# Recommendations for Making

Attribution Determinations

in the Context of

Reasonably Attributable BART

Prepared by: The WESTAR Council

Prepared for: The Air Managers Commit tee, 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 Sundbl om, 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 Qua lity; Alan Newman, Washington Department of

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

Park Service; and Kristin Ga ston and Bob Lebens, WESTAR.

Prepared for:

The Air Managers Committee of the Western Regional Air Partnership

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

May 2003Acknowledgements

The recommendations regarding the attribut ion 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 ackn owledges the generous support of all the Working Group

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

Arizona Department of Environmental Qual ity; Deb Wolfe, Mont ana Department of

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

Mexico Environment Department; Colleen Delane y, Utah Department of Environment Quality;

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

and Kristin Gaston and Bob Lebens, WESTAR.

Authors of the Attribution Techniques (S ection VI) are Mike S undblom (Monitoring);

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

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

drafted the certification s ection and provided liaison betw een the Western Regional Air

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

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

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

with Bob Lebens, edited the report.

### EXECUTIVE SUMMARY

This report recommends a genera l procedure and applicable te chnical approaches that may

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

(FLM) certification of visibi lity impairment in a Cl ass I area (Certification).

WESTAR formed the Reasonably Attributab le 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 knowledg eable about RA BART and associated monitoring

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

information about both the certification process and the attribution de termination process.

However, the recommendations focus on the gene ral principles of the attribution assessment

process and the technical criter ia 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 a nd within a reasonable time frame.

 No more complex than necessary.

 Performed by state or tribal agency staff.

 Adequate to determine whether or not visibi lity 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 pollutant s of concern must be identified.

 Factors to consider in assessing impa irment 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 impairm ent 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. Backgr ound....................................................................................................... 1

A. Introduction and Purpose

B. Regulatory Context



### II. Federal Land Manager Cer tification of Impairment ........................................ 7

III. The Attribution Determ ination Process.............................................11

IV. General Principle s and Crite ria ...................................................................... 17

A. General Principl es of the Attribution Process

B. Technical Criteria

C. Examples of the Attribution Process



### V. Techni ques...................................................................................................... 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 Retr ofit Technology (RA BART ) is the portion of

EPA’s visibility rule pub lished in 19 80 and codified in 40 CF R 51.302–51.306 that d eals with

visibility im pacts from one source or a sm all gr oups of sources. RA BART refers to reasonably

attributable visibility im pairm e nt and best av ailable retrofit t echnology for eligible sources and

em ission lim its, and em issions controls as define d by the statute and the rules. Som e confusion

exists rega rd ing the app l ication of RA BART, the process of a ssessing sources of visibility

im pair m e nt, and the technical t ools available for an RA BART attribution determ ination. RA

BART is a statutory requirem ent, although cer tain of the requirem ents m a y no longer be

applic able w h en a sourc e com p lies with BART or insta lls BA RT-like con t rols or af ter a sta t e

im ple m ents a trading program under 51.308(e)(2) or any trading program under 51.309, and if no

rem a ining visibility im pa cts continue from one source or a small group of sources.

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

studies W E STAR developed in 2001 to exam in e and document how Reasonably Attributable

Visibility Impairm e nt (RAVI) had been addressed in previous assessm ents. 1

The Federal land m a nagers (FLMs) advocate m a intaining RA BART as a tool because RA

BART is effective when new m onitoring indicat es that a previously un-m o nitored area has

visibility problem s diffe ring from the regional visi bility im pa irm e nt conditions at other areas. In

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

are cau sing or contr i buting to iden tif ied vis i bility im pairm e nt. 2

First, this re port re comm ends attrib ution pr ocess principles and a ssessm ent techniqu es a

state or tribe m a y consider in an attribution as sessm e nt to identify if, and to what degree, an

existing source or sm all group of sources causes or contributes to visibility im pairm e nt. The

report focus e s on RAVI and does not specifically address im pairm e nt due to reg i onal h aze.

Although this report includes referen ces to region al haze, such references serve only to place the

attribution process in the larger context of the broad regulatory fra m ework that addresses

visibility im pairm e nt, including the regional haze regulations.

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

level at which reasonab l y attributable im pair m e nt exists. Instead, the work ing group ou tlin ed a

recomm ended general process and recomm ende d t echnical procedures that m a y be used as

guidelines if an attribu tio n assessm ent is nece ssary. Due to th e circum stances unique to each

attribution assessm ent and the requirem ent that the assessm ent be conducted on a case-by-case

basis, recomm ending one specific an alysis for ev er y situation was not possible. A state or tribe

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

exam ples of the attribution assessm ent process, each providing a ra nge of techniques to be used

1 W E STAR C o unci l

, RA B A R T and RA BAR T-like Case St udies , J u ne 2001.

2 Th is do cu m e n t sp ecifically relates to RA B A RT an d do es not a d dres s br o a der i ssues rel a t i ng t o l o ng -t er m

vi si bi l i t y

strateg i es. FLMs g e n e rally do n o t i n tend t o issu e Certificat io n s citin g sp ecific sou r ces ex cep t fo r situ atio n s inv o l v i ng

BART so urces.

1based on av ailab l e data. The exam pl es also reco mmend m o r e refined analys es that m a y require

additional data.

Third, this report includes specific infor m ati on about the current poli cy of FLM agencies

regard ing ce rtif ic ation s o f im pairm e nt (Cer tif ication), but m a kes no recom m e ndations regarding

the ce rtif ic ation proces s. This repor t m a ke s no recommendations regarding state process

following an attribution determ in ation nor exam ines options f o r perform i ng the Best A v ailable

Retrofit Technology (BART) analys is or the incorporation of BART requirem e nts into State

Im ple m entation Plans (S IPs).

Tribal Implementation

The recommendation s in this docum ent are in tended to help stat es as sess reasonable

attribution following Certification by an FLM. S ubject to the requirem e nts of the Clean Air Act,

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

responsibility for m a king reasonably attributable determ inations in response to a Certification by

an FLM when a BART-elig ible sou r ce iden tif ied by the FLM in the Cer tif ication is on the tribe ’ s

land. The regional haze rule also ex p licitly recog n izes the authority of trib es to im plem ent the

provisions of that rule on tribal lands. T hose provisions create the following fra m ework:

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

2. Federally recognized tribes m a y apply for a nd receive delegation of federal authority to

im ple m ent CAA program s (including visibility regulation), or “rea sonably severable”

elem ents of such program s. The m e chan ism f

o r this de lega tion is a Tr iba l

Im plem entation Plan (T I P ). A reason ably seve rab l e elem ent is one that is n o t integra lly

rela ted to p r ogram elements th at a r e not inc l uded in the p l an s u bm ittal, an d is consiste nt

with applicable statutory and regulatory requirem e nts.

3. Where a tribe does not seek delegation th rough a TIP, the Environm ental Protection

Agency (EPA), as neces sary and app r opriate , will prom ulgate a Federal Implem entation

Plan (FIP) w ithin a reaso n able tim e fr am e to protect air quality on tribal lands.

Accordingly, these recommendations also m a y assist a federally recognized tribe that

chooses to adopt a TIP to im ple m ent the RA BA RT provisions. In som e cases, a tribe m a y be

able to u t iliz e the r ecomm endations in m u ch the sam e way as states. In m a ny other cases,

however, the recomm endations m a y be m odified to m eet the unique situati on of the tribe and the

nature of its air program

, incl uding its m a nner of defining “rea sonably severable elem ents” and

its m e thod of dividing responsibilities between th e tribe and E P A. Because of these differences,

the recommendations that follow do not refer to tribes ev ery tim e states are referenced

.



### B. Regulatory Context

The national goal for visibility is set f o rth in the C l ean Air Act (CAA) at 42 USC 7491: “the

prevention of any future, and the remedying of any existing, impairm ent of visibility in

m a ndatory Class I Federal areas which im pairm e nt results from m a nm ad e air pollution.” The

requirem e nts apply to 156 designated Class I areas.

2As stated previously, Congress required that states provide a rem e dy for visibility

im pair m e nt that was “reasonably attributable” to one source or a sm all group of sources. (42

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

m a ke reasonable progress toward m e eting th e national goal, including requirem ents for

identifying m a jor sources of e m issions causing or contributing to visibility im pairm e nt, and

requirem e nts for the application of BART on suc h sources. E P A prom ulgated rules pursuant to

Congress’s directive to provide guidelines to the st ates on appropriate te chniques and m e thods

for im ple m e n ting the SIP requirem e nts. (40 CFR 51.302( c)). (Note: when this report refers to a

“source” within the m eaning of this regulati on, the phrase “or a sm all group of sources” is

im plied.)

The require m e nt to install Bes t Availab l e Retr of it Technology controls on existing sources

is a key elem ent of the visibili ty protection provisions in the CAA de monstrating the need to

f o cus on pollution em itte d f r om a specif i c se t of ex isting sour ces. Sources are potentia lly subject

to BART controls if they m eet the f o llowing cr ite ria :

1. A m a jor stationary source from 1 of 26 source c a t egor ies id e n tif ied in the CAA and

regulations (see Appendix A);

2. Potential to Em it (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 stati onary facility” to define a

facility that m e t the above criteria. However, to avoid any confusion about whether that term

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

term “BART-elig ible so urce” is used throughou t th is repo rt for sim ilar reasons, but the reader

should be aware that som e sections of the visibi lity rule still refer to “existing stationary

facilities.” For purposes of th e attribution discussion in th is paper, th ese term s are

interchangeable.

In 1999, EPA added two new section s to the v i si b ility ru le to address regional haze (4 0 CFR

51.308 and 51.309). Pollutan ts causing regional haze m a y be transpo r ted hundreds of m iles, and

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

focused on direct visibility im pacts of an indi vidual source or sm all group of sources. The 1999

revisions are commonly referred to as the reg i ona l haze ru le, although the 1 999 rule incorporates

the earlier requirem e nts for BART as we ll as the n e w regional haze prov isions.

To rem e dy RAVI, the regulations o u tlin e a proc ess to id entif y and contro l em issions f r om

sources that are directly impac ting visibility at specific Clas s I areas (40 CFR 51.302). Three

prim ary step s in this pro cess are:

1. The Federal land m a nager cer tif ies impairm e nt;

2. The state identifies ex isting sources that cause or contribut e to the vis i bility

im pairm e nt; and

33. The state perform s a BART analysis to de term ine what controls , if any, are required

on any existing source that m eets the BART criter i a and has b een iden tif ie d as

contributing to the im pairm e nt.

Attribution Process

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

will r e ly dur ing the a t trib ution pro ces s. That sec t i on states tha t the a t tribution m u st indicate each

[ B ART-eligible sourc e ] “ which may reasonably be anticipated to cause or contribute to

impairment of visibility .” W h ether or not the impairm e nt is “reason ably attributable ” is

determ ined by “visual observation or other t echn i que the state deem s appropriate.” (40 CFR

51.301(s)). Because the state is resp onsible for identifying sources, th is report p r ovides

recomm endations for the state to consider wh en it undertakes an attribution determ ination.

Once visib i lity im pairment is identif i ed, RAVI is addressed on a case-by-case basis. Under

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

existence of visibility im pairm e nt. However, in the context of the current regional haze rule,

states m u st also address BART requi rem e nts for regional haze (RH BART). 3 Several options

exist for add r essing RH BART. For exam ple, ra ther than req u ire a sou r ce-specific BART

em ission lim it, a state m a y choose to develop a tr ading program

, either regionally or within its

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

If a state develops a trading program , the tim e pe riod to achieve the em issions reductions

m a y be extended. As a r e sult, v i sib i lity cond ition s at a spe c if ic Class I a r e a initia lly m a y rem a in

static or even deteriorate dur ing the early im ple m entation period. During this tim e, FLMs have

indicated that the RAVI process m a y be utili zed to provide steady a nd continuing improvem e nt

in visib i lity. W ithin the context of the regi onal haze rule, this m a y be an e x am ple of a

“geographic enhancem ent.” As noted above, the recomm endations in this report apply only to

case-by -cas e application s of BART and are not in tended to ap ply to th e broader region al haze

rule such as a m a rket-based trading progr am i m plem ented as part of the 1999 rule.

A state m a y find it difficult to d e termine a source/recep tor link for RAVI difficult when

there are o t h e r sources lo cated in the area, includ ing inte rnatio nal sourc e s. However, the regional

haze ru le provisions do not alter the requirem e nt to undertake the attribution assessm ent.

State determines what contro ls, if any, are required

After the attribution determ ination, the state is required to perfor m a BART analysis to

determ ine what types of contro ls, if any, should b e placed on the source(s) found to be

contributing to the im pairm e nt. The following factors affect the BART determ ination:

1. Available technology;

2. Costs of compliance;

3. Energy and non air quality envir onmental im pacts of com p liance;

3 The R H B A R T p r o v i s i o n wa s rem a nded t o EPA by t h e U. S. Court of App eals as a resu l t o f th e ru li n g in Am e r ic an

Corn Gr owers Associ ation v. EPA , No . 99 013 48 ( D C Cir

. May 2 4 , 2 002 )

44. Re m a ining useful life of the source; and,

5. Degree of improvem e nt that can be anticipated to result from the use of the controls.

As noted above, this report does not provide gu id ance reg a rding state process followin g an

attribution determ ination nor does it exam ine op tions f o r im plem enting th e BART requirem ent.

56

### II. FEDERAL LAND MANAGER CERT IFICATION OF IMPAIRMENT

The Federal land m a nagers m onitor visibili ty through a nationwid e m onitoring network,

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

IMPROVE network has recen tly been expanded and the FL Ms 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 w e ll as trends occurring over tim e to identify

areas where visibility is not im proving.

The FLMs generally will use a screening proces s to identify Class I areas that m a y be

affected by RAVI. This screen ing process will b e influenced by the approach the s t ate relies

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

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

No distinction exists between em ission re ductions needed to address RH BART and

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

im pair m e nt.

2. Trading Program under 51.308

The FLMs antic ipate tha t the f o llowing screen in g criteria m a y be appropriate, but will not

m a ke a final decision until a 308 trading program has been developed. Th e screening criteria

associa t ed w ith th is app r oach m a y be sim ilar to th e screening criteria asso ciated with the trad ing

program option under 51.309. Howe ver, the FL Ms have indicated that the screening criteria

ultim ately s e lected for 5 1.308 will d e pend on how the trad in g program is structured, the selected

em issions cap, and other aspect s of the trading program

.

Potential Sc reening Pro c ess Criter ia:

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

area are not decreasing. 4

(ii) One or m o re BART-eligible sources of SO 2 , NO

x , VOC, PM 10 , etc., a r e lo cated

within 100 m iles of the m a ndato ry federal Class I area.

(iii) The BART-elig ible sou r ces iden tif ie d in (ii) are not already well-con t ro lled for

pollutants that contribute to visib i lity im pairm e nt.

3. Milestones and Backstop Trading Program under 51.309

The FLMs plan to sign a Mem o randum of U nderstanding (MOU) with the particip ating

states to def i ne the sc ree n ing cr ite ria the FLMs will us e to c e r tif y im pairment.

4 The dec r ease of a pol l u t a nt ( o r seco n d ary s p eci es of t h at p o l l u t a nt ) w o ul d be m easure d f r om t h e be gi n n i ng o f t h e

market or em is sions tra d ing program

, and s u ch a t r adi n g pr og ram wo ul d t a ke a l o n g t i m e ( 1 0 t o 1 5 y ear s) t o reac h

th e lev e l o f redu ctio n th at “m eets” BART . T h e decrease would be teste d over th e f i r s t 5 t o 1 0 year s. A ver y qu ick

tim e fram e for red u ctio ns wo u l d ne gate t h e n eed fo r R A BA RT criteria.

7Screening P r ocess criteria: 5

(i) Sulfate levels in the Clas s I area are n o t decreasin g.

(ii) One or m o re BART-eligible sources of SO 2 are located within 100 m iles of the

m a ndatory federa l Class I are a

. (iii) The BART-elig ible sou r ces iden tif ie d in (ii) are not alrea dy well-controlled for

SO 2 (85% or better SO 2 c ontrol for co al-fir ed utility boilers).

Goal: For F L Ms to complete the cer tification process between 2006 and 2008. 6

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

reduction estim ates, shape of the declining em i ssion cap, inclusion of sources that w e re not

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

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

they m u st be addressed in the two tradi ng program s described above because em ission

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

anticipate certifying im pairm e nt under these circum stances but do intend to notify the state as

part of the S I P developm ent process if concerns arise regarding “hot spot ” im pacts from sources

that m a y directly affect specific Clas s I areas.

Note: BART for regional haze has been addressed for SO 2 under this option. Regional haze

BART for NO

x and PM will be addr essed in S I

P revis i ons th at are due in 2008.

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

im pair m e nt is neces sary, the FLM will send an official Certification to the state. The

Certif ica tion will gene rally inc l ude th e f o llowing inf o rm ation:

1. Class I area(s) im pacted;

2. Basis of certification (photographic docu m e ntation, m onitoring, m odeling, etc.);

3. Type of i m pairm e nt certified: plum e impact, or layered or unifor m haze;

4. Pollutant(s) of interest; and,

5. Prelim inary identification of source( s) believed responsible for im pact.

FLM, State, and EPA r o les in RA BART

FLMs are responsible for certifying impairm ent. The Certification dem onstrates the F L M’s

determ ination that there is evidence of visibil ity impairm ent from one source or a sm a ll group of

sources.

5 W i t h i n t h e co nt ext of est a bl i s he d re gi o n al m i l e st ones f o r SO 2 a n d a bac k st op t r adi n g p r og ram

, t h e FL M s ha ve sai d

it is ap propriate to u s e t h e fo l l o w ing screen in g pro cess i n m a ki ng t h ese r ecom m e ndat i o ns as pa rt of t h e C e rt i f i cat i on.

Vol unt a r y Em issi ons R e d u ct i o n Pr o g ram fo r M a j o r I n d u st ri al So urces o f S u l f ur Di o x i d e i n Ni ne West er n St at es an d

A Back stop Trad ing Pro g ram. An Ann e x to th e Rep o rt of t h e Grand Can y o n Visi b ility Tran spo r t C o mmissio n

. Su

bm it t e d by t h e West er n R e gi o n al Ai r Pa rt ners hi p t o t h e U.S

. E nvi ro nm ent a l Pr ot ect i o n Age n cy

, p. 6 1

, Sept em ber

29, 2000.

6 This goal will not i n a n y way restrict the abi lity of the FLMs to ce rtify im pair m e nt at a later date i f it is necessary

to fu lfill th eir statu t o r y o b lig at io n s

. ib i d

.

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

f acilities tha t “em it an air pollu tant w h ich m a y be reasonab l y anticipated to cause or contribute to

any visibility im pair m e nt” in that Class I area, and (2) for sources subject to BART, the state

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

in plac e, the state m u st establish a BART lim it in the SIP.

EPA has two m a jor resp onsibilities f o r the RA BA RT requirem e nt. First, in the states that do

not have a S I P in place to address the RA BART requirem e nts, but where the program is

im ple m ented through a FIP, EPA wi ll conduct th e BART ana l ysis and establish any BART

em ission lim its. Second, for the states with SIP s that includ e RA BART regulations, EPA will

provide federal enforcement of stat e- estab lished BART em ission lim its.

910

### III. THE ATTRIBUTION DETERMINATION PROCESS

This section recomm ends a process fo r the s t ate to f o llow in or der to com p lete an a t trib ution

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

The Certif ic ation f o cuse s on the ex is tence of vis i bility im pair m e nt. W h ile the FLM may

identify sources or ev en sources areas that contr i bute to the Certif ica tion

, the f o rm al

identification of sources is a state responsibility.

The state should m a ke a detailed review of th e data supporting the Cert ification to determ ine

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

specific sou r ces or sou r ce areas, the s t ate m a y req u est th at th e FLM perform m o re detailed

analyses to further substantiate the imp airm ent set forth in the Certification.

If

the sta t e d e term ines su f f i cient inf o r

m ation exists to proceed, the state should begin the

process by: (1) evaluating which sources are BA RT-eligible, and (2) reviewing the im pairm e nt

inform ation provided by the FLM in support of the Certification.

1. Evaluate which sources are BART

- e l i g i b l

e

Congress and EPA established criteria to dete rm ine which sources are subject to BART. The

catego r ies o f sources su bject to BART are lis ted in 40 CFR 51.301, the definition of “exis ting

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

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

BART proc ess by examining the potential sour ce em issions and the dates of operation.

The criteria for determ ining if a source is BA RT-eligible m a y be com p licated if a source has

multiple un its tha t were construc ted at dif f e rent tim e s and a state m a y be unable to d e term ine if a

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

in im ple m enting the 1980 rule. 7 In addition to the 1980 guidelines for determ ining BARTeligibility, E P A also has proposed gu idelines that include criteria for determ ining if a source is

BART-eligible. 8 These guidelines are expected to be re-proposed in 2004, prom ulgated in 2005

and codified at 40 CFR Part 51 as A ppendix Y. Th e state sh ould refer to both sets of guidelines

to dete rm ine if a source is BART-eligible, a lt hough the proposed guidelines are not binding until

final prom ulgation occurs.

When using the applicability criteria in Appendix Y, the st ate should look for infor m ation

readily ava i lable f r om existing sta t e d a ta such as p e rm itting his t ory, em ission invento r y

, or other

sim ilar databases.

7 U.S. Env i ro nmen tal Pro t ectio n Ag en cy, Guid elin es fo r Determin ing Best Ava ilab l e Retro fit Techn o l ogy fo r C o a l

fired Po wer p l a n s and Oth e r Existin g S t a tion a r y Fa cilities, EPA-4 5 0 / 3-8 0 -009 (b ), Office of Ai r Qu ality Plann i ng

and Standa rds

, Research Tria ngle Pa rk, N. D., N ovem b er 1 9 8 0 ( 1 98 0 B A R T G u i d el i n es ).

8 66 FR 381 08 Proposed Guidelines for Best Av ailable Retrofit Technol ogy (BART) (Ju l y 20

, 20 01). EPA will repr o pose t h ese gui del i n es as a resul t of t h e re m a nd i n A m erican Cor n Gr owers v. E P

A

.

111. If a database of Potential To Em it (PTE) is not available, state em ission inventories

can be a useful screening tool to determ ine whether a source m eets the size criteria

(for exam ple, identify all sources with em issions greater than 100 tons/year of

visibility im pairing pollutants). Operati ng perm it applications m a y also contain

inform ation about the PT E of a source, or individual units within a source.

2. State business records may be useful to determ ine when the f acility was co nstruc ted

or when it comm enced operation. Newspaper arti cle searches or a detailed historical

review of each source m a y also provide useful infor m ation.

3. The state should require the source to provide inform ati on regarding the construction

dates of individual em ission units.

