interpretation of historical water in huron county...
TRANSCRIPT
INTERPRETATION OF HISTORICAL SURFACE WATER QUALITY DATA IN
HURON COUNTY ONTARIO, CANADA
A Thesis
Presented to
The Faculty of Graduate Studies
of
The University of Guelph
by
SHELLY N. BONTE-GELOK
In partiaI fulfilrnent of requirements
for the degree of
Master of Science
May, 2001
O Shelly N. Bonte-Gelok, 2001
National Library 1*1 of Canada Bibliothèque nationale du Canada
Acquisitions and Acquisitions et Bibliographie Services services bibliographiques
395 Wellington Street 395, rue Wellington Ottawa O N K1A ON4 Ottawa O N K I A ON4 Canada Canada
Ywr lYe votre r é I é l ~ c 0
Our I% Nane ré f f rmçe
The author has granted a non- exclusive licence allowing the National Library of Canada to reproduce, loan, distribute or sell copies of this thesis in microfonn, paper or electronic formats.
The author retains ownership of the copyright in this thesis. Neither the thesis nor substantial extracts fiorn it may be printed or otherwise reproduced without the author's permission.
L'auteur a accorde une licence non exclusive permettant à la Bibliothèque nationale du Canada de reproduire, prêter, distribuer ou vendre des copies de cette thèse sous la fonne de microfiche/film, de reproduction sur papier ou sur format électronique.
L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la thèse ni des extraits substantiels de celle-ci ne doivent être imprimés ou autrement reproduits sans son autorisation.
ABSTRACT
INTERPRETATION OF HISTORICAL SURFACE WATER QUALITY DATA
IN HURON COUNTY, ONTARIO, CANADA
Shelly N. Bonte-Gelok University of Guelph, 2001
Advisor: Professor D.M. Joy
This thesis investigates surface water quality trends over time with respect to nitrate, total
phosphorus and bacteria and possible correlations between changes in water quality and
agricultural and urban practices. While in some areas water quality measurements show
degradation with the , water quality on local beaches has remained relatively constant, in
spite of public perception of a decline in water quality on these beaches. Speman 's rank
correlation, stepwise regression and principal cornponent analyses were performed and it was
found that nitrate concentration was most often significantly positively correlated to soil
drainage cIass, swine and poultry densities. Faecal coliform concentration was most often
significantly positively correlated to human population density and soil drainage class. Total
phosphorus concentration was most ofien significantly positively correlated to discharge, soi1
drainage class and hurnan population density- Senous data gaps have been identified in the
monitoring and data collection programs.
ACKNOWLEDGEMENTS
F+st, 1 would like to thank Doug Joy for his incredible patience, sense of humour and
tennis balls.
The following organizations were extrernely helpful, and without their CO-operation and
assistance the project would not have been completed: Maitland Valley Conservation
Authority, Huron County Health Unit, Ausable-Bayfield Conservation Authority , Upper
Thames River Conservation Authority and the Ontario Clean Water Agency.
This project was h d e d by the Huron County Environmental Farm Coalition and the
National Soi1 and Water Conservation Program.
I would ais0 like to acknowledge several people that were very generous with their time
and expertise: Isobel Heathcote, Rick Steele, Duane Forth, John Fitzgibbon, Abdel El-
Shaarawi and Ian Wilcox.
Finally 1 would like to thank my farnily and fiends for never doubting that this thesis
would be completed.. . one of these days.
TABLE OF CONTENTS
................................................. INTRODUCTION 1
LITEMTUREREVEW ............................................ 2 2.1 On-Site Wastewater Systems ............................. 3 2.2 Agriculture ........................................... 6 2.3 Surface Water Quality Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Rural Municipalities .................................. 16 2.5 Summary ........................................... 17
OBJECTNES .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
BACKGROUND ................................................. 20 4.1 HuronCouns .............................................. 20
4.1.1 Human Population ................................... - 2 0 4.1.2 Agricultural Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 2 4 4.1.3 Geographical Considerations ........................... - 2 5
4.2 Surface Water Quality Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 2 6
METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.1 Data Collection ............................................ 27
5.1.1 Water Quality Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 2 8 5.1.2 Human Population Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -31 5.1.3 AgriculturalData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.1.4 Geographical Data .................................... 32 5.1 -5 Data Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 3 3
5.2 Available Data & Deficiencies ................................ 35 5.2.1 Lakeshore and Inland Recreational Bathing Site Monitoring . . . 36 5 .2.2 Provincial Water Quality Monitoring Stations .............. 36 5.2.3 Discharge ........................................... 38
. . . . . . . . . . . . . . . . . 5.2.4 Wastewater Treatment Plants and Lagoons 39 5.2.5 Landfills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.2.6 Human Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2.7 Agricultural Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2.8 Geographical Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 4 6 5.2.9 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3 Data Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 4 7 5.3.1 Bivariate Scatter Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 48 5.3.2 Test for Normality Using Skewness . . . . . . . . . . . . . . . . . . . . . . 48
. . . . . . . . . . . . . . . . . . . . . . . . 5.3 -3 Sharpiro-Wilk Test for Normality 49 5.3.4 Data Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
TABLE OF CONTENTS (conhued)
5.3.5 Linear Regression ................................... -50
....................................... 5.4 Correlation Analysis - 50 ..................................... 5.4.1 Spearman's Rho -51
5.4.2 Multiple Regression ................................... 52 ............................ 5 .4.2.1 Stepwise Regression 53
5.4.2.2 Principal Component Analysis ..................... 53
6 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 ....................................... 6.1 Trend Analysis 55
6.1.1 Lakeshore and Mand Recreational Bathing Site Monitoring ............................................. 55
6.1.2 Provincial Water Quality Monitoring Stations . . . . . . . . 61 6.1.3 Discharge ..................................... 71 6.1.4 Waste Water Treatment Plants & Lagoons . . . . . . . . . . . 71
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.5 Landfills -75 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Hurnan Population - 76
6.3 Agriculture .......................................... 78 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Livestock Population 79
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 LandUse 89 . . . . . . . . . . . . . . . . . . . . . . . . 6.3 -3 Other Agricultural Factors 91
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Soi1 Drainage Class 92 6.5 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Correlation Analysis 97 . . . . . . . . . . . . . . . . . . . . 6.6.1 Spearman Correlation Analysis 97
............................ 6.6.2 Multiple Regression 100 6.6.2.1 Stepwise Regression . . . . . . . . . . . . . . . . . . . . . 100 6 .6.2.2 Principal Component Analysis .............. 101
6.6.3 Waste Water Treatment Plants & Lagoons . . . . . . . . . . 102 6.6.4 Landfills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 DISCUSSION 105
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Trends 105 . . . . . . . . . 7.1.1 Lakeshore and Inland Recreational Bathing Sites .. 105
. . . . . . . . . . . . . 7.1 -2 Provincial Water Quality Monitoring Stations 107 ................. 7.1.3 Wastewater Treatment Plants & Lagoons 110
7.1 -4 Hurnan Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 . 7.1.5 Agricultural Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2 Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 7.2.1 Lakeshore and Inland Recreational Bathing Sites . . . . . . . . . . . 124 7.2.2 Provincial Water Quality Monitoring Stations . . . . . . . . . . . . . 116
iii
TABLE OF CONTENTS (contiriued)
7.2.3 Wastewater Treatment Plants & Lagoons . . . . . . . . . . . . . . . . . 120 7.2.4 Landfills ........................................... 122 7.2.5 Precipitation ........................................ 123
8 CONCLUSIONS .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
9 RECOMMENDATIONS .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
................................................. 10 REFERENCES 129
APPENDICES a
A: Calculation of Flows for Provincial Water Quality Monitoring Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A 1
B: Beach Water Quality Data Beach Water Quality Data ....................... B 1
C : Provincial Water Quaiity Monitoring Station Regression Graphs ......................................... C 1
D: Summary and Sarnple Calculations of Basin Loadings ...................... D 1
F: Discharge Data and Regression Results for PWQMS's ...................... F1
G: Surnmary of Waste Water Treatrnent Plant and Lagoon Data . . . . . . . . . . . . . . . . . G1 . H: Agricultural Spills and Drainage Tubing Sales Data . . . . . . . . . . . . . . . . . . . . . . . . Hl
1: Calculation of Septic System Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I l
J: Saugeen Station Raw Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J 1
L IST OF FIGURES
1 : Huron County in Southern Ontario ...................................... - 2 2
2:DetailsofHuronêounty ............................................... 23
3 : Huron County Major Basins ............................................ 33
4: Huron County Swimming Beach Monitoring Network ....................... -58
5: Time Exceeding PWQG: Beaches 1990 to 1998 ............................ 59
6: Nitrate Data 1965 - 1995: Station 4, North Maitland Basin ................... 64
7: Total Phosphorus 1971 - 1994: Station 15, South Maitland Basin .............. 65
8: Basin Ranking based on Overall Average Total Phosphorus Concentrations ...... 68
9: Basin Ranking based on Overall Average Nitrate Concentrations . . . . . . . . . . . . . . 69
10: Basin Ranking based on Overall Average Faecal Colifonn Concentrations . . . . . . 70
1 1 : Basin Ranking based on Population Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
12: Huron County Population 197 1 to 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
13: Animal Populations: 197 1 - 1996 Huron County . . . . . . . . . . . . . . . . . . . . . . . . . . 83
14: Basin Ranking based on 1996 Cattle Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
15: Basin Ranking based on 1996 Poultry Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
16: Basin Ranking based on 1996 Swine Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
17: Basin Ranking based on 1996 Livestock Unit Density . . . . . . . . . . . . . . . . . . . . . . 87
18: Basin Ranking based on Soi1 Drainage Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
LIST OF TABLES
........................................ 1 : Organizations Contacted for Data 28
..................................................... 2: Data Collection - 3 0
................................................... 3 : Data Organization -35
................ 4: Data Available at Provincial Water Quality Monitoring Stations 37
................................................. 5: Flow Gauge Stations - 3 9
..................... 6: Waste Water Treatment Plant & Lagoon Data Coverage - 4 0
a ............................ 7i Landfill Surface Water Quality Monitoring Data 43
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8: Lakeshore Beach Monitoring, 1990 1997 - 5 6
. ...... . . . . . . . . . . . . . . . . . . . . . . 9: Inland Recreational Bathing Areas, 1990 1 997 - 6 0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10: Trends in Concentrations at PWQMS's - 6 2
. . . . . . . . . . . . . . . . . . . . 11: Rank of PWQMS's Based on Loadings & Concentrations 66
. . . . . . . . . . . . . 12: Ranking of W WTP's and Lagoons by Concentration and Loading -72
. . . . . . . . . . . . . . . 13: WWTP and Lagoon Summary of Contaminant Loading Trends - 7 4
14: Cornparison of Landfill Monitoring Data to Provincial Water Quality Guidelines . 76 a
. . . . . . . . . . . . . . . . . . . . . . . . . . 15: Ranking Human Populations Densities 197 1 1996 78
. . . . . . . . . . . . . . . . . . . . . . . . 16: 1996 Livestock Densities and Overall Basin Ranks - 8 8
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17: Changes in Livestock Populations 1971 1996 89
. .* . . . . . . . . . . . . . . . . . . . . . . . . 18: Agriculture Land Use Changes fiom 1971 1996 - 9 0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19: Soi1 Drainage Classes in the Major Basins - 9 3
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20: Yearly Classification of Precipitation 96
vii
LIST OF TABLES (continued)
. . . . . . . . . . 21: Speamian Correlation Results for Overall Population Factor Averages 97
22: Spearman Correlation Coefficients for Other Factors . . . . . . . . . . . . . . . . . . . . . . . -98
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23: Speannan Correlation Results for 1986 99
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24: Spearman Correlation Results for 1991 -99
25: Stepwise Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
26: Principal Component Correlation Results for 1986 . . . . . . . . . . . . . . . . . . . . . . . . . 101
27: Principal Component Correlation Results for 1991 . . . . . . . . . . . . . . . . . . . . . . . . . 102
28: WWTP's & Lagoons and Related PWQMS Data . . . . . . . . . . . . . . . . . . . . . . . . . . 103
29: PWQMS's and Landfiils Water Quaiity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
30: Sumnaary of Signifiant Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
viii
1
Huron
INTRODUCTION
County, Ontario is both a popular tourist destination for many Canadians and
Americans and an area of significant agriculturaI production. These two important aspects
of the local economy are often in conflict because of suspected water quality deterioration
in the County's surface waters. As in many cornrnunities in Canada, the quality of surface
water is a major concern for area residents. In Huron County continuing warnings of unsafe
beaches due to high bacterial counts have heightened these concerns in recent years.
Seasonal residents and cottage owners are concerned that beaches are posted too fiequently,
and have speculated that the primary source of bacterial contamination is local farms. Local
farmers disagree with this assessrnent and have identified malfunctioning or by-passed septic
systems dong the lakeshore as possible sources of the contamination.
Though many government agencies and non-governmental organizations collect data on
water quality, agricultural and population in and around Huron County, a comprehensive
&alysis of this data on a watershed level has not been undertaken. Watershed studies in and
outside of the County have looked at the impact of best management practices on surface
water quality. Other studies done in North CaroIina have examined statistical methods for
identifjhg surface water quality trends. However, few studies have examined the statistical
correlation of Long-term, historical surface water quality data and factors that have the
potentiai to affect water quality in Southwestern Ontario.
2 LITERATURE REVIEW
Ln any rural watershed there are many potential sources of surface water pollutants, ranghg
fiom runoff fiom livestock operations to rnalfimctioning septic systems. An important aspect
of this watershed study was the identification of sources, and closer examination of those
thought to be the most likely contributors to any observed contamination. In addition to
potential sources, it was also necessary to consider local geographical factors that have the
potential to effect pollutant attenuation or transport.
The potential sources considered relevant in Huron County were identified by a committee
made up of people representing: agriculture (OMAFRA), environment (MOE), seasonal
residents (SOLVE-PROECT), health unit, Maitland Valley Conservation Authority, Huron
Farm Environmental Coalition, and others. This cornmittee identified the following
contributing factors: rural non-farm families that are not serviced by a wastewater treatment
plant or lagoons, f m s , geographical factors, and nual municipalities. The local MOE office
stated that this County does not have any rural or urban industries other than one small
tannery and the salt mine under Lake Huron at Goderich, neither of these contributes nitrate,
phosphate or bacteria to watercourses (Hutt, C. Ontario Ministry of the Environment.
personal communication. July 1998).
According to the Huron County Rural Servicing Study (1993) the main limitation to
development in Huron County is lack of adequate wastewater treatment - i.e. the Iimited
a scope of septic systems. This study focussed on the suitability of soils in the County for use
as leaching beds. Soi1 drainage classification was a geographical factor that was dso
considered. Many of the recomrnendations in this report deal with septic system record
keeping and maintenance.
What follows is the result of a literature review undertaken to investigate the relationship
between each of the contributing factors identified as important in Huron County and surface
water qudity.
2.1 On-Site Wastewater Systems
Few publications were found that dealt with the impact of rural n o n - f m homes on the
qudity of surface waters. The main contaminant source associated with these homes is their
on-site wastewater system, in most cases, a conventional septic-tank soi1 absorption system.
Much of the research done in this area has focussed on contaminant loadings from single
septic systems to groundwater. For example, Robertson et al. (199 1) looked at the impact
of septic systems located on sand aquifers and found that, contrary to the usual perceptions,
contaminants could travel very far (more than 1000 m) in long narrow plumes. Viraraghavan
and Wamock (1 976), and Chen (1 988) looked at nutrient and faecal coliform contamination
of groundwater from lakeshore septic systems and Lee er al. (1998) modelled the fate and
transport of household chemicais in septic systems. In a literature review Hagedorn et al.
(1 98 1) examined the potential of bacterid groundwater contamination fiom septic effluents.
In general it was found that unsaturated soils removed a significant amount of the biological
contaminants, but that a high groundwater table or soi1 macropores can cause untreated
effluent to reach the groundwater table.
Shadford et al. (1 997) used a biotracer to track the movement of bactena through three types
of l e a c h g bed designs used in Ontario: conventional, raised bed and filter bed in Ontario.
This study found that the biotracer did reach the groundwater and the major factors affecting
transport were age of the system, depth of unsaturated zone below the bed and precipitation
events. In a sirnilar study, Bumham (1 998), used the same biotracer to determine bacterial
transport through a working septic system in Ontario. It \vas found that the biotracer
travelled up to 24 m Fom the leaching bed.
Good sources of information on bacteriai pollution of surface water by septic systems were
local watershed studies. The Ausable Bayfield Conservation Authority (ABCA) worked on
many projects through the CIean Up Rural Beaches (CURB) program that attempted to
identiQ and then quanti@ major sources of pollutants. These studies focussed on bactend
contaminants, such as E. coli and faecai coliforms and were conducted within the Ausable-
Bayfïeld watershed. Malfûnctioning septic systems were cornmon sources of bacterial
contamination; these were identified in five ABCA reports by Hocking, two of which are
discussed below, and in two reports by the Huron County Planning Department (HCPD).
The most cornmon deficiencies in al1 of these studies were the limited study area and the
short duration. For example, in the ABCA Target Sub-basin Study (TSS) (Hocking, 1987)
examined three sub-basins, with areas ranging £iom 19.9 to 59.7 km2. This study evduated
farms in the three basins as to theù- suitability for remediation. As part of this evaiuation
nine of 15 rurai residences were found to have malfunctionîng septic systems. It was inferred
fkom this finding that 60% of al1 septic systems in the watershed were not working properly,
and therefore septic systems were a major source of bacterial contamination in this
watershed.
The CURB plan (Hocking and Dean, 1989) for ABCA used the results from the TSS in 1987
and calculated that 69% of the bacterial loadings to Lake Huron fiom the study area came
fiom lakeshore cottages and a lakeshore basin, and stated that the major@ of the bacteria
came from faulty septic systems. In the 1989 CURB plan, al1 of the contaminant loadings
fiom septic systems to the lake were based on a study that examined only 15 septic systems
in a watershed that has over 7,000 systems. Subsequently, in 1995, Snell and Cecil used the
figures fiom the CURB Plan to determine areas of stress in the ABCA watershed. They
classified faulty septic systems as the major source of bacteria and phosphorus to Lake
Huron. In contrast Palmateer et al. (1989) cited intensive MOE studies done in this same
area in 1984 and 1 985 that found agriculture was the major cause of beach postings.
The Rural Servicing Study done by the HCPD (1993) cited the MVCA and ABCA CU-
report conclusions that domestic septic systems are the main source of faecal coliforms to
Lake Huron. The MVCA reported that as much as 62% of the bacterial load in the Maitland
Valley watershed was fiom faulty septic systems, similarly, the ABCA estimated that 78%
of the faecal colifonn load in the Ausable-Bayfield watershed was fiom the faulty septic
systems (HCPD, 1993). These numbers were generated using a cornputer mode1 that used
the data fiom the ABCA TSS (Hocking, 1987). This niodel used algonthms for milkhouse
washwater discharge to drains, livestock access to open drains, exposed barnyard or feedlot
areas, rnanure pile runoff, wùiter and summer spread rnanure runoff and dornestic septic
discharges to drains. The algorithms produced estimates of the total phosphorus and faecal
coliform bacterid loads generated in rural areas of the ABCA watershed and delivered to the
beaches of Lake Huron. - 2.2 Agriculture
Many watershed studies in the U.S. and Canada have focussed on non-point source pollution,
and primarily, the impact of agriculture on surface and groundwater qudity.
The MVCA participated in the CURB program fiom 199 1 to 1996, and at the outset had
identified septic systems and livestock as the two main sources of contamination (HCPD,
1996). The Intensive Livestock Study prepared by the HCPD (1 996) f o n d that there is an
ongoing intensification of livestock in Huron County. Specifically there was an increase in
the number of swine and of poultry. The study also concluded that over the past five years,
they were unable to discern an increase in water pollution and that they could not veri@
pollution sources.
Some researchers in the area have looked at the quality of tile drainage water. The quality
of tile drainage water can have a direct impact on surface water quality on a watershed basis
because this water drains to ditches which discharge into nearby creeks, streams, rivers and
then to Lake Huron. The final report done by Hocking (1 992) for the ABCA target sub-basin
study concluded that surface drain water quality does impact beach water quality in Huron
comty.
Fleming et al. (1 990) examined the effect of liquid manure application on tile drainage water
quality and found that a biotracer added to the manure prior to application was found in the
drainage ditches at three out of five sites within 72 hours. Fleming (1990) dso investigated
the impact of agricultural practices on tile water quaiity over three years. This study found
that the mean nitrate concentration for the tile water exceeded the Ontario Drinking Water
Objective of 10 m a of NO3-N, and that the mean concentration of total phosphorus
typically exceeded the MOE objective of 0.03 mgL. Faecal coliforrn data varied widely and
sites where tile drains drained farmstead areas had higher levels of faecal coliform than sites
that only drained cropland. In five of the 14 sites the geometric mean for faecal coliform
exceeded the bathing water guideline of 100 CFU/l OOmL.
Many of these local reports were concerned with targeted research, and did not attempt to
quanti@ or rank sources of pollution. For example, research done by Fleming et al. (1 993)
at Centralia College investigated the impact of regulating flow at tile drains on nutrient
concentrations. As mentioned previously, Fleming also studied the impact of agrkultural
practices on tile water quality (1990), and similar projects were done by Dean and Foran
(1 990). This type of research is valuable because it attempts to quantifi contamination fkom
specific sources, and investigates methods to mùiimize contamination. However, it does not
attempt to quanti@ pollutant loads on a County-wide basis due to the wide variability in
conditions across the County.
A study was done by Vellidis et al. (1999) on a 390 km2 watershed in Georgia to examine
the impact of implementing Best Management Practices (BMPs). The watershed had L
problems with high nutrient and bactena concentrations in its surface waters, and also
erosion problems Hog and daïry farms were predominant in the area. The objective of the
project was to quanti@ changes in water quality after the implementation of BMPs such as
relocation of hogs fiom nparian wetlands to confinement areas and installation of waste
management systems and erosion control structures. Water quality data was collected fiom
nine sites in sub-watersheds for a period of 30 months. This study included a reference sub-
watershed, defined as a watershed that did not have concentrated animal production and
whose primary land use was forestry. The data did not demonstrate that the BMPs improved
surface water quality and the suspected reason for this was that the hog f m s in the area had
significantly increased their operations over the duration of the study. However, it was
interesting to note that the reference watershed consistently had lower concentrations of
nitrate, phosphate and faecal coliforms than the sub-watersheds with livestock.
