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

Key Points

• Effective machine learning can optimize control of ozone and PM2.5 levels in urban settings.
• Key performance indicators reveal significant improvements in air quality management with these strategies.
• Utilizing source apportionment techniques, insights on pollutant origins enhance the overall machine learning framework.
• This intervention may enable better air quality policies, though external validation is necessary.