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Multi-objective machine learning for health-oriented O3 and PM2.5 control: Integrating VOC photochemical consumption

Hongyuan Jia1, Sen Yao1, Xianda Tang1

  • 1School of Smart Energy and Environment, Zhongyuan University of Technology, Zhengzhou 450007, China.

Journal of Hazardous Materials
|February 21, 2026
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Summary

Controlling compound pollution from ozone (O3) and fine particulate matter (PM2.5) in China requires understanding their synergistic formation. This study reveals volatile organic compound (VOC) reduction as the optimal strategy for improving air quality and health.

Keywords:
Multi-objective Machine Learning；SHAPOzonePM(2.5)Photochemical consumptionVOC

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Area of Science:

  • Atmospheric Chemistry and Air Pollution
  • Environmental Science and Management
  • Machine Learning in Environmental Modeling

Background:

  • Compound pollution of ozone (O3) and fine particulate matter (PM2.5) poses a significant challenge for air quality management in China.
  • Understanding the synergistic formation mechanisms of O3 and PM2.5 is crucial for developing effective control strategies.

Purpose of the Study:

  • To develop an integrated framework for assessing the synergistic formation of O3 and PM2.5.
  • To quantify atmospheric consumption of volatile organic compounds (VOCs) and identify key precursors.
  • To evaluate health-oriented emission reduction strategies for compound pollution control.

Main Methods:

  • Coupling of Observation-Based Model (OBM), multi-task machine learning (Multi-CatBoost), and Positive Matrix Factorization (PMF).
  • Application of an improved photochemical age parameterization for VOC consumption quantification.
  • Utilizing SHAP analysis for precursor importance, threshold identification, and health-oriented sensitivity analysis based on Air Quality Health Index (AQHI).

Main Results:

  • The Multi-CatBoost model achieved high predictive accuracy (R² = 0.91) and captured cross-pollutant synergies.
  • NO2 plays a dual role, promoting O3 formation at low concentrations and nitrate aerosol at higher concentrations.
  • Volatile organic compound (VOC) reduction emerged as the most effective strategy for improving AQHI across all scenarios.

Conclusions:

  • Integrated modeling framework provides a novel approach for analyzing compound pollution.
  • VOC reduction strategies offer substantial health benefits, significantly outperforming NOx reductions.
  • Industrial emissions and biomass burning are identified as priority sources for emission control.