300 15/10/2003 65 20 EMC 1 year 20 min 55 0.018 0.53 7 1y20min 151-300 15/10/2003 65 20 EMC 1 year 20 min 55 0.018 0.53 7 1y20min 76-150 15/10/2003 65 20 EMC 1 year 20 min 55 0.018 0.53 7 1y20min 0.45-75 15/10/2003 65 20 EMC 1 year 20 min 55 0.018 0.53 7

1y20min Dis 15/10/2003 65 20 EMC 1 year 20 min 55 0.018 0.53 7 2y35min >300 15/10/2003 65 35 EMC 2 year 35 min 97 0.018 0.53 7

2y35min 151-300 15/10/2003 65 35 EMC 2 year 35 min 97 0.018 0.53 7 2y35min 76-150 15/10/2003 65 35 EMC 2 year 35 min 97 0.018 0.53 7 2y35min 0.45-75 15/10/2003 65 35 EMC 2 year 35 min 97 0.018 0.53 7



10y65min Dis 15/10/2003 65 65 EMC 10 year 65 min 174 0.018 0.53 7 1y10m >300 15/10/2003 86 10 EMC 1 year 10 min 39 0.005 0.53 7

1y10m 151-300 15/10/2003 86 10 EMC 1 year 10 min 39 0.005 0.53 7 1y10m 76-150 15/10/2003 86 10 EMC 1 year 10 min 39 0.005 0.53 7 1y10m 0.45-75 15/10/2003 86 10 EMC 1 year 10 min 39 0.005 0.53 7 1y10min Dis 15/10/2003 86 10 EMC 1 year 10 min 39 0.005 0.53 7 2y20m >300 15/10/2003 86 20 EMC 2 year 20 min 81 0.005 0.53 7


10y40m 151-300 15/10/2003 86 40 EMC 10 year 40 min 157 0.005 0.53 7 10y40m 76-150 15/10/2003 86 40 EMC 10 year 40 min 157 0.005 0.53 7 10y40m 0.45-75 15/10/2003 86 40 EMC 10 year 40 min 157 0.005 0.53 7 10y40min Dis 15/10/2003 86 40 EMC 10 year 40 min 157 0.005 0.53 7

Dry daysbefore


Slope [m/m]

Texture depth[mm]



Duration [min]

248

TABLE B2 - Industrial wash-off data PART 1 (Parameters continued from previous page)

1y20min >300 11.20 14 5.11 6.65 317.00 0.220 0.940 5.80 1y20min 151-300 11.20 14 12.04 6.65 317.00 1.107 1.013 11.90 1y20min 76-150 11.20 14 16.61 6.65 317.00 0.025 1.028 11.40 1y20min 0.45-75 11.20 14 64.7 6.65 317.00 1.513 <0.001 24.90

1y20min Dis 11.20 14 1.54 6.65 317.00 7.495 0.511 70.00 2y35min >300 11.20 14 6.78 6.56 335.00 1.907 0.621 4.80

2y35min 151-300 11.20 14 14.03 6.56 335.00 0.198 0.563 6.60 2y35min 76-150 11.20 14 23.12 6.56 335.00 <0.001 0.487 11.60 2y35min 0.45-75 11.20 14 54.64 6.56 335.00 0.364 <0.001 21.90

2y35min Dis 11.20 14 1.71 6.56 335.00 7.123 1.108 60.00 10y65min >300 11.20 14 15.89 6.52 390.00 0.007 0.507 2.10

10y65min 151-300 11.20 14 30.3 6.52 390.00 0.491 <0.001 3.80 10y65min 76-150 11.20 14 17.21 6.52 390.00 <0.001 <0.001 5.50 10y65min 0.45-75 11.20 14 35 6.52 390.00 1.206 0.160 14.30

10y65min Dis 11.20 14 1.6 6.52 390.00 6.024 1.820 130.00 1y10m >300 11.20 14 6.4 6.76 665.00 0.309 0.269 5.70

1y10m 151-300 11.20 14 8.1 6.76 665.00 0.945 0.685 9.50 1y10m 76-150 11.20 14 11.09 6.76 665.00 0.975 0.015 17.20 1y10m 0.45-75 11.20 14 71.62 6.76 665.00 0.947 <0.001 64.20 1y10min Dis 11.20 14 2.79 6.76 665.00 7.123 1.373 250.00 2y20m >300 11.20 14 14.05 6.75 287.00 0.189 0.576 4.70

2y20m 151-300 11.20 14 19.57 6.75 287.00 1.570 <0.001 10.70 2y20m 76-150 11.20 14 17.54 6.75 287.00 1.543 <0.001 12.80 2y20m 0.45-75 11.20 14 46.86 6.75 287.00 <0.001 <0.001 27.60 2y20min Dis 11.20 14 2.4 6.75 287.00 6.717 0.675 80.00 10y40m >300 11.20 14 15.53 6.80 583.00 1.331 <0.001 2.60

10y40m 151-300 11.20 14 19.57 6.80 583.00 0.491 0.497 5.60 10y40m 76-150 11.20 14 23.12 6.80 583.00 0.270 0.027 7.20 10y40m 0.45-75 11.20 14 40.24 6.80 583.00 <0.001 0.170 12.70 10y40min Dis 11.20 14 1.54 6.80 583.00 6.417 0.528 230.00

TSS [mg/L]

2996.90

4370.30

pH EC [uS/cm]

TOC [ppm]

IC [ppm] Total solids [mg]

4401.80


Last rainfall[mm]

Last swept[days]


4480.40

3738.40

4500.20

249


1y20min >300 1y20min 151-300 1y20min 76-150 2.11 0.26 0.35 <0.01 0.09 <0.01 <0.01 <0.01 <0.01 1y20min 0.45-75 2.17 0.40 0.28 0.12 0.16 0.08 <0.01 <0.01 0.16

1y20min Dis 0.08 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 2y35min >300

2y35min 151-300 2y35min 76-150 2.07 <0.01 0.26 <0.01 <0.01 0.13 0.17 0.09 <0.01 2y35min 0.45-75 2.24 0.23 0.37 <0.01 0.23 <0.01 0.23 0.18 0.09

2y35min Dis 0.07 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y65min >300

10y65min 151-300 10y65min 76-150 1.98 0.10 0.21 <0.01 0.03 0.09 0.14 <0.01 0.09 10y65min 0.45-75 2.14 0.19 0.32 0.02 0.12 0.07 0.18 0.12 0.12

10y65min Dis 0.04 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 1y10m >300

1y10m 151-300 1y10m 76-150 2.03 0.16 0.09 0.02 0.09 <0.01 0.10 0.09 0.12 1y10m 0.45-75 2.12 0.15 0.33 0.13 0.15 0.05 0.20 0.25 0.13 1y10min Dis 0.07 0.03 0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 2y20m >300

2y20m 151-300 2y20m 76-150 2.03 0.26 0.21 0.05 0.06 0.07 0.08 0.11 0.13 2y20m 0.45-75 1.87 0.19 0.30 0.10 0.13 0.06 0.16 0.24 0.10 2y20min Dis 0.05 0.02 0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 10y40m >300

10y40m 151-300 10y40m 76-150 1.65 0.20 0.32 0.06 0.05 0.03 0.08 0.08 0.08 10y40m 0.45-75 1.96 0.19 0.27 0.09 0.12 0.05 0.17 0.21 0.10 10y40min Dis 0.06 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.050.01 <0.01 0.09 0.090.73 0.08 0.03 0.02


NAP [ppm] ACY [ppm] ACE [ppm] FLU [ppm] PHE [ppm] ANT [ppm]


0.99 0.17 0.08 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

1.05 <0.01 <0.01 <0.01 <0.01 <0.01 0.11 0.09 0.04

0.85 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.85 0.13 0.05 0.03 0.01 <0.01 0.11 0.10 0.06

0.72 0.12 0.06 0.03 0.08<0.01 <0.01 0.09 0.12

250


1y20min >300 0.176 1y20min 151-300 0.080 1y20min 76-150 <0.01 <0.01 0.09 <0.01 <0.01 <0.01 0.152 1y20min 0.45-75 <0.01 <0.01 0.12 0.02 <0.01 <0.01 0.312

1y20min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.005 2y35min >300 0.050

2y35min 151-300 0.067 2y35min 76-150 <0.01 <0.01 0.12 <0.01 <0.01 <0.01 0.096 2y35min 0.45-75 <0.01 <0.01 0.10 0.02 <0.01 0.02 0.136

2y35min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.005 10y65min >300 0.026

10y65min 151-300 0.046 10y65min 76-150 <0.01 0.01 0.11 <0.01 <0.01 0.02 0.152 10y65min 0.45-75 <0.01 0.05 0.14 0.01 <0.01 0.02 0.152

10y65min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.005 1y10m >300 0.080

1y10m 151-300 0.058 1y10m 76-150 <0.01 0.05 0.07 <0.01 <0.01 0.02 0.288 1y10m 0.45-75 0.20 0.05 0.13 0.04 <0.01 0.02 0.504 1y10min Dis <0.01 <0.01 0.09 <0.01 <0.01 0.01 <0.005 2y20m >300 0.044

2y20m 151-300 0.044 2y20m 76-150 0.10 0.02 0.08 <0.01 <0.01 0.02 0.022 2y20m 0.45-75 0.26 0.08 0.12 0.04 <0.01 0.02 0.504 2y20min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.005

10y40m >300 0.042 10y40m 151-300 0.065 10y40m 76-150 0.10 0.04 0.07 <0.01 <0.01 0.01 0.192 10y40m 0.45-75 0.22 0.08 0.12 <0.01 <0.01 <0.01 0.264 10y40min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.005

<0.01 <0.010.07 0.02 0.05 <0.01


CHR [ppm] BbF [ppm] BaP [ppm] IND [ppm] DbA [ppm] BgP [ppm] Fe [ppm]

<0.01 <0.01 <0.01 <0.01

<0.01 <0.01

<0.01 <0.01 <0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

0.09 <0.01 0.10 <0.01

<0.01 <0.01

<0.01 <0.010.07 <0.01 0.12 <0.01

251


1y20min >300 0.009 0.036 0.001 0.014 <0.001 0.006 0.001 1y20min 151-300 0.009 0.051 0.001 0.016 <0.001 0.004 0.004 1y20min 76-150 0.017 0.073 0.004 0.015 <0.001 0.004 0.002 1y20min 0.45-75 0.026 0.152 0.007 0.025 <0.001 0.003 0.004

1y20min Dis 0.300 0.011 0.001 0.012 <0.001 <0.001 0.012 2y35min >300 0.006 0.022 0.001 0.006 <0.001 0.003 0.001

2y35min 151-300 0.012 0.039 0.002 0.015 0.001 0.004 0.002 2y35min 76-150 0.014 0.053 0.002 0.020 <0.001 0.003 0.001 2y35min 0.45-75 0.014 0.067 0.003 0.018 <0.001 0.002 0.002

2y35min Dis 0.270 0.007 <0.001 0.010 <0.001 <0.001 0.009 10y65min >300 0.005 0.019 0.001 0.005 <0.001 0.002 <0.001

10y65min 151-300 0.010 0.030 0.001 0.019 <0.001 0.003 0.001 10y65min 76-150 0.018 0.076 0.002 0.016 <0.001 0.014 0.002 10y65min 0.45-75 0.019 0.096 0.004 0.022 <0.001 0.002 0.002

10y65min Dis 0.320 0.007 <0.001 0.010 <0.001 <0.001 0.007 1y10m >300 0.013 0.041 0.002 0.006 <0.001 0.003 0.001

1y10m 151-300 0.008 0.036 0.002 0.010 <0.001 0.001 0.001 1y10m 76-150 0.026 0.160 0.005 0.020 <0.001 0.018 0.004 1y10m 0.45-75 0.037 0.304 0.017 0.022 <0.001 0.004 0.006 1y10min Dis 0.360 0.007 0.001 0.011 <0.001 <0.001 0.014 2y20m >300 0.010 0.028 0.001 0.005 <0.001 0.001 0.001

2y20m 151-300 0.008 0.032 0.002 0.008 <0.001 0.002 0.001 2y20m 76-150 0.003 0.013 0.001 0.002 <0.001 <0.001 <0.001 2y20m 0.45-75 0.036 0.280 0.015 0.024 <0.001 0.002 0.006 2y20min Dis 0.330 <0.005 <0.001 0.009 <0.001 <0.001 0.010 10y40m >300 0.010 0.022 0.001 0.006 <0.001 0.001 0.001

10y40m 151-300 0.012 0.030 0.001 0.013 <0.001 0.001 0.001 10y40m 76-150 0.024 0.104 0.004 0.026 <0.001 0.012 0.002 10y40m 0.45-75 0.023 0.160 0.007 0.017 <0.001 0.004 0.003 10y40min Dis 0.290 <0.005 <0.001 0.007 <0.001 <0.001 0.010

Cu

[ppm]

Cd

[ppm]

Cr

[ppm]

