Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Delp WW

PROVIDER: S-EPMC7374346 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM<sub>2.5</sub> Monitors with Optical Sensors.

Delp William W WW Singer Brett C BC

Sensors (Basel, Switzerland) 20200630 13

Air quality monitors using low-cost optical PM<sub>2.5</sub> 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 w ...[more]

PMID: 32630124

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Project description:Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 micrometers (PM2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM2.5 >12 μg/m3 occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m3), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375-880nm, were: summer - wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer - no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2-1.8), summer - wildfire smoke 1.4 (1.0-1.8), fall 1.3 (1.1-1.4), spring 1.2 (1.1-1.4), and summer - no wildfire smoke 1.2 (1.0-1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further understanding of how to interpret aethalometer data in regions with complex emissions would contribute to much-needed research in communities impacted by air pollution from agricultural as well as residential sources of combustion.

Project description:The availability of low-cost monitors marketed for use in homes has increased rapidly over the past few years due to the advancement of sensing technologies, increased awareness of urban pollution, and the rise of citizen science. The user-friendly packages can make them appealing for use in research grade indoor exposure assessments, but a rigorous scientific evaluation has not been conducted for many monitors on the open market, which leads to uncertainty about the validity of the data. Furthermore, many previous sensor studies were conducted for a relatively short period of time, which may not capture the changes this type of instrument may exhibit over time (known as sensor aging). We evaluated three monitors (AirVisual Pro, Speck, and AirThinx) in an occupied, non-smoking residence over a 12-month period in order to assess the sensors, the built-in calibrations, and the need for additional data to achieve high accuracy for long deployments. Two units of each type of monitor were evaluated in order to assess the precision between units, and a personal DataRAM (pDR-1200) with a filter was placed in the home for about 20% of the sampling period (e.g., about a week each month) to evaluate the accuracy over time. The average PM2.5 mass concentration from the periods of colocation with the pDR were 5.31 μg/m3 for the gravimetric-corrected pDR (hereafter pDR-corrected), 5.11 and 5.03 μg/m3 for the AirVisual Pro units, 13.58 and 22.68 μg/m3 for the Speck units, and 7.56 and 7.57 μg/m3 for the AirThinx units. The AirVisual Pros exhibited the best accuracy compared to the filter at about 86%, which was slightly better than the nephelometric component of the pDR compared to the filter weight (84%). The accuracies of the Speck (-174 and -405%) and AirThinx (42 and 40%) monitors were much lower. When the 1-minute averaged PM2.5 mass concentrations were categorized by air quality index (AQI), the pDR-corrected matched the AirVisual Pro, Speck, and AirThinx bins about 97, 40, and 87% of the time, respectively. The Pearson correlation coefficients (R2) between the unit pairs and the pDR were 0.90/0.90, 0.50/0.27, and 0.92/0.93 for the AirVisual Pro, Speck, and AirThinx units, respectively. The R2 between units of the same type were 0.99, 0.17, and 1.00 for the AirVisual Pro, Speck, and AirThinx, respectively. All of the monitors could achieve better accuracy by adding filter corrections and post-processing to correct for known biases in addition to the manufacturer's correction routine. Monthly calibrations yielded the highest accuracies, but nearly as high of accuracies could be achieved with only one or two calibrations for the Air Visual Pro and the AirThinx for many applications. In general, this type of new low-cost monitor shows exciting potential for use in scientific research. However, only one of the three monitors exhibited high accuracy (within 20% of the true mass concentration) without any post processing or additional measurements, so an evaluation of each monitor is essential before the data can be used to confidently evaluate residential exposures.

Project description:BackgroundThere is increasing evidence that long-term exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] may adversely impact cognitive performance. Wildfire smoke is one of the biggest sources of PM2.5 and concentrations are likely to increase under climate change. However, little is known about how short-term exposure impacts cognitive function.ObjectivesWe aimed to evaluate the associations between daily and subdaily (hourly) PM2.5 and wildfire smoke exposure and cognitive performance in adults.MethodsScores from 20 plays of an attention-oriented brain-training game were obtained for 10,228 adults in the United States (U.S.). We estimated daily and hourly PM2.5 exposure through a data fusion of observations from multiple monitoring networks. Daily smoke exposure in the western U.S. was obtained from satellite-derived estimates of smoke plume density. We used a longitudinal repeated measures design with linear mixed effects models to test for associations between short-term exposure and attention score. Results were also stratified by age, gender, user behavior, and region.ResultsDaily and subdaily PM2.5 were negatively associated with attention score. A 10 μg/m3 increase in PM2.5 in the 3 h prior to gameplay was associated with a 21.0 [95% confidence interval (CI): 3.3, 38.7]-point decrease in score. PM2.5 exposure over 20 plays accounted for an estimated average 3.7% (95% CI: 0.7%, 6.7%) reduction in final score. Associations were more pronounced in the wildfire-impacted western U.S. Medium and heavy smoke density were also negatively associated with score. Heavy smoke density the day prior to gameplay was associated with a 117.0 (95% CI: 1.7, 232.3)-point decrease in score relative to no smoke. Although differences between subgroups were not statistically significant, associations were most pronounced for younger (18-29 y), older (≥70y), habitual, and male users.DiscussionOur results indicate that PM2.5 and wildfire smoke were associated with reduced attention in adults within hours and days of exposure, but further research is needed to elucidate these relationships. https://doi.org/10.1289/EHP10498.

Dataset Information

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

Publications

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM<sub>2.5</sub> Monitors with Optical Sensors.

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