United States
Environmental Prat
LAgency
Guidance for Tracking Progress Under the
Regional Haze Rule
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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
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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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Guidance for Tracking Progress
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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-l
7. Appendix B - Analysis of the Effect of Correlation
Between f(RH) and SO4 nndf(RH) and NO3 on Deciview
Calculations Usingf(RH) B-l
in
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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-l Boxplots of yearly percentage errors pSO4ysirly for 20
selected IMPROVE sites B-10
Figure B-2 Boxplots of pSO4monthly for 20 selected IMPROVE sites B-l 1
Figure B-3 Boxplots of pSO3ymly for 20 selected IMPROVE sites B-l2
Figure B-4 Boxplots of pNO3—y for 20 selected IMPROVE sites B-l3
Figure B-5 Boxplots of pSO4NO3y=,,ly for 20 selected IMPROVE sites B-l4
Figure B-6 Boxplots of pSO4NO3—y for 20 selected IMPROVE sites B-l5
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
IV
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List of Tables
Table 1-1
Table A-1
Table A-2
Table A-3
Deployment of IMPROVE Sites, 1999-2001 1-4
Values for f(RH) determinted from the growth of ammonium sulfate A-4
Recommended Monthly Site-Specific f(RH) Values for Each Mandatory
Federal Class I Area, Based on the Representative IMPROVE Site
Location A-6
Monthly Site-Specific f(RH) Values for Each Mandatory Federal
Class I Area, Based on the Centroid of the Area
(Supplemental Information)
A-ll
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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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NFS - 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 In (bj 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.
Participate 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
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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
'Areas 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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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
2 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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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
1999
2000
2001
TOTAL
Number of
IMPROVE Sites
Deployed
60
41
9
110
Number of Years of Data for
Calculating Baseline
Conditions (2000-2004)
5
4
3
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.
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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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=10ln(bJ10)
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:
• provide for an improvement in visibility for the most impaired (i.e., 20% worst)
days over the period of the implementation plan, and
• ensure no degradation invisibility 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 five-
year 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 to 18 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:
Current worst day conditions - estimated natural conditions
(2064 - current year)
= yearly improvement (in dv)
or,
(2064-2004) GQyears
= 03dv/yr
Baseline 29 "
Conditions
Haze Index
(Deciview)
Estimated
-vr < | 1 -.
rn a ui i ai 11-
C ond tt ions
Required Analysis for
^ , 1st Implementation
>/ Peno d = 4 . 2 deciviews
V,
2000-4 2018
2064
Yeara
Figure 1-1 Example of method for determining Mandatory Federal Class I area rate of progress to
be analyzed in SIP development process (" 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:
0.3 dv/year x 14 years = 4.2 dv.
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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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.
7 See CAA section 169A (g).
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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
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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 l-l.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
864 Federal Register 35746 (Thursday, July 1, 1999).
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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:
• 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.
• 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).
• 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.
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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.
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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.
83
IMPROVE Sites (1
Pratocol Sites (11'
QA Sites (201-2 02)
105
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
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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:
• Current visibility conditions (i.e., the most recent 5-year average) for the most
impaired and least impaired days;
• The difference between current conditions and baseline conditions (2000-2004) for
most impaired and least impaired days;
• The change in visibility conditions over the past 5 years for the most impaired and
least impaired days;
• 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
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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.
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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 PM25, 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 PM25
Several of the particle components needed to assess PM25 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
maybe determined based upon either measurements of p articulate 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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Assemble Composite
Components of P M2 5
Assess Missing
V ariab les
D eterm ine Quarterly
Average Concentrations
forMissing Variables
0 btain f(R H ) Valu es
Evaluate Feasibility of
Substituting Average
V alues
Calculate Deciview
Values
Check D ata
C o m p leten ess
Id entify Best and
W o rst Days
C ale u late Annual
Average Deciviews
C alcu late 5-Year
Deciview Averages
Figure 2-1 Summary of Step-by-Step Process
for Tracking Progress Calculations
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Fine Soil is calculated by summing the five crustal elements (Al, Si, Ca, Fe, and Ti),
accounting for their presence as oxides (e.g., A12O3), 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, October-
December), 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/(7?//) 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
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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
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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
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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
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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 Mm1 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 |j,m aerodynamic diameter)
and PM10 (particulate matter < 10 |j,m 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
bexl) are then used to calculate the haze index in terms of deciviews. Figure 3-1 summarizes this
process.
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Site-Specific
f(RH) Factors
Estimates of the
Concentrations
of the Primary
Components of PM2 5
