  Considerable financial resources are allocated for measuring ambient air
pollution in the United States, yet the locations for these monitoring sites
may not be optimized to capture the full extent of current pollution
variability. Prior research on best sensor placement for monitoring fine
particulate matter (PM2.5) pollution is scarce: most studies do not span areas
larger 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 the
contiguous United States. This novel approach incorporates the variation of
PM2.5 on timescales ranging from one day to over a decade to capture air
pollution variability. We find that the mrDMD algorithm identifies
high-priority sensor locations in the western United States, but a
significantly 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 to
only 32% in the current EPA network. The mrDMD sensor locations can capture
PM2.5 from wildfires and high pollution events, with particularly high skill in
the West. These results suggest significant gaps in the current EPA monitoring
network in the San Joaquin Valley in California, northern California, and in
the Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our framework
diagnoses where to place air quality sensors so that they can best monitor
smoke from wildfires. Our framework may also be applied to urban areas for
equitable placement of PM2.5 monitors.
