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SAMI's stated mission is: "Through a cooperative effort, identify and recommend reasonable measures to remedy existing and to prevent future adverse health effects from humaninduced air pollution on the air quality related values (AQRVs) of the Southern Appalachians, primarily of Class I parks and wilderness areas, weighing the environmental and socioeconomic implications of any recommendations" (SAMI, 1993). In order to achieve this goal, SAMI and its committees have developed various work plans for establishing the technical foundations for making informed decisions regarding emission management options. SAMI's Technical Oversight Committee (TOC) was established to provide technical support toward SAMI's mission. The TOC has primary responsibility for providing projections of the environmental, social, and economic consequences of emission management options considered by SAMI's Policy Committee. The TOC's Effects Subcommittee is responsible for overseeing the assessment of air pollution effects on visibility, streams, soil, and vegetation in response to changes in pollutant exposure. The objectives of the current study are to (1) evaluate existing information concerning the effects of air pollutants on visibility in the Southern Appalachians, particularly in Class I (park and wilderness) areas and (2) recommend methodologies to assess alternative emission management options with regard to their effects on visual air quality, taking into account the efforts of SAMI modeling, monitoring, and emission inventory efforts. These objectives have been met by conducting a comprehensive review of the literature to evaluate the adequacy of existing data and study results. Following the literature review, recommendations were then developed concerning the methodologies SAMI should use to address the following three questions: (1)X!What are the current status and historical trends of visibility in the Southern Appalachians and what is the contribution of anthropogenic activity to visibility impairment?t" (2)X!What is the relationship between air pollutant exposures (concentrations) and visibility responses?t" (3)X!What changes in visibility are projected to occur as a result of changes in exposures due to implementation of the 1990 Clean Air Act Amendments or other emission management options being considered by SAMI's Policy Committee?t"+0,,Ԍ X  STUDY APPROACH All available literature was reviewed to identify existing information concerning the effects of air pollutants on visibility in the SAMI region, particularly in Class I (park and wilderness) areas. The technical and scientific literature was examined to identify publications, studies, and data pertaining to historical visibility trends, current visibility conditions, contributions of anthropogenic emissions to current and historic conditions, and the relationships between pollutant concentrations and visibility impairment. This review was conducted to establish (1) the current status and historical trends of visibility impairment in the Southern Appalachians, (2) the contribution of anthropogenic activity to visibility impairment, (3) the relationships between visibility impairment and pollutant concentrations, and (4) models that can be used to estimate the effects on future visibility conditions due to various emission management options. An objective assessment was made of all relevant research, focusing on the likely application of the work to the current assessment objectives. The literature review began with the larger visibility studies that have been conducted throughout the United States during the last two decades. In addition to providing insights into the causes and effects of visibility impairment, summarizing air quality data and relating those observations to an understanding of optical physics, these major studies often represented compilations or distillations of a large body of previously published research. An example of this kind of study was performed for the National Acid Precipitation Assessment Program (NAPAP; Trijonis et al., 1990), in which the current knowledge regarding atmospheric visibility was clearly summarized. The bibliography (Appendix A) contains, for each significant visibility study, the study title, date of publication, authors (or agency) responsible for preparation of the study or report, for whom the study or report was prepared, the purpose of the study, data and models (if any) that were used in the study, and a brief description of the major findings and conclusions generated by the study. The major reports obtained were divided into three (overlapping) groups: (1) studies that summarized previous study results and/or made recommendations regarding data analysis and modeling, (2) those that characterized visibility conditions (particularly in the SAMI region), and (3) those that addressed visibility modeling issues. Following review of the major studies, a much larger body of literature was scrutinized to support the findings, conclusions, and recommendations presented in this report. A complete bibliography of relevant literature, reviewed as part of this investigation, may be found in the reference list following Section 6. This report summarizes the results of our investigation. First a discussion of visibility is presented in Section 2, identifying the important physical processes and factors that affect atmospheric visibility. Section 3 presents a characterization of the current and historical visibility conditions in the SAMI region, obtained from the review of previous study results. In Section 4, the modeling tools that are available for relating the chemical and physical characteristics of air pollutants to visibility are discussed. From an understanding of the important factors that affect visual air quality, the available databases and modeling tools are examined in Section 5 to identify critical gaps in our+0,, knowledge of visibility in the SAMI region. Lastly, recommendations are presented in Section 6 for characterizing visibility and for estimating the effects on visibility in the SAMI region due to various emission management options. The following section contains a technical description of the physical processes that govern visual air quality, its relationship to air pollutants, and the methods used for characterizing and evaluating visual air quality. It is presented to establish the scientific basis for the assessment of visibility data and models. Some readers may choose to skip Section 2 and proceed directly to the characterization of visibility in the SAMI region, presented in Section 3.  0,, 0,,  Y <K 2 WHAT IS VISIBILITY? ă Visibility in the atmosphere can be defined as our ability to detect and recognize objects using our sense of sight. The information obtained through our sense of sight depends on perception of differences in brightness and color. Even in the absence of air pollutants, the brightness of objects decreases as the distance between object and observer increases due to the loss of transmitted light scattered or absorbed by the intervening atmosphere. The presence of air pollutants increases the scattering and absorption of the intervening atmosphere so as to obscure objects that would otherwise (in a clean atmosphere) be recognizable. The "haze" of the intervening air affects the observer by changing the brightness of viewed objects, their perceived texture, and their colors. The human sensations of brightness and color are psychophysical in nature and cannot be measured by one instrument. The associated components that can be measured physically are the amount of light energy entering the eye (brightness) and its distribution among different wavelengths (color). Therefore, two separate phenomena are involved in visibility: (1) the optical properties of the atmosphere (acting as a semitransparent medium), and (2) the psychophysics of the human eyebrain system. A third element in visibility involves the psychology of value judgment. It is precisely the aesthetic quality of visual perception that has led to Clean Air Act (CAA) legislation to protect visibility in pristine areas (such as Class I parks and wilderness) of the United States (Hidy, 1984). A stepwise set of relationships can be used to estimate the effects of a deliberate plan of air pollution control (i.e., implementation of the 1990 CAA Amendments [CAAA] or some other emission management option [EMO]) on the human perception of visibility (Gray et al., 1993): X` hp x (#%'0*,.8135@8:Operation"b b \Measurements"  ~Data Analyses  $ National Weather Service Airport Visibility Data (Trijonis and Shapland, 1979) National Weather Service; urban and rural airports throughout US; since 1918 Human estimates of prevailing visibility historical visibility trends$ Sulfate Regional Experiment (Mueller and Hidy, 1983)D  EPRI; nonurban sites in NC, TN, VA and WV; 1977!1978D  3hr aerosol PM mass, sulfate concentrationsD  compute extinction coefficient, visual range using VISMAP| Interagency Monitoring of Protected Visual Environments (Sisler et al., 1993)  Natl Park Service; 36 Class I area sites including Shenandoah, Great Smoky NPs (since 1987), Dolly Sods, James River Face, and Shining Rock W (since 1992). 