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Map of the IMPROVE sites used in the analysis. Site names and coordinates can be found in Table S1 in the Supporting Information. Boundaries for the regions are included. Grey shading represents the humid temperate domain; white is the dry domain.
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In this study we have evaluated the role of wildfires on concentrations of fine particle (d < 2.5 microm) organic carbon (OC) and particulate mass (PM2.5) in the Western United States for the period 1988-2004. To do this, we examined the relationship between mean summer PM2.5 and OC concentrations at 39 IMPROVE sites with a database of fires develo...
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... 3. Correlation of PM2.5 concentrations among sites. Correlation among annual summer mean (June - Aug) PM2.5 for 39 sites in the contiguous United States. A “1” indicates the correlation is significant with P e 0.05. All significant relationships had positive correlations. The regional groupings, shown in Figure 1, are highlighted with bold lines.
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... California See figure 1 Average Biomass OC Area burned ( μ g/m 3 )- (acres) burned (kg) ok ...
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... ) averaged over the Western U.S. Using the same model and satellite constrained fire emissions, Park et al. ( 22 ) estimate emissions of 0.6 Tg of OC for U.S. wildfires in 1998, which is larger than the fossil fuel emissions of OC. Park et al. ( 21 ) extended their analysis using PM data from the IMPROVE network of observations in the U.S. ( 22 ). Using the ratio of observed fine particle total carbon to “non-soil” potassium, Park et al. ( 23 ) were able to estimate the contribution of fires to total carbon concentrations at each site in the IMPROVE network for the years 2001–2004. As part of our analysis, we have derived a similar quantity and compare our results with those of Park et al. ( 23 ) in the Discussion section of this paper. The goals of this paper are to (1) identify regions where multiple sites show a high degree of correlation in seasonal mean PM2.5; (2) quantify the extent to which these sites are influenced by fires; and (3) develop quantitative relationships that can be used to predict seasonal mean PM2.5 and OC enhancements due to fires. To do this, PM2.5 and OC concentrations are correlated to the area burned and fire emissions for five regions in the Western United States. In a separate analysis, we have evaluated the role of fires on ozone (O 3 ) concentrations in the region as well ( 25 ). Area Burned and Biomass Consumption. The forest wildland fire database was developed based on reports from multiple government agencies, including the U.S. Forest Service, Bureau of Land Management, National Park Service, and Bureau of Indian Affairs. These reports incorporate approximately 90% of all wildland area burned reported for the Western U.S. ( 22 ). The database spans from 1980 through 2004 with a 1 ° × 1 ° resolution ranging from 101 ° - 125 ° West longitude and 31 ° - 49 ° North latitude ( 14 ). In each grid cell, the number of acres burned was reported for the month of the fire start date. Fires which burned over the monthly divisions were only accounted for once. In other words, a fire that burned from July 29 through August 5 would be fully assigned only to the month of July. This results from the fact that the fire reports have only consistently reported start dates. Since much of the annual area burned comes from a number of large fires, this simplification can pose a problem if monthly PM data are used. For this reason, we combined fire and PM data for the summer months of June, July, and August. These three months are responsible for 70 - 93% of annual acres burned in the Western U.S., depending on the year. One potential problem with the use of area burned for our analysis is that this does not consider variations in emissions from different biomass types: forest fires consume more biomass and emit more PM2.5 per acre burned than grass fires. Using ecosystem-specific fuel loadings and maps of the regional ecosystem type from the U.S. Forest Service ( the area burned was converted to kilograms of biomass burned in each 1 ° × 1 ° grid cell. This conversion was done assuming that fires occur with 25% high, 25% medium, and 25% low severity. The final 25% was assumed to be unburned ( 27 ). PM2.5 and OC Data. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network began making particulate matter measurements in 1988 at nearly 200 sites across the United States (( 27 ); . colostate.edu/improve/). Samples are collected for 24 h, 2–3 times per week, and analyzed for fine (particle diameter, d < 2.5 μ m) and coarse mass ( d ) 2.5–10 μ m), as well as an array of chemical species on the fine aerosol. PM2.5 is determined by filter weights and fine particle ( d < 2.5 μ m) OC is determined by a multistep thermal oxidation to CO 2 ( 27 ). We used data from 39 of these sites that have more than 10 years of observations and fall within the spatial range of the fire database. Figure 1shows a map of the 39 sites and Table S1 (in the Supporting Information) provides the full names, coordinates, and other information. Prior to 2001, an IMPROVE sample was collected at each site two days per week. Starting in 2001, this was changed to every three days. For each year, PM2.5 and OC mass concentration at each site were averaged for the summer months (June, July, and August). A total of up to 31 samples could be collected for the 3-month