Appendix B.4

Natural Background Visibility and Regional Haze 

Goals in the Southeastern United States 

Tennessee Department of Environment and Conservation 

Division of Air Pollution Control 

9th Floor L&C Annex 

401 Church Street 

Nashville, TN 37243

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1 Natural Background Visibility and Regional Haze 

2 Goals in the Southeastern United States 

3 Ivar Tombach

4 Consultant, 753 Grada Ave., Camarillo, CA 93010 

5 Patricia Brewer

6 VISTAS, 2090 U.S. 70 Highway, Swannanoa, NC 28778 

7 ABSTRACT 

8 The goal of the regional haze mitigation program in the United States is to attain “natural 

9 conditions” in national parks and wilderness areas by 2064. Results of research investigations on 

10 background concentrations of sea salt and biogenic organic matter, of episodic Saharan and 

11 Asian dust, and of carbon from natural fires were reviewed to provide a basis for making site12 specific estimates of what the concentrations of atmospheric fine particulate matter components 

13 might be under natural conditions in the southeastern United States. Based on this review, rough 

14 estimates were made of potential contributions of these aerosol components to natural 

15 background visibility. Natural organic particles were the dominant influence on the rate of 

16 visibility improvement required to reach natural conditions at an inland, mountainous location 

17 and organic particles and sea salt were the dominant influences on the rate at a coastal location. 

18 African dust also had a large episodic effect, but the current regulatory approach is not designed 

19 to address episodic background variations. Insufficient data exist to quantify the contributions of 

20 wildfires with any detail, although global air pollution modeling provides insight and their 

21 emissions can be locally dominant. Conservative regional refinements to the default natural 

22 background estimates do not greatly alter the region-wide rates of reduction of ambient 

23 particulate matter concentrations that will be needed to accomplish the first phase of the regional 

24 haze program. However, refinements at specific Class I areas may have considerable influence 

25 on defining the nature (magnitude and spatial and temporal distribution) of local emission 

26 reduction efforts there.

27 INTRODUCTION 

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The United States Environmental Protection Agency’s Regional Haze Rule128 prescribes that 

29 States are to make efforts to improve visibility in 156 national parks and wilderness areas (“Class 

30 I areas”) at such rates that "natural conditions" would be achieved in each Class I area by 2064. 

31 A "reasonable rate of progress" corresponds to linear improvement in visibility, as characterized 

32 in units of deciviews, between current conditions during the base period of 2000-2004 and 

33 natural conditions at the end point of 2064. 

34 To provide a starting point for the regional haze planning process, the U.S. EPA has 

35 issued guidance for estimating natural visibility conditions based on assumed natural background 

concentrations of the chemical components of particulate matter.236 Recognizing that these 

37 “default” concentration estimates may not be appropriate for every Class I area, the guidance 

38 document also provides a State with the option of developing a "refined" estimate of natural 

39 conditions that reflects local circumstances. 

40 This paper discusses the attributes of the default approach, identifies issues that might 

41 suggest consideration of a refined method, and provides a survey of the current scientific 

42 understanding of background and natural particulate matter (PM) component concentrations. 

43 (Note that the concept of achieving “natural conditions” by a regulatory program in the United 

44 States is an idealistic one, in that it assumes that the only pollutants ultimately transported into 

45 the U.S. across its borders will be natural ones and ignores potential contributions from 

46 anthropogenic sources outside the country. Although the issue of transboundary transport is 

47 important in the implementation of U.S. regional haze policy, it will not be addressed here. 

48 Rather, we focus on the scientific issues involved in estimating natural conditions in the absence 

49 of transboundary anthropogenic contributions.) 

50 The discussion here focuses on the geographic region encompassed by the VISTAS 

51 Regional Planning Organization, essentially the southeastern United States. VISTAS (Visibility 

52 Improvement – State and Tribal Association of the Southeast) is an association of ten States, the 

53 Eastern Band of Cherokee Indians, and local air pollution control agencies. The VISTAS States 

54 and the Class I areas within them are shown in Figure 1. 

55 Of importance to VISTAS is whether the limitations in the default concentration 

56 estimates are significant, particularly for the development of emission control strategies designed 

57 to reach a first phase goal by 2018 -- i.e., could the decisions that VISTAS will be making about 

58 initial emission management strategies be affected by uncertainties in the natural background 

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59 concentration estimates? And, as a corollary, will it be worthwhile to consider refining the 

60 estimates for some components of particulate matter using the new information that has become 

61 available since the default natural concentration estimates were developed? 

62 Fortuitously, the global climate change research community has carried out substantial 

63 research on global background and natural aerosols in the past decade. Their interest is to 

64 separate the natural and anthropogenic contributions by particulate matter to global radiation 

65 balance, a focus that neatly parallels the regional haze interests here. Consequently, the 

66 information base now available for estimating natural background concentrations greatly exceeds 

67 that of the pre-1990 research upon which the EPA’s default guidance is based. 

68 To address the questions raised and to take advantage of the new data, this paper reviews 

69 the basis for the default concentration estimates, discusses two technical issues concerning 

70 estimating light extinction from those concentrations, surveys relevant research findings that 

71 provide insight into natural conditions PM component concentrations in the Southeast, and 

72 discusses the implications of this new information on control strategy planning. 

73 BACKGROUND -- THE DEFAULT ESTIMATES OF NATURAL CONDITIONS 

74 In September 2003, the U.S. EPA issued Guidance for Estimating Natural Visibility Conditions 

Under the Regional Haze Rule,

2

75 to provide initial guidance for estimating natural conditions. 

76 That document recommends that natural extinction levels be estimated from the default 

77 component concentrations by using the formula established by IMPROVE for estimating current 

78 light extinction from measurements of the concentrations of the main components of fine 

79 particulate matter. (IMPROVE stands for Interagency Monitoring of Protected Visual 

80 Environments, the national visibility-related PM component monitoring program.) The 

81 IMPROVE formula estimates light extinction from the concentrations of fine ammonium sulfate, 

82 ammonium nitrate, particulate organic matter, light absorbing carbon, and fine soil, plus coarse 

83 particles: 

bext 84 = 3f(RH)[SULFATE] + 3f(RH)[NITRATE] + 4[POM] + 1[SOIL] 

85 + 0.6[CM] + 10[LAC] + 10, (1)

where the concentrations, indicated by square brackets, are in μg/m3; bext is in Mm-1 86 ; and the 

87 numerical coefficients before each term, which represent extinction efficiencies (light extinction 

per unit mass), are in m288 /g. SULFATE is assumed to be in the form of ammonium sulfate; 

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89 NITRATE is assumed to be ammonium nitrate; and POM is calculated as 1.4[OC] where [OC]

90 represents the organic carbon concentration in particulate organic matter [POM]. (The 

91 IMPROVE program labels POM as OMC and, as with POM here, uses that label to describe only 

92 the fine fraction.) CM represents coarse matter, particles between 2.5 and 10 μm diameter. Light 

93 absorbing carbon (LAC) is sometimes interchanged with elemental carbon (EC). Although the 

94 two are not exactly equivalent, we will use the terms interchangeably here. The constant 10 at the 

95 end approximates Rayleigh scattering by air; it is the correct value at an altitude of about 1500 m 

96 (5000 ft). A humidity-dependent enhancement factor, f(RH), is used to account for the increased 

97 light scattering by sulfates and nitrates as the relative humidity increased. Monthly-average 

98 "climatologically-representative" values of f(RH) for each Class I area have been published by 

the U.S. EPA and are to be used for the purposes of the Regional Haze Rule.299 

100 Much of the Regional Haze Rule describes visibility in terms of a haze index (HI), in 

101 units of deciviews (dv). The haze index is determined via the formula

102 HI = 10 ln(bext/10), (2)

using the bext (in Mm-1 103 ) derived in eq 1. 

104 Estimates of natural background concentrations of the particulate matter components in 

105 eq 1 were developed in 1990 by Trijonis for the National Acid Precipitation Assessment Program 

(NAPAP).3106 He estimated annual average concentrations of chemical components of particulate 

107 matter for both the eastern United States and the mountain/desert regions of the western United 

108 States. The East was defined as extending up to one tier of states west of the Mississippi River, 

109 thus the VISTAS domain falls entirely within that region. 

110 The U.S. EPA has adopted Trijonis’ estimates of natural background concentrations, after 

adjusting his values to make them consistent with current IMPROVE measurement conventions.2111 

112 The resulting default concentrations for the East are listed in Table 1, along with Trijonis’ 

113 estimates of the errors in his values. The last two columns list the dry extinction efficiencies (the 

114 numerical coefficients in eq 1) for each component and the estimated contribution of each 

115 component to light extinction under “dry” conditions (relative humidity ≤ 36%), as calculated 

116 using eq 1 with f(RH) = 1. 

117 Note that particulate organic matter is overwhelmingly the largest contributor to natural 

118 background extinction, in contrast to the present situation in the East where sulfates usually 

119 dominate light extinction. 

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120 Trijonis’ error factors show that there are large uncertainties in his concentration 

121 estimates, typically by a factor of two or so. These uncertainties reflect the limited information 

122 he had available for developing his estimates and the result of assigning of a single set of 

123 numbers to characterize concentrations throughout the year over the large geographic area of the 

124 eastern United States. 

125 Trijonis arrived at his estimates for the eastern United States as follows, using the 

126 information that was available to him in 1990: 

127 • Fine organic particulate matter (POM) – He made ad hoc adjustments to 

128 measurements then available for the rural East (i.e., he assumed that 1/3 of the 

average measured concentration of 4 μg/m3129 is natural), which were supported by 

130 measurements of organic aerosol concentrations in remote areas (which suggest a 

background of 1.5 to 2.25 μg/m3131 in the northern hemisphere). (Note that there is a 

132 difference between background concentrations in remote areas today and 

133 concentrations under natural conditions. The former can include global background 

134 from anthropogenic emissions and thus may differ from the natural value.) 

135 • Fine sulfates – He estimated that 3% of sulfur emissions in the East are due to natural 

136 sources (based on the NAPAP emission inventory), considered rural measurements in 

137 the southern hemisphere, and considered background estimates made for central 

138 Alaska. 

139 • Fine elemental carbon – He estimated that 2.8% of EC emissions are due to wildfires 

140 and prescribed burning (from fire emissions information in the NAPAP emission 

141 inventory) and considered that global background EC concentrations are very small. 

• Fine ammonium nitrate – He considered nitrate levels (about 0.1 μg/m3142 ) in tropical 

143 forests and observed that the NAPAP emission inventory indicated that natural NOx

144 emissions are 4.5 times as great as those of gaseous sulfur (on a molar basis) but that 

145 atmospheric nitrate production should be relatively less that that of sulfur. 

146 • Fine soil -- Measurements suggested an average fine soil concentration of about 1 

μg/m3147 in the East. He arbitrarily assumed that half of that amount is natural. 

148 • Coarse matter – He used the lower end of the range of coarse mass concentration 

149 measurements at "remote" sites in the U.S. 

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150 Thus, Trijonis based his estimates on the limited base of data that was available at the 

151 time and interpreted it with a healthy dose of intuition, so the large error factors he assigned to 

152 his own estimates are not surprising. 

153 Figure 2 displays the annual average estimates of bext that result from applying the default 

154 concentrations of Table 1 together with the EPA’s site-specific, climatologically-representative 

155 f(RH) at four of the Class I areas in the VISTAS region, and identifies the contribution of each 

156 particulate matter component (and any water associated with it) to that extinction. The bars in the 

157 figure are labeled with the IMPROVE abbreviations for the names of the Class I areas the 

158 monitors represent – GRSM = Great Smoky Mountains National Park; MACA = Mammoth 

159 Cave National Park; ROMA = Cape Romain Wilderness within the Cape Romain National 

160 Wildlife Refuge; and EVER = Everglades National Park. 

161 The extinction coefficient and haze index values that result from the application of eqs 1 

162 and 2 (using the concentrations in Table 1 and site-specific, climatologically-representative 

163 monthly f(RH) values) are intended to represent default estimates of the average natural 

164 conditions light extinction during each month, though based on annually constant default 

165 concentrations. Since the Regional Haze Rule specifies that reasonable progress in mitigating 

166 regional haze is to consist of improvement on the 20% of the days that are most hazy and of no 

167 deterioration on the 20% of the days that are least hazy (clearest), the natural conditions 

168 extinction value that is most relevant is the one that applies to the 20% of the days that are the 

169 most hazy. 

