2002-2017 anthropogenic emissions data for air quality modeling over the United States.

The United States Environmental Protection Agency (US EPA) has developed a set of annual North American emissions data for multiple air pollutants across 18 broad source categories for 2002 through 2017. The sixteen new annual emissions inventories were developed using consistent input data and methods across all years. When a consistent method or tool was not available for a source category, emissions were estimated by scaling data from the EPA's 2017 National Emissions Inventory with scaling factors based on activity data and/or emissions control information. The emissions datasets are designed to support regional air quality modeling for a wide variety of human health and ecological applications. The data were developed to support simulations of the EPA's Community Multiscale Air Quality model but can also be used by other regional scale air quality models. The emissions data are one component of EPA's Air Quality Time Series Project which also includes air quality modeling inputs (meteorology, initial conditions, boundary conditions) and outputs (e.g., ozone, PM2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

Performance Evaluation of the Meteorology and Air Quality Conditions From Multiscale WRF-CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS).

The Long Island Sound (LIS) Tropospheric Ozone Study was a multi-agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF-CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33-km grid spacing, going from -0.12 to 0.08 K. Additionally, 4-km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution. Case studies of two high ozone concentration events (July 10 and August 6) revealed that sound breezes and low-level jets had a critical role in transporting pollutant-rich, shallow marine air masses from the LIS inland over the Connecticut coast. Modifications were made to the representation of sea surface temperatures, which subsequently improved the simulation of surface ozone predictions.

An evaluation of empirical and statistically based smoke plume injection height parametrisations used within air quality models.

Air quality models are used to assess the impact of smoke from wildland fires, both prescribed and natural, on ambient air quality and human health. However, the accuracy of these models is limited by uncertainties in the parametrisation of smoke plume injection height (PIH) and its vertical distribution. We compared PIH estimates from the plume rise method (Briggs) in the Community Multiscale Air Quality (CMAQ) modelling system with observations from the 2013 California Rim Fire and 2017 prescribed burns in Kansas. We also examined PIHs estimated using alternative plume rise algorithms, model grid resolutions and temporal burn profiles. For the Rim Fire, the Briggs method performed as well or better than the alternatives evaluated (mean bias of less than ±5-20% and root mean square error lower than 1000 m compared with the alternatives). PIH estimates for the Kansas prescribed burns improved when the burn window was reduced from the standard default of 12 h to 3 h. This analysis suggests that meteorological inputs, temporal allocation and heat release are the primary drivers for accurately modelling PIH.

The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation.

The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O3 and daily PM2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O3, CMAQ531 has higher O3 in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O3 bias (2-4 ppbv monthly average). MDA8 O3 is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O3 from the lateral boundary conditions (BCs), which generally increases MDA8 O3 bias in spring and fall ( 0.5 µg m-3). For daily 24 h average PM2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O3 mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O3 dry deposition to snow in CMAQ53) and lower O3 mixing ratios in middle and lower latitudes year-round (due to reduced O3 over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O3 mixing ratios and higher PM2.5 concentrations (0.1-0.25 µg m-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O3 is generally higher with M3Dry outside of summer, while PM2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH3, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2.

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).

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

In November 2016, a large area of wildfire occurred in the southeastern United States, concomitant with the occurrence of severe drought during the same period. Whereas the previous studies on biomass burning over this region mainly focused on the prescribed fire, this study investigated the impact of wildfire using the two-way-coupled Weather Research and Forecasting model and Community Multiscale Air Quality model. Two episodic wildfire burning events (November 6 to 9 and November 13 to 16, 2016) were selected, and the mean contribution to fine particulate matter (PM2.5) in the southeastern United States from wildfires reached 9.6 to 42.5 µg m-3 and 10.9 to 26.1 µg m-3, with mean relative contributions of 41% and 49%, respectively, during these two events. The effect of wildfire propagates along the path of the smoke plume, which is determined by the wind speed and direction. For instance, during the first event, the dominant low-altitude wind vector displayed an anticyclonic-type flow with low wind speed, resulting in relatively localized influence and high intensity. In contrast, during the second event, relatively fast eastward wind, particularly over the latter part of the event, strengthened the diffusion and affected larger areas in comparison with the first event. Moreover, differently from the previous studies, this study took a further step to reveal the mechanism of the aerosol direct effect on the deterioration of air quality during wildfire, mainly through the modulation of reduction in surface downward shortwave radiation, planetary boundary layer height and wind speed, subsequently, facilitating pollution accumulation. Quantification analysis showed an average of 10% to 14% extra enhancement of PM2.5 during the November 6 to 8 episode. Considering that more frequent drought is projected to occur in the southeastern United States, wildfire may play an even more important role in modulating the air quality in this region.

