Analysis of Recreational Water Characteristics

A Thesis

Submitted to the Faculty of Graduate Studies and Research

in Partial Fulfillment of the Requirements

for the Degree of

Master of Applied Science

in Environmental Systems Engineering

University of Regina

Christopher Frederick Seeley

Regina, Saskatchewan

March 2015

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Christopher Frederick Seeley, candidate for the degree of Master of Applied Science in Environmental Systems Engineering, has presented a thesis titled, Analysis of Recreational Water Characteristics, in an oral examination held on February 13, 2015. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material.

External Examiner: Dr. Biplob Das, Saskatchewan Water Security Agency

Supervisor: Dr. Dena McMartin, Environmental Systems Engineering

Committee Member: Dr. Guo H. Huang, Environmental Systems Engineering

Committee Member: Dr. Satish Sharma, Environmental Systems Engineering

Chair of Defense: Dr. Christopher Oriet, Department of Psychology

Abstract Users of natural recreational waters may be exposed to physical hazards and pathogens

that are present in the environment. These pathogens may be natural or resulting from

human activities, which in turn can be from point source and non-point source pollution.

In Canada, recreational water quality monitoring generally falls under provincial and

territorial jurisdictions. An environmental health monitoring program developed through

this research attempts to characterize and communicate the physical and biological risks associated with recreational water use.

Some items addressed during this research include:

1) Determination of the parameters correlated to water quality at beaches

2) Selection of beaches for further monitoring

Within the current research and monitoring approaches, there are gaps in knowledge with respect to the factors that affect water quality at a recreational beach setting. While many research papers have considered time series data from one or two beaches, there remain questions with respect to which beaches should be selected for monitoring. Thus, the following hypothesis was formulated and tested.

That the physical characteristics of a beach area are not correlated with water quality

when considered in conjunction with environmental factors (null hypothesis).

The hypothesis, which was rejected, is important as many jurisdictions only monitor a select number of beaches. Based on the results from testing and subsequently rejecting, the hypothesis, two models were developed.

The first model attempts to explain the geometric mean of E. coli and includes such

parameters of significance as turbidity and Secchi disc data, residential density, water

temperature, pH, beach grooming, wind speed, stormwater runoff, parking facilities,

presence of seaweed and algae, numbers of swimmers present, bird populations and

whether or not pets are permitted at the beach area. The second model describes the

probability of detecting E. coli and can be represented by the same parameters including

turbidity (both average and maximum), water temperature, parking facilities, and

presence of seaweed and algae. Parameters that differ in this model include conductivity, number of toilets, occurrence of rainfall in the previous 24-hours, and prevailing winds both parallel and onshore to the beach area. Based on these two models, a process for ranking beaches to select those that should be sampled in any given year was developed.

The following criteria were applied for making those environmental monitoring and

resource allocation decisions:

 A significant residential density should surround the beach. In addition, the area

surrounding the beach can drain to the beach area

 The beach should be popular with swimmers. This could perhaps be measured by the

number of toilets.

 The beach should allow pets on the beach.

 Waterfowl should frequent the beach area.

 High amounts of seaweed and algae in the swimming area should be common. This

could be due to blue-green algae blooms.

 The beach should have a parking lot available for users.

 Beaches in areas with more wind should be preferred

 Beaches where the water temperature is typically higher should be preferred.

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Acknowledgement I would like to thank all those that contributed to this project. This includes the Director

of Environmental Health, Tim Macaulay, with the Saskatchewan Ministry of Health.

Without his vision, sampling of recreational beaches in Saskatchewan would never have

occurred. Further, the Ministry organized, paid for and completed all the sampling efforts

required for this project. A sampling effort as large as this one would never be possible

without the cooperation of field staff, in particular the summer employees at the Ministry

who coordinated and sampled the majority of the locations over the time period. Due to

their dedication, the quality and quantity of data is sufficient to complete the analysis

contained herein. Beach managers, health regions and their field staff as well as others at

the Ministry of Health also played a large role in ensuring the sampling program’s

success.

I would also like to thank Dr. McMartin for her patience and support through my long journey through graduate studies. Her advice and supervision was critical in completing this project.

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Dedication Lastly and most importantly, I would like to thank my family for their love and support.

Many weekends, while I was writing my thesis, and evenings, while I was completing course work, were spent without me. My wife looked after all the things I couldn’t during this time. Most importantly, she has taken care of our children during those times I was away. I am in their debt as without her and the children’s support I could never have completed this program.

Contents

Abstract ...... ii Acknowledgement ...... iii Dedication ...... iv List of Tables ...... vii List of Figures ...... x List of Appendices ...... xiii List of Abbreviations ...... xiv 1 Introduction ...... 1 2 Objectives ...... 3 3 Background ...... 4 3.1 Field of Interest ...... 4 3.1.1 EH Program development description ...... 4 3.1.2 Recreational Water Illness ...... 5 Sources of Contamination ...... 6 3.1.3 Factors Affecting Recreational Water Quality ...... 8 Time Independent Variables ...... 9 Time dependent variables ...... 10 3.1.4 Previous Modeling ...... 13 3.2 Modeling Techniques ...... 14 4 Experimental Methods and Materials ...... 17 4.1 Study Area ...... 17 4.2 Sampling Methodology ...... 20 4.2.1 Standard Equipment ...... 20 4.2.2 Sample Site Selection ...... 21 4.2.3 Surface Water Sampling Procedure ...... 21 4.2.4 Storage and Shipping of Samples ...... 22 4.2.5 Forms and Records ...... 23 4.3 Instrumentation/Simulation ...... 23 4.4 Laboratory and/or Field Techniques ...... 23 4.4.1 Environmental Health Sanitary Survey ...... 23

4.4.2 Sample Collection Form ...... 28 4.5 Information about the Analysis Technique ...... 32 Regression Techniques ...... 32 Dependent Variable ...... 32 Independent Variable Selection ...... 33 Testing the Linear Model Assumptions ...... 34 Testing Logistic Model Assumptions ...... 36 Performance metrics ...... 36 5 Results ...... 38 5.1.1 Independent Variables ...... 38 5.1.2 Dependant Variables ...... 41 E.coli Geometric Mean ...... 41 5.2 Model 1 ...... 42 5.2.1 Model Assumption Tests ...... 45 Correctly Specified model ...... 45 Zero population mean ...... 47 Autocorrelation ...... 47 Heteroskedasticity ...... 48 Multicollinearity ...... 57 5.2.2 Model interpretation...... 58 5.3 Model 2 ...... 59 5.3.1 Model Assumptions ...... 64 5.3.2 Model Interpretation ...... 65 5.4 Discussion ...... 66 5.5 Beach Selection ...... 69 6 Conclusions & Recommendations...... 72 6.1 Limitations ...... 73 7 References ...... 74

List of Tables

Table 1: U.S. EPA Recreational Fresh Water Criteria ...... 4 Table 2: Health Canada Recreational Water Quality Guidelines ...... 4 Table 3 - Turgeon et al Variables Examined ...... 10 Table 5: Saskatchewan Watersheds and Their Status ...... 24 Table 6: Beaufort Wind Speed ...... 29

Table 4: Direction of Bias if X2 omitted ...... 35 Table 7: Beaches & Samples by Health Region ...... 38 Table 8: Statistically Significant T-Test Results ...... 39 Table 9: Model 1 Coefficients Steps 1 through 8 ...... 42 Table 10: Model 1 Coefficients Steps 9 through 16 ...... 43 Table 11: Linear Regression Summary Statistics ...... 44 Table 12: Linear Regression Results ...... 44 Table 13: Linear Regression Sum of Squares ...... 45 Table 14: Linear Regression Results with Robust Errors ...... 56 Table 15: Linear Regression with Robust Errors Summary Statistics ...... 57 Table 16: Variance Inflation Factors ...... 57 Table 17: Model 1 Parameters Interpretations ...... 58 Table 18: Model 2 Coefficients Steps 1 through 6 ...... 59 Table 19: Model 2 Coefficients Steps 7 through 12 ...... 61 Table 20: Model 2 Backwards elimination ...... 63 Table 21: Logistical Regression Results ...... 63 Table 22: Marginal Effects Model 2 ...... 65 Table 23: Model 2 Parameters Interpretations ...... 65 Table 24: Beach Ranking System ...... 70 Table 25: Top 20 Beaches...... 71 Table 26: Models ...... 244 Table 27: R2 between the Turbidity Parameters and E.coli Parameters ...... 258 Table 28: Turbidity R2 with Selected Variables ...... 259 Table 29: Turbidity Parameters and E.coli Detected T-Test Results ...... 259 Table 30: Air and Water Temperatures and E.coli Detected T-Test Results ...... 262 Table 31: Temperatures R2 versus Selected Variables ...... 262 Table 32: Sechi Disk and E.coli Detected T-Test Results ...... 264 Table 33: R2 between Sechi Disc and Selected Parameters ...... 264 Table 34: Wind Direction and E.coli Detected T-Test Results ...... 267 Table 35: R2 between Wind Direction and E. coli Parameters ...... 267 Table 36: Two Tailed T-test between Geometric Mean E.coli and Wind Speed 4&5 ... 270 Table 37: Wind Direction and E.coli Detected T-Test Results ...... 270 Table 38: : R2 between Wind Speed and E. coli Parameters ...... 270 Table 39: R2 between Wind Speed and Direction ...... 271

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Table 40: Swimmer Density and E.coli Geometric Mean T-Test Results ...... 273 Table 41: R2 between Swimmer Density and E.coli Parameters ...... 273 Table 42: Sunlight and E.coli Geometric Mean T-Test Results ...... 275 Table 43: Rainfall and E.coli Geometric Mean T-Test Results ...... 278 Table 44: R2 between Rainfall Parameters and E.coli Parameters ...... 278 Table 45: Maximum Wave Height and E.coli Geometric Mean T-Test Results ...... 280 Table 46: R2 between Maximum Wave Height and E.coli Parameters ...... 280 Table 47: Flooding and E.coli Geometric Mean T-Test Results ...... 281 Table 48: Number of Birds and E.coli Geometric Mean T-Test Results ...... 283 Table 49: R2 between the Number of Birds and E.coli Parameters ...... 283 Table 50: Sample pH parameters and E.coli Geometric Mean T-Test Results ...... 286 Table 51: R2 between the Sample pH and E.coli Parameters ...... 286 Table 52: Conductivity and E.coli Detected T-Test Results ...... 289 Table 53: R2 between the Sample Conductivity and E.coli Parameters ...... 289 Table 54: Time of Day and E.coli Detected T-Test Results ...... 291 Table 55: R2 between time of day and E.coli Parameters ...... 291 Table 56: Month and E.coli Detected T-Test Results ...... 294 Table 57: R2 between Sample Month and E. Coli Parameters ...... 295 Table 58: Seaweed and Algae levels in water and E.coli Detected T-Test Results ...... 298 Table 59: R2 between Seaweed and Algae amounts and E. coli Parameters ...... 299 Table 60: Pets Allowed and E.coli Parameters T-Test Results ...... 300 Table 61: R2 between Pets Allowed and E.coli Parameters ...... 300 Table 62: Residential Density Parameters and E.coli Parameters T-Test Results ...... 304 Table 63: R2 between the Residential Density Parameters and E.coli Parameters ...... 305 Table 64: Holding Tanks and E.coli Parameters T-Test Results ...... 307 Table 65: R2 between Holding Tanks and E.coli Parameters ...... 307 Table 66: Parking Lot Presence and E.coli Parameters T-Test Results ...... 309 Table 67: R2 between Parking Lot Availability and E.coli Parameters ...... 309 Table 68: Accessible by Road and E.coli Parameters T-Test Results ...... 311 Table 69: R2 between Road Accessibility and E.coli Parameters ...... 311 Table 70: Number of Toilets and E.coli Detected T-Test Results ...... 313 Table 71: R2 between Number of Toilets and E.coli Parameters ...... 313 Table 72: Runoff from Urban Areas and E.coli Parameters T-Test Results ...... 315 Table 73: R2 between Stormwater Runoff from Urban Areas and E.coliParameters .... 315 Table 74: Beach Materials and E.coli Detection T-Test Results ...... 319 Table 75: R2 between Beach Material and E.coli Parameters ...... 319 Table 76: Beach Size and E.coli Detection T-Test Results ...... 322 Table 77: R2 between Beach Size and E.coli Parameters ...... 322 Table 78: Surrounding Land Use and E.coli Detection T-Test Results ...... 322 Table 79: Land Use Parameter Interpretations ...... 324

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Table 80: R2 between Selected Land Uses and E.coli Parameters ...... 324 Table 81: Watershed Characteristics and E.coli Parameters T-Test Results ...... 327 Table 82: R2 between Watershed Status and E.coli Parameters ...... 327 Table 83: Beach Grooming and E.coli Geometric Mean T-Test Results ...... 330 Table 84: Strong Onshore Winds and E.coli Parameters T-Test Results ...... 331

List of Figures

Figure 1: Ecozones and Ecoregions of Saskatchewan ...... 18 Figure 2: Health of watersheds based on condition indicators ...... 19 Figure 3: Beach Locations ...... 20 Figure 4: Beach Sampling Locations ...... 21 Figure 5: E.coli Geometric Mean Histogram ...... 41 Figure 6: Log of Maximum E.coli Histogram ...... 41 Figure 7: Maximum E.coli Histogram ...... 42 Figure 8 : Residuals versus Time ...... 47 Figure 9: Residual versus Previous Residual ...... 48 Figure 10: Square of Residual versus Birds ...... 49 Figure 11: Square of Residual versus Water Temperature ...... 49 Figure 12: Square of Residual versus Sechi Disk ...... 50 Figure 13: Square of Residual versus Residential Density - Medium ...... 50 Figure 14: Square of Residual versus Stormwater from Residential Areas ...... 51 Figure 15: Square of Residual versus Parking Lot Available ...... 51 Figure 16: Square of Residual versus Wind Speed ...... 52 Figure 17: Square of Residual versus Swimmer Number ...... 52 Figure 18: Square of Residual versus Wave Height To ...... 53 Figure 19: Square of Residual versus Beach Grooming in more than 24 Hours ...... 53 Figure 20: Square of Residual versus Seaweed & Algae - High ...... 54 Figure 21: Square of Residual versus pH ...... 54 Figure 22: Square of Residual versus Log of Average Turbidity ...... 55 Figure 23: Square of Residual versus Pets Allowed ...... 55 Figure 24: Average Turbidity Histogram ...... 255 Figure 25: Average Turbidity +1 Log Histogram ...... 256 Figure 26: Maximum Turbidity Histogram ...... 256 Figure 27: Natural Log of Maximum Turbidity +1 ...... 257 Figure 28: Turbidity versus Geometric Mean of E.coli ...... 258 Figure 29: Sample Turbidity versus E.coli Parameters ...... 259 Figure 30: Water and Air Temperature versus E. coli Parameters ...... 260 Figure 31: Box Plot of Air Temperature over E. coli Detected ...... 261 Figure 32: Box Plot of Water Temperature over E. coli Detected ...... 261 Figure 33: Sechi Disk Histogram ...... 263 Figure 34: Sechi Disk Versus E.coli & Turbidity Parameters ...... 264 Figure 35: Onshore Wind Versus E.coli Geometric Mean ...... 265 Figure 36: Offshore Wind Versus E.coli Geometric Mean ...... 266 Figure 37: Parallel Wind Versus E.coli Geometric Mean ...... 266 Figure 38: Wind Speed Versus E.coli Parameters ...... 268 Figure 39: Histogram of Wind Speed ...... 269

Figure 40: Geometric Mean of E.coli over Wind Speed ...... 269 Figure 41: E.coli Geometric Mean versus low Swimmer Density ...... 271 Figure 42: E.coli Geometric Mean versus Medium Swimmer Density ...... 272 Figure 43: E.coli Geometric Mean versus High Swimmer Density ...... 272 Figure 44: E.coli Geometric Mean versus Overcast Sun ...... 274 Figure 45: E.coli Geometric Mean versus Cloudy ...... 274 Figure 46: E.coli Geometric Mean versus Sunny ...... 275 Figure 47: Rainfall Parameters versus E.coli Parameters ...... 276 Figure 48: E.coli Geometric Mean versus Rainfall during Sampling ...... 277 Figure 49: E.coli Geometric Mean versus Rainfall in Last 24 Hours ...... 277 Figure 50: Maximum Wave Height Histogram ...... 279 Figure 51: Maximum Wave Height versus E.coli Parameters ...... 279 Figure 52: Presence of flooding versus E.coli Parameters ...... 281 Figure 53: Number of Birds on the Beach Histogram ...... 282 Figure 54: Number of Birds versus E.coli Parameters ...... 283 Figure 55: pH Histogram ...... 284 Figure 56: H+ Histogram ...... 285 Figure 57: Sample pH versus E.coli Parameters ...... 286 Figure 58: Conductivity Histogram ...... 287 Figure 59: Log of Conductivity Histogram ...... 288 Figure 60: Sample Conductivity versus E.coli Parameters ...... 289 Figure 61: Time of Day Histogram ...... 290 Figure 62: Sample Time of Day versus E.coli Parameters ...... 291 Figure 63: Geometric Mean of E.coli versus the Sample Month of June ...... 292 Figure 64: Geometric Mean of E.coli versus the Sample Month of July ...... 293 Figure 65: Geometric Mean of E.coli versus the Sample Month of August ...... 293 Figure 66: Geometric Mean of E.coli versus the Sample Month of September ...... 294 Figure 67: No Seaweed or Algae in the Swimming Area Box Plot ...... 296 Figure 68: Low Seaweed and Algae in Water Box Plot ...... 296 Figure 69: Medium Seaweed and Algae in Water Box Plot ...... 297 Figure 70: High Seaweed and Algae in Water Box Plot ...... 297 Figure 71: Pets Allowed Box Plot ...... 300 Figure 72: No Residential Density Box Plot ...... 301 Figure 73: Low Residential Density Box Plot ...... 302 Figure 74: Medium Residential Density Box Plot ...... 302 Figure 75: High Residential Density Box Plot ...... 303 Figure 76: Any Residential Density Box Plot ...... 303 Figure 77: Residential Parameter Parameters versus E.coli Parameters ...... 304 Figure 78: Holding Tanks Box Plot ...... 306 Figure 79: Presence of Holding Tanks versus E.coli Parameters ...... 307

Figure 80: Parking Available Box Plot ...... 308 Figure 81: Presence of a Parking Lot versus E.coli Parameters ...... 309 Figure 82: Accessible by Road Box Plot ...... 310 Figure 83: Accessible by Road versus E.coli Parameters ...... 311 Figure 84: Number of Toilets Histogram ...... 312 Figure 85: Number of Toilets versus E.coli Parameters ...... 313 Figure 86: Runoff from Residential Areas Box Plot ...... 314 Figure 87: Runoff from Urban Areas versus E.coli Parameters ...... 315 Figure 88: Beach Material -Mucky Box Plot ...... 316 Figure 89: Beach Material - Rocky Box Plot ...... 317 Figure 90: Beach Material - Sandy Box Plot ...... 317 Figure 91: Beach Material - Other Box Plot ...... 318 Figure 92: Beach Materials versus E.coli Parameters ...... 318 Figure 93: Beach Length Histogram ...... 320 Figure 94: Beach Width Histogram ...... 321 Figure 95: Beach Size versus E.coli Parameters ...... 321 Figure 96: Geometric Mean of E. coli versus Healthy Watershed ...... 325 Figure 97: Geometric Mean of E.coli versus Stressed Watershed ...... 326 Figure 98: Geometric Mean of E.coli versus Impacted Watershed ...... 326 Figure 99: Beach Grooming in the last 24 hours Box Plot ...... 328 Figure 100: Any Beach Grooming Box Plot...... 328 Figure 101: Beach Grooming in more than 24 hours Box Plot ...... 329 Figure 102: Beach Grooming Parameters versus E.coli Parameters ...... 329 Figure 103: Strong Onshore Winds Box Plot ...... 331

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List of Appendices Appendix A: Map of Beach Locations

Appendix B: Environmental Health Sanitary Survey Form

Appendix C: Sample Collection Form

Appendix D: Calculation of Geometric Mean

Appendix E: Raw Data

Appendix F: Previous Modeling

Appendix G: Descriptive Statistics

Appendix H: Analysis of Variables

Appendix I: Linear Regression Process Results

Appendix J: Logistic Regression Process Results

Appendix K: Beach Ranking Results

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List of Abbreviations α Alpha – significance level

AIC Akaike information criterion

ANN Artificial neural networks

CDC Centers for Disease Control

CFU Colony Forming Units

E.coli Escherichia coli

EC Escherichia coli

EN Enterococcus

EHSS Environmental Health Sanitary Survey

FC Fecal Coliform

FFSGA Fixed Functional set algorithms

FN False Negative

FP False Positive

GM Geometric Mean

LR Logistical Regression

Max Maximum

MLR Multivariate Linear Regression

OLS Ordinary Least Squares p-value (1-α) level of confidence

R2 Coefficient of determination

R2-Adjusted Adjusted coefficient of determination

STV Statistical Threshold Value of 90%

TC Total Coliform

TN True Negative

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TP True Positive

TSS Total Suspended Solids

U.S. EPA United States Environmental Protection Agency

VIF Variance Inflation Factor

WHO World Health Organization

WSA Water Security Agency

1 Introduction Users of natural recreational waters may be exposed to physical hazards and pathogens that are present in the environment. These pathogens may be natural or resulting from human activities, which in turn can be from point source and non-point source pollution. Many North American jurisdictions have created recreational water monitoring programs to attempt to characterize the risk of gastroenteritis illnesses and to advise beach goers of the risks. In Canada, recreational water quality monitoring generally falls under provincial and territorial jurisdictions (Health Canada, 2012). The responsibility for overseeing recreational water quality falls under different authorities in the provinces and territories depending on provincial policies. However, due to the health focus of the activities, the provincial Ministry of Health or regional health authorities often carry out these programs.

Health hazards in recreational water environments include drowning and near-drownings; injuries; and illnesses. Drowning and near-drownings account for a significant death toll (WHO, 2000). Injuries range for traumatic spinal cord injuries and other severe injuries to less severe incidents related to discarded materials on the beach (WHO, 2000). Lastly, pathogenic microorganisms found in water bodies can cause a wide range of illnesses. The primary concern is gastro-intestinal illnesses and infections of the eyes, ears, nose and throat. However, there is also some evidence to suggest that more serious illnesses such as meningitis, hepatitis A, typhoid fever and poliomyelitis may be transferred through recreational waters.

Water-borne disease can be acquired during recreational water activities such as swimming, boating or other water sports (Sinclair, et al., 2009). The link between health outcomes and recreational swimming has been well documented (Marion, et al., 2010). This is important because the usage and attendance at rivers, lakes and coastal waters for recreational activities has been increasing and is expected to continue to increase due to climate change (Turgeon, et al., 2011).

An environmental health monitoring program attempts to characterize and communicate the physical and biological risks associated with recreational water usage. To date, Saskatchewan has not had a formal environmental health beach monitoring program. Program activities have consisted of complaint follow-up and occasional one-of sampling of recreational waters, primarily in response to complaints. Over the 2012 and 2013 calendar years, the Ministry of Health began the development of Recreational Water Quality Monitoring Program called “Healthy Beach”. In the initial year of development, program development activities focused on research, sampling and monitoring criteria and informational needs. The second year of the program collected descriptive information regarding approximately 200 beaches in Saskatchewan and included water sampling. Future program years will reduce the number of beaches monitored to a

manageable number of beaches. The intent is to regularly monitor beaches where there is a greater likelihood of adverse microbial test results and therefore a higher likelihood of the presence of pathogens. In addition, physical hazard information will be collected periodically from all beaches and communicated to the public in a meaningful manner.

2 Objectives In the development of the Health Beach program, a number of elements need to be considered. While the scope of the program development is quite broad, this particular research is focused on providing an indication of beaches that should be monitored on a more frequent basis.

Some items addressed during this research include:

1) Determination of the parameters correlated to water quality at beaches 2) Selection of beaches for further monitoring

There are gaps in knowledge with respect to the factors that affect water quality at a recreational beach setting. While many research papers have considered time series data from one or two beaches, there remain questions with respect to which beaches should be monitored in the first place.

The following hypothesis was tested:

That the physical characteristics of a beach area are not correlated with water quality when considered in conjunction with environmental factors (null hypothesis).

The hypothesis is important as many jurisdictions only monitor a select number of beaches. For example, more than 200 public beaches were identified in Saskatchewan. Due to fiscal restraints, only a small subset of these beaches can be monitored on an ongoing basis.

3 Background

3.1 Field of Interest

3.1.1 EH Program development description The World Health Organization and others recommend evaluation of the source of fecal contamination and environmental characteristics influencing the contamination of recreational waters (Turgeon, 2012). These factors are usually stable over time. Monitoring those beaches with a higher risk of fecal contamination is desirable from a public health perspective.

In 1986, the U.S. Environmental Protection Agency (U.S. EPA) recommended the use of Escherichia coli (E. coli) and/or enterococci as fecal indicator(s) to assess pathogen concentrations associated with gastrointestinal illness (U.S. EPA, 2012). In 2000, the Beaches Environmental Assessment and Costal Health Act required certain areas to adopt bacterial levels that provided the same or better levels of protection. In 2012, the U.S. EPA adopted new criteria that are based on fecal indicator bacteria.

Table 1: U.S. EPA Recreational Fresh Water Criteria Indicator Limits Enterococci 35 cfu/100 mls GM 130 cfu/100 mls STV E.coli 126 cfu/100 mls GM 410 cfu/100 mls STV *STV indicates a Statistical Threshold Value of 90% *GM indicates a geometric mean to be calculated over a rolling 30 day period.

In Canada, Health Canada recently published the Guidelines for Canadian Recreational Water Quality (3rd Edition). Generally, the responsibility for managing recreational waters falls under provincial and territorial jurisdictions with the specific duties split between municipalities, health authorities and other stakeholders varying from jurisdiction to jurisdiction. The Health Canada recommendation is for a multi-barrier approach that includes the identification and control of water quality hazards before people can be exposed (Health Canada, 2012). Health Canada recommends a number of guideline values including fecal indicator guidelines provided in Table 2.

Table 2: Health Canada Recreational Water Quality Guidelines Indicator Limits Enterococci 35 cfu/100 mls GM 70 cfu/100 mls Max E.coli 200 cfu/100 mls GM 400 cfu/100 mls Max *Max indicated a single sample maximum concentration *GM indicates a geometric mean of at least five samples.

Most research attempts to predict the specific conditions under which a positive fecal indicator bacteria test will occur. Often this research is an attempt to decrease the response time between identifying the presence of fecal indicator bacteria and providing health advice to recreational users. While the same type of empirical modeling is proposed for this research, the focus is different. The intent of this research is to develop a methodology useful for the selection of a limited number of beaches for focused monitoring and assessment from the larger pool of recreational beaches based on the likelihood of higher fecal indicator bacteria results.

It is generally accepted that the physical processes that determine water quality and the indicators of high microbial density are location specific (Jones, et al., 2013). Because of this, researchers often use significant monitoring equipment at a specific beach in order to collect information on a number of physical processes to increase the statistical validity of results (Jones, et al., 2013). The equipment often monitors environmental factors such as wind, near shore waves and chemical and physical measurements of the water. The modeling technique then develops a physically-based model that describes processes related to microbial generation, transport and fate. These approaches are resource-intensive and increase knowledge of processes affecting recreational water quality. However, it is not realistic to expect all the required parameters at all beaches in a recreational water quality programs to be monitored for a significant number of physical parameters. Therefore physical based models do not provide recreational water program managers with adequate information for decision making. Rather than relying on resource intensive physical based models, an empirical model that predicts the response of fecal indicator bacteria based on various independent variables can be used to understand the distributions of fecal indicator bacteria (Jagupilla, et al., 2010). In fact, successful predictive models can be developed using only basic land use data (Crowther, et al., 2003).

3.1.2 Recreational Water Illness Numerous illnesses related to swimming in recreational water have been reported (Pruss, 1998). Investigations historically focus on gastro-intestinal illness, eye infections, skin complaints, ear, nose, and throat infections and respiratory illness. These investigations concluded that the risks of these symptoms are greater in swimmers than in non- swimmers (Pruss, 1998; Graczyk, et al., 2010).

The rate of certain symptoms, with gastro-intestinal symptoms being the most frequent adverse health outcome, has been shown to be significantly related to the count of fecal indicator bacteria in recreational water with a significant dose-response association (Pruss, 1998; McLellan & Salmore, 2003).

For 1971-2000, the U.S. EPA, the U.S. Centers for Disease Control and Prevention (CDC) and the United States Council of State and Territorial Epidemiologists

surveillance system for reporting waterborne outbreaks contained a record of 259 outbreaks including an estimate 21,740 cases of illness (Craun, et al., 2005). In some studies, swimming in a lake or pond has resulted in 44.8% of the outbreaks and 34.8% of the illnesses (Craun, et al., 2005). The most commonly identified organism was Shigella, with N. fowleri, E.coli O157:H7; Schistomosoma, norovirus (Craun, et al., 2005). Similarly, between 1991 and 2001, The Netherlands reported 742 recreational water related outbreaks involving at least 5,623 individuals where the significant health outcomes included skin conditions and gastroenteritis (Schets, et al., 2001).

As few as 5 pathogenic E. coli organisms, a single Giardia cyst, or 80 to 140 Cryptosporidium oocysts can cause illness if ingested (Health Canada, 2012). Due to the low numbers of organisms required to cause illness, the unintentional ingestion of a single mouthful of contaminated water while swimming may be sufficient to cause illness (Craun, et al., 2005). Wading, playing or swimming in recreational waters has been observed to be a significant risk factor for gastrointestinal illness (Marion, et al., 2010).

Recreational water monitoring programs often use microorganisms such as E. coli or Enterococci as indicators of fecal contamination rather than attempting to monitor the great diversity of pathogenic organisms at significant difficulty and cost of monitoring. Indicator organisms are those found in the intestinal tract of warm-blooded animals like the pathogens of interest. They are found in high concentration in fecal matter and are easy to culture. The indicators often do not directly cause illness. However, they are associated with fecal contamination that may contain pathogens (McLellan & Salmore, 2003). For Example, E.coli is a fecal coliform bacterium that lives in the human intestinal tract and is excreted in large numbers in feces. The bacteria are typically nonpathogenic but are used as an indicator of other pathogens (Viessman & Hammer, 1998).

These bacterial indicators are unreliable indicators for the presence of virus (Sinclair, et al., 2009). However, E. coli density has been found to be significantly associated with elevated gastrointestinal illness (Marion, et al., 2010). In addition, there is an association between gastrointestinal illness and single daily E. coli measurements in recreational water (Marion, et al., 2010). Although some studies have indicated that traditional fecal indicators (i.e. enterococcus, total coliforms and fecal coliforms) are not associated with health risks at beaches that not predominately contaminated by human sewage (Colford, et al., 2007), it is still common practice to use indicator organisms when monitoring recreational waters.

Sources of Contamination In 1971-2000, the sources of contamination associated with untreated recreational water outbreaks were feces in water or ill bathers (31%); bather overloading or crowding (34%); diaper aged children (25%); seepage or overflow of sewage (21%); animals (18%); and flooding or heavy rainfall (3%) (Craun, et al., 2005).

The sources of fecal contamination can be placed into several categories as laid out by Turgeon et al (Turgeon, 2012):

1. Urban activities Urban fecal pollution can occur in a number of ways. First, during a rainstorm, overland flow and storm sewers can carry domestic wastes, animal waste and other fecal contamination into surface water bodies. Secondly, during significant rainstorms, sanitary sewers and treatment systems may be overloaded and result in the discharge of partially treated or raw sewage from the wastewater treatment plants or through combined sewage overflows. Other urban sources may include onsite wastewater treatment systems, sewage from land spreading of domestic waste and effluent from wastewater treatment plants (Crowther, et al., 2002).

The number of bathers and the levels of waterborne pathogens have been shown to be significantly related (Graczyk, et al., 2010). This is believed to be partially due to the re-suspension of bottom sediments by bathers.

2. Wildlife Many types of wild animals can contribute to fecal pollution of recreational waters. However, waterfowl have been specifically studied and it has been shown that these birds can be carriers of campylobacter, salmonella and cryptosporidium. Fecal contamination from wildlife can be directly deposited into water, deposited on to beach sands or enter the water through runoff.

3. Agricultural activities Agricultural activities affect recreational waters in many ways. Fecal contamination can come from animals and manure piles on farms. The various pathogens contained in animal feces can reach water bodies and recreational areas following rain events. In addition, direct access of livestock to water can introduce fecal contamination. Rainfall can also wash spread manure into recreational waters. It is generally accepted that runoff from pasture land can be a significant source of fecal indicator bacteria. In addition, high-flow conditions in farming catchments can have higher fecal indicator bacteria present (Crowther, et al., 2002).

There are a number of factors that affect the levels of fecal contamination in recreational waters as described by Turgeon et al (Turgeon, 2012):

1. Vegetation Riparian zones reduce the concentration of fecal pathogens from runoff by more than 99% (Sullivan, et al., 2007). Wetlands can also help to reduce incoming E. coli contamination by 60-77% (Knox, et al., 2008). 7

2. Climatic Conditions Rainfall and runoff are one of the primary means that non-water deposited fecal contamination reaches water courses. The timing of various activities in a watershed can also impact the amount of fecal contamination reaching the water. For example, rainfall immediately following agricultural field manure spreading would result in higher fecal contamination than if several days passed prior to rainfall.

3. Soil Type and Topography Soil type can influence the quantity of microbes that reach water bodies. High clay content can offer protection against environmental stresses, while a sandy soil will not. High soil humidity will also facilitate microorganism transport. Lastly, a steep slope of the surrounding land will increase the runoff speed which increases soil erosion and particle, including microorganisms, movement.

4. Environmental Conditions Environmental conditions will control the rates of fecal indicator bacteria die-off and therefore the amount of fecal contamination that reaches water bodies. These factors include solar isolation, temperature, dissolved organic nutrients, dissolved oxygen and protistan grazing (Rippy, et al., 2013; Marsalek & Rochfort, 2004). Some studies have indicated that die-off within the stream during transit or in the sediments is not a major factor in some watersheds (Crowther, et al., 2002).

Fecal contamination of recreational waters is complex and can result from a number of sources that may result in a large amount of organisms in water. Multiple mechanisms have been identified that introduce fecal contamination to beaches. Some of these are tidal pumping, groundwater, river flow, re-suspension from sediments (Rippy, et al., 2013). Both contact with the recreational waters and contact between sand and individuals (Heaney, et al., 2012) have been shown to be related to illness but the relationship between fecal indicators in sediments and the water is not necessarily related. However, migration of the fecal contamination from the sediments to the water column can take place. Wave action, including that caused by boats, may reintroduce fecal indicators into the water column from sand (Zhu, et al., 2011).The highest concentrations of fecal indicators in sediments are often found in locations close to the mouth of rivers, and in beaches with low water renewal and a high accumulation of fine sediments (Garrido-Perez, et al., 2008).

3.1.3 Factors Affecting Recreational Water Quality Two broad classes of variables have been shown to be important to influence fecal contamination of recreational waters. These include time-dependant meteorological

conditions like rainfall, wind direction and temperature as well as time independent land use and environmental conditions.

Time Independent Variables The relative importance for the two classes is not well studied; however, Turgeon et al determined that 40% of the total variation in fecal coliforms was due to time-independent variables related to the beach itself.

Ruminants production within 2 km of the beach and urban area have been shown to be significant (Turgeon, et al., 2011). This is consistent with other findings that there are highly significant relationships between mean fecal indicator bacteria and the percentage land use and land affected by livestock related management practices (Crowther, et al., 2002; Hampson, et al., 2010) such as improved grassland (Kay, et al., 2010) related to intensive livestock farming. The concentration of dairy farming has also been shown to be a dominant source of fecal indicator especially under high flow conditions (Hampson, et al., 2010). This is likely due to a large quantity of manure washed off farmland during these events. Conversely, human population density has been found to be more significant in low flow conditions likely due to a continual low loading rate of fecal contamination (Hampson, et al., 2010).

Several other variables may also have a significant impact including swine production in 2 km, wastewater treatment plant within 2 km upstream, topographical index greater than 3, proximity to septic tanks, and lake area greater than 1 km2 (Turgeon, et al., 2011; Eleria & Vogel, 2005). In addition, the presence of sewers, household density, population, percent impervious area, domestic wastewater spreading practices and domestic animal density may all be correlated with fecal indicator concentrations (Eleria & Vogel, 2005; Schoonover & Lockaby, 2006). Significant increases in fecal coliforms can occur in a watershed that is greater than 20% impervious surfaces, which has significant implications for urbanization practices (Schoonover & Lockaby, 2006).

Turgeon et al perhaps completed the most complete examination of time independent variables to date. They examined 24 time independent variables related to agricultural activities, human activities and geo-hydrological characteristics.

Table 3 - Turgeon et al Variables Examined Type of activity Variable Definition Agricultural Swine production in the area of Absence, presence in 2 km influence or presence in 5 km

Ruminant production in the area of Absence, presence in 2 km interest or presence in 5 km

Crop lands in the area of influence Absence, presence in 2 km or presence in 5 km

Human activities Urban area Most of the area of interest is an urban area

Wastewater treatment plant Absence, presence in 2 km upstream or presence in 5 km upstream

Geo-hydrological Plant hardiness zone Zone covering the larger characteristics area of the area of interest

Tributaries Number of lake tributaries

Lake area Lake area in km2

Topographical index Topographical index value at the beach localization

Interestingly, only two variables were found to be statistically significant. These were presence of ruminant production in the area of influence and whether it was an urban area.

Time dependent variables Numerous studies have been completed that attempt to relate time dependent environmental variables to the concentration of fecal indicator bacteria found in recreational waters. The variables found to be significant in studies includes: turbidity, solar radiation, air temperature, water temperature, rainfall, combined sewer overflows, bottom sediments re-suspension processes, salinity, presence of animals, time of the year, and wind factors.

Turbidity has been shown to be an important factor in a number of studies (Chandramouli, et al., 2007; Francy, et al., 2006). Turbidity affects the penetration of solar radiation, which, if the fecal indicator bacteria are solar sensitive, will result in less die off of the indicator bacteria (Rippy, et al., 2013; Eleria & Vogel, 2005). In particular,

log10 turbidity has been found to be correlated with E. coli concentrations at some beaches (Francy, et al., 2006). Turbidity is very much impacted by wave height, wind speed, rainfall, runoff and other environmental variables (He & He, 2008; Ferguson, et al., 1996) therefore the inclusion of turbidity and other impacting variables may introduce co-linearity.

Cumulative solar radiation prior to the time of sample collection has been identified as an

important variable for predicting log10(E. coli) densities (Jones, et al., 2013; Crowther, et al., 2001). Some studies have identified solar radiation as less important for E. coli (Noble, et al., 2004; Sinton, et al., 2002) as compared to enterococci. While solar radiation will increase the rate of decay for E. coli, the change in rate is less that the change in rate for enterococci. However, for both organisms sunlight increases inactivation rates by a factor of 5 to 10 (Noble, et al., 2004; Sinton, et al., 2002).

The average monthly air temperature in June, July and August and the number of summer days (maximum temperature ≥25°C) and the number of tropical days (maximum temperature ≥30°C) in a summer has been shown to be related to the number of outbreaks (Schets, et al., 2001) and the presence of fecal indicator bacteria (Schoonover & Lockaby, 2006). However, the effect of temperature on the survival of indicator bacteria is not consistent in all studies (Noble, et al., 2004). In some cases, a higher temperature increases the inactivation rate, in others temperature has no effect, while some indicate that a lower temperature increases the inactivation rate (Mas & Ahlfeld, 2007). Air temperature and water temperature have an obvious relationship. However, water temperature is also likely related to solar radiation (He & He, 2008) in that higher water temperatures often result from increase solar radiation, which also may increase fecal indicator die-off.

In one study, an increase in water temperature was moderately correlated with only one fecal indicator (somatic coliphages) (Love, et al., 2010). Other findings indicate a positive relationship between water temperature and E. coli concentrations (Francy, et al., 2006). Some other studies have indicated that natural inactivation rates of fecal indicators increase with increasing temperature (Noble, et al., 2004), which would suggest lower fecal indicators in higher temperature waters. There does not to be a consensus on the impact of water temperature on the presence or concentration of fecal indicator bacteria.

Rainfall may increase microbe densities by introducing bacteria via surface runoff and storm water discharge and resuspension of sediments as well as other mechanisms (Jones, et al., 2013; Love, et al., 2010). Rainfall in the previous three days has been shown to be a significant explanatory variable (Love, et al., 2010), and other studies have found other rainfall related variables important (Motamarri & Boccelli, 2012; Crowther, et al., 2001; Francy, et al., 2006). The appropriate rainfall variable is very likely site-specific (Jones, et al., 2013) due to the time it takes rainfall to reach the water body and various

environmental factors. The amount and occurrence of runoff may be related to the amount of water required to exceed the holding capacity of the surrounding land (He & He, 2008). This explains the variation in time since rainfall and the different rainfall amounts that are found to be important in various studies. It is also apparent that the rainfall itself is not the direct contributor to increased fecal indicators. Instead, it is the runoff water that carries a variety of contaminants from surrounding landuse. For example, large paved parking lots near the beach have been shown to influence the E. coli counts in the recreational water (McLellan & Salmore, 2003).

Rainfall is also related to the flow rate of a river or the stage of a lake. High rainfall periods naturally coincide with high flow periods due to the increased water volumes. Fecal indicator bacteria concentrations increase by an order of magnitude or more during high flow periods (Crowther, et al., 2003). This is potentially due to two causes. First the number of organisms entering the water is increased due to increased water runoff, entrainment of organisms from sediments, and extension of the water to formerly dry areas (i.e. flooding). Second, there is an increased water depth, velocity and turbidity that reduces the chances of die off and sedimentation (Crowther, et al., 2003).

Combined sewer overflows variables have been demonstrated as important; however some have been it has been suggested that there may not be a cause and effect relationship (Jones, et al., 2013; Marsalek & Rochfort, 2004; Haack, et al., 2003; Rijal, et al., 2009). The typical levels of indicator bacteria in storm water (103-105 E.coli/100 mls) and in combined sewer overflows (105 – 106 E.coli/100 mls) are significantly greater than recreational water quality guidelines (Marsalek & Rochfort, 2004) and therefore, if a combined sewer overflow is present, indicator bacteria levels can be elevated.

Fecal coliforms in recreational water can also originate from the re-suspension of bottom sediments and from the bathers themselves (Graczyk, et al., 2010)(Gerba, 2000)(Graczyk, et al., 2007). Therefore, beaches will a higher bather load will tend to have greater amounts of indicator bacteria. Bottom sediments may also contain significant amounts of fecal indicator bacteria (He & He, 2008). Increased wind speed will often increase mixing and turbidity which may increase fecal indicator bacteria concentrations (Crowther, et al., 2001). Wave height also impacts the movement of nearshore sediment and may also increase the fecal indicator bacteria found in the water.

The presence of domestic or wild animals may increase the concentration of fecal indicator bacteria. Dog fecal events can have a major impact of fecal indicator bacteria (i.e. enterococci) (Zhu, et al., 2011). The presence of birds has also been shown to have a significant positive relationship with E. coli at some beaches (McLellan & Salmore, 2003; Francy, et al., 2006).

Studies generally indicated increasing inactivation rates of fecal indicators with increasing salinity (Noble, et al., 2004; Sinton, et al., 2002). Therefore, there is often a negative correlation between salinity and fecal indicators (Love, et al., 2010; Ferguson, et al., 1996; Eleria & Vogel, 2005). This is relatively unimportant when considering freshwater lakes such as those found in Saskatchewan.

Water collection time was moderately negatively correlated with E. coli. (Love, et al., 2010). Water collected later in the day may have lower E. coli. concentrations.

Higher concentrations at certain times of year have been shown in some studies (Crowther, et al., 2001; Christensen, 2001). It is likely that the time of year is correlated with weather variables such as rainfall and solar radiation or a flushing effect mechanism (Eleria & Vogel, 2005). In addition, peak summer holiday time periods may increase bacterial inputs due the presence of significant numbers of bathers.

Wind factors such as speed and direction have been shown to influence water quality (McLellan & Salmore, 2003; Hellweger, 2007; Francy, et al., 2006). In particular, this can be due to re-suspension of bacteria in the sediment due to higher wave action. Water found in the beach sand at the water/sand interface can potentially have significant E. coli concentrations (Francy, et al., 2003). The relationship has been found to be a positive relationship (Francy, et al., 2006).

3.1.4 Previous Modeling Modeling of water quality in surface water has been completed and reviewed using a number of methods. Empirical models, such as linear regression techniques, are particularly useful where limited data is available (Tufail, et al., 2008).

Some of the differences between different models are due to different water and environmental characteristics but also the time-frequency characteristics of explanatory variables (Ge & Frick, 2009). Specifically, models that consider that consider data from a short time period often have high R2 values and compared to much lower values achieved in multi-year models (Ge & Frick, 2009).

Generally, the analysis approach is to model time dependent environmental variables in order to predict recreational water quality (see Appendix F). These investigations usually examine one or more sampling locations on the same water system over a period of time. In a minority of cases, investigations attempt to focus on time independent environmental variables such as land use characteristics.

Model significance has been shown to improve significantly when logarithmic transformed data are used in model development (Tufail, et al., 2008; He & He, 2008;

Ferguson, et al., 1996). Log10 transformations are applied to bacterial concentrations in order to improve their parametricity (McLellan & Salmore, 2003). The geometric mean is

calculated as the antilog of the log10 values (Crowther, et al., 2001) as shown in Appendix D.

3.2 Modeling Techniques Two primary modeling techniques have been used in past analysis: multivariate linear regression and multivariate logistical linear regression. Other techniques such as learning vector quantization; artificial neural based forecasting; and simple correlation coefficients have been used but not extensively. Often the purpose of the modeling is to develop prediction techniques that can be used to predict water quality in near real time versus waiting for bacterial lab results that take at least 18 hours. In other cases, the studies explore the importance of various explanatory variables. ANNs can provide a superior modeling technique if the application of the model is to predict violations of water quality standards for fecal bacteria. However, ordinary least squares (OLS) regression models provide more transparent information about the nature of the relationships between indicator bacteria and the explanatory variables (Mas & Ahlfeld, 2007).

Multivariate linear regression is a method that attempts to analyze relationships between data. The methodology to calculation a multivariate linear regression is usually the ordinary least squares method. The basic equation for multivariate linear regression for k explanatory variables and i predictions of y.

⋯

Where:

th yi: the i dependent variable

th xik: the i explanatory variables

th βik: the i slope factors

th ϵi: the i error term

The slope factors are a measure of the effect that the associated dependent variable (x) has on y holding all other dependent variables constant.

The classical linear model has the following assumptions (Kahane, 2008):

1. Correctly specified: The regression model is linear, correctly specified and the error term is additive. 2. Zero population mean: The error term has a zero population mean 3. No measurement error: There are no measurement errors in the explanatory or dependent variables.

4. Uncorrelated error term: The explanatory variables are uncorrelated to the error term. 5. No Autocorrelation: Observations of the error term are uncorrelated with each other. 6. No heteroskedasticity: The error term has a constant variance. 7. No multicollinearity: There is no perfect relationship between explanatory variables. 8. Normally distributed error: The error term is normally distributed.

Where the first 6 assumptions are shown to be true, the linear regression is considered “BLUE”. “Blue” stands for best (i.e. minimum error), linear (i.e. the slope coefficients are to the power of 1), unbiased (the parameters truly reflect the effect or the slope factors calculated from the sample truly represent the population slope factors), and estimator (i.e. of available estimations).

Multivariate logistic regression is based on the logistic function, which provides that all estimates must lie in the range between zero and one, and that there is an s-shaped description on the combined effect of several explanatory variables on the probability of a ‘positive’ result. It is used to estimate regressions where there is a dummy dependent variable. That is, where the dependent variable is either 0 or 1. The general equation is:

1 1 1⋯ The method used to derive estimates of slope factors and intercepts is called maximum likelihood. There are two different methods: unconditional and conditional methods. Generally, the unconditional method is used when the number of explanatory variables or parameters is large when compared to the number of dependent observations whereas the conditional method is preferred when the number of parameters is small when compared to the number of observations. For the purpose of this research, the number of parameters (~10) is relatively large when compared to the number of observations (~200)

Logistic regressions resolve issues with a dummy dependent variable. That is where the value of the dependent variable is either 1 or 0. In this case, the ordinary least squares method is not a good methodology due to the violation of a number assumptions and values. Specifically, the ordinary least squares method will produces results outside the range of 0 and 1; there is built in heteroskedasticity; the distribution of errors is not normal and R2 is not effective for measuring the goodness of fit. The assumptions of logistic linear regressions (UCLA, 2013; Kleinbaum & Klein, 2010) are:

1. The resulting model is linear; 2. There is no specification error: no omitted variables and no extraneous variables; 3. There is no measurement error in the dependent or explanatory variables;

4. The dependent variable is a dummy variable; and 5. The observations are independent and not linear combinations of each other.

4 Experimental Methods and Materials 4.1 Study Area The Province of Saskatchewan is located in the center of Canada. It is primarily part of the Great Plains region of North America. Four ecozones are present in Saskatchewan: prairie, boreal plains, boreal shield and taiga shield (Figure 1). The shield areas are primarily Precambrian rocks and are located in the north. To the south of the shield area is the boreal plain with prairie being even further south. Each of these ecozones is further divided into several ecoregions. The boreal shield includes exposed bedrock and glacial tills resulting in a rolling topography. This area includes forests of mostly black spruce. The boreal plain ecozone is an area of thick glacial deposits. It is mostly forested with a small amount of land being used for agriculture (Canadian Plains Research Center, 2013). The Prairie ecozone is the area of Saskatchewan dominated by agriculture and therefore most modified by humans (Canadian Plains Research Center, 2013). Glaciation has affected all areas of Saskatchewan except the Cypress Hills in the far southwest. Glaciation landforms dominate Saskatchewan with erosional features present in the shield ecozones and depositional features common in the other ecozones.

The beaches sampled during this study are located mainly in the prairie and boreal plains ecozones with only a few beaches located in the boreal shield ecozone.

Saskatchewan’s climate is characterized by long cold winters and dry warm summers. The climate is relatively dry and is considered semi-arid.

Within Saskatchewan, there are also 29 watersheds. In 2010, the Saskatchewan Watershed Authority completed an assessment of the health of these watersheds. Based on this analysis, six watersheds were identified as healthy, 19 were stressed and four were impacted (Figure 2) (Saskatchewan Watershed Authority, 2010). A healthy watershed was defined as a watershed that has no apparent change in function or services provided by water, and the system is both resistant and resilient to change. A stressed watershed was one that has no degradation in function and services it provides, but has lost resistance to change. A watershed was considered impacted if the watershed has a change or degradation in function or services.

Figure 1: Ecozones and Ecoregions of Saskatchewan

(Canadian Plains Research Center, 2013)

Figure 2: Health of watersheds based on condition indicators

(Saskatchewan Watershed Authority, 2010)

The beaches included in the test were located as shown in Figure 3 and in Appendix A Eighteen of the watersheds included beaches that were tested. The watersheds that did not contain a beach were: Athabasca River (far north); Tazin River (far north); Black Lake (far north); Kasbsa Lake (far north); Reindeer River Wollaston Lake (far north); Lake Athabasca (far north); Old Wives Lake; Big Muddy Creek; Moose Jaw River; Lower Souris River; and Quill Lakes.

Figure 3: Beach Locations

4.2 Sampling Methodology

4.2.1 Standard Equipment The following standard equipment was used.

 1 – 1L bottle per beach to collect composite samples for microcystin and physical parameters testing  5 – 250 ml sterile bacteriological bottle per beach to collect samples for bacterial analyses  1 – turbidimeter, regularly inspected and calibrated to ensure accuracy.

 1 – Global Positioning System for determining the location of the beach.  1 – thermometer for testing both water and air temperature  1 – cooler with cold packs for storing water samples until delivery to laboratory  1– 250 ml sample bottle for performing onsite turbidity testing  1 – pair of hip waders for water collection  1 – secchi disk for measuring light penetration  1 – digital camera for documenting the beach environment

4.2.2 Sample Site Selection Samples sites were selected at five (5) equal intervals along the beach unless the beach is over 1 kilometre in length. Health Canada specifies that the geometric mean of E. Coli should be calculated from a minimum of 5 samples (Health Canada, 2012). This also conforms to American (U.S. EPA, 2002) and Ontario (Ontario Minister of Health and Long-Term Care, 2008) guidance for beach sampling.

Figure 4: Beach Sampling Locations

4.2.3 Surface Water Sampling Procedure External contamination was avoided while collecting samples. Care was taken to not touch the top of the bottle during removal or replacement of the cap. The sterilized sample bottle was opened with the opening facing downward, held by the base and submerged. At the appropriate depth (see Sample Site Selection) the bottle was turned upwards with the opening facing the current, if any.

The samplers used waders to enter the water to a depth of 1-1.5 metres. Where the depth of water is less than 1 metre, samples were obtained as far offshore as possible, but within the swimming area.

The sample was collected from 25 to 30 cm below the water surface as far away from the sampler as possible. Shallow water samples have been shown to be well correlated with

deeper water samples, however, the shallow water samples result in higher E. coli results (Olyphant & Whitman, 2004). All samples were uniquely identified with a number or code.

For bacteriological samples, some water was discarded to allow for an air space of at least 6-7 mm for mixing.

For microcystin and chemical tests, a 1000 mL composite sample was taken that consists of 200 mL from each of the five sites.

The following precautions were taken when collecting surface water samples.

 The time of day, sampling method, location, and depth were kept constant for each beach. Sampling east to west or west to east should also be kept constant.  Special care was taken not to contaminate samples. This includes storing samples in a secure location to preclude conditions which could alter the properties of the sample. Samples were sealed during long-term storage or shipment.  Documentation of field sampling was completed on the appropriate lab forms and beach inspection forms.  All shipping documents, such as air bills, bills of lading, etc., were retained.

Digital pictures were taken of each beach during each sampling event.

4.2.4 Storage and Shipping of Samples Whenever possible, samples were stored for no more than 8-hours prior to analysis to avoid changes in concentration of indicator organisms. Collected samples were handled as follows:

1. Transfer the sample(s) into suitable, labeled sample containers. 2. Preserve the sample if appropriate, or use pre-preserved sample bottles. Do not overfill bottles if they are pre-preserved. 3. Cap the container, place in a Ziploc plastic bag and cool to 10˚C or less. Place samples should be stored in insulated boxes with cooling packs or ice. Samples should not be frozen. 4. Record all pertinent data in the site logbook and on field data sheets. Retain Sample Collection (Appendix C) forms. 5. Complete the laboratory requisitions. Send laboratory requisitions with the samples. 6. Attach custody seals to cooler prior to shipment. 7. Decontaminate all sampling equipment prior to the collection of additional samples with that sampling device.

The temperature and time of storage were recorded to aid in interpretation of results. Samples were mailed to Saskatchewan Disease Control Laboratory in Regina, SK.

4.2.5 Forms and Records Each site inspection or sample included information on the location and sampling site, date, time, weather conditions, water temperature, visual inspections for abnormal conditions, sources of contamination, and the name of the sampler. A sample form is found in Appendix C.

The EHSS (Appendix B) was completed on the initial sampling event of the season (i.e. one per beach per year). A single sample collection form was used for each beach with all sample specific information filled out on the last table (one per beach per sampling event). A lab requisition was filled out for each sample site (five per beach per sample event).

There are two forms found in Appendix C, each were filled out for each sample site at a beach.

4.3 Instrumentation/Simulation Hach 2100P turbidimeters were used for measuring the turbidity of each sample. At each beach, a 250 ml water sample was collected at each of the five sampling locations. This sample was used for turbidity measurements and to make the composite sample required for microcystin and chemical lab tests. Turbidity measurements were completed in the field within 10 minutes of being collected following the standard procedures found in the Hach operating manuals. The turbidimeters were calibrated at the beginning of the season and were checked using secondary standards periodically throughout the year.

4.4 Laboratory and/or Field Techniques All laboratory testing was completed using the services of the Saskatchewan Disease Control Laboratory, an accredited laboratory in Regina, Saskatchewan. 4.4.1 Environmental Health Sanitary Survey Environmental and environmental health data on the beaches visited were collected in order to characterize them. Detailed information was collected once over the 2013 bathing season regardless of the number of times the beach was visited.  Health Region o The health region in which the beach was located was noted for reporting purposes and for follow-up of adverse test results.  Beach Name o The locally known beach name was recorded for tracking and reporting to stakeholders.  Lake Name

o The name of the lake was tracked for each beach in order to assist with locating and so that results from individual beaches could be examined by program staff.  EHSS GPS Coordinates o The GPS coordinates were recorded for the center of each beach.  Owner Type o The type of ownership or the primary stakeholder was defined for each beach. The variable was limited to regional park, provincial park, village, rural municipality, or private owner.  Water body type o The type of water body that the beach was located on was defined. This variable was limited to lake, river, slough, man-made, or other type.  Watershed o The watershed that the beach was located on was recorded as appropriate from Table 4.  Status of the watershed o The status of the watershed was determined and two variables (“Watershed Impacted” and “Watershed Stressed”) were used to track this information. If either variable was equal to “1”, then the watershed was impacted or stressed as appropriate. If both variables are 0, then the watershed is considered healthy.  EHS Date o The date that the Environmental Health Survey was completed was recorded.  EHS Time o The time of date that the EHS survey was completed was recorded and converted to 24 hour format. The time was also converted to a decimal between 0 and 1, with 1 being 24:00 H and 0 being 0:00 H.  Person Conducting Survey o The individual conducting the environmental health survey was recorded. The individuals were either employed by the health region or the Ministry of Health.  Dimensions of Beach (m) o “Beach Length” was measured parallel to beach o “Beach Width” was measured perpendicular to beach  Dimensions of swimming area (m) o “Swimming Area Length” was measured parallel to beach o “Swimming Area Width” was measured perpendicular to beach

Table 4: Saskatchewan Watersheds and Their Status Watersheds Status Assiniboine River Impacted Athabasca River Stressed Battle River Stressed Beaver River Stressed Big Muddy Creek Stressed Black Lake Healthy Carrot River Stressed Churchill River Healthy Cypress Hills North Slope Stressed Eagle Creek Stressed Kasba Lake Healthy Lake Athabasca Healthy Lake Winnipoegosis Stressed Lower Qu'Appelle River Stressed Lower Souris River Stressed Milk River Stressed Moose Jaw River Impacted North Saskatchewan River Stressed Old Wives Lake Stressed Poplar River Stressed Quill Lakes Impacted Reindeer River Wollaston Lake Healthy Saskatchewan River Stressed South Saskatchewan River Stressed Swift Current Creek Stressed Tazin River Healthy Upper Qu'Appelle River Stressed Upper Souris River Stressed Wascana Creek Impacted (Saskatchewan Watershed Authority, 2010)

 Unmarked Swimming Area o If the swimming area was not marked with buoys or other means, it was considered unmarked and this variable was selected as yes (converted to “1”)  Beach access o Beach access was tracked through three independent variables: “General Public Access”, “Residents and Guest Access”, and “Itinerant Use Access”. General public was used when the area was marked as public. Residents and guest access was used when the area was not marked or advertised as public but was instead most likely used for the purposes of the nearby residents and their guests. Itinerant use access was used when

the beach area was located directly adjacent to an itinerant use facility such as a hotel or resort.  EHSS Average Bather Density o The average bather density was recorded for the time at which the EHSS was completed. “EHSS Average Bather Density – Low” was recorded when 1 - 5 bathers were present. “EHSS Average Bather Density – Med” and “EHSS Average Bather Density – High” were used for 6 - 20 bathers and 21 or more bathers respectively. If all three variables are zero, then no bathers were present during EHSS completion.  Residential Density o The residential density immediately surrounding the beach was recorded for the time at which the EHSS was completed. “Residential Density – Low” was recorded when less than 50 residents (<20 residences) were estimated to be present. “Residential Density – Med” and “Residential Density – High” were used for 51-100 residents (21-40 residences) and more than 100 residents (>40 residences) respectively. If all three variables are zero, then no residences were present during EHSS completion.  Beach Material o The beach material immediately of the beach was recorded for the time at which the EHSS was completed. “Beach Material - Mucky” was recorded when the beach was muddy. “Beach Material - Rocky” and “Beach Material - other” were used for rocky beaches and non-rocky, non-sandy, and non-muddy beaches respectively. If all three variables are zero, then the beach was sandy and “Beach Material - Sandy” equals 1.  Beach Grooming o If grooming of the beach was apparent, it was considered groomed and this variable was selected as yes (converted to “1”)  Surrounding land uses o A number of surrounding land uses were examined based on a visual examination from the beach and the road access. In order to be considered present, the land use would have to be in the immediate area of the beach. These were: “urban”, “residential”, “field”, “march/swamp”, “harbor”, “rural”, “forest”, “hills/uplands”, “landfill”, “agricultural”, “commercial”, “industrial”, “river/stream/ditch”, and “other”.  Presence of chemical hazards o The potential for chemical discharges was recorded during the EHSS. In order to be considered present, the item would have to be in the immediate area of the beach. These were: “Commercial/Industrial Discharges”, “Marinas”, “Motorized Water Craft”, “Other”, “Stormwater Runoff from

area subject to pesticide application” and “Stormwater Runoff from Urban Areas”  Presence of microbial hazards o The potential for biological contamination was recorded during the EHSS. In order to be considered present, the item would have to be in the immediate area of the beach. These were: “Municipal Sewage Discharges”, Stormwater Drains/Discharges”, “Wastes from Animal Feeding Operations”, “Combined Sewer Overflow”, “Discharging Private Sewage Systems”, “Holding Tanks”, “Communal Collection Systems”, “Other Fecal Waste Discharges”, “Stormwater Runoff from Agricultural Areas”, “Stormwater Runoff from Areas receiving Sewage Sludge”, “Stormwater Runoff from Beach and Surrounding Areas”, “Stormwater Runoff from Surrounding Facilities”, “Stormwater Runoff from Residential Areas”, and “Natural Drainage” o “Other Microbial sources” and “Stormwater Runoff from other areas” were free text fields. o “Birds” and “Other Wild Animals” were recorded as a number present during the EHSS o “Pets Allowed” was based on whether the beach was posted as ‘pets allowed’. A “yes” or “1” means that pets are allowed.  Presence of physical hazards o Physical hazards were recorded as they are important environmental health factors. However, they are non-germane to the research undertaken. The variables included “Steep Slopes or Drop-offs”, “Depths Greater than 4.5 m”, “Large Rocks”, “Slippery or uneven bottom”, “Dense Aquatic Plants”, Strong Currents or Rip Tides”, and “Undertows”  Aesthetic considerations o Aesthetic concerns were recorded as they are important environmental health factors. However, they are non-germane to the research undertaken. The variables included: the amount of refuse on the beach (“EHSS Amount of Refuse – Low”, “EHSS Amount of Refuse – Med”, and “EHSS Amount of Refuse – High”); the type of refuse (“EHSS Food Related Litter”, “ EHSS Medical Litter”, “EHSS Sewage Litter”, “EHSS Household Waste”, “EHSS Building Materials”, “EHSS Fishing Related Refuse”, and “EHSS Dead Fish”); the amount algae on the beach (“EHSS Amount of algae on beach – Low”, “EHSS Amount of algae on beach – Medium”, “EHSS Amount of algae on beach – High”); the amount algae in the swimming area (“EHSS Amount of algae in swimming area – Low”, “EHSS Amount of algae in swimming area – Medium”, “EHSS

Amount of algae in swimming area – High”); “automobiles permitted near the beach”; and “boats permitted near the beach”.  Other bacteriological hazards o If the presence of certain other bacteriological hazards were known, they were marked as present. These included: “Cyanobacterial blooms”, “Schistosomes” and “Large Number of Aquatic Plants”.  Facility and Safety provisions o Facility and Safety concerns were recorded as they are important environmental health factors. However, they are mostly non-germane to this research undertaken. The variables included: “Number of Toilets” (number); “Number of Showers” (number); “Number of Drinking Water Fountains” (number); “Number of Litter Bins” (number); “Number of Lifeguard Stations” (number); “Number of Lifesaving Equipment” (number); “Number of First Aid Stations” (number); “Access for persons with Disabilities”, “Toilets and Showers may contaminate bathing area”; “Animal Proof Litter Bins”; “Accessible by Road”; “Accessible by Path”; “Parking Area Available”; “Emergency Number Posted”; “Beach Posted for Swimming Suitability”; “Spill Procedure”; “Swimmer Injury Procedure”; “Waterborne Disease Outbreaks Procedure”; “Other Procedures”; and Public Notification Methods”

4.4.2 Sample Collection Form A sample event occurred when the beach was visited for the purpose of collecting water samples for analysis. This may have occurred at the same time as EHSS information was collected, in the case where the beach was only visited once over the 2013 bathing season. However, where the beach was sampled more than once during the season, additional sample collection forms were completed.

 Sample Date o The date that the sampling was completed was recorded.  Sample Week of the Year o The week of the year that the sampling was completed was recorded.  Sample Time o The time of date that the EHS survey was completed was recorded and converted to 24 hour format (“Sample Time - 24H”) and a decimal format where 1 was midnight.  Sampler o The individual conducting the sampling was recorded. The individuals were either employed by the health region or the Ministry of Health.  Sample Location Description o A text description of the sample location was included. 28

 GPS Coordinates o The GPS Coordinates were collected from the center of the beach.  Air temperature o The air temperature was recorded in degrees Celsius.  Water temperature o The water temperature was recorded in degrees Celsius.  Secchi disk depth o The secchi disk depth was recorded at the center sampling location in centimetres.  Prevailing winds o The prevailing winds at the time of beach sampling were recorded. “Prevailing Wind - onshore” was recorded when the wind was blowing onshore. “Prevailing Wind - offshore” and “Prevailing Wind - parallel” were used for winds from the water to beach and winds blowing parallel to the beach respectively. If all three variables are zero, then there was no wind.  Wind speed (Beaufort wind speed) o The wind speed was estimated using the Beaufort wind speed.

