article · Asian Journal of Research and Reviews in Physics
Estimating ground-level fine particulate matter (PM2.5) in data-sparse areas like West Africa is difficult due to scarce monitoring stations and variable atmospheric conditions. Research evaluated statistical and machine-learning models across seven monitoring stations covering diverse ecological zones in Nigeria. Using satellite-derived aerosol optical depth, meteorological factors, gaseous pollutants, and temporal features, models including Ordinary Least Squares, Random Forest, XGBoost, Long Short-Term Memory networks, and stacked ensembles were compared. Random Forest delivered the highest predictive accuracy, achieving the lowest regional mean root mean square error of 10.56 micrograms per cubic metre and leading at four stations. MERRA-2 reanalysis showed considerably larger errors. Model performance varied by season and region, proving more stable along the Guinea Coast than in the Sahel. Tree-based machine-learning methods offer reliable air quality predictions where ground data are sparse.
Accurate air quality data is essential for managing pollution and safeguarding public health. In regions with few monitoring stations, standard global reanalysis tools can be unreliable. Developing verified machine-learning models that utilise satellite and meteorological data offers a dependable way to track harmful particulate pollution, assess population exposure, and design targeted pollution control policies in data-scarce settings.
The framework could enable air quality assessment tools, exposure estimation services, and evidence-based environmental management systems for public authorities and environmental monitoring organisations. Judged against the abstract, this represents applied and tested computational research evaluated across real-world monitoring stations in Nigeria, making it suitable for integration into regional air quality forecasting platforms or environmental compliance programmes.
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Accurate estimation of fine particulate matter (PM2.5) remains a major challenge in data-sparse regions such as West Africa because of limited ground-based monitoring networks and highly variable atmospheric conditions. This study evaluated statistical and machine-learning models for predicting ground-level PM2.5 concentrations across seven monitoring stations representing diverse ecological zones in Nigeria. Predictor variables included satellite-derived aerosol optical depth (AOD), meteorological parameters, gaseous pollutants, and temporal features. Ordinary Least Squares (OLS), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and stacked ensemble models were developed and evaluated using a consistent time-based training and testing strategy. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r), bias, normalized mean bias (NMB), and the Seasonal Stability Index (SSI). The results revealed substantial spatial and seasonal variability in model performance across Nigeria. Random Forest consistently achieved the highest predictive accuracy, producing the lowest regional mean RMSE (10.56 µg m⁻3) and ranking as the best-performing model at four of the seven monitoring stations. OLS demonstrated competitive performance in several locations, indicating that linear relationships remained important under certain environmental conditions, whereas XGBoost and LSTM generally exhibited lower predictive performance. In contrast, the MERRA-2 reanalysis dataset showed considerably larger prediction errors than the developed models. Seasonal analysis further demonstrated that model performance varied across ecological zones, with greater instability observed in the Sahel and more consistent predictions in the Guinea Coast. Overall, the findings demonstrated that ensemble tree-based machine learning models provided robust and reliable PM2.5 predictions in Nigeria and outperformed conventional statistical models and coarse-resolution reanalysis products. The proposed framework provides a practical approach for improving air quality assessment, exposure estimation, and evidence-based air pollution management in Nigeria and other data-sparse regions.
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DOI: 10.9734/ajr2p/2026/v10i3238
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