Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

González, Raúl; Conn, Kathleen E.; Crosswell, Joseph R.; Noble, Rachel T.

doi:10.1016/j.watres.2012.07.050

Cited by 46 publications

(34 citation statements)

References 44 publications

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“…The positive conductivity correlation with microbial indicators in this study conflicted with previously reported inverse correlations in estuary waters in North Carolina and surface waters in Central Florida possibly due to changes in salinity and other dissolved solids, and dilution of E . coli concentrations in waters after storm and run-off water exposure [11, 29, 30]. This might be explained in that the pond waters in this study are close-system surface water sources feed with well water, and that the length of the sampling period was longer, more frequent, and concentrated on only six ponds.…”

Section: Discussionmentioning

confidence: 98%

“…This might be explained in that the pond waters in this study are close-system surface water sources feed with well water, and that the length of the sampling period was longer, more frequent, and concentrated on only six ponds. McEgan et al [11] and Gonzales et al [29] sampled a total of 202 and 151 times, respectively, compared to 540 data points in this study. In a study conducted in South Africa, total and fecal coliforms did not correlate with chemical oxygen demand; enterococci correlated with pH, turbidity, and rainfall [31].…”

Section: Discussionmentioning

confidence: 99%

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Microbial quality of agricultural water in Central Florida

2017

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The microbial quality of water that comes into the edible portion of produce is believed to directly relate to the safety of produce, and metrics describing indicator organisms are commonly used to ensure safety. The US FDA Produce Safety Rule (PSR) sets very specific microbiological water quality metrics for agricultural water that contacts the harvestable portion of produce. Validation of these metrics for agricultural water is essential for produce safety. Water samples (500 mL) from six agricultural ponds were collected during the 2012/2013 and 2013/2014 growing seasons (46 and 44 samples respectively, 540 from all ponds). Microbial indicator populations (total coliforms, generic Escherichia coli, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. Salmonella isolates were serotyped and analyzed by pulsed-field gel electrophoresis. Following rain events, coliforms increased up to 4.2 log MPN/100 mL. Populations of coliforms and enterococci ranged from 2 to 8 and 1 to 5 log MPN/100 mL, respectively. Microbial indicators did not correlate with environmental variables, except pH (P<0.0001). The invA gene (Salmonella) was detected in 26/540 (4.8%) samples, in all ponds and growing seasons, and 14 serotypes detected. Six STEC genes were detected in samples: hly (83.3%), fliC (51.8%), eaeA (17.4%), rfbE (17.4%), stx-I (32.6%), stx-II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from Salmonella (invA-4.8%) or STEC (stx-I-32.6%, stx-II-9.4%) was detected in each pond. Water quality for tested agricultural ponds, below recommended standards, did not guarantee the absence of pathogens. Investigating the relationships among physicochemical attributes, environmental factors, indicator microorganisms, and pathogen presence allows researchers to have a greater understanding of contamination risks from agricultural surface waters in the field.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Microbial quality of agricultural water in Central Florida

2017

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Section: Figmentioning

confidence: 99%

“…Conductivity of surface water is an indirect measure of salinity and total dissolved solids and may be affected by storm water and runoff (42). An inverse correlation between indicator bacteria (E. coli, enterococci, and Bacteroides) and conductivity or salinity was reported for estuary waters in eastern North Carolina (42). The inverse correlation noted in the current study may be due to storm water and runoff effects in which the addition of the storm water or runoff in carrying salinity or other total dissolved solids from the surrounding area also dilutes the E. coli concentration.…”

Section: Figmentioning

confidence: 99%

Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters

McEgan

Mootian

Goodridge

et al. 2013

Appl Environ Microbiol

104

107

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c Coliforms, Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration of Salmonella and biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n ‫؍‬ 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) for Salmonella, E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) and Salmonella levels (R 2 < 0.1) and between physicochemical indicators and Salmonella levels (R 2 < 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed that E. coli concentration can predict the probability of enumerating selected Salmonella levels. The lack of good correlations between biological indicators and Salmonella levels and between physicochemical indicators and Salmonella levels shows that the relationship between pathogens and indicators is complex. However, Escherichia coli provides a reasonable way to predict Salmonella levels in Central Florida surface water through logistic regression.

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“…In order to address the problem in current Beach Monitoring Programs and to implement the BEACH ACT or other regulations in a more effective and efficient way, increasing efforts have been made to develop predictive models for assessing fecal pollution of beach waters (Chigbu et al 2005;López et al 2013;Pandey et al 2012;Gonzalez et al 2012;Srinivas and Dominic 2012;Zhang et al 2012). Grant and Sanders (2010) proposed a conceptual and mathematical framework, called the "beach boundary layer model," for understanding and quantifying the relative impact of beach-side and bay-side sources of fecal pollution on nearshore water quality.…”

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confidence: 99%

Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

Zhang

Deng

Rusch

2014

Water Qual Expo Health

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This study presents and compares two models for predicting fecal coliform levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB toolbox and the other one was developed based on the multiple linear regression method (MLR). A total of six independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with eight different combinations of the independent variables are capable of explaining about 76 % of variation in fecal coliform levels for model training data and 44 % for independent data. The findings are obtained from the ANN model and the MLR model using six years of bacteriological and environmental monitoring data. The results show that the ANN model performs consistently better than the MLR model. Applications of the ANN model can significantly reduce potential health risks of fecal pollution to beachgoers. The paper provides new insights into environmental processes responsible for the variation in levels of fecal coliform bacteria in coastal beach waters as well.

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Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

Cited by 46 publications

References 44 publications

Microbial quality of agricultural water in Central Florida

Microbial quality of agricultural water in Central Florida

Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters

Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches

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