# 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 9 th 

### Floor L&C Annex 401 Church Street Nashville, TN 37243

# Natural Background Visibility and Regional Haze

1 

# G oals in the Southeastern United States 2

Ivar Tombach 3 Consultant, 753 Grada Ave., Camarillo, CA 93010 4
Patricia Brewer 5 VISTAS, 2090 U.S. 70 Highway, Swannanoa, NC 28778 6

ABSTRACT 7 The goal of the regional haze mitigation program in the United States is to attain “natural 8 conditions” in national parks and wilderness areas by 2064. Results of research investigations on 9 background concentrations of sea salt and biogenic organic matter, of episodic Saharan and 10 Asian dust, and of carbon from natural fires were reviewed to provide a basis for making site11 specific estimates of what the concentrations of atmospheric fine particulate matter components 12 might be under natural conditions in the southeastern United States. Based on this review, rough 13 estimates were made of potential contributions of these aerosol components to natural 14 background visibility. Natural organic particles were the dominant influence on the rate of 15 visibility improvement required to reach natural conditions at an inland, mountainous location 16 and organic particles and sea salt were the dominant influences on the rate at a coastal location. 17 African dust also had a large episodic effect, but the current regulatory approach is not designed 18 to address episodic background variations. Insufficient data exist to quantify the contributions of 19 wildfires with any detail, although global air pollution modeling provides insight and their 20 emissions can be locally dominant. Conservative regional refinements to the default natural 21 background estimates do not greatly alter the region-wide rates of reduction of ambient 22 particulate matter concentrations that will be needed to accomplish the first phase of the regional 23 haze program. However, refinements at specific Class I areas may have considerable influence 24 on defining the nature (magnitude and spatial and temporal distribution) of local emission 25
reduction efforts there. 26

INTRODUCTION 27

### 2

The United States Environmental Protection Agency’s Regional Haze Rule 1 

### prescribes that 28 States are to make efforts to improve visibility in 156 national parks and wilderness areas (“Class 29 I areas”) at such rates that "natural conditions" would be achieved in each Class I area by 2064. 30 A "reasonable rate of progress" corresponds to linear improvement in visibility, as characterized 31 in units of deciviews, between current conditions during the base period of 2000-2004 and 32 natural conditions at the end point of 2064. 33 To provide a starting point for the regional haze planning process, the U.S. EPA has 34 issued guidance for estimating natural visibility conditions based on assumed natural background 35 concentrations of the chemical components of particulate matter. 2 

### Recognizing that these 36 “default” concentration estimates may not be appropriate for every Class I area, the guidance 37 document also provides a State with the option of developing a "refined" estimate of natural 38
conditions that reflects local circumstances. 39 This paper discusses the attributes of the default approach, identifies issues that might 40 suggest consideration of a refined method, and provides a survey of the current scientific 41 understanding of background and natural particulate matter (PM) component concentrations. 42 (Note that the concept of achieving “natural conditions” by a regulatory program in the United 43 States is an idealistic one, in that it assumes that the only pollutants ultimately transported into 44 the U.S. across its borders will be natural ones and ignores potential contributions from 45 anthropogenic sources outside the country. Although the issue of transboundary transport is 46 important in the implementation of U.S. regional haze policy, it will not be addressed here. 47 Rather, we focus on the scientific issues involved in estimating natural conditions in the absence 48 of transboundary anthropogenic contributions.) 49 The discussion here focuses on the geographic region encompassed by the VISTAS 50 Regional Planning Organization, essentially the southeastern United States. VISTAS (Visibility 51 Improvement – State and Tribal Association of the Southeast) is an association of ten States, the 52 Eastern Band of Cherokee Indians, and local air pollution control agencies. The VISTAS States 53 and the Class I areas within them are shown in Figure 1. 54 Of importance to VISTAS is whether the limitations in the default concentration 55 estimates are significant, particularly for the development of emission control strategies designed 56 to reach a first phase goal by 2018 -- i.e., could the decisions that VISTAS will be making about 57 initial emission management strategies be affected by uncertainties in the natural background 58

### 3

concentration estimates? And, as a corollary, will it be worthwhile to consider refining the 59 estimates for some components of particulate matter using the new information that has become 60 available since the default natural concentration estimates were developed? 61 Fortuitously, the global climate change research community has carried out substantial 62 research on global background and natural aerosols in the past decade. Their interest is to 63 separate the natural and anthropogenic contributions by particulate matter to global radiation 64 balance, a focus that neatly parallels the regional haze interests here. Consequently, the 65 information base now available for estimating natural background concentrations greatly exceeds 66 that of the pre-1990 research upon which the EPA’s default guidance is based. 67 To address the questions raised and to take advantage of the new data, this paper reviews 68 the basis for the default concentration estimates, discusses two technical issues concerning 69 estimating light extinction from those concentrations, surveys relevant research findings that 70 provide insight into natural conditions PM component concentrations in the Southeast, and 71 discusses the implications of this new information on control strategy planning. 72

BACKGROUND -- THE DEFAULT ESTIMATES OF NATURAL CONDITIONS 73 In September 2003, the U.S. EPA issued Guidance for Estimating Natural Visibility Conditions 74 Under the Regional Haze Rule , 2 

### to provide initial guidance for estimating natural conditions. 75
That document recommends that natural extinction levels be estimated from the default 76 component concentrations by using the formula established by IMPROVE for estimating current 77 light extinction from measurements of the concentrations of the main components of fine 78 particulate matter. (IMPROVE stands for I nteragency M onitoring of Pro tected V isual 79
E nvironments, the national visibility-related PM component monitoring program.) The 80 IMPROVE formula estimates light extinction from the concentrations of fine ammonium sulfate, 81 ammonium nitrate, particulate organic matter, light absorbing carbon, and fine soil, plus coarse 82 particles: 83

b ext



### = 3f(RH)[SULFATE] + 3f(RH)[NITRATE] + 4[POM] + 1[SOIL] 84 + 0.6[CM] + 10[LAC] + 10, (1) 85
where the concentrations, indicated by square brackets, are in μg/m 3 

### ; b ext 

### is in Mm -1 

### ; and the 86 numerical coefficients before each term, which represent extinction efficiencies (light extinction 87 per unit mass), are in m 2 

### /g. SULFATE is assumed to be in the form of ammonium sulfate; 88

### 4

NITRATE is assumed to be ammonium nitrate; and POM is calculated as 1.4[OC] where [OC] 89 represents the organic carbon concentration in particulate organic matter [POM] . (The 90 IMPROVE program labels POM as OMC and, as with POM here, uses that label to describe only 91 the fine fraction.) CM represents coarse matter, particles between 2.5 and 10 μm diameter. Light 92 absorbing carbon ( LAC ) is sometimes interchanged with elemental carbon ( EC ). Although the 93 two are not exactly equivalent, we will use the terms interchangeably here. The constant 10 at the 94 end approximates Rayleigh scattering by air; it is the correct value at an altitude of about 1500 m 95 (5000 ft). A humidity-dependent enhancement factor, f(RH) , is used to account for the increased 96 light scattering by sulfates and nitrates as the relative humidity increased. Monthly-average 97 "climatologically-representative" values of f(RH) for each Class I area have been published by 98 the U.S. EPA and are to be used for the purposes of the Regional Haze Rule. 2 

### 99 Much of the Regional Haze Rule describes visibility in terms of a haze index ( HI ), in 100 units of deciviews (dv). The haze index is determined via the formula 101
HI = 10 ln(b ext 

### /10) , (2) 102
using the b ext



### (in Mm -1 

### ) derived in eq 1. 103 Estimates of natural background concentrations of the particulate matter components in 104 eq 1 were developed in 1990 by Trijonis for the National Acid Precipitation Assessment Program 105 (NAPAP). 3 

### He estimated annual average concentrations of chemical components of particulate 106 matter for both the eastern United States and the mountain/desert regions of the western United 107 States. The East was defined as extending up to one tier of states west of the Mississippi River, 108 thus the VISTAS domain falls entirely within that region. 109 The U.S. EPA has adopted Trijonis’ estimates of natural background concentrations, after 110 adjusting his values to make them consistent with current IMPROVE measurement conventions. 2 

### 111 The resulting default concentrations for the East are listed in Table 1, along with Trijonis’ 112 estimates of the errors in his values. The last two columns list the dry extinction efficiencies (the 113 numerical coefficients in eq 1) for each component and the estimated contribution of each 114 component to light extinction under “dry” conditions (relative humidity ≤ 36%), as calculated 115 using eq 1 with f(RH) = 1

. 116 Note that particulate organic matter is overwhelmingly the largest contributor to natural 117 background extinction, in contrast to the present situation in the East where sulfates usually 118 dominate light extinction. 119

### 5 Trijonis’ error factors show that there are large uncertainties in his concentration 120 estimates, typically by a factor of two or so. These uncertainties reflect the limited information 121 he had available for developing his estimates and the result of assigning of a single set of 122 numbers to characterize concentrations throughout the year over the large geographic area of the 123
eastern United States. 124 Trijonis arrived at his estimates for the eastern United States as follows, using the 125
information that was available to him in 1990: 126

• Fine organic particulate matter (POM) – He made a d hoc adjustments to 127 measurements then available for the rural East (i.e., he assumed that 1/3 of the 128 average measured concentration of 4 μg/m 3 

### is natural), which were supported by 129 measurements of organic aerosol concentrations in remote areas (which suggest a 130 background of 1.5 to 2.25 μg/m 3 

### in the northern hemisphere). (Note that there is a 131 difference between background concentrations in remote areas today and 132 concentrations under natural conditions. The former can include global background 133 from anthropogenic emissions and thus may differ from the natural value.) 134

• Fine sulfates – He estimated that 3% of sulfur emissions in the East are due to natural 135 sources (based on the NAPAP emission inventory), considered rural measurements in 136 the southern hemisphere, and considered background estimates made for central 137
Alaska. 138

• Fine elemental carbon – He estimated that 2.8% of EC emissions are due to wildfires 139 and prescribed burning (from fire emissions information in the NAPAP emission 140 inventory) and considered that global background EC concentrations are very small. 141

• Fine ammonium nitrate – He considered nitrate levels (about 0.1 μg/m 3 

### ) in tropical 142 forests and observed that the NAPAP emission inventory indicated that natural NO x 

### 143 emissions are 4.5 times as great as those of gaseous sulfur (on a molar basis) but that 144 atmospheric nitrate production should be relatively less that that of sulfur. 145

• Fine soil -- Measurements suggested an average fine soil concentration of about 1 146 μg/m 3 