4. Institutional m e mory within the air ag ency can b e invalu able if records of

construction dates of m a jor em ission units are not available, although m e mories will

not withstand legal challenges like hard docum entary data.

5. New Source Review per m itting reco rds m a y be usef ul to ide n tif y new units th at wer e

constructed after 1977, determ ine the P TE of the source, or determ ine if

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

modifications at a source do not affect a pplicability 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 stat e should look in the vicini ty of all the sources

suspected of causing or contributing to the im pa irm e nt identified in th e Certification. If no

BART-eligible sourc e s exist, an at tribution assessm ent is unnecessary.

2. Review Support Information in the FLM Certification

Initially, the state should acquire all the supporting inform ation the FLM used in the

Certif ica tion and indepen d ently ev alu a te th e da ta. This initia l r e view will h e lp the sta t e

determ ine if the inform ation is sufficient to support a reasonable attr ibution determ ination.

The state should consider the t ype of im pair m e nt in the Cer tif ication : (a ) plum e im pact

visibility im pairm e nt, (b ) unif o rm haze vis i bil ity im pairm e nt, or (c) laye re d haze vis i bility

im pairm e nt:

(a) Plum e i m pa ct is the im pairm e nt addre ssed by the original visibility protection

program under which the state m u st m a ke a reasonable attribution

determ inatio n before pro ceeding to the BART analysis (40 CFR 51.301-

51.307).

(b) In som e instances, unifo rm haze m a y be thought to be source-specific haze

from a BART-elig ible so urce. If a s t ate can su ccessfully demonstrate th ere is a

source em itting any air pollutant that m a y reasonably be anticipated to cause or

contribute to any im pairm e nt of visibilit y, the state should c onsider that portion

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

consider the source to be subject to B A RT.

(c) The v i sib ility im pairm e nt m a y al so be define d as laye red haze, a cond ition that

results when aerosols are “trapp ed” u nder stagn a n t air m a ss conditions.

12Once the FLM has issued a Certification, the responsibility f o r the attribution assessm e nt

shifts to the state. If a state doe s attribute im pairm e nt to a source, the state m u st be able to defend

its finding that one or m o re BART-eligible so urces did cause or c ontribute to visibility

im pair m e nt in the Class I area. However, the Ni nth Circuit Court of Ap peals es tab l ished a low

thresho l d for unaccep tab l e vis i bility impairm ent. 9

3. Evaluate Existing Data not include d in the Cer tif ication

When analyzing supporting data, the state should determ ine if additional infor m ation exists

that was no t available to the FLM. Exam ples in clude special projec t cam era stud ies o r am bient

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

PSD perm it applic ant, 10 or the university. In certain situ ations, another ag ency m a y have

previously collected inform ation that could be used in an attribution determ ination. 11 These data

should be reviewed to determ ine whet her they support the FLM Certification.

To conclude this portion of the attrib ution pro ces s, the state m a y wish to prepare a report th at

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

docum ent the state’s findings and c o nc l u s i ons a b out i t s de c i s i ons

. The report would address two

questions:

First, the report would assess whether any BA RT-elig ible sou r ce(s) exis t th at poten tia lly

contributed to visibility impairm ent as described in the Certifi cation. The report would contain a

prelim inary determ ination of whether the source s are BART-eligible and, if possible, analyze

each source’s relativ e co ntribu tion to the im pa irment (bas ed o n available s ource/recep tor

inform ation).

Second, the report would assess the im pair m e nt data. This assessm ent would include the

state’s ev alu a tion of the supporting d a ta in the Ce rtification and an anal ys is of any addition a l

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

along with any recomm e ndations for strategies to obtain the missing inform ation. The state m a y

use these recomm endations to determine the n e xt steps in the at tribution process.

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

attribution assessm ent and m a kes a determ ination that the data does or does not indicate a source

subjec t to B A RT, the attribution pro cess is com p lete. Howev e r, if the s t ate dete rm ines the data is

insufficient to com p lete an attribution assessm en t, the evaluation process ends, and the state m a y proceed to the next s t ep —identification of data g a ps and the studies n ecessary to ob tain this

inform ation.

9 See Cen t ra l Ariz. Wa ter Conserva tion Dist v. EPA

, 99 0 F. 2d 1 5 3 1 ( 9 t h C i r. 19 9 3 ); See a l so di scussi on i n RA BAR T

and RA BA RT-like Case St udies

. 10 For ex am p l e, a PSD p e rm it a p p lican t m a y be req u i red t o ob tain p r e-app l i catio n am b i en t air q u a lity in fo rm atio n

, and t h at i n f o r

m at i on m a y not have bee n av ai l a bl e t o or k n o w n t o t h e FL M

. 11 I n Washi ngt o n st at e, t h e pl u m e of a de fu nc t cop p e r sm el t e r has been t r ac ed by i t s arse ni c de po si t i on. I f t h i s were

an activ e facility, th is groun d tracin g of the plu m e co u l d b e u s ed to supp ort a so urce/recepto r con n ection in an

attrib u tion d e term in atio n

.

134. Identify data gaps and necessary studies to fill the gaps

As part of the data review, th e state should note gaps or incons istencies in the available data.

Exam ples of data gaps and inconsistencies m a y include:

1. Missing photos in a sequence of photos,

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

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

4. IMPROVE monitor data m i ssing at critical periods,

5. Lack of association betw een the sou r ce’s em issions and m oni tored data at the receptor,

6. Special studies perform e d dur ing a tim e when a source th at potentially contributed to

the im pairment was not operating,

7. Contem pora n eous studies with contradi ctory results that cannot be explained,

8. Am bient studies that use naturally occurri ng tracers and, when th e suspected source(s)

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

indicated by the am bient study results,

9. Tracer studies where th e tracer was n o t found at the anticipated recep tors,

10. No explicit exploration of whether wildfire or other natu ral events signif i cantly caused

or contributed to one or more of the im pair m e nt episodes,

11. Other differences or inconsis tencies in the available data.

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

to obta i n the m i ssing inf o rm ation suf f i cient to co mplete an a t tribu tion a s s e ssm ent. The sta t e

should design potential studies a nd determ ine the resource needs of those studies. The am ount of

work necess a ry will dep e nd on the availab i lity o f the information and ho w critical th e

inf o rm ation is to th e attr ibution ass e s s m e nt. S ection IV provides technica l criteria and exam ples

of the technical pro cess the state m a y c onsider at this point in the process.

5. Consultatio n Process

The W E STAR review of previous RAVI a ssessm e nts de m o nstrated the im portance of

including all stakeholders in the design of a ny data collection plans or m odeling protocols.

Com p eting studies can be very inefficient, expensive, and ti m e -consum i ng, and ultim ately m a y not help the state m a ke a final decision.

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

protocols, disagreem ents m a y be resolved be fore the state or s ource con tin ues with any

additional studies. The state should consider invo lving th e f o llowing stakeholders in the

consultation process:

1. Affected so urces

2. Neighboring states and tribes

3. Environm ental Protection Agency

4. Federal land m a nagers

5. Local environm ental groups

6. Local perm itting agencies

147. Local governm e nt representatives

6. Completion and Review of Addition a l Visib ility Studies

When the addition a l visibility stud ies, if any, are com p lete, the state sho u ld evalu a te the

resulting data. The techn i cal st af f m a y wish to f i naliz e the re port and m a ke recomm endations

based on the scien tif ic aspects of the data.

7. Final Determination of Reasonably Attributab le Visibility I m

pairment

The state m a y reach any one of the following con c lusions :

1. The im pairm e nt certif ie d by the FLM is re asona bly attribu t a b le to the id entified BARTelig ible sou r ce(s) f o r sp e c if ic polluta nts;

2. The im pairm e nt certified by the FLM is re asonably attributable to the source(s)

identified by the FLM and additional sour ces no t identified by the FLM for specific

pollutants;

3. There is inadequate data to support a dete rm ination that the impairm ent is due to the

source(s) identified by the FLM;

4. The im pairm e nt is reas onably attrib utable to oth e r adjacent BART-eligible sources for

specific po llutants ; or

5. The im pairm e nt certified by the FLM is not rea s onably a ttr ibutable to a BART-eligible

source.

1516

### IV. GENERAL PRINCIPLES AND CRI TERIA

This section provides general prin cip l es and tech nical criteria fo r the sta t e to consid er af ter

receipt of an FLM Certification. After receipt of such a Certifi cation, the state m u st determ ine

whether BART-eligib le sources “ e mit any a i r po llutan t which m a y reason ably be an tic ipated to

cause or contribute to any visibi lity impairm ent in any” Class I area. W h ile these p r inciples and

technical criteria should generally be useful and applicable, each state m u s t decide what is

required to support its individual determ ination.

Part A contains general princi ples to guide the developm ent of a conceptu al fram ewor k for

the attribution assessm ent process. Part B contai ns evaluatio n criteria to guide the decisions

regarding a technical fram e w ork for the attribution assessm ent proces s.

Conceptually, the statutory fram ework for an attribution determ ination (“m a y reasonably be

anticipated to cause or contribu te to visibility im pairm e nt”) should be the general principle

guiding the attribution process. EP A m a intains, a nd the Ninth Circui t agreed, that an affir m ative

attribution decision is possible ev en with consid e r able un cer tainty and a low triggering threshold.

However, each state sho u ld decid e individually wh at is requ ired to supp ort its determ ination.

A. General Principles o f

th e Attribu tio n P r o c e s s

Attribu tion process c r ite r ia add r ess f actors lik ely to influence the perform a nce of the state

attribution assessm ent. These crite ria provide param e ters to define and im plem ent the a ttr ibution

assessm ent process given resource constraint s, legal considerations, adm i nistrative

decisionm aking requirem ents, and other relevant factors. The general principles include the

following:

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

relies on existing data with a m i ni m u m of additional analysis ( see se ction III). If

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

process between affected states and trib es, identified sources, Federal land m a nagers,

EPA, and the general public. Past experi en ce has shown that com p eting technical

studies often result in an unnecessarily expensive and unduly com p licated process.

2. Attribution assessm ents should be technically and legally defensible.

3. Attribution assessm ents should be accom p lished at a reasonable cost and within a

reasonab l e tim e fram e.

4. Attribution assessm ents shoul d be no m o re com p l e x than necessary, recognizing that

the circum stances surrou nding a Certification m a y vary greatly. State or tribal agency

staff should be capable of perfor m i ng the attribution assessm ent and m a king the final

determ ination, although contractors m a y be used for certain types of m o deling or

monitoring.

5. Reasonably attributable visibility im pairm e nt can only be identifie d if a source/receptor

17links a potentially BART-eligi ble source or sm all group of sources to a Class I area.

B. Technical C r iter i a

The technical criteria ad dress th e appropriateness of availabl e analy tical techniques from a

scien tif ic pe rspective. T h e rule d e f i nes visib i l ity im pair m e nt to m ean “any hum a nly perceptible

change in visibility (light ex tinction, visual range, c ontrast, coloration) from that which would

have existed under natural conditions.”

The technic a l cr ite ria in clude th e f o llowing:

1. The BART s ource m u st em it an air pollutant that m a y reasonably be anticipated to

cause or contribute to any of the im pairm e nt. The visibi lity-impairing pollutants of

concern m u st be identified.

2. The attribution assessm ent should address th e unique visibility im pair m e nt in the FLM

Certif ica tion f

o r the Clas s I are a

. Som e factors to consider include:

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

b. Frequency: how often episodes of impairm ent occur;

c. Geographic extent: how much of the Cl ass I area is affected b y the im pairm e nt;

d. Magnitude: how m u ch visibility im pairm e nt is due to the source’s em issions; and

e. Tim e of occurrence: in cl uding tim e of day and tim e of year.

These factors need to be considered togeth er, b e cause they affect each oth e r. An

infrequent o ccurrence of a larg e m a gnitude ep iso d e m a y m e et the criteria. A frequent

occurrence of a sm all magnitude episode m a y also m eet the criteria. The FLM m a y provide inform ation about thes e criteria in the Ce rtification, and the state should review

this inf o rm ation as a sta r ting poin t f o r its assessment. The state m a y consider other

factors and data, as appropriate.

3. The attribution assessm ent should identify th e distance from the source to the Class I

area. This distance will affect the cho i ce of tools appropriate fo r characterizing the

specific source’s im pact on visibility im pair m e nt.

4. To the extent possible, the attribution a ssessm ent should be quantitative. Under certain

circum stances, qualitative inform ation, such as photographs or tim e-lapse video of

distinct plum es or source-specif ic haze events, may be adequate.

5. The state should use as m a ny approaches or indicators of im pair m e nt as practicab le

12 B a sed o n pre v i ous cases, m o st im pai r m e nt epi s o d es a r e relativ ely sh ort—lastin g on e t o sev e ral ho urs—wh ich m a y

affect m oni t o ri ng an d ot he r as sessm ent techniques

.

18rather than relying on a single m e thod. The assessm e nt m a y r e ly on air monitoring and

modeling techniques and other supporting scie ntific data. Consistency between source

and observational techniques strengthens the analysis. 13

6. The state should consider the leve l of uncertainty in the a ssess m e nt.

7. EPA guideline m odels should be used when ever practicable. When other models are

used, additional technical discussi ons with EPA may be necessary.

C. Examples o f

the Attr ibu tion Process

The state should consult this se ction after evaluation of the FL M Certification. At this point,

the state will already have decided w h at gaps n e e d to be f illed to com p lete the attr ibution

assessm ent.

The recommendation s in this report recognize th at each attribution de term ination will be

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

not possible to recomm e nded a sing le techn i cal approach for a stat e to follow. A combination of

monitoring and m odeling techniques is usually appropriate, but inst ead of attem p ting to cover all

possible combinations of technique s, this report provides five se lected s c enarios. Th e general

principles and technical criteria iden tified in sections A and B are in corporated in to these

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

individualized tech n i cal procedure.

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

techniques that m a y be consider ed and/or substitute d for the techniques in the scenarios (see

Section V).

Scenario 1 : Limited data/one source

Data input: Sm all am ount of existing data (i.e ., one IMPROVE site with data for 2-3 years)

Evidence of “local” im pact.

Lim ited m e teorological data.

Where it is likely that on e m a jor source cont ributes to im pairm e nt, a combination of the

following techniques is suggested:

1. Exam ine IMPROVE data, in cluding quality assurance

2. Look at extinction budget to identi fy visibility-impairing pollutants

3. Perform som e analysis of when the episodes are occurring

4. Perform a si mple back trajectory an alysis using a m e thod such as HYSPLIT

5. Look at relationships be tween particulate species

6. Exam ine source em issio n s data for co rrelations with am bient m onitoring

7. Perform dis p ersion m odel based on data a nd capabilities such as VISCREEN or

CALPUFF Lite

13 W E STAR C o unci l

, RA B A R T and RA BAR T-like Case St udies , J u ne 2001.

198. Exam ine other data such as source owne r data, deposition data, or photographic

evidence

The results of this initial analysis m a y not alwa ys be conclusive. This level of review m a y be

adequate if a source/receptor relationship can be identified. H o wever, if the results are

inconclusive, m o re data and/or m o re refined an alyses m a y be necessary and m a y help the state

reach an attribution d e term ination. The state sho u ld consid er how the availab l e data and choice

of techniques m i ght affe ct its abil ity to asses s the effectivenes s of the controls in rem e dying the

im pair m e nt.

It m a y be efficient to loo k ahead to the da ta and analytical need s of a potential BART

analys is. For exam ple, the state m a y wish to use a m o re refined m odel that would be useful for

both attribution and rem e dy assessm ent.

Assum i ng there is a two-year tim e period 14 to collect data and analyze additional information,

the followin g techniqu es also can b e considered:

1. Better source characterization, including m easurem ent of e m i ssions of trace elem ents

(source profiles) for use in CMB m o deling

2. Met m onitoring or m odeling

3. Ca m e ra site, possibly with tim e-lapse video

4. Additional aerosol m onitori ng, episodic or saturation

5. In plum e trajectory by aircraft, if available

6. Fine tim e-resolved optical m onitoring, such as ne phelom e ter, transm issometer,

aethelom e ter com b ined with pollutant m onitoring (e.g., SO 2 , NOx, real tim e PM) for

monitoring of episodes

7. More refined chem ical visibility m o del, such as C A LPUFF

8. Repeat initial techniques with new data

The technic a l cr ite ria ar e lis ted be lo w, f o llowed by t echniques that m a y be used to obtain results

about the criteria.

1. The im pairm e nt m u st be related to em i ssions fromspecific sources. The visibilityim pairing pollutants of concern should be identified.

Techniques : Back trajectory, species relations hips, relationship between source

em issions and am bient monitoring, dispersi on model refined by cam era, aerosol, in

plum e, optical m onitoring, a nd refined dispersion models.

2. The attribution assessm ent m u st address the unique visibility im pair m e nt certified by

14 The 1999 re gional haze rule revise d the re quirem ents for general plan re quirem ents for visibility protection. As a

result, plan re visions a r e requi r ed once e v ery five y ear s, rath er tha n t h e L o n g Te rm Strategy revie w re q u ir em ent of

every t h ree y e a r s. If an FLM c e rt i f i e s R A VI at l east 6 m ont hs pri o r t o a pl an revi si o n

, se ct i on 51 .3 0 2 re qui res t h at

the State Pla n revision a d dress suc h Ce rtifications . Gi ve n thi s 5-year cycle, we have pres um ed f o r t h is re po rt that a n

attrib u tion an alysis sh ou l d take abo u t t

w o years to co m p lete in o r d e r to all o w ti m e fo r th e BART eng i n e ering

analysis, if nee d ed.

20the FLM for the Class I area. Som e factors to con s ider includ e:

a. Duration : so urce’s leng th of effect per episode

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

b. Frequency: how often the im pairm e nt occurs

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

c. Geographic extent: how much of the Class I a r ea is im paired

Techniques : Monitoring, dispersion model, addi tional data on deposition and other

studies

d. Magnitude: how m u ch impair m e nt is due to the source

Techniques : Dispersion model, receptor m odel

e. Tim e of occurrence

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

3. Uncertainty of results : Each analy t ical m e thod will have its own level o f uncertain ty

. These individual uncertainties should be kept in m i nd as outputs ar e compared within

the over a ll a ssessm ent. If the results of different technique s are the sam e or sim ilar

(within the u n certainties of the techniques), th en the overall level of uncertainty is likely

to decrease.

Scenario 2: Moderate d a ta/multip l e sources

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

more years )

Evidence of an increasin g trend in su lfate

Meteorological data likely from several stations

Assum i ng the Certification identi fies m u ltiple B A RT sources of different types within a 100-

m ile radius, a com b ination of the following techniques is suggested:

1. Exam ine IMPROVE data, in cluding quality assurance

2. Look at extinction budget to identi fy visibility-impairing pollutants

3. Perform som e analysis of when the episodes are occurring

4. Perform multiple back trajectory anal yses us ing a m e thod such as HYSPLIT

5. Look at relationships be tween particulate species

6. Exam ine source em issio n s data for co rrelations with am bient m onitoring

7. Perform dis p ersion m odel based on data a nd capabilities such as CALMET/CALPUFF

8. Exam ine other data such as source owne r data, deposition data, or photographic

evidence

9. W i nd fields/Synoptic analyses

10. Com p arison of different episodes

11. UNMIX, PMF and/or CMB

2112. Analysis of any regional m odeli ng already conducted, such as CMAQ

13. Long term visual m onitoring, such as cam era data or transm issom e ter data, com p ared to

m e t data to identify quadrants of concern

The results of this initial analysis m a y not be conclusive. This level of review m a y be

adequate if a source/receptor re lationship can be identified.

However, if the resu lts ar e in conclusive, m o re data and/or m o re refined analyses m a y be

necessary and m a y help the state reach an attribution determ ina tion. The state should consider

how the available d a ta m i ght affect th e ability to a ssess th e ef f ectiv eness o f the contro ls in

rem e dying the im pairm e nt. It m a y be efficient to l ook ahead to the data and analytical needs of a

potential B A RT analysis. For example, the stat e m a y wish to use a m o re refined m o del that

would be useful for both attribution and rem e dy assessm ent.

Assum i ng there is a two-year tim e period to coll ect data and analyze additional information, the

following techniques also can be considered:

1. Em issions inventory

2. Analysis of a nested dom a in within the reg i onal model

3. Source profiles (natural tracers)

4. Additional source m easurem en ts, such as stack testing

5. Met m onitoring or m odeling

6. Ca m e ra

7. Additional aerosol m onitori ng, episodic or saturation

8. Monitor i ng to m easure a ddition a l pa r a m e ters, suc h as precu rs ors, amm onia and

oxidants

9. In plum e trajectory by aircraft, if ava ilab l e, to ch aracterize p l um e chem istry

10. Fine tim e-resolved optical m onitoring, such as nephelom e ter, transm issometer,

aethelom e ter com b ined with pollutant m onitoring (e.g., SO 2 , NOx, real tim e PM) for

monitoring episodes.

11. Repeat initial techniques with new data

The technic a l cr ite ria ar e lis ted be lo w, f o llowed by t echniques that m a y be used to obtain results

about the criteria.

1. The im pairm e nt m u st be related to em i ssions fromspecific sources. The visibilityim pairing pollutants of concern should be identified.

Techniques : Back trajectory, species relations hips, relationship between source

em issions and am bient monitoring, dispersi on model refined by cam era, aerosol, in

plum e, optical m onitoring, a nd refined dispersion models.

2. The attribution assessm ent m u st address th e unique visibility im pair m e nt certified by

the FLM for the Class I area. Som e factors to con s ider includ e:

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

22Technique s: Monitoring, dispersion mode l, back trajectory, cam e ra

b. Frequency: how often the im pairm e nt occurs by source

Techniques : Monitoring, dispersion model, back trajectory, receptor m o del, cam e ra

c. Geographic extent: how much of the Class I a r ea is im paired

Techniques : Monitoring, dispersion model, a dditional data on deposition and other

studies

d. Magnitude: how m u ch impairm e nt is due to each individual source

Techniques : Dispersion model, receptor m odel

e. Tim e of occurrence

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

Scenario 3 : No IMPROVE data, Certifica t ion by modelin g evidence

Data input: Detailed m o deling of a source a nd specific visibility im pairment using a

CALPUFF r un, or detailed (neste d) run of regional m odel.

Detailed emissions data and m e teorologi cal data were input to the m odel.

