Recommendations for Making 

Attribution Determinations 

in the Context of 

Reasonably Attributable BART 

Prepared by: The WESTAR Council 

Prepared for: The Air Managers Committee, Western Regional Air Partnership 

May 2003 

Recommendations for Making 

Attribution Determinations 

in the Context of 

Reasonably Attributable BART 

Prepared by: 

The WESTAR Council RA BART Phase II Working Group: Alice Edwards, Alaska Department 

of Environmental Conservation; Mike Sundblom, Arizona Department of Environmental 

Quality; Deb Wolfe, Montana Department of Environmental Quality; Dana Mount, North 

Dakota Department of Health; Dave DuBois, New Mexico Environment Department; Colleen 

Delaney, Utah Department of Environment Quality; Alan Newman, Washington Department of 

Ecology; Lisa Reiner, Quinalt Indian Nation; Bruce Polkowsky and Kristi Gebhart, National 

Park Service; and Kristin Gaston and Bob Lebens, WESTAR. 

Prepared for: 

The Air Managers Committee of the Western Regional Air Partnership 

in fulfillment of the Western Governors’ Association contract #30203-13 

May 2003 
Acknowledgements 

The recommendations regarding the attribution process, the applicable technical 

approaches and the examples in this report are the product of the RA BART Phase II Working 

Group. The WESTAR Council acknowledges the generous support of all the Working Group 

members. Alice Edwards, Alaska Department of Environmental Conservation; Mike Sundblom, 

Arizona Department of Environmental Quality; Deb Wolfe, Montana Department of 

Environmental Quality; Dana Mount, North Dakota Department of Health; Dave DuBois, New 

Mexico Environment Department; Colleen Delaney, Utah Department of Environment Quality; 

Lisa Reiner, Quinalt Indian Nation; Bruce Polkowsky and Kristi Gebhart, National Park Service; 

and Kristin Gaston and Bob Lebens, WESTAR. 

Authors of the Attribution Techniques (Section VI) are Mike Sundblom (Monitoring); 

Kristi Gebhart (Receptor Modeling); and Dave DuBois (Source Modeling), with assistance from 

Ajith Kaduwela, California Air Resources Board. Bruce Polkowsky and Colleen Delaney 

drafted the certification section and provided liaison between the Western Regional Air 

Partnership Market Trading Forum and the Working Group. Alan Newman, Washington 

Department of Ecology drafted the portion of the report outlining the example process to which 

Dana Mount also contributed. Kristin Gaston drafted the regulatory context section and along 

with Bob Lebens, edited the report. 
EXECUTIVE SUMMARY 

This report recommends a general procedure and applicable technical approaches that may 

be used by states and tribes to assess reasonable attribution in response to a Federal land manager 

(FLM) certification of visibility impairment in a Class I area (Certification). 

WESTAR formed the Reasonably Attributable Best Available Retrofit Technology (RA 

BART) Phase II Working Group with Federal land management agency staff and members of 

state and tribal air quality agencies knowledgeable about RA BART and associated monitoring 

and modeling techniques. To provide the necessary framework, the report provides background 

information about both the certification process and the attribution determination process. 

However, the recommendations focus on the general principles of the attribution assessment 

process and the technical criteria used in the assessment. 

The recommendations are summarized below: 

General Principles: 

 The attribution assessment should be: 

 

 A collaborative process that relies on existing data with minimal additional 

analyses. 

 Technically and legally defensible. 

 Accomplished at a reasonable cost and within a reasonable time frame. 

 No more complex than necessary. 

 Performed by state or tribal agency staff. 

 Adequate to determine whether or not visibility impairment is attributable to an 

existing stationary source potentially subject to BART. 

Technical Criteria: 

 Emissions from BART-eligible sources must “cause or contribute to” visibility 

impairment. Visibility-impairing pollutants of concern must be identified. 

 Factors to consider in assessing impairment include: duration, frequency, 

geographic extent, magnitude, and time of occurrence. 

 Identify distance from source to Class I area to determine appropriate tools for 

characterization of the impairment. 

 Quantitative results are preferable, although qualitative results such as 

photographs may be adequate. 

 Use as many different indicators of impairment as practicable rather than relying 

on a single indicator. 

 Consider level of uncertainty in the assessment.

 Use EPA guideline models whenever practicable. 

Table of Contents 

I. Background....................................................................................................... 1

 A. Introduction and Purpose 

 B. Regulatory Context 

II. Federal Land Manager Certification of Impairment ........................................ 7 

III. The Attribution Determination Process.............................................11 

IV. General Principles and Criteria ...................................................................... 17

 A. General Principles of the Attribution Process 

B. Technical Criteria 

C. Examples of the Attribution Process 

V. Techniques...................................................................................................... 31 

A. Monitoring 

B. Source Modeling 

C. Observational Modeling 

VI. Appendix 

 RA BART Rule (40 CFR 51.300-306) 

I. BACKGROUND 

A. Introduction and Purpose 

Reasonably Attributable Best Available Retrofit Technology (RA BART) is the portion of 

EPA’s visibility rule published in 1980 and codified in 40 CFR 51.302–51.306 that deals with 

visibility impacts from one source or a small groups of sources. RA BART refers to reasonably 

attributable visibility impairment and best available retrofit technology for eligible sources and 

emission limits, and emissions controls as defined by the statute and the rules. Some confusion 

exists regarding the application of RA BART, the process of assessing sources of visibility 

impairment, and the technical tools available for an RA BART attribution determination. RA 

BART is a statutory requirement, although certain of the requirements may no longer be 

applicable when a source complies with BART or installs BART-like controls or after a state 

implements a trading program under 51.308(e)(2) or any trading program under 51.309, and if no 

remaining visibility impacts continue from one source or a small group of sources. 

This report, therefore, addresses the RA BART attribution process and builds upon case 

studies WESTAR developed in 2001 to examine and document how Reasonably Attributable 

Visibility Impairment (RAVI) had been addressed in previous assessments.1

 

The Federal land managers (FLMs) advocate maintaining RA BART as a tool because RA 

BART is effective when new monitoring indicates that a previously un-monitored area has 

visibility problems differing from the regional visibility impairment conditions at other areas. In 

addition, RA BART is effective when BART-eligible sources in the vicinity of the protected area 

are causing or contributing to identified visibility impairment.2

 

First, this report recommends attribution process principles and assessment techniques a 

state or tribe may consider in an attribution assessment to identify if, and to what degree, an 

existing source or small group of sources causes or contributes to visibility impairment. The 

report focuses on RAVI and does not specifically address impairment due to regional haze. 

Although this report includes references to regional haze, such references serve only to place the 

attribution process in the larger context of the broad regulatory framework that addresses 

visibility impairment, including the regional haze regulations. 

Second, this report includes no recommendations with regard to establishing a threshold 

level at which reasonably attributable impairment exists. Instead, the working group outlined a 

recommended general process and recommended technical procedures that may be used as 

guidelines if an attribution assessment is necessary. Due to the circumstances unique to each 

attribution assessment and the requirement that the assessment be conducted on a case-by-case 

basis, recommending one specific analysis for every situation was not possible. A state or tribe 

should select from the techniques summarized in Section IV. Section IV(C) includes five 

examples of the attribution assessment process, each providing a range of techniques to be used 

 

1

 WESTAR Council, RA BART and RA BART-like Case Studies, June 2001. 2

 This document specifically relates to RA BART and does not address broader issues relating to long-term visibility

strategies. FLMs generally do not intend to issue Certifications citing specific sources except for situations involving

BART sources. 

1 
based on available data. The examples also recommend more refined analyses that may require 

additional data. 

Third, this report includes specific information about the current policy of FLM agencies 

regarding certifications of impairment (Certification), but makes no recommendations regarding 

the certification process. This report makes no recommendations regarding state process 

following an attribution determination nor examines options for performing the Best Available 

Retrofit Technology (BART) analysis or the incorporation of BART requirements into State 

Implementation Plans (SIPs). 

Tribal Implementation 

The recommendations in this document are intended to help states assess reasonable 

attribution following Certification by an FLM. Subject to the requirements of the Clean Air Act, 

42 USC 7601(d) and the Tribal Authority Rule, 40 CFR 49.1– 49.11, a tribe may accept 

responsibility for making reasonably attributable determinations in response to a Certification by 

an FLM when a BART-eligible source identified by the FLM in the Certification is on the tribe’s 

land. The regional haze rule also explicitly recognizes the authority of tribes to implement the 

provisions of that rule on tribal lands. Those provisions create the following framework: 

1. Absent special circumstances, reservation lands are not subject to state jurisdiction. 

2. Federally recognized tribes may apply for and receive delegation of federal authority to 

implement CAA programs (including visibility regulation), or “reasonably severable” 

elements of such programs. The mechanism for this delegation is a Tribal

Implementation Plan (TIP). A reasonably severable element is one that is not integrally 

related to program elements that are not included in the plan submittal, and is consistent 

with applicable statutory and regulatory requirements. 

3. Where a tribe does not seek delegation through a TIP, the Environmental Protection 

Agency (EPA), as necessary and appropriate, will promulgate a Federal Implementation 

Plan (FIP) within a reasonable timeframe to protect air quality on tribal lands. 

Accordingly, these recommendations also may assist a federally recognized tribe that 

chooses to adopt a TIP to implement the RA BART provisions. In some cases, a tribe may be 

able to utilize the recommendations in much the same way as states. In many other cases, 

however, the recommendations may be modified to meet the unique situation of the tribe and the 

nature of its air program, including its manner of defining “reasonably severable elements” and 

its method of dividing responsibilities between the tribe and EPA. Because of these differences, 

the recommendations that follow do not refer to tribes every time states are referenced. 

B. Regulatory Context 

The national goal for visibility is set forth in the Clean Air Act (CAA) at 42 USC 7491: “the 

prevention of any future, and the remedying of any existing, impairment of visibility in 

mandatory Class I Federal areas which impairment results from manmade air pollution.” The 

requirements apply to 156 designated Class I areas. 

2 
As stated previously, Congress required that states provide a remedy for visibility 

impairment that was “reasonably attributable” to one source or a small group of sources. (42 

USC 7491). Congress directed EPA to ensure that all SIPs contained measures necessary to 

make reasonable progress toward meeting the national goal, including requirements for 

identifying major sources of emissions causing or contributing to visibility impairment, and 

requirements for the application of BART on such sources. EPA promulgated rules pursuant to 

Congress’s directive to provide guidelines to the states on appropriate techniques and methods 

for implementing the SIP requirements. (40 CFR 51.302(c)). (Note: when this report refers to a 

“source” within the meaning of this regulation, the phrase “or a small group of sources” is 

implied.) 

The requirement to install Best Available Retrofit Technology controls on existing sources 

is a key element of the visibility protection provisions in the CAA demonstrating the need to 

focus on pollution emitted from a specific set of existing sources. Sources are potentially subject 

to BART controls if they meet the following criteria:

1. A major stationary source from 1 of 26 source categories identified in the CAA and 

regulations (see Appendix A); 

2. Potential to Emit (PTE) 250 tons per year of any air pollutant; and, 

3. Not in operation prior to August 7, 1962 and “in existence” on August 7, 1977. 

In the 1980 visibility rule, EPA used the term “existing stationary facility” to define a 

facility that met the above criteria. However, to avoid any confusion about whether that term

encompassed a larger group of sources, EPA now uses the term “BART-eligible source.” The 

term “BART-eligible source” is used throughout this report for similar reasons, but the reader 

should be aware that some sections of the visibility rule still refer to “existing stationary 

facilities.” For purposes of the attribution discussion in this paper, these terms are 

interchangeable. 

In 1999, EPA added two new sections to the visibility rule to address regional haze (40 CFR 

51.308 and 51.309). Pollutants causing regional haze may be transported hundreds of miles, and 

therefore, regional haze must be addressed as a broader regional issue. The 1980 visibility rule 

focused on direct visibility impacts of an individual source or small group of sources. The 1999 

revisions are commonly referred to as the regional haze rule, although the 1999 rule incorporates 

the earlier requirements for BART as well as the new regional haze provisions. 

To remedy RAVI, the regulations outline a process to identify and control emissions from

sources that are directly impacting visibility at specific Class I areas (40 CFR 51.302). Three 

primary steps in this process are: 

1. The Federal land manager certifies impairment; 

2. The state identifies existing sources that cause or contribute to the visibility 

impairment; and 

3 
3. The state performs a BART analysis to determine what controls, if any, are required 

on any existing source that meets the BART criteria and has been identified as 

contributing to the impairment. 

Attribution Process 

The language of 40 CFR 51.302(c)(4)(i) provides the basic principle upon which the state 

will rely during the attribution process. That section states that the attribution must indicate each 

[BART-eligible source] “which may reasonably be anticipated to cause or contribute to 

impairment of visibility.” Whether or not the impairment is “reasonably attributable” is 

determined by “visual observation or other technique the state deems appropriate.” (40 CFR 

51.301(s)). Because the state is responsible for identifying sources, this report provides 

recommendations for the state to consider when it undertakes an attribution determination. 

Once visibility impairment is identified, RAVI is addressed on a case-by-case basis. Under 

the 1980 regulation, a state evaluates BART-eligible sources only after an FLM certifies the 

existence of visibility impairment. However, in the context of the current regional haze rule, 

states must also address BART requirements for regional haze (RH BART).3

 Several options 

exist for addressing RH BART. For example, rather than require a source-specific BART 

emission limit, a state may choose to develop a trading program, either regionally or within its 

own jurisdiction, that achieves greater reasonable progress than case-by-case RH BART. 

If a state develops a trading program, the time period to achieve the emissions reductions 

may be extended. As a result, visibility conditions at a specific Class I area initially may remain 

static or even deteriorate during the early implementation period. During this time, FLMs have 

indicated that the RAVI process may be utilized to provide steady and continuing improvement 

in visibility. Within the context of the regional haze rule, this may be an example of a 

“geographic enhancement.” As noted above, the recommendations in this report apply only to 

case-by-case applications of BART and are not intended to apply to the broader regional haze 

rule such as a market-based trading program implemented as part of the 1999 rule. 

A state may find it difficult to determine a source/receptor link for RAVI difficult when 

there are other sources located in the area, including international sources. However, the regional 

haze rule provisions do not alter the requirement to undertake the attribution assessment. 

State determines what controls, if any, are required 

After the attribution determination, the state is required to perform a BART analysis to 

determine what types of controls, if any, should be placed on the source(s) found to be 

contributing to the impairment. The following factors affect the BART determination: 

1. Available technology; 

2. Costs of compliance; 

3. Energy and non air quality environmental impacts of compliance; 

 

3

 The RH BART provision was remanded to EPA by the U.S. Court of Appeals as a result of the ruling in American

Corn Growers Association v. EPA, No. 9901348 (DC Cir. May 24, 2002) 

4 
4. Remaining useful life of the source; and, 

5. Degree of improvement that can be anticipated to result from the use of the controls. 

As noted above, this report does not provide guidance regarding state process following an 

attribution determination nor does it examine options for implementing the BART requirement. 

5 
 

6 
II. FEDERAL LAND MANAGER CERTIFICATION OF IMPAIRMENT 

The Federal land managers monitor visibility through a nationwide monitoring network, 

known as Interagency Monitoring of PROtected Visual Environments (IMPROVE). The 

IMPROVE network has recently been expanded and the FLMs anticipate reliable trend data for 

the new IMPROVE sites between the years 2006 and 2008. The FLMs plan to evaluate the 

current visibility conditions in Class I areas as well as trends occurring over time to identify 

areas where visibility is not improving. 

 

The FLMs generally will use a screening process to identify Class I areas that may be 

affected by RAVI. This screening process will be influenced by the approach the state relies 

upon to address RH BART in its SIP. There are three approaches that may be used. 

1. Case-by-Case Review of RH BART under 51.308

No distinction exists between emission reductions needed to address RH BART and 

reductions to address RAVI, therefore, the BART process will address both types of visibility 

impairment. 

2. Trading Program under 51.308

The FLMs anticipate that the following screening criteria may be appropriate, but will not 

make a final decision until a 308 trading program has been developed. The screening criteria 

associated with this approach may be similar to the screening criteria associated with the trading 

program option under 51.309. However, the FLMs have indicated that the screening criteria 

ultimately selected for 51.308 will depend on how the trading program is structured, the selected 

emissions cap, and other aspects of the trading program. 

 

Potential Screening Process Criteria: 

(i) Sulfate, nitrate, organic carbon, other fine particulate, etc., levels in the Class I 

area are not decreasing.4

(ii) One or more BART-eligible sources of SO2, NOx, VOC, PM10, etc., are located 

within 100 miles of the mandatory federal Class I area. 

(iii) The BART-eligible sources identified in (ii) are not already well-controlled for 

pollutants that contribute to visibility impairment. 

3. Milestones and Backstop Trading Program under 51.309 

 

The FLMs plan to sign a Memorandum of Understanding (MOU) with the participating 

states to define the screening criteria the FLMs will use to certify impairment. 

 

4

 The decrease of a pollutant (or secondary species of that pollutant) would be measured from the beginning of the

market or emissions trading program, and such a trading program would take a long time (10 to 15 years) to reach 

the level of reduction that “meets” BART. The decrease would be tested over the first 5 to 10 years. A very quick 

time frame for reductions would negate the need for RA BART criteria. 

7 
Screening Process criteria:5

(i) Sulfate levels in the Class I area are not decreasing. 

(ii) One or more BART-eligible sources of SO2 are located within 100 miles of the 

mandatory federal Class I area. 

(iii) The BART-eligible sources identified in (ii) are not already well-controlled for 

SO2 (85% or better SO2 control for coal-fired utility boilers). 

Goal: For FLMs to complete the certification process between 2006 and 2008.6

These criteria were influenced by the design of the 309 trading program including emission 

reduction estimates, shape of the declining emission cap, inclusion of sources that were not 

BART-eligible, and inclusion of new source growth under the cap. 

Although geographic enhancements do not need to be addressed under the first approach, 

they must be addressed in the two trading programs described above because emission 

reductions may occur more gradually in the context of a trading program. The FLMs do not 

anticipate certifying impairment under these circumstances but do intend to notify the state as 

part of the SIP development process if concerns arise regarding “hot spot” impacts from sources 

that may directly affect specific Class I areas. 

Note: BART for regional haze has been addressed for SO2 under this option. Regional haze 

BART for NOx and PM will be addressed in SIP revisions that are due in 2008.

In all three cases discussed above, if the FLM determines that a certification of visibility 

impairment is necessary, the FLM will send an official Certification to the state. The 

Certification will generally include the following information: 

1. Class I area(s) impacted; 

2. Basis of certification (photographic documentation, monitoring, modeling, etc.); 

3. Type of impairment certified: plume impact, or layered or uniform haze; 

4. Pollutant(s) of interest; and, 

5. Preliminary identification of source(s) believed responsible for impact. 

FLM, State, and EPA roles in RA BART 

FLMs are responsible for certifying impairment. The Certification demonstrates the FLM’s 

determination that there is evidence of visibility impairment from one source or a small group of 

sources. 

 

5 Within the context of established regional milestones for SO2 and a backstop trading program, the FLMs have said

it is appropriate to use the following screening process in making these recommendations as part of the Certification.

Voluntary Emissions Reduction Program for Major Industrial Sources of Sulfur Dioxide in Nine Western States and 

A Backstop Trading Program. An Annex to the Report of the Grand Canyon Visibility Transport Commission. 

Submitted by the Western Regional Air Partnership to the U.S. Environmental Protection Agency, p. 61, September 

29, 2000. 

6

 This goal will not in any way restrict the ability of the FLMs to certify impairment at a later date if it is necessary 

to fulfill their statutory obligations. ibid. 

8 
Following the Certification, states have the following regulatory obligations: (1) identify 

facilities that “emit an air pollutant which may be reasonably anticipated to cause or contribute to 

any visibility impairment” in that Class I area, and (2) for sources subject to BART, the state 

must identify the BART level of control technology. If the source does not have adequate control 

in place, the state must establish a BART limit in the SIP. 

EPA has two major responsibilities for the RA BART requirement. First, in the states that do 

not have a SIP in place to address the RA BART requirements, but where the program is 

implemented through a FIP, EPA will conduct the BART analysis and establish any BART 

emission limits. Second, for the states with SIPs that include RA BART regulations, EPA will 

provide federal enforcement of state-established BART emission limits. 

9 
10 
III. THE ATTRIBUTION DETERMINATION PROCESS 

This section recommends a process for the state to follow in order to complete an attribution 

determination after receipt of a Certification from an FLM under 40 CFR 51.302(c)(1).

The Certification focuses on the existence of visibility impairment. While the FLM may 

identify sources or even sources areas that contribute to the Certification, the formal 

identification of sources is a state responsibility. 

The state should make a detailed review of the data supporting the Certification to determine 

which sources or source areas require further evaluation. If the data are insufficient to identify 

specific sources or source areas, the state may request that the FLM perform more detailed 

analyses to further substantiate the impairment set forth in the Certification. 

If the state determines sufficient information exists to proceed, the state should begin the 

process by: (1) evaluating which sources are BART-eligible, and (2) reviewing the impairment 

information provided by the FLM in support of the Certification. 

1. Evaluate which sources are BART-eligible

 

Congress and EPA established criteria to determine which sources are subject to BART. The 

categories of sources subject to BART are listed in 40 CFR 51.301, the definition of “existing 

stationary facility,” and also are included in Appendix A of this report. 

The state must confirm that the potential source or group of sources is subject to the RA 

BART process by examining the potential source emissions and the dates of operation. 

The criteria for determining if a source is BART-eligible may be complicated if a source has 

multiple units that were constructed at different times and a state may be unable to determine if a 

specific source would qualify as BART-eligible. EPA published guidelines in 1980 to aid states 

in implementing the 1980 rule.7

 In addition to the 1980 guidelines for determining BARTeligibility, EPA also has proposed guidelines that include criteria for determining if a source is 

BART-eligible.8

 These guidelines are expected to be re-proposed in 2004, promulgated in 2005 

and codified at 40 CFR Part 51 as Appendix Y. The state should refer to both sets of guidelines 

to determine if a source is BART-eligible, although the proposed guidelines are not binding until 

final promulgation occurs. 

