# FEDERAL LAND MANAGERS’ AIR QUALITY RELATED VALUES WORKGROUP (FLAG)

PHASE I REPORT

(December 2000)

U.S. FOREST SERVICE – AIR QUALITY PROGRAM

NATIONAL PARK SERVICE – AIR RESOURCES DIVISION U.S. FISH AND WILDLIFE SERVICE – AIR QUALITY BRANCH

## Table of Contents

A. EXECUTIV E SUMM ARY ......................................................................................................... ....iii

1. RECOMMENDATIONS FOR EVALUATIN G VISIBILITY IMPACTS .........................iv

2. RECOMMENDATIONS FOR EVALUAT ING OZONE IM PACTS.................................vii

3. RECOMMENDATIONS FOR EVALUATIN G DEPOSITION IMPACTS .......................ix

B. BACK GROUND .................................................................................................................. .............1

1. HI STORY ................................................................................................................... .................1 a. FLAG Ap proach ............................................................................................................... ......1 b. FLAG Orga nization........................................................................................................... .....2

2. OVERVIEW OF RES OURCE ISSUES....................................................................................2

a. Visibility..................................................................................................................... .............3
b. Vegetation ..................................................................................................................... ..........3

c. Soils and Surf ace Waters....................................................................................................... .4

3. LEGAL RESPONSIB ILITIES ..................................................................................................4

4. COMMONALITIES AMONG FEDERA L LAND M ANAGERS..........................................5 a. Identify ing AQRVs .......................................................................................................... ......6
b. Determining the Leve ls of Pollution that Trigger Concern for the Well-Being of AQRVs...................................................................................................................... ..........6 c. Visibility.................................................................................................................. ................6 d. Biological and Physical Effects............................................................................................. .6

e. Determining the Leve l of Pollution Li kely to Cause an “Adverse Impact” on AQRVs...................................................................................................................... .........7
f. FLM Da tabases .............................................................................................................. ........7

C. FEDERAL LAND MANAGERS' AP PROACH TO AQRV PROTECTION................................8 1. AQRV PROTECTI ON AND IDENTI FICATION...................................................................8

2. NEW SOURCE REVIEW ...................................................................................................8 a. Roles and Responsib ilities of FLMs .....................................................................................9
b. Elements of Pe rmit Review .................................................................................................1 0
c. FLM Permit Review Process ..............................................................................................13 d. Criteria for Decision Making (A dverse Impact Considerations)........................................15
e. Air Pollution Permit Conditions that Benefit Class I Areas ................................................16 f. Reducing Pollution in Nonattainment Areas (Nonattainment Permit Process) ................17 3. OTHER AIR QUALITY REVIEW CONSID ERATIONS ...................................................17 a. Remedying Existing Adverse Impacts.................................................................................18 b. Requesting SIP Revi sions to Address AQRV Adverse Impacts .......................................18
c. Periodic Increment Co nsumption Review ..........................................................................19

4. MANAGING EM ISSIONS GENERATED IN AND NEAR FLM AREAS........................20
a. Prescrib ed Fire ............................................................................................................. .........20 b. Strategies to Minimize Emissions from Sources In and Near FLM Areas........................21 c. Conformity Requirements in Nonattainment Areas............................................................23

D. SUBGROUP REPORTS: TECHNICAL ANALYSES AND RECOMMENDATIONS...........24

1. SUBGROUP OBJECT IVES AND TASKS ............................................................................24

2. VISIBILITY................................................................................................................. ..............24

a. Intr oductio n.............................................................................................................. .............24 b. Analysis Techniques....................................................................................................... ......29 c. Air Quality Models and Vi sibility Assessment Procedures................................................29 d. Summary ................................................................................................................... ............35ii e. Appendix 2.A Vi sibility Parameters ..................................................................................36 f. Appendix 2.B Estima te of Natura l Conditions...................................................................40 g. Appendix 2.C Example Problem ........................................................................................69

3. OZ ONE ..................................................................................................................... ................75
a. Introd uction................................................................................................................ ...........75
b. Ozone Effects on Vegetation ...............................................................................................7 5
c. Recommended Metric to Determine Phytotoxic Ozone Co ncentrations ..........................77
d. Identification of Ozone Sensitive AQRVs or Sensitiv e Receptors ...................................79

e. Review Process for Sources that Could A ffect Ozone Levels or Vegetation in

FLM Areas .................................................................................................................... .......79
f. Further Guidance to FLMs ............................................................................................82 g. Ozone Air Pollution Websites.......................................................................................83
h. Appendix 3.A A Preliminary Listing for Selected USDA/FS, NPS, and FWS
Class I Areas of Plant Species that have been Show n to be Sensitive Receptors
for Ozone.................................................................................................................... ...........84 i. Appendix 3.B List of Representati ve Low and High W126 and N100 Values
for SELECTED NPS and FWS Areas.........................................................................103

4. DEPOSITION..................................................................................................................... .....119
a. Introd uction................................................................................................................ .........119 b. Current Trends in Deposition.............................................................................................120
c. Identification and As sessment of AQRVs.........................................................................125 d. Determining Critical Loads ...............................................................................................12 9 e. Other AQRV Identification and Assessment Tools..........................................................132
f. Recommendations and Guidance for Ev aluating Potential Effects from Proposed Increases in De position to an FLM Area .........................................................134 g. Summary ..................................................................................................................... ........142 h. Websites for Deposition and Related In formation............................................................143

E. FUTURE FLAG WORK .......................................................................................................151

1. IMPLEMENTING FL AG RECOMME NDATIONS...........................................................151

2. PHASE I UP DATES...............................................................................................................1 51

3. PHASE II TASKS................................................................................................................. ..151

APPENDIXES

A. GLO SSARY ................................................................................................................... ...............152

B. LEGAL FRAMEWORK FOR MANAGING AIR QUALITY AND AIR QUALITY EFFECTS ON FEDERAL LANDS .................................................................157

C. GENERAL POLICY FOR MANA GING AIR QUALITY RELATED

VALUES IN CLAS S I AREAS....................................................................................................166 D. BEST AVAILABLE CONTROL TECHNOLOGY (BACT) ANALYSIS ..........................168

E. MAPS OF FEDERAL CLASS I AREAS..............................................................................169

F. CLASS I AREA CONTAC TS......................................................................................................1 73

G. FLAG PART ICIPANT S .......................................................................................................... ....195

H. BIBLIOGRAPHY................................................................................................................ ..........199iii A. EXECUTIVE SUMMARY

The F ederal L and Managers' A ir Quality Related Values Work G roup (FLAG) was formed to develop a more consistent approach for the Federal Land Managers (FLMs) to evaluate air pollution effects on their resources. Of particular im portance is the New Source Review (NSR) program, es pecially in the
review of Prevention of Significant Deterioration (PSD) of air quality permit applications. The goals of FLAG have been to provide consistent policies and processes both for identifying air quality related values (AQRVs) and for evaluating the effects of air pollution on AQRVs, primarily those in Federal Class I air quality areas, but in some instances, in Class II areas. Fe deral Class I areas are defined in the Clean Air Act as national parks over 6,000 acres and wilderness areas a nd memorial parks over 5,000 acres, established as of 19 77. All other FLM areas are designa ted Class II. Maps of Federal Class I areas are provided in Appendix E. Lists of Class I Area contacts are provided in Appendix F.

FLAG members include representatives from the th ree FLMs that administer the nation's Federal Class I areas: the U.S. Department of Agricultu re Forest Service (USDA/FS), the National Park Service (NPS), and the U.S. Fish and Wildlife Servic e (FWS). (Subsequently in this report, these three agencies collectively will be referred to as “FLMs.” Class I and Class II air quality areas are called "FLM areas" in this report.) Appendix G contains a list of FLAG Participants.

This report describes the work accomplished in Ph ase I of the FLAG effort. That work includes identifying policies and processes common to the FLMs (herein called “commonalities”) and developing new policies and processes using readily available information. This report provides State permitting authorities and potential permit app licants a consistent and predictable process for assessing the impacts of new a nd existing sources on AQRVs, includi ng a process to identify those AQRVs and potential adverse impacts. The report also discusses non-new source review considerations and managing emissions in Federal areas. In Phase II, FLAG will address unresolved issues including those that will requi re research and the collection of new data.

This FLAG Phase I Report consolidates the results of the FLAG Visibility, Oz one, and Deposition subgroups. The chapters prepared by these subgr oups contain issue-specific technical and policy analyses, recommendations for ev aluating AQRVs, and guidelines for completing and evaluating NSR permit applications. These recommendations and guidelines are intended for use by the FLMs, permitting authorities, NSR permit applicants, and other interested parties. The report includes background information on the roles a nd responsibilities of the FLMs under the NSR

program.

This document includes guidelines for completing a nd evaluating NSR applications that may affect FLM areas. It does not provide a universal formul a that would, in all s ituations, allow one to determine whether or not a source of air pollution do es, or would, cause or c ontribute to an adverse impact. That determinati on remains a project-speci fic management decision, the re sponsibility for which remains with the FLM, as delegated by Congre ss. The FLM's assessment of whether or not an adverse impact would occur is based on the sensitivity of the AQRVs at the particular FLM area under
consideration.

To provide information for the FLM’s assessment of adverse impacts on AQRVs, the permit applicant should identify the poten tial impacts of the source on all appli cable AQRVs of that area. An FLMiv may ask that an applicant address an y or all of the areas of concern. The primar y areas of concern to the FLMs with respect to air pollution emissions are visibility impairment, ozone effects on vegetation, and effects of pollutant depo sition on soils an d surface waters.

The FLAG Phase I Report also describes the FLAG effort–including the FLAG approach, organization, and plans for future FLAG work. Appendix A of the report contains a glossary of technical terms, abbreviations, an d acronyms used in the report along with asso ciated definitions. Appendix H provides a list of all re ferences cited in the FLAG report.

The key recomme ndations developed by th e Visibility, Ozone , and Deposition subgroups are
summarized below. Howe ver, for all three subject matter areas, FLAG recommends that the permit applicant consult with the appropriate regulatory ag ency and with the FLM for the affected area(s) for confirmation of preferred procedures . This consultation should take place in the early stages of the permit application process.

1. RECOMMENDATIONS FOR EVALUAT ING VISIBILITY IMPACTS

FLAG has provided guidance in the form of recommendations, specific prescriptions, and interpretation of results for asse ssing visibility impacts of source s near Class I areas (although this guidance is generally appli cable to Class II areas, as well). The guidance addresses assessments for sources proposed for locations near (generally with in 50 km) and at large distances (greater than 50 km) from these areas. It also recommends impa irment thresholds and id entifies the conditions for which cumulative analyses of all increment-c onsuming sources would be necessary. The key components of the recommendations are highlighted below.

In general, FLAG recommends that an applicant:

• Consult with the appropriate regulatory agency an d with the FLM for the affected Class I area(s)
or other affected area for confir mation of preferred procedures a nd for the need for a cumulative analysis.

• Obtain FLM recommendation for th e specified reference levels (e stimate of natural conditions) and, if applicable, FLM recommended plume/obse rver geometries and model receptor locations.

• Apply the applicable EPA Guidelin e, steady-state models for region s within the Class I area that are affected by plumes or layers that are viewed against a backgrou nd (generally within 50 km of the source).

Calculate hourly estimates of ∆ E and plume contrast, with resp ect to natural conditions, and compare these estimates with the th resholds given in Section D.2.c.

• For regions of the Class I area where visibility im pairment from the source would cause a general alteration of the appearance of th e scene (generally 50 km or mo re away from the source or from
the interaction of the emissions from multiple sources), apply a non-steady-state air quality model
with chemical transformation ca pabilities (refer to IWAQM guida nce documents), which yields
ambient concentrations of vi sibility-impairing po llutants. At each Class I receptor:v Calculate the change in extinc tion due to the source being an alyzed, compare these changes with the reference conditions, a nd compare these results with th e thresholds given in Section

D.2.c.

If necessary, calculate the cumu lative change in extinction due to new source growth.

This prescription is portrayed schematically in Figure V-1.vi Figure V-1. Prescription fo r visibility assessment for dist ant/multi-source applications (source greater than or equal to 50 km from the Class I area)

Prescription for New Source Review Visibility Analysis

(Distant/Multi-source)

Single-source
contribution to

change in
extinction ≥

10.0%

? FLM likely to object to the permit

FLM not likely to object to the permit Has a cumulative analysis been

conducted (or has the source triggered a need for a cumulative impact analysis) or is its
contribution to extinction ≥ 5.0%

?

Conduct cumulative visibility analysis of PSD increment consuming

sources

Cumulative change in
extinction ≥ 10% and single-source
contribution ≥ 0.4%

?

FLM likely to object to the permit Yes

No

No Yes

Yes Novii 2

. RECOMMENDATIONS FOR E VALUATING OZONE IMPACTS

• FLAG agrees with the EPA contention that sing le source-receptor modeling for ozone is not feasible at this time. FLM actions or specific requests on a permit application will be based on the existing air pollution situation at the area they manage. These conditions include (1) whether or not actual ozone damage has occurre d in the area, and (2) whether or not ozone exposure levels occurring in the area are high enough to cau se damage to vegetation ( i.e., phytotoxic O 3 exposures). Figure O-1 shows the various responses an FLM would have to a permit application. (Note: th e term “Ozone exposur e currently recognized as phytotoxic” is determined based on data from exposure response studies and ambient ozone levels at the site. The FLM may ask the applicant to calculate the ozone exposure values if these data are not already available. “Ozone damage to vegetation ” is determined from field observations at the impacted site.)

• Oxidant stipple necrosis on plant foliage and ozone-induced sene scence infer adverse physiological or ecological effects, and are considered to be damage if they are determined to have a negative impact on aesthetic value.

• The W126 ozone metric is re commended to describe ozone exposure, based on a 24-hour, seasonal (April through October) period of measuremen t. The number of hours in this period of time greater than or equal to 100 ppb (N100) will al so be determined, in recognition of the importance of peak concentrations in plant response.

• NO

x and VOC are of concern because they are precursors of oz one. Current information indicates

most FLM areas are NO

x limited. Until we de termine the VOC or NO

x status of each area, we will
focus on control of NO

x emission sources.viii Figure O-1. FLM response to potential oz one effects from new emissions source.

No

Unknown Unknown Yes

Yes

No

Unknown Yes

No

Items referenced in Figure O-1: a. The FLM may recommend one or more of the following: - That the proposed source use st ricter than BACT controls ( e.g. , Lowest Achievable Emission Rate [LAER]). - That the proposed source obtain NO

x emission offsets th at will benefit the potentially affected FLM area (as demonstrated by dispersion modeling). - That the permi tting authority ( i.e. , state or EPA) conduct regiona l modeling to id entify sources that are contributing significan tly to ozone-associated impacts in the FLM area , and that the permitting authority then undertake actions necess ary to reduce emissions from those sources ( e.g. , SIP revision). b. The applicant calculate the oz one exposure for vegetation (usi ng W126 and N1 00 metrics) for the affected FLM area(s) where such info rmation is not al ready available. c. The permitting authority or app licant fund post-construction ambi ent ozone monitoring in or near
the FLM area. d. The applicant conduct or fund post-construction ozone effects surveys in the FLM area and/or exposure/response effects research. a

FLM not likely to object to permit,; FLM may ask for d

FLM not likely to object to permit Ozone damage to vegetation FLM not likely to object to permit; FLM may ask for

c and/or d

a ; FLM ma y ask for c FLM not likely to object to permit; FLM may ask for c 

### START

Ozone damage to vegetation a

Ozone

exposure currently recognized as

p hytotoxic ; FLM

may ask for bix 3. RECOMMENDATIONS FOR EVALUAT ING DEPOSITION IMPACTS

The permit applicant should consult with the appropriate regulat ory agency and FLM for the affected area(s) to determine if a deposition impact analysis should be done. If an analysis is advised, the permit applicant should obtain ava ilable information on Class I AQRV s, critical loads, and concern
thresholds from the FLM. In addition, the applicant should refe r to the “Recommendations and Guidance for Evaluating Po tential Effects from Prop osed Increases in Depos ition to an FLM Area” section of the Deposition Chapte r (Section D.4.f). The following steps summarize that guidance.

• Estimate the current deposition rate to the FLM area. A list of moni toring sites providing data to characterize deposition in FL M areas is included in the De position Chapter (Table D-2).

• Estimate the future depo sition rate by adding the existing rate , the new emissions’ contribution to deposition, and the contribution of sources perm itted but not yet operating. Modeling of new and permitted but not yet operating em issions’ contributi on to deposition sh ould be conducted following IWAQM r ecommendations.

• Compare the future depositi on rate with the recommen ded screening criteria ( e.g., critical load, concern threshold, or screening level value) fo r the affected FLM area . A list of documents summarizing these screening criteri a, where available, can be found in Appendix H. Information
for USDA/FS Class I areas is also available at:

http://www.fs.fed.u s/r6/aq/natarm

.

A website with NPS and FWS Class I area informatio n is currently under development.

• In consultation with the FLM, use the followi ng flowchart (Figure D-1) to determine whether mitigation is recommended.x No/Unknown

Yes START

Current

adverse
effects to AQRVs from deposition/

Critical load

exceeded
? Yes

Unknown No New

emissions

cause or contribute to
adverse effects/Critical

load exceeded

? FLM may recommend
elements of "a" or "b"

or (if necessary) "c"

a. The applicant should use one or more of the following: - Stricter (than BACT) controls (e.g., Lowest Achievable Emission Rate [LAER]). - Emission offsets located in an area that, considering geographic and meteorological factors, will benefit the impacted wilderness or park, as demostrated by modeling. - Regional modeling to identify sources contributing significantly to deposition adverse effects; SIP revision to reduce emissions contributing to adverse effects. (See text for discussion of mitigation options.) b. Deposition and deposition effects monitoring/research in the FLM area. c. Denial of permit. FLM unlikely to object to permit FLM unlikely to object to permit; may recommend "b" Figure D-1. FLM resonse to potential deposition effects from new
emissions sources. Figure D-1. FLM response to pote ntial deposition effects from new

emissions sources.B. BACKGROUND

1. HISTORY

The Clean Air Act Amendments of 1977 give Fe deral Land Managers (FLMs) an “affirmative responsibility” to protect the na tural and cultural res ources of Class I areas from the adverse impacts of air pollution. (See Appendix B. “LEGAL FR AMEWORK FOR MANAGING AIR QUALITY AND AIR QUALITY EFFECTS ON FEDERA L LANDS.”) FLM responsibilities include the review of air quali ty permit applications from pr oposed new or modified major pollution sources near these Class I areas. If, in its permit review, an FLM demonstrates that emissions from a proposed source will cause or contribute to adverse im pacts on the air quality related values (AQRVs) of a Class I area, the pe rmitting authority, typically the State, can deny the permit.

Individually, FLMs have developed different a pproaches to identifying AQRVs and defining adverse impacts on AQRVs in Class I areas. Fo r example, in 1988, the U.S. Department of Agriculture Forest Service (USDA/FS) conducted a national screening process to identify the AQRVs for each of its Class I areas. Using this national process as a starting point, each USDA/FS region refined the screening parameters and iden tified sensitive AQRVs fo r many Class I areas. However, this resulted in differe nces in the approaches and levels used by USDA/FS regions. The U.S. Department of the Interior National Park Service (NPS) and the U.S. Fish and Wildlife Service (FWS) have adopted a case-by-case approach to permit review, considering the most recent
information available for each area. NPS and FW S have not completed lists of sensitive AQRVs nor defined adverse impact levels for all of their Class I areas.

a. FLAG Approach

Air resource managers from the USDA/FS, NPS, and FWS recognized the need for a more consistent approach among their agencies with resp ect to their efforts to protect AQRVs. In April 1997, an interagency workgroup was formed whose obj ective was “to achieve greater consistency in the procedures each agency uses in iden tifying and evaluating AQRVs.” The workgroup named
itself the F ederal L and Managers' A ir Quality Related Values Work G roup, or FLAG. Although FLAG membership comprises air resource manage rs and subject matter experts from the three agencies, representatives from the U.S. Environm ental Protection Agency (EPA), U.S. Geological Survey, and State air agencies have al so participated in FLAG efforts.

FLAG participants have collaborated to:

• define sensitive AQRVs,

• identify the critical loads (or pollutant levels ) that would protect an area and identify the criteria that define adverse impacts, and

• standardize the methods and proced ures for conducting AQRV analyses.

To accomplish its objective, FLAG started with (a nd will continue to build on) the procedures, terms, definitions, and screen ing levels common to the three agencies. Many such “commonalities” were identified early in the FLAG planning sessions. (See section B.4. “COMMONALITIES AMONG FEDERAL LAND MANAGERS.”)2 FLAG’s “Action Plan” stipulates a phased approach. Phase I addresse d issues that could be resolved
without research or the collection of new data. Phase II will addre ss the more comple x and unresolved issues from Phase I that may require additional data collection. (See section E. “FUTURE FLAG WORK.”)

The FLAG effort focuses on the effe cts of the air pollutants that could affect the health of resources in Class I areas, primarily pollutants such as oz one, particulate matter, nitrogen dioxide, sulfur dioxide, nitrates, and sulfates. In Phase I, FLAG c oncentrated on four issues : (1) terrestrial effects of ozone; (2) aquatic and terrestrial effects of wet and dry pol lutant deposition; (3) visibility impairment; and (4) process and policy issues. Four subgroups, one for each of these issues, were formed and charged with developing a set of recommendations for consistent policies and

processes.

In Phase I, FLAG findings and te chnical recommendations underwent scientific peer review, as well as review by agency decisionmakers such as Class I area Park Superintendents, Refuge Managers, and Forest Supervisors; Regional Forest ers; and the Assistant Secretary for Fish and Wildlife and Parks. (Note: USDA/FS has designate d the FLM as the Regional Foresters and, in some cases, Forest Supervisors. However, the A ssistant Secretary for Fish and Wildlife and Parks holds FLM responsibilities for NPS and FWS.) FL AG products have also undergone public review and comment. [A “notice of availability” of the draft FLAG report was published in the Federal Register, and the FLMs conducted a public meeting to discuss the draft FLAG report and provided a 90-day public comment period.]

b. FLAG Organization

In addition to the four subgroups (policy, deposit ion, ozone, and visibility ), the FLAG organization included Leadership and Coordinating Committees and a Project Manager. The Leadership Committee, which includes the air quality program chiefs from the three FLM agencies, was responsible for providing direction to the wo rkgroup and the resources necessary for FLAG to accomplish its objective. The Coordinating Committ ee, which also includes representatives from each agency, was responsible for communications within the workgroup, including coordination among the agencies and subgroups. The FLAG Pr oject Manager coordinated FLAG activities, served as a single point-of-contact for the subgroups, and performed other administrative functions.

2. OVERVIEW OF RESOURCE ISSUES

Research conducted on Federal la nds by FLMs and others has characterized natural resource effects associated with air pollution, and ha s helped identify thos e particular resources that are vulnerable to pollution. This effort does not address the impact s from air pollution on cultural resources. Documented effects include impairment of visibility, injury and reduced growth of vegetation, and
acidification and fertilization of soil s and surface waters. Air pollution effects on resources have been
identified in a number of FLM areas; a few examples ar e provided below. It is important to note that similar, or even more serious, air pollution effects may be occu rring on all Federal lands, but FLMs have not had the financial resources to perform the inventorying, monitoring, and/or research necessary to documen t such effects.3 a. Visibility

Visitors to national parks and wildernesses list th e ability to view unobscur ed scenic vistas as a significant part of a satisfying experience. Unfortunate ly, visibility im pairment has be en documented
in most Class I ar eas with visibility monitoring. Most visibility im pairment is in the form of regional haze. The greatest visibility impairment due to regi onal haze occurs in the eastern United States and in southern California, while the least impairment oc curs in the Colorado Plateau and Nevada Great Basin areas, and in Alaska. Sulfate is primarily responsible for visibility impairme nt in the eastern United States ( e.g., Shenandoah National Park in Virginia); in southern Californ ia the majority of visibility impairment is a ttributable to nitrates ( e.g., San Gorgonio Wilderness) ; in the Northern Rocky
Mountains and Pacific Northw est, impairment is prim arily due to organics ( e.g., Glacier National Park in Montana); and in the intermountain West, sulfate, organics and elemental carbon are the main cause of impairment ( e.g., Grand Canyon National Park in Arizona) (Sisler et al. , 1993).

Visibility impairme nt on Federal lands can also result from plume intrusi on and has been documented in Mount Zirkel Wilderness, Moos ehorn National Wildlife Refuge, and Grand Canyon National Park.

b. Vegetation

While several componen ts of air pollution ( e.g. , sulfur dioxide, nitrogen dioxide, and peroxyacyl nitrates) can affect vegetation, ozone is generally acknowledged as the air pollutant causing the greatest amount of injury and damage to vegetatio n. The most common visi ble effects are stipple (dark colored lesions on leaves resulting from pigmen tation of injured cells), fleck (collapse of a few cells in isolated areas of the up per layers of the leaf, resulting in tiny light-colore d lesions), mottle (degeneration of the chlorophyll in certain areas of the leaf giving the leaf a blotchy appearance), necrosis (death of tissue) , and in extreme cases, mortality. Aside from visible injury, ozone exposure can result in less obvious physiolo gical impairment such as decreased growth or altered carbon
allocation.

Ozone fumigation experi ments have identified a nu mber of plant species that are sensitive to ozone. For example, fumigations were conducted in Gr eat Smoky Mountains Nati onal Park (Tennessee and North Carolina) from 1987 to 1992. On the basis of foliar injury, thirty specie s were rated as sensitive
to ozone levels that occu rred in the park. The species with fo liar injury included black cherry ( Prunus

serotina ) and American sycamore ( Platanus occidentalis ). Additional observations and physiological measurements indicated elevated ozone reduced leaf, root, and total dry weights, and increased the severity of leaf stipple and prem ature leaf abscission in these two species (Neufeld and Renfro, 1993a,b). Field observations have documented foliar injury of these species in other eastern United States areas such as Brigantine Wilderness (New Jersey) and Cape Romain Wilderness (South Carolina).

Ponderosa pine ( Pinus ponderosa ) and Jeffrey pine ( Pinus jeffreyi ) are recognized as good candidates for ozone-injury surveys in the western United States, based on th eir documented sensitivity. For example, these species were examin ed for ozone injury in national parks and national forests in the
California Sierra Nevada from 19 91 to 1995. The sites surveyed included Lassen Volcanic, Yosemite, and Sequoia/Kings Canyon National Parks and the Tahoe, Eldorado, Stanislaus, Sierra, and Sequoia
National Forests. Foliar in jury attributable to ozone was found at all areas, and the extent of injury generally increased in a southward directio n along the Sierra Nevada (Miller et al. , 1995).4 c. Soils and Surface Waters

Acidity in rain, snow, cloudwat er, and dry deposition can affect soil fertility and nutrient cycling processes in watersheds and can result in acidif ication of lakes and streams with low buffering capacity. Deposition of sulfate to sensitive waters heds results in leaching of base cations, soil acidification, and surface-water acidification. In so me soils, sulfate adsorptio n results in "delayed" acidification of surface waters. Deposition of exce ss nitrogen species (nitrate and ammonium) to both terrestrial and aquatic systems can result in acidifying streams, lakes, and soils. There is also evidence that nitrogen depositio n can cause shifts in phyt oplankton composi tion in lakes in which biological activity is limited by nitrogen availability, i.e. , increased nitrogen depos ition can cause phytoplankton species that use nitrogen more efficien tly to eventually dominate the lake.

Water chemistry surveys and on-going monitoring s how that many high elev ation lakes on Federal lands in the Sierra Nevada, Cascades, and Rocky Mo untains are sensitive to acid deposition. In general, these lakes are on bedrock that provides th em with very little buffe ring capacity. Some of these lakes, for example, Loch Va le in Rocky Mountain National Park (Colorado) experience episodic acidification during Spring snowme lt (Baron and Campbell, 1997).

Through funding provid ed by the Southern Appalachian Mountain s Initiative, Herlihy et al. (1996) compiled information on surf ace water sensitivity of str eams in nine of the elev en Class I areas in the Southern Appalachians. The nine Class I areas were gro uped according to geology, physiography, and stream chemistry, then the groupings were ranked in terms of effects. Cl ass I areas in the West Virginia Plateau (Otter Creek and Dolly Sods Wildernesses) had the highest percentage of acidic stream length and lowest pH values. Class I ar eas in the Northern and Southern Blue Ridge ( e.g. , Shenandoah National Park in Virginia and Joyce Ki lmer/Slickrock Wilderness in North Carolina) had a lower percentage of acidic st ream length, however, streams with low buffering capacity were
common. The Alabama Plateau Cl ass I area (Sipsey Wilderness) had streams with the highest buffering capacity. (Note that th e authors based their report on su rveys conducted by others and did not account for potential differences in methods of data collection.)

A number of Federal areas contain estuarine and coas tal areas that may experience eutrophication as a result of excess nitrogen depositi on. For example, symptoms of eutrophication, including nutrient enrichment and algal blooms, have been observed in Everglades National Park and Chassahowitzka Wilderness (Florida).

3. LEGAL RESPONSIBILITIES

The specific legal responsib ilities that Congress ha s given FLMs to pr otect natural, cultu ral, and scenic resources on the public lands from air pollution ar e identified in Appendix B. Statutes described in Appendix B. include agency orga nic acts, the Wilderness Act, and the Clean Air Act (CAA).

The fundamental Congressional dir ection for managing public lands arises out of respective organic
acts. Each of these laws is essentially a charter from Congre ss to the Executive Branch providing a purpose for parks, wildernesses, and refuges, re spectively, and establishing broad management objectives for these areas. The Wild erness Act sets aside a subset of these public lands where natural processes are allowed to dominate. The agency stewards develop specific management objectives5 building on the organic acts using public involvem ent, regulations, best available science, and additional direction provided by Congress.

Among this additional Congressional direction is th e Clean Air Act (CAA). It further characterizes some of the public lands as Class I areas and di rects the land managers to take an affirmative responsibility to protect these ar eas from air pollution. The CAA dire cts that the FLMs identify and protect air quality related values, in cluding visibility. This direction is consiste nt with the underlying charters provided by the organi c acts and the Wilderne ss Act. The simila rities of management objectives, and of the policies and pr ocedures necessary for protecting Cl ass I areas, are at the core of the FLAG process.

In implementing laws, it is essential to understand th e “intent of Congress.” In the case of the CAA, the FLM gleans additional in sight from a passage in Se nate Report No. 95-127, 95 th Congress, 1st Session, 1977 which states,

“The Federal Land Manager holds a powerful to ol. He is required to protect Federal lands
from deterioration of an establ ished value, even wh en Class I [increment s] are not exceeded. ... While the general scope of th e Federal Government 's activities in pr eventing significant deterioration has been carefully limited, the FLM should assume an aggressive role in protecting the air quality values of land areas un der their jurisdiction. In cases of doubt the land manager should er r on the side of protecting the ai r quality-related va lues for future generations.”

Although the FLMs have an "affirmative respons ibility" to protect AQRVs, they have no permitting authority under the CAA, and they have no authority under the CAA to establish air quality-related rules or standards. The FLM role consists of considering whether emissions from a new source may have an adverse impact on AQRVs and providing comments to permitting authorities (States or EPA). It is important to emphasize that the FLAG report is only a guidance document that explains factors and information the FLMs expect to use when carrying out their consultative role. It is separate fr om Federal regulato ry programs.

The FLAG report describes the st eps and process that the FLMs in tend to go through in order to perform their statutory duties. Consequently, the scope of the FLAG report is to provide a more consistent approach for the three FLM agencies to evaluate air pollution effects on their resources, and to provide guidance to permitting authorities and permit applicants regarding necessary AQRV analyses. Although FLAG strives to be consistent with regulatory pr ograms and initiatives such as the Regional Haze Rule and New Source Review Reform, no direct ties exist between FLAG and these regulatory requirements.

4. COMMONALITIES AMONG FE DERAL LAND MANAGERS

If a new source is propos ed near two or more areas managed by different FLMs, the FLMs generally try to coordinate in their inter actions with the permitti ng authority and with the applicant. For example, two or more FLMs involved in pre-applic ation meetings typically try to minimize the workload for the applicant by reaching agreement on the types of analyses the application should contain. Beyond coordinating durin g permit review, FLMs currently base reques ts and decisions on similar principles regarding res ource protection and FL M responsibilities. Li sted below are the6 common principles in five areas of air resource management . In addition, Appe ndix C provides the

FLM’s “GENERAL POLICY FOR MANAGING AIR QUALITY RELATED VALUES IN CLASS

I AREAS.”

a. Identifying AQRVs

FLMs agree on the following definition of an AQRV:

A resource, as identified by the FLM for one or more Federal areas, th at may be adversely affected by a change in air quality. The resource may include visi bility or a specific scenic, cultural, physical, biological, ecologica l, or recreational res ource identified by the FLM for a particular area.

This definition is comp atible with the general definiti on of AQRV that appears in the Federal Register (FR 15016, April 10, 1978). That definition includes visibility, flora, fauna, odor, water, soils, geologic features, and cultural resources. FLMs have the re sponsibility to identify specific AQRVs of areas they manage. To this end, FLMs further refine AQRVs beyond the above definition to be more site-specific ( i.e. , area specific) by using on-site information. FLMs have developed inventories of specific AQRVs for many Class I ar eas and recognize that, ideally, inventories should be developed
for all Class I areas. FLMs can be contacted for copies of site -specific AQRV lists. Finally, FLMs agree on the need for continued inventory, resear ch, and monitoring to improve their ability to determine which AQRVs are most sensitive to air pollution and the sensitivity of these AQRVs.

b. Determining the Levels of Pollution that Trigger Concern for the Well-Being of AQRVs

FLMs believe that it should be poss ible to agree among themselves on the levels of pollution that trigger concerns for AQRVs. FL Ms recognize the need to assess cumulative impacts and the
difficulties associated wi th this process. Diffi culties arise when a large number of minor source impacts eventually lead to an unacceptable cumulati ve impact or when a new source applies for a PSD permit in an area that has a high background concentration of pollu tion from existing sources. This means that a proposed new source should be evaluate d within the context of th e total impacts that are occurring or that potentially could occur from pe rmitted/existing sources on the AQRVs of the area.

c. Visibility

FLMs use EPA-approved models to evaluate visibility impacts. The models use thresholds of visibility degradation measured in light ex tinction to evaluate source impacts to haze (far-field/multisource impacts), and EPA established criteria for coherent plume impacts (near-field impacts). Currently all FLMs use Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data to dete rmine current cond itions for visibilit y in FLM areas.

d. Biological and Physical Effects

All FLMs rely on research, monito ring, models, and effe cts experts to identify and understand physical, biological, and chemical changes resulting from air pollution and rela ting them to changes in AQRVs. Further, they focus on se nsitive AQRVs (defined as either species or processes) to assess this biological/physi cal/chemical change.7 e. Determining the Level of Pollution Likely to Cause an "Adverse Impact" on AQRVs

FLMs rely on the best scientific information available in the published literature and best available data to make informed decisions regarding levels of pollution likely to cause adverse impacts. FLMs re-evaluate, update, and assess this information as appropriate. They cons ider specific Agency and Class I area legislative mandates in their decisions and, in cases of doubt, "err on the side of protecting the AQRVs for future generation s." (Senate Report No. 95-127, 95th Congress, 1st Session, 1977)

For air quality dispersion modeling analyses, FLMs follow 40 CFR §52.21(l) (App endix W of Part 51,

EPA's Guideline on Air Quality Models , revised 1996) and the recomm endations of the Interagency Workgroup on Air Quality Modeling (IWAQM). FLMs recommend protocols for modeling analyses to permit applicants on a case-by-case basis considering types and amount of emissions, location of source, and meteorology. When reviewing modeling and impact analysis results, all FLMs consider frequency, magnitude, duration, and location of impacts.

f. FLM databases

Air Synthesis (formerly Air Quality Info rmation Management System – AQUIMS) Air Synthesis is an information management and decision-support co mputer system under development by NPS and FWS. Air Synthesis is designed to assist FLMs in determining potential effects of pollutants on AQRVs. It contains information on air quality and its effects in Class I parks
and wildernesses as well as natural resource data a nd annotated bibliographies of current literature on ozone and deposition. The system will also contai n an interactive expert system module that will
allow FLMs to assess the current st atus of freshwaters an d determine if these resources are affected by deposition of sulfur or nitrogen.

Natural Resource Information Sy stem – Air Module (NRIS-AIR) The Air Module is part of the USDA/FS’ Natural Resource Information System that integrates various physical, biological, and soci oeconomic data within an integr ated system of database, mapbased spatial information, and analytical tools. Version 1.0 of NRIS-AIR, released in November 1998, tracks AQRVs, sensitive receptors, and indicators for each of the USDA/FS Class I areas. The water submodule provides data storage, reports, and tools for evaluating locally entered water quality and wet deposition data. Future NRIS-A IR versions (currently under development) will provide the information structure for visibility, flora, fauna, so il, geologic resources, cultural resources, and air quality data, as well as providing a PSD permit tracking system.8 C. FEDERAL LAND MANAGERS' A PPROACH TO AQ RV PROTECTION

FLM responsibilities for resource prot ection on Federal lands are cl ear and there should be no misunderstanding regarding the tools the FLM uses to fulfill these responsi bilities. Opportunities to influence decisions regarding pollution sources exte rnal to the park or wilderness are limited. However, FLMs strive to minimize emissions from in ternal sources and their ef fects. Approaches for minimizing air pollution from ex ternal and internal sources ar e discussed in detail below.

1. AQRV PROTECTION AND IDENTIFICATION

Congress assigned the FLMs an affirm ative responsibility to protect AQR Vs in Federal Class I areas. The FLMs interpret this assignm ent as a resp onsibility to:

1. Identify AQRVs in each of the Class I areas.

2. Establish inventorying and mo nitoring protocols for AQRVs.

3. Prioritize AQRV inventorying and monitoring (because of constrained budgets).

4. Specify a process for evaluating air pollu tion effects on AQRVs, including the use of
sensitive indicators.

5. Specify adverse effect s for each AQRV.

To the extent possible, AQRVs have been identifi ed for each Class I area. Additional AQRVs may be
identified in the future as more is learned through science about the sensitivity of resources to air pollution. Public involvement in this process is necessary an d will be accomplished through participation in the land management planning process or reply to an announcement in the Federal Register

.

While the sensitivity of an AQRV to air pollution may be known, th e long term monitoring of its health or status may not have been accompli shed. The expense of monitoring all AQRVs simultaneously is prohibi tive. Consequently, FL Ms seek opportunities thr ough the permitting process and through partners hips to gather more inform ation about condition of AQRVs.

Because AQRVs themselves are ofte n difficult to measure, surrogates are used as indicators, or sensitive indicators, of the health or status of th e AQRV. Designing a working process for Class I area management and AQRV protection is outlined ahead in this document.

Finally, an adverse impact is determined for each AQRV. An adverse impact from air pollution
results in a diminishment of the Class I area’s nationa l significance, that is, th e reason the Class I area was created. Adverse impacts can also be an im pairment of the structure or functioning of the ecosystem as well as an impairme nt of the quality of the visitor experience. The FLMs make an adverse impact determina tion on a case-by-case ba sis, based on technical and other information.

2. NEW SOURCE REVIEW

Section 165 of the CAA spells out the roles and responsibilities for FLMs in New Source Review, including the Prevention of Signifi cant Deterioration (PSD) permittin g program. Other laws, such as the respective agency organic acts and the Wilderness Act, provide the fundame ntal underpinning of land management directio n to land managers. Th e following discussion merges this complex9 labyrinth of legal responsibilities as it relates to air resource management . A pendi ng regulation revision from EPA which contains many of the items in this section addres sing NSR may add more specificity to the Class I area protection pr ocess from the perspe ctive of the CAA.

a. Roles and Respon sibilities of FLMs

The FLM. The federal official directly responsible fo r the national parks, natio nal wildlife refuges, and national forests ( e.g. , park superintendents, refuge managers , and forest supervisors, respectively) derive their responsibility from th e respective agency organic acts. Furthermore, thes e officials, and the FLM for the respective agencies , have an affirmative responsibi lity under Section 165 of the CAA to protect and enhance the AQRVs of Class I areas from the adverse effects of air pollution. The FLM for the USDA/FS is the Regional Forester or the Forest Supervisor depending on the specific location.
The FLM for the NPS and FW S is the Department of the Interior’s Assistant Secretary for Fish and
Wildlife and Parks.

Visibility Protection Program for New and Modified Sources. The FLMs have visibility protection responsibility under 40 CFR §51.307 (New source review ), which spells out the requirements for State Implementation Plan (SIP) vi sibility protection programs, as well as 40 CFR §52.27 (Protection of visibility fr om sources in attainment areas) and 40 CFR §52.28 (Protection of visibility from sources in non-at tainment areas). These three pr ovisions, taken together along with the SIP-approved rules, establish the visibility protection program for ne w and modified sources throughout the country.

Notification. Section 165 (42 USC, 7475) of the CAA requi res the EPA, or the State/local permitting authority, to notify the FLM if em issions from a proposed project may impact a Class I area. The permitting authority should forward PSD applications to th e FLM for review and analysis as soon as possible after receipt, giving the FLM an opportunity to review the a pplication concurrently with the permitting authority.

Generally, the permitting authority should notify the FLM of all new or modified major facilities proposing to locate within 100 km (62 miles) of a Class I area. In additio n, the permitting authority should notify the FLM of "very larg e sources" with the potential to affect Class I ar eas proposing to locate at distances greater than 100 km. (Reference Marc h 19, 1979, memorandum from EPA Assistant Administrator for Air, Noise, and Radi ation to Regional Administ rators, Regions I - X). Given the multitude of possible size/distance combinations, the FLMs can not precisely define in advance what constitutes a "very large source" locat ed more than 100 km away that may impact a particular Class I area. Therefore, the FLM and permitting authority should work together to determine which PSD applications the FLM is to be made aware of in excess of 100 km. The FLM and permitting authority should make this determin ation on a case-by-case basis, considering such
factors as:

• Current conditions of sensitive AQRVs;

• Magnitude of emissions;

• Distance from the Class I area;

• Potential for source growth in an area/region;

• Existing/prevailing meteorological conditions;

• Cumulative effects of several sources to AQRVs.10 Additionally, such dialogue facilitates coordina tion between permitting authorities and the FLMs. The significance of the impact to AQRVs is more impor tant than the distance of the source. Not all PSD permit applications that the FLM is notified of will be analyzed in-depth by the FLM. FLM notification of a PSD permit app lication for a project located greater than 100 km does not mean that that application will be reviewed by the FLM in detail. Notification of PSD permit applications in excess of 100 km by the permitting au thority allows the FLM to gauge the level of potential cumulative effects. As indicated above, the FLM decide s which PSD perm it applications to review on a case-by-case basis depe nding on the potential impacts to AQRVs.

Pre-Application Meetings. To expedite the PSD permit review process, the FLM encourages preapplication meetings with permitting authorities and permit applicants to disc uss air quality concerns for a specific Class I area in question. Given prelim inary information, such as the source's location and the types and quantity of projected air emissions , the FLM can discuss speci fic AQRVs for an area and advise the applicant of the analyses needed to assess potential impact s on these resources.

Completeness Determination. To further minimi ze delays, the FLMs en courage the permitting authority to use comments provide d by the FLM concerning the comple teness of the a pplication, and to not deem the applicat ion complete until the app licant performs all necessa ry air quality impact analyses, including all relevant AQRV impact informatio n. The permitting authority should then notify the FLM when they deem the application to be complete.

Visibility Protect ion Procedures. Additional procedural requirement s apply when a proposed source has the potential to impair visi bility in a Class I ar ea (40 CFR §52.27(d)(19 98)). Specifically, the permitting authority must, upon receiving a permit appli cation for a source that ma y affect visibility in any Class I area, notify the FLM in writing. Such notifica tion should include a co py of all information relevant to the permit application, including the proposed source's antici pated impacts on visibility in a Class I area. The permitting authority should notify the FLM within 30 days of receipt and at least 60 days prior to the close of the comment period.

If the FLM notifies the pe rmitting authority that the proposed source may adversely imp act visibility in a Class I area, or may adversely impact visibility in a previously identified integral (scenic) vista, then the permitting authority is to work with the FLM to address their conc erns. If the permitting authority agrees with the FLM's finding that visi bility in a Class I area may be advers ely affected, the permit may not be issued. Even though the perm itting authority may agree with the FLM's adverse impact finding regarding integral vistas, the perm itting authority may still issue a permit if the
emissions from the source are consistent with reasonable progress toward the national goal of preventing or remedyi ng visibility impairment. In making this decision, th e permitting authority may take into account the costs of compliance, the ti me needed for compliance, the energy and non-air quality environmental impacts of compliance, and the usef ul life of the source.

The FLM will make a preliminary de termination regarding possible a dverse visibility im pacts within a prescribed time of receipt of all relevant information.

b. Elements of Permit Review

The FLM review of a PSD application for a proposed project that may impact a Class I area generally
consists of thr ee main analyses:11 1. Air quality impact an alysis to ensure that predicted pollutant levels in Class I areas do
not exceed National Ambi ent Air Quality Standards (NAAQS) and PSD increments, and to provide sufficient information for the FLM to conduct an AQRV impact analysis. Ensuring that permit applicants meet these requirements is the direct responsibility of the pe rmitting authority (see discussion below);

2. AQRV impact analysis to ensure that the Class I area resources ( i.e. , visibility, flora, fauna, etc.) are not adversely affected by the proposed emissions. The AQRV impact analysis includes interp reting the significance of the results from the applicant’s air quality impact analysis and is the responsibility of th e FLM (see discussion below);
and

3. Best Available Control Technology (BACT) analysis to help ensure that the source
installs the best control t echnology to minimize emission increases from the proposed project (See Appendix D for a summary of this analysis). The final BACT determination is a direct respons ibility of the permitting authority.

Air Quality Impact Analysis . The permit applicant must perform an air quality impact analysis for each pollutant subject to PSD review. This analysis should show the contribution of the proposed emissions to increment consumption and to the existing ambi ent pollution levels in a Class I park or wilderness area. The applicant shou ld perform a cumulative incremen t analysis for each pollutant and averaging time for which the propos ed source will have a significant impact. Because proposed sources are not yet operating, the air quality analysis must rely on mathematical dispersion models to estimate the air quality impact of the pro posed emissions. The FLMs provide the applicants with guidance on where to place model receptors with in the Class I area. The appli cant is responsible to provide
sufficient information for the FLM to make a decision about the acceptability of potential AQRV impacts as a consequence of the new source.

The applicant should perform the ai r quality impact analysis using approved models and procedures as specified in 40 CFR §52.21(l) and 40 CF R §51.166(l) (Appendix W of Part 51, EPA's Guideline on Air Quality Models , revised 1996 and in revision again as of the date of this writing, December 2000). The applicant should e xplicitly state all assumptions for the analysis, and furnish sufficient information on modeling input so that the FLM ca n validate and duplicate th e model results. FLMs encourage the permit applicant to submit a modeli ng protocol for review before performing the Class I modeling analyses. This protocol shoul d include the proposed air quality analysis methodology and model input ( i.e. , emissions, stack data, meteor ological data, etc.), and the proposed location of the receptors in the FLM area.

AQRV Impact Analysis. According to the CAA’s legislativ e history and curren t EPA regulations and guidance, the air quality impact analysis that provide s sufficient information to enable the FLM to conduct the AQRV impact analysis is one part of a permit application just as are the BACT analysis and the air quality impact analysis relative to the incr ements and NAAQS. The applicant bears the entire cost of prepari ng the permit application including the complete air quality impact analysis.

