Climate and Land Use Influences on Bacteria Levels in Stormwater

Modeling solutions for microbial water contamination in the global south for public health protection

Microbial contamination of water sources is a pressing global challenge, disproportionately affecting developing regions with inadequate infrastructure and limited access to safe drinking water. In the Global South, waterborne pathogens such as bacteria, viruses, protozoa, and helminths contribute to diseases like cholera, dysentery, and typhoid fever, resulting in severe public health burdens. Predictive modeling emerges as a pivotal tool in addressing these challenges, offering data-driven insights to anticipate contamination events and optimize mitigation strategies. This review highlights the application of predictive modeling techniques-including machine learning, hydrological simulations, and quantitative microbial risk assessment -to identify contamination hotspots, forecast pathogen dynamics, and inform water resource allocation in the Global South. Predictive models enable targeted actions to improve water safety and lower the prevalence of waterborne diseases by combining environmental, socioeconomic, and climatic factors. Water resources in the Global South are increasingly vulnerability to microbial contamination, and the challenge is exacerbated by rapid urbanization, climate variability, and insufficient sanitation infrastructure. This review underscores the importance of region-specific modeling approaches. Case studies from sub-Saharan Africa and South Asia demonstrated the efficacy of predictive modeling tools in guiding public health actions connected to environmental matrices, from prioritizing water treatment efforts to implementing early-warning systems during extreme weather events. Furthermore, the review explores integrating advanced technologies, such as remote sensing and artificial intelligence, into predictive frameworks, highlighting their potential to improve accuracy and scalability in resource-constrained settings. Increased funding for data collecting, predictive modeling tools, and cross-sectoral cooperation between local communities, non-governmental organizations, and governments are all recommended in the review. Such efforts are critical for developing resilient water systems capable of withstanding environmental stressors and ensuring sustainable access to safe drinking water. By leveraging predictive modeling as a core component of water management strategies, stakeholders can address microbial contamination challenges effectively, safeguard public health, and contribute to achieving the United Nations' Sustainable Development Goals.

OPMS - A web-based ocean pollution monitoring system

Marine pollution poses significant risks to both marine ecosystems and human health, requiring effective monitoring and control measures. This study presents the Ocean Pollution Monitoring System (OPMS), a web application designed to visualize the seasonal and annual fluctuations of marine pollutants along coastal regions in Canada. The pollutants include fecal coliform and biotoxins such as paralytic shellfish poisoning (PSP), and amnesic shellfish poisoning (ASP). The OPMS utilizes 20 years of data from nearly 15,000 shellfish harvesting sites across six provinces of Canada, allowing users to explore trends and the impact of these pollutants in user-selected geographical regions. The seasonal fluctuation patterns of fecal coliform and biotoxin levels were extracted by Functional Principal Component Analysis (FPCA) previously. OPMS visualizes these results in finer granularity to provide environmental managers and policymakers with a decision-support tool in shellfish safety and water quality management. The tool is accessible at http://opms.uvic.ca.

Land use and rainfall influences on bacterial levels and sources in stormwater ponds

Urban stormwater runoff is a known source of microbial contamination of stormwater ponds. However, less is known about the influences of land use and rainfall on microbial quality over time in these receiving waters. In this study, two fecal indicator bacteria (FIB), namely Escherichia coli and thermotolerant coliforms, were monitored in three stormwater ponds in Calgary, Alberta, Canada. The stormwater ponds were selected due to their potential as water sources for beneficial uses such as irrigation, which requires lower water quality than drinking water, thereby alleviating the pressure on the city's potable water demands. The selected stormwater ponds vary in size and shape, contribution catchment size, and percentages of several primary land use types. Microbial source tracking for human, dog, seagull, Canada goose, ruminant, and muskrat was also conducted to determine sources of bacterial contamination in the stormwater ponds. Sampling was conducted near the pond surface and adjacent to the shoreline, specifically near the outfalls that discharge stormwater runoff into the ponds and the inlets that convey water out of the ponds. Overall, the FIB concentrations in the vicinity of pond outfalls were significantly or relatively higher than those near pond inlets. The contamination in the McCall Lake and the Country Hills stormwater ponds showed higher amounts of human markers (40 to 60%) compared to the Inverness stormwater pond (< 20%), which coincided with their higher FIB concentration medians. The results revealed that stormwater drained from catchments with a higher percentage of commercial land use was more contaminated than those with primary residential land use, while the impacts of residential development on the FIB levels in the Inverness stormwater pond were not obvious. Furthermore, FIB concentrations in the ponds increased in response to both rain events and inter-event dry periods, with human-specific markers being predominant despite the high levels of animal markers during inter-event dry periods. Human-origin sources might be among the main microbial loading contributors in the pond catchments in general. All these findings can inform the development or improvement of measures for mitigating microbial pollution, strategies for reusing stormwater, and maintenance programs.

Evaluating the impact of hydrometeorological conditions on E. coli concentration in farmed mussels and clams: experience in Central Italy

Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r = 0.69) than between E. coli concentration and rainfall maxima (r = 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1-6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence.

Urbanization under a Changing Climate–Impacts on Hydrology

On a global scale, urbanization and climate change are two powerful forces that are reshaping ecosystems and their inhabitants [...]

Urban stormwater characterization, control, and treatment

This review summarizes over 280 studies published in 2019 related to the characterization, control, and management of urban stormwater runoff. A summary of quantity and quality concerns is provided in the first section of the review, serving as the foundation for the following sections which focus on the control and treatment of stormwater runoff. Finally, the impact of stormwater control devices at the watershed scale is discussed. Each section provides a self-contained overview of the 2019 literature, common themes, and future work. Several themes emerged from the 2019 literature including exploration of substrate amendments for improved water quality effluent from stormwater controls, the continued study of the role of vegetation in green infrastructure practices, and a call to action for the development of new models which generate reliable, computationally efficient results under the physical, chemical, biological, and social complexity of stormwater management. PRACTITIONER POINTS: Over 280 studies were published in 2019 related to the characterization, control, and treatment of urban stormwater. Studies on bioretention and general stormwater characteristics represented the two most common subtopics in 2019. Trends in 2019 included novel substrate amendments, studies on the role of vegetation, and advancements in computational models.

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

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.