Table 5: Beaufort Wind Speed Number Indicators of Wind Speed Wind Speed (km/hr) 0 Smoke rises vertically <2 1 Wind direction shown by smoke drift 2-5 2 Wind felt on face; leaves rustle 6-12 3 Leaves; small twigs in constant motion; light flag extended 13-19 4 Raises dust and loose paper; small branches are moved 20-29 5 Small trees in leaf sway; crested wavelets on inland waters 30-38

 Average Swimmer Density o The average bather density was recorded during sampling. “Average Swimmer Density – Low” was recorded when 1 - 5 bathers were present. “Swimmer Bather Density – Med” and “Average Swimmer Density – High” were used for 6 - 20 bathers and 21 or more bathers respectively. If all three variables are zero, then no swimmers were present during sampling.  Swimmer Number o The number of swimmers observed during the sampling was recorded.  Average boater density

o The average boater density was recorded during sampling. “Average Boater Density – Low” was recorded when 1 - 5 bathers were present. “Boater Bather Density – Med” and “Average Boater Density – High” were used for 6 - 20 bathers and 21 or more bathers respectively. If all three variables are zero, then no boaters were present during sampling.  Boater Number o The number of boaters observed during the sampling was recorded.  Sunlight o The sunlight at the time of beach sampling was recorded. “Sunlight - overcast” was recorded when it was overcast. “Sunlight – partially cloudy” and “Sunlight - rainy” were used when it was partially sunny and raining respectively. If all three variables are zero, then it was sunny.  Rainfall during sampling o Whether rainfall occurred during the sampling was noted.  Rainfall in last 24 hours o Whether there was evidence of rainfall occurring in the last 24 hours was noted.  Wave height o The average wave height was recorded during sampling. “Wave Height – Low” was recorded when waves were more than 0 but less than or equal to 0.5 m. “Wave Height – Med” and “Wave Height – High” were used for waves more than 0.5 m but less than or equal to 1.5 m and waves more than 1.5 m respectively. If all three variables are zero, then there were no waves present during sampling.  Wave height range o To variables were used to characterize the range of wave heights. These were “Wave Height Range From” and “Wave Height Range To”.  Flooding o The Flooding variable was recorded as yes (or “1”) when there was evidence of current flooding during sampling.  Refuse on the beach o The amount of refuse on the beach was recorded with three variables. These were “Amount of Refuse – Low” (1-20% of the beach surface), “Amount of Refuse – Med” (21-50% of the beach surface), and “Amount of Refuse – High” (greater than 50% of the beach surface). If all three variables are zero, then there was no refuse present during sampling. The type of refuse was also tracked with several variables including “Food Related Litter”, “Medical Litter”, “Sewage Litter”, “Household Waste”, “Building Materials”, “Fishing Related Refuse”, and “Dead Fish”.

 Beach Grooming Last 24 hours o If grooming of the beach (e.g. raking or harrowing) was apparent and fresh, it was considered groomed in the last 24 hours and this variable was selected as yes (converted to “1”)  Beach Grooming more than 24 hours o If grooming of the beach was apparent but obscured by other beach activities, it was considered groomed more than 24 hours ago and this variable was selected as yes (converted to “1”)  Algae on the beach o The amount of algae on the beach. “Beach Seaweed&Algae – Low” was recorded when 1-20% of the beach surface was covered is seaweed and algae. “Beach Seaweed&Algae – Med” and “Beach Seaweed&Algae – High” were used for when 21% to 50% of the beach surface was covered and more than 50% of the beach area was covered in seaweed and algae respectively. If all three variables are zero, then no seaweed or algae was noted on the beach during sampling.  Algae in the swimming area o The amount of algae on the beach. “Swimming area Seaweed&Algae – Low” was recorded when 1-20% of the swimming area surface was covered is seaweed and algae. “Swimming area Seaweed&Algae – Med” and “Swimming area Seaweed&Algae – High” were used for when 21% to 50% of the swimming area was covered and more than 50% of the swimming area was covered in seaweed and algae respectively. If all three variables are zero, then no seaweed or algae was noted in the swimming area during sampling.

At each sample site for each beach, the following information was collected:  Turbidity (NTU)  Depth of water sample (m);  Presence of Algae (yes=1, no=0);  E. coli (# CFU/100 mls); and,  Total Coliforms (# CFU/100 mls)

Any results less than the detection limit were converted to the detection limit value. Any values that were over range were converted to the highest resolution of the analytical test.

A single composite sample was created from combining equal amounts from each of the five sample locations. From this sample, the following information was collected.

 pH (pH units);

 Alkalinity (mg/L as CaCO3);

 Conductivity (μS/m); and  Total Microcystin (µg/L)

The following parameters were analyzed by the Saskatchewan Disease Control Laboratory from samples collected from the recreational area water:

 Fecal Coliforms/ E. coli (Most Probably Number);  pH;  Conductivity ;  Alkalinity ;  Total Microcystin o completed using the ELISA test strips to screen for microcystins. Any samples that showed the presence of microcystin were tested by an analytical methodology determined by the accredited laboratory.

4.5 Information about the Analysis Technique STATA will be used to complete all modeling and analysis.

Regression Techniques Regression modeling is simple but useful. Some researchers indicate that they should be attempted before using more complicated methods (Tufail, et al., 2008). Specifically for modeling water quality results, it has been found that a regression can be developed that uses a unique set of explanatory variables for each beach (Francy, et al., 2006).

Dependent Variable The focus of the sampling was the collection of E. coli bacteria, therefore it was selected as the dependent variable.

In Canada, the recreational water standards are based on both a single sample maximum and the geometric mean of five samples. In general, the geometric mean is lower than any single sample measurement (Motamarri & Boccelli, 2012). However, it is a conservative estimate of the fecal indicator bacteria levels and is consistent with other studies (Motamarri & Boccelli, 2012; Eleria & Vogel, 2005).

Water samples from any single monitoring station are unlikely to be representative of the beach as a whole (Olyphant & Whitman, 2004) therefore, the geometric mean of samples collected on the same day is used.

The geometric mean, single maximum and the natural logarithm of the geometric mean of E. coli were used as the dependent variables in a number of ananlysis. For the purposes

of the natural-logarithm of the geometric mean of E. coli, a value of one was added to all results prior to applying the logarithm.

In addition, a logistic regression was completed using the 0 as no E. coli detected and 1 as E. coli detected. Other studies have utilized the odds of exceeding a recreational water quality limit as the dependent variable in the logistic regression (Herberger, et al., 2008; Motamarri & Boccelli, 2012; Mas & Ahlfeld, 2007; Francy, et al., 2006; Eleria & Vogel, 2005). However, in this case, the data had very few results over a recreational water limit and therefore the analysis would lack robustness.

Independent Variable Selection Prior knowledge from the scientific literature is seen as the most important rationale for including or excluding variables in a statistical analysis (Walter & Tiemeier, 2009).

Prior to beginning modeling, the variables were examined graphically. Person’s R correlation between the selected independent continuous variables and the dependent variables were developed. In addition, whisker box plots were developed to examine the relationships between categorical independent variables and the dependent variables.

Log10 transformations were applied for variables where the skewness exceeded 1.0 (Hampson, et al., 2010) unless there was a significant number of zero values in the data set.

Statistical science has developed several methods to achieve appropriate variable selection. These include: 1) change in the effect estimate; 2) stepwise selection; 3) shrinkage and penalized regression (Walter & Tiemeier, 2009). For this research, a stepwise procedure was selected. A subset of variables to examine in the regression model was selected based on the theory. These parameters were evaluated for a significant correlation with the geometric mean of E. coli using Pearson’s correlation and by statistically determining which variables were related to changes to E. coli results.

The multiple stepwise selection method used to determine the important independent variables for the multivariate linear regression is described below.

1) Begin by selecting as per the literature review. (x variables) 2) Complete forward step-wise procedure a. Try all variables individually. b. Record the R2-adjusted, Sum of Squared Errors (SSE) & F-Test. Select the variable that improves the model the most based on R2-adjusted. c. Repeat step 2 until two consecutive additions improves the R2-adjusted by less than 0.01 3) Complete backwards step-wise procedure a. Remove each variable in the model individually.

b. Record the R2-adjusted, Sum of Squared Errors (SSE) & F-Test. Remove the variable that improves the model the most based on R2-adjusted. c. Repeat step 3 until the R2-adjusted improves by less than 0.01 4) Complete forward step-wise procedure a. Try all variables individually. b. Record the R2-adjusted, Sum of Squared Errors (SSE) & F-Test. Select the variable that improves the model the most based on R2-adjusted. c. Repeat step 4 until two consecutive additions improves the R2-adjusted by less than 0.01

Variables with a variance inflation factor > 5 were excluded to minimize collinearity and the R2-adjusted for a variable to enter was set to 0.01 (or as described above). The level of explained variance was assessed using the coefficient of determination (R2 expressed as a percentage), adjusted for the degrees of freedom. The normal probability plot of standardized residuals was examined to confirm the validity of each model. All statistical tests were assessed at α = 0.05 (i.e. 95% confidence level).

A multiple stepwise selection method was used to determine the important independent variables for the logistic regression.

1) Begin by selecting as per the literature review. 2) Try all variables, one at a time. 3) Record the pseudo-R2 (McFadden’s R2). Select the variable that improves the model the most. 4) Complete forward stepwise procedure until the measure for goodness of fit indicate that the model is sufficient.

The improvement in pseudo-R2 for a variable to enter was set to 0.01.

All statistical tests were assessed at α = 0.05 (i.e. 95% confidence level).

Testing the Linear Model Assumptions

1. Correctly Specified Assumption The omission of a relevant variable will result in biased estimates of β’s. In order to detect this, the coefficients were examined to determine whether they are significantly different from expectations. In addition, omitted variables that have been shown to be important in the literature are discussed and the direction of bias determined.

Table 6: Direction of Bias if X2 omitted

Y and X2 are positively Y and X2 are negatively related related X1 & X2 are positively related Upward bias in β1 Downward bias in β1 X1 & X2 are negatively Downward bias in β1 Upward bias in β1 related

An upward bias in β1 results in β1 being larger than it should be (i.e. the actual).

The Ramsey Specification test was used to determine if a relevant variable that is a power of an included explanatory variable has been omitted.

2. Zero population mean The average error term must equal zero.

∑ ̅ 0 If this is not the case, there is very little that can be done but it does bring into question the results of the regression.

3. No measurement error

There are no measurement errors in the explanatory or dependent variables. A detailed sampling procedure has been developed to alleviate the concern of measurement error. No further analysis was undertaken.

4. Uncorrelated error term The explanatory variables are uncorrelated to the error term. The explanatory variables were plotted versus the error terms to investigate whether a relationship was present.

5. No Autocorrelation Auto correlation is typically a problem for time series data. In general, it is present when the sign of the error term is related to the sign of the preceding error term either negatively (negative autocorrelation) or positively (positive autocorrelation). The data collected is cross-sectional data so this should not be a concern. However, the error term was plotted against time to test for autocorrelation.

6. No heteroskedasticity: The error term must have a constant variance. If the variance of the error term is not constant from observation to observation, heteroskedasticity is present. The heteroskedasticity can either be pure, which is when the model is correctly specified and the error term doesn’t have a constant variance, or impure, which means the model is not

correctly specified. In the case of pure heteroskedasticity, the model is unbiased but not considered BLUE. The standard errors are biased therefore statistical testing is unreliable.

The square of the error term was plotted versus each explanatory variable in order to visually detect heteroskedasticity. In addition, the Breusch-Pagan test was run on the model.

7. No Multicollinearity If there is perfect multicollinearity between two explanatory variables, the linear regression model will fail. Where there is imperfect multicollinearity, where two or more explanatory variable are strongly related but not perfectly, the regression model will be unbiased and is still considered BLUE. However, there will be inflated standard errors, which makes finding significance with the statistical tests more difficult, and the estimates will be sensitive to changes in specification.

To detect multicollinearity, simple correlation coefficients and the Variance Inflation Factor (VIF) was calculated and examined for multicollinearity. If correlation coefficients was greater than 0.8 or the VIF > 5 (Hampson, et al., 2010), the issue was addressed through discussion or dropping a redundant variable.

8. Normally distributed error The error term is assumed to be normally distributed.

Testing Logistic Model Assumptions The assumptions for logistic models as given above are assumed to have been met based on the sampling procedure and study design. They are discussed for the model under the results however; specific statistical tests are not utilized.

Performance metrics The predictive performance of the linear regression models were evaluated using adjusted-R2 and the F-test.

The adjusted R2 adjusts the regular R2 downwards to correct for the addition of independent variables. The regular R2 is a measure of the variation that can be explained by a model.

The F-test is a test for the significance of the model. The null hypothesis is that the entire model is not significant. If the null hypothesis is rejected, then the model is considered significant.

Each logistic model was evaluated using a number of measures for goodness of fit that all attempts to determine whether the fitted model adequately describes the observed outcomes in the data. This included McFadden’s R2, Hosmer-Lemeshow goodness of fit and Akaike information criterion (AIC).

The McFadden’s R2 is inversely related to a reduction in the error variance. The value varies from 0 to 1. The value should be maximized.

The Hosmer-Lemeshow test is a statistical test used for assessing the goodness of fit for a logistical regression model (Kleinbaum & Klein, 2010). The null hypothesis that there is no evidence to indicate lack of fit (Kleinbaum & Klein, 2010). If the HL goodness-of-fit is greater than α, the null hypothesis, which is that there is no difference between observed and model-predicted values, is not rejected, implying that the model's estimates fit the data at an acceptable level. In other words, when the null hypothesis is not rejected (i.e., large p-values), the model is a good fit for the data. The HL statistic should be minimized and the HL test should not reject the null hypothesis.

AIC measures the prediction error. A model with a lower AIC is said to reduce the prediction error. Therefore, the AIC should be reduced (Gelman & Hill, 2007).

It is noted that the deviance is a typical goodness of fit measure for many models that has been used in some modeling attempts (Mas & Ahlfeld, 2007). However, the use of this test is problematic when assessing the goodness of fit in a binary logistic regression model (Kleinbaum & Klein, 2010).

5 Results The following table shows the number of beaches and sample events by health region. Samples were taken at beaches in each health region.

Table 7: Beaches & Samples by Health Region Health Region Sample events Beaches Cypress Health Region 5 5 Five Hills Health Region 5 4 Heartland Health Region 9 9 Kelsey Trail Health Region 11 10 Mamawetan Churchill River Health Region 34 10 Prince Albert Parkland Health Region 29 29 Prairie North Health Region 31 27 Regina Qu’Appelle Health Region 44 36 Sun Country Health Region 8 6 Sunrise Health Region 22 19 Saskatoon Health Region 14 13 Total 212 168

There were 4 instances where the recreational water quality guidelines for E. coli were exceeded. A number of the locations did not have complete information. Appendix E contains all raw data (available in electronic version only).

5.1.1 Independent Variables An analysis was completed for each variable and can be found in Appendix H. Each variable was graphed versus E. coli parameters in order to visually examine the potential existence of relationships. In addition, categorical variables had box plots completed using the geometric mean of E. coli and the dependent variable to visually examine whether a difference between E. coli was present based on the category. For continuous variables, a box plot was completed based on the ‘E. coli detected’ categorical variable to look for differences in the independent variable based on the presence or absence of E. coli. Further correlation coefficients were calculated for each independent variable against other likely related independent variables and the potential dependent variables. Lastly, Student T-Tests were conducted against either E. coli detected for continuous variable or the continuous E. coli variables (e.g. geometric means) for categorical variables.

Table 8: Statistically Significant T-Test Results Parameters Tested Student T-Test Results Log of Average Turbidity & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000 Log of Maximum Turbidity & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.000 Pr(|T| > |t|) = 0.0001 Pr(T > t) = 1.0000 Air Temperature & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.0013 Pr(T > t) = 0.999 Water Temperature & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.002 Pr(|T| > |t|) = 0.0046 Pr(T > t) = 0.998 Sechi Disk & E.coli Detected Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 T-Test Pr(T < t) = 0.999 Pr(|T| > |t|) = 0.0003 Pr(T > t) = 0.0001 Offshore Winds & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.032 Pr(|T| > |t|) = 0.0636 Pr(T > t) = 0.9682 Rainfall in Last 24 Hours & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.978 Pr(|T| > |t|) = 0.045 Pr(T > t) = 0.0225 Test Wave Height Maximum & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli detected T-Test Pr(T < t) = 0.0023 Pr(|T| > |t|) = 0.0046 Pr(T > t) = 0.9977 Sample pH & E.coli Detected Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 T-Test Pr(T < t) = 0.011 Pr(|T| > |t|) = 0.021 Pr(T > t) = 0.9894 Sample [H+] & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.987 Pr(|T| > |t|) = 0.026 Pr(T > t) = 0.0128 Log of Conductivity & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.0001 Pr(|T| > |t|) = 0.0002 Pr(T > t) = 0.9999 No Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.022 Pr(|T| > |t|) = 0.0448 Pr(T > t) = 0.9776 No Seaweed/Algae & Log of Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.006 Pr(|T| > |t|) = 0.0109 Pr(T > t) = 0.9945 Low residential density & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.024 Pr(|T| > |t|) = 0.0488 Pr(T > t) = 0.9756 Test Medium Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Log of Maximum E.coli T- Pr(T < t) = 0.978 Pr(|T| > |t|) = 0.0446 Pr(T > t) = 0.0223 Test Mucky Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.969 Pr(|T| > |t|) = 0.062 Pr(T > t) = 0.0311 Beach Width & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.003 Pr(T > t) = 0.9988 Beach Area & E.coli Detected Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 T-Test Pr(T < t) = 0.011 Pr(|T| > |t|) = 0.023 Pr(T > t) = 0.9886 Residential & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric mean T-Test Pr(T < t) = 0.975 Pr(|T| > |t|) = 0.0498 Pr(T > t) = 0.0249 Residential & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli T-Test Pr(T < t) = 0.986 Pr(|T| > |t|) = 0.0274 Pr(T > t) = 0.0137 Marsh or Swamp & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric mean T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.0025 Pr(T > t) = 0.9987 Forest & maximum E.coli T- Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Test Pr(T < t) = 0.989 Pr(|T| > |t|) = 0.0226 Pr(T > t) = 0.0113 Forest & E.coli Geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.956 Pr(|T| > |t|) = 0.0890 Pr(T > t) = 0.0445 Beach Grooming greater than Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 24 hours ago & E.coli Pr(T < t) = 0.036 Pr(|T| > |t|) = 0.0710 Pr(T > t) = 0.9645 Geometric Mean T-Test

The above significant T-tests provide information on potential relationships that may be found in the model after development.

‘E. coli detected’ is related to the following parameters at levels that are statistically significant.

 Log of Average Turbidity  Log of Maximum Turbidity  Air Temperature  Water Temperature  Wave Height Maximum  Sechi Disk  pH  Sample [H+]  Log of conductivity  Beach Width  Beach Area

The geometric mean of E. coli is related to the following parameters at levels that are statistically significant.

 Offshore Winds  Rainfall in the last 24 hours  No Seaweed/Algae  Residential Density - Low  Medium Seaweed/Algae  Mucky Beach Material  Residential Surrounding Land Use  Marsh or Swamp  Forest  Beach Grooming greater than 24 hours ago

Many of the above variables or related variables appear in the final models.

5.1.2 Dependant Variables

E.coli Geometric Mean Figure 5: E.coli Geometric Mean Histogram .03 .02 Density .01 0 0.00 100.00 200.00 300.00 400.00 500.00 Ecoli Geometric Mean

Figure 6: Log of Maximum E.coli Histogram 2 1.5 1 Density .5 0 0.00 0.50 1.00 1.50 2.00 2.50 Log Max Ecoli add 1

Figure 7: Maximum E.coli Histogram .02 .015 .01 Density .005 0 0.00 100.00 200.00 300.00 400.00 500.00 Max Ecoli

None of the three E. coli variables have a well defined normal distribution.

5.2 Model 1 The process described in previous sections were used to develop a linear regression model. The two tables below shows the included coefficients through the model development.

Table 9: Model 1 Coefficients Steps 1 through 8 Coefficients Step Step Step Parameter Step 4 Step 5 Step 6 Step 7 Step 8 1 2 3 Log of Turbidity 10.0 7.88 5.28 6 5.76 4.37 4.29 -0.32 Average Beach Grooming in 33.37 34.1 more than 24 hours Birds 0.51 0.51 0.53 0.45 0.51 Parking Area Available Pets Allowed Rainfall Last 24 -26.0 -32.52 -32.38 -27.82 -24.57 -24.33 Hours Residential Density - -19.11 -24.21 -25.05 -28.63 Medium

Coefficients Step Step Step Parameter Step 4 Step 5 Step 6 Step 7 Step 8 1 2 3 Sample1pH -32.09 -39.55 -55.57 Seaweed & Algae in Water - High Sechi Disc Stormwater Runoff From Residential Swimmer Number Water Temperature 3.62 Wave Height To 76.47 82.15 78.79 83.09 100.92 101.24 124.13 WindSpeed

Table 10: Model 1 Coefficients Steps 9 through 16 Coefficients Parameter Step Step Step Step Step Step Step Step 9 10 11 12 13 14 15 16 Log of Turbidity 0.05 -6.5 -7.33 -19.24 -23.6 -22.79 -20.4 -18.36 Average Beach Grooming in 27.37 27.9 23.86 27.22 27.44 35.06 28.62 28.62 more than 24 hours Birds 0.48 0.43 0.4 0.35 0.3 0.3 0.29 0.28 Parking Area -30.04 -29.9 -30.3 -31.47 -32.25 -30.27 -42.56 -49.09 Available Pets Allowed 19.92 24.29 24.35 Rainfall Last 24 -20.12 -23.9 -25.21 -25.7 19.72 -20.87 -14.04 Hours Residential Density - -34.18 -37.0 -37.67 -34.09 -34.88 -34.24 -36.38 -37.29 Medium Sample1pH -58.4 -79.5 -37.67 -72.35 -68.16 -58.93 -56.59 -59.49 Seaweed & Algae in 24.94 32.71 34.29 38.13 40.24 39.56 Water - High Sechi Disc -0.39 -0.37 -0.74 -0.92 -0.83 -0.8 -0.79 Stormwater Runoff -14.17 -15.88 -18 -19.58 -19.5 From Residential Swimmer Number 0.23 0.28 Water Temperature 3.67 2.8 2.5 1.28 -0.23 -1.12 -2.42 -2.78 Wave Height To 119.99 110.2 115.1 106.6 127.15 140.66 140.08 147.65 WindSpeed -7.87 -10.53 -10.67 -12.33

The table below shows the summary statistics from the completed model. There were 74 observations in the final model. The original 126 observations were reduced due to fields

that were blank. There were 14 degrees of freedom in the numerator and 59 degrees of freedom in the denominator for the F-test calculation. The resulting F-test (statistic equals 4.23 and Prob > F equals 0) indicates that the model is significant and a 95% level of confidence. In other words, at least one of the variables is significant. Furthermore, the R2 value is 0.501 which indicates that the model explains 50.1 % of the model variation. The adjusted R2 is 0.3826.

Table 11: Linear Regression Summary Statistics Statistic Result Number of obs 74 F( 14, 59) 4.23 Prob > F 0 R-squared 0.501 Adj R-squared 0.3826 Root MSE 52.513

Table 12: Linear Regression Results E. coli Geometric Mean Coef. Std. t P>t [95% Conf. Err. Interval] Log of Average Turbidity -18.361 10.098 -1.82 0.074 -38.567 1.845 Wave Height Range To 147.653 44.608 3.31 0.002 58.392 236.914 Birds 0.275 0.226 1.22 0.229 -0.1780.728 Residential Density - Medium -37.290 13.816 -2.70 0.009 -74.580 -9.645 pH -59.494 31.745 -1.87 0.066 -123.0154.028 Beach Grooming in over 24 hours 28.620 19.045 1.50 0.138 -9.489 66.728 Water Temperature -2.775 3.814 -0.73 0.470 -10.406 4.857 Parking Area Available -49.088 19.492 -2.52 0.015 -88.090 -10.085 Sechi Disc -0.789 0.347 -2.27 0.027 -1.483 -0.095 Seaweed and Algae in Swimming 39.564 18.838 2.10 0.040 1.869 77.258 Area - High Stormwater runoff from Residential -19.501 18.683 -1.04 0.301 -56.886 17.885 Areas Wind Speed -12.332 8.540 -1.44 0.154 -29.420 4.757 Pets Allowed 24.348 15.827 1.54 0.129 -7.323 56.018 Number of Swimmers 0.275 0.204 1.35 0.182 -0.132 0.682 _cons 738.341 284.752 2.59 0.012 168.5541308.128 T- Test results are shown above. To re-iterate, T-Test is not a test for importance, it is a test for statistical significance. Therefore in some cases, an independent variable may be statistically insignificant but still important due to the theory and therefore left in the model.

Table 13: Linear Regression Sum of Squares Source SS df MS Model 163381.153 14 11670.1 Residual 162700.094 59 2757.63 Total 326081.247 73 4466.87

The final model can be represented as

738 18.4 147.65 0.28 37.3 59.5 28.6 2.8 49.1 0.79 39.6 19.5 12.3 24.3 0.28

Where:

χ1= Log of average turbidity χ2= Weight height range to χ3= Birds χ4= Residential density – medium χ5= pH χ6= Beach grooming in over 24 hours χ7= Water temperature χ8= Parking area available χ9= Secchi disc χ10= Seaweed and algae in swimming area – high χ11= Stormwater runoff from residential areas χ12= Wind speed χ13= Pets allowed χ14= Number of swimmers

5.2.1 Model Assumption Tests

Correctly Specified model Exclusion of a relevant variable

Log of average turbidity is expected to be directly correlated to the geometric mean of E. coli. However in this case, the sign is negative which is not expected.

By examining Table 33 in Appendix H, one can see that there is a strong correlation between log of average turbidity and Secchi disk results (-0.66) and rainfall in the last 24 hours (-0.27). While this correlation is not significant enough to discard either variable, this may result in the unexpected sign. By examining Tables 9 and 10, one can see that the sign of log of average turbidity switched when water temperature was added, then became positive again when parking area was added, but became negative again when

secchi disk results were added to the model. Clearly there are interactions between the log of average turbidity and other model parameters.

Beach grooming is generally believed to decrease the E. coli concentrations in the near beach water environment due to the exposure of bacteria to the surface environment. However, in this case, beach grooming that occurred more than 24 hours before sampling results in a higher geometric mean of E. coli. An alternative hypothesis is that beach grooming can bring buried indicator bacteria to the surface and they make their way to the water through wave action.

Generally, research has shown that the availability of a parking area will increase the fecal indicator bacteria. In this case, the availability of a parking area was negatively related to E. coli geometric mean concentrations. This may either indicate an omitted variable or that the presence of a parking area indicates other environmental parameters. For example, it could be that parking lots are only constructed at beaches with better water quality.

Stormwater runoff from medium density residential areas also has an unexpected sign. One would expect that with increasing residential density, there would be increasing E. coli geometric mean concentrations. Again, this could result from an omitted variable or represent an actual relationship. Based on the data collected, it may be that beaches surrounded by a medium residential density possessed a characteristic that was related to lower E. coli geometric mean concentrations. It’s also notable that other residential density measurements did not enter the model.

Increasing wind speed is believed to the related to increasing E. coli concentrations. In this case, the opposite is being represented. While the direction of the wind does not appear in the model, wind speed is positively correlated most strongly to onshore winds. Given that onshore winds typically result in a decrease of fecal indicator bacteria (McLellan & Salmore, 2003), the coefficient for wind speed has a downward bias, which means that it is lower than expected. This may explain the sign that is inconsistent with the theory for the wind speed coefficient.

Based on the above analysis, there may be omitted relevant variables. However, either these variables were not included based on the model development procedure or data on them not collected. Like all models that use a subset of all possible variables, this indicates that the estimates of coefficients may be biased.

Inclusion of irrelevant variable

The Ramsey specification test was completed as described below. The null hypothesis is that the model has no omitted variables that are powers of included variable. The

alternate hypothesis is that there is at least one omitted variable. At a 95% level of confidence, the null hypothesis is accepted.

Ramsey RESET test using powers of the fitted values of E.coli Geometric Mean Ho: model has no omitted variables F(3, 56) = 225.72 Prob > F = 0.0000 Based on the above analysis, there are no omitted variables that are powers of variables already in the model.

Zero population mean The average error term is calculated as 5.3 x 10-8. While not zero, this result is quite low and therefore this model condition can be considered met.

Autocorrelation Figure 8 : Residuals versus Time 300 200 100 0 -100 01jul2013 01jun2013 01sep2013 01aug2013 Sample Date

Residuals Fitted values

The above graph is an informal test of autocorrelation. While there is a slight increase in residual over time, this is likely the nature of the data (i.e. pure autocorrelation). The nature of the data is such that as the summer progressed, one would expect to see greater 47

variance in the fecal indicator bacteria. The implication is that the model produces unbiased estimated of the coefficients but the standard error may be biased. This means that hypothesis testing may not be valid.

Figure 9: Residual versus Previous Residual 300 200 100 Residuals 0 -100 -100 0 100 200 300 relag

The above figure shows the relationship between the residual and the previous residual (relag). In this case, the results are clustered about the origin. As this is an informal test, a conclusive result cannot be proven, however, it is likely that there is no significant correlation between these two residuals, which means there is little autocorrelation.

Heteroskedasticity The square of the residual against each explanatory variable has been plotted below.

Figure 10: Square of Residual versus Birds 60000 40000 res2 20000 0

0.00 50.00 100.00 150.00 Birds

Figure 11: Square of Residual versus Water Temperature 60000 40000 res2 20000 0

10.00 15.00 20.00 25.00 Water Temperature

Figure 12: Square of Residual versus Sechi Disk 60000 40000 res2 20000 0

0.00 50.00 100.00 150.00 200.00 Sechi Disc

Figure 13: Square of Residual versus Residential Density - Medium 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Residential Density - Med

Figure 14: Square of Residual versus Stormwater from Residential Areas 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Stormwater runoff from residential Areas

Figure 15: Square of Residual versus Parking Lot Available 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Parking Area Available

Figure 16: Square of Residual versus Wind Speed 60000 40000 res2 20000 0

0.00 1.00 2.00 3.00 4.00 5.00 Wind Speed

Figure 17: Square of Residual versus Swimmer Number 60000 40000 res2 20000 0

0.00 100.00 200.00 300.00 Swimmer Number

Figure 18: Square of Residual versus Wave Height To 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Wave Height Range To

Figure 19: Square of Residual versus Beach Grooming in more than 24 Hours 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Beach Grooming more than 24 hours

Figure 20: Square of Residual versus Seaweed & Algae - High 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Seaweed&Algae in water High

Figure 21: Square of Residual versus pH 60000 40000 res2 20000 0

8.00 8.50 9.00 9.50 Sample 1 pH

Figure 22: Square of Residual versus Log of Average Turbidity 60000 40000 res2 20000 0

0.00 2.00 4.00 6.00 TurbidityAveLog

Figure 23: Square of Residual versus Pets Allowed 60000 40000 res2 20000 0

0.00 0.20 0.40 0.60 0.80 1.00 Pets Allowed

The above figures do not indicate that there is significant heteroskedasticity

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

The null hypothesis is that there is relationship between the square of the residual and explanatory variables. The alternate hypothesis is that the square of the residual is related to at least one explanatory variable. The test results are shown below.

chi2(1) = 120.69

Prob > chi2 = 0.0000

The p-value (Prob > chi2) is less than the level of significance (α = 10%) so reject the null hypothesis is rejected. All regression coefficients between the error squared and independent variables are not zero. Therefore, at a 90% level of confidence there is heteroskedasticity. This violates the assumptions of the model. To address heteroskedasticity, robust standard errors are used. The following two tables show the resulting model based on robust standard errors.

Table 14: Linear Regression Results with Robust Errors E. coli Geometric Mean Coef. Std. t P>t [95% Conf. Err. Interval] Log of Average Turbidity -18.361 12.473 -1.470 0.146 -43.318 6.597 Wave Height Range To 147.653 56.864 2.600 0.012 33.868 261.438 Birds 0.275 0.329 0.840 0.406 -0.383 0.933 Residential Density - Medium -37.290 16.417 -2.270 0.027 -70.141 -4.439 pH -59.494 34.746 -1.710 0.092 -129.021 10.034 Beach Grooming in over 24 hours 28.620 18.854 1.520 0.134 -9.107 66.346 Water Temperature -2.775 2.452 -1.130 0.262 -7.681 2.131 Parking Area Available -49.088 27.667 -1.770 0.081 -104.450 6.275 Sechi Disc -0.789 0.429 -1.840 0.071 -1.647 0.069 Seaweed and Algae in Swimming Area - High 39.564 26.151 1.510 0.136 -12.765 91.893 Stormwater runoff from Residential Areas -19.501 22.935 -0.850 0.399 -65.394 26.393 Wind Speed -12.332 6.883 -1.790 0.078 -26.104 1.441 Pets Allowed 24.348 14.355 1.700 0.095 -4.377 53.072 Number of Swimmers 0.275 0.084 3.250 0.002 0.106 0.444 _cons 738.341 348.923 2.120 0.039 40.147 1436.535

Table 15: Linear Regression with Robust Errors Summary Statistics Statistic Result Number of obs 74 F( 14, 59) 7.52 Prob > F 0 R-squared 0.501 Root MSE 52.513

Multicollinearity Table 16: Variance Inflation Factors Variable VIF 1/VIF SechiDisc 4.92 0.203275 Log of Average Turbidity 4.39 0.227995 Water Temperature 2.9 0.345094 Wave Height To 2.24 0.446394 Wind Speed 2.06 0.48511 Pets Allowed 1.58 0.632459 Beach Grooming in more than 24hours 1.41 0.709494 Parking Area Available 1.39 0.721931 Sample1pH 1.37 0.732391 Swimmer Number 1.36 0.73479 Residential Density - Medium 1.22 0.819222 Seaweed & Algae in Swimming Area - High 1.21 0.829796 Birds 1.2 0.830826 Stormwater Runoff From Residential Areas 1.19 0.843579 Mean VIF 2.03

The VIF for all variables is less than five therefore significant multicollinearity is not present. However, the top VIF results belong to variables that may have an opposite sign or are theoretically related to such variables. This adds weight to the argument that correlations between variables contributes to signs that are opposite from those expected.

Due to the extremely high correlation between the log of the average turbidity and the log of the maximum turbidity, when one of the parameters enters the model, the other was not used. This was to prevent multicollinearity.

5.2.2 Model interpretation Table 17: Model 1 Parameters Interpretations Parameter Statistically Interpretation Significant Log of Average No A 1% increase in the average turbidity results in a Turbidity decrease of 0.184 in the geometric mean of E. coli. Wave Height Range To Yes A single unit increase in the wave height results in an increase of 147.6 in the geometric mean of E. coli. Birds No A single unit increase in the number of birds results in an increase of 0.28 in the geometric mean of E. coli. Residential Density - Yes The presence of a medium residential density results Medium in a decrease of 37.3 in the geometric mean of E. coli as compared to those beaches with no surrounding residential density. pH No A single unit increase in the pH results in a decrease of 59.5 in the geometric mean of E. coli. Beach Grooming in over No Where beach grooming has occurred on a beach more 24 hours than 24 hours ago, an increase of 28.6 in the geometric mean of E. coli results as compared to those beaches with no beach grooming. Water Temperature No A single unit increase in the water temperature results in a decrease of 2.8 in the geometric mean of E. coli. Parking Area Available No Where a parking area is available, a decrease of 49.1 in the geometric mean of E. coli results as compared to those beaches with no parking area. Sechi Disc No A single unit increase in the sechi disk results in a decrease of 0.8 in the geometric mean of E. coli. Seaweed and Algae in No Where the amount of seaweed and algae is high in a Swimming Area - High swimming area, an increase of 39.6 in the geometric mean of E. coli results as compared to those beaches with no seaweed or algae in the swimming area. Stormwater runoff from No Where there is stormwater runoff possible from a Residential Areas residential area, a decrease of 19.5 in the geometric mean of E. coli results as compared to those beaches with no potential runoff from residential areas. Wind Speed No A single unit increase in the wind speed results in a decrease of 12.3 in the geometric mean of E. coli. Pets Allowed No Where pets are allowed on the beach, an increase of 24.3 in the geometric mean of E. coli results as compared to those beaches where pets are prohibited. Number of Swimmers Yes A single unit increase in the number of swimmers results in a decrease of 0.28 in the geometric mean of E. coli.

It is important to note that only the number of swimmers, the wave height maximum and the medium residential density parameters are statistically significant.

5.3 Model 2 Model 2 was developed using a stepwise process. The forward process took 12 steps while the backwards process was only one step.

The forward stepwise process was stopped at step 12 as there was a significant improvement in the HL statistics from step 10 to step 11. A small degradation occurred from step 11 to step 12. This process is shown in the three following tables.

Table 18: Model 2 Coefficients Steps 1 through 6 Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Accessible by Road 0.0076 0.1078 0.164 0.2156 0.246 0.3018 Air Temperature 0.0404 0.188 0.234 0.2797 0.3067 0.3197 Water Temperature 0.0292 0.1538 0.2477 0.2826 0.3095 0.3303 Beach Grooming - Any 0.0051 0.1049 0.1701 0.2209 0.245 0.3047 Beach Area 0.0182 0.1126 0.2324 0.2703 0.2954 0.3256 Beach Grooming in more than 24 0.0025 0.1075 0.1819 0.227 0.2473 0.3063 hours Beach Grooming in the Last 0.0015 0.1057 0.1709 0.2218 0.2453 0.3067 24hours Beach Length 0.0039 0.0996 0.2313 0.2697 0.2964 0.3258 Beach Material - Mucky 0.0154 0.1055 0.1606 0.215 0.2495 0.3054 Beach Material - Other 0 0.1039 0.1646 0.2184 0.2441 0.2963 Beach Material - Rocky 0.0016 0.1066 0.1643 0.2156 0.246 0.3057 Beach Material - Sandy 0.0026 0.1029 0.1653 0.2208 0.2514 0.3018 Beach Width 0.0327 0.1136 0.233 0.2707 0.2954 0.3264 Birds 0 0.107 0.1878 0.234 0.2562 0.3099 Flooding 0.0311 0.1045 0.1642 0.2162 0.2465 0.3049 Holding Tanks 0.0001 0.1201 0.193 0.2408 0.2683 0.3083 Number of Toilets 0.0003 0.1031 0.2374 0.2791 0.3103 0.3396 Parking Area Available 0.0019 0.0844 0.234 0.2736 0.2972 0.3522 No Pets Allowed 0.0016 0.1045 0.1638 0.2162 0.2475 0.3018 Prevailing Winds - Offshore 0.0035 0.1102 0.1823 0.2371 0.2718 0.3279 Prevailing Winds - Onshore 0.001 0.1219 0.1878 0.2436 0.2809 0.3397 Prevailing Winds - Parallel 0.0001 0.1219 0.1743 0.2289 0.2589 0.3174 Rainfall During Sampling 0.0001 0.0976 0.172 0.2129 0.2439 0.3086 Rainfall Last 24 Hours 0.0016 0.097 0.2216 0.2751 0.3256 Residential Density - Any 0.0106 0.1074 0.1703 0.2213 0.2538 0.3102 Residential Density - High 0.0001 0.1031 0.1638 0.2157 0.2461 0.3019 Residential Density - Low 0 0.1028 0.1638 0.2169 0.246 0.3007 Residential Density - Medium 0.0042 0.1041 0.167 0.2167 0.2481 0.3034 Residential Density - None 0.0106 0.1074 0.1703 0.2213 0.2538 0.3102 Sample August 0.0111 0.1375 0.2561 0.2954 - - Sample July 0.0056 0.1158 0.2413 0.2796 0.3051 0.3471

Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Sample June 0.0066 0.1158 0.248 0.2926 0.3119 0 Sample Sept 0.0005 0.0996 0.2332 0.2729 0.3011 0.3299 Sample Time Decimal 0.003 0.1167 0.2378 0.2757 0.3009 0.3276 Sample1 Alkalinity 0.002 0.1461 0.2557 0.2926 0.318 0.3417 Sample1 Conductivity 0.0031 0.205 Sample1H 0.02 0.1662 0.2317 0.2705 0.2955 0.3404 Sample1Log Conductivity 0.0538 0.2312 - Sample1pH 0.0203 0.1556 0.2348 0.2775 0.2982 0.3285 Seaweed & Algae in Water - High 0.0068 0.1094 0.1785 0.2448 0.2733 0.3368 Seaweed & Algae in Water - Low 0.0069 0.1114 0.1856 0.2437 0.2653 0.3123 Seaweed & Algae in Water - 0.0132 0.1318 0.1781 0.2309 0.259 0.3075 Medium Seaweed & Algae in Water - None 0.0149 0.1337 0.1885 0.2375 0.2593 0.303 Sechi Disc 0.0614 0.1382 0.1763 0.2307 0.2643 0.323 Stormwater Runoff From 0.0002 0.1063 0.1861 0.2512 0.2679 0.3245 Residential Sunlight Overcast 0.0089 0.1128 0.1967 0.2495 0.2781 0.3139 Sunlight Rainy 0 0.0986 0.1636 0.2146 0.2442 0.2955 Sunlight Sunny 0.0178 0.1216 0.2012 0.2545 0.2826 0.3163 Swimmer Density - Medium 0 0.0998 0.176 0.2202 0.2488 0.3016 Swimmer Density - High 0.0012 0.0999 0.171 0.2146 0.2399 0.2906 Swimmer Density - Low 0.0001 0.1035 0.1848 0.2169 0.2501 0.3007 Swimmer Number 0.0012 0.1035 0.1851 0.2367 0.2795 0.3293 Turbidity Average 0.0169 0.1165 0.2528 - Maximum Turbidity 0.0034 0.1272 0.2696 - Log of Maximum Turbidity 0.0932 0.1022 0.2317 - Log of Turbidity Average 0.0993 - - Watershed Healthy 0.0417 0.1591 0.2364 0.277 0.3012 0.3135 Watershed Impacted 0.0035 0.0993 0.2312 0.2696 0.2954 0.3256 Watershed Stressed 0.0104 0.1591 0.2364 0.277 0.3012 0.3135 Wave Height To 0.0298 0.1484 0.2515 0.2857 0.2983 0.337 WindSpeed 0.0001 0.0913 0.227 0.2663 0.2898 0.3236 WindSpeed 4 or 5 0.0016 0.1007 0.2485 0.2872 0.3155 0.3508 Strong onshore Winds 0.0074 0.1058 0.1738 0.2318 0.263 0.3208 Maximum Adj-R2 0.0993 0.2312 0.2696 0.2954 0.3256 0.3522 Adj R2 Improvement 0.1319 0.0384 0.0258 0.0302 0.0266 H-L 12.94 8.87 15.51 13.08 4.99 2.94 Prob>chi2 0.114 0.353 0.0517 0.1091 0.7583 0.9383 AIC 1.28 1.116 1.079 1.061 1.048 1.039 BIC -442.404 -422.97 -424.47 -423.91 -358.14 -324.94

Table 19: Model 2 Coefficients Steps 7 through 12 Parameters Step 7 Step 8 Step 9 Step Step Step 10 11 12 Accessible by Road 0.3336 0.3593 0.4545 0.5369 0.5986 0.639 Air Temperature 0.3496 0.4034 0.4606 0.5417 0.597 0.6387 Water Temperature 0.3718 0.4408 - Beach Grooming - Any 0.35 0.3587 0.4577 0.5373 0.5961 0.6389 Beach Area 0.3522 0.3793 0.4431 0.5016 0.6045 0.6404 Beach Grooming in more than 24 0.3495 0.3586 0.4543 0.5364 0.5975 0.6416 hours Beach Grooming in the Last 24hours 0.3501 0.3591 0.4595 0.5364 0.596 0.6394 Beach Length 0.3532 0.3821 0.4411 0.4976 0.596 0.6388 Beach Material - Mucky 0.3325 0.3584 0.4523 0.5335 0.5913 0.6338 Beach Material - Other 0.3274 0.3539 0.4504 0.5304 0.5907 0.6334 Beach Material - Rocky 0.3341 0.3591 0.4543 0.5362 0.5959 0.6389 Beach Material - Sandy 0.3322 0.3596 0.4563 0.5379 0.5964 0.6388 Beach Width 0.3538 0.381 0.4496 0.5025 0.6018 0.6388 Birds 0.3414 0.3674 0.4777 0.5414 0.5959 0.6404 Flooding 0.3412 0.3749 0.4615 0.5411 0.596 0.641 Holding Tanks 0.3231 0.3362 0.4289 0.5109 0.5726 0.6217 Number of Toilets 0.3735 0.4006 0.4975 - Parking Area Available - No Pets Allowed 0.3333 0.3589 0.4608 0.5533 0.602 0.64 Prevailing Winds - Offshore 0.3465 0.373 0.4698 0.5607 0.6214 0.6596 Prevailing Winds - Onshore 0.3656 0.3891 0.4769 0.5426 0.5934 0.6855 Prevailing Winds - Parallel 0.3328 0.3563 0.4552 0.5563 0.6387 Rainfall During Sampling 0.3336 0.3595 0.4204 0.4786 0.5834 0.6276 Rainfall Last 24 Hours Residential Density - Any 0.334 0.3851 0.4662 0.5512 0.61 0.6439 Residential Density - High 0.3329 0.3593 0.4596 0.5358 0.5988 0.6408 Residential Density - Low 0.3331 0.3623 0.4553 0.5361 0.5987 0.6417 Residential Density - Medium 0.3328 0.3635 0.4641 0.5361 0.6016 0.64 Residential Density - None 0.334 0.3851 0.4662 0.5512 0.61 0.6439 Sample August Sample July 0.3723 0.3976 0.4562 0.526 0.6229 0.6804 Sample June Sample Sept 0.3572 0.3856 0.4429 0.4975 0.6021 0.6507 Sample Time Decimal 0.3578 0.3866 0.4412 0.5094 0.6319 0.6602 Sample1 Alkalinity 0.3638 0.3975 0.4428 0.5 0.5959 0.6388 Sample1 Conductivity Sample1H 0.3741 0.3892 0.4657 0.5337 0.6 0.6446 Sample1Log Conductivity

Parameters Step 7 Step 8 Step 9 Step Step Step 10 11 12 Sample1pH 0.3617 0.3816 0.4468 0.509 0.6005 0.6449 Seaweed & Algae in Water - High 0.372 0.3969 0.4952 0.5958 Seaweed & Algae in Water - Low 0.3432 0.3798 0.4816 0.5941 0.6314 0.6765 Seaweed & Algae in Water - Medium 0.3377 0.3694 0.4548 0.5359 0.6046 0.649 Seaweed & Algae in Water - None 0.3328 0.3676 0.4564 0.5443 0.6182 0.6626 Sechi Disc 0.339 0.3634 0.4619 0.5507 0.6089 0.6545 Stormwater Runoff From Residential 0.3384 0.3668 0.4494 0.5364 0.5971 0.6575 Sunlight Overcast 0.3442 0.3702 0.4659 0.542 0.5958 0.6392 Sunlight Rainy 0.325 0.3509 0.447 0.5291 0.5897 0.6333 Sunlight Sunny 0.346 0.372 0.4679 0.5427 0.5958 0.6392 Swimmer Density - Medium 0.328 0.344 0.4257 0.5105 0.5758 0.625 Swimmer Density - High 0.3142 0.3412 0.435 0.5127 0.5877 0.6312 Swimmer Density - Low 0.326 0.3525 0.4454 0.5241 0.5963 0.6388 Swimmer Number 0.3432 0.3681 0.4767 0.5677 0.6216 0.6582 Turbidity Average Maximum Turbidity Log of Maximum Turbidity Log of Turbidity Average Watershed Healthy 0.332 0.3585 0.4543 0.5358 0.5958 0.6387 Watershed Impacted 0.3522 0.3793 0.4408 0.4975 0.5958 0.6387 Watershed Stressed 0.332 0.3585 0.4543 0.5358 0.5958 0.6387 Wave Height To 0.3793 - WindSpeed 0.3531 0.3841 0.4395 0.5009 0.6003 0.6458 WindSpeed 4 or 5 0.3753 0.3882 0.4458 0.5033 0.6052 0.6458 Strong onshore Winds 0.3501 0.3657 0.4571 0.5459 0.6014 0.6458 Maximum Adj-R2 0.3793 0.4408 0.4975 0.5958 0.6387 0.68 Adj R2 Improvement 0.0271 0.0615 0.0567 0.0983 0.0429 0.047 H-L 5.42 6.26 6.16 6.58 1.6 1.7 Prob>chi2 0.7114 0.6185 0.6293 0.5822 0.9909 0.989 AIC 1.024 0.967 0.917 0.814 0.784 0.744 BIC -310.5 -304.4 -297.5 -277.1 -272.7 -273.6

Table 20: Model 2 Backwards elimination Parameters Coeficients Step 1 TurbidityAveLog 0.8773274 0.672 Sample1LogConductivity -0.7237653 0.5221 TurbidityMax -0.01997 0.6074 SampleAugust -2.660183 0.6546 RainfallLast24hours -7.171062 0.5169 ParkingAreaAvailable 7.333699 0.5462 WaveHeightRangeTo 4.126892 0.5339 WaterTemperature 1.944441 0.4558 NumberofToilets -0.2325118 0.5452 SeaweedAlgaeinwaterHigh 4.913609 0.6188 PrevailingWindparallel 4.980328 0.5934 PrevailingWindonshore 3.111646 0.6387 Maximum Adj-R2 0.6855 0.672 Adj R2 Improvement -0.0135

The results of the logistical regression are below.

Table 21: Logistical Regression Results Ecoli Detected Coef. Std. z P>z [95% Interval] Err. Conf. Log of Average Turbidity 0.877 0.740 1.190 0.236 -0.572 2.327 Log of Conductivity -0.724 3.202 -0.230 0.821 -6.999 5.551 Maximum of Turbidity -0.020 0.011 -1.870 0.061 -0.041 0.001 Sample August -2.660 1.585 -1.680 0.093 -5.768 0.447 Rainfall Last 24 hours -7.171 2.725 -2.630 0.008 -12.512 -1.830 Parking Area Available 7.334 3.048 2.410 0.016 1.361 13.307 Wave Height Range To 4.127 3.438 1.200 0.230 -2.611 10.865 Water Temperature 1.944 0.633 3.070 0.002 0.704 3.185 Number of Toilets -0.233 0.125 -1.870 0.062 -0.477 0.012 Seaweed Algae in water - High 4.914 2.379 2.070 0.039 0.251 9.576 Prevailing Wind - parallel 4.980 1.971 2.530 0.012 1.117 8.844 Prevailing Wind - onshore 3.112 1.511 2.060 0.039 0.150 6.073 _cons -40.341 15.792 -2.550 0.011 -71.293 -9.389

The final model for the probability of detecting E. coli can be represented as

40.34 0.884 0.72 0.02 2.7 7.2 7.3 4.1 1.9 0.23 4.9 5.0 3.1

Where:

χ1= Log of average turbidity χ2= Log of conductivity χ3= Maximum of turbidity χ4= Sample August χ5= Rainfall in the last 24 hours χ6= Parking area available χ7= Wave height range to χ8= Parking area available χ9= Water temperature χ10= Number of toilets χ11= Seaweed and algae in the swimming area - high χ12= Prevailing wind – parallel χ13= Prevailing wind – onshore

5.3.1 Model Assumptions 1. The resulting model is linear

The resulting model must be linear in order to be valid. There is not test for this and it has to be assumed that this is true.

2. There is no specification error: no omitted variables and no extraneous variables

Like with the linear regression model, it is important that the model is correctly specified. Obviously, it is very likely that some important variable has been omitted as there are an infinite number of possible variables and only a subset were considered. However, this is true of every model developed.

3. There is no measurement error in the dependent or explanatory variables

A detailed sampling and measurement procedure was followed for this program. A significant effort was expended to ensure that measurement error was minimized.

4. The dependent variable is a dummy variable

The dependent variable was selected as whether E. coli was detected. In many studies, the exceedance of the recreational water quality guidelines is used as the dependent variable. In this case, this was a very rare situation and therefore not used.

5. The observations are independent and not linear combinations of each other.

The observations are all independent and not linear combinations of each other.

5.3.2 Model Interpretation Table 22: Marginal Effects Model 2 Variable dy/dx Std. z P>z [ 95% C.I. ] X Err. Log of Average Turbidity 0.177 0.139 1.270 0.202 -0.095 0.450 1.949 Log of Conductivity -0.146 0.629 -0.230 0.816 -1.378 1.086 2.993 Maximum of Turbidity -0.004 0.002 -1.770 0.076 -0.008 0.000 35.151 Sample August -0.557 0.268 -2.080 0.038 -1.083 -0.032 0.337 Rainfall Last 24 hours -0.823 0.132 -6.230 0.000 -1.083 -0.564 0.651 Parking Area Available 0.885 0.097 9.160 0.000 0.695 1.074 0.843 Wave Height Range To 0.833 0.703 1.190 0.236 -0.544 2.210 0.157 Water Temperature 0.393 0.123 3.200 0.001 0.152 0.633 20.060 Number of Toilets -0.047 0.025 -1.890 0.059 -0.096 0.002 3.675 Seaweed Algae in water - 0.437 0.152 2.880 0.004 0.140 0.734 0.145 High Prevailing Wind - parallel 0.498 0.164 3.040 0.002 0.177 0.819 0.193 Prevailing Wind - onshore 0.538 0.224 2.400 0.016 0.099 0.977 0.434

Table 23: Model 2 Parameters Interpretations Parameter Statistically Interpretation Significant Log of Average No Evaluated at the means, the impact of a 1 unit Turbidity increase in the log of the average turbidity (or a increase from 89 to 99 NTU) results in a 17.7% increase in the probability of detecting E. coli. Log of Conductivity No Evaluated at the means, the impact of a 1 unit increase in the log of the conductivity (or a increase from 984 to 994 NTU) results in a 14.6% decrease in the probability of detecting E. coli. Maximum of No Evaluated at the means, the impact of a 1 unit Turbidity increase in the maximum turbidity (or a increase from 35 to 36 NTU) results in a 0.4% decrease in the probability of detecting E. coli. Sample August No Evaluated at the means and compared to a sample not taken in August, a sample taken in August has a 55.6% lower probability of having E. coli detected. Rainfall Last 24 hours Yes Evaluated at the means and compared to a sample where rainfall did not occur in the last 24 hours, a sample taken where rainfall occurred in the last 24 hours has a 82.3% lower probability of having E. coli detected.

Parameter Statistically Interpretation Significant Parking Area Yes Evaluated at the means and compared to a sample Available taken from a location with no parking lot present, a sample taken from a location with a parking lot has a 88.5% higher probability of having E. coli detected. Wave Height Range No Evaluated at the means, the impact of a 1 unit To increase in the wave height (or a increase from 0.157 to 1.157 m) results in a 83.3% increase in the probability of detecting E. coli. Water Temperature Yes Evaluated at the means, the impact of a 1 unit increase in the water temperature (or a increase from 20.06 to 21.06 °C) results in a 39.3% increase in the probability of detecting E. coli. Number of Toilets No Evaluated at the means, the impact of a 1 unit increase in the number of toilets (or a increase from 3.68 to 4.68) results in a 4.7% decrease in the probability of detecting E. coli. Seaweed Algae in Yes Evaluated at the means and compared to a sample water - High taken from a location with less than a high amount of algae and seaweed, a sample taken from a location high levels of seaweed and algae has a 43.7% higher probability of having E. coli detected. Prevailing Wind - Yes Evaluated at the means and compared to a sample parallel taken from a location with no wind or an offshore wind, a sample taken from a location there is a parallel wind has a 49.8% higher probability of having E. coli detected. Prevailing Wind - Yes Evaluated at the means and compared to a sample onshore taken from a location with no wind or the an offshore wind, a sample taken from a location there is a onshore wind has a 53.8% higher probability of having E. coli detected.

5.4 Discussion The parameters that appear in model 1 are correlated to the magnitude of E. coli results found in during the sampling program. The following were statistically significant

 Number of swimmers Previous studies have indicated that higher swimmer density results in higher E. coli concentrations. The results of this study corroborate this. The process by which this occurs could be that the E. coli results from direct swimmer

contamination or that swimmers cause increased sand and water mixing that releases E. coli from the sand.  Wave height range to Previous studies have indicated that increased wave height results in higher E. coli concentrations. The often hypothesized process is that the increased waves causes increase sand-water mixing and wash back material from the beach. Both of which could result in higher E. coli results.  Residential Density – Medium (negative relationship) The analysis should that a medium residential density resulted in decreased E. coli concentrations. This is compared to all other residential densities (none, low and high). This is difficult to interpret but may be masking other relationships. Perhaps a medium density residential development is large enough to manage the beach environment, lowering the E. coli concentrations from a low residential density beach, but not yet large enough that there is significant inputs from swimmers, allowing for a lowered E. coli concentrations as compared to beaches with high density residential development.

The parameters that appear in the second model are correlated to whether E. coli will be detected at a particular beach during sampling. The following were statistically significant.

 Rainfall in the last 24 hours (negative relationship) Rainfall is generally considered likely to increase E. coli concentrations. However, in this case, a negative relationship was discovered. This may be that the beach waters were generally sampled after the E. coli was washed through the system and therefore E. coli was not detected. Another theory is that this is a negative impact due to the positive impact of parking lots or winds.  Parking area available With respect to the presence of parking lots, there are a number of processes that could be present. It could be that a parking lot is present at beaches with higher usage. The higher usage causes more E. coli detections. Another theory is that a parking lot close to the beach allows E. coli contaminated stormwater runoff to directly enter the water without being treated in a riparian zone resulting in more frequent E.coli detections. It may be that the parking lot itself is not the direct contributor to the detection of E.coli but is instead correlated with a direct factor.  Water temperature Previous studies have shown a varying impact of water temperature on E. coli. In this case, it is hypothesized that water temperature is also related to beaches in impacted beaches as well as the level of usage.  A high amount of seaweed and algae in the water

A high amount of seaweed and algae in the water may indicate a lake under going eutrophication; a swimming area where the wind is blowing in algae, a beach location with poor water circulation characteristics. All of these factors can logically result in an increased likelihood of detecting E. coli.  Prevailing winds onshore and winds parallel to the beach. Two separate wind conditions are incorporated into the model. Both onshore winds and parallel winds result in an increased probability of discovering E. coli. This is compared to conditions where there was no wind or the wind was offshore. Onshore winds could result in moving algae or other floating material into the swimming area; whereas offshore winds can result in materials being moved away from the beach.

The following parameters appeared in both models:

 Log of average turbidity.  Wave height range to  Water temperature  Seaweed and algae in swimming area – high  Parking area available

It can be said that they are important factors for both determining whether E. coli will be detected and the magnitude of the E.coli results.

Both models are empirical models that attempt to correlate various independent parameters with a dependent parameter. It is important to note that correlation does not mean causation. Though it would be hard to believe that increased amounts of E. coli cause larger waves, and much more likely that larger waves result in addition E. coli being present, these models do not prove either interpretation. Instead, the model demonstrates that the presence and magnitude of E. coli is correlated to maximum wave height.

The various independent variables may also be masking some other information. For example, while the status of the watershed did not appear in any developed models. The combination of water temperature and high levels of seaweed and algae in the swimming area may describe impacted beaches in impacted watersheds more accurately that the broad description. An impacted watershed may have beach locations that are not experiencing the conditions that caused the impacted status. Impacted watersheds tend to be in southern Saskatchewan, which would indicate warmer water temperatures as compared to northern Saskatchewan. In addition, impacted watersheds may be experiencing eutrophication, which would result in increased algae and seaweed in the swimming area.

Further, it is again stressed that this research is in support of decision making for an environmental health monitoring program. The number of samples was fixed by resources available for sampling. Initial estimates indicated that the sample size should be sufficient for the analysis completed. Unfortunately, some sampling was not submitted with complete data. Therefore, the power of the resulting modeling is lower than expected.

5.5 Beach Selection One of the desired outcomes for this research was to develop a ranking of beaches for the Saskatchewan Ministry of Health to be able to select a lesser number of beaches for more frequent monitoring.

An approach was devised based on the following parameters.

 A significant residential density should surround the beach. In addition, the area surrounding the beach can drain to the beach area  The beach should be popular with swimmers. This could perhaps be measured by the number of toilets.  The beach should allow pets on the beach.  Waterfowl should frequent the beach area.  High amounts of seaweed and algae in the swimming area should be common. This could be due to blue-green algae blooms.  The beach should have a parking lot available for users.  Beaches in areas with more wind should be preferred  Beaches where the water temperature is typically higher should be preferred.

& ∗ 1

Table 24: Beach Ranking System Parameter Value Variable Logic Surrounding Medium or High Res If the residential density is medium or Residential high, add 1 to rank. Density

Stormwater True Run If stormwater runoff from the runoff from residential area impacts the beach, add residential area 1 to rank. Pets allowed True Pet If pets are allowed on the beach then add 1 to the rank.

Birds Normalized Bird Add the number of birds divided by birds 150 (i.e. the maximum number of birds of all beaches) to the rank.

Seaweed and Medium or High S&A If the seaweed and algae in the Algae in the swimming area is medium or high, add swimming area 1 to rank.

Parking Lot True Park If a parking lot is available for users available then add 1 to the rank.

Wind Normalized Wind Add the wind value divided by 5 (i.e. wind the maximum wind of all beaches) to the rank. This also accounts for wave height.

Water Normalized Temp Add the water temperature value temperature water divided by 26 (i.e. the maximum temperature temperature of all beaches) to the rank.

Exceedance Max E. coli and Exceed If the Maximum E. Coli value exceeds Geometric Mean 400 c.f.u./100 mls or the geometric of E. coli mean of E. Coli exceeds 200 c.f.u./100 mls, add 10 to the rank.

Toilets Normalized Toilets Multiply the number of toilets divided toilets by 22 (i.e. the maximum number of toilets of all beaches) plus 1 and the rank.

The “Toilets” variable is multiplied into the ranking as it is correlated with the potential for a higher geometric mean E.coli and it is a measure of exposure. It is likely that beaches with additional toilets have a higher usage and therefore a larger population exposed. This is very important from an environmental health perspective.

The values were normalized by dividing the result by the maximum value for that parameter. This was done to give each parameter an equal weight in the ranking scheme.

The ranking system resulting in the following.

 The above ranking system ensures that all beaches that experienced an exceedance of the Guidelines for Canadian Recreational Water Quality are at the top of the ranking system.  Some characteristics such as birds, wind, water temperature are specific to the time of day or year that the beach is visited. However, if a highly ranked beach has a value that is actually much lower than those recorded, future year sampling will result in a lowering of the rank.

In addition to using the ranking system to select a certain number of beaches for monitoring, other beaches not highly ranked should be selected for monitoring in a given year to ensure that all beaches are part of the full sampling program for a year over a period of a number of years.

The full ranking can be found in the appendix; the top 20 beaches are provided in the table below.

Table 25: Top 20 Beaches Beach Name Lake Name Rank Regina Beach Last Mountain Lake 21.73 Poplar Beach Resort Beach Wakaw Lake 16.22 Etter's Beach Last Mountain Lake 14.88 Eldora Beach Beach Last Mountain Lake 12.01 Katepwa Beach Katepwa Lake 9.85 Doran Park Beach Christopher Lake 7.86 Echo Bible Camp Beach Echo Lake 7.51 Crystal Bay/Sunset Beach Public Beach Brightsand Lake 7.39 Fort Campground Beach Echo Lake 7.31 Sunset Beach Beach Crooked lake 6.80 La Ronge Beach Lac La Ronge 6.79 Chitek Lake Campground Beach Chitek Lake 6.76 Bird's Point Beach Round Lake 6.59 St. Brieux Beach St. Brieux Lake 6.44 Murray Point Campground Beach Emma Lake 6.34 Sundale Beach Last Mountain Lake 6.32 First Mustus Lake Campground Beach First Mustus Lake 6.21 Wakaw Lake Regional Park Main Beach Wakaw Lake 6.20 Pebble BM Beach Iroquois Lake 6.17 Greenwater Beach Greenwater Lake 6.13

6 Conclusions & Recommendations Model 1 was developed to better explain a significant amount of the variation in the geometric mean of E. coli. Model 2 explains factors affecting the detection of E. coli.

The thesis that the physical characteristics of a beach area are not correlated to water quality when considered in conjunction with environmental factors was proven false as several time independent environmental variables were shown to be statistically significant through data interpretation and analyses. The physical characteristics of the beach, such as residential density, do impact the resulting E. coli concentrations. In Model 1, the number of swimmers, wave height range to, and residential density (medium – negative relationship) were noted as statistically significant and positive correlations except where noted.

In model 2, several more parameters were established as having statistically significant and positive correlations, except where noted:  Rainfall in the last 24 hours (negative relationship)  Parking area available  Water temperature  A high amount of seaweed and algae in the water  Prevailing winds onshore  Prevailing winds parallel to the beach.

A number of parameters that were not statically significant on their own were maintained in the model since these improved the models’ overall significance. It is important in these cases to not confuse statistically significant with important. For example, in many intermediate steps for Model 2, the log of average turbidity was statistically significant. Only when additional parameters were added did the statistical significance decrease. Therefore, it is hypothesized that the factors that remain in the model may not be statistically significant but are still important factors that affect model accuracy.

Ultimately, the purpose of developing this model was to assist in determining those beaches most in need of regular monitoring. Based on the results, the characteristics of a beach more likely to have higher levels of E. coli and therefore should be monitored can be described as below.

 High amounts of seaweed and algae in the swimming area should be common. This could be due to blue-green algae blooms.  The beach should have a parking lot available for users.  Beaches in areas with more wind should be preferred  Beaches where the water temperature is typically higher should be preferred.

Further it is recommended that the models be further refined, particularly as related to the predictive ability of the model. Future data sets are required to evaluate the performance of the developed models and to develop additional models. The classification performance of all linear and logistic regressions should be evaluated using true negative (TN), true positive (TP), false negative (FN), and false positive (FP) rates. TN and TP rates are also considered specificity and sensitivity while FP and FN rates are Type I and Type II errors. FN is more important than FP due to the increased health risk associated with a FN (Motamarri & Boccelli, 2012; Mas & Ahlfeld, 2007). The can be used as an indication of specificity, which is the proportion of non-exceedances that were predicted correctly, and sensitivity, which is the proportion of exceedances that were predicted correctly.

Lastly, it is recommended that further work be done to in completing a power analysis and consider the effect size. A power analysis helps to determine the probability of correctly rejecting a null hypothesis or of correctly detecting a true effect. It is related to the effect size, which is the degree to which the null is false. The effect size depends on the standard error and the amount between the mean of the population and the mean of the samples. This also can be used to assist in determining sample size. Some initial calculations determine that the number of variables for a power of 80% is 87.

6.1 Limitations The limitations of the statistical analysis are many as for any analysis. A discussion of the various assumptions and their impact are given throughout this research.

One item of specific note is that certain beaches were sampled more than once. Typically this occurred when an adverse sample result was detected in the first sample event. This may skew the results towards those beaches that had the adverse results.

A second major limitation is data availability. The availability of data is a limiting factor when producing beach rankings. The data that is available is generally cross-sectional data from across seasons. It is challenging to produce robust results when the data is limited to one or two occurrences at each beach as these occurrences may be influenced by specific events that have occurred at the time of sampling.

7 References Canadian Plains Research Center, 2013. EcoZones and EcoRegions of Saskatchewan. [Online] Available at: http://esask.uregina.ca/management/app/assets/img/enc2/PDF/51BDF440- 1560-95DA-43C27F4AB5F620471.pdf

Canadian Plains Research Center, 2013. Geography of Saskatchewan. [Online] Available at: http://esask.uregina.ca/entry/geography_of_saskatchewan.html

Chandramouli, V., Brion, G., Neelakantan, T. R. & Lingireddy, S., 2007. Backfilling missing microbial concentraitons in a riverine database using artificail networks. Water Research, pp. 217-227.

Christensen, V. G., 2001. Characterization of Surface-Water Quality based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, South-Central Kansas, December 1998 Through June 2001, Lawrence: United States Geological Survey.

