The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory: Wildland fire emissions developed for the 2011 and 2014 U.S. National Emissions Inventory

Impact of the 2022 New Mexico, US wildfires on air quality and health

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM2.5: diameter ≤ 2.5 μm) and ozone (O3) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM2.5 across New Mexico was 0.3 μg/m3, though 1 h maximum exceeded 120 μg/m3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O3 (MDA8-O3) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM2.5 and MDA8-O3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15-21) and 4 (95 % CI: 3-6) deaths. Additionally, we estimate that wildfire PM2.5 was responsible for 171 (95 %: 124-217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States

BACKGROUND

Forests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.

RESULTS

The summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.

CONCLUSIONS

The WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Estimating annual GHG and particulate matter emissions from rural and forest fires based on an integrated modelling approach

Rural and forest fires represent one of the most significant sources of emissions in the atmosphere of trace gases and aerosol particles, which significantly impact carbon budget, air quality, and human health. This paper aims to illustrate an integrated modelling approach combining spatial and non-spatial inputs to provide and enhance the estimation of GHG and particulate matter emissions from surface fires using Italy as a case study over the period 2007-2017. Three main improvements characterize the approach proposed in this work: (i) the collection and development of comprehensive and accurate data inputs related to burned area; (ii) the use of the most recent data on fuel type and load; and (iii) the modelling application to estimate fuel moisture, burning efficiency, and fuel consumption considering meteorological factors and combustion phases. On average, Italy's GHG and particulate matter emissions were 2621 Gg yr-1, ranging from a minimum of 772 Gg yr-1 in 2013 to a maximum of 7020 Gg yr-1 in 2007. Emissions from fire disturbances in broadleaf forests, shrublands, and agricultural fuel types account for about 76 % of the total. Results were compared with global and national inventories and showed good agreement, especially considering CO2 and particulate matter. The approach of this study added confidence in emission estimates, and the results can be utilized in decision support systems to address air quality management and fire impact mitigation policies.

An Analysis of Prescribed Fire Activities and Emissions in the Southeastern United States from 2013 to 2020

Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.

Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m3). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m3, transitioning to a quadradic fit above 400 µg/m3. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes.

Beyond Particulate Matter Mass: Heightened Levels of Lead and Other Pollutants Associated with Destructive Fire Events in California

As the climate warms, wildfire activity is increasing, posing a risk to human health. Studies have reported on particulate matter (PM) in wildfire smoke, yet the chemicals associated with PM have received considerably less attention. Here, we analyzed 13 years (2006-2018) of PM_2.5 chemical composition data from monitors in California on smoke-impacted days. Select chemicals (e.g., aluminum and sulfate) were statistically elevated on smoke-impacted days in over half of the years studied. Other chemicals, mostly trace metals harmful to human health (e.g., copper and lead), were elevated during particular fires only. For instance, in 2018, lead was more than 40 times higher on smoke days on average at the Point Reyes monitoring station, due mostly to the Camp Fire, burning approximately 200 km away. There was an association between these metals and the combustion of anthropogenic material (e.g., the burning of houses and vehicles). Although still currently rare, these infrastructure fires are likely becoming more common and can mobilize trace metals in smoke far downwind, at levels generally unseen except in the most polluted areas of the country. We hope a better understanding of the chemicals in wildfire smoke will assist in the communication and reduction of public health risks.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018)

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM2.5 in California (2007-2018) was 5.16 μg/m3 (S.D. 4.66 μg/m3). The average concentration of fire-PM2.5 in California by year was 1.61 μg/m3 (~30% of total PM2.5). The contribution of fire-source PM2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m3. Based on model-derived fire-PM2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

Effects of wildfire smoke exposure on innate immunity, metabolism, and milk production in lactating dairy cows

Wildfires are particularly prevalent in the Western United States, home to more than 2 million dairy cows that produce more than 25% of the nation's milk. Wildfires emit fine particulate matter (PM_2.5) in smoke, which is a known air toxin and is thought to contribute to morbidity in humans by inducing inflammation. The physiological responses of dairy cows to wildfire PM_2.5 are unknown. Herein we assessed the immune, metabolic, and production responses of lactating Holstein cows to wildfire PM_2.5 inhalation. Cows (primiparous, n = 7; multiparous, n = 6) were monitored across the wildfire season from July to September 2020. Cows were housed in freestall pens and thus were exposed to ambient air quality. Air temperature, relative humidity, and PM_2.5 were obtained from a monitoring station 5.7 km from the farm. Animals were considered to be exposed to wildfire PM_2.5 if daily average PM_2.5 exceeded 35 µg/m³ and wildfire and wind trajectory mapping showed that the PM_2.5 derived from active wildfires. Based on these conditions, cows were exposed to wildfire PM_2.5 for 7 consecutive days in mid-September. Milk yield was recorded daily and milk components analysis conducted before, during, and after exposure. Blood was taken from the jugular vein before, during, and after exposure and assayed for hematology, blood chemistry, and blood metabolites. Statistical analysis was conducted using mixed models including PM_2.5, temperature-humidity index (THI), parity (primiparous or multiparous), and their interactions as fixed effects and cow as a random effect. Separate models included lags up to 7 d to identify delayed and persistent effects from wildfire PM_2.5 exposure. Exposure to elevated PM_2.5 from wildfire smoke resulted in lower milk yield during exposure and for 7 d after last exposure and higher blood CO₂ concentration, which persisted for 1 d following exposure. We observed a positive PM_2.5 by THI interaction for eosinophil and basophil count and a negative PM_2.5 by THI interaction for red blood cell count and hemoglobin concentration after a 3-d lag. Neutrophil count was also lower with a combination of higher THI and PM_2.5. We found no discernable effect of PM_2.5 on haptoglobin concentration. Effects of PM_2.5 and THI on metabolism were contingent on day of exposure. On lag d 0, blood urea nitrogen (BUN) was reduced with higher combined THI and PM_2.5, but on subsequent lag days, THI and PM_2.5 had a positive interaction on BUN. Conversely, THI and PM_2.5 had a positive interacting effect on nonesterified fatty acids (NEFA) on lag d 0 but subsequently caused a reduction in circulating NEFA concentration. Our results suggest that exposure to high wildfire-derived PM_2.5, alone or in concert with elevated THI, alters systemic metabolism, milk production, and the innate immune system.