Calculated^
(IMPROVE
Methodology)
Deciview
Values
Species Specific
Extinction
Efficiencies
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 |_im), and D collects PM10 particles (0-10 |_im). 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 * [SO42~], 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:
[OMC] = (14)[OC] (1)
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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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., A12O3, SiO2, CaO, K2O, FeO, Fe2O3, TiO2) as shown in Equation 2:
[ SOIL] = 2.2[ Al ] + 2A9[ Si] +1.63[ Ca ]
+2A2[Fe]+W4[Ti]
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, NajO, 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?
(3)
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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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
J_J
!'
(4)
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
McMurryetal. [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 (cc;), the relative humidity adjustment term that
varies as a function of relative humidity for that species (ft(RH)), and the dry concentration of that
species (m,):
where a,., 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, ft(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.,
ft(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/D0, 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/D0 vs. ^//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/fT?//) 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/(7?/f) curves. This smooth curve is
illustrated in Figure 3-2, which shows theffRH) for ammonium sulfate as a function of relative
humidity. The data are listed in Appendix A-l.
The value off(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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10
o
ro
Li-
en
gi
QJ
20
40 60
Relative Humidity(%)
80
100
Figure 3-2 Smoothed Ammonium Sulfatef(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 semi-
theoretical 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 ofBP2000 (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, ihef(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:
(5)
+(0.6)[CM]
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 fs04(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 |_im and a geometric standard
deviation, og, of 2.0, ihQfs04(RH) values are calculated using D/D0 curves that are smoothed
between the crystallization and deliquescent points. The fN03(RH) associated with nitrates is
assumed to be the same as for sulfates, wh\[Qforg(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
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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 iof(RH) values using a non-
linear weighting factor curve (see Figure 3-2). Values above 95% RH were set equal to theffRH)
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/fT?//) values, as in Appendix A. The 12 monthly-
avQragQdf(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-averagedy(7?//) 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 ofmonthly-averagey(7?//) 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 off(RH) vs. 24 hr values, is within 10% for 20
IMPROVE sites studied.
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.
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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:
(6)
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 lOMm"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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Combining all of the factors discussed above, the following equation converts particle
species concentration data in units of ng/nf for a sample period at a monitoring location to total
light extinction in units of Mm1.
($)f(RH)[NlTRATE]
+ (4)[OMC]
(7)
(Q.(5)[C'M]
+ 10
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 dechiew 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:
HI = 10ln(b^/10) (8)
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.
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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.
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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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
p - y
-' ^-
where P020: y is the 20th %ile for year y, Xi = the ith 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
p - y
^ ~ ^ [z'+l]
where POM y is the 80th %ile for year y, X[i+1] = the (z'+l)th number in the ordered series of n
numbers, and i is the integer part of the product of 0.80 and n.
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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
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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.
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APPENDIX A
Monthly Site-Specific/f^^ Values
for Each Mandatory Federal Class I Area
A-l
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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
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.
-------
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 iof(RH) values using a non-
linear weighting factor curve, based on a modified ammonium sulfate growth curve. Values
above 95% RH were set equal to thef(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 &omf(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/fT?//) 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 monthly-
averaged/(7?7:/) 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 iwof(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 24f(RH) values for use in calculating extinction for aerosol data.
A-3
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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 SO42", NO3" and [SO42~ + 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 off(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-4
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Table A-l Values for f(RH) Determined from the Growth of Ammonium Sulfate
RH
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
f(RH)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
RH
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
f(RH)
1.00
1.00
1.00
1.02
1.04
1.06
1.08
1.10
1.13
1.15
1.18
1.20
1.23
1.26
1.28
1.31
1.34
1.37
1.41
1.44
1.47
1.51
1.54
1.58
1.62
1.66
1.70
1.74
1.79
1.83
1.88
1.93
1.98
RH
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
f(RH)
2.03
2.08
2.14
2.19
2.25
2.31
2.37
2.43
2.50
2.56
2.63
2.70
2.78
2.86
2.94
3.03
3.12
3.22
3.33
3.45
3.58
3.74
3.93
4.16
4.45
4.84
5.37
6.16
7.40
9.59
14.1
26.4
A-5
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Recommended Monthly
Class 1 Area
Acadia
Agua Tibia
Alpine Lakes
Anaconda - Pintler
Ansel Adams
Arches
Badlands
Bandelier
Bering Sea (a)
Big Bend
Black Canyon of the Gunnison
Bob Marshall
Bosque del Apache
Boundary W aters Canoe Area
Breton
Bridger
Brigantine
Bryce Canyon
Cabinet Mountains
Caney Creek
Canyonlands
Cape Romain
Capitol Reef
Caribou
Carlsbad Caverns
Chassahowitzka
Chiricahua NM
Chiricahua W
Cohutta
Crater Lake
Craters ofthe Moon
Cucamonga
Denali
S ite Name
Acadia
Agua Tibia
Snoqualmie Pass
Sula
Kaiser
Canyonlands
Badlands
Bandelier
Big Bend
W eminuche
Monture
Bosque del Apache
Boundary W aters
Breton
Bridger
Brigantine
Bryce Canyon
Cabinet Mountains
Caney Creek
Canyonlands
Cape Romain
Capitol Reef
Lassen Volcanic
Guadalupe Mountains
Chassahowitzka
Chiricahua
Chiricahua
Cohutta
Crater Lake
Craters ofthe Moon
San Gabriel
Denali
1
100
80
71
110
50
59
33
31
55
73
38
23
20
65
5
49
75
29
50
15
52
90
32
18
39
39
12
86
69
93
102
Table A-2
Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,
