Improving photochemical indicators for attributing ozone sensitivities in source apportionment analysis.

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O3) sensitivity to nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O3-sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O3 concentration contributions in the newly added transition regime were apportioned to NOx and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O3 sensitivity. The source contributions of O3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O3 concentration contributions changed significantly in the NOx-limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O3 to precursor emissions and may have far-reaching implications for informing O3 pollution control policy.

Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: Consistent S/IVOCs contribution but large differences in SOA aging.

Secondary organic aerosol (SOA) is an important component of atmospheric fine particulate matter (PM2.5), with contributions from anthropogenic and biogenic volatile organic compounds (AVOC and BVOC) and semi- (SVOC) and intermediate volatility organic compounds (IVOC). Policymakers need to know which SOA precursors are important but accurate simulation of SOA magnitude and contributions remain uncertain. Findings from existing SOA modeling studies have many inconsistencies due to differing emission inventory methodologies/assumptions, air quality model (AQM) algorithms, and other aspects of study methodologies. To address some of the inconsistencies, we investigated the role of different AQM SOA algorithms by applying two commonly used models, CAMx and CMAQ, with consistent emission inventories to simulate SOA concentrations and contributions for July and November 2018 in China. Both models have a volatility basis set (VBS) SOA algorithm but with different parameters and treatments of SOA photochemical aging. SOA generated from BVOC (i.e., BSOA) is found to be more important in southern China. In contrast, SOA generated from anthropogenic precursors is more prevalent in the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin and Central China. Both models indicate negligible SOA formation from SVOC emissions compared to other precursors. In July, when BVOC emissions are abundant, SOA is predominantly contributed by BSOA (except for NCP), followed by IVOC-SOA (i.e., SOA produced from IVOC) and ASOA (i.e., SOA produced from anthropogenic VOC). In contrast, in November, IVOC became the leading SOA contributor for all selected regions except PRD, illustrating the important contribution of IVOC emissions to SOA formation. While both models generally agree in terms of the spatial distributions and seasonal variations of different SOA components, CMAQ tends to predict higher BSOA, while CAMx generates higher ASOA concentrations. As a result, CMAQ results suggest that BSOA concentration is always higher than ASOA in November, while CAMx emphasizes the importance of ASOA. Utilizing a conceptual model, we found that different treatment of SOA aging between the two models is a major cause of differences in simulated ASOA concentrations. The step-wise SOA aging scheme implemented in the CAMx VBS (based on gas-phase reactions with OH radical and similar to other models) exhibits a strong enhancement effect on simulated ASOA concentrations, and this effect increases with the ambient organic aerosol (OA) concentrations. The CMAQ aerosol module implements a different SOA aging scheme that represents particle-phase oligomerization and has smaller impacts on total OA. Different structures and/or parameters of the SOA aging schemes are being used in current models, which could greatly affect model simulations of OA in ways that are difficult to anticipate. Our results indicate that future control policies should aim at reducing IVOC emissions as well as traditional VOC emissions. In addition, aging schemes are the major driver in CMAQ vs. CAMx treatments of ASOA and their resulting predicted mass. More sophisticated measurement data (e.g., with resolved OA components) and/or chamber experiments (e.g., investigating how aging influences SOA yields) are needed to better characterize SOA aging and constrain model parameterizations.

Economy-wide evaluation of CO2 and air quality impacts of electrification in the United States.

Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO2 and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO2 and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM2.5), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM2.5 sources-such as fugitive dust and agricultural emissions-can offset electrification benefits, suggesting that additional measures beyond CO2 policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification.

Chemical Sensitivity Analysis and Uncertainty Analysis of Ozone Production in the Comprehensive Air Quality Model with Extensions Applied to Eastern Texas.

