w e s t v ir g in ia de par t m en t of en vi r on m en t al pr ot ec t i on



# W es t V i rgi ni a R egiona l H aze Stat e I m pl em ent at i on P l an

to Pres er ve, Prot ec t and Im prove V i si bi l i t y i n C l ass I Federal A re as

Fi nal J un e 20 08

Promoting a healthy env ironmen t.

### Pa g e I nt e nt i o na l l y Le f t Bl a nkW est V i rgi ni a R egi o nal H aze S IP P age i 

### T ab l e of C on te n ts
EXE CUTIVE SUMMARY................................................................................................ Pa g e xiii ACK NOWL EDGMENTS: VI STAS Cont rac tors.
............................................................. Pa g e xiv 1.0. I NTRODUCTI ON...................................................................................................... Pag e 1 1.1. What is reg ional haze?
. ........................................................................................ Pag e 1 1.2. What are the require ments under the Clea n Air Ac t for addr essingreg ional haze?

.............................................................................................................................. Pag e 1 1.3. Gene ral ove rview of reg ional haze SI P requireme nts.
......................................... Pag e 3 1.4. Class Iare as within West Virginia.
...................................................................... Pag e 4 1.5. State and F eder al L and Mana g er Coordination.
.................................................. Pag e 7 2.0. ASSES SMENT OF B ASEL I NE AND CURRENT CONDI TI ONS AND ESTI MATE OF NATURAL

BA CKGROUND COND I TI ONS I N CLASS IAREAS.
.................... Pag e 10 2.1. Estimating Natural Conditions for Dolly Sods and Otter Cree k Wil derne ss Area s

............................................................................................................................ Pag e 13 2.2. Estimating B aseline Conditions for Dolly Sods and Otter Cree k Wil derne ss Area s

............................................................................................................................ Pag e 14 2.3. Summary

of Natural B ackground and B aseline Conditions for Dolly Sods and Otter Creek Wilderne ss Area s. .................................................................................... Pag e 15 2.4. Pollut ant Contributions t o Visibili ty Impairment (2000- 2004 Ba seline Data)

............................................................................................................................ Pag e 16 3.0. UNI FORM RATE OF PROGRES S TO NATURAL CONDI TI ONS I

N 2064.
...... Pag e 21 3.1. Glidepath for

the Dolly Sods and Otter Cree k Wil derne ss Area s. ..................... Pag e 21 4
. 0
. N A T U R E O F T H E P R O B LE M : C H IE F C A U S E S O F V IS IB ILIT Y IM P A IRM EN T IN DOL LY SODS AND OTTE R CR EEK WI L DERNESS AREAS.
.......................... Pag e 23 4.1. Ba seline Emissions I nventory
. ........................................................................... Pag e 23 4.1. a. Stationary

Point S ource s. ............................................................................. Pag e 24 4.1. a.1. Electric Gene ratingUnits (EGUs).
........................................................ Pag e 24 4.1. a.2. Other Industrial Point S ource s (Non-EGUs).
........................................ Pag e 25 4.1. b. Stationary

Are a Source s. .............................................................................. Pag e 25 4.1. c. Off- Road Mobile Sources.
........................................................................... Pag e 26 4.1. d. Hig hway

Mobile Sources.
............................................................................ Pag e 27 4.1. e. Biog enic Emissions Sources.
....................................................................... Pag e 27 4.1. f. Summary

2002 Ba se G2 (F ebrua

ry 2007 version) B aseline Emission Inventory for West Virg inia.
........................................................................................ Pag e 27 4. 1. g . M od e l Pe rf

or manc e Impr ov e me nts thr ou gh E mis sio ns Inv e nto ry Impr ov e me nts

............................................................................................................................ Pag e 28 4.2. Assessment of Relative Contributions from Specific Pollut ants and Source Categ ories (2002-04 B aseline D ata)
. ................................................................................... Pag e 29W est V i rgi ni a R egi o nal H aze S IP P a g e ii

5.0. REGI ONAL

HAZ E MODEL I

NG METHO DS AND I

NPUTS.
.............................. Pag e 31 5.1. Analy sis Method.
............................................................................................... Pag e 31 5.2. Model Selection.
................................................................................................ Pag e 32 5.2. a. Selection of Photochemica l Grid Model.
..................................................... Pag e 32 5.2. a.1. Criteria.
............................................................................................ Pag e 32 5.2. a.2. Over

view of CMAQ.
....................................................................... Pag e 32 5.2. b. Selection of Mete orolog ical Model.............................................................. Pag e 33 5.2. b.1. Criteria.
............................................................................................ Pag e 33 5.2. b.2. Over

view of MM5.
.......................................................................... Pag e 33 5.2. c. Selection of Emissions ProcessingSy stem.
................................................ Pag e 34 5.2. c.1. Criteria.
.................................................................................................. Pag e 34 5.2. c.2. Over

view of SMOKE.
..................................................................... Pag e 35 5.3. Selection of the Mode ling Ye ar. ........................................................................ Pag e 36 5.4. ModelingDomains.
............................................................................................ Pag e 37 5.4. a. Horizontal Modeling Doma in.
..................................................................... Pag e 37 5.4. b. Ver

tical ModelingDomain.
......................................................................... Pag e 39 6.0. MODEL

PERFORMANCE EVALUATI ON.
......................................................... Pag e 40 6.1. ModelingPerfor

mance Goals, Criteria a

nd Results.
.......................................... Pag e 41 6.2. VI STAS Domain-wide Perf ormanc e. ................................................................ Pag e 42 6.3. West Virginia Class I Are as Perf

ormanc e. ........................................................ Pag e 45 7.0. L ONG- TERM STRATEGY FOR WEST VI RGI NIA’S CL ASS IAREAS.
.......... Pag e 48 7.1. Over

view of the L ong -term Strate gy Deve lopment Process.
............................. Pag e 48 7.2. Ex pecte d Visibili ty Results in 2018 for West Virginia Class I Are as under

exis ting and planne d emissions controls (Ba se G2 Inventory ).
....................................... Pag e 49 7.2. a. Fe dera l and State Control Requirements.
.................................................... Pag e 49 7.2. b. Additional State prog rams to re duce e missions
. .......................................... Pag e 52 7.2. c. Projected 2009 a

nd 2018 Ba se G2 Emissions Inventories .
......................... Pag e 52 7.2. c.1. Summary

of F inal Ba se G2 Emission Inventories f or 2009 and 2018

.......................................................................................................... Pag e 54 7.2. d. Model Results for the 2018 B ase G 2 Inventory Compared to the Unif

orm Rate of Prog ress Glidepaths for Dolly Sods and Otter Cree k Wil derne ss Area s. .... Pag e 55 7.3. Relative Contribution from International Emissi ons to Visibil ity Impairment in 2018 at VI STAS Cl ass I

are as. .................................................................................... Pag e 60 7.4. Relative Contributions to Vis ibilit y Impairment: Pollut ants, Source Categ ories and Geographic Are as. .............................................................................................. Pag e 64 7. 5. Re lativ e Contr ibutio ns to V isi bil ity Impa ir me nt: Ge ogra

ph ic L oc atio ns of

the L arg e st Emissions S ource s Contributi ngto Visibil ity Impairment at Dolly

Sods and Otter Creek Wilderne ss Area s. .................................................................................... Pag e 66 7.5. a. Ba ck Tra

jectory Analy ses. ........................................................................... Pag e 66 7.5. b. Residence Time Plots.
.................................................................................. Pag e 67

2 7.5. c SO Are as of Influenc e. ................................................................................ Pag e 68 2 7.5. d. Emission S ource s within S O Are as of Influenc e. ....................................... Pag e 70 7.5. e. Specific Sourc e Ty pes in the Are a of I

nfluenc e for

Dolly Sods and Otter Cree k Wil derne ss Area s. ........................................................................................ Pag e 84W est V i rgi ni a R egi o nal H aze S IP P a g e ii i 2 7.6. EvaluatingControl Options for Specific SO Emissions S ource s in Each Ar ea of Influenc e............................................................................................................. Pag e 88 7.6. a. Cost of Compl iance
...................................................................................... Pag e 89 7.6. b. Time Nec essar

y for Compliance
. ................................................................. Pag e 90 7.6. c. Energy

and Non- Air Qua lity

Environmental Impacts.
................................. Pag e 90 7.6. d. RemainingUseful L ife.
................................................................................ Pag e 90 7. 7. Whic h Co ntr ol M e a sure s Re pr e se nt R e a so na ble Pr ogre ss f

or Ind ivi du a l So ur c e s?

............................................................................................................................ Pag e 90 7.8. What Cont rol Dete rminations Represe nt Be st Available Retrof it Technology

(B ART) for Individual Sources?
. ..................................................................................... Pag e 99 7.8. a. BA RT-Eligible Sourc es in West Virg inia.
................................................... Pag e 99 7.8. b. Deter

mination of Sources Subject to B ART in West Virginia.
................. Pag e 101 7.8. c. Deter

mination of BART Require ments for Subject-to- BA RT Sources.
.... Pag e 107 7.9. What Additional Emi ssions Cont rols Were Considere d as par t of the L ong -Ter

m Strateg y for Visibility

Improvement by

2018?
................................................. Pag e 111 8.0. REASONAB L E PROGRESS GOAL S.
................................................................. Pag e 114 9.0. MONI TORI

NG STRATEGY.
............................................................................... Pag e 115 10.0
. I NTERSTATE CONSUL TATI ON REGARDI NG WEST VI RGI

NIA CONTRI BU TI ON TO VI SI BI L I TY I MPAI RMENT I N CLASS IAREAS I

N NEI GHB ORI

NG STATES

................................................................................................................................ Pag e 118 11.0
. COMPR EHENSIVE PERI ODI C I MPL EMENTATI ON PLAN REVI SI ONS.
.... Pag e 123 12.0
. DETERMI

NATI ON OF THE ADE QUACY OF THE EXI STI NG PLAN.
.......... Pag e 125W est V i rgi ni a R egi o nal H aze S IP P a g e iv [This pag e intentionally

left blank.]W est V i rgi ni a R egi o nal H aze S IP P age v 

### L i st of A p p en d i ce s Appendix A: VI STAS Memorandum of Unde rstandingand B ylaws.............................. Pag e A- 1 Appendix B: Conceptual De scription.
............................................................................. Pag e B -1 Appendix C : ModelingAnaly sis Protocol and Quality Assuranc e Projec t Plan.
............ Pag e C-1 Appendix D: Emissions P repa ration and Results.
........................................................... Pag e D- 1 Appendix E: Air Qua lity and Mete orolog ical Prepa rations and Results.
........................ Pag e E-1 Appendix F: Model Perfor mance Evaluation.
................................................................ Pag e F -1 Appendix G: ModelingResults and Supplemental Analy sis.
.......................................... Pag e G- 1 Appendix H: Reasonable Prog ress Eva luation/L ongTerm Strate g y . .............................. Pag e H- 1 Appendix I : Data A cce ss. ................................................................................................ Pag e I -1 Appendix J : Consultation P roce ss. .................................................................................. Pag e J-1 Appendix K: Recommende d Improvements.................................................................... Pag e K- 1 Appendix L : BA RT Related Doc umentation.
................................................................. Pag e L -1 Appendix M: VI STAS Modeling Reports.
...................................................................... Pag e M-1 Appendix N: Public Participation Documentation.
......................................................... Pag e N- 1W est V i rgi ni a R egi o nal H aze S IP P age vi 

### L i st of T ab l es an d F i gu re s Figure 1.4- 1. West Virginia Ca ss Iare as. .......................................................................... Pag e 5 Figure 1.5- 1. Class IAre as in the VI STAS Region.
.......................................................... Pag e 7 Tab le 1.5-1. Mandatory Fe dera l Class IAre as in the VI STAS Region Where Visibility

is an Important Value
. .......................................................................... Pag e 9 Figure 2.0- 1. Important fac tors involved in seeinga sce nic vista are outlined..
............... Pag e 11 Figure 2.0- 2. I MPROVE Monitoring sites in the VI STAS Region.
................................ Pag e 12 Ta b le 2. 3- 1. Natur a l B a c kgro un d a nd B a se lin e Con dit ion s for Dolly Sods and Otter Cree k Wil derne ss Area s. ....................................................... Pag e 15 Figure 2.4- 1. Aver ag e lig ht ext inction for the 20% Haziest Day s in 2000-2004 at VI STAS and

neig hboringClass Iare as usingNew IMPROVE alg orithm.
....................... Pag e 16 Figure 2.4- 2. Aver ag e lig ht ext inction for the 20% Cleare st Day s in 2000-2004 at VI STAS and

neig hboringClass Iare as usingNew IMPROVE alg orithm.
....................... Pag e 17 F igur e 3
. 1

- 1
. U n if

o r m R ate o f Pr o g r e s s G li d e p ath

f o r th e 2 0 % Wo r s t D ay s at D o ll y So d s , WV

........................................................................................................................................ Pag e 22 Tab le 4.1. Summary

of 2002 B aseline Emissions Inventory for West Virg inia.
.............. Pag e 28 2 Figure 4.2- 1. SO Emissions i n 2002 in the VI STAS S tates.
.............................................. Pag e 30 2 Figure 4.2- 2. SO Emissions i n 2002 in West Virginia.
...................................................... Pag e 30 Figure 5.4.a -1. The MM5 horizontal domain is the outer most, blue g rid, with the CMAQ 36-km

domain nested in the MM5 domain.
............................................................ Pag e 38 Figure 5.4.a -2. A more de tailed view of the 12-km grid over the VI STAS reg ion.
............. Pag e 39 Figure 6.0- 1. Monitoring Ne tworks used for VI STAS 2002 model performa nce e valuation, and their location within t he VI STAS 12-km domain..
...................................... Pag e 40 Ta b le 6. 1- 1. Mo de l Pe rf

or manc e g oa ls a

nd c ri te ri a fo r c omp on e nts of

fi ne pa rt ic le ma ss

........................................................................................................................................ Pag e 41 Figure 6.2- 1. Soccer

plot depicting both the me an fr actional e rror and fr actional bias f

or c omp on e nt c on c e ntr atio n f or a ll V I STA S sit e s b a se d o n B a se G2 re su lts
.

...................................................................................................................... Pag e 43 Figure 6.2- 2. A zoomed view of the soc cer plot depicting both the me an fr actional e rror and fr a c tio na l bi a s f

or c omp on e nt c on c e ntr atio n f or a ll V I STA S sit e s b a se d o n B a se G2 re sults..
................................................................................................... Pag e 43 Figure 6.2- 3. Seasonal socc er plots base d on Ba se G1 re sults for all VI STAS I MPROVE

monitors..
..................................................................................................... Pag e 44 Figure 6.2- 4. Bug le plot of the mea n fra ction bias for pa

rticulate matter and its component conce ntrations for a ll VI STAS sit es base d on Ba se G2 re sults..
................. Pag e 44 Figure 6.2- 5. Bug le plot of mean f rac tion error

for pa rticulate ma

tter and its component spe cies for a ll VI STAS sit es base d on Ba se G2 re sults..
.......................................... Pag e 45 F igure 6. 3- 1: Sta c ke d b ar c ha rt fo r D oll y Sod s o n th e 20 % b e st d ay s ( top ) a nd the 20 % w or st day s (bottom)..
............................................................................................. Pag e 46 2 F igure 7. 2. c -1
. B a se G2 An nu a l SO e mis sio ns fo r 2 00 2, 20 09 , a nd 20 18 in t he VI STA S state s.

...................................................................................................................... Pag e 53

x F igure 7. 2. c -2
. B a se G2 An nu a l N O e mis sio ns in 2 00 2, 20 09 and 20 18 in t he VI STA S state s.

...................................................................................................................... Pag e 54 Tab le 7.2.c- 1. 2009 Emission I nventory Summary

for West Virg inia............................. Pag e 54 Tab le 7.2.c- 2. 2018 Emission I nventory Summary

for West Virg inia............................. Pag e 55W est V i rgi ni a R egi o nal H aze S IP P a g e vii

F igure 7. 2. d- 1. CMA Q 2 01 8 B a se G2 re su lts c omp are d to the Un if or m Rate of Pr ogre ss at D oll y Sods, W V.
.................................................................................................... Pag e 56 Figure 7.2.d- 2. Projected visibility improvement on 20% wor st visibi lity day s at VI STAS and neig hboringClass Iare as for the 2018 B ase G 2a CMAQ run ( 12 km gr id)

...................................................................................................................... Pag e 57 Figure 7.2.d- 3. CMAQ 2018 Ba se G2 re sults at Dolly Sods..
.............................................. Pag e 58 Figure 7.2.d- 4. Projected visibility improvement on 20% be st visibi lity day s at VI STAS and neig hboringClass Iare as for the 2018 B ase G 2a CMAQ run ( 12 km gr id)

...................................................................................................................... Pag e 58 Figure 7.2.d- 5. Visibili ty

improvement on 20% ha ziest day s at Dolly Sods between 2000- 2004 b as el i n e c o n d i t i o n s (l ef

t ) an d 2 0 1 8 Ba s e G 2 a p ro

j ec t edv i s i b i l i ty

(r i gh t ). Im age g ener ated usingWI

NHAZ E..
....................................................................... Pag e 59 F igure 7. 2. d- 6. Pr oje c te d v isi bil ity on 20 % h a zie st d ay s f

or natur a l ba c kgro un d v isi bil ity conditions at Dolly

Sods. I mag e g ener ated usingWI NHAZ E..
................... Pag e 60 Figure 7.3- 1. Estimated international e missions contribution to sulfate at VI STAS and

neig hboringClass Iare as. ............................................................................ Pag e 62 Figure 7.3- 2. Measur ed PM component conc entration on 20% worst visibili ty day s in 2002 (left ba rs ) a nd CMA Q- sim

ulate d inte rn atio na l c ontr ibutio ns (r ight b ars ) a t D oll y Sod s, WV..
............................................................................................................. Pag e 63 F igur e 7
. 4

- 1
. CM A Q p r o je c ti o n s o f

v is ib il it y r e s p o n s e s o n2 0 % w o r s t d ay s at D o ll y So d s , WV to 3 0% re du c tio ns fr om 2 00 9 B a se D i nv e nto ry fo r v isi bil ity -r e du c ingpo llutants in differ ent source cate g ories and g eographic are as. .................................... Pag e 65 Figure 7.5.a -1. Ex ample bac k trajec tories for 20% worst visibili ty day s in 2002 for Dolly

Sods

...................................................................................................................... Pag e 67 F igure 7. 5. b- 1. Re sid e nc e tim e plo t f or 20 % w or st v isi bil ity

da y s in 20 00 -2 00 4 f

or Do lly Sod s. Ba sed on traje ctories with 100m start height..
............................................. Pag e 68 2 Figure 7.5.c -1. SO Are a of Influenc e plot for sulfate extinction weighted r esidenc e time for 20%

wo rs t vi sib ili ty

da y s in 20 00 -2 00 4 f

or Do lly Sod s. B a se d o n tr a

je c tor ie s w ith 100m start heig ht..
........................................................................................ Pag e 69 2 4 F igure 7. 5. d- 1. Do lly

Sod s 2 00 2 ( le ft ) a

nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * SO exti nction-we ighte d residenc e time plots..
................................................... Pag e 71

2 F igure 7. 5. d- 2. 20 18 SO dis tanc e we ighte d e mis sio ns * SO 4 e xtinc tio n w e ighte d r e sid e nc e tim e

plot for Dolly

Sods, W V..
............................................................................ Pag e 72 2 Tab le 7.5.d-1. 2018 SO Point S ource Contribution t o Dolly Sods, W V by

State.
......... Pag e 73 Tab le 7.5.d-2. Point Source Units Contributing at lea st 0.5% to Sulfate at Dolly Sods.
... Pag e 74 Tab le 7.5.d-3. West Virg inia Coal-Fir ed Elec tric Ge nerating Units and Existi ngand Schedule d Controls.
......................................................................................................................... Pag e 76 2 F igure 7. 5. d. 3. B ri g anti ne , N J 200 2 ( le ft ) a nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * 4 SO exti nction weighted re sidence time plots.
............................................. Pag e 78

2 Tab le 7.5-4. 2018 SO Point S ource Contribution t o Br iga ntine, NJ by

State.
............... Pag e 78

2 Figure 7.5.d.4. Gre at Smokey Mountains, NC and TN 2002 (le ft) and 2018 ( rig ht) SO distance

4 we ig hte d e mis sio ns * SO e xtinc tio n w e ighte d r e sid e nc e tim e plo ts

...................................................................................................................... Pag e 79

2 Tab le 7.5-5. 2018 SO Point S ource Contribution t o Gre at Smokey Mountains, NC and TN by State................................................................................................................................ Pag e 79W est V i rgi ni a R egi o nal H aze S IP P a g e viii 2 Figure 7.5.d.5. J ames River F ace , VA 2002 (le ft) and 2018 ( rig ht) SO distance w eig hted 4 e mis sio ns * SO e xtinc tio n w e ighte d r e sid e nc e tim e plo ts

...................................................................................................................... Pag e 80

2 Tab le 7.5-6. 2018 SO Point S ource Contribution t o J ames River F ace , VA by State.
... Pag e 80 2 Figure 7.5.d.6. L inville Gorg e, NC 2002 (lef t) and 2018 (r ight) SO distance w eig hted emissions 4 * SO exti nction weighted re sidence time plots.
.......................................... Pag e 81

2 Tab le 7.5-7. 2018 SO Point S ource Contribution t o L inville Gorg e, NC by State.
........ Pag e 81 2 Figure 7.5.d.7. Mammoth Cave, KY 2002 ( left) a nd 2018 (right) SO distance w eig hted 4 emissions * S O exti nction weighted re sidence time plots.
......................... Pag e 82

2 Tab le 7.5-8. 2018 SO Point S ource Contribution t o Mammoth Cave, KY by State.
..... Pag e 82 2 F igure 7. 5. d. 8. She na

nd oa h, VA20 02 (l e ft ) a

nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * 4 SO e xtinc tio n w e ig hte d r e sid e nc e tim e plo ts

...................................................................................................................... Pag e 83

2 Tab le 7.5-9. 2018 SO Point S ource Contribution t o Shenandoah, VA by State.
........... Pag e 83 Tab le 7.5.e.- 1. 2018 Emissions C ontributions from Major Source Categ ories in the Ar ea of

Influenc e for

Dolly Sods, W V.
.............................................................. Pag e 85 2 Tab le 7.5.e- 2. 2018 SO Emissions contributi ons from Major Sourc e Categ ories in the Ar ea

of Influenc e for

Dolly Sods, W V.
.......................................................... Pag e 86

2 Ta b le 7. 5. e -3 . Numb e rs and Pe rc e nta g e s o f 2 01 8 SO Em iss ion Un its that Co ntr ibute to Sulfate Visibility

Impairment at Dolly

Sods.
................................................................. Pag e 87 Tab le 7.7-1. EGUSources with Contribution $ 1% to any Class IAre a......................... Pag e 91 Tab le 7.7-2. Non- EGU Source s to be Evaluate d for Rea sonable F urther Progress. ........ Pag e 92 2 TAB L E IV-3. – Ave rag e Costs per Ton of Annual SO Controls.
...................................... Pag e 94 2 Tab le IV-4.– Mar g inal Costs per Ton of Annua l SO Controls.
.......................................... Pag e 94 2 Tab le IV-5. – Estimated Costs per Tons of SO Controlled Under CAI R, Cap L evels B eg inning in 2010 and 2015.
........................................................................................................... Pag e 95 2 Tab le 7.7-3. SO Control Cost s for L arg e Prehe ater Kilns (2006 Dollars).
..................... Pag e 98 Tab le 7.8.a- 1. BA RT-Eligible Sourc es in West Virg inia.
.............................................. Pag e 99 Ta b le 7. 8. b- 1. B AR T E xemp tio n M od e lin gRe su lts 12 km Modeling - Maxi mum Predicted Visibility Impact (de lta-dec iview) pe r Class IAre as

...................................................................................................................................... Pag e 101 Ta b le 7. 8. b- 2. B AR T E xemp tio n M od e lin gRe su lts 4km Modeling - 98 Perce ntile Visibil ity Impact (de lta-dec iview) pe r Class IAre a th

...................................................................................................................................... Pag e 104 Tab le 7.8.b-3. Exemption Modeling Results for F acilities with > 0.5 dec iview of Impact 4km Modeling - 98 Perce ntile Visibil ity Impact (de lta-dec iview) pe r Class IAre a th

...................................................................................................................................... Pag e 105 Tab le 7.8.b-4. Summary of B ART Ex emption Requirements for PPG (54-051-00002)

...................................................................................................................................... Pag e 106 Tab le 7.8.c- 1. Summary of B ART Requirements for Mt. St orm (54-023- 00003).
......... Pag e 109 Tab le 7.8.c- 2. Summary of B ART Requirements for Capitol Cement (54-003-00006)

...................................................................................................................................... Pag e 110 Tab le 7.9-1. VI STAS Ammoni a Emissions.
.................................................................. Pag e 112 3 Tab le 7.9-2. West Virg inia NH Emissions.
.................................................................. Pag e 113 Tab le 8.0-1. West Virg inia Reasonable Prog ress Goa ls.
............................................... Pag e 114 Figure 9.0- 1. PM2.5 Monit oringNetwor k in West Virginia..
....................................... Pag e 117W est V i rgi ni a R egi o nal H aze S IP P a g e ix

Tab le 10.0-1. 23 WV Electr ic Gene ratingUnits I dentified in MANE- VU “E GU ASK”

...................................................................................................................................... Pag e 120 Tab le 10.0-2. Contribution of West Virginia 2018 Point Source Sulfate Emissions t o Br iga ntine

...................................................................................................................................... Pag e 122W est V i rgi ni a R egi o nal H aze S IP P age x 

### L i s t o f Ac r o n y ms AoI - Are a of Influenc e BA R T - B es tAv ai l ab l e R et ro fi tT ec h n o l o gy B R E T 1 - B r e t o nW i l d e r

n e s s A r e a , LA BRI G - B rig antine Wilderness Are a, NJ CAA - Clean A ir Act CACR1 - Caney

Creek Wilderne ss Area , AR CA I R - Cle an A ir

Inte rs tate Rul e CA MR - Cle an A ir

Me rc ur y Rul e CEM - Continuous Em issions Moni toring CERR - Con so lid ate d E mis sio ns Re po rt ingRul e CH AS 1 - C has so ho wi tz ka W il dern ess Area, FL CMA Q - Commun ity

Mu lti sc a le Ai r Q ua lity COHU1 - Cohutta Wilderness Are a, GA DO E - D ep artm en to f E n er gy D O SO 1 - D o ll y

So d s Wi ld e r

n e s s A r e a , WV DV - D eciview EAC - Ear ly

Action Compact EG U - El e c tr ic Ge ne ratin gUn it EPA - Environmental Protec tion Age ncy EVE R1 - Ev erglad es N ati on al P ark, FL FAA- F eder al Aviation Administration FDD A - F our Dimensional Data Assimilation FL M - Fe dera l L and Mana g er GRSM1 - Great Smokey

