Ambient air pollution — driven by emissions from transport, industry, power generation, and biomass burning — is a major source of health damage and economic loss, particularly in developing countries. Evidence from the World Health Organization and global monitoring initiatives such as IQAir shows that rapid urban growth has pushed ambient concentrations of fine particulates (PM₂.₅), ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO) well beyond recommended guidelines in many regions (WHO 2021; IQAir 2025; Health Effects Institute 2024). Recent global assessments attribute 8.1 million premature deaths and 120 million disability-adjusted life years to air pollution, and approximately US$8.1 trillion in annual economic losses to PM₂.₅ exposure alone (Health Effects Institute 2024; Feng et al. 2025; World Bank 2022). Yet sparse monitoring systems in many countries limit accurate pollution exposure assessment and policy evaluation, highlighting the need for consistent, high-resolution air quality data.
What is needed? High-resolution and comparable air quality data
Reliable air pollution data are critical for effective environmental and public health policy, yet consistent, high-resolution estimates remain scarce across much of the world.
Accurate local observations are essential for identifying exposure hotspots, distinguishing local from transboundary sources, and assessing chronic exposure across populations and ecosystems. Recent advances in publicly-available satellite measurements, ground monitoring reports and computer tools now make such analyses feasible that are global in reach, near real-time frequency, and standardized in methodology.
Building on this progress, we developed and piloted a machine-learning framework to estimate daily ambient air quality by integrating satellite-measured pollutant concentrations, meteorological data, emissions flux estimates, and geographic information. The result is a harmonized dataset for each pollutant that can be updated continuously and applied consistently across countries and regions with a one-week lag. Because PM₂.₅, O₃, NO₂, and CO differ in sources, atmospheric behavior, health impacts, and mitigation pathways, pollutant-specific estimation is essential for accurate exposure assessment and targeted policy design. Estimating each pollutant separately gives decision-makers the granularity to move from general air quality concerns to specific interventions.
Our work is based entirely on open-access, freely-available information, including data from European Space Agency’s (ESA) Sentinel-5P (TROPOMI) satellite platform and thousands of IQAir ground monitoring units. For global consistency, our approach combines all needed datasets into a common 0.1° spatial grid for the pilot period April 10, 2025, to November 17, 2025.
