Research Project: Improving Pre-harvest Produce Safety through Reduction of Pathogen Levels in Agricultural Environments and Development and Validation of Farm-Scale Microbial Quality Model for Irrigation Water Sources

Location: Environmental Microbial & Food Safety Laboratory

2022 Annual Report

Accomplishments
1. Zero-valent iron sand filtration can improve agricultural water quality by reducing bacterial pathogens. Agricultural water quality and availability are critical factors in the production of fruits and vegetable that are safe to consume. Smaller farms which use surface water for irrigation may not be able to invest in expensive water treatment technologies. ARS researchers in Beltsville, Maryland, utilize zero-valent iron sand (ZVI) filtration to reduce E. coli levels in pond water and laboratory experiments. The removal and inactivation of E. coli was based on the percentage of zero-valent iron used in the filter and the turbidity of the water. These results provide practical irrigation water quality improvements for small farmers.

2. Salmonella enterica is more prevalent than Listeria monocytogenes the Eastern shore of Maryland. Salmonella enterica and Listeria monocytogenes are bacterial foodborne pathogens of concern on produce. Surface water is increasingly relied upon to provide irrigation water to preserve critical groundwater resources but can introduce pathogens to fruits and vegetables if not appropriately disinfected. Of water samples analyzed over a two-year period, sixty-five percent of water samples contained Salmonella while forty percent contained L. monocytogenes. Recycled wastewater contained lower levels of pathogens compared to river water, indicating its potential suitability to irrigate fruits and vegetables.

3. Improved recovery of antibiotic-resistance Salmonella enterica from surface water. The National Antimicrobial Resistance Monitoring System Environmental Working Group (NARMS EWG) prioritized the recovery and isolation of antibiotic-resistant Salmonella from surface water. ARS researchers in Beltsville, Maryland, Riverside, California, Clay Center and Lincoln, Nebraska, and Athens, Georgia, refined and improved four different protocols for the recovery of Salmonella from surface water. Protocols were shared with EPA and FDA members of the NARMS EWG and posted on protocols.io.

4. Science-based guidance on when and where to take irrigation water samples have been developed. Irrigation water quality from a variety of sources (ponds, rivers, etc.) is commonly determined through microbial testing and sampling. Previous work has shown that the time/location of water sample collection can influence the levels of Escherichia coli, a common bacterial microbial quality indicator. ARS researchers In Beltsville, Maryland, analyzed the results of an extensive multi-year E. coli monitoring assessment in irrigation ponds and proposed the first science-based methodology to design a monitoring regime for site-specific indicator microorganisms for irrigation ponds. Results of this work will benefit agricultural stakeholders and water resource managers who design and implement microbial water quality monitoring.

