A species-specific QSAR-based framework for PFAS hazard identification and ecological risk mapping

Area of Science:

• Environmental Chemistry
• Toxicology
• Computational Science

Background:

• Per- and polyfluoroalkyl substances (PFAS) present a growing regulatory challenge due to their vast chemical diversity and limited toxicological data.
• Assessing the ecological risks of these data-poor compounds is crucial for effective environmental management.

Purpose of the Study:

• To develop species-specific quantitative structure-activity relationship (QSAR) models for predicting the acute and chronic toxicity of untested PFAS.
• To identify high-potency PFAS and assess ecological risks in Chinese surface freshwater, focusing on emerging contaminants.

Main Methods:

• Developed and validated species-specific QSAR models for predicting PFAS toxicity across four aquatic taxa (fish, water fleas, midges, green algae).
• Utilized 2D descriptors to link PFAS structural characteristics to toxicity and derived predicted no-effect concentrations (PNEC).
• Assessed ecological risks of 25 PFAS in Chinese surface waters, identifying risk hotspots.

Main Results:

• QSAR models demonstrated high accuracy and outperformed existing predictive tools.
• Nineteen PFAS were identified as high-potency compounds with sub-micromolar toxicity.
• Emerging PFAS like PFODA and PFHxDA showed greater chronic risks than legacy PFAS, with risk hotspots concentrated in the Yangtze and Yellow River basins.

Conclusions:

• The study provides a scalable computational framework for rapid ecological hazard screening of data-poor PFAS.
• Findings facilitate evidence-based environmental risk management and highlight the risks posed by emerging PFAS in Chinese freshwater ecosystems.