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Assessment of dissolved organic matterfluorescence PARAFAC components before and aftercoagulationefiltration in a full scale water treatmentplant

Nancy P. Sanchez*, Andrew T. Skeriotis, Christopher M. Miller

Department of Civil Engineering, Auburn Science and Engineering Center (ASEC), 210, The University of Akron, Akron, OH 44325, USA

a r t i c l e i n f o

Article history:

Received 6 September 2012

Received in revised form

19 December 2012

Accepted 20 December 2012

Available online 3 January 2013

Keywords:

Water treatment plants

Coagulation

Fluorescence spectroscopy

Parallel factor (PARAFAC) analysis

Uncorrected Matrix Correlation

(UMC)

Abbreviations: DOM, dissolved organic mUMC, Uncorrected Matrix Correlation; NOMfluorescence regional integration; EEM, exccarbon; UV254, ultraviolet absorbance at 254 nIR, individual raw water model; IT, individuafluorescence model; SVD, singular value deunits; PS, PARAFAC sample loadings summa* Corresponding author. Tel.: þ1 330 972 244E-mail address: ns40@uakron.edu (N.P. S

0043-1354/$ e see front matter ª 2012 Elsevhttp://dx.doi.org/10.1016/j.watres.2012.12.032

a b s t r a c t

Fluorescence monitoring of the raw and treated water after coagulationefiltration in

a drinking water treatment plant in Northeast Ohio was conducted during a period of 32

months. Principal fluorophore groups present in the dissolved organic matter (DOM) of the

raw, treated, raw-treated combined water and differential fluorescence data sets com-

prising over 680 samples were determined through Parallel Factor (PARAFAC) analysis.

Four components (two humic-like and two with protein nature) were identified in each

model and their degree of similarity was evaluated using the Uncorrected Matrix Corre-

lation (UMC), a measure of spectral overlapping. Results show that spectral characteristics

of the components in the independent models are comparable (average UMC > 0.98),

indicating that from a PARAFAC perspective, components in the raw water are not expe-

riencing major transformations beyond removal through the treatment process and new

fluorescent components are not being formed. Coagulation assessment based on PARAFAC

application to the differential excitation-emission matrices (DEEM), representing the por-

tion of fluorescence removed after treatment, is introduced in this paper along with the

volumetric evaluation of the components present in a sample as an alternative approach to

determine their relative contribution. Volumetric analysis revealed a predominance of

humic components, constituting about 80% in the raw and treated water. Results of the

DEEM model indicated that the most amenable component to be removed by coagulation

(removal w50%) at full scale operation is a humic-like fluorophore with predominance in

the raw water, while removal of the protein-like components was about 30%. Results also

show that the PARAFAC sample loadings exhibit a higher association with the total EEM

signal in the raw and treated water samples when compared with alternative analysis

atter; DEEM, differential excitationeemission matrix; PARAFAC, parallel factor analysis;, natural organic matter; DBP, disinfection by-products; TOC, total organic carbon; FRI,itationeemission matrix; DWTP, drinking water treatment plant; DOC, dissolved organicm; DI, deionized; IFE, inner filter effect; RSD, relative standard deviation; R.U, Raman units;l treated water model; CM, raw-treated combined water model; IDF, individual differentialcomposition; SSE, sum of squared error; OFI, overall fluorescence intensity; AU, Arbitrarytion; DEEMs, differential excitationeemission matrices (EEMraweEEMtreated).4; fax: þ1 330 972 6020.anchez).ier Ltd. All rights reserved.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01680

techniques. These results support the analysis of the PARAFAC components present in the

raw and treated samples as a viable measure for assessment of the coagulation process in

a drinking water treatment plant.

ª 2012 Elsevier Ltd. All rights reserved.

1. Introduction present in the sample (Bro, 1997). The basic formulation of

Natural organicmatter (NOM) present in freshwaters has been

defined as a complex and not fully characterized mixture of

a variety of aliphatic and aromatic compounds with a broad

range of molecular weights (�Swietlik and Sikorska, 2005).

Composition and characteristics of the aquatic NOM are

highly location-dependent and are determined by the source

of the organicmatter, the water chemistry and environmental

conditions (e.g., temperature and pH) and the biological pro-

cesses occurring in the water source (Leenheer and Croue,

2003). NOM plays a major role in any drinking water treat-

ment facility because of its influence on the performance of

treatment stages such as the coagulation-flocculation process

(Edzwald, 1993; Owen et al., 1995; Rebhun and Lurie, 1993) and

the formation of disinfection by-products (DBP) derived from

the NOM-chlorine reaction in the disinfection step (Rook,

1974). NOM removal optimization in a drinking water treat-

ment plant requires an understanding of its specific charac-

teristics and seasonal variability in the water source, which

will likely determine its treatability (Parsons et al., 2004).

Fluorescence spectroscopy analysis, based on the presence

of fluorophores associated with humic-fulvic and protein like

compounds, has emerged among other techniques as a rapid

characterization tool with high sensitivity towards NOM,

requiring minimal sample preparation and with potential for

on-line implementation (Ahmad and Reynolds, 1999; Belzile

et al., 2006; Carstea et al., 2010; Hudson et al., 2007). Fluo-

rescence spectroscopy offers insight on the characteristics

and composition of the NOM unlike aggregate parameters

typically used in drinking water treatment plants (e.g., total

organic carbon (TOC) and ultraviolet absorbance) (Bridgeman

et al., 2011; Leenheer and Croue, 2003).

Excitationeemission pairs (peak picking) (Coble, 1996),

fluorescence indexes (Hood et al., 2003; Huguet et al., 2009;

McKnight et al., 2001), fluorescence regional integration (FRI)

(Chen et al., 2003) and Parallel Factor (PARAFAC) analysis

(Stedmon and Markager, 2005; Stedmon et al., 2003) have been

used as the main methods to analyze the fluorescent signal

emitted by the fluorophoresmoieties present in theNOM. Peak

location tracking constitutes a useful approachwhen a limited

number of excitationeemission pairs are being monitored;

however, as excitationeemissionmatrices (EEM)have emerged

as a rapid and standard fluorescence procedure (Coble, 1996),

FRI and PARAFAC have become more common techniques for

the analysis of the specific fluorescence information.

PARAFAC offers a higher degree of information about the

specific components in the EEM (Baghoth et al., 2011) and

approaches to the modeling of the excitationeemission ma-

trix by considering this as the result of the contribution of the

fluorescence of a specific number of different fluorophore

groups (components) with common spectral characteristics

PARAFAC has been well-documented in previous studies

(Baghoth et al., 2011; Stedmon et al., 2003). Each PARAFAC

component is the product of three factors: a sample, an

excitation and an emission loading. The contribution of the

different components present in a sample explains the fluo-

rescence intensity at any excitationeemission pair in the EEM.

According to this, the number of components that contribute

to the fluorescence in the sample; an estimate of the excita-

tion/emission spectra (excitation and emission loadings) of

each component and a respective estimate of the concentra-

tion of each fluorophore group in the form of the samplemode

loading (sample scores) can be obtained by PARAFAC.

