Microbial Water Quality: Monitoring and Modeling

Abstract

Microbial water quality lies in the nexus of human, animal, and environmental health. Multidisciplinary efforts are under way to understand how microbial water quality can be monitored, predicted, and managed. This special collection of papers in the Journal of Environmental Quality was inspired by the idea of creating a special section containing the panoramic view of advances and challenges in the arena of microbial water quality research. It addresses various facets of health‐related microorganism release, transport, and survival in the environment. The papers analyze the spatiotemporal variability of microbial water quality, selection of predictors of the spatiotemporal variations, the role of bottom sediments and biofilms, correlations between concentrations of indicator and pathogenic organisms and the role for risk assessment techniques, use of molecular markers, subsurface microbial transport as related to microbial water quality, antibiotic resistance, real‐time monitoring and nowcasting, watershed scale modeling, and monitoring design. Both authors and editors represent international experience in the field. The findings underscore the challenges of observing and understanding microbial water quality; they also suggest promising research directions for improving the knowledge base needed to protect and improve our water sources. Core Ideas Environmental factors are known to control microbial water quality in natural systems. This collection of papers presents a panoramic view of microbial water quality research. With this special section, the editors hope to stimulate interdisciplinary efforts in the field.

Quantifying the effect of overland flow on Escherichia coli pulses during floods: Use of a tracer-based approach in an erosion-prone tropical catchment

“…In developing countries, untreated surface water is often used for drinking, cooking, bathing and washing, thus exposing the population to a high health risk (Boithias et al, 2016). Catchment microbial response depends on complex interactions between spatial patterns of land use, soil types, antecedent conditions of soil, and rainfall characteristics (Kim et al, 2018;McKergow and Davies-Colley, 2010;Pachepsky et al, 2018;Strauch et al, 2014). In the tropics, the presence in stream water of Fecal Indicator Bacteria (FIB), such as Escherichia coli (E. coli), is known to be partly driven by surface runoff (Causse et al, 2015;Ribolzi et al, 2016a) and groundwater flow (Chuah and Ziegler, 2018).…”

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

“…Identifying observations with uncertain predictions is an approach to determine the set of data requiring optimization and thus find a way to optimize the collection and to efficiently complete our training set, allowing for a better prediction in the future by re-running the model with the newly collected measurements. Indeed, recent studies have shown that increasing the quality and quantity of the dataset by adding complementary measures allows to effectively increase the training set and to improve prediction accuracy [20,43].…”

Antibacterial Performance Evaluation of Silver Coated River Sand for Water Decontamination Application

“…9 Drinking water if drawn from such sources, generally contains various pathogenic microorganisms and it turns out difficult to treat and deliver clean drinking water as the pathogens vary depending on the source of the contamination. 10 The Jia-Bharali river basin is one of the most evolved regions in the north Brahmaputra plain of India. 11 The area has broad tea-estates and paddy fields.…”