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

“representative site”)

Som e optical data, photographic data, and lim ited aerosol data from other

networks

Assum i ng the Certification points to a specific so urce, a com b ination of the following techniqu es

is suggested:

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

modeling dom a in) to better understand the re gional conditions and com p a r e with m odel

outputs of the unique effect

2. Review the model’s outputs on “high im pact” days and com p are with actual

m e teorological data of the area (to confirm transport, inversi ons, or other aspects of the

model which m a y create the unique impact)

3. Review source em issions data used in the m odel

4. Review other m onitoring data (optical m easurem e n ts, deposition data, ozone data, etc.)

to collaborate m odel predictions

5. Exam ine any photographic evidence

The results of this initial analysis m a y not alwa ys be conclusive. This level of review m a y be

adequate if a source/receptor relationship can be identified. B ecause no visibi lity -spec i f i c par tic le

monitoring is available in this case, the key to attribution is to dete rm ine that the source

em issions are indeed reaching the Class I area on days when impairm ent exists. If the r e sults are

inconclusive, m o re data and/or m o re refined an alyses m a y be necessary and m a y help the state

reach an attribution d e term ination. The state sho u ld consid er if the avai lable data and modeling

inf o rm ation supplied with this Cer tif ication could be used to d e term ine the ef f ectivenes s of the

23controls in rem e dying the im pairm e nt. It m a y be e fficient to look ahead to the data and analytical

needs of a potential BART analysis if additio n a l or dif f e rent modeling will be ne cessa ry.

Additiona l d a ta co llec tio n and analys es that m i ght be considered:

1. Better source profiles or newer em issions inform ation

2. Met m onitoring or m odeling

3. Ca m e ra

4. New aerosol m onitoring (e pisod i c or satura tion )

5. In plum e trajectory by aircraft, if available

6. Repeat in itial analy tical techniques w ith new data

The technic a l cr ite ria ar e lis ted be lo w, f o llowed by t echniques that m a y be used to obtain results

about the criteria.

1. The im pairm e nt m u st be related to em i ssions fromspecific sources. The visibilityim pairing pollutants of concern should be identified.

Techniques : Confir m model inputs, co m p are with any new particle m onitoring at Class I

area. Exam ine perform a nce of the mode l, and perform uncertainty analyses.

2. The attribution assessm ent m u st address the un ique visibility im pair m e nt certified by the

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

a. Duration : so urce’s leng th of effect per episode

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

b. Frequency: how often the im pairm e nt occurs

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

c. Geographic extent: how much of the Class I a r ea is im paired

Techniques : New m onitoring and exam inati on of dispersion model outputs,

additional data on deposition and other studies

d. Magnitude: how m u ch impair m e nt is due to the source

Techniques : Dispersion model

e. Tim e of occurrence

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

Scenario 4 : Direct Photographic Evidence

Data input: The FLM certif ied im pairm e nt in a wilderness area based on photographic

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

in a valley located in the wildernes s area when th e wind is fro m the east. A

series of photographs during two epis odes showed a distinct plum e that

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

m iles east of the wilderness area. The photographic series showed that the

plum e traveled into the wildernes s area.

Aerial photos during one episode also s how a distinct plum e that travels into

the wilderness area. Th e wildernes s ar ea does not have an IMP R OVE site. A

Class I area located 50 m iles to the nor th has an IMPROVE site that was

repres entative for the area. This m o nitoring site does not show decreased

visibility on the days wh en haze was photographed in the wild erness valley.

Assum i ng the Certification identifies one BART source, the following steps are suggested:

1. Review the photographic evidence and dete rm ine whether the photogr aphs show a clear

connection between the source and the h aze docum ented in the wildern e s s area.

In this example, the pho tographic eviden ce showed a clear co nnection b e tween the

source and the haze in th e wildern e ss ar ea. Qualitative information, su ch as

photograph i c evidence that establish e s a source/recepto r link, is allowed u nder EPA

regulations that define reas onable attribution as determ in ed by “visual observation or

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

The results of this initial analysis m a y not always be conclusive. If the photographic

evidence is not com p elling, the state m a y reach a different co nclusion. Th e state m a y determ ine

that the im pairm e nt, as docum ented by the FLM, was not reasonably attributable to the source

identified in the Certification. The FL M would th en need to gather additional inform ation to

support a Certification. Alte rnatively, if the state determ ines that the source m a y be causing the

haze but the qualitative evidence is n o t quite suffi cient to determ ine attrib ution, th e state cou l d

initiate a da ta collection ef f o rt to pro v ide better infor m ation regarding vi sibility im pairm e nt at

the Class I area. Such techniques m a y include:

1. Better source characterization, including m easurem ent of e m i ssions of trace elem ents

(source profiles) for use in CMB m o deling

2. Met m onitoring or m odeling

3. Additional aerosol m onitori ng (episodic or saturation)

4. In plum e trajectory by aircraft, if available

5. Fine tim e-resolved optical m onitoring, su ch as nephelom e ter, transm issometer, or

aethelom e ter com b ined with pollutant m onitoring (e.g., SO 2 , NOx, real tim e PM) for

monitoring of episodes.

6. More refined chem ical visibility m o del, such as C A LPUFF

The technic a l cr ite ria ar e lis ted be lo w, f o llowed by t echniques that m a y be used to obtain results

about the criteria.

1. The im pairm e nt m u st be related to em i ssions fromspecific sources. The visibilityim pairing pollutants of concern should be identified.

Techniques : Back trajectory, species relations hips, relationship between source

em issions and am bient monitoring, dispersi on model refined by cam era, aerosol, in

25plum e, optical m onitoring, a nd refined dispersion models.

2. The attribution assessm ent m u st address the unique visibility im pair m e nt certified by

the FLM for the Class I area. Som e factors to con s ider includ e:

a. Duration : so urce’s leng th of effect per episode

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

b. Frequency: how often the im pairm e nt occurs

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

c. Geographic extent: how much of the Class I a r ea is im paired

Techniques : Monitoring, dispersion model, addi tional data on deposition and other

studies

d. Magnitude: how m u ch impair m e nt is due to the source

Techniques : Dispersion model, receptor m odel

e. Tim e of occurrence

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

3. Uncertainty of results : Each analy t ical m e thod will have its own level o f uncertain ty

. These individual uncertainties should be kept in m i nd as outputs ar e compared within

the over a ll a ssessm ent. If the results of different technique s are the sam e or sim ilar

(within the u n certainties of the techniques), th en the overall level of uncertainty is likely

to decrease.

The final step in the attribution process scen ario would be to veri fy that the source w a s

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

the source was experiencing unusual upset cond itions during the tim es identified. In this

exam ple, the source was determ ined to have been operating norm ally. The state would then issue

a dete rm ination tha t the visibility im pairm e nt at the Class I area was reaso n ably attribu t able to

the existing stationary facility.

Scenario 5 : Data Rich

Data input: A large special visib ility study recently was conducted.

Four m onths of data spanning two seasons was collected including:

1. At one “rec e p tor” s ite in a Class I a r e a ther e were m u ltiple m e teoro l ogica l, optica l , and

particulate sam p lers including extensive m eas urem ents of par ticle com positions, size

distributions, scattering, abso rption, light extinction, ions, oxidants, relative hum idity,

tem p erature, vertical wind profiles, S O 2 , photographs, and concentr ations of four unique

tracers released from four di fferent sources of interest. Th ere were co-loc ated sam p lers

m easuring several param e ters in more than on e way. There are at least som e one, six,

and twelve-hour partic le m easurem e n ts in addition to twenty-four hour sam p les.

262. This receptor site has been an IMPROVE s ite f o r severa l yea r s a nd these data are also

available.

3. At thir ty-f iv e other s i tes in the region, called “satellit e” sites there was an IMPROVE

Module-A sam p ler taking daily twenty-four hour sam p les which were analyzed for fine

m a ss, S, soil elem ents, trace m e tals, and elem ental H from which organ i cs can be

estim ated. The unique tracers were also m easured at m a ny of these s ites.

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

5. An extensiv e current em i ssions inventory was created.

6. W i nd profilers were dep l oyed at two to th ree sites in addition to the recep tor site.

7. Measurem ents of the ch em ical compositi on of resuspended local dust, smoke from

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

collected in order to creat e “source profiles” to be us ed in receptor m odeling.

Assum i ng the cer tif ica tio n identif i es a BART source with in 1 00 m iles of the Class I a r ea

from which a unique tracer was released, a co m b ination of the following techniques is

suggested:

1. Exam ine fine particle data, including quali ty assu rance. Use the collo cated data to

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

m a tch the twenty-four hour data and if reasonable, be us ed to exam ine the diurnal

cycles in the fine particle concentrations.

2. Look at the extinction b udget to identify vi sib i lity-im pairing pollutants. Because size

distributions were m easured, the extinction budget can be estim a ted from Mie scattering

calculations as well as by using sim p le techniques that assume bulk scattering and

absorption efficiencies. Check to see if m easured scattering p l us absorption add up to

the m easured and reconstructed extinction.

3. Com p are the special study data to histor ical IMP R OVE and a r chived m e teorological

data to determ ine the representativeness of the study period.

4. Perform som e analysis of when the episode s are occurring. EOF analysis can be helpful

to summ arize the m a ssive data s e t. U s e the pho to graphs to create a tim e-lapse

visualization of the scen e at the recep tor site.

5. Perform bac k trajectory analyses usi ng HYSPLI T , CAPITA Monte Carlo or ATAD.

Exam ine whether the trajector ies change substantially when the study’s w i nd-profiler

data a r e inc l uded. Deter m ine whether tr aje c tor i e s are cons istent with trac er

concentrations.

6. Look at relationships between particulate species (f actor analysis) to s ee if they identify

source types.

7. Look at the spatial and tem poral patterns of trace elem ents and the m a jor constituents of

the fine m a ss. This m a y suggest dominan t source areas and transport patterns for

different source types. Use EOF analysis to de te r m ine the patterns th at explain m o st of

the covarian ce in th e data. Check to see how well thes e are reconciled with the back

trajectory modeling and the determ inistic m ode ling. Check to see if the sam e dom i nant

sources were indicated.

8. Exam ine source em issio n s data for co rrelations with am bient m onitoring.

9. Perform dis p ersion m odel such as C A LMET/CALPUFF.

2710. Analyze wind fields, synoptic conditions, a nd satellite photos. Check to see if they w e re

consistent w ith m o re sophisticated meteorological m odeling. Look for unusual

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

modeled and observed cloud patterns were com p arable.

11. Com p are the m e teorological and chem ical characteristics of different episodes

12. UNMIX, PMF and CMB can help determ ine m a jor source types and when they were

im portant.

If, at this point, sim ilar s ource/recep tor relati onsh i ps have been identified using several

different techniques, this level of rev i ew is adequ a te. If the resu lts are inconclusiv e , and there are

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

BART-eligible source, more analyses m a y be necessary. Often in a large study, m easurem ents

are m a de, but not imm e diately analyzed in the lab due to cost. If this is the case, m o re sam p les

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

The technic a l cr ite ria ar e lis ted be lo w, f o llowed by techniques that m a y be used to obtain

results abou t the criteria.

1. The im pairm e nt m u st be related to em i ssions fromspecific sources. The visibilityim pairing pollutants of concern should be identified.

Techniques : Reconstructed particle m a ss and light extinction including exam ination of

the reconstructions in different size ranges a nd at different sites in the region, analysis

of responses of light scattering to rela tive hum idity, back trajectory analyses,

exam ination of inter-species relationships com p ared to m easured source profiles,

dispersion modeling with sophisticated m ode ls such as REMSAD and CMAQ, analysis

of wind fields and dispersion by co mparis on to m easured tracer con centrations,

com puter anim ation of spatial patterns of species of interes t , use of receptor m odels

such as TMBR, DMB, a nd TAGIT that ut ilize unique tracer inform ation, or other

recep tor m o dels such as CMB, UNMIX, and PMF that do not require tracer

inform ation, but can use it. W h en so m a ny different m odels can be used, model

reconciliation is usually a pa rt of a data-rich scenario.

2. The attribution assessm ent m u st address th e unique visibility im pair m e nt certified by

the FLM for the Class I area. Som e factors to con s ider includ e:

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

Technique s: Monitoring, dispersion mode l, back trajectory, cam e ra

b. Frequency: how often the im pairm e nt occurs by source

Techniques : Monitoring, dispersion model, back trajectory, receptor m o del, cam e ra

c. Geographic extent: how much of the Class I a r ea is im paired

Techniques : Monitoring, dispersion m odel, analys is of spatial patterns using data

f r om the satellite s ite s.

28d. Magnitude: how m u ch impairm e nt is due to each individual source

Techniques : Dispersion model, receptor m odel

e. Tim e of occurrence

Techniques : Monitoring, dispersion mode l, back trajectory, cam e ra

3. Uncerta inty of results : I n this c a se, the abil ity of a m odel to predic t the o b served trac er

concentratio ns is an ind i cator of the model’s uncertainty.

2930

### V. TECHNI QUES

A. Monitoring

Introductio n

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

m i ght be used to support an attribution analys is. T h e m e thods described include those most

commonly used in pollution studies or am bi ent monitoring program s and should not be

considered an exhaustive list of poten tial m e thods. Methods in clude, IMPR OVE, filter based

aerosol, continuous aerosol (incl udes optical m e thods such as the nephelom e ter), continuous gas

and canister sam p ling, transm issom e ter, scene (i ncludes film , video, digital), and tracer or

aircraft m e thods.

Several of the m e thods described ar e currently in use as a part of national, st ate, local, tribal,

or private ambient m oni toring networks. These si tes are generally located in urban areas, but

those near the area of study could be used in th e a ttr i bution analysis thereby reducing the cost of

monitoring.

The IMPROVE network is designed to assess v i sibility in Cla ss I ar eas an d routin ely

m easure visibility im pair ing pollu tants. These site s of f e r value to the a t trib ution ana l ys is if they

are located in the area of study.

The RA BART case stu d ies show addition a l ex am ples of the m e thods used during s ource

attribution studies. The case studies also show that m e thods, other than ambient m onitoring, m a y be used to support the analysis . Existing program s su ch as CASTNET ( d ry deposition network),

surface water deposition studies, o r s now deposition studies m a y provide additional d a ta for the

attribution analysis.

Although not specifically listed as a monitoring technique, m e teorological m onitoring is an

im portant elem ent the attribu tion an a l ysis. Coll ection of m e teorological data can range from

sim p le wind, tem p erature, and relative hum idity m easure m ent to an array of acoustic wind

profilers. The com p lexity of the m e t e orological monitoring program depends on data needs and

the availability of existing da ta. As with am bient monitori ng, m e teorological data m a y be

available at sites located near th e stu dy area and offers reduced m onitoring cost.

1. IMPROVE

Overview : T h e Interagency Monitoring of PROtect ed Visual Environm ents (IMPROVE)

program is a cooperative m easurem e n t effort governed by a s t eering comm ittee com p osed of

representatives from Federa l and regional-state organiza tions. The IMP R OVE moni toring

program wa s established in 1985 to aid the crea tion of Federal and State implem e ntation plans

for the protection of visibility in Class I areas (1 56 national parks and wilderness areas) as

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

establish current visibility and aerosol conditi ons in m a ndatory class I areas; (2) identify

chem ical species and em ission sources respon sibl e f o r exis ting m a n-m a de visib i lity impairm ent;

31(3) docum ent long-term trends for assessing progre ss towards the national vi sibility goal; and (4)

with the en actm ent of the region al h aze ru le, pro v ide reg i onal haze m onitoring rep r es enting all

visibility-protected federal class I areas where practical.

Magnitud e : Possible when used in conjunction with appropriate analytical techniques.

Frequency : Possible when used in conj unction with appropriate anal ytical techniques; Lim ited

by twenty-four hour sample integration, and one-in-three day sam p ling frequency.

Duration : Possible when used in conjunction with a ppropriate analytical techniques; Lim ited by

24 hour sample integration, and one -in-three day sam p ling frequency.

Principle : T w enty-four hour integrated filter-based am bient monitor; Gravim etric analysis for

PM 2. 5 (Module D) and P M 10 ); S from Particle Induced X-ray Em ission (PIXE); NO 3 from ion

chrom a tography (denuded nylon filter from Modu le C); Organic and elemental carbon from

Therm a l Optical Reflectance (TOR); H fr om Prot on Elastic S cattering (P ESA).

Uncerta inty : All elem ental S if f

r om sulf ate; All s u lf ate is ammonium sulfate; NO 3 (D enuder

efficiency is close to 100 %. All ni trate is from a mmonium nitrate);

Average organic m o lecule is 70% carbon; Carbon (organic and elem ental) is defined by the

analytical method; Fin e soil bas e d o n elem ental com position; Course m a ss (PM 10 – P M 2. 5 )

consists only of insoluble soil particles.

Strengths : R e gulatory indicator for regional haze rule ; Long term data record in or near m a ny

f e deral Clas s I are a s; Ex tensiv e netw ork currently in place.

Lim itations : Integrated twenty-four hour sam p le; one -in-three day sam p li ng interval; Nitrate

losses due to volatilizatio n f r om f

ilter ; Not capab le of distingu ishing be twe e n prim ary particula t e

and atm o spherically transfor m e d particulate.

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

m a intenance; Chem ical analysis m u st be conducted by appropriate laboratory.

Regulatory Context : Regional Haze rule; Included in SIP Vis i bility Plan

2. FILTE R-BASE D AE ROSOL

Overview : F ilter-b ased aerosol m onitoring is u s ed to iden tify c h em ical species and ob tain

concentratio n m easurements of atm o spheric cons titu ents that contr i bute to visibility im pairm e nt.

Prim ary tec hniques in clude f ilte r-ba s ed aeroso l s a m p lers that colle ct sam p les on var i ous

substrates in various size ranges such as PM 2. 5 or PM 10 . Aerosol m onitoring can provide fine

m a ss concentration, course m a ss concentration, optical absorption, m a jor and trace elem ents,

organic and elem ental carbon, and su lfate, nitrate, and chloride ions. A variety of m e thods are

available to conduct filter ba sed aerosol m onitoring. Many methods are approved as an EPA

reference m e thod, while som e are not, but m a y of fer variability that the ref e rence m e thod does

not.

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

Frequency : Possible when used in conj unction with appropriate anal ytical techniques; Lim ited

by 24-hour sam p le integration and sampling fre quency (daily sam p ling is possible depending on

instrum e nt s e lec t ed).

Duration : Possible when used in conjunction with a ppropriate analytical techniques; Lim ited by

24-hour sam p le integration and sam p ling freque ncy (daily sampling is possible depending on

instrum e nt s e lec t ed).

Principle : T h e m e thods used for analyses of thes e filter m e dia includ e gravim etry (electro

m i crobalance) for m a ss; X-ray fluorescence (XRF) and particle induced X-ray em ission (PIXE)

for trace elem ents; Ion chrom a tography (IC) for anions and selected cation s ; Controlledcom bustion for carbon; Gas chrom a tography/m a ss spectroscopy (GC/MS) for sem i -volatile

organic p a rticles ; Special m easurem ent needs m a y include determ ining particle size and

morphology through optical a nd/or electron m i croscopy.

Strengths : L a rge network of urban m onitoring available; Filter ba sed sampling allows for a

variety of chem ical/elem e ntal analysis.

Lim itations : Integrated twenty-four hour sam p le; Ge nerally operated on one -in-three or one-insix day sam p ling interval (daily sam p ling is po ssible depending on instrum e nt selected); Not

capable of distinguishing between prim ary particulate and atmospherically transformed

particulate.

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

m a intenance; Chem ical analysis m u st be conducted by appropriate laboratory.

Regulatory Context : Commonly used in NAAQS/SIP com p l i ance m onitoring networks.

3. CONTINUOUS AE ROSOL

Overview : Aerosols can be m easured continuously using several different m e thods. Optical

m easure m ent of aerosols can be m easured usi ng an instrum e nt such as a nephelom e ter to

m easure light scattering (bscat) or a aethelom e ter to m easure light absorption (babs) by aerosols

(black carbon). Continuous instrum e nts such as TEOM or BETA can provide PM 10 or PM 2. 5

aerosol concentration.

Magnitud e : Possible when used in conjunction with appropriate analytical techniques.

Frequency : Possible when used in conj unction with appropriate anal ytical techniques; Lim ited

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

depending on instrum e nt selected).

33Duration : Possible when used in conjunction with a ppropriate analytical techniques; Lim ited by

twenty-four hour sam p le integration and sam p ling frequency (daily sam p ling is possible

depending on instrum e nt selected).

Strengths : C ontinuous m easurem ent with at least hour ly tim e resolution; Nephelom e ters and

Aethelom eters are commonly used to support Cla ss I area m o nitoring program s and an existing

data record m a y be available in som e areas.

Lim itations : Continuous aerosol m easurem ent with m e thods such as TEOM and BETA are

generally made in urban areas to suppor t NAAQS co m p liance networks; Not capable of

distinguishing between prim ary particulate a nd atm o spherically transform e d particulate.

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

m a intenance.

Regulatory Context : Commonly used in Class I area m onitoring networks or urban N AAQS

com p liance networks.

4. CONTINUOUS GAS E OUS

Overview : A variety of gaseous pollu tants, such as Ozone (O 3 ), Carbon Monoxide (CO),

Nitrogen Dioxide (NOx), nitr ic oxide (NO), ammonia (NH 3 ), hydrogen peroxide (H 2 O 2 ), sulfur

dioxide (SO 2) , Organics, and HAPS/ TOXICS, ca n be m easured continuously. Gaseous

monitoring is generally conducte d with instrum e nts that continuously draw sa m p le air and

periodically (as frequently as once per second) analy ze the sample. Canister system s c o llect an

am bient air sam p le over a specific p e riod of tim e in clean evacuated canis t ers. The canisters are

then subject to subsequent analysis at a labora tory using a m e thod such as GC/FID. This m e thod

is able to provide tim e -integrated samples fr om several hours to twen ty-four hours or m o re.

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

instrum e nts must be conducted on a regular schedule in order to ensure data quality.

Magnitud e : 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 : C ontinuous m easurem ent with hourly tim e reso lution; Monitors available to identify

variety of pollutants.

Weaknesses/Lim itations : Generally require conditioned envi ronm ent and frequent perfor m ance

checks.

Practical Co nsideration s : Data m a y be available f r om established urban area m onitoring

networks and som e Class I area m o nitoring netw orks; Additional cost ($/year) for new sites.

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

m a intenance.

Regulatory Context : Commonly used in urban N AAQ S compliance and HAPS/TOXICS

networks.

5. TRANS M ISSOMETER

Overview : T r ansm issometers m easure the am ount of light transm itted through the atmosphere

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

intensity and a receiver. The transm ission m easur em ents are electronically converted to hourly

averaged lig ht extinction (b

ext ).

Magnitud e : 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.

Uncerta intie s : A transm issom e ter m u st be ins t a lle d in stab le location s with a clea r and

unobstructed path to avoid in terference with the signal.

Strengths : C ontinuous m easurem ent of b

ext in m a ny Class I areas and some urban areas.

Lim itations : Not capable of identifying pollutants c ontributing to visibility im pair m e nt.