Ames and Malm4170 developed a statistical procedure to estimate the average of the 

background extinction on the 20% haziest days (which they assumed is represented by the 90th 171 

172 percentile of extinction values) from the annual average default values. They assumed that the 

haze index under natural conditions would be normally distributed, in which case the 10th 173 and 

90th 174 percentile values can be related to the mean and standard deviation of the distribution. The 

175 mean values were calculated using Trijonis' default concentrations. To estimate the standard 

176 distribution of natural conditions, they adjusted IMPROVE-measured frequency distributions of 

177 today's haze indices at each Class I area by reducing actual sulfate and nitrate concentrations 

178 until, on average, they matched the Trijonis default values in the East and the West. Measured 

179 concentrations of organic carbon, elemental carbon, fine soil, and coarse matter were not reduced 

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180 similarly, under the assumption that at Class I areas they represent local concentrations that 

181 already derive largely from natural sources. 

182 From such synthetic frequency distributions at several Class I areas, Ames and Malm 

183 estimated one representative value of the standard deviation for the East and another one for the 

West, enabling calculation of 10th and 90th 184 percentile estimates. They concluded that, for the 

East, the 90th 185 percentile value for any given Class I area is 3.84 dv greater than the annual mean 

186 haze index (in deciviews) that is calculated from the default concentrations and f(RH). Based on 

this procedure, Appendix B of EPA's guidance document2187 presents default estimates of the haze 

188 index that are intended to represent the 20% haziest and 20% clearest days under natural 

189 conditions at each Class I area. 

190 SOURCES OF NATURAL BACKGROUND PARTICULATE MATTER 

191 Sources of gas and particulate emissions that contribute to natural particulate matter can be 

192 grouped into two classes depending on their variability during the year – essentially steady or 

193 episodic. Since haze calculations under the Regional Haze Regulations are based on 24-hour 

194 averages, diurnal variations are not of import to us here. 

195 Considering potential contributors, relatively steady-state natural sources (i.e., ones that 

196 vary slowly throughout the year or within a season, though they may reflect diurnal variability 

197 and even flare up at times) include the following: 

198 • Oceans and tidal zones– sources of sea salt (affected by the amount of wind-raised 

199 spray) and also sulfates and organic carbon (both primary and secondary). The 

200 primary on-land effect occurs within tens of kilometers to, at most, a few hundred 

201 kilometers of the shoreline. 

202 • Forests and vegetation – sources of organic carbon (both primary and secondary), 

203 coarse organic matter (from plant detritus), sulfates, and nitrates. Emissions result 

204 from plant photosynthesis and respiration and also from decaying vegetation. 

205 Ammonia produced in forests plays a role in the formation of ammonium sulfate, 

206 ammonium nitrate, and even organic particles. 

207 • Lightning – source of nitrates (relatively steady from day to day in some regions and 

208 seasons). 

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209 • The earth's crust – source of fine and coarse soil, including sulfates and nitrates, due 

210 to wind and natural disturbances. 

211 • Volcanoes – sources of sulfates and nitrates (both primary and secondary) and of soil 

212 particles. The main volcano currently affecting the VISTAS region is Popocatepetl in 

213 southern Mexico, whose SO2 emissions alone were estimated by the BRAVO Study

to average 3500 tonnes (3800 tons) per day.5214 These emissions are from the ultimate 

215 "tall stack" at 5465 m (18050 feet) above sea level (before plume rise), and thus can 

216 be transported very long distances along different pathways than lower-elevation 

217 emissions. 

218 The principal episodic natural sources include: 

219 • Windstorms – sources of soil particles, which can be transported long distances, even 

220 intercontinentally (examples are documented North African and Asian dust impacts 

221 on the United States). 

222 • Wildfires – sources of organic carbon and, to a lesser degree, elemental carbon and 

223 soil, which can also be transported long distances (an example is the observed effect 

224 of transported Central American biomass burning emissions on the U.S. South, 

225 although most of this burning is man-caused). 

226 If EPA’s default natural concentration values are valid, the contributions of all of these 

227 steady-state and episodic sources should already be reflected in those default concentration 

228 estimates, at least on average. At any specific location and time, however, conditions could 

229 deviate considerably from the default values. 

230 Subsequent to the development of the default values, natural background concentrations 

231 over the entire globe have been estimated by applying air quality models. As examples, such 

232 simulations have produced estimates for the southeastern U.S. of concentrations of OC and EC 

from natural sources, biomass burning, and all sources, with the Grantour model;6233 of 

concentrations of all components, with the GEOS-CHEM model;7, 8 234 and of concentrations of 

235 anthropogenic and biogenic secondary organic aerosol (SOA, the secondary fraction of POM), 

with the MOZART-2 model.9236 Specifically, the most recent application of the GEOS-CHEM 

model8237 yielded the following estimates of annual average natural background concentrations in 

the U.S. East: ammonium sulfate, 0.11 μg/m3; ammonium nitrate, 0.03 μg/m3238 ; organics, 1.17 

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μg/m3, and elemental carbon, 0.04 μg/m3239 . These can be compared with the default values in 

240 Table 1, with the caveat that the GEOS-CHEM modeling values include effects of worldwide 

241 “climatological” (i.e., multi-year average) fire emissions, both natural or anthropogenic, and thus 

242 may overstate the natural portion of carbon concentrations alone. 

243 Such global modeling could provide finer spatial resolution than the East-West split of 

244 the default values and could be used to develop seasonal or monthly estimates of the natural 

245 background. As uncertainties in emission inventories, particularly for natural emissions, are 

246 being reduced, global modeling is improving in accuracy and its results should be considered in 

247 parallel with the default estimates of Trijonis when establishing refined natural background 

248 values. 

249 We conducted a review of the scientific understanding of the emissions and ambient 

250 concentrations of all the source types mentioned above, to evaluate which of those source types 

251 are most likely to cause appreciable impact to natural conditions concentrations in any part of the 

VISTAS region.10 252 An important consideration was whether the scientific information base is 

253 strong enough to develop credible quantitative estimates of aerosol concentrations resulting from 

254 each of the above emissions categories. The potential impact of each emission category on 

255 natural visibility in the VISTAS region, and the credibility and importance associated with each 

256 impact relative to the default conditions, was estimated. 

257 As a result of that review, contributions of the following four categories of sources were 

258 considered for potential inclusion in refined estimates of natural conditions and thus have been 

259 investigated further: 

260 • organic and elemental carbon from wildfires, 

261 • volatile organic compounds (VOCs) and POM from biological processes in forests, 

262 • oceanic sea salt and POM, and 

263 • long range transport of windblown dust from Asia and North Africa. 

264 Impacts from other source categories mentioned earlier were considered to be too small 

265 to be important or our understanding of them at this time was too limited to enable development 

266 of credible concentration estimates. 

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267 In addition, the statistical procedure used by the EPA to derive extinction under natural 

268 conditions for the average 20%-haziest days and the empirical factor used to calculate POM 

269 concentrations from measured OC concentrations were considered worthy of review. 

270 Alternative assumptions for extinction efficiencies in eq 1 would also affect calculated 

271 light extinction under both current and natural background conditions. These alternatives are not 

discussed in this paper and the reader is referred to recent reviews of the topic.11 - 13 272 

273 The rest of this paper discusses the following issues: (1) statistical estimation of 

274 extinction under natural conditions for the average of the haziest 20% days, (2) the choice of the 

275 scaling factor for OC mass, and (3) current knowledge about the four categories of sources listed 

276 above. Included are initial estimates (sometimes crude) of the potential magnitudes of these 

277 issues on natural background concentrations and their potential impacts on developing 

278 reasonable progress goals. The questions then addressed in conclusion are whether such effects 

279 are substantial enough at some locations to materially affect the slope of the required "reasonable 

280 progress" line, and might there be areas where additional effort to refine natural background 

281 estimates would be justified? 

282 POTENTIAL REFINEMENTS TO THE DEFAULT VALUES

283 Statistical Estimation of the Haziest 20% of Days 

Ames and Malm4284 assumed that the frequency distribution of haze index values over a year is 

normally distributed. For a normal distribution, the haze indices at the 10th and 90th 285 percentiles of 

286 the haze distribution under natural conditions can be calculated using the following formula: 

287 HI = <HI> ± 1.28σ, (3) 

288 where HI is the haze index (in deciviews), <HI> is the annual mean haze index for the Class I 

area of interest (from the EPA guidance document2

289 ), and σ is the standard deviation of the 

assumed normal distribution of HI. Using the plus sign produces the 90th 290 percentile value and 

using the minus sign produces the 10th 291 percentile value. The term 1.28σ represents the distance 

from the mean to the 10th and 90th 292 percentiles. 

293 Using the synthesized natural concentrations frequency distribution described earlier, 

294 Ames and Malm estimated σ to average 3 in the East and 2 in the West under natural conditions, 

295 so the formula for the East becomes simply 

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296 HI = <HI> ± 3.84. (4) 

However, since the HI frequency distribution from the 80th to the 100th 297 percentiles is not 

uniform in a normal distribution, the HI value at the midpoint (90th 298 ) percentile of that range does 

299 not actually represent the mean of the 20% haziest days that is prescribed by the Regional Haze 

300 Regulations. Rather, the mean value for that range in a normal distribution is represented by the 

haze index at the 92nd percentile.14 301 Consequently, the 1.28 factor should be 1.42 if the 

302 distribution is normal, in which case the formula for the East becomes 

303 HI = <HI> ± 4.26. (5) 

304 Thus, for the East, the haze indices for the average of the haziest 20% of the natural days should 

305 be 0.42 dv greater than the default values that are presented in the EPA's guidance document 

306 (and those for the clearest 20% of the natural days should be 0.42 dv less), if a normal 

307 distribution is assumed. 

308 

309 Mass Concentration of Particulate Organic Matter 

310 Trijonis estimated the natural background concentration of organic carbon (OC) in the East to 

average 1 μg/m3311 . This estimate should include both emissions from natural fires and biogenic 

312 emissions from vegetation. To account for the oxygen, hydrogen, nitrogen, and other elements 

313 that are contained in POM, he multiplied his OC value by 1.5; to conform with current 

314 IMPROVE practice, the EPA has used a factor of 1.4 for the same adjustment for Table 1. The 

315 global aerosol research community generally appears to use a factor of 1.6 to 1.8 (see, for 

316 example, references 15 and 16). Considering individual species, the known range of the 

multiplier spans from 1.17 for large saturated hydrocarbons to 3.75 for oxalic acid.17 317 

318 A comprehensive review of experimental and theoretical research results by Turpin and 

Lim18 319 led to the conclusions that the appropriate multiplier for ambient OC will vary depending 

320 on the origin and age of the aerosol and that the value of 1.4 does not accurately represent a non321 urban aerosol. Rather, they concluded that the appropriate factor for a non-urban aerosol is 2.1 ± 

322 0.2. They also concluded that the appropriate value for an urban aerosol is 1.6 ± 0.2, which 

323 approximates the 1.5 and 1.4 factors used by Trijonis and the EPA and IMPROVE. Turpin and 

324 Lim’s value for an aerosol heavily impacted by wood smoke is 2.4 ± 0.2. 

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325 It thus seems reasonable that the OC multiplier that would best represent natural 

326 conditions most of the time would be about 2.1 (Turpin and Lim’s value for a non-urban 

327 aerosol), while 2.4 could represent natural conditions during episodic fire events. Using the 2.1 

factor, the estimated mass concentration of POM in Table 1 would be 2.1 μg/m3328 and the 

extinction contribution would become 8.4 Mm-1 329 , which increases the overall dry extinction of 

9.09 Mm-1 to 11.89 Mm-1, an increase of 2.8 Mm-1 330 or 31%. This corresponds to an increase of 

331 2.7 deciviews under dry conditions. 

332 A different multiplicative factor than 2.1 is needed for deducing current POM 

333 concentrations from IMPROVE OC measurements, though, because today's organic aerosol may 

334 include an urban component or reflect smoke from intentional burning of vegetation. For 

335 example, ratios of POM concentration to OC concentration in Class I areas have been found to 

be 1.8 and 2.0 in recent research studies.19, 20 336 A recent evaluation of the literature recommends a 

value of 1.8 for IMPROVE monitoring sites.11 337 

338 Concentrations of POM mentioned in the next few sections will be based on the factor of 

339 1.4, unless otherwise indicated. If the factor used by the investigator to scale measured or 

340 modeled OC concentrations is different or if the model directly presents POM concentrations, 

341 such a situation will be identified. The implications of changing to a different factor for natural 

342 conditions will be addressed after we have surveyed results of scientific research concerning the 

343 contributions of the sources listed earlier.

