Guidance for Estimating Natural Visibility

Conditions Under the Regional Haze Rule

Guidance for Tracking Progress Under the 

Regional Haze Rule
 EPA-454/B-03-004

 September 2003

 Guidance for Tracking Progress

Under the Regional Haze Rule

Contract No. 68-D-02-0261

Work Order No. 1-06

 U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Emissions, Monitoring and Analysis Division

Air Quality Trends and Analysis Group

Research Triangle Park, NC 
DISCLAIMER

This report is a work prepared for the United States Government by Battelle. In no event

shall either the United States Government or Battelle have any responsibility or liability for any

consequences of any use, misuse, inability to use, or reliance upon the information contained

herein, nor does either warrant or otherwise represent in any way the accuracy, adequacy,

efficacy, or applicability of the contents hereof.

ACKNOWLEDGMENTS

The Environmental Protection Agency wishes to acknowledge the assistance and input

provided by the following advisors in the preparation of this guidance document: 

Rodger Ames, National Park Service (Cooperative Institute for Research in the Atmosphere

(CIRA); Shao-Hang Chu/USEPA; Rich Damberg, USEPA; Tammy Eagan, Florida Dept. of

Environmental Protection; Neil Frank, USEPA; Eric Ginsburg, USEPA; Dennis Haddow, U.S.

Fish and Wildlife Service; Ann Hess, Colorado State University; Hari Iyer, Dept. of Statistics,

Colorado State University; Mike Koerber, Lake Michigan Air Directors Consortium; Bill

Leenhouts, U.S. Fish and Wildlife Service; William Malm, National Park Service (CIRA); Debbie

Miller, National Park Service; Tom Moore , Western Regional Air Partnership; Janice Peterson,

U.S. Department of Agriculture, Forest Service; Marc Pitchford, National Oceanic and

Atmospheric Administration, Air Resources Laboratory; Rich Poirot, State of Vermont, Dept. of

Environmental Conservation; Bruce Polkowsky, National Park Service; Tom Rosendahl, 

USEPA; David Sandberg, U.S. Fish and Wildlife Service; Jim Sisler, National Park Service

(CIRA); Tim Smith USEPA; Tammy Eagan, Florida Dept of the Environment ; Ken Walsh,

SAIC.
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TABLE OF CONTENTS

Abbreviations and Acronyms ................................................ vi

Glossary of Terms ........................................................ viii

1. INTRODUCTION

1.1 What is regional haze? .......................................... 1-1

1.2 What is the purpose of this Tracking Progress guidance document 

for the regional haze program? .................................... 1-1

1.3 Does this guidance document apply to Tribal Class I areas as well 

as mandatory Federal Class I areas? ................................ 1-2

1.4 What is the statutory and regulatory basis for the regional haze program? ... 1-3

1.5 What are the initial milestones of the regional haze program? ............. 1-3

1.6 What visibility metric will be used for setting goals and tracking progress? ... 1-5

1.7 What key requirements in the regional haze rule relate to progress

goals for mandatory Federal Class I areas? ........................... 1-5

1.8 How does a State determine the rate of progress it must analyze in 

the progress goal development process? ............................ 1-6

1.9 What other factors should be considered in developing Class I area 

progress goals? ............................................... 1-8

1.10 Would EPA accept a progress goal providing for visibility degradation? ..... 1-9

1.11 What are the regional haze rule requirements for progress reviews 

and future SIP revisions? ........................................ 1-9

1.12 What are the major analytical tasks involved in addressing specific 

requirements in the regional haze rule regarding tracking progress? ....... 1-10
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1.13 What air quality monitoring is under way to support tracking progress 

toward improving visibility conditions under the regional haze rule? ....... 1-12

1.14 Why haven’t particulate matter monitors been deployed at all mandatory

Federal Class I areas? ......................................... 1-13

1.15 Does this guidance pertain to tracking of Class I area changes in visibility 

by western States submitting regional haze plans under Section 51.309 

of the regional haze rule? ....................................... 1-14

1.16 Does this guidance on Tracking Progress address all of the required 

elements of the 5-year progress reviews required under the regional 

haze rule? .................................................. 1-14

1.17 What information is provided in the rest of this guidance document? ....... 1-15

2. SUMMARY OF TRACKING PROGRESS CALCULATION PROCEDURES

2.1 What is the purpose of this section of the guidance document? ............ 2-1

2.2 What is the sequence of steps needed to calculate data for tracking progress? . 2-1

2.3 This 10-step process focuses on using complete years of data. What 

if an incomplete year would obviously have been a particularly bad or 

good visibility year? ............................................ 2-9

3. CALCULATION OF HAZE

3.1 What causes haze? ............................................. 3-1

3.2 How are haze levels calculated? ................................... 3-1

3.3 How are the monitoring data used for the calculation of bext obtained? ...... 3-2

3.4 What are the species specific scattering efficiencies for aerosol components? . 3-4

3.5 What effect does relative humidity have on the haze levels? .............. 3-6

3.6 How are the f(RH) values determined? ............................. 3-10
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3.7 How does light absorption contribute to light extinction? ............... 3-12

3.8 How is the total light extinction calculated? ......................... 3-12

3.9 How are deciview values calculated? .............................. 3-13

3.10 Should outliers in the data be excluded? ............................ 3-14

4. PROCEDURES FOR COMPARING 5-YEAR PERIODS

4.1 How are the daily deciview values, calculated as described in Section 3, 

used to track progress in improving visibility? ......................... 4-1

4.2 How are the selection and averaging of the best and worst days in 

each year done? ............................................... 4-1

4.3 How are the 5-year deciview averages determined? .................... 4-2

4.4 What is the nature of the comparison between 5-year average 

deciview values? ............................................... 4-2

4.5 What if siting or procedural changes are implemented at an IMPROVE site? . 4-2

4.6 What if changes are made in the sites selected to cover a mandatory 

Federal Class I area? ............................................ 4-3

4.7 Are trends in the individual species important, as well as the overall 

trend in visibility? .............................................. 4-3

5. References ............................................................ 5-1

6. Appendix A - Monthly Site-Specific f(rh) Values for Each

 Mandatory Federal Class I Areas ..................................... A-1

7. Appendix B - Analysis of the Effect of Correlation 

Between f(RH) and SO4 and f(RH) and NO3 on Deciview 

Calculations Using f(RH) ............................................. B-1
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List of Figures

Figure 1-1 Example of method for determining Mandatory Federal Class I area rate of 

progress to be analyzed in SIP development process .................... 1-7

Figure 1-2 Expanded IMPROVE Visibility Monitoring Network .................. 1-13

Figure 2-1 Summary of Step-by-Step Process for Tracking Progress Calculations ...... 2-3

Figure 3-1 Summary of Process to Calculate Haze Index ......................... 3-2

Figure 3-2 Smoothed Ammonium Sulfate Growth Curve ......................... 3-8

Figure B-1 Boxplots of yearly percentage errors pSO4yearly for 20 

selected IMPROVE sites ....................................... B-10

Figure B-2 Boxplots of pSO4mo nth ly for 20 selected IMPROVE sites ................. B-11

Figure B-3 Boxplots of pSO3yearly for 20 selected IMPROVE sites .................. B-12

Figure B-4 Boxplots of pNO3monthly for 20 selected IMPROVE sites ................. B-13

Figure B-5 Boxplots of pSO4NO3yearly for 20 selected IMPROVE sites .............. B-14

Figure B-6 Boxplots of pSO4NO3monthly for 20 selected IMPROVE sites .............. B-15

Figure B-7 Comparison of percentage errors in average extinction for 

LOWEST 20% extinction days ................................... B-16

Figure B-8 Comparison of percentage errors in average extinction 

for HIGHEST 20% ........................................... B-17

Figures B-9 Comparison of percentage errors in average deciview 

for LOWEST 20% extinction days. ............................... B-18

Figure B-10 Comparison of percentage errors in average deciview 

for HIGHEST 20% extinction days ............................... B-19
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List of Tables 

Table 1-1 Deployment of IMPROVE Sites, 1999-2001 ......................... 1-4

Table A-1 Values for f(RH) determinted from the growth of ammonium sulfate ....... A-4

Table A-2 Recommended Monthly Site-Specific f(RH) Values for Each Mandatory 

Federal Class I Area, Based on the Representative IMPROVE Site

Location ..................................................... A-6

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal 

 Class I Area, Based on the Centroid of the Area

 (Supplemental Information) ..................................... A-11
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Abbreviations and Acronyms

bag - Light extinction coefficient associated with the absorption by gases

bap - Light extinction coefficient associated with the absorption by particles

bext - Total light extinction coefficient

bsg - Light extinction coefficient associated with the scattering by gases

bsp - Light extinction coefficient associated with the scattering by particles

BART - best available retrofit technology

CAA – Clean Air Act

CAAA – 1990 Clean Air Act Amendments

CASTNet - Clean Air Status and Trends Network

CIRA – Cooperative Institute for Research in the Atmosphere, Colorado State University

CM - Coarse mass

dv - Deciview - unit of the haze index

EPA – United States Environmental Protection Agency

f(RH) – Relative humidity adjustment factor

GCVTC – Grand Canyon Visibility Transport Commission

IMPROVE – Interagency Monitoring of Protected Visual Environments

LAC - Light absorbing carbon

Mm-1 - Inverse megameter (10-6 m-1)

MARAMA - Mid-Atlantic Regional Air Management Association

NAAQS – National Ambient Air Quality Standards

NESCAUM - Northeast States for Coordinated Air Use Management

NO2 – Nitrogen dioxide

NOAA - National Oceanic and Atmospheric Administration
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NPS – United States Department of the Interior, National Park Service

OC - Organic carbon

OMC - Organic carbon mass

OP - Pyrolized organics

PIXE - Proton induced x-ray emission spectroscopy

PM – Particulate matter

PM2.5 – Particulate matter with an aerodynamic diameter less than 2.5 microns

PM10 – Particulate matter with an aerodynamic diameter less than 10 microns

RH - Relative humidity

RPO - Regional Planning Organization

SIP – State Implementation Plan

STAPPA - State and Territorial Air Pollution Program Administrators

TDMA - Tandem differential mobility analyzers

TIP - Tribal implementation plan

TOR - Thermal optical reflectance

WESTAR - Western States Air Resources Council
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Glossary of Terms

Aerosols – Suspensions of tiny liquid and/or solid particles in the air.

Coarse mass – Mass of particulate matter with an aerodynamic diameter greater than 2.5 microns but less

than 10 microns.

Deciview (dv) - The unit of measurement of haze, as in the haze index (HI) defined below.

Fine particulate matter – particulate matter with an aerodynamic diameter less than 2.5 microns(PM2.5).

Fine soil – Particulate matter composed of pollutants from the Earth’s soil, with an aerodynamic diameter

less than 2.5 microns. The soil mass is calculated from chemical mass measurements of fine aluminum,

fine silicon, fine calcium, fine iron, and fine titanium as well as their associated oxides.

Haze index (HI) – A measure of visibility derived from calculated light extinction measurements that is

designed so that uniform changes in the haze index correspond to uniform incremental changes in visual

perception, across the entire range of conditions from pristine to highly impaired. The haze index [in units

of deciviews (dv)] is calculated directly from the total light extinction [bext expressed in inverse megameters

(Mm-1)] as follows:

HI = 10 ln (bext/10)

Light absorbing carbon - Carbon particles in the atmosphere that absorb light; also reported as elemental

carbon.

Least-impaired days – Data representing a subset of the annual measurements that correspond to the

clearest, or least hazy, days of the year.

Light extinction - A measure of how much light is absorbed or scattered as it passes through a medium,

such as the atmosphere. The aerosol light extinction refers to the absorption and scattering by aerosols,

and the total light extinction refers to the sum of the aerosol light extinction, the absorption of gases (such

as NO2

), and the atmospheric light extinction (Rayleigh scattering).

Mandatory Federal Class I areas - Certain national parks (over 6,000 acres), wilderness areas (over

5,000 acres), national memorial parks (over 5,000 acres), and international parks that were in existence as

of August 1977. Appendix A lists the mandatory Federal areas. 

Most impaired days – Data representing a subset of the annual measurements that correspond to the

dirtiest, or haziest, days of the year.
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Nitrate – Solid or liquid particulate matter containing ammonium nitrate [NH4NO3

] or other nitrate salts. 

Atmospheric nitrate aerosols are often formed from the atmospheric oxidation of oxides of nitrogen (NOx

).

Organic carbon – Aerosols composed of organic compounds, which may result from emissions from

incomplete combustion processes, solvent evaporation followed by atmospheric condensation, or the

oxidation of some vegetative emissions.

Particulate matter – Material that is carried by liquid or solid aerosol particles with aerodynamic

diameters less than 10 microns (in the discussions of this report). The term is used for both the in situ

atmospheric suspension and the sample collected by filtration or other means.

Rayleigh scattering – Light scattering of the natural gases in the atmosphere. At an elevation of 1.8

kilometers, the light extinction from Rayleigh scattering is approximately 10 inverse megameters (Mm-1).

Relative humidity – Partial pressure of water vapor at the atmospheric temperature divided by the vapor

pressure of water at that temperature, expressed as a percentage.

Sulfate – Solid or liquid particulate matter composed of sulfuric acid [H2SO4

], ammonium bisulfate

[NH4HSO4], or ammonium sulfate [(NH4)2SO4]. Atmospheric sulfate aerosols are often formed from the

atmospheric oxidation of sulfur dioxide.

Total carbon – Sum of the light absorbing carbon and organic carbon.

Visibility impairment – Any humanly perceptible change in visibility (light extinction, visual range,

contrast, coloration) from that which would have existed under natural conditions. This change in

atmospheric transparency results from added particulate matter or trace gases.
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1. INTRODUCTION

1.1 What is regional haze?

Regional haze is visibility impairment caused by the cumulative air pollutant emissions

from numerous sources over a wide geographic area. Visibility impairment is caused by particles

and gases in the atmosphere. Some particles and gases scatter light while others absorb light. 

The net effect is called “light extinction.” The result of these processes is a reduction of the

amount of light from a scene that is returned to the observer, as well as an addition of scattered

light to the sight path, creating a hazy condition. To a viewer, haze can be perceived as a

reduction in the visual clarity of an object.

The primary cause of regional haze in many parts of the country is light scattering

resulting from fine particles (i.e., particulate matter less than 2.5 microns in diameter, referred to

as PM2.5) in the atmosphere. These fine particles can contain a variety of chemical species

including carbonaceous species (i.e., organics and elemental carbon), as well as ammonium

nitrate, sulfates, and soil. Additionally, coarse particles between 2.5 and 10 microns in diameter

can contribute to light extinction. Each of these components can be naturally occurring or the

result of human activity. The natural levels of these species result in some level of visibility

impairment in the absence of any human influences and will vary with season, daily meteorology,

and geography. 

1.2 What is the purpose of this Tracking Progress guidance document for the regional haze

program?

This document provides guidance to EPA Regional, State, and Tribal air quality

management authorities and the general public, on how EPA intends to exercise its discretion in

implementing Clean Air Act provisions and EPA regulations, concerning the tracking of progress

under the regional haze program. The guidance is designed to implement national policy on these

issues. Sections 169A and 169B of the Clean Air Act (42) U.S.C. § § 7491,7492 and

implementing regulations at 40 CFR 51.308 and 51.309 contain legally binding requirements. 

This document does not substitute for those provisions or regulations, nor is it a regulation itself. 

Thus, it does not impose binding, enforceable requirements on any party, nor does it assure that

EPA may approve all instances of its application, and thus the guidance may not apply to a

particular situation based upon the circumstances. The EPA and State decision makers retain the

discretion to adopt approaches on a case-by-case basis that differ from this guidance where

appropriate. Any decisions by EPA regarding a particular State implementation plan (SIP)

demonstration will only be made based on the statute and regulations and will only be made

following notice and opportunity for public review and comment. Therefore, interested parties

are free to raise questions and objections about the appropriateness of the application of this
Guidance for Tracking Progress 

Under the Regional Haze Rule 

1Areas designated as mandatory Class I areas are those National Parks exceeding 6,000

acres, wilderness areas and national memorial parks exceeding 5,000 areas, and all international parks

which were in existence on August 7, 1977. Visibility has been identified as an important value in 156 of

these areas. See 40 CFR part 81, subpart D. The extent of a Class I area includes subsequent changes in

boundaries, such as park expansions. [CAA section 162 (a)]. States and tribes may designate additional

areas as Class I, but the requirements of the visibility program under section 169A of the CAA apply only

to "mandatory Class I areas," and do not affect these additional areas. For the purpose of this guidance

document, the term “Class I area” will be used interchangeably with “mandatory Federal Class I area.”

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guidance to a particular situation; EPA will, and States should, consider whether or not the

recommendations in this guidance are appropriate in that situation. This guidance is a living

document and may be revised periodically without public notice. The EPA welcomes public

comments on this document at any time and will consider those comments in any future revision

of this guidance document. 

Readers of this document are cautioned not to regard statements recommending the use of

certain procedures or defaults as either precluding other procedures or information or providing

guarantees that using these procedures or defaults will result in actions that are fully approvable. 

As noted above, EPA cannot assure that actions based upon this guidance will be fully approvable

in all instances, and all final actions may only be taken following notice and opportunity for public

comment.

1.3 Does this guidance document apply to Tribal Class I areas as well as mandatory

Federal Class I areas? 

Not directly, although the procedures for calculating light extinction and tracking visibility

changes over time that are described in this guidance can be used by Tribes that are conducting

their own air quality monitoring using the Interagency Monitoring of Protected Visual

Environments (IMPROVE) protocol. The CAA and the regional haze rule call for the protection

of visibility in 156 “mandatory Federal Class I areas.”1

 Tribes can establish Class I areas for the

purposes of the prevention of significant deterioration program, but the CAA does not provide for

the inclusion of Tribal areas as mandatory Federal Class I areas subject to section 169A and 169B

of the CAA. For this reason, progress goals do not have to be established for Tribal Class I areas. 

However, Tribes may find it advantageous for a number of reasons to participate in

regional planning organizations (RPO) for regional haze and to develop regional haze Tribal

implementation plans (TIPs). Participation in an RPO may allow some Tribes to build capacity 
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See 63 Federal Register 7254 (February 12, 1998), and 40 CFR Part 49.

3

See 45 Federal Register 80084 (December 2, 1980).

4

See 64 Federal Register 35713 (July 1, 1999). See also 40 CFR 51.300-309.

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and enhance their air quality management capabilities. Under the Tribal Air Rule, Tribal

governments may elect to implement air programs in much the same way as States, including

development of Tribal implementation plans.2 

In this way, Tribes can work with other States and Tribes on the development and

adoption of specific emissions reduction strategies designed to protect air quality across a broad

region including Tribal and State lands. 

1.4 What is the statutory and regulatory basis for the regional haze program? 

Section 169A of the 1977 Clean Air Act amendments (CAAA) set forth legislative

requirements for addressing visibility impairment due to air pollution. It established a national

visibility goal to remedy existing impairment and prevent future impairment in 156 National Parks

and wilderness areas across the country designated as mandatory Federal Class I areas. It also

called for EPA to develop regulations requiring State implementation plans (SIPs) to address

visibility. These plans must include a long-term strategy and Best Available Retrofit Technology

(BART) on certain existing sources for making “reasonable progress” toward this goal.

The EPA issued initial visibility regulations in 19803

 that addressed visibility impairment in

a mandatory Federal Class I area that is “reasonably attributable” to a single source or small group

of sources. The EPA subsequently issued regulations to address regional haze (i.e., visibility

impairment caused by emissions from numerous sources located over a broad geographic region),

in 1999.4

 The regional haze rule requires States with mandatory Federal Class I areas to develop

SIPs that include reasonable progress goals for improving visibility in each mandatory Federal

Class I area and emission reduction measures to meet those goals.

1.5 What are the initial milestones of the regional haze program?

After publication of the regional haze rule in 1999, the first step in the implementation

process was the upgrade and expansion of the IMPROVE visibility monitoring network to 110

sites nationally. These sites were selected to represent all mandatory Federal Class I areas, except
Guidance for Tracking Progress 

Under the Regional Haze Rule 

5Bering Sea Wilderness is too remote for routine measurements of the kind employed by the

IMPROVE visibility monitoring network.

6

40CFR51.308 (d) (2) (i). Also, as discussed in the preamble to the regional haze rule (64

FR 35728-9, July 1, 1999), representative monitoring data collected from this network will be used to

establish baseline conditions (for the 2000-2004 period) for each Class I area and to track progress toward

goals established in future SIPs.

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for the Bering Sea Wilderness site5. The expanded IMPROVE monitoring network was deployed

during the 1999-2001 time frame in accordance with Table 1-1.

Representative monitoring data collected from this network will be used to establish

baseline conditions (for the 2000-2004 period) for each Class I area and to track progress toward

goals established in future SIPs.6 One can see from Table 1-1 that 101 (or 92%) of the 110 sites

are expected to have at least 4 complete years of data for the purpose of determining baseline

conditions. Only 9 sites are expected to use 3 years of data to establish baseline conditions.

Table 1-1 Deployment of IMPROVE Sites, 1999-2001

Year

Number of

IMPROVE Sites

Deployed

Number of Years of Data for

Calculating Baseline

Conditions (2000-2004)

1999 60 5

2000 41 4

2001 9 3

TOTAL 110

Most States (and Tribes as appropriate) are expected to submit regional haze SIPs in the

2007-2008 time frame. Nine western States have the option under Section 51.309 of the regional

haze rule to implement the recommendations of the Grand Canyon Visibility Transport

Commission (GCVTC) within the framework of the regional haze rule, provided they submit

initial regional haze SIPs in 2003. Progress reviews are to be conducted every 5 years after SIP

submittal, and comprehensive SIP revisions are required in 2018 and every 10 years thereafter.
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1.6 What visibility metric will be used for setting goals and tracking progress?

As stated at 40 CFR 51.308 (d) (1), baseline visibility conditions, progress goals, and

changes in visibility must be expressed in terms of deciview (dv) units. The deciview is a unit of

measurement of haze, implemented in a haze index (HI) that is derived from calculated light

extinction, and that is designed so that uniform changes in haziness correspond approximately to

uniform incremental changes in perception, across the entire range of conditions, from pristine to

highly impaired. 

