Evaluation of statistical models for predicting Escherichia coli particle attachment in fluvial systems

Piorkowski, Gregory S.; Jamieson, Rob; Bezanson, G. S.; Hansen, Lisbeth Truelstrup; Yost, Christopher K.

doi:10.1016/j.watres.2013.09.003

Cited by 13 publications

(13 citation statements)

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“…Classification and regression trees were used to identify environmental and land use factors associated with pathogens, finding distinct indicators of their sporadic distribution (Wilkes et al, 2011). Classification and regression trees, regularized regression, and multivariate adaptive splines were used to investigate factors driving E. coli attachment to particles and virulence (Piorkowski et al, 2013). Models provide information to overcome the difficulties and deficiencies associated with using FIOs to assess pathogen impairment, providing a flexible approach that can be implemented in diverse watersheds.…”

Section: Canonical Variable Selection For Ecological Modeling Of Fecamentioning

confidence: 99%

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

et al. 2018

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More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+‐somatic bacteriophage detections. Models were validated using a bootstrapping cross‐validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation. Core Ideas Maxent models were developed to infer source and transport of two fecal indicators. Canonical correlation analysis was applied to select parameters for Maxent models. Canonical selection and sensitivity analysis improved E. coli models. Elevated concentrations of E. coli are associated with hardness and heterotrophic activity. Bacteriophage detection is inhibited by sediment coliforms and enzyme activity.

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Section: Canonical Variable Selection For Ecological Modeling Of Fecamentioning

confidence: 99%

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

et al. 2018

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“…Strain-specific attachment has been observed in E. coli particle attachment in other studies (Cook et al, 2011;Pachepsky et al, 2008), and E. coli strains derived from human feces has been associated with greater particle attachment compared to those derived from livestock manure (Boutilier et al, 2009). Piorkowski et al (2013) reported higher particle attachment percentages of waterborne E. coli in stream reaches dominated by human versus livestock fecal inputs. Thus, the stronger relationship between sediment E. coli contributions and sediment transport observed at Stn3 may be in part be due to the greater particle attachment efficiency of human-derived E. coli.…”

Section: Comparative Contribution Of Sediment Versus Catchment Sourcementioning

confidence: 66%

“…Triplicate sediment samples (200 to 300 g) were retrieved from the sediment-water interface (0 to 5 cm depth) using a lever-action grab sampler with a 950 cm 3 bucket that was rinsed with stream water between each sample. Samples were collected from depositional areas (pools or point bars), riffles and runs of each stream reach and composited in a single sterile container to accommodate heterogeneity in E. coli populations existing within stream reaches (Piorkowski et al, 2013). Sediment particle size distribution and organic matter content were determined as reported by Piorkowski et al (2014).…”

Section: Site Description and Sampling Designmentioning

confidence: 99%

Reach specificity in sediment E. coli population turnover and interaction with waterborne populations

Piorkowski

Jamieson

Bezanson

et al. 2014

Science of The Total Environment

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“…Modeling provides flexible approaches to infer sources and processes associated with FIOs and other pathogens, overcoming some of the issues of the single indicator paradigm. Various statistical and machine learning models have been used to approach such problems of incorporating age of fecal pollution for source tracking or detection of viruses (Brion et al 2002;Black et al, 2007); identifying land use, environmental, and water quality parameters associated with FIOs and pathogens (Brion and Lingireddy, 1999;Viau et al, 2011;Wilkes et al, 2011;Gonzalez et al, 2012;Gonzalez and Noble, 2014;Hall et al, 2014;Herrig et al, 2015;Lušić et al, 2017); determining factors influencing particle attachment and virulence (Piorkowski et al, 2013); and optimizing microbial source tracking (Belanche-Muñoz and Blanch, 2008;Ballestè et al, 2010;Smith et al, 2010;Molina et al, 2014). Some other applications of modeling include using turbidity or rainfall to predict E. coli concentrations at unmonitored sites (Money et al 2009, Coulliete et al 2009, estimating E. coli loads using physical, chemical, and biological factors within a neural network (Dwivedi 2013), and hyporheic-groundwater interactions associated with transport of E. coli within sediments porewater (Dwivedi 2016).…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

“…alkalinity, conductivity, and hardness. Conductivity was selected because of its use in previously developed fecal indicator models (Wilkes et al, 2011;Gonzalez et al, 2012;Gonzalez and Noble, 2014;Piorkowski et al, 2013 Manuscript to be reviewed Manuscript to be reviewed Generally, the sites influenced by the greatest amount of developed or agricultural land use (SC5 -SC1) had the highest probability of impairment. August had the highest probability of impairment, followed by May, November, and February.…”

Section: Multivariate Model Performancementioning

confidence: 99%

Peer Review #1 of "Maxent estimation of aquatic Escherichia coli stream impairment (v0.1)"

2018

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Background. The leading cause of surface water impairment in United States' rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods. We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model's ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results. Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO 3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion. Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural runoff , stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator's utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.

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Evaluation of statistical models for predicting Escherichia coli particle attachment in fluvial systems

Cited by 13 publications

References 37 publications

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

Reach specificity in sediment E. coli population turnover and interaction with waterborne populations

Peer Review #1 of "Maxent estimation of aquatic Escherichia coli stream impairment (v0.1)"

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