Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia.

BACKGROUND: Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. OBJECTIVES: Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. METHODS: BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO2), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. RESULTS: Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. DISCUSSION: Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Source-Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts.

Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.

Photochemical model assessment of single source NO2 and O3 plumes using field study data.

Single source contribution to ambient O3 and PM2.5 has been estimated with photochemical grid models to support policy demonstrations for National Ambient Air Quality Standards, regional haze, and permit related programs. Limited field data exists to evaluate model representation of the spatial extent and chemical composition of plumes emitted by specific facilities. New tropospheric column measurements of NO2 and in-plume chemical measurements downwind of specific facilities allows for photochemical model evaluation of downwind plume extent, grid resolution impacts on plume concentration gradients, and source attribution methods. Here, photochemical models were applied with source sensitivity and source apportionment approaches to differentiate single source impacts on NO2 and O3 and compare with field study measurements. Source sensitivity approaches (e.g., brute-force difference method and decoupled direct method (DDM)) captured the spatial extent of NO2 plumes downwind of three facilities and the transition of near-source O3 titration to downwind production. Source apportionment approaches showed variability in terms of attributing the spatial extent of NO2 plumes and downwind O3 production. Each of the Community Multiscale Air Quality (CMAQ) source apportionment options predicted large O3 contribution from a large industrial facility in the flight transects nearest the facility when measurements and source sensitivity approaches suggest titration was outpacing production. In general, CMAQ DDM tends to attribute more O3 to boundary inflow and less to within-domain NOX and VOC sources compared to CMAQ source apportionment. The photochemical modeling system was able to capture single source plumes using 1 to 12 km grid resolution with best representation of plume extent and magnitude at the finer resolutions. When modeled at 1 to 12 km grid resolution, primary and secondary PM2.5 impacts were highest at the source location and decrease as distance increases downwind. The use of coarser grid resolution for single source attribution resulted in predicted impacts highest near the source but lower peak source specific concentrations compared to finer grid resolution simulations because impacts were spread out over a larger area.

Evaluating reduced-form modeling tools for simulating ozone and PM2.5 monetized health impacts.

Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by making simplifying assumptions about pollutant source characteristics, transport and chemistry. Two reduced form tools used by the Environmental Protection Agency in recent assessments are source apportionment-based benefit per ton (SA BPT) and source apportionment-based air quality surfaces (SABAQS). In this work, we apply these two reduced form tools to predict changes in ambient summer-season ozone, ambient annual PM2.5 component species and monetized health benefits for multiple sector-specific emission control scenarios: on-road mobile, electricity generating units (EGUs), cement kilns, petroleum refineries, and pulp and paper facilities. We then compare results against photochemical grid and standard health model-based estimates. We additionally compare monetized PM2.5 health benefits to values derived from three reduced form tools available in the literature: the Intervention Model for Air Pollution (InMAP), Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 (AP2) and Estimating Air pollution Social Impact Using Regression (EASIUR). Ozone and PM2.5 changes derived from SABAQS for EGU scenarios were well-correlated with values obtained from photochemical modeling simulations with spatial correlation coefficients between 0.64 and 0.89 for ozone and between 0.75 and 0.94 for PM2.5. SABAQS ambient ozone and PM2.5 bias when compared to photochemical modeling predictions varied by emissions scenario: SABAQS PM2.5 changes were overpredicted by up to 46% in one scenario and underpredicted by up to 19% in another scenario; SABAQS seasonal ozone changes were overpredicted by 34% to 83%. All tools predicted total PM2.5 benefits within a factor of 2 of the full-form predictions consistent with intercomparisons of reduced form tools available in the literature. As reduced form tools evolve, it is important to continue periodic comparison with comprehensive models to identify systematic biases in estimating air pollution impacts and resulting monetized health benefits.

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.

Hydrogen chloride (HCl) at ground sites during CalNex 2010 and insight into its thermodynamic properties.

Gas phase hydrogen chloride (HCl) was measured at Pasadena and San Joaquin Valley (SJV) ground sites in California during May and June 2010 as part of the CalNex study. Observed mixing ratios were on average 0.83 ppbv at Pasadena, ranging from below detection limit (0.055 ppbv) to 5.95 ppbv, and were on average 0.084 ppbv at SJV with a maximum value of 0.776 ppbv. At both sites, HCl levels were highest during midday and shared similar diurnal variations with HNO3. Coupled phase partitioning behavior was found between HCl/Cl- and HNO3/NO3 - using thermodynamic modelling and observations. Regional modeling of Cl- and HCl using CMAQ captures some of the observed relationships but underestimates measurements by a factor of 5 or more. Chloride in the 2.5-10 µm size range in Pasadena was sometimes higher than sea salt abundances, based on co-measured Na+, implying that sources other than sea salt are important. The acid-displacement of HCl/Cl- by HNO3/NO3 - (phase partitioning of semi-volatile acids) observed at the SJV site can only be explained by aqueous phase reaction despite low RH conditions and suggests the temperature dependence of HCl phase partitioning behavior was strongly impacted by the activity coefficient changes under relevant aerosol conditions (e.g., high ionic strength). Despite the influence from activity coefficients, the gas-particle system was found to be well constrained by other stronger buffers and charge balance so that HCl and Cl- concentrations were reproduced well by thermodynamic models.

