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Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM 2.5) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research. Implications: In the recent decade the area of wildfires in the United States has increased dramatically and the resulting smoke has exposed millions of people to unhealthy air quality. In this critical review we examine the key factors and impacts from fires including natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry and human health.

With harmful ozone concentrations tied to meteorological conditions, EPA investigates the U.S. air quality implications of a changing climate. Consequently, the 03 NAAQS are most often exceeded during summertime hot spells in places with large natural or anthropogenic precursor emissions (e.g., cities and suburban areas). Table 2 The average maximum or minimum temperature and/or changes in their spatial distribution and duration, leading to a change in reaction rate coefficients and the solubility of gases in cloud water solution;The frequency and pattern of cloud cover, leading to a change in reaction rates and rates of conversion of S02to acid deposition;The frequency and intensity of stagnation episodes or a change in the mixing layer, leading to more or less mixing of polluted air with background air;Background boundary layer concentrations of water vapor, hydrocarbons, NOx, and 03, leading to more or less dilution of polluted air in the boundary layer and altering the chemical transformation rates;

Smoke from fire is a local, regional and often international issue that is growing in complexity as competition for airshed resources increases. BlueSky is a smoke modeling framework designed to help address this problem by enabling simulations of the cumulative smoke impacts from fires (prescribed, wildland, and agricultural) across a region. Versions of BlueSky have been implemented in prediction systems across the contiguous US, and land managers, air-quality regulators, incident command teams, and the general public can currently obtain BlueSky-based predictions of smoke impacts for their region. A highly modular framework, BlueSky links together a variety of state-of-the-art models of meteorology, fuels, consumption, emissions, and air quality, and offers multiple model choices at each modeling step. This modularity also allows direct comparison between similar component models. This paper presents the overall model framework Version 2.5 – the component models, how they are linked together, and the results from case studies of two wildfires. Predicted results are affected by the specific choice of modeling pathway. With the pathway chosen, the modeled output generally compares well with plume shape and extent as observed by satellites, but underpredicts surface concentrations as observed by ground monitors. Sensitivity studies show that knowledge of fire behavior can greatly improve the accuracy of these smoke impact calculations.

Field and laboratory emission factors (EFs) of wildland fire emissions for 276 known air pollutants sampled across Canada and the US were compiled. An online database, the Smoke Emissions Repository Application (SERA), was created to enable analysis and summaries of existing EFs to be used in smoke management and emissions inventories. We evaluated how EFs of select pollutants (CO, CO 2 , CH 4 , NO x , total particulate matter (PM), PM 2.5 and SO 2 ) are influenced by combustion phase, burn type and fuel type. Of the 12 533 records in the database, over a third (n ¼ 5637) are represented by 23 air pollutants, most designated as US Environmental Protection Agency criteria air pollutants, greenhouse gases, hazardous air pollutants or known air toxins. Among all pollutants in the database, including the most common pollutants PM, CO, CO 2 and CH 4 , records are unevenly distributed with a bias towards flaming combustion, prescribed burning and laboratory measurements. Across all EFs, records are most common for south-eastern and western conifer forests and western shrubland types. Based on identified data gaps, we offer recommendations for future studies, including targeting underrepresented air pollutants, smouldering combustion phases and improved source characterisation of wildland fire emissions.Additional keywords: air quality, greenhouse gas emissions, particulate matter, prescribed fire emissions, smoke management, wildfire emissions.

Fine particulate matter (PM 2.5 ) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM 2.5 exposures. This review article surveys publicly available exposure datasets for surface PM 2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM 2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM 2.5 exposure data are: ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM 2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM 2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.

Abstract.A comprehensive numerical modeling framework was developed to estimate the effects of collective global changes upon ozone pollution in the US in 2050. The framework consists of the global climate and chemistry models, PCM (Parallel Climate Model) and MOZART-2 (Model for Ozone and Related Chemical Tracers v.2), coupled with regional meteorology and chemistry models, MM5 (Mesoscale Meteorological model) and CMAQ (Community Multi-scale Air Quality model). The modeling system was applied for two 10-year simulations: 1990-1999 as a present-day base case and 2045-2054 as a future case. For the current decade, the daily maximum 8-h moving average (DM8H) ozone mixing ratio distributions for spring, summer and fall showed good agreement with observations. The future case simulation followed the Intergovernmental Panel on Climate Change (IPCC) A2 scenario together with business-as-usual US emission projections and projected alterations in land use, land cover (LULC) due to urban expansion and changes in vegetation. For these projections, US anthropogenic NO x (NO+NO 2 ) and VOC (volatile organic carbon) emissions increased by approximately 6% and 50%, respectively, while biogenic VOC emissions decreased, in spite of warmer temperatures, due to decreases in forested lands and expansion of croplands, grasslands and urban areas. A stochastic model for wildfire emissions was applied that projected 25% higher VOC emissions in the future. For the global and US Correspondence to: B. Lamb (blamb@wsu.edu) emission projection used here, regional ozone pollution becomes worse in the 2045-2054 period for all months. Annually, the mean DM8H ozone was projected to increase by 9.6 ppbv (22%). The changes were higher in the spring and winter (25%) and smaller in the summer (17%). The area affected by elevated ozone within the US continent was projected to increase; areas with levels exceeding the 75 ppbv ozone standard at least once a year increased by 38%. In addition, the length of the ozone season was projected to increase with more pollution episodes in the spring and fall. For selected urban areas, the system projected a higher number of pollution events per year and these events had more consecutive days when DM8H ozone exceed 75 ppbv.

[1] The Air Indicator Report for Public Access and Community Tracking (AIRPACT) real-time numerical air quality forecast system operates daily in the Pacific Northwest region to predict hourly ozone, PM 2.5 and related precursor and pollutant species. In an update to the existing AIRPACT forecast system, the MM5/SMOKE/CMAQ Eulerian modeling system replaces the MM5/CALMET/CALGRID model framework. The new system, AIRPACT-3, has a larger domain that encompasses Washington, Oregon, Idaho, and bordering areas. The system includes a highly dynamic emission processing subsystem which incorporates anthropogenic and biogenic emissions, as well as real-time wildfire emission estimates, and a dynamic dairy ammonia emissions module. As an initial evaluation of the system, forecast results were compared against measurement data for the August-November 2004 period. Analyses showed that the system is skillful in predicting episodic ozone conditions (8-h daily maxima) above 50 ppbv, but systematically over-predicts levels less than 40 ppbv. For fine particulate matter, PM 2.5 , the system captures the concentration differences between urban and rural regions, and captures qualitatively the speciated distribution of fine PM 2.5 component concentrations. A separate emission sensitivity study shows the system can adequately simulate the PM pollution impacts from fire events; however, the new dairy ammonia emission module has lesser impact on the overall PM 2.5 forecast performance.