Spatial-temporal variations of microbial water quality in surface reservoirs and canals used for irrigation

Won, Gayeon; Kline, Terence R.; LeJeune, Jeffrey T.

doi:10.1016/j.agwat.2012.10.007

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…While some studies identified an association between rainfall and microbial water quality (Wilkes et al, 2011;Pandey et al, 2012;Won et al, 2013;Francy et al, 2013;Stocker et al, 2016), other studies did not (Benjamin et al, 2013;McEgan et al, 2013a;Won et al, 2013). Interestingly, a survey of Florida water sources, which did not find an association between Salmonella levels and rainfall, hypothesized that rainfall did not have a direct effect on microbial water quality, and instead interacted with other factors to affect microbial water quality (McEgan et al, 2013a); our findings support this hypothesis.…”

Section: Weather and Physicochemical Water Quality Were Associated Wisupporting

confidence: 60%

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

et al. 2020

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Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural

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Section: Weather and Physicochemical Water Quality Were Associated Wisupporting

confidence: 60%

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

et al. 2020

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“…While some studies have identified an association between rainfall and microbial water quality (Francy et al, 2013; Pandey et al, 2012; Stocker et al, 2016; Wilkes et al, 2011; Won et al, 2013), other studies did not (Benjamin et al, 2013; McEgan et al, 2013a; Won et al, 2013). For instance, Won et al (Won et al, 2013) reported that E. coli levels in Ohio reservoirs were not associated with rainfall 0-1 d BSC, while E. coli levels in Ohio canals were higher on days when >2 cm of rain fell. Interestingly, a survey of Florida water sources, which did not find an association between Salmonella levels and rainfall over the day, week, or month preceding sampling, hypothesized that rainfall did not have a direct effect on microbial water quality, and instead interacted with other factors to affect microbial water quality (McEgan et al, 2013a).…”

Section: Discussionmentioning

confidence: 99%

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

Weller

Brassill

Rock

et al. 2020

Preprint

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19 20 21 22 23 produce farms. Managing water-associated risks does not lend itself to one-size-fits-all 24 approaches due to the heterogeneous nature of freshwater environments, and because 25 environmental conditions affect the likelihood of pathogen contamination and the relationship 26 between indicator organism levels (e.g., E. coli) and pathogen presence. To improve our ability 27 to develop location-specific risk management practices, a study was conducted in two produce-28 growing regions to (i) characterize the relationship between E. coli levels and pathogen presence 29 in agricultural water, and (ii) identify environmental factors associated with pathogen detection. 30Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 31 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds 32 Ratio [OR]=2.18), and eaeA-stx codetection (OR=6.49) was significantly greater for MS 33 compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also 34 showed that eaeA-stx codetection in AZ (OR=50.2) and NY (OR=18.4), and Salmonella 35 detection in AZ (OR=4.4) were significantly associated with E. coli levels, while Salmonella 36 detection in NY was not. Random forest analysis indicated that interactions between 37 environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of 38 pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of 39 pathogen detection. Our findings suggest that (i) environmental heterogeneity, including 40 interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not 41 be a suitable indicator of the food safety risks. Instead, targeted methods that utilize 42 environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict 43 when there is a high or low risk of surface water being contaminated by pathogens) are needed to 44 assess and mitigate the food safety risks associated with preharvest water use. By identifying 45 environmental factors associated with an increased likelihood of detecting pathogens in 46 agricultural water, this study provides information that (i) can be used to assess when pathogen 47 contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted 48 interventions for individual water sources, providing an alternative to existing one-size-fits-all 49 approaches. 50 51 52 3 3Preharvest surface water use for produce production (e.g., irrigation, fertigation, pesticide 53 application, dust abatement) has repeatedly been identified as a factor associated with an 54

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“…Many studies have found that diarrhoeal infections are caused by the use of wastewater or by drinking contaminated water and/or consumption of contaminated food [ 33 ]. Pathogens, such as Escherichia coli , could be involved in diarrhoeal contamination due to runoff and the use of wastewater for irrigation, as well as because of the accidental release of effluents in the open drainage network by poorly managed sanitation facilities [ 14 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Ecohealth Approach to Urban Waste Management: Exposure to Environmental Pollutants and Health Risks in Yamoussoukro, Côte d’Ivoire

Kouamé

Dongo

Nguyen-Viet

et al. 2014

IJERPH

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Poor waste management is a key driver of ill-health in urban settlements of developing countries. The current study aimed at assessing environmental and human health risks related to urban waste management in Yamoussoukro, the political capital of Côte d'Ivoire. We undertook trans-disciplinary research within an Ecohealth approach, comprised OPEN ACCESS Int. J. Environ. Res. Public Health 2014, 1110293 of a participatory workshop with stakeholders and mapping of exposure patterns. A total of 492 randomly selected households participated in a cross-sectional survey. Waste deposit sites were characterised and 108 wastewater samples were subjected to laboratory examinations. The physico-chemical parameters of the surface water (temperature, pH, conductivity, potential oxidise reduction, BOD5, COD, dissolved oxygen, nitrates, ammonia and total Kendal nitrogen) did not comply with World Health Organization standards of surface water quality. Questionnaire results showed that malaria was the most commonly reported disease. Diarrhoea and malaria were associated with poor sanitation. Households having dry latrines had a higher risk of diarrhoea (odds ratio (OR) = 1.8, 95% confidence interval (CI) 1.2-2.7) compared to latrines with septic tanks and also a higher risk for malaria (OR = 1.9, 95% (CI) 1.1-3.3). Our research showed that combining health and environmental assessments enables a deeper understanding of environmental threats and disease burdens linked to poor waste management. Further study should investigate the sanitation strategy aspects that could reduce the environmental and health risks in the study area.

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Spatial-temporal variations of microbial water quality in surface reservoirs and canals used for irrigation

Cited by 20 publications

References 22 publications

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

Ecohealth Approach to Urban Waste Management: Exposure to Environmental Pollutants and Health Risks in Yamoussoukro, Côte d’Ivoire

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