A limited number of studies have reported the use of flu-

orescence analysis for the assessment of engineered systems

including drinkingwater treatment processes (Ishii and Boyer,

2012). Excitationeemission pairs (Cheng et al., 2004) and

location and intensity change of the peaks in the EEM before

and after coagulation have been used to evaluate the perfor-

mance of this process at different pH levels in a full scale plant

(Bieroza et al., 2011a; Bieroza et al., 2010) and at laboratory

scale (Gone et al., 2009). Analyses of the EEM at different stages

in full scale water treatment plants located in UK using sur-

face waters were conducted based on the variation of the in-

tensity of the peaks and shifts in the location of their

excitation-emission maxima (Bieroza et al., 2009b, 2011a).

Self-organizing maps and comparison of different analysis

methods applied to fluorescence data collected at full-scale

operation in UK have also been reported for the study of the

performance of the drinking water treatment processes

(Bieroza et al., 2009a, 2011b; Bieroza et al., 2012). The use of

PARAFAC for the study of the fate of the NOM in drinking

water treatment plants was also reported for a combined set

of samples collected at two treatment facilities in the Neth-

erlands (Baghoth et al., 2011). PARAFAC components in sets

and subsets of water and leachate samples have been repor-

ted previously suggesting that the degree of similarity in the

retained components can differ for different data sets and

according to the number of fluorophore groups included in the

model (Beggs, 2010; Bieroza et al., 2011b; Bieroza et al., 2012).

To the best of our knowledge and with the exception of some

short-term monitoring studies conducted exclusively during

summer periods (Johnstone et al., 2009; Pifer and Fairey, 2012),

PARAFAC has not been commonly reported as an alternative

for the process monitoring of drinking water treatment plants

(DWTPs) in the United States and no long-term fluorescence

studies accounting for the seasonal variability inherent to the

NOM character have been reported.

The purpose of this study was to conduct a comprehensive

assessment of the components resulting from PARAFAC

analysis of a large set of samples collected before and after

coagulation-filtration in a full scale drinking water treatment

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1681

plant. The study explored the generation and validation of

independent PARAFACmodels on raw and treated water data

sets and the comparison of the resulting components based

on a UMC analysis in order to establish if from a PARAFAC

perspective, notable changes could be observed in the NOM

character after treatment or if new fluorescence components

could be detected. Significant changes in the NOM structure

after treatment would imply that the use of PARAFAC models

based on combined data sets (e.g., raw and treated water

samples) to track the NOM fate in a drinking water treatment

train should be evaluated. To address this point, components

from a composite PARAFAC model fit on the combined raw

and treated water data set were also analyzed on a UMC basis.

A novel approach based on PARAFAC application to the dif-

ferential fluorescence matrices and the examination of the

volume based distribution of the components in the raw and

treated water was used to determine the coagulation effect on

the components identified in the water before treatment and

to establish the changes in the distribution of the fluorophores

in the DOM pool. A further objective was to study the associ-

ation between the total fluorescence signal in the EEM and the

PARAFAC sample loadings when compared with the normal-

ized volumes obtained through FRI application. Specific

capability of both approaches to describe the EEM signal for

a large set of raw and treated water samples was examined.

2. Materials and methods

2.1. Sample collection

Sampling was conducted during 32 months comprised be-

tween October 2009 and July 2012. Raw and filter effluent

water samples were obtained one to three times a week at the

Ravenna DWTP located in Ravenna (Ohio). The treatment

process consists of a typical train comprising pre-oxidation

(e.g., potassium permanganate and chlorine dioxide), rapid

mix (e.g., coagulant addition), clarification, filtration and

chlorination. Ferric chloride was used as coagulant for an

average treated water flow around 7.6 ML/d.

Raw water samples were obtained from Lake Hodgson

(Ravenna, OH), which serves as the water source for the water

treatment facility. No previous treatment was applied to the

raw water samples. Treated water samples were collected

after filtration before any final chlorine application. Samples

were collected in Teflon-lined caps amber bottles and stored

at 4 �C for a maximum of 3 days until analysis. Samples were

filtered through 0.45 mm Whatman nylon membrane filters

and analyzed for dissolved organic carbon (DOC), pH, ultra-

violet absorbance at 254 nm (UV254) and fluorescence excita-

tioneemission matrices.

2.2. Raw and treated water characterization

Non-purgeable dissolved organic carbon was measured using

a TOC-5000A Total carbon analyzer (Shimadzu, Japan). Sam-

ples were acidified to pH 2with 1 MHCl and spargedwith pure

air for removal of the purgeable fraction of the inorganic

carbon. DOC levels varied from 4.5 to 7.8 mg/L (mean:

5.64 � 0.57) and 2.9e6.7 mg/L (mean: 4.10 � 0.56) for the raw

and treated water respectively during the sampling period.

Ultraviolet absorbance at 254 nmwas determined using a 1 cm

quartz cell in a 1601 UVevisible spectrophotometer (Shi-

madzu, Japan) and ranged from 0.10 to 0.14 cm�1 (mean:

0.12 � 0.009) and 0.05e0.1 cm�1 (mean: 0.073 � 0.01) for the

raw and treated water respectively. Accumet Basic AB15 pH

meter (Fisher Scientific, USA) was used for the pH measure-

ments. Raw and treatedwater pH varied from 6.9 to 8.3 (mean:

7.6 � 0.3) and 7.6 to 8.6 (mean: 8.4 � 1.7) respectively.

2.3. Fluorescence analysis and data processing

Filtered raw and treated water samples were diluted (1:2 ratio)

using deionized (DI) water in order to prevent any inner

filter effect (IFE). DI water was obtained using a Barnstead

ROpureLP system (Barnstead/Thermolyne, USA). The ultravi-

olet absorbance spectrum between 220 and 400 nm was

recorded for the diluted raw and treated water samples. The

spectra showed levels of absorption below 0.1 cm�1 in the

specific wavelength range and therefore no corrections for IFE

were applied (Lakowicz, 2006; Larsson et al., 2007). Ionic

strength and pH of the filtered samples were adjusted ac-

cording to procedures presented previously (Baghoth et al.,

2011; Chen et al., 2003; Westerhoff et al., 2001). Ionic strength

was adjusted by addition of a 1 M KCl solution resulting in

0.01 M KCl. Sample pH was adjusted to 3.0 � 0.15 using 0.01 M

H2SO4.

Fluorescence excitation-emission matrices were recorded

using an F-7000 fluorescence spectrophotometer (Hitachi,

Japan). Excitation and emission wavelength ranges were set

from 204 to 404 nm and 290e550 nm respectively. Excitation

and emission scanning intervals were set at 5 nm and 2 nm

respectively. Scan speed was set at 60,000 nm/min (Table S1),

and excitation and emission slit widths were fixed at 10 nm.

Photomultiplier detector voltage was fixed at 400 V. Instru-

ment spectral corrections were applied according to the

manufacturer instructions (e.g., Rhodamine B as the quantum

counter for excitation spectrum and quartz diffuser for emis-

sion spectra) in order to have corrected excitation and emis-

sion spectra accounting for specific instrumental response. A

blank solution consisting of DI water with adjusted pH and

ionic strength according to the sample preparation protocol

was examinedwith each set of samples for analysis. The blank

was subtracted fromthe sample 3Dscan in order to account for

the effects of the KCl and acid added to the sample and to

remove Raman and Rayleigh scatter. Variation in the lamp

light intensity was measured by recording the Raman peak

area for DI water at an excitation wavelength of 349 nm

(emission wavelength range: 376e420 nm) each day the in-

strument was used. Relative standard deviation (RSD) for this

parameter was below 5% for the period of analysis. As an

additional quality control for the spectrophotometer oper-

ation, a quinine sulfate solution (7 mg/L in 0.1 M H2SO4) was

excited at 310 nm and fluorescence intensity was recorded at

450 nm each day prior to the analysis of the corresponding set

of samples. RSD for the intensity of this solutionwas below 5%

during the period of analysis. Regions of elastic and inelastic

scatter (first and second order Rayleigh and Raman scatter) in

the EEMs were replaced with missing values covering 20 nm

beyond the limit of the scatter area. Results of the Raman peak

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01682

area for the day of analysis of the sample were used to nor-

malize the results of the fluorescence intensity and therefore

all fluorescence results in this paper are reported as Raman

Units (R.U).

