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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Research Project #445760

Research Project: Develop a Quantitative, Usable Database of PFAS Concentrations in Agricultural Samples Including Before & After Treatment or Practice Implementation

Location: Water Management and Conservation Research

Project Number: 2020-13000-005-019-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Dec 22, 2023
End Date: May 31, 2027

Build upon the systematic review previously completed for the USDA NRCS, focusing on extracting data and conditions related to the concentrations of PFAS in agricultural samples (e.g., biosolids, soils, manures, and other organic residuals) and removal rates after treatment or conservation practice implementation (e.g., after a buffer strip). The goal of developing this database will be to help NRCS to develop future management strategies for addressing PFAS contamination in existing agricultural systems and to provide a platform for future data collection.

The team will perform a systematic literature review and create a quantitative database from the data in the reviewed literature. This would be done to capture papers that were not included in the original review, plus papers published since the review was completed. After systematic literature review using Covidence software and the PRISMA process, data will be extracted manually and using plot digitizing software (e.g., Alcasa Digitize It). The occurrence database will be expanded according to standard fields (e.g., publication title, DOI, organic residual, PFAS species, concentration measurement, measurement units, etc.). Two reviewers will check each entry. The team will assemble descriptive statistics of PFAS concentrations and removals among different matrices and synthesize the information in data visualizations (e.g., box plots of PFAS occurrence) and a final report and academic publications. Gaps for agriculture research will be identified.