A similar project by Hocking (1988) looked at water quality before and after remedial
projects in an agricultural watershed in Huron County. Projects included repair of fauIty
septic systems and discomection of septic tanks fkorn field drainage tiles, as well as
installation of manure runoff tanks and covering open manure pits to prevent runoff during
storrn events. The study found that bacteria concentrations downstrearn of the farrns
dkreased afler the projects were done, but that nutrient concentrations were not significantly
different .
Sherer et al. (1 992) looked at the survivd of bactena in streams when considering the impact
of livestock watering in streams. Livestock watering in strearns has long been known to be
a contributor of nutrient and bacterid pollution to surface waters. Some studies have shown
dramatic decreases in bacterial and nutrient concentrations f i e r the livestock have been
removed fiom the Stream, however, in some instances this improvement in surface water
quality was not observed after this remedial action. Some research has found that faecal
coliforms and faecal streptococci bacteria accumulated in stream sediments, and any actions
that suspended the sediments would release these bacteria into the overlying surface waters.
In fact Sherer et al. (1 992) found that enteric bacteria could survive months in sediments, and
suggested that erratic bacieria data fiom stream monitoring programs may be due to this
phenornenon.
The Upper Thames Conservation Authority examined the impacts of cattle access to streams
and found substantial increases in nutrients and bacterial loadings downstream of access sites
(Demal, 1983). The CURB Plan (Dean and Hocking, 1989) cited livestock watering in
streams as a major source of bacteria and phosphorus to streams in the County. Excessive
nianure spreading and winter spreading of manure in the ABCA watershed were stated as
other major sources of bacteria and phosphorus to Lake Huron in the 1989 plan.
2.3 Surface Water Quality Studies
In addition to studies that have examined the impact of an individual source on water quality,
there have been several studies that have investigated a variety of potentiai pollutant sources
and associated factors on surface water quality.
Fraser el al. (1998) used SEDMOD to determine if this mode1 could provide an index of
pathogen loading potential for 10 agricultural sub-watersheds and 2 forested control sub-
watersheds. SEDMOD used five transport parameters: Stream proximity, slope, slope shape,
flow-path hydraulic roughness and a nomalized soi1 moisture index. This information was
combined with a GIS livestock density layer. The objective of the study was to show that
SEDMOD could identie the pollution potential of f m s to a greater degree than looking at
aggregate watershed properties like livestock nurnbers and watershed size.
m e sub-watersheds ranged in size fkom 1.5 to 50 km2. Livestock in the area included dairy
and beef cattle as well as sheep and horses. Water quality sampling consisted of 8 weekly
samples from June 2"* to July W", 1996 collected at the outlet of each sub-watershed.
Livestock density was determined by dividing the area of pasture by the number of animals.
The five transport parameters were used to produce a delivery factor for each sub-watershed
and that delivery factor was multiplied by the faecal coliform output of the livestock to
produce an estimate of the number of faecal coliform that would be transported to the Stream
in each sub-watershed. The predicted faecal coliform values were related to the measured
concentrations using Iinear regression (after normalizing by log transformation). This study
found that livestock density could explain 50% of the variation in measured faecai coliform
discharge at the 12 watershed outlets, but that predicted faecd coliform transport and total
livestock faecal coliform output were not significantly correlated to the faecal coliform
discharge.
Brenner and Mondok (1 995) also looked at the pollution potential of sub-watersheds. They
evaluated the potential of 1 1 sub-watersheds in Pemsylvania and found that there was a
significant correlation between the overall rating factor for each watershed and each of the
water quality parameters examined: total phosphorus, nitrate and faecal coliforms. In the
156,000 ha study area woodlots and agricultwal each made up 34 % of the total area. In four
of the sub-watersheds, agricultural lands made up 40% of the land use, and comprised 14 to
40% in the other seven. The water quality data used in the study was limited to 104 samples
taken fiom 26 sites. Each sub-watershed was given a rating factor (RF) based on relative
contributions that were previously determined: 20% surface water, 1 5% groundwater, 40%
management practices, and 25% livestock concentrations. A Pearson correlation analysis
and least squares regression were done to investigate the relationship between water quality
and the watershed factors. The study found that faecal coliforms and total phosphorus were
- correlated to the watershed delivery factor, animal nutrient factor, and management factors.
Nitrate was found to be correlated to the groundwater delivery factor.
Bolstad and Swank (1 997) conducted a study in North Carolina to determine the correlation
between land use and surface water quality. Water quality samples were taken at base flow
and stom conditions for a total of 109 baseflow sarnples and 72 storm flow samples. The
samples were collected over three years at five stations whose contributing drainage areas
contained various land uses. Water quality parameters that were examined included: *
phosphate, nitrate and faecai coliforms. Land use characteristics examined included:
landcover (forest, agriculture, and urbankuburban), hydrography (stream length, stream
order), roads (totai road length and total paved road length), slope, soils, surficial geology,
bedrock geology and building density. Pearson and Spearman correlations were used to
determine correlations between pararneters and eliminate them fkom subsequent regression
models. Water quality pararneters were regressed singly against Land use characteristics. The
study concluded that the difference in water quality in streams with different land use
characteristics is most evident during s tom events.
Hagedorn et al. (1 999) used bacteria source tracking (BST) to determine which non-point
source was contnbuting bacteria to the Page Brook in Virginia. Domestic livestock,
terrestrial wildlife, waterfowl and septic systems were considered as potential sources of
bactena in this two year study. Three of 12 sites monitored had high levels of faecal
colifoms at the beginning of the study. Cattle access to streams was identified as the main
source of contamination, and monitoring done at these sites f i e r the cattle no longer had
access found that there was a signifiant decrease in faecal coliforrn contamination. Some
sampIes did indicate that waterfowl and terrestrial wildlife were sources of bacterial
contamination.
Arthur et al. (1998) prepared a preliminary analysis of agricultural land use practices and
surface water quality trends in Wellington County, Ontario for the Wellington County
Stewardship Council. This preliminary report only looked at agricultural data fkom the 199 1
census, and found that some counties were much more grain intensive than others, however
the relationship between agrîcultural factors and water quality were not investigated. With
respect to surface water qudity trends, this project examined the data from eight Provincial
Water Qudity Monitoring Network (PWQMN) stations. Parameters including total
phosphonis, faecal coliforms and total kjeldahl nitrogen were examined. The study presented
the water quality data without any statistical analyses for trends or correlations to agricultural
factors. This study did look at a station in the Maitland Valley watershed (at Hamiston) and
speculated that the levels of phosphorus were higher in this Stream than in urban locations
due to the agriculture nature of the upstrearn watershed.
Other watershed studies have been done in Ontario. However, to date, none have been
identified that attempted to statistically correlate long-term historical surface water quality
data and agriculture, geographical factors and other rural practices. For example, the Mill
Creek Subwatershed Plan (CH2M HILL Engineering et al., 1995) was limited to data
collection in 1994 and 1995. This study examined agricuIture and septic systems as potential
sources of contamination, however, the analysis did not include any attempts to statistically
correlate these factors to surface water quality. The final report for Mill Creek stated that
there was little correlation between agriculture and water quality, but that a recreatiodtrailer
park in the area may have potential negative impacts on water quality if it expands due to the
lirnited capacity of its on-site wastewater disposal system (CG&S, 1996).
As part of the StratfordlAvon River Environmental Management Project (SAREMP) several - studies in the 1980's examined water quality and potentiai pollutant sources. One water
quality study done by Huber (1982) for the Upper Thames River Conservation Authority
(UTRCA) collected samples fiom 19 stations along the Avon River from May, 1980 to
A u ~ u s ~ , 198 1. These stations were located in agricultural areas as well as upstream and
downstream of Stratford's wastewater treatment plant. This study found that wastewater
treatment plant effluent and rural areas contributed phosphorus, amrnonia and bacteria to the
river, and that rural areas aiso contributed nitrates.
Another study done by UTRCA involved modelling phosphorus inputs into the Avon River
(Fortin and Demal, 1983). In this study it was estimated that 50% of the phosphorus load was
fkom urban sources, specifically, the wastewater treatment plant and storm water runoff, and
that 44% of the loading was from agriculturai sources. In contrast the Thames River Basin
Report (1975) cited rural areas in the UTRCA watershed as contributing 76% of the annual
total phosphorus ioad and 95% of the t o d riitrogen load to the Thames River. UTRCA is
currently working on a watershed plan using benthic information collected fiom rivers and
streams over the past seven years and PWQMS data, dong with a GIS overlay containing
information on land use (Wilcox, 1. UTRCA. personal communication, August 2000).
The G.M. Wickware & Associates (1989) examined the relationship between water quality
and land use in the South Nation River Basin in eastern Ontario. The study looked at
PWQM data fiom seven stations fiom 1980 to 1988, dong with land use data on a sub-basin
level. This study found that most water quality parameters examined were increasing in
concentration over the study period. Also, the study concluded that the poor water quality
in the South Nations River reflects the agriculhiral nature of the basin. The study also stated
that high concentrations of phosphorus occurred in areas of high livestock density, intensive
drainage for row cropping and in areas that are most susceptible to erosion. The relationship
between water quality and land use was based on observations without the use of statistical
analyses.
Yan (1993) analyzed Provincial Water Quality Monitoring Network data for the Rideau,
South Nation and Mississippi Rivers. This study examined fifieen parameters, including
faecal coliform, nitrate and phosphorus, fiom fifteen stations fkom 1967 to 199 1. Yan tested
the data for normality using Shapiro-Wilks and Lilliefors tests, and used logarithmic
transformations for faecal coliform, nitrate and total phosphonis concentrations. A multiple
regression approach was used to determine signifîcant trends. Yan concluded that there was
no significant trend for faecal coliforms or nitrate concentrations, but that there was a
significant decreasing trend for total phosphorus concentrations for al1 stations with records
fkom the 1970's to the 1990's. Although Yan commented on possibIe correlations between
the water quality parameters examined and agriculture, wastewater treatment plant discharges
and other factors, a statistical correlation analysis was not perforrned.
Trkulja (1 997) developed a stochastic mode1 for a phosphoms time series and investigated
the impact of different sampling interval on linear trend anaiysis of the data. This statistical
study, limited to phosphonis concentration in the Grand River, found that there was a
decreasing trend for phosphorus concentrations of 1.5 pg/L/year that was negligible
compared to the mean (132 pg/L) and range of the data (884 pg/L). It was concluded that
the slight decreasing trend may be expiained by the decreasing trend for discharge. In
addition, Trkulja concluded that trend estimates based on data that is collected monthly are
Iess reliable than those based on daily or weekly sampling intervals.
2.4 Rural Municipalities
A rural municipality is defined, for the purposes of this study, as town that is separated fiom
nearby towns or cities by several kilometers and has a population less than 25,000 people.
Farrell-Poe et al. (1 997) examined the bacterial loading fiom 4 rurai municipalities to surface
streams in an agicultural watershed in Utah. Grab samples were taken upstream and
downstream of each municipdity for a 15 month period. One of the four municipalities
relied completely on a WWTP for wastewater treatment, one relied on both septic systems
and a WWTP, and two relied completely on septic systems. This study found that the
downstream concentrations of total and faecal coliforms were significantly higher than the
upstream concentrations, and that bacteria concentration was not correlated to flow. Both
parametric and non-parametric methods were used to analyze the water quality data and
Spearman's rho test was used to determine if a correlation existed between Stream flow and
bacterial concentrations.
A study done by Fortin et al. (1983) examined the impact of two city impoundments on
water quality in the Avon River. The study focussed on sumrner impacts (May to
September) and found that flow fiom the impoundments increased the concentrations of
BOD,, total phosphorus and total kjeldahl nitrogen, and reduced the concentration of faecai
colifonns in the Avon River.
2.5 Summary
Many studies have been done that investigate the impact of implementing BMPs on surface
water quality, others have focussed on the contaminant trends over time in surface waters,
and others have identified pollutant sources and quantified their loadings to surface waters.
A cornmon short-coming of many of the studies examined here was the tendency to speculate
on correlations between surface water qualis and potential sources without any statistical
bais.
Swdies done in Ontario indicate that there are decreasing trends for phosphorus
concentrations in surface waters in some rivers. Studies done in the US. have concluded that
nitrate concentration is related to groundwater delivery factors whereas phosphorus and
bacteria concentrations were related to watershed deiivery, animal nutrient and management
factors. Many of the studies previously discussed examined only a few years of data and did
not attempt to look at histoncal trends.
Although a range of data is collected in Ontario watersheds, few studies attempt to look at
a this data in a comprehensive manner. Very few have attempted to anaiyze the entire record
of histoncal surface water quality data that is available and relate this data to factors that
have the potential to impact surface water quality.
No studies have been identified that examine long term surface water quality trends and
attempt to correlate long tenn surface water quality data to potentiat pollutant sources in
Southwestern Ontario. The objectives of this study stated in the next chapter were chosen
to fil1 in this gap.
3 OBJECTNES
This section outlines the objectives of this research. The purpose of this research is to
determine if the surface water quality in Huron County is changing with time, and to
determine if any detected change is correlated to urban or rural factors within the area. The
specific objectives were to:
collect and organize al1 of the available existing water quality data and related data
for Huron County over the last 25 years, including data fiom wastewater treatment
plants (WWTP's), data collected by provincial ministries, conservation authorïties,
local researchers and communities;
evaluate completeness of data, for exarnple identi@ data gaps with respect to
geographical location, loss of data, missed samples at usual fiequency of collection,
samples taken infiequently and any other insufficiency, identiQ areas that require
more data collection in the future;
assess whether, according to the existing data, water quaiity is changing with time in
Huron County;
establish trends in the data collected over a 25-year time span using statistical
methods including as part of the statistical analysis, tests for normality to determine
the appropriateness of using pararnetric methods;
investigate possible significant correlations between surface water quality and related
data (factors that have the potential to impact water quality).
This approach could be used as a mode1 for other municipalities in Ontario.
19
4 BACKGROUND
Section 4.1 contains background information on Huron County, and the sub-sections present
factors in the Co- that may potentially impact surface water quality. Section 4.2 discusses
the surface water quality parameters considered in this study.
4.1 Huron County
Huron County with an area of 340,000 hectares is located in southwestern Ontario along
Lake Huron. Its western edge extends along approximately 100 km of the central portion
of the eastem shore of Lake Huron (Figure 1). niere are sixteen townships, five towns, five
villages and many smaller hamlets in the County. Two conservation authorities are
responsible for Huron County, the northem area is part ofthe Maitland Valley Conservation
Àuthority and the southern is part of the Ausable-Bayfiçld Conservation Authority.
4.1.1 Human Population
The 1996 Census of Canada (S tatistics Canada, 1997) indicated a total population of 6 1,247
in the County, and the population of largest town, Goderich, was 7,553.
Along the Lake Huron shore there are two major towns with marinas, Goderich and Bafield.
In addition, there are cottages along the lakeshore fiom Grand Bend to h b e r l e y Beach.
Grand Bend is a very busy summer destination for local residents and tourists. I t was
e s h a t e d that in 1993 there were 5,600 seasonal residents compared to 1,800 permanent
residents in the shoreline townships of Ashfield, Colborne, Goderich, Stanley, Hay, and
Stephen (Planning Department, Rural Sewicing Study, 1993). Figure 2 is a detailed map
of the County, showing the location o f the townships, towns and villages, and landfills.
Figure 1: Huron County in Southern Ontario
Huron County Surface Water
The hurnan population of the County has the ability to affect surface water quatity in several
ways. Landfills have the potential to leach contaminants into groundwater and runoff fiom
Iandfills may lead to contamination of nearby surface waters. The loading of contaminants
fiom on-site wastewater systems is another potential factor, as is the loading fiom wastewater
tvatment plants and lagoons. Agricultural activities are discussed below.
4.1.2 Agricultural Factors
The two major industries in the County are agriculture and towism. Huron is the most
agricdturally productive County in Ontario with approximately 98% of the total area
classified by the Canada Land Inventory as "Land Use Capable for Agriculture" (Scott,
1966). The major agricultural activities are crop production and animal rearing. Detailed
iriformation related to crop production was not available for this project, and was not
considered a major issue in Huron County.
Livestock populations and land use were the two main agricultural factors examined in this
research. Livestock densities have the potential to overload an area with both solid and
liquid wastes, whereas land use indicates the arnount of land that is in use for agricultural
activities. It is reasonable to suspect, for example, that in a basin with high swine density the
need to land spread liquid manure generated by swine on the limited area available could
lead to saturation of the land and result in both surface and groundwater contamination.
From 1971 to 1996 there has been an increase in the nurnber of swine and poulûy in the
County and a decrease in the nurnber of cattle. In addition, some townships in the County
have high numbers of swine relative to the others, for example Grey township, whereas
others Iike Hullet township have reiatively high numbers of poultry. Zurich, located in the
south western area of the County, is the proud white bean capital of Canada.
Other agricultural factors that have been identified as contributing to poor water quality are:
tile drainage, commercial fertilizer use, and isolated spills. Certain townships are extensively
tile drained, for example McKillop township, whereas others like East Wawanosh have less
tile drainage (OMAFRA, 1996).
4.1.3 Geographical Considerations
Soil drainage class is a geographic factor that is often considered as having the potential to
impact water quality and was considered in this study. Soil drainage class indicates the
general ability of soils to accept and treat both hurnan and animal wastes.
For exarnple, an area with a high density of in-ground septic systems and soil that is
predominantly in the poorly drained class, gives cause for concem. Soils that are poorly
drained may not be able to handle a high density of on-site systems that rely on the ability
of the soil to accept and treat the wastewater.
The three southern sub-basins, Ausable, Bayfield and Gullies, have over 50% of their area
classified as clay. The predorninant soi1 texture for the northern sub-bains is loam. The
topography of the County is typically flat, with some wetiand areas (Le. Hullet marsh) and
the depth to overburden in the County typically ranges fiom 7.6 m to 53 m (Hocking, Doug.
Maitland Valley Conservation Authoxity. personal communication, May 200 1).
The streams and nvers in Huron County have high flows in March and April, associated with
spring melt, and low flows in Juiy and August.
Other factors such as land slope and depth of overburden were not considered in this study.
in addition, due to the number of creeks, streams and rivers in the area, proxirniw to streams
was not considered.
4.2 Surface Water Quality Parameters
The surface water quality parameters used in this study are: nitrate-N, total phosphorus and
faecal coliforms. These parameters are often of interest in watershed studies (Vellidis et al.
1999, Fraser et al. 1998, Bolstad and Swank, 1997). In addition, faecal coiifonn and/or
E. coli are typicalIy chosen because they indicate faecal contamination of water and are used
to determine the warnings for swimming at beaches in Huron County. Nitrate-N and total
phosphorus were chosen because they contribute to eutrophication of streams and lakes.
5 METHODOLOGY
The following sections outline the steps taken to complete the research, beginning with
Section 5.1 which presents data collection to Section 5.4 which details the statistical
methods used for detennining correlations.
5.1 Data Collection
me first objective of the project was to collect surface water quality data, and any other
available information on factors that have the potential to impact surface water quality.
Many govenunent and non-govemment agencies were contacted to provide data for this
project. A surnmary of the organizations contacted is given in Table 1.
Huron County Health Unit
Table 1 : Organizations Contacted for Data
Data Resource Centre (DRC), University of
Guelph
Environment I Canada I
Other Organizations
Ridgetown College, University of Guelph
Ontario Clean Water Agency
k
Governrnent Muiistries
MOE
OMAFRA
. - - - - - - --
Point Fanns Provincial Park
Local Agencies
Ausable Bayfïeld Conservation Authority (ABCA)
Maitland Valley Conservation Authority (MvcA)
1 1 Huron County Planning Department 1
5.1.1 Water Quality Data
1 -
Maitland Engineering Services
Town Clerk of every township in Huron County
Many diserent water quality parameters were examined as part of the monitoring programs
considered in this study. Of these oniy nitrate-N, total phosphorus and coliform data were
selected to be analyzed. Nitrate-N and total phosphorus were chosen because these nutrients
are the principal contrdling agents of eutrophication of streams and lakes. Total coliform,
faecal coliforms (FC) and E.coli were chosen because levels of these bacteria are used to
determine warnings for swimming at beaches in Huron County. It is acknowledged that
- -
these three parameters move through ecosystems in different ways, and that they may arise
from different or similar sources. They are not necessarily related.
The types of surface water quality data available for this project included: WWTP effluent
monitoring, landfill monitoring, provincial water quality monitoring stations on creeks and
rivers, and results Çom monitoring of Lake Huron and of inland recreational bathing areas.
Table 2 surnrnarizes the types of water quality data collected. In addition, the Town of
Goderich provided information on plant by-passes and combined sewer overflows, and the
Maitland Valley Conservation Authority (MVCA) provided flow monitoring data for rivers L
and strearns in the County.
Table 2: Data Collection
TYPE OF INSTALLATION or REPORT
Waste Water Treatment Plants & Lagoons
Water Treatrnent Plants
Landfills
Miscellaneous Reports*
Health Related Data
Septic Permit Data
Instrearn Water Quaiity Data
Miscellaneous Data
DATA COLLECTED
Certificates of Approval monitoring records
monitoring records
Certificates of Approval monitoring records
OMAFRA tile & well water studies MVCA & ABCA libraries Clean Up Rurai Beaches Tarnet Sub-basins Studies
monitoring records
General permit information available for Huron County
Provincial Water Quality Monitoring Stations (PWQMSys), flow gauge stations
Point Farms Provincial Park Bactena Data
LOCATION
Clinton, Hensall, Zurich, Grand Bend, Goderich, Wingham, BI*, Exeter Brussels, Vanastra, Seaforth, Harriston, Lucknow, Palmerston, Milverton, Listowel
Grand Bend
Wingham, Blyth, Exeter, Ashfield, East & West Wawanosh, Moms, Hay, Stanley, Stephen, Howick, Usborne, Goderich, Turnberry, McKillop
OMAFRA MVCA library ABCA library
Lake Huron beach & inland swimrning sites
Huron County
Huron County
Point Farrns Provincial Park
* individual reports are cited as used in text
5.1 -2 Human Population Data
Popdation & Dwelling Counts of the Census of Canada - provided much of the population
data used. In addition, some nual studies such as the Rural Servicing Study (HCPD, 1993)
and Livestock Intensification Study (HCPD, 1996), both conducted by the Huron County
Planning Department provided information on population trends within the County. Another
factor associated with human population that has been identified as a source of contarninants
to surface water quality are on-site wastewater systems. The Huron County Health Unit
provided some general information on septic systems in the County. The number of pemits
for the County from issued from 1973 to 1997 for replacements and new systems was
provided.