2.4. PARAFAC application

N-way v.3.00 Toolbox (Andersson and Bro, 2000) and DOM-

Fluor v.1.7 Toolbox (Stedmon and Bro, 2008) for MATLAB were

used to fit four different and independent PARAFAC models

over a data set comprising 688 water samples. MATLAB

R2009a (Mathworks, USA) was used to perform the modeling

task.

The first and second models were fit exclusively over the

raw (individual-raw model-IR) and treated water (individual-

treated model-IT ) samples respectively. A third model was

generated from the composite data set comprising the raw

and treated water samples (composite model-CM ). A fourth

model was fit on the set of differential fluorescence matrices,

representing the portion of the fluorescence signal being

removed by the coagulation-filtration process (individual-

differential fluorescence model-IDF ). Excitation wavelengths

below 224 nmwere removed from themodel, considering that

wavelengths lower than 220 nm are usually associated with

high levels of noise and do not contribute relevant fluo-

rescence information (Stedmon et al., 2003; Yamashita and

Tanoue, 2003). A triangle of zeros was included in the region

of higher excitation and lower emission to increase the speed

of the calculation (Stedmon et al., 2003; Thygesen et al., 2004).

The number of samples in the initial data sets, the outliers

and the final number of samples used in each model after

outliers were removed are presented in Table 1. Samples with

unusual fluorescence signal (e.g., extremely high or low in-

tensity and atypical contour plots when compared with his-

torical data) were classified as outliers and removed from the

data set. Further examination of the residual variance and

leverage values after preliminary PARAFAC application was

also conducted for determining potential outliers.

One to six components were retained for each PARAFAC

model. Non-negativity constraints to the excitation, emission

and samples modes were applied. Random values and sin-

gular value decomposition (SVD) were used for initialization

of each model. The convergence to a unique solution for

multiple runs was examined in each case and used as

Table 1 eWater samples included in each data set for the fittinOctober 2009 to July 2012.

Model Number of samples-initi

Individual-raw (IR) 344

Individual-treated (IT) 344

Composite (CM) 688

Individual-differential fluorescence (IDF) 344

a Only 29 samples (8 raw and 21 treated) were identified as outliers in the e

these outliers were also removed from the data set before fitting the CM

fluorescence removal. As samples for two dates were common outliers i

excluded.

b Outliers removed from the IDF model corresponded to 27 pairs of raw

Total number of samples excluded was 54.

a criterion showing the adequacy of the model and its con-

vergence to a global minimum. Model validation based on

split half analysis (Harshman and DeSarbo, 1984) was con-

ducted. Each data set was divided in two sub-datasets (first

and second randomhalves) and an independentmodel was fit

over each new group of samples. Concordance of the excita-

tion and emission loadings for the sub-datasets indicates that

true components pertaining to the entire group of samples are

being retained and that they are not a product of noise but

systematic variation in the data set (Andersen and Bro, 2003;

Harshman and DeSarbo, 1984; Stedmon et al., 2003).

Core consistency diagnostic (CORCONDIA) (Bro, 1998) was

used as an additional criterion to select the appropriate

number of components to be included in the differentmodels.

CORCONDIA evaluates the degree of trilinearity of the PAR-

AFAC loadings by comparison of the least squares Tucker3

(Tucker, 1966) core calculated for these and a superdiagonal

core of ones according to the assumption that the model can

be represented as a constrained Tucker3 model with the core

corresponding to a superdiagonal of ones (Andersen and Bro,

2003; Bro and Kiers, 2003). Core consistency, expressed as

a percentage, indicates the degree of fitting of the Tucker3

core with respect to the assumption of the model (Bro and

Kiers, 2003). Core consistency generally decreases as the

number of components in themodel increases, being 100% for

a model including one component and exhibiting a significant

reduction when a component is added after the appropriate

number of constituents has been reached (Bro and Kiers,

2003). As final criteria, the examination of the meaningful-

ness of the resulting excitation and emission loadings and the

evaluation of the sum of squared error (SSE) of the integrated

spectra on the excitation and emission side (Stedmon and

Markager, 2005) were used to select the number of compo-

nents to be retained in each model.

3. Results and discussion

3.1. PARAFAC components

Fig. 1 presents the explained variance and core consistency

values determined for the IR, IT, CM and IDF models varying

from one to six components. Explained variance ranged

g of independent PARAFACmodels. Samples collected from

al Removed outliers/samples Number of samples-final

8 336

21 323

54a 634

27b 317

ntire data set. However, the respective treated/rawwater samples for

model in order to have consistent pairs of samples for calculation of

n the raw and treated water data sets, 54 instead of 58 samples were

and respective treated water samples (54 samples removed in CM).

1 2 3 4 5 60

25

50

75

100

1 2 3 4 5 60

25

50

75

100

1 2 3 4 5 60

25

50

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100

Number of components

Exp

lain

ed v

aria

nce/

Cor

e co

nsis

tenc

y (%

)

1 2 3 4 5 60

25

50

75

100

Explained varianceCore consistency

a)

d)

b)

c)

Fig. 1 e Explained variance and core consistency for PARAFAC models with different number of components fitted to the

individual and composite data sets. (a) Individual-raw water (IR model), (b) individual-treated water (IT model), (c) combined

raw-treated water (CM model), (d) individual-differential fluorescence (IDF model).

0

0.2

0.4

0.6

0.8

Excitation/Emission wavelength (nm)

0

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Entire data set

First half

Second half

a) b)

C1

C2

C3

C4

C1

C3

C4

C2

Fig. 2 e Split half validation of the four components

obtained through PARAFAC application for (a) composite

data set (CM model) and (b) individual-differential

fluorescence data set (IDF model). Excitation and emission

loadings located to the left and right side respectively.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1683

between 97.8 and 99.8%, 97.5 and 99.7%, 97.6 and 99.8% and

95.6 and 99.3% for the individual-raw, individual-treated,

composite and individual-differential fluorescence model

respectively. The inclusion of a fifth component caused

a reduction in the core consistency from 77.4 to 21.5%, 83.2 to

23.3%, 83.1 to 13.1% and 89.5 to 36.8% for the IR, IT, CM and IDF

model respectively. As shown in Fig. 1, for all models the

largest decrease in core consistency occurred when adding

a fifth component. The model convergence to a unique solu-

tion, an explained variance over 99.5% for most of the data

sets and the significant reduction in the core consistency

when a fifth component is included in the models, indicated

that four components are adequate for explaining these spe-

cific data sets. SSE results, obtained when the integrated

excitation and emission spectra were evaluated for models

containing from 2 to 6 components, support the inclusion of

only four components in the independently generated PAR-

AFAC models.

From a validation perspective, models includingmore than

four components could not be validated through the split half

technique for any of the data sets, indicating that as shown by

the core consistency analysis, four components adequately

explain the variability in each set of samples. Fig. 2 presents

the results of the split half analysis validation for the CM and

IDFmodels. Overlapping of the spectra for the first and second

half and the entire data set demonstrates that the four com-

ponents are valid explaining the fluorescence signal for the

samples and correspond to real constituents. Spectral char-

acteristics of the retained components include a broad emis-

sion spectrawith a singlemaximum, and an excitation spectra

with multiple maxima as exhibited by NOM fluorophores

reported by previous studies (Hall et al., 2005; Stedmon and

Markager, 2005; Stedmon et al., 2003; Yamashita and Tanoue,

2003). Similar validation results were obtained for each of the

fourmodels. Four components were retained for each data set

because core consistency, explained variance, algorithm con-

vergence to a repeated single output, meaning of the spectral

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01684

loadings and SSE of the integrated spectra supported the se-

lection of this number of constituents. Core consistency and

explained variance were 77.4 and 99.8, 83.2 and 99.6, 83.1 and

99.7 and 89.5 and 99.0% for the IR, IT, CM and IDF models

respectively.

Fig. 3 compares the contour plots of the excitation emis-

sion matrices representing each one of the components

retained for the individual and composite data sets models,

and Fig. 4 presents the contour plots of the components

retained for the individual-differential fluorescence model.

Although a visual comparison is possible (Figs. 3 and 4), no

definite conclusions can be drawn regarding the degree of

similarity of the EEM for the retained components in each data

set until a quantitative assessment is conducted. Uncorrected

Matrix Correlation (Burdick and Tu, 1989) has been previously

used as a measure of the spectral overlapping of extracted

and reference components in multicomponent systems; to

examine the effect of micellar media and solvents on the

fluorescence analysis of coal liquids and to compare the effect

of parameters such as the concentration, pH and ionic

strength on the fluorescence analysis of humic substances

(Burdick and Tu, 1989; Hertz and McGown, 1992; Millican and

McGown, 1989; Mobed et al., 1996). The UMC calculation and

its mathematical formulation have been presented in detail

elsewhere (Burdick and Tu, 1989). Complete coincidence of

two spectra under comparison corresponds to a UMC of 1 with

a lower limit of 0 when the overlap is null. Table 2 presents the

inter-model comparison for the components retained for each

data set. UMC values above 0.980 were observed for compo-

nents one to four (C1 to C4) in most of the models, indicating

a high degree of overlapping and only minor variations in the

3D scans representing the components obtained after fitting

Excitation w

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IR ModelUMC=0.970

UMC=0.993

UMC=0.994

CM Model

IT Model

C1

C3 UMC=0.999

UMC=0.987

UMC=0.998

Fig. 3 e Contour plots and uncorrected matrix correlation (UMC)

the individual-raw model (IR), individual-treated model (IT) and

of the independentmodels. As presented in Table 2, an overall

inter-model comparison for each component shows an aver-

age UMC above 0.980 with RSD not exceeding 2% in any case.

UMC values corresponding to the comparison between IR and

ITmodels were above 0.993 for C1 to C4 showing a high degree

of resemblance between the independently identified com-

ponents in these data sets. Lower UMC levels were noticed

when C4 in the IDF model was compared with the last com-

ponent retained in the IR, IT and CMmodels. Considering that

C4 is located in a region of short wavelengths, some instru-

mental noise is likely included in this component and sup-

pressed when the differential EEMs are obtained, which could

explain the moderate UMC values. Assessment of the degree

of variability of the contribution of C4 to the fluorescence in

the raw and treatedwater during the period ofmonitoring will

provide additional support for this observation (Section 3.2).

3.2. Treatment process effect on fluorophores

Visual examination of Figs. 3 and 4 and the low UMC values

obtainedwhencomponents 1 and2 (C1 andC2) in the IR, IT and

CM models are compared with the IDF model (Table 2) in-

dicates a change in the order of these components for this last

model (i.e., C1 and C2 in IR, IT and CM are C2 and C1 in IDF

respectively). Considering that components are ordered ac-

cording to their contribution to the explained variance of the

fluorescence signal in the data set (e.g., C1 has the maximum

contribution to the overall variance in the set of samples),

Component 2 in the rawwater appears as themain constituent

in the data set explaining the fluorescence portion that is

removed by the treatment process (IDF model). However,

a higher contribution to the explained variance and higher

avelength (nm)

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350

400

450

500

550

UMC=0.994

UMC=0.993

C2

C4

UMC=0.987

UMC=0.999

UMC=0.990

UMC=0.998

based comparison of PARAFAC components determined in

composite model (CM).

250 300 350 400

300

350

400

450

500

550

250 300 350 400

300

350

400

450

500

550

Excitation wavelength (nm)

Em

issi

on

wav

elen

gth

(nm

)

250 300 350 400

300

350

400

450

500

550

250 300 350 400

300

350

400

450

500

550

C1

C3 C4

C2

Fig. 4 e Contour plots of the components identified through

PARAFAC application to the individual-differential

fluorescence data set (IDF model).

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1685

sample loadings do not necessarily imply that the first com-

ponent presents the highest concentration in the sample,

considering that a calibration procedure has not been per-

formed. As the sample loadings can not be compared on an

inter-component basis, a more certain measure of the distri-

bution of the components in the differential EEMs can be

obtained using the volume of the components in the sample

and its relative distribution. The volume under the 3D surface

(i.e., EEM) corresponding to each component in a specific

Table 3 e Spectral characteristics of the components retained

Component Excitation wavelengthof maximum intensity (nm)a

1 224 (314)

2 <224 (344)

3 <224 (289)

4 <224(279)

a Values in parentheses show the secondary excitation maxima.

Table 2 e Uncorrected Matrix Correlation (UMC) for the compoand composite data sets.

Component Model compariso

IR/IT IR/CM IR/IDF IT/CM

1 0.993 0.970 0.639 (0.971)b 0.987

2 0.993 0.987 0.421 (0.980)c 0.990

3 0.994 0.999 0.988 0.998

4 0.994 0.999 0.971 0.998

a IR, IT, IDF and CM refer to the models fit over the raw water, treated wa

CM as an example, presents the UMC obtained by comparison of the PAR

b Value in parentheses shows the comparison between component 1 in

c Value in parentheses shows the comparison between component 2 in

sample was calculated using a Riemann sum algorithm. Once

the individual volumes for the components were calculated,

their relative distribution was established for each sample.

Volume based distribution of the PARAFAC components in the

IDF model showed that C1, C2, C3 and C4 correspond to an

average of 53.9, 33.9, 10.8 and 1.40% respectively for the 32

month sampling period. This indicates that C2 in the IR, IT and

CM models (C1 in the IDF model) is the most amenable to be

removed through the specific coagulation treatment. Although

itwouldbepossible todraw the sameconclusionby comparing

the average of the samples loadings for C1 and C2 in the

samples of the entire data set, the PARAFAC application to the

differential EEMarrangement offers a novel approachdiffering

from a sample by sample basis and allowing insight on the

composition of the portion of the DOM being removed during

treatment.