period, but at most sites a few samples were missed. We excluded the summer mean for a site if fewer than 11 samples were included in the average. Because of the 3 - 4 day delay between samples, some fires may be missed. In addition, even fires that are near a sampling site may be missed, depending on local winds. However, the largest fires, which make up the majority of biomass consumption in high fire years, can burn for several days or weeks ( 3 ), and therefore result in widespread PM enhancement. So we hypothesize that the regional averaged summer mean PM2.5 and OC concentrations should correlate with large fires in the Western United States. Figure 1 shows a map of the IMPROVE sites used in this analysis and the boundaries of the five regions used in our analysis. Figure 2a and b show the summer mean PM2.5 concentration in the Northern Rocky Mountain (Region 1) and Central Rocky Mountain regions (Region 2), respectively. Both also show the natural log of the biomass burned by fires within each region, which is discussed below. In Regions 1 and 2, the seasonal mean PM2.5 concentrations show excellent correspondence. Figure 3 is a correlation matrix that shows the significant correlations between summer PM2.5 at each site with all other sites. Significant correlations were defined as those with a P value less than 0.05, the correlation coefficient depends on the number of data points considered. As a point of comparison, summer mean PM2.5 data from Denali National Park in Alaska were included, and found not to be significantly correlated with any of the sites we examined in the Western United States (Denali data are not shown). Within Regions 1, 2, and 5, most sites show a significant correlation ( P < 0.05) with most other sites in the region. An exception is Crater Lake in Region 5, which shows a poor correlation with other sites in the Pacific Northwest and a better correlation with two sites in California. The significant correlation in seasonal mean PM2.5 within one region suggests that there are large-scale factors responsible for these interannual variations across the region. Regions 3 and 4 have fewer significant correlations between sites. This suggests that local influences play a greater role in explaining the interannual variations at these sites. There are also numerous significant correlations outside of one region. For example Craters of the Moon (CRMO1) and Bridger Wilderness (BRID1) show numerous significant correlations with sites in Colorado. This suggests that transport of PM from one region to the other is likely also important. Our hypothesis is that fires play a primary role in explaining the interannual variations in PM2.5 concentrations. Therefore, it is important to group sites based on their geographic proximity as well as their correlation in seasonal mean PM2.5. For Regions 1, 2, and 5, the choice of regional boundaries is fairly straightforward. However, for the Southwest and California regions, the poor correlation between sites suggests that any regional boundaries will be somewhat arbitrary. by Region. The IMPROVE sites were grouped by region, as shown in Figure 1. To evaluate the role of fires, we calculated the number of acres burned and the quantity of biomass burned (kg) in each region for each summer period. Between 1988 and 2004, these five regions accounted for 88% of the biomass burned in the entire Western U.S. The lowest percentage occurred in 1997 (34%), a year with a very low amount of burning in the Western U.S. In all other years, these five regions accounted for at least 70% of the total biomass burned in the Western U.S. For this time period, Regions 1 through 5 contained 50%, 4%, 2%, 10%, and 23%, respectively, of all biomass burned by fires each summer in the Western U.S. Regional PM2.5 and OC concentrations were calculated as the average of the individual site averages for each summer. Area burned and biomass burned were calculated as the sum over each summer period. An analysis of the area burned and biomass burned data shows that for all regions the data are not normally distributed. This reflects the fact that a few years have a much larger amount of burning. The annual PM2.5 data for each region are very close to a normal distribution. For OC, the data are also normally distributed with the exception of one outlier (Region 4, 2002), which had very high OC concentrations at several sites in the region. In this year, the sites in Northern California appear to have been strongly influenced by the Biscuit fire in Southern Oregon/Northern California. For this reason, all further calculations on both area burned and biomass burned are computed on the natural log of these quantities. Annual summer mean PM2.5 and OC concentrations along with annual fire data for each region are given in Table S2 in the Supporting Information. Figure 2a and b show time series of the summer PM2.5 concentration and biomass burned by fires for the Northern Rocky Mountain and Central Rocky Mountain regions, respectively. Table 1 gives the regression parameters for PM2.5 and OC in each region with the natural log of biomass burned and area burned. These regressions were calculated using Ordinary Least Squares (OLS), which assumes that errors in the X values are small compared to errors in Y . We also computed regressions using the reduced major axis method (RMA) method ( 30 ) and found that the slopes were significantly (30%–50%) greater. However, consideration of the data suggests that the Y values (regional mean PM concentration) do have a much greater uncertainty. This is because the regional average PM or OC ...