When using the applicability criteria in Appendix Y, the state should look for information 

readily available from existing state data such as permitting history, emission inventory, or other 

similar databases. 

 

7

 U.S. Environmental Protection Agency, Guidelines for Determining Best Available Retrofit Technology for Coalfired Power plans and Other Existing Stationary Facilities, EPA-450/3-80-009(b), Office of Air Quality Planning

and Standards, Research Triangle Park, N.D., November 1980 (1980 BART Guidelines). 8

 66 FR 38108 Proposed Guidelines for Best Available Retrofit Technology (BART) (July 20, 2001). EPA will repropose these guidelines as a result of the remand in American Corn Growers v. EPA. 

11 
1. If a database of Potential To Emit (PTE) is not available, state emission inventories 

can be a useful screening tool to determine whether a source meets the size criteria 

(for example, identify all sources with emissions greater than 100 tons/year of 

visibility impairing pollutants). Operating permit applications may also contain 

information about the PTE of a source, or individual units within a source. 

2. State business records may be useful to determine when the facility was constructed 

or when it commenced operation. Newspaper article searches or a detailed historical 

review of each source may also provide useful information. 

3. The state should require the source to provide information regarding the construction 

dates of individual emission units. 

4. Institutional memory within the air agency can be invaluable if records of

construction dates of major emission units are not available, although memories will 

not withstand legal challenges like hard documentary data. 

5. New Source Review permitting records may be useful to identify new units that were

constructed after 1977, determine the PTE of the source, or determine if 

reconstruction has occurred. (Note: the draft guidelines in Appendix Y state that 

modifications at a source do not affect applicability unless the change qualifies as 

reconstruction of the source). 

If the source or group of sources identified in the Certification is not BART-eligible, the 

state should inform the FLM of its finding. The state should look in the vicinity of all the sources 

suspected of causing or contributing to the impairment identified in the Certification. If no 

BART-eligible sources exist, an attribution assessment is unnecessary. 

2. Review Support Information in the FLM Certification 

Initially, the state should acquire all the supporting information the FLM used in the 

Certification and independently evaluate the data. This initial review will help the state 

determine if the information is sufficient to support a reasonable attribution determination. 

The state should consider the type of impairment in the Certification: (a) plume impact 

visibility impairment, (b) uniform haze visibility impairment, or (c) layered haze visibility 

impairment: 

 

(a) Plume impact is the impairment addressed by the original visibility protection 

program under which the state must make a reasonable attribution 

determination before proceeding to the BART analysis (40 CFR 51.301-

51.307). 

(b) In some instances, uniform haze may be thought to be source-specific haze 

from a BART-eligible source. If a state can successfully demonstrate there is a 

source emitting any air pollutant that may reasonably be anticipated to cause or 

contribute to any impairment of visibility, the state should consider that portion 

of the uniform haze to be impact from the identified source(s) and, therefore, 

consider the source to be subject to BART. 

(c) The visibility impairment may also be defined as layered haze, a condition that 

results when aerosols are “trapped” under stagnant air mass conditions.

12 
Once the FLM has issued a Certification, the responsibility for the attribution assessment 

shifts to the state. If a state does attribute impairment to a source, the state must be able to defend 

its finding that one or more BART-eligible sources did cause or contribute to visibility 

impairment in the Class I area. However, the Ninth Circuit Court of Appeals established a low 

threshold for unacceptable visibility impairment.9

3. Evaluate Existing Data not included in the Certification

When analyzing supporting data, the state should determine if additional information exists 

that was not available to the FLM. Examples include special project camera studies or ambient 

monitoring data collected by the state or local air pollution control agency, the potential or actual 

PSD permit applicant,10 or the university. In certain situations, another agency may have

previously collected information that could be used in an attribution determination.11 These data 

should be reviewed to determine whether they support the FLM Certification. 

To conclude this portion of the attribution process, the state may wish to prepare a report that 

summarizes its initial evaluation of the Certification. The purpose of the report is to carefully 

document the state’s findings and conclusions about its decisions. The report would address two 

questions: 

First, the report would assess whether any BART-eligible source(s) exist that potentially 

contributed to visibility impairment as described in the Certification. The report would contain a 

preliminary determination of whether the sources are BART-eligible and, if possible, analyze 

each source’s relative contribution to the impairment (based on available source/receptor 

information). 

Second, the report would assess the impairment data. This assessment would include the 

state’s evaluation of the supporting data in the Certification and an analysis of any additional 

data used in the attribution assessment. The data gaps found in this review would be identified 

along with any recommendations for strategies to obtain the missing information. The state may 

use these recommendations to determine the next steps in the attribution process. 

If the state believes the Certification is supported by sufficient data, proceeds with an 

attribution assessment and makes a determination that the data does or does not indicate a source 

subject to BART, the attribution process is complete. However, if the state determines the data is 

insufficient to complete an attribution assessment, the evaluation process ends, and the state may 

proceed to the next step—identification of data gaps and the studies necessary to obtain this 

information. 

 

 

9

 See Central Ariz. Water Conservation Dist v. EPA, 990 F.2d 1531 (9th Cir. 1993); See also discussion in RA BART 

and RA BART-like Case Studies. 10 For example, a PSD permit applicant may be required to obtain pre-application ambient air quality information,

and that information may not have been available to or known to the FLM. 11 In Washington state, the plume of a defunct copper smelter has been traced by its arsenic deposition. If this were 

an active facility, this ground tracing of the plume could be used to support a source/receptor connection in an 

attribution determination.

13 
4. Identify data gaps and necessary studies to fill the gaps 

As part of the data review, the state should note gaps or inconsistencies in the available data. 

Examples of data gaps and inconsistencies may include: 

1. Missing photos in a sequence of photos, 

2. Poor resolution of the plume or its source in the photos, 

3. Back trajectory analyses done with a very large grid resolution or poor techniques, 

4. IMPROVE monitor data missing at critical periods, 

5. Lack of association between the source’s emissions and monitored data at the receptor, 

6. Special studies performed during a time when a source that potentially contributed to 

the impairment was not operating, 

7. Contemporaneous studies with contradictory results that cannot be explained, 

8. Ambient studies that use naturally occurring tracers and, when the suspected source(s) 

are tested, the tracers do not exist or exist at levels far below (or above) the level 

indicated by the ambient study results, 

9. Tracer studies where the tracer was not found at the anticipated receptors,

10. No explicit exploration of whether wildfire or other natural events significantly caused 

or contributed to one or more of the impairment episodes, 

11. Other differences or inconsistencies in the available data. 

Once any data gaps have been identified, the state should decide which studies are necessary 

to obtain the missing information sufficient to complete an attribution assessment. The state 

should design potential studies and determine the resource needs of those studies. The amount of 

work necessary will depend on the availability of the information and how critical the 

information is to the attribution assessment. Section IV provides technical criteria and examples 

of the technical process the state may consider at this point in the process. 

5. Consultation Process 

The WESTAR review of previous RAVI assessments demonstrated the importance of 

including all stakeholders in the design of any data collection plans or modeling protocols. 

Competing studies can be very inefficient, expensive, and time-consuming, and ultimately may 

not help the state make a final decision. 

If stakeholders are involved in the design of any data collection efforts or modeling 

protocols, disagreements may be resolved before the state or source continues with any

additional studies. The state should consider involving the following stakeholders in the 

consultation process: 

1. Affected sources 

2. Neighboring states and tribes 

3. Environmental Protection Agency 

4. Federal land managers 

5. Local environmental groups 

6. Local permitting agencies 

14 
7. Local government representatives 

6. Completion and Review of Additional Visibility Studies 

When the additional visibility studies, if any, are complete, the state should evaluate the 

resulting data. The technical staff may wish to finalize the report and make recommendations 

based on the scientific aspects of the data. 

7. Final Determination of Reasonably Attributable Visibility Impairment 

The state may reach any one of the following conclusions: 

1. The impairment certified by the FLM is reasonably attributable to the identified BARTeligible source(s) for specific pollutants; 

2. The impairment certified by the FLM is reasonably attributable to the source(s) 

identified by the FLM and additional sources not identified by the FLM for specific 

pollutants; 

3. There is inadequate data to support a determination that the impairment is due to the 

source(s) identified by the FLM; 

4. The impairment is reasonably attributable to other adjacent BART-eligible sources for 

specific pollutants; or 

5. The impairment certified by the FLM is not reasonably attributable to a BART-eligible 

source. 

15 
16 
IV. GENERAL PRINCIPLES AND CRITERIA 

This section provides general principles and technical criteria for the state to consider after 

receipt of an FLM Certification. After receipt of such a Certification, the state must determine 

whether BART-eligible sources “emit any air pollutant which may reasonably be anticipated to 

cause or contribute to any visibility impairment in any” Class I area. While these principles and 

technical criteria should generally be useful and applicable, each state must decide what is 

required to support its individual determination. 

Part A contains general principles to guide the development of a conceptual framework for 

the attribution assessment process. Part B contains evaluation criteria to guide the decisions 

regarding a technical framework for the attribution assessment process. 

Conceptually, the statutory framework for an attribution determination (“may reasonably be 

anticipated to cause or contribute to visibility impairment”) should be the general principle 

guiding the attribution process. EPA maintains, and the Ninth Circuit agreed, that an affirmative 

attribution decision is possible even with considerable uncertainty and a low triggering threshold. 

However, each state should decide individually what is required to support its determination. 

A. General Principles of the Attribution Process

Attribution process criteria address factors likely to influence the performance of the state 

attribution assessment. These criteria provide parameters to define and implement the attribution 

assessment process given resource constraints, legal considerations, administrative 

decisionmaking requirements, and other relevant factors. The general principles include the 

following: 

1. Whenever possible, an attribution assessment should be a collaborative process that 

relies on existing data with a minimum of additional analysis (see section III). If 

supplemental data are needed, field studies should be designed in a collaborative 

process between affected states and tribes, identified sources, Federal land managers, 

EPA, and the general public. Past experience has shown that competing technical 

studies often result in an unnecessarily expensive and unduly complicated process. 

2. Attribution assessments should be technically and legally defensible. 

3. Attribution assessments should be accomplished at a reasonable cost and within a 

reasonable time frame. 

4. Attribution assessments should be no more complex than necessary, recognizing that 

the circumstances surrounding a Certification may vary greatly. State or tribal agency 

staff should be capable of performing the attribution assessment and making the final 

determination, although contractors may be used for certain types of modeling or 

monitoring. 

5. Reasonably attributable visibility impairment can only be identified if a source/receptor 

17 
links a potentially BART-eligible source or small group of sources to a Class I area. 

B. Technical Criteria 

The technical criteria address the appropriateness of available analytical techniques from a 

scientific perspective. The rule defines visibility impairment to mean “any humanly perceptible 

change in visibility (light extinction, visual range, contrast, coloration) from that which would 

have existed under natural conditions.” 

The technical criteria include the following: 

1. The BART source must emit an air pollutant that may reasonably be anticipated to 

cause or contribute to any of the impairment. The visibility-impairing pollutants of 

concern must be identified. 

2. The attribution assessment should address the unique visibility impairment in the FLM 

Certification for the Class I area. Some factors to consider include: 

a. Duration: source’s length of effect on visibility per episode;12

b. Frequency: how often episodes of impairment occur; 

 

c. Geographic extent: how much of the Class I area is affected by the impairment; 

d. Magnitude: how much visibility impairment is due to the source’s emissions; and 

e. Time of occurrence: including time of day and time of year. 

These factors need to be considered together, because they affect each other. An 

infrequent occurrence of a large magnitude episode may meet the criteria. A frequent 

occurrence of a small magnitude episode may also meet the criteria. The FLM may 

provide information about these criteria in the Certification, and the state should review 

this information as a starting point for its assessment. The state may consider other 

factors and data, as appropriate. 

3. The attribution assessment should identify the distance from the source to the Class I 

area. This distance will affect the choice of tools appropriate for characterizing the 

specific source’s impact on visibility impairment. 

4. To the extent possible, the attribution assessment should be quantitative. Under certain 

circumstances, qualitative information, such as photographs or time-lapse video of 

distinct plumes or source-specific haze events, may be adequate. 

5. The state should use as many approaches or indicators of impairment as practicable 

12 Based on previous cases, most impairment episodes are relatively short—lasting one to several hours—which may 

affect monitoring and other assessment techniques.

18 
rather than relying on a single method. The assessment may rely on air monitoring and 

modeling techniques and other supporting scientific data. Consistency between source 

and observational techniques strengthens the analysis.13 

6. The state should consider the level of uncertainty in the assessment. 

7. EPA guideline models should be used whenever practicable. When other models are 

used, additional technical discussions with EPA may be necessary. 

C. Examples of the Attribution Process

The state should consult this section after evaluation of the FLM Certification. At this point, 

the state will already have decided what gaps need to be filled to complete the attribution 

assessment. 

The recommendations in this report recognize that each attribution determination will be 

unique. Because an attribution determination is a fact-specific and individual determination, it is 

not possible to recommended a single technical approach for a state to follow. A combination of 

monitoring and modeling techniques is usually appropriate, but instead of attempting to cover all 

possible combinations of techniques, this report provides five selected scenarios. The general 

principles and technical criteria identified in sections A and B are incorporated into these 

scenarios. The goal is to provide the state with references for use when it is creating its own 

individualized technical procedure. 

In addition to the scenarios, this report provides detailed tables and narrative descriptions of 

techniques that may be considered and/or substituted for the techniques in the scenarios (see 

Section V). 

Scenario 1: Limited data/one source 

Data input: Small amount of existing data (i.e., one IMPROVE site with data for 2-3 years) 

Evidence of “local” impact. 

Limited meteorological data. 

Where it is likely that one major source contributes to impairment, a combination of the 

following techniques is suggested: 

1. Examine IMPROVE data, including quality assurance 

2. Look at extinction budget to identify visibility-impairing pollutants 

3. Perform some analysis of when the episodes are occurring 

4. Perform a simple back trajectory analysis using a method such as HYSPLIT 

5. Look at relationships between particulate species 

6. Examine source emissions data for correlations with ambient monitoring 

7. Perform dispersion model based on data and capabilities such as VISCREEN or 

CALPUFF Lite 

 

13 WESTAR Council, RA BART and RA BART-like Case Studies, June 2001. 

19 
8. Examine other data such as source owner data, deposition data, or photographic 

evidence 

The results of this initial analysis may not always be conclusive. This level of review may be 

adequate if a source/receptor relationship can be identified. However, if the results are 

inconclusive, more data and/or more refined analyses may be necessary and may help the state 

reach an attribution determination. The state should consider how the available data and choice 

of techniques might affect its ability to assess the effectiveness of the controls in remedying the 

impairment. 

It may be efficient to look ahead to the data and analytical needs of a potential BART 

analysis. For example, the state may wish to use a more refined model that would be useful for 

both attribution and remedy assessment. 

 

Assuming there is a two-year time period14 to collect data and analyze additional information, 

the following techniques also can be considered: 

1. Better source characterization, including measurement of emissions of trace elements 

(source profiles) for use in CMB modeling 

2. Met monitoring or modeling 

3. Camera site, possibly with time-lapse video 

4. Additional aerosol monitoring, episodic or saturation 

5. In plume trajectory by aircraft, if available 

6. Fine time-resolved optical monitoring, such as nephelometer, transmissometer, 

aethelometer combined with pollutant monitoring (e.g., SO2, NOx, real time PM) for 

monitoring of episodes 

7. More refined chemical visibility model, such as CALPUFF 

8. Repeat initial techniques with new data 

The technical criteria are listed below, followed by techniques that may be used to obtain results 

about the criteria. 

1. The impairment must be related to emissions from specific sources. The visibilityimpairing pollutants of concern should be identified. 

Techniques: Back trajectory, species relationships, relationship between source 

emissions and ambient monitoring, dispersion model refined by camera, aerosol, in 

plume, optical monitoring, and refined dispersion models. 

2. The attribution assessment must address the unique visibility impairment certified by 

 

14 The 1999 regional haze rule revised the requirements for general plan requirements for visibility protection. As a 

result, plan revisions are required once every five years, rather than the Long Term Strategy review requirement of

every three years. If an FLM certifies RAVI at least 6 months prior to a plan revision, section 51.302 requires that

the State Plan revision address such Certifications. Given this 5-year cycle, we have presumed for this report that an

attribution analysis should take about two years to complete in order to allow time for the BART engineering

analysis, if needed. 

20 
the FLM for the Class I area. Some factors to consider include: 

a. Duration: source’s length of effect per episode 

Techniques: Monitoring, dispersion model, back trajectory, camera 

b. Frequency: how often the impairment occurs 

Techniques: Monitoring, dispersion model, back trajectory, camera 

c. Geographic extent: how much of the Class I area is impaired 

Techniques: Monitoring, dispersion model, additional data on deposition and other 

studies 

d. Magnitude: how much impairment is due to the source 

Techniques: Dispersion model, receptor model 

e. Time of occurrence 

Techniques: Monitoring, dispersion model, back trajectory, camera 

3. Uncertainty of results: Each analytical method will have its own level of uncertainty. 

These individual uncertainties should be kept in mind as outputs are compared within 

the overall assessment. If the results of different techniques are the same or similar 

(within the uncertainties of the techniques), then the overall level of uncertainty is likely 

to decrease.

Scenario 2: Moderate data/multiple sources 

Data input: Moderate amount of existing data (i.e., one IMPROVE site with data for six or 

more years)

Evidence of an increasing trend in sulfate 

Meteorological data likely from several stations 

Assuming the Certification identifies multiple BART sources of different types within a 100-

mile radius, a combination of the following techniques is suggested: 

1. Examine IMPROVE data, including quality assurance 

2. Look at extinction budget to identify visibility-impairing pollutants 

3. Perform some analysis of when the episodes are occurring 

4. Perform multiple back trajectory analyses using a method such as HYSPLIT 

5. Look at relationships between particulate species 

6. Examine source emissions data for correlations with ambient monitoring 

7. Perform dispersion model based on data and capabilities such as CALMET/CALPUFF 

8. Examine other data such as source owner data, deposition data, or photographic 

evidence 

9. Wind fields/Synoptic analyses 

10. Comparison of different episodes 

11. UNMIX, PMF and/or CMB 

21 
12. Analysis of any regional modeling already conducted, such as CMAQ 

13. Long term visual monitoring, such as camera data or transmissometer data, compared to 

met data to identify quadrants of concern 

The results of this initial analysis may not be conclusive. This level of review may be 

adequate if a source/receptor relationship can be identified. 

However, if the results are inconclusive, more data and/or more refined analyses may be 

necessary and may help the state reach an attribution determination. The state should consider 

how the available data might affect the ability to assess the effectiveness of the controls in 

remedying the impairment. It may be efficient to look ahead to the data and analytical needs of a 

potential BART analysis. For example, the state may wish to use a more refined model that 

would be useful for both attribution and remedy assessment. 

 

Assuming there is a two-year time period to collect data and analyze additional information, the 

following techniques also can be considered: 

1. Emissions inventory 

2. Analysis of a nested domain within the regional model 

3. Source profiles (natural tracers) 

4. Additional source measurements, such as stack testing 

5. Met monitoring or modeling 

6. Camera 

7. Additional aerosol monitoring, episodic or saturation 

8. Monitoring to measure additional parameters, such as precursors, ammonia and 

oxidants 

9. In plume trajectory by aircraft, if available, to characterize plume chemistry 

10. Fine time-resolved optical monitoring, such as nephelometer, transmissometer, 

aethelometer combined with pollutant monitoring (e.g., SO2, NOx, real time PM) for 

monitoring episodes. 

11. Repeat initial techniques with new data 

The technical criteria are listed below, followed by techniques that may be used to obtain results 

about the criteria. 

1. The impairment must be related to emissions from specific sources. The visibilityimpairing pollutants of concern should be identified. 

Techniques: Back trajectory, species relationships, relationship between source 

emissions and ambient monitoring, dispersion model refined by camera, aerosol, in 

plume, optical monitoring, and refined dispersion models. 

2. The attribution assessment must address the unique visibility impairment certified by 

the FLM for the Class I area. Some factors to consider include: 

a. Duration: each source’s length of effect per episode 

22 
Techniques: Monitoring, dispersion model, back trajectory, camera 

b. Frequency: how often the impairment occurs by source 

Techniques: Monitoring, dispersion model, back trajectory, receptor model, camera 

c. Geographic extent: how much of the Class I area is impaired 

Techniques: Monitoring, dispersion model, additional data on deposition and other 

studies 

d. Magnitude: how much impairment is due to each individual source 

Techniques: Dispersion model, receptor model 

e. Time of occurrence 

Techniques: Monitoring, dispersion model, back trajectory, camera 

Scenario 3: No IMPROVE data, Certification by modeling evidence 

Data input: Detailed modeling of a source and specific visibility impairment using a 

CALPUFF run, or detailed (nested) run of regional model. 

Detailed emissions data and meteorological data were input to the model. 

No IMPROVE data available for this specific Class I area (monitoring may be at a 

“representative site”) 

Some optical data, photographic data, and limited aerosol data from other 

networks 

Assuming the Certification points to a specific source, a combination of the following techniques 

is suggested: 

1. Examine IMPROVE data for any nearby Class I areas (especially those included in the 

modeling domain) to better understand the regional conditions and compare with model 

outputs of the unique effect 

2. Review the model’s outputs on “high impact” days and compare with actual 

meteorological data of the area (to confirm transport, inversions, or other aspects of the 

model which may create the unique impact) 

3. Review source emissions data used in the model 

4. Review other monitoring data (optical measurements, deposition data, ozone data, etc.) 

to collaborate model predictions 

5. Examine any photographic evidence 

The results of this initial analysis may not always be conclusive. This level of review may be 

adequate if a source/receptor relationship can be identified. Because no visibility-specific particle 

monitoring is available in this case, the key to attribution is to determine that the source 

emissions are indeed reaching the Class I area on days when impairment exists. If the results are 

inconclusive, more data and/or more refined analyses may be necessary and may help the state 

reach an attribution determination. The state should consider if the available data and modeling 

information supplied with this Certification could be used to determine the effectiveness of the 

23 
controls in remedying the impairment. It may be efficient to look ahead to the data and analytical 

needs of a potential BART analysis if additional or different modeling will be necessary. 