The FLM then uses the results from the applicant's air quality impact analysis and other information to conduct the AQRV impact analysis and make an informed decision about whether or not AQRVs will be adversely affected. If the FLM concludes that AQRVs are or will be12 adversely affected, the FLM must so demonstrate to the permitting authority. The following sections of this document give guidance to applicants on how to conduct an air quality impact analysis and how the FLM uses this inform ation to make an AQRV impact decision.

Cumulative Impact Analysis . The applicant’s air quality imp act analysis should include both the permit applicant’s contribution to the AQRV impacts, as well as the cumulative source impacts on AQRVs. A cumulative air qualit y analysis in which the proposed source and any recently permitted (but not yet operating) sources in the ar ea are modeled is an important part of any AQRV impact analysis. This cumulative modeled impact is then added to measured ambient levels (to the extent that such monitoring data are available) so that the FLM can assess the total effect of the anticipated ambient concentrations on AQRVs. If no representative monitoring data are available, the applicant should estimate th e total pollutant con centrations by modeling emissions from all contributing sources in the area.

Information Provided by the FLM to the Applicant. To assist the permit applicant in performing air quality impact anal yses, the FLMs will provide a ll available information about AQRVs for a particular Class I ar ea that may be adversely affect ed by emissions from the proposed
source. FLMs will recommend available methods th e applicant should use to analyze the potential effects to the receptor(s) located in the Class I area. In addition to identifying AQRVs, FLMs will, to the extent possible:

(1) identify inventories, surveys, monitori ng data, scientific studi es, or other published reports that are the basis for identification of AQRVs;

(2) identify specific receptors known to be mo st sensitive to air pollution and the pollutant or pollutants that individually or in combin ation can cause or contribute to an adverse effect on each receptor;

(3) Identify the critical pollutant concentrat ions above which adverse effects are known or suspected to occur;

(4) Recommend methods the applicant shoul d use for predicting ambient pollutant concentrations and other related impacts ( e.g. , deposition, visibility) which may cause or contribute to an adverse effect on each receptor; and

(5) Suggest screening level values or criter ia that would be used to assess whether a proposed emissions increase would have a de minimis impact on AQRVs.

It is important to highlight the distinction between the air quality im pact analyses th at the applicant performs and the AQRV impact analyses that FLMs perform. Whereas the perm it applicant calculates changes in pollutant concentrations , deposition rates, or visibility extinction, the FLM assesses the extent to which these impacts affect sensitive visual , aquatic, or terrestrial resources. Given the FLM’s statutory responsibilities a nd expertise, the FLM must have resp onsibility to consider whether the amount of pollution dispersed into the air or depo sited on the ground (or in water) would have an adverse impact on any AQRV, and if so, to demonstrate th at claim to the perm itting authority. In making an adverse impact findin g, FLMs consider such factors as magnitude, frequency, duration, location, and timing of impacts, as well as curr ent and projected conditions of AQRVs based on cumulative impacts.13 c. FLM Permit Review Process

The FLM's current permit review process for any app lication that may impact a FLM area is described
below.

1. Pre-application . If possible, participate in any pre-appl ication meeting to learn specifics of the proposed project (size, emissions, location, etc.) and to provide information regarding recommended Class I analyses.

2. Completeness Determination. Upon receipt, the FLM will re view the application and provide comments to th e permitting authority rega rding the completeness of the application
and the need for additional in formation regarding the BACT, Air Quality Impacts, and AQRV Impacts analyses. The FLM will coordinate wi th the permitting auth ority and the permit applicant to ensure that all the necessary info rmation to enable the FLM to make an impact
determination is included.

3. Public Comment Period . After review of all relevant information, the FLM will provide pertinent comments to the perm itting authority, before or duri ng the official public comment period, and/or at scheduled public hearings.

4. N o Class I Increment Violated and No Adverse Impacts. If no Class I increment is
violated and no adverse impacts to AQRVs are expected, th e FLM will inform the permitting authority of this dete rmination and no further FLM action is necessary. Th e FLM may still provide BACT comments.

5. No Class I Increment Violated but AQRV Impact Uncertainty. If no Class I increment is violated but uncertainty exists regarding pot ential adverse impacts to AQRVs, the FLM may request that the permitting auth ority include a permit condition th at requires the permittee to conduct relevant post-construction AQRV or air quality monitoring. The FLM may also request certain control technologies or methods to reduce impacts.

6. Class I Increment Violated, bu t No Adverse AQRV Impacts. If the Class I increment is violated, but no adverse AQRV impacts are antic ipated, the applicant requests the FLM to "certify" no adverse impact under Section 165(d)(2)C)(iii) of the Clean Air Act [42 USC 7475(d)(2)(C)(iii)(1998)]. If the FLM concurs, (s)he makes a preliminary dete rmination that no adverse impacts will occur.

a. The FLM will inform the a pplicant, the State/local perm itting authority, and EPA of the preliminary no adverse impact determination.

b. The FLM will notify the public of its prel iminary no adverse impact determination either through the pe rmitting authority's notice procedur es, or through separate notice
in the Federal Register . Such notice should in clude a statement as to the availability of supporting documentation for in spection and copying, and an an nouncement of at least a 30- day public comment period on issues directly relevant to the determ ination in question.

c. The FLM will review and prepar e response to public comments.14 d. The FLM will make a final determination regarding no ad verse impacts, with a clear
and concise statement of reasons supporting that determination.

e. The FLM will inform the pe rmit applicant, th e permitting authorit y, and EPA of its
final determination and if the final determination is "no ad verse impact," the FLM shall so "certify" in a letter to the affected parties.

f. Simultaneous with step e, the FLM will publish a final de termination in the "Notice"
section of the Federal Register , including a clear and conc ise statement of reasons supporting that determ ination, statement as to availability of supp orting documentation for inspection and copy ing, and statement as to immediat e effective date (date signed)
of final determination.

g. The FLM will contact the pe rmitting authority a nd request a revision to the State Implementation Plan (SIP) to elimin ate the Class I incr ement violations.

7. Adverse Impact Determination. Regardless of increment status, the FLM may make a preliminary determin ation that the proposed project will cause, or co ntribute to, an adverse impact on AQRVs. Before officially declaring an advers e impact, the FLM will inform the proposed new source and the permitting authority that an adverse impact determination is imminent and suggest that the permit be modifie d. If the permit is mo dified to satisfy the concerns of the FLM, then an adverse determination is avoided.

a. The FLM will inform the a pplicant, the permitting authorit y, and EPA of a preliminary adverse impact determination.

b. The FLM will notify the public of the preliminary adve rse impact determination either through the permitting authority 's notice procedures, or thro ugh separate notice in the Federal Register . Such notice should in clude a statement as to the availability of supporting documentation for inspection and copying, and an announcement of at least a 30-day public comment period on issues dire ctly relevant to the determination in question.

c. The FLM will review and prepar e response to public comments.

d. The FLM will make a final determination regarding adverse impacts, with a clear and concise statement of reasons supporting that determination.

e. The FLM will inform the pe rmit applicant, th e permitting authorit y, and EPA of its
final determination.

f. Simultaneous with step e, the FLM will publish a final de termination in the "Notice"
section of the Federal Register , including a clear and conc ise statement of reasons supporting that determ ination, statement as to availability of supp orting documentation for inspection and copy ing, and statement as to immediat e effective date (date signed)
of final determination.15 g. If the FLM makes a final de termination that a source will have an adverse impact, the FLM will oppose the perm it. However, the permit app licant may propos e to mitigate any adverse impacts (via reduci ng emissions, obtaining emissi on offsets, etc.). If the applicant adequately mitigates the adverse im pacts to the satisfaction of the FLM, the
FLM will withdraw hi s objection to the permit. If the adverse impacts are not adequately mitigated and the permitting authority nevertheless issues the permit, the FLM may appeal the permit.

Note: If the permitting authorit y's SIP makes execution of the above listed steps impossible ( e.g. , inadequate time allotments for the FLM's determination or lack of timely FLM notice ) the procedures shall be adjusted as approp riate. In addition, the above procedures (6 an d 7) could also be modified to
accommodate those situations when the FLM chooses to certify that existing impacts are adverse, absent a proposed new source. Su ch an action would alert potential permit applicants that adverse impacts exist and any new source wo uld need to mitigate its potentia l impacts. Although each FLM may implement the above procedures somewhat di fferently, the FLAG goal is to reduce the differences in implementing the above steps.

Furthermore, FLMs intend to coordinate on air pe rmit modeling requirements for new or modified sources that are geographically near more than on e FLM area. For example, a proposed source in
eastern Tennessee that lie s equidistant from NPS-administered Great Smoky Mounta ins National Park and the FS-administered Joyce Ki lmer/Slickrock Wilderness would receive coordinated guidance on modeling requirements from the FLMs. The FLMs may or may not have common AQRVs at different Class I areas, making coordination beneficial. Th e FLMs may also coordinate on potential permit conditions and mitig ation strategies.

d. Criteria for Decision Making (Adverse Impact Considerations)

As previously mentioned, the legislative history of the CAA provides direction to the FLM on how to comply with the affi rmative responsibility to pr otect AQRVs in Cl ass I areas, and in cases of doubt, the land manager should e rr on the side of protecting air quality -related values for future generations.

The FLMs define adverse impact on AQRVs as:

An unacceptable effect, as identified by an FLM , that results from current, or would result from predicted, deterioration of air quality in a Federal Class I or Class II area. A determination of unacceptable effect shall be made on a case-by-case basis for each area taking
into account existi ng air quality conditions. It should be based on a demonstration that the current or predicted deterioration of air quality will cause or contribute to a diminishment of the area's national significance , impairment of the structure and functioning of the area's ecosystem, or impairment of the quality of the visitor experience in the area. Also, the Federal visibility protection regulations (40 CFR §51.300, et seq., §52.27) define adverse impact on visibility as:

[V]isibility impairment which in terferes with the management , protection, pr eservation or enjoyment of the visitor's visual experience of the Federal class I area. This determination must be made on a case-by-case basis taking into acco unt the geographic extent, intensity, duration, frequency, and time of vi sibility impairment, and how these factors correla te with: (1) times of16 visitor use of the Fede ral class I area, and (2) the freque ncy and timing of natural conditions
that reduce visib ility. (Id. §51.301(a))

FLMs typically address adverse impacts on a cas e-by-case basis in resp onse to PSD permit applications. When an adverse impact is predicted, FLMs recommend that permits either be modified to protect AQRVs or be denied. FLMs ca n also address adverse cond itions outside of the PSD process. To do so, they: certify vi sibility impairment; part icipate in regional assessments; informally
collaborate with States a nd EPA; review lease perm its, SIP revisions, Nati onal Environmental Policy Act (NEPA) analyses, Park/Refuge/Forest ma nagement plans, CERCLA (Comprehensive Environmental Response, Compen sation, and Liability Act) reviews, and other documents.

In some States, FLMs use screening proc edures or thresholds that indicate when the condition of an AQRV is acceptable or unacce ptable. The pollutant concentration or loading rate that will adversely impact an AQRV can vary among Cl ass I areas, and depe nds on current conditions . After a threshold is reached, an increase in pollutant concentrations is likely to be unacceptable. A concern threshold can be an adverse impact threshold or other quantifiable level in resource condition or pollutant exposure identifi ed by the FLM.

e. Air Pollution Perm it Conditions that Benefit Class I Areas

The FLM does not dete rmine what permit conditions will be required or admini ster permit conditions; that is the responsibility of th e permitting authority. However, the FLMs may request permit conditions or agree to withdraw ob jections to permit is suance if requested co nditions are included.
The FLMs view the inclusion of certain PSD permit conditions by the permitting authority as a means to help protect or enhance the condition of AQRVs when:

1. Air pollution source(s) ma y cause impacts that exceed protection thresholds for AQRVs; 2. Terrestrial resources, a quatic resources, and/ or visibility are cu rrently adversely impacted by air pollution and proposed em issions will exacerbate these adverse conditions; 3. FLM policies require improvement or restoration of AQR Vs in parks and wildernesses; and 4. There is uncertainty on the extent and magnitude of air pollution effects on AQRVs.

Permit conditions may require emission offsets, AQRV a nd/or air quality monito ring, inventories, reopeners , LAER (or other improved contro l technologies), or other measur es to protect, enhance, or restore resources and values of parks a nd wildernesses. Perm it conditio ns may:

1. Result in net air quality benefits at a protected ar ea or within a region; 2. Contribute to a reduction of air pollution within a region; 3. Promote ecosystem inventories and/or moni toring to evaluate physical and biological resource damage caused by air pollution emissions; and 4. Promote ecosystem restoration or improv e the condition of resources damaged by air pollution emissions.17 The basis of an air permit condition may be identified in the public notice for the draft permit. To be effective, permit conditions must be federally enforceable and guara nteed. Air permit provisions may be temporary or permanent depending on the natu re of the permit requirements. Procedures to implement an air perm it condition must be acc eptable to the FLM ( e.g. , an agreement between parties [memorandum of understanding, in teragency agreement] is an option to accomplish inventory, monitoring, or other requirements).

f. Reducing Pollution in Nonattainmen t Areas (Nonattainment Permit Process)

The PSD program does not apply with respect to a particular pollutant when the source locates in an area designated non-attainment for that pollu tant. Instead, pollution sources are regulated by Non-attainment Area New Source Review (NNS R). NNSR includes air quality planning and regulation of stationary sources. Air quality planning addresses issu es such as lowest achievable emission rate (LAER), offsets, reasonably available control tec hnology (RACT), and mobile and stationary source control strategies. New major stationary sour ces and major modifications of sources in designated non-attainment areas must satisfy NNSR before c onstruction begins. For visibility protection, SIPs must include either EPA-approve pr ovisions to comply with 40 CFR §51.307 for the non-attainment pollutant, otherwise, th e federally promulgated visibility provisions at 40 CFR §52.28 would apply to all sources located in non-attainment areas. Therefore, FLMs can provide suggestions to the permitting authority regarding these conditions during the permitting and planning processes.

SIPs provide a mechanism to addres s AQRV impacts for when the source or the Class I area is located
in a non-attainment area . Land managers should recommend that States adopt policies, rules, or regulations in their SIPs requiring a demonstr ation that offsets will result in a net air quality benefit within any Class I area likely to be impacted by emissions from the source to be permitted. FLMs may also request emissions reductions greater than 1: 1, perhaps offset rates of 1.5 or 2.0 to 1, or higher, depending on the impacts to be offset. Such recommendation s can be developed jointly in a meeting with the regulatory authorit y or in a letter from the FLM.

Mitigation measures recommended by FLMs may include stringent control technologies to minimize the increase in emissions and the impact on AQRVs. Monitoring can determine whether predicted resource conditions are observed. Offsets ensure that net emissions reductions from all sources will occur within a geographic area and their resulting air quality impacts at the Class I area will be mitigated.

3. OTHER AIR QUALITY RE VIEW CONSIDERATIONS

At all Class I areas where visibility has been monito red, visibility conditions have been found to be impaired (by human-caused pollution). The impair ment comes primarily from older sources, not new sources. From a regional perspective, new or modified sources (using new/cleaner technologies) contribute far less to impaired AQRV conditions than old sources. New programs, such as EPA’s NAAQS for fine particulate matter a nd 8-hour ozone levels have be en legally challenged (as of December 2000) so their effectiveness in reducing ov erall regional pollutant le vels from older sources
is uncertain at this time. EPA has implemented a call for reducing NO

x emissions from older sources
in the eastern U.S. to meet existin g ozone standards, however, this ac tion is being appealed to the U.S. Supreme Court. In addition to national ambient st andards, most States ar e just now beginning the planning process to implement EPA’s Regional Haze Regulations. If all of these requirements are18 implemented, then progress toward remedying im paired AQRVs is likely. However, given the sensitivity of some AQRVs to low levels of po llution, programs focused on reaching national goals, such as the NAAQS or visibility, ma y not fully remedy impacts on AQRVs in all locations. It is for this reason that the FLM should pursue all othe r reasonable strategies to protect AQRVs. The following sections discuss FLM issues that go beyond NSR.

a. Remedying Existing Adverse Impacts

The existence of adverse impacts is unacceptable to FLMs and cont rary to the mandates of their specific agencies. Consequently, FLMs may request or participate in regional assessments to protect AQRVs, as appropria te. Regional assess ments often use a multi-face ted approach to remedy impairment. For example, cat egories addressed by the Gra nd Canyon Visibi lity Transport Commission (GCVTC) include air pollution prevention; clean air co rridors; stationary sources;
sources in and near Class I areas; mobile sources; road dust; fire; and future regi onal coordination.

Clean Air Act requirements for remedying existing visibility impairment provide a mechanism for addressing impacts from specific sources or groups of sources. Negotiations at the Centralia Power Plant in the state of Washington provide an example of how to build partnerships and work collaboratively to obtain retrofit cont rols or more stringent control technologies for sources that affect a FLM area. Through a collaborative decisionmaking pr ocess, owners of the Centralia plant agreed to reduce sulfur dioxide emissions at the plant by 90%. In another case, the USDA/FS asked the state of Colorado to remedy existing impa irment at Mt. Zirkel Wilderne ss. Following USDA/FS testimony about the Mt. Zirkel Wilderness, te rms of a court-ordered consent decr ee that specified controls for the Hayden Power Plant were incl uded in Colorado’ s long-term visib ility strategy.

FLMs may also coordinate with others to ensure that emission reductions in nonattainment areas will improve air quality in FLM areas. Recommendations on urban planning were developed with FLM involvement to address nonattainment areas in California. Data documenting ozone effects on vegetation were provided to the planning authority.

b. Requesting SIP Revisions to Address AQRV Adverse Impacts

A SIP is the key vehicle a state uses to develop th e pollution control programs that will be used to
achieve and maintain the NAAQS as well as prevent significant de terioration of air quality. It is important for FLMs to be involved in SIP developmen t, as participation prov ides an opportunity to influence planning of pollution control programs that can benefit air quality in FLM areas. Once a SIP is fully approved by EPA, it is le gally enforceable under both Stat e and Federal law. FLMs can use the SIP process to address existing impacts that are unacceptable by requesting a SIP revision. This approach is particularly useful for addressing impacts on AQRVs ot her than visibility, since the Clean Air Act does not provide specific requirements for ot her AQRVs. In an Octobe r 16, 1996, letter from the Deputy Assistant Administrator of the EPA to th e Assistant Secretary of the Department of the Interior (DOI), the EPA acknowledged that the CAA provides authority to address adverse impacts on AQRVs in Class I areas from both new and existing sources. EP A committed to initi ate rulemaking
that will set forth the affirmative obligation for States to protect AQRVs as part of their CAA responsibility to prevent significant deterioration of air quality. EPA st ates this approach would require the protection of AQRVs as part of SIPs.19 In an October 17, 1996, response, DOI offered to assist EPA in developing this adverse impact SIP rulemaking. In add ition, DOI urged EPA "to requir e State Implementation Plan s to prevent significant deterioration of air quality by adopting mitigation measures to address adve rse impacts on AQRVs in
Class I areas." These SIP revisions could be used to address mu ltiple sources and re gional pollution that adversely affect AQRVs in a ll Class I areas. DOI sent a follow -up letter to EPA in July 2000 reiterating the need for an AQRV “r estoration and protection” rulemaki ng. EPA solicited public input regarding an AQRV rule, as well as a request from Northeastern stat es for more stri ngent secondary NAAQS. EPA will consid er comments received and then d ecide on a course of action.

South Coast and San Diego, California, SIP revisi ons included FLM recommen dations to reduce the impact of minor sources on AQRVs. South Coast recommen dations addressed visi bility while the San Diego recommendations addressed all AQRVs. EPA's NO X SIP Call in the east is another example of obtaining emission reduc tions through the SIP revision process. The NO X SIP Call is directed at 20
eastern States and the District of Columbia to address NO X emissions from existi ng large sources. Once this action is implem ented, significant reductions in ozone formation and nitr ogen deposition are anticipated.

c. Periodic Incremen t Consumption Review

As mentioned above, EPA has in dicated its intention to the FL Ms to establish a SIP revision requirement to address existing adverse impa cts on AQRVs. The FLMs strongly support EPA exercising its authority in this way. In the in terim, however, there ar e existing SIP revision requirements that are not being fully utiliz ed. EPA's current regulations require States to conduct a periodic review of the adequacy of their PSD plan and program. [ 40 CFR §51.166(a)(4)] This would
include an assessment of increment consumption in Class I and Class II areas. Few States have ever conducted a comprehensiv e, cumulative increment consumption analysis for one or more Class I areas. In addition, many PSD sources have not exceeded the significant impact levels for increment consumption; thus, few PSD per mit applicants have had to pe rform a cumulati ve increment consumption analysis for Class I areas. Such a periodic increment consumptio n review would be beneficial given that the burden of proof for AQRV adverse impact determinations shifts from the FLM to the applicant when the increment has been consumed.

In its 1990 report Air Pollution: Protecting Parks and Wild erness From Nearby Pollution Sources the U.S. General Accounting Office (GAO ) found that only 1 pe rcent of the sources within 100 kilometers of five Class I areas it investigat ed were required to have permit s under the PSD program, with 99 percent of the sources being minor or grandfathere d sources. It also found that “non-PSD sources contribute from 53 to 90 percent of five of the six criteria pollutant s emitted within a 100-kilometer
radius of each of the 5 Class I areas.” As part of its investiga tion, GAO noted that “a significant portion of total emissions of volatile organic com pounds generally comes fr om small sources...and suggested that as part of the overall control strate gy, States may want to consider lowering thresholds for regulating new sources to 25 tons of volat ile organic compou nds a year.” According to the investigation, 55 percen t of anthropogenic VOC emissions come from ne w sources or modifications totaling 5 tons per year or less. In a review of PSD permit applications near Mesa Verde National Park (a Class I area in Colo rado), a cumulative modeli ng analysis of incremen t-consuming sources found that approximately 80 percent of the NO 2 Class I increment at the park had been consumed, but much of it by minor sources.20 The FLMs have encouraged EPA to provide clearer direction on how often these periodic reviews must occur as the lack of a pres cribed time-frame for conducting su ch analyses has clearly led to noncompliance with this requirement over the past twenty years by States. The FLMs believe EPA should revise 40 CFR §51. 166 to require that the periodic revi ews be conducted no less frequently than every five years.

4. MANAGING EMISSIONS GENERAT ED IN AND NEAR FLM AREAS

Specific strategies need to be developed and impl emented for reducing and pr eventing pollution from the many diverse source s and activities in comm unities surrounding FLM areas , including “gateway” communities. Accountability mechanisms are needed to ensure th at appropriate actions are taken, reported and incorporated into SIPs, visibility protection plans, and Federa l land manage ment plans. Various forums ( e.g. , the Western Regional Air Partnership, and the Southern Appalachian Mountains Initiative) are addressing some of the emissions sources of conc ern and developi ng appropriate regional strategies. In additio n, EPA has formed other “regio nal planning organizations” for implementing its regional haze rule. FLMs should participate in these forums, consistent with Federal law ( e.g. , Federal Advisory Committee Act), to the maximum extent possible and should coordinate
their activities with in those forums to ensure that comprehensive strategies are developed and implemented to address all the key emissions so urces near FLM areas.

A systematic assessment of emission sources in a nd near FLM areas would be extremely helpful for formulating strategies ai med at mitigating or eli minating adverse impacts on area resources. Without this assessment it is not possible to accurately quantify the extent to which these emis sions contribute to the overall problem. Nevertheless, FLMs can, and should, take steps to minimize emissions generated on FLM lands even without an accurate inventory of emissions sources.

a. Prescribed Fire

Prescribed fire is a la nd management tool used for multiple landscape obj ectives. Prescribed fire
allows the FLM to mimic natura l fire return interv als under controlled co nditions where smoke management can minimize air quality impacts. The a lternative is wildfires, wh ich can be very difficult to control and may cause much more severe air qua lity impacts. A modeling assessment suggests that using prescribed fire to minimize wildfires can result in a net reduction in fine particle (PM 2.5 ) emissions in the long term. In the Pacific Northwest wildfire emissions were found to be greater than prescribed fire emissions in the same airshed (Ottmar, 1996).

Since the turn of the century, wild fire has been aggressively suppre ssed on most of the nation's public lands to protect public safety, prop erty, and to prevent what was thou ght to be the destruction of our natural and cultural resources. Fire-exclusion practi ces have resulted in forests, shrub lands, and grasslands plagued with a variety of proble ms, including overcrowding, resulting from the encroachment of species nor mally suppressed by fire; vulnerability of trees to insects and disease; and inadequate reproduction of certai n species. In addition, heavy accumulation of fuels (such as dead vegetation on the forest floor) can ca use fires to be catastr ophic, which threatens firefighter and public safety, impairs forest and ecosystem health, destroys property and natural and cultural resources, and degrades air quality. The intense or extended peri ods of smoke associated with wildfires can also
cause serious health effects and significantly decr ease visibility.21 FLMs recognize prescribed fire as a valuable tool; th ey also recognize that em issions from prescribed fire can be a significant source of air pollution. Smoke particles are al so in the size range (< 2.5 μm) that they play a significant role in visibility impair ment. Particulate matter is the main pollutant of
concern from smoke because it ca n cause serious health problems, especially for people with respiratory illness.

The FLMs are committed to minimizing the imp acts from smoke by following sound smoke management practices, and if practical, using a lternative methods to ach ieve land management objectives. Each prescribed burn site will have unique charac teristics, but in ge neral, smoke impacts can be greatly mini mized by burning duri ng weather conditions that provide optima l humidity levels and dispersion conditions for the type of materials be ing burned, in addition to limiting the amount of materials and acreage burned at one time.

Generally, fire inside wilderness is considered natura l–there is a need whenever possible to allow these fires to burn out naturally when the fires do not thr eaten private property or air quality conditions do
not threaten human healt h. Visibility impa irments caused by na turally ignited fire s in wilderness should similarly be classified as natural. Unlike stationa ry source emissions, whic h are continuous, fire emissions are spatially and temporally sporadic.

EPA has worked in partnership with land mana gement agencies in th e U.S. Departments of Agriculture, Defense, and the Interi or; State Foresters; State air regulat ors; Tribes; and others to obtain
recommendations and develop a national po licy that addresses how best to improve th e quality of wildland ecosystems (including forests and grasslan ds) and reduce threats of catastrophic wildfires through the increased use of manage d fire, while achieving national cl ean air goals (U.S. EPA, 1998). EPA’s wildland fire policy describe s criteria for wildland managers (fed eral, state, tribal, and private), and state and tribal air pollution ag encies, to use in planning for and implementing prescribed fires, and recommends a variety of smoke management tec hniques that land managers can use to help reduce smoke impacts from prescribed fires. The policy is available at EPA’s website: http://www.epa.gov/ttn /faca/fa08.html

.

b. Strategies to Minimize Emissions fr om Sources In and Near FLM Areas

Aside from prescribed fire, othe r activities that genera te air pollution in FL M areas include road building, operation of generators, oil and gas develo pment, etc. Developing strategies for addressing natural resource impacts in or near an FLM area should not only take into cons ideration the type of activities generating the emi ssions and their amount, but also the existing cond ition of the resources of that area. More stringent measures should be required for sources in and near FLM areas that are already experiencing advers e effects from air pollution.

Examples of potential air pollution prevention practices that FLM agen cies may encourage or develop and use are categorized under th e following three strategies:

Pollution Preven tion Strategies

 Review land management plans for affected FLM areas to assess whether they include strategies to limit and reduce air pollution emissions and incorporate protective measures into planning and
decision documents.

 Place priority on po llution prevention.22  Encourage zero and near- zero emitting technologies.

 Promote energy conservation and the use of renewable energy sources.

 Promote use of clean fuels.

Mobile Source Strategies

 Promote the adoption of Low Emis sion Vehicle standards or the co nversion of Federal fleets to
alternative fuels.

 Improve control of ev aporative emissions.

 Adopt and enforce more stringent emission stan dards for the tour bus industry and other highemitting vehicles ( e.g. , snowmobiles).

 Retire high-emitting vehicles from Federal fleets as quickly as possible and/or relocate highemitting vehicles to less sensitive areas until they can be retired.

 Establish emission budgets from the transportation sect or for selected FLM areas.

 Develop mass transit system s in some NPS units ( e.g. , light rail in Grand Canyon NP and a bus system in Zion NP).

Minor Source Strategies

 Apply RACT, BACT, LAER, best a nd reasonably available control measures, etc., to existing sources, as appropriate.

 Go beyond conformity requireme nts to include the protection of AQRVs in FLM areas by ensuring all actions FLMs can pr acticably control in and near FLM areas will not cause, or contribute to, an adverse impact on any AQRV.

Improved involvement with interest ed parties in gateway communities (those adjacent to FLM areas) will likely be required to ensure growth in these comm unities occurs in a mann er that mitigates the impact on natural resources. These communities may need to enhance thei r participation in the planning processes of FLMs. Similarly, FLMs must participate in planning activities for public lands
located in the FLM area and commun ities adjacent to FLM areas to en sure air quality concerns are adequately addressed. Mechanisms must be identi fied and developed for community involvement in developing, implementing, and enforc ing emission management strategies for sources near and in

FLM areas.

Implementing strategies to achieve emission reductions in and near FLM areas will require efforts in at least three specific areas:

1. FLMs should ensure that sufficie nt emphasis is placed in agency planning documents requiring the minimization of air pollution emission s from new activities or practices.

2. FLM agencies should inventory air pollution emi ssions within FLM areas. After emissions have been quantified, FLMs, States, an d adjacent communities will be able to assess the impact of these emissions through the use of ap propriate models. Knowledge of Class I area emissions will also improve FLM ability to consult with States du ring the development and review of their SIPs (especially visibility SIPs).

3. FLMs should cooperate with Stat es and local communities in asse ssing the need for, and the development of, appropriate emission reduction stra tegies in and near FLM areas that address non-23 PSD sources. Without an acknowle dgment from States and local co mmunities that these sources may pose a threat to FLM areas and a systematic assessment of these pot ential impacts, current efforts to protect FLM area resources may be insufficient.

c. Conformity Requirements in Nonattainment Areas

Conformity criteria and procedures ensure that actions on lands admi nistered by Federal agencies do
not cause a violation of the NAAQS, increase the fr equency of any standard s violations, or delay attainment. Conformity to SIPs is only required for activitie s within nonattainment areas for nontransportation related sources if emissions are above de minimis levels and regionally significant. Any activity that represents 10 percent, or more, of the emission inventory for that pollutant in the nonattainment or maintenan ce area is regionally significant. Exam ples of actions that may require a conformity determination include road paving project s, ski area development, or mining. Activities such as prescribed fire, that are included in a conforming land management plan, are exempt from conformity requirements.

The FLM should define the process to be used in conformity determinations and perform the conformity analysis before a pr oject is implemented. A conformi ty analysis typically includes emission calculations, public part icipation, mitigation measures/implementation schedules, and reporting methods. The Paci fic Southwest Region of th e USDA/FS has published a Conformity Handbook for FLMs to assist in conformity compliance. In an approved Plan of Operation, FLMs can require monitoring. For example, in the case of Carlota Mine, loca ted on National Forest land in Arizona, the USDA/FS reque sted additional mitigati on measures to protect AQRVs in the Superstition
Wilderness.

Transportation projects in FLM areas classified as nonattainment are subject to a more complicated transportation conformity process. Consultation with State and lo cal air quality and transportation agencies will be required to comply with applicable regulations.24 D. SUBGROUP REPORTS:

TECHNICAL ANALYSES AND RECOMMENDATIONS

1. SUBGROUP OBJECT IVES AND TASKS

Subgroups were formed to address the four key issues relevant to AQRV identification and evaluation issues: policy (and procedures), vi sibility, ozone, and deposition. Each of th ese subgroups reviewed the commonalities among the FLMs then addr essed the tasks assigned to them by FLAG. One of their
first tasks was to differentiate betw een Phase I tasks, those which coul d be resolved in the short term without significant additional resource s, and Phase II i ssues, those that would require a longer period or greater effort.

Subgroups were asked to reach co mmon ground among the FLMs on the issues. The intent was to develop, to the extent po ssible, consistent policies, processes, and terminology th at could be used when identifying AQRVs and eval uating impacts on AQRVs. This invol ves recommending consistent approaches for identifying air pollution effects on AQRVs, for determining ad verse impacts, and for attributing adverse impacts to specific pollution sour ces. In addition, the FLMs consider that AQRV protection from visibility, ozone, and deposition impacts are equally important. Howe ver, we also recognize that given the current stat e of the science, attrib uting adverse impacts to specific sources are
easier to document for visib ility than for deposition an d ozone, and easier for depo sition than ozone.

The individual subgroup report s document the common policies, procedures, and definitions identified or develope d during Phase I activities. The Visibility, Ozone, and Deposition subgroup reports are included below. The FLAG Policy Subgrou p Report was used as th e basis for much of the
rest of this FLAG Phase I Report , including much of Section B. "Background" and Section C. "Federal Land Managers' Approach to AQRV Protection."

2. VISIBILITY

a. Introduction

This chapter describes methods fo r analyzing the impacts on visibil ity from new or modified air pollution sources. This includes sources that fall under the purview of the Prevention of Significant Deterioration (PSD) regulations and sources that are being analyzed for Environmental Impact
Statements under the National E nvironmental Policy Act (NEPA). The basis for some of the decisions outlined in this chapter is section 169A of the Clean Air Act. The opening statement of this section states: “Congr ess hereby declares as a national goal the prevention of any future, and the remedying of any existing, impairment of visi bility in mandatory class I Federal areas which impairment results from manmade air pollution.” Under the regulations promulgated for visibility protection (40 CFR §51.301 (x)) visibi lity impairment is defined as “...any humanly perceptible change in visibility (visual rang e, contrast, coloration) from th at which would have existed under
natural conditions.” The remainde r of this chapter describes me thods that allow for new source growth to be analyzed against the constraint of preventing visibility impairment as defined in 40 CFR §51.301 (x), that is, new sour ce growth should not allow any humanly perceptible change in visibility as compared against natural conditions.

There are three different visibility impact thresholds that are discussed: leve ls of concern, analysis25 thresholds, and decision thresholds. These are all interrelated. The levels of concern are visibility impact levels that would alert the FLM to a need for closer scrutiny. The analysis thresholds parallel these levels of concern in that if visibility impacts appro ach the levels of concern, the FLM would need to see further analyses to make an informed judgement about those impacts. The decision thresholds correspond to the visibility im pacts, below which the FLM is not likely to object to an increase in visibility impairing polluta nts. It is important to note that the decision thresholds can not be absolute; the FLM is requi red to make a determination on a “...case-by-case basis taking into account the ge ographic extent, intensity, duration, frequency and time of visibility impairments...” (40 CFR §51.301 (a)). However, the decision thresholds shoul d be useful as an initial benchmark for analysts to judge whether visibility impacts would likely cause the FLM to object to a proposed action.

Natural Conditions

Comparing the impacts of new source growth ag ainst natural conditions implies that natural
conditions are defined. At the time of this writing (December 2000) the EPA is working on defining natural conditions in support of their visibi lity regulations, but that work has not yet been completed. An estimate of natural conditions ha s been made (NAPAP, 1990). These estimates are only differentiated by the broad categories of the eastern and western United States. FLAG has adopted the appropriate aerosol c oncentrations from the NAPAP as estimates of natural conditions for each Class I area (Appendix 2.B). These estimates are a surrogate to be used until more
definitive values for natural conditions are established.

Visibility Impairment

Before proceeding with the discussi on, it is useful to identify the wa ys that visibility impairment can manifest itself. First, the pollutant loading of a section of the atmosphere can become visible, by the contrast or color differe nce between a layer or plume and a viewed background, such as a landscape feature or the sky. The se cond way that visibility is impaired is a general alteration in the appearance of landscape features or the s ky, changing the color or the contrast between landscape features or causing features of a view to disappear. The first phenomenon is commonly referred to as plume impairment, whereas the second phenomenon is sometimes referred to as uniform haze impairment. As plumes are transpor ted within a stable atmospheric layer, they may become a layered haze. As plumes and other mo re diffuse emission sources are transported and become well mixed in the atmosphere, they may develop into a uniform haze.

Visibility Parameters

The analysis methods for new source growth, described in this chapte r, only deal with the visibility effects of discrete plumes and the aggregation of discrete plumes. The difference in these phenomena, as treated in this chapter, is whether th e visibility effect is primarily seen as a section of the atmosphere which exhibits a change in contra st or color as compared with a viewed background, or whether the effect is due to an altera tion of the appearance of the background features themselves. For the first situation, the contrast (C) and color difference index ( ∆ E) of the plume and the viewing background are calculate d. For the second situ ation, the change in atmospheric light extinction ( ∆ b ext ), relative to natural conditions, is calculated. An approximation for which situation applie s is the distance from the point of emission. (Distance serves as an indicator of where steady state conditions may appl y.) The visibility impairment from sources26 within 50 kilometers of a view is usually calc ulated using contrast and color difference, where visibility impairment from sources greater than 50 kilometers from a view, or the aggregation of a number of plumes, regardless of distance, is usually calculated using the change in light extinction. The distance approximation is useful for dis tinguishing these two phenomena; the terms “near
field” and “distant/multi-source” are sometimes used in the remainder of this document to make
this distinction. More information on visibi lity parameters can be found in Appendix 2.A.

Levels of Concern

The crucial level of concern for visibility impair ment is whether it is humanly perceptible as compared against natural conditions (40 CFR §51.301( x)). As noted above, different visibility parameters are applied for different phenomena. A summary of the thresholds of perceptibility for the case of a plume viewed against a background i ndicates that contrast values (C) of ±0.01 to ±0.05 (note that the sign denotes whether the pl ume is brighter (+) or darker (-) than the background) are perceptible (NAPAP, 1990). A change in the color difference index ( ∆ E) of less
than 1 to 4 has been identified as the range of perceptib ility for this parameter. The Workbook for Plume Visual Impact Screening and Analysis (U SEPA, 1992a) suggests that a level of 0.05 for the absolute value of contrast (|C| = 0.05) and ∆ E = 2 be used as thresholds in screening analyses; these levels were set in the mid-range of the pe rceptibility thresholds, in part, because of the conservative nature of the screening calculations. These levels also constitute the FLM’s level of concern for screening analyses of plumes viewed against a backgr ound. Under circumstances of a more refined analysis, |C| = 0.02 and ∆ E = 1 are the levels of c oncern (USEPA, 1992b). These levels are usually applied for near field analys es where single sources are locating within 50
kilometers of a view.

For the case of visibility impairment which changes the appearance of a viewed background feature, thresholds of perceptibi lity, where a just noticeable change occurs in the scene, have been found to correspond to a change in extinction ( ∆ b ext ) as low as 2% under ideal conditions, up to 20% (NAPAP, 1990; Pitchford and Malm, 1994). A ∆ b ext of 5% will evoke a just noticeable change in most landscapes (NAP AP, 1990). The FLMs are concer ned about situations where a change in extinction from new source growth is greater than 5% as compared against natural conditions. Changes in extinction greater than 10% are generally considered unacceptable by the FLMs and will likely raise objections to further po llutant loading without mitigation. These levels are usually applied for distant/multi-source analyses where sources are located more than 50 kilometers from a view or for analyzing the visib ility impairment from an aggregation of plumes from multiple sources, regardless of distance.

Cumulative Analyses

A cumulative effects analysis of new source grow th (defined as all PSD increment-consuming sources) on visibility impairment should be performed. The change in extinction ( ∆ b ext ) will usually be the visibility parameter examined. Th e FLMs recognize that cumu lative analyses of the effects of new source growth on vi sibility impairment have only rarely been carried out. Until cumulative analyses are performed for an area, th e FLMs are suggesting some analysis thresholds

to either trigger a cumulative analysis or allo w a source to be permitted if its impact is below certain prescribed levels.27 If a cumulative analysis has already been perfor med for the area, or if other considerations ( i.e.

, NEPA, PSD increments, or other AQRV analyses) re quire that a cumulative analysis be performed for the proposed source, then the visibility impact s of the source are expected to be considered as part of the cumulative visibility impairment, as compared against natural conditions. When these conditions are met, the inclusion of the proposed source is expected regardless of the predicted visibility impairment of the source, unless its impacts are considered below de minimis

.

Analysis Thresholds fo r New Cumulative Analyses

The analysis thresholds outlined here are interim levels to be used until such time as cumulative analyses are conducted for an area. Change in extinction ( ∆ b ext ) is usually the visibility parameter analyzed for a cumulative analysis. If the visi bility impact of a proposed project is below 0.4% ∗ change in extinction, the impact s would be considered below de minimis and would not require further analysis. For situations where a cumulativ e visibility analysis has not been done or is not required because of other considerations, the fo llowing analysis thresholds will apply. If the visibility impact of a proposed s ource is less than a 5% change in extinction a cumulative analysis would not be expected. For visibi lity impairment predicted to be above 5%, but less than 10%, change in extinction from a proposed source, a cumu lative analysis is expected. If the visibility impairment is predicted to be gr eater than 10% from a proposed sour ce, the FLM is likely to object to the project regardless of other source growth, unless there is mitigation.

Decision Thresholds

Each determination of whether the impacts from a new source or major modification will be considered adverse must, by re gulation, be made on a case-by- case basis (40 CFR §51.301(a)). Therefore, the decision thresholds specified here ar e strictly a guideline. More refined visibility analyses may indicate that the visibility parameters used ( i.e., C, ∆ E, ∆ b ext ) do not adequately characterize the visibility for a particular situat ion; the FLMs will consid er such information in making their decision. The decision thresholds para llel the FLM levels of concern. For near field situations where a section of the atmosphere is polluted and is viewed ag ainst a scenic background, screening analysis values of contrast with an absolute value less than 0.05 (|C| < 0.05) would not likely result in an objection by the FLM. Similarly, a value of ∆ E < 2 from a screening analysis would not likely result in an obj ection. If a refined near field an alysis is performed, values of |C| < 0.02 or ∆ E < 1 would not likely result in an objection by the FLM.

For distant or multi-source situations, if a cumulati ve visibility analysis has not previously been conducted and is not required for other analyses, a single-source ch ange in extinction less than 5% would not generally trigger a need for a cumulativ e analysis. Under those circumstances, the FLM would not likely object to the proposed action. If the forecast single-source contribution to extinction is between 5% and 10%, or if a cumulative analyses is required or already exists, a special decision threshold applie s. If the visibility impairme nt from the proposed action, in combination with cumulative new source growth, is le ss than a change in extinction of 10% for all time periods, the FLMs will not likely object to the proposed action. If the visibility impairment

∗ The de minimis level of 0.4% is defined as 4% of the unacceptable change in extinction ( i.e. , 10%), paralleling the discussion of significant impact levels in the proposed new source review modifications. (FR 61 38291-38293)28 from the proposed action, in comb ination with cumulative new sour ce growth, is greater than or equal to a change in extinction of 10% for any time periods, the FLMs will likely object to the proposed action, unless the contribution fr om the proposed action is less than a de minimis value of 0.4% for these time periods.

Relationship to Regional Haze Rule

The FLAG recommendations are complimentary to the regional haze rule. However, the visibility
recommendations of FLAG are intended for new s ource review and NEPA type applications, whereas the regional haze rule addresses the effect s of existing sources of visibility impairment in conjunction with new source review. The FL AG recommendation is designed to prevent new sources from causing visibility impairment, and the criteria for developi ng these recommendations do not necessarily apply to existing sources. At the time of this writing, new source review is an ongoing effort, but it will be several years before State Implementation Plans (SIPs) under the regional haze rule are submitted. If the new visi bility SIPs adequately account for new source growth, the FLMs may reconsider the FLAG recommendations.

The visibility parameters for cumulative impact anal ysis, outlined here, are related to those in the regional haze rule. However, an assumption inherent in regional haze is that the pollution is fairly evenly distributed over a broad ge ographic extent. By contrast, th e analysis techniques, described herein, at most deal with the aggregation of a subs et of the plumes that mi ght affect regional haze,
but do not meet the criteria of being a regional haze.

The levels of concern and de minimis levels described in this document were arrived at, in part, with the knowledge they apply to a limited number of sources under new source review and that the analyses are always compared to natural conditions. The de minimis levels described here should not be used for determining whether em issions from an existing source are reasonably anticipated to cause or contribute to visibility impairment. Those cr iteria have been laid out in the regulations (40 CFR §51 Subpart P Protection of Visibil ity) and through interp retations of those regulations by EPA and courts.

While there are some distinct differences betwee n this document and the regional haze rule, there
are also some similarities. One of these is the need for conducting a cumulative assessment of visibility impairment. This wi ll include the need for evaluati ng the effects of sources beyond an
individual state’s bounda ries. Therefore, it is anticipated that when modeling centers are established for SIP development work, the tools th ey use may be applicab le to analyzing both existing impairment as well as the potential impacts of new source growth.29 b. Analysis Techniques

There are two fundamentally different approach es one could adopt to determine visibility impairment. One is a technically rigorous, comple x, and situation-specific method, while the other is a more generalized approach. The more rigor ous approach requires de termination of particle
concentrations and size distributi ons, calculation of particle grow th dynamics, and application of Mie Theory to determine the opti cal characteristics of the aerosol distribu tion. Sophisticated
radiative transfer models are then applied, using aerosol optical characteristics, lighting and scene characteristics, and spatial distribution of the po llutants to calculate the path and wavelength of image-forming and non-image-forming light that r eaches a specific observer from all points in the scene being viewed.

While such a detailed analysis may be useful for assessing specific cases, it is usually impractical for situations in which visibility could be experienced in a nearly infinite va riety of circumstances. Practical limitations frequently dictate that it is more reasonable to use a generalized approach to determine the change in extinction by using bulk-av eraged aerosol-specific extinction efficiencies rather than trying to reproduce the complex optic al phenomena that may occur in the atmosphere.

Consequently, FLAG recommends the generalized approach for determining the effects on visibility from a proposed new source's emissions. The procedure is to estimate the atmospheric concentrations of visibility impairing pollutant s, apply representative visibility parameters, calculate the change from specified reference leve ls, and compare this change with prescribed

threshold values.

FLAG is using estimates of natural conditions as re ference levels for Class I visibility analyses. Comparison with natural conditions will help ensure that those conditions will not be impaired in keeping with Section 169A of the CAA. Because of the different requireme nts of the two modeling approaches discussed below, natu ral conditions must be expresse d using two different metrics:

• Standard visual range (visual range ad justed to a Rayleigh condition of 10 Mm -1 ), for near field modeling. Present EPA guideline visibility models traditionally accept visibility conditions expressed in these terms.

• Extinction, for distant/multi-source modeling. Vi sibility conditions should be expressed in terms of the averaged extinction efficiencies of the individual atmospheric constituents that comprise the total extinction. The relative humidity effects of the hygroscopic particles must be accounted for when the change in extinction is calculated.

Information needed to calculate the above indice s is provided in Appendi x 2.B for all 156 Class I areas for which visibility is an important attribute. If estimates are needed for Class II areas, the FLM can provide them.

c. Air Quality Models and Visibility Assessment Procedures

The modeling discussion will be di vided into two parts to address the very different requirements for 1) near field modeling where plumes or laye rs are compared against a viewing background and 2) distant/multi-source modeling for plumes and aggr egations of plumes that affect the general

appearance of a scene. Note that both of the ab ove analyses might apply depending on the source’s30 proximity to all portions of the Class I area or multiple Class I areas.