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... for 70 - 93% of annual acres burned in the Western U.S., depending on the year. One potential problem with the use of area burned for our analysis is that this does not consider variations in emissions from different biomass types: forest fires consume more biomass and emit more PM2.5 per acre burned than grass fires. Using ecosystem-specific fuel loadings and maps of the regional ecosystem type from the U.S. Forest Service ( the area burned was converted to kilograms of biomass burned in each 1 ° × 1 ° grid cell. This conversion was done assuming that fires occur with 25% high, 25% medium, and 25% low severity. The final 25% was assumed to be unburned ( 27 ). PM2.5 and OC Data. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network began making particulate matter measurements in 1988 at nearly 200 sites across the United States (( 27 ); . colostate.edu/improve/). Samples are collected for 24 h, 2–3 times per week, and analyzed for fine (particle diameter, d < 2.5 μ m) and coarse mass ( d ) 2.5–10 μ m), as well as an array of chemical species on the fine aerosol. PM2.5 is determined by filter weights and fine particle ( d < 2.5 μ m) OC is determined by a multistep thermal oxidation to CO 2 ( 27 ). We used data from 39 of these sites that have more than 10 years of observations and fall within the spatial range of the fire database. Figure 1shows a map of the 39 sites and Table S1 (in the Supporting Information) provides the full names, coordinates, and other information. Prior to 2001, an IMPROVE sample was collected at each site two days per week. Starting in 2001, this was changed to every three days. For each year, PM2.5 and OC mass concentration at each site were averaged for the summer months (June, July, and August). A total of up to 31 samples could be collected for the 3-month period, but at most sites a few samples were missed. We excluded the summer mean for a site if fewer than 11 samples were included in the average. Because of the 3 - 4 day delay between samples, some fires may be missed. In addition, even fires that are near a sampling site may be missed, depending on local winds. However, the largest fires, which make up the majority of biomass consumption in high fire years, can burn for several days or weeks ( 3 ), and therefore result in widespread PM enhancement. So we hypothesize that the regional averaged summer mean PM2.5 and OC concentrations should correlate with large fires in the Western United States. Figure 1 shows a map of the IMPROVE sites used in this analysis and the boundaries of the five regions used in our analysis. Figure 2a and b show the summer mean PM2.5 concentration in the Northern Rocky Mountain (Region 1) and Central Rocky Mountain regions (Region 2), respectively. Both also show the natural log of the biomass burned by fires within each region, which is discussed below. In Regions 1 and 2, the seasonal mean PM2.5 concentrations show excellent correspondence. Figure 3 is a correlation matrix that shows the significant correlations between summer PM2.5 at each site with all other sites. Significant correlations were defined as those with a P value less than 0.05, the correlation coefficient depends on the number of data points considered. As a point of comparison, summer mean PM2.5 data from Denali National Park in Alaska were included, and found not to be significantly correlated with any of the sites we examined in the Western United States (Denali data are not shown). Within Regions 1, 2, and 5, most sites show a significant correlation ( P < 0.05) with most other sites in the region. An exception is Crater Lake in Region 5, which shows a poor correlation with other sites in the Pacific Northwest and a better correlation with two sites in California. The significant correlation in seasonal mean PM2.5 within one region suggests that there are large-scale factors responsible for these interannual variations across the region. Regions 3 and 4 have fewer significant correlations between sites. This suggests that local influences play a greater role in explaining the interannual variations at these sites. There are also numerous significant correlations outside of one region. For example Craters of the Moon (CRMO1) and Bridger Wilderness (BRID1) show numerous significant correlations with sites in Colorado. This suggests that transport of PM from one region to the other is likely also important. Our hypothesis is that fires play a primary role in explaining the interannual variations in PM2.5 concentrations. Therefore, it is important to group sites based on their geographic proximity as well as their correlation in seasonal mean PM2.5. For