Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor.

Andrea L. Clements,Karoline K. Barkjohn,Brett Gantt

doi:10.5194/amt-14-4617-2021

Andrea L. Clements, Karoline K. Barkjohn

Open Access

https://doi.org/10.5194/amt-14-4617-2021

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Abstract

PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40% in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 μg m-3 to 3 μg m-3 with an average FRM or FEM concentration of 9 μg m-3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

Highlights

Fine particulate matter (PM2.5, the mass of particles with aerodynamic diameters smaller than 2.5 μm) is associated with a number of negative health effects (Schwartz et al, 1996;Pope et al, 2002;Brook et al, 2010)
From this list of public PurpleAir sensors potentially collocated with regulatory PM2.5 monitors, we reached out to the appropriate SLT air monitoring agency to understand if these units were operated by the air monitoring agency and their interest in partnering in this research effort
We first considered a number of redundant parameters using linear regression; once we selected the parameters that explained the most variance, we considered a number of increasingly complex models where parameters were included as additive terms with coefficients or where they were multiplied with each other to form more complex models

Summary

Introduction

Fine particulate matter (PM2.5, the mass of particles with aerodynamic diameters smaller than 2.5 μm) is associated with a number of negative health effects (Schwartz et al, 1996;Pope et al, 2002;Brook et al, 2010). In addition 35 to health effects, PM2.5 can harm the environment, reduce visibility, and damage materials and structures (Al-Thani et al, 2018;Ford et al, 2018). Understanding PM2.5 at fine spatial and temporal resolutions can help mitigate risks to human health and the environment, but the high cost and complexity of conventional monitoring networks can limit network density (Snyder et al, 2013;Morawska et al, 2018)

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