Impact of wildfire on particulate matter in the southeastern United States in November 2016

Unveiling the Impact of Wildfires on Nanoparticle Characteristics and Exposure Disparities through Mobile and Fixed-Site Monitoring in Toronto, Canada

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign and fixed-site measurements during the unprecedented 2023 wildfire season. Our results reveal changes in particle characteristics during wildfire days, with particle number concentrations decreasing by 60% and particle diameter increasing by 30% compared to nonwildfire days. Moreover, the median lung deposited surface area (LDSA) levels rose by 31% during wildfire events. We employed gradient boosting models to estimate near-road LDSA levels on both wildfire and nonwildfire days. The LDSA ratio (wildfire/nonwildfire) exceeded 2.0 in certain areas along highways and in downtown Toronto. Furthermore, our findings show that marginalized communities faced greater LDSA increases than less marginalized ones. Under wildfire conditions, the LDSA ratio difference between the most and least marginalized groups was 16% for recent immigrants and visible minorities and 7% for seniors and children, both statistically significant. This study delivers critical insights into the spatiotemporal variations of nanoparticle characteristics during wildfire and nonwildfire periods, demonstrating the substantial health risks posed by increased LDSA levels and the inequitable distribution of these risks among Toronto's diverse population.

Evaluating the ability of the Wind Erosion Prediction System (WEPS) to simulate near-surface wind speeds in the Inland Pacific Northwest, USA

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil-vegetation-atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly.

Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 in the contiguous U.S

Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM_2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM_2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM_2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.

Striking impacts of biomass burning on PM2.5 concentrations in Northeast China through the emission inventory improvement

Biomass burning exerts substantial influences on air quality and climate, which in turn to further aggravate air quality. The biomass burning emissions in particular of the agricultural burning may suffer large uncertainties which limits the understanding of their impact on air quality. Based on an improved emission inventory of the Visible Infrared Imaging Radiometer Suite (VIIRS) relative to commonly used Global Fire Emissions Database (GFED), we thoroughly evaluate the impact of biomass burning on air quality and climate during the episodes of November 2017 in Northeast China which is rich in agriculture burning. The results first indicate substantial underestimates in simulated PM_2.5 concentrations without the inclusion of biomass burning emission inventory, based on a regional air quality model Weather Research and Forecasting model and Community Multiscale Air Quality model (WRF-CMAQ). The addition of biomass burning emissions from GFED then reduces the bias to a certain extent, which is further reduced by replacing the agricultural fires data in GFED with VIIRS. Numerical sensitivity experiments show that based on the improved emission inventory, the contribution of biomass burning emissions to PM_2.5 concentrations in Northeast China reaches 32%, contrasting to 15% based on GFED, during the episode from November 1 to 7, 2017. Aerosol direct radiative effects from biomass burning are finally elucidated, which not only reduce downward surface shortwave radiation and planetary boundary layer height, but also affect the vertical distribution of air temperature, wind speed and relative humidity, favorable to the accumulation of PM_2.5. During November 1-7, 2017, the mean daily PM_2.5 enhancement due to aerosol radiative effects from VIIRS_G is 16 μg m^-3, a few times higher than that of 2.8 μg m^-3 from GFED. The study stresses the critical role of biomass burning, particularly of small fires easily missed in the traditional low-resolution satellite products, on air quality.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Impact of Wildfires on Meteorology and Air Quality (PM2.5 and O3) over Western United States during September 2017

In this study, we investigated the impact of wildfires on meteorology and air quality (PM2.5 and O3) over the western United States during the September 2017 period. This is done by using Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate scenarios with wildfires (base case) and without wildfires (sensitivity case). Our analysis performed during the first half of September 2017 (when wildfire activity was more intense) reveals a reduction in modelled daytime average shortwave surface downward radiation especially in locations close to wildfires by up to 50 W m−2, thus resulting in the reduction of the diurnal average surface temperature by up to 0.5 °C and the planetary boundary layer height by up to 50 m. These changes are mainly attributed to aerosol-meteorology feedbacks that affect radiation and clouds. The model results also show mostly enhancements for diurnally averaged cloud optical depth (COD) by up to 10 units in the northern domain due to the wildfire-related air quality. These changes occur mostly in response to aerosol–cloud interactions. Analysis of the impact of wildfires on chemical species shows large changes in daily mean PM2.5 concentrations (exceeding by 200 μg m−3 in locations close to wildfires). The 24 h average surface ozone mixing ratios also increase in response to wildfires by up to 15 ppbv. The results show that the changes in PM2.5 and ozone occur not just due to wildfire emissions directly but also in response to changes in meteorology, indicating the importance of including aerosol-meteorology feedbacks, especially during poor air quality events.

Potential impacts of prescribed fire smoke on public health and socially vulnerable populations in a Southeastern U.S. state

Prescribed fire is an essential tool for wildfire risk mitigation and ecosystem restoration in the Southeastern United States. It is also one of the region's largest sources of atmospheric emissions. The public health impacts of prescribed fire smoke, however, remain uncertain. Here, we use digital burn permit records, reduced-complexity air quality modeling, and epidemiological associations between fine particulate matter concentrations and multiple health endpoints to assess the impacts of prescribed burning on public health across Georgia. Additionally, we examine the social vulnerability of populations near high prescribed burning activity using a demographic- and socioeconomic-based index. The analysis identifies spatial clusters of burning activity in the state and finds that areas with intense prescribed fire have levels of social vulnerability that are over 25% higher than the state average. The results also suggest that the impacts of burning in Georgia can potentially include hundreds of annual morbidity and mortality cases associated with smoke pollution. These health impacts are concentrated in areas with higher fractions of low socioeconomic status, elderly, and disabled residents, particularly vulnerable to air pollution. Estimated smoke-related health incidence rates are over 3 times larger than the state average in spatial clusters of intense burning activity, and over 40% larger in spatial clusters of high social vulnerability. Spatial clusters of low social vulnerability experience substantially lower negative health effects from prescribed burning relative to the rest of the state. The health burden of smoke from prescribed burns in the state is comparable to that estimated for other major emission sectors, such as vehicles and industrial combustion. Within spatial clusters of socially-vulnerable populations, the impacts of prescribed fire considerably outweigh those of other emission sectors. These findings call for greater attention to the air quality impacts of prescribed burning in the Southeastern U.S. and the communities most exposed to fire-related smoke.

Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management

Wildfire risks and losses have increased over the last 100 years, associated with population expansion, land use and management practices, and global climate change. While there have been extensive efforts at modeling the probability and severity of wildfires, there have been fewer efforts to examine causal linkages from wildfires to impacts on ecological receptors and critical habitats. Bayesian networks are probabilistic tools for graphing and evaluating causal knowledge and uncertainties in complex systems that have seen only limited application to the quantitative assessment of ecological risks and impacts of wildfires. Here, we explore opportunities for using Bayesian networks for assessing wildfire impacts to ecological systems through levels of causal representation and scenario examination. Ultimately, Bayesian networks may facilitate understanding the factors contributing to ecological impacts, and the prediction and assessment of wildfire risks to ecosystems. Integr Environ Assess Manag 2021;17:1168-1178. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.