Chemical sensitivity analysis (CSA) is a new probing tool for sampling sensitivities to chemistry parameters during a three-dimensional (3-D) simulation. CSA was applied to rank all of the parameters in the Carbon Bond 6 revision 4 (CB6r4) mechanism and to create an ensemble of six chemical mechanisms representing higher and lower O3 formations than CB6r4. This ensemble of mechanisms was used to estimate the uncertainty from the chemistry in a 3-D simulation and combined with uncertainties from other model inputs obtained from calculations of their sensitivities. The overall uncertainty (1σ) in O3 predictions for eastern Texas was 10-11 ppb in the Gulf of Mexico near Galveston and 7-8 ppb in much of the rest of the domain on the higher O3 days of June 2012. As a percent of the O3 concentration, the uncertainty was more uniform over the domain, 11-14%. Chemistry and emissions make the largest contributions to the O3 uncertainty. Uncertainty in the dry deposition velocities is less important in urban areas and the Gulf, but it is similar in importance to the uncertainty in chemistry and emissions at most other locations. Uncertainty in O3 boundary concentrations is the least important.

Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3.

In the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), and as contribution to the second phase of the Hemispheric Transport of Air Pollution (HTAP2) activities for Europe and North America, the impacts of a 20 % decrease of global and regional anthropogenic emissions on surface air pollutant levels in 2010 are simulated by an international community of regional-scale air quality modeling groups, using different state-of-the-art chemistry and transport models (CTMs). The emission perturbations at the global level, as well as over the HTAP2-defined regions of Europe, North America and East Asia, are first simulated by the global Composition Integrated Forecasting System (C-IFS) model from European Centre for Medium-Range Weather Forecasts (ECMWF), which provides boundary conditions to the various regional CTMs participating in AQMEII3. On top of the perturbed boundary conditions, the regional CTMs used the same set of perturbed emissions within the regional domain for the different perturbation scenarios that introduce a 20 % reduction of anthropogenic emissions globally as well as over the HTAP2-defined regions of Europe, North America and East Asia. Results show that the largest impacts over both domains are simulated in response to the global emission perturbation, mainly due to the impact of domestic emission reductions. The responses of NO2, SO2 and PM concentrations to a 20 % anthropogenic emission reduction are almost linear (~ 20 % decrease) within the global perturbation scenario with, however, large differences in the geographical distribution of the effect. NO2, CO and SO2 levels are strongly affected over the emission hot spots. O3 levels generally decrease in all scenarios by up to ~ 1 % over Europe, with increases over the hot spot regions, in particular in the Benelux region, by an increase up to ~ 6 % due to the reduced effect of NOx titration. O3 daily maximum of 8 h running average decreases in all scenarios over Europe, by up to ~ 1 %. Over the North American domain, the central-to-eastern part and the western coast of the US experience the largest response to emission perturbations. Similar but slightly smaller responses are found when domestic emissions are reduced. The impact of intercontinental transport is relatively small over both domains, however, still noticeable particularly close to the boundaries. The impact is noticeable up to a few percent, for the western parts of the North American domain in response to the emission reductions over East Asia. O3 daily maximum of 8 h running average decreases in all scenarios over north Europe by up to ~ 5 %. Much larger reductions are calculated over North America compared to Europe. In addition, values of the Response to Extra-Regional Emission Reductions (RERER) metric have been calculated in order to quantify the differences in the strengths of nonlocal source contributions to different species among the different models. We found large RERER values for O3 (~ 0.8) over both Europe and North America, indicating a large contribution from non-local sources, while for other pollutants including particles, low RERER values reflect a predominant control by local sources. A distinct seasonal variation in the local vs. non-local contributions has been found for both O3 and PM2.5, particularly reflecting the springtime long-range transport to both continents.

Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3.

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O3, CO, SO2 and PM2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry-transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ~ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ~ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ~ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.

Attributing differences in the fate of lateral boundary ozone in AQMEII3 models to physical process representations.

