Next, we estimate, for each city, the same two parameters as in the first step (total PM2.5 and contributions from each sector) but in this case only considering within-city emissions (i.e. emissions originating within a city boundary developed in partnership with city officials for each of the 96 cities). For these city simulations, we use a different InMAP configuration designed to take advantage of the fact that all emissions are in the relatively small area of one city; this allows us to reduce computational expense while maintaining high spatial resolution in areas where it is beneficial. Specifically, for each city we use a modeling spatial domain of 20×25 degrees (~2220×2775 km 2 , which is ~1% of the surface area of the earth), centered at the center of the city in question. This size was chosen with the expectation of capturing >90% of the total exposure to PM2.5 pollution resulting from emissions originating in the city . We use maximum and minimum grid cell sizes of 2×2.5 degrees and 0.01×0.013 degrees (~220×280 km 2 and ~1.2×1.4 km 2 at the equator). As expected, these individual city simulations often end up with higher spatial resolution in the city in question (relative to the global simulations) owing to the decreased overall population in the model spatial domain as compared to the global simulation. The resulting 1,152 individual-city simulations (i.e., 96 cities × 12 source sectors) each take ~2-30 hours on a circa-2019 computer with 8 CPU cores, depending on the population density of the region in question.