Correlation between E. coli levels and the presence of foodborne pathogens in surface irrigation water: Establishment of a sampling program

Truchado, Pilar; Hernández, Noel Batista; Gil, María I.; Ivanek, Renata; Allende, Ana

doi:10.1016/j.watres.2017.10.041

Cited by 40 publications

(28 citation statements)

References 30 publications

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“…For instance, temperature was correlated with year-day and solar radiation among other factors, which obfuscates our ability to interpret the relationship between temperature and likelihood of pathogen detection. Moreover, based on the findings of this and other studies, the strength and direction of the relationship between temperature and microbial water quality appear to be pathogen, region, and/or waterway-specific (Francy et al, 2013; Luo et al, 2015; Truchado et al, 2018). In the study reported here, the likelihood of detecting Salmonella in AZ GS, and of codetecting eaeA and stx in NY MS increased as avg.…”

Section: Discussionsupporting

confidence: 56%

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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Section: Discussionsupporting

confidence: 56%

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

Self Cite

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“…Full saturation of soil with water is conducive to microbial transport, but subsurface travel distances are usually limited. This is illustrated by Weldeyohannes et al (2018), who applied naturally occurring E. coli in secondarily treated wastewater to the soil surface under seasonally changing conditions in central Alberta, Canada. When the vadose zone increased from 0.4 to 0.9 m, E. coli levels in the monitoring wells decreased dramatically despite continued high surface application.…”

Section: Subsurface Microbial Transport and Microbial Water Qualitymentioning

confidence: 99%

“…Each control can be characterized by several parameters that may serve as microbial water quality predictors. There is often a need to select and compare statistical techniques that can reduce the number of predictors and create more-robust predictive models (Singh et al, 2004;Truchado et al, 2018). One such technique-canonical correlation analysis-was applied as the input-selection procedure for a machine learning model in the work of Gilfillan et al (2018), aimed at identifying potential drivers of the impairment of microbial water quality using E. coli and F+ and somatic bacteriophages as indicators in the mixeduse stream in East Tennessee.…”

Section: Predictors Of Spatiotemporal Variationsmentioning

confidence: 99%

Microbial Water Quality: Monitoring and Modeling

et al. 2018

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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.

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“…The complexity of reproducibly measuring microbiological contamination of irrigation water has made monitoring difficult. Several different types of pathogens have been detected in diverse irrigation water sources including bacteria (e.g., Salmonella and Escherichia coli), protozoa (e.g., Cryptosporidium and Giardia), as well as viruses (e.g., noroviruses)) [118,119]. Irrigation of food crops with surface water clearly has the highest potential for contaminating freshly eaten produce, and this topic has had the greatest research and regulatory effort in recent years.…”

Section: Pathogensmentioning

confidence: 99%

Irrigation Water Quality—A Contemporary Perspective

Malakar

Snow

Ray

2019

Water

109

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In the race to enhance agricultural productivity, irrigation will become more dependent on poorly characterized and virtually unmonitored sources of water. Increased use of irrigation water has led to impaired water and soil quality in many areas. Historically, soil salinization and reduced crop productivity have been the primary focus of irrigation water quality. Recently, there is increasing evidence for the occurrence of geogenic contaminants in water. The appearance of trace elements and an increase in the use of wastewater has highlighted the vulnerability and complexities of the composition of irrigation water and its role in ensuring proper crop growth, and long-term food quality. Analytical capabilities of measuring vanishingly small concentrations of biologically-active organic contaminants, including steroid hormones, plasticizers, pharmaceuticals, and personal care products, in a variety of irrigation water sources provide the means to evaluate uptake and occurrence in crops but do not resolve questions related to food safety or human health effects. Natural and synthetic nanoparticles are now known to occur in many water sources, potentially altering plant growth and food standard. The rapidly changing quality of irrigation water urgently needs closer attention to understand and predict long-term effects on soils and food crops in an increasingly fresh-water stressed world.

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Correlation between E. coli levels and the presence of foodborne pathogens in surface irrigation water: Establishment of a sampling program

Cited by 40 publications

References 30 publications

Complex interactions between weather, and microbial and physiochemical 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

Microbial Water Quality: Monitoring and Modeling

Irrigation Water Quality—A Contemporary Perspective

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