### in the East. He arbitrarily assumed that half of that amount is natural. 147

• Coarse matter – He used the lower end of the range of coarse mass concentration 148
measurements at "remote" sites in the U.S. 149

### 6 Thus, Trijonis based his estimates on the limited base of data that was available at the 150 time and interpreted it with a healthy dose of intuition, so the large error factors he assigned to 151 his own estimates are not surprising. 152 Figure 2 displays the annual average estimates of b ext 

### that result from applying the default 153 concentrations of Table 1 together with the EPA’s site-specific, climatologically-representative 154 f(RH) at four of the Class I areas in the VISTAS region, and identifies the contribution of each 155 particulate matter component (and any water associated with it) to that extinction. The bars in the 156 figure are labeled with the IMPROVE abbreviations for the names of the Class I areas the 157 monitors represent – GRSM = Great Smoky Mountains National Park; MACA = Mammoth 158 Cave National Park; ROMA = Cape Romain Wilderness within the Cape Romain National 159 Wildlife Refuge; and EVER = Everglades National Park. 160 The extinction coefficient and haze index values that result from the application of eqs 1 161 and 2 (using the concentrations in Table 1 and site-specific, climatologically-representative 162 monthly f(RH) values) are intended to represent default estimates of the average natural 163 conditions light extinction during each month, though based on annually constant default 164 concentrations. Since the Regional Haze Rule specifies that reasonable progress in mitigating 165 regional haze is to consist of improvement on the 20% of the days that are most hazy and of no 166 deterioration on the 20% of the days that are least hazy (clearest), the natural conditions 167 extinction value that is most relevant is the one that applies to the 20% of the days that are the 168 most hazy. 169
Ames and Malm 4 

### developed a statistical procedure to estimate the average of the 170 background extinction on the 20% haziest days (which they assumed is represented by the 90 th 

### 171 percentile of extinction values) from the annual average default values. They assumed that the 172 haze index under natural conditions would be normally distributed, in which case the 10 th 

### and 173
90 th 

### percentile values can be related to the mean and standard deviation of the distribution. The 174 mean values were calculated using Trijonis' default concentrations. To estimate the standard 175 distribution of natural conditions, they adjusted IMPROVE-measured frequency distributions of 176 today's haze indices at each Class I area by reducing actual sulfate and nitrate concentrations 177 until, on average, they matched the Trijonis default values in the East and the West. Measured 178 concentrations of organic carbon, elemental carbon, fine soil, and coarse matter were not reduced 179

### 7

similarly, under the assumption that at Class I areas they represent local concentrations that 180 already derive largely from natural sources. 181 From such synthetic frequency distributions at several Class I areas, Ames and Malm 182 estimated one representative value of the standard deviation for the East and another one for the 183 West, enabling calculation of 10 th 

### and 90 th 

### percentile estimates. They concluded that, for the 184 East, the 90 th 

### percentile value for any given Class I area is 3.84 dv greater than the annual mean 185 haze index (in deciviews) that is calculated from the default concentrations and f(RH) . Based on 186 this procedure, Appendix B of EPA's guidance document 2 

### presents default estimates of the haze 187 index that are intended to represent the 20% haziest and 20% clearest days under natural 188
conditions at each Class I area. 189

SOURCES OF NATURAL BACKGROUND PARTICULATE MATTER 190 Sources of gas and particulate emissions that contribute to natural particulate matter can be 191 grouped into two classes depending on their variability during the year – essentially steady or 192 episodic. Since haze calculations under the Regional Haze Regulations are based on 24-hour 193 averages, diurnal variations are not of import to us here. 194 Considering potential contributors, relatively steady-state natural sources (i.e., ones that 195 vary slowly throughout the year or within a season, though they may reflect diurnal variability 196 and even flare up at times) include the following: 197

• Oceans and tidal zones– sources of sea salt (affected by the amount of wind-raised 198 spray) and also sulfates and organic carbon (both primary and secondary). The 199 primary on-land effect occurs within tens of kilometers to, at most, a few hundred 200
kilometers of the shoreline. 201

• Forests and vegetation – sources of organic carbon (both primary and secondary), 202 coarse organic matter (from plant detritus), sulfates, and nitrates. Emissions result 203 from plant photosynthesis and respiration and also from decaying vegetation. 204 Ammonia produced in forests plays a role in the formation of ammonium sulfate, 205 ammonium nitrate, and even organic particles. 206

• Lightning – source of nitrates (relatively steady from day to day in some regions and 207 seasons). 208

### 8 • The earth's crust – source of fine and coarse soil, including sulfates and nitrates, due 209
to wind and natural disturbances. 210

• Volcanoes – sources of sulfates and nitrates (both primary and secondary) and of soil 211 particles. The main volcano currently affecting the VISTAS region is Popocatepetl in 212 southern Mexico, whose SO 2 

### emissions alone were estimated by the BRAVO Study 213 to average 3500 tonnes (3800 tons) per day. 5 

### These emissions are from the ultimate 214 "tall stack" at 5465 m (18050 feet) above sea level (before plume rise), and thus can 215 be transported very long distances along different pathways than lower-elevation 216
emissions. 217 The principal episodic natural sources include: 218

• Windstorms – sources of soil particles, which can be transported long distances, even 219 intercontinentally (examples are documented North African and Asian dust impacts 220 on the United States). 221

• Wildfires – sources of organic carbon and, to a lesser degree, elemental carbon and 222 soil, which can also be transported long distances (an example is the observed effect 223 of transported Central American biomass burning emissions on the U.S. South, 224 although most of this burning is man-caused). 225
If EPA’s default natural concentration values are valid, the contributions of all of these 226 steady-state and episodic sources should already be reflected in those default concentration 227 estimates, at least on average. At any specific location and time, however, conditions could 228 deviate considerably from the default values. 229 Subsequent to the development of the default values, natural background concentrations 230 over the entire globe have been estimated by applying air quality models. As examples, such 231 simulations have produced estimates for the southeastern U.S. of concentrations of OC and EC 232 from natural sources, biomass burning, and all sources, with the Grantour model; 6 

### of 233 concentrations of all components, with the GEOS-CHEM model; 7, 8 

### and of concentrations of 234 anthropogenic and biogenic secondary organic aerosol (SOA, the secondary fraction of POM), 235
with the MOZART-2 model. 9 

### Specifically, the most recent application of the GEOS-CHEM 236
model 8 

### yielded the following estimates of annual average natural background concentrations in 237 the U.S. East: ammonium sulfate, 0.11 μg/m 3 

### ; ammonium nitrate, 0.03 μg/m 3 

### ; organics, 1.17 238

### 9

μg/m 3 

### , and elemental carbon, 0.04 μg/m 3 

### . These can be compared with the default values in 239 Table 1, with the caveat that the GEOS-CHEM modeling values include effects of worldwide 240 “climatological” (i.e., multi-year average) fire emissions, both natural or anthropogenic, and thus 241 may overstate the natural portion of carbon concentrations alone. 242 Such global modeling could provide finer spatial resolution than the East-West split of 243 the default values and could be used to develop seasonal or monthly estimates of the natural 244 background. As uncertainties in emission inventories, particularly for natural emissions, are 245 being reduced, global modeling is improving in accuracy and its results should be considered in 246 parallel with the default estimates of Trijonis when establishing refined natural background 247
values. 248 We conducted a review of the scientific understanding of the emissions and ambient 249 concentrations of all the source types mentioned above, to evaluate which of those source types 250 are most likely to cause appreciable impact to natural conditions concentrations in any part of the 251 VISTAS region. 10 

### An important consideration was whether the scientific information base is 252 strong enough to develop credible quantitative estimates of aerosol concentrations resulting from 253 each of the above emissions categories. The potential impact of each emission category on 254 natural visibility in the VISTAS region, and the credibility and importance associated with each 255 impact relative to the default conditions, was estimated. 256 As a result of that review, contributions of the following four categories of sources were 257 considered for potential inclusion in refined estimates of natural conditions and thus have been 258 investigated further: 259

• organic and elemental carbon from wildfires, 260

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

• oceanic sea salt and POM, and 262

• long range transport of windblown dust from Asia and North Africa. 263 Impacts from other source categories mentioned earlier were considered to be too small 264 to be important or our understanding of them at this time was too limited to enable development 265
of credible concentration estimates. 26610 In addition, the statistical procedure used by the EPA to derive extinction under natural 267 conditions for the average 20%-haziest days and the empirical factor used to calculate POM 268 concentrations from measured OC concentrations were considered worthy of review. 269 Alternative assumptions for extinction efficiencies in eq 1 would also affect calculated 270 light extinction under both current and natural background conditions. These alternatives are not 271 discussed in this paper and the reader is referred to recent reviews of the topic. 11 - 13 272 The rest of this paper discusses the following issues: (1) statistical estimation of 273 extinction under natural conditions for the average of the haziest 20% days, (2) the choice of the 274 scaling factor for OC mass, and (3) current knowledge about the four categories of sources listed 275 above. Included are initial estimates (sometimes crude) of the potential magnitudes of these 276 issues on natural background concentrations and their potential impacts on developing 277 reasonable progress goals. The questions then addressed in conclusion are whether such effects 278 are substantial enough at some locations to materially affect the slope of the required "reasonable 279 progress" line, and might there be areas where additional effort to refine natural background 280 estimates would be justified? 281

POTENTIAL REFINEMENTS TO THE DEFAULT VALUES 282 Statistical Estimation of the Haziest 20% of Days 283
Ames and Malm 4 assumed that the frequency distribution of haze index values over a year is 284 normally distributed. For a normal distribution, the haze indices at the 10 th and 90 th percentiles of 285 the haze distribution under natural conditions can be calculated using the following formula: 286

HI = <HI> ± 1.28 σ , (3) 287

where HI

is the haze index (in deciviews), <HI> is the annual mean haze index for the Class I 288 area of interest (from the EPA guidance document 2 ), and σ is the standard deviation of the 289 assumed normal distribution of HI. Using the plus sign produces the 90 th percentile value and 290 using the minus sign produces the 10 th percentile value. The term 1.28 σ represents the distance 291
from the mean to the 10 th and 90 th percentiles. 292 Using the synthesized natural concentrations frequency distribution described earlier, 293
Ames and Malm estimated σ to average 3 in the East and 2 in the West under natural conditions, 294 so the formula for the East becomes simply 29511 HI = <HI> ± 3.84

. (4) 296
However, since the HI frequency distribution from the 80 th to the 100 th percentiles is not 297 uniform in a normal distribution, the HI value at the midpoint (90 th ) percentile of that range does 298 not actually represent the mean of the 20% haziest days that is prescribed by the Regional Haze 299 Regulations. Rather, the mean value for that range in a normal distribution is represented by the 300
haze index at the 92 nd percentile. 14 Consequently, the 1.28 factor should be 1.42 if the 301 distribution is normal, in which case the formula for the East becomes 302