Mn

[ppm]

Sample name [ARI and

particle size (µm)]

Zn [ppm] Al [ppm] Pb

[ppm]

252

TABLE B2 - Industrial Wash-off data PART 2 (Data for simulated rainfall events with intensities 115 and 133mm/hr)

1y5m >300 15/10/2003 115 5 EMC 1 year 5 min 25 0.05 0.53 7 11.20 1y5m 151-300 15/10/2003 115 5 EMC 1 year 5 min 25 0.05 0.53 7 11.20 1y5m 76-150 15/10/2003 115 5 EMC 1 year 5 min 25 0.05 0.53 7 11.20 1y5m 0.45-75 15/10/2003 115 5 EMC 1 year 5 min 25 0.05 0.53 7 11.20 1y5min Dis 15/10/2003 115 5 EMC 1 year 5 min 25 0.05 0.53 7 11.20 2y10m >300 15/10/2003 115 10 EMC 2 year 10 min 50 0.05 0.53 7 11.20




10y17m 151-300 15/10/2003 133 17 EMC 10 year 17 min 110 0.06 0.53 7 11.20 10y17m 76-150 15/10/2003 133 17 EMC 10 year 17 min 110 0.06 0.53 7 11.20 10y17m 0.45-75 15/10/2003 133 17 EMC 10 year 17 min 110 0.06 0.53 7 11.20 10y17min Dis 15/10/2003 133 17 EMC 10 year 17 min 110 0.06 0.53 7 11.20



Duration [min]

Dry days b f

Last rainfall[mm]

Event Total volume[L]

Slope [m/m]

Texture depth [mm]

253


1y5m >300 14 7.89 6.53 661.00 0.775 0.135 6.20 1y5m 151-300 14 12.06 6.53 661.00 0.262 0.068 6.30 1y5m 76-150 14 23.9 6.53 661.00 1.273 0.157 15.60 2.12 0.28 1y5m 0.45-75 14 53.78 6.53 661.00 0.160 0.244 86.00 1.94 0.13 1y5min Dis 14 2.37 6.53 661.00 9.106 0.290 240.00 0.09 0.03 2y10m >300 14 10.67 6.70 550.00 2.871 0.849 7.50

2y10m 151-300 14 19.93 6.70 550.00 0.311 0.582 8.30 2y10m 76-150 14 20.71 6.70 550.00 0.382 0.249 13.55 1.45 0.24 2y10m 0.45-75 14 47 6.70 550.00 <0.001 <0.001 40.40 1.83 0.25 2y10min Dis 14 1.69 6.70 550.00 7.916 0.106 220.00 0.08 0.03 2y7m >300 14 12.62 6.71 629.00 0.199 0.255 10.00

2y7m 151-300 14 18.47 6.71 629.00 0.241 0.197 26.60 2y7m 76-150 14 14.49 6.71 629.00 0.283 0.083 24.70 1.21 0.18 2y7m 0.45-75 14 52.56 6.71 629.00 0.011 0.047 60.10 1.73 0.13 2y7min Dis 14 1.86 6.71 629.00 7.496 0.170 230.00 0.07 0.03 5y13m >300 14 4.63 6.79 509.00 0.399 0.503 2.90

5y13m 151-300 14 14.78 6.79 509.00 0.017 0.135 12.70 5y13m 76-150 14 23.97 6.79 509.00 1.658 0.048 24.10 1.76 0.19 5y13m 0.45-75 14 55.03 6.79 509.00 <0.001 0.111 28.00 1.34 0.12 5y13min Dis 14 1.59 6.79 509.00 7.044 0.209 210.00 0.06 <0.01

10y17m >300 14 6.9 6.73 461.00 0.045 0.271 9.90 10y17m 151-300 14 6.46 6.73 461.00 0.176 0.010 11.40 10y17m 76-150 14 20.04 6.73 461.00 0.219 0.021 19.90 1.02 0.10 10y17m 0.45-75 14 63.83 6.73 461.00 0.012 0.218 13.20 1.64 0.09 10y17min Dis 14 2.77 6.73 461.00 6.561 0.079 200.00 0.05 <0.01


Last swept[days]


pH EC [uS/cm]

TOC [ppm]

IC [ppm] Total solids[mg]

TSS [mg/L]

NAP [ppm]

ACY [ppm]

2885.47

0.54 0.12

3501.73

0.65 0.12

5323.51

0.49 0.06

5665.07

0.58 0.12

5986.28

0.39 0.06

254


1y5m >300 1y5m 151-300 1y5m 76-150 0.25 0.05 0.05 0.05 0.07 0.09 0.09 0.06 0.05 0.09 1y5m 0.45-75 0.31 0.06 0.07 0.06 0.20 0.21 0.13 0.23 0.07 0.11 1y5min Dis 0.02 0.01 <0.01 <0.01 0.02 <0.01 0.01 <0.01 <0.01 0.01 2y10m >300

2y10m 151-300 2y10m 76-150 0.26 0.05 0.06 0.06 0.07 0.09 0.09 0.14 0.04 0.07 2y10m 0.45-75 0.31 0.10 0.08 0.06 0.19 0.24 0.12 0.25 0.07 0.10 2y10min Dis 0.02 <0.01 <0.01 <0.01 0.02 <0.01 0.01 <0.01 <0.01 0.01 2y7m >300

2y7m 151-300 2y7m 76-150 0.16 0.05 0.05 0.05 0.06 0.08 0.06 0.09 0.03 0.04 2y7m 0.45-75 0.21 0.05 0.08 0.03 0.12 0.13 0.10 0.21 0.06 0.04 2y7min Dis 0.02 <0.01 <0.01 <0.01 0.02 <0.01 <0.01 <0.01 <0.01 <0.01 5y13m >300

5y13m 151-300 5y13m 76-150 0.18 0.07 0.05 0.05 0.06 0.03 0.06 0.10 0.03 0.03 5y13m 0.45-75 0.20 0.05 0.09 0.04 0.12 0.21 0.10 0.24 0.06 0.10 5y13min Dis 0.02 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y17m >300

10y17m 151-300 10y17m 76-150 0.10 0.06 0.05 0.03 0.05 0.02 0.06 0.10 0.04 0.04 10y17m 0.45-75 0.21 0.04 0.05 0.03 0.10 0.20 0.11 0.27 0.07 0.11 10y17min Dis <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01


ACE [ppm] FLU [ppm] PHE [ppm] CHR [ppm]

BbF [ppm] BaP [ppm]ANT [ppm]


<0.01 0.03 0.01 <0.01

0.10 0.05 0.08

0.08 0.07 0.07

<0.01 0.03 0.01 <0.01 0.08 0.02 0.07

0.02 0.090.05

<0.01 <0.01 <0.01 <0.01

0.05 <0.01 0.06

0.05 0.03 0.06

0.02 0.01 <0.01 <0.01 0.07 0.02 0.06

0.01 0.050.03

<0.01 <0.01 <0.01 <0.01 <0.01 0.050.03 0.01 0.04 0.07

255


1y5m >300 0.088 0.015 0.043 0.002 0.012 <0.001 0.003 0.001 1y5m 151-300 0.080 0.013 0.040 0.002 0.014 <0.001 0.001 0.001 1y5m 76-150 <0.01 <0.01 0.02 0.176 0.026 0.104 0.005 0.016 <0.001 0.006 0.002 1y5m 0.45-75 0.03 <0.01 0.02 0.488 0.035 0.280 0.014 0.018 <0.001 0.002 0.005 1y5min Dis <0.01 <0.01 0.01 <0.005 0.510 0.014 0.002 0.010 <0.001 <0.001 0.017 2y10m >300 0.057 0.013 0.025 0.002 0.005 <0.001 0.001 0.001

2y10m 151-300 0.073 0.014 0.036 0.002 0.014 <0.001 0.001 0.001 2y10m 76-150 <0.01 <0.01 0.02 0.144 0.018 0.072 0.004 0.016 <0.001 0.003 0.002 2y10m 0.45-75 0.02 <0.01 0.02 0.304 0.026 0.168 0.010 0.017 <0.001 0.001 0.003 2y10min Dis <0.01 <0.01 <0.01 <0.005 0.380 0.009 0.001 0.008 <0.001 <0.001 0.011 2y7m >300 0.078 0.012 0.034 0.002 0.007 <0.001 <0.001 0.001

2y7m 151-300 0.078 0.011 0.036 0.002 0.011 <0.001 <0.001 0.001 2y7m 76-150 <0.01 <0.01 0.01 0.208 0.019 0.104 0.006 0.013 <0.001 0.001 0.002 2y7m 0.45-75 0.01 <0.01 <0.01 0.880 0.051 0.416 0.025 0.039 <0.001 0.002 0.010 2y7min Dis <0.01 <0.01 <0.01 <0.005 0.340 0.007 0.001 0.008 <0.001 <0.001 0.013 5y13m >300 0.058 0.008 0.027 0.001 0.004 <0.001 <0.001 0.001

5y13m 151-300 0.066 0.011 0.034 0.002 0.009 <0.001 0.001 0.001 5y13m 76-150 <0.01 <0.01 <0.01 0.224 0.018 0.104 0.005 0.011 <0.001 0.001 0.004 5y13m 0.45-75 0.01 <0.01 <0.01 0.600 0.034 0.280 0.014 0.026 <0.001 0.001 0.006 5y13min Dis <0.01 <0.01 <0.01 <0.005 0.260 <0.005 <0.001 0.008 <0.001 <0.001 0.017

10y17m >300 0.041 0.015 0.034 0.004 0.003 0.001 0.001 0.002 10y17m 151-300 0.054 0.011 0.032 0.002 0.010 0.001 0.001 0.002 10y17m 76-150 <0.01 <0.01 <0.01 0.152 0.017 0.078 0.005 0.010 0.001 0.001 0.003 10y17m 0.45-75 <0.01 <0.01 <0.01 0.488 0.028 0.248 0.014 0.027 <0.001 0.001 0.005 10y17min Dis <0.01 <0.01 <0.01 <0.005 0.290 <0.005 <0.001 0.006 <0.001 <0.001 0.009

<0.01 <0.01

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

<0.01 <0.01

Cu [ppm] Cd [ppm] Cr [ppm] Mn [ppm]

Fe [ppm] Zn [ppm] Al [ppm] Pb [ppm]Sample name [ARI andparticle size (µm)]

IND [ppm] DbA [ppm] BgP [ppm]

256

TABLE B2 – Industrial Wash-off data PART 3 (Blanks and replicates)

Field blank 15/10/2003 115 - Field BLANK - - - - - -Field blank 15/10/2003 115 - Field BLANK - - - - - -Field blank 15/10/2003 86 - Field BLANK - - - - - -Field blank 15/10/2003 86 - Field BLANK - - - - - -Field blank 15/10/2003 133 - Field BLANK - - - - - -Field blank 15/10/2003 133 - Field BLANK - - - - - -Field blank 15/10/2003 65 - Field BLANK - - - - - -Field blank 15/10/2003 65 - Field BLANK - - - - - -

Laboratory blank 15/10/2003 - - Distilled Blank - - - - - -Laboratory blank 15/10/2003 - - Distilled Blank - - - - - -

Build-up U/S St 76-150 15/10/2003 - - Replicate - - - - - -Build-up U/S St <0.45 15/10/2003 - - Replicate - - - - - -

1y5m 76-150 15/10/2003 115 5 Replicate - - - - - -10y65min 0.45-75 15/10/2003 65 65 Replicate - - - - - -


Duration [min]

Classification

Dry daysbefore

Last rainfall


Slope [m/m]

Texture depth

Field blank - - 6.65 530.00 4.274 1.005 <0.01 <0.01 <0.01Field blank - - 6.65 530.00 <0.001 <0.001 0.04 0.065 <0.01Field blank - - 6.75 460.00 1.001 1.276 <0.01 <0.01 <0.01Field blank - - 6.75 460.00 <0.001 <0.001 0.02 0.057 <0.01Field blank - - 6.72 460.00 0.740 0.650 <0.01 <0.01 <0.01Field blank - - 6.72 460.00 <0.001 <0.001 <0.01 0.023 <0.01Field blank - - 6.61 340.00 3.426 0.294 <0.01 <0.01 <0.01Field blank - - 6.61 340.00 <0.001 <0.001 0.02 0.032 <0.01

Laboratory blank - - 6.27 9.00 <0.001 <0.001 <0.01 <0.01 <0.01Laboratory blank - - 6.27 9.00 <0.001 <0.001 <0.01 0.012 <0.01

Build-up U/S St 76-150 - - - - 0.639 <0.001 225.1 2.202 0.351Build-up U/S St <0.45 - - - - 8.25 <0.001 30 0.062 0.023

1y5m 76-150 - - - - 1.197 0.152 17.1 2.119 0.27610y65min 0.45-75 - - - - 1.21 0.158 14.2 2.102 0.191

ACY [ppm]TOC [ppm]

IC [ppm]

TSS [mg/L]

pH EC [uS/cm]

