Coupled Effects of Pore Water Velocity and Soil Heterogeneity on Bacterial Transport: Intact vs. Repacked Soils

Chen, Jing; Yang, Liandong; Chen, Xijuan; Ripp, Steven; Zhuang, Jie

doi:10.3389/fmicb.2022.730075

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…This observation aligns with previous research on the influence of ionic strength and pore water velocity on attachment/detachment dynamics (J. Chen et al, 2022;Knappett et al, 2008). Compared to the traditional method, the values of both 𝑘 a and 𝑘 d obtained through the RP method were marginally lower.…”

Section: Comparison Of Traditional Methods and Rp Methodssupporting

confidence: 91%

Estimating bacterial breakthrough behaviors based on bacterial retention profiles in porous media

Chen,

Yang,

Chen

et al. 2024

Soil Science Soc of Amer J

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Traditional methods for assessing bacterial transport in soil or permeable sand aquifers, such as flow‐through experiments, breakthrough curve (BTC), and retention profile (RP) analysis, face challenges due to their complexity and the labor‐intensive nature of in situ implementations. This study seeks to address the question: How can the transport behavior of bacteria in soil be predicted in a simpler and more cost‐effective manner using RPs? We introduce an RP method designed to overcome these challenges by utilizing soil sampling at various depths to model bacterial breakthrough behaviors with greater efficiency. By employing the one‐site attachment/detachment model within the Hydrus 1D framework, our research compares the RP method (three RPs) against the conventional approach (BTC + RP), showcasing its efficacy through column experiments with bioluminescent Escherichia coli in humic acid‐coated sand. The results indicate that the accuracy of RP method aligns closely with traditional methods in predicting bacterial transport. This technique allows for the use of solid samples collected from a limited number of depths to predict breakthrough behaviors accurately, making it suitable for evaluating bacterial transport in settings such as farmlands, contaminated lands, riverbanks, and soil aquifer treatment systems. Our findings underscore the RP method's role in streamlining experimental procedures and its potential application in environmental science and agronomy.

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Section: Comparison Of Traditional Methods and Rp Methodssupporting

confidence: 91%

Estimating bacterial breakthrough behaviors based on bacterial retention profiles in porous media

Chen,

Yang,

Chen

et al. 2024

Soil Science Soc of Amer J

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“…Packing and/or differences in grain size may have also contributed to increasing pore-scale complexity which has been observed to widen pore-water velocity distributions − and could lead to more colloids reaching the grain surface based on previous pore-scale simulation studies. ,− Under unfavorable attachment conditions, bacteria attachment is largely driven by local variations in velocity. This has been observed in experiments and models at grain–grain contacts, near grain surfaces where roughness leads to enhanced attachment, , and regions of decreasing pore water velocity. ,,− …”

Section: Resultsmentioning

confidence: 58%

In Situ Measurements of Dynamic Bacteria Transport and Attachment in Heterogeneous Sand-Packed Columns

Le,

Thompson,

Roden

et al. 2023

Environ. Sci. Technol.

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Prevention, mitigation, and regulation of bacterial contaminants in groundwater require a fundamental understanding of the mechanisms of transport and attachment in complex geological materials. Discrepancies in bacterial transport behaviors observed between field studies and laboratory experiments indicate an incomplete understanding of dynamic bacterial transport and immobilization processes in realistic heterogeneous geologic systems. Here, we develop a new experimental approach for in situ quantification of dynamic bacterial transport and attachment distribution in geologic media that relies on radiolabelingEscherichia coliwith positron-emitting radioisotopes and quantifying transport with three-dimensional (3D) positron emission tomography (PET) imaging. Our results indicate that the highest bacterial attachment occurred at the interfaces between sand layers oriented orthogonal to the direction of flow. The predicted bacterial attachment from a 3D numerical model matched the experimental PET results, highlighting that the experimentally observed bacterial transport behavior can be accurately captured with a distribution of a first-order irreversible attachment model. This is the first demonstration of the direct measurement of attachment coefficient distributions from bacterial transport experiments in geologic media and provides a transformational approach to better understand bacterial transport mechanisms, improve model parametrization, and accurately predict how local geologic conditions can influence bacterial fate and transport in groundwater.

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“…Pore water velocity showed highest importance in the predictions of k and λ . This is because bacterial transport could be increased by increasing pore water velocity ( Bradford et al, 2006 ; Choi et al, 2007 ; Chen et al, 2022 ). Higher pore water velocity is accompanied by higher water shear force and less bacteria-soil contact time, which can reduce bacterial mechanical filtration and bacterial attachment, respectively ( Hendry et al, 1999 ; Li et al, 2005 ; Syngouna and Chrysikopoulos, 2011 ).…”

Section: Resultsmentioning

confidence: 99%

“…For porous medium type, bacterial concentration, organic content, and saturated hydraulic conductivity, the SHAP contribution shows relatively aggregated distribution. Many studies showed that the intact soil could greatly facility bacterial transport because of preferential flow in macropore-dominated pathways ( McLeod et al, 1998 ; Safadoust et al, 2011 ; Chen et al, 2022 ). Nevertheless, the effect of intact soil or disturbed soil was not obvious in our predictive models.…”

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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Coupled Effects of Pore Water Velocity and Soil Heterogeneity on Bacterial Transport: Intact vs. Repacked Soils

Abstract: Graphical AbstractDepth profile of pore water velocity effect in differently structured soils.

Cited by 9 publications

References 48 publications

Estimating bacterial breakthrough behaviors based on bacterial retention profiles in porous media

Estimating bacterial breakthrough behaviors based on bacterial retention profiles in porous media

In Situ Measurements of Dynamic Bacteria Transport and Attachment in Heterogeneous Sand-Packed Columns

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

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