Confronting Uncertainties of Simulated Air Pollution Concentrations during Persistent Cold Air Pool Events in the Salt Lake Valley, Utah.

Air pollutant accumulations during wintertime persistent cold air pool (PCAP) events in mountain valleys are of great concern for public health worldwide. Uncertainties associated with the simulated meteorology under stable conditions over complex terrain hinder realistic simulations of air quality using chemical transport models. We use the Community Multiscale Air Quality (CMAQ) model to simulate the gaseous and particulate species for 1 month in January 2011 during the Persistent Cold Air Pool Study (PCAPS) in the Salt Lake Valley (SLV), Utah (USA). Results indicate that the temporal variability associated with the elevated NOx and PM2.5 concentrations during PCAP events was captured by the model (r = 0.20 for NOx and r = 0.49 for PM2.5). However, concentrations were not at the correct magnitude (NMB = -35/12% for PM2.5 during PCAPs/non-PCAPs), where PM2.5 was underestimated during PCAP events and overestimated during non-PCAP periods. The underestimated PCAP strength is represented by valley heat deficit, which contributed to the underestimated PM2.5 concentrations compared with observations due to the model simulating more vertical mixing and less stable stratification than what was observed. Based on the observations, the dominant PM2.5 species were ammonium and nitrate. We provide a discussion that aims to investigate the emissions and chemistry model uncertainties using the nitrogen ratio method and the thermodynamic ammonium nitrate regime method.

Satellite Formaldehyde to Support Model Evaluation.

Formaldehyde (HCHO), a known carcinogen classified as a hazardous pollutant by the United States Environmental Protection Agency (U.S. EPA), is measured through monitoring networks across the U.S. Since these data are limited in spatial and temporal extent, model simulations from the U.S. EPA Community Multiscale Air Quality (CMAQ) model are used to estimate ambient HCHO exposure for the EPA National Air Toxics Assessment (NATA). Here, we employ satellite HCHO retrievals from the Ozone Monitoring Instrument (OMI)-the NASA retrieval developed by the Smithsonian Astrophysical Observatory (SAO), and the European Union Quality Assurance for Essential Climate Variables (QA4ECV) retrieval-to evaluate three CMAQ configurations, spanning the summers of 2011 and 2016, with differing biogenic emissions inputs and chemical mechanisms. These CMAQ configurations capture the general spatial and temporal behavior of both satellite retrievals, but underestimate column HCHO, particularly in the western U.S. In the southeastern U.S., the comparison with OMI HCHO highlights differences in modeled meteorology and biogenic emissions even with differences in satellite retrievals. All CMAQ configurations show low daily correlations with OMI HCHO (r = 0.26 - 0.38), however, we find higher monthly correlations (r = 0.52 - 0.73), and the models correlate best with the OMI-QA4ECV product. Compared to surface observations, we find improved agreement over a 24-hour period compared to afternoon-only, suggesting daily HCHO amounts are captured with more accuracy than afternoon amounts. This work highlights the potential for synergistic improvements in modeling and satellite retrievals to support near-surface HCHO estimates for the NATA and other applications.

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.

Evaluation of 15 years of modeled atmospheric oxidized nitrogen compounds across the contiguous United States.

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke.

Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52-0.95), but overpredicted PM2.5 concentrations (normalized root mean square errors, NRMSE = 80-167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.

The recent and future health burden of the U.S. mobile sector apportioned by source.