Code
ACAD1
AGTI1
SNPA1
SULA1
KAIS1
CANY1
BADL1
BAND1
BIBE1
WEMI1
MONT1
BOAP1
BOWA1
BRET1
BRID1
BRIG1
BRCA1
CAB 11
CACR1
CANY1
ROMA1
CAP 11
LAV01
GUMO1
CHAS1
CHIR1
CHIR1
COHU1
CRLA1
CRM01
SAGA1
DENA1
Site St
ME
CA
WA
MT
CA
UT
SD
NM
TX
CO
MT
NM
MN
LA
WY
NJ
UT
MT
AR
UT
SC
UT
CA
TX
FL
AZ
AZ
GA
OR
ID
CA
AK
LAT
44.38
33.38
47.38
45.88
37.13
38.38
43.63
35.88
29.38
37.63
47.13
33.88
47.88
29.13
42.88
39.38
37.63
47.88
34.38
38.38
32.88
38.38
40.63
31.88
28.63
32.13
32.13
34.88
42.88
43.38
34.38
63.75
LONG
-68.37
-116.87
-121.37
-114.12
-119.12
-109.87
-101.87
-106.37
-103.12
-107.87
-113.12
-106.87
-91.62
-89.12
-109.87
-74.37
-112.12
-115.62
-94.12
-109.87
-79.62
-111.37
-121.62
-104.87
-82.62
-109.37
-109.37
-84.62
-122.12
-113.62
-118.12
-148.75
Jan
f(RH)
3.2
2.4
5.3
3.4
3.0
2.6
2.8
2.3
1.8
2.5
3.3
2.2
2.9
3.5
2.5
2.9
2.6
3.7
3.3
2.6
3.2
2.7
3.7
2.4
3.5
2.0
2.0
3.4
4.6
3.1
2.6
2.5
Feb
f(RH)
2.8
2.3
5.0
3.0
2.7
2.3
2.8
2.1
1.7
2.3
2.9
2.0
2.6
3.3
2.3
2.6
2.4
3.2
3.0
2.3
2.9
2.5
3.1
2.0
3.2
1.9
1.9
3.1
4.0
2.7
2.5
2.3
Mar
f(RH)
2.8
2.4
3.7
2.6
2.5
1.8
2.8
1.8
1.5
2.0
2.6
1.6
2.6
3.3
2.3
2.7
2.0
2.8
2.7
1.8
2.8
2.0
2.8
1.6
3.1
1.6
1.6
2.9
3.7
2.3
2.5
2.1
Apr
f(RH)
3.2
2.2
3.6
2.3
2.1
1.6
2.6
1.6
1.4
1.7
2.4
1.4
2.3
3.3
2.1
2.6
1.6
2.5
2.8
1.6
2.7
1.7
2.4
1.4
3.0
1.2
1.2
2.7
3.5
2.0
2.2
1.9
May
f(RH)
3.2
2.2
4.2
2.3
2.0
1.5
2.8
1.6
1.5
1.7
2.4
1.4
2.5
3.4
2.1
2.9
1.5
2.5
3.2
1.5
2.9
1.6
2.3
1.6
3.0
1.2
1.2
3.2
3.2
2.0
2.2
1.8
Jun
f(RH)
3.3
2.2
3.1
2.2
1.7
1.2
2.7
1.4
1.5
1.5
2.4
1.3
2.8
3.6
1.8
3.0
1.3
2.4
3.2
1.2
3.3
1.4
2.1
1.5
3.5
1.1
1.1
3.6
2.9
1.8
2.1
2.1
Jul
f(RH)
3.7
2.2
3.5
1.9
1.7
1.3
2.4
1.7
1.6
1.7
2.1
1.7
3.0
3.8
1.5
3.2
1.3
2.1
3.0
1.3
3.3
1.4
2.0
1.9
3.5
1.7
1.7
3.6
2.6
1.4
2.2
2.5
Aug
f(RH)
3.7
2.3
3.4
1.8
1.7
1.5
2.4
2.0
1.8
2.0
2.0
1.9
3.1
3.8
1.5
3.4
1.5
2.1
3.0
1.5
3.6
1.6
2.0
2.2
3.7
2.0
2.0
3.7
2.7
1.4
2.2
2.9
Sep
f(RH)
3.9
2.3
3.8
2.0
1.8
1.5
2.3
1.9
1.9
1.9
2.3
1.9
3.2
3.6
1.8
3.4
1.5
2.4
3.2
1.5
3.5
1.6
2.1
2.4
3.7
1.7
1.7
3.7
2.9
1.6
2.3
2.8
Oct
f(RH)
3.5
2.2
4.9
2.5
1.9
1.6
2.3
1.7
1.7
1.7
2.7
1.6
2.7
3.4
2.0
3.2
1.6
2.9
3.2
1.6
3.4
1.7
2.3
1.7
3.5
1.5
1.5
3.5
3.6
2.0
2.2
3.0
Nov
f(RH)
3.4
2.1
5.5
3.3
2.3
2.0
2.9
2.0
1.7
2.2
3.2
1.8
3.1
3.4
2.5
2.8
2.0
3.6
3.1
2.0
3.1
2.1
3.1
1.9
3.4
1.6
1.6
3.2
4.6
2.7
2.2
2.9
Dec
f(RH)
3.5
2.2
5.3
3.4
2.7
2.3
2.8
2.3
1.7
2.4
3.3
2.2
3.1
3.5
2.4
2.9
2.4
3.8
3.3
2.3
3.1
2.5
3.5
2.3
3.6
2.1
2.1
3.4
4.7
3.0
2.3
3.0
A-6
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Recommended Monthly
Class 1 Area
Desolation
Diamond Peak
Dolly Sods
Dom e Land
Eagle Cap
Eagles Nest
Emigrant
Everglades
Fitzpatrick
Flat Tops
Galiuro
Gates of the Mountains
Gearhart Mountain
Gila
Glacier
Glacier Peak
Goat Rocks
Grand Canyon
Grand Teton
Great Gulf
Great Sand Dunes
Great Smoky Mountains
Guadalupe Mountains
Haleakala
Hawaii Volcanoes
Hells Canyon
Hercules - Glade
Hoover
Isle Royale
James River Face
Jarbidge
John Muir
Joshua Tree
S ite N am e
Bliss
Crater Lake
Dolly Sods
Dom e Land
Starkey
White River
Yosem ite
Everglades
Bridger
White River
Chiricahua
Gates of the Mountains
Crater Lake
Gila Cliffs
Glacier
North Cascades
White Pass
Grand Canyon, Hance
Yellowstone
Great Gulf
Great Sand Dunes
Great Smoky Mountains
Guadalupe Mountains
Haleakala
Hawaii Volcanoes
Hells Canyon
Hercules - Glade
Hoover
Isle Royale
James River Face
Jarbidge
Kaiser
Joshua Tree
95
86
8
109
76
56
96
19
65
56
39
74
86
42
72
81
79
48
66
4
53
10
32
108
107
77
28
97
25
7
68
110
101
Table A-2
Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,
Code
BLIS1
CRLA1
DOS01
DOME1
STAR1
WHRI1
YOSE1
EVER1
BRID1
WHRI1
CHIR1
GAM01
CRLA1
GICL1
GLAC1
NOCA1
WHPA1
GRCA2
YELL2
GRGU1
GRSA1
GRSM1
GUM01
HALE1
HAVO1
HECA1
HEGL1
HOOV1
ISLE1
JAR 11
JARB1
KAIS1
JOSH1
Site St
CA
OR
WV
CA
OR
CO
CA
FL
WY
CO
AZ
MT
OR
NM
MT
WA
WA
AZ
WY
NH
CO
TN
TX
HI
HI
OR
MO
CA
Ml
VA
NV
CA
CA
LAT
38.88
42.88
39.13
35.63
45.13
39.13
37.63
25.38
42.88
39.13
32.13
46.88
42.88
33.13
48.63
48.63
46.63
35.88
44.63
44.38
37.63
35.63
31.88
20.75
19.25
44.88
36.63
38.13
47.38
37.63
41.88
37.13
34.13
LONG
-120.12
-122.12
-79.37
-118.12
-118.62
-106.87
-119.62
-80.62
-109.87
-106.87
-109.37
-111.62
-122.12
-108.12
-114.12
-121.12
-121.37
-111.87
-110.37
-71.12
-105.62
-83.87
-104.87
-156.25
-155.25
-116.87
-92.87
-119.12
-88.12
-79.62
-115.37
-119.12
-116.37
Jan
f(RH)
3.2
4.6
3.0
2.6
4.3
2.2
3.0
2.6
2.5
2.2
2.0
2.8
4.6
2.1
3.9
4.5
4.8
2.5
2.5
2.8
2.4
3.6
2.4
2.7
3.0
3.7
3.2
3.1
3.1
2.9
2.9
3.0
2.4
Feb
f(RH)
2.8
4.0
2.7
2.3
3.8
2.2
2.9
2.5
2.3
2.2
1.9
2.5
4.0
1.9
3.4
4.1
4.2
2.4
2.3
2.6
2.3
3.0
2.0
2.6
2.9
3.1
2.9
2.7
2.6
2.7
2.6
2.7
2.3
Mar
f(RH)
2.5
3.7
2.7
2.2
3.2
2.0
2.7
2.5
2.3
2.0
1.6
2.4
3.7
1.6
3.1
3.6
3.8
2.0
2.2
2.6
2.0
3.0
1.6
2.5
2.9
2.4
2.6
2.5
2.7
2.6
2.1
2.5
2.3
Apr
f(RH)
2.0
3.5
2.5
1.9
2.9
2.0
2.2
2.3
2.1
2.0
1.2
2.3
3.5
1.3
2.9
3.4
3.6
1.6
2.1
2.8
1.9
2.8
1.4
2.5
2.9
2.1
2.6
2.0
2.5
2.4
2.1
2.1
2.0
May
f(RH)
1.9
3.2
3.5
1.9
2.7
2.0
2.1
2.3
2.1
2.0
1.2
2.3
3.2
1.3
3.0
3.3
3.4
1.4
2.1
2.9
1.9
3.2
1.6
2.4
2.9
2.0
3.0
1.9
2.4
2.9
2.2
2.0
2.0
Jun
f(RH)
1.6
2.9
3.1
1.8
2.4
1.7
1.7
2.6
1.8
1.7
1.1
2.2
2.9
1.2
3.0
3.0
3.1
1.2
1.9
3.0
1.7
3.6
1.5
2.3
2.9
1.8
3.0
1.6
2.9
3.1
2.0
1.7
1.9
Jul
f(RH)
1.5
2.6
3.2
1.8
2.0
1.8
1.5
2.5
1.5
1.8
1.7
2.0
2.6
1.9
2.5
2.9
2.9
1.4
1.7
3.3
1.9
3.6
1.9
2.4
3.0
1.5
3.0
1.5
3.2
3.2
1.6
1.7
1.5
Aug
f(RH)
1.5
2.7
3.5
1.8
2.1
2.1
1.5
2.8
1.5
2.1
2.0
1.9
2.7
1.9
2.5
3.1
3.0
1.7
1.6
3.5
2.3
3.6
2.2
2.4
3.0
1.4
3.0
1.5
3.4
3.3
1.4
1.7
2.0
Sep
f(RH)
1.7
2.9
3.5
1.9
2.4
2.1
1.6
2.9
1.8
2.1
1.7
2.1
2.9
1.8
3.0
3.5
3.5
1.7
1.8
3.6
2.2
3.7
2.4
2.3
3.0
1.6
3.2
1.6
3.5
3.4
1.4
1.8
2.0
Oct
f(RH)
1.8
3.6
3.1
1.9
3.3
1.8
1.8
2.7
2.0
1.8
1.5
2.4
3.6
1.6
3.3
4.2
4.3
1.7
2.1
3.2
1.9
3.4
1.7
2.5
3.0
2.2
2.9
1.8
2.9
3.0
1.6
1.9
2.0
Nov
f(RH)
2.4
4.6
2.8
2.0
4.2
2.1
2.3
2.5
2.5
2.1
1.6
2.7
4.6
1.8
3.7
4.7
4.9
2.0
2.4
3.0
2.3
3.3
1.9
2.7
3.3
3.4
3.0
2.3
3.3
2.7
2.4
2.3
1.9
Dec
f(RH)
3.0
4.7
3.1
2.2
4.5
2.1
2.7
2.6
2.4
2.1
2.1
2.7
4.7
2.2
3.8
4.7
5.0
2.3
2.5
2.9
2.4
3.5
2.3
2.6
3.0
3.8
3.2
2.8
3.3
3.0
2.8
2.7
2.1
A- 7
-------
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,
Class 1 Area
Joyce Kilmer - Slickrock
Kaiser
Kalm iopsis
Kings Canyon
La G arita
Lassen Volcanic
Lava Beds
Linville Gorge
Lostwood
Lye Brook
Mam moth Cave
Marble Mountain
Maroon Bells - Snowmass
Mazatzal
Medicine Lake
Mesa Verde
Mingo
Mission Mountains
Mokelumne
Moosehorn
Mount Adam s
Mount Baldy
Mount Hood
Mount Jefferson
Mount Rainier
Mount Washington
Mount Zirkel
Mountain Lakes
North Absaroka
North Cascades
Okefenokee
Olym pic
Otter Creek
S ite N am e
Great Smoky Mountains
Kaiser
Kalm iopsis
Sequoia
W eminuche
Lassen Volcanic
Lava Beds
Linville Gorge
Lostwood
Lye Brook
Mam moth Cave
Trinity
White River
Ike's Backbone
Medicine Lake
Mesa Verde
Mingo
Monture
Bliss
Moosehorn
White Pass
Mount Baldy
Mount Hood
Three Sisters
Mount Rainier
Three Sisters
Mount Zirkel
Crater Lake
North Absoraka
North Cascades
Okefenokee
Olym pic
Dolly Sods
10
110
89
98
55
90
87
13
62
3
9
104
56
46
63
54
26
73
95
2
79
43
85
84
78
84
58
86
67
81
16
83
8
Code
GRSM1
KAIS1
KALM1
SEQU1
WEMI1
LAV01
LABE1
LIG01
LOST1
LYBR1
MACA1
TRIN1
WHRI1
IKBA1
MELA1
MEVE1
MING1
MONT1
BLIS1
MOOS1
WHPA1
BALD1
MOH01
THSI1
MORA1
THSI1
MOZI1
CRLA1
NOAB1
NOCA1
OKEF1
OLYM1
DOSO1
Site St
TN
CA
OR
CA
CO
CA
CA
NC
ND
VT
KY
CA
CO
AZ
MT
CO
MO
MT
CA
ME
WA
AZ
OR
OR
WA
OR
CO
OR
WY
WA
GA
WA
WV
LAT
35.63
37.13
42.63
36.38
37.63
40.63
41.63
35.88
48.63
43.13
37.13
40.88
39.13
34.38
48.38
37.13
36.88
47.13
38.88
45.13
46.63
34.13
45.38
44.38
46.88
44.38
40.63
42.88
44.63
48.63
30.63
48.13
39.13
LONG
-83.87
-119.12
-124.12
-118.87
-107.87
-121.62
-121.62
-81.87
-102.37
-73.12
-86.12
-122.87
-106.87
-111.62
-104.37
-108.37
-90.12
-113.12
-120.12
-67.37
-121.37
-109.37
-121.87
-122.12
-122.12
-122.12
-106.62
-122.12
-109.37
-121.12
-82.12
-122.87
