Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling

While mobile, vehicle-based sampling of air quality has provided insights into pollutant distributions within cities, previous research has been limited in using observed pollution hotspots to identify and localize unknown emission sources away from the road. Such "hyperlocal source identification" requires hotspots identified through mobile sampling to be linked to emission sources by tracing atmospheric transport upwind from observed hotspots. Here we analyze almost one year's worth of pollutant observations from two Google Street View cars driven around the Salt Lake area, with repeated sampling in select neighborhoods. We then present a method to bridge the gap in hyperlocal source identification by using a high-resolution atmospheric transport model, as part of a new Stochastic Lagrangian Monte Carlo (SLMC) method to identify emission sources based on mobile observations. Case studies with both known and unknown sources are presented to illustrate the efficacy of the SLMC method in hyperlocal source identification. With the availability of techniques like SLMC, we envision mobile air quality monitoring vehicles can serve as "roving sentinels" in identifying unknown pollution sources.