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                    U.S. DEPARTMENT OF AGRICULTURE
 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** 2019 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**
                                

1. For Sub-objective 2.C, significant progress was made in the study of coupled dynamics of E. coli and Listeria monocytogenes in the Conococheague Creek, Pennsylvania, and irrigation ponds, Maryland, which generated the unique, highly-resolved temporal and spatial genetic relationship map among L. monocytogenes strains in conjunction with actual pathogen levels, E. coli concentrations, and water quality parameters. Twenty-five novel clones of L. monocytogenes were identified. The strains do not match strains from recent U.S. outbreaks. However, some strains belonged to clones (CC1, CC4 and CC6) that have been strongly associated with clinical cases and are considered hypervirulent. These novel data are critical for microbial source tracking, modeling and risk assessment. They are also essential in re-assessing the significance of the current Bacterial Fecal Indicators-based microbiological quality standards proposed for recreational and irrigation waters. 
2.	For Sub-objective 2.A, progress was made in the study of periphyton as a habitat for fecal indicator and pathogenic bacteria. Bacteria in bottom sediments can survive multiply, and strongly affect microbial water quality.  However, nothing is known about the fecal indicators and pathogens in periphyton.  We studied a small creek in MD and discovered that concentrations of fecal bacteria cells in periphyton were higher than in sediment, and were much higher than in creek water. Substantial populations of L. monocytogenes, but not Salmonella, were found in periphyton. 
3.	For Sub-objective 1.A, progress was made in the study of the effect of the commercial algicide on algal and E. coli populations in microcosms. Interactions of phytoplankton and E. coli are not well understood, and it is not known if algicide applications improve or worsen the microbial water quality.  The label application rates caused the die-off of both Microcystis aeruginosa and E. coli. The algae outcompeted E. coli in the absence of the algicide. 
4.	For Sub-objective 2.A, significant progress was made in assessing the effect of diurnal temperature oscillations on E. coli survival in stream bottom sediments in a microcosm study.  All existing data on fecal indicator bacteria survival in sediment were obtained under constant temperature conditions. In our pioneer study, there were significantly higher populations of both E. coli and enterococci found to be surviving in the sandy sediment under oscillating temperature conditions. No such effect was seen in clay loam sediments.  
5.	For Sub-objective 1.A, progress was made in the first study of the spatiotemporal variability of phytoplankton populations in irrigation ponds, carried out at two ponds. Eight phytoplankton groups—three of cyanobacteria, two of diatoms, and three of green algae—were detected and enumerated from a total of 342 samples of irrigation pond water taken during the summer season. These measurements were coupled with those for water quality parameters, including E. coli concentration, and the data analysis was started.
6.	For Sub-objective 1.A, significant progress was made in evaluating the common practice of taking irrigation pond water samples from locations near the banks, with the compilation of a database of 306 nearshore and 204 interior in situ sensor measurements and samples taken during 18 campaigns during the summer seasons in two irrigation ponds. In most instances (69%), a statistically significant (P<0.05) difference was seen between the measurements of the nearshore sampling locations and those of the interior waters. Even in instances where the statistical significance was not achieved, probabilities of water quality parameters having the same distribution in the interior and nearshore areas were low. This has serious implications for the microbial water quality monitoring. If one were to sample only from the banks, the samples would not accurately reflect the characteristics of the interior waters that will be pumped onto fields and crops. 
7.	For Sub-objectives 1.A, 1.C, 2.A, and 2C, substantial conceptual contributions were made to the collaborative projects on modeling the somatic and F+RNA coliphages and Escherichia coli at a Great Lakes Beach (with the U.S. EPA), simpler modeling in environmental studies and predictions (Madrid Technical University, Spain), evaluating the influence of climate change on the fate and transport of fecal coliform bacteria using the modified SWAT model (UNIST, Korea), using artificial intelligence methods to develop predictive models with imagery data (UNIST, Korea), and developing the global database on water infiltration in soils (ISMC, Germany).
8.	For Sub-objective 1.B, progress was made on field testing for the multi-depth robotic water sampler STRIDER. The GPS-based control module for the sampler is being developed to allow the automated visits of waypoints according mission plans. **Accomplishments**
                                