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Appendix A: Map of Beach Locations

(Saskatchewan Ministry of Health, 2012)

Appendix B: Environmental Health Sanitary Survey Form

Environmental Health and Safety Survey (EHSS) Identification Date: ______Time:______Premise ID:______Beach Name:______Address: ______GPS Coordinates: ______Owner/Operator: ______Tel.: ______Fax: ______E-mail: ______Person Conducting Survey: ______Health Region: ______Background Information Water Body Type: Lake / River / Slough / Man-made pond / Other: ______Dimensions Beach: Length (m): ______Width (m): ______Swimming Area: Length (m): ______Approx Width (m): ______Unmarked: Y/N (Attach Map or Aerial Photo of Suitable Scale [include location of sample sites])

Average Bather Density (None Low Med High [circle one]) Residential Density (None Low Med High [circle one])

Access (check all that apply) □ General Public □ Residents & Guests □ Itinerant residents □ Other (specify): ______

Beach Material □ Sandy □ Mucky □ Rocky □ Other (specify): ______Beach grooming occurs? □ Yes □ No

Surrounding Land Uses (check all that are present): □ Urban □ Rural □ Landfill □ Agricultural (specify): ______□ Residential □ Forest □ Field □ Commercial (specify): ______□ Marsh/Swamp □ Hills/Uplands □ Harbour □ Industrial (specify): ______□ River/Stream/Ditch □ Other: ______□ Other: ______Chemical Hazards - Potential Sources of Chemical Contamination (check all that are present): □ Commercial/Industrial Discharges □ Marinas □ Motorized Watercraft □ Other: ______□ Other: ______

Stormwater Runoff From (check all that are present): □ Areas subject to pesticide application □ Urban Areas □ Areas subject to fertilizer application □ Other: ______

Microbiological Hazards - Potential Sources of Faecal Contamination (check all that are present in the vicinity of the beach) □ Municipal Sewage Discharge □ Combined Sewer Overflows (CSOs) □ Stormwater Drains/Discharges □ Wastes from Animal Feeding Operations □ Discharging Private Sewage Systems □ Holding Tanks □ Communal Collection System □ Other Discharges Containing Faecal Wastes (List): ______□ Other Sewage Collection/Disposal/ Treatment Systems (List):______

Stormwater Runoff from (check all that are present): □ Agricultural Areas □ Areas Receiving Sewage Sludge □ Beach and Surrounding Area □ Facilities (e.g., parking, pathways) □ Residential Areas □ Other: ______□ Other: ______

Other Environmental Sources (check all that are present): □ Discharging River/Streams/Creeks □ Birds (e.g., gulls, ducks, geese): #______□ Other wild animals: #______□ Pets allowed | not allowed [circle one] □ Other: ______□ Other: ______Physical Hazards and Aesthetic Considerations Subsurface Hazards (check all that are present): □ Steep slopes or dropoffs □ Depths greater than 4.5 m □ Large rocks □ Slippery or uneven bottom □ Dense aquatic plants □ Other: ______

Water Conditions (check all that are present): □ Strong Currents or Rip Tides □ Undertows

Other: Amount of Refuse on the beach (None Low Med High [circle one]) Type of Refuse: □ Food-related □ Medical □ Sewage-related □ Household waste □ Building Materials □ Fishing-related □ Dead fish Amount of Seaweed/Algae on beach (None Low Med High [circle one]) Amount of Seaweed/Algae (None Low Med High [circle one]) in swimming area

Vehicles Permitted on Beach or Near Bathing Area: Automobiles Yes / No Boats/Watercraft Yes / No Specify: ______

Other Biological Hazards Other Biological Hazards Known to Affect the Recreational Water Area

(Presence may be continuous, seasonal or sporadic) (check all that are present):

□ Cyanobacterial Blooms □ Schistosomes (Swimmer’s Itch) □ Large Numbers of Aquatic Plants □ Other (specify): ______□ Other (specify): ______

Facilities and Safety Provisions (check all that are present) □ Toilets #: ____ □ Showers #: ____ Is wastewater from toilets, showers, etc likely to contaminate the bathing area? Yes / No □ Drinking Water Fountain #: ____ □ Litter Bins #: ____ Are they animal proof? Yes / No □ Other:______#: ____ □ Other:______#: ____ □ Access for Persons with Disabilities

Accessibility (check all that are present): □ Road □ Path □ No access  Parking area is available? Yes / No

Safety Provisions (check all that are present): □ Lifeguard Stations #: ____ □ Lifesaving Equipment #: ____ □ First Aid Stations #: ____

Signs/Communication Materials (check all that are present): □ Emergency Contact Information □ Beach Posting/Suitability for Swimming □ Other: ______□ Other: ______

Formal Procedures or Reporting Mechanisms in Place to Deal with (check all that are present): □ Municipal or Industrial Spills/Discharges/Treatment Bypasses □ Swimmer Injuries □ Waterborne Disease Outbreaks □ Other: ______Are methods in place to warn the public of danger/water quality or other concerns? Yes / No

(Saskatchewan Ministry of Health, 2012)

Appendix C: Sample Collection Form

Identification Date: ______Time:______Premise ID:______Beach Name:______Health Region: ______Sampler Name:______Sample Location Description: ______GPS (beach centre): ______Background Information Air Temp: Measured (˚C): ______Water Temp: Measured (˚C): ______Sechi Disk: Measured (cm): ______

Prevailing Winds  Onshore (from water to shore)  Offshore (shore to water)  None  Parallel to shore Beaufort Wind Speed (circle) : 0 1 2 3 4 5 Swimmer Density  None  Low  Medium  High #:______Boater Density  None  Low  Medium  High #:______Sunlight  Sunny  Overcast  Partially Cloudy  Rainy Rainfall: During sampling (circle): Yes/ No Last 24-h (circle): Yes/ No Wave Height: (circle) None/Low/Medium/High Range (m): _____ to _____ Flooding:  Yes  No

Amount of Refuse on the beach (None Low Med High [circle one]) Type:  Food-related  Medical  Sewage-related  Household waste  Building Materials  Fishing-related  Dead fish Beach Grooming  Last 24 hours  >24 hours  None apparent Seaweed/Algae on beach (None Low Med High [circle one]) Seaweed/Algae in swimming area (None Low Med High [circle one]) Birds (on beach) #:______Other Observations: ______

Sample taken (list identification number given): ______

Sample Notes Identification NTU GPS Depth(m) Algae Present Comments (y/N)

(Saskatchewan Ministry of Health, 2012)

Lab Requisition Form

(Saskatchewan Ministry of Health, 2012)

Appendix D: Calculation of Geometric Mean

(The information found below is summarized from Ontario’s Beach Management Guidance Document)

Uneven distribution of bacteria through the recreational water means that the count of microorganisms in a single “grab sample” may not represents the average concentration.

To determine an accurate estimate of the quality of recreational water, the results of a number of samples must be combined so that unrepresentative samples will not unduly influence the average. The geometric mean of all the samples is often used for this purpose.

The formula for the geometric mean Gx is:

(1/n) Gx = (X1,X2,X3,...... Xn) that is, the geometric mean of n readings is the nth root of their product.

This calculation, a cumbersome one in arithmetic terms, can be simplified considerably by means of logarithms (logs), as described below.

Calculating the Geometric Mean

The logarithm of the geometric mean is:

log Gx = (log X1 + log X2 + log X3 + .... + log Xn)/n

that is, to find the log of the geometric mean of a set of readings, add up the logs of all the readings and divide by the number of readings. The geometric mean will then be the antilog of log Gx.

It should be noted that the daily geometric mean provides historical data by indicating the water quality at the time of sampling, rather than the present water quality.

For counts that are below the limit of detection, transformation count by adding 1 before taking logarithms. The estimated geometric mean is obtained by subtracting 1 from the antilog of log G.

(Ontario Minister of Health and Long-Term Care, 2008)

Appendix E: Raw Data

Unique Bea HR Health Healt Beach Name Lake Name EHSS GPS Owner/Ope Watershed Watersh Watershe Identifi ch # Region h Coordinates rator Type ed d er Regio Stressed Impacted n No CHR 1 1 CH Cypress 1 Onas Beach Lac Pelletier Lake Lat 49° 57.922 N, Reg Park Swift Current Creek 1 0 R Long 107° 55.742 W CHR 2 2 CH Cypress 1 Darlings Beach Lac Pelletier Lake Lat 49° 58.945 N, Reg Park Swift Current Creek 1 0 R Long 107° 55.668 W CHR 3 3 CH Cypress 1 Camp Lemieux Beach Lac Pelletier Lake Lat 49° 58.722 N, Private Swift Current Creek 1 0 R Long 107° 56.206 Owner W CHR 4 4 CH Cypress 1 Camp Elim Beach Lac Pelletier Lake Lat 50° 0.561 N, Private Swift Current Creek 1 0 R Long 107° 56.125 Owner W CHR 5 5 CH Cypress 1 Cabri Regional Park Beach Lake Diefenbaker Lat 50° 40.083 N, Reg Park South Saskatchewan 1 0 R Long 108° 15.875 River W FH 1 1 FH FHHR 2 Buffalo Pound Provincial Buffalo Pound Lake Lat 50° 35. 801N, Prov Park Upper Qu'Appelle 1 0 Park Beach Long 105° 24.787 River W FH 2 2 FH FHHR 2 Rockin Beach Fife Lake Lat 49° 11.864 N, Reg Park Poplar River 1 0 Long 105° 51.524 W FH 2 2 FH FHHR 2 Rockin Beach Fife Lake Lat 49° 11.864 N, Reg Park Poplar River 1 0 Long 105° 51.524 W FH 3 3 FH FHHR 2 Palliser Regional Park Lake Diefenbaker Lat 50° 53.310 N, Reg Park South Saskatchewan 1 0 Beach Long 106° 57.426 River W FH 4 4 FH FHHR 2 Lovering Lake Beach Lovering Lake Lat 50° 50.503 N, Prov Park Upper Qu'Appelle 1 0 Long 105° 37.262 River W HL 1 1 HL HRHA 3 Clearwater Lake Regional Clearwater Lake 50.8706-107.9322 Reg Park North Saskatchewan 1 0 Park Beach River

HL 10 10 HL HRHA 3 Danielson Visitor’s Center Lake Diefenbaker Lat 51° 15.522 N, Prov Park Saskatchewan River 1 0 Beach Long 106° 52.566 W HL 11 11 HL HRHA 3 Coteau Beach Lake Diefenbaker Lat 51° 9.59 N, Village Saskatchewan River 1 0 Long 106° 49.128 W HL 2 2 HL HRHA 3 Hitchcock Bay Beach Lake Diefenbaker Lat 51° 2.786 N, RM Saskatchewan River 1 0 Long 106° 51.33 W HL 3 3 HL HRHA 3 Prairie Lake Regional Park Lake Diefenbaker Lat 50° 42.513 N, Reg Park Saskatchewan River 1 0 Beach Long 107° 23.123 W HL 4 4 HL HRHA 3 Macklin Lake Regional Macklin Lake Lat 52°3256 N, Reg Park Battle River 1 0 Park Beach Long 109°9479 W HL 5 5 HL HRHA 3 Suffern Lake Beach Suffern Lake Lat 52°631 N, Long Reg Park Battle River 1 0 109°8998 W HL 6 6 HL HRHA 3 Sask Landing Provincial Lake Diefenbaker Lat 50° 39.473 N, Prov Park Saskatchewan River 1 0 Park Beach (Cottonwood) Long 108° 00.269 W HL 7 7 HL HRHA 3 Sask Landing Provincial Lake Diefenbaker Lat 50° 40.328 N, Prov Park Saskatchewan River 1 0 Park Beach (Bearpaw) Long 107° 57.044 W KT 10 10 KT KTHR 4 RV Tobin Lake Beach Tobin Lake Lat 53°.518 688 N, Village Saskatchewan River 1 0 Long 103°.7439 W KT 11 11 KT KTHR 4 Pruden's Point Beach Tobin Lake Lat 53°.51036 N, Private Saskatchewan River 1 0 Long 103°.80295 W Owner KT 12 12 KT KTHR 4 Lower Fishing Beach Lower Fishing Lake Lat 54°.04 N, Long Prov Park Saskatchewan River 1 0 104°.63 W KT 13 13 KT KTHR 4 Ranger's Beach Lower Fishing Lake Lat 54°.04 N, Long Prov Park Saskatchewan River 1 0 104°.63 W KT 4 4 KT KTHR 4 Marean Lake Beach Marean Lake Lat 52.514680 N, Private Lake Winnipoegosis 1 0 Long 103.593521 W Owner KT 5 5 KT KTHR 4 Barrier Ford Beach Barrier Lake Lat 52.519756 N, Private Lake Winnipoegosis 1 0 Long 103.79992 W Owner KT 6 6 KT KTHR 4 Greenwater Beach Greenwater Lake Lat 52.495310 N, Prov Park Lake Winnipoegosis 1 0 Long 103.512132 W KT 7 7 KT KTHR 4 Lake Charron Beach Lake Charron Lat 53.403725 N, Reg Park Lake Winnipoegosis 1 0

Long 104.30345 W KT 8 8 KT KTHR 4 St. Brieux Beach St. Brieux Lake Lat 52.632386 N, Reg Park Carrot River 1 0 Long 104.906130 W KT 8 8 KT KTHR 4 St. Brieux Beach St. Brieux Lake Lat 52.632386 N, Reg Park Carrot River 1 0 Long 104.906130 W KT 9 9 KT KTHR 4 Kipabiskau Beach Kipabiskau Lake Lat 52.571564 N, Reg Park Lake Winnipoegosis 1 0 Long 104.18423 W MC 1 1 MC MCRRH 5 Ramsey Bay Beach Weyakwin Lake 54°27'34.93"N/ Village Churchill River 0 0 A 105°59'7.09"W MC 1 1 MC MCRRH 5 Ramsey Bay Beach Weyakwin Lake 54°27'34.93"N/ Village Churchill River 0 0 A 105°59'7.09"W MC 1 1 MC MCRRH 5 Ramsey Bay Beach Weyakwin Lake 54°27'34.93"N/ Village Churchill River 0 0 A 105°59'7.09"W MC 1 1 MC MCRRH 5 Ramsey Bay Beach Weyakwin Lake 54°27'34.93"N/ Village Churchill River 0 0 A 105°59'7.09"W MC 1 1 MC MCRRH 5 Ramsey Bay Beach Weyakwin Lake 54°27'34.93"N/ Village Churchill River 0 0 A 105°59'7.09"W MC 10 10 MC MCRRH 5 Buffalo Narrows Beach Churchill Lake Lat 55˚89367 N, Village Churchill River 0 0 A Long 108˚61352 W MC 2 2 MC MCRRH 5 Ile-a-la-Crosse Beach La Ile-a-la-Crosse Lat 55° 42.896 N, Village Churchill River 0 0 A Lake Long 107° 88.924 W MC 2 2 MC MCRRH 5 Ile-a-la-Crosse Beach La Ile-a-la-Crosse Lat 55° 42.896 N, Village Churchill River 0 0 A Lake Long 107° 88.924 W MC 3 3 MC MCRRH 5 Jan Lake Beach Jan Lake Lat 54° 53.3116 N, Private Saskatchewan River 1 0 A Long 102° 49.4579 Owner W MC 3 3 MC MCRRH 5 Jan Lake Beach Jan Lake Lat 54° 53.3116 N, Private Saskatchewan River 1 0 A Long 102° 49.4579 Owner W MC 3 3 MC MCRRH 5 Jan Lake Beach Jan Lake Lat 54° 53.3116 N, Private Saskatchewan River 1 0 A Long 102° 49.4579 Owner W MC 3 3 MC MCRRH 5 Jan Lake Beach Jan Lake Lat 54° 53.3116 N, Private Saskatchewan River 1 0 A Long 102° 49.4579 Owner

W MC 3 3 MC MCRRH 5 Jan Lake Beach Jan Lake Lat 54° 53.3116 N, Private Saskatchewan River 1 0 A Long 102° 49.4579 Owner W MC 4 4 MC MCRRH 5 La Ronge Beach Lac La Ronge Lat 55˚6'7.73'' N, Village Churchill River 0 0 A Long 105˚17'12.24''W MC 5 5 MC MCRRH 5 Wadin Bay Beach Lac La Ronge Lat 55˚15'35.20''N, Prov Park Churchill River 0 0 A Long 105˚11'41.83''W MC 6 6 MC MCRRH 5 Missinipi Beach Churchill River Lat 58°36.986 N, Village Churchill River 0 0 A Long 104°46.1001 W MC 6 6 MC MCRRH 5 Missinipi Beach Churchill River Lat 58°36.986 N, Village Churchill River 0 0 A Long 104°46.1001 W MC 6 6 MC MCRRH 5 Missinipi Beach Churchill River Lat 58°36.986 N, Village Churchill River 0 0 A Long 104°46.1001 W MC 6 6 MC MCRRH 5 Missinipi Beach Churchill River Lat 58°36.986 N, Village Churchill River 0 0 A Long 104°46.1001 W MC 6 6 MC MCRRH 5 Missinipi Beach Churchill River Lat 58°36.986 N, Village Churchill River 0 0 A Long 104°46.1001 W MC 7 7 MC MCRRH 5 Angler's Trail Resort Lac La Plonge Lat 55°10.1623 N, Prov Park Beaver River 1 0 A Beach Long 107°30.1800 W MC 7 7 MC MCRRH 5 Angler's Trail Resort Lac La Plonge Lat 55°10.1623 N, Prov Park Beaver River 1 0 A Beach Long 107°30.1800 W MC 7 7 MC MCRRH 5 Angler's Trail Resort Lac La Plonge Lat 55°10.1623 N, Prov Park Beaver River 1 0 A Beach Long 107°30.1800 W MC 7 7 MC MCRRH 5 Angler's Trail Resort Lac La Plonge Lat 55°10.1623 N, Prov Park Beaver River 1 0 A Beach Long 107°30.1800

W MC 7 7 MC MCRRH 5 Angler's Trail Resort Lac La Plonge Lat 55°10.1623 N, Prov Park Beaver River 1 0 A Beach Long 107°30.1800 W MC 8 8 MC MCRRH 5 Pinehouse Lake Beach Pinehouse Lake 55°30'50.61"N/ Village Churchill River 0 0 A 106°34'21.69"W MC 8 8 MC MCRRH 5 Pinehouse Lake Beach Pinehouse Lake 55°30'50.61"N/ Village Churchill River 0 0 A 106°34'21.69"W MC 8 8 MC MCRRH 5 Pinehouse Lake Beach Pinehouse Lake 55°30'50.61"N/ Village Churchill River 0 0 A 106°34'21.69"W MC 8 8 MC MCRRH 5 Pinehouse Lake Beach Pinehouse Lake 55°30'50.61"N/ Village Churchill River 0 0 A 106°34'21.69"W MC 8 8 MC MCRRH 5 Pinehouse Lake Beach Pinehouse Lake 55°30'50.61"N/ Village Churchill River 0 0 A 106°34'21.69"W MC 9 9 MC MCRRH 5 Michele Pt. Campground Dore Lake Lat 54°42.3471 N, Village Beaver River 1 0 A Beach Long 107°14.1363 W MC 9 9 MC MCRRH 5 Michele Pt. Campground Dore Lake Lat 54°42.3471 N, Village Beaver River 1 0 A Beach Long 107°14.1363 W MC 9 9 MC MCRRH 5 Michele Pt. Campground Dore Lake Lat 54°42.3471 N, Village Beaver River 1 0 A Beach Long 107°14.1363 W MC 9 9 MC MCRRH 5 Michele Pt. Campground Dore Lake Lat 54°42.3471 N, Village Beaver River 1 0 A Beach Long 107°14.1363 W PA 10 10 PA PAPHR 6 Macintosh beach Emma Lake Lat 53.90414 N, RM North Saskatchewan 1 0 Long 105.90414 W River PA 11 11 PA PAPHR 6 Murray Point Campground Emma Lake Lat 53.603695 N, RM North Saskatchewan 1 0 Beach Long 105.91502 W River PA 12 12 PA PAPHR 6 Big Shell Lake Beach Big Shell Lake Lat 53.199345 N, Village North Saskatchewan 1 0 Long 107.1385 W River PA 13 13 PA PAPHR 6 Murray Point Beach Emma Lake Lat 53.609802 N, RM North Saskatchewan 1 0 Long 105.92252 W River PA 14 14 PA PAPHR 6 Doran Park Beach Christopher Lake Lat 53.55908 N, RM North Saskatchewan 1 0 Long 105.80793 W River

PA 15 15 PA PAPHR 6 Bells Beach Christopher Lake N/A RM North Saskatchewan 1 0 River PA 16 16 PA PAPHR 6 Martin's Lake Regional Martin's Lake Lat 52.99585 N, Reg Park North Saskatchewan 1 0 Park Beach Long 106.99745 W River PA 18 18 PA PAPHR 6 Sturgeon Lake Regional Sturgeon Lake Lat 53.42141 N, Reg Park North Saskatchewan 1 0 Park Beach Long 106.1564 W River PA 2 2 PA PAPHR 6 Morin Lake Regional Park Morin Lake Lat 53.498383 N, Reg Park North Saskatchewan 1 0 Beach Long 107.05836 W River PA 20 20 PA PAPHR 6 Chitek Lake Campground Chitek Lake Lat 53°45'24.41 N, Prov Park Battle River 1 0 Beach Long 107°45'49.05 W PA 21 21 PA PAPHR 6 Minnowaka Beach Candle Lake Lat 53.77557 N, Prov Park North Saskatchewan 1 0 Long 105.16196 W River PA 22 22 PA PAPHR 6 Sandy Bay Campground Candle Lake Lat 53.797794 N, Prov Park North Saskatchewan 1 0 Beach Long 105.329666 W River PA 23 23 PA PAPHR 6 Memorial Lake Regional Memorial Lake Lat 53.296726 N, Reg Park North Saskatchewan 1 0 Park Beach Long 107.055244 W River PA 24 24 PA PAPHR 6 Pelican Cove Beach Iroquois Lake Lat 53.168945 N, RM North Saskatchewan 1 0 Long 107.052704 W River PA 25 25 PA PAPHR 6 Pebble BM Beach Iroquois Lake N/A RM North Saskatchewan 1 0 River PA 26 26 PA PAPHR 6 Shady BM Beach Meeting Lake N/A RM North Saskatchewan 1 0 River PA 27 27 PA PAPHR 6 Crescent Beach Meeting Lake N/A RM North Saskatchewan 1 0 River PA 28 28 PA PAPHR 6 Meeting Lake Regional Meeting Lake Lat 53.20983 N, RM North Saskatchewan 1 0 Park Beach Long 107.70473 W River PA 29 29 PA PAPHR 6 Emerald Lake Regional Emerald Lake Lat 53.180126 N, Reg Park North Saskatchewan 1 0 Park Beach Long 106.9635 W River PA 31 31 PA PAPHR 6 Lac La Peche Beach Lac La Pache N/A RM North Saskatchewan 1 0 River PA 32 32 PA PAPHR 6 Redberry Lake Regional Redberry Lake Lat 52.711105 N, Reg Park North Saskatchewan 1 0 Park Beach Long 107.21622 W River PA 33 33 PA PAPHR 6 Anderson Point Anglin Lake Lat 53˚42.933 N, Prov Park North Saskatchewan 1 0 Campground Beach Long 105˚56.508 W River PA 34 34 PA PAPHR 6 Anglin Lake Cabin Beach Anglin Lake N/A Prov Park North Saskatchewan 1 0

River PA 4 4 PA PAPHR 6 Waskateena Beach Candle Lake Lat 53.74966 N, Village North Saskatchewan 1 0 Long 105.26151 W River PA 5 5 PA PAPHR 6 McPhail Cove Beach Emma Lake Lat 53.60117 N, RM North Saskatchewan 1 0 Long 105.901215 W River PA 6 6 PA PAPHR 6 Birch Bay Beach Emma Lake Lat 53.590225 N, RM North Saskatchewan 1 0 Long 105.88892 W River PA 7 7 PA PAPHR 6 Nels Beach Emma Lake Lat 53.572727 N, RM North Saskatchewan 1 0 Long 105.85859 W River PA 8 8 PA PAPHR 6 Sunset Bay Beach Emma Lake Lat 53.569714 N, RM North Saskatchewan 1 0 Long 105.86714 W River PA 9 9 PA PAPHR 6 Sunnyside Beach Emma Lake Lat 53.58166 N, RM North Saskatchewan 1 0 Long 105.88215 W River PN 1 1 PN PNHR 7 Atton's Lake Regional Park Atton's Lake Lat 52°50.246 N, Reg Park Battle River 1 0 Public Beach Long 108°51.693 W PN 1 1 PN PNHR 7 Atton's Lake Regional Park Atton's Lake Lat 52°50.246 N, Reg Park Battle River 1 0 Public Beach Long 108°51.693 W PN 10 10 PN PNHR 7 Meota Regional Park Jackfish Lake Lat 53° 2.204 N, Reg Park North Saskatchewan 1 0 Public Beach Long 108° 26.801 River W PN 12 12 PN PNHR 7 Turtle Lake Sunset View Turtle Lake Lat 53.5479 N, Long RM North Saskatchewan 1 0 Public Beach 108.6474 W River PN 13 13 PN PNHR 7 Turtle Lake South Bay Turtle Lake Lat 53.5188 N, Long RM North Saskatchewan 1 0 Public Beach 108.6966 W River PN 14 14 PN PNHR 7 Jumbo Beach Jumbo Lake Lat 54.0278 N, Long Prov Park Beaver River 1 0 109.1909 W PN 15 15 PN PNHR 7 Peck Lake Campground Peck Lake Lat 53.8824 N, Long Prov Park North Saskatchewan 1 0 Beach 109.5871 W River PN 16 16 PN PNHR 7 Little Fishing Lake Beach Little Fishing Lake Lat 53.8533 N, Long Prov Park North Saskatchewan 1 0 109.5623 W River PN 17 17 PN PNHR 7 Ministikwan Lake Ministikwan Lake Lat 54.0278 N, Long Prov Park Beaver River 1 0 Campground/Picnic Beach 109.5871 W PN 18 18 PN PNHR 7 Kimball Lake Campground Kimball Lake Lat 54.4055 N, Long Prov Park Beaver River 1 0 Beach 108.8154 W PN 19 19 PN PNHR 7 First Mustus Lake First Mustus Lake Lat 54.4347 N, Long Prov Park Beaver River 1 0 Campground Beach 109.8146 W

PN 2 2 PN PNHR 7 Glenburn Regional Park Manmade Lake Lat 52°29.836 N, Reg Park North Saskatchewan 1 0 Main Beach Long 107°41.962 W River PN 2 2 PN PNHR 7 Glenburn Regional Park Manmade Lake Lat 52°29.836 N, Reg Park North Saskatchewan 1 0 Main Beach Long 107°41.962 W River PN 20 20 PN PNHR 7 South Flotten Lake Flotten Lake Lat 54.5948 N, Long Prov Park Beaver River 1 0 Campgound Beach 108.5121 W PN 21 21 PN PNHR 7 Matheson Lake Matheson Lake Lat 54.4202 N, Long Prov Park Beaver River 1 0 Campground Beach 108.9154 W PN 22 22 PN PNHR 7 Jeannette Lake Subdivsion Jeannette Lake Lat 54.5366 N, Long Prov Park Beaver River 1 0 Public Beach 108.5374 W PN 23 23 PN PNHR 7 Greig Lake Public Beach Greig Lake Lat 54.4493 N, Long Prov Park Beaver River 1 0 108.6887 W PN 24 24 PN PNHR 7 Waterhen Lake South Waterhen Lake Lat 54.4638 N, Long Prov Park Beaver River 1 0 Campground Beach 108.5122 W PN 26 26 PN PNHR 7 Howe Bay Campground Pierce Lake Lat 54.4783 N, Long Prov Park Beaver River 1 0 Beach 109.6461 W PN 27 27 PN PNHR 7 Sandy Beach Campground Pierce Lake Lat 54.4784 N, Long Prov Park Beaver River 1 0 Beach 109.747 W PN 28 28 PN PNHR 7 Murray Doell Camground Lac Des Isles Lake Lat 54.4637 N, Long Prov Park Beaver River 1 0 Beach 109.369 W PN 3 3 PN PNHR 7 Brightsand Lake Regional Brightsand Lake Lat 53.6496 N, Long Reg Park North Saskatchewan 1 0 Park Public Beach 108.844 W River PN 4 4 PN PNHR 7 Crystal Bay/Sunset Beach Brightsand Lake Lat 53.5479 N, Long RM North Saskatchewan 1 0 Public Beach 108.8685 W River PN 4 4 PN PNHR 7 Sandy Beach Lake Sandy Beach Lake Lat 53°27.178 N, Reg Park North Saskatchewan 1 0 Regional Park Public Long 109° 59.623 River Beach W PN 4 4 PN PNHR 7 Sandy Beach Lake Sandy Beach Lake Lat 53°27.178 N, Reg Park North Saskatchewan 1 0 Regional Park Public Long 109° 59.623 River Beach W PN 5 5 PN PNHR 7 Silver Lake Regional Park Silver Lake Lat 53°13.716, Long Reg Park North Saskatchewan 1 0 Public Beach 109°16.226 W River PN 5 5 PN PNHR 7 Silver Lake Regional Park Silver Lake Lat 53°13.716, Long Reg Park North Saskatchewan 1 0 Public Beach 109°16.226 W River PN 6 6 PN PNHR 7 Picnic Lake Municipal Picnic Lake 53.2125, -108.6739 RM North Saskatchewan 1 0 Park Public Beach River

PN 7 7 PN PNHR 7 Resort Village of Cochin Jackfish Lake Lat 53° 4.74 N, Village North Saskatchewan 1 0 Public Beach Long 108° 20.57 W River PN 8 8 PN PNHR 7 Battlefords Provincial Park Jackfish Lake Lat 53° 7.852 N, Prov Park North Saskatchewan 1 0 Public Beach Long 108° 23.849 River W PN 9 9 PN PNHR 7 Resort Village of Aquadeo Jackfish Lake Lat 53.136257 N, Village North Saskatchewan 1 0 Public Beach Long 108.42184 W River RQ 1 1 RQ RQHR 8 Alice Beach Last Mountain Lake Lat 50° 55.824 N, Village Upper Qu'Appelle 1 0 Long 105° 9.84 W River RQ 10 10 RQ RQHR 8 Valley View Beach - Buffalo Pound Lake N/A RM Upper Qu'Appelle 1 0 Buffalo Pound River RQ 12 12 RQ RQHR 8 Alta Vista Beach Last Mountain Lake Lat 50° 48.165 N, RM Upper Qu'Appelle 1 0 Long 104° 58.249 River RQ 13 13 RQ RQHR 8 Eldora Beach Beach Last Mountain Lake Lat 50° 52.017, RM Upper Qu'Appelle 1 0 Long 105° 6.366 River RQ 14 14 RQ RQHR 8 Katepwa Beach Katepwa Lake Lat 50° 41.626 N, Prov Park Lower Qu'Appelle 1 0 Long 103° 37.753 River W RQ 14 14 RQ RQHR 8 Katepwa Beach Katepwa Lake Lat 50° 41.626 N, Prov Park Lower Qu'Appelle 1 0 Long 103° 37.753 River W RQ 14 14 RQ RQHR 8 Katepwa Beach Katepwa Lake Lat 50° 41.626 N, Prov Park Lower Qu'Appelle 1 0 Long 103° 37.753 River W RQ 15 15 RQ RQHR 8 B-Say Tah Beach Echo Lake Lat 50° 47.492 N, Village Lower Qu'Appelle 1 0 Long 103° 51.090 River W RQ 15 15 RQ RQHR 8 B-Say Tah Beach Echo Lake Lat 50° 47.492 N, Village Lower Qu'Appelle 1 0 Long 103° 51.090 River W RQ 16 16 RQ RQHR 8 Etter's Beach Last Mountain Lake Lat 51° 13.964 N, Village Upper Qu'Appelle 1 0 Long 105° 18.241 River W RQ 16 16 RQ RQHR 8 Etter's Beach Last Mountain Lake Lat 51° 13.964 N, Village Upper Qu'Appelle 1 0 Long 105° 18.241 River W

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RQ 17 17 RQ RQHR 8 Katepwa Camp Beach Katepwa Lake Lat 50° 44.138 N, Private Lower Qu'Appelle 1 0 Long 103° 40.897 Owner River RQ 18 18 RQ RQHR 8 Saskatchewan Beach Last Mountain Lake Lat 50° 47.91 N, Village Upper Qu'Appelle 1 0 Long 104° 5.67 W River RQ 19 19 RQ RQHR 8 Buena Vista Beach Last Mountain Lake Lat 50° 47.195 N, Village Upper Qu'Appelle 1 0 Long 104°56.750 River RQ 2 2 RQ RQHR 8 Highwood Beach Last Mountain Lake Lat 50° 49.394 N RM Upper Qu'Appelle 1 0 Long 105°04.407 W River RQ 20 20 RQ RQHR 8 Fort Campground Beach Echo Lake Lat 50° 46.54N, Private Lower Qu'Appelle 1 0 Long 103° 47.976 Owner River W RQ 20 20 RQ RQHR 8 Fort Campground Beach Echo Lake Lat 50° 46.54N, Private Lower Qu'Appelle 1 0 Long 103° 47.976 Owner River W RQ 21 21 RQ RQHR 8 Kedleston Beach Last Mountain Lake Lat 50° 49.137 N, RM Upper Qu'Appelle 1 0 Long 105° 04.136 River W RQ 23 23 RQ RQHR 8 Wee Too Beach Last Mountain Lake Lat 50° 56.049 N, Village Upper Qu'Appelle 1 0 Long 105° 9.962 W River RQ 24 24 RQ RQHR 8 Camp Monahan Beach Katepwa Lake Lat 50° 43.098 N, Private Lower Qu'Appelle 1 0 Long 103° 39.8 W Owner River RQ 25 25 RQ RQHR 8 Glen Harbour Beach Last Mountain Lake Lat 50° 53.12 N, Village Upper Qu'Appelle 1 0 Long 105° 5.75 W River RQ 26 26 RQ RQHR 8 Lipp's Beach Last Mountain Lake Lat 50° 56.254 N, Village Upper Qu'Appelle 1 0 Long 105° 10.232 River W RQ 28 28 RQ RQHR 8 Circle Square Ranch Beach Manmade Lake Lat 50° 17.059 N, Private Upper Qu'Appelle 1 0 long 103° 11.766 W Owner River RQ 29 29 RQ RQHR 8 Camp Lutherland Beach Pasqua Lake Lat 50° 47.875 N, Private Lower Qu'Appelle 1 0 Long 103° 56.218 Owner River W RQ 3 3 RQ RQHR 8 Pelican Pointe Beach Last Mountain Lake Lat 50° 48.113 N, Village Upper Qu'Appelle 1 0 Long 105° 2.18 W River RQ 30 30 RQ RQHR 8 Grandview Beach Last Mountain Lake Lat 50° 52.247 N, Village Upper Qu'Appelle 1 0 Long 105° 6.356 W River RQ 31 31 RQ RQHR 8 Lumsden Beach Last Mountain Lake Lat 50° 45.857 N, Village Upper Qu'Appelle 1 0

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Long 104°53.850 W River RQ 32 32 RQ RQHR 8 Shore Acres Beach Last Mountain Lake Lat 50° 48.065 N, RM Upper Qu'Appelle 1 0 Long 104° 57.497 River W RQ 33 33 RQ RQHR 8 Fieldstone Campground Manmade Lake Lat 50° 10.234, Private Assiniboine River 0 1 Beach Long 101° 40.437 Owner W RQ 36 36 RQ RQHR 8 Mohr's Beach Last Mountain Lake Lat 50° 52.81 N, RM Upper Qu'Appelle 1 0 Long 105° 5.636 W River RQ 37 37 RQ RQHR 8 Sorenson Beach Last Mountain Lake Lat 50° 50.155 N, RM Upper Qu'Appelle 1 0 Long 105° 3.668 W River RQ 38 38 RQ RQHR 8 Moosomin Regional Park Moosomin Lake Lat 50° 04.571, Reg Park Assiniboine River 0 1 Beach Long 101° 42 397 W RQ 39 39 RQ RQHR 8 Echo Bible Camp Beach Echo Lake Lat 50° 48.079 N, Private Lower Qu'Appelle 1 0 Long 1103° 49.588 Owner River RQ 39 39 RQ RQHR 8 Echo Bible Camp Beach Echo Lake Lat 50° 48.079 N, Private Lower Qu'Appelle 1 0 Long 1103° 49.588 Owner River RQ 39 39 RQ RQHR 8 Echo Bible Camp Beach Echo Lake Lat 50° 48.079 N, Private Lower Qu'Appelle 1 0 Long 1103° 49.588 Owner River RQ 4 4 RQ RQHR 8 Sunset Cove Beach Last Mountain Lake Lat 50° 48.711 N, Village Upper Qu'Appelle 1 0 Long 105° 00.194 River RQ 41 41 RQ RQHR 8 Pasqua Lake Beach Pasqua Lake Lat 50° 47.878 N, Prov Park Lower Qu'Appelle 1 0 Long 103° 53.836 River W RQ 42 42 RQ RQHR 8 Sundale Beach Last Mountain Lake Lat 50° 48.662 N, RM Upper Qu'Appelle 1 0 Long 105° 0.811 W River RQ 43 43 RQ RQHR 8 Welwyn Centennial Qu'Appelle River Lat 50° 20.384, Village Assiniboine River 0 1 Regional Park Beach Long 101° 30.997 W RQ 44 44 RQ RQHR 8 Echo Provincial Park Echo Lake Lat 50° 47.826 N, Prov Park Lower Qu'Appelle 1 0 Beach Long 103° 53.529 River W RQ 5 5 RQ RQHR 8 Kannata Valley Beach Last Mountain Lake Lat 50° 47.195, Village Upper Qu'Appelle 1 0 Long 104° 54.772 River RQ 6 6 RQ RQHR 8 Regina Beach Last Mountain Lake Lat 50° 47.767 N, Village Upper Qu'Appelle 1 0

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Long 104° 59.06 W River RQ 6 6 RQ RQHR 8 Regina Beach Last Mountain Lake Lat 50° 47.767 N, Village Upper Qu'Appelle 1 0 Long 104° 59.06 W River RQ 8 8 RQ RQHR 8 Sarnia Beach Last Mountain Lake Lat 50° 59.558 N, RM Upper Qu'Appelle 1 0 Long 105° 13.062 River W SC 1 1 SC Sun 9 Nickle Lake Reg Pk Nickle Lake Lat 49° 35.333, Reg Park Upper Souris River 1 0 Country Beach Long 103° 46.819 W SC 2 2 SC Sun 9 Mainprize Reg Pk Beach Rafferty Reservoir 49*22’20” N Reg Park Upper Souris River 1 0 Country 103*35’02”W SC 2 2 SC Sun 9 Mainprize Reg Pk Beach Rafferty Reservoir 49*22’20” N Reg Park Upper Souris River 1 0 Country 103*35’02”W SC 3 3 SC Sun 9 Boundary Dam Beach Boundary Dam 49* 04’ 28” N 103* Reg Park Upper Souris River 1 0 Country Reservoir 01” 50”W SC 3 3 SC Sun 9 Boundary Dam Beach Boundary Dam 49* 04’ 28” N 103* Reg Park Upper Souris River 1 0 Country Reservoir 01” 50”W SC 4 4 SC Sun 9 Village of Kenosee Lake Kenosee Lake Lat 49° 49.850, Village Upper Souris River 1 0 Country Beach Long 102° 16.962 W SC 5 5 SC Sun 9 Moose Mtn Prov Park Kenosee Lake Lat 49° 49.880, Prov Park Upper Souris River 1 0 Country Beach Long 102° 17.903 W SC 6 6 SC Sun 9 Moose Creek Regional Alameda Dam N/A Reg Park Upper Souris River 1 0 Country Park Beach SR 1 1 SR Sunrise 10 Crystal Lake Beach Crystal Lake 51° 51' 0.72", -102° RM Assiniboine River 0 1

25' 47.92" SR 1 1 SR Sunrise 10 Crystal Lake Beach Crystal Lake 51° 51' 0.72", -102° RM Assiniboine River 0 1

25' 47.92" SR 10 10 SR Sunrise 10 Lady Beach Lady Lake Lat 52° 1.66 N, Reg Park Assiniboine River 0 1

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Long 102° 39.054

W SR 11 11 SR Sunrise 10 Annie Laurie Beach Annie Laurie Lake Lat 51° 57.962 N, RM Assiniboine River 0 1 Long 102° 39.983

W SR 12 12 SR Sunrise 10 KC Beach Regional Park Fishing Lake Lat 51° 48.306 N, Reg Park Assiniboine River 0 1 Beach Long 103° 29.472 W SR 15 15 SR Sunrise 10 Leslie Beach Regional Fishing Lake Lat 51° 48.763 N, Reg Park Assiniboine River 0 1 Park Beach Long 103° 33.635 W SR 17 17 SR Sunrise 10 Good Spirit Lake Good Spirit Lake Lat 51° 30.38 N, Prov Park Assiniboine River 0 1 Provincial Park Beach Long 102° 39.933W SR 18 18 SR Sunrise 10 Canora Beach Good Spirit Lake Lat 51° 35.684 N, RM Assiniboine River 0 1 Long 102°40.25 W SR 19 19 SR Sunrise 10 Burgis Beach Good Spirit Lake Lat 51° 32.939 N, RM Assiniboine River 0 1 Long 102° 37.673 W SR 2 2 SR Sunrise 10 Crooked Lake Provincial Crooked Lake Lat 50° 36.274 N, Prov Park Lower Qu'Appelle 1 0 Park Beach Long 102° 40.382 River SR 20 20 SR Sunrise 10 Sandy Beach Good Spirit Lake Lat 51° 31.574 N, RM Assiniboine River 0 1 Long 102° 36.87 W SR 21 21 SR Sunrise 10 Melville Beach Crooked Lake Lat 50° 36.829 N, Village Lower Qu'Appelle 1 0 Long 102° 43.480 River SR 22 22 SR Sunrise 10 Moose Bay Beach Crooked Lake Lat 50°36.831 N, RM Lower Qu'Appelle 1 0

Long 102°41.486 W River SR 3 3 SR Sunrise 10 Sunset Beach Beach Crooked lake Lat 50° 35.546, RM Lower Qu'Appelle 1 0 Long 102° 39.662 River SR 4 4 SR Sunrise 10 Pickerel Point Beach Madge Lake Lat 51° 39.365 N, Prov Park Lake Winnipoegosis 1 0 Long 101° 36.712 W SR 5 5 SR Sunrise 10 West End Resort Beach Round Lake Lat 50° 32.842 N, Village Lower Qu'Appelle 1 0 Long 102° 24.917 River W SR 6 6 SR Sunrise 10 Bird's Point Beach Round Lake Lat 50° 32.663 N, Village Lower Qu'Appelle 1 0 Long 102° 22.205 River

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W SR 7 7 SR Sunrise 10 Carlton Trail Regional Carlton Trail Lake Lat 50° 67.508 N, Reg Park Assiniboine River 0 1 Park Beach Long 101° 70.124

W SR 8 8 SR Sunrise 10 Ministik Beach Madge lake Lat 51° 38.329 N, Prov Park Lake Winnipoegosis 1 0 Long 101° 37.967 W SR 9 9 SR Sunrise 10 Kamsack Beach Madge Lake Lat 51° 38.238 N, Prov Park Lake Winnipoegosis 1 0 Long 101° 38.681 W ST 1 1 ST Saskatoon 11 Aspen Grove Beach Blackstrap Lake Lat 51° 46.934 N, Prov Park South Saskatchewan 1 0 Long 106° 26.044 River W ST 10 10 ST Saskatoon 11 Domremy Beach Resort Wakaw Lake N/A RM Carrot River 1 0 Beach ST 11 11 ST Saskatoon 11 Last Mountain Lake Last Mountain Lake Lat 51° 21.182 N, Reg Park Upper Qu'Appelle 1 0 Regional Park Main Beach Long 105° 13.23 W River Area ST 11 11 ST Saskatoon 11 Last Mountain Lake Last Mountain Lake Lat 51° 21.182 N, Reg Park Upper Qu'Appelle 1 0 Regional Park Main Beach Long 105° 13.23 W River Area ST 12 12 ST Saskatoon 11 Lucien Lake Regional Park Lucien Lake N/A Reg Park Carrot River 1 0 Main Beach Area ST 13 13 ST Saskatoon 11 Rowan's Ravine Provincial Last Mountain Lake Lat 50° 59.56 N, Prov Park Upper Qu'Appelle 1 0 Park Main Beach Area Long 105°.11.11 W River ST 14 14 ST Saskatoon 11 Island View Main Beach Last Mountain Lake Lat 50° 58.52 N, Village Upper Qu'Appelle 1 0 Area Long 105°. 9.96 W River ST 15 15 ST Saskatoon 11 Leroy Leisureland Main Quill Lakes Lat 51° 59.828 N, Reg Park Upper Qu'Appelle 1 0 Beach/Pool Area Long 104°50.067 W River ST 16 16 ST Saskatoon 11 Pike Lake Main Beach Pike Lake N/A Prov Park South Saskatchewan 1 0 River ST 2 2 ST Saskatoon 11 Saskin Beach Fishing Lake Lat 51° 50.946 N, RM Assiniboine River 0 1 Long 103° 31.108 W ST 4 4 ST Saskatoon 11 Kevin Misfeldt Beach Area Blackstrap Lake Lat 51° 44.741 N, Prov Park South Saskatchewan 1 0 Beach Long 106° 27.865 River

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W ST 7 7 ST Saskatoon 11 Thode Subdivision Beach Blackstrap Lake Lat 51° 47.349 N, Prov Park South Saskatchewan 1 0 Area Long 106° 26.306 River W ST 8 8 ST Saskatoon 11 Wakaw Lake Regional Wakaw Lake N/A Village Carrot River 1 0 Park Main Beach ST 9 9 ST Saskatoon 11 Poplar Beach Resort Beach Wakaw Lake N/A Village Carrot River 1 0

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Unique EHS EHS Person Beach Beach Swimming Swimming Unmarked General Residents and Guest Itinerant Identifier Date Time Conducting Length Width Area Area Swimming Public Access use Survey Length Width Area Access Access

CHR 1 7-Aug- 13:45:00 Adam 75.0 5.3 75.0 1 0 1 13 CHR 2 7-Aug- 12:30:00 Adam 33.8 7.5 33.8 15.0 0 1 1 0 13 CHR 3 7-Aug- 11:30:00 Adam 18.8 9.0 18.8 0 0 1 1 13 CHR 4 7-Aug- 13:00:00 Adam 22.5 7.5 22.5 10.0 0 0 0 1 13 CHR 5 3-Jul-13 13:50:00 Britt 38.0 12.0 105.0 8.0 0 1 0 0 FH 1 28-Jul- 11:45:00 Adam 102.8 12.0 102.8 8.0 0 1 1 0 13 FH 2 16-Jul- 11:15:00 Adam 147.0 11.3 147.0 0.0 1 0 0 0 13 FH 2 31-Jul- 12:00:00 Other 500.0 15.0 1 1 0 0 13 FH 3 3-Jul-13 15:30:00 Britt 305.0 25.0 1 1 1 FH 4 28-Jul- 1:30:00 Adam 40.5 7.5 40.0 8.0 0 1 1 0 13 HL 1 29-Jul- 9:30:00 other 200.0 15.0 200.0 50.0 1 1 1 1 13 HL 10 4-Jul-13 10:30:00 Adam 165.0 30.8 165.0 10.0 0 1 0 0 HL 11 4-Jul-13 13:00:00 Adam 56.3 9.0 56.3 5.0 0 1 1 0 HL 2 4-Jul-13 14:00:00 Adam 97.5 9.0 97.5 8.0 0 0 1 0 HL 3 3-Jul-13 11:45:00 Britt 65.0 15.0 110.0 0 1 1 1 HL 4 28- 11:45:00 Other 85.0 3.0 80.0 50.0 0 1 1 1 Aug-13 HL 5 27- 10:50:00 Other 95.0 30.0 80.0 30.0 0 1 1 1 Aug-13 HL 6 3-Jul-13 19:30:00 Britt 125.0 15.0 66.0 0 1 1 0 HL 7 3-Jul-13 17:30:00 Britt 200.0 12.6 120.0 0 1 1 0 KT 10 17-Jul- 13:00:00 Other 80.0 20.0 80.0 50.0 0 1 1 1

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13 KT 11 29-Jul- 11:25:00 Other 106.0 30.0 106.0 91.0 0 1 1 1 13 KT 12 24-Jul- 11:00:00 Other 110.0 30.0 110.0 30.0 0 1 1 0 13 KT 13 24-Jul- 11:30:00 Other 10.0 3.0 70.0 55.0 0 1 1 1 13 KT 4 7-Aug- 9:30:00 Arden 74.0 1.0 72.0 80.0 0 1 1 13 KT 5 11- 14:50:00 Arden 40.0 9.0 50.0 60.0 0 1 1 Aug-13 KT 6 7-Aug- 11:20:00 Arden 296.0 20.0 240.0 100.0 0 1 1 13 KT 7 11-Jul- 11:30:00 Arden 17.0 5.0 17.0 1 1 1 13 KT 8 29- 12:00:00 Other 25.0 24.0 55.0 45.0 1 1 0 Aug-13 KT 8 9-Jul-13 13:00:00 Arden 19.0 14.0 24.0 30.0 0 1 1 KT 9 11-Jul- 13:00:00 Arden 90.0 16.0 85.0 25.0 0 1 1 13 MC 1 29-Jul- 10:50:00 other 100.0 30.5 61.0 38.0 13 MC 1 29-Jul- 10:50:00 other 100.0 30.5 61.0 38.0 13 MC 1 29-Jul- 10:50:00 other 100.0 30.5 61.0 38.0 13 MC 1 29-Jul- 10:50:00 other 100.0 30.5 61.0 38.0 13 MC 1 29-Jul- 10:50:00 other 100.0 30.5 61.0 38.0 13 MC 10 30- Other 8.0 1.0 1.0 Aug-13 MC 2 22- 15:00:00 Other 100.0 20.0 100.0 20.0 Aug-13 MC 2 30- Other 100.0 10.0 100.0 10.0 0 Aug-13

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MC 3 31-Jul- 3:30:00 Other 150.0 10.0 150.0 25.0 1 0 1 0 13 MC 3 31-Jul- 3:30:00 Other 150.0 10.0 150.0 25.0 1 0 1 0 13 MC 3 31-Jul- 3:30:00 Other 150.0 10.0 150.0 25.0 1 0 1 0 13 MC 3 31-Jul- 3:30:00 Other 150.0 10.0 150.0 25.0 1 0 1 0 13 MC 3 31-Jul- 3:30:00 Other 150.0 10.0 150.0 25.0 1 0 1 0 13 MC 4 20- 11:30:00 Arden 69.0 32.0 69.0 80.0 0 1 1 Aug-13 MC 5 20- 10:00:00 Arden 145.0 10.0 145.0 80.0 0 1 1 Aug-13 MC 6 30-Jul- 10:30:00 Other 75.0 7.0 75.0 15.0 13 MC 6 30-Jul- 10:30:00 Other 75.0 7.0 75.0 15.0 13 MC 6 30-Jul- 10:30:00 Other 75.0 7.0 75.0 15.0 13 MC 6 30-Jul- 10:30:00 Other 75.0 7.0 75.0 15.0 13 MC 6 30-Jul- 10:30:00 Other 75.0 7.0 75.0 15.0 13 MC 7 31-Jul- 0:00:00 other 22.0 7.9 1 13 MC 7 31-Jul- 0:00:00 other 22.0 7.9 1 13 MC 7 31-Jul- 0:00:00 other 22.0 7.9 1 13 MC 7 31-Jul- 0:00:00 other 22.0 7.9 1 13 MC 7 31-Jul- 0:00:00 other 22.0 7.9 1 13 MC 8 31-Jul- 0:00:00 Other 61.0 3.1 1 13

109

MC 8 31-Jul- 0:00:00 Other 61.0 3.1 1 13 MC 8 31-Jul- 0:00:00 Other 61.0 3.1 1 13 MC 8 31-Jul- 0:00:00 Other 61.0 3.1 1 13 MC 8 31-Jul- 0:00:00 Other 61.0 3.1 1 13 MC 9 7-Aug- 10:30:00 Other 76.2 6.1 1 0 1 1 13 MC 9 7-Aug- 10:30:00 Other 76.2 6.1 1 0 1 1 13 MC 9 7-Aug- 10:30:00 Other 76.2 6.1 1 0 1 1 13 MC 9 7-Aug- 10:30:00 Other 76.2 6.1 1 0 1 1 13 PA 10 17-Jul- 18:00:00 Arden 45.0 14.0 1 1 1 13 PA 11 17-Jul- 17:10:00 Arden 107.0 23.0 100.0 30.0 0 1 1 13 PA 12 22-Jul- 10:30:00 Arden 122.0 16.0 122.0 50.0 0 1 1 13 PA 13 17-Jul- 16:30:00 Arden 50.0 14.0 40.0 60.0 0 1 1 13 PA 14 15-Jul- 12:36:00 Arden 100.0 5.0 100.0 1 1 1 13 PA 15 10-Jul- 16:00:00 Arden 100.0 12.0 100.0 1 1 1 13 PA 16 22-Jul- 11:20:00 Arden 45.0 10.0 60.0 100.0 0 1 1 13 PA 18 10-Jul- 14:20:00 Arden 40.0 20.0 45.0 25.0 0 1 1 13 PA 2 18-Jul- 12:30:00 Arden 44.0 26.0 50.0 120.0 0 1 1 13 PA 20 16-Jul- 18:00:00 Arden 980.0 7.0 280.0 110.0 0 1 1 13

110

PA 21 16-Jul- 16:00:00 Arden 360.0 12.0 230.0 100.0 0 1 1 13 PA 22 18-Jul- 14:00:00 Arden 100.0 9.0 100.0 60.0 0 1 1 13 PA 23 18-Jul- 14:00:00 Arden 100.0 9.0 100.0 60.0 0 1 1 13 PA 24 24-Jul- 14:10:00 Arden 45.0 1.0 45.0 20.0 0 1 1 13 PA 25 24-Jul- 15:00:00 Arden 15.0 2.0 20.0 25.0 0 1 1 13 PA 26 24-Jul- 10:25:00 Arden 1 1 1 13 PA 27 24-Jul- 11:00:00 Arden 27.0 8.0 35.0 40.0 0 1 1 13 PA 28 24-Jul- 9:00:00 Arden 65.0 20.0 60.0 80.0 0 1 1 13 PA 29 24-Jul- 13:00:00 Arden 57.0 1.0 55.0 100.0 0 1 1 13 PA 31 12- 12:00:00 Arden 39.0 8.0 37.0 18.0 0 1 1 Aug-13 PA 32 22-Jul- 13:50:00 Arden 128.0 65.0 60.0 130.0 0 1 1 13 PA 33 8-Aug- 13:00:00 Arden 55.0 20.0 40.0 15.0 0 1 1 13 PA 34 8-Aug- 12:00:00 Arden 47.0 20.0 45.0 1 1 1 13 PA 4 16-Jul- 14:30:00 Arden 357.0 7.0 400.0 200.0 0 1 1 13 PA 5 17-Jul- 15:30:00 Arden 130.0 2.0 120.0 60.0 0 1 1 13 PA 6 17-Jul- 15:00:00 Arden 10.0 2.0 10.0 1 1 1 13 PA 7 17-Jul- 14:30:00 Arden 80.0 20.0 70.0 60.0 0 1 1 13 PA 8 15-Jul- 14:00:00 Arden 58.0 10.0 48.0 60.0 0 1 1 13

111

PA 9 17-Jul- 13:20:00 Arden 216.0 18.0 200.0 110.0 0 1 1 13 PN 1 13-Jul- 18:15:00 Other 150.0 28.0 150.0 1 1 0 0 13 PN 1 13-Jul- 18:15:00 Other 150.0 28.0 150.0 1 1 0 0 13 PN 10 3-Jul-13 11:00:00 Adam 101.3 18.8 101.3 12.0 0 1 1 0 PN 12 29-Jul- 8:30:00 Arden 79.0 10.0 71.0 80.0 0 1 1 13 PN 13 29-Jul- 10:00:00 Arden 43.0 10.0 35.0 40.0 0 1 1 13 PN 14 30-Jul- 4:30:00 Arden 108.0 30.0 1 1 1 13 PN 15 29-Jul- 8:00:00 Arden 262.0 20.0 180.0 60.0 0 1 1 13 PN 16 29-Jul- 9:00:00 Arden 160.0 14.0 50.0 30.0 0 1 1 13 PN 17 30-Jul- 6:30:00 Arden 290.0 11.0 280.0 1 1 1 13 PN 18 31-Jul- 10:30:00 Arden 50.0 13.0 370.0 70.0 0 1 1 13 PN 19 31-Jul- 11:30:00 Arden 20.0 2.0 1 1 1 13 PN 2 14-Jul- 14:10:00 Other 65.0 9.0 0 1 0 0 13 PN 2 14-Jul- 14:10:00 Other 65.0 9.0 0 1 0 0 13 PN 20 1-Aug- 4:35:00 Arden 700.0 11.0 700.0 1 1 1 13 PN 21 31-Jul- 9:00:00 Arden 212.0 14.0 100.0 80.0 0 1 1 13 PN 22 1-Aug- 6:00:00 Arden 50.0 10.0 50.0 80.0 0 1 1 13 PN 23 1-Aug- 8:00:00 Arden 108.0 22.0 108.0 50.0 0 1 1 13 PN 24 1-Aug- 7:00:00 Arden 60.0 21.0 60.0 1 1 1

112

13 PN 26 30-Jul- 6:30:00 Arden 65.0 2.0 75.0 90.0 0 1 1 13 PN 27 30-Jul- 5:30:00 Arden 190.0 7.0 190.0 80.0 0 1 1 13 PN 28 31-Jul- 7:30:00 Arden 88.0 10.0 88.0 60.0 0 1 1 13 PN 3 29-Jul- 11:30:00 Arden 295.0 38.0 50.0 70.0 0 1 1 13 PN 4 29-Jul- 11:00:00 Arden 200.0 25.0 100.0 150.0 0 1 1 13 PN 4 13-Jul- 12:45:00 Other 90.0 12.0 90.0 70.0 1 0 0 13 PN 4 13-Jul- 12:45:00 Other 90.0 12.0 90.0 70.0 1 0 0 13 PN 5 13-Jul- 15:20:00 Other 120.0 21.0 120.0 60.0 0 1 0 0 13 PN 5 13-Jul- 15:20:00 Other 120.0 21.0 120.0 60.0 0 1 0 0 13 PN 6 22- 11:50:00 Arden 18.0 15.0 18.0 1 1 1 Aug-13 PN 7 3-Jul-13 12:30:00 Adam 187.5 16.5 187.5 9.0 0 1 0 0 PN 8 3-Jul-13 13:30:00 Adam 292.5 7.5 292.0 10.0 0 1 1 0 PN 9 24-Jul- 7:00:00 Arden 600.0 10.0 100.0 300.0 0 1 1 13 RQ 1 10-Jul- 11:15:00 Adam 37.5 15.0 37.5 15.0 0 1 1 0 13 RQ 10 22- 14:15:00 Adam 30.0 15.0 30.0 1 0 1 0 Aug-13 RQ 12 9-Jul-13 11:35:00 Britt 18.0 10.0 49.0 25.0 1 0 1 0 RQ 13 14- 12:00:00 Adam 11.3 4.5 11.3 1 0 1 0 Aug-13 RQ 14 11-Jul- 10:00:00 Adam 232.0 22.5 232.0 20.0 0 1 1 0 13 RQ 14 11-Jul- 10:00:00 Adam 232.0 22.5 232.0 20.0 0 1 1 0 13

113

RQ 14 11-Jul- 10:00:00 Adam 232.0 22.5 232.0 20.0 0 1 1 0 13 RQ 15 17-Jul- 12:18:00 Britt 77.0 9.0 80.5 56.0 0 1 1 0 13 RQ 15 17-Jul- 12:18:00 Britt 77.0 9.0 80.5 56.0 0 1 1 0 13 RQ 16 14- 10:00:00 Adam 105.0 22.5 105.0 15.0 0 1 1 1 Aug-13 RQ 16 14- 10:00:00 Adam 105.0 22.5 105.0 15.0 0 1 1 1 Aug-13 RQ 17 11-Jul- 11:20:00 Adam 52.5 7.5 5.2 10.0 0 0 0 1 13 RQ 18 6-Jun- 9:30:00 Adam 90.0 6.8 90.0 4.0 0 1 13 RQ 19 11-Jul- 11:30:00 Britt 101.5 14.0 70.0 49.0 0 1 1 0 13 RQ 2 11-Jul- 17:30:00 Britt 52.5 9.8 49.0 10.0 1 1 1 0 13 RQ 20 26- 12:15:00 Adam 199.5 13.5 199.5 1 1 1 0 Aug-13 RQ 20 24-Jul- 1:30:00 Other 200.0 15.0 1 1 1 1 13 RQ 21 11-Jul- 15:23:00 Britt 128.1 28.0 35.0 42.0 0 0 1 1 13 RQ 23 21- 11:45:00 Adam 75.0 9.0 75.0 15.0 0 0 1 0 Aug-13 RQ 24 11-Jul- 12:30:00 Adam 42.0 10.5 42.0 15.0 0 0 0 1 13 RQ 25 25-Jun- 13:30:00 Adam 56.3 16.5 56.3 10.0 0 1 1 1 13 RQ 26 21- 11:00:00 Adam 22.5 7.5 22.5 1 0 1 0 Aug-13 RQ 28 27- 11:40:00 Britt 39.2 10.5 43.4 10.5 0 0 1 1 Aug-13 RQ 29 21- 10:24:00 Britt 33.6 13.3 33.6 14.0 0 0 1 1 Aug-13

114

RQ 3 6-Jun- 11:30:00 Adam 99.0 8.3 1 1 13 RQ 30 21- 12:30:00 Adam 52.5 22.5 52.5 12.0 0 1 1 0 Aug-13 RQ 31 11-Jul- 9:30:00 Britt 157.5 51.0 80.5 42.0 0 1 1 0 13 RQ 32 27- 11:00:00 Adam 22.5 7.5 22.5 1 0 1 0 Aug-13 RQ 33 13- 12:57:00 Britt 122.5 9.8 56.0 56.0 1 1 1 1 Aug-13 RQ 36 25-Jun- 14:30:00 Adam 32.9 9.8 32.9 10.0 0 1 13 RQ 37 12- 14:30:00 Adam 51.0 4.5 51.0 1 1 1 0 Aug-13 RQ 38 13- 10:40:00 Britt 130.2 52.5 123.2 49.0 0 0 0 1 Aug-13 RQ 39 17-Jul- 14:25:00 Britt 24.5 10.5 14.0 10.5 1 0 1 1 13 RQ 39 17-Jul- 14:25:00 Britt 24.5 10.5 14.0 10.5 1 0 1 1 13 RQ 39 17-Jul- 14:25:00 Britt 24.5 10.5 14.0 10.5 1 0 1 1 13 RQ 4 9-Jul-13 10:15:00 Britt 14.0 6.3 14.0 21.0 1 0 1 0 RQ 41 21- 11:50:00 Britt 213.5 8.4 120.4 19.6 0 0 0 1 Aug-13 RQ 42 27- 10:00:00 Adam 36.0 37.5 36.0 10.0 0 0 1 0 Aug-13 RQ 43 13- 14:05:00 Britt 21.0 18.2 21.0 17.5 1 1 0 1 Aug-13 RQ 44 17-Jul- 10:00:00 Britt 315.0 14.0 150.5 84.0 0 1 1 0 13 RQ 5 9-Jul-13 13:55:00 Britt 22.4 15.0 59.0 36.0 1 0 1 0 RQ 6 10-Jul- 13:30:00 Adam 170.3 84.0 170.3 15.0 0 1 1 1 13 RQ 6 10-Jul- 13:30:00 Adam 170.3 84.0 170.3 15.0 0 1 1 1 13

115

RQ 8 10-Jul- 10:15:00 Adam 112.5 8.3 112.5 10.0 1 0 1 1 13 SC 1 30-Jul- 11:20:00 Britt 84.0 33.0 84.0 14.0 0 0 0 1 13 SC 2 3-Jul-13 10:15:00 Other 50.0 18.0 50.0 25.0 0 1 0 1 SC 2 3-Jul-13 10:15:00 Other 50.0 18.0 50.0 25.0 0 1 0 1 SC 3 2-Jul-13 11:00:00 Other 75.0 50.0 40.0 20.0 1 1 0 1 SC 3 2-Jul-13 11:00:00 Other 75.0 50.0 40.0 20.0 1 1 0 1 SC 4 30-Jul- 15:15:00 Britt 56.0 21.0 56.0 17.5 1 1 1 1 13 SC 5 30-Jul- 16:40:00 Britt 134.4 65.0 144.0 28.0 0 0 1 1 13 SC 6 3-Jul-13 10:00:00 Other 38.0 23.0 38.0 1 1 0 0 SR 1 30-Jul- 12:00:00 Other 55.0 10.0 30.0 50.0 0 1 1 1 13 SR 1 30-Jul- 12:00:00 Other 55.0 10.0 30.0 50.0 0 1 1 1 13 SR 1 30-Jul- 12:00:00 Other 55.0 10.0 30.0 50.0 0 1 1 1 13 SR 1 30-Jul- 12:00:00 Other 55.0 10.0 30.0 50.0 0 1 1 1 13 SR 1 30-Jul- 12:00:00 Other 55.0 10.0 30.0 50.0 0 1 1 1 13 SR 10 25-Jul- 9:30:00 Adam 21.5 9.0 21.5 1 1 0 0 13 SR 11 25-Jul- 10:00:00 Adam 75.0 34.5 75.0 1 1 0 0 13 SR 12 23-Jul- 13:30:00 Adam 37.5 30.0 37.5 9.0 0 0 1 1 13 SR 15 23-Jul- 14:30:00 Adam 258.0 15.0 258.0 15.0 0 1 1 1 13 SR 17 24-Jul- 8:45:00 Adam 206.3 18.8 206.8 25.0 0 1 1 1 13 SR 18 24-Jul- 12:30:00 Adam 48.0 11.3 48.0 1 0 1 1 13