Significant ground-level ozone attributed to lightning-induced nitrogen oxides during summertime over the Mountain West States.

Using lightning flash data from the National Lightning Detection Network with an updated lightning nitrogen oxides (NOx) emission estimation algorithm in the Community Multiscale Air Quality (CMAQ) model, we estimate the hourly variations in lightning NOx emissions for the summer of 2011 and simulate its impact on distributions of tropospheric ozone (O3) across the continental United States. We find that typical summer-time lightning activity across the U.S. Mountain West States (MWS) injects NOx emissions comparable to those from anthropogenic sources into the troposphere over the region. Comparison of two model simulation cases with and without lightning NOx emissions show that significant amount of ground-level O3 in the MWS during the summer can be attributed to the lightning NOX emissions. The simulated surface-level O3 from a model configuration incorporating lightning NOx emissions showed better agreement with the observed values than the model configuration without lightning NOx emissions. The time periods of significant reduction in bias in simulated O3 between these two cases strongly correlate with the time periods when lightning activity occurred in the region. The inclusion of lightning NOx increased daily maximum 8 h O3 by up to 17 ppb and improved model performance relative to measured surface O3 mixing ratios in the MWS region. Analysis of model results in conjunction with lidar measurements at Boulder, Colorado during July 2014 corroborated similar impacts of lightning NOx emissions on O3 emissions estimated for other summers is comparable to the 2011 air quality. The magnitude of lightning NOx estimates suggesting that summertime surface-level O3 levels in the MWS region could be significantly influenced by lightning NOx.

Reflecting on progress since the 2005 NARSTO emissions inventory report.

Emission inventories are the foundation for cost-effective air quality management activities. In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American emissions inventories and made recommendations for improving their effectiveness. This paper reviews the recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. The findings reveal that all emissions inventory improvement areas identified by the 2005 NARSTO publication have been explored and implemented to some degree. The U.S. National Emissions Inventory has become more detailed and has incorporated new research into previously under-characterized sources such as fine particles and biomass burning. Additionally, it is now easier to access the emissions inventory and the documentation of the inventory via the internet. However, many emissions-related research needs exist, on topics such as emission estimation methods, speciation, scalable emission factor development, incorporation of new emission measurement techniques, estimation of uncertainty, top-down verification, and analysis of uncharacterized sources. A common theme throughout this retrospective summary is the need for increased coordination among stakeholders. Researchers and inventory developers must work together to ensure that planned emissions research and new findings can be used to update the emissions inventory. To continue to address emissions inventory challenges, industry, the scientific community, and government agencies need to continue to leverage resources and collaborate as often as possible. As evidenced by the progress noted, continued investment in and coordination of emissions inventory activities will provide dividends to air quality management programs across the country, continent, and world. Implications: In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American air pollution emissions inventories. This paper reviews the eight recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. Although progress has been made, many opportunities exist for the scientific agencies, industry, and government agencies to leverage resources and collaborate to continue improving emissions inventories.

Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States.

