# Technical Memorandum #1

Updated Visibility Statistics for the MANE-VU Region

Prepared by Northeast States for Coordinated Air Use Management (NESCAUM) For the Mid-Atlantic/Northeast Visibility

Union (MANE-VU) Regional Planning Organization

February 15, 2002

0 20 40 60 80 100 120 140 160 180

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh

## ii NESCAUM Northeast States for Coordinated Air Use Management 129 Portland Street Boston, MA 02114

TEL: 617-367-8540 FAX: 617-742-9162 http://www.nescaum.org

## iii 

# Technical Memorandum #1

Updated Visibility Statistics for the MANE-VU Region

February 15, 2002

Submitted to the United States Environmental Protection Agency, Region III in partial fulfillment of requirements for EPA grant X-983384-01-0 to the Ozone Transport Commission

Project Manager

Gary Kleiman

Editors

Gary Kleiman, Arthur Marin

Principal Contributors (NESCAUM)

Gary Kleiman Ingrid Ulbrich

USEPA Project Officer

Russ S. Bowen (USEPA Region III)

## iv

## v 

# Table of Contents

Executive Summary................................................................................vii

I. Introduction ................................................................................................................. 1 II. Data Sources and Methodologies................................................................................ 2 III. Visibility Across the Northeastern U.S. –1999........................................................... 6 A. MANE-VU Class I Areas........................................................................................ 6 B. Nearby Areas........................................................................................................... 7 IV. Visibility Trends.......................................................................................................... 9 A. Comparison of Timescales for Averaging Relative Humidity................................ 9 B. Five-year average trends ....................................................................................... 17 Trends in Composition on the Best and Worst Visibility Days .................................... 18 V. Comparing Optical vs. Aerosol Monitoring Techniques .......................................... 23 VI. Summary ................................................................................................................... 27 Appendix A – Site specific, climatologically averaged relative humidity and relative humidity adjustment factors and other visibility statistics................................................ 29

## vi

## vii 

# Executive Summary

Under the U.S. Environmental Protection Agency’s (USEPA) 1999 regional haze rule, states and tribes are required to submit implementation plans which must include calculations of current and estimated natural visibility conditions. Recent monitoring data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network has been used to examine visibility conditions in the Mid-Atlantic/Northeast Visibility Union (MANE-VU) region. These results suggest that across the MANE-VU region, visibility impairment on the 20 percent of days with the worst visibility in 1999 was reduced by approximately 2 dv relative to conditions in 1997 with the worst visibility conditions occurring further south and west. Further research is required to determine the cause of this improvement; however, meteorological conditions may have played a role. A slight decrease in nitrate composition appears to have occurred between 1995 and 2000, however, no substantive reductions in sulfate or organic material are apparent on the twenty percent worst visibility days during this same time period.

Recent guidance issued by USEPA for calculating visibility conditions suggests the use of climatological monthly mean values of the relative humidity adjustment factor to account for differences in scattering properties of fine particulate with increased relative humidity. An examination of relative humidity adjustment factor averaging time suggests that use of annual or monthly mean values may understate visibility conditions calculated using daily average relative humidity data where available. Comparison with optical data confirms that measured visibility conditions (i.e. transmissometer and nephelometer data) may be substantially different from those conditions obtained through USEPA recommended procedures. Further study is required to determine the impact these differences are likely to have on calculated rates of progress.

## viii

## 1 

# I. Introduction

States and tribes must submit implementation plans by December 31, 2008 outlining control measures needed over the subsequent ten years (2009-2018) in order to improve visibility conditions in Federal Class I areas within and near the MANE-VU region in compliance with the Environmental Protection Agency’s (USEPA) regional haze rule. These plans must include calculations of baseline 1 

## and estimated natural visibility conditions. These plans must also estimate the necessary rate of progress for the 10-year compliance period needed to achieve the overall goal of natural visibility conditions by 2064. This document provides a survey of speciated fine particle and monitoring data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) program and how it can be used in this process. This work builds upon a previous NESCAUM report (NESCAUM, 2001a) and is intended to update and expand upon the technical discussion presented in that document. New figures are presented which describe the nature and extent of visibility impairment in the region. Data used to generate each figure are presented in tabular form in Appendix A.

Two documents have recently been drafted by USEPA in order to guide states and tribes in performing reasonable progress and natural visibility calculations (USEPA 2001a, 2001b). While the methodology for calculating reconstructed light extinction used in this document is consistent with the approach taken in these guidance documents, NESCAUM has not rigorously applied the guidance recommendations for the substitution of missing values. When the proposed guidance documents are finalized, those aspects of the calculations presented here that differ from the guidance should be harmonized to provide a consistent assessment of current visibility conditions and trends in the MANE-VU region.

1 

### Calculations of baseline conditions are based on monitored data from the years 2000 to 2004.

## 2 

# II. Data Sources and Methodologies

Data from the IMPROVE program represent a crucial input to state and tribal planning efforts under the 1999 regional haze rule. The National Park Service manages the IMPROVE program with the support of several contractors who perform specific data collection and analysis functions. These contractors include the University of California at Davis (site selection, filter management, gravimetric and elemental analyses and database management), the Desert Research Institute (elemental and organic carbon analyses), the Research Triangle Institute (ion analyses), Atmospheric Resource Specialists, Inc. (optical, scene, and meteorological data collection), and the Cooperative Institute for Research in the Atmosphere (data analysis and website support).

The IMPROVE web site provides speciated data for all sampling days at IMPROVE monitors. 2 

## Total particle light extinction can be calculated using this information and the methodology described below. Table 1 lists the particle species, formulae and assumptions used by IMPROVE to calculate particle concentrations. Ambient concentrations are in turn used to calculate reconstructed particle light extinction coefficients. The bracketed symbols in the second column of Table 1 correspond to species concentrations and to the labeling conventions used in the IMPROVE database. The labeling convention is: [S] = Elemental sulfur [NO3] = Nitrate [EC#] = Detailed elemental carbon species measured by thermal optical reflectance (TOR) with three bins (# = 1,2,3) [OC#] & [OP] = Detailed TOR organic species with bins (# = 1,2,3,4) [AL] = Aluminum [SI] = Silicon [CA] = Calcium [FE] = Iron [TI] = Titanium [MT] = Total mass (PM 10 

## ) [MF] = Fine mass (PM 2.5 

## )

2 

### IMPROVE data are available via an ftp link located at the web address http://alta_vista.cira.colostate.edu/. The website is part of a cooperative program on visibility in Class I areas between the National Park Service Air Resources Division and the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University in Ft. Collins.

## 3 Total light extinction is a function of the individual light absorption and light scattering properties of particles present in the atmosphere. This total is frequently expressed as a light extinction coefficient (b ext 

## ) in units of inverse length (such as Mm -1 

## ). In simple terms, the light extinction coefficient is a measure of the proportion of light extinguished per unit of distance traveled through the atmosphere. The light extinction coefficient, b ext 

## , can be measured directly with a transmissometer or determined empirically by “reconstructing” extinction as the sum of the scattering and absorption coefficients of the relevant particle constituents, as indicated by the following equation.: 3,4

3 

### Absorption by nitrogen dioxide gas is not generally significant on a regional scale, though it can play a role in coherent pollution plumes (FLAG, 2000). Hence the discussion in this chapter considers elemental carbon as the only contributor to atmospheric light absorption.



## Table 1: Formulae and assumptions used with IMPROVE sampling measurements to derive reconstructed particle light extinction (adapted from Sisler and Malm, 2000).

Species Formula Assumptions

SULFATE 

## 4.125[S] All elemental S is from sulfate. All sulfate is from ammonium sulfate.

NITRATE 1.29[NO3] Denuder efficiency is close to 100%. All nitrate is from ammonium nitrate.

LAC (Light absorbing carbon) [EC1] + [EC2] + [EC3] – [OP] All high temperature carbon is elemental.

OMC (Organic mass from carbon) 1.4{[OC1] + [OC2] + [OC3] + [OC4] + [OP]} Average organic molecule is 71% carbon.

SOIL (Fine Soil) 2.2[AL] + 2.49[SI] + 1.63[CA] + 2.42[FE] + 1.94[TI] [Soil K] = 0.6[Fe]. FeO and Fe 2 

## O are equally abundant. A factor of 1.16 is used for MgO,

Na 2 

## O, H 2 

## O, CO 2


## . RCFM (Reconstructed fine mass) [SULFATE] + [NITRATE] + [LAC] + [OMC] + [SOIL] Represents dry ambient fine aerosol mass for

continental sites.

CM (Coarse Mass) [MT] – [MF] Consists only of insoluble soil particles.

## 4 b ext 

## = b SO4 

## + b NO3 

## + b OrgC 

## + b Soil 

## + b Coarse 

## + b ElemC 

## + b Ray


## Note that this equation includes Rayleigh scattering, b Ray 

## , which is a measure of scattering due to air molecules. The Federal Land Managers’ Air Quality Related Values Workgroup (FLAG) uses a Rayleigh scattering value of 10 Mm -1 

## for the entire U.S. (FLAG, 2000). This value corresponds to Rayleigh conditions at about 1800 meters above sea level (Sisler and Malm, 2000). However, Rayleigh scattering varies with altitude and at sea level is estimated to be about 12 Mm -1 

## (Trijonis et al., 1990). To avoid understating “natural” background visibility impairment at coastal sites (which could result in setting unrealistic goals for haze reduction efforts), the analysis conducted here assumes a Rayleigh coefficient of 12 Mm -1 

## for the Acadia, Brigantine, Moosehorn, and Roosevelt Campobello Class I areas as well as the Washington D.C. and the James River Face IMPROVE sites. All of these sites have a mean altitude below 300 meters. This assumption reduces calculated background extinction levels by 2 Mm -1 

## but leads to a change of only 0.3 dv in estimated natural background conditions on the deciview scale. 5



## The calculation of extinction coefficients for each individual chemical species can be described by the following equations (FLAG, 2000):

b SO4 

## = 3[(NH 4 

## ) 2 

## SO 4 

## ]f(RH) 6


## b NO3 

## = 3[NH 4 

## NO 3 

## ]f(RH) b OrgC 

## = 4[OrgC] b Soil 

## = 1[Soil] b Coarse 

## = 0.6[Coarse] b ElemC 

## = 10[ElemC]. The bracketed quantities represent ambient air concentrations expressed in micrograms per cubic meter ( μ g/m 3 

## ). The numeric coefficients represent “dry” scattering efficiencies 7 

## (m 2 

## /g), while the relative humidity adjustment factor f(RH) accounts for the hygroscopic properties of sulfate and nitrate (i.e., their tendency to absorb water in the atmosphere). As relative humidity increases this factor becomes larger, which in turn produces a higher coefficient of light extinction for the hygroscopic particles. Provided concentrations and humidity levels are known, the light extinction coefficients for individual particle constituents can be calculated and summed to estimate the overall light extinction coefficient, b ext 

## .

4 

### Particles in the atmosphere may exist as an internal mixture of several chemical species. IMPROVE assumes that the contribution of each particle constituent can be determined separately and summed to determine total light extinction.

5 

### This assumption is in contrast to the recent USEPA guidance on this point which recommends using 10 Mm -1 

### consistently at all Class I areas, regardless of altitude (USEPA, 2001a).
6 

### IMPROVE assumes that all sulfate is in the form ammonium sulfate ((NH 4 

### ) 2 

### SO 4 

### ) and that all nitrate is in the form ammonium nitrate (NH 4 

### NO 3 

### ). Other forms of these species exist in nature as detailed in Section D of this chapter. These differing forms may have different scattering efficiencies and relative humidity adjustment factors.

7 

### Dry scattering efficiencies were determined for light at 550 nm (0.55 μ m; green). There may be discrepancies between this value and those determined by integrating over the entire visible spectrum (400- 700 nm).

## 5 It should be noted that a number of uncertainties are embedded in these calculations; hence, reconstruction of light extinction will not be accurate for every sample. 8 

## For example, the equations reflect simplified assumptions about the role of relative humidity and may not adequately account for the non-linear relationship between humidity and particle growth rate. Moreover, the relative humidity values traditionally used by IMPROVE represent an average over large geographic areas and long periods of time. Ideally, relative humidity should be recorded and stored with each concentration measurement so that an appropriate factor can be calculated for each observation. Second, different humidity adjustment factors should be used for the sulfate and nitrate fraction of aerosol particles given differences in the growth rates for these two constituents with increasing relative humidity. Third, the above equations assume that organic carbons are non-hygroscopic and do not require a relative humidity adjustment. In many instances little information is available about the specific constituents of secondary organic aerosol particles and of their potential affinity for water. Whether or not a relative humidity adjustment factor should be applied to the organic fraction is therefore an issue of current debate (Saxena et al., 1995). Finally, the IMPROVE calculations make an assumption that the particles are externally mixed as opposed to the more likely case that each particle is a homogenous (internal) mixture of the individual components. This difference will affect the physical properties of the particle and in turn how they impact visibility. The sensitivity of reconstructed light extinction to each of these assumptions is an area that warrants further investigation. 9 

## It should be noted that the IMPROVE program has taken efforts to quantify the precision of the techniques used for their calculations and has deemed it sufficient for the IMPROVE program objectives.

The recent guidance documents issued by USEPA (in draft form) discuss some of the uncertainty present in reconstructed light extinction, but do not resolve the issue by performing a detailed sensitivity analysis for each assumption. Section V of this report describes some comparisons of reconstructed light extinction with measured light extinction and attempts to quantify the discrepancy between the two techniques and thus provide a measure of the overall uncertainty.

The regional haze rule requires that visibility conditions be measured in deciview, a metric that is approximately linear with human perception of visibility impairment. The deciview is related to atmospheric extinction through the following relationship:

dv = 10 ln(b ext 

## /10).

Extinction is used in this memorandum to explore the contribution of individual component of fine particulate to overall visibility impairment. The deciview is used to examine trends in visibility conditions.

8 

### In fact, reconstructed light extinction is not expected to be accurate for every sample as it was designed to provide a consistent and replicable process for approximating light extinction based on observed relationships.
9 

### The sensitivity of reconstructed light extinction to relative humidity adjustment factor averaging time is explored in Section IV. The FLAG (FLAG, 2000) has suggested that annually-averaged adjustment factors are adequate for screening analyses; however, the recent USEPA guidance (USEPA, 2001a) has recommended monthly average factors be used for SIP work.

## 6 

# III. Visibility Across the Northeastern U.S. –1999

A. MANE-VU Class I Areas

The regional haze rule calls for a steady improvement of visibility on the 20 percent of days with the worst visibility and the prevention of deterioration in visibility on the 20 percent of days with the best visibility at Federal Class I areas across the country. In 1999, visibility experienced at MANE-VU Class I areas was somewhat better than that which has been documented previously for 1997 (NESCAUM, 2001a). As Figure 1 shows, the 20 percent of days with the worst visibility 10 

## (left bars; based on calculations of reconstructed light extinction) range from 89 to 174 Mm -1 

## of total light

10 

### The terms “worst” or “best” visibility as well as “20 percent worst” or “20 percent best” visibility conditions are defined throughout this report as the simple average of the upper or lower 20 percentile of a cumulative frequency distribution of reconstructed light extinction for days in which all particle species were successfully measured, respectively. 

## Figure 1: Speciated contribution to total atmospheric light extinction in or near Class I Areas in the Northeast and Mid-Atlantic states on 20 percent of days with the worst (left bar) and best (right bar) visibility conditions during 1999.

115 Mm -1 

### / 28 Mm -1 

### Great Gulf (Presidential Range) 89 Mm -1 

### / 25 Mm -1 

### Moosehorn (Roosevelt Campobello) 110 Mm -1 

### / 23 Mm -1 

### Acadia

119 Mm -1 

### / 21 Mm -1 

### Lye Brook

174 Mm -1 

### / 50 Mm -1 

### Washington D.C. 161 Mm -1 

### / 39 Mm -1 

### Brigantine 

### Sulfate

Elemental Carbon Nitrate
Organic Carbon

Rayleigh Crustal Material

## 7 extinction (particle light extinction plus Rayleigh scattering). 11 

## This corresponds to 22-29 dv and is approximately 2 dv lower than visibility conditions on the worst days in 1997. Visibility impairment is not, however, uniform across the region, with the worst visibility conditions occurring further south and west.

The majority of this visibility impairment can be attributed to sulfate aerosol, which was responsible for over two-thirds of the extinction on the days with the worst twenty percent visibility at most sites. Organic Carbon (OC) is formed from the byproducts of literally hundreds of precursor organic molecules including Volatile Organic Compounds (VOCs) and biogenically emitted species. After sulfate, organic carbon is responsible for the greatest atmospheric extinction on the days with the worst and best visibility days. Nitrate, elemental carbon (or soot) and crustal material (i.e. dust and soil) are responsible for the remaining particle extinction. Rayleigh scattering, due to natural scattering of air molecules, is shown in blue.

Substantially lower levels of visibility impairment are present on the 20 percent of days with the best visibility conditions (see right bars in Figure 1) relative to the worst days. However, it should be noted that some areas (especially those further south and west) still have significant visibility impairment relative to natural conditions, estimated to be approximately 21-24 Mm -1 

## or 8-9 dv (NESCAUM, 2001a).



# B. Nearby Areas

Areas just to the south of the MANE-VU region also saw improved visibility conditions in 1999 over what has been reported previously for 1997. Figure 2 shows average reconstructed light extinction for the 20 percent of days with the worst and best visibility conditions at IMPROVE monitors at Class I areas in Virginia and West Virginia. These values represent, on average, a 3 dv improvement in the worst 20 percent of visibility conditions in 1999 relative to 1997 values. Similar to the MANE-VU Class I areas, an analysis of the 2000 data will be required to know whether or not the trend has continued.

11 

### These calculations use a climatological monthly mean relative humidity adjustment factor consistent with recent USEPA guidance on the subject. Discussion of how these factors differ from alternatives is presented in section IV of this memorandum.

## 8 Figure 2: Speciated contribution to total atmospheric light extinction in or near Class I areas in Virginia and West Virginia on 20 percent of days with the worst (left bar) and best(right bar) visibility conditions during 1999.

Sulfate

Elemental Carbon Nitrate
Organic Carbon

Rayleigh Crustal Material 146 Mm -1 

### / 42 Mm -1 

## Dolly Sods/Otter Creek

191 Mm -1 

### / 49 Mm -1 

## James River Face 151 Mm -1 

### / 32 Mm -1 

## Shenandoah

## 9 

# IV. Visibility Trends

As discussed in the last section, 1999 visibility conditions appear to have been better than those of other recent years. We caution the reader, however, that an analysis of the meteorological conditions in 1999 and earlier years should be performed before concluding that a significant improvement in visibility conditions (on the order of 2 dv) has occurred as a result of reductions in anthropogenic emissions. The one-in-three day data collection schedule of the IMPROVE program may have also contributed, in part, to an improved trend if the worst visibility days happened to occur mostly on days when no sampling was taking place. The trend charts shown in this report (see Figure 3-12 later in this section) reflect the improved visibility relative to prior years, but additional years of data will be required to determine if this trend is sustained or whether the improvement was temporary.

Determining what role meteorology plays in year to year variation of average visibility conditions is complicated. Individual terms in the equation for reconstructed light extinction are proportional to the fine particle mass as well as the relative humidity correction factor and, therefore, reconstructed extinction will be sensitive to year-to-year variation in both of these factors. 12 

## In simplified terms, ambient fine particle mass is a function of emissions, transport, photochemistry, and deposition, which vary in time.

Relative humidity also varies year-to-year, but recent guidance on tracking reasonable progress (USEPA, 2001a) suggests a method for removing this variability. By using climatological mean relative humidity adjustment factors, the sensitivity of reconstructed light extinction to interannual changes in relative humidity is removed completely. Essentially, the same humidity conditions are assumed to be experienced every year and thus any difference in visibility conditions is due to emissions or other meteorological factors. The advantage is that we can remove one confounding factor for verification of a regional haze control. The disadvantage is that we are not calculating precise visibility conditions in any given year.



# A. Comparison of Timescales for Averaging Relative Humidity

In order to understand the differences between the use of climatological annual, climatological monthly or day-specific relative humidity adjustment factors, we have calculated reconstructed light extinction in three different ways. Consistent with previous NESCAUM reports and recommendations presented in the FLAG report, annual average relative humidity adjustment factors 13 

## have been used to calculate average visibility (in

12 

### Terms are also proportional to the dry scattering efficiency of the particular material, but this is a physical characteristic of each component and not subject to temporal variation.
13 

### FLAG recommend the use of annual average relative humidity adjustment factor for screening analysis only. For detailed analyses, the use of daily average factors is preferred, when such data is available. The FLAG final report (FLAG, 2000) presents seasonal average factors for use when daily humidity data is unavailable. It should be noted that the annual and seasonal average factors presented in the FLAG final

## 10 deciviews) on the twenty percent of days with the worst and best visibility conditions. 14 

## These calculations have been repeated using monthly site-specific adjustment factors developed and recommended by USEPA in their recent guidance on tracking progress under the regional haze rule (USEPA, 2001a). In addition, daily relative humidity adjustment factors (based on actual humidity data as opposed to climatological means) were used to calculate reconstructed extinction values. All three techniques are compared in the following series of charts showing trends in the best and worst visibility at IMPROVE sites throughout the Northeast and Mid-Atlantic States (presented from northeast to southwest).

All of the values shown in these charts represent the simple average of visibility values (in dv) from the twenty percent of days with the worst and best visibility conditions each year. The difference between the two (or sometimes three) curves shown for worst visibility conditions at each site has to do with the averaging time used for relative humidity adjustment factor. For the blue curves, a single site-specific annual mean value for the relative humidity adjustment factor was used for the whole year. These values were obtained from the final Phase I Report of the Federal Land managers’ Air quality related values workGroup (FLAG, 2000). For the red curves, 12 site-specific climatological monthly mean values were used for days in the corresponding month. These values were provided by the USEPA contractor (SAIC, personal communication) and were the basis for relative humidity adjustment factors listed in the recent USEPA guidance document (USEPA, 2001a). When measured daily relative humidity data was available (see Acadia and Camp Dodge Charts) the calculations were repeated using these data directly (green curves). Reconstructed light extinction was then calculated, converted to deciviews, and ranked from greatest to least. The twenty percent of days with the highest and lowest deciview values were usually the same, but not always. There were some instances when the use of a monthly (or daily) average relative humidity adjustment factor elevated a day (or dropped a day) into (or out of) the top twenty percent of the ranking. Appendix A contains a table of annual and monthly climatological mean values for relative humidity and the appropriate adjustment factors which were used for these calculations. Note that the FLAG reports do not list an annual correction factor nor relative humidity values for Washington, D.C. We have assumed that relative humidity and the associated correction factor for the Washington, D.C. IMPROVE monitor would be relatively close to values estimated for Shenandoah National Park and have used 3.0 for the annual value of the relative humidity correction factor for these calculations.

Results differ between sites. At most sites, visibility impairment is consistentlyunderestimated on the worst days and overestimated on the best days using the annuallyaveraged factors relative to the values derived using monthly factors. However, the degree of difference is relatively small and at many sites (e.g. Lye Brook, Brigantine and

phase I report differ substantially from those presented in the draft phase I report (FLAG, 1999) andsubsequently used by NESCAUM in earlier assessments of visibility conditions in the Northeast and MidAtlantic region. The factors presented in the final phase I report are used here and are compared to the earlier values in Table A.1 and A.2 in Appendix A.
14 

### See footnote 10 for a specific definitions of 20 percent worst and best conditions.

## 11 Washington D.C.) the two methods produce very similar results on the worst days. The two methods are in strong agreement at virtually all sites on the best visibility days (exceptions being Washington D.C. and James River Face). The two sites for which daily humidity data were available show that use of a daily factor produces values that are significantly higher (in dv) than those calculated using other methods. These results show that averaging humidity across a month or a year will tend to moderate the influence of hygroscopic aerosol in calculations of extinction. However, such a relationship has not been observed elsewhere (USEPA, 2001a, pg. 6-4) and is worthy of additional investigation.

## 12 Figure 3: Worst (top) and best (bottom) visibility trends at Moosehorn Wilderness Area

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best



## Figure 4: Worst (top) and best (bottom) visibility trends at Acadia National Park

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst Daily Worst
Annual Best Monthly Best Daily Best

## 13 Figure 6: Worst (top) and best (bottom) visibility trends at Lye Brook Wilderness Area

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best 

## Figure 5: Worst (top) and best (bottom) visibility trends at Camp Dodge (IMPROVE monitor near Great Gulf and Presidential Range – Dry River Wilderness Areas)

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst Daily Worst
Annual Best Monthly Best Daily Best



### note: The Camp Dodge IMPROVE monitor collects data in summertime only.

## 14 Figure 8: Worst (top) and best (bottom) visibility trends at Washington D.C.

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best



### note: The Washington D.C. IMPROVE data reflect urban conditions 

## Figure 7: Worst (top) and best (bottom) visibility trends at Brigantine Wilderness Area

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best

## 15 Figure 10: Worst (top) and best (bottom) visibility trends at Dolly Sods Wilderness Area

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best 

## Figure 9: Worst (top) and best (bottom) visibility trends at Shenandoah National Park

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best

## 16 Figure 12: Worst (top) and best (bottom) visibility trends at Great Smoky Mountain National Park

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best 

## Figure 11: Worst (top) and best (bottom) visibility trends at James River Face Wilderness Area

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Deciviews (DV) Annual Worst Monthly Worst
Annual Best Monthly Best

## 17 

# B. Five-year average trends

USEPA’s Draft Guidance on Tracking Progress Under the Regional Haze Rule (USEPA, 2001a) specifically requires the use of five-year averages for determining success with visibility goals in State Implementation Plans. As opposed to the trends shown in the preceding section, trends in five-year averages are more difficult to discern as each point represents five years worth of data, and thus any interannual variability is smoothed to a much greater degree.

Figure 13 shows trend lines for the twenty percent worst visibility days calculated using climatological mean monthly relative humidity adjustment factors as advocated in the guidance document. The year specified on the x-axis represents the middle year of the five used for each data point. Thus the data record ends with 1997 which is based on data from the years 1995-1999. Trends for the twenty percent best days have also been calculated using five-year averages and are shown in Figure 14. In general, we see that visibility has remained unchanged or improved at most IMPROVE monitoring sites on the best and worst visibility days. The exception is Great Smoky Mountain National Park where significant degradation of visibility conditions is apparent.

Figure 13: Worst day visibility trends based on five-year average values of visibility (dv) for MANE-VU and nearby Class I areas.

20 22 24 26 28 30 32 34

1990 1991 1992 1993 1994 1995 1996 1997Deciviews (DV)

ACAD BRIG

CAMP DOSO

GRSM JEFF

LYBR MOOS

SHEN WASH

## 18 

# C. Trends in Composition on the Best and Worst Visibility Days

In addition to looking at trends in overall visibility conditions, it is interesting to look at trends in extinction from individual components of fine particles. Figures 13-18 show speciated contribution to extinction for three IMPROVE sites in the MANE-VU region across years for which data was available. The three sites shown are Acadia National Park, Lye Brook Wilderness Area, and Brigantine Wilderness Area. Climatological monthly mean relative humidity adjustment factors were used for these calculations of species specific atmospheric extinction.

On the best visibility days, a slight reduction in the extinction due to sulfates and nitrates is evident. 15 

## No apparent trends are evident in organic carbon or crustal material; however, crustal material does exhibit a significant amount of variability. This variability in what is ostensibly a natural phenomenon, may be indicative of meteorological

15 

### This finding is further supported by EPA’s 1999 Emissions and Air Quality Trends Report which found visibility trends in sulfate and nitrate levels were decreasing on the best and “middle” twenty percent of days. 

## Figure 14: Best day visibility trends based on five-year average values of visibility (dv) for MANE-VU and nearby Class I areas.

0 5 10 15 20 25

1990 1991 1992 1993 1994 1995 1996 1997Deciviews (DV)

ACAD BRIG

CAMP DOSO

GRSM JEFF

LYBR MOOS

SHEN WASH

## 19 variability. Elemental carbon may have decreased slightly at Acadia over the 1988-99 time period.

There appears to be a slight reduction in nitrate extinction on the worst days, but trends in sulfate – the principal contributor to visibility impairment at all sites – are difficult to discern. The second largest contributor to extinction at all three sites, organic material, has remained relatively unchanged during the period.

## 20 Figure 13: Speciated contribution to extinction observed at Acadia National Park on the 20 percent of days with the worst visibility between 1988 and 1999.

0 20 40 60 80 100 120 140 160 180

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh



## Figure 14: Speciated contribution to extinction observed at Acadia National Park on the 20 percent of days with the best visibility (note difference in scale) between 1988 and 1999.

0 10 20 30 40 50 60

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh

## 21 Figure 15: Speciated contribution to extinction observed at Lye Brook Wilderness Area on the 20 percent of days with the worst visibility between 1993 and 1999.

0 20 40 60 80 100 120 140 160 180

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh



## Figure 16: Speciated contribution to extinction observed at Lye Brook Wilderness Area on the 20 percent of days with the best visibility (note difference in scale) between 1993 and 1999.

0 10 20 30 40 50 60

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh

## 22 Figure 17: Speciated contribution to extinction observed at Brigantine Wilderness Area on the 20 percent of days with the worst visibility between 1993 and 1999.

0 20 40 60 80 100 120 140 160 180

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh



## Figure 18: Speciated contribution to extinction observed at Brigantine Wilderness Area on the 20 percent of days with the best visibility (note difference in scale) between 1993 and 1999.

0 10 20 30 40 50 60

Extinction (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sulfate
Nitrate
OC
Crustal
EC Rayleigh

## 23 

# V. Comparing Optical vs. Aerosol Monitoring Techniques

The transmissometer and nephelometer are the most common instruments used for optical monitoring. Transmissometers operate over an open path of 1 km to 10 km and measure total light extinction (b ext 

## ) by determining the loss of light (due to scattering and absorption of the intervening atmosphere) from an artificial light source of known luminescence at a fixed distance. Nephelometers measure light scattering (b scat 

## ), which is responsible for most light extinction in rural areas of the eastern U.S. Nephelometers operate over a very short open path (few inches) and are easier to install and maintain than transmissometers. However, they measure only a portion (albeit a large majority) of total light extinction. Light absorption (b abs 

## ) can be measured continuously by aethalometers and other instruments, which measure the amount of light absorbed by particles collected on a filter. Absorption is typically estimated from the concentration of elemental carbon collected on IMPROVE filters or by subtracting nephelometer data (b scat 

## ) from transmissometer data (b ext 

## ). Relatively few aethalometers have been deployed in the field.

Reconstructed light extinction obtained from aerosol monitoring techniques compares fairly well with light attenuation as measured by transmissometers and nephelometers; however, the level of agreement is dependant on how relative humidity is treated in the calculation (Malm, 2000). In order to understand the differences between optical and aerosol techniques, reconstructed light extinction was compared to measured extinction obtained by the IMPROVE program.

Figure 19 shows reconstructed light extinction plotted against measured extinction derived from transmissometer data at Acadia National Park between 1988 and 1993. The reconstructed extinction values plotted in Figure 19 are based on calculations using climatological monthly mean relative humidity factors. Significantly more data were available for this type of comparison from the Shenandoah National Park IMPROVE monitor. Figure 20 shows similar data for this more southerly location during 1999 only. A review of the data indicates that some values which would be considered among the twenty percent worst based on reconstructed light extinction would not qualify among the twenty percent worst days based on measured light extinction and vice versa. While this conclusion can not be definitively drawn due the high number of unpaired values, it does point to a potential inaccuracy incurred by the use of reconstructed light extinction. 16

16 

### It should be noted that perfect agreement between reconstructed light extinction based on aerosol measurements and light extinction measured directly by a transmissometer are not expected to give perfect agreement since aerosol measurments are collected at a single ground-level site, whereas a transmissometer provides the integrated visibility conditions across a line of site, usually somewhat above the surface.

## 24 Figure 19: Comparison of measured extinction (transmissometer data) to reconstructed

light extinction (b ext 

## ; calculated using climatological mean monthly relative humidity adjustment factors) at Acadia National Park between 1988 and 1993.

0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

Transmisometer b extReconstructed b ext



## Figure 20: Comparison of measured extinction (transmissometer data) to reconstructed

light extinction (b ext 

## ; calculated using climatological mean monthly relative humidity adjustment factors) at Shenandoah National Park during 1999.

0 50 100 150 200 250 300

0 50 100 150 200 250 300

Transmisometer b extReconstructed b ext

## 25 Some IMPROVE sites also have nephelometers deployed which measure total scattering (b scat 

## ). Figure 21 compares the reconstructed scattering coefficient calculated using climatological mean monthly relative humidity adjustment factors with measured scattering coefficients derived from nephelometer data collected at Acadia during 1997. Figure 22 shows the same figure, with reconstructed scattering coefficients that have been calculated using actual relative humidity data measured at Acadia. This figure shows that the agreement is clearly much better when actual data is used, and suggests that the use of monthly average factors may bias the reconstructed scattering values high.

The issues that these figures raise with respect to the use of climatological mean monthly factors are complex. By using climatological monthly values, any year to year variation in visibility which can be attibuted to interannual relative humidity variation is removed. Hence, reasonable progress calculations based on climatological mean factors will specifically track that portion of any visibility improvement which is due to emissions reductions of haze contributing pollutants. If climatological mean values are used consistently for the baseline period and for future calculations of visibility conditions, then any bias due to the use of monthly average factors on reconstructed extinction values is likely to be consistent across both time periods (i.e. extinction calculated for the baseline period and for out years are both likely to be off in the same direction). Given that the deciview is the metric upon which “rate of progress” calculations are based, and it is logarithmically related to extinction, the difference between two equally biased extinction values, will not necessarily translate into two equally biased deciview values. Therefore, further study is required to understand the effect that these potential biases may introduce in “rate of progress” calculations for setting visibility goals.

## 26 Figure 21: Comparison of measured scattering (nephelometer data) to reconstructed scattering (b scat 

## ; calculated using climatological mean monthly relative humidity adjustment factors) at Acadia National Park during 1997.

0 50 100 150 200 250

0 50 100 150 200 250 Nephalometer bsp (Mm-1)Reconstructed bsp (Mm-1)



## Figure 22: Comparison of measured scattering (nephelometer data) to reconstructed scattering (b scat 

## ; calculated using daily relative humidity adjustment factors) at Acadia National Park during 1997.

0 30 60 90 120 150

0 30 60 90 120 150 Nephalometer bsp (Mm-1)Reconstructed bsp (Mm-1)

## 27 

# VI. Summary

The IMPROVE network has generated a significant quantity of data for the MANE-VU Class I areas and those nearby. Analysis of these data continues to be an important source of information on baseline visibility conditions and year-to-year variability. The latest IMPROVE results indicate that MANE-VU Class I areas experienced significantly improved visibility in 1999 over 1997, the most recent year previously assessed by MANE-VU. The reasons for this improvement are not known definitively; however, meteorological variability and the IMPROVE sampling schedule may have played a role in addition to changes in anthropogenic emissions. Analysis of data from subsequent years will be required to determine if a statistically significant trend has been established or whether 1999 was a temporary improvement.

Differences in annual average visibility conditions (calculated in deciviews) are apparent depending on how relative humidity adjustment factors are handled in the calculations. Annual and monthly climatological mean values of the relative humidity adjustment factor have been compared against factors calculated for daily average relative humidity measured at two sites. This limited analysis of the data indicates that climatological mean values may underestimate visibility conditions on the worst days, however, other studies have not supported this conclusion.

While overall trends in visibility have shown marginal improvements, trends in some of the individual components of fine particulate matter measured at IMPROVE sites in the MANE-VU region have not been observed. Continued analysis of IMPROVE data from the regional haze baseline period (2000-2004) should help in identifying any trends in specific components of fine particulate if they exist. Ideally, such analysis should attempt to account for variations in meteorology not accounted for by the use of climatological relative humidity adjustment factors.

Finally, a comparison of measured and reconstructed light extinction has shown reasonable agreement between these techniques at some locations. Further study is required to fully understand the sensitivity of calculated rates of progress to the use of climatological mean values of relative humidity adjustment factors versus daily averages of observed humidity.

## 28 References

FLAG (Federal Land Managers’ Air Quality Related Values Workgroup), Draft Phase I Report, U.S. Forest Service, National Park Service, and U.S. Fish and Wildlife Service, October, 1999.

FLAG (Federal Land Managers’ Air Quality Related Values Workgroup), Phase I Report, U.S. Forest Service, National Park Service, and U.S. Fish and Wildlife Service, available online: http://www2.nature.nps.gov/ard/flagfree/FLAG--FINAL.pdf, December, 2000.

Malm, W.C. et al., Spatial and Seasonal Patterns and Temporal Variability of Haze and Its Constituents in the United States: Report III , Cooperative Institute for Research in the Atmosphere. Colorado State University, Ft. Collins, CO, 2000.

NESCAUM, Regional Haze and Visibility in the Northeast and Mid-Atlantic States

, Northeast States for Coordinated Air Use Management, Boston, MA, January, 2001a.

NESCAUM, A Basis for Control of BART-Eligible Sources , Northeast States for Coordinated Air Use Management, Boston, MA, July, 2001b.

Saxena, P., L.M. Hildemann, P.H. McMurry, and J.H. Seinfeld, “Organics alter hygroscopic behavior of atmospheric particles.”, Journal of Geophysical Research

, 100

, 18,755-18,770, 1995.

Sisler, J.F., and W.C. Malm. “Interpretation of Trends of PM 2.5 

## and Reconstructed Visibility from the IMPROVE Network.” J. Air & Waste Mgmt. Assn

. 50 , 775-789, 2000.

Trijonis, J.C., W.C. Malm, M. Pitchford, and W.H. White, Visibility: Existing and Historical Conditions – Causes and Effects , State of Science and Technology Report 24, Volume III of the National Acid Precipitation Assessment Program (NAPAP), Washington: Oct. 1990.

USEPA, Draft Guidance for Tracking Progress Under the Regional Haze Rule

, Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, September 27, 2001a.

USEPA, Draft Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule , Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, September 27, 2001b.

## 29 

# Appendix A – Site specific, climatologically averaged relative humidity and relative humidity adjustment factors and other visibility statistics30Table A.1 – Site specific, climatological average relative humidity (data listed in percent)Site FLAG1999FLAG2000Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecACAD87 82 69 66 64 64 65 66 71 74 74 73 72 72BRIG88 82 67 64 64 62 66 69 72 74 74 71 67 68CAMP88 82 68 65 63 63 64 68 71 73 74 71 70 69DOSO89 83 69 66 65 61 67 70 72 74 74 69 68 71GRSM85 84 70 66 64 62 70 77 76 77 76 72 70 71JEFF83 82 66 63 62 59 67 71 72 74 73 68 65 68LYBR87 82 68 65 64 62 64 67 70 73 74 70 69 69MOOS88 82 70 66 63 64 64 67 70 73 74 72 72 72SHEN87 82 67 64 63 60 66 71 73 75 74 69 65 68WASHN/A N/A 65 61 62 61 67 68 69 71 71 70 65 65Notes: Annual Data based on FLAG draft phase I report (1999) and final report in 2000. Monthly data provided by SAICunder contract to USEPA (see USEPA, 2001a). Note that significant differences exist between the draft and final FLAG annualestimates. Additionally, large differences exist between annual FLAG and the monthly USEPA estimates of relative humidity.However, the differences between FLAG and USEPA correction factors (Table A.2) are much smaller suggesting differentrelationships were used for calculating correction factors from relative humidity data.31Table A.2 – Site specific, climatological average relative humidity adjustment factor (unitless)Site FLAG1999FLAG2000Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecACAD3.8 3.0 3.26 2.94 2.84 3.37 3.11 2.98 3.41 3.83 4.04 3.82 3.56 3.53BRIG3.9 3.0 2.83 2.64 2.73 2.6 3.03 3.16 3.44 3.72 3.64 3.34 2.85 2.83CAMP3.9 3.0 2.78 2.56 2.58 2.77 2.93 3.22 3.49 3.81 3.98 3.42 3.06 2.92DOSO4.3 3.1 2.98 2.79 2.81 2.56 3.12 3.39 3.54 3.87 3.85 3.27 2.97 3.1GRSM3.4 3.2 3.31 3.04 2.91 2.7 3.17 3.86 3.82 3.96 4.24 3.77 3.29 3.44JEFF3.1 3.0 2.83 2.64 2.66 2.43 2.98 3.28 3.39 3.67 3.64 3.15 2.81 2.96LYBR3.8 3.0 2.74 2.56 2.61 2.59 2.82 3.03 3.27 3.56 3.66 3.25 2.93 2.83MOOS3.9 3.0 2.97 2.69 2.66 3.01 2.96 3.1 3.41 3.8 3.91 3.54 3.24 3.2SHEN3.8 3.0 3.07 2.83 2.79 2.53 3.05 3.41 3.54 3.93 3.85 3.21 2.95 3.07WASHN/A N/A 2.74 2.47 2.62 2.42 3.03 2.89 2.98 3.05 3.31 3.14 2.69 2.64Notes: Annual Data based on FLAG draft phase I report (1999) and final report (2000). Monthly data provided by SAIC undercontract to USEPA (see USEPA, 2001a). Note that significant differences exist between the draft and final FLAG annual estimates.Additionally, large differences exist between annual FLAG and monthly USEPA estimates of relative humidity (Table A.1). Asshown here, the FLAG and USEPA correction factors are in much better agreement suggesting different relationships were used forcalculating correction factors from relative humidity data.32Table A.3 – Speciated contribution to atmospheric light extinction in or near Class I Areas in the Northeast and Mid-Atlanticstates on 20% of days with the worst visibility conditions in 1999.Contribution to Extinction fromSulfate(Mm-1)Nitrate(Mm-1)OrganicCarbon(Mm-1)CrustalMaterial(Mm-1)ElementalCarbon(Mm-1)RayleighScattering(Mm-1)TotalExtinction(Mm-1)TotalExtinction(dv)Acadia National Park, ME 78.9 4.3 9.1 4.1 3.9 12.0 112.2 24.2Brigantine Wildlife Refuge, NJ 110.6 7.2 18.5 7.7 6.7 12.0 162.7 27. 9Dolly Sodds /Otter Creek Wilderness,WV 107.2 5.2 15.3 2.4 5.4 10.0 145.5 26.8Great Gulf Wilderness Area, NH 84.2 1.4 11.6 4.3 3.8 10.0 115.2 24.4Great Smoky Mountains NationalPark, NC 168.2 2.0 19.4 5.4 5.3 10.0 210.2 30.5Jefferson/James River FaceWilderness Area, VA 142.7 3.5 23.7 3.3 8.2 12.0 193.5 29.6Lye Brook Wilderness Area, VT 85.9 4.8 11.3 2.1 4.8 10.0 118.9 24.8Moosehorn Wildlife Refuge, ME 59.5 4.6 8.7 2.2 3.5 12.0 90.5 22.0Shenandoah National Park, VA 110.5 4.1 17.3 4.1 5.3 10.0 151.2 27.2Washington, DC 113.2 10.1 24.5 3.7 12.8 12.0 176.3 28.733Table A.4 – Speciated contribution to atmospheric light extinction in or near Class I Areas in the Northeast and Mid-Atlanticstates on 20% of days with the best visibility conditions in 1999.Contribution to Extinction fromSulfate(Mm-1)Nitrate(Mm-1)OrganicCarbon(Mm-1)CrustalMaterial(Mm-1)ElementalCarbon(Mm-1)RayleighScattering(Mm-1)TotalExtinction(Mm-1)TotalExtinction(dv)Acadia National Park, ME 7.6 0.9 2.2 1.7 1.0 12.0 25.4 9.3Brigantine Wildlife Refuge, NJ 13.3 3.7 4.6 4.8 2.7 12.0 41.0 14.1Dolly Sodds /Otter Creek Wilderness,WV 18.0 3.0 5.4 2.7 2.4 10.0 41.6 14.3Great Gulf Wilderness Area, NH 6.8 0.7 5.5 3.0 1.5 10.0 27.6 10.1Great Smoky Mountains NationalPark, NC 19.8 2.2 6.9 3.5 3.1 10.0 45.6 15.2Jefferson/James River FaceWilderness Area, VA 19.2 3.2 9.0 2.7 4.6 12.0 50.6 16.2Lye Brook Wilderness Area, VT 6.1 1.1 2.3 0.8 1.0 10.0 21.4 7.6Moosehorn Wildlife Refuge, ME 7.7 1.5 2.4 2.1 1.0 12.0 26.8 9.8Shenandoah National Park, VA 12.3 3.1 3.0 1.9 1.8 10.0 32.1 11.7Washington, DC 18.2 5.0 7.1 2.8 6.8 12.0 52.0 16.534Table A.5 – Worst and best visibility trends at Acadia National Park, ME (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 24.2 24.5 24.8 23.5 23.2 24.0 24.8 22.6 22.8 22.3 22.4 22.4Monthly 24.7 25.0 25.4 24.1 24.2 24.9 25.7 23.2 23.5 23.1 23.2 23.1

WorstDaily 28.3 27.4 27.7 25.9 26.8Annual 10.0 11.4 10.9 11.4 10.4 10.8 10.4 10.2 8.9 10.2 9.5 8.9Monthly 10.2 11.5 10.7 11.2 10.2 10.6 10.2 9.7 8.6 9.9 9.1 8.4

BestDaily 8.7 9.4 8.7 8.4 8.4Table A.6 – Worst and best visibility trends at Brigantine Wildlife Refuge, NJ (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 27.2 28.1 27.5 27.1 27.3 27.4 27.0WorstMonthly 27.7 28.4 28.1 27.2 28.0 28.2 27.5Annual 17.0 16.4 15.4 16.4 15.6 14.5 13.9BestMonthly 16.8 15.9 14.9 16.0 15.2 14.1 13.4Table A.7 – Worst and best visibility trends at Dolly Sodds/Otter Creek Wilderness, WV (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 28.5 28.8 29.0 27.5 28.3 28.5 25.7WorstMonthly 29.7 30.0 30.1 28.2 29.7 29.6 26.5Annual 15.8 12.1 13.8 15.1 14.9 12.9 14.2BestMonthly 15.6 11.9 13.3 14.9 14.6 12.8 13.935Table A.8 – Worst and best visibility trends at Great Gulf Wilderness, NH (Total extinction in deciviews, summer only)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 24.9 21.0 24.5 24.7 22.6Monthly 26.6 22.7 25.9 26.4 24.2

WorstDaily 30.7 27.2 27.2 28.7Annual 10.6 9.5 10.0 8.6 9.8Monthly 11.2 9.8 10.4 8.6 10.1

BestDaily 11.2 10.3 10.2 9.4Table A.9 – Worst and best visibility trends at Great Smoky Mountains National Park, NC (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 26.5 28.7 30.2 27.4 28.4 27.8 28.4 28.5 28.8 29.3 30.4 28.6WorstMonthly 27.1 30.3 31.8 28.7 30.0 29.1 29.8 29.9 30.5 30.9 32.0 30.2Annual 15.1 15.8 15.7 13.3 13.6 14.5 14.0 13.4 15.3 14.8 14.6 15.0BestMonthly 14.3 15.7 15.9 13.2 13.6 14.7 14.0 13.6 15.4 15.0 14.7 15.1Table A.10 – Worst and best visibility trends at Jefferson/James River Face Wilderness, VA (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 29.3 28.5 29.5 29.4 28.7WorstMonthly 30.1 29.1 30.4 30.4 29.4Annual 17.2 17.5 17.4 16.7 16.5BestMonthly 16.6 16.8 16.8 16.0 15.736Table A.11 – Worst and best visibility trends at Lye Brook Wilderness, VT (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 24.5 25.5 24.5 22.1 24.0 24.5 23.6WorstMonthly 24.7 25.7 25.2 22.5 24.1 25.0 24.4Annual 7.9 9.3 8.5 8.2 8.2 6.9 7.5BestMonthly 8.0 9.1 8.5 8.1 8.1 6.8 7.4Table A.12 – Worst and best visibility trends at Moosehorn Wildlife Refuge, ME (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 21.8 21.5 20.8 20.1 20.9WorstMonthly 22.3 21.9 21.6 21.0 21.5Annual 10.3 9.5 9.7 9.5 9.6BestMonthly 9.8 9.0 9.1 8.8 9.0Table A.13 –Worst and best visibility trends at Shenandoah National Park, VA (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 28.5 27.9 28.7 29.1 28.2 27.7 28.1 28.3 27.5 27.4 28.6 26.0WorstMonthly 29.3 29.1 30.1 30.7 29.7 29.3 29.6 29.5 28.8 28.9 30.4 26.9Annual 14.9 12.6 14.6 13.8 12.4 14.5 12.2 13.5 14.4 13.2 11.4 11.6BestMonthly 14.4 12.8 14.6 13.7 12.3 14.5 12.2 13.3 14.5 13.2 11.3 11.537Table A.14 – Worst and best visibility trends at Washington, DC (Total extinction in deciviews)HumidityFactor1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Annual 29.9 28.7 32.6 32.4 32.2 30.8 30.0 30.5 29.8 29.1 28.6WorstMonthly 29.6 28.5 32.6 32.4 32.0 30.8 29.8 30.1 29.8 29.1 28.4Annual 20.7 20.8 21.1 19.5 21.5 20.5 18.2 18.7 18.4 17.3 16.8BestMonthly 20.2 19.9 20.5 18.8 20.7 19.5 17.3 18.0 17.5 16.6 15.9Table A.15 – Worst visibility trends based on five-year average (Total extinction in deciviews)Site 1988-19921989-19931990-19941991-19951992-19961993-19971994-19981995-1999Acadia National Park, ME 24.68 24.73 24.86 24.41 24.30 24.08 23.73 23.21Brigantine Wildlife Refuge, NJ 27.89 27.98 27.81Dolly Sodds /Otter Creek Wilderness,WV25.16Great Gulf Wilderness Area, NH 29.56 29.54 28.82Great Smoky Mountains NationalPark, NC 29.58 29.97 29.88 29.49 29.84 30.03 30.61 30.70Jefferson/James River FaceWilderness Area, VA29.90Lye Brook Wilderness Area, VT 24.42 24.48 24.23Moosehorn Wildlife Refuge, ME21.66Shenandoah National Park, VA 29.77 29.78 29.88 29.76 29.37 29.21 29.43 28.89Washington, DC 31.03 31.26 31.53 31.02 30.50 29.91 29.4338Table A.16 – Best visibility trends based on five-year average (Total extinction in deciviews)Site 1988-19921989-19931990-19941991-19951992-19961993-19971994-19981995-1999Acadia National Park, ME 10.75 10.84 10.56 10.36 9.84 9.78 9.48 9.13Brigantine Wildlife Refuge, NJ 15.75 15.22 14.74Dolly Sodds /Otter Creek Wilderness,WV10.02Great Gulf Wilderness Area, NH 14.07 13.51 13.91Great Smoky Mountains NationalPark, NC 14.55 14.63 14.29 13.83 14.25 14.52 14.52 14.74Jefferson/James River FaceWilderness Area, VA16.37Lye Brook Wilderness Area, VT 8.36 8.13 7.79Moosehorn Wildlife Refuge, ME9.14Shenandoah National Park, VA 13.55 13.57 13.46 13.19 13.34 13.53 12.91 12.77Washington, DC 20.03 19.88 19.37 18.87 18.59 17.78 17.0739Table A.17 – Speciated contribution to extinction observed at Acadia National Park, ME on the 20 percent of days with theworst and best visibility using monthly relative humidity factors (Mm-1)1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Sulfate71.1 83.3 82.0 66.8 86.1 83.3 91.2 64.9 78.7 64.3 76.9 78.9Nitrate9.0 7.6 13.5 9.5 6.7 8.5 8.7 7.6 4.5 6.3 3.9 4.3OrganicCarbon 16.6 15.5 12.3 16.2 10.6 13.8 14.3 14.2 10.7 13.1 10.1 9.1CrustalMatter 5.3 2.5 5.5 4.5 2.2 4.2 5.7 4.7 3.4 4.7 5.6 4.1ElementalCarbon 7.6 7.1 8.0 6.6 5.2 5.4 6.0 4.7 4.4 4.4 4.8 3.9RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0WorstTotalExtinction 121.7 127.9 133.3 115.5 122.8 127.3 137.9 108.1 113.7 104.8 113.3 112.2Sulfate9.9 13.2 11.0 11.4 10.4 11.2 9.2 8.8 7.9 9.0 8.3 7.6Nitrate1.7 1.8 1.4 1.4 1.4 1.4 1.6 1.5 0.9 1.1 1.0 0.9OrganicCarbon 3.1 3.6 2.8 4.1 2.5 2.9 3.4 2.6 2.4 3.1 3.0 2.2CrustalMatter 1.2 1.3 2.8 2.3 1.9 1.9 2.3 2.4 1.7 2.8 1.9 1.7ElementalCarbon 2.2 2.2 1.7 1.7 1.6 1.7 1.6 1.4 1.1 1.1 1.1 1.0RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0BestTotalExtinction 28.0 32.1 29.7 30.8 27.8 29.1 28.2 26.7 23.9 27.0 25.2 23.440Table A.18 – Speciated contribution to extinction observed at Lye Brook Wilderness, VT on the 20 percent of days with theworst and best visibility using monthly relative humidity factors (Mm-1)1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Sulfate84.2 91.5 97.7 59.8 89.1 98.9 85.9Nitrate7.1 9.3 5.5 7.2 5.1 4.2 4.8OrganicCarbon 11.8 12.4 12.0 11.3 10.6 12.0 11.3CrustalMatter 3.0 4.7 2.3 2.5 2.4 0.0 2.1ElementalCarbon 5.5 5.6 5.4 4.8 4.6 5.7 4.8RayleighScattering 10.0 10.0 10.0 10.0 10.0 10.0 10.0WorstTotalExtinction 121.5 133.5 132.9 95.6 121.8 130.8 118.9Sulfate7.1 6.9 6.9 7.8 7.5 5.6 6.1Nitrate1.2 1.8 2.2 1.2 1.3 1.0 1.1OrganicCarbon 2.1 3.0 1.9 2.0 2.2 1.8 2.3CrustalMatter 1.4 2.3 1.4 0.9 1.0 1.2 0.8ElementalCarbon 0.7 1.2 1.2 1.1 0.8 0.7 1.0RayleighScattering 10.0 10.0 10.0 10.0 10.0 10.0 10.0BestTotalExtinction 22.6 25.2 23.7 23.0 22.8 20.3 21.441Table A.19 – Speciated contribution to extinction observed at Brigantine Wildlife Refuge, NJ on the 20 percent of days withthe worst and best visibility using monthly relative humidity factors (Mm-1)1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999Sulfate104.8 91.2 121.7 100.5 122.4 134.3 110.6Nitrate14.5 8.7 13.0 13.2 7.2 2.0 7.2OrganicCarbon 18.1 14.3 15.1 14.3 15.1 17.1 18.5CrustalMatter 5.9 5.7 7.4 12.1 13.2 5.6 7.7ElementalCarbon 8.0 6.0 7.3 7.0 6.0 7.7 6.7RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0WorstTotalExtinction 163.4 137.9 176.4 159.1 175.9 178.7 162.7Sulfate24.6 17.8 19.2 21.3 18.7 17.7 13.3Nitrate5.4 5.5 4.9 4.1 3.6 3.0 3.7OrganicCarbon 6.3 5.5 3.7 4.6 3.8 3.7 4.6CrustalMatter 5.0 6.9 4.9 6.9 7.8 4.4 4.8ElementalCarbon 3.3 3.6 2.2 2.8 2.1 2.5 2.7RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0BestTotalExtinction 54.5 49.3 45.0 49.8 46.1 41.3 39.042