Increasing emphasis has been placed on characterizing the contributions and the uncertainties of ozone imported from outside the US. In chemical transport models (CTMs), the ozone transported through lateral boundaries (referred to as LB ozone hereafter) undergoes a series of physical and chemical processes in CTMs, which are important sources of the uncertainty in estimating the impact of LB ozone on ozone levels at the surface. By implementing inert tracers for LB ozone, the study seeks to better understand how differing representations of physical processes in regional CTMs may lead to differences in the simulated LB ozone that eventually reaches the surface across the US. For all the simulations in this study (including WRF/CMAQ, WRF/CAMx, COSMO-CLM/CMAQ, and WRF/DEHM), three chemically inert tracers that generally represent the altitude ranges of the planetary boundary layer (BC1), free troposphere (BC2), and upper troposphere-lower stratosphere (BC3) are tracked to assess the simulated impact of LB specification. Comparing WRF/CAMx with WRF/CMAQ, their differences in vertical grid structure explain 10 %-60 % of their seasonally averaged differences in inert tracers at the surface. Vertical turbulent mixing is the primary contributor to the remaining differences in inert tracers across the US in all seasons. Stronger vertical mixing in WRF/CAMx brings more BC2 downward, leading to higher BCT (BCT = BC1+BC2+BC3) and BC2/BCT at the surface in WRF/CAMx. Meanwhile, the differences in inert tracers due to vertical mixing are partially counteracted by their difference in sub-grid cloud mixing over the southeastern US and the Gulf Coast region during summer. The process of dry deposition adds extra gradients to the spatial distribution of the differences in DM8A BCT by 5-10 ppb during winter and summer. COSMO-CLM/CMAQ and WRF/CMAQ show similar performance in inert tracers both at the surface and aloft through most seasons, which suggests similarity between the two models at process level. The largest difference is found in summer. Sub-grid cloud mixing plays a primary role in their differences in inert tracers over the southeastern US and the oceans in summer. Our analysis of the vertical profiles of inert tracers also suggests that the model differences in dry deposition over certain regions are offset by the model differences in vertical turbulent mixing, leading to small differences in inert tracers at the surface in these regions.

Seasonal ozone vertical profiles over North America using the AQMEII3 group of air quality models: model inter-comparison and stratospheric intrusions.

This study evaluates simulated vertical ozone profiles produced in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) against ozonesonde observations in North America for the year 2010. Four research groups from the United States (US) and Europe have provided modeled ozone vertical profiles to conduct this analysis. Because some of the modeling systems differ in their meteorological drivers, wind speed and temperature are also included in the analysis. In addition to the seasonal ozone profile evaluation for 2010, we also analyze chemically inert tracers designed to track the influence of lateral boundary conditions on simulated ozone profiles within the modeling domain. Finally, cases of stratospheric ozone intrusions during May-June 2010 are investigated by analyzing ozonesonde measurements and the corresponding model simulations at Intercontinental Chemical Transport Experiment Ozonesonde Network Study (IONS) experiment sites in the western United States. The evaluation of the seasonal ozone profiles reveals that, at a majority of the stations, ozone mixing ratios are underestimated in the 1-6 km range. The seasonal change noted in the errors follows the one seen in the variance of ozone mixing ratios, with the majority of the models exhibiting less variability than the observations. The analysis of chemically inert tracers highlights the importance of lateral boundary conditions up to 250 hPa for the lower-tropospheric ozone mixing ratios (0-2 km). Finally, for the stratospheric intrusions, the models are generally able to reproduce the location and timing of most intrusions but underestimate the magnitude of the maximum mixing ratios in the 2-6 km range and overestimate ozone up to the first kilometer possibly due to marine air influences that are not accurately described by the models. The choice of meteorological driver appears to be a greater predictor of model skill in this altitude range than the choice of air quality model.

Air Quality Impacts of Electrifying Vehicles and Equipment Across the United States.

