Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

Abstract

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.

Hot Spots and Persistence of Nitrate in Aquifers Across Scales

“…Moreover, the interpolated value may be different from the measurement itself [29]. Similarly, PCA requires certain assumptions, such as linearity and large variances being the only important structure in the dataset [30]. On the other hand, entropy is a non-parametric approach and a robust measure of variability.…”

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

“…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.…”

“…headwater or lower reaches) and temperature all showed significant correlations with either concentrations or loadings of E. coli in streams. Dwivedi et al (2013) investigated the correlation between 13 water quality factors with E. coli loading and concluded that dissolved oxygen, phosphate, ammonia, chlorophyll, suspended solids, and temperature, were most correlated. Diurnal variability (Meays et al, 2006) and dissolved carbon dioxide (Gray, 1975) have also been found to be relevant.…”

Comparison of Electrostatic and Non‐Electrostatic Models for U(VI) Sorption on Aquifer Sediments

“…The traditional grid search method, which incorporates an exhaustive search of the entire parameter space in finite steps, was applied to minimize the objective function in iTOUGH2 (Press et al 1992;Finsterle 2000). Two commonly used goodness-of-fit criteria were used for evaluating the best fit of parameters in the inverse analysis: the Nash Sutcliffe efficiency (NSE) and coefficient of determination (R 2 ) (Nash and Sutcliffe 1970;Legates and McCabe 1999;Dwivedi et al 2013).…”