Fitting of the individual-differential fluorescence model

(IDF) also offers a visual perspective that demonstrates the

fact that all four components identified in the raw water data

set are being removed by the treatment process following the

order C2 > C1 > C3 > C4. The absence of one or more of the

components identified in the rawwater in the IDFmodel could

indicate a highly recalcitrant component(s) to be removed

through coagulation.

Table 3 presents the spectral features of the retained

components and their character compared toprevious studies.

Components 1 and 2 have a single emissionpeakwithmaxima

above 398 nm and two excitation maxima. The first compo-

nent is comparable to humic-like components previously

reported by Stedmon and colleagues, corresponding to com-

ponent 4 (Stedmon et al., 2003) and resembling components 3

and 6 in Stedmon and Markager (2005) particularly in the

after application of PARAFAC to the composite data set.

Emission wavelengthof maximum intensity (nm)

Character

398 Humic like

466 Humic like-

terrestrial origin

344 Protein like

294 Protein like

nents determined by PARAFAC application over individual

na Average RSD (%)

IT/IDF IDF/CM

0.574 (0.981)b 0.447 (0.994)b 0.983 1.07

0.476 (0.978)c 0.470 (0.997)c 0.987 0.75

0.984 0.988 0.992 0.61

0.960 0.964 0.981 1.83

ter, differential fluorescence and composite data sets respectively. IR/

AFAC component 1 retained in the IR and CM models.

the IR, IT and CM models and component 2 in the IDF model.

the IR, IT and CM models and component 1 in the IDF model.

Table

4e

Distributionofth

ePARAFACco

mponents

inth

era

wandtreatedwateraccord

ingto

CM

modelresu

lts.

a,b

Com

ponent

Average

F max(R.U

)Average

F maxrem

oval(%

)Averageco

mponent

distributione

F maxbasis(%

)Average

volum

e(�

10�3)(nm

)Averageco

mponent

distributionevolum

ebasis(%

)Average

volum

ereduction(%

)

Raw

water

Treatedwater

Raw

water

Treatedwater

Raw

water

Treatedwater

Raw

water

Treatedwater

10.538�

0.047

0.334�

0.082

37.9

�12.7

37.3

�5.2

38.4

�7.8

3.45�

0.30

2.14�

0.52

39.4

�1.7

43.0

�2.7

37.9

�12.7

20.386�

0.047

0.193�

0.045

50.0

�9.9

26.8

�4.9

22.2

�5.0

3.94�

0.48

1.97�

0.46

44.9

�3.1

39.5

�3.3

50.0

�9.9

30.323�

0.071

0.204�

0.075

36.9

�19.3

22.4

�3.9

23.4

�6.3

1.28�

0.28

0.81�

0.29

14.6

�2.8

16.2

�4.3

36.9

�19.3

40.194�

0.192

0.139�

0.129

28.3

�26.5

13.5

�10.2

16.0

�13.0

0.090�

0.088

0.064�

0.060

1.02�

0.97

1.29�

1.2

28.3

�26.5

Total

1.44

0.870

39.5

100

100

8.76

4.98

100

100

43.1

aCM

refers

toth

ePARAFACmodelfitonth

eraw-treatedwaterco

mbineddata

set(n

¼634watersa

mples).

bResu

ltsco

rresp

ondto

averagevaluesforth

e32month

samplingperiod.Associatedstandard

deviationis

includedforeach

value.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01686

emission side. Component 1 also mimics a fulvic-like compo-

nent reported previously for Occoquanwatershed in Northern

Virginia (Holbrook et al., 2006) and humic-like components

found in previous studies in anOhio reservoir (Johnstone et al.,

2009) and lakes in the Yungui Plateau in China (Zhang et al.,

2010). Component 2 shows high resemblance with terres-

trially derived humic acid components reported previously

(Holbrook et al., 2006; Stedmon et al., 2003; Stedmon et al.,

2007; Zhang et al., 2011; Zhang et al., 2010) and has some cor-

respondence with the fulvic acid fluorophore group found by

Stedmon and Markager (2005), although the emission and

excitation maxima are blue shifted. Components 3 and 4 are

located in the simple protein region according to the bound-

aries defined by Chen and colleagues (Chen et al., 2003).

Component 3 resembles protein like compounds, specifically

tryptophan, reported in previous studies (Stedmon and

Markager, 2005; Stedmon et al., 2003; Yamashita and Jaffe,

2008; Yamashita and Tanoue, 2003). Component 4 shows

agreement with tyrosine like components previously reported

(Coble, 1996; Hall et al., 2005; Stedmon and Markager, 2005;

Yamashita and Tanoue, 2003; Zhang et al., 2010).

Component distribution in the raw and treated water was

evaluated in terms of the fluorescence maximum intensities

(Fmax) and using a new volumetric approach. Fmax-based

analysis uses the point of maximum intensity for each com-

ponent in a sample in order to establish the relative con-

tribution of the PARAFAC fluorophore moieties; however, it

does not involve a thorough evaluation of the component

configuration (i.e., a higher Fmax does not necessarily imply

a major presence of the component in the sample). A more

comprehensive approach to determine the predominant

components in the raw water and the variation in the distri-

bution after treatment might be offered by a volume based

quantification. Analysis of the volume under the matrix that

represents each component allows determining its specific

contribution in a particular sample, through examination of

the total volume of the component and not only of a specific

location on it. This approach offers a new basis to overcome

the non-comparability of the sample loadings in an inter-

component basis. Table 4 presents the average component

distribution based on Fmax and on a volumetric basis for the 32

month sampling period. Similar apportionment of the com-

ponents in the raw and treated water was observed. Average

Fmax based distribution corresponded to 64.1% and 60.6% of

humic character components (C1 and C2) in the raw and trea-

ted water respectively, while protein like components (C3 and

C4) represented 35.9 and39.4% in the rawwater andwater after

coagulation-filtration respectively. The average volumetric

based distribution showed that the aggregate contribution of

C1 and C2 was 84.3 and 82.5% in the raw and treated water

respectively. Protein-like components (C3 and C4) constituted

an average of 15.6 and 17.5% in the water before and after

treatment respectively. Thevariability in thedistributionof the

components in the raw and treated water during the period of

monitoring is included in Table 4. Standard deviation asso-

ciated to the component distribution indicates minor varia-

bility for the humic-like substances in the raw water,

particularly in terms of the volumetric contribution. Higher

levels of fluctuationwere observed for C1 and C2 in the treated

water, while fluorophores with protein character, specifically

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1687

C4, exhibited the highest variation in their contribution to the

water before and after coagulationefiltration.

The volume based distribution indicates that C2 is the

predominant component in the raw water, while C1 consti-

tutes the major portion in the treated water. This differs from

what can be observed based on the Fmax results, which show

that C1 is the principal component in the raw and treated

water. According to the Fmax-based distribution, C3 in the

treated water presents a higher contribution to the fluo-

rescence when compared with C2. However, the volumetric

results reveal a predominance of C2 when compared with C3

in this data set. Volumetric results are in accordance with the

output of the IDF model regarding the predominant removal

of C2 in the treatment process.

