Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO₂ concentrations in Accra, Ghana

Abstract

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in space-time mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation, and predictions were summarized as annual and seasonal (Harmattan vs non-Harmattan) mean concentrations. The predictions were used to examine population level distribution of, and socioeconomic inequalities in, exposure at the census enumeration area. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg/m3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg/m3). Road traffic was the dominant factor for both pollutants, but NO2 had much higher spatial heterogeneity than NO. For both pollutants, the levels were much higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the WHO guideline of 10 µg/m3. Significant disparity in NO2 concentrations existed across socioeconomic gradient, with residents in the poorest communities exposed to levels about 15 µg/m3 higher compared to the richest (p < 0.001). The results showed the important role of road traffic emissions in air pollution exposure in the GAMA, with major implication for the health of the city poor. The data could support climate and health impact assessment as well as policy evaluation in the city.