NAP [ppm]Sample name Last swept


257

TABLE B2 – Industrial Wash-off data PART 3 (Blanks and replicates continued from previous page)

Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Build-up U/S St 76-150 0.40 0.12 0.14 0.10 0.10 0.16 0.16 0.32 0.12Build-up U/S St <0.45 0.02 <0.01 <0.01 <0.01 0.02 <0.01 <0.01 <0.01 <0.01

1y5m 76-150 0.25 0.05 0.05 0.06 0.07 0.09 0.09 0.06 0.05 10y65min 0.45-75 0.32 0.02 0.12 0.07 0.18 0.12 0.12 <0.01 0.05

Sample name ACE [ppm] FLU [ppm]

PHE [ppm] CHR [ppm]

BbF [ppm]ANT [ppm] FLA [ppm] PYR [ppm]

BaA [ppm]

Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.015 <0.005 <0.001 <0.001 <0.001 <0.001 0.001Field blank <0.01 <0.01 <0.01 <0.01 0.016 0.008 <0.005 0.001 0.001 0.001 <0.001 0.001Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.016 <0.005 <0.001 <0.001 <0.001 <0.001 <0.001Field blank <0.01 <0.01 <0.01 <0.01 0.008 0.005 <0.005 <0.001 <0.001 <0.001 <0.001 <0.001Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.015 <0.005 <0.001 <0.001 <0.001 <0.001 <0.001Field blank <0.01 <0.01 <0.01 <0.01 0.006 0.005 0.005 <0.001 <0.001 <0.001 <0.001 <0.001Field blank <0.01 <0.01 <0.01 <0.01 <0.005 0.007 <0.005 <0.001 <0.001 <0.001 <0.001 0.001Field blank <0.01 <0.01 <0.01 <0.01 0.006 0.002 0.008 0.001 <0.001 <0.001 <0.001 <0.001

Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.005 0.004 <0.005 <0.001 <0.001 <0.001 <0.001 <0.001Laboratory blank <0.01 <0.01 <0.01 <0.01 0.002 0.007 <0.005 <0.001 <0.001 <0.001 <0.001 <0.001

Build-up U/S St 76-150 0.15 <0.01 <0.01 0.02 2.230 0.113 0.630 0.036 0.037 <0.001 0.003 0.021Build-up U/S St <0.45 <0.01 <0.01 <0.01 <0.01 <0.005 0.180 0.009 0.002 0.007 <0.001 <0.001 0.023

1y5m 76-150 0.09 <0.01 <0.01 0.02 0.178 0.026 0.103 0.005 0.016 <0.001 0.006 0.002 10y65min 0.45-75 0.14 0.01 <0.01 0.02 0.150 0.019 0.099 0.004 0.022 <0.001 0.002 0.002

Sample name Fe [ppm]

Zn [ppm]

BaP [ppm]

IND [ppm]

DbA [ppm]

BgP [ppm]

Cr [ppm]

Mn [ppm]

Al [ppm] Pb [ppm]

Cu [ppm]

Cd [ppm]

258

TABLE B3 – Commercial Wash-off data PART 1 (Data for simulated rainfall events with intensities 65 and 86mm/hr)

1y20min >300 11/01/2004 65 20 EMC 1 year 20 min 24 0.05 0.92 0 1y20min 151-300 11/01/2004 65 20 EMC 1 year 20 min 24 0.05 0.92 0 1y20min 76-150 11/01/2004 65 20 EMC 1 year 20 min 24 0.05 0.92 0 1y20min 0.45-75 11/01/2004 65 20 EMC 1 year 20 min 24 0.05 0.92 0





10y65min Dis 11/01/2004 65 65 EMC 10 year 65 min 73 0.05 0.92 0 1y10m >300 11/01/2004 86 10 EMC 1 year 10 min 40 0.02 0.92 0



10y40m 151-300 11/01/2004 86 40 EMC 10 year 40 min 145 0.02 0.92 0 10y40m 76-150 11/01/2004 86 40 EMC 10 year 40 min 145 0.02 0.92 0 10y40m 0.45-75 11/01/2004 86 40 EMC 10 year 40 min 145 0.02 0.92 0 10y40min Dis 11/01/2004 86 40 EMC 10 year 40 min 145 0.02 0.92 0



Duration [min]

Dry daysbefore


Slope [m/m]

Texture depth [mm]

259

TABLE B3 - Commercial wash-off data PART 1 (Parameters continued from previous page)

1y20min >300 Unknown 0 6.62 26.90 1.225 1.930 17.00 1y20min 151-300 Unknown 7.14 6.62 26.90 <0.001 0.532 19.30 1y20min 76-150 Unknown 19.22 6.62 26.90 <0.001 <0.001 22.90 1y20min 0.45-75 Unknown 73.64 6.62 26.90 1.062 <0.001 33.80

1y20min Dis Unknown <0.01 6.62 26.90 5.815 <0.001 20.00 2y35min >300 Unknown 9.54 7.20 31.10 <0.001 <0.001 42.10

2y35min 151-300 Unknown 14.91 7.20 31.10 <0.001 <0.001 49.50 2y35min 76-150 Unknown 26.13 7.20 31.10 <0.001 <0.001 16.60 2y35min 0.45-75 Unknown 42.59 7.20 31.10 2.934 <0.001 23.00

2y35min Dis Unknown 6.83 7.20 31.10 1.621 <0.001 20.00 10y65min >300 Unknown 10.73 7.09 32.70 3.885 <0.001 22.90

10y65min 151-300 Unknown 12.32 7.09 32.70 <0.001 <0.001 25.40 10y65min 76-150 Unknown 23.65 7.09 32.70 <0.001 <0.001 20.20 10y65min 0.45-75 Unknown 50.57 7.09 32.70 <0.001 <0.001 16.80

10y65min Dis Unknown 3.87 7.09 32.70 1.621 <0.001 10.00 1y10m >300 Unknown 7.06 7.66 46.40 0.968 0.602 14.70

1y10m 151-300 Unknown 12.74 7.66 46.40 0.189 <0.001 30.60 1y10m 76-150 Unknown 32.31 7.66 46.40 <0.001 <0.001 27.60 1y10m 0.45-75 Unknown 41.89 7.66 46.40 <0.001 <0.001 21.80 1y10min Dis Unknown 1 7.66 46.40 7.340 <0.001 20.00 2y20m >300 Unknown 21.02 7.54 52.60 0.171 0.068 20.80

2y20m 151-300 Unknown 15.01 7.54 52.60 0.080 0.211 23.00 2y20m 76-150 Unknown 26.66 7.54 52.60 <0.001 0.031 28.80 2y20m 0.45-75 Unknown 36.1 7.54 52.60 0.601 0.046 27.70 2y20min Dis Unknown 1.21 7.54 52.60 6.606 0.793 40.00 10y40m >300 Unknown 11.3 6.82 46.40 <0.001 <0.001 13.50

10y40m 151-300 Unknown 19.5 6.82 46.40 <0.001 0.106 29.10 10y40m 76-150 Unknown 28.87 6.82 46.40 <0.001 0.087 22.80 10y40m 0.45-75 Unknown 38.91 6.82 46.40 0.687 <0.001 21.40 10y40min Dis Unknown 1.42 6.82 46.40 5.667 0.286 30.00

TSS [mg/L]

2232.00

5248.00

pH EC [uS/cm]

TOC [ppm]

IC [ppm]

Total solids[mg]

12586.00

Sample name [ARIand particle size (µm)]

Last swept[days]


6226.90

3788.00

7823.40

260


1y20min >300 1y20min 151-300 1y20min 76-150 1.08 0.34 0.06 0.05 0.12 0.02 0.06 0.10 0.32 0.23 1y20min 0.45-75 4.05 0.45 0.10 0.10 0.12 0.03 0.17 0.34 0.41 0.12

1y20min Dis 0.12 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.02 <0.01 2y35min >300

2y35min 151-300 2y35min 76-150 0.87 0.21 0.04 0.03 0.08 <0.01 <0.01 0.09 0.21 0.12 2y35min 0.45-75 6.45 0.57 0.76 0.53 1.03 0.08 0.20 0.66 0.43 0.20

2y35min Dis 0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 0.01 <0.01 10y65min >300

10y65min 151-300 10y65min 76-150 0.76 0.21 0.07 0.08 0.04 <0.01 <0.01 0.03 0.22 0.12 10y65min 0.45-75 3.46 0.35 0.34 0.10 0.10 <0.01 0.16 0.24 0.25 0.43

10y65min Dis 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 1y10m >300

1y10m 151-300 1y10m 76-150 1.65 0.54 0.15 0.02 <0.01 <0.01 <0.01 0.04 0.15 0.14 1y10m 0.45-75 4.76 0.45 0.10 0.08 0.09 0.02 0.12 0.28 0.32 0.09 1y10min Dis 0.24 0.02 0.01 <0.01 <0.01 0.01 0.01 <0.01 0.02 0.01 2y20m >300

2y20m 151-300 2y20m 76-150 0.95 0.42 0.12 <0.01 <0.01 <0.01 <0.01 0.02 0.03 0.01 2y20m 0.45-75 4.97 0.54 0.12 0.10 0.11 0.16 0.14 0.31 0.41 0.12 2y20min Dis 0.10 0.01 <0.01 <0.01 <0.01 0.01 0.01 <0.01 0.01 <0.01 10y40m >300

10y40m 151-300 10y40m 76-150 0.87 0.25 0.07 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 10y40m 0.45-75 4.01 0.54 0.13 0.09 0.10 0.17 0.14 0.27 0.39 0.22 10y40min Dis 0.02 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01

BaP

[ppm]

<0.01

<0.01

<0.01

<0.01

0.22

0.10

<0.01<0.01 <0.01 0.06 0.140.96 0.25 0.02 0.02

0.13

1.35 0.33 0.10 0.10 0.08 0.02 0.16 0.23 0.10

0.04 0.08 0.13 0.241.12 0.35 0.12 0.07

0.08

0.76 0.17 0.03 <0.01 <0.01 <0.01 0.10 0.12 <0.01

PYR [ppm] BaA [ppm]

1.03 0.32 0.09 0.04 <0.01 <0.01 0.12 0.23

FLU [ppm] PHE [ppm] ANT

[ppm]

FLA [ppm]Sample name [ARI and


NAP [ppm] ACY [ppm] ACE [ppm]

0.27 0.13 <0.01 <0.01 <0.01<0.01 <0.01 0.06 0.08

261


1y20min >300 0.192 0.024 0.050 0.002 0.015 <0.001 0.001 0.002 1y20min 151-300 0.376 0.034 0.136 0.005 0.038 <0.001 0.001 0.006 1y20min 76-150 0.23 0.15 0.06 <0.01 <0.01 0.344 0.032 0.112 0.005 0.014 <0.001 0.001 0.005 1y20min 0.45-75 0.12 0.35 0.08 0.03 <0.01 0.571 0.042 0.126 0.005 0.023 <0.001 0.003 0.005

1y20min Dis <0.01 <0.01 <0.01 <0.01 <0.01 0.112 0.650 0.023 0.001 0.091 <0.001 <0.001 0.009 2y35min >300 0.360 0.027 0.112 0.004 0.046 <0.001 0.001 0.004

2y35min 151-300 0.216 0.015 0.070 0.002 0.014 <0.001 0.001 0.003 2y35min 76-150 0.12 0.09 <0.01 <0.01 <0.01 0.304 0.021 0.096 0.003 0.008 <0.001 0.001 0.004 2y35min 0.45-75 0.20 0.37 0.25 0.13 0.05 0.453 0.028 0.093 0.004 0.017 <0.001 0.002 0.003

2y35min Dis <0.01 <0.01 <0.01 <0.01 <0.01 0.076 0.630 0.012 <0.001 0.084 <0.001 <0.001 0.005 10y65min >300 0.088 0.014 0.035 0.001 0.006 <0.001 0.001 0.001

10y65min 151-300 0.168 0.014 0.074 0.002 0.014 <0.001 0.001 0.003 10y65min 76-150 0.12 0.12 <0.01 <0.01 <0.01 0.208 0.016 0.080 0.003 0.006 <0.001 0.001 0.003 10y65min 0.45-75 0.43 0.32 0.08 <0.01 0.02 0.208 0.013 0.058 0.002 0.008 <0.001 0.001 0.002

10y65min Dis <0.01 <0.01 <0.01 <0.01 <0.01 0.050 0.590 0.010 <0.001 0.057 <0.001 <0.001 0.008 1y10m >300 0.312 0.037 0.120 0.006 0.011 <0.001 0.001 0.007

1y10m 151-300 0.520 0.028 0.208 0.009 0.024 <0.001 0.002 0.010 1y10m 76-150 0.14 0.10 <0.01 <0.01 0.02 0.664 0.038 0.264 0.011 0.016 <0.001 0.002 0.012 1y10m 0.45-75 0.09 0.27 0.04 0.02 0.05 0.260 0.020 0.100 0.003 0.016 <0.001 0.002 0.004 1y10min Dis 0.01 <0.01 <0.01 <0.01 <0.01 0.033 0.310 0.009 <0.001 0.080 <0.001 <0.001 0.004 2y20m >300 0.152 0.020 0.065 0.003 0.009 <0.001 0.001 0.004