344 

345 Carbon from Wildfires

346 Wildfires can affect air pollutant concentrations over large geographic areas. Effects of large 

347 wildfires in northwestern Canada have been observed on air pollutant concentrations in 

Tennessee21 348 and transport and impact of smoke from wildfires and biomass burning in southern 

349 Mexico and Central America have been observed in the central and southeastern portions of the 

United States.22, 23 350 

351 The contributions of natural fires to background carbon concentrations have just begun to 

352 be quantified. They are, of course, highly episodic at any given location. In the VISTAS domain, 

using a combination of chemical mass balance (CMB) modeling and 14 353 C analyses of particulate 

354 matter, average wood smoke contributions to POM were estimated to lie between 0.8 and 2.4 

μg/m3 at a rural location near Atlanta, Georgia.24 355 These values would also include the 

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356 contribution of intentional burning of vegetation, and thus are likely to overstate the natural 

357 contribution. 

Another estimate is provided by the global air pollution model GEOS-CHEM,7, 8 358 which 

359 was described earlier. The mean concentration of EC in the East, attributable almost entirely to 

biomass fires (natural and anthropogenic, worldwide), was estimated to be 0.04 μg/m3360 , twice the 

361 natural default EC value in Table 1. The modeled estimate for POM from the same fires and 

from all other natural sources is 1.17 μg/m3362 for the East, which is slightly less than the 

corresponding natural default value of 1.4 μg/m3 363 for POM in Table 1. 

364 Both the default POM concentration in Table 1 and the upper bound GEOS-CHEM value 

365 cited above lie in the range determined by the measurements at the rural location near Atlanta, so 

366 there is no inconsistency between the three sources of information. 

Another type of bounding estimate is provided by 14 367 C analyses at Great Smoky 

Mountains National Park during two recent summers.25, 26 368 Those measurements, which are 

369 described in the next section, found average modern carbon concentrations of 2.45 and 3.42 

μg/m3370 , respectively. Modern carbon would be derived naturally from wildfires and as a result of 

371 emissions from vegetation (discussed below) and from anthropogenic agricultural and prescribed 

372 biomass burning. It is likely that most of the modern carbon at Great Smoky Mountains in 

373 summer is attributable to biogenic emissions from vegetation, thus the natural burning 

374 contribution to carbon at these times will necessarily be substantially less than 2.45 or 3.42 

μg/m3375 (or the corresponding POM concentration will be substantially less than 3.43 or 4.79 

μg/m3376 , if the 1.4 factor is used).

377 The average seasonal regional contributions of natural wildfires throughout the Southeast 

378 need to be better quantified before default concentrations can be adjusted to reflect their 

379 contributions. 

380 As an example of a potential approach that could be used for this purpose, the total 

381 current biomass-burning contribution could be measured by creating and using local biomass382 burning particle signatures analogous to those developed for the Intercontinental Transport and 

Chemical Transformation (ITCT) mission from measurements in a forest fire plume in Utah.27 383 

384 Once the total biomass burning contribution is quantified, the anthropogenic and natural biomass 

385 burning contributions could be separated based on their proportions in (rapidly improving) 

386 emission inventories for wildfires, prescribed burning, and agricultural burning. 

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387 Alternatively, the contribution of biomass-burning smoke could be estimated by 

388 speciation of organic matter in particulate matter samples and then using organic species tracers, 

14 389 C dating, and CMB analysis of the data to deduce source types. VISTAS is currently carrying 

390 out such a study; one year of measurements at five locations was completed in May 2005 and 

391 sample and data analyses are now under way. 

392 

393 Emissions of POM from Forests and Other Vegetation

394 Forest vegetation is the principal global source of atmospheric organic carbon in particles 

395 (both primary and secondary), with global biogenic organic emissions exceeding anthropogenic 

emissions by as much as an order of magnitude.28, 29 396 In the United States, biogenic sources 

account for 1/2 to 3/4 of the hydrocarbon emissions.29, 30 397 The tropospheric burden of fine 

398 particulate carbon in low latitude regions of the globe is dominated by long-range transport from 

the tropics.31 399 

400 A visible manifestation of biogenic organic aerosol is the blue haze often observed over 

401 forested regions, which results from oxidative particle formation, probably from biogenic 

402 terpenes. It is unclear, though, to what extent such secondary terpenoid aerosols contribute to the 

total biogenic organic aerosol burden.32 403 

404 The largest biogenic emissions occur in the tropics, where there is a combination of high 

405 temperatures, high biomass density, and ample sunlight. During summer in the southeastern 

406 United States, though, the emission flux (emissions rate per unit area of land) of biogenic 

hydrocarbon emissions is estimated to be as large as at that in the tropics30 407 , such as in the 

Amazon basin.9, 29 408 

409 Studies at several locations on the globe have concluded that the formation of biogenic 

410 organic aerosol begins with the formation of minute particles (a few nanometers in size) through 

411 nucleation involving sulfuric acid, ammonia, water, and possibly some volatile organic 

412 compounds. These particles grow to optically-important sizes via condensation of both inorganic 

413 and photochemically-oxidized organic vapors. Aspects of the formation process in different parts 

of the world are described in several articles.33 - 40 414 

415 The exact role of VOCs in the nucleation process is a subject of debate, although the fact 

that biogenic VOCs are much more reactive than anthropogenic VOCs41 416 suggests that they play a 

417 role from the outset. Also, the acidity of seed particles appears to enhance the formation of 

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SOA,42-44 418 which may mean that current organic particle formation rates from natural emissions 

419 are greater than they would be under natural conditions because of the presence today of global 

420 anthropogenic inorganic acidic pollution. 

421 Concentrations of POM have been found to be quite high in tropical forests. 

422 Measurements of submicrometer (< 1 μm) particulate matter in an equatorial forest in the Ivory 

423 Coast (west coast of Africa) found the average particulate carbon concentration (i.e., before 

multiplication by a factor such as the 1.4 used by EPA and IMPROVE) to be 9 μg/m3424 during the 

wet (growing) season.31 425 Since biomass burning, which would have contributed carbonaceous 

426 particles during the dry season, did not take place during the wet season, the measured 

427 particulate carbon was attributed to natural emissions from the forest. 

428 Measurements made by low-flying aircraft over a tropical Amazon Basin forest in Brazil 

resulted in similar conclusions.45 429 The measurements were made during the early to middle "dry" 

430 season, when the forest was moist during the measurements and biomass burning did not yet 

431 appear to be an important source. Their sampler used for carbonaceous particles did not have a 

432 specific size cut, although it is likely that most of the particles sampled were in the fine size 

433 fraction. The average particulate organic carbon concentration in the mixed layer was 740 

nanomoles C/m3, which works out to 8.9 μg/m3434 of carbon (or a POM concentration of 12.5 

μg/m3435 , if a factor of 1.4 is used). 

436 In another study in the Amazon Basin, Absolute Principal Component Analysis (APCA) 

437 of five years of field measurements at two remote locations yielded that biogenic particles 

accounted for 57-63% of the total PM2.5 mass during the wet (non-burning) season.46 438 The 

439 average biogenic particle concentrations during those seasons were estimated to be 3.9 and 5.5 

μg/m3440 at the two sites. The biogenic material was considered to be composed principally of 

441 organic carbon compounds plus a small amount of sulfur compounds. 

442 These measured concentrations are consistent with concentrations simulated with the 

MOZART-2 model9443 , which estimated that SOA concentrations in the Amazon basin were as 

much as 10 μg/m3444 in summer. 

445 These tropical data show that the potential concentrations of biogenic POM can be quite 

446 large in and near forests. The question is how to translate these tropical research results into 

447 biogenic POM concentrations that would apply in the VISTAS region. Because of the cooler 

448 climate, it is likely that the average concentrations over the year will be lower in forests in the 

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449 much of the VISTAS region than in the tropics. As noted above, though, several analyses have 

450 concluded that summertime (June to September) biogenic VOC emission fluxes in the Southeast 

(except near the coast) are comparable to those in the Amazon basin.9, 29 451 

452 In a more northerly setting, research carried out on formation of POM in remote forests in 

453 Finland provides further insight. Overall PM2.5 mass concentrations observed during the spring 

there were lowest (2.6 μg/m3454 ) when the air mass was polar or arctic, and higher concentrations 

455 were observed when the air came from other directions that reflected warmer air and impacts 

from distant anthropogenic sources.47 456 

457 Carbon concentrations in June, measured by high-volume sampler (needed to collect 

458 sufficient sample for speciation of the organic carbon compounds) at the same location in 

Finland, were 3.2 μg/m3 for OC and 0.19 μg/m3459 for elemental carbon (or, more rigorously, black 

carbon).48 460 Direct comparison with the values in Table 1 is not possible, though, because the 

461 particle size range collected by the hi-vol is much broader than that of PM2.5. 

Estimates of natural SOA in the southeastern U.S. have been made via the 14 462 C 

measurements and CMB analyses that were cited earlier.26 463 Based on measurements made during 

464 one month of each quarter during one year at a rural, predominantly agricultural, location near 

Atlanta, Georgia, the average natural SOA concentration was 1.6 μg/m3465 , the peak concentration 

observed was about 2.5 μg/m3466 in July, and the concentration in winter (January) was zero. Data 

from another 14C study,49 467 this time in Nashville, Tennessee, allow us to conclude that biogenic 

468 OC concentrations there during a one-month period from mid-June to mid-July ranged from 

about 2 to about 5 μg/m3, which correspond to POM concentrations of 2.8 to 7 μg/m3469 or more. 

Two 14C studies at Great Smoky Mountains National Park in August 200225 470 and the 

summer of 200426 471 concluded that averages of 83% and 90% of the sampled POM concentrations 

472 were of modern origin, respectively. These translate into average concentrations of modern OC 

during those periods of 3.42 and 2.45 μg/m3473 , which are considered most likely to be mainly SOA 

derived from biogenic VOC emissions.26 474 (The corresponding POM concentrations are 4.78 and 

3.43 μg/m3475 , respectively, calculated with a factor of 1.4.) 

476 Simulation with the MOZART-2 model finds current fine SOA concentrations 

(attributable to both biogenic and anthropogenic emissions) to be around 2 μg/m3477 throughout the 

inland South in June and September, and 1 μg/m3478 or less in March and December. These values 

479 are somewhat less than the average POM concentrations (sum of both primary and secondary) 

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measured by IMPROVE samplers in these regions,50 480 but not greatly so, which suggests that they 

481 are not far off the mark. These modeled values may thus provide an indication of the upper 

482 bound to the typical biogenic contribution alone in the Southeast today. 

483 The information given here suggests that biogenic POM concentrations in forested 

portions of the VISTAS region could reach 4 μg/m3484 or more in summer. This value is 

485 comparable to currently observed summertime levels of organic carbon in forested areas in the 

southeastern U.S.50 486 Since biogenic contributions will be less the rest of the year, the annual 

average impact is likely in the range of 1 to 2 μg/m3487 . Such a biogenic contribution alone is 

comparable to the 1.4 μg/m3488 average default concentration for all organics, while the 

489 summertime peak is many times the default average. 

490 

491 Oceanic Emissions of Sea Salt and Organics

492 Sea Salt. Sea salt contributes to ambient particulate matter near the ocean. Over the ocean in 

493 remote areas it accounts for about half of the submicrometer (< 1 μm) aerosol in the boundary 

layer and for about 3⁄4 of the aerosol that is larger than 1 μm and smaller than 10 μm.16 494 The 

495 concentration will vary with the state of the ocean; average concentrations measured in the 

remote southern hemisphere ocean during the ACE 1 research study were 0.6 μg/m3496 for the 

submicrometer sea salt and about 9.5 μg/m3 for the salt larger in sizes between 1 and 10 μm.16 497 

498 The chemical composition of sea salt, which is a mixture of NaCl and other constituents, 

499 is complex and variable. In particular, sea salt loses chloride as it ages because exposure to 

500 atmospheric H2SO4 and HNO3 and certain non-acidic gases results in the release of gaseous HCl. 

501 Consequently, the mass scattering efficiency of aged sea salt differs from that of fresh sea salt 

(whose scattering is similar to that of pure NaCl).51 502 Being hygroscopic, sea salt has the potential 

503 to contribute disproportionately to haze when the humidity is high. 

504 Although IMPROVE measures elemental Na and Cl, and hence estimating the amount of 

505 sea salt at Class I areas is conceptually feasible, its effect is not included in the EPA/IMPROVE 

506 formula for estimating light extinction (eq 1). An assessment of the effect of this omission, using 

507 IMPROVE data taken at 59 sites from 1993 to 1999, led to the conclusion that including the 

508 mass concentration of NaCl consistently improved the comparison between reconstructed fine 

mass concentrations and measured PM2.5 at all sites.52 509 Fine NaCl accounted for 1% to 33% of the 

510 reconstructed fine mass concentrations (the 33% was at Point Reyes National Seashore, 

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511 California). In the VISTAS region, NaCl accounted for 5% of the fine mass concentration at 

512 Chassahowitzka National Wildlife Refuge (NWR) in Florida, 3% at the more inland Okefenokee 

513 NWR in Georgia, and 1-2% elsewhere. (Another VISTAS coastal site, Cape Romain NWR in 

514 South Carolina, was not included in the analysis.) 

515 The average reconstructed PM2.5 concentrations between 1996 and 1998 at 

Chassahowitzka and Okefenokee were 9.8 and 9.7 μg/m3, respectively.50 516 We can thus infer from 

517 the percentages above that the average fine sea salt concentration at those two locations is in the 

range of 0.3 to 0.5 μg/m3518 , which corresponds to 13 to 22% of the default natural fine mass 

519 concentrations there. Such concentrations are comparable to the default natural concentration of 

520 soil. Further, since the sea salt size distribution approximates that of soil (see, for example, a 

521 comparison of size distributions in reference 30), the extinction impact of comparable amounts 

522 of fine soil and dry fine sea salt can be expected to be comparable. Sea salt is hygroscopic 

523 however, the effect of which will be addressed later in this section. 