24hr samples twice a week:  xPH PM10, PM2.5 concentrations, chemical compositions, scene monitoring (camera), light extinction and absorption, meteorology (RH) source contributions to light extinction; visibilities expressed in deciviews; compare measured and estimated extinction values|\D  Eastern Fine Particle Visibility Network (Trijonis et al., 1990)T U.S. EPA; five eastern rural sites; 1988!1989T 24hr samples, fine particle mass, chemical composition, light extinction and absorption, photography supplement to IMPROVE network\ Southeastern Aerosol and Visibility Study (Saxena et al., 1995) EPRI, NPS; single monitoring location in Great Smoky Mountains NP; summer 1995@  xPX 1224hr PM10 and PM2.5 mass, chemical compositions, particle water, size distributions; optical extinction parameters; meteorology (T, RH); human perception measurements data collection in progress; plan is to relate aerosol properties to optical properties$ U.S. Forest Service Visibility and Monitoring (Air Resource Spec., 1995, Huber et al., 1990) U.S. Forest Service; 10 sites (8 Class I) in AL, AR, GA, NC, VA; 1987!1993 Photographic archives (scene monitoring) photographs collected at 9 am, 12 noon, and 3 pm daily standard visual range (from slides), deciview, extinction coefficient; trend analysis$\ Shenandoah Visibility Study (U.C. Davis, 1991)  U.C. Davis; two sites in VA; July!Sept 1991   xP0 12hr (6 am ! 6 pm) PM10  xP and PM2.5 mass, elements,  xP ions, carbon, NH3; light absorptionP! characterize air quality, visibility impairment\$ Great Smoky Mountains National Park Visibility & Air Quality Study (Trijonis et al., 1990)t% Tennessee Valley Authority; single airshed study; 1980!1983t% Fine and coarse aerosol mass, elements; scattering and extinction coefficients, photographyt% characterize visibility and air quality$\P! Shenandoah Valley Studies (Stevens et al., 1984)( General Motors; single location; 15 July ! 15 Aug 1980( Fine and coarse aerosol mass, ions, elements; extinction; human visual range estimates( characterize visibility and air quality\ t% Great Smoky Mountains Study (Stevens et al., 1980)t+ EPA; single location; 20!26 Sept 1978t+ Fine and coarse aerosol mass, elements; gasest+ aerosol characterization (t+0,,  X #Xw P7[hXP# AIR QUALITY IN THE SAMI REGION Theoretical empirically derived extinction budgets have consistently shown that fine atmospheric aerosols are the major contributors to anthropogenic light extinction. Fine particle scattering typically accounts for between 75 and 95 percent of nonRayleigh scattering in the eastern United States (Trijonis et al., 1990). It has also been observed that the eastern U.S. typically experiences higher concentrations of fine aerosols than in the western U.S. For example, the average fine particle mass concentrations between 1983 and 1986 at Shenandoah NP and Great Smoky Mountains NP were 10.2 and 11.5  Y1 g/m3, respectively. These two sites had the highest fine mass concentrations over the entire National Park Service (NPS) network of rural monitors (Eldred et al., 1987). The average fine mass concentrations at almost all rural locations in the western U.S. were  Y under 5 g/m3. Examination of IMPROVE data reveals that fine aerosol mass concentrations are highest in summer (Trijonis et al., 1990). In many locations throughout the U.S., and particularly in the rural east, fine particle sulfur is a significant contributor to the fine particle mass loadings. Particulate sulfur concentrations are a factor of about six higher in the rural East than in the rural West (Trijonis et al., 1990). The average fine particle sulfur concentrations at Shenandoah  YK NP and Great Smoky Mountains NP during 1983!1986 were 1.55 and 1.61 g/m3, respectively (Eldred et al., 1987). Sulfur particles (often in the form of fine sulfate aerosols) are the largest single species contributing to light extinction at most locations, especially in the East. This is due to the large affinity of sulfate for water, which increases light scattering efficiency (scattering per particle mass). Therefore the higher humidities occurring in the East play a large role in the effect that anthropogenic air pollution has on visibility in this area. Extinction budgets estimated by Trijonis et al. (1990) were used to show that of the 500 percent increase in extinction for the East relative to the West, about 300 percent is due to the increased contribution from sulfates. Extinction budgets assembled by Gray et al. (1993) using NPS IMPROVE data for 1983!1989 suggest that particle sulfur was responsible for over 50 percent of the total extinction in the SAMI region.  X  CURRENT VISIBILITY CONDITIONS Visibility in the rural East has been observed to be significantly lower than in the rural West (Trijonis, 1982). Using photographic records from 1986 to 1988, median visual range in the mountain areas of the Southwest was observed to be about 160 km, three times the median visual range at Shenandoah NP (54 km) or Great Smoky Mountains NP (43 km) (Trijonis et al., 1990). Photographic slide data from six National Forest Class I areas in Georgia, North Carolina, Virginia, and West Virginia collected between 1987 and 1993 indicate that the seasonal median standard visual range is between 25 and 30 km in summer and between 106 and 244 km in winter (SAMAB, 1996). Summertime visibility in the eastern U.S. has been consistently observed to be much worse than wintertime visibility (Trijonis et al., 1990). The natural background visual range (in the absence of anthropogenic pollution) for the eastern U.S. has been estimated to be about 95  45 km by Trijonis (1982) and 150 +0,, 45 km by the EPA (1994). Comparing these values to the median annual visual ranges in the SAMI national parks indicates that anthropogenic emissions are responsible for a significant reduction in visibility in the SAMI region.  X  HISTORICAL VISIBILITY TRENDS Airport observations made since the 1940s indicate that visibility has decreased in the Eastern United States, particularly in the SAMI region (Figure 4, from Husar and Wilson, 1993). Over a 35year period from 1948 through 1983, annual haziness has increased by 60 percent and the summer extinction coefficient has increased nearly 80 percent in the Southeast (Scott and Wayland, 1994). Also during this period, sulfur emissions have increased moderately in winter and strongly in summer. Historic trends data show a strong correlation between sulfur emissions and haze in the Southeast (Figure 5, sulfur emission and extinction trends for  Y summer and winter; emissions are for a 1.1 million km2 region of the Southeast, expressed as million tons of sulfur/year; from Trijonis et al., 1990). From this figure, it can be seen that both sulfur emissions and visibility degradation increased sharply in the Southeast during the 1960s, especially during the summer months. These data, while not providing conclusive evidence of a causeeffect relationship, show that trends in the seasonal sulfur emissions can provide a plausible explanation for the observed seasonal trends of atmospheric extinction coefficient in the Southeast. Data from the IMPROVE monitoring network (Eldred et al., 1993a) show statistically significant increases in particulate sulfur concentration between 1982 and 1992 at Shenandoah NP and Great Smoky Mountains NP. The increase is largest in summer, increasing about 4 percent per year. In spring and fall, the particulate sulfur yearly increase is about 2 percent. The winter rates were decreasing, but not significantly (Figure 6; from Eldred et al., 1993b). The MidAtlantic region, including the Virginias and Carolinas, has shown a strong summer increase in the extinction coefficient (75th  Y7 percentile of bext) between 1960 and 1973, followed by a decline (Husar et al., 1994). Over the 30 year period (1960!1990), the winter haze was virtually unchanged. A comparison of data from 1961 and 1989 shows a doubling of the summer extinction coefficient in data from Raleigh, Greensboro, and Charlotte (Husar et al., 1994). The ratio of the median summertime visibility to the median visibility during the rest of the year has decreased steadily from the early 1950s to the mid 1970s (Trijonis, 1982).  X#  CONTRIBUTIONS TO LIGHT EXTINCTION In order to evaluate the effects of EMOs on extinction, it is necessary to determine the contributions of air pollutants to anthropogenic light extinction. Extinction budgets have been developed, consisting of the contributions of the various pollutant species to total anthropogenic extinction. While it is not always possible to construct definitive extinction budgets given available data, certain conclusions can be drawn. Sulfates are the dominant source of light extinction in the East, contributing more than half of the total extinction (Trijonis et al., 1990). Organic compounds contribute about 20 percent  Y+ to bext, Rayleigh scattering about 10 percent, with the remaining extinction contributed +0,, fig 5 here0,, figs 6 & 7 here0,, by other particles (scattering by nitrates, coarse dust particles, etc.), particle absorption,  Y and NO2 absorption. Other estimates of extinction budgets for the eastern U.S. are similar. Sisler et al. (1993) reported that, for IMPROVE sites in the East, sulfate scattering accounts for about 2/3 of the total aerosol light extinction annually, and about 3/4 in summer. The National Academy of Sciences (NAS) has estimated that, in the East, anthropogenic sulfates account for 65 percent of visibility impairment, organics 14 percent, elemental carbon 11 percent, nitrates 5 percent, nitrogen dioxide 3 percent, and suspended dust 2 percent (EPA, 1995a). Once the major pollutant species contributing to extinction are known, the next step in the attribution process is to apportion each pollutant species to its emission sources (or in the case of secondary particulate matter such as sulfates and nitrates, to the source of its precursors). To accurately determine source attributions, one must construct an appropriate sourcereceptor relationship, which is generally the purpose of air quality models. The sources of visibilityimpairing