Near Field Analysis Technique for Analyzing Plumes or Layers Viewed Against a Background

The Model (Near Field – Steady State Conditions Applicable)

EPA has recommended a methodology to assess impacts due to cohe rent plumes. A guideline, for when these steady state conditions apply, is the distance from the s ource to the view of concern. This technique is usually applied for sources locating less than 50 km from a Class I area. Applicants must model their potential plume impact s using the screening model, VISCREEN (USEPA, 1992a), or, if the next level of anal ysis is called for, PLUVUE II (USEPA 1992b and 1996c). Both of these models us e steady-state, gaussian-based plume dispersion techniques to
calculate one-hour concentrations within an elevated plume. These two models calculate the change in the color difference index ( ∆Ε ) and contrast between the plume and the viewing background. Values of ∆Ε and plume contrast are based on th e concentrations of fine primary particulates (including sulf ates), nitrogen dioxide (NO 2 ), and the geometry of the observer, target, plume, and the position of the sun. PLUVUE II also allows c onsideration of the effects of secondarily formed sulfates. Pl ume contrast results from an in crease or decrease in light transmitted from the viewing background through the plume to the observer. The specifics of the emission scenarios and plume/observer geometries fo r modeling should be selected in consultation with the appropriate FLM. At the present tim e there is no recommended procedure for conducting analyses of multiple sources with these modeling tools, so multiple coherent plumes must be treated individually, or combined into a representative single source if reasonable.

The Recommended Prescription (Near Field – Steady State Conditions)

Until better modeling tools are ava ilable, FLAG recommends using the present EPA techniques for plume visual impact screening analyses (USEPA 1992a). However, unlike those procedures, which suggest the use of current average annual visibil ity conditions, FLAG recommends that the visual range corresponding to natural conditions be used to generate the hourly estimates of ∆ E and plume contrast. FLAG recommends this change in order for the analysis technique to be consistent with the national visibility goal. For screening-level an alyses, FLAG recommends th e use of the annual average reconstructed natural conditions given in the last column in Table 2.B-1 in Appendix 2.B. The table entry gives the specified reference level (including the effects of relative humidity) expressed in Mm -1 . The conversion to standard visual range can be made using Equation 1 in Appendix 2.A. For the refined analyses, the rec onstructed natural condition is derived from the relative humidity used in the modeling, the corre sponding relative humidity adjustment factor (Table 2.A-1), and estimated natural ae rosol concentrations (Table 2.B-1).

If a screening analysis of a new or modified source can demonstrate that its em issions will not cause a plume with any hourly estimates of ∆ E greater than or equal to 2.0, or the absolute value of the contrast values (|C|) greater than or equal to 0.0 5, the FLM is not likely to object to the issuance of the PSD permit based on near field visibility impacts and no further near field visibility analyses will be requested. More refined analyses ( i.e. , PLUVUE II) would be undertaken if the above conditions are not met and would be compared against lower levels of concer n; the FLM would not likely object

if ∆ E < 1.0 and |C| < 0.02.31 If the estimated plume parameters exceed the afor ementioned values, the FLM would rely on a caseby-case effects-based test (NPS 1993), taking in to account magnitude, frequ ency, duration, and other factors, to decide whet her to make an adverse impact determ ination.

Distant/Multi-Source Techniques for Analyzing Whether a Plume or an Aggregation of Plumes Alters the General Ap pearance of a Scene

This application is generally more complex than the near field, coherent plume modeling analyses and the guidance from EPA is less definitive, though it is evolving. The modeling system must include the capability to assess single and multipl e sources in a temporally and spatially varying meteorological domain, accommodate modeling domains measuring hundreds of kilometers, include rough and complex terrai n, provide pollutant concentra tion estimates for averaging times from one-hour to annual, and address inert and secondarily formed pollutants and dry and wet deposition. In the early 1990s the FLMs and th e EPA recognized the need for a consistent, technically credible technique to estimate contri butions to air quality of multiple new sources locating more than 50 km from Class I areas. Toward that end, the Interagency Workgroup on Air Quality Modeling (IWAQM) was established to develo p a modeling protocol for this application. FLAG proposes to rely on the IWAQM recomme ndations and modeling guidance for long range pollutant transport (present guidance, USEPA 1998 ∗ ). This technique is usually applied when sources are located more than 50 kilometers from por tions of a Class I area, when an aggregation of plumes may impact an area, or when the assumpti ons inherent in steady st ate visibility models do not apply.

The Model (Distant/Multi-Source)

Revised IWAQM guidance (USEPA 1998 * ) recommends non-steady state air quality modeling systems for screening and refined analyses. The IWAQM recommendations are adaptations and refinements of the CALPUFF dispersion modeli ng system, including the CALMET meteorological model (USEPA 1996a, http://www.src.com/calpuff/calpu ff1.htm). This modeling system consists of diagnostic meteorological models, a gaussi an puff dispersion model with algorithms for chemical transformation, wet and dry depositi on, and complex terrain, and a post processor (CALPOST) for calculating concentration and deposition fields and visibility impacts.

The modeling systems/techniques outlined in this recommendation provide ground level concentrations of visibility impairing pollutants. These concentrations can then be used to calculate the extinction due to these pollutants, using the relationships outlined in Appendix 2.A.
The results should be compared against a reference level deri ved from aerosol in formation (relative humidity adjusted hygroscopic and non-hygroscopic concentrations plus Ra yleigh extinction) given in Appendix 2.B for each Class I area. This refere nce level is a function of relative humidity. To achieve the best temporal and spatial resolu tion, relative humidity data included in the meteorological data base of the air quality model and the data provided in Table 2.A-1 is the preferred basis for making the necessary calculation of the relative humidity adjustment term f(RH) for refined visibility analyses. The approach, for screening level analyses, is to use the quarterly averaged reference levels given in Table 2.B

- 1 that are based on spatially interpolated seasonal relative humidity values and empirically derived f(RH) adjust ment factors (IMPROVE 2000). In

∗ At the time of this writing, USEPA is considering sim ilar procedures for incorporation into the Guideline on Air Quality Models (40 CFR §51 Appendix W). This should be consulted for the latest information.32 either approach, the same relative humidity adjustment factor, f(RH), is applied to determine both
the reference level and the effect of the incrementa l increase associated with the new source(s). An example model application is given in Appendix 2.C.

For the purposes of the following prescription, FL AG recommends basing th e analyses on block 24- hour averages ( i.e ., daily) of modeled visibility. The 24-ho ur average was selected over the 1-hour average time because:

• Our confidence in model performance for 24-hours is higher than for shorter time periods.

• The combined visibility effect of emissions from multiple sources transported over long distances is better represented over 24-hours than for shorter time periods.

• It avoids detailed day/nigh t visibility considerations.

• It avoids developing and implemen ting site-specific, comp lex visibility analyti cal methods that are not available at this time (see disc ussion under Analysis Techniques).

The Recommended Prescrip tion (Distant/Multi-Source)

The FLMs are concerned with the cumulative e ffects of new source gr owth on visibility; cumulative analyses need to be conducted. Th e FLMs recognize, however, that few cumulative visibility analyses have been done, therefore, the following pr escription is su ggested. If a cumulative analysis has not been performed for an area and if a single project’s visibility impairment, compared against natural conditions, is below certain analysis thresholds, then the FLMs are not likely to object to the project or ask that a cumulative analysis be performed before the project proceeds. If a cumulative analysis has already been done or if a cumulative analysis is required because of other considerations ( i.e. , increment consumption, NEPA, or other AQRVs), or if the analysis thresholds are ex ceeded, then the impacts of the propos ed project are expected to be considered as part of a cu mulative visibility analysis.

The prescription is as follows:

1. Calculate the single-source contribution. Compare results with the distant/multi-source Decision
Threshold.

• Determine whether a cumulative analysis has be en done for the Class I area(s) in question, and if it has been done, use the input files from the cumulative analysis to perform this step.

• If the estimated increase in visibility impairment attributed to the proposed project is

≥ 10%, compared against natural conditions, fo r at least one modeled day, then the FLM will consider the magnitude, frequency, duration, and other factors to assess the impact, but is likely to object to th e issuance of the permit.

• If the estimated increase in vi sibility impairment attributed to the proposed project is <10%, then the analysis should proceed to the next st ep (Note that if the si ngle-source contribution is always <0.4%, no further analyses are required).

2. If a cumulative analysis does not exist, compar e the single-source contri bution with distant/multisource Analysis Threshold and assess the need for a cumulative analysis.33 • If a cumulative analysis does not exist, a nd if there are no othe r requirements for a cumulative analysis, and if a new or modified source can demonstrate that its contribution to a change in extinction is <5.0%, compared against natural conditions, for all days, then the FLM is not likely to object to the issuance of the PSD permit based on visibility impacts.

• If the single-source contribution to a change in extinction is ≥ 5.0% or if a cumulative analysis already exists or is required for some other reason, then the analysis should proceed to the next step and estimate its contribution to cumulative impacts.

3. Conduct a cumulative analysis and compare results with cumu lative, distant/multi-source
Decision Threshold.

• If cumulative change in extinction is ≥ 10%, for all modeled block 24-hour periods, and the
new source contributes at least a 0.4% change in extinction to any of these periods, then the FLM will consider the magnitude, frequency, dura tion, and other factors to assess the impact, but is likely to object to the issuance of the permit.

• If cumulative modeling results indicate that the effects from the combined sources are expected to cause a change in extinction th at is < 10%, for all modeled block 24-hour periods, then the FLM is not likely to object to the issuance of the permit.

This prescription is portrayed schematically in Figure V-1.34 Figure V-1. Prescription for visi bility assessment for distant/multi-source applications (source greater than or eq ual to 50 km from the Class I area)

Prescription for New Source Review Visibility Analysis

(Distant/Multi-source)

Single-source
contribution to

change in
extinction ≥

10.0%

? FLM likely to object to the permit

FLM not likely to object to the permit Has a cumulative analysis been

conducted (or has the source triggered a need for a cumulative impact analysis) or is its
contribution to extinction ≥ 5.0%

?

Conduct cumulative visibility analysis of PSD increment consuming

sources

Cumulative change in
extinction ≥ 10% and single-source
contribution ≥ 0.4%

?

FLM likely to object to the permit Yes

No

No Yes

Yes No35 d. Summary

FLAG has provided guidance in the form of recommendations, specific prescriptions, and interpretation of results for assessing visibility impacts near Class I areas (although this guidance is generally applicable to Class II areas, as well ). The guidance addresses assessments for sources proposed for locations near and at large distances from these areas. It also recommends impairment thresholds and identifies the condition s for which cumulative analyses of all incrementconsuming sources would be necessary. Th e key components of the recommendations are highlighted below.

In general, FLAG recommends that an applicant:

• Consult with the appropriate regulatory agency an d with the FLM for the affected Class I area(s)
or other affected area for confir mation of preferred procedures a nd for the need for a cumulative analysis.

• Obtain FLM recommendation for th e specified reference levels (e stimate of natural conditions) and, if applicable, FLM recommended plume/obse rver geometries and model receptor locations.

• Apply the applicable EPA Guidelin e, steady-state models for region s within the Class I area that are affected by plumes or layers that are viewed against a backgrou nd (generally within 50 km of the source).

Calculate hourly estimates of ∆ E and plume contrast, with resp ect to natural conditions, and compare these estimates with the th resholds given in Section D.2.c.

• For regions of the Class I area where visibility im pairment from the source would cause a general alteration of the appearance of th e scene (generally 50 km or mo re away from the source or from
the interaction of the emissions from multiple sources), apply a non-steady-state air quality model
with chemical transformation ca pabilities (refer to IWAQM guida nce documents), which yields
ambient concentrations of vi sibility-impairing po llutants. At each Class I receptor:

Calculate the change in extinc tion due to the source being an alyzed, compare these changes with the reference conditions, a nd compare these results with th e thresholds given in Section

D.2.c.

If necessary, calculate the cumu lative change in extinction due to new source growth.36 Appendix 2.A Visibility Parameters

Visibility is usually characterized by either visu al range (VR) (the greatest distance that a large dark object can be seen) or by th e light-extinction coefficient (b ext ) (the attenuation of light per unit distance due to scattering and absorption by gase s and particles in the atmosphere) (IMPROVE, 1996). Under certain assumed conditions, these parameters are inversely related to each other by Equation 1; a long visual range corresponds to a lo w extinction. Visual range is useful for safety
reasons such as to direct aircraft traffic near air ports, but is not particularly useful for assessing the quality of scenic vistas. Nonethel ess, visual range remains a useful measure for describing visibility, especially for communication with the general public. The dimens ions of VR are length and the
dimensions of b ext are 1/length. Visual range is usually expressed in kilometers. The extinction coefficient is sometimes expressed as “inverse kilometers” (km -1 ) or as “inverse megameters” (Mm -1 ) (the reciprocal of 1 million meters). If b ext is expressed in Mm -1 the coefficient 3.912 becomes 3912 as in Equation 1.

Equation 1. Relationship between visual range and light-extinction coefficient.

Other visibility parameters frequently used include ∆ E and contrast. These metrics relate to the color difference or contrast, respectively, of a plume or haze with respect to some viewing background.

Calculating the Extinction Coefficient

Visibility is degraded by vi sible light scattered into and out of the line of si ght and by light absorbed along the line of sight. Light ex tinction is the sum of light scattering and absorption, and is usually quantified using the light extinction coefficient (b ext ). Extinction can be measured directly or it can be calculated from representative aero sol measurements. Using a gene ralized approach to estimating visibility effects, one can calculate the extinction coeffi cient as the sum of its parts, i.e. , b ext = b scat +
b abs , where b scat and b abs are the light scattering and absorption coefficients. The light scattering and absorption coefficients can be further broken down by their respective compone nts. The scattering coefficient is affected by light scattering (Rayleigh scattering (b Ray )) from air mole cules and from particle scattering (b sp ); the particles can be natural aerosol or result from air pollutants. The absorption coefficient is aff ected by gaseous absorption (b ag ) and particulate absorption (b ap ). Nitrogen dioxide is the only major light-absorbing gas in the lower atmosphe re; it generally does not affect hazes, although it can be an important element in a coherent pl ume assessment. Therefore, only particle absorption is considered in the suggested far-field visibility analyses, although nitrogen dioxide absorption should be considered when this technique is app lied to sources in the near field.

Particle scattering can be broken down by the contribu tions of different partic ulate species. It has
been convenient to consider the scatteri ng coefficients of fine particles (PM 2.5 ) (particles with mass mean diameters less than or equal to 2.5 μ m) and coarse particles (mass mean diameters greater

than 2.5 μ m but less than or equal to 10 μ m). The fine particle scatte ring coefficient can be further defined by the sum of the scattering coefficient due to sulfates (b SO4 ), nitrates (b NO3 ), organic ) Mm ( b 3912
) km ( b 912

. 3 ) km ( VR
1

ext 1

ext − − = =37 aerosols (b OC ), and soil (b Soil ); the coarse scattering coefficient (b Coarse ) is not refined any further. Thus the particle scattering coefficient (b sp ) can be expressed as in Equation 2.

Equation 2. Components of particle scattering.

Each of the particle scattering coefficients can be related to the mass of the components using the relationships in Equation 3.

Equation 3. Relationship between particle scattering and mass of each species.

The quantities in brackets are the masses expressed in μ g/m 3 . (It is assumed that the forms of the

SO 4 = and NO 3 - are ammonium sulfate [(NH 4 ) 2 SO 4 ] and ammonium nitrate [NH 4 NO 3 ].) The
numeric coefficients are the “d ry” scattering efficiencies (m 2 /g). The term f(RH) is the relative humidity adjustment factor. The extinction coefficients are in Mm -1 . If the “dry” scattering efficiencies are divided by 1000 ( i.e. , 0.003 instead of 3) the result ant extinction coefficients will
be in km -1

.

Particle absorption (b ap ) is primarily due to elemental carbon (soot). Similarly, absorption by gases (b ag ) is primarily from nitrogen dioxide (NO 2 ). For purposes of analyzing the effects of soot or

NO 2 on visibility in a mode ling analysis, the re lationships in Equation 4 s hould be used. Again, the quantities in brackets are the masses of el emental carbon or nitrogen dioxide in μ g/m 3 and 10 and
0.17 are the extinction efficiencies . Nitrogen dioxide absorption is usually only an issue in the near-field, therefore, it is usually not cons idered in an analysis for distant sources.

b ap = 10[EC]

b ag = 0.17[NO 2 ]

Equation 4. Relationship between partic le absorption and elemental carbon.

The total atmospheric extinction can be expressed as in Equation 5, where b Ray is the Rayleigh scattering component, which is assumed to be 10 Mm -1

.

b ext = b SO4 + b NO3 +b OC + b soil + b Coarse + b ap (+ b ag ) * + b Ray

Equation 5. Components of Extinction ( * b ag is usually only considered in near-field analyses). Coarse Soil OC 3 NO 4 SO sp b b b b b b + + + + =



## [ ] [ ] [ ] [ ] [ ] Mass Coarse 6

. 0 b Soil 1 b OC 4 b ) RH ( f NO NH 3 b ) RH ( f SO ) (NH 3 b

Coarse

Soil OC 3 4 3 NO 4 2 4 4 SO

= = = = =38 To the extent that a source contributes to the formation of some of these constituents, those
contributions can be summed to yield the source’s contribution to extinction. This will be
discussed in more detail below.

Examination of Equation 3 reveals that the su lfate and nitrate compone nts of the extinction coefficient are dependent upon relative humidity. These aerosols are hygros copic and the addition of water enhances their scattering efficiencies. It is sometimes c onvenient to consid er the sulfate and nitrate components of extinc tion separately from the remaining components of Equation 5 and to keep the relative humidity adjustment factor (f (RH)) separate. Equation 5 can then be rewritten as in Equation 6, where b hygro is the combined extinction coefficient of sulfate and nitrate, excluding the relative humid ity adjustment factor, and b non-hygro is the sum of b OC , b Soil , b Coarse , b ap , and b ag . b ext = b hygro f(RH) + b non-hygro + b Ray Equation 6. Extinction coefficient expresse d as the sulfate and nitrate contribution (b hygro =3[(NH 4 ) 2 SO 4 + NH 4 NO 3 ]) and non-hygroscopic components (b non-hygro = b OC +b Soil +b Coarse +b ap +b ag ).

The relative humidity adjustment factor requires some further explanation. Th e variation of the effect of relative humidity on the extinction efficiency, f(RH), of sulfates and nitrates is given numerically in Table 2.B-1. As can be seen, th e effect of relative humidity on the extinction efficiency of these aerosols is non-linear, and is several times greater at higher relative humidity than at lower humidity.

FLAG proposes that the relative humidity adjust ment to the “dry” sca ttering efficiencies (unadjusted for relative humi dity) for hygroscopic particles are made as follow:

• The preferred alternative is to apply day-by-day f( RH) adjustment factors to the analysis. For this alternative hourly relative humidit y data are needed. Hourly f(RH ) values should be averaged to generate a 24-hour relevant f(RH) factor. FLAG recommen ds, however, that if the hourly relative humidity exceeds 98%, that it be rolled back to 98%, so that th ere will be no f(RH) factors applied that are greater than f(98).

• For screening analyses the adjustme nt factor can be based on hist oric averages of f(RH) for the Class I area(s) of concern (Table 2.B-1).39 Table 2.A-1. f(RH) values for various values of relative humidity ∗

RH(%) f(RH) RH(% ) f(RH) RH(%) f( RH) RH(%) f(RH)

1 1.0 26 1.0 51 1.2 76 2.3

2 1.0 27 1.0 52 1.3 77 2.4

3 1.0 28 1.0 53 1.3 78 2.5

4 1.0 29 1.0 54 1.3 79 2.6

5 1.0 30 1.0 55 1.3 80 2.7

6 1.0 31 1.0 56 1.3 81 2.8

7 1.0 32 1.0 57 1.3 82 3.0

8 1.0 33 1.0 58 1.4 83 3.1

9 1.0 34 1.0 59 1.4 84 3.2

10 1.0 35 1.0 60 1.4 85 3.4

11 1.0 36 1.0 61 1.5 86 3.6

12 1.0 37 1.1 62 1.5 87 3.8

13 1.0 38 1.1 63 1.5 88 4.0

14 1.0 39 1.1 64 1.6 89 4.4

15 1.0 40 1.1 65 1.7 90 4.7

16 1.0 41 1.1 66 1.7 91 5.3

17 1.0 42 1.1 67 1.7 92 5.9

18 1.0 43 1.1 68 1.8 93 7.0

19 1.0 44 1.2 69 1.9 94 8.4

20 1.0 45 1.2 70 1.9 95 9.8

21 1.0 46 1.2 71 2.0 96 12.4

22 1.0 47 1.2 72 2.0 97 15.1

23 1.0 48 1.2 73 2.1 98 18.1

24 1.0 49 1.2 74 2.1 99 18.1 ♦

25 1.0 50 1.2 75 2.2 100 18.1 ♦

∗ The values in Table 2.A-1 are only appropriate for averaging times of 1 hour or less.

♦ The values for 99% and 100% RH ar e rolled back to the value for 98%.40 Appendix 2.B

Estimate of Natural Conditions

Table 2.B-1 provides natural background estimates fo r visibility reference levels for each Class I area; these will serve until better es timates of natural conditions are available. The estimates for natural background aerosol concentrations provide d in the State of Science and Technology No.24 (NAPAP, 1990), shown in Table 2.B-2 provide the ba sis for these estimates. The seasonal and annual means for the relative humidity ad justment factor f(RH) given in the table are computed using spatially interpolated and quarterly averaged Nationa l Weather Service relative humidity data and the following empirical relationships derived from site-s pecific relative humidity collected at some Class I areas (IMPROVE 2000).

2 )) 1 /( 1 ( 09

. 0 )) 1 /( 1 ( 59

. 0 34

. 0 ) ( RH RH RH f

− +

− + = (Annual)

)) 1 /( 1 ( 82

. 0 35

. 0 ) ( RH RH f

− + = (Winter)

2 )) 1 /( 1 ( 08

. 0 )) 1 /( 1 ( 78

. 0 01

. 0 ) ( RH RH RH f

− +

− +

− = (Spring)

)) 1 /( 1 ( 99

.. 0 19

. 0 ) ( RH RH f

− +

− = (Summer)

2 )) 1 (/( 01

. 0 )) 1 /( 1 ( 02

. 1 25

. 0 ) ( RH RH RH f

− +

− +

− = (Fall)

For annual, winter (Dec, Jan, Feb), spring (Mar , Apr, May), summer (J un, July, Aug), and fall (Sep, Oct, Nov), respectively. The visibility impairment (b ext ) due to this assumed distribution of background aerosol is calculated us ing Equation 6, Appendix 2.A.

The source of the relative humidity data is 10 years of monthly averaged historic National Weather Service data (over 250 sites) spatia lly interpolated and gridded (0. 25 degree grid size) and further interpolated to specific Class I areas. The annual and quarterly means are shown in Figure 2.B-1.41AverageAnnualRelativeHumidityValuesNA050010001500MilesLegendClassIArtas70RHValutslnttrpolaud){\JRHContoursProjectionAlbersEqual-AreaConicDataSoLrcesRHdatafromEPAAnalysisofNWSdata(reportinprogress)ClassIareacentroidsfromNPSAirResourcesDivision,1993Dateofmap6/20/2000CartographerK.Clifford42AverageWinterRelativeHumidityValuesNA1500Miles0500100068LegendClassI.Areas70RHValuts(lntorpol<Ud)(\JRHComoursProjectionAlbersEqual-AreaConicDataSourcesRHdatafromEPAAnalysisofNWSdata(reportinprogressClassIareacentroidsfromNPSAirResourcesDivision,1993Dateofmap6/16/2000CartographerK.Clifford43AverageSpringRelativeHumidityValuesNA1500Miles0500100064LegendClassIArtas70RHValuts(lntorpol<Ud)('JRHComoursProjectionAlbersEqual-AreaConicDataSourcesRHdatafromEPAAnalysisofNWSdata(reportinprogress)ClassIareacentroidsfromNPSAirResourcesDivision,1993Dateofmap6/20/2000CartographerK.Clifford44AverageSummerRelativeHumidityValuesNA01500Miles5001000LegendClassIArtas70RHValues(JntupoJaud)(\JRHComoursProjectionAlbersEqual-AreaConicDataSourcesRHdatafromEPAAnalysisofNWSdata(reportinprogress)ClassIareacentroidsfromNPSAirResourcesDivision,1993Dateofmap6/20/2000CartographerK.Clifford45NAAverageFallRelativeHumidityValuesMiles70LegendClassIArtas70RHVtb.lts(lntupol<Ud)(\JRHComoursProjectionAlbersEqual-AreaConicDataSourcesRHdatafromEPAAnalysisofNWSdata(reportinprogressClassIareacentroidsfromNPSAirResourcesDivision,1993Dateofmap6/20/2000CartographerK.Clifford46 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Acadia NP Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.2 11.4 21.4
Fall 0.9 8.5 10.0 3.1 11.3 21.3 Agua Tibia W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 2.4 5.9 15.9 Spring 0.6 4.5 10.0 2.6 6.1 16.1

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Alpine Lakes W Annual 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 4.5 7.2 17.2 Spring 0.6 4.5 10.0 3.3 6.5 16.5

Summer 0.6 4.5 10.0 2.7 6.1 16.1
Fall 0.6 4.5 10.0 5.1 7.6 17.6

Anaconda – Pintlar W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 3.7 6.7 16.7 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 3.1 6.4 16.4

Ansel Adams W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.0 6.3 16.3 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Arches NP Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 3.3 6.5 16.5 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.4 5.3 15.3
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Badlands NP (W) Annual 0.6 4.5 10.0 2.6 6.1 16.1
Winter 0.6 4.5 10.0 3.1 6.4 16.4 Spring 0.6 4.5 10.0 2.6 6.1 16.1

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 2.8 6.2 16.247 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Bandelier NM (W) Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.0 5.7 15.7 Big Bend NP Annual 0.6 4.5 10.0 2.1 5.8 15.8
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.9 5.6 15.6

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 2.2 5.8 15.8 Black Canyon NP Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 1.6 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Bob Marshall W Annual 0.6 4.5 10.0 2.7 6.1 16.1
Winter 0.6 4.5 10.0 3.9 6.8 16.8 Spring 0.6 4.5 10.0 2.5 6.0 16.0

Summer 0.6 4.5 10.0 2.0 5.7 15.7
Fall 0.6 4.5 10.0 3.5 6.6 16.6 Bosque del Apache W Annual 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.2 5.8 15.8 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 1.8 5.6 15.6 Boundary Waters Annual 0.9 8.5 10.0 3.3 11.5 21.5

Canoe Area W Winter 0.9 8.5 10.0 3.6 11.7 21.7 Spring 0.9 8.5 10.0 2.6 10.8 20.8

Summer 0.9 8.5 10.0 3.4 11.6 21.6
Fall 0.9 8.5 10.0 3.9 12.0 22.0

Breton Island W Annu al 0.9 8.5 10.0 3.8 11.9 21.9
Winter 0.9 8.5 10.0 3.4 11.6 21.6 Spring 0.9 8.5 10.0 4.0 12.1 22.1

Summer 0.9 8.5 10.0 4.1 12.2 22.2
Fall 0.9 8.5 10.0 3.6 11.7 21.748 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Bridger W Annual 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 1.9 5.6 15.6

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.4 5.9 15.9 Brigantine W Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.8 11.0 21.0 Spring 0.9 8.5 10.0 2.9 11.1 21.1

Summer 0.9 8.5 10.0 3.4 11.5 21.5
Fall 0.9 8.5 10.0 3.0 11.2 21.2 Bryce Canyon NP Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 1.4 5.4 15.4

Summer 0.6 4.5 10.0 1.4 5.3 15.3
Fall 0.6 4.5 10.0 2.0 5.7 15.7

Cabinet Mountains W Annu al 0.6 4.5 10.0 2.7 6.1 16.1
Winter 0.6 4.5 10.0 4.2 7.0 17.0 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 1.9 5.7 15.7
Fall 0.6 4.5 10.0 3.8 6.8 16.8 Caney Creek W Annual 0.9 8.5 10.0 3.1 11.3 21.3
Winter 0.9 8.5 10.0 3.1 11.3 21.3 Spring 0.9 8.5 10.0 3.3 11.5 21.5

Summer 0.9 8.5 10.0 3.0 11.2 21.2
Fall 0.9 8.5 10.0 3.1 11.3 21.3 Canyonlands NP Annu al 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 3.2 6.4 16.4 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Cape Romain W Annual 0.9 8.5 10.0 3.3 11.5 21.5
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 3.3 11.4 21.4

Summer 0.9 8.5 10.0 3.9 12.0 22.0
Fall 0.9 8.5 10.0 3.3 11.5 21.549 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Capitol Reef NP Annu al 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 3.0 6.3 16.3 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Caribou W Annual 0.6 4.5 10.0 2.3 5.9 15.9
Winter 0.6 4.5 10.0 3.6 6.6 16.6 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.7 6.1 16.1

Carlsbad Caverns NP An nual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.9 5.6 15.6
Fall 0.6 4.5 10.0 1.8 5.6 15.6

Chassahowitzka W Annual 0.9 8.5 10.0 3.9 12.0 22.0
Winter 0.9 8.5 10.0 3.4 11.6 21.6 Spring 0.9 8.5 10.0 3.7 11.8 21.8

Summer 0.9 8.5 10.0 4.1 12.2 22.2
Fall 0.9 8.5 10.0 3.9 12.0 22.0 Chiricahua NM (W) Annual 0.6 4.5 10.0 1.6 5.4 15.4
Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.2 5.2 15.2

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.6 5.5 15.5

Chiricahua W Annual 0.6 4.5 10.0 1.5 5.4 15.4
Winter 0.6 4.5 10.0 2.1 5.7 15.7 Spring 0.6 4.5 10.0 1.2 5.2 15.2

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.6 5.5 15.5

Cohutta W Annual 0.9 8.5 10.0 3.2 11.4 21.4
Winter 0.9 8.5 10.0 3.0 11.2 21.2 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.6 11.7 21.7
Fall 0.9 8.5 10.0 3.3 11.4 21.450 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Crater Lake NP Annual 0.6 4.5 10.0 3.3 6.5 16.5
Winter 0.6 4.5 10.0 4.7 7.3 17.3 Spring 0.6 4.5 10.0 3.1 6.4 16.4

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 4.4 7.2 17.2

Craters of the Annual 0.6 4.5 10.0 2.2 5.8 15.8 Moon NM (W) Winter 0.6 4.5 10.0 3.8 6.8 16.8 Spring 0.6 4.5 10.0 1.9 5.7 15.7

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.8 6.2 16.2 Cucamonga W Annual 0.6 4.5 10.0 2.3 5.9 15.9
Winter 0.6 4.5 10.0 2.4 5.9 15.9 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Desolation W Annual 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 3.3 6.5 16.5 Spring 0.6 4.5 10.0 1.8 5.6 15.6

Summer 0.6 4.5 10.0 1.6 5.4 15.4
Fall 0.6 4.5 10.0 2.4 6.0 16.0

Diamond Peak W Annual 0.6 4.5 10.0 3.5 6.6 16.6
Winter 0.6 4.5 10.0 4.9 7.4 17.4 Spring 0.6 4.5 10.0 3.2 6.4 16.4

Summer 0.6 4.5 10.0 2.3 5.9 15.9
Fall 0.6 4.5 10.0 4.8 7.4 17.4 Dolly Sods W Annual 0.9 8.5 10.0 3.1 11.3 21.3
Winter 0.9 8.5 10.0 3.0 11.2 21.2 Spring 0.9 8.5 10.0 3.0 11.2 21.2

Summer 0.9 8.5 10.0 3.4 11.6 21.6
Fall 0.9 8.5 10.0 3.2 11.4 21.4

Dome Land W Annual 0.6 4.5 10.0 1.9 5.7 15.7
Winter 0.6 4.5 10.0 2.4 6.0 16.0 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.7 15.751 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Eagle Cap W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 4.3 7.1 17.1 Spring 0.6 4.5 10.0 2.2 5.8 15.8

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 3.5 6.6 16.6 Eagles Nest W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 1.9 5.6 15.6

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8 Emigrant W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.2 6.4 16.4 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Everglades NP Annual 0.9 8.5 10.0 3.9 12.0 22.0
Winter 0.9 8.5 10.0 3.6 11.8 21.8 Spring 0.9 8.5 10.0 3.7 11.9 21.9

Summer 0.9 8.5 10.0 3.8 12.0 22.0
Fall 0.9 8.5 10.0 4.0 12.1 22.1 Fitzpatrick W Annual 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 1.9 5.7 15.7

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.4 6.0 16.0 Flat Tops W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.6 6.1 16.1 Spring 0.6 4.5 10.0 1.8 5.6 15.6

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Galiuro W Annual 0.6 4.5 10.0 1.6 5.5 15.5
Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.2 5.2 15.2

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.7 5.5 15.552 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Gates of the Annual 0.6 4.5 10.0 2.4 5.9 15.9
Mountains W Winter 0.6 4.5 10.0 3.1 6.4 16.4 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.8 6.2 16.2

Gearhart Mountain W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 4.0 6.9 16.9 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 3.2 6.4 16.4

Gila W Annual 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.2 5.8 15.8 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 1.8 5.6 15.6

Glacier NP Annual 0.6 4.5 10.0 2.8 6.2 16.2
Winter 0.6 4.5 10.0 2.8 6.2 16.2 Spring 0.6 4.5 10.0 2.5 6.0 16.0

Summer 0.6 4.5 10.0 2.1 5.8 15.8
Fall 0.6 4.5 10.0 3.8 6.8 16.8

Glacier Peak W Annual 0.6 4.5 10.0 3.8 6.8 16.8
Winter 0.6 4.5 10.0 4.4 7.2 17.2 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.8 6.2 16.2
Fall 0.6 4.5 10.0 5.1 7.6 17.6

Goat Rocks W Annual 0.6 4.5 10.0 3.6 6.6 16.6
Winter 0.6 4.5 10.0 4.6 7.3 17.3 Spring 0.6 4.5 10.0 3.2 6.4 16.4

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 5.0 7.5 17.5 Grand Canyon NP Annual 0.6 4.5 10.0 1.6 5.5 15.5
Winter 0.6 4.5 10.0 2.4 5.9 15.9 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.4 5.3 15.3
Fall 0.6 4.5 10.0 1.7 5.5 15.553 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Grand Teton NP Annual 0.6 4.5 10.0 2.1 5.8 15.8
Winter 0.6 4.5 10.0 3.0 6.3 16.3 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.5 6.0 16.0

Great Gulf W Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.8 11.0 21.0 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.3 11.5 21.5
Fall 0.9 8.5 10.0 3.1 11.3 21.3

Great Sand Annual 0.6 4.5 10.0 2.0 5.7 15.7 Dunes NM (W) Wint er 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.2 5.8 15.8 Great Smoky Annual 0.9 8.5 10.0 3.2 11.4 21.4
Mountains NP Winter 0.9 8.5 10.0 3.0 11.2 21.2 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.6 11.8 21.8
Fall 0.9 8.5 10.0 3.2 11.4 21.4 Guadalupe Annual 0.6 4.5 10.0 1.8 5.6 15.6
Mountains NP Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.9 5.6 15.6
Fall 0.6 4.5 10.0 1.8 5.6 15.6 Hells Canyon W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 4.3 7.1 17.1 Spring 0.6 4.5 10.0 2.2 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 3.5 6.6 16.6

Hercules Glades W An nual 0.9 8.5 10.0 3.1 11.3 21.3
Winter 0.9 8.5 10.0 3.2 11.4 21.4 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.2 11.4 21.4
Fall 0.9 8.5 10.0 3.1 11.3 21.354 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Hoover W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.1 6.4 16.4 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Isle Royale NP Annu al 0.9 8.5 10.0 3.5 11.6 21.6
Winter 0.9 8.5 10.0 3.4 11.6 21.6 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.6 11.7 21.7
Fall 0.9 8.5 10.0 4.1 12.2 22.2

James River Face W Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 3.0 11.2 21.2

Summer 0.9 8.5 10.0 3.5 11.7 21.7
Fall 0.9 8.5 10.0 3.0 11.2 21.2 Jarbidge W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.5 6.6 16.6 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.3 5.3 15.3
Fall 0.6 4.5 10.0 2.4 6.0 16.0

John Muir W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.2 5.8 15.8 Joshua Tree NM (W) Annual 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 2.0 5.7 15.7
Fall 0.6 4.5 10.0 2.0 5.7 15.7 Joyce Kilmer Annual 0.9 8.5 10.0 3.2 11.4 21.4
Slickrock W Winter 0.9 8.5 10.0 3.0 11.2 21.2 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.6 11.7 21.7
Fall 0.9 8.5 10.0 3.2 11.4 21.455 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Kaiser W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.0 6.3 16.3 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.3 5.9 15.9 Kalmiopsis W Annual 0.6 4.5 10.0 3.4 6.5 16.5
Winter 0.6 4.5 10.0 4.7 7.3 17.3 Spring 0.6 4.5 10.0 3.2 6.4 16.4

Summer 0.6 4.5 10.0 2.3 5.9 15.9
Fall 0.6 4.5 10.0 4.4 7.2 17.2 Kings Canyon NP Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.8 6.2 16.2 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8

La Garita W p 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 1.6 5.5 15.5

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Lassen Volcanic NP An nual 0.6 4.5 10.0 2.3 5.9 15.9
Winter 0.6 4.5 10.0 3.6 6.7 16.7 Spring 0.6 4.5 10.0 2.1 5.7 15.7

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.7 6.1 16.1 Lava Beds NM (W) Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 3.9 6.9 16.9 Spring 0.6 4.5 10.0 2.2 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 3.1 6.4 16.4 Linville Gorge W Annual 0.9 8.5 10.0 3.2 11.3 21.3
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.7 11.8 21.8
Fall 0.9 8.5 10.0 3.1 11.3 21.356 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Lostwood W Annual 0.6 4.5 10.0 2.9 6.2 16.2
Winter 0.6 4.5 10.0 3.2 6.4 16.4 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 2.3 5.9 15.9
Fall 0.6 4.5 10.0 3.5 6.6 16.6 Lye Brook W Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.3 11.4 21.4
Fall 0.9 8.5 10.0 3.1 11.3 21.3

Mammoth Cave NP Annual 0.9 8.5 10.0 3.2 11.4 21.4
Winter 0.9 8.5 10.0 3.3 11.5 21.5 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.4 11.6 21.6
Fall 0.9 8.5 10.0 3.3 11.5 21.5

Marble Mountain W Annu al 0.6 4.5 10.0 3.1 6.3 16.3
Winter 0.6 4.5 10.0 4.4 7.1 17.1 Spring 0.6 4.5 10.0 2.9 6.2 16.2

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 3.8 6.8 16.8

Maroon Bells Annual 0.6 4.5 10.0 1.9 5.7 15.7

Snowmass W Winter 0.6 4.5 10.0 2.6 6.0 16.0 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.8 15.8

Mazatzal W Annual 0.6 4.5 10.0 1.6 5.5 15.5
Winter 0.6 4.5 10.0 2.2 5.8 15.8 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.7 5.5 15.5

Medicine Lake W Annual 0.6 4.5 10.0 2.8 6.2 16.2
Winter 0.6 4.5 10.0 3.2 6.4 16.4 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 3.5 6.6 16.657 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Mesa Verde NP Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.6 6.1 16.1 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.8 15.8 Mingo W Annual 0.9 8.5 10.0 3.2 11.3 21.3
Winter 0.9 8.5 10.0 3.3 11.4 21.4 Spring 0.9 8.5 10.0 3.0 11.2 21.2

Summer 0.9 8.5 10.0 3.3 11.4 21.4
Fall 0.9 8.5 10.0 3.2 11.4 21.4

Mission Mountains W Annual 0.6 4.5 10.0 2.7 6.1 16.1
Winter 0.6 4.5 10.0 4.0 6.9 16.9 Spring 0.6 4.5 10.0 2.5 6.0 16.0

Summer 0.6 4.5 10.0 2.0 5.7 15.7
Fall 0.6 4.5 10.0 3.6 6.7 16.7

Mokelumne W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.3 6.5 16.5 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.4 5.9 15.9

Moosehorn W Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.9 11.2 21.2 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.2 11.4 21.4
Fall 0.9 8.5 10.0 3.2 11.4 21.4

Mount Adams W Annual 0.6 4.5 10.0 3.6 6.7 16.7
Winter 0.6 4.5 10.0 4.7 7.3 17.3 Spring 0.6 4.5 10.0 3.2 6.4 16.4

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 5.0 7.5 17.5 Mount Baldy W Annual 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 1.8 5.6 15.658 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Mount Hood W Annual 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 4.7 7.3 17.3 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.6 6.1 16.1
Fall 0.6 4.5 10.0 5.2 7.6 17.6

Mount Jefferson W Annual 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 4.8 7.4 17.4 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 5.2 7.6 17.6

Mount Rainier NP Annual 0.6 4.5 10.0 3.8 6.8 16.8
Winter 0.6 4.5 10.0 4.6 7.3 17.3 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.7 6.1 16.1
Fall 0.6 4.5 10.0 5.2 7.6 17.6 Mount Washington W Annu al 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 4.8 7.4 17.4 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 5.1 7.6 17.6

Mount Zirkel W Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 1.9 5.6 15.6

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Mountain Lakes W Annual 0.6 4.5 10.0 2.8 6.2 16.2
Winter 0.6 4.5 10.0 4.4 7.2 17.2 Spring 0.6 4.5 10.0 2.6 6.1 16.1

Summer 0.6 4.5 10.0 1.9 5.7 15.7
Fall 0.6 4.5 10.0 3.8 6.8 16.8

North Absoraka W Annual 0.6 4.5 10.0 2.2 5.8 15.8
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 2.1 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.5 6.0 16.059 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

North Cascades NP Annu al 0.6 4.5 10.0 3.8 6.8 16.8
Winter 0.6 4.5 10.0 4.2 7.0 17.0 Spring 0.6 4.5 10.0 3.4 6.5 16.5

Summer 0.6 4.5 10.0 2.8 6.2 16.2
Fall 0.6 4.5 10.0 5.1 7.6 17.6

Okefenokee W Annual 0.9 8.5 10.0 3.5 11.7 21.7
Winter 0.9 8.5 10.0 3.2 11.3 21.3 Spring 0.9 8.5 10.0 3.4 11.5 21.5

Summer 0.9 8.5 10.0 3.9 12.0 22.0
Fall 0.9 8.5 10.0 3.6 11.7 21.7 Olympic NP Annual 0.6 4.5 10.0 4.5 7.2 17.2
Winter 0.6 4.5 10.0 4.4 7.2 17.2 Spring 0.6 4.5 10.0 4.1 7.0 17.0

Summer 0.6 4.5 10.0 3.4 6.5 16.5
Fall 0.6 4.5 10.0 5.8 8.0 18.0

Otter Creek W Annual 0.9 8.5 10.0 3.2 11.3 21.3
Winter 0.9 8.5 10.0 3.0 11.2 21.2 Spring 0.9 8.5 10.0 3.0 11.2 21.2

Summer 0.9 8.5 10.0 3.5 11.6 21.6
Fall 0.9 8.5 10.0 3.2 11.4 21.4 Pasayten W Annual 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 3.8 6.8 16.8 Spring 0.6 4.5 10.0 3.3 6.5 16.5

Summer 0.6 4.5 10.0 2.7 6.1 16.1
Fall 0.6 4.5 10.0 5.0 7.5 17.5

Pecos W Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.0 5.7 15.7

Petrified Forest NP Annual 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 1.9 5.6 15.660 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Pine Mountain W Annual 0.6 4.5 10.0 1.6 5.5 15.5
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.7 5.5 15.5 Pinnacles NM (W) Annual 0.6 4.5 10.0 2.4 5.9 15.9
Winter 0.6 4.5 10.0 3.4 6.5 16.5 Spring 0.6 4.5 10.0 2.1 5.8 15.8

Summer 0.6 4.5 10.0 2.0 5.7 15.7
Fall 0.6 4.5 10.0 2.7 6.1 16.1 Point Reyes NS (W) Annual 0.6 4.5 10.0 3.0 6.3 16.3
Winter 0.6 4.5 10.0 3.9 6.9 16.9 Spring 0.6 4.5 10.0 2.7 6.1 16.1

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 3.1 6.4 16.4 Presidential Range Annual 0.9 8.5 10.0 3.0 11.2 21.2 Dry River W Winter 0.9 8.5 10.0 2.8 11.0 21.0 Spring 0.9 8.5 10.0 2.8 11.0 21.0

Summer 0.9 8.5 10.0 3.3 11.5 21.5
Fall 0.9 8.5 10.0 3.1 11.3 21.3

Rawah W Annual 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.2 5.8 15.8

Red Rock Lakes W Annual 0.6 4.5 10.0 2.2 5.8 15.8
Winter 0.6 4.5 10.0 3.2 6.4 16.4 Spring 0.6 4.5 10.0 2.1 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.7 6.1 16.1

Redwood NP Annual 0.6 4.5 10.0 4.2 7.0 17.0
Winter 0.6 4.5 10.0 4.6 7.3 17.3 Spring 0.6 4.5 10.0 4.3 7.1 17.1

Summer 0.6 4.5 10.0 3.2 6.4 16.4
Fall 0.6 4.5 10.0 4.6 7.3 17.361 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Rocky Mountain NP Annu al 0.6 4.5 10.0 2.1 5.7 15.7
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.2 5.8 15.8 Saguaro NP Annual 0.6 4.5 10.0 1.6 5.4 15.4
Winter 0.6 4.5 10.0 2.1 5.7 15.7 Spring 0.6 4.5 10.0 1.2 5.2 15.2

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.6 5.5 15.5

Salt Creek W Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.9 5.6 15.6
Fall 0.6 4.5 10.0 1.8 5.6 15.6

San Gabriel W Annual 0.6 4.5 10.0 2.4 5.9 15.9
Winter 0.6 4.5 10.0 2.4 5.9 15.9 Spring 0.6 4.5 10.0 2.4 6.0 16.0

Summer 0.6 4.5 10.0 2.4 5.9 15.9
Fall 0.6 4.5 10.0 2.2 5.8 15.8 San Gorgonio W Annual 0.6 4.5 10.0 2.2 5.8 15.8
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 2.2 5.8 15.8

Summer 0.6 4.5 10.0 2.1 5.8 15.8
Fall 0.6 4.5 10.0 2.1 5.8 15.8

San Jacinto W Annual 0.6 4.5 10.0 2.3 5.9 15.9
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 2.2 5.8 15.8
Fall 0.6 4.5 10.0 2.1 5.8 15.8

San Pedro Parks W Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.4 6.0 16.0 Spring 0.6 4.5 10.0 1.5 5.4 15.4

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.0 5.7 15.762 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

San Rafael W Annual 0.6 4.5 10.0 2.6 6.1 16.1
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 2.6 6.0 16.0

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 2.5 6.0 16.0

Sawtooth W Annual 0.6 4.5 10.0 2.2 5.8 15.8
Winter 0.6 4.5 10.0 4.0 6.9 16.9 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.9 6.2 16.2 Scapegoat W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 3.5 6.6 16.6 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 1.9 5.6 15.6
Fall 0.6 4.5 10.0 3.1 6.3 16.3 Selway – Bitterroot W An nual 0.6 4.5 10.0 2.6 6.0 16.0
Winter 0.6 4.5 10.0 4.0 6.9 16.9 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 3.4 6.5 16.5 Seney W Annual 0.9 8.5 10.0 3.6 11.8 21.8
Winter 0.9 8.5 10.0 3.7 11.8 21.8 Spring 0.9 8.5 10.0 2.9 11.1 21.1