U.S.-wide air quality impacts of electrifying vehicles and off-road equipment are estimated for 2030 using 3-D photochemical air quality model and detailed emissions inventories. Electrification reduces tailpipe emissions and emissions from petroleum refining, transport, and storage, but increases electricity demand. The Electrification Case assumes approximately 17% of light duty and 8% of heavy duty vehicle miles traveled and from 17% to 79% of various off-road equipment types considered good candidates for electrification is powered by electricity. The Electrification Case raises electricity demand by 5% over the 2030 Base Case but nitrogen oxide (NOx) emissions decrease by 209 thousand tons (3%) overall. Emissions of other criteria pollutants also decrease. Air quality benefits of electrification are modest, mostly less than 1 ppb for ozone and 0.5 µg m-3 for fine particulate matter (PM2.5), but widespread. The largest reductions for ozone and PM occur in urban areas due to lower mobile source emissions. Electrifying off-road equipment yields more benefits than electrifying on-road vehicles. Reduced crude oil imports and associated marine vessel emissions cause additional benefits in port cities. Changes in other gas and PM emissions, as well as impacts on acid and nutrient deposition, are discussed.

Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown.

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overallsense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluaion methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

Recommendations on statistics and benchmarks to assess photochemical model performance.

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance "benchmarks" for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future. IMPLICATIONS: We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.

Source apportionment of emissions from light-duty gasoline vehicles and other sources in the United States for ozone and particulate matter.

UNLABELLED: Federal Tier 3 motor vehicle emission and fuel sulfur standards have been promulgated in the United States to help attain air quality standards for ozone and PM2.5 (particulate matter with an aerodynamic diameter <2.5 µm). The authors modeled a standard similar to Tier 3 (a hypothetical nationwide implementation of the California Low Emission Vehicle [LEV] III standards) and prior Tier 2 standards for on-road gasoline-fueled light-duty vehicles (gLDVs) to assess incremental air quality benefits in the United States (U.S.) and the relative contributions of gLDVs and other major source categories to ozone and PM2.5 in 2030. Strengthening Tier 2 to a Tier 3-like (LEV III) standard reduces the summertime monthly mean of daily maximum 8-hr average (MDA8) ozone in the eastern U.S. by up to 1.5 ppb (or 2%) and the maximum MDA8 ozone by up to 3.4 ppb (or 3%). Reducing gasoline sulfur content from 30 to 10 ppm is responsible for up to 0.3 ppb of the improvement in the monthly mean ozone and up to 0.8 ppb of the improvement in maximum ozone. Across four major urban areas-Atlanta, Detroit, Philadelphia, and St. Louis-gLDV contributions range from 5% to 9% and 3% to 6% of the summertime mean MDA8 ozone under Tier 2 and Tier 3, respectively, and from 7% to 11% and 3% to 7% of the maximum MDA8 ozone under Tier 2 and Tier 3, respectively. Monthly mean 24-hr PM2.5 decreases by up to 0.5 µg/m(3) (or 3%) in the eastern U.S. from Tier 2 to Tier 3, with about 0.1 µg/m(3) of the reduction due to the lower gasoline sulfur content. At the four urban areas under the Tier 3 program, gLDV emissions contribute 3.4-5.0% and 1.7-2.4% of the winter and summer mean 24-hr PM2.5, respectively, and 3.8-4.6% and 1.5-2.0% of the mean 24-hr PM2.5 on days with elevated PM2.5 in winter and summer, respectively. IMPLICATIONS: Following U.S. Tier 3 emissions and fuel sulfur standards for gasoline-fueled passenger cars and light trucks, these vehicles are expected to contribute less than 6% of the summertime mean daily maximum 8-hr ozone and less than 7% and 4% of the winter and summer mean 24-hr PM2.5 in the eastern U.S. in 2030. On days with elevated ozone or PM2.5 at four major urban areas, these vehicles contribute less than 7% of ozone and less than 5% of PM2.5, with sources outside North America and U.S. area source emissions constituting some of the main contributors to ozone and PM2.5, respectively.

