1a. Objectives (from AD-416):
Objective 1: Elucidate and quantify mechanisms and factors of pathogen and indicator bacteria fate and transport from animal sources to irrigation waters. Evaluate the effects of soil and vegetation properties on parameters of pathogen fate and transport with surface runoff to irrigation water sources. Assess contribution of bottom sediments as potential pathogen reservoirs in agricultural landscapes. Research the fate and transport of pathogen and indicator bacteria in irrigation water delivery systems. Objective 2: Develop models and computer-based tools to recommend and implement site-specific diagnostics, monitoring, and prediction of the fate and transport of pathogen and indicator bacteria that affect the microbiological condition of irrigation water. Develop bacteria fate and transport components for USDA-ARS hydrologic models to simulate the effect of bottom sediments, periphyton, and bank soils on microbiological quality of surface waters intended for irrigation. Develop a farm-scale irrigation system model that will use site specific environmental and management data to provide input data for quantitative microbial risk assessment of irrigation waters.
1b. Approach (from AD-416):
An integrated approach including laboratory research, field research on irrigation systems, and mathematical modeling will be used. Experiments and monitoring will be carried out to (a) evaluate the effects of soil and vegetation properties on pathogen fate and transport with surface runoff to irrigation water sources, (b) understand and quantify pathogen and indicator bacteria fate in potential pathogen reservoirs associated with irrigation systems, such as bottom sediments in surface waters, and biofilms in irrigation equipment, and (b) microbial exchange between these reservoirs and flowing or stagnant waters. Mechanistic models will be developed to allow for (a) analyzing possible changes in pathogen and indicator bacteria concentrations along hydrologic pathways from animal sources to fields, and (b) improving resource allocation to monitor pathogen and indicator bacteria occurrence along the pathways.
3. Progress Report:
Field experiments were carried out to evaluate the effect of pipe ageing on biofilm formation in pipes and changes in microbial populations as surface water passes through the irrigation water delivery system. Identical irrigation systems were assembled with new and old aluminum pipes that were outfitted with coupons. Coupon extraction and irrigations occurred weekly. Biofilms in pipes significantly modified the microbial water quality in the majority of cases. This happened more often in new pipes. These results will contribute to the risk assessment of pathogen presence in irrigation waters. A novel computer code was developed to predict in-stream changes of indicator microorganism concentrations during and after high flow events. We simulated our multiyear monitoring data from artificial high flow events, and found that persistently high E. coli concentration in stream water after the events could be explained by the temporary microbe sequestration at the bottom. These results indicate that the role of runoff from manured fields and grazing lands as the dominant reason for high E. coli high concentrations in surface waters is overstated, and needs to be re-evaluated. To improve the characterization of freshwater bottom sediments as reservoirs of microorganisms, we used a “smart tracer”, a chemical that decomposes under the influence of microbial respiration. We carried out a controlled “smart tracer” point-source injection at a rural Maryland creek and monitored concentrations of the “smart tracer” and the product of its transformation along the creek. We are currently evaluating the data to see if a relationship between physiological activity of the microbial community and E. coli can be found. Laboratory microcosm experiments were initiated to evaluate the effect of nutrients in the water column on survival of E. coli in bottom sediments. This information is absent in existing literature and is critical for developing a model of microbial quality of irrigation water sources. Results of these experiments will be used in developing a model of microbial quality of irrigation water sources. We created the worlds largest dataset on E. coli inactivation in surface waters with the goal of evaluating the Arrhenius equation to simulate the dependence of E. coli inactivation rates on water temperature. Current microbial water quality models use this equation for all types of waters with parameters derived 35 years ago from only 20 experimental data sets. With our database of 220 experiments, we have shown that different values of parameters of the Arrhenius equation have to be applied to different types of surface waters. A large-scale field experiment was carried out to evaluate the effect of soil conditions on the runoff of microorganisms from a manured field. A unique irrigation setup at the USDA-ARS OPE3 research site was used to run two irrigation events for freshly applied and aged manure on the same field. These results will contribute to further validation of our microbial release and transport model STWIR, which is being adopted by EPA and will be linked with the legacy water quality model.
