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Updated: May 19, 2026

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
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What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing

Pengcheng Tu1, Junhao Xie2, Xueqing Li1

  • 1Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou 310051, China.

Environmental Science & Technology
|December 30, 2025
PubMed
Summary

Microplastics are found in human blood and feces. Drinking water source and socioeconomic factors significantly influence microplastic levels, particularly for poly(vinyl chloride) (PVC).

Keywords:
human exposuremachine learningmicroplasticspoly(vinyl chloride)py-GCMSsocioeconomic factors

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Area of Science:

  • Environmental Science
  • Toxicology
  • Analytical Chemistry

Background:

  • Microplastics are ubiquitous environmental pollutants with unavoidable human exposure.
  • Previous biomonitoring studies had small sample sizes, limiting understanding of microplastic exposure.
  • Key factors influencing microplastic levels in humans remain underexplored.

Purpose of the Study:

  • To quantify microplastic exposure in human blood and feces.
  • To identify factors influencing microplastic levels using machine learning.
  • To establish a framework for microplastic biomonitoring and mitigation.

Main Methods:

  • Analysis of 229 blood and 227 fecal samples using pyrolysis-gas chromatography-mass spectrometry.
  • Detection and quantification of seven polymer types: polyethylene, poly(vinyl chloride) (PVC), polypropylene, polystyrene, polyamide 66, poly(ethylene terephthalate), and poly(methyl methacrylate).
  • Machine learning modeling and explainable AI to identify influencing factors from demographic, lifestyle, socioeconomic, and dietary data.

Main Results:

  • Polyethylene, PVC, and polystyrene were the most prevalent polymers detected in both matrices.
  • A significant negative correlation was found between blood and fecal PVC levels.
  • Drinking water source, socioeconomic status (income, education), age, sex, and geographic location were significant predictors of microplastic levels.

Conclusions:

  • Microplastic exposure is widespread and influenced by various factors, including water source and socioeconomic status.
  • Machine learning and AI provide powerful tools for understanding microplastic exposure.
  • This study offers a comprehensive approach to microplastic biomonitoring and mitigation strategies.