The HI is expressed by the following formula:

HI = 10 ln(bext/10)

where bext represents total light extinction expressed in inverse megameters (i.e., Mm-1 =10-6 m-1). 

See Section 3 of this document for further details on calculating HI in dv units from IMPROVE

monitoring data. 

1.7 What key requirements in the regional haze rule relate to progress goals for mandatory

Federal Class I areas? 

In their initial control strategy SIPs due in 2007-8, States are required to adopt progress

goals for improving visibility from baseline conditions (represented by 2000-2004) to 2018

(represented by 2014 to 2018) for each Class I area in the State. A State that does not have any

Class I areas will not establish any progress goals in its SIP, but it is required to consult with other

States having Class I areas that may be impacted by emissions from the State. A State without

any Class I areas will also need to adopt emission reduction strategies to address its contribution

to visibility impairment problems in Class I areas located in other States. 

Specifically, a State is required to set progress goals for each Class I area in the State that:

C provide for an improvement in visibility for the most impaired (i.e., 20% worst)

days over the period of the implementation plan, and

C ensure no degradation in visibility for the least impaired (i.e., 20% best) days over

the same period.

In Class I areas with higher levels of visibility impairment, the conditions on the best days

may still be several deciviews higher than estimated natural conditions. The EPA expects that for

most of these areas, emission reduction strategies to improve visibility conditions on the worst
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days should also lead to improvements on the best days. States should track progress on the best

days as well as the worst days in order to determine if emission reduction strategies lead to an

improvement in the overall distribution of visibility conditions. If a degradation in best day

conditions is observed over time, States should re-evaluate their emission reduction strategies.

The reasonable progress goals must provide for a rate of improvement sufficient to attain

natural conditions by 2064, or justify any alternative to this rate based upon factors listed in 169A

(g) (1) of the Clean Air Act and 308 (d) (1) (i) (A) of the regional haze rule. States will

determine whether they are meeting their goals by comparing visibility conditions from one fiveyear average to another (e.g., 2000-2004 to 2013-2017). In order to conduct the analysis for

setting progress goals, the State should use this Tracking Progress guidance document for

determining 5-year baseline conditions. A separate guidance document addresses methods for

estimating Natural Visibility Conditions (i.e., the ultimate goal of the visibility improvement

program).

1.8 How does a State determine the rate of progress it must analyze in the progress goal

development process? 

In developing any progress goal, the State will need to analyze and consider in its set of

options the rate of improvement between 2004 (when 2000-2004 baseline conditions are set), and

future periods (such as 2018) that, if maintained in subsequent implementation periods, would

result in achieving estimated natural conditions by the year 2064. For example, for an eastern

Class I area for which the 20% worst visibility baseline condition is 29 dv and the estimated

natural condition is 11 dv, the rate of improvement that the State must analyze for establishing the

2018 progress goal is equal to18 dv (i.e., the difference between current and estimated natural

conditions) divided by 60 years (i.e., 2004 to 2064), which equals 0.3 dv per year. Carried out

over 14 years (i.e., 2004 to 2018), this rate of improvement would lead to a reduction in the 20%

worst average value by 4.2 dv.
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For this example, the rate of improvement is calculated as:

or,

Figure 1-1 Example of method for determining Mandatory Federal Class I area rate of progress to

be analyzed in SIP development process ( a HI values for 2004 are based on 2001-2004 data, etc.)

and, carried out over 14 years, this rate would achieve an improvement on the worst days of: 

The State must demonstrate in the SIP whether it finds that this rate of improvement is

reasonable or not, taking into consideration the relevant statutory factors (see next question). If it

finds that this rate is not reasonable, the State shall evaluate alternative rates of progress and

include a demonstration supporting its finding that an alternate rate is reasonable. In order to
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See CAA section 169A (g).

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determine the 2004-2018 progress rate for these analyses, the State will need to calculate 2000-

2004 baseline conditions in accordance with this guidance document and use separate EPA

guidance for estimating Natural Visibility Conditions.

1.9 What other factors should be considered in developing Class I area progress goals? 

Other important issues to be considered in developing Class I area progress goals include

the reasonable progress factors in the CAA, consultation with other States, Tribes and Federal

land managers, and emission reductions due to other Clean Air Act programs. The reasonable

progress factors7 to consider in developing any progress goal are: 

- the costs of compliance;

- the time necessary for compliance;

- the energy and non-air quality environmental impacts of compliance; and

- the remaining useful life of any existing source subject to such requirements.

In this context, non-air quality environmental impacts might include effects on aquatic, terrestrial

or materials damaged from acidic deposition, eutrophication of coastal estuaries from nitrogen

deposition, changes in the deposition of toxic trace metals or organics that may result from

emissions changes. The EPA plans to develop additional guidance on how to address these

factors in the goal-setting process. 

States with mandatory Federal Class I areas are required to develop Class I area progress

goals and consult with other States in developing Class I area progress goals and long-term

strategies to meet these goals. If one State is reasonably anticipated to cause or contribute to

visibility impairment in a Class I area located in another State, the two States are required to

consult with one another on the development of progress goals for the affected Class I area. 

Furthermore, these States must include strategies in their SIPs that address their respective

contributions to the haze in the affected Class I area. A State can take projected emissions

reductions from other States into account in setting specific Class I area goals. This consultation

process is essential because of the regional nature of the haze problem. The EPA supports the

regional planning organization process currently under way to implement the regional haze

program. We expect that much of the consultation, strategy development, apportionment

demonstrations, and technical documentation needed for SIPs of participating States will be

facilitated and developed through the RPO process.
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In developing progress goals, the regional haze rule also requires in paragraph 51.308 (d)

(1) (vi) that States must take into account any emission reduction strategies in place or on the way

in order to meet other Clean Air Act requirements. For example, emission reduction strategies

(e.g., strategies to attain the PM2.5 and ozone NAAQS and national mobile source measures such

as the Tier II or heavy duty diesel regulations) implemented in the State and/or in regions

contributing to visibility impairment in the State’s Class I areas should be taken into account by

the State, as it develops Class I area progress goals for regional haze. Progress goals for regional

haze certainly cannot be any less than the level of visibility improvement expected due to

implementation of emission reduction measures for other programs.

1.10 Would EPA accept a progress goal providing for a reduced rate of visibility

degradation?

Section 169A (a) (4) and other subsections of the Clean Air Act call for reasonable

progress "toward meeting the national goal" of eliminating man-made impairment of visibility. 

Since any progress goal calling for degradation of visibility, even at a modest rate, would not be

progress toward the goal, it is unlikely that EPA could propose to approve any demonstrations

that purport to show further visibility degradation as reasonable progress, (e.g., in situations

where visibility would be expected to degrade, and such projected degradations would be lessened

but not reversed thru proposed emission control strategies).

1.11 What are the regional haze rule requirements for progress reviews and future SIP

revisions?

After the initial SIPs are approved, States will conduct formal progress reviews, in the

form of a SIP revision, every 5 years from the date of SIP submittal (e.g., in 2013 if the initial SIP

is submitted in 2008). Progress will be reviewed for each Class I area by comparing “current”

conditions based on the most recent 5 consecutive years of data to the 2000-2004 baseline value

to determine whether air quality improvements are consistent with the progress goals established

in the SIP. Progress reviews in 2018 and beyond shall also compare the current visibility

conditions to visibility conditions 5 years prior, and to the 2000-2004 baseline value. In each 5-

year review, the State will also check progress in terms of emissions reductions to determine

whether emissions reductions measures contained in the plan have occurred in a timely and

effective manner. 

If progress is not consistent with the visibility and emission reduction goals established in

the previous SIP, the State must evaluate the reason for lack of progress and take any appropriate

action. If the lack of progress is primarily due to emissions from within the State, then the State

must revise its implementation plan within 1 year to include additional measures to make

progress. If the lack of progress is primarily due to emissions from other States, then the State
Guidance for Tracking Progress 

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8

64 Federal Register 35746 (Thursday, July 1, 1999).

1 - 10

must reinitiate the regional planning process to address this problem in the next major SIP

revision (e.g., in 2018). If the State finds that international emissions sources are responsible for a

substantial increase in emissions in any Class I area or causing a deficiency in visibility progress,

the State must submit a technical demonstration to EPA in support of its finding. Similarly, the

State should submit a technical demonstration if the State finds that unusual events (e.g., large

wildfires), have affected visibility progress during the 5-year period.8 Given that progress is

determined based upon long-term averaging, the EPA believes that it is unlikely that such events

will have a significant effect in most cases. See Section 3.10 regarding treatment of outliers and

other data associated with unusual events.

States will be required to conduct a comprehensive SIP revision in 2018 and every 10

years thereafter. This process will involve re-evaluating rates of progress for each mandatory

Federal Class I area within the State and establishing new visibility improvement goals for these

areas. Using the previous example, suppose that the eastern Class I area made only 2 dv of the

planned 4.2 dv of improvement on the worst days (e.g., from 29 to 27 dv) by 2018. If the lack of

progress is due to planned emission reductions that were not implemented or were ineffective,

then the revised SIP must include revised emission reduction measures needed to meet the

original progress goal for 2028 illustrated by Figure 1-1.8 This corresponds to 3 dv per 10 years

plus the 2.2 dv not achieved during the previous implementation period. The revised SIP must

also include revised emission reduction measures needed to meet the new Class I area progress

goals.

1.12 What are the major analytical tasks involved in addressing specific requirements in

the regional haze rule regarding tracking progress?

As noted above, the first step in tracking progress for the regional haze rule is collecting

and analyzing filter samples from IMPROVE network sites. In order to identify the 20% most

impaired and 20% least impaired days in a particular year, a haze index value (in deciview units)

needs to be determined for each 24-hour sample period, and then these values should be sorted

from highest to lowest. Averages (in deciviews) for that year can be calculated for HI values

associated with the 20% most impaired and 20% least impaired days. 

The average HI values for the 20% most impaired and 20% least impaired days in each

year should then be averaged for the five consecutive years 2000-2004 to define baseline

conditions. Similarly, when checking mid-course progress (e.g., in 2013), or for calculation of

current conditions for future SIPs, the annual average values for the 20% most impaired and 20%

least impaired days will be averaged for the 5 most recent years of data available, and then those
Guidance for Tracking Progress 

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values should be compared to the baseline values for that site. For mandatory Federal Class I

areas with multiple representative monitors, separate visibility values and progress goals should be

established for each site representing the area.

In order to facilitate this tracking process, States having one or more mandatory Federal

Class I areas are required by the rule to establish, and update as necessary, three important 

visibility parameters for the 20% best and 20% worst visibility days at each mandatory Federal

Class I area within the State:

C Baseline conditions - Baseline conditions represent visibility for the 20% best and worst

days for the initial 5-year period of the regional haze program. Baseline conditions are

calculated based on monitored data collected during the 2000-2004 period. 

C Current conditions - Current conditions for the best and worst days are calculated from a

5-year average (in deciviews), based on the most recent 5-year block of monitored data. 

Calculations of current conditions for each mandatory Federal Class I area are revised

every 5 years at the time of each periodic SIP revision and would be used to evaluate:

(1) the amount of progress made in relation to the reasonable

progress goals established for that mandatory Federal Class I area; 

(2) the amount of progress made since the last 5-year progress

review, and 

(3) the amount of progress made from the baseline period of the

program (2000-2004).

C Estimate of natural visibility conditions - The CAA sets a national visibility goal of

“remedying existing impairment and preventing future impairment.” Following from the

national goal, the regional haze rule calls for improvements on the worst days to remedy

existing impairment, and no degradation on the best days to prevent future impairment. 

Thus, the ultimate goal of the regional haze program is “natural conditions,”or the

visibility conditions that would be experienced in the absence of human-caused

impairment. Under the haze rule, natural conditions need to be estimated for the 20% best

and 20% worst days. These estimates should represent long-term averages, analogous to

the 5-year averages used to determine baseline conditions and current conditions. A

separate guidance document provides a methodology for developing estimates of natural

visibility conditions for each Class I area. Potential approaches for refining those

estimates are also discussed in that document. 
Guidance for Tracking Progress 

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1.13 What air quality monitoring is under way to support tracking progress toward

improving visibility conditions under the regional haze rule?

The IMPROVE visibility monitoring program was initiated in two mandatory Federal

Class I areas in 1986 and grew to include 30 sites in 1988. The IMPROVE program has been

coordinated and funded through a cooperative multi-organizational approach, with participation

by EPA, the Federal land managers (Department of Agriculture, U.S. Forest Service; Department

of Interior, National Park Service, Fish & Wildlife Service, Bureau of Land Management),

National Oceanic and Atmospheric Administration (NOAA), and multi-state organizations such as

the Mid-Atlantic Regional Air Management Association (MARAMA), Northeast States for

Coordinated Air Use Management (NESCAUM), Western States Air Resources Council

(WESTAR), and The State and Territorial Air Pollution Program Administrators (STAPPA). 

The IMPROVE monitoring protocols include aerosol monitoring of particulate matter mass and

its chemical components, optical monitoring of light scattering or overall light extinction, and

photographic monitoring. Some but not all sites include on-site monitoring of relative humidity. 

Through calendar year 1999, the IMPROVE sampling schedule was one 24-hour aerosol sample

twice a week, on Wednesdays and Saturdays.

In 1999, EPA provided funding for a significant expansion of the IMPROVE network. 

Fully deployed, the network includes aerosol monitoring at a total of 110 mandatory Federal

Class I area sites. The new sites in the expanded network were selected in order to provide

“representative” monitoring for all but one of 156 mandatory Federal Class I areas. New

IMPROVE sites began coming on-line in 1999. Most sites were fully deployed by the end of

2000, although a few did not come online until 2001 (see Table 1-1). In the expanded IMPROVE

network, one 24-hour sample is collected every 3 days, consistent with the sampling schedule for

the Federal Reference Method for the PM-2.5 National Ambient Air Quality Standard (NAAQS). 

Under this schedule, a total of 122 aerosol samples can be collected for each IMPROVE site each

year.

Most of the new IMPROVE sites include aerosol monitoring only. With limited network

funds, priority was given to aerosol monitoring with chemical composition analysis of collected

particulate matter samples. This allows the States, Tribes and Federal land managers to evaluate

changes in visibility impairment and to identify the principal types of emission sources contributing

to the visibility impairment there. Figure 1-2 shows the locations of the monitoring sites in the

expanded IMPROVE network. It should be noted that some States, Tribes, and Federal Land

Managers have funded the operation of additional IMPROVE sites to represent mandatory

Federal Class I areas or other areas of the country. At the time of publication of this guideline,

there are approximately 50 such additional sites known as IMPROVE protocol sites.
Guidance for Tracking Progress 

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1.14 Why haven’t particulate matter monitors been deployed at all mandatory Federal

Class I areas? 

Because of the broad spatial distributions of regional haze, and in order to use monitoring

resources efficiently, EPA determined, in conjunction with State and Federal land managers, that

some neighboring mandatory Federal Class I areas could be represented by a single monitoring

site. 

Figure 1-2 Expanded IMPROVE Visibility Monitoring Network

(Site 106 represents the US Virgin Islands)

In addition, one isolated mandatory Federal Class I area (Bering Sea, an uninhabited and

infrequently visited island 200 miles from the coast of Alaska), was considered to be so remote

from electrical power and people that it would be impractical to collect routine aerosol samples. 

The EPA consulted with the States in order to design a network that was as representative of all
Guidance for Tracking Progress 

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mandatory Federal Class I areas as possible. All mandatory Federal Class I areas (except Bering

Sea) are currently covered by at least one IMPROVE monitoring site, and some are covered by

additional IMPROVE protocol sites. If changes are made in the sites which cover specific

mandatory Federal Class I areas, all the calculations discussed in this document should be

performed for each site the State chose in the SIP’s monitoring strategy to represent the Class I

area.

1.15 Does this guidance pertain to tracking of Class I area changes in visibility by western

States submitting regional haze plans under Section 51.309 of the regional haze rule? 

Yes, any State with a mandatory Federal Class I area should track changes in visibility

according to this guidance, regardless of whether the State has submitted a regional haze

implementation plan under Section 51.308 or Section 51.309. Western States (and Tribes as

appropriate) that are implementing Section 51.309 to improve air quality at the 16 mandatory

Federal Class I areas on the Colorado Plateau, will not be required to set progress goals for these

areas for the 2003-2018 period. But they will be required to track progress in these 16 areas

every five years according to Section 51.309 (d) (10). Progress reviews and implementation plan

revisions are required in 2008, 2013, and 2018. For each mandatory Federal Class I area in the

State, the progress review should include an assessment of the following:

C Current visibility conditions (i.e., the most recent 5-year average) for the most

impaired and least impaired days;

C The difference between current conditions and baseline conditions (2000-2004) for

most impaired and least impaired days;

C The change in visibility conditions over the past 5 years for the most impaired and

least impaired days;

C The change in visibility conditions as compared to the State’s projection of

visibility improvement required in Section 51.309 (d) (2).

1.16 Does this guidance on Tracking Progress address all of the required elements of the

5-year progress reviews required under the regional haze rule? 

No, the primary focus of this document is to describe a recommended methodology for

calculating total light extinction values for a mandatory Federal Class I area, based on ambient

monitoring data. The document also provides basic guidance on the types of visibility

assessments needed as part of the 5-year progress reviews. However, the State will need to

evaluate both ambient monitoring information and the effectiveness of emission reduction
Guidance for Tracking Progress 

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measures in the 5-year progress reviews. The EPA will develop guidance for the full progress

review process at a later date. 

1.17 What information is provided in the rest of this guidance document?

The remainder of this document provides guidance on procedures to measure regional

haze and track progress towards meeting the national visibility goals. Section 2 of this document

provides a summary step-by-step description of recommended calculations for tracking progress

in regional haze improvement. Section 3 elaborates on that process and presents equations and

supporting information needed to perform the calculations. Section 4 discusses the final

comparisons used for tracking progress in visibility. 

An appendix is included in this document which lists the monthly relative humidity

correction factors for each mandatory Federal Class I area. These factors are used for calculating

light extinction at each mandatory Federal Class I area. 
Guidance for Tracking Progress 

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2. SUMMARY OF TRACKING PROGRESS CALCULATION PROCEDURES

2.1 What is the purpose of this section of the guidance document?

This section of the guidance document describes the process that could be carried out to

track progress in improving visibility in mandatory Federal Class I areas, using data from the

IMPROVE monitoring sites. The required calculation procedures should be carried out in a

nationally consistent manner in order to facilitate inter-comparability among States and national

reporting by EPA. The IMPROVE program will perform these calculations for all IMPROVE

and IMPROVE protocol monitoring sites and provide the results through the IMPROVE website

at:

http://vista.cira.colostate.edu/improve

as a service for those agencies who do not wish to implement the process themselves. The

IMPROVE program will ensure that all data and calculations are available in a timely manner,

consistent with SIP schedules and will reduce the burden on States which do not wish to perform

their own calculations. Data provided to the web site from the IMPROVE monitoring efforts will

be used to calculate light extinction and deciview values, 5-year average results, and visibility

trends at all IMPROVE sites. This centralized approach will assure consistent treatment of all

composite components of PM2.5, missing data, data substitution, and averaging, and will reduce

the effort needed from Federal, State, Tribes, and other interested parties or agencies doing

assessments. The calculations should be done according to the equations and procedures

presented in Section 3 of this guidance document, which are also detailed on the IMPROVE web

site. All monitoring data will be accessible for review by the responsible agencies so that data

flagging or adjustments for special occurrences or other factors can be implemented effectively. 

However, this service in no way usurps or relieves individual States from their regulatory

responsibility to assess the change in visibility in each mandatory Federal Class I area. The aim of

this approach is to promote consistency in the calculation procedures while making the process

easier for those States, Tribes, and other parties or agencies, who choose to do their own

assessments.

2.2 What is the sequence of steps needed to calculate data for tracking progress?

Figure 2-1 summarizes the step-by-step process for assessing visibility trends. The

process begins with the transfer of quality-assured, State-reviewed, IMPROVE PM2.5 monitoring

data to the IMPROVE web site. Then the following sequence of steps will be carried out on data

from each IMPROVE site, leading to the data needed for calculation of trends in visibility at each

site.
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Step 1 - Assemble Composite Components of PM2.5

Several of the particle components needed to assess PM2.5 light extinction are termed

composite components. Such variables may be a composite of multiple measured species, or may

be derived from measured species by appropriate conversion factors. Composite components

include Fine Soil, which is the sum of several crustal elements; Organic Carbon (OC), which is the

sum of four measured OC fractions and the pyrolyzed organics (OP); Light Absorbing Carbon

(LAC), which is the sum of three measured elemental carbon (EC) fractions less the OP fraction;

Coarse Mass, which is the difference between measured PM10 and PM2.5 mass; Sulfate, which

may be determined based upon either measurements of particulate sulfur or of sulfate ion, with

correction for associated ammonium ion; and Nitrate, which is calculated as the mass of

ammonium nitrate. The first step in the data process should be to complete these component

variables, using a procedure such as that summarized below: Note: All of these recommended

calculations are performed by the IMPROVE program and are made available on the IMPROVE

website.
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Figure 2-1 Summary of Step-by-Step Process

for Tracking Progress Calculations
Guidance for Tracking Progress 

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2 - 4

Fine Soil is calculated by summing the five crustal elements (Al, Si, Ca, Fe, and Ti),

accounting for their presence as oxides (e.g., Al2O3), and applying adjustment factors to correct

for factors such as non-soil potassium, and the presence of other soil components. If any of the

five primary crustal elements is below the minimum detection limit, it is assigned a value of half

the minimum detection limit. If any of the five crustal elements is missing from the data set,

generally all five will be missing because of the analytical method used for these elements. In that

case the Fine Soil data are flagged as missing.

Organic Carbon is calculated by summing the five OC fractions after each has been blank

corrected. If the resultant sum is negative, a value of zero is assigned for Organic Carbon.

Light Absorbing Carbon is calculated in a similar way, by first summing the three blank

corrected elemental carbon fractions, and subtracting the OP fraction. If the result after

subtraction is negative, a value of zero is assigned for Light Absorbing Carbon.

Coarse Mass is calculated by subtracting the PM2.5 value from the corresponding PM10

value. If the result after blank subtraction is negative, a value of zero is assigned for Coarse

Mass.