Source Apportionment of the Anthropogenic Increment to Ozone, Formaldehyde, and Nitrogen Dioxide by the Path-Integral Method in a 3D Model.

The anthropogenic increment of a species is the difference in concentration between a base-case simulation with all emissions included and a background simulation without the anthropogenic emissions. The Path-Integral Method (PIM) is a new technique that can determine the contributions of individual anthropogenic sources to this increment. The PIM was applied to a simulation of O3 formation in July 2030 in the U.S., using the Comprehensive Air Quality Model with Extensions and assuming advanced controls on light-duty vehicles (LDVs) and other sources. The PIM determines the source contributions by integrating first-order sensitivity coefficients over a range of emissions, a path, from the background case to the base case. There are many potential paths, with each representing a specific emission-control strategy leading to zero anthropogenic emissions, i.e., controlling all sources together versus controlling some source(s) preferentially are different paths. Three paths were considered, and the O3, formaldehyde, and NO2 anthropogenic increments were apportioned to five source categories. At rural and urban sites in the eastern U.S. and for all three paths, point sources typically have the largest contribution to the O3 and NO2 anthropogenic increments, and either LDVs or area sources, the smallest. Results for formaldehyde are more complex.

Trends in on-road vehicle emissions and ambient air quality in Atlanta, Georgia, USA, from the late 1990s through 2009.

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO(x)), and volatile organic compounds (VOCs) during 1995-2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO(x), and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995-2009 period despite an increase in total vehicle distance traveled. The CO and NO(x) emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO(x) in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001-2009. Although this trend coexists with the declining trends in on-road NO(x), VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors. Implications: Large reductions in on-road vehicle emissions of CO and NO(x) in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO(x) during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.

Implementation and application of sub-grid-scale plume treatment in the latest version of EPA's third-generation air quality model, CMAQ 5.01.

UNLABELLED: The Community Multiscale Air Quality (CMAQ) modeling system Version 5.0 (CMAQv5.0) was released by the US. Environmental Protection Agency (EPA) in February 2012, with an interim release (v5.01) in July 2012. Because CMAQ is a community model, the EPA encourages the development of proven alternative science treatments by external scientists and developers that can be incorporated as part of an official CMAQ release. This paper describes the implementation, evaluation, and testing of a plume-in-grid (PinG) module in CMAQ 5.01. The PinG module, also referred to as Advanced Plume Treatment (APT), provides the capability of resolving sub-grid-scale processes, such as the transport and chemistry of point-source plumes, in a grid model. The new PinG module in CMAQ 5.01 is applied and evaluated for two 15-day summer and winter periods in 2005 to the eastern United States, and the results are compared with those from the base CMAQ 5.01. Eighteen large point sources of NO(x) in the eastern United States were selected for explicit plume treatment with APT in the PinG simulation. The results show that overall model performance is negligibly affected when PinG treatment is included. However the PinG model predicts significantly different contributions of the 18 sources to pollutant concentrations and deposition downwind of the point sources compared to the base model. IMPLICATIONS: This study describes the incorporation of a plume-in-grid (PinG) capability within the latest version of the EPA grid model, CMAQ. The capability addresses the inherent limitation of the grid model to resolve processes, such as the evolution of point-source plumes, which occur at scales much smaller than the grid resolution. The base grid model and the PinG version predict different source contributions to ozone and PM2.5 concentrations that need to be considered when source attribution studies are conducted to determine the impacts of large point sources on downwind concentrations and deposition of primary and secondary pollutants.

Response of fish tissue mercury in a freshwater lake to local, regional, and global changes in mercury emissions.