Summer 0.9 8.5 10.0 3.6 11.7 21.7
Fall 0.9 8.5 10.0 4.2 12.3 22.3 Sequoia NP Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 1.8 5.6 15.6

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.8 15.8

Shenandoah NP Annual 0.9 8.5 10.0 3.0 11.2 21.2
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 2.9 11.1 21.1

Summer 0.9 8.5 10.0 3.4 11.6 21.6
Fall 0.9 8.5 10.0 3.1 11.3 21.363 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Shining Rock W Annual 0.9 8.5 10.0 3.2 11.4 21.4
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.7 11.9 21.9
Fall 0.9 8.5 10.0 3.2 11.3 21.3

Sierra Ancha W Annual 0.6 4.5 10.0 1.6 5.5 15.5
Winter 0.6 4.5 10.0 2.2 5.8 15.8 Spring 0.6 4.5 10.0 1.3 5.3 15.3

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.7 5.5 15.5 Sipsey W Annual 0.9 8.5 10.0 3.3 11.5 21.5
Winter 0.9 8.5 10.0 3.2 11.4 21.4 Spring 0.9 8.5 10.0 3.3 11.4 21.4

Summer 0.9 8.5 10.0 3.5 11.7 21.7
Fall 0.9 8.5 10.0 3.3 11.5 21.5

South Warner W Annual 0.6 4.5 10.0 2.1 5.8 15.8
Winter 0.6 4.5 10.0 3.6 6.7 16.7 Spring 0.6 4.5 10.0 1.9 5.6 15.6

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.6 6.1 16.1

St. Marks W Annual 0.9 8.5 10.0 3.6 11.8 21.8
Winter 0.9 8.5 10.0 3.2 11.4 21.4 Spring 0.9 8.5 10.0 3.5 11.7 21.7

Summer 0.9 8.5 10.0 4.0 12.1 22.1
Fall 0.9 8.5 10.0 3.6 11.8 21.8 Strawberry Mountain

W Annual 0.6 4.5 10.0 2.6 6.1 16.1

Winter 0.6 4.5 10.0 4.4 7.1 17.1 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 3.7 6.7 16.7 Superstition W Annual 0.6 4.5 10.0 1.6 5.4 15.4
Winter 0.6 4.5 10.0 2.1 5.8 15.8 Spring 0.6 4.5 10.0 1.2 5.2 15.2

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 1.6 5.5 15.564 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Swanquarter W Annual 0.9 8.5 10.0 3.3 11.5 21.5
Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 3.3 11.4 21.4

Summer 0.9 8.5 10.0 3.8 11.9 21.9
Fall 0.9 8.5 10.0 3.2 11.4 21.4 Sycamore Canyon W Annu al 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.3 5.9 15.9 Spring 0.6 4.5 10.0 1.4 5.3 15.3

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 1.8 5.6 15.6

Teton W Annual 0.6 4.5 10.0 2.1 5.8 15.8
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.5 6.0 16.0

Theodore Roosevelt

NP Annual 0.6 4.5 10.0 2.7 6.1 16.1

Winter 0.6 4.5 10.0 3.7 6.7 16.7 Spring 0.6 4.5 10.0 2.5 6.0 16.0

Summer 0.6 4.5 10.0 2.1 5.8 15.8
Fall 0.6 4.5 10.0 3.2 6.4 16.4

Thousand Lakes W Annual 0.6 4.5 10.0 2.3 5.9 15.9
Winter 0.6 4.5 10.0 3.7 6.7 16.7 Spring 0.6 4.5 10.0 2.1 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.8 6.2 16.2

Three Sisters W Annual 0.6 4.5 10.0 3.7 6.7 16.7
Winter 0.6 4.5 10.0 4.9 7.4 17.4 Spring 0.6 4.5 10.0 3.4 6.6 16.6

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 5.1 7.6 17.6

U.L. Bend W Annual 0.6 4.5 10.0 2.4 5.9 15.9
Winter 0.6 4.5 10.0 3.3 6.5 16.5 Spring 0.6 4.5 10.0 2.3 5.9 15.9

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.8 6.2 16.265 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Upper Buffalo W Annual 0.9 8.5 10.0 3.1 11.3 21.3
Winter 0.9 8.5 10.0 3.2 11.4 21.4 Spring 0.9 8.5 10.0 3.1 11.3 21.3

Summer 0.9 8.5 10.0 3.2 11.4 21.4
Fall 0.9 8.5 10.0 3.2 11.3 21.3

Ventana W Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 3.4 6.5 16.5 Spring 0.6 4.5 10.0 2.2 5.8 15.8

Summer 0.6 4.5 10.0 2.1 5.8 15.8
Fall 0.6 4.5 10.0 2.7 6.1 16.1 Voyageurs NP Annual 0.9 8.5 10.0 3.4 11.5 21.5
Winter 0.9 8.5 10.0 3.7 11.8 21.8 Spring 0.9 8.5 10.0 2.6 10.8 20.8

Summer 0.9 8.5 10.0 3.4 11.6 21.6
Fall 0.9 8.5 10.0 4.1 12.2 22.2

Washakie W Annual 0.6 4.5 10.0 2.1 5.8 15.8
Winter 0.6 4.5 10.0 2.8 6.2 16.2 Spring 0.6 4.5 10.0 2.0 5.7 15.7

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.5 6.0 16.0

Weminuche W Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.5 6.0 16.0 Spring 0.6 4.5 10.0 1.6 5.4 15.4

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.8 15.8

West Elk W Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.6 6.1 16.1 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.6 5.5 15.5
Fall 0.6 4.5 10.0 2.1 5.8 15.8

Wheeler Peak W Annual 0.6 4.5 10.0 1.9 5.6 15.6
Winter 0.6 4.5 10.0 2.4 5.9 15.9 Spring 0.6 4.5 10.0 1.6 5.5 15.5

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 2.1 5.8 15.866 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

White Mountain W Annual 0.6 4.5 10.0 1.8 5.6 15.6
Winter 0.6 4.5 10.0 2.2 5.8 15.8 Spring 0.6 4.5 10.0 1.4 5.4 15.4

Summer 0.6 4.5 10.0 1.8 5.6 15.6
Fall 0.6 4.5 10.0 1.8 5.6 15.6

Wichita Mountain W Annual 0.6 4.5 10.0 2.6 6.1 16.1
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 2.8 6.2 16.2

Summer 0.6 4.5 10.0 2.5 6.0 16.0
Fall 0.6 4.5 10.0 2.6 6.1 16.1

Wind Cave NP Annual 0.6 4.5 10.0 2.5 6.0 16.0
Winter 0.6 4.5 10.0 2.9 6.2 16.2 Spring 0.6 4.5 10.0 2.5 6.0 16.0

Summer 0.6 4.5 10.0 2.1 5.7 15.7
Fall 0.6 4.5 10.0 2.6 6.1 16.1

Wolf Island W Annual 0.9 8.5 10.0 3.5 11.6 21.6
Winter 0.9 8.5 10.0 3.1 11.3 21.3 Spring 0.9 8.5 10.0 3.3 11.5 21.5

Summer 0.9 8.5 10.0 3.9 12.0 22.0
Fall 0.9 8.5 10.0 3.6 11.7 21.7

Yellowstone NP Annual 0.6 4.5 10.0 2.2 5.8 15.8
Winter 0.6 4.5 10.0 3.0 6.3 16.3 Spring 0.6 4.5 10.0 2.1 5.8 15.8

Summer 0.6 4.5 10.0 1.7 5.5 15.5
Fall 0.6 4.5 10.0 2.5 6.0 16.0 Yolla Bolly – Annual 0.6 4.5 10.0 2.6 6.1 16.1
Middle Eel W Winter 0.6 4.5 10.0 3.8 6.8 16.8 Spring 0.6 4.5 10.0 2.4 5.9 15.9

Summer 0.6 4.5 10.0 2.0 5.7 15.7
Fall 0.6 4.5 10.0 3.0 6.3 16.3

Yosemite NP Annual 0.6 4.5 10.0 2.0 5.7 15.7
Winter 0.6 4.5 10.0 3.1 6.4 16.4 Spring 0.6 4.5 10.0 1.7 5.5 15.5

Summer 0.6 4.5 10.0 1.5 5.4 15.4
Fall 0.6 4.5 10.0 2.3 5.9 15.967 Site Season Components of Dr y Extinction (Mm -1 ) f(RH) Particle

bext w/f(RH) (Mm -1 ) Reference

Level (Mm -1 )

Hygro Non Hygro Rayleigh

Zion NP Annual 0.6 4.5 10.0 1.7 5.5 15.5
Winter 0.6 4.5 10.0 2.7 6.1 16.1 Spring 0.6 4.5 10.0 1.4 5.3 15.3

Summer 0.6 4.5 10.0 1.3 5.3 15.3
Fall 0.6 4.5 10.0 1.9 5.6 15.6

Seasonal RH data not available; contact FLM Bering Sea W 0.6 4.5 10.0 Contact the FLM
Denali NP 0.6 4.5 10.0 Contact the FLM
Haleakala NP 0.6 4.5 10.0 Contact the FLM
Hawaii Volcanoes NP 0.6 4.5 10.0 Contact the FLM Roosevelt Campobello IP 0. 9 8.5 10.0 Contact the FLM
Simeonof W 0.6 4.5 10.0 Contact the FLM
Tuxedni W 0.6 4.5 10.0 Contact the FLM Virgin Islands NP 0.9 8.5 10.0 Contact the FLM

Not included:

Rainbow Lakes Bradbury Bay68 Table 2.B-2 Estimated Annual Average Na tural Background Le vels of Aerosols Annual Average Concentration Particulate Aerosol Component East ( μ g/m 3 ) West ( μ g/m 3 )

Sulfates (as ammonium sulfate) 0.2 0.1

Ammonium Nitrate 0.1 0.1 Organics 1.5 0.5

Elemental Carbon 0.02 0.02

Soil Dust 0.5 0.5

Coarse 3.0 3.0

Taken from NAPAP 1990. We st refers to those States beyond the first tier of States west of the Mississippi River.69 Appendix 2.C Example Problem

Example applications for coherent plumes that ar e viewed against a scenic background are provided
in the Workbook for Plume Visual Im pact Screening and Analysis (USEPS, 1992a), so no specific example needs to be supplied here. The analysis of a plume or aggregation of plumes that affects the appearance of a scene does involve some new concep ts, so an example application is being provided. The example is given for two case s, first for a general model app lication where a visibility post processor is not available, and a second case for the CALMET/CALP UFF/CALPOST modeling
system.

General Model Application

For the purposes of general application, let us assume that a dispersion model has been run and yielded daily (24-hour) concentrations of SO 4 = (sulfate) and soot (eleme ntal carbon). From these concentrations the analyst can calculate a change in extinction from some specified reference level using the procedures given in Appendix 2.A. The first step is to calculate the visibility reference
level for the Class I area of interest from the information provided in A ppendix 2.B. Then, one
calculates the new source’s contri bution to extinction and the expect ed change in extinction from the reference level. This example will only ad dress one 24-hour time period. The calculation would, of course, have to be repeated for the other 24-hour time periods as well as, accounting for
the seasonal differences.

Calculation of the Reference Level

The determination of the reference level for a si ngle 24-hour period in January visibility condition can be made by examining the example table below (f or an actual case, the applicant would turn to Appendix 2.B). While the reference extinction fo r Acadia NP is provided in the table (21.1Mm -1 ), it is useful to go through the calculation to see how to apply the different numbers in the table.

Site Season Components of Dry Extinction (Mm -1 ) Particle b ext w/f(RH) (Mm -1 ) Reference Level (Mm -1 ) Hygro Non Hygro Rayleigh f(RH)

b ref Acadia
NP Annual 0.9 8.5 10.0 3.0 11.2 21.2

Winter 0.9 8.5 10.0 2.9 11.1 21.1 Spring 0.9 8.5 10.0 2.8 11.0 21.0
Summer 0.9 8.5 10.0 3.2 11.4 21.4
Fall 0.9 8.5 10.0 3.1 11.3 21.3

The reference extinction (b ref ), expressed in the form of E quation 6 (Appendix 2.A) would be
b ref = 0.9 f(RH) + 8.5 + 10.0 (see Equa tion 6, Appendix 2.A). The f(RH) term in the example table (for January) is 2.9, yielding an extinction coefficient of 21.1 Mm -1 . If one were using site specific, hourly relative humidity data, one would have to calculate the average f(RH) for that 24-hour period. To do this, one needs to look up the f(RH) value corresponding to each hour’s relative humidity in Table 2.A-1 (Appendix 2.A) and take the average of those f(RH) values. One can not take the average of the relative humidity and look up the f(RH) in the table; that would yield an70 incorrect result. (Similarly, the f(RH) values sh own in the example table and in Appendix 2.B are generated using annual and season al average relative humidity es timates and the empirical curve for f(RH) given in IMPROVE 2000. Annual and seasonal averages of f(RH) do not directly correspond to the relative humidity values in Table 2.A-1.)

Calculation of Singl e-Source Contribution

In a typical modeling analysis, IWAQM recommends, and the FLMs endorse, the use of five years of meteorological data. This will produce a co rresponding number of block 24-hour averaging periods, which will each need to be compared with the reference condition.

Again for purposes of illustration we will only s how the calculation of extinction for one modeled 24-hour period in January. This calculation would th en have to be repeated for all other 24-hour periods. For this example we will assume that the sources in the analysis contributed 0.3 μ g/m 3 of sulfate (SO 4 = ) and 0.10 μ g/m 3 of soot (elemental carbon), 24-hour average. The first step is to
convert the mass of SO 4 = to ammonium sulfate ((NH 4 ) 2 SO 4 ), which is accomplished by multiplying by the ratio of the molecular weights of (NH 4 ) 2 SO 4 to SO 4 = , which is 1.375. This yields a concentration of (NH 4 ) 2 SO 4 of 0.4 μ g/m 3 . This is then multiplied by the “dry” scattering efficiency of (NH 4 ) 2 SO 4 (which is 3, from Appendix 2.A, Equati on 3), yielding an extinction coefficient for
the sulfate of 1.2 Mm -1 ; the relative humidity adjustment has not yet been applied.

In this example our mode ling does not require any conversion of the mass of soot, so we will just multiply the soot concentration (0.10 μ g/m 3 ) by the extinction efficiency of elemental carbon (which is 10, from Appendix 2.A, Equation 4). This yields an extinction coefficient of 1.0 Mm -1

. Therefore, following the form of Equations 3 an d 5 (Appendix 2.A), the source contribution would
be:



### b

source 

### = 1.2 f(RH) + 1.0

A representative relative humidity adjustment term, f( RH), must be applied. It is important that the same adjustment be made to both the source contri bution to extinction and the reference level. For a screening level analysis, the relative humidity ad justment factors listed in Appendix 2.B can be applied to the source contributions . For example, if we are analyzing for Acadia NP, the average winter f(RH) is 2.9. With the winter quarterly average relative humidity adjustment factor (f(RH)) of 2.9, b
source would be 4.5 Mm -1

.

Calculation of the Change in Extinction

The resulting percent change in extinction is found from:



### ∆ b ext 

### = (b

source 

### /b ref 

### ) × 100%

For the example here, b
source = 4.5 Mm -1 and b ref = 22.1 Mm -1 , yielding ∆ b ext = 20%. This calculation must be repeated for each 24-hour av eraging period. To portray the frequency, magnitude, and geographic extent of expected impair ment, this calculation w ill have to be repeated for all days and many receptors in the modeling domain. FLAG expects a robust selection of model receptor locations in the Class I ar ea be included in the analyses, i.e. , one receptor representing the entire area, or just the nearest boundary, will not be sufficient.71 Example using the CALMET/CALPUFF/CALPOST modeling system

For the refined analysis, it is necessary to calculate the change in extinction for the relative humidity conditions on a specific day. To accomplis h this, the representative, hourly RH values for this day need to be obtained. For each hour, th e corresponding f(RH) must be obtained from Table 2.A-1. These f(RH) values are then averaged t ogether. These calculati ons would have to be repeated for each 24-hour average concentration, at each receptor, in the analysis, using the corresponding average f(RH), and be applied to bot h the aerosol data in Ta ble 2.B-1 (to determine the reference level) and the sour ce contribution to extinction.

In the case of a CALPUFF application, the post-pr ocessor, CALPOST, has been set up to directly calculate the combined visibility effects from different visibility impairing pollutants. Refer to Figure 2.C-1 for an example of the visibility parameters to set in the CALPOST input file. Most of the parameters set in CALPOST are application sp ecific. The pollutants in the example are sulfate and soot (elemental carbon). CALPOST allows for the specification of sulfate (SO4), but not
elemental carbon in the sour ce portion of the visibility calculation. (This s hould be rectified in the next update of the modeling system .) Therefore elemental carbon will be modeled as PM fine (PMF). In CALPOST the variable s LVSO4 and LVPMF are set to tr ue. Since elemental carbon is being modeled as PMF, the extinction efficiency fo r PMF must be set to that for elemental carbon (EEPMF = 10.0). FLAG is recommending that the de fault f(RH) values in Appendix 2.B be used for screening analyses for each Class I area. Therefore, MVISBK would be set to 6. When MVISBK is set to 6, RHFAC would be set to the f(RH) value in A ppendix 2.B or in this example, it would be set to 2.9 for the winter months (Dec, Jan, Feb). CALPOST does not explicitly allow for the input of the hygroscopic a nd non-hygroscopic components to exti nction at this time; it only allows for the input of the concentrations of pa rticulate species. To properly input the components to extinction, the hygroscopic component of extincti on is divided by 3 (the extinction efficiency of sulfate and nitrate) and is input to the vari able BKSO4. In this example the hygroscopic component of extinction is 0.9; after dividing by 3 we get a valu e of 0.3, which is input to CALPOST (BKSO4 = 0.3). For the non-hygroscopic component, enter its valu e into the variable BKSOIL (BKSOIL = 8.5) (also make sure that EESOIL is set to 1.0). All other background concentrations must be set to zero (0.0). Fi nally, a value for the extinction due to Rayleigh scattering must be entered (BEXTRAY = 10.0).

If MVISBK=2 (hour-by-hour calculation of f(RH)) in the above example, the hour-by-hour relative humidity values from CALMET would be used to calculate the 24-hour aver age extinction, rather than the long-term average f(RH) values supplied in Appendix 2.B. The only modification to the input would be that the value of RHMAX (set to 98.0 in the example) would be used to cap the maximum f(RH) used in the averages, and the value of RHFAC would not be us ed. A “vis.dat” file (not shown in Figure 2.C-1) would also be specified . It must be generated when CALPUFF is run.72 Figure 2.C-1. Segment of CALPOST input file corresponding to example problem.

---------------------------------------------------------------------------

INPUT GROUP: 2 -- Visibility Parameters (ASPEC = VISIB)

--------------

Maximum relative humidity (%) used in particle growth curve
(RHMAX) -- Default: 98 ! RHMAX = 98.0 !

Modeled species to be included in computing the light extinction
Include SULFATE? (LVSO4) -- Default: T ! LVSO4 = T !
Include NITRATE? (LVNO3) -- Default: T ! LVNO3 = F !
Include ORGANIC CARBON? (LVOC) -- Default: T ! LVOC = F !
Include COARSE PARTICLES? (LVPMC) -- Default: T ! LVPMC = F !
Include FINE PARTICLES? (LVPMF) -- Default: T ! LVPMF = T !

And, when ranking for TOP-N, TOP-50, and Exceedance tables,
Include BACKGROUND? (LVBK) -- Default: T ! LVBK = T !

Species name used for particulates in MODEL.DAT file
COARSE (SPECPMC) -- Default: PMC ! SPECPMC = PMC !
FINE (SPECPMF) -- Default: PMF ! SPECPMF = PMF !

Extinction Efficiency (1/Mm per ug/m**3)

---------------------------------------- MODELED particulate species:
PM COARSE (EEPMC) -- Default: 0.6 ! EEPMC = 0.6 !
PM FINE (EEPMF) -- Default: 1.0 ! EEPMF = 10.0 ! BACKGROUND particulate species:
PM COARSE (EEPMCBK) -- Default: 0.6 ! EEPMCBK = 0.6 ! Other species:
AMMONIUM SULFATE (EESO4) -- Default: 3.0 ! EESO4 = 3.0 !
AMMONIUM NITRATE (EENO3) -- Default: 3.0 ! EENO3 = 3.0 !
ORGANIC CARBON (EEOC) -- Default: 4.0 ! EEOC = 4.0 !
SOIL (EESOIL)-- Default: 1.0 ! EESOIL = 1.0 !
ELEMENTAL CARBON (EEEC) -- Default: 10. ! EEEC = 10.0 ! Set RHMAX = 98.0 (for MVISBK=2)

Set the flags for the pollu tants modeled. In the example problem SO4 and EC (Modeled as PMF)

Note: in the example problem, the source’s elemental carbon (EC) is modeled as PMFINE (PMF). To calculate the extinction for EC

as PMF the variable EEPMF needs to be reset to 10.073 Figure 2.C-1 (Cont). Segment of CALPOST in put file corresponding to example problem.

Background Extinction Computation

---------------------------------

Method used for background light extinction
(MVISBK) -- Default: 6 ! MVISBK = 2 !

1 = Supply single light extinction and hygroscopic fraction IWAQM (1993) RH adjustment applied to hygroscopic background

and modeled sulfate and nitrate 2 = Compute extinction from speciated PM measurements (A) - Hourly RH adjustment applied to observed and modeled

sulfateand nitrate - RH factor is capped at RHMAX 3 = Compute extinction from speciated PM measurements (B) - Hourly RH adjustment applied to observed and modeled

sulfate and nitrate - Receptor-hour excluded if RH>RHMAX - Receptor-day excluded if fewer than 6 valid receptor-hours 4 = Read hourly transmissometer background extinction

measurements - Hourly RH adjustment applied to modeled sulfate and

nitrate - Hour excluded if measurement invalid (missing, interference, or large RH) - Receptor-hour excluded if RH>RHMAX - Receptor-day excluded if fewer than 6 valid receptor-hours 5 = Read hourly nephelometer background extinction measurements - Rayleigh extinction value (BEXTRAY) added to measurement - Hourly RH adjustment applied to modeled sulfate and

nitrate - Hour excluded if measurement invalid (missing, interference, or large RH) - Receptor-hour excluded if RH>RHMAX - Receptor-day excluded if fewer than 6 valid receptor-hours 6 = Compute extinction from speciated PM measurements - FLAG RH adjustment factor applied to observed and

modeled sulfate and nitrate For Screening Analysis MVISBK = 6
Set RHFAC below

For Refined Analysis use MVISBK = 2

Specify a “vis.dat” file in CALPUFF74 Figure 2.C-1 (Cont). Segmen t of CALPOST input file corr esponding to example problem.

Additional inputs used for MVISBK = 6:

-------------------------------------- Extinction coefficients for hygroscopic species (modeled and background) are computed using a monthly RH adjustment factor in place of an hourly RH factor (VISB.DAT file is NOT needed). Enter the 12 monthly factors here (RHFAC). Month 1 is January.

(RHFAC) -- No default ! RHFAC = 2.9, 2.9, 2.8, 2.8, 2.8, 3.2, 3.2, 3.2, 3.1, 3.1, 3.1, 2.9 !

Additional inputs used for MVISBK = 2,3,6:

---------------------------------------- Background extinction coefficients are computed from monthly CONCENTRATIONS of ammonium sulfate (BKSO4), ammonium nitrate (BKNO3), coarse particulates (BKPMC), organic carbon (BKOC), soil (BKSOIL),

and elemental carbon (BKEC). Month 1 is January. (ug/m**3)

(BKSO4) -- No default ! BKSO4 = 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3 ! (BKNO3) -- No default ! BKNO3 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ! (BKPMC) -- No default ! BKPMC = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ! (BKOC) -- No default ! BKOC = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ! (BKSOIL) -- No default ! BKSOIL= 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5 ! (BKEC) -- No default ! BKEC = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 !

Additional inputs used for MVISBK = 2,3,5,6:

------------------------------------------ Extinction due to Rayleigh scattering is added (1/Mm)
(BEXTRAY) -- Default: 10.0 ! BEXTRAY = 10.0 ! For screening analysis set monthly f(RH) values from Appendix 2.B

Background extinction values (Appendix 2.B) set to: BKSO4 = Hygroscopic / 3 BKSOIL = Non-Hygroscopic BKNO3, BKPMC, B KOC, BKEC = 0.075 3. OZONE

a. Introduction

Ozone is a toxic air pollutant th at is formed on warm, sunny da ys when its precursors nitrogen oxides (NO

x ) and volatile organic compounds (VOC) react in the presence of sunlight. Because ozone is a regional pollutant, precursor sources both near and far from FLM areas can contribute to
ozone formation.

High ozone exposure can harm human health (U.S. EPA, 1996). Ozone is also phytotoxic, causing considerable damage to vegetation throughout the worl d. Some plant species are more sensitive to ozone than are humans (U.S. EPA, 1996). The pr imary National Ambient Air Quality Standard (NAAQS) for ozone is designed to protect huma n health, and the secondary NAAQS is set to achieve protection of the public welfare, in cluding vegetation. The primary and secondary
standards for ozone are the same. The new 8-hour , 0.08 ppm NAAQS for ozone is expected to be more protective of vegetation than the 1-hour, 0.12 ppm NAAQS. 1 Attaining and maintaining compliance with the NAAQS is the responsibility of states and EP A rather than the FLMs. FLAG guidelines are not for regulatory purposes, but provide guidance for the FLM to identify ozone impacts on lands they manage.

FLAG recognizes that specific re lationships between precursor emissions and ambient ozone concentrations at a FLM area are difficult to quantif y. Further, it is difficult to quantify the specific relationship between ambient ozone at a FLM area and vegetation re sponse. Therefore, FLAG has
chosen to focus on the effects of ozone on vegetation and the levels of ozone generally known to be phytotoxic in FLM areas as indicators of c oncern regarding ozone impacts on AQRVs.

The objectives of this chapte r are to document information currently known about vegetation response to ozone exposure, and to describe FLM procedures for responding to new source review (NSR) permit applications. If the FLMs have evid ence that ozone is adversely impacting an area they manage, they will work to restrict further emissions of ozone precur sors until those adverse impacts are mitigated.

b. Ozone Effects on Vegetation

Most ozone effects research has focused on agricultural crops beca use of the large economic losses
that have been documented. Neve rtheless, research has identified many native plants in natural ecosystems that are sensitive to oz one (U.S. EPA, 199 6). Some of these ozone -sensitive plant species
have been used as “bioindicators ” of ozone to document phytotoxic ity of ozone in the field due to ambient ozone. A listing of key literature describi ng known ozone effects on native vege tation is provided in Appendix H.

The definitions for ozone injury and damage used by FL Ms are based on the classical definitions (for example, see Guderian 1977). Inju ry is all physical or biological responses to pollutants, such as change in metabolism, reduced photosynthesis, leaf necrosis, premature leaf drop, and chlorosis. Damage is reduction in the intended use or value of the biological or physical resour ce; for example,

1 The new 8-hour standard is currently being challenged in co urt and is not yet enforced; th e 1-hour standard is still in
effect.76 economic production, ecological st ructure and function, aesthetic va lue, and biological or genetic diversity that may be altered thro ugh the impact of pollutants.

Ozone enters plants through leaf stomata. It oxidi zes plant tissue, causing ch anges in biochemical and physiological processes. These biochemical and physiological chan ges occur within the leaf long before visible necrotic sy mptoms appear (Guderian et al. 1985). Plants must expend energy to detoxify ozone and repair injured tissue that could otherwise be used for growth or for maintenance of plant health. The injured plant cells eventually die if de toxification and repair cannot keep up with ozone uptake. The mesophyll cells under the upper epidermis of leaves are the most sensitive to ozone, and those are the first cells to die. The adjacent epider mal cells then die, formi ng a small black or brown
interveinal necrotic lesion that becomes visible on th e upper surface of the leaf . These visible lesions most frequently begin to develop on leaves that have just become fully matured, with older leaves on a stem showing increased amounts of injury . These lesions, term ed oxidant stipple 2 , are quite specific indicators that the plant has been exposed to oz one. Other plant symptoms that can result from exposure to ozone, with or with out the presence of oxidant stipple, include chlorosis, premature senescence, and reduced growth. However, these symptoms are non -specific for ozone since other stressors can also cause them to occur. Further, th ese non-specific symptoms are difficult to quantify in natural ecosystems, although li mited data are available from exposure response experiments to estimate growth losse s from specific ozone exposur es. In general, the only indicator that a FLM has to document that ozone has impacted vegetation is visibl e symptoms of injury such as oxidant stipple.

In addition to affecting individual plants, ozone can also affect entire ecos ystems. Research shows that plants growing in areas with high exposure to am bient ozone may undergo natural selection for ozone tolerance (U.S. EPA, 1996). The final resu lt could be the elimination of the most ozonesensitive genotypes from the area. Regardless of the amount of ozone expo sure, the magnitude of plant response may vary depending on the geographi c area because of change s in meteorological and climatic conditions, and differences in plant conditions in space and ti me. Factors of most importance that influence plant response to ozone are the species/genotyp e, soil moisture, and nitrogen availability. Other factor s influencing plant respons e to ozone include nutrie nt status, atmospheric humidity, temperature, solar radiation, phenologi cal stage of development, day length, regional climatic differences, other pollutant interactions, and population/ecosystem interactions (U.S. EPA, 1996).

Ozone-induced physiological cha nges and/or growth reductions in plants may exist long before necrotic lesions appear on foliage; however, it is ve ry difficult to attribute these effects directly to ozone. Similarly, changes in growth, ecosystem form or function, or biological or genetic diversity caused by ozone are difficult to doc ument in natural ecosys tems. Limited data ar e available regarding injury and growth response to sp ecific ozone exposures. Given the difficulty in determining ozoneinduced physiological or growth changes in natural ecosystems, FLMs will utilize as indicators of ozone effects on vegetation (1) symptoms that are cl early ozone induced such as oxidant stipple, and (2) ozone exposures that have be en shown to be phytotoxic.

2 Specific symptoms of ozone injury in some plant species ar e different. A few species develop white or tan rather than
brown or black lesions. This is termed “fleck” or “weather fleck” instead of oxidant stipple. In conifers, ozone causes banding of necrotic and green tissue near the tip s of older needles, termed “chlorotic mottle.”77 c. Recommended Metric to Determin e Phytotoxic Ozone Concentrations

Various metrics have been used to relate ozone ex posure to plant response. Biologically relevant ozone metrics for plants cannot be directly related to, nor can they be calculated from, the 8-hour NAAQS for ozone. The NAAQS ozone metric does not directly account for peak concentrations, nor does it accumulate exposure, important parameters in any biologically relevant ozone metric. Biologically relevant metrics considered by FLAG include the W126, SUM06, AOT40, and ozone
flux. The W126 is an index that uses a sigmoidal weighted func tion to weight each hourly ozone
concentration. The W126 index is determined by summing all the si gmoidal weighted concentrations for a specified time period (Lefohn and Runeckles, 1987). The W126 index was described and used in

EPA’s Air Quality Criteria for Ozone and Related Photoc hemical Oxidants, Vol. II (U.S. EPA, 1996)
to characterize ozone trends. FLMs will use the W126 metric to determine phytotoxic ozone
concentrations in FLM areas. Th e W126 is preferred to other cu mulative metrics for a couple of reasons. First, the W126 preferenti ally weights the peak exposures, wh ereas other metrics, such as the AOT40 or the SUM06, do not. Second, the W126 accumulates ozone exposures at lower
concentrations than does the AOT40 or SUM06. The AOT40 and SUM06 only accumulate concentrations above their particular threshold, e.g. , 40 ppb for AOT40 and 60 ppb for SUM06. Phytotoxic effects have been shown to occur at exposure concentrations below 60 ppb (U.S. EPA 1996). The AOT40 metric is comm only used in Europe. Some European scientists recently have
concluded that the AOT40 metric is useful for exceedance mappin g but not for assessment of biomass loss (Kaerenlampi and Skaerby 1996). Therefore, FLAG does not recommend the AOT40 for FLM

assessments.

The SUM06, W126, and AOT 40 are ambient ozone exposure para meters, whereas flux is an ozone dose parameter for internal uptake. Flux is determin ed from ambient ozone co ncentration at the leaf
surface and stomatal conductance. Ozone uptake relates more closely to plant response than does ambient ozone exposure. However, detoxification of ozone once it enters the plant is also an important component of plant resp onse, and measuring uptake alone will not necessarily reflect the potential plant response. A benef it of flux is that it might allow differential weighting of daytime versus nighttime exposure (with daytime being weighted more heavily in most cases). Science has not advanced sufficiently for FLAG to recommend use of flux as a metric for plan t response to ozone at this time. However, research on the use of flux as an ozon e metric is continuing (Massman et al. 2000) and it will be examined for possible future use.

To use the W126 metric, the daily an d seasonal time periods of measur ement must also be determined. Although most ozone uptake occu rs during the day, many plant species can have nighttime stomatal conductance resulting in ozone uptake (Musselman and Minnick 2000). Nightti me uptake is a function of many variables, including species, region ( e.g. , desert, deciduous fore st, etc.), season, and elevation. In addition, many FLM areas, particularly those in mountainous regions, have high nighttime ozone exposures. Further, plants may be more sensitive to ozone at night (Musselman and Minnick 2000). Therefore, FLAG endorses use of a 24-hour time period for the W126 metric.

Plant sensitivity and exposure to ozone will change throug hout the growing season . Use of a rolling 90-day cumulative value for the W126 metric would account for changes in exposure over the season. However, some vegetation exposure /response and ozone monitoring da ta are currently available using 7-month (April through October) season al cumulative W126 values. In order to take advantage of this existing information, FLAG will use the Apr il-October time period for the W126 metric.78 FLAG recommends that peak concen trations (hourly ozone values gr eater than 100 ppb or N100) be included as a parameter of meas urement in conjunction with the W126 parameter. Experimental evidence confirms that peak co ncentrations are important (U.S. EPA 1996). Accounting for peak concentrations also provides impo rtant information regarding the timi ng of events an d helps determine if a response is due to chronic or acute exposure. Also, the quantitative exposure /growth response information used by FLAG for dete rmination of critical exposure ozone levels was generated from experimentation based on fumigation treatments c ontaining numerous occurrences of high hourly average concentrations. FL AG recognizes that oxidant stipple injury can occur at zero N100 for sensitive plant species; but the N1 00 should not be used alone as an indicator of sensitivity of vegetation to ozone.

W126 and N100 values for injury an d growth loss for selected eastern U.S. vegetation are presented in
Table O-1 and Table O-2. Data for Table O-1 were derived under favorable environmental conditions, and report the lowest exposure level treatment wh ere visible ozone symptoms were first observed. Thus, threshold exposure levels for ozone symptom response could be lower than those exposures reported here. Data from Table O-2 were calculated from exposure response relationships for a 10 percent growth loss when plants are grown under favorable environmen tal conditions. It is recognized
that data for other eastern U.S. plant species, and for pl ant species growing in the Western U.S., are not currently available. Howeve r, some additional exposure/resp onse data for other species are
available from which these values ca n be calculated. It is important to note that the critical level for injury or growth loss to vegetation from ozone is highly depende nt on plant species and environmental conditions when the plants are exposed. The results obtained in Tables O-1 and O-2 could vary under
different combinations of enviro nmental conditions. Additional res earch under varying environmental
conditions and ozon e exposures should be conducted.

Table O-1. W126 (ppm-h) and N100 ( ≥ 0.1 ppm) exposure levels th at result in foliar necrotic symptoms for selected plant species (from Lefohn 1998.)

Name W126 N100

Table mountain pine 20.0 2 Sweetgum 5.6 3 Sycamore 31.2 89 Winged sumac 3.3 5 Black cherry 11.5 10
Tall milkweed 0.3 0 Black-eyed Susan 12.8 50
Dwarf dandelion 0.3 0 Yellow buckeye 4.7 3 Virginia pine 30.0 50
Cutleaf coneflower 5.5 379 Table O-2. W126 (ppm-h) and N100 ( ≥ 0.1 ppm) exposure levels that resulted in a 10 percent growth loss for selected pl ant species (from Lefohn 1998.)

Name W126 N100

Aspen 259 6.4 4 Aspen wild 71.4 243 Black Cherry 6.5 1 Red Maple 85.4 245
Whorled-wood aster 8.2 10 Yellow poplar 14.4 4 Eastern white pine 30.2 66 Sugar maple 44.7 131 Sycamore 15.4 27 Winged sumac 9.7 4

Ambient W126 and N100 values are available for many Class I areas in the eastern U.S. , with values
soon to be available for additi onal FLM areas. A table showin g representative hi gh and low W126
and N100 values for selected FLM ar eas is appended to this report (Appendix 3.B). Unfortunately, ambient ozone data are lacking fo r many western U.S. FLM areas, and large differences in terrain and elevation may limit the us e of nearby data.

d. Identification of Ozone Sensit ive AQRVs or Sensitive Receptors

FLMs have determined that given the high ecological, aesthetic, and intrinsic va lue of federal lands, all native species are significant and warrant protectio n. Ideally, protection efforts would focus on the identification and protection of the most sensiti ve species in an area. Unfortunately, AQRV
identification is lim ited by incomplete species in ventories and/or lack of exposure/response data for most species of native ve getation. Sensitive species identification will im prove as more information becomes available. In the meantime, FLAG is providing a preliminary list of sensitive plant species for each Class I area, i.e. , those species that have been observed to exhibit ozone symptoms at ambient ozone exposures (Appendix 3.A). Th ose ambient levels have not neces sarily occurred at the specific Class I area where the plan ts occur. AQRV lists will be available in the Air Synthesis and NRIS-AIR databases (See Section B.4.f. of this report) and will be updated as necessary.

e. Review Process for Sources that Could Affe ct Ozone Levels or Ve getation in FLM Areas

As mentioned above, NO

x and VOC are ozone precursors. Stat es and the EPA have based ozone control strategies in various parts of the country on the determination of which precursor is most likely
to influence the formation of ozone. Information suggests that in areas where ozone formation is driven by VOC emissions, i.e. , VOC-limited areas, VOC to NO

xratios are less than 4:1. In VOClimited areas, minimizing or reducing VOC emissi ons is the most effectiv e means of limiting or lowering ozone concentra tions. Conversely, in NO

x -limited areas, where VOC to NO

x ratios are greater than 15:1, controlling NO

x emissions is most effective. It is generally thought that most rural
areas of the U.S. are NO

x -limited, most or all of the time, with the possible exception of the rural areas
of southern California. The FLMs do not have current data to show that all areas are NO

x limited, nor do they consider VOCs to be un important as ozone pr ecursors. However, un til there is enough
information available for FLAG to determine whether ozone form ation in each FLM area is primarily limited by NO

x or VOC emissions, we will assume all FLM areas are NO

x -limited and will focus on80 control of NO

x emissions. Where FLMs have information in dicating a specific area is VOC limited, they will shift ozone prot ection strategy to focus on VOC rather than NO

x emissions.

Source/receptor modeling is require d in most NSR permit applications for particulate matter, sulfur dioxide, and nitrogen dioxide. FLAG is aware of attempts by EPA and others to develop dispersion models that can relate emissions from a single source to changes in ozone concentrations. We recognize that there is currently no model available that can provi de this kind of single source
attribution information for ozone. Nevertheless, b ecause of existing and susp ected ozone c oncerns in a number of FLM areas ( e.g. , evidence of phytotoxic effects and high ambient concentr ations), we will consider ozone effects when reviewing NSR permit applications. However, because single source attribution modeling is possi ble for both visibility a nd deposition, FLMs will be more concerned about ozone if modeling indicates NO

x emissions are likely to cause an adverse impact on vi sibility, soils,
and/or surface waters.

The FLMs recognize that oxidant stipple can occur at hourly ozon e concentrations that can be
considered natural back ground levels (Singh et al. 1978). Many of the high hourly background
concentrations can be a ttributed to stratospheri c intrusions or stratos pheric mixing in the upper troposphere (Singh et al. 1978); but stratospheric intrusions rare ly occur in the middle and southern latitudes after May (Singh et al. 1980, Wooldridge et al. 1997), and thus do not coincide with the major portion of the growing season . However, oxidant stipple has be en observed on foliage in the spring when these intrusions can occur. In genera l, oxidant stipple observed on foliage from June through September cannot be attributed to natura l background ozone from st ratospheric sources. Low levels of ambient ozone may occa sionally occur in the troposphere from non-anthropogenic and nonstratospheric sources.

The occurrence of oxidant stipple necrosis on pl ant foliage may indicate further ozone induced physiological and growth impacts. Point sources em it precursors that could pr oduce ozone at the FLM area, and increased ozone could induce further injury or damage to vegetation. However, we assume
that restriction on increa ses in ozone precursors will prevent additional ambi ent ozone and subsequent increases in injury or da mage to vegetation in FLM managed areas . It is important that ambient ozone monitoring be conducted by the State or Local air pollution cont rol agency or by the FLM to determine the seasonal ozone exposure.

FLM actions or specific requests on a permit application will be base d on the existing air pollution situation at the FLM area(s) that ma y be affected by the source. So me FLMs may rely on growth loss
rather than foliar necrosis to make an adverse imp act. Each FLM will dete rmine if actions are
warranted to limit em issions that might lead to increased ambient ozone, based on the expected impact of ozone in their particular area.

FLM response will depend on whether or not:

1. ozone vegetation effects have been documented in the area (as evidenced by foliar injury or damage to vegetation);

2. ozone exposure levels occurring in the area are high enough that they could affect vegetation ( i.e. , ozone exposures ar e at levels shown to be phytotoxic).

Figure O-1 outlines the general FLM process for re sponding to NSR permit applications based on ozone exposure and vegetation effects at the r eceptor site. Management decisions regarding acceptance of an existing or future ozone e xposure will be area-sp ecific and may differ81 significantly between agencies, or even regionally within agencies. Each FLM will determine if injury and/or damage are necessary to warrant action, based on the expected im pact in the area they manage. The decisions are based on the FLM interp retation of regulations, past experience in the NSR arena, availability of ozone effect exposure/response informati on for species that occur in the area, and other factors. The FLM will negotiate with the NSR permit applicant and the permitting authority regarding the options listed in Figure O-1.

Figure O-1. FLM response to potential ozone effects from new emissions source.

No

U Unknown Unknown Yes

Yes

No

Unknown Yes

No

Items referenced in Figure O-1: a. The FLM may recommend one or more of the following: - That the proposed source use st ricter than BACT controls ( e.g. , Lowest Achievable Emission Rate [LAER]). - That the proposed source obtain NO

x emission offsets that will be nefit the potentially affected FLM area (as demonstrated by dispersion modeling). - That the permi tting authority ( i.e. , state or EPA) conduct regiona l modeling to identify sources that are contributing significan tly to ozone-associated impacts in the FLM area , and that the permitting authority then undertake actions necess ary to reduce emissions from those sources ( e.g. , SIP revision). a

FLM not likely to object to permit; FLM may ask for d

FLM not likely to object to permit Ozone damage to vegetation FLM not likely to object to permit; FLM may ask for

c and/or d

a ; FLM ma y ask for c FLM not likely to object to permit; FLM may ask for c 

### START

Ozone damage to vegetation a

Ozone

exposure currently recognized as phytotoxic; FLM

may ask for b82 b. The applicant calculate the oz one exposure for vegetation (usi ng W126 and N1 00 metrics) for the affected FLM area(s) where such info rmation is not already available. c. The permitting authority or app licant fund post-construction ambi ent ozone monitoring in or near
the FLM area. d. The applicant conduct or fund post-construction ozone effects surveys in the FLM area and/or exposure/response effects research.

Note: “Ozone exposure currently recognized as phytotoxic” is determined based on data from exposure response studies and am bient ozone at the site. The FLM may ask the applicant to calculate the ozone exposure values if these data are not already available. “Ozone damage to vegetation” is determined from fiel d observations at the impacted site.

f. Further Guidance to FLMs

As mentioned above, limited inform ation about ozone exposure/respons e relationships in plants and lack of an ozone source/receptor model make it difficult to protect FLM areas from the effects of
ozone from new sources. However, there are other area-specific gaps in information that also limit protection efforts. It is important for local la nd managers to attempt to collect the missing information. This section provides guidance specif ically to FLMs on what types of data should be
collected and how the data could be collected.

Identifying and Monitoring Ozone-sensitive AQRVs

Many FLM areas need more details regarding plant species presence, location, and abundance. FLAG recommends FLMs gather this info rmation, where needed, and refine their lists of area-specific ozonesensitive plants. FLMs are currently developing list s of sensitive species to crosscheck with the plant species list for their area to determin e potential sensitivity to ozone. In the future, th e FLMs will place ozone sensitive plant species lists in the NRIS-AIR or Air Synthesis databases for comparison to plant species lists for each wilderne ss area or national park.

FLAG recommends that once loca l FLMs have develo ped lists of poten tially sensitive AQRVs specific for their site, they conduct surveys to detect the presence of ozone-induced foliar injury on the selected species. The USDA/FS Fo rest Health Monitoring (FHM) Program has developed foliar injury survey protocols and QA/QC proced ures that can be used to coll ect this information. Another resource is the foliar injury tr aining module developed by the NPS Air Resources Division and Pennsylvania State University. This module helps field staff identify and quantify ozone injury symptoms on plant foliage. Field cr ews must obtain proper training and field experience in identifying foliar injury symptoms before surveys can be conducted.

Ideally, to verify ozone-induced foliar injury sy mptoms in the field, ex posure/response fumigation
studies should be conducted on these species, using concentrations that reflect current ambient exposure. Plants should also be tested at higher exposures, simu lating increased levels of ambient ozone that might occur in the future . Due to the expense of constructing and operat ing such systems, it would be most appropriate for agen cies to join resources and deve lop regional fumigation facilities. At a minimum, such facilities shou ld be constructed both in the eastern and western U.S., since
ambient conditions at an eas tern facility might not be appropriate for western species and vice versa.83 Ambient Ozone Monitoring

Many FLM areas do not currently have either on-site or nearby ambient ozone monitoring data. FLAG recommends that local FLMs make every effort to collect this inform ation and that they use quality-assured ambient ozone monitoring protocols developed by the EPA an d the state air quality agency. Continuous (activ e) monitoring is preferred since this t ype of data is necessary to determine compliance with the ozone NAAQS. Continuous monitoring is al so necessary to determine the temporal dynamics of ozone exposure for vegetation, and is necessary to calculate the W126 and N100 parameters. Unfortunately, con tinuous monitoring is expensive and requires electric power that
is often not available in or near remote FLM areas. When installing a continuous monitor is not an option, FLAG recommends use of passive monitors. Passive monito rs give total exposure loading values (SUM00) for a specified period of time. The data are useful for indicating year-to-year changes in total ozone exposure at an i ndividual site, and for indicating wher e continuous monitors should be installed. However, FLMs recogni ze the limitation of pa ssive samplers in relating ozone exposure to plant response.

g. Ozone Air Pollution Web Sites

U.S. EPA ozone information: http://www.e pa.gov/airlinks http://www.e pa.gov gov/oar/oaqps/cleanair.html http://www.e pa.gov/naaqsfin/o3h ealth.htm http://www.epa.g ov/acidrain/castnet

NPS ozone information: http://www2.nature.nps.gov/ard/gas/network.htm; http://www2.nature.nps.gov/ard/veginj.htm Ozone effects research, USDA ARS, No rth Carolina: http://www.ces.n csu.edu/depts/pp/notes/Ozone/ozone.html Ozone effects research, England: http://www.n cl.ac.uk/airweb/ozone/ozone.htm Ozone effects research, Switzerland: http://www.wsl.ch/forest/risks /wsidb/projects/ozone/ozoneENG.html Ozone exposure metr ics for vegetation: http://www.asl-associates.com/

Ozone W126 calculator: http://216.48.37.155/c alculator/index.htm84 Appendix 3.A: A Preliminary Listing for Sel ected USDA/FS, NPS, and FWS Class I Areas of Plant Species that have been Shown to be Sensitive Receptors for Ozone.

