Unequal spatio-temporal distribution of population-weighted pollution extremes through deep learning

Exposure to fine particulate matter (PM2.5) poses a significant global health risk, yet extreme concentration patterns remain underexplored. This study estimates daily PM2.5 concentrations from 1980–2023, validated against the WHO ambient air quality database. An ensemble of deep learning models (CNN, LSTM, DNN) incorporating meteorological inputs achieved robust predictive accuracy (RMSE < 17.85 µg/m³, R² > 0.894). Global and regional variations in population-weighted PM2.5 extremes [average annual, annual maximum, 99th percentile, days exceeding the USEPA standard of 35.5 μg/m³ (AQI > 100) weighted by population density] were analysed. Results reveal persistently high PM2.5 extremes in China, India, and Pakistan, contrasted with declining levels in Europe and North America. Significant variability in African nations like Rwanda and Benin was also observed. 79.7% of the global population and 66.3% of land areas exceeded the USEPA annual standards (9 μg/m³). Seasonal disparities underscore region-specific pollution trends. These findings advocate for phased, locally adaptive air quality strategies, especially in low-income and emerging economies.