Review of water quality criteria for water reuse and risk-based implications for irrigated produce under the FDA Food Safety Modernization Act, produce safety rule

Rock, Channah; Brassill, Natalie; Dery, Jessica L.; Carr, Dametreea; McLain, Jean E.; Bright, Kelly R.; Gerba, Charles P.

doi:10.1016/j.envres.2018.12.050

Cited by 58 publications

(50 citation statements)

References 14 publications

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“…Due to the continued use of E. coli to assess food safety risks associated with preharvest surface water use (California Leafy Greens Marketing Agreement, 2017; Food and Drug Administration, 2015; Jones and Shortt, 2010; Rock et al, 2018), understanding the associations between specific environmental factors and the relationship between E. coli levels and pathogen contamination of surface water is critical. As such, we used two-way PDPs to identify potential associations between the likelihood of detecting foodborne pathogens in AZ and NY waterways, and two-way interactions between environmental factors and E. coli levels.…”

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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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

Self Cite

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“…For safe human contact, it is recommended that recreational waters have a geometric mean (GM) below 35 CFU/100 mL for Enterococci and below 126 CFU/100 mL for E. coli, as specified by the Recreational Water Quality Criteria from the U.S. EPA [3]. This limit (126 CFU/100 mL) is identical to the hazard threshold set by the Food and Drug Administration for the irrigation of fresh produce according to the Food Safety Modernization Act (FSMA) [6]. In drinking water, the maximum level is set at 1 CFU/100 mL for E. coli [5], which is near the detection limit of many detection systems.…”

Section: Introductionmentioning

confidence: 99%

A High-Throughput Microfluidic Magnetic Separation (µFMS) Platform for Water Quality Monitoring

2019

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The long-term aim of this work is to develop a biosensing system that rapidly detects bacterial targets of interest, such as Escherichia coli, in drinking and recreational water quality monitoring. For these applications, a standard sample size is 100 mL, which is quite large for magnetic separation microfluidic analysis platforms that typically function with <20 µL/s throughput. Here, we report the use of 1.5-µm-diameter magnetic microdisc to selectively tag target bacteria, and a high-throughput microfluidic device that can potentially isolate the magnetically tagged bacteria from 100 mL water samples in less than 15 min. Simulations and experiments show~90% capture efficiencies of magnetic particles at flow rates up to 120 µL/s. Also, the platform enables the magnetic microdiscs/bacteria conjugates to be directly imaged, providing a path for quantitative assay.From the large-volume samples experiments with IONs, the magnetic separation microfluidic device was capable of processing > 50 mL samples (Figure 7). The 50 mL sample (IONs concentration of 100 µg/mL) was filtered in~7 min at a flow rate of 120 µL/s and the estimated capture efficiency was 70.0 ± 2.3%. Similarly, the 100 mL sample (IONs concentration 12.5 µg/mL, 100 mL) was filtered in Micromachines 2020, 11, 16 9 of 13 15 min at a flow rate of 120 µL/s and the estimated capture efficiency was 72.2 ± 2.0%. Figure 7 show 100 mL sample experiment images of the solutions before filtering (A), after filtering (B), the µFMS device with all captured IONs (C), and the VSM data to estimate capture efficiency for 100 mL sample (D).

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“…The quality of surface water, which includes lakes, rivers, ponds, springs, and creeks, fluctuates due to wastewater discharge, agricultural or urban runoff, domesticated and wild animal intrusion, human waste, and other contributing factors [14]. Consequently, the use of surface water is considered a higher risk practice than groundwater [30], which, when properly managed is less prone to contamination [14]. Reclaimed water is sourced from domestic or industrial wastewater that receives treatment and disinfection [19].…”

Section: Water As a Vehicle For Foodborne Pathogen Transmission Durinmentioning

confidence: 99%

“…In a recent study, a quantitative microbial risk assessment was conducted to identify potential contamination risks associated with foodborne pathogens on lettuce using different irrigation systems applying water in compliance with the PSR standards. Results showed that the risks of developing gastrointestinal (GI) illnesses when the water contains 126 CFU generic E. coli/100 mL corresponds to 0.000009% for subsurface irrigation systems, 0.0011% for furrow and 0.11% for sprinkler systems [30]. While the risk of illness from consuming lettuce containing 126 CFU generic E. coli/100 mL seems relatively low, several other factors should be taken into consideration when examining the risks of foodborne illness.…”

Section: Microbial Indicators Of Irrigation Water Contaminationmentioning

confidence: 99%

Factors Impacting the Prevalence of Foodborne Pathogens in Agricultural Water Sources in the Southeastern United States

Rodrigues

Silva

Dunn

2019

Water

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Surface water poses a great risk to fruit and vegetable crops when contaminated by foodborne pathogens. Several factors impact the microbial quality of surface waters and increase the risk of produce contamination. Therefore, evaluating the factors associated with the prevalence of pathogenic microorganisms in agricultural water sources is critical to determine and establish preventive actions that may minimize the incidence of foodborne outbreaks associated with contaminated production water. In the Southeastern U.S. environmental factors such as rainfall, temperature, and seasonal variations have been associated with the prevalence of pathogens or microbial indicators of fecal contamination in water. Also, the geographical location of the irrigation sources as well as surrounding activities and land use play an important role on the survival and prevalence of pathogenic bacteria. Therefore, these factors may be determinants useful in the evaluation of production water quality and may help to preemptively identify scenarios or hazards associated with the incidence of foodborne pathogenic microorganisms.

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Review of water quality criteria for water reuse and risk-based implications for irrigated produce under the FDA Food Safety Modernization Act, produce safety rule

Cited by 58 publications

References 14 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

A High-Throughput Microfluidic Magnetic Separation (µFMS) Platform for Water Quality Monitoring

Factors Impacting the Prevalence of Foodborne Pathogens in Agricultural Water Sources in the Southeastern United States

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