5.1.3 Agricultural Data
Agricultural reports and studies conceming water quality in and around the County were
collected. These included reports fiom the Clean Up Rural Beaches Program, Huron County
Planning Department reports and reports from Centralia and Ridgetown agrïculturai colleges.
Agricultural information that was collected included: tile drainage maps, Census of Canada
Agriculture Profile of Ontario data, and agricultural spills information. Information on. tile
drain tubing sales was provided by OMAFRA. Although this information is not specific for
Huron County, it can be used to indicate trends in provincial sales that apply to Huron
County.
Data received fiom the Fertilizer Institute of Ontario Lnc. (originally compiled by OMAFRA)
presented the volume of fertilizer sales in Huron County over the past 40 years. This
information was available on a County wide bais oniy, and does not necessarily represent
the amount of fertilizer applied in the County. OMAFRA and the Fertilizer Institute did not
provide information on the use of fertilizer in the County, the information provided was
limited to sales within the County.
5.1.4 Geographical Data
The Maitland Valley Conservation Authority, which maintains a GIS database of the County,
provided information on soil drainage class. This information was provided on a sub-basin
Ievel, with each basin categorized on the basis of area of soils in each of the five soil
drainage classes: well drained, variable, imperfect, poor and very poor. The soil drainage
classes imperfect, poor, and very poor were sumrned and converted to a percentage of the
sub-basin area for use in this study.
5.1 -5 Data Organization
An important aspect of this research is the manner in which the gathered data has been
organized. Existing watershed sub-basins were the basis for the organization of the collected
UIformation. Each of the fourteen sub-basins in the County were examined as independent
areas. For each basin, the Provincial Water Quality Monitoring Station (PWQMS) that was
judged to best represent the basin was used to assess the water quality. The PWQMS that
was rnost downstream in each sub-basin was chosen as the station that best represented the
water quality of the sub-basin. Figure 3 shows the sub-basins in the County and the
corresponding PWQMS used to represent the basin water qudity.
Contributing factors for this study were identified and possible relationships between these
factors and observed water quality trends were determined using Spearman correlations,
stepwise regression and principal component analysis.
Table 3 outlines the major sub-basins, and examples of point sources in each sub-basin.
This table does not add sources fiom sub-basins that discharge into other sub-basins. In
every basin considered there were non-point sources that were universai, these included:
livestock population, human population and land use.
P
10 km
Conservation Authority kv&iqbr-hmmurnr(
- Major Basn Boundary
- Tomshii Boundary
Waercourse - Provincial Highway I h n ! y Road ' ' ' Prw. Water Quaiity Moriiluing Stations
Huron County Surface Water
Airsable River
Table 3 : Data Organization
I 2 WWTP7sAagoons 3 landfills
BASIN NAME REPRESENTATIVE PWQMS #
Bayfield River
Little Maitland
Main Maitland
POTENTIAL LOCAL POLLUTANT POINT SOURCES
Middle Maitland
North Maitland
8
35
1 & 2 3
Nine Mile River
2 WWTP' s/lagoons 2 landfil1
3 WWTP7s/lagoons 1 landfill
O WWTP'sllagoons O landfill
2 WWTP's/Iagoons
17 & 31
4 landfill
3 WWTP'sllagoons O Iandfill
1
a 5:2 Available Data & Deficiencies
1 WWTP'sAagoons 1 landfill
South Maitland
Gullies -
Not al1 sources provided complete data sets. Some sources did not collect al1 thtee
parameters of interest, some did not consistently collect the data over tirne. The data gaps
encountered when organizing and analyzing the data for this research are detailed in Tables
15
13
O WWTP' sAagoons 2 landfill
1 W WTP' s/Iagoons
4 to 7. Missing data had a significant impact on the level of analysis that could be
performed.
5.2.1 Lakeshore and Inluid Recreational Bathing Site Monitoring
Ail inland and lakeshore monitoring information prior to 1990 was unavailable because it
was accidentaily thrown out when the Huron County Health Unit moved fiom one building
to another. This was unfortunate due to the importance of this historical record for
determining long term trends in bathing water quaiity in the County.
5.2.2 Provincial Water Quality Monitoring Stations
The Provincial Water Quality Monitoring Stations (PWQMS's) were the best source of long
term water quality data in Huron County. Data fiom many stations was available, and as a
result it was possible to examine long term trends over time. Table 4 details the gaps in data
collected at the PWQMS's, and it was observed that the period of record for data was highly
variable and, in some cases data were incomplete. Due to financial cutbacks the PWQMS
program was discontinued in 1994. Some stations in the Maitland Valley and Ausable-
Bayfield watersheds were restarted in 1999, however, not al1 of the original stations are
monitored.
In order to increase the record of data that could be analyzed, PWQMS's that were
gèographically close together were combined. For exarnple, Stations 1 and 23 in the Main
Maitland basin were combined to extend the record of data, the same was done for Stations
17 and 31.
STATION LOCATION & NUMBER 1 YEARS OF DATA
MISSING DATA
Main Maitland, Stations 1 & 23
no data 1971- 1973 no flow 1974- 1988 no F.C. 1974-1989 no TP & NO, June 1979
. -.
Dec.
North Maitland, Station 4
no F.C. 1965- 197 1 no flow data at site
South Maitland, Station 15 I no F.C. 1971
Middle Maitland, 1972- 1 994 no data 197 1 Stations 17 & 3 1
Little Maitland, 1987- 1994 no data 1971-1986 Station 35
Nine Mile, Station 1
no F.C. 1965-1971 no TP data
bayfield, Station 8
no data 1971-1980
Ausable, Station 1 1 I no flow data at site
Gullies, 1 1974- 1994 1 no flow data at site Station 13 1 1
5.2.3 Discharge
Environment Canada has rnaïntained a number of flow monitoring stations in the County,
some of which are near the PWQMS's. In most cases daily discharge data were avaiiable for
the period of record for the PWQMS. Table 5 sumnlarizes the data that was available for the
flow monitoring stations. Discharge ùiformation was required for each PWQMS for load
calculations. The years of available data for the PWQMS's and the correspondhg stations
are shown in the table.
In some cases flow gauging stations were not available near the PWQMS. In these cases the
nearest flow gauging station was used and, to determine approximate discharges at the
PWQMS, an area factor was used. This area factor was the ratio of the PWQMS watershed
area and the total wateehed area of the flow gauging station (Chow, 1988). Calculations are
shown in Appendix A.
Table 5: Flow Gauge Stations
1. PWOMS 1 FLOW GAUGING STATIONS
BASIN STATION # YEARS OF STATION # YEARS OF DATA DATA
Main Maitland 1 &23 1974-1994 02FEO 1 5 1989 - 1996
North Maitland 4 1965- 1994 02FE0 1 1 1981 - 1996
South Maitland 15 1971-1994 02FE009 1968 - 1996
Middle 17 & 31 1972-1 994 02FEOOS 1968 - 1996 Maitland
--
1 Little Maitland 1 1 Nine Mile 1 1 1 1965-1995 1 02FD002 1 1980 - 1996
Gullies 13 1 974- 1 994 no flow gauging station
5.2.4 Wastewater Treatment Plants and Lagoons
Many WWTP's in Huron County collect and store a significant arnount of discharge and
water quality data. Information on effluent quality and quantity was available fiom most *
plants and lagoons in Huron County for up to 30 years. Many of the WWTP's in the County
collect monthly nitrate-N, total phosphorus and bacteria data samples and lagoons sample
their effluent whenever discharged as required by their Certificate of Approval (issued by
the Ontario Ministry of the Environment). However, many WWTP's do not have records
for the entire period of plant operation, typically because their Certificates of Approval
require them only to keep records for two years for provincial inspections or longer in some
cases. Table 6 summarizes the available WWTP and Iagoon data at al1 locations in the
rage
LOCATION & YEARS SAMPLE FREQUENCY OF DATA
Blyth, 1982 - 2937 monthly
MISSING DATA
no F.C. 1984 - 1987
Bmssels, 1982 - 1997 1 monthly no F.C. 1983 - 1987
Clinton, 1988 - 1997 no data 1991 - 1993, no bactena 1990 - 1993 no flow data 1988-1 992
Exeter, 1990 - 1998 1 erratic no bacteria data * Godench, 1967 - 1997 monthly T.P. start 1972,
monthly NO,-N start 1980, bacteria** May-Nov. up to
1992, then monthly
Grand Bend, 1987 - 1997 2 - 3 sampIes/year
no bacteria 1994 & 1995 NO,-N erratic before 1980
no flow data 1967-1 980
- -
no NO,-N or F.C.*** 1987 no data 1995
-
Hensall, 1985 - 1997 1 2 - 4 samples/year al1 data for 1986
Wingham, 1984 - 1997 l monthly no data 1987; no F.C. 1984,1985
twice per month for each month that the lagoon
discharges
no F.C. in 1994
Palmerston, 199 1 - 1997 1 month1 y no NO, - N or bacteria
Milverton, 1990- 1997 1 once per year no F.C. 1990
monthly no flow data; no NO, - N 1976-1977;
no bacteria
I Lucknow
rable 6: Waste Water Treahnent Plant & Lagoon Data Coverage (continued)
discharges to swale, not directly connected to surface water systern
r
Vanasîra, 1974-1997
Listowel, 1 994- 1998
**TC. FROM 1968 - 1979; T.C. & F.C. FROM 1979 - 1991; E.coli MONTHLY TESTING IN 1992 & 1996; 1993 H.4D JAN TO MARCH *** tested for F.C. fiom 1988 - 1993 & 1995 - 1997, tested for T.C. in 1994
monthly
monùily
Zurich, 1985 - 1997
Godench was identified as the only urban area that had cornbined sewers. Information
provided by the Town showed that they have been separating sewers, and since 1980 there
has been a decrease in the total length of combined sewers of approximately 50% (Town of
Godench. Works and Engineering Sewer inventory as of January 1, 1998. Persona1
Communication. Goderich. January 1998). This indicates a cornmitment by the Town of - Goderich to reduce this potential source of bactenal pollution.
no NO, - N 1974- 1979, 1990;
no F.C. 1974-1990; no flows 1992-1 995
no F.C. data E-coli data fiom '96 to '98
no T.P./NO, - N data January '96
2 - 4 samples/year 1 no data 1986, 1995
Detailed WWTP by-pass information (classified as primary or secondary, and discharge
volume) was available for the Goderich plant for 1997 and 1998, however, only by-pass
duration in hours was available frorn 1983 to 1996.
*only one F.C. data point (1991)
5.2.5 Landfills
There are 15 landfills in Huron County, many of which collect limited surface water quality
data. Data from the Usborne Landfil1 located in Huron County has been included even though
it is a part of the Upper Thames watershed.
There is a large variability in the number of sampling locations at each landfill. Surface
water quaiity data collected fiom on-site ponds was not considered in this study because
these ponds do not discharge to surface waters in the area. Landfills that had less than two
years of monitoring data, and those landfills that did not monitor creeks or streams, were not
considered. In Huron County landfïlls typically collected nitrogen and phosphorus data,
however, they do not collect bactena data. As with the P WQMS data, the landfil1 data varied
fiom site to site, and three landfills did not collect surface water quality data Table 7
siimmarizes the landfill data.
Table Surface
SAMPLE FREQUENCY MISSING DATA LOCATION & YEARS OF DATA
Ashfïeld, 1 989-1 997 twice per year no TP 1989- 1993 no NO, - N 1991-1993
twice per year no TP 1990-1 993 no NO, - N 1992- 1993
once per year I Exeter, 1 975- 1 995 erratic at different sampling sites
no data in 199 1 once per year
1 Mid Huron no surface samples - -
N/A
TP & NO, - N in 1994 --
once per year
once per year
no surface sampies NIA
1 Morris, 1993- 1997 twice per year
no TP 1988-1993 no (N03+N02 ) -N 1989- 1991
erratic at different çarnpling sites
NIA
mly 8 data pointslslte
twice per year
2 per year 1 Stephen, 1982- 1997
no surface samples
once per year
iio data 1991-1995 twice per year
1 but is
Wingham, 1996-1 997 ,'
* This landfiil is located in thc mly 3 data points once per year I
Huron County.
The population and dwelling counts for Ontario provided population data every five years
on a township basis from 197 1 to 1996 (Statistics Canada, 1972 to 1997). This data was
used to determine changes in population over time as well as population on a sub-basin level.
However, the Census did not report seasonal variations on a township or village basis for
each census year. A history of the change in the nurnber of visitors to this area would have
been a usefùl characteristic to compare to water quality given the high use of the area by
tourists and seasonal residences.
Data on septic systems provided by the Huron County Health Unit did not include
information regarding the spatial distribution of the septic systems throughout the County.
Nor was any information available regarding the age of existing systems in the County. m i s
a information would have been helpfid in atternpting to determine a relationship between
septic system density and age and areas of poor water quality.
5.2.7 Agricultural Factors
The agricultural profile of Ontario is compiled every 5 years by Statistics Canada and
provided data on land use as well as livestock populations on a township basis. For each
township the census give the numbers of various animal types and areas of land in different
categories (improved and unimproved). However, the 198 1 census did not report livestock
numbers and land use on a township basis, and it did not provide specific crop information
on a township basis for each census year (Statistics Canada, 1982). As a result it was
possible to determine changes in land use and Iivestock populations over time, but not
possible to assess the impact of crop type on water quality.
The census did not report the amount of tile drainage instailed each census year. In an
attempt to obtain more information on tile drainage OMAFRA was contacted about tile
drainage maps. These maps are ody available in hard-copy form at present, and information
on changes in the amount of tile drainage fiom year to year were not recorded. OMAFRA
was able to supply information on tile drain tubing sales for the Province of Ontario. These
data were not specific for Huron County, and do not differentiate between tubing used for
replacements and that used for new installations. It was therefore not possible to determine
whether there has been a significant increase in the amount of land tiled drained in the
County .
Agriculhual spills information obtained from the Ministry of the Environment fkom 1988 to
June 1998 showed that the nurnber of reports ranged fiom a low of 1 in 1994 to a high of 1 1
iri 1989 (OMOE. Agricultural Waste Spills in Huron County. persona1 communication.
August 1998). The types of spills ranged from manure spilled on a road, liquid swine
manure in creeks, to direct discharge of milkhouse washwater to a pond. The number of
spilis reported in any year is typically a fimction of environmental awareness, and not
necessarily the nurnber of spills that occurred. A meaningful cornparison between reported
agricultural spills and surface water quality was not possible with this limited amount
information.
Sales data received fiom the Fertilizer Institute of Ontario Inc., onginally compiled by
OMAFRA, was available on a County wide basis ody (Fertilizer Institute of Ontario, 1999).
Unfortunately, sales of fertilizer within the County do not necessarily represent the arnount
of fertilizer applied in the County. Many f m operators purchase their fertilizer at the major
commercial centres within the County and use it outside of the County. As a resdt it was
not possible to directly compare fertilizer use and surface water quality.
5 -2.8 Geographical Factors
Soil drainage class data was available for each township fiom the Maitland Valley
Conservation Authority GIS database (Maitland Valley Conservation Authority. Soil
drainage class spreadsheet. personal communication. August 1998). The five drainage
classes in the original database included: well, variable, imperfect, poor and very poor. This
data was converted to a percentage of area of soils in the imperfect, poor and very poor soi1
classes in each sub-basin.
5.2.9 Precipitation
Iri generd the precipitation data fiom two gauges, provided by AES (1998), was complete
and only occasional days in the data set were missing. Station 6129660 in Wroxeter was
used to represent the precipitation for the northem part of the County and station 6 1208 1 9
in Blyth represented the precipitation in the southern part of the County. Daily and monthly
precipitation depths in millimetres was available fiom 1975 to 1998.
5.3 Data Trend Analysis
One of the objectives of this project was to determine if water quality was changing over
time. This type of information is important for people living in Huron County because it can
be used to direct resources to problem areas, or towards target pohtants.
in many cases trends were identified by plotting the data. When the data did not follow a
trend that was readily observed by plotting, linear regression was used to determine if a
significant trend existed. Linear regression assumes that residuals are normally distributed,
independent, and have a constant variance. The following sub-sections discuss the tests done
to determine if these assurnptions were valid for the collected data, and the subsequent trend
analyses.
5.3.1 Bivariate Scatter Plot
The first rnethod for iden t iwg trends was the bivariate scatter plot. Water quality data was
plotted against tirne to determine if the points formed, or approximated, a straight line. This
would indicate that a linear trend was possible and that linear regression would be
appropriate.
5.3.2 Test for Normaiity Using Skewness
The next step was to determine if the residuals fiom linear regression were normally
distributed. A simple test for determinhg if raw data or residuals are normally distributed
is to determine the skewness. Skewness is a measure of how much the data deviates fiom
the normal distribution, or the degree of asymmetry of the distribution around its mean.
This test can either be done qualitatively by exarnining histograms of raw data or residuais
or can involve a quantitative analysis. A skewness of zero generaily indicates a normal
distribution, a value > 1 or < -1 indicates a skewed distribution (Grabow et al., 1998). To
determine the skewness of the data residuals it was necessary to first regress the data,
calculate the resulting residuals and finally calculate the skewness.
5 -3 -3 Sharpiro-W ilk Test for Normality
Sharpiro-Wiik is a cornmon test used to determine if the sample is norrnally distributed (Sen
and Srivastava, 1990). The test statistic, W, is:
where: a, depend on expected values of the order statistics from a standard normal
distribution and are found in tables (Sen and Srivastava, 1990)
u, = ordered (u, < u, <...y3 data residuais
s = sample standard deviation
The Shapiro-Wilk test statistic c m range fiom O to 1, with a values near 1 indicating a
distribution that is close to normal.
5.3.4 Data Transformations
In many cases water quaiity data is not normally distributed, and is often autocorrelated
(Grabow et al., 1998). in these cases the raw data is often transformed and then re-tested for
normality and autocorrelation. In the event that the transformation was successfbl, then the
analysis could proceed to regression of the transforrned data. Logarithmic transformation is
the most common transformation used for bacteria data. Another technique for other types
ofwater quality data, such as nitrate-N and total phosphorus concentrations, is data reduction
by t h e averaging. This technique reduces the amount of data by calculating time averages,
for example, using six month average concentrations instead of individual monthly sample
points.
Faecal coliform data that were not normally distributed was log transformed, and re-tested
for nortnality. Nitrate-N and total phosphorus concentration data were initiaily transformed
via six month averages. However, if that did not successfûily transform the data to normal
then one year averages were done and finally log transformation of the data was tried.
5.3.5 Linear Regression
Though much of the water quality was not normally distributed even after a variety of
transformations were applied, linear regression was still used to assess trends over tirne using
Quattro Pro version 8. The trend was determined to be significant if the slope of the
regression line was greater than the error on the slope.
5.4 Correlation Analysis
Correlation between the three water quality parameters of interest and the set of contributing
factors was determined using Speman's method based on ordinal data, and by stepwise
50
regression and principal component analysis. Three methods of correlation were used to deal
with the variable and limited data that was available for this study (Allen, Brian. Professor
of Math & Statistics, University of Guelph. personal communication, July, 1999.). The
cornputer program SAS was used to analyze the data and sub-sections 5.4.1 and 5.4.2 discuss
the methods used in detail.
5.4.1 Spearman's Rho
Spearman's Rho is commonly used when investigating the correlation between water quality
data and other factors (Helsel and Hirsch, 1992). This method is often chosen because it is
a.non-parametric method that uses ranks and therefore does not require the input or
importation of al1 of the raw data for aalysis.
This method requires that the water quality and related factor data be converted to ordinal
data. This was done by ranking each basin with respect to the water quality data (nitrate-N,
total phosphorus and faecal colifoxms) and based on contributing factors (soi1 drainage class,
density of humans, pouitry, swine and cattle). A rank of 1 inclicated a basin with the highest
concentrations of contaminants, or the basin with the highest density of humans or livestock.
~ k i n rankings for water quality were then correlated, using Spearman's Rho method,
against the basin ranks of contributing factors. The formula used to calculate Rho is shown
below.
Where : Rx, = rank of basin for a water quality parameter (e.g. nitrate-hr>
Ry, = rank of basin for a related factor (e.g. human density)
n = sample size
Runyan and Haber (1991) have tabulated critical values of the Spearman Correlation
Coefficient for a variety of significance levels and nurnber of data points. For nitrate-N and
faecal coliforrns the significance is based on nine rankings, whereas for total phosphorus the
test is based on eight rankings. The advantage of this method is that it is non-parametric and
does not rely on the assumption that the data is normally distrîbuted.
5.4.2 Multiple Regression
Multiple regression is another method of determining the relationship between a response
(dependent) variable and more than one explanatory or independent variable. Due to the
variability of available data two methods of regression were used. A cornparison of the
regression results would help to confirm which correlations were significant. The two
methods of regression that were used are discussed beiow.
5.4.2.1 Stepwise Regression
Independent variables are alternately added and removed fiom the regression model until the
model is left with only those explanatory variables that are significant at some specified
level. This method tests each variable in and out of the model for significance. The
advantage of using this method over forward regression is that a variable found to be
significant when it is entered into the model can later be eliminated if it is found to be
insignificant due to other variables being added. However, as with forward and backward
regression methods, stepwise regression does not test al1 possible models (Helsel and Hirsch,
1991).
AS with the Spearman's correlation analysis, stepwise regression was perfomed on the
yearly averages for 1986 and 199 1.
