Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California

Liang, Yutong; Sengupta, Deep; Campmier, Mark Joseph; Lunderberg, David M.; Apte, Joshua S.; Goldstein, Allen H.

doi:10.1073/pnas.2106478118

Cited by 115 publications

(94 citation statements)

References 42 publications

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“…A study of 1400 buildings taken from the PurpleAir sensor network in two California areas during times of wildfires calculated infiltration ratios for days influenced or not influenced by fires [ 37 ]. The ratios were lower during wildfire days indicating that residents took action to protect themselves from the worsened outdoor air.…”

Section: Discussionmentioning

confidence: 99%

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Wallace¹

2022

Sensors

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Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m3, compared to approximately 3–10 µg/m3 using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.

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Section: Discussionmentioning

confidence: 99%

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Wallace¹

2022

Sensors

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“…Therefore, we have not attempted to incorporate a correction for RH. Another recent study of 1400 PurpleAir monitors in northern and southern California at times of wildfires found the RH effect was too small to include in their model [12].…”

Section: Limitationsmentioning

confidence: 99%

“…Published calibration factors based on the Plantower CF_1 algorithm, such as the value of 0.53 [12] and the EPA value of 0.54 [11], all indicate that the CF_1 algorithm overestimates PM 2.5 concentrations, perhaps by nearly a factor of 2. Similarly, the ALT-CF3 algorithm, which is not based on CF_1, is 0.55 times CF_1.…”

Section: Comparison With Other Algorithms and Effect On The Aqimentioning

confidence: 99%

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Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021

Wallace¹,

Zhao

Klepeis

2022

Sensors

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Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

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“…Prior studies on particulate-bound PAHs have found different results, wherein outdoor air concentrations were consistently higher than indoor air concentrations. 65 − 67 Since PAHs can be present in both the vapor phase and particulate phase, 19 and vapor-phase PAHs can account for up to 86% of the cancer risk from total inhalation exposure (vapor phase and particulate matter), 20 − 23 our results provide an important consideration for public health recommendations during wildfire events.…”

Section: Resultsmentioning

confidence: 78%

Wildfire Impact on Indoor and Outdoor PAH Air Quality

Ghetu

Rohlman

Smith

et al. 2022

Environ. Sci. Technol.

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Air quality impacts from wildfires are poorly understood, particularly indoors. As frequencies increase, it is important to optimize methodologies to understand and reduce chemical exposures from wildfires. Public health recommendations use air quality estimates from outdoor stationary air monitors, discounting indoor air conditions, and do not consider chemicals in the vapor phase, known to elicit adverse effects. We investigated vapor-phase polycyclic aromatic hydrocarbons (PAHs) in indoor and outdoor air before, during, and after wildfires using a community-engaged research approach. Paired passive air samplers were deployed at 15 locations across four states. Twelve unique PAHs were detected only in outdoor air during wildfires, highlighting a PAH exposure mixture for future study. Heavy-molecular-weight (HMW) outdoor PAH concentrations and average Air Quality Index (AQI) values were positively correlated ( p < 0.001). Indoor PAH concentrations were higher in 77% of samples across all sampling events. Even during wildfires, 58% of sampled locations still had higher indoor PAH air concentrations. When AQI values exceeded 140 (unhealthy for sensitive groups), outdoor PAH concentrations became similar to or higher than indoors. Cancer and noncancer inhalation risk estimates from vapor-phase PAHs were higher indoors than outdoors, regardless of the wildfire impact. Consideration of indoor air quality and vapor-phase PAHs could inform public health recommendations regarding wildfires.

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Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California

Cited by 115 publications

References 42 publications

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021

Wildfire Impact on Indoor and Outdoor PAH Air Quality

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