1. Improved the capability to estimate microbial water quality of stream water. Microbial water quality of irrigation and recreation water is known to be affected by wildlife inputs and solar radiation, which are not currently accounted for in water quality predictive models. ARS researchers in Beltsville, MD, in collaboration with Korean scientists, developed models to take into account variations in solar radiation and in wildlife contribution to surface water fecal contamination. Testing these models with the long-term microbiological water quality monitoring data has demonstrated improvement of the model accuracy. Results of this work will be useful in development of watershed management programs in which the effect of agriculture on water quality will be estimated more accurately than it is currently.
Pan, F., Pachepsky, Y.A., Guber, A.K., Hill, R. 2012. Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds. Journal of Hydrology. 414-415:99-107.
Pachepsky, Y.A., Garzio-Hadzick, A., Shelton, D.R., Hadzick, Z., Hull, R. 2011. Survival of E. coli O157:H12 in creek sediments after inoculation and re-inoculation. International Journal of Environment and Pollution. 46:234-245.
Cho, K., Sthiannopkao, S., Pachepsky, Y.A., Kim, K., Kim, J. 2011. Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Research. 45(17):5535-5544.
Guber, A.K., Gish, T.J., Pachepsky, Y.A., Mckee, L.G., Nicholson, T., Cady, R. 2011. Event-based estimation of water budget componets using the network of multi-sensor capacitance probes. Hydrological Sciences Journal. 56:1227-1241.
Pachepsky, Y.A., Shelton, D.R., Mclain, J.E., Patel, J.R., Mandrell, R.E. 2011. Irrigation waters as a source of pathogenic microorganisms in produce: a review. Advances in Agronomy. 113:73-138.
Sadeghi, A.M., Cardosol, F., Shelton, D.R., Shirmohammadi, A., Pachepsky, Y.A., Dulaney, W.P. 2012. Effectiveness of vegetated filter stripsin retention of E. coli and salmonella from swine manure slurry. Environmental Management. 110:1-7.
Pan, F., Pachepsky, Y.A., Guber, A.K., Hill, R. 2011. Information and Complexity Measures Applied to Observed and Simulated Soil Moisture Time Series. Hydrological Sciences Journal. 56:1027-1039.
Guber, A.K., Pachepsky, Y.A., Yakirevich, A., Shelton, D.R., Sadeghi, A.M., Goodrich, D.C., Unkrich, C.L. 2011. Uncertainty in modeling of fecal coliform overland transport associated with manure application in Maryland. Hydrological Processes. 25:2393-2404.
Shelton, D.R., Karns, J.S., Sadeghi, A.M., Coppock, C.R., Pachepsky, Y.A. 2011.Relationship between eae and stx virulence genes and Escherichia coli in an agricultural watershed: Implications for irrigation water standards and leafy green commodities. Journal of Food Protection. 74(1):18-23.
Pan, F., Pachepsky, Y.A., Jacques, D., Guber, A.K., Hil, R. 2012. Data assimilation with soil water content sensors and pedotransfer functions in soil water flow modeling. Soil Science Society of America Journal. 76:829-844.
Gonzalo, M., Vanderlinden, K., Pachepsky, Y.A., Giraldez, J., Espejo, A. 2012. Estimating topsoil water content of clay soils from with data from time-lapse electrical conductivity surveys. Soil Science. 177(6):369-376.
Kim, J., Heechul, C., Perfect, E., Pachepsky, Y.A., Sukop, M. 2011. Geometric and Hydrodynamic Characteristics of Three-dimensional Saturated Prefractal Porous Media Determined with Lattice Boltzmann Modeling. Transport in Porous Media. 90:831-846.
Hadzick, Z., Guber, A.K., Pachepsky, Y.A., Hill, R., Gish, T.J. 2011. Pedotransfer functions in soil electrical resistivity estimation. Geoderma. 164:195-202.
Zhu, P., Shelton, D.R., Li, S., Adams, D., Karns, J.S., Amstutz, P., Tang, C. 2011. Detection of E. coli O157:H7 by immunomagnetic separation coupled with fluorescence immunoassay. Biosensors and Bioelectronics. 30:337-341.