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ABSTRACT 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 [...]

ABSTRACT 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 [...]

ABSTRACT 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 [...]

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]

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]

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.