pollutant species include both natural and  Yb anthropogenic emissions of SO2, NOx, volatile organic compounds (VOCs), and fine  YK particles. The majority of SO2 emissions are from anthropogenic sources, most notably point sources such as utility and industrial boilers. The use of high sulfur content fuels  Y is largely responsible for the elevated levels of SO2 emissions in the East. In the  Y atmosphere, gaseous SO2 emissions are transformed into particulate sulfate through chemical reaction (oxidation). Different source regions have potentially variable impacts on visibility in Class I areas due to prevalent transport patterns combined with variable meteorological conditions associated with different transport regimes. For example, both the chemistry of sulfate formation and the lightscattering efficiency of sulfate particles are profoundly affected by atmospheric humidity. Higher levels of relative humidity may be associated with  YN certain transport patterns, and therefore SO2 emitted along these trajectories could have a  Y7 larger impact on visibility than SO2 emitted along trajectories of drier air. Gebhart and Malm (1990) statistically compared trajectory calculations with sulfate measurements at Shenandoah NP and Great Smoky Mountains NP to locate and evaluate the important source regions contributing sulfate to the parks. Their results indicate that the source areas contributing the largest fractions of sulfate to Shenandoah NP are the PittsburghCleveland area, followed by the Piedmont!Northeast Tennessee region and the ColumbusDaytonCincinnati region. The Cincinnati area (Ohio River Valley) was also estimated to contribute the largest fraction of sulfate to Great Smoky Mountains NP. A statistical scoping model (ASTRAP) was used to estimate the source regions (by state) that contribute to elevated sulfur levels in the same two national parks (Trexler, 1992, plus additional personal communication with E. Trexler provided source apportionment data). The results of this investigation were consistent with the findings of Gebhart and Malm (1990). Although these apportionment results were obtained using fairly simple air quality modeling tools, the results confirm that visibility impairment in the SAMI region is due to a regional sulfate haze. This finding is consistent with an examination of the typical time scales for both transport and sulfur oxidation rates in the region.+0,,Ԍ X ԙ SPATIAL REPRESENTATIVENESS OF VISIBILITY DATA To assess the spatial representativeness of visibility data, ideally one would examine concurrent air quality and/or visibility data collected at a group of monitoring sites located throughout a particular region. The amount of correlation observed between sites would indicate the degree to which any site could be considered representative of the surrounding region. The number of locations in most routine monitoring networks is limited, so it usually requires a special monitoring program to provide such a database (see, for example, Chow et al., 1989). Without that direct observational evidence, one must rely on an examination of cause and effect relationships, such as the spatial variability of source regions, meteorology of the region (typical transport patterns), and results of air quality model simulations. Model results may not, by themselves, present a realistic picture of the spatial variability of air quality (or visibility) if the resolution of the model (indicated by both the emission input data resolution and the transport algorithm resolution) is too coarse to capture small scale variations in concentrations, meteorology, and aerosol dynamics. The presence of clouds or fog can have a profound impact on the growth of sulfate aerosols. When clouds or fog (concentrated areas of high humidity) are not uniform in the atmosphere, there may exist significant spatial variations in sulfate size distribution and other optical properties of the aerosol. To the extent that areas of high humidity are present at a particular location for any length of time (for example, valley fogs), there is the potential for measurable variations in visibility to occur. This phenomenon has been observed in the Grand Canyon (Richards et al., 1991), when, during early winter mornings, canyon fog dissipates leaving a visible haze below the rim of the canyon and clearer air outside the canyon. In the SAMI region, relatively few data exist representing aerosol and visibility measurements. The IMPROVE network operates single sites in each of the two SAMI national parks, and the visibility at each location has generally been considered to be representative of the two parks, even though there is no direct monitoring evidence to demonstrate the representativeness of these sites. Despite the lack of direct monitoring evidence, there is some considerable indication that the visibility at these individual locations may be suitable representations for a large geographical area surrounding each monitor, especially when considering seasonal or annual average visibility conditions. Examination of the nature of the important source regions of sulfur emissions (and the spatial distribution of major sulfur point sources), combined with review of the spatial distributions obtained from the few air quality model simulations performed in the area, show that fine particle sulfur concentrations exhibit a fair amount of spatial homogeneity. This is consistent with the regional nature of the secondary formation of sulfate aerosols, which dominate light scattering at these parks. It should also be noted that, although light extinction is often estimated from air quality conditions measured at a single location, visibility is, in fact, a measure of air quality integrated along a sight path that is generally many kilometers in length. The regional haze that is responsible for so much of the light extinction in Class I areas within the SAMI region is made up of aerosols that have often migrated for many hours+0,, or even days. In addition to the regional haze, consisting mostly of aged sulfate aerosol, there is the potential for some increase in visibility impairment due to local, or nearby, sources of pollutants (Gray et al., 1994). Local sources can increase the levels of  Y visibility impairment by contributing primary sulfates, appreciable amounts of NOx, or  Y other fine particulate matter. The SO2 emissions from a large utility may occasionally, during stagnant periods, have an appreciable contribution to visibility in nearby areas; however, sulfate oxidation is relatively slow. In the SAMI region, during periods of significant regional haze (i.e., during summer haze events), it is not likely that local sources will have much effect on the already  Y elevated levels of visibility impairment. Apart from possible plume blight  Y ԍ #&m P7#{&P#Plume blight refers to a direct plume impact at a receptor within a Class I area. The occurrence of this type of visibility impact would be dealt with in a different regulatory manner than regional haze. from a single source, local sources will have little impact on visibility within SAMI Class I areas and therefore, it is likely that there is not a large degree of spatial variability in visibility in and around the parks. Therefore, the individual monitoring locations can be expected to be considered reasonably representative of the surrounding areas (at least for similar elevations within the parks), especially during the worst visibility episodes. It is less evident, however, that the visibility data collected at one location within a national park is particularly representative of other Class I areas in the Southeast. Emery et al. (1994) examined the similarities of groups of Class I areas throughout the U.S. Source regions, aerosol compositions, visibility impairment levels, extinction budgets, and simple regional transport calculations were used to group Class I areas (and to select representative Class I areas within each group). A score was assigned to each grouping that indicated the percentage of similarity between source attributions of parks within the group. The Class I areas of Dolly Sods W, Otter Creek W, Shenandoah NP, and James River Face W were placed in a subregional group (score = 29.9); Linville Gorge W, Shining Rock W, Great Smoky Mountains NP, Joyce Kilmer!Slickrock W, and Cohutta W were in a second group (score = 25.9). Sipsey W was in a low scoring group that included Mingo W (MO) and Mammoth Cave NP (KY) (score = 17.4). The Class I areas in the East exhibited much less similarity in source attributions as compared to the West, where group scores were generally over 50. Therefore, significant spatial variation in visibility is likely to occur between Class I areas in the SAMI region.  