524 In an experiment in which the composition of particulate matter arriving at Miami from 

525 over the Atlantic ocean was studied for 23 years, the annual mean sea salt concentration in TSP 

(calculated from the measured Na concentration and the Na/Cl ratio of seawater) was 8.67 μg/m3526 

for those periods when the flow arrived from over the ocean.53 527 The highest values were in the 

528 winter (attributed to stronger winds creating more sea spray then) and lower than average 

529 concentrations were found from May through September. The minimum daily concentration 

measured during every month of the year exceeded 4.5 μg/m3530 . 

531 The size cut point of the sampler used for these measurements is not known, but the 

investigator estimated that 1/3 to 1/2 of the sampled mass was in the fine (PM2.5) range.53 532 If the 

533 same fraction applied to the sea salt, that would suggest an annual average fine sea salt 

contribution in the range of 3 to 4.5 μg/m3534 . The corresponding estimate of minimum daily fine 

sea salt concentration (on days with onshore flow) would be in the range of 1.5 to 2.2 μg/m3535 . 

536 These concentrations are greater than the highest averages inferred above from analyses of 

IMPROVE samples.52 537 A possible reason for the discrepancy is that the Miami concentrations 

538 only reflect those measured at the shoreline during the times when airflow came from over the 

539 ocean, while the IMPROVE data represent all wind directions. Also, the Miami measurements 

540 were taken much closer to the shoreline than the IMPROVE measurements at Okefenokee, and 

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541 thus they could be influenced by sea spray from breaking surf, although the investigator states 

that the concentrations were not strongly influenced by salt spray from the surf zone.53 542 

543 Sea salt concentrations from IMPROVE measurements at Cape Romain and Okefenokee 

were also calculated by Paine and colleagues.54 544 Their estimate of current sea salt concentrations 

545 included both NaCl and Na2SO4, where the sulfate was assumed to have replaced chloride that 

546 was lost due to chemical reactions of NaCl with acidic gases and other substances in the 

547 atmosphere. Because such reactants might not be present under natural conditions, they also 

548 calculated the concentration of NaCl alone. Considering the latter concentration, which is the 

lower one, they estimated annual average fine sea salt concentrations to be 1.3 μg/m3549 at Cape 

Romain and 0.85 μg/m3 at Okefenokee. The Cape Romain figure of 1.3 μg/m3550 is consistent with 

the slightly higher range of 1.5 to 2.0 μg/m3551 that was deduced above from measurements at 

552 Miami during onshore flow alone. The Okefenokee value is somewhat larger than the 0.3 to 0.5 

μg/m3553 range inferred above for Okefenokee from data in reference 52. 

Another study24 554 estimated the fine sea salt concentrations along the coast of the Gulf of 

Mexico (the southern boundary of the VISTAS domain), to be in the range of 0.4 to 0.5 μg/m3555 at 

556 the coast of the Gulf of Mexico. This range of concentrations is lower than the ranges cited 

557 above, but the investigators point out that the Gulf may generate less sea salt spray than the more 

558 highly energetic ocean along the Atlantic coastline. 

559 Meaningful sea salt concentrations can extend well inland from the shoreline. Particulate 

560 matter measurements by low-flying aircraft (at about 150 m altitude) in the Amazon Basin of 

Brazil found that that, assuming that aerosol Na+561 was derived exclusively from sea salt, the 

average concentration of sea salt larger than 1 μm in size in the mixed layer was about 4 μg/m3562 

near the coast, 1.5 μg/m3 250 km inland, and less than 1 μg/m3 more than 400 km inland.45 563 

564 Without further information on size distribution, one cannot say how much of this material 

565 would show up as PM2.5 and how much would be coarser. A corresponding description for 

566 submicrometer sea salt was not possible because concentrations were below the detection 

567 method for the analytical method used. 

Studies of the light scattering efficiency and hygroscopic growth of sea salt particles51, 55 568 

569 don't, unfortunately, provide information on the scattering of light by sea salt particles in the 

570 actual size range found in the atmosphere. Over the Indian Ocean, an extinction efficiency of 1.0 

to 1.2 m2/g was estimated for submicrometer (< 1 μm diameter) sea salt at 55% RH,15 571 which is in 

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the same range as the 1.0 m2572 /g used by IMPROVE for dry fine soil particles. The estimate for 

supermicrometer (> 1 μm diameter) particles was 5.1 to 6.0 m2573 /g at 55% RH, which is well 

above the 0.6 m2574 /g used by IMPROVE for dry coarse particles, even after allowance is made for 

575 hygroscopic growth, although some of the difference may be explained by the difference 

576 between the 1 μm lower size cuts used for this work and the 2.5 μm size cut of IMPROVE. 

577 In the absence of better information, the light scattering efficiency of fine dry sea salt is 

sometimes taken to be the same as the 1 m2578 /g used by IMPROVE for fine soil, and that of the 

coarse fraction of dry sea salt is taken to be the 0.6 m2579 /g scattering efficiency of coarse 

particulate matter. This is the approach used by Paine et al.,54 580 who also deduced a humidity 

581 scaling function f(RH) for fine sea salt based on measurements made over the Atlantic Ocean 

582 during the ACE 2 experiment. The resulting function is similar to the EPA/IMPROVE f(RH) for 

583 sulfates and nitrates, except that hygroscopic growth begins at a lower relative humidity for sea 

584 salt than for sulfate and nitrate and the increase above 90% RH is not as rapid for sea salt. The 

585 f(RH) curves for fine sea salt and sulfate/nitrate are almost identical from 60% RH to 90% RH. 

586 Humidity-related growth can also be expected for the coarse fraction of sea salt, though not with 

the same f(RH) curve as for the fine sea salt.56 587 

588 Using their empirical f(RH) curve and their lower sea salt concentration estimates at Cape 

589 Romain and Okefenokee, Paine et al. calculated seasonal and annual average extinction impacts 

of fine sea salt concentrations.45 590 They concluded that the contribution of natural background fine 

sea salt to extinction was 4.3 Mm-1 at Cape Romain and 3.0 Mm-1 591 at Okefenokee. Using the data 

592 in Table 1 and the same annual f(RH) that Paine et al. used, we calculate that the annual default 

particulate matter extinctions without sea salt are 11.4 Mm-1 at Cape Romain and 11.6 Mm-1 593 at 

594 Okefenokee. Adding the above sea salt contribution to these values increases the estimated 

595 annual-average natural extinction due to particulate matter by 38% at coastal Cape Romain and 

596 26% at the more-inland Okefenokee. 

597 

598 Oceanic POM. Turning to organic particles from the ocean, mass concentrations of fine organic 

599 particles at the northeastern tip of Puerto Rico (and thus exposed to the easterly trade winds 

600 coming over the ocean) and also during ship sampling away from the coast there were found to 

be in the range of 0.3 to 0.4 μg/m3 during a February to June study.57 601 Black (elemental) carbon 

602 was not found in the samples, which indicated that the aerosol was relatively free of combustion 

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603 products. Consequently, a substantial fraction of this organic material may be associated with 

natural oceanic emissions.57, 58 604 

605 As another indication of background levels of oceanic carbon, average POM 

606 concentrations (defined as 1.6 x OC) in marine air masses over the Indian Ocean south of the 

equator were found to be 0.07 to 0.13 μg/m3 in submicrometer particles and 0.14 to 0.56 μg/m3 607 

in supermicrometer particles during the February-March monsoon season.15 608 These 

609 concentrations again represent values obtained for oceanic air masses, with the lowest 

610 concentrations associated with transport from the south. According to the investigators, these 

611 values are comparable with what has been measured in remote areas of the Atlantic and Pacific 

612 Oceans. In contrast, in the North Atlantic during the phytoplankton bloom periods, 

submicrometer OC concentrations in clean marine air at a coastal site averaged 0.9 μg/m3613 (which 

corresponds to a POM concentration of about 1.4 μg/m3, using the same 1.6 factor).59 614 

615 It is not clear how to relate open-ocean POM concentrations to concentrations of natural 

616 POM of oceanic origin at coastal IMPROVE sites such as Cape Romain. Available data aren’t 

617 sufficient to give insight into variability due to temperature or season. Nor do the oceanic 

618 concentrations reflect enhanced POM emissions from breaking waves at the shoreline and 

619 biological tidal zone POM formation, both of which are likely to be very site-specific. 

As a first guess, if one assumes 0.4 μg/m3620 for natural fine POM over the ocean and 

621 assumes that the wind blows onshore from the ocean half of the time (a good assumption at Cape 

622 Romain), then the annual average contribution of fine oceanic POM at Cape Romain might be 

0.2 μg/m3623 . This corresponds to an increase in the default POM concentration by about 15% and 

the default light extinction by 0.8 Mm-1 624 . The oceanic POM contribution would be smaller at 

625 more inland non-coastal sites such as Okefenokee. 

626 

627 Long Range Transport of Windblown Dust

628 To estimate the average fine soil concentration, Trijonis elected to "arbitrarily assume" (his 

words) that half of the current average measured fine soil concentrations are natural.3 629 The limited 

data available to him indicated that average rural fine soil concentrations were about 1 μg/m3630 in 

631 both the East and the desert/mountainous West, which he divided by two to arrive at his natural 

background estimate of 0.5 μg/m3632 for both regions. Recent measurements and data analyses 

633 allow us to assess the applicability of this estimate to the VISTAS region. 

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634 

635 IMPROVE Fine Soil Measurements in the VISTAS Region. IMPROVE measurements of fine 

636 soil concentrations at nine locations throughout the VISTAS region during 1996-1998 produce a 

composite annual average fine soil concentration of 0.62 μg/m3.

50 637 The annual average values are 

least at the northern sites (0.38 μg/m3 at Dolly Sods Wilderness, West Virginia and 0.42 μg/m3638 at 

Shenandoah National Park, Virginia) and greatest at the southern sites (0.77 μg/m3639 at 

Okefenokee NWR, 0.80 μg/m3 at Sipsey Wilderness, Alabama, and 1.1 μg/m3640 at 

641 Chassahowitzka NWR). The measured soil values in the southern part of the VISTAS region 

approximate the 1 μg/m3642 assumed by Trijonis as representative of the current fine soil 

643 concentration in the East, but the current measured values in the northern portion are closer to his 

default value of 0.5 μg/m3644 for natural conditions alone. Thus the default soil concentration may 

645 overstate natural fine soil concentrations in the northern portion of the VISTAS region. 

646 The soil concentrations measured by IMPROVE vary greatly by season. Winter 

647 concentrations are relatively uniform throughout the VISTAS domain, in the vicinity of 0.2 to 

0.3 μg/m3, while summer concentrations range from around 0.5 μg/m3 to around 2 μg/m3648 as one 

goes from north to south in the VISTAS region.50 649 As we discuss below, long-range transport of 

650 dust appears to be a major contributor to the large spatial variability in the summer. Such dust 

651 has two origins – Asia (including the Gobi Desert) and Africa (especially the Sahara Desert). 

652 

653 Asian Dust Contributions to IMPROVE Samples. The elemental composition of IMPROVE 

654 filter samples taken throughout the United States from 1989 to 1999 was evaluated by VanCuren 

and Cahill to determine the origins of the soil elements in those samples.60 655 Using cluster analysis 

656 of known Asian air masses to develop signatures for the Asian aerosols, they concluded that dust 

657 transported across the Pacific Ocean from Asia affects most of the U.S. during most of the year. 

658 Satellite images have demonstrated that Asian aerosols can easily cross North America and reach 

659 the western Atlantic Ocean. 

Asian dust impacts are greatest in the western U.S.61 660 and subside toward the East Coast, 

661 and are generally greater in the northern part of the country than they are to the south 

662 (presumably because westerly flow is more prevalent in the northern latitudes). This geographic 

663 distribution is not universal, however. A large Asian dust episode in mid April 2001 (discussed 

664 further below) had its greatest impacts along the central West Coast, the Rocky Mountains, and 

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665 in the Southeast, while the impacts along the northern boundary of the United States were 

smaller, though still significant.62 666 

667 The frequency of appearance of Asian dust in the United States is greatest in the spring 

668 and summer months, with the most noticeable seasonal variation in northern states and, 

669 generally, a flatter temporal distribution in the southeastern U.S. 