HI = <HI> ± 4.26

. (5) 303
Thus, for the East, the haze indices for the average of the haziest 20% of the natural days should 304 be 0.42 dv greater than the default values that are presented in the EPA's guidance document 305 (and those for the clearest 20% of the natural days should be 0.42 dv less), if a normal 306
distribution is assumed. 307
308 Mass Concentration of Particulate Organic Matter 309 Trijonis estimated the natural background concentration of organic carbon (OC) in the East to 310 average 1 μg/m 3 . This estimate should include both emissions from natural fires and biogenic 311 emissions from vegetation. To account for the oxygen, hydrogen, nitrogen, and other elements 312 that are contained in POM, he multiplied his OC value by 1.5; to conform with current 313 IMPROVE practice, the EPA has used a factor of 1.4 for the same adjustment for Table 1. The 314 global aerosol research community generally appears to use a factor of 1.6 to 1.8 (see, for 315 example, references 15 and 16). Considering individual species, the known range of the 316 multiplier spans from 1.17 for large saturated hydrocarbons to 3.75 for oxalic acid. 17 317 A comprehensive review of experimental and theoretical research results by Turpin and 318
Lim 18 led to the conclusions that the appropriate multiplier for ambient OC will vary depending 319 on the origin and age of the aerosol and that the value of 1.4 does not accurately represent a non320 urban aerosol. Rather, they concluded that the appropriate factor for a non-urban aerosol is 2.1 ± 321 0.2. They also concluded that the appropriate value for an urban aerosol is 1.6 ± 0.2, which 322 approximates the 1.5 and 1.4 factors used by Trijonis and the EPA and IMPROVE. Turpin and 323 Lim’s value for an aerosol heavily impacted by wood smoke is 2.4 ± 0.2. 32412 It thus seems reasonable that the OC multiplier that would best represent natural 325 conditions most of the time would be about 2.1 (Turpin and Lim’s value for a non-urban 326 aerosol), while 2.4 could represent natural conditions during episodic fire events. Using the 2.1 327 factor, the estimated mass concentration of POM in Table 1 would be 2.1 μg/m 3 and the 328
extinction contribution would become 8.4 Mm -1 , which increases the overall dry extinction of 329
9.09 Mm -1 to 11.89 Mm -1 , an increase of 2.8 Mm -1 or 31%. This corresponds to an increase of 330 2.7 deciviews under dry conditions. 331 A different multiplicative factor than 2.1 is needed for deducing current POM 332 concentrations from IMPROVE OC measurements, though, because today's organic aerosol may 333 include an urban component or reflect smoke from intentional burning of vegetation. For 334 example, ratios of POM concentration to OC concentration in Class I areas have been found to 335
be 1.8 and 2.0 in recent research studies. 19, 20 A recent evaluation of the literature recommends a 336 value of 1.8 for IMPROVE monitoring sites. 11 337
Concentrations of POM mentioned in the next few sections will be based on the factor of 338 1.4, unless otherwise indicated. If the factor used by the investigator to scale measured or 339 modeled OC concentrations is different or if the model directly presents POM concentrations, 340 such a situation will be identified. The implications of changing to a different factor for natural 341 conditions will be addressed after we have surveyed results of scientific research concerning the 342
contributions of the sources listed earlier. 343
344
Carbon from Wildfires 345 Wildfires can affect air pollutant concentrations over large geographic areas. Effects of large 346 wildfires in northwestern Canada have been observed on air pollutant concentrations in 347
Tennessee 21 and transport and impact of smoke from wildfires and biomass burning in southern 348 Mexico and Central America have been observed in the central and southeastern portions of the 349
United States. 22, 23 350 The contributions of natural fires to background carbon concentrations have just begun to 351 be quantified. They are, of course, highly episodic at any given location. In the VISTAS domain, 352 using a combination of chemical mass balance (CMB) modeling and 14 C analyses of particulate 353 matter, average wood smoke contributions to POM were estimated to lie between 0.8 and 2.4 354 μg/m 3 at a rural location near Atlanta, Georgia. 24 These values would also include the 35513

contribution of intentional burning of vegetation, and thus are likely to overstate the natural 356
contribution. 357 Another estimate is provided by the global air pollution model GEOS-CHEM, 7, 8 which 358 was described earlier. The mean concentration of EC in the East, attributable almost entirely to 359 biomass fires (natural and anthropogenic, worldwide), was estimated to be 0.04 μg/m 3 , twice the 360
natural default EC value in Table 1. The modeled estimate for POM from the same fires and 361 from all other natural sources is 1.17 μg/m 3 for the East, which is slightly less than the 362 corresponding natural default value of 1.4 μg/m 3 for POM in Table 1. 363 Both the default POM concentration in Table 1 and the upper bound GEOS-CHEM value 364 cited above lie in the range determined by the measurements at the rural location near Atlanta, so 365 there is no inconsistency between the three sources of information. 366 Another type of bounding estimate is provided by 14 C analyses at Great Smoky 367 Mountains National Park during two recent summers. 25, 26 Those measurements, which are 368 described in the next section, found average modern carbon concentrations of 2.45 and 3.42 369 μg/m 3 , respectively. Modern carbon would be derived naturally from wildfires and as a result of 370 emissions from vegetation (discussed below) and from anthropogenic agricultural and prescribed 371 biomass burning. It is likely that most of the modern carbon at Great Smoky Mountains in 372 summer is attributable to biogenic emissions from vegetation, thus the natural burning 373 contribution to carbon at these times will necessarily be substantially less than 2.45 or 3.42 374 μg/m 3 (or the corresponding POM concentration will be substantially less than 3.43 or 4.79 375 μg/m 3 , if the 1.4 factor is used). 376 The average seasonal regional contributions of natural wildfires throughout the Southeast 377 need to be better quantified before default concentrations can be adjusted to reflect their 378
contributions. 379 As an example of a potential approach that could be used for this purpose, the total 380 current biomass-burning contribution could be measured by creating and using local biomass381 burning particle signatures analogous to those developed for the Intercontinental Transport and 382 Chemical Transformation (ITCT) mission from measurements in a forest fire plume in Utah. 27 383 Once the total biomass burning contribution is quantified, the anthropogenic and natural biomass 384 burning contributions could be separated based on their proportions in (rapidly improving) 385 emission inventories for wildfires, prescribed burning, and agricultural burning. 38614 Alternatively, the contribution of biomass-burning smoke could be estimated by 387 speciation of organic matter in particulate matter samples and then using organic species tracers, 388

14 C dating, and CMB analysis of the data to deduce source types. VISTAS is currently carrying 389 out such a study; one year of measurements at five locations was completed in May 2005 and 390 sample and data analyses are now under way. 391
392 Emissions of POM from Forests and Other Vegetation 393 Forest vegetation is the principal global source of atmospheric organic carbon in particles 394 (both primary and secondary), with global biogenic organic emissions exceeding anthropogenic 395 emissions by as much as an order of magnitude. 28, 29 In the United States, biogenic sources 396 account for 1/2 to 3/4 of the hydrocarbon emissions. 29, 30 The tropospheric burden of fine 397 particulate carbon in low latitude regions of the globe is dominated by long-range transport from 398 the tropics. 31 399 A visible manifestation of biogenic organic aerosol is the blue haze often observed over 400 forested regions, which results from oxidative particle formation, probably from biogenic 401 terpenes. It is unclear, though, to what extent such secondary terpenoid aerosols contribute to the 402 total biogenic organic aerosol burden. 32 403 The largest biogenic emissions occur in the tropics, where there is a combination of high 404 temperatures, high biomass density, and ample sunlight. During summer in the southeastern 405 United States, though, the emission flux (emissions rate per unit area of land) of biogenic 406 hydrocarbon emissions is estimated to be as large as at that in the tropics 30 , such as in the 407
Amazon basin. 9, 29 408 Studies at several locations on the globe have concluded that the formation of biogenic 409 organic aerosol begins with the formation of minute particles (a few nanometers in size) through 410 nucleation involving sulfuric acid, ammonia, water, and possibly some volatile organic 411 compounds. These particles grow to optically-important sizes via condensation of both inorganic 412 and photochemically-oxidized organic vapors. Aspects of the formation process in different parts 413
of the world are described in several articles. 33 - 40 414 The exact role of VOCs in the nucleation process is a subject of debate, although the fact 415 that biogenic VOCs are much more reactive than anthropogenic VOCs 41 suggests that they play a 416 role from the outset. Also, the acidity of seed particles appears to enhance the formation of 41715

SOA, 42-44 which may mean that current organic particle formation rates from natural emissions 418 are greater than they would be under natural conditions because of the presence today of global 419 anthropogenic inorganic acidic pollution. 420 Concentrations of POM have been found to be quite high in tropical forests. 421 Measurements of submicrometer (< 1 μm) particulate matter in an equatorial forest in the Ivory 422 Coast (west coast of Africa) found the average particulate carbon concentration (i.e., before 423 multiplication by a factor such as the 1.4 used by EPA and IMPROVE) to be 9 μg/m 3 during the 424 wet (growing) season. 31 Since biomass burning, which would have contributed carbonaceous 425 particles during the dry season, did not take place during the wet season, the measured 426 particulate carbon was attributed to natural emissions from the forest. 427 Measurements made by low-flying aircraft over a tropical Amazon Basin forest in Brazil 428
resulted in similar conclusions. 45 The measurements were made during the early to middle "dry" 429 season, when the forest was moist during the measurements and biomass burning did not yet 430 appear to be an important source. Their sampler used for carbonaceous particles did not have a 431 specific size cut, although it is likely that most of the particles sampled were in the fine size 432 fraction. The average particulate organic carbon concentration in the mixed layer was 740 433
nanomoles C/m 3 , which works out to 8.9 μg/m 3 of carbon (or a POM concentration of 12.5 434 μg/m 3 , if a factor of 1.4 is used). 435 In another study in the Amazon Basin, Absolute Principal Component Analysis (APCA) 436 of five years of field measurements at two remote locations yielded that biogenic particles 437
accounted for 57-63% of the total PM 2.5 mass during the wet (non-burning) season. 46 The 438 average biogenic particle concentrations during those seasons were estimated to be 3.9 and 5.5 439 μg/m 3 at the two sites. The biogenic material was considered to be composed principally of 440 organic carbon compounds plus a small amount of sulfur compounds. 441
These measured concentrations are consistent with concentrations simulated with the 442
MOZART-2 model 9 , which estimated that SOA concentrations in the Amazon basin were as 443 much as 10 μg/m 3 in summer. 444 These tropical data show that the potential concentrations of biogenic POM can be quite 445 large in and near forests. The question is how to translate these tropical research results into 446 biogenic POM concentrations that would apply in the VISTAS region. Because of the cooler 447 climate, it is likely that the average concentrations over the year will be lower in forests in the 44816