116

SR 19 24-Jul- 11:00:00 Adam 90.0 8.3 90.0 1 1 1 1 13 SR 2 24-Jul- 11:00:00 Britt 74.2 14.0 74.2 14.0 1 0 1 1 13 SR 20 24-Jul- 10:00:00 Adam 90.0 11.3 90.0 1 1 1 1 13 SR 21 24-Jul- 14:00:00 Britt 70.0 34.3 70.0 28.0 1 1 1 0 13 SR 22 24-Jul- 12:20:00 britt 28.0 15.4 31.5 24.5 1 0 1 1 13 SR 3 24-Jul- 9:44:00 Britt 49.0 14.7 49.0 21.0 1 1 1 0 13 SR 4 14- 14:05:00 Britt 105.0 1.4 210.0 70.0 0 1 0 1 Aug-13 SR 5 23-Jul- 13:42:00 Britt 42.0 6.3 42.0 14.0 1 1 1 0 13 SR 6 23-Jul- 12:05:00 Britt 308.0 16.8 308.0 21.0 1 1 1 0 13 SR 7 7-Aug- 11:30:00 Other 400.0 12.0 150.0 12.0 1 1 0 0 13 SR 8 14- 11:40:00 Britt 157.5 7.7 122.5 70.0 0 1 0 1 Aug-13 SR 9 14- 13:05:00 Britt 24.5 2.8 24.5 21.0 1 0 1 0 Aug-13 ST 1 27-Jun- 11:30:00 Britt 133.0 9.1 1 1 1 0 13 ST 10 8-Jul-13 16:00:00 Arden 17.0 8.0 20.0 35.0 0 0 1 ST 11 12- 12:00:00 Adam 57.0 15.0 57.0 1 1 0 1 Aug-13 ST 11 12- 12:00:00 Adam 57.0 15.0 57.0 1 1 0 1 Aug-13 ST 12 9-Jul-13 11:00:00 Arden 18.0 21.0 75.0 40.0 0 1 1 ST 13 25-Jun- 10:20:00 Adam 450.0 33.8 450.0 12.0 0 1 1 1 13 ST 14 25-Jun- 12:30:00 Adam 42.0 6.3 42.0 9.0 0 1 1 0 13

117

ST 15 26- 14:25:00 Other 50.0 2.0 1 0 0 Aug-13 ST 16 4-Jul-13 15:00:00 Arden 170.0 7.0 160.0 50.0 0 1 1 ST 2 23-Jul- 1:00pm Adam 70.5 13.5 70.5 0.0 1 0 1 1 13 ST 4 27-Jun- 12:42:00 Britt 36.0 32.0 36.0 1 1 1 0 13 ST 7 27-Jun- 15:10:00 Britt 39.0 7.0 116.0 0 0 1 1 13 ST 8 8-Jul-13 11:30:00 Arden 80.0 21.0 80.0 80.0 0 1 1 ST 9 8-Jul-13 14:00:00 Arden 80.0 17.0 80.0 50.0 0 0 1

118

Unique EHSS EHSS EHSS EHSS Residential Residential Residential Residential Beach Beach Material - Beach Beach Identifier Average Average Average Average Density Density - Density - Density - Material Mucky Material Material Bather Bather Bather Bather Low Med High - Rocky - other Density Density Density - Density - Low Med - High CHR 1 None 0 0 0 low 1 0 0 Sandy 0 0 0 CHR 2 None 0 0 0 none 0 0 0 Sandy 0 0 0 CHR 3 low 1 0 0 Medium 0 1 0 Other 0 0 1 CHR 4 none 0 0 0 none 0 0 0 sandy 0 0 0 CHR 5 Medium 0 1 0 none 0 0 0 Sandy 0 0 0 FH 1 low 1 0 0 none 0 0 0 Sandy 0 0 0 FH 2 None 0 0 0 none 0 0 0 Sandy 0 0 0 FH 2 None 0 0 0 none 0 0 0 Sandy 0 0 0 FH 3 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 FH 4 low 1 0 0 low 1 0 0 Sandy 0 0 0 HL 1 low 1 0 0 High 0 0 1 Sandy 0 0 0 HL 10 None 0 0 0 none 0 0 0 Sandy 0 0 0 HL 11 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 HL 2 low 1 0 0 high 0 0 1 Sandy 0 0 0 HL 3 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 HL 4 medium 0 1 0 low 1 0 0 Sandy 0 0 0 HL 5 high 0 0 1 High 0 0 1 Sandy 0 0 0 HL 6 low 1 0 0 none 0 0 0 Sandy 0 0 0 HL 7 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 KT 10 low 1 0 0 high 0 0 1 Sandy 0 0 0 KT 11 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 KT 12 low 1 0 0 low 1 0 0 sandy 0 0 0 KT 13 low 1 0 0 low 1 0 0 mucky 1 0 0 KT 4 None 0 0 0 High 0 0 1 Sandy 0 0 0 KT 5 low 1 0 0 medium 0 1 0 Sandy 0 0 0 KT 6 low 1 0 0 medium 0 1 0 Sandy 0 0 0

119

KT 7 None 0 0 0 low 1 0 0 mucky 1 0 0 KT 8 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 KT 8 medium 0 1 0 low 1 0 0 Sandy 0 0 0 KT 9 low 1 0 0 low 1 0 0 Sandy 0 0 0 MC 1 None 0 0 0 MC 1 None 0 0 0 MC 1 None 0 0 0 MC 1 None 0 0 0 MC 1 None 0 0 0 MC 10 sandy 0 0 0 MC 2 Low 1 0 0 MC 2 low 1 0 0 MC 3 medium 0 1 0 MC 3 medium 0 1 0 MC 3 medium 0 1 0 MC 3 medium 0 1 0 MC 3 medium 0 1 0 MC 4 low 1 0 0 High 0 0 1 Sandy 0 0 0 MC 5 None 0 0 0 medium 0 1 0 Sandy 0 0 0 MC 6 low 1 0 0 MC 6 low 1 0 0 MC 6 low 1 0 0 MC 6 low 1 0 0 MC 6 low 1 0 0 MC 7 low 1 0 0 MC 7 low 1 0 0 MC 7 low 1 0 0 MC 7 low 1 0 0 MC 7 low 1 0 0 MC 8 low 1 0 0 MC 8 low 1 0 0

120

MC 8 low 1 0 0 MC 8 low 1 0 0 MC 8 low 1 0 0 MC 9 low 1 0 0 MC 9 low 1 0 0 MC 9 low 1 0 0 MC 9 low 1 0 0 PA 10 None 0 0 0 High 0 0 1 Sandy 0 0 0 PA 11 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PA 12 None 0 0 0 low 1 0 0 Sandy 0 0 0 PA 13 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PA 14 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PA 15 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PA 16 medium 0 1 0 High 0 0 1 mucky 1 0 0 PA 18 medium 0 1 0 low 1 0 0 Sandy 0 0 0 PA 2 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PA 20 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PA 21 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PA 22 medium 0 1 0 medium 0 1 0 Sandy 0 0 0 PA 23 medium 0 1 0 medium 0 1 0 Sandy 0 0 0 PA 24 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PA 25 low 1 0 0 High 0 0 1 Sandy 0 0 0 PA 26 None 0 0 0 medium 0 1 0 rocky 0 1 0 PA 27 None 0 0 0 low 1 0 0 mucky 1 0 0 PA 28 None 0 0 0 High 0 0 1 Sandy 0 0 0 PA 29 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PA 31 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PA 32 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PA 33 None 0 0 0 low 1 0 0 Sandy 0 0 0 PA 34 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PA 4 low 1 0 0 High 0 0 1 Sandy 0 0 0

121

PA 5 low 1 0 0 High 0 0 1 Sandy 0 0 0 PA 6 None 0 0 0 High 0 0 1 Sandy 0 0 0 PA 7 low 1 0 0 High 0 0 1 Sandy 0 0 0 PA 8 low 1 0 0 High 0 0 1 Sandy 0 0 0 PA 9 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PN 1 low 1 0 0 High 0 0 1 Sandy 0 0 0 PN 1 low 1 0 0 High 0 0 1 Sandy 0 0 0 PN 10 low 1 0 0 High 0 0 1 Sandy 0 0 0 PN 12 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PN 13 None 0 0 0 High 0 0 1 Sandy 0 0 0 PN 14 None 0 0 0 High 0 0 1 Sandy 0 0 0 PN 15 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PN 16 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PN 17 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PN 18 low 1 0 0 High 0 0 1 Sandy 0 0 0 PN 19 None 0 0 0 low 1 0 0 mucky 1 0 0 PN 2 medium 0 1 0 None 0 0 0 Sandy 0 0 0 PN 2 medium 0 1 0 None 0 0 0 Sandy 0 0 0 PN 20 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PN 21 None 0 0 0 low 1 0 0 Sandy 0 0 0 PN 22 None 0 0 0 High 0 0 1 Sandy 0 0 0 PN 23 None 0 0 0 High 0 0 1 Sandy 0 0 0 PN 24 None 0 0 0 low 1 0 0 Sandy 0 0 0 PN 26 None 0 0 0 medium 0 1 0 Sandy 0 0 0 PN 27 None 0 0 0 low 1 0 0 Sandy 0 0 0 PN 28 None 0 0 0 low 1 0 0 Sandy 0 0 0 PN 3 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PN 4 low 1 0 0 medium 0 1 0 Sandy 0 0 0 PN 4 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PN 4 medium 0 1 0 High 0 0 1 Sandy 0 0 0 PN 5 low 1 0 0 None 0 0 0 Sandy 0 0 0

122

PN 5 low 1 0 0 None 0 0 0 Sandy 0 0 0 PN 6 low 1 0 0 low 1 0 0 Sandy 0 0 0 PN 7 low 1 0 0 High 0 0 1 Sandy 0 0 0 PN 8 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 PN 9 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 1 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 10 None 0 0 0 low 1 0 0 sandy 0 0 0 RQ 12 none 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 13 None 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 14 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 RQ 14 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 RQ 14 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 RQ 15 low 1 0 0 High 0 0 1 rocky 0 1 0 RQ 15 low 1 0 0 High 0 0 1 rocky 0 1 0 RQ 16 none 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 16 none 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 17 None 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 18 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 19 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 RQ 2 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 RQ 20 None 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 20 low 1 0 0 Medium 0 1 0 rocky 0 1 0 RQ 21 high 0 0 1 Low 1 0 0 Sandy 0 0 0 RQ 23 None 0 0 0 Medium 0 1 0 sandy 0 0 0 RQ 24 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 RQ 25 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 26 low 1 0 0 Medium 0 1 0 sandy 0 0 0 RQ 28 Medium 0 1 0 Low 1 0 0 Sandy 0 0 0 RQ 29 None 0 0 0 low 1 0 0 rocky 0 1 0 RQ 3 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 30 low 1 0 0 Medium 0 1 0 Sandy 0 0 0

123

RQ 31 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 RQ 32 None 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 33 low 1 0 0 none 0 0 0 Sandy 0 0 0 RQ 36 None 0 0 0 High 0 0 1 Sandy 0 0 0 RQ 37 none 0 0 0 low 1 0 0 Sandy 0 0 0 RQ 38 None 0 0 0 none 0 0 0 Sandy 0 0 0 RQ 39 low 1 0 0 Low 1 0 0 rocky 0 1 0 RQ 39 low 1 0 0 Low 1 0 0 rocky 0 1 0 RQ 39 low 1 0 0 Low 1 0 0 rocky 0 1 0 RQ 4 none 0 0 0 High 0 0 1 rocky 0 1 0 RQ 41 None 0 0 0 none 0 0 0 rocky 0 1 0 RQ 42 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 RQ 43 low 1 0 0 none 0 0 0 Sandy 0 0 0 RQ 44 None 0 0 0 Low 1 0 0 Sandy 0 0 0 RQ 5 Medium 0 1 0 high 0 0 1 sandy 0 0 0 RQ 6 high 0 0 1 high 0 0 1 Sandy 0 0 0 RQ 6 high 0 0 1 high 0 0 1 Sandy 0 0 0 RQ 8 None 0 0 0 Medium 0 1 0 sandy 0 0 0 SC 1 low 1 0 0 none 0 0 0 Sandy 0 0 0 SC 2 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 SC 2 Medium 0 1 0 Medium 0 1 0 Sandy 0 0 0 SC 3 high 0 0 1 low 1 0 0 Sandy 0 0 0 SC 3 high 0 0 1 low 1 0 0 Sandy 0 0 0 SC 4 None 0 0 0 high 0 0 1 rocky 0 1 0 SC 5 low 1 0 0 low 1 0 0 sandy 0 0 0 SC 6 low 1 0 0 none 0 0 0 Sandy 0 0 0 SR 1 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 1 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 1 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 1 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 1 low 1 0 0 Medium 0 1 0 Sandy 0 0 0

124

SR 10 low 1 0 0 none 0 0 0 Sandy 0 0 0 SR 11 None 0 0 0 none 0 0 0 Sandy 0 0 0 SR 12 low 1 0 0 High 0 0 1 Sandy 0 0 0 SR 15 high 0 0 1 High 0 0 1 Sandy 0 0 0 SR 17 None 0 0 0 none 0 0 0 Sandy 0 0 0 SR 18 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 SR 19 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 2 low 1 0 0 Low 1 0 0 Sandy 0 0 0 SR 20 None 0 0 0 none 0 0 0 Sandy 0 0 0 SR 21 low 1 0 0 High 0 0 1 Sandy 0 0 0 SR 22 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 3 None 0 0 0 Medium 0 1 0 rocky 0 1 0 SR 4 Medium 0 1 0 low 1 0 0 rocky 0 1 0 SR 5 low 1 0 0 Medium 0 1 0 rocky 0 1 0 SR 6 low 1 0 0 Medium 0 1 0 Sandy 0 0 0 SR 7 low 1 0 0 medium 0 1 0 Sandy 0 0 0 SR 8 Medium 0 1 0 none 0 0 0 Sandy 0 0 0 SR 9 None 0 0 0 High 0 0 1 mucky 1 0 0 ST 1 low 1 0 0 Low 1 0 0 Sandy 0 0 0 ST 10 None 0 0 0 medium 0 1 0 mucky 1 0 0 ST 11 none 0 0 0 Medium 0 1 0 Sandy 0 0 0 ST 11 none 0 0 0 Medium 0 1 0 Sandy 0 0 0 ST 12 low 1 0 0 medium 0 1 0 Sandy 0 0 0 ST 13 None 0 0 0 Low 1 0 0 Sandy 0 0 0 ST 14 None 0 0 0 Medium 0 1 0 Sandy 0 0 0 ST 15 low 1 0 0 None 0 0 0 Sandy 0 0 0 ST 16 high 0 0 1 medium 0 1 0 Sandy 0 0 0 ST 2 None 0 0 0 high 0 0 1 Sandy 0 0 0 ST 4 Medium 0 1 0 none 0 0 0 Sandy 0 0 0 ST 7 none 0 0 0 high 0 0 1 sandy 0 0 0 ST 8 low 1 0 0 medium 0 1 0 Sandy 0 0 0

125

ST 9 low 1 0 0 High 0 0 1 Sandy 0 0 0

126

Unique Beach Urban Resid- Field Marsh/ Harbour Rural Forest Hills/ Land- Agri- Comm- Indus- River/ Other Comm- Marinas Motor- Other ID Groom- ential Swamp Uplands fill cultural ercial trial Stream/ ercial/ ized ing Ditch Indus- Water trial Craft Dis- charges CHR 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 CHR 2 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 CHR 3 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 CHR 4 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 CHR 5 0 1 1 1 1 FH 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 FH 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 FH 2 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 FH 3 1 1 1 1 1 1 1 1 FH 4 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 HL 1 1 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 HL 10 1 1 1 1 HL 11 0 1 1 1 HL 2 0 1 1 1 HL 3 1 1 1 1 1 1 1 HL 4 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 HL 5 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 HL 6 1 1 1 1 1 1 HL 7 0 1 1 1 KT 10 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 KT 11 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 KT 12 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 KT 13 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 KT 4 0 1 1 1 1 KT 5 1 1 1 1 1 KT 6 1 1 1 1 1

127

KT 7 0 1 1 1 1 KT 8 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 KT 8 0 1 1 1 1 1 KT 9 1 1 1 1 MC 1 0 0 0 0 0 0 1 0 0 0 0 0 0 MC 1 0 0 0 0 0 0 1 0 0 0 0 0 0 MC 1 0 0 0 0 0 0 1 0 0 0 0 0 0 MC 1 0 0 0 0 0 0 1 0 0 0 0 0 0 MC 1 0 0 0 0 0 0 1 0 0 0 0 0 0 MC 10 0 0 1 1 0 1 1 0 0 0 0 0 1 1 MC 2 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 MC 2 1 1 MC 3 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 MC 3 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 MC 3 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 MC 3 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 MC 3 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 MC 4 0 1 1 1 1 MC 5 0 1 1 1 1 MC 6 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 MC 6 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 MC 6 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 MC 6 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 MC 6 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 MC 7 1 1 1 1 1 MC 7 1 1 1 1 1 MC 7 1 1 1 1 1 MC 7 1 1 1 1 1 MC 7 1 1 1 1 1 MC 8 1 1 1 1 1 1 1 1 MC 8 1 1 1 1 1 1 1 1

128

MC 8 1 1 1 1 1 1 1 1 MC 8 1 1 1 1 1 1 1 1 MC 8 1 1 1 1 1 1 1 1 MC 9 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 MC 9 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 MC 9 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 MC 9 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 PA 10 0 1 1 1 PA 11 0 1 1 1 PA 12 0 1 1 1 1 PA 13 1 1 1 1 PA 14 0 1 1 1 1 PA 15 0 1 1 1 1 PA 16 1 1 1 1 1 PA 18 0 1 1 1 PA 2 0 1 1 1 PA 20 0 1 1 1 PA 21 0 1 1 1 PA 22 0 1 1 1 1 PA 23 0 1 1 1 1 PA 24 0 1 1 1 1 1 PA 25 0 1 1 1 1 1 PA 26 0 1 1 1 1 1 PA 27 1 1 1 1 1 1 PA 28 0 1 1 1 1 1 PA 29 0 1 1 1 1 1 PA 31 0 1 1 1 1 PA 32 0 1 1 1 PA 33 0 1 1 1 PA 34 1 1 1 1 PA 4 0 1 1 1 1

129

PA 5 0 1 1 1 PA 6 0 1 1 1 PA 7 0 1 1 1 PA 8 1 1 1 1 1 PA 9 1 1 1 1 PN 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 PN 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 PN 10 1 1 1 1 PN 12 0 1 1 1 1 1 PN 13 0 1 1 1 1 PN 14 1 1 1 1 1 PN 15 1 1 1 1 1 1 PN 16 1 1 1 1 1 PN 17 1 1 1 1 1 PN 18 0 1 1 1 PN 19 0 1 1 1 PN 2 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 PN 2 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 PN 20 0 1 1 1 PN 21 0 1 1 1 PN 22 0 1 1 1 PN 23 1 1 1 1 PN 24 1 1 1 1 PN 26 0 1 1 1 PN 27 0 1 1 PN 28 0 1 1 1 PN 3 1 1 1 1 1 1 PN 4 0 1 1 1 1 1 PN 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 PN 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 PN 5 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0

130

PN 5 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 PN 6 0 1 1 1 1 PN 7 0 1 1 PN 8 1 1 1 1 PN 9 0 1 1 1 1 1 1 1 1 RQ 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 RQ 10 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 12 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 RQ 13 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 14 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 RQ 14 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 RQ 14 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 RQ 15 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 15 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 16 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 RQ 16 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 RQ 17 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 RQ 18 0 1 1 1 1 0 1 1 RQ 19 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 2 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 20 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 20 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 RQ 21 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 RQ 23 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 24 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 RQ 25 1 1 1 1 1 1 1 RQ 26 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 28 0 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 RQ 29 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 3 1 1 1 1 1 RQ 30 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1

131

RQ 31 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 32 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 33 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 RQ 36 1 1 1 1 RQ 37 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 38 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 39 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 RQ 39 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 RQ 39 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 RQ 4 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 41 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 RQ 42 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 43 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 RQ 44 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 5 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 RQ 6 1 1 1 0 0 1 0 0 1 0 0 0 0 0 1 1 RQ 6 1 1 1 0 0 1 0 0 1 0 0 0 0 0 1 1 RQ 8 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 SC 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 SC 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 SC 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 SC 3 1 1 1 1 1 SC 3 1 1 1 1 1 SC 4 0 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 SC 5 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 SC 6 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 SR 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 SR 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 SR 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 SR 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 SR 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1

132

SR 10 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 SR 11 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 SR 12 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 SR 15 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 SR 17 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 SR 18 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 SR 19 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 SR 2 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 SR 20 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 SR 21 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 SR 22 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 SR 3 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 SR 4 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 SR 5 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 SR 6 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 SR 7 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SR 8 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 SR 9 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 ST 1 1 1 1 1 1 1 ST 10 0 1 1 1 1 1 ST 11 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 ST 11 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 ST 12 1 1 1 1 1 ST 13 1 1 1 1 1 1 1 ST 14 0 1 1 1 ST 15 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 ST 16 1 1 1 1 ST 2 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 ST 4 1 1 1 1 1 ST 7 0 1 1 1 1 1 ST 8 1 1 1 1

133

ST 9 1 1 1 1 1

134

Unique Other Storm Storm Storm Muni- Storm Wastes Com- Disch- Holding Comm- Other Other Storm Storm Storm Storm Storm Storm ID water water water cipal water from bined arging Tanks unal Fecal Micr- water water water water water water Runoff Runoff Runoff Sewage Drains Animal Sewer Private Coll- Waste obial runoff runoff runoff runoff runoff runoff from from from Dis- /Disch- Feed- Over- Sewage ection Disc- sources from from from from from from Areas Areas Urban charges arges ing flows Systems Systems harges Agricul- areas Beach Surro- reside- other subject subject Areas Opera- tural recei- and unding ntial areas to to tions Areas ving Surro- Fac- Areas pesticide ferti- Sewage unding ilities appl- lizer Sludge areas ication application CHR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 CHR 2 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 CHR 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 CHR 4 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 CHR 5 1 1 1 FH 1 0 1 1 0 0 FH 2 1 1 FH 2 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 FH 3 1 1 1 1 1 1 FH 4 0 1 1 1 1 HL 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 HL 10 1 1 HL 11 1 1 1 1 HL 2 1 1 1 1 HL 3 1 1 1 1 1 1 HL 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 HL 5 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 HL 6 1 1 1 HL 7 1 1 KT 10 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 KT 11 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0

135

KT 12 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 KT 13 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 KT 4 1 1 1 1 1 KT 5 1 1 1 1 KT 6 1 1 1 1 1 KT 7 1 1 1 1 KT 8 1 1 1 0 1 0 0 0 1 0 0 1 0 1 1 1 KT 8 1 1 1 1 KT 9 1 1 1 1 1 MC 1 MC 1 MC 1 MC 1 MC 1 MC 10 1 1 MC 2 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 MC 2 1 1 1 MC 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 3 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 4 1 1 1 1 1 1 MC 5 1 1 1 1 MC 6 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 MC 6 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 MC 6 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 MC 6 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 MC 6 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 MC 7 1 MC 7 1

136

MC 7 1 MC 7 1 MC 7 1 MC 8 1 1 MC 8 1 1 MC 8 1 1 MC 8 1 1 MC 8 1 1 MC 9 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 9 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 9 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 MC 9 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 PA 10 1 1 1 1 1 PA 11 1 1 1 1 1 PA 12 1 1 1 1 PA 13 1 1 1 1 1 PA 14 1 1 1 1 PA 15 1 1 1 1 1 PA 16 1 1 1 1 PA 18 1 1 1 1 PA 2 1 1 1 1 PA 20 1 1 1 1 PA 21 1 1 1 1 1 PA 22 1 1 1 1 PA 23 1 1 1 1 PA 24 1 1 1 1 PA 25 1 1 1 1 PA 26 1 0 1 1 PA 27 1 1 1 1 PA 28 1 1 1 1 PA 29 1 1 1 1

137

PA 31 1 1 1 1 PA 32 1 1 1 1 PA 33 1 1 1 1 PA 34 1 1 1 1 PA 4 1 1 1 1 1 PA 5 1 1 1 1 1 PA 6 1 1 1 1 1 PA 7 1 1 1 1 1 PA 8 1 1 1 1 1 PA 9 1 1 1 1 1 PN 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 PN 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 PN 10 1 1 1 PN 12 1 1 1 1 1 PN 13 1 1 1 1 1 PN 14 1 1 1 1 1 1 PN 15 1 1 1 1 PN 16 1 1 1 1 1 PN 17 1 1 1 1 PN 18 1 1 1 1 1 PN 19 1 1 1 1 PN 2 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 PN 2 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 PN 20 1 1 1 PN 21 1 1 1 1 PN 22 1 1 1 1 PN 23 1 1 1 1 1 PN 24 1 1 1 1 PN 26 1 1 1 1 PN 27 1 1 1 1 PN 28 1 1 1 1

138

PN 3 1 1 1 1 1 PN 4 1 1 1 1 PN 4 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 PN 4 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 PN 5 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 PN 5 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 PN 6 1 1 1 1 PN 7 1 1 1 1 PN 8 1 1 1 1 PN 9 1 1 1 1 1 RQ 1 1 1 1 RQ 10 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 RQ 12 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 RQ 13 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 RQ 14 1 1 1 1 RQ 14 1 1 1 1 RQ 14 1 1 1 1 RQ 15 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 15 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 16 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 RQ 16 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 RQ 17 1 1 1 RQ 18 1 1 1 1 RQ 19 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 2 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 RQ 20 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 RQ 20 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 RQ 21 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 RQ 23 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 RQ 24 1 1 1 1 RQ 25 1 1 1 1

139

RQ 26 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 RQ 28 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 RQ 29 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 3 1 1 1 1 RQ 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 RQ 31 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 32 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 RQ 33 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 RQ 36 1 1 1 1 RQ 37 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 38 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 RQ 39 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 39 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 39 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 4 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 41 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 RQ 42 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 RQ 43 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 RQ 44 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 RQ 5 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 RQ 6 1 1 1 RQ 6 1 1 1 RQ 8 1 1 1 1 SC 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 SC 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 SC 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 SC 3 1 1 1 1 SC 3 1 1 1 1 SC 4 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 SC 5 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 SC 6 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0

140

SR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 10 0 1 1 0 0 SR 11 0 1 1 1 0 SR 12 1 1 1 1 1 SR 15 1 1 1 1 1 SR 17 1 1 1 1 1 SR 18 1 1 1 1 SR 19 1 1 1 1 1 SR 2 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 SR 20 0 1 1 0 0 SR 21 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 22 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 SR 3 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 SR 4 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 SR 5 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 SR 6 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 SR 7 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 SR 8 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 SR 9 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 ST 1 1 1 1 1 ST 10 1 1 1 1 ST 11 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 ST 11 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 ST 12 1 1 1 1 ST 13 1 1 1 1 ST 14 1 1 1 1 ST 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

141

ST 16 1 1 1 0 ST 2 1 1 1 1 1 ST 4 1 1 ST 7 1 1 1 ST 8 1 1 1 1 ST 9 1 1 1 1

142

Unique Natural Birds Other Pets Steep Depths Large Slippery Dense Other Strong Under- EHSS EHSS EHSS EHSS EHSS Id drainage Wild All- Slopes Greater Rocks or Aquatic physical Currents tows Amount Amount Amount Amount Food Animals owed or than uneven Plants Hazard or rip of of of of Related Drop- 4.5m bottom tides Refuse Refuse - Refuse - Refuse - Litter offs on Low Med High Beach CHR 1 0 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 CHR 2 0 0 0 0 1 0 1 0 0 0 0 0 low 1 0 0 0 CHR 3 0 0 0 0 1 0 1 0 0 0 0 0 None 0 0 0 0 CHR 4 0 0 0 0 1 0 1 0 0 0 0 0 low 1 0 0 1 CHR 5 0 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 FH 1 20 0 0 0 0 1 1 0 0 0 0 Low 1 0 0 1 FH 2 4 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 FH 2 0 12 0 0 1 0 0 0 0 0 0 0 None 0 0 0 0 FH 3 3 0 1 0 0 0 0 0 0 0 0 None 0 0 0 FH 4 10 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 HL 1 0 0 1 0 0 0 0 1 0 0 0 none 0 0 0 0 HL 10 6 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 HL 11 5 0 0 0 0 1 0 0 0 0 0 None 0 0 0 HL 2 0 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 HL 3 0 0 1 0 0 1 0 0 0 0 0 None 0 0 0 HL 4 25 3 0 0 1 0 1 0 0 0 0 none 0 0 0 0 HL 5 4 0 0 0 1 0 0 0 0 0 0 none 0 0 0 0 HL 6 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 HL 7 0 0 0 0 0 0 0 1 0 0 0 Low 1 0 0 1 KT 10 0 1 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 KT 11 0 5 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 KT 12 0 1 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 KT 13 0 1 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 KT 4 16 0 0 1 low 1 0 0 1 KT 5 12 0 0 1 low 1 0 0 1 KT 6 20 0 0 low 1 0 0 1

143

KT 7 4 0 1 1 1 1 low 1 0 0 KT 8 0 1 1 1 0 0 0 0 0 0 0 None 0 0 0 0 KT 8 0 0 1 low 1 0 0 1 KT 9 2 0 0 1 1 1 low 1 0 0 1 MC 1 50 0 1 MC 1 50 0 1 MC 1 50 0 1 MC 1 50 0 1 MC 1 50 0 1 MC 10 2 0 1 low 1 0 0 0 MC 2 0 1 0 1 0 0 1 0 0 0 0 0 Low 1 0 0 1 MC 2 2 0 1 1 1 low 1 0 0 1 MC 3 none 0 0 0 0 MC 3 none 0 0 0 0 MC 3 none 0 0 0 0 MC 3 none 0 0 0 0 MC 3 none 0 0 0 0 MC 4 1 0 1 1 low 1 0 0 1 MC 5 0 0 1 low 1 0 0 1 MC 6 0 0 0 1 0 0 0 0 0 none 0 0 0 0 MC 6 0 0 0 1 0 0 0 0 0 none 0 0 0 0 MC 6 0 0 0 1 0 0 0 0 0 none 0 0 0 0 MC 6 0 0 0 1 0 0 0 0 0 none 0 0 0 0 MC 6 0 0 0 1 0 0 0 0 0 none 0 0 0 0 MC 7 1 1 1 low 1 0 0 MC 7 1 1 1 low 1 0 0 MC 7 1 1 1 low 1 0 0 MC 7 1 1 1 low 1 0 0 MC 7 1 1 1 low 1 0 0 MC 8 medium 0 1 0 1 MC 8 medium 0 1 0 1

144

MC 8 medium 0 1 0 1 MC 8 medium 0 1 0 1 MC 8 medium 0 1 0 1 MC 9 0 1 0 0 0 0 0 0 none 0 0 0 0 MC 9 0 1 0 0 0 0 0 0 none 0 0 0 0 MC 9 0 1 0 0 0 0 0 0 none 0 0 0 0 MC 9 0 1 0 0 0 0 0 0 none 0 0 0 0 PA 10 0 0 1 1 1 low 1 0 0 1 PA 11 2 0 0 low 1 0 0 1 PA 12 0 0 0 1 low 1 0 0 1 PA 13 0 0 0 low 1 0 0 1 PA 14 12 0 1 1 low 1 0 0 PA 15 0 0 0 low 1 0 0 1 PA 16 0 0 0 1 1 low 1 0 0 1 PA 18 0 0 0 low 1 0 0 1 PA 2 4 0 0 1 low 1 0 0 1 PA 20 100 0 0 low 1 0 0 1 PA 21 30 0 0 1 low 1 0 0 1 PA 22 0 0 0 1 1 1 low 1 0 0 1 PA 23 0 0 0 1 1 1 low 1 0 0 1 PA 24 0 0 0 1 low 1 0 0 1 PA 25 0 0 1 1 low 1 0 0 PA 26 0 0 1 1 PA 27 0 0 1 low 1 0 0 PA 28 0 0 0 1 1 low 1 0 0 1 PA 29 0 0 0 low 1 0 0 PA 31 0 0 0 low 1 0 0 1 PA 32 1 0 0 1 low 1 0 0 1 PA 33 0 0 0 1 1 1 low 1 0 0 1 PA 34 0 0 0 low 1 0 0 1 PA 4 2 0 0 low 1 0 0 1

145

PA 5 0 0 0 low 1 0 0 1 PA 6 0 0 1 low 1 0 0 PA 7 0 0 0 low 1 0 0 1 PA 8 0 0 0 1 low 1 0 0 1 PA 9 2 0 0 low 1 0 0 1 PN 1 4 0 0 0 0 0 0 0 0 0 0 low 1 0 0 0 PN 1 4 0 0 0 0 0 0 0 0 0 0 low 1 0 0 0 PN 10 0 0 0 0 0 0 0 0 0 0 0 none 0 0 0 PN 12 3 0 0 1 low 1 0 0 1 PN 13 12 0 1 low 1 0 0 1 PN 14 110 0 0 1 1 low 1 0 0 1 PN 15 4 0 0 low 1 0 0 1 PN 16 0 0 0 low 1 0 0 1 PN 17 80 0 1 low 1 0 0 1 PN 18 1 0 0 low 1 0 0 1 PN 19 0 0 1 none 0 0 0 PN 2 0 0 1 0 0 0 0 0 0 0 0 none 0 0 0 0 PN 2 0 0 1 0 0 0 0 0 0 0 0 none 0 0 0 0 PN 20 1 0 1 low 1 0 0 1 PN 21 0 0 0 low 1 0 0 1 PN 22 6 0 1 low 1 0 0 PN 23 40 0 0 low 1 0 0 1 PN 24 6 0 1 low 1 0 0 1 PN 26 2 0 0 low 1 0 0 1 PN 27 150 0 0 none 0 0 0 PN 28 80 0 0 low 1 0 0 1 PN 3 15 0 0 low 1 0 0 1 PN 4 1 0 1 low 1 0 0 PN 4 5 0 0 0 0 0 0 1 0 0 0 none 0 0 0 0 PN 4 5 0 0 0 0 0 0 1 0 0 0 none 0 0 0 0 PN 5 2 0 0 0 0 0 0 0 0 0 0 none 0 0 0 0

146

PN 5 2 0 0 0 0 0 0 0 0 0 0 none 0 0 0 0 PN 6 0 0 1 low 1 0 0 1 PN 7 3 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 PN 8 12 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 PN 9 5 0 0 1 1 low 1 0 0 1 RQ 1 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 RQ 10 0 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 12 0 1 0 1 0 0 1 0 0 0 0 0 none 0 0 0 0 RQ 13 0 0 0 1 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 14 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 RQ 14 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 RQ 14 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 RQ 15 0 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 15 0 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 16 0 100 0 0 0 0 0 0 1 0 0 0 low 1 0 0 1 RQ 16 0 100 0 0 0 0 0 0 1 0 0 0 low 1 0 0 1 RQ 17 0 0 1 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 18 2 0 1 Low 1 0 0 RQ 19 0 0 0 1 0 0 0 0 0 0 0 0 Low 1 0 0 1 RQ 2 0 1 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 20 0 0 0 1 0 0 0 0 1 0 0 0 low 1 0 0 1 RQ 20 0 5 0 1 0 0 0 0 0 0 0 0 medium 0 1 0 0 RQ 21 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 23 0 0 0 0 1 0 0 0 0 0 0 0 low 1 0 0 1 RQ 24 5 0 0 1 0 0 0 0 0 0 0 low 1 0 0 1 RQ 25 0 0 1 Low 1 0 0 RQ 26 0 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 28 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 29 0 0 0 1 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 3 2 1 1 Low 1 0 0 RQ 30 0 15 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1

147

RQ 31 0 0 0 1 0 0 0 0 0 0 0 0 Low 1 0 0 1 RQ 32 0 20 0 1 0 0 0 0 0 0 0 0 none 0 0 0 0 RQ 33 0 1 0 0 0 0 0 0 0 0 0 0 none 0 0 0 0 RQ 36 15 0 1 1 0 0 None 0 0 0 RQ 37 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 38 0 60 0 0 0 0 0 1 1 0 0 0 medium 0 1 0 1 RQ 39 0 0 0 1 0 0 1 0 0 0 0 0 None 0 0 0 0 RQ 39 0 0 0 1 0 0 1 0 0 0 0 0 None 0 0 0 0 RQ 39 0 0 0 1 0 0 1 0 0 0 0 0 None 0 0 0 0 RQ 4 0 3 0 1 0 0 0 0 0 0 0 0 medium 0 1 0 0 RQ 41 0 30 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 RQ 42 0 60 0 1 0 0 0 0 1 0 0 0 none 0 0 0 0 RQ 43 0 0 0 1 0 0 0 1 0 0 0 0 none 0 0 0 0 RQ 44 0 20 0 0 0 0 0 0 0 0 0 0 medium 0 1 0 1 RQ 5 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 RQ 6 15 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 6 15 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 RQ 8 6 0 1 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 1 0 0 0 0 0 0 0 0 1 0 0 0 low 1 0 0 1 SC 2 0 3 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 2 0 3 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 3 3 2 1 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 3 3 2 1 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 4 0 0 0 1 0 0 0 0 1 0 0 0 low 1 0 0 1 SC 5 0 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 SC 6 0 4 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 1 0 0 0 1 0 0 0 1 0 0 0 0 None 0 0 0 0 SR 1 0 0 0 1 0 0 0 1 0 0 0 0 None 0 0 0 0 SR 1 0 0 0 1 0 0 0 1 0 0 0 0 None 0 0 0 0 SR 1 0 0 0 1 0 0 0 1 0 0 0 0 None 0 0 0 0 SR 1 0 0 0 1 0 0 0 1 0 0 0 0 None 0 0 0 0

148

SR 10 0 0 0 0 0 0 0 0 0 0 0 none 0 0 0 0 SR 11 0 1 0 0 0 0 1 0 0 0 0 low 1 0 0 0 SR 12 2 0 0 0 0 1 0 0 0 0 0 low 1 0 0 1 SR 15 8 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 SR 17 4 0 0 0 0 1 0 0 0 0 0 low 1 0 0 1 SR 18 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 0 SR 19 2 0 1 0 0 0 0 0 0 0 0 Low 1 0 0 1 SR 2 0 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 20 0 0 1 0 0 0 0 0 0 0 0 low 1 0 0 1 SR 21 0 0 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 SR 22 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 3 0 15 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 4 0 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 5 0 0 0 1 0 0 0 0 0 0 0 0 None 0 0 0 0 SR 6 0 50 0 1 0 0 0 0 0 0 0 0 Low 1 0 0 1 SR 7 3 0 0 0 0 0 0 1 0 0 0 none 0 0 0 0 SR 8 0 0 0 0 0 0 0 0 0 0 0 0 low 1 0 0 1 SR 9 0 0 0 0 0 0 0 0 1 0 0 0 None 0 0 0 0 ST 1 0 0 0 0 0 0 0 0 0 0 0 None 0 0 0 ST 10 0 0 0 1 low 1 0 0 ST 11 0 25 0 0 0 0 0 0 1 0 0 0 low 1 0 0 1 ST 11 0 25 0 0 0 0 0 0 1 0 0 0 low 1 0 0 1 ST 12 0 0 0 low 1 0 0 ST 13 50 0 0 1 1 1 0 0 Low 1 0 0 1 ST 14 0 0 1 1 0 0 None 0 0 0 ST 15 0 0 0 0 0 0 0 0 0 0 none 0 0 0 0 ST 16 1 0 0 1 low 1 0 0 1 ST 2 0 0 1 1 0 0 0 1 0 0 0 medium 0 1 0 1 ST 4 0 0 0 0 0 0 0 0 0 0 0 Low 1 0 0 1 ST 7 2 0 0 0 0 0 0 1 0 0 0 None 0 0 0 ST 8 12 0 0 1 1 low 1 0 0 1

149

ST 9 8 0 1 1 1 low 1 0 0

150

Unique EHSS EHSS EHSS EHSS EHSS Dead amount EHSS EHSS EHSS Amount Amount Amount Amount Auto- boats Cyano- Id Med- Sewage House- Building Fishing Fish of Amount Amount Amount of algae of algae of algae of algae mobiles permitted bacterial ical Litter hold Materials related algae of algae of algae of algae in in in in perm- near blooms Litter Waste refuse on on on on swim- swim- swim- swim- itted beach beach beach - beach - beach - ming ming ming ming near Low Med High area area - area - area - beach Low Med High CHR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 CHR 2 0 0 0 1 0 0 low 1 0 0 low 1 0 0 0 1 0 CHR 3 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 CHR 4 0 0 0 1 0 0 low 1 0 0 low 1 0 0 0 1 0 CHR 5 None 0 0 0 None 0 0 0 1 1 0 FH 1 0 0 0 0 0 0 medium 0 1 0 medium 0 1 0 0 1 0 FH 2 None 0 0 0 none 0 0 0 0 1 0 FH 2 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 FH 3 None 0 0 0 None 0 0 0 1 1 0 FH 4 0 0 1 0 0 0 Low 1 0 0 low 1 0 0 0 1 0 HL 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 1 HL 10 None 0 0 0 None 0 0 0 0 1 0 HL 11 None 0 0 0 None 0 0 0 0 1 0 HL 2 medium 0 1 0 low 1 0 0 0 1 0 HL 3 None 0 0 0 None 0 0 0 1 0 0 HL 4 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 0 0 HL 5 0 0 0 0 0 0 None 0 0 0 low 1 0 0 0 0 0 HL 6 None 0 0 0 None 0 0 0 0 1 0 HL 7 None 0 0 0 None 0 0 0 0 0 0 KT 10 0 0 0 0 0 0 None 0 0 0 low 1 0 0 0 0 0 KT 11 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 KT 12 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 KT 13 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 0 0 KT 4 1 medium 0 1 0 medium 0 1 0 1 1 KT 5 1 1 low 1 0 0 high 0 0 1 1 1

151

KT 6 1 1 high 0 0 1 medium 0 1 0 0 0 KT 7 1 low 1 0 0 medium 0 1 0 0 0 KT 8 0 0 0 0 0 0 None 0 0 0 medium 0 1 0 1 1 0 KT 8 1 low 1 0 0 medium 0 1 0 0 0 KT 9 1 1 high 0 0 1 high 0 0 1 1 1 MC 1 None 0 0 0 medium 0 1 0 0 0 MC 1 None 0 0 0 medium 0 1 0 0 0 MC 1 None 0 0 0 medium 0 1 0 0 0 MC 1 None 0 0 0 medium 0 1 0 0 0 MC 1 None 0 0 0 medium 0 1 0 0 0 MC 10 0 0 1 0 0 0 None 0 0 0 None 0 0 0 MC 2 0 0 0 1 0 0 Low 1 0 0 None 0 0 0 1 1 0 MC 2 None 0 0 0 None 0 0 0 1 MC 3 0 0 0 0 0 0 low 1 0 0 0 1 0 MC 3 0 0 0 0 0 0 low 1 0 0 0 1 0 MC 3 0 0 0 0 0 0 low 1 0 0 0 1 0 MC 3 0 0 0 0 0 0 low 1 0 0 0 1 0 MC 3 0 0 0 0 0 0 low 1 0 0 0 1 0 MC 4 1 1 medium 0 1 0 high 0 0 1 0 0 MC 5 1 None 0 0 0 low 1 0 0 1 0 MC 6 0 0 0 0 0 0 low 1 0 0 1 1 MC 6 0 0 0 0 0 0 low 1 0 0 1 1 MC 6 0 0 0 0 0 0 low 1 0 0 1 1 MC 6 0 0 0 0 0 0 low 1 0 0 1 1 MC 6 0 0 0 0 0 0 low 1 0 0 1 1 MC 7 none 0 0 0 1 1 MC 7 none 0 0 0 1 1 MC 7 none 0 0 0 1 1 MC 7 none 0 0 0 1 1 MC 7 none 0 0 0 1 1 MC 8 None 0 0 0 1 1

152

MC 8 None 0 0 0 1 1 MC 8 None 0 0 0 1 1 MC 8 None 0 0 0 1 1 MC 8 None 0 0 0 1 1 MC 9 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 MC 9 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 MC 9 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 MC 9 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 PA 10 1 low 1 0 0 medium 0 1 0 0 1 PA 11 1 low 1 0 0 medium 0 1 0 0 1 PA 12 1 high 0 0 1 low 1 0 0 1 1 PA 13 1 medium 0 1 0 high 0 0 1 0 0 PA 14 1 low 1 0 0 medium 0 1 0 0 1 PA 15 1 low 1 0 0 low 1 0 0 0 1 PA 16 1 low 1 0 0 medium 0 1 0 1 1 PA 18 1 low 1 0 0 medium 0 1 0 1 1 PA 2 1 low 1 0 0 medium 0 1 0 1 1 PA 20 1 low 1 0 0 medium 0 1 0 0 1 PA 21 1 low 1 0 0 low 1 0 0 1 1 PA 22 low 1 0 0 medium 0 1 0 0 0 PA 23 low 1 0 0 medium 0 1 0 0 0 PA 24 1 1 low 1 0 0 low 1 0 0 1 1 PA 25 1 low 1 0 0 medium 0 1 0 0 1 PA 26 high 0 0 1 1 1 PA 27 1 medium 0 1 0 low 1 0 0 1 1 PA 28 1 low 1 0 0 low 1 0 0 0 1 PA 29 1 medium 0 1 0 medium 0 1 0 0 1 PA 31 1 1 low 1 0 0 high 0 0 1 0 1 PA 32 1 medium 0 1 0 high 0 0 1 1 0 PA 33 1 low 1 0 0 medium 0 1 0 0 0 PA 34 1 1 low 1 0 0 medium 0 1 0 0 1

153

PA 4 1 1 low 1 0 0 low 1 0 0 0 0 PA 5 1 medium 0 1 0 medium 0 1 0 0 1 PA 6 1 medium 0 1 0 low 1 0 0 0 1 PA 7 1 None 0 0 0 low 1 0 0 0 1 PA 8 1 None 0 0 0 low 1 0 0 1 1 PA 9 1 low 1 0 0 low 1 0 0 0 1 PN 1 0 0 1 0 0 0 low 1 0 0 None 0 0 0 0 0 0 PN 1 0 0 1 0 0 0 low 1 0 0 None 0 0 0 0 0 0 PN 10 none 0 0 0 none 0 0 0 0 1 0 PN 12 1 low 1 0 0 low 1 0 0 1 1 PN 13 1 low 1 0 0 low 1 0 0 1 1 PN 14 1 low 1 0 0 low 1 0 0 1 1 PN 15 1 medium 0 1 0 high 0 0 1 1 1 PN 16 1 low 1 0 0 low 1 0 0 0 1 PN 17 1 1 low 1 0 0 low 1 0 0 1 1 PN 18 1 low 1 0 0 medium 0 1 0 0 1 PN 19 low 1 0 0 medium 0 1 0 0 1 PN 2 0 0 0 0 0 0 None 0 0 0 low 1 0 0 1 0 0 PN 2 0 0 0 0 0 0 None 0 0 0 low 1 0 0 1 0 0 PN 20 1 high 0 0 1 medium 0 1 0 1 1 PN 21 1 high 0 0 1 medium 0 1 0 0 1 PN 22 1 low 1 0 0 low 1 0 0 1 1 PN 23 None 0 0 0 None 0 0 0 0 1 PN 24 1 1 high 0 0 1 medium 0 1 0 1 1 PN 26 1 high 0 0 1 medium 0 1 0 1 1 PN 27 low 1 0 0 low 1 0 0 1 1 PN 28 1 low 1 0 0 medium 0 1 0 1 1 PN 3 1 None 0 0 0 low 1 0 0 1 1 PN 4 1 high 0 0 1 high 0 0 1 0 1 PN 4 0 0 0 0 0 0 low 1 0 0 medium 0 1 0 0 0 0 PN 4 0 0 0 0 0 0 low 1 0 0 medium 0 1 0 0 0 0

154

PN 5 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 PN 5 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 PN 6 1 low 1 0 0 low 1 0 0 0 1 PN 7 low 1 0 0 none 0 0 0 0 1 0 PN 8 low 1 0 0 low 1 0 0 0 1 0 PN 9 1 high 0 0 1 high 0 0 1 1 1 RQ 1 none 0 0 0 none 0 0 0 0 1 0 RQ 10 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 RQ 12 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 RQ 13 0 0 0 1 0 0 low 1 0 0 none 0 0 0 0 1 0 RQ 14 None 0 0 0 None 0 0 0 0 1 0 RQ 14 None 0 0 0 None 0 0 0 0 1 0 RQ 14 None 0 0 0 None 0 0 0 0 1 0 RQ 15 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 15 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 16 0 0 1 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 16 0 0 1 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 17 None 0 0 0 low 1 0 0 0 1 0 RQ 18 1 Low 1 0 0 None 0 0 0 1 1 RQ 19 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 RQ 2 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 RQ 20 0 0 1 0 0 0 low 1 0 0 low 1 0 0 0 1 0 RQ 20 0 0 1 0 0 0 medium 0 1 0 high 0 0 1 1 1 0 RQ 21 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 0 0 RQ 23 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 0 0 RQ 24 low 1 0 0 low 1 0 0 0 1 0 RQ 25 1 None 0 0 0 None 0 0 0 1 1 RQ 26 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 RQ 28 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 RQ 29 0 0 0 0 0 0 Low 1 0 0 low 1 0 0 1 1 0 RQ 3 1 None 0 0 0 None 0 0 0 1 1

155

RQ 30 0 0 1 0 0 0 low 1 0 0 low 1 0 0 0 1 0 RQ 31 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 RQ 32 0 0 0 0 0 0 low 1 0 0 high 0 0 1 0 1 0 RQ 33 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 0 0 RQ 36 None 0 0 0 None 0 0 0 1 1 RQ 37 0 0 0 0 0 0 none 0 0 0 none 0 0 0 1 1 0 RQ 38 0 0 0 0 0 0 medium 0 1 0 medium 0 1 0 0 0 0 RQ 39 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 39 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 39 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 4 0 0 0 1 0 0 None 0 0 0 None 0 0 0 1 1 0 RQ 41 0 0 0 0 0 0 Low 1 0 0 low 1 0 0 0 0 0 RQ 42 0 0 0 0 0 0 low 1 0 0 high 0 0 1 0 1 0 RQ 43 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 RQ 44 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 RQ 5 0 0 0 0 0 0 none 0 0 0 none 0 0 0 1 1 0 RQ 6 None 0 0 0 None 0 0 0 0 1 0 RQ 6 None 0 0 0 None 0 0 0 0 1 0 RQ 8 None 0 0 0 None 0 0 0 0 1 0 SC 1 0 0 0 0 0 0 None 0 0 0 low 1 0 0 0 0 0 SC 2 0 0 0 0 0 0 none 0 0 0 none 0 0 0 0 1 0 SC 2 0 0 0 0 0 0 none 0 0 0 none 0 0 0 0 1 0 SC 3 low 1 0 0 low 1 0 0 0 0 0 SC 3 low 1 0 0 low 1 0 0 0 0 0 SC 4 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SC 5 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 SC 6 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0

156

SR 1 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 10 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 SR 11 0 0 0 0 1 0 medium 0 1 0 medium 0 1 0 0 1 0 SR 12 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 15 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 SR 17 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 1 0 SR 18 0 0 1 0 0 0 None 0 0 0 None 0 0 0 0 1 0 SR 19 0 0 0 0 1 1 None 0 0 0 None 0 0 0 1 1 0 SR 2 0 0 0 0 0 0 medium 0 1 0 medium 0 1 0 0 0 0 SR 20 0 0 1 0 0 0 None 0 0 0 None 0 0 0 0 1 0 SR 21 0 0 0 0 0 0 Low 1 0 0 low 1 0 0 1 1 0 SR 22 0 0 0 0 0 0 low 1 0 0 low 1 0 0 0 1 0 SR 3 0 0 0 0 0 0 medium 0 1 0 low 1 0 0 0 1 0 SR 4 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 SR 5 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 SR 6 0 0 0 0 1 0 None 0 0 0 0 1 0 SR 7 0 0 0 0 0 0 None 0 0 0 low 1 0 0 0 0 0 SR 8 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 SR 9 0 0 0 0 0 0 None 0 0 0 None 0 0 0 1 1 0 ST 1 medium 0 1 0 high 0 0 1 0 0 0 ST 10 1 low 1 0 0 medium 0 1 0 0 0 ST 11 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 ST 11 0 0 0 0 0 0 medium 0 1 0 high 0 0 1 0 1 0 ST 12 1 low 1 0 0 medium 0 1 0 0 0 ST 13 1 1 None 0 0 0 None 0 0 0 0 1 ST 14 None 0 0 0 None 0 0 0 0 1 ST 15 0 0 0 0 0 0 None 0 0 0 None 0 0 0 0 0 0 ST 16 1 low 1 0 0 medium 0 1 0 0 0 ST 2 0 1 1 1 0 0 medium 0 1 0 medium 0 1 0 0 1 0 ST 4 1 Low 1 0 0 high 0 0 1 1 0 0 ST 7 medium 0 1 0 medium 0 1 0 0 1 0

157

ST 8 1 low 1 0 0 medium 0 1 0 0 0 ST 9 1 None 0 0 0 high 0 0 1 0 1

158

Unique Schistosomes Large Number Number Access For Toilets and Number Number Aanimal Accessible Accessible Parking Number Identifier Number of Toilets of persons Showers of of proof by Road by Path Area of Life- of Showers with may Drinking Litter Litter Available guard Aquative Disabilities contaminate Water Bins Bins Stations Plants bathing Fountains area

CHR 1 0 1 6 4 1 0 0 2 1 0 1 1 0 CHR 2 0 1 0 0 0 0 0 0 0 0 1 0 0 CHR 3 0 0 12 14 0 0 7 16 0 0 1 1 1 CHR 4 0 1 5 4 0 0 1 3 1 0 1 1 1 CHR 5 0 0 2 1 0 0 1 4 1 1 0 1 0 FH 1 0 0 10 6 1 0 0 8 1 0 1 1 1 FH 2 0 0 7 2 1 0 0 2 0 0 1 1 0 FH 2 0 0 1 1 0 0 0 0 0 0 1 1 0 FH 3 0 0 0 0 0 0 0 0 1 1 0 FH 4 0 0 2 0 0 0 0 1 0 0 1 1 0 HL 1 0 0 4 0 0 0 0 4 1 0 1 1 0 HL 10 0 0 4 0 1 0 0 5 1 1 1 1 0 HL 11 0 0 2 0 0 0 0 2 1 1 1 1 0 HL 2 0 0 2 0 0 0 0 0 0 0 1 1 0 HL 3 0 0 2 2 1 0 0 0 0 1 0 1 0 HL 4 0 0 5 8 1 0 0 2 1 0 0 1 0 HL 5 1 0 1 0 1 0 0 3 1 1 1 1 0 HL 6 0 0 4 0 1 0 0 6 1 1 1 1 0 HL 7 0 0 2 0 1 0 0 0 0 0 1 1 0 KT 10 0 1 4 0 0 0 0 1 1 0 1 0 0 KT 11 0 0 0 0 0 0 0 0 0 1 1 1 0 KT 12 0 0 2 0 0 0 1 1 1 1 1 1 0

159

KT 13 0 0 2 0 0 0 0 0 0 1 0 0 0 KT 4 2 4 1 0 0 8 0 1 1 1 0 KT 5 1 0 0 1 0 0 3 0 1 1 1 0 KT 6 1 4 2 1 0 3 6 1 1 1 1 0 KT 7 2 4 1 1 1 1 0 1 1 0 0 KT 8 0 0 6 4 1 0 0 3 1 1 1 1 0 KT 8 4 4 1 0 1 3 0 1 1 1 0 KT 9 1 2 4 1 0 0 2 0 1 1 1 0 MC 1 1 0 1 1 MC 1 1 0 1 1 MC 1 1 0 1 1 MC 1 1 0 1 1 MC 1 1 0 1 1 MC 10 0 0 0 0 0 MC 2 0 0 1 0 1 0 0 3 0 1 MC 2 1 1 0 1 3 1 MC 3 0 0 MC 3 0 0 MC 3 0 0 MC 3 0 0 MC 3 0 0 MC 4 2 0 1 0 0 2 0 1 1 1 0 MC 5 2 0 0 0 0 3 1 1 1 1 0 MC 6 MC 6 MC 6 MC 6 MC 6 MC 7 2 1 1 MC 7 2 1 1 MC 7 2 1 1

160

MC 7 2 1 1 MC 7 2 1 1 MC 8 0 0 0 0 1 0 MC 8 0 0 0 0 1 0 MC 8 0 0 0 0 1 0 MC 8 0 0 0 0 1 0 MC 8 0 0 0 0 1 0 MC 9 0 0 4 0 0 2 MC 9 0 0 4 0 0 2 MC 9 0 0 4 0 0 2 MC 9 0 0 4 0 0 2 PA 10 1 0 0 0 0 0 1 0 1 1 1 0 PA 11 6 10 1 0 0 4 1 1 1 1 0 PA 12 2 0 1 0 0 4 0 1 1 1 0 PA 13 2 0 1 0 0 1 0 1 1 1 0 PA 14 1 4 0 0 0 0 4 0 1 1 1 0 PA 15 2 0 1 0 0 2 0 1 1 1 0 PA 16 4 2 1 0 0 2 0 1 1 1 0 PA 18 6 2 1 0 0 4 0 0 1 1 0 PA 2 2 0 1 0 0 3 0 1 1 1 0 PA 20 4 0 1 0 1 1 0 1 1 1 0 PA 21 4 0 1 0 0 4 0 1 1 1 0 PA 22 2 1 0 0 0 7 0 1 1 1 0 PA 23 2 1 0 0 0 7 0 1 1 1 0 PA 24 1 2 0 1 1 0 1 0 1 1 1 0 PA 25 1 2 0 1 0 0 1 0 1 1 1 0 PA 26 1 2 4 1 0 0 0 0 1 1 0 0 PA 27 0 0 1 0 0 2 0 1 1 1 0 PA 28 2 4 1 0 0 1 0 1 1 1 0 PA 29 2 0 1 0 0 2 0 1 1 1 0 PA 31 1 2 0 1 0 0 1 0 1 1 0 0

161

PA 32 5 5 1 0 1 3 0 1 1 1 0 PA 33 2 0 0 0 0 1 0 1 1 1 0 PA 34 0 0 0 0 0 0 0 1 1 0 0 PA 4 4 0 1 0 0 3 0 1 1 1 0 PA 5 0 0 0 0 0 0 0 1 1 0 0 PA 6 0 0 0 0 0 0 0 1 1 0 0 PA 7 2 0 1 0 0 1 0 1 1 1 0 PA 8 2 0 1 0 0 1 0 1 1 1 0 PA 9 2 0 1 0 0 8 0 1 1 1 0 PN 1 0 0 2 4 1 0 11 0 1 0 PN 1 0 0 2 4 1 0 11 0 1 0 PN 10 0 0 1 0 0 0 0 8 1 1 1 1 0 PN 12 0 0 1 0 0 1 0 1 1 1 0 PN 13 0 0 0 0 0 2 0 1 1 1 0 PN 14 1 0 0 3 0 1 1 1 0 PN 15 4 0 1 0 1 4 0 1 1 1 0 PN 16 4 0 0 0 0 3 0 1 1 1 0 PN 17 0 0 0 0 0 1 0 1 1 1 0 PN 18 4 1 0 2 6 1 1 1 1 0 PN 19 4 0 0 0 3 2 1 1 1 1 0 PN 2 0 0 5 4 1 0 0 1 1 1 0 1 0 PN 2 0 0 5 4 1 0 0 1 1 1 0 1 0 PN 20 4 0 0 0 0 1 1 1 1 0 0 PN 21 4 0 1 0 1 1 1 1 1 1 0 PN 22 0 0 0 0 1 0 0 1 1 1 0 PN 23 4 0 1 0 1 2 1 1 1 1 0 PN 24 4 0 1 0 0 1 0 1 1 1 0 PN 26 2 2 1 0 0 2 0 1 1 1 0 PN 27 4 0 1 0 0 1 1 1 1 1 0 PN 28 4 0 0 0 1 2 1 1 1 1 0 PN 3 2 2 1 0 1 5 0 1 1 1 0

162

PN 4 1 2 0 1 0 1 2 0 1 1 1 0 PN 4 0 1 5 1 0 0 6 0 0 1 1 0 PN 4 0 1 5 1 0 0 6 0 0 1 1 0 PN 5 0 0 1 0 0 0 2 0 0 1 0 PN 5 0 0 1 0 0 0 2 0 0 1 0 PN 6 0 0 0 0 0 2 0 1 1 1 0 PN 7 0 0 4 0 0 0 0 4 0 0 1 1 0 PN 8 0 0 12 4 1 0 0 8 0 0 1 1 0 PN 9 1 4 0 1 0 0 10 0 1 1 1 0 RQ 1 0 0 2 0 0 0 0 2 1 0 1 1 0 RQ 10 0 0 0 0 0 0 0 0 0 1 0 1 0 RQ 12 0 0 0 0 0 0 0 2 1 1 1 0 0 RQ 13 0 0 0 0 0 0 0 0 0 0 0 0 0 RQ 14 0 0 22 2 1 0 0 18 1 0 1 1 1 RQ 14 0 0 22 2 1 0 0 18 1 0 1 1 1 RQ 14 0 0 22 2 1 0 0 18 1 0 1 1 1 RQ 15 0 0 3 0 1 0 0 4 1 0 1 1 0 RQ 15 0 0 3 0 1 0 0 4 1 0 1 1 0 RQ 16 0 1 2 4 0 0 0 1 1 1 1 0 0 RQ 16 0 1 2 4 0 0 0 1 1 1 1 0 0 RQ 17 0 0 5 9 1 0 0 1 1 0 1 1 1 RQ 18 2 2 1 0 0 2 0 1 0 0 0 RQ 19 0 0 1 0 0 0 0 2 1 0 1 1 0 RQ 2 0 0 0 0 0 0 0 0 0 1 1 1 0 RQ 20 0 0 6 2 1 0 0 2 1 0 1 1 0 RQ 20 0 0 5 2 0 0 1 4 0 0 1 1 0 RQ 21 0 0 6 4 1 0 0 3 0 1 1 1 1 RQ 23 0 0 0 0 0 0 0 2 1 0 0 0 0 RQ 24 0 0 6 4 0 0 0 6 0 0 1 0 1 RQ 25 2 0 0 0 0 3 0 1 1 1 0 RQ 26 0 0 0 0 0 0 0 0 0 0 0 0 0

163

RQ 28 0 0 5 8 0 0 0 0 0 0 1 1 2 RQ 29 0 0 0 0 0 0 0 0 0 1 0 1 0 RQ 3 1 0 0 0 0 2 0 0 1 0 0 RQ 30 0 0 2 0 0 0 0 2 1 0 1 0 0 RQ 31 0 0 2 1 1 0 0 1 0 1 1 1 0 RQ 32 0 1 0 0 0 0 0 0 0 0 1 1 0 RQ 33 0 0 0 0 1 0 0 8 1 1 1 1 0 RQ 36 0 0 0 0 1 1 1 1 0 0 RQ 37 0 0 1 0 0 0 0 1 0 1 1 1 0 RQ 38 0 0 4 4 0 0 0 2 1 1 1 1 0 RQ 39 0 0 10 8 0 0 0 1 0 0 1 1 0 RQ 39 0 0 10 8 0 0 0 1 0 0 1 1 0 RQ 39 0 0 10 8 0 0 0 1 0 0 1 1 0 RQ 4 0 0 0 0 0 0 0 0 0 1 1 1 0 RQ 41 0 0 4 1 1 0 0 0 0 0 1 1 0 RQ 42 0 1 0 0 1 0 0 2 1 1 1 1 0 RQ 43 0 0 1 0 0 0 0 1 1 1 1 1 0 RQ 44 0 0 6 1 1 0 0 3 1 0 1 1 0 RQ 5 0 0 1 0 0 0 0 1 0 0 1 1 0 RQ 6 0 0 12 10 1 0 2 14 0 1 1 1 0 RQ 6 0 0 12 10 1 0 2 14 0 1 1 1 0 RQ 8 0 0 0 0 0 0 0 0 0 0 0 0 0 SC 1 0 1 6 0 1 0 0 2 0 0 1 1 0 SC 2 0 0 2 2 0 0 0 3 1 0 1 1 0 SC 2 0 0 2 2 0 0 0 3 1 0 1 1 0 SC 3 0 0 2 0 0 0 0 3 1 0 1 1 0 SC 3 0 0 2 0 0 0 0 3 1 0 1 1 0 SC 4 0 0 0 0 0 0 0 1 1 0 1 1 0 SC 5 0 0 4 2 1 0 0 10 0 0 1 1 0 SC 6 0 0 2 0 0 0 0 2 1 1 1 1 0 SR 1 0 0 2 0 0 0 0 1 0 0 1 0 0

164

SR 1 0 0 2 0 0 0 0 1 0 0 1 0 0 SR 1 0 0 2 0 0 0 0 1 0 0 1 0 0 SR 1 0 0 2 0 0 0 0 1 0 0 1 0 0 SR 1 0 0 2 0 0 0 0 1 0 0 1 0 0 SR 10 0 0 8 4 0 0 1 2 1 0 1 1 0 SR 11 0 0 4 0 0 0 0 1 0 0 1 0 0 SR 12 0 0 2 0 0 0 0 2 0 0 1 1 0 SR 15 0 0 6 4 1 0 0 5 0 1 1 1 0 SR 17 0 0 6 2 1 0 2 6 1 1 1 1 0 SR 18 0 0 0 0 0 0 0 2 0 0 1 1 0 SR 19 0 0 0 0 0 0 0 2 1 1 1 1 0 SR 2 0 0 0 1 0 0 0 7 1 1 1 1 0 SR 20 0 0 0 0 0 0 0 0 0 0 1 0 0 SR 21 0 0 2 1 0 0 0 4 1 1 1 1 0 SR 22 0 0 2 0 0 0 0 2 0 0 1 1 0 SR 3 0 0 1 0 0 0 0 2 1 1 1 1 0 SR 4 0 0 2 1 0 0 0 3 1 0 1 1 0 SR 5 0 0 2 2 0 0 0 0 0 1 0 1 0 SR 6 0 0 6 2 1 0 1 4 1 1 1 1 0 SR 7 0 1 7 5 0 1 2 1 1 0 1 0 SR 8 0 0 5 0 1 0 0 3 1 0 1 1 0 SR 9 0 1 2 0 1 0 0 0 0 1 1 0 0 ST 1 0 0 4 0 0 0 0 5 1 0 1 1 0 ST 10 1 0 0 0 0 0 1 0 1 0 0 ST 11 0 1 4 4 0 0 0 2 1 0 1 1 0 ST 11 0 1 4 4 0 0 0 2 1 0 1 1 0 ST 12 2 0 1 0 1 1 0 1 1 0 ST 13 3 2 1 0 0 7 1 0 1 1 0 ST 14 2 0 0 0 0 2 0 1 0 0 0 ST 15 0 0 1 1 1 1 ST 16 1 4 4 1 0 1 7 0 1 1 1 0

165

ST 2 0 1 0 0 0 0 0 0 0 0 1 1 0 ST 4 0 0 2 2 0 0 0 1 1 1 1 1 0 ST 7 0 0 0 0 0 0 0 1 0 0 1 1 0 ST 8 4 6 1 0 0 2 0 1 1 0 ST 9 0 0 1 0 0 3 0 0 1 1 0

166

Unique Emergency Number Number Emergency Beach Spill Swimmer Waterborne Other Public Notifcation Identifier Number of of First Contact posted for Procedure Injuries Disease Procedures Methods Posted Lifesaving Aid information swimming Procedure Outbreaks equipment Stations posted suitability Procedure

