This paper develops a conceptual model of vehicle trips in a region plagued by weather-dependent, mobilesource

air pollution, and numerically estimates optimal trips for one of the nation’s perennially worst air quality

regions in terms of short-term particulate matter. Based upon data-driven parameters and damage estimates,

our numerical model generates optimal values for region-wide vehicle trips and associated PM2:5 concentrations

along with their corresponding time paths. Our dataset includes a host of pertinent weather variables that determine PM2:5 concentrations both independently as well as interacted with vehicle trips. As a result, our empirical model enables us to isolate the conditions under which vehicle travel most affects air pollution levels. Our results suggest that maximizing net social welfare in the presence of mobile-source pollution requires substantial reductions in traditional, emissions-generating vehicle usage on days experiencing critical weather conditions – in the case of our study area, temperature inversions. Because they are socially optimal and targeted solely for days with temperature inversions, the estimated reductions in vehicle usage are substantially larger than those proposed by Moscardini and Caplan (2017) to attain the daily National Ambient Air Quality Standard (NAAQS) for particulate matter on an average basis. We find that the optimal reduction in vehicle trips result in corresponding particulate matter concentrations that are roughly six to 13 percent of the NAAQS.