much of the VISTAS region than in the tropics. As noted above, though, several analyses have 449
concluded that summertime (June to September) biogenic VOC emission fluxes in the Southeast 450 (except near the coast) are comparable to those in the Amazon basin. 9, 29 451 In a more northerly setting, research carried out on formation of POM in remote forests in 452 Finland provides further insight. Overall PM 2.5 mass concentrations observed during the spring 453 there were lowest (2.6 μg/m 3 ) when the air mass was polar or arctic, and higher concentrations 454 were observed when the air came from other directions that reflected warmer air and impacts 455 from distant anthropogenic sources. 47 456 Carbon concentrations in June, measured by high-volume sampler (needed to collect 457 sufficient sample for speciation of the organic carbon compounds) at the same location in 458 Finland, were 3.2 μg/m 3 for OC and 0.19 μg/m 3 for elemental carbon (or, more rigorously, black 459 carbon). 48 Direct comparison with the values in Table 1 is not possible, though, because the 460 particle size range collected by the hi-vol is much broader than that of PM 2.5 . 461
Estimates of natural SOA in the southeastern U.S. have been made via the 14 C 462 measurements and CMB analyses that were cited earlier. 26 Based on measurements made during 463 one month of each quarter during one year at a rural, predominantly agricultural, location near 464 Atlanta, Georgia, the average natural SOA concentration was 1.6 μg/m 3 , the peak concentration 465 observed was about 2.5 μg/m 3 in July, and the concentration in winter (January) was zero. Data 466
from another 14 C study, 49 this time in Nashville, Tennessee, allow us to conclude that biogenic 467 OC concentrations there during a one-month period from mid-June to mid-July ranged from 468 about 2 to about 5 μg/m 3 , which correspond to POM concentrations of 2.8 to 7 μg/m 3 or more. 469
Two 14 C studies at Great Smoky Mountains National Park in August 2002 25 and the 470
summer of 2004 26 concluded that averages of 83% and 90% of the sampled POM concentrations 471 were of modern origin, respectively. These translate into average concentrations of modern OC 472 during those periods of 3.42 and 2.45 μg/m 3 , which are considered most likely to be mainly SOA 473 derived from biogenic VOC emissions. 26 (The corresponding POM concentrations are 4.78 and 474 3.43 μg/m 3 , respectively, calculated with a factor of 1.4.) 475
Simulation with the MOZART-2 model finds current fine SOA concentrations 476 (attributable to both biogenic and anthropogenic emissions) to be around 2 μg/m 3 throughout the 477 inland South in June and September, and 1 μg/m 3 or less in March and December. These values 478 are somewhat less than the average POM concentrations (sum of both primary and secondary) 47917

measured by IMPROVE samplers in these regions, 50 but not greatly so, which suggests that they 480 are not far off the mark. These modeled values may thus provide an indication of the upper 481 bound to the typical biogenic contribution alone in the Southeast today. 482 The information given here suggests that biogenic POM concentrations in forested 483 portions of the VISTAS region could reach 4 μg/m 3 or more in summer. This value is 484 comparable to currently observed summertime levels of organic carbon in forested areas in the 485
southeastern U.S. 50 Since biogenic contributions will be less the rest of the year, the annual 486 average impact is likely in the range of 1 to 2 μg/m 3 . Such a biogenic contribution alone is 487 comparable to the 1.4 μg/m 3 average default concentration for all organics, while the 488 summertime peak is many times the default average. 489
490 Oceanic Emissions of Sea Salt and Organics 491 Sea Salt. Sea salt contributes to ambient particulate matter near the ocean. Over the ocean in 492 remote areas it accounts for about half of the submicrometer (< 1 μm) aerosol in the boundary 493 layer and for about 3⁄4 of the aerosol that is larger than 1 μm and smaller than 10 μm. 16 The 494 concentration will vary with the state of the ocean; average concentrations measured in the 495 remote southern hemisphere ocean during the ACE 1 research study were 0.6 μg/m 3 for the 496 submicrometer sea salt and about 9.5 μg/m 3 for the salt larger in sizes between 1 and 10 μm. 16 497 The chemical composition of sea salt, which is a mixture of NaCl and other constituents, 498 is complex and variable. In particular, sea salt loses chloride as it ages because exposure to 499 atmospheric H 2 SO 4 and HNO 3 and certain non-acidic gases results in the release of gaseous HCl. 500 Consequently, the mass scattering efficiency of aged sea salt differs from that of fresh sea salt 501 (whose scattering is similar to that of pure NaCl). 51 Being hygroscopic, sea salt has the potential 502 to contribute disproportionately to haze when the humidity is high. 503 Although IMPROVE measures elemental Na and Cl, and hence estimating the amount of 504 sea salt at Class I areas is conceptually feasible, its effect is not included in the EPA/IMPROVE 505 formula for estimating light extinction (eq 1). An assessment of the effect of this omission, using 506 IMPROVE data taken at 59 sites from 1993 to 1999, led to the conclusion that including the 507 mass concentration of NaCl consistently improved the comparison between reconstructed fine 508
mass concentrations and measured PM 2.5 at all sites. 52 Fine NaCl accounted for 1% to 33% of the 509 reconstructed fine mass concentrations (the 33% was at Point Reyes National Seashore, 51018

California). In the VISTAS region, NaCl accounted for 5% of the fine mass concentration at 511 Chassahowitzka National Wildlife Refuge (NWR) in Florida, 3% at the more inland Okefenokee 512 NWR in Georgia, and 1-2% elsewhere. (Another VISTAS coastal site, Cape Romain NWR in 513 South Carolina, was not included in the analysis.) 514 The average reconstructed PM 2.5 concentrations between 1996 and 1998 at 515 Chassahowitzka and Okefenokee were 9.8 and 9.7 μg/m 3 , respectively. 50 We can thus infer from 516 the percentages above that the average fine sea salt concentration at those two locations is in the 517 range of 0.3 to 0.5 μg/m 3 , which corresponds to 13 to 22% of the default natural fine mass 518 concentrations there. Such concentrations are comparable to the default natural concentration of 519 soil. Further, since the sea salt size distribution approximates that of soil (see, for example, a 520 comparison of size distributions in reference 30), the extinction impact of comparable amounts 521 of fine soil and dry fine sea salt can be expected to be comparable. Sea salt is hygroscopic 522 however, the effect of which will be addressed later in this section. 523 In an experiment in which the composition of particulate matter arriving at Miami from 524 over the Atlantic ocean was studied for 23 years, the annual mean sea salt concentration in TSP 525 (calculated from the measured Na concentration and the Na/Cl ratio of seawater) was 8.67 μg/m 3 526 for those periods when the flow arrived from over the ocean. 53 The highest values were in the 527 winter (attributed to stronger winds creating more sea spray then) and lower than average 528 concentrations were found from May through September. The minimum daily concentration 529 measured during every month of the year exceeded 4.5 μg/m 3

. 530 The size cut point of the sampler used for these measurements is not known, but the 531 investigator estimated that 1/3 to 1/2 of the sampled mass was in the fine (PM 2.5 ) range. 53 If the 532 same fraction applied to the sea salt, that would suggest an annual average fine sea salt 533 contribution in the range of 3 to 4.5 μg/m 3 . The corresponding estimate of minimum daily fine 534 sea salt concentration (on days with onshore flow) would be in the range of 1.5 to 2.2 μg/m 3

. 535 These concentrations are greater than the highest averages inferred above from analyses of 536 IMPROVE samples. 52 A possible reason for the discrepancy is that the Miami concentrations 537 only reflect those measured at the shoreline during the times when airflow came from over the 538 ocean, while the IMPROVE data represent all wind directions. Also, the Miami measurements 539 were taken much closer to the shoreline than the IMPROVE measurements at Okefenokee, and 54019

thus they could be influenced by sea spray from breaking surf, although the investigator states 541 that the concentrations were not strongly influenced by salt spray from the surf zone. 53 542 Sea salt concentrations from IMPROVE measurements at Cape Romain and Okefenokee 543 were also calculated by Paine and colleagues. 54 Their estimate of current sea salt concentrations 544
included both NaCl and Na 2 SO 4 , where the sulfate was assumed to have replaced chloride that 545 was lost due to chemical reactions of NaCl with acidic gases and other substances in the 546 atmosphere. Because such reactants might not be present under natural conditions, they also 547 calculated the concentration of NaCl alone. Considering the latter concentration, which is the 548 lower one, they estimated annual average fine sea salt concentrations to be 1.3 μg/m 3 at Cape 549 Romain and 0.85 μg/m 3 at Okefenokee. The Cape Romain figure of 1.3 μg/m 3 is consistent with 550 the slightly higher range of 1.5 to 2.0 μg/m 3 that was deduced above from measurements at 551 Miami during onshore flow alone. The Okefenokee value is somewhat larger than the 0.3 to 0.5 552 μg/m 3 range inferred above for Okefenokee from data in reference 52. 553 Another study 24 estimated the fine sea salt concentrations along the coast of the Gulf of 554 Mexico (the southern boundary of the VISTAS domain), to be in the range of 0.4 to 0.5 μg/m 3 at 555 the coast of the Gulf of Mexico. This range of concentrations is lower than the ranges cited 556 above, but the investigators point out that the Gulf may generate less sea salt spray than the more 557 highly energetic ocean along the Atlantic coastline. 558 Meaningful sea salt concentrations can extend well inland from the shoreline. Particulate 559 matter measurements by low-flying aircraft (at about 150 m altitude) in the Amazon Basin of 560 Brazil found that that, assuming that aerosol Na + was derived exclusively from sea salt, the 561 average concentration of sea salt larger than 1 μm in size in the mixed layer was about 4 μg/m 3 562 near the coast, 1.5 μg/m 3 250 km inland, and less than 1 μg/m 3 more than 400 km inland. 45 563 Without further information on size distribution, one cannot say how much of this material 564 would show up as PM 2.5 and how much would be coarser. A corresponding description for 565 submicrometer sea salt was not possible because concentrations were below the detection 566 method for the analytical method used. 567 Studies of the light scattering efficiency and hygroscopic growth of sea salt particles 51, 55 568 don't, unfortunately, provide information on the scattering of light by sea salt particles in the 569 actual size range found in the atmosphere. Over the Indian Ocean, an extinction efficiency of 1.0 570
to 1.2 m 2 /g was estimated for submicrometer (< 1 μm diameter) sea salt at 55% RH, 15 which is in 57120