Regions 1, 2, and 5, the choice of regional boundaries is fairly straightforward. However, for the Southwest and California regions, the poor correlation between sites suggests that any regional boundaries will be somewhat arbitrary. by Region. The IMPROVE sites were grouped by region, as shown in Figure 1. To evaluate the role of fires, we calculated the number of acres burned and the quantity of biomass burned (kg) in each region for each summer period. Between 1988 and 2004, these five regions accounted for 88% of the biomass burned in the entire Western U.S. The lowest percentage occurred in 1997 (34%), a year with a very low amount of burning in the Western U.S. In all other years, these five regions accounted for at least 70% of the total biomass burned in the Western U.S. For this time period, Regions 1 through 5 contained 50%, 4%, 2%, 10%, and 23%, respectively, of all biomass burned by fires each summer in the Western U.S. Regional PM2.5 and OC concentrations were calculated as the average of the individual site averages for each summer. Area burned and biomass burned were calculated as the sum over each summer period. An analysis of the area burned and biomass burned data shows that for all regions the data are not normally distributed. This reflects the fact that a few years have a much larger amount of burning. The annual PM2.5 data for each region are very close to a normal distribution. For OC, the data are also normally distributed with the exception of one outlier (Region 4, 2002), which had very high OC concentrations at several sites in the region. In this year, the sites in Northern California appear to have been strongly influenced by the Biscuit fire in Southern Oregon/Northern California. For this reason, all further calculations on both area burned and biomass burned are computed on the natural log of these quantities. Annual summer mean PM2.5 and OC concentrations along with annual fire data for each region are given in Table S2 in the Supporting Information. Figure 2a and b show time series of the summer PM2.5 concentration and biomass burned by fires for the Northern Rocky Mountain and Central Rocky Mountain regions, respectively. Table 1 gives the regression parameters for PM2.5 and OC in each region with the natural log of biomass burned and area burned. These regressions were calculated using Ordinary Least Squares (OLS), which assumes that errors in the X values are small compared to errors in Y . We also computed regressions using the reduced major axis method (RMA) method ( 30 ) and found that the slopes were significantly (30%–50%) greater. However, consideration of the data suggests that the Y values (regional mean PM concentration) do have a much greater uncertainty. This is because the regional average PM or OC concentration is based on only a small sample of points in each region. In a later section we estimate the uncertainty in our PM values based on the regression slope uncertainty, and these uncertainties overlap with the PM values that would be calculated by using RMA regression. Note that because the correlations are calculated on the natural log of burned area or biomass consumed, the intercepts can not be used to give information on the background concentrations in the absence of fires. Using OLS regressions, Regions 1, 2, and 4 show statistically significant relationships between PM2.5 and biomass burned. For Regions 3 and 5, a statistically significant relationship between fires and PM2.5 is not found. In Regions 1 and 2, statistically significant relationships are found using either biomass burned or area burned; however, in both cases the regressions have a greater R 2 and lower P value using biomass burned. This indicates that correction of the area burned by ecosystem type is a useful procedure to estimate the overall impact of fires in the Western U.S. In Region 4 (California) the relationship between fires and PM2.5 was relatively weak ( R 2 of 0.25). This is somewhat surprising given that, in some years, there are significant fires in the region. However, for the California sites the mean summer PM2.5 concentration over this period was 6.6 μ g/ m 3 , which is almost 2 μ g/m 3 higher than other regions. ...
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Citations
... The average summer-long surge in PM2.5 due to fires is 1.84 µg/m 3 in Forest Service Region 1 (which includes Montana) and is approximately doubled during large-fire years [30]. Since 1950, the western US has seen near exponential growth in fire frequency and size, as well as the increased occurrence of megafires (burns of more than 100,000 acres) [31]. ...
Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure.