-79.37
Jan
f(RH)
3.6
3.0
4.5
2.9
2.5
3.7
4.0
3.2
3.0
2.8
3.3
4.0
2.2
2.2
3.0
2.8
3.2
3.3
3.2
3.0
4.8
2.2
4.6
5.3
5.3
5.3
2.2
4.6
2.4
4.5
3.3
4.2
3.0
Feb
f(RH)
3.0
2.7
3.9
2.6
2.3
3.1
3.4
3.0
2.9
2.6
3.0
3.4
2.2
2.0
2.9
2.6
2.9
2.9
2.8
2.7
4.2
2.1
4.1
4.6
4.7
4.6
2.2
4.0
2.2
4.1
3.0
3.9
2.7
Mar
f(RH)
3.0
2.5
3.7
2.5
2.0
2.8
3.1
2.9
3.0
2.7
2.9
3.2
2.0
1.8
2.9
2.2
2.7
2.6
2.5
2.7
3.8
1.7
3.7
4.4
4.4
4.4
2.0
3.7
2.2
3.6
3.2
3.6
2.7
Apr
f(RH)
2.8
2.1
3.5
2.2
1.7
2.4
2.8
2.7
2.3
2.6
3.0
2.9
2.0
1.4
2.2
1.7
2.6
2.4
2.0
2.9
3.6
1.4
3.6
4.3
4.3
4.3
2.1
3.5
2.1
3.4
3.0
3.5
2.5
May
f(RH)
3.2
2.0
3.3
2.1
1.7
2.3
2.6
3.2
2.2
2.8
4.1
2.8
2.0
1.3
2.2
1.7
2.9
2.4
1.9
2.9
3.4
1.3
3.2
3.8
3.9
3.8
2.2
3.2
2.1
3.3
3.2
2.9
3.5
Jun
f(RH)
3.6
1.7
3.1
1.8
1.5
2.1
2.4
3.6
2.5
2.9
4.7
2.6
1.7
1.2
2.4
1.3
3.0
2.4
1.6
3.1
3.1
1.2
3.0
3.4
3.7
3.4
1.8
2.9
1.9
3.0
3.8
3.2
3.1
Jul
f(RH)
3.6
1.7
2.9
1.7
1.7
2.0
2.2
3.6
2.5
3.1
4.6
2.5
1.8
1.4
2.4
1.7
3.1
2.1
1.5
3.5
2.9
1.6
2.7
2.7
3.4
2.7
1.7
2.6
1.6
2.9
3.4
2.7
3.2
Aug
f(RH)
3.6
1.7
3.0
1.7
2.0
2.0
2.2
3.9
2.3
3.3
3.5
2.6
2.1
1.7
2.1
2.0
3.1
2.0
1.5
3.6
3.0
1.9
2.8
2.7
3.6
2.7
1.8
2.7
1.5
3.1
3.6
3.3
3.5
Sep
f(RH)
3.7
1.8
3.1
1.8
1.9
2.1
2.4
3.9
2.2
3.4
3.5
2.7
2.1
1.6
2.2
1.9
3.2
2.3
1.7
3.8
3.5
1.7
3.2
3.1
4.2
3.1
2.0
2.9
1.8
3.5
3.6
3.8
3.5
Oct
f(RH)
3.4
1.9
3.6
1.9
1.7
2.3
2.8
3.4
2.4
3.2
3.2
2.9
1.8
1.5
2.3
1.8
2.9
2.7
1.8
3.3
4.3
1.6
4.1
4.3
5.1
4.3
1.9
3.6
2.0
4.2
3.4
4.3
3.1
Nov
f(RH)
3.3
2.3
4.4
2.3
2.2
3.1
3.6
3.1
3.2
2.9
3.1
3.6
2.1
1.8
3.1
2.2
3.0
3.2
2.4
3.2
4.9
1.9
4.8
5.2
5.5
5.2
2.1
4.6
2.3
4.7
3.3
4.5
2.8
Dec
f(RH)
3.5
2.7
4.4
2.5
2.4
3.5
4.0
3.2
3.2
2.9
3.4
3.9
2.1
2.1
3.1
2.6
3.2
3.3
3.0
3.2
5.0
2.3
4.8
5.3
5.6
5.3
2.1
4.7
2.4
4.7
3.4
4.4
3.1
A-8
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Recommended Monthly
Class 1 Area
Pasayten
Pecos
Petrified Forest
Pine Mountain
Pinnacles
Point Reyes
Presidential Range - Dry River
Rawah
Red Rock Lakes
Redwood
Rocky Mountain
Roosevelt Campobello
Saguaro
Saint Marks
Salt Creek
San Gabriel
San G orgon io
San Jacinto
San Pedro Parks
San Rafael
Sawtooth
Scapegoat
Selway - Bitterroot
Seney
Sequoia
Shenandoah
Shining Rock
Sierra Ancha
Simeonof
Sipsey
South Warner
Strawberry Mountain
Superstition
S ite Name
Pasayten 82
WheelerPeak 35
Petrified Forest 41
Ike's Backbone 46
Pinnacles 92
Point Reyes 91
Great Gulf 4
Mount Zirkel 58
Yellowstone 66
Redwood 88
Rocky Mountain 57
Moosehorn 2
Saguaro 40
Saint Marks 17
SaltCreek 36
San Gabriel 93
San G orgon io 99
San Gorgonio 99
San Pedro Parks 34
San Rafael 94
Sawtooth 70
Monture 73
Sula 71
Seney 22
Sequoia 98
Shenandoah 6
Shining Rock 11
Sierra Ancha 45
Simeonof 1 05
Sipsey 21
Lava Beds 87
Starkey 76
Tonto 44
Table A-2
Site-Specific f(RH) Values for Each Mandatory Federal Class I Area,
Code
PASA1
WHPE1
PEF01
IKBA1
PINN1
PORE1
GRGU1
MOZI1
YELL2
REDW1
ROMO1
MOOS1
SAGU1
SAMA1
SACR1
SAGA1
SAG01
SAGO1
SAPE1
RAFA1
SAWT1
MONT1
SULA1
SENE1
SEQU1
SHEN1
SHRO1
SIAN1
SIME1
SIPS1
LABE1
STAR1
TONT1
Site St
WA
NM
AZ
AZ
CA
CA
NH
CO
WY
CA
CO
ME
AZ
FL
NM
CA
CA
CA
NM
CA
ID
MT
MT
Ml
CA
VA
NC
AZ
AK
AL
CA
OR
AZ
LAT
48.38
36.63
35.13
34.38
36.38
38.13
44.38
40.63
44.63
41.63
40.38
45.13
32.13
30.13
33.38
34.38
34.13
34.13
36.13
34.63
44.13
47.13
45.88
46.38
36.38
38.63
35.38
34.13
55.25
34.38
41.63
45.13
33.63
LONG
-119.87
-105.37
-109.87
-111.62
-121.12
-122.87
-71.12
-106.62
-110.37
-124.12
-105.62
-67.37
-110.62
-84.12
-104.37
-118.12
-116.87
-116.87
-106.87
-120.12
-114.87
-113.12
-114.12
-85.87
-118.87
-78.37
-82.87
-110.87
-160.75
-87.37
-121.62
-118.62
-111.12
Jan
f(RH)
4.6
2.4
2.4
2.2
3.4
3.6
2.8
2.2
2.5
3.8
1.9
3.0
1.8
3.5
2.2
2.6
2.5
2.5
2.4
3.0
3.3
3.3
3.4
3.3
2.9
2.9
3.3
2.2
4.2
3.3
4.0
4.3
2.1
Feb
f(RH)
4.1
2.2
2.1
2.0
3.4
3.2
2.6
2.2
2.3
3.6
2.0
2.7
1.6
3.3
1.9
2.5
2.6
2.6
2.2
2.8
2.8
2.9
3.0
2.8
2.6
2.6
3.0
2.0
4.2
3.0
3.4
3.8
1.9
Mar
f(RH)
3.5
1.9
1.7
1.8
3.5
3.1
2.6
2.0
2.2
3.8
2.0
2.7
1.4
3.2
1.5
2.5
2.4
2.4
1.9
2.8
2.3
2.6
2.6
2.9
2.5
2.7
2.9
1.7
3.8
2.8
3.1
3.2
1.6
Apr
f(RH)
3.3
1.8
1.4
1.4
2.6
2.6
2.8
2.1
2.1
3.6
2.1
2.9
1.1
3.1
1.5
2.2
2.1
2.1
1.6
2.5
2.0
2.4
2.3
2.7
2.2
2.4
2.7
1.4
4.0
2.7
2.8
2.9
1.3
May
f(RH)
3.2
1.8
1.3
1.3
2.4
2.5
2.9
2.2
2.1
3.8
2.3
2.9
1.1
3.2
1.6
2.2
2.1
2.1
1.6
2.5
2.0
2.4
2.3
2.6
2.1
2.9
3.2
1.3
4.2
3.1
2.6
2.7
1.2
Jun
f(RH)
2.9
1.6
1.2
1.2
2.2
2.3
3.0
1.8
1.9
3.9
2.0
3.1
1.0
3.6
1.5
2.1
1.8
1.8
1.4
2.4
1.8
2.4
2.2
3.0
1.8
3.1
3.6
1.1
4.6
3.4
2.4
2.4
1.1
Jul
f(RH)
2.8
1.8
1.5
1.4
2.1
2.4
3.3
1.7
1.7
4.2
1.9
3.5
1.4
3.8
1.7
2.2
1.7
1.7
1.7
2.5
1.4
2.1
1.9
3.3
1.7
3.2
3.6
1.5
5.0
3.5
2.2
2.0
1.4
Aug
f(RH)
2.9
2.1
1.8
1.7
2.2
2.4
3.5
1.8
1.6
4.2
1.9
3.6
1.7
3.8
1.9
2.2
1.8
1.8
2.0
2.6
1.4
2.0
1.8
3.6
1.7
3.5
3.9
1.8
5.2
3.5
2.2
2.1
1.7
Sep
f(RH)
3.4
2.1
1.6
1.6
2.2
2.5
3.6
2.0
1.8
3.7
2.0
3.8
1.5
3.7
2.0
2.3
1.9
1.9
1.9
2.7
1.5
2.3
2.0
3.7
1.8
3.5
3.9
1.6
4.5
3.5
2.4
2.4
1.6
Oct
f(RH)
4.1
1.8
1.6
1.5
2.4
2.5
3.2
1.9
2.1
3.4
1.8
3.3
1.4
3.5
1.7
2.2
1.8
1.8
1.7
2.6
2.0
2.7
2.5
3.3
1.9
3.0
3.5
1.5
3.7
3.3
2.8
3.3
1.5
Nov
f(RH)
4.7
2.2
2.0
1.8
2.4
2.9
3.0
2.1
2.4
3.6
2.0
3.2
1.5
3.4
1.8
2.2
1.9
1.9
2.1
2.4
2.9
3.2
3.3
3.5
2.3
2.7
3.2
1.8
3.9
3.1
3.6
4.2
1.7
Dec
f(RH)
4.8
2.4
2.3
2.1
2.9
3.3
2.9
2.1
2.5
3.4
1.9
3.2
2.0
3.6
2.0
2.3
2.1
2.1
2.3
2.6
3.3
3.3
3.4
3.4
2.5
2.9
3.3
2.2
4.2
3.3
4.0
4.5
2.1
A-9
-------
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,
Class 1 Area
Swanquarter
Sycamore Canyon
Teton
Theodore Roosevelt
Thousand Lakes
Th ree Sisters
Tuxedni
UL Bend
Upper Buffalo
Ventana
Virgin Islands (b)
Voyageurs
Washakie
W eminuche
West Elk
Wheeler Peak
W hite Mou ntain
W ichita Mountains
Wind Cave
Wolf Island
Yellowstone
Yolla Bolly- Middle Eel
Yos em ite
S ite N am e
Swanquarter
Sycamore Canyon
Yellowstone
Theodore Roosevelt
Lassen Volcanic
Th ree Sisters
Tuxedni
UL Bend
Upper Buffalo
Pinnacles
Virgin Islands
Voyageurs
North Absoraka
W eminuche
White River
Wheeler Peak
W hite Mou ntain
W ichita Mountains
Wind Cave
Okefenokee
Yellowstone
Trinity
Yos em ite
14
47
66
61
90
84
103
64
27
92
106
24
67
55
56
35
37
30
60
16
66
104
96
Code
SWAN1
SYCA1
YELL2
THR01
LAVO1
THSI1
TUXE1
ULBE1
UPBU1
PINN1
VIIS1
VOYA2
NOAB1
WEMI1
WHRI1
WHPE1
WHIT1
WIMO1
WICA1
OKEF1
YELL2
TRIN1
YOSE1
Site St
NC
AZ
WY
ND
CA
OR
AK
MT
AR
CA
VI
MN
WY
CO
CO
NM
NM
OK
SD
GA
WY
CA
CA
LAT
35.38
35.13
44.63
46.88
40.63
44.38
59.75
47.63
35.88
36.38
18.75
48.38
44.63
37.63
39.13
36.63
33.38
34.63
43.63
30.63
44.63
40.88
37.63
LONG
-76.12
-111.87
-110.37
-103.37
-121.62
-122.12
-152.75
-108.62
-93.12
-121.12
-155.75
-92.87
-109.37
-107.87
-106.87
-105.37
-105.62
-98.62
-103.37
-82.12
-110.37
-122.87
-119.62
Jan
f(RH)
2.9
2.4
2.5
2.9
3.7
5.3
3.6
2.6
3.2
3.4
2.7
2.4
2.5
2.2
2.4
2.2
2.8
2.5
3.3
2.5
4.0
3.0
Feb
f(RH)
2.7
2.4
2.3
2.8
3.1
4.6
3.4
2.4
2.9
3.4
2.4
2.2
2.3
2.2
2.2
1.9
2.6
2.5
3.0
2.3
3.4
2.9
Mar
f(RH)
2.6
2.0
2.2
2.8
2.8
4.4
2.9
2.4
2.6
3.5
2.3
2.2
2.0
2.0
1.9
1.6
2.4
2.5
3.2
2.2
3.2
2.7
Apr
f(RH)
2.4
1.6
2.1
2.4
2.4
4.3
2.8
2.3
2.7
2.6
2.2
2.1
1.7
2.0
1.8
1.5
2.4
2.5
3.0
2.1
2.9
2.2
May
f(RH)
2.7
1.5
2.1
2.4
2.3
3.8
2.8
2.2
3.1
2.4
2.2
2.1
1.7
2.0
1.8
1.5
2.7
2.6
3.2
2.1
2.8
2.1
Jun
f(RH)
3.0
1.2
1.9
2.5
2.1
3.4
2.9
2.1
3.1
2.2
2.8
1.9
1.5
1.7
1.6
1.4
2.5
2.5
3.8
1.9
2.6
1.7
Jul
f(RH)
3.1
1.5
1.7
2.4
2.0
2.7
3.6
1.9
3.0
2.1
2.5
1.6
1.7
1.8
1.8
1.7
2.2
2.2
3.4
1.7
2.5
1.5
Aug
f(RH)
3.2
2.0
1.6
2.2
2.0
2.7
3.9
1.8
3.0
2.2
2.7
1.5
2.0
2.1
2.1
1.9
2.4
2.2
3.6
1.6
2.6
1.5
Sep
f(RH)
3.1
1.9
1.8
2.2
2.1
3.1
3.8
1.9
3.2
2.2
2.9
1.8
1.9
2.1
2.1
2.0
2.7
2.1
3.6
1.8
2.7
1.6
Oct
f(RH)
3.0
1.8
2.1
2.3
2.3
4.3
3.4
2.2
3.0
2.4
2.5
2.0
1.7
1.8
1.8
1.7
2.5
2.2
3.4
2.1
2.9
1.8
Nov
f(RH)
2.7
2.0
2.4
3.0
3.1
5.2
3.5
2.6
3.0
2.4
2.8
2.3
2.2
2.1
2.2
1.8
2.6
2.6
3.3
2.4
3.6
2.3
Dec
f(RH)
2.9
2.3
2.5
3.0
3.5
5.3
3.7
2.6
3.2
2.9
2.7
2.4
2.4
2.1
2.4
2.1
2.8
2.5
3.4
2.5
3.9
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.