2y20m 151-300 0.344 0.017 0.136 0.005 0.016 <0.001 0.001 0.006 2y20m 76-150 0.01 <0.01 <0.01 <0.01 <0.01 0.376 0.022 0.144 0.006 0.006 <0.001 0.001 0.007 2y20m 0.45-75 0.12 0.31 0.09 0.03 0.06 0.224 0.018 0.098 0.003 0.016 <0.001 0.001 0.004 2y20min Dis <0.01 <0.01 <0.01 <0.01 <0.01 0.047 0.380 0.009 <0.001 0.049 <0.001 <0.001 0.003 10y40m >300 0.096 0.012 0.049 0.002 0.005 <0.001 0.001 0.002

10y40m 151-300 0.104 0.010 0.046 0.002 0.010 <0.001 0.001 0.002 10y40m 76-150 <0.01 <0.01 <0.01 <0.01 <0.01 0.304 0.019 0.136 0.004 0.019 <0.001 0.001 0.006 10y40m 0.45-75 0.22 0.30 0.09 0.03 0.02 0.154 0.019 0.070 0.003 0.036 <0.001 0.002 0.002 10y40min Dis <0.01 <0.01 <0.01 <0.01 <0.01 0.016 0.360 0.008 <0.001 0.110 <0.001 <0.001 0.003

Mn

[ppm]

Pb

[ppm]

Cu

[ppm]

Cd

[ppm]

Cr

[ppm]

Fe

[ppm]

<0.01 <0.01<0.01 0.01 <0.01

Al

[ppm]

<0.01 <0.01

Sample name [ARI and


CHR

[ppm]

BbF

[ppm]

Zn

[ppm]

IND

[ppm]

DbA

[ppm]

BgP

[ppm]

<0.01

<0.01 0.12 <0.01

<0.01 0.09 <0.01 <0.01

<0.01 <0.01

0.22 0.15

0.10 0.12 <0.01

0.07 0.02 <0.01

<0.01 <0.01<0.01 0.05 <0.01

262

TABLE B3 - Commercial Wash-off data PART 2 (Data for simulated rainfall events with intensities 115 and 133mm/hr)

1y5m >300 11/01/2004 115 5 EMC 1 year 5 min 11 0.03 0.92 0 Unknown 1y5m 151-300 11/01/2004 115 5 EMC 1 year 5 min 11 0.03 0.92 0 Unknown 1y5m 76-150 11/01/2004 115 5 EMC 1 year 5 min 11 0.03 0.92 0 Unknown 1y5m 0.45-75 11/01/2004 115 5 EMC 1 year 5 min 11 0.03 0.92 0 Unknown 1y5min Dis 11/01/2004 115 5 EMC 1 year 5 min 11 0.03 0.92 0 Unknown 2y10m >300 11/01/2004 115 10 EMC 2 year 10 min 23 0.03 0.92 0 Unknown

2y10m 151-300 11/01/2004 115 10 EMC 2 year 10 min 23 0.03 0.92 0 Unknown 2y10m 76-150 11/01/2004 115 10 EMC 2 year 10 min 23 0.03 0.92 0 Unknown 2y10m 0.45-75 11/01/2004 115 10 EMC 2 year 10 min 23 0.03 0.92 0 Unknown 2y10min Dis 11/01/2004 115 10 EMC 2 year 10 min 23 0.03 0.92 0 Unknown 10y25m >300 11/01/2004 115 25 EMC 10 year 25 min 48 0.03 0.92 0 Unknown

10y25 151-300 11/01/2004 115 25 EMC 10 year 25 min 48 0.03 0.92 0 Unknown 10y25 76-150 11/01/2004 115 25 EMC 10 year 25 min 48 0.03 0.92 0 Unknown 10y25 0.45-75 11/01/2004 115 25 EMC 10 year 25 min 48 0.03 0.92 0 Unknown 10y25min Dis 11/01/2004 115 25 EMC 10 year 25 min 48 0.03 0.92 0 Unknown

2y7m >300 11/01/2004 133 7 EMC 2 year 7 min 25 0.04 0.92 0 Unknown 2y7m 151-300 11/01/2004 133 7 EMC 2 year 7 min 25 0.04 0.92 0 Unknown 2y7m 76-150 11/01/2004 133 7 EMC 2 year 7 min 25 0.04 0.92 0 Unknown 2y7m 0.45-75 11/01/2004 133 7 EMC 2 year 7 min 25 0.04 0.92 0 Unknown 2y7min Dis 11/01/2004 133 7 EMC 2 year 7 min 25 0.04 0.92 0 Unknown 5y13m >300 11/01/2004 133 13 EMC 5 year 13 min 76 0.04 0.92 0 Unknown

5y13m 151-300 11/01/2004 133 13 EMC 5 year 13 min 76 0.04 0.92 0 Unknown 5y13m 76-150 11/01/2004 133 13 EMC 5 year 13 min 76 0.04 0.92 0 Unknown 5y13m 0.45-75 11/01/2004 133 13 EMC 5 year 13 min 76 0.04 0.92 0 Unknown 5y13min Dis 11/01/2004 133 13 EMC 5 year 13 min 76 0.04 0.92 0 Unknown 10y17m >300 11/01/2004 133 17 EMC 10 year 17 min 138 0.04 0.92 0 Unknown

10y17m 151-300 11/01/2004 133 17 EMC 10 year 17 min 138 0.04 0.92 0 Unknown 10y17m 76-150 11/01/2004 133 17 EMC 10 year 17 min 138 0.04 0.92 0 Unknown 10y17m 0.45-75 11/01/2004 133 17 EMC 10 year 17 min 138 0.04 0.92 0 Unknown 10y17min Dis 11/01/2004 133 17 EMC 10 year 17 min 138 0.04 0.92 0 Unknown

Dry days

before

Last swept

[days]

Event Total volume

event [L]

Slope

[m/m]

Texture depth [mm]

Sample name [ARIand particle size(µm)]

Sample date Intensity

[mm/hr]

Duration

[min]

263


1y5m >300 17.44 7.62 56.90 0.461 <0.001 42.40 1y5m 151-300 20.66 7.62 56.90 1.981 <0.001 10.50 1y5m 76-150 26.18 7.62 56.90 <0.001 <0.001 10.60 3.57 0.66 1y5m 0.45-75 32.77 7.62 56.90 2.383 <0.001 9.50 9.57 1.02 1y5min Dis 2.95 7.62 56.90 8.887 1.386 40.00 0.43 0.09 2y10m >300 9.35 7.23 48.00 0.289 <0.001 17.70

2y10m 151-300 15.32 7.23 48.00 <0.001 0.213 9.00 2y10m 76-150 27.23 7.23 48.00 0.237 0.102 14.10 2.77 0.57 2y10m 0.45-75 43.89 7.23 48.00 1.913 0.147 16.70 7.46 0.87 2y10min Dis 4.21 7.23 48.00 7.801 1.545 40.00 0.25 0.08 10y25m >300 19.76 7.26 44.70 0.083 0.630 33.40 10y25 151-300 24.62 7.26 44.70 0.372 0.106 31.20 10y25 76-150 23.8 7.26 44.70 <0.001 <0.001 19.40 1.43 0.12 10y25 0.45-75 30.06 7.26 44.70 1.322 0.143 12.80 4.58 0.35 10y25min Dis 1.76 7.26 44.70 6.945 1.340 30.00 0.15 0.03

2y7m >300 22.69 7.25 39.90 1.647 <0.001 10.30 2y7m 151-300 12.33 7.25 39.90 <0.001 0.379 9.00 2y7m 76-150 17.22 7.25 39.90 0.586 0.031 12.00 3.12 0.68 2y7m 0.45-75 46.35 7.25 39.90 0.723 0.077 10.30 5.45 0.73 2y7min Dis 1.41 7.25 39.90 6.184 0.271 30.00 0.05 0.04 5y13m >300 10.37 7.12 41.50 0.435 <0.001 33.50

5y13m 151-300 19.56 7.12 41.50 <0.001 0.072 22.60 5y13m 76-150 24.11 7.12 41.50 0.141 <0.001 23.80 1.02 0.34 5y13m 0.45-75 43.49 7.12 41.50 0.565 <0.001 23.20 3.43 0.53 5y13min Dis 2.47 7.12 41.50 6.423 0.273 20.00 0.01 <0.01 10y17m >300 17.76 7.11 38.00 <0.001 <0.001 23.70

10y17m 151-300 21.37 7.11 38.00 <0.001 <0.001 23.30 10y17m 76-150 20.87 7.11 38.00 <0.001 <0.001 17.20 0.91 0.14 10y17m 0.45-75 37.66 7.11 38.00 <0.001 <0.001 12.70 4.03 0.15 10y17min Dis 2.34 7.11 38.00 1.621 0.360 20.00 <0.01 <0.01

10612.20

0.57 <0.01

1040.00

1.53 0.12

7835.60

0.55 0.02

1322.50

1.01 0.10

4646.40

0.59 <0.01

NAP [ppm] ACY [ppm]

803.00

2.09 0.43

TOC [ppm]

IC [ppm]

Total solids[mg]

TSS [mg/L]



pH EC [uS/cm]

264


1y5m >300 1y5m 151-300 1y5m 76-150 0.16 0.12 0.98 0.07 0.25 0.12 0.31 0.27 0.13 0.18 1y5m 0.45-75 0.19 0.65 1.34 0.12 0.66 0.34 0.51 0.34 0.53 0.23 1y5min Dis 0.04 0.02 0.05 0.07 0.06 0.01 0.03 0.02 0.01 0.21 2y10m >300

2y10m 151-300 2y10m 76-150 0.16 0.07 0.24 <0.01 0.24 0.09 0.23 <0.01 0.06 0.13 2y10m 0.45-75 0.19 0.61 0.85 0.13 0.56 0.31 0.44 0.12 0.45 0.26 2y10min Dis 0.02 0.01 0.02 0.04 0.05 <0.01 0.01 0.01 <0.01 0.10 10y25m >300

10y25 151-300 10y25 76-150 0.15 <0.01 0.07 0.02 0.22 0.09 0.12 0.06 <0.01 0.02 10y25 0.45-75 0.19 0.61 0.75 0.13 0.76 0.31 0.34 0.11 0.12 0.18 10y25min Dis 0.02 0.01 <0.01 0.03 0.01 <0.01 <0.01 0.01 <0.01 0.04

2y7m >300 2y7m 151-300 2y7m 76-150 0.16 0.10 0.14 <0.01 0.18 <0.01 0.10 <0.01 <0.01 0.15 2y7m 0.45-75 0.20 0.51 1.14 0.12 0.66 0.32 0.45 0.15 0.56 0.26 2y7min Dis <0.01 <0.01 0.01 0.01 0.02 <0.01 <0.01 0.01 <0.01 0.05 5y13m >300

5y13m 151-300 5y13m 76-150 0.07 0.24 0.14 0.01 0.12 0.02 0.07 <0.01 0.23 0.13 5y13m 0.45-75 0.08 0.23 0.46 0.15 0.46 0.21 0.34 0.34 0.58 0.32 5y13min Dis <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.02 10y17m >300

10y17m 151-300 10y17m 76-150 <0.01 0.05 <0.01 <0.01 0.05 <0.01 <0.01 <0.01 0.22 <0.01 10y17m 0.45-75 0.08 0.02 0.53 0.12 0.34 0.22 0.23 0.13 0.53 0.22 10y17min Dis <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

<0.01 0.170.20 0.01 <0.01 <0.010.09 <0.01 <0.01 <0.01

0.03 <0.01 0.06

<0.01 0.080.02

0.15 0.10 0.01 0.10 0.11 0.09 0.06

0.03 <0.01 0.010.02 0.02 <0.01 0.01

<0.01 <0.01 0.08

0.10 0.110.10

0.05 0.01 <0.01 <0.01 0.10 0.09 <0.01

0.17 0.17 0.120.12 0.19 0.07 <0.01

CHR

[ppm]

BbF [ppm] BaP [ppm]ANT

[ppm]

FLA [ppm] PYR [ppm] BaA [ppm]Sample name [ARI and


ACE [ppm] FLU [ppm] PHE [ppm]

<0.01 <0.01 <0.01 <0.01 <0.01 0.010.12 <0.01 <0.01 <0.01

265


1y5m >300 0.248 0.096 0.104 0.006 0.014 <0.001 0.001 0.008 1y5m 151-300 0.288 0.029 0.120 0.006 0.018 <0.001 0.002 0.008 1y5m 76-150 0.01 <0.01 0.05 0.264 0.023 0.120 0.005 0.011 <0.001 0.001 0.006 1y5m 0.45-75 0.06 0.03 0.08 0.187 0.025 0.085 0.003 0.023 <0.001 0.003 0.003 1y5min Dis <0.01 <0.01 0.01 0.030 0.390 0.017 <0.001 0.080 <0.001 <0.001 0.008 2y10m >300 0.112 0.042 0.049 0.002 0.008 <0.001 0.001 0.003