... Such transient smoke effects were particularly evident during the 2023 Canadian wildfires, where smoke was directed southward to the Midwest and Northeastern US by coastal storms [6]. Wildfire smoke is a complex mixture of gasses and particles, yet concentrations of fine particulate matter (PM 2.5 ) are commonly used as a proxy measurement for the mixture because it is a dominant constituent in smoke and it has known deleterious impacts on the lower airways [7,8]. Wildfire smoke can increase PM 2.5 levels to several times those seen during non-wildfire periods [4,9]. ...
Wildfire activity is increasing around the world, concurrent with climate change, and mitigation strategies for protecting vulnerable populations are desperately needed. Because inhaled particles are deleterious to respiratory health, particularly among older adults with co-morbidities, we engaged maintenance personnel working in long term care facilities located in the Western US. Our objective was to identify opportunities to build resilience during wildfire smoke events. We implemented a virtual workshop that addressed wildfire smoke health impacts as well as strategies to assess and maintain indoor air quality. A total of 24 maintenance personnel attended the virtual workshop and 14 participated in a quantitative survey. Workshop attendees found value in the material and there was enthusiasm for educational resources and enhancing indoor air quality. Four months later, four maintenance staff participated in a follow-up interview. Our qualitative assessment revealed the following themes: awareness and prioritization, application of knowledge, barriers, and educational resources. Access to real-time actionable air quality data was a consistent feature across these themes. Maintenance personnel could play a key role in a facility’s ability to prepare for and respond to wildfire smoke events, and this study highlights potential challenges and opportunities to involving them in resilience building strategies.
... In all data sets, particulate air quality alert days are predominantly concentrated in California but are not exclusive to California (Figure 2). The spatial distribution of particulate air quality alert days will likely be different for different years as there is high interannual variability in wildfire driven-PM 2.5 (Jaffe et al., 2008;Spracklen et al., 2007), which is likely contributing to many of the alert days (Childs et al., 2022;David et al., 2021). ...
Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national‐level alert‐day identification relies predominately on surface monitor networks and forecasters. Satellite‐based estimates of surface air quality have rapidly advanced and have the capability to inform exposure‐reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite‐based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m⁻³) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person‐alerts (alert days × population) in 2020 over polar‐orbiting satellite data. We apply pre‐existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person‐alerts may lead to 1,200 (800–1,500) or 54% more averted PM2.5‐attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8–17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.
... 3.1.1. PM 2.5 PM 2.5 , airborne particles less than 2.5 µm in diameter [15] has been found to increase the risk of circulatory and lower respiratory diseases, particularly for children and the elderly and cardiovascular disease in adults [16]. PM 2.5 has also been linked to infant mortality and low birth weights [17]. ...
Background
Tulare County is located in the Central Valley region of California. Its population is exposed to stressors that include high levels of air, water, and soil pollution, socioeconomic strain, and poor access to walkable areas and healthy foods. As a result, this population suffers from a high disease burden compared to other California counties.
Objective
We hypothesize that environmental and socioeconomic stressors interact in complex ways to raise the burden of disease in the Tulare population beyond additive impacts.
Method
We used CalEnviroScreen to select Tulare County as the subject of the study and characterized the geographical interaction of stressors. The CalEnviroScreen indicators provided the basis for population-weighted average calculations to determine the most critical environmental and socioeconomic stressors in Tulare County. We also analyzed and interpreted walkability and dietary access through open-source data. In addition, we compared disease-based mortality in Tulare County to California state averages.
Results
Our evaluation reveals that the population living within the census tracts of Tulare County is exposed to environmental stressors at significantly higher levels relative to many other Californian census tracts, specifically for PM2.5, ozone, and drinking water quality. Relatively high exposures to socioeconomic stressors can compound resulting health impacts. We use dose-response curves and stressor mapping to characterize how multiple stressors may augment a population's vulnerability and effective doses from exposure to multiple stressors.
Significance
Previous health-impact studies have linked individual environmental stressors to their respective measures of disease. However, many communities continue to be exposed daily to numerous stressors that individually are within regulatory limits but could significantly magnify risk due to the synergistic effects. Dose-response curves tailored to population vulnerability provide a basis for quantifying the synergistic risks of multiple stressors on specific measures of disease.
