Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah G.; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

doi:10.3133/sir20135166

Cited by 14 publications

(33 citation statements)

References 6 publications

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“…Between the time of sampling and subsequent results, beach conditions will often change so the water sample is not relevant to today's beach conditions (Whitman, Nevers, and Gerovac 1999;Boehm et al 2002). To get around this, researchers have developed statistical models-dubbed "nowcast" models-to estimate FIB for the day based on the previous day E. coli levels and factors such as precipitation, wind, and water conditions (Francy et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting E. coli concentrations using limited qPCR deployments at Chicago beaches

Lucius¹,

Rose

Osborn

et al. 2018

Preprint

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9Culture-based methods to measure Escherichia coli (E. coli) are used by beach administrators to inform whether bacteria levels represent an elevated risk to swimmers. Since results take up to 12 hours, statistical models are used to forecast bacteria levels in lieu of test results; however they underestimate days with elevated fecal indicator bacteria levels. Quantitative polymerase chain reaction (qPCR) tests return results within 3 hours but are 2 to 5 times more expensive than culture-based methods. This paper presents a prediction model which uses limited deployments of qPCR tested sites with inter-beach correlation to predict when bacteria will exceed acceptable thresholds. The model can be used to inform management decisions on when to warn residents or close beaches due to exposure to the bacteria. Using data from Chicago collected between 2006 and 2016, the model proposed in this paper increased sensitivity from 3.4 percent to 11.2 percent-a 230 percent increase. We find that the correlation between beaches are substantial enough to provide higher levels of precision and sensitivity to predictive models. Thus, limited deployments of qPCR testing can be used to deliver better predictions for beach administrators at lower cost and less complexity.

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Section: Introductionmentioning

confidence: 99%

Predicting E. coli concentrations using limited qPCR deployments at Chicago beaches

Lucius¹,

Rose

Osborn

et al. 2018

Preprint

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“…Antecedent rainfall, wind, and water temperature were also utilized in models developed at inland lakes in Ohio (Francy et al 2013b). Day of year, rainfall, wind, and humidity were also used in combination with solar radiation at a Lake Michigan beach in Illinois (Francy et al 2013a). While waves were included in the 2012-2015 model, the coefficient was fairly low.…”

Section: Model Resultsmentioning

confidence: 99%

“…The grooming method chosen has also been shown to have an effect. Mechanical grooming with a tractor, as opposed to hand raking, has been shown in some cases to increase E. coli counts, possibly due to mixing in of bird feces (Kinzelman et al 2003 , Francy et al 2013a. This poor temporal resolution causes swimmers to be exposed unknowingly to contaminated waters (type II errors) or can cause beaches to have advisories when water quality is actually acceptable (type I errors) (Nevers and Whitman 2011).…”

Section: Beach Groomingmentioning

confidence: 99%

“…R24 is the 24-hour antecedent rainfall, in inches; R48 is the 48-hour antecedent rainfall, in inches; and R72 is the 72-hour antecedent rainfall, in inches. (Francy et al 2013a) Antecedent hourly wind speed and direction as well as relative humidity and solar radiation were averaged over the previous six hours to each sample. The most recent observations of air temperature, wind speed and direction were also included in the analysis.…”

Section: Compiling Data and Calculating Variablesmentioning

confidence: 99%

“…Models developed with Virtual Beach have been effectively implemented at many coastal and Great Lakes beaches, and to a lesser extent at inland lake beaches (Francy et al 2013b, Francy et al 2013a).…”

Section: Introductionmentioning

confidence: 99%

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Source determination and predictive model development for Escherichia coli concentrations at F.W. Kent Park Lake, Oxford, Iowa

Simmer¹

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Fecal contamination of Iowa recreational water bodies poses a threat to water quality as well as human health. Concern regarding the health effects of waterborne pathogens resulted in 149 beach advisories across 39 state-owned beaches during the 2015 beach season alone. While the presence of pollution is often clear, its cause and source may be difficult to identify. Furthermore, the current practice in Iowa of sampling once per week leads to high uncertainty and inadequately protects swimmers from exposure. The objective of this study was to determine the influential environmental factors and sources causing spikes in fecal contamination at F.W. Kent Park Lake in Oxford, IA, and to develop a predictive model of beach E. coli concentrations. Water samples were collected at the swimming beach as well as throughout the watershed from May to October, 2015. All samples were analyzed for Escherichia Coli using the IDEXX Colilert enumeration method. Together with weekly data from 2012 through 2014, two predictive models of E. coli based upon influential environmental and water quality variables were developed using EPA Virtual Beach software. These models proved to be more accurate than the current method used to assess risks to swimmers that assumes bacterial concentrations remain constant between samples. In addition, through statistical analysis and modeling, this study found evidence that the main source of fecal contamination were wild geese that frequent the beach. v

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Evaluating multiple predictive models for beach management at a freshwater beach in the Great Lakes region

Madani

Seth

2020

J of Env Quality

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Recreational water quality is currently monitored at Sandpoint Beach on Lake St. Clair using culture‐based enumeration of Escherichia coli. Using water quality and weather data collected over 4 yr, several multiple linear regression (MLR)‐based models were developed for near real‐time prediction of E. coli concentration and were tested using independent data from the fifth year. Model performance was assessed by the determination of metrics such as RMSE, accuracy, specificity, sensitivity, and area under the receiver operating characteristic curve (AUROC). Each of the developed MLR models described herein resulted in increased correct responses for both exceedance and non‐exceedance of the applicable standard as compared to predictions based on E. coli measurements (persistence models, using the previous day's E. coli concentration), which is the method currently being used. The AUROC values for persistence models are between 0.5 and 0.6, as compared to >0.7 for all the MLR models described herein. Among the MLR models, model performance improved when qualitative sky weather condition, which is commonly reported but was not previously used in similar models, was included. To select the best model, a principal coordinate analysis was used to combine multiple model performance metrics and provide a more sensitive tool for model comparison. Although models developed using 2, 3, and 4 yr of monitoring data provided reasonable performance, the model developed using the most recent 2‐yr data was marginally better. Thus, data from the most recent 2 yr are likely sufficient as a training dataset for updating the MLR model for Sandpoint Beach in the future.

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Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

Cited by 14 publications

References 6 publications

Predicting E. coli concentrations using limited qPCR deployments at Chicago beaches

Predicting E. coli concentrations using limited qPCR deployments at Chicago beaches

Source determination and predictive model development for Escherichia coli concentrations at F.W. Kent Park Lake, Oxford, Iowa

Evaluating multiple predictive models for beach management at a freshwater beach in the Great Lakes region

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