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

m a intenance; Data processing/quality assu rance requires high level of expertise.

Regulatory Context : Commonly used in Class I area m on itoring networks and in some urban

areas.

6. SCENE

Overview : S cene m onitoring refers to the use of still and/or tim e-lapse photography (including

digital im agery) to provide a qualitative repres entation of visual air quality. Scene m o nitoring

data qua lity objective r e commendations are to d o cum e nt the appearan ce of scenes of inte rest

under a variety of air qu ality and illu m i nation cond itions at d i fferent tim es of day and different

seasons. Scene m onitoring docum ents the visual condition observed at a monitoring site. The

data co llec t ion schedule can be ta ilo r e d to cap tur e the pe riods when visibility im pairment is m o st

likely to occur at specific sites. Tim e -lapse m ovies (generally tim e-lapse video or super 8 mm

film ) can be used at m onitoring sites and during special studies to docum ent the visual dynam i cs

of a scene or source.

Magnitud e : Cannot provide a quantita tive m easurem ent of vis i bility im pairm e nt but can

qualitatively illus t r a te va rious le vels of visibility im pairm e nt.

35Frequency : Possible depending on the m e thod used. Tim e -lapse video m a y be able to

dem onstrate the frequency of the impairm e nt . The use of still photography to docum e n t

im pair m e nt frequency depends upon the num ber of i m ages acquired over tim e

.

Duration : Possible depending on the m e thod used. Tim e -l apse video m a y be able to dem onstrate

the im pairment frequency. The use of still pho tog r aphy to docum ent im pairm e nt frequency

depends upon the num be r of im ages acquired over tim e

.

Uncerta intie s : n/a

Strengths : P r ovides im a g e/video record

Lim itations : Not capable of identifying pollutants c ontributing to visibility im pair m e nt.

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

m a intenance.

Regulatory Context : Commonly used in Class I area m on itoring networks and in some urban

areas.

7. TRACER METHODS, LIDAR SYSTEM S

, AIRCRAF T BAS E D MEASURE M ENTS

Overview : T h e m e thods described in this section are m o re specialized m e thods generally

reserved for special studies as opposed to the pr eviously described m e thods that are used m o re

routin ely. Tracer m e thods, lidar system s and airc raft based m e asurem ents will be described

individually.

Tracer Meth ods :

A tracer elem ent or compound is a s ubstance with unique characteristics that allows p o sitiv e

identificatio n at very lo w concentrations. Th e tracer com pound of choice will vary b y source

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

natural background concentrations and ideally would have the sam e chem ical for m a nd

properties as the com pound being tracked. In am bien t applications where use of the ideal tracer

is not poss i b l e due to po tential enviro nm ental risk , a near-subs titu te com pound m a y be used.

When used to determ ine potential source im p acts, the chosen com pound i s released from a

source stack and downwind m onitors are used to detect the presence of the com pound.

Lidar Methods :

A lidar trans m its short p u lses of lase r ligh t in to the atm o sphere. The la ser beam loses light to

scattering as it travels. At each range, som e of th e light is backscattered in to a detecto r . Because

the light takes longer to return from the m o re di stant ranges, the tim e dela y of the return pulses

can be converted to the corres ponding distance between the atm o spheric scatterer and the lidar.

The end res u lt is a profile of atm o spheric s catteri ng versus distance. Analysis of this signal can

yield information about the distri bution of aerosols in the atm o s phere. The am ount of backscatter

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

36plum es of pollution. Other properties of the atm o sphe re can also be deduced from the lidar return

signals. A frequency shif t in th e ligh t because of the Doppler effect perm i t s m easurement of

wind speeds. By detecting the am ount of depolar ization, one can discrim i nate between liquid

droplets and nonspherical ice partic les. Differential Absorption Li dar (DIAL) uses absorption, as

evidenced b y reduced backscatter fro m greater d i stances, to m easure the concentratio n of

atm o spheric gases. A Ram a n lidar detects partic u l ar atm o spheric com ponents (su c h as water

vapor) by measuring the wavelength-shifted return fro m selected m o lecules (NOAA).

Aircraft Based Measurem ents :

Aircraft bas e d m easurem ent systems utilize specia lly equipped aircraft to m a ke pollutant and

m e teorological m easurem ents at various elevat ions throughout the study dom a in. The aircraft

can be equip p ed with a multitude of gaseous, ae rosol, optica l and m e teorologica l equ i pm ent.

This m easurem ent m e thod provides the advantage of vertical profiles of various param e ters, the

ability to track point source plum es, and the ab ility to establish boundary conditions for future

analytical and m odeling exercises.

References :

NOAA, At mospheric L i ght Division, Environmental Technology Laboratory,

http://www2.etl.noaa.gov/DIAL_lidar.htm l

U.S. Environm ental Protection Agency, Visibi lity Monitoring Guidance (EPA-454/R-99-003),

June 1999.

National Research Council, Pr o t ec tin g Visibility in Nationa l P a rks and W ilderness Areas,

National Academ y Press, W a shington D.C., 1993, pp. 315-357.

3738Table 1. Monitoring TechniquesCriteriaMonitoring MethodIMPROVEFilterBasedAerosolContinuous AerosolGaseous;Continuousmethods, canistersampling, etc.TransmissometerScene35mmfilm/8mmvideo, digitalTracer Methods, Lidarsystems, Aircraft basedmeasurementsPollutantPM10YesYesYesNoNoNoAll PossiblePM2.5YesYesYesYesNoNoNOx(Site specific)NoNoYesNoNoSO2(Site specific)NoNoYesNoNoSulfateYesYesNoNoNoNoNitrateYesYesNoNoNoNoCarbonYesYesNoNoNoNoOrganicsYesYesNoYesNoNoOtherReconstructedextinctionNephelometer -BscatAethelometer - BabsHAPS, TOXICSTotalExtinctionVisualParametersVisibility/Concentration ChangesMagnitudeYesYesAll PossibleAll PossibleAll PossibleNoAll PossibleFrequencyYesYesYesAll PossibleDurationYesYesYesTimeofDayNoNoAll PossibleAll PossibleAll PossibleYesLikely Part ofFLMCertification?PossibleNo39Monitoring Techniques (continued)PrincipalQualitative/QuantitativeQuantitativeQualitativeQualitative/QuantitativeStrengthsReconstructedextinctioncalculation,historicalrecordvarious chemicalanalysis, largenetworknationwideContinuousmeasurement,nephelometercommonly located inor near Class I areasContinuousmeasurement,variety of possiblepollutantsContinuousmeasurement,provides lightextinctionvalue,historicalrecordRelativelyinexpensive,uncomplicatedoperationalrequirementsLidar and airplane basedsystems can providedtemporal and spatialresults, tracer methodscan provide sourcereceptor dataWeaknesses/LimitationsFixed locationinornearClassI areas, alsolocated inseveral urbanareasIntegrated 24 hoursample generallycollected on onein-three schedule,commonly inurban areasTEOM, BETA, etc,commonly located inurban areasGenerally requireconditionedenvironment forproper operation,commonly locatedin urban areasFixed locationinornearClassI areasNumber of stillimages limited,video requiresstorage androutine reviewComplex and relativelyexpensive to operate,requires specializedtraining and knowledgeto operate and analyzeresultsDistinguishBART Sources?Possible withother analysisPossiblewithotheranalysisNoPossiblewithotheranalysisNoPossible withother analysisPossible with otheranalysisPractical ConsiderationsCostModerate; varies depending on the number of instrumentsLowHigh40

### B. Source Modeling

Introductio n

Given the complex natural environment, the st ate m u st choose the conf iguration of modeling

techniques that will provide the m o st inform ation on the contributing source or sources of

im pairm e nt within th e lim ited resour ces of cu rrent technology, data, budgets, tim e and staff

availability.

1. Physical Models

Physical m odels are thos e that sim u late the m e teo r ology and air quality ov er an area.

Modeling relies on a num e rical or analytical m odel to estim ate particu l ate concentrations in

space and tim e . Because of its natu re and sources

, particulate m a tter is dif f icult to m odel over all

spatial scales. Many air quality m o dels th at a r e c u rrently ava i labl e were designed to be applied

over the regional scale w ith grid sizes from four t o forty kilo m e ters. Modeling requires detailed

m e teorological fields and em issi ons inventory over the entire dom a in. The com p ilation of data

required to run these m odels can require m u ch effort and expertise. Efforts are underway by

governm e nt agencies in the U.S. to generate and archive both em issions and estim ated activity

levels of m a ny source types in ge ographical inform ation system s.

Num e rical s ource-o r ien t ed m odels are desi gned to sim u late atmospheric diffusion or

dispers i on and estim ate concentratio ns at defi ned recep tors. Num e rical s ource m odels can be

grouped as kinem a tic, first-order closure, or second-order closure m odels (Bowne and

Lundergan, 1983). Kinem a tic m odels are the sim p le st both mathem atically and conceptually.

These m ode ls sim p lify the non-linear equations of turbulent motion, thereby perm itting a closed

analytical approxim a tion to describe pollutant concentration (Green et a l

. , 1980). First-order

closure m odels are based on the assumption of an isotropic pollutant concentration field.

Consequently, turbulent eddy fluxes are estim ated as being proportional to the local spatial

gradient of the transport quantities. T h e Eulerian grid m odels, Lagrangian particle m odels, and

trajectory puff/plum e m o dels are included in this category. Second- order closure m odels involve

a series of algorithm transfor m a ti ons of the equations of state, m a ss continuity, m o m e ntum , and

energy by us ing the Bous sinesque ap proxim a ti on and Reynold’s decom p osition theo ry (Holton,

1992; Stull, 1988).

2. Spatial Scales

The m odel’s applic able s p atia l sca l es play a large role m eeting the analysis ' objectives and

its ab ility to accurately a ssess spatial variab ility. PM 10 and P M 2. 5 concentrations m o deled or

m easured at any recep tor result from the com p lex interaction of m e teorology, chem ical

transform a tions and em issions from nearby and di stan t sour c e s. For exa m ple, a m o nitor loca ted

near an ope r a ting con s tr uction s ite w ill b e im pacted m o re by the daily con s truc tion a c tivity than

the surrounding area. That site m a y be classified as representing an area of a few tens of m e ters

to no m o re than one kilo m e ter depending on the size of the c onstruc tion area and f u g itiv e dust

control m e a s ures.

The dim e nsions given below are nominal rather th an exact and are presented as defined in

40 CFR part 58.

41a. Micro-Scale (10 to 100 m ) : This scale does not apply to scenarios releva nt to the attribution

problem . Modeling at the m i croscale is usually d one by sim p le Gaussian plum e m ode ls such as

ISCST3. Measurem ents in urban areas can show cons iderab le varia tions at this sc ale w h ile those

in pris tin e ar eas would n o t. Varia tion s of ten occu r when m onitors a r e lo ca ted clo s e to a low-lev e l

em issions source, such as a busy roadway, constr uction site, within a co mmunity that uses wood

stoves, or a short industrial stack. Fortunately, compliance mon itoring site exposure criteria

avoids m i croscale influences even fo r source-oriented m onitoring sites.

b. Middle Scale (100 to 500 m ) : Middle-scale m onitors show significant differences between

locations that are ~0.1 to 0.5 km apart. These di fferences m a y occur near large industrial areas

with m a ny different operations or near large cons truction sites. Monitors with m i ddle-scale zones

of

represen tation a r e of ten source -or i ented, u s ed to dete rm ine the contribu tions f r om em itting

activ ities with m u ltiple, individu al s ources to n e arby comm unity exposu r e m onitors.

c. Neighborhood Scale (500 m to 4 km): Nei ghborhood-scale monitors do not show significant

differences in particulate concentrations with spacing of a few kilom e ters. This dim e nsion is

often the size of e m issions and m ode ling grid s used in large urban areas for PM source

assessm ent, so this zone of representation of a monitor is the only one th at should be used to

evaluate such m odels. Sources affecting nei ghborhood-scale sites typically consist of sm all

individu al em itters, such as clean, paved, curbed roads, uncon gested traffic flow without a

significant fraction of heavy-dut y vehicles, or neighborhood use of residential heating devices

such as firep l aces and wood stoves.

d. Urban Scale (4 to 100 km ): Urban-scale m onitors show consistency am ong m easurements

with m onitor separations of tens of kilom e ters. These m onitors repres ent a m i xture of particle s

from m a ny s ources within the urban com p lex, including those from the sm aller scales. PM

m easure m ents at urban-scale locations are not dom i nated by any pa rticular neighborhood,

however. Urban-scale s i tes are often located at h i gher elevations and away from highly traveled

roads, industries, and residential heating.

e. Regional-Scale Background (100 to 1,000 km ): Regional-scale background m onitors show

consistency am ong measurem ents for m onitor separations of a few hundred kilom e ters.

Background concentrations are often m o re consis tent for specific chem ical com pounds, such as

sulfate or n i trate, than th ey are for P M m a ss co ncentrations. Regional-scal e PM is a com b ination

of naturally-occurring aerosol from windblown dust and m a rine aerosol as well as particles

generated in urban and industrial areas that m a y be m o re than 1,000 km distant. Regional-scale

sites are b e st located in rural areas away from local sources, a nd at higher elevations. National

parks, national wilderness areas, and m a ny stat e and county parks and re serves are appropriate

areas for regional-scale sites. Many of the IM PROVE sites characterize PM regional scale

background in different regi ons of the United States.

f. Continental-Scale Background (1,000 to 10,000 km ): Continental-scale background

monitors sh ow little variation ev en when they are separated b y m o re than 1,000 km . They are

hundreds of kilom e ters from the nearest sign ificant em itters. Though thes e sites m e asure a

m i xture of natural and diluted m a nmade source contributions, the m a nm a d e com pone nt is at its

m i ni m u m expected concentration. T h e Jarbidge W ilderness IMPROVE si te in northern Nevada

is a good exam ple of a c ontinental- scale background site for particulate m a tter in North Am erica.

42e. Global-Scale Background (>10,000 km ): Global- scale background m onitors are intended to

quantif y con centr ation s transpor ted b e tween dif f e rent con tine n ts as well a s natura lly -e m itted

particles and precursors from sea spray, vol canoes, and windblown dust. Yellow sand from

China has b een detected at the Maun a Loa, HI, laboratory (Darzi and W i nchester, 1982; Braaten

and Cahill, 1986), as well as on the North Am erican con tinent. Red dust from Africa’s Sahara

desert h a s b een detected at Mt . Yunque, Puerto R i co and over the southeastern United States.

Other global-scale sites include McMurdo, Palm er , and Ahmundson-Scott stations in Antarctica

(Lowenthal et al. , 1996 ), Pt. Barrow, Alaska, and Mace Head, Ireland.

3. Chemical Composition

This sec tion illus t r a tes h o w the chemical com position of aeros ols is an im portan t

consideration in the choice of particulate m a tte r models. The knowledge of how the aerosol' s

com position varies over an area will play a key role in the attr ibution study design.

The relative abundance of che m ical com ponent s in the atm o sphere closely reflect the

characteristics of e m ission sources. Major ch emical com ponents of PM 2. 5 and PM 10 m a ss in

urban and rural areas consist of nitrate, sulfat e, ammonium , c a rbon, geological m a terial, sodium

chloride, and liquid water.

Che m ical compositions can vary spatially in all scales of the atmosphere and depend on

sources surrounding the monitoring s ite. For example, on the continen tal scale, the eastern U.S.

fine particulate chem ical com positions are differe nt than those of the western states. In the

eastern portion of the U.S., nonurban PM 2. 5 is dom i nated by secondary sulfate, organics and

elem ental carbon (EPA, 1996). The data to supp ort this conclusion are based on the IMPROVE

and CASTNET networks. These networks provide a background fine-fraction aerosol database

because the monitoring s ites are p r imarily loca ted in national parks and wildernes s areas.

Analysis of this network shows that the western U.S. nonurban PM 2. 5 aerosol is predom inantly

carbon in nature. Nitrate also contributes si gnificantly to the f i ne particle m a ss budget

particularly in central and coas tal California. W ithin these ge neralizations, obvious departures

will be f oun d especially near sour ces such as nea r the oc ean a nd urban ar e a s where th e aerosol

will be p r imarily inf l uen ced by sea s a lt and combustion pa rticles, r e spec tively.

The typical PM 2. 5 chem ical com positions vary by season (Chow et al., 1993a; 1996a,

Watson et al., 1997), and consist of the following m a jor com p onents:

a. Organic Carbon: Organic carbon is com posed of gases and particles containing

com b inations of carbon and hydrogen atom s. Or ganic com pounds found in am bient air m a y also

be associated with other elem ents and co m pounds, particularly oxygen, nitrogen, sulfur,

halogens, and m e tals. Particulat e organic carbon consists of hundreds, possibly thousands, of

separate compounds (Rogge et al ., 1993a). The m a ss concentration of organic carbon can be

accurately m easured, as can carbon ate carbon (C how et al., 1993b), but only about ten percen t o f

the specific organic compounds th at it contains have been m eas ured. Vehicle exhaust (Rogge et

al., 1993b), residential and agricu ltural burning (Rogge et al., 1998) , m eat cooking (R ogge et al.,

1991), fuel com bustion (Rogge et al., 1997), ro ad dust (Rogge et al., 1993c), and particle

for m ation from heavy hydrocarbon gases (Pandis et al., 1992), are the m a jor sources of organic

carbon in P M 2. 5

.

43b. Elem ental Carbon: Elemental carbon is bl ack, often called “soot.” Elem ental carbon

contains pure, graphitic carbon, but it also contai ns high m o lecular weight, dark-colored,

nonvolatile organic m a terials such as tar, biological m a terial (e.g., coffee), and coke. Elem ental

carbon usually accom p anies organ i c carbon in co mbustion em issions, with diesel exh a ust

(W atson et al., 1994a, 1998) bei ng the largest contributor.

c. Sulfate: Ammonium sulfate ((NH 4 SO 4 ), ammonium bisulfate (NH 4 HSO 4 ), and sulfuric acid

(H 2 SO 4 ), are the m o st common sulfate com pounds in PM 2. 5 . T h ese com p ounds are water-soluble

and resid e almost exclus ively in the P M 2. 5 size fraction. Sodium sulfate (Na 2 SO 4 ) has been found

in coas tal areas where su lfuric acid h a s been ne utralized by sodium chloride (NaCl) in sea salt.

Although gypsum (Ca 2 SO 4 ) and some other geological com p ounds contain sulfate, these are not

easily dissolved in water for chem ical analysis an d are m o re abundant in the coarse fraction than

in PM 2. 5 ; they are usually class i fied in the geologi cal fraction.

d. Nitrate: Ammonium nitrate (NH 4 NO 3 ) is the m o st abundant nitrate com pound, a large

fraction of PM 2. 5 occurs during winter, and a m o derate fr action occurs during fall. Sodium nitrate

(NaNO 3 ) is found in the PM 2. 5 and coarse fractio ns near th e o ceans and s a lt playas. S m all

quantities of sodium nitrate have been found in summ ertim e particulate m a tter inland owing to

transpo r t fro m the ocean (Chow et al.

, 1996b).

e. Ammonium : Ammonium sulfate (NH 4 SO 4 ) and ammonium nitrate (NH 4 NO 3 ) are the m o st

common compounds contain i ng ammonium from reactio n s between sulf uric acid, nitric acid, and

ammonia gases. W h ile most of the sulfur dioxi de and oxides of nitrogen originate from fuel

com bustion in stationary and m obile sources, m o st of the ammonia derives from living things,

especially anim al husbandry prac tic ed in dairie s and f eedlots

.

f. Geological Material: Su spended dus t consis ts m a inly of oxid e s of alum inum , silicon,

calcium , titanium , iron, and other m e tal oxides. In areas su rrou nded by substantial terrain (i. e

., mountains), eons of runoff produce m i neral co mposition s in soils that can be fairly

hom ogeneous, with the exception of places where dr y lak e b e ds exis t that have accu mulated salt

deposits. Ind u strial p r ocesses su ch as steel m a king, sm elting, and m i ning have dis tinct geological

com positions. For instance, cem ent production an d distribution facilities m a y use alcareous,

siliceous, argillaceous, and ferriferous m i nerals that m a y not be natural to the region, w ith

lim estone (CaCO 3 ) being the m o st abundant (G reer et al ., 1992). Suspended geological m a terial

resides m o stly in th e coarse particle f r action (Hou ck et al, 198 9,1990), and typically co nstitu tes

~50% of PM 10 while only contributing 5 to 15% of PM 2. 5 (Watson et al., 1994b).

g. Sodium Chloride: Salt is found in suspended particles near oceans, open playas, and after

de-icing m a terials are applied. Bulk s ea water con t ains 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 of ten neutralized by nitric or sulfur ic acid in urban air where it is

encountered as sodium nitrate or sodium sulfate.

h. Liquid W a ter: Soluble nitrates , sulfates, amm onium, sodium , ot her inorganic ions, and som e

organic m a terial (Saxena and Hildem ann, 1997) absorb water vapor from the atm o sphere,

especially w h en relativ e hum i dity ex ceeds 70% (Tang and Munkelwitz, 1 993). Sulfuric acid

absorbs som e water or deliquesces at all hum id ities. Particles containing these com p ounds grow

44into th e drop let m ode as they ta ke on liquid water. Som e of this water is re tained when particle s

are sam p led and weighed for m a ss concentration. The precise am ount of water quantified in a

PM 2. 5 depen d s on its ion i c com position and the e qu ilib ration rela tive hum idity applied prior to

laboratory w e ighing.

Am bient m a ss concentrations contain both pr im ary and secondary particles. Prim ary

particles are directly em itted by sources and usually undergo few changes between source and

recep tor. Atmospheric concentration s of prim ar y particles are, on average, proportional to th e

quantities th at ar e em itte d.

Secondary particles are those th at form in the atm o sphere from gases that are directly

em itted by sources. Sulf ur dioxid e , a mmonia, and oxides of nitrog en are the pre c urso rs f o r

sulfuric acid, ammonium bisulfat e, ammonium s u lfate, and ammoni um nitrate particles. “Heavy”

volatile organic com pounds or HVO C (those containing m o re than eight carbon atom s) m a y also

change in to particle s. The m a jority o f these tr ans f orm a tions r e sult f r om intense pho toc h em ical

reactions that also create high ozone levels. Secondary particle s usually form over several hours

or days and attain aerod ynam i c diam eter s in th e accum u lation m ode between 0.1 and 1 μ m.

Several of these particles, notab ly those containing ammonium n itrate, are volatile an d transfer

m a ss between the gas and particle phase to m a inta in a chem ical equilib riu m . This vola tility ha s

im plications for a m bient concentration m eas urem ents as we ll as for gas and particle

concentrations in the atmosphere.

