Spatiotemporal Variability in Microbial Quality of Western US Agricultural Water Supplies: A Multistate Study

Scale and detection method impacted Salmonella prevalence and diversity in ponds

Site-specific approaches for managing food safety hazards in agricultural water require an understanding of foodborne pathogen ecology. This study identified factors associated with Salmonella contamination in Virginia ponds. Grab samples (250 mL, N = 600) were collected from 30 sites across nine ponds. Culture- and culture-independent (CIDT)-based methods were used to detect Salmonella in each sample. Salmonella isolated by culture-based methods were serotyped by Kauffman-White classification. Environmental data were collected for each sample. McNemar's χ2 was used to determine if Salmonella detection differed by testing method. Separate mixed effect models were used to identify environmental factors associated with culture and CIDT-based Salmonella detection. Separate models were built for each pond, and for all ponds combined. Salmonella detection differed significantly (p < 0.001) between CIDT (31 %; 183/600)- and culture (13 %; 77/600)-based methods. Culture-based methods yielded 11 different serovars. All cultured Salmonella samples were confirmed by CIDT; 42.1 % of CIDT Salmonella-positive samples could be cultured. Associations between environmental factors and Salmonella detection also varied substantially by pond and detection method. In the all-pond model, associations were observed for five factors (total coliforms, Escherichia coli, air temperature, UV, rain) for both culture- and CIDT-based Salmonella detection. Rain prior to sampling (24 h) increased odds of Salmonella detection for culture (OR = 5.09) and CIDT (OR = 3.62) in the all-pond model. When all the pond data were used, models masked associations at the individual pond level, as there were noticeable differences between ponds and the odds of isolating Salmonella by environmental factors. Ponds were within a 187-ha area in this study, emphasizing water management needs to be individualized (i.e., assess hazards/risks by pond). Results also highlight detection methods and scale strongly affect observed water quality and should be considered when developing monitoring programs to develop guidance for growers.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Spatial Versus Nonspatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds

Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio [VR] = 1.55) while intrapond models were pond dependent (VR: 0.65-18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56-87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.

Environmental and anthropogenic factors associated with the likelihood of detecting Salmonella in agricultural watersheds

Surface water is one of the primary sources of irrigation water for produce production; therefore, its contamination by foodborne pathogens, such as Salmonella, may substantially impact public health. In this study, we determined the presence of Salmonella in surface water and characterized the relationship between Salmonella detection and environmental and anthropogenic factors. From April 2019 to February 2020, 120 samples from 30 sites were collected monthly in four watersheds located in two different central Chile agricultural regions (N = 1080). Water samples from rivers, canals, streams, and ponds linked to each watershed were obtained. Surface water (10 L) was filtrated in situ, and samples were analyzed for the presence of Salmonella. Salmonella was detected every month in all watersheds, with a mean detection percentage of 28% (0%-90%) across sampling sites, regardless of the season. Overall, similar detection percentages were observed for both regions: 29.1% for Metropolitan and 27.0% for Maule. Salmonella was most often detected in summer (39.8% of all summer samples tested positive) and least often in winter (14.4% of winter samples). Random forest analysis showed that season, water source, and month, followed by latitude and river, were the most influential factors associated with Salmonella detection. The influences of water pH and temperature (categorized as environmental factors) and factors associated with human activity (categorized as anthropogenic factors) registered at the sampling site were weakly or not associated with Salmonella detection. In conclusion, Salmonella was detected in surface water potentially used for irrigation, and its presence was linked to season and water source factors. Interventions are necessary to prevent contamination of produce, such as water treatment before irrigation.