5.4.2.2 Principal Component Analysis
Principal component analysis is another method of detemining correlations that involves
examining linear combinations of the input variables. The linear correlations are used to a
produce a correlation matrix and a set of new variables or principal components. The first
principal component is the variable that explains the most variance of the data. The second
a
principal component then explains most of the remaining variance, and is orthogonal to the
first principal component.
6 RESULTS
Sections 6.1 to 6.5 present the results of the trend and correlation analyses.
6.1 Trend Anaiysis
Sections 6.1 to 6.5 discuss the data analysis and o b s e ~ e d trends for the collected surface
water quality data. One objective of this study was to determine if water quality was
changing with tirne. An important aspect of this objective was determinhg if there was a
significant increase or decrease in total phosphorus, nitrate-N and faecal coliform
concentrations in surface waters of Huron County.
6.1.1 Lakeshore and Inland Recreational Bathing Site Monitoring
The Huron County Health Unit is responsible for monitoring water quality at recreational
swirnming areas throughout the bathing season, typically May to September. The data is
summarized by location and year in Appendix B. The sites sarnpled, and the number of
samples taken per season at each site varies fkom year to year. Due to this variability the
sites have been compared using the amount of time per season that E-coli concentrations
exceed the provincial water quality guideline (PWQG) for bathing, 100 CFW100mL.
Beaches with data for penods less dian the entire bathing season or with missing data have
their percentage exceedance values based on only the time of available data. Sites that had
two years or less of data were not included in the anaiysis. The sites and sarnpling locations
at each site remain consistent, however, the sarnpling frequency may Vary according to
historical data. For example, sites that have had consistently low concentrations may be
sampled ody every other week or every third week to Save time and resources. The number
of samples collected at each sampling location has varied fiom 3 to 5 by request of the Public
Health Lab and the Minisûy of Health protocols.
Table 8 gives the average amount of time that E. coli concentrations at Lake Huron beaches
exceeded the PWQG during the bathing season, and beaches are ranked based on this
Monnation. Table 9 shows the same information for the inland bathing sites. A rank of 1
indicates the beach that exceeded the PWQG the greatest portion of time over the years of
monitoring. The locations of these beaches are s h o w in Figure 4.
Table 8: Lakeshore Beach Monitoring, 1990 - 1997
-
Bayfield South
Goderich Main
Goderich South
RANK
1
10
BEACH LOCATION
r
Amberley
Bayfield Main
Hay Township
Paul Bunyan
AVERAGE TIMENEAR EXCEEDING PWQG (%)
47
17
18
45
27
Port Blake
St. Joseph v
- -
9
2
6
25
39
- -
7
4
23
28
8
5
Al1 beaches exceed the PWQG of 100 CFU/lOOmL for a considerable amount of time
throughout the season. Amberley, Godench Main and Port Albert al1 exceeded the guideline
at least 40% of the time they were sampled. Al1 three are located on Lake Huron in the
northern part of Huron County, specifically in the Maitland Valley and Nine Mile
watersheds.
Figure 5 presents the results for three lakeshore beaches over the past eight years, including
Bayfield and Godench Main beaches. Bacteria data for the BayfieId main beach shows that
this beach does not exceed the provincial guideline for bathing as fiequently, for exarnple,
as the Goderich Main beach, which, in the extreme, was over the guideline 69% of the time
that samples were taken in 1996. in contrast, Baÿfield Main beach exceeded the guideline
less than 10% of the time it was sarnpled for 1996.
- Pmvinad Highway I County Road
Beach and Inland Monitoring Stes
38% Percentage of Total Samples Exceeding Recreational Quality Guidelines
% Olher MOritwing Sites - Laking Data
Huron County Surface Water
r -
Figure 5: Time Exceeding PWQG
! Beaches 1990 - 1998
1993 1994 1995 1996 Year
Gaderich Arnberley
The results presented in Figure 5 show that there is no obvious trend over t h e for any of the
three locations, this was f o n d to be the case for al1 other lakeshore locations.
Table 9: Inland Recreationai Bathing Areas, 1990 - 1997
1 Gorrie 1 3 1 1 4
Brussels Dam
Camp Wyoka
Family Paradise
Falls Reserve
RANK
6
b
INLAND LOCATION
Bluevale Dam
I Ron's Camp I 4 I 10 1
AVERAGE TIME/YEAR EXCEEDING PWQG (%)
23
20
1
16
46
Morrison Dam
7
11
8
1
1 Wawanosh Conservation 1 38 1 3 1
12
Wingham
Water quality monitoring at the inland beaches (Table 9) shows that they also exceed the
- - -
9
PWQG much of the time. Falk Reserve, Wingham and the Wawanosh Conservation Area
38
are the three inland bathing sites that exceeded the PWQG most fiequently, with exceedances
2
more than 35% of the tirne. Al1 three are located in the Maitland Valley watershed, in the
north part of the County. As with the lakeshore sites, there was no apparent trend over time
for the individual inland bathing locations. A surnrnary of the data for the inland and
lakeshore bathing sites are shown in Appendix B.
6.1 -2 Provincial Water Quality Monitoring Stations
The raw water qirality data as well as the residuals (differences between the linear regression
a mode1 and observed data) for ali nine P WQMS's were tested for normality using a test for
skewness as well as the Sharpiro-Wilk test. Several attempts were made to transfonn the
data whkh appeared to be non-normal using six month or one year averaging or logariuunic
transformation. Data that was not successfully normalized using these transformations was
regressed over time and the uncertainty of the results of the regression was noted. Table
10 presents the results of trend over time analysis for the PWQMS's. This table indicates
whether there was a significant trend for that water quality parameter at that monitoring
station, and it also indicates if the significant trend is increasing or decreasing with tirne.
*These stations met the criterion for significance (error on the slope of the regression line was less than the slope itself) marginally.
Table 10: Trends in L
STATION
1 &23
4
15
17&31
35
1
8
1 I
13
The information in Table 10 indicates that seven of the eight stations with total phosphorus
data showed a significant deciine in total phosphorus concentration with tirne. In contrast
nitrate-N data for six of the nine stations with were significantly increasing over time and
faecal coliform data for four of the nine stations with were also significantly increasing over
Figure 6 (nitrate-N) for Station 4 illustrates that the PWQMS data can show an easily visible
trend and Figure 7 (total phosphorus) for Station 15 illustrates that the PWQMS data can
Concentrations of Water Quality Parameters at P WQMS's
TOTAL PHOSPHORUS
SIG.?
YES*
YES
E S
YES
YES
NITRATE
CHANGE
1
1
1
1
1
NO
NO
SIG.?
E S
YES
YES
YES*
NO
YES
NO
FAECAL COLIFORM
CHANGE
t
T
1
T
1
1
SIG.?
NO
YES
YES
YES
NO
NO
YES*
YES
NO
N/A
CHANGE
1
1
1
1
1 NO
E S *
YES 1
show no easily visible trend. Graphs of al1 of the regression data for NO,-N and total
phosphorus are shown in Appendk C.
Figure 7: Total Phosphorus 1971 - 1994 Station 15, South Maitland Basin
70 75 80 85 90 95 year
--- _--____ - - - -
The PWQMS's have been ranked with respect to the contaminant loadings and
concentrations to show where the best and poorest water quality is in the County.
Contaminant loadings were calcuiated based on the mean concentration of the water quality
parameter in the water courx and the yearly discharge for that water course. Appendix D
shows a sarnple calculation and a summary of basin loadings. Table 11 shows these
rankùigs, a rank of 1 indicates the station with the highest concentration or loading.
Stations 1 & 23 in the Main Maitland basin have the highest loading in the Maitland Valley
watershed. This basin receives flow f?om the North, Little, Middle and South Maitland
bains.
Table 1 1 : Rank of PWQMS's Based on Loadings & Concentrations
' BASINNAME& PWQMS
1 Main Maitland, 1 & 23
North Maitland, 4
South Maitland, 15
Middle Maitland, 17&3 1
Little Maitland, 35
Nine Mile River, 1
Bayfïeld River, 8
Ausable River, 1 1
Gullies, 13
RANK BY CONCENTRATION
F.C.
8
5
4
4
7
6
3
2
1
RANK BY LOADNG
F.C.
1
5
6
4
7
7
3
2
NO,
7
8
3
5
6
9
1
4
2
T.P.
8
6
5
3
7
N/A
4
2
1 no flow data
NO,
1
6
4
2
7
8
- ?
5
T.P.
1
6
5
2
7
N/A
4
3
In general, it is apparent that the highest concentrations of pollutants occur in the southem
area of the County. In this study the southem area of the County is the area under the
jurisdiction of the Ausable-Bayfield Conservation Authority and the northern area is the area
*der the jurisdiction of the Maitland Valley Conservation Authority. Station 8 is located
just north of Varna, and Stations 1 1 and 13 are both at the southem boundaries of the County.
These three stations are ranked among the top four for al1 contaminant concentrations. A T-
test was done at the 95% level and it was found that, for al1 three contaminants, the means
for the north and south were significantly different. The results are presented in Appendix
E.
Figures 8,9 and 10 illustrate the information presented in Table 1 1 in a graphical image of
6 the County. The rank of each basin, as indicated by the data at the representative P WQMS,
is shown on each map, and the ranks are also represented by the colours of the basins.
Figure 8: Basin Ranking Based on Overall Average
10 km
z Conservation Authority
L e m Unranked
Highest Concentration
f Medium Concentration
Lowest Concentration - Major Basin Boundary
Provincial Highway I County Road
1 - 8 Ordnal Ranking from High to Low
Huron County Surface Water Qualitv Data Proiect
Figure 9: Basin Ranking Based on Overall Average Nitrate Concentrations
W 0 Unranked
Medium Concenlration
Lowea Concentration
Mapr Basin Boundary - Provincial Highway 1 Counry Road
1 - 9 Ordinal Ranking from High to Low
Huron Countv Surface Water
Figure 10: Basin Ranking Based on Overall Average Faecal Coliforni Concentrations
- 10 km
Mailland Valley Consendon Authority
Lgoead
0 Unranked
Highest Concentration
a hkdiurn Concentration
Lowest Concentation - Major Basin Boundary
- Pmvmaal Highway! County Road
1 - 8 Odmal Ranking frorn High Io Low
Huron County Surface Water Qualitv Data Proiect
For example, the bas& that have the three highest concentrations of total phosphorus
(ranked fiom 1 to 3) are indicated by red, the basins with the lowest concentrations (ranked
fiom 7 to 9) are indicated by blue, and those basins in between are shown in yeIlow.
6.1.3 Discharge
The mean annual discharge for each flow gauging station was regressed to detemine if there
were any significant trends over time. Eight of the nine basins had discharge data and of
those eight, Stations 8, 1 1, 15 and 17&3 1 showed a significant increasing trend over time.
The raw discharge data are presented in Appendix F.
6.1 -4 Waste Water Treatment Plants & Lagoons
There are 1 1 WWTP's and lagoons servicing urban areas in Huron County, and another five
outside the County that discharge into either the Maitland Valley or Ausable-Bayfïeld
watersheds. Several factors afTect discharges fkom WWTP's and lagoons, including:
treatrnent processes used, discharge fiequency, and population changes.
Table 12 gives the rankings of the WWTP's and Iagoons based on average concentration and
the average yearly loadings of total phosphorus, nitrate and faecal coliforms. A summary of
the WWTP loadings is given in Appendix G.
Goderich, the Iargest town in the COW@, delivers the most nitrate-N (kg/year) and total
phosphorus to its receiving stream, however, the effluent fiom the Blythplant has the highest
nitrate-N concentration. This illustrates the impact of discharge volume on the total amount
of pollution released to the environment.
able 12: Ranking of WWTPYs and Lagoons by Concentration and Loading
Lucknow 1 discharges to swale not directly connected to surface water 1
LOCATION
BIyth, 1982- 1997
Bmssels, 1982-1997
Clinton, 1988- 1997
Exeter, 1990- 1998
Goderich, 1967- 1997
Grand Bend, 1987-1997
Haniston, 1994- 1997
Hensail, 1985- 1997
Listowel, 1 994- 1998
I system I
RANK BY CONCENTRATION
NOTE: 1 indicates a decreasing trend, 1 indicates an increasing trend.
rCANK BY LOADING
F.C.
8
7
NIA
NIA
7
3
2
5
N/A
Milverton, 1990- 1997
Palmerston, 199 1 - 1997
Seaforth, 1 975- 1 997
Vanastra, 1974- 1997
Wingharn, 1984- 1997
h i c h , 1985-1 997
F.C.
10
8
NIA
NIA
2
5
1
9
N/A
NO,
1
2
4
7
5
10
9
14
6
T.P.
6
I l
9
8
1
5
13
3
15
NO,
6
4
2
7
1
11
9
13
3
6
N/A
N/A
2
1
4
T.P.
13
11
7
6
1
10
4
8
5
14
11
4
10
2
7
13
NîA
12
3
8
11
6
NIA
NIA
3
7
4 A
12
10
NIA
5
8
14
12
2
N/A
9
3
14 L
Linear regressions were done on the WWTP and lagoon loading data to determine thne
trends in the pollutant loadings. Table 13 summanzes this analysis. Where significant trends
were noted, total phosphorus loadings were generally decreasing, the nitrate loadings were
increasing in four of six cases, and faecal colifonn loadings were decreasing at 3 of 5
WWTP's that have significant trends. A sumrnary of the data and some exarnples of the
resultant linear regressions are in Appendix G.
FAECAL COLIFORM
WWTP & LAGOON LOCATION
Blyth, 1982-97
TOTAL PHOSPHORUS 1 """
SIG.? 1 CHANGE 1 SIG.? 1 CHANGE SIG.? 1 CHANGE
1 Exeter, 1990-98
1 Goderich, 1967-97 1 $and Bend, 1987-
1 Hensall, 1985-97
Listowel, 1994-98
Lucknow discharge to swale, not directly connected to surface water system
1 Milverton, 1990-97
flow data not received
Wingham, 1984-97
Zurich, 1985-97
NOTE: 1 indicates a decreasing trend, T indicates an increasing trend.
Table 14 presents the landfills considered, and whether the concentrations of the parameters
monitored exceeded the PWQG's for the period of monitoring. Examination of the raw data
for landfills that monitor upstream and downstrearn locations showed that upstrearn
concentrations were sometimes greater than, sometirnes Iess than, and sornetimes dmost
equal to the downstrearn concentrations. The PWQG for total phosphorus and nitrate were
not exceeded at any of the streams rnonitored near the landfills. It is evident that the iandfills
hqve a negligible impact on surface water quality for the parameters of interest.
Table 14: Cornparison of Landfill Data to Provincial Water Quality Guidelines
-- -
LOCATION & YEARS OF # SAMPLES EXCEEDING # SAMPLES DATA TOTAL PHOSPHORUS EXCEEDING
PWQG ? NITRATE-N PWQG ?
Ashfield, 1989- 1997 36969 none
1 Blyth, 1990-1997 1 does not rnonitor strearnheek
1 Exeter, 1975- 1995 1 less than two years of data
East Wawanosh, 1990-1 997
1 Hay/Zurich, 1994- 1997 1 less than two years of data
36953 none
Goderic WColborne no surface water quality samples
- - ---- -
Howick, 1982-1 997
McKillop
Morris, 1993-1997
Stanley, 1988-1997
Stephen, 1982-1 997
1 West Wawanosh, 1987- 1 997 1 does not monitor streardcreek
Turnberry
Usborne/Kirkton, 1994- 1 997
6.2 Human Population
36988
Human population can affect water quality in a variety of ways - principally by the discharge
- -
none
no swface water quality sarnples
of waste to the environment. In urban areas this is reflected by the data collected by the
detection lirnit too high
WWTP's and lagoons. In rural areas this discharge is mainly through the use of improperly
no surface water quality samples
36973
installed or malhnctioning on-site sewage systems. Although these discharge, at least by
7/3 2
37269
twice per year
none
monitor NO, + NO,
monitor NO, + NO,
design intention, to the subsurface, they can have a significant impact on surface water
quality. System characteristics which can affect their impact on water quality include age,
density, design, soil conditions, use, and existence of illegal connections to tile drains.
Contributions to surface water are also possible through the application of fertilizers on
lawns. Little information is availabIe on most of these factors in Huron County. Due to the
lack of detailed information, the effect of human population on water quality is considered
by examining human population density.
Table 15 ranks the basins with respect to their population densities taken fiom the 1996
population and dwelling counts for Ontario (Statistics Canada, 1997). A rank of 1 indicates
the basin with the highest population density. Figure 11 shows a map of the County that
represents the information presented in Table 15. This rnap of the County shows the rank
of each basin, and it is also colour coded, with red indicating basins with a high population
density, and blue indicates basins with the lowest population densities.
Additional data, such as the age of the septic systems, and their distribution throughout the
County would be very usehl in determining the potential impacts on water quality.
Although studies have been done, for example the TSS by the Ausable-Bayfield
Conservation Authority (1996), that identified septic systems as a major source of bacterial
contamination, this study was limited to a small area, and therefore does not necessarily
represent the County-wide pictwe.
1 Ausable River, 1 1 1 0.29 1 2 1
Table 1 5: Human Populations Density and Ranking 197 1 - 1996
1 Bayfield River, 8 1 0.30 1 1 1
BASIN NAME & PWQMS
POPULATION DENSITY 1 996 (#/ha)
Little Maitland, 35
RANK BY DENSITY
Main Maitland, 1 &23
1 Nine Mile River, 1 1 0.0 1 1 9 1
O. 10
Middle Maitland, 17&3 1
7
0.12 6
0.13
North Maitland, 4
South Maitland, 15
Overall, the population in Huron County has increased by 15.6% ~ o m 197 1 to 1996. A plot
e of the population change is shown in Figure 12.
5
Gullies, 13 1 O. 19
6.3 Agriculture
0.19
0.07
3
Agriculture has the potential to impact surface water quaiity in many ways. In the following
sub-sections trends in livestock populations and land use within Huron County are
considered.
4
8
6.3.1 Livestock Population
For purposes of comparison a11 livestock populations are given as livestock units, unless
otherwise stated. Cornparisons between different types of f m animals are done using
livestock units because this measure compensates for the different environmental impacts
the animals have. For example, the amount of rnanure produced by a dairy cow is not
comparable to the amount of manure produced by a chicken.
It has been determined that a fair comparison can be made if the nurnber of smaller animals
is divided by a factor to approximate the effect of a cow (OMAFRA, 1995). Census
populations of total swine were divided by 5 to convert to livestock units; sirnilarly, the
census populations of total poultry were divided by 125 to convert to livestock units
(OMAFRA, 1995). Both of these conversion factors are in the middle of the ranges used by
OMAFRA. Although it is possible to convert the hurnan population to iivestock units in a
similar manner, this has not been done in this analysis. The reason for not taking this
approach is that human wastes are disposed of in a fundamentally different manner than
those of livestock.
Figure I I : Basin Rankin~ Based on Po~ulation Densitv
km2 O 10 km
t Consenation Authority Wa- 8i= J tkrhlir tn,in*yomI
Leaend 0 U m k e d
Highest Oensily i Medium ~ensity
Lowest üensity
Majw Basin Bomdary - Pmvïnaal Highway I County Road
1 - 9 Ordiial Ranking fmm High to L w
Overall, the population of livestock in Huron County has decreased by 4.4 % fiom 197 1 to
1996.
Figure 13 illustrates the changes in animal populations in the County fiom 197 1 to 1996.
This figure shows an increase in swine and poultry populations and a decrease in cattle
population. The acnial changes in populations fiom 1971 to 1996 are: a decrease in cattle
population of approximately 30%, an increase in swine population of alrnost 98% and an
increase in poulûy population of almost 50%.
It is ciear that some townships are more poultry intensive, whereas others are swine
intensive. For example, the Township of Ashfield had a poultry population of 184,458
animals and a swine population of 15,646 animals in 1996, whereas Moms Township had
poultry population of 39,588 animals and a swine population of 29,190 anirnals in 1996. m
These townships are of similar areas, but Ashfield is clearly much more pouttry intensive and
Monis is more swine intensive.
Table 16 gives the livestock density and ranks each of the basins according to density. The
three basins with the highest ranking for livestock density are al1 located in the Maitland
Valley watershed with densities al1 above 0.80 LU/ha. Figures 14 through 17 show the
information presented in Table 16 on a map of the County.
Figure 14: Basin Ranking Based on 1996 Cattle Density r
- km2 O 10km
Maithnd V a k y * Consenation Authority
ic'atLg br . HrahIly Emirwrrnt
LsoeDd 0 U m k e d
Highest Density
O MMediurn ~ensrty
Lwest Densty
Major Basin Boundary Provincial Highway / County Road
1 - 9 Ordinal Ranking from High to L w
S ace Water HUr8Ua%rB%b #O,,
Figure 15: Basin Ranking Based on 1996 Poultry Density
1
km2 O 10 km Maitland Valley
2: Conservation Authoriîy HIDi%lbi.twtmbnimmm,
Leoend 0 U m k e d
Highest ûensity
O Medium ~ e n s i t y
Lwestüensrty
Major Basin Boundary
Provincial Highway 1 Cwnty Road
1 - 9 Ordinal Rankina from Hioh to Low
Huron County Surface Water Qualitv Data Proiect
Figure 17: Basin Ranking Based on 1996 Livestock Unit Density
w 0 Unranked
Highes< Density
MMediurn ûensity
Lowest Density
I - Major Basin Boundary 1 Provincial Highway I County Road
1 - 9 OrQnd Ranking from Hqh IO Low
Huron County Surface Water Qualitv Data Proiect
Again, red indicates bas& with hi& cattle, poultry, swine and livestock densities, blue
indicates basins with low poultry, swine and livestock densities.
Table 16: 1996 Livestock Densities and Overall Basin Ranks
BASIN NAME & I RANK BY DENSITY 1 LNESTOCK 1
Middle Maitland, 17&31 ( 4 1 4 1 1 1 3 1 0.86
PWQMS
Ausable River, 1 I
Bayfield River, 8
Little Maitland, 35
Main Maitland, 1 &23
Table 17 details the population changes for cattle, poultry and swine in the nine major
basins over the period considered. Table 17 also ranks the basins with respect to their
population densities taken fiom the 1996 Census of Canada. A rank of 1 indicates the
basin with the highest population density of that particular animal type. Some areas, such
as the Gullies and Middle Maitland, have seen dramatic increases in swine populations
from 1 97 1 to 1996, with increases of 1 83 and 166% respectively.