Mountains National Park, NC and TN HAP - Ha zardous Air Pollutant HE GL 1 - He rc ule s G la de Wild e rn e ss Are a , M O I MPRO VE - I

nte ra g e nc y

Mo nit or ingof

Pr ote c te d V isu a l E nv ir onme nts I PM - Integ rate d Planning Mode l J AR1 - J ames River F ace Wil derne ss Area , VA L I GO1 - L inville Gorg e Wilderness Are a, NC L SD - L ow Sulfur Diese l L T S - Lon g-T ermS tra t egy MACA1 - Mammoth Cave National Park, KY MI NG 1 - Min g o N atio na l Wild lif e Re fu g e , M O MM5 - Mesosca le Meteor ologic al Model NAAQS - N ational Ambient Air Qua lity Standard

x NO - Nitrog en Oxides OKEF 1 - Oke fenoke e Wilderness Are a, GA OTB /OTW - On the books/On the Way PBL - Planetar

y Bounda ry L ayer PM - Particulate Matter 2.5 PM - P art ic ulate Matte r w itha dia me te r o f 2 .5 mic ro me te rs or le ss ( fi ne pa

rt ic le s) 10 PM - Particluate Matter with a dia meter of 10 micrometer s or less (c oarse matter) POM - Particulate O rg anic MatterW est V i rgi ni a R egi o nal H aze S IP P age xi

P S AT - P M S o urc e A p p o rt i o n m en tT ec h n o l o gy PSU/NC AR - Pennsylvania State Univer sity /National Center f

or Atmospheric Resear ch RAMS - Regiona l Air ModelingSy stem RH R - Re g ion a l H a ze R

ule RO MA 1 - Ca

pe Rom a in Wi lde rn e ss Are a , SC RPG - Reasonable Progress Goa l RPO - Reg ional Planning Or g anization SAMI- Southern Appa lachian Mounta in Initiative S AM A1 - S ain t M arks Nat io nal W il dl if e R efuge, FL SCC - Source Classification Code SHEN1 - Shenandoa

h National Park, VA SHRO1 - Shining Rock Wilderness Are a, NC SI P - State Implementation Plan SI PS1- S ipsey

Wil derne ss Area , AL SMO KE - S pa

rs e Matr ix Op e rator

Ke rn e l E mis sio ns (m od e l) SMP - Smoke Manag ement Plan SOA- S e c on da

ry Or g anic Ae ro so ls 2 SO - Sulfur Dioxi de STN - Speciate d Trends N etwork SW AN1 - Swa

nquarte r National Wi ldlife Are a, NC TDM - Tr avel De mand Model UAM - Ur

ban Airshe d Model UPBU - Upper

Buf falo Wilderness Ar ea, A R EPA - United States Environmenta l Protection Ag ency VI STA S - Vi sib ili ty

Impr ov e me nt - State and Tr iba l A sso c iatio n o f t

he Southe a st WVDEP - West Virginia De partment of E nvironmental Protection WVD AQ - We st V ir g ini a Di vis ion of Ai r Q ua lity VOC - Volatile Or g anic Compound VMT - Ve hicle Miles Tra veledW est V i rgi ni a R egi o nal H aze S IP P a g e xii [This pag e intentionally

left blank.]W est V i rgi ni a R egi o nal H aze S IP P a g e xiii 

### E X E CU T I V E S UMMARY Reg ional haze is defined a s visibil ity impairment that is produce d by a multitude of source s and activities which e mit fine particles a nd their pre cursor s, and which a re loc ated a cross a

broad g e ogra ph ic are a . T he se e mis sio ns are tr ans po rt e d o ve r l arg e re g ion s, inc lud ingnatio na l pa rk s, fore sts and wilderne ss are as (“ Class I ” fe dera l are as). The Clean Air Ac t mandates prote ction of vis ibi lity , e sp e c ia lly

in C la ss I

are a s. In 1999, the U.S. Environmental Protec tion Age ncy (EPA) fina liz ed the Reg ional Haze Rule. The rule calls for

state, tribal and f

eder al ag encie s to work tog ether to improve visibili ty

in 156 natio na l pa

rk s and wi lde rn e ss a

re a s. Th e state s a

re re qu ir e d to de ve lop and imp le me nt a ir

qu a lity pr ote c tio n p lans (S tate Impl e me ntatio n Pl ans ) t o r e du c e po llutio n th at c a us e s v isi bil ity impairment. These plans should establish goa ls and emissions reduction strate g ies based on trends fr om various source s including point, ar ea, a nd mobile (both on-roa d and non-r oad) so ur c e e mis sio ns , a

nd c omm e rc ia l ma

ri ne , b iog e nic , a

nd wi ldf ir e and a g ri c ult ur a l e mis sio ns . Un de r t he Re g ion a l H a ze R

ule , s tate s a

re re qu ir e d to de ve lop SI Ps to re du c e vis ibi lity impairment with the express intent that by 2064, the visibili ty

in the Class Iare as will be re tur ne d to natur a l c on dit ion s. F ive mul ti- state re g ion a l pl ann ingor g anizatio ns (R POs ) a re c ur re ntl y wo rk ingtog e the r t o develop the te chnica l basis for these state Plans. The produc ts of the re g ional planning or g anizatio ns wi ll b e us e d b y state s to e sta

bli shmon ito ri ngstr ate g ie s f or e va luatin gvis ibi lity c on dit ion s, ba se lin e s and

tr e nd s, and

to d e ve lop lon g -t e rm

str ate g ie s f

or makin g“ re a so na

ble pr ogre ss” tow ard

e lim inatin ga ll m anma de vis ibi lity imp a ir me nt f ro m ma nd ator y Cla ss I are a s. Withthe he lp o f t

he Vi sib ili ty

Impr ov e me nt - State and Tr iba l A sso c iatio n o f t

he Southe a st (VI STAS) RP O, West Virg inia has deve loped a SI P to address visibilit y impairment in the State’s Class IFe dera l Area s – Dolly Sods W ilderness Ar ea a

nd Otter Cre ek Wilderness Are a – located in the e astern mountains of West Virg inia. Ind ev el o p i n g th e S I P , W es tVi rgi n i a h as s o u gh tt o d ev el o p re as o n ab l e p ro gre s s go al s an d l o n gte rm str ate g ie s, a s w e ll a s to e xami ne the re a so na ble ne ss o f B AR T ( B e st Ava ila ble Re tr of

it Te c hn olo g y

) r e du c tio ns . A s w ill be dis c us se d f ur the r, fo r t he 20 pe rc e nt w or st d ay s, Wes t Vi rg ini a ha s a do pte d a

re a so na ble pr ogre ss g oa l of

7. 3 d e c ivi e w ( dv ) r e du c tio n in vis ibi lity impairment by

2018, which is consistent with the uniform rate of progress ne eded to a chieve a natura l background condition of 10.4 dv by 2064. L ikewise, West Virg inia has adopte d a re a so na ble pr ogre ss g oa l f

or the 20 pe rc e nt b e st d ay s whic h w ou ld r e su lt i n a 1. 2 dv r e du c tio n in visibil ity impairment. A cha

ng e of one deciview

is normally

perc eptible to the human eye. The afor ementioned pr edicted r eductions in visibili ty impairment are expected to re sult from implementation of exist ing a nd planned e mission controls which will be discussed in further de ta il thr ou g ho ut t

his SI P do c ume nt. In addition to consultation among the VI STAS states, W est Virg inia has eng ag ed in consultation with states outside the VI STAS reg ion as well as with fe dera l stakeholders inc luding F eder al L and Mana g ers r esponsible for the Class Iare as and the EPA.W est V i rgi ni a R egi o nal H aze S IP P a g e xiv 

### A C K N O W L E D G M E N T S : V IS T A S C on tr actors Ai r R e so ur c e Spe c ia lis ts: Mo nit or ingData An a ly sis Alpine Geophy sics: Emissi ons and Air Qua lity Modeling , Tec hnical Advisor f or Emissions In v e n t o r y Atmospheric Rese arc h and Ana ly sis, I nc.: Oper ation of continuous monitors at Millbrook, NC Ba ron Applied Mete orolog y : Meteorolog ical Modeling Dese rt Resea

rch Institut e: Carbon Sourc e Attribution including sample filter pre paration, sample ana ly sis using Ga s Chromatog raphy and Mass Spec trometry , source attribution using Chemical Mass B alanc e ana ly ses and Positive Matrix Fa ctorization Earth Te ch, Inc: B ART Modeling , CAL PUFF Training ENVI RON: Emissions and Air Q uality Modeling Geor g ia Institut e of Te chnolog y : Emissi ons and Air Qua lity modeling se nsitivi ties Har vard University : GEOS-Chem g lobal model I CF Consulting: Integ rate d Planning Mode l for future elec tric utility g ener ation MACTEC: 2002, 2009, and 2018 Emissions Inventory and Projec tions, CAL PUFF training E.H. Pecha

n and Associates: 2002 Mobile Inventory Resear ch Tria

ng le Institut e: Chemical Ana ly sis of Monitoring Samples Sy stem Applications International: Meteorolog ical Chara cter ization Te nn e sse e Va lle y

Autho ri ty : O pe ratio n o f c ontin uo us mon ito rs at G re at Smo ky Mo unta in Natio na l Park

and pr e pa ratio n o f f

ina l pr oje c t r e po rt

and dr a ft manu sc ri pt Ivar Tomba ch, Environmenta l Consult ant TRC, I nc: B ART Modeling University

of California, River side: Emissions and Air Q uality Modeling VI STAS Technica l Coordinator, Pat Br ewe r Woods Hole: Analy sis of Carbon 14 isotope in car bon samples as pa

rt of ca rbon sourc e attribution projectW est V i rgi ni a R egi o nal H aze S IP P age 1



## West Virginia Regi onal Haz e State Impleme ntation Plan to Pres erve, P rot ect a n d I mp rov e V is ib ilit y

in Class I Feder al Areas 1 . 0 . I NT RODUCT I ON 1.1. What is regional haze? Reg ional haze is defined a s visibil ity impairment that is produce d by a multitude of source s and activities which e mit fine particles a nd their pre cursor s, and which a re loc ated a cross a

broad g e ogra ph ic are a . T he se e mis sio ns are tr ans po rt e d o ve r l arg e re g ion s, inc lud ingnatio na l pa rk s, fore sts and wilderne ss are as (“ Class I ” F eder al ar eas)
. F ine pa rt ic le s ma y e ith e r b e e mitte d d ir e c tly or fo rm e d f ro m e mis sio ns of

pr e c ur so rs , th e mos t 2 x imp or tant o f whic h a

re su lf ur dio xide (S O ) a

nd nit ro g e n o xide s (NO ). Part ic le s a ff e c t vi sib ili ty throug h the scatteringand absor ption of light, and f

ine par ticles - par

ticles similar in siz e to the wave leng th of light - a

re most ef

ficient, pe r unit of mass, at re ducingvisibil ity . There fore , re du c ingfi ne pa

rt ic le s ( pa rt ic le s w itha

dia me te r l e ss t

ha n 2 .5 μm) , in

the atmo sp he re is g ener ally considere d to be an e ffe ctive method of r educingreg ional haze, and thus improving 2.5 vis ibi lity . T he mos t im

po rt ant s ourc e s o f P M and its pr e c ur so rs are c oa l- fi re d p ow e r p lants , 2.5 industrial boilers and other combustion sources. Othe r sig nificant c ontributors to PM and vis ibi lity imp a ir me nt i nc lud e mob ile so ur c e e mis sio ns , a re a so ur c e s, fi re s, and

wi nd blo wn du st. Fine pa rticles also a dverse ly

impact human hea lth, especia lly respiratory

and ca rdiovascula

r sy ste ms. Th e Un ite d St ate s E nv ir onme nta l Pr ote c tio n A g e nc y (E PA) ha s se t natio na l ambi e nt a ir quality standards (NAAQS) f

or daily and annua l levels of fine particle s with a diameter smaller 2.5 that 2.5 μm (PM ). 1.2. What are t he requirem ents und er t he Clean Air Ac t for addressing regional h aze? In Section 169A of the 1977 Amendments to the Clean Air A ct (CAA) , Congr ess set for th a prog ram for

protec ting visibility

in Class Iare as which c alls for the “ preve ntion of any future, and the r emedy

ing of any exis ting, impairme nt of visibili ty in mandatory

Class IFe dera l are as wh ic h im pa ir me nt r e su lts fr om m anma de a ir po llutio n. ” Con g re ss a do pte d th e vis ibi lity provisions to protect visibili ty in 156 Class IFe dera l are as or national parks (ove r 6,000 ac res) , wilderne ss are as (ove r 5,000 ac res) , national memorial pa rks (ove r 5,000 ac res) , and international pa

rks that wer e in exist ence as of August 1977. Among the m are some of the nation’s most scenic a

rea s, such as the Grand Canyon. On Dec ember 2, 1980, EPA promulgated regulations to address visibilit y impairment (45 F R 80084). The 1980 re gulations were develope d to addre ss visibi lity impairment that is “re asonably attributable” to a sing le source or sma ll gro up of

so ur c e s. Th e se re gulatio ns re pr e se nte d th e fi rs t ph a se in a dd re ssi ngvis ibi lity imp a ir me nt a nd de fe rr e d a c tio n o n r e g ion a l haze t hat e manate s f

ro m a va ri e ty of so ur c e s unti l monitoring, modelingand scie ntific knowledg e about the r elationships betwee n pollutants and visibil ity impairment improved.W est V i rgi ni a R egi o nal H aze S IP P age 2

In the 1990 Amendments to the CAA, Congress a dded Section 169B and ca lled on EPA to issue reg ional haze rules. The Reg ional Haze rule (RHR) that EPA promulg ated on July 1, 1999 (64 FR 35713), r evised the e x isting visibilit y regulations in order to integrate provisions addressing re g ion a l haze i mpa ir

me nt a nd e sta bli sha c omp re he ns ive vis ibi lity pr ote c tio n p ro g ra

m f or Cla ss I Fe dera l are as. States ar e re quired to submit state implementation plans (SI Ps) to EPA that set out eac h states’ plan f

or complying w ith the reg ional haze rule, inc luding c onsultation and c oo rd inatio n w ithoth e r s tate s and wi th f e de ra l la nd mana g e rs . T he tim ingof the SI P su

bmi tta l is 2.5 tied to EPA’s promulga

tion of desig nations for the PM National Ambient Air Q uality Standard (NAA QS) at 40 CFR Part 81. States ar e re quired to submit a reg ional haze implementation plan to EPA within t hree y ear s after the date of

desig nation. Be cause EPA promulga ted designation dates on De cembe r 17, 2004, re g ional haze SI Ps are due by Dec ember

17, 2007. The re g ional haze rule a ddresse d the combined visibility eff ects of va rious pollution s ource s over a wide g eographic reg ion. This wide-re achingpollution net meant that all states – even tho se wi tho ut C la ss I

Ar e a s – wo uld be re qu ir e d to pa

rt ic ipate in h a ze r e du c tio n e ff or ts. EPA desig nated f

ive Reg ional Planning Or g anizations (RP O) to assist with the coordination and cooper ation neede d to addre ss the visibil ity issue. West Virginia is among those states that make up the so uth e a ste rn

po rt ion of the c ontiguo us Un ite d St ate s k no wn a s V I STA S ( Vi sib ili ty Improvement – State a

nd Tribal Association of the Southeast), and inc ludes the ea stern band of th e C h e r o k e e In d i an s i na d d i t i o nt o th e f o l l o w i n gs t at e s : A l a

b am a , F l o r i d a , G e o r g i a , K e n tu c ky, Mississ ippi, North Carolina, South Carolina, Tenne ssee, a nd Virg inia.

F igu r e 1. 2- 1. G e og r aphi c al A r e as of R e gio nalP lan ning Or ga niz at ion sW est V i rgi ni a R egi o nal H aze S IP P age 3

1.3. Ge neral over view of regional haze SIP requirem ents Th e re g ion a l haze r

ule (RHR) at 40 CF R 51 .3 08 (d ) r e qu ir e s st ate s to de mon str ate re a so na ble prog ress towa

rd mee ting the national goa l of a re turn to natural visibility conditions by

2064. As a guide for r easona

ble progress, the RHR direc ts St ates to graphic ally show what would be a “uniform r ate of

prog ress” toward natural conditions for e ach ma ndatory Fe dera l Class Iare a within the State and/or for eac h mandatory

Fe dera l Class Iare a located outside the State which may be af fec ted by emissions from source s within the St ate. States a re to e stablish baseline visibil ity conditions for 2000-2004; natur al bac kg round visibilit y in 2064; and the rate of un if or m pr ogre ss b e tw e e n b a se lin e and ba c kgro un d c on dit ion s. Th e un if or m r ate of pr ogre ss i s also known as the “ g lidepath”. The re g ional haze rule the n require s States to establish reasona ble progress g oals (RPGs), expressed in dec iviews, for visibility improvement at e ach a ffe cted Class I are a cove ringeac h (a pp ro ximate ly

) 1 0- y e ar p e ri od

until 20 64 . T he g oa ls m

us t pr ov ide fo r r e a so na

ble pr ogre ss towards a chievingnatura l visibi lity conditions; provide for improve ment in visibi lity for the mos t im pa ir e d d ay s o ve r t

he pe ri od of the imp le me ntatio n p lan; a nd e ns ur e no de g ra datio n in v is ib il it y f o r th e le a s t i mp a ir e d

d ay s o v e r

th e s ame p e r io d

[s e e 4 0 CF R § 5 1
. 3 0 8 ( d ) ( 1 ) ]. In o rd e r t o e ns ur e that vi sib ili ty g oa ls a re pr op e rl y

me t and se t, s tate imp le me ntatio n p lans mus t inc lud e de te rm inatio ns , f

or e a c h Cl a ss I

are a , o f t he ba se lin e vis ibi lity

c on dit ion s ( e xpre sse d in deciview s) for the most impaire d and lea st impaired day s. SI Ps mustalso contain supporting documentation for

all require d analy ses used to ca lculate the de g ree of visibilit y impairment un der n atu ral vi si bi li ty

con di ti on s fo r the m os t i mp air ed an d l eastimpai red d ay s [ see 4 0 C FR § 5 1
. 3 0 8 (d )( 2 )]
. Inad d i t i o n , s t at es mu s ti n cl u d e am o n i t o ri n g s tra t egy fo r m ea s uri n g, c ha ra c te ri zingand

re po rt ingof

re g ion a l haze v isi bil ity

imp a ir me nt t hat is re pr e se ntativ e of

a ll ma n d ato r y F e d e r a l C la s s Iar e a s w it h in th e s tate [s e e 4 0 CF R § 5 1
. 3 0 8 ( d ) ( 4 ) ]. This first set of re asonable prog ress g oals must be met throug h measure s contained in the state ’s l o n g-t erms tra t egy

co v er i n g th e p er i o d

fr o mth e p re s en tun t i l 2 0 1 8
. T h e l o n g t erms tra t egy includes enf

orce able e mission l imitations , compliance schedule s, and other me asure s as nece ssary to achieve the re asonable progress g oals, includingall controls re quired or e x pecte d un d er al l fe d er al an d

s t at e r egu l at i o n s b y

2 0 0 9 an d b y 2 0 1 8
. Du ri n g d ev el o p m en to f t h e l o n gterm strate g y , states ar e also re quired to consider specific fac tors such as the a bove-me ntioned ong oing c ontrol prog rams; measur es to mitigate construction ac tiviti es; source retire ment and repla ceme nt schedules; smoke mana g ement programs for agriculture a

nd fore stry ; and e n f o r c e a b il it y o f s p e c if ic me a s ur e s [s e e 4 0 CF R § 5 1
. 3 0 8 ( d ) ( 3 ) ]. In addition, a spec ific compone nt of ea ch state’ s first long- term strate g y is dictated by

the specific best ava ilable re trofit technology (B ART) re quirements of 40 CF R 51.308(e) w ithin the RHR. The RHR at 40 CFR §51.308(e) require s states to include a de termination of B ART for eac h BA RT-elig ible source in the State that emits any air pollutant which may

rea sonably

be anti c ipate d to c a

us e or c ontr ibute to a

ny imp a ir me nt o f v isi bil ity

in a ny mand ator

y F e de ra l Cla ss Iare a. The Clean Air Ac t section 169A(b) define s BART- elig ible source s as source s in 26 specific source cate g ories, in oper ation within a 15-year period prior

to the enac tment of the 1977 Clean Air Ac t Amendments. States must determine B ART acc ordingto five fa ctors set outW est V i rgi ni a R egi o nal H aze S IP P age 4

in section 169A(g )(7) of the Clean A ir Act. Emission limi tations repre sentingBA RT, and schedule s for c ompliance with B ART for e ach sour ce subje ct to BA RT, must be included in the l o n g - t e r ms tr at e g y. State Implementation Plans for the fir st review pe riod are due De cembe r 17, 2007. These plans will cover long -term strateg ies for visibility improvement betwe en base line conditions in 2000- 2004 and 2018. States ar e re quired to eva luate progress towa rd re asonable progress g oals eve ry five (5) y ear s to assure that installed e missions controls ar e on tra ck with emissions reduction fore casts in ea ch SI P. The first interim revie w is due to EPA five year s after submitt al of the initial S I P. I f emissions controls fail to meet SI P forec asts, then states should implement additional emissions controls by 2018 to be consistent with the SI P or revise the SI P to be c on sis te nt w iththe up date d e mis sio ns fo re c a st. Th e EPApr ov ide d s e ve ra l guid anc e do c ume nts lis te d b e low to a ssi st t he state s in implementation of the re g ional haze rule r equire ments. West Virginia f ollowed these guidance documents in deve loping the te chnica l analy ses re ported in this plan.

• Guidance for Tr ackingProg ress Unde r the Reg ional Haze Rule (EPA-454/B -03-004, September 2003)
.

• Gu ida nc e fo r E sti matin gNatur a l V isi bil ity Con dit ion s Und e r t

he Re g ion a l H a ze R

ule (EPA-454/B -03-005, Septembe r 2003).

• Gu ida nc e on the Us e of

Mo de ls a nd Othe r Ana ly se s f

or De mon str atin gAtta inm e nt o f A ir Quality Goals for Ozone, PM2.5, and Reg ional Haze (EPA, 2007)
.

• Guidance for SettingReasonable Prog ress Goa ls Under the Re g ional Haze Program (EPA, J une 2007). 1. 4. Cl as s I a r e as with in W e stVi r gin ia West Virginia ha s two Class Iare as within its borders: the Dolly Sods W ilderness Ar ea a

nd the Otter Cre ek Wilderness Are a. The United States Cong ress de signated the Dolly Sods W ilderness in 1975 and it now ha s a total of 10 ,2 15 a c re s. Al l of

the wi lde rn e ss i s in

Wes t V ir g ini a

and it i s ma na g e d b y the F or e st Service
. In th e mid -1 80 0s , th e Da lhe fa mil y us e d o pe n gra ssy

fi e lds c a lle d “ so ds ” fo r grazingsh e e p in this area , which now be ars the na me Dolly Sods wilderness. The r eg ion, located high on the A l l e g h e ny

P l at e a u , i s kn o w nf

o r i t s e x t e n s i v e r o c ky

p l a i n s , u p l an d b o g s , an d

s w e e p i n gv i s t a s . In the lower elevations, y ou’ll find a for est of northe rn har dwoods and laur el thickets. Higher up, g ro ve s o f w ind

stunte d r e d s pr

uc e sta nd ne ar h e ath b arr e ns wh e re a zal e a s, mou nta in l a ur e ls, rh od od e nd

ro n, and blu e be rr ie s gro w. Th e bo g s a re un iqu e de pr e ssi on s o f s ph a g num mo ss, cra nberr ies, and the insec t-eating sunde w plant – an e cosy stem you’d expect to see in nor thern Canada . Be aver

ponds dot the Wi lderne ss and the hea dwater s of Red Cree k spill outof the a rea .W est V i rgi ni a R egi o nal H aze S IP P age 5

The United States Cong ress de signated the Otter Creek Wilderne ss in 1975 and it now has a total of 20,000 ac res. All of the wilderne ss is in W est Virg inia and is manag ed by the For est Service
. In a natur al bowl betwe en Shaver s Mountain (on the ea st side) and McG owan Mountain (on the west side) lies Otter

Creek Wilderne ss. Most of the numerous strea ms in the area

flow into Otter Cr e e k, wh ic h r

un s n or th a c ro ss t

he Wild e rn e ss i nto the Dr y F or k Ri ve r. Th e se str e ams f

re qu e ntl y fl a shfl oo d d

ur ingpe ri od s o f h e avy

ra in. F ro m th e mouth o f O tte r C re e k, the te rr a in r ise s to about 3,900 fe et on McGow an Mountain. The a rea , logg ed extensively

betwee n 1897 and 1914, now sports a sec ond-g rowth for est, dense thicke ts of rhododendr on and mountain laure l along the strea ms, and a va

riety of mosses in damper

reg ions. Spruce dominate the higher c ountry and g ive wa to ha

rdwoods such a s black c herr y and yellow birch lowe r down. B lack be ars have returne d and ar e re united with white-tailed de er, w ild turkey s, hare s, rabbits, g rouse, a nd sever al specie s of squirre ls. Bea

vers a re a ctive in seve ral spots. Timber ra ttlesnakes may

be see n Although D olly

Sods and Otter Cree k are separ ate a nd distinct wilderness ar eas, a nd both are mandatory

Fe dera l Class Iare as, they are beingtreated as one are a for reg ional haze purposes due to their c lose proxim ity to eac h other, a

nd the fa ct that there is only one I MPROVE – Inte ra g e nc y

Mo nit or ingof

Pr ote c te d V isu a l E nv ir onme nts – mo nit or wh ic h is loc ate d at D oll y Sods. The West Virginia D ivision of Air Quality (WVDAQ) w ithin the W V Depa rtment of Envir onme nta l Pr ote c tio n ( WVD EP) is r e sp on sib le fo r d e ve lop ingthe Re g ion a l H a ze S I P. T his SI P establishes rea sonable progress g oals for visibility improvement at these Class Iare as, and pr e sc ri be s a

lon gte rm

str ate g y that w ill a c hie ve tho se re a so na ble pr ogre ss g oa ls w ith in t he fi rs t reg ional haze planningperiod.

