What the study found
A structured, tiered PFAS fingerprinting framework can use target-only data to distinguish overlapping PFAS sources and describe source, process, and transport information. In the demonstration dataset, the framework identified distinct mixture patterns linked to manufacturing era, formulation chemistry, and hydrologic context.
Why the authors say this matters
The authors conclude that the approach could assist PFAS investigations when source histories are complex and compound coverage is limited. The study suggests that multiple complementary analytical metrics can support defensible source differentiation under data-limited conditions.
What the researchers tested
The researchers developed a tiered PFAS fingerprinting framework that combines compound-level concentrations, class- and carbon-number-resolved composition, diagnostic ratios, isomer distributions, precursor-product relationships, multivariate clustering, and geospatial pattern analysis. They applied it to groundwater datasets from a complex industrial setting at two time points, 2018 and 2024.
What worked and what didn't
The framework resolved sulfonate-rich mixtures consistent with electrochemical fluorination-era inputs, telomer-associated industrial mixtures characterized by fluorotelomer sulfonates and carboxylates, and short-chain-enriched profiles influenced by wastewater-related transport and mixing. Temporal evaluation showed changes in precursor abundance and terminal perfluoroalkyl carboxylic acids between sampling events, and diagnostic ratios and isomer patterns added temporal context where quantifiable. Unsupervised clustering also corroborated compositional similarity and hydraulic connectivity among site domains.
What to keep in mind
The abstract describes a demonstration in one complex industrial groundwater setting, so the scope is limited to that context. The authors also note that the approach is designed for target-only analytical datasets, which means it depends on the analytes and metrics available in such data.
Key points
- The framework is designed to differentiate overlapping PFAS sources using only target analytes.
- It combines several lines of evidence, including ratios, isomer patterns, clustering, and geospatial patterns.
- In the example dataset, it identified sulfonate-rich, telomer-associated, and short-chain-enriched PFAS profiles.
- The study observed temporal changes in precursor abundance and terminal perfluoroalkyl carboxylic acids between 2018 and 2024.
- Unsupervised clustering supported similarities and hydraulic connectivity among site domains.
Disclosure
- Research title:
- Target-only PFAS data can still differentiate overlapping sources
- Authors:
- Jenny E. Zenobio, Faezeh Pazoki, Adam Forsberg, Sheau-Yun Dora Chiang
- Institutions:
- Jacobs (United States)
- Publication date:
- 2026-02-23
- OpenAlex record:
- View
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