Health impact assessment of air pollutants in simulated temperature scenarios in the largest coal mining region of Brazil

Abstract

This study examines the impact of projected temperature increases on air pollutant levels and associated health outcomes in Candiota, Brazil, a major coal mining region. Using meteorological and air pollutant data (PM2.5, PM10, O3) and machine learning, we simulated pollutant levels under 2 °C and 4 °C increases and assessed the consequent health impacts. Results indicate increases in O3, PM10, and PM2.5 levels across all investigated cities, with marked seasonal differences, leading to higher mortality and hospitalizations due to cardiovascular and respiratory diseases. Underscoring the need for targeted interventions in coal mining areas as climate change exacerbates air pollution health impacts.

Keywords

• Candiota
• Machine learning
• Predictive models
• Climate change