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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** 2021 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**
                                

This is the final report for project 8042-12630-011-00D, which was terminated in February 2021.
1. For Sub-objective 1A, progress was made in observing and quantifying the diurnal changes of spatial patterns of E. coli concentrations in irrigation ponds in Maryland. The model was developed to simulate the decrease of E. coli concentrations during the morning hours. The model provided the possibility to tailor water sampling times to the time of water use for irrigation. The effect of water quality parameters on the deactivation rate of E. coli requires further study. 
2. For Sub-objective 1A, progress was made in analyzing the interactions of algae populations and E. coli in freshwater sources. The first review on this subject matter was written with co-authors from U.S. FDA and Korea. The review was submitted to a prestigious journal.
3. For Sub-objective 1A, progress was made in establishing the effect of algal populations on E. coli concentration patterns in irrigation ponds. The database was compiled on six major phytoplankton groups belonging to green algae, diatoms, and cyanobacteria. Phytoplankton was sampled concurrently with water quality parameters and E. coli concentrations. The unique database contains more than 7000 phytoplankton concentration measurements. 
4. For Sub-objective 1A, progress was made in researching the temporal stability of E. coli concentrations in irrigation ponds. Two major characterization methods of finding persistent spatial patterns were applied to the three years of monitoring performed at irrigation ponds in Maryland. We observed the well-expressed temporal stability and made preliminary recommendations on using temporal stability to guide the microbial water quality monitoring.  
5. For Sub-Objective 1A, progress has been made in understanding the effect of the artificial intelligence method selection and water quality parameter availability on estimating E. coli concentrations with in-situ and laboratory measurements. We found that, of the tested 10 methods, the random forest algorithm provided the best results. Surprisingly, the most readily available water quality measurements provided the best predictability of E. coli concentrations.  The data analysis continues.
6. For Sub-objective 2A, the efficiency of using the reflectance instead of the digital numbers to predict E. coli concentrations was researched. Using reflectance appeared to be an efficient method to weed out overexposed images and improve the reliability of image data used for E. coli predictions.  
7. For Sub-objectives 1.A, 1.C, 2.A, and 2C, substantial conceptual contributions were made to the collaborative projects on in-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models (UNIST, Korea), prediction of infiltration parameters of soils (Cankiri U. Turkey, EMFSL), recognition of patterns in pathogenic bacteria spatial distributions among different freshwater sources in the Mid-Atlantic (EMFSL). **Accomplishments**
                                

1. The method to assess the movement of pathogens and indicators from bottom sediment to water in streams. E. coli concentrations in streams used for irrigation must be assessed to prevent the spread of microbes that can cause disease in humans because of the produce consumption. High E. coli and pathogen bacteria concentrations are commonly related to land-use practices. However, these bacteria also live in bottom sediments and can move to water, thus distorting the land practices evaluation. ARS scientists from Beltsville, Maryland, proposed and tested the method to measure the stream-averaged flux of bacteria from sediment to water by labeling water in the stream and measuring bacteria concentrations in labeled water at the beginning and the end of a stream reach. The application of this method demonstrated an increase of E. coli cell numbers in water in the absence of any E. coli influx from the land or animals. Results of this work illustrate the need to reevaluate the recommendations on changing land-use practices to prevent  pathogen impairment of streams. **Review Publications**
                            *Kim, G., Baek, I., Stocker, M., Smith, J., Van Tessel, A., Qin, J., Chan, D.E., Pachepsky, Y.A., Kim, M.S. 2020. Hyperspectral imaging from a multipurpose floating platform to estimate chlorophyll-a concentrations in irrigation pond water. Remote Sensing. 13(12):2070. https://doi.org/doi:10.3390/rs12132070.*
                                *Gummatov, N., Pachepsky, Y.A. 2020. Analysis of spatial variability of soil water retention using the CDF matching. Soil Science. https://doi.org/10.1139/cjss-2019-0130.*
                                *Kim, S., Daughtry, C.S., Russ, A.L., Pedrera-Padilla, A., Pachepsky, Y.A. 2020. Analysis of spatiotemporal variability of corn yields using empirical orthogonal functions. Journal of Environmetrics. 12(12):3339. https://doi.org/10.3390/w12123339.*
                                *Kim, S., Karahan, G., Sharma, M., Pachepsky, Y.A. 2021. The site-specific selection of the infiltration model based on the global dataset and random forest algorithm. Vadose Zone Journal. https://doi.org/10.1002/vzj2.20125.*
                                *Martin, M., San Jose Martinez, F., Giraldez, J.V., Pachepsky, Y.A., Vogel, H. 2020. Editorial for the special issue on “Advances in soil scaling: Theories, techniques and applications”. European Journal of Soil Science. https://doi.org/10.1111/ejss.13063.*
                                *Pachepsky, Y.A., Martinez, G., Pan, F., Wagener, T., Nicholson, T. 2016. Evaluating hydrological model performance using information theory-based metrics. Hydrology and Earth System Sciences. doi:0.5194/hess-2016-46.*
                                *Pyo, J., Park, L., Pachepsky, Y.A., Baek, S., Kim, K., Cho, K., Kim, S. 2020. Using convolutional neural network for predicting cyanobacteria concentrations in river water. Water Research. https://doi.org/10.1016/j.watres.2020.116349.*
                                *Smith, J.L., Stocker, M., Wolny, J.L., Hill, R.L., Pachepsky, Y.A. 2020. Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-020-08664-w.*
                                *Stocker, M.D., Hill, R., Pachepsky, Y.A. 2020. Modeling the kinetics of the rainfall-induced release of manure-borne fecal indicator bacteria as affected by manure consistency and manure weathering. Journal of Environmental Quality. https://doi.org/10.1002/jeq2.20164.*
                                *Vanderlinden, K., Pachepsky, Y.A., Pedrera-Padilla, A., Martinez, G., Espejo-Perez, A.J., Perea, F., Giraldez, J.V. 2021. Water retention and field soil water states in a vertisol under long-term direct drill and conventional tillage. European Journal of Soil Science. https://doi.org/10.1111/ejss.12967.*
                                *Cho, K., Pachepsky, Y.A., Ligaray, M., Kwon, Y. 2020. Data assimilation in surface water quality modeling: a review. Water Research. https://doi.org/10.1016/j.watres.2020.116307.*


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