Two decades of kilometer-scale daily PM 2.5 from satellite observations and machine learning reveal geographically diverging exposure in Ghana

Exposure to fine particulate matter (PM 2.5 ) is a major contributor to the global burden of disease, yet air quality data remain sparse in many low- and middle-income countries, limiting nationwide monitoring and effective policy development. We address this gap by developing a high-resolution (1 km×1 km) gridded dataset of daily surface PM 2.5 concentrations across Ghana from 2005 to 2025 by training multiple machine learning (ML) models built on ground-based monitoring, satellite observations, and reanalysis products of atmospheric composition and meteorology. Estimates from these models were evaluated with measurements from reference-grade monitors and a large network of calibrated low-cost sensors deployed across Ghana. XGBoost showed the strongest performance among all ML algorithms and best captured spatial and temporal variability in PM 2.5 levels. SHapley Additive exPlanations (SHAP) analysis identifies meteorological variables and aerosol optical properties among the important drivers of model-estimated PM 2.5 . The long-term gridded PM 2.5 dataset suggests a pronounced north-south exposure disparity in Ghana, with northern regions of the country experiencing substantially higher PM 2.5 concentrations compared to the South, and this difference may be widening at a rate of around 0.15 µg m −3 annually over the 21-year period. This study provides the first long-term high-resolution PM 2.5 exposure estimates for Ghana and presents a scalable framework for generating air quality information in data-sparse regions to support air pollution relevant health impact assessments and evidence-based mitigation policies.