the same range as the 1.0 m 2 /g used by IMPROVE for dry fine soil particles. The estimate for 572 supermicrometer (> 1 μm diameter) particles was 5.1 to 6.0 m 2 /g at 55% RH, which is well 573
above the 0.6 m 2 /g used by IMPROVE for dry coarse particles, even after allowance is made for 574 hygroscopic growth, although some of the difference may be explained by the difference 575 between the 1 μm lower size cuts used for this work and the 2.5 μm size cut of IMPROVE. 576 In the absence of better information, the light scattering efficiency of fine dry sea salt is 577
sometimes taken to be the same as the 1 m 2 /g used by IMPROVE for fine soil, and that of the 578 coarse fraction of dry sea salt is taken to be the 0.6 m 2 /g scattering efficiency of coarse 579 particulate matter. This is the approach used by Paine et al., 54 who also deduced a humidity 580 scaling function f(RH) for fine sea salt based on measurements made over the Atlantic Ocean 581 during the ACE 2 experiment. The resulting function is similar to the EPA/IMPROVE f(RH) for 582 sulfates and nitrates, except that hygroscopic growth begins at a lower relative humidity for sea 583 salt than for sulfate and nitrate and the increase above 90% RH is not as rapid for sea salt. The 584 f(RH) curves for fine sea salt and sulfate/nitrate are almost identical from 60% RH to 90% RH. 585 Humidity-related growth can also be expected for the coarse fraction of sea salt, though not with 586
the same f(RH) curve as for the fine sea salt. 56 587 Using their empirical f(RH) curve and their lower sea salt concentration estimates at Cape 588 Romain and Okefenokee, Paine et al. calculated seasonal and annual average extinction impacts 589
of fine sea salt concentrations. 45 They concluded that the contribution of natural background fine 590
sea salt to extinction was 4.3 Mm -1 at Cape Romain and 3.0 Mm -1 at Okefenokee. Using the data 591
in Table 1 and the same annual f(RH) that Paine et al. used, we calculate that the annual default 592 particulate matter extinctions without sea salt are 11.4 Mm -1 at Cape Romain and 11.6 Mm -1 at 593 Okefenokee. Adding the above sea salt contribution to these values increases the estimated 594 annual-average natural extinction due to particulate matter by 38% at coastal Cape Romain and 595
26% at the more-inland Okefenokee. 596
597
Oceanic POM. Turning to organic particles from the ocean, mass concentrations of fine organic 598 particles at the northeastern tip of Puerto Rico (and thus exposed to the easterly trade winds 599 coming over the ocean) and also during ship sampling away from the coast there were found to 600 be in the range of 0.3 to 0.4 μg/m 3 during a February to June study. 57 Black (elemental) carbon 601 was not found in the samples, which indicated that the aerosol was relatively free of combustion 60221

products. Consequently, a substantial fraction of this organic material may be associated with 603
natural oceanic emissions. 57, 58 604 As another indication of background levels of oceanic carbon, average POM 605 concentrations (defined as 1.6 x OC) in marine air masses over the Indian Ocean south of the 606 equator were found to be 0.07 to 0.13 μg/m 3 in submicrometer particles and 0.14 to 0.56 μg/m 3 607 in supermicrometer particles during the February-March monsoon season. 15 These 608 concentrations again represent values obtained for oceanic air masses, with the lowest 609 concentrations associated with transport from the south. According to the investigators, these 610 values are comparable with what has been measured in remote areas of the Atlantic and Pacific 611 Oceans. In contrast, in the North Atlantic during the phytoplankton bloom periods, 612 submicrometer OC concentrations in clean marine air at a coastal site averaged 0.9 μg/m 3 (which 613 corresponds to a POM concentration of about 1.4 μg/m 3 , using the same 1.6 factor). 59 614 It is not clear how to relate open-ocean POM concentrations to concentrations of natural 615 POM of oceanic origin at coastal IMPROVE sites such as Cape Romain. Available data aren’t 616 sufficient to give insight into variability due to temperature or season. Nor do the oceanic 617 concentrations reflect enhanced POM emissions from breaking waves at the shoreline and 618 biological tidal zone POM formation, both of which are likely to be very site-specific. 619 As a first guess, if one assumes 0.4 μg/m 3 for natural fine POM over the ocean and 620 assumes that the wind blows onshore from the ocean half of the time (a good assumption at Cape 621 Romain), then the annual average contribution of fine oceanic POM at Cape Romain might be 622 0.2 μg/m 3 . This corresponds to an increase in the default POM concentration by about 15% and 623 the default light extinction by 0.8 Mm -1 . The oceanic POM contribution would be smaller at 624
more inland non-coastal sites such as Okefenokee. 625
626 Long Range Transport of Windblown Dust 627 To estimate the average fine soil concentration, Trijonis elected to "arbitrarily assume" (his 628 words) that half of the current average measured fine soil concentrations are natural. 3 The limited 629 data available to him indicated that average rural fine soil concentrations were about 1 μg/m 3 in 630 both the East and the desert/mountainous West, which he divided by two to arrive at his natural 631 background estimate of 0.5 μg/m 3 for both regions. Recent measurements and data analyses 632 allow us to assess the applicability of this estimate to the VISTAS region. 63322

634 IMPROVE Fine Soil Measurements in the VISTAS Region. IMPROVE measurements of fine 635 soil concentrations at nine locations throughout the VISTAS region during 1996-1998 produce a 636 composite annual average fine soil concentration of 0.62 μg/m 3

. 50 The annual average values are 637 least at the northern sites (0.38 μg/m 3 at Dolly Sods Wilderness, West Virginia and 0.42 μg/m 3 at 638 Shenandoah National Park, Virginia) and greatest at the southern sites (0.77 μg/m 3 at 639 Okefenokee NWR, 0.80 μg/m 3 at Sipsey Wilderness, Alabama, and 1.1 μg/m 3 at 640 Chassahowitzka NWR). The measured soil values in the southern part of the VISTAS region 641 approximate the 1 μg/m 3 assumed by Trijonis as representative of the current fine soil 642 concentration in the East, but the current measured values in the northern portion are closer to his 643 default value of 0.5 μg/m 3 for natural conditions alone. Thus the default soil concentration may 644 overstate natural fine soil concentrations in the northern portion of the VISTAS region. 645 The soil concentrations measured by IMPROVE vary greatly by season. Winter 646 concentrations are relatively uniform throughout the VISTAS domain, in the vicinity of 0.2 to 647 0.3 μg/m 3 , while summer concentrations range from around 0.5 μg/m 3 to around 2 μg/m 3 as one 648 goes from north to south in the VISTAS region. 50 As we discuss below, long-range transport of 649 dust appears to be a major contributor to the large spatial variability in the summer. Such dust 650 has two origins – Asia (including the Gobi Desert) and Africa (especially the Sahara Desert). 651
652 Asian Dust Contributions to IMPROVE Samples. The elemental composition of IMPROVE 653 filter samples taken throughout the United States from 1989 to 1999 was evaluated by VanCuren 654 and Cahill to determine the origins of the soil elements in those samples. 60 Using cluster analysis 655 of known Asian air masses to develop signatures for the Asian aerosols, they concluded that dust 656 transported across the Pacific Ocean from Asia affects most of the U.S. during most of the year. 657 Satellite images have demonstrated that Asian aerosols can easily cross North America and reach 658
the western Atlantic Ocean. 659 Asian dust impacts are greatest in the western U.S. 61 and subside toward the East Coast, 660 and are generally greater in the northern part of the country than they are to the south 661 (presumably because westerly flow is more prevalent in the northern latitudes). This geographic 662 distribution is not universal, however. A large Asian dust episode in mid April 2001 (discussed 663 further below) had its greatest impacts along the central West Coast, the Rocky Mountains, and 66423

in the Southeast, while the impacts along the northern boundary of the United States were 665 smaller, though still significant. 62 666 The frequency of appearance of Asian dust in the United States is greatest in the spring 667 and summer months, with the most noticeable seasonal variation in northern states and, 668 generally, a flatter temporal distribution in the southeastern U.S. 669 Focusing now on the VISTAS region, graphs in the Van Curen and Cahill paper 60 show 670 that, on those days when Asian dust was a dominant contributor to fine soil concentrations at 671 Shenandoah National Park, the average concentration of that dust exceeded 0.6 μg/m 3 during 672 every month from March through October and was less the rest of the year. The average 673 contribution during Asian dust events during that 7-month period was about 0.7 μg/m 3 and the 674 peak monthly average was about 1.2 μg/m 3 , in August. Episodic concentrations (24-hr average) 675 were much higher. 676 As an extreme example, which occurred after the period analyzed by VanCuren and 677 Cahill, the Asian dust event of mid April 2001 (the largest such event ever observed in the U.S.) 678
caused 24-hr PM 10 concentrations to increase by 20-30 μg/m 3 (!) at the Shenandoah and 679 Okefenokee IMPROVE samplers. 62 This was followed by two additional episodes during the 680 first half of May. All three episodes also significantly impacted particulate matter concentrations 681 in the northeastern U.S. Interestingly, the trajectories to the Northeast for the first May episode 682 first passed through the VISTAS region. They entered the VISTAS states from the northwest, 683 circled clockwise through the VISTAS region, and then headed northeast to the northeastern 684
U.S. 63 685 Based on VanCuren’s and Cahill’s analysis, Asian dust events occurred at Shenandoah 686 during every month of the year, with frequencies between about 20 to 36% of the time in the 687 spring months, 16% of the time in October, and less than 10% of the time in the remaining 688 months. Combining these concentrations and frequencies of episodes, we estimate the average 689 Asian dust concentration (averaged over all days, irrespective of whether Asian dust impact 690 occurred on any given day) at Shenandoah from March through October is in the range of 0.1 to 691 0.2 μg/m 3 . The Asian dust statistics at Dolly Sods Wilderness are similar to those at Shenandoah. 692 Farther to the south, frequencies of Asian dust events were highest during summer at 693 Everglades National Park, Florida (averaging around 20% from May through September). The 694 average concentrations during those events were smaller than at Shenandoah (averaging about 69524

0.5 μg/m 3 from March to September), which suggests an average Asian dust contribution there 696 of around 0.1 μg/m 3 over the late spring and summer. 697 The fine soil concentrations measured by IMPROVE in the Appalachian region (which 698 includes Shenandoah and Great Smoky Mountains National Parks) average 0.45 μg/m 3 over the 699 year and peak at 0.61 μg/m 3 over the summer season. 50 The episodic fine dust concentrations at 700 Shenandoah that were attributed by VanCuren and Cahill to Asian dust events (and were 701 described above) are comparable to or greater than these measured averages. This implies that 702 Asian dust plays a dominant role in Shenandoah soil concentrations at times from spring into 703 fall, and its average 0.1 to 0.2 μg/m 3 contribution accounts for roughly 20 to 40% of the 704 measured fine soil concentration during that period. 705 At southeastern IMPROVE sites (encompassing Chassahowitzka, Okefenokee, and Cape 706 Romain), the average fine soil concentration in 1996-1998 was 0.81 μg/m 3 and the summer 707 average was 1.86 μg/m 3

. 03 As expected, the influence of Asian dust is much less noticeable here 708 than at the more northerly locations. Rather, as discussed next, African dust transport plays a 709 major role in the summertime peak. 710
711 African Dust Contributions to IMPROVE Samples. Dust transported across the Atlantic Ocean 712 from the Sahara Desert is another contributor to fine soil concentrations in the VISTAS region. 713 African dust impacts, which can be identified by their elemental signature, occur less frequently 714 in the VISTAS region than those from Asian dust but the magnitudes of the African 715 contributions are much larger whenever those impacts occur. 60 716 African dust impacts tend to be greatest along the Caribbean and diminish toward the 717 north. Specifically, at the Everglades, African dust events occurred regularly from June through 718 September over an 11-year period, with the soil composition on more than 40% of the days in 719 July dominated by African dust. 60 The mean 24-hr average African fine soil dust contribution 720 there during events occurring in June through August was between 7 and 9 μg/m 3 . The pattern is 721 similar at Okefenokee, but with somewhat lower frequency and concentrations than at the 722 Everglades. Farther north, the average African dust event contributed 4.5 μg/m 3 at Shenandoah 723 and Dolly Sods, but those events were infrequent and occurred almost exclusively in August. 724 IMPROVE measurements at the three southeastern monitoring sites, Chassakhowitzka, 725 Okefenokee, and Cape Romain do indeed show an average soil concentration of 1.86 μg/m 3 in 72625

the summer, compared to about 0.6 μg/m 3 in the spring and autumn. 50 The high frequency of 727 occurrence and the high concentration impacts of African dust events there imply that such dust 728 is the dominant cause of that summertime peak, which suggests that African dust alone accounts 729 for most of the summer increase, perhaps roughly 1 μg/m 3 of fine soil, averaged over the 730 summer. This amount alone, without considering other natural sources, is well in excess of the 731 0.5 μg/m 3 average natural concentration estimated by Trijonis. 732 Perry and colleagues 64 used IMPROVE samples from 1988 through 1995 to estimate the 733 impacts of transported North African dust at various locations in the Southeast. They concluded 734 that 57% of the samples taken at the Everglades in July showed an African dust impact greater 735 than 3 μg/m 3 . Corresponding percentages at other VISTAS locations were 27% at Okefenokee, 736 19% at Chassahowitzka, 16% at Sipsey and Great Smoky Mountains, 14% at Dolly Sods, and 737 8% at Shenandoah. A surprising 23% of the observations at Mammoth Cave National Park, 738 Kentucky in July contained African dust above 3 μg/m 3 , which is reinforced by an even higher 739 37% at Upper Buffalo Wilderness in Arkansas (outside the VISTAS region). Further outside the 740 VISTAS region, 19% of the observations at Big Bend National Park in western Texas in July 741 contained more than 3 μg/m 3 of African dust and the corresponding percentage was 76% at the 742 Virgin Islands. Percentages of African dust impact were less in other months, with the period of 743 impact concentrated on June through August in the southern locations in the VISTAS region and 744 including only July at the northern locations. 745 In contrast, the period of impact at the Virgin Islands covered the nine months from 746 February through October. As a result, Perry et al. found the annual mean fine soil concentration 747 at the Virgin Islands was 3.25 μg/m 3 , versus values in the vicinity of 1 μg/m 3 that were given 748 earlier for the Southeast, and the summertime fine soil concentrations there were a substantial 749 7.58 μg/m 3 , about four times as large as the highest concentrations in the VISTAS region. 750 From 23 years of measurements of the composition of particulate matter transported over 751 the Atlantic Ocean onto the coast at Miami, the mean concentration of soil dust in TSP during 752 onshore flow was 4.72 μg/m 3

. 53 The maximum concentrations occurred from June through 753 August. The concentration during onshore flow averaged 16.3 μg/m 3 in July. The investigator 754
estimated that one-third to one-half of these concentrations is in the PM 2.5 range. Based on 755 correlation with climate conditions (e.g., drought cycles) in Africa and satellite observations of 756 dust cloud transport, this soil dust is attributed to African emissions 75726 The BRAVO Study also found African dust at the Big Bend National Park, Texas, 758 IMPROVE site during July-October 1999. 65 Based on analyses of speciated particulate matter 759 measurements taken during that study, 66 we estimated that the average African dust contribution 760 at Big Bend National Park in July (averaged over all days, not just Saharan dust events) was 1.4 761 μg/m 3 and in the first half of August it was 0.9 μg/m 3 . Although Big Bend is well west of the 762 VISTAS domain, on several days the African dust there still accounted for more than 80% of the 763 measured fine soil concentration and contributed over 2.5 μg/m 3

. 764 African dust contributions over the VISTAS region can be expected to range between 765 those reported at Shenandoah, Everglades, and Big Bend. Although precise quantification is not 766 possible from the information provided here, it is reasonable to conclude that African dust 767 contributes substantially to fine soil concentrations at most VISTAS locations during the summer 768 (specifically, June through August). As a broad average for the region, daily impacts during 769 African dust events during those months may average 3 to 5 μg/m 3 and such events might occur 770 roughly 20% of the time. Thus, a rough estimate of the average Saharan fine dust contribution 771 during the summer could be in the range of 0.6 to 1.0 μg/m 3 -- more in Florida and a bit less at 772 Shenandoah and Dolly Sods. 773 These analyses of IMPROVE and BRAVO data provide strong evidence that the natural 774 contribution to fine soil during the summer is substantially greater than Trijonis’ average natural 775 soil estimate of 0.5 μg/m 3 for the year. If African dust is the only natural contributor, then the 776 foregoing data suggest that a summertime natural conditions estimate would be in the range of 777 0.6 to 1 μg/m 3 , out of a measured summertime average range of 0.5 to 2 μg/m 3 for all fine soil at 778 IMPROVE sites. Other natural sources will further increase the natural contribution, with Asian 779 dust alone adding another 0.1 to 0.2 μg/m 3 . Thus, at least for the summertime, it appears that 780 natural sources account for the overwhelming majority of the measured fine soil concentration in 781 the VISTAS area, not just the half postulated by Trijonis, and the concentration of natural soil 782 during that season far exceeds the default annual estimate of 0.5 μg/m 3

. 783
784 Implications of Transported Dust on Natural Background Extinction. Because of its episodic 785 nature, it is difficult to suggest an approach for dealing with the contributions of Asian and 786 African dust to the natural background. The annual average impact is relatively small, but its 787 monthly contribution can be substantial during the summer months (a range of 0.6 to 1.0 μg/m 3 78827

was estimated above) and the episodic contribution can be much larger at times. Some of the 789 average effect is probably reflected in Trijonis’ average estimate of 0.5 μg/m 3 , but the 790 seasonality, episodicity, and geographic variation is not. Since the scattering efficiency of fine 791
soil is assumed to be 1 m 2 /g, the average summertime impact translates to an extinction 792
increment of 0.6 to 1.0 Mm -1 . Some portion of this amount could be added to the default natural 793 conditions estimate for specific sites for those months. The effect during the rest of the year is 794 substantially smaller. 795

IMPLICATIONS FOR MANAGING REGIONAL HAZE 796 The discussion above has identified a variety of potential refinements to the default estimates of 797 natural background concentrations of PM components and light extinction in the VISTAS 798 region. The question to be addressed at this point is whether such refinements should be pursued 799 by VISTAS. Developing refined estimates could be worthwhile if they might materially affect 800 the slope of the "reasonable progress" line and perhaps affect the soundness of the emission 801 management strategies that are pursued. On the other hand, from a practical point of view, 802 developing and justifying refined estimates at this time may not be worthwhile if their impact on 803 the first round of emissions management steps (which have a 2018 target) is so small that it does 804 not materially affect the emissions management choices. Even in the latter case, there is still 805 merit in applying the best science available, even at this point, because the process of doing so 806 will lay the groundwork for the increasing importance of scientifically sound natural background 807 estimates to making technically and financially responsible emissions management policy 808
decisions in the future. 809 This issue is illustrated schematically in Figure 3. Changing the natural conditions 810 estimate changes the slope of the line required to achieve progress toward the natural conditions 811 goal. But the difference in the 2018 regional haze goal, in deciviews, is much smaller than the 812 change in the 2064 natural background estimate. 813 Note that, because of the nature of the refinements discussed here and the limitations in 814 the default estimates, it is likely, but not always necessary, that a natural conditions refinement 815 will increase the estimated natural extinction as illustrated in Figure 3. A refinement that 816 decreases a default estimate is not precluded, however. Also, some of the refinements under 817 consideration here for natural conditions (such as for the OC mass multiplier and sea salt) also 81828

imply parallel adjustments to corresponding estimates of current conditions, and therefore the 819 baseline at the top of the figure may also be subject to adjustment. 820 To put some numbers into this discussion, it will be useful to recap the estimated impacts 821 resulting from each of the natural contributions discussed in this paper: 822

• Adjusting the 90 th percentile extinction to the 92 nd percentile, to more properly 823 represent the mean of the haziest 20% of the days in a normal distribution, would 824 increase the default haze index by 0.42 dv. (The impact on the extinction coefficient, 825
in Mm -1 will vary depending on the extinction level.) 826

• Increasing the organic carbon multiplier to 2.1 would increase the default natural 827 extinction by 2.8 Mm -1 . An even greater multiplier would be appropriate for organic 828 carbon that originates from fires. Since the biogenic contribution to organics is likely 829 greater on a seasonal basis than the fire contribution, the 2.1 value seems appropriate 830 for the Southeast, instead of a higher value. One should also consider increasing the 831 present day organic carbon multiplier, but probably to a lesser value (say, 1.8) 832 because of the anthropogenic carbon content of the ambient aerosol today. 833

• Preliminary information on wildfire impacts from global modeling results suggests 834
that overall POM and elemental carbon concentrations for the East are about the same 835 as the default values. Presumably, Trijonis' estimates included average wildfire 836 impacts. The global modeling is able to improve on the default averages by providing 837 insight into spatial and seasonal variability, however. 838

