Tracking Air Pollution from Space: High-Resolution Daily Estimates for PM2.5, O3, NO2, and CO

Air pollution imposes severe health and economic costs worldwide, yet ground-based monitoring data are difficult and expensive to collect, limiting the information available for policy analysis. This paper develops machine learning models to estimate daily air quality using satellite observations, meteorological data, emissions inventories, and geographic information. Data sources include IQAir ground station reports; atmospheric pollutant concentrations from the European Space Agency’s tropospheric monitoring instrument, TROPOMI; and meteorological data from the Copernicus ERA5 Land database. The paper estimates separate random forest models for fine particulate matter (PM2.5), ozone, nitrogen dioxide, and carbon monoxide, using a random forest algorithm that quantifies variable importance. The results are robust: TROPOMI dominates predictions for fine particulates (PM2.5), nitrogen dioxide, and carbon monoxide, with the Emissions Database for Global Atmospheric Research and meteorological variables providing secondary contributions. For ozone, the meteorological variables are most influential, followed by TROPOMI and the Emissions Database for Global Atmospheric Research. Geographic features matter across all pollutants—elevation, latitude, and coastal proximity consistently rank high, while Köppen-Geiger climate zones vary in importance by pollutant. Temporal tracking is validated by correlating daily monitored versus predicted pollutant levels across urban areas using IQAir ground station data. Median intertemporal correlations exceed 0.69 for all pollutants, with u pper quartiles above 0.82 and lower quartiles above 0.53. Although the pilot uses data from April to November 2025, TROPOMI has been available since 2018 and the Copernicus ERA5 Land database provides matching coverage, enabling daily estimates from April 2018 onward on a global terrestrial grid at 0.1-degree resolution.