Likelihood of achieving air quality targets under model uncertainties.

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States.

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O3), fine particulate matter (PM2.5), and regional haze in the Southeastern United States. O3 and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO2, primary carbon (PC), NH3, NO(x) or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case. The sensitivity of summertime O3 to VOC emissions is small in the Southeast and ground-level NO(x) controls are generally more beneficial than elevated NO(x) controls (per unit mass of emissions reduced). SO2 emission reduction is the most beneficial control strategy in reducing summertime PM2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO2. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH3 emissions is an effective strategy to reduce wintertime ammonium nitrate (NO3NH4) levels and improve visibility; NO(x) emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.

Assessment of biomass burning emissions and their impacts on urban and regional PM2.5: a Georgia case study.

Biomass burning is a major and growing contributor to particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5). Such impacts (especially individual impacts from each burning source) are quantified using the Community Multiscale Air Quality (CMAQ) Model, a chemical transport model (CTM). Given the sensitivity of CTM results to uncertain emission inputs, simulations were conducted using three biomass burning inventories. Shortcomings in the burning emissions were also evaluated by comparing simulations with observations and results from a receptor model. Model performance improved significantly with the updated emissions and speciation profiles based on recent measurements for biomass burning: mean fractional bias is reduced from 22% to 4% for elemental carbon and from 18% to 12% for organic matter; mean fractional error is reduced from 59% to 50% for elemental carbon and from 55% to 49% for organic matter. Quantified impacts of biomass burning on PM2.5 during January, March, May, and July 2002 are 3.0, 5.1, 0.8, and 0.3 microg m(-3) domainwide on average, with more than 80% of such impacts being from primary emissions. Impacts of prescribed burning dominate biomass burning impacts, contributing about 55% and 80% of PM2.5 in January and March, respectively, followed by land clearing and agriculture field burning. Significant impacts of wildfires in May and residential wood combustion in fireplaces and woodstoves in January are also found.

Regional air quality: local and interstate impacts of NO(x) and SO2 emissions on ozone and fine particulate matter in the eastern United States.

While the U.S. air quality management system is largely designed and managed on a state level, many critical air quality problems are now recognized as regional. In particular, concentrations of two secondary pollutants, ozone and particulate matter, are often above regulated levels and can be dependent on emissions from upwind states. Here, impacts of statewide emissions on concentrations of local and downwind states' ozone and fine particulate matter are simulated for three seasonal periods in the eastern United States using a regional Eulerian photochemical model. Impacts of ground level NO(x) (e.g., mobile and area sources), elevated NO(x) (e.g., power plants and large industrial sources), and SO2 emissions are examined. An average of 77% of each state's ozone and PM(2.5) concentrations that are sensitive to the emissions evaluated here are found to be caused by emissions from other states. Delaware, Maryland, New Jersey, Virginia, Kentucky, and West Virginia are shown to have high concentrations of ozone and PM(2.5) caused by interstate emissions. When weighted by population, New York receives increased interstate contributions to these pollutants and contributions to ozone from local emissions are generally higher. When accounting for emission rates, combined states from the western side of the modeling domain and individual states such as Illinois, Tennessee, Indiana, Kentucky, and Georgia are major contributors to interstate ozone. Ohio, Indiana, Tennessee, Kentucky, and Illinois are the major contributors to interstate PM(2.5). When accounting for an equivalent mass of emissions, Tennessee, Kentucky, West Virginia, Virginia, and Alabama contribute large fractions of these pollutants to other states.

An integrated framework for multipollutant air quality management and its application in Georgia.

Air protection agencies in the United States increasingly confront non-attainment of air quality standards for multiple pollutants sharing interrelated emission origins. Traditional approaches to attainment planning face important limitations that are magnified in the multipollutant context. Recognizing those limitations, the Georgia Environmental Protection Division has adopted an integrated framework to address ozone, fine particulate matter, and regional haze in the state. Rather than applying atmospheric modeling merely as a final check of an overall strategy, photochemical sensitivity analysis is conducted upfront to compare the effectiveness of controlling various precursor emission species and source regions. Emerging software enables the modeling of health benefits and associated economic valuations resulting from air pollution control. Photochemical sensitivity and health benefits analyses, applied together with traditional cost and feasibility assessments, provide a more comprehensive characterization of the implications of various control options. The fuller characterization both informs the selection of control options and facilitates the communication of impacts to affected stakeholders and the public. Although the integrated framework represents a clear improvement over previous attainment-planning efforts, key remaining shortcomings are also discussed.

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains: Part II. Fine particulate matter and visibility.

As part of the Southern Appalachian Mountains Initiative, a comprehensive air quality modeling system was developed to evaluate potential emission control strategies to reduce atmospheric pollutant levels at the Class I areas located in the Southern Appalachian Mountains. Six multiday episodes between 1991 and 1995 were simulated, and the skill of the modeling system was evaluated. Two papers comprise various parts of this study. Part I details the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. This paper (part II) addresses issues involved with modeling particulate matter (PM) and its relationship to visibility. For most of the episodes, the fractional error was approximately 50% or less for the major constituents of the fine PM (i.e., sulfate [SO4] and organics) in the region. The mean normalized errors and fractional errors are generally larger for the NO3 and soil components, but these components are relatively small. Variations in modeling bias with pollutant levels were also examined. The model showed a systematic overestimation for low levels and an underestimation for high levels for most PM species. For ammonium, the model showed better performance at lower SO4 concentrations when the measured SO4 was assumed to be completely neutralized (ammonium sulfate) and better performance at higher SO4 concentrations when the partially neutralized (ammonium bisulfate) assumption was made. The contributions of various components of PM to reductions in visibility were also calculated; SO4 was found to be the major contributor.

Preliminary evaluation of the Community Multiscale Air Quality model for 2002 over the Southeastern United States.

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) is one of five Regional Planning Organizations that is charged with the management of haze, visibility, and other regional air quality issues in the United States. The VISTAS Phase I work effort modeled three episodes (January 2002, July 1999, and July 2001) to identify the optimal model configuration(s) to be used for the 2002 annual modeling in Phase II. Using model configurations recommended in the Phase I analysis, 2002 annual meteorological (Mesoscale Meterological Model [MM5]), emissions (Sparse Matrix Operator Kernal Emissions [SMOKE]), and air quality (Community Multiscale Air Quality [CMAQ]) simulations were performed on a 36-km grid covering the continental United States and a 12-km grid covering the Eastern United States. Model estimates were then compared against observations. This paper presents the results of the preliminary CMAQ model performance evaluation for the initial 2002 annual base case simulation. Model performance is presented for the Eastern United States using speciated fine particle concentration and wet deposition measurements from several monitoring networks. Initial results indicate fairly good performance for sulfate with fractional bias values generally within +/-20%. Nitrate is overestimated in the winter by approximately +50% and underestimated in the summer by more than -100%. Organic carbon exhibits a large summer underestimation bias of approximately -100% with much improved performance seen in the winter with a bias near zero. Performance for elemental carbon is reasonable with fractional bias values within +/- 40%. Other fine particulate (soil) and coarse particular matter exhibit large (80-150%) overestimation in the winter but improved performance in the summer. The preliminary 2002 CMAQ runs identified several areas of enhancements to improve model performance, including revised temporal allocation factors for ammonia emissions to improve nitrate performance and addressing missing processes in the secondary organic aerosol module to improve OC performance.

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains. Part I: hourly and seasonal ozone.

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI's assessment.