F igure 1.4-1. West Virginia Ca ss Iare asW est V i rgi ni a R egi o nal H aze S IP P age 6

In d e ve lop ingthi s SI P, WV DA Q h a s a lso c on sid e re d th at e mis sio n s ourc e s o uts ide of Wes t Vi rg ini a may a ff e c t vi sib ili ty

at D oll y Sod s a nd Otte r C re e k, and

that e mis sio ns so ur c e s w ith in West Virginia ma

y aff ect visibility

at Class Iare as in neighboringstates. Through VI STAS, the so uth e a ste rn

state s h ave wo rk e d to g e the r t o a sse ss s tate -b y -s tate c ontr ibutio ns to v isi bil ity impairment in specific Class Iare as, includingthose in West Virginia, a s well as those a ffe cted by emissions from West Virginia. This tec hnical wor k is discussed further

in Chapters 5, 6 and 7 below. A f

ull discussion of the proce ss and outcome of consultations betwee n West Virginia a nd other states is conta ined in Chapter 10. Prior to VI STAS, the southern states coope rate d in a voluntary reg ional partne rship “to identify and re commend re asonable measure s to remedy exis ting a

nd preve nt future a dverse eff ects fr om human-induce d air pollution on the air quality related values of

the Southern Appa lachian Mountains”. States coope rate d with fede ral land ma nag ers, the E nvironmental Protection Ag ency , industry , environmenta l org anizations and aca demia to complete a technica l assessment of the impac ts of acid de position, oz one, and f ine par ticles on sensitive resour ces in the Southern Appalac hians. The Southern A ppalac hian Mountain Initiative (SAMI ) F inal Report was delivere d in August 2002. The SAMIAssessment conc luded that ammonium sulfate is the major contributor to visibili ty

impairment in the Southern Appala chian Mountains and, to improve visibil ity , it is m ost important to reduce sulfur diox ide emissions. SAMIalso conc luded that re du c ingammo nia

e mis sio ns wo uld

be he lpf ul t o r e du c e ammo nium ni tr ate c ontr ibutio ns to visibil ity impairment. Emissions controls for org anic c arbon, e lemental ca rbon, and soil wer e e xpe c te d to be le ss i mpo rt ant f or imp ro vin gvis ibi lity . SA MI mod e lin gfo un d th at on

the ha zie st day s, much of the be nefit of e missions reduc tions would occur in the state wher e emissions re du c tio ns we re ma de . E mis sio ns in s ur ro un din gSAM Istate s a

nd state s o uts ide the SAM I reg ion also contribute to air qua lity in the SAMIClass Iare as. The SAMI states supported strong national multi-pollut ant leg islation to accomplish its m ission. Emi ssions reductions to meet natio na l he a lthsta

nd ard s f or ozo ne and

fi ne pa rt ic le s w e re e xpe c te d to a lso imp ro ve a ir qu a lity in the reg ion. SAMIstates committed to consider a ir quality benef its in t he Southern Ap pa la c hia

ns a s th e y de ve lop e d St ate Impl e me ntatio n Pl ans fo r t he he a lthsta

nd ard s. In 2002, the North Car olina leg islature passe d the North Car olina Clean Smokestack Ac t (CSA) the re qu ir e s N or

th C aro lin a

uti lit ie s to re du c e e mis sio ns of su lf ur dio xide and

nit ro g e n o xide s. The SAMI

analy ses and r ecommenda tions supported development of the North Carolina CSA. Whil e not direc tly

aff ectingcoal- fired pow er pla nts in W est Virg inia, the NC CSA, by reduc ing e mis sio ns that ma

y be tr ans fe rr e d a c ro ss s tate bo rd e rs , w ill re du c e vis ibi lity imp a ir me nt i n We st Vi rg ini a and is the re fo re me nti on e d thro ugho ut t he SI P a s a

n e ff e c tiv e int e r- state op tio n to c ontr ol e mis sio ns . Congr ess subsequently

considere d sever al leg islative bills t o reduc e sulfur dioxide and nitrog en ox ides from e lectric g ener atingutilit ies but ultim ately did not adopt any

of them. In 2004, the EPA promulga

ted the Clean A ir Interstate Rule ( CAI R) to require emissions reductions for sulfur diox ide and nitrog en oxides from elec tric g ener atingutilit ies in 28 easter n states and the

2 District of Columbia. West Virginia is a CAI R participatingstate. The CAIR rule ca ps SO and

x NO and a llo ws fo r i nte rs tate tr a din gof e mis sio ns to y

ie ld c os t e ff

e c tiv e re du c tio ns . T he se reduc tions will signific antly improve visibilit y

in Class Iare as in West Virg inia.W est V i rgi ni a R egi o nal H aze S IP P age 7

1.5. State and F ederal Land Manager Coor din ation As requir ed by

40 CFR §51.308(i), the r eg ional haze SI P mustinclude proc edure s for c ontinuing consultation betwee n the States and F eder al L and Mana g ers on the implementation of the visibil ity protec tion prog ram, includingdevelopment a nd revie w of implementation plan revisions and 5-year

prog ress re ports; and on the implementation of other programs havingthe po te nti a l to c ontr ibute to i mpr ov e me nt o f v isi bil ity in a ny mand ator

y F e de ra l Cla ss I

are a wi thi n the State. Successf ul implementation of a re g ional haze program will involve long -term r eg ional c oo rd inatio n amon gState s. VI STA S wa s f or me d in 20 01 to a dd

re ss r e g ion a l haze a

nd vis ibi lity problems in the southeaster n United States. J urisdictions repre sented by VI STAS members include: the Ea stern B and of Cher okee I ndians; the States of Alaba ma, Flor ida, Geor g ia, Kentucky , Mississ ippi, North Carolina, South Carolina, Tenne ssee, Vir g inia and West Virg inia; and the loca l air pollution control prog rams located in these States. A c opy of the VI STAS Memorandum of

Ag ree ment and B ylaws is enclose d as Appe ndix A. The objec tives of the VI STAS project ar e to establish natura l background visibilit y conditions acr oss the mandatory

Fe dera l Class Iare as; identify curr ent visibili ty

levels, ana ly

ze emission c ontr ol l e ve ls that w ill a c hie ve int e ri m vi sib ili ty g oa ls; and

pr ov ide a de qu ate do c ume ntatio n to member a g encie s so that they can de velop their re g ional haze State/Tribal Implementation Plans (SI P/TI P). Figure 1.5- 1 shows the 18 mandator y Fe dera l Class Iare as in the VI STAS Region, wh e re vis ibi lity

is a n im po rt ant v a lue . T a

ble 1. 5- 1 li sts the se Cla ss I are a s.

F igure 1.5-1. Class I Are as in the VIST AS RegionW est V i rgi ni a R egi o nal H aze S IP P age 8

A technic al support document f or the re g ional haze state implementation plans wa s prepa red cooper atively

by VI STAS staff and state s to chara cter ize reg ional haze in the southeaster n United States. The r eport include s a re view of the scienc e and situation; ca lculation of initial baseline visibility ; reviewof monitoring data/data g aps and r ecommendations for additional mon ito ri ng ; in iti a l e mis sio n inve nto ry c ha ra c te ri zat ion

and pr oje c tio ns ; and

c omp lia nc e wi th exis ting c ontrol prog rams. Source contributions to VI STAS mandatory Fe dera l Class Iare as ar e also assesse d.W est V i rgi ni a R egi o nal H aze S IP P age 9



### T ab l e 1. 5- 1. M an d at or y F e d e r al C l as s I A r e as i n t h e V I S T A S R e gi on

W h er e V i si b i l i ty i s an Im p ortan t V al u e 1 State Are a Nam e Acr eage F ederal Land Manager 40 CFR §81.401 Al a bama S ip sey W il dern ess Area 24 ,9 22 US DA-FS 40 CFR §81.407 Florida Ch a s s ah o w it zk a Wi ld e r n e s s A r e a 2 3 , 5 7 9 U SD I
- F WS Ev e rg la de s N atio na l Pa

rk 1, 39 7, 42 9 USD I -N PS St
. M ar k s Wi ld e r n e s s A r e a 1 7 , 3 5 0 U SD I
- F WS 40 CFR §81.408 Ge or g ia Co ho tt a W il dern ess Area 36 ,9 77 US DA-FS O k e f e n o k e e Wi ld e r

n e s s A r e a 3 5 3 , 9 8 1 U SD I
- F WS Wo lf I s la n d Wi ld e r n e s s A r e a 5 , 1 2 6 U SD I
- F WS 40 CFR §81.411 Kentucky Mammo th C ave Natio na l Pa rk 51 ,3 03 USD I -N PS 40 CFR §81.422 North Carolina Gre at Smoky Mountains National Park 27 3, 55 1 USD I -N PS 2 J oyce Kilmer-Slickrock Wilderness Ar ea 13 ,5 62 US DA-FS 3 L in vi ll e Go rge W il dern ess Area 11 ,7 86 US DA-FS S hi ni ng Ro ck W il dern ess Area 18 ,4 83 US DA-FS Sw an qu arte r

Wi ld e r n e s s A r e a 8 , 7 8 5 U SD I
- F WS 40 CFR §81.426 South C arolina Ca p e Ro ma in

Wi ld e r n e s s A r e a 2 9 , 0 0 0 U SD I
- F WS 40 CFR §81.428 Tenne ssee Gre at Smoky Mountains National Park 24 1, 20 7 USD I -N PS 2 J oy ce Ki lm er-S li ckro ck W il dern ess Area 3, 83 2 US DA-FS 3 40 CFR §81.433 Vi rg ini a J ame s R iv er Face W il dern ess Area 8, 88 6 US DA-FS She na nd oa h N atio na l Pa

rk 19 0, 53 5 USD I -N PS 40 CFR §81.435 Wes t V ir g ini a Dol ly S od s W il dern ess Area 10 ,2 15 US DA-FS Ot ter Cr eek W il dern ess Area 20 ,0 00 US DA-FS 4 0 C FR Sub p art D , §§ 8 1
. 4 0 1 t hrough 8 1
. 4 3 7 l i st s t ho se mand at o ry Fed eral C l ass I areas, est ab l i shed und er t he 1 C l ean Ai r Act Am endment s of 197 7, wh er e t he A dm i ni s t r at or, i n c ons ul t at i on wi t h t he Secr et ar y of t he I nt er i or, ha s d e te rm ine d visib ili ty t o b e a n im p o rta n t va lue . T h e li sting o f ar e a s w h e re visib ili ty i s a n im p o rta n t va lue re p re se n ts an eva l uat i on of

al l i nt er nat i onal par ks ( I P ) , na t i onal wi l dern es s ar eas ( W i l d) e xce edi ng 5, 00 0 acr es , na t i onal memor i al pa r k s ( N M P) ex ce edi n g 5, 000 a cr es , a n d n at i on al pa r k s ( N P) ex ce edi n g 6, 000 a cr es , i n e x i s t en ce on Aug u s t 7, 1977. Cons u l t at i on by EP A wi t h t h e Fe der al La n d M an ag er s i n v ol v ed: T h e D epa r t men t of I n t er i or (U SD I) , N at i o nal P ark Servi ce (N P S), and Fis h and W i l d l i f e Se rvi ce (FW S); and t he D ep art m ent of A gri cul t ure (U SD A ), Fo res t Servi ce (FS ). G reat Sm o ky M o unt ai ns N at i o nal P ark, 51 4 , 7 5 8 acres o veral l , of whi ch 2 7 3 , 5 5 1 acres are i n N o rt h C aroli na, and 2 24 1, 20 7 acr es ar e i n T enn es s ee. J oy ce K i l mer - Sl i ck r ock W i l der n es s , 14, 033 a cr es ov er al l , of w h i ch 10, 201 a cr es a r e i n N or t h Car ol i n a, a n d 3, 832 3 acr es ar e i n T enn es s ee.W est V i rgi ni a R egi o nal H aze S IP Pag e 10



### 2 . 0 . AS S E S S ME NT OF B AS E L I NE AN D C UR RE NT CO ND I T I ON S AN D E S T IM A T E O F N A T U R A L B A C K G R O U N D C O N D IT IO N S IN CL AS S I AR E AS Th e g oa l of

the re g ion a l haze r

ule is t o r e sto re natur a l vi sib ili ty c on dit ion s to the 15 6 Cl a ss I are as identified in the 1977 Clea n Air Ac t Amendments. 40 CFR -51.301(q) define s natural conditions: “Natura l conditions includes naturally occur ringphenomena

that reduc e visibilit y

as me a s ur e d in te r ms o f li g h t e xti n c ti o n , v is u a l r an g e , c o n tr a s t o r c o lo r ati o n . ” T h e r e g io n a l h a ze SI Ps mustcontain mea sures that make “re asonable progress” toward this g oal by reduc ing anthropog enic e missions that cause haze. The re g ional haze rule de fines re g ional haze as “ visibil ity impairment that is cause d by the emission of air pollutants from numerous sour ces loc ated ove r a w ide g eographic are a”. T he re g ion a l haze r

ule g oe s o n to de fi ne vis ibi lity imp a ir me nt a s “ any

humanly pe rc e pti ble c ha

ng e in visibil ity (lig ht ext inction, visual rang e, contr ast, coloration) from that which w ould have e x isted under natural conditions”. An ea sily understood mea sure of

visibil ity to most people is visual rang e. Visual ra ng e is the g r e at e s td i s t an c e , i nk i l o m e t e r s o r m i l e s , atw h i c ha

d ar k o b j e c tc an b e v i e w e d

a g a i n s tth e s ky. F or e va luatin gthe re lativ e c ontr ibutio ns of po llutants to v isi bil ity

imp a ir me nt, ho we ve r, the mos t useful mea sure of

visibil ity impairment is light extinction, which is usually expressed in units of inverse me g ameter s (Mm ). L ight e x tinction affe cts the cla rity and color of objec ts being -1 viewed. The mea sure use d by

the re g ional haze rule is the de ciview ( dv). De civiews a re c alculate d direc tly from lig ht ext inction using a loga rithmic scale. T he dec iview is a usef ul measure for trac king pr og ress in improvingvisibil ity , beca use ea ch dec iview cha

ng e is equa l to an incre mental cha

ng e in visibili ty

perc eived by

the human eye. Most people c an ea sily detec t a one
ext de c ivi e w c ha

ng e in v isi bil ity . Ano the r u se fu l me a sure is l ight e xtinc tio n, b , whic h is inv e rs e ly related to the dec iview. Figure 2.0- 1. Shows the important fac tors involved in seeinga sce nic vista.W est V i rgi ni a R egi o nal H aze S IP Pag e 11

F igure 2.0-1. Important fac tors involved in seeinga sce nic vista are outlined. Imag e-f orming information from a

n object is reduc ed (sc attere d and absor bed) a s it passes throug h the atmosphere to the human observe r. Air lig ht is also added to the sig ht path by scatter ing pr ocesse s. Sunli g ht, light from c louds, and g round-r efle cted light impinge on and scatter fr om particulate s located in the sight path. Some of this scattere d light re mains in the sight path, a nd at times it can bec ome so brig ht that the imag e esse ntially disappea rs. A fina l important factor in seeingand appre ciatinga sce nic vista are the cha rac teristics of the human obse rver . [ Source : Introduction to Visibili ty by Wil liam C. Malm, Air Resource D ivision, National Park Ser

vice]
To a ssi st s tate s in

c on du c tin gvis ibi lity -r e late d r e se arc h a nd in c re atin gF e de ra l and

State implementation plans for the pr otection of visibility in Class Iare as, the Intera g ency Monitoring of Pr ote c te d V isu a l E nv ir onme nts (IMPRO VE ) p ro gra m w a s e sta

bli sh e d in 19 85 . I MPRO VE is a coope rative me asure ment eff ort g overne d by

repr esentatives from F eder al, State and r eg ional or g anizatio ns . I MPRO VE ’s ob je c tiv e s a re , amon goth e r t

hin g s, to e sta

bli shc ur re nt v isi bil ity c on dit ion s in

Cla ss I are a s; t o tr a c k c ha

ng e s in vis ibi lity ; to ide nti fy

e mis sio n s ourc e s r e sp on sib le fo r m an- ma de vis ibi lity

imp a ir me nt; and to p ro vid e re g ion a l haze m on ito ri ngdata fo r a ll Cl a ss I are as, whe re pr actica l. I MPROVE’s monitoring networ k consists of aer osol and optical samplers. The network be g an in 1988 with 20 monitoring sites in Class Iare as and quic kly g rew to 30 sites by

1999. Finally , with the enac tment of the re g ional haze rule, the network ha s beenW est V i rgi ni a R egi o nal H aze S IP Pag e 12

e xpand e d to 80 a dd iti on a l si te s ( fo r a tot a l of 11 0 s ite s) wi th a dd iti on a l si te s f or sp e c ia l pr oje c ts. Figure 2.0- 2 depicts the location of I MPROVE monit oringsites in the VI STAS reg ion. 

### 18 Cl as s I A r e as i n VIS T A S St at e s and M oni t or Loca t i ons

F igure 2.0-2. I MPROVE Monitoring sites in the VI STAS Region Some Class Iare as employ specia l optical equipment that measur es visibilit y direc tly . More common, howeve r, ar e instruments which sample the air for the var ious fine par

ticulate matter which ca uses visibili ty

impairment. A mathematical re lationship (ext inction alg orithm) has bee n established betwe en these differ ent kinds of par ticulates and visibility . When the amounts ofW est V i rgi ni a R egi o nal H aze S IP Pag e 13

sulfates, nitrate s, car

bonace ous matter and othe r sample e lements are measure d, visibil ity can be estimated (r econstruc ted). I t is the avera g e of the I MPROVE monit oringdata f or 2000 through 2004 that can be thought of as “c urre nt” visibili ty

conditions for this initi al per iod. The compa rison of initial baseline c on dit ion s to natur a l vi sib ili ty c on dit ion s in dic ate s th e amou nt o f i mpr ov e me nt n e c e ssa ry to atta in n atur a l vi sib ili ty

by 20 64 . E a c h s tate mus t e sti mate natur a l vi sib ili ty le ve ls f or Cla ss I areas wi th in it s b ord ers in con su lt ati onwi th Federa l L andMan agers an d o th er s tat es [ 40 CFR 51.308(d)( 2)]. “Current c onditions” are a ssessed eve ry five year s as par

t of the SI P review where a c tua l pr ogre ss i n r e du c ingvis ibi lity imp a ir me nt i s c omp are d to the re du c tio ns c omm itt e d to in the SI P. For eac h Class Iare a, ther e ar e thre e (3) metrics of visibility that are part of the deter

mination of re a so na ble pr ogre ss: 1) natur a l c on dit ion s, 2) baseline c onditions, and 3) c ur re nt c on dit ion s. Eac h of the thre e metric s includes the conc entration data of the visibility pollutants as differ ent terms in the light e x tinction algor ithm, with respective e x tinction coeff icients and re lative humidity fac tors. Total light e x tinction, when conve rted to dec iviews, is calculated for the ave ra g e of

the 20 pe rc e nt b e st a

nd 20 pe rc e nt w or st v isi bil ity da y s.

“ Natur a l” vis ibi lity

is d e te rm ine d b y

e sti matin gthe natur a l c on c e ntr atio ns of vis ibi lity po llutants and then c alculatingtotal light extinction wit h the light e x tinction algor ithm. “Ba seline” visibil ity is the startingpoint for the improveme nt of visibili ty conditions. I t is the avera g e of the I MPRO VE mon ito ri ngdata fo r 2 00 0 thro ugh 2 00 4 a

nd is e qu iva le nt t o “ c ur

re nt” vis ibi lity conditions for this initi al re view per iod. The compa rison of initial baseline conditions to natural conditions indicates the amount of improve ment nece ssary to attain natura l visibi lity by 2064. Ea c h s tate mus t e sti mate natur a l vi sib ili ty le ve ls f or Cla ss I are a s w ith in i ts b or de rs in c o n s u lt ati o nw it

hth e F e d e r a l L an d

M an a g e r s ( F L M s ) an d

o th e r s tate s [4 0 CF R 5 1
. 3 0 8 ( d ) ( 2 ) ]. “Curre nt conditions” are assessed e very five year s as par

t of the SI P review where actua l pr ogre ss i n r e du c ingvis ibi lity imp a ir me nt i f c omp are d to the re du c tio ns c omm itt e d to in t he SI P. 2.1. Estim ating Natural Condition s for Dolly S ods an d Otter C ree k Wild erness Ar eas Natura l background visibilit y , as def ined in Gu ida nc efor

Es tim ati ng

Natural V

isi bil ity Conditi ons Under the Re

gional Haze Program , EPA-454/B- 03-005, September 2003, is based o nan nu al av er age co n ce n tra t i o n s o f f i n e p art i cu l at e c o mp o n en t s . T h e s am e annu al av er age natura l background visibilit y is assumed for a ll Cl ass I are as in the ea stern United States (sepa rate values a

re e stimated for the w estern United States). Natur al bac kg round visibilit y for the 20 per cent wor st day s is estimated by assuming that the fine pa rticle c oncentr ation for natural ba c kgro un d a

re no rm a lly

dis tr ibute d a

nd the 90 pe rc e nti le of

the annua l di str ibutio n r e pr e se nts th natur a l ba c kgro un d v isi bil ity on the 20 % w or st d ay s.W est V i rgi ni a R egi o nal H aze S IP Pag e 14

In th e 20 03 guid anc e , th e EPAa lso pr ov ide d th at st ate s ma y us e a “ re fi ne d a

pp ro a c h” to e sti mate the values that char acte rize the natura l visibi lity conditions of the Class Iare as. The pur pose of such a r efine ment would be to provide more acc urate estimates with chang es to the extinction a lg or ithm th at ma

y inc lud e the c on c e ntr atio n v a lue s; f a c tor s to c a lc ulate e xtinc tio n f ro m a me a sure d p art ic ula

r s pe c ie s and

pa rt ic le size ; th e e xtinc tio n c oe ff ic ie nts fo r c e rt a in c omp ound s; g e ogra ph ic a l va ri atio n ( by

a lti tud e ) o f a

fi xed va lue ; and

the a dd iti on

of vis ibi lity po llutants
. In 2005, the I MPROVE St eer ing Committee made rec ommendations for a ref ined equation that modifies the terms of the orig inal equation to ac count for the most rece nt data. The choice be tw e e n u se of

the old or the ne w e qu atio n f

or c a lc ulatin gthe vis ibi lity

me tr ic s f or e a c h Cl a ss I are a is made by

the state in which the Class Iare a is locate d. The ne w I MPROVE equation ac counts for the eff ect of particle siz e distribution on light e xtinc tio n e ff

ic ie nc y of su lf ate , n itr ate , a

nd or g anic c arb on . T he ma ss m ult ipl ie r f or or g anic car bon (par ticulate org anic matter) is incre ased f rom 1.4 to 1.8. New te rms are added to the equation to ac count for light ext inction by sea sa lt and light absor

ption by g aseous nitrog en dio xide . Si te -s pe c if

ic va lue s are us e d f

or Rayle igh s c atte ri ngto a c c ount f or the sit e -s pe c if

ic eff ects of e levation and tempe rature . Separate re lative humidity enhanc ement fa ctors ar e used f or sma ll a nd larg e size dis tr ibutio ns of ammo nium su lf

ate and ammo nium ni tr ate and

fo r s e a sa lt. T h e e l em en t al carb o n(l i gh t -ab s o rb i n g carb o n ), fi n e s o i l , an d co ar s e m as s t erm s d o n o tch an ge betwee n the orig inal and new I MPROVE equation. The VI STAS states chose to use the ne w I MPROVE Equation as the basis for the conc eptual de sc ri pti on

be c a us e it t ake s into a c c ount t he mos t r e c e nt r e vie w o f t he sc ie nc e and

be c a us e it i s rec ommended by

the I MPROVE St eer ing Committee. F or a more detailed discussion of the two I MP RO VE equat io ns , s ee Ap pen di x B. Th e “ old ” I MPRO VE e qu atio n is us e d to e sti ma te light s c atte ri ngby

pa rt ic le s:

ext b . 3 x f

( RH ) x [S u lf

ate ] + 3 x f

( RH ) x [N it

r ate ] + 4 x [O r g an ic M a s s ] + 10 x [ Elemental Car

bon] + 1 x [Fine Soil] + 0.6 x [ Coarse Ma ss] + 10. Th e “ ne w” I MPRO VE e qu atio n is us e d to e sti ma te light s c atte ri ngby

pa rt ic le s:

ext S L b . 2
. 2 x f ( RH ) x [S ma ll Su lf ate ] + 4
. 8 x f (RH) x [ L arg e Sulfate] S L + 2
. 4 x f ( RH ) x [S ma ll N it r ate ] + 5
. 1 x f (RH) x [ L arg e Nitrate] + 2. 8 x [Sma ll O rg anic Ma ss] +6. 1 x [L arg e Or g anic Ma ss] + 10 x [Ele me nta l Ca

rb on ] + 1 x [Fin e Soi l] SS + 1.7 f (RH) x [Sea

Sa lt] +0. 6 x [Coa

rs e Ma ss] + Rayleig h Scattering(Site Specific)

2 + 0
. 3 3 x [N O ( p p b ) ]. 2.2. Estim ating Baselin e Conditions f or Dolly Sods an d Otter C ree k Wild erness Ar eas Ba seline visibilit y

conditions at Dolly

Sods and Otter Cree k Wil derne ss Area s are deter mined from samplingdata c ollected at the I MPROVE monit oringsite located in the D olly SodsW est V i rgi ni a R egi o nal H aze S IP Pag e 15

Wil derne ss Area . A five year aver ag e (2000 to 2004) w as ca lculated f or the 20% w orst and the 20% best visibilit y

day s in acc ordanc e with 40 CFR 51.308(d)( 2) and Guidance for Tracking Prog re ss U nd e r th eRe gio na l H az eRu le , E PA- 45 4/B -0 3- 00 4, Se pte mbe r 2 00 3. I MPRO VE data re c or ds fo r D oll y

Sod s f or the pe ri od

20 00 to 2 00 4 e xce e d E PA r e qu ir e me nts fo r d ata completene ss. I MPROVE does not operate a monitor at the O tter Cree k Wil derne ss Area and considers the I MPROVE monit or data from Dolly

Sods to be repre sentative of visibility at Otter Creek. T he lig ht ext inction and dec iview visibili ty values for

the 20 per cent be st and 20 perc ent wo rs t vi sib ili ty

da y s at th e Cla ss I

are a s a re ba se d o n d ata

and c a lc ulatio ns inc lud e d in

Ap pe nd ix B of thi s SI P. 2.3. Summ ary of Natural Background and B aseline Condi tions f or Dolly Sods an d Otter Cre ek Wilderness Areas Ta ble 2. 3- 1 p re se nts e sti mate d n atur a l ba c kgro un d a nd ba se lin e vis ibi lity

me tr ic s f or the Do lly Sods and Otter Cree k Wil derne ss Area s. Note that West Virg inia is not consideringinternational e mis sio ns to b e a

c omp on e nt o f n atur a l ba c kgro un d. B a se lin e vis ibi lity on the 20 pe rc e nt w or st da y s at D oll y

Sod s is 29 .0 dv . N atur a l ba c kgro un d v isi bil ity on the 20 pe rc e nt w or st d ay s is predic ted to be 10.4 dv. Va lues are shown to a pre cision of one de cimal place for tec hnical acc urac y . Howeve r, cha

ng es of less than ±0.5 dVare likely to be imperc eptible.