Am bient concentrations of secondary aerosols are not necessarily pr oportional to quantities

of e m issions since the rate at which they for m m a y be lim ited by factors other than the

concentration of the precursor gases. Secondary particulate a mmonium nitrate concentrations

depend on gaseous ammonia and nitric acid concen tra tions as well as temperatu r e and rela tive

hum i dity. A nearby source of ammonia m a y cause a localized increase in PM 2. 5 concentrations

by shifting the equilibrium from the gas to th e particulate am m onium nitrate phase (Watson et

al., 1994c). Ammonium sulfate m a y for m rapidly fr om sulfur dioxide and ammonia gases in the

presence of clouds and fogs, or slow ly in dry ai r. Because fin e particle deposition v e locities are

slower than those of th e gaseous precursors, PM 2. 5 m a y travel m u ch farther than the p r ecurso r s,

and secondary particles precursors are often f ound far from their em issions sources and m a y extend over scales exceeding 1,000 km.

4. Particle Formation

Ammonium nitrate and ammoni um sulfate aeros o ls are th e most prevalent second ary

particles found at urban and non-urban sites throughout the U.S. during the winter. These

particles can for m when gas m o lecules are attr acted to and adhere to exis ting par tic les

.

Sulfur dioxide gas changes to particulat e sulfate through gas- and aqueous-phase

transform a tion pathways. In the gas-phase path w a y, ultraviolet sunlight induces photochem i cal

reac tions c r e a ting ox idiz ing species that re act w ith a wide var i ety of atm o spheric cons titu ents.

The gas-phase transformation rate ap pears to b e controlled more by the p r esence o r ab sence of

the hydroxyl radical and its com p eting reactions of other gases than by the sulfur dioxide

concentratio ns.

45In the presence of fogs or cl ouds, sulfur dioxide dissolves in droplets where it experiences

aqueous reactions that are m u ch faster than gas-phase reac tions. W h en ozone and hydrogen

peroxide are dissolved in the dr oplet, the sulfur dioxide is qu ickly oxidized to sulfuric acid.

W h en ammonia is d i sso lved in the d r ople t , the s u lf uric acid is neutralized to amm onium sulfate.

If the fog or cloud evaporates and relative hum idity decreases below 100 percent, the sulfate

particle exists as a sm all droplet that includes a portion of liquid water. As the relative hum i dity

further decreases below 70 percent, the droplet ev aporates an d a sm all, solid sulf a t e p a rticle

rem a ins. The reac tions within th e fog droplet are very fast, a nd the rate is controlled by the

solubility of the precursor gases. Aqueous transform a tion rates of sulfur dioxide to sulfate are 10

to 100 tim es as fast as gas-phase rates. These ch em ical reactions are crit ical to und erstanding PM

concentrations in areas and downwind of areas that em its large am ounts of SO 2 . The location and

SO 2 e m issions output of large point sources such as coal and oil fired power plants need to be

m a pped and com p ared with transport patterns in order to determ ine the impact of ammonium

sulfate particles on am bien t surface concentration s

.

Fogs serve as an environm ent for creating part icles and as vehicles for particle rem o val.

During heavy fogs, particles and precursor gases ar e scavenged as fog droplets grow to sizes that

settle rapidly to the su rface. The extent and in te nsity of these fogs is so poorly characterized,

however, that it is not yet possible to determ in e where and when particle for m ation overtakes

particle deposition, thereby adding to the PM 2. 5 concentration loading.

Nitrogen oxide converts to nitrogen dioxide, prim a r ily by reaction with ozone. Nitrogen

dioxide can: 1) change back to nitrogen oxide in the presen ce of ultravio le t radiation; 2) change

to short-lived species which take place in other ch em ical reactions; 3) form organic nitrates; or 4)

oxidize to form nitric acid. The m a jor pathway to nitric acid is a reaction with hydroxyl radicals

that transform s nitrogen dioxide to nitric acid. Nitric acid deposits from the atm o sphere fairly

rapidly but, in the presence of ammo nia, it is neutralized to partic ulate ammonium nitrate. This is

an im portant process in secondary partic le production because m a ny agricultural areas

surrounding populated urban areas contain larg e ammonia sources. Chow and Egam i (1997)

show that San Joaquin Valley a mmonia concentrations are large during w i nter. Conversion rates

for nitrogen dioxide to nitric aci d, ranging from le ss than one per cent per hour to ninety percent

per hour. These rates are typically five to ten tim es the conversion rates for sulfate form ation.

Though they vary throughout a twenty-four hour period, these rates are significant during both

daytim e and nighttim e hours, in contrast to the g a s-phase sulfate chem istry that is m o st active

during day l ight hours. T h e im portant nitric acid -ammonia reaction has imp lications to network

design by the need to locate and m a p possible sources of ammonia. Significant sources of

ammonia are associated with anim al husbandr y a nd f e rtilize r applic ation s

. Locating an d

estim ating a mmonia em issions will b e a dif f i cult ta sk bec a use it is no t trad ition a lly tab u lated in

em ission inventories and requires further resear ch to refine the m e thodology to m easure the

em issions.

While ammonium sulfate is a fairly stab le com p ound, ammonium nitrate is not. Its

equilibrium with gaseous ammonia and nitric ac id is strongly influenced by tem p erature and

rela tive hum idity. Atm o spheric p a rtic le nitra t e ca n occur in atmospheric a e rosol pa rtic les as so lid

ammonium nitrate o r as ionized ammonium nitrate in aerosol par ticles containing water. In both

46the solid and ionized form s, a mmoni um nitrate is in equilibrium with ga s-phase nitric acid and

ammonia.

For fixed relative hum idity, increasing tem p erat ure decreases the particle n itrate frac tio n.

This is a consequence of the direct relation be tween the equilibrium constants and tem p erature.

As tem p erature increas es, the equ ilibrium cons tants increase, which m e ans higher gas-phase

pressures can be supported, thereby reducing the particle nitrate fract ion. For fixed hum idity,

decreasing tem p erature increases the particle nitr ate fraction. As tem p eratur es approach 0°C, the

curves app r oach lim itin g values. Pa rticle f r ac tio ns of one are used f o r io n ratios gre a ter than or

equal to one, and particle fractions are determ ined by the amount of available amm o nia for ion

ratios less than one. For the higher temperatur e s , incre a sing r e lative hum idity in crea se s the

particle nitrate fraction. This is a consequence of liquid wate r present for the 60% and 80%

relative hum idity cases. When there is sufficient ammonia present with 30% relative hum i dity,

more than 9 0 % of the nitra t e is in th e particle ph ase for tem p eratu r es less than 20°C. More than

half of the particle nitrate is gone at tem p er atures above 30°C, and all of it disappears at

tem p eratures above 40°C.

At m o spheric water is another im portant com ponent of suspended pa rticu l ate m a tte r. The

sharp rise in liquid water cont ent at r e lative hum idities betw een 55% and 75% is known as

deliquescence. A precise hum idity at which solubl e particles take on liquid water depends on the

chem ical m i xture and temperature. P a rticles c ontaining these com pounds gr ow into the droplet

mode as they take on liquid water, so the sam e concentration of sulfate or nitrate m a kes a m u ch

larger contribution to light extinc tion when hum i dities are high (> 70 percent) than when they are

low (<30 percent). Excess liquid water is also m easured as part of the PM 2. 5 m a ss whe n sam p led

by light scattering continuous m onitors or when fi lters have not been eq uilibrated at relative

hum i dities less than 30 % prio r to w e ighing.

Som e of the organic carbon in suspended particles is also of secondary origin. Secondary

organic compounds in particulate m a tter include aliphatic acids, alcohols, arom atic acids, nitroarom atics, carbonyls, esters, phenols, and alip hatic nitrates (G rosjean and Seinfeld, 1989;

Grosjean, 1992, Pandis et al., 1992, 1993; Sein feld and Pandis, 1998). Norm ally, prim a r y organic carbon particles are m o re prevalent than secondary orga nics with exceptions such as

those found in Los Angeles where co nditions of clear sk ies an d high photo c hem i cal smog are

frequent. Although secondary organi c aerosol was thought to be m i ni m a l during winter in central

California, recent an alyses (Strader et al., 1998 ) d e m onstrate that it could be as m u ch as 20% of

twenty-four hour organic carbon in som e sam p le s. This occu rs because low wintertim e

tem p eratures lower the saturation vapor pressure for sem i -volatile organic com pounds. This is

probably m i nor during winter and f a ll when photochem i cal reactions are not dom inant.

The exact precursors of secondary organics ar e not well understood, but they are believed to

consist of heavy hydrocarbons with more than seven carbon atom s. Odum (1997) identifies

arom atics as the m a jor group of commonly m easur ed reactive organic ga ses that affect both

ozone and secondary aerosol form ation.

475. Source Modeling Tech niques

Before selecting a m odeling technique, it is wise to estab lish a conceptua l model. A

conceptual model describes the re levant physical and chem ical pr ocesses that affect em issions,

transpo r t, an d transf orm a tion spe c if ic to the regi o n of interes t

. It is the starting point for any

source apportionm ent process. Conceptual m odels take advantage of the large body of scientific

knowledge already acquired. They id entify the sources that are likel y to be present and elim inate

those that are not. They exam ine m e teorological conditions that a ffect concentrations and focus

further m o deling on the conditions c onducive to the high concentrations.

Modeling te chniques r e levant to attribution a r e s p lit into s e v e ral catego ries dependin g

com p lexity, physical attributes, purpose and cost to execute. The following tables on pages 55 to

62 provide a m a trix of detailed inform ation on specific m o dels grouped in these categories.

a. Puff Modeling Techniques

These m odels are b a sed on a Lagran gian fram ew ork where air parcels are track ed spatially

and tem porally. They can include ch em ical m echan ism s as well as deposition effects. The m o st

commonly used puff m o del is the C A LMET/CALPUFF (Scire et al., 2000).

b. Grid Modeling Techniques

For estim ating PM 2. 5 levels, Eulerian m odels that in clude aerosol m odules sim u lating the

physical and chem ical processes governing particulat e concentrations in th e atm o sphere are m o re

suitable than Lagrangian models such as plum e trajectory m o dels. Eulerian three-dim e nsional

models m a y use either a sim p lifie d treatm e nt of atm o spheric chem istry (usually used to address

long-term particulate concentrations at urban sites) or include a m o re detailed atm o spheric

chem istry tr eatm e nt (us u ally us ed to sim u la te only a few days of episodes due to their

com positional cost).

Commonly used long-term Eulerian models w ith sim p lified atm o spheric processes include

(Seigneur et al., 1997):

• Urban Airshed Model V e rsion V (U AM-V).

• Urban Airshed Model with version V with Linea r Chem istry ( UAM-LC)

• Regulatory Modeling S y stem for Aerosol and D e position (R EMSAD).

Short-term Eulerian m o dels with co mplex atm o spheric processes include:

• Urban Airshed Model V e rsion V with Aerosols (UAM-AER O),

• Urban Airshed Model with Aeroso l Inorganic Module (UAM-AIM).

• SARMAP Air Quality Model w ith A e rosols (SAQM-AERO).

• Community Multi-sc a le Air Quality Model (CM AQ)

48• Com p rehensive Air Quality Mode l with ex tensio ns (CAMx)

All of the above m e ntioned Eulerian models ha ve been developed by va rio u s scien tis ts f r om

univers ities, f e deral and s t ate agencie s , and th e pr ivate secto r . These par tic ulate air qu a lity

models provide a three-dim e nsional treatm e nt to sim u late the fate and transport of atm o spheric

contam inants. All of thes e Euler i an models in clud e gas phase chem istr y and aerosol dynam i cs

and sim u late atm o spheric inorganics (such as sulf ate, nitrate, and ammonium ), but som e of these

models do not include the treatm ent of organics (i.e., REMSAD and UAM-LC).

c. Lagrangian Trajectory Model Tech niques

The advantages of using Lagrangian models ar e the ease of u s e, the ab ility to perform m a ny

trajecto r ies and perform back trajectories. Commonly used Lagrangian models include

HYSPLIT ( D raxler and Hess, 1997) and FLEXPART (Stohl and Siebert, 2001).

d. Meteoro log i cal Modeling Techniques

Meteorological m odels describe transport, disp ersion, vertical m i xing, a nd moisture in tim e

and space. Meteoro l ogical m odels consist of strai ght line, in terpolation (term e d diagnostic), and

first principle (term e d prognostic) form ulations, with increasing levels of com p lexity and

requirem e nts for com put ational and data resources.

The straight line m odel is applied to hourly wind directions from a si ngle monitor, assum i ng

an air m a ss travels a distance equal to the wind velo city in the m easured di rection, regardless of

the dis t ance f r om the m o nitor i ng site. This m odel is applicable for a few hour s of transport in flat

terrain, typically for evaluating a single em issions source.

Interpo l a tio n m odels integrate wind speed and directions f r o m m u ltiple m easurem ent

locations, including upper air m easurem ents provi de by rem o te sensors or balloon launches. The

more advan ced of these models allo w barriers, s u ch as m ountains, to be p l aced between

monitors. Wind fields, therefore, show different directions and ve locitie s at dif f e rent horizon tal

and vertica l position s . In terpo l ation wind m odels are app lic a b le to dom ains with a lar g e num ber

of well-placed m onitors and for estim a ting the move m e nt of air m a sses from m a ny s ources over

transpo r t tim e s of m o re than ha lf a day. The number and placem ent of monitors, es pecially upp er

air m onitors, is especially im portant in m ountai nous terrain and in coastal areas where winds are

unusual.

First principle m odels (Stauffer and Seam an, 1994; Seam an et al., 1995; Koracin and Enger,

1994) em body scientists’ best knowledge of atmospheric physics and therm odyna m i cs,

em ploying basic equations for conservation and transfer of energy and m o m e ntum . Also known

as “prognostic m odels,” first principle m odels pur port to need no data other than values from a

sparse upper air network for interpolation. They are com putationally intensive, often requiring

supercom puters but have becom e m o re practical and cost-effectiv e as workstation and desktop

com puters becom e m o re powerful. Modern versions use “four-dim ensional data assim ilation” or

FDDA that com p are m o del-calculated wind, humidity, and temperature fields with

m easure m ents and “nudge” m odel outputs toward observations.

49A m o re complex m e teorologica l m o del is no t ne cessar ily a b e tte r m odel for a spec if ic

application. One of the most widely used fi rst principle m o del is the Fifth-Generation

NCAR/Penn State Mesoscale Model or MM5 m o del (Grell et al., 1995). T h e MM5

m e teorological m odel has been adopted as the plat form for central California air quality studies

(Seam an et. al., 1995). MM5 input data consist of wind speed, wind direction, tem p e r ature,

atm o spheric pressure, and relative hum i dity at ground level, within th e boundary layer, and

above the boundary layer. In m a ny cases in valle y situations ten-m e ter vertical resolution is

needed within the surfac e 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 (W atson et al., 1998). Tim e

resolu tion is at le ast hou rly f o r thes e m easur em ents. Measu r em ents are needed where large

differences are expected, although th is is largely unknown for winter.

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53Source Modeling and Back Trajectory Attribution TechniquesTable 2. Puff, Visibility and Trajectory Modeling Attribution TechniquesAir Quality ModelsVisibilityModelsLagrangian Trajectory ModelsEvaluation CriteriaCALPUFFVISCREENPLUVUEIIHYSPLITRAPTADFLEXPARTChemical MechanismsDoes the modelsimulateaqueous phase chemistry?(Ifit does whatchemistrymechanismisused anddoesitinclude fog/cloud chemistry)Yes; reactions forSO4and NO3NONONONONODoes the modelsimulate gasphase chemistry?(ifit does,what chemicalmechanismisimplemented)YesNONONONONOSimulate secondaryorganicaerosols4 species modelNONONONONOInorganic PM (i.e.ions, SO4,NO3, etc)SO4, NO3NONONONONOSizedistributions (sectional ormodal)CoarseandfinemodesNONOOnlyfor depositionNOApplicable spatial scalesMicro- toregionalscaleNeighbor-hood tourban scaleNeighborhood toglobal scaleNeighborhood toglobalscaleNeighborhood toglobal scaleApplicable temporalscales(episodic orlongtermapplications)Episodic orLongtermN/AN/AEpisodicorLongtermEpisodic orLongtermEpisodic orLongtermDoes the modelhavethecapabilitytodistinguishBART sources?YESYESYESYESYESYESDoes the modelhavethecapabilitytoingestfieldmeasurements(PM,HNO3,H2O2, NH3, etc)NO; onlyasbackground valuesNONONONONO55AirQualityModels (continued)VisibilityModelsLagrangian Trajectory ModelsEvaluation CriteriaCALPUFFVISCREENPLUVUEIIHYSPLITRAPTADFLEXPARTVisibility ModelingDoes the modelsimulatebackground regional hazeNONONONONONOPoint sourcetreatment (plumerise, plume ingrid)YESYESYESYESYESYESDoes the modelhavethecapabilitytocalculate wetanddrydeposition?YESNONOYESNOYESVisibility treatment:(extinction,deciview, visualrange)Extinction(totaland forSO4, NO3,EC,OC, fine,coarse),Deciviews**NONONOInput RequirementsMeteorological datarequired(single site, gridded, numberof levels,etc)Gridded; abletorun with singlesite**Gridded(FNL,EDAS,MM5)Gridded(UseHOTMAC’sprediction)Gridded(ECMWF, MM5)Emission data required (singlestack,multiplepoint sources,gridded, etc)Multiplepoint,area, volume**Multiplepointsources, griddedinventorySingle stack ormultiplepoint sources*Allow for initialand boundaryconditions(is it requiredornot applicable)YESNONONONONOHasthe model beencomparedagainst field program data?Hasthe model been peerreviewed?YESYESYESYESYESNODoes enough data exist nowto runthe model (Does dataexistin a formatreadyfor themodel?Are currentdatabasesadequate for themodel?)Use CALMETwith existingstations orMMdataYESYESGridded met dataavailable on NOAAARL ftp site (FNL,EDAS); adaptableto readin MM5Use HOTMAC to obtainwind andturbulence data*56AirQualityModels (continued)VisibilityModelsLagrangian Trajectory ModelsEvaluation CriteriaCALPUFFVISCREENPLUVUEIIHYSPLITRAPTADFLEXPARTPracticalConsiderationsCoststo run (hardwareplatforms,filestorage,operating system)Inexpensive; PC,Windows, up to20GB (withCALMET)Inexpensive; PC,DOS,1MBInexpensive;PC, DOS,1MBInexpensive; PC,Windows, 20MBInexpensive: LinuxPC,RedhatInexpensive;Linux PC, Redhat; can becompiled onPCHave protocolsor proceduresbeen developedto run andinterpret themodel?FLAG for ClassIAQRVsFLAG for ClassIAQRVsFLAG forClass IAQRVsNONONOIsthe sourcecodeavailable?YESYESYESNOYESYESAre beginning user trainingclassesavailable?YES; byEarthTech, BEELineEPA; APTIEPA; APTINOYESNOAre user support groupsavailable?YES; onelist-serveYES; throughEPASCRAMYES; throughEPA SCRAMNOYESNOLevel ofexpertise requiredtorun andinterpret results(Level ofLinux, UNIX, PCskills required)Moderate; abletorun PC DOSprograms;knowledge ofatmosphericchemistry &physicsSimple; ableto run onPC DOS and windowsSimple; ableto run onPCDOS andWindowsSimple; ableto runon PC runningWindowsSimple torun:GUIallowsthe usertoeasilyrun the programKnowledge isneededtointerpret resultsModerate; mustcompile sourcecode forPC, Linuxor UNIX; knowledge of metinput formatsOutput visualization requiredto interpret output numbersYESNONOYES; has built invisualizationYES; has built invisualizationYESOther strengths****Applicable from buildingscaletoterrainscale.*AvailabilityEarthTechwww.src.comEPAEPANOAAAirResourceLaboratory;www.arl.noaa.gov/ss/models/hysplit.htmlYamadaScience &Art;http://www.ysasoft.comhttp://www.forst.unimuenchen.de/EXT/LST/METEO/stohl/flexpart.html*No information available atthistime57Source Modeling and Back Trajectory Attribution TechniquesTable 3. Eulerian Grid Based Modeling Attribution TechniquesAir Quality ModelsGrid ModelsEvaluation CriteriaCMAQREMSADCAMxUAM-AEROUAM-VPMURMCalGridChemical MechanismsDoes the modelsimulateaqueous phasechemistry?(If itdoes,whatchemistrymechanismisused anddoesitincludefog/cloudchemistry)35 equilibriaand99reactionsforSO4andNO31 reactionforSO41 reactionforSO435 equilibriaand99reactionsforSO4andNO3*2 reactionsfor sulfateNODoes the modelsimulate gas phasechemistry?(ifitdoes,whatchemicalmechanismisimplemented)CBM-IV (93reactions) orRADM2(158 reactions)CBM-IV(93 reactions) orRADM2 (158reactions)CB-IV with enhancedisoprene orSAPRC97SAPRC97(185 reactions)CBLCCYES (both CBM-IVand SAPRC)(about 100reactions)Simulate secondary organicaerosolsPrimaryfromemissions; secondaryfrom organicsPrimaryfromemissionsPrimaryfromemissions; secondaryfrom gas phasereactions oforganicprecursors usingyieldsSecondaryfrom gasphase reactionsoforganic precursorsusing yieldsPrimaryfromemissionsPrimaryfromemissions; secondaryfrom gas phasereactions oforganicprecursors usingproductionfractionsNOInorganic PM (i.e.ions, SO4, NO3, etc)SO4, NO3and othermaterialSO4, NO3, NH4, andother materialSO4, NO3, NH4, Cl,other ionsandmaterialsSO4, NO3, NH4, Cl,other ionsandmaterialsSO4, NO3, NH4, andother materialsSO4, NO3, NH4, Cl,other ionsandmaterialsNOSize distributions(sectional or modal)Lognormal;threemodes: Aitken,AccumulationandcoasePM2.5fraction,coarsemodeDiscrete bins, userspecified upto 10Discrete bins, userspecified upto 10Lognormal bins; userspecifiedDiscrete bins; userspecifiedNOApplicable spatial scalesMesoscaleMesoscaleMesoscaleUrban scaleUrban scaleMesoscaleUrban to RegionalApplicabletemporal scales (episodic orlongtermapplications)EpisodicLongtermEpisodicEpisodicEpisodicEpisodicEpisodicDoesthe modelhavethecapabilitytodistinguish BARTsources?YES; using plume ingridNOYESNONONONO58Air Quality Models (continued)Grid ModelsEvaluation CriteriaCMAQREMSADCAMxUAM-AEROUAM-VPMURMCalGridChemical Mechanisms(continued)Does the modelhavethe capabilitytoingestfieldmeasurements(PM,HNO3,H2O2,NH3,etc)YES; as initialandboundaryconditionsUses default profilesYES; as initialandboundaryconditionsYES; as initialandboundaryconditionsYES; as initialandboundaryconditionsYES; as initialandboundaryconditionsYES; as initialandboundaryconditionsVisibility ModelingDoes the modelsimulate background regionalhazeYESYESYESYES(withprocessing)YESYESNOPoint sourcetreatment (plumerise, plume ingrid)Plume in gridNOPlume in gridNONONONODoesthe modelhavethecapabilitytocalculate wet and drydeposition?YESYESYESYESYESYESYESVisibility treatment:(extinction,deciview,visual range)Extinction(totalandfor SO4, NO3, EC,OC, fine,coarse),DeciviewsExtinction(totalandfor SO4, NO3, EC,OC, fine,coarse),DeciviewsNOYES (withprocessing)**NOInput RequirementsMeteorological datarequired (singlesite,gridded, numberof levels,etc)GriddedGriddedGriddedGriddedGriddedGriddedGriddedCALMETdateEmission datarequired (singlestack,multiplepoint sources, gridded, etc)GriddedGriddedGriddedGriddedGriddedGriddedGriddedAllow for initialand boundaryconditions (isit requiredornot applicable)YESYESYESYESYESYESYESHasthe model beencomparedagainst fieldprogram data?Has the model been peerreviewed?YESYESYESYESYESYESYESDoes enough data exist nowto run the model(Does data existin a format readyfor themodel?Arecurrent databasesadequateforthe model?)Extensive need fordetailedemissionsand meteorologicalfieldsExtensive need fordetailedemissionsand meteorologicalfieldsExtensive need fordetailedemissionsand meteorologicalfieldsExtensive need fordetailedemissionsand meteorologicalfieldsExtensive need fordetailedemissionsand meteorologicalfields*Extensive needfordetailedemissionsand meteorologicalfields59AirQualityModels (continued)Grid ModelsEvaluation CriteriaCMAQREMSADCAMxUAM-AEROUAM-VPMURMCalGridPracticalConsiderationsCoststo run (hardware platforms,filestorage,operating system)Can be runinexpensively;LinuxPC, up to1TB(annual runs)Can be runinexpensive; LinuxPCCan be runinexpensively;LinuxPCCan be runinexpensively;LinuxPCCan be runinexpensively;LinuxPCCan be runinexpensively;LinuxPCCan be runinexpensively;LinuxPCHas a protocol orprocedures been developedto runthe model?NO; but RPOs haveregional hazeprotocolsNO; but RPOs haveregional hazeprotocolsNONONONONOIsthe sourcecodeavailable?YES*YESYESYESYESYESAre beginning user trainingclasses available?YES; throughEPAand RPOsNOYES; through SAIand RPOsNONONONOAre user support groups available?YES; throughEPAand RPOsNONONONONONOLevel ofexpertise requiredtorunandinterpret results(LevelofLinux,UNIX, PCskills required)Considerableexpertise inUNIX orLinux; knowledge ofatmosphericchemistry &physics*Moderate toextensiveModeratetoextensive**ModerateOutput visualization requiredtointerpretoutput numbersYES; use PAVEYES; usePAVEYESYESYESYESYESAvailabilityEPA; Sharon LeDuc919-541-1335SAI; Sharon Douglas415-507-7108ENVIRON; RalphMorris 415-899-0700SAI; Sharon Douglas415-507-7108SAI; Sharon Douglas415-507-7108Georgia Tech; TedRussel, Talat OdmanCalifornia AirResourcesBoard*No information available atthistime60Table 4. Meteorological Modeling Attribution TechniquesDiagnostic ModelsMesoscale Prognostic ModelsEvaluation CriteriaCALMETDiagnostic WindModelRAMSMM5Eta*ARPSHOTMACApplicable spatial scalesUrban toregionalscaleUrban toregionalscaleUrban to globalscaleUrban to globalscaleUrban to globalscaleUrban to globalscaleUrban tomesoscaleApplicable temporalscalesHourlytoannualHourlytoannualHourlytoannualHourlytoannualHourlyto annualHourlytoannualHourlytoannualDoes enough data exist inorganizationnow to runthe model?Aretheexistingmonitoringnetworks adequate withinthe domain?Depends onareaDepends onareaDependsonareaDepends onareaDepends onareaDependsonareaDependsonareaFor the prognosticmodels: Capable ofFDDA?N/AN/AYesYesYesYesYesFor the prognosticmodels: Are filesavailable (archived) fromreal-time?N/AN/ANOAA is runningthe model fortheeast coastSeveral federalagenciesandregionalconsortiums arerunning MM5NOAA is runningthe Etamodel andfieldsavailablethrough NCEP ftpsiteIt can beinitializedusingNCEPanalysesfilesaswellasindividualobservationsNoData storage/archivalrequirements forsimulation ofepisodicand annualeventsDepends onmodeling domain,grid size, number ofspeciesand lengthof simulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationDepends onmodeling domain,grid size, numberof speciesandlengthofsimulationSimulateclouds/precipitationfields?Atwhattimeintervals?NoNoYes – minutestodaysYes – minutestodaysYes – minutestodaysYes – minutestodaysYes – minutestodaysHasthe model beencompared againstfieldprogramdata?Hasthemodel beenpeerreviewed?YesYesYesYesYesYesYes61Meteorological Modeling Attribution Techniques (continued)Diagnostic ModelsMesoscale Prognostic ModelsEvaluation CriteriaCALMETDiagnostic WindModelRAMSMM5Eta*ARPSHOTMACToolsavailable tovisualize outputfields?What operatingsystems?Yes – Unix, Linux,WindowsYes – Unix,Linux, WindowsYes – Unix, LinuxYes – Unix, LinuxYes – Unix, LinuxYes – Unix, LinuxYes – Built-inGUI; Unix,Linux,WindowsAre user groups,listservers availablewhenproblems arise?YesYesYesYesNoYesYesCostto run(hardware,software)PC Windows, PCLinux; Free sourcecodePC Linux, UNIX;FreePC Linux; licensedsource codePC Linux, UNIX;Free sourcecodePC Linux, UNIX;Free sourcecodePC Linux, UNIX;Free sourcecodePC Linux; licensedsource codeLevel ofexpertiserequiredtorunandinterpret results(LevelofLinux, UNIX, PC skillsrequired)ModerateModerateHighHighHighHighHighAvailability ofusertrainingYesYesYesYesNoYesYesIsthe sourcecodeavailable?YesYesYesYesNoYesYesHas a protocol orproceduresbeendeveloped torunthemodel?YesYesYesYesYesYesYesStrengthsRelativelyeasytouse; little observeddata neededRelativelyeasytouse; little observeddata neededDetailed 3-D,complex flowintimeDetailed 3-D,complex flowintimeDetailed 3-D,complex flowintimeDetailed 3-D,complex flowintimeDetailed 3-D,complex flowintimeWeaknesses/LimitationsParameterizationdepended;maynotcapturevariousflowsParameterizationdepended;maynotcapturevariousflowsLargecomputertime; timeconsuming todebugLargecomputertime; timeconsuming todebugLargecomputertime; timeconsuming todebugLargecomputertime; timeconsuming todebugNo realtimedatainitialization* WhiletheEtaisnot considereda mesoscale model,ithasbeenused to simulatemeteorology downto 10kmgridscales.The workstationEtaisavailable fromNOAA and used experimentallybysome NWS offices.* No information availableatthistime62