Average reduction in Fmax and component volume after

treatment corresponded to 38.0, 50.0, 36.9 and 28.3% for C1 to

C4 respectively. As Fmax and component volumes are exten-

sions of the sample loadings, it is expected that the results in

terms of removal should be in agreement. These removal

levels concur with the order of the components observed in

the IDF model, although the outcome of this model should be

interpreted in terms of removed fluorescence and not on

a percentage basis. Results indicate that C2, a componentwith

humic character and terrestrial origin is themost amenable to

be removed through coagulation (50%) followed by another

humic-like component (C1) whose fluorescence signal

decreased approximately 38%. Protein-like components were

impacted the least during the treatment process with removal

levels of 36.9% and 28.3% for C3 and C4 respectively. The

preferential removal of humic-like components was expected

and concurs with the results of previous coagulation studies

based on PARAFAC application (Baghoth et al., 2011; Beggs,

2010; Beggs and Summers, 2011).

Establishing that independently identified components in

the raw and treated water data sets are similar (Table 2), in-

dicates that minimal transformation beyond physical removal

is taking place in the coagulationefiltration process. New

PARAFAC components are not being formed and the four

components present in the raw water without any pre-

treatment are amenable to be removed by coagulation-

filtration according to the IDF model results. The high degree

of similarity observed for the PARAFAC components in the in-

dependent data sets of raw and treated water was a generally

untested but presumed condition in studies using PARAFAC

(i.e., Fmax) for the evaluation of the NOM behavior in a drinking

water treatment plant. An important contribution of this study

is that this assumption has been proved quantitatively using

a large set of samples collected in a frequency that reflects the

seasonalvariabilityof thewatersourceand thepossiblevarying

conditions in the treatment process. Average values for the

fluorescenceremovalanddistributionof thecomponents in the

raw and treated water during the sampling period have been

presented in thispaper.A furtherand thoughtful analysis of the

temporal variability of the coagulation efficiency and its asso-

ciationwith the seasonal dynamics of the DOMfluorescence in

the raw water will be included in a future publication.

Previous studies have reported a change in the molecular

composition of the remaining NOM after coagulation reflected

in the variation of the fluorescence index (ratio between in-

tensities at 450 and 500 nm at an excitation wavelength of

370 nm) (Beggs et al., 2009) and observed by electrospray ion-

ization Fourier-transform ion cyclotron resonance mass

spectroscopywhenAl and Fe salts were used as coagulants for

a peat leachate (Riedel et al., 2012). FromaPARAFACviewpoint,

a change in the distribution of the components before and

after treatment is established, but only minor variations (as

reflected by the UMC values) are noted in the components

coming from independently fit models (IR and IT models). The

lackof specificity of PARAFAC to reveal the changes in theNOM

observed by Riedel and Bister (2012) might be due to the fact

that this technique decomposes the fluorescence signal in

components representing common fluorophores groups and

therefore gives a more global approach to the NOM constitu-

ents. Also, the fact that the fluorescent moieties only con-

stitute a fraction of the aromatic structures in the NOM must

be considered. Molecular changes in the NOM resulting in new

fluorescence moieties being formed should be reflected in the

IT model in two different ways: (i) the identification of sub-

stantially different components from those retained for the

rawwater data set and (ii) the necessity of inclusion of a higher

number of validated components to explain the variability in

the set comprising the treated water samples. However, as

indicatedby theUMCvalueswhencomponents in the IRand IT

models were compared (Table 2) and according to the final

validation of 4 components in the raw and treated water data

sets, neither a different number of components was identified

in the IT model nor the character of the components in the IT

and IR data sets showed significant differences.

According to these results, PARAFAC might offer a valid

alternative to assess the dynamics and efficiency of the

coagulation process in a full scale drinking water facility. The

feasibility of the use of composite models based on a com-

bined data set (i.e., raw and treated water after coagulation-

filtration) to evaluate the removal levels attained in the

treatment process has been supported by the findings repor-

ted in this paper. However, the application of PARAFAC to

combined data sets in order to determine levels of removal in

systems involving changes in the character of the DOM has to

be further studied.

3.3. Association with the overall fluorescence intensity(OFI)

Alternative approaches to the analysis of the 3D fluorescence

matrices such as the FRI technique could also represent an

option for the evaluation of the coagulation process, as they

allow obtaining a quantitative measure of the EEM for the raw

and treated water. The OFI (Beggs et al., 2009), which corre-

sponds to the total summation of the fluorescence intensities

in an EEM expressed as Raman units (R.U), was used to com-

pare the representativeness of the PARAFAC and FRI ap-

proaches as descriptors of the fluorescence signal in the data

set under analysis.

TheFRI approachpresentedbyChenet al. (2003)wasused to

calculate the total normalized excitation-emission area vol-

ume (FT,n) for the EEMmatrices. This volume is the result of the

summationof theareanormalizedvolumes (Fi,n) of five regions

within operationally defined boundaries that correspond to

different excitation and emission areas within the EEM. The

EEMs used for the calculation of Fi,n are obtained for samples

5 10 15 20 25 30 35

600

800

1000

1200

OFI

(R

.U)

PS

0 0.2 0.4 0.6 0.8 1

600

800

1000

1200

5 10 15 20 25 30 35

200

400

600

800

1000

PS

0 0.2 0.4 0.6 0.8 1

200

400

600

800

1000

R =0.95

R =0.98 R =0.91

R =0.72

b)

c)

a)Raw water Raw water

d)Treated water Treated water

T,n*

T,n* (x 10 ) (nm)-5

(x 10 ) (nm)-5

2 2

22

Φ

Φ

Fig. 5 e Comparison of the representativeness of the summation of the PARAFAC sample loadings (PS)-(a) and (c) and the

total normalized excitation-emission area volume (FT,n*)-(b) and (d) in the description of the overall fluorescence intensity

(OFI) for raw and treated water samples (n[ 317 in each data set). FT,n* is calculated after normalization to Raman units and

at the actual DOC level of the sample.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01688

normalized to DOC¼ 1mg/L and therefore, FT,n is expressed in

units of AU-nm2[mg/L C]�1. As the EEMs of our samples were

determined at the actual DOC levels and fluorescence signal

was normalized to Raman units (nm�1) before calculation of

Fi,n, the reported values of FT,n differ from those that would be

obtained by normalizing the DOC to 1 mg/L and using AU for

the fluorescence intensity. The total normalized excitation-

emission area volume as determined in this paper, has been

labeled as FT,n* and is reported in nm (i.e., R.U-nm2).

The summation of the PARAFAC sample loadings (PS)

(Beggs et al., 2009) for C1 to C4 (i.e., summation of the scores of

all the components present in a sample) was calculated for

each sample as a measure of the PARAFAC findings for the

data set. The linear relationships between the OFI andFT,n* are

presented in Fig. 5b and d) for the raw and treated water

respectively. Fig. 5a) presents the linear association between

the OFI and the PS for the set of raw water samples, while the

respective results for treated water are depicted in Fig. 5c).

PARAFAC sample loadings exhibited better agreement with

the total OFI in each data set. Coefficients of determination

corresponding to 0.95 and 0.98 were observed for the sample

scores summation in the raw and treated water, while lower

levels of association (R2 ¼ 0.72 and 0.91 respectively) were

identified for the correlations including the FRI volumes. This

observation strengthens the idea that when compared with

different approaches, PARAFAC application results better to

explain the characteristic fluorescence signal of samples with

different DOC content in a drinking water treatment train.

4. Conclusions

A long-term monitoring based assessment of the PARAFAC

components before and after coagulation-filtration treatment

in a DWTP has been presented. The generation of different

PARAFAC models, the analysis of the distribution of the

components before and after treatment and the evaluation of

the coagulation effect on the identified fluorophore moieties

lead to the following conclusions:

� Four components are present in each independently fitted

PARAFAC model. Two components of humic character

(C1 and C2) and two components with protein-like nature

(C3 and C4) were found to explain the raw, treated, com-

bined raw-treated and differential fluorescence data sets.