Tabl e 2. 3- 1. Na tu r al B ac kgr ound a nd Ba se line Co ndit ion s for Dolly S ods an d Otter C ree k Wild erness Ar eas Na tu r al B ac kgr ound C ondi ti ons Class I Area Aver age for

20 pe r c e nt Worst Days (d e c ivi e ws) Aver age for

20 pe r c e nt Best Days (d e c ivi e ws) Aver age for

20 pe r c e nt Worst Days Bext (M m )

-1 Aver age for 20 pe r c e nt Best Days Bext (M m )

-1 Dolly Sods W ilderness 10.4 3.6 18.5 4.4 Otter Cre ek Wilderness 10.4 3.6 18.5 4.4 Baseline Visib ility Cond itions (2000-2004) Class I Area Aver age for

20 pe r c e nt Worst Days (d e c ivi e ws) Aver age for

20 pe r c e nt Best Days (d e c ivi e ws) Aver age for

20 pe r c e nt Worst Days Bext (M m )

-1 Aver age for 20 pe r c e nt Best Days Bext (M m )

-1 Dolly Sods W ilderness 29.0 12.3 194.6 35.4 Otter Cre ek Wilderness 29.0 12.3 194.6 35.4W est V i rgi ni a R egi o nal H aze S IP Pag e 16

2.4. P ollutan t Contributions to Visib ility Im pairm ent (2000- 2004 Baseline Data) The 20 pe rce nt worst visibil ity day s at Dolly

Sods and Otter Cree k g ener ally occur duringthe period May

to September. The peak hazy day s occur

in the summer under

stag nant weather conditions with high re lative humidity , high te mperatures, and low w ind speeds. The 20 perc ent best visibili ty

day s can oc cur at any

time of the year .

Figure 2.4- 1. Aver ag e lig ht ext inction for the 20% Haziest Day s in 2000-2004 at VI STAS and

neig hboringClass Iare as usingNew IMPROVE alg orithmW est V i rgi ni a R egi o nal H aze S IP Pag e 17

F igure 2.4-2. Aver age light extinction for the 20% Cleare st Days in 2000-2004 at VISTAS

and ne igh bor ing Cl as s I a r e as usin g N e w IM P RO VE alg or it hm

4 2 4 Am mo niu m s ulf

ate , (NH ) SO , is the most important contributor to visibility impairment and fine pa rticle mass on the 20 pe rce nt worst and 20 per cent be st visibi lity day s at Dolly

Sods and Otte r C re e k. Sul fa

te le ve ls o n th e 20 pe rc e nt w or st d ay s a c c ount f or 87 % o f t he vis ibi lity impairment. Acr oss the VI STAS reg ion, sulfate leve ls are higher at the VI STAS inl and sites than at the VI STAS coastal sites (F igur e 2.4.1). O n the 20 per cent c lear est day s, sulfate leve ls are mor e un if or m a c ro ss t

he re g ion (F igur e 2. 4. 2) . [N ote tha t in the se tw o f

igur e s, the le ve ls at D oll y Sods are a lso considere d to be re prese ntative of the le vels at Otter Cre ek.] The be st avera g e visibilit y and lowest sulfate values on the clea rest day s occur

red at Shini ng Rock, NC, at 1621 meter

elevation, and is likely

influence d on the clea

rest day s by

reg ional transport of a ir masses above the boundar y layer. Sulfate particle s are formed in the atmosphere 2 4 from sulfur dioxide emissions. S ulfate pa rticles occ ur as hydrog en sulfate , H (SO ), ammonium 4 4 4 2 4 bisulfate, HNH SO , and ammonium sulfate, (NH ) SO , dependingon the ava ilability of 3 ammonia NH , in the atmosphere
.W est V i rgi ni a R egi o nal H aze S IP Pag e 18

Particulate Organic Matter ( POM) is t he se c on d mo st i mpo rt ant c ontr ibutor to f ine pa rt ic le ma ss and light e xtinc tio n o n th e 20 pe rc e nt h a zie st a nd the 20 pe rc e nt c le are st d ay s at D oll y Sod s. Elevate d levels of POM and e lemental ca rbon (EC), indicate impact fr om wildfires or pr escr ibed fire. Sig nificant fir e impacts a re infr equent at Dolly

Sods. VI STAS collected a dditional samples of ca rbon at five sites, including Mammoth C ave, K Y; Gre at Smokey

Mountains, NC; Mil lbrook (Raleigh, NC); Cape Romain, SC; and Shenandoah, VAto better unde rstand sourc es contributingto car

bon in rural a

nd urban a

rea s. At Shenandoah a

nd other mountain Class Iare as th e m aj o ri ty

o f c ar b o ni s fr o mb i o gen i c e m i s s i o n s , ei th er fr o mv ege t at i o no r f

ro mwo o d

b urn i n g. Woo d b ur

nin gis m or e imp or

tant i n th e fa ll, wi nte r a

nd e arl y sp ri ngmonths . B iog e nic e mis sio ns , chief ly emissions of volatile org anic c ompounds (VOC) fr om veg etation, peak in spring a nd summer. B ioge nic ca

rbon emissions at Cape Romain and other coasta l sites were lower tha n e mis sio ns at th e fo re ste d mo unta in s ite s. Carb on fr om g a so lin e and

die se l e ng ine s is a

re lativ e ly small contributor at the rur al sites. At Millbrook, an urban site, c arbon f rom fossil fuel combustion has a lar g er pe rce ntag e contr ibution than at the rura l sites, but st ill less than 50 perc ent of total ca

rbon mea sured. The se re sults sugg est that controllinganthropog enic sourc es of c arb on wi ll h ave little be ne fi t in imp ro vin gvis ibi lity

in C la ss I

are a s. Contr oll inganthro po g e nic

2.5 so ur c e s o f c arb on

wi ll l ike ly

be mor e e ff

e c tiv e at r e du c ingle ve ls o f P M in u rb an a re a s.

4 3 Am mo niu m n itrate , NH NO , is formed in the atmosphere by rea ction of ammonia a

nd nitrog en ox ides. In the VI STAS reg ion, nitrate for mation is li mited by

the ava ilability

of ammonia a

nd by te mpe ratur e . A mmo nia

pr e fe re nti a lly

re a c ts w ithsu lf

ur dio xide and

su lf ate be fo re re a c tin gwi th nitrog en oxides. P article nitrate is forme d at lower temperatures; at ele vated tempe rature s nitrates re mains in the g aseous f orm. For this reason, pa

rticle nitrate levels ar e ver y low in the summer and a

minor contributor to visibili ty impairment. Particle nitrate conc entrations are highe r o n w int e r d ay s and are mor e imp or

tant f or the c oa sta l si te s whe re the 20 pe rc e nt w or st day s can oc cur on w inter day s. Modeling da ta (see section 7) indicate that in the VI STAS reg ion ammonium nitrate formation is l imited by ammonia conc entrations and sugg est that for w inter da y s, c ontr ols of

ammo nia so ur c e s w ou ld b e mor e e ff e c tiv e in r e du c ingammo nium ni tr ate le ve ls tha n c ontr ols of

nit ro g e n o xide s. Ele me nta l Ca rb on (

EC) , is a compar atively

minor contributor to visibili ty impairment. Sources include agriculture, pr escr ibed, and wild fire s and incomplete c ombustion of fossil fuels. EC le ve ls a re highe r at ur

ba n mo nit or s th an at th e Cla ss I are a s. Th is s ug g e sts that c ontr ol o f f os sil 2.5 fu e l c omb us tio n s ourc e s w ou ld b e mor e e ff e c tiv e at r e du c ingPM in u rb an a re a s th an to imp ro ve vis ibi lity

in C la ss I

are a s. So il fine pa rticles ar e minor contributors to visibilit y at most sites on mostday s. Occ asional episodes of e levated f

ine soil can be attributed to Sahara n dust episodes, particula rly at the Everg lades, F L , but are rar ely seen at Dolly

Sods. No control strateg ies are indicated f or fine so il. Se a s alt , NaCl, is observed a t the coasta l sites. Sea salt contributions to visi bility impairment are mos t im po rt ant o n th e 20 pe rc e nt c le are st d ay s whe n s ulf ate and POM le ve ls a

re low . Se a

sa lt le ve ls d o n ot c ontr ibute signif ic antl y to v isi bil ity

imp a ir me nt o n th e 20 pe rc e nt w or st v isi bil ity day s. The new

I MPROVE equation uses Chloride ion, Cl , from routine IMPROVE - measure ments to calculate sea salt levels. Cl may

rea ct with nitrate a

nd volatiliz e off

the filter, -W est V i rgi ni a R egi o nal H aze S IP Pag e 19

so that Cl may

undere stimate sea sa lt levels. VI STAS funded sodium ion, Na , analy ses of the - + I MPROVE filters from Oc tober 2003 to De cembe r 2004 for nine sites. Sodium i on measure ments may

overe stimate sea sa lt levels beca use sodium nitrate is an additional sourc e of so dium io n th at c ann ot b e dis tin guis he d f ro m so dium c hlo ri de . O ur be st und e rs tand ingto d ate su g g e sts that us ingCl may un de re sti mate se a

sa lt l e ve ls b y

a s mu c h a s a

fa c tor of tw o. Th is - un c e rt a int y ha s gre at im

pli c atio n f

or c a lc ulatin gse a

sa lt l e ve ls und e r n atur a l vi sib ili ty c on dit ion s. Coa rsepa rti c lema ss (p art ic le s w ithdia me te rs be tw e e n 2 .5 and

10 mic ro ns ) h a s a

re lativ e ly sma ll c ontr ibutio n to vis ibi lity imp a ir me nt b e c a

us e the light e xtinc tio n e ff ic ie nc y of c oa rs e ma ss is very low compar ed to the extinction efficienc y for sulfa te, nitrate a

nd car bon.

2.5 An unidentified c omp on e nt i s r e po rt e d b y I MPRO VE a s th e dif

fe re nc e be tw e e n th e tot a l PM ma ss m e a sure d o n th e fi lte r a nd the sum of

the me a sure d c omp on e nts . T his un ide nti fi e d ma ss may be positive or neg ative and is attributable to water and/or the f

actor s used to calc ulate the mol e c ula r w e ig hts of

the oth e r c omp on e nts
. The new IMPR OVE equation g e ne ra lly re su lts in h ighe r c a lc ulate d li g ht e xtinc tio n o n d ay s w ith highe r m a ss a

nd low e r l ig ht e xtinc tio n o n d ay s w ithlow e r m a ss. Th is t e nd s to inc re a se calc ulated lig ht ext inction for cur rent c onditions and to decre ase c alculate d light extinction for natura l visibi lity

conditions. Adding se a salt to the newI MPROVE equation incre ases lig ht exti nction for both cur

rent a nd natura l visibi lity

conditions. Incre asingthe mass multipli er f or particulate org anic matter in the new I MPROVE equation incre ase light ext inction for cur rent conditions more than for

natura l conditions. The new a lgor ithm does not chang e the c onclusion that in the VI STA S r e g ion

and in We st V ir g ini a , th e mos t e ff e c tiv e me ans to i mpr ov e vis ibi lity is t o r e du c e su lf

ate c on c e ntr atio ns .
2.5 PM trends in urban and Clas s I areas : I MPRO VE data we re c omp are d to mon ito ri ngdata from the Spec iated Tre nds Network ( STN) in near by urban a rea s to understand the similarities and diffe renc es in composition of fine par ticle mass. Howe ver, the two networks use differ ent 2.5 me a sure me nt a

nd ana ly tic a l te c hn iqu e s, makin gdata c omp ari so n d if

fi c ult . Se ve ra l PM nonattainment ar eas a

re in c lose proxim ity

to the Class Iare as in the southeaste rn US, including Atlanta, GA; B irmingha

m, AL ; Charleston, WV; Chattanooga , TN; L ouisvill e, KY; a nd Knoxvi lle, TN. Ammonium sulfate conc entrations are c ompara ble betwe en urba

n and nea rby Cla ss I are a s, wh ile or g anic c arb on , e le me nta l c arb on and nit rate c on c e ntr atio ns are g e ne ra lly highe r i n urb an a

re a s th an th e Cla ss I

are a s. Th e se re su lts su g g e st that su lf ate is w ide ly distributed reg ionally

while urba

n are as see additional incre mental pollutant loadings f rom local e mis sio ns so ur c e s. Role of mete orology in determining visibility conditions : sp e c ia lize d mathe matic a l te c hn iqu e s, including Classifica

tion and Reg ression Tre e Ana ly ses (CART) we re use d to char acte rize the re latio ns hip be tw e e n me te or olo g ic a l c on dit ion s a nd vis ibi lity c on dit ion s at th e Cla ss I

are a s. D ays w e r e a s s i g n e d

t o o n e o f f i v e v i s i b i l i ty c l a s s e s r an g i n gf r o mp o o r t o g o o d v i s i b i l i ty. D ays we re the n a ssi g ne d to bin s b a se d o n me te or olo g ic a l c on dit ion s. F or Do lly Sod s p oo r v isi bil ity day s were most li kely

to occur on day s with high tempe rature s, high r elative humidity , low wind 2.5 speeds a nd elevated PM mass at upwind urba n are as. Prec ipitation was not a g ood predic tor ofW est V i rgi ni a R egi o nal H aze S IP Pag e 20

v i s i b i l i ty c o n d i t i o n s . W e i g h t s w e r e a s s i g n e d t o d ays b a s e d

o nf r e qu e n c y o f

o c c ur r e n c e o f d ays wi th s imi lar m e te or olo g ic a l c on dit ion s. Th e abo ve an aly ses are fu rther d is cus sedin App end ix B.W est V i rgi ni a R egi o nal H aze S IP Pag e 21



### 3 . 0 . U N IF O R M R A T E O F P R O G R E S S T O N A T U R A L C O N D IT IO N S IN 2064 As stated in Section 1.3 above , the re g ional haze rule dire cts states to g raphic ally show what constitutes a “unifor

m rate of prog ress” toward natural conditions for e ach ma ndatory Fe dera l Class Iare a within the State, as we ll as for e ach ma ndatory Fe dera l Class Iare a located outside the State that may

be af fec ted by emissions from source s within the St ate. The uniform rate of prog ress is also known a s the “g lidepath.” The g lidepath is simply a straight gr aphica l line drawn from the ba seline leve l of visibili ty impairment for 2002- 2004 to the level re prese nting no anthropog enic ( man-made ) impairment in 2064. Eac h state must set g oals for e ach Class I are a that provide for r easona ble progress towa

rds achie ving natural visibilit y conditions by

2064. Section 40 CFR §51.308(d)( 1) of the r eg ional haze rule r equire s that rea sonable progress g oals must both: 1) provide for

improvement in visibili ty for the most impaired da y s over the pe riod of the implementation plan; and 2) ensure no degradation in vis ibilit y for the le ast impaired da

y s over the sa

me per iod. Uniform rate of progress graphs ( g lidepaths), we re de veloped f or ea ch Class Iare a in the VI STAS reg ion. The g lidepaths wer e deve loped in acc ordanc e with EPA’s guidance for tra cking prog ress a

nd used data

collecte d from the IMPROVE monit oringsites as descr ibed in Section 2 of thi s d oc ume nt. Th e g lid e path i s o ne of the ind ic ator s u se d in

se tti ngre a so na

ble pr ogre ss g oa ls.
3.1. Glidepath for the Dolly Sod s and OtterCre ek Wilderness Areas Th e fo llo wi ngis the g lid e path f or the Do lly Sod s a nd Otte r C re e k Wil de rn e ss Are a s. As no te d in Section 1.4, althoug h Dolly Sods and Otter Cree k are separ ate a nd distinct wilderness ar eas, a nd both are mandatory

Class Iare as, they

are beingtreated as one are a for reg ional haze purposes due to their c lose proxim ity to eac h other, a

nd the fa ct that there is only one I MPROVE monit or, which is locate d at Dolly

Sods. Natural bac kg round visibilit y is predicted to be 10.4.W est V i rgi ni a R egi o nal H aze S IP Pag e 22

F igur e 3
. 1

- 1
. U n if

o r m R ate o f Pr o g r e s s G li d e p ath

f o r th e 2 0 % Wo r s t D ay s at D o ll y So d s , WVW est V i rgi ni a R egi o nal H aze S IP Pag e 23



### 4 . 0 . N A TU R E O F TH E PR O BLEM : C H I EF C A U SES O F VI SI BI LI TY I M PA I R M EN T I N D O LLY SO D S A N D O TTER C R EEK W I L DE RN E S S AR E AS Th e re g ion a l haze r

ule at 40 CF R 51 .3 08 (d )( 4) state s:

. . . T h e i mp l em en t at i o np l anmu s tal s o p ro v i d e f o r t h e f o l l o wi n g: (i) . . . . (v) A statewide inventory of emiss ions of pol lutants that are rea sonably anticipated to c ause or contribute to visibility impairment in any mandatory

Class IFe dera l are a. Th e inv e ntor

y mus t in c lud e e mis sio ns fo r a ba se lin e y e ar, e mis sio ns fo r t he mos t re c e nt ye ar f

or wh ic h d ata are ava ila

ble , a nd e sti mate s o f f

utu re pr oje c te d e mis sio ns . T h e S t at e mu s ta l s o i n c l u d e a c o m m i tm e n tt o u

p d at e th e i n v e n t o r

y p e r i o d i c a l l y. T h i s S e c t i o na d dr e s s e s th e e m i s s i o n s i n v e n t o r y r e qu i r e m e n t s o f

4 0 C F R 5 1
. 3 0 8 ( d ) ( 4 ) ( v )
. In addition, West Virginia c ommits to periodica lly updatingthe inventory

as re quired by

the Consolidated Emissi ons ReportingRule 40 CFR 51.30 [60FR39602, 10J UN2002]. 4.1. Baseline Emissions In ventory The re g ional haze rule at 40 CFR 51.308(d)(4) (v) r equire s a statewide emissions inventory of pollutants that are r easona bly anticipated to c ause or

contribute to visibilit y

impairment in any mandatory

Class Iare a. An inventor y was de veloped f

or the ba seline year

2002 and estimated for 2009 a

nd 2018. The pollutants inventoried include volatile org anic c ompounds, nitroge n 2.5 10 ox ides, fine pa

rticulate ( PM ), coa

rse pa rticulate ( PM ), ammonia a nd sulfur diox ide. A more detailed discussion of the ba seline emissions inventory for 2002 is conta ined in Appendix D. There are five diff

ere nt emission inventory

source classifications: st ationary point; st ationary are a ; on -r oa d ( highw ay

); of f- ro a d; a nd bio g e nic so ur c e s. Statio na ry po int so ur c e s in c lud e tho se source s that emit gr eate r than a specifie d tonnag e per

y ear , for whic h the data is provide d at the proce ss level. Elec tric g ener atingutilit ies and industrial sourc es comprise the major c ateg ories for stationar

y point sources. Stationary are a sourc es ar e those sourc es whose e missions are relatively small, but due to the larg e number of these sour ces, the collective e missions could be signif ic ant ( i.e . d

ry c le ane rs , s e rv ic e statio ns , a g ri c ult ur a l so ur c e s, fi re s e mis sio ns , e tc .) . T he se ty pes of e missions are estimated on a c ounty level. On- road ( highw ay ) mobile source s include car s, trucks, and motorc ycles that use the roadw ay sy stem. The emissions from these sour ces a re estimated by

vehicle ty

pe and r oad ty

pe and a re summed to the c ounty

level. Non- road ( or off

ro a d) mob ile so ur c e s are e qu ipm e nt t hat c an mo ve but do no t us e ro a dw ay s, i.e ., lawn mow e rs , c on str uc tio n e qu ipm e nt, ra ilr oa d lo c omo tiv e s, a ir c ra ft , e tc . T he e mis sio ns fr om t he se so ur c e s, like stationary

are a sourc es, ar e estimated on a county level. B ioge nic source s are the natura l so ur c e s li ke tr e e s, c ro ps , gra sse s and the natur a l de c ay

of pla nts . T he e mis sio ns fr om t he se so ur c e s are e sti mate d o n a c ounty le ve l. In a dd iti on

to t he va ri ou s so ur c e c la ssi fi c atio ns , th e re are va ri ou s ty pe s o f e mis sio n inve nto ri e s. The fir st is the actual base y ear inventory . This inventory ref lects the ba se year

emissions thatW est V i rgi ni a R egi o nal H aze S IP Pag e 24

c or re sp on d to the me te or olo g ic a l data us e d in the mod e lin ge ff

or t, i n th is c a se , 2 00 2. Th e se emissions are use d for e valuatingair qua lity model perf

ormanc e. The sec ond ty

pe of inve ntory

is the ty pical base y ear

inventory . This inventory is simi lar to the a c tua l ba se y e ar. Ho we ve r, fo r s ourc e s th at ma y have signif

ic ant e mis sio ns c ha

ng e s f ro m ye ar t o y ear , a more ty

pical emission value is used. In this modeling ef fort, ty pical emissions were de ve lop e d f

or the e le c tr ic g e ne ratin gun its (E GU s) and the wi ldf ir e e mis sio ns . T he a ir

qu a lity modeling r esults, using the ty pical base y ear

inventory , are used to ca lculate r elative r eduction fa c tor s f or futur e y e ars . T he se re lativ e re du c tio n f a c tor s f or futur e y e ars are the n u se d to de mon str ate re a so na

ble pr ogre ss t ow ard

vis ibi lity g oa ls. A sy nopsis of the inventories used f or ea ch sourc e cla ssification is provided below. Amore detailed discussion of the e missions inventory development c an be f

ound in Appendix D. 4.1.a. Stationary

Point S ource s P o i n ts o ur c e e m i s s i o n s ar e e m i s s i o n s f r o mi n d i v i d

u a l s o ur c e s h av i n ga

f i x e d l o c at i o n . G e n e r a l l y, these sourc es must have pe rmits to operate a nd their emissions are inventoried on a regular schedule . Major sourc es emitting at least 100 tons per year (tpy ) of a criter ia pollutant, 10 tpy

of a sing le hazardous a ir pollutant (HAP), or 25 tpy total HAP are inventoried a nnually . Minor (smaller) source s have be en inventorie d less fre quently . The point source emissions data ca n be g ro up e d a s E GUso ur c e s and oth e r i nd us tr ia l po int

so ur c e s, a lso c a lle d n on -E GUso ur c e s. 4. 1. a .1
. El e c tr ic Ge ne ratin gUn its (E GU s) The a ctual base y ear

inventory

for the E GU sourc es used 2002 c ontinuous emissi ons monitoring (CEM) data

repor ted to the EPA’s Ac id Rain prog ram or 2002 hour ly emissions data provided 2 x by the stakeholde rs. These data pr ovide hourly emissions profiles for SO and NO that can be used in air qua lity

modeling. E missions profiles a re use d to estimate emissions of other pollutants (volatile org anic c ompounds, carbon monoxide, ammonia, fine pa rticles, soil) base d 2 x on measure d emissions of SO and NO
. EGU sourc e emissions are a sig nificant portion of the emissions inventory . They vary daily and seasona lly

as a f unction of ene rg y

demand a

nd utiliz ation as we ll as outag e sche dules. Ther efor e, a ty pical base y ear

inventory

was de veloped to avoid potential a nomalies in future year e mis sio ns that mi g ht b e c re ate d b y re lyingon

a sin g le y e ar ( 20 02 ) t o r e pr e se nt o pe ra tio ns . T his approa ch is consistent with the EPA’s modelingguidance . To deve lop a ty pical year 2002 em i s s i o n s i n v en t o ry

fo r E GU s o urc es , ea ch un i t ’s av er age C E M h eati np utfo r 2 0 0 0 th ro u gh 2004 was divided by

the 2002 ac tual heat input to g ener ate a unit specific nor

malizi ngfac tor. This normalizi ngfac tor was then multiplied by the 2002 ac tual emissions. The heat inputs for the pe ri od

20 02 thr ou g h 2 00 4 w e re us e d s inc e themod e lin gc ur re nt d e sign v a lue s u se monitoring data from this same 5- y ear period. I f a unit wa s shutdown for an e ntire year during 20 00 thr ou g h 2 00 4 p e ri od , th e ave ra g e of the y e ars the un it w a s o pe ra tio na l w a s u se d. I f a

un it was shutdown in 2002, but not permane ntly shutdown, the emissions and heat inputs 2001 (or 20 00 ) w e re us e d in

the no rm a lizin gc a lc ulatio ns .W est V i rgi ni a R egi o nal H aze S IP Pag e 25

As part of the VI STAS air quality

modeling, V I STAS, in cooperation with the other easte rn RPOs, contracte d with I CF Resourc es, L .L .C., to ge nerate future y ear emission inventories for the elec tric g ener atingsector of the contiguous United States using the Integ rate d Planning Mo de l ( I PM) . I PM is a

dy namic lin e ar o pti mizatio n mo de l th at c an b e us e d to e xami ne a ir pollution control poli cies for various pollutants throug hout the contiguous United States for the entire e lectric power

sy stem. The dy

namic natur e of IPM enables proje ction of the be havior of the po we r s y ste m ov e r a

sp e c if ie d f utu re pe ri od . O pti mizatio n lo g ic in IPM d e te rm ine s th e le a stc os t me ans of

me e tin ge le c tr ic g e ne ratio n a nd c a pa c ity re qu ir e me nts wh ile c omp lyingwi th specifie d constraints includingair pollution regulations, transmission bot tlenecks, a nd plant sp e c if ic op e ratio na l c on str a int s. Th e ve rs atil ity of I PM a llo ws us e rs to s pe c if y wh ic h c on str a int s to exercise, and to populate I PM with t heir own datasets. The EPA a lso relies on I PM for re gulator y purp os e s. The I PM modeling runs took into consideration both C AI R impl ementation and N orth Carolina’s C S Ar e qu i r e m e n t s f

o r D uk e P o w e r

an d P r o g r e s s E n e r g y. 4. 1. a .2
. Othe r I

nd us tr ia l Po int

Sourc e s (No n- EGUs ) For the non-EGUsource s, the same inventor y is used for both the a ctual and ty pical base y ear emissions inventories. The non-EGU categ ory uses annua l emissions as reported f or the Consolidated Emissi ons ReportingRule (CERR) for the year 2002. These e missions wer e temporally

allocate d to month, day

and hour usingsource cate g ory code ( SCC ) base d allocation fa c tor s. The g ener al appr oach f

or asse mbling future y ear data wa s to use rec ently

updated growth and c ontr ol d ata

c on sis te nt w iththe EPA ’s CA I R ana ly se s. Th is d ata wa s su pp le me nte d w ithstate sp e c if ic g ro wth f

a c tor s and

sta ke ho lde r i np ut o n gro wth a ssumpt ion s. 4.1.b. Stationary

Are a Source s Stationary

are a sourc es include sour ces whose individual emissions are re latively small but due to the larg e number

of these sour ces, the collective e missions could be significant (i.e ., combustion of fuels for

heating , structure fires, se rvice stations, etc.). Emissions are e stimated by multipl ying a

n emission factor by

some known indicator of collective a ctivity , such as f uel usag e, number

of households or population. Stationary are a sourc e emissions are estimated at the c ounty le ve l. W e s tV i r g i n i a

u s e d th e 2 0 0 2 C E RR

d at a a s th e b a s i s f

o r th e s t at i o n ar

y ar e a s o ur c e i n v e n t o r

y . In addition, the VI STAS contractor calc ulated emissions for a nimal husbandry , wild fires a nd particulate matter f

rom pave d and unpave d roads. F or other states within the modeling doma in, the state supplied data

or the CERR data for

2002 was used. West Virg inia updated e stimates for the Residential Fue l/W ood Combusti on cate g ories for coal, distillate oil, natural g as, and w ood from the 2002 pr eliminary NEI file. These data we re posted to EPA’s CDX site a nd made available to VI STAS’ contrac tors. Another c omponent of the a rea source inventory

that VI STAS fo c us e d o n w a s r e late d to the de ve lop me nt o f a mmo nia e mis sio ns fr om l ive sto c k a nd fe rt ili zer s, a s w e ll a s PM e mis sio ns fr om p ave d r oa ds . V e rs ion 3. 6 o f t he Carn e g ie Me llo n Univ e rs ityW est V i rgi ni a R egi o nal H aze S IP Pag e 26