1. USDA/FS CLASS I WILDERNESS AREAS

Alabama Sipsey Black Cherry Prunus serotina Blackberry Rubus canadensis Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera

Arkansas Upper Buffalo Blackberry Rubus canadensis

Arizona
Chiricahua Serviceberry Amelanchier alnifola Chokecherry Prunus virginiana
Globemallow Sphaeralcea
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata
Galiuro Serviceberry Amelanchier alnifola Chokecherry Prunus virginiana
Globemallow Sphaeralcea
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata

Mazatzal Aspen Populus tremuloides
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata Mount Baldy
Arizona Willow Salix
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Pine Mountain Aspen Populus tremuloides
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata
Sierra Ancha Aspen Populus tremuloides
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata Superstition Aspen Populus tremuloides
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata Sycamore Canyon Aspen Populus tremuloides
Ponderosa Pine Pinus ponderosa
Skunkbush Rhus trilobata85 California Agua Tibia Jeffrey Pine Pinus jeffreyi
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Ansel Adams Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Caribou
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta Cucamonga Jeffrey Pine Pinus jeffreyi
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Desolation
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Dome Land Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola Emmigrant Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis
Ponderosa Pine Pinus ponderosa

Hoover
Ponderosa Pine Pinus ponderosa
John Muir Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Kaiser Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Marble Mountain Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Mokelumme Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola86 San Gorgonio Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
San Jacinto Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
San Rafael Jeffrey Pine Pinus jeffreyi
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
South Warner Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola
Thousand Lakes Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola Yolla Bolly - Middle Eel Jeffrey Pine Pinus jeffreyi
Limber Pine Pinus flexilis Lodgepole Pine Pinus contorta
Ponderosa Pine Pinus ponderosa
Western White Pine Pinus monticola

Colorado Eagles Nest Subalpine fir Abies lasiocarpa
White clover Trifolium repens Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp. Flat Tops Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.87 Maroon Bells – Snowmass
White fir Abies concolor Subalpine fir Abies lasiocarpa Sagebrush Artemesia sp. Trembling aspen Populus tremuloides
Mount Zirkel Subalpine fir Abies lasiocarpa Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.
Rawah
White fir Abies concolor Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.
Weminuche
White fir Abies concolor Subalpine fir Abies lasiocarpa Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Huckleberry Vaccinium sp.
West Elk
White fir Abies concolor Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Saskatoon serviceberry Amelanchier alnifolia Sagebrush Artemesia sp Hybrid poplar Populus deloides x trichocarpa
Ninebark Pysocarpus sp. Chockcherry Prunus vierginiana Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.

Florida Bradwell Bay Blackberry Rubus canadensis Staghorn Sumac Rhus typhina Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera88 Georgia
Cohutta Black Cherry Prunus serotina Blackberry Rubus canadensis Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera

Idaho
Sawtooth
Ponderosa Pine Pinus ponderosa

Minnesota Boundary Waters Canoe Area Aspen Populus tremuloides Chokecherry Prunus virginiana
Eastern White Pine Pinus strobus

Missouri
Hercules-Glades Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana

Nevada Jarbridge
Ponderosa Pine Pinus ponderosa

New Hampshire
Great Gulf Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana Presidential Range - Dry River Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Red Spruce Picea rubens
White Ash Fraxinus americana

North Carolina Joyce Kilmer - Slickrock American Sycamore Platinus occidentalis Black Cherry Prunus serotina Blackberry Rubus canadensis Elderberry Sambucus canadensis Flowering Dogwood Cornus florida Pin Cherry Prunus pensylvanica Poison Ivy Rhus radicans Red Maple Acer rubrum
Red Oak Quercus rubra
Sassafrass Sassafras albidum Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana
Eastern White Pine Pinus strobus89 Linville Gorge American Sycamore Platinus occidentalis Black Cherry Prunus serotina Blackberry Rubus canadensis Flowering Dogwood Cornus florida Red Maple Acer rubrum
Red Oak Quercus rubra
Sassafrass Sassafras albidum Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera
Eastern White Pine Pinus strobus Shining Rock Black Cherry Prunus serotina Black-Eyed Susan Rudbeckia hirta Blackberry Rubus canadensis Chokecherry Prunus virginiana Flowering Dogwood Cornus florida
Milkweed Asclepias syriaca Poison Ivy Rhus radicans
Red Oak Quercus rubra
Sassafrass Sassafras albidum Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana
Eastern White Pine Pinus strobus

Oregon *

Diamond Peak Western Serviceberry Amelancier alnifolia Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Stink Currant Ribes bracteosum Eagle Cap Western Serviceberry Amelancier alnifolia
Ponderosa Pine Pinus ponderosa Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Gearhart Mountain Western Serviceberry Amelancier alnifolia Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata

* Plant species listed for Oregon have been identified as ozone sensitive in laboratory fumigations. Ozone injury to some of these species has not been verified in the field.90 Hells Canyon Western Serviceberry Amelancier alnifolia
Ponderosa Pine Pinus ponderosa Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Black Twin-berry Lonicera involucrata
Mallow Ninebark Physocarpus malvaceus
Black Cottonwood Populus trichocarpa Kalmiopsis Western Serviceberry Amelancier alnifolia
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Mount Hood Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa
Stink Currant Ribes bracteosum
Mount Jefferson Western Serviceberry Amelancier alnifolia Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa
Stink Currant Ribes bracteosum Mount Washington Western Serviceberry Amelancier alnifolia
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Stink Currant Ribes bracteosum
Mountain Lakes Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus
Ponderosa Pine Pinus ponderosa Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa91 Strawberry Mountain Western Serviceberry Amelancier alnifolia
Ponderosa Pine Pinus ponderosa Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Three Sisters Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus
Ponderosa Pine Pinus ponderosa Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata

Vermont Lye Brook Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Red Spruce Picea rubens
White Ash Fraxinus americana

Virginia
James River Face Black Cherry Prunus serotina Blackberry Rubus canadensis Chokecherry Prunus virginiana Flowering Dogwood Cornus florida
Milkweed Asclepias syriaca Poison Ivy Rhus radicans
Red Oak Quercus rubra
Sassafrass Sassafras albidum Sweetgum Liquidambar styraciflua Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana
Eastern White Pine Pinus strobus

Washington * Alpine Lakes Western Serviceberry Amelancier alnifolia
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Stink Currant Ribes bracteosum

* Plant species listed for Washington have been identified as ozone sensitive in laboratory fumigations. Ozone injury to some of these species has not been verified in the field.92 Glacier Peak Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa
Stink Currant Ribes bracteosum
Goat Rocks Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa
Stink Currant Ribes bracteosum
Mount Adams Western Serviceberry Amelancier alnifolia
Pacific ninebark Physocarpus capitatus Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Red Alder Alnus rubra
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata
Black Cottonwood Populus trichocarpa
Stink Currant Ribes bracteosum Paysayten Western Serviceberry Amelancier alnifolia Quaking Aspen Populus tremuloides
Scouler Willow Salix scouleriana Thin-leaved Huckleberry Vaccinium membranaceum
Mountain Ash Sorbus sitchensis Black Twin-berry Lonicera involucrata

West Virginia Dolly Sods Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana
Otter Creek Black Cherry Prunus serotina Elderberry Sambucus canadensis
Milkweed Asclepias syriaca Red Spruce Picea rubens Tulip Poplar Liriodendron tulipifera
White Ash Fraxinus americana93 Wisconsin
Rainbow Lake Aspen Populus tremuloides Chokecherry Prunus virginiana
Eastern White Pine Pinus strobus

Wyoming Bridger
Ponderosa Pine Pinus ponderosa Fitzpatrick (probable species) Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Serviceberry Amelanchier alnifolia Sagebrush Artemesia sp. Hybrid poplar Populus deloides x trichocarpa Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana
Ninebark Pysocarpus sp. Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.
North Absoroka (probable species) Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Serviceberry Amelanchier alnifolia Sagebrush Artemesia sp. Hybrid poplar Populus deloides x trichocarpa Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana
Ninebark Pysocarpus sp. Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.

Teton
Ponderosa Pine Pinus ponderosa
Washakie (probable species) Subalpine fir Abies lasiocarpa
Boxelder Acer negundo Serviceberry Amelanchier alnifolia Sagebrush Artemesia sp. Hybrid poplar Populus deloides x trichocarpa Trembling aspen Populus tremuloides Chockcherry Prunus vierginiana
Ninebark Pysocarpus sp. Thimbleberry Rubus parviflorus Squawberry Rhus trilobata Huckleberry Vaccinium sp.94 2. NPS CLASS I AREAS

Alaska
Denali NP Quaking aspen Populus tremuloides Black poplar Populus balsamifera

Arizona
Chiricahua NM Arizona pine Pinus ponderosa
Skunkbush Rhus trilobata White stem blazingstar Mentzelia albicaulis Black cherry Prunus serotina
White clover Trifolium repens Grand Canyon NP
Cottonwood Populus fremontii Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Single-leaf ash Fraxinus anomala
Skunkbush Rhus trilobata Serviceberry Amelanchier alnifolia White stem blazingstar Mentzelia albicaulis
Smooth desert dandelion Malacothrix glabrata
Desert dandelion Malacothrix glabrata
White clover Trifolium repens
Petrified Forest NP
Skunkbush Rhus trilobata Cheat grass Bromus tectorum
Red-stem stork’s bill Erodium cicutarium Perrenial rye grass Lolium perenne White stem blazingstar Mentzelia albicaulis Saguaro NP Ponderosa pine Pinus ponderosa
Skunkbush Rhus trilobata Quaking aspen Populus tremuloides

California
Joshua Tree NP Single-leaf ash Fraxinus anomala
Skunkbush Rhus trilobata White stem blazingstar Mentzelia albicaulis Kings Canyon NP Jeffrey pine Pinus jeffreyi Ponderosa pine Pinus ponderosa Twinberry Lonicera involucrata Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
Skunkbush Rhus trilobata Butterweed groundsel Senecio serra
White clover Trifolium repens Black poplar Populus balsamifera trichocarpa
Lassen Volcanic NP
Skunkbush Rhus trilobata Chokecherry Prunus virginiana Ponderosa pine Pinus ponderosa
White clover Trifolium repens Jeffrey pine Pinus jeffreyi95 Lava Beds NM Chokecherry Prunus virginiana Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Jeffrey pine Pinus jeffreyi Point Reyes National Seashore Ledebour’s honeysuckle Lonicera involucrata
White clover Trifolium repens
Redwood NP
White clover Trifolium repens Black poplar Populus balsamifera trichocarpa Chokecherry Prunus virginiana Jeffrey pine Pinus jeffreyi Sequoia NP Jeffrey pine Pinus jeffreyi Ponderosa pine Pinus ponderosa Twinberry Lonicera involucrata Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
Skunkbush Rhus trilobata Butterweed groundsel Senecio serra
White clover Trifolium repens Black poplar Populus balsamifera trichocarpa
Yosemite NP Ponderosa pine Pinus ponderosa
White clover Trifolium repens
Skunkbush Rhus trilobata Twinberry honeysuckle Lonicera involucrata Chokecherry Prunus virginiana

Colorado Black Canyon of the Gunnison NP Serviceberry Amelanchier alnifolia Quaking aspen Populus tremuloides
Skunkbush Rhus trilobata
White clover Trifolium repens Chokecherry Prunus virginiana
Great Sand Dunes NM Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Serviceberry Amelanchier alnifolia
Mountain ninebark Physocarpus monogynus
Skunkbush Rhus trilobata White stem blazingstar Mentzelia albicaulis
White clover Trifolium repens
Mesa Verde NP Ponderosa pine Pinus ponderosa Spreading dogbane Apocynum androsaemifolium
Box elder Acer negundo Cheat grass Bromus tectorum Poison ivy Toxicodendron radicans
Skunkbush Rhus trilobata Serviceberry Amelanchier alnifolia Quaking aspen Populus tremuloides White stem blazingstar Mentzelia albicaulis
White clover Trifolium repens96 Rocky Mountain NP Wild strawberry Fragaria virginiana
Mountain ninebark Physocarpus monogyna Ponderosa pine Pinus ponderosa Balsam poplar Populus balsamifera Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
White clover Trifolium repens

Hawaii
Haleakala NP Ponderosa pine Pinus ponderosa
White clover Trifolium repens
Hawaii Volcanoes NP
White clover Trifolium repens

Idaho
Craters of the Moon NM Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides

Kentucky
Mammoth Cave NP Yellow-poplar Liriodendron tulipifera Red maple Acer rubrum
Poke milkweed Asclepias exaltata American sycamore Platanus occidentalis Black cherry Prunus serotina Black-eyed susan Rudbeckia hirta
Cutleaf coneflower Rudbeckia laciniata
Sassafras Sassafras albidum
Green ash Fraxinus pennsylvanica
White clover Trifolium repens

Maine
Acadia NP
Broad-leaf aster Aster macrophyllus Black cherry Prunus serotina Quaking aspen Populus tremuloides
White ash Fraxinus americana Spreading dogbane Apocynum androsaemiolium

Michigan Isle Royale NP Common snowberry Symphoricarpos albus Quaking aspen Populus tremuloides Twinberry honeysuckle Lonicera involucrata Black poplar Populus balsamifera
White clover Trifolium repens Red maple Acer rubrum Chokecherry Prunus virginiana Bigleaf aster Aster macrophyllus Paper birch Betula papyrifera97 Minnesota Voyageurs NP
Green ash Fraxinus pennsylvanica Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
White clover Trifolium repens Snowberry Symphoricarpos albus Paper birch Betula papyrifera
Common milkweed Asclepias syriaca Black poplar Populus balsamifera Red maple Acer rubrum

Montana
Glacier NP Paper birch Betula papyrifera Wild strawberry Fragaria virginiana Twinberry Lonicera involucrata Rock-spiraea Holodiscus discolor Ponderosa pine Pinus ponderosa Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides Chokecherry Prunus virginiana Snowberry Symphoricarpos albus
White clover Trifolium repens

New Mexico
Bandelier NM Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides
Skunkbush Rhus trilobata
White clover Trifolium repens
Carlsbad Caverns NP Ponderosa pine Pinus ponderosa
Fremont’s cottonwood Populus fremontii Quaking aspen Populus tremuloides White stem blazingstar Mentzelia albicaulis Black cherry Prunus serotina

North Carolina/Tennessee Great Smoky Mountains NP Yellow-poplar Liriodendron tulipifera Red maple Acer rubrum
Tall milkweed Asclepias exaltata Table-mountain pine Pinus pungens American sycamore Platanus occidentalis Black cherry Prunus serotina Winged sumac Rhus copallina Black-eyed susan Rudbeckia hirta
Cutleaf coneflower Rudbeckia laciniata
Crown-beard Verbesina occidentalis
Sassafras Sassafras albidum98 North Dakota
Theodore Roosevelt NP Paper birch Betula papyrifera Wild strawberry Fragaria virginiana Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
White clover Trifolium repens

Oregon
Crater Lake NP Ponderosa pine Pinus ponderosa Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides Lodgepole pine Pinus contorta

South Dakota
Badlands NP
Green ash Fraxinus pennsylvanica Ponderosa pine Pinus ponderosa Chokecherry Prunus virginiana Snowberry Symphoricarpos albus
Western wormwood Artemisia ludoviciana
Wind Cave NP Wild strawberry Fragaria virginiana
Green ash Fraxinus pennsylvanica Quaking aspen Populus tremuloides Ponderosa pine Pinus ponderosa Chokecherry Prunus virginiana

Texas Big Bend NP Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Black cherry Prunus serotina
Skunkbush Rhus trilobata Guadalupe Mountains NP Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides Black cherry Prunus serotina White stem blazingstar Mentzelia albicaulis Chokecherry Prunus virginiana

Utah
Arches NP
Cottonwood Populus fremontii Single-leaf ash Fraxinus anomala
Skunkbush Rhus trilobata White stem blazingstar Mentzelia albicaulis Bryce Canyon NP Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides
Skunkbush Rhus trilobata
White clover Trifolium repens99 Canyonlands NP
Cottonwood Populus fremontii Single-leaf ash Fraxinus anomala Serviceberry Amelanchier alnifolia Quaking aspen Populus tremuloides White stem blazingstar Mentzelia albicaulis Capitol Reef NP Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides
Cottonwood Populus fremontii Single-leaf ash Fraxinus anomala Serviceberry Amelanchier alnifolia
Skunkbush Rhus trilobata White stem blazingstar Mentzelia albicaulis
White clover Trifolium repens
Smooth desert dandelion Malacothrix glabrata
Zion NP
Cottonwood Populus fremontii Ponderosa pine Pinus ponderosa Quaking aspen Populus tremuloides
Skunkbush Rhus trilobata Serviceberry Amelanchier alnifolia White stem blazingstar Mentzelia albicaulis
White clover Trifolium repens

Virginia
Shenandoah NP Black cherry Prunus serotina Quaking aspen Populus tremuloides
White ash Fraxinus americana
Common milkweed Asclepias syriaca Yellow-poplar Liriodendron tulipifera American sycamore Platanus occidentalis
White clover Trifolium repens

Washington
Mount Rainier NP Ponderosa pine Pinus ponderosa Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides Twinberry Lonicera involucrata Serviceberry Amelanchier alnifolia Snowberry Symphoricarpos albus
North Cascades NP Ponderosa pine Pinus ponderosa Black poplar Populus balsamifera trichocarpa Paper birch Betula papyrifera
Box elder Acer negundo Twinberry Lonicera involucrata Serviceberry Amelanchier alnifolia Snowberry Symphoricarpos albus Olympic NP Black poplar Populus balsamifera trichocarpa Twinberry Lonicera involucrata Serviceberry Amelanchier alnifolia Snowberry Symphoricarpos albus100 Wyoming
Grand Teton NP Wild strawberry Fragaria virginiana Twinberry Lonicera involucrata Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
Skunkbush Rhus trilobata Bitterweed groundsel Senecio serra
White clover Trifolium repens
Yellowstone NP Wild strawberry Fragaria virginiana Twinberry Lonicera involucrata White stem blazingstar Mentzelia albicaulis Black poplar Populus balsamifera trichocarpa Quaking aspen Populus tremuloides Chokecherry Prunus virginiana
Skunkbush Rhus trilobata Butterweed groundsel Senecio serra
White clover Trifolium repens101 3. FWS CLASS I WILDERNESS AREAS

Florida
Chassahowitzka
Eastern redbud Cercis canadensis Sweetgum Liquidambar styraciflua Loblolly pine Pinus taeda Black cherry Prunus serotina
Dwarf sumac Rhus copallina Blackeyed Susan Rudbeckia hirta

Georgia
Okefenokee American sycamore Platanus occidentalis Black cherry Prunus serotina
Dwarf sumac Rhus copallina Eastern poison ivy Toxicodendron radicans
Eastern redbud Cercis canadensis Flowering dogwood Cornus florida Loblolly pine Pinus taeda Red maple Acer rubrum Sweetgum Liquidambar styraciflua
Tree-of-heaven Ailanthus altissima Virginia creeper Parthenocissus quinquefolia

Maine
Moosehorn Spreading dogbane Apocynum androsaemifolium Swamp milkweed Asclepias incarnata
Whorled wood aster Aster acuminatus Bigleaf aster Aster macrophyllus
Parasol aster Aster umbellatus
White ash Fraxinus americana
Green ash Fraxinus pennsylvanica Quaking aspen Populus tremuloides Pin cherry Prunus pennsylvanica Black cherry Prunus serotina Common chokecherry Prunus virginiana Staghorn sumac Rhus typhina Allegheny blackberry Rubus allegheniensis
American elder Sambucus canadensis

Michigan Seney
American basswood Tilia americana Bigleaf aster Aster macrophyllus Blackeyed Susan Rudbeckia hirta
Common milkweed Asclepias syriaca Eastern poison ivy Toxicodendron radicans Quaking aspen Populus tremuloides Red maple Acer rubrum Sambucus nigra Sambucus canadensis Scarlet elderberry Sambucus racemosa Spreading dogbane Apocynum androsaemifolium102 Missouri Mingo Tuliptree Liriodendendron tulipifera Virginia creeper Parthenocissus quinquefolia

Montana
Medicine Lake Cheatgrass Bromus tectorum Quaking aspen Populus tremuloides
White ash Fraxinus americana

New Jersey Brigantine
Boxelder Acer negundo
Tree-of-heaven Ailanthus altissima
Common milkweed Asclepias syriaca Cheatgrass Bromus tectorum Sweet gum Liquidambar styraciflua Virginia creeper Parthenocissus quinquefolia Pitch pine Pinus rigida Black cherry Prunus serotina Winged sumac Rhus copallina
American elder Sambucus canadensis
Sassafras Sassafras albidum
Common lilac Syringa vulgaris Poison-ivy Toxicodendron radicans Grape Vitis spp

.

North Dakota
Lostwood
Boxelder Acer negundo Quaking aspen Populus tremuloides Raspberry Rubus idaeus Saskatoon serviceberry Amelanchier alnifolia

Oklahoma
Wichita Mountains Blackeyed Susan Rudbeckia hirta Cheatgrass Bromus tectorum
Eastern redbud Cercis canadensis
Green ash Fraxinus pennsylvanica
Smooth sumac Rhus glabra Sugar Maple Acer saccharum Virginia creeper Parthenocissus quinquefolia

South Carolina Cape Romain Sweetgum Liquidambar styraciflua Virginia creeper Parthenocissus quinquefolia Winged sumac Rhus copallina
American elder Sambucus canadensis
Chinese tallow tree Sapium sabiferum Poison-ivy Toxicodendron radicans Labrusca grape Vitis labrusca103 Appendix 3.B. List of Representative Low and High W126 and N100 Values

for SELECTED NPS and FWS Areas.

SITE = ACADIA NATIONAL PARK

AIRS SITE NUMBER = 23-009-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

ACAD 1988 43925 89 4485 5136 87.3

ACAD 1996 10052 4 5085 5136 99.0

______________________________________________________________________________________

SITE = ARCHES NATIONAL PARK

AIRS SITE NUMBER = 49-019-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

ARCH 1989 21199 0 4260 5136 82.9

ARCH 1990 1713 0 4639 5136 90.3

______________________________________________________________________________________

SITE = BADLANDS NATIONAL PARK

AIRS SITE NUMBER = 46-071-1001

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

BADL 1988 13332 0 4791 5136 93.3

BADL 1990 4766 0 4783 5136 93.1

______________________________________________________________________________________104 SITE = BANDELIER NATIONAL MONUMENT

AIRS SITE NUMBER = 35-028-1002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

BAND 1991 44945 0 4997 5136 97.3

BAND 1993 21854 0 4566 5136 88.9

_____________________________________________________________________________________

SITE = BIG BEND NATIONAL PARK

AIRS SITE NUMBER = 48-043-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

BIBE 1992 12169 0 4366 5136 85.0

BIBE 1994 26667 0 4702 5136 91.5

______________________________________________________________________________________

SITE = BIG THICKET NATIONAL PRESERVE

AIRS SITE NUMBER = 48-457-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

BITH 1987 21554 16 4401 5136 85.7

BITH 1991 6763 1 3383 5136 65.9

______________________________________________________________________________________

SITE = BRIGANTINE WILDERNESS AREA

AIRS SITE NUMBER = 34-001-0005

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

BRIG 1991 67729 109 4963 5136 96.6

BRIG 1994 24901 2 4980 5136 97.0

______________________________________________________________________________________105 SITE = CANYONLANDS NATIONAL PARK

AIRS SITE NUMBER = 49-037-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CANY 1993 21278 0 4390 5136 85.5

CANY 1996 49676 0 4373 5136 85.1

______________________________________________________________________________________

SITE = CAPE COD NATIONAL SEASHORE

AIRS SITE NUMBER = 25-001-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CACO 1988 70427 174 4856 5136 94.5

CACO 1993 23439 10 4675 5136 91.0

______________________________________________________________________________________

SITE = CAPE ROMAIN WILDERNESS AREA

AIRS SITE NUMBER = 45-019-0046

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CARO 1991 4377 0 4943 5136 96.2

CARO 1993 19019 0 4945 5136 96.3

______________________________________________________________________________________

SITE = CHAMIZAL NATIONAL MEMORIAL

AIRS SITE NUMBER = 48-141-0044

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CHAM 1993 5310 1 4549 5136 88.6

CHAM 1995 19564 14 4890 5136 95.2

______________________________________________________________________________________106 SITE = CHANNEL ISLANDS NATIONAL PARK

AIRS SITE NUMBER = 06-111-0006

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CHIS 1991 14943 0 3268 5136 63.6

CHIS 1992 33809 6 4549 5136 88.6

______________________________________________________________________________________

SITE = CHIRICAHUA NATIONAL MONUMENT

AIRS SITE NUMBER = 04-003-8001

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CHIR 1992 20023 0 4521 5136 88.0

CHIR 1994 36119 0 5005 5136 97.4

______________________________________________________________________________________

SITE = COLORADO NATIONAL MONUMENT

AIRS SITE NUMBER = 08-077-0600

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

COLM 1987 35812 0 4418 5136 86.0

COLM 1990 12229 0 5012 5136 97.6

______________________________________________________________________________________

SITE = CONGAREE SWAMP NATIONAL MONUMENT

AIRS SITE NUMBER = 45-079-1006

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

COSW 1990 25577 14 4004 5136 78.0

COSW 1994 5329 0 4221 5136 82.2

______________________________________________________________________________________107 SITE = COWPENS NATIONAL BATTLEFIELD

AIRS SITE NUMBER = 45-021-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

COWP 1988 58732 69 4135 5136 80.5

COWP 1990 22474 0 4784 5136 93.1

______________________________________________________________________________________

SITE = CRATERS OF THE MOON NATIONAL MONUMENT

AIRS SITE NUMBER = 16-023-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CRMO 1993 8563 0 4506 5136 87.7

CRMO 1994 25462 0 4752 5136 92.5

______________________________________________________________________________________

SITE = CUYAHOGA VALLEY NATIONAL RECREATION AREA

AIRS SITE NUMBER = 39-153-2004

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

CUVA 1990 15249 15 3267 5136 63.6

CUVA 1991 39670 33 4193 5136 81.6

______________________________________________________________________________________

SITE = DEATH VALLEY NATIONAL PARK

AIRS SITE NUMBER = 06-027-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

DEVA 1994 68630 1 4125 5136 80.3

DEVA 1996 46223 0 4380 5136 85.3

______________________________________________________________________________________108 SITE = DENALI NATIONAL PARK

AIRS SITE NUMBER = 02-290-0003

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

DENA 1993 2590 0 4773 5136 92.9

DENA 1996 4144 0 4831 5136 94.1

______________________________________________________________________________________

SITE = EVERGLADES NATIONAL PARK

AIRS SITE NUMBER = 12-025-0030

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

EVER 1987 7968 0 3693 5136 71.9

EVER 1991 1568 0 4202 5136 81.8

______________________________________________________________________________________

SITE = GLACIER NATIONAL PARK

AIRS SITE NUMBER = 30-029-8001

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GLAC 1989 5871 0 5136 5136 100.0

GLAC 1993 2314 0 5136 5136 100.0

______________________________________________________________________________________

SITE = GREAT BASIN NATIONAL PARK

AIRS SITE NUMBER = 32-033-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GRBA 1995 22881 0 4836 5136 94.2

GRBA 1996 38342 0 4800 5136 93.5

______________________________________________________________________________________109 SITE = GRAND CANYON NATIONAL PARK

AIRS SITE NUMBER = 04-005-2003 AND 04-005-8001

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GRCA 1990 15430 0 3827 5136 74.5

GRCA 1996 47476 0 4633 5136 90.2

______________________________________________________________________________________

SITE = GREAT SAND DUNES NATIONAL MONUMENT

AIRS SITE NUMBER = 08-003-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GRSA 1989 10777 0 4436 5136 86.4

GRSA 1991 16966 0 4130 5136 80.4

______________________________________________________________________________________

SITE = GREAT SMOKY MTS NATIONAL PARK - CADES COVE SITE

AIRS SITE NUMBER = 47-009-0102

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GSCC 1994 14879 5 3988 5136 77.6

GSCC 1996 24268 0 4805 5136 93.6

______________________________________________________________________________________

SITE = GREAT SMOKY MTS NATIONAL PARK - CLINGMANS DOME SITE

AIRS SITE NUMBER = 47-155-0102

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GSCD 1993 40757 0 4087 5136 79.6

GSCD 1996 74162 3 4104 5136 79.9

______________________________________________________________________________________110 SITE = GREAT SMOKY MTS NATIONAL PARK - COVE MT SITE

AIRS SITE NUMBER = 47-155-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GSCM 1992 49135 3 4620 5136 90.0

GSCM 1996 98657 8 4879 5136 95.0

______________________________________________________________________________________

SITE = GREAT SMOKY MTS NATIONAL PARK - LOOK ROCK SITE

AIRS SITE NUMBER = 47-009-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GSLR 1991 36430 0 4504 5136 87.7

GSLR 1996 76608 5 4650 5136 90.5

______________________________________________________________________________________

SITE = GUADALUPE MOUNTAINS NATIONAL PARK

AIRS SITE NUMBER = 48-109-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

GUMO 1990 13795 0 4531 5136 88.2

GUMO 1992 25368 0 4120 5136 80.2

______________________________________________________________________________________

SITE = HALEAKALA NATIONAL PARK

AIRS SITE NUMBER = 15-009-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

HALE 1992 609 0 4842 5136 94.3

HALE 1993 1197 0 4668 5136 90.9

______________________________________________________________________________________111 SITE = HAWAII VOLCANOES NATIONAL PARK

AIRS SITE NUMBER = 15-001-0005

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

HAVO 1987 3176 0 4068 5136 79.2

HAVO 1991 244 0 4775 5136 93.0

______________________________________________________________________________________

SITE = INDIANA DUNES NATIONAL LAKESHORE

AIRS SITE NUMBER = 18-127-0020

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

INDU 1989 11619 0 3708 5136 72.2

INDU 1990 64667 66 3944 5136 76.8

______________________________________________________________________________________

SITE = ISLE ROYALE NATIONAL PARK

AIRS SITE NUMBER = 26-061-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

ISRO 1988 11169 0 2341 5136 45.6

ISRO 1991 6804 0 2631 5136 51.2

______________________________________________________________________________________

SITE = JOSHUA TREE NATIONAL PARK

AIRS SITE NUMBER = 06-065-9002 AND 06-071-9002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

JOTR 1990 57422 32 3310 5136 64.4

JOTR 1994 151025 224 4839 5136 94.2

______________________________________________________________________________________112 SITE = LASSEN VOLC ANIC NATIONAL PARK

AIRS SITE NUMBER = 06-089-3003

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

LAVO 1993 11637 0 4586 5136 89.3

LAVO 1994 38104 0 4845 5136 94.3

______________________________________________________________________________________

SITE = MAMMOTH CAVE NATIONAL PARK

AIRS SITE NUMBER = 21-061-0500

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

MACA 1988 51761 74 4828 5136 94.0

MACA 1993 15306 1 449 4 5136 87.5

______________________________________________________________________________________

SITE = MESA VERDE NATIONAL PARK

AIRS SITE NUMBER = 08-083-0101

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

MEVE 1994 20007 0 4832 5136 94.1

MEVE 1996 29698 0 4860 5136 94.6

______________________________________________________________________________________

SITE = MOUNT RAINIER NATIONAL PARK

AIRS SITE NUMBER = 53-053-1010

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

MORA 1993 2845 0 4565 5136 88.9

MORA 1994 6900 1 4224 5136 82.2

______________________________________________________________________________________113 SITE = NORTH CASCADES NATIONAL PARK

AIRS SITE NUMBER = 53-057-0013

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

NOCA 1996 2173 0 4198 5136 81.7

______________________________________________________________________________________

SITE = OLYMPIC NATIONAL PARK

AIRS SITE NUMBER = 53-009-0012

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

OLYM 1993 712 0 4585 5136 89.3

OLYM 1995 1858 0 4667 5136 90.9

______________________________________________________________________________________

SITE = PETRIFIED FOREST NATIONAL PARK

AIRS SITE NUMBER = 04-001-0012

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

PEFO 1988 14446 1 4830 5136 94.0

PEFO 1989 25791 1 4696 5136 91.4

______________________________________________________________________________________

SITE = PINNACLES NATIONAL MONUMENT

AIRS SITE NUMBER = 06-069-0003

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

PINN 1987 78874 86 4737 5136 92.2

PINN 1994 32951 0 4771 5136 92.9

______________________________________________________________________________________114 SITE = POINT REYES NATIONAL SEASHORE

AIRS SITE NUMBER = 06-041-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

PORE 1989 4813 0 4577 5136 89.1

PORE 1990 1784 0 4856 5136 94.5

______________________________________________________________________________________

SITE = REDWOOD NATIONAL PARK

AIRS SITE NUMBER = 06-015-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

REDW 1988 1814 0 4825 5136 93.9

REDW 1989 1015 0 4624 5136 90.0

______________________________________________________________________________________

SITE = ROCKY MOUNTAIN NATIONAL PARK

AIRS SITE NUMBER = 08-069-0007

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

ROMO 1990 5592 0 4091 5136 79.7

ROMO 1996 37033 0 4810 5136 93.7

______________________________________________________________________________________

SITE = SAGUARO NATIONAL PARK

AIRS SITE NUMBER = 04-019-0021

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SAGU 1987 9184 0 3970 5136 77.3

SAGU 1993 46792 1 4761 5136 92.7

______________________________________________________________________________________115 SITE = SANTA MONICA MTNS NATIONAL RECREATION AREA

AIRS SITE NUMBER = 06-037-1902

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SAMO 1989 73919 238 4770 5136 92.9

SAMO 1991 63864 145 4700 5136 91.5

______________________________________________________________________________________

SITE = SEQUOIA/KINGS CANYON NATIONAL PARKS - ASH MOUNTAIN

AIRS SITE NUMBER = 06-107-0005

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SEAM 1987 165538 245 4786 5136 93.2

SEAM 1995 97343 73 4812 5136 93.7

______________________________________________________________________________________

SITE = SEQUOIA/KINGS CANYON NATIONAL PARKS - GRANT GROVE

AIRS SITE NUMBER = 06-107-0007

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SEGG 1994 135176 163 4814 5136 93.7

SEGG 1995 80484 32 4264 5136 83.0

______________________________________________________________________________________

SITE = SEQUOIA/KINGS CANYON NATIONAL PARKS - LOWER KAWEAH

AIRS SITE NUMBER = 06-107-0006

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SELK 1993 130157 137 4847 5136 94.4

SELK 1995 71759 34 4786 5136 93.2

______________________________________________________________________________________116 SITE = SHENANDOAH NATIONAL PARK - BIG MEADOWS

AIRS SITE NUMBER = 51-113-0003

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SVBM 1988 81013 63 4448 5136 86.6

SVBM 1989 29297 0 449 9 5136 87.6

______________________________________________________________________________________

SITE = SHENANDOAH NATIONAL PA RK - DICKEY RIDGE

AIRS SITE NUMBER = 51-187-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SVDR 1988 99239 160 3784 5136 73.7

SVDR 1992 26841 0 4351 5136 84.7

______________________________________________________________________________________

SITE = SHENANDOAH NATIONAL PARK - SAWMILL RUN

AIRS SITE NUMBER = 51-015-0042

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

SVSR 1988 56538 47 4722 5136 91.9

SVSR 1989 16943 0 4490 5136 87.4

______________________________________________________________________________________

SITE = THEODORE ROOSEVELT NATIO NAL PARK - NORTH UNIT

AIRS SITE NUMBER = 38-053-0002

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

TRNO 1989 14184 0 4 206 5136 81.9

TRNO 1993 4573 0 4281 5136 83.4

______________________________________________________________________________________117 SITE = VOYAGEURS NATIONAL PARK

AIRS SITE NUMBER = 27-071-0101 AND 27-137-0034

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

VOYA 1988 10026 3 4643 5136 90.4

VOYA 1990 2234 0 4658 5136 90.7

______________________________________________________________________________________

SITE = YELLOWSTONE NATIONAL PARK

AIRS SITE NUMBER = 56-039-1010 AND 56-039-1011

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

YELL 1990 3794 0 4663 5136 90.8

YELL 1994 15212 0 4825 5136 93.9

______________________________________________________________________________________

SITE = YOSEMITE NATIONAL PA RK - CAMP MATHER

AIRS SITE NUMBER = 06-109-0004

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

YOCM 1989 21184 0 4321 5136 84.1

YOCM 1994 52334 0 4567 5136 88.9

______________________________________________________________________________________

SITE = YOSEMITE NATIONAL PA RK - TURTLEBACK DOME

AIRS SITE NUMBER = 06-043-0003

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

YOTD 1994 103128 21 4881 5136 95.0

YOTD 1995 65291 10 4467 5136 87.0

______________________________________________________________________________________118 SITE = YOSEMITE NATIONAL PARK - WAWONA VALLEY

AIRS SITE NUMBER = 06-043-0004

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

YOWV 1987 70513 44 4742 5136 92.3

YOWV 1994 27911 0 4720 5136 91.9

______________________________________________________________________________________

SITE = YOSEMITE NATIONAL PARK - YOSEMITE VALLEY

AIRS SITE NUMBER = 06-043-0005

APR-OCT

CUMULATIVE APR-OCT APR-OCT TOTAL PERCENT

W126 NO. OF HOURS NO. OF NO. OF DATA

EXPOSURE WITH OZONE VALID HOURS HOURS IN CAPTURE FOR

PARK YEAR PPB-HRS >=100PPB OF OZONE APR-OCT APR-OCT

YOYV 1991 15175 0 4620 5136 90.0

YOYV 1994 31740 1 4780 5136 93.1

______________________________________________________________________________________119 4. DEPOSITION

a. Introduction

Atmospheric deposition has been st udied extensively throughout the world, beginning in the 1800’s in England, Sweden, Norway, and Germany. Res earch has primarily focused on the deposition of acidic pollutants and long-term acidification. Many publications describe current conditions, monitoring and modeling methods, and the results of acidification experiments. In the United States, research on acidificati on was first begun in 1962 at Hubbard Brook, New Hampshire. Subsequent work in the Adirondack lakes and other areas furthered the understanding of acid deposition effects. It is now recognized that, in addition to causing acidification, deposition of pollutants can affect many ecosystem characteris tics, including nutrient cycling and biological diversity.

Although much progress has been made to control sulfur dioxide and nitr ogen oxide emissions, deposition of sulfur (S) and nitr ogen (N) compounds continues to be a problem in North America and Europe (Hedin and Likens 1996). As a result, certain sensitive freshw ater lakes and streams continue to lose acid-neutralizing capacity (ANC) and sensitive soils continue to be acidified. Other ecosystems, including forests, grasslands, es tuaries, and N-limited lakes exhibit unwanted
fertilization and other effect s from excess N deposition.

Federal Land Managers (FLMs) have documente d the effects of S and N deposition on many air quality related values (AQRVs). Documented eff ects include acidification of lakes, streams, and soils; leaching of nutrients from so ils; injury to high-elevation spruce forests; changes in terrestrial and aquatic species composition and abundance; changes in nutrient cycling; unnatura l fertilization of terrestrial ecosystems; and eu trophication of estuarine and some lake systems. FLMs recognize that other undocumented effects may also be occurring.

The FLAG deposition subgroup was formed to iden tify common approaches among these agencies for evaluating atmospheric deposition and its effects on AQRVs. In addition, the subgroup was
directed to recommend methods for establishing critical deposition loading values (“critical loads”) and, where possible, recommend such critical loads for specific area s. These tasks were assigned to Phase I or Phase II, depending on their degree of difficulty.

During the scoping process, the FLAG Deposition S ubgroup determined that Phase I tasks would include the summarization of information currently available about deposition and its effects on FLM areas and the development of recommendati ons on methods to model and evaluate current and future deposition and its effects on AQRVs. In addition, critical load values, where available from previous FLM guidance documents, would be referenced. FLMs agreed that site-specific AQRV and critical load information would be main tained on FLM web sites, rather than included in the Phase I report. In this way, the inform ation can be updated and the most recent versions made quickly available to the public. Some of this information is already available on FLM web sites, and the FLMs are committed to entering rema ining available information as soon as possible.

The subgroup recognizes that the development and refi nement of site-specific critical load values for all FLM areas are crucial for AQRV protection. However, because of the complexity of this undertaking, and the l ack of information for many areas , it was deferred to Phase II.120 During Phase II, the subgroup will focus efforts on developing methods for es tablishing critical deposition loading values for FLM areas, and estab lishing critical loads for areas with adequate information. For areas lacking sufficient informati on to determine critical loads, strategies will be developed to obtain needed information. Previously established critical loads will be reviewed and refined as necessary. The subgroup will also explor e alternative methods for estimating background deposition rates, including extrapol ation techniques or modeling that considers the spatial scale of ecosystems and differences in elevation. Method s for addressing problems with dry deposition and cloud and fog deposition measurements will also be considered. In addition, Phase II will provide research or monitoring recommendations to improve our understanding of deposition and its effects, including effects on cultural resources.

b. Current Trends in Deposition

From 80%-99% of S emissions an d from 83%-95% of nitrogen oxid es emissions are anthropogenic (NAPAP 1991). As a result, most S and N deposition is anthropogeni c in origin. The Clean Air Act
mandated reductions in S and N emi ssions that should result in decreases in S and N deposition. Deposition monitoring data can be used to identify decreases in de position. The Nation al Atmospheric Deposition Program ( NADP) provides one of the be st and most comprehensiv e long-term records of wet deposition chemistry in the U. S. Results from a trends analysis of 1983-1994 data from 153 NADP sites (Lynch et al. 1996) are shown on Figur es D-2, D-3, and D-4.

Figure D-2 shows that, between 1983-1994, wet precipitation sulf ate concentrations declined significantly at 38% of monitore d sites, presumably as a result of the Clean Air Act and mandated
emissions reductions. Numeric declines were observed at the majority of the remaining sites. Sulfate concentrations increased signific antly at only one site . A more recent anal ysis of 1995-1997 NADP
data from eastern sites sh owed continuing declines in sulfate c oncentrations and wet depositions over
much of the northea stern U.S. (Lynch et al. 2000). The EPA’s “National Air Quality and Emissions Trends Report, 1998,” also reported declines in the concentrations of sulfate in wet deposition, noting that the reductions were directly related to the la rge regional decreases in sulfur dioxide emissions resulting from Phase I of the Ac id Rain program (U.S. EPA 2000).

Despite emissions reductions, many FLM areas are located where current sulfate, and therefore total S, deposition remains high and where deposition is in excess of estim ated critical loads. A workgroup convened by the Ecological Societ y of America (ESA) in 1999 concluded that in some regions and ecosystems, current reductions of S emissions may be insufficient for ecologi cal recovery to occur (ESA 2000). Surface water sulfate concentrati ons are generally not decreasing because of the desorption of previously deposited S from soils (Johnson and Henderson 1979), and surface S mineralization with pond and lake water level ch anges (Schindler 1998). Elevated surface water sulfate concentrations from past deposition is expected to persist for decades even with continued emission declines. Chronic high surface water sulf ate levels reduce ANC, making ecosystems more vulnerable to chemical change during episodic events.

Figure D-3 shows that there is no consistent tr end in nitrate concentrat ions. The number of sites exhibiting increasing tr ends is nearly equal to the number exhib iting decreasing tren ds. However, the number of sites that expe rienced a statistically significant incr ease in nitrate (14%) was much greater than the number of sites that experienced a significan t decrease in nitrate (1.3%). Figure D-4 shows an
increase in ammonium conc entrations at most sites, with statis tically significant in creases at 22% of121 the sites, mostly in the West. Only one site had a significant d ecreasing trend. Given the increases in nitrate and ammonium, total N concentrations are clearly increasing at some locations.

Estimates of natural ba ckground S a nd N precipitation concentrations and deposition can be made from certain reliable early precipita tion chemistry data (Junge 1958), precipitation data from carefully
selected remote areas such as Al aska and Argentina, a nd to some extent from present NADP data from coastal Oregon and Alaska (NADP 1 982-1997). Except for coastal Or egon, present precipitation S and N concentrations throughout the co ntiguous states exceed these estimates of natural background levels, primarily due to anthropogeni c emissions of S and N compounds.

In this chapter, it is assumed that S is deposited in to the environment primarily as sulfate ion and N is deposited primarily as nitrate and ammonium i ons. Other ionic forms of S and N occur in the atmosphere, but information on their deposition into ecosystems is limited. For example, organic N may be important in some areas , but reliable measurement method s for organic N in atmospheric deposition are not widely available.122 Figure D-2. Trends in sulfate io n concentration, 1983-1994 (Lynch et al. 1996).

National Atmospheric Deposition Program I National Trends Network



### Trends in Sulfate lon Concentration

1 983 through 1994

'V Negative slope estimate, p>O .05

"'Negative slope estimate, p<O .05 I" £:.Positive slope estimate, p>0.05

.&. Positive slope estimate, p<0.05123 Figure D-3. Trends in nitrate io n concentration, 1983-1994 (Lynch et al. 1996).

National Atmospheric Deposition Program I National Trends Network



### Trends in Nitrate I on Concentration

1 983 through 1994

'V Negative slope estimate, p>O .05

"'Negative slope estimate, p<O .05 

## , £:.Positive slope estimate, p>0.05

.&. Positive slope estimate, p<0.05124 Figure D-4. Trends in ammonium io n concentration, 1983-1994 (Lynch et al. 1996).125 c. Identification and Assessment of AQRVs

AQRVs sensitive to pollutant deposition have been identified in various documents published by the USDA/FS, NPS, and FWS, which are listed in th e “General References” of Appendix H of this report. The FLMs have previously used a combination of approaches to identify AQRVs, including national and regional wo rkshops, regional reviews, and site-specific studies. AQRV identification was based on information from pee r-reviewed scientific literature and expert judgment. Because information on AQRVs may cha nge as new data becomes available, the FLMs agree that AQRV information will be made ava ilable on FLM web sites to allow for updating and improve accessibility, as discussed in the Introduction to this chapter.

Information on AQRVs for many USDA/F S Class I areas ca n be found at

http://www.fs.fed.u s/r6/aq/natarm

.

The USDA/FS is currently adding to and updating this information.

NPS and FWS are currently developing a web site wi th AQRV information that will be linked to the
NPS AirWeb at

http://www.nature.nps.gov/ard

and to the FWS National Wildlife Refuge System web site at

http://refuges.fws.gov.

FLMs recommend that permit applicants consult with the appropriate FLM (Appendix F) to determine the need for an AQRV analysis and, if applicable, the methods for the analysis.

All FLMs use a similar conceptual approach to identify AQRVs that reflects th e FLMs’ interest in maintaining the integrity of ec osystem structure and function a nd protecting the most sensitive ecosystem components. AQRVs can be categorized by the type of ecos ystem in which they are found, such as terrestrial, freshwater, and estuarine ec osystems. Each ecosystem and its AQRVs responds somewhat differently to de position and appro aches to evaluating deposition effects mu st therefore be developed accordingly. In terrestr ial ecosystems, detection of change s in production, decomposition, and nutrient cycling pro cesses provide inform ation on deposition stress. In aquatic and estuarine ecosystems, detection of changes in water ch emistry and aquatic community composition and structure provide similar informa tion. Table D-1 summari zes AQRV indicators that may be used to assess effects in various ecosystems.126 Table D-1. Indicators for monito ring and evaluating effects

from deposition of S and N.

