Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter
(PM2.5) concentrations in cities around the world. One of the most commonly deployed
LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States. PurpleAir
measurements are widely used by the public to evaluate PM2.5 levels in their
neighborhoods. PurpleAir measurements are also increasingly being integrated into
models by researchers to develop large-scale estimates of PM2.5. However, the change
in sensor performance over time has not been well studied. It is important to understand
the lifespan of these sensors to determine when they should be replaced, and when
measurements from these devices should or should not be used for various
applications. This paper fills in this gap by leveraging the fact that: (1) Each PurpleAir
sensor is comprised of two identical sensors and the divergence between their
measurements can be observed, and (2) There are numerous PurpleAir sensors within
~ 50 meters of regulatory monitors allowing for the comparison of measurements
between these instruments. We propose empirically-derived degradation outcomes for
the PurpleAir sensors and evaluate how these outcomes change over time. On
average, we find that the number of ‘flagged’ measurements, where the two sensors
within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of
operation. Approximately, 2 percent of all PurpleAir sensors were permanently
degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to
be in the hot and humid climate zone, suggesting that sensors in this location may need
to be replaced sooner. We also find that the bias of PurpleAir sensors, or the difference
between corrected PM2.5 levels and the corresponding reference measurements,
changed over time by -0.12 μg/m3 (95% CI: -0.13 μg/m3, -0.11 μg/m3) per year. The
average bias increases dramatically after 3.5 years. Climate zone is a significant
modifier of the association between degradation outcomes and time.
This article can be cited before page numbers have been issued, to do this please use: P. N. deSouza, K.
deSouza, P., K. Barkjohn, A. Clements, J. Lee, R.A. Kahn, B. Crawford, and P. Kinney (2023), This article can be cited before page numbers have been issued, to do this please use: P. N. deSouza, K., View Article Online, doi:10.1039/D2EA00142J.
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Research Program
Applied Sciences Program (ASP)
Atmospheric Composition
Atmospheric Composition Modeling and Analysis Program (ACMAP)