Numerical Modeling of Microbial Fate and Transport in Natural Waters: Review and Implications for Normal and Extreme Storm Events

Abstract

Degradation of water quality in recreational areas can be a substantial public health concern. Models can help beach managers make contemporaneous decisions to protect public health at recreational areas, via the use of microbial fate and transport simulation. Approaches to modeling microbial fate and transport vary widely in response to local hydrometeorological contexts, but many parameterizations include terms for base mortality, solar inactivation, and sedimentation of microbial contaminants. Models using these parameterizations can predict up to 87% of variation in observed microbial concentrations in nearshore water, with root mean squared errors ranging from 0.41 to 5.37 log10 Colony Forming Units (CFU) 100 mL−1. This indicates that some models predict microbial fate and transport more reliably than others and that there remains room for model improvement across the board. Model refinement will be integral to microbial fate and transport simulation in the face of less readily observable processes affecting water quality in nearshore areas. Management of contamination phenomena such as the release of storm-associated river plumes and the exchange of contaminants between water and sand at the beach can benefit greatly from optimized fate and transport modeling in the absence of directly observable data.

Modeling Untreated Wastewater Evolution and Swimmer Illness for Four Wastewater Infrastructure Scenarios in the San Diego‐Tijuana (US/MX) Border Region

“…This is because of its abundance in untreated wastewater (Eftim et al., 2017), being long‐lived in seawater (unlike EC and ENT) with a median decay constant of (10‐day decay time‐scale) based on data available to date (e.g., Boehm et al., 2015, 2019), and its low infectious dose. Effects of sunlight, turbidity, and sedimentation, which can be important controls on FIB fate (e.g., Weiskerger & Phanikumar, 2020), are not considered here for NoV. Thus, with specified source NoV concentration C src (copy L −1 ), the shoreline NoV concentration is C = DC src .…”

“…Coupled wave and circulation hydrodynamic models can concurrently simulate the three‐dimensional (3D) flow of estuaries, the breaking‐wave driven surfzone, and the shelf (e.g., Kumar et al., 2015, 2012; Olabarrieta et al., 2011; Wu et al., 2020). Hydrodynamic models have previously been coupled to FIB (e.g., E. coli [EC] or Enterococcus [ENT]) models, particularly in lake systems (e.g., Weiskerger & Phanikumar, 2020). For example, sunlight, temperature, and sedimentation induced FIB loss was coupled to a two‐dimensional (2D) hydrodynamic model to simulate EC and ENT in southern Lake Michigan over a month (Liu et al., 2006).…”

Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

“…The regulatory monitoring of the bathing waters is based on the enumeration of culturable fecal indicator bacteria, Escherichia coli and intestinal enterococci (e.g., European Bathing directive 2006/7/EC). Such surveys are costly, time-consuming, and labor-intensive, as a consequence weekly or monthly sampling strategies are routinely implemented with additional event-based sampling [11,12].…”

“…Each individual sensor may present a slightly greater error margin than the costly high precision equipment, however the multitude of sensors allows to build a dense network which in average is capable of providing enough information for the machine learning models [24]. However, enrichment of training datasets with high quality data of extreme events is particularly important in the context of climate change with the expected rise of temperature and increase in the frequency and intensity of storm events [12]. Therefore, the objective of this study is to explore these three strategies to improve the input datasets for training and testing machine learning models, particularly study the relevance of the active learning strategy.…”

Analysis of Storage Effects in the Recirculation Zone Based on the Junction Angle of Channel Confluence

“…The flow structure at a confluence and its effects on solute mixing were simulated using the quasi-three-dimensional model Environmental Fluid Dynamics Code (EFDC). The EFDC model has been used frequently in many riverine, estuary, flow, and solute mixing simulations, and it has been proven to be reasonably accurate [34][35][36][37]. The EFDC was applied to various junction angles to determine their effect on the size of the recirculation zone.…”