A-10
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I
Based on the Centroid of the Area (Supplemental Information)
Class 1 Area
Acadia
Agua Tibia
Alpine Lakes
Anaconda - Pintler
Ansel Adams
Arches
Badlands
Bandelier
Bering Sea (a)
Big Bend
Black Canyon of the Gunnison
Bob Marshall
Bosque del Apache
Boundary W aters Canoe Area
Breton
Bridger
Brigantine
Bryce Canyon
Cabinet Mountains
Caney Creek
Canyonlands
Cape Romain
Capitol Reef
Caribou
Carlsbad Caverns
Chassahowitzka
Chiricahua NM
Chiricahua W
Cohutta
Crater Lake
Craters ofthe Moon
Cucam onga
Denali
Desolation
Diamond Peak
Dolly Sods
Site Name
Acadia
Agua Tibia
Snoqualmie Pass
Sula
Kaiser
Canyonlands
Badlands
Bandelier
Big Bend
W eminuche
Monture
Bosque del Apache
Boundary W aters
Breton
Bridger
Brigantine
Bryce Canyon
Cabinet Mountains
Caney Creek
Canyonlands
Cape Romain
Capitol Reef
Lassen Volcanic
Guadalupe Mountains
Chassahowitzka
Chiricahua
Chiricahua
Cohutta
Crater Lake
Craters ofthe Moon
San Gabriel
Denali
Bliss
Crater Lake
Dolly Sods
Map ID
1
100
80
71
110
50
59
33
31
55
73
38
23
20
65
5
49
75
29
50
15
52
90
32
18
39
39
12
86
69
93
102
95
86
8
Code
ACAD1
ACT 11
SNPA1
SULA1
KAIS1
CANY1
BADL1
BAND1
BIBE1
WEMI1
MONT1
BOAP1
BOWA1
BRET1
BRID1
BRIG1
BRCA1
CAB 11
CACR1
CANY1
ROMA1
CAP 11
LAVO1
GUMO1
CHAS1
CHIR1
CHIR1
COHU1
CRLA1
CRMO1
SAGA1
DENA1
BLIS1
CRLA1
DOS01
Site
St
ME
CA
WA
MT
CA
LIT
SD
NM
TX
CO
MT
NM
MN
LA
WY
NJ
LIT
MT
AR
LIT
SC
LIT
CA
TX
FL
AZ
AZ
GA
OR
ID
CA
AK
CA
OR
WV
LAT
44.37
33.41
47.42
45.98
37.65
38.64
43.74
35.78
60.45
29.31
38.58
47.75
33.79
47.95
29.73
42.98
39.46
37.62
48.21
34.41
38.46
32.94
38.36
40.50
32.14
28.75
32.01
31.84
34.92
42.90
43.47
34.25
63.72
38.98
43.53
39.11
LONG
68.26
116.98
121.42
113.42
119.20
109.58
101.94
106.27
172.79
103.19
107.70
113.38
106.83
91.50
88.88
109.76
74.45
112.17
115.71
94.08
109.82
79.66
111.05
121.18
104.48
82.55
109.39
109.27
84.58
122.13
113.55
117.57
148.97
120.12
122.10
79.43
Jan
f(RH)
3.3
2.4
4.3
3.3
3.0
2.6
2.6
2.2
2.0
2.4
3.6
2.1
3.0
3.7
2.5
2.8
2.6
3.8
3.4
2.6
3.3
2.7
3.7
2.1
3.8
2.0
2.0
3.3
4.6
3.1
2.5
2.5
3.2
4.5
3.0
Feb
f(RH)
2.9
2.4
3.8
2.9
2.7
2.3
2.7
2.1
1.9
2.2
3.1
1.9
2.6
3.5
2.4
2.6
2.4
3.3
3.1
2.3
3.0
2.4
3.1
2.0
3.5
2.0
1.9
3.1
3.9
2.7
2.4
2.3
2.8
4.0
2.8
Mar
f(RH)
2.8
2.4
3.5
2.5
2.4
1.8
2.6
1.8
1.6
1.9
2.8
1.6
2.7
3.7
2.3
2.7
1.9
2.9
2.9
1.7
2.9
2.0
2.8
1.6
3.4
1.6
1.6
3.0
3.7
2.3
2.4
2.1
2.4
3.6
2.8
Apr
f(RH)
3.4
2.2
3.9
2.4
2.1
1.6
2.4
1.6
1.5
1.9
2.6
1.4
2.4
3.6
2.2
2.6
1.6
2.6
3.0
1.6
2.8
1.7
2.5
1.5
3.2
1.3
1.2
2.8
3.4
2.0
2.2
1.9
2.0
3.7
2.6
May
f(RH)
3.1
2.2
2.9
2.4
1.9
1.6
2.8
1.6
1.6
1.9
2.7
1.4
2.3
3.8
2.1
3.0
1.5
2.7
3.6
1.5
3.2
1.6
2.4
1.6
3.3
1.3
1.3
3.4
3.2
2.0
2.1
1.9
1.8
3.2
3.1
Jun
f(RH)
3.0
2.2
3.2
2.3
1.7
1.3
2.7
1.4
1.6
1.6
2.7
1.3
2.9
4.0
1.8
3.2
1.3
2.7
3.6
1.2
3.7
1.4
2.2
1.6
3.9
1.1
1.1
3.8
3.0
1.8
2.1
2.2
1.6
3.1
3.4
Jul
f(RH)
3.4
2.3
2.9
2.0
1.6
1.4
2.5
1.7
1.7
1.7
2.3
1.8
3.1
4.3
1.5
3.4
1.3
2.3
3.4
1.3
3.6
1.4
2.1
1.8
3.9
1.8
1.8
4.0
2.8
1.4
2.1
2.5
1.5
2.9
3.5
Area,
Aug
f(RH)
3.8
2.3
3.1
1.9
1.6
1.5
2.4
2.1
2.0
1.9
2.2
2.0
3.4
4.3
1.5
3.7
1.5
2.2
3.4
1.5
4.1
1.6
2.1
2.1
4.2
2.1
2.1
4.2
2.9
1.4
2.2
3.0
1.6
2.9
3.9
Sep
f(RH)
4.0
2.3
3.3
2.1
1.6
1.6
2.2
1.9
2.1
2.0
2.6
1.9
3.5
4.2
1.7
3.6
1.5
2.6
3.6
1.6
4.0
1.6
2.2
2.2
4.1
1.8
1.8
4.2
3.1
1.6
2.2
2.8
1.7
3.1
3.9
Oct
f(RH)
3.8
2.3
3.9
2.5
1.8
1.6
2.3
1.7
1.9
1.8
2.9
1.6
2.8
3.7
2.0
3.3
1.6
3.0
3.5
1.6
3.7
1.7
2.4
1.8
3.9
1.5
1.5
3.8
3.6
2.0
2.2
2.9
1.9
3.7
3.3
Nov
f(RH)
3.6
2.1
4.5
3.2
2.3
2.0
2.7
2.0
1.8
2.1
3.5
1.8
3.2
3.7
2.4
2.9
2.0
3.7
3.4
2.0
3.4
2.1
3.0
1.9
3.7
1.6
1.6
3.4
4.6
2.8
2.1
3.0
2.4
4.6
3.0
Dec
f(RH)
3.5
2.2
4.5
3.3
2.7
2.3
2.7
2.2
1.9
2.3
3.5
2.2
3.2
3.7
2.4
2.8
2.4
3.9
3.5
2.3
3.2
2.5
3.4
2.1
3.9
2.2
2.2
3.5
4.6
3.0
2.2
3.1
3.0
4.6
3.1
A-11
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I
Based on the Centroid of the Area (Supplemental Information)
Class 1 Area
Dom e Land
Eagle Cap
Eagles Nest
Emigrant
Everglades
Fitzpatrick
Flat Tops
Galiuro
Gates of the Mountains
Gearhart Mountain
Gila
Glacier
Glacier Peak
Goat Rocks
Grand Canyon
Grand Teton
Great Gulf
Great Sand Dunes
Great Smoky Mountains
Guadalupe Mountains
Haleakala
Hawaii Volcanoes
Hells Canyon
Hercules - Glade
Hoover
Isle Royale
James River Face
Jarbidge
Jon n Muir
Joshua Tree
Joyce Kilmer - Slickrock
Kaiser
Kalm iopsis
Kings Canyon
La G arita
Lassen Volcanic
Site Name
Dom e Land
Starkey
White River
Yos em ite
Everglades
Bridger
White River
Chiricahua
Gates of the Mountains
Crater Lake
Gila Cliffs
Glacier
North Cascades
White Pass
Grand Canyon, Hance
Yellowstone
Great Gulf
Great Sand Dunes
Great Smoky Mountains
Guadalupe Mountains
Haleakala
Hawaii Volcanoes
Hells Canyon
Hercules - Glade
Hoover
Isle Royale
James River Face
Jarbidge
Kaiser
Joshua Tree
Great Smoky Mountains
Kaiser
Kalm iopsis
Sequoia
W eminuche
Lassen Volcanic
Map ID
109
76
56
96
19
65
56
39
74
86
42
72
81
79
48
66
4
53
10
32
108
107
77
28
97
25
7
68
110
101
10
110
89
98
55
90
Code
DOME1
STAR1
WHRI1
YOSE1
EVER1
BRID1
WHRI1
CHIR1
GAM01
CRLA1
GICL1
GLAC1
NOCA1
WHPA1
GRCA2
YELL2
GRGU1
GRSA1
GRSM1
GUM01
HALE1
HAV01
HECA1
HEGL1
HOOV1
ISLE1
JAR 11
JARB1
KAIS1
JOSH1
GRSM1
KAIS1
KALM1
SEQU1
WEMI1
LAV01
Site
St
CA
OR
CO
CA
FL
WY
CO
AZ
MT
OR
NM
MT
WA
WA
AZ
WY
NH
CO
TN
TX
HI
HI
OR
MO
CA
Ml
VA
NV
CA
CA
TN
CA
OR
CA
CO
CA
LAT
35.70
45.10
39.69
38.20
25.39
43.27
39.97
32.56
46.87
42.49
33.22
48.51
48.21
46.54
35.97
43.68
44.31
37.73
35.63
31.83
20.81
19.43
45.34
36.69
38.14
47.99
37.62
41.89
37.39
34.03
35.43
37.28
42.27
36.82
37.96
40.54
LONG
118.19
117.29
106.25
119.75
80.68
109.57
107.25
110.32
111.81
120.85
108.25
114.00
121.04
121.48
111.98
110.73
71.22
105.52
83.94
104.80
156.28
155.27
116.57
92.90
119.45
88.83
79.48
115.43
118.84
116.18
84.00
119.18
123.93
118.76
106.81
121.57
Jan
f(RH)
2.5
3.8
2.2
3.2
2.7
2.5
2.3
2.0
2.9
4.0
2.1
4.0
4.2
4.3
2.4
2.6
2.8
2.4
3.3
2.0
2.7
3.2
3.7
3.2
3.1
3.1
2.8
3.0
2.9
2.4
3.3
3.0
4.5
2.8
2.3
3.8
Feb
f(RH)
2.3
