Sensitivity of air quality to vehicle ammonia emissions in the United States.

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH3 emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM2.5) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppbv in January and 3.0 ppbv in July. The updated on-road NH3 emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM2.5 concentrations increased in urban areas, including enhancements of up to 0.5 µg/m3 in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

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.

CABOT-O3: An Optimization Model for Air Quality Benefit-Cost and Distributional Impacts Analysis.

Macpherson et al. (2017) presented a mathematical programming model that identifies minimum-cost control strategies that reduce emissions regionally to meet ambient air quality targets. This project introduces the Cost And Benefit Optimization Tool for Ozone (CABOT-O3), which extends the previous model by updating emissions and air quality relationships, adding a health impacts module, and quantifying distributional impacts. The tool draws upon source apportionment photochemical air quality modeling to characterize the contribution of emissions reductions to ambient ozone concentrations across the contiguous United States. The health impacts analysis module estimates the change in the number and economic value of premature deaths using modeled changes in ozone levels resulting from the application of emission control strategies. These extensions allow us to evaluate strategies to attain ozone air quality standards at minimum cost or to maximize net benefit, while assessing the change in the distribution of health impacts. In a case study applied to stationary pollution sources, we find that, when compared to minimizing costs to meet a uniform ozone standard, maximizing net benefits results in greater emissions and ozone concentration reductions in some parts of the country and fewer in others. Our results highlight potential equity-efficiency trade-offs in designing air quality policies.

An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps.

The United States (US) Environmental Protection Agency (EPA)'s SPECIATE database contains speciated particulate matter (PM) and volatile organic compound (VOC) emissions profiles. Emissions profiles from anthropogenic combustion, industry, wildfires, and agricultural sources among others are key inputs for creating chemically-resolved emissions inventories for air quality modeling. While the database and its use for air quality modeling are routinely updated and evaluated, this work sets out to systematically prioritize future improvements and communicate speciation data needs to the research community. We first identify the most prominent profiles (PM and VOC) used in the EPA's 2014 emissions modeling platform based on PM mass and VOC mass and reactivity. It is important to note that the on-road profiles were excluded from this analysis since speciation for these profiles is computed internally in the MOVES model. We then investigate these profiles further for quality and to determine whether they were being appropriately matched to source types while also considering regional variability of speciated pollutants. We then applied a quantitative needs assessment ranking system which rates the profile based on age, appropriateness (i.e. is the profile being used appropriately), prevalence in the EPA modeling platform and the quality of the reference. Our analysis shows that the highest ranked profiles (e.g. profile assignments with the highest priority for updates) include PM2.5 profiles for fires (prescribed, agricultural and wild) and VOC profiles for crude oil storage tanks and residential wood combustion of pine wood. Top ranked profiles may indicate either that there are problems with the currently available source testing or that current mappings of profiles to source categories within EPA's modeling platform need improvement. Through this process, we have identified 29 emissions sourcecategories that would benefit from updated mapping. Many of these mapping mismatches are due to lack of emissions testing for appropriate source categories. In addition, we conclude that new source emissions testing would be especially beneficial for residential wood combustion, nonroad gasoline exhaust and nonroad diesel equipment.

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.

The Impact of Iodide-Mediated Ozone Deposition and Halogen Chemistry on Surface Ozone Concentrations Across the Continental United States.

The air quality of many large coastal areas in the United States is affected by the confluence of polluted urban and relatively clean marine airmasses, each with distinct atmospheric chemistry. In this context, the role of iodide-mediated ozone (O3) deposition over seawater and marine halogen chemistry accounted for in both the lateral boundary conditions and coastal waters surrounding the continental U.S. is examined using the Community Multiscale Air Quality (CMAQ) model. Several nested simulations are conducted in which these halogen processes are implemented separately in the continental U.S. and hemispheric CMAQ domains, the latter providing lateral boundary conditions for the former. Overall, it is the combination of these processes within both the continental U.S. domain and from lateral boundary conditions that lead to the largest reductions in modeled surface O3 concentrations. Predicted reductions in surface O3 concentrations occur mainly along the coast where CMAQ typically has large overpredictions. These results suggest that a realistic representation of halogen processes in marine regions can improve model prediction of O3 concentrations near the coast.

Ozone trends across the United States over a period of decreasing NOx and VOC emissions.

In this work, we evaluate ambient ozone trends at urban, suburban, and rural monitoring sites across the United States over a period of decreasing NOx and VOC emissions (1998-2013). We find that decreasing ozone trends generally occur in the summer, in less urbanized areas, and at the upper end of the ozone distribution. Conversely, increasing ozone trends generally occur in the winter, in more urbanized areas, and at the lower end of the ozone distribution. The 95(th) percentile ozone concentrations decreased at urban, suburban, and rural monitors by 1-2 ppb/yr in the summer and 0.5-1 ppb/yr in the winter. In the summer, there are both increasing and decreasing trends in fifth percentile ozone concentrations of less than 0.5 ppb/yr at urban and suburban monitors, while fifth percentile ozone concentrations at rural monitors decreased by up to 1 ppb/yr. In the winter, fifth percentile ozone concentrations generally increased by 0.1-1 ppb/yr. These results demonstrate the large scale success of U.S. control strategies targeted at decreasing peak ozone concentrations. In addition, they indicate that as anthropogenic NOx emissions have decreased, the ozone distribution has been compressed, leading to less spatial and temporal variability.

Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment.

Diagnostic air quality model evaluation of source-specific primary and secondary fine particulate carbon.

Ambient measurements of 78 source-specific tracers of primary and secondary carbonaceous fine particulate matter collected at four midwestern United States locations over a full year (March 2004-February 2005) provided an unprecedented opportunity to diagnostically evaluate the results of a numerical air quality model. Previous analyses of these measurements demonstrated excellent mass closure for the variety of contributing sources. In this study, a carbon-apportionment version of the Community Multiscale Air Quality (CMAQ) model was used to track primary organic and elemental carbon emissions from 15 independent sources such as mobile sources and biomass burning in addition to four precursor-specific classes of secondary organic aerosol (SOA) originating from isoprene, terpenes, aromatics, and sesquiterpenes. Conversion of the source-resolved model output into organic tracer concentrations yielded a total of 2416 data pairs for comparison with observations. While emission source contributions to the total model bias varied by season and measurement location, the largest absolute bias of -0.55 µgC/m(3) was attributed to insufficient isoprene SOA in the summertime CMAQ simulation. Biomass combustion was responsible for the second largest summertime model bias (-0.46 µgC/m(3) on average). Several instances of compensating errors were also evident; model underpredictions in some sectors were masked by overpredictions in others.

Magnetic resonance imaging subtraction vs. pre- and post-contrast 3D gradient recalled echo fat suppressed imaging for evaluation of the canine and feline brain.

Subtraction magnetic resonance imaging (MRI) has been reported to increase accuracy in the diagnosis of meningeal and inflammatory brain diseases in small animals. 3D T1W gradient recalled echo (GRE) techniques have been proposed as a suitable alternative to conventional spin echo sequences in imaging the canine brain. The aim of this study was to compare subtraction images and paired pre- and post-contrast 3D T1W GRE fat suppressed (FS) images in canine and feline MRI studies using clinical diagnosis as the gold standard. Paired pre- and post-contrast T1W 3D FS GRE images and individual subtraction images of 100 small animal patients were randomized and independently evaluated by 2 blinded observers. Diagnosis categories were "normal," "inflammatory," "neoplastic," and "other." Clinical diagnosis was made in the same categories and served as the gold standard. Image interpretation results were compared to the clinical diagnosis. Interobserver agreement was determined. Clinically, 41 studies were categorized as "normal," 18 as "inflammatory," 28 as "neoplastic," and 13 as "other." The agreement of the pre- and post-contrast GRE images with the gold standard was significantly higher than that of the subtraction images (k = 0.7491 vs. k = 0.5924; p = 0.0075). The largest sources of error were misinterpretation of "other" as "normal" and "normal" as "inflammatory." There was no significant difference between the two observers (p = 0.8820). Based on this study, subtraction images do not provide an advantage to paired pre- and post-contrast FS GRE images when evaluating the canine and feline brain.

A direct sensitivity approach to predict hourly ozone resulting from compliance with the National Ambient Air Quality Standard.

In setting primary ambient air quality standards, the EPA's responsibility under the law is to establish standards that protect public health. As part of the current review of the ozone National Ambient Air Quality Standard (NAAQS), the US EPA evaluated the health exposure and risks associated with ambient ozone pollution using a statistical approach to adjust recent air quality to simulate just meeting the current standard level, without specifying emission control strategies. One drawback of this purely statistical concentration rollback approach is that it does not take into account spatial and temporal heterogeneity of ozone response to emissions changes. The application of the higher-order decoupled direct method (HDDM) in the community multiscale air quality (CMAQ) model is discussed here to provide an example of a methodology that could incorporate this variability into the risk assessment analyses. Because this approach includes a full representation of the chemical production and physical transport of ozone in the atmosphere, it does not require assumed background concentrations, which have been applied to constrain estimates from past statistical techniques. The CMAQ-HDDM adjustment approach is extended to measured ozone concentrations by determining typical sensitivities at each monitor location and hour of the day based on a linear relationship between first-order sensitivities and hourly ozone values. This approach is demonstrated by modeling ozone responses for monitor locations in Detroit and Charlotte to domain-wide reductions in anthropogenic NOx and VOCs emissions. As seen in previous studies, ozone response calculated using HDDM compared well to brute-force emissions changes up to approximately a 50% reduction in emissions. A new stepwise approach is developed here to apply this method to emissions reductions beyond 50% allowing for the simulation of more stringent reductions in ozone concentrations. Compared to previous rollback methods, this application of modeled sensitivities to ambient ozone concentrations provides a more realistic spatial response of ozone concentrations at monitors inside and outside the urban core and at hours of both high and low ozone concentrations.

