Air quality models and other decision support tools that relate pollution emissions to the resulting ambient concentrations and health impacts can be important for designing effective policies to improve air quality. Mechanistically detailed Eulerian chemical transportation models (CTMs) are commonly used for this purpose, but owing to high requirements for user training, computational resources, and input data, are often unavailable for urban-level policy analysis. Multiple reduced-complexity air quality models (RCMs) have been designed to fill this gap ). Among these RCMs, the Intervention Model for Air Pollution (InMAP; has proven useful in health impact assessment and environmental justice applications owing in part to its use of a variable spatial resolution computational grid which focuses computational effort on areas with high population density. The recent creation of a globalthrough-urban version of the InMAP model now provides the opportunity to estimate the exposure consequences of primary and secondary PM2.5 concentrations at high spatial resolution in nearly all densely populated areas globally.