This study analyzes simulated regional-scale ozone burdens both near the surface and aloft, estimates process contributions to these burdens, and calculates the sensitivity of the simulated regional-scale ozone burden to several key model inputs with a particular emphasis on boundary conditions derived from hemispheric or global-scale models. The Community Multiscale Air Quality (CMAQ) model simulations supporting this analysis were performed over the continental US for the year 2010 within the context of the Air Quality Model Evaluation International Initiative (AQMEII) and Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) activities. CMAQ process analysis (PA) results highlight the dominant role of horizontal and vertical advection on the ozone burden in the mid-to-upper troposphere and lower stratosphere. Vertical mixing, including mixing by convective clouds, couples fluctuations in free-tropospheric ozone to ozone in lower layers. Hypothetical bounding scenarios were performed to quantify the effects of emissions, boundary conditions, and ozone dry deposition on the simulated ozone burden. Analysis of these simulations confirms that the characterization of ozone outside the regional-scale modeling domain can have a profound impact on simulated regional-scale ozone. This was further investigated by using data from four hemispheric or global modeling systems (Chemistry - Integrated Forecasting Model (C-IFS), CMAQ extended for hemispheric applications (H-CMAQ), the Goddard Earth Observing System model coupled to chemistry (GEOS-Chem), and AM3) to derive alternate boundary conditions for the regional-scale CMAQ simulations. The regional-scale CMAQ simulations using these four different boundary conditions showed that the largest ozone abundance in the upper layers was simulated when using boundary conditions from GEOS-Chem, followed by the simulations using C-IFS, AM3, and H-CMAQ boundary conditions, consistent with the analysis of the ozone fields from the global models along the CMAQ boundaries. Using boundary conditions from AM3 yielded higher springtime ozone columns burdens in the middle and lower troposphere compared to boundary conditions from the other models. For surface ozone, the differences between the AM3-driven CMAQ simulations and the CMAQ simulations driven by other large-scale models are especially pronounced during spring and winter where they can reach more than 10 ppb for seasonal mean ozone mixing ratios and as much as 15 ppb for domain-averaged daily maximum 8 h average ozone on individual days. In contrast, the differences between the C-IFS-, GEOS-Chem-, and H-CMAQ-driven regional-scale CMAQ simulations are typically smaller. Comparing simulated sur face ozone mixing ratios to observations and computing seasonal and regional model performance statistics revealed that boundary conditions can have a substantial impact on model performance. Further analysis showed that boundary conditions can affect model performance across the entire range of the observed distribution, although the impacts tend to be lower during summer and for the very highest observed percentiles. The results are discussed in the context of future model development and analysis opportunities.

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2 km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy.

Modeling emissions for three-dimensional atmospheric chemistry transport models.

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date. IMPLICATIONS: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.

The health impacts and economic value of wildland fire episodes in the U.S.: 2008-2012.

INTRODUCTION: Wildland fires degrade air quality and adversely affect human health. A growing body of epidemiology literature reports increased rates of emergency departments, hospital admissions and premature deaths from wildfire smoke exposure. OBJECTIVE: Our research aimed to characterize excess mortality and morbidity events, and the economic value of these impacts, from wildland fire smoke exposure in the U.S. over a multi-year period; to date no other burden assessment has done this. METHODS: We first completed a systematic review of the epidemiologic literature and then performed photochemical air quality modeling for the years 2008 to 2012 in the continental U.S. Finally, we estimated the morbidity, mortality, and economic burden of wildland fires. RESULTS: Our models suggest that areas including northern California, Oregon and Idaho in the West, and Florida, Louisiana and Georgia in the East were most affected by wildland fire events in the form of additional premature deaths and respiratory hospital admissions. We estimated the economic value of these cases due to short term exposures as being between $11 and $20B (2010$) per year, with a net present value of $63B (95% confidence intervals $6-$170); we estimate the value of long-term exposures as being between $76 and $130B (2010$) per year, with a net present value of $450B (95% confidence intervals $42-$1200). CONCLUSION: The public health burden of wildland fires-in terms of the number and economic value of deaths and illnesses-is considerable.

The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012.

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency's National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008-2012. Adding fires to CMAQ increases the number of 'grid-cell days' with PM2.5 above 35 µg m-3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

Influence of uncertainties in burned area estimates on modeled wildland fire PM2.5 and ozone pollution in the contiguous U.S.