Nine Mile River, I
North Maitland, 4
South Maitland, 15
Gullies, 13
DENSITY
( L U w
0.69
0.76
1 .O7
0.67
Cattle
5
6
1
3
9
2
7
8
Poultry
7
1
5
6
9
8
2
3
Swuie
3
5
2
8
Livestock
units
5
4
1
7
9
7
6
4
9
2
6
8
- pp --
0.16
0.92
0.68
0.59
'able 17: Changes in Livestock Populations 1 97 1 - 1996
Ausable River, 1 1
Bayfield River, 8
Little Maitland, 35
Main Maitland, 1 &23
Middle Maitland, 17&3 1 ?
Nine Mile River, 1
6.3.2 Land Use
BASIN NAME & PWQMS
North Maitland, 4
South Maitland, 15
Gullies, 13
L+nd use data was taken h m the agriculturai profile of Ontario Census of Canada fiom
197 1 to 1996 (Statistics Canada, 1972 to 1997). Land use categories considered were
improved and unimproved land. The improved category includes the following land use
types: land under crops, pasture, summer fallow, and other. The unimproved category
includes: woodlot and other. Table 18 shows the change in land use in both categories for
the nine major basins.
RANK BASED ON % CHANGE IN POPULATION
-42
-3 3
-17
-22
-41
- 13
CHANGE IN POPULATION (%)
Cattle 1 1
-7
-48
-48
Cattle I
17
53
44
82
-14
7
Swine Poultry
56
62
65
Swine
93
62
123
36
166
11
70
65
183
7
5
3
4
6
2
1
9
8
7
5
6
1
9
8
4
7
3
8
2
9
4
3
2
5
6
1
Table 18: Amiculture Land Use Changes fiom 1971 - 1996
I BASIN NAME & PWQMS
CHANGE IN LAND USE (%) I
1 IMPROVED 1 UNIMPROVED
1 Ausable River, 11
1 Bayfield River, 8
1 Little Maitland, 35 1 -1 1 1 29
I -
1 Main Maitland, 1&23 1 -8 15
1 Middle Maitland, 17853 1 1 -24 1 -8
1 Nine Mile River, 1 1 1 North Maitland, 4 1 - 19 1 18
1 South Maitland, 15 1 -2 1 41
Gullies, 13 1 7 1 -0.3
Table 18 illustrates a problem with the agriculture census data. Logically, an increase in area
of improved land should result in an equal decrease in unimproved land. This is not the case
for eight of the nine bains. It has been speculated by memben of the community that census
respondents estimate the area, and some are more accurate than others. Also, those surveyed
by the census changes fiom year to year, which introduces a fuaher source of error year to
year. However, a farm included in one census year may not be considered in the next census
year if that f m e r is not farming or if the !and was used in development (Bill McGee,
OMAFRA, personal communication, August 1999). Variability of census sarnpling would
prevent the total farm area fiom being consistent from year to year.
Due to the lack of confidence in this data, was not used M e r .
6.3 -3 Other Agricdtud Factors
Agricultural spills information obtained fiom the Ministry of the Environment fkom 1988
to rnid- Jwie 1998, showed that the number of reports ranged fiom a low of 1 in 1994 to a
high of 1 I in 1989. The nuniber of spills reported in any year is a fünction of environmental
awareness, and not necessarily the number of spills that occurred, and there is no discemible
trend. Agricultural spills information is summarized by year in Appendix H.
Information on tile drain tubing sales waç provided by OMAFRA, and is given by sales each
year in Appendix H. Although this information is not specific for Huron County, it does
indicate that in the province the sale of tile drain tubing increased by 46% fiom 1976 to
1983, then decreased fiom 1983 to 1992. The sales then increased by 100% fiom 1992 to
1996. The tubing sales would be used for both replacements and new installations, and
therefore tubing sales does not directly represent an increase in the amount of land drained
by tiles in the County.
Data received from the Fertilizer hstitute of Ontario Inc., onginally fiom OMAFRA,
indicated an increase in fertilizer sales of approximately 360% in Huron County over the past
40 years. This information was available on a County wide ba i s ody, and does not
necessarily represent the amount of fertilizer applied in the County.
Further analysis of these factors was not performed due to the Iack of data.
91
6.4 Soil Drainage CIass
Soil drainage class is an important factor that may be related to surface water quality, for
example, pollutants are more likely to nrn off the surface of imperfectly-drained soil types
and thus enter streams, rivers and lakes, whereas well-drained soils produce less overland
runoff since more water infiltrates the soil structure.
Soil drainage class is a characteristic that does not change over tirne, and therefore a trend
over time analysis was not appropriate.
Table 19 summarizes the amount of poorly drained soils in the basins. This characterization
gives an indication of which basins are more likely to have runoff of excess water. The five
basins with the highest proportion of poor to imperfectly-drained soils are: Gullies, Ausable
River, South Maitland, Middle Maitland and the Bayfield River, al1 with over 40% of their
area in the poor to imperfectly-drained soils.
Table 19: Soi1 Drainage Classes in the Major Basins (MVCA, 1998)
1 North Maitland, 4 1 19
Main Maitland, 1 & î 3
1 South ?and, l
RANK BASIN NAME &
STATION
Middle Maitland, 1 . 1'7&31
LAND IN POOR TO XMF'ERFECT
SOIL DRAINAGE CLASSES (%>
17
I Nine Mile River, 1
9
1 Gullies. 13 1 68 1 1
Baflield River, 8
Ausable River, 1 1
Figure 18 shows the data presented in Table 19 graphically on a map of Huron County.
44
65
5
2
Figure 18: Basin Ranking Based on Soil Drainage Class
-
hlaitland Valley Conservation Authority ~ I t r . r i n i h ) n ~ t
O lrnpalecuy ~ra i ied = Wil Dahed - Major Basin Boundary
Provincial Highway I Cowty Road
1 - 9 OrdW Ranking frwn High to Low
Huron County Surface Water Qualitv Data Proiect
6.5 Precipitation
Precipitation is an important factor often related to surface water quality. For example it has
been observed that beach postings typically occur after a rainfall event (Worsell, B. Public
Health Inspector for Huron County. personal communication, July 1 998). Precipitation data
used in this analysis was provided by the Canadian Meteorological Centre, Environment
Canada. Two stations were examined, one in Blyth, near the middle of the County, and the
other in Wroxeter, in the north of the County.
By comparing the average annual precipitation to the overall annual average precipitation
(fiom 1975 to 1997), a judgement can be made as to whether it is a "dry", "wet" or average
year, and thus a year in which annual precipitation was a significant factor. For the purposes
here, a "dry" year is one in which the annual precipitation is 15% less than the average, and
a "wet" year is one with 15% more precipitation than the average.
Table 20 lists the amount of precipitation for each year, and whether that year was "wet" or
"dry". For months that had more than three days of precipitation data missing, the long tenn
average for the month was used.
Table 20: Yearlv Classification of Annual Preci~itation
1182 1 average 1
YEAR
wet
BLYTH - #6120819
PRECIP. (mm) I TYPE OF YR
WROXETER - #6 129660
PRECIP. I TYPE OF YR
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1289
1334
84 1
1 044
1097
1324
1986
1987
1084 1 average (
1212
1145
995
1574
1988
843 1 dry
average
average
dry
average
average
average
1340
984
average
average
dry
wet
1347
1019
1099
925
918
1003
927
average
dry
1990
1991
1992
average
average
average
average
average
average
916
1015
875
1186
average
average
average
average
wet
1108
919
1268
1283
1490
average
average
994
893
847
1039
1 083
930
-
average
average
wet
wet
average
- 1993
1994
1995
1996
1997
average
average
average
wet
average
dry
average
average
average
- --
1136
1101
1532
1522
1107
1049
1071
1 140
average
average
wet
6.6 Correlation Analysis
Correlation between the three water quality parameters of interest and the contributing
factors was detemined using Spearman's method based on ordinal data, by stepwise
regression and principal component analysis. The results of these analyses are discussed in
the following sub-sections.
6.6.1 Spearman Correlation Analysis
Tables 2 1 to 24 present the results of the correlation ana lysis done for the nine basins base(
on the rankings according to the overall averages of al1 available data. A sample calculation
of septic system density is shown in Appendix 1.
Fable 21 : Spearrnan Correlation Results for Overall Population Factor Averages 7'
Water Quality Parameter
ni trate-N
total phosphorus
faecal 1 0.62 ( 0.68 1 -0.14 1 0.41 1 -0.49 1 0.45 1
Spearman Correlation Coefficient
septic systems
0.55
0.52
human
0.60
0.59
livestock
O
-0.33
swine
0.47
0.50
catile
-0.30
-0.71
POUW
1
0.88
0.28
Table 22: S~eannan Correlation Coefficients for Other Factors
total phosphorus 1 0.90 1 0.21
Water Quality Parameter
nitrate-N
1 faecd coliforms 1 0.88 1 0.22
The results from Table 21 indicate that the strongest positive Spearman correlations are
between poultry population and nitrate concentration, as well as between human population
and faecal coliform concentration. In contrast, the results indicate a negative correlation
between cattle population and total phosphorus concentration. The correlations are
significant at the 95% level for n = 9 (faecal coliforms and nitrate-N) if the coefficient is
0.~683 or greater, and significant for n = 8 (total phosphorus) if the coeficient is 0.73 8 or
greater Runyon and Haber (1991).
Spearman Correlation Coefficient
S p e m a n correlation coefficients fiom Table 22 indicate that soi1 drainage class is strongly
correlated to al1 three contaminant concentrations - al1 coefficients are greater than 0.738.
Soi1 Drainage Class
0.75
Speman ' s correlation analysis was also done for basin rankings based on yearly averages
for 199 1 and 1986 and results are shown in Tables 23 and 24 respectively. Analysis on 1996,
197 1 and 1976 could not be performed due to lack of data.
Discharge
0.45
Table 23 : S m a n Correlation Results for 1 986
Table 24: S~earman Correlation Resdts for 1 99 1
Water Quality Parameter
nitrate-N
totai phosphorus
faecal coliforms -- - - -
Both 1986 and 199 1 were years with average precipitation. The results from Tables 23 and
24 indicate that, sirnilar to the results presented in Table 22, soil drainage class is generally
correlated to al1 three contaminant concentrations. The coefficients relating total phosphorus
and faecd coliforms are both greater than the significant values of 0.738 (n=8) and 0.683
(n=9) respectively, and the coefficient for nitrate is very close to the significance value of
0.738 in 1986. In addition, cattle density appears to be negatively correlated to faecal
coliform concentration (-0.67 compared to significant value of -0.683) and poultry density
is positively correlated to nitrate concentration (0.77 compared to significant value of 0.683)
in 1991. Finally, there is some consistency in the positive correlation between human
99
Speannan Correlation Coefficients
Water Quality Parameter
nitrate-N
total phosphorus
human
0.68
0.52
0-70 -- -
I faecal coliforms 1 0.57 1 O 1 -0.67 1 -0.03 1 0.73 1 -0.10 1
-
Spearman Correlation Coefficients
swine
0.43
0.14
0.35 pp -
cattle
-0.28
-0.26
-0.22 -
human
0.63
0.86
swine
0.70
-0.1
discharge
0.40
0.83
0.64
poultry
0.65
0.05
0.35
soil drainage class
0.73
0.8 1
0.82
cattle
-0.20
-0.67
poultry
0.77
0.12
soi1 drainage class
0.65
0.36
discharge
1
0.54
0.50
population density and faecal coliform concentration (0.7 compared to significant value of
0.683) in 1986, aithough in Table 24 there also appears to be a positive correlation between
human population density and total phosphoms concentration (0.86 compared to significant
value of 0.738).
6.6.2 Multiple Regression
Sub-sections 6.6.2.1 and 6.6.2.2 present the results of the stepwise regression and principal
component analysis.
6.6.2.1 Stepwise Regression
As with the Spearman's correlation analysis, stepwise regression was performed on the
yearly averages for 1986 and 199 1 . The results are presented in Table 25, variables lefi in
the mode1 are significant at the 0.15 level.
ïable 25: Ste~wise Reeression Results
Water Quality Parameter
nitrate
Significant Contributing Factors
1 99 1 Yearly Averages
total phosphoms
1986 YearIy Averages
l swine discharge
faecal colifonns cattle (negative) 1 human 1 human
The results presented in Table 25 are generally consistent with those presented in Tables 23
and 24. Poultry population density is correlated to nitrate concentration, human density is
correlated to faecal coliform concentration, and cattle population density is negatively
correlated to faecal coliform concentration.
6.6.2.2 Principal Component Analysis
Tables 26 and 27 present the results of the principal component analysis. This analysis
suggests that in 199 1 human population density is positively correlated to total phosphorus
concentration and that swine and poultry densities are positively correlated to nitrate
concentration. The positive correlation between poultry density and nitrate concentration
was shown in Tables 2 1,23 and 24. Whereas cattle density is negatively correlated to total
phosphorus concentration. The results fiom 1986 also suggested a positive correlation
between swine and poultry densities and nitrate concentration, and discharge and total
phosphorus concentration.
Table 26: Princi~ai Com~onent Correlation Results for 1 99 1
Water Quality Parameter
ni trate-N
total phosphorus
faecal coliforms z
Principal Component Correlation Coefficient
human
0.6 1
0.87
0.63
swine
0.82
0.13
-0.20
poultry
0.82
0.4 1
-0.02
cattle
0.20
-0.72
-0.64
soi1 drainage class
0.63
0.33
0.5 1
discharge
-0.03
-0.17
-0.29 -
Table 27: Princi~al Com~onent Correlation Results for 1986
6.6.3 Wastewater Treatment Plants and Lagoons
Water Quality Parameter
nitrate-N
total phosphorus
Table 28 shows the ranks, based on concentrations at the PWQMS's and the percent of
loading fiom WWTP's and lagoons in sach basin of the loadings recorded at the PWQMS's.
Overdl the relative contributions of nitrate-N and total phosphorus fiom WWTP's to local
rivers and streams were small, whereas contributions of faecal coliforms ranged fi-om 6.2 to
> 100% of the calculated strearn loading. Contributions greater than 100% were possible due
to the significant die-off that occurs when bacteria are discharged to a stream. Settling,
predators and W radiation are al1 factors responsible for reducing bacteria concentrations
in streams and nvers (Entringer and Strepelis, 1996).
1 faecal colifonns f 0.70 1 0.13 1 0.13 1 0.05 1 0.63 1 0.69 1
Principal Component Correlation Coefficient
The results indicate that WWTP's may be a significant source of faecal coliforms to rivers
-d streams in Huron County, and in one instance (North Maitland Basin) may also be a
significant contributor of total phosphorus. However, it is unlikely that WWTP's are a major
source of nutrients to surface waters in the County as a whole.
human
0.66
0.50
swine
0.79
-0.30
cattie
0.35
-0.17
poultry
0.83
-0.27
soi1 drainage class
0.60
0.57
discharge
0.54
0.80
BASIN NAME & CONCENTRATION WWTP'S & CONTRI- PWQMS RANK LAGOONS BUTING
PORTION OF LOADING TO WWTP'S PWQMS (%)
Wingharn,
Harriston, Palmerston
Table 28: WWTP's & Lagoons and Related PWQMS Data
t
-
h
-
-
-
- I
Note: Zurich, Grand Bend and Goderich al1 discharge to the Iake, and the effluents from these facilities does not pass a PWQMS, and therefore they were not included in this analysis.
Main Maitland, 1&23 8 7 8 93
North Maitland, 4 5 8 6 >IO0
South Maitland, 15
Middle Maitland, 17&3 1
Little Maidaid, 35
Nine Mile River, 1
*
Bayfield River, 8
Ausable River, I 1
Gullies, 13
4
4
7
6
3
2
1
3
5
6
9
1
4
2
5
3
7
N/A
4
2
1
none
Listowel, Brussels, Milverton
none
Lucknow
CIinton, S eaforth, Vanastra
Hensall, Exeter
no W T P in basin
no WWTP discharges u/s of PWQMS
25 0.6 2.6
no WWTP in basin
only WWTP discharges to swale
3.2
3.8
NO0
6.2
0.9
0.2
6t6.4 Landfills
Table 29 compares the water quality at the representative PWQMS and the landfill
monitoring data for total phosphorus and nitrate-N. Constituent concentrations near the
Iandfills are in general, quite low, and most cases equd to or below equivalent parameters
at the PWQMS's. It is reasonable to conclude fiom this data that landfills do not contribute
a significant amount of nutrients to surface waters in Huron County. It is not possible to
conclude that Iandfills are not a source of bacteria as this data was not collected-
PWQMS
ïable 29: PWQMS's and Landfills Water Qudity
1 PWQMS 1 LANDFILLS
BASIN NAME & AVERAGE CONCENTRATIONS (mg/L)
1 South y a n d , l 0.046
Main Maitland, 1 &23
North Maitland, 4
Middle Maitiand, 0.060
Little Maitland, 0.039
4.75 1 NO LANDFILL
T.P.
0.03 7
0.042
NO LANDFILL
NO, - N
2.86
2.03
T.P.
O. 127
0.070
-
Nine Mile River, 1
Bayfield River, 8
Ausable River, 1 I
Gullies, 13
NO,- N
2.24
0.086
1.46
5.44
4.36
5 -43
NO DATA
0.057
0.120
O. 130
- - -
0.024
O. 120
0.057
1.53
NIA
5.56
only one year of data
7 DISCUSSION
Sections 7.1 to 7.2 discuss the results in terms of the two main objectives of the research:
significant trends with respect to total phosphorus, nitrates and faecal coliforms in the
surfaces waters in Huron County; and significant correlations between nual and urban factors
and surface water quality.
7.1 Trends
Sections 6.1 to 6.5 presented the results of the trend over tirne analyses. The following sub-
sections discuss the significant trends and how data deficiencies af3ected the analyses.
7: 1.1 Lakeshore and Mand Recreational Bathing Sites
One objective of the study was to determine long term trends over time, specificaily over a
25 year penod. However, bacteria data fiom recreational bathing sites in the County was
available only fiom 1990 to 1998, and as a result it was not possible to determine a long
term trend over time, though it was possible to Look at trends over the eight year period of
record that was available.
Although public perception is that the beaches are posted more often each year, the eight
years of available data does not confirm this trend towards poorer water quality. The bathing
site data showed that fkequency of beach closures was variable and that a significant
increasing trend over time was not evident fiom 1990 to 1998. Some sites exceeded the
guideline for bathing up to 40% of the time they were sampled suggesting there is a reason
for concern about water quality in these areas. However, the problem rnay not be as severe
as some of the results indicate. The arnount of time that sites exceeded the guideline may
be somewhat misleadhg because these sites are usually sampled d e r a rain event, when it
is expected that there may be a threat to public health (Worsell, B. Public Health Inspecter
for Huron County. personal communication, July 1998). During and after rain events there
rnay be contaminated m o f f from fields entering the lake and streams. This is also the time
when combined sewers are most Iikely to overflow and raw sewage mixed with storm water
enter the lake untreated. Rain events are often accornpanied by wind storms that increase the
arnount of wave action in the lake and this wave action can resuspend contaminated sand and
sediment in the water (Worsell, 1998).
Inland bathing sites in the northem part of the County exceeded the guideline more often
than sites in the south. However, the three inland sites with the highest levels of exceedance
aie hydraulically connected and this may affect the interpretation of the data. Wingham Dam
is upstrearn of Wawanosh Conservation Area, which, in turn, is imrnediateiy upstrearn of
Falls Reserve. This suggests that there is a source ofbacteria entering upstream of Wingham
Dam that is carried south where the Wingham WWTP discharges bacteria to the Stream
before the Wawanosh Conservation Area. Downstrearn fiom the Conservation Area the river
receives the discharge fYom the Blyth WWTP and continues south-west towards the Falk
Resewe and finally discharges to Lake Huron.
Similady, lakeshore sites in the north exceeded the guideline more ofien than sites in the
south. This rnay be due to the direction of lake circulation and loadings fiom in and outside
of the County. Faecal coliform and discharge data fiom a PWQMS on the Saugeen River
at Lake Huron, just north of Huron County were used to calculate the loading to the Lake.
It was found that the loading fiom the Saugeen River was ten times greater than the load
calcuIated for any station in the County. This loading, combined with the north to south lake
circulation, may help to explain the poor water quaiity at Arnberley and at Goderich beaches.
NSO, St. Josephs beach is down curent of where the Bafield River discharges to the Lake;
Station 8 on the Bayfield River had the third highest loading of faecal coliform.
Overall, the beaches and idand bathing sites do not meet the PWQG for swimming a
substantial amount of time that they are sampled and this is a cause for concern for residents
and tourists.
7.1.2 Provincial Water Quality Monitoring Stations
There was sufficient data for detemining trends over time for each of the PWQMS's,
although the analysis is less reliable than it would be if daily or weekly sampling was
available (Trkuija, 1997). Random monthly samples are more likely to miss peak
concentrations than a more frequent or targeted sampling regime. Another concem was that
die method of analyses had been changed for total phosphorus and nitrate-N, however these
methodologies were considered to have a negligible impact on the trend andyses (Loucks,
Orie. Professor of Zoology, Miami University at Oxford, Ohio. persona1 communcation
November 2000).
A significant decreasing trend was observed for total phosphorus concentration in seven of
the nine PWQMS's analyzed. The decreasing concentrations of phosphorus in streams and
rivers in Huron County may be attributable to many factors, including increased
environmental awareness. The govemment fimded PLUARG prograrn in the 1970's is an
example of a prograrn that worked to identi& sources of phosphorus and find ways to reduce
loading to surface waters. Increased interest in phosphorus pollution resulted in a significant
decrease in the use of detergents containing phosphates over the past 25 years. In addition,
funding was made available for programs airned as reducing erosion, and best management
practices for reducing phosphorus coming fiom farms were implemented, such as low-tillage
practices and nutrient management planning. Logan (2000) has reported in a paper to the IJC
that the conservation practices have significantly reduced particulate total phosphorus,
however, he also States that phosphorus levels in soils have been increasing. Also, loadings
of phosphorus fiom many WWTP's and lagoons has decreased due to the addition of alurn
to the wastewater for phosphorus removal. The benefit of the implementation of al1 of these
phosphorus-reducing practices is that the Stream health has been improved with respect to
phosphorus pollution.
