RESUMO
This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.