(CMU) NH3 mode l was used to improve the NH3 emissions from livestock and f ertilizer application. EPA deve loped more c urre nt paved r oad PM emission estimates using an improve d methodology . VI STAS obtained the new data and incor porate d it into t he inventory . The improved data

was re submitt ed to the NEI duringthe final c omment period. The a ctual base y ear

inventory

serve s as the ty

pical base y ear

inventory

for a ll area

source c ate g or ie s, e xce pt f

or wi ld f ir e s. F or wi ldf ir e s, a ty pic a l ye ar i nv e nto ry

wa s g e ne rate d to avo id anomalies in wildfire activity in 2002 compare d to longe r term avera g es. De velopment of a ty pical year fire inve ntory

provided the c apability

of usinga compa

rable data se t for both the base year and future y ear s. Thus, fire e missions would rema in the same for air qua lity

modeling in b oth the ba se and

futur e y e ars . T he VI STA S F ir e Spe c ia l Inte re st Wo rk Gr ou p u se d s tate rec ords to ratio the number of ac res bur

ned over a long er time pe riod (thre e or more y ear s, as av ai l ab l e f

ro ms t at e r ec o rd s ) t o 2 0 0 2 fo r e ac hfi re ty p e. Ba s edo nth es e r at i o s , th e 2 0 0 2 ac re age w a s s c a l e d

u p o r d o w nt o d e v e l o p a

typ i c a l ye ar

i n v e n t o r y. The VI STAS contractor

g ener ated the f

uture year emissions inventories for 2009 a nd 2018 for the re g ional haze modeling . Growth fa ctors, supplied by the states or taken fr om the EPA’s CA I R e mis sio ns pr oje c tio ns , w e re a pp lie d to pr oje c t th e c ontr oll e d e mis sio ns to t he a pp ro pr iate y ear . I f no g rowth fa ctor wa s available from either the states or the CAI R gr owth fac tor files the EPA’s Economic Gr owth and Ana ly sis Sy stem Version 5 growth fa ctors we re use d. 4.1.c. Off- Road Mobile Sources Off- road, or

“non-r oad”, mobile sourc es include e quipment that can move but do not use the roadw ay sy stem, such as c onstruction equipment, airc raf t, railroad loc omotives, lawn and g arde n equipment, etc. F or the majority of the non-r oad mobile source s, the emissions were estimated using the EPA’s NONROAD 2005c model. F or the thre e sourc e categ ories not included in the NONROAD model, i.e., airc raf t eng ines, railroa d locomotives and commer cial mar ine, more traditional methods of estimatingemissions were use d. The same inventory is used for both the a c tua l and

ty pic a l ba se y e ar e mis sio ns inv e nto ri e s. F or the so ur c e c ate g or ie s e sti mate d u sin gthe EPA ’s NO NR OA D m od e l, t he mod e l gro wth assumptions were use d to cre ate the 2009 a nd 2018 future year

inventories. The NONROAD model takes into consider ation regulations affe ctingemissions from these sourc e categ ories. F or the commer cial mar ine vessels, ra ilroad locomotive and a irport emissions, the VI STAS contra ctor c alculate d the future y ear emissions using detailed inve ntory data ( both before and afte r controls) f

or 1996 and 2010 obta ined from the CAIR Technica l Support Document. When available , state spec ific g rowth fa ctors we re use d. Preliminary data in the NE Ilisted all of the CMV river tra ffic e missions from seve ral West Virg inia, Ohio and Ke ntucky counties at the Port of Hunti ng ton . E mis sio ns we re a llo c ate d to the re sp e c tiv e c ounti e s in the Wes t V ir g ini a c or re c tio n to the NE I . T he data a lso inc or

re c tly re po rt e d r e sid

ua l oi l us a g e fo r t

he CMV cate g ory . Spatial emissions allocations improvements were made by the DAQ f

or seve ral business and nonroa d eng ine categ ories as w ell. Review of the Preliminary NEI showed 0.0 tons of e mis sio ns fr om t

he c oa l mi nin gc ate g or

y in s e ve ra l c ounti e s whe re c oa l mi nin gis p re va le nt. Fur ther a naly sis revea led that County Business Patter

ns data is often r eporte d as numeric rang es rathe r than a s distinct values. When the NONROAD mode l encounter s rang es, it interpre ts themW est V i rgi ni a R egi o nal H aze S IP Pag e 27

as non-numer ic and pr ocesse s them as 0.0 values. The se er rors we re c orre cted in the 2002 NE I and pr ov ide d to the VI STA S’ c ontr a c tor s. 4.1.d. Hig hway

Mobile Sources For onroad ve hicles, the ne west ver sion of the MOB I L E model, MOB I L E6.2, was use d. Key inputs for MOB I L E include infor mation on the ag e of ve hicles on the roa ds, the ave rag e spee ds on the roa ds, the mix of vehic les on the roa ds, any programs in place in an ar ea to r educe emissions from motor vehicles (e .g ., emissions inspection prog rams), a nd temperature. The MOB I L E model takes into consider ation regulations that affe ct emissions from this source sector. T he same MOBI L E run is used to represe nt the actua l and ty pical year emissions for onroad ve hicles usinginput data re flec tive of 2002. The MO BI L E model then wa s run for 2009 and 2018 usinginput data re flec tive of those year s. For West Virg inia, the spee d, vehicle a g e and vehicle mix data are all MOBI L E 6.2 defa ults. The 2002 vehicle miles traveled ( VMT) wa s ob ta ine d f ro m th e Wes t V ir g ini a

De pa rtme nt o f T ra

ns po rt atio n ( WVD OT ) a

nd pr ov ide d to VI STAS along w ith W est Virg inia-spec ific temper ature data. 4.1.e. Biog enic Emissions Sources Biog enic e missions wer e pre pare d with the SMOKE-B EI S3 (Biog enic Emissions Inventory Sy stem 3 version 0.9) pr eproc essor. SMOKE-B EI S3 is a modified version of the Urban A irshed Model (UAM) -B EI S3 model. Modifications include use of MM5 data, g ridded land use data, and imp ro ve d e mis sio ns c ha

ra c te ri zat ion . T he e mis sio n f a c tor s th at are us e d in

SMO KE -B EI S3 are the same a s the emission factor s in UAM-B EI S3. Th e ba sis fo r t

he g ri dd e d la

nd us e data us e d b y B EI S3 i s th e c ounty

land us e data in t he B iog e nic Emission L andcove r Database version 3 (B EL D3) provide d by the EPA. A sepa

rate land classification scheme, ba sed upon satellite (A dvance d Ver y Hig h Resolution R adiometer [AV HRR], 1 km s patia l r e so lut ion ) a nd c e ns us inf

or matio n, a ide d in

de fi nin gthe fo re st, a g r i c u l tur e an d

ur b anp o rt i o n s o f

e a c hc o un ty. 4.1.f. Summary

2002 Ba se G2 (F ebrua

ry 2007 version) B aseline Emission Inventory for Wes t V ir g ini a Table 4.1 is a summar

y of the 2002 ba seline emissions inventory for West Virg inia. The complete inventor

y and discussion of the methodology is contained in Appe ndix D. The emissions summ arie s for other VI STAS states can a lso be found in Appendix D.W est V i rgi ni a R egi o nal H aze S IP Pag e 28

Tabl e 4. 1. Sum m ar y o f 200 2 Ba se line Em iss ion s In ve nt or y forWe stVi r gin ia VO C (tpy) NH 3

(tpy) 10

PM (tpy) 2.5

PM (tpy) x

NO (tpy) 2

SO (tpy) Point 15,775 453 22,076 15,523 277,589 570,153 Are a 60,443 9,963 115,346 21,049 12,687 11,667 Non-Road Mobile 18,566 9 1,850 1,728 33,239 2,112 On-Road Mobile 45,284 2,036 1,481 1,068 63,525 2,635 B iog e nic s 35 7, 85 0 N/A N/A N/A 2, 77 6 N/A TOTAL 499,976 12,461 143,771 42,385 390,703 586,568
4. 1. g . M od e l Pe rf

or manc e Impr ov e me nts thr ou gh E mis sio ns Inv e nto ry Impr ov e me nts Since the initial model perfor mance evaluation, VI STAS has made seve ral improveme nts to the e mis sio ns inv e nto ry

wh ic h in tur n im pr ov e mod e l pe rf

or manc e . T he se inv e nto ry

imp ro ve me nts are detailed in the VI STAS emissi ons inventory repor t and Appendix D, and ar e summarized below:

• For elec tric g ener atingutilit ies, the Integ rate d Planning Mode l (I PM) was used to provide estimates for f

uture year utilit y production and e missions . Continuous Em issions Monitoring data was use d to define se asonal va riability in production and e missions . For B a se G2 e mis sio ns , s tate s u pd ate d I PM mo de l pr oje c tio ns fr om 2 00 5 w ithc ontr ol d ata provided by

utilit y companies in 2006 through winter 2007.

• F o r o n - r o a d v e h i c l e e m i s s i o n s , s t at e s an d l o c a l a g e n c i e s p r o v i d e d

u p d at e d M O B ILE model input and vehicle- miles-trave led data.

• F or ammo nia e mis sio ns fr om a g ri c ult

ur a l so ur c e s, the Carn e g ie Me llo n Univ e rs ity ammo nia mod e l w a s u se d to imp ro ve annua l and monthl y e sti mate s.
• For fires, the VI STAS states provided fire activity data f or 2002 wildfire s, presc ribed fire, la nd clea ringand agricultural bur

ning a nd MACTEC developed a 2002 fire inv e nto ry . Wh e re data

a llo we d, Al pin e Ge op hy sic s mo de le d f ir e e ve nts a s p oint so ur c e s. In 2006, the United States F orest Servic e and F ish and Wil dlife Service provided projec tions of increa sed pre scribed bur ning in 2009 a

nd 2018; these data

wer e incorpora ted in the B ase Ginventory for a ll states except Florida.

• F or no n- ro a d e ng ine s, the up date d E PA N ON RO AD 20 05 e mis sio ns mod e l w a s u se d in Ba se G.

• F or c omm e rc ia l ma

ri ne e mis sio ns in s hip pin glane s in the Gu lf of Me xico and

At lanti c Oce an, ENV I RON cre ated gridded e missions for the V I STAS modeling domain usingW est V i rgi ni a R egi o nal H aze S IP Pag e 29

inv e nto ry data ne wl y de ve lop e d f

or EPAby Cor be tt at th e Un ive rs ity

of De lawa re . T he se em i s s i o n s we re i n co rp o ra t edi nth e B as e G m o d el i n g.

• Updated inve ntories from the ne ighbor ing RPOs, Mexico and Canada wer e incor

porate d as ava ilable.
4.2. Assessm ent of Relative Contr ibu tions f romSpecif

ic P ollutan ts and S ource Cate gories (2002- 04 Baseline Data) Ammonium sul fate is the larg est contributor to visibilit y impairment at Dolly

Sods and Otter Creek a nd reduc tion of sulfur diox ide emissions would be the most effe ctive mea ns of re ducing 2 ammonium sulfate. As illustrated in Figure 4.2- 1, 96% of the SO emissions in t he VI STAS state s are attr ibuta

ble to e le c tr ic g e ne ra

tin gfa c ili tie s a

nd ind us tr ia l po int

so ur c e s. As sh own in 2 Figure 4.2- 2, 98% of the SO emissions in W est Virg inia are attributable to ele ctric utilities and ind us tr ia l po int

so ur c e s.

[This space intentionally blank.]W est V i rgi ni a R egi o nal H aze S IP Pag e 30 2 Figure 4.2- 1. SO Emissions i n 2002 in the VI STAS S tates

2 F igur e 4. 2- 2. SO Em iss ion s in 20 02 in We st V ir g ini aW est V i rgi ni a R egi o nal H aze S IP Pag e 31



### 5 . 0 . R EG I O N A L H A ZE M O D ELI N G M ETH O D S A N D I N PU TS Modelingfor r eg ional haze was pe rfor med by VI STAS for the ten Southeaster n states, including West Virginia. T he sec tions below outline the methods and inputs used by VI STAS for the re g ion a l mo de lin g . A dd iti on a l de ta ils are pr ov ide d in Ap pe nd ic e s C, D a

nd M. 5.1. Analysis Method The modelinganaly sis is a complex technica l evaluation that beg ins by selec tion of the modeling sy ste m. V I STA S c ho se the fo llo wi ngmod e lin gsy ste m:

• Meteorolog ical Model: The Pennsy lvania State Univer sity

/National Center f

or Atmos ph e ri c Re se arc h ( PSU/N CA R) Me so sc a le Me te or olo g ic a l Mo de l ( MM5 ) i s a no nhydr os tatic , p ro g no sti c me te or olo g ic a l mo de l r outin e ly us e d f or ur ba n- and re g ion a lscale photochemica l, fine par ticulate matter , and re g ional haze re gulatory modeling stu die s.

• Emissions M odel: The Sparse Matrix Operator Ke rnel Emissions (SMOKE) modeling sy stem is an emissions modeling sy stem that g ener ates hourly g ridded spec iated emission inputs of mobile, nonroad mobile, are a, point, fire a nd bioge nic emission sources f or ph oto c he mic a l g ri d mo de ls.

• Air Qua lity

Model: EPA’s Models-3/ Communit y Multiscale Air Qua lity

(CMAQ) modeling sy stem is an ‘One -Atmosphere ’ photoche mical g rid model ca pable of a dd re ssi ngozo ne , p a

rt ic ulate matte r ( PM) , v isi bil ity and a c id d e po sit ion

at r e g ion a l sc a le for pe riods up to one y ear . Th e EPA ’s 20 07 Mo de lin gGu ida nc e re c omm e nd s mo de lin gan e nti re y e ar o r, at a min imum, sever al day s in eac h quarte r of a y ear , to adequately

repr esent the r ang e of me teorolog ical conditions that contribute to elevated levels of f ine par ticulate matter . VI STAS selected the year 2002 as the modelingy ear for this demonstration. Meteor ologic al inputs were develope d for 2002 using the meteorolog ical model. Emissions inventories wer e also de veloped f or 2002 and pr oc e sse d thro ugh th e e mis sio ns mod e l. T he se inp uts we re the n u se d in the a ir qu a lity mod e l to pr e dic t f ine pa rt ic le ma ss a

nd vis ibi lity . T he mod e l r e su lts fo r 2 00 2 w e re c omp are d w ith observe d meteorolog ical and a ir quality data to eva luate model per formanc e. Sever al c on fi gur atio ns of

the me te or olo g ic a l and

a ir qu a lity mod e l w e re e va luate d to se le c t a configuration that g ave the best overa ll perfor mance for the V I STAS reg ion. Once model perf

ormanc e wa s deeme d adequate, the cur rent a nd future year

emissions were proce ssed throug h the emissions model. The air qua lity modeling r esults are used to deter mine a re lativ e re du c tio n in

futur e vis ibi lity

imp a ir me nt, wh ic h is us e d to de te rm ine re a so na ble pr ogre ss. Th e c omp le te mod e lin gpr oto c ol u se d f or thi s a na ly sis c an b e fo un d in

Ap pe nd ix C.W est V i rgi ni a R egi o nal H aze S IP Pag e 32

5.2. Model Select ion To e ns ur e that a

mod e lin gstu dy

is d e fe ns ibl e , c are mus t be take n in the se le c tio n o f t

he mod e ls to be used. The models selecte d must be scientifica lly appropr iate for

the intended a

pplication and be fr e e ly a c c e ssi ble to a ll s take ho lde rs . Sc ie nti fi c a lly a pp ro priate me ans that th e mod e ls addre ss important phy sical and c hemical phe nomena in suff icient detail, usingpeer -re viewed methods. Fre ely

acc essible means that model formulations and codingare fre ely available for revie w and that the mode ls are a vailable to stake holders, and the ir consultants, for e x ecution and verific ation at no or low c ost. Th e fo llo wi ngse c tio ns outli ne the c ri te ri a fo r s e le c tin ga

mod e lin gsy ste m th at is bo th d e fe ns ibl e and ca pable of meetingthe study ' s g oals. These criter ia wer e used in sele ctingthe modeling sy stem used for this modelingattainment demonstration. 5.2.a. Selection of Photochemica l Grid Model 5. 2. a .1
. Cr ite ri a F or a ph oto c he mic a l g ri d mo de l to qu a lif y a s a c and idate fo r u se in a

re g ion a l haze S I P, a

State needs to show that it meets the sa me seve ral g ener al cr iteria a s a model for an attainment de mon str atio n f

or a natio na l ambi e nt a ir

qu a lity sta nd ard (N AA QS) :

• The model ha s rec eived a scientific pe er r eview,

• Th e mod e l c an b e de mon str ate d a pp lic a ble to t he pr ob le m on

a the or e tic a l ba sis ,

• Databa ses nee ded to per form the a naly sis are a

vailable a nd adequate,

• Available pa st appropriate per formanc e eva luations have shown the mode l is not bi ased tow ard un de re sti mate s o r o ve re sti mate s,

• A protocol on methods and pr ocedur es to be followe d has bee n established, and

• The de veloper

of the model must be willing to make the source code a vailable to user s f o r f r e e o r f o r a r e a s o n a b l e c o s t , an d

th e m o d e l c ann o to th e r

w i s e b e p r o p r i e t ar

y. 5.2.a.2. Over

view of CMAQ The photoche mical model selec ted for this study was CMAQ ve rsion 4.5. For more than a deca de, the EPA ha s been de velopingthe Models-3 CMAQ modelingsy stem with the overa rchingaim of produc ing a ‘One -Atmosphere ’ air qua lity modeling sy stem capa

ble of addre ssing ozone, fine particulate matter, visibility and ac id deposition withi n a common platform. The orig inal justification for the Mode ls-3 development e merg ed fr om the challeng es posed by the 1990 CAAA a

nd the EPA’s desire to develop an a dvance d modeling f rame work for ‘h oli sti c ’ e nv ir onme nta l mo de lin guti lizin gstate -o f- sc ie nc e re pr e se ntatio ns of atmo sp he ri cW est V i rgi ni a R egi o nal H aze S IP Pag e 33

p ro ce s s es i na h i ghp er fo rm an ce co mp u t i n g en v i ro n m en t
. T h e E P A c o mp l et edth e i n i t i al s t age of deve lopment with Models-3 and re lease d the CMAQ model in mid-1999 as the initial operating sc ience model under the Models-3 fr amewor k. The most rec ent re ndition is CMAQ version 4.5, which w as re lease d in September 2005. An a dv anta g e of

c ho os ingCMA Q a s th e atmo sp he ri c mod e l is the a bil ity to d o o ne -a

tmo sp he ri c modeling. T he same modeling c onfiguration is beingapplied for the ozone and PM2.5 atta inm e nt d e mon str atio n SI Ps, a s w e ll a s th e re g ion a l haze S I P. Anumbe r o f f e atur e s in CMAQ’s theore tical formulation and te chnica l implementation make the model we ll suit ed for an nu al P M m o d el i n g. The c onfiguration used f

or this modeling de monstration, as well as a more de tailed desc ription of the CMAQ model, c an be f ound in the ModelingProtocol (Appendix C). 5.2.b. Selection of Mete orolog ical Model 5. 2. b. 1. Cr ite ri a Meteorolog ical models, either throug h objective, diagnostic, or prognostic analy sis, ex tend available information about the state of the atmosphere to the grid upon which photoche mical g rid modelingis to be car ried out. The c riteria f

or selec ting a

meteorolog ical model ar e base d on both the models ability

to acc urate ly

replicate important meteorolog ical phenome na in the re g ion of stu dy , a nd the mod e l' s a

bil ity to i nte rf a c e wi th t he re st o f t

he mod e lin gsy ste ms - - p art ic ula rl y the photochemica l gr id model. Wi th these issues in mind, the following c riteria w ere established f o r th e m e t e o r o l o g i c a l m o d e l t o b e u s e d i nth i s s tu dy:

• Non-Hydrostatic F ormulation

• Reasonably

curr ent, pee r re viewed f

ormulation

• Simul ates Cloud Phy sics

• Pub lic ly

ava ila ble on no or low c os t

• Output ava ila

ble in I /O API fo rm at

• Supports Four Dimensional Data Assimilation (FDDA )

• Enhanc ed tre atment of Planeta ry Bounda ry L ayer he ights for

AQ modeling
5. 2. b. 2. Ov e rv ie w o f M M5 The non-hydrostatic MM5 model is a three -dimensional, limited-are a, primitive equation, pr ogno sti c mod e l th at ha s b e e n u se d w ide ly in r e g ion a l a ir

qu a lity mod e l a pp lic atio ns . T he ba sicW est V i rgi ni a R egi o nal H aze S IP Pag e 34

model has bee n under c ontinuous development, improvement, testingand open pe er- revie w for more than 20 year s and has be en used w orldwide by hundreds of

scientists for a va

riety of mesoscale studies. MM5 us e s a

te rr a in- fo llo wi ngno n- dim e ns ion a lize d p re ssu re , o r " sigma " , v e rt ic a l c oo rd inate simil ar to that used in many operational and re sear ch models. In the non-hy drostatic MM5, the sigma levels ar e def

ined ac cordingto the initial hy drostatically -bala nced r efe renc e state so that the sigma le ve ls a re a lso tim e -i nv ari ant. Th e g ri dd e d me te or olo g ic a l f ie lds pr od uc e d b y MM5 are direc tly

compatible with the input require ments of ‘one atmosphere ’ air- quality models using thi s c oo rd inate . M M5 fi e lds c an b e e a sil y us e d in oth e r r e g ion a l a ir

qu a lity mod e ls w ith dif fe re nt c oo rd inate sy ste ms b y pe rf or min ga ve rt ic a l inte rp olatio n, fo llo we d b y a ma ssconser vation re- adjustment. Di sti nc t pl ane tary

bo un da ry lay e r ( PB L ) p ara

me te ri zat ion s a

re ava ila ble fo r a ir -q

ua lity applications, both of which r epre sent sub-grid-sca le turbulent fluxes of heat, moisture a nd momentum. One sche me uses a f irst-order eddy diffusivity

formulation for sta

ble and ne utral e nv ir onme nts and

a mod if ie d f ir st- or de r s c he me fo r u

ns ta ble re g ime s. Th e oth e r s c he me us e s a prog nostic equation for

the sec ond-orde r turbulent kinetic e nergy , while diagnosing the other ke y boundary lay er te rms. Initial and latera l boundary conditions are spe cified f or re al-data ca ses from mesosc ale thre edimensional analy ses per formed a t 12-hour interva ls on the outermost g rid mesh selec ted by the us e r. Sur fa c e fi e lds are ana ly zed at thre e -h our i nte rv a ls. A C re ssm an- ba se d te c hn iqu e is u se d to analy ze standard surf

ace and ra diosonde observations, using the National Meteorolog ical C en t er 's s p ec tra l an al y s i s , as a f i rs tgu es s . T h e l at er al b o un d ary d at a are i n tro d

u ce d u s i n g a relax ation technique a

pplied in the outermost five row s and columns of the c oarse st gr id domain. Th e MM5 mod e lin gsy ste m in re gulator y a ir qu a lity a pp lic atio n s tud ie s h ave be e n w ide ly repor ted in the literature (e.g ., Emery et al., 1999; Tesc he et a l., 2000, 2003) and many have involved compar isons with other prog nostic models such as the Reg ional Atmospheric Mode ling Sy ste m ( RA MS) and

the Sy ste ms A

pp lic atio n Inte rn atio na l Me so sc a le Mo de l. M M5 e njo y s a far riche r application hist ory in regulatory

modeling studies c ompare d with RAMS or other mod e ls. F ur

the rm or e , in

e va luatio ns of

the se mod e ls i n o ve r 6 0 r e c e nt r e g ion a l sc a le a ir qu a lity application studies since 1995, it has g ener ally been f ound that the MM5 model tends to produce somewhat be tter photoche mical model inputs than alternative models. The c onfiguration used f

or this modeling de monstration, as well as a more de tailed desc ription of the MM5 model, ca

n be found in the Mode ling Protocol (A ppendix E). 5.2.c. Selection of Emissions ProcessingSy stem 5. 2. c .1
. Cr ite ri a The princ ipal criter ion for an e missions proce ssing sy stem is that it accurately prepa res emissions files in a format suitable f or the photoche mical g rid model beingused. The f ollowingW est V i rgi ni a R egi o nal H aze S IP Pag e 35

list i ncludes c larifica

tion of this criterion and a dditional desirable c riteria f or ef fec tive use of the sy ste m.