X  VISIBILITY IN THE FUTURE Assessment of 1990 CAAA controls on anthropogenic emission sources suggests that these controls will cause a moderate improvement in the summer visibility for the eastern U.S. (Trexler, 1992). Figure 7 (from Trexler and Laulainen, 1993) shows the distribution of hourly deciviews for current conditions and for the modeled CAAA scenario (estimated using the ASTRAP air quality model) at Shenandoah NP. The effects on visibility in the Great Smoky Mountains National Park due to implementation of the CAAA, as well as for other federal land manager (FLM) proposed control#')0,, scenarios, were assessed using a combination of emissionbased air quality models (ICF, 1993). It was estimated that the CAAA provisions are expected to improve visibility by about 25 percent (reduction in total light extinction in 2005). The FLM proposed program, which includes an offset program for new sources and a statewide Reasonable Available Control Technology (RACT) requirement on existing sources, would improve visibility by approximately an additional 10 percent. The visibility improvements are  Yv due almost entirely to reductions in SOx emissions in the surrounding states. In the EPA's report to Congress (EPA, 1993a), the effects of the CAAA on visibility were estimated using (1) a preliminary analysis conducted by SAI (Gray et al., 1993), and (2) RADM modeling of the eastern U.S. The preliminary analysis indicated that future (2005) extinction at Class I areas in the SAMI region would be reduced by about 25 percent (relative to current levels) due to the CAAA. The RADM modeling predicted that, in 2010, the CAAA would be responsible for lowering the median extinction by 30 percent at Shenandoah NP and 33 percent at Great Smoky Mountains NP. Extinction at the Sipsey Wilderness in eastern Alabama would be reduced by 16 percent in 2010 due to the CAAA. The 90th percentile estimate (i.e., visibility is better only 10 percent of the time) for the visual range in the Great Smoky Mountains National Park (computed using the RADM model) is 94 km under the CAAA scenario in 2010, which represents a 15 percent increase over 2010 base case (without CAAA) conditions. The effects of the CAAA are predicted to be greater in summer than in winter (EPA, 1993a). #&m P7#{&P# FIGURE 7. Perceived visual impairment at Shenandoah NP for current conditions and projected 2001 conditions under the CAAA. #Xw P7[hXP#u!0,,  Y K 4 VISIBILITY MODELING ă It is necessary to understand the relationship between air pollutant concentrations and visibility to evaluate the effects on visibility of a control strategy (emission management option). As discussed in Section 2, the optical effects in the atmosphere due to air pollution can be adequately described by examination of the atmospheric light extinction. The total light extinction is computed by summing up the scattering and absorption by both particles and gases. In terms of air pollutant concentrations, this summation can be approximated by:  Yz bext = I Ci ei + R,`!t"(4)  YN where Ci are the concentrations and ei are the extinction efficiencies for each pollutant,  Y9 i, and R represents Rayleigh scattering (of air). Equation 4 is a commonly used simplification of the actual complex relationship between the composition of the atmosphere and the total light extinction. It assumes that the increased extinction due to an incremental increase in a specific air pollutant species is directly proportional to the increase in mass of the species. The proportionality constants are the extinction  Y efficiencies, ei. The actual relationship is particularly complicated for particulate matter species because the shape, size, and chemical nature of individual particles can greatly affect the particle scattering properties of a given mass of particulate matter. The assumptions required for the extinctionmass relationship implied by equation 4 are generally never met. There are an infinite number of possible aerosol mixtures that are consistent with the ambient data. Because different mixtures of chemically identical aerosols have different optical and thermodynamic properties, a variety of feasible formulations of equation 4 are possible that would be consistent with ambient conditions  Y (Sisler and Malm, 1994). Nevertheless, equation 4 can be relied upon if the ei are appropriately selected to represent the atmospheric conditions under consideration. Total atmospheric extinction has been estimated by applying equation 4 to observed mass concentrations of particulate species. Comparisons of extinction computed using equation 4 and direct optical measurements reveal that there is considerable uncertainty in the extinction relationships for particulate matter (Trijonis et al., 1990). However, the simplicity of representing extinction using equation 4 is appealing because it is common practice to measure (or estimate with air quality models) particulate species mass concentrations. If equation 4 is to be the basis for modeling the relationships between extinction and atmospheric composition, then it is essential that the extinction efficiencies be accurately determined for all important species, and that estimation of the particle efficiencies account for factors such as atmospheric water (relative humidity) that will have significant impacts on particulate species efficiencies. Visibility models employing equation 4 differ in the manner in which the extinction efficiencies are derived or applied.+0,,Ԍ X ԙ VISIBILITY MODELING OBJECTIVES The two functions that are required of visibility modeling tools for SAMI's assessment of visibility impacts are: (1)^to characterize current visibility (using pollutant concentration observations) to establish baseline conditions, and to compare measured light extinction to computed extinction in order to derive relationships between observed air quality and atmospheric optical parameters, andt" (2)^to estimate changes in visibility (by examining changes in optical properties of the atmosphere) corresponding to modeled air quality representing possible future scenarios.t" The first objective, characterizing current (or baseline) visibility conditions, can be accomplished entirely without the use of visibility modeling. That is, one can merely observe the optical properties (e.g., extinction or visual range) of the atmosphere using optical instrumentation (e.g., nephelometers or cameras). However, to accomplish the second objective, the relationships between extinction and concentrations must be well understood. So the results of the first objective are often used to carry out the second, unless visibility is modeled using extinction relationships derived from previous studies, possibly at other locations. It is necessary to establish baseline visibility conditions that correspond to current air quality conditions. Therefore, the visibility modeling tools must be used either to establish those conditions or to recreate observed visibility, and hence provide verification that the model is accurate. Then the visibility model, relating atmospheric composition to extinction, can be used to estimate the atmospheric optical effects of predicted changes in air quality (pollutant concentrations).  X7  VISIBILITY INFLUENCE DIAGRAM The two different visibility modeling objectives, (1) computing extinction from air quality observations, and (2) estimating extinction from modeled air quality, can be accomplished with the same general modeling tools. The function of the visibility model can be described by the influence diagram in Figure 8. The right side of this diagram represents the relationships between visibility impairment due to air pollution and human perception and hence, valuation of visibility impacts. As discussed in Section 2, these relationships, although ultimately important to the evaluation of the benefits of an EMO, are a function of the specific scene and observer and are not affected by air pollutants. The focus of this study is on methods for estimating the effects of air pollutants on visibility, and therefore assessing the effects on the optical properties of the atmosphere will provide sufficient information to evaluate various EMOs. For the first modeling objective (computing extinction from air quality observations), the air quality concentrations represent measured species concentrations of particles and gases, typically at one location for a duration not to exceed 24 hours. The concentration data are matched with corresponding observed (or estimated) relative humidity records. +0,,  T  Figure 1  Figure 1 y!3m d>ddHAG002.CGMy$ddt"t"ddt"t" !ds"$#&m P7#{&P#: @FIGURE 8. Influence diagram for visibility.  Y] #Xw P7[hXP#Using extinction efficiencies for each species, the visibility model then estimates the total extinction, or some other measure of visibility impairment, corresponding to the observed conditions. For the second modeling objective (computing extinction from modeled air quality), the air quality concentrations that are the input to the visibility model are the output from an air quality model (a model that typically relates emissions to air pollutant concentrations) for a given emission scenario (EMO). In this case, the meteorological data (relative humidity) may be obtained from observations or from the output of a meteorological model (a model that predicts meteorological properties of the atmosphere, including winds, temperatures, and relative humidities). Once again, using extinction efficiencies for each species, the visibility model then estimates the total extinction, or some other measure of visibility impairment, corresponding to the modeled air quality conditions. Table 3 describes the data that are represented in Figure 8. The air quality and meteorological data that are the input to the visibility model may be obtained either through observations or as a result of modeling. Extinction efficiencies used by the visibility model may be obtained from empirical analysis, theoretical analysis, or a combination of the two. Visibility impairment (the optical response to the input data) can be described by various parameters, including extinction, deciviews, and visual range. For each data type, the dimensions, temporal and spatial resolutions, and other details required to define the data are specified in Table 3.  X5$  ALTERNATIVE APPROACHES FOR RELATING ATMOSPHERIC  X% COMPOSITION TO OPTICAL PROPERTIES Two basic approaches have been used to establish the relationships between atmospheric composition and optical properties (i.e., visibility impairment): (1)^estimating light extinction using extinction efficiencies derived theoretically, andt" (2)^estimating light extinction using extinction efficiencies derived empirically.t" 0}+0,,ds"<!0  d l t$|,!