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

Abstract

Wildfires have become an important source of particulate matter (PM_2.5 < 2.5-µm diameter), leading to unhealthy air quality index occurrences in the western United States. Since people mainly shelter indoors during wildfire smoke events, the infiltration of wildfire PM_2.5 into indoor environments is a key determinant of human exposure and is potentially controllable with appropriate awareness, infrastructure investment, and public education. Using time-resolved observations outside and inside more than 1,400 buildings from the crowdsourced PurpleAir sensor network in California, we found that the geometric mean infiltration ratios (indoor PM_2.5 of outdoor origin/outdoor PM_2.5) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM_2.5 nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.

Keywords: PM2.5; biomass burning; exposure; indoor air; low-cost PM2.5 sensors.

Fig. 1.

Number of publicly accessible indoor PurpleAir sensors in the United States and California. The shadings show major wildfire periods (start date to containment date of fires with >50,000 total acres burned) in California. Wildfire periods are from the Cal Fire website (https://www.fire.ca.gov/incidents/).

Fig. 2.

Relationship of indoor and outdoor PM_2.5 for an example house. (A) Scatterplots of calibrated PM_2.5 measured at 10-min resolution by an indoor PurpleAir sensor against the nearest outdoor PurpleAir measurement, differentiating fire days (red) and non-fire days (blue), illustrative of the levels of PM_2.5 pollution of buildings in the NC 2020 case. (B) Scatterplots of calibrated indoor PM_2.5 of outdoor origin against outdoor PM_2.5. (C) Concentration time profile of calibrated indoor and outdoor PM_2.5 measured by the two sensors. (D) Concentration time profile of calibrated infiltrated PM_2.5 and outdoor PM_2.5. The figures demonstrate the indoor PM_2.5 were clearly affected by the outdoor smoke, and our algorithm can effectively remove the indoor peaks because of indoor emissions.

Fig. 3.

Distribution of the indoor/outdoor ratio and the infiltration ratio in the San Francisco Bay Area in August and September 2020. (A) Mean indoor/outdoor PM_2.5 ratio of buildings during fire days and non-fire days and (B) mean infiltrated PM_2.5/outdoor PM_2.5 ratio of buildings during fire days and non-fire days. Buildings have lower indoor/outdoor PM_2.5 ratio and infiltration ratio on fire days.

Fig. 4.

Violin plots of particle infiltration ratios during fire and non-fire periods. n = 1,274 buildings, 2.1 × 10⁶ sensor hours for NC 2020, n = 115 buildings, 2.8 × 10⁵ sensor hours for SC 2020 and n = 52 buildings, 4.4 × 10⁴ sensor hours for NC 2018. Each violin plot shows the probability density of the infiltration ratio and a boxplot of interquartile range with whiskers extended to 1.5 times the interquartile range. Circles indicate the median, and horizontal lines indicate the mean.

Fig. 5.

Frequency distribution of indoor PM_2.5 total loss rate constants (λ_t) in buildings in the San Francisco Bay Area on the fire days and non-fire days in August to September 2020 (decay peaks were found in n = 1,000 buildings). A reduced total PM_2.5 loss rate constant on the fire days indicates a reduction in ventilation.