2y10m 151-300 0.200 0.021 0.096 0.003 0.013 <0.001 0.001 0.005 2y10m 76-150 0.01 <0.01 0.02 0.200 0.017 0.080 0.003 0.010 <0.001 0.001 0.004 2y10m 0.45-75 0.07 0.02 0.05 0.100 0.018 0.046 0.002 0.018 <0.001 0.002 0.002 2y10min Dis <0.01 <0.01 <0.01 0.008 0.370 0.008 <0.001 0.072 <0.001 <0.001 0.006 10y25m >300 0.128 0.028 0.052 0.002 0.007 <0.001 0.001 0.003 10y25 151-300 0.144 0.012 0.066 0.002 0.010 <0.001 0.001 0.003 10y25 76-150 <0.01 <0.01 <0.01 0.096 0.010 0.038 0.001 0.003 <0.001 0.001 0.002 10y25 0.45-75 0.08 <0.01 0.03 0.046 0.010 0.023 0.001 0.005 <0.001 0.001 0.001 10y25min Dis <0.01 <0.01 <0.01 0.010 0.410 0.008 <0.001 0.064 <0.001 <0.001 0.005

2y7m >300 0.296 0.050 0.120 0.006 0.014 <0.001 0.001 0.008 2y7m 151-300 0.264 0.017 0.104 0.004 0.014 <0.001 0.001 0.006 2y7m 76-150 <0.01 <0.01 <0.01 0.160 0.012 0.074 0.002 0.006 <0.001 0.001 0.003 2y7m 0.45-75 0.06 0.02 0.04 0.087 0.012 0.040 0.001 0.011 <0.001 0.002 0.001 2y7min Dis 0.01 <0.01 <0.01 0.011 0.270 0.008 <0.001 0.075 <0.001 <0.001 0.005 5y13m >300 0.104 0.014 0.057 0.002 0.007 <0.001 0.001 0.003

5y13m 151-300 0.144 0.012 0.075 0.002 0.010 <0.001 0.001 0.004 5y13m 76-150 <0.01 <0.01 0.02 0.120 0.010 0.060 0.002 0.005 <0.001 0.001 0.002 5y13m 0.45-75 <0.01 <0.01 0.02 0.053 0.011 0.027 0.001 0.011 <0.001 0.001 0.001 5y13min Dis <0.01 <0.01 <0.01 0.053 0.210 0.009 <0.001 0.071 <0.001 <0.001 0.005 10y17m >300 0.184 0.023 0.080 0.003 0.008 <0.001 0.001 0.011

10y17m 151-300 0.128 0.010 0.056 0.002 0.009 <0.001 0.001 0.007 10y17m 76-150 <0.01 <0.01 <0.01 0.080 0.011 0.041 0.001 0.005 <0.001 0.001 0.002 10y17m 0.45-75 <0.01 <0.01 0.02 0.078 0.009 0.016 <0.001 0.004 <0.001 <0.001 <0.001 10y17min Dis <0.01 <0.01 <0.01 0.039 0.240 0.008 <0.001 0.063 <0.001 <0.001 0.012

<0.01

<0.01 <0.01

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01 <0.01

<0.01 <0.01

Cu [ppm]

Cd [ppm]

Cr [ppm]

Mn [ppm]

Fe [ppm] Zn [ppm] Al [ppm] Pb [ppm]

<0.01 <0.01 <0.01


IND [ppm]

DbA [ppm] BgP [ppm]

<0.01

<0.01 <0.01

266

TABLE B3 – Commercial Wash-off data PART 3 (Blanks and replicates)

Field blank 11/01/2004 115 - Field BLANK - - - - - -Field blank 11/01/2004 115 - Field BLANK - - - - - -Field blank 11/01/2004 86 - Field BLANK - - - - - -Field blank 11/01/2004 86 - Field BLANK - - - - - -Field blank 11/01/2004 133 - Field BLANK - - - - - -Field blank 11/01/2004 133 - Field BLANK - - - - - -Field blank 11/01/2004 65 - Field BLANK - - - - - -Field blank 11/01/2004 65 - Field BLANK - - - - - -

Laboratory blank 11/01/2004 - - Distilled Blank - - - - - -Laboratory blank 11/01/2004 - - Distilled Blank - - - - - -Build-up 76-150 11/01/2004 - - Replicate - - - - - -Build-up <0.45 11/01/2004 - - Replicate - - - - - - 1y5m 0.45-75 11/01/2004 115 5 Replicate - - - - - - 2y7m 76-150 11/01/2004 133 7 Replicate - - - - - -

Classification

Dry daysbefore

Last rainfall

Event Total volume[L]

Slope [m/m]

Texture depth [mm]


Duration [min]

Field blank - - 7.19 34.90 1.478 0.143 <0.01 0.01 <0.01Field blank - - 7.19 34.90 <0.001 <0.001 0.05 0.02 0.02Field blank - - 7.14 34.70 4.904 0.172 <0.01 <0.01 <0.01Field blank - - 7.14 34.70 <0.001 <0.001 0.07 0.05 <0.01Field blank - - 7.08 6.74 1.621 <0.001 <0.01 0.01 <0.01Field blank - - 7.08 6.74 <0.001 <0.001 0.12 0.04 <0.01Field blank - - 6.88 7.59 1.621 <0.001 <0.01 <0.01 <0.01Field blank - - 6.88 7.59 <0.001 <0.001 0.05 0.05 <0.01

Laboratory blank - - 6.27 5.64 1.621 <0.001 <0.01 <0.01 <0.01Laboratory blank - - 6.27 5.64 <0.001 <0.001 0.01 <0.01 <0.01Build-up 76-150 - - - - 1.109 <0.001 608.6 4.80 1.26Build-up <0.45 - - - - 18.792 6.464 150 0.89 0.09 1y5m 0.45-75 - - - - 2.295 <0.001 9.4 9.76 1.02 2y7m 76-150 - - - - 0.578 0.031 10.5 3.15 0.68

pH EC [uS/cm]

NAP [ppm]Sample name Last swept


ACY [ppm]TOC [ppm]

IC [ppm]

TSS [mg/L]

267

TABLE B3 – Commercial Wash-off data PART 3 (Parameters continued from previous page)

Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 0.02 <0.01 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Field blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01

Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Laboratory blank <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01Build-up 76-150 0.79 0.64 1.57 0.18 0.69 0.54 0.64 0.68 0.37 0.65 0.21 0.06 0.05Build-up <0.45 0.05 0.02 0.02 <0.01 0.02 0.01 0.03 <0.01 <0.01 0.12 <0.01 <0.01 <0.011y5m 0.45-75 0.19 0.65 1.38 0.12 0.67 0.34 0.51 0.34 0.54 0.22 0.06 0.03 0.08 2y7m 76-150 0.17 0.10 0.14 <0.01 0.18 <0.01 0.10 <0.01 <0.01 0.16 <0.01 <0.01 <0.01

BaP [ppm]

IND [ppm]

DbA [ppm]

BgP [ppm]

Sample name ACE [ppm]

FLU [ppm]

PHE [ppm]

CHR [ppm]

BbF [ppm]

ANT [ppm]

FLA [ppm]

PYR [ppm]

BaA [ppm]

Field blank 0.017 0.017 0.019 <0.001 0.006 <0.001 <0.001 0.002Field blank 0.012 0.004 0.006 <0.001 <0.001 <0.001 0.001 <0.001Field blank 0.013 0.015 0.013 <0.001 0.008 <0.001 <0.001 0.001Field blank 0.009 0.005 0.007 <0.001 <0.001 <0.001 <0.001 <0.001Field blank 0.010 0.013 0.018 <0.001 0.006 <0.001 <0.001 <0.001Field blank 0.010 0.005 0.006 <0.001 <0.001 <0.001 0.001 <0.001Field blank 0.070 0.014 0.014 <0.001 0.005 <0.001 <0.001 0.004Field blank 0.096 0.004 0.069 <0.001 <0.001 <0.001 <0.001 <0.001

Laboratory blank <0.005 0.090 0.009 <0.001 0.002 <0.001 <0.001 <0.001Laboratory blank 0.018 0.004 0.006 <0.001 <0.001 <0.001 <0.001 <0.001Build-up 76-150 0.769 0.012 0.459 0.002 0.001 <0.001 0.001 0.020Build-up <0.45 0.950 0.835 0.069 0.014 0.120 0.003 <0.001 0.2681y5m 0.45-75 0.029 0.002 0.051 0.003 0.002 <0.001 0.001 0.0012y7m 76-150 0.158 0.012 0.071 0.002 0.006 <0.001 0.001 0.003

Mn [ppm]Pb [ppm] Cu [ppm] Cd [ppm] Cr [ppm]Sample name Fe [ppm] Zn [ppm] Al [ppm]

268

TABLE B4 – Mean concentrations [mg/kg] of PAHs in the different particle size classes in wash-off samples at the residential site <0.45μm 0.45-75μm 76-150μm >150μm PAH DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD

NAP 89 0.05 0.02 100 0.94 0.25 100 1.01 0.10 100 0.93 0.09 ACY 22 0.01 0.01 100 0.12 0.05 55 0.12 0.07 33 0.12 0.07 ACE 0 <0.01 <0.01 67 0.07 0.04 11 0.13 0.04 11 0.07 0.02 FLU 0 <0.01 <0.01 44 0.08 0.06 11 0.11 0.04 0 <0.01 <0.01 PHE 0 <0.01 <0.01 55 0.12 0.10 33 0.16 0.08 0 <0.01 <0.01 ANT 0 <0.01 <0.01 67 0.09 0.05 55 0.15 0.08 0 <0.01 <0.01 FLA 0 <0.01 <0.01 55 0.18 0.11 55 0.20 0.12 67 0.18 0.10 PYR 0 <0.01 <0.01 55 0.14 0.09 55 0.17 0.12 67 0.15 0.09 BaA 0 <0.01 <0.01 67 0.13 0.09 33 0.18 0.10 22 0.06 0.03

CHR 0 <0.01 <0.01 11 0.10 0.03 11 0.16 0.05 0 <0.01 <0.01

BbF* 0 <0.01 <0.01 33 0.12 0.06 0 <0.01 <0.01 11 0.12 0.04 BaP 11 0.01 0.01 78 0.11 0.07 44 0.17 0.10 33 0.13 0.08 IND 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01

DbA 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01

BgP 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01 DF = Detection Frequency; SD = Standard deviation from mean;

* Benzo[b]flouranthene (BbF) and Benzo[k]flouranthene (BkF) was measured as one parameter

269

TABLE B5 – Mean concentrations [mg/kg] of PAHs in the different particle size classes in wash-off samples at the industrial site <0.45μm 0.45-75μm 76-150μm >150μm PAH DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD

NAP 100 0.07 0.01 100 1.91 0.27 100 1.77 0.39 100 0.71 0.21 ACY 55 0.02 0.01 100 0.19 0.09 91 0.20 0.09 82 0.11 0.05 ACE 64 0.02 0.01 100 0.28 0.06 100 0.22 0.08 45 0.05 0.03 FLU 9 0.01 0.01 91 0.08 0.04 73 0.05 0.03 55 0.03 0.02 PHE 0 <0.01 <0.01 100 0.12 0.05 91 0.06 0.03 36 0.01 0.01 ANT 0 <0.01 <0.01 91 0.05 0.02 82 0.06 0.04 0 <0.01 <0.01 FLA 64 0.01 0.01 91 0.17 0.06 91 0.09 0.05 82 0.08 0.04 PYR 0 <0.01 <0.01 91 0.20 0.07 82 0.07 0.04 73 0.07 0.05 BaA 18 0.01 0.01 100 0.12 0.02 82 0.09 0.04 22 0.06 0.03

CHR 0 <0.01 <0.01 73 0.24 0.11 64 0.10 0.05 73 0.07 0.03

BbF* 0 <0.01 <0.01 82 0.07 0.03 82 0.04 0.02 45 0.02 0.01 BaP 27 0.04 0.03 100 0.11 0.03 100 0.07 0.03 73 0.08 0.04 IND 0 <0.01 <0.01 82 0.02 0.01 0 <0.01 <0.01 0 <0.01 <0.01

DbA 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01 0 <0.01 <0.01

BgP 18 0.01 0.01 55 0.02 0.01 64 0.02 0.01 0 <0.01 <0.01 DF = Detection Frequency; SD = Standard deviation from mean;


270

TABLE B6 – Mean concentrations [mg/kg] of PAHs in the different particle size classes in wash-off samples at the commercial site <0.45μm 0.45-75μm 76-150μm >150μm PAH DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD DF [%] Mean SD

NAP 92 0.13 0.13 100 5.18 1.83 100 1.58 0.99 100 0.99 0.50 ACY 58 0.04 0.03 100 0.55 0.24 100 0.37 0.20 83 0.22 0.15 ACE 33 0.02 0.01 100 0.21 0.19 92 0.11 0.06 83 0.08 0.05 FLU 25 0.01 0.01 100 0.30 0.25 75 0.09 0.07 67 0.07 0.06 PHE 42 0.02 0.02 100 0.55 0.46 67 0.23 0.27 33 0.05 0.03 ANT 58 0.02 0.02 92 0.11 0.06 33 0.03 0.02 33 0.05 0.03 FLA 58 0.03 0.02 100 0.36 0.24 58 0.16 0.10 100 0.11 0.05 PYR 8 0.01 0.01 100 0.32 0.12 75 0.07 0.05 83 0.14 0.09 BaA 50 0.02 0.01 100 0.38 0.08 83 0.18 0.11 50 0.08 0.05

CHR 42 0.01 0.01 100 0.20 0.11 58 0.14 0.10 42 0.10 0.07

BbF* 8 0.01 0.01 100 0.39 0.14 67 0.14 0.08 58 0.09 0.06 BaP 75 0.06 0.06 100 0.26 0.10 83 0.13 0.09 83 0.09 0.06 IND 8 0.01 0.01 83 0.09 0.06 25 0.03 0.02 8 0.07 0.01

DbA 0 <0.01 <0.01 67 0.04 0.04 0 <0.01 <0.01 8 0.02 0.01

BgP 8 0.01 0.01 92 0.04 0.02 33 0.03 0.02 0 <0.01 <0.01 DF = Detection Frequency; SD = Standard deviation from mean;


271

FIGURE B1 Particle size distribution graph for build-up sample taken at the

residential site


industrial site

Particle Diameter (µm.)