CHR 1 1 0 0 1 0 0 0 0 0 0 CHR 2 0 0 0 0 0 0 0 0 0 0 CHR 3 1 1 2 1 0 0 1 0 0 0 CHR 4 1 1 1 0 0 0 1 0 0 0 CHR 5 0 0 1 0 0 0 0 0 0 0 FH 1 1 1 1 1 1 0 1 0 0 1 FH 2 0 1 1 0 0 0 0 0 0 0 FH 2 0 0 0 0 0 0 0 0 0 0 FH 3 0 0 0 0 0 0 0 0 0 0 FH 4 0 0 0 0 0 0 0 0 0 0 HL 1 0 0 0 0 0 0 0 0 0 0 HL 10 0 0 0 0 1 0 0 1 0 1 HL 11 0 0 0 0 0 0 0 0 0 0 HL 2 0 0 0 0 0 0 0 0 0 0 HL 3 0 0 1 1 1 0 0 0 0 1 HL 4 1 0 0 1 0 0 0 0 0 0 HL 5 0 0 0 0 0 0 0 0 0 0 HL 6 0 0 0 0 0 0 0 0 0 0 HL 7 0 0 0 0 0 0 0 0 0 0 KT 10 0 0 0 0 1 0 0 0 0 0 KT 11 0 0 0 0 0 0 0 0 0 0 KT 12 0 0 0 0 1 0 0 0 0 0 KT 13 0 0 0 0 0 0 0 0 0 0

167

KT 4 0 0 0 0 0 0 0 0 0 0 KT 5 0 0 0 0 0 0 0 0 0 0 KT 6 1 1 0 1 1 0 0 0 0 1 KT 7 1 0 0 1 1 0 0 0 0 1 KT 8 0 0 0 0 0 0 0 0 0 0 KT 8 1 2 0 1 0 0 0 0 0 1 KT 9 0 0 0 0 0 0 0 0 0 1 MC 1 MC 1 MC 1 MC 1 MC 1 MC 10 0 0 0 0 0 0 0 0 0 0 MC 2 0 0 0 0 1 MC 2 MC 3 MC 3 MC 3 MC 3 MC 3 MC 4 0 0 0 0 0 0 0 0 0 0 MC 5 0 0 0 0 0 0 0 0 0 1 MC 6 MC 6 MC 6 MC 6 MC 6 MC 7 MC 7 MC 7 MC 7

168

MC 7 MC 8 0 0 MC 8 0 0 MC 8 0 0 MC 8 0 0 MC 8 0 0 MC 9 MC 9 MC 9 MC 9 PA 10 0 0 0 0 0 0 0 0 0 0 PA 11 0 2 0 0 0 0 0 0 0 0 PA 12 0 0 0 0 0 0 0 0 0 0 PA 13 0 1 0 0 0 0 0 0 0 0 PA 14 0 0 0 0 0 0 0 0 0 0 PA 15 0 0 0 0 1 0 0 0 0 1 PA 16 0 0 0 0 0 0 0 0 0 0 PA 18 0 0 0 0 0 0 0 0 0 1 PA 2 0 0 0 0 0 0 0 0 0 0 PA 20 0 0 0 0 0 0 0 0 0 0 PA 21 0 0 0 0 0 0 0 0 0 0 PA 22 0 0 0 0 0 0 0 0 0 0 PA 23 0 0 0 0 0 0 0 0 0 0 PA 24 0 0 0 0 0 0 0 0 0 0 PA 25 0 2 0 0 0 0 0 0 0 0 PA 26 0 0 0 0 0 0 0 0 0 0 PA 27 0 0 0 0 0 0 0 0 0 0 PA 28 0 0 0 0 0 0 0 0 0 0 PA 29 0 0 0 0 0 0 0 0 0 0 PA 31 0 0 0 0 0 0 0 0 0 0 PA 32 0 0 0 0 0 0 0 0 0 0

169

PA 33 1 0 0 1 0 0 0 0 0 0 PA 34 1 1 0 1 0 0 0 0 0 0 PA 4 0 0 0 0 0 0 0 0 0 0 PA 5 0 0 0 0 0 0 0 0 0 0 PA 6 0 0 0 0 0 0 0 0 0 0 PA 7 1 0 0 1 0 0 0 0 0 0 PA 8 0 0 0 0 0 0 0 0 0 0 PA 9 0 0 0 0 0 0 0 0 0 0 PN 1 0 1 1 0 0 0 0 0 0 0 PN 1 0 1 1 0 0 0 0 0 0 0 PN 10 0 0 0 0 0 0 0 0 0 0 PN 12 0 0 0 0 0 0 0 0 0 0 PN 13 0 0 0 0 0 0 0 0 0 0 PN 14 1 1 0 1 1 0 0 0 0 0 PN 15 0 1 0 0 0 0 0 0 0 0 PN 16 0 0 0 0 0 0 0 0 0 0 PN 17 0 0 0 0 0 0 0 0 0 0 PN 18 0 0 0 0 0 0 0 0 0 0 PN 19 0 0 0 0 0 0 0 0 0 0 PN 2 0 0 0 0 0 0 0 0 0 0 PN 2 0 0 0 0 0 0 0 0 0 0 PN 20 0 0 0 0 0 0 0 0 0 0 PN 21 1 0 0 1 1 0 0 0 1 1 PN 22 0 0 0 0 0 0 0 0 0 0 PN 23 1 0 0 1 0 0 0 0 0 0 PN 24 0 0 0 0 0 0 0 0 0 0 PN 26 0 0 0 0 0 0 0 0 0 0 PN 27 1 0 0 1 1 0 0 0 1 1 PN 28 1 0 0 1 0 0 0 0 0 0 PN 3 0 0 0 0 0 0 0 0 0 0 PN 4 0 0 0 0 0 0 0 0 0 0

170

PN 4 0 0 1 0 0 0 1 0 0 0 PN 4 0 0 1 0 0 0 1 0 0 0 PN 5 0 0 1 0 0 0 0 0 0 0 PN 5 0 0 1 0 0 0 0 0 0 0 PN 6 0 0 0 0 0 0 0 0 0 0 PN 7 0 0 0 0 0 0 0 0 0 0 PN 8 0 0 0 0 0 0 0 0 0 0 PN 9 0 0 0 0 0 0 0 0 0 0 RQ 1 0 0 0 0 0 0 0 0 0 0 RQ 10 0 0 0 0 0 0 0 0 0 0 RQ 12 0 0 0 0 0 0 0 0 0 0 RQ 13 0 0 0 0 0 0 0 0 0 0 RQ 14 1 1 1 1 0 0 1 1 0 1 RQ 14 1 1 1 1 0 0 1 1 0 1 RQ 14 1 1 1 1 0 0 1 1 0 1 RQ 15 0 0 0 0 0 0 0 0 0 0 RQ 15 0 0 0 0 0 0 0 0 0 0 RQ 16 0 0 0 0 0 0 0 0 0 0 RQ 16 0 0 0 0 0 0 0 0 0 0 RQ 17 1 4 3 1 1 0 1 0 0 1 RQ 18 0 0 0 0 1 0 0 0 0 0 RQ 19 0 0 0 0 0 0 0 0 0 0 RQ 2 0 0 0 0 0 0 0 0 0 0 RQ 20 0 0 0 0 0 0 0 0 0 0 RQ 20 0 0 0 1 0 0 0 0 0 0 RQ 21 0 1 1 0 0 0 0 0 0 0 RQ 23 0 0 0 0 0 0 0 0 0 0 RQ 24 1 1 1 1 1 0 1 0 0 1 RQ 25 0 0 0 0 0 0 0 0 0 0 RQ 26 0 0 0 0 0 0 0 0 0 0 RQ 28 0 5 1 0 0 0 0 0 0 1

171

RQ 29 0 0 0 0 0 0 0 0 0 0 RQ 3 0 0 0 0 0 0 0 0 0 0 RQ 30 0 0 0 0 0 0 0 0 0 0 RQ 31 0 0 0 0 1 0 0 0 0 0 RQ 32 0 0 0 0 0 0 0 0 0 0 RQ 33 0 0 0 0 0 0 0 0 0 0 RQ 36 0 0 0 0 0 0 0 0 0 0 RQ 37 0 0 0 0 0 0 0 0 0 0 RQ 38 0 0 0 0 0 0 0 0 0 0 RQ 39 0 0 0 0 0 0 0 0 0 0 RQ 39 0 0 0 0 0 0 0 0 0 0 RQ 39 0 0 0 0 0 0 0 0 0 0 RQ 4 0 0 0 0 0 0 0 0 0 0 RQ 41 0 1 0 0 0 0 0 0 0 0 RQ 42 0 0 0 0 0 0 0 0 0 0 RQ 43 0 0 0 0 0 0 0 0 0 0 RQ 44 0 1 0 0 0 0 0 0 0 0 RQ 5 0 0 0 0 0 0 0 0 0 0 RQ 6 1 2 1 1 1 0 1 1 0 1 RQ 6 1 2 1 1 1 0 1 1 0 1 RQ 8 0 0 0 0 0 0 0 0 0 0 SC 1 0 0 0 0 0 0 0 0 0 0 SC 2 0 0 0 0 0 0 0 0 0 0 SC 2 0 0 0 0 0 0 0 0 0 0 SC 3 0 0 0 0 0 0 0 0 0 0 SC 3 0 0 0 0 0 0 0 0 0 0 SC 4 0 0 0 0 0 0 0 0 0 0 SC 5 0 0 0 0 0 0 0 0 0 0 SC 6 0 0 0 0 0 0 0 0 0 0 SR 1 0 0 0 0 0 0 0 0 0 0 SR 1 0 0 0 0 0 0 0 0 0 0

172

SR 1 0 0 0 0 0 0 0 0 0 0 SR 1 0 0 0 0 0 0 0 0 0 0 SR 1 0 0 0 0 0 0 0 0 0 0 SR 10 0 0 1 0 0 0 1 0 0 0 SR 11 0 0 0 0 0 0 0 0 0 0 SR 12 0 0 0 0 0 0 0 0 0 0 SR 15 0 0 0 0 0 0 0 0 0 0 SR 17 1 1 1 1 1 0 0 0 0 1 SR 18 0 0 0 0 0 0 0 0 0 0 SR 19 0 0 0 0 0 0 0 0 0 0 SR 2 0 0 0 0 0 0 0 0 0 0 SR 20 0 0 0 0 0 0 0 0 0 0 SR 21 0 0 0 0 0 0 0 0 0 0 SR 22 0 0 0 0 0 0 0 0 0 0 SR 3 0 0 0 0 0 0 0 0 0 0 SR 4 0 0 0 0 0 0 0 0 0 0 SR 5 0 0 0 0 0 0 0 0 0 0 SR 6 0 0 0 0 0 0 0 0 0 0 SR 7 0 0 0 0 0 0 0 0 0 0 SR 8 0 0 0 0 0 0 0 0 0 0 SR 9 0 0 0 0 0 0 0 0 0 0 ST 1 0 0 0 0 0 0 0 0 0 0 ST 10 0 0 0 0 0 0 0 0 0 0 ST 11 1 1 1 1 0 0 1 0 0 0 ST 11 1 1 1 1 0 0 1 0 0 0 ST 12 0 0 0 0 0 0 0 0 0 0 ST 13 1 0 0 1 0 0 0 0 0 1 ST 14 0 0 0 0 0 0 0 0 0 0 ST 15 0 0 0 0 0 0 0 0 ST 16 1 0 1 1 1 0 0 0 0 1 ST 2 0 0 0 0 0 0 0 0 0 0

173

ST 4 0 0 0 0 0 0 0 0 0 0 ST 7 0 0 0 0 0 0 0 0 0 0 ST 8 0 0 0 0 0 0 0 0 0 0 ST 9 0 0 0 0 0 0 0 0 0 0

174

Unique Sample Sample Sample Sample Sample Sample GPS Air Water Sechi Prevailing Prevailing Prevailing Prevailing Wind Identifier Date Week Time Time - Time Location Coordinates Temp Temp Disc Winds Wind - Wind - Wind - Speed of the 24H Decimal Desc- onshore offshore parallel year ription CHR 1 7-Aug- 1:45:00 13:45 0.57 Main Lat 49° 57.922 N, 18 21 120 Onshore 1 0 0 1 13 32.00 PM Beach Long 107° 55.742 W CHR 2 7-Aug- 12:30:00 12:30 0.52 Main Lat 49° 58.945 N, 18 21 80 Parallel 0 0 1 1 13 32.00 PM Beach Long 107° 55.668 W CHR 3 7-Aug- 11:30:00 11:30 0.48 Main Lat 49° 58.722 N, 16 20 100 None 0 0 0 0 13 32.00 AM Beach Long 107° 56.206 W CHR 4 7-Aug- 1:00:00 13:00 0.54 Main Lat 50° 0.561 N, 18 21 80 Parallel 0 0 1 1 13 32.00 PM Beach Long 107° 56.125 W CHR 5 3-Jul- 2:15:00 14:15 0.59 Main Lat 50° 40.083 N, 23 23 120 offshore 0 1 0 2 13 27.00 PM Beach Long 108° 15.875 W FH 1 28-Jul- 11:45:00 11:45 0.49 Main Lat 50° 35. 801N, 16 21 100 Parallel 0 0 1 0 13 31.00 AM Beach Long 105° 24.787 W FH 2 16-Jul- 11:47:00 11:47 0.49 Main Lat 49° 11.864 N, 19 20 20 Onshore 1 0 0 1 13 29.00 AM Beach Long 105° 51.524 W FH 2 31-Jul- 12:00:00 12:00 0.50 Main 49° 121 43 N, 21 21 60 Onshore 1 0 0 0 13 31.00 PM Beach 105° 54 33 W FH 3 4-Jul- 3:30:00 15:30 0.65 Main Lat 50° 53.310 N, 30 23 120 None 0 0 0 13 27.00 PM Beach Long 106° 57.426 W FH 4 28-Jul- 1:30:00 13:30 0.56 Main Lat 50° 50.503 N, 16 21 120 onshore 1 0 0 1 13 31.00 PM Beach Long 105° 37.262 W HL 1 29-Jul- 9:30:00 9:30 0.40 Main Beach 15 15 Onshore 1 0 0 2

175

13 31.00 AM HL 10 4-Jul- 10:30:00 10:30 0.44 Main Lat 51° 15.522 N, 23 120 None 0 0 0 0 13 27.00 AM Beach Long 106° 52.566 W HL 11 4-Jul- 1:00:00 13:00 0.54 Main Lat 51° 9.59 N, 23 120 None 0 0 0 0 13 27.00 PM Beach Long 106° 49.128 W HL 2 4-Jul- 2:00:00 14:00 0.58 Main Lat 51° 2.786 N, 26 80 None 0 0 0 0 13 27.00 PM Beach Long 106° 51.33 W HL 3 4-Jul- 12:15:00 12:15 0.51 Main Lat 50° 42.513 N, 26 22 30 onshore 1 0 0 1 13 27.00 PM Beach Long 107° 23.123 W HL 4 28- 12:05:00 12:05 0.50 Main Lat 52°3256 N, 29 23 None 0 0 0 0 Aug-13 35.00 PM Beach Long 109°9479 W HL 5 27- 10:30:00 10:30 0.44 Main Lat 52°631 N, 23 21 Onshore 1 0 0 2 Aug-13 35.00 AM Beach Long 109°8998 W HL 6 3-Jul- 7:50:00 19:50 0.83 Main Lat 50° 39.473 N, 23 24 120 Offshore 0 1 0 2 13 27.00 PM Beach Long 108° 00.269 W HL 7 3-Jul- 5:40:00 17:40 0.74 Main Lat 50° 40.328 N, 28 23 120 Onshore 1 0 0 2 13 27.00 PM Beach Long 107° 57.044 W KT 10 17-Jul- 12:45:00 12:45 0.53 Main Lat 53°.518 688 25 23 80 Parallel 0 0 1 3 13 29.00 PM Beach N, Long 103°.7439 W KT 11 29-Jul- 11:25:00 11:25 0.48 Main Lat 53°.51036 N, 24 18 100 Parallel 0 0 1 3 13 31.00 AM Beach Long 103°.80295 W KT 12 24-Jul- 11:00:00 11:00 0.46 Main Lat 54°.04 N, 21 18 150 onshore 1 0 0 3 13 30.00 AM Beach Long 104°.63 W KT 13 24-Jul- 11:30:00 11:30 0.48 Main Lat 54°.04 N, 21.5 18.5 150 onshore 1 0 0 3 13 30.00 AM Beach Long 104°.63 W KT 4 7-Aug- 10:00:00 10:00 0.42 Main Lat 52.514680 N, 12 17 130 Onshore 1 0 0 1

176

13 32.00 AM Beach Long 103.593521 W KT 5 11-Jul- 2:50:00 14:50 0.62 Main Lat 52.519756 N, 20 17 130 Parallel 0 0 1 2 13 28.00 PM Beach Long 103.79992 W KT 6 7-Aug- 11:20:00 11:20 0.47 Main Lat 52.495310 N, 12 17 130 Onshore 1 0 0 2 13 32.00 AM Beach Long 103.512132 W KT 7 11-Jul- 12:15:00 12:15 0.51 Main Lat 53.403725 N, 20 22 130 Onshore 1 0 0 3 13 28.00 PM Beach Long 104.30345 W KT 8 29- 12:00:00 12:00 0.50 Main Beach 29 23 30 Parallel 0 0 1 0 Aug-13 35.00 PM KT 8 9-Jul- 1:45:00 13:45 0.57 Main Lat 52.632386 N, 18 22 40 Onshore 1 0 0 3 13 28.00 PM Beach Long 104.906130 W KT 9 11-Jul- 1:50:00 13:50 0.58 Main Lat 52.571564 N, 20 23 130 Onshore 1 0 0 4 13 28.00 PM Beach Long 104.18423 W MC 1 29-Jul- 10:50:00 10:50 0.45 Main Beach 16 15 1 13 31.00 AM MC 1 7-Aug- 9:51:00 9:51 0.41 Main Beach 13 16 2 13 32.00 AM MC 1 13- 10:00:00 10:00 0.42 Main Beach 19 16.5 1 Aug-13 33.00 AM MC 1 20- 10:00:00 10:00 0.42 Main Beach 18 18 3 Aug-13 34.00 AM MC 1 27- 2:10:00 14:10 0.59 Main Beach 13 18.5 3 Aug-13 35.00 PM MC 10 29- 1:10:00 1:10 0.05 Main Lat 55˚89367 N, Long 20 1 Aug-13 35.00 AM Beach 108˚61352 W MC 2 22- 3:00:00 15:00 0.63 Main Lat 55° 42.896 N, Long 19 1 Aug-13 34.00 PM Beach 107° 88.924 W MC 2 29- 3:05:00 15:05 0.63 Main Beach 22 Parallel 0 0 1 0 Aug-13 35.00 PM MC 3 31-Jul- 12:45:00 12:45 0.53 Main Lat 54° 53.3116 N, Long 19 1

177

13 31.00 PM Beach 102° 49.4579 W MC 3 8-Aug- 3:15:00 15:15 0.64 Main Lat 54° 53.3116 N, Long 19 1 13 32.00 PM Beach 102° 49.4579 W MC 3 15- 12:07:00 12:07 0.50 Main Lat 54° 53.3116 N, Long 18 1 Aug-13 33.00 PM Beach 102° 49.4579 W MC 3 20- 5:23:00 17:23 0.72 Main Lat 54° 53.3116 N, Long 20.5 4 Aug-13 34.00 PM Beach 102° 49.4579 W MC 3 27- 3:15:00 15:15 0.64 Main Lat 54° 53.3116 N, Long 21.5 1 Aug-13 35.00 PM Beach 102° 49.4579 W MC 4 20- 11:30:00 11:30 0.48 Main Lat 55˚6'7.73'' N, 22 16 130 Onshore 1 0 0 3 Aug-13 34.00 AM Beach Long 105˚17'12.24''W MC 5 20- 10:00:00 10:00 0.42 Main Lat 21 16 130 Offshore 0 1 0 Aug-13 34.00 AM Beach 55˚15'35.20''N, Long 105˚11'41.83''W MC 6 30-Jul- 12:30:00 12:30 0.52 Main Lat 58°36.986 N, 19.5 1 13 31.00 PM Beach Long 104°46.1001 W MC 6 26- 2:30:00 14:30 0.60 Main Lat 58°36.986 N, Long 18 Aug-13 35.00 PM Beach 104°46.1001 W MC 6 12- 2:15:00 14:15 0.59 Main Lat 58°36.986 N, Long 18.5 1 Aug-13 33.00 PM Beach 104°46.1001 W MC 6 7-Aug- 10:15:00 10:15 0.43 Main Lat 58°36.986 N, Long 19.5 1 13 32.00 AM Beach 104°46.1001 W MC 6 19- 2:20:00 14:20 0.60 Main Lat 58°36.986 N, Long 16 1 Aug-13 34.00 PM Beach 104°46.1001 W MC 7 6-Aug- 12:45:00 12:45 0.53 Main Lat 55°10.1623 N, Long 16.4 1 13 32.00 PM Beach 107°30.1800 W MC 7 31-Jul- 11:45:00 11:45 0.49 Main Lat 55°10.1623 N, Long 17.8 1 13 31.00 AM Beach 107°30.1800 W MC 7 13- 11:21:00 11:21 0.47 Main Lat 55°10.1623 N, Long 17.4 Aug-13 33.00 AM Beach 107°30.1800 W MC 7 28- 12:00:00 12:00 0.50 Main Lat 55°10.1623 N, Long 21.2 1 Aug-13 35.00 PM Beach 107°30.1800 W MC 7 20- 12:00:00 12:00 0.50 Main Lat 55°10.1623 N, Long 20 1

178

Aug-13 34.00 PM Beach 107°30.1800 W MC 8 28- 9:50:00 9:50 0.41 Main Lat 55°30.5061 N, Long 22.8 1 Aug-13 35.00 AM Beach 106°34.2169 W MC 8 20- 2:02:00 14:02 0.58 Main Lat 55°30.5061 N, Long 18 1 Aug-13 34.00 PM Beach 106°34.2169 W MC 8 13- 2:40:00 14:40 0.61 Main Lat 55°30.5061 N, Long 17.2 1 Aug-13 33.00 PM Beach 106°34.2169 W MC 8 6-Aug- 2:45:00 14:45 0.61 Main Lat 55°30.5061 N, Long 18 1 13 32.00 PM Beach 106°34.2169 W MC 8 31-Jul- 2:05:00 14:05 0.59 Main Lat 55°30.5061 N, Long 17 1 13 31.00 PM Beach 106°34.2169 W MC 9 7-Aug- 10:30:00 10:30 0.44 Main Lat 54°42.3471 N, Long 16.2 1 13 32.00 AM Beach 107°14.1363 W MC 9 14- 11:40:00 23:40 0.99 Main Lat 54° 42.3471 N, Long 15.8 1 Aug-13 33.00 PM Beach 107° 14.1363 MC 9 21- 11:20:00 11:20 0.47 Main Lat 54° 42.3471 N, Long 16 2 Aug-13 34.00 AM Beach 107° 14.1364 MC 9 28- 3:20:00 15:20 0.64 Main Lat 54° 42.3471 N, Long 20 1 Aug-13 35.00 PM Beach 107° 14.1365 PA 10 17-Jul- 6:30:00 18:30 0.77 Main Lat 53.90414 N, 24 20 130 Parallel 0 0 1 2 13 29.00 PM Beach Long 105.90414 W PA 11 17-Jul- 5:30:00 17:30 0.73 Main Lat 53.603695 N, 24 20 130 Parallel 0 0 1 1 13 29.00 PM Beach Long 105.91502 W PA 12 22-Jul- 10:40:00 10:40 0.44 Main Lat 53.199345 N, 17 19 130 None 0 0 0 0 13 30.00 AM Beach Long 107.1385 W PA 13 17-Jul- 4:50:00 16:50 0.70 Main Lat 53.609802 N, 23 21 130 Parallel 0 0 1 2 13 29.00 PM Beach Long 105.92252 W PA 14 15-Jul- 1:30:00 13:30 0.56 Main Lat 53.55908 N, 23 20 130 Parallel 0 0 1 4 13 29.00 PM Beach Long 105.80793 W PA 15 10-Jul- 4:00:00 16:00 0.67 Main Beach 29 21 130 Offshore 0 1 0 1 13 28.00 PM

179

PA 16 22-Jul- 12:00:00 12:00 0.50 Main Lat 52.99585 N, 17 20 130 None 0 0 0 0 13 30.00 PM Beach Long 106.99745 W PA 18 10-Jul- 2:20:00 14:20 0.60 Main Lat 53.42141 N, 22 20 130 Onshore 1 0 0 2 13 28.00 PM Beach Long 106.1564 W PA 2 18-Jul- 1:00:00 13:00 0.54 Main Lat 53.498383 N, 19 20 130 Onshore 1 0 0 3 13 29.00 PM Beach Long 107.05836 W PA 20 16-Jul- 6:00:00 18:00 0.75 Main Lat 53.77557 N, 15 17 120 Onshore 1 0 0 2 13 29.00 PM Beach Long 105.16196 W PA 21 16-Jul- 5:10:00 17:10 0.72 Main Lat 53.797794 N, 15 18 130 Parallel 0 0 1 2 13 29.00 PM Beach Long 105.329666 W PA 22 18-Jul- 2:45:00 14:45 0.61 Main Lat 53.296726 N, 19 20 130 Parallel 0 0 1 2 13 29.00 PM Beach Long 107.055244 W PA 23 9-Sep- 5:00:00 17:00 0.71 Main Beach 13 37.00 PM PA 24 24-Jul- 2:40:00 14:40 0.61 Main Lat 53.168945 N, 13 19 130 None 0 0 0 0 13 30.00 PM Beach Long 107.052704 W PA 25 24-Jul- 3:30:00 15:30 0.65 Main Beach 13 17 130 None 0 0 0 0 13 30.00 PM PA 26 24-Jul- 10:40:00 10:40 0.44 Main Beach 13 18 130 None 0 0 0 0 13 30.00 AM PA 27 24-Jul- 11:30:00 11:30 0.48 Main Beach 14 17 100 Onshore 1 0 0 1 13 30.00 AM PA 28 24-Jul- 9:40:00 9:40 0.40 Main Lat 53.20983 N, 14 18 130 None 0 0 0 0 13 30.00 AM Beach Long 107.70473 W PA 29 24-Jul- 1:40:00 13:40 0.57 Main Lat 53.180126 N, 13 19 130 Onshore 1 0 0 2 13 30.00 PM Beach Long 106.9635 W PA 31 12- 12:00:00 12:00 0.50 Main Beach 21 19 130 Onshore 1 0 0 1 Aug-13 33.00 PM PA 32 22-Jul- 2:20:00 14:20 0.60 Main Lat 52.711105 N, 17 19 130 None 0 0 0 0

180

13 30.00 PM Beach Long 107.21622 W PA 33 8-Aug- 1:00:00 13:00 0.54 Main Lat 53˚42.933 N, 13 16 70 Parallel 0 0 1 1 13 32.00 PM Beach Long 105˚56.508 W PA 34 8-Aug- 12:00:00 12:00 0.50 Main Beach 13 18 130 Parallel 0 0 1 1 13 32.00 PM PA 4 16-Jul- 3:20:00 15:20 0.64 Main Lat 53.74966 N, 15 13 130 Onshore 1 0 0 3 13 29.00 PM Beach Long 105.26151 W PA 5 17-Jul- 4:10:00 16:10 0.67 Main Lat 53.60117 N, 24 20 130 Parallel 0 0 1 3 13 29.00 PM Beach Long 105.901215 W PA 6 17-Jul- 3:10:00 15:10 0.63 Main Lat 53.590225 N, 24 20 130 Onshore 1 0 0 2 13 29.00 PM Beach Long 105.88892 W PA 7 17-Jul- 2:45:00 14:45 0.61 Main Lat 53.572727 N, 24 21 130 Parallel 0 0 1 2 13 29.00 PM Beach Long 105.85859 W PA 8 15-Jul- 2:50:00 14:50 0.62 Main Lat 53.569714 N, 23 18 130 Onshore 1 0 0 1 13 29.00 PM Beach Long 105.86714 W PA 9 17-Jul- 2:00:00 14:00 0.58 Main Lat 53.58166 N, 24 21.1 130 Offshore 0 1 0 2 13 29.00 PM Beach Long 105.88215 W PN 1 13-Jul- 6:15:00 18:15 0.76 Main Lat 52°50.246 N, 19 22 5 13 28.00 PM Beach Long 108°51.693 W PN 1 17- 2:30:00 14:30 0.60 Main Lat 52° 50.246 N, 32 26 140 Onshore 1 0 0 3 Aug-13 33.00 PM Beach Long 108° 51.693 W PN 10 3-Jul- 11:00:00 11:00 0.46 Main Lat 53° 2.204 N, 20 16 20 Offshore 0 1 0 2 13 27.00 AM Beach Long 108° 26.801 W PN 12 29-Jul- 9:00:00 9:00 0.38 Main Lat 53.5479 N, 14 15 130 Onshore 1 0 0 4 13 31.00 AM Beach Long 108.6474 W

181

PN 13 29-Jul- 10:00:00 10:00 0.42 Main Lat 53.5188 N, 14 16 130 Offshore 0 1 0 3 13 31.00 AM Beach Long 108.6966 W PN 14 30-Jul- 5:00:00 5:00 0.21 Main Lat 54.0278 N, 9 15 130 Onshore 1 0 0 1 13 31.00 AM Beach Long 109.1909 W PN 15 29-Jul- 8:30:00 8:30 0.35 Main Lat 53.8824 N, 12 17 130 Onshore 1 0 0 2 13 31.00 AM Beach Long 109.5871 W PN 16 29-Jul- 9:20:00 9:20 0.39 Main Lat 53.8533 N, 12 16 130 Onshore 1 0 0 1 13 31.00 AM Beach Long 109.5623 W PN 17 30-Jul- 7:00:00 7:00 0.29 Main Lat 54.0278 N, 12 16 130 Parallel 0 0 1 3 13 31.00 AM Beach Long 109.5871 W PN 18 31-Jul- 11:30:00 11:30 0.48 Main Lat 54.4055 N, 17 21 130 Onshore 1 0 0 1 13 31.00 AM Beach Long 108.8154 W PN 19 31-Jul- 11:30:00 11:30 0.48 Main Lat 54.4347 N, 15 17 80 Onshore 1 0 0 3 13 31.00 AM Beach Long 109.8146 W PN 2 14-Jul- 2:10:00 14:10 0.59 Main Lat 52°29.836 N, 24 23 2 13 29.00 PM Beach Long 107°41.962 W PN 2 17- 11:30:00 11:30 0.48 Main Lat 52° 29.836 N, 28 25 120 Onshore 1 0 0 5 Aug-13 33.00 AM Beach Long 107° 41.952 W PN 20 1-Aug- 5:00:00 5:00 0.21 Main Lat 54.5948 N, 10 15 130 None 0 0 0 0 13 31.00 AM Beach Long 108.5121 W PN 21 31-Jul- 9:20:00 9:20 0.39 Main Lat 54.4202 N, 12 16 130 None 0 0 0 0 13 31.00 AM Beach Long 108.9154 W PN 22 1-Aug- 6:30:00 6:30 0.27 Main Lat 54.5366 N, 12 16 130 Onshore 1 0 0 1 13 31.00 AM Beach Long 108.5374 W PN 23 1-Aug- 8:30:00 8:30 0.35 Main Lat 54.4493 N, 15 18 130 None 0 0 0 0 13 31.00 AM Beach Long 108.6887 W PN 24 1-Aug- 7:30:00 7:30 0.31 Main Lat 54.4638 N, 15 18 130 None 0 0 0 0 13 31.00 AM Beach Long 108.5122 W PN 26 30-Jul- 7:10:00 7:10 0.30 Main Lat 54.4783 N, 10 15 130 None 0 0 0 0 13 31.00 AM Beach Long 109.6461 W PN 27 30-Jul- 6:20:00 6:20 0.26 Main Lat 54.4784 N, 11 15 130 None 0 0 0 0 13 31.00 AM Beach Long 109.747 W PN 28 31-Jul- 8:00:00 8:00 0.33 Main Lat 54.4637 N, 10 16 130 None 0 0 0 0 13 31.00 AM Beach Long 109.369 W

182

PN 3 29-Jul- 12:00:00 12:00 0.50 Main Lat 53.6496 N, 22 17 130 Parallel 0 0 1 1 13 31.00 PM Beach Long 108.844 W PN 4 13-Jul- 12:45:00 12:45 0.53 Main Lat 53°27.178 N, 17 20 Onshore 1 0 0 5 13 28.00 PM Beach Long 109° 59.623 W PN 4 19- 3:30:00 15:30 0.65 Main Lat 53° 27.178, 20 22 130 None 0 0 0 Aug-13 34.00 PM Beach Long 109° 59.623 W PN 4 29-Jul- 11:30:00 11:30 0.48 Main Lat 53.5479 N, 15 17 130 Onshore 1 0 0 1 13 31.00 AM Beach Long 108.8685 W PN 5 13-Jul- 3:20:00 15:20 0.64 Main Lat 53°13.716, 22 20 Parallel 0 0 1 5 13 28.00 PM Beach Long 109°16.226 W PN 5 17- 4:00:00 16:00 0.67 Main Lat 53° 13.716 N, 25 26 40 Onshore 1 0 0 Aug-13 33.00 PM Beach Long 109° 16.226 W PN 6 22- 11:50:00 11:50 0.49 Main Beach 25 18 130 Onshore 1 0 0 2 Aug-13 34.00 AM PN 7 3-Jul- 12:30:00 12:30 0.52 Main Lat 53° 4.74 N, 22 16 120 Parallel 0 0 1 1 13 27.00 PM Beach Long 108° 20.57 W PN 8 3-Jul- 1:30:00 13:30 0.56 Main Lat 53° 7.852 N, 25 17 120 onshore 1 0 0 1 13 27.00 PM Beach Long 108° 23.849 W PN 9 24-Jul- 7:50:00 7:50 0.33 Main Lat 53.136257 N, 13 17 130 None 0 0 0 0 13 30.00 AM Beach Long 108.42184 W RQ 1 10-Jul- 11:15:00 11:15 0.47 Main Lat 50° 55.824 N, 24 21 120 None 0 0 0 0 13 28.00 AM Beach Long 105° 9.84 W RQ 10 22- 2:15:00 14:15 0.59 Main No GPS No 25 22 60 None 0 0 0 0 Aug-13 34.00 PM Beach signal RQ 12 9-Jul- 12:10:00 12:10 0.51 Main Lat 50° 48.165 N, 21 22 100 Onshore 1 0 0 1 13 28.00 PM Beach Long 104° 58.249 RQ 13 14- 12:00:00 12:00 0.50 Main Lat 50° 52.017, 21 21 80 Onshore 1 0 0 1 Aug-13 33.00 PM Beach Long 105° 6.366

183

RQ 14 11-Jul- 10:00:00 10:00 0.42 Main Lat 50° 41.626 N, 23 22 20 Onshore 1 0 0 2 13 28.00 AM Beach Long 103° 37.753 W RQ 14 23-Jul- 10:55:00 10:55 0.45 Main Lat 50° 41.601 N, 19 24 Onshore 1 0 0 13 30.00 AM Beach Long 103° 37.727 W RQ 14 10-Sep- 12:00:00 12:00 0.50 Main Lat 50° 41.601 N, 18 20 100 Parallel 0 0 1 2 13 37.00 PM Beach Long 103° 37.727 W RQ 15 17-Jul- 12:50:00 12:50 0.53 Main Lat 50° 47.492 N, 25 25 40 Parallel 0 0 1 1 13 29.00 PM Beach Long 103° 51.090 W RQ 15 24-Jul- 12:00:00 12:00 0.50 Main Beach 20 22 Offshore 0 1 0 13 30.00 PM RQ 16 14- 10:00:00 10:00 0.42 Main Lat 51° 13.964 N, 19 20 20 Onshore 1 0 0 1 Aug-13 33.00 AM Beach Long 105° 18.241 W RQ 16 21- 10:00:00 10:00 0.42 Main Beach 19 21 40 none 0 0 0 0 Aug-13 34.00 AM RQ 17 11-Jul- 11:20:00 11:20 0.47 Main Lat 50° 44.138 N, 24 22 80 None 0 0 0 1 13 28.00 AM Beach Long 103° 40.897 RQ 18 6-Jun- 9:30:00 9:30 0.40 Lat 50° 47.91 17 Onshore 1 0 0 3 13 23.00 AM N, Long 104° 5.67 W RQ 19 11-Jul- 11:55:00 11:55 0.50 Main Lat 50° 47.195 N, 21 22 66 Parallel 0 0 1 3 13 28.00 AM Beach Long 104°56.750 RQ 2 11-Jul- 6:00:00 18:00 0.75 Main Lat 50° 49.394 N 19 21 100 None 0 0 0 0 13 28.00 PM Beach Long 105°04.407 W RQ 20 26- 12:15:00 12:15 0.51 Main Lat 50° 46.54N, 29 24 20 None 0 0 0 0 Aug-13 35.00 PM Beach Long 103° 47.976 W RQ 20 24-Jul- 1:30:00 13:30 0.56 Main Beach 22 24 onshore 1 0 0 13 30.00 PM RQ 21 11-Jul- 4:00:00 16:00 0.67 Main Lat 50° 49.137 N, 27 21 80 Parallel 0 0 1 3 13 28.00 PM Beach Long 105° 04.136

184

W RQ 23 21- 11:45:00 11:45 0.49 Main Lat 50° 56.049 N, 20 21 60 Onshore 1 0 0 1 Aug-13 34.00 AM Beach Long 105° 9.962 W RQ 24 11-Jul- 12:30:00 12:30 0.52 Main Lat 50° 43.098 N, 23 22 80 None 0 0 0 0 13 28.00 PM Beach Long 103° 39.8 W RQ 25 25-Jun- 1:30:00 13:30 0.56 Main Lat 50° 53.12 N, 20 16 120 Onshore 1 0 0 2 13 26.00 PM Beach Long 105° 5.75 W RQ 26 21- 11:00:00 11:00 0.46 Main Lat 50° 56.254 N, 20 21 60 Onshore 1 0 0 1 Aug-13 34.00 AM Beach Long 105° 10.232 W RQ 28 27- 11:40:00 11:40 0.49 Main Lat 50° 17.059 N, 27 23 20 Parallel 0 0 1 0 Aug-13 35.00 AM Beach long 103° 11.766 W RQ 29 21- 10:24:00 10:24 0.43 Main Lat 50° 47.875 N, 20 21 90 Offshore 0 1 0 2 Aug-13 34.00 AM Beach Long 103° 56.218 W RQ 3 6-Jun- 11:33:00 11:33 0.48 Lat 50° 48.113 17 Offshore 0 1 0 2 13 23.00 AM N, Long 105° 2.18 W RQ 30 21- 12:30:00 12:30 0.52 Main Lat 50° 52.247 N, 21 21 100 Onshore 1 0 0 1 Aug-13 34.00 PM Beach Long 105° 6.356 W RQ 31 11-Jul- 10:15:00 10:15 0.43 Main Lat 50° 45.857 N, 21 21 70 Onshore 1 0 0 3 13 28.00 AM Beach Long 104°53.850 W RQ 32 27- 11:00:00 11:00 0.46 Main Lat 50° 48.065 N, 26 24 20 None 0 0 0 0 Aug-13 35.00 AM Beach Long 104° 57.497 W RQ 33 13- 3:57:00 15:57 0.66 Main Lat 50° 10.234, 21 21 100 Parallel 0 0 1 1 Aug-13 33.00 PM Beach Long 101° 40.437 W RQ 36 25-Jun- 2:30:00 14:30 0.60 Main Lat 50° 52.81 N, 18 16 120 Offshore 0 1 0 1 13 26.00 PM Beach Long 105° 5.636

185

W RQ 37 12- 2:30:00 14:30 0.60 Main Lat 50° 50.155 N, 24 22 120 None 0 0 0 0 Aug-13 33.00 PM Beach Long 105° 3.668 W RQ 38 13- 10:40:00 10:40 0.44 Main Lat 50° 04.571, 19 19 45 Onshore 1 0 0 2 Aug-13 33.00 AM Beach Long 101° 42 397 W RQ 39 17-Jul- 2:55:00 14:55 0.62 Main Lat 50° 48.079 25 25 20 Onshore 1 0 0 1 13 29.00 PM Beach N, Long 1103° 49.588 RQ 39 24-Jul- 3:00:00 15:00 0.63 Main Beach 23 24 Parallel 0 0 1 13 30.00 PM RQ 39 10-Sep- 10:00:00 10:00 0.42 Main Lat 50° 48.079 17 19 40 Onshore 1 0 0 1 13 37.00 AM Beach N, Long 1103° 49.588 RQ 4 9-Jul- 10:40:00 10:40 0.44 Main Lat 50° 48.711 N, 20 21 100 None 0 0 0 0 13 28.00 AM Beach Long 105° 00.194 RQ 41 21- 11:50:00 11:50 0.49 Main Lat 50° 47.878 N, 20 21 100 Onshore 1 0 0 3 Aug-13 34.00 AM Beach Long 103° 53.836 W RQ 42 27- 10:00:00 10:00 0.42 Main Lat 50° 48.662 N, 25 24 20 onshore 1 0 0 0 Aug-13 35.00 AM Beach Long 105° 0.811 W RQ 43 13- 2:05:00 14:05 0.59 Main Lat 50° 20.384, 22 22 60 Onshore 1 0 0 1 Aug-13 33.00 PM Beach Long 101° 30.997 W RQ 44 17-Jul- 11:10:00 11:10 0.47 Main Lat 50° 47.826 N, 23 22 75 None 0 0 0 0 13 29.00 AM Beach Long 103° 53.529 W RQ 5 9-Jul- 2:30:00 14:30 0.60 Main Lat 50° 47.195, 21 21 76 Onshore 1 0 0 1 13 28.00 PM Beach Long 104° 54.772 RQ 6 10-Jul- 1:30:00 13:30 0.56 Main Lat 50° 47.767 N, 27 25 80 None 0 0 0 0 13 28.00 PM Beach Long 104° 59.06 W RQ 6 25-Jul- 9:45:00 9:45 0.41 Main Lat 50° 47.779 14 20 75 Parallel 0 0 1 3 13 30.00 AM Beach N, Long 104°

186

59.056 W RQ 8 10-Jul- 10:15:00 10:15 0.43 Main Lat 50° 59.558 N, 23 22 120 None 0 0 0 0 13 28.00 AM Beach Long 105° 13.062 W SC 1 30-Jul- 11:20:00 11:20 0.47 Main Lat 49° 35.333, 18 20 90 None 0 0 0 0 13 31.00 AM Beach Long 103° 46.819 W SC 2 3-Jul- 11:00:00 11:00 0.46 Main Lat 49° 22.20 N 30 21 onshore 1 0 0 4 13 27.00 AM Beach SC 2 13- 10:00:00 10:00 0.42 Main Beach 23 20 3 Aug-13 33.00 AM SC 3 2-Jul- 11:00:00 11:00 0.46 Main Lat 49° 04.28N 32 24.8 None 0 0 0 2 13 27.00 AM Beach SC 3 13- 11:00:00 11:00 0.46 Main Beach 25 22 Offshore 0 1 0 Aug-13 33.00 AM SC 4 30-Jul- 3:15:00 15:15 0.64 Main Lat 49° 49.850, 18 20 46 Parallel 0 0 1 2 13 31.00 PM Beach Long 102° 16.962 W SC 5 30-Jul- 4:40:00 16:40 0.69 Main Lat 49° 49.880, 18 20 90 Parallel 0 0 1 1 13 31.00 PM Beach Long 102° 17.903 W SC 6 3-Jul- 10:00:00 10:00 0.42 Main Beach 24 23 Parallel 0 0 1 1 13 27.00 AM SR 1 30-Jul- 12:00:00 12:00 0.50 Main Lat 51°.51 N, 20 19 onshore 1 0 0 1 13 31.00 PM Beach Long 102° SR 1 19- 1:35:00 13:35 0.57 Main Lat 51°51.072 N, 31 25 200 Onshore 1 0 0 1 Aug-13 34.00 PM Beach Long 102° SR 1 12- 1:50:00 13:50 0.58 Main Lat 51°51.072 N, 25 22 onshore 1 0 0 1 Aug-13 33.00 PM Beach Long 102° SR 1 7-Aug- 12:00:00 12:00 0.50 Main Lat 51°51.072 N, 20 20 Onshore 1 0 0 3 13 32.00 PM Beach Long 102° SR 1 26- 1:05:00 13:05 0.55 Main Lat 51°51.072 N, 30 25 200 None 0 0 0 0 Aug-13 35.00 PM Beach Long 102° SR 10 25-Jul- 9:30:00 9:30 0.40 Main Lat 52° 1.66 N, 12 20 120 Parallel 0 0 1 2 13 30.00 AM Beach Long 102° 39.054 W

187

SR 11 25-Jul- 10:00:00 10:00 0.42 Main Lat 51° 57.962 N, 13 20 20 Onshore 1 0 0 2 13 30.00 AM Beach Long 102° 39.983 W SR 12 23-Jul- 1:30:00 13:30 0.56 Main Lat 51° 48.306 N, 21 21 120 None 0 0 0 0 13 30.00 PM Beach Long 103° 29.472 W SR 15 23-Jul- 2:30:00 14:30 0.60 Main Lat 51° 48.763 N, 23 21 120 None 0 0 0 0 13 30.00 PM Beach Long 103° 33.635 W SR 17 24-Jul- 8:45:00 8:45 0.36 Main Lat 51° 30.38 N, 15 12 20 Onshore 1 0 0 1 13 30.00 AM Beach Long 102° 39.933W SR 18 24-Jul- 12:30:00 12:30 0.52 Main Lat 51° 35.684 N, 19 18 80 Onshore 1 0 0 1 13 30.00 PM Beach Long 102°40.25 W SR 19 24-Jul- 11:00:00 11:00 0.46 Main Lat 51° 32.939 N, 17 17 20 Onshore 1 0 0 1 13 30.00 AM Beach Long 102° 37.673 W SR 2 24-Jul- 11:30:00 11:30 0.48 Main Lat 50° 36.274 N, 20 21 45 Onshore 1 0 0 1 13 30.00 AM Beach Long 102° 40.382 SR 20 24-Jul- 10:00:00 10:00 0.42 Main Lat 51° 31.574 N, 14 16 40 Onshore 1 0 0 2 13 30.00 AM Beach Long 102° 36.87 W SR 21 24-Jul- 2:25:00 14:25 0.60 Main Lat 50° 36.829 N, 23 21 75 None 0 0 0 0 13 30.00 PM Beach Long 102° 43.480 SR 22 24-Jul- 12:45:00 12:45 0.53 Main Lat 50°36.831 N, 20 21 70 Offshore 0 1 0 1 13 30.00 PM Beach Long 102°41.486 W SR 3 24-Jul- 9:44:00 9:44 0.41 Main Lat 50° 35.546, 20 21 20 Onshore 1 0 0 3 13 30.00 AM Beach Long 102° 39.662 SR 4 14- 2:05:00 2:05 0.09 Main Lat 51° 39.365 N, 23 22 100 None 0 0 0 0 Aug-13 33.00 AM Beach Long 101° 36.712 W SR 5 23-Jul- 2:25:00 14:25 0.60 Main Lat 50° 32.842 19 23 70 Onshore 1 0 0 1 13 30.00 PM Beach N, Long 102° 24.917 W

188

SR 6 23-Jul- 1:25:00 13:25 0.56 Main Lat 50° 32.663 N, 20 22 70 None 0 0 0 0 13 30.00 PM Beach Long 102° 22.205 W SR 7 7-Aug- 11:45:00 11:45 0.49 Main Lat 50° 67.508 N, 26 120 offshore 0 1 0 2 13 32.00 AM Beach Long 101° 70.124 W SR 8 14- 11:40:00 11:40 0.49 Main Lat 51° 38.329 N, 21 22 100 Parallel 0 0 1 1 Aug-13 33.00 AM Beach Long 101° 37.967 W SR 9 14- 1:05:00 13:05 0.55 Main Lat 51° 38.238 N, 22 21 100 None 0 0 0 0 Aug-13 33.00 PM Beach Long 101° 38.681 W ST 1 27-Jun- 11:30:00 11:30 0.48 Main Lat 51° 46.934 N, 23 20 100 Onshore 1 0 0 1 13 26.00 AM Beach Long 106° 26.044 W ST 10 8-Jul- 4:48:00 16:48 0.70 Main Beach 16 20 130 Parallel 0 0 1 1 13 28.00 PM ST 11 12- 12:00:00 12:00 0.50 Main Lat 51° 21.182 N, 23 22 0 None 0 0 0 0 Aug-13 33.00 PM Beach Long 105° 13.23 W ST 11 21- 12:35:00 12:35 0.52 Main Beach Aug-13 34.00 PM ST 12 9-Jul- 11:50:00 11:50 0.49 Main Beach 18 21 130 Onshore 1 0 0 2 13 28.00 AM ST 13 25-Jun- 10:30:00 10:30 0.44 Main Lat 50° 59.56 N, 20 16 120 Onshore 1 0 0 1 13 26.00 AM Beach Long 105°.11.11 W ST 14 25-Jun- 12:30:00 12:30 0.52 Main Lat 50° 58.52 N, 18 16 120 Offshore 0 1 0 1 13 26.00 PM Beach Long 105°. 9.96 W ST 15 26- 3:32:00 15:32 0.65 Main Lat 51° 59.828 N, 26 21 100 Onshore 1 0 0 1 Aug-13 35.00 PM Beach Long 104°50.067 W ST 16 4-Jul- 3:00:00 15:00 0.63 Main Beach 24 21 10 Onshore 1 0 0 1 13 27.00 PM ST 2 23-Jul- 1:00:00 13:00 0.54 Main Lat 51° 50.946 N, 22 21 20 Onshore 1 0 0 1

189

13 30.00 PM Beach Long 103° 31.108 W ST 4 27-Jun- 12:42:00 12:42 0.53 Main Lat 51° 44.741 N, 27 20 120 Onshore 1 0 0 1 13 26.00 PM Beach Long 106° 27.865 W ST 7 27-Jun- 3:10:00 15:10 0.63 Main Lat 51° 47.349 N, 28 20 100 Onshore 1 0 0 1 13 26.00 PM Beach Long 106° 26.306 W ST 8 8-Jul- 12:15:00 12:15 0.51 Main Beach 16 20 130 Onshore 1 0 0 2 13 28.00 PM ST 9 8-Jul- 2:00:00 14:00 0.58 Main Beach 16 20 130 Onshore 1 0 0 2 13 28.00 PM

190

Unique Wind Swimmer Swimmer Swimmer Swim- Swim- Boater Boater Boater Boater Boater Sunlight Sun- Sun- Sun- Rain- Rain- ID 4_5 x Density Density - Density - mer mer Den- Density Density Density No. light light - light - fall fall On- Low Med Den- Num- sity - Low - Med - High – part rainy During Last shore sity - ber over- cloudy Sam- 24 High cast pling hours CHR 1 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 CHR 2 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 CHR 3 0.00 low 1 0 0 10 None 0 0 0 0 Overcast 1 0 0 0 1 CHR 4 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 CHR 5 0.00 Medium 0 1 0 20 None 0 0 0 0 Partially 0 1 0 0 0 Cloudy FH 1 0.00 low 1 0 0 4 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy FH 2 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 FH 2 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 FH 3 0.00 Medium 0 1 0 25 Low 1 0 0 3 sunny 0 0 0 0 0 FH 4 0.00 low 1 0 0 4 None 0 0 0 0 Partially 0 1 0 0 0 Cloudy HL 1 0.00 low 1 0 0 medium 0 1 0 Partially 0 1 0 0 0 Cloudy HL 10 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 HL 11 0.00 low 1 0 0 7 Low 1 0 0 1 Sunny 0 0 0 0 0 HL 2 0.00 low 1 0 0 12 None 0 0 0 0 sunny 0 0 0 0 0 HL 3 0.00 None 0 0 0 0 None 0 0 0 0 sunny 0 0 0 0 0 HL 4 0.00 None 0 0 0 0 None 0 0 0 0 sunny 0 0 0 0 0 HL 5 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 HL 6 0.00 low 1 0 0 8 None 0 0 0 0 sunny 0 0 0 0 0 HL 7 0.00 low 1 0 0 10 Low 1 0 0 1 sunny 0 0 0 0 0 KT 10 0.00 low 1 0 0 3 None 0 0 0 0 sunny 0 0 0 0 0 KT 11 0.00 low 1 0 0 6 None 0 0 0 0 sunny 0 0 0 0 0 KT 12 0.00 None 0 0 0 0 None 0 0 0 0 rainy 0 0 1 1 1

191

KT 13 0.00 None 0 0 0 0 None 0 0 0 0 rainy 0 0 1 1 1 KT 4 0.00 None 0 0 0 0 High 0 0 1 50 Sunny 0 0 0 0 0 KT 5 0.00 Low 1 0 0 15 Low 1 0 0 1 Sunny 0 0 0 0 0 KT 6 0.00 Low 1 0 0 4 medium 0 1 0 20 Sunny 0 0 0 0 0 KT 7 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 KT 8 0.00 Low 1 0 0 Low 1 0 0 Sunny 0 0 0 0 0 KT 8 0.00 Medium 0 1 0 50 Low 1 0 0 1 Sunny 0 0 0 0 0 KT 9 1.00 Low 1 0 0 7 Low 1 0 0 2 Sunny 0 0 0 0 0 MC 1 None 0 0 0 0 Partially 0 1 0 Cloudy MC 1 Rainy 0 0 1 1 1 MC 1 MC 1 MC 1 MC 10 MC 2 MC 2 0.00 low 1 0 0 1 None 0 0 0 0 0 0 MC 3 Medium 0 1 0 MC 3 MC 3 MC 3 MC 3 MC 4 0.00 Low 1 0 0 5 Low 1 0 0 1 Sunny 0 0 0 0 0 MC 5 0.00 None 0 0 0 0 Low 1 0 0 1 Sunny 0 0 0 0 0 MC 6 low 1 0 0 Low 1 0 0 MC 6 MC 6 MC 6 0 MC 6 MC 7 0 MC 7 low 1 0 0 0

192

MC 7 0 MC 7 0 MC 7 0 MC 8 0 0 MC 8 0 0 MC 8 0 0 MC 8 0 0 MC 8 Low 1 0 0 0 0 MC 9 low 1 0 0 0 0 MC 9 MC 9 0 0 MC 9 0 0 PA 10 0.00 None 0 0 0 0 Low 1 0 0 5 Partially 0 1 0 0 0 Cloudy PA 11 0.00 Medium 0 1 0 90 medium 0 1 0 10 Partially 0 1 0 0 0 Cloudy PA 12 0.00 None 0 0 0 0 Low 1 0 0 1 Partially 0 1 0 0 0 Cloudy PA 13 0.00 Low 1 0 0 10 Low 1 0 0 7 Sunny 0 0 0 0 0 PA 14 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 PA 15 0.00 Medium 0 1 0 60 Low 1 0 0 7 Sunny 0 0 0 0 1 PA 16 0.00 Medium 0 1 0 70 Low 1 0 0 1 Sunny 0 0 0 0 0 PA 18 0.00 Medium 0 1 0 30 Low 1 0 0 4 Sunny 0 0 0 0 1 PA 2 0.00 Low 1 0 0 15 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy PA 20 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 PA 21 0.00 Medium 0 1 0 65 Low 1 0 0 4 Sunny 0 0 0 0 1 PA 22 0.00 Medium 0 1 0 100 Low 1 0 0 2 Partially 0 1 0 0 1 Cloudy PA 23 PA 24 0.00 None 0 0 0 0 Low 1 0 0 8 Overcast 1 0 0 0 1 PA 25 0.00 Low 1 0 0 10 medium 0 1 0 20 Overcast 1 0 0 0 1

193

PA 26 0.00 None 0 0 0 0 Low 1 0 0 2 Overcast 1 0 0 0 1 PA 27 0.00 None 0 0 0 0 Low 1 0 0 1 Overcast 1 0 0 0 1 PA 28 0.00 None 0 0 0 0 medium 0 1 0 27 Overcast 1 0 0 0 1 PA 29 0.00 Low 1 0 0 12 medium 0 1 0 26 Overcast 1 0 0 0 1 PA 31 0.00 Low 1 0 0 6 High 0 0 1 30 Sunny 0 0 0 0 0 PA 32 0.00 Low 1 0 0 25 None 0 0 0 0 Overcast 1 0 0 0 1 PA 33 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 PA 34 0.00 Low 1 0 0 5 Low 1 0 0 8 Sunny 0 0 0 0 0 PA 4 0.00 Low 1 0 0 15 Low 1 0 0 2 Sunny 0 0 0 0 1 PA 5 0.00 Low 1 0 0 10 Low 1 0 0 2 Partially 0 1 0 0 0 Cloudy PA 6 0.00 None 0 0 0 0 Low 1 0 0 4 Sunny 0 0 0 0 0 PA 7 0.00 Low 1 0 0 15 Low 1 0 0 5 Sunny 0 0 0 0 0 PA 8 0.00 Low 1 0 0 6 Low 1 0 0 1 Overcast 1 0 0 0 1 PA 9 0.00 High 0 0 1 135 Low 1 0 0 5 Sunny 0 0 0 0 0 PN 1 low 1 0 0 None 0 0 0 0 sunny 0 0 0 0 0 PN 1 0.00 Medium 0 1 0 75 Low 1 0 0 Sunny 0 0 0 0 0 PN 10 0.00 low 1 0 0 6 Low 1 0 0 3 Partially 0 1 0 0 0 Cloudy PN 12 1.00 None 0 0 0 0 Low 1 0 0 10 Overcast 1 0 0 0 1 PN 13 0.00 None 0 0 0 0 medium 0 1 0 30 Overcast 1 0 0 1 1 PN 14 0.00 None 0 0 0 0 Low 1 0 0 4 Partially 0 1 0 0 1 Cloudy PN 15 0.00 None 0 0 0 0 Low 1 0 0 3 Overcast 1 0 0 0 1 PN 16 0.00 Low 1 0 0 8 Low 1 0 0 9 Overcast 1 0 0 0 1 PN 17 0.00 None 0 0 0 0 Low 1 0 0 10 Overcast 1 0 0 0 1 PN 18 0.00 Low 1 0 0 12 medium 0 1 0 15 Overcast 1 0 0 0 1 PN 19 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 PN 2 Medium 0 1 0 20 None 0 0 0 0 sunny 0 0 0 0 0 PN 2 1.00 Medium 0 1 0 24 None 0 0 0 0 Sunny 0 0 0 0 0 PN 20 0.00 None 0 0 0 0 Low 1 0 0 5 Partially 0 1 0 0 1 Cloudy

194

PN 21 0.00 None 0 0 0 0 Low 1 0 0 1 Overcast 1 0 0 0 1 PN 22 0.00 None 0 0 0 0 High 0 0 1 50 Partially 0 1 0 0 1 Cloudy PN 23 0.00 None 0 0 0 0 High 0 0 1 50 Sunny 0 0 0 0 1 PN 24 0.00 None 0 0 0 0 Low 1 0 0 5 Sunny 0 0 0 0 1 PN 26 0.00 None 0 0 0 0 medium 0 1 0 30 Overcast 1 0 0 0 1 PN 27 0.00 None 0 0 0 0 medium 0 1 0 20 Overcast 1 0 0 0 1 PN 28 0.00 None 0 0 0 0 Low 1 0 0 11 Rainy 0 0 1 1 1 PN 3 0.00 Low 1 0 0 13 Low 1 0 0 8 Sunny 0 0 0 0 1 PN 4 1.00 low 1 0 0 Low 1 0 0 sunny 0 0 0 0 1 PN 4 0.00 Low 1 0 0 9 None 0 0 0 0 Overcast 1 0 0 0 1 PN 4 0.00 Low 1 0 0 5 Low 1 0 0 10 Sunny 0 0 0 0 1 PN 5 0.00 low 1 0 0 None 0 0 0 Sunny 0 0 0 0 1 PN 5 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 0 PN 6 0.00 Low 1 0 0 4 Low 1 0 0 1 Sunny 0 0 0 0 0 PN 7 0.00 low 1 0 0 5 None 0 0 0 0 Partially 0 1 0 0 0 Cloudy PN 8 0.00 low 1 0 0 39 Low 1 0 0 1 Partially 0 1 0 0 0 Cloudy PN 9 0.00 None 0 0 0 0 High 0 0 1 35 Overcast 1 0 0 0 0 RQ 1 0.00 None 0 0 0 0 Low 1 0 0 4 sunny 0 0 0 0 0 RQ 10 0.00 None 0 0 0 0 low 1 0 0 3 sunny 0 0 0 0 0 RQ 12 0.00 None 0 0 0 Low 1 0 0 2 Sunny 0 0 0 0 1 RQ 13 0.00 None 0 0 0 0 Low 1 0 0 4 Partially 0 1 0 0 0 Cloudy RQ 14 0.00 Medium 0 1 0 30 Low 1 0 0 2 Overcast 1 0 0 0 1 RQ 14 0.00 Medium 0 1 0 10 Medium 0 1 0 5 Partially 0 1 0 0 0 Cloudy RQ 14 0.00 None 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy RQ 15 0.00 None 0 0 0 0 Low 1 0 0 1 sunny 0 0 0 0 1 RQ 15 0.00 Medium 0 1 0 30 Low 1 0 0 2 sunny 0 0 0 0 1 RQ 16 0.00 None 0 0 0 0 Low 1 0 0 2 Partially 0 1 0 0 0

195

Cloudy RQ 16 0.00 none 0 0 0 0 none 0 0 0 0 sunny 0 0 0 0 0 RQ 17 0.00 None 0 0 0 0 low 1 0 0 1 Overcast 1 0 0 0 1 RQ 18 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 0 RQ 19 0.00 low 1 0 0 5 None 0 0 0 0 Overcast 1 0 0 0 1 RQ 2 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 RQ 20 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 0 RQ 20 0.00 low 1 0 0 3 Low 1 0 0 1 Sunny 0 0 0 0 1 RQ 21 0.00 high 0 0 1 30 None 0 0 0 0 Sunny 0 0 0 0 0 RQ 23 0.00 None 0 0 0 0 low 1 0 0 1 Sunny 0 0 0 0 0 RQ 24 0.00 None 0 0 0 0 medium 0 1 0 8 Overcast 1 0 0 0 1 RQ 25 0.00 None 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy RQ 26 0.00 None 0 0 0 0 low 1 0 0 3 Sunny 0 0 0 0 0 RQ 28 0.00 Medium 0 1 0 10 none 0 0 0 0 Sunny 0 0 0 0 0 RQ 29 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 RQ 3 0.00 None 0 0 0 0 Low 1 0 0 3 Partially 0 1 0 0 0 Cloudy RQ 30 0.00 None 0 0 0 0 low 1 0 0 2 Sunny 0 0 0 0 0 RQ 31 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 RQ 32 0.00 None 0 0 0 0 low 1 0 0 2 Sunny 0 0 0 0 0 RQ 33 0.00 low 1 0 0 2 None 0 0 0 0 Sunny 0 0 0 0 0 RQ 36 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 1 1 RQ 37 0.00 None 0 0 0 0 Low 1 0 0 4 Partially 0 1 0 0 0 Cloudy RQ 38 0.00 None 0 0 0 0 Low 1 0 0 1 Sunny 0 0 0 0 0 RQ 39 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 RQ 39 0.00 None 0 0 0 0 None 0 0 0 0 sunny 0 0 0 0 1 RQ 39 0.00 None 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy RQ 4 0.00 None 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1 RQ 41 0.00 None 0 0 0 0 None 0 0 0 0 Sunny 0 0 0 0 1

196

RQ 42 0.00 None 0 0 0 0 low 1 0 0 2 Sunny 0 0 0 0 1 RQ 43 0.00 low 1 0 0 4 None 0 0 0 0 Partially 0 1 0 0 0 Cloudy RQ 44 0.00 None 0 0 0 0 Low 1 0 0 1 Sunny 0 0 0 0 1 RQ 5 0.00 low 1 0 0 7 None 0 0 0 0 Sunny 0 0 0 0 1 RQ 6 0.00 high 0 0 1 300 medium 0 1 0 7 sunny 0 0 0 0 0 RQ 6 0.00 low 1 0 0 10 None 0 0 0 0 Overcast 1 0 0 0 1 RQ 8 0.00 None 0 0 0 0 None 0 0 0 0 sunny 0 0 0 0 0 SC 1 0.00 low 1 0 0 5 None 0 0 0 0 Sunny 0 0 0 0 1 SC 2 1.00 low 1 0 0 5 none 0 0 0 0 Sunny 0 0 0 0 0 SC 2 low 1 0 0 5 None 0 0 0 0 Sunny 0 0 0 0 0 SC 3 0.00 Medium 0 1 0 6 Low 1 0 0 2 sunny 0 0 0 0 0 SC 3 0.00 Medium 0 1 0 10 None 0 0 0 0 Sunny 0 0 0 0 0 SC 4 0.00 none 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy SC 5 0.00 low 1 0 0 8 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy SC 6 0.00 None 0 0 0 0 None 0 0 0 0 sunny 0 0 0 0 0 SR 1 0.00 low 1 0 0 2 medium 0 1 0 3 sunny 0 0 0 0 1 SR 1 0.00 Medium 0 1 0 50 Low 1 0 0 3 Sunny 0 0 0 0 0 SR 1 0.00 Low 1 0 0 6 Low 1 0 0 2 sunny 0 0 0 1 SR 1 0.00 low 1 0 0 1 Low 1 0 0 1 Overcast 1 0 0 0 SR 1 0.00 low 1 0 0 22 Low 1 0 0 2 Sunny 0 0 0 0 0 SR 10 0.00 low 1 0 0 4 None 0 0 0 0 Overcast 1 0 0 1 0 SR 11 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 1 1 SR 12 0.00 low 1 0 0 3 medium 0 1 0 7 Overcast 1 0 0 0 1 SR 15 0.00 high 0 0 1 50 medium 0 1 0 6 Overcast 1 0 0 0 1 SR 17 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 SR 18 0.00 None 0 0 0 0 medium 0 1 0 6 Overcast 1 0 0 0 1 SR 19 0.00 low 1 0 0 1 None 0 0 0 0 Overcast 1 0 0 1 1 SR 2 0.00 None 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy

197

SR 20 0.00 None 0 0 0 0 none 0 0 0 0 Overcast 1 0 0 1 1 SR 21 0.00 low 1 0 0 7 None 0 0 0 0 sunny 0 0 0 0 1 SR 22 0.00 low 1 0 0 2 None 0 0 0 0 Partially 0 1 0 0 1 Cloudy SR 3 0.00 None 0 0 0 0 Low 1 0 0 2 Partially 0 1 0 0 1 Cloudy SR 4 0.00 Medium 0 1 0 20 Low 1 0 0 1 Sunny 0 0 0 0 0 SR 5 0.00 low 1 0 0 1 Low 1 0 0 1 Partially 0 1 0 0 1 Cloudy SR 6 0.00 low 1 0 0 1 None 0 0 0 0 sunny 0 0 0 0 1 SR 7 0.00 Low 1 0 0 4 none 0 0 0 0 Partially 0 1 0 0 0 Cloudy SR 8 0.00 Medium 0 1 0 12 none 0 0 0 0 Sunny 0 0 0 0 0 SR 9 0.00 None 0 0 0 0 none 0 0 0 0 Sunny 0 0 0 0 0 ST 1 0.00 low 1 0 0 4 Low 1 0 0 2 sunny 0 0 0 0 1 ST 10 0.00 None 0 0 0 0 Low 1 0 0 1 Sunny 0 0 0 0 1 ST 11 0.00 None 0 0 0 0 None 0 0 0 0 Partially 0 1 0 0 0 Cloudy ST 11 ST 12 0.00 Low 1 0 0 5 None 0 0 0 0 Sunny 0 0 0 0 0 ST 13 0.00 None 0 0 0 0 None 0 0 0 0 Overcast 1 0 0 0 1 ST 14 0.00 None 0 0 0 0 Low 1 0 0 2 Overcast 1 0 0 0 1 ST 15 0.00 Low 1 0 0 None 0 0 0 0 Overcast 1 0 0 0 0 ST 16 0.00 High 0 0 1 100 Low 1 0 0 1 Sunny 0 0 0 0 0 ST 2 0.00 None 0 0 0 0 medium 0 1 0 6 Partially 0 1 0 0 1 Cloudy ST 4 0.00 Medium 0 1 0 12 None 0 0 0 0 sunny 0 0 0 0 1 ST 7 0.00 None 0 0 0 0 Low 1 0 0 1 sunny 0 0 0 0 1 ST 8 0.00 Low 1 0 0 12 None 0 0 0 0 Sunny 0 0 0 0 1 ST 9 0.00 Low 1 0 0 5 Low 1 0 0 3 Sunny 0 0 0 0 1

198

Uniqu Wave Wave Wave Wave Wave Wave Floo Amoun Refuse Refuse Refuse Food Med- Sewage House Building Fishing Dead e Id Height Height Heigh Heigh Heigh Height d- t of Amoun Amount Amoun Relate ical - -hold Material Related Fish - Low t - t - t Range ing Refuse t - Low - Med t - High d Refuse Related Waste s Refuse refuse Med High Range To on Refuse Refuse From Beach CHR 1 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 CHR 2 None 0 0 0 0.0 0.0 0 Low 1 0 0 0 0 0 0 1 0 0 CHR 3 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 CHR 4 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 1 0 0 CHR 5 low 1 0 0 0.1 0.2 0 Low 1 0 0 1 FH 1 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 FH 2 Low 1 0 0 0.0 0.5 0 Low 1 0 0 1 FH 2 low 1 0 0 0.0 0.5 0 None 0 0 0 0 0 0 0 0 0 0 FH 3 low 1 0 0 0.0 0.1 0 None 0 0 0 FH 4 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 1 0 0 0 HL 1 None 0 0 0 0.0 0.0 0 none 0 0 0 0 0 0 0 0 0 0 HL 10 None 0 0 0 0 Low 1 0 0 1 HL 11 None 0 0 0 0.0 0.0 0 None 0 0 0 HL 2 low 1 0 0 0.0 0.5 0 Low 1 0 0 1 HL 3 low 1 0 0 0.0 0.1 0 None 0 0 0 HL 4 None 0 0 0 0.0 0.0 1 None 0 0 0 0 0 0 0 0 0 0 HL 5 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 HL 6 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 HL 7 Low 1 0 0 0.2 0.4 0 Low 1 0 0 1 KT 10 Low 1 0 0 0.0 0.5 0 None 0 0 0 0 0 0 0 0 0 0 KT 11 low 1 0 0 0.0 0.3 0 Low 1 0 0 1 0 0 0 0 0 0 KT 12 Mediu 0 1 0 0.0 1.0 0 None 0 0 0 0 0 0 0 0 0 0 m KT 13 Mediu 0 1 0 0.0 1.0 0 None 0 0 0 0 0 0 0 0 0 0 m KT 4 None 0 0 0 0.0 0.0 1 Low 1 0 0 1 1 KT 5 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1

199

KT 6 Low 1 0 0 0.0 0.1 0 Low 1 0 0 1 KT 7 Low 1 0 0 0.0 0.3 1 Low 1 0 0 1 KT 8 None 0 0 0 0 None 0 0 0 KT 8 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 1 KT 9 Low 1 0 0 0.0 0.1 0 Low 1 0 0 1 1 MC 1 Low 1 0 0 0.01 0.02 MC 1 Mediu 0 1 0 0.3 0.3 m MC 1 Low 1 0 0 0.0 0.03 MC 1 Low 1 0 0 0.0 0.02 MC 1 Low 1 0 0 0.0 0.05 MC 10 Low 1 0 0 0 0 0 1 0 0 0 MC 2 Low 1 0 0 0.0 0.5 1 low 1 0 0 1 0 0 0 1 0 0 MC 2 None 0 0 0 0.0 0.0 0 low 1 0 0 0 0 0 0 0 0 0 MC 3 Low 1 0 0 0.0 0.2 none 0 0 0 0 0 0 0 0 0 0 MC 3 low 1 0 0 0.2 0.2 MC 3 none 0 0 0 0.0 0.0 MC 3 Mediu 0 1 0 0.2 0.4 m MC 3 None 0 0 0 0.0 0.0 MC 4 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 1 1 MC 5 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 MC 6 None 0 0 0 0.0 0.0 None 0 0 0 0 0 0 0 0 0 0 MC 6 None 0 0 0 0.0 0.0 MC 6 None 0 0 0 0.0 0.0 MC 6 None 0 0 0 0.0 0.0 MC 6 none 0 0 0 0.0 0.0 MC 7 MC 7 none 0 0 0 0.0 0.0 low 1 0 0 MC 7 low 1 0 0 0.1 0.2 MC 7 none 0 0 0 0.0 0.0

200

MC 7 None 0 0 0 0.0 0.0 MC 8 none 0 0 0 0.0 0.0 MC 8 None 0 0 0 0.0 0.0 MC 8 none 0 0 0 0.0 0.0 MC 8 None 0 0 0 0.0 0.0 MC 8 none 0 0 0 0.0 0.0 Medium 0 1 0 1 MC 9 None 0 0 0 0.0 0.0 none 0 0 0 0 0 0 0 0 0 0 MC 9 None 0 0 0 0.0 0.0 MC 9 Mediu 0 1 0 0.2 0.3 m MC 9 none 0 0 0 0.0 0.0 PA 10 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 11 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 12 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 13 None 0 0 0 0.0 0.0 1 Low 1 0 0 1 1 PA 14 Low 1 0 0 0.0 0.4 1 Low 1 0 0 1 PA 15 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 16 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 18 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 2 Low 1 0 0 0.0 0.3 0 Low 1 0 0 1 1 PA 20 Low 1 0 0 0.0 0.4 0 Low 1 0 0 1 1 PA 21 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 22 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 PA 23 PA 24 None 0 0 0 0.0 0.0 1 Low 1 0 0 1 1 1 PA 25 None 0 0 0 0.0 0.0 1 Low 1 0 0 1 PA 26 None 0 0 0 0.0 0.0 0 None 0 0 0 PA 27 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 PA 28 None 0 0 0 0.0 0.0 1 Low 1 0 0 1 1 PA 29 Low 1 0 0 0.0 0.4 0 Low 1 0 0 1 PA 31 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 1

201

PA 32 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 33 Low 1 0 0 0.0 0.1 1 Low 1 0 0 1 1 PA 34 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 1 PA 4 Mediu 0 1 0 0.0 0.8 0 Low 1 0 0 1 1 1 m PA 5 Low 1 0 0 0.0 0.4 1 Low 1 0 0 1 1 PA 6 Low 1 0 0 0.0 0.2 1 Low 1 0 0 1 PA 7 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 8 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PA 9 Low 1 0 0 0.0 0.3 0 Low 1 0 0 1 1 PN 1 Low 1 0 0 0.0 0.1 0 Low 1 0 0 0 0 0 1 0 0 0 PN 1 None 0 0 0 0 None 0 0 0 0 0 0 0 0 0 0 PN 10 Low 1 0 0 0.0 0.8 0 None 0 0 0 PN 12 Mediu 0 1 0 0.0 0.5 0 Low 1 0 0 1 1 m PN 13 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 14 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 15 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 1 PN 16 Low 1 0 0 0.0 0.1 0 Low 1 0 0 1 1 PN 17 Low 1 0 0 0.0 0.3 0 Low 1 0 0 1 1 1 PN 18 Low 1 0 0 0.0 0.2 0 Low 1 0 0 1 1 PN 19 Low 1 0 0 0.0 0.2 1 None 0 0 0 PN 2 None 0 0 0 0.0 0.0 0 none 0 0 0 0 0 0 0 0 0 0 PN 2 None 0 0 0 0 None 0 0 0 PN 20 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 21 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 22 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 PN 23 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 PN 24 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 1 PN 26 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 27 None 0 0 0 0.0 0.0 0 None 0 0 0

202

PN 28 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 3 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 4 Low 1 0 0 0.1 0.2 0 None 0 0 0 0 0 0 0 0 0 0 PN 4 0 None 0 0 0 PN 4 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 PN 5 low 1 0 0 0.0 0.1 1 None 0 0 0 0 0 0 0 0 0 0 PN 5 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 PN 6 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 PN 7 low 1 0 0 0.0 0.5 0 Low 1 0 0 1 PN 8 Low 1 0 0 0.0 0.5 0 Low 1 0 0 1 PN 9 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 RQ 1 None 0 0 0 0.0 0.0 0 None 0 0 0 RQ 10 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 RQ 12 Low 1 0 0 0.0 0.1 0 None 0 0 0 RQ 13 low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 0 1 0 0 RQ 14 Low 1 0 0 0.0 0.5 0 none 0 0 0 RQ 14 0 None 0 0 0 0 0 0 0 0 0 0 RQ 14 low 1 0 0 0.0 0.1 0 None 0 0 0 0 0 0 0 0 0 0 RQ 15 low 1 0 0 0.2 0.4 0 None 0 0 0 0 0 0 0 0 0 0 RQ 15 low 1 0 0 0 Low 1 0 0 0 0 0 1 0 0 0 RQ 16 low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 0 0 0 0 RQ 16 none 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 1 0 0 RQ 17 None 0 0 0 0.0 0.0 0 low 1 0 0 1 RQ 18 Low 1 0 0 0.0 0.1 0 Low 1 0 0 1 RQ 19 Low 1 0 0 0.1 0.3 0 Low 1 0 0 1 0 0 0 0 0 0 RQ 2 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 20 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 1 0 0 0 RQ 20 Mediu 0 1 0 0 Medium 0 1 0 0 0 0 1 0 0 0 m RQ 21 Low 1 0 0 0.1 0.3 0 None 0 0 0 RQ 23 low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 0 0 0 0

203

RQ 24 None 0 0 0 0.0 0.0 0 low 1 0 0 1 RQ 25 Low 1 0 0 0.0 0.5 0 None 0 0 0 0 RQ 26 low 1 0 0 0.0 0.5 0 None 0 0 0 0 0 0 0 0 0 0 RQ 28 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 29 low 1 0 0 0.0 0.1 0 low 1 0 0 1 0 0 0 0 0 0 RQ 3 None 0 0 0 0 None 0 0 0 RQ 30 low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 1 0 0 0 RQ 31 Mediu 0 1 0 0.3 0.5 0 Low 1 0 0 1 m RQ 32 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 33 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 36 Low 1 0 0 0.0 0.5 0 None 0 0 0 RQ 37 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 38 low 1 0 0 0.0 0.2 0 Medium 0 1 0 1 0 0 0 0 0 0 RQ 39 low 1 0 0 0.0 0.2 0 None 0 0 0 0 0 0 0 0 0 0 RQ 39 Mediu 0 1 0 0 Low 1 0 0 0 0 0 0 1 0 0 m RQ 39 low 1 0 0 0.2 0.4 0 None 0 0 0 0 0 0 0 0 0 0 RQ 4 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 RQ 41 Mediu 0 1 0 0.4 0.6 0 low 1 0 0 1 0 0 0 0 0 0 m RQ 42 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 RQ 43 Low 1 0 0 0.0 0.1 0 None 0 0 0 0 0 0 0 0 0 0 RQ 44 None 0 0 0 0.0 0.0 0 Medium 0 1 0 1 0 0 0 0 0 0 RQ 5 Low 1 0 0 0.0 0.1 0 None 0 0 0 RQ 6 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 RQ 6 Mediu 0 1 0 0.3 0.5 0 None 0 0 0 m RQ 8 None 0 0 0 0.0 0.0 1 low 1 0 0 1 SC 1 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 SC 2 Low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 0 0 0 0 SC 2 None 0 0 0 0 low 1 0 0 1 0 0 0 0 0 0

204

SC 3 Low 1 0 0 0.0 0.5 0 None 0 0 0 SC 3 None 0 0 0 0 Low 1 0 0 1 SC 4 low 1 0 0 0.1 0.3 0 Low 1 0 0 1 0 0 0 0 0 0 SC 5 low 1 0 0 0.0 0.1 0 low 1 0 0 1 0 0 0 0 0 0 SC 6 Low 1 0 0 0.0 0.5 0 none 0 0 0 0 0 0 0 0 0 0 SR 1 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 SR 1 low 1 0 0 0.3 0.5 0 None 0 0 0 0 0 0 0 0 0 0 SR 1 low 1 0 0 0.3 0.4 0 low 1 0 0 0 0 0 0 0 0 1 SR 1 low 1 0 0 0.2 0.3 0 None 0 0 0 0 0 0 0 0 0 0 SR 1 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 SR 10 Low 1 0 0 0.0 0.5 0 None 0 0 0 0 0 0 0 0 0 0 SR 11 Low 1 0 0 0.0 0.5 0 low 1 0 0 0 0 0 0 0 1 0 SR 12 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 0 0 0 0 0 0 SR 15 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 SR 17 Low 1 0 0 0.0 0.5 0 Low 1 0 0 1 0 0 0 0 0 0 SR 18 low 1 0 0 0.0 0.5 0 low 1 0 0 0 0 0 1 0 0 0 SR 19 low 1 0 0 0.0 0.5 0 Low 1 0 0 1 0 0 0 0 1 1 SR 2 Low 1 0 0 0.1 0.2 0 None 0 0 0 0 0 0 0 0 0 0 SR 20 Low 1 0 0 0.0 0.5 0 low 1 0 0 1 0 0 0 0 0 0 SR 21 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 0 0 0 0 0 0 SR 22 Low 1 0 0 0.0 0.1 0 None 0 0 0 0 0 0 0 0 0 0 SR 3 Low 1 0 0 0.1 0.3 0 None 0 0 0 0 0 0 0 0 0 0 SR 4 None 0 0 0 0.0 0.0 1 None 0 0 0 0 0 0 0 0 0 0 SR 5 low 1 0 0 0.0 0.1 0 None 0 0 0 0 0 0 0 0 0 0 SR 6 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 0 0 0 0 1 0 SR 7 none 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 SR 8 None 0 0 0 0.0 0.0 0 low 1 0 0 1 0 0 0 0 0 0 SR 9 None 0 0 0 0.0 0.0 1 None 0 0 0 0 0 0 0 0 0 0 ST 1 Low 1 0 0 0.0 0.1 0 None 0 0 0 ST 10 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 ST 11 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 0 0 0 0 0 0

205

ST 11 ST 12 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 ST 13 Low 1 0 0 0.0 0.5 0 Low 1 0 0 1 1 ST 14 None 0 0 0 0 None 0 0 0 ST 15 None 0 0 0 0.0 0.0 0 None 0 0 0 0 0 0 0 0 0 0 ST 16 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 ST 2 none 0 0 0 0.0 0.0 0 Medium 0 1 0 1 1 1 1 ST 4 Low 1 0 0 0.0 0.1 0 Low 1 0 0 1 1 ST 7 None 0 0 0 0.0 0.0 0 None 0 0 0 ST 8 None 0 0 0 0.0 0.0 0 Low 1 0 0 1 1 ST 9 None 0 0 0 0.0 0.0 0 Low 1 0 0 1

206

Unique Beach Beach Beach Seaweed/Al Beach Beach Beach Seaweed/Al Swimming Swimming Swimming Samp Identifi Groomin Groomi Groomi gae on Seaweed&Al Seaweed&Al Seaweed&Al gae In Area Area Area le 1 er g ng last ng more Beach gae Amount - gae Amount - gae Amount - Swimming Seaweed&Al Seaweed&Al Seaweed&Al GPS 24 hours than 24 Low Med High Area gae Amount - gae Amount - gae Amount - hours Low Med High

CHR 1 >24 0 1low 1 0 0 low 1 0 0 hours CHR 2 >24 0 1low 1 0 0 low 1 0 0 hours CHR 3 None 0 0None 0 0 0 None 0 0 0 CHR 4 None 0 0low 1 0 0 low 1 0 0 CHR 5 None 0 0None 0 0 0 None 0 0 0 FH 1 >24 0 1 Medium 0 1 0 medium 0 1 0 hours FH 2 none 0 0none 0 0 0 none 0 0 0 FH 2 None 0 0low 1 0 0 low 1 0 0 FH 3 None 0 0None 0 0 0 None 0 0 0 FH 4 None 0 0low 1 0 0 low 1 0 0 HL 1 last 24 1 0low 1 0 0 low 1 0 0 hours HL 10 Last 24 1 0None 0 0 0 None 0 0 0 hours HL 11 None 0 0None 0 0 0 None 0 0 0 HL 2 None 0 0 Medium 0 1 0 low 1 0 0 HL 3 Last 24 1 0None 0 0 0 None 0 0 0 hours HL 4 None 0 0 Low 1 0 0 low 1 0 0 HL 5 None 0 0 None 0 0 0 low 1 0 0 HL 6 None 0 0None 0 0 0 None 0 0 0 HL 7 None 0 0None 0 0 0 None 0 0 0 KT 10 >24 0 1 None 0 0 0 low 1 0 0

207

hours KT 11 >24 0 1 None 0 0 0 none 0 0 0 hours KT 12 none 0 0none 0 0 0 none 0 0 0 KT 13 None 0 0None 0 0 0 None 0 0 0 KT 4 none 0 0 medium 0 1 0 Medium 0 1 0 KT 5 Last 24 1 0 Low 1 0 0 High 0 0 1 Hours KT 6 Last 24 1 0 High 0 0 1 Medium 0 1 0 Hours KT 7 none 0 0High 0 0 1 High 0 0 1 KT 8 Last 24 1 0None 0 0 0 None 0 0 0 hours KT 8 none 0 0 Low 1 0 0 Medium 0 1 0 KT 9 Last 24 1 0High 0 0 1 High 0 0 1 Hours MC 1 None 0 0 0 Medium 0 1 0 MC 1 MC 1 MC 1 MC 1 MC 10 none 0 0 0 none 0 0 0 MC 2 low 1 0 0 low 1 0 0 MC 2 None 0 0None 0 0 0 None 0 0 0 MC 3 low 1 0 0 MC 3 MC 3 MC 3 MC 3 MC 4 none 0 0 medium 0 1 0 High 0 0 1 MC 5 none 0 0 None 0 0 0 Low 1 0 0 MC 6 low 1 0 0

208

MC 6 MC 6 MC 6 MC 6 MC 7 MC 7 none 0 0 0 MC 7 MC 7 MC 7 MC 8 MC 8 MC 8 MC 8 MC 8 none 0 0 0 MC 9 MC 9 MC 9 MC 9 PA 10 none 0 0 None 0 0 0 Low 1 0 0 PA 11 none 0 0 Low 1 0 0 Medium 0 1 0 PA 12 none 0 0 High 0 0 1 Low 1 0 0 PA 13 none 0 0 medium 0 1 0 High 0 0 1 PA 14 none 0 0 Low 1 0 0 Medium 0 1 0 PA 15 none 0 0Low 1 0 0 Low 1 0 0 PA 16 >24 0 1Low 1 0 0 Low 1 0 0 hours PA 18 none 0 0 Low 1 0 0 Medium 0 1 0 PA 2 none 0 0Low 1 0 0 Low 1 0 0 PA 20 none 0 0 Low 1 0 0 Medium 0 1 0 PA 21 none 0 0Low 1 0 0 Low 1 0 0 PA 22 none 0 0 Low 1 0 0 Medium 0 1 0

209

PA 23 PA 24 none 0 0Low 1 0 0 Low 1 0 0 PA 25 >24 0 1 Low 1 0 0 Medium 0 1 0 hours PA 26 none 0 0High 0 0 1 High 0 0 1 PA 27 >24 0 1 medium 0 1 0 Low 1 0 0 hours PA 28 none 0 0 Low 1 0 0 Medium 0 1 0 PA 29 none 0 0 medium 0 1 0 Medium 0 1 0 PA 31 none 0 0 Low 1 0 0 High 0 0 1 PA 32 none 0 0 medium 0 1 0 High 0 0 1 PA 33 none 0 0 Low 1 0 0 Medium 0 1 0 PA 34 >24 0 1 Low 1 0 0 Medium 0 1 0 hours PA 4 none 0 0Low 1 0 0 Low 1 0 0 PA 5 none 0 0 medium 0 1 0 Medium 0 1 0 PA 6 none 0 0 medium 0 1 0 Low 1 0 0 PA 7 Last 24 1 0Low 1 0 0 Low 1 0 0 Hours PA 8 Last 24 1 0 None 0 0 0 Low 1 0 0 Hours PA 9 none 0 0Low 1 0 0 Low 1 0 0 PN 1 None 0 0 low 1 0 0 None 0 0 0 PN 1 None 0 0 Low 1 0 0 None 0 0 0 PN 10 >24 0 1none 0 0 0 none 0 0 0 hours PN 12 none 0 0Low 1 0 0 Low 1 0 0 PN 13 none 0 0Low 1 0 0 Low 1 0 0 PN 14 Last 24 1 0Low 1 0 0 Low 1 0 0 Hours PN 15 >24 0 1 medium 0 1 0 High 0 0 1 hours PN 16 >24 0 1 Low 1 0 0 Medium 0 1 0 hours 210

PN 17 Last 24 1 0Low 1 0 0 Low 1 0 0 Hours PN 18 none 0 0Low 1 0 0 Low 1 0 0 PN 19 none 0 0 High 0 0 1 Medium 0 1 0 PN 2 None 0 0 None 0 0 0 low 1 0 0 PN 2 None 0 0 0 None 0 0 0 PN 20 none 0 0 High 0 0 1 Medium 0 1 0 PN 21 none 0 0 High 0 0 1 Medium 0 1 0 PN 22 none 0 0Low 1 0 0 Low 1 0 0 PN 23 >24 0 1None 0 0 0 None 0 0 0 hours PN 24 none 0 0 High 0 0 1 Medium 0 1 0 PN 26 none 0 0 High 0 0 1 Medium 0 1 0 PN 27 none 0 0Low 1 0 0 Low 1 0 0 PN 28 none 0 0 Low 1 0 0 Medium 0 1 0 PN 3 Last 24 1 0 None 0 0 0 Low 1 0 0 Hours PN 4 Last 24 1 0 low 1 0 0 medium 0 1 0 hours PN 4 Last 24 1 0Low 1 0 0 Low 1 0 0 hours PN 4 none 0 0High 0 0 1 High 0 0 1 PN 5 Last 24 1 0None 0 0 0 None 0 0 0 hours PN 5 Last 24 1 0None 0 0 0 None 0 0 0 hours PN 6 none 0 0Low 1 0 0 Low 1 0 0 PN 7 none 0 0 low 1 0 0 none 0 0 0 PN 8 Last 24 1 0low 1 0 0 low 1 0 0 hours PN 9 none 0 0High 0 0 1 High 0 0 1 RQ 1 None 0 0None 0 0 0 None 0 0 0 RQ 10 None 0 0low 1 0 0 low 1 0 0

211

RQ 12 None 0 0None 0 0 0 None 0 0 0 RQ 13 None 0 0low 1 0 0 low 1 0 0 RQ 14 last 24 1 0None 0 0 0 None 0 0 0 hours RQ 14 Last 24 1 0High 0 0 1 High 0 0 1 hours RQ 14 None 0 0None 0 0 0 None 0 0 0 RQ 15 None 0 0 Medium 0 1 0 high 0 0 1 RQ 15 >24 0 1 High 0 0 1 low 1 0 0 hours RQ 16 >24 0 1 Medium 0 1 0 high 0 0 1 hours RQ 16 >24 0 1low 1 0 0 low 1 0 0 hours RQ 17 None 0 0 None 0 0 0 low 1 0 0 RQ 18 None 0 0None 0 0 0 None 0 0 0 RQ 19 None 0 0None 0 0 0 None 0 0 0 RQ 2 None 0 0 none 0 0 0 None 0 0 0 RQ 20 None 0 0low 1 0 0 low 1 0 0 RQ 20 >24 0 1 Medium 0 1 0 high 0 0 1 hours RQ 21 None 0 0None 0 0 0 None 0 0 0 RQ 23 None 0 0low 1 0 0 low 1 0 0 RQ 24 None 0 0low 1 0 0 low 1 0 0 RQ 25 Last 24 1 0None 0 0 0 None 0 0 0 hours RQ 26 None 0 0low 1 0 0 low 1 0 0 RQ 28 none 0 0none 0 0 0 none 0 0 0 RQ 29 None 0 0low 1 0 0 low 1 0 0 RQ 3 None 0 0 0 None 0 0 0 RQ 30 >24 0 1low 1 0 0 low 1 0 0 hours RQ 31 None 0 0None 0 0 0 None 0 0 0

212

RQ 32 None 0 0 low 1 0 0 high 0 0 1 RQ 33 Last 24 1 0None 0 0 0 None 0 0 0 hours RQ 36 >24 0 1None 0 0 0 None 0 0 0 hours RQ 37 None 0 0None 0 0 0 None 0 0 0 RQ 38 None 0 0 Medium 0 1 0 medium 0 1 0 RQ 39 None 0 0 Medium 0 1 0 high 0 0 1 RQ 39 None 0 0 Medium 0 1 0 low 1 0 0 RQ 39 None 0 0 Medium 0 1 0 low 1 0 0 RQ 4 None 0 0None 0 0 0 None 0 0 0 RQ 41 None 0 0low 1 0 0 low 1 0 0 RQ 42 Last 24 1 0 low 1 0 0 high 0 0 1 hours RQ 43 None 0 0None 0 0 0 None 0 0 0 RQ 44 None 0 0 Medium 0 1 0 high 0 0 1 RQ 5 None 0 0None 0 0 0 None 0 0 0 RQ 6 >24 0 1None 0 0 0 None 0 0 0 hours RQ 6 Last 24 1 0None 0 0 0 None 0 0 0 hours RQ 8 None 0 0None 0 0 0 None 0 0 0 SC 1 Last 24 1 0 None 0 0 0 low 1 0 0 hours SC 2 none 0 0none 0 0 0 none 0 0 0 SC 2 >24 0 1 low 1 0 0 Medium 0 1 0 hours SC 3 >24 0 1low 1 0 0 low 1 0 0 hours SC 3 None 0 0low 1 0 0 low 1 0 0 SC 4 None 0 0low 1 0 0 low 1 0 0 SC 5 Last 24 1 0low 1 0 0 low 1 0 0 hours SC 6 None 0 0low 1 0 0 low 1 0 0

213

SR 1 None 0 0low 1 0 0 low 1 0 0 SR 1 None 0 0 0 None 0 0 0 SR 1 none 0 0 0 low 1 0 0 SR 1 low 1 0 0 low 1 0 0 SR 1 None 0 0 0 None 0 0 0 SR 10 None 0 0None 0 0 0 None 0 0 0 SR 11 None 0 0 Medium 0 1 0 medium 0 1 0 SR 12 None 0 0low 1 0 0 low 1 0 0 SR 15 >24 0 1None 0 0 0 None 0 0 0 hours SR 17 >24 0 1None 0 0 0 None 0 0 0 hours SR 18 >24 0 1None 0 0 0 None 0 0 0 hours SR 19 >24 0 1None 0 0 0 None 0 0 0 hours SR 2 None 0 0 Medium 0 1 0 medium 0 1 0 SR 20 None 0 0None 0 0 0 None 0 0 0 SR 21 None 0 0low 1 0 0 low 1 0 0 SR 22 None 0 0low 1 0 0 low 1 0 0 SR 3 Last 24 1 0Medium 0 1 0 Medium 0 1 0 hours SR 4 None 0 0None 0 0 0 None 0 0 0 SR 5 None 0 0None 0 0 0 None 0 0 0 SR 6 Last 24 1 0None 0 0 0 None 0 0 0 hours SR 7 >24 0 1 None 0 0 0 low 1 0 0 hours SR 8 >24 0 1none 0 0 0 none 0 0 0 hours SR 9 none 0 0none 0 0 0 none 0 0 0 ST 1 Last 24 1 0 Medium 0 1 0 high 0 0 1 hours ST 10 none 0 0 Low 1 0 0 Medium 0 1 0

214

ST 11 >24 0 1 Medium 0 1 0 high 0 0 1 hours ST 11 ST 12 Last 24 1 0 Low 1 0 0 Medium 0 1 0 Hours ST 13 >24 0 1None 0 0 0 None 0 0 0 hours ST 14 None 0 0None 0 0 0 None 0 0 0 ST 15 >24 0 1 None 0 0 0 low 1 0 0 hours ST 16 none 0 0 Low 1 0 0 Medium 0 1 0 ST 2 none 0 0Medium 0 1 0 Medium 0 1 0 ST 4 >24 0 1 low 1 0 0 high 0 0 1 hours ST 7 None 0 0 Medium 0 1 0 medium 0 1 0 ST 8 Last 24 1 0 Low 1 0 0 Medium 0 1 0 Hours ST 9 Last 24 1 0 None 0 0 0 High 0 0 1 Hours

215

Unique 1 1 1 1 1 1 1 1 1 1 2 2 2 2 Ecoli 2 Coli- 2 3 NTU 3 3 3 ID NTU Depth Algae Micro- Ecoli Colif- Fecal pH Alkal- Condu- NTU Depth Algae forms Fecal Depth Algae Ecoli cystin orms Strep inity ctivity Strep CHR 1 4.5 1.2 1 <0.1 <10 1081 1 9.1 321 681 3.6 1.2 1 <10 749 4.8 1.2 1 <10 CHR 2 10 1.2 1 1 <10 1553 9.1 321 685 13.4 1.2 1 <10 727 7.4 1.2 1 <10 CHR 3 4.7 1.2 0 <0.1 <10 1515 0 9.1 321 825 4.4 1.2 0 10 1607 4.5 1.2 0 <10 CHR 4 4.8 1.2 1 <0.1 10 12997 9 320 689 5.7 1.2 1 10 19863 4.5 1.2 1 31 CHR 5 288 1.2 0 <0.1 209 5794 8.2 152 361 302.0 1.2 0 285 6867 302.0 1.2 0 389 FH 1 15.2 1.2 1 1 20 8164 9.1 139 593 14.7 1.2 1 10 12033` 16.5 1.2 1 <10 FH 2 114 1.2 0 5.9 31 58300 9 903 3091 113.0 1.2 0 <10 90600 97.4 1.2 0 10 FH 2 1.5 2.8 <10 19863 60 9 909 3039 1.5 <10 69700 40 1.5 10 FH 3 21.8 1.2 0 <0.1 41 591 8.4 139 403 21.3 1.2 0 10 512 20.6 1.2 0 86 FH 4 2.7 1.2 1 0.2 10 1450 8.6 134 864 2.3 1.2 0 20 1296 2.3 1.2 0 41 HL 1 1.5 0 <10 11199 10 1.3 0 10 9804 10 1.8 0 <10 HL 10 1.5 1.2 0 <0.1 <10 1850 8.7 147 510 1.3 1.2 0 20 1401 1.4 1.2 0 <10 HL 11 2.7 1.2 0 <0.1 10 1720 8.7 145 437 2.4 1.2 0 20 1333 2.8 1.2 0 <10 HL 2 6.9 1.2 0 <0.1 41 1872 8.4 141 415 6.5 1.2 0 10 959 6.9 1.2 0 <10 HL 3 122 1.2 0 <0.1 41 4352 8.1 138 341 120.0 1.2 0 86 1956 120.0 1.2 0 109 HL 4 1.0 1 <0.1 <10 5012 <10 8.9 473 1530 1.0 0 10 691 1.0 0 <10 HL 5 1.2 0 10 160 <10 1.2 0 10 85 1.2 0 <10 HL 6 211 1.2 0 <0.1 31 2481 8.2 150 353 202.0 1.2 0 86 1842 192.0 1.2 0 132 HL 7 213 1.2 0 <0.1 110 63100 8.2 148 350 199.0 1.2 0 121 27900 184.0 1.2 0 181 KT 10 1.0 0 <0.1 <10 836 8.6 163 460 1.0 0 <10 624 1.0 0 <10 KT 11 1.0 0 <0.1 <10 1722 10 8.4 164 461 1.0 0 <10 1455 10 1.0 0 10 KT 12 1.2 0 <0.1 <10 637 8.5 161 300 1.2 0 <10 759 1.2 0 <10 KT 13 1.2 0 <0.1 <10 323 8.5 164 298 1.2 0 <10 132 1.2 0 <10 KT 4 1 1.4 0 <0.1 <10 75 8.8 189 820 1.9 1.4 0 <10 97 1.1 1.4 0 <10 KT 5 1.4 0 1 10 408 8.7 228 784 1.4 0 <10 813 1.4 0 <10 KT 6 3.3 1.4 0 <0.1 63 637 8.5 185 626 3.2 1.4 0 30 776 3.0 1.4 0 31 KT 7 1.2 1 <0.1 <10 4611 8.8 444 6966 1.2 1 <10 12997 1.2 1 <10 KT 8 1 <0.1 <10 14136 <10 8.8 364 3391 1 <10 19863 1 <10

216

KT 8 1.4 0 <0.1 <10 722 9 382 3211 1.4 0 <10 1396 1.4 0 <10 KT 9 1.3 0 <0.1 <10 2603 8.7 220 897 1.3 0 <10 2282 1.3 0 <10 MC 1 7.1 1.0 1 <0.1 <10 86 <10 8.2 63.9 127 4.9 1.0 1 <10 108 <10 4.2 1.0 1 <10 MC 1 6.3 1.0 <10 228 20 8.3 63.5 126 5.8 1.0 <10 121 6.6 1.0 <10 MC 1 4.6 1.0 <0.1 <10 305 <10 8 64.3 129 3.8 1.0 <10 393 4.1 1.0 <10 MC 1 3.8 1.0 <0.1 <10 228 <10 8.3 65.2 130 3.1 1.0 <10 175 3.9 1.0 <10 MC 1 3.4 1.0 <0.1 <10 480 <10 8.3 64.5 130 3.8 1.0 <10 359 3.7 1.0 <10 MC 10 1.2 1.8 10 183 <10 8.3 86.3 174 1.2 <10 272 1.2 <10 MC 2 <0.1 <10 749 <10 8.2 98.3 206 <10 573 <10 MC 2 1.2 <0.1 <10 231 <10 8.4 101 212 <10 160 10 MC 3 3.3 1.0 <0.1 <10 727 <10 8.1 58 123 4.2 1.0 <10 1012 <10 3.3 1.0 <10 MC 3 5 1.0 <0.1 <10 1515 <10 8.3 57.7 121 3.3 1.0 <10 1274 3.0 1.0 <10 MC 3 3.4 1.0 <0.1 <10 399 <10 8.4 58.8 123 3.4 1.0 <10 839 3.2 1.0 <10 MC 3 2.3 1.0 <10 1935 10 1.4 1.0 <10 836 2.3 1.0 10 MC 3 1.0 <0.1 <10 1789 <10 8.2 58.9 125 1.0 <10 1500 1.0 <10 MC 4 1.4 0 <0.1 52 1860 8.2 105 202 1.4 0 10 1616 1.4 0 63 MC 5 1.4 0 0.1 <10 233 8.2 87.6 173 1.4 0 <10 86 1.4 0 <10 MC 6 2.9 1.0 <0.1 <10 495 <10 7.9 59.9 125 2.6 1.0 <10 374 <10 3.2 1.0 <10 MC 6 2.3 1.0 <0.1 <10 2481 <10 8.1 61.6 130 2.3 1.0 <10 1664 2.3 1.0 <10 MC 6 0.9 1.0 <0.1 <10 857 <10 8.4 59.9 126 2.5 1.0 <10 744 2.8 1.0 <10 MC 6 3.1 1.0 <0.1 10 1723 <10 8 59.4 124 3.4 1.0 <10 1565 4.4 1.0 10 MC 6 2.9 1.0 <10 2613 2.7 1.0 <10 1014 <10 2.4 1.0 <10 MC 7 0.6 1.0 <0.1 <10 465 10 8.4 8.5 164 0.9 1.0 <10 602 0.5 1.0 <10 MC 7 2.1 1.0 <0.1 41 393 20 8.2 84.2 162 2.1 1.0 <10 309 <10 1.0 1.0 10 MC 7 0.7 1.0 <0.1 <10 538 <10 8.4 85.4 165 0.6 1.0 <10 703 0.7 1.0 <10 MC 7 0.6 1.0 <0.1 <10 521 <10 8.3 85.4 170 0.6 1.0 <10 11199 0.6 1.0 <10 MC 7 1.9 1.0 <10 1989 <10 0.9 1.0 10 1187 1.0 1.0 10 MC 8 9.8 1.0 <.1 <10 1187 <10 7.9 60.2 120 7.0 1.0 <10 2851 7.0 1.0 <10 MC 8 7.2 1.0 <0.1 20 7701 7.9 60.9 119 7.6 1.0 <10 4352 7.5 1.0 <10 MC 8 11.8 1.0 <0.1 <10 1010 10 8.1 60.2 118 7.5 1.0 <10 798 7.9 1.0 <10 MC 8 8.4 1.0 <0.1 <10 228 <10 8.7 58.2 113 7.8 1.0 <10 272 8.6 1.0 <10

217

MC 8 7.8 1.0 1 <10 218 <10 8 58.7 116 8.0 1.0 <10 142 <10 9.0 1.0 <10 MC 9 1.5 1.0 <0.1 10 31 <10 8.5 173 317 1.6 1.0 <10 75 1.7 1.0 <10 MC 9 1.3 1.0 1 <10 63 <10 8.7 173 311 1.3 1.0 10 249 1.2 1.0 <10 MC 9 8.1 1.0 <0.1 <10 1130 20 8.5 169 314 12.6 1.0 20 1223 11.6 1.0 63 MC 9 1.6 1.0 <0.1 <10 512 <10 8.6 169 313 1.5 1.0 <10 384 1.7 1.0 <10 PA 10 1.4 0 1 173 216 8.9 330 817 1.4 0 <10 41 1.4 0 <10 PA 11 1.4 0 1 20 243 8.7 295 717 1.4 0 20 146 1.4 0 <10 PA 12 1.4 0 <0.1 <10 63 8.7 233 453 1.4 <10 41 1.4 <10 PA 13 1.4 0 1 <10 20 8.8 295 713 1.4 0 <10 20 1.4 0 <10 PA 14 1.4 <0.1 <10 1250 9 419 1084 1.4 <10 1333 1.4 <10 PA 15 1.4 0 <0.1 20 435 9 415 1105 1.4 0 31 393 1.4 0 10 PA 16 1.4 0 <0.1 <10 75 8.6 252 711 1.4 0 <10 275 1.4 0 31 PA 18 1.4 0 <0.1 <10 581 7.9 228 471 1.4 0 <10 842 1.4 0 10 PA 2 1.4 0 <0.1 <10 2755 8.9 274 616 1.4 0 10 1022 1.4 0 20 PA 20 1.3 0 <0.1 20 464 8.6 179 322 1.3 0 10 1274 1.3 0 20 PA 21 1.4 0 <0.1 <10 97 8.5 178 324 1.4 0 <10 146 1.4 0 <10 PA 22 1.4 0 <0.1 <10 487 8.6 338 1246 1.4 0 <10 285 1.4 0 <10 PA 23 <10 285 <10 <10 404 <10 PA 24 1.4 0 <0.1 <10 120 8.8 266 525 1.4 0 <10 135 1.4 0 <10 PA 25 1.4 0 <0.1 <10 110 8.8 266 524 1.4 0 <10 122 1.4 0 <10 PA 26 1.4 0 <0.1 10 703 8.7 200 462 1.4 0 <10 839 1.4 0 <10 PA 27 1.4 0 <0.1 <10 860 8.7 201 463 1.4 0 <10 908 1.4 0 <10 PA 28 1.4 0 <0.1 <10 211 8.7 200 466 1.4 0 <10 189 1.4 0 <10 PA 29 1.4 0 <0.1 <10 538 8.9 443 1245 1.4 0 <10 886 1.4 0 <10 PA 31 3.6 1.4 0 <0.1 <10 1989 8.8 235 459 2.1 1.4 0 10 1500 2.7 1.4 0 <10 PA 32 1.4 1.7 <10 576 8.8 622 14569 1.4 <10 323 1.4 <10 PA 33 5.1 1.4 0 <0.1 <10 1483 8.5 168 382 2.6 1.4 0 <10 573 2.1 1.4 0 <10 PA 34 2.6 1.4 0 <0.1 <10 4884 8.3 161 346 2.2 1.4 0 <10 4352 1.8 1.4 0 <10 PA 4 1.2 0 <0.1 10 432 8.5 178 324 1.2 0 10 613 1.2 0 <10 PA 5 1.4 0 1 <10 <10 8.8 296 709 1.4 0 <10 10 1.4 0 <10 PA 6 1.4 0 <0.1 <10 63 9 333 826 1.4 0 <10 20 1.4 0 <10

218

PA 7 1.4 0 1 <10 373 8.9 333 828 1.4 0 <10 408 1.4 0 <10 PA 8 1.4 0 <0.1 <10 512 8.9 334 843 1.4 <10 262 1.4 0 <10 PA 9 1.4 0 <0.1 <10 52 8.9 334 833 1.4 0 10 31 1.4 0 <10 PN 1 1.7 1.4 0 <0.1 <10 3255 8.9 494 2087 1.2 0 <10 3255 1.4 0 <10 PN 1 2.8 1.3 0 <0.1 <10 4352 9 504 2146 1.7 1.3 0 <10 309 2.0 1.3 0 <10 PN 10 29.3 1.2 <0.1 160 24196 9 481 2392 37.1 1.2 97 17329 16.8 1.2 256 PN 12 1.9 1.2 0 <0.1 <10 135 8.7 327 613 2.1 1.2 0 10 135 2.4 1.2 0 <10 PN 13 1.4 1.4 0 <0.1 <10 97 8.6 325 611 1.7 1.4 0 <10 148 1.4 1.4 0 <10 PN 14 2 1.4 0 <0.1 <10 435 8.8 307 575 1.8 1.4 0 10 350 1.8 1.4 0 <10 PN 15 1.3 1.4 0 <0.1 <10 185 8.9 447 746 1.5 1.4 0 <10 301 1.2 1.4 0 <10 PN 16 1.1 1.4 0 <0.1 <10 187 8.7 245 424 1.3 1.4 0 <10 98 1.1 1.4 0 <10 PN 17 1.1 1.4 0 <0.1 <10 275 8.8 372 728 1.5 1.4 0 <10 256 1.1 1.4 0 <10 PN 18 1.1 1.4 0 <0.1 <10 20 8.9 326 546 1.2 1.4 0 10 146 1.1 1.4 0 10 PN 19 8.2 1.4 0 <0.1 10 448 8.7 167 303 7.4 1.4 0 <10 373 6.9 1.4 0 <10 PN 2 1 1.4 0 <0.1 <10 4611 8.9 149 1664 0.8 1.3 0 20 4611 0.9 1.1 0 <10 PN 2 3.6 1.1 1 <0.1 <10 <10 8.2 251 2316 2.9 1.2 1 <10 <10 2.5 1.2 1 <10 PN 20 2.7 1.4 0 <0.1 <10 84 8.7 139 256 2.4 1.4 0 <10 109 2.9 1.4 0 <10 PN 21 1.7 1.4 1 <0.1 10 359 8.7 216 385 2.5 1.4 1 <10 185 1.8 1.4 1 <10 PN 22 0.7 1.4 0 <0.1 <10 20 8.8 203 353 0.8 1.4 0 <10 41 1.1 1.4 0 <10 PN 23 1.1 1.4 0 <0.1 <10 74 8.9 285 503 1.3 1.4 0 <10 122 1.8 1.4 0 <10 PN 24 12.1 1.4 0 <0.1 <10 1211 8.3 150 276 6.1 1.4 0 <10 179 10.2 1.4 0 <10 PN 26 2.8 1.4 0 <0.1 <10 697 8.7 137 258 1.2 1.4 0 10 428 2.1 1.4 0 <10 PN 27 0.7 1.4 0 <0.1 <10 226 8.6 143 266 1.2 1.4 0 <10 161 0.9 1.4 0 10 PN 28 2 1.4 0 <0.1 <10 41 8.7 147 269 1.9 1.4 0 <10 75 2.1 1.4 0 <10 PN 3 1.6 1.4 0 <0.1 <10 865 9.1 602 1000 1.1 1.4 0 20 489 0.9 1.4 0 <10 PN 4 7.1 1.4 0 <0.1 <10 426 9.1 381 932 2.6 1.2 0 <10 323 3.0 1.2 0 <10 PN 4 7 1.2 1 0.666 <10 4884 9.1 369 914 7.6 1.2 0 <10 2613 7.3 1.2 0 10 PN 4 2.1 1.4 0 <0.1 <10 63 9 606 1011 2.2 1.4 0 <10 20 2.0 1.4 0 <10 PN 5 10 1.4 0 <0.1 10 1354 8.5 360 1131 8.2 1.2 0 30 1187 7.8 1.2 0 41 PN 5 10 1.2 <0.1 31 3199 8.8 383 1102 10.0 1.2 10 2042 10.0 1.2 20 PN 6 1.4 0 <0.1 10 598 8.9 265 573 1.4 0 <10 554 1.4 0 <10

219

PN 7 2.2 1.2 <0.1 <10 2014 8.9 483 2186 3.0 1.2 10 6867 2.6 1.2 <10 PN 8 2 1.2 <0.1 <10 3873 8.8 497 2051 2.1 1.2 <10 2909 1.9 1.2 <10 PN 9 1.4 0 <0.1 <10 5172 9.1 511 2142 1.4 0 20 473 1.4 0 <10 RQ 1 2.3 1.2 0 <0.1 <10 8.8 259 1812 2.5 1.2 0 <10 1086 2.9 1.2 0 10 RQ 10 38.5 1.2 1 4 30 17329 9 140 614 28.9 1.2 1 63 19863 29.0 1.2 1 62 RQ 12 2.9 1.0 0 0.5 <10 1236 8.7 254 1785 3.4 1.0 0 <10 613 4.0 1.0 0 <10 RQ 13 3.4 1.2 0 1 211 17329 8.8 262 1842 3.9 1.2 0 276 8664 3.7 1.2 0 262 RQ 14 26.1 1.2 0 27.8 10 4611 9 233 1434 30.7 1.2 0 <10 2909 24.1 1.2 0 <10 RQ 14 1.2 1 2096 <10 63 9.1 237 1419 1.2 1 <10 41 1.2 1 <10 RQ 14 4.9 1.0 0 <0.1 <10 657 9.1 197 1340 4.9 1.0 0 <10 309 4.9 1.0 0 <10 RQ 15 54.5 1.0 1 36.2 <10 565 9 202 1165 49.7 1.0 1 20 2481 44.5 1.0 1 <10 RQ 15 0.7 <10 573 9 207 1192 <10 988 <10 RQ 16 14.3 1.2 1 1 528 9800 8.9 265 1888 16.1 1.2 1 530 8600 15.4 1.2 1 512 RQ 16 17.8 1.2 1 <10 7270 9.1 252 1884 15.7 1.2 <10 9208 16.0 1.2 <10 RQ 17 12.9 1.2 1 1 10 5475 8.9 222 1334 10.1 1.2 1 <10 5172 9.8 1.2 1 <10 RQ 18 15.6 1.2 <0.1 <10 75 8.7 13.5 1.2 <10 85 10.9 1.2 <10 RQ 19 5.8 1.0 0 <0.1 10 4352 8.6 261 1808 4.3 1.0 0 20 4884 5.1 1.0 0 <10 RQ 2 1.9 1.0 0 <0.1 <10 987 8.6 262 1833 2.0 1.0 0 <10 816 1.7 1.0 0 30 RQ 20 20.9 1.2 1 <0.1 <10 6488 9.1 196 1240 16.1 1.2 1 10 12033 19.9 1.2 1 30 RQ 20 3.4 <10 1274 9.3 203 1175 <10 855 <10 RQ 21 5.9 1.0 0 <0.1 279 4884 8.7 262 1839 5.0 1.0 0 189 6488 4.0 1.0 0 <10 RQ 23 10.5 1.2 0 5 <10 5794 9 267 1863 11.8 1.2 0 <10 6488 11.2 1.2 0 10 RQ 24 12.6 1.2 1 1 <10 8664 8.9 228 1410 9.4 1.2 1 <10 3609 10.3 1.2 1 <10 RQ 25 1.1 1.2 <0.1 <10 98 1.2 1.2 <10 86 1.0 1.2 <10 RQ 26 14.9 1.2 1 4.6 <10 5172 9 266 1868 16.0 1.2 1 10 6488 14.7 1.2 1 <10 RQ 28 23.9 1.0 0 <0.1 31 420 9.4 186 821 22.4 1.0 0 <10 389 28.3 1.0 0 20 RQ 29 3.7 1.0 1 3 <10 333 9.1 223 1325 4.1 1.0 1 <10 148 4.4 1.0 1 <10 RQ 3 8.6 0.5 <0.1 <10 98 10.5 <10 145 8.4 <10 RQ 30 5.4 1.2 1 3 20 17329 9 268 1861 7.6 1.2 1 <10 19863 7.9 1.2 1 <10 RQ 31 6.5 1.0 0 <0.1 10 1291 8.6 260 1792 6.8 1.0 0 <10 17329 6.8 1.0 0 <10 RQ 32 160 1.2 1 132.6 41 7270 9.2 272 1864 174.0 1.2 1 30 3441 180.0 1.2 1 20

220

RQ 33 1.0 0 <0.1 <10 404 8.3 184 1800 1.0 0 <10 52 1.0 0 <10 RQ 36 2.4 1.2 <0.1 <10 175 2.0 1.2 10 323 1.7 1.2 <10 RQ 37 14.4 1.2 0 <0.1 10 8164 9.1 262 1802 20.2 1.5 0 31 8664 15.6 1.5 0 <10 RQ 38 1.0 1 <0.1 <10 3873 8.7 240 1103 1.0 1 187 6867 1.0 1 10 RQ 39 80.1 1.0 1 35.1 63 3873 8.8 207 1193 81.0 1.0 1 63 4106 75.2 1.0 1 20 RQ 39 1.2 0.2 <10 3448 9 207 1192 1.2 <10 2382 1.2 <10 RQ 39 14.8 1.0 1 <0.1 10 9.1 198 1259 16.5 1.0 1 31 12033 15.8 1.0 1 10 RQ 4 3.7 1.0 0 <0.1 <10 2909 8.7 255 1804 3.2 1.0 0 <10 2014 3.6 1.0 0 <10 RQ 41 9.8 1.0 1 1.9 31 3448 9.2 221 1323 11.4 1.0 1 31 6488 11.4 0.9 1 <10 RQ 42 25.6 1.2 1 5.1 109 8164 9 268 1892 26.2 1.2 1 31 3654 24.8 1.2 1 31 RQ 43 1.0 0 1 <10 368 8.8 251 1092 1.0 0 <10 364 1.0 0 <10 RQ 44 5.7 1.0 1 8 <10 309 8.8 204 1154 6.2 1.0 1 <10 122 7.5 1.0 1 <10 RQ 5 8 1.0 0 1 20 2247 8.6 256 1785 6.6 1.0 0 52 3076 8.4 1.0 0 85 RQ 6 3.8 1.2 0 <0.1 20 908 8.7 258 1759 1.2 0 52 1670 4.8 1.2 0 120 RQ 6 10 1.0 0 1.7 <10 3873 9 262 1815 10.8 1.0 0 10 5475 11.3 1.0 0 <10 RQ 8 2.1 1.2 0 <0.1 31 4106 8.7 259 1901 2.2 1.2 0 <10 908 2.3 1.2 0 <10 SC 1 11.7 1.0 1 <0.1 <10 11199 8.6 151 547 8.2 1.0 1 <10 7701 8.7 1.0 1 <10 SC 2 1.3 0 <0.1 10 990 20 8.3 117 470 1.3 0 <10 1334 10 1.3 0 <10 SC 2 <0.1 <10 122 8.8 137 490 52 886 <10 SC 3 1.2 0 <0.1 <10 3448 <10 8 169 966 1.2 0 20 2382 30 1.2 0 52 SC 3 <0.1 41 4352 8.3 193 1065 20 4352 63 SC 4 7.6 0.8 1 3.1 31 5172 8.8 439 2639 10.0 1.0 1 52 7270 7.2 1.0 1 <10 SC 5 6.8 1.0 0 0.7 75 3873 8.8 442 2666 5.9 1.0 0 52 4106 6.6 1.0 0 63 SC 6 1.2 0 <0.1 <10 399 1.2 0 122 855 1.2 0 20 SR 1 1.5 0 <10 52 1.5 0 <10 187 1.5 0 <10 SR 1 1.5 0 <10 315 1.5 0 <10 243 1.5 0 10 SR 1 1.5 0 <10 122 1.5 0 30 119 1.5 0 <10 SR 1 1.5 0 <10 246 1.5 0 <10 218 1.5 0 <10 SR 1 1.5 0 10 231 1.5 0 <10 1291 1.5 0 10 SR 10 1.2 0 <0.1 <10 272 8.5 180 439 1.2 0 <10 457 1.2 0 10 SR 11 1.2 1 11.11 <10 19863 8.5 196 512 1.2 1 31 18500 1.2 1 <10

221

SR 12 1.2 0 0.2 20 2481 8.8 280 2679 1.2 0 10 6131 1.2 0 10 SR 15 1.2 0 0.8 10 4611 8.8 279 2636 1.2 0 <10 2247 1.2 0 <10 SR 17 0.5 0 0.1 75 417 8.8 206 861 0.5 0 86 369 0.5 0 31 SR 18 0.5 0 <0.1 <10 1145 8.8 203 851 0.5 0 <10 933 0.5 0 <10 SR 19 0.5 0 <0.1 31 389 8.7 208 876 0.5 0 <10 402 0.5 0 10 SR 2 26.5 1.0 1 <0.1 <10 >24196 9 262 1476 21.9 1.0 1 20 19863 17.6 1.0 1 <10 SR 20 0.5 0 <0.1 <10 309 8.6 208 954 0.5 0 <10 420 0.5 0 10 SR 21 3.7 1.0 1 <0.1 <10 4884 8.8 256 1493 3.5 1.0 1 <10 2481 3.5 1.0 1 <10 SR 22 4.7 1.0 1 <0.1 <10 1872 8.7 257 1491 4.4 1.0 1 <10 2098 4.8 1.0 1 <10 SR 3 28.5 0.8 1 1.9 <10 6867 8.9 258 1494 21.3 0.8 1 10 4352 19.1 0.8 1 <10 SR 4 1.0 0 <0.1 <10 265 8.6 181 715 1.0 0 <10 529 1.0 0 <10 SR 5 4.6 1.0 0 <0.1 20 3879 8.8 264 1479 4.7 1.0 0 <10 12033 5.2 1.0 0 10 SR 6 5.1 1.0 0 <0.1 <10 1500 8.7 264 1487 3.8 1.0 0 <10 2046 3.4 1.0 0 10 SR 7 1.2 0 10 122 1.2 0 20 241 1.2 0 10 SR 8 1.0 0 <0.1 <10 355 8.6 182 715 1.0 0 <10 457 1.0 0 <10 SR 9 1.0 0 <0.1 10 528 8.6 183 711 1.0 0 10 432 1.0 0 <10 ST 1 2.2 1.2 1 <0.1 10 246 8.9 168 937 2.2 1.0 1 10 132 1.2 1.2 1 <10 ST 10 1.4 0 <0.1 <10 5475 8.4 188 2329 1.4 0 <10 4884 1.4 0 <10 ST 11 886 1.2 1 623.9 <10 5475 8.6 259 1891 428.0 1.5 1 <10 5794 261.0 1.5 1 <10 ST 11 1.2 1.7 20 86200 9.2 234 1898 1.2 10 29900 1.2 <10 ST 12 1.4 0 <0.1 <10 1187 8.8 341 1427 1.4 0 <10 573 1.4 0 <10 ST 13 2.1 1.2 <0.1 10 173 1.7 1.2 52 175 1.7 1.2 10 ST 14 4.2 1.2 <0.1 74 820 2.0 1.2 85 657 2.6 1.2 52 ST 15 1.2 0 <0.1 <10 8.4 240 1333 1.2 0 <10 31 1.2 0 <10 ST 16 1.4 0 <0.1 10 1178 8.4 269 700 1.4 0 <10 414 1.4 0 <10 ST 2 1.2 1 5.1 41 24196 8.8 279 2801 1.2 1 10 14136 1.2 1 <10 ST 4 1.2 1.0 1 <0.1 10 63 8.9 165 929 1.1 1.0 1 <10 85 1.3 1.0 1 <10 ST 7 2.6 1.2 1 <0.1 20 1025 8.9 171 944 1.7 1.2 1 52 1223 2.7 1.2 1 97 ST 8 1.4 0 <0.1 <10 1607 8.3 182 2307 1.4 0 <10 1354 1.4 0 10 ST 9 1.4 0 <0.1 <10 959 8.5 184 2276 1.4 0 10 496 1.4 0

222

223

Unique 3 3 Fecal 4 NTU 4 Depth 4 Algae 4 Ecoli 4 4 Fecal 5 NTU 5 Depth 5 Algae 5 Ecoli 5 5 Fecal Identifier Coliforms Strep Coliforms Strep Coliforms Strep

CHR 1 959 3.8 1.2 1 <10 350 4.4 1.2 1 <10 426 CHR 2 1145 7.2 1.2 1 <10 933 7.2 1.2 0 <10 1314 CHR 3 1354 6.9 1.2 0 <10 1565 6.8 1.2 0 <10 1850 CHR 4 17329 4.7 1.2 1 10 17329 4.7 1.2 1 <10 9804 CHR 5 4884 295.0 1.2 0 74 4884 270.0 1.2 0 30 3448 FH 1 4611 15.4 1.2 1 10 4611 15.2 1.2 1 10 7270 FH 2 67000 93.5 1.2 0 10 67600 108.0 1.2 0 20 83900 FH 2 24196 40 1.5 <10 1203300 60 1.5 <10 26200 50 FH 3 1019 21.1 1.2 0 74 1650 22.4 1.2 0 41 884 FH 4 464 3.1 1.2 1 74 473 2.6 1.2 0 10 1935 HL 1 8664 10 1.3 0 <10 14136 50 1.5 0 <10 12033 40 HL 10 2143 1.3 1.2 0 <10 2014 1.2 1.2 0 <10 2359 HL 11 1309 2.0 1.2 0 10 1054 2.3 1.2 0 10 932 HL 2 1872 7.1 1.2 0 20 1430 11.5 1.2 0 <10 1430 HL 3 3076 117.0 1.2 0 97 2603 118.0 1.2 0 86 2603 HL 4 10462 <10 1.0 0 <10 19863 1.0 1 <10 12997 HL 5 197 <10 1.2 0 <10 173 1.2 0 <10 135 HL 6 2046 196.0 1.2 0 31 2603 192.0 1.2 0 41 2247 HL 7 51200 185.0 1.2 0 86 12100 198.0 1.2 0 10 19863 KT 10 697 1.0 0 <10 813 1.0 0 <10 1169 KT 11 1789 30 1.0 0 10 2143 <10 1.0 0 <10 1354 <10 KT 12 504 1.2 0 10 402 1.2 0 <10 298 KT 13 109 1.2 0 <10 96 1.2 0 <10 228 KT 4 86 1.4 1.4 0 <10 75 1.8 1.4 0 <10 145 KT 5 594 1.4 0 <10 932 1.4 0 <10 422 KT 6 426 3.0 1.4 0 31 759 2.6 1.4 0 <10 279

224

KT 7 9208 1.2 1 <10 3873 1.2 1 <10 6867 KT 8 15531 <10 1 <10 24196 1 <10 45000 KT 8 4884 1.4 0 <10 8164 1.4 0 <10 2755 KT 9 3255 1.3 0 10 1236 1.3 0 30 2909 MC 1 110 <10 3.5 1.0 1 <10 187 <10 3.9 1.0 1 <10 145 20 MC 1 160 70 3.6 1.0 <10 211 5.9 1.0 <10 448 MC 1 282 <10 3.9 1.0 <10 292 <10 305 MC 1 187 2.7 1.0 <10 313 <10 3.2 1.0 <10 435 MC 1 441 4.9 1.0 <10 583 4.9 1.0 <10 563 MC 10 246 <10 <10 74 <10 109 MC 2 586 <10 <10 684 <10 1607 MC 2 145 <10 1.2 <10 253 1.2 <10 173 MC 3 323 <10 4.1 1.0 <10 336 <10 5.6 1.0 <10 538 <10 MC 3 1553 <10 3.5 1.0 <10 1500 3.3 1.0 <10 1515 MC 3 886 <10 3.4 1.0 <10 776 3.2 1.0 <10 794 MC 3 1725 3.0 1.0 10 2046 3.7 1.0 146 5475 160 MC 3 1376 <10 1.0 <10 1236 1.0 <10 988 MC 4 1607 1.4 0 31 1296 1.4 0 10 1421 MC 5 63 1.4 0 <10 97 1.4 0 10 1421 MC 6 233 <10 3.2 1.0 <10 336 10 2.9 1.0 <10 341 <10 MC 6 754 <10 2.0 1.0 <10 809 2.5 1.0 20 1785 MC 6 496 <10 2.2 1.0 <10 480 1.0 <10 420 MC 6 987 40 2.6 1.0 <10 988 3.2 1.0 <10 1106 MC 6 771 3.6 1.0 10 1333 <10 1.0 <10 959 MC 7 364 1.6 1.0 <10 609 1.1 1.0 <10 820 MC 7 364 <10 1.0 1.0 10 231 40 0.9 1.0 <10 309 20 MC 7 537 <10 0.6 1.0 10 657 1.0 1.0 <10 663 MC 7 988 0.7 1.0 10 905 0.7 1.0 <10 1076 MC 7 689 <10 0.9 1.0 <10 428 2.1 1.0 <10 624 MC 8 1058 <10 7.3 1.0 <10 7270 <10 <10 1058 <10 MC 8 3654 6.3 1.0 <10 2382 5.7 1.0 <10 1722 <10

225

MC 8 383 <10 9.3 1.0 <10 987 6.6 1.0 <10 733 MC 8 529 7.6 1.0 <10 457 8.9 1.0 <10 754 MC 8 63 <10 7.6 1.0 <10 86 <10 8.6 1.0 <10 262 <10 MC 9 41 10 1.7 1.0 <10 97 1.5 1.0 <10 75 MC 9 41 1.1 1.0 <10 75 1.3 1.0 <10 146 MC 9 1467 9.3 1.0 20 1607 11.0 1.0 52 1785 MC 9 318 <10 2.0 1.0 <10 359 1.6 1.0 <10 317 PA 10 107 1.4 0 <10 73 1.4 0 <10 155 PA 11 31 1.4 0 <10 135 1.4 0 <10 31 PA 12 41 1.4 <10 20 1.4 <10 160 PA 13 86 1.4 0 <10 169 1.4 0 <10 221 PA 14 1483 1.4 <10 1274 1.4 <10 2481 PA 15 683 1.4 0 31 471 1.4 0 <10 504 PA 16 243 1.4 0 <10 228 1.4 0 <10 408 PA 18 1017 1.4 0 <10 1439 1.4 0 <10 1334 PA 2 809 1.4 0 <10 462 1.4 0 <10 723 PA 20 441 1.3 0 <10 548 1.3 0 <10 481 PA 21 134 1.4 0 <10 259 1.4 0 10 161 PA 22 2909 1.4 0 41 410 1.4 0 10 355 PA 23 279 <10 <10 281 <10 309 PA 24 146 1.4 0 <10 145 1.4 0 <10 213 PA 25 74 1.4 0 <10 63 1.4 0 <10 158 PA 26 591 1.4 0 10 620 1.4 0 <10 676 PA 27 1012 1.4 0 10 888 1.4 0 <10 794 PA 28 160 1.4 0 <10 355 1.4 0 <10 108 PA 29 457 1.4 0 <10 309 1.4 0 <10 368 PA 31 1789 2.3 1.4 0 20 2359 5.0 1.4 0 20 6867 PA 32 556 1.4 <10 265 1.4 <10 331 PA 33 683 2.8 1.4 0 <10 959 8.7 1.4 0 <10 657 PA 34 4611 1.9 1.4 0 10 3255 3.8 1.4 0 <10 3448 PA 4 10462 1.2 0 10 426 1.2 0 <10 1850

226

PA 5 31 1.4 0 <10 20 1.4 0 <10 41 PA 6 31 1.4 0 <10 52 1.4 0 <10 185 PA 7 345 1.4 0 <10 320 1.4 0 <10 246 PA 8 313 1.4 0 <10 295 1.4 0 <10 171 PA 9 31 1.4 0 10 52 1.4 0 <10 41 PN 1 5794 1.3 0 <10 2613 1.3 1.4 0 <10 1956 PN 1 1553 2.5 1.3 0 10 5172 2.7 1.3 0 <10 2723 PN 10 8664 19.3 1.2 148 6867 24.1 1.2 275 9208 PN 12 199 4.0 1.2 0 <10 984 3.0 1.2 0 <10 121 PN 13 52 2.2 1.4 0 <10 85 1.9 1.4 0 <10 74 PN 14 465 2.4 1.4 0 20 345 3.2 1.4 0 <10 727 PN 15 121 1.1 1.4 0 <10 213 1.3 1.4 0 <10 253 PN 16 63 1.2 1.4 0 <10 75 1.2 1.4 0 <10 41 PN 17 218 1.0 1.4 0 <10 241 1.1 1.4 0 <10 384 PN 18 216 1.0 1.4 0 <10 131 1.0 1.4 0 <10 52 PN 19 364 6.4 1.4 0 <10 473 6.9 1.4 0 <10 435 PN 2 2143 1.0 1.1 0 20 2282 1.4 1.1 0 41 2382 PN 2 <10 2.9 1.2 1 <10 <10 3.1 1.1 1 <10 <10 PN 20 109 3.8 1.4 0 <10 120 3.2 1.4 0 <10 74 PN 21 83 1.5 1.4 1 <10 160 1.6 1.4 1 <10 213 PN 22 20 0.8 1.4 0 <10 31 0.8 1.4 0 <10 20 PN 23 41 1.0 1.4 0 <10 158 1.8 1.4 0 <10 74 PN 24 246 4.2 1.4 0 <10 155 5.1 1.4 0 <10 275 PN 26 389 1.3 1.4 0 <10 488 1.2 1.4 0 <10 538 PN 27 110 0.8 1.4 0 <10 98 1.4 1.4 0 <10 109 PN 28 41 1.6 1.4 0 <10 109 1.8 1.4 0 <10 85 PN 3 670 0.9 1.4 0 <10 1541 0.8 1.4 0 <10 329 PN 4 771 10.0 1.3 0 <10 1043 6.8 1.4 0 10 1421 PN 4 1723 6.7 1.3 0 <10 1467 6.5 1.2 1 <10 24196 PN 4 63 1.8 1.4 0 <10 63 1.9 1.4 0 10 41 PN 5 1789 7.9 1.3 0 52 2046 8.4 1.4 0 122 2909