#4&(*<-/1D468L;=?TB d 0 l  $|,!#4&(*<-/1D468L;=?TBdd  T #&m P7#{&P#TABLE 3. Parameters of visibility influence diagram (Figure 8).   yddddy  T AIR QUALITY0 0 4ObservationsJ  NQg/m3, ppmt" CONCENTRATIONSl l 5h <HJ  NQ24hr, hourlyt"  TY   +0 0 4 <HJ  NQSO4, NO3, elemental and organic carbon (EC and  T1 OC), other PM2, PM10, PM15, TSP, NO2, size distribution by speciest"   +0 0 4 <HJ  NQlocation dependent (interpolation)t"  T   +0 0 4ModeledJ  NQg/m3, ppmt"   +0 0 4ConcentrationsJ  NQ24hr, hourlyt"  TA   +0 0 4 <HJ  NQSO4, NO3, EC, OC, other fine, coarse PM, NO2, source attribution, size distribution by species (and source)t"   +0 0 4 <HJ  NQselected receptor locationst" METEOROLOGY0 0 4Observed RHJ  NQpercentt"   +0 0 4 <HJ  NQhourly, daily avg, daily max, seasonal avg diurnal patternst"   +0 0 4 <HJ  NQlocation dependent (interpolation)t"   +0 0 4Modeled RHJ  NQpercentt"   +0 0 4 <HJ  NQhourlyt"   +0 0 4 <HJ  NQmeteorological model grid resolutiont"  T EXTINCTION  +0 0 4EmpiricalJ  NQm2/gt" EFFICIENCIES0 0 4 <HJ  NQby species (with RH dependence)t"   +0 0 4 <HJ  NQregression resultst"   +0 0 4 <HJ  NQregionally location dependentt"  TI   +0 0 4TheoreticalJ  NQm2/gt"   +0 0 4 <HJ  NQby species and particle size distribution, particle morphology, external, internal mixtures, water contentt"   +0 0 4 <HJ  NQMie scattering theoryt"  TY   +0 0 4Hybrid Approach  NQm2/gt"   +0 0 4 <HJ  NQcombination of Mie theory and empirical data, assumptionst"  T" VISIBILITY  +0 0 4Total Extinction  NQMmé1; hourly; represents fractional lightt" IMPAIRMENT0 0 4 <HJ  NQenergy absorbed and scattered by pollutants (and air)t"   +0 0 4DeciviewsJ  NQlogarithmic scale; hourly; a 1deciview change corresponds approx. to a 10% reduction in extinctiont"   +0 0 4Visual RangeJ  NQkm; hourly; estimate of distance black object can be seen against white background; assumes viewer contrast thresholdt"  Q* ydY-dddyQ*0,, #&m P7#{&P#TABLE 3. Concluded.   yddddy SCENE  +0 0 4 <J8  NQdefinition of scene geometry: objects, colors (wavelenth), distances, light intensity (footlamberts), contrast (cycles per degree), solar angle, clouds, fogt"   +0 0 4 <HJ  NQlocation, time dependentt" PERCEPTION  +0 0 4Noticeable Change  NQtime, location, viewer dependentt"   +0 0 4Quality/ValueJ  NQaesthetic, economic valuation ($)t"  0 yd8 dddy#Xw P7[hXP#  d 0 l  $|,!#4&(*<-/1D468L;=?TB ddd #Xw P7[hXP#The most reliable estimates of extinction due to individual chemical species are those computed using theoretical light scattering models (Mietheory) fitted to the sizeresolved composition of the aerosols observed in the region of interest. Such models explicitly simulate the physical causeandeffect relationship and best utilize all of the data on the aerosol's properties that affect scattering efficiencies (Trijonis et al., 1990). However, even the most fundamental of these models incorporate important assumptions concerning particle structure, condensed water, and other unobserved aspects of the aerosol (White, 1986). Many researchers have developed versions of Mie scattering models (e.g., Sloane et al., 1991, Wilson and Reist, 1994, Zhang et al., 1994). A significant limitation to the use of theoretically derived extinction efficiencies is that they have not been developed and tested for many locations, including the SAMI region. In fact, very little research has been done to model extinction relationships for aerosols collected in the eastern U.S. A much wider range of extinction efficiency estimates is available from multiple regression analyses of the empirical relationship between extinction and aerosol composition (Trijonis et al., 1990). In this approach, the observed light scattering is statistically compared to observed aerosol concentration and composition to estimate the scattering efficiencies of each particulate species. The hygroscopic species (e.g., sulfate and nitrate) concentrations are usually corrected to account for water uptake using a function of the ambient relative humidity. This approach has generated statistically significant extinction relationships for many applications, and the resulting coefficients have been used to accurately estimate light scattering from aerosol concentration data (Sloane, 1988). The physical and chemical nature of aerosols are not uniform spatially or temporally, and therefore a set of statistically derived relationships from a given database may not be appropriate for aerosols at other locations or at other times. Variabilities in ambient sulfate size distribution, chemical structure, and atmospheric humidity (causing aerosol growth and change in optical phase) will cause substantial variations in sulfate scattering efficiencies. Another approach that has been used (Trijonis et al., 1990) is to select scattering efficiencies based on a synthesis of a number of previous regression (and/or Mietheory) analyses. While this approach has been widely used, unfortunately, the majority of+0,, regression applications (and syntheses of previous results) have been performed for data collected in the West, and there is considerable uncertainty in relying on the resulting scattering efficiencies for application to the SAMI region. A number of visibility modeling applications were identified during the literature review. The computational tools used to estimate visibility from aerosol concentrations and compositions are often found as postprocessing modules attached to air quality models. In this way, the air quality model is used to simulate the relationship between emissions and air quality, and then the visibility modeling component relates the resulting air quality into visibility impacts. Table 4 identifies various modeling approaches that have been used to relate air quality to visibility. Many modelers have developed tools to relate particulate species concentrations to visibility using a set of extinction efficiencies selected from the literature to, as closely as possible, represent the conditions of the aerosol being modeled. Many of the scattering efficiencies used in these models are empirical, being derived entirely from regression analysis. Some of the scattering efficiencies used are theoretical, based on Mie scattering calculations; for example, by exercising a model such as the Elastic Light Scattering Interactive Efficiencies model (ELSIE) using as much real data as possible concerning the physical and chemical nature of the aerosol in question. A third, popular approach, is to develop a composite set of extinction efficiencies from a variety of empirical and theoretical analyses. Empirically derived extinction efficiencies obtained using multiple linear regression models are subject to a number of uncertainties. Errors in the resulting extinction  Y efficiencies can be related to errors in light scattering (bsp) measurements, correlations between chemical concentrations, and nonlinearities caused by variations in size distribution and mixing state from sample to sample (Lowenthal et al., 1995). There are also a diverse assortment of relative humidity correction algorithms available which tend to differ greatly at high relative humidities. Therefore, caution must be exercised when applying these extinction efficiencies in a visibility model. The high degree of intercorrelation commonly found among aerosol species can make regression estimates very sensitive to the choice of species to be included in the analysis (Sloane, 1983a). An example of the problem of intercorrelation occurs, for example, when sulfate and nitrate concentrations are strongly correlated. Then the scattering efficiency computed for sulfate may include portions of the scattering effect due to the nitrate, and vice versa. A strong correlation between independent variables will likely cause one of the variables to be dropped from the regression model. If nitrate is not used in the resulting regression equation, then the sulfate concentration is acting as a surrogate for total ions, and while the model may reproduce observed extinction values, the sulfate extinction efficiency will not be accurate (and would not be appropriate for use in a model that also includes scattering by nitrates). Despite these objections, regressionderived extinction efficiencies have often been taken from the literature and applied in visibility postprocessing modules to compute extinction (see Table 4). The uncertainties in following this approach have been estimated (Trijonis et al., 1990) by using a range of efficiencies synthesized from a variety of estimates. It has been found that certain features of the particle scattering budget are quite insensitive to the details of the accounting method. +0,, ^ Ad<<<C  adddx% 0 ^  P(  Tx #&m P7#{&P#TABLE 4. Visibility modeling components.Pp  ""Study"= Reference"`Description"UOutputp  P  VASM/ ASTRAP  DOE, 1994  postprocessor to statistical AQ model uses RHdependent literature extinction efficienciesp  extinction, deciviews MESOPUFF II EPO, 1993b postprocessor to Lagrangian AQ model uses nominally selected RHdependent extinction efficienciesH  extinction, visual range(p CALPUFF Scire et al., 1995 postprocessor similar to MESOPUFF IIp  extinction(H  NPAQMS (RTM II) Gray et al., 1993 postprocessor to Eulerian dispersion model uses RHdependent literature extinction coefficientsH extinction, visual range, deciviewsp  VISCREEN PLUVUE IIp EPA, 1992p plume optics computed theoreticallyp contrast (scenedependent), color change(H STAGHAZE, HAZEPUFFp Latimer, 1993ap visibility screening toolsp contrast, visual range(H RIVAD Latimer, 1990 postprocessor to Lagrangian