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One of the largest rail yard facilities in the Southeastern US, the Inman and Tilford yards, is located in the northwestern section of Atlanta, Georgia alongside other industries, schools, businesses, and dwellings. It is a significant source of fine particulate (PM2.5) and black carbon (BC) (Galvis, Bergin, & Russell, 2013). We calculate 2011 PM2.5 and BC emissions from the rail yards and primary industrial and on-road mobile sources in the area and determine their impact on local air quality using Gaussian dispersion modeling. We determine the change in PM2.5 and BC concentrations that could be accomplished by upgrading traditional switcher locomotives used in these rail yards to a lower emitting technology and evaluate the health benefits for comparison with upgrade costs. Emissions from the rail yards were estimated using reported fuel consumption data (GAEPD, 2012b) and emission factors previously measured in the rail yards (Galvis et al., 2013). Model evaluation against 2011 monitoring data found agreement between measured and simulated concentrations. Model outputs indicate that the line-haul and switcher activities are responsible for increments in annual average concentrations of approximately 0.5±0.03 μg/m(3) (39%) and 0.7±0.04 μg/m(3) (56%) of BC, and for 1.0±0.1 μg/m(3) (7%) and 1.6±0.2 μg/m(3) (14%) of PM2.5 at two monitoring sites located north and south of the rail yards respectively. Upgrading the switcher locomotives at the yards with a lower emitting technology in this case "mother slug" units could decrease PM2.5 and BC emissions by about 9 and 3 t/year respectively. This will lower annual average PM2.5 concentrations between 0.3±0.1 μg/m(3) and 0.6±0.1 μg/m(3) and BC concentrations between 0.1±0.02 μg/m(3) and 0.2±0.03 μg/m(3) at monitoring sites north and south of the rail yards respectively, and would facilitate PM2.5 NAAQS attainment in the area. We estimate that health benefits of approximately 20 million dollars per year could be gained., (Copyright © 2015 Elsevier B.V. All rights reserved.)

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.

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.

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.

A detailed sensitivity analysis was conducted to help to quantify the impacts of various emission control options in terms of potential visibility improvements at class I national parks and wilderness areas in the southeastern United States. Particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) model, and light extinctions were calculated using the modeled PM concentrations. First, likely changes in visibility at class I areas were estimated for 2018. Then, using emission projections for 2018 as a starting point, the sensitivity of light extinction was evaluated by reducing emissions from various source categories by 30%. Source categories to be analyzed were determined using a tiered approach: any category that showed significant impact in one tier was broken into subcategories for further analysis in the next tier. In the first tier, sulfur dioxide (SO2), nitrogen oxides, ammonia, volatile organic compound, and primary carbon emissions were reduced uniformly over the entire domain. During summer, when most class I areas experience their worst visibility, reduction of SO2 emissions was the most effective control strategy. In the second tier, SO2 sources were separated as ground level and elevated. The elevated sources in 10 southeastern states were differentiated from those in the rest of the domain and broken into three subcategories: coal-fired power plant (CPP), other power plant, and other than power plant [i.e., non–electric generating unit (non EGU)]. The SO2 emissions from the CPP subcategory had the largest impact on visibility at class I areas, followed by the non-EGU subcategory. In the third tier, emissions from these two subcategories were further broken down by state. Most class I areas were affected by the emissions from several states, indicating the regional nature of the haze problem. Here, the visibility responses to all of the aforementioned emission reductions are quantified and deviations from general trends are identified. [ABSTRACT FROM AUTHOR]

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 ˜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. [ABSTRACT FROM AUTHOR]

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 over-estimation 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. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor on air pollution.