Depth-Dependent Concentrations of E. coli in Agricultural Irrigation Ponds

Microbial water quality surveys of irrigation sources are conducted by measuring the concentrations of generic E. coli. The objective of this study was to evaluate the dependence of E. coli concentrations on the water sampling depth at different times of the day. Three irrigation ponds were sampled in Maryland eleven times during the growing seasons of 2019–2021. Water was collected in replicates at the surface (0 cm) and then in 50 cm depth intervals at 9:00, 12:00, and 15:00. Ponds 1 and 2 were sampled to 150 cm, whereas Pond 3 was only sampled to the 50 cm depth due to it having a shallower average depth. An analysis of variance test revealed that E. coli concentrations significantly differed by depth in only one pond (p > 0.05) but on multiple dates. Additionally, the sampling time of day was significant at only two of eleven of the observation dates across ponds; in those cases, the average concentrations across the pond increased in the order of 9:00 > 12:00 > 15:00. This study shows that E. coli concentrations measured in irrigation ponds may substantially differ depending on the sampling depth and time of day, and that these factors should be accounted for in the monitoring design.

Wastewater reuse for irrigation of produce: A review of research, regulations, and risks

The burden of disease caused by the contamination of ready-to-eat produce with common waterborne microbial pathogens suggests that irrigation supplies should be closely monitored and regulated. Simultaneously freshwater resources have become increasingly scarce worldwide while global demand continues to grow. Since the turn of the 20th century with the advent of modern wastewater treatment plants, the reuse of treated wastewater is considered a safe and viable water source for irrigation of ready-to-eat vegetables. However strict, and often costly, treatment regimens mean that only a fraction of the world's wastewater supplies are being put to reuse. The purpose of this review is to explore the available literature on the risks associated with reuse water for ready-to-eat produce production including different approaches to reducing those risks as the demand for reuse water increases. It is not the intent of the authors to determine which methods of treatment should be applied, which pathogens should be considered of greatest concern, or which regulations should be applied. Rather, it is meant to be a discussion of the evolving guidelines governing irrigation with reuse water, potential risks from known pathogens common to produce production and recommendations for improving the adoption of water reuse moving forward. To date, there is little evidence to suggest that adequately treated reuse water poses more risk for produce-related illness or outbreaks than other sources of irrigation water. However, multiple epidemiological and quantitative risk assessment models suggest that guidelines for the use of reuse water should be regionally specific and based on local growing practices, available technologies for wastewater treatment, and overall population health. Though research suggests water reuse is generally safe, the assumptions of risk are both personal and of public interest, they should be considered carefully before water reuse is either allowed or disallowed in produce production environments.

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml-1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Comparative stability of assay results of enterococci measured by culture and qPCR over time in bathing beach waters

The diurnal presence of the culturable bacterial indicators of fecal contamination in the water environment has been shown to be highly variable over time due to natural die-off and injury from effects of sunlight and other environmental stressors. Molecular analytes of a quantitative polymerase chain reaction (qPCR) method for measuring fecal contamination degrade considerably slower than the alternative of culturable fecal indicator bacteria. The rapid qPCR method holds the promise of more timely notification decisions with respect to postings or closure being made on the basis of microbial water quality samples collected earlier on the same day. In the case of culture-based methods requiring a 24 h or longer incubation period, decisions must be based on samples collected no sooner than the previous day. To examine the effect of this lag in assay results, temporal stability of a molecular Enterococci target analyte with that of traditional culture-based cells is compared using data from USEPA studies conducted between 2003 and 2007 on seven freshwater and marine beaches that were impacted by publicly-owned treatment works. Generally, levels of the molecular indicator were more consistent throughout the day between 8:00 am and 3:00 pm. The difference in temporal consistency is even more pronounced when the 24-h lag in culture-based results is taken into account.

Microbial and physicochemical assessment of irrigation water treatment methods

AIMS

The presence of foodborne pathogens in preharvest agricultural water has been identified as a potential contamination source in outbreak investigations, driving markets and auditing bodies to begin requiring water treatment for high-risk produce. Therefore, it is essential that we identify water treatment methods which are effective as well as practical in their application on farm.