227

PN 5 1937 10.0 1.2 41 1811 10.0 1.2 10 1014 PN 6 571 1.4 0 <10 697 1.4 0 10 512 PN 7 1162 2.7 1.2 10 2014 2.7 1.2 20 1616 PN 8 2359 2.6 1.2 <10 2755 1.7 1.2 <10 2098 PN 9 2382 1.4 0 10 6867 1.4 0 <10 7270 RQ 1 1664 2.2 1.2 0 20 1723 2.2 1.2 0 <10 2046 RQ 10 24196 31.9 1.2 1 63 12997 30.2 1.2 1 <10 12977 RQ 12 1354 4.4 1.0 0 <10 1050 3.4 1.0 0 10 776 RQ 13 19863 4.0 1.2 0 350 11199 3.4 1.2 0 331 17329 RQ 14 2224 21.9 1.2 0 <10 1935 29.1 1.2 0 108 4352 RQ 14 63 1.2 <10 145 1.2 1 20 3654 RQ 14 359 4.5 1.0 0 10 836 4.7 1.0 0 <10 216 RQ 15 471 59.0 1.0 1 <10 712 81.9 1.0 1 10 487 RQ 15 759 <10 691 <10 733 RQ 16 16000 14.8 1.2 1 345 8400 15.6 1.2 1 450 10112 RQ 16 6131 15.8 1.2 <10 7270 16.2 1.2 <10 12997 RQ 17 3654 8.8 1.2 1 <10 4106 10.9 1.2 1 <10 4352 RQ 18 160 14.2 1.2 <10 120 23.2 1.2 <10 96 RQ 19 2046 4.8 1.0 0 10 1860 5.1 1.0 0 20 1187 RQ 2 627 1.5 1.0 0 10 733 1.8 1.0 0 20 882 RQ 20 >24196 20.6 1.2 1 <10 8164 16.6 1.2 1 <10 104620 RQ 20 1187 <10 420 <10 388 RQ 21 1650 3.6 1.0 0 <10 2495 3.7 1.0 0 <10 2613 RQ 23 5794 10.8 1.2 0 <10 3873 10.5 1.2 0 <10 4352 RQ 24 3654 9.7 1.2 1 <10 3654 5.8 1.2 1 <10 2723 RQ 25 157 1.4 1.2 <10 109 2.9 1.2 <10 121 RQ 26 7270 15.2 1.2 1 10 4884 16.0 1.2 1 <10 5794 RQ 28 836 49.9 0.6 0 20 410 28.5 0.5 0 10 389 RQ 29 657 4.5 1.0 1 <10 620 4.8 1.0 1 <10 158 RQ 3 31 7.9 <10 63 7.1 1.2 <10 31 RQ 30 11199 7.6 1.2 1 <10 9208 6.5 1.2 1 10 11199

228

RQ 31 554 7.8 0.8 0 <10 712 16.0 0.8 0 10 1529 RQ 32 11199 188.0 1.2 1 <10 8164 215.0 1.2 1 20 2163 RQ 33 52 1.0 0 <10 110 1.0 0 <10 30 RQ 36 121 1.9 1.2 <10 246 2.5 1.2 10 218 RQ 37 4611 14.8 1.5 0 <10 4611 16.4 1.5 0 <10 3873 RQ 38 6131 1.0 1 20 4884 1.0 1 20 6488 RQ 39 5172 64.3 1.0 1 41 3255 79.5 1.0 1 52 4611 RQ 39 4884 1.2 <10 3448 1.2 <10 4352 RQ 39 19863 15.7 1.0 1 86 9208 17.9 1.0 1 31 9208 RQ 4 1467 3.5 1.0 0 <10 2064 3.6 1.0 0 10 1722 RQ 41 1850 11.1 0.9 1 <10 1374 10.4 0.8 1 <10 816 RQ 42 8164 25.0 1.2 1 <10 5172 15.8 1.2 1 31 7270 RQ 43 512 1.0 0 <10 295 1.0 0 10 839 RQ 44 355 6.2 1.0 1 10 426 7.2 1.0 1 10 670 RQ 5 1785 8.6 1.0 0 63 1467 8.2 1.0 0 97 1723 RQ 6 3654 6.4 1.2 0 536 4611 7.0 1.2 0 226 6867 RQ 6 2909 11.8 1.0 0 <10 3076 11.0 1.0 0 122 3448 RQ 8 1153 2.4 1.2 0 20 3654 2.6 1.2 0 <10 2723 SC 1 5475 7.8 1.0 1 <10 6867 7.7 0.8 1 10 8164 SC 2 1354 1.3 0 <10 1333 <10 1.3 0 <10 2851 <10 SC 2 712 20 75 <10 31 1291 110 SC 3 74900 210 1.2 1 173 3076 50 1.2 1 <10 2909 <10 SC 3 6488 70 73 8664 75 5172 10 SC 4 1607 9.7 1.0 1 10 6867 9.5 0.8 1 31 12997 SC 5 5172 5.6 1.0 0 41 4884 6.8 1.0 0 <10 4884 SC 6 435 1.2 0 10 441 1.2 0 10 420 SR 1 31 1.5 0 <10 109 1.5 0 <10 86 SR 1 388 1.5 0 <10 259 1.5 0 <10 315 SR 1 121 1.5 0 <10 <10 1.5 0 <10 63 SR 1 199 1.5 0 <10 548 1.5 0 <10 211 SR 1 556 1.5 0 10 565 1.5 0 <10 464

229

SR 10 288 1.2 0 10 332 1.2 0 <10 455 SR 11 19863 1.2 1 20 24196 1.2 1 <10 19863 SR 12 3873 1.2 0 10 4884 1.2 0 31 7270 SR 15 2909 1.2 0 <10 3654 1.2 0 <10 2755 SR 17 341 0.5 0 10 323 0.5 0 10 776 SR 18 1019 0.5 0 10 1050 0.5 0 <10 638 SR 19 631 0.5 0 <10 259 0.5 0 10 459 SR 2 7701 19.6 1.0 1 <10 9804 21.7 1.0 1 10 14136 SR 20 452 0.5 0 10 173 0.5 0 <10 359 SR 21 5475 3.4 1.0 1 <10 3448 3.6 1.0 1 10 3448 SR 22 4352 4.9 1.0 1 <10 7270 4.7 1.0 1 <10 2046 SR 3 9804 20.2 0.8 1 <10 4106 22.2 0.8 1 10 4884 SR 4 1076 1.0 0 <10 960 1.0 0 <10 432 SR 5 8664 5.0 1.0 0 10 9804 4.9 1.0 0 10 9804 SR 6 1450 4.1 1.0 0 <10 2613 3.8 1.0 0 10 1793 SR 7 345 1.2 0 <10 146 1.2 0 <10 109 SR 8 246 1.0 0 <10 530 1.0 0 <10 185 SR 9 443 0.8 0 <10 1019 0.8 0 <10 388 ST 1 63 2.4 1.2 1 <10 41 1.8 1.2 1 <10 134 ST 10 9208 1.4 0 <10 3654 1.4 0 <10 3076 ST 11 2987 530.0 1.5 1 <10 3609 173.0 1.5 1 <10 3255 ST 11 19863 1.2 <10 24196 1.2 <10 24196 ST 12 657 1.4 0 <10 448 1.4 0 <10 364 ST 13 52 2.2 1.2 41 420 2.7 1.2 20 520 ST 14 488 2.5 1.2 86 689 2.2 1.2 75 644 ST 15 20 <10 1.2 0 <10 51 1.2 0 <10 10 ST 16 420 1.4 0 <10 450 1.4 0 <10 389 ST 2 57600 1.2 1 10 17329 1.2 1 10 27900 ST 4 31 1.2 1.0 1 30 160 4.7 1.0 1 10 146 ST 7 1081 2.2 1.2 1 41 933 2.2 1.2 120 1169 ST 8 1842 1.4 0 <10 1553 1.4 0 <10 5794

230

ST 9 1.4 0 <10 798 1.4 0 <10 420

231

Unique Turbidity Turbidty Turbidity Turbidity Max Coliform Max Log Ecoli Ecoli Identifier Ave Max MaxLog Ave Coliform Geometric Ecoli Max Geometric Detected Mean Ecoli Mean add 1 CHR 1 4.1 4.4 0.6 4.2 639.17 0. 0 959.00 00 CHR 2 7.2 7.2 0.9 9.0 1226.59 0. 0 1,314.00 00 CHR 3 6.8 6.9 0.8 5.5 1582.69 1. 0 1,850.00 04 CHR 4 4.7 4.7 0.7 4.9 13034.3 1. 1 17,329.00 2 51 CHR 5 282.5 295.0 2.5 291.4 4103.66 2. 1 4,884.00 59 FH 1 15.3 15.4 1.2 15.4 5789.82 1. 1 7,270.00 32 FH 2 100.8 108.0 2.0 105.2 74975.3 1. 1 83,900.00 3 51 FH 2 25178.0 1. 0 1,203,300.00 7 04 FH 3 21.8 22.4 1.4 21.4 949.10 1. 1 1,650.00 94 FH 4 2.9 3.1 0.5 2.6 947.54 1. 1 1,935.00 88 HL 1 10210.4 1. 0 14,136.00 8 04 HL 10 1.2 1.3 0.1 1.3 2248.41 1. 0 2,359.00 32 HL 11 2.1 2.3 0.4 2.4 1104.53 1. 1 1,309.00 32 HL 2 9.3 11.5 1.1 7.8 1636.14 1. 1 1,872.00 62 HL 3 117.5 118.0 2.1 119.4 2829.63 2. 1 3,076.00 04 HL 4 11660.8 1. 0

232

19,863.00 2 04 HL 5 163.08 1. 1 197.00 04 HL 6 194.0 196.0 2.3 198.6 2144.15 2. 1 2,603.00 12 HL 7 191.5 198.0 2.3 195.8 31890.2 2. 1 51,200.00 1 26 KT 10 902.66 0. 0 1,169.00 00 KT 11 1556.38 1. 1 2,143.00 04 KT 12 387.55 1. 0 504.00 04 KT 13 157.65 0. 0 228.00 00 KT 4 1.6 1.8 0.3 1.4 111.67 0. 0 145.00 00 KT 5 500.67 1. 0 932.00 04 KT 6 2.8 3.0 0.5 3.0 344.75 1. 1 759.00 81 KT 7 7951.81 0. 0 9,208.00 00 KT 8 26436.6 0. 0 45,000.00 2 00 KT 8 3668.16 0. 0 8,164.00 00 KT 9 3077.14 1. 1 3,255.00 49 MC 1 3.7 3.9 0.6 4.7 126.29 0. 0 187.00 00 MC 1 4.7 5.9 0.8 5.6 267.73 0. 0 448.00 00 MC 1 293.27 0. 0 305.00 00 MC 1 2.9 3.2 0.5 3.3 285.21 0. 0

233

435.00 00 MC 1 4.9 4.9 0.7 4.1 498.28 0. 0 583.00 00 MC 10 163.75 1. 0 246.00 04 MC 2 970.41 0. 0 1,607.00 00 MC 2 158.38 1. 0 253.00 04 MC 3 4.9 5.6 0.7 4.1 416.86 0. 0 538.00 00 MC 3 3.4 3.5 0.5 3.6 1533.88 0. 0 1,553.00 00 MC 3 3.3 3.4 0.5 3.3 838.74 0. 0 886.00 00 MC 3 3.4 3.7 0.6 2.6 3073.17 2. 1 5,475.00 17 MC 3 1165.97 0. 0 1,376.00 00 MC 4 1511.14 1. 1 1,607.00 81 MC 5 299.20 1. 0 1,421.00 04 MC 6 3.0 3.2 0.5 2.9 281.87 0. 0 341.00 00 MC 6 2.3 2.5 0.4 2.3 1160.12 1. 0 1,785.00 32 MC 6 456.42 0. 0 496.00 00 MC 6 2.9 3.2 0.5 3.3 1044.81 1. 1 1,106.00 04 MC 6 859.88 1. 0 1,333.00 04 MC 7 1.4 1.6 0.2 0.9 546.33 0. 0 820.00 00 MC 7 0.9 1.0 0.0 1.4 335.37 1. 1

234

364.00 62 MC 7 0.8 1.0 0.0 0.7 596.68 1. 0 663.00 04 MC 7 0.7 0.7 -0.2 0.6 1031.06 1. 0 1,076.00 04 MC 7 1.5 2.1 0.3 1.4 655.70 1. 1 689.00 04 MC 8 7.3 7.3 0.9 7.8 1058.00 0. 0 7,270.00 00 MC 8 6.0 6.3 0.8 6.8 2508.42 1. 0 3,654.00 32 MC 8 7.9 9.3 1.0 8.6 529.85 0. 0 987.00 00 MC 8 8.2 8.9 0.9 8.3 631.56 0. 0 754.00 00 MC 8 8.1 8.6 0.9 8.2 128.48 0. 0 262.00 00 MC 9 1.6 1.7 0.2 1.6 55.45 1. 0 97.00 04 MC 9 1.2 1.3 0.1 1.2 77.37 1. 0 146.00 04 MC 9 10.1 11.0 1.0 10.5 1618.21 1. 1 1,785.00 81 MC 9 1.8 2.0 0.3 1.7 317.50 0. 0 359.00 00 PA 10 128.78 2. 1 155.00 24 PA 11 31.00 1. 1 135.00 32 PA 12 80.99 0. 0 160.00 00 PA 13 137.86 0. 0 221.00 00 PA 14 1918.16 0. 0 2,481.00 00 PA 15 586.71 1. 1

235

683.00 51 PA 16 314.87 1. 0 408.00 51 PA 18 1164.77 1. 0 1,439.00 04 PA 2 764.79 1. 1 809.00 32 PA 20 460.57 1. 1 548.00 32 PA 21 146.88 1. 0 259.00 04 PA 22 1016.22 1. 1 2,909.00 62 PA 23 293.62 0. 0 309.00 00 PA 24 176.35 0. 0 213.00 00 PA 25 108.13 0. 0 158.00 00 PA 26 632.07 1. 1 676.00 04 PA 27 896.40 1. 0 1,012.00 04 PA 28 131.45 0. 0 355.00 00 PA 29 410.09 0. 0 457.00 00 PA 31 3.6 5.0 0.7 3.1 3505.01 1. 1 6,867.00 32 PA 32 428.99 0. 0 556.00 00 PA 33 5.8 8.7 0.9 4.3 669.87 0. 0 959.00 00 PA 34 2.8 3.8 0.6 2.5 3987.32 1. 0 4,611.00 04 PA 4 4399.40 1. 1

236

10,462.00 04 PA 5 35.65 0. 0 41.00 00 PA 6 75.73 0. 0 185.00 00 PA 7 291.32 0. 0 345.00 00 PA 8 231.35 0. 0 313.00 00 PA 9 35.65 1. 1 52.00 04 PN 1 1.3 1.3 0.1 1.5 3366.46 0. 0 5,794.00 00 PN 1 2.6 2.7 0.4 2.3 2056.41 1. 0 5,172.00 04 PN 10 21.7 24.1 1.4 25.3 8931.86 2. 1 9,208.00 44 PN 12 3.5 4.0 0.6 2.7 155.17 1. 0 984.00 04 PN 13 2.0 2.2 0.3 1.7 62.03 0. 0 85.00 00 PN 14 2.8 3.2 0.5 2.2 581.42 1. 1 727.00 32 PN 15 1.2 1.3 0.1 1.3 174.97 0. 0 253.00 00 PN 16 1.2 1.2 0.1 1.2 50.82 0. 0 75.00 00 PN 17 1.1 1.1 0.0 1.2 289.33 0. 0 384.00 00 PN 18 1.0 1.0 0.0 1.1 105.98 1. 1 216.00 04 PN 19 6.7 6.9 0.8 7.2 397.92 1. 0 473.00 04 PN 2 1.2 1.4 0.1 1.0 2259.34 1. 1 2,382.00 62 PN 2 3.0 3.1 0.5 3.0 - #NUM! 0. 0

237

00 PN 20 3.5 3.8 0.6 3.0 89.81 0. 0 120.00 00 PN 21 1.5 1.6 0.2 1.8 132.96 1. 0 213.00 04 PN 22 0.8 0.8 -0.1 0.8 20.00 0. 0 31.00 00 PN 23 1.4 1.8 0.3 1.4 55.08 0. 0 158.00 00 PN 24 4.7 5.1 0.7 7.5 260.10 0. 0 275.00 00 PN 26 1.2 1.3 0.1 1.7 457.47 1. 0 538.00 04 PN 27 1.1 1.4 0.1 1.0 109.50 1. 0 110.00 04 PN 28 1.7 1.8 0.2 1.9 59.03 0. 0 109.00 00 PN 3 0.9 0.9 0.0 1.1 469.50 1. 0 1,541.00 32 PN 4 8.4 10.0 1.0 5.9 1046.70 1. 0 1,421.00 04 PN 4 6.6 6.7 0.8 7.0 6456.76 1. 0 24,196.00 04 PN 4 1.8 1.9 0.3 2.0 50.82 1. 0 63.00 04 PN 5 8.1 8.4 0.9 8.5 2281.27 2. 1 2,909.00 09 PN 5 10.0 10.0 1.0 10.0 1401.47 1. 1 1,937.00 62 PN 6 540.70 1. 1 697.00 04 PN 7 2.7 2.7 0.4 2.6 1370.33 1. 1 2,014.00 32 PN 8 2.2 2.6 0.4 2.1 2224.68 0. 0 2,755.00 00 PN 9 4161.39 1. 1

238

7,270.00 32 RQ 1 2.2 2.2 0.3 2.4 1845.14 1. 1 2,046.00 32 RQ 10 31.1 31.9 1.5 31.7 17719.8 1. 1 24,196.00 1 81 RQ 12 3.9 4.4 0.6 3.6 1025.04 1. 0 1,354.00 04 RQ 13 3.7 4.0 0.6 3.7 18552.7 2. 1 19,863.00 9 55 RQ 14 25.5 29.1 1.5 26.4 3111.08 2. 1 4,352.00 04 RQ 14 479.79 1. 0 3,654.00 32 RQ 14 4.6 4.7 0.7 4.8 278.47 1. 0 836.00 04 RQ 15 70.5 81.9 1.9 57.9 478.93 1. 1 712.00 32 RQ 15 745.89 0. 0 759.00 00 RQ 16 15.2 15.6 1.2 15.2 12719.7 2. 1 16,000.00 5 73 RQ 16 16.0 16.2 1.2 16.3 8926.62 0. 0 12,997.00 00 RQ 17 9.9 10.9 1.0 10.5 3987.76 1. 0 4,352.00 04 RQ 18 18.7 23.2 1.4 15.5 123.94 0. 0 160.00 00 RQ 19 5.0 5.1 0.7 5.0 1558.40 1. 1 2,046.00 32 RQ 2 1.6 1.8 0.2 1.8 743.65 1. 1 882.00 49 RQ 20 18.6 20.6 1.3 18.8 104620. 1. 1 104,620.00 00 49 RQ 20 678.64 0. 0 1,187.00 00 RQ 21 3.7 3.7 0.6 4.4 2076.40 2. 1

239

2,613.00 45 RQ 23 10.7 10.8 1.0 11.0 5021.50 1. 0 5,794.00 04 RQ 24 7.7 9.7 1.0 9.6 3154.34 0. 0 3,654.00 00 RQ 25 2.1 2.9 0.5 1.5 137.83 0. 0 157.00 00 RQ 26 15.6 16.0 1.2 15.4 6490.18 1. 1 7,270.00 04 RQ 28 39.2 49.9 1.7 30.6 570.27 1. 1 836.00 51 RQ 29 4.6 4.8 0.7 4.3 322.19 0. 0 657.00 00 RQ 3 7.5 7.9 0.9 8.5 31.00 0. 0 63.00 00 RQ 30 7.0 7.6 0.9 7.0 11199.0 1. 1 11,199.00 0 32 RQ 31 11.9 16.0 1.2 8.8 920.36 1. 1 1,529.00 04 RQ 32 201.5 215.0 2.3 183.4 4921.73 1. 1 11,199.00 62 RQ 33 39.50 0. 0 110.00 00 RQ 36 2.2 2.5 0.4 2.1 162.41 1. 1 246.00 04 RQ 37 15.6 16.4 1.2 16.3 4225.92 1. 1 4,611.00 51 RQ 38 6306.97 2. 1 6,488.00 27 RQ 39 71.9 79.5 1.9 76.0 4883.45 1. 1 5,172.00 81 RQ 39 4610.33 0. 0 4,884.00 00 RQ 39 16.8 17.9 1.3 16.1 13524.0 1. 1 19,863.00 0 94 RQ 4 3.5 3.6 0.6 3.5 1589.39 1. 0

240

2,064.00 04 RQ 41 10.8 11.1 1.0 10.8 1228.66 1. 1 1,850.00 51 RQ 42 20.4 25.0 1.4 23.5 7704.04 2. 1 8,164.00 04 RQ 43 655.41 1. 0 839.00 04 RQ 44 6.7 7.2 0.9 6.6 487.70 1. 1 670.00 04 RQ 5 8.4 8.6 0.9 8.0 1753.73 1. 1 1,785.00 99 RQ 6 6.7 7.0 0.8 5.5 5009.19 2. 1 6,867.00 73 RQ 6 11.4 11.8 1.1 11.0 3167.05 2. 1 3,448.00 09 RQ 8 2.5 2.6 0.4 2.3 1771.90 1. 1 3,654.00 51 SC 1 7.7 7.8 0.9 8.8 6685.65 1. 0 8,164.00 04 SC 2 1964.75 1. 0 2,851.00 04 SC 2 958.75 1. 1 1,291.00 72 SC 3 14760.9 2. 1 74,900.00 0 24 SC 3 5792.75 1. 1 8,664.00 88 SC 4 9.6 9.7 1.0 8.8 4570.14 1. 1 12,997.00 72 SC 5 6.2 6.8 0.8 6.3 5025.94 1. 1 5,172.00 88 SC 6 427.43 2. 1 441.00 09 SR 1 51.63 0. 0 109.00 00 SR 1 349.60 1. 0

241

388.00 04 SR 1 87.31 1. 0 121.00 49 SR 1 204.91 0. 0 548.00 00 SR 1 507.92 1. 1 565.00 04 SR 10 361.99 1. 1 455.00 04 SR 11 19863.0 1. 1 24,196.00 0 51 SR 12 5306.29 1. 1 7,270.00 51 SR 15 2830.95 1. 0 3,654.00 04 SR 17 514.41 1. 1 776.00 94 SR 18 806.30 1. 0 1,050.00 04 SR 19 538.17 1. 1 631.00 51 SR 2 20.7 21.7 1.3 21.5 10433.6 1. 1 14,136.00 6 32 SR 20 402.83 1. 1 452.00 04 SR 21 3.5 3.6 0.6 3.5 4344.86 1. 0 5,475.00 04 SR 22 4.8 4.9 0.7 4.7 2983.99 0. 0 7,270.00 00 SR 3 21.2 22.2 1.3 22.3 6919.74 1. 1 9,804.00 04 SR 4 681.79 0. 0 1,076.00 00 SR 5 5.0 5.0 0.7 4.9 9216.39 1. 1 9,804.00 32 SR 6 3.9 4.1 0.6 4.0 1612.41 1. 1

242

2,613.00 04 SR 7 193.92 1. 1 345.00 32 SR 8 213.33 0. 0 530.00 00 SR 9 414.59 1. 1 1,019.00 04 ST 1 2.1 2.4 0.4 1.9 91.88 1. 1 134.00 04 ST 10 5322.01 0. 0 9,208.00 00 ST 11 351.5 530.0 2.7 455.6 3118.12 0. 0 3,609.00 00 ST 11 21922.7 1. 1 24,196.00 1 32 ST 12 489.03 0. 0 657.00 00 ST 13 2.5 2.7 0.4 2.1 164.44 1. 1 520.00 72 ST 14 2.4 2.5 0.4 2.7 560.60 1. 1 689.00 94 ST 15 14.14 0. 0 51.00 00 ST 16 404.20 1. 0 450.00 04 ST 2 40087.9 1. 1 57,600.00 0 62 ST 4 2.9 4.7 0.7 1.9 67.28 1. 1 160.00 49 ST 7 2.2 2.2 0.4 2.3 1124.14 2. 1 1,169.00 08 ST 8 3266.89 1. 0 5,794.00 04 ST 9 420.00 1 798.00

243

244

Appendix F: Previous Modeling

243

Table 26: Models Study Indicator Model Time- Model fit Significant variables Bacteria Type independent test variables Ferguson et FC, Fecal MLR 9 R2: 0.52- Precipitation; al 1996 Strep; 0.8 Distance from river; Clostridium date Perfingens; F-RNA, etc Crowther, TC, FC, FS MLR 10 R2: 0.06- Precipitation; Kay & 0.53 Tide height at time of Wyer, 2001 sampling; Sunshine; Proportion of onshore winds; Turbidity Christensen, FC MLR 121 R2: 0.661 Turbidity 2001 Water Temperature Haack et al EC; Correlation 11 TSS, Rainfall 24 hours 2003 TC; Coefficient or 48 hours; Wave EN height; Wind Speed Mclellan EC MLR 7 R2: 0.03- Precipitation; and Salmore 0.29 Time since last rainfall; (2003) Wind speed; Wind direction Francy et al. EC LR 14 R2: 0.17- Stream flow; 2003 0.58 Precipitation; Wave height; Birds; Turbidity; Wind Direction; Date Crowther et EC MLR 24 R2: 0.73- Improved Pasture; al. (2003) 0.858 Mean slope gradient; Rough grazing Olyphant & EC MLR 12 R2: 0.71 Onshore wind vector; Whitman Rainfall; (2004) Average lake stage; Solar radiation; Water temperature; Turbidity Eleria & FC MLR 31 R2: 0.54- Rainfall; Vogel LR 0.69 R2: Wind speed;

244

(2005) 0.46-0.56 Flow rate; Previous bacteria concentration Francy et al. EC MLR 11 R2: 0.35- Turbidity; (2006) 0.44 Rainfall; Wave Height; Water Temperature; Day of the year; Wind Direction; Lake Level Schoonover FC MLR 4 R2: 0.69 % impervious surface; & Lockaby (2006) Hellweger EC MLR 7 R2: 0.6 Flow variables (3) (2007) 80/90 Combined sewer Mechanistic TN/TP overflow Ensemble 93/70 Wind speed (50/50) Wind Direction Ensemble 97/77 (max) 74/99 Chandramou FC ANN 49 R2: 0.63- River Flow; li et al. 0.94 Total coliforms; (2007) 97/61 background colonies; (TN/TP) Fecal Streptococci; turbidity; calcium hardness Mas & FC LR 7 51-38 Water temperature; Ahlfeld (TN/TP) Precipitation; (2007) LR R2<0.5 Precipitation -1 day; ANN 58-75/46 Conductivity; 61- Stream flow; 81/46-62

He & He EN, TC, FC ANN 16 R2: Water temperature; (2008) 0.845- Conductivity; 0.932 Turbidity; Last Rain; Rainfall Tide Height; Wave height; pH; Flow rate Heberger et EN LR R2: 0.42- Stream flow; al. (2008) 0.82 Precipitation; 88,84 Time since last rainfall;

245

(TN) hysteresis 89,100 (TP) Tufail et al. 30-day LR 4 R2: 0.58- Daily turbidity; (2008) geometric ANN .69 Daily stream flow mean EC FFSGA R2: 0.58- 0.73 R2: 0.66- 0.7 Daily EC LR 4 R2: 0.26 Daily turbidity; ANN R2: 0.27 Daily stream flow FFSGA R2: 0.28 Jagupilla at FC MLR R2: 0.6- Temperature, al. 2010 0.96 Daily mean discharge, Precipitation in 24 hours previous, Fecal coliform concentration upstream, Daily mean discharge (t-1), D.C. Love et FC; MLR 6 R2: 0.19- Water temp; Salinity; al. (2010) EC; 0.69 dissolved oxygen; pH; EN; depth; collection time Somatic coliphage F+ coliphage Hampson et FC MLR 3 R2: Dairy/km2; al (2010) EN 0.311- Sheep/km2; 0.624 Human/km2 Turgeon et FC MLR 24 Hosmer- Ruminant production in al. (2011) Lemesho the area of interest w test (<2km) p=0.523 Urban

S. FC MLR; 18 TN: Log Discharge * Motamarri ANN; >92% all Rainfall in previous 168 & D. Learning FN: hours* Boccelli Vector >40% Time since Storm >0.5 (2012) Quantizatio MLR in* n FN: 30- Time since Storm >0.25 40% in* ANN,LV Log Discharge at t-1* Q Jones et al EC Linear 29 R2: Location; (2013) EN Mixed 0.355- Rainfall & Combined Effects 0.658 sewer overflow;

246

Cumulative solar radiation

 FFSGA – Fixed Functional set algorithms; MLR – Multivariate Linear Regression; ANN - Artificial Neural Networks; LR – Logistical Regression  EN – Enterococcus; FC – Fecal Coliform; TC – Total Coliform  FN – False Negative; FP – False Positive  Only one interaction term was noted in the studies reviewed. This was rainfall (24 hour total), onshore wind vector (4 hour average) and turbidity (4 hour) (Olyphant & Whitman, 2004).  Motamarri & Boccelli (2012) did not determine significance however, found the variables that were most important across four different models. The top five are provided here.

247

Appendix G: Descriptive Statistics

248

Categorical Variables

Positive Total Results Blanks Results Watershed Stressed 175 213 0 Watershed Impacted 18 213 0 Watershed Healthy 20 213 0 Unmarked Swimming Area 77 195 18 General Public Access 144 188 25 Residents and Guest Access 155 189 24 Itinerant use Access 61 125 88 EHSS Average Bather Density - Low 90 211 2 EHSS Average Bather Density - Med 33 211 2 EHSS Average Bather Density - High 8 211 2 Residential Density - Low 41 182 31 Residential Density - Med 64 182 31 Residential Density - High 52 182 31 Beach Material - Mucky 7 182 31 Beach Material - Rocky 14 182 31 Beach Material - other 1 182 31 Beach Material – Sandy 160 182 31 Beach Grooming 68 177 36 Urban 45 129 84 Residential 150 198 15 Field 65 123 90 Marsh/Swamp 7 120 93 Harbour 19 123 90 Rural 73 168 45 Forest 129 196 17 Hills/Uplands 95 145 68 Landfill 1 119 94 Agricultural 16 122 91 Commercial 20 128 85 Industrial 3 121 92 River/Stream/Ditch 11 122 91 Other 1 82 131 Commercial/Industrial Discharges 9 98 115 Marinas 20 104 109 Motorized Water Craft 195 206 7 Stormwater Runoff from Areas subject to 7 93 120 pesticide application Stormwater Runoff from Areas subject to 8 94 119 fertilizer application

249

Stormwater Runoff from Urban Areas 60 126 87 Municipal Sewage Discharges 8 90 123 Stormwater Drains/Discharges 10 94 119 Wastes from Animal Feeding Operations 0 91 122 Combined Sewer Overflows 0 91 122 Discharging Private Sewage Systems 3 93 120 Holding Tanks 190 200 13 Communal Collection Systems 21 109 104 Other Fecal Waste Discharges 2 91 122 Stormwater runoff from Agricultural 9 93 120 Areas Stormwater runoff from areas receiving 2 91 122 Sewage Sludge Stormwater runoff from Beach and 192 201 12 Surrounding areas Stormwater runoff from Surrounding 123 174 39 Facilities Stormwater runoff from residential Areas 137 185 28 Stormwater runoff from other areas 0 0 213 Natural drainage 0 64 149 Pets Allowed 67 181 32 Steep Slopes or Drop-offs 19 134 79 Depths Greater than 4.5m 12 129 84 Large Rocks 34 139 74 Slippery or uneven bottom 26 139 74 Dense Aquatic Plants 41 147 66 Strong Currents or rip tides 0 126 87 Undertows 0 126 87 EHSS Amount of Refuse - Low 122 207 6 EHSS Amount of Refuse - Med 10 207 6 EHSS Amount of Refuse - High 0 207 6 EHSS Food Related Litter 105 174 39 EHSS Medical Litter 0 103 110 EHSS Sewage Litter 6 108 105 EHSS Household Waste 70 161 52 EHSS Building Materials 9 106 107 EHSS Fishing related refuse 4 104 109 Dead Fish 6 108 105 EHSS Amount of algae on beach - Low 86 211 2 EHSS Amount of algae on beach - Med 29 211 2 EHSS Amount of algae on beach - High 9 211 2 Amount of algae in swimming area - 58 193 20 Low Amount of algae in swimming area - 43 193 20 Med Amount of algae in swimming area - 26 193 20 250

High automobiles permitted near beach 71 212 1 boats permitted near beach 158 211 2 Cyanobacterial blooms 1 121 92 Schistosomes 4 126 87 Large Number of Aquative Plants 27 134 79 Access For persons with Disabilities 85 178 35 Toilets and Showers may contaminate 2 177 36 bathing area Animal proof Litter Bins 67 179 34 Accessible by Road 103 178 35 Accessible by Path 165 181 32 Parking Area Available 160 192 21 Emergency Number Posted 28 183 30 Emergency Contact information posted 29 183 30 Beach posted for swimming suitability 20 183 30 Spill Procedure 0 182 31 Swimmer Injuries Procedure 15 182 31 Waterborne Disease Outbreaks Procedure 6 182 31 Other Procedures 2 182 31 Public Notification Methods 23 182 31 Prevailing Wind - onshore 81 177 36 Prevailing Wind - offshore 15 177 36 Prevailing Wind - parallel 36 177 36 Sunlight - overcast 46 181 32 Sunlight - partially cloudy 35 181 32 Sunlight - rainy 4 181 32 Rainfall During Sampling 10 193 20 Rainfall Last 24 hours 96 189 24 Wave Height - Low 85 207 6 Wave Height - Med 12 207 6 Wave Height - High 0 207 6 Flooding 17 181 32 Beach Grooming last 24 hours 31 174 39 Beach Grooming more than 24 hours 32 174 39 Seaweed/Algae on Beach 0 0 26 Seaweed/Algae In Swimming Area 0 0 30

251

Continuous and Integer Variables

Average MedianStd DevCou- Nulls Skew- Max Min nt ness Beach Length 109.4 75.0 115.9 211 2 3.63 700.0 10.0 Beach Width 17.2 13.5 12.8 211 2 2.49 84.0 1.0 Swimming Area 96.1 60.0 85.8 187 26 3.03 700.0 5.2 Length Swimming Area 41.8 25.0 38.6 156 57 2.8 300.0 - Width Birds 9.3 - 22.2 194 19 3.52 150.0 - Number of Toilets 3.2 2.0 3.4 200 13 2.99 22.0 - Number of 1.4 - 2.4 194 19 2.28 10.0 - Showers Number of Litter 3.0 2.0 3.3 202 11 2.55 18.0 - Bins Sample Time - 24H 11:59 12:00 2:48 213 0 -0.20 0.8 0.1 Air Temperature 20.0 20.0 4.9 186 27 0.006 32.0 9.0 Water Temperature 20.0 21.0 2.7 206 7 -0.14 26.0 12.0 Sechi Disc 94.3 100.0 40.3 158 55 -0.68 200.0 - Wind Speed 1.3 1.0 1.2 200 13 0.91 5.0 - Swimmer Number 9.9 - 29.9 173 40 6.3 300.0 - Boater Number 4.3 1.0 8.7 175 38 3.59 50.0 - Sample 1 Depth 1.2 1.2 0.2 206 7 1.5 0.5 Sample 1 15.2 - 154.6 200 13 2,096.0 - Microcystin Sample 1 E. coli 15.4 - 50.0 213 0 528.0 - Sample 1 3505.7 857.0 9,088.7 210 3 86,200 - Coliforms Sample 1 Fecal 4.7 - 11.0 41 172 60.0 - Strep Sample 1 pH 8.7 8.7 0.3 195 18 -0.52 9.4 7.9 Sample 1 237.5 220.5 133.5 194 19 1.73 909.0 8.5 Alkalinity Sample 1 1094.8 823.0 1,299.6 194 19 6.36 14,569 113.0 Conductivity Sample 2 NTU 19.1 3.8 55.3 127 86 428.0 0.6 Sample 2 Depth 1.2 1.2 0.2 204 9 1.5 0.5 Sample 2 E. coli 16.7 - 51.5 213 0 530.0 - Sample 2 3566.7 816.0 9,052.4 213 0 90,600 - Coliforms Sample 3 NTU 17.0 4.0 45.7 128 85 302.0 0.5 Sample 3 Depth 1.2 1.2 0.2 204 9 1.8 0.5 Sample 3 0.0 - - 16 197 - - Microcystin

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Sample 3 E. coli 15.9 - 54.9 212 1 512.0 - Result Sample 3 3675.1 754.0 9,406.9 212 1 74,900 - Coliforms Sample 3 Fecal 15.0 - 40.6 32 181 210.0 - Strep Sample 4 NTU 19.3 4.0 60.5 129 84 530.0 0.6 Sample 4 Depth 1.2 1.2 0.2 203 10 1.5 0.5 Sample 4 E. coli 15.7 - 53.8 213 0 536.0 - Sample 4 8430.2 809.0 82,485.1 213 0 1,203,300 - Coliforms Sample 5 NTU 17.5 4.7 43.4 126 87 270.0 - Sample 5 E. coli 15.0 - 49.5 213 0 450.0 - Sample 5 3799.8 794.0 10,485.4 213 0 104,620 - Coliforms Average Turbidity 16.08 4.56 56.65 213 87 5.3 455.6 Log of Average 1.68 1.52 1.31 213 87 6.12 Turbidity Maximum 23.64 5.16 88.99 213 87 7.75 886 Turbidity Log of Maximum 0.83 0.71 0.56 213 87 2.95 Turbidity Maximum E. coli 37.4 10 73.57 213 1 4.42 536 E. coli Geometric 10.9 0.6 42.2 213 1 7.8 467.1 - Mean

253

Appendix H: Analysis of Variables

254

Variables

Descriptive statistics of all parameters are found in the appendix

Time dependent variables

Turbidity Figure 24: Average Turbidity Histogram

TurbidityAve .025 .02 .015 Density .01 .005 0 0.00 100.00 200.00 300.00 400.00 TurbidityAve

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Figure 25: Average Turbidity +1 Log Histogram

TurbidityAveLog .4 .3 .2 Density .1 0 0.00 2.00 4.00 6.00 TurbidityAveLog

Figure 26: Maximum Turbidity Histogram

TurbidtyMax .015 .01 Density .005 0 0.00 200.00 400.00 600.00 800.00 TurbidtyMax

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Figure 27: Natural Log of Maximum Turbidity +1

TurbidityMaxLog .8 .6 .4 Density .2 0 0.00 1.00 2.00 3.00 TurbidityMaxLog

A visual inspection of the four figures above shows that the log figures are more normal in shape and should be preferred. In addition, the average turbidity and the maximum turbidity have a skewness greater than five and therefore, as noted earlier, log transformation of the variables should be used.

257

Figure 28: Turbidity versus Geometric Mean of E.coli

Ecoli Geometric Mean

3.00 2.00 TurbidityMaxLog 1.00 0.00 1000.00

500.00 TurbidtyMax

0.00 500.00

TurbidityAve

0.00 6.00 4.00 TurbidityAveLog 2.00 0.00 0.00 500.000.00 1.00 2.00 3.000.00 500.00 1000.000.00 500.00

Visually, there seems to be a positive correlation between turbidity and the geometric mean of E. coli. The table below provides the correlation coefficients between turbidity variables and E. coli variables. There is a relatively high positive correlation between the bacteria variables and the log of the turbidity parameters. This is further demonstrated in the figure following.

Table 27: R2 between the Turbidity Parameters and E.coli Parameters Average Log of Maximum Log of Turbidity Average Turbidity Maximum Turbidity Turbidity E.coliGeometric Mean 0.1596 0.2449 0.0952 0.2283 E.coliDetected 0.1434 0.3537 0.0671 0.3428 LogMax E.coli 0.164 0.3302 0.065 0.3092 Maximum E. coli 0.2255 0.3052 0.1327 0.2899

258

Figure 29: Sample Turbidity versus E.coli Parameters

Ecoli Geometric Mean

3.00

2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli

0.00 500.00

TurbidityAve

0.00 6.00 4.00 TurbidityAveLog 2.00 0.00 1000.00

500.00 TurbidityMax

0.00 3.00 2.00 TurbidityMaxLog 1.00

0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 500.000.00 2.00 4.00 6.000.00 500.00 1000.00

Table 28: Turbidity R2 with Selected Variables Turbidity Turbidity Max Ave Log Log Turbidity Ave Log 1.0000 Turbidity Max Log 0.9928 1.0000 Sample1pH -0.0502 -0.0590 Sample1 Alkalinity -0.0715 -0.0838 Sample1 Conductivity 0.1895 0.1651 Rainfall Last24 hours -0.2688 -0.2699 Rainfall During Sampling -0.1431 -0.1477 Sechi Disc -0.6618 -0.6585

The above table describes the correlation between turbidity parameters and various other selected parameters. As one can see, there are a number of other correlations. Sechi disk and turbidity are both measures of optic light penetration. For example, as the turbidity increases, the sechi disk value will decreases resulting from a smaller depth at which the disk can be seen.

Table 29: Turbidity Parameters and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Average Turbidity & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

259

Detected T-Test Pr(T < t) = 0.054 Pr(|T| > |t|) = 0.1091 Pr(T > t) = 0.9454 Log of Average Turbidity Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 & E.coli Detected T-Test Pr(T < t) = 0.000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000 Maximum Turbidity & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.228 Pr(|T| > |t|) = 0.4554 Pr(T > t) = 0.7723 Log of Maximum Turbidity Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 & E.coli Detected T-Test Pr(T < t) = 0.000 Pr(|T| > |t|) = 0.0001 Pr(T > t) = 1.0000 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected for all except the log of the maximum and average turbidity and there is a significant difference between the remaining three data sets. In fact, it can be said that when the turbidity (log of average and log of maximum) is higher when E.coliis detected.

Water and Air Temperature The figure below shows the plots of air and water temperature versus various E. coli parameters. The maximum E. coli appears to be related to temperature. The geometric mean of E. coli appears to have a weak relationship to the temperature variables.

Figure 30: Water and Air Temperature versus E. coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 30.00 Air 20.00 Temperature 10.00 25.00 20.00 Water 15.00 Temperature 10.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.0010.00 20.00 30.00

260

Figure 31: Box Plot of Air Temperature over E. coli Detected

0 1 30 25 20 Air Temperature Air 15 10

Graphs by EcoliDetected

Figure 32: Box Plot of Water Temperature over E. coli Detected

0 1 25 20 Water Temperature 15 10

Graphs by EcoliDetected

261

The above box plots indicate that there is a relationship between temperatures and whether E. coli is detected.

Table 30: Air and Water Temperatures and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Air Temperature & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.0013 Pr(T > t) = 0.999 Water Temperature & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.002 Pr(|T| > |t|) = 0.0046 Pr(T > t) = 0.998 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected and there is a significant difference between the data sets. In fact, it can be said that the temperature (water and air) when E.coli is not detected is lower than when it is detected.

Table 31: Temperatures R2 versus Selected Variables Parameter E.coli Air Water Sechi Turbidity Geometric Temperature Temperature Disc Ave Log Mean E.coli Geometric 1.0000 Mean Air Temperature 0.1315 1.0000 Water Temperature 0.0841 0.7671 1.0000 Sechi Disc -0.2367 -0.3790 -0.4814 1.000 Turbidity Ave Log 0.2262 0.4602 0.5747 -0.66 1.000

The correlation between water temperature and air temperature is approximately 77% which indicates that the two variables are very closely related. However, neither are correlated well with the geometric mean of E. coli. At this stage both variables are retained due to R2 being less than 0.8.

Sechi Disk A Sechi disk measures the depth to which one can view a white and black disc of a known size. Intuitively this should be related to turbidity. In other words, the more turbid the water, the shallower the depth at which one can view the disc. The histogram below indicates that the distribution of sechi disk results is not normally distributed.

262

Figure 33: Sechi Disk Histogram .03 .02 Density .01 0 0.00 50.00 100.00 150.00 200.00 Sechi Disc

The graphs below indicate a negative relationship between turbidity parameters and the sechi disk result, which is as expected. There does not appear to be a strong relationship between sechi disk results and E.coli parameters.

263

Figure 34: Sechi Disk Versus E.coli & Turbidity Parameters

Sechi Disc

500.00 Ecoli Geometric Mean 0.00 3.00

2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli

0.00 500.00

TurbidityAve

0.00 1000.00

500.00 TurbidtyMax

0.00 3.00 2.00 TurbidityMaxLog 1.00

0.00 0.00 100.00 200.000.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 500.000.00 500.00 1000.00

Table 32: Sechi Disk and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Sechi Disk & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.999 Pr(|T| > |t|) = 0.0003 Pr(T > t) = 0.0001 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected and there is a significant difference between the data sets. In fact, it can be said that the sechi disc value when E.coli is not detected is higher than when it is detected.

Table 33: R2 between Sechi Disc and Selected Parameters Sechi Air Water Turbidity Turbidity Turb- Turb- Disc Temp Temp MaxLog Max idity idity AveLog Ave SechiDisc 1 Air -0.379 1 Temperature Water -0.4814 0.7671 1 Temperature Turbidity -0.6612 0.4522 0.5389 1 MaxLog Turbidity Max -0.3205 0.2106 0.2371 0.6756 1 Turbidity -0.6661 0.46020.5747 0.9923 0.6477 1

264

AveLog Turbidity Ave -0.3072 0.2621 0.301 0.7682 0.957 0.7528 1

The Sechi Disc values does so a large negative correlation between both the log of the maximum turbidity (R2=66%) and the log of the average turbidity (R2=67%) . However, the value is less than 0.8, therefore these variables cannot be eliminated at this stage due to multicollinearity.

Prevailing Winds The following three box plots show the relationship between the detection of E.coli and wind direction. Offshore winds seem to be correlated to an increased geometric mean of E. coli.

Figure 35: Onshore Wind Versus E.coli Geometric Mean

Prevailing Winds - onshore 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

265

Figure 36: Offshore Wind Versus E.coli Geometric Mean

Prevailing Winds - offshore 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 37: Parallel Wind Versus E.coli Geometric Mean

Prevailing Winds - parallel 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

266

Table 34: Wind Direction and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Onshore Winds & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.151 Pr(|T| > |t|) = 0.3025 Pr(T > t) = 0.8487 Offshore Winds & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.032 Pr(|T| > |t|) = 0.0636 Pr(T > t) = 0.9682 Parallel Winds & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.896 Pr(|T| > |t|) = 0.2073 Pr(T > t) = 0.104 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the presence of onshore winds does not affect E.coli results.

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the presence of parallel winds does not affect E.coli results.

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is a not significant difference between the data sets. However, if one sets the null hypothesis to the difference being less than 0, the null hypothesis is rejected and the presence of off shore winds does result in higher E.coli results.

Table 35: R2 between Wind Direction and E. coli Parameters Onshore Offshore Parallel E. coli Detected 0.0311 0.0715 -0.0109 E. coli Geometric Mean 0.0782 0.1401 -0.0955 LogMax E. coli 0.056 0.0438 -0.0433 Max E. coli -0.0023 0.1216 -0.0216

The correlation coefficient demonstrates again that the highest correlation between E.coli parameters and wind direction is with offshore winds.

Wind Speed The figure below does not show a significant relationship between wind speed and E. coli parameters.

267

Figure 38: Wind Speed Versus E.coli Parameters

Wind Speed

500.00

Ecoli Geometric Mean

0.00 3.00

2.00 Log Max Ecoli 1.00 add 1

0.00 600.00

400.00 Max 200.00 Ecoli

0.00 0.00 5.000.00 500.000.00 1.00 2.00 3.00

The histogram below shows a generally normal distribution of wind speed with a slight skew to the right.

268

Figure 39: Histogram of Wind Speed 1 Density .5 0 0.00 1.00 2.00 3.00 4.00 5.00 Wind Speed

Figure 40: Geometric Mean of E.coli over Wind Speed

Wind Speed 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

012345

269

From the above diagram there appears to be a difference in the geometric mean of E.coli when the wind speed is high. However, when wind speeds 4 and 5 are lumped together and a t-test completed, the null hypothesis is rejected and no significance is found at the 95% significance level.

Table 36: Two Tailed T-test between Geometric Mean E.coli and Wind Speed 4&5 Group Obs Mean Std. Err.Std. [95% Interval] Dev. Conf. 0 203 11.12818 3.020737 43.03888 5.17196 17.0844 1 9 5.378239 4.161803 12.48541 -4.2189 14.97537 combined 212 10.88408 2.898109 42.19711 5.171123 16.59704 diff 5.74994314.40284 -22.6427 34.14262 diff = mean(0) - mean(1) t = 0.3992

Ho: diff = 0 degrees of freedom = 210

Table 37: Wind Direction and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Wind Speed 4&5 & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.655 Pr(|T| > |t|) = 0.6901 Pr(T > t) = 0.3451 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the presence of wind at a Beaufort Speed of 4 or 5 does not affect E.coli results.

Table 38: : R2 between Wind Speed and E. coli Parameters Wind Air Sechi Turb- Turb- Turb- Turb- Speed Temp Disc idity idity idity idity Max Max Ave Ave Log Log Wind Speed 1 Air Temperature 0.001 1 Sechi Disc 0.081 -0.3848 1 Turbidity MaxLog -0.012 0.4267 -0.684 1 Turbidity Max -0.061 0.2116 -0.328 0.6764 1 Turbidity AveLog -0.009 0.4312 -0.69 0.9925 0.6514 1 Turbidity Ave -0.022 0.2612 -0.315 0.7686 0.957 0.7563 1 Ecoli Geometric 0.024 0.1197 -0.243 0.2101 0.0778 0.2278 0.1399 Mean

Based on the above analysis, wind speed as an individual parameter does not seem overly important to E.coli levels in recreational waters.

270

Table 39: R2 between Wind Speed and Direction Prevailing Prevailing Prevailing Winds Parameter Wind Speed Winds Winds Parallel Offshore Onshore Wind Speed 1 Prevailing Winds 0.1775 1 Parallel Prevailing Winds 0.0976 -0.1423 1 Offshore Prevailing Winds 0.3529 -0.4776 -0.2582 1 Onshore

Onshore winds are slightly correlated to wind speed meaning that higher wind speeds are expected when there are onshore winds.

Swimmer Density The following three box plots do not visually show a relationship between the geometric mean of E. coli and swimmer density categories.

Figure 41: E.coli Geometric Mean versus low Swimmer Density

SwimmerDensityLow 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

271

Figure 42: E.coli Geometric Mean versus Medium Swimmer Density

SwimmerDensityMed 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 43: E.coli Geometric Mean versus High Swimmer Density

SwimmerDensityHigh 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

272

From the above figures and a series of two tail t-tests shown below, swimmer density as an individual parameter appears to have little relationship to E.coli results.

Table 40: Swimmer Density and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Swimmer Density Low & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.802 Pr(|T| > |t|) = 0.3960 Pr(T > t) = 0.1980 Test Swimmer Density Medium Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 & E.coli Geometric Mean Pr(T < t) = 0.500 Pr(|T| > |t|) = 0.9995 Pr(T > t) = 0.5003 T-Test Swimmer Density High & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.278 Pr(|T| > |t|) = 0.5564 Pr(T > t) = 0.7218 Test Note: diff = mean(0) - mean(1)

Table 41: R2 between Swimmer Density and E.coli Parameters Swimmer Swimmer Swimmer Density Density Density Low Medium High Swimmer Density Low 1 Swimmer Density Medium -0.2906 1 Swimmer Density High -0.1285 -0.0628 1 E.coliGeometric Mean -0.0628 0 0.0435 E.coliDetected -0.0184 0.002 0.0415 Log Max E. coli -0.0455 0.0794 0.1444 Max E. coli -0.0971 0.0577 0.2853

From the above figures and the series of two tail t-tests, swimmer density as an individual parameter appears to have little relationship to E.coli results.

Sunlight The following three box plots do not visually show a relationship between the geometric mean of E. coli and sunlight density categories.

273

Figure 44: E.coli Geometric Mean versus Overcast Sun

Sunlightovercast 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 45: E.coli Geometric Mean versus Cloudy

Sunlightrainy 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

274

Figure 46: E.coli Geometric Mean versus Sunny

Sunlightsunny 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Table 42: Sunlight and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Cloudy & E.coli Geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Mean T-Test Pr(T < t) = 0.709 Pr(|T| > |t|) = 0.582 Pr(T > t) = 0.2910 Overcast & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.918 Pr(|T| > |t|) = 0.164 Pr(T > t) = 0.0819 Sunny & E.coli Geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Mean T-Test Pr(T < t) = 0.062 Pr(|T| > |t|) = 0.124 Pr(T > t) = 0.9381 Note: diff = mean(0) - mean(1)

From the above figures and the series of two tail t-tests, the degree of sunlight as an individual parameter appears to have little relationship to E.coli results. However, it is noted for future model building that overcast skies might be related to higher E.coli results. In addition, sunny skies might be related to lower E.coli results.

Rainfall During Sampling The following graph shows relationships between E. coli parameters and rainfall categories. No relationships are apparent.

275

Figure 47: Rainfall Parameters versus E.coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 1.00 Rainfall 0.50 During Sampling 0.00 1.00 Rainfall 0.50 Last 24 hours 0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 0.50 1.00

The following two box plots do not visually show a relationship between the geometric mean of E. coli and rainfall categories.

276

Figure 48: E.coli Geometric Mean versus Rainfall during Sampling

RainfallDuringSampling 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 49: E.coli Geometric Mean versus Rainfall in Last 24 Hours

RainfallLast24hours 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

277

Table 43: Rainfall and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Rainfall during Sampling & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.784 Pr(|T| > |t|) = 0.432 Pr(T > t) = 0.2161 Test Rainfall in Last 24 Hours & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.978 Pr(|T| > |t|) = 0.045 Pr(T > t) = 0.0225 Test Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected and there is a significant difference between the data sets. In fact, it can be said that the E.coli geometric mean lower when it has rained in the last 24 hours. This does appear to be counterintuitive.

Table 44: R2 between Rainfall Parameters and E.coli Parameters Rainfall during Rainfall in the last 24 E.coli Sampling hours Geometric Mean Rainfall during 1 Sampling Rainfall in the last 24 0.1881 1 hours E.coli Geometric Mean -0.0589 -0.1474 1

The correlation between rainfall and the geometric mean of E. coli is not strong.

Maximum Wave Height The histogram below indicates that the wave height to parameters is not normally distributed. It is skewed to the right.

278

Figure 50: Maximum Wave Height Histogram 8 6 4 Density 2 0 0.00 0.20 0.40 0.60 0.80 1.00 Wave Height Range To

The figure below shows the relationships between E.coli parameters and maximum wave height. From this graph it is apparent that there are some relationships.

Figure 51: Maximum Wave Height versus E.coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 1.00

0.50 EcoliDetected

0.00 1.00 Wave 0.50 Height Range To 0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 0.50 1.00

279

Table 45: Maximum Wave Height and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Wave Height Maximum & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli detected T-Test Pr(T < t) = 0.0023 Pr(|T| > |t|) = 0.0046 Pr(T > t) = 0.9977 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected and there is a significant difference between the data sets. In fact, it can be said that the E.coli is detected more frequently when there are higher wave heights.

Table 46: R2 between Maximum Wave Height and E.coli Parameters Maximum E.coli E.coli LogMax Maximum Wave Geometric Detected E.coli E. coli Height Mean Maximum Wave Height 1 E.coliGeometric Mean 0.2042 1 E.coliDetected 0.2064 0.273 1 LogMax E.coli 0.209 0.4372 0.7543 1 Maximum E. coli 0.2062 0.8028 0.4348 0.6642 1

Based on calculated correlations, there are relationships between maximum wave height and E.coli parameters.

Flooding The graph below does not indicate any relationship between whether flooding was observed and E.coli parameters.

280

Figure 52: Presence of flooding versus E.coli Parameters

Ecoli Geometric Mean

3.00

2.00 Log Max Ecoli 1.00 add 1

0.00 600.00

400.00 Max 200.00 Ecoli

0.00 1.00

0.50 Flooding

0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00 400.00 600.00

Table 47: Flooding and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Evidence of Flooding & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli detected T-Test Pr(T < t) = 0.829 Pr(|T| > |t|) = 0.3415 Pr(T > t) = 0.1707 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the presence of flooding does not affect E.col iresults.

Birds on Beach The number of birds on the beach is not normally distributed but is skewed to the right.

281

Figure 53: Number of Birds on the Beach Histogram .08 .06 .04 Density .02 0 0.00 50.00 100.00 150.00 Birds

However, the chart below shows a relationship between the number of birds and E.coli parameters.

282

Figure 54: Number of Birds versus E.coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 1.00

0.50 EcoliDetected

0.00 150.00 100.00 Birds 50.00 0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 0.50 1.00

Table 48: Number of Birds and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Number of birds & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.508 Pr(|T| > |t|) = 0.984 Pr(T > t) = 0.4921 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) for all tests is accepted and there is not a significant difference between the data sets tested. In fact, it can be said that the the number of birds does not affect the detection of E. coli.

Table 49: R2 between the Number of Birds and E.coli Parameters E.coli Number of Geometric E.coli LogMax Maximum Birds Mean Detected E.coli E. coli Number of Birds 1 E.coliGeometric Mean 0.1766 1 E.coliDetected -0.0011 0.283 1 LogMax E.coli -0.0116 0.4489 0.7517 1 Maximum E. coli 0.1126 0.8081 0.432 0.6611 1

283

The number of birds is positively correlated with the E. coli geometric mean and the Maximum E.coli variables, as would be expected. pH For this analysis, [H+] was calculated.

Figure 55: pH Histogram 2 1.5 1 Density .5 0 8.00 8.50 9.00 9.50 Sample 1 pH

The above diagram is relatively normal in its distribution. The histogram below for H+ is not.

284

Figure 56: H+ Histogram 4.0e+08 3.0e+08 Density 2.0e+08 1.0e+08 0 0.00e+00 5.00e-09 1.00e-08 1.50e-08 Sample 1 [H+]

The figure below indicates that there may be a relationship between pH and E.coli parameters.

285

Figure 57: Sample pH versus E.coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 9.50

9.00 Sample 8.50 1 pH 8.00 1.50e-08

1.00e-08 Sample 5.00e-09 1 [H+] 0.00e+00 0.00 500.000.00 1.00 2.00 3.000.00200.00400.00600.008.00 8.50 9.00 9.50

Table 50: Sample pH parameters and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Sample pH & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 detected T-Test Pr(T < t) = 0.011 Pr(|T| > |t|) = 0.021 Pr(T > t) = 0.9894 Sample [H+] & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 detected T-Test Pr(T < t) = 0.987 Pr(|T| > |t|) = 0.026 Pr(T > t) = 0.0128 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected for all tests and there is a significant difference between the data sets. In fact, it can be said that the pH is higher and the [H+] is lower when E.coli is detected.

Table 51: R2 between the Sample pH and E.coli Parameters Sample E.coli Sample [H+] Geometric E.coli LogMax Maximum pH Mean Detected E.coli E. coli Sample pH 1 Sample [H+] -0.9168 1 E.coli Geometric Mean 0.0126 -0.01551 E.coli Detected 0.1692 -0.1615 0.2876 1 LogMax E.coli 0.1031 -0.0944 0.4445 0.7603 1

286

Maximum E. coli 0.0001 -0.0045 0.8109 0.4447 0.6583 1

The above correlations show a positive correlation between the sample pH and the log of the maximum E.coli. Other than the direction of the correlation, using the hydrogen ion concentration changes the results very little.

Conductivity Figure 58: Conductivity Histogram 6.0e-04 4.0e-04 Density 2.0e-04 0 0.00 5000.00 10000.00 15000.00 Sample 1 Conductivity

The above figure is does not appear to be a normal distribution and the skewness of conductivity was greater than 5 so conductivity was converted by applying the logarithm. The figure below results and is closer to a normal distribution.

287

Figure 59: Log of Conductivity Histogram 1 .8 .6 Density .4 .2 0 2.00 2.50 3.00 3.50 4.00 Sample 1 Log Conductivity

The figures below do not indicate a strong relationship between conductivity and E.coli parameters.

288

Figure 60: Sample Conductivity versus E.coli Parameters

EcoliDetected

500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 15000.00

10000.00 Sample 1 5000.00 Conductivity 0.00 0.00 0.50 1.000.00 500.000.00 1.00 2.00 3.000.00200.00400.00600.00

Table 52: Conductivity and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Conductivity & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.185 Pr(|T| > |t|) = 0.370 Pr(T > t) = 0.8152 Log of Conductivity & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.0001 Pr(|T| > |t|) = 0.0002 Pr(T > t) = 0.9999 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted for conductivity and there is not a significant difference between the data sets. In fact, it can be said that the conductivity is not significantly different when E.coli is detected. However, at a 95% degree of confidence, the null hypothesis (Ho) is rejected for the logarithm of conductivity and there is a significant difference between the data sets. In fact, it can be said that the logarithm of conductivity is significantly higher when E.coli is detected.

Table 53: R2 between the Sample Conductivity and E.coli Parameters Log of Cond- E. coli E.coli Log of Max Cond- uctivity Detected Geometric Max E. coli uctivity Mean E.coli Log of Conductivity 1 Sample Conductivity 0.7309 1 E.coli Geometric Mean 0.261 0.0597 1

289

E.coli Detected 0.129 0.0644 0.287 1 LogMax E.coli 0.2367 0.0255 0.7592 0.4446 1 Maximum E. coli 0.1728 0.0654 0.4438 0.8109 0.65831

Sample conductivity and the log of conductivity are not well correlated with E.coli parameters.

Time of day Figure 61: Time of Day Histogram 4 3 2 Density 1 0 0.00 0.20 0.40 0.60 0.80 1.00 Sample Time Decimal

The above histogram appears relatively normal in distribution. The figure below does not indicate that there is a relationship between the time of day and E.coli parameters.

290

Figure 62: Sample Time of Day versus E.coli Parameters

Sample Time Decimal

1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 0.00 0.50 1.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

Table 54: Time of Day and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Time of Day & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.175 Pr(|T| > |t|) = 0.350 Pr(T > t) = 0.8249 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the time of day is not significantly different when E.coliis detected.

Table 55: R2 between time of day and E.coli Parameters E.coli Ecoli Log Maximum Time Detected Geometric Max E. coli of Day Mean E. coli E.coli Detected 1 Ecoli Geometric 0.2854 1 Mean Log Max E. coli 0.7537 0.4391 1 Maximum E. coli 0.4442 0.8032 0.6606 1 Time of Day 0.0616 0.0062 0.0846 0.0799 1

Sample time of day is not well correlated with E.coli parameters.

291

Month The following four box plots do not suggest a relationship between month and E.coli parameters.

Figure 63: Geometric Mean of E.coli versus the Sample Month of June 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

292

Figure 64: Geometric Mean of E.coli versus the Sample Month of July 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 65: Geometric Mean of E.coli versus the Sample Month of August 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

293

Figure 66: Geometric Mean of E.coli versus the Sample Month of September 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Table 56: Month and E.coli Detected T-Test Results Parameters Tested Student T-Test Results June & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Mean T-Test Pr(T < t) = 0.317 Pr(|T| > |t|) = 0.634 Pr(T > t) = 0.6831 June & maximum E.coli T- Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Test Pr(T < t) = 0.490 Pr(|T| > |t|) = 0.980 Pr(T > t) = 0.5100 July & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Mean T-Test Pr(T < t) = 0.747 Pr(|T| > |t|) = 0.507 Pr(T > t) = 0.2533 July & maximum E.coli T- Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Test Pr(T < t) = 0.243 Pr(|T| > |t|) = 0.4850 Pr(T > t) = 0.7575 August & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Mean T-Test Pr(T < t) = 0.307 Pr(|T| > |t|) = 0.6129 Pr(T > t) = 0.6935 August & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.763 Pr(|T| > |t|) = 0.4745 Pr(T > t) = 0.2373 September & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 geometric Mean T-Test Pr(T < t) = 0.541 Pr(|T| > |t|) = 0.9176 Pr(T > t) = 0.4588 September & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.763 Pr(|T| > |t|) = 0.4745 Pr(T > t) = 0.2373 Note: diff = mean(0) - mean(1)

294

At a 95% degree of confidence, the null hypothesis (Ho) is accepted and there is not a significant difference between the data sets. In fact, it can be said that the month is not significantly different when E.coli is detected.

Table 57: R2 between Sample Month and E. Coli Parameters E.coli Ecoli Log Maximum Detected Geometric Max E. coli Mean E. coli E.coliDetected 1 Ecoli Geometric Mean 0.2854 1 Log Max E. coli 0.7537 0.4391 1 Maximum E. coli 0.4442 0.8032 0.6606 1 Sample Date -0.1535 -0.0514 -0.1311 -0.1337 Sample June 0.0967 0.0329 0.0244 0.0017 Sample July 0.0836 -0.0459 0.1016 0.0482 Sample August -0.1195 0.0349 -0.1158 -0.0494 Sample September -0.0254 -0.0072 0.0073 -0.0026

Sample month is not well correlated with E.coli parameters.

Water Seaweed and Algae Amounts The following four box plots do not suggest a relationship between the amount of algae and seaweed in the swimming area and E.coli parameters.

295

Figure 67: No Seaweed or Algae in the Swimming Area Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Seaweed&Algae in water None

Figure 68: Low Seaweed and Algae in Water Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Seaweed&Algae in water Low

296

Figure 69: Medium Seaweed and Algae in Water Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Seaweed&Algae in water Medium

Figure 70: High Seaweed and Algae in Water Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Seaweed&Algae in water High

297

Table 58: Seaweed and Algae levels in water and E.coli Detected T-Test Results Parameters Tested Student T-Test Results No Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric Mean T- Pr(T < t) = 0.186 Pr(|T| > |t|) = 0.3718 Pr(T > t) = 0.8141 Test No Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.022 Pr(|T| > |t|) = 0.0448 Pr(T > t) = 0.9776 No Seaweed/Algae & Log Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 of Maximum E.coli T-Test Pr(T < t) = 0.006 Pr(|T| > |t|) = 0.0109 Pr(T > t) = 0.9945 Low Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric Mean T- Pr(T < t) = 0.818 Pr(|T| > |t|) = 0.3639 Pr(T > t) = 0.1820 Test Low Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.874 Pr(|T| > |t|) = 0.2517 Pr(T > t) = 0.1259 Low Seaweed/Algae & Log Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 of Maximum E.coli T-Test Pr(T < t) = 0.861 Pr(|T| > |t|) = 0.2776 Pr(T > t) = 0.1388 Medium Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric Mean T- Pr(T < t) = 0.899 Pr(|T| > |t|) = 0.2029 Pr(T > t) = 0.1015 Test Medium Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.923 Pr(|T| > |t|) = 0.1539 Pr(T > t) = 0.0770 Medium Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Log of Maximum E.coli T- Pr(T < t) = 0.978 Pr(|T| > |t|) = 0.0446 Pr(T > t) = 0.0223 Test High Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric Mean T- Pr(T < t) = 0.056 Pr(|T| > |t|) = 0.1123 Pr(T > t) = 0.9439 Test High Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.300 Pr(|T| > |t|) = 0.6005 Pr(T > t) = 0.6997 High Seaweed/Algae & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Log of Maximum E.coli T- Pr(T < t) = 0.355 Pr(|T| > |t|) = 0.7108 Pr(T > t) = 0.6446 Test Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted for all except three data sets and there is not a significant difference between all but these three data sets.

