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

Abstract

Exposure to fine particulate matter (PM<inf>2.5</inf>) poses a significant global health risk, yet extreme concentration patterns remain underexplored. This study estimates daily PM<inf>2.5</inf> 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 PM<inf>2.5</inf> 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 PM<inf>2.5</inf> 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. © 2025 Elsevier B.V., All rights reserved.