Additional data collection and analyses that might be considered: 

1. Better source profiles or newer emissions information 

2. Met monitoring or modeling 

3. Camera 

4. New aerosol monitoring (episodic or saturation)

5. In plume trajectory by aircraft, if available 

6. Repeat initial analytical techniques with new data 

The technical criteria are listed below, followed by techniques that may be used to obtain results 

about the criteria. 

1. The impairment must be related to emissions from specific sources. The visibilityimpairing pollutants of concern should be identified. 

Techniques: Confirm model inputs, compare with any new particle monitoring at Class I 

area. Examine performance of the model, and perform uncertainty analyses. 

2. The attribution assessment must address the unique visibility impairment certified by the 

FLM for the Class I area. Some factors to consider include: 

a. Duration: source’s length of effect per episode 

Techniques: Monitoring, dispersion model, back trajectory, camera 

b. Frequency: how often the impairment occurs 

Techniques: Monitoring, dispersion model, back trajectory, camera 

c. Geographic extent: how much of the Class I area is impaired 

Techniques: New monitoring and examination of dispersion model outputs, 

additional data on deposition and other studies 

d. Magnitude: how much impairment is due to the source 

Techniques: Dispersion model 

e. Time of occurrence 

Techniques: Monitoring, dispersion model, back trajectory, camera 

Scenario 4: Direct Photographic Evidence 

Data input: The FLM certified impairment in a wilderness area based on photographic 

evidence. Photographs taken over a period of two years showed a distinct haze 

in a valley located in the wilderness area when the wind is from the east. A 

series of photographs during two episodes showed a distinct plume that 

24 
originates at an existing stationary source constructed in 1965 and located ten 

miles east of the wilderness area. The photographic series showed that the 

plume traveled into the wilderness area. 

Aerial photos during one episode also show a distinct plume that travels into 

the wilderness area. The wilderness area does not have an IMPROVE site. A 

Class I area located 50 miles to the north has an IMPROVE site that was

representative for the area. This monitoring site does not show decreased 

visibility on the days when haze was photographed in the wilderness valley. 

Assuming the Certification identifies one BART source, the following steps are suggested: 

1. Review the photographic evidence and determine whether the photographs show a clear 

connection between the source and the haze documented in the wilderness area. 

In this example, the photographic evidence showed a clear connection between the 

source and the haze in the wilderness area. Qualitative information, such as 

photographic evidence that establishes a source/receptor link, is allowed under EPA 

regulations that define reasonable attribution as determined by “visual observation or 

other technique the State deems appropriate.” (40 CFR 51.301(s)). 

The results of this initial analysis may not always be conclusive. If the photographic 

evidence is not compelling, the state may reach a different conclusion. The state may determine 

that the impairment, as documented by the FLM, was not reasonably attributable to the source 

identified in the Certification. The FLM would then need to gather additional information to 

support a Certification. Alternatively, if the state determines that the source may be causing the 

haze but the qualitative evidence is not quite sufficient to determine attribution, the state could 

initiate a data collection effort to provide better information regarding visibility impairment at 

the Class I area. Such techniques may include: 

1. Better source characterization, including measurement of emissions of trace elements 

(source profiles) for use in CMB modeling 

2. Met monitoring or modeling 

3. Additional aerosol monitoring (episodic or saturation) 

4. In plume trajectory by aircraft, if available 

5. Fine time-resolved optical monitoring, such as nephelometer, transmissometer, or 

aethelometer combined with pollutant monitoring (e.g., SO2, NOx, real time PM) for 

monitoring of episodes. 

6. More refined chemical visibility model, such as CALPUFF 

The technical criteria are listed below, followed by techniques that may be used to obtain results 

about the criteria. 

1. The impairment must be related to emissions from specific sources. The visibilityimpairing pollutants of concern should be identified. 

Techniques: Back trajectory, species relationships, relationship between source 

emissions and ambient monitoring, dispersion model refined by camera, aerosol, in 

25 
plume, optical monitoring, and refined dispersion models. 

2. The attribution assessment must address the unique visibility impairment certified by 

the FLM for the Class I area. Some factors to consider include: 

a. Duration: source’s length of effect per episode 

Techniques: Monitoring, dispersion model, back trajectory, camera 

b. Frequency: how often the impairment occurs 

Techniques: Monitoring, dispersion model, back trajectory, camera 

c. Geographic extent: how much of the Class I area is impaired 

Techniques: Monitoring, dispersion model, additional data on deposition and other 

studies 

d. Magnitude: how much impairment is due to the source 

Techniques: Dispersion model, receptor model 

e. Time of occurrence 

Techniques: Monitoring, dispersion model, back trajectory, camera 

3. Uncertainty of results: Each analytical method will have its own level of uncertainty. 

These individual uncertainties should be kept in mind as outputs are compared within 

the overall assessment. If the results of different techniques are the same or similar 

(within the uncertainties of the techniques), then the overall level of uncertainty is likely 

to decrease.

The final step in the attribution process scenario would be to verify that the source was 

operating on the days when the haze was observed in the area. The state should also determine if 

the source was experiencing unusual upset conditions during the times identified. In this 

example, the source was determined to have been operating normally. The state would then issue 

a determination that the visibility impairment at the Class I area was reasonably attributable to 

the existing stationary facility. 

Scenario 5: Data Rich 

Data input: A large special visibility study recently was conducted. 

Four months of data spanning two seasons was collected including: 

1. At one “receptor” site in a Class I area there were multiple meteorological, optical, and

particulate samplers including extensive measurements of particle compositions, size 

distributions, scattering, absorption, light extinction, ions, oxidants, relative humidity, 

temperature, vertical wind profiles, SO2, photographs, and concentrations of four unique 

tracers released from four different sources of interest. There were co-located samplers 

measuring several parameters in more than one way. There are at least some one, six, 

and twelve-hour particle measurements in addition to twenty-four hour samples. 

26 
2. This receptor site has been an IMPROVE site for several years and these data are also

available. 

3. At thirty-five other sites in the region, called “satellite” sites there was an IMPROVE 

Module-A sampler taking daily twenty-four hour samples which were analyzed for fine 

mass, S, soil elements, trace metals, and elemental H from which organics can be 

estimated. The unique tracers were also measured at many of these sites. 

4. Aircraft sampling of plumes from source(s) of interest was conducted. 

5. An extensive current emissions inventory was created. 

6. Wind profilers were deployed at two to three sites in addition to the receptor site. 

7. Measurements of the chemical composition of resuspended local dust, smoke from

burning local fuels, and emissions from local point and area sources of interest were 

collected in order to create “source profiles” to be used in receptor modeling. 

Assuming the certification identifies a BART source within 100 miles of the Class I area 

from which a unique tracer was released, a combination of the following techniques is 

suggested: 

1. Examine fine particle data, including quality assurance. Use the collocated data to 

assess accuracy and precision. The one, six, and twelve-hour data should average up to 

match the twenty-four hour data and if reasonable, be used to examine the diurnal 

cycles in the fine particle concentrations. 

2. Look at the extinction budget to identify visibility-impairing pollutants. Because size 

distributions were measured, the extinction budget can be estimated from Mie scattering 

calculations as well as by using simple techniques that assume bulk scattering and 

absorption efficiencies. Check to see if measured scattering plus absorption add up to 

the measured and reconstructed extinction. 

3. Compare the special study data to historical IMPROVE and archived meteorological 

data to determine the representativeness of the study period. 

4. Perform some analysis of when the episodes are occurring. EOF analysis can be helpful 

to summarize the massive data set. Use the photographs to create a time-lapse 

visualization of the scene at the receptor site. 

5. Perform back trajectory analyses using HYSPLIT, CAPITA Monte Carlo or ATAD. 

Examine whether the trajectories change substantially when the study’s wind-profiler 

data are included. Determine whether trajectories are consistent with tracer 

concentrations. 

6. Look at relationships between particulate species (factor analysis) to see if they identify 

source types. 

7. Look at the spatial and temporal patterns of trace elements and the major constituents of 

the fine mass. This may suggest dominant source areas and transport patterns for 

different source types. Use EOF analysis to determine the patterns that explain most of 

the covariance in the data. Check to see how well these are reconciled with the back 

trajectory modeling and the deterministic modeling. Check to see if the same dominant 

sources were indicated. 

8. Examine source emissions data for correlations with ambient monitoring. 

9. Perform dispersion model such as CALMET/CALPUFF. 

27 
10. Analyze wind fields, synoptic conditions, and satellite photos. Check to see if they were 

consistent with more sophisticated meteorological modeling. Look for unusual 

conditions such as wild fires, hurricanes, stagnation episodes, etc. Look to see if 

modeled and observed cloud patterns were comparable. 

 11. Compare the meteorological and chemical characteristics of different episodes 

12. UNMIX, PMF and CMB can help determine major source types and when they were 

important. 

If, at this point, similar source/receptor relationships have been identified using several 

different techniques, this level of review is adequate. If the results are inconclusive, and there are 

differing points of view about the frequency, duration, or significance of the attribution to the 

BART-eligible source, more analyses may be necessary. Often in a large study, measurements 

are made, but not immediately analyzed in the lab due to cost. If this is the case, more samples 

could be analyzed. It is unlikely that any additional field monitoring would be required. 

The technical criteria are listed below, followed by techniques that may be used to obtain 

results about the criteria.

1. The impairment must be related to emissions from specific sources. The visibilityimpairing pollutants of concern should be identified. 

Techniques: Reconstructed particle mass and light extinction including examination of 

the reconstructions in different size ranges and at different sites in the region, analysis 

of responses of light scattering to relative humidity, back trajectory analyses, 

examination of inter-species relationships compared to measured source profiles, 

dispersion modeling with sophisticated models such as REMSAD and CMAQ, analysis 

of wind fields and dispersion by comparison to measured tracer concentrations, 

computer animation of spatial patterns of species of interest, use of receptor models 

such as TMBR, DMB, and TAGIT that utilize unique tracer information, or other 

receptor models such as CMB, UNMIX, and PMF that do not require tracer 

information, but can use it. When so many different models can be used, model 

reconciliation is usually a part of a data-rich scenario. 

2. The attribution assessment must address the unique visibility impairment certified by 

the FLM for the Class I area. Some factors to consider include: 

a. Duration: each source’s length of effect per episode 

Techniques: Monitoring, dispersion model, back trajectory, camera 

b. Frequency: how often the impairment occurs by source 

Techniques: Monitoring, dispersion model, back trajectory, receptor model, camera 

c. Geographic extent: how much of the Class I area is impaired 

Techniques: Monitoring, dispersion model, analysis of spatial patterns using data 

from the satellite sites. 

28 
d. Magnitude: how much impairment is due to each individual source 

Techniques: Dispersion model, receptor model 

e. Time of occurrence 

Techniques: Monitoring, dispersion model, back trajectory, camera 

3. Uncertainty of results: In this case, the ability of a model to predict the observed tracer 

concentrations is an indicator of the model’s uncertainty. 

29 
30 
V. TECHNIQUES 

A. Monitoring 

Introduction 

This section describes a variety of ambient air monitoring methods that, if appropriate, 

might be used to support an attribution analysis. The methods described include those most 

commonly used in pollution studies or ambient monitoring programs and should not be 

considered an exhaustive list of potential methods. Methods include, IMPROVE, filter based 

aerosol, continuous aerosol (includes optical methods such as the nephelometer), continuous gas 

and canister sampling, transmissometer, scene (includes film, video, digital), and tracer or 

aircraft methods. 

Several of the methods described are currently in use as a part of national, state, local, tribal, 

or private ambient monitoring networks. These sites are generally located in urban areas, but 

those near the area of study could be used in the attribution analysis thereby reducing the cost of 

monitoring. 

The IMPROVE network is designed to assess visibility in Class I areas and routinely 

measure visibility impairing pollutants. These sites offer value to the attribution analysis if they 

are located in the area of study. 

The RA BART case studies show additional examples of the methods used during source 

attribution studies. The case studies also show that methods, other than ambient monitoring, may 

be used to support the analysis. Existing programs such as CASTNET (dry deposition network), 

surface water deposition studies, or snow deposition studies may provide additional data for the 

attribution analysis. 

Although not specifically listed as a monitoring technique, meteorological monitoring is an 

important element the attribution analysis. Collection of meteorological data can range from

simple wind, temperature, and relative humidity measurement to an array of acoustic wind 

profilers. The complexity of the meteorological monitoring program depends on data needs and 

the availability of existing data. As with ambient monitoring, meteorological data may be 

available at sites located near the study area and offers reduced monitoring cost. 

1. IMPROVE

Overview: The Interagency Monitoring of PROtected Visual Environments (IMPROVE) 

program is a cooperative measurement effort governed by a steering committee composed of 

representatives from Federal and regional-state organizations. The IMPROVE monitoring 

program was established in 1985 to aid the creation of Federal and State implementation plans 

for the protection of visibility in Class I areas (156 national parks and wilderness areas) as 

stipulated in the 1977 amendments to the Clean Air Act. The objectives of IMPROVE are to: (1) 

establish current visibility and aerosol conditions in mandatory class I areas; (2) identify 

chemical species and emission sources responsible for existing man-made visibility impairment; 

31 
(3) document long-term trends for assessing progress towards the national visibility goal; and (4) 

with the enactment of the regional haze rule, provide regional haze monitoring representing all 

visibility-protected federal class I areas where practical. 

Magnitude: Possible when used in conjunction with appropriate analytical techniques. 

Frequency: Possible when used in conjunction with appropriate analytical techniques; Limited 

by twenty-four hour sample integration, and one-in-three day sampling frequency. 

Duration: Possible when used in conjunction with appropriate analytical techniques; Limited by 

24 hour sample integration, and one-in-three day sampling frequency. 

Principle: Twenty-four hour integrated filter-based ambient monitor; Gravimetric analysis for 

PM2.5 (Module D) and PM10); S from Particle Induced X-ray Emission (PIXE); NO3 from ion 

chromatography (denuded nylon filter from Module C); Organic and elemental carbon from

Thermal Optical Reflectance (TOR); H from Proton Elastic Scattering (PESA). 

Uncertainty: All elemental S if from sulfate; All sulfate is ammonium sulfate; NO3 (Denuder 

efficiency is close to 100%. All nitrate is from ammonium nitrate); 

Average organic molecule is 70% carbon; Carbon (organic and elemental) is defined by the 

analytical method; Fine soil based on elemental composition; Course mass (PM10 – PM2.5) 

consists only of insoluble soil particles. 

Strengths: Regulatory indicator for regional haze rule; Long term data record in or near many 

federal Class I areas; Extensive network currently in place. 

Limitations: Integrated twenty-four hour sample; one-in-three day sampling interval; Nitrate 

losses due to volatilization from filter; Not capable of distinguishing between primary particulate 

and atmospherically transformed particulate. 

Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance; Chemical analysis must be conducted by appropriate laboratory. 

Regulatory Context: Regional Haze rule; Included in SIP Visibility Plan 

2. FILTER-BASED AEROSOL

Overview: Filter-based aerosol monitoring is used to identify chemical species and obtain 

concentration measurements of atmospheric constituents that contribute to visibility impairment. 

Primary techniques include filter-based aerosol samplers that collect samples on various 

substrates in various size ranges such as PM2.5 or PM10. Aerosol monitoring can provide fine 

mass concentration, course mass concentration, optical absorption, major and trace elements, 

organic and elemental carbon, and sulfate, nitrate, and chloride ions. A variety of methods are 

available to conduct filter based aerosol monitoring. Many methods are approved as an EPA 

reference method, while some are not, but may offer variability that the reference method does 

not. 

32 
Magnitude: Possible when used in conjunction with appropriate analytical techniques. 

Frequency: Possible when used in conjunction with appropriate analytical techniques; Limited 

by 24-hour sample integration and sampling frequency (daily sampling is possible depending on 

instrument selected). 

Duration: Possible when used in conjunction with appropriate analytical techniques; Limited by 

24-hour sample integration and sampling frequency (daily sampling is possible depending on 

instrument selected). 

Principle: The methods used for analyses of these filter media include gravimetry (electromicrobalance) for mass; X-ray fluorescence (XRF) and particle induced X-ray emission (PIXE) 

for trace elements; Ion chromatography (IC) for anions and selected cations; Controlledcombustion for carbon; Gas chromatography/mass spectroscopy (GC/MS) for semi-volatile 

organic particles; Special measurement needs may include determining particle size and 

morphology through optical and/or electron microscopy. 

Strengths: Large network of urban monitoring available; Filter based sampling allows for a 

variety of chemical/elemental analysis. 

Limitations: Integrated twenty-four hour sample; Generally operated on one-in-three or one-insix day sampling interval (daily sampling is possible depending on instrument selected); Not 

capable of distinguishing between primary particulate and atmospherically transformed 

particulate. 

Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance; Chemical analysis must be conducted by appropriate laboratory. 

Regulatory Context: Commonly used in NAAQS/SIP compliance monitoring networks. 

3. CONTINUOUS AEROSOL 

Overview: Aerosols can be measured continuously using several different methods. Optical 

measurement of aerosols can be measured using an instrument such as a nephelometer to 

measure light scattering (bscat) or a aethelometer to measure light absorption (babs) by aerosols 

(black carbon). Continuous instruments such as TEOM or BETA can provide PM10 or PM2.5

aerosol concentration. 

Magnitude: Possible when used in conjunction with appropriate analytical techniques. 

Frequency: Possible when used in conjunction with appropriate analytical techniques; Limited 

by twenty-four hour sample integration and sampling frequency (daily sampling is possible 

depending on instrument selected). 

33 
Duration: Possible when used in conjunction with appropriate analytical techniques; Limited by 

twenty-four hour sample integration and sampling frequency (daily sampling is possible 

depending on instrument selected). 

Strengths: Continuous measurement with at least hourly time resolution; Nephelometers and 

Aethelometers are commonly used to support Class I area monitoring programs and an existing 

data record may be available in some areas. 

Limitations: Continuous aerosol measurement with methods such as TEOM and BETA are 

generally made in urban areas to support NAAQS compliance networks; Not capable of 

distinguishing between primary particulate and atmospherically transformed particulate. 

Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance. 

Regulatory Context: Commonly used in Class I area monitoring networks or urban NAAQS 

compliance networks. 

4. CONTINUOUS GASEOUS 

Overview: A variety of gaseous pollutants, such as Ozone (O3), Carbon Monoxide (CO), 

Nitrogen Dioxide (NOx), nitric oxide (NO), ammonia (NH3), hydrogen peroxide (H2O2), sulfur 

dioxide (SO2), Organics, and HAPS/TOXICS, can be measured continuously. Gaseous 

monitoring is generally conducted with instruments that continuously draw sample air and 

periodically (as frequently as once per second) analyze the sample. Canister systems collect an 

ambient air sample over a specific period of time in clean evacuated canisters. The canisters are 

then subject to subsequent analysis at a laboratory using a method such as GC/FID. This method 

is able to provide time-integrated samples from several hours to twenty-four hours or more. 

Regular checks of the flow rate, stability, reproducibility, precision, and accuracy of these 

instruments must be conducted on a regular schedule in order to ensure data quality. 

Magnitude: Possible when used in conjunction with appropriate analytical techniques. 

Frequency: Possible when used in conjunction with appropriate analytical techniques. 

Duration: Possible when used in conjunction with appropriate analytical techniques. 

Strengths: Continuous measurement with hourly time resolution; Monitors available to identify 

variety of pollutants. 

Weaknesses/Limitations: Generally require conditioned environment and frequent performance 

checks. 

Practical Considerations: Data may be available from established urban area monitoring 

networks and some Class I area monitoring networks; Additional cost ($/year) for new sites. 

34 
Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance. 

Regulatory Context: Commonly used in urban NAAQS compliance and HAPS/TOXICS 

networks. 

5. TRANSMISSOMETER 

Overview: Transmissometers measure the amount of light transmitted through the atmosphere 

over a known distance (generally between 0.5 km and 10.0 km) between a light source of known 

intensity and a receiver. The transmission measurements are electronically converted to hourly 

averaged light extinction (bext). 

Magnitude: Possible when used in conjunction with appropriate analytical techniques. 

Frequency: Possible when used in conjunction with appropriate analytical techniques. 

Duration: Possible when used in conjunction with appropriate analytical techniques. 

Uncertainties: A transmissometer must be installed in stable locations with a clear and

unobstructed path to avoid interference with the signal. 

Strengths: Continuous measurement of bext in many Class I areas and some urban areas. 

Limitations: Not capable of identifying pollutants contributing to visibility impairment. 

Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance; Data processing/quality assurance requires high level of expertise. 

Regulatory Context: Commonly used in Class I area monitoring networks and in some urban 

areas.

6. SCENE

Overview: Scene monitoring refers to the use of still and/or time-lapse photography (including 

digital imagery) to provide a qualitative representation of visual air quality. Scene monitoring 

data quality objective recommendations are to document the appearance of scenes of interest 

under a variety of air quality and illumination conditions at different times of day and different 

seasons. Scene monitoring documents the visual condition observed at a monitoring site. The 

data collection schedule can be tailored to capture the periods when visibility impairment is most 

likely to occur at specific sites. Time-lapse movies (generally time-lapse video or super 8 mm

film) can be used at monitoring sites and during special studies to document the visual dynamics 

of a scene or source. 

Magnitude: Cannot provide a quantitative measurement of visibility impairment but can 

qualitatively illustrate various levels of visibility impairment. 