A suite of mechanistic atmospheric and mercury (Hg) cycling and bioaccumulation models is applied to simulate atmospheric Hg deposition and Hg concentrations in the water column and in fish in a Hg-impaired freshwater lake located in the northeastern United States that receives its Hg loading primarily through deposition. Two future-year scenarios evaluate the long-term response of fish tissue Hg concentrations to reductions in local and nationwide coal-fired electric-generating utility and other Hg emissions and an increase or decrease in global (non-US) Hg emissions. Results indicate that fish tissue Hg concentrations in this ecosystem could require approximately 3 yr to 8 yr to begin to respond to declines in US emissions and deposition with a fish Hg reduction proportional to deposition reduction requiring over 50 yr. Furthermore, recovery could potentially be partially or completely offset by growth in non-US Hg emissions.

Establishing policy relevant background (PRB) ozone concentrations in the United States.

Policy Relevant Background (PRB) ozone concentrations are defined by the United States (U.S.) Environmental Protection Agency (EPA) as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America (i.e., the U.S, Canada, and Mexico). Estimates of PRB ozone have had an important role historically in the EPA's human health and welfare risk analyses used in establishing National Ambient Air Quality Standards (NAAQS). The margin of safety for the protection of public health in the ozone rulemaking process has been established from human health risks calculated based on PRB ozone estimates. Sensitivity analyses conducted by the EPA have illustrated that changing estimates of PRB ozone concentrations have a progressively greater impact on estimates of mortality risk as more stringent standards are considered. As defined by the EPA, PRB ozone is a model construct, but it is informed by measurements at relatively remote monitoring sites (RRMS). This review examines the current understanding of PRB ozone, based on both model predictions and measurements at RRMS, and provides recommendations for improving the definition and determination of PRB ozone.

Ozone impacts of natural gas development in the Haynesville Shale.

The Haynesville Shale is a subsurface rock formation located beneath the Northeast Texas/Northwest Louisiana border near Shreveport. This formation is estimated to contain very large recoverable reserves of natural gas, and during the two years since the drilling of the first highly productive wells in 2008, has been the focus of intensive leasing and exploration activity. The development of natural gas resources within the Haynesville Shale is likely to be economically important but may also generate significant emissions of ozone precursors. Using well production data from state regulatory agencies and a review of the available literature, projections of future year Haynesville Shale natural gas production were derived for 2009-2020 for three scenarios corresponding to limited, moderate, and aggressive development. These production estimates were then used to develop an emission inventory for each of the three scenarios. Photochemical modeling of the year 2012 showed increases in 2012 8-h ozone design values of up to 5 ppb within Northeast Texas and Northwest Louisiana resulting from development in the Haynesville Shale. Ozone increases due to Haynesville Shale emissions can affect regions outside Northeast Texas and Northwest Louisiana due to ozone transport. This study evaluates only near-term ozone impacts, but the emission inventory projections indicate that Haynesville emissions may be expected to increase through 2020.

Comparison of source apportionment and sensitivity analysis in a particulate matter air duality model.

Two efficient methods to study relationships between particulate matter (PM) concentrations and emission sources are compared in the three-dimensional comprehensive air quality model with extensions (CAMx). Particulate source apportionment technology (PSAT) is a tagged species method that apportions concentrations of PM components to their respective primary precursors, e.g., sulfate is apportioned to SOx, nitrate to NOx, etc. The decoupled direct method (DDM) calculates first-order sensitivities of PM concentrations to model inputs. Both tools were applied to two month long (February and July) PM modeling episodes and evaluated against changes in PM concentrations due to various emission reductions. The results show that source contributions calculated by PSAT start to deviate from the actual model responses as indirect effects from limiting reactants or nonprimary precursor emissions become important The DDM first-order sensitivity is useful for determining source contributions only if the model response to input changes is reasonably linear. For secondary inorganic PM, the response is linear for emission reductions of 20% in all cases considered and reasonably linear for reductions of 100% inthe case of on-road mobile sources. The model response for secondary organic aerosols and primary PM remains nearly linear to 100% reductions in anthropogenic emissions.