METHODS AND RESULTS

In this work, we evaluated two sanitizers which are most prominent in preharvest agricultural water treatment (calcium hypochlorite (free chlorine: 3-5 ppm) and peracetic acid (PAA: 5 ppm)), an EPA registered antimicrobial device (ultraviolet light (UV)), in addition to a combination approach (chlorine + UV, PAA + UV). Treatments were evaluated for their ability to inactivate total coliforms and generic Escherichia coli and consistency in treatment efficacy over 1 h of operation. Physicochemical variables were measured along with microbial populations at 0, 5, 15, 30, 45 and 60 min of operation. Escherichia coli and coliform counts showed a significant (P < 0·05) reduction after treatment, with combination and singular treatments equally effective at inactivating E. coli and coliforms. A significant increase (P < 0·05) in oxidation-reduction potential was seen during water treatment (Chlorine; UV + Chlorine), and a significant reduction (P < 0·05) in pH was seen after PAA and PAA + UV treatments (60 min).

CONCLUSION

Overall, the results indicate that all treatments evaluated are equally efficacious for inactivating E. coli and coliforms present in surface agricultural water.

SIGNIFICANCE AND IMPACT OF THE STUDY

This information when paired with challenge studies targeting foodborne pathogens of interest can be used to support grower decisions when selecting and validating a preharvest agricultural water treatment programme.

Outbreaks of Escherichia coli O157:H7 Infections Linked to Romaine Lettuce in Canada from 2008 to 2018: An Analysis of Food Safety Context

ABSTRACT

Foodborne diseases are a major cause of illness in Canada. One of the main pathogens causing cases and outbreaks of foodborne illness in Canada is Escherichia coli O157:H7. From 2008 to 2018, 11 outbreaks of E. coli O157:H7 infection in Canada were linked to leafy greens, including 7 (63.6%) linked to romaine lettuce, 2 (18.2%) linked to iceberg lettuce, and 2 (18.2%) linked to other or unspecified types of leafy greens. The consumption of lettuce in Canada, the behavior of E. coli O157:H7 on lettuce leaves, and the production practices used for romaine and iceberg lettuce do not seem to explain why a higher number of outbreaks of E. coli O157:H7 infection were linked to romaine than to iceberg lettuce. However, the difference in the shape of iceberg and romaine lettuce heads could be an important factor. Among the seven outbreaks linked to romaine lettuce in Canada between 2008 and 2018, an eastern distribution of cases was observed. Cases from western provinces were reported only twice. The consumption of romaine and iceberg lettuce by the Canadian population does not seem to explain the eastern distribution of cases observed, but the commercial distribution, travel distances, and the storage practices used for lettuce may be important factors. In the past 10 years, the majority of the outbreaks of E. coli O157:H7 infection linked to romaine lettuce occurred during the spring (March to June) and fall (September to December). The timing of these outbreaks may be explained by the availability of lettuce in Canada, the growing region transition periods in the United States, and the seasonality in the prevalence of E. coli O157:H7. The consumption of romaine lettuce by the Canadian population does not explain the timing of the outbreaks observed.

HIGHLIGHTS

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

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 water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

There is a need for science-based tools to (i) help manage microbial produce safety hazards associated with preharvest surface water use, and (ii) facilitate comanagement of agroecosystems for competing stakeholder aims. To develop these tools an improved understanding of foodborne pathogen ecology in freshwater systems is needed. The purpose of this study was to identify (i) sources of potential food safety hazards, and (ii) combinations of factors associated with an increased likelihood of pathogen contamination of agricultural water Sixty-eight streams were sampled between April and October 2018 (196 samples). At each sampling event separate 10-L grab samples (GS) were collected and tested for Listeria, Salmonella, and the stx and eaeA genes. A 1-L GS was also collected and used for Escherichia coli enumeration and detection of four host-associated fecal source-tracking markers (FST). Regression analysis was used to identify individual factors that were significantly associated with pathogen detection. We found that eaeA-stx codetection [Odds Ratio (OR) = 4.2; 95% Confidence Interval (CI) = 1.3, 13.4] and Salmonella isolation (OR = 1.8; CI = 0.9, 3.5) were strongly associated with detection of ruminant and human FST markers, respectively, while Listeria spp. (excluding Listeria monocytogenes) was negatively associated with log10 E. coli levels (OR = 0.50; CI = 0.26, 0.96). L. monocytogenes isolation was not associated with the detection of any fecal indicators. This observation supports the current understanding that, unlike enteric pathogens, Listeria is not fecally-associated and instead originates from other environmental sources. Separately, conditional inference trees were used to identify scenarios associated with an elevated or reduced risk of pathogen contamination. Interestingly, while the likelihood of isolating L. monocytogenes appears to be driven by complex interactions between environmental factors, the likelihood of Salmonella isolation and eaeA-stx codetection were driven by physicochemical water quality (e.g., dissolved oxygen) and temperature, respectively. Overall, these models identify environmental conditions associated with an enhanced risk of pathogen presence in agricultural water (e.g., rain events were associated with L. monocytogenes isolation from samples collected downstream of dairy farms; P = 0.002). The information presented here will enable growers to comanage their operations to mitigate the produce safety risks associated with preharvest surface water use.

