Predicting the Geospatial Distribution of Urban Population Health Risks Based on Air Pollutants, Climate, and Health Facilities in African Cities

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Abstract
Air pollution and inadequate or poorly equipped health infrastructure are drivers in determining the health risks of urban populations globally. The spatial impacts of these drivers on the health risks of densely populated areas remain unexplored, particularly in the global south. Air pollutants such as fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), tropospheric ozone (O3), and nitrogen dioxide (NO2) vary spatially along gradients in maximum temperature (Tmax), relative humidity (RH), and wind speed (WS). The current study spatially predicts the combined impact of air pollutants within varying climatic conditions (Tmax, RH and WS) and the distribution of healthcare facilities on Ghana’s urban population health risk in the Greater Accra Metropolitan Area (GAMA). The study employed spatial correlation and regression kriging using district-level population density per km2 as the primary variate (dependent variable) with the air pollutants, climate, and distribution of health facilities as covariates (drivers) of the population health risk. Preliminary results revealed specific spots (areas) of high and low health risks to communities in GAMA. The Accra and Tema Metropolitan Areas and Ablekuma Central, Ablekuma North, Korle Klottey, and Ashaiman District Assemblies had high population health risk spots (>25,000 persons/km2) with a mean dry season PM2.5 of 75.0 µg m-3, five times higher than the World Health Organization (WHO) recommendation of 15 µg m-3, as the main significant driver. Other determining risk factors were monthly mean PM2.5 (36.5 µg m-3), NO (68.4 µg m-3), NO2 (70.5 µg m-3), O3 (16 ppb), SO2 (3 ppb), CO (327 ppb) and Tmax (26℃), HR (80%) and WS (9 km h-1). The concentration and spatial autocorrelation of the pollutants diminished towards peri-urban areas such as Kpone-Katamanso, Ga East, and Ga West. These results underscore the critical role of applying geospatial tools to monitoring, understanding and managing population health risks induced by air pollution and adverse climate of densely populated areas. The results highlight the need to manage and address the combined effects of air pollutants and the role of climate and inadequate health facilities in the health risks of the GAMA population with spatial precision and district-level policies. It also contributes to global efforts toward achieving spatial equity in universal healthcare coverage, aligning with the United Nations Sustainable Development Goal 3.8 and strengthening policy and practical relevance of geospatial approaches for sustainable interventions.Keywords: Urban climate condition and informatics; Environment and social challenges; Spatial dependency; Urbanization; spatial regression