1. Using imagery from unmanned aerial vehicles (drones) for microbial water quality assessment in irrigation ponds. Microbial quality of water in irrigation water sources must be assessed to prevent spread of microbes that can cause disease in humans because of the produce consumption. Microbial quality of irrigation water is evaluated based on concentrations of the indicator bacterium E. coli. No recommendations have existed so far on where the pond water samples should be taken for microbial analysis. ARS scientists from Beltsville, Maryland, proposed and tested the method of using the drone-based imagery and artificial intelligence techniques to obtain representative water samples for E. coli enumeration across irrigation ponds. Reflectance in different parts of the spectra are combined to characterize E. coli habitat in water. Results of this work provide the knowledge base for efficient microbial water quality sampling, and indicate the novel direction of monitoring microbial water quality, thus contributing to the improvements in food safety. **Review Publications**
                            *Pachepsky, Y.A., Allende, A., Boithias, L., Cho, K., Hofstra, N., Jamieson, R., Molina, M. 2018. Microbial water quality: monitoring and modeling. Environmental Quality. 47(5):931-938.*
                                *Rahmatil, M., Weihermuller, L., Vanderborght, J., Pachepsky, Y.A., Mao, L., Moosavi, N., Montzka, K., Looy, K. 2018. Challenges in matching permeability observed in macroporous soil with lattice Boltzmann and image analysis methods using segmented pore structures. Earth System Science Data.  https://doi.org/10.5194/essd-2018-11.*
                                *Stocker, M., Pachepsky, Y.A., Hill, R. 2018. E. coli export from the manured field depends on the time from rainfall start to runoff initiation. Journal of Environmental Quality. 47(5):1293-1297.*
                                *Garcia-Gutierrez, C., Pachepsky, Y.A., Martin, M.A. 2018. Saturated hydraulic conductivity and textural heterogeneity of soils. Hydrology and Earth System Sciences. 22(7):3923-3932.*
                                *Jeon, D., Pachepsky, Y.A., Kim, B., Kim, J. 2019. New methodology to develop high-resolution rainfall data using weather radar for watershed-scale water quality model. Desalination and Water Treatment. 138:248-256.*
                                *Vereecken, H., Pachepsky, Y.A., Bogena, H., Javaux, M., Montzka, C. 2019. Upscaling issues in ecohydrological observations. In: Li X., Vereecken H., editors. Observation and Measurement of Ecohydrological Processes. Ecohydrology.  Berlin, Heidelberg: Springer. p. 435-454.*
                                *Jeon, D., Ligaray, M., Kim, M., Kim, G., Lee, G., Pachepsky, Y.A., Cha, D., Cho, K. 2018. Evaluating the influence of climate change on the fate and transport of fecal coliform bacteria using the modified SWAT model. Science of the Total Environment. 658:753-762.*
                                *Nam, W., Tadesse, T., Wardlow, B., Hayes, M., Svoboda, M., Hong, E., Pachepsky, Y.A., Jang, M. 2018. Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events. International Journal of Applied Earth Observation and Geoinformation. 39(5):1548-1574.*
                                *Griffith, B.J., Daughtry, C.S., Russ, A.L., Dulaney, W.P., Gish, T.J., Pachepsky, Y.A. 2019. Effect of shallow subsurface flow pathway networks on corn yield spatial variation under different weather and nutrient management. International Agrophysics. 33:271-276.*
                                *Valdes-Abellan, J., Pachepsky, Y.A., Martinez, G., Pla, C. 2019. How critical is the frequency of water content measurements for obtaining soil hydraulic properties with data assimilation? Vadose Zone Journal. https://doi.org/10.2136/vzj2018.07.0142.*


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