Sulfate is preferably calculated from the particulate sulfur determination. If the sulfur

value is below the minimum detection limit, a value of half the minimum detection limit is assigned

for sulfate but if that analysis is missing, then the ionic SO4

=

 determined by ion chromatography is

used. The total mass of sulfate present is then calculated assuming it exists in the aerosol as

ammonium sulfate [(NH4

)2SO4

]. Nitrate is calculated directly from the measured nitrate ion

values, with a factor of 1.29 applied to account for associated ammonium ion. Both Sulfate and

Nitrate measurements are blank corrected. If the result is negative for either measurement, a

value of zero is assigned for Sulfate or Nitrate accordingly.

Step 2 - Assess Missing Variables

Once the calculations outlined in the first step above have been completed, the entire data

set should be reviewed to identify any missing data for the composite components. Those

variables for which one or more results are missing should be addressed as in the following steps

to fill in the missing data with long-term average values. Days for which no data at all are

available are not included in any further calculations.
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Step 3 - Determine Quarterly Median Concentrations for Missing Variables

For each variable for which one or more data points were found missing in Step 2,

quarterly median values are calculated for the current year and each of the previous 4 years of

data. For each calendar quarter (i.e., January-March, April-June, July-September, OctoberDecember), the calculated median values for the corresponding complete quarters from the

current year and the previous 4 years are then averaged to obtain an average of up to five median

values. All data for each variable in the data set are used to calculate the medians, including data

that have been set to zero (e.g., for Organic Carbon, Light Absorbing Carbon), and those that are

based partly on assigned non-detect values (e.g., Fine Soil). The resulting averages of the

quarterly median concentrations are then used in subsequent steps to determine whether missing

data can be replaced with the average values.

In this context, a complete quarter is defined as one in which data for a species are

available for at least 50% of the sampling days and which has no more than 10 consecutive

sampling days with data missing for that species. With a sampling schedule of every third day,

this requirement means that no more than one consecutive month of data can be missing. 

Quarters which do not meet these criteria should not be used to calculate the quarterly average

values.

In carrying out this step, care must be taken that the sampling and analytical procedures

are uniform throughout the data period being considered. For example, it must be determined

that monitors have not been moved, that filter mask sizes have not been changed, etc. Such

determinations require a careful review of the history of any siting changes as well as changes in

the monitoring procedures for the site. If siting or procedural changes are made, it is important to

establish that comparability in the monitoring data has been maintained throughout the changes. 

Also see Section 4.5.

Step 4 - Obtain f(RH) Values

Calculations of light extinction and deciview values require f(RH) factors, which adjust the

light scattering effect of hygroscopic aerosol species to account for particle growth caused by

water vapor in the atmosphere. It is recommended that the f(RH) factors used be site-specific and

be associated with monthly, rather than, e.g., seasonal or annual time frames. A table of

recommended monthly f(RH) values for the Class I areas is included as Appendix A of this

guidance document. The appropriate f(RH) values are used with monitoring data from each

IMPROVE site in all visibility calculations.
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Step 5 - Evaluate Feasibility of Substituting Average Values 

The reason the guidance includes a process for testing the feasibility of substituting

average species concentration values for missing values is to maximize the number of sample

periods upon which haze trends can be assessed without significantly degrading the haze

calculation. The alternative, a rule that valid data for each species must be available for every

sample period, both tends to reduce the number of valid haze calculations and in extreme cases

can result in the introduction of substantial biases in annual average worst-case calculated haze

values. For example in the mid-1990s the eastern United States summertime IMPROVE fine

particulate nitrate data were occasionally invalidated as a result of the sampler’s nylon filter

clogging, typically during periods of high sulfate concentration. Invalidating these sample periods

would produce a biased annual average of the worst-case haze conditions since sulfates contribute

significantly to the haze in the eastern United States during the summer. However, substitution of

an average nitrate value for the missing nitrate would avoid the bias and would not significantly

degrade the haze calculation because summer fine particulate nitrate concentrations are minor

contributors to haze in the eastern United States. The process described below is an objective

approach for determining whether a substitution can be made for missing species data without

significantly degrading the calculated haze values.

In this step, light extinction calculations by equation 8 in Section 3 are carried out in two

ways for each IMPROVE site having any missing data: (1) using the original data from the past 1

to 5 “complete” years, as defined below in Step 7, and (2) by substituting the appropriate average

of the quarterly median concentrations determined in Step 3 for the individual species

concentrations in the data set. Comparison of the two sets of results then determines whether the

average of quarterly median concentrations can be used to fill in any missing data. This step in the

overall process requires several steps in itself, as described below.

First, for a given IMPROVE site, the total light extinction values (bext, see Section 3) for

all days with no missing data are calculated. This calculation is done as described in Section 3 of

this guidance document, using the appropriate f(RH) factors and the appropriate calculations of

the individual composite components. This calculation produces a list of bext values for that

portion of the original data set that had no missing values.

The second step is to recalculate the bext values for the same sampling days, but with the

appropriate average of the quarterly median values for a single species (from Step 3 above),

substituted in place of all of the individual values of that species. For example, the average of the

quarterly median values of sulfate at a site would be substituted for the corresponding individual

sulfate values, for all the days from that site with no missing data. The bext values are then
Guidance for Tracking Progress 

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calculated for the resulting data set. The product of this step is a second set of bext values,

corresponding one-to-one with those calculated from the original values.

Next, these two sets of bext values are compared for each sample day on a calendar year

basis. If the difference between the 2nd bext value relative to the 1st bext value is less than 10% for at

least 90% of the sample days, then it is acceptable to replace any missing values for that species

with the appropriate average of the quarterly medians for that species. If this criterion is not met,

then any missing values for that species must remain missing for that calendar year. 

The preceding process should be repeated as necessary for each species with any missing

data, one at a time, e.g., producing a set of bext values with the average of the quarterly median

sulfate concentration substituted for all individual sulfate values, then another set of bext values

with an average of the quarterly median nitrate concentration substituted for all individual nitrate

values, etc. Each such set of bext values is compared to the original set, to make a judgment about

substitution of averages for just one particle species. Note that this process is to be carried out

for each composite species for each year, i.e., producing up to 30 tests for each 5-year period,

depending on the extent of missing data. It is expected that at any given IMPROVE site, it may

be reasonable and appropriate to replace missing data with quarterly medians for some species,

but not for others. 

In calculating bext values in this step of the overall process, a value of 10 inverse

megameters (i.e., 10/106

 m, or 10 Mm-1) for Rayleigh scattering should be used for all sites.

Instances in which data on more than one aerosol component are missing in the same

sample are likely to be rare. As a result, the process for dual substitution is not presented at

length here. However, substitution of two variables in the same sample could be done, subject to

adequate justification and testing, such as in the substitution test described previously. The same

acceptance criterion of less than 10% difference in bext values in 90% of the data should apply. 

For example, currently, light absorbing carbon and organic carbon data are likely to either be

present or missing in the same samples because of the common analysis method for these species. 

As a result, this substitution test could also be carried out for those two species simultaneously. 

That is, the quarterly median values for both species could be substituted for their individual

values at a site, the bext values could be calculated, and the comparison made to assess whether

simultaneous replacement of missing LAC and OC data with averages is appropriate. 

Once the suitability of replacing missing data with medians has been assessed as described

above, all missing data for those species meeting the acceptance criterion should be 
Guidance for Tracking Progress 

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2 - 8

replaced with the appropriate quarterly average values, and the bext values for those samples

should then be calculated. Missing data for species not meeting the criterion should be left

missing, and no bext values should be calculated for those samples.

Step 6 - Calculate Deciview Values

In this step the bext values calculated in Step 5 are converted to deciview values using

equation 9 in Section 3. Note that the appropriateness of substituting averages for missing data

could just as easily be evaluated in terms of deciview values, instead of the bext values, since a

difference of 10% in bext is equivalent to a difference of approximately 1 deciview. That is, if the

deciview values calculated with substituted averages differ from those of the original data by less

than 1 deciview for at least 90% of the samples, then replacement of missing data with annual

average values is appropriate. Otherwise, the missing data should remain missing. 

Step 7 - Check Data Completeness

In this step the data sets resulting from previous steps are reviewed for completeness.

In order for a year of data from a site to be used to track progress in improving visibility, all four

quarters of that year should be at least 50% complete, and overall, the year should be 75%

complete. That is, complete data (including that filled in by substitution of averages), should be

available for at least 50% of the sampling days in each quarter of the year and for 75% of all

scheduled sampling days for the year. In addition, there should be no more than 10 missing

sampling days in a row at any time during the calendar year. With a sampling schedule of every

third day, this requirement means that a site should not be out of operation for any period of more

than one consecutive month during the calendar year. 

Annual data sets meeting these completeness criteria should be used in subsequent steps to

calculate 5-year average visibility results for tracking progress. Every attempt should be made to

get 5 years of complete data within each 5-year period, and EPA expects that failure to meet this

goal will be rare. However, if maximum data recovery is not achieved, EPA believes that a

minimum of 3 years of data meeting these completeness requirements is sufficient to calculate the

5-year averages within each 5-year period. This recommendation for at least 3 years out of 5 is

consistent with the policy established in EPA’s regulations governing monitoring and analysis of

PM2.5, which establishes minimum data requirements for PM2.5 NAAQS comparisons. Because of

the close relationships between visibility impairment and fine particle concentrations, as well as

between the regional haze program and efforts to attain national ambient air quality standards, we

believe that similar data completeness policies should apply. Due to delays in deployment, some

of the 110 IMPROVE monitoring sites will have no more than 3 or 4 years of complete data at

the time when baseline conditions are calculated (Table 1-1). The 3 year completeness criterion
Guidance for Tracking Progress 

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will allow calculation of baseline conditions at these sites. If 3 years with complete data are not

available, estimates for baseline or current conditions should be prepared in consultation with the

Environmental Protection Agency’s Office of Air Quality Planning and Standards (EPA/OAQPS). 

In any case, all complete years should be used in these calculations.

Step 8 - Identify Best and Worst Days

In this step, the 20% best and 20% worst visibility days within a year are identified, based

on the deciview values. This step is conducted only for those years of data that meet the data

completeness requirements stated in Step 7 above.

Step 9 - Calculate Annual Average Deciviews

In this step, an annual average deciview value is calculated for the best 20% of the days in

a year and for the worst 20% of the days in the year. This process uses the best and worst days

identified in the previous step and should be carried out only for years meeting the data

completeness requirements.

Step 10 - Calculate 5-Year Deciview Averages

Once the annual average deciview values are calculated for the 20% best and 20% worst

days in each year, those values should be then averaged to produce best and worst average

deciview values over the prescribed 5-year periods. As noted above, a minimum of 3 years of

complete data should be available before a 5-year average is calculated. If 3 years with complete

data are not available, estimates for baseline or current conditions should be prepared in

consultation with the EPA/OAQPS. The resulting estimates for the 5-year period then should be

used as the basis for tracking progress (Section 4).

2.3 This 10-step process focuses on using complete years of data. What if an incomplete

year would obviously have been a particularly bad or good visibility year?

This potential occurrence is an indication of one instance in which it would be appropriate

to include data from incomplete years in calculations. For example, suppose that for a given year,

in which data completeness overall fell below the recommendation stated above, there were 25

deciview values (out of 122 sampling periods) that were above the average for 20% worst days

for the other years in a 5-year period. The inclusion of these results in the calculation of the 5-

year averages (i.e., Step 10 above) would necessarily increase the 5-year average for the 20%

worst days, regardless of the deciview values for the other 97 sampling episodes in that year. 

That increase would bring the 5-year average closer to its true value for that 5-year period. Thus
Guidance for Tracking Progress 

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2 - 10

it is reasonable to include the highest 20% deciview readings from an incomplete year, if those

values increase the 5-year average of the highest 20% of deciviews, relative to that based on

complete years only. For similar reasons, it is also appropriate to include the lowest 20% of

deciview readings from an incomplete year, if those values decrease the 5-year average for the

lowest 20% of deciviews, relative to that based on complete years only. As a result, the highest

and lowest deciview readings from incomplete years may be included in tracking progress

calculations, provided they meet the criteria outlined above. This inclusion is analogous to the

policy represented in provisions of Appendices K, M, and N to 40 CFR 50 regarding particulate

matter and makes use of incomplete years to provide more accurate estimates for tracking

progress. 

In any 5-year period of baseline or current conditions, there should be at most 2

incomplete years of data. One process for using an incomplete year of data is as follows. First,

calculate quarterly average deciview values from those years with complete data (i.e., the 3 or

more years meeting the data completeness criteria). Second, substitute the appropriate quarterly

average deciview values for all sampling days in an incomplete year that have some missing data,

or even days with no data at all. The purpose of this substitution is to fill in the middle of the data

set from the incomplete year to define the 20% highest and lowest values. Consequently,

substituting even for days with no data is appropriate. Third, sort all deciview values within each

incomplete year and calculate the averages of the 20% best and 20% worst visibility days. Finally,

if the average deciview value of the 20% worst days in the incomplete year is higher than the

corresponding average calculated from all the complete years, then include the average from the

incomplete year along with those from the complete years and calculate a new 5-year average. 

Similarly, if the average deciview value of the 20% best days in the incomplete year is lower than

the corresponding average calculated from all the complete years, then include the average from

the incomplete year along with those from the complete years, and calculate a new 5-year

average.
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3. METHOD TO CALCULATE THE HAZE INDEX

3.1 What causes haze?

Haze is caused by the presence of particles and gases in the air which either absorb or

scatter light. Light reflected from landscape features is scattered and absorbed (attenuated) as it

passes through the atmosphere toward the observer, and other light is scattered into the

observer’s sight path by the intervening atmosphere. The degree to which light is attenuated by

these scattering and absorption processes can be expressed in terms of a coefficient of light

extinction, bext. Absorption of light due to gases, bag, is caused primarily by the presence of

nitrogen dioxide (NO2) in the atmosphere, and absorption due to particles, bap, is caused primarily

by elemental carbon (also called light absorbing carbon). Scattering by gases in the atmosphere,

bsg, is described by the Rayleigh scattering theory [van de Hulst, 1957] and is referred to as

Rayleigh scattering. The magnitude of Rayleigh scattering varies depending on air density and the

wavelength of light. To simplify comparisons of values among sites at a variety of elevations, the

IMPROVE program assumes a standard value of 10 Mm-1 for Rayleigh scattering in visibility

calculations regardless of site elevation. Scattering by particles, bsp, is caused by both fine and

coarse aerosol species and is the largest contributor to total light extinction in most rural locations

[Malm et al., 1994a]. The sum of these individual coefficients provides the overall light extinction

coefficient, bext, which is used to calculate the haze level.

3.2 How are haze levels calculated?

Tracking of trends for the regional haze rule requires the calculation of haze levels, in

deciview units, from measured particle species concentrations representative of each mandatory

Federal Class I area. The species concentrations needed are routinely measured by the

IMPROVE network at selected mandatory Federal Class I areas across the United States. Under

IMPROVE protocols, particle measurements are made every third day on a 24-hour integrated

sampling interval, starting at midnight. PM2.5 (particulate matter < 2.5 μm aerodynamic diameter)

and PM10 (particulate matter < 10 μm aerodynamic diameter) mass are measured at all sites, with

chemical speciation provided for the PM2.5 fraction. The chemical speciation results provide

concentration values for the major chemical constituents of PM2.5 (i.e., sulfate, nitrate, organic

carbon, elemental carbon, and soil). The species concentrations are used along with site-specific

correction factors to correct for the effects of relative humidity, and species-specific extinction

efficiencies to account for the different degree to which each species causes light extinction, to

determine daily overall light extinction values. These total light extinction values (expressed as

bext) are then used to calculate the haze index in terms of deciviews. Figure 3-1 summarizes this

process.
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Figure 3-1 Summary of Process to Calculate Haze Index

3.3 How are the monitoring data used for the calculation of bext obtained?

The IMPROVE network has monitoring sites at 110 locations to monitor conditions

representative of the 155 mandatory Federal Class I areas. At each of the sites, an IMPROVE

sampler is operated. These samplers each have 4 modules (identified as A, B, C, and D) which

are used to collect particulate matter samples for chemical or gravimetric analysis. Modules A, B,

and C collect fine particles (0-2.5 :m), and D collects PM10 particles (0-10 :m). The Module A

Teflon filter is the primary filter for providing the fine particle mass data. Module B, with a

denuder before the nylon filter to remove acidic gases, is used primarily for nitrate. Module C 

collects samples on quartz filters which are analyzed for carbon in eight temperature fractions and

used to determine both organic carbon and light absorbing carbon concentrations. At some sites

Module C uses a single quartz filter to collect samples whereas other sites use tandem quartz

filters for quality assurance purposes.
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Sulfate ion concentration is determined by multiplying the concentration of elemental

sulfur, as determined from proton induced x-ray emission (PIXE) analysis of the Module A

sample, by 3 to account for the oxygen in the sulfate ion. When elemental sulfur data are not

available, sulfate measured by ion chromatographic analysis of the Module B sample can be used

to determine the dry sulfate concentration. 

Fine particle sulfate content originates predominantly from atmospheric oxidation of sulfur

dioxide to sulfuric acid, either by aqueous reactions in cloud droplets or through gas-phase

photochemistry. If there is inadequate ammonia in the atmosphere to fully neutralize the sulfuric

acid, then the resulting aerosols are acidic. Depending on the ammonia available, solutions of

varying acidity may be formed, ranging from ammonium sulfate (fully neutralized) to sulfuric acid. 

If only the sulfate ion is measured, as is the case at nearly every IMPROVE site, then one must

assume a form of sulfate (i.e., a degree of neutralization by ammonia) and multiply by an

appropriate multiplication factor, for instance, 1.375 * [SO4

2-], if ammonium sulfate is assumed as

is the case for the IMPROVE program. 

 The mass of organic material present can be calculated from the measured PM2.5 OC

mass, which is determined by thermal optical reflectance (TOR) analysis [Chow, et al., 1993]. An

average ambient particulate organic compound is assumed to have a constant fraction of carbon

by weight. Organic carbon mass concentration (OMC) is simply:

where the factor of 1.4 was selected to adjust the organic carbon mass for other elements

assumed to be associated with the organic carbon molecule [White and Roberts, 1977; Japar et

al., 1984].

Light absorbing carbon (LAC), sometimes referred to as elemental carbon (EC), is also

determined by TOR analysis and is calculated from the sum of elemental carbon fractions minus

the pyrolized fraction. 

Nitrate ion concentration is determined by ion chromatographic analysis of the sample

collected in Module B. Assuming that the nitrate ion is associated with ammonium nitrate

aerosol, [NH4NO3], the ammonium nitrate mass, [NITRATE], can be estimated from the nitrate

ion mass concentration by using a multiplication factor of 1.29, which accounts for the mass ratio

of NH4NO3 to NO3

-

.
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(2)

(3)

Soil mass concentration, [SOIL], is estimated by summing the mass of those elements

predominantly associated with soil, with allowance for oxygen present in the common compounds

(e.g., Al2O3, SiO2, CaO, K2O, FeO, Fe2O3, TiO2) as shown in Equation 2:

Since potassium can originate from wood smoke as well as from soil, iron is used as a surrogate

for determining [SOIL]. The concentrations of these elements are determined by analysis of the

Module A sample by PIXE. In addition, a correction is applied for other compounds such as

MgO, Na2O, and carbonate [Malm, et. al., 1994a]. 

Coarse particle mass (CM) is estimated gravimetrically by subtracting the gravimetric fine

mass (PM2.5 from Module A) from total gravimetric mass (PM10 from Module D); - i.e., in the

IMPROVE program, no additional chemical analyses are carried out on the coarse fraction. 

3.4 What are the species mass extinction efficiencies for aerosol components?

The goal of tracking progress guidance is to evaluate changes in haze or visibility

attributed to various aerosol species. However, as noted by White [1986], attribution of

atmospheric extinction to aerosol species is an ill-defined problem. Whereas the sum of mass

associated with each aerosol species is roughly equal to gravimetric mass, and therefore fractional

contribution of each species to total mass can be calculated, the same is not generally true for

extinction. Because two or more species can be mixed together in a variety of ways into a single

particle with different optical properties (internally mixed) it is not possible to state, in a general

way, the amount of extinction attributed to the individual species. Moreover, a review of the

literature reveals that single particle efficiency as it relates to the removal of radiant energy as it

passes through the atmosphere is defined in a multiplicity of ways [van de Hulst, 1957; Ouimette

and Flagan, 1982; White, 1986; Malm and Kreidenweis, 1997]. Ouimette and Flagan [1982]

define an extinction efficiency as the change in extinction associated with the addition or removal

of a fraction of a specific species and White [1986] argues that the most meaningful species

extinction efficiency is associated with the decrease in total extinction resulting from an

incremental removal of that species from the atmosphere. These parameters can be highly 
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(4)

dependent on assumptions concerning particle size, mixing characteristics, and chemical and

optical properties which are rarely if ever measured. A full discussion of the various concepts is

beyond the scope of this guidance.

For tracking progress, a simple and straightforward approach for estimating aerosol

species contribution to extinction is outlined. Most routine aerosol monitoring programs and

many special study visibility characterization programs were designed to measure bulk aerosol

species mass concentrations such as sulfates, nitrates, carbonaceous material, and selected

elements [White and Roberts, 1977; Heisler et al., 1980; Malm et al., 1994b; Tombach and

Thurston, 1994; Watson et al., 1990; Macias et al., 1981]. They were not designed to determine

the microphysical and chemical characteristics of these species. Any particle in the atmosphere

scatters and/or absorbs a specific fraction of radiant energy, whether it is externally or internally

mixed. When computing total extinction, the microscopic structure of the aerosol (that is, the

extent of internal or external mixing) is found to be relatively unimportant, so that the assumption

of internally vs. externally mixed particles does not have much impact on the predicted results. 

This insensitivity to total scattering/extinction has been demonstrated by a number of authors,

including Hasan and Dzubay [1983], Sloane [1983], and, more recently, Pilinis et al. [1995], and

McMurry et al. [1996].

Therefore, the calculation of light extinction from aerosol species concentrations treats

each species contribution separately and merely sums them. This formulation implies no

interaction between the various aerosol species with respect to their contributions to extinction. 