AQ model, literature extinction efficienciesp extinction, visual rangep GCVTC (VISHWA) EPA, 1995a RHdependent extinction coefficients selected by consensus p extinction, deciviewsp NGSVS Richards et al., 1991 RHdependent extinction coefficients derived from local aerosol datap extinction, visual rangep ELSIE Sloane et al., 1991 Mie scattering using size, chemistry, water for internal mixturesp" extinction (scattering)(p MARS# Wilson and Reist, 1994$ Mie scattering using size distribution and RH data$ extinction (scattering)((p" ImageBased Modeling& Eldering et al., 1993& Mie scattering& synthetic photographs( $ SEAVS ( Saxena et al., 1995 ( Mie scattering using size, chemistry, water uptake ) extinction, perception & +0,,  Y #Xw P7[hXP#Lowenthal et al. (1995) evaluated the sensitivity of the theoretically derived extinction coefficients to model inputs and assumptions using the ELSIE model (a Mie scattering algorithm). One conclusion was that scattering is relatively insensitive to assumptions concerning choice of sulfate speciation or particle configuration. On the other hand, particle scattering was highly sensitive to the choice of empirical liquid water growth function, especially for high relative humidities. If the evolution of sulfur particles were known, then the particle formation, growth, and water uptake occurring between source and receptor, including transport through fogs or clouds, could be adequately characterized so that Mie scattering theory could be definitively applied. Since particles are rarely characterized to this detail (even using the most sophisticated air quality models), one must use surrogate variables (such as ambient relative humidity), and also make various assumptions about the nature of the aerosols, in order to apply Mie theory. For the regression approach, ambient relative humidity is used as a surrogate variable (through use of the humidity correction factor) to represent the state of the sulfate aerosol with regards to its water content. y0,,  Y  H 5 CRITICAL GAPS IN KNOWLEDGE ă The previous sections presented a description of the important factors that affect visual air quality, the available databases and visibility characterization for the SAMI region, and a review of the modeling tools that are used to relate air quality to visibility. The information in those sections, obtained through a comprehensive review of the literature, was assessed to identify critical gaps in our knowledge of visibility in the SAMI region. These critical information gaps limit the capability to define current visibility conditions and to project the effects of changes in pollutant exposures on visual air quality. This section presents a number of conclusions regarding the tools available for characterizing and modeling visibility in the SAMI region. The conclusions are presented specifically to identify the existing critical gaps and to consider what remedies may be available for filling those gaps. Critical knowledge gaps have been divided into two groups: those that are related to the characterization of visibility in the SAMI region, and those that are related to visibility modeling.  X  VISIBILITY CHARACTERIZATION There is a lack of comprehensive data for the Southeast, including the SAMI region, necessary for evaluating patterns and causeeffect relationships for visibility. Existing data (e.g., from the IMPROVE network) are sufficient for a basic analysis (examination of 24hour average aerosol species data, light scattering and absorption data, and comparisons between chemical speciescomputed extinction and measured light extinction). However, there are only five IMPROVE sites in the SAMI region, and only two were operating before 1992. The data from these sites are marginally sufficient in representing visibility in the entire region. Although the majority of the visibility impairment in the SAMI region is due to regional haze, there may be a need to consider local "hot spot" contributions, and other possible variations in visibility. Despite the qualitative nature of photographic records, there is reasonably good evidence of historical trends. However, longterm aerosol and visibility measurements to support these observations do not exist. Databases need to be assembled for the SAMI region to allow for the extinction budgets to be estimated representing worstcase and bestcase conditions, as well as for average conditions. Ideally, a complete distribution of visibility conditions should be used to characterize the contributions to extinction. In order to characterize future visibility conditions in the SAMI area, an accurate detailed dispersion model, simulating aerosol chemistry and dynamics, needs to be+0,, applied. The inputs to the model should including a larger source region than the eightstate SAMI region to account for longrange transport and regional haze. Such a model could be used to determine future air quality and visibility under various emission management scenarios, including an evaluation of the effects of the 1990 CAAA.  Xv  VISIBILITY MODELING The fundamental physics that relate light extinction and other optical parameters to air quality is well established. Light extinction is a simple linear sum of scattering and absorption by particles and gases. In addition, further subdivisions of light extinction contributions are either exactly additive (e.g., coarse versus fine particles), or approximately additive (e.g., allocations among various chemical species of particulate matter). The precise amount of light extinction attributable to each of the particulate species is somewhat uncertain though, especially in the Southeast. Currently, an accurate estimation of light extinction in the SAMI region due to atmospheric aerosols must rely, to a great extent, on data from other locations due to lack of locally collected and analyzed extinction efficiency data. This represents the most significant gap in our ability to assess the effects on visibility of various emission management options. In addition to the general lack of data representing local aerosol properties, other specific gaps exist in our ability to model the relationships between aerosols and visibility. The composition, water content, and optical properties of organic particulate matter are largely unknown, even though organic carbon constitutes a substantial fraction of fine particle concentrations. Also, measurements have indicated that water may constitute more than half the particle mass when relative humidities exceed about 85 percent (Zhang et al., 1993); yet water content is not measured with current routine methods. The contributions of both organics and water to visibility impairment in the Southeast is not well characterized. The humidity dependence on extinction efficiencies for the hygroscopic particulate species (sulfates, nitrates, and organics) is not well characterized. Current formulations are quite variable and must be considered suspect, especially at high relative humidities. Of course, this uncertainty is particularly important in the SAMI region, which experiences very high summer humidity levels. Growth of atmospheric sulfate aerosols in the presence of water is an extremely important factor affecting the optical properties of the sulfate aerosol. Relative humidity is used as a surrogate variable to represent the evolution of sulfate formation and transport through which water is taken up by the aerosol. The water content is an extremely important factor affecting the optical properties (light extinction) of sulfates and other aerosols. Another significant uncertainty associated with the use of relative humidity data arises from the common practice of combining daily average or daily peak relative humidity measurements with 24hour average concentration measurements. The uncertainties in some particle properties (such as particle shape, internal versus external mixtures, and size distribution to a lesser extent) are generally less than the+ 0,, typical variations observed in aerosol concentrations (from day to day), and therefore extinction computed using assumed Miescattering properties will usually result in a reasonable approximation. However, to reduce these uncertainties, experiments and subsequent analysis are needed in the SAMI region to quantify the reliability of current visibility modeling methods. To fill these information gaps, there is a need for a comprehensive characterization of the aerosols present in the atmosphere of the Southeast. This would include both physical and chemical measurements of particulate matter, collected on shorter averaging times than 24 hours, and under a range of conditions, including high relative humidity haze episodes. The resulting data should include aerosol size and detailed chemical distributions, particulate water content, and concurrent (continuous) measurements of atmospheric optical properties, such as total light extinction, light scattering and absorption, and relative humidity. The comprehensive aerosol data could then be used to compute theoretical light scattering efficiencies from Mie theory. A model of extinction could be then be developed and the results compared to observations to verify model performance. Some of the information gaps discussed above may be filled by research conducted as part of the Southeastern Aerosol and Visibility Study (SEAVS; Saxena et al., 1995). It is one of SEAVS's major objectives to conduct field experiments collecting aerosol and visibility data, and to perform the analyses necessary to characterize the optical properties of atmospheric aerosols present in the SAMI region. Both theoretically and empirically derived extinction efficiencies are to be developed (and compared), and the results of this study (if successful) will likely allow for the development of a much more reliable visibility modeling tool for the SAMI region. Light extinction is used to compute transmittance through the atmosphere. Missing from the analysis is an understanding and modeling of path radiance and the effects on path radiance from particulate matter. The relative importance of fine and coarse particle scattering, and absorption, on path radiance should be characterized, and an empirical approach should be developed for combining path radiance and extinction into a total visibility index (Trijonis et al., 1990). If the path radiance and the transmittance (computed using extinction) were both considered, one would need to identify the distribution (average and range) of scene conditions and evaluate visibility in the context of that distribution. This gap is not likely to be filled in the near future, and therefore it is not possible to completely describe the effects of air pollution on all scenes. Instead, the current approach is to only compute extinction and assume that the other factors remain constant. Comparisons are generally made between data obtained from 24hour chemical (aerosol) measurements and continuous or individual daytime optical measurements. There is a need to remove nighttime and other naturally obscuring conditions from consideration in visibility analyses. (!0,, "0,,  X  HM6 RECOMMENDATIONS ă Based on an evaluation of existing information, recommendations were developed for methodologies that can be used to assess the effects of emission management options on visibility in the SAMI region. The literature on visibility data and modeling approaches was examined to determine the adequacy of existing information for characterizing visibility impairment and for developing models that relate air quality to visibility. The information obtained from this review was synthesized into a body of knowledge concerning visibility impairment in the SAMI region and visibility modeling approaches. Finally, the information was evaluated in the context of SAMI's objectives (to develop tools for assessing the effects of EMOs on visibility). The selected air quality and visibility models will first be used to simulate the visibility corresponding to base case (current) conditions (using basecase air quality data). It is important to verify that this parameterization closely matches actual basecase visibility conditions (at the few monitored locations), or else predictions of future visibility scenarios will be questionable, and even the relative visibility changes predicted by the model between two future scenarios may be inaccurate. Verification of the model should include a species by species comparison of ambient and modeled spatial and temporal concentration trends. Once the modeling approach has been verified, it can be used to estimate visibility at any receptor site within the modeling domain, under various emission scenarios. A set of receptor sites will be selected for model output, including one or more sites in each Class I area. The air quality and visibility (postprocessing) models will be applied to estimate the visibility at each receptor for each time period modeled. The periods modeled should include as many episodes as possible, representing a range of meteorological (and visibility) conditions. Ideally, an entire annual cycle should be modeled, but this may not be practical with many air quality models. A preliminary set of recommendations was prepared corresponding to three levels of complexity. Each level of complexity is associated with somewhat different data sets, key indicators, spatial and temporal scales, and modeling approaches. Figure 8 and Table 3 (presented in Section 4) defined the inputs and outputs to the air quality model. The specific data that are recommended for use in each modeling approach (level of complexity) are described below.  X(  MINIMUM MODELING APPROACH This approach represents an elementary use of available data and modeling tools to estimate average extinction levels. Species extinction efficiencies will be obtained from+#0,, literature values, representing both empirical and theoretical derivations. Efficiency data and relative humidity relationships will be sought from previous studies such as NAPAP and GCVTC. The data will be synthesized into the most representative set of efficiencies for use in the SAMI region. A significant disadvantage with this approach is that the majority of the available efficiency data are not particularly applicable for the SAMI region. The daily average or daily maximum observed relative humidity from the closest measuring station will be used in the extinction model. Air quality measurements will consist of 24hour average aerosol species data from the IMPROVE monitoring network, used to establish basecase visibility conditions. Either the air quality model will output hourly averaged air quality concentrations or modeled daily averaged concentrations will be combined with typical diurnal concentration patterns, allowing for an approximation of diurnal patterns of extinction. The model will combine these data to produce daily average extinction for the observed aerosol data and hourly extinction for the modeled scenarios. Of course, extinction values can be easily converted into deciviews or visual range (meteorological range) for alternative presentation purposes.  X  BASIC MODELING APPROACH This approach is similar to the minimum modeling approach, except that locally derived species extinction efficiency values will be used. These can be obtained from two possible sources: (1) from the results of the SEAVS study, which will compute theoretical scattering efficiencies for all important aerosol species using measurements taken during 1995 summer haze episodes in Great Smoky Mountains NP, or (2) from an empirical regression analysis of recent IMPROVE data for Shenandoah NP and Great Smoky Mountains NP. Either of these choices, but especially the first option, if achieved, would represent a significant improvement over the minimum modeling approach. As another improvement for the basic modeling program relative to the minimum approach, hourly relative humidity data should be sought (observed or modeled) so that the hourly species concentrations output from the air quality model can be matched in time with diurnally varying humidity levels. As in the minimum modeling approach, this approach will generate daily average extinction for the observed aerosol data and hourly extinction for the modeled scenarios. Alternative visibility measures that can be readily computed from the distribution of total extinction values are deciviews or visual range (meteorological range).  X#'  FULL MODELING APPROACH This approach will employ a comprehensive Mie scattering treatment to estimate total extinction from aerosol data. In order to apply Mie theory, the output of the air quality model will need to be much more detailed than merely hourly particulate species concentrations (as required by the minimum and basic modeling approaches). The air+$0,, quality model will supply concentrations and size distributions of each particulate species (including organics), and possibly other important aerosol properties, such as the liquid water content associated with each hygroscopic species, on an hourly basis. The comprehensive aerosol data will be supplied to a Mie theory computer code (such as ELSIE) to compute time and locationdependent scattering efficiencies for all species. For any parameter that is required by the Mie theory code but is not generated by the air quality model, assumed values will be based, as much as possible, on data from locally collected aerosols (such as results from SEAVS). This approach represents the stateoftheart modeling technique for visibility. The capabilities of the air quality model (chemical and aerosol mechanics) will need to be matched with the requirements of the Mietheory model. Necessary assumptions will be made for any unknown (not measured or modeled) aerosol quantity/property. If the air quality model can predict liquid water, there will be no direct need for relative humidity data in this modeling approach. There are few if any validated regionalscale transport models capable of simulating aerosol chemistry and dynamics in sufficient detail to provide all the information required by the Mie theory calculations. However, some reduced form models may be used to approximate many of the processes and provide some of these detailed data. If an air quality model that considers aerosol dynamics is to be used, the performance of the model must be evaluated to ensure that the model can accurately reproduce conditions in the SAMI region. This may be a difficult obstacle to overcome within the SAMI assessment time frame for most, if not all, available air quality modeling approaches. The results of this modeling approach will be, like the other two approaches, hourly estimates of total light extinction (or alternatively, deciviews or visual range) at all receptor locations of interest.  