Mobile sources emit particulate matter as well as precursors to particulate matter (PM2.5) and ground-level ozone, pollutants known to adversely impact human health. This study uses source-apportionment photochemical air quality modeling to estimate the health burden (expressed as incidence) of an array of PM2.5- and ozone-related adverse health impacts, including premature death, attributable to 17 mobile source sectors in the US in 2011 and 2025. Mobile sector-attributable air pollution contributes a substantial fraction of the overall pollution-related mortality burden in the U.S., accounting for about 20% of the PM2.5 and ozone-attributable deaths in 2011 (between 21 000 and 55 000 deaths, depending on the study used to derive the effect estimate). This value falls to about 13% (between 13 000 and 37 000 deaths) by 2025 due to regulatory and voluntary programs reducing emissions from mobile sources. Similar trends across all morbidity health impacts can also be observed. Emissions from on-road sources are the largest contributor to premature deaths; this is true for both 2011 (between 12 000 and 31 000 deaths) and 2025 (between 6700 and 18 000 deaths). Non-road construction engines, C3 marine engines and emissions from rail also contribute to large portions of premature deaths. Across the 17 mobile sectors modeled, the PM2.5-attributable mortality and morbidity burden falls between 2011 and 2025 for 12 sectors and increases for 5. Ozone-attributable mortality and morbidity burden increases between 2011 and 2025 for 10 sectors and falls for 7. These results extend the literature beyond generally aggregated mobile sector health burden toward a representation of highly-resolved source characterization of both current and future health burden. The quantified future mobile source health burden is a novel feature of this analysis and could prove useful for decisionmakers and affected stakeholders.

A database for evaluating the InMAP, APEEP, and EASIUR reduced complexity air-quality modeling tools.

Policy analysts and researchers often use models to translate expected emissions changes from pollution control policies to estimates of air pollution changes and resulting changes in health impacts. These models can include both photochemical Eulerian grid models or reduced complexity models; these latter models make simplifying assumptions about the emissions-to-air quality relationship as a means of reducing the computational time needed to simulate air quality. This manuscript presents a new database of photochemical- and reduced complexity-modelled changes in annual average particulate matter with aerodynamic diameter less than 2.5 µm and associated health effects and economic values for five case studies representing different emissions control scenarios. The research community is developing an increasing number of reduced complexity models as lower-cost and more expeditious alternatives to full form Eulerian photochemical grid models such as the Comprehensive Air-Quality Model with eXtensions (CAMx) and the Community Multiscale Air Quality (CMAQ) model. A comprehensive evaluation of reduced complexity models can demonstrate the extent to which these tools capture complex chemical and physical processes when representing emission control options. Systematically comparing reduced complexity model predictions to benchmarks from photochemical grid models requires a consistent set of input parameters across all systems. Developing such inputs is resource intensive and consequently the data that we have developed and shared (https://github.com/epa-kpc/RFMEVAL) provide a valuable resource for others to evaluate reduced complexity models. The dataset includes inputs and outputs representing 5 emission control scenarios, including sector-based regulatory policy scenarios focused on on-road mobile sources and electrical generating units (EGUs) as well as hypothetical across-the-board reductions to emissions from cement kilns, refineries, and pulp and paper facilities. Model inputs, outputs, and run control files are provided for the Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 and 3, Intervention Model for Air Pollution (InMAP), Estimating Air pollution Social Impact Using Regression (EASIUR), and EPA's source apportionment benefit-per-ton reduced complexity models. For comparison, photochemical grid model annual average PM2.5 output is provided for each emission scenario. Further, inputs are also provided for the Environmental Benefits and Mapping Community Edition (BenMAP-CE) tool to generate county level health benefits and monetized health damages along with output files for benchmarking and intercomparison. Monetized health impacts are also provided from EASIUR and APEEP which can provide these outside the BenMAP-CE framework. The database will allow researchers to more easily compare reduced complexity model predictions against photochemical grid model predictions.

Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California.

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007-2013) was 4.91 µg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 µg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 µg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California's population lived in a county that experienced at least one episode of high smoke exposure ("smokewave") from 2007-2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (-2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

Volatile Organic Compound Emissions from Prescribed Burning in Tallgrass Prairie Ecosystems.

Prescribed pasture burning plays a critical role in ecosystem maintenance in tallgrass prairie ecosystems and may contribute to agricultural productivity but can also have negative impacts on air quality. Volatile organic compound (VOC) concentrations were measured immediately downwind of prescribed tallgrass prairie fires in the Flint Hills region of Kansas, United States. The VOC mixture is dominated by alkenes and oxygenated VOCs, which are highly reactive and can drive photochemical production of ozone downwind of the fires. The computed emission factors are comparable to those previous measured from pasture maintenance fires in Brazil. In addition to the emission of large amounts of particulate matter, hazardous air pollutants such as benzene and acrolein are emitted in significant amounts and could contribute to adverse health effects in exposed populations.

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.

Fire behavior and smoke modeling: Model improvement and measurement needs for next-generation smoke research and forecasting systems.