The three data sets where there was a significant difference are as follows:

 No Seaweed/Algae & Maximum E.coli T-Test indicates that when there was no algae or seaweed in the swimming water, there is a significantly lower maximum E.coli results.  No Seaweed/Algae & Log of Maximum E.coli T-Test indicates that when there were no algae or seaweed in the swimming water, there is a significantly lower logarithm of maximum E.coli results.

298

 Medium Seaweed/Algae & Log of Maximum E.coli T-Test indicates that when there was a medium amount of algae or seaweed in the swimming water, there is a significantly higher logarithm of maximum E.coli results.

The correlation coefficients shown below indicate that there may be some weak relationships between the amount of algae and seaweed in a swimming area and the following four box plots do not suggest a relationship between month and E.coli parameters.

Table 59: R2 between Seaweed and Algae amounts and E. coli Parameters High Medium Low No Seaweed Seaweed Seaweed Seaweed and and and and Algae in Algae in Algae in Algae in Water Water Water Water High Seaweed and Algae in 1 Water Medium Seaweed and Algae in -0.1809 1 Water Low Seaweed and Algae in -0.2764 -0.3637 1 Water No Seaweed and Algae in Water -0.26 -0.3422 -0.5227 1 E.coli Detected 0.0838 -0.132 -0.0935 0.1478 E.coli Geometric Mean 0.1181 -0.0948 -0.0677 0.0666 LogMax E.coli 0.0277 -0.1491 -0.0809 0.1882 Maximum E. coli 0.0391 -0.1061 -0.0854 0.1489

299

Time independent variables

Pets Allowed The box plot below indicates that there may be a small difference in the geometric mean of E.coli and whether pets are allowed on a beach. “1” means that pets are allowed.

Figure 71: Pets Allowed Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Pets Allowed

Table 60: Pets Allowed and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Pets Allowed & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.519 Pr(|T| > |t|) = 0.963 Pr(T > t) = 0.4815 Pets Allowed & Log of Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Max E.coli T-Test Pr(T < t) = 0.512 Pr(|T| > |t|) = 0.977 Pr(T > t) = 0.4883 Pets Allowed & Max E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 T-Test Pr(T < t) = 0.578 Pr(|T| > |t|) = 0.845 Pr(T > t) = 0.4224 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted for all tests and there is not a significant difference between data sets. In fact, it can be said that the E. coli parameters are statistically equal in all cases.

Table 61: R2 between Pets Allowed and E.coli Parameters E.coli E.coli LogMax Maximum Pets Detected Geometric E.coli E. coli Allowed Mean

300

E.coli Detected 1 E.coli Geometric Mean 0.2776 1 LogMax E.coli 0.7495 0.4526 1 Maximum E. coli 0.4241 0.8074 0.6643 1 Pets Allowed 0.04 -0.0035 -0.0022 -0.0147 1 There are not large correlation coefficients between E. coli parameters and whether pets are allowed on a beach.

Residential Density The following five box plots do not show an apparent relationship between residential density categories and E. coli parameters.

Figure 72: No Residential Density Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Residential Density - None

301

Figure 73: Low Residential Density Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Residential Density - Low

Figure 74: Medium Residential Density Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Residential Density - Med

302

Figure 75: High Residential Density Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Residential Density - High

Figure 76: Any Residential Density Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Residential Density - Any

303

The chart below does not show a significant relationship between residential categories and E. coli parameters.

Figure 77: Residential Parameter Parameters versus E.coli Parameters

Ecoli Geometric Mean

3.00

2.00 Log Max Ecoli 1.00 add 1

0.00 600.00

400.00 Max 200.00 Ecoli

0.00 1.00

0.50 EcoliDetected

0.00 1.00 Residential 0.50 Density - None

0.00 1.00 Residential 0.50 Density - Low

0.00 1.00 Residential 0.50 Density - Med

0.00 1.00 Residential 0.50 Density - High

0.00 1.00 Residential 0.50 Density - Any

0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00 400.00600.000.00 0.50 1.000.00 0.50 1.000.00 0.50 1.000.00 0.50 1.000.00 0.50 1.00

Table 62: Residential Density Parameters and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results No residential density & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.430 Pr(|T| > |t|) = 0.8609 Pr(T > t) = 0.5696 Test Low residential density & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.024 Pr(|T| > |t|) = 0.0488 Pr(T > t) = 0.9756 Test Medium residential density Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 & E.coli Geometric Mean Pr(T < t) = 0.919 Pr(|T| > |t|) = 0.1612 Pr(T > t) = 0.0806 T-Test High residential density & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.682 Pr(|T| > |t|) = 0.6360 Pr(T > t) = 0.3180 Test Any residential density & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.570 Pr(|T| > |t|) = 0.8609 Pr(T > t) = 0.4304 Test Note: diff = mean(0) - mean(1)

304

At a 95% degree of confidence, the null hypothesis (Ho) is accepted for all test except the low residential density test and there is not a significant difference between the remaining four data sets. In fact, it can be said that when the geometric mean is statistically equal in all cases except where the residential density is low. In this case, the geometric mean of E.coli is greater than when the residential density is not low.

Table 63: R2 between the Residential Density Parameters and E.coli Parameters No Low Medium High Any Resid- Resid- Resid- Resid- Resid- ential ential ential ential ential density density density density density No Residential density 1 Low Residential density -0.2166 1 Medium Residential density -0.2961 -0.4002 1 High Residential density -0.2507 -0.339 -0.4632 1 Any Residential density -1 0.2166 0.2961 0.2507 1 E.coli Geometric Mean 0.0131 0.1467 -0.1046 -0.0354 -0.0131 E.coli Detected 0.1232 0.0018 -0.072 -0.0195 -0.1232 LogMax E.coli 0.1354 0.0306 -0.0757 -0.0519 -0.1354 Maximum E. coli 0.0887 0.0989 -0.1479 -0.0028 -0.0887

The table above shows correlations between E.coli parameters and residential density. Although the correlations are relatively low, the results are interesting in that all the signs are opposite the expected. In other words, the E.coli results are higher where there is no or low residential density and lower where the residential density is high or medium.

Holding tanks The bow plot below does not show a difference in the geometric mean of E. coli and the presence of holding tanks.

305

Figure 78: Holding Tanks Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Holding Tanks

The graph below does not show a relationship between the presence of holding tanks and E. coli parameters.

306

Figure 79: Presence of Holding Tanks versus E.coli Parameters

Ecoli Geometric Mean

3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 1.00

0.50 EcoliDetected

0.00 1.00

0.50 Holding Tanks

0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 0.50 1.00

Table 64: Holding Tanks and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Holding Tanks present & Ha: diff < 0 Ha: diff !=0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.312 Pr(T > t) =0.6242 Pr(T > t) = 0.6879 Test Holding Tanks present & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 log of the maximum of Pr(T < t) = 0.767 Pr(|T| > |t|) = 0.4662 Pr(T > t) = 0.2331 E.coli T-Test Holding Tanks present & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum of E.coli T-Test Pr(T < t) = 0.410 Pr(|T| > |t|) = 0.8200 Pr(T > t) = 0.5900

Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is accepted for all tests and there is not a significant difference between the geometric mean when holding tanks are present or when they are not present. In fact, it can be said that when the geometric mean is statistically equal.

Table 65: R2 between Holding Tanks and E.coli Parameters E.coli Ecoli Log Maximum Holding Detected Geometric Max E. coli Tanks Mean E. coli

307

E.coli Detected 1 Ecoli Geometric 0.2784 1 Mean Log Max E. coli 0.75 0.4422 1 Maximum E. coli 0.4338 0.8026 0.6629 1 Holding Tanks -0.0151 0.0349 -0.0519 0.0162 1

The table above shows correlations between E.coli parameters and the presence of holding tanks. All correlations are low.

Parking Available The following two figures provide a graphical representation of the relationship between the availability of parking and E. coli parameters. In both cases, no relationship is apparent.

Figure 80: Parking Available Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Parking Area Available

308

Figure 81: Presence of a Parking Lot versus E.coli Parameters

Parking Area Available

1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 0.00 0.50 1.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

Table 66: Parking Lot Presence and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Parking Lot & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.982 Pr(|T| > |t|) = 0.0369 Pr(T > t) = 0.0185 Parking Lot & E.coli Log Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 of Max E.coli T-Test Pr(T < t) = 0.060 Pr(|T| > |t|) = 0.1191 Pr(T > t) = 0.9405 Parking Lot & Maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 of E.coli T-Test Pr(T < t) = 0.582 Pr(|T| > |t|) = 0.8370 Pr(T > t) = 0.4185 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is not rejected for all except geometric mean of E.coli and there is a significant difference between these data sets. In fact, it can be said that when there is a parking lot present, the geometric mean of E.coli is greater.

Table 67: R2 between Parking Lot Availability and E.coli Parameters E.coli Ecoli Log Maximum Parking Detected Geometric Max E. coli Area Mean E. coli Available

E.coli Detected 1

309

Ecoli Geometric 0.2784 1 Mean Log Max E. coli 0.7555 0.4459 1 Maximum E. coli 0.4283 0.8078 0.6589 1 Parking Area 0.049 -0.1511 0.1132 -0.015 1 Available

The table above shows correlations between E.coli parameters and the presence of holding tanks. All correlations are low.

Accessible by Road The following two figures provide a graphical representation of the relationship between the accessibility by road and E. coli parameters. In both cases, no relationship is apparent.

Figure 82: Accessible by Road Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Accessible by Road

310

Figure 83: Accessible by Road versus E.coli Parameters

Accessible by Road

1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 0.00 0.50 1.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

Table 68: Accessible by Road and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Road Accessibility & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Geometric Mean T-Test Pr(T < t) = 0.450 Pr(|T| > |t|) = 0.9006 Pr(T > t) = 0.5497 Road Accessibility & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliLog of Max E.coli T- Pr(T < t) = 0.947 Pr(|T| > |t|) = 0.1062 Pr(T > t) = 0.0531 Test Road Accessibility & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum of E.coli T-Test Pr(T < t) = 0.323 Pr(|T| > |t|) = 0.6450 Pr(T > t) = 0.6775 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected for all tests and there is a not significant difference between data sets.

Table 69: R2 between Road Accessibility and E.coli Parameters E.coli Ecoli Log Maximum Accessibility Detected Geometric Max E. coli by Road Mean E. coli E.coli Detected 1 Ecoli Geometric 0.2756 1 Mean Log Max E. coli 0.7522 0.4532 1 Maximum E. coli 0.4216 0.8071 0.6651 1

311

Accessibility by -0.0964 0.0095 -0.1218 0.0349 1 Road

The table above shows correlations between E.coli parameters and accessibility by road. All correlations are low.

Number of toilets The histogram below indicates that the number of toilets is skewed to the right and is not normally distributed.

Figure 84: Number of Toilets Histogram .2 .15 .1 Density .05 0 0.00 5.00 10.00 15.00 20.00 Number of Toilets

As shown in the figure below, there is no apparent relationship between the number of toilets and E. coli parameters.

312

Figure 85: Number of Toilets versus E.coli Parameters

Number of Toilets

1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 0.00 10.00 20.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

Table 70: Number of Toilets and E.coli Detected T-Test Results Parameters Tested Student T-Test Results Number of Toilets & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Detected T-Test Pr(T < t) = 0.383 Pr(|T| > |t|) = 0.7669 Pr(T > t) = 0.6166 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected for all tests and there is a not significant difference between data sets.

Table 71: R2 between Number of Toilets and E.coli Parameters E.coli Ecoli Log Maximum Number Detected Geometric Max E. coli of Mean E. coli Toilets E.coli Detected 1 Ecoli Geometric 0.2839 1 Mean Log Max E. coli 0.7502 0.4441 1 Maximum E. coli 0.4366 0.8076 0.6586 1 Number of Toilets 0.0261 -0.0345 0.1236 0.0973 1

313

The table above shows correlations between E.coli parameters and number of toilets. All correlations are low.

Runoff residential areas The following two figures do not indicate a strong relationship between runoff from residential areas and E. coli parameters.

Figure 86: Runoff from Residential Areas Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Stormwater runoff from residential Areas

314

Figure 87: Runoff from Urban Areas versus E.coli Parameters

Stormwater Runoff from Urban Areas 1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00 2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli 0.00 0.00 0.50 1.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

Table 72: Runoff from Urban Areas and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Runoff Urban Areas & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.748 Pr(|T| > |t|) = 0.5045 Pr(T > t) = 0.2523 Test Runoff Urban Areas & log Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 of maximum E.coli T-Test Pr(T < t) = 0.187 Pr(|T| > |t|) = 0.3740 Pr(T > t) = 0.8130 Runoff Urban Areas & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum E.coli T-Test Pr(T < t) = 0.688 Pr(|T| > |t|) = 0.6242 Pr(T > t) = 0.3121 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected for all tests and there is a not significant difference between data sets.

Table 73: R2 between Stormwater Runoff from Urban Areas and E.coliParameters E.coli Ecoli Log Max Stormwater Detected Geometric Max E. coli Runoff Mean E. coli from Urban Areas E.coli Detected 1

315

Ecoli Geometric Mean 0.2364 1 Log Max E. coli 0.7529 0.4035 1 Maximum E. coli 0.422 0.8556 0.6531 1 Stormwater Runoff from Urban 0.2074 -0.06 0.0799 -0.044 1 Areas

The table above shows correlations between E.coli parameters and runoff from residential areas. All correlations are low except the relation between E.coli being detected and residential area stormwater runoff.

Beach Material The following four box plots do not show an apparent relationship between beach material and the geometric mean of E. coli.

Figure 88: Beach Material -Mucky Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Material - Mucky

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Figure 89: Beach Material - Rocky Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Material - Rocky

Figure 90: Beach Material - Sandy Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Material - Sandy

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Figure 91: Beach Material - Other Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Material - other

The graph below does not show any relationships between beach materials and E.coli parameters.

Figure 92: Beach Materials versus E.coli Parameters

Beach Material - Mucky

1.00 Beach 0.50 Material - Rocky

0.00 1.00 Beach 0.50 Material - other

0.00 1.00 Beach 0.50 Material - Sandy

0.00 1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean

0.00 3.00

2.00 Log Max Ecoli 1.00 add 1

0.00 600.00

400.00 Max 200.00 Ecoli

0.00 0.00 0.50 1.000.00 0.50 1.000.00 0.50 1.000.00 0.50 1.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

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Table 74: Beach Materials and E.coli Detection T-Test Results Parameters Tested Student T-Test Results Mucky Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.761 Pr(|T| > |t|) = 0.478 Pr(T > t) = 0.2388 Test Rocky Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.683 Pr(|T| > |t|) = 0.634 Pr(T > t) = 0.3171 Test Sandy Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.193 Pr(|T| > |t|) = 0.385 Pr(T > t) = 0.8073 Test Mucky Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.969 Pr(|T| > |t|) = 0.062 Pr(T > t) = 0.0311 Rocky Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.262 Pr(|T| > |t|) = 0.524 Pr(T > t) = 0.7383 Other Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.835 Pr(|T| > |t|) = 0.329 Pr(T > t) = 0.1647 Sandy Beach Materials & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Detected T-Test Pr(T < t) = 0.213 Pr(|T| > |t|) = 0.427 Pr(T > t) = 0.7867 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is not rejected for all except the E.coli detected and mucky beach materials. In this case, there is a significant difference between data sets. In fact, it can be said that when there is mucky beach material, the E.coli is more likely to be detected.

Table 75: R2 between Beach Material and E.coli Parameters E.coli Ecoli Log Maximum Detected Geometric Max E. coli Mean E. coli E.coli Detected 1 Ecoli Geometric 0.2783 1 Mean Log Max E. coli 0.751 0.4523 1 Maximum E. coli 0.4253 0.8074 0.6641 1 Mucky -0.1378 -0.0531 -0.0948-0.0736 Rocky 0.0494 -0.0356 -0.0414-0.0585 Other -0.0725 -0.0196 0.0023 -0.0261 Sandy 0.0574 0.0649 0.0892 0.0972

The table above shows correlations between E.coli parameters and the beach materials. All correlations are low.

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Beach Length and Width The following two histograms show that beach length and beach width are relatively normally distributed with a skew to the right.

Figure 93: Beach Length Histogram .008 .006 .004 Density .002 0 0.00 200.00 400.00 600.00 800.00 1000.00 Beach Length

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Figure 94: Beach Width Histogram .08 .06 .04 Density .02 0 0.00 20.00 40.00 60.00 80.00 Beach Width

The figure below shows only minimal relationships between beach size characteristics and E.coli parameters.

Figure 95: Beach Size versus E.coli Parameters

Beach Length

100.00

50.00 Beach Width

0.00 1.00

0.50 EcoliDetected

0.00 500.00 Ecoli Geometric Mean 0.00 3.00

2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli

0.00 0.00 500.00 1000.000.00 50.00 100.000.00 0.50 1.000.00 500.000.00 1.00 2.00 3.00

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Table 76: Beach Size and E.coli Detection T-Test Results Parameters Tested Student T-Test Results Beach Width & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 detected T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.003 Pr(T > t) = 0.9988 Beach Length & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 detected T-Test Pr(T < t) = 0.143 Pr(|T| > |t|) = 0.287 Pr(T > t) = 0.8566 Beach Area & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 detected T-Test Pr(T < t) = 0.011 Pr(|T| > |t|) = 0.023 Pr(T > t) = 0.9886 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is not rejected for all except the E.coli detected and beach width and beach area. In this case, there is a significant difference between data sets. In fact, it can be said that when E.coli is more likely to be detected at narrower or smaller beaches.

Table 77: R2 between Beach Size and E.coli Parameters E.coli Ecoli Log Max E. Beach Beach Beach Detected Geometric Max coli Length Width Area Mean E. coli E.coli Detected 1 E.coli Geometric 0.2869 1 Mean Log Max E. coli 0.7543 0.4395 1 Maximum E. coli 0.4465 0.8031 0.661 1 Beach Length 0.0752 -0.0328 0.0575-0.0028 1 Beach Width 0.2073 0.101 0.2718 0.339 0.1176 1 Beach Area 0.1582 0.0618 0.2298 0.2571 0.6477 0.7354 1

The table above shows correlations between E.coli parameters and the beach size parameters. Correlations between beach size parameters (width and area) are relatively high with E.coli parameters.

Surrounding Land Use Table 78: Surrounding Land Use and E.coli Detection T-Test Results Parameters Tested Student T-Test Results Urban & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.653 Pr(|T| > |t|) = 0.6942 Pr(T > t) = 0.3471 Urban & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.434 Pr(|T| > |t|) = 0.8687 Pr(T > t) = 0.5656 Residential & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 geometric mean T-Test Pr(T < t) = 0.975 Pr(|T| > |t|) = 0.0498 Pr(T > t) = 0.0249

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Residential & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli T-Test Pr(T < t) = 0.986 Pr(|T| > |t|) = 0.0274 Pr(T > t) = 0.0137 Field & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.304 Pr(|T| > |t|) = 0.6088 Pr(T > t) = 0.6956 Field & maximum E.coli T- Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Test Pr(T < t) = 0.303 Pr(|T| > |t|) = 0.6060 Pr(T > t) = 0.6970 Marsh or Swamp & E.coli Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 geometric mean T-Test Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.0025 Pr(T > t) = 0.9987 Marsh or Swamp & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum E.coli T-Test Pr(T < t) = 0.078 Pr(|T| > |t|) = 0.1558 Pr(T > t) = 0.9221 Habour & E.coli geometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.425 Pr(|T| > |t|) = 0.8495 Pr(T > t) = 0.5752 Habour & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.148 Pr(|T| > |t|) = 0.2960 Pr(T > t) = 0.8520 Rural & E.coligeometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.915 Pr(|T| > |t|) = 0.1700 Pr(T > t) = 0.0850 Rural & maximum E.coliT- Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Test Pr(T < t) = 0.951 Pr(|T| > |t|) = 0.0979 Pr(T > t) = 0.0489 Forest & E.coligeometric Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 mean T-Test Pr(T < t) = 0.956 Pr(|T| > |t|) = 0.0890 Pr(T > t) = 0.0445 Forest & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.989 Pr(|T| > |t|) = 0.0226 Pr(T > t) = 0.0113 Hills or Uplands & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coligeometric mean T- Pr(T < t) = 0.410 Pr(|T| > |t|) = 0.8208 Pr(T > t) = Test 0.5896 Hills or Uplands & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum E.coliT-Test Pr(T < t) = 0.109 Pr(|T| > |t|) = 0.2175 Pr(T > t) = 0.8913 Agricultural & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coligeometric mean T- Pr(T < t) = 0.710 Pr(|T| > |t|) = 0.5804 Pr(T > t) = Test 0.2902 Agricultural & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.597 Pr(|T| > |t|) = 0.8060 Pr(T > t) = 0.4030 Commercial & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

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E.coligeometric mean T- Pr(T < t) = 0.828 Pr(|T| > |t|) = 0.3441 Pr(T > t) = Test 0.1721 Commercial & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.929 Pr(|T| > |t|) = 0.1424 Pr(T > t) = 0.0712 Industrial & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coligeometric mean T- Pr(T < t) = 0.391 Pr(|T| > |t|) = 0.7821 Pr(T > t) = Test 0.6089 Industrial & maximum Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coliT-Test Pr(T < t) = 0.144 Pr(|T| > |t|) = 0.2876 Pr(T > t) = 0.8562 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is rejected and the alternate hypothesis accepted for the cases shown in the table below.

Table 79: Land Use Parameter Interpretations Parameters Tested Student T-Test Results where Ho Interpretation Residential & E.coli Ha: diff != 0 Ha: diff > 0 The E.coli geometric mean is geometric mean T- Pr(|T| > |t|) = 0.0498 Pr(T > t) = 0.025 less where there is a residential Test land use. Residential & Ha: diff != 0 Ha: diff > 0 The maximum E.coli is less maximum E.coli T- Pr(|T| > |t|) = 0.027 Pr(T > t) = 0.0137 where there is a residential land Test use. Marsh or Swamp & Ha: diff < 0 Ha: diff != 0 The E.coli geometric mean is E.coli geometric Pr(T < t) = 0.001 Pr(|T| > |t|) = 0.0025 greater where there is a marsh mean T-Test or swamp adjacent. Rural & maximum Ha: diff > 0 The maximum E.coli is less E.coli T-Test Pr(T > t) = 0.0489 where there is a rural land use. Forest & E.coli Ha: diff > 0 The E.coli geometric mean is geometric mean T- Pr(T > t) = 0.0445 less where there is a forest land Test use. Forest & maximum Ha: diff != 0 Ha: diff > 0 The maximum E.coli is less E.coli T-Test Pr(|T| > |t|) = 0.023 Pr(T > t) = 0.0113 where there is a forest land use.

Table 80: R2 between Selected Land Uses and E.coli Parameters E.coli Ecoli Log Maximum Detected Geometric Max E. coli Mean E. coli E.coli Detected 1 E.coli Geometric Mean 0.2365 1 Log Max E. coli 0.7484 0.4062 1 Maximum E. coli 0.4071 0.79 0.6471 1 Residential -0.0971 -0.1366 -0.0878 -0.1218 Marsh or Swamp -0.035 0.3391 -0.0162 0.1844

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Rural -0.0903 -0.0628 0.0185 -0.026 Forest -0.1407 -0.1312 -0.1765 -0.1753 The R2 values from the selected land uses are given in the table above. From this it’s apparent that the presence of a marsh or swamp is relatively highly correlated with E.coli parameters.

Watershed Characteristics The following three box plots do not show an apparent relationship between watershed status and E.coli parameters.

Figure 96: Geometric Mean of E. coli versus Healthy Watershed

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Watershed Healthy

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Figure 97: Geometric Mean of E.coli versus Stressed Watershed 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Figure 98: Geometric Mean of E.coli versus Impacted Watershed 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

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Table 81: Watershed Characteristics and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Stressed watershed & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric mean T- Pr(T < t) = 0.105 Pr(|T| > |t|) = 0.2107 Pr(T > t) = 0.8947 Test Stressed watershed & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum E.coli T-Test Pr(T < t) = 0.073 Pr(|T| > |t|) = 0.1463 Pr(T > t) = 0.9268 Impacted watershed & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli geometric mean T- Pr(T < t) = 0.737 Pr(|T| > |t|) = 0.5255 Pr(T > t) = 0.2628 Test Impacted watershed & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 maximum E.coli T-Test Pr(T < t) = 0.603 Pr(|T| > |t|) = 0.7943 Pr(T > t) = 0.3972 Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) is not rejected for all. In fact, it can be said that E.coli is not more likely to be detected based on the watershed status.

Table 82: R2 between Watershed Status and E.coli Parameters E.coli Ecoli Log Max E. Watershed Watershed Detected Geometric Max coli Stressed Impacted Mean E. coli E.coli Detected 1 E.coli Geometric 0.2854 1 Mean Log Max E. coli 0.7537 0.4391 1 Maximum E. coli 0.4442 0.8032 0.6606 1 Watershed 0.1157 0.0863 0.095 0.1001 1 Stressed Watershed 0.0717 -0.0438 0.0756 -0.018 -0.6518 1 Impacted

The table above shows correlations between E.coli parameters and the watershed status. All correlations are low.

Beach Grooming The following three box plots do not provide an indication of a relationship between beach grooming and E.coli parameters.

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Figure 99: Beach Grooming in the last 24 hours Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Grooming last 24 hours

Figure 100: Any Beach Grooming Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Any Beach Grooming

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Figure 101: Beach Grooming in more than 24 hours Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Beach Grooming more than 24 hours

The figure below does not indicate a relationship between beach grooming and E.coli parameters.

Figure 102: Beach Grooming Parameters versus E.coli Parameters

Ecoli Geometric Mean

3.00

2.00 Log Max Ecoli 1.00 add 1 0.00 600.00

400.00 Max 200.00 Ecoli

0.00 1.00 Beach 0.50 Grooming last 24 hours 0.00 1.00 Beach 0.50 Grooming more than 24 hours 0.00 1.00 Any 0.50 Beach Grooming 0.00 0.00 500.000.00 1.00 2.00 3.000.00 200.00400.00600.000.00 0.50 1.000.00 0.50 1.00

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Table 83: Beach Grooming and E.coli Geometric Mean T-Test Results Parameters Tested Student T-Test Results Beach Grooming in the last Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 24 hours & E.coli Pr(T < t) = 0.772 Pr(|T| > |t|) = 0.4566 Pr(T > t) = 0.2283 Geometric Mean T-Test Beach Grooming greater Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 than 24 hours ago & E.coli Pr(T < t) = 0.036 Pr(|T| > |t|) = 0.0710 Pr(T > t) = 0.9645 Geometric Mean T-Test Any Beach Grooming & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.193 Pr(|T| > |t|) = 0.3850 Pr(T > t) = 0.8075 Test Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) for all tests is not rejected and there is not a significant difference between the data sets tested. In fact, it can be said that the beach grooming does not affect E.coli results.

Compound Terms

High Onshore Winds This is defined as a wind speed of 4 or 5 and onshore winds. While one would expect a relationship between E.coli parameters and this compound term, none is apparent in the box plot below.

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Figure 103: Strong Onshore Winds Box Plot

0 1 500 400 300 200 Ecoli Geometric Mean Geometric Ecoli 100 0

Graphs by Wind4_5 x Onshore

Table 84: Strong Onshore Winds and E.coli Parameters T-Test Results Parameters Tested Student T-Test Results Strong Onshore Winds & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Maximum E.coli T-Test Pr(T < t) = 0.769 Pr(|T| > |t|) = 0.4623 Pr(T > t) = 0.2312 Strong Onshore Winds & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Log of Maximum E.coli T- Pr(T < t) = 0.614 Pr(|T| > |t|) = 0.7712 Pr(T > t) = 0.3856 Test Strong Onshore Winds & Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 E.coli Geometric Mean T- Pr(T < t) = 0.722 Pr(|T| > |t|) = 0.5557 Pr(T > t) = 0.2779 Test Note: diff = mean(0) - mean(1)

At a 95% degree of confidence, the null hypothesis (Ho) for all tests is not rejected and there is not a significant difference between the data sets tested. In fact, it can be said strong onshore winds does not affect E.coli results.

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Appendix I: Linear Regression Process Results

332

Model 1 Forward Stepwise Results Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 Step 15 ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 Accessible by -0.0056 0.0455 0.1078 0.1552 0.1938 0.2103 0.2228 0.2504 0.2816 0.301 0.3155 0.3255 0.345 0.3696 0.3734 Road Air 0.0026 0.0378 0.0996 0.1417 0.1891 0.2049 0.2257 0.2601 0.282 0.3022 0.3179 0.3245 0.3408 0.3569 0.3643 Temperature Beach -0.0014 0.0436 0.1011 0.1534 0.1909 0.2087 0.2326 0.249 0.2792 0.3022 0.3135 0.3227 0.3402 0.3575 0.3669 Grooming - Any Beach Area -0.001 0.048 0.108 0.1379 0.186 0.2036 0.2203 0.2493 0.2791 0.2983 0.3148 0.325 0.3463 0.3606 0.3725 Beach 0.0132 0.0796 0.1325 0.1754 0.2088 0.2224 0.258 - Grooming in more than 24 hours Beach -0.0026 0.0379 0.099 0.1428 0.1955 0.2057 0.2219 0.249 0.2792 0.298 0.3135 0.3227 0.3402 0.3575 0.3669 Grooming in the Last 24hours Beach Length -0.0037 0.0454 0.1124 0.1388 0.1963 0.2078 0.2239 0.2521 0.2805 0.2974 0.3136 0.3227 0.3405 0.357 0.364 Beach Material -0.0027 0.0344 0.0944 0.1414 0.1889 0.2039 0.2208 0.2501 0.2795 0.2977 0.3149 0.3239 0.3411 0.3567 0.364 - Mucky Beach Material -0.0052 0.0341 0.0928 0.1411 0.1894 0.2055 0.2243 0.2555 0.2889 0.3091 0.3307 0.3427 0.3567 0.3703 0.3793 - Other Beach Material -0.0043 0.0344 0.1101 0.1443 0.1905 0.2065 0.2229 0.2502 0.2835 0.3009 0.3186 0.3288 0.3446 0.3601 0.3714 - Rocky Beach Material -0.0014 0.044 0.1113 0.1442 0.1899 0.2052 0.2213 0.2492 0.2802 0.2978 0.3145 0.3238 0.3409 0.3569 0.3655 - Sandy Beach Width -0.0014 0.0509 0.1107 0.1445 0.1881 0.2056 0.2211 0.2491 0.2806 0.2972 0.3136 0.323 0.3405 0.3572 0.3679 Birds 0.0261 0.0748 0.1418 0.1949 - Flooding -0.0005 0.0339 0.0928 0.1427 0.1894 0.2047 0.223 0.2509 0.2806 0.3029 0.3256 0.3346 0.3532 0.3665 0.3729 Holding Tanks -0.0038 0.0494 0.1023 0.1392 0.193 0.2099 0.2212 0.2501 0.2856 0.2971 0.3145 0.3254 0.3442 0.3583 0.367

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 Step 15 ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 Log of 0.0445 - Maximum Turbidity Log of 0.0524 - Turbidity Average Maximum 0.0011 - Turbidity Number of -0.0039 0.0455 0.1132 0.1377 0.1914 0.2108 0.2271 0.2538 0.2811 0.2969 0.3135 0.3229 0.358 0.3554 0.3671 Toilets Parking Area 0.0177 0.092 0.1417 0.1621 0.203 0.2266 0.253 0.27 0.3064 - Available Pets Allowed -0.0056 0.0338 0.0933 0.1413 0.1911 0.2072 0.2252 0.2688 0.2907 0.3067 0.3154 0.3264 0.354 0.3738 Prevailing 0.014 0.0472 0.0981 0.1496 0.1992 0.2114 0.2278 0.2542 0.2875 0.3048 0.3261 0.3389 0.3459 0.3639 0.3672 Winds - Offshore Prevailing 0.0004 0.0484 0.0943 0.1464 0.1939 0.2128 0.2289 0.2572 0.2877 0.3035 0.3198 0.3246 0.3415 0.3605 0.367 Winds - Onshore Prevailing 0.0034 0.0403 0.1024 0.1501 0.1933 0.2112 0.2273 0.2568 0.2919 0.3082 0.3266 0.3326 0.3525 0.3645 0.3681 Winds - Parallel Rainfall During -0.002 0.0408 0.1052 0.1369 0.1906 0.2073 0.2224 0.2508 0.2799 0.3 0.3141 0.3232 0.3402 0.3566 0.3634 Sampling Rainfall Last 24 0.0162 0.0668 0.1451 - Hours Residential -0.0054 0.0343 0.0928 0.1413 0.1903 0.2035 0.2199 0.2518 0.2865 0.3063 0.3148 0.3231 0.3406 0.3568 0.3636 Density - Any Residential -0.0043 0.0333 0.0928 0.1411 0.189 0.2081 0.2263 0.2559 0.2819 0.2983 0.3157 0.325 0.3451 0.3619 0.3676 Density - High Residential 0.0161 0.0532 0.1259 0.1717 0.2058 0.2083 0.2288 0.2642 0.2939 0.3087 0.3186 0.3265 0.3463 0.3625 0.3662

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 Step 15 ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 Density - Low Residential 0.0054 0.0465 0.1186 0.1693 0.2124 Density - Medium Residential -0.0054 0.0343 0.0928 0.1413 0.1903 0.2035 0.2199 0.2518 0.2865 0.3063 0.3148 0.3231 0.3406 0.3568 0.3636 Density - None Sample August -0.0035 0.0451 0.1147 0.1368 0.1862 0.2043 0.2269 0.2521 0.2844 0.2975 0.3139 0.3232 0.3412 0.3596 0.3649 Sample July -0.0026 0.0457 0.1108 0.1365 0.1861 0.2035 0.2302 0.2541 0.2905 0.3013 0.3183 0.3258 0.3421 0.3603 0.3657 Sample June -0.0037 0.0461 0.1091 0.1367 0.186 0.2056 0.2203 0.2491 0.2809 0.2995 0.3159 0.3227 0.3459 0.3642 0.3689 Sample Sept -0.0047 0.0451 0.1093 0.1366 0.186 0.2036 0.2202 0.2507 0.2824 0.3015 0.3189 0.33 0.348 0.3634 0.3696 Sample Time -0.0047 0.0447 0.108 0.1366 0.1912 0.2059 0.2227 0.2533 0.2793 0.2981 0.3154 0.3257 0.3406 0.3566 0.3647 Decimal Sample1 -0.0044 0.0493 0.1174 0.142 0.1922 0.2191 0.224 0.2524 0.2796 0.2973 0.3135 0.3227 0.3408 0.3577 0.3643 Alkalinity Sample1 -0.0011 0.0658 0.118 0.1449 0.192 0.2162 0.2262 0.2557 0.2791 0.2974 0.3192 0.3276 0.3413 0.3569 0.365 Conductivity Sample1H -0.005 0.0471 0.116 0.1404 0.1934 0.2272 CL Sample1Log 0.0115 0.0625 0.1181 0.1476 0.192 0.2162 0.2294 0.2598 0.2792 0.2972 0.3192 0.3262 0.3416 0.3567 0.3639 Conductivity Sample1pH -0.005 0.0459 0.1182 0.14 0.1949 0.2287 - Seaweed & 0.0085 0.0487 0.1173 0.1725 0.2112 0.2265 0.2501 0.2699 0.2989 0.3173 0.3321 Algae in Water - High Seaweed & -0.0009 0.0361 0.0985 0.1725 0.198 0.2161 0.2346 0.2596 0.2989 0.3144 0.327 0.3284 0.3449 0.3605 0.3657 Algae in Water - Low Seaweed & 0.0035 0.0403 0.0953 0.1413 0.1889 0.2036 0.2196 0.2499 0.2795 0.2976 0.3137 0.3227 0.3407 0.3569 0.3635 Algae in Water - Medium Seaweed & -0.0011 0.0345 0.0941 0.1428 0.1889 0.2034 0.2195 0.2496 0.28 0.2973 0.3142 0.3321 0.3491 0.3638 0.3672

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 Step 15 ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 Algae in Water - None Sechi Disc 0.0447 0.0453 0.098 0.1601 0.2028 0.2184 0.2459 0.2755 0.3033 0.3229 Stormwater -0.0054 0.0131 0.0896 0.1224 0.1884 0.1972 0.2056 0.2377 0.2669 0.284 0.3293 0.3504 - Runoff From Residential Sunlight 0.0053 0.0419 0.1082 0.1445 0.1888 0.2055 0.2206 0.2517 0.28 0.2973 0.3136 0.3236 0.3408 0.3587 0.368 Overcast Sunlight Rainy -0.0039 0.0349 0.0938 0.1415 0.1919 0.2062 0.2242 0.252 0.2813 0.2992 0.3149 0.3238 0.3406 0.3575 0.3639 Sunlight Sunny 0.0077 0.0435 0.1104 0.1448 0.1922 0.2069 0.222 0.2532 0.2809 0.2978 0.3135 0.3231 0.3405 0.3579 0.367 Swimmer -0.0055 0.0366 0.0988 0.1406 0.1897 0.2042 0.2206 0.25 0.2792 0.2981 0.3182 0.3273 0.344 0.3576 0.3642 Density - Medium Swimmer -0.0036 0.0466 0.1087 0.1437 0.1924 0.2052 0.2206 0.249 0.2807 0.2977 0.3159 0.3241 0.3428 0.3568 0.3636 Density - High Swimmer -0.0015 0.037 0.0986 0.1427 0.1889 0.2045 0.2195 0.2491 0.2791 0.2987 0.3154 0.3242 0.3409 0.3572 0.3662 Density - Low Swimmer 0.0003 0.0504 0.1177 0.1546 0.2051 0.2168 0.2288 0.25 0.2791 0.3094 0.323 0.3335 0.35 0.3663 0.3804 Number Turbidity 0.0176 - Average Water 0.0006 0.0452 0.1159 0.152 0.2052 0.2249 0.2571 0.2881 Temperature Watershed 0.0004 0.0534 0.1098 0.1459 0.1903 CL Healthy Watershed -0.0028 0.0524 0.1155 0.1451 0.1949 CL Impacted Watershed 0.0027 0.0534 0.1098 0.1459 0.1903 CL Stressed Wave Height 0.0367 0.1155 -

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 Step 15 ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ ADJ R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 To WindSpeed -0.0048 0.042 0.1167 0.1421 0.1858 0.2034 0.2222 0.2494 0.2838 0.2974 0.3168 0.3261 0.3669 WindSpeed 4 or -0.004 0.045 0.1103 0.1371 0.1861 0.2034 0.2197 0.249 0.2791 0.2983 0.3135 0.3227 0.3403 0.3583 0.3665 5 Strong onshore -0.0037 0.0331 0.0969 0.1443 0.1894 0.2042 0.2194 0.2474 0.2816 0.2975 0.3135 0.3227 0.3403 0.3669 0.3665 Winds Maximum ADJ- 0.0524 0.1155 0.1451 0.1949 0.2124 0.2287 0.258 0.2881 0.3064 0.3229 0.3321 0.3504 0.3669 0.3738 0.3804 R2 ADJ R2 0.0631 0.0296 0.0498 0.0175 0.0163 0.0293 0.0301 0.0183 0.0165 0.0092 0.0183 0.0165 0.0069 0.0066 Improvement

CL – Collinearity

337

Model 1 Backwards Stepwise Results Step 1 Step 2 Parameters Coefficients Adj R2 if removed Coeficients Adj R2 if removed Log of Turbidity Average -20.4 0.2859 -18.4 0.2736 Beach Grooming in more than 24 28.62 0.3673 28.6 0.3697 hours Birds 0.287 0.3741 0.28 0.3777 Parking Area Available -42.56 0.3472 -49.09 0.3277 Pets Allowed 24.3 0.3663 24.3 0.3686 Rainfall Last 24 Hours -14.035 0.3826 Residential Density - Medium -36.38 0.3187 -37.3 0.318 Sample1pH -56.6 0.3016 -59.5 0.2842 Seaweed & Algae in Water - High 40.24 0.3432 39.56 0.3476 Sechi Disc -0.802 0.337 -0.79 0.342 Stormwater Runoff From -19.58 0.3246 -19.5 0.3214 Residential Swimmer Number 0.232 0.3738 0.275 0.3643 Water Temperature -2.42 0.3595 -2.77 0.3582 Wave Height To 140.08 0.2914 147.7 0.2802 WindSpeed -10.67 0.3599 -12.3 0.3513 Maximum Adj-R2 0.3804 0.3826 0.3804 0.3777 Adj R2 Improvement 0.0022 -0.0027

338

Appendix J: Logistic Regression Process Results

339

Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Adjusted R2 Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 Accessible by Road 0.0076 0.1078 0.164 0.2156 0.246 0.3018 Air Temperature 0.0404 0.188 0.234 0.2797 0.3067 0.3197 Water Temperature 0.0292 0.1538 0.2477 0.2826 0.3095 0.3303 Beach Grooming - Any 0.0051 0.1049 0.1701 0.2209 0.245 0.3047 Beach Area 0.0182 0.1126 0.2324 0.2703 0.2954 0.3256 Beach Grooming in more than 24 hours 0.0025 0.1075 0.1819 0.227 0.2473 0.3063 Beach Grooming in the Last 24hours 0.0015 0.1057 0.1709 0.2218 0.2453 0.3067 Beach Length 0.0039 0.0996 0.2313 0.2697 0.2964 0.3258 Beach Material - Mucky 0.0154 0.1055 0.1606 0.215 0.2495 0.3054 Beach Material - Other 0 0.1039 0.1646 0.2184 0.2441 0.2963 Beach Material - Rocky 0.0016 0.1066 0.1643 0.2156 0.246 0.3057 Beach Material - Sandy 0.0026 0.1029 0.1653 0.2208 0.2514 0.3018 Beach Width 0.0327 0.1136 0.233 0.2707 0.2954 0.3264 Birds 0 0.107 0.1878 0.234 0.2562 0.3099 Flooding 0.0311 0.1045 0.1642 0.2162 0.2465 0.3049 Holding Tanks 0.0001 0.1201 0.193 0.2408 0.2683 0.3083 Number of Toilets 0.0003 0.1031 0.2374 0.2791 0.3103 0.3396 Parking Area Available 0.0019 0.0844 0.234 0.2736 0.2972 0.3522 Pets Allowed 0.0016 0.1045 0.1638 0.2162 0.2475 0.3018 Prevailing Winds - Offshore 0.0035 0.1102 0.1823 0.2371 0.2718 0.3279 Prevailing Winds - Onshore 0.001 0.1219 0.1878 0.2436 0.2809 0.3397 Prevailing Winds - Parallel 0.0001 0.1219 0.1743 0.2289 0.2589 0.3174 Rainfall During Sampling 0.0001 0.0976 0.172 0.2129 0.2439 0.3086 Rainfall Last 24 Hours 0.0016 0.097 0.2216 0.2751 0.3256 Residential Density - Any 0.0106 0.1074 0.1703 0.2213 0.2538 0.3102 Residential Density - High 0.0001 0.1031 0.1638 0.2157 0.2461 0.3019

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Adjusted R2 Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 Residential Density - Low 0 0.1028 0.1638 0.2169 0.246 0.3007 Residential Density - Medium 0.0042 0.1041 0.167 0.2167 0.2481 0.3034 Residential Density - None 0.0106 0.1074 0.1703 0.2213 0.2538 0.3102 Sample August 0.0111 0.1375 0.2561 0.2954 - - Sample July 0.0056 0.1158 0.2413 0.2796 0.3051 0.3471 Sample June 0.0066 0.1158 0.248 0.2926 0.3119 0 Sample Sept 0.0005 0.0996 0.2332 0.2729 0.3011 0.3299 Sample Time Decimal 0.003 0.1167 0.2378 0.2757 0.3009 0.3276 Sample1 Alkalinity 0.002 0.1461 0.2557 0.2926 0.318 0.3417 Sample1 Conductivity 0.0031 0.205 Sample1H 0.02 0.1662 0.2317 0.2705 0.2955 0.3404 Sample1Log Conductivity 0.0538 0.2312 - Sample1pH 0.0203 0.1556 0.2348 0.2775 0.2982 0.3285 Seaweed & Algae in Water - High 0.0068 0.1094 0.1785 0.2448 0.2733 0.3368 Seaweed & Algae in Water - Low 0.0069 0.1114 0.1856 0.2437 0.2653 0.3123 Seaweed & Algae in Water - Medium 0.0132 0.1318 0.1781 0.2309 0.259 0.3075 Seaweed & Algae in Water - None 0.0149 0.1337 0.1885 0.2375 0.2593 0.303 Sechi Disc 0.0614 0.1382 0.1763 0.2307 0.2643 0.323 Stormwater Runoff From Residential 0.0002 0.1063 0.1861 0.2512 0.2679 0.3245 Sunlight Overcast 0.0089 0.1128 0.1967 0.2495 0.2781 0.3139 Sunlight Rainy 0 0.0986 0.1636 0.2146 0.2442 0.2955 Sunlight Sunny 0.0178 0.1216 0.2012 0.2545 0.2826 0.3163 Swimmer Density - Medium 0 0.0998 0.176 0.2202 0.2488 0.3016 Swimmer Density - High 0.0012 0.0999 0.171 0.2146 0.2399 0.2906 Swimmer Density - Low 0.0001 0.1035 0.1848 0.2169 0.2501 0.3007 Swimmer Number 0.0012 0.1035 0.1851 0.2367 0.2795 0.3293 Turbidity Average 0.0169 0.1165 0.2528 - Maximum Turbidity 0.0034 0.1272 0.2696 -

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Parameters Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Adjusted R2 Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 Log of Maximum Turbidity 0.0932 0.1022 0.2317 - Log of Turbidity Average 0.0993 - - Watershed Healthy 0.0417 0.1591 0.2364 0.277 0.3012 0.3135 Watershed Impacted 0.0035 0.0993 0.2312 0.2696 0.2954 0.3256 Watershed Stressed 0.0104 0.1591 0.2364 0.277 0.3012 0.3135 Wave Height To 0.0298 0.1484 0.2515 0.2857 0.2983 0.337 WindSpeed 0.0001 0.0913 0.227 0.2663 0.2898 0.3236 WindSpeed 4 or 5 0.0016 0.1007 0.2485 0.2872 0.3155 0.3508 Strong onshore Winds 0.0074 0.1058 0.1738 0.2318 0.263 0.3208 Maximum Adj-R2 0.0993 0.2312 0.2696 0.2954 0.3256 0.3522 Adj R2 Improvement 0.1319 0.0384 0.0258 0.0302 0.0266 H-L 12.94 8.87 15.51 13.08 4.99 2.94 Prob>chi2 0.114 0.353 0.0517 0.1091 0.7583 0.9383 AIC 1.28 1.116 1.079 1.061 1.048 1.039 BIC -442.404 -422.971 -424.477 -423.916 -358.148 -324.943

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Parameters Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Adjusted Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 R2 Accessible by Road 0.3336 0.3593 0.4545 0.5369 0.5986 0.639 Air Temperature 0.3496 0.4034 0.4606 0.5417 0.597 0.6387 Water Temperature 0.3718 0.4408 - Beach Grooming - Any 0.35 0.3587 0.4577 0.5373 0.5961 0.6389 Beach Area 0.3522 0.3793 0.4431 0.5016 0.6045 0.6404 Beach Grooming in more than 24 hours 0.3495 0.3586 0.4543 0.5364 0.5975 0.6416 Beach Grooming in the Last 24hours 0.3501 0.3591 0.4595 0.5364 0.596 0.6394 Beach Length 0.3532 0.3821 0.4411 0.4976 0.596 0.6388 Beach Material - Mucky 0.3325 0.3584 0.4523 0.5335 0.5913 0.6338 Beach Material - Other 0.3274 0.3539 0.4504 0.5304 0.5907 0.6334 Beach Material - Rocky 0.3341 0.3591 0.4543 0.5362 0.5959 0.6389 Beach Material - Sandy 0.3322 0.3596 0.4563 0.5379 0.5964 0.6388 Beach Width 0.3538 0.381 0.4496 0.5025 0.6018 0.6388 Birds 0.3414 0.3674 0.47770.5414 0.5959 0.6404 Flooding 0.3412 0.3749 0.46150.5411 0.596 0.641 Holding Tanks 0.3231 0.3362 0.4289 0.5109 0.5726 0.6217 Number of Toilets 0.3735 0.4006 0.4975 - Parking Area Available - Pets Allowed 0.3333 0.3589 0.4608 0.5533 0.602 0.64 Prevailing Winds - Offshore 0.3465 0.373 0.4698 0.5607 0.6214 0.6596 Prevailing Winds - Onshore 0.3656 0.3891 0.4769 0.5426 0.5934 0.6855 Prevailing Winds - Parallel 0.3328 0.3563 0.4552 0.5563 0.6387 Rainfall During Sampling 0.3336 0.3595 0.4204 0.4786 0.5834 0.6276 Rainfall Last 24 Hours Residential Density - Any 0.334 0.3851 0.4662 0.5512 0.61 0.6439 Residential Density - High 0.3329 0.3593 0.4596 0.5358 0.5988 0.6408 Residential Density - Low 0.3331 0.3623 0.4553 0.5361 0.5987 0.6417 Residential Density - Medium 0.3328 0.3635 0.4641 0.5361 0.6016 0.64

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Parameters Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Adjusted Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 R2 Residential Density - None 0.334 0.3851 0.4662 0.5512 0.61 0.6439 Sample August Sample July 0.3723 0.3976 0.4562 0.526 0.6229 0.6804 Sample June Sample Sept 0.3572 0.3856 0.4429 0.4975 0.6021 0.6507 Sample Time Decimal 0.3578 0.3866 0.4412 0.5094 0.6319 0.6602 Sample1 Alkalinity 0.3638 0.3975 0.4428 0.5 0.5959 0.6388 Sample1 Conductivity Sample1H 0.3741 0.3892 0.46570.5337 0.6 0.6446 Sample1Log Conductivity Sample1pH 0.3617 0.3816 0.44680.509 0.6005 0.6449 Seaweed & Algae in Water - High 0.372 0.3969 0.4952 0.5958 Seaweed & Algae in Water - Low 0.3432 0.3798 0.4816 0.5941 0.6314 0.6765 Seaweed & Algae in Water - Medium 0.3377 0.3694 0.4548 0.5359 0.6046 0.649 Seaweed & Algae in Water - None 0.3328 0.3676 0.4564 0.5443 0.6182 0.6626 Sechi Disc 0.339 0.3634 0.4619 0.5507 0.6089 0.6545 Stormwater Runoff From Residential 0.3384 0.3668 0.44940.5364 0.5971 0.6575 Sunlight Overcast 0.3442 0.3702 0.4659 0.542 0.5958 0.6392 Sunlight Rainy 0.325 0.3509 0.447 0.5291 0.5897 0.6333 Sunlight Sunny 0.346 0.372 0.4679 0.5427 0.5958 0.6392 Swimmer Density - Medium 0.328 0.344 0.4257 0.5105 0.5758 0.625 Swimmer Density - High 0.3142 0.3412 0.435 0.5127 0.5877 0.6312 Swimmer Density - Low 0.326 0.3525 0.4454 0.5241 0.5963 0.6388 Swimmer Number 0.3432 0.3681 0.4767 0.5677 0.6216 0.6582 Turbidity Average Maximum Turbidity Log of Maximum Turbidity Log of Turbidity Average

344

Parameters Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Adjusted Adjusted Adjusted Adjusted Adjusted Adjusted R2 R2 R2 R2 R2 R2 Watershed Healthy 0.332 0.3585 0.4543 0.5358 0.5958 0.6387 Watershed Impacted 0.3522 0.3793 0.4408 0.4975 0.5958 0.6387 Watershed Stressed 0.332 0.3585 0.4543 0.5358 0.5958 0.6387 Wave Height To 0.3793 - WindSpeed 0.3531 0.3841 0.43950.5009 0.6003 0.6458 WindSpeed 4 or 5 0.3753 0.3882 0.4458 0.5033 0.6052 0.6458 Strong onshore Winds 0.3501 0.3657 0.4571 0.5459 0.6014 0.6458 Maximum Adj-R2 0.3793 0.4408 0.4975 0.5958 0.6387 0.6855 Adj R2 Improvement 0.0271 0.0615 0.0567 0.0983 0.0429 0.0468 H-L 5.42 6.26 6.166.58 1.6 1.7 Prob>chi2 0.7114 0.6185 0.62930.5822 0.9909 0.9889 AIC 1.024 0.967 0.9170.814 0.784 0.744 BIC -310.495 -304.378 -297.491-277.076 -272.695 -273.589

345

Step 1 Parameters Coefficients Adj R2 if removed TurbidityAveLog 0.8773274 0.672 Sample1LogConductivity -0.7237653 0.5221 TurbidityMax -0.01997 0.6074 SampleAugust -2.660183 0.6546 RainfallLast24hours -7.171062 0.5169 ParkingAreaAvailable 7.333699 0.5462 WaveHeightRangeTo 4.126892 0.5339 WaterTemperature 1.944441 0.4558 NumberofToilets -0.2325118 0.5452 SeaweedAlgaeinwaterHigh 4.913609 0.6188 PrevailingWindparallel 4.980328 0.5934 PrevailingWindonshore 3.111646 0.6387 Maximum Adj-R2 0.6855 0.672 Adj R2 Improvement -0.0135

346

Appendix K: Beach Ranking

347

Rank Unique Beach Name Lake Name Rank Identifier 1 RQ 6 Regina Beach Last Mountain Lake 21.73 2 ST 9 Poplar Beach Resort Beach Wakaw Lake 16.22 3 RQ 16 Etter's Beach Last Mountain Lake 14.88 4 RQ 13 Eldora Beach Beach Last Mountain Lake 12.01 5 RQ 14 Katepwa Beach Katepwa Lake 9.85 6 PA 14 Doran Park Beach Christopher Lake 7.86 7 RQ 39 Echo Bible Camp Beach Echo Lake 7.51 8 PN 4 Crystal Bay/Sunset Beach Public Beach Brightsand Lake 7.39 9 RQ 20 Fort Campground Beach Echo Lake 7.31 10 SR 3 Sunset Beach Beach Crooked lake 6.80 11 MC 4 La Ronge Beach Lac La Ronge 6.79 12 PA 20 Chitek Lake Campground Beach Chitek Lake 6.76 13 SR 6 Bird's Point Beach Round Lake 6.59 14 KT 8 St. Brieux Beach St. Brieux Lake 6.44 15 PA 11 Murray Point Campground Beach Emma Lake 6.34 16 RQ 42 Sundale Beach Last Mountain Lake 6.32 17 PN 19 First Mustus Lake Campground Beach First Mustus Lake 6.21 18 ST 8 Wakaw Lake Regional Park Main Beach Wakaw Lake 6.20 19 PA 25 Pebble BM Beach Iroquois Lake 6.17 20 KT 6 Greenwater Beach Greenwater Lake 6.13 21 PN 8 Battlefords Provincial Park Public Beach Jackfish Lake 6.08 22 ST 2 Saskin Beach Fishing Lake 6.01 23 PN 15 Peck Lake Campground Beach Peck Lake 6.00 24 PN 4 Sandy Beach Lake Regional Park Public Beach Sandy Beach Lake 6.00 25 RQ 31 Lumsden Beach Last Mountain Lake 5.90 26 RQ 15 B-Say Tah Beach Echo Lake 5.87 27 PA 32 Redberry Lake Regional Park Beach Redberry Lake 5.81 28 PN 17 Ministikwan Lake Campground/Picnic Beach Ministikwan Lake 5.75 29 RQ 19 Buena Vista Beach Last Mountain Lake 5.69

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30 PN 16 Little Fishing Lake Beach Little Fishing Lake 5.69 31 ST 12 Lucien Lake Regional Park Main Beach Area Lucien Lake 5.68 32 PA 13 Murray Point Beach Emma Lake 5.68 33 PA 22 Sandy Bay Campground Beach Candle Lake 5.64 34 PA 29 Emerald Lake Regional Park Beach Emerald Lake 5.60 35 PN 24 Waterhen Lake South Campground Beach Waterhen Lake 5.59 36 SR 5 West End Resort Beach Round Lake 5.55 37 PN 9 Resort Village of Aquadeo Public Beach Jackfish Lake 5.54 38 HL 3 Prairie Lake Regional Park Beach Lake Diefenbaker 5.50 39 RQ 25 Glen Harbour Beach Last Mountain Lake 5.47 40 SR 22 Moose Bay Beach Crooked Lake 5.46 41 PN 20 South Flotten Lake Campgound Beach Flotten Lake 5.42 42 KT 4 Marean Lake Beach Marean Lake 5.41 43 PA 18 Sturgeon Lake Regional Park Beach Sturgeon Lake 5.31 44 PN 13 Turtle Lake South Bay Public Beach Turtle Lake 5.30 45 RQ 5 Kannata Valley Beach Last Mountain Lake 5.24 46 PA 10 Macintosh beach Emma Lake 5.17 47 SC 4 Village of Kenosee Lake Beach Kenosee Lake 5.17 48 KT 5 Barrier Ford Beach Barrier Lake 5.13 49 KT 9 Kipabiskau Beach Kipabiskau Lake 5.13 50 PA 28 Meeting Lake Regional Park Beach Meeting Lake 5.12 51 PA 26 Shady BM Beach Meeting Lake 5.12 52 PA 21 Minnowaka Beach Candle Lake 5.07 53 RQ 32 Shore Acres Beach Last Mountain Lake 5.06 54 MC 5 Wadin Bay Beach Lac La Ronge 5.03 55 PN 26 Howe Bay Campground Beach Pierce Lake 5.01 56 RQ 17 Katepwa Camp Beach Katepwa Lake 4.97 57 SR 15 Leslie Beach Regional Park Beach Fishing Lake 4.91 58 FH 3 Palliser Regional Park Beach Lake Diefenbaker 4.90 59 PN 28 Murray Doell Camground Beach Lac Des Isles Lake 4.90 60 KT 7 Lake Charron Beach Lake Charron 4.88

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61 SR 19 Burgis Beach Good Spirit Lake 4.87 62 PN 2 Glenburn Regional Park Main Beach Manmade Lake 4.86 63 PA 4 Waskateena Beach Candle Lake 4.86 64 PN 22 Jeannette Lake Subdivsion Public Beach Jeannette Lake 4.86 65 RQ 4 Sunset Cove Beach Last Mountain Lake 4.83 66 RQ 2 Highwood Beach Last Mountain Lake 4.81 67 PA 2 Morin Lake Regional Park Beach Morin Lake 4.80 68 SC 2 Mainprize Reg Pk Beach Rafferty Reservoir 4.79 69 SR 1 Crystal Lake Beach Crystal Lake 4.77 70 PN 18 Kimball Lake Campground Beach Kimball Lake 4.74 71 ST 16 Pike Lake Main Beach Pike Lake 4.74 72 ST 11 Last Mountain Lake Regional Park Main Beach Last Mountain Lake 4.74 Area 73 HL 1 Clearwater Lake Regional Park Beach Clearwater Lake 4.70 74 ST 1 Aspen Grove Beach Blackstrap Lake 4.69 75 PN 23 Greig Lake Public Beach Greig Lake 4.68 76 PA 9 Sunnyside Beach Emma Lake 4.61 77 PA 7 Nels Beach Emma Lake 4.59 78 PN 7 Resort Village of Cochin Public Beach Jackfish Lake 4.53 79 PN 14 Jumbo Beach Jumbo Lake 4.51 80 SR 7 Carlton Trail Regional Park Beach Carlton Trail Lake 4.51 81 PA 16 Martin's Lake Regional Park Beach Martin's Lake 4.45 82 PN 12 Turtle Lake Sunset View Public Beach Turtle Lake 4.40 83 PA 15 Bells Beach Christopher Lake 4.37 84 PA 5 McPhail Cove Beach Emma Lake 4.37 85 RQ 21 Kedleston Beach Last Mountain Lake 4.34 86 KT 11 Pruden's Point Beach Tobin Lake 4.33 87 PN 3 Brightsand Lake Regional Park Public Beach Brightsand Lake 4.31 88 PA 31 Lac La Peche Beach Lac La Pache 4.29 89 CHR 3 Camp Lemieux Beach Lac Pelletier Lake 4.28 90 FH 1 Buffalo Pound Provincial Park Beach Buffalo Pound Lake 4.28

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91 PN 21 Matheson Lake Campground Beach Matheson Lake 4.27 92 PA 8 Sunset Bay Beach Emma Lake 4.25 93 PN 27 Sandy Beach Campground Beach Pierce Lake 4.23 94 PN 1 Atton's Lake Regional Park Public Beach Atton's Lake 4.22 95 RQ 29 Camp Lutherland Beach Pasqua Lake 4.21 96 PN 10 Meota Regional Park Public Beach Jackfish Lake 4.20 97 RQ 38 Moosomin Regional Park Beach Moosomin Lake 4.17 98 PA 6 Birch Bay Beach Emma Lake 4.17 99 SR 12 KC Beach Regional Park Beach Fishing Lake 4.17 100 PA 33 Anderson Point Campground Beach Anglin Lake 4.16 101 ST 14 Island View Main Beach Area Last Mountain Lake 4.16 102 RQ 1 Alice Beach Last Mountain Lake 4.15 103 SR 21 Melville Beach Crooked Lake 4.15 104 PN 6 Picnic Lake Municipal Park Public Beach Picnic Lake 4.09 105 PA 24 Pelican Cove Beach Iroquois Lake 4.07 106 RQ 12 Alta Vista Beach Last Mountain Lake 4.05 107 RQ 37 Sorenson Beach Last Mountain Lake 4.02 108 ST 7 Thode Subdivision Beach Area Blackstrap Lake 3.98 109 ST 10 Domremy Beach Resort Beach Wakaw Lake 3.97 110 RQ 36 Mohr's Beach Last Mountain Lake 3.92 111 PA 34 Anglin Lake Cabin Beach Anglin Lake 3.89 112 SR 18 Canora Beach Good Spirit Lake 3.89 113 RQ 8 Sarnia Beach Last Mountain Lake 3.89 114 PA 27 Crescent Beach Meeting Lake 3.85 115 RQ 44 Echo Provincial Park Beach Echo Lake 3.79 116 CHR 4 Camp Elim Beach Lac Pelletier Lake 3.69 117 SC 3 Boundary Dam Beach Boundary Dam 3.68 Reservoir 118 RQ 24 Camp Monahan Beach Katepwa Lake 3.66 119 KT 12 Lower Fishing Beach Lower Fishing Lake 3.60 120 HL 7 Sask Landing Provincial Park Beach (Bearpaw) Lake Diefenbaker 3.58

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121 RQ 3 Pelican Pointe Beach Last Mountain Lake 3.57 122 RQ 28 Circle Square Ranch Beach Manmade Lake 3.54 123 SC 5 Moose Mtn Prov Park Beach Kenosee Lake 3.51 124 SR 17 Good Spirit Lake Provincial Park Beach Good Spirit Lake 3.42 125 RQ 30 Grandview Beach Last Mountain Lake 3.39 126 HL 5 Suffern Lake Beach Suffern Lake 3.38 127 HL 11 Coteau Beach Lake Diefenbaker 3.31 128 HL 2 Hitchcock Bay Beach Lake Diefenbaker 3.27 129 PA 23 Memorial Lake Regional Park Beach Memorial Lake 3.27 130 MC 1 Ramsey Bay Beach Weyakwin Lake 3.25 131 ST 4 Kevin Misfeldt Beach Area Beach Blackstrap Lake 3.24 132 RQ 43 Welwyn Centennial Regional Park Beach Qu'Appelle River 3.18 133 SR 4 Pickerel Point Beach Madge Lake 3.10 134 RQ 41 Pasqua Lake Beach Pasqua Lake 3.08 135 MC 2 Ile-a-la-Crosse Beach La Ile-a-la-Crosse Lake 3.07 136 SR 9 Kamsack Beach Madge Lake 3.06 137 SR 2 Crooked Lake Provincial Park Beach Crooked Lake 3.01 138 RQ 26 Lipp's Beach Last Mountain Lake 3.01 139 RQ 23 Wee Too Beach Last Mountain Lake 3.01 140 PA 12 Big Shell Lake Beach Big Shell Lake 2.98 141 SR 10 Lady Beach Lady Lake 2.96 142 KT 10 RV Tobin Lake Beach Tobin Lake 2.94 143 RQ 18 Saskatchewan Beach Last Mountain Lake 2.85 144 RQ 10 Valley View Beach - Buffalo Pound Buffalo Pound Lake 2.85 145 HL 6 Sask Landing Provincial Park Beach (Cottonwood) Lake Diefenbaker 2.75 146 FH 2 Rockin Beach Fife Lake 2.63 147 SR 11 Annie Laurie Beach Annie Laurie Lake 2.56 148 CHR 1 Onas Beach Lac Pelletier Lake 2.56 149 KT 13 Ranger's Beach Lower Fishing Lake 2.53 150 HL 4 Macklin Lake Regional Park Beach Macklin Lake 2.52 151 SR 8 Ministik Beach Madge lake 2.51

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152 CHR 5 Cabri Regional Park Beach Lake Diefenbaker 2.49 153 ST 13 Rowan's Ravine Provincial Park Main Beach Area Last Mountain Lake 2.44 154 SC 6 Moose Creek Regional Park Beach Alameda Dam 2.30 155 FH 4 Lovering Lake Beach Lovering Lake 2.26 156 SC 1 Nickle Lake Reg Pk Beach Nickle Lake 2.25 157 MC 7 Angler's Trail Resort Beach Lac La Plonge 2.20 158 MC 8 Pinehouse Lake Beach Pinehouse Lake 2.08 159 SR 20 Sandy Beach Good Spirit Lake 2.02 160 RQ 33 Fieldstone Campground Beach Manmade Lake 2.01 161 CHR 2 Darlings Beach Lac Pelletier Lake 2.01 162 ST 15 Leroy Leisureland Main Beach/Pool Area Quill Lakes 2.01 163 MC 10 Buffalo Narrows Beach Churchill Lake 1.98 164 PN 5 Silver Lake Regional Park Public Beach Silver Lake 1.86 165 MC 3 Jan Lake Beach Jan Lake 1.59 166 HL 10 Danielson Visitor’s Center Beach Lake Diefenbaker 1.23 167 MC 9 Michele Pt. Campground Beach Dore Lake 1.20 168 MC 6 Missinipi Beach Churchill River 0.95

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