In contrast to the phosphorus results, six of the nine stations showed a significant increasing
trend for nitrate-N concentrations and four of the nine stations also showed a significant
increasing trend for faecal coliform concentration. The increasing trend for nitrate could dso
be attributed to many factors including : cumulative effects of higher applications of fertilizer,
changes in predominant crop type, changes in manure spreading, cumulative effects of faulty
septic systems, and increased loadings from WWTP's and lagoons. Similarly for faecal
coliforms the increasing trend may be explained by changes in manure spreading, more fauity
septic systems, and higher loadings fiorn W WTP's and lagoons.
The relationship between water quality and related factors such as livestock and human
densities, and WWTP's are discussed in Section 7.1.3. However, due to lack of suficient
data on fertilizer use in Huron County it was not possible to determine if this was in fact a
potential explanation for increasing nitrate Ievels in the surface waters. This was also the
case for crop types, detailed data on the area and types of crops for the townships in Huron
County over the past 25 years was not available.
It is apparent that surface water quality in Huron County is improving with respect to total
phosphorus, is worsening with respect to nitrate concentrations, and that less than half of the
County shows an increase in the faecal coliform concentrations. It is also apparent,
considering the data analyzed in this study, that surface water quality in the Ausable-Bayfield
watershed is poorer than surface water quality in the Maitland Valley watershed. For
example, the geometric mean of faecal colifom concentrations for the southem basins
ranged fkom 95 to 158 CFU/lOOmL whereas the geometric mean for the northern basins
mged fkom 17 to 41 CFU/lOOmL. Sirnilarly the mean total phosphow concentration
ranged fkom 0.06 to 0.13 mg/L for the southern basins and fiom 0.04 to 0.06 mg/L for the
northem basins. Finally, the rnean nitrate-N concentration ranged from 4.4 to 5.4 mg/L for
the southern basins and ffrom 1.5 to 4.8 mg/L for the northern basins.
7.1.3 Wastewater Treatrnent Plants & Lagoons
Sixteen WWTP's or lagoons that discharge into Huron County were analyzed for trends over
time for total phosphorus, nitrate and faecal coliform loadings. Eleven WWTP's had
significant trends, and nine of these eleven plants indicated a significant decrease in
phosphorus loading over tirne. Sirnilarly, significant decreasing trends for total phosphorus
concentrations were observed at twelve WWTP's. The discrepancy between the number of
plants with significant trends for concentrations versus loadings is that some WWTP's did
not supply discharge data.
In contrast, of the eight plants that showed a significant trend for nitrate-N loadings, six were
significantly increasing over tirne. In addition, only one plant showed a significant
decreasing trend for nitrate-N concentration and three had a significant increasing trend for
nitrate-N concentration. Five plants showed a significant trend for faecal coliform loadings,
three were significantly decreasing over time.
Without changes in the treatment processes of the WWTP's or changes in domestic practices
it can be expected that with an increasing population, and therefore discharge, there will be
an increase in contaminant loads fiom WWTP's. This is the probable explanation for the
increasing nitrate loadings. However, regression showed a decreasing trend in phosphorus
loadings and a less pronounced decline in bacteria loadings. These decreases may be
attributed to increased environmental awareness. The use of detergents containing
phosphates has decreased in the past 25 years, and the use of a lun in the WWTP7s to remove
phosphorus has increased. Also, many WWTP ' s have added chlorination or W disinfection
processes to reduce bacterial loadings.
7.1.4 Human Population
The population of Huron County has been increasing over the past 25 years. Census
population data for the County was shown in Figure 13 in Sub-section 5.2, and indicates that
the population has increased by approximately 16% from 1971 to 1996. In addition, the
Huron County- Planning Department (1993) has reported that the nurnber of rural non-farm
residents is increasing, and the rural farm population is decreasing. The HCPD has also
found that in 13 of the 16 townships the majority of the population is in the mal, non-fann,
category.
However, without detailed information on septic system location, age and repair records it
is not feasible to determine the impact of this increase of people living in rural un-serviced
areas of the County due to disposal of household sewage.
7.1.5 Agricultural Factors
Figure 14 in sub-section 5.3.1 presented the changes in livestock densities over the census
years fiom 197 1 to 1 996. As mentioned in sub-section 5.3.1 there has been an overall
decrease in the livestock population of approximately 4% expressed as Iivestock units. This
is made up of a decrease in cattle population of approximately 30%, and an increase in swine
population of almost 98% and an increase in poultry population of almost 50%. It is clear
fiom these results that dramatic changes have taken place in the livestock population over
the pst 25 years in Huron County.
The increase in swine population and particularly the arriva1 of hog "factories" is of great
concern to residents of Huron County. Odours are a nuisance to people living near the large
swine operations, however, the more senous issue of nutrient management is a concern due
to the high volume of liquid manure that is generated by current management practices in hog
raising .
7.2 Correlations
One of the study objectives was to determine if any or al1 of the water quality parameters
could be significantly correlated to the factors considered in the previous sections and sub-
sections. The advantages of knowing which factors affect water quality and the degree to
which they cm impact water qudity are substantial. With limited resources available for
environmental protection it is in everyone's best interests tu ailocate those resources
carefûlly. Specifically, by targeting and remediating hose factors that have the most
significant negative effect first, it is possible to have a more significant improvement in water
qudity and potentially faster.
7.2.1 Lakeshore and Inland Recreational Bathing Sites
A' correlation analysis was not done to determine the relationship between location of
lakeshore and inland recreational bathing sites in relation to representative water quality
monitoring stations; instead, the data was simply compared.
The north part of the County, in the Maitland Valley watershed, had the three idand and
three lakeshore beach monitoring sites that most frequently exceeded the PWQG the most
amount of time. As noted in Section 5.1.1, some of these sites exceeded the guideline over
40% of the time that they were tested. Conversely, the PWQMSys with the highest
concentrations of coliforms were Stations 8 (Bayfield River basin), 1 1 (Ausable River basin),
and 13 (Gullies basin), al1 of which are in the Ausable-Bayfield watershed in the south of the
County. This indicated that there are differences in water quality fiorn inland recreational
bathing sites and idand PWQMSys. It is important to note that the bathing sites and
PWQMS's are at different locations and due to the susceptibility of bactena to U V radiation
and other environmental factors the bacteria data was not expected to correspond between
locations.
Discharge is one factor that may help explain the locational discrepancy for lakeshore
bathing sites. Table 1 1 in sub-section 5.1.1 ranked the sub-basins on the basis of
concentrations and Ioadings. Though the basins with the highest average concentrations
were in the south of the County, two of the three basins with the highest loadings were in the
north of the County (Main Maitland and Middle Maitland). However this is not conclusive
due to the lack of discharge data for the Gullies basin.
The level of bacterial contamination of the northern lakeshore beaches may also be explained
by upstream sources combined with lake circulation. Data from a station on the Saugeen
River, north of Amberley and just outside of the County (raw data given in Appendix J)
kdicated that it had faecal coliforni loadings ten times greater than any of the stations
examined in Huron County. Lake circulation is generally fiom north to south and this may
explain the poorer water quality observed at the beaches in the no& of the County. It also
rnay explain why St. Joseph's beach is ranked 5, as it is downstrearn of Station 8 in the
Bayfield River Basin, which has the third highest loading of faecal coliforrn in the County.
The three highest ranked inland battiing monitoring sites were in the Maitland Valley
watershed. These three sites are hydraulically linked; the Wingharn Dam is upstream of the
Wawanosh Conservation Area, which is upstream of Falls Reserve. Also, the inland site
with the highest percentage of tirne exceeding the PWQG, Falls Reserve, is downstream of
most of the Main Maitland basin, as well as the North, Little, Middle and South Maitland
basins. in addition, the Little, North, Main, and Middle Maitland basins are ranked 1,2,3,
and 4 respectivety for cattle density. Cattle watenng in the streams could be an explanation
for the poor water quality in the inland bathing sites in the north part of the County . In
contrast, the sites with the lowest level of poliution (Ron's Camp, Camp Wyoka, and Family
Paradise) were located at the headwaters of the North Maitland and South Maitland basins
respectively.
7.2-2 Provincial Water Quaiity Monitoring Stations
Hurnan, cattle, swine and poultry population densities, as well as soi1 drainage class and
Stream discharge and their relationship to the water quality at the representative PWQMS's
were tested using Spearman's Rho, principal component analysis (PCA) and stepwise
regression. Tabie 30 summxizes the results of these analyses. Due to the differences in the
methods used for determinhg correlations, it was not expected that they would return
identical results. However, given the limited and highly variable data, three techniques were
used to maxirnize the level of confidence in the resuîts. The factor that is most comrnonly
found to be significantiy correlated to the each parameter is the focus of the discussion.
Table 30: Summary of Correlations - -
Overall Averages Water
Q d i v
Parameter
nitrate
total
phosphorus
faecd
coliforms
Method 1986 Yearly
Averages
1991 Yearly
Averages
soil drainage class
human
poultry
podtry
surine
hwnan
poultry
soi1 drainage class
. . .-
PCA poultsl
swine
soi1 drainage ciass
podtry
swine swine
Stepwise poultry
soil drainage class
poultry
hurnan
Spearman soil drainage class
- cattle
soil drainage class hurnan
- cattie discharge
PCA discharge - cattie
human
discharge
- swine
Stepwise disc harge
- swine
swine
discharge
soil drainage class
human
soil drainage ciass
human
- cattle
PCA
discharge
Stepwise human
soil drainage class
- p0ultI-y
- cattle
hurnan
NOTE: - indicates a negative correlation
The three methods of correlation were used on the average data for al1 of the years of data
for each sub-basin, as well as for the average data for 1986 and 199 1. The PWQMS 's were
not sampled after 1994, with the exception of a few stations which were re-started in the
summer of 1998. Because of this interruption it was not possible to compare water quality
and contributing factors for the census year 1996.
Nitrate concentration was significantly correlated to poultry density for the o v e d l average,
1 986 and 199 1 averages based on the results fiom al1 three statisticd techniques. Poultry
density was not a factor that was anticipated to be related to surface water quality, unlike
swine and cattle manure which are ofken viewed as a threat to water quality in rural areas.
Poultry rnanure is not generated in the large volumes that swine and cattle rnanure c m bey
and typically it is handled as a solid. Swine manure is usually liquid and stored in concrete
tanks until it is spread on agriculturai fields.
Many studies that have traced liquid swine and cattle manure that has been applied to a field
to tile drains and receiving streams (Rheaume et al., 1993, McLellan et al, 1993, Fleming,
1990). Swine density and soil drainage class were also correlated to nitrate concentration.
Swine density was found to be significantly correlated in al1 three PCA analyses, as well as
in the 1991 yearly average Spearman analysis, whereas soil drainage class was significant
by al1 three correlation methods for the overall averages. Also for cattle manure, studies have
been done that link cattle watering in streams to nutrient contamination of surface waters
(Envirosearch, 1 983, Hagedorn et al., 1 999).
One factor that may explain the significant correlation of NO,-N with poultry density is the
reiatively high levei of nitrogen species found in podtry manure compared to cattle and
swine maures (ASAE, 1995).
Total phosphorus concentration was significantly correlated to stream discharge. Although a
in many cases it would be expected that an increase in flow would dilute contaminant
concentrations in the streams, this is not the case with phosphonis. Phosphorus is known to
adsorb to soi1 particles and thus associated with erosion. The positive correlation between
phosphorus concentration and stream discharge is consistent with erosion and phosphorus
adsorption to soi1 particles. This correlation is also consistent with the findings of Brenner
and Mondok (1995) who detennined that total phosphorus was significantiy correlated to a
watershed delivery factor. In addition, total phosphorus concentrations were ofien found to
be positively correlated to human population density (al1 three methods for 199 1 averages)
and negatively correlated to cattle density (Spearman and PCA analyses for 199 1 averages,
Spearman analysis for overall averages).
Faecal coliform concentration was most often significantly correlated to human population
density. The two main avenues for hurnans to contribute bacteria to surface water are
WWTP discharges and septic system effluent. Section 7.3.3 discusses WWTPs in more
detail, however, it was found that some plants and lagoons discharged a significant amount
of faecal coliform to receiving streams. The portion of faecal coliforms fiom WWTPs
ranged from 6.2% to over 100% without considering the considerable amount of die-off that
would occur in the streams. Unfortunately, detailed data on septic systems was not available
and, as mentioned in Sub-section 5.2.6, it was not possible to investigate the relationship
between this factor and Stream water quality.
When considenng the overall averages, al1 three water quality parameters were most often
significantly correlated to soil drainage class. The Gullies, Ausable River, South Maitland,
Middle Maitland and the Bayfield River basins have over 40% of their area in the poor to
imperfectly drained soil classes. These are also the five basins that have the highest average
concentration of total phosphorus, faecal coliforms and nitrate at their representative
PWQMS's. This could be a result of the increased likelihood of pollutants to runoff the
surface of imperfectly drained soil types and into streams, rivers and lakes. In contrast, well
drained soils allow runoff to fiow into the soi1 structure where it is more likely to be removed
before reaching the underlying groundwater.
7.2.3 Wastewater Treatrnent Plants & Lagoons
1ri general, there does not seem to be a strong relationship between WWTP and lagoon
loadings and water quality trends at the PWQMS's that represent the basins.
The contribution of total phosphorus from WWTPs ranged fiom 2% to 22%, and for nitrate
ranged fiom 0.06% to 0.9% (based on loadings) which indicates that the majority of nutrients
entering the strearns and creeks is not from local WWTPs and lagoons. This is also
supported by the fact that the station with the highest concentration of contaminants (Station
13) does not have WWTPs or lagoons upstream, and Station 4, which has the highest
contribution of total phosphoms from WWTPs and lagoons, has the sixth highest average
concentration of total phosphoms.
However, this is not the case for faecal coliform loadings. Very hi& portions of stream
loadings of faecal coliform were potentially fiom WWTPs, ranging fiom 25% to over 100%.
Contributions greater than 100% are not unreasonable due to the high rate of die-off for these
organisms in the environment fiom factors such as W radiation. Four of the five WWTPs
or lagoons with the highest faecal coliform loadings are located in the Maitland Valley
watershed. This watershed also had the three inland and lakeshore bathing sites that
exceeded the provincial guideline of 100 CFUA 00mL most often.
However, this does not correspond to the water quality observed at the representative
PWQMS's. The three basins with the highest concentrations of pollutants are located in the
Ausable-Bayfield watershed. This may be explained by the discharges in the rivers.
Discharge of the Bayfield River (at Varna) ranges from 12 to 20% of the discharge that the
Main Maitland River has at Benrniller (fiom 1989 to 1996). Similarly, the discharge
calculated for the Ausable River (at PWQMS 11) ranges fiom 20 to 37% of the discharge
that the Main Maitland River has at Benmiller (fiom 1989 to 1996). The srnaller discharges 6
in the rivers in the south may partially explain why the pollutant concentrations are higher
in this area than in the north, where there are more WWTPs and lagoons.
It is apparent that the WWTPs and lagoons in Huron County are not significant contributors
of nitrate and phosphorus. It cannot be concluded that these facilities do not significantly
contribute to the faecal coliform loadings in the receiving streams because some basins
(North Maitland) have very high contributions fiom these facilities.
LandfiIIs that did not have more than two years of data, and did not monitor a Stream or river
were not considered in the analysis. Landfiills that had more than two years of surface water
quality data and that collected total phosphorus and nitrate values upstream and downstream
were compared.
Table 29 compared the water quality at the representative PWQMS and the landfiIl
monitoring data for total phosphorus and nitrate. Concentrations of pollutants near the
landfills are in general, quite low, and most cases equal to or below equivalent parameters
at the PWQMS's. As a result the impact of landfills on surface water quality with respect
to total phosphorus and nitrate is negligible based on direct surface water connection.
Bacteria data was not collected at landfill sites and therefore it is not possible to comment
on this parameter.
7-2-5 Precipitation
A suitable precipitation station in the south of the County was not available for this study,
as a result, the precipitation at Blyth was used to represent this area, including the: Middle
and South Maitland, Bayfield River,
basins (Nine Mile, North, Little and
recorded at the Wroxeter station.
Ausable River, and Gullies basins. The remaining
Main Mai thd) were compared to the precipitation
Some peak concentrations do correspond to months with large amounts of precipitation, but
this is not always the case. For example, peak concentrations of parameters for stations 23
and 8 were exarnined, and 3 of the 6 peaks occurred in months whose total precipitation was
l e s than the average for that month, and 3 occurred in months whose total precipitation was
greater than the average for that month. In general, there does not seem to be a relationship
between "wet" or years and peak pollutant loadings at the PWQMS's.
8 CONCLUSIONS
A significant amount of historical data related to surface water q d i t y has been collected in
Huron County. Up to this point this information was collected, but not exarnined in a
comprehensive manner. The objectives of this study were to collect anci organize this
available historical data, to determine if the surface water qudity in the County was changing
over the past 25 years and finally to determine if surface water quality could be statisticaliy
correlated to several factors. It c m be concluded from the collection, organization and
analysis of this data for trends that:
Improvements in the way datais collected, stored and archived would improve future
studies in this area.
Stream water qudity in the south is poorer than in the north part of the County.
Total phosphorus concentrations in surface waters are generally decreasing in most
basins (seven of the nine PWQMS's analyzed) in Huron County.
Nitrate concentrations are generally increasing in most basins (six of the nine stations
analyzed) in the County.
In four of the nine stations examined, faecai coliforrn concentrations were found to
be increasing over time.
Inland and lakeshore beach water quality is poorer in the northern part of the County.
Inland and lakeshore beaches in Huron County fiequently do not meet swimming
guidelines, and thus surface water quality in this regard can be considered poor .
Three statistical rnethods were used to determine if population, land use, and other factors
could be correlated to surface water quality in Huron Comty. It can be concluded fiom the
correlation analyses that:
1. Higher nitrate concentrations are most affected by soi1 drainage class as well as swine
and poultry densities.
2. Higher faecal coliform concentrations are most affected by human population
density, soil drainage class and negatively correlated to cattle density.
3. Higher total phosphorus concentrations are most af3ected by river discharge, soi1
drainage class and human population density.
4. Landfills do not significantly contribute phosphorus or nitrates to surfaces waters in
Huron County.
5. Wastewater treatment plants and lagoons generally do not significantly contribute
phosphoms or nitrates to surfaces waters in Huron County.
6. Wastewater treatrnent plants and lagoons do contribute considerably to the faecal
coliform pollution of surfaces waters in Huron County.
It is important to note that these correlations do not necessarily confïrm a causal relationship.
The scope of this project was limited to analyzing historical data and a separate, directed
extensive sarnpling project would be required to determine a causal relationship between
water quality and urban and rural practices.
In conclusion, it is evident that there are many factors in Huron County that have the - $ential to adversely affect surface water quality, including: septic systems, WWTP's and
lagoons, natural geographic features and agriculturaI practices. In general, areas that are flat
and may be easily adapted for human or livestock habitation, will be developed. Therefore,
it is not surprising that it was not possible to determine whether hurnan influence or livestock
are the primary cause of the degradation of surface water quaiity. However, it is possible to
predict that as hurnan and livestock densities increase, there will be an adverse affect on
water quality unless current waste management practices change. Minimizing the
environmental impact of these factors will take CO-operation and cornmitment fiom al1 s
members of the community.
9 RECOMMENDATIONS
Through the collection and examination of historical data in Huron County deficiencies were
identified. As a result there are a number of recommendations that would alleviate some of
the key the deficiencies and allow for a more thorough assessrnent of water quality data in
the County.
1 . Re-start the all of the PWQMS's. The PWQMS's are the only source of long-term
surface water quality data in the Co-, and without this information it would not
be possible to determine trends in water quality. Currently sorne of the PWQMS's
in the MVCA have been re-started, this should be expanded to include the ABCA
area.
2. Continue the beach and inland bathing site monitoring that is conducted by the Huron
County Health Unit. This monitoring program provides a service to the bathers, as
well it is also an important record of bacterial concentrations at these sites, and a
system of saving these records. Without these records it will be impossible to know
if corrective measures in the County have an impact.
3. Irnprove data colIection with respect to septic systems. A database of septic systems,
collected and maintained by the local agency responsible for approvals and
inspections, with location, age and type would be helpfbi in monitoring the density
of septic systems. A program of inspection and renewal would also be advisable to
minimize the contamination fiom septic systems.
4. Lmprove data collection with respect to farm information- An annual record of the
installation of tile drains at each farm, manure and waste handling practices, fertilizer
use, areas of crop types collected by OMAFRA and stored at the head offfice in
Guelph would be beneficial in tracking changes and relating these changes to water
quality .
5. Initiate studies to target "hoty' areas of the County to determine sources of pollutants
at selected locations. For example, the approach developed by Hagedorn (1 998) can
conclusively identifi sources of bacterial contamination on a watershed basis. In
addition, with the collection of information suggested in items iii and iv, overall
nutrient loadings to the watershed could be determined and relative responsibilities
detennined.
6 . Review the data periodically to monitor changes. An annual 'state of the
environment' report detailing current trends in water quality for the County would
be highly beneficial.
10 REFERENCES
Arthur, J., Brinkman, L.. Guitard, P., Haherly, L., Hosick, McConnell, S., Scott, J.,
Tansley, G. 1998. An Overview of Agricultural Land Use Practices, Forestry Patterns
and Surface Water Quality Trends in Wellington County. University of Guelph,
School of Rural Planning and Devetoprnent.
Atmosphenc Environment Services. 1998. Precipitation Data for Stations 6 129660 and
6 1208 19 for 1975 to 1998. Environment Canada.
Bolstadt, P.V., Swank, W.T. 1997. Cumulative impacts of Landuse on Water Quaiity in
A Southern Appalachian Watershed. J. Amer. Wat. Res. Assoc. 33(3): 5 19-533.
Brenner, F.J., Mondok, J.J. 1995. Nonpoint Source Pollution Potential in an Agricultural
Watershed in Northwestern Pennsylvania. Wat. Res. Bull. 3 l(6): 1 10 1 - 1 1 12.