• F i l e S y s t e mC o mp at i b i l i ty w i thth e I/ O A P I,
• F i l e P o rt a

b i l i ty,
• Ability

to gr id emissions on a L amber t Conformal projec tion,

• R e p o rtC a

p a b i l i ty,
• G r a p h i c a l A n a l ys i s C a

p a b i l i ty,
• MO B I L E6 Mo bil e Sourc e Em iss ion s,
• Biog enic Emissions Inventory Sy stem version 2 (B EI S-3),

• Ability

to proce ss emissions for the proposed doma in in a rea sonable amount of time,

• Ab ili ty

to p ro c e ss c ontr ol s tr ate g ie s,
• No or low c ost for ac quisiti on and maintena nce, a nd

• Expa nd a ble to s up po rt

oth e r s pe c ie s a

nd me c ha nis ms 5.2.c.2. Over

view of SMOKE The SMOKE Emissions ProcessingSy stem Prototy pe wa s orig inally

develope d at the Microc omp uti ngCe nte r o f N or th C aro lin a . A s w ithmos t ‘ e mis sio ns mod e ls’ , SM OKE i s p ri nc ipa lly an emission processing system and not a true emissions modeling system in which emissions e sti mate s are sim

ulate d f ro m ‘ fi rs t pr inc ipl e s’ . T his me ans that, w iththe e xce pti on of mob ile and biog enic sourc es, its purpose is to provide an e fficie nt, modern tool for c onvertingemissions inventory data into the for

matted emission files require d by an air quality simulation m odel. For mobile sources, SMOKE a ctually simulates emissions rates based on input mobile-sourc e activity data, e mission factors and outputs from tra nsportation trave l-demand mode ls. SMOKE was orig inally

designed to allow emissions data proc essingmethods to util ize emerg ent high- perf ormanc e-c omputing as a

pplied to sparse- matrix alg orithms. Indeed, SMOKE is the fastest e missions proce ssing tool cur rently available to the air qua lity modeling c ommunity . The sparse matrix approa ch utiliz ed throughout SMOKE permits both rapid and fle x ible proce ssing of e mis sio ns data . T he pr oc e ssi ngis r a pid be c a us e SMO KE uti lize s a

se ri e s o f m atr ix c a lc ulatio ns ins te a d o f l e ss e ff ic ie nt a lg or ithms u se d in

pr e vio us sy ste ms. Th e pr oc e ssi ngis flexibl e bec ause the proce ssing steps of temporal proje ction, controls, che mical spec iation, temporal a llocation, and spatial a llocation have be en sepa rate d into independent oper ations wher ever possible. The re sults from these steps ar e mer g ed tog ether at a f inal stag e of p ro ce s s i n g. SMOKE contains a numbe r of major features that make it an attrac tive component of the modeling sy stem. The model supports a va riety of input formats fr om other emissions proce ssing sy stems and models. It supports both gridde d and county total land use sche me for bioge nic emissions modeling. SMOKE c an ac commodate e missions files from up to 10 c ountr ie s and

any po llutant c an b e pr oc e sse d b y thesy ste m. For additional information about the SMOKE model plea se re fer to Modeling Protocol (Appe ndix C).W est V i rgi ni a R egi o nal H aze S IP Pag e 36

5.3. Select ion of

the Modeling Year A cr ucial step to SI P modeling is the selec tion of the period of time to model to represe nt curre nt a ir qu a lity c on dit ion s and

to p ro je c t c ha ng e s in a ir qu a lity in r e sp on se to c ha

ng e s in e mis sio ns . The year 2002 was sele cted a s the base year for se vera l rea sons. The EPA’s Apr il 2007 Guidance on theuse of Models and Othe r Analyses for Dem onstrating 2.5 Attainment of Air Qualit

y Goals for Ozone, PM , and Regional Haze ide nti fi e s sp e c if ic g oa ls t o c on sid e r whe n s e le c tin gon e or mor e e pis od e s f

or us e in d e mon str atin gre a so na ble pr ogre ss i n attainingthe re g ional haze air qua lity g oals. The EPA re commends that episode se lection der ive from the f ollowing pr incipal cr iteria:

• Simu late a

va ri e ty of me te or olo g ic a l c on dit ion s;
• Mo de l ti me pe ri od s in wh ic h o bs e rv e d c on c e ntr atio ns are c los e to t he a pp ro ximate ba se lin e de sign v a lue or

vis ibi lity imp a ir me nt;
• Model periods for

which extensive air qua lity /meteorolog ical data

bases e x ist; and

• Model a suff

icient number of

day s so that the modeled attainment test a pplied at ea ch mon ito r v iol atin gthe NAAQ S is b a se d o n mu lti ple da y s. For reg ional haze modeling , the guidance g oes fur ther by sug g estingthat the pre fer red a pproac h is to model a full, represe ntative y ear. Mo reov er, t he re qu ir ed re lat iv e reduct io n fac to r (R RF) values should be ba sed on model re sults avera g ed over the 20 per cent wor st and the 20 per cent best visibili ty

day s determined f

or ea ch Class Iare a base d on monitoring data from the 2000 - 2004 baseline pe riod. The EPA also lists severa l other consider ations to bear in mind when c hoosing pote ntial reg ional ha ze e pis od e s in c lud ing : ( a ) c ho os e pe ri od s whic h h ave a lr e a dy be e n mo de le d, (b ) c ho os e periods which a

re dr awn f

rom the year s upon which the c urre nt desig n values a re ba sed, (c ) include we ekend da

y s amongthose chosen, a nd (d) c hoose modelingperiods that mee t as many episode sele ction criter ia as possible in the maxim um number of nonattainment or Class Iare as as possible. Fina lly , the EPA expli citly rec ommended in its 2007 guida nce the use of 2002 a s the baseline inve ntory

y ear . VI STA S a do pte d a

log ic a l, s te pw ise a

pp ro a c h in

imp le me nti ngthe EPAguid anc e in o rd e r t o identify the most prefe rable , repr esentative y ear for r eg ional haze modeling . These ste ps include th e f o l l o wi n g:

• Represe ntativeness of Me teorolog ical Conditions: The VI STAS meteorolog ical c ontr a c tor

(B AM S) ide nti fi e d im

po rt ant m e te or olo g ic a l c ha ra c te ri sti c s a

nd data se ts i n the VI STAS reg ion directly rele vant to the eva luation of ca ndidate annua l modeling e pis od e s.

• Init ia l E pis od e Ty

pin g : A t th e tim e of

se le c tio n in

20 03 , me te or olo g ic a l and a ir qu a lity data we re a

vailable f or 2002 model inputs and model per formanc e eva luation. VI STAS us e d Cl a ssi fi c atio n a

nd Re g re ssi on

Tr e e (C AR T) ana ly se s to e va luate vis ibi lityW est V i rgi ni a R egi o nal H aze S IP Pag e 37

conditions for 2000, 2001 and 2002, the candidate modelingy ear s. The year 2002 was fo un d to be re pr e se ntativ e of c on dit ion s in

the oth e r t

wo y e ars . Su

bs e qu e ntl y , th e se ana ly se s w e re re pe ate d w iththe me te or olo g ic a l and a ir qu a lity mon ito ri ngdata

fo r 2 00 to 2004 to evaluate how well the 2002 modelingy ear repr esente d the full 2000-2004 baseline pe riod. This analy sis confirmed that visibility and PM2.5 mass in 2002 were re pr e se ntativ e of

the fi ve -ye ar b a se lin e pe ri od fo r t he VI STA S Cla ss I

are a . T his ana ly sis is di scu ss ed i n m ore d eta il in th e pro jec t r epo rtinApp end ix B.

• Data Av a ila bil ity : In p ara lle l w iththe CA RT a na ly sis , e pis od e c ha

ra c te ri zat ion

ana ly se s, c oll a bo rativ e inv e sti g atio ns by VI STA S state s ( e .g ., NC DA Q, Ge or g ia DN R, F LDE P) intensively

studied the ava ilability

of PM2.5, meteorolog ical, and e missions data a nd re pr e se ntativ e ne ss o f a lte rn ativ e ba se lin e mod e lin gpe ri od s f ro m a re gulator y sta nd po int
. Additionally , 2002 was the year that EPA was re quiringstates to provide e missions inv e nto ry data fo r t he Com pr e he ns ive Em iss ion s Re po rt ingRul e (C ERR), it m a de se ns e t o u s e 2 0 0 2 a s th e m o d e l i n gye ar t o t ak e a dv an t a g e o f

th e 2 0 0 2 i n v e n t o r

y.

• Ye ars to b e us e d b y

oth e r RPOs : V I STA S a lso c on sid e re d what ye ars oth e r RPOs wo uld be modeling , and seve ral ha d alre ady chosen c alenda

r year 2002 as the modelingy ear . After a leng thy proce ss of integ rate d studies, the episode sele ction proce ss culminated in the se le c tio n o f c a le nd ar ye ar 2 00 2 ( Janu ary 1 thro ugh D e c e mbe r 3 1) a s th e mos t c ur re nt, re pr e se ntativ e and

pr a g matic c ho ic e fo r V I STA S r e g ion a l haze m od e lin g . A ll o f t he EPAc ri te ri a for r eg ional haze episode se lection wer e dire ctly considere d in this process tog ether with many other c onsiderations (e .g ., timing of ne w emissions or aer ometric data deliver ies by the EPA or the States to the modelingteams).
5. 4. M ode ling Do m ain s 5. 4. a . Ho ri zonta l Mo de lin gDo ma in The EPA’s 2007 modelingguidance rec ommends a 12-km modelingg rid resolution for PM2.5 modeling while a 36-km grid resolution is considere d acc eptable f or re g ional haze. For

the VI STAS modeling, a c oarse 36-km g rid resolution was use d for modelingthe entire United State s and

a fi ne 12 -k m gri d w a s u se d to mod e l th e e a ste rn Un ite d St ate s. The CMAQ model wa s run in one-w ay nested grid mode. This allowed the larg er oute r domains to fee d conce ntration data to the inne r nested doma in. One-w ay nestingis believed to be appropr iate for

the g ener ally

stag nant conditions experience d duringWest Virginia ozone e pis od e s. Tw o- wa

y ne sti ngwa s n ot c on sid e re d d ue to n ume ri c a l and

c omp utatio na l un c e rt a int y associate d with the technique. The horizontal coar se g rid modelingdomain boundarie s were deter mined throug h a national eff ort to develop a

common g rid projec tion and boundary . A smaller 12- km gr id, modeling domain was sele cted in an a ttempt to balance loc ation of ar eas of interest, such a s ozone and fineW est V i rgi ni a R egi o nal H aze S IP Pag e 38

particulate matter nonattainment are as, as we ll as Class 1 and wilderne ss are as for reg ional haze. Processingtime was also a f actor in choosinga smaller 12- km gr id, modeling domain. The c oarse 36-km horizontal gr id domain cover s the continental United States. This domain wa s used as the oute r g rid domain for MM5 modelingwith the CMAQ domain nested within the MM5 do ma in. F igur e 5. 4. a -1 sh ow s th e MM5 ho ri zonta l do ma in a s th e oute r m os t, b lue g ri d with the CMAQ 36-km domain nested in the MM5 domain.

F igure 5.4.a-1. The MM5 horizontal domain is the outer most, blue g rid, with the CMAQ 36-

km d oma in n e ste d in

the MM5 do ma in
To ac hieve f

iner spatial r esolution in the VI STAS states, a one-w ay nested high resolution (12-km grid resolution) wa s used. Figure 5.4.1- 2 shows the 12-km grid, modelingdomain for the VI STAS reg ion. This is t he modelingdomain for whic h the re asonable progress g oals are assessed.W est V i rgi ni a R egi o nal H aze S IP Pag e 39

F igure 5.4.a-2. A more de tailed view of the 12-km grid over the VI STAS reg ion 5. 4. b. Ve rt ic a l Mo de lin gDo ma in Th e CMA Q v e rt ic a l stru c tur e is p ri mari ly de fi ne d b y

the ve rt ic a l gri d u se d in

the MM5 modeling. T he MM5 model employed a te rra in following c oordinate sy stem defined by pressure , using 34 la

y ers that e x tend from the sur fac e to the 100 mb level. A la y er- aver ag ing scheme was used to g ener ate 19 ve rtical layers f or CMAQ to reduc e the c omputational costs of the CMAQ simulations. The eff ects of la y er a vera g ing w ere evaluated in conjunction with the VI STAS modeling ef

fort and w as found to have a re latively minor eff

ect on the mode l pe rf or manc e me tr ic s whe n b oth the 34 lay e r a

nd the 19 lay e r C MA Q m od e ls w e re c omp are d to ambient monitoring data.W est V i rgi ni a R egi o nal H aze S IP Pag e 40



### 6 . 0 . M ODE L P E RF ORM ANCE E V AL UAT I ON The initial modeling e ffor t focused on e valuatingprevious re g ional air qua lity modeling applications and te sting c andidate mode l configurations for the SMOKE emissions and CMAQ model for the V I STAS 36-km and 12-km modelingdomains. This effor t resulted in a r eport rec ommending the model configuration for the annua l emissions and air quality modeling, which is included as par

t of the VI STAS Emis sions and Air Qua lity ModelingProtocol. The eva luation of the mete orolog ical modelingconfiguration ca n be found in Appe ndix F.1, with a summar y of the final mete orolog ical and a ir quality modeling c onfiguration in the modelingprotocol c o n t a i n e d i nAp p e n d i x E an d

Ap p e n d i x C , r e s p e c t i v e l y. Air quality

model perf

ormanc e for

the 2002 modelingy ear

was initially

tested in 2004 using a

n ear ly version of the VI STAS emissi ons inventory . In keepingwith the one-atmosphere objec tive of the CMAQ modelingplatform, model per formanc e wa s evaluated base d on measure d ozone, fine pa rticles, and a cid deposition in the Air Quality Sy stem (AQS), I MPROVE, Speciated Trends N etwork ( STN), Southern Ae rosol Resear ch and Cha rac terization (SEARCH), National Ac id D e po sit ion

Pr ogra m (NA DP) and

Cle an A ir

Status and

Tr e nd s N e tw or

k ( CA STN e t) monitoring ne tworks (F igur e 6.0-1) . A detailed e x amination of the re sults was published in 2005 in t he Jour na l of Ai r a nd Was te Mana g e me nt ( se e Ap pe nd ix B .3 ) a s w e ll a s b e ingsumma ri zed in Appendix B.1.

F igu r e 6. 0- 1. M oni to r ing Ne two r ks us e d forVI STA S 20 02 m ode l pe r for m anc e e va lua ti on,

and t he irloc at ion with in t he VI STA S 12 -k m dom ain .W est V i rgi ni a R egi o nal H aze S IP Pag e 41

6.1. Modeling Pe rform ance G oals, Criter ia and Results In 2004, VI STAS established model perf ormanc e g oals and c riteria f or compone nts of fine pa rt ic le ma ss ( Ta

ble 6. 1- 1) ba se d o n p re vio us mod e l pe rf or manc e fo r o zon e and

fi ne pa rt ic le s. The EPA modelingguidance for f ine par ticulate matter

at the time noted that PM models may not be able to a chieve the same le vel of pe rfor mance as ozone models. VI STAS’ evaluation considere d sever al statistical perf ormanc e mea sures a

nd display s. Fra ctional bias and me an fra ctional er

ror we re se lected a s the most appropriate metrics to summarize model perf ormanc e; other metric s were also ca lculated a nd are included in the full model per formanc e eva luation (Appe ndix F.2)
. Tabl e 6. 1- 1. M ode l P e r for m anc e go als and c r it e r ia fo rc om pone nt s o f fine par ti c le m as s F ract ional Bias F ract ional

Error Co m m e nt

# 15% # 35% Goal for PM model performa

nce ba sed on ozone model perf ormanc e, conside red e x cellent pe rfor mance # 30% # 50% Goal for PM model performa

nce, c onsidered g ood perf ormanc e # 60% # 7 5 % C ri t er i a f o r P M m o d el p er fo rm an ce , co n s i d er edav er age perf ormanc e. Exceedingthis level of per formanc e indicate d fundame ntal conce rns with the modelingsy stem and trig g ere d diag nostic evaluation. S ev er al grap h i c d i s p l ay s o f m o d el p er fo rm an ce we re p re p ar edi n cl u d i n g: 1. Sc atte r p lot s p re dic te d a nd ob se rv e d c on c e ntr atio ns and de po sit ion

by sp e c ie s, monitoring ne twork and month; 2. Ti me se ri e s p lot s o f p re dic te d a nd ob se rv e d c on c e ntr atio ns and de po sit ion

by sp e c ie s, monitoring site and month; 3. Spatia lly

ave ra g e tim e se ri e s p lot s; 4. Time serie s plots of monthl y fra ctional bias and e rror

for a specie s, reg ion and networ k; 5. Pe rf or manc e g oa l pl ots (“ so c c e r p lot s” ) t hat summa

ri ze m od e l pe rf

or manc e by sp e c ie s, reg ion and sea son; and 6. Con c e ntr atio n p e rf

or manc e plo ts ( “ bu g le plo ts” ) t hat di sp lay

fr a c tio na l bi a s o r e rr or

a s a fu nc tio n o f c on c e ntr atio n b y sp e c ie s, re g ion , mo nit or ingne tw or k a nd month Th e “ so c c e r p lot s” and

“ bu g le plo ts” are re lativ e ly ne w t oo ls i n mo de l pe rf

or manc e e va luatio ns , and have rec ently

been inc luded as model pe rfor mance evaluation displ ay s in the EPA’s 2007 mod e lin gguid anc e fo r O zon e , PM 2. 5 a nd Re g ion a l H a ze. B oth

“ so c c e r p lot s” and

“ bu g le plo ts” are convenie nt way s to ex amine model per formanc e with re spect to set g oals and c riteria. T he bug le plots have the a dded bene fit of adjustingthe g oals and c riteria to c onsider the c on c e ntr atio n o f t

he sp e c ie s. An a ly sis of

“ bu g le plo ts” g e ne ra lly

su g g e sts that gre ate r e mph a sis sh ou ld b e pla c e d o n p e rf

or manc e of tho se c omp on e nts wi th t he g re ate st c ontr ibutio n to PM ma ssW est V i rgi ni a R egi o nal H aze S IP Pag e 42

and vis ibi lity imp a ir me nt ( e .g . s ulf

ate and or g anic c arb on ) a

nd that gre ate r b ia s a

nd e rr or c ou ld be ac cepte d for c omponents with smaller contributions to total P M mas (e.g . elementa l car bon, nitrate a nd soil). 6.2. VISTAS Domain-wid e P erform ance Fur ther discussion of model pe rfor mance in this document will focus on the compar ison of observational data fr om the I MPROVE monit ors and model output data f rom the 2002 VI STAS Ba se G2-A ctual annua l air quality modeling. F ocus is limit ed to the I MPROVE monit oring network a s these sites ar e the loca tions used in projectingvisibil ity improvement g oals in the Cla ss I are a s. Th e e va luatio n w ill pr ima ri ly fo c us on the a ir qu a lity mod e l’ s p e rf

or manc e wi th r e sp e c t to individual components of fine pa rticulate matter (PM2.5), as g ood model perf ormanc e of the c omp on e nt s pe c ie s w ill dic tate g oo d mo de l pe rf or manc e of tot a l or re c on sti tut e d f

ine pa rt ic ulate matter. Model pe rfor

mance of the total fine pa rticulate matter and the resultingtotal light e xtinc tio n w ill a lso be pr ov ide d a s a me ans to d isc us s th e ov e ra ll m od e l pe rf or manc e fo r t

his Implementation Plan. In this analy sis, mean fra ctional bias (e rror) is used in lieu of mea n bias (er ror) , to preve nt low observations and model predictions fr om skewingthe metrics. A f ull lis t of model perf

ormanc e statistics is found in Appendix F.2. The socce r and bug le plots for all of the V I STAS I MPROVE monitors are inc luded her e for summary purposes. The g oal and c riteria le vels used for reg ional haze model per formanc e we re 30% and 60%, r espec tively , for mea n fra ctional bias and 50% and 75 %, re sp e c tiv e ly , f

or me an f ra c tio na l e rr or . Pl ots have be e n d e ve lop e d f or the ave ra g e monthl y conce ntrations and the pe rfor mance statistics for all of the most signif icant lig ht scattering component spec ies (sulfate , nitrate a nd org anic c arbon) for the 20 pe rce nt best day s and the 20 perc ent worst day s. Plot s for individual I MPROVE monit ors associated with West Virginia’ s Class Iare as ar e included in Appe ndix F.2. Th e so c c e r p lot s o f m onthl y c on c e ntr atio ns (F igur e s 6 .2 -1 and 6. 2- 2) sh owthat va lue s f or nit rate g ener ally fall outside of the pe rfor

mance criter ia thresholds (r ed box). Sul fate s and org anics g ener ally fall within g oal thresholds (gree n box ), with a c ouple of months fallingjust outsi de the g oa ls thr e sh old s b ut w e ll w ith in t he c ri te ri a

thr e sh old s. In th e se fi gur e s, e a c h p oint r e pr e se nts a mo n th
. F igur e 6
. 2

- 3 c o n ta in s s e p ar ate s o c c e r p lo ts f o r e a c hs e a s o n . T h e s e a s o n a l p lo ts e mp h a s iz e poorer nitrate pe rfor

mance in the summer, when obse rved nitrate is quite low and pre dicted nit rate is e ve n lo we r. Whe n c on c e ntr atio n is fa c tor e d into p e rf or manc e c ri te ri a , n itr ate perf ormanc e improves with re spect to mea n fra ctional bias (MF B) and mea n fra ctional er

ror (MFE ) (F igur es 6.2-4 a

nd 6.2-5). Ad dit ion a lly , p e rf

or manc e a sse sse d at th e “ on e atmo sp he re ” le ve l w a s a lso de e me d a c c e pta ble for ozone and pa

rticulate matter at va

rious monitoring sites (STN, F RM, CAS TNet, e tc.). Ov e ra ll, VI STA S f

ound B a se G2 mod e lin gre su lts to b e re pr e se ntativ e and a c c e pta ble fo r u se in modeling pr ojection for ozone, pa rticulate matter and r eg ional haze.W est V i rgi ni a R egi o nal H aze S IP Pag e 43

F igure 6.2-1. Soccer

plot depicting both the me an fr actional e rror and fr actional bias f

or component conc entration for all VI STAS sit es base d on Ba se G2 re sults. Each po int re pr e se nts a

monthl y va lue a s c omp are d to the mod e l pe rf or manc e c ri te ri a (re d box ) and mode ling pe rfor mance g oals (gree n box ).

F igure 6.2-2. A zoomed view of the soc cer plot depicting both the me an fr actional e rror and fr a c tio na l bi a s f

or c omp on e nt c on c e ntr atio n f or a ll V I STA S sit e s b a se d o n B a se G2 re sults. Each point repr esents a monthly value a s compare d to the model perf ormanc e cr iteria (r ed box) and modelingperf ormanc e g oals (gree n box ).W est V i rgi ni a R egi o nal H aze S IP Pag e 44

F igure 6.2-3. Seasonal socc er plots base d on Ba se G1 re sults for all VI STAS I MPROVE

mon ito rs .

F igure 6.2-4. Bug le plot of the mea n fra ction bias for pa

rticulate matter and its component conce ntrations for a ll VI STAS sit es base d on Ba se G2 re sults. Each point repr esents a monthly

mean f rac tion bias value as c ompare d to the model perf ormanc e cr iteria (r ed lines) a nd modeling pe rfor

mance g oals (gree n lines).W est V i rgi ni a R egi o nal H aze S IP Pag e 45

F igure 6.2-5. Bug le plot of mean f rac tion error

for pa rticulate ma

tter and its component spe cies fo r a ll V I STA S sit e s b a se d o n B a se G2 re su lts . E a c h p oint r e pr e se nts a monthl y mean f rac tion bias value as c ompare d to the model perf ormanc e cr iteria (r ed lines) and modelingperf

ormanc e g oals (gree n lines).
6.3. West Vir ginia Clas s I Areas P erform ance The followingsection provides stac ked bar char ts comparingobserve d and modeled f ine 2.5 pa rt ic ulate matte r ( PM )c omp os iti on . T he data have be e n s pli t into t

wo c ha rt s, wi th t he fi rs t displaying the 20 perc ent best day s followed by the cha rt for the 20 perc ent worst day s. Stacked bar c harts showingmass composition have bee n develope d for the D olly Sods I MPROVE monitoring site (se e F igur e 6.3-1) . The Dolly Sods W ilderness Ar ea IMPROVE monit or is used to repre sent the Otter Cre ek Wilderness Are a. The stac ked bar

char t allows a side by

side compar ison of eac h of the da y s observe d and modeled composition and total light e x tinction. W ithin each ba r, the c olor code s are :

• Yellow = light ext inction due to sulfates (be x tSO4)

• Red = lig ht ext inction due to nitrates (be x tNO3)

• Gr e e n =light e xtinc tio n d ue to o rg anic c arb on

(b e xtOC)

• B la c k =light e xtinc tio n d ue to e le me nta l c arb on

(b e xtEC)

• B ro wn

= light e xtinc tio n d

ue to s oil (b e xtSoil )

• Gr e y = light e xtinc tio n d

ue to c oa rs e matte r (be xtCM)
The c omponents are prese nted in the same or der f or both the obser

ved (le ft hand ba

r) a nd modeled bar

(right hand bar), so it is easy to identify

day s when the pr edicted light ext inction forW est V i rgi ni a R egi o nal H aze S IP Pag e 46

the component diff

ers f rom the obser

ved. The total heig ht of the bar provides the total rec onstructed pa

rticulate matter lig ht ext inction value.