ECOSYSTEM INDICATORS FOR SULFUR DEPOSITION
Freshwater Chemical change (ANC depression), changes in phytoplankton and benthic community composition, species diversity, biomass Terrestrial Leaching of soil cations , soil acidification, mobilization of
aluminum ions

Estuarine Saltwater not sensitive to S deposition ; leaching of nutrients may occur in sandy nearshore soils

ECOSYSTEM INDICATORS FOR NITROGEN DEPOSITION
Freshwater Chemical change (ANC depression), changes in phytoplankton and benthic community composition, species diversity, biomass Terrestrial Changes in: litter and soil carbon and N dynamics; biomass; soil N proc esses; litter decompos ition rates; soil microbe functional groups; so il organic matter quality and quantity; soilwater chemistry Estuarine Changes in: phytoplan kton species composition and biomass; aquatic invertebrates; seagrass health and distribution; nutrient ratios ; dissolved oxygen; trophic

status

Terrestrial, freshwater, and estuarine AQRVs are discussed below. Inaddition, methods to evaluate Sand N-induced deposition st ress are discussed.

Terrestrial Ecosystems

Terrestrial ecosystem AQRVs include flora, fauna, and soils. FLMs have identified, where possible, AQRVs, or characteristics of AQRVs , most likely to be sensitive to S and N deposition (“sensitive receptors”). For example, high-elevation spruce fore sts may be sensitive receptors. FLMs assess the
condition of these sensitive receptors by ev aluating some aspect of the receptor (the “sensitive receptor indicator”, or “indicator”). For example, an indi cator for high-elevation red spruce forests is the occurrence and extent of winter foliar injury. In ge neral, the FLM has focused on deposition effects to vegetation and chemical receptors in terrestrial ecosystems, with little emphasis on fauna. In addition, there is increasing awareness amo ng FLMs that cert ain soil fauna ( e.g. , microorganisms and invertebrates) are very sensitive to deposit ion and can be used as sensitive receptors.

In terrestrial ecosystems, sulfate production is regulated primarily by chemical processes (Johnson et al. 1983) and it is rarely a limiting nutrient. Soil re sponse to acidic depositi on can be ev aluated by monitoring the leaching of e ssential soil cations, so il acidification, and mobilization of ionic aluminum. These processes have been studied both in field and laboratory experiments, and are
defined in detail in th e literature (Mollitor a nd Raynal 1983, Richter et al. 1983, Johnson et al. 1983,
Reuss and Johnson 1986). Effects of S deposition can be detected by monitoring calcium and127 magnesium ions and S in the litte r layer and surface so ils; calcium, magnesium, potassium, and sulfate ions in soil solution; cation exchange capacity (C EC); and base saturation.

In general, biological AQRVs do not provide reliable indicators of S deposition in terrestrial ecosystems except under extreme S de position. Lichens have been used in some areas as biomonitors to demonstrate spatial trends in S deposition, particularly in ar eas with pronounced S deposition gradients. For example, isotopic analysis of liche ns from Mt. Zirkel Wilder ness, Colorado, indicated that power plants in the nearby Yampa Valley were the so urce of elevated S in the lichens (Jackson et al. 1996).

Unlike S, the production and mobil ity of N in ecosystems is regulat ed almost entirely by biological processes. N is a limiting nutrient in most terres trial and estuarine ecosys tems, and is seasonally limiting in many freshwat er ecosystems. Most ecosystems can re tain and process significant additions of N, with resulting increases in production and ch anges in species diversity, biomass, and nutrient cycling. However, these changes ar e usually considered to be undesir able in natural ecosystems. The ability to retain and process N varies significantly depending on waters hed successional status, site and fire history, soil conditions, vegetation, and ot her non-human factors. When N inputs exceed an ecosystem’s assimilation capacity, N is lost or leached, usually as n itrate, from the soil and can be detected in adjacent streams or lakes. This may occur following a major disturbance such as fire, logging, land use change, grazing, agriculture, or where atmospheri c N deposition or experimental inputs exceed what the ecosystem can assi milate (Fenn and Dunn 1989, Fenn 1991, Fenn et al. 1996,
Adams et al. 1997).

Studies in northern Europe (Dise and Wright 1995) found that European fore sts leached detectable levels of nitrate at inputs of about 10-25 kilograms N per hectare per year (kg N ha -1 yr -1 ). Tundra and high-elevation alpine site s may leach N at much lower levels of input. Mountain wa tersheds in the western U.S. show signs of N leakage at wet deposition levels of 3-5 kg N ha -1 yr -1 (Eilers et al. 1994;
Williams et al. 1996; Williams and Tonnessen, in review). However, even high elevation, poorly vegetated ecosystems with limited soil developmen t can process more than 80% of the atmospheric N input before it reaches the aquatic system (Campbell et al. 1995, Kendall et al. 1995). Although nitrogen leaching has often been used as an indi cator of excess N deposition, major changes occur in below- and aboveground biomass, species diversity, and nutrient cycling l ong before N input levels
are sufficient to cause nitrat e leaching (NAPAP 1993, Tilman et al. 1997, Vitousek et al. 1997). For example, with ambient deposition rates of 7-10 kg N ha -1 yr -1 , a Minnesota Long-Term Ecological Research (LTER) grassland study observed shifts from native, warm-season grasses to low diversity mixtures dominated by cool-season grasses and a greater than 50% d ecline in species richness (Wedin and Tilman 1996, Tilman et al. 1997). Significant losses in terrestrial diversity may have already
occurred over extensive areas of the U.S., particularly in forest understories, sh rublands, grasslands,
and in soil microbial communities.

Because significant ecological changes may occur befo re nitrate loss can be detected, more sensitive indicators than nitrate leaching are needed to eval uate N deposition effects. Such indicators include changes in carbon and N dynamic s of litter and soil and biomass (Aber and Driscoll 1997, Magill et al. 1997). With knowled ge of inputs and small-scale N fertilization studies, change s in soil organic matter quality and quantity in resp onse to N deposition can be evaluate d. Soil microbial communities control the quantity and quality of N available to ecosyste ms and may be very sensitive indicators of N deposition. Changes in soil microb e functional groups or biomass may provide good estimates of ecosystem critical loads and incremental effects. Soil N mineralization, small root growth, and carbon:nitrogen ratios of soil and microbial biomass are also sensit ive to N deposition. Evidence128 suggests that current deposition rates may alte r the production of dissolved organic carbon and organic N compounds in soils, whic h are important nutrient and energy sources for both terrestrial and aquatic ecosystems. These co uld also be used as indicators of N deposition effects. However, because there are many other variable s that also affect soil processes, it ma y be very difficult to discern effects on any soil indicators that are solely attributable to N.

Freshwater Ecosystems

AQRVs in freshwater ecosystems include lakes and streams and th eir associated flora and fauna. Sensitive receptors include water chemistry and clar ity, phytoplankton, zooplank ton, fish, amphibians, macroinvertebrates, and benthic organisms. Water chemistry indicators that respond to deposition include pH, ANC, conductance, cations and anions, me tals, and dissolved oxygen. Physical indicators, such as water clarity, and biological indicators, including species diversity , abundance, condition factor and productivity of fish, amphibians, macroinvertebrates, and plankton can also be used to detect deposition eff ects in aquatic ec osystems. Much resear ch has been done on the sensitivity of aquatic species to deposi tion, many of which are discussed in the 1990 National Ac id Precipitation Assessment Program (NAPAP) State of Science re port (NAPAP 1991a) and the 1998 NAPAP report (NAPAP 1998).

Sulfur is not a limiting nutrient in freshwater ecosystems. However, there are small regions of the U.S., including some FLM areas, where a relatively high percentage of surface water is sensitive to present acidic inputs. In these areas, S deposition can cause decreases in ANC and pH. For these sensitive or low-ANC waters, the be st approach to quanti fy S deposition effects is the procedure currently used, monitoring changes in ANC and pH.

Nitrogen deposition, like S deposition, can cause ep isodic acidification of surface water in certain sensitive high-elevation ecosystems that have lo w-ANC headwater lakes and streams. Episodic
acidification occurs in these areas when deposition is as low as 3-5 kg N ha -1 yr -1 (Williams et al. 1996).

Estuarine Ecosystems

AQRV sensitive receptors in estuarine ecosystems in clude plankton, seagrasses, and water chemistry and clarity. Associated coastal fore st and dune soils may also be usef ul as sensitive receptors. Water and soil nutrient concentrations, phytoplankton species co mposition and abundance, seagrass health, and dissolved oxygen concentrations can be used to evaluate deposition effects.

In estuaries, S is not a limiting nutrient. In addition, estuarine waters are highly buffered and, therefore, not subject to acidific ation. However, many coastal forest and dune soils are dominated by sandy soils that are sensitive to leaching of limiting nutrients beca use of very low cation exchange capacity (Au 1974). Monitoring for change in estuar ine areas with high S deposition should therefore focus on soil ion mobility. As so il calcium and magnesium levels are generally adequate because of deposition from marine sources, potassium is likel y the only limiting nutrient subject to significant loss by sulfate leaching.

The role of N in estuaries is pr obably the best-documented example of anthropogenic alteration with a literature record dating back to the 1950s. Production an d use of fertilizers, land use cha nges, and fossil fuel combustion have greatly increased the availa ble N, normally a li miting nutrient, which
enters coastal waters. This has increased estu arine production and accel erated the process of129 eutrophication. Eutrophication can result in dramatic algae blooms, anoxia, the production of toxic hydrogen sulfide gas, and species extirpation in estuarine ecosyste ms. Human induced eutrophication has been documented for many areas along the Atlantic and Gulf coasts, including the Chesapeake Bay, Tampa Bay, Sarasota Bay, Flor ida Bay, and Long Island Sound.

A number of FLM areas along the Atla ntic and Gulf coasts contain sign ificant coastal wa ters that may be sensitive to eutrophication. L ittle is known about excess N effects in most of these areas, although eutrophication is well documented in Florida Bay, located in Evergl ades National Park. Also, recent
evidence indicates that coastal waters in Chassahowitzka Wilder ness (Florida) experience N-induced algal blooms (Dixon and Estevez in draft). In most coastal waters , 10-45% of the N entering the system is atmospheric, e ither from direct depositi on to surface water or depo sition to the watershed. Complete elimination of atmosphe ric N inputs would not entirely mitigate ecosyst em change due to N because of the substantial contributions from ag ricultural and urban runo ff. However, for most estuaries, any reduction in N input would be benefi cial in restoring ecosyste m structure and function.

The monitoring procedures recommended, and currently used, in estuaries are similar to those used in freshwater, with emphasis on incremental changes in plankton, aquatic plant, benthic, and invertebrate community composition; species diversity, distributio n, and biomass; and ec osystem trophic status.

Significance of Long-Term Monitoring to Evaluate Tr ends and Validate Modeling

Long-term monitoring is critical to evaluate trends in deposition and deposition effects. Monitoring programs should concentrate not only on areas with high past and/or present sulfate, nitrate, or ammonium deposition, but also in areas that are very sensitive to depos ition and in areas where deposition is expected to increase. For selected monitoring sites, the FLM should (1) obtain ion deposition data for the site, as fr om NADP or CASTNet, (2) identify sensitive AQRVs and appropriate variables to monitor, (3) evalua te the present conditi on of the sensitive AQRVs, (4) determine the degree to which results from one site can be extrapolated to other FLM areas in the region, and lastly (5) implement a long-term mon itoring program, using carefu lly selected variables.

Long-term monitoring data are also needed to support and validate m odels used to pr edict deposition and deposition effects, including the effects of incr eases or decreases of S and N on ecosystems. Long term studies in both aquatic and terrestrial ecosys tems such as Hu bbard Brook, Lake Tahoe, and the Experimental Lakes Area have pr ovided useful information for mo deling (Bormann and Likens 1967,
Holm-Hanson et al. 1976, Likens and Bormann 1977, Leonard et al. 1979, Byron and Eloranta 1984,
Schindler et al. 1985, Schindler 1987, Schindler et al. 1990, Jassby et al. 1995). NAPAP and the
National Science Foundation LTER program have addressed monitori ng to meet modeling needs in both terrestrial and aquatic ecosystems.

Data requirements to support models vary, but the qua lity of input data will determine the quality of a model’s predictions. Modeling is further discu ssed in the “Other AQ RV Identification and
Assessment Tools” secti on of this chapter.

d. Determining Critical Loads

Critical load is defined by FLMs as “the conc entration of air pollution above which a specific deleterious effect may occur.” Critical loads have been widely accepted in Europe and Canada as a basis for negotiating control strategies for transboundary air pollution (Posch et al. 1997).130 In Canada, researchers have estima ted the critical loads of S in we t deposition necessary to protect moderately sensitive lakes in eastern provinces. That value, equivalent to 6.7 kg ha -1 yr -1 of S in wet deposition, was used by Canada to argue for the U. S. to implement the Clean Air Act Amendments of 1990, which call for the initial reduction of sulfur dioxide emissions in th e eastern U.S. and later
from all electric utilities nationwide. With additional data on lake a nd stream chemistry available for sensitive systems in Nova Scotia, Ontario, and Qu ebec, the Canadians are now recommending a more stringent critical load, equivalent to 2.7 kg ha -1 yr -1 of wet deposition S.

In both European countries and in North America, attention has expanded beyond ecosystem damage caused by S deposition to ecosystem damage caused by N deposition. In some European forests, chronically high N deposition has ex ceeded the assimilation capacity of local ecosystems, resulting in the release of nitrate into surface waters (Dise an d Wright 1995). Watersheds that are leaking nitrate into surface waters during the growing season , are referred to as "N saturated" (Aber et al. 1989). Nitrogen saturation has been linked to forest de cline in Europe (Schulze 1989). Based on a set of regional N addition experiments conducted at sites in northern Europe (NIT REX), Wright (1995) recommended a N critical load of less than 10 kg ha -1 yr -1 to protect European forests and freshwaters from N saturation. However, this critical load do es not protect ecosystems from the changes caused by N deposition prior to actual N saturation, includi ng shifts in composition and abundance of soil fauna species and alterations in soil chemistry.

In the United States, two states have attempted to set deposition standards or critical loads to protect sensitive ecosystems. In 1982, the State of Minnesota passed the Acid Deposition Control Act to limit wet sulfate deposition to 11 kg ha -1 yr -1 , which is equivalent to 3.7 kg S ha -1 yr -1 . At this sulfate level, precipitation pH was likely to remain above 4.7, which would protect lake s with ANC less than 50 microequivalents per liter (μeq l -1 ). This critical load was to be achieved by controls on large sources
of sulfur dioxide in Minnesota. As of 1990, monitoring by state offi cials showed no evidence of lake acidification under the sulfur dioxide control program. However, the efficacy of this control strategy is still uncertain, because as much as 90% of the sulfate deposite d in northern Minnesota may have sources outside of the state (Orr et al. 1992).

In 1989, the California legislature adopted the Atmospheric Acidity Protection Act, which required the Air Resources Board (CARB) to "develop and adopt standards, to the extent supportable by scientific data, at levels which are necessary and appropriate to pr otect public hea lth and sensitive ecosystems from adverse effects resulting from at mospheric acidity" (CARB 1993). An assessment of existing data identified the high elevation waters heds, surface waters, and mixed conifer forests of the Sierra Nevada and the Los Angeles Basin as se nsitive ecosystems. CARB analyses suggested that appropriate standards would include a critical load value for inorganic N to protect forests, and
critical loads for both N and S to protect poorly buffered lakes an d streams. However, no acidity standards to protect human health or critical loads to protect ecosystems have been set in California to
date.

The Clean Air Act Amendments of 1990, Title IV, section 404, called on the Environmental Protection Agency (EPA) to prepare a report on the feasibility and effectiveness of setting deposition standards nationwide to protect sensitive aquatic and terrestrial resources. The completed report includes a number of modeling an alyses that project the effect of reductions in both S and N deposition in areas studied during NAPAP. EPA concluded that depositi on standards could not be set at this time because of 1) the l ack of clearly defined policy regardi ng appropriate or desired goals for protecting sensitive aquatic or terrestrial resources, and 2) key scientific un certainties, particularly regarding nitrogen watershed processes. In a ddition, EPA recognized that a national deposition131 standard might be inappropriate because of differ ences among ecosystems. However, in response to public comments on the report, EP A stated that “Given an adequate level of monitoring and assessment data, Class I areas could serve as pote ntial targets for standard setting activities.” (U.S. EPA 1995)

Critical Loads in FLM Areas

In the Clean Air Act, as amende d in 1977, Congress gave FLMs an “affirmative responsibility” to protect AQRVs from the adverse effe cts of air pollution. Congress’ in tent was, “...In cases of doubt the land manager should err on th e side of protecting the air qu ality-related values for future generations...” (Senate Report No. 95- 127, 95th Congress, 1st Session, 1977). In an effort to ensure AQRV protection, FLMs have established critical loads for many FLM ar eas. FLMs agree that a critical load should protect the most sensitive AQR Vs within each FLM area an d should be based on
the best science available. As ne w scientific information becomes av ailable, critical loads should be reviewed and updated. Critical loads should ensu re that no unacceptable change occurs to the

resource.

FLMs have previously used a comb ination of approaches to establish critical loads, including national and regional workshops, re gional reviews, and site-specific studie s (see Appendix H) . In all cases, the FLMs have used peer-revie wed scientific literature an d expert judgment to make their decisions. For example, the NPS has established critical loads for several national parks through regional reviews that have evaluated existing information on air quality, deposition, and effects on AQRVs in national parks. For these reviews, NPS grouped parks by region and ecosystem type, including the Pacific Northwest, the Colorado Plateau, and the Rocky M ountains, and conducted an empirical assessment of the status of aquatic and terrestrial resources . An analysis of deposition effects was done, using current deposition data for S and N and effects in formation from field observations and research. In the Pacific Northwest region, this analysis led re searchers to recommend guidelines for critical loads of S and N to protect sensitive resources, particularly low- ANC lakes, streams and ponds. These guidelines for critical loads will be available on the NPS AirWeb site in the near future at:

http://www.nature.nps.gov/ard

.

The FWS is also committed to establishing critical load guidelines to protect sensitive resources. These guidelines for critical loads will be ava ilable through the FWS National Wildlife Refuges site

at: http://refuges.fws.gov

.

The USDA/FS has conducted a series of national a nd regional workshops to establish critical loads
and concern thresholds. In the la te 1980s, the USDA/F S published prototype methods for evaluating the effects of acid deposition on AQRVs, including A Screening Procedure to Evaluate Air Pollution Effects on Class I Wilderness Areas (Fox et al. 1989) and Guidelines for Measuring the Physical, Chemical, and Biological Condition of Wilderness Ecosystems (Fox et al. 1987)

. Subsequently, the USDA/FS held regional workshops to develop scr eening procedures for new air pollutant emissions sources. These workshops were comprised of national and regional USDA/FS land managers, deposition experts from the academic and air pollu tion research community, and agency air quality professionals. Dependent on the workshop leadership , each regional worksh op followed a slightly different process and a variety of outputs and fo rmats resulted. However, all workshops used a collaborative process to de termine S and N deposition rates that would pose a risk to the aquatic and terrestrial ecosystems protected in FLM areas, while addressing the scientific uncertainty inherent in132 ecosystem response to acid ic deposition. Critical load guideli nes for many USDA/FS Class I areas are published in workshop reports (see Appendix H) and are available at:

http://www.fs.fed.u s/r6/aq/natarm

.

The USDA/FS is currently adding to and updating this information.

As resources permit, during Phase II of FLAG, the subgroup will develop methods for establishing critical deposition loading values for all FLM areas and recommend critical loads for areas where adequate information exists. For areas lacking sufficient informat ion to determine critical loads, strategies will be developed to obtain needed information.

e. Other AQRV Identificati on and Assessment Tools

In addition to AQRV monitoring, there are several tools available to the FLM for identifying AQRVs and assessing the response of sensitive AQRVs to pollutant deposition. These include the aquatic effects expert system component of the FWS/NPS Air Synthesis, th e Natural Resource Information System – Air Module (NRIS-Air), and deposition models such as the Model of Acidification of Groundwater in Catchments (MAGIC) and MAGIC- With Aggregated Nitrogen Dynamics (MAGICWAND).

Air Synthesis

Air Synthesis is an information management and decision-support co mputer system under development by NPS and FWS. Air S ynthesis is designed to assist FLMs in determining potential effects of pollutants on AQRVs. It contains information on air qua lity and its effects in parks and
wildernesses as well as natural resource data and annotated bibli ographies of current literature on deposition. An interactive expert system module is under developmen t for inclusion in Air Synthesis
to allow FLMs to assess the current status of fr eshwaters and determine if these resources are likely to be affected by deposition of S or N. The aqua tic effects expert system is being developed by regional scientists. This system will allow FLMs to input existing surface water data for lakes and streams to determine: (1) the aci dification status of the waters , (2) the likely cause of high concentrations of acid anions ( e.g., deposition, land use, organic input s) and, (3) the sensitivity of the
waters to increases in N or S de position. Results can be displayed in a geographic information system (GIS) image that color-codes the acidification status of lakes and streams. In addition, the expert system evaluates the completeness and the amount of uncertainty in water chemistry data sets. Air Synthesis will be available through the NPS AirWeb at:

http://www.nature.nps.gov/ard

or the FWS National Wildlife Re fuge System web site at:

http://refuges.fws.gov

.

Natural Resource Information Sy stem – Air Module (NRIS-Air)

The Air Module is part of the US DA/FS Natural Res ource Information System th at integrates various physical, biological and socioeconomic data with in a system of database, map-based spatial information, and analytical tool s. Version 1.0 of NRIS-Air, re leased in November 1998, tracks133 AQRVs, sensitive receptor s and indicators for each of the US DA/FS Class I areas. The water submodule provides data storage, reports, and tools fo r evaluating locally en tered water quality and wet deposition data. It also integrates the NADP data set a nd the entire National Surface Water Survey including the Eastern and Western Lakes Su rveys and the National St ream Survey. Future
NRIS-Air versions under development will provide the information structur e for visibility, flora, fauna, soil, geologic resources, cultural resour ces, and air quality data, as well as providing an air pollution permit tracking system.

Information from NRIS-Air, including USDA/FS Cl ass I area AQRV information, is available at:

http://www.fs.fed.us/r6/aq/natarm

.

Deposition Effects Models

A number of watershed process models have been de veloped and tested in an attempt to simulate the effects of S and N on soils, forests, and surface waters. Th ese models are used by FLMs to predict effects from increases in deposition and vary from detailed, compartment models of watersheds to lumped parameter models that do not track different ions thr ough each soil compartment. For a review of models developed under NAPAP see NAPAP 1991.

A commonly applied watershed model is MAGIC. MAGIC was first devel oped for eastern U.S. watersheds and then extensively tested and validat ed throughout Europe a nd North America (Cosby
et al. 1985, 1995, 1996). The model was used by NAPAP in its 1990 Integrated Assessment to project surface water chemistry resulting from va rious deposition scenar ios (NAPAP 1991b). In another application in the easte rn U.S., MAGIC has been linked with a simple, empirical, dose/response fish model developed at University of Virginia, that make s it possible to predict changes in fish productivity based on modeled changes in streamwater chemistry.

As a result of NAPAP, there was increased awar eness of the potential impacts of inorganic N deposition on watersheds and surface waters. In response, the MAGIC mo del was updated with a module called With Aggregated Nitrogen Dynamics (WAND). MA GIC-WAND is a process-based model that uses site-specific information on hydrology, soils, and hydr ochemistry. The model predicts changes through time in lake or stream chemistry. These time-serie s of changes in pH and ANC can subsequently be used by FLMs to cal culate critical S or N loads for watersheds.

MAGIC-WAND has been extensively tested in the Adirondacks and at watersheds in Maine. For example, the Bear Brook Waters hed Manipulation Project uses MA GIC-WAND to predict the effects of experimentally added N and S on a test watershed. MAGIC-WAND has also been applied to watersheds in FLM areas in the Cascades, the Sierra Nevada, the Rocky Mountains, and the Wind River Range in an effort to quant ify critical S and N loads to aquatic and terrestrial resources. In the southeastern U.S., MAGIC-WAND is being used under the auspices of the Southern Appalachian Mountains Initiative (SAMI) to predict the effects of future deposition s cenarios on FLM areas. Future SAMI modeling efforts will link watershed m odel results with fish dose/response models. The ultimate goal is to calibrate MAGI C-WAND with landscape level data in order to set regional critical
loads.

Other models are also in use. For example, the USDA/FS Rocky Mounta in Region recommends using either CALPUFF or ISCST (or other approve d models) to estimate S and N deposition. The Screening Methodology for Calculating ANC Change to Hi gh Elevation Lakes (USDA Forest Service134 2000) summarizes procedures for estimating tota l deposition of S and N. The document also recommends computations for estimating alkalinity changes in lakes caused by increases in S and N deposition. Another model, the Nutr ient Cycling Model (NuCM) has been used in the East to predict the effect of changes in de position on nutrient concentrati ons in soils and vegetation.

f. Recommendations and Guidance for Evaluati ng Potential Effects from Proposed Increases in Deposition to an FLM Area

FLMs often request that proponents of new emissi ons sources or modifications of existing sources near FLM areas provide sufficient information for the FLM to evaluate the potential effects of emissions increases on AQRVs. FLMs have provide d guidance for applican ts through guidance documents, correspondence, meetings, and phone co nsultations. This chapter summarizes current guidance for the evaluation of new emissions on deposition and sensitive AQRVs and includes
recommendations for:

• the types of data, information, and analysis needed before a pe rmit can be considered complete, including analytical and modeli ng protocols for a prop onent’s use in conduc ting an AQRV impact analysis;

• approaches and sources of appropriate values for estimating wet and dry deposition; and

• permit conditions to mitigate source impacts.

These recommendations can most easily be described using a flow chart. Figure D-1 summarizes the approaches to be taken to evaluate a proposed action.135 No/Unknown

Yes START

Current

adverse
effects to AQRVs from deposition/

Critical load

exceeded
? Yes

Unknown No New

emissions

cause or contribute to
adverse effects/Critical

load exceeded

? FLM may recommend
elements of "a" or "b"

or (if necessary) "c"

a. The applicant should use one or more of the following: - Stricter (than BACT) controls (e.g., Lowest Achievable Emission Rate [LAER]). - Emission offsets located in an area that, considering geographic and meteorological factors, will benefit the impacted wilderness or park, as demostrated by modeling. - Regional modeling to identify sources contributing significantly to deposition adverse effects; SIP revision to reduce emissions contributing to adverse effects. (See text for discussion of mitigation options.) b. Deposition and deposition effects monitoring/research in the FLM area. c. Denial of permit. FLM unlikely to object to permit FLM unlikely to object to permit; may recommend "b" Figure D-1. FLM resonse to potential deposition effects from new
emissions sources. Figure D-1. FLM response to pote ntial deposition effects from new

emissions sources.136 The flowchart begins with the question, “Are there currently adverse effects from pollutant deposition to AQRVs in the FLM area?” To answer this question, the FL M needs information on deposition-sensitive AQRVs, deposition load s at which these AQRVs are affected ( i.e., critical loads), and the current pollutant deposition rates in the ar ea. In areas where no info rmation is available, information from a nearby, or eco logically similar area, may be us ed. An adverse effect may be expected to occur if the critical load is exceeded for an area. AQR V and critical load information are discussed earlier in this report. Procedures fo r estimating current pollutant deposition rates are summarized in the section, “Estim ation of Current and Future Depos ition Rates.” After considering this information, the FLM determines if adverse effects to AQRVs already exist at an area. If adverse effects are present, the FLM may recommend that “a” or “b” or both of Figure D-1 are included as permit conditions. If these recommendations, or some combination of them, cannot be implemented, the FLM is likely to recommend denial of the permit.

If there are no current documented adverse effects from pollutant de position to AQRVs, or there is a lack of information on deposition and deposition effects in the ar ea (and informatio n from nearby or ecologically similar area s is unavailable), the FLM may ask, “Will the proposed action cause an adverse effect to AQRVs?” The information needed to answer this question includes the information listed above regarding AQRVs, critical loads, and current deposition rates. In addition, an estimate is needed of the future predicted deposition rate. Procedures for this esti mate are found in the “Estimation of Current and Future Depo sition Rates” sectio n of this report.

With this information, the FLM can determine if the proposed action is lik ely to cause an adverse effect to AQRVs. If the answer is no, or unknown, the FLM would not object to the action because of potential deposition effects. The FLM may still, however, object to the action for other reasons including an inadequate best av ailable control technology analysis , predicted National Ambient Air Quality Standards violati ons, predicted Class I increment impact s, or other predicted AQRV impacts.
If the available information is insufficient for the FL M to determine if the pr oposed action will cause an adverse effect to AQRVs, th e FLM may ask for depo sition and deposition effects monitoring and/or research in the FLM area ( i.e., item “b”). If the answ er is yes and the pro posed action will likely cause an adverse effect to AQ RVs, the FLM may re commend permit conditions that ensure mitigation, including stricter emissions controls a nd effective emissions offsets ( i.e., item “a”). If no mitigation is possible, the FLM is likely to recommend denial of the permit.

Available Deposition Monitoring Data

Atmospheric pollutants are deposited to ecosyst ems primarily through wet deposition and dry deposition. FLMs participate in national monitori ng programs to monitor wet and dry deposition, including the National At mospheric Deposition Progr am (NADP) and the Clean Air Status and Trends Network (CASTNet). A 1999 repor t, “The Role of Monitoring Netw orks in the Management of the Nation’s Air Quality,” (CENR, 1999) identified these two networks as being critical for characterizing baseline air quality data in the U.S.

Wet Deposition

Wet deposition includes ra in, snow, fog, cloudwater , and dew. In most FLM areas, rain and snow are the primary contributors to wet de position. However, in some high elevation ar eas, fog, cloudwater, and dew are significant contributors, as discussed below.

Because rain and snow are the pr imary constituents of wet depos ition at most FLM areas, the FLM generally relies on data from NADP to evaluate wet deposition of pollutants. NADP samplers collect137 rain and snow and NADP has documented deposition for many y ears in a nationwi de network that currently includes over 22 0 monitoring sites. The network coll ects data to evaluate spatial and temporal long-term trends in precip itation chemistry. The precipitation at each site is collected weekly and sent to a central analytical la boratory for analysis of hydrogen ( acidity as pH), sulfate, nitrate, ammonium, chloride, and base catio ns, including calcium, magnesium, potassium, and sodium. Data and isopleth maps of pollutant conc entrations and depositi on are available on the NADP web site at:

http://nadp.sws.uiuc.edu/

FLMs agree that it is preferable to obtain NADP data from the web site, rather than summarizing wet deposition data in this report. In this way, current data can be easily accesse d by FLMs and the public.

Approximately 50 FLM area s have NADP samplers in or immedi ately adjacent to them. Because
some of these areas are classified as wilderness, FLMs install sa mpling equipment in adjacent nonwilderness areas in order to preserve the wilderness character of the ar ea. Ambient air in these adjacent areas is considered representative of air in the wilderness area.

A number of FLM areas do not have an NADP sample r in or adjacent to th em. Where possible, the
FLM has identified an NADP site wh ose data may be used to characte rize deposition at the area. This information is appended to this Deposition chapter (Table D-2). Deposition rate s generally increase with elevation and deposition in high-elevation areas may be difficult to characterize with data from a
lower-elevation NADP site. FLM consulta tion may be necessary to estimate depositi on in these areas.

Areas that experience si gnificant depositio n from fog and cloudwater or large amounts of snow may need to use alternate sampling me thods and data in ad dition to NADP protocol s and NADP data to characterize them. Wet deposition in these areas may need to be sampled with alternate methods, including cloudwater samplers and snowpack samplin g or estimated by modeling . At sites where such
data or modeled estimates are availa ble, they should be used to calcu late total depositi on. At mountain sites frequented by clouds and fog, deposition from clouds may equal or exceed that from precipitation. Cloud water is generally more acidic an d contains higher concentrations of base cations than rain water; therefore, it can contribute significantly to total loadings of S and N (Hemmerlein and Perkins 1992). Various methods have been developed to measure deposition from cloudwater. The Mountain Acid Deposition Progra m (MADPro) used automated cloud water collectors to sample at three high-elevation eastern sites (Anderson et al. 1999). Forests covered by fog for significant periods of time may be especially susceptible to injury from acid deposition. Acidic cloud water has predisposed red spruce in the high elevations of the northeast U.S. Appalachians to winter injury and cumulative impacts with other bio tic and abiotic stresses have cau sed mortality. The contribution of clouds and fog to deposition at hi gh elevations may overshadow bo th deposition from precipitation and dry deposition (Hidy 1998). The U.S. EPA esti mated that as a result of cloud cover, high elevation forests might experience four times the amount of total pollutant deposition as lower
elevation forests without cloud cove r (NAPAP 1991). High elevation lakes are also impacted by fog and clouds, as well as rain and snow. Measurements in high el evation areas that do not include all
contributions to wet deposition will result in unde r-estimates.

Modeling has been used to estimate total wet deposition in some areas. For example, the Southern Appalachian Man and the Biosphere Cooperative (as part of the Southern Appalachian Assessment) has used NADP data, topographical da ta, and meteorological data to model wet deposition loading at locations in the southeastern U.S.138 Dry Deposition

Dry deposition includes gases, aerosols and partic les. The primary gases involved with N and S deposition are ammonia (NH 3 ), nitric oxide (NO), nitrogen dioxide (NO 2 ), nitric acid (HNO 3 ), and sulfur dioxide (SO 2 ), while the primary particles are nitrate (NO 3 - ), ammonium (NH 4 + ), and sulfate (SO 4 2- ) ions (Hanson and Lindberg , 1991). Ammonia, NO, NO 2 and SO 2 are taken up by plants through stomat a, while HNO 3 , due to its high depos ition velocity, is deposite d to plant surfaces in addition to being taken up by stomat a. Nitrate, ammonium, and sulf ate particles deposit to surfaces (Bytnerowicz and Fenn, 1996).

Dry deposition is much mo re difficult to estimate than wet deposition. The estimation of dry deposition rates requires information on the ambient concentrations of pollutants, meteorological data, and information on land use, vegetation, and surface conditions, all of which ar e site-specific. Because of this site-specificity, it is diffic ult to spatially extrapolate dry depos ition data as is often done for wet deposition data.

In general, FLMs rely on data from CASTNet for estimates of dry deposition in FLM areas http://www.epa.gov/castnet/ CASTNet was developed by EPA, as a
result of the Clean Air Act Amendmen ts of 1990, and currently includes over 70 sites. These include a combination of former National Dry Deposition Network sites, Park Research and Intensive Monitoring of Ecosystems Network sites (PRIMENet), and others. Dry deposition is measured at 26 NPS areas and 2 USDA/FS areas. FLMs agree that it is preferable to obtain CASTNet data from the web site, rather than summar izing dry deposition data in this report . In this way, cu rrent data can be easily accessed by FL Ms and the public.

Other methods for measuring dry de position are avai lable. For example, information on vertical changes in concentrations of major gases and particle s of interest over plant canopies can be used for calculation of deposition of th ese compounds to forests and other ecosystems (Hicks et al. , 1987). Models, such as “Big-Leaf” (Baldocchi et al. , 1987) allow estimating dry deposition to uniform canopies, such as agricultural crop s or lowland forests. However, no models have been developed so
far for reliable estimates of de position of gases and particles to forests and other ecosystems in complex mountain terrain (Bytnerowicz et al. , 1997). Therefore, no good large-scale estimates of dry deposition are available for western U.S. forests.

Another approach to evaluating dry deposition is net throughfall techni que. By measuring concentrations of ions in throughfall (bulk precip itation) and after subtracting concentrations of the same ions in precipitation in an op en area, fluxes of ions such as nitrate, ammonium, and sulfate can be calculated. A branch washing technique is simila r to the net throughfall a pproach and is used when no wet precipitation is present. The pre-washed branch es are exposed to ambien t air for a certain time period and then carefully rinsed with water (Lin dberg and Lovett, 1985). Information about amounts of nitrate, ammonium and sulfate rinsed from branch es of a known surface area , time of exposure, and leaf area index of a given forest stand allow the calculation of fluxes of the measured ions to trees. Adding stomatal uptake of gase s (calculated from information on gas concentration and stomatal conductance), and estimates of deposition to other la ndscape forms (such as so ils and rocks) allow for quite reliable estimates of dry deposition at a forest stand level (Bytnerowicz et al. , 2000). Such
estimates have been made for the subalpine zone of the eastern Sierra Neva da and mixed conifer139 forests on the western Sierra Nevada and the Sa n Bernardino Mountains (Bytnerowicz and Fenn, 1996; Bytnerowicz et al. , 1999). Both the net th roughfall and branch washing techniques, although providing relatively accurate estima tes of deposition to certain ecosys tems, cannot be applied to every type of vegetation. These techniques work well for conifers with rela tively thick cuti cles. For plants with thinner cuticle, extraction of i ons from plant interior or transc uticular uptake of deposited ions may not allow for making good estimate s of dry deposition to plant surfaces.

Recent developments, such as passive samplers that allow for relatively inexpe nsive determinations of nitric oxide, nitrogen dioxide, ammonia, nitric ac id and sulfur dioxide concentrations, provide some promising opportunities for large-scale estimates of distribution of these pollu tants. This, together with information on landscape-level vegetation covera ge, leaf area index, and deposition velocity of the monitored pollutants, will allow calculating deposition of the measured gases to various landscape forms. Although this approach would not include deposition fluxes of particulate pollutants, a large portion of dry N and S de position (gases) would be covered. In formation on fluxes of the N and S particulate component (nitrate, ammonium, and sulf ate ion concentrations) can be estimated based on
their concentrations from annular denuder/filter pack systems or other co mparable techniques and literature values of depositio n velocities of these ions.

For many FLM areas, detailed site-specific inform ation and monitoring needed for dry deposition measurements are not available. Therefore, the FLM may choose to recommend a reasonable estimate of dry deposition. NAPAP’s 1991 summary report concluded that dr y deposition of sulfur is 30-60% of the total (wet plus dry) deposition at regionally re presentative sites; dry depo sition of nitrogen is 30- 70% of the total (wet plus dry) deposition at re gionally representative s ites (NAPAP 1991a). An analysis of one year (1991) of NADP, CASTNet, and IMPROVE (Interagency Monitoring of Protected Visual Environments) data from national parks and wildernesses found that wet deposition dominated total deposition in both the East and the West. Dry deposition of sulfur was 20-50% of the total; dry deposition of nitrogen wa s 30-60% of the total (Hidy 1998). These estimates, and similar ones, have led to the common as sumption that dry deposition is approximately 50% of the total deposition. Therefore, for many FLM areas without on-site or nearby representative dry deposition sampling, the FLM may recommend that dry depo sition is equal to we t deposition. The FLM
recommends this as a “best availabl e estimate,” recognizing that in some areas it may result in underor over-estimating total deposition. Total deposition, which is the sum of wet plus dry deposition, therefore equals twic e the wet deposition.

In summary,

Total Deposition = Wet Deposition + Dry Deposition

Or,

Total Deposition = 2 x Wet Deposition, assuming Dry Deposition = Wet Deposition

Table D-2 identifies monitoring stations in or ne ar FLM areas for estimating wet and dry deposition
values. For some areas the FLM as sumes that dry deposition equals wet deposition, r ecognizing that this may result in under- or over- estimates of total deposition. Th e table provides information on the appropriate dry depo sition data to use at sites where data are available.

FLMs will continue to participat e in monitoring and re search to furt her our understanding of dry deposition dynamics and improve ou r measurements of dry deposition.140 Other Deposition Measurement Methods

Pollutant deposition, particularly in areas where traditional wet an d dry deposition sampling is impractical, can also be estimated by other methods. These methods include bul k samplers that collect both wet and dry deposition and snow pack measurements that estimate the total amount of pollutants
in the snow column at the time of maximum snow accumulation. Special methods have also been developed for collecting fog and cloud water (Anderson et al. 1999).

In addition, methods are being developed to estimate dry deposition rates from pollutant concentrations obtained by IMPROVE fine particle samplers. IMPROVE samp lers are located at many FLM areas and ex panded coverage is planned for 1999.

Modeling Deposition Rates

Deposition from existi ng sources can be estimated from deposition moni toring data, but contributions to deposition from the proposed source and other sources permitte d but not yet operating must be
modeled.

Modeling should be done in acc ordance with recommendations developed by the Interagency Workgroup on Air Quality Mo deling (IWAQM) Phase 2:

http://www.epa.gov/scram001/7thconf/calpuff/phase2.pdf

.

IWAQM provides the procedures that can be used to estimate S and N depos ition from a proposed source and other sources permitted bu t not yet operating. The FLMs pr opose that these procedures be used to estimate S and N deposition. For S depos ition, the wet and dry fluxe s of sulfur dioxide and sulfate are calculated, normalized by the molecular weight of S, and expressed as total S. For N deposition, IWAQM recommends that the wet and dry fluxes of nitric acid (HNO 3 ) and nitrate (NO 3 - ) and the dry flux of nitrogen oxides (NO

x ) be calculated, normalized by th e molecular weight of N, and expressed as total N. In additio n, the FLMs agree that wet and dry fluxes of ammonium sulfate ((NH 4 ) 2 SO 4 )) and ammonium nitrate (NH 4 NO 3 ) should be calculated, normalized by the molecular weight of N, and added to the estimate of total N. Therefore, total N de position is the sum of N contributed by dry and wet fluxes of HNO 3 , NO 3 - , (NH 4 ) 2 SO 4 , and NH 4 NO 3 and the dry flux of NO

x .

The FLMs recognize that the ammonia (NH 3 ) in these compounds is deri ved from both man-made and natural sources. Free gaseous NH 3 has a high deposition velocity and tends to deposit quickly. However, if sulfates and nitrates (which are primarily man-made) ar e present in the atmosphere, free

NH 3 quickly reacts to form (NH 4 ) 2 SO 4 and NH 4 NO 3 . These compounds, because of their fine particle size and slower deposition velocity than free gaseous NH 3 , can be transported long distances and deposited in a FLM area, adding to the total N deposition loading.

An appropriate estimate of ambient free gaseous NH 3 is needed for the modeling analysis. IWAQM refers to Langford et al. (1992), who suggest that typical (within a factor of 2) background values of

NH 3 are: 10 parts per billion (ppb) fo r grasslands, 0.5 ppb for forest , and 1 ppb for arid lands at 20 o C. Langford et al. (1992) provide strong evidence that background levels of NH 3 show strong dependence with ambient temperature (variations of a factor of 3 or 4) and a strong dependence on the soil pH. However, given all the uncertainties in NH 3 data, IWAQM recommends use of the background levels provided above, unless better data are available for the specific mo deling domain. IWAQM notes that141 in areas where there are hi gh ambient levels of sulf ate, values such as 10 ppb might overestimate the formation of particulate nitrate from a given sour ce, for these polluted conditions. IWAQM further notes that areas in the vicinity of strong point sources of NH 3, such as feed lots or other agricultural areas, may experience locally hi gh levels of background NH 3

.

Questions regarding these recomme ndations should be resolved through consultation with the appropriate FLM and the appropriat e State and/or EPA modeling repres entative. Applicants should provide a modeling protocol to the appropriate FLM prior to conducting modeling analyses.

Estimation of Current and Future Deposition Rates

In order to evaluate a proposed source’s contribution to total (wet + dry) depo sition in a FLM area, it is necessary to first estimate current pollutant deposition rates. The current rate is a result of deposition from all existing natural and anthropogenic sour ces. FLMs use two approaches to estimating the current rate of deposition. One approach estima tes the current rate by av eraging data from an appropriate monitoring site for the pollutant of interest, using all years with complete data records. The second, more conservativ e, approach assumes that the current rate is equiva lent to the highest rate for the pollutant of intere st in the data record.

The method for estima ting future total deposition rates is:

1. Identify in table D-2 available on-site or representative wet and dry deposition data for the FLM area. Wet deposition data can be obtai ned through NADP ( http://nadp.sws.uiuc.edu/ ).

Dry deposition data can be obtained through CASTNet at http://www.epa.gov/castnet/

Table D-2 will indicate if dry deposition is assume d to equal wet depositionfor the site. For highelevation sites, consult with th e FLM to determine if deposition from cloudwater, fog, dew, or snowpack should be considered. For sites without on-site data, consult FLM for further guidance.

2. After consulting with the FLM, estimate either:

a. the average annual or seasonal wet and dry deposition rates for the appropriate pollutant using all years with complete data records; or
b. the highest annual or seasonal wet and dry de position rates for the appropriate pollutant using all years with complete data records.

3. Calculate current total depos ition (wet + dry = total).

4. Estimate, using the appropriate dispersion model as described in the “Modeling Deposition Rates” section above, the proposed source’s contributi on to future total depos ition on an annual or
seasonal basis.

5. Estimate, using appropriate dispersion model as described in the “Modeling Deposition Rates” section above, the contribution of any sources permitted but not yet ope rating to future total deposition. This estimate may be available from the State permitting authority.142 6. The current pollutant deposition rate plus the pr oposed source’s contribution to deposition plus the contribution from other sources permitted but not yet operating equa ls the future total deposition rate.

Current + Proposed + Permitted (not ye t operating) = Future Total Deposition

This future total deposition rate for a given pollutant can then be used to determin e the potential for adverse effects to AQRVs. If appropriate, the change in deposition rate can be used to estimate changes in pH or ANC in an ecosystem. If the fu ture total deposition rate is expected to cause an adverse effect to AQRVs and/or exceeds the criti cal load established for a FLM area, the FLM may recommend mitigation, as outlined in the flowchar t on Figure D-1. If no critical load has been established for the FLM area, the FLM will use the best information available in determining whether
to recommend mitigation.

g. Summary

• Deposition of S and N has the potential to affect terrestria l, freshwater, and estuarine ecosystems on FLM lands.

• The FLM has identified, where possible, AQRVs sens itive to deposition of S and N on FLM
lands and the critical loads as sociated with those AQRVs.

• A proponent of a source of new emissions with the potential to contribute to S or N deposition
in an FLM area should consult with the FLM to determine what analyses are needed to assess AQRV effects. The FLM may request a deposition impact analysis, described in detail in this chapter and summarized below.

1. Estimate the current deposition rate to the FLM area. A list of monitoring sites providing data to characterize deposition in FLM areas is include d in Table D-2.

2. Estimate the future deposit ion rate by adding the existi ng rate, the new emissions’ contribution to deposition, and the contribution of sources perm itted but not yet operating. Modeling of new and permitted but not yet oper ating emissions’ contribution to deposition should be conducted following IWAQM Phase 2 recommendations.

3. Compare the future depos ition rate with the recomm ended screening criteria ( e.g., critical load, concern threshold, or screening level value) for the affected FLM area. A list of documents summarizing these screening criteria, where av ailable, can be found in Appendix H. Information for USDA/FS Class I areas is also available at:

http://www.fs.fed.u s/r6/aq/natarm

.

A web site with NPS and FWS Class I area information is cu rrently under development.
The web site will be available at

http://www.nature.nps.gov/ard and http://refuges.fws.gov

.

