|HONG, EUNMI - Orise Fellow|
|NAM, WON-HO - Hankyong National University|
|MUIRHEAD, RICHARD - Collaborator|
Submitted to: Journal of Environmental Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/24/2016
Publication Date: 1/5/2017
Citation: Hong, E., Shelton, D.R., Pachepsky, Y.A., Nam, W., Coppock, C.R., Muirhead, R. 2017. Modeling the interannual variability of microbial quality metrics of irrigation water in a Pennsylvanian stream. Journal of Environmental Management. 187:253-264.
Interpretive Summary: The number of foodborne diseases in the U.S. has been steadily increasing every year. Over 40% of these cases are linked to fresh produce. The Food and Drug Administration has issued regulations mandated by the Food Safety Modernization Act (FSMA) that include microbial water quality standards for irrigation water based on E. coli concentration. In this study, we randomly sampled simulated water time series and evaluated the effect of the interannual weather variability on the assessment results of the microbial quality of the streamwater using the modified Soil and Water Assessment Tool model in the Little Cove Creek Watershed in Pennsylvania. Our modeling results showed that substantial interannual variability in microbial water quality metrics can be expected due to differences in location, weather pattern, and seasonality with 15.7-70.1% regulatory threshold exceedance rates. The results are useful in determining the role and impact of site specific factors and designing monitoring of microbial water quality to assess the compliance of irrigation management practices to FSMA-based regulations.
Technical Abstract: Knowledge of the microbial quality of irrigation waters is extremely limited. For this reason, the US FDA has promulgated the Produce Rule, mandating the testing of irrigation water sources for many farms. The rule requires the collection and analysis of at least 20 water samples over two to four years to adequately evaluate the quality of water intended for produce irrigation. The objective of this work was to evaluate the effect of interannual weather variability on surface water microbial quality. We used the Soil and Water Assessment Tool model to simulate E. coli concentrations in the Little Cove Creek; this is a perennial creek located in an agricultural watershed in south-eastern Pennsylvania. The model performance was evaluated using regulatory microbial water quality metrics, i.e. the geometric mean and the statistical threshold value. Using the 90-year time series of weather observations, we simulated and randomly sampled the time series of E. coli concentrations. Five samples per year were taken for four consecutive years. We found that weather conditions of a specific year may strongly affect the evaluation of microbial quality and that the long-term assessment of microbial water quality may be quite different from the evaluation based on short-term observations. The variations in microbial concentrations and water quality metrics were affected by location, wetness of the hydrological years, and seasonality, with 15.7 – 70.1% of samples exceeding the regulatory threshold. The results of this work demonstrate the value of using modeling to design and evaluate monitoring protocols to assess the microbial quality of water used for produce irrigation.