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

• Published in: Atmospheric measurement techniques Vol. 14; no. 6; pp. 4617 - 4637
• Main Authors: Barkjohn, Karoline K, Gantt, Brett, Clements, Andrea L
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 22.06.2021; Copernicus Publications
• Subjects: Accuracy; Air; Air monitoring; Air pollution; Air quality; Airborne particulates; Bias; Cleaning; Data; Environmental impact; Error correction; Fires; Government; Humidity; Indoor air quality; Instruments; Outdoor air quality; Particulate emissions; Particulate matter; Performance evaluation; Public health; Regions; Relative humidity; Root-mean-square errors; Sensors; Smoke; Statistical methods; Suspended particulate matter; United States > US
• ISSN: 1867-1381; 1867-8548
• DOI: 10.5194/amt-14-4617-2021

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 h 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 (US), 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 US. A simple linear regression reduces much of this bias across most US 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 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 US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.