The appropriate FLM should be cont acted for additiona l information.143 h. Websites for Deposition and Related Information

Clean Air Stat us and Trends Network (CAS TNet) dry deposition data: http://www.epa.gov/castnet/

IWAQM guidance for deposition modeling: http://www.epa.gov/scram001/7thconf/calpuff/phase2.pdf

National Acid Precipitation Assessment Program: No longer Available, CENR overseas AIRMoN Dry http://www.arl.noaa.gov/research/projects/airmon_dry.html

National Atmospheric Deposition Program (NADP) wet deposition data: http://nadp.sws.uiuc.edu/

National Park Service Airweb: http://www.nature.nps.gov/ard/

Natural Resources Conservation Service, Snow Water Equivalent In formation (SNOTEL): www.wcc.nrcs.usda.gov/factpub/sntlfct1.html

Southern Appalachian Man and the Biosphere Co operative, Southern Appalachian Assessment: http://samab.org/

USDA Forest Service National Air Resource Management Web Site: http://www.fs.fed.u s/r6/aq/natarm

U.S. EPA Office of Air and Radiation: http://www.epa.gov/oar

U.S. EPA, Depositio n to Estuaries: http://www.epa.gov/owow/oceans/airdep

U.S. EPA, STOrage and RETrieva l System for Water and Biologica l Monitoring Da ta (STORET): http://www.epa.gov/OWOW/STORET/

U.S. Fish and Wildlife Se rvice Air Quality Branch: http://www.fws.gov/refuges/habitats/airQuality.html

U.S. Geological Survey, National Water-Q uality Assessment (NAWQA) Program: http://water.usgs.gov/nawqa/

U.S. Geological Survey, Acid Rain Program: http://bqs.usgs.gov/acidrain

U.S. Geological Survey, Water Data Stor age and Retrieval System (WATSTORE): http://h2o.er.usgs.gov/public /nawdex/wats/intro.html

Fact Sheet: http://water.usgs.gov/public/pubs/FS/FS-013-97/144 Table D-2. Sites used for estimating wet and dry deposition in Class I Areas (distance and direction, when noted, refer to the location of the monitoring site relative to the Class I area).

ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

National Park
Service Class I
Units On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Acadia NP Y ME98 Y ACAD

A rches NP N UT98 N dry = wet Badlands NP N SD08 N dry = wet Bandelier NM Y NM07 N dry = wet Big Bend NP Y TX04 Y BIBE Black Canyon of
the Gunnison NP N CO08 N dry = wet

Bryce Canyon NP Y UT99 N dry = wet Canyonlands NP Y UT98 Y CANY Capitol Reef NP N UT99 N dry = wet
Carlsbad Caverns
NP N TX22 N dry = wet

Chiricahua NM Y AZ98 Y CHIR Crater Lake NP N OR09 N dry = wet
Craters of the
Moon NM Y ID03 N dry = wet

Denali NP Y AK03 Y DENA Everglades NP Y FL11 Y EVER
Glacier NP Y MT05 Y GLAC Grand Canyon NP Y AZ03 Y GRCA Grand Teton NP N WY08 N dry = wet
Great Sand
Dunes NM N CO00 N dry = wet

Great Smoky
Mountains NP Y TN11 Y GRSM Use background value N dep=17.4 kg/ha/yr S dep=35.7

kg/ha/yr (includes wet+dry+cloud/fog) (SAMI) Guadalupe
Mountains NP Y TX22 N dry = wet

Haleakala NP N N/A N dry = wet

Hawaii Volcanoes
NP Y HI99 N dry = wet

Isle Royale NP Y MI97 N dry = wet
Joshua Tree NP Y CA67 Y JOTR

Lassen Volcanic
NP Y CA96 Y LAVO

Lava Beds NM N CA76 N dry = wet145 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

National Park
Service Class I
Units On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Mammoth Cave
NP N KY99 N dry = wet

Mesa Verde NP Y CO99 Y MEVE

Mount Rainier NP Y WA99 Y MORA

North Cascades
NP Y WA19 Y NOCA

Olympic NP Y WA14 Y OLYM

Petrified Forest
NP N AZ03 N dry = wet

Pinnacles NM Y CA66 Y PINN Point Reyes NS N N/A N dry = wet Redwood NP N N/A N dry = wet Rocky Mountain
NP Y CO98/19 Y ROMO

Saguaro NP N AZ99 N dry = wet Sequoia / Kings Canyon NPs Y CA75 Y SEKI

Shenandoah NP Y VA28 Y SHEN

Theodore
Roosevelt NP Y ND07 Y THRO

Virgin Islands NP Y VI01 Y VIIS Voyageurs NP Y MN23 Y VOYA Wind Cave NP N SD08 N dry = wet

Yellowstone NP Y WY08 Y YELL

Yosemite NP Y CA99 Y YOSE Zion NP N UT99 N dry = wet

ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Forest Service
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Agua Tibia N N/A N N/A

Alpine Lake N WA19/21 70 miles N/70
mi SW N dry = wet

Anaconda-Pintler N MT97 15 mi NE N dry = wet

Ansel Adams N N/A N N/A

Bob Marshall N MT05 65 mi NW N 468 GNP 65 mi NW146 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Forest Service
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Boundary Waters Y MN18/08 adjacent N dry = wet Bradwell Bay N FL14/* 30 mi N/
17 mi SW N 156 SUM 17 mi SW

Bridger N WY06/
WY98 6 mi SW/
4 mi W N 165 PND 6 mi W, bulk dep
available

Cabinet
Mountains N MT05 85 mi E N dry = wet

Caney Creek N AR03 60 mi SE N 150 CAD 60 mi SE

Caribou N N/A N N/A

Chiricahua N AZ99 92 mi NW Y 467
CNM/167

CNM adjacent

Cohutta N TN11 N GRSM

Cucamonga N N/A N N/A

Desolation N N/A N N/A

Diamond Peak N OR10 45 mi N N dry = wet

Dolly Sods N WV18 17 mi W N 107 PAR 17 mi W

Dome Land N N/A N N/A

Eagle Cap N OR18 35 mi W N dry = wet

Eagles Nest N CO02/94 40 mi NE N dry = wet

Emigrant N N/A N N/A

Fitzpatrick N WY06/
WY98 13 mi SW/
17 mi W N 165 PND 13 mi W, bulk dep
available Flat Tops N CO92/
CO08 25 mi SW N dry = wet

Galiuro N AZ99 40 mi N/NE N 467
CNM/167

CNM 75 mi SE

Gates of the
Mountains N MT05 30 mi S N dry = wet

Gearhart
Mountain N OR9 45 mi N N dry = wet

Gila Y NM01 adjacent N dry = wet

Glacier Peak N WA19 10 mi NW N dry = wet147 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Forest Service
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Goat Rocks N WA21 40 mi NW N dry = wet

Great Gulf N NH02/ME0
2 30 mi SW/35
mi SE N 109 WST 30 mi SW

Hells Canyon N OR18/ID04 90 mi W/85 mi
NE N dry = wet

Hercules-Glades N AR16/27 50 mi SE/90
mi SW N dry = wet

Hoover N N/A N N/A

James River Face N VA13 65 mi W N 120 VPI 65 mi W Jarbidge N N/A N 164 SAV 50 mi SW

John Muir N N/A N N/A

Joyce-KilmerSlickrock N N/A N 137 COW 45 mi E

Kaiser N N/A N N/A

Kalmiopsis N N/A N dry = wet

La Garita N CO91 30 mi S N dry = wet

Linville Gorge N N/A N 126 PNF 15 mi N

Lye Brook Y VT01/* 20 mi SW/adjacent Y 145 LYE adjacent

Marble Mountain N N/A N N/A

Maroon BellsSnowmass N * 3 mi S N 161 GTH 3 mi S

Mazatzal N AZ03 142 mi N/NW N 474
GCN/174

GCN 142 mi N/NW

Mission
Mountains N MT05 60 mi N N dry = wet

Mokelumne N N/A N N/A

Mount Adams N OR98 55 mi NE N

Mount Baldy N AZ99 67 mi SW N dry = wet

Mount Hood N OR98 20 mi NW N dry = wet

Mount Jefferson N OR10 30 mi SW N dry = wet

Mount Washington N OR10 15 mi SW N dry = wet148 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Forest Service
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Mountain Lakes N CA76/OR10 50 mi SW/125
mi N N dry = wet

North Absaroka N WY08 30 mi W N dry = wet

Otter Creek N WV18 4 mi N N 107 PAR 4 mi N

Pasayten N WA19 30 mi SW N dry = wet

Pecos N NM07 38 mi SW N 405 MEV 163 mi NW

Pine Mountain N AZ03 125 mi N N 474
GCN/174

GCN 125 mi N

Presidential Range-Dry River N NH02/ME02 25 mi SW/30
mi SE N 109 WST 25 mi SW

Rainbow Lake N WI37/36 45 mi SW/70
mi E N 134 PRK 70 mi S

Rawah N CO98/19 25 mi SW N 169
CNT/406

ROM 30 mi NW/20 mi
SE

San Gabriel N N/A N N/A

San Gorgonio N N/A N N/A

San Jacinto N N/A N N/A

San Pedro Parks N NM09 4 mi SW N 405 MEV 113 mi NW

San Rafael N N/A N N/A

Sawtooth N ID15/03 50 mi NW/80
mi SE N dry = wet

Scapegoat N MT05 100 mi NW N dry = wet

Selway-Bitterroot N MT97 20 mi SE N dry = wet

Shining Rock N N/A N 137 COW 20 mi W

Sierra Ancha N AZ03 175 mi NW N 474
GCN/174

GCN 175 mi NW

Sipsey N N/A N dry = wet

South Warner N AL99/10 80 mi E/140
mi S N N/A

Strawberry
Mountain N OR18 75 mi N N dry = wet

Superstition N AZ06 142 mi NW N dry = wet149 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Forest Service
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Sycamore Canyon N AZ06 79 mi N N 474
GCN/174

GCN 79 mi N

Teton N WY08/
WY98 NW/SE N dry = wet

Thousand Lakes N N/A N N/A

Three Sisters N OR10 5 mi NW N dry = wet

Upper Buffalo N AR16/27 60 mi NE/45
mi NW N dry = wet

Ventana N N/A N dry = wet

Washakie N WY08/98 45 mi NW/60
mi S N dry = wet

Weminuche Y CO96/91 6 mi NW/adjacent N 405 MEV 45 mi SW

West Elk N * 20 mi NE N 161 GTH 20 mi NE

Wheeler Peak N NM12 83 mi NE N 405 MEV 130 mi NW

White Mountain N NM08 42 mi SE N dry = wet

Yolla Bolly Middle
Eel N N/A N N/A

ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Fish and Wildlife
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Bering Sea N AK03 1100 km E N dry = wet

Bosque del Apache N NM08 163 km SE N dry = wet

Breton N LA30 175 km NW N dry = wet

Brigantine Y NJ00 on-site N dry = wet

Cape Romain starts 9/2000 SC18 87 km NW;
use on-site
data when
available N dry = wet

Chassahowitzka Y FL05 on-site N dry = wet

Lostwood N MT13 130 km SW N dry = wet150 ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

Fish and Wildlife
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

Medicine Lake N MT13 47 km W N dry = wet

Mingo N MO05 1 km SW N dry = wet

Moosehorn N ME98 107 km SW N dry = wet

Okefenokee Y GA09 On-site N dry = wet

Red Rock Lakes N WY08 106 km NE N dry = wet

Salt Creek N NM08 120 km SW N dry = wet

Seney starts 9/2000 MI98 98 km E; use
on-site data
when avail. N dry = wet

Simeonof N AK03 1200 km NE N dry = wet

St. Marks N FL23 50 km W N dry = wet

Swanquarter N NC06 80 km S N dry = wet

Tuxedni N AK03 450 km NE N dry = wet

UL Bend N MT98 180 km NW N dry = wet

Wichita Mountains N OK17 103 km NE N dry = wet

Wolf Island N GA09 103 km SW N dry = wet

ESTIMATION OF WET DEPOSITION –
NADP ESTIMATION OF DRY DEPOSITION -
CASTNet

International
Class I Areas On-site monitoring Cal Code Comments On-site monitoring Site/ID Comments

RooseveltCampobello N ME98 N dry = wet

Distance and direction, when noted, refer to the location of the monitoring site relative to the Class I

area. N/A is not available
NADP data are available at http://nadp.sws.uiuc.edu
CASTNet data for NPS are available from john_ray@nps.gov
CASTNet data for USDA/FS are available at http://www.epa.gov/acidrain/castnet Wet deposition data at CAST Net site available from frank.neil@epa.gov151 E. FUTURE FLAG WORK

1. IMPLEMENTING FL AG RECOMMENDATIONS

FLAG participants believe that th e recommendations in this document should be implemented as soon as possible. Therefore, an atte mpt has been made to present th orough and clear guidance of the processes that will be us ed to protect and improv e AQRVs in FLM areas.

Many of the issues and recommendations discu ssed herein are complex and require specialized knowledge. Consequently, State agen cies and others who intend to us e this information in NSR/PSD permitting, land planning and use, and other activities, deserve furt her guidance and implementation assistance. FLAG members anticipa te that much of this guidance and assistance will be provided locally through established formal and informal links between FLMs, States, EPA and others. However, FLAG members also se e the need to conduct workshop s and training sessions and to provide guidance on the Internet.

2. PHASE I UPDATES

The FLAG Phase I report is intended to clearly st ate FLM guidance regardi ng NSR/PSD as it exists
in December 2000. As the FLMs learn more about how to better assess the health and status of AQRVs, and as EPA produces new modeling t ools, the FLAG guidance will be revisedaccordingly. As periodic revisions become necessary, any such revisions will be made to the webbased FLAG report. Any revision s to the report will be clearl y stated on the FLAG web site. Additionally, once EPA promulgates the New Sour ce Review Reform regulations, the FLMs may need to revise the FLAG Phase I report to a ddress any inconsistencie s that may result.

3. PHASE II TASKS

Phase I has addressed issues that could be resolved relatively quickly, withou t extensive research or the collection of new data. During Phase II, FLAG will addr ess the more complex issues and concerns, including those that may requir e additional data collection.

In Phase II, to the extent resource s permit, FLAG members intend to f ill information gaps identified in Phase I, recommend methods for es tablishing critical loads of poll utants, and attempt to resolve remaining differences in the policie s and processes FLMs use to evalua te the effects of pollutants on AQRVs. In Phase II FLAG will also provide additional resear ch and monitoring recommendations.

Other potential Phase II tasks include:

 Refine policies to prevent adverse AQRV impa cts, and restore adversely impacted AQRVs.

 Refine procedures for cumula tive AQRV impact analyses.

 Promoting internal (FLM) emi ssion reductions through im plementation of sustainable practices.

 Recommending a policy on redesignation of Class II federal lands to Class I.

 Clarifying FLM roles and responsibi lities on Clas s II lands.

 Recommending a consistent policy regard ing increment tracki ng and enforcement.

Issue-specific Phase II tasks are discusse d in the individual subgroup reports.152 APPENDIX A. GLOSSARY

The list below contains definitions for some of the terms used in the FLAG Phase I Report . These terms are defined in the sense that they relate to the work of the Federal Land Managers (FLMs) in protecting air resources.

For terms whose definition is lengthy or complex, the associated Code of Federal Regulations (CFR)
section or other reference is cited.

AIR QUALITY RELATED VALUE (AQRV). A resource, as identified by the FLM for one or more Federal areas, that may be adversely affect ed by a change in air quality. The resource may include visibility or a specific scen ic, cultural, physical, biological, ecolog ical, or recreational resource identified by the FLM for a particular area.

ADVERSE IMPACT ON AN AQRV . An unacceptable effect, as identified by an FLM , that results from current, or would result from predicted, deterior ation of air quality in a Federal Class I or Class II area. A determination of unacceptable effect shall be made on a case-by- case basis for each area taking into account existing air quality conditions. It shou ld be based on a demonstr ation that th e current or predicted deterioration of air quality will cause or contribu te to a diminishment of the area's national significance, impairment of the stru cture and functioning of the area's ecosystem, or impairment of the quality of the visitor ex perience in the area.

ADVERSE IMPACT ON VISIBILITY . Visibility impairment wh ich interferes with the management, protection, preservation, or enjoyment of a visitor's visual experi ence of a Federal Class
I or Class II area. This determination must be ma de on a case-by-case basis taking into account the geographic extent, intensity, duratio n, frequency and time of visibi lity impairments, and how these factors correlate with (1) times of visitor use of the Class I area, and (2) the frequency and timing of
natural conditions that reduce visib ility. This term does not include effects on integral vistas. [40 CFR §51.301(a)]

ABSORPTION. The process by which incident light is re moved from the atmosphere and retained by a particle.

ABSORPTION COEFFICIENT. A number that is proporti onal to the “amount” of light removed from a sight path by ab sorption per unit distance.

ACIDIFICATION. The decrease of acid neutralizing capacity in water or base saturation in soil caused by natural or anthropogenic processes.

AEROSOL. A mixture of microscopic so lid or liquid particles in a gaseous medium. Smoke, haze, and fog are aerosol examples.

AIRSHED. A geographic area that, because of topography, meteorology, and/or climate, is frequently affected by th e same air mass.

AOT40. Sum of all hourly average concentrations afte r subtracting 40 ppb from each hourly value.153 BACT (BEST AVAILABLE CONTROL TECHNOLOGY). The control level (or control measures) required for sources subject to PSD . (See 40 CFR §52.21 (b)(12), or 40 CFR §51.166(b)(12)).

CLASS I AREA. As defined in the Clean Air Act, the follow ing areas that were in existence as of August 7, 1977: national parks ove r 6,000 acres, national wilderne ss areas and national memorial parks over 5,000 acres, an d international parks.

CRITICAL LOAD. The concentration of air pollution above which a specif ic deleterious effect may

occur.

CUMULATIVE EFFECT . The impact on an AQRV resulting from the total pollutant loading from all sources including the contributing effects of kn own and reasonably fores eeable new an d modified sources of air pollution. A singl e source may cause indivi dually minor, but cu mulatively significant, effects on AQRVs.

DAMAGE. Any reduction in the intended use or valu e of a biological or physical resource. For example, economic production, ecological structure or function, aesthetic value, or biological or genetic diversity that may be altered by a pollutant.

EMISSION OFFSET. A Federally enforceable re duction in emissions from an existing source that mitigates the impacts of a proposed new or modified source on AQRVs , PSD increments , and/or NAAQS. Also, Federally en forceable reductions in actual em issions from existin g sources in a
nonattainment area such that the total allowable emissions from a new or modified source and existing sources will be sufficiently less than the total emissions from ex isting sources before the application for a permit to construct so as to represent reasonable further progress towards attainment of the NAAQS. (See 42 U.S.C. § 7503(a)(1)(A))

EXTINCTION . The attenuation of light due to scattering and absorption as it passes through a
medium.

FUGITIVE EMISSIONS . Emissions which do not pass through a stack, chimney, vent, or other functionally equivalent opening.

FEDERAL LAND MANAGER (FLM) . The Secretary of the Depart ment with authority over such lands. [40 CFR §51.166(b)(24)] The FLM for the Department of the Interior has been delegated to the Assistant Secretary for Fish an d Wildlife and Parks; the FLM for the Department of Agriculture has been delegated to the Forest Service, and has been redelegated to the Regional Forester or individual Forest Supervisor.

FLUX. Gaseous uptake in to plant tissue.

GREEN LINE. The total pollutant loading (contributions from existing and proposed sources) below which there is a very high degree of certainty that no AQRV will be adversely affected.

HAZE . An atmospheric aerosol of sufficient concentra tion to be visible. The particles are so small that they cannot be seen individually, but are still effective attenuating light and reducing vi sual range.154 HYDROCARBONS . Compounds containing only hydrogen and carbon. Examples: methane, benzene, and decane.

HYGROSCOPIC . Readily absorbing moisture, as from the atmosphere.

INJURY. Any physical or biological response to pollutant s, such as a change in metabolism, reduced photosynthesis, leaf necrosis, prem ature leaf drop, or chlorosis.

LAER (LOWEST ACHIEVABLE EMISSIONS RATE). The control level required of a source subject to nonattainment review. (See 40 CFR §51.165(a)(1)(xiii))

LIMIT OF ACCEPTABLE CHANGE. The amount of change th at could occur without significantly altering an AQRV or sensitive receptor

.

MICROMETER . A unit of length eq ual to one millionth of a meter; the unit of measur e for particle
size.

MIE THEORY . A complex mathematical mode l that allows the computatio n of the amount of energy (light) scattered by spherical particles.

N100. Number of hourly average concentrations ≥ 100 ppb.

NATURAL CONDITIONS . Conditions substantially unaltered by humans or human activities. As applied in the context of visib ility, natural conditions include na turally occurring phenomena that reduce visibility as measur ed in terms of light ex tinction, visual range, contrast, or coloration.

NATURAL VISIBILITY CONDITIONS. Visibility conditi ons attributable to Rayleigh scattering
and aerosol associated wi th natural processes.

NEPHELOMETER . An instrument that measures the amount of light scattered.

NITRATES . Those gases and aerosols that have origins in the gas-to-aerosol conversion of nitrogen oxides, e.g ., NO 2 ; of primary interest are nitric acid and ammonium nitrate. Ammo nium nitrate is very hygroscopic so its contribu tion to visibility impairment is magnified in the pr esence of water vapor.

NITROGEN DIOXIDE . A gas (N0 2 ) consisting of one nitrogen and two oxygen atom s It absorbs blue light and therefore has a reddish -brown color asso ciated with it.

NONATTAINMENT AREA. An area designated by the EPA Ad ministrator pursuant to Section 107(d) of the Clean Ai r Act as having air quality which does not meet one or more National Ambient Air Quality Standard (NAAQS). Fo r a list of nonattainment areas, see 40 CFR Part 81, Subpart C.

OXIDANT STIPPLE. Small brown or black interveinal necro tic lesions on the adaxial surface of
leaf tissue that can be attributed to exposure to ozone.

PHYTOTOXIC. Poisonous to plants.

POST-CONSTRUCTION MONITORING. Monitoring required as a permit condition that the permitting authority considers necessary to determine the effect emissions from a stationary source155 may have, or are having, on the ai r quality or on the AQRVs of an area. Such monitoring includes both “ambient” monitoring and “AQRV” monitori ng and may involve short-term and long-term measurements made at locations representa tive of the greatest expected impacts.

PSD INCREMENTS . The maximum increases in ambient po llution concentrati ons allowed over baselines concentrations. See 40 CFR §51.16 6 (c) for increments fo r specific pollutants.

RACT (REASONABLY AVAILABLE CONTRO L TECHNOLOGY). The lowest emissions limit that a particular source ca n meet by the application of cont rol technology that is reasonably available considering technolo gical and econom ic feasibility.

RAYLEIGH SCATTERING . The scattering of light by partic les much smaller than the wavelength of the light, e.g. , molecular scattering in the natural atmosphere.

RECONSTRUCTED EXTINCTION . Extinction estimate that results from summing up the product of the mass of each measured particle speci es and the appropriate absorption or extinction
coefficient.

RED LINE. The total pollutant loadi ng (contributions from existing a nd proposed sources) at which there is a very high degree of certainty that at least one AQRV will be adversely affected.

REGIONAL HAZE VISI BILITY IMPAIRMENT . Any humanly pe rceptible change in visibility (light extinction, visual range, contrast, coloration) from that which would have existed under natural conditions, caused predominantly by a combination of many sources from, and occurring over, a wide geographic area.

RE-OPENER

. A permit condition that requires the permitti ng authority, at a specified time after permit issuance, to review and revi se, if necessary, the permit base d on new information such as the findings from post-construction m onitoring, updated emissions in ventories, updated modeling, research, or information on air pollution effects to terrestrial, aquatic, and visibility resources.

SCATTERING . An interaction of a light with an object ( e.g ., a fine particle) that causes the light to be redirected in its path.

SCATTERING COEFFICIENT . Measure of the ability of partic les to scatter li ght; measured in number proportional to th e “amount” of light scat tered per unit distance.

SCREENING LEVEL OR SCR EENING LEVEL VALUE (SLV). The concentration or dose of air pollution below which es timated impacts from a proposed new or modified source are considered insignificant. The SLV is depe ndent on existing air quality and on the condition of the AQRV of

concern.

SENSITIVE RECEPTOR. The AQRV, or part thereof, that is the most responsive to, or the most easily affected by the type of air pollution in question. For ex ample, at Great Smoky Mountains National Park, spruce-fir forest is a sensitive receptor of the AQRV flora.

SENSITIVE RECEPTOR INDICATOR. A measurable physical, chemi cal, biological, or social ( e.g. , odor) characteristic of a sensitive receptor. For example, for the sensitive receptor, Crater Lake, water clarity is a sensiti ve receptor indicator.156 STATIONARY SOURCE . A source of pollution that is well defi ned, such as the smokestack of a coal-fired power plant or smelter.

SULFATES . Those aerosols that have origins in the gas-to-aerosol conversion of sulfur dioxide; of primary interest are sulfuric acid and ammonium su lfate. Sulfuric acid and ammonium sulfate are very hygroscopic so their contribution to visibility impairme nt is magnified in the presence of water

vapor.

SULFUR DIOXIDE . A gas (S0 2 ) consisting of one sulfur and tw o oxygen atoms. Of interest
because sulfur dioxide converts to an aerosol.

SUM00. The sum of all hourly averag e concentrations above 0 ppb.

SUM06. The sum of all hourly average concentrations at or above 60 ppb.

TARGET LOAD. The acceptable concentration or dose of an air pollutant that provides a reasonable margin of safety below the critic al load. The target load sh ould be achievabl e under existing
conditions.

TRANSMISSOMETER . An instrument that measures the am ount of light extinction over a fixed, specified path length.

VISIBILITY IMPAIRMENT . Any humanly perceptible change in visibility (visual range, contrast, coloration) from that which w ould have existed under natural conditions. [40 CFR §5 1.301(x)]

VISUAL RANGE . The distance at which a large black object would just disappear from view.

VOLATILE ORGANIC COMPOUND (VOC). Any compound of carbon, except those excluded by EPA, that participat es in atmospheric phot ochemical reactions. (S ee 40 CFR §51.100(s))

W126. An ozone index that multiplies each specific concentr ation by a sigmoidal weighted function,
then sums all values. W i = 1/[1 + M e -(A x Ci) ] , where M and A are co nstants 4403 and 126 ppm -1

, respectively, w i is the weighting factor for c i , and c i is concentration in ppm.157 APPENDIX B.

LEGAL FRAMEWORK FOR MANAGING AIR QUALITY AND AIR QUALITY EFFECTS

ON FEDERAL LANDS

Introduction

The regulation of air pollution sources has clearly been delegated to EPA, and as applicable, the States. However, Federal Land Managers (FLMs) have the responsibility to protect the particular
values of the lands over which th ey have jurisdiction, to the exte nt they have been delegated the authority, from the adverse impacts of ac tivities inside and outside these areas.

This Appendix sets out the basi c legal authorities and responsib ilities with which the FLMs comprising FLAG must comply, in addition to thos e authorities which they can utilize to protect AQRVs on public lands.

For the purposes of this Appendix only, the term " public lands" is defined to include units of the National Park, National Wildlife Refuge , and National Forest Systems.

I. AGENCY ORGANIC ACTS

A. Department of the Interior : National Park Service (NPS):

This Organic Act is very specific in that it mandates national park unit managers:

[T]o conserve the scenery and the natural and hist oric objects and wild lif e therein and to provide for the enjoyment of the same in such manner and by such means as will leave them unimpaired for the enjoyment of future generations.

16 U.S.C. §1(1997); and

[T]he authorization of act ivities shall be construed and the protection, manage ment, and administration
of these areas shall be conducted in light of the high public value a nd integrity of th e National Park System and shall not be exercised in derogation of the values and purposes for which these various areas have been established, except as may have been or shall be di rectly and specifically provided for by Congress.

16 U.S.C. § 1a-1 (1997)

B. Department of the Interior: Fish and Wildlife Service (FWS):

With respect to National Wildlife Refuge System lands (Refuge System lands under the jurisdiction
of the United States Fish and Wildlife Service (FWS), FWS managers are required to manage Refuge System lands so to:

[E]nsure that the biological integrity, diversity, and environmental health of the System are maintained for the benefit of present and future generations of Americans.158 16 U.S.C. §668dd(a)(4)(B)(1997)

C. Department of Agriculture: Forest Service (Forest Service)

National Forest System lands are defined as:

[A]ll National Forests reserved or withdrawn from the public domain of the United States, all national forests acquired through purchase, exchange, donation, or other means, all national grasslands and land utilization pr ojects...and all lands waters, and ot her interests administered by
the Forest Service.

16 U.S.C. §1609(a)(1997)

The Forest Service's Organic Admi nistration Act of 1897 directs the Secretary of Agriculture to:

[M]ake provisions for the protection against destru ction by fire and depr edations upon the public
forests and national forests...

16 Sec. §551(1997)

The National Forest units are managed consiste nt with Land and Resource Management Plans (LRMPs) under the provisions of the National Fo rest Management Act (NFMA). 16 §U.S.C. 1604 (1997). Any measures addressi ng AQRVs on National Forest System lands will be implemented through, and be consistent with, the provisions of an applicable LRMP or its revision (16 U.S.C. §1604(i)).

The Secretary of Agriculture is required by la w to prepare a Renewable Resource Assessment by 1979, and every 10 years thereafter. By law th is Assessment is required to address:

3. A description of Forest Service programs in re search, cooperative programs and management of the National Forest System, their relationships , and the relationships of these programs and responsibilities to public and private activities; and

5. An analysis of the potential effects of gl obal climate change on th e condition of renewable resources on the Forests and range lands of the United States; and

6. An analysis of the rural and urban forestry opportunities to mitigate the buildup of atmospheric carbon dioxide and reduce the risk of global climate change.

16 U.S.C. §1601(a) (1997)

In addition, the Secretary of Agriculture is required to prepare and transmit to the President, a Renewable Resource Program (the Program) every 5 years. This Program must include program recommendations which recognize the fundamental need to protect, and where appropriate, improve the quality of ... air re sources. 16 U.S.C. §1602(5)(C).

The Forest Service's implemen ting regulations for NFMA are found at 36 C.F.R. §219 et seq. LRMPs are, in part, specifically based on:159 [R]ecognition that the National Forests are ec osystems and their management for goods and services requires an awareness and consideratio n of the interrelationships among plants, animals, soil, water, air, and other environmen tal factors within such ecosystems.

36 C.F.R. §219.1(b)(3)

II. The Wilderness Act. 16 U.S.C. §1131 (1997).

AQRVs in Wilderness areas may receive further pr otection by the language of the Wilderness Act
itself which states:

Wilderness areas... shall be administered for the us e of the American people in such a manner as will leave them unimpaired for future use and en joyment as wilderness .... (16 U.S.C. Sec. §1131).

For Wilderness Areas in the National Forest Syst em, the Act's implementi ng regulations are found at 36 C.F.R. §293. These Wilderness Areas shall be administered:

...[For] such other purposes for which it may have b een established in such a manner as to preserve and protect [their] wilderness character. In carry ing out such purposes, National Forest Wilderness resources shall be managed to promote, perpetua te, and, where necessary, restore the wilderness
character of the land...

36 C.F.R. §293.2 (1997)

III. The Clean Air Act, 42 U.S.C. §7401 et seq.

Because of a perceived need for national and regional air quality research to support State programs, Congress passed its first federal air qua lity initiative in 1955. (A ir Pollution Control Act of 1955, ch. 360, 69 Stat. 322). In response to incr easing harm to public heal th and welfare and to inadequate controls and enforcement, Congress has slowly but steadily expanded and refined the law, now known as the Clean Air Act (CAA), to cover more types of pollutants and emitters; e.g. , stationary and mobile sources of pollution. These efforts have culminated in the 1990 Amendments to the CAA, which represent the most comprehensive and detailed set of measures to date to both prevent and curtail air pollution.

The declaration of purpose, as re vised in 1990 states in part:

The purposes of this subchapter are: to protect a nd enhance the quality of the Nation’s air resources so as to promote the public heal th and welfare and the producti ve capacity of its population.

42 U.S.C. § 7401(b)(1); and

A primary goal of this Act is to encourage or otherwise promote reasonable Federal, State, and local government actions, consistent with the provisions of this Act, for pollution prevention.

42 U.S.C. §7401(c)160 The CAA provides an additional legal framework for FLMs to preserve and protect AQRVs from pollution sources emanating both within and outside National Park, Forest, and Refuge boundaries.

A. National Ambient Air Quality Standards (NAAQS) and State Im plementation Plans (SIPs): The CAA establishes a regulatory program with the goal of achieving and maintaining "national ambient air quality standards" (NAA QS) through state or, if necessary, federal implementation plans (SIPs or FIPs). 1

The U.S. Environmental Protection Agency (EPA) is charged with promulgating:

1. "primary" NAAQS for "criteria" pollutants "to prot ect the public health," allowing an adequate margin of safety;" and

2. "secondary" NAAQS "to protect th e public welfare from any known or anticipated adverse effects associated with the presence of such air pollutant in the ambient air." 2

The above secondary standards ma y help protect public land AQRVs. 3 To date, EPA has promulgated NAAQS for six criteria pollutants: sulfur dioxide, part iculate matter, nitrogen dioxide, carbon monoxide, ozone and lead. In July of 1997, EPA issued revised, and more stringent NAAQS for ozone and "fine particul ate matter" to address human h ealth concerns. However, EPA openly acknowledged that these revised NAAQS were not fully adequate to protect the above "secondary" values, in particular th ose sensitive AQRVs on public lands.

B. Prevention of Signif icant Deterioration (PSD):

The CAA, as amended in 1977, includes the followi ng major purposes regardi ng the "prevention of significant deterioration" (PSD) provisions:

[T]o protect public health and welfar e from any actual or potential adve rse effect . . . from air pollution . . . notwithstanding attainment and maintenance of all national ambient air quality standards.

42 U.S.C. § 7470(1)

[T]o preserve, protect, and enha nce the air quality in national pa rks, national wilderness areas, national monuments, national seashores, and other areas of special national or regional natural, recreational, scenic, or historic value.

42 U.S.C. §7470(2)

The PSD section provides some protection for park and wilderness AQRVs through establishment of ceilings on additional amounts of air polluti on over baseline levels in clean air areas (increments). It requires EPA or the State to provide to the FLM notice of any proposed major emitting facility 4 whose emissions may affect a Class I ar ea (42 U.S.C. §7475(d)(2)(A), and also by charging:

[T]he Federal Land Manager 1 and the Federal official charged with direct responsibility for management of such lands with "an affirmative responsibility to protect the air quality related values (including visibility) of any such lands with in a class I area and to consider, in consultation161 with the Administrator, whether a proposed major emitting facility will have an adverse impact on
such values.

42 U.S.C. §7475(d)(2)(B).

Class I areas include national pa rks larger than 6,000 acres a nd national wilderness areas and national memorial parks which exceed 5,000 acre s, in existence on August 7, 1977. The 1990 Amendments provided that subsequent additions to the boundaries of such areas are also Class I areas. Currently, 48 areas in the National Park sy stem, 21 Refuge System units, and 88 areas under the administration of the Forest Service are designated as Class I.

Under the PSD provisions and implementing regulati ons (40 C.F.R. §51.166(p)), for Class I areas,
once baseline concentrations come under review by submission of a PSD preconstruction permit application for a major new or modified emissions source, only the smallest increment of certain pollutants -- sulfur dioxide, nitr ogen oxide and particulate matter -- may be added to the air by the proposed new source, and other "i ncrement consuming" sources.

Under the PSD provisions a FLM has several tools he/she may use to protect AQRVs.

A state may not issue a PSD permit to allow c onstruction or modification of a major emitting facility when the applicable Federal Land Manager files a notice alleging the facility may cause or contribute to a change in the Class I area's air quality and by identifying the potential adverse impact of such a change, unless:

The facility owner demonstrates that the facility's emissions of particulate matter, sulfur dioxide, and nitrogen oxides will not cause or contribute to concentrati ons which will exceed the maximum
allowable increases for that Class I area.

42 U.S.C. §7475(d)(2)(C)(i)(paraph rased) and 42 U.S.C. §7476.

Even if no increment violation is predicted,

[T]he state may not issue a PSD permit, if th e Federal Land Manager demonstrates to the
satisfaction of the State that the emissions from su ch facility will have an adverse impact on the air quality-related values (including visibility) of Class I lands.

42 U.S.C. §7475(d)(2)(C)(ii)(paraphrased)

Neither the CAA nor the implementing regulations sp ecify criteria for the FLM to "satisfy" state permitting agencies. Consequently, some states ha ve taken a liberal view of their discretion to reject an FLM's adverse impact determination. However, EPA's Environmental Appeals Board (the Board) has ruled that state discretion in re jecting a FLM's finding of adverse impacts is not "unfettered" (see the Board's decisions regardin g the permit appeals for the Old Dominion Electric Cooperative and Hadson Power project s in Virginia). Nevertheless, th e appropriate role of the FLM in the PSD permit process is cu rrently being addressed in EP A's proposed New Source Review Reform regulations. The final regulations are expected to be promulgated in 2001.162 C. Visibility Protection. Subpart II, 42 U.S.C. §7491 et seq. (1997)

The Visibility portion of the CAA:

"... [D]eclares as a national goa l the prevention of any future, a nd the remedying of any existing, impairment of visibility in mandatory class I Federal areas which impairment results from manmade air pollution."

42 U.S.C. §7491(a)(1).

To help carry out this goal, the Secretaries of the Interior and Agricu lture are charged with identifying Class I areas where visibility is an im portant value. EPA is ch arged with reporting to Congress on methods to implement the national goal and with promulgating regulations to ensure reasonable progress toward meeting the goal.

In 1980, EPA issued enfo rceable regulations for visibility impair ment “reasonably at tributable” to a specific source or small group of sources. In particular, major stationary sources emitting any pollutant which may “reasonably be anticipated to cau se or contribute to any impairment of visibility” is required to install best av ailable retrofit technology (BART). In addition, in April 1999 EPA promulgated final regulations addres sing regional haze. Th e regional haze rule protects air quality in Class I areas by requiri ng States to plan to achieve “natural ” visibility conditions over a 60-year
timeframe.

The 1990 Amendments add a new section on visibil ity, which authorizes EPA in conjunction with NPS and other federal agencies, to conduct visibility research and to evaluate clean air corridors and emissions sources and source regions causing visi bility impairment in Class I areas. In this regard, EPA was required to establish the Grand Canyon Visibility Transport Commission (GCVTC) by 1991 and consider the recommendati ons GCVTC would make (42 U.S.C. §7492(f). NPS, FS, FWS, and BLM played a vital role in the work of the GCVTC and committees in an effort to improve air quality in the Grand Ca nyon and other parks and wilderness areas in the
"Golden Circle" on the Colorado Plateau.

As part of the visibility protecti on process, states are required to promulgate a plan to prevent any future, and remedy any existing impairment of visibility in Class I areas... 40 C.F.R. §51.300 (1997). EPA has defined "vis ibility impairment" as:

[A]ny humanly perceptible change in visibility (visual range, contra st, coloration) from that which
would have existed under natural conditions.

40 C.F.R. §51.301(x)(1997).7

However, EPA has promulgated its visibility regul ations to allow FLMs to use their existing authorities to address "visibility impairment" (rath er than "significant impairment") so that "the affected Federal Land Manager may certify to the Stat e, at any time, that there exists impairment of visibility in any mandatory Class I Federal area." 40 C.F.R. §51.302(c).163 D. Nonattainment Areas, 42 U.S.C. §7501 et seq.:

Areas that have failed to meet NAAQS for one or more criteria pollutants are designated as
"nonattainment" areas. Under th e 1990 Amendments, Congress provides for further classification
of nonattainment areas base d on severity of the nonattainment a nd availability and feasibility of appropriate pollution control measures and fo r a compliance schedule ranging from 1993 in marginal nonattainment areas to 2010 for Los Angeles.

The 1990 Amendments authorize EPA to issue c ontrol technique guidance documents (CTGs) covering a variety of topics, such as control of idling vehicles and voluntary removal of pre-1980 model year light duty vehicles ( cash for clunker programs). (42 U.S.C. §7408.) EPA is authorized to issue CTGs, in lieu of regulations, to re duce "volatile organic compounds" (VOC) emissions from any consumer or commerci al product. (42 U.S.C. §7511b.)

Proposed new or modified major stationary sour ces within nonattainment areas are required to meet emissions limits based on "lowest achievable emission reduction" technology (LAER) and may be constructed only if their emissions are su fficiently offset by reducti ons in emissions from other sources. The 1990 Amendments also require analysis of alternative sites, sizes, production processes, and control techniqu es and a finding that the benef its of the source outweigh its environmental and social costs. (42 U.S.C. §7501-15.)

E. General

CAA Subchapter III 42 U.S.C. §7601 et seq. cont ains definitions, requirements for reports to Congress, authorizations for appropriations, a nd procedures for EPA rulemaking and judicial
review. Citizen suits are author ized: 1) against EPA for failure to perform a nondiscretionary duty under the CAA, or 2) against others for alleged violations of an emission limitation, standard, or order. (42 U.S.C.§7601 et seq.)

F. Acid Deposition

The 1990 Amendments add Title IV, which contains requirements for electric utilities to reduce
emissions associated with acid rain. To reduce the adverse effects of aci d deposition, Title IV requires a reduction in annual emi ssions of sulfur dioxide of ten million tons from 1980 emission levels and a reduction of nitrogen oxides emissions of approximately two million tons from 1980 emission levels, in the 48 conti guous states and the District of Columbia. (42 U.S.C. §7651.) The Title creates a system of market-based emission allowances, which can be traded among sources. See (42 U.S.C. §7651a-o.)

G. Operating Permits

The 1990 Amendments add Subchapter V, 42 U.S. C. §7661 et seq., which establishes a nationwide permit program for existing stationary sour ces. Permit requirements will include emission limitations. EPA may veto state permits, which do not comply with provisions of the CAA. (42 U.S.C. §7661a-f.)164 H. Conformity, 42 U.S.C. §7506 (1997)

(Paraphrased) No federal agency may engage in, support in any way,... license or permit, or otherwise approve any activity which does not conform to an approved state (or federal) implementation plan. Conformity shall be an affirm ative responsibility of the head of each agency. Conformity means:

(A) Conforming to the SIP's purpose of eliminati ng or reducing the number of NAAQS violations;

(B) That any such activities will not:

(i) Cause or contribut e to new violations in any area; or

(ii) Increase the frequency or severity of any existing standard violation...

EPA, in its "criteria and procedures" for impl ementing "conformity" has decided that only those activities that "a federal agency can practicably control, and will maintain control over due to a continuing program responsibility" are s ubject to same. 40 C.F.R. §93.152.

Although required to comply with the conform ity provisions (42 U.S.C. §7618(1997)), the FLM cannot use these provisions to protect AQRVs fr om adverse impacts from offsite sources.

IV. IMPACT ON FEDERAL LAND MANAGERS

The CAA reinforces the FLMs' Or ganic Act and Wilderness Act roles as protectors of AQRVs on public lands.

The CAA also imposes on FLMs an obligation to comply with the Act's many provisions regarding the abatement of air pollution to the same exte nt as any private person (42 U.S.C. §7418).

Thus, under various authorities, FLMs are responsible for pr otecting AQRVs within their respective unit boundaries and taking appropriate ac tion to do so, when reviewing emission sources both within units, and in pr oximity to unit boundaries.

FLMs, under the CAA, have an affirmative re sponsibility for protecting AQRVs (including visibility) in reviewing proposed PSD permits. Ho wever, because of the uncertainty involved in "satisfying" State permitting agencies in the PSD process, and the appropriate delegated role for FLMs in non-PSD situations, the existing framewor k may provide an inadequate means for FLMs to protect AQRVs from adverse impacts ca used by sources outside unit boundaries.165 References

1) Clean Air Act, 42 U.S.C. §7401-7671q (as amended 1990). 2) Clean Air Deskbook , The Environmental Law Reporter, En vironmental Law Institute, 1992. 3) Managing National Park System Resources: A Handbook on Legal Duties, Opportunities, and
Tools , Chap. 4 "The Clean Air Act" by Molly Ross at pp. 51-65, The Conservation Foundation,

1990. 4) Atmospheric Environment Vol. 27B, No. 1, "The 20-Year History of the Evolution of Air Pollution Control Legislation in the U.S. A." by Richard H. Schulze at pp. 15-25. 5) Wilderness Act of 1964, 16 U.S.C. §1131 et seq , P.L. 577, 78 stat 890 as amended. 6) The Principal Laws Relating to Forest Service Activities , USDA - Forest Service ISBN 0-16- 041927-1, 1993 7) Organic Administration Act of 1897, 16 U.S.C. §473-475, §477-482, §551.166 APPENDIX C.

GENERAL POLICY FOR MANAGING AIR QUALITY

RELATED VALUES IN CLASS I AREAS

Most Federal Land Manager (FLM) enabling legisl ation and regulations developed to implement Federal Laws do not directly address air quality, or air pollution effects on Parks or Wildernesses. They do, however, provide broad direction on what should be protected in Parks and Wildernesses (the earth and its commun ity of life) and to what degree (pre serve natural conditi ons or conserve resources unimpaired). Accordingly, FLMs have developed the following policies related to air quality and Class I areas:

1. Class I areas are not merely a commodity for human use and consumption. Park and Wilderness ecosystems have intrinsic values other than user/public concerns.

2. A principle objective of FLM management is to offer a natural user experience, rather than strictly an enjoyable one. The amount of en joyment is purely a personal matter for the

individual user to decide.

3. All Class I components are equally importan t; none is of lesser value than another.

4. A Class I component is important even if us ers of the area are unaware of its existence.

5. All life forms are equally important. For example, microorganisms are as essential as elk, wild flowers, or grizzly bears.

6. The goal of Class I management is to prot ect not only resources w ith immediate aesthetic appeal ( i.e. , sparkling clean streams) but also unseen ecological processe s (such as natural biodiversity and gene pools).

7. The most sensitive Class I components are to be emphasized more than those of “average” or “normal” sensitivity. Sensitivity is generally dete rmined by inertia (resistance to change), elasticity (how far the component can be stre tched from its natural condition without being permanently modified), and resiliency (the number of times it can revert to its natural condition after experiencing human-caused change).

8. Each Class I component is important in itself; as well as in te rms of how it inte racts with other components of the ecosystem. That is, the individual parts of the Class I ecosystem are as significant as the sum of the parts.

9. The physical components of the ecosystem (for in stance, lake chemistry) are as essential as its biological constituents ( i.e. , salamanders). That is, the earth is as essential as the community of

life.167 10. Class I components are to be pr otected from “human-caused change” rather than from “damage.” Terms such as “damage” and “har m” are prejudicial, whereas “human-caused change” is value-neutral. (For example, deposits of nitrogen in a lake from nitrogen oxide, a common air pollutant, might result in more plant growth and larger fish. This would, however, be an unnatural - and therefore unacceptabl e - change in the aquatic ecosystem).

11. The goal of Class I management is to protect natural conditions, rather than the conditions when first monitored. That is, if initial moni toring in a Class I area identifies human-caused changes, appropriate actions shou ld be taken to remedy them, in order to move towards a more

natural condition.

12. The designation of a Park or Wilderness as Class I or II does not dictate the management goals for it; these are identified in the enabling legi slation. The designation only determines which options are available to meet the goals. Class I Parks or Wildernesses, for instance, can be protected through AQRV analysis, whereas the pr otection of Class II Parks and Wildernesses can be achieved using BACT requirements.

13. While it may not be possible to manage every Cl ass I area in a natural or near-natural state, each should be managed in as pristine a condi tion as the specific (local) biophysical, legal, scientific, and social/political si tuation will allow. That is, FLMs will do the best job possible of Park and Wilderness management, based on local constraints and opportunities. The extent of actual protection, therefore, may vary.