We developed a comprehensive air quality modeling system for use in integrated assessment studies. We evaluated the ability of the modeling system to reproduce observed PM2.5 levels in Class I areas of the Southern Appalachians Mountains using IMPROVE data during 9 week-long episodes between 1991 and 1995. The mean normalized error was less than 40% for the sulfate, ammonium, elemental carbon and organic components. The error was larger for nitrates and soils but these components constitute a small fraction of PM2.5 in the Southeast. Using meteorological inputs for the same episodes and emission projections, we estimated the PM2.5 levels in the year 2010. In addition, using the direct sensitivity analysis method, we estimated the sensitivity of PM2.5 levels to SO2, NOx and NH3 emissions from various sub-regions. These sensitivities give a first-order estimate of the responses to emissions controls beyond those already factored into the 2010 projections. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

To meet the requirements of the Clean Air Act's "good neighbor" provision, it is necessary to quantify the interstate transport of air pollution and determine if upwind states significantly contribute to noncompliance with the National Ambient Air Quality Standards (NAAQS) at downwind states. This study aims to estimate the contributions of ten southeastern states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) to ozone (O 3), and identify significant associations with nonattainment or maintenance at monitors across the eastern United States (US) in 2017. Two widely used air quality models, the Comprehensive Air quality Model with extensions (CAMx) and the Community Multiscale Air Quality model (CMAQ), using different versions of the Carbon Bond (CB) chemical mechanism, were applied to predict 2017 O 3 design values (DVFs) from 2011 O 3 design values (DVCs) at the US Environmental Protection Agency's (EPA) Air Quality System (AQS) sites throughout the eastern US. Since 2017 design values are already published, the accuracy of the EPA-recommended modeling methodology for projecting current design values into the future only by changing the emissions was evaluated. The assumption that good model performance in the base year would result in accurate future year ozone projections was tested. In general, CAMx/CB6r2 simulated higher DVFs than CMAQ/CB05, with an average difference of 0.5 ppb. As a result, this study identified 24 sites in the eastern US as nonattainment (DVF ≥ 76 ppb) with CAMx/CB6r2 compared to only 16 sites with CMAQ/CB05. Using monitor cell relative response factors (RRFs) instead of 3×3 cell matrix maximum RRFs (the approach used in EPA's proposed Cross-State Air Pollution Rule (CSAPR) modeling) (EPA, 2015a), led to higher DVFs at some monitors and lower DVFs at others. Source apportionment analysis with the CAMx Anthropogenic Precursor Culpability Assessment (APCA) probing tool revealed that NO x emissions dominated over VOC emissions in contributing to O 3 in downwind states. Contributions of total anthropogenic NO x emissions and contributions of Electric Generating Unit (EGU) NO x emissions from the southeastern states to O 3 at nonattainment and maintenance sites were quantified separately using CAMx/APCA as well as CMAQ/zero-out simulations. Contributions obtained from CAMx/APCA were typically larger than the contributions obtained from CMAQ/zero-out. Therefore, a "significant contribution" determined by CAMx/APCA may be insignificant according to CMAQ/zero-out. "Significant contribution" was defined as an impact larger than 1% of the 2008 O 3 NAAQS (i.e., ≥ 0.76 ppb). Alabama, Kentucky, Mississippi, Tennessee, Virginia and West Virginia were linked to 2, 3, 1, 1, 6 and 5 nonattainment sites with CAMx/APCA, while only Kentucky, Virginia and West Virginia were linked to 2, 3 and 2 nonattainment sites with CMAQ/zero-out. Additionally, for significant linkages, EGU NO x contributions were 10–55% of the total anthropogenic NO x contributions. The information produced in this study can be used by southeastern states to help develop "good neighbor" State Implementation Plans (SIPs). • 2017 modeled ozone design values are below 76 ppb in Southeast US, except Kentucky. • CAMx simulates higher future ozone design values than CMAQ, 0.5 ppb on average. • Using monitor cell rather than 3×3 matrix maximum yields higher O 3 design values. • Southeast US NO x emissions contribute above 1% to O 3 exceedances in neighbor states. • CAMx-APCA predicts more significant contributions than emission zero-out with CMAQ. [ABSTRACT FROM AUTHOR]