35 
Frequency: Possible depending on the method used. Time-lapse video may be able to 

demonstrate the frequency of the impairment. The use of still photography to document 

impairment frequency depends upon the number of images acquired over time. 

Duration: Possible depending on the method used. Time-lapse video may be able to demonstrate 

the impairment frequency. The use of still photography to document impairment frequency 

depends upon the number of images acquired over time. 

Uncertainties: n/a 

Strengths: Provides image/video record 

Limitations: Not capable of identifying pollutants contributing to visibility impairment. 

Level of Expertise Required: Standard operating procedures available for routine operation and 

maintenance. 

Regulatory Context: Commonly used in Class I area monitoring networks and in some urban 

areas. 

7. TRACER METHODS, LIDAR SYSTEMS, AIRCRAFT BASED MEASUREMENTS 

Overview: The methods described in this section are more specialized methods generally 

reserved for special studies as opposed to the previously described methods that are used more 

routinely. Tracer methods, lidar systems and aircraft based measurements will be described 

individually. 

Tracer Methods: 

A tracer element or compound is a substance with unique characteristics that allows positive 

identification at very low concentrations. The tracer compound of choice will vary by source 

type and composition of the plume being tracked. In general, the tracer must have very low 

natural background concentrations and ideally would have the same chemical form and 

properties as the compound being tracked. In ambient applications where use of the ideal tracer 

is not possible due to potential environmental risk, a near-substitute compound may be used. 

When used to determine potential source impacts, the chosen compound is released from a 

source stack and downwind monitors are used to detect the presence of the compound. 

Lidar Methods: 

A lidar transmits short pulses of laser light into the atmosphere. The laser beam loses light to 

scattering as it travels. At each range, some of the light is backscattered into a detector. Because 

the light takes longer to return from the more distant ranges, the time delay of the return pulses 

can be converted to the corresponding distance between the atmospheric scatterer and the lidar. 

The end result is a profile of atmospheric scattering versus distance. Analysis of this signal can 

yield information about the distribution of aerosols in the atmosphere. The amount of backscatter 

indicates the density of the scatters. This can be used to measure cloud base height or track 

36 
plumes of pollution. Other properties of the atmosphere can also be deduced from the lidar return 

signals. A frequency shift in the light because of the Doppler effect permits measurement of 

wind speeds. By detecting the amount of depolarization, one can discriminate between liquid 

droplets and nonspherical ice particles. Differential Absorption Lidar (DIAL) uses absorption, as 

evidenced by reduced backscatter from greater distances, to measure the concentration of 

atmospheric gases. A Raman lidar detects particular atmospheric components (such as water 

vapor) by measuring the wavelength-shifted return from selected molecules (NOAA).

Aircraft Based Measurements: 

Aircraft based measurement systems utilize specially equipped aircraft to make pollutant and 

meteorological measurements at various elevations throughout the study domain. The aircraft 

can be equipped with a multitude of gaseous, aerosol, optical and meteorological equipment. 

This measurement method provides the advantage of vertical profiles of various parameters, the 

ability to track point source plumes, and the ability to establish boundary conditions for future 

analytical and modeling exercises. 

References:

NOAA, Atmospheric Light Division, Environmental Technology Laboratory, 

http://www2.etl.noaa.gov/DIAL_lidar.html

U.S. Environmental Protection Agency, Visibility Monitoring Guidance (EPA-454/R-99-003), 

June 1999. 

National Research Council, Protecting Visibility in National Parks and Wilderness Areas, 

National Academy Press, Washington D.C., 1993, pp. 315-357. 

37 
38 
Table 1. Monitoring Techniques 

Criteria Monitoring Method 

IMPROVE Filter Based

Aerosol 

Continuous Aerosol Gaseous; 

Continuous 

methods, canister 

sampling, etc. 

Transmissometer Scene 

35mmfilm/8

video, dig

PollutantPM10 Yes Yes Yes No No No PM2.5 Yes Yes Yes Yes No No NOx (Site specific) No No Yes No No SO2 (Site specific) No No Yes No No Sulfate Yes Yes No No No No Nitrate Yes Yes No No No No Carbon Yes Yes No No No No Organics Yes Yes No Yes No No Other Reconstructed 

extinction 

Nephelometer - 

Bscat 

Aethelometer - Babs 

HAPS, TOXICS Total 

Extinction 

Visual 

Paramete

Visibility/Concentration ChangesMagnitude Yes Yes All Possible All Possible All Possible No Frequency Yes Yes Yes Duration Yes Yes Yes Time of Day No No 

All Possible All Possible All Possible

Yes 

Likely Part of FLM Certification?

Possible 

39 
Monitoring Techniques (continued)

PrincipalQualitative/Quantitative

Quantitative Qualitati

Strengths Reconstructed 

extinction calculation, historical record 

various chemical analysis, large network nationwide 

Continuous measurement, nephelometer commonly located in or near Class I areas 

Continuous measurement, variety of possible pollutants 

Continuous measurement, provides light extinction value, historical record 

Relatively inexpensivuncomplicaoperational requiremen

Weaknesses/ Limitations 

Fixed location in or near ClassI areas, also located in several urban areas 

Integrated 24 hour sample generally collected on onein-three schedule, commonly in urban areas 

TEOM, BETA, etc, commonly located in urban areas 

Generally require conditioned environment for proper operation, commonly located in urban areas 

Fixed location in or near ClassI areas 

Number of images limvideo requistorage and routine rev

Distinguish BART Sources?

Possible with other analysis 

Possible with otheranalysis 

No Possible with other

analysis 

No Possible wi

other analy

Practical ConsiderationsCost Moderate; varies depending on the number of instruments Low 

40 
 B. Source Modeling 

Introduction

Given the complex natural environment, the state must choose the configuration of modeling 

techniques that will provide the most information on the contributing source or sources of 

impairment within the limited resources of current technology, data, budgets, time and staff 

availability.

1. Physical Models

Physical models are those that simulate the meteorology and air quality over an area. 

Modeling relies on a numerical or analytical model to estimate particulate concentrations in 

space and time. Because of its nature and sources, particulate matter is difficult to model over all 

spatial scales. Many air quality models that are currently available were designed to be applied 

over the regional scale with grid sizes from four to forty kilometers. Modeling requires detailed 

meteorological fields and emissions inventory over the entire domain. The compilation of data 

required to run these models can require much effort and expertise. Efforts are underway by 

government agencies in the U.S. to generate and archive both emissions and estimated activity 

levels of many source types in geographical information systems. 

Numerical source-oriented models are designed to simulate atmospheric diffusion or 

dispersion and estimate concentrations at defined receptors. Numerical source models can be 

grouped as kinematic, first-order closure, or second-order closure models (Bowne and 

Lundergan, 1983). Kinematic models are the simplest both mathematically and conceptually. 

These models simplify the non-linear equations of turbulent motion, thereby permitting a closed 

analytical approximation to describe pollutant concentration (Green et al., 1980). First-order 

closure models are based on the assumption of an isotropic pollutant concentration field. 

Consequently, turbulent eddy fluxes are estimated as being proportional to the local spatial 

gradient of the transport quantities. The Eulerian grid models, Lagrangian particle models, and 

trajectory puff/plume models are included in this category. Second-order closure models involve 

a series of algorithm transformations of the equations of state, mass continuity, momentum, and 

energy by using the Boussinesque approximation and Reynold’s decomposition theory (Holton, 

1992; Stull, 1988). 

2. Spatial Scales 

The model’s applicable spatial scales play a large role meeting the analysis' objectives and 

its ability to accurately assess spatial variability. PM10 and PM2.5 concentrations modeled or 

measured at any receptor result from the complex interaction of meteorology, chemical 

transformations and emissions from nearby and distant sources. For example, a monitor located 

near an operating construction site will be impacted more by the daily construction activity than 

the surrounding area. That site may be classified as representing an area of a few tens of meters 

to no more than one kilometer depending on the size of the construction area and fugitive dust 

control measures. 

The dimensions given below are nominal rather than exact and are presented as defined in 

40 CFR part 58. 

41 
a. Micro-Scale (10 to 100 m): This scale does not apply to scenarios relevant to the attribution 

problem. Modeling at the microscale is usually done by simple Gaussian plume models such as 

ISCST3. Measurements in urban areas can show considerable variations at this scale while those 

in pristine areas would not. Variations often occur when monitors are located close to a low-level 

emissions source, such as a busy roadway, construction site, within a community that uses wood 

stoves, or a short industrial stack. Fortunately, compliance monitoring site exposure criteria 

avoids microscale influences even for source-oriented monitoring sites. 

b. Middle Scale (100 to 500 m): Middle-scale monitors show significant differences between 

locations that are ~0.1 to 0.5 km apart. These differences may occur near large industrial areas 

with many different operations or near large construction sites. Monitors with middle-scale zones 

of representation are often source-oriented, used to determine the contributions from emitting 

activities with multiple, individual sources to nearby community exposure monitors. 

c. Neighborhood Scale (500 m to 4 km): Neighborhood-scale monitors do not show significant 

differences in particulate concentrations with spacing of a few kilometers. This dimension is 

often the size of emissions and modeling grids used in large urban areas for PM source 

assessment, so this zone of representation of a monitor is the only one that should be used to 

evaluate such models. Sources affecting neighborhood-scale sites typically consist of small 

individual emitters, such as clean, paved, curbed roads, uncongested traffic flow without a 

significant fraction of heavy-duty vehicles, or neighborhood use of residential heating devices 

such as fireplaces and wood stoves. 

d. Urban Scale (4 to 100 km): Urban-scale monitors show consistency among measurements 

with monitor separations of tens of kilometers. These monitors represent a mixture of particles 

from many sources within the urban complex, including those from the smaller scales. PM 

measurements at urban-scale locations are not dominated by any particular neighborhood, 

however. Urban-scale sites are often located at higher elevations and away from highly traveled 

roads, industries, and residential heating. 

e. Regional-Scale Background (100 to 1,000 km): Regional-scale background monitors show 

consistency among measurements for monitor separations of a few hundred kilometers. 

Background concentrations are often more consistent for specific chemical compounds, such as 

sulfate or nitrate, than they are for PM mass concentrations. Regional-scale PM is a combination 

of naturally-occurring aerosol from windblown dust and marine aerosol as well as particles 

generated in urban and industrial areas that may be more than 1,000 km distant. Regional-scale 

sites are best located in rural areas away from local sources, and at higher elevations. National 

parks, national wilderness areas, and many state and county parks and reserves are appropriate 

areas for regional-scale sites. Many of the IMPROVE sites characterize PM regional scale 

background in different regions of the United States. 

f. Continental-Scale Background (1,000 to 10,000 km): Continental-scale background 

monitors show little variation even when they are separated by more than 1,000 km. They are 

hundreds of kilometers from the nearest significant emitters. Though these sites measure a 

mixture of natural and diluted manmade source contributions, the manmade component is at its 

minimum expected concentration. The Jarbidge Wilderness IMPROVE site in northern Nevada 

is a good example of a continental-scale background site for particulate matter in North America. 

42 
e. Global-Scale Background (>10,000 km): Global-scale background monitors are intended to 

quantify concentrations transported between different continents as well as naturally-emitted 

particles and precursors from sea spray, volcanoes, and windblown dust. Yellow sand from

China has been detected at the Mauna Loa, HI, laboratory (Darzi and Winchester, 1982; Braaten 

and Cahill, 1986), as well as on the North American continent. Red dust from Africa’s Sahara 

desert has been detected at Mt. Yunque, Puerto Rico and over the southeastern United States. 

Other global-scale sites include McMurdo, Palmer, and Ahmundson-Scott stations in Antarctica 

(Lowenthal et al., 1996), Pt. Barrow, Alaska, and Mace Head, Ireland. 

3. Chemical Composition 

This section illustrates how the chemical composition of aerosols is an important 

consideration in the choice of particulate matter models. The knowledge of how the aerosol's 

composition varies over an area will play a key role in the attribution study design. 

The relative abundance of chemical components in the atmosphere closely reflect the 

characteristics of emission sources. Major chemical components of PM2.5 and PM10 mass in 

urban and rural areas consist of nitrate, sulfate, ammonium, carbon, geological material, sodium

chloride, and liquid water. 

Chemical compositions can vary spatially in all scales of the atmosphere and depend on 

sources surrounding the monitoring site. For example, on the continental scale, the eastern U.S. 

fine particulate chemical compositions are different than those of the western states. In the 

eastern portion of the U.S., nonurban PM2.5 is dominated by secondary sulfate, organics and 

elemental carbon (EPA, 1996). The data to support this conclusion are based on the IMPROVE 

and CASTNET networks. These networks provide a background fine-fraction aerosol database 

because the monitoring sites are primarily located in national parks and wilderness areas. 

Analysis of this network shows that the western U.S. nonurban PM2.5 aerosol is predominantly 

carbon in nature. Nitrate also contributes significantly to the fine particle mass budget 

particularly in central and coastal California. Within these generalizations, obvious departures 

will be found especially near sources such as near the ocean and urban areas where the aerosol 

will be primarily influenced by sea salt and combustion particles, respectively. 

The typical PM2.5 chemical compositions vary by season (Chow et al., 1993a; 1996a, 

Watson et al., 1997), and consist of the following major components: 

a. Organic Carbon: Organic carbon is composed of gases and particles containing 

combinations of carbon and hydrogen atoms. Organic compounds found in ambient air may also 

be associated with other elements and compounds, particularly oxygen, nitrogen, sulfur, 

halogens, and metals. Particulate organic carbon consists of hundreds, possibly thousands, of 

separate compounds (Rogge et al., 1993a). The mass concentration of organic carbon can be 

accurately measured, as can carbonate carbon (Chow et al., 1993b), but only about ten percent of

the specific organic compounds that it contains have been measured. Vehicle exhaust (Rogge et 

al., 1993b), residential and agricultural burning (Rogge et al., 1998), meat cooking (Rogge et al., 

1991), fuel combustion (Rogge et al., 1997), road dust (Rogge et al., 1993c), and particle 

formation from heavy hydrocarbon gases (Pandis et al., 1992), are the major sources of organic 

carbon in PM2.5. 

43 
b. Elemental Carbon: Elemental carbon is black, often called “soot.” Elemental carbon 

contains pure, graphitic carbon, but it also contains high molecular weight, dark-colored, 

nonvolatile organic materials such as tar, biological material (e.g., coffee), and coke. Elemental 

carbon usually accompanies organic carbon in combustion emissions, with diesel exhaust 

(Watson et al., 1994a, 1998) being the largest contributor. 

c. Sulfate: Ammonium sulfate ((NH4SO4), ammonium bisulfate (NH4HSO4), and sulfuric acid 

(H2SO4), are the most common sulfate compounds in PM2.5. These compounds are water-soluble 

and reside almost exclusively in the PM2.5 size fraction. Sodium sulfate (Na2SO4) has been found 

in coastal areas where sulfuric acid has been neutralized by sodium chloride (NaCl) in sea salt. 

Although gypsum (Ca2SO4) and some other geological compounds contain sulfate, these are not 

easily dissolved in water for chemical analysis and are more abundant in the coarse fraction than 

in PM2.5; they are usually classified in the geological fraction.

d. Nitrate: Ammonium nitrate (NH4NO3) is the most abundant nitrate compound, a large 

fraction of PM2.5 occurs during winter, and a moderate fraction occurs during fall. Sodium nitrate 

(NaNO3) is found in the PM2.5 and coarse fractions near the oceans and salt playas. Small 

quantities of sodium nitrate have been found in summertime particulate matter inland owing to 

transport from the ocean (Chow et al., 1996b). 

e. Ammonium: Ammonium sulfate (NH4SO4) and ammonium nitrate (NH4NO3) are the most 

common compounds containing ammonium from reactions between sulfuric acid, nitric acid, and

ammonia gases. While most of the sulfur dioxide and oxides of nitrogen originate from fuel 

combustion in stationary and mobile sources, most of the ammonia derives from living things, 

especially animal husbandry practiced in dairies and feedlots. 

f. Geological Material: Suspended dust consists mainly of oxides of aluminum, silicon, 

calcium, titanium, iron, and other metal oxides. In areas surrounded by substantial terrain (i.e., 

mountains), eons of runoff produce mineral compositions in soils that can be fairly 

homogeneous, with the exception of places where dry lake beds exist that have accumulated salt 

deposits. Industrial processes such as steel making, smelting, and mining have distinct geological 

compositions. For instance, cement production and distribution facilities may use alcareous, 

siliceous, argillaceous, and ferriferous minerals that may not be natural to the region, with 

limestone (CaCO3) being the most abundant (Greer et al., 1992). Suspended geological material 

resides mostly in the coarse particle fraction (Houck et al, 1989,1990), and typically constitutes 

~50% of PM10 while only contributing 5 to 15% of PM2.5 (Watson et al., 1994b). 

g. Sodium Chloride: Salt is found in suspended particles near oceans, open playas, and after 

de-icing materials are applied. Bulk sea water contains 57±7% chloride, 32±4% sodium, 8±1% 

sulfate, 1.1±0.1% soluble potassium, and 1.2±0.2% calcium (Pytkowicz and Kester, 1971). As 

noted above, sodium chloride is often neutralized by nitric or sulfuric acid in urban air where it is 

encountered as sodium nitrate or sodium sulfate. 

h. Liquid Water: Soluble nitrates, sulfates, ammonium, sodium, other inorganic ions, and some

organic material (Saxena and Hildemann, 1997) absorb water vapor from the atmosphere, 

especially when relative humidity exceeds 70% (Tang and Munkelwitz, 1993). Sulfuric acid 

absorbs some water or deliquesces at all humidities. Particles containing these compounds grow 

44 
into the droplet mode as they take on liquid water. Some of this water is retained when particles 

are sampled and weighed for mass concentration. The precise amount of water quantified in a 

PM2.5 depends on its ionic composition and the equilibration relative humidity applied prior to 

laboratory weighing. 

Ambient mass concentrations contain both primary and secondary particles. Primary 

particles are directly emitted by sources and usually undergo few changes between source and 

receptor. Atmospheric concentrations of primary particles are, on average, proportional to the 

quantities that are emitted. 

Secondary particles are those that form in the atmosphere from gases that are directly 

emitted by sources. Sulfur dioxide, ammonia, and oxides of nitrogen are the precursors for 

sulfuric acid, ammonium bisulfate, ammonium sulfate, and ammonium nitrate particles. “Heavy” 

volatile organic compounds or HVOC (those containing more than eight carbon atoms) may also 

change into particles. The majority of these transformations result from intense photochemical 

reactions that also create high ozone levels. Secondary particles usually form over several hours 

or days and attain aerodynamic diameters in the accumulation mode between 0.1 and 1 μm.

Several of these particles, notably those containing ammonium nitrate, are volatile and transfer 

mass between the gas and particle phase to maintain a chemical equilibrium. This volatility has 

implications for ambient concentration measurements as well as for gas and particle 

concentrations in the atmosphere. 

Ambient concentrations of secondary aerosols are not necessarily proportional to quantities 

of emissions since the rate at which they form may be limited by factors other than the 

concentration of the precursor gases. Secondary particulate ammonium nitrate concentrations 

depend on gaseous ammonia and nitric acid concentrations as well as temperature and relative 

humidity. A nearby source of ammonia may cause a localized increase in PM2.5 concentrations 

by shifting the equilibrium from the gas to the particulate ammonium nitrate phase (Watson et 

al., 1994c). Ammonium sulfate may form rapidly from sulfur dioxide and ammonia gases in the 

presence of clouds and fogs, or slowly in dry air. Because fine particle deposition velocities are 

slower than those of the gaseous precursors, PM2.5 may travel much farther than the precursors, 

and secondary particles precursors are often found far from their emissions sources and may 

extend over scales exceeding 1,000 km. 

4. Particle Formation 

Ammonium nitrate and ammonium sulfate aerosols are the most prevalent secondary 

particles found at urban and non-urban sites throughout the U.S. during the winter. These 

particles can form when gas molecules are attracted to and adhere to existing particles.

Sulfur dioxide gas changes to particulate sulfate through gas- and aqueous-phase 

transformation pathways. In the gas-phase pathway, ultraviolet sunlight induces photochemical 

reactions creating oxidizing species that react with a wide variety of atmospheric constituents. 

The gas-phase transformation rate appears to be controlled more by the presence or absence of 

the hydroxyl radical and its competing reactions of other gases than by the sulfur dioxide 

concentrations. 

45 
In the presence of fogs or clouds, sulfur dioxide dissolves in droplets where it experiences 

aqueous reactions that are much faster than gas-phase reactions. When ozone and hydrogen 

peroxide are dissolved in the droplet, the sulfur dioxide is quickly oxidized to sulfuric acid. 

When ammonia is dissolved in the droplet, the sulfuric acid is neutralized to ammonium sulfate. 

If the fog or cloud evaporates and relative humidity decreases below 100 percent, the sulfate 

particle exists as a small droplet that includes a portion of liquid water. As the relative humidity 

further decreases below 70 percent, the droplet evaporates and a small, solid sulfate particle 

remains. The reactions within the fog droplet are very fast, and the rate is controlled by the 

solubility of the precursor gases. Aqueous transformation rates of sulfur dioxide to sulfate are 10 

to 100 times as fast as gas-phase rates. These chemical reactions are critical to understanding PM 

concentrations in areas and downwind of areas that emits large amounts of SO2. The location and 

SO2 emissions output of large point sources such as coal and oil fired power plants need to be 

mapped and compared with transport patterns in order to determine the impact of ammonium

sulfate particles on ambient surface concentrations. 

Fogs serve as an environment for creating particles and as vehicles for particle removal. 

During heavy fogs, particles and precursor gases are scavenged as fog droplets grow to sizes that 

settle rapidly to the surface. The extent and intensity of these fogs is so poorly characterized, 

however, that it is not yet possible to determine where and when particle formation overtakes 

particle deposition, thereby adding to the PM2.5 concentration loading. 