F igure 6.3-1: Sta c ke d b ar c ha rt fo r D oll y Sod s o n th e 20 % b e st d ay s ( top ) a nd the 20 % w or st da y s ( bo tto m) . O bs e rv e d c omp os iti on is p re se nte d in

the le ft ha nd ba r, wi th modeled composition repr esente d by the right hand bar
.W est V i rgi ni a R egi o nal H aze S IP Pag e 47

A re view of the stacke d bar c harts re iterate s that sulfates ar e a la rg e contr ibutor to light exti nction in the West Virginia Class I are as on both the 20 per cent be st day s and the 20 pe rce nt worst day s. The bar

char ts also sugg est that org anic c arbon a nd nitrates ar e important on the 20 perc ent best day s at Dolly Sods. The bar c harts for

the 20 per cent be st day s reiter ate the g ener al under- predic tion seen in previous sec tions. The under- predic tion of sulfate on most of the 20 perc ent best day s appea

rs to be the c rux of the under-pr ediction, with the under -pre diction of nit rate fa c tor ingin h e avil y

on so me da y s. Comparingthe 20 per cent be st day s char t to the 20 perc ent worst day s char t, one notices that the va ri ou s c omp on e nts of

pa rt ic ulate po llutio n p lay

a mor e pr omi ne nt r ole in t he 20 pe rc e nt b e st d ays th anw i thth e 2 0 p e r c e n tw o r s td ays . A l s o , th e s p e c i e s m ak e u p o nth e 2 0 p e r c e n tb e s td ays va ri e s mo re wi de ly

c omp are d to the 20 pe rc e nt w or st d ay s. Th is s ug g e sts that a c c ur ate ly mod e lin ge a c h s pe c ie s is e sp e c ia lly imp or tant o n th e 20 pe rc e nt b e st d ay s. Wit h the bar

char t for the 20 pe rce nt worst day s, it can be se en that model per formanc e does improve. L ight e x tinction due to org anic c arbon a lso becomes more important to total li g ht exti nction. Much like the sulfate component, the or g anic c arbon c omponent acc urac y has improved per formanc e over the 20 per cent be st day s series, though some day s are still under predic ted. Over all, the WVDAQ found mode l perfor mance to fall within acc eptable limits. The WVDAQ fu rt he r a sse rt s th at th e on e atmo sp he re mod e lin gpe rf or me d b y the VI STA S c ontr a c tor s is repr esentative of conditions in the southeaster n states and is applica

ble for use in setting re a so na ble pr ogre ss g oa ls f

or the Cla ss I

are a s.W est V i rgi ni a R egi o nal H aze S IP Pag e 48



### 7 . 0 . LO N G - TER M STR A TEG Y FO R W EST VI R G I N I A ’ S C LA SS I AR E AS As state d in

Se c tio n 1 .3 a

bo ve , th e re g ion a l haze r

ule re qu ir e s State s to e sta bli shre a so na

ble prog ress g oals, expressed in dec iviews, for visibility improvement at e ach a ffe cted Class I are a, c ov e ri nge a c h ( a

pp ro ximate ly

) t e n- y e ar p e ri od

until 20 64 . T his fi rs t se t of re a so na ble pr ogre ss g oals must be met throug h measure s contained in the state ’s long -term strateg y cover ing the p er i o d fr o mth e b as el i n e u n t i l 2 0 1 8
. T h i s s ec t i o nd i s cu s s es d ev el o p m en to f W es tVi rgi n i a’ s l o n gt e r ms tr at e g y. 7. 1. Ove r vie w of th e Long -t e r m St r at e gyDe ve lop m e ntP r oc e ss The monitored data and modelinganaly ses cited in sec tions 2 and 5 above e stablish that, for the VIS T AS re gi o n , th e k ey

co n tri b ut o rs t o re gi o n al h az e i nth e 2 0 0 2 -0 4 b as el i n e p er i o d we re l ar ge stationary source s of sulfur dioxi de emissions. Kee ping that key conclusion in mind, this s ection a dd re sse s th e fo llo wi ngqu e sti on s:
a . As sumin gimp le me ntatio n o f e xistin gfe de ra l and state a ir re gulator y re qu ir e me nts in Wes t V ir g ini a

and the VI STA S r e g ion , h owmuc h v isi bil ity imp ro ve me nt, c omp are d to the g lidepath, would be e x pecte d at Class Iare as in West Virg inia betwee n now and 2018? b. What additional emission controls represe nt BART in West Virg inia?
c. I f additional emission reduc tions were ne eded, f rom what pollutants and sourc e cate g ories would the greatest visibil ity benef its be rea liz ed betwe en the ba seline and 2018? d. Nar rowingdown furthe r, in what g eographic locations are the emissions found which have the g re ate st i mpa c t on vis ibi lity in s pe c if ic Cla ss I are a s?
e . What ty

pe s o f e mis sio ns so ur c e s d o a re fo un d in

tho se g e ogra

ph ic loc atio ns ? f. Which specific individual source s in those ge og raphic locations have the g reatest impact o nv i s i b i l i ty? g . What a dd iti on a l e mis sio n c ontr ols re pr e se nt r e a so na ble pr ogre ss f

or tho se sp e c if

ic so ur c e s? h. Given the a dditional emission reductions expected from r easona ble progress a

nd BA RT, how much additional visibilit y improvement, compa

red to the g lidepath, is expected at Dolly Sods and Otter Cree k betwee n the base line and 2018?W est V i rgi ni a R egi o nal H aze S IP Pag e 49

7. 2. Expe c te d Vi sibi lity R e sult s in 2 01 8 forWe stVi r gin ia C las s I A r e as unde re xis ti ng and p lann ed em ission s controls (Base G 2 Inventory) There are signific ant control programs beingimplemented betwe en the ba seline per iod and 2018. These pr og rams ar e desc ribed in more de tail below. 7. 2. a . F e de ra l and

State Contr ol R e qu ir e me nts Cle an A ir Inte rs tate Rul e (C AIR) + Cle an A ir Me rc ur y Rul e (C AM R) . CA I R w ill pe rm ane ntl y 2 x cap e missions of sulfur dioxide (SO ) and nitrog en oxides (NO ) in the ea stern United States by 2 2015. When fully

implemented, CAI R will reduce SO emissions in t hese state s by more than

x 70 pe rc e nt a

nd NO e mis sio ns by

mor e tha n 6 0 p e rc e nt f

ro m 20 03 le ve ls. Th e CA MR w ill reduc e utility

emissions of mercur

y from 48 tons a year

to 15 tons, a reduc tion of near ly 70 pe rc e nt.

x x NO SI P Ca ll. Pha se Iof the NO SI P c a ll a

pp lie s to c e rt a in E le c tr ic Ge ne ra tin gUn its (E GU s) and larg e no n- EG Us , in c lud inglarg e ind us tr ia l bo ile rs and

tur bin e s, and

c e me nt k iln s. Th os e

x States aff ecte d by the NO SI P Call i n the VI STAS reg ion have de veloped r ules for the control

x x of NO emissions that have bee n approve d by the EPA. This NO SI P Call has resulted in a 68

x pe rc e nt r e du c tio n in

NO e mis sio ns fr om l arg e statio na ry c omb us tio n s ourc e s. F or thi s a na ly sis ,
x the emissions for NO SI P call aff ecte d source s were cappe d at 2007 levels a nd car ried for war d at the ca pped leve ls for the 2009/2018 future y ear inventories. North Carolina Clea

n Smokestacks Act ( CSA). Under the act, e nacte d in 2002, coal-f ired powe r

x plants in North Carolina must achie ve a 77- perc ent cut in nitrog en oxide (NO ) emissions by 2 2009 and a 73- perc ent cut in sulfur dioxi de (SO ) emissions by 2013. This leg islation establishes 2 x annua l c a ps on bo th S O and

NO e mis sio ns fo r t

he tw o p ri mary

uti lity c omp anie s in

No rt h Carolina, Duke Energy

and Prog ress Ene rgy . These r eductions must be made in Nor th Carolina, and allowa

nces a re not sale able. Geor g ia Multi-Poll utant Bill. Geor g ia ena cted r egulations in summ er 2007 to re quire c oal fire d 2 x power plants in Georg ia to reduc e SO by approximately

90%, NO by

approximately

50%, and merc ury by an estimated 75- 85% by

2015. Reductions will affe ct 21 units at seven f acilities. The rule require s specific controls on spec ific units acc ordingto a spec ific sche dule. This is not a ca p and tra de rule , thus there a re no a llowance s to be banke d, sold or trade d. Consent Ag ree ments (Santee Coope r, TECO, VEPCO, Gulf Power , EKPC, Dupont, and Stone Container).

• Santee Cooper

[US District Court of South Carolina, Charleston Division] : A 2004 consent a g ree ment calls for Santee Cooper

to install and commence operation of

2 x c ontin uo us e mis sio n c ontr ol e qu ipm e nt f or PM/S O /N O e mis sio ns ; c omp ly wi th s y ste m2 x 2 x wide annua l PM/ SO /NO emissions li mits; agree not to buy , sell or trade SO /NO a llo wa nc e s a llo c ate d to Sante e Coo pe r S y ste m a s a re su lt o f s a id a g re e me nt; and to c omp ly wi th e mis sio n unit

lim its of sa id a gre e me nt.W est V i rgi ni a R egi o nal H aze S IP Pag e 50

• TECO [US Dis trict Court, Middle District of Florida ]: Under a settlement a g ree ment, by 2008, Tampa Ele ctric w ill ins tall permane nt emissions-control equipment to meet str ing e nt p oll uti on

lim its ; im

ple me nt a se ri e s o f i nte ri m po llutio n- re du c tio n me a sure s to reduc e emissions while the per manent contr ols are de signe d and installed; and re tire po llutio n e mis sio n a llo wa nc e s th at T ampa

El e c tr ic or

oth e rs c ou ld u se , o r s e ll t o o the rs , x 2 to emit additional NO , SO and PM.

• VEPCO [ US District Court, Eastern District of V irg inia]: Virg inia Electric and Power 2 Co. ag ree d to spend $1.2 billion between by 2013 to eliminate 237,000 tons of SO and

x NO e mis sio ns e a c h ye ar f ro m e ight c oa l- fi re d e le c tr ic ity g e ne ratin gpla

nts in V ir g ini a and West Virg inia.

• Gu lf Pow e r 7 [State of

F lor ida “ Agre e me nt f

or the Pur po se of En suri ngCom

pli anc e wi th the Ozone Ambient Air Qua lity Standards”, dated August 28, 2002] : A 2002 agree ment

x calls for Gulf Power to upgrade its operation to cut NO emission rates by 61 perc ent at its Crist7 g ener atingplant by 2007 with major reduc tions beg inning in ea rly 2005. The Crist plant is a significa

nt source of

nitrog en oxide emissions in the Pensacola are a.

• EKPC [US Di str ic t Co ur t, E a ste rn Di str ic t of

Ke ntu c ky , Ce ntr a l D ivi sio n, L e xing ton ]: A J uly 2, 2007 consent a g ree ment betwee n the EPA and Ea st Kentucky Power Cooper ative 2 re qu ir e s th e uti lity

to r e du c e its e mis sio ns of SO by 54 ,0 00 ton s p e r ye ar a

nd its

x e mis sio ns of

NO by 8, 00 0 to ns pe r ye ar, by

ins ta lli ngand

op e ratin gse le c tiv e c ata ly tic reduc tion (SCR ) tec hnology ; low-NOx burners, and PM and me rcur y Continuous EMissions Moni tors at the utility ’s Spurlock, Da le and Cooper Plants. Accordingto the EPA, total emissions from the plants will decrea se betwe en 50 and 75 pe rce nt from 2005 levels. As with all fe dera l consent dec ree s, EKPC is precluded fr om using re ductions require d under othe r programs, such a s CAI R, to meet the reduc tion requireme nts of the c on se nt d e c re e . E KPC is e xpe c te d to sp e nd $6 54 mil lio n to ins ta ll p oll uti on c ontr ols
.

• Dupont [US District Court for the Southern D istrict of Ohio]: A 2007 agr eeme nt calls for E. I . Dupont Nemours & Company ’s J ames River pla nt to inst all dual absorption pollution control equipment by September 1, 2009, r esulting in e mission reductions of 2 a p p r o x i m at e l y 1 , 0 0 0 t o n s S O annu a l l y.

• Stone Container [US Dist rict Court, Easter n District of Virg inia]: A 2004 agree ment calls for the West Poi nt Paper Mill owned by Smurfit/S tone Container to contr ol with a 2 we t sc ru bb e r t he SO e mis sio ns of

the #8 Pow e r B oil e r. Th is c ontr ol d e vic e sh ou ld r e su lt 2 in reduc tions of over 3,500 tons of SO in 2018.

• A la b ama Po w e r [U S D is tr ic t C o urt, N o rth e r nD is tr ic t o f

A la b ama , So uth e r

nD iv is io n ]: Under a 2006 Partial Consent De cre e APC agree d to spend approximately $200 milli on by 20 12 to i ns ta ll p oll uti on

c ontr ols on

Pla nt M ill e r Unit s 3 and

4. Up on fu ll implementation, the agree ment will cause A PC t o reduc e appr ox imately 28,000 tons per 2 x y ear of emissions of sulfur dioxi de (SO ) and nitrog en oxides (NO ) fr om Plant Mi ller.W est V i rgi ni a R egi o nal H aze S IP Pag e 51

• AEP [US District Court for the Southern D istrict of Ohio, Easter n Division] : America

n

x Electric Power a g ree d to spend $4.6 billion dol lars to eliminate 72,000 tons of NO 2 emissions each year

by 2016 and 174,000 tons of SO emissions each year by 2018 from six teen plants located in Indiana, Ke ntucky , Ohio, Virg inia and West Virg inia. H e av y D u ty D i e s e l ( 2 0 0 7 ) E n g i n e S t an d ar d
. E P As e ta P M e m i s s i o n s s t an d ar d

f o r n e wh e av yduty eng ines of 0.01 grams per brake -horse power -hour ( g/bhp-hr), to take full eff ect f or diesel

x eng ines in the 2007 model y ear . This rule also includes standa rds for N O and non-me thane

x hydroca rbons (NMHC) of

0.20 g/bhp-hr and 0.14 g/bhp-hr, re spectively . These N O and NMHC standards w ill be phased in tog ether betwee n 2007 and 2010 for diesel eng ines. Sulfur in diesel fu e l mu st b e low e re d to e na ble mod e rn

po llutio n- c ontr ol t e c hn olo gy to b e e ff e c tiv e on the se trucks and buse s. EPA will require a 97 perc ent re duction in the sulfur conte nt of hig hway diesel fuel fr om its current leve l of 500 parts pe r million (low sulfur diesel, or L SD) to 15 parts per milli on (ultra- low sulfur diesel, or UL SD). T i e r 2 T a i l p i p e . T i e r

2 i s a f l e e tav e r a g i n gp r o g r am , m o d e l e d a f t e r th e C a l i f o r n i a LE VII sta nd ard s. Manu fa c tur e rs c an p ro du c e ve hic le s w ithe mis sio ns ra ng ingfr om r e lativ e ly

dir ty to

x zero, but the mix of vehic les a manuf actur er se lls each year

must have ave rag e NO emissions below a spe cified va lue. Tier 2 sta ndards be came eff

ective in the 2005 mode l y ear . L arg e Spark I g nition and Recre ational Vehic le Rule. EPA has adopte d new standa rds for

x emissions of NO , hydroca rbons (HC), a

nd car bon monox ide (CO) f

rom sever al g roups of previously

unregulated nonroa d eng ines. Included in these are larg e industrial spark- ignition eng ines and re cre ational vehicle s. Nonroad spa rk-ignition engine s are those power ed by g asoline, liquid propane, or compre ssed natura l ga s rated ove r 19 kilowatts (kW) (25 horsepowe r). The se eng ines are used in commerc ial and industrial applications, including forklifts, elec tric g ener ators, air port bag g ag e tra

nsport vehicles, a

nd a var iety

of fa rm and construction applications. Nonroad re cre ational vehicle s include snowmobiles, off- highw ay motorcycles, a

nd all terra in vehicles. The se rules w ere initially eff ective in 2004 a

nd will be fully phased- in by

2012. Nonroa d Diesel Rule. This rule se ts standards that will reduc e emissions by more than 90 perc ent from nonr oad diese l equipment, and re duce sulfur levels by 99 perc ent from c urre nt le ve ls i n n onro a d d ie se l f ue l st art ingin 2 00 7. Th is s te p w ill a pp ly to m os t no nr oa d d ie se l f

ue l in 2010 and to fue l used in locomotives and marine vessels in 2012. ( http:// www/epa /gov/nonr oaddie se l/ ) Ind us tr ia l B oil e r/Pr oc e ss H e ate r/RI CE M AC T. Th e EPAiss ue d f ina l r ule s to su bs tanti a lly re du c e e mis sio ns of

toxic a ir po llutants fr om i nd

us tr ia l, c omm e rc ia l and

ins tit uti on a l bo ile rs , pr oc e ss h e ate rs and

fr om s tatio na

ry re c ipr oc atin gint e rn a l c omb us tio n e ng ine s ( RI CE) . T he se rules re duce e missions of a numbe r of toxic air pollutants, including hydrog en chlor ide, 2 mang ane se , le a d, are se nic and me rc ur y by 20 09 . T his ru le a lso re du c e s e mis sio ns of SO and PM in conjunction with the tox ic air pollutant re ductions. The applied Max imum Achievable Control 2 1 0 Tec hnology

(MACT) c ontrol eff

iciencie s were 4 perc ent for SO and 40 per cent f or PM and 2.5 PM . ( EPA ’s ind

us tr ia l bo ile r M AC T r

ule s w e re va c ate d o n Jun e 8, 20 07 . T he VI STA S “ be stW est V i rgi ni a R egi o nal H aze S IP Pag e 52

and fi na l” mod e lin gru

n w ill make a dju stm e nts to t he c ontr ol a ssumpt ion s d

ue to t his MA CT rule ba sed on input from ea ch state. West Virg inia has dete rmined that the c ontrols assumptions sh ou ld r e ma in, sin c e c a se -b y -c a se MA CT u nd e r C AASe c tio n 1 12

j is tr ig g e re d a

nd the state does not expect the controls to be a ny less stringe nt than require d by the vac ated r

ule.)

x Com bu sti on Turb ine MA CT. Th e pr oje c tio n inve nto ri e s d o n ot i nc lud e the NO c o- be ne fi t eff ects of the MACT regulations for Ga s Turbines or stationar y Reciproc atingInterna l C o mb u s t i o nE n g i n e s , w h i c hE P Ae s t i m at e s t o b e s m a l l c o mp ar e d t o th e o v e r a l l i n v e n t o r

y. VOC 2-, 4-, 7- , and 10-year MACT Standards. Va

rious point source MACTs and a ssociated emission reductions wer e implemented. Reduc tions occurr ing be fore 2002 wer e not counted. 7.2.b. Additional State prog rams to re duce e missions In a dd iti on

to a c c ounti ngfo r s pe c if

ic e mis sio n r e du c tio ns du e to o ng oin ga ir po llutio n p ro g ra

ms as re quired unde r the Reg ional Haze Rule 308(d) (3)( v)(A) , states ar e also re quired to consider the air qua lity

benef its of measure s to mit iga

te the impacts of c onstruction activities [section 3 0 8 ( d ) ( 3 ) ( v ) ( B ) ] an d

a g r ic u lt ur a l a n d f o r e s tr y s mo k e ma

n a g e me n t [ s e c ti o n

3 0 8 ( d ) ( 3 ) ( v ) ( E ) ]. These state measur es ar e discussed in more detail in Section 7.9 and in Appendix H. 7. 2. c . Pr oje c te d 2 00 9 a

nd 20 18 B a se G2 Em iss ion s Inv e nto ri e s The B ase Ginventories for 2009 and 2018 ac count for post-2002 e mission reductions from promulg ated a

nd proposed f

eder al, state, loca l and site-spec ific control pr og rams as of J uly 1, 2004. In g ener al, emissions inventories wer e deve loped for 2009 a nd 2018 using c urre nt control information in West Virginia, a nd g rowingthe 2002 inventory using EG AS6, MOBI L E6.2, and I PM f or EGUs
. For EGUs, I PM results were adjusted base d on State and loca l (S/L ) ag ency knowledg e of planned e mission controls at specific EGUs. These updates for Ba se G2 a

re doc umented in the MACTEC emissions i nventory repor t “Docume ntation of the 2002 B ase Y ear and 2009 and 2018 Projection Yea

r Emissions I nventories f or VI STAS” dated January 2007. For non-EGUs, r ece ntly

updated growth and c ontrol data c onsistent with the data used in EPA’s CA I R a na ly sis wa s u se d a

nd su pp le me nte d b y

State and loc a l a g e nc y

data and up date d f

or e c a sts from the US De partment of E nergy (US DOE)
. Are a sourc e contr ols were estimated usingknown state-le vel Stag e I controls on g asoline dispensing f

acilities and ope n burningestimates, as we ll as controls used to projec t emissions for E P A’ s He av y

Du ty Di es el ru l em ak i n g an d

fo r t h e C AIR r

u l em ak i n g. Mo bil e so ur c e c ontr ols inc lud e d lo c a l c ontr ols un de rl yingthe 20 02 ba se lin e inv e nto ry (v e hic le emission inspection, St ag e I Ivapor r ecove ry , anti-tamper ing, e tc.) with cha ng es base d on specific State input. W est Virg inia does not requir e mobile source controls, other tha n Stage I vapor r ecove ry

in five (5) counties: Kana wha, Putnam, Cabell, Way ne and Wood. Nonroa dW est V i rgi ni a R egi o nal H aze S IP Pag e 53

control data

and proje ctions for 1996, 2010, 2015 a nd 2020 wer e obtained f

rom the EPA’s Clean Ai r I nte rs tate (C AIR) Te c hn ic a l Su pp or t D oc ume nt, and

str a ight l ine pr oje c tio ns we re us e d to e sti mate 20 09 and

20 18 le ve ls.

2 x The followingbar c harts show e x pecte d decr ease s in emissions of S O and NO acr oss the VI STA S state s f

ro m 20 02 thr ou g h 2 01 8. (S imi lar c ha rt s f or oth e r v isi bil ity

imp a ir ingpo llutants 2 are c onta ine d in

Ap pe nd ix H) . N ote that f or SO e mis sio ns in p art ic ula r, wh ic h a re the larg e st c ontr ibutor

to h a ze, e mis sio ns fr om e le c tr ic g e ne ra tin gfa c ili tie s a

re e xpe c te d to de c re a se 2 dr amatic a lly

(7 0% ) b e tw e e n 2 00 2 a

nd 20 18 , w ithSO re du c tio ns fr om E GU s in Wes t V ir g ini a of 83 %. Ho we ve r, e ve n a ft e r i mpl e me ntatio n o f C AI R, E GUe mis sio ns are pr oje c te d to re ma in 2 the larg e st c ontr ibutor

to h a ze, c omp ri sin gmor e tha n h a lf of the re ma ini ngSO e mis sio ns in ever y state.

2 F igure 7.2.c- 1. Base G2 A nnu al SO em ission s for 2002, 2009, and 2018 in the VIST AS

st at e s.W est V i rgi ni a R egi o nal H aze S IP Pag e 54 x F igure 7.2.c- 2. Base G2 A nnu al NO em ission s in 2002, 2009 an d 2018 in the VISTAS

st at e s. 7.2.c.1. Summary

of F inal Ba se G2 Emission Inventories f or 2009 and 2018 Table 7.2.c -1 is a summary of the 2009 B ase G 2 emission inventory . See Ta ble 4.1 for a compar ison to the 2002 baseline emissions inventory . The c omplete inventory and discussion of the methodolog y

is contained in Appe ndix D. Tabl e 7. 2. c -1 . 2 00 9 Em iss ion Inv e nt or y Sum m ar y forWe stVi r gin ia

VO C (tpy) 3

NH (tpy) 10

PM (tpy) 2.5

PM (tpy) x

NO (tpy) 2

SO (tpy) Point 15,171 671 22,747 15,709 124,358 333,463 Are a (inc ludes fire s) 57,346 10,625 118,106 23,680 14,326 12,284 Non-Road Mobile 18,069 11 1,640 1,528 30,133 359 On-Road Mobile 26,584 2,292 1,145 734 33,392 299 B iog e nic s 35 7, 85 0 N/A N/A N/A 2, 77 6 N/A TOTAL 475,021 13,598 143,637 41,651 209,986 346,404W est V i rgi ni a R egi o nal H aze S IP Pag e 55

Table 7.2.c -2 is a summary of the 2018 e mission i nventory . The c omplete inventory and discussion of the methodolog y is contained in Appe ndix D. Tabl e 7. 2. c -2 . 2 01 8 Em iss ion Inv e nt or y Sum m ar y forWe stVi r gin ia

VO C (tpy) 3

NH (tpy) 10

PM (tpy) 2.5

PM (tpy) x

NO (tpy) 2

SO (tpy) Point 17,952 593 28,084 20,165 94,600 177,517 Are a (inc ludes fire s) 62,806 11,504 124,566 24,507 15,716 12,849 Non-Road Mobile 14,086 13 1,292 1,198 25,710 56 On-Road Mobile 14,652 2,268 747 369 15,530 231 B iog e nic s 35 7, 85 0 N/A N/A N/A 2, 77 6 N/A TOTAL 467,347 14,377 154,688 46,239 154,332 190,653
7.2.d. Model Results for the 2018 B ase G 2 Inventory Compared to the Unif

orm Rate of Prog ress Glidepaths for Dolly Sods and Otter Cree k Wil derne ss Area s Us ing20 00 -2 00 4 I MPRO VE mon ito ri ngdata , th e de c ivi e w v a lue fo r t he 20 pe rc e nt b e st d ay s in e a c h ye ar a

re ave ra g e d to g e the r, pr od uc inga sin g le ave ra g e de c ivi e w v a lue fo r t he be st d ay s. Simi larly , the dec iview values f or the 20 pe rce nt worst day s in eac h y ear are aver ag ed tog ether , pr od uc inga sin g le ave ra g e de c ivi e w v a lue fo r t he wo rs t da

y s. F igur e 7. 2. d- 1 il lus tr ate s th e pr e dic te d v isi bil ity imp ro ve me nt b y

20 18 re su lti ngfr om t

he B a se G2 inventory

that repr esents implementation of e x isting fe dera l and state re gulations, compare d to the Uniform Rate of

Prog ress for

Dolly Sods (DOSO). The r ed lines show the tar g et natura l condition in 2064 for the 20 per cent wor st day s, and the incr emental de ciview c hang es re sulting from a unif

orm rate of progress be tween c urre nt and natura l conditions in 2064, for aver ag e 20 perc ent worst visibilit y

day s in the 2000-2004 base line period. The purple lines show the improvement expected f

rom exist ing a

nd planned e missions controls duringthe per iod of the f i r s tl o n gt e r

ms tr at e g y.W est V i rgi ni a R egi o nal H aze S IP Pag e 56

F igure 7.2.d-1. CMAQ 2018 Ba se G2 re sults compared to the Uniform Rate of Progress at

D o ll y So d s , WVW est V i rgi ni a R egi o nal H aze S IP Pag e 57

F igure 7.2.d-2. Projected visibility improvement on 20% wor st visibi lity day s at VI STAS and neig hboringClass Iare as for the 2018 B ase G 2a CMAQ run ( 12 km gr id)
No te that f

or Do lly Sod s, vis ibi lity

imp ro ve me nts on

the 20 pe rc e nt w or st d ay s a re e xpe c te d to be better than the uniform r ate of

prog ress g lidepath by

2018, based solely

on reduc tions from e xistin gand

pla nn e d e mis sio ns c ontr ols . A t D oll y Sod s, a 4. 3 dv im

pr ov e me nt i n v isi bil ity would meet the unifor

m rate of prog ress in 2018; expected e missions reduc tions by 2018 are pr oje c te d to a c hie ve a

7. 3 dv im

pr ov e me nt. In f

a c t, a s il lus tr ate d in

F igur e 7. 2. d- 2, vis ibi lity improvements at all the VI STAS mount ain Class Iare as and most of the c oastal Class Iare as ar e projec ted to be better

than the uniform r ate of

progress. In Figure 7.2.d- 2, the per centa g e of the targ e t r e du c tio n a c hie ve d f or Do lly Sod s u sin gthe ne w I MPRO VE e qu atio n is 16 5% . T his means that the r ate of

improvement is 65% g reater than the uniform rate of progress by 2018. In addition to improving visibilit y on the 20 per cent wor st visibi lity day s, states are also requir ed to protect visibility

on the 20 per cent be st day s at the Class Iare as. As illustrated in Figure 7.2.d3, vis ibi lity

on the 20 pe rc e nt b e st d ay s is pr oje c te d to imp ro ve at a ll V I STA S Cla ss I are a s a s a result of the 2018 B ase G 2 emissions reductions. In Figure 7.2.d- 3, the per centa g e of the targ et a c h i e v e d f o r D o l l y S o d s i s a

b o ut- 1 2 %
. Z e r o p e r c e n tc h an g e w o u l d m e ann o c h an g e i nv i s i b i l i ty; -12% mea

ns that visibil ity

is better than no c hang e, or a 12% improvement ( values lowe r than curr ent conditions).W est V i rgi ni a R egi o nal H aze S IP Pag e 58

F igure 7.2.d-3. CMA Q 2 01 8 B a se G2 re su lts at D oll y Sod s.

F igure 7.2.d-4. Projected visibility improvement on 20% be st visibi lity day s at VI STAS and neig hboringClass Iare as for the 2018 B ase G 2a CMAQ run ( 12 km gr id)W est V i rgi ni a R egi o nal H aze S IP Pag e 59

The e x pecte d chang e in visibili ty at Dolly Sods between 2000- 2004 baseline c onditions on t he 2 0 p e r c e n tw o r s td ay s an d 2 0 1 8 p r o j e c t i o n s f o r B a s e G 2 a

i s i l l u s tr at e d

i nF i gur e 7
. 2
. d

- 4
. In contra st, natural bac kg round conditions for the 20 pe rce nt worst day s are illust rate d in Figure 7.2.d-5. These imag es we re g ener ated usingWI NHAZ E, a photographic imag ing tool that acc ounts for the e ffe ct of c oncentr ations of tine par ticle components a

nd relative humidity

on visibil ity . These imag es illustrate that notable improve ments in visibi lity are expected by

2018 and that si g nif

ic antl y g re ate r i mpr ov e me nts are ne e d to re a c h n atur a l ba c kgro un d c on dit ion s.