This would be the case if each of the particles were composed of only one species (e.g., sulfate

particles separate from nitrate particles which are separate from organic carbon particles, etc.). In

general the extinction contribution for each species is modeled as the product of three factors: the

dry mass extinction efficiency for that species ("i

), the relative humidity adjustment term that

varies as a function of relative humidity for that species (f

i

(RH)), and the dry concentration of that

species (mi

): 

where "i, the dry mass extinction efficiency, is defined to be the ratio of the total

extinction associated with species i divided by its mass and the relative humidity

adjustment factor, f

i

(RH), is defined to be the ratio of scattering by a species at

some relative humidity to scattering by that species under dry conditions, i.e.,

f

i

(RH) = bspi(RH)/bspi(RH=0) where i refers to the ith species.
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The extinction efficiencies for soil and coarse mass are taken from a literature review by

Trijonis and Pitchford [1987]. For soil, the dry extinction efficiency of 1 m2/g is used, and for

coarse mass, a value of 0.6 m2/g is used. For both nitrate and sulfate, a dry extinction efficiency

of 3 m2/g is based on literature reviews by Trijonis et al. [1990] and by White [1990]. Trijonis’

best estimate for sulfates and nitrates is 2.5 m2/g with an error factor of 2, while White’s average

low and high estimates for the rural West are 3.0 and 3.7 m2/g respectively. For organic carbon

mass, Trijonis estimates a dry extinction efficiency of 3.75 m2/g, again with an error factor of 2,

and White’s low and high average estimates for the rural West are 1.8 and 4.1 m2

/g respectively. 

Based on these estimates, a dry extinction efficiency of 4 m2/g is used. More recently, Malm et al.

[1996] and Chow et al. [2002] demonstrated that the assumption of the dry specific scattering

values yielded good agreement between measured and calculated extinction across the entire

IMPROVE monitoring network.

3.5 What effect does relative humidity have on the haze levels?

 Some aerosol components are hygroscopic (principally sulfates and nitrates), meaning that

particles composed of those materials grow in size by accumulating water from the atmosphere

under moist conditions. This causes an enhanced amount of light scattering that is directly related

to the atmospheric relative humidity. Implicit to the use of Equation (4) is an assumed linear

relationship between aerosol species mass and extinction. However, the relationship between

measured light scattering and hygroscopic species mass can be quite nonlinear because of water

uptake as a function of relative humidity. A number of authors have attempted to linearize the

model, in an empirical way, by multiplying the hygroscopic species by such a factor as 1/(1-RH)

to account for the presence of water mass [White and Roberts, 1977; Malm et al., 1989]. 

However, Malm et al. [1989] and Gebhart and Malm [1989] proposed a different approach. They

multiplied the hygroscopic species by a relative humidity scattering enhancement factor, f(RH),

that is calculated on a sampling-period-by-sampling-period basis using Mie theory, an assumed

size distribution, and laboratory measured aerosol growth curves which illustrate the size of

aerosol particles as a function of relative humidity.

Tang [1996] published growth curves showing the ratio of particle diameter to particle

diameter at zero relative humidity, D/Do

, as a function of increasing and decreasing relative

humidity for a number of inorganic salts. For increasing or decreasing RH, many salts exhibit a

hysteresis in the D/Do vs. RH relationship, with sharp discontinuities at the deliquescence (relative

humidity at which the crystal abruptly absorbs water) and crystallization (relative humidity at

which particles abruptly lose water and recrystalize) humidities. Because mixtures of ammoniated

sulfate compounds with other species have been shown to be hygroscopic below the deliquescent

values [Sloane, 1984; Sloane, 1986; Stelson and Seinfeld, 1982; Chow et al., 2002] and because

the growth factor and light-scattering efficiency for ambient aerosols has previously been
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observed to be rather smooth [Sloane, 1984; Sloane, 1986; Sloane, 1983; Wexler and Seinfeld,

1991; Waggoner et al., 1981; Day et al., 2000; Malm et al., 2000a; Malm et al., 2000b; Malm and

Day, 2001], it is not known whether the upper or lower limb of the hysteresis curve applies for a

particular aerosol sample. Therefore, as a “best estimate” for the sulfate species growth, the

curves are smoothed between the deliquescence and crystallization points.

Malm et al. [2000 a, b] and Malm and Day [2001] have demonstrated that in both the East

and West, the best estimate growth curves yield good agreement between measured and

theoretically predicted f(RH) functions and between measured and predicted ambient fine particle

scattering. It is recognized that the sulfate f(RH) function is quite different for the East than West

because of sulfate ammoniation. In the East where sulfates can be quite acidic, average growth of

the sulfate aerosol begins at much lower relative humidities (<30%) than in the West. In the

Colorado Plateau region of the West, growth does not typically initiate until about 40-50%

relative humidity. However, because ammonium mass concentration is not routinely measured in

the IMPROVE program, ammonium sulfate is assumed as the form of sulfate and the “smoothed”

ammonium sulfate growth curve is used for estimating sulfate f(RH) curves. This smooth curve is

illustrated in Figure 3-2, which shows the f(RH) for ammonium sulfate as a function of relative

humidity. The data are listed in Appendix A-1.

The value of f(RH) rises very slowly from 1 as the relative humidity increases, only

reaching 2 at about 70% relative humidity. However, f(RH) is non-linear and increases rapidly as

it approaches 100% relative humidity (at which point it is undefined). For example, f(RH) is 4 at

about 90% relative humidity and increases to 7.5 at about 95% relative humidity. The importance

of this effect is illustrated by considering that the same concentration of sulfate aerosol is

responsible for 4 times the haze at 95% relative humidity as at 70% relative humidity. 
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Figure 3-2 Smoothed Ammonium Sulfate f(RH) Curve

Various functions for the hygroscopicity of particulate organic compounds have also been 

proposed. Assumptions must be made about the fraction of organics that are soluble. Models

that treat water uptake for non-ideal multicomponent solutions using theoretical and semitheoretical thermodynamic relationships have been developed and have been applied to both

visibility and climate forcing problems [Saxena and Peterson, 1981; Pilinis et al., 1995; Saxena et

al., 1986, 1993]. The correct treatment of the hygroscopicity of species in multicomponent

mixtures (especially organic species) remains problematic, not only because of the lack of suitable

mixture thermodynamic data but also because of the lack of information about other critical

mixture properties. 

Scientists have experimentally measured growth of ambient particles as a function of

relative humidity using tandem differential mobility analyzers (TDMA) in non-urban settings
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[Zhang et al., 1993; 1994; Swietlicki et al., 1999]. One study was carried out in Meadview,

Arizona (west end of Grand Canyon) over a 31-day period during the summer of 1991, a second

at Hopi Point, Arizona (midpoint of Grand Canyon), over a 13-day period during the winter of

1990, and a third at Claremont, California, over an 11-day period during the summer of 1987 [Cai

et al., 1993; Zhang et al., 1993, 1994; McMurry and Zhang, 1991]. A TDMA consists of two

DMAs operated in series. The first DMA is used to select a size, while the second is used to

measure the change in particle size as relative humidity is varied. Usually, a MOUDI size sampler

[Marple et al., 1991] is run concurrent with the TDMA to derive estimates of particle

composition. 

Based on their modeling assumptions, Saxena et al. [1995] concluded that at the Grand

Canyon, aerosol organic species increased water absorption by inorganic species, while at

Claremont the net effect of organics was to diminish water absorption by inorganics. On the other

hand, Pitchford and McMurry [1994] showed that on 6 of the 8 sampling days at the Grand

Canyon study cited above, if it is assumed that nitrates and sulfates uptake water at the same rate

as measured in the laboratory, they alone could account for all of the measured water absorption.

Swietlicki et al.[1999] made TDMA measurements in Northern England and found growth

to take place in two modes, one mode being less hygroscopic than the other. They concluded that

growth could be attributed to the inorganic content of the aerosol. Cocker et al. [2001] measured

hygroscopic properties of Pasadena, California, aerosol and concluded that growth factors

increased when forest fires were present. However, they were unable to attribute the growth to

any single species because concurrent aerosol speciation was not carried out simultaneously.

McDow et al. [1994] measured water uptake by diesel soot, automobile exhaust, and

wood smoke particles. They found all three emission types absorbed water, with the wood smoke

sample weight increasing by about 10% as sample relative humidities increased, whereas diesel

soot sample weight increased by only 2%-3%.

Chughtai et al. [1999] examined the hydration characteristics of BP2000 (commercially

available carbon black), and of carbon produced from n-hexane, diesel fuel, JP8 (aviation fuel),

pine needles, Utah coal, and acetylene. They examined water adsorption isotherms between 20%

and 85% relative humidity and concluded that the ability of black carbons, produced from a

variety of fuel types, to adsorb water generally increased with age and surface oxidation. At high

relative humidity (83%), large surface areas determine the adsorption capacity. At lower relative

humidity, however, the surface functional groups determine the extent of hydration. Even at 83%

relative humidity, the water uptake was less than 10% of total mass for all carbon species 
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other than BP2000. Because of its large surface area, BP2000 absorbed about 40% of its mass in

water. Consequently, they concluded that commercial carbon blacks are not acceptable models

for fuel-produced carbons.

Field experiments and subsequent data analysis at the Great Smoky Mountains and Grand

Canyon National Parks [Malm et al., 1997; Malm and Kreidenweis, 1997; Malm et al., 2000 a, b]

and, more generally, data collected in the IMPROVE network [Malm et al., 1996] show that 

within the uncertainty of the measurements and modeling assumptions, ambient organics in rural

areas of the United States are at most only weakly hygroscopic. Therefore, based on the available

data, the f(RH) for aerosol organics can be reasonably set equal to one.

The additive extinction by chemical species embodied in Equation 4 can be combined with

the effect of RH discussed above, to estimate the scattering of light by fine particles. The

following equation is used to estimate calculated particle light scattering:

The brackets in Equation 5 indicate the species concentration, 3 m2

/g is the dry specific scattering

efficiency for sulfates and nitrates, 4 m2/g is the dry specific scattering efficiency for organic mass,

and 1 m2/g and 0.6 m2/g are the respective scattering efficiencies for soil and coarse mass.

3.6 How are the f(RH) values determined?

Average fSO4(RH) values for each sampling period are calculated based on the ambient

humidity, using Tang’s [1996] ammonium sulfate growth curves. Assuming a lognormal sulfate

mass size distribution, with a geometric mass mean diameter of 0.3 :m and a geometric standard

deviation, Fg

, of 2.0, the fSO4(RH) values are calculated using D/Do curves that are smoothed

between the crystallization and deliquescent points. The fNO3(RH) associated with nitrates is

assumed to be the same as for sulfates, while f

org(RH) for organics is set equal to 1.0.

To assess the changes in man-made pollution contributions to visibility impairment, it is

appropriate to use relative humidity that is the same for the baseline period and future periods 
Guidance for Tracking Progress 

Under the Regional Haze Rule 

9

 U.S. EPA, Interpolating Relative Humidity Weighting Factors to Calculate Visibility

Impairment and the Effects of IMPROVE Monitor Outliers, prepared by Science Applications

International Corporation, Raleigh, NC, EPA Contract No. 68-D-98-113, August 30, 2001.

3 - 11

with changed emissions. In other words, it is more appropriate to eliminate the confounding

effects of interannual variations in relative humidity, while maintaining typical regional and

seasonal humidity patterns.

To that end, the U.S. EPA recently sponsored a project to examine measured hourly

relative humidity data over a 10-year period (1988-1997) within the United States to derive

month-specific climatological mean humidity correction factors for each mandatory Federal Class

I area.9

 The hourly RH measurements from each site were converted to f(RH) values using a nonlinear weighting factor curve (see Figure 3-2). Values above 95% RH were set equal to the f(RH)

corresponding to 95% RH. 

The results of that work are the values presented in Appendix A. These relative humidity

factors have been calculated from available hourly relative humidity data from 292 National

Weather Service stations across the 50 States and the District of Columbia as well as from 29

IMPROVE and IMPROVE protocol monitoring sites, 48 Clean Air Status and Trends Network

(CASTNet) sites, and 13 additional sites administered by the National Park Service. Using a

software tool available from EPA, monthly f(RH) values can be calculated for any location in the

United States. In most regions there is a seasonal cycle of relative humidity, which is accounted

for by generating the appropriate monthly f(RH) values, as in Appendix A. The 12 monthlyaveraged f(RH) values are listed for each IMPROVE or IMPROVE protocol site and their

corresponding Class I areas. The site specific values associated listed for each mandatory Federal

Class I area in this way are recommended to be used for all visibility and tracking progress

calculations for that Class I area. These values are provided in Table A-2. A supplemental table

of 12 monthly-averaged f(RH) values for each mandatory Federal Class I area is also provided in

Table A-3 for informational purposes.

Appendix B examines within-month correlations that may exist between RH and inorganic

aerosol concentrations and the potential effect on the computed haze index. If the correlations

are significant, then the use of monthly-average f(RH) could systematically over- or under-predict

the contribution of sulfate or nitrate to visibility impairment The results in Appendix B show that

monthly average f(RH) values are appropriate. The difference in computed haze index values

resulting from the use of monthly values of f(RH) vs. 24 hr values, is within 10% for 20

IMPROVE sites studied.
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3.7 How does light absorption contribute to light extinction?

Light absorption by gaseous species, primarily NO2, is generally negligible in mandatory

Federal Class I areas and not included in calculations of light extinction. However, estimating the

total light extinction also requires a knowledge of light absorption by particles. Light absorption

by particles is primarily due to elemental carbon (also called light-absorbing carbon). Horvath

[1993] has reviewed the measurement of light absorption by elemental carbon, while Fuller et al.

[1999] has explored theoretically the variability of absorption efficiency as a function of carbon

morphology. Estimated mass absorption efficiencies of elemental carbon vary by more than a

factor of two, as do direct measurements. Although particle light absorption can be estimated in a

variety of ways, there is no one method that is generally accepted by the scientific community. 

For purposes of this guidance, elemental carbon light absorption is estimated using:

were LAC is the concentration of light-absorbing carbon as measured using the Thermal Optical

Reflectance (TOR) analysis method [Chow et al., 1993], and 10 is the specific absorption

efficiency for LAC, which has been used by a number of scientists [Horvath, 1993]. 

3.8 How is the total light extinction calculated?

In addition to particle scattering and particle absorption, total light extinction needs to

include a term bsg, i.e., for Rayleigh scattering, which is scattering by the gas molecules in the

atmosphere. Thus, bext = bsp + bap + bsg. As indicated in Section 3.7, carbon light absorption is

estimated as ten times the concentration of light-absorbing carbon for the purposes of the

guidance. A standard value of 10Mm-1 for Rayleigh scattering is used in visibility calculations

regardless of site elevation in keeping with the practice of rounding each constant in the aerosol

extinction calculation to one significant digit and to simplify comparisons of values among sites at

a variety of elevations.
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(8)

Combining all of the factors discussed above, the following equation converts particle

species concentration data in units of μg/m3 for a sample period at a monitoring location to total

light extinction in units of Mm-1

.

Malm et. al. [1996] used this IMPROVE algorithm to successfully reconstruct scattering at nine

sites, namely Grand Canyon, Petrified Forest, Guadalupe Mountains, Yellowstone, Rocky

Mountain, Glacier, Pinnacles, Bandelier National Parks, and the Bridger Wilderness Area. 

Additionally, comparisons were made between calculated and measured extinction at Acadia and

Shenandoah National Parks for the time period 1988-1991. Those results were reported in the

February 1993 IMPROVE report "Spatial and Temporal Patterns and the Chemical Composition

of the Haze in the United States,” ISSN No. 0737-5352-26. Finally, Chow et al. [2002] has

compared measured and calculated scattering at the Great Smoky Mountains National Park for

the time period 1994-2000 and found that, on average, nephelometer measured scattering is about

6% larger than calculated scattering, and the RMS error was 23%.

3.9 How are haze index values in deciview units calculated?

Once the light extinction has been calculated for a monitoring site, using Equation 7, the

haze index (HI) in deciview (dv) units can be calculated. The HI is a visibility metric based on the

light-extinction coefficient that expresses incremental changes in perceived visibility [Pitchford

and Malm, 1994]. Because the HI expresses a relationship between changes in light extinction

and perceived visibility, it can be useful in describing visibility trends. A change in the HI of one

dv is equivalent to about a 10% change in extinction coefficient, which is a small but perceptible

scenic change under a wide range of visibility conditions. The HI is defined by the following

equation:

The value of the haze index is near zero dv for a pristine atmosphere (dv = 0 for a pure Rayleigh

scattering condition) and increases as visibility is degraded.
Guidance for Tracking Progress 

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10 Walsh, K. “OUTLIERS AND THEIR EFFECTS ON AVERAGE VISIBILITY

IMPAIRMENT CONDITIONS,” EPA Contract 68-D-98-113 with Science Applications International

Corporation. March 2001.

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3.10 Should outliers in the data be excluded?

Each annual estimate of best and worst days should be based on all valid measured aerosol

concentrations during the calendar year. This includes high concentrations associated with

regional forest fires or other unusual events. An analysis of IMPROVE data10 collected during

1994-1998 revealed that by excluding outliers (measurements greater than 2 standard deviations

from the mean of the 20% worst days) from the calculation of the 5-year mean for the 20% worst

visibility days, the mean haze index changed by 0.3% at Great Smoky Mountains and by 2.5% at

Great Sand Dunes. Changes at other sites were between these two values. Similarly, changes in

the calculated 5-year mean haze index resulting from the exclusion of outliers in the 20% best

visibility days (measurements greater than 2 standard deviations from the mean of the 20% best

days) were between 0.6% (Point Reyes) and 2.9% (Big Bend). Thus the impact from a small

number of days tends to average out when the visibility is examined on a deciview scale over a 5-

year period. It is important to include these extreme concentrations in the estimates for 5-year

baseline and current visibility conditions because the impact from these events may be part of

natural background and is thus reflected in the estimate for the target visibility levels. When an

outlier in the data is clearly not representative of the regional haze levels, the result should be

flagged and an explanation provided of the cause of the outlier. If a very localized fire (for

example, a nearby structural fire) severely impacts the loading of a specific sampler but does not

degrade the visibility outside of the immediate vicinity (e.g., within 1 mile), the data should be

flagged in all data files and calculations. Such occurrences may not be appropriate for inclusion in

visibility trends analysis. On the other hand, events which result in apparent outliers in the data

and do have an impact on the regional visibility (e.g., forest fires) should be included in

subsequent trends analysis. The data should be flagged and explained, if possible, but should

remain in the data set. Any supporting evidence which may be used to help quantify the impact of

the episode causing the outlier should be collected, if possible.
Guidance for Tracking Progress 

Under the Regional Haze Rule 

4 - 1

4. PROCEDURES FOR COMPARING 5-YEAR PERIODS

4.1 How are the daily deciview values, calculated as described in Section 3, used to track

progress in improving visibility?

The daily deciview values from the best and worst days in each year are first selected, then

averaged over annual and 5-year periods, and finally those 5-year averages are compared to assess

progress. This section of the guidance document describes the procedures for carrying out this

process. 

4.2 How are the selection and averaging of the best and worst days in each year done?

Once the daily deciview values have been calculated for each sampling day at a site,

including those days for which missing data were replaced by appropriate averages, the deciview

values for each year are ranked from lowest to highest. Then the lowest 20% of the deciview

values for the year (i.e., the best 20% of the days in terms of visibility) are averaged, to produce

an annual average deciview value for the best 20% of the days. Similarly, the highest 20% of the

deciview values for the year (i.e., the worst 20% of the days in terms of visibility) are averaged,

giving the annual average deciview value for the worst 20% of the days. The methods used to

calculate %iles are based on those found in Interpretation of the National Ambient Air Quality

Standards for Particulate Matter (40 CFR 50, Appendix N). After sorting the values from lowest

to highest, the 20th %ile value for year y, is given by

where P0.20, y is the 20th %ile for year y, Xi

 = the i

th number in the ordered series of n numbers, and

i is the integer part of the product of 0.20 and n.

Similarly, the 80th %ile is given by

where P0.80, y is the 80th %ile for year y, X[i+1] = the (i+1)th number in the ordered series of n

numbers, and i is the integer part of the product of 0.80 and n.
Guidance for Tracking Progress 

Under the Regional Haze Rule 

4 - 2

This process should be repeated for each year of data available. Note that the data

completeness recommendations stated earlier in this document may eliminate some years from

being included in this calculation. For each complete year of data for a site, the results of this

calculation are two values, i.e., the average deciview values for the best and worst days,

respectively.

4.3 How are the 5-year deciview averages determined?

The annual average deciview values for the 20% best and 20% worst visibility days in

each year are further averaged over the 5-year periods specified in the regional haze rule. For

example, the baseline 5-year period is 2000-2004. The annual average deciview values for the

20% best days in each year in that period are averaged together, producing a single average

deciview value for the best days. Similarly, the annual average deciview values for the 20% worst

days in each year in that period are averaged together, producing a single average deciview value

for the worst days. Thus each 5-year period is characterized by two values, i.e., the average

deciview values for the best and worst days, respectively. These averages over the 2000-2004

time period are the basis against which improvements in worst day visibility and lack of

degradation for the best day visibility are judged. Corresponding averages are to be calculated

over successive 5-year periods, i.e., 2005-2009, 2010-2014, etc. 

Within any specified 5-year period, there should be at least 3 complete years of data from

which annual averages are drawn for this calculation of 5-year averages. The calculation of 

5-year averages should include all complete years in that period. If a 5-year period has less than

three complete years of data, then estimates should be prepared through consultation with

EPA/OAQPS.

4.4 What is the nature of the comparison between 5-year average deciview values?

The comparison should be a simple arithmetic comparison of the current 5-year average

deciview values to those from the baseline (i.e., 2000-2004) period. The 5-year average deciview

values for the 20% worst days are compared to judge progress in improving visibility, and the 

5-year average deciview values for the 20% best days are compared to check whether any

degradation of visibility on the best days has occurred. The first such SIP comparison will take

place in 2018, with an interim progress check in 2013.

4.5 What if siting or procedural changes are implemented at an IMPROVE site?

If siting or procedural changes that may affect the monitoring data at a site occur, care

must be taken to ensure the comparability of the monitoring data before and after the change is
Guidance for Tracking Progress 

Under the Regional Haze Rule 

4 - 3

implemented. When possible, the monitoring agency should conduct comparative sampling

adequate to demonstrate data comparability. 