Humic-like constituents were predominant in the raw and

treated water, constituting 84.3 and 82.5% of the samples on

a volumetric basis respectively.

� UMC based analysis demonstrated that from a PARAFAC

perspective, no changes in themolecular composition of the

fluorophores occur and no new components are being

generated in the coagulation process. UMC values also

indicated the feasibility of the use of PARAFACmodels fit on

composite data sets to determine the NOM fate after

coagulationefiltration.

� The PARAFAC models fitted on the differential excita-

tioneemission matrices constitute a valuable tool to study

the effect of the coagulation process on the DOM present in

the raw water. The results of this model allow: (i) deter-

mining the most amenable component to be removed in

a specific treatment process, (ii) establishing the order of

preferred removal and (iii) identifying recalcitrant compo-

nents to be removed in a particular treatment stage.

� The volumetric approach to analyze the contribution of the

PARAFAC components in the raw and treated water, offers

a more comprehensive evaluation of the fluorophore dis-

tribution in the sample than that obtained based on Fmax.

The use of a volumetric analysis might overcome the non-

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1689

comparable character of the sample loadings when differ-

ent components are analyzed.

� The most amenable component to be removed through the

specific coagulation process based on FeCl3 was a humic-

like fluorophore with allochthonous origin (C2), while

protein-like components exhibited the lowest removal

levels.

� The sample loadings summation showed to be an effective

parameter to describe the OFI in the EEMs when compared

with the FRI approach. Higher coefficients of determination

were observed for the PS particularly regarding the raw

water data set.

Appendix A. Supplementary data

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.watres.2012.12.032.

r e f e r e n c e s

Ahmad, S.R., Reynolds, D.M., 1999. Monitoring of water qualityusing fluorescence technique: prospect of on-line processcontrol. Water Research 33 (9), 2069e2074.

Andersen, C.M., Bro, R., 2003. Practical aspects of PARAFACmodeling of fluorescence excitation-emission data. Journal ofChemometrics 17 (4), 200e215.

Andersson, C.A., Bro, R., 2000. The N-way Toolbox for MATLAB.Chemometrics and Intelligent Laboratory Systems 52 (1), 1e4.

Baghoth, S.A., Sharma, S.K., Amy, G.L., 2011. Tracking naturalorganic matter (NOM) in a drinking water treatment plantusing fluorescence excitationeemission matrices andPARAFAC. Water Research 45 (2), 797e809.

Beggs, K.M.H., 2010. Characterizing Temporal and SpatialVariability of Watershed Dissolved Organic Matter andDisinfection Byproduct Formation with FluorescenceSpectroscopy. University of Colorado, Boulder.

Beggs, K.M.H., Summers, R.S., 2011. Character and chlorinereactivity of dissolved organic matter from a Mountain PineBeetle impacted watershed. Environmental Science &Technology 45 (13), 5717e5724.

Beggs, K.M.H., Summers, R.S., McKnight, D.M., 2009.Characterizing chlorine oxidation of dissolved organic matterand disinfection by-product formation with fluorescencespectroscopy and parallel factor analysis. Journal ofGeophysical Research 114 (G4), G04001.

Belzile, C., Roesler, C.S., Christensen, J.P., Shakhova, N.,Semiletov, I., 2006. Fluorescence measured using the WETStarDOM fluorometer as a proxy for dissolved matter absorption.Estuarine. Coastal and Shelf Science 67 (3), 441e449.

Bieroza, M., Baker, A., Bridgeman, J., 2009a. Exploratory analysisof excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organicmatter removal efficiency at water treatment works. Journalof Geophysical Research 114, G00F07.

Bieroza, M., Baker, A., Bridgeman, J., 2009b. Relating freshwaterorganic matter fluorescence to organic carbon removalefficiency in drinking water treatment. Science of The TotalEnvironment 407 (5), 1765e1774.

Bieroza, M.Z., Bridgeman, J., Baker, A., 2010. Fluorescencespectroscopy as a tool for determination of organic matterremoval efficiency at water treatment works. Drinking WaterEngineering and Science 3 (1), 63e70.

Bieroza, M., Baker, A., Bridgeman, J., 2011a. Assessment of low pHcoagulation performance using fluorescence spectroscopy.Journal of Environmental Engineering 137 (7), 596e601.

Bieroza, M., Baker, A., Bridgeman, J., 2011b. Classification andcalibration of organic matter fluorescence data with multiwayanalysis methods and artificial neural networks: anoperational tool for improved drinking water treatment.Environmetrics 22 (3), 256e270.

Bieroza, M., Baker, A., Bridgeman, J., 2012. New data mining andcalibration approaches to the assessment of water treatmentefficiency. Advances in Engineering Software 44 (1), 126e135.

Bridgeman, J., Bieroza, M., Baker, A., 2011. The application offluorescence spectroscopy to organic matter characterisationin drinking water treatment. Reviews in EnvironmentalScience and Biotechnology 10 (3), 277e290.

Bro, R., 1997. PARAFAC. Tutorial and applications. Chemometricsand Intelligent Laboratory Systems 38 (2), 149e171.

Bro, R., 1998. Multi-way analysis in the food Industry. Models,algorithms, and applications. PhD thesis, Royal Veterinary andAgricultural University, Denmark.

Bro, R., Kiers, H.A.L., 2003. A new efficient method fordetermining the number of components in PARAFAC models.Journal of Chemometrics 17 (5), 274e286.

Burdick, D.S., Tu, X.M., 1989. The wavelength componentvectorgram: a tool for resolving two-component fluorescentmixtures. Journal of Chemometrics 3 (2), 431e441.

Carstea, E.M., Baker, A., Bieroza, M., Reynolds, D., 2010.Continuous fluorescence excitationeemission matrixmonitoring of river organic matter. Water Research 44 (18),5356e5366.

Chen, W., Westerhoff, P., Leenheer, J.A., Booksh, K., 2003.Fluorescence excitation�emission matrix regional integrationto quantify spectra for dissolved organic matter.Environmental Science & Technology 37 (24), 5701e5710.

Cheng, W.P., Chi, F.H., Yu, R.F., 2004. Evaluating the efficiency ofcoagulation in the removal of dissolved organic carbon fromreservoir water using fluorescence and ultravioletphotometry. Environmental Monitoring and Assessment 98(1), 421e431.

Coble, P.G., 1996. Characterization of marine and terrestrial DOMin seawater using excitationeemission matrix spectroscopy.Marine Chemistry 51 (4), 325e346.

Edzwald, J.K., 1993. Coagulation in drinking water treatment:particles, organics and coagulants. Water Science &Technology 27 (11), 21e35.

Gone, D.L., Seidel, J.-L., Batiot, C., Bamory, K., Ligban, R., Biemi, J.,2009. Using fluorescence spectroscopy EEM to evaluate theefficiency of organic matter removal duringcoagulationeflocculation of a tropical surface water (Agboreservoir). Journal of Hazardous Materials 172 (2e3), 693e699.

Hall, G.J., Clow, K.E., Kenny, J.E., 2005. Estuarial fingerprintingthrough multidimensional fluorescence and multivariateanalysis. Environmental Science & Technology 39 (19),7560e7567.