3.2
2.2
2.8
2.6
2.3
2.2
1.8
2.6
3.4
1.9
3.5
3.7
3.8
2.3
2.4
2.6
2.3
3.0
2.0
2.6
2.9
3.1
2.9
2.8
2.5
2.6
2.6
2.6
2.3
3.1
2.7
3.9
2.6
2.2
3.2
Mar
f(RH)
2.2
2.5
2.0
2.5
2.6
2.2
2.0
1.5
2.4
3.1
1.6
3.2
3.4
3.4
1.9
2.2
2.6
2.0
2.9
1.6
2.6
3.0
2.5
2.7
2.5
2.7
2.7
2.1
2.4
2.2
2.9
2.5
3.8
2.4
1.9
2.9
Apr
f(RH)
1.9
2.1
2.0
2.1
2.4
2.1
2.0
1.2
2.3
2.8
1.3
3.1
3.8
4.2
1.5
2.1
2.8
1.9
2.7
1.5
2.5
3.0
2.2
2.7
2.1
2.4
2.4
2.1
2.1
2.0
2.7
2.1
3.5
2.1
1.8
2.5
May
f(RH)
1.8
2.0
2.1
1.9
2.4
2.1
2.0
1.2
2.3
2.7
1.4
3.2
2.9
2.8
1.4
2.1
2.9
1.9
3.2
1.6
2.4
3.0
2.1
3.3
1.9
2.2
3.0
2.2
1.9
2.0
3.3
1.9
3.5
1.9
1.8
2.4
Jun
f(RH)
1.8
1.9
1.9
1.7
2.7
1.8
1.8
1.1
2.3
2.5
1.2
3.4
3.2
3.4
1.2
1.8
3.2
1.8
3.9
1.5
2.3
2.9
2.0
3.3
1.6
2.6
3.3
2.2
1.7
1.9
3.8
1.7
3.3
1.8
1.6
2.2
Jul
f(RH)
1.8
1.6
1.8
1.5
2.6
1.5
1.7
1.5
2.0
2.3
2.1
2.8
2.9
3.0
1.4
1.5
3.5
1.9
3.8
1.9
2.5
3.1
1.6
3.3
1.5
3.0
3.4
1.6
1.7
2.0
4.0
1.6
3.2
1.7
1.7
2.1
Area,
Aug
f(RH)
1.8
1.6
2.0
1.6
2.9
1.5
1.9
1.8
1.9
2.3
2.0
2.6
3.1
3.2
1.7
1.5
3.8
2.3
4.0
2.2
2.4
3.2
1.6
3.3
1.5
3.2
3.7
1.4
1.7
2.0
4.2
1.7
3.2
1.7
2.1
2.1
Sep
f(RH)
1.8
1.6
2.0
1.6
3.0
1.7
1.9
1.6
2.1
2.4
1.8
3.2
3.3
3.1
1.6
1.7
4.0
2.2
4.2
2.2
2.4
3.2
1.8
3.4
1.6
3.8
3.6
1.4
1.7
2.0
4.2
1.7
3.3
1.8
2.0
2.2
Oct
f(RH)
1.9
2.3
1.9
1.9
2.8
2.0
1.8
1.5
2.4
2.8
1.6
3.5
3.9
3.8
1.6
2.0
3.4
1.9
3.8
1.8
2.5
3.2
2.4
3.1
1.8
2.7
3.2
1.6
1.9
2.0
3.8
1.9
3.6
1.9
1.8
2.4
Nov
f(RH)
2.0
3.4
2.1
2.4
2.6
2.4
2.2
1.6
2.8
3.7
1.8
3.8
4.4
4.4
1.9
2.4
3.1
2.4
3.3
1.9
2.8
3.7
3.5
3.1
2.3
3.3
2.8
2.4
2.2
1.9
3.3
2.3
4.4
2.3
2.2
3.1
Dec
f(RH)
2.2
4.0
2.1
2.9
2.7
2.4
2.2
2.1
2.8
3.8
2.2
3.9
4.4
4.6
2.3
2.6
2.9
2.4
3.4
2.2
2.7
3.2
3.9
3.3
2.8
3.3
3.0
2.8
2.6
2.0
3.5
2.7
4.3
2.5
2.3
3.5
A-12
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I
Based on the Centroid of the Area (Supplemental Information)
Class 1 Area
Lava Beds
Linville Gorge
Lostwood
Lye Brook
Mam moth Cave
Marble Mountain
Maroon Bells - Snowmass
Mazatzal
Medicine Lake
Mesa Verde
Mingo
Mission Mountains
Mokelumne
Moosehorn
Mount Adams
Mount Baldy
Mount Hood
Mount Jefferson
Mount Rainier
Mount Washington
Mount Zirkel
Mountain Lakes
North Absaroka
North Cascades
Okefenokee
Olym pic
Otter Creek
Pasayten
Pecos
Petrified Forest
Pine Mountain
Pinnacles
Point Reyes
Presidential Range - Dry River
Rawah
Red Rock Lakes
Site Name
Lava Beds
Linville Gorge
Lostwood
Lye Brook
Mam moth Cave
Trinity
White River
Ike's Backbone
Medicine Lake
Mesa Verde
Mingo
Monture
Bliss
Moosehorn
White Pass
Mount Baldy
Mount Hood
Th ree Sisters
Mount Rainier
Th ree Sisters
Mount Zirkel
Crater Lake
North Absoraka
North Cascades
Okefenokee
Olym pic
Dolly Sods
Pasayten
Wheeler Peak
Petrified Forest
Ike's Backbone
Pinnacles
Point Reyes
Great Gulf
Mount Zirkel
Yellowstone
Map ID
87
13
62
3
9
104
56
46
63
54
26
73
95
2
79
43
85
84
78
84
58
86
67
81
16
83
8
82
35
41
46
92
91
4
58
66
Code
LABE1
LIG01
LOST1
LYBR1
MACA1
TRIN1
WHRI1
IKBA1
MELA1
MEVE1
MING1
MONT1
BLIS1
MOOS1
WHPA1
BALD1
MOH01
THSI1
MORA1
THSI1
MOZI1
CRLA1
NOAB1
NOCA1
OKEF1
OLYM1
DOS01
PASA1
WHPE1
PEFO1
IKBA1
PINN1
PORE1
GRGU1
MOZI1
YELL2
Site
St
CA
NC
ND
VT
KY
CA
CO
AZ
MT
CO
MO
MT
CA
ME
WA
AZ
OR
OR
WA
OR
CO
OR
WY
WA
GA
WA
WV
WA
NM
AZ
AZ
CA
CA
NH
CO
WY
LAT
41.71
35.89
48.60
43.15
37.22
41.52
39.15
33.92
48.50
37.20
36.98
47.40
38.58
45.12
46.19
34.12
45.38
44.55
46.76
44.30
40.55
42.34
44.77
48.54
30.74
47.32
39.00
48.85
35.93
35.08
34.31
36.49
38.12
44.21
40.70
44.67
LONG
121.34
81.89
102.48
73.12
86.07
123.21
106.82
111.43
104.29
108.49
90.20
113.85
120.03
67.26
121.50
109.57
121.69
121.83
122.12
121.87
106.70
122.11
109.78
121.44
82.13
123.35
79.65
120.52
105.64
109.77
111.80
121.16
122.90
71.35
105.94
111.70
Jan
f(RH)
4.0
3.3
3.0
2.7
3.4
4.4
2.2
2.1
3.0
2.5
3.3
3.6
3.2
3.0
4.3
2.2
4.3
4.4
4.4
4.4
2.2
4.3
2.4
4.1
3.5
4.5
3.0
4.2
2.3
2.4
2.2
3.2
3.6
2.8
2.1
2.7
Feb
f(RH)
3.4
3.0
2.9
2.6
3.1
3.8
2.1
1.9
2.9
2.3
3.0
3.1
2.8
2.7
3.8
2.0
3.8
3.9
4.0
3.9
2.2
3.6
2.3
3.7
3.2
4.1
2.8
3.7
2.1
2.2
2.0
2.8
3.3
2.6
2.1
2.5
Mar
f(RH)
3.1
3.0
2.9
2.6
2.9
3.7
2.0
1.7
2.9
1.9
2.8
2.7
2.4
2.7
3.4
1.7
3.5
3.6
3.6
3.6
2.0
3.3
2.2
3.4
3.1
3.8
2.8
3.4
1.8
1.7
1.7
2.6
3.1
2.6
2.0
2.3
Apr
f(RH)
2.7
2.7
2.3
2.6
2.6
3.3
2.0
1.3
2.3
1.5
2.6
2.5
2.0
3.0
4.4
1.4
3.9
3.7
4.7
3.7
2.1
3.0
2.2
3.7
3.0
4.1
2.6
3.7
1.7
1.4
1.4
2.4
2.7
2.8
2.1
2.1
May
f(RH)
2.6
3.3
2.3
2.8
3.2
3.4
2.1
1.3
2.2
1.5
3.0
2.6
1.9
3.0
2.9
1.3
3.0
3.1
3.1
3.1
2.2
2.9
2.1
2.9
3.6
3.2
3.2
2.9
1.7
1.3
1.3
2.3
2.5
3.0
2.3
2.1
Jun
f(RH)
2.4
3.9
2.6
3.0
3.5
3.2
1.7
1.1
2.5
1.3
3.2
2.6
1.6
3.1
3.5
1.2
3.2
3.1
3.7
3.1
1.9
2.6
1.9
3.2
3.7
3.5
3.5
3.2
1.5
1.2
1.1
2.0
2.3
3.4
2.0
1.9
Jul
f(RH)
2.3
4.1
2.7
3.3
3.7
3.2
1.9
1.5
2.5
1.6
3.3
2.3
1.5
3.4
3.1
1.6
2.9
2.9
3.3
3.0
1.7
2.5
1.7
2.9
3.7
3.1
3.7
2.9
1.8
1.5
1.4
2.0
2.5
3.7
1.8
1.7
Area,
Aug
f(RH)
2.3
4.5
2.4
3.6
3.9
3.2
2.2
1.7
2.2
2.0
3.5
2.2
1.6
3.8
3.3
1.9
3.0
2.9
3.5
2.9
1.9
2.5
1.6
3.2
4.1
3.5
4.1
3.2
2.1
1.8
1.8
2.1
2.6
4.0
2.0
1.6
Sep
f(RH)
2.4
4.4
2.3
3.7
3.9
3.2
2.1
1.6
2.2
1.9
3.5
2.5
1.7
3.9
3.1
1.7
3.1
3.0
3.4
3.0
2.0
2.6
1.8
3.5
4.0
3.7
4.0
3.3
2.0
1.7
1.6
2.1
2.6
4.3
2.0
1.8
Oct
f(RH)
2.7
3.7
2.4
3.3
3.4
3.4
1.8
1.5
2.4
1.7
3.1
2.9
1.9
3.5
3.9
1.6
3.9
3.8
4.1
3.8
1.9
3.1
2.0
3.9
3.8
4.4
3.3
3.9
1.7
1.6
1.5
2.3
2.7
3.5
1.9
2.1
Nov
f(RH)
3.5
3.2
3.2
2.9
3.2
4.1
2.1
1.7
3.2
2.1
3.1
3.5
2.4
3.2
4.5
1.8
4.5
4.6
4.7
4.6
2.1
4.1
2.4
4.4
3.5
4.8
3.0
4.4
2.0
1.9
1.7
2.5
2.9
3.1
2.1
2.6
Dec
f(RH)
3.8
3.4
3.2
2.8
3.5
4.2
2.1
2.1
3.2
2.3
3.3
3.6
2.9
3.2
4.6
2.2
4.6
4.5
4.7
4.6
2.1
4.3
2.4
4.4
3.6
4.8
3.1
4.5
2.2
2.3
2.1
2.9
3.3
3.0
2.0
2.7
A-13
-------
Guidance for Tracking Progress Under the Regional Haze Rule
Table A-3 Monthly Site-Specific f(RH) Values for Each Mandatory Federal Class I
Based on the Centroid of the Area (Supplemental Information)
Class 1 Area
Redwood
Rocky Mountain
Roo sevelt Ca m pobe No
Saguaro
Saint Marks
Salt Creek
San Gabriel
San G orgon io
San Jacinto
San Pedro Parks
San Rafael
Sawtooth
Scapegoat
Selway - Bitterroot