Wildland fires are a major source of fine particulate matter (PM2.5), one of the most harmful ambient pollutants for human health globally. To represent the influence of wildland fire emissions on atmospheric composition, regional and global chemical transport models rely on emission inventories developed from estimates of burned area (i.e. fire size and location). While different methods of estimating annual burned area agree reasonably well in the western U.S. (within 20-30% for most years during 2002-2014), estimates for the southern U.S. can vary by more than a factor of 5. These differences in burned area lead to significant variability in the spatial and temporal allocation of emissions across fire emission inventory platforms. In this work, we implement wildland fire emission estimates for 2011 from three different products - the USEPA National Emission Inventory (NEI), the Fire Inventory of NCAR (FINN), and the Global Fire Emission Database (GFED4s) - into the Community Multiscale Air Quality (CMAQ) model to quantify and characterize differences in simulated PM and ozone concentrations across the contiguous U.S. (CONUS) due to the fire emission inventory used. The NEI is developed specifically for the U.S., while both FINN and GFED4s are available globally. We find that NEI emissions lead to the largest increases in modeled annual average PM2.5 (0.85 µg m-3) and April-September maximum daily 8-h ozone (0.28 ppb) nationally compared to a "no fire" baseline, followed by FINN (0.33 µg m-3 and 0.22 ppb) and GFED4s (0.12 µg m-3 and 0.17 ppb). Annual mean enhancements in wildland fire pollution are highest in the southern U.S. across all three inventories (over 4 µg m-3 and 2 ppb in some areas), but show considerable spatial variability within these regions. We also examine the representation of five individual fire events during 2011 and find that of the two global inventories, FINN reproduces more of the acute changes in pollutant concentrations modeled with NEI and shown in surface observations during each of the episodes investigated compared to GFED4s. Understanding the sensitivity of modeling fire-related PM2.5 and ozone in the U.S. to burned area estimation approaches will inform future efforts to assess the implications of present and future fire activity for air quality and human health at national and global scales.

Community Vulnerability to Health Impacts of Wildland Fire Smoke Exposure.

Identifying communities vulnerable to adverse health effects from exposure to wildfire smoke may help prepare responses, increase the resilience to smoke and improve public health outcomes during smoke days. We developed a Community Health-Vulnerability Index (CHVI) based on factors known to increase the risks of health effects from air pollution and wildfire smoke exposures. These factors included county prevalence rates for asthma in children and adults, chronic obstructive pulmonary disease, hypertension, diabetes, obesity, percent of population 65 years of age and older, and indicators of socioeconomic status including poverty, education, income and unemployment. Using air quality simulated for the period between 2008 and 2012 over the continental U.S. we also characterized the population size at risk with respect to the level and duration of exposure to fire-originated fine particulate matter (fire-PM2.5) and CHVI. We estimate that 10% of the population (30.5 million) lived in the areas where the contribution of fire-PM2.5 to annual average ambient PM2.5 was high (>1.5 µg/m3) and that 10.3 million individuals experienced unhealthy air quality levels for more than 10 days due to smoke. Using CHVI we identified the most vulnerable counties and determined that these communities experience more smoke exposures in comparison to less vulnerable communities.

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications.

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1.

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

Development of the crop residue and rangeland burning in the 2014 National Emissions Inventory using information from multiple sources.

Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory (NEI). In the 2011 NEI, wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM2.5. This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We focus on both the postharvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning excluding all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay occurred over approximately 1.5 million acres of land and produced 19,600 short tons of PM2.5. For areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay, biomass burning emissions occurred over approximately 1.6 million acres of land and produced 30,000 short tons of PM2.5. This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,650 short tons and 2011: 141,180 short tons. Note that in the previous two NEIs rangeland burning was not well defined and so the comparison is not exact. The remote sensing data also provided verification of our existing diurnal profile for crop residue burning emissions used in chemical transport modeling. In addition, the entire database used to estimate this sector of emissions is available on EPA's Clearinghouse for Inventories and Emission Factors (CHIEF, http://www3.epa.gov/ttn/chief/index.html ). IMPLICATIONS: Estimates of crop residue burning and rangeland burning emissions can be improved by using satellite detections. Local information is helpful in distinguishing crop residue and rangeland burning from all other types of fires.