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Related Concept Videos

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Related Experiment Video

Updated: Feb 23, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation
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An integrated framework coupling process driven model and machine learning to simulate soil heavy metal(loid)s

Shiyi Yi1, Xiaonuo Li2, Weiping Chen1

  • 1State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of Hazardous Materials
|February 21, 2026
PubMed
Summary

This study introduces a new model to track soil heavy metal(oid)s (HMs) accumulation, revealing increased atmospheric deposition and declining soil carrying capacity, especially for arsenic and copper.

Keywords:
Heavy metal(loid)sInput-output fluxesLong-term spatiotemporal dynamicsMachine learningWater-solute transport models

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Area of Science:

  • Environmental Science
  • Geochemistry
  • Computational Modeling

Background:

  • Regional soil heavy metal(oid)s (HMs) assessment is challenged by difficulties in linking spatiotemporal processes and quantifying fluxes without extensive data.
  • Existing methods lack the ability to accurately simulate long-term soil HM dynamics and predict environmental carrying capacity.

Purpose of the Study:

  • To develop and apply an integrated modeling framework for simulating soil HM dynamics and assessing environmental risks at a regional scale.
  • To quantify HM input and output fluxes, predict changes in soil carrying capacity, and identify high-risk areas.

Main Methods:

  • Coupling a multi-source emission inventory, AERMOD atmospheric dispersion model, and HYDRUS-based water-solute transport module.
  • Incorporating empirical approaches for output processes and a machine learning module for calibrating net HM fluxes.
  • Application to an industrial agglomeration in Zhejiang province, China, for long-term simulation.

Main Results:

  • Atmospheric deposition of HMs increased with industrialization, while agricultural inputs decreased, with high deposition zones near point sources.
  • Leaching and soil erosion were dominant HM loss pathways; runoff increased with impervious surfaces.
  • A significant decline in soil environmental carrying capacity was predicted within 50 years, with expanding overload zones for As, Cu, Cd, Pb, and Hg.

Conclusions:

  • The developed framework effectively simulates soil HM dynamics, identifies key input/output pathways, and predicts future environmental risks.
  • Arsenic and copper pose significant ecotoxicity risks in forestland; carrying capacity declined sharply in cultivated and residential lands.
  • The framework serves as a robust tool for emission source tracing, risk zoning, and regional environmental management.