Wildfires have become an important source of particulate matter (PM2.5 < 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 PM2.5 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 PM2.5 of outdoor origin/outdoor PM2.5) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM2.5 nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.

<p>Wildfires have become the dominant 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_2.5into 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 over 1400 buildings from the crowdsourced PurpleAir sensor network in California, we found that infiltration ratios (indoor PM­_2.5of outdoor origin/outdoor PM_2.5) were reduced on average from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, mean indoor concentration of PM_2.5nearly tripled during wildfire events, with lower infiltration in newer buildings and those utilizing air conditioning or filtration. </p>

polluted seasons in Delhi when PM 1 concentrations steadily increase throughout the season and can exceed 1000 µgm -3 during episodic events. Positive matrix factorization on the organic aerosol (OA) spectrum suggests comparable seasonal average contributions from HOA (Hydrocarbon-like OA), BBOA (Biomass-Burning OA) and OOA (Oxidized-OA), with BBOA dominating during episodic events. We demonstrate the influence of regional sources such as agricultural burning during this season through temporal trends of pollutants, PMF factors, meteorology, and non-parametric wind regression analysis. We use inorganic fragment ratios to show the influence of metals during the festival of Diwali. Furthermore, we demonstrate the influence of transitioning meteorology in governing PM 1 composition through the season. Overall, our analysis provides novel insights into the factors controlling PM 1 during one of the most polluted seasons in Delhi.

We assess impacts of the 2020 COVID-19 lockdown on ambient air quality in Delhi, building on over three years of real-time measurements of black carbon (BC) and nonrefractory submicrometer aerosol (NR-PM1) composition from the Delhi Aerosol Supersite and public data from the regulatory monitoring network. We performed source apportionment of organic aerosol (OA) and robust statistical analyses to differentiate lockdown-related impacts from baseline seasonal and interannual variability. The primary pollutants NO x , CO, and BC were most reduced, primarily due to lower transportation emissions. Local and regional emissions such as agricultural burning decreased during the lockdown. PM2.5 declined but remained well above WHO guidelines. Despite the lockdown, NR-PM1 changed only moderately compared to prior years. Differences in the trends of hydrocarbon-like OA and BC suggest that some sources of primary aerosol may have increased. Despite notable reductions in some primary pollutants, the lockdown restrictions led to rather small perturbations in the primary fraction of NR-PM1, with secondary aerosol continuing to dominate. Overall, our results demonstrate the impact of secondary and primary pollution on Delhi’s air quality and show that large changes in emissions within Delhi alone are insufficient to bring about needed improvements in air quality.

Abstract. We report on the long-term performance of a popular low-cost PM2.5 sensor, the PurpleAir PA-II, at multiple sites in India, with the aim of identifying robust calibration protocols. We established 3 distinct sites in India (North India: Delhi, Hamirpur; South India: Bangalore), where we collocated PA-II with reference beta-attenuation monitors to characterize sensor performance and to model calibration relationships between PA-IIs and reference monitors for hourly data. Our sites remained in operation across all major seasons of India. Without calibration, the PA-IIs had high precision (Normalized Root Mean Square Error [NRMSE] among replicate sensors ≤ 15 %) and tracked the overall seasonal and diurnal signals from the reference instruments well (Pearson’s r ≥ 0.9) but were inaccurate (NRMSE ≥ 40 %). We used a comprehensive feature selection process to create optimized site-specific calibrations. Relative to the uncalibrated data, parsimonious least-squares long-term calibration models improved PA-II performance at all sites (cross-validated NRMSE: 20–30 %, R2: 0.82–0.95), particularly by reducing seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed by site. Likewise, using a moving-window calibration, we find a calibration scheme using seasonally specific information somewhat improves performance relative to a static long-term calibration model. In contrast, we demonstrate that a successful short-term calibration exercise for one season may not transfer reliably to other seasons. Overall, we demonstrate how the PA-II, when paired with a careful calibration scheme, can provide actionable information on PM2.5 in India with only modest irreducible uncertainty.

Wildfires have become the dominant source of particulate matter (PM2.5, < 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 PM2.5 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 over 1400 buildings from the crowdsourced PurpleAir sensor network in California, we found that infiltration ratios (indoor PM2.5 of outdoor origin/outdoor PM2.5) were reduced on average from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, mean indoor concentration of PM2.5 nearly tripled during wildfire events, with lower infiltration in newer buildings and those utilizing air conditioning or filtration. Significance StatementWildfires have become the dominant source of particulate matter in the western United States.Previous characterizations of exposure to wildfire smoke particles were based mainly on ambient concentration of PM2.5. Since people mainly shelter indoors during smoke events, the infiltration of wildfire PM2.5 into indoor environments determines exposure. We present analysis of infiltration of wildfire PM2.5 into more than 1400 buildings in California using more than 2.4 million sensor-hours of data from the PurpleAir low-cost sensor network. Findings reveal that infiltration of PM2.5 during wildfire days was substantially reduced compared with non-fire days, related to people's behavioral change. These results improve understanding of exposure to wildfire particles and facilitate informing the public about effective ways to reduce their exposure.

The complexity of urban boundary layer dynamics poses challenges to those responsible for the design and regulation of buildings and structures in the urban environment. Lidar systems in the New York City Metropolitan region have been used extensively to study urban boundary layer dynamics. These systems, in conjunction with other sensing platforms can provide an observatory to perform research and analysis of turbulent and inclement weather patterns of interest to developers and agencies.

Table S1. Most relevant parameters selected through sequential feature selection (SFS) for each PM2.5 channel (CF1, ATM, ALT) from Delhi collocation. Parameters: relative humidity (RH), temperature (T), and dew point (D). As expected, the PM2.5 was selected as the most relevant, followed by RH features.