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Considerable financial resources are allocated for measuring ambient airpollution in the United States, yet the locations for these monitoring sitesmay not be optimized to capture the full extent of current pollutionvariability. Prior research on best sensor placement for monitoring fineparticulate matter (PM2.5) pollution is scarce: most studies do not span areaslarger than a medium-sized city or examine timescales longer than one week.Here we present a pilot study using multiresolution modal decomposition (mrDMD)to identify the optimal placement of PM2.5 sensors from 2000-2016 over thecontiguous United States. This novel approach incorporates the variation ofPM2.5 on timescales ranging from one day to over a decade to capture airpollution variability. We find that the mrDMD algorithm identifieshigh-priority sensor locations in the western United States, but asignificantly lower density of sensors than expected along the eastern coast,where a large number of EPA PM2.5 monitors currently reside. Specifically, 53%of mrDMD optimized sensor locations are west of the 100th meridian, compared toonly 32% in the current EPA network. The mrDMD sensor locations can capturePM2.5 from wildfires and high pollution events, with particularly high skill inthe West. These results suggest significant gaps in the current EPA monitoringnetwork in the San Joaquin Valley in California, northern California, and inthe Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our frameworkdiagnoses where to place air quality sensors so that they can best monitorsmoke from wildfires. Our framework may also be applied to urban areas forequitable placement of PM2.5 monitors.

A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States