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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: Design and Implementation of Monitoring and Modeling Methods to Evaluate Microbial Quality of Surface Water Sources Used for Irrigation **Location:
                            Environmental Microbial & Food Safety Laboratory** 2016 Annual Report **Objectives**
                                

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. **Progress Report**
                                

Two irrigation ponds and two water accumulation ponds were surveyed biweekly for concentrations of E. coli and enterococci, suspended particulate matter, and chlorophyll A concentrations. Each pond was sampled on a 20 point grid to include both stagnant and mobile parts of water body. Preliminary results show that there is a stable spatial structure in distributions of both E. coli and chlorophyll, and particulate matter, i.e., concentrations in some parts of the pond are consistently below average while in other parts they are consistently above average. Should this stability persist during the irrigation season, it will present a good indication where water has to be sampled to characterize the E. coli distribution in pond water. The hydrodynamic model FVCOM is being adapted to simulate water flow in ponds during the pumping for irrigation. The USDA-ARS big data initiative staff is providing necessary computer tools and facilities to run this model.

The watershed of Conococheague Creek in Southern Pennsylvania was instrumented to obtain monitoring data sufficient to set and run the watershed-scale water quality model SWAT for subsequent research on monitoring optimization. Sampling sites include pristine, predominantly crop land, and mixed pastures/crops areas; these land uses are typical of the U.S. Mid-Atlantic. The unique set up includes evaluation of E. coli inputs into the creek from wildlife and monitoring of sediment E. coli concentrations, along with traditional water flow and E. coli concentrations in the water column. In addition, experiments have been designed to monitor regrowth of E. coli populations in the creek bottom sediment after high flow events. The artificial high flow events will be created by releasing 60 tons of water in 20 minutes to the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) creek at the Beltsville Agricultural Research Center (BARC). E. coli concentrations and environmental variables will be measured along the creek in 30 locations for 10 days. This experiment will allow for modifying the SWAT model to account for E. coli regrowth after high flow events.  

A field experiment was performed to research survival of manure borne indicator bacteria in the soil mixing zone. Solid manure was applied on the field at the BARC OPE3 research watershed, irrigation was applied, and runoff and E. coli concentrations in runoff were monitored. Irrigation events were repeated after one and two weeks and a sampling of top soil mixing zone and manure was performed. Preliminary data analysis shows that the manure weathering, results in decreases of release and removal rates of manure-borne E. coli compared with the irrigation immediately after application. **Accomplishments**
                                

1. Improved capability to estimate microbial water quality with predictive microbiology models.  Reliability of modeling microbial water quality in irrigation water sources depends on the accuracy of simulating microorganism survival in major environmental media including soil, water, manure, stream bed sediment. ARS scientists from Beltsville, Maryland, together with the U.S. Environmental Protection Agency (EPA) scientists from Athens, Georgia, developed the microbial database editor software that allows to modify physico-microbial properties related to microbial indicators and pathogens and to populate metadata standards, so the properties are available for consumption by microbial source, fate, transport, and risk modules. Results of this work are intended to be used by water resource and water use managers and consultants who currently employ modeling to follow regulatory guidance on microbial water quality and to meet regulatory microbial quality standards of irrigation and recreation waters. None. **Review Publications**
                            *Pyo, J., Haseong, Pachepsky, Y.A., Lee, H., Nam, G., Kim, M.S., Im, J., Cho, K.H. 2016. Chlorophyll-a concentration estimation with three bio-optical algorithms: correction for the low concentration range for the Yiam Reservoir, Korea. Remote Sensing Letters. 7(5):407–416.*


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