### C. Observational Modeling Techniques

Introductio n

Observation a l or recep to r m odeling refers to a g r oup of analysis techniqu es in which

monitoring data co llected at or in the region of a recep tor are analyzed in various way s in orde r

to infer inform ation about the pollutants and th e sources of the polluta nts causing visibility

im pair m e nt.

These types of m odels often are used as the fi rst technique for source apportionm ent in order

to get an initial understanding of the source-receptor relationships in a region. They are also used

to verify or reconcile determ inistic m odels, a nd to aid in planning intensive m onitoring studies.

Results of observationa l m odels can eithe r be qu antitative or qualitative. Quantita tive results

are es tim ates of the fractions of a m e asured species that can b e attribu t ed to a single so urce or

attributed among several sources or source areas

. Qualitative results in clu d e such inform ation as

the wind directions and other m e teorologica l conditions m o st associated with high

concentrations, or inferences about probable source types based on the relationships between

trace elem ents at a single s ite, or inform ation about source areas based on the spatial and

tem poral patterns in the concentrations of a single species. Often seve ral observational m odels

are used together to form hypotheses about the important source areas a nd source types affecting

the concen trations at a receptor.

Advantages of receptor models are that they are generally qu ick and in ex pensive to run and

requir e re latively little in put data. Disadvantages include the necessity of employing simplifying

assum p tions such as linear relationships and ofte n the results are lim ited to averages over long

periods of tim e or large spatial areas. Subjective user judgm ent is required to choose appropriate

input data and/or in terp ret th e resu lts of m a ny receptor m odels. As an example, suppose the

UNMIX m o del data located a source associated w ith high concentrations of Br, K, elem ental

carbon, and organic carbon, and anothe r source associated with high Se and S. It is the judgm ent

of the m odel e r regarding the re lationship of the individual spec ies to a spe c if ic sour ce that

determ ines that first source is “sm oke” a nd the second is “coal-fired power plants.”

Receptor m odels cou l d potentially b e grouped in several different ways, based on their

different attributes. Here they are somewhat ar bitrarily put into four categ ories, with each

catego r y req u iring increm entally m o re particu l at e data at the recep tor: 1 ) back-trajectory

analyses ; 2) analyses of interspe cies relationships; 3) analys es of spatial an d tem poral patterns;

and 4) analyses that require a un ique trac er. Som e m odels that f a ll into eac h catego ry a r e listed

below:

Category 1. Back-Trajectory Analyses

a. Residence T i m e Analyses including re sidence tim e, source contribution function,

conditional probability, and so forth. These give qualitative info r m ation about source

areas and transport patterns.

63b. Trajectory Mass Balance – Regression of reside nce tim e of back trajec tories in se le cted

source areas agains t con centration s yi elding quantitative so urce attributions.

Category 2. Analyses of Interspecies Relationships

a. Chem ical M a ss Balance (CMB) - Quantitati ve source attributions are ob tained by a

weighted regression of known s ource profiles against m easured concentrations of several

species.

b. UNMIX – By looking for “edges” in th e relationships between species, U N MIX

estim ates bo th the sou r ce profiles and th e quantitative sou r ce contribu tions from each

source.

c. Positive Matrix Fac t oriza tion (PMF) – PMF, like UNMIX, uses the r e la tio nships betw een

species to es tim ate the n u m b er and com position of the sources and the quantitative source

contributions.

d. Enrichm e nt Factors (EF) – The “enrichm e nt” of certain ratio s of trace elem ents is used to

qualitatively infer source types im pacting a recep tor.

Category 3. Analyses of Spati al and Temporal Patterns

a. Em pirical O r thogonal Function Analysis (E OF) – Analysis of spatial and tem poral

patte rns lead ing to qua litative inf o rmation about locations of dom i nant source areas,

frequency and tim ing of source im pacts and m e teorological conditions associated with

them

. b. Receptor M odel Applie d to Patterns in Space (R MAPS) – W ith add ition a l assum p tions

applied to E O Fs, quantitative s ource attributions are es tim ated.

Category 4. Tracer Analyses

a. Tracer Mass Balance Regression (T MBR) – Tracer concentrations, pos sibly weighted by

other facto r s

, are regress e d agains t concentra tion s of the spec ies of inte res t to giv e a

quantitative estim ate of the contribution from the source th at e m itted the tracer.

b. Differential Mass Balan ce (DMB) - The differen tia l ra tios of trac er to po llutant be twee n

source and recepto r are adjusted b a sed on sim p le chem istry and m e teorology to give an

estim ate of the contribution of th e tracer source to the recepto r.

c. Tracer-Aero s ol Gradien t Interp retive Technique (TAGIT) – By com p aring the ratios of

tracer to concentration of in terest at “background” sites to the ratios at tracer-affected

sites, a quan titativ e attr ibution of the trac er sou r c e to the r eceptor is estimated.

Following are brief des c rip tions of each of thes e m odels and a few references giv i ng further

details and exam ples of t h eir use.

1a. Qualitative Back Trajector y Residence Time Analyses

There are several m e thods of statistically analyzing the re lationships between where air

m a sses arrived from and the concentrations m eas ured at a receptor. Th ese include, but are not

lim ited to: 1 ) where was air m o st like l y to a rriv e f r om when concentr ation s are high ; 2 ) if air

arriv e d f r om a given a r ea , what is th e probability that the conc entra tion a t the recep t or was high

when the air m a ss arriv e d there; 3) w h at is the m ean (or m e dian or m a xi m u m or distribution) of

64concentratio ns at th e receptor when air m a sses arrived from a given area. Selected References:

Ashbaugh et al. (1985), Gebhart et al. (2001), Poirot and W i s h inski (1986).

Data Needed : A tim e series of concentrations of the species of interest. One or m o re back

trajectories of three to five da ys duration corresponding to each c oncentration. Back trajectories

can be calcu l ated by any of several m e thods including ATAD, Hysplit, the CAPITA Monte

Carlo Model, as well as others. Standard National W eather Service upper air data can be used as

input, though m o re detailed m e te orological data can be input if available. Dispersion can be

included in som e of these m odels.

Model Assumptions : Errors in trajectory placem ent ar e random and uncorrelated. Variations

in deposition, chem istry, em issions, and so forth, have less influence on m easured concentration s

on average than variatio ns in tr anspo r t direc tions.

Biggest Potential Problem s: Results of these types of anal yses are qualitative rath er th an

quantitative. Results are more st atistically robust when averaged over long tim e periods, usually

a m i ni m u m of one season and preferably several y ears. Nearby sources cannot be resolved. User

judgm ent is required to choose trajectories of an appropriate type, height, and length and also to

choose appropriate defin itions of “h igh” con centrations. Some concentrations that vary

seasonally m a y have all “high” concentra tions in a single season, necessitating som e

com p ensation in the an alysis. Model results are probably m o re appropriate for species such as

particulate s u lfate that are rela tively uniform ove r large spatial sc ales, rather than, for exam ple,

particulate nitrate, which is m o re volatile and s e e m s to be m o re re lated to local source s than

long-range transport.

1b. TrMB (Trajectory Mass Balance)

This is a m u ltip le linear regres sion o f the fre quency of occurrence of trajectory endpo ints in

each of several sou r ce areas against the correspo ndi ng concentrations at the recep tor. The result

is the average attribu tion of a single species am ong up to about 25 source areas over a long tim e,

for exam ple, one season or year, or several year s. Selected ref e rences: Gebhart and Malm (1989),

Stohl (1998).

Data Needed : Tim e series of concentra tions of the pollutant of in teres t at a single site. One

or m o re back trajecto r ies associated with each co ncentration. Input data for these are u pper air

winds, tem p eratu r es, and m o isture ov er a large ar ea. Often data are obtained from the standard

National W eather Service observa tions, but other data such as higher resolution wind fields,

wind profiler data specif i c to a given study, can also be used if available. Em ission data can be

used if available, but m u st vary in tim e to be useful. Si m p le chem istry and/or deposition can be

used if data are availab l e. The user d e fines the size and locations of the source areas to be

considered.

Model Assumptions : Average contributions of each sour c e area can b e written as a linear

com b ination of the contributions from severa l source areas. Average chem istry and deposition

are adequ a te to explain average sou r ce contributio ns. Errors in back traj ectories are ran d om and

norm a lly distributed.

65Biggest Potential Problem s: No attribution to single sources—only to source areas.

Attribu tions must be aver ag ed over long tim e periods. Nearby sources cannot be m ode led

accurately. Subjectivity in choosi ng source areas. Violation o f a ssum p tions of linear chem istry.

2a. Chemical Mass Balance (CMB)

CMB is a multip le linea r regres sion o f m easured concentr ation s agains t kn own source

profiles. It is used for the attribution of all m easured chem ical species am ong several sources for

each concen tration m easurem ent period for a sing le m onitorin g site. Reg r essions a r e weighted by

the uncertainties in both the source profiles and the concentrations. Selected References: W a tson

et al. (1984 and 2001).

Data Needed : Concentrations and measurem ent uncertain ties of both the chem ical species of

interest and of as m a ny trace elem ents as possib l e are necessa ry for each tim e period and location

for which attributions are desi red. IMPROVE data can be used. A source profile is needed for

each source. These are the relativ e amounts of each em itted chem ical species and th e

uncertainties in thes e values.

Model Assumptions : Compositions of source em issions ar e constant over the period of

am bient and source sam p ling. Chemical species do not react with each o t her, for example, they

add linearly. All sources with a poten tial for significantly con t ribut ing to the recep tor have been

identif i ed an d have had their em issio n s chara c te rized. The sou r ces’ com p osition s ar e linearly

independen t of each oth e r. The number of sources or source catego r ies is less th an or equal to the

num b er of chem ical species. Measurem ent un certainties are random , unc orrelated and norm a lly

distributed.

Biggest Potential Problem s: The m o del cannot directly appor tion secondary species such as

sulfates, nitrates, and secondary organics. There are som e workar ounds for this. The usual tactic

is to apportion these species between the known prim ary sources and a source designated as

“secondary particles.” It is also possible to use “fract ionated” or “aged” source profiles where an

attem p t is made to pr e-d e term ine the chem ical processes that occurr ed between source and

recep tor and then adju st the source p r ofile acco rd ingly. Obtaining all n ecessary sou r ce profiles

can be difficult. In som e studies, other rece ptor m odels have estim ated sou r ce profiles.

2b. UNMIX

For a selection of m easured species, UNMIX uses singular value decom position with

additional non-negativity constrai nts to estim a te the num ber of sources, the source compositions,

and the source contributions to each sam p le at a single m onitoring site. U N MIX attempts to find

the “edge s” in the rela tio nshi ps betw een species and relates these to sou r ces. Selected

References: Henry (1997a, 1999), Lewis et al. (1998).

Data Needed : A tim e series of concentrations of se veral species m easured at a s i ngle site.

IMPROVE data can be used.

66Model Assumptions : Concentrations are linear com b inations of an unknown num b er of

sources of u nknown composition. Contribution s fr om sources are pos itive. Source com positions

are app r oxim a tely constant in tim e. For each sou r ce th ere a r e som e sam p l e s that contain little or

no contribution from tha t source.

Biggest Potential Problem s : A maximum of seven sources ca n be identified. There is som e

subjectivity in choosing fitting sp ecies, num ber of sources, h o w to deal with m i ssing or below

detection lim it values, and which tim e periods a nd species should be analyzed together. Sources

of secondary species will prob ably v i olate the as sum p tion of constan t sou r ce com position. This

can cause m u ltip le sou r ces to be iden tified for a s i ngle phys ical source that im pacts the recep t or

under differing conditions. Supplem e n tal analysis m a y be required to deconvolute these.

2c

. Positive Matrix Factoriz ation (PMF)

PMF uses an iterative weighted least squares m e thod to deco mpose a time-by-species m a trix

to estim a te the num ber and com position of the so urces and th e contributio ns of each source to

each m easured species. It will also c a l cula te e rror estim ates for these v a lue s . Selec t ed

References: Paatero and Tappe r (1994), Paatero (1997); Xie et al. (1999).

Data Needed : A tim e series of conce n tra tions an d their uncer tain ties f o r s e vera l spec ies at a

single m onitoring location. IMPROVE data can be used.

Model Assumptions : Concentrations are linear com b inations of an unknown num b er of

sources of u nknown composition. Contribution s fr om sources are pos itive. Source com positions

are approxim a tely constant in tim e.

Biggest Potential Problem s: Correlations in de tec tion lim its or uncer tain ties as well as in

concentratio ns can inf l ue nce the results. For exam ple, PMF may detec t po sitiv e cor r elations

between spe c ies e ithe r d

u e to sourc e activ ity (des irab le) o r measurem ent protoco l cha nges

(undesirable).

2d. Enrichment Facto r s (EF)

The differences in ratios of elem ental con centrations between a reference sam p le and a

m easured sample are used to determ ine how sour ces m a y have “enriched” the concentrations of

certain species. Som e exam ples inclu d e: high A l/Ca has been linked to Saharan dust, high Br/Pb

m a y indicate lead is linked to autos rather than industry, high Se is linke d to coal burning, heavy

m e tals are linked to sm elting, V and Ni are linked to residual oil com bustion. Selected

References: Lawson and W i nchester (1979), Pare kh et al. (1989), Perru (1997), Roshid and

Griffiths (1993).

Data Needed : Tim e series of concen trations of tr ace elem ents and the sp ecies of in te rest.

Som e historical inform ation about the “standard” crusta l, sea salt, or other ratios for a region.

Model Assumptions : Ele m ental ratio s depend m o stly on en richm e nt of trace elem ents by a

source and have less dependence on m e teor ology. The reference ratios are constant.

67Biggest Potential Problem s: Attributions are generally to so urce areas, not to single sources.

3a. Empirical Orthogonal Function Analysis (EOF)

A f e w (typically two to s i x) spa tia l p a tte rns th at explain m o st of the c ovariance in the s p atia l

and tem poral patterns of a m easured species are o b tained by singular value decom position.

Associations between the spatial patterns and source areas and/or tr ansport of air pollutants into

the study area can often be infe rred, but are qualitative. The or iginal data m a trix can be

approxim a tely reconstructed by linearly recom b in ing thes e few patterns. Selected Reference s :

Gebhart and Malm (1997), Henry et al. (1991), Malm et al. ( 1990), Malm and Gebhart (1997).

Data Needed : Measurem ents of a single air pollutant of interest at sev e ral sites for several

tim e periods. Typically used are c oncentrations m easured at fifteen to f o rty sites f o r th irty o r

more tim e periods. There m u st be more tim e peri ods than sites. Data from special studies are

often analyzed in this way.

Model Assumptions : On ly a f e w spatial pa tte rns a r e requ ir ed to explain a large m a jority of

the covariance in the spa tial and tem poral patterns. These pa tterns have a physical m e aning that

can be inferred, such as transport of em issions from a source into the study area or local

stagnation.

Biggest Potential Problem s : Source attributions are qualit ative, not quantitative and

inte rpre tatio n of the spatial pa tte rns is subjec tiv e. The m odel requires a si te by tim e matr ix with

no m i ssing values, so som e method of elim inating or filling in both m i ssing and below detection

lim it values is neces sary.

3b. Receptor Model Applied to Patterns in Spa ce (RMAPS)

Determ ines the average attribution o f a single sp ecies am ong a few source areas by

decom posing the tim e by site m a trix of concentr ations into a source m a trix and a time weighting

m a trix. Sim i lar to UNMIX, the edges in the s catterplots between s ources and non-negativity

requirem e nts are used to constrain the identifi cation of sources. Selected References: Henry

(1997 b, c, d), W h ite (1999).

