Location: Environmental Microbial & Food Safety Laboratory
2024 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
Significant progress has been made on all Objectives of the project, which fall under National Program 108.
For Sub-objective 1A, ZVI-sand filters were designed for systems with higher water flow rates targeting foodborne parasites. Addition of ZVI to sand filtration improved the removal of foodborne parasite surrogates (Eimeria tenella, acervulina) from water.
For Sub-objective 1A, the ZVI-sand filters were placed in-line with drip systems to improve the quality of rainwater for irrigation of spring mix.
For Sub-objective 1B, cells exposed to sodium hypochlorite are evaluated for their resistance to sodium hypochlorite after repeated cycles exposure to hypochlorite and then survival in soils.
For Sub-objective 1B, pathogen dynamics of E. coli O157:H7 and Salmonella Infantis were evaluated in soils receiving biological soil amendments as side-dressing. Transfer from soils to growing lettuce plants were also investigated.
For Sub-objective 2A, the first use of the unmanned surface vehicle (USV) to map microbial water quality in irrigation ponds was demonstrated. Microbiome structure was evaluated as a determinant of the fecal indicator bacteria habitat in water. Here, we report that depth-dependent gradients of water quality parameters (e.g., pH, dissolved oxygen, temperature) correlate with water microbiome diversity in a model irrigation pond. Composite sampling protocol assessed the generic and antibiotic-resistant fecal indicator bacteria in ponds. Results indicate the applicability of the composite sampling for the overall microbial water quality assessment. Antibiotic-resistant E. coli levels in irrigation ponds based on water quality variables were assessed. Microcystin concentrations in Georgia irrigation ponds were determined in a unique spatiotemporal data set.
For Sub-objective 2C, autonomous calibration of models of fate and transport of pollutants in streams was developed. Method is based on the AI technique of reinforcement learning and allows to automatically select periods for model parameter improvement. The AI method of the long short-term memory was first applied to uncouple predictive modeling of water flow and pollutant concentration. The method provided remarkable improvement of the estimation accuracy of the pollutant flux.
Accomplishments
1. A cost-effective and efficient method to detect Salmonella enterica from water was developed and provided to other federal agencies. Antibiotic-resistant Salmonella enterica is a pathogen of critical concern in multiple food commodities. One route of contamination of antibiotic-resistant Salmonella enterica onto fruits and vegetables is through agricultural or irrigation water. The National Antimicrobial Resistance Monitoring and Surveillance (NARMS), a collaboration between the CDC, FDA, and USDA, has developed consensus microbiological methods for the recovery of several antibiotic resistant pathogens from clinical, animal and food but not environmental water samples. USDA-ARS scientists in Riverside, California, Athens, Georgia, Beltsville, Maryland, and Lincoln and Clay Center, Nebraska, collaborated and compared four different microbiological methods on 60 water samples to recover low levels of Salmonella from surface water. Research showed that the modified standard method 9260.B2 (SM) consistently recovered low levels of Salmonella from water samples at frequencies equivalent to dead-end ultrafiltration (DEUF), a method used by CDC and FDA. The modified standard method identified was used in EPA NRSA (National Rivers and Streams Assessment). This research benefits NARMS and its federal partners (FDA, CDC) as it expands its monitoring of antibiotic resistant Salmonella in water.
2. Rapid methods of detection reliably predicted the absence of bacterial foodborne pathogens in irrigation water. Quickly detecting foodborne pathogens in irrigation water can inform decisions by farmers on strategies to reduce risk of contamination and illness associated with fresh fruits and vegetables. Currently most traditional testing methods take up to five days to confirm the presence of a pathogen in agricultural water. The use of rapid (culture independent) methods like real-time PCR (qPCR) has the potential to accelerate the testing process. USDA-ARS researchers in Beltsville, Maryland, compared results from qPCR and traditional culture methods from multiple irrigation water sources, analyzing almost 2000 samples for Salmonella and L. monocytogenes. Overall, qPCR methods could be more confidently utilized to determine the absence of in irrigation water samples examined in this study. Results indicated that pond and reclaimed water showed higher levels of agreement between traditional and rapid methods than river water, and that volume of the sample size also influenced the results. This work aids farmers and regulators by evaluating factors that affect rapid detection methods of pathogens in irrigation waters.