Volume (%)

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272


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FIGURE B4 Particle size distribution at each site for 1year ARI with intensity

of 65mm/hr and duration 20 min


Volume (%)

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279

APPENDIX C

CHEMOMETRIC ANALYSIS USING PCA

280

The method of principal component analysis (adapted from Jackson (1991))

The method of PCA is based on a key result from matrix algebra: A p x p symmetric,

non-singular matrix, such as the covariance matrix S, may be reduced to a diagonal

matrix L by premultiplying and postmultiplying it by a particular orthonormal matrix

U such that:

LSUU =′

The diagonal elements of L (l1, l2, ….lp) are called the eigenvalues of S. The columns

of U (u1, u2, …, up) are called the eigenvectors of S.

The starting point for PCA is the sample covariance matrix S. For a p-variable

problem,

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

221

22212

11221

.......

...

...

ppp

p

p

sss

ssssss

S

The principal axis transformation will transform p correlated variables (x1, x2, …, xp)

into p new uncorrelated variables (z1, z2, …, zp). The coordinate axes of these new

variables are described by the characteristic vectors ui which make up the matrix U

of direction cosines used in the transformation:

][ xxUz −′=

Where x and x are p x 1 vectors of observations on the original variables and their

means (loadings and scores). The transformed variables are called the principal

components of x of PCs for short. The ith principal component is

][ xxuz ii −′=

281

PCA Algorithm used in MatLab

Res; Data matrix loaded (Res)

[vs,vl,veigen] = pca(Res,’yes’); Scores (vs), loadings(vl) and

eigenvalues (veigen) are calculated

based on the original data matrix. The

data is also pre-treated using the

standardise option (‘yes’)

scree(veigen); A scree plot of the eigenvalues is plotted

to determine the number of principal

components to use in the analysis

pcaplota(vl,veigen,Res_var,1,2); The loadings of each variable (Res_var

contain variable names) are plotted on

the first two principal components

pcaplot3d(vl,veigen,Res_var,1,2,3); The loadings of each variable are

plotted on the first three principal

components in a three-dimensional plot

pcaploto(vs,veigen,Res_obj,1,2); The scores of each object (Res_obj

contain object names) are plotted on the

first two principal components

pcaplot3d(vs,veigen,Res_obj,1,2,3); The scores of each object are plotted on

the first three principal components in a

three-dimensional plot

biplot(Res,Res_var,Res_obj,’yes’); Loadings and scores are combined in a

two-dimensional biplot

biplot3d(Res,Res_var,Res_obj,’yes’); Loadings and scores are combined in a

three-dimensional biplot

282

The PCA function in MatLab (adapted from Kramer (1993))

function [vs, vl, veigen] = pca(x, standardise)

% [vs, vl, veigen] = PCA(x, standardise)

%

% This function performs principal component analysis using eigenvalue/

% eigenvector decomposition.

%

% returned are: the scores matrix, vs

% the loadings matrix, vl

% the normalised eigenvalues, veigen

%

% Standardise: 'none' = absent

% 'yes'

% 'no'

%

% A "standardise" option of "no" mean centers the x data but does not

% normalise to unit variance. Whereas the "yes" option mean centers and

% then normalises the x data to unit variance. When the "standardise"

% option is or omitted, then neither centering nor normalisation

% is carried out.

%

if nargin == 1

xc = x' * x;

elseif nargin == 2

[m, n] = size(x);

mx = mean(x);

if strcmp(standardise, 'yes')

xc = corrcoef(x);

% column centre and normalise to unit variance

sx = std(x);

for i=1:n

x(:,i) = (x(:,i) - mx(i))./sx(i);

end

283

elseif strcmp(standardise, 'no')

xc = cov(x);

% column centre

for i=1:n

x(:,i) = x(:,i) - mx(i);

end

elseif ~strcmp(standardise, 'none')

disp('Error: Invalid option specified')

end

else

disp('Error: Invalid number of input arguments')

end

[v, d] = eig(xc);

[veigen, order] = sort(diag(d)); % sort eigenvalues into ascending order

order = flipud(order); % we require principal components in descending order

veigen = flipud(veigen);

veigen = veigen./sum(veigen); % evals as fraction of total variance

vl = v(:, order); % arrange eigenvectors into same order as eigenvalues

vs = (vl' * x')'; % project eigenvalues to find scores

284

The Scree function in MatLab (adapted from Kramer (1993))

function [S] = scree(veigen)

% scree(veigen)

%

% Constructs a scree plot for the number of significant PC's from the output

% data of the PCA or SPCA functions.

%

% input: veigen is a matrix of eigenvectors returned from PCA or SPCA

%

% output: S is the vector containing the sum of the eigenvectors squared

%

[m,n] = size(veigen)

Z = veigen.^2

S = sum(Z)

plot(Z,'b-')

set(gca,'xtick',[1 2 3 4 5 6 7 8 9 10])

title('Scree Plot')

xlabel('Principal Component')

ylabel('Residual Variance, V')

285

The pcaplota function in MatLab (adapted from Kramer (1993))

function pcaplota(y, veigen, lbls, pc1, pc2)

% PCAPLOT(y, lbls, pc1, pc2)

%

% Constructs a plot from the output data of the PCA or SPCA functions. It

% can be used to create either a loadings vs loading plot or a scores vs

% scores plot.

%

% input: y is a loadings or scores matrix returned from PCA or SPCA

%

% lbls is: vector of strings containing text labels for the variables

% (columns)

%

% optional arguments (if these argument are omitted then a PC1 vs PC2

% plot is assumed:

%

% pc1 is the first principal component to plot

% pc2 is the second principal component to plot

%

[m, n]=size(y);

if (nargin == 3) % assume a PC1 vs PC2 plot is required

pc1 = 1;

pc2 = 2;

elseif (nargin ~= 5) % pc1 and pc2 contain PCs to plot

disp('Error: Incorrect number of input arguments')

end

plot(y(:,pc1), y(:,pc2), 'ro', 0, 0, 'b+') % include (0, 0) in plot

text(y(:,pc1), y(:,pc2), lbls) % annotate plot

for i=1:n

line([ 0 y(i,pc1)], [0 y(i,pc2)],'color', [1 0 0])

end

286

title('Principal Component Analysis')

str1 = ['PC1 (' num2str(veigen(1)*100,3) ' %)'];


xlabel(str1)

ylabel(str2)

k = axis; % find axis coordinates

% draw in axis lines to mark quadrants

line([k(1) k(2)], [0 0],'color',[0 0 1])

line([0 0], [k(3) k(4)],'color',[0 0 1])

287

The pcaplot3d function in MatLab (adapted from Kramer (1993))

function pcaplot3d(y, veigen, lbls, pc1, pc2, pc3)

% PCAPLOT(y, lbls, pc1, pc2)

%



% scores plot.

%


%

% lbls is: vector of strings containing text labels for the variables or

% objects

% %

% optional arguments (if these argument are omitted then a PC1, PC2, PC3

% plot is assumed:

%



% pc3 is the third principal component to plot


pc1 = 1;

pc2 = 2;

pc3 = 3;



end

plot3(y(:,pc1), y(:,pc2), y(:,pc3), 'ro', 0, 0, 0, 'b+') % include (0, 0) in plot

text(y(:,pc1), y(:,pc2), y(:,pc3), lbls) % annotate plot





288

xlabel(str1)

ylabel(str2)

zlabel(str3)

v = axis; % find axis coordinates


line([v(1) v(2)], [0 0], [0 0])

line([0 0], [v(3) v(4)], [0 0])

line([0 0], [0 0], [v(5) v(6)])

%Turn grid on

Grid on

289

The pcaploto function in MatLab (adapted from Kramer (1993))

function pcaploto(y, veigen, lbls, pc1, pc2)

% PCAPLOTo(y, lbls, pc1, pc2)

%



% scores plot.

%


%

% lbls is: vector of strings containing text labels for the variables

% (columns)

%

% optional arguments (if these argument are omitted then a PC1 vs PC2

% plot is assumed:

%



%


pc1 = 1;

pc2 = 2;



end

plot(y(:,pc1), y(:,pc2), 'ro', 0, 0, 'b+') % include (0, 0) in plot

text(y(:,pc1), y(:,pc2), lbls) % annotate plot




xlabel(str1)

ylabel(str2)

v = axis; % find axis coordinates

290


line([v(1) v(2)], [0 0],'color',[0 0 1])

line([0 0], [v(3) v(4)],'color',[0 0 1])

291

The biplot function in MatLab (adapted from Kramer (1993))

function biplot(x, lblsv, lblso, standardise)

% BIPLOT(x, lbls, standardise)

%

% Constructs a biplot from the input matrix x for principal component

% analysis using singular value decomposition algorithm.

%

% input: x is a (nxp) data matrix whose columns represent variables and

% whose rows represent samples (or observations).

%

% lblsv is: vector of strings containing text labels for the variables

% (columns)

% lblso is: vector of strings containing text labels for the objects

% (columns)

%

%

% Standardise is one of: 'none'

% 'yes'

% 'no'

%


% standardise to unit variance. Whereas the "yes" option mean centers (by

% removing the mean from each column) and then standardises the x data to unit

% variance. When "none" is specified, then neither centering nor

% standardisation is carried out.

%

if (nargin == 4)

[m ,n] = size(x);

mx = mean(x);



sx = std(x);

for i=1:n

292

x(:,i) = (x(:,i) - mx(i))./sx(i);

end


% just column centre

for i=1:n

x(:,i) = x(:,i) - mx(i);

end



end

else


end

[u, s, v] = svd(x);

evals = diag(s).^2;

evals = evals./sum(evals);

plot(u(:,1), u(:,2), 'bo', v(:,1), v(:,2), 'ro')

legend('objects', 'variables')

title('Principal Component Analysis Biplot')

str1 = ['PC1 (' num2str(evals(1)*100,3) ' %)'];


xlabel(str1)

ylabel(str2)

% draw vectors from origin to variables

for i=1:n

line([ 0 v(i,1)], [0 v(i,2)], 'Color', [1 0 0])

end

text(v(:,1),v(:,2),lblsv) % annotate variables

text(u(:,1),u(:,2),lblso) % annotate objects

a = axis; % find axis coordinates

% draw in axis lines (in lt. grey) to mark quadrants

line([a(1) a(2)], [0 0], 'Color', [0.75 0.75 0.75]);

line([0 0], [a(3) a(4)], 'Color', [0.75 0.75 0.75]);

293

The biplot3d function in MatLab (adapted from Kramer (1993))

function biplot3d(x, lblsv, lblso, standardise)

% BIPLOT(x, lbls, standardise)

%

% Constructs a biplot from the input matrix x for principal component

% analysis using singular value decomposition algorithm.

%

% input: x is a (nxp) data matrix whose columns represent variables and

% whose rows represent samples (or observations).

%

% lblsv is: vector of strings containing text labels for the variables

% (columns)

% lblso is: vector of strings containing text labels for the objects

% (columns)

%

%

% Standardise is one of: 'none'

% 'yes'

% 'no'

%


% standardise to unit variance. Whereas the "yes" option mean centers (by

% removing the mean from each column) and then standardises the x data to unit

% variance. When "none" is specified, then neither centering nor

% standardisation is carried out.