Seney
Sequoia
Shenandoah
Shining Rock
Sierra Ancha
Simeonof
Sipsey
South Warner
Strawberry Mountain
Superstition
Swanquarter
Sycamore Canyon
Teton
Theodore Roosevelt
Thousand Lakes
Three Sisters
Tuxedni
UL Bend
Upper Buffalo
Ventana
Virgin Islands (b)
Voyageurs
Site Name
Redwood
Rocky Mountain
Moosehorn
Saguaro
Saint Marks
Salt Creek
San Gabriel
San G orgon io
San G orgon io
San Pedro Parks
San Rafael
Sawtooth
Monture
Sula
Seney
Sequoia
Shenandoah
Shining Rock
Sierra Ancha
Simeonof
Sipsey
Lava Beds
Starkey
Tonto
Swanquarter
Sycamore Canyon
Yellowstone
Theodore Roosevelt
Lassen Volcanic
Three Sisters
Tuxedni
UL Bend
Upper Buffalo
Pinnacles
Virgin Islands
Voyageurs
Map ID
88
57
2
40
17
36
93
99
99
34
94
70
73
71
22
98
6
11
45
105
21
87
76
44
14
47
66
61
90
84
103
64
27
92
106
24
Code
REDW1
ROM01
MOOS1
SAGU1
SAMA1
SACR1
SAGA1
SAG01
SAG01
SAPE1
RAFA1
SAWT1
MONT1
SULA1
SENE1
SEQU1
SHEN1
SHRO1
SIAN1
SIME1
SIPS1
LABE1
STAR1
TONT1
SWAN1
SYCA1
YELL2
THRO1
LAV01
THSI1
TUXE1
ULBE1
UPBU1
PINN1
VIIS1
VOYA2
Site
St
CA
CO
ME
AZ
FL
NM
CA
CA
CA
NM
CA
ID
MT
MT
Ml
CA
VA
NC
AZ
AK
AL
CA
OR
AZ
NC
AZ
WY
ND
CA
OR
AK
MT
AR
CA
VI
MN
LAT
41.56
40.28
44.88
32.25
30.12
33.61
34.27
34.18
33.75
36.11
34.78
44.18
47.17
45.86
46.26
36.50
38.52
35.39
33.82
54.92
34.34
41.33
44.30
33.63
35.31
34.03
44.09
47.30
40.70
44.29
60.15
47.55
35.83
36.22
18.33
48.59
LONG
124.08
105.55
66.95
110.73
84.08
104.37
117.94
116.90
116.65
106.81
119.83
114.93
112.73
114.00
86.03
118.82
78.44
82.78
110.88
159.28
87.34
120.20
118.73
111.10
76.28
116.18
110.18
104.00
121.58
122.04
152.60
107.87
93.21
121.59
64.79
93.17
Jan
f(RH)
4.4
1.7
3.0
1.8
3.7
2.1
2.5
2.7
2.5
2.3
2.8
3.3
3.2
3.5
3.3
2.5
3.1
3.3
2.1
4.3
3.4
3.6
3.9
2.1
2.9
2.4
2.5
2.9
3.8
4.5
3.5
2.7
3.3
3.2
2.8
Feb
f(RH)
3.9
1.9
2.7
1.6
3.4
1.9
2.5
2.8
2.4
2.1
2.7
2.9
2.8
3.0
2.8
2.4
2.8
3.0
2.0
4.1
3.1
3.1
3.3
1.9
2.7
2.3
2.4
2.8
3.2
4.0
3.3
2.5
3.0
2.9
2.4
Mar
f(RH)
4.6
1.9
2.7
1.4
3.4
1.5
2.4
2.6
2.4
1.8
2.7
2.3
2.6
2.6
2.9
2.4
2.8
2.9
1.7
3.6
2.9
2.7
2.8
1.6
2.6
2.2
2.2
2.8
2.9
3.6
2.9
2.5
2.7
2.8
2.4
Apr
f(RH)
3.9
2.1
3.0
1.1
3.4
1.5
2.2
2.3
2.2
1.6
2.4
2.0
2.4
2.3
2.7
2.2
2.5
2.7
1.3
3.9
2.8
2.4
2.9
1.3
2.5
2.0
2.1
2.3
2.5
3.7
2.7
2.3
2.8
2.4
2.3
May
f(RH)
4.5
2.3
3.0
1.1
3.5
1.7
2.2
2.2
2.1
1.6
2.3
2.0
2.5
2.4
2.6
1.9
3.1
3.4
1.3
3.9
3.3
2.3
2.3
1.3
2.9
2.0
2.1
2.3
2.4
3.1
2.7
2.2
3.4
2.3
2.3
Jun
f(RH)
4.7
2.0
3.1
1.1
4.0
1.6
2.1
1.9
2.0
1.4
2.3
1.8
2.4
2.3
3.1
1.8
3.4
3.9
1.1
4.3
3.7
2.1
2.4
1.1
3.2
1.9
1.9
2.5
2.2
3.1
2.9
2.2
3.4
2.1
3.1
Jul
f(RH)
4.9
1.8
3.4
1.4
4.1
1.8
2.2
1.8
2.1
1.7
2.5
1.4
2.1
1.9
3.6
1.7
3.5
4.1
1.5
5.0
3.9
1.9
2.0
1.5
3.4
2.0
1.6
2.4
2.1
3.0
3.6
2.0
3.4
2.2
2.7
Area,
Aug
f(RH)
4.7
2.0
3.8
1.8
4.4
2.0
2.2
1.9
2.1
2.0
2.5
1.4
2.0
1.9
4.0
1.6
3.9
4.5
1.8
5.2
3.9
1.9
2.0
1.7
3.5
2.0
1.5
2.2
2.1
2.9
4.0
1.8
3.4
2.3
3.0
Sep
f(RH)
4.3
1.9
3.9
1.6
4.2
2.1
2.2
1.9
2.1
1.9
2.4
1.5
2.3
2.1
4.1
1.8
3.9
4.4
1.6
4.5
3.9
2.0
1.9
1.6
3.4
2.0
1.7
2.2
2.2
3.0
3.9
1.9
3.6
2.2
3.2
Oct
f(RH)
3.7
1.8
3.5
1.4
3.8
1.8
2.3
1.9
2.1
1.7
2.5
2.0
2.6
2.6
3.4
1.9
3.2
3.8
1.5
3.8
3.6
2.3
2.6
1.5
3.1
2.0
2.0
2.3
2.4
3.8
3.5
2.2
3.3
2.4
2.6
Nov
f(RH)
3.8
1.8
3.3
1.6
3.7
1.8
2.1
1.9
2.0
2.1
2.3
2.9
3.1
3.3
3.6
2.3
3.0
3.3
1.7
4.0
3.3
3.1
3.7
1.7
2.8
1.9
2.4
3.0
3.1
4.6
3.5
2.7
3.2
2.5
2.9
Dec
f(RH)
3.4
1.7
3.2
2.1
3.8
2.1
2.2
2.2
2.1
2.2
2.5
3.3
3.1
3.5
3.5
2.3
3.1
3.4
2.1
4.3
3.4
3.4
4.1
2.1
2.9
2.0
2.5
3.0
3.5
4.6
3.7
2.7
3.3
2.9
2.8
A-14
-------
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)
Class 1 Area
Washakie
W eminuche
West Elk
WheelerPeak
White Mountain
W ichita Mountains
Wind Cave
Wolf Island
Yellowstone
Yolla Bolly- Middle Eel
Yos em ite
Zion
Site Name
North Absoraka
W eminuche
White River
WheelerPeak
White Mountain
W ichita Mountains
Wind Cave
Okefenokee
Yellowstone
Trinity
Yos em ite
Zion
Map ID
67
55
56
35
37
30
60
16
66
104
96
51
Code
NOAB1
WEMI1
WHRI1
WHPE1
WHIT1
WIM01
WICA1
OKEF1
YELL2
TRIN1
YOSE1
ZION1
Site
St
WY
CO
CO
NM
NM
OK
SD
GA
WY
CA
CA
UT
LAT
43.95
37.65
38.69
36.57
33.49
34.74
43.55
31.31
44.55
40.11
37.71
37.25
LONG
109.59
107.80
107.19
105.42
105.83
98.59
103.48
81.30
110.40
122.96
119.70
113.01
Jan
f(RH)
2.5
2.4
2.3
2.3
2.1
2.7
2.5
3.4
2.5
4.0
3.3
2.7
Feb
f(RH)
2.3
2.2
2.2
2.2
1.9
2.6
2.5
3.1
2.4
3.4
3.0
2.4
Mar
f(RH)
2.2
1.9
1.9
1.9
1.6
2.4
2.5
3.1
2.3
3.1
2.8
2.0
Apr
f(RH)
2.1
1.7
1.9
1.8
1.5
2.4
2.5
3.0
2.2
2.8
2.3
1.6
May
f(RH)
2.1
1.7
1.9
1.8
1.5
3.0
2.7
3.3
2.2
2.7
2.1
1.5
Jun
f(RH)
1.8
1.5
1.7
1.6
1.4
2.7
2.5
3.7
1.9
2.5
1.8
1.3
Jul
f(RH)
1.6
1.6
1.8
1.8
1.8
2.3
2.3
3.7
1.7
2.4
1.5
1.2
Aug
f(RH)
1.5
2.0
2.1
2.2
2.0
2.5
2.3
4.1
1.6
2.5
1.5
1.4
Sep
f(RH)
1.8
1.9
2.0
2.1
2.0
2.9
2.2
4.0
1.8
2.6
1.5
1.4
Oct
f(RH)
2.0
1.7
1.8
1.8
1.7
2.6
2.2
3.7
2.1
2.7
1.8
1.6
Nov
f(RH)
2.4
2.1
2.1
2.2
1.8
2.7
2.6
3.5
2.5
3.3
2.4
2.0
Dec
f(RH)
2.5
2.3
2.2
2.3
2.1
2.8
2.6
3.5
2.5
3.6
2.8
2.4
a: No participate 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.
A-15
-------
APPENDIX B
Analysis of the Effect of Correlation Between
f(RH) and SO4 andf(RH) and NO3 on
Deciview Calculations using f(RH)
B-l
-------
Guidance for Tracking Progress
Under the Regional Haze Rule
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 monthly-
avQragQdf(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 andf(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/(7?//) 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 forf(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 off(RH) and SO4, and, likewise,
separate averages off(RH) and NO3. Let us denote the errors introduced due to such substitution
of averages by dSO4 and dNO3, respectively.
B-2
-------
Guidance for Tracking Progress
Under the Regional Haze Rule
That is,
dSO4 = average f(RH) *average SO4 -average [f(RH)*SOA] (1)
and
dNO3 = *\&ragef(RH) *average NO3 - average \f(RH) *NO3]. (2)
These errors will be zero if, and only if, the sample Pearson correlation bet ween f(RH) and SO4,
respectively f(RH) andNO3, is zero.
