Estimated human health risks from recreational exposures to stormwater runoff containing animal faecal material

Soller, Jeffrey A.; Bartrand, Timothy A.; Ravenscroft, John; Molina, Marirosa; Whelan, G.; Schoen, Mary E.; Ashbolt, Nicholas J.

doi:10.1016/j.envsoft.2015.05.018

Cited by 60 publications

(46 citation statements)

References 86 publications

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“…Undertaking QMRA for various exposures to stormwater can nevertheless be challenging due to difficulties in discerning the sources and concentrations of pathogen contamination in stormwater, and assumptions regarding pathogen sources, fate, and transport are needed depending on the availability of site-specific information. Several (n = 16) QMRA studies have relied upon concentrations of pathogens observed in stormwater-impacted coastal, recreational waters, or drinking source waters for assessment of health risks (Donovan et al, 2008;Soller et al, 2010;ten Veldhuis et al, 2010;Fewtrell et al, 2011;Tseng and Jiang, 2012;Andersen et al, 2013;McBride et al, 2013;de Man et al, 2014;Sales-Ortells and Medema, 2014;Schoen et al, 2014;Soller et al, 2014;Adell et al, 2016;Krkosek et al, 2016;Lim et al, 2017;Soller et al, 2015;Soller et al, 2017), and two have used other modelling approaches for microbial health risks such as Bayesian network modelling (Goulding et al, 2012) or disease transmission models (Soller et al, 2006). These recreational water QMRAs are reviewed in detail by Federigi et al (2019).…”

Section: Health Risk Assessment Approachesmentioning

confidence: 99%

“…Pathogenic bacteria, viruses and protozoa can be found in stormwater runoff and subsequently transported to environmental water bodies through sewer overflows, defective septic systems, agricultural runoff, defecation from wild animals and discharge of treated sewage (Ahmed et al, 2005;Noble et al, 2006;Rajal et al, 2007). The pathogens present in various animal fecal sources will differ from those in sewage Soller et al, 2015;Federigi et al, 2019), and therefore stormwater is likely to contain a different pathogen profile than sewage. Studies have reported a high prevalence of fecal indicator bacteria (FIB) and enteric pathogens in stormwater (Noble et al, 2006;Rajal et al, 2007;AWQC, 2008;Sidhu et al, 2012a;Cizek et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

Ahmed

Hamilton

Toze

et al. 2019

Science of The Total Environment

119

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Editor: Patricia HoldenDemands on global water supplies are increasing in response to the need to provide more food, water, and energy for a rapidly growing population. These water stressors are exacerbated by climate change, as well as the growth and urbanisation of industry and commerce. Consequently, urban water authorities around the globe are exploring alternative water sources to meet ever-increasing demands. These alternative sources are primarily treated sewage, stormwater, and groundwater. Stormwater including roof-harvested rainwater has been considered as an alternative water source for both potable and non-potable uses. One of the most significant issues concerning alternative water reuse is the public health risk associated with chemical and microbial contaminants. Several studies to date have quantified fecal indicators and pathogens in stormwater. Microbial source tracking (MST) approaches have also been used to determine the sources of fecal contamination in stormwater and receiving waters. This review paper summarizes occurrence and concentrations of fecal indicators, pathogens, and MST marker genes in urban stormwater. A section of the review highlights the removal of fecal indicators and pathogens through water sensitive urban design (WSUD) or Best Management Practices (BMPs). We also discuss approaches for assessing and mitigating health risks associated with stormwater, including a summary of existing quantitative microbial risk assessment (QMRA) models for potable and non-potable reuse of stormwater. Finally, the most critical research gaps are identified for formulating risk management strategies.Crown

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Section: Health Risk Assessment Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

Ahmed

Hamilton

Toze

et al. 2019

Science of The Total Environment

119

View full text Add to dashboard Cite

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“… Determine the requirements for meeting water quality standards (Soller et al., ; Wanda, Mamba, & Msagati, ; Zhu et al., ); Calculate the effectiveness of actions to limit pollutant sources for a designated use (Jeznach et al., ; Lindim et al., ; Matisoff, Watson, Guo, Duewiger, & Steely, ); Measure impacts associated with changes in dominate land‐use types within lake catchments (Dunalska et al., ; Elliott et al., ; Erol & Randhir, ); and Support water quality management decisions (Ali & Khairy, and Kim, Noh, Son, & Kim, ). …”

Section: Introductionmentioning

confidence: 99%

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

et al. 2018

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This study describes similarity‐based modelling techniques used to develop a spatially based water quality prediction model to facilitate sustainable lake water quality management. The lentic nature of lakes allows them to slowly absorb pollutants over a long period of time with readily noticeable signs, causing symptoms to appear only when the water quality has significantly degraded, meaning the risk of improper water quality management can be very high. Thus, failure to establish sustainable planning at the watershed scale was found to be a major threat of water quality degradation, from extemporary approaches often practised in lake management. Accordingly, the developed model is tailored for lakes facing moderate to serious water quality data limitation. The geodatabase integrates the identified driving factors of physical, social and water quality with significant influences on the status of lake water quality. A 1 km buffer radius with percentages of built‐up area, population, lake surface area and rainfall is measured. Calculation of a water quality index was then quantified on the basis of a similarity‐modelling technique. Lake Putrajaya was chosen as a control point for developing the indicator index. A total of 93 recreational lakes within Selangor and Kuala Lumpur were selected as modelling points. The results of this study indicated the similarity technique of spatial modelling is sufficiently reliable to be applied as an early assessment indicator. From the 93 lakes in this study, none feel in the category of either “bad” or “excellent,” with the majority being in class 3 (medium water quality status) and only four considered as having a good water quality condition. The balance of 35 lakes was considered to exhibit poor water quality. The model output is an indicator index, providing classification guidelines for the water quality status of the assessed lake as an early assessment tools.

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“…Presence of fecally-derived bacteria in water indicates a potential human health risk due to direct contact with water during recreational activities or eating produce that was irrigated or washed using the water [1][2][3] . The U. S. Environmental Protection Agency (EPA) is currently using and the U. S. Food and Drug Administration (FDA) has suggested to use generic Escherichia coli (E. coli) and enterococci as indicators of fecal contamination for recreational water and irrigation water 4 .…”

mentioning

confidence: 99%

Effect of the time scale on the uncertainty of geometric mean concentrations of fecal indicators in creek under baseflow conditions

Jeon

Pachepsky

Harriger

et al. 2020

Sci Rep

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Geometric mean concentrations of fecal indicator bacteria E. coli and enterococci are commonly used to evaluate the microbial quality of irrigation, recreation, and other types of waters, as well in watershed-scale microbial water quality modeling. It is not known how the uncertainty of those geometric mean concentrations depends on the time period between sampling. We analyzed data collected under baseflow conditions from three years of weekly and several daily sampling campaigns at Conococheague Creek in Pennsylvania. Standard deviations of logarithms of geometric mean concentrations were computed over weeks, months, and seasons. The increase in standard deviations from weekly to seasonal time scale was on average about 0.1 and 0.2 for log(E. coli) and log(enterococci), respectively, and in most cases was statistically significant. This may need to be accounted for when evaluating the uncertainty of measurements for modeling purposes and in risk assessment of microbial water quality.

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Estimated human health risks from recreational exposures to stormwater runoff containing animal faecal material

Cited by 60 publications

References 86 publications

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

Effect of the time scale on the uncertainty of geometric mean concentrations of fecal indicators in creek under baseflow conditions

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