It is worthwhile to consider possible uncertainties, biases, and limitations associated with our approach. Our modeling framework is built around the InMAP reduced-complexity model and its associated InMAP source-receptor matrix (ISRM, 28). The computational efficiency of this model enabled us to interactively execute thousands of unique model runs representing distinct vehicle fleets for twenty individual years, while maintaining sufficiently fine scale (down to 1 km 2 ) to capture spatially sharp exposure disparities . However, our modeling approaches have notable limitations. First, as with any atmospheric modeling, our results rest on the validity of underlying emissions inventories, including how they represent patterns over time and space (e.g., 74), as discussed briefly below. Second, InMAP makes make simplifying assumptions that can lead to somewhat higher bias than traditional chemical transport models (CTMs), which model the underlying atmospheric chemistry and dynamics with higher fidelity.