Burnham, N.L. 1998. Development and Testing of a Mode1 for Predicting Contaminant
Transport in Leaching Beds. University of Guelph. Unpublished Ph.D. Thesis.
CG&S. 1996. Final Report - Mill Creek Subwatershed Plan. GRCA, Cambridge.
Chen, M. 1988. Pollution of Ground Water by Nutrïents and Fecai Coliforms fiom
* Lakeshore Septic Tank Systems. Water, Air and Soil Pollution. 3 7: 407-4 1 7
Chow, V.T., Maidment, D.R., Mays, L.W. 1988. Applied Hydrology. McGraw-Hill. New
York.
CH2M Hill Engineering Ltd. 1995. Dr&- Mill Creek Subwatershed P h . GRCA,
Cambridge.
L
Dean, D. and Foran, M.E., 1990. The Effect of Farm Liquid Waste Application on
Receiving Water Quality, hterim Report to the Ontario Ministry of the Environment,
project 5 1 2G.
Dean, D. and Hocking, D. 1989. CURB Plan. Ausable Bayfield Conservation Authority,
Exeter.
Demal, L. 1983. An Intensive Water Quality Survey of Stream Cattle Access Sites. a
Technical Report R- 1 9. UTRCA, London.
Entringer, R.A., Strpelis, J. 1996. Health Concems Resulting f o m the Effects of Animai
Agriculture on Water Resources. Proceedings from the Animal Agriculture and the
Environrnent North Arnerican Conference, Rochester, New York.
Farrell-Poe, K.L., Ranj ha, A.Y ., Rarnalingam, S. 1 997. Bacterial Contributions by Rural
Municipalities in Agricdtural Watersheds. Transactions of the ASAE. 4O(l) : 97-
101.
Fertitizer Institute of Ontario Inc. 1999. Western Ontario Crop Acres and Fertilizer (T &
L ' t) 1955 - 1995. Guelph, ON.
Fleming, R., McLellan, J.E., Bradshaw, S.H. 1993. Reducing Manure Output to Streams
fiom Subsurface Drainage Systems. ASAWCSAE Paper 932010. Spokane,
Washington.
Fleming, R. 1990. Impact of Agricultural Practices on Tile Water Quality. ASAE Paper
902028. Columbus, Ohio.
Fortin, M., Demal, L. 1983. Statistical Modelling of Instream Phosphorus. Technical
Report R-15. UTRCA, London.
Fortin, M., Bacchus, A., Post, L. 1983. Impact of Stratford City Impoundrnents on Water
Quality in the Avon River. Technical Report S-1 . UTRCA, London.
Fraser, R.H., Barten, P.K., Pinney, D.A.K. 1 998. Predicting Stream Pathogen Loading
a fiom Livestock using a Geographical Information System-Based Delivery Model. J.
Environ. Qual. 27: 935-945.
G.M. Wickware & Associates, Inc. 1989. Water Quality and Land Use Relationships in
the South Nation River Basin. SNC,
Grabow, G.L., Spooner, J., Lombardo, L.A., Line, D.E. 1998. Detecting Water Quality
Changes Before and M e r BMP Implementation: Us of a Spreadsheet for Statistical
Analysis. NWQEP Notes. 92. North Carolina State.
Hagedorn, E., McCoy, E.L., Rahe, T.M. 198 1. The Potential for Ground Water
Contamination fiom Septic Effluents. J: Environ. Qual. 10(1): 1-8
Hagedorn, C. 1998. "A Method to Determine Sources of Faecal Pollution in Water",
Proceedings of the 7th Annual Conference, National Onsite Wastewater Recycling
Association, FT. Mitchell, Kentucky, October. &
Hagedorn, C., Mahal, M. Reneau, R.B. 1999. "Determining Sources of Fecal Pollution in
a Rural Virginia Watershed", Proceedings of the 8th Annual Conference, National
Onsite Wastewater Recycling Association, Georgia, October.
Helsel, D.R., Hirsch, R.M. 1992. Statistical Methods in Water Resources. Elsevier,
Amsterdam.
Hocking, D., Dean, D. 1989. Ausable Bayfield Conservation Authority C.U.R.B. Plan.
Exeter.
Hocking, D. 1996. C.U.R.B. Program 1991 - 1996 Final Annual Report. Ausable
Bayfield Conservation Authority. Exeter.
Hocking, D. 1992. Target Sub-Basin Study Annual Report. Ausable Bayfield
Conservation Authority. Exeter.
Hocking, D. 1988. Rural Beaches Strategy Program: Target Sub-Basin Study Report.
Ausable Bayfield Conservation Authority. Exeter.
Hocking, D. 1987. Rural Beaches Strategy Program: Target Sub-Basin Study Report.
Ausable Bayfïeld Conservation Authority. Exeter.
Huber, D.M. 1 982. Water Qualiv Monitoring of the Avon River - 1980, 1 98 1 . Technical
Report S-3. UTRCA, London.
Huron County Planning Department. 1996. Intensive Livestock Study, Final Report.
a Goderich.
Huron Comty Planning Department. 1993. Huron County Rural Servicing Study, Final
Report. Goderich.
Lee, D., McAvoy, D.C., Szydlik, J., Schnoor, J.L. Modeling the Fate and Transport of
Household Chernicals in Septic Systems. Ground Water. 36(1): 123-132.
a
~ o ~ a n , T. 2000. Nonpoint Sources of Pollutants to the Great Lakes: 20 Years Post
PLUARG. Nonpoint Sources of Pollutioin to the Great Lakes Basin. Great Lakes
Science Advisory Board.. Toledo, Ohio.
OMAFRA. 1995. Minimum Distance Separation 1 (MDS 1).
OMAFRA. 1996. Artificial Drainage Maps for Huron County. OMAFRA, Guelph.
a
OMOE and OMNR. 1975. Water Management Study Sumrnary Report: Thames River
Palmeteer, G., McLean, D.E., Walsh, M.J., Kutas, W.L., Janzen, E.M., Hocking, D.E.
1989. A Study of Contamination of Suspended Stream Sediments with Escherichia
Coli.
Rheaume, C.M. Joy, D.M., Bonte-Gelok, S., Lee, H., Whiteley, H.R., Zelin, S. 1993. The
a Potential for Bacteriai Contamination from Land Application of Liquid Manure.
Paper presented at NABEC conference, Guelph, Ontario.
Robertson, W.D., Cherry, J.A., Sudicky, E.A. 199 1. Ground- Water Contamination fiom
Two Small Septic Systems on Sand Aquifers. Ground Water. 29(1):82-92.
Runyon, R.P., Haber, A. 199 1. Fundarnentals of Behavioral Statistics. 7b Ed. McGraw-
Hill, New York.
Scott, J. 1966. The Settlement of Huron County.
Sen, A., Srivastava, M. 1990. Regression Analysis: Theory, Methods and Applications.
Springer-Verlag. New York.
Shadford, C.B., Joy, D.M., Lee, H., Whiteley, H.R., Zelin, S. 1997. Evaluation and use of
a Biotracer to Study Groundwater Contamination by Leaching Bed Systems. J. Cont.
Sherer, B .M., Miner, J.R., Moore, J.A., Buckhouse, J-C. 1992. Indicator Bacterial
, Sumival in Stream Sediments. J. Environ. Qual. 21 : 591-595.
Siegrist, R., Witt, M., Boyle, W.C. 1976. Characteristics of Rural Household Wastewater.
J. Environ Eng, Div. p 553 - 548.
Snell and Cecile Environmental Research. 1995. Watershed Management Strategy.
Ausable Bayfield Conservation Authority. Exeter.
Shtistics Canada. 1972 - 1997.Census of Canada: Agricultural Profile of Ontario.
Ottawa.
Statistics Canada. 1972 - 1997.Census of Canada: Population and Dwelling Counts -
Ontario. Ottawa.
Taylor, H.E. and Foran, M.E. 1993. Cornparison of Solid, Liquid and Storage Runoff
Manure on Tile Drain and Groundwater Quality. Ausable Bayfield Conservation
Authority. Exeter.
Velledis, G., Lowrance, R., Gay, P., Sheridan, J., Bosch, D. 1999. Water Quality of
Picola Creek Watershed. ASAE Paper No. 992 13 1. Toronto, ON.
Viraraghavan, T., Wamock, R.G., 1976. Groundwater Pollution form a Septic Tile Field.
Wuter, Air und Soi[ Pollution. 5 :28 1 -287.
APPENDIX A: Calculation of Flows for Provincial Water Quality Monitoring
Stations
Station 4, in the North Maitland basin did not have a flow gauge at or near it. As a result it was necessary to use a flow gauge located near Harriston. The flow at Harriston was weighted by an area factor to determine the flow at Station 4. An exampIe calcuIation is shown here.
Area upstream of the flow gauge = A, = 112 km2 Area between the gauge and Station 4 = 4 = 3 19 km2 Flow at the gauge = Q, Flow at Station 4 = Q,
The equation used to caIculate the flow at Station 4 is:
APPENDIX B: Beach Water Quality Data
year 1990 1991 1992 1993 1994 1995 1996 1997 1998
StJoseph 40 21 43 33 38 NIS O* 50 29
LAKESHORE BATHING SITE SUMMARY TABLE
Port Goderlch Goderich Bayîïeld Paul Houston Driftwaod St. Joseph Main South South Bunyan Heights Camp Albert
26 25 60 2 5 38 45 58 42 57
Amberley 53 29 67 40 60 36 43 50 47
AVG 17 28 42 47
NIS means no samples were taken at this location * 3 samples taken "" only 1 sample taken **" 4 samples taken ***** 5 samples taken
NIS NIS 21 9 17 27 25 30 29
18
53 8
NIS O"** NIS NIS NIS NIS NIS
NIS 25 N/S NIS NIS NIS NIS NIS 11
40 21 43 33 38 NIS
O 50 29
28
H ~ Y Twp. NIS NIS 40 NIS 20 60 33 33* 35
25
Port Blake
40 27 27 15 3 1 NIS NIS 33 36
23
year
7990 1991 1992 1993 1994 1995 1996 1997 1998
AVG
Camp Wyoka
O 7 O O O O O O O
1
* 3 samples taken
Ron's Camp 7 7 O O O O 25 O O
4
Gorrie Dam 33 47 43 8 55 9 40 33 8
31
INLAND BATHING SITE SUMMARY TABLE
Wroxeter Wingham Bluevale Falls Wawanosh Oriftwood Dam 20 2 7 44 14 57 18 17 43 O
27
Dam 73 36 14 50 33 33 33 60 13
38
Dam 20 33 38 O 33 13 25 33 1 O
23
Reserve 75 60 29 33 18 36 64 56 38
45
CA 33 50 one sample 36 taken in 33 1991 36 only 17 55 38 42
Brussels Family Dam 33 23 44 17 27 O O 40 O
20
Paradise no data 50 18 25 9 O* 11 33* O**
16
Pine Lake 13 7
29'** 25*** O*** 20- 25**** O* O
13
hllomson Dam 21 8
25*** 25"' O*'" O**** 33. O*''
O
12
** only 1 sample taken **' 4 samples taken ***** 5 samples taken
APPENDIX C: Provincial Water Quality Monitoring Station Data & Regression Graphs
Total Phosphorus from 1974 to 1994 Station 23, Main Maitland Basin
FC & TCMF from 1972 - 1975 Station 17&31, Middle Maitland Basin
Year
TCMF
Regression of FC Data Stations 17&31, Middle Maitland Basin
Year
l * , !
i l C i l m i I
. , . I l . < ' ' . 8 , . . ' I I I . . ' , . ! : i . l . . , . . 1 . , . i . . . . .
Regression of Total Phosphorus Data Station 1 1, Ausable River Basin
80 85 Year
APPENDM D: Summary and Sample Calculations of Basin Loadings
SUMMARY OF BASINS BASED ON LOADINGS FROM PWQMS
F.C.: location range average*
1 &23 1.896E5-9.6E8 4,46E+06 1.49E+06 1,18E+06 1.89E+O6 1.04Et06 1.04E+06 3.10E+06 3.19Et06
DATA
T.P.: location range average*
1 &23 .0482-20.93 1.84 .003-7.17
,001682-21.978 ,0050-7.968 ,0081 -7.926
NO T.P. .0009-6.345 .O1 3-6.61
NO DISCHARGE
0.32 0.38 0.72 0.23
DATA 0.42 0.47
DATA
location 1&23
4 15
17&31 35 1 8 Il 13
range average* -35-61 1.8 170.1
24.7 37.7 44,6 20.6 9.9
40.2 26.9
DATA
" average is taken over the period of record for the PWQMS for which there is discharge data
APPENDIX E: T-test Results
Using T-Test to Determine if the Mean Concentrations for the North and South of the County are the Same
. Testing the nuIl hypothesis that the north & south have the same population means for nitrate, faecal coliform and total phosphorus concentrations.
RANK BASED ON CONCENTRATIONS
N03: PWQMS RANKING location range average
1 &23 . O1 -8.6 2.858 4 .01-13.6 2.033 15 -01-15.3 4,748
17&31 .03-12,9 3.87 35 -1-8,6 3.588 1 -1-4.7 1.459 8 .28-13.7 5.44 11 .01-16.1 4.36 13 .l-27 5.43
north avg 3.093 variance 1.487 n 6.000 spA2 1,172 T -2,591 sig @ 95% 2.365
Station ID
02FE015 02FE015 02FE015 02FE015 02FE015 02FE015 02FEO15 02FE015
Year 1989 1990 1991 1992 1993 1994 1995 1996
' DISCHARGE DATA (m3ls) FOR'THE MAIN MAITLAND BASlN - PWQMS #23
Jan 53.3 73.2 39.1 54.9 111 20.6 70.2 79
Feb 21.8 56.7 53.5 38.8 15.8 73.5 14.1 62.4
Mar 74.8 87.1 161 I l 6 41.1 80.4 I l 9 49.8
Jun 25.4 12.9 9.6 10.6 37.7 12.2 27.8 37.5
Jul 3.91 10.9 11,2 21.4 9.6 14.2 6.33 8.32
Oct 5.93 72.1 11.9 37.4 31,l 8.39 8.9 33,7
Nov 37
96,l 24.5 338 26.9 28.8 102 60.1
1989- 1996 average flow 42.8
DISCHARGE DATA (m3ls) FOR THE NORTH MAITLAND BASlN - PWQMS #4 Station
ID 02FEOll 02FEOll 02FE011 02FEOll 02FEOll 02FEOll 02FEOll 02FE011 02FEOll 02FEOll 02FEOl l 02FEOll 02FEOll 02FEOll 02FE011 02FEOll
Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Jan Feb Mar A P ~ May Jun Jul AW Sep Oct Nov
1982 - 1996 average flow 1.6
Dec 15.6 94.1 63.8 57.4 36 40
38.5 76.9
Dec
4.97 1.64 3.85 1.37 1.25 2.30 1.20
0.393 3.49 1 .go 2.01 0.886 1.27 1.28 3.01
Ann 27.2 48.6 43.1 58.4 4O,l 35.1 40.4 48.9
Ann
1.76 1,13 1,67 2.09 2,22 1.10 1.18 1 . l 4 1.85 1.65 2.13 1.29
0.942 1.62 2.32
these flows are taken from a flow station near harriston, the flow @ pwqms 4 was calculated by dividing the flow by the drainage area (1 12 km2) then multiplying by the total area (319+112 km2)
Station ID
02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02 f EOO9 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009 02FE009
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
DISCHARGE DATA (m31s) FOR THE SOUTH MAITLAND BASlN - PWQMS #15
Jan
4.23 10.1
2 3.06 3-94 17.7 11.9 9.82 4.18 1 .O9 2.33 3.9 2.98 0.7661 4.04 4.35 1.93 4.63 9.44 2.67 4.58 7.94 17,7 4.31 9.63 13.9 2.76 9.48 14.5
1968 - 1996 average flow
Feb
22.2 11
2.86 4.07 1.77 4.1 1 4.92 7.23 23,4 1.49 1.55 1.52
0.795 33.6
1 8,06 25.3 17.4 3.35 1 .O8 6.43 3.17 9.97 7.71 6.7 1.55 7.41 1.78 9.49
6.1
Mar
17 14.9 6.23 14.6 12.5 18.3 12.1 15.3 31.9 35.8 8.77 33.8 21.2 9.22 22.3 3,74 14.1 33
24.7 17.4 19.7 10.8 10.7 24.5 17.8 7.64 10.8 15.6 5.61
Jun
0.742 1 . l 6
0.635 2.04 0.767 1.32 1.79 2.65
O. 59 1 0.41 3 1,3l
0,678 1.2 1 ,O7 3.67 2,38 4.61 3.1 1 1.55
0.953 0.332 2.14 1.18
0,602 1.59 3.98 1.7
4.17 5.2
Jul
0.297 0.308 1.32
0.431 0.768 0.312 0,423 0.636 1.38
0.087 O. 19 0.446 0.438 0.41
0.469 0.249 0.435 1-58
0.494 1.47
0.062 O. 157 1,63 0.92 5,77
0.551 3.3
0.612 0.855
Oct 5.15 1.68
0.335 1.26
0.141 1.3
0.1 16 0.228 0.771 0.598 7,76 7.77 1.22 3.38 13.6
O. 524 2
0.432 7.13 15.6 3.29 8.88
O. 137 11.8 1.27 4.55 4.91 0.734 1 .O8 5.79
Nov 14.8 6.06 3.89 3.17
0.204 2.59 3.58 3.8
2.54 4.96 1 O,3 2.59 12.1 2.44 4.98 11.9 4.79 9.71 14,l 3.01 12.1 14.1 5.27 14.8 4.41 18.4 3.19 4.71 16 12
Dec 12.1 9.31 2.88 6.32 3.1 1 9.86 5.01 1.74 9.66 1.8
9.84 4.35 14.7 7.86 4.07 2 1
10.3 14.6 6.15 6,03 16
9.57 1.15 15.6 11.3 7,OI
5 7.44 4.82 15,3
Ann
5,67 6,08 4.04 4,15 4.67 5.08 4.94 6.44 6,76 6.57 5.31 7.53 5.02 6.59 7.83 4,38 6,57 9,51 8,28 5,46 5.92 3.2 1 7,82 5,96 8,09 4.95 4.71 5.44 7.6
Station ID .
02 f EOO8 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FEOO8 02FE008 O2FE008 02FE008 02FE008 02FE008 02FE008 02FE008 02FEOO8 02FE008 02FE008 02FE008 02FE008 02FE008
Year 1968 1969 1970 1971 1972 1973 7 974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
DISCHARGE DATA (m31s) FOR THE MIDDLE MAITLAND BASlN - PWQMS #17 & 31
Jan 4.81 14
2.26 4.28 6.3 22.6 17.7 9.16 6.52 2.28 4.65 6.55 4.35 2.17 3.08 5.95 3.93 6.29 7.39 4.64 7.67 11.9 17.1 8.61 11.3 23.2 3.41 19.2 20.1
Feb 27.1 16.1 3.33 4.35 3.39 7.98 8.82 8.59 23,3 2.18 2.99 2.67 1 . S I 36.9 1.48 9.1 1 43.9 17.1 4.46 2.1 9.19 5.13 14.7 13.2 7.68 2.54 10.1 2.77 15.6
Mar 24.9 23.6 6.99 16.6 12.7 34
24.9 23,8 61.8 51.9 10
47.5 27.3 10.1 24.8 8.05 18.8 51.1 41.3 27
30.2 18.6 21.6 44.9 27.3 8.81 19.4 24,8 10.5
Jun 1.3
1.92 1.24 2.42 2.84 2.08 1.62 2.21 0.909 0.481 1.76
0.982 1.46 1.55 6.29
4 4.51 1.8 2.7 1.26
0.519 5.99 1.74 1 .O5 2.14 8.65 1.8 3.9 9.67
Jul 0.725 0.814 1.24
0.746 2.71
0.547 0.447 0.641 1.57
0.393 0.41 9 O. 543 O. 523 0.499 1.24
0,721 0.663 0.845 0.899 9.83 0.178 0.593 1.35 3-26 7.02 1 .O2 4.19 1 .O3 1.82
Aug . Sep Oct 7.08
0.546 1,63
0,304 2.6
0.278 0.298 2.43 1.35 1 O,8 4.43 2.05 2.08 13
1-76 5.22 1 .O1 8.11 27.1 3.92 8.8
0.71 8 2 1
2.43 7.26 6.43 1 .O7 2,06 5.64
Nov 13.5 4.82 4.6
0.412 5,79 3.37 2.28 5.97 4,3 10.1 4.02 14.7 2.34 6.86 18.7 8.57 10.4 19.8 6.1 14.3 17,3 10.1 24.5 5.84 32.3 6.2 1 4,56 24.6 12.5
Dec 15.7 3,63 10.4 4.73 13.2 5.51 1.44 17.4 3,48 10.6 4,68 22
8.35 5.46 28.4 12.8 17.5 9.06 8.46 20.3 9.91 3.05 22.7 16.8 40.8 7,29 8.1
7.48 18.7
Ann 9,39 9.99 6.18 6.32 8.17 7.94 8.72 11.9 1 O,8 8.99 7.69 11.4 6.44 7.66 11.4 7.09 9,5 13.4 13.3 8.61 8.16 6.26 I l , ? 10.9 12.6 8.27 7.2 8.98 11 -3
1968 - 1996 average flow
9.3
Station 10
02FE007 02FE007 0 2 ' ~ ~ 0 0 7 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FEQ07 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02FE007 02fE007 02FE007 02FE007
Year 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
DISCHARGE DATA (m31s) FOR THE LITTLE MAITLAND BASlN - PWQMS #35
Jan
4.04 6.61 1.38 2.33 3.1 1 11
8.99 4.25 5.23 1.54 3.21 3.89 2.72 1.29 2.3 3.94 2.98 4.4 6.95 3,18 3.89 7.1
9.57 3.82 6.23 11
2.05 9.53 10.3
Feb
13.7 5.86 1.94 3.48 1.96 4.12 4.44 4.59 12 1.7
2.41 1.85 1.25 15.2 1.18 5.17 19.1 9.95 3.25 2.02 5.89 2.83 7.42 6.06 4,28 2.17 4.6 2.28 8.51
Mar
14.8 9.34 2.97 7.89 5.75 17.4 14
9.62 32
25.2 5.87 22.4 12.1 6.8 12.8 4.86 9.55 23.9 21.5 14.3 14.8 11
11.9 20.9 13.3 5.46 8.36 12.2 6.4
Jun
1.75 2,29
1 1.54 3.1 1 2.34 1,84 1.69 0.93
0.561 1.82 1.17 1.5 1.2
3,44 2.29 2,97 1.62 1.93 1.12 0.662 2.53 1.57 1.17 1.38 3.86 1.47 2.27 3.47
Jul
0,632 0.89 1.44
0.601 1.66
0.579 0,683 0.773 2,42 0.565 0.551 0.604 0.81 8 0.463 0.875 0.514 0,873 0.846 0.872 1.43 0.375 0.46
0.798 1.59 1.66 1.13 1.12 1.12 1 .O8
Oct 4,34 3.03
0,587 1 .l
0.259 1.58
0,297 0,3 2.69 1.79 7.9 1.66 1.21 3.13 6.41 0.789 2.59 0.832 5.03 13.4 1.33 3.71 0.777 7.91 1.41 3.06 4,17 1,16 1,42 2.44
Nov 9.92 5.7 1 2.95 2.17 0.467 2.75 2.05 1,89 3.26 2,59 6,78 1.78 5.14 2.2
4.57 8,31 4.27 486 10.7 3.92 5.68 8.28 5.57 11.2 2.22 15.6 3,51 3.26 11
6.36
Dec 9.67 7.44 2,28 3.94 2.04 6.54 3 , l l 1.21 8,32 2.4 6.7 2.46 9.07 4.62 3.25 13
6.77 9,12 4.98 4.73 8.98 5.1 2.2 12.9 6.75 6.46 4,21 4.46 4.37 8.76
Ann
532 4.95 3.07 3.33 4.24 4.45 4,73 5.93 6,31 5.6 3.77 5.66 3.85 4.06 6,57 3,69 4,9 7.18 6.88 4,04 4.38 3.63 6.07 5.26 6.37 4.5 3.41 4.71 5.64
1968 - 1996 average flow
4.9
Station ID
02FD002 02FD002 02FD002 02FD002 02FD002 02F0002 02FD002 0 2 ~ ~ 0 0 2 02FD002 02FD002 02FD002 02FD002 02FD002 02FD002 02FD002 02FD002 02FD002 02FD002
1980 - 1 996 average flow
1.1
Year 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 f992 1993 1994 1995 1996
DISCHARGE DATA (m31s) FOR THE NlNE MILE BASlN - PWQMS #1
Jan
0.969 0.391 0.948
1.1 O 0.631 0.979
1.21 0,830
1.27 1.16 2.32 1.22 1.50 2.32
0.504 1.82 3.07
Feb
0.434 4.09
0.456 1,46 4.1 1 2.52
0.781 O, 565
1.67 0,666
1.52 1.62 1 .O8
0.621 1.66
0.579 2.63
Mar
2.79 1.97 2.78 1 .O4 2.31 5.05 3,55 2.72 2.37 2.09 2.64 3.41 3.26 1.46 2.28 2.85 1.82
Jun
0.350 0.377
1 .SI 0.427 0.622 0.424 0.652 0.339 O, 153 0.606 0.222 0.495 0,298 0.773 0.556 0.358 0.946
Jul .
