Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches

Brooks, Wesley; Fienen, Michael N.; Corsi, Steven R.

doi:10.1016/j.jenvman.2012.09.033

Cited by 18 publications

(18 citation statements)

References 19 publications

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“…The E. coli concentrations at GW had positive relations with the tidal level, air temperature, and suspended solid concentration (SS), as well as the rainfall-associated variables, wind direction, and WWTP discharge (Table 2). In this study, the dependence of the two FIB on the tidal level and air temperature was not statistically significant, except for E. coli at GW, even though several studies have considered the wave height (Brooks et al, 2013;Feng et al, 2015;Francy, 2009;Molina et al, 2014;Nevers and Whitman, 2011;Shively et al, 2016;Telech et al, 2009;Thoe et al, 2014) and air temperature (Brooks et al, 2013;Gonzalez et al, 2012;Molina et al, 2014;Nevers and Whitman, 2011;Shively et al, 2016;Telech et al, 2009;Thoe et al, 2014) as inputs. No published studies have used SS as an input variable; however, some studies have used turbidity, which could be a proxy for SS (Brooks et al, 2013(Brooks et al, , 2016Francy, 2009;Gonzalez et al, 2012;Molina et al, 2014;Shively et al, 2016;Telech et al, 2009).…”

Section: Dependence Of Fecal Indicator Bacteria On the Independent Vacontrasting

confidence: 58%

“…The precipitation variables were significant variables for both ENT and E. coli at both HA and GW (Table 2). A number of previous studies used rainfall‐associated variables as input data to develop statistical models to simulate the FIB levels in beach water (Brooks et al, 2013, 2016; Feng et al, 2015; Francy, 2009; Gonzalez et al, 2012; Jones et al, 2013; Molina et al, 2014; Nevers and Whitman, 2011; Shively et al, 2016; Telech et al, 2009; Thoe et al, 2014). Similar to the results of this study (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…This variable has been considered as a factor affecting the mortality of bacteria in marine water (Boehm et al, 2009) and freshwater (Cho et al, 2010). This variable is commonly implemented to predict FIB concentrations in beach water using statistical models (Brooks et al, 2013; Feng et al, 2015; Jones et al, 2013; Nevers and Whitman, 2011; Shively et al, 2016; Thoe et al, 2014) and deterministic models (Feng et al, 2013; Ki et al, 2007; Liu et al, 2006; Russell et al, 2013; Sanders et al, 2005; Zhu et al, 2011). Shively et al (2016) demonstrated that the solar radiation, along with wind direction and rainfall, was the first important regressor to predict E. coli densities in their regression tree.…”

Section: Discussionmentioning

confidence: 99%

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Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

et al. 2018

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Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and Escherichia coli) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations (p < 0.01), whereas solar radiation was negatively correlated (p < 0.01). The performance of the ANN model for predicting ENT and E. coli at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset (p < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use. Core Ideas Enterococcus and E. coli concentrations were predicted using machine learning models. Nine variables collected from two beach waters were tested as input for the models. The ANN performed better than SVR for predicting fecal indicator bacteria concentrations.

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Section: Dependence Of Fecal Indicator Bacteria On the Independent Vacontrasting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

et al. 2018

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“…The goal of this research was to identify significant influences on enterococci concentrations by measuring a wide variety of variables. To distill this information, we used a PSLR model, which has been shown to out-perform similar multiple linear regression and principle components regression analyses (56) and has gained popularity in the water quality field (57, 58). Results from the PLSR analysis in this study showed that particle-associated enterococci and concentrations of mammal fecal sources were the driving force behind variation in enterococci concentrations, as described by both PLSR models constructed.…”

Section: Discussionmentioning

confidence: 99%

Effects of Fecal Source Input, Environmental Conditions, and Environmental Sources on Enterococci Concentrations in a Coastal Ecosystem

Rothenheber¹,

Jones

2018

Preprint

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21Fecal pollution at coastal beaches in the Northeast, USA requires management efforts to address 22 public health and economic concerns. Concentrations of fecal-borne bacteria are influenced by 23 different fecal sources, environmental conditions, and ecosystem reservoirs, making their public 24 health significance convoluted. In this study, we sought to delineate the influences of these 25 factors on enterococci concentrations in southern Maine coastal recreational waters. Weekly 26 water samples and water quality measurements were conducted at freshwater, estuarine, and 27 marine beach sites from June through September 2016. Samples were analyzed for total and 28 particle-associated enterococci concentrations, total suspended solids, and microbial source 29 tracking markers for multiple sources. Water, soil, sediment, and marine sediment samples were 30 also subjected to 16S rRNA sequencing and SourceTracker analysis to determine the influence 31 from these environmental reservoirs on water sample microbial communities. Enterococci and 32 particle-associated enterococci concentrations were elevated in freshwater, but suspended solids 33 concentrations were relatively similar. Mammal fecal contamination was significantly elevated 34 in the estuary, with human and bird fecal contaminant levels similar between sites. A partial least 35 squares regression model indicated particle-associated enterococci and mammal marker 36 concentrations had the most significant positive relationships with enterococci concentrations 37 across marine, estuary, and freshwater environments. Freshwater microbial communities were 38 significantly influenced by underlying sediment while estuarine/marine beach communities were 39 influenced by freshwater, high tide height, and estuarine sediment. We found elevated 40 enterococci levels are reflective of a combination of increased fecal source input, environmental 41 3 sources, and environmental conditions, highlighting the need for encompassing MST approaches 42 for managing water quality issues. 43 IMPORTANCE 44Enterococci have long been the federal standard in determining water quality at estuarine and 45 marine environments. Although enterococci are highly abundant in the fecal tracts of many 46 animals they are not exclusive to that environment and can persist and grow outside of fecal 47 tracts. This presents a management problem for areas that are largely impaired by non-point 48 source contamination, as fecal sources might not be the root cause of contamination. This study 49 employed different microbial source tracking methods to delineate influences from fecal source 50 input, environmental sources, and environmental conditions to determine which combination of 51 variables are influencing enterococci concentrations in recreational waters at a historically 52 impaired coastal town. Results showed that fecal source input, environmental sources and 53 conditions all play a role in influencing enterococci concentrations. This highlights the need to 54 include an encompassing microb...

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“…c o m / l o c a t e / s c i t o t e n v Schriewer et al, 2010;Wilkes et al, 2013). Escherichia coli (E. coli) is a commonly used indicator organism for pathogens in freshwater (Brooks et al, 2013;Dwivedi et al, 2013;Vidon et al, 2008). Understanding the factors controlling instream E. coli levels is critical to developing strategies to reduce disease risks due to contaminated drinking and recreational waters.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Explaining and modeling the concentration and loading of Escherichia coli in a stream—A case study

Wang

Schneider

Parlange

et al. 2018

Science of The Total Environment

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Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R = 0.85 for log (E. coli concentration) and R = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak.

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Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches

Cited by 18 publications

References 19 publications

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

Effects of Fecal Source Input, Environmental Conditions, and Environmental Sources on Enterococci Concentrations in a Coastal Ecosystem

Explaining and modeling the concentration and loading of Escherichia coli in a stream—A case study

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