4.6 What if changes are made in the sites selected to cover a mandatory Federal Class I

area?

Currently, all mandatory Federal Class I areas are covered by at least one IMPROVE

monitoring site. The sites chosen to represent the different mandatory Federal Class I areas were

chosen in consultation between EPA and the States. If a different site or additional sites are

selected to represent a given mandatory Federal Class I area, the calculations presented in this

document for trends assessment must be performed using the data from the newly selected

monitoring site(s). 

4.7 Are trends in the individual species important, as well as the overall trend in visibility?

Though the regional haze regulation calls for tracking of calculated haze expressed in

deciview units, the implementation of the haze regulation can only be accomplished by reducing

the concentrations of the particulate species that are responsible for the man-made portion of the

worst haze days. Towards that end it is especially helpful to also track trends for all of the

particle species that contribute to haze. Apportioning the haze to the various particle species

contributors is an important first step to assessing which pollutants offer the best haze reduction

opportunities for emissions controls. In the long-term, tracking trends of species contributions to

haze provides information that can be useful in determining whether implemented emission

controls are having the expected effects. Ultimately as the man-made contributions to specific

particle species are reduced below those from natural source, natural haze level estimates can be

refined.

The contribution to haze by each particle species cannot be determined in terms of the

haze index expressed in deciview units, which because of its logarithmic nature cannot

appropriately be broken into components. The best approach to assign haze contribution to

particle species is by their share of the light extinction expressed in units of inverse megameters

(Mm-1) as defined by the individual terms in equation 8. The IMPROVE web site reports the

species contribution to light extinction for each site and sample period as well as annual trends at

each site for the worst, best, and middle haze days.
Guidance for Tracking Progress 

Under the Regional Haze Rule 

5 - 1

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Heisler, S.L., Henry, R.C., Watson, J.G., and Hidy, G.M., “The 1978 Denver winter haze study

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Malm, W.C., Day, D., and Kreidenweis, S.M., Comparison of measured and reconstructed

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Zhang, X.Q.; McMurry, P.H.; Hering, S.V.; Casuccio, G.S. Mixing characteristics and water

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A - 1

APPENDIX A

Monthly Site-Specific f(RH) Values 

for Each Mandatory Federal Class I Area
1 U.S. EPA, Interpolating Relative Humidity Weighting Factors to Calculate Visibility Impairment

and the Effects of IMPROVE Monitor Outliers, prepared by Science Applications International

Corporation, Raleigh, NC, EPA Contract No. 68-D-98-113, August 30, 2001.

A - 2

 APPENDIX A

Origin of Relative Humidity and f(RH) Values

In terms of visibility reduction caused by fine particles, it is appropriate to treat relative

humidity differently for different objectives. If the objective is the most reliable short-term

estimate of visibility, then the measured or estimated relative humidity for the specific time and

location of the aerosol speciation data is most appropriate. On the other hand, if the objective is

to assess the long-term changes in man-made visibility impairment, it is appropriate to use relative

humidity that is the same for the baseline period and future periods. In other words, it is more

appropriate to eliminate the confounding effects of varying relative humidity, if the purpose is to

track the visibility effects of air pollution emissions over extended time periods.

A number of approaches were considered to prevent variations in the relative humidity

adjustment factor from confounding efforts to track progress related to emission controls. The

simplest approach would use the same typical or overall average adjustment factor for all Class I

areas at all times. However, this would enhance the contributions of hygroscopic particle species

in dry locations and during typically dry seasons above what they truly should be, while reducing

their contributions in moist locations and seasons. Such distortions of the contributions to haze

by hygroscopic particle species are unnecessary if a set of Class I area-specific adjustment factors

are used that reflect seasonal changes in relative humidity. 

A second approach would be to review relative humidity data over a long period of time

to derive climatological estimates for relative humidity adjustment factors. These climatological

estimates would then be used to estimate visibility extinction coefficients. These estimates are

more likely to reflect “typical” relative humidity at the different mandatory Federal Class I areas

during different times of year and, thus, are more likely to be more appropriate for establishing

trends in visibility at the mandatory Federal Class I areas. 

Recently, the U.S. EPA sponsored a project to examine measured hourly relative humidity

data over a 10-year period within the United States, to derive month-specific climatological mean

humidity correction factors for each mandatory Federal Class I area.1 The results of that work are

presented in the table below. These relative humidity factors have been calculated from available

hourly relative humidity data from 292 National Weather Service 
A - 3

stations across the 50 States and the District of Columbia as well as from 29 IMPROVE and

IMPROVE protocol monitor sites, 48 CASTNet sites, and 13 additional sites administered by the

National Park Service. 

The hourly RH measurements from each site were converted to f(RH) values using a nonlinear weighting factor curve, based on a modified ammonium sulfate growth curve. Values

above 95% RH were set equal to the f(RH) corresponding to 95% RH. For days in which at least

16 hours of valid RH data were available, daily averages were determined from these hourly

f(RH) values at each site. Monthly averages were then calculated from the daily f(RH) averages

at each site.

The monthly average f(RH) values were interpolated at 1/4-degree increments using the

inverse distance weighting technique (with a distance interpolation exponent of 1):

where the monthly f(RH)g of the grid cell is calculated from f(RH)w at the weather station, and the

horizontal distance between the grid cell center and the weather station, xwg, summed over all the

weather stations within a 250-mile radius with valid f(RH) values for that month.

In most regions there is a seasonal cycle of relative humidity which is accounted for by this

process of appropriate f(RH) values for each month of the year from the daily-averaged values. 

Thus, the 12 monthly-averaged f(RH) values determined in this way for each Class I area should

be used for all aerosol speciation data or model predictions for that location. However, a more

complicated approach has also been investigated, as described below.

The regional haze regulation requires separate tracking of visibility changes for the worst

20% and best 20% of visibility days. If there is a significant correlation in any month at any site

between daily relative humidity and the sulfate or nitrate concentrations, then use of the monthlyaveraged f(RH) will systematically over- or under-predict the contribution to visibility impairment

of the aerosol species. Fortunately, this concern can be tested at a number of locations in all

regions of the country using the IMPROVE database. If the use of monthly-averaged values were

found to cause large systematic biases in any region of the country, the Class I areas in those

regions would require two f(RH) values for each month. One value would be the average f(RH)

associated with relative humidity conditions that correspond to the worst 20% and the other value

associated with relative humidity conditions that correspond to the best 20% of the light

extinction values. Therefore, there is the potential that some Class I area locations could require

up to 24 f(RH) values for use in calculating extinction for aerosol data.
A - 4

The U.S. National Park Service has tested this possibility by examining data for each of

the 12 months from 20 mandatory Federal Class I areas where relative humidity measurements are

made. In nearly all cases, no statistically significant correlations were found between measured

concentrations of SO4

2-, NO3

-

 and [SO4

2- + NO3

-

] vs. daily values of relative humidity in a large

majority of months. Furthermore, deciview calculations were made using day-specific vs.

climatological values for the relative humidity adjustment factor for each of 10 years in 15

mandatory Federal Class I areas. In 14 of the 15 areas, little if any difference was observed in the

year-to-year calculations for the mean deciview values for the 20% worst and 20% best days, nor

was there any difference in the trends. Some difference in the mean deciview value for the worst

20% days was observed in one mandatory Federal Class I area. However, the overall trend in the

mean worst and best deciview values for this site was similar using the two types of f(rh) values. 

These results suggest there is a relatively weak correlation between hygroscopic components of

PM and relative humidity and that the choice of a “climatological” vs. “day-specific” method for

computing f(RH) has little apparent effect on observed trends in visibility. Consequently, the

simpler climatological approach is used in regional haze calculations.
A - 5

Table A-1 Values for f(RH) Determined from the Growth of Ammonium Sulfate

RH f(RH) RH f(RH) RH f(RH)

1 1.00 34 1.00 67 2.03

2 1.00 35 1.00 68 2.08

3 1.00 36 1.00 69 2.14

4 1.00 37 1.02 70 2.19

5 1.00 38 1.04 71 2.25

6 1.00 39 1.06 72 2.31

7 1.00 40 1.08 73 2.37

8 1.00 41 1.10 74 2.43

9 1.00 42 1.13 75 2.50

10 1.00 43 1.15 76 2.56

11 1.00 44 1.18 77 2.63

12 1.00 45 1.20 78 2.70

13 1.00 46 1.23 79 2.78

14 1.00 47 1.26 80 2.86

15 1.00 48 1.28 81 2.94

16 1.00 49 1.31 82 3.03

17 1.00 50 1.34 83 3.12

18 1.00 51 1.37 84 3.22

19 1.00 52 1.41 85 3.33

20 1.00 53 1.44 86 3.45

21 1.00 54 1.47 87 3.58

22 1.00 55 1.51 88 3.74

23 1.00 56 1.54 89 3.93

24 1.00 57 1.58 90 4.16

25 1.00 58 1.62 91 4.45

26 1.00 59 1.66 92 4.84

27 1.00 60 1.70 93 5.37

28 1.00 61 1.74 94 6.16

29 1.00 62 1.79 95 7.40

30 1.00 63 1.83 96 9.59

31 1.00 64 1.88 97 14.1

32 1.00 65 1.93 98 26.4

33 1.00 66 1.98
Guidance for Tracking Progress Under the Regional Haze Rule

A - 6

Table A-2 

Recommended Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Code Site St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

Acadia Acadia 1 ACAD1 ME 44.38 -68.37 3.2 2.8 2.8 3.2 3.2 3.3 3.7 3.7 3.9 3.5 3.4 3.5

Agua Tibia Agua Tibia 100 A G T I1 CA 33.38 -116.87 2.4 2.3 2.4 2.2 2.2 2.2 2.2 2.3 2.3 2.2 2.1 2.2

Alpine Lakes Snoqualmie Pass 80 SNPA1 W A 47.38 -121.37 5.3 5.0 3.7 3.6 4.2 3.1 3.5 3.4 3.8 4.9 5.5 5.3

Anaconda - Pintler Sula 71 SULA1 MT 45.88 -114.12 3.4 3.0 2.6 2.3 2.3 2.2 1.9 1.8 2.0 2.5 3.3 3.4

A ns el Adam s Kaiser 110 KAIS1 CA 37.13 -119.12 3.0 2.7 2.5 2.1 2.0 1.7 1.7 1.7 1.8 1.9 2.3 2.7

Arches Canyonlands 50 CANY1 UT 38.38 -109.87 2.6 2.3 1.8 1.6 1.5 1.2 1.3 1.5 1.5 1.6 2.0 2.3

Badlands Badlands 59 BADL1 SD 43.63 -101.87 2.8 2.8 2.8 2.6 2.8 2.7 2.4 2.4 2.3 2.3 2.9 2.8

Bandelier Bandelier 33 BAND1 NM 35.88 -106.37 2.3 2.1 1.8 1.6 1.6 1.4 1.7 2.0 1.9 1.7 2.0 2.3

Bering Sea (a)

Big Bend Big Bend 31 BIBE1 T X 29.38 -103.12 1.8 1.7 1.5 1.4 1.5 1.5 1.6 1.8 1.9 1.7 1.7 1.7

Black Canyon of the Gunnison W eminuche 55 W EM I1 CO 37.63 -107.87 2.5 2.3 2.0 1.7 1.7 1.5 1.7 2.0 1.9 1.7 2.2 2.4

Bob Marshall Monture 73 MONT1 MT 47.13 -113.12 3.3 2.9 2.6 2.4 2.4 2.4 2.1 2.0 2.3 2.7 3.2 3.3

Bosque del Apache Bosque del Apache 38 BOAP1 NM 33.88 -106.87 2.2 2.0 1.6 1.4 1.4 1.3 1.7 1.9 1.9 1.6 1.8 2.2

Boundary W aters Canoe Area Boundary W aters 23 BOW A1 MN 47.88 -91.62 2.9 2.6 2.6 2.3 2.5 2.8 3.0 3.1 3.2 2.7 3.1 3.1

Breton Breton 20 BRET1 LA 29.13 -89.12 3.5 3.3 3.3 3.3 3.4 3.6 3.8 3.8 3.6 3.4 3.4 3.5

Bridger Bridger 65 BRID1 W Y 42.88 -109.87 2.5 2.3 2.3 2.1 2.1 1.8 1.5 1.5 1.8 2.0 2.5 2.4

Brigantine Brigantine 5 B RIG 1 NJ 39.38 -74.37 2.9 2.6 2.7 2.6 2.9 3.0 3.2 3.4 3.4 3.2 2.8 2.9

Bryce Canyon Bryce Canyon 49 BRCA1 UT 37.63 -112.12 2.6 2.4 2.0 1.6 1.5 1.3 1.3 1.5 1.5 1.6 2.0 2.4

Cabinet Mountains Cabinet Mountains 75 CAB I1 MT 47.88 -115.62 3.7 3.2 2.8 2.5 2.5 2.4 2.1 2.1 2.4 2.9 3.6 3.8

Caney Creek Caney Creek 29 CACR1 AR 34.38 -94.12 3.3 3.0 2.7 2.8 3.2 3.2 3.0 3.0 3.2 3.2 3.1 3.3

Canyonlands Canyonlands 50 CANY1 UT 38.38 -109.87 2.6 2.3 1.8 1.6 1.5 1.2 1.3 1.5 1.5 1.6 2.0 2.3

Cape Romain Cape Romain 15 ROMA1 SC 32.88 -79.62 3.2 2.9 2.8 2.7 2.9 3.3 3.3 3.6 3.5 3.4 3.1 3.1

Capitol Reef Capitol Reef 52 CAP I1 UT 38.38 -111.37 2.7 2.5 2.0 1.7 1.6 1.4 1.4 1.6 1.6 1.7 2.1 2.5

Caribou Lassen Volcanic 90 LAVO1 CA 40.63 -121.62 3.7 3.1 2.8 2.4 2.3 2.1 2.0 2.0 2.1 2.3 3.1 3.5

Carlsbad Caverns Guadalupe Mountains 32 GUMO1 T X 31.88 -104.87 2.4 2.0 1.6 1.4 1.6 1.5 1.9 2.2 2.4 1.7 1.9 2.3

Chassahowitzka Chassahowitzka 18 CHAS1 FL 28.63 -82.62 3.5 3.2 3.1 3.0 3.0 3.5 3.5 3.7 3.7 3.5 3.4 3.6

Chiricahua NM Chiricahua 39 CHIR1 AZ 32.13 -109.37 2.0 1.9 1.6 1.2 1.2 1.1 1.7 2.0 1.7 1.5 1.6 2.1

Chiricahua W Chiricahua 39 CHIR1 AZ 32.13 -109.37 2.0 1.9 1.6 1.2 1.2 1.1 1.7 2.0 1.7 1.5 1.6 2.1

Cohutta Cohutta 12 COHU1 G A 34.88 -84.62 3.4 3.1 2.9 2.7 3.2 3.6 3.6 3.7 3.7 3.5 3.2 3.4

Crater Lake Crater Lake 86 CRLA1 O R 42.88 -122.12 4.6 4.0 3.7 3.5 3.2 2.9 2.6 2.7 2.9 3.6 4.6 4.7

Craters of the Moon Craters of the Moon 69 CRMO1 ID 43.38 -113.62 3.1 2.7 2.3 2.0 2.0 1.8 1.4 1.4 1.6 2.0 2.7 3.0

Cucamonga San Gabriel 93 SAGA1 CA 34.38 -118.12 2.6 2.5 2.5 2.2 2.2 2.1 2.2 2.2 2.3 2.2 2.2 2.3

Denali Denali 102 DENA1 AK 63.75 -148.75 2.5 2.3 2.1 1.9 1.8 2.1 2.5 2.9 2.8 3.0 2.9 3.0
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-2 

Recommended Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Code Site St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 7

Desolation Bliss 95 BLIS1 CA 38.88 -120.12 3.2 2.8 2.5 2.0 1.9 1.6 1.5 1.5 1.7 1.8 2.4 3.0

Diamond Peak Crater Lake 86 CRLA1 O R 42.88 -122.12 4.6 4.0 3.7 3.5 3.2 2.9 2.6 2.7 2.9 3.6 4.6 4.7

Dolly Sods Dolly Sods 8 DOSO1 W V 39.13 -79.37 3.0 2.7 2.7 2.5 3.5 3.1 3.2 3.5 3.5 3.1 2.8 3.1

Dome Land Dome Land 109 DOME1 CA 35.63 -118.12 2.6 2.3 2.2 1.9 1.9 1.8 1.8 1.8 1.9 1.9 2.0 2.2

Eagle Cap Starkey 76 STAR1 O R 45.13 -118.62 4.3 3.8 3.2 2.9 2.7 2.4 2.0 2.1 2.4 3.3 4.2 4.5

Eagles Nest W hite River 56 W HRI1 CO 39.13 -106.87 2.2 2.2 2.0 2.0 2.0 1.7 1.8 2.1 2.1 1.8 2.1 2.1

Emigrant Yosem ite 96 YOSE1 CA 37.63 -119.62 3.0 2.9 2.7 2.2 2.1 1.7 1.5 1.5 1.6 1.8 2.3 2.7

Everglades Everglades 19 EVER1 FL 25.38 -80.62 2.6 2.5 2.5 2.3 2.3 2.6 2.5 2.8 2.9 2.7 2.5 2.6

Fitzpatrick Bridger 65 BRID1 W Y 42.88 -109.87 2.5 2.3 2.3 2.1 2.1 1.8 1.5 1.5 1.8 2.0 2.5 2.4

Flat Tops W hite River 56 W HRI1 CO 39.13 -106.87 2.2 2.2 2.0 2.0 2.0 1.7 1.8 2.1 2.1 1.8 2.1 2.1

Galiuro Chiricahua 39 CHIR1 AZ 32.13 -109.37 2.0 1.9 1.6 1.2 1.2 1.1 1.7 2.0 1.7 1.5 1.6 2.1

Gates of the Mountains Gates of the Mountains 74 GAMO1 MT 46.88 -111.62 2.8 2.5 2.4 2.3 2.3 2.2 2.0 1.9 2.1 2.4 2.7 2.7

Gearhart Mountain Crater Lake 86 CRLA1 O R 42.88 -122.12 4.6 4.0 3.7 3.5 3.2 2.9 2.6 2.7 2.9 3.6 4.6 4.7

Gila Gila Cliffs 42 GICL1 NM 33.13 -108.12 2.1 1.9 1.6 1.3 1.3 1.2 1.9 1.9 1.8 1.6 1.8 2.2

Glacier Glacier 72 GLAC1 MT 48.63 -114.12 3.9 3.4 3.1 2.9 3.0 3.0 2.5 2.5 3.0 3.3 3.7 3.8

Glacier Peak North Cascades 81 NOCA1 W A 48.63 -121.12 4.5 4.1 3.6 3.4 3.3 3.0 2.9 3.1 3.5 4.2 4.7 4.7

Goat Rocks W hite Pass 79 W HPA1 W A 46.63 -121.37 4.8 4.2 3.8 3.6 3.4 3.1 2.9 3.0 3.5 4.3 4.9 5.0

Grand Canyon Grand Canyon, Hance 48 GRCA2 AZ 35.88 -111.87 2.5 2.4 2.0 1.6 1.4 1.2 1.4 1.7 1.7 1.7 2.0 2.3

Grand Teton Yellowstone 66 YELL2 W Y 44.63 -110.37 2.5 2.3 2.2 2.1 2.1 1.9 1.7 1.6 1.8 2.1 2.4 2.5

Great Gulf Great Gulf 4 GRGU1 NH 44.38 -71.12 2.8 2.6 2.6 2.8 2.9 3.0 3.3 3.5 3.6 3.2 3.0 2.9

Great Sand Dunes Great Sand Dunes 53 GRSA1 CO 37.63 -105.62 2.4 2.3 2.0 1.9 1.9 1.7 1.9 2.3 2.2 1.9 2.3 2.4

Great Smoky Mountains Great Smoky Mountains 10 GRSM1 T N 35.63 -83.87 3.6 3.0 3.0 2.8 3.2 3.6 3.6 3.6 3.7 3.4 3.3 3.5

Guadalupe Mountains Guadalupe Mountains 32 GUMO1 T X 31.88 -104.87 2.4 2.0 1.6 1.4 1.6 1.5 1.9 2.2 2.4 1.7 1.9 2.3

Haleakala Haleakala 108 HALE1 HI 20.75 -156.25 2.7 2.6 2.5 2.5 2.4 2.3 2.4 2.4 2.3 2.5 2.7 2.6

Hawaii Volcanoes Hawaii Volcanoes 107 HAVO1 HI 19.25 -155.25 3.0 2.9 2.9 2.9 2.9 2.9 3.0 3.0 3.0 3.0 3.3 3.0

Hells Canyon Hells Canyon 77 HECA1 O R 44.88 -116.87 3.7 3.1 2.4 2.1 2.0 1.8 1.5 1.4 1.6 2.2 3.4 3.8

Hercules - Glade Hercules - Glade 28 HEGL1 MO 36.63 -92.87 3.2 2.9 2.6 2.6 3.0 3.0 3.0 3.0 3.2 2.9 3.0 3.2

Hoover Hoover 97 HOOV1 CA 38.13 -119.12 3.1 2.7 2.5 2.0 1.9 1.6 1.5 1.5 1.6 1.8 2.3 2.8

Isle Royale Isle Royale 25 ISLE1 MI 47.38 -88.12 3.1 2.6 2.7 2.5 2.4 2.9 3.2 3.4 3.5 2.9 3.3 3.3

James River Face James River Face 7 JAR I1 VA 37.63 -79.62 2.9 2.7 2.6 2.4 2.9 3.1 3.2 3.3 3.4 3.0 2.7 3.0

Jarbidge Jarbidge 68 JARB1 NV 41.88 -115.37 2.9 2.6 2.1 2.1 2.2 2.0 1.6 1.4 1.4 1.6 2.4 2.8

John Muir Kaiser 110 KAIS1 CA 37.13 -119.12 3.0 2.7 2.5 2.1 2.0 1.7 1.7 1.7 1.8 1.9 2.3 2.7

Joshua Tree Joshua Tree 101 JOSH1 CA 34.13 -116.37 2.4 2.3 2.3 2.0 2.0 1.9 1.5 2.0 2.0 2.0 1.9 2.1
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-2 