Nitrogen oxide converts to nitrogen dioxide, primarily by reaction with ozone. Nitrogen 

dioxide can: 1) change back to nitrogen oxide in the presence of ultraviolet radiation; 2) change 

to short-lived species which take place in other chemical reactions; 3) form organic nitrates; or 4) 

oxidize to form nitric acid. The major pathway to nitric acid is a reaction with hydroxyl radicals 

that transforms nitrogen dioxide to nitric acid. Nitric acid deposits from the atmosphere fairly 

rapidly but, in the presence of ammonia, it is neutralized to particulate ammonium nitrate. This is 

an important process in secondary particle production because many agricultural areas 

surrounding populated urban areas contain large ammonia sources. Chow and Egami (1997) 

show that San Joaquin Valley ammonia concentrations are large during winter. Conversion rates 

for nitrogen dioxide to nitric acid, ranging from less than one percent per hour to ninety percent 

per hour. These rates are typically five to ten times the conversion rates for sulfate formation. 

Though they vary throughout a twenty-four hour period, these rates are significant during both 

daytime and nighttime hours, in contrast to the gas-phase sulfate chemistry that is most active 

during daylight hours. The important nitric acid-ammonia reaction has implications to network 

design by the need to locate and map possible sources of ammonia. Significant sources of 

ammonia are associated with animal husbandry and fertilizer applications. Locating and 

estimating ammonia emissions will be a difficult task because it is not traditionally tabulated in 

emission inventories and requires further research to refine the methodology to measure the 

emissions. 

While ammonium sulfate is a fairly stable compound, ammonium nitrate is not. Its 

equilibrium with gaseous ammonia and nitric acid is strongly influenced by temperature and 

relative humidity. Atmospheric particle nitrate can occur in atmospheric aerosol particles as solid 

ammonium nitrate or as ionized ammonium nitrate in aerosol particles containing water. In both 

46 
the solid and ionized forms, ammonium nitrate is in equilibrium with gas-phase nitric acid and 

ammonia. 

For fixed relative humidity, increasing temperature decreases the particle nitrate fraction. 

This is a consequence of the direct relation between the equilibrium constants and temperature. 

As temperature increases, the equilibrium constants increase, which means higher gas-phase 

pressures can be supported, thereby reducing the particle nitrate fraction. For fixed humidity, 

decreasing temperature increases the particle nitrate fraction. As temperatures approach 0°C, the 

curves approach limiting values. Particle fractions of one are used for ion ratios greater than or 

equal to one, and particle fractions are determined by the amount of available ammonia for ion 

ratios less than one. For the higher temperatures, increasing relative humidity increases the 

particle nitrate fraction. This is a consequence of liquid water present for the 60% and 80% 

relative humidity cases. When there is sufficient ammonia present with 30% relative humidity, 

more than 90% of the nitrate is in the particle phase for temperatures less than 20°C. More than 

half of the particle nitrate is gone at temperatures above 30°C, and all of it disappears at 

temperatures above 40°C. 

Atmospheric water is another important component of suspended particulate matter. The 

sharp rise in liquid water content at relative humidities between 55% and 75% is known as 

deliquescence. A precise humidity at which soluble particles take on liquid water depends on the 

chemical mixture and temperature. Particles containing these compounds grow into the droplet 

mode as they take on liquid water, so the same concentration of sulfate or nitrate makes a much 

larger contribution to light extinction when humidities are high (>70 percent) than when they are 

low (<30 percent). Excess liquid water is also measured as part of the PM2.5 mass when sampled 

by light scattering continuous monitors or when filters have not been equilibrated at relative 

humidities less than 30% prior to weighing. 

Some of the organic carbon in suspended particles is also of secondary origin. Secondary 

organic compounds in particulate matter include aliphatic acids, alcohols, aromatic acids, nitroaromatics, carbonyls, esters, phenols, and aliphatic nitrates (Grosjean and Seinfeld, 1989; 

Grosjean, 1992, Pandis et al., 1992, 1993; Seinfeld and Pandis, 1998). Normally, primary 

organic carbon particles are more prevalent than secondary organics with exceptions such as 

those found in Los Angeles where conditions of clear skies and high photochemical smog are 

frequent. Although secondary organic aerosol was thought to be minimal during winter in central 

California, recent analyses (Strader et al., 1998) demonstrate that it could be as much as 20% of 

twenty-four hour organic carbon in some samples. This occurs because low wintertime 

temperatures lower the saturation vapor pressure for semi-volatile organic compounds. This is 

probably minor during winter and fall when photochemical reactions are not dominant. 

The exact precursors of secondary organics are not well understood, but they are believed to 

consist of heavy hydrocarbons with more than seven carbon atoms. Odum (1997) identifies 

aromatics as the major group of commonly measured reactive organic gases that affect both 

ozone and secondary aerosol formation. 

47 
5. Source Modeling Techniques 

Before selecting a modeling technique, it is wise to establish a conceptual model. A 

conceptual model describes the relevant physical and chemical processes that affect emissions, 

transport, and transformation specific to the region of interest. It is the starting point for any 

source apportionment process. Conceptual models take advantage of the large body of scientific 

knowledge already acquired. They identify the sources that are likely to be present and eliminate 

those that are not. They examine meteorological conditions that affect concentrations and focus 

further modeling on the conditions conducive to the high concentrations. 

Modeling techniques relevant to attribution are split into several categories depending

complexity, physical attributes, purpose and cost to execute. The following tables on pages 55 to 

62 provide a matrix of detailed information on specific models grouped in these categories. 

a. Puff Modeling Techniques 

These models are based on a Lagrangian framework where air parcels are tracked spatially 

and temporally. They can include chemical mechanisms as well as deposition effects. The most 

commonly used puff model is the CALMET/CALPUFF (Scire et al., 2000). 

b. Grid Modeling Techniques 

For estimating PM2.5 levels, Eulerian models that include aerosol modules simulating the 

physical and chemical processes governing particulate concentrations in the atmosphere are more 

suitable than Lagrangian models such as plume trajectory models. Eulerian three-dimensional 

models may use either a simplified treatment of atmospheric chemistry (usually used to address 

long-term particulate concentrations at urban sites) or include a more detailed atmospheric 

chemistry treatment (usually used to simulate only a few days of episodes due to their 

compositional cost). 

Commonly used long-term Eulerian models with simplified atmospheric processes include 

(Seigneur et al., 1997): 

• Urban Airshed Model Version V (UAM-V). 

• Urban Airshed Model with version V with Linear Chemistry (UAM-LC) 

• Regulatory Modeling System for Aerosol and Deposition (REMSAD). 

Short-term Eulerian models with complex atmospheric processes include: 

• Urban Airshed Model Version V with Aerosols (UAM-AERO), 

• Urban Airshed Model with Aerosol Inorganic Module (UAM-AIM). 

• SARMAP Air Quality Model with Aerosols (SAQM-AERO). 

• Community Multi-scale Air Quality Model (CMAQ) 

48 
• Comprehensive Air Quality Model with extensions (CAMx) 

All of the above mentioned Eulerian models have been developed by various scientists from

universities, federal and state agencies, and the private sector. These particulate air quality 

models provide a three-dimensional treatment to simulate the fate and transport of atmospheric 

contaminants. All of these Eulerian models include gas phase chemistry and aerosol dynamics 

and simulate atmospheric inorganics (such as sulfate, nitrate, and ammonium), but some of these 

models do not include the treatment of organics (i.e., REMSAD and UAM-LC). 

c. Lagrangian Trajectory Model Techniques 

The advantages of using Lagrangian models are the ease of use, the ability to perform many 

trajectories and perform back trajectories. Commonly used Lagrangian models include 

HYSPLIT (Draxler and Hess, 1997) and FLEXPART (Stohl and Siebert, 2001). 

d. Meteorological Modeling Techniques 

Meteorological models describe transport, dispersion, vertical mixing, and moisture in time

and space. Meteorological models consist of straight line, interpolation (termed diagnostic), and 

first principle (termed prognostic) formulations, with increasing levels of complexity and 

requirements for computational and data resources. 

The straight line model is applied to hourly wind directions from a single monitor, assuming 

an air mass travels a distance equal to the wind velocity in the measured direction, regardless of 

the distance from the monitoring site. This model is applicable for a few hours of transport in flat 

terrain, typically for evaluating a single emissions source. 

Interpolation models integrate wind speed and directions from multiple measurement 

locations, including upper air measurements provide by remote sensors or balloon launches. The 

more advanced of these models allow barriers, such as mountains, to be placed between 

monitors. Wind fields, therefore, show different directions and velocities at different horizontal 

and vertical positions. Interpolation wind models are applicable to domains with a large number 

of well-placed monitors and for estimating the movement of air masses from many sources over 

transport times of more than half a day. The number and placement of monitors, especially upper 

air monitors, is especially important in mountainous terrain and in coastal areas where winds are 

unusual. 

First principle models (Stauffer and Seaman, 1994; Seaman et al., 1995; Koracin and Enger, 

1994) embody scientists’ best knowledge of atmospheric physics and thermodynamics, 

employing basic equations for conservation and transfer of energy and momentum. Also known 

as “prognostic models,” first principle models purport to need no data other than values from a 

sparse upper air network for interpolation. They are computationally intensive, often requiring 

supercomputers but have become more practical and cost-effective as workstation and desktop 

computers become more powerful. Modern versions use “four-dimensional data assimilation” or 

FDDA that compare model-calculated wind, humidity, and temperature fields with 

measurements and “nudge” model outputs toward observations. 

49 
A more complex meteorological model is not necessarily a better model for a specific

application. One of the most widely used first principle model is the Fifth-Generation 

NCAR/Penn State Mesoscale Model or MM5 model (Grell et al., 1995). The MM5 

meteorological model has been adopted as the platform for central California air quality studies 

(Seaman et. al., 1995). MM5 input data consist of wind speed, wind direction, temperature, 

atmospheric pressure, and relative humidity at ground level, within the boundary layer, and 

above the boundary layer. In many cases in valley situations ten-meter vertical resolution is 

needed within the surface layer, 30-50 m resolution is needed in the valley wide layer, and 100 m

resolution is needed above the valleywide layer up to ~2000 m agl (Watson et al., 1998). Time

resolution is at least hourly for these measurements. Measurements are needed where large 

differences are expected, although this is largely unknown for winter.

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Darzi, M.; and Winchester, J.W. (1982). Aerosol characteristics at Mauna Loa Observatory, 

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50 
Green, A.E.; Singhal, R.P.; and Venkateswar, R. (1980). Analytic extensions of the Gaussian 

plume model. JAPCA, 30:773-776. 

Greer, W.L.; Johnson, M.D.; Morton, E.L.; Raught, E.C.; Steuch, H.E.; and Trusty, C.B. (1992). 

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Eds. Van Nostrand Reinhold, New York, NY, pp. 746-766. 

Grell, G.A.; Dudhia, J.; and Stauffer, D.R. (1995). A description of the fifth-generation Penn 

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Grosjean, D.; and Seinfeld, J.H. (1989). Parameterization of the formation potential of secondary 

organic aerosols. Atmos. Environ., 23:1733-1747. 

Grosjean, D. (1992). In-situ organic aerosol formation during a smog episode: estimated 

production and chemical functionality. Atmos. Environ., 26A:953-963. 

Holton, J.R. (1982). An Introduction to Dynamic Meteorology. Academic Press, New York, NY. 

Houck, J.E.; Chow, J.C.; and Ahuja, M.S. (1989). The chemical and size characterization of 

particulate material originating from geological sources in California. In Transactions, 

Receptor Models in Air Resources Management, Watson, J.G., Ed. Air & Waste 

Management Association, Pittsburgh, PA, pp. 322-333. 

Houck, J.E.; Goulet, J.M.; Chow, J.C.; Watson, J.G.; and Pritchett, L.C. (1990). Chemical 

characterization of emission sources contributing to light extinction. In Transactions, 

Visibility and Fine Particles, Mathai, C.V., Ed. Air and Waste Management Association, 

Pittsburgh, PA, pp. 437-446. 

Koracin, D.; and Enger, L. (1994). A numerical study of boundary-layer dynamics in a mountain 

valley. Part 2: Observed and simulated characterisitcs of atmospheric stability and local 

flows. Boundary Layer Meteorology, 69:249-283. 

Lowenthal, D.H.; Chow, J.C.; Watson, J.G.; Dipple, W.A.; and Mazzera, D.M. (1996). PM10

source apportionment at McMurdo Station, Antarctica. EM, 28-30. 

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(1997). Aromatics, reformulated gasoline, and atmospheric organic aerosol formation. 

Environ. Sci. Technol., 31:1890-1897. 

Pandis, S.N.; Harley, R.A.; Cass, G.R.; and Seinfeld, J.H. (1992). Secondary organic aerosol 

formation and transport. Atmos. Environ., 26A:2269-2282. 

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51 
Pytkowicz, R.M.; and Kester, D.R. (1971). The physical chemistry of sea water. Oceanogr. Mar. 

Biol., 9:11-60. 

Rogge, W.F.; Hildemann, L.M.; Mazurek, M.A.; and Cass, G.R. (1991). Sources of fine organic 

aerosol. 1. Charbroilers and meat cooking operations. Environ. Sci. Technol., 25:1112-

1125. 

Rogge,W.F.; Mazurek, M.A.; Hildemann, L.M.; Cass, G.R.; and Simoneit, B.R.T. (1993a). 

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and seasonal variation. Atmos. Environ., 27A:1309-1330. 

Rogge, W.F.; Hildemann, L.M.; Mazurek, M.A.; Cass, G.R.; and Simoneit, B.R.T. (1993b). 

Sources of fine organic aerosol. 2. Noncatalyst and catalyst-equipped automobiles and 

heavy-duty diesel trucks. Environ. Sci. Technol., 27:636-651. 

Rogge, W.F.; Hildemann, L.M.; Mazurek, M.A.; Cass, G.R.; and Simoneit, B.R.T. (1993c). 

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dust: Roads as sources and sinks. Environ. Sci. Technol., 27:1892-1904. 

Rogge, W.F.; Hildemann, L.M.; Mazurek, M.A.; Cass, G.R.; and Simoneit, B.R.T. (1997). 

Sources of fine organic aerosol. 8. Boilers burning No. 2 distillate fuel oil. Environ. Sci. 

Technol., 31:2731-2737. 

Rogge, W.F.; Hildemann, L.M.; Mazurek, M.A.; Cass, G.R.; and Simoneit, B.R.T. (1998). 

Sources of fine organic aerosol. 9. Pine, oak, and synthetic log combustion in residential 

fireplaces. Environ. Sci. Technol., 13-22. 

Scire, J.S.; Strimaitis, D.G.; and Yamartino, R.J. (2000). A user's guide for the CALPUFF 

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assimilation system applied in the San Joaquin Valley during SARMAP. Part I: Modeling 

design and basic performance characteristics. Appl. Meteorol., 34:1739-1761. 

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quality models for particulate matter. Report No. CP015-97-1a. Prepared for American

Petroleum Institute, Washington, D.C., by Atmospheric & Environmental Research, Inc., 

San Ramon, CA. 

Seinfeld, J.H.; and Pandis, S.N. (1998). Atmospheric Chemistry and Physics. John Wiley & 

Sons, New York. 

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Meteorol., 33:416-434. 

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guide. June 4, 2001. 

52 
Strader, R.; Pandis, S.N.; and Lurmann, F.W. (1998). Evaluation of secondary organic aerosol 

formation in winter. Prepared for California Air Resources Board, Sacramento, CA, by 

Carnegie Mellon University, Pittsburgh, PA. 

Stull, R.B. (1988). An Introduction to Boundary Layer Meteorology. Kluwer Academic 

Publishers, Dordrecht. 

Tang, I.N.; and Munkelwitz, H.R. (1993). Composition and temperature dependence of the 

deliquescence properties of hygrosopic aerosols. Atmos. Environ., 27A:467-473. 

Watson, J.G.; Chow J.C.; Lowenthal, D.H.; Pritchett, L.C.; Frazier, C.A.; Neuroth, G.R.; and 

Robbins,R. (1994a). Differences in the carbon composition of source profiles for dieseland gasoline-powered vehicles. Atmos. Environ., 28:2493-2505. 

Watson, J.G.; Chow, J.C.; Lu, Z.; Fujita, E.M.; Lowenthal, D.H.; and Lawson, D.R. (1994b). 

Chemical mass balance source apportionment of PM10 during the Southern California Air 

Quality Study. Aerosol Sci. Technol., 21:1-36. 

Watson, J.G.; Chow, J.C.; Lurmann, F.W.; and Musarra, S. (1994c). Ammonium nitrate, nitric 

acid, and ammonia equilibrium in wintertime Phoenix, Arizona. JAWMA, 44:405-412. 

Watson, J.G.; Chow ,J.C.; Rogers, C.F.; Dubois, D.; and Cahill, C.F. (1997). Analysis of 

historical PM10 and PM2.5 measurements in Central California. Prepared for California 

Air Resources Board, Sacramento, CA, by Desert Research Institute, Reno, NV. 

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Research Institute, Reno, NV.

53 

Source Modeling and Back Trajectory Attribution Techniques 

Table 2. Puff, Visibility and Trajectory Modeling Attribution Techniques 

Air Quality Models

Visibility Models Lagrangian Trajectory Models Evaluation Criteria 

CALPUFF VISC

R

E

E

N

P

L

U

V

U

EII H

YSP

LIT

RA

P

T

A

D FL

E

X

Chemical Mechanisms

Does the model simulate aqueous phase chemistry? (Ifit does what chemistrymechanis

m is used and does it

include fog/cloud chemistry) 

Yes; reactions for 

SO4 and NO3

N

O

N

O

N

O

N

O

N

Does the model simulate gas phase chemistry? (if it does,what chemical mechanis

m is

implemented) 

Yes N

O

N

O

N

O

N

O

N

Simulate secondary organic aerosols 

4 species model NO NO NO NO N

Inorganic PM (i.e. ions, SO4, 

NO3, etc) 

SO4, N

O3 N

O

N

O

N

O

N

O

N

Size distributions (sectional ormodal) 

Coarse and fine

modes 

NO NO Only for deposition NO 

Applicable spatial scales

Micro- to regional 

scale 

Neighbor-hood to 

urban scale 

Neighborhood to

global scale 

Neighborhood to global 

scale 

Neighborhood 

Applicable temporal scales(episodic or long term applications) 

Episodic or Long

term

N/A

N/A

Episodic or Long

term

Episodic or Long term Episodic o

Does the model have the capability to distinguish BART sources? 

Y

ES Y

ES Y

ES Y

ES Y

ES Y

Does the model have the capability to ingest fieldmeasurements (

P

M, HNO3, 

H2O2, N

H3, etc)

NO; only as

background values 

N

O

N

O

N

O

N

O

N

55 
Air Quality Models (continued) 

Visibility Models Lagrangian Trajectory Models Evaluation Criteria 

CALPUFF VISCREEN PLUVUEII HYSPLIT RAPTAD FLEX

Visibility Modeling 

Does the model simulate background regional haze 

NO NO NO NO NO N

Point source treatment (plumerise, plume in grid) 

YES YES YES YES YES Y

Does the model have the capability to calculate wet and dry deposition?

YES NO NO YES NO Y

Visibility treatment:(extinction, deciview, visualrange) 

Extinction (total

and for SO4, NO3, 

EC, OC, fine, 

coarse), Deciviews

* * NO NO N

Input Requirements

Meteorological data required (single site, gridded, numberof levels, etc) 

Gridded; able to

run with single site

* * Gridded 

(FNL, EDAS,

MM5) 

 Gridded 

(Use HOTMAC’s 

prediction) 

 Gridded (EC

Emission data required (single stack, multiple point sources, gridded, etc) 

 Multiple point,

area, volume

* * Multiple point

sources, gridded 

inventory

Single stack or multiple

point sources 

Allow for initial and boundaryconditions (is it required or not applicable) 

YES NO NO NO NO N

Has the model been comparedagainst field program data?Has the model been peerreviewed?

YES YES YES YES YES N

Does enough data exist now to run the model (Does data exist in a format ready for the model? Are current databasesadequate for the model?) 

Use CALMET 

with existing 

stations or MM 

data 

YES YES Gridded met data

available on NOAA 

ARL ftp site (FNL, 

EDAS); adaptable 

to read in MM5 

Use HOTMAC to obtain 

wind and turbulence data 

56 
Air Quality Models (continued) 

Visibility Models Lagrangian Trajectory Models Evaluation Criteria 

CALPUFF VISCREEN PLUVUEII HYSPLIT RAPTAD FLEX

Practical Considerations

Costs to run (hardware platforms, file storage,operating system) 

Inexpensive; PC, 

Windows, up to 

20GB (with 

CALMET) 

Inexpensive; PC, 

DOS, 1MB 

Inexpensive; 

PC, DOS, 

1MB 

Inexpensive; PC, 

Windows, 20MB 

Inexpensive: Linux PC, 

Redhat 

Inexpensive; Linux compiled on PC 

Have protocols or procedures been developed to run andinterpret the model?

FLAG for Class I 

AQRVs 

FLAG for Class I 

AQRVs 

FLAG for 

Class I 

AQRVs 

NO NO N

Is the source code available? YES YES YES NO YES YAre beginning user trainingclasses available?

YES; by

EarthTech, BEELine 

EPA; APTI EPA; APTI NO YES N

Are user support groups available?

YES; one list-serve YES; through EPA 

SCRAM 

YES; through 

EPA SCRAM 

NO YES N

Level of expertise required torun and interpret results (Level of Linux, UNIX, PC skills required) 

Moderate; able to

run PC DOS 

programs; 

knowledge of

atmospheric 

chemistry &

physics 

Simple; able to run on 

PC DOS and windows 

Simple; able

to run on PC 

DOS and 

Windows 

Simple; able to run 

on PC running 

Windows 

 Simple to run: GUI 

allows the user to easily

run the program

Knowledge is needed to 

interpret results 

Moderate; must com

PC, Linux or UNIX

input 

Output visualization requiredto interpret output numbers 

YES NO NO YES; has built in 

visualization 

YES; has built in 

visualization 

Y

Other strengths * * * * Applicable from building

scale to terrain scale.

