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March 2, 2026Journal of Hazardous Materials Advances0 citationsOpen Access

Enhancing PFAS Data Integrity: an LLM-Based FAIR+Environmental Principle for Improved Evaluation of Environmental Contaminants and Related Constituent Databases

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JDJialin DongSYSean D. YoungHLHao Li

Key Points

  • The aim is to enhance the integrity and evaluation of PFAS datasets using an LLM-based FAIR framework.
  • Assessment of over 100 PFAS datasets using a semi-automated LLM pipeline.
  • Implementation of FAIR principles tailored for environmental data.
  • Evaluation of data quality, integration metrics, and scoring criteria for different water matrices.
  • PFOA concentrations are significantly higher in groundwater compared to surface and drinking water.
  • Approximately 34% of drinking water samples exceed the 4 ng/L maximum contaminant level for PFOA.
  • Surface water datasets achieved the highest FAIR score of 53.6%, indicating better data management practices.

Abstract

• PFAS datasets assessed with FAIR principles and data quality, integration metrics. • A semi-automated LLM assessment pipeline reliably assessed >100 PFAS datasets. • PFOA in groundwater is higher compared to surface, drinking water. • ∼34% of drinking water samples exceed PFOA MCL (4 ng/L). • Public PFAS soil occurrence datasets are limited. Per- and polyfluoroalkyl substances (PFAS) are persistent, bioaccumulative contaminants of emerging concern, yet data sharing around their environmental occurrence and monitoring remains fragmented. We propose a “FAIR+Environmental” framework that extends the Findable, Accessible, Interoperable, Reusable (FAIR) principles to environmental-specific matrices, assessed by a semi-automated Large Language Model (LLM) pipeline that uses rule-based scoring, few-shot prompting, and Chain-of-Thought (CoT) reasoning. We applied the framework to >100 U.S. PFAS datasets across groundwater, surface water, drinking water, and soil matrices. Few-shot CoT LLMs streamlined FAIR evaluations, reducing the manual effort required for expert assessments. Among environmental matrices, surface water datasets achieved the highest FAIR-Score (53.6%), followed by drinking water (52.6%), soil (49.3%), and groundwater (45.2%). Multi-state datasets consistently outperformed single-state datasets, particularly in Interoperability and Reusability criteria. Compared to geoscience databases, PFAS environmental datasets lag in FAIR adherence, highlighting the urgent need for centralized, standardized, and FAIR-compliant data management in the field. PFOA occurrence indicated overall PFAS pollution hotspots because of its frequent detection. PFOA contamination was most severe in soil and groundwater. Surface water and drinking water showed lower concentrations but remain critical public health exposures, with ∼34% of drinking water samples exceeding the 4 ng/L maximum contaminant level. Temporal trends indicated little significant change in PFOA concentrations across most states.

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Cite This Study

Dong et al. (2026) studied this question.

synapsesocial.com/papers/69a52df3f1e85e5c73bf12e8https://doi.org/10.1016/j.hazadv.2026.101103
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