XN  REDUCEDFORM MODEL During the development of SAMI's integrated assessment, there has been some interest raised concerning construction of a "reducedform" model. A reducedform model would incorporate all components of the integrated assessment into a single model that could be exercised on a small to mediumsized computer (Lumina, 1995). Ideally, reducedform model components would be extrapolated from empirical data or developed from the results of the more detailed models for each component. A reducedform model corresponding to the "full modeling approach" for estimating visibility impacts within the SAMI region could be developed by exercising a Mietheory extinction model over a range of input data corresponding to the likely range of conditions encountered in the SAMI region. Then by interpolating these results, one could estimate the extinction efficiencies for each visibilityrelated species for any set of conditions. It would be necessary to establish a set of parameters to simplify the set of input conditions. For example, the complex evolution of sulfate particles could be reduced to simple measures that characterize the degree of humidity and temperatures encountered, the levels of solar radiation (and clouds), and the interaction with ammonium ions (which, together with sulfate ion, form ammonium sulfate aerosols).+%0,,ԌAlternatively, a reducedform model using the "basic modeling approach" might consist of using the air quality transport and chemistry model to generate sourcereceptor transfer coefficients and then applying locally derived extinction efficiencies to the computed transfer coefficients corresponding to each visibilityrelated species concentration. It would be necessary to account for the levels of relative humidity encountered by the ambient aerosol that correspond to the time periods being modeled. The results of this calculation would be an extinction efficiencyweighted average of the visibilityrelated species concentrations that would indicate the visibility impact associated with the pollutant levels predicted by the reducedform air quality model for each EMO.  X  FINAL RECOMMENDATIONS The preliminary recommendations have been updated to reflect the integration of the visibility modeling approach with (1) the recommended air quality transport and chemistry modeling approach (which provides input to the visibility assessment model), and (2) the effects benefits evaluation model (which employs output of the visibility assessment model). Further details of the modeling approach and data used have been identified. Through consultation with SAMI staff and contractors working to develop related components of the integrated assessment, and by incorporating comments and suggestions received from reviewers of the draft report, a final recommendation has been developed for the methodology to be used to assess the effects of EMOs on visibility in the SAMI region. SAMI has determined that the overall budget for the integrated assessment is to be between $2 and $3 million, of which as much as $900,000 would be used for developing and applying the air quality transport and chemistry model. Table 5 displays a description of the recommended approach to be used for evaluating visibility effects. This approach is similar to the "basic modeling approach" presented above. The visibility module is estimated to cost between $70,000 and $80,000, which includes developing an extinctionbased postprocessing module using results of the SEAVS study, applying the module for all EMOs evaluated by the air quality model, and preparing and interpreting the visibility results for the SAMI audience. Results should be presented in the form of distributions of hourly visual range (and deciviews) representing an annual cycle for each EMO, with key parameters of the distributions identified, such as median, 10th and 90th percentiles, etc. The use of SEAVS data to generate SAMIspecific extinction efficiencies will result in a reliable modeling tool for assessing visibility impacts in the SAMI region that are associated with the air quality conditions predicted for each EMO. In fact, it is likely that the uncertainties associated with the estimation of pollutant species concentrations by the air quality transport and chemistry model will be considerably larger than those associated with estimation of light extinction. Since uncertainties are propagated through each step of the assessment process, the recommended visibility module can be considered to be a suitable tool for assessing visibility impacts. (&0,,  dhp x(08"$&@)+-H024P79@d O adddx% 0  dddH' O & #&m P7#{&P# TABLE 5. Visibility Effects Module  #&m P7#{&P#Description of Module:` 88BThe visibility effects module is intended to accept information on particulate pollutant concentrations and meteorology and provide quantified measures of the visibility effects in Class I areas within the SAMI region."8 "   Estimated Cost: 7X88B$70,000 ! $80,000 (for development of module, application, preparation/presentation of results; assuming one base year and 10 EMOs evaluated)."8 X00#"0 Projected Delivery Date:` 88BMarch 1, 1997 (module development), results within 6!8 weeks of transport model application."8 Other Timing Issues: 7X88BDelivery of this module depends on completion of SEAVS analysis (draft results expected by October/November 1996)."8 Input:X00#X -X 7X88BHourly (preferred; alternatively, 3 hour average) concentrations of particulate sulfate, nitrate, organic, elemental, other fine, and coarse mass; hourly relative humidity data."8 Output: -X 7X88BFrequency distribution of (1) visual range (miles or kilometers), and (2) deciviews."8 Geographic Coverage: 7X88BTransport/chemistry model module should provide gridded output covering entire eightstate SAMI region; visibility calculations will be done using same resolution (relative humidity data will be interpolated using NWS and other available data). One or more receptors will be chosen within each SAMI Class I area (depending on grid resolution; grid cells should be less than about 2050 km)."8 Period of Coverage: 7X88BOne annual cycle should be modeled as base year, matching year of emission inventory preparation (1990)."8 Assumptions: -X 7X88BThe visibility module assumes that the size and chemical structure of the ambient particulate matter in the southeastern U.S. can be represented by the data collected by the SEAVS analysis (conducted during six weeks in 1995). The module further assumes that this characterization will be consistent with future emission scenarios."8 Level of Confidence: 7X88BHigh to very high level of confidence in visibility results (depends on uncertainty of transport model)."8 " #Xw P7[hXP# ('0,, (0,,  Y R GLOSSARY ă  Y2  absorption coefficient . a component of the extinction coefficient  Y X!dd aerosol . generic term describing a system of small particles suspended in air (or another gas); often referred to as particulate mattert"  Y X!dd air quality model . a model that relates emissions to air pollutant concentrations; often refers to a dispersion model that is used to simulate the emissions, transport and atmospheric chemistry of air pollutantst"  Yf  anthropogenic . created as a result of human activity  Y9  AQ . air quality  Y   AQRV . Air Quality Related Values  Y  ASTRAP . atmospheric transport model developed at Argonne National Laboratory  Y  bext . light extinction coefficient; the fractional light attenuation per unit of distance  Y  CAA . Clean Air Act (originally 1970, includes many subsequent amendments)  Y[  CAAA . Clean Air Act Amendments (1990)  Y. X!dd Class I areas . National parks and wilderness areas that have been designated for mandatory protection by the U.S. Congresst"  Y  dV . deciview; a measure of the clarity of the atmosphere  Y!  ei . extinction efficiency; light extinction per particle mass  Y#  ELSIE . Elastic Light Scattering Interactive Efficiencies model  Yf%  EMO . emission management option  Y9'  EPA . U.S. Environmental Protection Agency  Y )  extinction . see light extinction  Y*  extinction coefficient . see bext +)0,,Ԍ Y  extinction efficiency . see ei  Y  extinction budget . the contributions of various pollutant species to total extinction  Y  GCVTC . Grand Canyon Visibility Transport Commission  Yz X!dd IMPROVE . Interagency Monitoring of Protected Visual Environments; Class I area visibility monitoring programt"  Y6 X!dd light extinction . a measure of visibility impairment; often refers to extinction  Y coefficient, bextt"  Y  Mie theory . theoretical aerosol light scattering model  Y  Mmé1 . inverse megameters; units used for bext  Y  NAPAP . National Acid Precipitation Assessment Program  Yn  nephelometer . instrument used to measure light scattering coefficient  YA  NOx . oxides of nitrogen  Y  NP . national park; Class I area  Y  PM . particulate matter  Y  PM10 . particulate matter smaller than 10 microns aerodynamic diameter  Y X!dd Rayleigh scattering . molecular light scattering by gases; may refer to the scattering by particlefree airt"  YI  SAMI . Southern Appalachian Mountains Initiative  Y  SEAVS . Southeastern Aerosol and Visibility Study  Y  scattering coefficient . a component of the extinction coefficient  Y! X!dd size distribution . a key property of atmospheric aerosols; the quantity of particulate matter that exists at each particle size (diameter)t"  Y~$ X!dd source attribution . an estimate of the contributions of various emission sources to pollutant concentrations; often the result of application of an air quality modelt"  Y:'  SO2 . sulfur dioxide  Y )  SOx . oxides of sulfur, including sulfur dioxide (SO2) and sulfate (SO4)  Y*  SO4 . sulfate +*0,,Ԍ Y  SVR . Standard Visual Range (sometimes referred to as "meteorological range")  Y  TOC . Technical Oversight Committee (SAMI)  Y  VR . visual range; see SVR  Yy  W . wilderness; Class I area c+0,, ,0,,  Y OQ REFERENCES ă X!ddAdams, K. M., L. I. Davis, Jr., S. M. Japar, and W. R. Pierson. 1989. Realtime in situ measurements of atmospheric optical absorption in the visible via photoacoustic spectroscopy II: Validation for atmospheric elemental carbon  Y aerosol. Atmos. Environ., 23:693700.t" X!ddAgrawal, K. M., I. Fernandez, and A. L. 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