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Related Concept Videos

Bioplastics01:27

Bioplastics

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Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
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Microbial Bioremediation of Pesticides01:28

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Pesticides often feature structurally complex chemical architectures, incorporating halogen groups and multiple aromatic rings. These characteristics confer high chemical stability, rendering many pesticides resistant to natural degradation processes. This resistance poses significant environmental concerns, as persistent pesticide residues can accumulate in ecosystems and affect non-target organisms.Despite the inherent stability of many pesticides, certain microorganisms possess the metabolic...
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Updated: Apr 8, 2026

Investigating Long-Distance Transport of Perfluoroalkyl Acids in Wheat via a Split-Root Exposure Technique
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Development of Bioaccessibility-Based Soil Environmental Criteria for PFAS through the Establishment of In Vitro

Xin Yang1, Mingxue Ren1, Albert Juhasz2

  • 1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, People's Republic of China.

Environmental Science & Technology
|April 7, 2026
PubMed
Summary

This study developed new methods to measure per- and polyfluoroalkyl substances (PFAS) bioaccessibility in soil for plants and earthworms. Machine learning models predict PFAS bioaccessibility, improving ecological risk assessment.

Keywords:
PFASbioaccessibilitymachine learningsoil environmental criteriaspecies sensitivity distribution

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Area of Science:

  • Environmental Chemistry
  • Ecotoxicology
  • Soil Science

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are widespread environmental contaminants, with soil being a major reservoir.
  • Current soil environmental criteria (SEC) rely on total pollutant concentrations, not bioavailable amounts, leading to potential underestimation of ecological risks.
  • Standardized in vitro methods for measuring PFAS bioaccessibility in soil are lacking.

Purpose of the Study:

  • To develop and validate extraction methods for assessing PFAS bioaccessibility in soil for plants and earthworms.
  • To build a machine learning model for predicting PFAS bioaccessibility in soil.
  • To derive bioaccessibility-based SEC for a more accurate ecological risk assessment of PFAS in soil.

Main Methods:

  • Developed water and C18 membrane extraction methods benchmarked against in vivo tests for plant and earthworm bioaccumulation.
  • Collected 3,474 data points from 44 soil samples across China.
  • Utilized machine learning to predict PFAS bioaccessibility and derive bioaccessibility-based ecotoxicity data.

Main Results:

  • Water extraction effectively predicted PFAS accumulation in plant shoots and short-chain PFAS in roots.
  • C18 membrane extraction was suitable for long-chain PFAS in roots and PFAS in earthworms.
  • Machine learning models accurately predicted PFAS bioaccessibility, leading to derived HC5 values (0.229-0.330 mg/kg) for PFOA, PFHxS, and PFOS.

Conclusions:

  • Established a novel framework for deriving bioaccessibility-based PFAS SEC.
  • Demonstrated the feasibility of using machine learning to predict PFAS bioaccessibility in soil.
  • Facilitated a shift from total concentration-based to bioaccessibility-based ecological risk assessment for PFAS in soil, reducing uncertainty and improving accuracy.