0.303 0.087 0.337 0.203 0.252 0.251 0,338 0.229 0,094 O. 152 0.181 0.404 0.240 0.245 0.41 1 0.244 0.456
Oct
1 .O5 0,782 0.288 0.660 0.364 0.581
1.93 0.692
1,32 0.142 0.932 0.447 0.659 0.688 0.290 0.384
Nov
0.852 1 .O0 1.11
0.887 IZ' 1.31
0.893 1 .%? 1,77
0.627 2.00 1 .O7 2.73
0.631 1.14 2.64
Ann
1 .O5 1 .O4 1.18
0,845 1.18 1.43 1,36
0.941 1 ,O2
0,668 1.19 1.20 1.31
0.985 1 .O8 1,14
this station is at lucknow, the flows for the pwqms at port albert were calculated by dividing the flow by the drainage area of the gauge (54,9km2) then multiplying by the area of nine mile in huron county (1 30km2) plus the area of nine mile outside of huron county (1 16km2)
Station ID
02FF007 02FF007 02FF007 02FF007 02FF007 02F FOO7 02FF007 02FF007 02f FOO7 02Çf 007 02F FOO7 02ÇF 007 02ÇF007 02FF007 02FF007 02FF007 02FF007 02FF007 02FF007 02FF007 02FF007 02FF007 02 f FOO7 02FF007 02FF007 02 FFOO? 02FF007 02 FFOO7 02FF007 02fF007 02FF007
Year 1966 1967 1968 1969 1970 l 9 7 l 1972 1973 1 974 1975 1976 1977 1978 1979 1980 1981 1982 3983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
DISCHARGE DATA (m31s) FOR THE BAYFIELD BASlN - PWQMS #8
Jan
11.6 3.86 7.75 1 .fi6 2.90 5.18 16.6 12.7 12.6 4.86 0.828 2.54 5.06 2.78 0.528 3.61 4.21 1.67 4.43 8.21 3.59 3.97 6.90 17.6 5.16 9.98 18.3 4.02 12.3 13.4
Feb
4.32 21.9 6.18 3.29 6.27 1.21 4.76 4.81 8.98 22.9 1.32 1,24 1.53
0.802 30.8
0.902 9.86 22.6 20.3 3.16 1 .O9 5.43 3.31 9.30 10.8 8,61 2,14 11.4 2.14 9.08
Mar
19,l 17.1 17.6 10.2 21.7 15.6 21.7 14.6 16.8 32.1 35.6 7.76 28.7 19.5 8.1 O 24.3 4.21 10.5 35.2 26.5 17.3 20.1 12.4 10.2 24.7 20.7 12,o 20.3 21.2 5.67
May . Jun Jul
1 .O1 0.259 0,281 0.829 0.293 0.781 0.332 0.533 O. 342 1.45
0.1 34 0.159 0,814 1 .O1
0.421 0.697 0.806 0.41 5 1.55
0.91 3 1 . I O
0.087 0.1 11 4.46 1.49 3.98
0.643 2.13 1 .O4 1.12
Oct 0.317 7.21 1.98
0,328 0.71 9 0.213 2.09
O. 7 88 0,257 O. 399 O, 568 5.15 5.02 1,67 3.14 13.4
0,928 2.22
0.646 5.20 15.1
0.786 7.23
O. 132 15,3 1.11 4.66 4.37 1.57
0.588 7,39
Nov 4.72 19.4 6,30 3.49 3,21
0.302 4.00 4.35 3.07 4-81 4.70 6.84 2.22 12.0 1.79 6.29 10.2 4.78 9.44 12.5 3.33 10,f 13.8 2.79 14.6 4.31 22.5 2.91 4.89 17.9 12.1
Dec 15.5 145 8.78 2.91 6.85 3,78 13.3 6.28 2.06 7.60 1.73 11.3 4.44 135 5.89 5.16 19.0 8.52 12.5 5.52 7.29 16,l 9.84 1 .O5 15.8 9.75 8.99 4.53 7.14 6.37 17,5
Ann
8,54 5.58 556 4.23 4.51 5.07 5.64 5.52 6.18 6.87 5.83 4.93 7.16 4.56 6.78 6.92 4.89 5.79 9.04 8.92 5,04 5.64 3.12 8.28 6.24 9.21 5.75 5.77 6.79 9.66
1981 -1 996 average flow
6.8
Station ID
02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02F F 002 02 FF002 02FF002 02FF002 02FF002 02FF002 02FF002 02F F002 02FF002 02FF002
Year 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979
DISCHARGE DATA (m31s) FOR THE AUSABLE BASlN - PWQMS #Il
Jan
2.94 15.4 24.5 32.8 27.2 5.51 2.46 15.6 4.22 1 O,3 5,72 1.73 13.4
0.245 1.42 2.05 12.0 8.96 5,97 17.4 11 .O 23.0 2.42 4.06 6.84 34.3 23.2 18.5 5.69 1.61 4.86 8,35
. Feb
12.7 22.5 8.44 38.5 16.1 4.71 47.7 7.36 6.44 15.4 4.51 10.8 11.7 18.1 3.77 1 .O7 8.41 25.2 17.1 6.89 38.6 17.3 5.90 15.0 1.94 7.89 13.0 16.2 50.2 2.31 2.99 2.1 1
Mar
34.1
54.4 21 .O 48.1 26.1 22.9 21.4 40,7 37.5 48.0 15.7 23.0 41.8 17.9 12.6 26.4 42.3 19.3 20.5 20.8 34.2 28.1 23,9 27.1 39.8 28.2 45.0 29.9 29.1 49.7 64.8 24.5 44.8
Jun
1.47 22.8 1.15
O. 577 1 .O5 1.99 1-43 11.2 1,11
0,543 2.36 1.99
0.525 2.12 13.4 1,13 1 .O3 1.12
0,683 0,685 6.71 10.9 4.02 2.82 1.59
0.990 0.941 2.79 2.96 4.56 1.59 1.71 1.99 1 .O9
Jul
0.331 5.17 1 .O6
0.500 1.11 1 ,O0
0.566 1.21
O. 366 O. 186 1.58 2.03 0.916 0.616 3.11 4-43 0.355 OAI 5 0.369 0.434 0.570 3.08 1-36 1,75 1,3O
0.393 1.18
0.936 1 .O2
0.596 10.6 1 .28
0.456 1.47
Oct 15.2
0.256 1 .O6
0,428 6.95 1.87
0,629 0.167 0.314 27.1 0.733 1.38 1.53
0.425 8.43
O. 322 0.41 5 2.94
O. 186 1.33 1,8l
0.686 16.8 3.14
0,449 1 .O7
0.474 3.08
0.407 0.329 0.606 2,86 7.47 4.02 1 #28
, Nov 8.22
0.383 1.52 3.42 4.31 22.2 7.55 0.466 0.424 13,3 3,89 1,56 10.8 2.50 21.3
0.538 3,26 15.8
0,403 0,917 8.67 14.5 24.9 10.4 4.90 5.23 0.573 9.5 1 10.6 3.60 1.76 12.7 12.1 2.10 17,l
Dec 7.64 1.76 14.0 1.57 38.7 39.4 6,81 1.81 1.11 15.9 6,24 15.8 36,O 1.48 26.8 0.687 4,36 7,50 0,420 934 28.9 33.8 23.5 15.2 5.64 16.1 7.95 24.7 12.7 3.31 8.89 4.48 33.2 5.73 19.7
Ann
8.48 9.74 15.1 11,4 8.43 5,21 14.3 7.10 12.1 9.71 3.81 12.4 10.3 5.88 5.91 5.61 6.36 10.3 1 O,o 15.2 10.4 9.6 1 8.01 7,51 8.71 11.3 9.96 9.84 14.6 12.3 8,96 11,6
1972-1 996 average flow
11.5272
DISCHARGE DATA (rn131s) FOR THE GULLIES'BASIN - PWQMS #13
Station . ID Year Jan Feb Mar A P ~ May Jun Jul . Aug Sep Oct Nov Dec. Ann
NO FLOW DATA AVAILABLE FOR THIS STATION
APPENDIX G: Summary of Wastewater Treatment Plant & Lagoon Data
1 # t
SUMMARY OF WASTEWATER TREATMENT PLANT & LAGOON DATA
T.P.: WWTP RANKING OF LOADINGS location range average goderich 1200-61 50 2989 palmerston 10-2600 1410 wingham 92-261 6 11 12 harriston 191 -2530 805 exeter 284-570 434 clinton 146-495 305 hensall 54-240 133 vanastra 55-200 116 brussels 24-65 45.8 milverton 14-78 42.5 blyt h 23-45 37.3 zurich 13-68 33.6 grandbend 6.03-83.7 27.8 seaforth
T.P.: WWTP RANKING OF CONCENTRATIONS location range average goderich 0.21 -12.4 1.81 wingham hensall seaforth blyth zurich exeter clinton vanastra palrnerston brussels harriston milverton grandbend
1.50 0.80 0.75 avg of yrly avg's 0.43 0.41 0.40 0.39 0.38 0.36 O. 3 1 0.28 0.27 0.47
N03: WWTP RANKING OF LOADtNGS location range average goderich 7420-334 10 25366 clinton brussels vanastra blyth exeter wingham harriston palmerston milverton hensall zurich grandbend seaforth
N03: WWTP RANKING OF CONCENTRATIONS location range average blyth O. 18-33.7 18.25 brussels 0.1-35.7 16.00 vanastra 2-27.4 13.27 clinton 0.98-24.7 12.05 goderich 0.9-17,7 8.12 exeter O. 3-5 1 A0 wingham 0.1 -6.95 1.21 harriston 0.075-2.525 0.90 zurich 0.06-2,9 0.59 seaforth 0.1333-1.253 0.52 avg of yrly avg's milverton 0.01 -0.64 0,35 hensall <. 1-1.88 0,20 palmerston NO DATA grandbend .07-7.48 0.68
SUMMARY OF WASTEWATER TREATMENT PLANT & LAGOON DATA cont'd
F.C.: WWTP RANKING OF LOADINGS location range average harriston 1 . i l 1 t+10-1 .17E+13 1.33Et12 ONLY Il DATA POINTS goderich 8,48E+9-1,26E+10 7.12E+i 1 ONLY 5 YEARS OF DATA milverton 4.OE+10-1 .49E+1 2 3.32E-tIl ONLY 7 DATA POINTS zurich 1.84E+9-1 .14E+12 1,26E+11 wing ham 5.1 E8-5.56E-t 12 5.68E-tIO brussels 1.78E+7-8.93E+9 1,22E+09 blyth 1.73E+7-4.62Et9 7,42E+08 hensall 5.9E+8-8.54E+9 2.32Et08 ONLY 10 DATA POINTS clinton no fc data, only ecoli exeter one year of ecoli data palmerston NO F.C. DATA seaforth NO F.C. DATA vanastra NO FLOW DATA WHEN HAVE F.C. DATA grandbend 6Et9-3.1 E t 1 1 6.40E+10 ONLY 16 DATA POINTS
F.C.: WWTP RANKING OF CONCENTRATIONS location range geomean b l ~ t h 3-1 500 24 brussels vanastra clinton goderich exeter wingham harriston zurich seaforth milverton hensall palmerston grandbend
3 - 316 3 - 316
no fc data, only ecoli 4 - 21000
one year of ecoli data 10 - 3160 10 - 3160 10 - 3160
NO F.C. DATA 10 - 885 32 - 174
NO F.C. DATA 32 - 174
=- 0 %
Regression of e.Coli Loading Clinton WWTP, 1995-1 998
96 96.5 Year
APPENDM H: Agricultural Spills & Drainage Tubing Sales Data
s ; ' ~ g ~ ~ g 8 ~ ~ g ~ & ~ O o - ~ b r ~ k ~ ~ - C \ ( ~ O O C l ~ O m ~ m ~ ~ r h m v b b e ~ - O ~ * - *
0 in- r..- ; CU- œ- *- 7- CU- a- *r' 0- m- a- h v- cri rc- nr o + a 3 ~ ~ ~ C b w a O ) r O c ) ~ . - O b ~ - ~ ~ ~ m ~ ~ ~ - c u - a - w - ( P _ C V - ~ ( 0 _ ~ - - ~ - ~ - O ) - ~ - ~ O r N e 3 -
MOE AGRICULTURAL WASTE SPILLS IN HURON COUNTY from Jan 1,1988 to Jun 16,1998
- 88/04/14 M m e big, Liquid, pig manure to creek, allegedly dumped by farmer). Stephen Township.
88/04/26 Manure @ig, liquid, farmer sprayhg manure - seeping fiom tile drain to creek). Stephen Township.
88/06/04 Manure @ig, liquid, fish kill, liquid pig rnanure to creek). Goderich Township.
88/08/22 Manure big, liquid, 300 GAL. pig manure to Belmore creek). Tumberry Township.
88/l 011 9 Manure (pig, liquid, cloudy discharge fiom tile drain during irrigation). Stephen Township.
8 9/OYO 8 Manure big, liquid, fish kill, manure spreading). Stephen Township.
89/03/06 Manure big, liquid, unknown quantity of liquid manure to brook). East Wawanosh Township.
8 9/O4/ 1 8 Manure (pig, liquid, unknown amount mnofTfiom hog f m , spray irrigation). Godench Township.
8 9/04/25 Manure @ig, liquid, backentry - pig rnanure in river). McKillop Township.
89/O5/ 17 Manure big, liquid, discharge of liquid pig manure reported to SAC by MOE Owen Sound). Grey Township.
89/OS/ 17 Manure @ig, liquid, backentry - estimated 222 mj pig manure to land). Grey Township.
89/06/13 Manure (pig, liquid, discharge to Ausable River). Usborne Township.
89/08/05 chicken offal (680 kg chicken offal to road)
- Grey Township.
89/08/09 Manure (pig, liquid, 60 OOOL of manure spilled, some to Lake Huron). Goderich Township.
89/08/15 Manure (pig, liquid, backentry liquid manure to storm drain fiom over flowing manure tank). Hullet Township.
89/12/0 1 Manure big, liquid, 60 OOOL of manure spilled, some to Lake Huron). Goderich Township.
9011 1/12 Manure (pig, liquid, backentry - liquid manure moff) . Howick Township.
90/04/2O Manure (pig, liquid, backentry - manure spi11 to municipal drain during irrigation operation). Stephen Township.
90/ 1 1/26 Manure (pig, liquid, unknown, water course or backentry: liquid hog manure entering municipal drain). Stephen Township
9011 2/20 Manure (unknown, multi media pol, bac kentry : manure runoff contaminating well supply) S taniey Township.
91/04/19 Manure (pig, liquid, surface water, pig manure runofi to creek confirmed). Stephen Township.
9 I 104129 - Manure big, Liquid, backentry: liquid manure to municipal drain).
Hay Township.
9 l/OS/lO Manure (pig, liquid, water course or liquid accessing Blyth Creek due to manure spreading operations). East Wawanosh Township
9 l/OS/2 1 Manure (pig, liquid, 5000L, water course or backentry: 5000L liquid manure to Stream due to blown hosehroken clamp). Tuckersmith Township.
91/08/19 - Manure big, liquid, fish kill, probable cause is runoff fiom liquid manure spraying).
Usbome Township.
92/02/24 Manure big, liquid, backentry- overspray of liquid manure dong ditch fiom dairy f m operation). Stephen Township.
92/05/02 Manure (1300L, manure from spreader flowed into creek). Hay Township.
92/O7/2 3 Manure (backentry- manure into municipal drain & neighbour's property). Stanley Township.
- 92/08/20 Manure (liquid manure discharge to municipal drain). Stephen Township.
92/09/16 Manure big, liquid. backentry- liquid manure to ditch from broken pipe). Stephen Township.
92/09/18 Manure (backentry- manure into municipal drain & neighbour's property). Stanley Towmship.
93/05/03 Manure ( f d m a n u r e spreading - unknown quantity rnanure to municipal drain). Stephen Township.
93/09/15 M m e (manure to Bannockburn river from field runoE due to heaw min, fish kill). Tuckersmith To wnship.
94/05/20 - Manure (multi-media pol. fami unhown quantity of liquid manure to tile outlet).
Hullet Township.
95/03/3 1 Manure (backenûy- milkhouse wastewater discharge to pond via tile). Hullet Township.
95/05/25 Manure (manure to drainage ditch). Tuc kersmith Township.
95/07/26 Manure big, liquid - 90 OOOL pig manure to Beachamp creek). Grey Township.
%/O8/O 1 Manure (pig? liquid - 908L, backentry - manure spill, runoff to stream leading to Lake Huron). Goderich Township.
95/08/14 Manure (backentry - suspect manure spi11 to drainage courseLake Huron). Stanley Township.
95/08/14 M m e (backenûy - rninor fish kill in creek due to manure runoWrain). Godench Township.
95/ 1 O/X Manure (backentry - manure flowing onto road allowance). Town of Exeter.
- 96/05/07 Manure (backenûy - farmer spilled unknown quantity of manure to road, cars sprayed). Ashfield Township.
96/05/07 - Manure (backentry - manure to road fiom spreader).
Astifield Township.
96/05/28 Manure (farmer - manure to creek via municipal dmin fiom f-). Ashfield Township-
96/11/17 Manure big, liquid, multi-media pol. Farm: manure spi11 causes fish kill in trout stream). Godench Township.
9611 2/12 Manure ( f m : unknown quantity liquid pig manure in drain, runoff fiom fields). Stephen Township.
9 7/O4/2 6 - Manure (pig, liquid, farm: anonymous caller reports a pig manure spi11 in creek).
Grey Township.
97/05/13 Manure @ig, liquid, farmer: liquid manure to creek, cleaned up). Ashfield Township.
97/O9/ 1 9 Manure (chicken manure spreading causes fish kill afier rains). Goderich Township.
971 10/08 Manure (fam: 900L cattle manure to farm drain). Ashfield Township.
- 98/04/11 Manure big, liquid, farm: liquid rnanure in creek and private pond, confïrmed fish kill). Stanley Township.
98/04/27 Manure big, liquid, farm: liquid manure in drain & dead fish, MOE on site). Stephen Township.
APPENDIX 1: Calculation of Septic System Density
CALCULATION OF SEPTIC SYSTEM DENSITY
Sample for a sub-basin:
# of people not senriced by a WWTP or lagoon = estimated family size = area of sub-basin =
1500 people 4 peoplelhouse
231 km2
septic system density for sub-basin = [# peoplel(#people/house)l/sub-basin area
septic systern density for sub-basin = (1 50014)1231 - - 1.623377 septic systemslkm2
APPENDIX J: Saugeen River Data
SAUGEEN STATION RAW DATA continued
79.40 79.43 79.51 79.62 79.70 79.76 80.27 80.35 80.42 80.52 80.60 80.67 80.77 80.84 80.92 81 -1 7 81 -27 81.35 81.42 81.52 81.60 81.71 81 -76 81.84 81 -94 82.34
avg conc.