POPULATION-WEIGHTED ASSESSMENT OF SPATIAL PATTERNS AND POPULATION-WEIGHTED EXPOSURE TO ATMOSPHERIC POLLUTION IN NORTHWESTERN NIGERIA

Abstract

Atmospheric pollution poses a significant environmental concern in Northwestern Nigeria, where differences in population concentration, human activities, biomass combustion, and regional dust processes create unequal exposure across states. This study assessed the spatial patterns and population-weighted exposure to major atmospheric pollutants across Northwestern Nigeria. Data on PM₂.₅, PM₁, SO₂, CO, O₃, and aerosol optical depth data were from NASA atmospheric and satellite products, while population estimates were obtained from the National Bureau of Statistics.  The analysis also used Ground-based air quality observations and administrative boundary data. The datasets underwent spatial alignment, extraction, aggregation, and population weighting. Descriptive statistics assessed pollutant exposure, whereas Pearson correlation analyzed relationships between population density and PM₂.₅ exposure as well as between aerosol optical depth (AOD) and PM₂.₅. The study employed RMSE to assess the agreement between gridded and ground-based observations. Spatial analysis revealed both population exposure vulnerability and overall exposure metrics.  Model evaluation employed RMSE, R², and prediction accuracy. Kano and Sokoto recorded the highest population-weighted exposure, whereas Kebbi had the lowest. Kano combined high population density with elevated particulate exposure, whereas exposure in Sokoto and Jigawa may reflect the additional influence of Harmattan dust, biomass combustion, and regional aerosol transport. Biomass fuel users had the highest estimated exposure among the vulnerable groups assessed. Extra Trees achieved the strongest predictive performance. The study highlights that population exposure results from the combined effects of population distribution, local emissions, and regional atmospheric dynamics. It recommends source-specific emission controls, cleaner household energy, stronger air quality monitoring, and targeted interventions in persistent exposure hotspots.