Recommended Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Code Site St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 8

Joyce Kilmer - Slickrock Great Smoky Mountains 10 GRSM1 T N 35.63 -83.87 3.6 3.0 3.0 2.8 3.2 3.6 3.6 3.6 3.7 3.4 3.3 3.5

Kaiser Kaiser 110 KAIS1 CA 37.13 -119.12 3.0 2.7 2.5 2.1 2.0 1.7 1.7 1.7 1.8 1.9 2.3 2.7

Kalm iopsis Kalm iopsis 89 KALM1 O R 42.63 -124.12 4.5 3.9 3.7 3.5 3.3 3.1 2.9 3.0 3.1 3.6 4.4 4.4

Kings Canyon Sequoia 98 SEQU1 CA 36.38 -118.87 2.9 2.6 2.5 2.2 2.1 1.8 1.7 1.7 1.8 1.9 2.3 2.5

La G arita W eminuche 55 W EM I1 CO 37.63 -107.87 2.5 2.3 2.0 1.7 1.7 1.5 1.7 2.0 1.9 1.7 2.2 2.4

Lassen Volcanic Lassen Volcanic 90 LAVO1 CA 40.63 -121.62 3.7 3.1 2.8 2.4 2.3 2.1 2.0 2.0 2.1 2.3 3.1 3.5

Lava Beds Lava Beds 87 LABE1 CA 41.63 -121.62 4.0 3.4 3.1 2.8 2.6 2.4 2.2 2.2 2.4 2.8 3.6 4.0

Linville Gorge Linville Gorge 13 L IG O 1 NC 35.88 -81.87 3.2 3.0 2.9 2.7 3.2 3.6 3.6 3.9 3.9 3.4 3.1 3.2

Lostwood Lostwood 62 LOST1 ND 48.63 -102.37 3.0 2.9 3.0 2.3 2.2 2.5 2.5 2.3 2.2 2.4 3.2 3.2

Lye Brook Lye Brook 3 LYBR1 VT 43.13 -73.12 2.8 2.6 2.7 2.6 2.8 2.9 3.1 3.3 3.4 3.2 2.9 2.9

Mam m oth C ave Mam m oth C ave 9 MACA1 KY 37.13 -86.12 3.3 3.0 2.9 3.0 4.1 4.7 4.6 3.5 3.5 3.2 3.1 3.4

Marble Mountain Trinity 104 TRIN1 CA 40.88 -122.87 4.0 3.4 3.2 2.9 2.8 2.6 2.5 2.6 2.7 2.9 3.6 3.9

Maroon Bells - Snowmass W hite River 56 W HRI1 CO 39.13 -106.87 2.2 2.2 2.0 2.0 2.0 1.7 1.8 2.1 2.1 1.8 2.1 2.1

Mazatzal Ike's Backbone 46 IKBA1 AZ 34.38 -111.62 2.2 2.0 1.8 1.4 1.3 1.2 1.4 1.7 1.6 1.5 1.8 2.1

Medicine Lake Medicine Lake 63 MELA1 MT 48.38 -104.37 3.0 2.9 2.9 2.2 2.2 2.4 2.4 2.1 2.2 2.3 3.1 3.1

Mesa Verde Mesa Verde 54 MEVE1 CO 37.13 -108.37 2.8 2.6 2.2 1.7 1.7 1.3 1.7 2.0 1.9 1.8 2.2 2.6

Mingo Mingo 26 M IN G 1 MO 36.88 -90.12 3.2 2.9 2.7 2.6 2.9 3.0 3.1 3.1 3.2 2.9 3.0 3.2

Mission Mountains Monture 73 MONT1 MT 47.13 -113.12 3.3 2.9 2.6 2.4 2.4 2.4 2.1 2.0 2.3 2.7 3.2 3.3

Mokelumne Bliss 95 BLIS1 CA 38.88 -120.12 3.2 2.8 2.5 2.0 1.9 1.6 1.5 1.5 1.7 1.8 2.4 3.0

Moosehorn Moosehorn 2 MOOS1 ME 45.13 -67.37 3.0 2.7 2.7 2.9 2.9 3.1 3.5 3.6 3.8 3.3 3.2 3.2

M ount Adam s W hite Pass 79 W HPA1 W A 46.63 -121.37 4.8 4.2 3.8 3.6 3.4 3.1 2.9 3.0 3.5 4.3 4.9 5.0

Mount Baldy Mount Baldy 43 BALD1 AZ 34.13 -109.37 2.2 2.1 1.7 1.4 1.3 1.2 1.6 1.9 1.7 1.6 1.9 2.3

Mount Hood Mount Hood 85 MOHO1 O R 45.38 -121.87 4.6 4.1 3.7 3.6 3.2 3.0 2.7 2.8 3.2 4.1 4.8 4.8

Mount Jefferson Three Sisters 84 THSI1 O R 44.38 -122.12 5.3 4.6 4.4 4.3 3.8 3.4 2.7 2.7 3.1 4.3 5.2 5.3

Mount Rainier Mount Rainier 78 MORA1 W A 46.88 -122.12 5.3 4.7 4.4 4.3 3.9 3.7 3.4 3.6 4.2 5.1 5.5 5.6

Mount W ashington Three Sisters 84 THSI1 O R 44.38 -122.12 5.3 4.6 4.4 4.3 3.8 3.4 2.7 2.7 3.1 4.3 5.2 5.3

Mount Zirkel Mount Zirkel 58 MO ZI1 CO 40.63 -106.62 2.2 2.2 2.0 2.1 2.2 1.8 1.7 1.8 2.0 1.9 2.1 2.1

Mountain Lakes Crater Lake 86 CRLA1 O R 42.88 -122.12 4.6 4.0 3.7 3.5 3.2 2.9 2.6 2.7 2.9 3.6 4.6 4.7

North Absaroka North Absoraka 67 NOAB1 W Y 44.63 -109.37 2.4 2.2 2.2 2.1 2.1 1.9 1.6 1.5 1.8 2.0 2.3 2.4

North Cascades North Cascades 81 NOCA1 W A 48.63 -121.12 4.5 4.1 3.6 3.4 3.3 3.0 2.9 3.1 3.5 4.2 4.7 4.7

Okefenokee Okefenokee 16 OKEF1 G A 30.63 -82.12 3.3 3.0 3.2 3.0 3.2 3.8 3.4 3.6 3.6 3.4 3.3 3.4

Olym pic Olym pic 83 OLYM1 W A 48.13 -122.87 4.2 3.9 3.6 3.5 2.9 3.2 2.7 3.3 3.8 4.3 4.5 4.4

Otter Creek Dolly Sods 8 DOSO1 W V 39.13 -79.37 3.0 2.7 2.7 2.5 3.5 3.1 3.2 3.5 3.5 3.1 2.8 3.1
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-2 

Recommended Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Code Site St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 9

Pasayten Pasayten 82 PASA1 W A 48.38 -119.87 4.6 4.1 3.5 3.3 3.2 2.9 2.8 2.9 3.4 4.1 4.7 4.8

Pecos W heeler Peak 35 W HPE1 NM 36.63 -105.37 2.4 2.2 1.9 1.8 1.8 1.6 1.8 2.1 2.1 1.8 2.2 2.4

Petrified Forest Petrified Forest 41 PEFO1 AZ 35.13 -109.87 2.4 2.1 1.7 1.4 1.3 1.2 1.5 1.8 1.6 1.6 2.0 2.3

Pine Mountain Ike's Backbone 46 IKBA1 AZ 34.38 -111.62 2.2 2.0 1.8 1.4 1.3 1.2 1.4 1.7 1.6 1.5 1.8 2.1

Pinnacles Pinnacles 92 PINN1 CA 36.38 -121.12 3.4 3.4 3.5 2.6 2.4 2.2 2.1 2.2 2.2 2.4 2.4 2.9

Point Reyes Point Reyes 91 PORE1 CA 38.13 -122.87 3.6 3.2 3.1 2.6 2.5 2.3 2.4 2.4 2.5 2.5 2.9 3.3

Presidential Range - Dry River Great Gulf 4 GRGU1 NH 44.38 -71.12 2.8 2.6 2.6 2.8 2.9 3.0 3.3 3.5 3.6 3.2 3.0 2.9

Rawah Mount Zirkel 58 MO ZI1 CO 40.63 -106.62 2.2 2.2 2.0 2.1 2.2 1.8 1.7 1.8 2.0 1.9 2.1 2.1

Red Rock Lakes Yellowstone 66 YELL2 W Y 44.63 -110.37 2.5 2.3 2.2 2.1 2.1 1.9 1.7 1.6 1.8 2.1 2.4 2.5

Redwood Redwood 88 REDW1 CA 41.63 -124.12 3.8 3.6 3.8 3.6 3.8 3.9 4.2 4.2 3.7 3.4 3.6 3.4

Rocky Mountain Rocky Mountain 57 ROMO1 CO 40.38 -105.62 1.9 2.0 2.0 2.1 2.3 2.0 1.9 1.9 2.0 1.8 2.0 1.9

Roosevelt Campobello Moosehorn 2 MOOS1 ME 45.13 -67.37 3.0 2.7 2.7 2.9 2.9 3.1 3.5 3.6 3.8 3.3 3.2 3.2

Saguaro Saguaro 40 SAGU1 AZ 32.13 -110.62 1.8 1.6 1.4 1.1 1.1 1.0 1.4 1.7 1.5 1.4 1.5 2.0

Saint Marks Saint Marks 17 SAMA1 FL 30.13 -84.12 3.5 3.3 3.2 3.1 3.2 3.6 3.8 3.8 3.7 3.5 3.4 3.6

Salt Creek Salt Creek 36 SACR1 NM 33.38 -104.37 2.2 1.9 1.5 1.5 1.6 1.5 1.7 1.9 2.0 1.7 1.8 2.0

San Gabriel San Gabriel 93 SAGA1 CA 34.38 -118.12 2.6 2.5 2.5 2.2 2.2 2.1 2.2 2.2 2.3 2.2 2.2 2.3

San G orgonio San G orgonio 99 SAGO1 CA 34.13 -116.87 2.5 2.6 2.4 2.1 2.1 1.8 1.7 1.8 1.9 1.8 1.9 2.1

San Jacinto San G orgonio 99 SAGO1 CA 34.13 -116.87 2.5 2.6 2.4 2.1 2.1 1.8 1.7 1.8 1.9 1.8 1.9 2.1

San Pedro Parks San Pedro Parks 34 SAPE1 NM 36.13 -106.87 2.4 2.2 1.9 1.6 1.6 1.4 1.7 2.0 1.9 1.7 2.1 2.3

San Rafael San Rafael 94 RAFA1 CA 34.63 -120.12 3.0 2.8 2.8 2.5 2.5 2.4 2.5 2.6 2.7 2.6 2.4 2.6

Saw tooth Saw tooth 70 SAW T1 ID 44.13 -114.87 3.3 2.8 2.3 2.0 2.0 1.8 1.4 1.4 1.5 2.0 2.9 3.3

Scapegoat Monture 73 MONT1 MT 47.13 -113.12 3.3 2.9 2.6 2.4 2.4 2.4 2.1 2.0 2.3 2.7 3.2 3.3

Selway - Bitterroot Sula 71 SULA1 MT 45.88 -114.12 3.4 3.0 2.6 2.3 2.3 2.2 1.9 1.8 2.0 2.5 3.3 3.4

Seney Seney 22 SENE1 MI 46.38 -85.87 3.3 2.8 2.9 2.7 2.6 3.0 3.3 3.6 3.7 3.3 3.5 3.4

Sequoia Sequoia 98 SEQU1 CA 36.38 -118.87 2.9 2.6 2.5 2.2 2.1 1.8 1.7 1.7 1.8 1.9 2.3 2.5

Shenandoah Shenandoah 6 SHEN1 VA 38.63 -78.37 2.9 2.6 2.7 2.4 2.9 3.1 3.2 3.5 3.5 3.0 2.7 2.9

Shining Rock Shining Rock 11 SHRO1 NC 35.38 -82.87 3.3 3.0 2.9 2.7 3.2 3.6 3.6 3.9 3.9 3.5 3.2 3.3

Sierra Ancha Sierra Ancha 45 SIAN1 AZ 34.13 -110.87 2.2 2.0 1.7 1.4 1.3 1.1 1.5 1.8 1.6 1.5 1.8 2.2

Simeonof Simeonof 105 SIME1 AK 55.25 -160.75 4.2 4.2 3.8 4.0 4.2 4.6 5.0 5.2 4.5 3.7 3.9 4.2

Sipsey Sipsey 21 SIPS1 AL 34.38 -87.37 3.3 3.0 2.8 2.7 3.1 3.4 3.5 3.5 3.5 3.3 3.1 3.3

South Warner Lava Beds 87 LABE1 CA 41.63 -121.62 4.0 3.4 3.1 2.8 2.6 2.4 2.2 2.2 2.4 2.8 3.6 4.0

Strawberry Mountain Starkey 76 STAR1 O R 45.13 -118.62 4.3 3.8 3.2 2.9 2.7 2.4 2.0 2.1 2.4 3.3 4.2 4.5

Superstition Tonto 44 TONT1 AZ 33.63 -111.12 2.1 1.9 1.6 1.3 1.2 1.1 1.4 1.7 1.6 1.5 1.7 2.1
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-2 

Recommended Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Code Site St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 10

Swanquarter Swanquarter 14 SWAN1 NC 35.38 -76.12 2.9 2.7 2.6 2.4 2.7 3.0 3.1 3.2 3.1 3.0 2.7 2.9

Sycamore Canyon Sycamore Canyon 47 SYCA1 AZ 35.13 -111.87 2.4 2.4 2.0 1.6 1.5 1.2 1.5 2.0 1.9 1.8 2.0 2.3

Teton Yellowstone 66 YELL2 W Y 44.63 -110.37 2.5 2.3 2.2 2.1 2.1 1.9 1.7 1.6 1.8 2.1 2.4 2.5

Theodore Roosevelt Theodore Roosevelt 61 THRO1 ND 46.88 -103.37 2.9 2.8 2.8 2.4 2.4 2.5 2.4 2.2 2.2 2.3 3.0 3.0

Thousand Lakes Lassen Volcanic 90 LAVO1 CA 40.63 -121.62 3.7 3.1 2.8 2.4 2.3 2.1 2.0 2.0 2.1 2.3 3.1 3.5

Three Sisters Three Sisters 84 THSI1 O R 44.38 -122.12 5.3 4.6 4.4 4.3 3.8 3.4 2.7 2.7 3.1 4.3 5.2 5.3

Tuxedni Tuxedni 103 TUXE1 AK 59.75 -152.75 3.6 3.4 2.9 2.8 2.8 2.9 3.6 3.9 3.8 3.4 3.5 3.7

UL Bend UL Bend 64 ULBE1 MT 47.63 -108.62 2.6 2.4 2.4 2.3 2.2 2.1 1.9 1.8 1.9 2.2 2.6 2.6

Upper Buffalo Upper Buffalo 27 UPBU1 AR 35.88 -93.12 3.2 2.9 2.6 2.7 3.1 3.1 3.0 3.0 3.2 3.0 3.0 3.2

Ventana Pinnacles 92 PINN1 CA 36.38 -121.12 3.4 3.4 3.5 2.6 2.4 2.2 2.1 2.2 2.2 2.4 2.4 2.9

Virgin Islands (b) Virgin Islands 106 VIIS1 VI 18.75 -155.75

Voyageurs Voyageurs 24 VOYA2 MN 48.38 -92.87 2.7 2.4 2.3 2.2 2.2 2.8 2.5 2.7 2.9 2.5 2.8 2.7

W ashakie North Absoraka 67 NOAB1 W Y 44.63 -109.37 2.4 2.2 2.2 2.1 2.1 1.9 1.6 1.5 1.8 2.0 2.3 2.4

W eminuche W eminuche 55 W EM I1 CO 37.63 -107.87 2.5 2.3 2.0 1.7 1.7 1.5 1.7 2.0 1.9 1.7 2.2 2.4

W est Elk W hite River 56 W HRI1 CO 39.13 -106.87 2.2 2.2 2.0 2.0 2.0 1.7 1.8 2.1 2.1 1.8 2.1 2.1

W heeler Peak W heeler Peak 35 W HPE1 NM 36.63 -105.37 2.4 2.2 1.9 1.8 1.8 1.6 1.8 2.1 2.1 1.8 2.2 2.4

W hite Mountain W hite Mountain 37 W H IT 1 NM 33.38 -105.62 2.2 1.9 1.6 1.5 1.5 1.4 1.7 1.9 2.0 1.7 1.8 2.1

W ichita Mountains W ichita Mountains 30 W IM O 1 O K 34.63 -98.62 2.8 2.6 2.4 2.4 2.7 2.5 2.2 2.4 2.7 2.5 2.6 2.8

W ind C ave W ind C ave 60 W ICA1 SD 43.63 -103.37 2.5 2.5 2.5 2.5 2.6 2.5 2.2 2.2 2.1 2.2 2.6 2.5

W olf Island Okefenokee 16 OKEF1 G A 30.63 -82.12 3.3 3.0 3.2 3.0 3.2 3.8 3.4 3.6 3.6 3.4 3.3 3.4

Yellowstone Yellowstone 66 YELL2 W Y 44.63 -110.37 2.5 2.3 2.2 2.1 2.1 1.9 1.7 1.6 1.8 2.1 2.4 2.5

Yolla Bolly - Middle Eel Trinity 104 TRIN1 CA 40.88 -122.87 4.0 3.4 3.2 2.9 2.8 2.6 2.5 2.6 2.7 2.9 3.6 3.9

Yosem ite Yosem ite 96 YOSE1 CA 37.63 -119.62 3.0 2.9 2.7 2.2 2.1 1.7 1.5 1.5 1.6 1.8 2.3 2.7

a: No particulate matter sampling or visibility monitoring is conducted in the Bering Sea Wilderness.

b: f(RH) values for Virgin Islands National Park were not calculated because of the limited RH data available.
Guidance for Tracking Progress Under the Regional Haze Rule

A - 11

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area, 

Based on the Centroid of the Area (Supplemental Information)

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Ma p ID Code St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

Acadia Acadia 1 ACAD1 ME 44.37 68.26 3.3 2.9 2.8 3.4 3.1 3.0 3.4 3.8 4.0 3.8 3.6 3.5

Agua Tibia Agua Tibia 100 A G T I1 CA 33.41 116.98 2.4 2.4 2.4 2.2 2.2 2.2 2.3 2.3 2.3 2.3 2.1 2.2

Alpine Lakes Snoqualmie Pass 80 SNPA1 W A 47.42 121.42 4.3 3.8 3.5 3.9 2.9 3.2 2.9 3.1 3.3 3.9 4.5 4.5

Anaconda - Pintler Sula 71 SULA1 MT 45.98 113.42 3.3 2.9 2.5 2.4 2.4 2.3 2.0 1.9 2.1 2.5 3.2 3.3

A ns el Adam s Kaiser 110 KAIS1 CA 37.65 119.20 3.0 2.7 2.4 2.1 1.9 1.7 1.6 1.6 1.6 1.8 2.3 2.7

Arches Canyonlands 50 CANY1 UT 38.64 109.58 2.6 2.3 1.8 1.6 1.6 1.3 1.4 1.5 1.6 1.6 2.0 2.3

Badlands Badlands 59 BADL1 SD 43.74 101.94 2.6 2.7 2.6 2.4 2.8 2.7 2.5 2.4 2.2 2.3 2.7 2.7

Bandelier Bandelier 33 BAND1 NM 35.78 106.27 2.2 2.1 1.8 1.6 1.6 1.4 1.7 2.1 1.9 1.7 2.0 2.2

Bering Sea (a) 60.45 172.79

Big Bend Big Bend 31 BIBE1 T X 29.31 103.19 2.0 1.9 1.6 1.5 1.6 1.6 1.7 2.0 2.1 1.9 1.8 1.9

Black Canyon of the Gunnison W eminuche 55 W EM I1 CO 38.58 107.70 2.4 2.2 1.9 1.9 1.9 1.6 1.7 1.9 2.0 1.8 2.1 2.3

Bob Marshall Monture 73 MONT1 MT 47.75 113.38 3.6 3.1 2.8 2.6 2.7 2.7 2.3 2.2 2.6 2.9 3.5 3.5

Bosque del Apache Bosque del Apache 38 BOAP1 NM 33.79 106.83 2.1 1.9 1.6 1.4 1.4 1.3 1.8 2.0 1.9 1.6 1.8 2.2

Boundary W aters Canoe Area Boundary W aters 23 BOW A1 MN 47.95 91.50 3.0 2.6 2.7 2.4 2.3 2.9 3.1 3.4 3.5 2.8 3.2 3.2

Breton Breton 20 BRET1 LA 29.73 88.88 3.7 3.5 3.7 3.6 3.8 4.0 4.3 4.3 4.2 3.7 3.7 3.7

Bridger Bridger 65 BRID1 W Y 42.98 109.76 2.5 2.4 2.3 2.2 2.1 1.8 1.5 1.5 1.7 2.0 2.4 2.4

Brigantine Brigantine 5 B RIG 1 NJ 39.46 74.45 2.8 2.6 2.7 2.6 3.0 3.2 3.4 3.7 3.6 3.3 2.9 2.8

Bryce Canyon Bryce Canyon 49 BRCA1 UT 37.62 112.17 2.6 2.4 1.9 1.6 1.5 1.3 1.3 1.5 1.5 1.6 2.0 2.4

Cabinet Mountains Cabinet Mountains 75 CAB I1 MT 48.21 115.71 3.8 3.3 2.9 2.6 2.7 2.7 2.3 2.2 2.6 3.0 3.7 3.9

Caney Creek Caney Creek 29 CACR1 AR 34.41 94.08 3.4 3.1 2.9 3.0 3.6 3.6 3.4 3.4 3.6 3.5 3.4 3.5

Canyonlands Canyonlands 50 CANY1 UT 38.46 109.82 2.6 2.3 1.7 1.6 1.5 1.2 1.3 1.5 1.6 1.6 2.0 2.3

Cape Romain Cape Romain 15 ROMA1 SC 32.94 79.66 3.3 3.0 2.9 2.8 3.2 3.7 3.6 4.1 4.0 3.7 3.4 3.2