Review Publications
Anderson Coughlin, B., Craighead, S., Kelly, A., Vanore, A., Johnson, G., Jiang, C., Haymaker, J., White, C., Foust, D., Duncan, R., East, C.L., Handy, E., Bradshaw, R., Murray, R., Kulkarni, P., Solaiman, S., Betancourt, W., Gerba, C., Allard, S., Parveen, S., Hashem, F., Micallef, S., Sapkota, A., Sapkota, A., Sharma, M., Kniel, K. 2021. Enteric viruses and Pepper Mild Mottle Virus show significant correlation in select Mid-Atlantic agricultural waters. Applied and Environmental Microbiology. 87:e00211-21. https://doi.org/10.1128/AEM.00211-21.
Kim, S., Eckart, K., Sabet, S., Chiu, P., Sapkota, A.R., Handy, E., East, C.L., Kniel, K.E., Sharma, M. 2021. Escherichia coli reductions in water by zero valent iron sand filtration is based on water quality parameters. Water. 13(19):2702.
Pachepsky, Y.A., Anderson, R.G., Harter, T., Jacques, D., Jamieson, R., Jeong, J., Kim, H., Ouyang, Y., Wan, Y., Zhang, W. 2021. Fate and transport in environmental quality. Journal of Environmental Quality. 50(6):1282-1289. https://doi.org/10.1002/jeq2.20300.
Smith, J.E., Wolny, J.L., Stocker, M.D., Hill, R.L., Pachepsky, Y.A. 2021. Temporal stability of phytoplankton functional groups within two agricultural irrigation ponds in Maryland, USA. Frontiers in Water. https://doi.org/10.3389/frwa.2021.724025.
Stocker, M., Pachepsky, Y.A., Smith, J., Morgan, B.J., Hill, R., Kim, M.S. 2021. Persistent patterns of E. coli concentrations in two irrigation ponds from three years of monitoring. Water, Air, and Soil Pollution. https://doi.org/10.1007/s11270-021-05438-z.
Abbas, A., Baek, S., Silvera, N., Soulileuth, B., Pachepsky, Y.A., Ribolzi, O., Boithias, L., Cho, K. 2021. In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models. Hydrology and Earth System Sciences. 25(12):6185-6202. https://doi.org/10.5194/hess-25-6185-2021.
Cho, K., Wolny, J., Kase, J., Unno, T., Pachepsky, Y.A. 2021. Interactions of E. coli with algae and aquatic vegetation in natural waters. Water Research. 209:117952. https://doi.org/10.1016/j.watres.2021.117952.
Kim, S., Pachepsky, Y.A., Karahan, G., Sharma, M. 2021. Estimating parameters of empirical infiltration models from the global data set using the machine learning algorithm. Journal of Hydrology. https://doi.org/10.31545/intagr/132922.
Gonzalez Jimenez, A., Pachepsky, Y.A., Gomez Flores, J., Ramons Rodriguez, M., Vanderlinden, K. 2022. Correcting coordinate-measurement mismatch of on-the-go field measurements by optimizing nearest neighbor statistics. Sensors. 22(4):1496. https://doi.org/10.3390/s22041496.
Stocker, M., Pachepsky, Y.A., Hill, R.L., Kim, M.S. 2022. Elucidating spatial patterns of E. coli in two irrigation ponds with empirical orthogonal functions. Journal of Hydrology. 609:127770. https://doi.org/10.1016/j.jhydrol.2022.127770.
Stocker, M., Smith, J., Hill, R., Pachepsky, Y.A. 2022. Intra-daily variation of E. coli concentrations in agricultural irrigation ponds. Journal of Environmental Quality. https://doi.org/10.1002/jeq2.20352.
Baek, S., Eun-Jung, Y., Pyo, J., Pachepsky, Y.A., Son, H., Cho, K. 2022. Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level. Water Research. https://doi.org/10.1016/j.watres.2022.118494.
Karahan, G., Pachepsky, Y.A. 2022. Parameters of infiltration models as affected by the measurement technique and land use. Catena. https://doi.org/10.36783/18069657rbcs20210147.
Malayil, L., Negahban-Azar, M., Rosenberg Goldstein, R., Sharma, M., Gleason, J., Muise, A., Murray, R., Sapoka, A.R. 2021. "Zooming" our way through virtual undergradate research training: a successful redesign of the CONSERVE summer internship program. Journal of Microbiology and Biology Education. 22:1. https://doi.org/10.1128/jmbe.v22i1.2625.
Pachepsky, Y.A., Karahan, G. 2022. On shapes of cumulative infiltration curves. Geoderma. 412:115715. https://doi.org/10.1016/j.geoderma.2022.115715.
Stocker, M., Pachepsky, Y.A., Hill, R. 2022. Prediction of E. coli concentrations in agricultural pond waters: application and comparison of machine learning algorithms. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2021.768650.
Anderson-Coughlin, B.L., Litt, P.K., Kim, S., Craighead, S., Kelly, A.J., Chiu, P., Sharma, M., Kniel, K.E. 2021. Zero-valent Iron filtration reduces microbial contaminants in irrigation water and transfer to raw agricultural commodities. Microorganisms. 9(2009):1-14. https://doi.org/10.3390/microorganisms9102009.
Limoges, M.A., Neher, D., Weicht, T.R., Millner, P.D., Sharma, M., Donnelly, C. 2021. Differential survival of generic E. coli and Listeria spp. in Northeastern U.S. soils amended with dairy manure compost, poultry litter compost, and heat-treated poultry pellets and fate in raw edible radish crops. Journal of Food Protection. https://doi.org/10.4315/JFP-21-261.
Acheamfour, C., Parveen, S., Hashem, F., Sharma, M., Gerdes, M., May, E.B., Rogers, K., Haymaker, J., Duncan, R., Foust, D., Tabodi, M., Bradshaw, R., Handy, E.T., East, C.L., Kim, S., Micallef, S., Callahan, M., Allard, S., Anderson-Coughlin, B., Craighead, S., Gartley, S., Vanore, A., Kniel, K.E., Solaiman, S., Bui, A., Craddock, H.A., Kulkarni, P., Rosenberg-Goldstein, R., Sapkota, A.R. 2021. Levels of Salmonella enterica and Listeria monocytogenes in alternative irrigation water vary nased on water source on the Eastern Shore of Maryland. Applied and Environmental Microbiology. 9:e00669-21. https://doi.org/10.1128/Spectrum.00669-21.
Ghanbaian, B., Pachepsky, Y.A. 2022. Machine learning in vadose zone hydrology: a flashback. Vadose Zone Journal. https://doi.org/10.1002/vzj2.20212.
Gartley, S., Anderson-Coughlin, B., Sharma, M., Kniel, K.E. 2022. Listeria monocytogenes in irrigation water: an assessment of outbreaks, sources, prevalence, and persistence. Microorganisms. https://doi.org/10.3390/microorganisms10071319.