[1] To simulate ozone (O 3 ) air quality in future decades over the eastern United States, a modeling system consisting of the NASA Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model, the Pennsylvania State University/National Center for Atmospheric Research mesoscale regional climate model (MM5), and the Community Multiscale Air Quality model has been applied. Estimates of future emissions of greenhouse gases and ozone precursors are based on the A2 scenario developed by the Intergovernmental Panel on Climate Change (IPCC), one of the scenarios with the highest growth of CO 2 among all IPCC scenarios. Simulation results for five summers in the 2020s, 2050s, and 2080s indicate that summertime average daily maximum 8-hour O 3 concentrations increase by 2.7, 4.2, and 5.0 ppb, respectively, as a result of regional climate change alone with respect to five summers in the 1990s. Through additional sensitivity simulations for the five summers in the 2050s the relative impact of changes in regional climate, anthropogenic emissions within the modeling domain, and changed boundary conditions approximating possible changes of global atmospheric composition was investigated. Changed boundary conditions are found to be the largest contributor to changes in predicted summertime average daily maximum 8-hour O 3 concentrations (5.0 ppb), followed by the effects of regional climate change (4.2 ppb) and the effects of increased anthropogenic emissions (1.3 ppb). However, when changes in the fourth highest summertime 8-hour O 3 concentration are considered, changes in regional climate are the most important contributor to simulated concentration changes (7.6 ppb), followed by the effect of increased anthropogenic emissions (3.9 ppb) and increased boundary conditions (2.8 ppb). Thus, while previous studies have pointed out the potentially important contribution of growing global emissions and intercontinental transport to O 3 air quality in the United States for future decades, the results presented here imply that it may be equally important to consider the effects of a changing climate when planning for the future attainment of regional-scale air quality standards such as the U.S. national ambient air quality standard that is based on the fourth highest annual daily maximum 8-hour O 3 concentration.

We investigated how climate change could affect ambient ozone concentrations and the subsequent human health impacts. Hourly concentrations were estimated for 50 eastern US cities for five representative summers each in the 1990s and 2050s, reflecting current and projected future climates, respectively. Estimates of future concentrations were based on the IPCC A2 scenario using global climate, regional climate, and regional air quality models. This work does not explore the effects of future changes in anthropogenic emissions, but isolates the impact of altered climate on ozone and health. The cities' ozone levels are estimated to increase under predicted future climatic conditions, with the largest increases in cities with present-day high pollution. On average across the 50 cities, the summertime daily 1-h maximum increased 4.8 ppb, with the largest increase at 9.6 ppb. The average number of days/summer exceeding the 8-h regulatory standard increased 68%. Elevated ozone levels correspond to approximately a 0.11% to 0.27% increase in daily total mortality. While actual future ozone concentrations depend on climate and other influences such as changes in emissions of anthropogenic precursors, the results presented here Climatic Change (2007) 82: [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76]

An accurate description of emissions is crucial for model simulations to reproduce and interpret observed phenomena over extended time periods. In this study, we used an approach based on activity data to develop a consistent series of spatially resolved emissions in the United States from 1990 to 2010. The state-level anthropogenic emissions of SO₂, NO_x, CO, NMVOC (non-methane volatile organic compounds), NH₃, PM₁₀ and PM_2.5 for a total of 49 sectors were estimated based on several long-term databases containing information about activities and emission controls. Activity data for energy-related stationary sources were derived from the State Energy Data System. Corresponding emission factors reflecting implemented emission controls were calculated back from the National Emissions Inventory (NEI) for seven years (i.e., 1990, 1995, 1996, 1999, 2001, 2002 and 2005), and constrained by the AP-42 (US EPA's Compilation of Air Pollutant Emissions Factors) dataset. Activity data for mobile sources including different types of highway vehicles and non-highway equipment were obtained from highway statistics reported by the Federal Highway Administration. The trends in emission factors for highway mobile source were informed by the 2011 National Transportation Statistics. Emissions for all non-energy-related sources were either scaled by the growth ratio of activity indicators or adjusted based on the NEI trends report.

Because of the strengthened control efforts, particularly for the power sector and mobile sources, emissions of all pollutants except NH₃ were reduced by half over the last two decades. The emission trends developed in this study are comparable with the NEI trend report and EDGAR (Emissions Database for Global Atmospheric Research) data, but better constrained by trends in activity data. Reductions in SO₂, NO_x, CO and EC (speciation of PM_2.5 by SMOKE, Sparse Matrix Operator Kernel Emissions) emissions agree well with the observed changes in ambient SO₂, NO₂, CO and EC concentrations, suggesting that the various controls on emissions implemented over the last two decades are well represented in the emission inventories developed in this study. These inventories were processed by SMOKE and are now ready to be used for regional chemistry transport model simulations over the 1990–2010 period

Abstract.Trends in air quality across the Northern Hemisphere over a 21-year period were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the observed trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe.The model successfully reproduced the observed decreasing trends in SO 2 , NO 2 , 8 h O 3 maxima, SO 2− 4 and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO − 3 in the US, potentially pointing to uncertainties of NH 3 emissions. The model failed to capture the 6-year trends of SO 2 and NO 2 in CN-API (China -Air Pollution Index) from 2005 to 2010, but reproduced the observed pattern of O 3 trends shown in three World Data Centre for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resolution employed in these calculations, predicted SO 2 and NO 2 concentrations are underestimated relative to all urban networks, i.e., US-AQS (US -Air Quality System; normalized mean bias (NMB) = −38 % and −48 %), EU-AIRBASE (European Air quality data Base; NMB = −18 and −54 %) and CN-API (NMB = −36 and −68 %). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Programme), SO 2 is overestimated (NMB from 4 to 150 %) while NO 2 is simulated well (NMB within ±15 %) in all seasons. Correlations between simulated and observed O 3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and observed SO 2− 4 was found compared to that for SO 2 . Underestimation of summer SO 2− 4 in the US may be associated with the uncertainty in precipitation and associated wet scavenging representation in the model. The model exhibits worse performance for NO − 3 predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO − 3 as well as seasonal variations of NH 3 emissions. There are high correlations (R > 0.5) between observed and simulated EC, although the model underestimates the EC concentration by 65 % due to the coarse grid resolution as well as uncertainties in the PM speciation profile associated with EC emissions.The almost linear response seen in the trajectory of modeled O 3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NO x and volatile organic compound (VOC) emissions with a ratio of 0.46 may provide the most effective means for O 3 reductions for the region devoid of nonlinear response potentially associated with NO x or VOC limitation resulting from alternate strategies. The response of O 3 is ...

Although considerable uncertainty exists in climate forecasts and future health vulnerability, the range of projections we developed suggests that by midcentury, acclimatization may not completely mitigate the effects of climate change in the New York City metropolitan region, which would result in an overall net increase in heat-related premature mortality.

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

Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O3 concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O3-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O3 in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O3-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O3 precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O3-related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change.

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting