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 baseline1

 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 (PM10)

[MF] = Fine mass (PM2.5)

 

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 (bext) 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, bext, 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 Fe2O are equally

abundant. A factor of

1.16 is used for MgO,

Na2O, H2O, CO2.

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

bext = bSO4 + bNO3 + bOrgC + bSoil + bCoarse + bElemC + bRay

Note that this equation includes Rayleigh scattering, bRay, 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):

bSO4 = 3[(NH4)2SO4]f(RH) 6

bNO3 = 3[NH4NO3]f(RH)

bOrgC = 4[OrgC]

bSoil = 1[Soil]

bCoarse = 0.6[Coarse]

bElemC = 10[ElemC].

The bracketed quantities represent ambient air concentrations expressed in micrograms

per cubic meter (μg/m3

). The numeric coefficients represent “dry” scattering

efficiencies7

 (m2/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, bext.

 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 ((NH4)2SO4) and that all nitrate is in

the form ammonium nitrate (NH4NO3). 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 μm; 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(bext/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 visibility10 (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 factors13 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 consistently

underestimated 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) and

subsequently 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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1999

Deciviews (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 1997

Deciviews (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 1997

Deciviews (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 (bext) 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 (bscat), 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 (babs) 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

(bscat) from transmissometer data (bext). 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 (bext; 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 ext

Reconstructed b ext

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

light extinction (bext; 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 ext

Reconstructed b ext
25

Some IMPROVE sites also have nephelometers deployed which measure total

scattering (bscat). 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 (bscat; 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 (bscat; 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 PM2.5 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 statistics
30

Table A.1 – Site specific, climatological average relative humidity (data listed in per

Site FLAG

1999

FLAG2000

Jan Feb Mar Apr May Jun Jul Aug Sep 

ACAD 87 82 69 66 64 64 65 66 71 74 BRIG 88 82 67 64 64 62 66 69 72 74 CAMP 88 82 68 65 63 63 64 68 71 73 DOSO 89 83 69 66 65 61 67 70 72 74 GRSM 85 84 70 66 64 62 70 77 76 77 JEFF 83 82 66 63 62 59 67 71 72 74 LYBR 87 82 68 65 64 62 64 67 70 73 MOOS 88 82 70 66 63 64 64 67 70 73 SHEN 87 82 67 64 63 60 66 71 73 75 WASH N/A N/A 65 61 62 61 67 68 69 71 

Notes: Annual Data based on FLAG draft phase I report (1999) and final report in 2000. Monthly data p

under contract to USEPA (see USEPA, 2001a). Note that significant differences exist between the draft and finaestimates. Additionally, large differences exist between annual FLAG and the monthly USEPA estimates of relaHowever, the differences between FLAG and USEPA correction factors (Table A.2) are much smaller suggestinrelationships were used for calculating correction factors from relative humidity data.
31

Table A.2 – Site specific, climatological average relative humidity adjustment factor (u

Site FLAG

1999

FLAG2000

Jan Feb Mar Apr May Jun Jul Aug Sep 

ACAD 3.8 3.0 3.26 2.94 2.84 3.37 3.11 2.98 3.41 3.83 4.BRIG 3.9 3.0 2.83 2.64 2.73 2.6 3.03 3.16 3.44 3.72 3.CAMP 3.9 3.0 2.78 2.56 2.58 2.77 2.93 3.22 3.49 3.81 3.DOSO 4.3 3.1 2.98 2.79 2.81 2.56 3.12 3.39 3.54 3.87 3.GRSM 3.4 3.2 3.31 3.04 2.91 2.7 3.17 3.86 3.82 3.96 4.JEFF 3.1 3.0 2.83 2.64 2.66 2.43 2.98 3.28 3.39 3.67 3.LYBR 3.8 3.0 2.74 2.56 2.61 2.59 2.82 3.03 3.27 3.56 3.MOOS 3.9 3.0 2.97 2.69 2.66 3.01 2.96 3.1 3.41 3.8 3.SHEN 3.8 3.0 3.07 2.83 2.79 2.53 3.05 3.41 3.54 3.93 3.WASH N/A N/A 2.74 2.47 2.62 2.42 3.03 2.89 2.98 3.05 3.

Notes: Annual Data based on FLAG draft phase I report (1999) and final report (2000). Monthly data pro

contract to USEPA (see USEPA, 2001a). Note that significant differences exist between the draft and final FLA

Additionally, large differences exist between annual FLAG and monthly USEPA estimates of relative humidity (shown here, the FLAG and USEPA correction factors are in much better agreement suggesting different relationcalculating correction factors from relative humidity data.
32

Table A.3 – Speciated contribution to atmospheric light extinction in or near Class I Areas in the Northea

states on 20% of days with the worst visibility conditions in 1999.

Contribution to Extinction from

Sulfate(Mm-1)

Nitrate(Mm-1)

OrganicCarbon(Mm-1)

CrustalMaterial(Mm-1)

ElementalCarbon(Mm-1)

RayleighScattering(Mm-1)

TE(M

Acadia National Park, ME 78.9 4.3 9.1 4.1 3.9 12.0 Brigantine Wildlife Refuge, NJ 110.6 7.2 18.5 7.7 6.7 12.0 Dolly Sodds /Otter Creek Wilderness,WV 107.2 5.2 15.3 2.4 5.4 10.0 Great Gulf Wilderness Area, NH 84.2 1.4 11.6 4.3 3.8 10.0 Great Smoky Mountains NationalPark, NC 168.2 2.0 19.4 5.4 5.3 10.0 Jefferson/James River FaceWilderness Area, VA 142.7 3.5 23.7 3.3 8.2 12.0 Lye Brook Wilderness Area, VT 85.9 4.8 11.3 2.1 4.8 10.0 Moosehorn Wildlife Refuge, ME 59.5 4.6 8.7 2.2 3.5 12.0 Shenandoah National Park, VA 110.5 4.1 17.3 4.1 5.3 10.0 Washington, DC 113.2 10.1 24.5 3.7 12.8 12.0 
33

Table A.4 – Speciated contribution to atmospheric light extinction in or near Class I Areas in the Northea

states on 20% of days with the best visibility conditions in 1999.

Contribution to Extinction from

Sulfate(Mm-1)

Nitrate(Mm-1)

OrganicCarbon(Mm-1)

CrustalMaterial(Mm-1)

ElementalCarbon(Mm-1)

RayleighScattering(Mm-1)

TE(M

Acadia National Park, ME 7.6 0.9 2.2 1.7 1.0 12.0 Brigantine Wildlife Refuge, NJ 13.3 3.7 4.6 4.8 2.7 12.0 Dolly Sodds /Otter Creek Wilderness,WV 18.0 3.0 5.4 2.7 2.4 10.0 Great Gulf Wilderness Area, NH 6.8 0.7 5.5 3.0 1.5 10.0 Great Smoky Mountains NationalPark, NC 19.8 2.2 6.9 3.5 3.1 10.0 Jefferson/James River FaceWilderness Area, VA 19.2 3.2 9.0 2.7 4.6 12.0 Lye Brook Wilderness Area, VT 6.1 1.1 2.3 0.8 1.0 10.0 Moosehorn Wildlife Refuge, ME 7.7 1.5 2.4 2.1 1.0 12.0 Shenandoah National Park, VA 12.3 3.1 3.0 1.9 1.8 10.0 Washington, DC 18.2 5.0 7.1 2.8 6.8 12.0 
34

Table A.5 – Worst and best visibility trends at Acadia National Park, ME (Total extinction in d

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Annual 24.2 24.5 24.8 23.5 23.2 24.0 24.8 22.6 22.8 22Monthly 24.7 25.0 25.4 24.1 24.2 24.9 25.7 23.2 23.5 23

Worst Daily 28.3 27.4 27.7 25

Annual 10.0 11.4 10.9 11.4 10.4 10.8 10.4 10.2 8.9 10Monthly 10.2 11.5 10.7 11.2 10.2 10.6 10.2 9.7 8.6 9

Best Daily 8.7 9.4 8.7 8

Table A.6 – Worst and best visibility trends at Brigantine Wildlife Refuge, NJ (Total extinction in 

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 27.2 28.1 27.5 27.1 2

Monthly 27.7 28.4 28.1 27.2 2

Best Annual 17.0 16.4 15.4 16.4 1

Monthly 16.8 15.9 14.9 16.0 1

Table A.7 – Worst and best visibility trends at Dolly Sodds/Otter Creek Wilderness, WV (Total extinc

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 28.5 28.8 29.0 27.5 2

Monthly 29.7 30.0 30.1 28.2 2

Best Annual 15.8 12.1 13.8 15.1 1

Monthly 15.6 11.9 13.3 14.9 1
35

Table A.8 – Worst and best visibility trends at Great Gulf Wilderness, NH (Total extinction in deciview

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Annual 24.9 21.0 2Monthly 26.6 22.7 2

Worst Daily 30.7 27.2 2

Annual 10.6 9.5 1Monthly 11.2 9.8 1

Best Daily 11.2 10.3 1

Table A.9 – Worst and best visibility trends at Great Smoky Mountains National Park, NC (Total extin

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 26.5 28.7 30.2 27.4 28.4 27.8 28.4 28.5 28.8 2

Monthly 27.1 30.3 31.8 28.7 30.0 29.1 29.8 29.9 30.5 3

Best Annual 15.1 15.8 15.7 13.3 13.6 14.5 14.0 13.4 15.3 1

Monthly 14.3 15.7 15.9 13.2 13.6 14.7 14.0 13.6 15.4 1

Table A.10 – Worst and best visibility trends at Jefferson/James River Face Wilderness, VA (Total extin

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 29.3 28.5 2

Monthly 30.1 29.1 3

Best Annual 17.2 17.5 1

Monthly 16.6 16.8 1
36

Table A.11 – Worst and best visibility trends at Lye Brook Wilderness, VT (Total extinction in 

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 24.5 25.5 24.5 22.1 2

Monthly 24.7 25.7 25.2 22.5 2

Best Annual 7.9 9.3 8.5 8.2 

Monthly 8.0 9.1 8.5 8.1 

Table A.12 – Worst and best visibility trends at Moosehorn Wildlife Refuge, ME (Total extinction 

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 21.8 21.5 2

Monthly 22.3 21.9 2

Best Annual 10.3 9.5 

Monthly 9.8 9.0 

Table A.13 –Worst and best visibility trends at Shenandoah National Park, VA (Total extinction i

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 28.5 27.9 28.7 29.1 28.2 27.7 28.1 28.3 27.5 2

Monthly 29.3 29.1 30.1 30.7 29.7 29.3 29.6 29.5 28.8 2

Best Annual 14.9 12.6 14.6 13.8 12.4 14.5 12.2 13.5 14.4 1

Monthly 14.4 12.8 14.6 13.7 12.3 14.5 12.2 13.3 14.5 1
37

Table A.14 – Worst and best visibility trends at Washington, DC (Total extinction in deciv

HumidityFactor 1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Worst Annual 29.9 28.7 32.6 32.4 32.2 30.8 30.0 30.5 2

Monthly 29.6 28.5 32.6 32.4 32.0 30.8 29.8 30.1 2

Best Annual 20.7 20.8 21.1 19.5 21.5 20.5 18.2 18.7 1

Monthly 20.2 19.9 20.5 18.8 20.7 19.5 17.3 18.0 1

Table A.15 – Worst visibility trends based on five-year average (Total extinction in decivi

Site 1988-

1992

1989-

1993

1990-

1994

1991-

1995

1992-

1996

1993-

1997

19

1

Acadia National Park, ME 24.68 24.73 24.86 24.41 24.30 24.08 23Brigantine Wildlife Refuge, NJ 27.89 27Dolly Sodds /Otter Creek Wilderness,WV Great Gulf Wilderness Area, NH 29.56 29Great Smoky Mountains NationalPark, NC 29.58 29.97 29.88 29.49 29.84 30.03 30Jefferson/James River FaceWilderness Area, VA Lye Brook Wilderness Area, VT 24.42 24Moosehorn Wildlife Refuge, ME Shenandoah National Park, VA 29.77 29.78 29.88 29.76 29.37 29.21 29Washington, DC 31.03 31.26 31.53 31.02 30.50 29
38

Table A.16 – Best visibility trends based on five-year average (Total extinction in decivie

Site 1988-

1992

1989-

1993

1990-

1994

1991-

1995

1992-

1996

1993-

1997

199

199

Acadia National Park, ME 10.75 10.84 10.56 10.36 9.84 9.78 9Brigantine Wildlife Refuge, NJ 15.75 15Dolly Sodds /Otter Creek Wilderness,WV Great Gulf Wilderness Area, NH 14.07 13Great Smoky Mountains NationalPark, NC 14.55 14.63 14.29 13.83 14.25 14.52 14Jefferson/James River FaceWilderness Area, VA Lye Brook Wilderness Area, VT 8.36 8Moosehorn Wildlife Refuge, ME Shenandoah National Park, VA 13.55 13.57 13.46 13.19 13.34 13.53 12Washington, DC 20.03 19.88 19.37 18.87 18.59 17
39

Table A.17 – Speciated contribution to extinction observed at Acadia National Park, ME on the 20 perce

worst and best visibility using monthly relative humidity factors (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Sulfate

71.1 83.3 82.0 66.8 86.1 83.3 91.2 64.9 78.7 6

Nitrate

9.0 7.6 13.5 9.5 6.7 8.5 8.7 7.6 4.5 

OrganicCarbon 16.6 15.5 12.3 16.2 10.6 13.8 14.3 14.2 10.7 1CrustalMatter 5.3 2.5 5.5 4.5 2.2 4.2 5.7 4.7 3.4 ElementalCarbon 7.6 7.1 8.0 6.6 5.2 5.4 6.0 4.7 4.4 RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 1

Worst

TotalExtinction 121.7 127.9 133.3 115.5 122.8 127.3 137.9 108.1 113.7 10

Sulfate

9.9 13.2 11.0 11.4 10.4 11.2 9.2 8.8 7.9 

Nitrate

1.7 1.8 1.4 1.4 1.4 1.4 1.6 1.5 0.9 

OrganicCarbon 3.1 3.6 2.8 4.1 2.5 2.9 3.4 2.6 2.4 CrustalMatter 1.2 1.3 2.8 2.3 1.9 1.9 2.3 2.4 1.7 ElementalCarbon 2.2 2.2 1.7 1.7 1.6 1.7 1.6 1.4 1.1 RayleighScattering 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 1

Best

TotalExtinction 28.0 32.1 29.7 30.8 27.8 29.1 28.2 26.7 23.9 2
40

Table A.18 – Speciated contribution to extinction observed at Lye Brook Wilderness, VT on the 20 perce

worst and best visibility using monthly relative humidity factors (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Sulfate

84.2 91.5 97.7 59.8 8

Nitrate

7.1 9.3 5.5 7.2 

OrganicCarbon 11.8 12.4 12.0 11.3 1CrustalMatter 3.0 4.7 2.3 2.5 ElementalCarbon 5.5 5.6 5.4 4.8 RayleighScattering 10.0 10.0 10.0 10.0 1

Worst

TotalExtinction 121.5 133.5 132.9 95.6 12

Sulfate

7.1 6.9 6.9 7.8 

Nitrate

1.2 1.8 2.2 1.2 

OrganicCarbon 2.1 3.0 1.9 2.0 CrustalMatter 1.4 2.3 1.4 0.9 ElementalCarbon 0.7 1.2 1.2 1.1 RayleighScattering 10.0 10.0 10.0 10.0 1

Best

TotalExtinction 22.6 25.2 23.7 23.0 2
41

Table A.19 – Speciated contribution to extinction observed at Brigantine Wildlife Refuge, NJ on the 20 p

the worst and best visibility using monthly relative humidity factors (Mm-1)

1988 1989 1990 1991 1992 1993 1994 1995 1996 19

Sulfate

104.8 91.2 121.7 100.5 12

Nitrate

14.5 8.7 13.0 13.2 

OrganicCarbon 18.1 14.3 15.1 14.3 1CrustalMatter 5.9 5.7 7.4 12.1 1ElementalCarbon 8.0 6.0 7.3 7.0 RayleighScattering 12.0 12.0 12.0 12.0 1

Worst

TotalExtinction 163.4 137.9 176.4 159.1 17

Sulfate

24.6 17.8 19.2 21.3 1

Nitrate

5.4 5.5 4.9 4.1 

OrganicCarbon 6.3 5.5 3.7 4.6 CrustalMatter 5.0 6.9 4.9 6.9 ElementalCarbon 3.3 3.6 2.2 2.8 RayleighScattering 12.0 12.0 12.0 12.0 1

Best

TotalExtinction 54.5 49.3 45.0 49.8 4
42