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Abstract. The Plantower PMS5003 sensors (PMS) used in the PurpleAir monitor PA-II-SD configuration (PA-PMS) are equivalent to cell-reciprocal nephelometers using a 657 nm perpendicularly polarized light source that integrates light scattering from 18 to 166∘. Yearlong field data at the National Oceanic and Atmospheric Administration's (NOAA) Mauna Loa Observatory (MLO) and Boulder Table Mountain (BOS) sites show that the 1 h average of the PA-PMS first size channel, labeled “> 0.3 µm” (“CH1”), is highly correlated with submicrometer aerosol scattering coefficients at the 550 and 700 nm wavelengths measured by the TSI 3563 integrating nephelometer, from 0.4 to 500 Mm−1. This corresponds to an hourly average submicrometer aerosol mass concentration of approximately 0.2 to 200 µg m−3. A physical–optical model of the PMS is developed to estimate light intensity on the photodiode, accounting for angular truncation of the volume scattering function as a function of particle size. The model predicts that the PMS response to particles > 0.3 µm decreases relative to an ideal nephelometer by about 75 % for particle diameters ≥ 1.0 µm. This is a result of using a laser that is polarized, the angular truncation of the scattered light, and particle losses (e.g., due to aspiration) before reaching the laser. It is shown that CH1 is linearly proportional to the model-predicted intensity of the light scattered by particles in the PMS laser to its photodiode over 4 orders of magnitude. This is consistent with CH1 being a measure of the scattering coefficient and not the particle number concentration or particulate matter concentration. The model predictions are consistent with data from published laboratory studies which evaluated the PMS against a variety of aerosols. Predictions are then compared with yearlong fine aerosol size distribution and scattering coefficient field data at the BOS site. Field data at BOS confirm the model prediction that the ratio of CH1 to the scattering coefficient would be highest for aerosols with median scattering diameters < 0.3 µm. The PMS detects aerosols smaller than 0.3 µm diameter in proportion to their contribution to the scattering coefficient. The results of this study indicate that the PMS is not an optical particle counter and that its six size fractions are not a meaningful representation of particle size distribution. The relationship between the PMS 1 h average CH1 and bsp1, the scattering coefficient in Mm−1 due to particles below 1 µm aerodynamic diameter, at wavelength 550 nm, is found to be bsp1 = 0.015 ± 2.07 × 10−5 × CH1, for relative humidity below 40 %. The coefficient of determination r2 is 0.97. This suggests that the low-cost and widely used PA monitors can be used to measure and predict the submicron aerosol light scattering coefficient in the mid-visible nearly as well as integrating nephelometers. The effectiveness of the PA-PMS to serve as a PM2.5 mass concentration monitor is due to both the sensor behaving like an imperfect integrating nephelometer and the mass scattering efficiency of ambient PM2.5 aerosols being roughly constant.

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Abstract. The Plantower PMS5003 sensors (PA-PMS) used in the PurpleAir (PA) monitor PA-II-SD configuration are equivalent to cell-reciprocal nephelometers using a 657 nm perpendicularly polarized light source that integrates light scattering from 18 to 166 degrees. Yearlong field data at the National Oceanic and Atmospheric Administration’s (NOAA) Mauna Loa Observatory (MLO) and Boulder Table Mountain (BOS) sites show that the 1 h average of the PA-PMS first size channel, labeled “> 0.3 μm” (“CH1”) is highly correlated with submicrometer aerosol scattering coefficients at the 550 nm and 700 nm wavelengths measured by the TSI 3563 integrating nephelometer, from 0.4 Mm−1 to 500 Mm−1. This corresponds to an hourly average submicrometer aerosol mass concentration of approximately 0.2 to 200 ug m−3. A physical-optical model of the PA-PMS is developed to estimate light intensity on the photodiode, accounting for angular truncation as a function of particle size. Predictions are then compared with yearlong fine aerosol size distribution and scattering coefficient field data at the BOS site. It is shown that CH1 is linearly proportional to the model-predicted intensity of the light scattered by particles in the PA-PMS laser to its photodiode over 4 orders of magnitude. This is consistent with CH1 being a measure of the scattering coefficient and not the particle number concentration or particulate matter concentration. Field data at BOS confirm the model prediction that the ratio of CH1 to the scattering coefficient would be highest for aerosols with median scattering diameters < 0.3 μm. The PA-PMS detects aerosols smaller than 0.3 μm diameter in proportion to their contribution to the scattering coefficient. The model predicts that the PA-PMS response to particles > 0.3 μm decreases relative to an ideal nephelometer by about 75 % for particle diameters ≥ 1.0 μm. This is a result of using a laser that is polarized, the angular truncation of the scattered light, and particle loss in the instrument before reaching the laser. The results of this study indicate that the PA-PMS is not an optical particle counter and that its six size fractions are not an accurate representation of particle size distribution. The relationship between the PA-PMS 1 h average CH1 and bsp1, the scattering coefficient in Mm−1 due to particles below 1 μm aerodynamic diameter, at wavelength 550 nanometers, is found to be bsp1 = 0.015 ± 2.07 × 10−5 × CH1, for relative humidity below 40 %. The coefficient of determination R2 is 0.97. This suggests that the low-cost and widely used PA monitors can be used to measure and predict the aerosol light scattering coefficient in the mid-visible nearly as well as integrating nephelometers.

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 has integrated corrected PAS data onto its AirNow website. This integration results in much better spatial coverage for PM2.5 (particulate matter with diameters less than 2.5 µm) across the US. 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 h over 40 µg m−3 and characterized the primary aerosol type as either typical urban, smoke, or long-range transported dust. For each event, we paired a 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. We then corrected the PAS data using either the correction equation from Barkjohn et al. (2021) or a new equation that is now being used by the U.S. EPA for the AirNow Fire and Smoke Map (U.S. EPA, 2022b). Both equations do a good job at correcting the data for smoke and typical pollution events but with some differences. Using the Barkjohn et al. (2021) equation, we find mean slopes of 1.00 and 0.99 for urban and smoke aerosol events, respectively, for the corrected data versus the regulatory data. 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 % underestimate in the corrected PAS data at these extremely high concentrations. Using the new EPA equation, we find slopes of 0.95 and 0.88 for urban and smoke events, respectively, indicating a slight underestimate in PM2.5 using this equation, especially for smoke events. For dust events, while the PAS and regulatory data still show significant correlations, the PAS data using either correction equation underestimate 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, California (CA), which experiences high concentrations of PM2.5 due to both smoke and locally emitted dust. For this site, we find similar results as above; the corrected PAS 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 / PM1 mass and 0.3 µm / 5 µm particle counts are significantly different for dust compared to smoke. Using this difference, we propose a modified correction equation that improves the PAS data for some dust events, but further work is needed to improve this algorithm.