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.

Simulating the degree of oxidation in atmospheric organic particles.

Modeled ratios of organic mass to organic carbon (OM/OC) and oxygen to carbon (n(O)/n(C)) in organic particulate matter are presented across the US for the first time and evaluated extensively against ambient measurements. The base model configuration systematically underestimates OM/OC ratios during winter and summer months. Model performance is greatly improved by applying source-specific OM/OC ratios to the primary organic aerosol (POA) emissions and incorporating a new parametrization to simulate oxidative aging of POA in the atmosphere. These model improvements enable simulation of urban-scale gradients in OM/OC with values in urban areas as much as 0.4 lower than in the surrounding regions. Modeled OM/OC and n(O)/n(C) ratios in January range from 1.4 to 2.0 and 0.2 to 0.6, respectively. In July, modeled OM/OC and n(O)/n(C) ratios range from 1.4 to 2.2 and 0.2 to 0.8, respectively. Improved model performance during winter is attributed entirely to our application of source-specific OM/OC ratios to the inventory. During summer, our treatment of oxidative aging also contributes to improved performance. Advancements described in this paper are codified in the latest public release of the Community Multiscale Air Quality model, CMAQv5.0.

Case report: MRI findings with CNS blastomycosis in three domestic cats.

Blastomycosis is a systemic mycotic infection caused by dimorphic fungi. The disease is rare in cats, and reports on imaging findings with central nervous system (CNS) involvement are limited. Magnetic resonance imaging (MRI) was performed antemortem in three feline patients. Imaging findings that may allow prioritization of intracranial blastomycosis over other differential diagnoses included focal or multifocal intra-axial mass lesions with dural contact, lesion hypointensity on T2-weighted images and diffusion-weighted imaging/apparent diffusion coefficient map (DWI/ADC), strong and homogeneous contrast enhancement of the lesion(s), concurrent meningeal enhancement, marked perilesional edema and mass-effect, and ocular abnormalities. One cat was managed successfully and had a recurrence of CNS blastomycosis more than 4.5 years after the initial diagnosis. Repeat MRI at that point revealed both new and persistent (chronic) abnormalities.

Changes in Ozone Chemical Sensitivity in the United States from 2007 to 2016.

Anthropogenic nitrogen oxide (NOx) and volatile organic compound (VOC) emissions in the U.S. have declined substantially over the last decade, altering the NOx-VOC chemistry and ozone (O3) production characteristics of many areas. In this work we use multiple air quality analysis tools to assess how these large reductions in NOx and VOC have affected O3 production regimes across the U.S. between 2007 and 2016. We first compare observed and modeled evolution of NOx-limited and NOx-saturated O3 formation regimes using a day-of-week (DOW) analysis. This comparison builds confidence in the model's ability to qualitatively capture O3 changes due to chemistry and meteorology both within years and across periods of large emissions decreases. DOW analysis, however, cannot definitively differentiate between emissions and meteorology impacts. We therefore supplement this analysis with sensitivity calculations from CAMx-HDDM to characterize modeled shifts in O3 formation chemistry between 2007 and 2016 in different regions of the U.S. We also conduct a more detailed investigation of the O3 chemical behavior observed in Chicago and Detroit, two complex urban areas in the Midwest. Both the ambient and modeling data show that more locations across the U.S. have shifted towards NOx-limited regimes between 2007 and 2016. The model-based HDDM sensitivity analysis shows only a few locations remaining NOx-saturated on high-O3 days in 2016 including portions of New York City, Chicago, Minneapolis, San Francisco and Los Angeles. This work offers insights into the current state of O3 production chemistry in large population centers across the U.S., as well as how O3 chemistry in these areas may evolve in the future.

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.

Variability in Observation-based Onroad Emission Constraints from a Near-road Environment.

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (ΔCO:ΔNOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNOx but not cross-road gradient ΔCO:ΔNOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

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.

Uncertainty associated with ambient ozone metrics in epidemiologic studies and risk assessments.

Epidemiologic studies relating ambient ozone concentrations to adverse health outcomes have typically relied on spatial averages of concentrations from nearby monitoring stations, referred to as "composite monitors." This practice reflects the assumption that ambient ozone concentrations within an urban area are spatially homogenous. We tested the validity of this assumption by comparing ozone data measured at individual monitoring sites within selected US urban areas to their respective composite monitor time series. We first characterized the temporal correlation between the composite monitor and individual monitors in each area. Next, we analyzed the heteroskedasticity of each relationship. Finally, we compared the distribution of concentrations measured at individual monitors to the composite monitor distribution. Individual monitors showed high correlation with the composite monitor over much of the range of ambient ozone concentrations, though correlations were lower at higher concentrations. The variance between individual monitors and the composite monitor increased as a function of concentration in nearly all the urban areas. Finally, we observed statistical bias in the composite monitor concentrations at the high end of the distribution. The degree to which these results introduce uncertainty into studies that utilize composite monitors depends on the contributions of peak ozone concentrations to reported health effect associations.

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.