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 PM_2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM_2.5 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 PM_2.5 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 PM_2.5 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.

Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plantower model PMS3003) for measuring PM _2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM _2.5 (mean ± SD) of 9±9 and 10±3 µg m ⁻³ , respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM _2.5 of 36±17 and 116±57 µg m ⁻³ during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental β attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM _2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM _2.5 increased. The precision of reference instruments (T640: ±0.5 µg m ⁻³ for 1 h; SHARP: ±2 µg m ⁻³ for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and β -attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27 % for 1 h to 9 % for 24 h) than in Durham (from 201 % to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35 % to 11 %); and (3) the higher T640–PMS3003 correlations ( R ² ≥0.63) than SHARP–PMS3003 ( R ² ≥0.25). A major RH influence was found in RTP (1 h RH =64±22 %) due to the relatively high precision of the T640 measurements that can explain up to ∼30 % of the variance in 1 min to 6 h PMS3003 PM _2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM _2.5 concentrations within ∼10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM _2.5 exceeded ∼125 µg m ⁻³ and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM _2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization.

Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM2.5) has, to date, been limited due to the cost and logistics involved with traditional monitoring approaches. New miniaturized particle sensors are a potential strategy to gather more time- and spatially-resolved data, to address data gaps in regions with limited monitoring and to address important air quality research priorities in a more cost-effective manner. This work presents field evaluations and lab testing of three models of low-cost (< $200) PM sensors (SHINYEI: models PPD42NS, PPD20V, PPD60PV) in three locations: urban background (average PM2.5: 8 µg m–3) and roadside in Atlanta, Georgia, USA (average PM2.5: 21 µg m–3), and a location with higher ambient concentrations in Hyderabad, India (average PM2.5: 72 µg m–3). Sensor measurements were compared against reference monitors in the lab using one-minute averages and in field locations using one-hour averages. At the Atlanta sites the sensors were weakly correlated with a tapered element oscillating microbalance (TEOM) at best (R2 ≤ 0.30). In Hyderabad, the PPD20V sensors had the highest correlation with the environmental beta attenuation monitor (E-BAM) (R2 > 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R2 = ~0, < 40 µg m–3). The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications (< ~250 µg m–3). These field-testing results provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions. The inconsistent performance results underscore the need for rigorous evaluation of optical particle sensors in the laboratory and in diverse field environments.

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m³). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m³, transitioning to a quadradic fit above 400 µg/m³. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes.

Air pollution, particularly fine particles of 2.5 μm or smaller in diameter (PM_2.5), is a known risk factor for asthma exacerbation.¹ Elevated PM_2.5 levels are found in polluted atmospheres throughout the world and numerous indoor environments where tobacco smoke is present or solid fuels are used for cooking or heating^2,3; PM_2.5 can penetrate the lung deeply to deposit in small airways^4-6 that have a physiology very important in asthma.⁷ Anatomically defined as the airways with inner diameters smaller than 2 mm, the small airways cumulatively represent a substantially greater cross-sectional area than the large airways.⁸ Compromised small airways contribute substantially to airflow obstruction^9-11 and airway inflammation^12-14 in asthma. Although PM_2.5 exposure has been associated with increased oxidative stress and pulmonary inflammation,^1,15,16 to our knowledge, no studies have examined whether reducing PM_2.5 exposure can be associated with improvements in small airway physiology in individuals with asthma.