• The contribution of vegetation to POM in forested areas is harder to quantify. The 839 potential impact within forests in the deep South during the warmer months appears 840
to be several times the default estimate of POM concentration and extinction and the 841 annual average impacts are comparable to the default values. If so, the impact on 842 background extinction in such areas could be as much as 10 Mm -1 during the warmest 843 months. More research is needed to ascertain the actual impact, which will depend on 844 the degree to which a given location is surrounded by forest and affected by forest 845 emissions. As an initial estimate for locations entirely in forested areas, adding 1 846 μg/m 3 of POM (for an extinction increase of 4 Mm -1 ) during spring and summer 847 seems conservative, or a corresponding annual average impact in the range of 1/3 to 84829 1/2 of that amount (i.e., 0.3 to 0.5 μg/m 3 , or 1.2 to 2 Mm -1 ) may be appropriate. If 849 geographic distribution is considered, the annual value in the northern part of the 850 VISTAS domain and at high elevation locations is likely to be smaller than that 851 farther south and in the lowlands. These estimates have high uncertainty. 852

• Sea salt has a major impact that can be estimated with fair confidence, at least near 853 the coast. The impact will vary with distance from the coast. Annual average fine 854 fraction concentration impacts of around 1.3 μg/m 3 at the coast and perhaps half that 855 at inland sites in Florida are suggested by current research results. It is unlikely that 856 much of this is reflected in Trijonis' estimates because of the limited monitoring data 857 that was available to him. The extinction impact of such an increase in fine 858
concentrations will be 1.3 Mm -1 or more (depending on relative humidity) at the coast 859 and less inland. There will also be a smaller coarse fraction impact. Sea salt is not 860 considered in the current IMPROVE algorithm for estimating visibility, so the same 861 impact should also be included in estimates of current extinction. 862

• POM from the sea may also affect coastal areas. An average mass concentration 863 impact of 0.2 μg/m 3 , as suggested earlier, translates into an extinction impact of 0.8 864
Mm -1 . Because few measurements are available except over open ocean, and coastal 865 values are dependent on the nature of the shoreline, this estimate has high uncertainty. 866

• In the northern portion of the VISTAS region, the average soil concentrations already 867 approximate the default background values, which suggests that the default values 868 there may be too high by, perhaps, 0.2 to 0.3 μg/m 3 (and thus that the default 869 extinction there may be too large by 0.2 to 0.3 Mm -1 ). Counterbalancing this, the 870 default concentrations may be too low at some locations, but this has not been 871
established. 872

• The 0.1 to 0.2 μg/m 3 of transported Asian dust during spring and summer would 873 impact the extinction by 0.1 to 0.2 Mm -1 then, and by perhaps 0.1 Mm -1 when 874 averaged over the full year. The question is whether that Asian dust is already 875 reflected in the default estimates, since it may have appeared in the measurement data 876 on which Trijonis based his estimates. 87730 • African dust has a very large episodic impact during the summer. If seasonal average 878 natural conditions concentrations were to be estimated, the summertime mass 879 concentration of soil could be increased by about 0.6 to 1.0 μg/m 3 over the annual 880 default values. (If different adjustments are made for different geographic areas, one 881 might use an even larger increase in the southern portion of the VISTAS region and a 882 smaller one in the northern portion.) The result would be to increase natural 883 background extinction by 0.6 to 1.0 Mm -1 during the summer months. One might 884 compensate with a small decrease during the rest of the year, or argue that the 885 episodic summer impacts were not reflected in the limited data base that Trijonis had 886 available. On an annual basis a net increase of 0.2 to 0.3 μg/m 3 (0.2 to 0.3 Mm -1 ) 887 might be added to the default soil value. 888 Table 2 summarizes the two suggested refinements to the method of estimating natural 889 conditions and the potential effects of the seven types of natural sources that are itemized in the 890
bulleted discussions above. The last column of the table indicates the authors’ assessment of 891 whether a refinement of a default method or concentration is warranted, based on the magnitude 892 of the expected impact, whether it might already be included in the default concentrations, and 893 the degree of scientific understanding of the impact (or the compensating degree of 894 conservativeness in the estimated impact). Note that the suggested refinements are within the 895 typical factor of two error estimates by Trijonis, as listed in Table 1. 896 Of course, it is unlikely that all of the refinements discussed above and summarized in 897 Table 2 should be applied simultaneously at every location. As already mentioned, some of the 898 contributions may already be included in the default estimates (possibly Asian dust, for 899 example). Also, some sources will not be applicable at some locations, e.g., sea salt in the inland 900 mountains or, perhaps, forest organics at the seashore. 901 One should also recognize that our conclusions are based on relatively uncritical review 902 of the literature. Measurement methods may be biased, measurements may be unrepresentative, 903 or scientific hypotheses may change with more information, Consequently, much of what is 904 involved in our assessment here has some degree of uncertainty and is itself subject to 905 refinement. In this light, any values that we assign to potential refinements should be taken as 906 approximations. 90731 To assess the significance of refinements that might realistically be considered for 908 specific locations, we consider next examples of some of their effects on the regional haze 909 progress goals at two specific locations -- Great Smoky Mountains National Park (inland, 910 mountainous) and Cape Romain National Wildlife Refuge (Atlantic coastal). 911
912 Implications at Great Smoky Mountains National Park 913 The average current extinction at Great Smoky Mountains National Park, at the eastern end of 914 Tennessee, can be approximated by the most recent five years (1998-2002) for which IMPROVE 915 particulate matter component concentration data were available on the VIEWS web site 67 on 10 916 October 2004. At the GRSM IMPROVE site adjacent to the park the average 20% haziest-days’ 917 haze index over those five years (using the EPA-prescribed protocols for the calculations 68 ) is 918 29.54 dv, which is equivalent to an extinction coefficient of 191.9 Mm -1 . From EPA's natural 919 conditions guidance document, 2 the long-term goal is default natural conditions estimated at 920 11.44 dv (31.4 Mm -1 ) for the 20% haziest days. 921 Consider four potential scenarios with different estimates of the natural conditions 922 endpoint. 923

• GRSM1: EPA's default natural conditions estimate. Calculate annual average from 924 Table 1 concentrations and f(RH) from U.S. EPA’s natural conditions guidance 925
document. 2 Estimate average of 20% haziest days based on estimated 90 th percentile, 926 which adds 3.84 dv to the annual average haze index value. 927

• GRSM2: Change OC mass multiplier from 1.4 to 2.1, which adds 2.8 Mm -1 to the 928 annual average. (Also, when computing progress slope, adjust the OC mass multiplier 929 for present day to 1.8, a value midway between the 1.6 and 2.1 values recommended 930 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/m 3 of 932 biogenic POM from the forests (the lower bound of the range proposed in Table 2) to 933 the annual default concentrations. This increases the natural background annual 934 average light scattering contribution by 1.2 Mm -1

. 935

• GRSM3*: As an alternative to GRSM3, if all of the haziest days are in the growing 936 season/summer (which pretty much holds true at Great Smoky Mountains), add the 93732 peak effects of African dust (0.6 μg/m 3 , 0.6 Mm -1 ) and the peak season impacts of 938 vegetation emissions of biogenic organics (conservatively 1 μg/m 3 , 4 Mm -1 ) to the 939 natural background of Scenario GRSM2 during summer only. Concentrations during 940
the other three seasons are taken to be the same as those in GRSM2. 941 For scenarios GRSM1 through GRSM3, the contribution of each PM component to 942 annual average light extinction is displayed graphically in Figure 4. The largest changes are 943 produced by changing the assumed mass concentration of organic matter. Since the annual 944 average effect of the added dust is small, the annual average of extinction for scenario GRSM3* 945 would be virtually the same as that of GRSM3. 946 Table 3 compares current and natural conditions values of light extinction, annual 947 averages versus averages of 20% haziest days, and haziest days estimates based on the 90 th 948 percentile of the distribution (the default approach) versus the 92 nd percentile (the approach 949 supported here), for all four scenarios. An assumption implicit in this data tabulation and similar 950
calculations here is that refinements to the default natural conditions concentrations do not alter 951 the standard deviation of the frequency distribution of the natural conditions haze index, so that 952 the statistical adjustment used by the EPA to determine average conditions on the 20% haziest 953 days from the annual averages 4 is the same for both the original and the refined conditions. 954 Emissions control strategies are intended to focus on the 20% haziest days rather than the 955 annual average conditions. The control strategy implications for the four scenarios on the 20% 956 haziest days are summarized in Table 4. The first data column is the current haze index that 957 represents the average of the 20% haziest days. It was calculated under the current EPA 958 protocol, 68 with OC multiplied by 1.4 and the 90 th percentile used to define the 20% haziest 959 natural days for Scenario GRSM1(90). The same scenario, except that the 92 nd percentile was 960 used, is labeled GRSM1(92). The remaining scenarios also use the 92 nd percentile to define the 961 20% haziest natural days and have natural OC multiplied by 2.1 and current OC multiplied by 962 1.8. Both the Current Haze Index and the Natural Background Haze Index columns reflect the 963
effects of the refinements stated above. The difference between these two columns is the 964 required improvement over 60 years, so 1/6 of that is required over a decade, as listed in the third 965 data column. The corresponding change in b ext over the first decade is in the next column. The 966 next-to-last column gives the percentage reduction in particulate matter concentrations that 96733