3. Substantial improvement of E. coli levels the retrieval in irrigation ponds with drone-based imagery and artificial intelligence. E. coli levels in ponds used for irrigation are an agricultural water quality indicator used to produce contamination. The suitability of drone-based imagery for assessing spatial patterns of E. coli levels was recently demonstrated. USDA-ARS scientists in Beltsville, Maryland, devised spectral indices from the collected images, related them to bacterial habitat characteristics, and then developed a predictive E. coli machine learning model. This research created powerful Artificial Inteligence models which appeared to be more accurate and robust compared with the models based on the raw imagery data. Results of this work improve the usability of drone-based imagery in monitoring and surveying of microbial water quality water quality in irrigation ponds.
Review Publications
Mcconn, B.R., Kraft, A.L., Durso, L.M., Ibekwe, A.M., Frye, J.G., Wells, J., Tobey, E.M., Ritchie, S.M., Williams, C.F., Cook, K.L., Sharma, M. 2024. An analysis of culture-based methods used for the detection and isolation of Salmonella spp., Escherichia coli, and Enterococcus spp. from surface water: a systematic review. Science of the Total Environment. 927. Article 172190. https://doi.org/10.1016/j.scitotenv.2024.172190.
Kraft, A., Wells, J., Frye, J.G., Ibekwe, A.M., Durso, L.M., Hiott, L.M., East, C.L., McConn, B., Franklin, A.M., Boczek, L.A., Garland, J.L., Kabrera, C., McDermott, P., Ottesen, A.R., Zheng, J., Cook, K.L., Sharma, M. 2023. A comparison of methods to detect low levels of Salmonella enterica in surface waters to support antimicrobial resistance surveillance efforts performed in multiple laboratories. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2023.167189.
Franklin, A.M., Weller, D.L., Durso, L.M., Bagley, M., Davis, B.C., Frye, J.G., Grim, C., Ibekwe, A.M., Jahne, M., Keely, S.P., Kraft, A.L., McConn, B.R., Mitchell, R., Ottesen, A., Sharma, M., Strain, E., Tadesse, D., Tate, H., Wells, J., Williams, C.F., Cook, K.L., Kabera, C., McDermott, P., Garland, J. 2024. A one health approach for monitoring antimicrobial resistance: Developing a national freshwater pilot effort. Frontiers in Water. 6. Article 1359109. https://doi.org/10.3389/frwa.2024.1359109.
Hong, S., Abbas, A., Kim, S., Kwon, D., Yoon, N., Yun, D., Lee, S., Pachepsky, Y.A., Pyo, J., Cho, K. 2023. Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system . Journal of Environmental Modeling and Software. 168. Article e105805. https://doi.org/10.1016/j.envsoft.2023.105805.
Stocker, M.D., Smith, J.E., Pachepsky, Y.A., Blaustein, R.A. 2024. Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water. Applied and Environmental Microbiology. 915: Article e170143. https://doi.org/10.1016/j.scitotenv.2024.170143.
Smith, J., Stocker, M.D., Wolny, J., Pachepsky, Y.A. 2024. Effects of sampling time and depth on phytoplankton metrics in irrigation ponds. Journal of Phycology. 11(4): Article e74. https://doi.org/10.3390/environments11040074.
Pachepsky, Y.A., Yakirevich, A., Ponizovsky, A., Gummatov, N. 2023. Estimating osmotic pressure in soil solutions of saline soils to evaluate the salinity effect on crop yields. Geoderma. 23(4); article e20299. https://doi.org/10.1002/vzj2.20299.
Hong, S., Morgan, B.J., Stocker, M.D., Smith, J.E., Kim, M.S., Cho, K., Pachepsky, Y.A. 2024. Estimating concentrations of Escherichia coli across a farm pond from the sUAS-based RGB imagery and water quality variables with machine learning techniques. Water Research. 260: Article e121861. https://doi.org/10.1016/j.watres.2024.121861.