Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers

Tryner, Jessica; L’Orange, Christian; Mehaffy, John; Miller‐Lionberg, Daniel; Hofstetter, Josephine C.; Wilson, Ander; Volckens, John

doi:10.1016/j.atmosenv.2019.117067

Cited by 136 publications

(127 citation statements)

References 23 publications

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“…Correlations and adjustment factors were evaluated with the 4-h data streams. (The cf_1 and cf_atm data streams were switched in PA-II reporting at the time of the fire [45], but we have subsequently confirmed that the stream used in this study was the stream that is currently labeled as cf_1).…”

Section: Analysis Of Data From Pa-ii Monitorssupporting

confidence: 54%

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Delp

Singer

2020

Sensors

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Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44–0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire.

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Section: Analysis Of Data From Pa-ii Monitorssupporting

confidence: 54%

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Delp

Singer

2020

Sensors

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“…The PA, unlike the other sensors evaluated here, reports multiple PM 2.5 measurements corresponding to the two identical sensors (a and b) and a correction factor (CF) for indoor (CF = 1) and outdoor (CF = atm) applications. For some versions of the PA firmware, there was an inconsistency between the PA CF = 1/atm label and the label reported by the OEM sensor used in the PA package (Plantower, Beijing, China, PMS5003) [ 25 ]. In this work, we refer to the data reported with the lower concentration here as CF = atm consistent with the PMS5003, despite being labeled as CF = 1 in the PA firmware (v2.50i) of the sensors used in this study.…”

Section: Methodsmentioning

confidence: 99%

Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

Holder

Mebust

Maghran

et al. 2020

Sensors

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Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52–0.95), but overpredicted PM2.5 concentrations (normalized root mean square errors, NRMSE = 80–167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.

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“…There is a rapidly growing literature on the performance of low-cost sensors and monitors for ambient particles 24 , but fewer studies focusing on IAQ. In ambient studies, sensors are compared against regulatory monitoring equipment over weeks to months [25][26][27][28][29][30][31][32][33][34][35][36][37] , and some have proposed sophisticated algorithms to account for humidity and other environmental factors 38 . The most extensive and systematic evaluations have been done by the South Coast Air Quality Management District's Air Quality Sensor Performance Evaluation Center (AQ-SPEC) (www.aqmd.org/aqspec) using methods described in a recent paper 39 .…”

Section: -Dec-2019mentioning

confidence: 99%

Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources

Wang

Delp

Singer

2020

Building and Environment

101

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Advances in particle sensor design and manufacturing have enabled the development of lowcost air quality monitors (LCMs). The sensors use light scattering to estimate mass concentration and thus require evaluation for aerosols of varied composition and size distribution. We tested the performance of six LCMs designed for home use and having a retail price under US$300 in October 2018. We assessed their performance by comparing their output to reference PM2.5 and PM10 measurements from 21 common residential sources and from infiltrated outdoor PM2.5. Reference data were obtained by using gravimetric measurements to adjust time-resolved output from an aerosol spectrometer with both electrical mobility and optical particle sensors. Compared by linear regression to reference measurements, LCMs had negative intercepts and slopes of 1-2 BAE-D-19-02765-R1: Performance of low-cost IAQ monitors for residential PM 12-Dec-2019 Wang, Delp, and Singer 2 for infiltrated outdoor PM2.5. Semi-quantitative responses (~50-200% of actual PM2.5) were obtained for varied aerosols including minerals (ultrasonic humidifier, vacuuming, test dust); combustion products (incense, mosquito coil, extinguished candles); microwave popcorn; and cooking involving frying or grilling. LCMs had low or no response to sources for which all mass was in particles smaller than 0.25 m, including steady candle flames and cooking without frying or grilling. PM10 data from LCMs was more variable than PM2.5.

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Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers

Cited by 136 publications

References 23 publications

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources

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