The InMAP configuration used here leverages pre-processed physical and chemical information from the output of GEOS-Chem and uses a variable spatial-resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than conventional CTMs, yet with spatial resolution that is higher than is typically possible using a conventional CTM for a given domain. Conventional CTMs create a three-dimensional Eulerian grid and simulate changes in pollutant concentration in each cell at a high temporal resolution (<1 hour) based on physical transport via wind flow and plume rise, emissions, physical removal (e.g., deposition), and interdependent non-linear physico-chemical transformation pathways. In contrast, InMAP uses time-averaged transport and reaction rates in its algorithms for emission, plume rise, transport, transformation, and removal of atmospheric pollution. To reduce computational intensity, the algorithms are in some cases simplified relative to similar algorithms in a conventional CTM; these simplified representations are calibrated using output from a conventional CTM (GEOS-Chem). This is the first detailed application of InMAP to global cities, but it has previously been used to study PM2.5 air pollution in the US, including racial-ethnic disparities in exposure , and exposure to air pollution from agriculture , electricity , and freight . Further details regarding the InMAP model, including model formulation and performance evaluation, for the US and globally, are described in detail elsewhere ).