Harshman, R.A., DeSarbo, W.S., 1984. In: Law, H.G., Snyder, C.W.,Hattie, J.A., McDonald, R.P. (Eds.), Research Methods for Multi-mode Data Analysis. Praeger, New York, pp. 602e642.

Hertz, P.M.R., McGown, L.B., 1992. Organized media forfluorescence analysis of complex samples: comparison of bilesalt and conventional detergent micelles in coal liquids.Analytical Chemistry 64 (23), 2920e2928.

Holbrook, R.D., Yen, J.H., Grizzard, T.J., 2006. Characterizingnatural organic material from the Occoquan watershed(Northern Virginia, US) using fluorescence spectroscopy andPARAFAC. Science of The Total Environment 361 (1e3),249e266.

Hood, E., McKnight, D.M., Williams, M.W., 2003. Sources andchemical character of dissolved organic carbon across an

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01690

alpine/subalpine ecotone, Green Lakes Valley, Colorado FrontRange, United States. Water Resources Research 39, 1e12.

Hudson, N., Baker, A., Reynolds, D., 2007. Fluorescence analysis ofdissolved organic matter in natural, waste and pollutedwaters e a review. River Research and Applications 23 (6),631e649.

Huguet, A., Vacher, L., Relexans, S., Saubusse, S., Froidefond, J.M.,Parlanti, E., 2009. Properties of fluorescent dissolved organicmatter in the Gironde Estuary. Organic Geochemistry 40 (6),706e719.

Ishii, S.K.L., Boyer, T.H., 2012. Behavior of reoccurring PARAFACcomponents in fluorescent dissolved organic matter in naturaland engineered systems: a critical review. EnvironmentalScience & Technology 46 (4), 2006e2017.

Johnstone, D.W., Sanchez, N.P., Miller, C.M., 2009. Parallel factoranalysis of excitationeemission matrices to assess drinkingwater disinfection byproduct formation during a peakformation period. Environmental Engineering Science 26 (10),1551e1559.

Lakowicz, J.R., September 15, 2006. Principles of FluorescenceSpectroscopy, third ed. Springer.

Larsson, T., Wedborg, M., Turner, D., 2007. Correction of inner-filter effect in fluorescence excitationeemission matrixspectrometry using Raman scatter. Analytica Chimica Acta583 (2), 357e363.

Leenheer, J.A., Croue, J.-P., 2003. Peer reviewed: characterizingaquatic dissolved organic matter. Environmental Science &Technology 37 (1), 18Ae26A.

McKnight, D.M., Boyer, E.W., Westerhoff, P.K., Doran, P.T.,Kulbe, T., Andersen, D.T., 2001. Spectrofluorometriccharacterization of dissolved organic matter for indication ofprecursor organic material and aromaticity. Limnology andOceanography 46 (1), 38e48.

Millican, D.W., McGown, L.B., 1989. Fluorescence lifetimeselectivity in excitation-emission matrices for qualitativeanalysis of a two-component system. Analytical Chemistry 61(6), 580e583.

Mobed, J.J., Hemmingsen, S.L., Autry, J.L., McGown, L.B., 1996.Fluorescence characterization of IHSS humic substances:total luminescence spectra with absorbance correction.Environmental Science & Technology 30 (10), 3061e3065.

Owen, D.M., Amy, G.L., Chowdhury, Z.K., Paode, R., McCoy, G.,Viscosil, K., 1995. NOM characterization and treatability.Journal of the American Water Works Association 87 (1),46e63.

Parsons, S.A., Jefferson, B., Goslan, E.H., Jarvis, P.R., Fearing, D.A.,2004. Natural organic matter e the relationship betweencharacter and treatability. Water Science and Technology:Water Supply 4 (5e6), 43e48.

Pifer, A.D., Fairey, J.L., 2012. Improving on SUVA254 usingfluorescence-PARAFAC analysis and asymmetric flow-fieldflow fractionation for assessing disinfection byproductformation and control. Water Research 46 (9), 2927e2936.

Rebhun, M., Lurie, M., 1993. Control of organic matter bycoagulation and floc separation. Water Science & Technology27 (11), 1e20.

Riedel, T., Biester, H., Dittmar, T., 2012. Molecular fractionation ofdissolved organic matter with metal salts. EnvironmentalScience & Technology 46 (8), 4419e4426.

Rook, J.J., 1974. Formation of haloforms during chlorination ofnatural waters. Water Treatment Examination 23,234e243.

Stedmon, C.A., Bro, R., 2008. Characterizing dissolved organicmatter fluorescence with parallel factor analysis:a tutorial. Limnology and Oceanography Methods 6,572e579.

Stedmon, C.A., Markager, S., 2005. Resolving the variability indissolved organic matter fluorescence in a temperate estuaryand its catchment using PARAFAC analysis. Limnology andOceanography 50 (2), 686e697.

Stedmon, C.A., Markager, S., Bro, R., 2003. Tracing dissolvedorganic matter in aquatic environments using a new approachto fluorescence spectroscopy. Marine Chemistry 82 (3e4),239e254.

Stedmon, C.A., Thomas, D.N., Granskog, M., Kaartokallio, H.,Papadimitriou, S., Kuosa, H., 2007. Characteristics of dissolvedorganic matter in Baltic coastal sea ice: allochthonous orautochthonous origins? Environmental Science & Technology41 (21), 7273e7279.

Thygesen, L.G., Rinnan, A., Barsberg, S., Møller, J.K.S., 2004.Stabilizing the PARAFAC decomposition of fluorescencespectra by insertion of zeros outside the data area.Chemometrics and Intelligent Laboratory Systems 71 (2),97e106.

Tucker, L., 1966. Some mathematical notes on three-mode factoranalysis. Psychometrika 31 (3), 279e311.

Westerhoff, P., Chen, W., Esparza, M., 2001. Fluorescence analysisof a standard fulvic acid and tertiary treated wastewater.Journal of Environmental Quality 30 (6), 2037e2046.

Yamashita, Y., Jaffe, R., 2008. Characterizing the Interactionsbetween trace metals and dissolved organic matterusing excitation�emission matrix and parallel factoranalysis. Environmental Science & Technology 42 (19),7374e7379.

Yamashita, Y., Tanoue, E., 2003. Chemical characterization ofprotein-like fluorophores in DOM in relation to aromaticamino acids. Marine Chemistry 82 (3e4), 255e271.

Zhang, Y., Zhang, E., Yin, Y., Van Dijk, M.A., Feng, L., Shi, Z.,Liu, M., Qin, B., 2010. Characteristics and sources ofchromophoric dissolved organic matter in lakes of the YunguiPlateau, China, differing in trophic state and altitude.Limnology and Oceanography 55 (6), 2645e2659.

Zhang, Y., Yin, Y., Feng, L., Zhu, G., Shi, Z., Liu, X., Zhang, Y., 2011.Characterizing chromophoric dissolved organic matter in LakeTianmuhu and its catchment basin using excitation-emissionmatrix fluorescence and parallel factor analysis. WaterResearch 45 (16), 5110e5122.

�Swietlik, J., Sikorska, E., 2005. Characterization of natural organicmatter fractions by high pressure size-exclusionchromatography, specific UV absorbance and totalluminescence spectroscopy. Polish Journal of EnvironmentalStudies 15 (1), 145e153.