Focusing now on the VISTAS region, graphs in the Van Curen and Cahill paper60 670 show 

671 that, on those days when Asian dust was a dominant contributor to fine soil concentrations at 

Shenandoah National Park, the average concentration of that dust exceeded 0.6 μg/m3672 during 

673 every month from March through October and was less the rest of the year. The average 

contribution during Asian dust events during that 7-month period was about 0.7 μg/m3674 and the 

peak monthly average was about 1.2 μg/m3675 , in August. Episodic concentrations (24-hr average) 

676 were much higher. 

677 As an extreme example, which occurred after the period analyzed by VanCuren and 

678 Cahill, the Asian dust event of mid April 2001 (the largest such event ever observed in the U.S.) 

caused 24-hr PM10 concentrations to increase by 20-30 μg/m3679 (!) at the Shenandoah and 

Okefenokee IMPROVE samplers.62 680 This was followed by two additional episodes during the 

681 first half of May. All three episodes also significantly impacted particulate matter concentrations 

682 in the northeastern U.S. Interestingly, the trajectories to the Northeast for the first May episode 

683 first passed through the VISTAS region. They entered the VISTAS states from the northwest, 

684 circled clockwise through the VISTAS region, and then headed northeast to the northeastern 

U.S.63 685 

686 Based on VanCuren’s and Cahill’s analysis, Asian dust events occurred at Shenandoah 

687 during every month of the year, with frequencies between about 20 to 36% of the time in the 

688 spring months, 16% of the time in October, and less than 10% of the time in the remaining 

689 months. Combining these concentrations and frequencies of episodes, we estimate the average 

690 Asian dust concentration (averaged over all days, irrespective of whether Asian dust impact 

691 occurred on any given day) at Shenandoah from March through October is in the range of 0.1 to 

0.2 μg/m3692 . The Asian dust statistics at Dolly Sods Wilderness are similar to those at Shenandoah. 

693 Farther to the south, frequencies of Asian dust events were highest during summer at 

694 Everglades National Park, Florida (averaging around 20% from May through September). The 

695 average concentrations during those events were smaller than at Shenandoah (averaging about 

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0.5 μg/m3696 from March to September), which suggests an average Asian dust contribution there 

of around 0.1 μg/m3697 over the late spring and summer. 

698 The fine soil concentrations measured by IMPROVE in the Appalachian region (which 

includes Shenandoah and Great Smoky Mountains National Parks) average 0.45 μg/m3699 over the 

year and peak at 0.61 μg/m3 over the summer season.50 700 The episodic fine dust concentrations at 

701 Shenandoah that were attributed by VanCuren and Cahill to Asian dust events (and were 

702 described above) are comparable to or greater than these measured averages. This implies that 

703 Asian dust plays a dominant role in Shenandoah soil concentrations at times from spring into 

fall, and its average 0.1 to 0.2 μg/m3704 contribution accounts for roughly 20 to 40% of the 

705 measured fine soil concentration during that period. 

706 At southeastern IMPROVE sites (encompassing Chassahowitzka, Okefenokee, and Cape 

Romain), the average fine soil concentration in 1996-1998 was 0.81 μg/m3707 and the summer 

average was 1.86 μg/m3.

03 708 As expected, the influence of Asian dust is much less noticeable here 

709 than at the more northerly locations. Rather, as discussed next, African dust transport plays a 

710 major role in the summertime peak. 

711 

712 African Dust Contributions to IMPROVE Samples. Dust transported across the Atlantic Ocean 

713 from the Sahara Desert is another contributor to fine soil concentrations in the VISTAS region. 

714 African dust impacts, which can be identified by their elemental signature, occur less frequently 

715 in the VISTAS region than those from Asian dust but the magnitudes of the African 

contributions are much larger whenever those impacts occur.60 716 

717 African dust impacts tend to be greatest along the Caribbean and diminish toward the 

718 north. Specifically, at the Everglades, African dust events occurred regularly from June through 

719 September over an 11-year period, with the soil composition on more than 40% of the days in 

July dominated by African dust.60 720 The mean 24-hr average African fine soil dust contribution 

there during events occurring in June through August was between 7 and 9 μg/m3721 . The pattern is 

722 similar at Okefenokee, but with somewhat lower frequency and concentrations than at the 

Everglades. Farther north, the average African dust event contributed 4.5 μg/m3 723 at Shenandoah 

724 and Dolly Sods, but those events were infrequent and occurred almost exclusively in August. 

725 IMPROVE measurements at the three southeastern monitoring sites, Chassakhowitzka, 

Okefenokee, and Cape Romain do indeed show an average soil concentration of 1.86 μg/m3726 in 

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the summer, compared to about 0.6 μg/m3 in the spring and autumn.50 727 The high frequency of 

728 occurrence and the high concentration impacts of African dust events there imply that such dust 

729 is the dominant cause of that summertime peak, which suggests that African dust alone accounts 

for most of the summer increase, perhaps roughly 1 μg/m3730 of fine soil, averaged over the 

731 summer. This amount alone, without considering other natural sources, is well in excess of the 

0.5 μg/m3732 average natural concentration estimated by Trijonis. 

Perry and colleagues64 733 used IMPROVE samples from 1988 through 1995 to estimate the 

734 impacts of transported North African dust at various locations in the Southeast. They concluded 

735 that 57% of the samples taken at the Everglades in July showed an African dust impact greater 

than 3 μg/m3736 . Corresponding percentages at other VISTAS locations were 27% at Okefenokee, 

737 19% at Chassahowitzka, 16% at Sipsey and Great Smoky Mountains, 14% at Dolly Sods, and 

738 8% at Shenandoah. A surprising 23% of the observations at Mammoth Cave National Park, 

Kentucky in July contained African dust above 3 μg/m3739 , which is reinforced by an even higher 

740 37% at Upper Buffalo Wilderness in Arkansas (outside the VISTAS region). Further outside the 

741 VISTAS region, 19% of the observations at Big Bend National Park in western Texas in July 

contained more than 3 μg/m3742 of African dust and the corresponding percentage was 76% at the 

743 Virgin Islands. Percentages of African dust impact were less in other months, with the period of 

744 impact concentrated on June through August in the southern locations in the VISTAS region and 

745 including only July at the northern locations. 

746 In contrast, the period of impact at the Virgin Islands covered the nine months from 

747 February through October. As a result, Perry et al. found the annual mean fine soil concentration 

at the Virgin Islands was 3.25 μg/m3, versus values in the vicinity of 1 μg/m3748 that were given 

749 earlier for the Southeast, and the summertime fine soil concentrations there were a substantial 

7.58 μg/m3750 , about four times as large as the highest concentrations in the VISTAS region. 

751 From 23 years of measurements of the composition of particulate matter transported over 

752 the Atlantic Ocean onto the coast at Miami, the mean concentration of soil dust in TSP during 

onshore flow was 4.72 μg/m3.

53 753 The maximum concentrations occurred from June through 

August. The concentration during onshore flow averaged 16.3 μg/m3754 in July. The investigator 

755 estimated that one-third to one-half of these concentrations is in the PM2.5 range. Based on 

756 correlation with climate conditions (e.g., drought cycles) in Africa and satellite observations of 

757 dust cloud transport, this soil dust is attributed to African emissions 

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758 The BRAVO Study also found African dust at the Big Bend National Park, Texas, 

IMPROVE site during July-October 1999.65 759 Based on analyses of speciated particulate matter 

measurements taken during that study,66 760 we estimated that the average African dust contribution 

761 at Big Bend National Park in July (averaged over all days, not just Saharan dust events) was 1.4 

μg/m3 and in the first half of August it was 0.9 μg/m3762 . Although Big Bend is well west of the 

763 VISTAS domain, on several days the African dust there still accounted for more than 80% of the 

measured fine soil concentration and contributed over 2.5 μg/m3764 . 

765 African dust contributions over the VISTAS region can be expected to range between 

766 those reported at Shenandoah, Everglades, and Big Bend. Although precise quantification is not 

767 possible from the information provided here, it is reasonable to conclude that African dust 

768 contributes substantially to fine soil concentrations at most VISTAS locations during the summer 

769 (specifically, June through August). As a broad average for the region, daily impacts during 

African dust events during those months may average 3 to 5 μg/m3770 and such events might occur 

771 roughly 20% of the time. Thus, a rough estimate of the average Saharan fine dust contribution 

during the summer could be in the range of 0.6 to 1.0 μg/m3772 -- more in Florida and a bit less at 

773 Shenandoah and Dolly Sods. 

774 These analyses of IMPROVE and BRAVO data provide strong evidence that the natural 

775 contribution to fine soil during the summer is substantially greater than Trijonis’ average natural 

soil estimate of 0.5 μg/m3776 for the year. If African dust is the only natural contributor, then the 

777 foregoing data suggest that a summertime natural conditions estimate would be in the range of 

0.6 to 1 μg/m3, out of a measured summertime average range of 0.5 to 2 μg/m3778 for all fine soil at 

779 IMPROVE sites. Other natural sources will further increase the natural contribution, with Asian 

dust alone adding another 0.1 to 0.2 μg/m3780 . Thus, at least for the summertime, it appears that 

781 natural sources account for the overwhelming majority of the measured fine soil concentration in 

782 the VISTAS area, not just the half postulated by Trijonis, and the concentration of natural soil 

during that season far exceeds the default annual estimate of 0.5 μg/m3783 . 

784 

785 Implications of Transported Dust on Natural Background Extinction. Because of its episodic 

786 nature, it is difficult to suggest an approach for dealing with the contributions of Asian and 

787 African dust to the natural background. The annual average impact is relatively small, but its 

monthly contribution can be substantial during the summer months (a range of 0.6 to 1.0 μg/m3788 

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789 was estimated above) and the episodic contribution can be much larger at times. Some of the 

average effect is probably reflected in Trijonis’ average estimate of 0.5 μg/m3790 , but the 

791 seasonality, episodicity, and geographic variation is not. Since the scattering efficiency of fine 

soil is assumed to be 1 m2792 /g, the average summertime impact translates to an extinction 

increment of 0.6 to 1.0 Mm-1 793 . Some portion of this amount could be added to the default natural 

794 conditions estimate for specific sites for those months. The effect during the rest of the year is 

795 substantially smaller. 

796 IMPLICATIONS FOR MANAGING REGIONAL HAZE 

797 The discussion above has identified a variety of potential refinements to the default estimates of 

798 natural background concentrations of PM components and light extinction in the VISTAS 

799 region. The question to be addressed at this point is whether such refinements should be pursued 

800 by VISTAS. Developing refined estimates could be worthwhile if they might materially affect 

801 the slope of the "reasonable progress" line and perhaps affect the soundness of the emission 

802 management strategies that are pursued. On the other hand, from a practical point of view, 

803 developing and justifying refined estimates at this time may not be worthwhile if their impact on 

804 the first round of emissions management steps (which have a 2018 target) is so small that it does 

805 not materially affect the emissions management choices. Even in the latter case, there is still 

806 merit in applying the best science available, even at this point, because the process of doing so 

807 will lay the groundwork for the increasing importance of scientifically sound natural background 

808 estimates to making technically and financially responsible emissions management policy 

809 decisions in the future. 

810 This issue is illustrated schematically in Figure 3. Changing the natural conditions 

811 estimate changes the slope of the line required to achieve progress toward the natural conditions 

812 goal. But the difference in the 2018 regional haze goal, in deciviews, is much smaller than the 

813 change in the 2064 natural background estimate. 

814 Note that, because of the nature of the refinements discussed here and the limitations in 

815 the default estimates, it is likely, but not always necessary, that a natural conditions refinement 

816 will increase the estimated natural extinction as illustrated in Figure 3. A refinement that 

817 decreases a default estimate is not precluded, however. Also, some of the refinements under 

818 consideration here for natural conditions (such as for the OC mass multiplier and sea salt) also 

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819 imply parallel adjustments to corresponding estimates of current conditions, and therefore the 

820 baseline at the top of the figure may also be subject to adjustment. 

821 To put some numbers into this discussion, it will be useful to recap the estimated impacts 

822 resulting from each of the natural contributions discussed in this paper: 

• Adjusting the 90th percentile extinction to the 92nd 823 percentile, to more properly 

824 represent the mean of the haziest 20% of the days in a normal distribution, would 

825 increase the default haze index by 0.42 dv. (The impact on the extinction coefficient, 

in Mm-1 826 will vary depending on the extinction level.) 