Data Needed : Tim e series of concentrati ons of th e pollu tant o f interes t at severa l sites within

a region. The m odel 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 m onths.

Model Assumptions : Av erage con t ributions of each sourc e area can b e written as a n onnegativ e lin ear com b ination of the m a jor principal com ponents of the d a ta. The spa t ial s cale of

the pollutan t is la rge co mpared to th e spacing of the sam p ling sites.

Biggest Potential Problem s : If the second m a jor assum p tion is violated, the concentrations

of the pollutant at each s ite will have little correlation with the other s ites; therefore the m odel

would not apply because it relies on the comm on variations am ong sites.

684a. Tracer Mass Balance Regress i on (TMBR) also called Multiple Linear Regression

(MLR) on Marker Species

Estim ates th e attribu tion of the aeros ol specie s of interest by a sour ce or source type, which

em itted or e m its a unique tra cer. Un certainty esti m a tes are also genera ted if included in the

regression. The m odel is a regressi on of the tracer, possibly weight ed by other factors against the

species of interest. Selected Refe rences: Malm et al. (1989 a, b, c).

Data Needed : Tim e series of a m bient concentrations and the i r uncer tain ties f o r the ae rosol

species being apportioned and also the tra cer species.

Model Assumptions : Th e tracer(s) are uniquely em itted by non-overlappin g groups of

sources. Sou r ce em ission s are cons tant over the p e riod of am bient s a m p ling. Depositio n and

conversion are constant f o r all sam p ling pe riods and can be estim ated by first-order

approxim a tions. In the WHITEX a pplication, sulfate oxidation rates were assum e d to be related

to RH, where RH was a surrogate for tim e the ai r m a ss spent in clouds. Measurem ent errors are

random , unc orrelated, and norm a lly distributed.

Biggest Potential Problem s : Tracer concentration s are not of ten available. Source profiles,

deposition, and conversion all vary in tim e and space.

4b. Differen tial Mass Balance (DMB)

DMB estimates the fraction of a spec ies of inte res t attribu t able to a single source that can be

tagged with a unique tracer. The ratio of m easured tracer to m easured sulf ate or n itr a t e is

assum e d to be related to the fr actional contribution of the traced source. The ratio is adjusted

based on the estim ated difference between the rati o at the sou r ce to the ratio at the receptor.

Travel tim es between the source and recep tor ar e estim ated ba sed on winds, and then b y using

sim p le estimates of dispersion, deposition and ox id ation, the tracer to seco ndary species ratio is

adjusted. Selected Refe rences: Malm (1989b, c).

Data Needed : Tim e series of tracer concentratio ns and concentrations and em ission rates of

the spec ies o f interes t an d its pr ecurs ors, f o r ex ample, sulfur dioxide a nd sulfate. Estim ates of

wind speed and direction, m i xing height s, deposition and oxidation rates.

Model Assumptions : W i nd direction does not change dur ing transport time. The rates for

deposition and conversion are firs t-order and in varian t in sp ace a nd tim e along the transport path

between the source and the recep tor. The ratio of the em ission rates for the species of interes t or

its parent species and the tracer is known.

Biggest Potential Problem s : Si m p le chem istry and m e te orology m a y not be adequate,

especially for long transport tim e s, complex terr ain, and/or changing chem ical regimes. Tracer

concentrations unique to a singl e sou r ce are often not available. The fraction of attribu t able

concentration m a y only be calcula ble to within a range based on the reasonable ranges of rate

coefficients.

694c. Tracer

- Aerosol Gr adient Inter p retive Tec hnique (TAGIT)

Results are the attribution of prim ary or s econdary species associated with the source

“tagged” by a tracer release. TAGIT com putes attributions on a sam p le period-by-sam p le period

basis. For each sam p le period, background concentrati on of the species of interest is determ ined

by averag in g the concen trations of th e speci es at nearby sites th at do not have tracer

concentrations and are significantly above background. These sites are presum ed to be

unaffected by the tracer-tagged source and t hus represent the av erage background. This

background for each sample period is then subtract ed from the concentration of the species of

interest at impacted receptor s ites for corr esponding sam p le periods. The difference is the

concentration attributable to th e tagged source. G r een (2001), K uhns et al. (1999), Pitchford et

al. (2000).

Data Needed : Concentrations of a unique tracer from a source of interest and sim u ltaneous

concentrations of a pollutant of in terest at several sites in a region.

Model Assumptions : There is no impact from the tagged so u r ce if the tra c er conc entr ation is

less than the level considered to be “s ignificantly” above it s’ background. Background

concentrations of the species of interest do not vary system atically in space.

Biggest Potential Problem s : Assum i ng no im pac t from the tagged source when tracer is not

statistically above background can lead to an und erestim a tion of attribu tion. Measured tracer

concentrations often have la rg e unce r ta inties. So m e sam p lin g periods will have a negative

concentratio n attributed to the tagg ed source.

References :

Ashbaugh, Lowell L., W illiam C. Malm , W illy Z. Sadeh (1985) “A Residence T i me Probability

Analysis of Sulfur Concentrati ons at Grand Canyon National Park,” Atmospheric

Environment , 19, 1263-1270.

Gebhart, Kristi A., Sonia A. Kreidenweiss, and W illiam C. Malm (2001) “Back-Trajectory

Analyses of Fine Particu l ate Matter Measured at Big Bend National Pa rk in the His t or ical

Database and the 1996 Scoping Study” Science of the Total E n vironment , 276, 185-204.

Gebhart, Kristi A. and William C. Ma lm (1989) “Source Apportionm ent of Particulate Sulfate

Concentrations at Three National Parks in th e 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. Ma lm (1997) “S patial and Tem poral Patterns in Particle Data

Measured during the MOHAVE Study,” J. of the Air & Waste Managem e nt Assoc. , 47,

119-135

Green, Mark, 2001 (Inform al paper, BRAVO)

70Henry, R.S. (1997a) “History and fu ndam e ntals of m u ltivariate air quality recep t or models,”

Chemometrics and Intelligent Laboratory Systems , 37

, 37-42.

Henry, R.C. (1997b) “Receptor Mod e l Applied to Patterns in S p ace (RMAPS) Part I – Model

Description” JA&WMA , 47, 216-219.

Henry, R.C. (1997c) “Receptor Mod e l Applied to Patterns in S p ace (RMAPS) Part II –

Apportionment of Airborne Particul ate Sulfur from Project MOHAVE” JA&WMA , 47,

220-225.

Henry, R.C. (1997d) “Receptor Mod e l Applied to Patterns in S p ace (RMAPS) Part III –

Apportionment of Airborne Particulat e Sulfur W e stern W a shington State” JA&WMA , 47,

226-230.

Henry, R.C. (1999) “Comparing a new algorithm with the classic m e thods for estim ating the

num b er of factors,” Chemometrics a nd Intellig en t Laboratory Systems , 48, 91-97.

Lewis, C.W

. , R.C. Henry, and J.H. Shreffler (1998) “An Exploratory L ook at Hydrocarbon Data

from the Photochem i cal Assessm en t Monitoring Stations Network” J. Air & Waste

Management Assoc . 48, 71-76.

Henry, Ronald C., Yi-Jin W a ng, and Kristi A. Gebhart (1991) “The Relationship Between

Em pirical O r thogonal Functions an d Sources of Air Pollution,” Atmospheric

Environment , 25A, 503-509.

Kuhns et al. (1999)

Lawson, D.R. and J.W

. W i nchest er (1979) “A Standard crusta l aerosol as a reference for

elem ental en richm e nt factors,” A t mospheric Environment , 13, 517-523.

Malm , W illiam C, Hari K. Iyer, and Kristi Gebh art (1989 a) “Application of Tracer M a ss Balance

Regression to W H ITEX Data” in Trans actions of the A&W M A/EPA Spec ialty

Conference: Visibility and Fine Particles, Estes Park, CO, Oct. 15-19, 1989, pp. 806-818,

C.V. Mathai, Ed.

Malm , W illiam C., Hari K. Iyer, Joh n W a tson, and Douglas A. Latim er (1989b) “Survey of a

Variety of Recepto r Mod e ling Techn i ques,” Trans action s of the A& W M A/EPA Specialty

Conference-Visibility & Fi ne Particles, Estes Park, CO, Oct. 15-19, 1989, pp. 781-805,

C.V. Mathai, Ed.

Malm , W illiam

, Kristi G e bhart, Dou g las Latim e r, Thom as Cahill, Robe rt Eldred, Rog e r Pie l ke,

Roger Stocker, and John Watson (1989) “Natio nal Park Service Report On the W i nter

Haze Inten s ive Tracer Experim e nt” Nationa l Park Service, A i r Quality Division,

Research Branch, Fort C o llins, CO, Dec. 4, 1989

71Malm , W illiam C. and Kristi A. Gebhart (1997 ) “Source app o rtionm ent of sulfur and light

extinction using receptor m odeling techniques,” Journal of the Air & Waste Management

Association , 47, 250-258.

Malm , W illiam C., Kristi A. Gebhart, and Ronald C. Henry (1990) “An In vestig ation of the

Dom i nant Source Regions of Fine Sulfur in th e Western United States an d their Areas of

Influence,” Atmospheric Environment , 24A, 3047-3060.

Paatero, P. (1997) “Least squares formulati on of robust non-negative factor analysis,”

Chemometrics and Intelligent Laboratory Systems , 37, 15-35.

Paatero, P. and U. Tapper (1994) “Po s itiv e Matrix Factorization – a Nonnegativ e Factor Model

with Optim al Utiliza tion of Error-Estim ates of Data Values,” E n vironmetr i cs , 5, 111-126.

Parekh, P.P., B. Ghauri, and L. Husain (1989) “Identification of Pollution Sources of

Anom alously Enriched Elem ents,” Atmospheric Environment , 23, 1435-1442.

Perru, K.D., T.A. Cahill, R.A. Eldred, and D.D. Dutcher (1997 ) “Long-ran g e Transport of North

African dust to the eastern United States,” J. Geophys. Res ., 102, 11,225-11,238.

Pitchford, Marc, Mark Green, Ham p den Kuhns, and R obert 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

m a y be a conference paper.)

Poirot, Richard L. and Paul R. W i shinski ( 1986) “Visibility, Sulfat e and Air Mass history

Associated with the Su mmertim e Aerosol in Northern Vermont,” Atmospheric

Environment , 20, 1457-1469.

Roshid, M. and R.F. Griffiths (1993) “Am b ient K, S, and Si in fine and coarse aerosols of Kuala

Lum pur, Malaysia,” Journal of Aerosol Science , 24, 597-601.

Stohl, A. (1998) “Com putation, Accuracy and A pplications of Traject ories - A Review and

Bibliography,” Atmospheric Environ m ent , 32, 947-966.

Watson, John G., John A. Cooper, and Jam e s J. Huntzicker (1984) “The E ffective Variance

Weighting for Least Squares Calculations Appl ie d to the M a s s Balance R ecepto r Mod e l,”

Atmospheric Environment , 18, 1347-1355.

Watson, John G., Judith C. Chow and Eric M. Fujita (2001) “Review of volatile organic

com pound s ource apportionm ent by chem ical m a ss balance” Atmospheric Environment

, 35, 1567-1584.

White, W a rren H. (1999) “Phantom Spatial Factors: An Example,” J. o f

th e Air & Was t e

Management Assoc. , 49, 345-349.

72Xie, Yu-Long, Philip K. Hopke, Pentti Paatero, L e onard A. Barrie, and Sh ao-Meng Li (1999)

“Identification of Source Natu re and Seasonal Variations of Arctic Aerosol by Positive

Matrix Factorization,” J. of the Atmospheric Sciences , 56, 249-260.

7374Table 5. Observational Modeling TechniquesAnalyses of Back TrajectoriesCriteriaResidenceTime&Source ContributionFunctionConditionalProbabilityConcentrationStatistics byAir MassHistory (Mean,Max,Median)ClusterAnalysisHit–No HitTrajectory Mass BalanceQuantitative Source Attribution?NoNoNoNoNoYesNumberof sources that can bedistinguished?Typicallyonly about10 orfewertransportpatterns aredistinguishableMaximumof 10-151/grid cell,typically50x50 (2500),thoughusuallyonlyabout 5-20transport patterns aredistinguishable2-15120-30maxAveraging TimeofResult?Weeks toyearsWeeksto yearsWeeksto yearsWeeksto yearsDaily,possiblyhourlyMonths to yearsPrevious use at Class I Areas?(SeeText for References)YesYesYesYesYesYesMagnitude ofimpacts?NoNoYesNoNoYesFrequency ofimpacts?YesNoNoYesYesYesDuration ofimpacts?YesNoNoYesYesYesTimeof Day of impacts?Depends on particledataDepends on particledataDepends on particledataDepends onparticle dataYesDepends on particle dataTimeof Year of impacts?YesYesYesYesYesYesAppropriate forwhat source-receptordistances?Regional ScaleRegional ScaleRegional ScaleRegional ScaleRegionalScaleRegional ScaleAttribution of SecondarySpecies?YesYesYesYesYesYesCost torun?MinimalMinimalMinimalMinimalMinimalMinimalCode Available?NoNoNoNoNoNoEquations and/oralgorithmsavailable?YesYesYesYesNoneneededYesComputerHardware NeededPCPCPCPCPCPCAdditional softwarenecessary?Anyprogramminglanguage orstatisticalpackage and graphicsand/ormappingsoftware that allowsoverlayof data onamapAnyprogramminglanguage orstatistical packageand graphics and/ormapping softwarethat allows overlayof data on amapAnyprogramminglanguage orstatisticalpackage and graphicsand/ormappingsoftware that allowsoverlayof data onamapAnyprogramminglanguage orstatisticalpackage andgraphics and/ormappingsoftware thatallows overlayof data on amapGraphing ormappingsoftware thatallowsoverlayofdata on amapAnyprogramminglanguage orstatistical packageand graphicsand/ormapping softwarethatallows overlay of data on amapEPA Approved Model?NoNoNoNoNoNo75Observational Modeling Techniques(continued)Analyses of inter-species relationshipsAnalyses of Spatial and TemporalPatternsCriteriaCMBPMFUNMIXFactorAnalysisEOFRMAPSQuantitative SourceAttribution?YesYesYesNoNoYesNumberof sources that can bedistinguished?Usually <10Usually <107< Number of speciesanalyzedUsually4-8transport patternsUsually 4-8transportpatternsAveragingTimeofResult?SameasmonitoringdataSameasmonitoringdataSameasmonitoringdataSameasmonitoringdataSameasmonitoringdataPrevioususe atClass IAreas?(SeeTextforReferences)YesNo?No?YesYesYesMagnitude ofimpacts?YesYesYesNoNoYesFrequencyofimpacts?YesYesYesYesYesYesDuration ofimpacts?YesYesYesNoYesYesTimeofDay ofimpacts?Dependsoninput dataDepends oninput dataDepends oninputdataDepends oninputdataDepends oninputdataDepends oninputdataTimeofYearofimpacts?YesYesYesYesYesYesAppropriate forwhat source-receptordistances?Urban toregionalscalesUrban toregionalscalesUrban toregionalscalesUrban toregionalscalesRegional ScaleRegional ScaleAttribution ofSecondary Species?WithadditionalinformationWithadditionalinformationWithadditionalinformationYesYesYesCost torun?Free topurchase,minimal torun$700topurchase,minimal torunCurrently freetopurchase,minimal torun,$100(student)-1500(full)forMATLABMinimalMinimalMinimalCodeAvailable?YesYesYesNoNoNoEquationsand/oralgorithmsavailable?YesYesYesYesYesYesComputer HardwareNeededPCPCPCPCPCPCAdditionalsoftwarenecessary?NoNoMATLABwithoptimizationtoolboxAny statisticspackageAny statisticspackage withability todosingular valuedecompositionsAny statisticspackagewithability todosingular valuedecompositionsEPA Model?YesNo?Yes?NoNoNo76Observational Modeling Techniques (continued)Analyses of Unique Tracer DataCriteriaTMBRDMBTAGITQuantitativeSource Attribution?YesYesYesNumber of sources that can bedistinguished?1-31-31/tracerAveraging Timeof Result?Sameasparticulate dataSameas particulate dataPrevious use at Class I Areas? (SeeText for References)YesYesYesMagnitudeofimpacts?YesYesYesFrequency of impacts?YesYesYesDuration of impacts?YesYesYesTimeof Day of impacts?Depends on particulate dataDepends onparticulate dataDepends on particulate dataTimeofYearof impacts?YesYesYesAppropriate for what source-receptordistances?Urban to Regional ScaleUrban to RegionalScaleRegional ScaleAttribution of SecondarySpecies?YesYesYesCost to run?MinimalMinimalMinimalCode Available?NoNoNoEquations and/or algorithmsavailable?YesYesYesComputer Hardware NeededPCPCPCAdditional software necessary?Any programming language,spreadsheet,or statisticalpackageAny programminglanguage,spreadsheet,orstatistical packageAny programming language, spreadsheet,or statistical packageEPA Model?NoNoNo77

### Appendix A : RA BART Rul e (40 CFR 51.300-306)

Sec. 51.300 Purpose and applicability.

Authority: Secs. 110, 114, 121, 160-169, 169A, a nd 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 prim ary purposes of this s ubp art are to req u ire States to develop program s to

assure reasonable progress toward meeting th e national goal of prev enting any future, and

rem e dying any existing, im pair m e nt of visibility in m a ndatory Class I Federal areas which

im pair m e nt results from m a nmade air pollu tion ; a nd to es tablish neces sary additional procedures

for new source perm it applicants, States and Fe deral Land Managers to use in conducting the

visibility im pact analysis required for new sources under Sec. 51.166. This subpart sets forth

requirem e nts addressing visibility im pairm e nt in its two principal form s: ``reasonably

attributable' ' im pair m e nt (i.e., im pairm e nt attrib utable to a single source/sm a ll group of sources)

and region al haze (i.e., widespread haze from a mu ltitude of s ources which im pairs visibility in

every direction over a large area).

(b) Applicability. (1) Gene ral Applicability. The provisions of this subpart pertaining to

im ple m entation plan requirem ents for assuring re asonable progress in preventing any f u ture and

rem e dying any existing visibility im pairm e nt are applic able to:

(i) Each State which has a m a ndatory Class I Federal area iden tified in part 81, subpart D, of

this title, an d Each State in which th ere is any source the em issions from which m a y r easonably

be anticipated to cause or contribute to any im pairm e nt of visibility in any such area.

(ii)The provisions of this subpart pertaini ng to implem e ntation plans to address reasonably

attributable visibility im pairm e nt are applic able to the following States : Alabam a, Alaska,

Arizona, Arkansas, California, Colorado, Florida, Georgia, Hawaii, Idaho, Kentucky, Louisiana,

Maine, Michigan, Minnesota, Missouri, Montan a, Nevada, New Ha m p s h ire, New Jersey, New

Mexico, North Carolina, North Dakota, Okla hom a

, Oregon, South Carolina, South Dakota,

Tennessee, Texas, Utah, Verm ont, Virginia, Vi rgin Islands, Washington, W e st Virginia,

Wyom ing.

(3) The provisions of this subpart p e rtaini ng to implem e ntation plans to address regio n al haze

visibility im pairm e nt are applicable to all States as defined in section 302( d) of the Clean Air Act

(CAA) except Guam , Puerto Rico, A m erican Samoa, and the Northern Mariana Islands.

[45 FR 80089, Dec. 2, 1980, as am ended at 64 F R 35763, July 1, 1999]

Sec. 51.301 Definitions.

For purposes of this subpart:

Adverse impact on visibility m eans, for purposes of section 307, visibility im pairm e nt which

interferes with the m a nagem e nt, protection, pres ervation, or enjoym ent of the visitor' s visual

experience o f the Federal Class I area. This dete rm ination m u st be m a de on a case-by-case basis

taking into account the geographi c extent, intensity, duration, fr equency and tim e of visibility

im pair m e nts, and how these factors correlate with (1) tim e s of visitor use of the Federal Class Iarea, and (2) the frequency and tim ing of natural conditions that reduce visibility. This term does

not include effects on integral vistas.

Agency means the U.S

. Environm e n tal Protection Agency.

BART-eligible source m eans an existing stationary facility as defined in this section. Best

Available Retrofit Technology (BAR T) m eans an em ission lim itation based on the degree of

reduction achievable through the application of th e best system of continuous em ission reduction

for each pollutant which is em itted b y an exis tin g stationary f acility. The em ission lim itation

must be established, on a case-by -case basis, taking into consid eration the technology available,

the costs of com p liance, the energy and nonair qu ality environm ental im pa cts of com p l i ance, any

pollution control equipm ent in use or in ex isten ce at the sou r ce, th e rem a ining us ef ul lif e of the

source, and the degree of im prove m e nt in visibi lity which m a y reasonably be anticipated to

result from t h e use of such technology.

Building, structure, or facility m eans all of the pollutant-emitt ing activities whic h belong to the

sam e industrial grouping, are located on one or more contiguous or adjacent properties, and are

under the co ntrol of the s a m e person (or pe rsons under common contro l). Pollutan t -em itting

activities must be considered as part of the sa m e industrial grouping if they belong to the sam e

Major Group (i.e., which have the sam e two-digit code) as described in the Standard Industrial

Classification Manual, 1972 as am ended by th e 1977 Supplem ent (U.S. Governm e nt Printing

Office stock num b ers 4101-0066 an d 003-005-00176-0 respectively).

Deciview m eans a m e asurem ent of visibility impairm ent. A deciview is a haze ind e x deriv e d

from calculated light extinction, such that uniform changes in haziness correspond to uniform

increm ental changes in p e rcep tion across the entir e range of conditions, from pristine to highly

im paired. The deciv i ew haze index is calcu la ted based on the following equation (for the

purposes of calculating deciview, th e atm o spheric light ex tin ction co efficient m u st be calcu lated

from aerosol m easurem ents):

Deciview haze index = 10 ln

e (b

ext /10Mm -1 ) W h ere b

ext = the atm o spheric light extinction

coefficient, expressed in inverse m e gam e ters (Mm -1 ).

Existing stationary facility m eans any of the following stationary sources of air pollutants,

including any reconstructed s ource, which was not in operati on prior to August 7, 1962, and was

in existence on August 7, 1977, and ha s the potential to em it 250 tons per year or m o re of any air

pollutant. In determ ining potential to em it, f ugitiv e em issions, to the ex ten t quantif iable, m u st be

counted.

