Hydro-epidemiological modelling of bacterial transport and decay in nearshore coastal waters

King, J. W.; Ahmadian, Reza; Falconer, Roger Alexander

doi:10.1016/j.watres.2021.117049

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…However, 3D modelling of the scheme at this stage would have required excessive computational resources and would potentially have necessitated limiting the simulation period and reducing the horizontal grid resolution. Furthermore, the flow structure in the Bristol Channel is well mixed [37,38] and the main focus of this study is preliminary on the far-field environmental assessment of this proposed scheme. It was therefore decided to refine TELEMAC-2D in this study due to the wellmixed nature of the estuary [37,38], computational efficiency and general applicability of the results [28,39].…”

Section: Numerical Modelmentioning

confidence: 99%

“…Furthermore, the flow structure in the Bristol Channel is well mixed [37,38] and the main focus of this study is preliminary on the far-field environmental assessment of this proposed scheme. It was therefore decided to refine TELEMAC-2D in this study due to the wellmixed nature of the estuary [37,38], computational efficiency and general applicability of the results [28,39]. However, a 3D analysis of the water renewal capacity will be undertaken in future planned studies.…”

Section: Numerical Modelmentioning

confidence: 99%

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Refined hydro-environmental modelling for tidal energy generation: West Somerset Lagoon case study

2021

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Section: Numerical Modelmentioning

confidence: 99%

Section: Numerical Modelmentioning

confidence: 99%

Refined hydro-environmental modelling for tidal energy generation: West Somerset Lagoon case study

2021

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“…These methods require a minimum of 18-24 hour (USEPA 2010) laboratory assay. However, FIO concentrations change continuously (Boehm et al 2002;Whitman et al 2004;Kim et al 2004;King et al 2021), causing culture-based warning systems to give outdated water quality alerts. More rapid FIO analysis methods such as quantitative polymerize chain reaction (qPCR) can determine FIO concentrations in less than 6 hours, but these methods require significant up-front investments and trained personnel (Zhang et al 2018) and still cannot be used as a predictive tool.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fecal-Indicator Organisms in Coastal Waters Using a Complex Nonlinear Artificial Intelligence Model

Lam

Ahmadian

2023

J. Environ. Eng.

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High levels of faecal indicator organisms (FIOs) at bathing water sites can cause disease and impose threats to public health. There is a need for predicting FIO levels to inform the public and reduce exposure. Data-driven models are one of the main tools being considered as predictive models. However, identifying the main inputs of the data-driven models is a major challenge in developing FIO predictor models. This paper develops a data-driven model for FIO concentration prediction based on a limited number of critical input variables. Essential variables were identified with be a combination of Gamma test and GA (Gamma-GA-test). Artificial Neural Networks (ANNs) and linear regression models were developed using these two variable identification approaches for comparison. The models were applied to a case study, and it was found that the model using the Gamma-GA test has a high potential to predict FIO levels more accurately, although this requires further investigation with different case studies. A correlation analysis was required prior to the variable identification approaches in this study. The need of this analysis highlights the significance of understanding the waterbody and the data set in the development and application of data-driven models. Models using a Gamma-GA test were more capable of predicting extreme (high) FIO concentrations, making a Gamma-GA test more suitable for a bathing water quality early warning system. The importance of nonlinearity in such predictive models was also demonstrated by the better performance of nonlinear ANN models compared 1Lam, October 3, 2022 to linear regression models regardless of the variable identification approaches used. This paper highlights the importance of nonlinearity in bathing water quality prediction and encourages further utilisation of nonlinear models for this application.

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“…In river and estuarine basins, the hydrodynamic process are often highly complex (Seo et al, 2022), and are driven by complex bathymetries, with pronounced spatiotemporal heterogeneity in the bottom roughness and often caused by: nonuniform sediment particles and salt marshes with different plants, unsteady upstream inflows, tide and wave processes from the open sea, wind‐driven currents, and density driven currents caused by turbidity, temperature and salinity differences, especially in deltaic zones between rivers and estuaries (Arevalo et al, 2022; Horner‐Devine et al, 2015). These complex hydrodynamic flow fields can cause complex transportation pathways of passive pollutants, such as fecal bacteria or FIOs (Abu‐Bakar et al, 2017; Huang et al, 2017; King et al, 2021). Likewise, along the coast there are generally sand beach regions, where numerous outfalls from sewage and riverine confluences often discharge untreated fecal bacteria, resulting in high concentrations and fluxes of FIOs along bathing beaches.…”

Section: Introductionmentioning

confidence: 99%

Dynamic tracing of fecal bacteria processes from a river basin to an estuary using a 2D/3D model

Huang

Falconer

Lin

et al. 2022

River

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In this study fecal bacteria processes have been investigated using the EFDC 2D/3D model, based on local refinements using an orthogonal curvilinear grid system and with the model being applied to Ribble river basin, through the estuary, and into the Fylde Coast. The input fluxes from numerous minor rivers and streams within the basin were solved using a grid‐based distributed hydrological model (GDHM) and a river network 1D model (RNM1D), developed by the authors. The detailed measured hydrodynamic data, included tide levels and nearshore ADCP 3D velocity field data at four sites and with data being recorded over a 2‐week period. During this period continuous monitoring using four types of tracers was also undertaken across the Ribble basin, which included sampling for E. coli (EC) and Intestinal Enterococci (IE) parameters. Monitoring devices, in the form of buoys, were also used and moved with currents, driven by upstream discharges, tides, waves and wind. The results showed: (i) the grid system gave a detailed topographical representation of the transition zones from the river system to the estuary and coast, with the hydrodynamic and related solute transport processes being well represented; and (ii) the model predicted results fit generally well with the water stage, 3D flow velocity profiles (with some errors in the bottom and surface layers), and E. coli concentrations. The tracer paths from the injection sites were simulated using a Lagrangian particle tracking method, which showed that the tracer from the north bank outfalls and the Ribble river could propagate to the highly popular bathing beaches at Blackpool, particularly under the action of South Westerly winds. More detailed solutions and refinements (e.g., wave driven by the wind and density flows caused by different salinities, temperatures, and suspended sediment concentrations) need to be considered in the next stage of this study.

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Hydro-epidemiological modelling of bacterial transport and decay in nearshore coastal waters

Cited by 13 publications

References 29 publications

Refined hydro-environmental modelling for tidal energy generation: West Somerset Lagoon case study

Refined hydro-environmental modelling for tidal energy generation: West Somerset Lagoon case study

Predicting Fecal-Indicator Organisms in Coastal Waters Using a Complex Nonlinear Artificial Intelligence Model

Dynamic tracing of fecal bacteria processes from a river basin to an estuary using a 2D/3D model

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