05 Oct 2022
05 Oct 2022
Status: this preprint is currently under review for the journal AMT.

An evaluation of the U.S. EPA’s correction equation for Purple Air Sensor data in smoke, dust and wintertime urban pollution events

Daniel Jaffe1,2, Colleen Miller1,a, Katie Thompson1, Manna Nelson3, Brandon Finley1, James Ouimetteb, and Elisabeth Andrews4,5 Daniel Jaffe et al.
  • 1University of Washington Bothell, School of STEM, Bothell, WA 98011
  • 2University of Washington Seattle, Department of Atmospheric Sciences, Seattle, WA 98195
  • 3Seattle University, Department of Civil and Environmental Engineering, Seattle, WA 98122
  • 4NOAA Global Monitoring Laboratory, Boulder, CO 80305
  • 5Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309
  • anow at: University of California-Irvine, Department of Chemistry, Irvine, CA 92697
  • bprivate address: Santa Rosa, CA 95409

Abstract. PurpleAir Sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations and are now widely used, especially in regions with few regulatory monitors. However, the raw PAS data have significant biases, so the sensors must be calibrated to generate accurate data. The U.S. EPA recently developed a national correction equation and have integrated corrected PAS data onto its AirNow website. This integration results in much better spatial coverage for PM2.5 across the U.S. The goal of our study is to evaluate the EPA correction equation for three different types of aerosols: typical urban wintertime aerosol, smoke from biomass burning, and mineral dust.

We identified 50 individual pollution events, each having a peak hourly PM2.5 concentration of at least 47 µg m-3 and a minimum of 3 hours over 40 µg m-3 and characterize the primary aerosol type as either typical urban, smoke or dust. For each event, we paired an PAS sampling outside air with a nearby regulatory PM2.5 monitor to evaluate the agreement. All 50 events show statistically significant correlations (R values between 0.71–1.00) between the hourly PAS and regulatory data, but with varying slopes. Using the standard EPA correction for the typical urban and smoke aerosols, we find average slopes of 1.00 and 0.99, respectively. This means that the standard EPA correction is highly effective at generating accurate data for these aerosol types. For heavy smoke events, we find a small change in the slope at very high PM2.5 concentrations (>600 µg m-3), suggesting a ~20 % under-estimate in the corrected PAS data at these extremely high concentrations. For dust events, while the PAS and regulatory data still show significant correlations, the PAS data using the standard correction underestimates the true PM2.5 by a factor of 5–6.

We also examined several years of co-located regulatory and PAS data from a site near Owens Lake, CA, which experiences high concentrations of PM2.5 due to both smoke and dust. For this site we find similar results as above; the PAS corrected data are accurate in smoke, but are too low by a factor of 5–6 in dust. Using these data we also find that the ratios of PAS measured PM10 to PM1 mass and 0.3 µm to 5 µm particle counts are significantly different for dust compared to smoke. Given the ability of the PAS data to identify dust aerosols, we propose a modified correction algorithm that significantly improves the PAS data for dust events.

Daniel Jaffe et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-265', Anonymous Referee #1, 26 Oct 2022
  • RC2: 'Comment on amt-2022-265', Anonymous Referee #2, 05 Nov 2022

Daniel Jaffe et al.

Daniel Jaffe et al.


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Short summary
PurpleAir Sensors (PAS) are low-cost tools to measure fine particulate matter (PM) concentrations. However, the raw PAS data have significant biases, so the sensors must be corrected. We analyzed data from numerous sites and found that the standard correction to the PAS Purple Air data is accurate in urban pollution events and smoke events, but leads to a 6 fold underestimate in the PM2.5 concentrations in dust events. We propose a new correction algorithim to address this problem.