14. Although monitoring is critical to many air resource manage ment decisions, it must not interfere needlessly with Park or Wilderness. Where possible, the most intrusive monitoring and instrumentation should be conducted adjacent to the Class I area - if such areas adequately represent the area of concern.168 APPENDIX D.

BEST AVAILABLE CONTROL TEC HNOLOGY (BACT) ANALYSIS

Given the need to minimize emissions and their re sulting air quality impacts, the FLMs recommend that the applicant conduct the BACT analysis using EPA’s t op-down approach. In brief, a topdown process ranks all available co ntrol technologies in descending order of control effectiveness. All of the available control sy stems for the source, including th e most stringent, must be considered. The applicant first examines the most eff ective, or top, alternative. That alternative is established as the BACT unless the applicant demonstrates, and the permitting authority agrees, that technical considerations, or energy, environmental, or econom ic impacts justify a conclusion that the most stringent technology is not ac hievable in that case. FLMs utilize EPA’s BACT/RACT/LAER Clearinghouse, a nd other information, for a ssessing control technologies proposed by permit applicants.

If the most stringent technology is eliminated in this fashion, then the next most stringent alternative is considered, and so on. Permit applicants should refer to chapter B of the EPA New Source Review Workshop Manual for a detailed discussion of the top-down policy.

The FLM reviews the applicant's BACT analysis to determine if the best available pollution control technology is being proposed, thereby minimizi ng the proposed emission increases and their corresponding impact on the FLM area in question. The FLM does this by comparing the proposed
controls to recent BACT determinations for sim ilar facilities. If the FLM disagrees with the applicant's BACT analysis, technical comments are submitted to the permitting authority who has the ultimate responsibility to make the BA CT determination and issue the permit.

The environmental impacts analysis of the BACT revi ew is not to be confus ed with the air qualityrelated analysis. The environmental impacts anal ysis of the BACT review should concentrate on impacts other than ambient air quality impacts of th e regulated pollutant in question, such as solid or hazardous waste generation, discharges of pollute d water from a control device, or emissions of unregulated pollutants. Thus, the fact that a give n control alternative would result in only a slight improvement in ambient concentrations of the po llutant in question when compared with a less stringent control alternative, should not be viewed as a basis for re jecting the more stringent control
alternative.

Regarding the economic im pact analysis, given the special protec tion Class I areas ar e afforded under the Clean Air Act, FLMs believe th at the need to minimize potentia l impacts on a Class I area should be a major consideration in the BACT determination for a project proposed near such an area. Therefore, if a source proposes to locate near a Class I area, additional costs to minimize impacts on sensitive Class I resources may be warranted, even though such costs may be considered economically unjustified under other circumstances.169 APPENDIX E. MA PS OF FEDERAL CLASS I AREAS170NATIONALPARKSERVICECLASSIAREASVoyageurs\RooseveltNParsbadCavernsNP\GuadalupeMountainsNPALASKANP\'"(';;Jand-oZJ.l.y.A);p.MapproducedbytheNationalParkServiceAirResourcesDivision02/22/1999HAWAIIEvergladesNP.t::::::;;HaleakalaNPeVirginIslandsNPVolcanoesNP171FISHANDWILDUFESERVICECLASSIAREASeWichitaMounjPts•stCreek1"0HAWAII....-\)<i:>"'producedbytheFishandWildlifeServiceAirQualityBranch2/27/1998\)172FORESTSERVICECLASSIWILDERNESSAREASassHAWAII._,....'·MapproducedbytheNationalParkServiceAirResourcesDivision08/23/1999173 APPENDIX F. CLAS S I AREA CONTACTS

USDA FOREST SERVICE CLASS I AREAS

REGION 1

Class I Areas:

Anaconda-Pintler Wilderness

Bob Marshall Wilderness

Cabinet Mo untains Wilderness

Gates of the Mountains Wilderness

Mission Moun tains Wilderness Scapegoat Wilderness Selway-Bitterroot Wilderness

Contact: Ann Acheson
Phone Number: (406) 329-3493
Fax Number : (406) 329-3132 Email Address: aacheson@fs.fed.us Mailing Address:

USDA Forest Service, Region 1

P.O. Box 7669 Missoula, MT 59807

REGION 2

Class I Areas: Eagles Nest Wilderness Fitzpatrick Wilderness Flat Tops Wilderness

La Garita Wilderness

Maroon Bells - Snowmass Wilderness

Mount Zirkel Wilderness

North Absaroka Wilderness

Rawah Wilderness

Washakie Wilderness

Weminuche Wilderness

West Elk Wilderness

Contact: Dennis Haddow
Phone Number: (303) 275-5759
Fax Number : (303) 275-5754 Email Address: dhaddow@fs.fed.us Mailing Address: USDA Forest Service, Region 2

P.O. Box 25127
Lakewo od, CO 80225-0127174 REGION 3

Class I Areas:

Chiricahua Wilderness

Galiur o Wilderness

Gila Wilderness

Mazatzal Wilderness Mount Baldy Wilderness

Pecos Wilderness

Pine Moun tain Wilderness

San Pedro Parks Wilderness

Sierra Ancha Wilderness Superstit ion Wilderness Sycamore Ca nyon Wilderness

Wheeler Peak Wilderness

White Mountain Wilderness

Contact: Debby Potter, Ph.D.
Phone Number: (505) 842-3143
Fax Number : (505) 842-3800 Email Address: dapotter@fs.fed.us

Mailing Address:

USDA Forest Service, Southwestern Region Federal Building 517 Gold Ave., S.W.

Albuquerque, NM 87102

REGION 4

Class I Areas: Bridger Wilderness Hells Cany on Wilderness*

Hoover Wilderness Jarbidge Wilderness

Sawtooth Wilderness*

Teton Wilderness

Contact: Dennis Haddow * Contact: Ann Acheson Phone Number: (303) 275-5759 Phon e Number: (406) 329-3493 Fax Number: (303) 275- 5754 Fa x Number: (406) 329-3132 Email Address: dhaddow@fs.fed.us Email Address: aacheson@fs.fed.us Mailing Address: Mailing Address: USDA Forest Service, Regi on 2 USDA Forest Se rvice, Region 1

P.O. Box 25127 P.O. Box 7669 Lakewood, CO 80225-0127 Missoula, MT 59807175 REGION 5

Class I Areas:

Caribou Wilderness

Marble Moun tain Wilderness

South Warner Wilderness

Thousand Lakes Wilderness Yolla Bolly - Middle Eel Wilderness

Contact: Suraj Ahuja
Phone Number : (916) 934-5630
Fax Number: (916) 934-7384 Email Address: sahuja@fs.fed.us Mailing Address:
Mendocino National Forest
420 E. Laurel Street Willows, CA 95988

Class I Areas: Agua Tibia Wilderness Cucamonga Wilderness

San Gabr iel Wilderness San Gorgonio Wilderness

San Jacinto Wilderness

San Rafael Wilderness

Ventana Wilderness

Contact: Mike McCorison
Phone Number: (818) 574-5286 Fax Number: (818) 574-5233

Em ail Address: mmccorison@fs.fed.us Mailing Address: Angeles National Forest
701 N. Santa Anita Ave. Arcadia, CA 91006176 Class I Areas:

Ansel Adams Wilderness

Desolation Wilderness

Dome Land Wilderness Emigrant Wilderness

John Muir Wilderness

Kaiser Wilderness

Mokelumne Wilderness

Contact: Trent Procter Phone Number: (559) 784 -1500, ext. 1114
Fax Number: (559) 781 - 4744
Email Addre ss: tprocter@fs.fed.us Mailing Address: Sequoia National Forest

900 W. Grand Ave. Porterville, CA 93257-2035

REGION 6

Class I Areas: Alpine Lakes Wilderness

Diamond Peak Wilderness Eagle Cap Wilderness

Gearhart Mo untain Wilderness

Glacier Pe ak Wilderness

Goat Rocks Wilderness

Hells Ca nyon Wilderness Kalmiopsis Wilderness

Mt. Adams Wilderness

Mt. Hood Wilderness

Mt. Jefferson Wilderness Mt. Washington Wilderness

Mountain Lakes Wilderness Pasayten Wilderness Strawberry Mountain Wilderness

Three Sisters Wilderness

Contact: Bob Bachman
Phone Number: (503) 808 - 2918 Fax Number: (503) 808 - 2973
Email Addre ss: rbachman@fs.fed.us Mailing Address:

USDA Fo rest Service, Region 6

P.O. Box 3623 Portland, OR 97208-3623177 REGION 8

Class I Areas: Dolly Sods Wilderness

James River Face Wilderness

Otter Creek Wilderness

Contact: Cindy Huber
Phone Number : (540) 265-5156
Fax Number : (540) 265-5145 Email Address: chuber@fs.fed.us Mailing Address:
Jefferson National Forest

5162 Valleypointe Parkway

Ro anoke, VA 24019

Class I Areas: Joyce Kilmer - Slickr ock Wilderness

Linville Gorge Wilderness Shining Rock Wilderness

Contact: Bill Jackson
Phone Number: (704) 257-4815
Fax Number : (704) 257-4263 Email Address: bjackson02@fs.fed.us Mailing Address:
National Fo rests in North Carolina

P.O. Box 2750
Ashe ville, NC 28802

Class I Areas: Caney Creek Wilderness Upper Buffalo Wilderness

Contact: Laura Hudnell
Phone Number : (501) 321-5235
Fax Number : (501) 321-5353
Email Address: lhudnell@fs.fed.us Mailing Address:
Ouach ita National Forest

Box 12 70, Federal Building Hot Spring s Natl. Park, AR 71902178 Class I Areas: Bradwell Bay Wilderness

Cohutta Wilderness Sipsey Wilderness

Contact: Dave Wergowske
Phone Number : (334) 241-8137
Fax Number : (334) 241-8111 Email Address: dwergowske@fs.fed.us Mailing Address:
National Forests in Alabama

2946 Chestnut Street Montgo mery, AL 36107-3010

REGION 9

Class I Area: Boundary Waters Canoe Area

Contact: Robert Berrisford
Phone Number : (218) 720-5385
Fax Number : (218) 720-5600 Email Address: bberrisford@fs.fed.us Mailing Address: Superi or National Forest

Box 33 8, Federal Building
515 W. First St. Duluth, MN 55802

Class I Area: Lye Brook Wilderness

Contact: Nancy Burt
Phone Number : (802) 747-6742
Fax Number : (802) 747-6766
Email Address: nburt@fs.fed.us Mailing Address:

Green Mo untain National Forest
231 N. Main Street
Rutl and, VT 05701179 Class I Areas:

Great Gulf Wilderness Presidential Range - Dry River Wilderness

Contact: Joan Carlson
Phone Number : (603) 528-8721
Fax Number: (603) 528-8783
Email Address: jcarlson@fs.fed.us Mailing Address:
White Mo untain National Forest
719 Main Street
Laconi a, NH 03246-0772

Class I Area:

Hercules - Glades Wi lderness

Contact: Laura Hudnell Phone Number: (501)321-5235
Fax Number : (501) 321-5353
Email Address: lhudnell@fs.fed.us Mailing Address:
Mark Twain National Forest

401 Fairgrounds Road Rolla, MO 65401

Class I Area:

Rainbow Lakes Wilderness

Contact: Dale Higgins
Phone Number : (715) 762-5181
Fax Number : (715) 762-5179 Email Address: dhiggins@fs.fed.us Mailing Address: Chequame gon National Forest

1170 4th Avenue South Park Falls, WI 54552180 NATIONAL PARK SERV ICE CLASS I AREAS

For inquiries regarding routine pe rmit issues, contact the NPS Air Re sources Division in Lakewood, Colorado: John Bunyak, Chief, Policy, Planning and Permit Review Br anch; (303) 969-2818; john_bunyak@nps.gov; P.O. Box 25287, Denver CO, 80225.

ALASKA REGION

Denali National Park and Preserve
Contact: Andrea Blakesley
Phone Number : (907) 683-2294
Fax Number : (907) 683-9612 Email Address: andrea_blakesley@nps.gov Mailing Address:

P.O. Box 9 Denali Park, AK 99755

INTERMOUNTAIN REGION

Arches National Park Contact: Superintendent
Phone Number : (801) 259-3911
Fax Number : (801) 259-8341 Mailing Address:

P.O. Box 907 Moab, UT 84532

Bandelier National Monument Contact: Superintendent
Phone Number : (505) 672-3861
Fax Number : (505) 672-9607 Mailing Address: HCR 1, Box 1, Suite 15 Los Alamos, NM 87544

Big Bend National Park Contact: Superintendent
Phone Number : (915) 477-2251
Fax Number : (915) 477-2357 Mailing Address:

P.O. Box 129 Big Bend National Park, TX 79834181 Black Canyon of the Gunnison National Park Contact: Superintendent
Phone Number : (970) 641-2337
Fax Number : (970) 641-2337 Mailing Address:

P.O. Box 1648 Montrose, CO 81402

Bryce Canyon National Park Contact: Superintendent
Phone Number : (435) 834-5322
Fax Number : (435) 834-4102 Mailing Address:

P.O. Box 170001 Bryce Canyon, UT 84717

Canyonlands National Park Contact: Superintendent
Phone Number : (801) 259-3911
Fax Number : (801) 259-8628 Mailing Address:

P.O. Box 907 Moab, UT 84532

Capitol Reef National Park Contact: Superintendent
Phone Number : (801) 425-3791
Fax Number : (801) 425-3026 Mailing Address: HC 70, Box 15 Torry, UT 84775

Carlsbad Caverns National Park Contact: Superintendent
Phone Number : (505) 785-2232
Fax Number : (505) 785-2133 Mailing Address: 3225 National Parks Highway Carlsbad, NM 88220

Chiricahua National Monument Contact: Superintendent
Phone Number : (520) 824-3560
Fax Number : (520) 824-3421 Mailing Address: Dos Cabezas Route, Box 6500 Willcox, AZ 85643182 Glacier National Park
Contact: Bill Michels
Phone Number : (406) 888-5441
Fax Number : (406) 888-7901 Email Address: bill_michels@nps.gov Mailing Address:

P.O. Box 128 West Glacier, MT 59936

Grand Canyon National Park
Contact: Carl Bowman
Phone Number : (520) 638-7817
Fax Number : (520) 638-7797 Email Address: carl_bowman@nps.gov Mailing Address:

P.O. Box 129 Grand Canyon, AZ 86023

Grand Teton National Park Contact: Superintendent
Phone Number : (307) 739-3300
Fax Number : (307) 739-3438 Mailing Address:

P.O. Box 170 Moose, WY 83012

Great Sand Dunes Na tional Monument Contact: Superintendent
Phone Number : (719) 378-2312
Fax Number : (719) 378-2594 Mailing Address: 11500 Highway 150 Mosca, CO 81146

Guadalupe Mountains National Park Contact: Superintendent
Phone Number : (915) 828-3251
Fax Number : (915) 828-3269 Mailing Address: HC 60, Box 400 Salt Flat, TX 79847-9400183 Mesa Verde National Park Contact: George San Miguel
Phone Number : (970) 529-4465
Fax Number: (970) 529- 4498 Email Address: george_sanmiguel@nps.gov Mailing Address:

P.O. Box 8
Mesa Verde Nati onal Park, CO 81330

Petrified Forest National Park Contact: Superintendent
Phone Number : (520) 524-6228
Fax Number : (520) 524-3567 Mailing Address:

Box 2217 Petrified Forest National Park, AZ 86028

Rocky Mountain National Park
Contact: Ken Czarnowski
Phone Number : (970) 586-1263
Fax Number : (970) 586-1310 Email Address: ken_czarnowski@nps.gov Mailing Address: Estes Park, CO 80517

Saguaro National Park Contact: Meg Weesner
Phone Number : (520) 733-5170
Fax Number : (520) 733-5183
Email Address: meg_weesner@nps.gov Mailing Address: 3693 South Old Spanish Trail Tucson, AZ 85730-5601

Yellowstone National Park Contact: Mary Hektner
Phone Number : (307) 344-2151
Fax Number : (307) 344-2323 Email Address: mary_hektner@nps.gov Mailing Address:

P.O. Box 168 Yellowstone National Park, WY 82190184 Zion National Park
Contact: Jeff Br adybaugh
Phone Number : (801) 772-0140
Fax Number : (801) 772-3426 Email Address: je ff_bradybaugh@nps.gov Mailing Address: Springdale, UT 84767

MIDWEST REGION

Badlands National Park Contact: Sandee Dingman
Phone Number : (605) 433-5262
Fax Number : (605) 433-5404 Email Address: sandee_dingman@nps.gov Mailing Address:

P.O. Box 6 Interior, SD 57750

Isle Royale National Park Contact: Superintendent
Phone Number : (906) 482-0986
Fax Number : (906) 482-7170 Mailing Address: 87 North Ripley Street Houghton, MI 49931

Theodore Roosevelt National Park Contact: Russell Runge
Phone Number : (701) 623-4466
Fax Number : (701) 623-4840 Email Address: russell_runge@nps.gov Mailing Address:

P.O. Box 7 Medora, ND 58645

Voyageurs National Park Contact: Roger Andrascik
Phone Number : (218) 283-9821
Fax Number : (218) 285-7407 Email Address: roger_andrascik@nps.gov Mailing Address:

P.O. Box 50 International Falls, MN 56649185 Wind Cave National Park Contact: Dan Roddy
Phone Number : (605) 745-1157
Fax Number : (605) 745-4207 Email Address: dan_roddy@nps.gov Mailing Address: RR 1, Box 190 Hot Springs, SD 57747

NORTHEAST REGION

Acadia National Park
Contact: Bob Breen
Phone Number : (207) 288-9561
Fax Number : (207) 288-5507
Email Address: bob_breen@nps.gov Mailing Address:

P.O. Box 177 Bar Harbor, ME 04609

Shenandoah National Park
Contact: Christi Gordon
Phone Number : (540) 999-3499
Fax Number : (540) 999-3601 Email Address: christi_gordon@nps.gov Mailing Address: 3655 U.S. Highway 211 East Luray, VA 22835

PACIFIC WEST REGION

Crater Lake National Park Contact: Superintendent
Phone Number : (541) 594-2211
Fax Number : (541) 584-2299 Mailing Address:

P.O. Box 7 Crater Lake, OR 97604

Craters of the Moon National Monument Contact: Superintendent
Phone Number : (208) 527-3257
Fax Number : (208) 527-3073 Mailing Address:

P.O. Box 29 Arco, ID 83213186 Haleakala National Park Contact: Superintendent
Phone Number : (808) 572-9306
Fax Number : (808) 572-1304 Mailing Address:

P.O. Box 369 Makawao, HI 96768

Hawaii Volcanoes National Park Contact: Superintendent
Phone Number : (808) 985-6025
Fax Number : (808) 967-8186 Mailing Address:

P.O. Box 52 Volcanoes, HI 96718

Joshua Tree National Park
Contact: Chris Holbeck
Phone Number : (760) 367-5501
Fax Number : (760) 367-6392 Email Address: chris_holbeck@nps.gov Mailing Address:
74485 National Monument Drive Twentynine Palms, CA 92277

Kings Canyon National Park Contact: Annie Esperanza
Phone Number : (559) 565-3341
Fax Number : (559) 565-3730 Email Address: annie_esperanza@nps.gov Mailing Address: 47050 Generals Highway Three Rivers, CA 93271

Lassen Volcanic National Park Contact: Superintendent
Phone Number : (530) 595-4444
Fax Number : (530) 595-3262 Mailing Address:

P.O. Box 100 Mineral, CA 96063-0100187 Lava Beds National Monument
Contact: Chuck Barat
Phone Number : (530) 667-2282
Fax Number : (530) 667-2737 Email Address: chuck_barat@nps.gov Mailing Address:

P.O. Box 867 Tulelake, CA 96134

Mount Rainier National Park
Contact: Barbara Samora
Phone Number: (360) 569-2177 x2301
Fax Number : (360) 569-2170 Email Address: barbara_samora@nps.gov Mailing Address: Tahoma Woods, Star Route Ashford, WA 98304-9801

North Cascades National Park Contact: Leigh Smith
Phone Number : (360) 856-5700
Fax Number : (360) 856-1934 Email Address: leigh_smith@nps.gov Mailing Address: 2105 Highway 20 Sedro Woolley, WA 98284

Olympic National Park
Contact: Cat Hoffman
Phone Number : (360) 452-3011
Fax Number : (360) 452-0335
Email Address: cat_hoffman@nps.gov Mailing Address:
600 East Park Avenue Port Angeles, WA 98362

Pinnacles National Monument
Contact: Chad Moore
Phone Number : (408) 389-4485
Fax Number : (408) 389-4489 Email Address: chad_moore@nps.gov Mailing Address: 5000 Highway 146 Paicines, CA 95043188 Point Reyes National Seashore Contact: Superintendent
Phone Number : (415) 663-8522
Fax Number : (415) 663-8132 Mailing Address: Point Reyes Station Point Reyes, CA 94956

Redwood National Park Contact: Superintendent
Phone Number : (707) 464-6101
Fax Number : (707) 464-1812 Mailing Address:
1111 Second Street Crescent City, CA 95531

Sequoia National Park Contact: Annie Esperanza
Phone Number : (559) 565-3341
Fax Number : (559) 565-3730 Email Address: annie_esperanza@nps.gov Mailing Address: 47050 Generals Highway Three Rivers, CA 93271

Yosemite National Park Contact: Superintendent
Phone Number : (209) 372-0265
Fax Number : (209) 372-0220 Mailing Address:

P.O. Box 577 Yosemite National Park, CA 95389

SOUTHEAST REGION Everglades National Park Contact: Superintendent
Phone Number : (305) 242-7710
Fax Number : (305) 242-7728 Mailing Address:

P.O. Box 279 Homestead, FL 33030189 Great Smoky Mountains National Park
Contact: Jim Renfro
Phone Number : (423) 436-1708
Fax Number : (423) 436-1220 Email Address: jim_renfro@nps.gov Mailing Address: 107 Park Headquarters Road Gatlinburg, TN 37738

Mammoth Cave National Park Contact: Bobby Carson
Phone Number : (502) 749-2508
Fax Number : (520) 749-2349
Email Address: bobby_carson@nps.gov Mailing Address:

P.O. Box 7 Mammoth Cave, KY 42259

Virgin Islands National Park Contact: Superintendent
Phone Number : (809) 775-6238
Fax Number : (809) 775-7025 Mailing Address:
6310 Estate Nazareth #10 St. Thomas, VI 00802190 U.S. FISH AND WILDLIFE SERVICE CLASS I AREAS

For inquiries regarding routine permit issues, c ontact the FWS Air Quality Branch in Lakewood, Colorado: Sandra Silva, Chief; (303) 969-2814; sandra_v_silva@ nps.gov; P.O. Box 25287, Denver CO, 80225.

REGION 2 Bosque Del Apache Wilderness

Contact: Refuge Manager Phone Number: (505) 835-1828 Fax Number: (505) 835-0314 Mailing Address: Bosque del Apache NWR

P.O. Box 1246 Socorro, NM 87801

Salt Creek Wilderness Contact: Refuge Manager Phone Number: (505) 662-6755 Fax Number: (505) 623-9039 Mailing Address:
Bitter Lake NWR

P.O. Box 7 Roswell, NM 88202-0007

Wichita Mountains Wilderness Contact: Refuge Manager Phone Number: (580) 429-3007 Fax Number: (580) 429-9323 Email Address: R2RW_WM@fws.gov Mailing Address:
Wichita Mountains NWR Rt. 1, Box 448 Indiahoma, OK 73552

REGION 3 Seney Wilderness Contact: Refuge Manager Phone Number: (906) 586-9851 Fax Number: (906)586-3800
Email Address: Mike_Tansy@fws.gov Mailing Address: Seney NWR HCR 2, Box 1 Seney, MI 49883191 Mingo Wilderness

Contact: Refuge Manager Phone Number: (573) 222-3589 Fax Number: (573) 222-6343 Email Address: Gerald_Clawson@fws.gov Mailing Address: Mingo NWR Route 1, Box 103 Puxico, MO 63960

REGION 4

Breton Wilderness Contact: Refuge Manager Phone Number: (504) 646-7555 Fax Number: (504) 646-7588 Email Address: howard_poitevint@fws.gov Mailing Address: Bogue Chitto NWR 1010 Gause Blvd., Bldg. 936 Slidell, LA 70458

Cape Romain Wilderness Contact: Refuge Manager Phone Number: (803) 928-3264 Fax Number: (803) 928-3803 Email Address: R4RW_SC.CRM@mail.fws.gov Mailing Address: Cape Romain NWR 5801 Highway 17 North Awendaw, SC 29429

Chassahowitzka Wilderness Contact: Refuge Manager Phone Number: (352) 563-2088 Fax Number: (352) 795-7961 Mailing Address:
Chassahowitzka NWR 1502 S. Kings Bay Drive Crystal River, FL 32629192 Okefenokee Wilderness Contact: Refuge Manager Phone Number: (912) 496-7366 Fax Number: (912) 496-7366 Mailing Address:
Okefenokee NWR Route 2, Box 3330 Folkston, GA 31537

St. Marks Wilderness Contact: Refuge Manager Phone Number: (850) 925-6121 Fax Number: (850) 925-6930 Email Address: R4RW_FL.SMK@mail.fws.gov Mailing Address:
St. Marks NWR

P.O. Box 68 St. Marks, FL 32355

Swanquarter Wilderness

Contact: Refuge Manager Phone Number: (252) 926-4021 Fax Number: (252) 926-1743 Email Address: R4RW_NC.MTK@mail.fws.gov Mailing Address:
Mattamuskeet NWR Route 1, Box N-2 Swanquarter, NC 27885

Wolf Island Wilderness Contact: Refuge Manager Phone Number: (912) 652-4415 Fax Number: (912) 652-4385 Mailing Address: Savannah Coastal Refuges 1000 Business Center Dr., Ste. 10 Savannah, GA 31405

REGION 5 Brigantine Wilderness Contact: Refuge Manager Phone Number: (609) 652-1665 Fax Number: (609) 652-1474 Email Address: R5RW_FBRNWR @fws.gov Mailing Address: Edwin B. Forsythe NWR P.O. Box 72, Great Creek Rd. Oceanville , NJ 08231193 Moosehorn Wilderness

Contact: Refuge Manager Phone Number: (207) 454-3521 Fax Number: (207) 454-2550 Email Address: Mark_Sweeny@fws.gov Mailing Address:
Moosehorn NWR R.R. 1 Box 202, Suite 1 Baring, ME 04694-9703

REGION 6

Lostwood Wilderness Contact: Refuge Manager Phone Number: (701) 848-2722 Fax Number: (701) 848-2702 Mailing Address:
Lostwood NWR

R.R. 2 Box 98 Kenmare, ND 58746-9046

Medicine Lake Wilderness

Contact: Refuge Manager Phone Number: (406) 789-2305 Fax Number: (406) 789-2350 Mailing Address:
Medicine Lake NWR
223 North Shore Road Medicine Lake, MT 59247

Red Rock Lakes Wilderness Contact: Refuge Manager Phone Number: (406) 276-3536 Fax Number: (406) 276-3538 Mailing Address:
Red Rock Lakes NWR Monida Star Route, Box 15 Lima, MT 59739

UL Bend Wilderness

Contact: Refuge Manager Phone Number: (406) 538-8706 Fax Number: (406) 538-7521 Mailing Address:
Charles M. Russell NWR

P.O. Box 110 Lewistown, MT 59457194 REGION 7

Bering Sea/Simeonof/Tuxedni Wilderness Areas Contact: Refuge Manager Phone Number: (907) 235-6546 Fax Number: (907) 235-7783 Mailing Address:
Alaska Maritime NWR 2355 Kachemak Bay Drive, Suite 101 Homer, AK 99603195 APPENDIX G. FLAG PARTICIPANTS

The individuals listed in th e attached table participated in the development of the FLAG Phase I Report . The abbreviations for the FL AG subgroup or committee on wh ich participants served are
shown below.

LC = Leadership Committee CC = Coordinating Committee P = Policy Subgroup
V = Visibilit y Subgroup O = Ozone Subgroup D = Deposition Subgroup T = Terminology (Glossary) Subgroup196FLAG PARTICIPANTSNameSubgroupOrganizationWork PhoneFaxEmail AddressAcheson, Ann V Forest Service(406) 329-3493 (406) 329-3132 aacheson@fs.fed.usAhuja, Suraj O,V,D Forest Service (530) 934-3316 (530) 934-7384 sahuja@fs.fed.usBachman, Bob CC,V,D,T Forest Service (503) 808-2918 (503) 808-2973 rbachman@fs.fed.usBayle, Bruce P Forest Service (404) 347-3872 (404) 347-6197 bbayle@fs.fed.us∗Benoit, Clif V,P Forest Service -- -- --Blanchard, Karen EPA (919) 541-5503 (919) 541-5509 blanchard.karen@epamail.epa.gov∗Blett, Tamara Forest Service -- -- --Boutcher, Steve Forest Service (503) 326-5434 (503) 808-2973 sboutcher@fs.fed.usBray, David T,V EPA (206) 553-4253 (206) 553-0110 bray.dave@epamail.epa.gov∗Breitenfeld, Dale Park Service -- -- --Bunyak, John P,T Park Service (303) 969-2818 (303) 969-2822 john_bunyak@nps.govBytnerowicz, A. O Forest Service (909) 680-1562 (909) 680-1501 andrzej@deltanet.com∗Carriero, Joe CC,T Fish and Wildlife -- -- --Copeland, Scott V CIRA (CSU) (970)491-3315 (970) 491-8598 COPELAND@CIRA.colostate.eduFisher, Rich CC,V,P,O,T Forest Service (970)498-1232 (970) 498-1010 rfisher@lamar.colostate.edu∗Flores, Miguel P,O Park Service -- -- --∗No longer works for the agency, or now works for a different office.197NameSubgroupOrganizationWork PhoneFaxEmail AddressHaddow, Dennis P,V,T Forest Service(303) 275-5759 (303) 275-5754 dhaddow@fs.fed.usHogsett, Bill O EPA (541) 754-4632 (541) 754-4739 bill@heart.cor.epa.govHuber, Cindy V Forest Service (540) 265-5156 (540) 265-5145 chuber@fs.fed.usIrwin, John V EPA (919) 541-5682 (919) 541-0044 irwin.john@epamail.epa.govJackson, Bill O Forest Service (704) 257-4815 (704) 257-4263 bjacksono2@fs.fed.usLamb, Donna LC Forest Service (202) 205-0800 (202) 205-1096 dlamb@fs.fed.usMalm, Bill V Park Service (970) 491-8292 (970) 491-8598 malm@CIRA.colostate.eduManiero, Tonnie CC,O Park Service (303) 969-2806 (303) 969-2822 tonnie_maniero@nps.govMorris, Kristi Park Service (303) 987-6941 (303) 969-2822 kristi_morris@nps.govMorse, Dee CC,V Park Service (303) 969-2817 (303) 969-2822 dee_morse@nps.govMusselman, Bob O,D Forest Service (970)498-1239 (970) 498-1010 bobm@lamar.colostate.eduNotar, John V Park Service (303) 969-2079 (303) 969-2822 john_notar@nps.gov∗Parker, Kim D,O Forest Service -- -- --Peterson, Dave O BRD (206) 543-1587 (206) 695-0790 wild@u.washington.eduPeterson, Janice O,D Forest Service (425) 744-3425 (425) 744-3255 jpeterson@fs.fed.usPitchford, Mark V NOAA (702) 895-0432 (702) 895-0507 MarcP@snsc.dri.eduPitt, Ken P USDA/OGC (303) 275-5539 (303) 275-5557 kenneth.pitt@usda.govPorter, Ellen CC,D Fish and Wildlife (303) 969-2617 (303) 969-2822 ellen_porter@nps.govPotter, Debby P,D Forest Service (505) 842-3143 (505) 842-3800 dapotter@fs.fed.usProcter, Trent O Forest Service (559) 784-1500 (559) 781-4744 tprocter@fs.fed.us∗No longer works for the agency ornow works for a different office.198NameSubgroupOrganizationWork PhoneFaxEmail AddressRenfro, Jim O Park Service (423) 436-1708 (423) 436-5598 jim_renfro@nps.govRiebau, Al D Forest Service (202) 205-1524 (202) 205-1054 ariebau@fs.fed.usRocchio, Judy O Park Service (415) 427-1431 (415) 427-1487 judy_rocchio@nps.govRolofson, Bud V Fish and Wildlife (303) 969-2804 (303) 969-2822 bud_rolofson@nps.govScruggs, Mark V Park Service (303) 969-2077 (303) 969-2822 mark_scruggs@nps.govShaver, Chris LC Park Service (303) 969-2074 (303) 969-2822 chris_shaver@nps.govShepherd, Don Park Service (303)969-2075 (303) 969-2822 don_shepherd@nps.govSilva, Sandra LC Fish and Wildlife (303) 969-2814 (303) 969-2822 sandra_silva@nps.govStottlemyer, Bob D USGS/BRD (970) 498-1017 (970) 498-1010 crhoades@lamar.colostate.eduThomas, Jerome Forest Service (803) 561-4000 (803) 561-4004 jthomas@fs.fed.us∗Tonnessen, Kathy D Park Service -- -- --Vimont, John V Park Service (303) 969-2808 (303) 969-2822 john_vimont@nps.gov∗No longer works for the agency ornow works for a different office.199 APPENDIX H. BIBLIOGRAPHY

Some of the documents cited below were referenced in this FLAG Phase I Report. Others are listed to provide background information.

General References

Adams, M.B., D.S. Nichols, C.A. Federe r, K.F. Jensen and H. Parrott. 1991. Screening Procedure to Evaluate Effects of Air Pollution on East ern Region Wildernesse s Cited as Class I Air Quality Areas

. General Technical Report NE-151. U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. Radnor, Pennsylvania.

Binkley, D., C. Giardina, I. Dockersmith, D. Morse, M. Scruggs an d K. Tonnessen. 1997. Status of Air Quality and Related Values in Class I National Parks and Monu ments of the Colorado Plateau . U.S. Department of the Interior, National Park Serv ice, Air Resources Division. Denver, Colorado.

Blankenship, J.O., D.U. Potter, R.W. Fisher and J.M. Eilers. In Press. Guidelines for Evaluating Air Pollution Impacts on Class I Ar eas in the Southwestern Region . U.S. Department of Agriculture, Forest Service, Rocky M ountain Research Station. Ft. Co llins, Colorado.

Bunyak, J. 1993. Permit Application Guidance for New Pollution Sources . Natural Resources Report NPS/NRAQD/NRR-93. U.S. Department of the Interi or, National Park Service, Air Quality Division. Denver, Colorado.

Eilers, J. M., C.L. Rose and T.J. Sullivan. 1994. Status of Air Quality a nd Effects of Atmospheric Pollutants on Ecosystems in the Pacific No rthwest Region of the National Park Service . Technical Report NPS/NRAQD/NRTR-94/160. U.S. Department of the Interior, National Park Service, Air Quality Division. Denver, Colorado.

Fox, D.G., A.M. Bartuska, J.G. Byrne, E. Cowling, R. Fisher, G.E. Likens , S.E. Lindberg, R.A. Linthurst, J. Messer and D.S. Nichols. 1989. A Screening Procedure to Eval uate Air Pollution Effects
on Class I Wilderness Areas . General Technical Report RM-168. U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Rang e Experiment Station. Ft. Collins, Colorado.

Fox, D., G.J.C. Bernabo and B. Hood. 1987. Guidelines for Measuring the Physical, Chemical, and Biological Condition of Wilderness Ecosystems . General Technical Report RM-146. U.S. Department of Agriculture, Forest Service, Rock y Mountain Forest and Range Experiment Station. Ft. Collins, Colorado.

Garner, J.H.B., T. Pagano and E.B. Cowling. 1989. An Evaluation of the Role of Ozone, Acid Deposition, and Other Airborne Pollutants in the Forests of Eastern North America . General Technical Report SE-59. U.S. Department of Agri culture, Forest Service, Southeastern Forest Experiment Station. Asheville, North Carolina.200 Haddow, D., R. Musselman, T. Blett and R. Fisher. 1998. Guidelines for Evaluating Air Pollution Impacts on Wilderness within the Rocky Moun tain Region: Report of a Workshop, 1990 . General Technical Report RMRS-GTR-4. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Ft. Collins, Colorado.

Ottmar, R. 1996. Linking recent historical and cu rrent forest vegetation patterns to smoke and
crown fire in the Interior Columbia River Basin. Paper presented at the 13th Conference of Fire and Forest Meteorology, October 27-31, 1996. Lorne, Australia.

Peterson, D.L., D.L. Schmoldt, J.M. E ilers, R.W. Fisher and R.D. Doty. 1993. Guidelines for Evaluating Air Pollution Impacts on Cl ass I Wilderness Areas in California . General Technical Report PSW-GTR-136. U.S. Department of Agricu lture, Forest Service, Pacific Southwest Research Station. Albany, California.

Peterson, D.L., T.J. Sullivan, J.M. Eilers, S. Brace, D. Horner and K. Savig. 1998. Assessment of Air Quality and Air Pollutant Im pacts in National Parks of th e Rocky Mountains and Northern
Great Plains . U.S. Department of the Interior, Nationa l Park Service, Air Resources Division.

Peterson, J., D. Schmoldt, D. Peterson, J. Eilers, R. Fisher and R. Bachman. 1992. Guidelines for Evaluating Air Pollution Impacts on Class I Wilderness Areas in the Pacific Northwest . General Technical Report PNW-GTR-299. U. S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, Oregon.

Porter, E. 1996. Air Quality and Air Quality Rela ted Values in Chassahowitzka National Wildlife Refuge and Wilderness Area. U.S. Fish and Wildlife Service, Air Quality Branch. Denver,
Colorado.

Stanford, J., A. Acheson, D. Brakke, S. Eversman, K. Savig and J. Eilers. 1997. A Screening Procedure to Evaluate Air Pollution Eff ects in Region 1 Wilderness Areas, 1991 . General Technical Report RM-GTR-294. U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. Ft. Collins, Colorado.

Turner, R. S., D. G. Fox, A. Bartus ka, W. Jackson and B. Bayle. Draft. Screening Procedures to Evaluate Effects of Air Pollution on Forest Service Wilderness in the Southern Region

.

USDA Forest Service. 2000. Screening Methodology for Calculatin g ANC Change to High Elevation
Lakes . Rocky Mountain Re gion. January 2000.

U.S. EPA. 1998. Interim Air Quality Policy on Wildland and Prescribed Fires . Office of Air Quality Planning and Standards. Research Triangle Park, North Carolina. 39 pp.201 Visibility References

Colorado State University. C ooperative Institute for Research in the Atmosphere. 1996 IMPROVE, Interagency Monitoring of Protected Environments. Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States: An Analysis of Data from the IMPROVE Network.

National Acid Precipitation Assessment Program. 1990. Acidic Deposition: State of Science and Technology, Report 24, Visibility: Existing and Historical Conditions – Causes and Effects.

National Park Service. 1993 . Permit Applicatio n Guidance for Ne w Pollution Sources. Natural Resources Report NPS/Nraqd/NRR-93.

Pitchford, M.L. and Malm, W.C. 1994. Developmen t and Applications of a Standard Visual Index. Atmospheric Environment 28, 1049-1054.

USEPA. 1992a . Workbook for Visual Impact Screening and Analysis (Revised). EPA-450/R-92-023

USEPA. 1992b. User’s Manual for th e Plume Visibility Mode l, PLUVUE II (Revised) . EPA-454/B92-008.

USEPA. 1996a . CALMET, CALPUFF, and CALPOST Modeling System . PB 96-502-083INC.

USEPA. 1996b. Proposed Rule, New Source Revi ew Reform, Federal Register/Vol. 61, No. 142/Tuesday, July 23, 1996.

USEPA. 1996c. Addendum to the User’s Manual for the Plume Visibility Model, PLUVUE II (Revised). EPA-454/B-95-001.

USEPA. 1998. Interagency Workgrou p on Air Quality Modeling (IWA QM) Phase 2 Summary Report and Recommendations for Modelin g Long-Range Tr ansport and Impacts on Regional Visibility

. EPA454/R-98-019.202 Ozone References

Ashmore, M.R. and A.W. Davison. 1996. Towards a critical level of ozone for natural vegetation. In : Kaerenlampi, L. and L. Skaerby, eds. Critical Levels for Ozone in Eu rope: Testing and Finalizing the Concepts . UN-ECE Workshop Report. University of Kuopio, De partment of Ecology and
Environmental Scienc e. p. 58-71.

Brace, S., D.L. Peterson and D. Horner. 1998. Diagnosing Ozone Injury in Vascular Plants of the Pacific Northwest . PNW-GTR-xxx (In Press). U.S. Department of Agriculture, Forest Service, Pacific
Northwest Research Station.

Chappelka, A.H., L.J. Samuelson, J.M. Skelly, A.S. Lefohn and D. Nesdill. 1995. Effects of Ozone on Vegetation in Southern Appalachians: an Annotated Bibliography . Auburn University, School of Forestry. Auburn, Alabama.

Davis, D.D. 1995. Evaluation of Ambient Ozone Injury on the Foliage of Vegetation in the Edwin B. Forsythe National Wildlife Refuge, Brigantine, New Jersey. Final Report

.

Davis, D.D. 1998. Evaluation of Ambient Ozone Injury on th e Foliage of Vegetation in the Cape Romain National Wildlife Refuge, South Carolina. 1997 Observations

.

Davis, D.D. 1998. Evaluation of Ozone Injury on Vegetatio n in the Okefenokee National Wildlife Refuge, Georgia. 1997 Observations

.

Eckert, R., R. Kohut, J. Lauren ce, P. King, T. Lee, B. Rock, D. Moss and A. Theisen. 1991. Studies to Assess the Effects of Ozone on Native Vegetation of Acadia National Park . Annual Report. University of New Hampshire. Durham, New Hampshire.

Guderian, R., D.T. Tingey, and R. Rabe. 1985. Effects of photochemi cal oxidants on plants. Part 2. In: Guderian, R., ed. Air Pollution by Photochemical Oxidants . Springer-Verlag. New York. p. 129-296.

Guderian, R. 1977. Air Pollution. Phytotoxicity of Acidic Gases and Its Significance in Air Pollution
Control. Springer-Verlag. New York.

Heck, W.W. and E.B. Cowl ing. 1997. The need for a long-term cumulative se condary ozone standard - an ecological perspective. Environmental Management . January.

Heck, W.W., C.S. Furiness, C.K. Simms and E.B. Cowling, eds. 1997. Report from Workshop on
Research Needs to Assess th e Effects of Ozone on Crop, Forest and Natural Ecosystems . May 18, 1997. Southern Oxidants Study, North Carolina State Universi ty. Raleigh, North Carolina.

Kaerenlampi, L. and L. Skaerby, eds. 1996. Critical Levels for Ozone in Europe: Testing and Finalizing the Concepts. UN-ECE Workshop Report . University of Kuopio, Department of Ecology
and Environmental Science.203 Lefohn, A.S. 1998. The Identification of Ozone Exposures that Result in Vegetati on Visible Injury and Growth Loss for Specific Sp ecies Grown in the Southern Appalachian Mountain Region . Southern Appalachian Mountains Initiative Report.

Lefohn, A.S. and V.C. Runeckles. 1987. Estab lishing a standard to protect vegetation - ozone exposure/dose considerations. Atmospheric Environment 21(3):561-568.

Massman, W.J., R.C. Musselman, and A.S. Lefohn . 2000. A conceptual ozon e dose-response model to develop a standard to protect vegetation. Atmospheric Environment 34:745-759.

Miller, P., R. Guthrey and S. Schilling. 1995 Draft. Third Progress Report of FOREST. Comparisons of 1991-1995 Ozone Injury Index at Sierra Nevada and San Bernardino Mountains Sites . 9 pp.

Miller, P.R., K.W. St olte, D.M. Duriscoe and J. Pronos. 1996. Evaluating Ozone Air Pollution Effects
on Pines in the Western United States . General Technical Report PSW-G TR-155. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. Albany, California.

Miller, P., R. Guthrey, S. Schilli ng and J. Carroll. 1996. Ozone in jury responses of ponderosa and Jeffrey pine in the Sierra Nevada and San Bernar dino Mountains in Califo rnia. Paper presented at International Symposium: Air Pollu tion and Climate Change Effects on Forest Ecosystems, Feb 5-9, 1996. U.S. Department of Agricultu re, Forest Service, Pacific Southwest Research Station. Riverside,
California.

Multi-stakeholder NO

x /VOC Science Program. 1997. Report of the Vegeta tion Objective Working Group: Canad ian 1996 NO

x /VOC Science Assessment . Atmospheric Environment Service, Environment Canada. Toronto, Ontario, Canada.

Musselman, R.C. and T.J. Minnick. 2000. Noctur nal stomatal conductance and ambient air quality
standards for ozone. Atmospheric Environment 34:719-733.

Neufeld, H.S. and J.R. Renfro. 1993a. Sensitivity of Black Cherry Seedlings (Prunus serotina Ehrh.) to Ozone in Great Smoky Mountains National Park. The 1989 Seedling Set . Natural Resources Report NPS/NRTR-93/112. U.S. Department of th e Interior, National Park Service, Air Quality Division. Denver, Colorado. 26 pp.

Neufeld, H.S. and J.R. Renfro. 1993b. Sensitivity of Sycamore Seedlings (Platanus occidentalis ) to Ozone in Great Smoky Mountains National Park. Data from 1989 . Natural Resources Report
NPS/NRTR-93/13 1. U.S. Department of the Interior, National Park Servic e, Air Quality Division. 39

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Singh, H.B.; Ludwig, F.L. ; Johnson, W.B. 19 78. Tropospheric oz one: concentrations and variabilities in clean remote atmospheres. Atmospheric Environment 12:2185-2196.

Singh, H.B.; Viezee, W.; Jo hnson, W.B.; Ludwig, F.L. 1980. The impact of stratospheric ozone on trpospheric air quality. Journal Air Pollution Control Association 30:1009-1017.204 Skaerby, L. and P.E. Karlsson. 1996. Critical levels for ozone to prot ect forest trees - best available knowledge from the nordic countr ies and the rest of Europe. In : Kaerenlampi, L. an d L. Skaerby, eds. Critical Levels for Ozon e in Europe: Testing a nd Finalizing the Concepts . UN-ECE Workshop Report. University of Kuopio, Department of Ecology and Envi ronmental Scienc e. p. 72-85.

U.S. EPA. 1992. Summary of Selected New Informatio n on Effects of Ozone on Health and Vegetation: Supplement to 1986 Air Quality Criteria for Ozone and Other Photoc hemical Oxidants

. EPA/600/8-88/105F. Research Triangle Park, NC: US Environmental Protect ion Agency, Office of
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U.S. EPA. 1996a. Review of National Ambient Air Qualit y Standards for Ozon e Assessment of Scientific and Tech nical Information

. OAQPS Staff Paper . EPA-452/R-96-007. U. S. EPA, Office of Air Quality Planning and Standards. Research Tria ngle Park, North Carolina.

U.S. EPA. 1996b. Air Quality Criteria for Ozone and Related Photochemical Ox idants. Vol. 2

. EPA/600/P-93/004bF. U.S. EPA, Office of Research and Deve lopment. Research Triangle Park,
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Wooldridge, G; Zeller, K.; Musselman, R. 1997 . Ozone concentration ch aracteristics at a highelevation forest site. Theoretical and Applied Climatology 56:153-164.205 Deposition References

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Au, S. 1974. Vegetation and Ecological Processes on Shackleford Bank , North Carolina . Scientific Monograph Series No. 6. U.S. Department of the In terior, National Park Serv ice. Washington, D.C. 86

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