%

if (nargin == 4)

[m ,n] = size(x);

mx = mean(x);



sx = std(x);

for i=1:n

294

x(:,i) = (x(:,i) - mx(i))./sx(i);

end


% just column centre

for i=1:n

x(:,i) = x(:,i) - mx(i);

end



end

else


end

[u, s, v] = svd(x);

evals = diag(s).^2;

evals = evals./sum(evals);

plot3(u(:,1), u(:,2), u(:,3), 'go', v(:,1), v(:,2), v(:,3), 'rd')

legend('objects', 'variables')

title('Principal Component Analysis Biplot')




xlabel(str1)

ylabel(str2)

zlabel(str3)

grid on

% draw vectors from origin to variables

for i=1:n

line([ 0 v(i,1)], [0 v(i,2)], [0 v(i,3)], 'Color', [1 0 0])

end

text(v(:,1),v(:,2),v(:,3),lblsv) % annotate variables

text(u(:,1),u(:,2),u(:,3),lblso) % annotate objects

a = axis; % find axis coordinates

% draw in axis lines (in lt. grey) to mark quadrants

295

line([a(1) a(2)], [0 0], [0 0], 'Color', [0 0 1])

line([0 0], [a(3) a(4)], [0 0], 'Color', [0 0 1])

line([0 0], [0 0], [a(5) a(6)], 'Color', [0 0 1])

296

FIGURE C1 Scores plot of the PAHs and heavy metals at the residential site

showing the score of each particle size class on PC1 and PC2

FIGURE C2 Scree plot of the PAH and heavy metal data in build-up sample

from industrial site showing elbow occurring at PC2

ELBOW

PC1 (81.4%)

PC2

(12.

7%)

297

FIGURE C3 Scores plot of the PAHs and heavy metals at the industrial site


FIGURE C4 Scree plot of the PAH and heavy metal data in build-up sample

from commercial site showing elbow occurring at PC2

ELBOW

PC1 (73.4%)

PC2

(18.

5%)

298

FIGURE C5 Scores plot of the PAHs and heavy metals at the commercial site


FIGURE C6 Scree plot of dissolved data at the residential site indicating elbow

occurring at PC3

ELBOW

PC1 (73.4%)

PC2

(18.

5%)

299

FIGURE C7 Three dimensional loadings plot of the PAHs and heavy metals

detected in the dissolved fraction of wash-off samples from the residential site

1y10 = 1 year 10 minutes ARI event

FIGURE C8 Scores of each rainfall event on PC1 and PC2 at the residential

site when using PCA on detected PAHs and heavy metals in the dissolved

fraction

300

FIGURE C9 Scree plot of the PAHs and heavy metals (0.45-75μm) at the

residential site (elbow occurring at PC3)


detected in washed-off particles 0.45-75µm from the residential site

ELBOW

301

FIGURE C11 Scores of each object on PC1 and PC2 at the residential site when

using PCA on detected PAHs and heavy metals in 0.45-75μm size particles

FIGURE C12 Scree plot of the PAHs and heavy metals (76-150μm) at the


ELBOW

302


detected in washed-off particles 76-150µm from the residential site


using PCA on detected PAHs and heavy metals in 76-150μm size particles

303

FIGURE C15 Scree plot of the PAHs and heavy metals (>150μm) at the



detected in washed-off particles >150µm from the residential site

ELBOW

304


using PCA on detected PAHs and heavy metals in >150μm size particles

FIGURE C18 Scree plot of dissolved data at the industrial site indicating elbow

occurring at PC3

ELBOW

305


detected in dissolved samples from the industrial site

FIGURE C20 Scores of each rainfall event on PC1 and PC2 at the industrial site

when using PCA on detected PAHs and heavy metals in the dissolved fraction

306

FIGURE C21 Scree plot of the PAHs and heavy metals (0.45-75μm) at the

industrial site (elbow occurring at PC2)


when using PCA on detected PAHs and heavy metals in particles 0.45-75µm

ELBOW

307

FIGURE C23 Scree plot of the PAHs and heavy metals (76-150μm) at the



detected in washed-off particles 76-150µm from the industrial site

ELBOW

308


when using PCA on detected PAHs and heavy metals in particles 76-150µm

FIGURE C26 Scree plot of the PAHs and heavy metals (>150μm) at the


ELBOW

309


when using PCA on detected PAHs and heavy metals in particles >150µm

FIGURE C28 Scree plot of dissolved data at the commercial site indicating

elbow occurring at PC3

ELBOW

310


detected in dissolved samples from the commercial site

FIGURE C30 Scores of each rainfall event on PC1 and PC2 at the commercial

site when using PCA on detected PAHs and heavy metals in the dissolved

fraction

311

FIGURE C31 Scree plot of the PAHs and heavy metals (0.45-75µm) at the

commercial site (elbow occurring at PC3)


site when using PCA on detected PAHs and heavy metals in particles 0.45-75µm

ELBOW

312

FIGURE C33 Scree plot of the PAHs and heavy metals (76-150µm) at the



detected in washed-off particles 76-150µm from the commercial site

ELBOW

313


site when using PCA on detected PAHs and heavy metals in particles 76-150µm

FIGURE C36 Scree plot of the PAHs and heavy metals (76-150µm) at the


ELBOW

314


detected in washed-off particles >150µm from the commercial site


site when using PCA on detected PAHs and heavy metals in particles >150µm

315

APPENDIX D

PREDICTION USING PLS

316

PLS and the underlying scientific model (adapted from Wold et al. 2001))

The PLS model is developed from a training set of N observations (objects, cases,

compounds, process time points) with K X variables denoted by xk (k = 1, …, K),

and M Y-variables ym (m = 1, 2 ,…M). These training data form the two matrices X

and Y of dimensions (N x K) and (N x M), respectively.

The PLS model finds a few ‘new’ variables, which are estimates of the latent

variables or their rotations. These new variables are called X-scores and denoted by

ta (a = 1, 2, …, A). The X-scores are predictors of Y. The X-scores are few (A in

number), and orthogonal. They are estimated as linear combinations of the original

variables xk with the coefficients, ‘weights’, wka (a = 1, 2, …, A).

∑= k ikkaia XWt ; (T = XW).

The X-scores (ta’s) have the following properties:

a) They are, multiplied by the loadings pak, good summaries of X, so that the X

residuals, eik, are small:

With multivariate Y (when M>1) the corresponding Y-scores (ua) are, multiplied

with the weights cam, good summaries of Y, so that the residuals, gim, are small:

b) the X-scores are good predictors of Y:

317

PLS1 algorithm used in the research

1. Load Data and pretreat

Wash_off;

[Xcent,xmean] = pretreat(Wash_off);

[Wash_off2,xsdev] = varscale(Xcent);

2. Remove unwanted variables

Wash_off3 = Wash_off2(:,2:6);

3. Split into X and Y matrices (X predictors, Y to be predicted)

Y = Wash_off3(:, 4); Note: If using calibration/validation, split

e = [1;2;3;5]; appropriately

X = Wash_off3(:,e);

4. Run PLS Algorithm

[p, q, w, b, t, u, x, y] = pls1(X, Y); (p = X loadings; q = Y loadings; w = weights;

b = inner relationships; t = X scores;

u = Y scores; x = X residuals; y = Y residuals

5. Run NPLS Algorithm to see error predictions

[rev, secv, press] = npls(X, Y, x, y, p, q, w, b); NOTE: Use X, Y for x,y if no

validation data, else use

validation x,y (in this case ½ of

the data matrix was used for

validation)

Select appropriate n (# of factors) to use in prediction

318

6. Run REGEN algorithm to regenerate data using specified # of factors n

[Xr, Xresid] = regen(X', p, 2); NOTE: Transpose X data matrix as

calculates row-wise

7. Transpose Xr and Xresid to make calculate Column-wise

Xr=Xr'; Xresid=Xresid';

8. Run PLS1 prediction algorithm with selected # of factors

[pred, resp] = pls1pred(Xr, p, q, w, b, 2);

9. Run Observed vs Predicted plot algorithm

opplot(pred,Y,2,1,0,0,1); Note: Leave last 4 numerals as is -these

refer to alternative plotting options

10. Run Predicted error algorithm to obtain errors of fit

[SSE, SSF, SSX, SSY, R2X, R2Y, Q2, RMSEP] = SEP(Xr, Y, Xresid, y, press', pred, 2);

319

pretreat function used in MatLab (adapted from Mathisen (2004)) function [A, m, v] = pretreat(a)

%MEANCENT mean centers the data in a column-wise matrix

% [X, m] = MEANCENT(A)

%

% where:

%

% A is the mean centered data

% m is a vector containing the means

% xvar is a vector containing the variances

% data is the absorbance or concentration matrix

%

% MEANCENT expects the data to be organized column-wise per the MLR

conventions.

% The mean centering is performed row by row.

% If your data is organized row-wise per the PLS conventions, use the transpose

% of A, A' as the argument to MEANCENT.

[i, j] = size(a);

if j < 2,

error('Cannot scale the variance of less than 2 variables');

end

m = mean(a')';

x = a - m * ones(1,j);

xbar = mean(a')';

a = a - xbar * ones(1,j);

v = sqrt(sum(a' .* a'))';

A = a ./ (v * ones(1,j));

320

varscale function used in MatLab (adapted from Mathisen (2004))

function [x, xvar] = varscale(a)

%VARSCALE scales variance of the data in a matrix to equal Variables / (Points - 1)

% [x, xvar] = varscale(a)

%

% where:

%

% x is the scaled data

% xvar is a vector containing the variances

% a is the data matrix to be scaled

%

% VARSCALE expects the data to be organized column-wise per the MLR

conventions.

% The variance scaling is performed row by row.

% The mean of the data is removed prior to scaling and restored after scaling.

% The resulting data is NOT mean-centered. The total variance in the resulting

% data will be equal to Number of Variables / (Number of Points - 1).

%

% If your data is organized row-wise per the PLS conventions, use the transpose

% of A, A' as the argument of VARSCALE.

[i, j] = size(a);

if j < 2,

error('Cannot scale the variance of less than 2 variables');

end

xbar = mean(a')';

a = a - xbar * ones(1,j);

xvar = sqrt(sum(a' .* a'))';

x = a ./ (xvar * ones(1,j));

x = x + xbar * ones(1,j);

321

pls1 function used in MatLab (adapted from Mathisen (2004))

function [p, q, w, b, t, u, x, y] = pls1(a, c, maxrank)

%PLS1 Partial Least Squares in latent variables

% Uses the Matlab's SVD routine on the smallest covariance matrix

% for computational efficiency. This function individually computes the

% PLS calibration each component (column) in the C matrix even if the C

% matrix contains more than one component (column). If you wish to calculate

% a PLS calibration using all component information simultaneously, then

% you should use the function PLS. Conforms to ASTM standard E 1655-97.

%

% [P, Q, W, B] = PLS1(A, C, MAXRANK) or,

%

% [P, Q, W, B, T, U, X, Y] = PLS1(A, C, MAXRANK)

%

% Where:

%

% P is the matrix of spectral factors

% Q is the matrix of concentration factors

% W is the matrix of weights

% B is the vector containing the inner relationships

% T is the matrix of spectral scores

% U is the matrix of concentration scores

% X is the matrix of spectral residuals

% Y is the matrix of concentration residuals

% A is the training set absorbance matrix

% C is the training set concentration matrix

% MAXRANK is optional limit to the number of factors calculated

[m,n] = size(a);

[m,l] = size(c);

if l ~= 1,

message=[ num2str(l), ' C variables. PLS1 requires a single variable in the C

matrix.'];

error(message);

322

end

if nargin == 3, i = min([n, m, maxrank]); else i = min([n m]); end

x = a;

y = c;

for h = 1:i,

w(:,h) = x' * y;

w(:,h) = w(:,h) / sqrt(w(:,h)' * w(:,h));

t(:,h) = x * w(:,h);

tsq = t(:,h)' * t(:,h);

p(:,h) = ( x' * t(:,h) ) / tsq;

q(:,h) = ( y' * t(:,h) ) / tsq;

u(:,h) = y * q(:,h);

x = x - (t(:,h) * p(:,h)');

y = y - (t(:,h) * q(:,h)');

end

b=ones(1,i);

323

npls function used in MatLab (adapted from Mathisen (2004))

function [rev, secv, press] = npls(X, Y, xv, yv, p, q, w, b)

rev = plsrev(X, p, q, w, b);

secv = plscross(X, Y);

press = plspress(xv, yv, p, q, w, b);

plot(rev);

title('Reduced Eigenvalues')

xlabel('Number of PCs')

ylabel('REV')

figure

plot(secv);

title('Standard Error of Cross-Validation')


ylabel('SECV')

figure

plot(press);

title('Predicted Residual Error Sum of Squares')


ylabel('PRESS')

324

regen function used in MatLab (adapted from Mathisen (2004))

function [reg, resid] = regen(a, vc, r)

%REGEN PCA short cicuit data reproduction using N factors.

