Two decades of kilometer-scale daily PM_2.5 from satellite observations and machine learning reveal geographically diverging exposure in Ghana

Abstract. Exposure to fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 dataset reveals a unique north-south exposure disparity in Ghana, with northern regions of the country experiencing substantially higher PM_2.5 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 PM_2.5 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.