There is an urgent need for next-generation smoke research and forecasting (SRF) systems to meet the challenges of the growing air quality, health, and safety concerns associated with wildland fire emissions. This review paper presents simulations and experiments of hypothetical prescribed burns with a suite of selected fire behavior and smoke models and identifies major issues for model improvement and the most critical observational needs. The results are used to understand the new and improved capability required for the next-generation SRF systems and to support the design of the Fire and Smoke Model Evaluation Experiment (FASMEE) and other field campaigns. The next-generation SRF systems should have more coupling of fire, smoke, and atmospheric processes to better simulate and forecast vertical smoke distributions and multiple sub-plumes, dynamical and high-resolution fire processes, and local and regional smoke chemistry during day and night. The development of the coupling capability requires comprehensive and spatially and temporally integrated measurements across the various disciplines to characterize flame and energy structure (e.g., individual cells, vertical heat profile and the height of well mixing flaming gases), smoke structure (vertical distributions and multiple sub-plumes), ambient air processes (smoke eddy, entrainment and radiative effects of smoke aerosols), fire emissions (for different fuel types and combustion conditions from flaming to residual smoldering), as well as night-time processes (smoke drainage and super-fog formation).

Monetized health benefits attributable to mobile source emission reductions across the United States in 2025.

By-products of mobile source combustion processes, such as those associated with gasoline- and diesel-powered engines, include direct emissions of particulate matter as well as precursors to particulate matter and ground-level ozone. Human exposure to fine particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5) is associated with increased incidence of premature mortality and morbidity outcomes. This study builds upon recent, detailed source-apportionment air quality modeling to project the health-related benefits of reducing PM2.5 from mobile sources across the contiguous U.S. in 2025. Updating a previously published benefits analysis approach, we develop national-level benefit per ton estimates for directly emitted PM2.5, SO2/pSO4, and NOX for 16 mobile source sectors spanning onroad vehicles, nonroad engines and equipment, trains, marine vessels, and aircraft. These benefit per ton estimates provide a reduced-form tool for estimating and comparing benefits across multiple mobile source emission scenarios and can be applied to assess the benefits of mobile source policies designed to improve air quality. We found the benefit per ton of directly emitted PM2.5 in 2025 ranges from $110,000 for nonroad agriculture sources to $700,000 for onroad light duty gas cars and motorcycles (in 2015 dollars and based on an estimate of PM-related mortality derived from the American Cancer Society cohort study). Benefit per ton values for SO2/pSO4 range from $52,000 for aircraft sources (including emissions from ground support vehicles) to $300,000 for onroad light duty diesel emissions. Benefit per ton values for NOX range from $2100 for C1 and C2 marine vessels to $7500 for "nonroad all other" mobile sources, including industrial, logging, and oil field sources. Benefit per ton estimates increase approximately 2.26-fold when using an alternative concentration response function to derive PM2.5-related mortality. We also report benefit per ton values for the eastern and western U.S. to account for broad spatial heterogeneity patterns in emissions reductions, population exposure and air quality benefits.

Mobile Source Contributions to Ambient Ozone and Particulate Matter in 2025.

The contribution of precursor emissions from 17 mobile source sectors to ambient ozone and fine particulate matter levels across the U.S. were evaluated, using the CAMx photochemical model, to identify which mobile source sectors are projected to have the largest impacts on air pollution in 2025. Both onroad and nonroad sectors contribute considerably to projected air pollution across much of the country. Summer ozone season ozone contributions between 2 and 5 ppb, which are among the highest levels presented on the maps of mobile source sectors, are largely found in the southeast United States from the onroad sectors, most notably light-duty and heavy-duty vehicles, and along the coastline from the Category 3 (C3) marine sector. Annual average PM2.5 contributions between 0.5 to 0.9 µg/m3, which are among the highest levels presented on the maps of mobile source sectors, are found throughout the Midwest and along portions of the east and west coast from onroad sectors as well as nonroad diesel and rail sectors. Additionally, contributions of precursor emissions to ambient ozone and PM2.5 levels were evaluated to understand the range of impacts from precursors in the various mobile source sectors. For most mobile source sectors, in most locations, NOX emissions contributed more to ozone than VOC emissions, and secondary PM2.5 contributed more to ambient PM2.5 than primary PM2.5. The largest ozone levels on the maps showing contributions from mobile source NOX emissions tended to be between 2 and 5 ppb, while the largest ozone levels on the maps showing contributions from mobile source VOC emissions tended to be between 0.9 and 2 ppb, except for southern California where ozone contributions from VOC emissions from onroad light duty vehicles were between 2 and 5 ppb. The largest contributions to ambient PM2.5 on the maps showing primary and secondary contributions from mobile source sectors tended to be between 0.1 and 0.5 µg/m3. The contribution from primary PM2.5 extended over localized areas (urban-scale) and the contribution from secondary PM2.5 extended over more regional (multi-state) areas.