% REG = REGEN(A, VC, R) or [REG, RESID] = REGEN(A, VC, R)

%

% Where:

%

% REG is the matrix containing the regenerated data

% RESID is the matrix containing the residuals of the regeneration

% A is the absorbance matrix

% VC is the matrix containing the factors

% R is the rank (number of factors) to use in the regeneration

[t, n] = size(vc);

if nargin == 3, n = min([n, r]);, end;

vc = vc(:, 1:n);

reg = (a' * vc * vc')';

resid = a - reg;

325

pls1pred function used in MatLab (adapted from Mathisen (2004))

function [c, x] = pls1pred(a, p, q, w, b, n, amean, cmean, ascal, cscal)

%PLS1PRED Predicts unknown concentrations using PLS1.

%

% [C, X] = PLS1PRED(A, P, Q, W, B, N, AMEAN, CMEAN, ASCAL,

CSCAL)

%

% C is the matrix of the unknown's concentrations

% X is the matrix of spectral residuals after decomposition

% A is the unknown spectrum (or spectra)

% P is the matrix of spectral factors

% Q is the matrix of concentration factors

% W is the matrix of weights

% B is the vector containing the inner relationships

% if B == 0, then all b's are set to unity

% N is the number of factors to use

%

% optional arguments:

%

% AMEAN vector of means of mean-centered calibration spectra

% CMEAN vector of means of mean-centered calibration concentrations

% ASCAL vector of scaling factors of scaled calibration spectra

% CSCAL vector of scaling factors of scaled calibration concentrations

%

% Specify 0 for amean, cmean, ascal, cscal if no scaling or centering

[maxrank, h] = size(p);

if nargin > 5, maxrank = min([n, maxrank]); end

if sum(size(b)==1) == 2,

if b==0,

b=ones(1,maxrank);

end

end

326

c=0;

[j, i] = size(a);

if nargin > 6,

if amean ~= 0,

a = a - amean * ones(1, j);

end

if nargin > 8,

if ascal ~= 0,

a = a ./ (ascal * ones(1, j));

end

end

end

x = a;

for h=1:maxrank

t(:,h) = x * w(:,h);

x = x - t(:,h) * p(:,h)';

c = c + b(h) * t(:,h) * q(:,h);

end

[l, k] = size(c);

if nargin > 9,

if cscal ~= 0

c = c .* (cscal * ones(1,l));

end

end

if nargin > 7,

if cmean ~=0,

c = c + cmean * ones(1,l);

end

end

327

opplot function used in MatLab (adapted from Mathisen (2004))

function opplot(pred,meas,lv,num,rmse,rmsestr,st)

%MVPMPLOT -- predicted vs. measured plot

%mvpmplot(pred,meas,lv,num,rmse,rmsestr,st)

%Inputs: pred predicted values

% meas measured values

% lv number of latent variables (optional)

% num if > 0, plot with numbers

% rmse root mean square error of [cal,crossval,pred] (optional)

% rmsestr root mean square error text string (optional)

% st if zero (0), omit statistics

% Description:

% This function creates a predicted/measured plot, with some

% statistics and a regression line.

%

[m,n] = size(pred); [p,q] = size(meas);

if max(m,n) ~= max(p,q), error('vectors must have same size!')

end

if min(m,n) ~= 1 | min(p,q) ~= 1,

error('input must be vectors!')

end

% select plot character depending of number of objects

if length(meas) > 75,

plotchar = '.b';

else plotchar = 'ob';

end

% plot max and min

328

pmax = max(max(pred),max(meas));

pmin = min(min(pred),min(meas));

% increment for spacing between text lines

txtinc = (pmax-pmin)/12;

% get statistics

[press,sep,rms,slope,bias,offset,r] = mvpmstats(pred,meas);

%slope and offset

(y=ax+b)

rmax = slope*max(meas) + offset;

rmin = slope*min(meas) + offset;

% print predicted/measured

if num > 0,

mvnumplot(meas,pred,[],num)

else

plot(meas,pred,plotchar)

end

hold on

axis([pmin pmax pmin pmax])

title('Observed vs Predicted PLS')

xlabel('Observed');ylabel('Predicted');

% print target line

plot([pmax pmin],[pmax pmin],':k')

% print trend line

329

plot([pmax pmin],[rmax rmin],'--m')

if exist('st') == 0 | st ~= 0,

% print statistics

txt1=text(pmin+txtinc,pmax-1/2*txtinc,['Objects: ' num2str(max(m,n))]);

txt2=text(pmin+txtinc,pmax-2/2*txtinc,['Slope: ' num2str(slope)]);

txt3=text(pmin+txtinc,pmax-3/2*txtinc,['Offset: ' num2str(offset)]);

txt4=text(pmin+txtinc,pmax-4/2*txtinc,['Bias: ' num2str(bias)]);

txt5=text(pmin+txtinc,pmax-5/2*txtinc,['Correlation: ' num2str(r)]);

txt6=text(pmin+txtinc,pmax-6/2*txtinc,['SEP: ' num2str(sep)]);

if exist('rmse'), if ~exist ('rmsestr'), rmsestr = 'RMSEP';

end

txt7=text(pmin+txtinc,pmax-7/2*txtinc,[rmsestr ': ' num2str(rmse)]);

else

txt7=text(pmin+txtinc,pmax-7/2*txtinc,['RMSEP: ' num2str(rmsep)]);

end

if exist('lv'),

txt8=text(pmin+txtinc,pmax-8/2*txtinc,['Latent var.: ' int2str(lv)]);

end

for i = 1:8, set(eval(['txt' int2str(i)]),'FontSize',8);

end

end

end of mvpmplot

330

SEP function used in MatLab (adapted from Mathisen (2004))

function [SSE, SSF, SSX, SSY, R2X, R2Y, Q2, RMSEP] = SEP(X, Y, x, y, press,

pred, n)

SSE = sum(x.^2);

SSF = sum(y.^2);

SSX = sum(X.^2);

SSY = sum(Y.^2);

P = sum(press.^2);

R2X = (1-(SSE/SSX))*100;

R2Y = (1-(SSF/SSY))*100;

Q2 = (1-(P/SSY))*100;

RMSEP = (sum(((pred-Y).^2)/n)).^0.5;

331

TABLE D1 Abbreviations of objects in Matrix A

Abbreviation Real name (Site, ARI event, particle size)

1 Residential Build-up >300µm

2 Residential Build-up 151-300µm


4 Residential Build-up 0.45-75µm

5 Residential Build-up <0.45µm

6 Residential 1y10min >300µm

7 Residential 1y10min 151-300µm


9 Residential 1y10min 0.45-75µm

10 Residential 1y10min <0.45µm





















332





















51 Industrial Build-up >300µm

52 Industrial Build-up 151-300µm


54 Industrial Build-up 0.45-75µm

55 Industrial Build-up <0.45µm

56 Industrial 1y20min >300µm

57 Industrial 1y20min 151-300µm


59 Industrial 1y20min 0.45-75µm

60 Industrial 1y20min <0.45µm



333

































334

















111 Commercial Build-up >300µm

112 Commercial Build-up 151-300µm


114 Commercial Build-up 0.45-75µm

115 Commercial Build-up <0.45µm

116 Commercial 1y20min >300µm

117 Commercial 1y20min 151-300µm


119 Commercial 1y20min 0.45-75µm

120 Commercial 1y20min <0.45µm







335

































336


















337

TABLE D2 Abbreviation of objects in Matrix B Abbreviation Real name (Site, ARI event, particle size)

1 Residential Build-up >150µm


3 Residential Build-up 0.45-75µm

4 Residential Build-up <0.45µm




























338










41 Industrial Build-up >150µm


43 Industrial Build-up 0.45-75µm

44 Industrial Build-up <0.45µm




















339


























89 Commercial Build-up >150µm


91 Commercial Build-up 0.45-75µm

92 Commercial Build-up <0.45µm




340





























124 Commercial 2y10min 0.45µm




341














342

TABLE D3 Loading coefficients of each variable in the predictions made

Predictors

Predicted pH EC TSS TDS DOC TOC IC Fe Mn

Al 0.1896 0.2260 0.0977 x x x x 0.2006 0.0269

Pb 0.5802 0.5216 0.3457 x x x x 1.1491 0.0247

Cu 0.2531 0.2740 x x x 0.3435 0.0809 x x

Cr 0.5147 0.4755 x x x 0.0153 0.0362 x x

Zn 0.9290 1.4437 x x 0.0081 x x x x

Cd 0.6454 0.5526 x x x 0.0775 0.7284 x x

PAH1 0.3582 0.5689 0.0391 x x 0.3251 0.2388 x x

PAH2 0.3175 0.6770 0.2117 x x 0.1610 0.2117 x x

PAH3 0.3175 0.6770 1.5231 x x 0.4958 0.4555 x x

x denotes not used in prediction; PAH1 = FLA and PYR; PAH2 = ACE and ACY; PAH3 = FLU, PHE, NAP, BaP, BaA and CHR

343

FIGURE D1 PCA scree plot of all the heavy metal data (175 objects) collected

from the three sites showing the elbow at the third principal component

-8 -6 -4 -2 0 2 4 6-3

-2

-1

0

1

2

3

001

002

003

004

005

006007

008

009010

011012013014

015016017

018019

020

021022023

024025

026027028

029030

031

032

033

034035

036037038

039040 041042043

044045

046047

048049

050

051

052053054

055

056057058

059060

061062

063

064

065066

067068

069070071072 073

074075 076

077078

079

080 081

082 083

084085

086087

088

089090

091092093

094

095

096097098

099100

101

102 103

104105 106

107108109110111112

113

114

115

116

117

118

119

120

121122123

124

125

126

127128129130

131

132

133134135

136137

138

139140

141

142143144

145

146147

148

149150

151152

153

154155 156157

158

159160

161162

163

164165

166167168

169170

171172173174

175

PC1 (41.1 %)

PC

2 (2

1.3

%)

FIGURE D2 Scores plot of PC1 vs PC3 from PCA on all heavy metal data

ELBOW

PC1 (41.1%)

PC3

(11.

2%)

344

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

pH

EC

TOC

IC

DOCTSS

TDS

Fe

Zn Al Pb

Cu

Cd Cr

Mn

PC1 (41.1 %)

PC

2 (2

1.3

%)

FIGURE D3 Loadings plot (PC1 vs PC3) showing separation of metals on PC3

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

pH EC

TOCIC

DOC

TSS

TDSFe

Zn

Al

Pb

Cu

Cd

Cr

Mn

PC1 (41.1 %)

PC

2 (2

1.3

%)

FIGURE D4 Loadings plot (PC1 vs PC2) showing separation of Zn from other

metals

PC1 (41.1%)

PC3

(11.

2%)

345

FIGURE D5 PCA scree plot of all the PAH data (140 objects) collected from

the three sites showing the elbow at the second principal component

FIGURE D6 PCA biplot of the PAH data collected from the three sites

ELBOW

Dissolved data

346

FIGURE D7 PCA scree plot of the 96 objects chosen for PAH prediction, elbow

occurring at the second principal component

FIGURE D8 PCA biplot of 96 data objects (PAH data in washed-off particles)

and 15 variables

ELBOW

347

FIGURE D9 Loadings plot (PC1 vs PC3) of the PAH data

FIGURE D10 Plot of observed versus predicted Cu concentrations using two

latent variables, with an SEP of 0.82

PC1 (41.1%)

PC3

(9.2

%)

Observed Cu concentrations log[mg/kg]

Pred

icte

d C

u co

ncen

tratio

ns lo

g[m

g/kg

]


348

FIGURE D11 Plot of observed versus predicted Cd concentrations using two


FIGURE D12 Plot of observed versus predicted Cr concentrations using two


Observed Cd concentrations log[mg/kg]

Pred

icte

d C

d co

ncen

tratio

ns lo

g[m

g/kg

]


Observed Cr concentrations log[mg/kg]

Pred

icte

d C

r con

cent

ratio

ns lo

g[m

g/kg

]


349

FIGURE D13 Plot of observed versus predicted Zn concentrations using two


FIGURE D14 Plot of observed versus predicted Pb concentrations using two


Observed Zn concentrations log[mg/kg]

Pred

icte

d Zn

con

cent

ratio

ns lo

g[m

g/kg

]


Observed Pb concentrations log[mg/kg]

Pred

icte

d Pb

con

cent

ratio

ns lo

g[m

g/kg

]


350

FIGURE D15 Plot of observed versus predicted concentrations of PAH group 1



using four latent variables, with an SEP of 0.80

Observed concentrations log[mg/kg]

Pred

icte

d co

ncen

tratio

ns lo

g[m

g/kg

]



Pred

icte

d co

ncen

tratio

ns lo

g[m

g/kg

]


351


using two latent variables, with an SEP of 0.86


Pred

icte

d co

ncen

tratio

ns lo

g[m

g/kg

]


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