would be required if all particulate matter components in Equation 1 were reduced by the same 968 percentage. 969 Since sulfates are the predominant contributors to haze in the VISTAS region most of the 970 time, the last column in Table 4 indicates the % reduction in non-natural sulfate concentrations 971 that would be needed during the first decade to produce the given b ext decrease if only sulfates 972 were reduced and all other components remained unchanged. These values were calculated using 973 an annual-average f(RH) of 3.32 (from Table A-2 in Reference 2), i.e., RH = 85%, and a 5-year 974 (1998-2002) average ammonium sulfate concentration of 14.25 μg/m 3 on the 20% haziest days, 975 of which 0.23 μg/m 3 is assumed uncontrollable natural background (from Table 1) . 976 The differences between scenarios GRSM1 through GRSM3 are relatively small, whether 977 equal control of all components is assumed, or only control of sulfates. Note, though, that the 978 percentage improvements given in the next-to-last column assume (unrealistically) that the 979 concentrations of all of the chemical components in Table 1 will be reduced by the same 980 percentage. The difference between a 27.5% and a 25.0% reduction in a given component 981 concentration will be much more significant if part of that component concentration is natural or 982 otherwise uncontrollable. For example, Trijonis postulated that fine soil is half natural and half 983 anthropogenic, so the percentage reductions in the anthropogenic part of the soil need to be twice 984 as large as the percentages just cited if the specified reduction in soil is to hold. This implies 985 requiring a 55% reduction of anthropogenic soil emissions under the default assumption versus 986 50% under the GRSM3 scenario. Similarly, if some of the universe of sources that emit a 987 specific pollutant is already well-controlled, the percentage reductions required of the other 988 sources will be greater than the values given in Table 4, in which case the differences between 989 the required reduction rates may be of economic importance when setting a control strategy. 990 The required percentage of concentration reduction decreases further when we consider 991 scenario GRSM3*. This scenario takes advantage of the fact that most of the haziest days at 992 Great Smoky Mountains occur in the summer, at the same time when the organic emissions from 993 vegetation and impacts from African dust are the greatest and thus the natural background is 994 high. In this situation, a smaller degree of emissions control is required to attain the required rate 995 of progress on the 20% haziest days. 996
99734 Implications at Cape Romain National Wildlife Refuge 998 A similar analysis, following the same protocols, can be carried out for the Cape Romain 999 National Wildlife Refuge, on the Atlantic coast of South Carolina. At the Cape Romain 1000 IMPROVE site (ROMA), the current extinction on the 20% haziest days can be approximated 1001 for our purposes here by the values in four recent years, 1999-2002. 67 (The data for 1998 are too 1002 incomplete to define the 20% worst days in that year.) The average 20% haziest days' haze index 1003 over those four years is 25.89 dv or 133.2 Mm -1 . The haziest days natural conditions target in the 1004 EPA guideline document 2 is 11.36 dv, or 31.1 Mm -1 , for this location. 1005 Similar to the previous analysis, consider five scenarios. The first two scenarios, ROMA1 1006 and ROMA2, are identical to those at Great Smoky Mountains, albeit with local data. The other 1007 three scenarios reflect the uniqueness of the coastal location of Cape Romain: 1008

• ROMA1: EPA's default natural conditions estimate, as described for GRSM1. 1009

• ROMA2: Adjust the OC mass multiplier for natural conditions to 2.1 and for present 1010 day data to 1.8, as described in GRSM2. 1011

• ROMA3: Add 1.3 μg/m 3 of sea salt to Scenario ROMA2. Using the annual average 1012 f(RH) of 3.15 at Cape Romain, this sea salt contributes 4.1 Mm -1 of light scattering. 1013 Since the EPA/IMPROVE formula (eq 1) does not consider sea salt, this amount is 1014
added to both current and natural conditions extinction estimates. 1015

• ROMA 4: Add 0.8 Mm -1 to natural conditions in Scenario ROMA3 to represent the 1016 contribution of 0.2 μg/m 3 of oceanic POM. 1017

• ROMA5: Add an annual average of 0.3 μg/m 3 of African dust, which increases 1018 natural extinction by 0.3 Mm -1 . (Being farther south than Great Smoky Mountains, 1019 the African dust impact here is greater and temporally more diffuse, although an 1020 annual average is still a crude approximation. Furthermore the 20% haziest days at 1021 Cape Romain are distributed throughout the year and thus are not well correlated with 1022 the summertime episodicity of African dust, so a seasonal analysis of the type used 1023 for Great Smoky Mountains is not viable here.) 1024 Figure 5 shows the effects of these scenarios on the contributions to natural conditions extinction 1025 by the various components of the particulate matter. The biggest changes result from increasing 102635

the organic carbon multiplier and from adding sea salt. The contributions of oceanic organics and 1027 African dust are relatively minor. 1028 Table 5 summarizes the extinction statistics for the various scenarios. Cape Romain is 1029 less hazy than Great Smoky Mountains, especially on the haziest 20% of the days. Note that the 1030 increased OC multiplier and the addition of sea salt affect the current extinction estimates as well 1031
as those of natural conditions. 1032 Table 6, which is in the same format as Table 4, shows the emissions control strategy 1033 implications of these four scenarios. The main difference from Table 4 is the smaller percentage 1034 concentration reductions that are needed because Cape Romain is substantially less hazy on 1035 average than Great Smoky Mountains, while the default natural conditions goals are virtually 1036 identical. Also, all of the refinements considered here have a bigger effect on the required 1037 percentage of reduction than did those considered for Great Smoky Mountains. Otherwise, the 1038 general conclusions concerning Table 4 also apply to Table 6. 1039 In overview, Figures 4 and 5 illustrate that most refinements in the annual average 1040 estimates of concentrations of soil, elemental carbon, and nitrates will have minimal effect 1041 because the underlying contributions of those components are quite small. Even the episodic 1042 African dust contribution in the summer in Scenario GRSM3* makes only about a 1% 1043 contribution to the extinction on the natural conditions 20% haziest days. On the other hand, 1044 changes in POM and sea salt concentrations can have important impacts on the light extinction 1045
and deserve first attention if a decision is made to refine the default concentrations. The 1046
combination of the use of the 92 nd percentile and the 2.1 multiplier for calculating POM from OC 1047 also has a material effect on the natural conditions extinction estimate. When the needed degree 1048 of emission control is considered, however, the impacts of some of these refinements to natural 1049 conditions are diluted because partially-compensating adjustments also need to be made to 1050
estimates of current extinction. 1051

CONCLUDING COMMENTS 1052 This review was prepared to illuminate potential approaches for refining natural conditions 1053 estimates and to provide some insight into the most promising approaches. The calculations in 1054 the previous section suggest that there is merit in establishing refined estimates of natural 1055 conditions at some Class I areas, even at this initial part of the 60-year period. 105636 We have found that conservative regional refinements to the default natural background 1057 estimates do not greatly alter the region-wide rates of reduction of ambient particulate matter 1058 concentrations that will be needed to attain the natural conditions goal. However, refinements at 1059 a specific Class I area may have considerable influence on defining the nature (magnitude and 1060 spatial and temporal distribution) of local emission reduction efforts to reach the natural 1061 conditions goal there. 1062 Three relatively non-controversial refinements that could be applied without extensive 1063 effort are (1) using a more-appropriate percentile to reflect the average of the haziest 20% of the 1064 days; (2) applying an organic carbon multiplier adjustment at most or all Class I areas and (3) 1065 adding the effect of sea salt at Class I areas that are relatively close to the ocean. In the last two 1066 cases, appropriate corresponding adjustments should also be made to present-day estimates 1067 before the rate of reasonable progress is calculated. In the case of the OC multiplier, though, the 1068 present day multiplier probably should not be the same as that for natural conditions. 1069 African dust is a large contributor episodically during the summer, but its episodicity 1070 means that creative thought will be needed to weave it into the mandated regional haze 1071 management process. Compared to other components, on an average basis it is not an important 1072 contributor to natural light extinction, though. 1073 POM created in forests and emitted from natural wildfires are two other promising 1074 contributors, but their effects are not well quantified and including them will be more 1075 challenging and require more research than the other refinements that have been mentioned. 1076 Asian dust has most of the complexities of African dust, but it has even smaller impact. 1077 Note that in this paper we have not considered changes to the numerical coefficients 1078 (extinction efficiencies) used in eq 1 to calculate light extinction. If the scaling factor for OC is 1079 increased, then total light extinction calculated via eq 1could possibly become an overestimate 1080 because of the greater POM concentration, unless the extinction efficiencies of carbon or other 1081 components in the equation are adjusted. These issues are addressed elsewhere 11 - 13 and have 1082
not been included in our calculations. 1083 As a closing perspective, one needs to remember that any refinements to the default 1084 natural conditions estimates will have to be made in a regulatory framework. In such a setting, 1085 refinements are most likely to be accepted if the scientific community and the regulators have 1086 confidence in the science supporting the changes and justifying the magnitudes of the changes, 108737

and there is minimal controversy concerning those aspects. This paper addresses the technical 1088 merits of potential refinements to natural background visibility at VISTAS Class I areas but does 1089 not make policy recommendations as to whether these refinements should be adopted by the 1090 VISTAS states as part of their state implementation plans for regional haze. 1091

ACKNOWLEDGMENTS 1092 The work reported in this paper was funded by the VISTAS regional planning organization, 1093 through its funding agency, the Southeastern State Air Resource Managers and grants from the 1094 Environmental Protection Agency. The ideas presented in this paper are those of the authors and 1095 do not represent policy decisions by the VISTAS states. We appreciate the comprehensive 1096 comments by an anonymous reviewer; they have substantially enhanced the quality of this paper. 1097

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128744

ABOUT THE AUTHORS 1288 Ivar Tombach is Technical Advisor to the VISTAS Regional Planning Organization. He is an 1289 independent air pollution consultant, located at 753 Grada Ave., Camarillo, CA 93010. Patricia 1290 Brewer is VISTAS’ Technical Coordinator at 2090 U.S. 70 Highway, Swannanoa, NC 28778. 1291
1292 1293 1294 TABLES 1295 1296 Table 1. Annual average default natural background concentration estimates for particle 1297 components in the East, and their estimated impacts on dry light extinction according to the 1298 IMPROVE algorithm (Eq 1). 1299 1300 Component Average Concentration, μg/m 3 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 130245

Table 2. Summary of suggested refinements to default estimates of natural conditions in the 1303 VISTAS region and their potential effects, with authors’ assessments of whether refinement of 1304 the default method or concentration is warranted. 1305 1306 Issue/Source PM 2.5 Component Affected Annual Average

Concentration Impact (μg/m 3 ) Annual Light

Extinction Impact (Mm -1 ) Refining

Default Warranted? Methods Refinements Use 92 nd %-ile instead of 90 th 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 130746

Table 3. Estimated annual and 20%-haziest days extinction averages (in Mm -1 ) for four 1308 hypothetical scenarios at Great Smoky Mountains National Park. 1309 1310 Annual Average b ext 20% Haziest Days b ext

Scenario 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

Table 4. Implications of four natural conditions scenarios on the emissions reductions required 1316 to decrease haze on the 20% haziest days during the first decade at Great Smoky Mountains 1317 National Park. 1318 1319 Scenario Current HI, dv Natural Backgnd HI, dv Improvmnt

Rate, dv/decade ∆b ext 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 132247

Table 5. Estimated annual and 20%-haziest days extinction averages (in Mm -1 ) for five 1323 hypothetical scenarios at Cape Romain National Wildlife Reserve. 1324 1325 Annual Average b ext 20% Haziest Days b ext

Scenario 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

Table 6. Implications of five scenarios on the emissions reductions required to decrease haze on 1331 the 20% haziest days during the first decade at Cape Romain National Wildlife Reserve. 1332 1333 Scenario Current HI, dv Natural Backgnd HI, dv Improvmnt

Rate, dv/decade ∆b ext 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

### F IGURES

F i gure 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.