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Title: Predicting characteristics of rainfall driven estrogen runoff and transport from swine AFO spray fields.

Authors: Lee, Boknam; Kullman, Seth W; Yost, Erin E; Meyer, Michael T; Worley-Davis, Lynn; Williams, C Michael; Reckhow, Kenneth H

Published In Sci Total Environ, (2015 Nov 01)

Abstract: Animal feeding operations (AFOs) have been implicated as potentially major sources of estrogenic contaminants into the aquatic environment due to the relatively minimal treatment of waste and potential mobilization and transport of waste components from spray fields. In this study a Bayesian network (BN) model was developed to inform management decisions and better predict the transport and fate of natural steroidal estrogens from these sites. The developed BN model integrates processes of surface runoff and sediment loss with the modified universal soil loss equation (MUSLE) and the soil conservation service curve number (SCS-CN) runoff model. What-if scenario simulations of lagoon slurry wastes to the spray fields were conducted for the most abundant natural estrogen estrone (E1) observed in the system. It was found that E1 attenuated significantly after 2 months following waste slurry application in both spring and summer seasons, with the overall attenuation rate predicted to be higher in the summer compared to the spring. Using simulations of rainfall events in conjunction with waste slurry application rates, it was predicted that the magnitude of E1 runoff loss is significantly higher in the spring as compared to the summer months, primarily due to spray field crop management plans. Our what-if scenario analyses suggest that planting Bermuda grass in the spray fields is likely to reduce runoff losses of natural estrogens near the water bodies and ecosystems, as compared to planting of soybeans.

PubMed ID: 26102057 Exiting the NIEHS site

MeSH Terms: Agriculture/methods; Animal Feed; Animals; Bayes Theorem; Environmental Monitoring/methods*; Estrogens/analysis*; Geologic Sediments; Manure; Models, Chemical; Rain*; Soil/chemistry; Swine; Water Movements*; Water Pollutants, Chemical/analysis*; Water Pollution, Chemical/statistics & numerical data*

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