Availability EarthTech

www.src.com

EPA EPA NOAA Air

Resource 

Laboratory;

www.arl.noaa.gov/s

s/models/hysplit.ht

ml

Yamada Science & Art;

http://www.ysasoft.com 

http://ww

muenchen.de/EXT/

lexpa

* No information available at this time

57 
Source Modeling and Back Trajectory Attribution Techniques 

Table 3. Eulerian Grid Based Modeling Attribution Techniques 

Air Quality Models

Grid Models Evaluation Criteria 

C

M

A

Q

R

E

MSA

D

C

A

Mx U

A

M-A

E

R

O UAM-V

P

M

Chemical Mechanisms

Does the model simulate aqueous phase chemistry? (If it does, what chemistry mechanis

m is used and does it include

fog/cloud chemistry)

35 equilibria and 99 

reactions for S

O4 and 

NO3

1 reaction for SO4 1 reaction for SO4 35 equilibria and 99 

reactions for S

O4 and 

NO3

* 2 

Does the model simulate gas phase chemistry? (if it does, what chemicalmechanis

m is implemented) 

CBM-IV (93 

reactions) or RADM2 

(158 reactions) 

CBM-IV 

(93 reactions) or

RADM2 (158 

reactions) 

CB-IV with enhanced

isoprene or

SAPRC97 

SAPRC97 

(185 reactions) 

C

B

(

Simulate secondary organic aerosols Primary from

emissions; secondary 

from organics 

Primary from 

emissions 

Primary from 

emissions; secondary 

from gas phase 

reactions of organic 

precursors using

yields 

Secondary from gas 

phase reactions of 

organic precursors 

using yields 

Primary from 

emissions 

e

Inorganic PM (i.e. ions, SO4, NO3, etc) SO4, N

O3 and other 

material

SO4, N

O3, N

H4, and 

other material 

SO4, N

O3, N

H4, Cl, 

other ions and

materials 

SO4, N

O3, N

H4, Cl, 

other ions and

materials 

SO4, N

O3, N

H4, and 

other materials 

Size distributions (sectional or modal) Lognormal; three 

modes: Aitken, 

Accumulation and 

coase 

PM2.5 fraction, coarse

mode 

Discrete bins, user 

specified up to 10

Discrete bins, user 

specified up to 10

Lognormal bins; user 

specified 

Applicable spatial scales

Mesoscale 

Mesoscale 

Mesoscale Urban scale Urban scale 

Applicable temporal scales (episodic or longterm applications) 

Episodic Long term

Episodic Episodic Episodic 

Does the model have the capability todistinguish BAR

T sources?

YES; using plu

me in 

grid 

N

O

Y

ES N

O

N

O

58 
Air Quality Models (continued) 

Grid Models Evaluation Criteria 

CMAQ REMSAD CAMx UAM-AERO UAM-VPM 

Chemical Mechanisms (continued) 

Does the model have the capability to ingest field measurements(PM,HNO3,H2O2,NH3,etc) 

YES; as initial and 

boundary conditions 

Uses default profiles YES; as initial and 

boundary conditions 

YES; as initial and 

boundary conditions 

YES; as initial and 

boundary conditions 

Visibility Modeling 

Does the model simulate background regional haze 

YES YES YES YES (with

processing) 

YES 

Point source treatment (plume rise, plume ingrid) 

Plume in grid NO Plume in grid NO NO 

Does the model have the capability tocalculate wet and dry deposition?

YES YES YES YES YES 

Visibility treatment: (extinction, deciview,visual range) 

Extinction (total and 

for SO4, NO3, EC, 

OC, fine, coarse), 

Deciviews

Extinction (total and 

for SO4, NO3, EC, 

OC, fine, coarse), 

Deciviews

NO YES (with 

processing) 

* 

Input Requirements

Meteorological data required (single site, gridded, number of levels, etc) 

Gridded Gridded Gridded Gridded Gridded 

Emission data required (single stack, multiplepoint sources, gridded, etc) 

Gridded Gridded Gridded Gridded Gridded 

Allow for initial and boundary conditions (isit required or not applicable) 

YES YES YES YES YES 

Has the model been compared against fieldprogram data? Has the model been peerreviewed?

YES YES YES YES YES 

Does enough data exist now to run the model (Does data exist in a format ready for the model? Are current databases adequate forthe model?) 

Extensive need for 

detailed emissions 

and meteorological 

fields 

Extensive need for 

detailed emissions 

and meteorological 

fields 

Extensive need for 

detailed emissions 

and meteorological 

fields 

Extensive need for 

detailed emissions 

and meteorological 

fields 

Extensive need for 

detailed emissions 

and meteorological 

fields 

59 
Air Quality Models (continued) 

Grid Models Evaluation Criteria 

CMAQ REMSAD CAMx UAM-AERO UAM-VPM 

Practical Considerations

Costs to run (hardware platforms, file storage,operating system) 

Can be run 

inexpensively; Linux 

PC, up to 1 TB 

(annual runs) 

Can be run 

inexpensive; Linux 

PC 

Can be run 

inexpensively; Linux 

PC 

Can be run 

inexpensively; Linux 

PC 

Can be run 

inexpensively; Linux 

PC 

i

Has a protocol or procedures been developedto run the model?

NO; but RPOs have 

regional haze 

protocols 

NO; but RPOs have 

regional haze 

protocols 

NO NO NO 

Is the source code available? YES * YES YES YES Are beginning user training classes available? YES; through EPA 

and RPOs 

NO YES; through SAI 

and RPOs 

NO NO 

Are user support groups available? YES; through EPA 

and RPOs 

NO NO NO NO 

Level of expertise required to run and interpret results (Level of Linux, UNIX, PC skills required) 

Considerable

expertise in UNIX or 

Linux; knowledge of 

atmospheric 

chemistry & physics 

 * Moderate to

extensive 

 Moderate to

extensive 

* 

Output visualization required to interpret output numbers 

YES; use PAVE YES; use PAVE YES YES YES 

Availability EPA; Sharon LeDuc 

919-541-1335 

SAI; Sharon Douglas 

415-507-7108 

ENVIRON; Ralph 

Morris 415-899-0700 

SAI; Sharon Douglas 

415-507-7108 

SAI; Sharon Douglas 

415-507-7108 

R

* No information available at this time 

60 
Table 4. Meteorological Modeling Attribution Techniques 

Diagnostic Models Mesoscale Prognostic Models

Evaluation Criteria CALMET Diagnostic Wind Model RAMS MM5 Eta * ARPS 

Applicable spatial scales Urban to regional 

scale 

Urban to regional 

scale 

Urban to global 

scale 

Urban to global 

scale 

Urban to global 

scale 

Urban to global 

scale 

Applicable temporal scales

Hourly to annual Hourly to annual Hourly to annual Hourly to annual Hourly to annual Hourly to annual H

Does enough data exist inorganization now to runthe model? Are the existing monitoring networks adequate within the domain?

Depends on area Depends on area Depends on area Depends on area Depends on area Depends on area D

For the prognostic models: Capable of FDDA?

N/A N/A Yes Yes Yes Yes 

For the prognostic models: Are files available (archived) fromreal-time?

N/A N/A NOAA is running 

the model for the 

east coast 

Several federal 

agencies and 

regional 

consortiums are 

running MM5 

NOAA is running 

the Eta model and 

fields available 

through NCEP ftp 

site 

It can be

initialized using 

NCEP analyses

files as well as

individual 

observations 

Data storage/archivalrequirements forsimulation of episodic and annual events

Depends on 

modeling domain, 

grid size, number of 

species and length 

of simulation 

Depends on 

modeling domain, 

grid size, number 

of species and

length of 

simulation 

Depends on 

modeling domain, 

grid size, number 

of species and

length of 

simulation 

Depends on 

modeling domain, 

grid size, number 

of species and

length of 

simulation 

Depends on 

modeling domain, 

grid size, number 

of species and

length of 

simulation 

Depends on 

modeling domain, 

grid size, number 

of species and

length of 

simulation 

m

g

Simulate clouds/precipitation fields? At what timeintervals?

No No Yes – minutes to 

days

Yes – minutes to

days

Yes – minutes to

days

Yes – minutes to

days

Y

Has the model been compared against field program data? Has the model been peerreviewed?

Yes Yes Yes Yes Yes Yes 

61 
Meteorological Modeling Attribution Techniques (continued) 

Diagnostic Models Mesoscale Prognostic Models

Evaluation Criteria CALMET Diagnostic Wind Model RAMS MM5 Eta * ARPS 

Tools available tovisualize output fields?What operating systems?

Yes – Unix, Linux, 

Windows 

Yes – Unix, 

Linux, Windows 

Yes – Unix, Linux Yes – Unix, Linux Yes – Unix, Linux Yes – Unix, Linux 

G

Are user groups, listservers available whenproblems arise?

Yes Yes Yes Yes No Yes 

Cost to run (hardware,software) 

PC Windows, PC 

Linux; Free source 

code 

PC Linux, UNIX; 

Free 

PC Linux; licensed 

source code

PC Linux, UNIX; 

Free source code

PC Linux, UNIX; 

Free source code

PC Linux, UNIX; 

Free source code

PC 

Level of expertise required to run and interpret results (Level ofLinux, UNIX, PC skills required) 

Moderate Moderate High High High High 

Availability of user training 

Yes Yes Yes Yes No Yes 

Is the source codeavailable?

Yes Yes Yes Yes No Yes 

Has a protocol orprocedures beendeveloped to run the model?

Yes Yes Yes Yes Yes Yes 

Strengths Relatively easy to 

use; little observed 

data needed 

Relatively easy to 

use; little observed 

data needed 

Detailed 3-D,

complex flow in 

time 

Detailed 3-D,

complex flow in 

time 

Detailed 3-D,

complex flow in 

time 

Detailed 3-D,

complex flow in 

time 

c

Weaknesses/ Limitations 

Parameterization

depended; may not 

capture various 

flows 

Parameterization

depended; may not 

capture various 

flows 

Large computer

time; time 

consuming to 

debug 

Large computer

time; time 

consuming to 

debug 

Large computer

time; time 

consuming to 

debug 

Large computer

time; time 

consuming to 

debug 

N

* While the Eta is not considered a mesoscale model, it has been used to simulate meteorology down to 10 km grid scales. The workstation Eta is ava

 NOAA and used experimentally by some NWS offices.

* No information available at this time

62 
C. Observational Modeling Techniques 

Introduction 

Observational or receptor modeling refers to a group of analysis techniques in which 

monitoring data collected at or in the region of a receptor are analyzed in various ways in order 

to infer information about the pollutants and the sources of the pollutants causing visibility 

impairment. 

These types of models often are used as the first technique for source apportionment in order 

to get an initial understanding of the source-receptor relationships in a region. They are also used 

to verify or reconcile deterministic models, and to aid in planning intensive monitoring studies. 

Results of observational models can either be quantitative or qualitative. Quantitative results 

are estimates of the fractions of a measured species that can be attributed to a single source or 

attributed among several sources or source areas. Qualitative results include such information as 

the wind directions and other meteorological conditions most associated with high 

concentrations, or inferences about probable source types based on the relationships between 

trace elements at a single site, or information about source areas based on the spatial and 

temporal patterns in the concentrations of a single species. Often several observational models 

are used together to form hypotheses about the important source areas and source types affecting 

the concentrations at a receptor. 

Advantages of receptor models are that they are generally quick and inexpensive to run and 

require relatively little input data. Disadvantages include the necessity of employing simplifying 

assumptions such as linear relationships and often the results are limited to averages over long 

periods of time or large spatial areas. Subjective user judgment is required to choose appropriate 

input data and/or interpret the results of many receptor models. As an example, suppose the 

UNMIX model data located a source associated with high concentrations of Br, K, elemental 

carbon, and organic carbon, and another source associated with high Se and S. It is the judgment 

of the modeler regarding the relationship of the individual species to a specific source that 

determines that first source is “smoke” and the second is “coal-fired power plants.” 

Receptor models could potentially be grouped in several different ways, based on their 

different attributes. Here they are somewhat arbitrarily put into four categories, with each 

category requiring incrementally more particulate data at the receptor: 1) back-trajectory 

analyses; 2) analyses of interspecies relationships; 3) analyses of spatial and temporal patterns; 

and 4) analyses that require a unique tracer. Some models that fall into each category are listed 

below: 

Category 1. Back-Trajectory Analyses 

a. Residence Time Analyses including residence time, source contribution function, 

conditional probability, and so forth. These give qualitative information about source 

areas and transport patterns. 

63 
b. Trajectory Mass Balance – Regression of residence time of back trajectories in selected 

source areas against concentrations yielding quantitative source attributions. 

Category 2. Analyses of Interspecies Relationships 

a. Chemical Mass Balance (CMB) - Quantitative source attributions are obtained by a 

weighted regression of known source profiles against measured concentrations of several 

species. 

b. UNMIX – By looking for “edges” in the relationships between species, UNMIX 

estimates both the source profiles and the quantitative source contributions from each 

source. 

c. Positive Matrix Factorization (PMF) – PMF, like UNMIX, uses the relationships between 

species to estimate the number and composition of the sources and the quantitative source 

contributions. 

d. Enrichment Factors (EF) – The “enrichment” of certain ratios of trace elements is used to 

qualitatively infer source types impacting a receptor. 

Category 3. Analyses of Spatial and Temporal Patterns 

a. Empirical Orthogonal Function Analysis (EOF) – Analysis of spatial and temporal 

patterns leading to qualitative information about locations of dominant source areas, 

frequency and timing of source impacts and meteorological conditions associated with 

them. 

b. Receptor Model Applied to Patterns in Space (RMAPS) – With additional assumptions 

applied to EOFs, quantitative source attributions are estimated. 

Category 4. Tracer Analyses 

a. Tracer Mass Balance Regression (TMBR) – Tracer concentrations, possibly weighted by 

other factors, are regressed against concentrations of the species of interest to give a 

quantitative estimate of the contribution from the source that emitted the tracer. 

b. Differential Mass Balance (DMB) - The differential ratios of tracer to pollutant between 

source and receptor are adjusted based on simple chemistry and meteorology to give an 

estimate of the contribution of the tracer source to the receptor. 

c. Tracer-Aerosol Gradient Interpretive Technique (TAGIT) – By comparing the ratios of

tracer to concentration of interest at “background” sites to the ratios at tracer-affected 

sites, a quantitative attribution of the tracer source to the receptor is estimated. 

Following are brief descriptions of each of these models and a few references giving further 

details and examples of their use. 

1a. Qualitative Back Trajectory Residence Time Analyses 

There are several methods of statistically analyzing the relationships between where air 

masses arrived from and the concentrations measured at a receptor. These include, but are not 

limited to: 1) where was air most likely to arrive from when concentrations are high; 2) if air 

arrived from a given area, what is the probability that the concentration at the receptor was high 

when the air mass arrived there; 3) what is the mean (or median or maximum or distribution) of 

64 
concentrations at the receptor when air masses arrived from a given area. Selected References: 

Ashbaugh et al. (1985), Gebhart et al. (2001), Poirot and Wishinski (1986). 

Data Needed: A time series of concentrations of the species of interest. One or more back 

trajectories of three to five days duration corresponding to each concentration. Back trajectories 

can be calculated by any of several methods including ATAD, Hysplit, the CAPITA Monte 

Carlo Model, as well as others. Standard National Weather Service upper air data can be used as 

input, though more detailed meteorological data can be input if available. Dispersion can be 

included in some of these models. 

Model Assumptions: Errors in trajectory placement are random and uncorrelated. Variations 

in deposition, chemistry, emissions, and so forth, have less influence on measured concentrations

on average than variations in transport directions. 

Biggest Potential Problems: Results of these types of analyses are qualitative rather than 

quantitative. Results are more statistically robust when averaged over long time periods, usually 

a minimum of one season and preferably several years. Nearby sources cannot be resolved. User 

judgment is required to choose trajectories of an appropriate type, height, and length and also to 

choose appropriate definitions of “high” concentrations. Some concentrations that vary 

seasonally may have all “high” concentrations in a single season, necessitating some

compensation in the analysis. Model results are probably more appropriate for species such as 

particulate sulfate that are relatively uniform over large spatial scales, rather than, for example, 

particulate nitrate, which is more volatile and seems to be more related to local sources than 

long-range transport. 

1b. TrMB (Trajectory Mass Balance) 

This is a multiple linear regression of the frequency of occurrence of trajectory endpoints in 

each of several source areas against the corresponding concentrations at the receptor. The result 

is the average attribution of a single species among up to about 25 source areas over a long time, 

for example, one season or year, or several years. Selected references: Gebhart and Malm (1989), 

Stohl (1998). 

Data Needed: Time series of concentrations of the pollutant of interest at a single site. One 

or more back trajectories associated with each concentration. Input data for these are upper air 

winds, temperatures, and moisture over a large area. Often data are obtained from the standard 

National Weather Service observations, but other data such as higher resolution wind fields, 

wind profiler data specific to a given study, can also be used if available. Emission data can be 

used if available, but must vary in time to be useful. Simple chemistry and/or deposition can be 

used if data are available. The user defines the size and locations of the source areas to be 

considered. 

Model Assumptions: Average contributions of each source area can be written as a linear 

combination of the contributions from several source areas. Average chemistry and deposition 

are adequate to explain average source contributions. Errors in back trajectories are random and 

normally distributed. 

65 
Biggest Potential Problems: No attribution to single sources—only to source areas. 

Attributions must be averaged over long time periods. Nearby sources cannot be modeled 

accurately. Subjectivity in choosing source areas. Violation of assumptions of linear chemistry. 

2a. Chemical Mass Balance (CMB)

CMB is a multiple linear regression of measured concentrations against known source 

profiles. It is used for the attribution of all measured chemical species among several sources for 

each concentration measurement period for a single monitoring site. Regressions are weighted by 

the uncertainties in both the source profiles and the concentrations. Selected References: Watson 

et al. (1984 and 2001). 

Data Needed: Concentrations and measurement uncertainties of both the chemical species of 

interest and of as many trace elements as possible are necessary for each time period and location 

for which attributions are desired. IMPROVE data can be used. A source profile is needed for 

each source. These are the relative amounts of each emitted chemical species and the 

uncertainties in these values. 

Model Assumptions: Compositions of source emissions are constant over the period of 

ambient and source sampling. Chemical species do not react with each other, for example, they 

add linearly. All sources with a potential for significantly contributing to the receptor have been 

identified and have had their emissions characterized. The sources’ compositions are linearly 

independent of each other. The number of sources or source categories is less than or equal to the 

number of chemical species. Measurement uncertainties are random, uncorrelated and normally 

distributed. 

Biggest Potential Problems: The model cannot directly apportion secondary species such as 

sulfates, nitrates, and secondary organics. There are some workarounds for this. The usual tactic 

is to apportion these species between the known primary sources and a source designated as 

“secondary particles.” It is also possible to use “fractionated” or “aged” source profiles where an 

attempt is made to pre-determine the chemical processes that occurred between source and 

receptor and then adjust the source profile accordingly. Obtaining all necessary source profiles 

can be difficult. In some studies, other receptor models have estimated source profiles.

2b. UNMIX

For a selection of measured species, UNMIX uses singular value decomposition with 

additional non-negativity constraints to estimate the number of sources, the source compositions, 

and the source contributions to each sample at a single monitoring site. UNMIX attempts to find 

the “edges” in the relationships between species and relates these to sources. Selected 

References: Henry (1997a, 1999), Lewis et al. (1998). 

Data Needed: A time series of concentrations of several species measured at a single site. 

IMPROVE data can be used. 

66 
Model Assumptions: Concentrations are linear combinations of an unknown number of 

sources of unknown composition. Contributions from sources are positive. Source compositions

are approximately constant in time. For each source there are some samples that contain little or 

no contribution from that source. 

Biggest Potential Problems: A maximum of seven sources can be identified. There is some

subjectivity in choosing fitting species, number of sources, how to deal with missing or below 

detection limit values, and which time periods and species should be analyzed together. Sources 

of secondary species will probably violate the assumption of constant source composition. This 

can cause multiple sources to be identified for a single physical source that impacts the receptor 

under differing conditions. Supplemental analysis may be required to deconvolute these. 

2c. Positive Matrix Factorization (PMF) 

PMF uses an iterative weighted least squares method to decompose a time-by-species matrix 

to estimate the number and composition of the sources and the contributions of each source to 

each measured species. It will also calculate error estimates for these values. Selected 

References: Paatero and Tapper (1994), Paatero (1997); Xie et al. (1999). 

Data Needed: A time series of concentrations and their uncertainties for several species at a 

single monitoring location. IMPROVE data can be used. 

Model Assumptions: Concentrations are linear combinations of an unknown number of 

sources of unknown composition. Contributions from sources are positive. Source compositions

are approximately constant in time. 

Biggest Potential Problems: Correlations in detection limits or uncertainties as well as in 

concentrations can influence the results. For example, PMF may detect positive correlations 

between species either due to source activity (desirable) or measurement protocol changes 

(undesirable). 

2d. Enrichment Factors (EF) 

The differences in ratios of elemental concentrations between a reference sample and a 

measured sample are used to determine how sources may have “enriched” the concentrations of 

certain species. Some examples include: high Al/Ca has been linked to Saharan dust, high Br/Pb 

may indicate lead is linked to autos rather than industry, high Se is linked to coal burning, heavy 

metals are linked to smelting, V and Ni are linked to residual oil combustion. Selected 

References: Lawson and Winchester (1979), Parekh et al. (1989), Perru (1997), Roshid and 

Griffiths (1993). 

Data Needed: Time series of concentrations of trace elements and the species of interest. 

Some historical information about the “standard” crustal, sea salt, or other ratios for a region. 

Model Assumptions: Elemental ratios depend mostly on enrichment of trace elements by a 

source and have less dependence on meteorology. The reference ratios are constant. 

