Two decades of kilometer-scale daily PM2.5 from satellite observations and machine learning reveal geographically diverging exposure in Ghana

Abhishek Anand , Joe A Amooli, Selina Amoah, Esi Nerquaye Tetteh, Nana Ama Browne Klutse, Clement Ackaah, Benjamin Essien, Mawuli Kingsley Amegah, Daniel McMahon Westervelt 

Exposure to fine particulate matter (PM2.5) is a major contributor to 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 gridded (1 km × 1 km) dataset for daily surface PM2.5 concentrations in Ghana from 2005 to 2025 by training multiple machine learning (ML) models built on ground-based monitoring, satellite observations, and reanalysis products for atmospheric composition and meteorological parameters. 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 PM2.5 levels. SHapley Additive exPlanations (SHAP) analysis for model predictors indicates that both meteorological variables and aerosol optical properties are key contributors to model performance. The long-term gridded PM2.5 dataset reveals a unique north-south exposure disparity in Ghana, with northern regions of the country experiencing substantially higher PM2.5 concentrations compared to the South, that may be widening over the 21-year period by over 0.2 µg m-3 yr-1. This study provides the first long-term high-resolution PM2.5 exposure levels for Ghana and presents a scalable framework for generating air quality information in data-sparse regions to support air pollution relevant health impact assessment and evidence-based mitigation policies.