Excess Morbidity and Mortality Associated with Air Pollution above American Thoracic Society Recommended Standards, 2017-2019

Rationale: Over the past year, the American Thoracic Society (ATS), led by its Environmental Health Policy Committee, has reviewed the most current air quality scientific evidence and has revised their recommendations to 8 µg/m³ and 25 µg/m³ for long- and short-term fine particulate matter (PM_2.5) and reaffirmed the recommendation of 60 ppb for ozone to protect the American public from the known adverse health effects of air pollution. The current EPA standards, in contrast, expose the American public to pollution levels that are known to result in significant morbidity and mortality. Objectives: To provide county-level estimates of annual air pollution-related health outcomes across the United States using the most recent federal air quality data, and to support the ATS's recent update to the long-term PM_2.5 recommended standard. This study is presented as part of the annual ATS/Marron Institute "Health of the Air" report. Methods: Daily air pollution values were obtained from the U.S. Environmental Protection Agency's (EPA) Air Quality System for monitored counties in the United States from 2017-2019. Concentration-response functions used in the EPA's regulatory review process were applied to pollution increments corresponding to differences between the rolling 3-year design values and ATS-recommended levels for long-term PM_2.5 (8 µg/m³), short-term PM_2.5 (25 µg/m³), and ground-level ozone (O₃; 60 ppb). Health impacts were estimated at the county level in locations with valid monitoring data. Results: Meeting ATS recommendations throughout the country prevents an estimated 14,650 (95% CI: 8,660 - 22,610) deaths; 2,950 (95% CI: 1,530 - 4,330) lung cancer incidence events; 33,100 (95% CI: 7,300 - 71,000) morbidities, and 39.8 million (95% CI: 14.6 - 63.3 million) impacted days annually (see Table 1). This prevents 11,850 more deaths; 2,580 more lung cancer incidence events; 25,400 more morbidities; and 27.2 million more impacted days than meeting EPA standards alone. Conclusions: Significant health benefits to be gained by U.S. communities that work to meet ATS-recommended air quality standards have now been identified under scenarios meeting the new ATS recommendation for long-term PM_2.5 (8 µg/m³). The "Health of the Air" report presents an opportunity for air quality managers to quantify local health burdens and EPA officials to update their standards to reflect the latest science.

Predicting wildfire particulate matter and hypothetical re-emission of radiological Cs-137 contamination incidents

Radiological release incidents can potentially contaminate widespread areas with radioactive materials and decontamination efforts are typically focused on populated areas, which means radionuclides may be left in forested areas for long periods of time. Large wildfires in contaminated forested areas have the potential to reintroduce these radionuclides into the atmosphere and cause exposure to first responders and downwind communities. One important radionuclide contaminant released from radiological incidents is radiocesium (¹³⁷Cs) due to high yields and its long half-life of 30.2 years. An Eulerian 3D photochemical transport model was used to estimate potential ambient impacts of ¹³⁷Cs re-emission due to wildfire following hypothetical radiological release scenarios. The Community Multiscale Air Quality (CMAQ) model did well at predicting levels and periods of increased PM2.5 carbon due to wildfire smoke at routine surface monitors in California during the summer of 2016. The model also did well at capturing the extent of the surface mixing layer compared to aerosol lidar measurements. Emissions from a large hypothetical wildfire were introduced into the wildland-urban interface (WUI) impacted by a hypothetical radiological release event. While ambient concentrations tended to be highest near the fire, the highest population committed effective dose equivalent by inhalation to an adult from ¹³⁷Cs over an hour was downwind where wind flows moved smoke to high population areas. Seasonal variations in meteorology (wind flows) can result in differential population impacts even in the same metropolitan area. Modeled post-incident ambient levels of ¹³⁷Cs both near these wildfires and further downwind in nearby urban areas were well below levels that would necessitate population evacuation or warrant other protective action recommendations such as shelter-in-place. These results suggest that 1) the modeling system captures local to regional scale transport and levels of PM2.5 from wildfire and 2) first responders and downwind population would not be expected to be at elevated risk from the initial inhalathion exposure of ¹³⁷Cs re-emission.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.