Machine Learning-Based Estimation of Surface PM2.5 in Nigeria Using Integration of Satellite, Ground Observations and Reanalysis Data

Abstract

Accurate estimation of surface PM2.5 across Nigeria remains challenging because satellite AOD and global reanalysis products alone cannot adequately represent near-surface particulate concentrations owing to complex aerosol-meteorology interactions, regional transport, and limited ground observations for calibration. This study addresses these limitations by developing a multi-source data fusion framework based on the Random Forest (RF) algorithm that integrates low-cost sensor measurements, satellite observations, atmospheric composition, meteorological variables, and reanalysis data to improve PM2.5 estimation. Ground-based observations from Purple Air and Clarity sensors were combined with satellite-derived aerosol optical depth, atmospheric trace gases, meteorological variables, and MERRA-2 reanalysis products. The RF model was trained and evaluated using a spatial cross-validation (leave location out) framework and assessed using RMSE, MAE, coefficient of determination (R²), Index of Agreement (IOA), and correlation coefficient. The RF model consistently outperformed MERRA-2 across all monitoring stations, yielding substantially lower prediction errors (RMSE: 14-35 µg m⁻³ versus 30-126 µg m⁻³; MAE: 9-31 µg m⁻³ versus 18-89 µg m⁻³) and stronger agreement with observations (IOA up to 0.68). Whereas MERRA-2 produced large negative R² values at several locations, the RF model achieved improved predictive performance, including a positive R² of 0.29 in Lagos and higher correlation coefficients across most stations. Feature importance analysis identified relative humidity as the dominant predictor, followed by MERRA-2 PM2.5, O3, NO2, and AOD, highlighting the combined influence of aerosol hygroscopic growth, atmospheric chemistry, and regional transport. These findings demonstrate that multi-source machine learning data fusion substantially improves surface PM2.5 estimation over Nigeria with scalable framework...