827 • Increasing the organic carbon multiplier to 2.1 would increase the default natural 

extinction by 2.8 Mm-1 828 . An even greater multiplier would be appropriate for organic 

829 carbon that originates from fires. Since the biogenic contribution to organics is likely 

830 greater on a seasonal basis than the fire contribution, the 2.1 value seems appropriate 

831 for the Southeast, instead of a higher value. One should also consider increasing the 

832 present day organic carbon multiplier, but probably to a lesser value (say, 1.8) 

833 because of the anthropogenic carbon content of the ambient aerosol today. 

834 • Preliminary information on wildfire impacts from global modeling results suggests 

835 that overall POM and elemental carbon concentrations for the East are about the same 

836 as the default values. Presumably, Trijonis' estimates included average wildfire 

837 impacts. The global modeling is able to improve on the default averages by providing 

838 insight into spatial and seasonal variability, however. 

839 • The contribution of vegetation to POM in forested areas is harder to quantify. The 

840 potential impact within forests in the deep South during the warmer months appears 

841 to be several times the default estimate of POM concentration and extinction and the 

842 annual average impacts are comparable to the default values. If so, the impact on 

background extinction in such areas could be as much as 10 Mm-1 843 during the warmest 

844 months. More research is needed to ascertain the actual impact, which will depend on 

845 the degree to which a given location is surrounded by forest and affected by forest 

846 emissions. As an initial estimate for locations entirely in forested areas, adding 1 

μg/m3 of POM (for an extinction increase of 4 Mm-1 847 ) during spring and summer 

848 seems conservative, or a corresponding annual average impact in the range of 1/3 to 

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1/2 of that amount (i.e., 0.3 to 0.5 μg/m3, or 1.2 to 2 Mm-1 849 ) may be appropriate. If 

850 geographic distribution is considered, the annual value in the northern part of the 

851 VISTAS domain and at high elevation locations is likely to be smaller than that 

852 farther south and in the lowlands. These estimates have high uncertainty. 

853 • Sea salt has a major impact that can be estimated with fair confidence, at least near 

854 the coast. The impact will vary with distance from the coast. Annual average fine 

fraction concentration impacts of around 1.3 μg/m3855 at the coast and perhaps half that 

856 at inland sites in Florida are suggested by current research results. It is unlikely that 

857 much of this is reflected in Trijonis' estimates because of the limited monitoring data 

858 that was available to him. The extinction impact of such an increase in fine 

concentrations will be 1.3 Mm-1 859 or more (depending on relative humidity) at the coast 

860 and less inland. There will also be a smaller coarse fraction impact. Sea salt is not 

861 considered in the current IMPROVE algorithm for estimating visibility, so the same 

862 impact should also be included in estimates of current extinction. 

863 • POM from the sea may also affect coastal areas. An average mass concentration 

impact of 0.2 μg/m3864 , as suggested earlier, translates into an extinction impact of 0.8 

Mm-1 865 . Because few measurements are available except over open ocean, and coastal 

866 values are dependent on the nature of the shoreline, this estimate has high uncertainty. 

867 • In the northern portion of the VISTAS region, the average soil concentrations already 

868 approximate the default background values, which suggests that the default values 

there may be too high by, perhaps, 0.2 to 0.3 μg/m3869 (and thus that the default 

extinction there may be too large by 0.2 to 0.3 Mm-1 870 ). Counterbalancing this, the 

871 default concentrations may be too low at some locations, but this has not been 

872 established. 

• The 0.1 to 0.2 μg/m3873 of transported Asian dust during spring and summer would 

impact the extinction by 0.1 to 0.2 Mm-1 then, and by perhaps 0.1 Mm-1 874 when 

875 averaged over the full year. The question is whether that Asian dust is already 

876 reflected in the default estimates, since it may have appeared in the measurement data 

877 on which Trijonis based his estimates. 

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878 • African dust has a very large episodic impact during the summer. If seasonal average 

879 natural conditions concentrations were to be estimated, the summertime mass 

concentration of soil could be increased by about 0.6 to 1.0 μg/m3880 over the annual 

881 default values. (If different adjustments are made for different geographic areas, one 

882 might use an even larger increase in the southern portion of the VISTAS region and a 

883 smaller one in the northern portion.) The result would be to increase natural 

background extinction by 0.6 to 1.0 Mm-1 884 during the summer months. One might 

885 compensate with a small decrease during the rest of the year, or argue that the 

886 episodic summer impacts were not reflected in the limited data base that Trijonis had 

available. On an annual basis a net increase of 0.2 to 0.3 μg/m3 (0.2 to 0.3 Mm-1 887 ) 

888 might be added to the default soil value. 

889 Table 2 summarizes the two suggested refinements to the method of estimating natural 

890 conditions and the potential effects of the seven types of natural sources that are itemized in the 

891 bulleted discussions above. The last column of the table indicates the authors’ assessment of 

892 whether a refinement of a default method or concentration is warranted, based on the magnitude 

893 of the expected impact, whether it might already be included in the default concentrations, and 

894 the degree of scientific understanding of the impact (or the compensating degree of 

895 conservativeness in the estimated impact). Note that the suggested refinements are within the 

896 typical factor of two error estimates by Trijonis, as listed in Table 1. 

897 Of course, it is unlikely that all of the refinements discussed above and summarized in 

898 Table 2 should be applied simultaneously at every location. As already mentioned, some of the 

899 contributions may already be included in the default estimates (possibly Asian dust, for 

900 example). Also, some sources will not be applicable at some locations, e.g., sea salt in the inland 

901 mountains or, perhaps, forest organics at the seashore. 

902 One should also recognize that our conclusions are based on relatively uncritical review 

903 of the literature. Measurement methods may be biased, measurements may be unrepresentative, 

904 or scientific hypotheses may change with more information, Consequently, much of what is 

905 involved in our assessment here has some degree of uncertainty and is itself subject to 

906 refinement. In this light, any values that we assign to potential refinements should be taken as 

907 approximations. 

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908 To assess the significance of refinements that might realistically be considered for 

909 specific locations, we consider next examples of some of their effects on the regional haze 

910 progress goals at two specific locations -- Great Smoky Mountains National Park (inland, 

911 mountainous) and Cape Romain National Wildlife Refuge (Atlantic coastal). 

912 

913 Implications at Great Smoky Mountains National Park 

914 The average current extinction at Great Smoky Mountains National Park, at the eastern end of 

915 Tennessee, can be approximated by the most recent five years (1998-2002) for which IMPROVE 

particulate matter component concentration data were available on the VIEWS web site67 916 on 10 

917 October 2004. At the GRSM IMPROVE site adjacent to the park the average 20% haziest-days’ 

haze index over those five years (using the EPA-prescribed protocols for the calculations68 918 ) is 

29.54 dv, which is equivalent to an extinction coefficient of 191.9 Mm-1 919 . From EPA's natural 

conditions guidance document,2920 the long-term goal is default natural conditions estimated at 

11.44 dv (31.4 Mm-1 921 ) for the 20% haziest days. 

922 Consider four potential scenarios with different estimates of the natural conditions 

923 endpoint. 

924 • GRSM1: EPA's default natural conditions estimate. Calculate annual average from 

925 Table 1 concentrations and f(RH) from U.S. EPA’s natural conditions guidance 

document.2 Estimate average of 20% haziest days based on estimated 90th 926 percentile, 

927 which adds 3.84 dv to the annual average haze index value. 

• GRSM2: Change OC mass multiplier from 1.4 to 2.1, which adds 2.8 Mm-1 928 to the 

929 annual average. (Also, when computing progress slope, adjust the OC mass multiplier 

930 for present day to 1.8, a value midway between the 1.6 and 2.1 values recommended 

for urban and rural areas, respectively, by Turpin and Lim.18 931 ) 

• GRSM3: Assume the conditions of Scenario GRSM2 and also add 0.3 μg/m3932 of 

933 biogenic POM from the forests (the lower bound of the range proposed in Table 2) to 

934 the annual default concentrations. This increases the natural background annual 

average light scattering contribution by 1.2 Mm-1 935 . 

936 • GRSM3*: As an alternative to GRSM3, if all of the haziest days are in the growing 

937 season/summer (which pretty much holds true at Great Smoky Mountains), add the 

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peak effects of African dust (0.6 μg/m3, 0.6 Mm-1 938 ) and the peak season impacts of 

vegetation emissions of biogenic organics (conservatively 1 μg/m3, 4 Mm-1 939 ) to the 

940 natural background of Scenario GRSM2 during summer only. Concentrations during 

941 the other three seasons are taken to be the same as those in GRSM2. 

942 For scenarios GRSM1 through GRSM3, the contribution of each PM component to 

943 annual average light extinction is displayed graphically in Figure 4. The largest changes are 

944 produced by changing the assumed mass concentration of organic matter. Since the annual 

945 average effect of the added dust is small, the annual average of extinction for scenario GRSM3* 

946 would be virtually the same as that of GRSM3. 

947 Table 3 compares current and natural conditions values of light extinction, annual 

averages versus averages of 20% haziest days, and haziest days estimates based on the 90th 948 

percentile of the distribution (the default approach) versus the 92nd 949 percentile (the approach 

950 supported here), for all four scenarios. An assumption implicit in this data tabulation and similar 

951 calculations here is that refinements to the default natural conditions concentrations do not alter 

952 the standard deviation of the frequency distribution of the natural conditions haze index, so that 

953 the statistical adjustment used by the EPA to determine average conditions on the 20% haziest 

days from the annual averages4954 is the same for both the original and the refined conditions.

955 Emissions control strategies are intended to focus on the 20% haziest days rather than the 

956 annual average conditions. The control strategy implications for the four scenarios on the 20% 

957 haziest days are summarized in Table 4. The first data column is the current haze index that 

958 represents the average of the 20% haziest days. It was calculated under the current EPA 

protocol,68 with OC multiplied by 1.4 and the 90th 959 percentile used to define the 20% haziest 

natural days for Scenario GRSM1(90). The same scenario, except that the 92nd 960 percentile was 

used, is labeled GRSM1(92). The remaining scenarios also use the 92nd 961 percentile to define the 

962 20% haziest natural days and have natural OC multiplied by 2.1 and current OC multiplied by 

963 1.8. Both the Current Haze Index and the Natural Background Haze Index columns reflect the 

964 effects of the refinements stated above. The difference between these two columns is the 

965 required improvement over 60 years, so 1/6 of that is required over a decade, as listed in the third 

data column. The corresponding change in bext 966 over the first decade is in the next column. The 

967 next-to-last column gives the percentage reduction in particulate matter concentrations that 

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968 would be required if all particulate matter components in Equation 1 were reduced by the same 

969 percentage. 

970 Since sulfates are the predominant contributors to haze in the VISTAS region most of the 

971 time, the last column in Table 4 indicates the % reduction in non-natural sulfate concentrations 

that would be needed during the first decade to produce the given bext 972 decrease if only sulfates 

973 were reduced and all other components remained unchanged. These values were calculated using 

974 an annual-average f(RH) of 3.32 (from Table A-2 in Reference 2), i.e., RH = 85%, and a 5-year 

(1998-2002) average ammonium sulfate concentration of 14.25 μg/m3975 on the 20% haziest days, 

of which 0.23 μg/m3976 is assumed uncontrollable natural background (from Table 1) . 

977 The differences between scenarios GRSM1 through GRSM3 are relatively small, whether 

978 equal control of all components is assumed, or only control of sulfates. Note, though, that the 

979 percentage improvements given in the next-to-last column assume (unrealistically) that the 

980 concentrations of all of the chemical components in Table 1 will be reduced by the same 

981 percentage. The difference between a 27.5% and a 25.0% reduction in a given component 

982 concentration will be much more significant if part of that component concentration is natural or 

983 otherwise uncontrollable. For example, Trijonis postulated that fine soil is half natural and half 

984 anthropogenic, so the percentage reductions in the anthropogenic part of the soil need to be twice 

985 as large as the percentages just cited if the specified reduction in soil is to hold. This implies 

986 requiring a 55% reduction of anthropogenic soil emissions under the default assumption versus 

987 50% under the GRSM3 scenario. Similarly, if some of the universe of sources that emit a 

988 specific pollutant is already well-controlled, the percentage reductions required of the other 

989 sources will be greater than the values given in Table 4, in which case the differences between 

990 the required reduction rates may be of economic importance when setting a control strategy. 

991 The required percentage of concentration reduction decreases further when we consider 

992 scenario GRSM3*. This scenario takes advantage of the fact that most of the haziest days at 

993 Great Smoky Mountains occur in the summer, at the same time when the organic emissions from 

994 vegetation and impacts from African dust are the greatest and thus the natural background is 

995 high. In this situation, a smaller degree of emissions control is required to attain the required rate 

996 of progress on the 20% haziest days. 

