Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/25/2012
Publication Date: 4/19/2012
Citation: Martinez, G., Pachepsky, Y.A., Shelton, D.R., Dao, T.H., Whelan, G., Kim, K., Williams, J., Jaehak, J. 2012. Survival and release of Escherichia coli in land-applied animal manure: models and parameters. [abstract]. BARC Poster Day. p. 40. Interpretive Summary:
Technical Abstract: Field- and farm-scale estimates of E. coli loads to surface water sources are needed to evaluate management decisions affecting microbiological quality of irrigation and recreation waters. This work reports initial steps of the development of a bacteria survival and release subroutine for the USDA-ARS-supported APEX model. Recently models of bacteria survival and release have been proposed and results of experiments to test these models have been published by various research groups. The objective of this work is to augment our experimental results with data from other sources to assess the applicability of the models and variability of parameters of those models. Knowledge of parameter variability has been recently shown to be critical in modern modeling technology including the Bayesian parameter estimation, sensitivity and uncertainty analysis, and multimodeling data assimilation. Experiments on survival and release of E. coli from bovine manure of different types were carried out at the Beltsville Agricultural Research Center. We utilized available data from experiments in Australia, New Zealand, the Netherlands, Nebraska, Virginia, and Georgia. Simulating the shoulder in bacterial inactivation curves appears to be necessary. E. coli inactivation rates were relatively low, compared to background environmental media. The modified Arrhenius model proved to be a viable tool to normalize inactivation data obtained at different temperature regimes. Released bacteria concentrations have been found relatively constant during an hour-long rainfall simulations. The Bradford-Schijven model of bacteria release has substantially different parameters, depending on animal manure type and the presence of vegetation. This work will be continued to provide a reliable module for field, farm, and watershed scale modeling of microbiological surface water quality.