  Environmental Protection Agency (EPA) air quality (AQ) monitors, the gold
standard for measuring air pollutants, are sparsely positioned across the US
due to their costliness. Low-cost sensors (LCS) are increasingly being used by
the public to fill in the gaps in AQ monitoring; however, LCS are not as
accurate as EPA monitors. In this work, we investigate factors impacting the
differences between an individual's true (unobserved) exposure to fine
particulate matter (PM2.5) and the exposure reported by their nearest AQ
instrument, which could be either an EPA monitor or an LCS. Three factors
contributing to these differences are (1) distance to the nearest AQ
instrument, (2) local variability in AQ, and (3) device measurement error. We
examine the contributions of each component to the overall error in reported AQ
using simulations based on California data. The simulations explore different
combinations of hypothetical LCS placement strategies (at schools, near major
roads, and in environmentally and socioeconomically marginalized census tracts)
for different numbers of LCS, with varying plausible amounts of LCS device
measurement error. For each scenario, we evaluate the accuracy of daily AQ
information available from individuals' nearest AQ instrument with respect to
absolute errors and misclassifications of the Air Quality Index, stratified by
socioeconomic and demographic characteristics. We illustrate how real-time AQ
reporting could be improved (or, in some cases, worsened) by using LCS, both
for the population overall and for marginalized communities specifically. This
work has implications for the integration of LCS into real-time AQ reporting
platforms.