Capitol Reef Capitol Reef 52 CAP I1 UT 38.36 111.05 2.7 2.4 2.0 1.7 1.6 1.4 1.4 1.6 1.6 1.7 2.1 2.5

Caribou Lassen Volcanic 90 LAVO1 CA 40.50 121.18 3.7 3.1 2.8 2.5 2.4 2.2 2.1 2.1 2.2 2.4 3.0 3.4

Carlsbad Caverns Guadalupe Mountains 32 GUMO1 T X 32.14 104.48 2.1 2.0 1.6 1.5 1.6 1.6 1.8 2.1 2.2 1.8 1.9 2.1

Chassahowitzka Chassahowitzka 18 CHAS1 FL 28.75 82.55 3.8 3.5 3.4 3.2 3.3 3.9 3.9 4.2 4.1 3.9 3.7 3.9

Chiricahua NM Chiricahua 39 CHIR1 AZ 32.01 109.39 2.0 2.0 1.6 1.3 1.3 1.1 1.8 2.1 1.8 1.5 1.6 2.2

Chiricahua W Chiricahua 39 CHIR1 AZ 31.84 109.27 2.0 1.9 1.6 1.2 1.3 1.1 1.8 2.1 1.8 1.5 1.6 2.2

Cohutta Cohutta 12 COHU1 G A 34.92 84.58 3.3 3.1 3.0 2.8 3.4 3.8 4.0 4.2 4.2 3.8 3.4 3.5

Crater Lake Crater Lake 86 CRLA1 O R 42.90 122.13 4.6 3.9 3.7 3.4 3.2 3.0 2.8 2.9 3.1 3.6 4.6 4.6

Craters of the Moon Craters of the Moon 69 CRMO1 ID 43.47 113.55 3.1 2.7 2.3 2.0 2.0 1.8 1.4 1.4 1.6 2.0 2.8 3.0

Cucamonga San Gabriel 93 SAGA1 CA 34.25 117.57 2.5 2.4 2.4 2.2 2.1 2.1 2.1 2.2 2.2 2.2 2.1 2.2

Denali Denali 102 DENA1 AK 63.72 148.97 2.5 2.3 2.1 1.9 1.9 2.2 2.5 3.0 2.8 2.9 3.0 3.1

Desolation Bliss 95 BLIS1 CA 38.98 120.12 3.2 2.8 2.4 2.0 1.8 1.6 1.5 1.6 1.7 1.9 2.4 3.0

Diamond Peak Crater Lake 86 CRLA1 O R 43.53 122.10 4.5 4.0 3.6 3.7 3.2 3.1 2.9 2.9 3.1 3.7 4.6 4.6

Dolly Sods Dolly Sods 8 DOSO1 W V 39.11 79.43 3.0 2.8 2.8 2.6 3.1 3.4 3.5 3.9 3.9 3.3 3.0 3.1
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area, 

Based on the Centroid of the Area (Supplemental Information)

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Ma p ID Code St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 12

Dome Land Dome Land 109 DOME1 CA 35.70 118.19 2.5 2.3 2.2 1.9 1.8 1.8 1.8 1.8 1.8 1.9 2.0 2.2

Eagle Cap Starkey 76 STAR1 O R 45.10 117.29 3.8 3.2 2.5 2.1 2.0 1.9 1.6 1.6 1.6 2.3 3.4 4.0

Eagles Nest W hite River 56 W HRI1 CO 39.69 106.25 2.2 2.2 2.0 2.0 2.1 1.9 1.8 2.0 2.0 1.9 2.1 2.1

Emigrant Yosem ite 96 YOSE1 CA 38.20 119.75 3.2 2.8 2.5 2.1 1.9 1.7 1.5 1.6 1.6 1.9 2.4 2.9

Everglades Everglades 19 EVER1 FL 25.39 80.68 2.7 2.6 2.6 2.4 2.4 2.7 2.6 2.9 3.0 2.8 2.6 2.7

Fitzpatrick Bridger 65 BRID1 W Y 43.27 109.57 2.5 2.3 2.2 2.1 2.1 1.8 1.5 1.5 1.7 2.0 2.4 2.4

Flat Tops W hite River 56 W HRI1 CO 39.97 107.25 2.3 2.2 2.0 2.0 2.0 1.8 1.7 1.9 1.9 1.8 2.2 2.2

Galiuro Chiricahua 39 CHIR1 AZ 32.56 110.32 2.0 1.8 1.5 1.2 1.2 1.1 1.5 1.8 1.6 1.5 1.6 2.1

Gates of the Mountains Gates of the Mountains 74 GAMO1 MT 46.87 111.81 2.9 2.6 2.4 2.3 2.3 2.3 2.0 1.9 2.1 2.4 2.8 2.8

Gearhart Mountain Crater Lake 86 CRLA1 O R 42.49 120.85 4.0 3.4 3.1 2.8 2.7 2.5 2.3 2.3 2.4 2.8 3.7 3.8

Gila Gila Cliffs 42 GICL1 NM 33.22 108.25 2.1 1.9 1.6 1.3 1.4 1.2 2.1 2.0 1.8 1.6 1.8 2.2

Glacier Glacier 72 GLAC1 MT 48.51 114.00 4.0 3.5 3.2 3.1 3.2 3.4 2.8 2.6 3.2 3.5 3.8 3.9

Glacier Peak North Cascades 81 NOCA1 W A 48.21 121.04 4.2 3.7 3.4 3.8 2.9 3.2 2.9 3.1 3.3 3.9 4.4 4.4

Goat Rocks W hite Pass 79 W HPA1 W A 46.54 121.48 4.3 3.8 3.4 4.2 2.8 3.4 3.0 3.2 3.1 3.8 4.4 4.6

Grand Canyon Grand Canyon, Hance 48 GRCA2 AZ 35.97 111.98 2.4 2.3 1.9 1.5 1.4 1.2 1.4 1.7 1.6 1.6 1.9 2.3

Grand Teton Yellowstone 66 YELL2 W Y 43.68 110.73 2.6 2.4 2.2 2.1 2.1 1.8 1.5 1.5 1.7 2.0 2.4 2.6

Great Gulf Great Gulf 4 GRGU1 NH 44.31 71.22 2.8 2.6 2.6 2.8 2.9 3.2 3.5 3.8 4.0 3.4 3.1 2.9

Great Sand Dunes Great Sand Dunes 53 GRSA1 CO 37.73 105.52 2.4 2.3 2.0 1.9 1.9 1.8 1.9 2.3 2.2 1.9 2.4 2.4

Great Smoky Mountains Great Smoky Mountains 10 GRSM1 T N 35.63 83.94 3.3 3.0 2.9 2.7 3.2 3.9 3.8 4.0 4.2 3.8 3.3 3.4

Guadalupe Mountains Guadalupe Mountains 32 GUMO1 T X 31.83 104.80 2.0 2.0 1.6 1.5 1.6 1.5 1.9 2.2 2.2 1.8 1.9 2.2

Haleakala Haleakala 108 HALE1 HI 20.81 156.28 2.7 2.6 2.6 2.5 2.4 2.3 2.5 2.4 2.4 2.5 2.8 2.7

Hawaii Volcanoes Hawaii Volcanoes 107 HAVO1 HI 19.43 155.27 3.2 2.9 3.0 3.0 3.0 2.9 3.1 3.2 3.2 3.2 3.7 3.2

Hells Canyon Hells Canyon 77 HECA1 O R 45.34 116.57 3.7 3.1 2.5 2.2 2.1 2.0 1.6 1.6 1.8 2.4 3.5 3.9

Hercules - Glade Hercules - Glade 28 HEGL1 MO 36.69 92.90 3.2 2.9 2.7 2.7 3.3 3.3 3.3 3.3 3.4 3.1 3.1 3.3

Hoover Hoover 97 HOOV1 CA 38.14 119.45 3.1 2.8 2.5 2.1 1.9 1.6 1.5 1.5 1.6 1.8 2.3 2.8

Isle Royale Isle Royale 25 ISLE1 MI 47.99 88.83 3.1 2.5 2.7 2.4 2.2 2.6 3.0 3.2 3.8 2.7 3.3 3.3

James River Face James River Face 7 JAR I1 VA 37.62 79.48 2.8 2.6 2.7 2.4 3.0 3.3 3.4 3.7 3.6 3.2 2.8 3.0

Jarbidge Jarbidge 68 JARB1 NV 41.89 115.43 3.0 2.6 2.1 2.1 2.2 2.2 1.6 1.4 1.4 1.6 2.4 2.8

John Muir Kaiser 110 KAIS1 CA 37.39 118.84 2.9 2.6 2.4 2.1 1.9 1.7 1.7 1.7 1.7 1.9 2.2 2.6

Joshua Tree Joshua Tree 101 JOSH1 CA 34.03 116.18 2.4 2.3 2.2 2.0 2.0 1.9 2.0 2.0 2.0 2.0 1.9 2.0

Joyce Kilmer - Slickrock Great Smoky Mountains 10 GRSM1 T N 35.43 84.00 3.3 3.1 2.9 2.7 3.3 3.8 4.0 4.2 4.2 3.8 3.3 3.5

Kaiser Kaiser 110 KAIS1 CA 37.28 119.18 3.0 2.7 2.5 2.1 1.9 1.7 1.6 1.7 1.7 1.9 2.3 2.7

Kalm iopsis Kalm iopsis 89 KALM1 O R 42.27 123.93 4.5 3.9 3.8 3.5 3.5 3.3 3.2 3.2 3.3 3.6 4.4 4.3

Kings Canyon Sequoia 98 SEQU1 CA 36.82 118.76 2.8 2.6 2.4 2.1 1.9 1.8 1.7 1.7 1.8 1.9 2.3 2.5

La G arita W eminuche 55 W EM I1 CO 37.96 106.81 2.3 2.2 1.9 1.8 1.8 1.6 1.7 2.1 2.0 1.8 2.2 2.3

Lassen Volcanic Lassen Volcanic 90 LAVO1 CA 40.54 121.57 3.8 3.2 2.9 2.5 2.4 2.2 2.1 2.1 2.2 2.4 3.1 3.5
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area, 

Based on the Centroid of the Area (Supplemental Information)

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Ma p ID Code St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 13

Lava Beds Lava Beds 87 LABE1 CA 41.71 121.34 4.0 3.4 3.1 2.7 2.6 2.4 2.3 2.3 2.4 2.7 3.5 3.8

Linville Gorge Linville Gorge 13 L IG O 1 NC 35.89 81.89 3.3 3.0 3.0 2.7 3.3 3.9 4.1 4.5 4.4 3.7 3.2 3.4

Lostwood Lostwood 62 LOST1 ND 48.60 102.48 3.0 2.9 2.9 2.3 2.3 2.6 2.7 2.4 2.3 2.4 3.2 3.2

Lye Brook Lye Brook 3 LYBR1 VT 43.15 73.12 2.7 2.6 2.6 2.6 2.8 3.0 3.3 3.6 3.7 3.3 2.9 2.8

Mam m oth C ave Mam m oth C ave 9 MACA1 KY 37.22 86.07 3.4 3.1 2.9 2.6 3.2 3.5 3.7 3.9 3.9 3.4 3.2 3.5

Marble Mountain Trinity 104 TRIN1 CA 41.52 123.21 4.4 3.8 3.7 3.3 3.4 3.2 3.2 3.2 3.2 3.4 4.1 4.2

Maroon Bells - Snowmass W hite River 56 W HRI1 CO 39.15 106.82 2.2 2.1 2.0 2.0 2.1 1.7 1.9 2.2 2.1 1.8 2.1 2.1

Mazatzal Ike's Backbone 46 IKBA1 AZ 33.92 111.43 2.1 1.9 1.7 1.3 1.3 1.1 1.5 1.7 1.6 1.5 1.7 2.1

Medicine Lake Medicine Lake 63 MELA1 MT 48.50 104.29 3.0 2.9 2.9 2.3 2.2 2.5 2.5 2.2 2.2 2.4 3.2 3.2

Mesa Verde Mesa Verde 54 MEVE1 CO 37.20 108.49 2.5 2.3 1.9 1.5 1.5 1.3 1.6 2.0 1.9 1.7 2.1 2.3

Mingo Mingo 26 M IN G 1 MO 36.98 90.20 3.3 3.0 2.8 2.6 3.0 3.2 3.3 3.5 3.5 3.1 3.1 3.3

Mission Mountains Monture 73 MONT1 MT 47.40 113.85 3.6 3.1 2.7 2.5 2.6 2.6 2.3 2.2 2.5 2.9 3.5 3.6

Mokelumne Bliss 95 BLIS1 CA 38.58 120.03 3.2 2.8 2.4 2.0 1.9 1.6 1.5 1.6 1.7 1.9 2.4 2.9

Moosehorn Moosehorn 2 MOOS1 ME 45.12 67.26 3.0 2.7 2.7 3.0 3.0 3.1 3.4 3.8 3.9 3.5 3.2 3.2

M ount Adam s W hite Pass 79 W HPA1 W A 46.19 121.50 4.3 3.8 3.4 4.4 2.9 3.5 3.1 3.3 3.1 3.9 4.5 4.6

Mount Baldy Mount Baldy 43 BALD1 AZ 34.12 109.57 2.2 2.0 1.7 1.4 1.3 1.2 1.6 1.9 1.7 1.6 1.8 2.2

Mount Hood Mount Hood 85 MOHO1 O R 45.38 121.69 4.3 3.8 3.5 3.9 3.0 3.2 2.9 3.0 3.1 3.9 4.5 4.6

Mount Jefferson Three Sisters 84 THSI1 O R 44.55 121.83 4.4 3.9 3.6 3.7 3.1 3.1 2.9 2.9 3.0 3.8 4.6 4.5

Mount Rainier Mount Rainier 78 MORA1 W A 46.76 122.12 4.4 4.0 3.6 4.7 3.1 3.7 3.3 3.5 3.4 4.1 4.7 4.7

Mount W ashington Three Sisters 84 THSI1 O R 44.30 121.87 4.4 3.9 3.6 3.7 3.1 3.1 3.0 2.9 3.0 3.8 4.6 4.6

Mount Zirkel Mount Zirkel 58 MO ZI1 CO 40.55 106.70 2.2 2.2 2.0 2.1 2.2 1.9 1.7 1.9 2.0 1.9 2.1 2.1

Mountain Lakes Crater Lake 86 CRLA1 O R 42.34 122.11 4.3 3.6 3.3 3.0 2.9 2.6 2.5 2.5 2.6 3.1 4.1 4.3

North Absaroka North Absoraka 67 NOAB1 W Y 44.77 109.78 2.4 2.3 2.2 2.2 2.1 1.9 1.7 1.6 1.8 2.0 2.4 2.4

North Cascades North Cascades 81 NOCA1 W A 48.54 121.44 4.1 3.7 3.4 3.7 2.9 3.2 2.9 3.2 3.5 3.9 4.4 4.4

Okefenokee Okefenokee 16 OKEF1 G A 30.74 82.13 3.5 3.2 3.1 3.0 3.6 3.7 3.7 4.1 4.0 3.8 3.5 3.6

Olym pic Olym pic 83 OLYM1 W A 47.32 123.35 4.5 4.1 3.8 4.1 3.2 3.5 3.1 3.5 3.7 4.4 4.8 4.8

Otter Creek Dolly Sods 8 DOSO1 W V 39.00 79.65 3.0 2.8 2.8 2.6 3.2 3.5 3.7 4.1 4.0 3.3 3.0 3.1

Pasayten Pasayten 82 PASA1 W A 48.85 120.52 4.2 3.7 3.4 3.7 2.9 3.2 2.9 3.2 3.3 3.9 4.4 4.5

Pecos W heeler Peak 35 W HPE1 NM 35.93 105.64 2.3 2.1 1.8 1.7 1.7 1.5 1.8 2.1 2.0 1.7 2.0 2.2

Petrified Forest Petrified Forest 41 PEFO1 AZ 35.08 109.77 2.4 2.2 1.7 1.4 1.3 1.2 1.5 1.8 1.7 1.6 1.9 2.3

Pine Mountain Ike's Backbone 46 IKBA1 AZ 34.31 111.80 2.2 2.0 1.7 1.4 1.3 1.1 1.4 1.8 1.6 1.5 1.7 2.1

Pinnacles Pinnacles 92 PINN1 CA 36.49 121.16 3.2 2.8 2.6 2.4 2.3 2.0 2.0 2.1 2.1 2.3 2.5 2.9

Point Reyes Point Reyes 91 PORE1 CA 38.12 122.90 3.6 3.3 3.1 2.7 2.5 2.3 2.5 2.6 2.6 2.7 2.9 3.3

Presidential Range - Dry River Great Gulf 4 GRGU1 NH 44.21 71.35 2.8 2.6 2.6 2.8 3.0 3.4 3.7 4.0 4.3 3.5 3.1 3.0

Rawah Mount Zirkel 58 MO ZI1 CO 40.70 105.94 2.1 2.1 2.0 2.1 2.3 2.0 1.8 2.0 2.0 1.9 2.1 2.0

Red Rock Lakes Yellowstone 66 YELL2 W Y 44.67 111.70 2.7 2.5 2.3 2.1 2.1 1.9 1.7 1.6 1.8 2.1 2.6 2.7
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area, 

Based on the Centroid of the Area (Supplemental Information)

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Ma p ID Code St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 14

Redwood Redwood 88 REDW1 CA 41.56 124.08 4.4 3.9 4.6 3.9 4.5 4.7 4.9 4.7 4.3 3.7 3.8 3.4

Rocky Mountain Rocky Mountain 57 ROMO1 CO 40.28 105.55 1.7 1.9 1.9 2.1 2.3 2.0 1.8 2.0 1.9 1.8 1.8 1.7

Roosevelt Campobello Moosehorn 2 MOOS1 ME 44.88 66.95 3.0 2.7 2.7 3.0 3.0 3.1 3.4 3.8 3.9 3.5 3.3 3.2

Saguaro Saguaro 40 SAGU1 AZ 32.25 110.73 1.8 1.6 1.4 1.1 1.1 1.1 1.4 1.8 1.6 1.4 1.6 2.1

Saint Marks Saint Marks 17 SAMA1 FL 30.12 84.08 3.7 3.4 3.4 3.4 3.5 4.0 4.1 4.4 4.2 3.8 3.7 3.8

Salt Creek Salt Creek 36 SACR1 NM 33.61 104.37 2.1 1.9 1.5 1.5 1.7 1.6 1.8 2.0 2.1 1.8 1.8 2.1

San Gabriel San Gabriel 93 SAGA1 CA 34.27 117.94 2.5 2.5 2.4 2.2 2.2 2.1 2.2 2.2 2.2 2.3 2.1 2.2

San G orgonio San G orgonio 99 SAGO1 CA 34.18 116.90 2.7 2.8 2.6 2.3 2.2 1.9 1.8 1.9 1.9 1.9 1.9 2.2

San Jacinto San G orgonio 99 SAGO1 CA 33.75 116.65 2.5 2.4 2.4 2.2 2.1 2.0 2.1 2.1 2.1 2.1 2.0 2.1

San Pedro Parks San Pedro Parks 34 SAPE1 NM 36.11 106.81 2.3 2.1 1.8 1.6 1.6 1.4 1.7 2.0 1.9 1.7 2.1 2.2

San Rafael San Rafael 94 RAFA1 CA 34.78 119.83 2.8 2.7 2.7 2.4 2.3 2.3 2.5 2.5 2.4 2.5 2.3 2.5

Saw tooth Saw tooth 70 SAW T1 ID 44.18 114.93 3.3 2.9 2.3 2.0 2.0 1.8 1.4 1.4 1.5 2.0 2.9 3.3

Scapegoat Monture 73 MONT1 MT 47.17 112.73 3.2 2.8 2.6 2.4 2.5 2.4 2.1 2.0 2.3 2.6 3.1 3.1

Selway - Bitterroot Sula 71 SULA1 MT 45.86 114.00 3.5 3.0 2.6 2.3 2.4 2.3 1.9 1.9 2.1 2.6 3.3 3.5

Seney Seney 22 SENE1 MI 46.26 86.03 3.3 2.8 2.9 2.7 2.6 3.1 3.6 4.0 4.1 3.4 3.6 3.5

Sequoia Sequoia 98 SEQU1 CA 36.50 118.82 2.5 2.4 2.4 2.2 1.9 1.8 1.7 1.6 1.8 1.9 2.3 2.3

Shenandoah Shenandoah 6 SHEN1 VA 38.52 78.44 3.1 2.8 2.8 2.5 3.1 3.4 3.5 3.9 3.9 3.2 3.0 3.1

Shining Rock Shining Rock 11 SHRO1 NC 35.39 82.78 3.3 3.0 2.9 2.7 3.4 3.9 4.1 4.5 4.4 3.8 3.3 3.4

Sierra Ancha Sierra Ancha 45 SIAN1 AZ 33.82 110.88 2.1 2.0 1.7 1.3 1.3 1.1 1.5 1.8 1.6 1.5 1.7 2.1

Simeonof Simeonof 105 SIME1 AK 54.92 159.28 4.3 4.1 3.6 3.9 3.9 4.3 5.0 5.2 4.5 3.8 4.0 4.3

Sipsey Sipsey 21 SIPS1 AL 34.34 87.34 3.4 3.1 2.9 2.8 3.3 3.7 3.9 3.9 3.9 3.6 3.3 3.4

South Warner Lava Beds 87 LABE1 CA 41.33 120.20 3.6 3.1 2.7 2.4 2.3 2.1 1.9 1.9 2.0 2.3 3.1 3.4

Strawberry Mountain Starkey 76 STAR1 O R 44.30 118.73 3.9 3.3 2.8 2.9 2.3 2.4 2.0 2.0 1.9 2.6 3.7 4.1

Superstition Tonto 44 TONT1 AZ 33.63 111.10 2.1 1.9 1.6 1.3 1.3 1.1 1.5 1.7 1.6 1.5 1.7 2.1

Swanquarter Swanquarter 14 SWAN1 NC 35.31 76.28 2.9 2.7 2.6 2.5 2.9 3.2 3.4 3.5 3.4 3.1 2.8 2.9

Sycamore Canyon Sycamore Canyon 47 SYCA1 AZ 34.03 116.18 2.4 2.3 2.2 2.0 2.0 1.9 2.0 2.0 2.0 2.0 1.9 2.0