... Historical fire perimeter geodatabase from the FRAP was imported to ArcGIS software and the wildfire events of Class E and above (i.e., 300 acres or more in total burnt area) were extruded out to include in the analysis. Wildfires with a total burnt area of 300 acres or more are considered large fires (National Interagency Fire Center, 2021) with a higher potential for particle pollution (Jaffe et al., 2008). Since the wildfire perimeters were sometimes spread across the county boundary, the wildfire perimeter polygons were intersected over the county boundary to get the wildfire perimeters within each county. ...
Exposure to wildfire-related smoke has serious implications for the health outcomes of outdoor occupational workers. As wildfire season is becoming more prolonged in California, outdoor building construction workers are at great risk of exposure to particulate matter (PM2.5) pollution caused by wildfire smoke. Wildfire smoke PM2.5 exposure has been shown to result in short-term health impairments, missed workdays, and long-term disease progression. Studies highlighting the exposure of outdoor building construction workers to wildfire-related PM2.5 is limited in the existing literature. Using historical wildfires data, employment data of outdoor building construction workers, and air quality data for PM2.5 pollution from 2010-2020, this paper investigates the potential exposure of outdoor building construction workers to wildfire smoke PM2.5 in California. Counties which experienced wildfire events were identified by intersecting wildfire perimeters with county boundaries using the geospatial analysis software ArcGIS. Monthly employment during wildfire months and the number of days when the PM2.5 air quality reached unhealthy levels during those months were evaluated to find the worker smoke exposure days of each county. Results show that outdoor building construction workers in California are vulnerable to wildfire PM2.5 exposure and associated health risks, as the typical wildfire season aligns with peak seasonal construction work.
... The model produced satisfactory results during the dryhot season (MAM) but failed during the rainy season (June-August). Finally, the concentrations of PM 2.5 in the study area are influenced by fire emissions, and incorporating fire data as an additional variable could enhance the model's performance during this time of year (Jaffe et al., 2008;Vega et al., 2021). ...
The Megalopolis of Central Mexico experiences high levels above the Official Mexican Standard (NOM) of PM2.5, leading to various respiratory diseases ranging from acute symptoms to chronic illnesses such as asthma and lung cancer. It is crucial to measure PM2.5 levels accurately to warn the public about the risks of exposure to particulate matter. Unfortunately, the Megalopolis of Central Mexico has a shortage of monitoring sites, limiting data availability. This study addresses this issue using satellite data to develop a multiple linear regression model. Our model uses aerosol optical depth (AOD), relative humidity (RH), temperature (T), the planetary boundary layer height (PBLH), and the normalized difference vegetation index (NDVI) as independent variables to estimate PM2.5 concentrations in the region under study. The relationship between AOD and PM2.5 concentrations was found to be strongly influenced by RH and T. However, this effect is compensated for by a low PBLH (< 400 m), which enables AOD and PM2.5 measurements to be similar in magnitude. Our findings have important implications for estimating PM2.5 concentrations using satellite data. This study could help improve air quality monitoring in the Megalopolis of Central Mexico by providing more spatial and temporal data on particle concentrations in the atmosphere.
... The arguments around the merits of using prescribed burning are complex, and a number of papers raise questions such as whether prescribed burning releases more carbon from forests than wildfires (Wiedinmyer & Hurteau, 2010), how mechanical chipping of residues prior to prescribed burning effects GHG emissions (Naeher et al., 2006) and whether forest thinning and prescribed burning help to develop wildfire resilient stands (Busse et al., 2009;Cabiyo et al., 2021). A number of the papers presented the results of regional-scale air quality monitoring associated with forest fires (e.g., Jaffe et al., 2008;May et al., 2014), while others studied the air quality impacts of specific fires, such as the 2002 McNally fire in the San Joaquin Valley (Cisneros et al., 2012), and the Rim "mega-fire" of 2000 (Navarro et al., 2016). Both wildfires and prescribed burning have impacts on emission of pollutants for which standards are set in California's NAAQS (Schweizer et al., 2017). ...
