Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Research Project #421065

Research Project: PATHOGEN FATE AND TRANSPORT IN IRRIGATION WATERS

Location: Environmental Microbial & Food Safety Laboratory

2014 Annual Report


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:
Research was continued to evaluate biofilm formation in irrigation systems and their impact on irrigation water quality. An irrigation system was assembled consisting of removable coupons in the pipes, allowing for evaluation of E. coli colonization of inner pipe surfaces. Antibiotic resistance was compared for waters entering and leaving the irrigation system. Preliminary data analysis indicates that the presence of biofilms alters the antibiotic resistance of microorganisms in irrigation waters. This confirms the need for monitoring water quality at the sprinkler head because microbial water quality assessments based solely on source waters can be misleading. Experiments were carried out to determine the effects of rainfall intensity and land slope on the release of E. coli and enterococci from dairy cattle manure, using partitioning boxes and soil boxes. No significant effect of the rainfall intensity was found for the release as a function of rainfall depth. This conclusion, if further confirmed, will substantially simplify modeling microbial release that is critical for risk assessment of microbial contamination in the environment. The linear semilogarithmic model (LSL) is commonly used to simulate the inactivation rate of bacteria concentration in waters over time. There were prior indications that the assumption of semilogarithmic linearity may not accurately reflect inactivation rates in waters. We compared performance of the LSL model with the two-parametric Weibull model using data on survival of E. coli in various types of waters from a representative database of 167 laboratory experiments. The Weibull model was preferred in more than 99% of all cases. Application of the Weibull model can improve predictive capabilities of the microbial water quality modeling. A comprehensive review was undertaken to evaluate the possibility of using coliforms for qualitative prediction of pathogen concentrations. Overall, coliform indicators alone cannot provide conclusive, non-site-specific and non-pathogen-specific information about the presence and/or concentrations of most important pathogens in surface waters suitable for irrigation. Standards of microbial water quality for irrigation cannot rely only on concentrations of indicators and/or pathogens, but must include references to crop management. This work has been done to inform and support development of the produce rule in FDA. Previous research has shown that vegetated filter strips (VFS) are very effective at minimizing microbial runoff from fields via enhanced infiltration. However, the wide variability observed in experimental data suggests that this is not the only mechanism responsible for microbial retention by VFS. In collaboration with scientists from University of Florida, we developed a theory of the retention that can be used to not only help construct and refine mathematical models of colloid transport in vegetation systems in overland flow, but also inform the development of theories of colloid deposition on natural organic material (NOM)-coated surfaces in natural, engineered, and biomedical systems. A large-scale field experiment was carried out to evaluate the effect of soil conditions on the run off of microorganisms from a manured field. An irrigation system at the USDA-ARS OPE3 research site was used to conduct irrigation events for applied fresh and aged manure, and companion plot-scale experiments were performed. Results will contribute to further validation of the microbial release and transport model STWIR, which is being adopted by EPA and will be linked with the legacy water quality model.


4. Accomplishments
1. Improved capability to estimate microbial water quality. Modeling of microbial water quality is dependent on estimates of microorganism survival after deposition on land in manure or animal waste. ARS scientists from Beltsville, MD, have created the world’s largest database on E. coli survival in soils and land deposited animal waste. The data have been analyzed using predictive microbiology to simulate the dependence of E. coli survival rates on temperature for different types of animal waste and manures. These results will contribute to improved accuracy of microbial water quality models used for assessment and predictions for recreational and irrigation water sources.


Review Publications
Guber, A., Pachepsky, Y.A., Dao, T.H., Shelton, D.R., Sadeghi, A.M. 2013. Evaluating manure release parameters for nonpoint contaminant transport model KINEROS2/STWIR. Ecological Modeling. 263:126-138.

Shelton, D.R., Kiefer, L., Pachepsky, Y.A., Martinez, G., Mccarty, G.W., Dao, T.H. 2013. Comparison of microbial quality of irrigation water delivered in aluminum and PVC pipes. Agricultural Water Management. 129:145–151.