Teton Yellowstone 66 YELL2 W Y 44.09 110.18 2.5 2.4 2.2 2.1 2.1 1.9 1.6 1.5 1.7 2.0 2.4 2.5

Theodore Roosevelt Theodore Roosevelt 61 THRO1 ND 47.30 104.00 2.9 2.8 2.8 2.3 2.3 2.5 2.4 2.2 2.2 2.3 3.0 3.0

Thousand Lakes Lassen Volcanic 90 LAVO1 CA 40.70 121.58 3.8 3.2 2.9 2.5 2.4 2.2 2.1 2.1 2.2 2.4 3.1 3.5

Three Sisters Three Sisters 84 THSI1 O R 44.29 122.04 4.5 4.0 3.6 3.7 3.1 3.1 3.0 2.9 3.0 3.8 4.6 4.6

Tuxedni Tuxedni 103 TUXE1 AK 60.15 152.60 3.5 3.3 2.9 2.7 2.7 2.9 3.6 4.0 3.9 3.5 3.5 3.7

UL Bend UL Bend 64 ULBE1 MT 47.55 107.87 2.7 2.5 2.5 2.3 2.2 2.2 2.0 1.8 1.9 2.2 2.7 2.7

Upper Buffalo Upper Buffalo 27 UPBU1 AR 35.83 93.21 3.3 3.0 2.7 2.8 3.4 3.4 3.4 3.4 3.6 3.3 3.2 3.3

Ventana Pinnacles 92 PINN1 CA 36.22 121.59 3.2 2.9 2.8 2.4 2.3 2.1 2.2 2.3 2.2 2.4 2.5 2.9

Virgin Islands (b) Virgin Islands 106 VIIS1 VI 18.33 64.79

Voyageurs Voyageurs 24 VOYA2 MN 48.59 93.17 2.8 2.4 2.4 2.3 2.3 3.1 2.7 3.0 3.2 2.6 2.9 2.8
Guidance for Tracking Progress Under the Regional Haze Rule

Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I Area, 

Based on the Centroid of the Area (Supplemental Information)

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Class I Area S ite N am e Ma p ID Code St LAT LONG f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH) f(RH)

A - 15

W ashakie North Absoraka 67 NOAB1 W Y 43.95 109.59 2.5 2.3 2.2 2.1 2.1 1.8 1.6 1.5 1.8 2.0 2.4 2.5

W eminuche W eminuche 55 W EM I1 CO 37.65 107.80 2.4 2.2 1.9 1.7 1.7 1.5 1.6 2.0 1.9 1.7 2.1 2.3

W est Elk W hite River 56 W HRI1 CO 38.69 107.19 2.3 2.2 1.9 1.9 1.9 1.7 1.8 2.1 2.0 1.8 2.1 2.2

W heeler Peak W heeler Peak 35 W HPE1 NM 36.57 105.42 2.3 2.2 1.9 1.8 1.8 1.6 1.8 2.2 2.1 1.8 2.2 2.3

W hite Mountain W hite Mountain 37 W H IT 1 NM 33.49 105.83 2.1 1.9 1.6 1.5 1.5 1.4 1.8 2.0 2.0 1.7 1.8 2.1

W ichita Mountains W ichita Mountains 30 W IM O 1 O K 34.74 98.59 2.7 2.6 2.4 2.4 3.0 2.7 2.3 2.5 2.9 2.6 2.7 2.8

W ind C ave W ind C ave 60 W ICA1 SD 43.55 103.48 2.5 2.5 2.5 2.5 2.7 2.5 2.3 2.3 2.2 2.2 2.6 2.6

W olf Island Okefenokee 16 OKEF1 G A 31.31 81.30 3.4 3.1 3.1 3.0 3.3 3.7 3.7 4.1 4.0 3.7 3.5 3.5

Yellowstone Yellowstone 66 YELL2 W Y 44.55 110.40 2.5 2.4 2.3 2.2 2.2 1.9 1.7 1.6 1.8 2.1 2.5 2.5

Yolla Bolly - Middle Eel Trinity 104 TRIN1 CA 40.11 122.96 4.0 3.4 3.1 2.8 2.7 2.5 2.4 2.5 2.6 2.7 3.3 3.6

Yosem ite Yosem ite 96 YOSE1 CA 37.71 119.70 3.3 3.0 2.8 2.3 2.1 1.8 1.5 1.5 1.5 1.8 2.4 2.8

Zion Zion 51 ZION1 UT 37.25 113.01 2.7 2.4 2.0 1.6 1.5 1.3 1.2 1.4 1.4 1.6 2.0 2.4

a: No particulate matter sampling or visibility monitoring is conducted in the Bering Sea Wilderness.

b: f(RH) values for Virgin Islands National Park were not calculated because of the limited RH data available.
B - 1

APPENDIX B

Analysis of the Effect of Correlation Between

f(RH) and SO4

 and f(RH) and NO3

 on 

Deciview Calculations using f(RH)
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 2

The regional haze regulation requires separate tracking of visibility changes for the worst

20% and best 20% visibility days. If there is a significant correlation in any month at any site

between daily relative humidity and the sulfate or nitrate concentrations, then use of the monthlyaveraged f(RH) will systematically over- or under-predict the contribution to visibility impairment

of the aerosol species.

This Appendix presents an analysis of potential biases associated with correlations

between relative humidity and inorganic hygroscopic aerosol species using data collected at 20

mandatory Federal Class I areas where relative humidity measurements were made. The effect of

any correlation that may exist between SO4

 and/or NO3

 and f(RH) appears to induce small errors

that are, on average, within 10% of the true value for every site included in the study. Generally

those sites in the West are slightly overestimated by using average f(RH) values while those in the

East are underestimated. The type of average f(RH) value used (monthly, yearly, average of top

20% extinction days, average of bottom 20% extinction days) can have a significant impact on the

amount of error induced in the calculation of a yearly deciview index based on the highest 20%

(respectively, lowest 20%) extinction days. In most instances, the use of a yearly or monthly

average f(RH) resulted in similar percentage errors and both procedures resulted in less error than

using average f(RH) values derived from the worst or best 20% days. Therefore, the simpler

climatological approach is used in regional haze calculations.

We assessed the effect of the correlation between daily f(RH) and SO4 and/or NO3 mass

concentrations. This is prompted by the observation that, in reconstructing extinction from

aerosol mass concentrations, often an average value is used for f(RH) due to the unavailability of

on-site relative humidity measurements from which f(RH) values can be calculated. The equation

for reconstructed extinction is an additive linear combination of contributions from various

aerosol species. The contribution to estimated atmospheric extinction due to SO4

 and NO3

(interpreted as ammonium sulfate and nitrate) occurs in the form ef(RH)*SO4 and ef(RH)*NO3,

where e is an average mass scattering efficiency. Calculating average extinctions over a given

time period results in averaging of the products ef(RH)*SO4 and ef(RH)*NO3. However, when

an average f(RH) is used in place of a daily f(RH), the contribution to extinction due to SO4

and/or NO3

 is estimated by the product of the separate averages of f(RH) and SO4

, and, likewise,

separate averages of f(RH) and NO3. Let us denote the errors introduced due to such substitution

of averages by dSO4 and dNO3, respectively.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 3

That is,

dSO4 = average f(RH)*average SO4 –average [f(RH)*SO4] (1)

and

dNO3 = average f(RH)*average NO3 – average [f(RH)*NO3]. (2)

These errors will be zero if, and only if, the sample Pearson correlation between f(RH) and SO4

,

respectively f(RH) and NO3

, is zero.

More generally, suppose n pairs of observations, say (x1, y1), (x2, y2),... , (xn, yn) are available

on the variable pair (x, y). If the sample Pearson correlation coefficient r between X and Y is zero,

it follows that the average of the product X*Y is equal to the product of the separate averages of X

and Y. Namely, 

implies (3)

An examination of the sample correlation between SO4 and f(RH), as well as NO3 and f(RH), for

various site/year combinations in the IMPROVE database reveals the presence of statistically

significant correlations between SO4

 and/or NO3

 and f(RH), in some instances, implying that the

differences dSO4 and dNO3, defined in equations (1) and (2) respectively, are not zero. Therefore,

to understand the practical implications of the presence of correlations between f(RH) and SO4/NO3,

one needs to examine the magnitudes of dSO4

 and dNO3

 relative to the target quantities

[average(f(RH)*SO4)] and [average(f(RH)*NO3)]. We have made this assessment using IMPROVE

data for sites/years for which daily f(RH) data is available. In addition to examining dSO4 and dNO3

individually, we have also examined the combined error

dSO4+dNO3

 = average f(RH)*(average of SO4+NO3

) – average [f(RH)*(SO4+NO3

)]. (4)
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 4

In our calculations we used the following input quantities and data completeness criteria:

• f(RH) is computed from hourly RH observations. Hourly f(RH) factors are

derived from Tang’s smoothed ammonium sulfate growth curves. For RH values

greater than 95%, the f(RH) value is flagged as missing. Finally, the daily average

of the hourly f(RH) observations is obtained. Note the daily average f(RH) is set

to missing on days for which there are fewer than 16 hourly f(RH) observations. 

• SO4

 is interpreted as ammonium sulfate and NO3

 as ammonium nitrate in units of

ng/m3. 

• f(RH)Monthly: Monthly average f(RH) for each month was computed for any given

site by averaging f(RH)Daily for that month across all years for which data were

available for the site under consideration.

• f(RH)Yearly = Yearly average f(RH) for any given year and site was calculated by

averaging all daily f(RH) values for the site for the given year. Days for which

f(RH) was “missing” were not included in the calculation of this average. 

The use of two different averages was considered: 

(1) f(RH)monthly and (2) f(RH)yearly .

The following %age errors were calculated: 

(a) (Figure 1) 

pSO4 yearly = The error dSO4, expressed as a %age, when f(RH)yearly is used in place of f(RH)daily
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 5

(b) (Figure 2)

pSO4 monthly = The error dSO4, expressed as a %age, when f(RH)monthly is used in place of

f(RH)daily

(c) (Figure 3)

pNO3 yearly = The error dNO3, expressed as a %age, when f(RH)yearly is used in place of

f(RH)daily

(d) (Figure 4)

pNO3 monthly = The error dNO3, expressed as a %age, when f(RH)monthly is used in place of

f(RH)daily

(e) (Figure 5)

pSO4NO3 yearly = The error (dSO4+dNO3), expressed as a %age, when f(RH)yearly is used in

place of f(RH)daily
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 6

(f) (Figure 6)

pSO4NO3 monthly = The error (dSO4+dNO3), expressed as a %age, when f(RH)monthly is used

in place of f(RH)daily

The results are displayed graphically in Figures 1 through 6. In each figure, box-andwhisker plots of the annual visibility indices are displayed for each site. The number of years of

data used in any given box-and-whisker plot ranges from 3 to 11 years, depending on the site,

based on data completeness for the site. Each box-and-whisker plot has a rectangular part called

the box. Lines extending out from the top and bottom edges of the box are called whiskers. The

box represents the middle 50% of the distribution of the yearly indices and the position of the 

horizontal line between the top and bottom edges of the box represents the position of the median

value. The red dot on the box-and-whisker plot indicates the position of the mean value for the

%age error. The value below the label for each site shown along the horizontal axis is the average

“true” f(RH)dailySO4

 in ng/m3

 for that site across the years of data included the study. The vertical

lines (whiskers) are drawn from the box to the most extreme point within 1.5 interquartile ranges

(an interquartile range is the distance between the 25th and the 75th sample %iles). Any value

more extreme than this is considered a potential outlier and marked with a plot symbol. 

Identification of such data values is done to assist the data analyst in carrying out exploratory data

analyses and gaining a better understanding of the data and does not imply that there is something

wrong with such data values. Often reasons can be found that help explain the “extremeness” of

potential outliers.

An examination of Figures 1 through 6 shows that, in general, the average and/or median

error is typically below ±10%. Generally those sites in the West are overestimated by using

average f(RH) values while those in the East are underestimated.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 7

Effect of f(RH) value on Yearly Visibility Index

A second study was carried out to examine the effect on yearly visibility indices of

different choices of f(RH) values that may be substituted for f(RH)daily. Five candidates were

considered for such substitution: 

• f(RH)daily = daily f(RH) value

• f(RH)monthly 

= monthly average f(RH) values computed as explained

earlier

• f(RH)yearly = yearly average f(RH) values computed as explained

earlier.

• f(RH)high20 = average of f(RH)daily values computed from the highest

20% extinction days for a given year. These highest 20%

extinction days were determined by computing

reconstructed extinction using f(RH)daily values, then sorting

these extinctions and identifying the highest 20% extinction

days. 

• f(RH)low20

= average of f(RH)daily values computed from the lowest

20% extinction days for a given year. These lowest 20%

extinction days were determined by computing

reconstructed extinction using f(RH)daily values, then sorting

these extinctions and identifying the lowest 20% extinction

days.

A sampling day was included in the analysis only when f(RH)daily and all of the aerosol

concentrations needed to calculate the reconstructed extinction were available for that day. Such

a day is referred to here as a “complete day.” Recall that f(RH)daily is available for a day only if at

least 16 of the hourly f(RH) averages were available for that day in the IMPROVE database. For

any given site, years with fewer than 50 “complete days” were not considered in this study. Note

that extinction is computed here in inverse kilometers (1/km).
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 8

The following quantities were computed for the highest 20% extinction days.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)daily. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by exthigh,daily and dvhigh,daily, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)monthly. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by exthigh,monthly and dvhigh,monthly, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)yearly. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by exthigh,yearly and dvhigh,yearly, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)high20. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by exthigh20 and dvhigh20, respectively.

The following quantities were computed for the lowest 20% extinction days:

• Sorting was done based on reconstructed daily extinction computed using

f(RH)daily. The average extinction and deciview for the lowest 20% of days were

computed. These are denoted by extlow,daily and dvlow,daily, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)monthly. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by extlow,monthly and dvlow,monthly, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)yearly. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by extlow,yearly and dvlow,yearly, respectively.

• Sorting was done based on reconstructed daily extinction computed using

f(RH)high20. The average extinction and deciview for the highest 20% of days

were computed. These are denoted by extlow20 and dvlow20, respectively.

As mentioned earlier, only “complete days,” i.e., days for which data were available for each and

every component of the equation for reconstructing extinction, were used in the above

calculations.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 9

The extinction index (respectively, deciview index) based on f(RH)daily was taken as the

"true" or “correct” value. Relative errors, in %, for the other three methods (f(RH)yearly,

f(RH)monthly, and one of f(RH)high20 and f(RH)low20, depending on whether the highest 20% extinction

days or the lowest 20% extinction days are of interest) of computing reconstructed extinction

were calculated as follows:

 (11)

Visual summaries of these %age errors are provided in Figures 7 and 8 for extinction and Figures

9 and 10 for deciview. We observed that, for extinction or deciview representing the lowest 20%

extinction days, the index computed with f(RH)low20 tends to underestimate the true index, whereas

the indices computed with f(RH)monthly or f(RH)yearly tend to overestimate it. For extinction or

deciview representing the highest 20% extinction days, the index computed with f(RH)high20 tends

to overestimate the true index, whereas the indices computed with f(RH)monthly or f(RH)yearly tend to

underestimate it. 

Conclusions:

(a) The effect of any correlation that may exist between SO4

 and/or NO3

 and f(RH) appears to

induce errors that are, on average, within 10% of the true value for every site included in the

study. Positive %age errors indicate that the true index is overestimated by the method under

consideration. That is, the method results in a positive bias. This will occur when the

corresponding correlation coefficient is negative. Likewise, negative %age errors indicate a

negative bias. This situation will occur when the corresponding correlation coefficient is positive. 

These implications follow from the definition of these %age errors. 

(b) The type of average f(RH) value used (monthly, yearly, average of top 20% extinction days,

average of bottom 20% extinction days) can have a significant impact on the amount of error

induced in the calculation of a yearly deciview index based on the highest 20% (respectively,

lowest 20%) extinction days. In most instances, the use of a yearly or monthly average f(RH)

resulted in similar %age errors and both procedures resulted in less error than using average

f(RH) values derived from the worst or best 20% days. Therefore, the simpler climatological

approach is used in regional haze calculations.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 10

Figure B-1 Boxplots of yearly %age errors pSO4yearly for 20 selected IMPROVE sites. The red dot indicates the

mean of the %age errors for a site computed based on years of data for that site included in the study. The value

shown below the site name along the horizontal axis is the mean of average[f(RH)daily *SO4] (ng/m3) for that site,

averaged across years. An explanation of how to interpret the box-and-whisker plot is given in the body of the

paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 11

Figure B-2 Boxplots of pSO4monthly for 20 selected IMPROVE sites. The red dot indicates the mean of the %age

errors for a site computed based on years of data for that site included in the study. The value shown below the site

name along the horizontal axis is the mean of average[f(RH)daily*SO4] (ng/m3

) for that site, averaged across years. 

An explanation of how to interpret the box-and-whisker plot is given in the body of the paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 12

Figure B-3 Boxplots of pNO3yearly for 20 selected IMPROVE sites. The red dot indicates the mean of the %age

errors for a site computed based on years of data for that site included in the study. The value shown below the site

name along the horizontal axis is the mean of average[f(RH)daily*NO3] (ng/m3

) for that site, averaged across years. 

An explanation of how to interpret the box-and-whisker plot is given in the body of the paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 13

Figure B-4 Boxplots of pNO3monthly for 20 selected IMPROVE sites. The red dot indicates the mean of the %age

errors for a site computed based on years of data for that site included in the study. The value shown below the site

name along the horizontal axis is the mean of average[f(RH)daily*NO3] (ng/m3) for that site, averaged across years. 

An explanation of how to interpret the box-and-whisker plot is given in the body of the paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 14

 Figure B-5 Boxplots of pSO4NO3yearly for 20 selected IMPROVE sites. The red dot indicates the mean of the

%age errors for a site computed based on years of data for that site included in the study. The value shown below

the site name along the horizontal axis is the mean of average[f(RH)daily*(SO4+NO3)] (ng/m3) for that site,

averaged across years. An explanation of how to interpret the box-and-whisker plot is given in the body of the

paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 15

Figure B-6 Boxplots of pSO4NO3monthly for 20 selected IMPROVE sites. The red dot indicates the mean of the

%age errors for a site computed based on years of data for that site included in the study. The value shown below

the site name along the horizontal axis is the mean of average[f(RH)daily*(SO4+NO3)] (ng/m3

) for that site,

averaged across years. An explanation of how to interpret the box-and-whisker plot is given in the body of the

paper.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 16

Figure B-7 Comparison of %age errors in average extinction for LOWEST 20% extinction days. These values

are computed using f(RH)yearly (red points), f(RH)monthly (green points), and f(RH)low20 (blue points). For any given

site, these values were computed by averaging the %age errors across those years for the site that were included in

the study. The value shown below a site name along the horizontal axis is the mean across years of the yearly

average lowest 20% extinction values for that site calculated using f(RH)daily.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 17

Figure B-8 Comparison of %age errors in average extinction for HIGHEST 20% extinction days. These values are

computed using f(RH)yearly (red points), f(RH)monthly (green points), and f(RH)high20 (blue points). For any given site,

these values were computed by averaging the %age errors across those years for the site that were included in the study.

The value shown below a site name along the horizontal axis is the mean across years of the yearly average highest

20% extinction values for that site calculated using f(RH)daily.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 18

Figure 9. Comparison of %age errors in average deciview for LOWEST 20% extinction days. These values are

computed using f(RH) yearly (red points), f(RH) monthly (green points), and f(RH)low20 (blue points). For any given site,

these values were computed by averaging the %age errors across those years for the site that were included in the

study. The value shown below a site name along the horizontal axis is the mean across years of the yearly average

lowest 20% deciview values for that site calculated using f(RH)daily.
Guidance for Tracking Progress

Under the Regional Haze Rule

B - 19

Figure B-10 Comparison of %age errors in average deciview for HIGHEST 20% extinction days. These values

are computed using f(RH)yearly (red points), f(RH)monthly (green points), and f(RH)high20 (blue points). For any given

site, these values were computed by averaging the %age errors across those years for the site that were included in

the study. The value shown below a site name along the horizontal axis is the mean across years of the yearly

average highest 20% deciview values for that site calculated using f(RH)daily.
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(Please read Instructions on reverse before completing)

1. REPORT NO.

 EPA-454/B-03-004

2. 3. RECIPIENT'S ACCESSION NO.

4. TITLE AND SUBTITLE

Guidance for Tracking Progress Under the Regional Haze Rule

5. REPORT DATE

 September 2003

6. PERFORMING ORGANIZATION CODE

7. AUTHOR(S)

U.S. EPA/OAR/OAQPS/EMAD/AQTAG

8. PERFORMING ORGANIZATION REPORT NO.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

Office of Air Quality Planning and Standards

Emissions, Monitoring and Analysis Division

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

10. PROGRAM ELEMENT NO.

11. CONTRACT/GRANT NO.

68-D-02-0261

Work Order No. 1-06 

12. SPONSORING AGENCY NAME AND ADDRESS

Director

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

13. TYPE OF REPORT AND PERIOD COVERED

Guidance document 

14. SPONSORING AGENCY CODE

EPA/200/04

15. SUPPLEMENTARY NOTES

16. ABSTRACT

The purpose of this document is to provide guidance to the States in implementing the regional haze program under the

Clean Air Act. As part of the program, this document provides guidance to EPA Regioal, State, and Tribal air quality

management authorities and the general public, on how EPA intends to exercise its discretion in implementing Clean Air Act

provisions and EPA regulations, concerning the tracking of progress under the regional haze program.

17. KEY WORDS AND DOCUMENT ANALYSIS

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

Regional Haze, Haze Index Air Pollution control

18. DISTRIBUTION STATEMENT

 Unlimited

19. SECURITY CLASS (Report)

 Unclassified

21. NO. OF PAGES

20. SECURITY CLASS (Page)

 Unclassified

22. PRICE

EPA Form 2220-1 (Rev. 4-77)PREVIOUS EDITION IS OBSOLETE
United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-03-004

Environmental Protection Emissions, Monitoring and Analysis Division September 2003

Agency Research Triangle Park, NC

Postal information in this section where appropriate.