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

Objectives
Objective 1. Reduction of microbial contamination in pre-harvest agricultural environments through improvement of microbial water quality. Sub-objective 1A. Develop and evaluate on-farm filtration technologies which can be implemented in a cost-effective manner to improve the microbial quality of surface irrigation water. Sub-objective 1B. Identify and prioritize microbial, agricultural, seasonal, and spatiotemporal factors which affect the survival of enteric bacterial pathogens in soils and on plants introduced through contaminated irrigation water. Objective 2. Develop and validate novel monitoring methods for the microbial quality of irrigation water sources. Sub-objective 2A. Research the application of the UAV-based hyperspectral imaging to quantify lateral patterns of indicator and pathogen bacteria concentrations in irrigation ponds. Sub-objective 2B. Quantify movement indicators and pathogens from bottom sediment to stream water column at base flow conditions. Sub-objective 2C. Develop the microbial fate and transport modeling capabilities for APEX and the microbial index modeling method for site-specific evaluation of risks exceeding microbial water quality standards in surface water sources for irrigation.

Approach
On-farm filtration technologies will be scaled up and modified based on previous sand and iron filtration designs. Designs will include the incorporation of the lytic bacteriophages as a pre-treatment before the filtration process or after the pre-filtration process. Survival of pathogens in water after filtration or undergoing no filtration will then be evaluated in soils amended with treated or untreated soil amendments, along with the transfer of these pathogens to growing food commodities. Hyperspectral imaging conducted by unmanned aerial vehicles will be used in conjunction with standard microbiological methods to quantify E. coli in ponds. Sensitive methods to recover bacterial fecal indicators and enteric pathogens will be used to characterize the movement of these microorganisms in ponds. Current modeling frames and software packages will then be used to model the fate and transport of these pathogens in ponds and creeks which serve as potential irrigation water sources.

Progress Report
Progress was made on all objectives and their sub-objectives, which fall under National Program 108, Component 1 Foodborne Contaminants – Problem Statements 1: Characterize the Movement, Structure, and Dynamics of Microbial Populations; and 5: Develop, Validate, and Implement Intervention and Control Strategies to Reduce or Eliminate Pathogens in the Food System; and 6: Develop Predictive Microbiology Models and Informational Databases. For Sub-Objective 1A, data continue to be collected and analyzed examining the survival of E. coli in manure-amended soils in the Northeast, Southeast and Southwest U.S., including the application of organic amendments. Data were collected and evaluated on the survival of E. coli in manure amended soils in Georgia, Florida, and California. Data were also collected on the transfer of E. coli from soils to Romaine lettuce close to harvesting. For Sub-Objective 1B, Zero-valent iron sand filtration was evaluated to inactivate and remove both bacterial (E. coli) and parasitic microorganisms (Eimeria tenella). In addition, four different recovery and isolation methods were prioritized, refined, identified, and evaluated for the recovery of Salmonella from surface water through the National Antimicrobial Resistance Monitoring Surveillance Environmental Working Group (NARMS-EWG) and in collaboration with the Federal Drug Administration (FDA) and the Environmenta Protection Agency (EPA). These methods will be incorporated into the EPA National Rivers and Streams Assessment (NRSA). Methods for the recovery of antibiotic resistant E. coli from surface water were also identified and evaluated with collaboration from ARS researchers in California, Nebraska, and Georgia. For Sub-Objective 2A, progress was made in observing and quantifying three-dimensional patterns of E. coli levels in irrigation ponds in Maryland and Georgia. Increases in levels of E. coli were observed with increases in depth of pond. Statistical models were developed that accounted for the differences between microbial quality in the water body and near the surface, near shore, and at the bottom to determine boundary zones. Those data indicated the need to monitor the microbial quality of pond waters not only at the surface or at a single depth as commonly done currently, but at several depths in the water body. The effect of the drone flight altitude on the image intensity was evaluated with data from both wide-band narrow-band imaging of irrigation ponds. For Sub-Objective 2B, a database on coupled E. coli observations in sediment and water columns is being assembled. Preliminary results show a strong seasonality component to sediment levels of E. coli. An assessment of the genetic diversity of Listeria monocytogenes isolates from riverine sites with different land use is currently ongoing with FDA. For the Sub-Objective 2C, progress has been made in researching the comparative performance of different machine learning AI methods in (a) estimating levels of phytoplankton and b) evaluating machine learning models of fate and transport parameters. A consistent spatiotemporal pattern of variability of E. coli was observed working with ARS researchers in Tifton, Georgia. The persistent spatial patterns in concentrations have been found in all three ponds selected for future use in developing the microbial submodel for the APEX model.

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.