California has large and diverse biomass resources and provides a pertinent example of how biomass use is changing and needs to change, in the face of climate mitigation policies. As in other areas of the world, California needs to optimize its use of biomass and waste to meet environmental and socioeconomic objectives. We used a systematic review to assess biomass use pathways in California and the associated impacts on climate and air quality. Biomass uses included the production of renewable fuels, electricity, biochar, compost, and other marketable products. For those biomass use pathways recently developed, information is available on the effects—usually beneficial—on greenhouse gas (GHG) emissions, and there is some, but less, published information on the effects on criteria pollutants. Our review identifies 34 biomass use pathways with beneficial impacts on either GHG or pollutant emissions, or both—the “good.” These included combustion of forest biomass for power and conversion of livestock‐associated biomass to biogas by anaerobic digestion. The review identified 13 biomass use pathways with adverse impacts on GHG emissions, criteria pollutant emissions, or both—the “bad.” Wildfires are an example of one out of eight pathways which were found to be bad for both climate and air quality, while only two biomass use pathways reduced GHG emissions relative to an identified counterfactual but had adverse air quality impacts. Issues of high interest for the “future” included land management to reduce fire risk, future policies for the dairy industries, and full life‐cycle analysis of biomass production and use.
... Wild re smoke contains large quantities of ne particulate matter (PM 2.5 , airborne particles with diameters smaller than 2.5 µm), and can adversely affect regional air quality in downwind communities that are tens to hundreds of kilometers away. For instance, Jaffe et al. (2008) reported that PM 2.5 levels have increased in summer due to wild res in the western U.S. (3) and Geng et. al observed an signi cant enhancement in PM 2.5 concentrations in intensive wild re years in Colorado (4). ...
As wildfires become more frequent and intense, fire smoke has significantly worsened ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM 2.5 concentrations attributed to both fire smoke and non-smoke sources across the Continental U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke affected daily PM 2.5 concentrations at 40% of all regulatory air monitors in EPA's Air Quality System (AQS) for more than one month each year. People residing outside the vicinity of an EPA AQS monitor were subject to 36% more smoke impact days compared to those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM 2.5 concentrations to between 9 and 10 µg/m ³ would result in approximately 29% to 40% of the AQS monitors falling in nonattainment areas without taking into account the contribution from fire smoke. When fire smoke impact is considered, this percentage would rise to 35% to 49%, demonstrating the significant negative impact of wildfires on air quality.
... Wild re smoke contains large quantities of ne particulate matter (PM 2.5 , airborne particles with diameters smaller than 2.5 µm), and can adversely affect regional air quality in downwind communities that are tens to hundreds of kilometers away. For instance, Jaffe et al. (2008) reported that PM 2.5 levels have increased in summer due to wild res in the western U.S. (3) and Geng et. al observed an signi cant enhancement in PM 2.5 concentrations in intensive wild re years in Colorado (4). ...
As wildfires become more frequent and intense, fire smoke has significantly worsened ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM 2.5 concentrations attributed to both fire smoke and non-smoke sources across the Continental U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke affected daily PM 2.5 concentrations at 40% of all regulatory air monitors in EPA's Air Quality System (AQS) for more than one month each year. People residing outside the vicinity of an EPA AQS monitor were subject to 36% more smoke impact days compared to those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM 2.5 concentrations to between 9 and 10 µg/m ³ would result in approximately 29% to 40% of the AQS monitors falling in nonattainment areas without taking into account the contribution from fire smoke. When fire smoke impact is considered, this percentage would rise to 35% to 49%, demonstrating the significant negative impact of wildfires on air quality.
... The increase in wildfire activity is believed to be tied to global warming, with climate predictions for North America's west coast indicating wetter winters, drier summers, and more heat in years to come, leading to conditions conducive to large wildfires (Williams et al. 2019;Liu et al. 2010). Wildfires impact air quality, cause damage through the destruction of property and harm to health, and negatively influence atmospheric composition (Jaffe et al. 2008;Wang et al. 2021;Aguilera et al. 2021;Solomon et al. 2022). With current trends expected to continue, it is important to further the advancement of wildfire prediction models to accurately forecast wildfire spread and resulting smoke dispersion. ...
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements to improve fire spread forecasts from numerical models through data assimilation. This work develops a method for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state in a physics-informed approach. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen's coefficient of 0.81 for the fire perimeters and an average ignition time error of 32 minutes suggest that the method is highly accurate.