Environmental inactivation and irrigation-mediated regrowth of Escherichia coli O157:H7 on romaine lettuce when inoculated in a fecal slurry matrix

Field trials were conducted in July-August and October 2012 to quantify the inactivation rate of Escherichia coli O157:H7 when mixed with fecal slurry and applied to romaine lettuce leaves. Lettuce was grown under commercial conditions in Salinas Valley, California. One-half milliliter of rabbit, chicken, or pig fecal slurry, containing an average of 4.05 × 107 CFU E. coli O157:H7 (C0), was inoculated onto the upper (adaxial) surface of a lower leaf on 288 heads of lettuce per trial immediately following a 2.5 h irrigation event. To estimate the bacterial inactivation rate as a function of time, fecal matrix, irrigation and seasonal climate effects, sets of lettuce heads (n = 28) were sampled each day over 10 days and the concentration of E. coli O157:H7 (Ct) determined. E. coli O157:H7 was detected on 100% of heads during the 10-day duration, with concentrations ranging from ≤340 MPN/head (∼5-log reduction) to >3.45 × 1012 MPN/head (∼5-log growth). Relative to C0, on day 10 (Ct = 12) we observed an overall 2.6-log and 3.2-log mean reduction of E. coli O157:H7 in July and October, respectively. However, we observed relative maximum concentrations due to bacterial growth on day 6 (maximum Ct = 8) apparently stimulated by foliar irrigation on day 5. From this maximum there was a mean 5.3-log and 5.1-log reduction by day 10 (Ct = 12) for the July and October trials, respectively. This study provides insight into the inactivation and growth kinetics of E. coli O157:H7 on romaine lettuce leaves under natural field conditions. This study provides evidence that harvesting within 24 h post irrigation has the potential to increase the concentration of E. coli O157:H7 contamination, if present on heads of romaine lettuce; foliar irrigation can temporarily stimulate substantial regrowth of E. coli O157:H7.

Meeting Report: Key Outcomes from a Collaborative Summit on Agricultural Water Standards for Fresh Produce

On February 27 to 28, 2018, the Produce Safety Alliance convened a national water summit in Covington, KY to discuss the requirements of the United States Food and Drug Administration's (FDA) Food Safety Modernization Act Standards for the Growing, Harvesting, Packing, and Holding of Produce for Human Consumption (Produce Safety Rule [PSR]). The goals of the meeting were to better understand the challenges growers face in implementing the requirements in Subpart E-Agricultural Water and work collaboratively to develop practical solutions to meet fruit and vegetable production needs while protecting public health. To meet these goals, the summit engaged a diverse group of stakeholders including growers, researchers, extension educators, produce industry members, and regulatory personnel. Key outcomes included defining implementation barriers due to diversity in water sources, distribution systems, commodity types, climates, farm size, and production activities. There was an articulated need for science-based solutions, such as the use of agricultural water system assessments and sharing of federal, state, and regional water quality data, to ensure qualitative and quantitative standards reduce microbial risks. These identified challenges and needs resulted in significant debate about whether reopening the PSR-Subpart E for modification or attempting to address concerns through guidance would provide the best mechanism for alleviating concerns. In addition, training, outreach, and technical assistance were identified as vital priorities once the concerns are formally addressed by FDA. The water summit highlighted the critical need for transparency of FDA's progress on reevaluating the Subpart E requirements to help guide growers' decisions regarding the use of agricultural water.

Microbial Water Quality: Monitoring and Modeling

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 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.