67 
Biggest Potential Problems: Attributions are generally to source areas, not to single sources. 

3a. Empirical Orthogonal Function Analysis (EOF) 

A few (typically two to six) spatial patterns that explain most of the covariance in the spatial 

and temporal patterns of a measured species are obtained by singular value decomposition. 

Associations between the spatial patterns and source areas and/or transport of air pollutants into 

the study area can often be inferred, but are qualitative. The original data matrix can be 

approximately reconstructed by linearly recombining these few patterns. Selected References: 

Gebhart and Malm (1997), Henry et al. (1991), Malm et al. (1990), Malm and Gebhart (1997). 

Data Needed: Measurements of a single air pollutant of interest at several sites for several 

time periods. Typically used are concentrations measured at fifteen to forty sites for thirty or

more time periods. There must be more time periods than sites. Data from special studies are 

often analyzed in this way. 

Model Assumptions: Only a few spatial patterns are required to explain a large majority of 

the covariance in the spatial and temporal patterns. These patterns have a physical meaning that 

can be inferred, such as transport of emissions from a source into the study area or local 

stagnation. 

Biggest Potential Problems: Source attributions are qualitative, not quantitative and 

interpretation of the spatial patterns is subjective. The model requires a site by time matrix with 

no missing values, so some method of eliminating or filling in both missing and below detection 

limit values is necessary.

3b. Receptor Model Applied to Patterns in Space (RMAPS) 

Determines the average attribution of a single species among a few source areas by 

decomposing the time by site matrix of concentrations into a source matrix and a time weighting 

matrix. Similar to UNMIX, the edges in the scatterplots between sources and non-negativity 

requirements are used to constrain the identification of sources. Selected References: Henry 

(1997 b, c, d), White (1999). 

Data Needed: Time series of concentrations of the pollutant of interest at several sites within 

a region. The model previously has been used in special studies such as Project MOHAVE and 

PREVENT where there are fifteen to forty sites within a one or two state region collecting data 

daily for several weeks or months. 

Model Assumptions: Average contributions of each source area can be written as a nonnegative linear combination of the major principal components of the data. The spatial scale of

the pollutant is large compared to the spacing of the sampling sites. 

Biggest Potential Problems: If the second major assumption is violated, the concentrations 

of the pollutant at each site will have little correlation with the other sites; therefore the model 

would not apply because it relies on the common variations among sites. 

68 
4a. Tracer Mass Balance Regression (TMBR) also called Multiple Linear Regression 

(MLR) on Marker Species 

Estimates the attribution of the aerosol species of interest by a source or source type, which 

emitted or emits a unique tracer. Uncertainty estimates are also generated if included in the 

regression. The model is a regression of the tracer, possibly weighted by other factors against the 

species of interest. Selected References: Malm et al. (1989 a, b, c). 

Data Needed: Time series of ambient concentrations and their uncertainties for the aerosol 

species being apportioned and also the tracer species. 

Model Assumptions: The tracer(s) are uniquely emitted by non-overlapping groups of 

sources. Source emissions are constant over the period of ambient sampling. Deposition and 

conversion are constant for all sampling periods and can be estimated by first-order 

approximations. In the WHITEX application, sulfate oxidation rates were assumed to be related 

to RH, where RH was a surrogate for time the air mass spent in clouds. Measurement errors are 

random, uncorrelated, and normally distributed. 

Biggest Potential Problems: Tracer concentrations are not often available. Source profiles, 

deposition, and conversion all vary in time and space. 

4b. Differential Mass Balance (DMB) 

DMB estimates the fraction of a species of interest attributable to a single source that can be 

tagged with a unique tracer. The ratio of measured tracer to measured sulfate or nitrate is 

assumed to be related to the fractional contribution of the traced source. The ratio is adjusted 

based on the estimated difference between the ratio at the source to the ratio at the receptor. 

Travel times between the source and receptor are estimated based on winds, and then by using 

simple estimates of dispersion, deposition and oxidation, the tracer to secondary species ratio is 

adjusted. Selected References: Malm (1989b, c). 

Data Needed: Time series of tracer concentrations and concentrations and emission rates of 

the species of interest and its precursors, for example, sulfur dioxide and sulfate. Estimates of 

wind speed and direction, mixing heights, deposition and oxidation rates. 

Model Assumptions: Wind direction does not change during transport time. The rates for 

deposition and conversion are first-order and invariant in space and time along the transport path

between the source and the receptor. The ratio of the emission rates for the species of interest or 

its parent species and the tracer is known. 

Biggest Potential Problems: Simple chemistry and meteorology may not be adequate, 

especially for long transport times, complex terrain, and/or changing chemical regimes. Tracer 

concentrations unique to a single source are often not available. The fraction of attributable 

concentration may only be calculable to within a range based on the reasonable ranges of rate 

coefficients.

69 
4c. Tracer-Aerosol Gradient Interpretive Technique (TAGIT) 

Results are the attribution of primary or secondary species associated with the source 

“tagged” by a tracer release. TAGIT computes attributions on a sample period-by-sample period 

basis. For each sample period, background concentration of the species of interest is determined 

by averaging the concentrations of the species at nearby sites that do not have tracer 

concentrations and are significantly above background. These sites are presumed to be 

unaffected by the tracer-tagged source and thus represent the average background. This 

background for each sample period is then subtracted from the concentration of the species of 

interest at impacted receptor sites for corresponding sample periods. The difference is the 

concentration attributable to the tagged source. Green (2001), Kuhns et al. (1999), Pitchford et 

al. (2000). 

Data Needed: Concentrations of a unique tracer from a source of interest and simultaneous 

concentrations of a pollutant of interest at several sites in a region. 

Model Assumptions: There is no impact from the tagged source if the tracer concentration is 

less than the level considered to be “significantly” above its’ background. Background 

concentrations of the species of interest do not vary systematically in space. 

Biggest Potential Problems: Assuming no impact from the tagged source when tracer is not 

statistically above background can lead to an underestimation of attribution. Measured tracer 

concentrations often have large uncertainties. Some sampling periods will have a negative 

concentration attributed to the tagged source. 

References: 

Ashbaugh, Lowell L., William C. Malm, Willy Z. Sadeh (1985) “A Residence Time Probability 

Analysis of Sulfur Concentrations at Grand Canyon National Park,” Atmospheric 

Environment, 19, 1263-1270. 

Gebhart, Kristi A., Sonia A. Kreidenweiss, and William C. Malm (2001) “Back-Trajectory 

Analyses of Fine Particulate Matter Measured at Big Bend National Park in the Historical 

Database and the 1996 Scoping Study” Science of the Total Environment, 276, 185-204. 

Gebhart, Kristi A. and William C. Malm (1989) “Source Apportionment of Particulate Sulfate 

Concentrations at Three National Parks in the Eastern United States,” in Transactions of 

the A&WMA/EPA Specialty Conference-Visibility and Fine Particles, Estes Park, CO, 

Oct. 15-19, 1989, pp. 898-913, C.V. Mathai, Ed. 

Gebhart, Kristi A. and William C. Malm (1997) “Spatial and Temporal Patterns in Particle Data 

Measured during the MOHAVE Study,” J. of the Air & Waste Management Assoc., 47, 

119-135 

Green, Mark, 2001 (Informal paper, BRAVO) 

70 
Henry, R.S. (1997a) “History and fundamentals of multivariate air quality receptor models,” 

Chemometrics and Intelligent Laboratory Systems, 37, 37-42. 

Henry, R.C. (1997b) “Receptor Model Applied to Patterns in Space (RMAPS) Part I – Model 

Description” JA&WMA, 47, 216-219. 

Henry, R.C. (1997c) “Receptor Model Applied to Patterns in Space (RMAPS) Part II –

Apportionment of Airborne Particulate Sulfur from Project MOHAVE” JA&WMA, 47, 

220-225. 

Henry, R.C. (1997d) “Receptor Model Applied to Patterns in Space (RMAPS) Part III – 

Apportionment of Airborne Particulate Sulfur Western Washington State” JA&WMA, 47, 

226-230. 

Henry, R.C. (1999) “Comparing a new algorithm with the classic methods for estimating the 

number of factors,” Chemometrics and Intelligent Laboratory Systems, 48, 91-97. 

Lewis, C.W., R.C. Henry, and J.H. Shreffler (1998) “An Exploratory Look at Hydrocarbon Data 

from the Photochemical Assessment Monitoring Stations Network” J. Air & Waste 

Management Assoc. 48, 71-76. 

Henry, Ronald C., Yi-Jin Wang, and Kristi A. Gebhart (1991) “The Relationship Between 

Empirical Orthogonal Functions and Sources of Air Pollution,” Atmospheric 

Environment, 25A, 503-509. 

Kuhns et al. (1999) 

Lawson, D.R. and J.W. Winchester (1979) “A Standard crustal aerosol as a reference for 

elemental enrichment factors,” Atmospheric Environment, 13, 517-523. 

Malm, William C, Hari K. Iyer, and Kristi Gebhart (1989a) “Application of Tracer Mass Balance 

Regression to WHITEX Data” in Transactions of the A&WMA/EPA Specialty 

Conference: Visibility and Fine Particles, Estes Park, CO, Oct. 15-19, 1989, pp. 806-818, 

C.V. Mathai, Ed. 

Malm, William C., Hari K. Iyer, John Watson, and Douglas A. Latimer (1989b) “Survey of a 

Variety of Receptor Modeling Techniques,” Transactions of the A&WMA/EPA Specialty 

Conference-Visibility & Fine Particles, Estes Park, CO, Oct. 15-19, 1989, pp. 781-805, 

C.V. Mathai, Ed. 

Malm, William, Kristi Gebhart, Douglas Latimer, Thomas Cahill, Robert Eldred, Roger Pielke, 

Roger Stocker, and John Watson (1989) “National Park Service Report On the Winter 

Haze Intensive Tracer Experiment” National Park Service, Air Quality Division, 

Research Branch, Fort Collins, CO, Dec. 4, 1989 

71 
Malm, William C. and Kristi A. Gebhart (1997) “Source apportionment of sulfur and light 

extinction using receptor modeling techniques,” Journal of the Air & Waste Management 

Association, 47, 250-258. 

Malm, William C., Kristi A. Gebhart, and Ronald C. Henry (1990) “An Investigation of the 

Dominant Source Regions of Fine Sulfur in the Western United States and their Areas of 

Influence,” Atmospheric Environment, 24A, 3047-3060. 

Paatero, P. (1997) “Least squares formulation of robust non-negative factor analysis,” 

Chemometrics and Intelligent Laboratory Systems, 37, 15-35. 

Paatero, P. and U. Tapper (1994) “Positive Matrix Factorization – a Nonnegative Factor Model 

with Optimal Utilization of Error-Estimates of Data Values,” Environmetrics, 5, 111-126. 

Parekh, P.P., B. Ghauri, and L. Husain (1989) “Identification of Pollution Sources of 

Anomalously Enriched Elements,” Atmospheric Environment, 23, 1435-1442. 

Perru, K.D., T.A. Cahill, R.A. Eldred, and D.D. Dutcher (1997) “Long-range Transport of North 

African dust to the eastern United States,” J. Geophys. Res., 102, 11,225-11,238. 

Pitchford, Marc, Mark Green, Hampden Kuhns, and Robert J. Farber (2000) “Characterization of 

Regional Transport and Dispersion Using Project MOHAVE Tracer Data” J. Air & 

Waste Manage. Assoc. 50: 733-745. (This is not exactly TAGIT, but the 1999 reference 

may be a conference paper.) 

Poirot, Richard L. and Paul R. Wishinski (1986) “Visibility, Sulfate and Air Mass history 

Associated with the Summertime Aerosol in Northern Vermont,” Atmospheric 

Environment, 20, 1457-1469. 

Roshid, M. and R.F. Griffiths (1993) “Ambient K, S, and Si in fine and coarse aerosols of Kuala 

Lumpur, Malaysia,” Journal of Aerosol Science, 24, 597-601. 

Stohl, A. (1998) “Computation, Accuracy and Applications of Trajectories - A Review and 

Bibliography,” Atmospheric Environment, 32, 947-966. 

Watson, John G., John A. Cooper, and James J. Huntzicker (1984) “The Effective Variance 

Weighting for Least Squares Calculations Applied to the Mass Balance Receptor Model,” 

Atmospheric Environment, 18, 1347-1355. 

Watson, John G., Judith C. Chow and Eric M. Fujita (2001) “Review of volatile organic 

compound source apportionment by chemical mass balance” Atmospheric Environment, 

35, 1567-1584. 

White, Warren H. (1999) “Phantom Spatial Factors: An Example,” J. of the Air & Waste 

Management Assoc., 49, 345-349. 

72 
Xie, Yu-Long, Philip K. Hopke, Pentti Paatero, Leonard A. Barrie, and Shao-Meng Li (1999) 

“Identification of Source Nature and Seasonal Variations of Arctic Aerosol by Positive 

Matrix Factorization,” J. of the Atmospheric Sciences, 56, 249-260. 

73 
74 
Table 5. Observational Modeling Techniques 

Analyses of Back Trajectories 

Criteria Residence Ti

me & 

Source Contribution Function 

Conditional Probability 

Concentration Statistics by Air Mass History (

Mean, Max,

Median)

Cluster Analysis 

Hit – No H

Quantitative Source Attribution?

No No No No No 

Nu

mber of sources that can be distinguished? Typically only about 

10 or fewer transport patterns are distinguishable 

Maximum of 10-15 1/grid cell, typically

50x50 (2500), though usually only about 5-20 transport patterns are distinguishable 

2-15 1 

Averaging Ti

me of Result? Weeks to years Weeks to years Weeks to years Weeks to years Daily, 

possiblyhourly

Previous use at Class I Areas? (See Text for References) Yes Yes Yes Yes Yes Magnitude of impacts? No No Yes No No Frequency of impacts? Yes No No Yes Yes Duration of impacts? Yes No No Yes Yes Time of Day of impacts? Depends on particle 

data 

Depends on particle data 

Depends on particle data 

Depends on particle data 

Yes 

Ti

me of Year of i

mpacts? Yes Yes Yes Yes Yes 

Appropriate for what source-receptor distances? Regional Scale Regional Scale Regional Scale Regional Scale Regional 

Scale 

Attribution of Secondary Species? Yes Yes Yes Yes Yes Cost to run? Minimal Minimal Minimal Minimal Minimal Code Available? No No No No No Equations and/or algorith

ms available? Yes Yes Yes Yes None need

Co

mputer Hardware Needed PC PC PC PC PC 

Additional software necessary? Any program

ming 

language or statistical package and graphics and/or mapping software that allowsoverlay of data on a map

Any program

ming 

language orstatistical package and graphics and/ormapping softwarethat allows overlay of data on a 

map 

Any program

ming 

language or statistical package and graphics and/or mapping software that allowsoverlay of data on a map

Any program

ming 

language orstatistical package and graphics and/ormapping software that allows overlay of data on a 

map 

Graphing mapping software tallows overlay of data on a map

EPA Approved Model? No No No No No 

75 
Observational Modeling Techniques(continued)

Analyses of inter-species relationships Analyses o

Patterns 

Criteria C

M

B

P

M

F UNMI

X Factor Analysis E

O

F 

Quantitative Source Attributio

n? Yes Yes Yes No No 

Number of sources that can be distinguished?

Usually < 10 

Usually < 10 

7 < Number of s

pecies 

analyzed 

Usually 4-8 transport patt

A

veraging Time of Result? Same as

monitoringdata

Same as monitoringdata

Same as monitoring data

Same as

monitoring d

Pre

vious use at Class I Areas? (See Text for 

References) 

Yes No? No? Yes Yes 

Magnitude of impacts? Yes Yes Yes No No Frequency of impacts? Yes Yes Yes Yes Yes Duration of impacts? Yes Yes Yes No Yes Ti

me of Day of impacts?

Depends on

input data 

Depends oninput data 

Depe

n

ds o

n input

data

Depe

n

ds o

n input

data

Depe

n

ds o

n i

data

Time of Year of impacts? Yes Yes Yes Yes Yes Appropriate for what source-receptor distances?

Urban toregionalscales 

Urban toregionalscales 

Urban to regionalscales 

Urban to regionalscales 

Regional Sca

Attribution of Secon

dary Species?

With

ad

ditional 

informatio

n

Withad

ditional 

informatio

n

With ad

ditional 

informatio

n

Yes Yes 

Cost to run? Free to

purchase, minimal torun 

$700 to purchase, minimal torun 

Currently free to purchase, minimal to run, $100 (student)-1500 (full) for MATLAB 

Minimal Minimal 

Code A

vailable? Yes Yes Yes No No 

Equations and/or algorithms available? Yes Yes Yes Yes Yes Computer Hardware Nee

ded

P

C

P

C

P

C

P

C

P

C

Additional software necessary? No No MATLAB with

optimizatio

n

tool

box

Any statistics packa

ge 

Any statistics packa

ge with 

ability to dosingular val

u

decompositio

EPA Model? Yes No? Yes? No No 

76 
Observational Modeling Techniques (continued)

Analyses of Unique Tracer Data 

Criteria 

T

M

B

R

D

M

B TAGIT

Quantitative Source Attribution? Yes Yes Yes Number of sources that can be distinguished?

1-3 1-3 1/tra

c

er

Averaging Tim

e of Result? Sa

m

e as 

particulate data 

Sa

m

e as particulat

Previous use at Class I Areas? (See Text for References) 

Y

es Y

es Y

es

Magnitud

e o

f i

mpacts? Yes Yes Yes 

Frequency of i

mpacts? Yes Yes Yes 

Duration of impacts? Yes Yes Yes Tim

e of Day of i

mpacts? Depends on particulate data Depends on 

particulate data 

Depends on partic

Tim

e o

f Year of impacts? Yes Yes Yes 

Appropriate for what source-receptor distances? 

Urban to Regional Scale Urban to Regional 

Scale 

Regional Scale 

Attribution of Secondary Species? Yes Yes Yes Cost to run? Minim

al Minim

al Minim

al 

Code Available? No No No Equations and/or algorithmsavailable? 

Y

es Y

es Y

es

Co

mputer Hardware Needed PC PC PC 

Additional software necessary? Any programming language, 

spreadshe

et, or statistical 

package 

Any programming language, spreadsheet, or statistical package 

Any programming or statistical packa

EPA Model? No No No 

77 

Appendix A: RA BART Rule (40 CFR 51.300-306) 

Sec. 51.300 Purpose and applicability. 

 Authority: Secs. 110, 114, 121, 160-169, 169A, and 301 of the Clean Air Act, (42 U.S.C. 

7410, 7414, 7421, 7470-7479, and 7601). 

 Source: 45 FR 80089, Dec. 2, 1980, unless otherwise noted. 

 (a) Purpose. The primary purposes of this subpart are to require States to develop programs to 

assure reasonable progress toward meeting the national goal of preventing any future, and 

remedying any existing, impairment of visibility in mandatory Class I Federal areas which 

impairment results from manmade air pollution; and to establish necessary additional procedures 

for new source permit applicants, States and Federal Land Managers to use in conducting the 

visibility impact analysis required for new sources under Sec. 51.166. This subpart sets forth 

requirements addressing visibility impairment in its two principal forms: ``reasonably 

attributable'' impairment (i.e., impairment attributable to a single source/small group of sources) 

and regional haze (i.e., widespread haze from a multitude of sources which impairs visibility in 

every direction over a large area). 

 (b) Applicability. (1) General Applicability. The provisions of this subpart pertaining to 

implementation plan requirements for assuring reasonable progress in preventing any future and 

remedying any existing visibility impairment are applicable to: 

 (i) Each State which has a mandatory Class I Federal area identified in part 81, subpart D, of 

this title, and Each State in which there is any source the emissions from which may reasonably 

be anticipated to cause or contribute to any impairment of visibility in any such area. 

 (ii)The provisions of this subpart pertaining to implementation plans to address reasonably 

attributable visibility impairment are applicable to the following States: Alabama, Alaska, 

Arizona, Arkansas, California, Colorado, Florida, Georgia, Hawaii, Idaho, Kentucky, Louisiana, 

Maine, Michigan, Minnesota, Missouri, Montana, Nevada, New Hampshire, New Jersey, New 

Mexico, North Carolina, North Dakota, Oklahoma, Oregon, South Carolina, South Dakota, 

Tennessee, Texas, Utah, Vermont, Virginia, Virgin Islands, Washington, West Virginia, 

Wyoming. 

 (3) The provisions of this subpart pertaining to implementation plans to address regional haze 

visibility impairment are applicable to all States as defined in section 302(d) of the Clean Air Act 

(CAA) except Guam, Puerto Rico, American Samoa, and the Northern Mariana Islands. 

[45 FR 80089, Dec. 2, 1980, as amended at 64 FR 35763, July 1, 1999] 

Sec. 51.301 Definitions.

 For purposes of this subpart: 

 Adverse impact on visibility means, for purposes of section 307, visibility impairment which 

interferes with the management, protection, preservation, 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 account the geographic extent, intensity, duration, frequency and time of visibility 

impairments, and how these factors correlate with (1) times of visitor use of the Federal Class I 
area, and (2) the frequency and timing of natural conditions that reduce visibility. This term does 

not include effects on integral vistas. 

 Agency means the U.S. Environmental Protection Agency. 

 BART-eligible source means an existing stationary facility as defined in this section. Best 

Available Retrofit Technology (BART) means an emission limitation based on the degree of 

reduction achievable through the application of the best system of continuous emission reduction 

for each pollutant which is emitted by an existing stationary facility. The emission limitation 

must be established, on a case-by-case basis, taking into consideration the technology available, 

the costs of compliance, the energy and nonair quality environmental impacts of compliance, any 

pollution control equipment in use or in existence at the source, the remaining useful life of the 

source, and the degree of improvement in visibility which may reasonably be anticipated to 

result from the use of such technology. 

