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 Environmental Microbial & Food Safety Laboratory: Beltsville, MD
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ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Environmental Microbial & Food Safety Laboratory Research





- Pachepsky, Yakov
- 2021
- 2020
- 2019
- 2018
- 2017
- 2016
- Publications
- Food Safety, (Animal and Plant Products) (108)

Research Project: **Location:
                            Environmental Microbial & Food Safety Laboratory** Project Number: 8042-12630-011-000-D
                        
                        Project Type: In-House Appropriated Start Date: Feb 16, 2016
                        
                        End Date: Feb 15, 2021 **Objective:**
                        
                        Objective 1 - Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability.
Sub-objective 1.A. Research and quantify lateral patterns of indicator bacteria concentrations in ponds and reservoirs, and evaluate the effect of algal populations, flow patterns and water quality parameters on these patterns.
Sub-objective 1.B. Research and quantify patterns of vertical indicator bacteria distributions in water column in ponds and reservoirs. 
Sub-objective 1.C. Develop a model to estimate indicator bacteria concentrations at the intake of irrigation water based on vertical and lateral indicator bacteria distributions in the water of pond or reservoir.
Objective 2 - Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models. 
Sub-objective 2.A. Develop a model to evaluate stream bottom sediment as an indicator bacteria source between rainfall events.
Sub-objective 2.B. Research survival of manure-borne indicator bacteria in soil to predict contribution of soil E. coli reservoir to runoff leaving fields and pastures. 
Sub-objective 2.C. Develop a modeling-based method for site-specific optimization of stream water sampling scheduling to provide the most representative indicator bacteria concentrations in irrigation water for a given annual number of samples. **Approach:**
                        
                        Taken as a whole, this project strives to acquire, package and disseminate the knowledge about microbial quality of irrigation water in the way that offers wide applicability of results. No resources can currently be made available to monitor a large enough number of sites across the country to build a reliable statistical model that would relate microbial water quality to a multitude of environmental variables that vary based on prevailing local conditions at specific sites. This project relies on mechanistic rather than statistical models. It is designed on the premise that processes affecting microbial water quality stay the same whereas rates of those processes vary as they reflect local conditions. The project will develop observation methods that will improve data collection to fine-tune the model to a specific site by finding the site-specific rates. Models will be tested to make sure that simulation results are quantitatively and qualitatively similar to results of measurements. Data for such testing will be collected at field sites that reflect represent major contrasting combinations of environmental and management factors affecting water quality in irrigation water sources. The satisfactory performance of the models will provide confidence that the models and the corresponding data collection will be applicable at sites other than observed. As a disclaimer, it is realized that the current knowledge about microbial water quality controls still is far from being exhaustive, and some sites may exhibit microbial water quality features that are not understood and modeled well. The project is designed to efficiently utilize the best current knowledge about the processes controlling the microbial water quality of surface water. The integrated monitoring and modeling approach of this project can be re-applied as new knowledge will become available about the processes and factors controlling the microbial quality of surface water used for irrigation.


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