More generally, suppose n pairs of observations, say (x,, y,), (x2, y^,---, (xn, yj are available
on the variable pair (x, y). If the sample Pearson correlation coefficient r betweenXand 7 is zero,
it follows that the average of the product X*Yis equal to the product of the separate averages of X
and Y. Namely,
r=
An examination of the sample correlation between SO4 andf(RH), as well as NO3 andf(RH), for
various site/year combinations in the IMPROVE database reveals the presence of statistically
significant correlations between SO4 and/or NO3 andf(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/fT?//) and SO4/NO3,
one needs to examine the magnitudes of dSO4 and dNO3 relative to the target quantities
[average(/(7?7:/) *SO4)] and [average(/(7?7:/) *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)
B-3
-------
Guidance for Tracking Progress
Under the Regional Haze Rule
In our calculations we used the following input quantities and data completeness criteria:
• f(KH) is computed from hourly RH observations. Ho\x\yf(RH) factors are
derived from Tang's smoothed ammonium sulfate growth curves. For RH values
greater than 95%, theffRH) value is flagged as missing. Finally, the daily average
of the hourly/(7?//) observations is obtained. Note the daily average/(7?//) is set
to missing on days for which there are fewer than 1 6 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/(7?//) for each month was computed for any given
site by avQragmgf(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 (I) f(RH) yearly .
The following %age errors were calculated:
(a) (Figure 1)
pSO4yearly = The error dSO4, expressed as a %age, whQnf(RH)yearIy is used in place off(RH)daily
xlOO
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Guidance for Tracking Progress
Under the Regional Haze Rule
(b) (Figure 2)
pSO4monthly = The error dSO4, expressed as a %age, whQnf(RH)monthIy is used in place of
f(RH) daily
SO4]- average[f (&ft) ^ SQ4 ]
" x 100
(c) (Figure 3)
pNO3yearly = The error dNO3, expressed as a %age, whQnf(RH)yearly is used in place of
averagel f(Rff)vear!v* NQ]- average[f (RH)iMv JVO. 1
"-^ ^ X10°
(d) (Figure 4)
pNO3 monthly = The error dNO3, expressed as a %age, whea.f(RH)monthly is used in place of
f(RH)daily
^
(e) (Figure 5)
pSO4NO3yearly = The error (dSO4+dNOi), expressed as a %age, whQnf(RH)yearly is used in
place off(RH)daiIy
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Guidance for Tracking Progress
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(f) (Figure 6)
pSO4NO3 monthly = The error (dSO4+dNO3\ expressed as a %age, whQnf(RH)monthly is used
in place off(RH)daify
+ NO, )] - mm^[f(RH)^ (SO, + JTC,)]
The results are displayed graphically in Figures 1 through 6. In each figure, box-and-
whisker 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 1 1 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
"tmQ"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.
B-6
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Guidance for Tracking Progress
Under the Regional Haze Rule
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 off(RH) values that may be substituted for f(RH)daily. Five candidates were
considered for such substitution:
= daily f(RH) value
= monthly average f(RH) values computed as explained
= yearly average f(RH) values computed as explained
f(RH) monthly
earlier
f(RH)yearly
earlier.
f(RH)low20
= average off(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.
= average off(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 thatf(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 (I/km).
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Guidance for Tracking Progress
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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 exthighrdaily and dvhighrdaily, 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:tnonthly 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 extloWidaily and dvlm,idaily, 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 extlowyearly and dvlowyearly, 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.
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Guidance for Tracking Progress
Under the Regional Haze Rule
The extinction index (respectively, deciview index) based onf(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 off(RH)high20 &n&f(KH)iOW2oi 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:
index computed by the method - index computed using
% error for a method = :—; ;—: , .n T„ x 100
index computed usi
(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 withf(RH)monthIy orf(RH)yearIy tend to overestimate it. For extinction or
deciview representing the highest 20% extinction days, the index computed wiihf(RH)high20 tends
to overestimate the true index, whereas the indices computed withf(RH)monthIy orf(RH)yearIy tend to
underestimate it.
Conclusions:
(a) The effect of any correlation that may exist between SO4 and/or NO3 andf(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.
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Guidance for Tracking Progress
Under the Regional Haze Rule
30-
- 10-
-20
ll
ACAD BADL BAND BRID CANY CHIB CRLA GLAC GRCA GRSM GUMO MEWE MORA PEFO PIMM SAGO SHEW TOUT YELL YOSE
8161 3552 1827 1350 1621 2475 1126 3385 1607 18712 2826 1573 5849 1902 2895 2519 17768 2054 1301 1471
SITE
Figure B-l 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^RH)^ *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.
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Guidance for Tracking Progress
Under the Regional Haze Rule
20
bJ 10
-10-
-20-
-30-
T
ACAD BADL BAND BHID CANY CHIR CRLA GLAC GRCA GRSM GUMO MEUE MORA PEFO PINN SAGO SHEN TONT YELL YOSE
81B1 3552 1827 1350 1B21 2475 112B 3385 1B07 18712 2826 1573 5849 1902 2895 2519 177B8 2054 1301 1471
SITE
Figure B-2 Boxplots of pSO4mon(hly 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.
B-ll
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Guidance for Tracking Progress
Under the Regional Haze Rule
30-
: -20-
-30-
-40-
rtCAD BrtDL BftND BRID CrtNY CHIR CRLft GLrtC GRCA GRSM GUMO MEWE MORA PEFO FINN SrtGO SHEN
988 1252 373 253 375 257 214 931 337 913 442 209 987 324 2460 5819 2037
TONT
449
YELL
373
YOSE
791
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.
B-12
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Guidance for Tracking Progress
Under the Regional Haze Rule
40-
30-
20-
io-
o-
-10-
-20-
-30-
-40"
-50-
AC
9
. ' H, 1 1 1 1
i ' n ' r
M " • r -
1 H [
If l| (Pi
U T
P T i
1
•
AD BADL BAND BRID CANY CHIR CRLA GLAC GRCA GRSM GUMO MEUE MORA PEFO FINN SAGO SHEN TONT YELL YDS
88 1252 373 253 375 257 214 931 337 913 442 209 987 324 2460 5819 2037 449 373 79
SITE
Figure B-4 Boxplots of pNO3mon(hly 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.
B-13
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Guidance for Tracking Progress
Under the Regional Haze Rule
10-
o-
-20
-30-
.
ftCAD BADL BftND BRID CANY CHIB CBLft GLftC GRCft GBSM GUMO MEUE MOBft PEFO FINN SAGO 8HEN TONT TELL YOSE
9149 4804 2200 1603 1996 2731 1341 4317 1944 19625 3267 1782 6837 2226 S3S6 8338 19805 2503 1674 2262
SITE
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.
B-14
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Guidance for Tracking Progress
Under the Regional Haze Rule
30^
20-
o 10
-10-
-20
-40-
e
ACAD BADL BAND BRID CANY CHIR CRLA GLAC GRCA GRSM GUMO MEUE MORA PEFO FINN SAGO SHEN TONT YELL YOSE
9149 4804 2200 1603 1996 2731 1341 4317 1944 1962S 3267 1782 6837 2226 53S6 8338 1980S 2S03 1674 2262
SITE
Figure B-6 Boxplots of pSO4NO3mon(hly 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.
B-15
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Guidance for Tracking Progress
Under the Regional Haze Rule
30-
20-
*
•
BADL BAND BRID CANY CHIR GLAC
0.013 0.010 0.009 0.004 0.007 0.009 0.01S
GRCA DRSM GUMO
O.OOE 0.029 0.011
MEWE PEFO
0.007 0.010
FINN SAGO SHEN TONT YELL
0.017 0.010 0.027 0.010 0.009
YOSE
0.007
SITE
* KError(EXT) using Yearly Average fCrh)
• XError(EXT) using Monthly f(rh)
1 XError(EXT) using Bottom 20X f(rh)
Figure B-7 Comparison of %age errors in average extinction for LOWEST 20% extinction days. These values
are computed using f(RH)yeal]y (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
B-16
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Guidance for Tracking Progress
Under the Regional Haze Rule
20-
10-
o-
*
*
: ' '
-20 -
ftCAD BrtDL BAND BRID CANY CHIR OLftC GRCft ORSM GUMO MEUE PEFO FINN SftGO SHEN TONT TELL TOSE
0.090 0.051 0.028 0.021 0.026 0.031 0.059 0.025 0.157 0.039 0.021 0.028 0.061 0.093 0.179 0.030 0.028 0.041
SITE
• • * XError(EXT) using Tearly Average f(rh) « • « XError(EXT) using Bottom 20J5 f(rh)
• • • KError(EXT) using Monthly f(rh)
Figure B-8 Comparison of %age errors in average extinction for HIGHEST 20% extinction days. These values are
computed using f(RH)yeal]y (red points), f(RH)mon(hly (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
B-17
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Guidance for Tracking Progress
Under the Regional Haze Rule
20
10
,
ACAD BADL BAND GRID CANY CHIR GLAC GRCA GRSM GUMO MEWE PEFO FINN SAGO SHEN TONT YELL YOSE
8.1 6.9 6.3 3.7 5.4 6.2 9.0 4.7 13.4 7.4 5.0 6.6 9.8 6.7 12.8 7.1 6.1 5.2
SITE
• • • KErroKDU) using Yearly Average f(rh) • • • KError(DW) using Bottom 202 f(rh)
• • • XError(DU) using Monthly f(rh)
Figure 9. Comparison of %age errors in average deciview for LOWEST 20% extinction days. These values are
computed using f(RH) yeal]y (red points), f(RH) mon(hiy (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
B-18
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Guidance for Tracking Progress
Under the Regional Haze Rule
5:
4 :
i 3:
i 2:
; 0:
-2
-3
-4
-5
-6
•
*
ACAD
22.5
BADL
17.5
BAND BRID
13.2 11.1
CANY
12.5
CHIB
13.8
GLAC
18.9
GBCA
12.4
GRSM
28.0
GUIIO
15.7
MEUE
11.1
PEFD FINN
13.2 19.4
SAGO
23.0
SHEW
28.9
TDNT
13.8
YELL
13.1
YOSE
15.9
SITE
• • • XError(DV) using Yearly Average f(rh)
• • • XError(DV) using Monthly f(rh)
1 JSErroKDV) using Bottom 20!! f(rh)
Figure B-10 Comparison of %age errors in average deciview for HIGHEST 20% extinction days. These values
are computed using f(RH)yeal]y (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
B-19
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TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1. REPORT NO.
EPA-454/B-03-004
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 REPORTNO.
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 AGENCYNAME 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.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Regional Haze, Haze Index
Air Pollution control
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
Unlimited
20. SECURITY CLASS (Page)
Unclassified
EPA Form 2220-1 (Rev. 4-77)PREVIOUS EDITION IS OBSOLETE
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United States
Environmental Protection
Agency
Office of Air Quality Planning and Standards
Emissions, Monitoring and Analysis Division
Research Triangle Park, NC
Publication No. EPA-454/B-03-004
September 2003
Postal information in this section where appropriate.
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