Fossil-fuel fired steam electric plan ts of m o re th an 250 m illion British th erm a l units per hour

heat input,

Coal cleaning plants (therm al dryers),

Kraf t pulp m ills,

Portland cem e nt plants,

Prim ary zinc sm elters,

Iron and s t eel m ill plan ts,

Prim ary alum inum ore reduction plants,

Prim ary copper sm elters,

Municipal incinerators capable of charging m o re than 250 tons of refuse per day,

Hydrofluoric, sulfuric , and nitric acid plants,

Petroleum refineries,

Lim e plants,

Phosphate rock processing plants,Coke oven batteries,

Sulfur recovery plants,

Carbon black plan ts (f urnace p r ocess),

Prim ary lead sm elters,

Fuel conversion plants,

Sintering plants,

Secondary m e tal production facilities,

Che m ical process plants,

Fossil-fuel boilers of more than 250 m illion British therm a l units per ho ur heat input,

Petroleum storage and transfer facili ties with a capacity exceeding 300,000 barrels,

Taconite ore processing facilities,

Glass fiber pr ocessing plants, and

Charcoal production facilities.

Federal Class I area m eans any Federal land that is clas sified or reclas s i fied Class I.

Federal Land Manager m eans the Secretary of th e departm e nt with auth ority over the Federal

Class I area (or the Secretary' s designee) or, with respect to R oosevelt- Campobello International

Park, the Chairm an of the Roosevelt-Ca m pobello International Park Comm ission.

Federally enforceab l e m eans all lim itations and conditi ons which are enforceable by the

Adm i nistrator under the Clean Air Act including those requirem ents developed pursuant to parts

60 and 61 of this title, requirem e nts within a ny applicable State Im plem e n tation Plan, and any

perm it requirem e nts established pursuant to S ec. 52.21 of this chapter or under regulations

approved pursuant to part 51, 52, or 60 of this title.

Fixed cap ital cost m eans the capital needed to provide all of the depreciable com ponents.

Fugitive Em issions m eans those em issions wh ich could not reasonably p a ss through a stack,

chim ney, vent, or other func tionally equivalent opening.

Geographic enhancem ent for the purpose of Sec. 51.308 means a m e thod, procedure, or

process to allow a broad regiona l strategy, such as an em issions trading program designed to

achieve g r eater reasonab l e progress than BART for region al h aze, to acco mmodate BART for

reasonably attributable impairm ent.

Implementation plan m eans, for the purposes of this part, any State Im pl em entation Plan,

Federal Im plem entation Plan, or Tribal Im plem entation Plan.

Indian tribe or tr ibe means any Indian tribe, band, na tion, or other organized group or

community, including any Alaska Na tive v illage, which is f e derally re cog n ized as elig ible f o r th e

special prog ram s and services prov id ed by the Unite d States to Indians b ecause of th eir s t atu s as

Indians.

In exis t ence m eans that the owner o r operator ha s obtain e d all necess a ry preconstruction

approvals or perm its required by Federal, S t ate, or local air pollution em issions and air quality

laws or regulations and either has (1) begun, or caused to begin, a continuous program of

physical on-site construction of th e facility or (2) entered into binding agreem ents or contractual

obligations, which cannot be cancelled or m odifi e d without substantial loss to the owner or

operator, to undertake a program of construction of the facility to be com p leted in a reasonable

tim e.

In operation m eans engaged in activity related to the prim ary design function of the source.

Installatio n m eans an identifiab l e p i ece of process equipm ent.Integral vista m eans a view perceived from wi thin th e m a ndatory Class I Federal area of a

specific landm a rk or panoram a located outside the boundary of the m a ndatory Class I Federal

area.

Least impaired days m eans the average vis i bility im pair m e nt (m easured in deciv i ews ) for the

twenty percent of m onit o red days in a calenda r year with the lowest am ount of visibility

im pair m e nt.

Major stationary source and m a jor modification m ean m a jor stationary source and m a jor

modification, respectively, as defined in Sec. 51.166.

Mandatory Class I Federal Area m eans any area identified in part 81, subpart D of this title.

Most impaired days m eans the average vis i bility im pair m e nt (m easured in deciv i ews ) for the

twenty percent of m onit o red days in a calendar year with the highest am ount of visibility

im pair m e nt.

Natural co nditions includes naturally occurring phenom ena that reduce visibility as m easured

in term s of l i ght extinction, visu al range, contrast, or coloration.

Potentia l to emit m eans the m a xi mum capacity of a sta tionary source to em it a pollutant under

its physical and operational design. Any physical or operational lim itation on the capacity of the

source to emit a pollutant includ ing air pollution control equipm en t and restrictions on hours of

operation or on the type or am ount of m a terial combusted, stored, or processed, shall be treated

as part of its design if the lim itation or the effect it would have on em issions is federally

enforceable.

Secondary em issions do not count in determ ining the potential to em it of a stationary source.

Reasonably attributable m eans attributable by visual obser vation or any other technique the

State deem s appropriate. Reasona bly attributable visibility im pairm e nt m e ans visibility

im pair m e nt that is caused by the em ission of air pollutants from one, or a sm all num b er of

sources.

Reconstru c tion will be presum ed to have taken place wh ere the fixed capital cost of the new

com ponent exceeds 50 percen t of the fixed capital co st of a com p arable entirely new source. Any

final decisio n as to whether reconstruction has o ccurred m u st be m a de in accordan ce with the

provisions of Sec. 60.15 (f) (1) through (3) of this title.

Regional haze m eans visibility im pairm e nt that is cause d by the em ission of air pollutants from

num e rous sources located over a wide geographic area. Such sources include, but are not lim ited

to, m a jor and m i nor stationary sources, m obile sources, and area sources.

Secondary emissions m eans em issions which o ccur as a re sult of the construction or operation

of an existing stationary facility but do not com e from the ex isting stationary facility. S econdary

em issions may include, but are not lim ited to, emi ssions from ships or trains com i ng to or from

the existing stationary facility.

Significant impairment m eans, for purposes of Sec. 51.303, vi sibility im pairm e nt which, in the

judgm ent of the Adm i nistrator, interf eres with the m a nagem e nt , protection, preservation, or

enjoym ent of the visitor' s visual exp e rien ce of the m a ndatory Class I Federal area. This

determ ination m u st be made on a case-by-case basis taking into account the geographic extent,

intensity, duration, frequency and tim e of the vi sibility im pa irm e nt, and how these factors

corre late with (1) tim e s of vi sitor use of the m a ndatory Cla ss I Federal area, and (2) the

frequency and tim ing of natural co nditions that reduce visibility.

State m eans ``State' ' a s defined in section 302(d) of the CAA.

Stationary Source m eans any building, struct ure, facility, or insta llation which em its or m a y

em it any air pollutant.Visibility impairment m eans any hum anly perceptible change in visibility (light extinction,

visual range, contrast, coloration) from that which would have existed under na tu ral c onditions.

Visibility in any mandatory Class I Federal area includes any integral vista associated with

that area.

[45 FR 80089, Dec. 2, 1980, as am ended at 64 F R 35763, 35774, July 1, 1999]

Sec. 51.302 Im ple m entation control strategies for r easonably attributable visibility im pairm e nt.

(a) Plan R e vision Procedures. (1) Each State identified in Sec. 51.300(b)(2) m u st have

subm itted, not later than Septem ber 2, 1981, an imple m e ntation plan m eeting th e requ irem ents of

this subpart pertaining to reasonably attributable visibility im pairm e nt.

(2)(i) The State, prior to adoption of any im ple m entation plan to address reasonably

attributable visibility im pairm e nt required by this subpart, m u st conduct one or m o re public

hearings on such plan in accordan ce with Sec. 51 .102.

(ii) In addition to the requirem e nts in S ec. 51.102, the State m u st pr ovide written notification

of such hearings to each affected Federal La nd Manager, and other aff ected States, and m u st

state where the public can inspect a s u mm ary prep ared by the Federal Land Managers of their

conclusions and recommendations, if a ny, on the proposed plan revision.

(3) Subm i ssion of plan s as requ ired by this subp art m u st be conducted in accordan ce with the

procedures in Sec. 51.103.

(b) State and Federal L a nd Manager Coordina tion. (1) The State m u st identify to the Federal

Land Managers, in writing and with in 30 days of the date of pr om ulgation of these regulations,

the title of the of f i cial to which the F e dera l La nd Manager of any m a ndatory Class I Federal area

can subm it a recomm endation on the im ple m entation of this subpart including, but not lim ited to:

(i) A list of integral vist as that are to be listed by the St ate for the purpose of im ple m enting

section 304,

(ii) Identification of impairm ent of visibil ity in any m a nda tory Class I Federal area(s), and

(iii) Identification of elem ents for inclusi on in the visibility mon itoring strategy required by

section 305.

(2) The State m u st provide opportunity for c onsultation, in person and at least 60 days prior to

holding any public hearing on the plan, with th e Federal Land Manager on the proposed SIP

revision required by this subpart. This consultation m u st include the opportunity for the affected

Federal Land Managers to discuss their:

(i) Assessm e nt of i m pairm e nt of visibi lity in any m a ndatory Cl ass I Federal area, and

(ii) Recommendations on the de velopm ent of the long-term strategy.

(3) The plan m u st provide procedures for continuing consultation between the State and

Federal Land Manager on the im plem entation of the visibility protection program required by

this subpart.

(c) General plan requirem e nts for reasonabl y attributable visibili ty im pairm e nt. (1) The

affected Federal Land Manager m a y certify to the St ate, at an y tim e, that there ex ists reasonably

attributable impairm ent of visibility in any m a nda tory Class I Federal area.

(2) The plan m u st contain the following to addr ess rea s onab l y attribu t ab le im pairm e nt:

(i) A long-term (10-15 years) strategy, as specif i ed in Sec. 51.305 and Sec. 51.306, including

such em ission lim itations, schedules of com p liance, and such other m easures including schedulesfor the im plem entation of the elem ents of the lo ng-term strategy as m a y be necessary to m a ke

reasonable progress toward the natio nal goal specified in Sec. 51.300(a).

(ii) An assessm ent of visibi lity im pairm e nt and a discussion of how ea ch elem ent of the plan

relates to the preventing of future or rem e dying of existing impairm e nt of visibility in any

m a ndatory Class I Fede ral a r ea with in the State.

(iii) Em ission lim itations representing BART and schedules for com p l i ance with BART for

each existin g stationary facility iden tified acco rding to parag r aph (c)(4) of this section.

(3) The plan m u st require each source to m a intain control equipm ent required by this subpart

and establish procedures to ensure such contro l equipm ent is properly operated and m a intained.

(4) For any existing reasonably attributable visibility im pairm e nt the Federal Land Manager

certifies to the State under para graph (c)(1) of this section, at least 6 m ont hs prior to plan

subm ission or revision:

(i) The S t ate m u st identify and an alyze for B A RT each existing stationary facility which m a y

reasonab l y b e anticipated to cause or contribute to im pair m e nt of visibility in any m a ndatory

Class I Fede ral a r ea whe r e the im pairm e nt in th e m a ndatory Class I Federal area is reasonably

attributable to that ex isting stat ionary 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 m onths before plan

subm ission.

(ii) If the State determ in es that technologicial or econom ic lim itations on the applicability of

m easure m ent m e thodology to a particular exis tin g stationary facility w ould m a ke the imposition

of an e m ission standard infeasible it m a y instead prescribe a design, equipm ent, work practice, or

other operational standard, or com b ination thereo f

, to require the application of BART. Such

standard, to the degree possible, is to set f o rth the em issi on reduction to be achieved by

im ple m entation of such design, equipm ent, work practice or operation, and m u st provide for

com p liance by m eans which achieve equivalent results.

(iii) BART m u st be determ ined for fossil-fu el fired generating plants having a total generating

capacity in excess of 750 m e gawatts pursuant to ``Guidelines for Determ ining Best Available

Retrofit Technology for Coal-fired P o wer Plants and Other E x isting S t ationary Facilities' '

(1980), which is incorporated by reference, exclus ive 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. Departm e nt of Commerce, National Techn i cal Inform ation Serv ice,

5285 Port Royal Road, Springfield, Virginia 22161. It is also available for inspection at the

Office of the Federal Register Informati on Center, 800 North Capitol NW ., suite 700,

Washington, DC.

(iv) The p l an m u st require that e a c h existi ng stationa ry f acility require d to install an d operate

BART do so as expeditiously as practicable but in no case late r than five years after plan

approval.

(v) The plan m u st provide for a BART analysis of any existing stationa ry facility that m i ght

cause or contribute to impairm e nt of visibility in any m a ndatory Class I Federal area identified

under this paragraph (c)(4) at such tim es, as de term ined by the Adm i nistrator, as new technology

for control of the pollutant becom e s reasonably available if:

(A) The pollutant is e m itted by that existing stationa ry f acility,

(B) Controls representing BA RT for the pollutant have not prev iously been required under this

subpart, and

(C) The impairm ent of visibility in a ny m a nda tory Class I Federal area is reasonably

attributable to the em issions of that pollutant.[45 FR 80089, Dec. 2, 1980, as am ende d at 57 F R 40042, Sept. 1, 1992; 64

FR 35764, 35774, July 1, 1999]

Sec. 51.303 Exem ptions from control.

(a)(1 ) Any existing stationary facility sub j ect to the requ irem ent under Sec. 51.302 to install,

operate, and m a intain B A RT m a y a pply to the Adm i nistrator for an exemption from that

requirem e nt.

(2) An application under th is section m u st include all availa ble docum entation relevant to the

im pact of the source' s em issions on

visibility in any m a ndatory Class I Federal area and a dem o nstration by the existing stationary

f acility tha t it does not o r will not, by itse l f or in c o m b ination with othe r s ources, em it any air

pollutant which m a y be reasonably anticipated to cause or co ntri bu te to a signif i can t impairm ent

of visibility in any m a nd atory Class I Federal area.

(b) Any fossil-fuel fired power plant with a total generating capaci ty of 750 m e ga watts or

more m a y re ceive an exemption from BART only if the owner or operato r of such power plant

dem onstrates to the satisfaction of the Adm i nistrator that such pow er plant is lo cated at such a

distan ce f r o m all m a ndatory Class I Federal area s that su ch p o wer plant d o es not or w ill no t, by

itself or in com b ination with other sources, em it any air pollutant which m a y reasonably be

anticipated to cause or contribu te to significant impairm ent of vi sibility in any such m a ndatory

Class I Fede ral a r ea.

(c) Application under this Sec. 51.303 m u st be accom p anied by a written concurrence from the

State with regulatory authority over the source.

(d) The existing stationary facility m u st gi ve prior wr itten notice to all affected Federal Land

Managers of any application fo r exemption under this Sec. 51.303.

(e) The F e deral Land Manager m a y provide an initial recommendation or comm ent on the

disposition of such application. Such recomm endation, where provided, must be part of the

exem ption application. T h is recommendation is not to be construed as th e concurrence required

under paragraph (h) of this section.

(f) The Adm i nistrator, within 90 d a ys of recei p t of an application for ex em ption from control,

will prov ide notice of receipt of an ex em ption application and notice of op portunity for public

hearing on the application.

(g) After notice and opport unity for public hearing, the Adm i nistrator m a y grant or deny the

exem ption. For purposes of judici al review, final EPA action on an application for an exem ption

under th is Sec. 51.303 will no t occu r until EPA approves or d i sapproves the State

Im ple m entation Plan revision.

(h) An ex em ption granted by the Adm i nist rator under th is Sec. 51.303 will be effective on ly

upon concurrence by all affected Federal Land Mana gers with the Adm i nistrator' s determ ination.

[45 FR 80089, Dec. 2, 1980, as am ended 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 m a y identify any integral

vista. The integral vista must be iden tif ied ac cord ing to c r iteria the Federal Land Managerdevelops. T h ese criteria must in clude, but are not lim ited to, wh ether th e integ r al v i sta is

im portant to the vis ito r' s visual experience of the m a ndatory Cl ass I Federal area. Adoption of

criteria m u st be preced ed by reasonab l e notice and opportunity for public comm ent on the

proposed criteria.

(b) The F e deral Land Manager m u st notify th e State of any integral vistas identified under

paragraph (a) of this secti on, and the reasons therefor.

(c) The State m u st list in its im plem entation plan any integral vista the Federal Land Manager

identifies at least six m onths pr ior to plan subm ission, and must li st in its im ple m entation plan at

its earliest opportunity, and in no case later than at th e tim e of the periodic review of the SIP

required by Sec. 51.306(c), any integr al vista the Federal Land Mana ger identifies after that tim e

. (d) The State need no t in its im plem entation plan lis t any integra l vis t a the iden tif ic ation of

which was not m a de in accordance with th e criteri a in paragraph (a) of this section. In m a king

this finding, the State m u st carefully consider the expertise of the Federal Land Manager in

m a king the judgm e nts called for by the criteria for identification. W h ere the State and the

Federal Land Manager disagree on the identification of any integral vista, the State m u st give the

Federal Land Manager an opportunity to c onsult with the Gover nor of the State.

[45 FR 80089, Dec. 2, 1980, as am ended by 64 FR 35774, July 1, 1999]

Sec. 51.305 Monitoring for reasonab l y at tribu t ab le vis i bility impairm ent.

(a) For the purposes of a ddressing reasonably attributable vi sibility im pairm e nt, each State

containing a m a ndatory Class I Fe deral area m u st include in the plan a strategy for evaluating

reasonably attributable visibility im pairm e nt in any m a ndatory Class I Federal area by visual

observation or other appropriate m onitoring techniques. Such stra tegy m u st take into account

curren t and anticipated vi sibility m o nitoring research, the ava ilab ility of appropria te monitoring

techniques, and such guidance as is provided by the Agency.

(b) The plan m u st provide for the consideratio n of available visibility data and m u st provide a

m echanis m for its use in deci sions required by this subpart.

[45 FR 80089, Dec. 2, 1980, as am ended at 64 F R 35764, July 1, 1999]

Sec. 51.306 Long-term strategy requirem ents for r easonably attributable visibility im pairm e nt.

(a)(1 ) For the purposes of addressing reasonab l y attributable visib i lity impairm ent, each plan

must include a long-term (10-15 years) strate gy for m a king reasonable progress toward the

national goal specified in Sec. 51.300(a). This strategy m u st cove r any existing im pairm e nt the

Federal Land Manager certifies to the State at least 6 m onths pr ior to plan subm ission, and any

integral vista of which the Federal Land Manager no tif ie s the State a t le as t 6 m onths prior to plan

subm ission.

(2) A lon g -term strategy m u st be develope d f o r each m a ndatory Cla s s I Federal area located

within th e State and each m a ndatory Class I Fede ral area located outside the State which m a y be

affected by sources with in the State. This does not preclude the de velopm ent of a single

com p rehensive plan for all such areas

.(3) The plan m u st set forth with reasonable specificity why the long-term strategy is adequate

f o r m a king reasonable pr ogress towa r d the nation al vis i bility goal, inc l uding rem e dying existing

and preventing future impairm e nt.

(b) The State m u st coordinate its long-term strategy for an area with existing plans and goals,

including those provided by the a ffected Federal Land Managers, th at may affect impairm e nt of

visibility in any m a ndato ry Class I Federal area

. (c) The plan m u st provide for periodic revi ew and revision, as appr opriate, of the long-term

strategy for addressing reasonably at tribu t able visibility im pairm e nt. The plan m u st provide for

such period ic review and revis i on not less f r equen tly th an ever y 3 years un til the da te o f

subm ission of the State' s first plan addressing re gional haze visibility im pairm e nt in accordan ce

with Sec. 51.308(b) and (c). On or before this da te, the State must revise its plan to provide for

review and revision of a coordi nated long-term strategy for addr essing reasonably attributable

and region al haze vis i bility im pairm e nt, and the S t ate m u st su bm it the firs t such coord i nated

long-term strategy. Fu tu re coord i nated long-term st rateg i es must be subm itted consistent with th e

schedule for period ic pro g ress reports set forth in Sec. 51.308(g). Until the State revis e s its plan

to m eet this requirem e nt, the State must continue to com p ly with existing requirem e nts for plan

review and revision, and with all em ission m a na gem e nt requirem e nts in the plan to address

reasonably attributable impairm ent. This requir em ent does not affect any preexisting deadlines

for State sub m ittal of a long-term strategy review (or elem ent thereof) b e tween August 30, 1999

, and the date required for subm ission of the State' s first region al haze p l an. In addition, the plan

must provide for review of the long-term strate gy as it applies to reasonably attributable

im pair m e nt, and revision as appropriate, within 3 years of State receip t of any certification of

reasonab l y attributab le impairm ent from a Fede ral Land Manager. The review process m u st

include cons ulta tion with the a ppropriate Federal Land Managers, and the State m u st provide a

report to the public and the Adm i nistrator on p r o g ress towa rd the national goal. This report m u st

include an assessm ent of:

(1) The progress achieved in rem e dying existi ng im pair m e nt of visibility in any m a ndatory

Class I Fede ral a r ea ;

(2) The ability of the long-term strategy to prevent future impairm ent of visibility in any

m a ndatory Class I Federal area;

(3) Any change in visibility since the last su ch report, o r , in the cas e of the first report, since

plan approval;

(4) Additional m easures, including the need fo r SIP revisions, that m a y be necessary to assure

reasonab l e p r ogress towa rd the na tion a l vis i bility goal;

(5) The progress achieved in im plem enting BA RT and m eeting other schedules set forth in the

long-term strategy;

(6) The impact of any exem ption granted under Sec. 51.303;

(7) The need for BART to rem e dy existing visi bility im pair m e nt of any integ r al v i sta lis ted in

the plan since the last su ch re port, or, in the case of the fi rst report, since plan approval.

(d) The long-term strategy m u st provide for review of the impacts from any new m a jor

stationary so urce or m a jor m odif i cations on visib i lity in any mandatory Class I Fede ral area. This

review of major stationary sour ces o r m a jor m o difications 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 m a nda tory Class I Federal

areas.(e) The State m u st consider, at a m i nim u m

, the following factors during the deve lop m ent of its

long-term strategy:

(1) Em ission reductions due to ongoing air pollution control program s,

(2) Additional em ission lim itations and schedules for com p l i ance,

(3) Mea s u r es to m itiga t e the im pacts of constru c tion a c tiv ities,

(4) Source retirem e nt and replacement schedules,

(5) Sm oke m a nagem e nt techniques for ag ricultural and forestry m a nagem e nt purposes

including such plans as cu rrently ex is t within the State f o r th e s e purposes, and

(6) Enforceability of em ission lim i t ations and control m easures.

(f) The plan m u st discuss th e reasons why the above and other reason able m easures considered

in the developm ent 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 st rategy, m u st take into account the effect of new

sources, and the costs of com p liance, the tim e necessary for compliance, the energy and nonair

quality environm ental im pacts of compliance, a nd the rem a ining useful life of any affected

existing sou r ce and equ i pm ent therein.

[45 FR 80089, Dec. 2, 1980, as am ended at 64 F R 35764, 35774, July 1, 1999]