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1. Residual impacts of a wildland urban interface fire on urban particulate matter and dust: a study from the Marshall Fire

   https://doi.org/10.1007/s11869-023-01376-3  

   Silberstein, Jonathan M.; Mael, Liora E.; Frischmon, Caroline R.; Rieves, Emma S.; Coffey, Evan R.; Das, Trupti; Dresser, William; Hatch, Avery C.; Nath, Jyotishree; Pliszka, Helena O.; et al (January 2023, Air Quality, Atmosphere & Health)

   The impacts of wildfires along the wildland urban interface (WUI) on atmospheric particulate concentrations and composition are an understudied source of air pollution exposure. To assess the residual impacts of the 2021 Marshall Fire (Colorado), a wildfire that predominantly burned homes and other human-made materials, on homes within the fire perimeter that escaped the fire, we performed a combination of fine particulate matter (PM2.5) filter sampling and chemical analysis, indoor dust collection and chemical analysis, community scale PurpleAir PM2.5 analysis, and indoor particle number concentration measurements. Following the fire, the chemical speciation of dust collected in smoke-affected homes in the burned zone showed elevated concentrations of the biomass burning marker levoglucosan (medianlevo = 4147 ng g−1), EPA priority toxic polycyclic aromatic hydrocarbons (median Σ16PAH = 1859.3 ng g−1), and metals (median Σ20Metals = 34.6 mg g−1) when compared to samples collected in homes outside of the burn zone 6 months after the fire. As indoor dust particles are often resuspended and can become airborne, the enhanced concentration of hazardous metals and organics within dust samples may pose a threat to human health. Indoor airborne particulate organic carbon (median = 1.91 μg m−3), particulate elemental carbon (median = .02 μg m−3), and quantified semi-volatile organic species in PM2.5 were found in concentrations comparable to ambient air in urban areas across the USA. Particle number and size distribution analysis at a heavily instrumented supersite home located immediately next to the burned area showed indoor particulates in low concentrations (below 10 μg m−3) across various sizes of PM (12 nm–20 μm), but were elevated by resuspension from human activity, including cleaning. 

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2. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes

   https://doi.org/10.1021/acs.est.0c02341  

   Kelly, Kerry E.; Xing, Wei W.; Sayahi, Tofigh; Mitchell, Logan; Becnel, Tom; Gaillardon, Pierre-Emmanuel; Meyer, Miriah; Whitaker, Ross T. (December 2020, Environmental Science & Technology)

   null (Ed.)

   Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3−7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3−21.5 μg/m3, n(normalized)-RMSE: 9−24%), the wildfire (n: 46, RMSE: 2.6−4.0 μg/m3; nRMSE: 13.1−22.9%), and the PCAP (n: 96, RMSE: 4.9−5.7 μg/m3; nRMSE: 20.2−21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency’s AirNow’s visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users. 

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3. Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape

   https://doi.org/10.3389/fbuil.2021.648620  

   Cummings, Lucas E.; Stewart, Justin D.; Reist, Radley; Shakya, Kabindra M.; Kremer, Peleg (May 2021, Frontiers in Built Environment)

   null (Ed.)

   Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m 3 (PM 1 ), 13.48 ± 5.59 μg/m 3 (PM 2.5 ), 16.13 ± 5.80 μg/m 3 (PM 10 ), and 1.56 ± 0.39 μg/m 3 (BC). We find that fine PM size fractions (PM 2.5 ) constitute approximately 84% of PM 10 and that BC comprises 11.6% of observed PM 2.5 . Air pollution hotspots across three size fractions of PM (PM 1 , PM 2.5 , and PM 10 ) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM. 

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4. Modern buildings act as a dynamic source and sink for urban air pollutants

   https://doi.org/10.1016/j.crsus.2024.100103  

   Wu, Tianren; Tasoglou, Antonios; Wagner, Danielle N; Jiang, Jinglin; Huber, Heinz J; Stevens, Philip S; Jung, Nusrat; Boor, Brandon E (May 2024, Cell Reports Sustainability)

   Science for Society Buildings account for a significant fraction of the land area in cities and actively exchange air with their proximate outdoor environments via mechanical ventilation systems. However, the direct impact of buildings on urban air pollution remains poorly characterized. Due to reductions in traffic-associated emissions of volatile organic compounds (VOCs), volatile chemical products, which are widely used inside buildings, have become a major VOC source in urban areas. Indoor-generated VOCs are likely to be an important contributor to the VOC burden of the urban atmosphere, and ventilation systems provide a pathway for VOCs to be released outdoors. Here, we show how modern buildings act as significant emission sources of VOCs for the outdoor environment. Our results demonstrate that future air quality monitoring efforts in cities need to account for direct VOC discharge from buildings in order to capture emerging sources of environmental pollution that can impact the climate and human health. Summary Urban air undergoes transformations as it is actively circulated throughout buildings via ventilation systems. However, the influence of air exchange between outdoor and indoor atmospheres on urban air pollution is not well understood. Here, we quantify how buildings behave as a dynamic source and sink for urban air pollutants via high-resolution online mass spectrometry measurements. During our field campaign in a high-performance office building, we observed that the building continually released volatile organic compounds (VOCs) into the urban air and removed outdoor ozone and fine particulate matter. VOC emissions from people, their activities, and surface reservoirs result in significant VOC discharge from the building to the outdoors. Per unit area, building emissions of VOCs are comparable to traffic, industrial, and biogenic emissions. The building source-sink behavior changed dynamically with occupancy and ventilation conditions. Our results demonstrate that buildings can directly influence urban air quality due to substantial outdoor-indoor air exchange. 

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5. PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

   https://doi.org/10.3390/rs13234788  

   Yu, Xiaohe; Lary, David J.; Simmons, Christopher S. (December 2021, Remote Sensing)

   In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations. 

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