Transport of E. coli in saturated and unsaturated porous media: effect of physiological state and substrate availability

Madumathi, G.; Philip, Ligy; Bhallamudi, S. Murty

doi:10.1007/s12046-017-0650-8

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…The first type of factors has small differences but may influence bacterial transport behaviors. For example, cell surface characteristics (e.g., physiological state, flagella type, and extracellular polymeric substances) were reported to affect bacterial transport in sand or soil ( Madumathi et al, 2017 ; Du et al, 2021 ; Zhang et al, 2021 ). These variables were not included into the AML model because they were not clearly defined in most literature.…”

Section: Resultsmentioning

confidence: 99%

Predicting bacterial transport through saturated porous media using an automated machine learning model

et al. 2023

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Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants’ transport in the environment.

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Section: Resultsmentioning

confidence: 99%