 Building, structure, or facility means all of the pollutant-emitting activities which belong to the 

same industrial grouping, are located on one or more contiguous or adjacent properties, and are 

under the control of the same person (or persons under common control). Pollutant-emitting 

activities must be considered as part of the same industrial grouping if they belong to the same

Major Group (i.e., which have the same two-digit code) as described in the Standard Industrial 

Classification Manual, 1972 as amended by the 1977 Supplement (U.S. Government Printing 

Office stock numbers 4101-0066 and 003-005-00176-0 respectively). 

 Deciview means a measurement of visibility impairment. A deciview is a haze index derived 

from calculated light extinction, such that uniform changes in haziness correspond to uniform

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

impaired. The deciview haze index is calculated based on the following equation (for the 

purposes of calculating deciview, the atmospheric light extinction coefficient must be calculated 

from aerosol measurements): 

Deciview haze index = 10 lne (bext/10Mm-1) Where bext = the atmospheric light extinction 

coefficient, expressed in inverse megameters (Mm-1). 

 Existing stationary facility means any of the following stationary sources of air pollutants, 

including any reconstructed source, which was not in operation prior to August 7, 1962, and was 

in existence on August 7, 1977, and has the potential to emit 250 tons per year or more of any air 

pollutant. In determining potential to emit, fugitive emissions, to the extent quantifiable, must be 

counted. 

 Fossil-fuel fired steam electric plants of more than 250 million British thermal units per hour 

heat input, 

 Coal cleaning plants (thermal dryers), 

 Kraft pulp mills, 

 Portland cement plants, 

 Primary zinc smelters, 

 Iron and steel mill plants, 

 Primary aluminum ore reduction plants, 

 Primary copper smelters, 

 Municipal incinerators capable of charging more than 250 tons of refuse per day, 

 Hydrofluoric, sulfuric, and nitric acid plants, 

 Petroleum refineries, 

 Lime plants, 

 Phosphate rock processing plants, 
 Coke oven batteries, 

 Sulfur recovery plants, 

 Carbon black plants (furnace process), 

 Primary lead smelters, 

 Fuel conversion plants, 

 Sintering plants, 

 Secondary metal production facilities, 

 Chemical process plants, 

 Fossil-fuel boilers of more than 250 million British thermal units per hour heat input, 

 Petroleum storage and transfer facilities with a capacity exceeding 300,000 barrels, 

 Taconite ore processing facilities, 

 Glass fiber processing plants, and 

 Charcoal production facilities. 

 Federal Class I area means any Federal land that is classified or reclassified Class I.

Federal Land Manager means the Secretary of the department with authority over the Federal 

Class I area (or the Secretary's designee) or, with respect to Roosevelt-Campobello International 

Park, the Chairman of the Roosevelt-Campobello International Park Commission. 

 Federally enforceable means all limitations and conditions which are enforceable by the 

Administrator under the Clean Air Act including those requirements developed pursuant to parts 

60 and 61 of this title, requirements within any applicable State Implementation Plan, and any 

permit requirements established pursuant to Sec. 52.21 of this chapter or under regulations 

approved pursuant to part 51, 52, or 60 of this title. 

 Fixed capital cost means the capital needed to provide all of the depreciable components. 

Fugitive Emissions means those emissions which could not reasonably pass through a stack, 

chimney, vent, or other functionally equivalent opening. 

 Geographic enhancement for the purpose of Sec. 51.308 means a method, procedure, or 

process to allow a broad regional strategy, such as an emissions trading program designed to 

achieve greater reasonable progress than BART for regional haze, to accommodate BART for 

reasonably attributable impairment. 

 Implementation plan means, for the purposes of this part, any State Implementation Plan, 

Federal Implementation Plan, or Tribal Implementation Plan. 

 Indian tribe or tribe means any Indian tribe, band, nation, or other organized group or 

community, including any Alaska Native village, which is federally recognized as eligible for the 

special programs and services provided by the United States to Indians because of their status as 

Indians. 

 In existence means that the owner or operator has obtained all necessary preconstruction 

approvals or permits required by Federal, State, or local air pollution emissions and air quality 

laws or regulations and either has (1) begun, or caused to begin, a continuous program of 

physical on-site construction of the facility or (2) entered into binding agreements or contractual 

obligations, which cannot be cancelled or modified without substantial loss to the owner or 

operator, to undertake a program of construction of the facility to be completed in a reasonable 

time. 

 In operation means engaged in activity related to the primary design function of the source. 

 Installation means an identifiable piece of process equipment. 
 Integral vista means a view perceived from within the mandatory Class I Federal area of a 

specific landmark or panorama located outside the boundary of the mandatory Class I Federal 

area. 

 Least impaired days means the average visibility impairment (measured in deciviews) for the 

twenty percent of monitored days in a calendar year with the lowest amount of visibility 

impairment. 

 Major stationary source and major modification mean major stationary source and major 

modification, respectively, as defined in Sec. 51.166. 

 Mandatory Class I Federal Area means any area identified in part 81, subpart D of this title. 

 Most impaired days means the average visibility impairment (measured in deciviews) for the 

twenty percent of monitored days in a calendar year with the highest amount of visibility 

impairment. 

 Natural conditions includes naturally occurring phenomena that reduce visibility as measured 

in terms of light extinction, visual range, contrast, or coloration. 

 Potential to emit means the maximum capacity of a stationary source to emit a pollutant under 

its physical and operational design. Any physical or operational limitation on the capacity of the 

source to emit a pollutant including air pollution control equipment and restrictions on hours of 

operation or on the type or amount of material combusted, stored, or processed, shall be treated 

as part of its design if the limitation or the effect it would have on emissions is federally 

enforceable. 

Secondary emissions do not count in determining the potential to emit of a stationary source. 

 Reasonably attributable means attributable by visual observation or any other technique the 

State deems appropriate. Reasonably attributable visibility impairment means visibility 

impairment that is caused by the emission of air pollutants from one, or a small number of 

sources. 

 Reconstruction will be presumed to have taken place where the fixed capital cost of the new 

component exceeds 50 percent of the fixed capital cost of a comparable entirely new source. Any 

final decision as to whether reconstruction has occurred must be made in accordance with the 

provisions of Sec. 60.15 (f) (1) through (3) of this title. 

 Regional haze means visibility impairment that is caused by the emission of air pollutants from

numerous sources located over a wide geographic area. Such sources include, but are not limited 

to, major and minor stationary sources, mobile sources, and area sources. 

 Secondary emissions means emissions which occur as a result of the construction or operation 

of an existing stationary facility but do not come from the existing stationary facility. Secondary 

emissions may include, but are not limited to, emissions from ships or trains coming to or from

the existing stationary facility. 

 Significant impairment means, for purposes of Sec. 51.303, visibility impairment which, in the 

judgment of the Administrator, interferes with the management, protection, preservation, or 

enjoyment of the visitor's visual experience of the mandatory Class I Federal area. This 

determination must be made on a case-by-case basis taking into account the geographic extent, 

intensity, duration, frequency and time of the visibility impairment, and how these factors 

correlate with (1) times of visitor use of the mandatory Class I Federal area, and (2) the 

frequency and timing of natural conditions that reduce visibility. 

 State means ``State'' as defined in section 302(d) of the CAA. 

 Stationary Source means any building, structure, facility, or installation which emits or may 

emit any air pollutant. 
 Visibility impairment means any humanly perceptible change in visibility (light extinction, 

visual range, contrast, coloration) from that which would have existed under natural conditions.

 Visibility in any mandatory Class I Federal area includes any integral vista associated with 

that area. 

[45 FR 80089, Dec. 2, 1980, as amended at 64 FR 35763, 35774, July 1, 1999] 

Sec. 51.302 Implementation control strategies for reasonably attributable visibility impairment. 

 (a) Plan Revision Procedures. (1) Each State identified in Sec. 51.300(b)(2) must have 

submitted, not later than September 2, 1981, an implementation plan meeting the requirements of 

this subpart pertaining to reasonably attributable visibility impairment. 

 (2)(i) The State, prior to adoption of any implementation plan to address reasonably 

attributable visibility impairment required by this subpart, must conduct one or more public 

hearings on such plan in accordance with Sec. 51.102. 

 (ii) In addition to the requirements in Sec. 51.102, the State must provide written notification 

of such hearings to each affected Federal Land Manager, and other affected States, and must 

state where the public can inspect a summary prepared by the Federal Land Managers of their 

conclusions and recommendations, if any, on the proposed plan revision. 

 (3) Submission of plans as required by this subpart must be conducted in accordance with the 

procedures in Sec. 51.103. 

 (b) State and Federal Land Manager Coordination. (1) The State must identify to the Federal 

Land Managers, in writing and within 30 days of the date of promulgation of these regulations, 

the title of the official to which the Federal Land Manager of any mandatory Class I Federal area 

can submit a recommendation on the implementation of this subpart including, but not limited to: 

 (i) A list of integral vistas that are to be listed by the State for the purpose of implementing 

section 304, 

 (ii) Identification of impairment of visibility in any mandatory Class I Federal area(s), and 

 (iii) Identification of elements for inclusion in the visibility monitoring strategy required by 

section 305. 

 (2) The State must provide opportunity for consultation, in person and at least 60 days prior to 

holding any public hearing on the plan, with the Federal Land Manager on the proposed SIP 

revision required by this subpart. This consultation must include the opportunity for the affected 

Federal Land Managers to discuss their: 

 (i) Assessment of impairment of visibility in any mandatory Class I Federal area, and 

 (ii) Recommendations on the development of the long-term strategy. 

 (3) The plan must provide procedures for continuing consultation between the State and 

Federal Land Manager on the implementation of the visibility protection program required by 

this subpart. 

 (c) General plan requirements for reasonably attributable visibility impairment. (1) The 

affected Federal Land Manager may certify to the State, at any time, that there exists reasonably 

attributable impairment of visibility in any mandatory Class I Federal area. 

 (2) The plan must contain the following to address reasonably attributable impairment: 

 (i) A long-term (10-15 years) strategy, as specified in Sec. 51.305 and Sec. 51.306, including 

such emission limitations, schedules of compliance, and such other measures including schedules 
for the implementation of the elements of the long-term strategy as may be necessary to make 

reasonable progress toward the national goal specified in Sec. 51.300(a). 

 (ii) An assessment of visibility impairment and a discussion of how each element of the plan 

relates to the preventing of future or remedying of existing impairment of visibility in any 

mandatory Class I Federal area within the State.

 (iii) Emission limitations representing BART and schedules for compliance with BART for 

each existing stationary facility identified according to paragraph (c)(4) of this section.

 (3) The plan must require each source to maintain control equipment required by this subpart 

and establish procedures to ensure such control equipment is properly operated and maintained. 

 (4) For any existing reasonably attributable visibility impairment the Federal Land Manager 

certifies to the State under paragraph (c)(1) of this section, at least 6 months prior to plan 

submission or revision: 

 (i) The State must identify and analyze for BART each existing stationary facility which may 

reasonably be anticipated to cause or contribute to impairment of visibility in any mandatory 

Class I Federal area where the impairment in the mandatory Class I Federal area is reasonably 

attributable to that existing stationary facility. The State need not consider any integral vista the 

Federal Land Manager did not identify pursuant to Sec. 51.304(b) at least 6 months before plan 

submission. 

 (ii) If the State determines that technologicial or economic limitations on the applicability of 

measurement methodology to a particular existing stationary facility would make the imposition 

of an emission standard infeasible it may instead prescribe a design, equipment, work practice, or 

other operational standard, or combination thereof, to require the application of BART. Such 

standard, to the degree possible, is to set forth the emission reduction to be achieved by 

implementation of such design, equipment, work practice or operation, and must provide for 

compliance by means which achieve equivalent results. 

 (iii) BART must be determined for fossil-fuel fired generating plants having a total generating 

capacity in excess of 750 megawatts pursuant to ``Guidelines for Determining Best Available 

Retrofit Technology for Coal-fired Power Plants and Other Existing Stationary Facilities''

(1980), which is incorporated by reference, exclusive of appendix E, which was published in the 

Federal Register on February 6, 1980 (45 FR 8210). It is EPA publication No. 450/3-80-009b 

and is for sale from the U.S. Department of Commerce, National Technical Information Service,

5285 Port Royal Road, Springfield, Virginia 22161. It is also available for inspection at the 

Office of the Federal Register Information Center, 800 North Capitol NW., suite 700, 

Washington, DC. 

 (iv) The plan must require that each existing stationary facility required to install and operate 

BART do so as expeditiously as practicable but in no case later than five years after plan 

approval. 

 (v) The plan must provide for a BART analysis of any existing stationary facility that might 

cause or contribute to impairment of visibility in any mandatory Class I Federal area identified 

under this paragraph (c)(4) at such times, as determined by the Administrator, as new technology 

for control of the pollutant becomes reasonably available if: 

 (A) The pollutant is emitted by that existing stationary facility, 

 (B) Controls representing BART for the pollutant have not previously been required under this 

subpart, and 

 (C) The impairment of visibility in any mandatory Class I Federal area is reasonably 

attributable to the emissions of that pollutant. 
[45 FR 80089, Dec. 2, 1980, as amended at 57 FR 40042, Sept. 1, 1992; 64 

FR 35764, 35774, July 1, 1999] 

 

Sec. 51.303 Exemptions from control. 

 (a)(1) Any existing stationary facility subject to the requirement under Sec. 51.302 to install, 

operate, and maintain BART may apply to the Administrator for an exemption from that 

requirement. 

 (2) An application under this section must include all available documentation relevant to the 

impact of the source's emissions on 

visibility in any mandatory Class I Federal area and a demonstration by the existing stationary 

facility that it does not or will not, by itself or in combination with other sources, emit any air 

pollutant which may be reasonably anticipated to cause or contribute to a significant impairment 

of visibility in any mandatory Class I Federal area. 

 (b) Any fossil-fuel fired power plant with a total generating capacity of 750 megawatts or 

more may receive an exemption from BART only if the owner or operator of such power plant 

demonstrates to the satisfaction of the Administrator that such power plant is located at such a 

distance from all mandatory Class I Federal areas that such power plant does not or will not, by 

itself or in combination with other sources, emit any air pollutant which may reasonably be 

anticipated to cause or contribute to significant impairment of visibility in any such mandatory 

Class I Federal area. 

 (c) Application under this Sec. 51.303 must be accompanied by a written concurrence from the 

State with regulatory authority over the source. 

 (d) The existing stationary facility must give prior written notice to all affected Federal Land 

Managers of any application for exemption under this Sec. 51.303. 

 (e) The Federal Land Manager may provide an initial recommendation or comment on the 

disposition of such application. Such recommendation, where provided, must be part of the 

exemption application. This recommendation is not to be construed as the concurrence required 

under paragraph (h) of this section. 

 (f) The Administrator, within 90 days of receipt of an application for exemption from control, 

will provide notice of receipt of an exemption application and notice of opportunity for public 

hearing on the application. 

 (g) After notice and opportunity for public hearing, the Administrator may grant or deny the 

exemption. For purposes of judicial review, final EPA action on an application for an exemption 

under this Sec. 51.303 will not occur until EPA approves or disapproves the State 

Implementation Plan revision. 

 (h) An exemption granted by the Administrator under this Sec. 51.303 will be effective only 

upon concurrence by all affected Federal Land Managers with the Administrator's determination. 

[45 FR 80089, Dec. 2, 1980, as amended by 64 FR 35774, July 1, 1999] 

Sec. 51.304 Identification of integral vistas. 

 (a) On or before December 31, 1985 the Federal Land Manager may identify any integral 

vista. The integral vista must be identified according to criteria the Federal Land Manager 
develops. These criteria must include, but are not limited to, whether the integral vista is 

important to the visitor's visual experience of the mandatory Class I Federal area. Adoption of 

criteria must be preceded by reasonable notice and opportunity for public comment on the 

proposed criteria. 

 (b) The Federal Land Manager must notify the State of any integral vistas identified under 

paragraph (a) of this section, and the reasons therefor. 

 (c) The State must list in its implementation plan any integral vista the Federal Land Manager 

identifies at least six months prior to plan submission, and must list in its implementation plan at 

its earliest opportunity, and in no case later than at the time of the periodic review of the SIP 

required by Sec. 51.306(c), any integral vista the Federal Land Manager identifies after that time. 

 (d) The State need not in its implementation plan list any integral vista the identification of

which was not made in accordance with the criteria in paragraph (a) of this section. In making 

this finding, the State must carefully consider the expertise of the Federal Land Manager in 

making the judgments called for by the criteria for identification. Where the State and the 

Federal Land Manager disagree on the identification of any integral vista, the State must give the 

Federal Land Manager an opportunity to consult with the Governor of the State. 

[45 FR 80089, Dec. 2, 1980, as amended by 64 FR 35774, July 1, 1999] 

 

Sec. 51.305 Monitoring for reasonably attributable visibility impairment. 

 (a) For the purposes of addressing reasonably attributable visibility impairment, each State 

containing a mandatory Class I Federal area must include in the plan a strategy for evaluating 

reasonably attributable visibility impairment in any mandatory Class I Federal area by visual 

observation or other appropriate monitoring techniques. Such strategy must take into account 

current and anticipated visibility monitoring research, the availability of appropriate monitoring 

techniques, and such guidance as is provided by the Agency. 

 (b) The plan must provide for the consideration of available visibility data and must provide a 

mechanism for its use in decisions required by this subpart. 

[45 FR 80089, Dec. 2, 1980, as amended at 64 FR 35764, July 1, 1999] 

Sec. 51.306 Long-term strategy requirements for reasonably attributable visibility impairment. 

 (a)(1) For the purposes of addressing reasonably attributable visibility impairment, each plan 

must include a long-term (10-15 years) strategy for making reasonable progress toward the 

national goal specified in Sec. 51.300(a). This strategy must cover any existing impairment the 

Federal Land Manager certifies to the State at least 6 months prior to plan submission, and any 

integral vista of which the Federal Land Manager notifies the State at least 6 months prior to plan 

submission. 

 (2) A long-term strategy must be developed for each mandatory Class I Federal area located 

within the State and each mandatory Class I Federal area located outside the State which may be 

affected by sources within the State. This does not preclude the development of a single 

comprehensive plan for all such areas.
 (3) The plan must set forth with reasonable specificity why the long-term strategy is adequate 

for making reasonable progress toward the national visibility goal, including remedying existing 

and preventing future impairment. 

 (b) The State must coordinate its long-term strategy for an area with existing plans and goals, 

including those provided by the affected Federal Land Managers, that may affect impairment of 

visibility in any mandatory Class I Federal area. 

 (c) The plan must provide for periodic review and revision, as appropriate, of the long-term

strategy for addressing reasonably attributable visibility impairment. The plan must provide for 

such periodic review and revision not less frequently than every 3 years until the date of

submission of the State's first plan addressing regional haze visibility impairment in accordance 

with Sec. 51.308(b) and (c). On or before this date, the State must revise its plan to provide for 

review and revision of a coordinated long-term strategy for addressing reasonably attributable 

and regional haze visibility impairment, and the State must submit the first such coordinated 

long-term strategy. Future coordinated long-term strategies must be submitted consistent with the 

schedule for periodic progress reports set forth in Sec. 51.308(g). Until the State revises its plan 

to meet this requirement, the State must continue to comply with existing requirements for plan 

review and revision, and with all emission management requirements in the plan to address 

reasonably attributable impairment. This requirement does not affect any preexisting deadlines 

for State submittal of a long-term strategy review (or element thereof) between August 30, 1999, 

and the date required for submission of the State's first regional haze plan. In addition, the plan 

must provide for review of the long-term strategy as it applies to reasonably attributable 

impairment, and revision as appropriate, within 3 years of State receipt of any certification of 

reasonably attributable impairment from a Federal Land Manager. The review process must 

include consultation with the appropriate Federal Land Managers, and the State must provide a 

report to the public and the Administrator on progress toward the national goal. This report must 

include an assessment of: 

 (1) The progress achieved in remedying existing impairment of visibility in any mandatory 

Class I Federal area; 

 (2) The ability of the long-term strategy to prevent future impairment of visibility in any 

mandatory Class I Federal area; 

 (3) Any change in visibility since the last such report, or, in the case of the first report, since 

plan approval; 

 (4) Additional measures, including the need for SIP revisions, that may be necessary to assure 

reasonable progress toward the national visibility goal; 

 (5) The progress achieved in implementing BART and meeting other schedules set forth in the 

long-term strategy; 

 (6) The impact of any exemption granted under Sec. 51.303; 

 (7) The need for BART to remedy existing visibility impairment of any integral vista listed in 

the plan since the last such report, or, in the case of the first report, since plan approval. 

 (d) The long-term strategy must provide for review of the impacts from any new major 

stationary source or major modifications on visibility in any mandatory Class I Federal area. This 

review of major stationary sources or major modifications must be in accordance with Sec. 

51.307, Sec. 51.166, Sec. 51.160, and any other binding guidance provided by the Agency 

insofar as these provisions pertain to protection of visibility in any mandatory Class I Federal 

areas. 
 (e) The State must consider, at a minimum, the following factors during the development of its 

long-term strategy: 

 (1) Emission reductions due to ongoing air pollution control programs, 

 (2) Additional emission limitations and schedules for compliance, 

 (3) Measures to mitigate the impacts of construction activities, 

 (4) Source retirement and replacement schedules, 

 (5) Smoke management techniques for agricultural and forestry management purposes 

including such plans as currently exist within the State for these purposes, and 

 (6) Enforceability of emission limitations and control measures. 

 (f) The plan must discuss the reasons why the above and other reasonable measures considered 

in the development of the long-term strategy were or were not adopted as part of the long-term

strategy. 

 (g) The State, in developing the long-term strategy, must take into account the effect of new 

sources, and the costs of compliance, the time necessary for compliance, the energy and nonair 

quality environmental impacts of compliance, and the remaining useful life of any affected 

existing source and equipment therein. 

[45 FR 80089, Dec. 2, 1980, as amended at 64 FR 35764, 35774, July 1, 1999]