997 

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998 Implications at Cape Romain National Wildlife Refuge 

999 A similar analysis, following the same protocols, can be carried out for the Cape Romain 

1000 National Wildlife Refuge, on the Atlantic coast of South Carolina. At the Cape Romain 

1001 IMPROVE site (ROMA), the current extinction on the 20% haziest days can be approximated 

for our purposes here by the values in four recent years, 1999-2002.67 1002 (The data for 1998 are too 

1003 incomplete to define the 20% worst days in that year.) The average 20% haziest days' haze index 

over those four years is 25.89 dv or 133.2 Mm-1 1004 . The haziest days natural conditions target in the 

EPA guideline document2 is 11.36 dv, or 31.1 Mm-1 1005 , for this location.

1006 Similar to the previous analysis, consider five scenarios. The first two scenarios, ROMA1 

1007 and ROMA2, are identical to those at Great Smoky Mountains, albeit with local data. The other 

1008 three scenarios reflect the uniqueness of the coastal location of Cape Romain: 

1009 • ROMA1: EPA's default natural conditions estimate, as described for GRSM1. 

1010 • ROMA2: Adjust the OC mass multiplier for natural conditions to 2.1 and for present 

1011 day data to 1.8, as described in GRSM2. 

• ROMA3: Add 1.3 μg/m31012 of sea salt to Scenario ROMA2. Using the annual average 

f(RH) of 3.15 at Cape Romain, this sea salt contributes 4.1 Mm-1 1013 of light scattering. 

1014 Since the EPA/IMPROVE formula (eq 1) does not consider sea salt, this amount is 

1015 added to both current and natural conditions extinction estimates. 

• ROMA 4: Add 0.8 Mm-1 1016 to natural conditions in Scenario ROMA3 to represent the 

contribution of 0.2 μg/m31017 of oceanic POM. 

• ROMA5: Add an annual average of 0.3 μg/m31018 of African dust, which increases 

natural extinction by 0.3 Mm-1 1019 . (Being farther south than Great Smoky Mountains, 

1020 the African dust impact here is greater and temporally more diffuse, although an 

1021 annual average is still a crude approximation. Furthermore the 20% haziest days at 

1022 Cape Romain are distributed throughout the year and thus are not well correlated with 

1023 the summertime episodicity of African dust, so a seasonal analysis of the type used 

1024 for Great Smoky Mountains is not viable here.) 

1025 Figure 5 shows the effects of these scenarios on the contributions to natural conditions extinction 

1026 by the various components of the particulate matter. The biggest changes result from increasing 

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1027 the organic carbon multiplier and from adding sea salt. The contributions of oceanic organics and 

1028 African dust are relatively minor. 

1029 Table 5 summarizes the extinction statistics for the various scenarios. Cape Romain is 

1030 less hazy than Great Smoky Mountains, especially on the haziest 20% of the days. Note that the 

1031 increased OC multiplier and the addition of sea salt affect the current extinction estimates as well 

1032 as those of natural conditions. 

1033 Table 6, which is in the same format as Table 4, shows the emissions control strategy 

1034 implications of these four scenarios. The main difference from Table 4 is the smaller percentage 

1035 concentration reductions that are needed because Cape Romain is substantially less hazy on 

1036 average than Great Smoky Mountains, while the default natural conditions goals are virtually 

1037 identical. Also, all of the refinements considered here have a bigger effect on the required 

1038 percentage of reduction than did those considered for Great Smoky Mountains. Otherwise, the 

1039 general conclusions concerning Table 4 also apply to Table 6. 

1040 In overview, Figures 4 and 5 illustrate that most refinements in the annual average 

1041 estimates of concentrations of soil, elemental carbon, and nitrates will have minimal effect 

1042 because the underlying contributions of those components are quite small. Even the episodic 

1043 African dust contribution in the summer in Scenario GRSM3* makes only about a 1% 

1044 contribution to the extinction on the natural conditions 20% haziest days. On the other hand, 

1045 changes in POM and sea salt concentrations can have important impacts on the light extinction 

1046 and deserve first attention if a decision is made to refine the default concentrations. The 

combination of the use of the 92nd 1047 percentile and the 2.1 multiplier for calculating POM from OC 

1048 also has a material effect on the natural conditions extinction estimate. When the needed degree 

1049 of emission control is considered, however, the impacts of some of these refinements to natural 

1050 conditions are diluted because partially-compensating adjustments also need to be made to 

1051 estimates of current extinction. 

1052 CONCLUDING COMMENTS 

1053 This review was prepared to illuminate potential approaches for refining natural conditions 

1054 estimates and to provide some insight into the most promising approaches. The calculations in 

1055 the previous section suggest that there is merit in establishing refined estimates of natural 

1056 conditions at some Class I areas, even at this initial part of the 60-year period. 

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1057 We have found that conservative regional refinements to the default natural background 

1058 estimates do not greatly alter the region-wide rates of reduction of ambient particulate matter 

1059 concentrations that will be needed to attain the natural conditions goal. However, refinements at 

1060 a specific Class I area may have considerable influence on defining the nature (magnitude and 

1061 spatial and temporal distribution) of local emission reduction efforts to reach the natural 

1062 conditions goal there. 

1063 Three relatively non-controversial refinements that could be applied without extensive 

1064 effort are (1) using a more-appropriate percentile to reflect the average of the haziest 20% of the 

1065 days; (2) applying an organic carbon multiplier adjustment at most or all Class I areas and (3) 

1066 adding the effect of sea salt at Class I areas that are relatively close to the ocean. In the last two 

1067 cases, appropriate corresponding adjustments should also be made to present-day estimates 

1068 before the rate of reasonable progress is calculated. In the case of the OC multiplier, though, the 

1069 present day multiplier probably should not be the same as that for natural conditions. 

1070 African dust is a large contributor episodically during the summer, but its episodicity 

1071 means that creative thought will be needed to weave it into the mandated regional haze 

1072 management process. Compared to other components, on an average basis it is not an important 

1073 contributor to natural light extinction, though. 

1074 POM created in forests and emitted from natural wildfires are two other promising 

1075 contributors, but their effects are not well quantified and including them will be more 

1076 challenging and require more research than the other refinements that have been mentioned. 

1077 Asian dust has most of the complexities of African dust, but it has even smaller impact. 

1078 Note that in this paper we have not considered changes to the numerical coefficients 

1079 (extinction efficiencies) used in eq 1 to calculate light extinction. If the scaling factor for OC is 

1080 increased, then total light extinction calculated via eq 1could possibly become an overestimate 

1081 because of the greater POM concentration, unless the extinction efficiencies of carbon or other 

components in the equation are adjusted. These issues are addressed elsewhere11 - 13 1082 and have 

1083 not been included in our calculations. 

1084 As a closing perspective, one needs to remember that any refinements to the default 

1085 natural conditions estimates will have to be made in a regulatory framework. In such a setting, 

1086 refinements are most likely to be accepted if the scientific community and the regulators have 

1087 confidence in the science supporting the changes and justifying the magnitudes of the changes, 

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1088 and there is minimal controversy concerning those aspects. This paper addresses the technical 

1089 merits of potential refinements to natural background visibility at VISTAS Class I areas but does 

1090 not make policy recommendations as to whether these refinements should be adopted by the 

1091 VISTAS states as part of their state implementation plans for regional haze. 

1092 ACKNOWLEDGMENTS 

1093 The work reported in this paper was funded by the VISTAS regional planning organization, 

1094 through its funding agency, the Southeastern State Air Resource Managers and grants from the 

1095 Environmental Protection Agency. The ideas presented in this paper are those of the authors and 

1096 do not represent policy decisions by the VISTAS states. We appreciate the comprehensive 

1097 comments by an anonymous reviewer; they have substantially enhanced the quality of this paper. 

1098 REFERENCES

1099 1. Regional Haze Regulations; Final Rule. Federal Register, 1999, 64, 35713-35774. 

1100 2. U.S. EPA. Guidance for Estimating Natural Visibility Conditions under the Regional Haze 

1101 Rule. EPA-454/B-03-005. September 2003. 

1102 3. Trijonis, J. C. Characterization of Natural Background Aerosol Concentrations. Appendix A 

1103 in Acidic Deposition: State of Science and Technology. Report 24. Visibility: Existing and 

1104 Historical Conditions -- Causes and Effects. J. C. Trijonis, lead author. National Acid 

1105 Precipitation Assessment Program, Washington, DC. 1990. 

1106 4. Ames, R. B.; Malm, W. C. Recommendations for Natural Condition Deciview Variability: 

1107 An Examination of IMPROVE Data Frequency Distributions. In (CD-ROM) Proceedings of 

1108 A&WMA/AGU Specialty Conference on Regional Haze and Global Radiation Balance -- 

1109 Aerosol Measurements and Models: Closure, Reconciliation and Evaluation, Bend, OR, 2-5 

1110 October 2001. 

1111 5. Pitchford, M. L.; Tombach, I.; Barna, M.; Gebhart, K. A.; Green, M. C.; Knipping, E.; 

1112 Kumar, N.; Malm, W.; Pun, B.; Schichtel, B. A.; Seigneur, C. Big Bend Regional Aerosol 

1113 and Visibility Observational Study. Final report, 

1114 http://vista.cira.colostate.edu/improve/Studies/BRAVO/reports/FinalReport/bravofinalreport.

1115 htm. September 2004.

1116 6. Liousse, C.; Penner, J. E.; Walton, J. J.; Eddleman, H. A global three-dimensional model 

1117 study of carbonaceous aerosols. J. Geophys. Res., 1996, 101, No. D14, 19411-19432. 

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1118 7. Park, R. J.; Jacob, D. J.; Chin, M.; Martin, R. V. (2003). Sources of carbonaceous aerosols 

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1177 doi:10.1029/2003JD0004398. 

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1184 Climate Change. John Wiley & Sons, New York. 1998. 

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1190 organic acids produced by forests. Nature, 1998, 395, 683-686. 

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1194 35. Rodhe, H. Foreword: Biogenic aerosol formation in the boreal forest (BIOFOR). Tellus, 

1195 2001, 53B, 323. 

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1197 particles. Nature, 2000, 404, 66-69. 

1198 37. Kulmala, M.; Hämeri, K.; Aalot, P. P.; Mäkelo, J. M.; Pirjola, L.; Douglas-Nilsson, E.; 

1199 Buzorius, G.; Rannik, Ü.; Dal Maso, M.; Seidl, W.; Hoffman, T.; Janson, R.; Hansson, H.-C.; 

1200 Viisanen, Y.; Laaksonen, A.; O'Dowd, C. D. Overview of the international project on 

1201 biogenic aerosol formation in the boreal forest (BIOFOR). Tellus, 2001, 53B, 324-343. 

1202 38. Kulmala, M.; Korhonen, P.; Napari, I.; Karlsson, A.; Berresheim, H.; O'Dowd, C. D. Aerosol 

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1204 Res., 2002, 107, No. D19, Article 8111. 

1205 39. Janson, R.; Rosman, K.; Karlsson, A.; Hansson, H.-C. Biogenic emissions and gaseous 

1206 precursors to forest aerosols. Tellus, 2001, 53B, 423-440. 

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1207 40. Held, A.; Nowak, A.; Birmili, W.; Wiedensohler, A.; Forkel, R.; Klemm, O. Observations of 

1208 particle formation and growth in a mountainous forest region in central Europe. J. Geophys. 

1209 Res., 2004, 109, D23204, doi:10.1029/2004JD005346. 

1210 41. O'Dowd, C. D.; Aalto, P.; Hämeri, K.; Kulmala, M.; Hoffman, T. Atmospheric particles from 

1211 organic vapours. Nature, 2002, 416, 497-498. 

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1215 43. Iinuma, Y.; Böge, O.; Gnauk, T.; Herrmann, H. Aerosol-chamber study of the α-pinene/O3

1216 reaction: influence of particle acidity on aerosol yields and products. Atmos. Environ., 2004, 

1217 38, 761-773.

1218 44. Gao, S.; Ng, N. L.; Keywood, M.; et al. Particle Phase Acidity and Oligomer Formation in 

1219 Secondary Organic Aerosol. Environ. Sci. Technol., 2004, 38, 24, 6582-6589, 

1220 doi:10.102/es049125k. 

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1222 of the Amazon Basin During the Dry Season. J. Geophys. Res., 1988, 93, No. D2, 1499-

1223 1508. 

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1225 atmospheric aerosols in the Amazon Basin: Source identification and apportionment. J. 

1226 Geophys. Res., 1998, 103, No. D24, 31,849-31,864. 

1227 47. Makela, J. M.; Yli-Koivisto, S.; Hiltunen, A.; Seidl, W.; Switlicki, E.; Teinilä, K.; Sillanpää, 

1228 M.; Koponen, K.; Paatero, J.; Rosman, K.; Hämeri, K. Chemical composition of aerosol 

1229 during particle formation events in boreal forest. Tellus, 2001, 53B, 380-393. 

1230 48. Alves, C.; Carvalho, A.; Pio, C. Mass balance of organic carbon fraction in atmospheric 

1231 aerosols. J. Geophys. Res., 2002, 107, D21, 8345, doi:10.1029/2001JD000616. 

1232 49. Lewis, C. W.; Klouda, G. A.; Ellenson, W. D. Radiocarbon measurement of the biogenic 

1233 contribution to summertime PM-2.5 ambient aerosol in Nashville, TN. Atmos. Environ., 

1234 2004, 38, 6053-6061, doi:10.1016/jatmosenv.2004.06.011. 

1235 50. Malm, W. C., Principal Author. Spatial and Seasonal Patterns and Temporal Variability of 

1236 Haze and Its Constituents in the United States: Report III. Cooperative Institute for Research 

1237 in the Atmosphere, Colorado State Univ., Ft. Collins, CO. May 2000. 

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1238 51. Tang, I. N.; Tridico, A. C.; Fung, K. H. Thermodynamic and optical properties of sea salt 

1239 aerosols. J. Geophys. Res., 1997, 102, No. D19, 23,269-23,275. 

1240 52. Lowenthal, D. H.; Kumar, N. PM2.5 Mass and Light Extinction Reconstruction in IMPROVE. 

1241 J. Air Waste Manage. Assoc., 2003, 53, 1109-1120. 

1242 53. Prospero, J. M. Long-term measurements of the transport of African mineral dust to the 

1243 southeastern United States: Implications for regional air quality. J. Geophys. Res., 1999, 104, 

1244 No. D13, 15,917-15,927. 

1245 54. Paine, R. J.; Heisler, S. L.; Heinold, D. W. Regional Haze Assessments with CALPUFF: 

Application of Refined Procedures. Paper 69470, Proceedings of the 96th 1246 Annual Conference 

1247 and Exhibition of the Air and Waste Management Assoc. San Diego, CA. 22-26 June 2003. 

1248 55. Hansson, H.-C.; Rood, M. J.; Koloutsou-Vakakis, S.; Hämeri, K., Orsini, D.; Wiedensohler, 

1249 A. NaCl Aerosol Particle Hygroscopicity Dependence on Mixing with Organic Compounds. 

1250 J. Atm. Chem., 1998, 31, 321-346.

1251 56. Lowenthal, D.; Kumar, N. Light Scattering from Sea Salt Aerosols in IMPROVE. Submitted 

1252 to J. Air Waste Manage. Assoc., 2005.

1253 57. Novakov, T.; Corrigan, C. E.; Penner, J. E.; Chuang, C. C.; Rosario, O.; Mayol-Bracero, O. 

1254 L. Organic aerosols in the Caribbean trade winds: A natural source? J. Geophys. Res., 1997, 

1255 102, No. D17, 21,307-21313. 

1256 58. Mayol-Bracero, O. L., Rosario, O., Corrigan, C. E., Morales, R., Torres, I.; Pérez, V. 

1257 Chemical characterization of submicrometer organic aerosols in the tropical trade winds of 

1258 the Caribbean using gas chromatography/mass spectrometry. Atmos, Environ., 2001, 35, 

1259 1735-1745. 

1260 59. Cavalli, F.; Facchini, M. C.; Decesari, S.; Mircea, M.; Emblico,L.; Fuzzi, S. Advances in 

1261 characterization of size-resolved organic matter in marine aerosol over the North Atlantic. J. 

1262 Geophys. Res., 2004, 109, D24215, Doi:10.1029/2004JD005137. 

1263 60. VanCuren, R. A.; Cahill, T. A. Asian Aerosols in North America: Frequency and 

1264 Concentration of Fine Dust. Journal of Geophysical Research, 2002, 107, D24, 4804, 

1265 doi:10.1029/2002JD002204. 

1266 61. VanCuren, R. A.; Cliff, S. S.; Perry, K. D.; Jimenez-Cruz, M. Asian continental aerosol 

1267 persistence above the marine boundary layer over the eastern North Pacific; Continuous 

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43

1268 aerosol measurements from Intercontinental transport and Chemical transformation 2002 

1269 (ITCT 2K2). J. Geophys. Res., 2005, 110, D09S90, doi:10.1029/2004JD004973. 

1270 62. Jaffe, D., Snow, J.; Cooper, O. (2003). The 2001 Asian Dust Events: Transport and Impact 

1271 on Surface Aerosol Concentrations in the U.S. EOS, 2003, 84, 501-507. 

1272 63. DeBell, L. J.; Vozzella, M.; Talbot, R. W.; Dibb, J. E. Asian dust storm events of spring 2001 

1273 and associated pollutants observed in New England by the Atmospheric Investigation, 

1274 Regional Modeling, Analysis and Prediction (AIRMAP) monitoring network. J. Geophys. 

1275 Res., 2004, 109, D01304, doi:10.1029/2003JD003733. 

1276 64. Perry, K. D.; Cahill, T. A.; Eldred, R. A.; Dutcher, D. D.; Gill, T. E. Long-range transport of 

1277 North African dust to the eastern United States. J. Geophys. Res., 1997, 102, No. D10, 

1278 11,225-11,238. 

1279 65. Al-Harbi, B. H. O. Transport of North African dust to Big Bend, Texas, during the 1999 Big 

1280 Bend regional aerosol and visibility study. M. S. thesis, Department of Atmospheric 

1281 Sciences, Colorado State University, 2003. 

1282 66. Schichtel, B. Personal communication, 2003. 

1283 67. VIEWS web site. http://vista.cira.colostate.edu/views. 

1284 68. U.S. EPA. Guidance for Tracking Progress under the Regional Haze Rule. EPA-454/B-03-

1285 004. September 2003. 

1286 

1287 

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44

1288 ABOUT THE AUTHORS 

1289 Ivar Tombach is Technical Advisor to the VISTAS Regional Planning Organization. He is an 

1290 independent air pollution consultant, located at 753 Grada Ave., Camarillo, CA 93010. Patricia 

1291 Brewer is VISTAS’ Technical Coordinator at 2090 U.S. 70 Highway, Swannanoa, NC 28778. 

1292 

1293 

1294 

1295 TABLES 

1296 

1297 Table 1. Annual average default natural background concentration estimates for particle 

1298 components in the East, and their estimated impacts on dry light extinction according to the 

1299 IMPROVE algorithm (Eq 1). 

 1300 

Component Average 

Concentration, 

μg/m3

Error 

Factor 

Dry Extinction 

Efficiency, 

m

2

/g 

Dry 

Extinction, 

Mm–1

Ammonium sulfate 0.23 2 3 0.69 

Ammonium nitrate 0.1 2 3 0.3 

POM 1.4 2 4 5.6 

Elemental carbon 0.02 2-3 10 0.2 

Fine soil 0.5 1.5-2 1 0.5 

Coarse matter 3.0 1.5-2 0.6 1.8 

Sum Fine = 2.25 

Coarse = 3.0 

9.09 

1301 

 1302 

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45

1303 Table 2. Summary of suggested refinements to default estimates of natural conditions in the 

1304 VISTAS region and their potential effects, with authors’ assessments of whether refinement of 

1305 the default method or concentration is warranted.

 1306 

Issue/Source PM2.5

Component 

Affected 

Annual Average 

Concentration 

Impact (μg/m3) 

Annual Light 

Extinction 

Impact (Mm-1) 

Refining 

Default 

Warranted? 

Methods Refinements 

Use 92nd %-ile 

instead of 90th

Extinction -- +0.42 dv Yes 

Increase OC 

multiplier to 2.1 

POM 0.7 2.8 Yes 

Component Concentration Impacts 

Wildfires POM and 

EC 

1.1 and 0.2 4.4 and 2 No 

Forest 

organics 

POM 0.3 – 0.5 

(or ~1 in spring 

and summer) 

1.2 – 2 

(or ~4 in spring 

and summer) 

Yes, in 

heavily 

forested 

areas 

Sea salt Sea salt 1.3 at coast, 

0.7 inland (e.g., in 

Florida) 

1.3 f(RH) 

0.7 f(RH) 

Yes, near 

coast; 

perhaps 

inland 

Ocean 

organics 

POM 0.2 0.8 Yes, near 

coast 

Fine soil Soil -0.2 in northern 

VISTAS states 

-0.2 No 

Asian dust Soil 0.1 

(or 0.1 to 0.2 in 

spring and 

summer) 

0.1 

(or 0.1 to 0.2 in 

spring and 

summer) 

No 

African dust Soil 0.2 – 0.3 

(or 0.6 to 1 in 

summer) 

0.2 – 0.3 

(or 0.6 to 1 in 

summer) 

Maybe, 

episodically 

at some 

sites 

 1307 

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Appendix B.4 - 46

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46

Table 3. Estimated annual and 20%-haziest days extinction averages (in Mm-1 1308 ) for four 

1309 hypothetical scenarios at Great Smoky Mountains National Park. 

1310 

Scenario Annual Average bext 20% Haziest Days bext 

Current Natural Current Natural 

(90%ile) 

Natural 

(92%ile) 

GRSM1 85.8 21.4 191.1 31.4 32.8 

GRSM2 89.0 24.2 198.0 35.5 37.1 

GRSM3 89.0 25.4 198.0 37.3 38.9 

GRSM3* 89.0 25.4 198.0 40.1 41.7 

 1311 

 1312 

1313 

 1314 

1315 

1316 Table 4. Implications of four natural conditions scenarios on the emissions reductions required 

1317 to decrease haze on the 20% haziest days during the first decade at Great Smoky Mountains 

1318 National Park. 

 1319 

Scenario Current 

HI, dv 

Natural 

Backgnd 

HI, dv 

Improvmnt 

Rate, 

dv/decade 

∆bext in 1st 

Decade, 

Mm-1

Required 

Reduction, 

Total Conc. 

Required 

Reduction, 

Sulfates 

GRSM1(90) 29.5 11.4 3.02 -52.2 27.5% 35.8% 

GRSM1(92) 29.5 11.9 2.95 -51.2 26.9% 35.1% 

GRSM2 29.9 13.1 2.79 -50.4 25.7% 34.6% 

GRSM3 29.9 13.6 2.71 -49.2 25.0% 33.7% 

GRSM3* 29.9 14.3 2.60 -47.4 24.1% 32.4% 

 1320 

 1321 

 1322 

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47

Table 5. Estimated annual and 20%-haziest days extinction averages (in Mm-1 1323 ) for five 

1324 hypothetical scenarios at Cape Romain National Wildlife Reserve. 

 1325 

Scenario Annual Average bext 20% Haziest Days bext 

Current Natural Current Natural 

(90%ile) 

Natural 

(92%ile) 

ROMA1 76.6 21.2 133.2 31.1 32.5 

ROMA2 79.7 24.0 138.4 35.3 36.8 

ROMA3 83.8 28.1 142.5 41.3 43.1 

ROMA4 83.8 28.9 142.5 42.5 44.3 

ROMA5 83.8 29.2 142.5 42.9 44.7 

 1326 

 1327 

 1328 

1329 

1330 

1331 Table 6. Implications of five scenarios on the emissions reductions required to decrease haze on 

1332 the 20% haziest days during the first decade at Cape Romain National Wildlife Reserve. 

 1333 

Scenario Current 

HI, dv 

Natural 

Backgnd 

HI, dv 

Improvmnt 

Rate, 

dv/decade 

∆bext in 1st 

Decade, 

Mm-1

Required 

Reduction, 

Total Conc. 

Required 

Reduction, 

Sulfates 

ROMA1(90) 25.9 11.4 2.42 -28.7 23.3% 32.1% 

ROMA1(92) 25.9 11.8 2.35 -27.9 22.7% 31.2% 

ROMA2 26.3 13.0 2.21 -27.4 21.4% 30.7% 

ROMA3 26.6 14.6 1.99 -25.8 19.4% 28.8% 

ROMA4 26.6 14.9 1.95 -25.2 19.0% 28.2% 

ROMA5 26.6 15.0 1.93 -25.0 18.9% 28.0% 

 1334 

1335 

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FIGURES 

Figure 1. States and Class I areas in the VISTAS domain. 

Figure 2. Component contributions to estimated annual average light extinction under 

default natural conditions for dry aerosol and for climatologically-representative relative 

humidities at four Class I areas. 

Figure 3. Illustration of effects of changes in 2064 natural conditions estimates on 2018 

regional haze goals. 

Figure 4. Component contributions to estimated annual average light extinction under 

three assumed natural conditions scenarios at Great Smoky Mountains National Park. 

Figure 5. Component contributions to estimated annual average light extinction under 

five assumed natural conditions scenarios at Cape Romain National Wildlife Refuge.

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