F igure 7.2.d-5. Visibili ty improvement on 20% ha ziest day s at Dolly Sods between 2000- 2004 baseline c onditions (left) and 2018 B ase G 2a proje cted visibility (right). Imag e g ener ated usingWI NHAZ E.W est V i rgi ni a R egi o nal H aze S IP Pag e 60

F igure 7.2.d-6. Pr oje c te d v isi bil ity on 20 % h a zie st d ay s f

or natur a l ba c kgro un d v isi bil ity conditions at Dolly

Sods. I mag e g ener ated usingWI NHAZ E.
7. 3. Re lat ive Co nt r ibut ion fro m Int e r nat ion al E m iss ion s t o V isi bili tyIm pai r m e ntin 2018 at VISTAS Class I areas

2.5 E m i s s i o n s fr o mM ex i co , C an ad a, C en tra l Am er i ca , As i a an d Af ri caco n tri b ut e t o P M l o ad i n gs and visibilit y

impairment at Class Iare as in the continenta l United States. To evaluate the relative contribution of international emissions t o visibil ity at Class Iare as in the southeaste rn United States, VI STAS used a combination of modelingresults from the G EOS-Chem g lobal model and CMAQ. VI STAS used the GEOS-Chem g lobal model to ge nerate initial and boundary conditions for the CMAQ modelingdomain. GEOS-Chem wa s run for the 2002 modeling year

using a 4 x 5 degree horizontal grid r esolution and a 3-hour temporal re solution. Em iss ion s w e re ba se d o n mo nth ly ave ra g e s, so the mod e l do e s n ot c a ptu re the e pis od ic varia bility in emissions. The GEOS-Chem outputs were used to ca lculate initial and boundar y conditions for the national CMAQ modeling domain, whic h included portions of Cana da and Mexi co. The emissions from these countr ies wer e included w ithin the national CMAQ modeling domain or as pa

rt of the bounda

ry conditions outside the national modeling doma in, as appropr iate. Two complimentary

methods were used to ca lculate the impac t of international e missions at Class Iare as. All international f ires ar e tre ated a s natural e missions in these runs, so both ca ses un d er es t i m at e t h e c o n tri b ut i o n s fr o mi n t ern at i o n al fi re s th atar e du e t o an th ro p o gen i c b urn i n g.W est V i rgi ni a R egi o nal H aze S IP Pag e 61

1. International emissions are re prese nted as the diff ere nce be tween two G EOS-Chem runs – In the first run United States a nthropog enic e missions wer e re moved, and in the se cond run both United States and inter national anthropog enic e missions wer e re moved. The differ ence repr esents international anthropog enic e missions , in the absenc e of United State s anthro po g e nic e mis sio ns (e .g . c omp are d to 20 64 le ve ls) . H arv ard Un ive rs ity p r o v id e d G E O S- Ch e m r e s u lt s to V I ST A S f o r 2 0 0 2 inte r

n ati o n a l c o n tr ib uti o no na 4 x 5 de g re e g ri d. Con c ur

re ntl y , H arv ard

mod e lin gfo r t

he El e c tr ic Pow e r R e se arc h Ins tit ute (EPRI ) provided G EOS-Chem results for 2001 inter national contribution on a more ref ined 1 x 1 degr ee grid scale
. 2. International emissions are re prese nted by the diffe renc e betwe en two CMAQ 36-km simulatio ns , b oth

us ing20 18 B a se F e mis sio ns and bo un da ry c on dit ion s f

ro m G EO SChem – In the first CMAQ run, a ll global natural and a nthropog enic e missions in 2018 are active. I

n the sec ond CMAQ run, only g lobal (United States and inter national) natural e mis sio ns are a c tiv e . H e re the imp a c ts o f i nte rn atio na l e mis sio ns are c omp are d a g a ins t 2018 conditions rather

than natura l background. S ep ar at e f ro mVIS T AS , th e C ENRAP RP O u s edP M S o urc e A p p o rt i o n m en tT ec h n o l o gy (PSAT) in the CAMx reg ional air qua lity model to calculate the impact of Canadian a

nd Mexi can e missions and boundar y conditions on visibi lity

at Class Iare as in 2002. VI STAS has compar ed its results to results from other RPOs on the impact of international e missions and boundary conditions on visibi lity

at Class Iare as in 2002; the results wer e similar. As illustrated in Figure 7.3- 1, for a nnual ave rag e contr ibutions t o sulfate at VI STAS and neig hboringClass Iare as, the e stimated international contr ibutions are hig her at Class Iare as near the Canadia

n and Mexican borde rs and a long the easte rn coa st. The estimated international c ontr ibutio n is highe r u sin gCMA Q a nd PSA T tha

n in the GE OS- Che m r

un s b e c a us e the g ri d scale is finer (mor e ac curate dispersion of e missions ) and be cause the bac kg round atmospher e includes loading s from cur rent United States a

nthropog enic e missions (greater photoche mical a c tiv ity ). Simi lar c ha

rt s f or nit rate and or g anic c arb on

ma ss, fo r i mpa c ts o n 2 0 p e rc e nt w or st visibil ity day s, and for

impacts of international emissions on calculated light ext inction are included in Appendix H.W est V i rgi ni a R egi o nal H aze S IP Pag e 62

F igu r e 7. 3- 1. Est im at e d int e r nat ion al e m iss ion s c ont r ibut ion to sulfa te atVI STA S and

neighb oring Class I areas
In Figure 7.3- 2 CMAQ projec tions of contributions from international e missions to PM m ass on 20 perc ent worst visibilit y day s in 2002 at Dolly

Sods, W est Virg inia are compar ed to United State s d ome sti c c ontr ibutio ns to P M c omp on e nts at th e sit e on tho se da y s.W est V i rgi ni a R egi o nal H aze S IP Pag e 63

F igure 7.3-2. Measure d PMcom ponent concentrat ion on 20% worst visib ility days i n 2002

(left bars) and CMAQ-sim ulated in ter national contributions (right bars) at Dolly Sod s, WV.
Al tho ugh V I STA S a sse sse d im pa c ts f ro m inte rn atio na l e mis sio ns at th e Cla ss I are a s, the se modeling r

uns were to provide information to the states to assist t hem in understandingthe po te nti a l im

pa c ts at th e ir

Cla ss I are a s. Th e mod e lin gsh ow e d th at at D oll y Sod s th e imp a c ts from international emissions were a pproxi mately

1 dv of the c urre nt conditions. S ince g ood projec ted emissions for ar eas outside the c ontinental United States are not rea dily available , the re is n o r e a l w ay

to a sse ss t

he imp a c ts f

ro m inte rn atio na l e mis sio ns in 2 01 8, oth e r t ha n to a ssume that th e imp a c t w ou ld b e a pp ro ximate ly

the sa me a s f or the ba se lin e c on dit ion s. There fore , WVDAQ will not be acc ounting f or international emissions i n setting the 2018 rea sonable progress g oals for D olly Sods and Otter Cree k. Neve rtheless, a s the atmosphere become s closer to natural bac kg round conditions in the future, the incre mental contribution from interna tional emissions wil l become mor e important. The information is included in this SI P documentation to provide re fer ence for f uture a ssessments of re a so na ble pr ogre ss.W est V i rgi ni a R egi o nal H aze S IP Pag e 64

7. 4. Re lat ive Co nt r ibut ion s t o V isi bili tyIm pai r m e nt : P oll ut ant s, Sour c e Ca te go r ie s a nd Ge ographic Areas An important step towar d identifying f uture re asonable progress mea sures is to identify the key pollutants contributing to visibili ty impairment at ea ch Class Iare a. To unde rstand the r elative be ne fi t of fu rt he r r e du c inge mis sio ns fr om d if fe re nt p oll uta

nts , s ourc e se c tor s a

nd g e ogra ph ic are a s, VI STA S e ng a g e d th e Ge or g ia Ins tit ute of Te c hn olo g y

to p e rf or m e mis sio n s e ns iti vit

y model runs usingCMAQ. Emissions s ensitiviti es we re initially perf ormed for three episodes repr esentingwinter a

nd summer conditions: J an 2002, Jul 2001 and Jul y 2002. These r

uns used the ini tia l 20 18 pr oje c tio n inve nto ry and c on sid e re d 3 0% re du c tio ns fr om s pe c if ic po llutants , source cate g ories and g eographic are as. Emissions sensiti vities were repe ated usingthe 2009 Ba se D proje ction inventory and two month-longepisodes fr om 2002; J un 1 - Jul 10 and Nov 19 - De c 19. Emissions in 2009 were r educe d by 30% for eac h pollutant sensitivi ty run. The pollutant contributions that were e valuated w ere :

2 • SO from EGUsource s in eac h VI STAS state, other RPOs in the VI STAS 12-km gr id, and B ound ary

Con dit ion s f

ro m outsi de the 12 -k

m do ma in 2 • SO from non-EGU point sources in e ach V I STAS state, other RPOs and Bounda ry Conditi ons

x • NO from ground leve l (on-roa d plus non-road a rea ) sourc es in ea ch VI STAS state and other RPOs

x • NO from point (EGU plus non-E GU) sour ces in e ach V I STAS state and other RPOs

3 • NH from all sourc es in VI STAS and other RPOs

• Vo latil e Or g anic Com po un ds fr om a

nthro po g e nic so ur c e s in

the 12 -k m mo de lin gdo ma in

• Primary

Carbon fr om all gr ound level sourc es in ea ch VI STAS state and other RPOs

• Primary

Carbon fr om all point source sin ea ch VI STAS S tate and othe r RPOs
• Primary

Carbon fr om all fires in ea ch VI STAS state and other RPOs R e s u l t s ar e s h o w ni nF i gur e 7
. 4

- 1 b e l o wf o r th e av e r a g e o f

th e 2 0 p e r c e n tw o r s tv i s i b i l i ty d ays for Dolly Sods. Responses for the 20 pe rce nt worst day s were calc ulated by aver ag ing the re sp on se s f

or the 20 pe rc e nt w or st d ay s th at w e re mod e le d in the tw o e pis od e s. F or

Do lly Sod s, response s on 9 of the 20 per cent wor st visibi lity day s were included in the graphic
.W est V i rgi ni a R egi o nal H aze S IP Pag e 65

F igure 7.4-1. CM A Q p r o je c ti o n s o f

v is ib il it y r e s p o n s e s o n2 0 % w o r s t d ay s at D o ll y So d s , WV to 3 0% re du c tio ns fr om 2 00 9 B a se D i nv e nto ry fo r v isi bil ity -r e du c ingpo llutants in d if fe re nt s ourc e c ate g or ie s a nd g e ogra ph ic are a s. As F igur e 7. 4- 1 il lus tr ate s, the g re ate st v isi bil ity be ne fi ts o n th e 20 pe rc e nt w or st d ay s f or Do lly 2 Sod s are pr oje c te d to re su lt f ro m f ur the r r e du c ingSO e mis sio ns fr om E GU s. At Do lly Sod s, 2 benef its are pr ojected f

rom SO reduc tions from EGUs in sever al VI STAS states, including Florida , Geor g ia, Kentuc ky , North Carolina, Te nnessee , Virg inia and West Virg inia. 2 Contr ibutio ns fr om o the r RPOs and SO c omi ngfr om o uts ide the bo un da ry are a lso signif

ic ant. The greatest benef

it would likely

be fr om further

EGU re ductions in W est Virg inia, the MRPO, 2 and fr om o uts ide the bo un da

ry . A dd iti on a l, s ma lle r b e ne fi ts w ou ld c ome fr om a dd iti on a l SO emission reductions from non- utilit y , industrial point sources. Withi n the VI STAS states, the 2 re lativ e imp or

tanc e of SO re du c tio ns fr om n on -E GU s is sim ila r t o th at f or EGUs
.
2.5 Ammonium nit rate is a small contributor to PM mass and visibilit y impairment on the 20 x 3 perc ent worst day s at Dolly Sods. Theref

ore the benef

its of reduc ing N O and NH are small. Vo latil e Or g anic Com

po un ds (V OC ) i n We st V ir g ini a or ig inate pr ima

ri ly fr om b iog e nic so ur c e s, a s v e g e tativ e e mis sio ns , a nd a lso c ontr ibute to v isi bil ity imp a ir me nt. Contr oll inganthro po g e nic source s of VOC has little, if any , visibil ity benef it at Dolly

Sods. Reducing pr imary

car bon fromW est V i rgi ni a R egi o nal H aze S IP Pag e 66

po int so ur c e s, g ro un d le ve l so ur c e s o r f ir e s is pr oje c te d to have min ima l vi sib ili ty be ne fi t. T his is c on sis te nt w iththe mon ito ri ngdata wh ic h s ho ws that mo st m e a sure d o rg anic c arb on is seconda ry in orig in and primary

car bon is only

a small fra ction of the total mea sured c arbon (A pp e nd ix B ). Re du c ingc arb on fr om f ir e s w a s n ot f

ound to b e e ff e c tiv e be c a

us e the re wa s li ttl e fi re a c tiv ity

at th e se sit e s o n th e da

y s mo de le d in

the se ns iti vit

y ana ly se s. Note that these results from the e mission s ensitivity runs ar e consistent with the conc lusions drawn from the 2000- 2004 baseline monitoringdata ( see Sec tion 2.4 above). The results indicate that sulfate is the dominate c ontributor to visibi lity impairment on the 20 per cent wor st day s at a ll V I STA S sit e s and

that ammo nium ni tr ate c an b e imp or tant f or sit e s whe re 20 pe rc e nt w or st 2 day s occur

in the winter. We conc lude that re ducingSO emissions from EGU and non- EGU po int so ur c e s in

Wes t V ir g ini a

and the Mid we st RPO w ou ld h ave the g re ate st v isi bil ity be ne fi t for Dolly Sods. Cont ributions from other VI STAS states and MANE-VU ar e also sig nificant for this area
. 7. 5. Re lat ive Co nt r ibut ion s t o V isi bili tyIm pai r m e nt : G e og r aphi c Loc at ion s o f the Lar ge stEm iss ion s So ur c e s C ont r ibut ing to Vi sibi lity I m pai r m e ntatDo llySods and OtterCre ek Wilderness Areas

2 On c e it w a s d e te rm ine d th at SO e mis sio n r e du c tio ns fr om E GUand no n- EGU p oint so ur c e s in the VI STAS states would be the most effe ctive sourc es to control to improve visibility at the Cla ss I are a s, the ne xt ste p w a s to ide nti fy the sp e c if

ic g e ogra ph ic are a s th at mo st l ike ly

2 influence visibil ity

in eac h Class Iare a, and the n to identify the major SO point sources located 2 in t ho se g e ogra

ph ic are a s. An

SO Ar e a

of Inf lue nc e (A oI ) w a s d e fi ne d f or e a c h Cl a ss I

are a to repr esent the g eographic are a conta ining sourc es that would likely have the g reatest impact on 2 visibil ity at that Class Iare a. All SO point sources within these Ar eas of Influenc e we re identified and r anked by

their 2018 B ase Gemissions. The followingsections contain a broad overvie w of the steps in the Area of Influenc e ana ly ses. See Appe ndix H for a more de tailed dis c us sio n o f t

he se ana ly se s and

plo ts f or a dd iti on a l Cla ss I

are a s. The Ar ea of

Influenc e ana ly sis is nota sourc e appor tionment modeling exerc ise, but rather a re lativ e me tr ic ba se d o n th e ma g nit ud e of e mis sio ns fr om a

so ur c e , it s d ist anc e to t

he Cla ss I are a(s) of conc ern, a

nd the sulfate e x tinction weig hted re sidence time plots, developed using back tr ajec tories. In other wor ds, it is notan exact quantific ation of sourc e-by-sourc e c ontr ibutio n to vis ibi lity

imp a ir me nt o n th e 20 % w or st d ay s at a sp e c if ic Cla ss I are a , b ut a relative metric used to infe r this information. 7.5.a. Ba ck Tra

jectory Analy ses Th e fi rs t st e p w a s to g e ne rate me te or olo g ic a l ba c k tr a je c tor ie s f

or I MPRO VE mon ito ri ngsit e s in West Virginia a

nd neig hboringClass Iare as for the 2000-2004 ba seline period. Ba ck tra jectory ana ly se s u se int e rp olate d, me a sure d o r m od e le d me te or olo g ic a l f ie lds to e sti mate the mos t li ke ly centr al path of a ir masses that ar rive at a re ceptor

at a g iven time. The method e ssentially follows a par cel of

air ba ckwa

rd in hourly

steps for a

specifie d leng th of time. Figure 7.5.a -1 is an example of a ba ck tra

jectory analy sis for Dolly

Sods for the 20 per cent wor st day s in 2002.W est V i rgi ni a R egi o nal H aze S IP Pag e 67 

### B ac k Traj e c t or i e s f

or 200 2 20 % W or s t D ays D ol l y S od s W i l dern ess A rea

F igu r e 7. 5. a- 1. Exa m ple bac k t r aj e c to r ie s for20 % wors t v isi bili tyday s in 2 00 2 forDo lly

Sods Traje ctories we re star

ted at 100 meter s and 500 meter s above the surfa ce a nd run bac kwar d from the site for 72 hour s. These individual bac k trajec tories for the 20 perc ent worst day s in 2002 we re a lso us e fu l in

e va luatin gmod e l pe rf

or manc e fo r i nd ivi du a l da

y s at th e Cla ss I

are a s. 7. 5. b. Re sid e nc e Ti me Plo ts The ne x t step was to plot residenc e time for e ach Class I are a usingfive year s of bac k trajec tories for the 20 perc ent worst visibilit y day s in 2000-2004. Residence time is the fr e qu e nc y that w ind s p a ss o ve r a

sp e c if ic g e ogra ph ic are a on the wa y to a Cla ss I are a . Se pa

rate residenc e time plots were g ener ated usingtrajec tories with 100m and 500m start heights. As ill us tr ate d in

F igur e 7. 5. b- 1, wi nd s in fl ue nc ingDo lly Sod s o n th e 20 pe rc e nt w or st d ay s c ome from all dire ctions, with a signif icant southwest-nor

theast gradie nt influencingthe 20 per cent worst visibili ty

day s.W est V i rgi ni a R egi o nal H aze S IP Pag e 68

F igu r e 7. 5. b- 1. Re side nc e ti m e plo t fo r20 % wors t v isi bili tyday s in 2 00 0- 20 04 forDo lly

Sods . Based on trajec torie s w ith 100m startheight.

2 7.5.c SO Are as of Influenc e The ne x t step was to deve lop sulfate extinction-weig hted re sidence time plots t o define the g e ogra ph ic are a wi th t he highe st p ro ba

bil ity of inf lue nc ingthe re c e pto r o n th e 20 pe rc e nt w or st da y s in 20 00 -2 00 4 th at w e re do min ate d b y su lf ate . E a c h b a c k tr a

je c tor y wa s w e ighte d b y

su lf ate exti nction for that da

y . This allowed us to focus on the 20 perc ent worst day s that are influence d by su lf ate and pla c e le ss i mpo rt anc e on da y s in fl ue nc e d b y

or g anic c arb on

fr om f ir e s. Sul fa

te weighted bac k trajec tories for 20 pe rce nt worst day s were combined for 5 y ear s of data. T he re su lti ngsu lf

ate e xtinc tio n- we ig hte d r e sid e nc e tim e plo ts w e re us e d to de fi ne the g e ogra ph ic 2 Are a of Influenc e for

source s of SO emissions. I n Figure 7.5.c -1 the a rea repr esenting10 perc ent or greater re sidence time is outl ined in red a nd the ar ea r epre senting5 perc ent or greater residenc e time is outlined in gra y . The VI STAS states focused the ir ana ly ses on the Ar ea of Influenc e def

ined by 5 perc ent or greater sulfate extinction-weig hted re sidence time.W est V i rgi ni a R egi o nal H aze S IP Pag e 69 2 F igure 7.5.c- 1. SO Are a of Influenc e plot for sulfate extinction weighted r esidenc e time for

20% worst visibility

day s in 2000-2004 for Dolly Sods. Base d on trajec tories wi th 1 00 m st art

he ight.W est V i rgi ni a R egi o nal H aze S IP Pag e 70 2 7.5.d. Emission S ource s within S O Are as of Influenc e Residence time plots were then c ombined with ge og raphic ally -gridded e mission data based on the 2002 base line and 2018 B ase Gemissions inventories. Plot s were g ener ated f or the Ar eas of Influenc e def

ined by trajec tories with 100m and 500m start heights. As a way of incorpor ating the ef fec ts of transport, de position and chemica l transformation of point source e missions along the path of the trajec tories, these data wer e we ighte d by 1/d, where d was c alculate d at the 2 dis tanc e be tw e e n gri d c e ll c e nte rs , in kil ome te rs . T he dis tanc e -w e ighte d p oint so ur c e SO emissions were the n combined with the gridded, e x tinction-weighted bac k-traje ctory residenc e tim e s at a sp atia l r e so lut ion

of 36 -k m. Th e fi na l st e p w a s to c omb ine the re sid e nc e tim e s a nd g ri dd e d e mis sio ns data

in p lot s a nd data 2 sets. The distance weighted (1/d), gridded point source SO emissions were multiplied by the tot a l e xtinc tio n- we ig hte d b a c k- tr a je c tor y re sid e nc e tim e s o n a

g ri d c e ll b y

g ri d c e ll b a sis . T he se results wer e then nor

malized by

the domain-wide total and display ed as a perc entag e. The ana ly sis wa s d on e us ingbo th t he 20 02 and

20 18 ba se y e ar i nv e nto ri e s. Dolly Sods Figure 7.5.d- 1 illust rate s 2002 and 2018 distance weighted, g ridded e missions multipl ied by sulfate- weighted re sidence time plots for Dolly Sods. These maps help visualize wher e 2 emissions reductions will be occ urringbetwee n 2002 and 2018. The chang e in SO emissions be tw e e n 2 00 2 a

nd 20 18 c an b e se e n b y

c omp ari nge mis sio n s ourc e str e ng ths in t he tw o p lot s. Note the e missions from ea ch sourc e ar e norma liz ed by the total emissions in t he domain. 2 Sources that r educe SO emissions by 2018 will show a lower c ontribution to emiss ions in the domain. On the 2018 map, the grid ce lls with t hese sour ces w ill show a lighter color gradie nt 2 tha n o n th e 20 02 ma

p. F or e xamp le , SO re du c tio ns fr om E GU s in we ste rn Wes t V ir g ini a resultingfrom CAI R can be seen by compar ing the 2002 and 2018 maps. B eca use the total emissions in t he domain ar e smaller in 2018, a source that does not cha

ng e emissions betwee n 2002 and 2018 may

actua lly appea r to incre ase in importanc e in 2018 compar ed to 2002.W est V i rgi ni a R egi o nal H aze S IP Pag e 71 2 F igure 7.5.d-1. Do lly

Sod s 2 00 2 ( le ft ) a

nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * 4 SO e xtinc tio n- we ighte d r e sid e nc e tim e plo ts.

2 F igur e 7. 5. d- 2 il lus tr ate s th e SO dis tanc e we ighte d e mis sio ns * s ulf ate we ighte d r e sid e nc e tim e plo ts f or 20 18 e mis sio ns fo r D oll y Sod s. Th is p lot

ill us tr ate s th e re lativ e imp or tanc e of Wes t Virg inia source s compare d to sources in ne ighbor ing states. Additional analy ses, including2002 and 20 18 dis tanc e we ig hte d e mis sio ns * r e sid e nc e tim e plo ts f or Do lly Sod s a nd the Cla ss I are as in neig hboringstates ar e conta ined in Appendix H. Th e se ana ly se s are se rv inga s th e ba sis fo r c on su ltatio n amon gthe VI STA S state s.W est V i rgi ni a R egi o nal H aze S IP Pag e 72 2 F igure 7.5.d-2. 2018 SO distance w eig hted emissions * SO4 ex tinction weig hted re sidence

time plot for Dolly

Sods, W V.

2 Table 7.5.d- 1 shows, in tabular for m, the relative contributions of point source SO emissions fr om n e arb y

state s to Do lly

Sod s. Ag a in i t sh ou ld b e no te d, a s st ate d in the int ro du c tio n to section 7.5, that the Ar ea of Influenc e ana ly sis is nota sourc e appor tionment modeling exerc ise, but rather a re lative metric ba sed on the magnitude of emissions from a sourc e, its distance to the Class Iare a(s) of conc ern, a nd the sulfate e x tinction weig hted re sidence time plots, developed using ba ck tra

jectorie s. In other wor ds, it is notan exact quantific ation of sourc e-by-sourc e c ontr ibutio n to vis ibi lity

imp a ir me nt o n th e 20 % w or st d ay s at a sp e c if ic Cla ss I are a , b ut a relative metric used to infe r this information.W est V i rgi ni a R egi o nal H aze S IP Pag e 73 2 Tabl e 7.5.d-1. 2018 SO P oint Sou rce Contribution to Dolly S ods, WV by S tate State Relative Contribution State Relative Contribution Kentucky 1.34% South C arolina 0.09% Maryland 11.81% Tenne ssee 1.25% North Carolina 0.80% Virg inia 5.73% Ohio 7.37% West Virginia 66.35% Pennsylvania 5.26% Th e re are 32 3 unit s id e nti fi e d w ith in t he Ar e a of Inf lue nc e wh ic h c ontr ibute to s ulf ate at D oll y Sods, including seve nty -seve n (77) units in West Virginia. Tw enty -nine (29) units have a relative contribution g reater than 0.5% and contr ibute 72.11% to sulfate, inc luding se ventee n (1 7) un its in We st V ir g ini a , 1 5 o f whic h a re EGUs . T a

ble 7. 5. d- 2 id e nti fi e s th e 29 un its wi thi n the AoI for Dolly Sods that contribute at least 0.5% to the total sulfate. Seventee n (17) units have a re lative contribution g reater than 1.0% and contr ibute 64.28% to sulfate, inc luding f ourtee n (1 4) un its in We st V ir g ini a , a ll o f whic h a re EGUs
. Mary land c ontr ibute s 1 1. 81 % t o s ulf ate at D oll y

Sod s, wi th 9 .8 6% attr ibuta ble to t wo (2 ) u nit s, one of which is subject to BA RT, at the New Pag e fa cility

located in L

uke, Mar yland. WVDAQ has consulted with the Ma ryland Depa

rtment of the Environme nt and Mary land indicated that they expect the New

Pag e fa cility

to install a scrubber

with at least a 90% control ef ficienc y on at le a st o ne of

the tw o unit s im

pa c tin gDo lly

Sod s. Ma

ry land ha s n ot ye t f

ina lize d th e ir

B AR T proce ss, and New

Pag e wa s require d to submittheir five ( 5) fa ctor a naly sis to the Maryland Depa rtment of the Environme nt by October 31. Ta ble 7. 5. d- 3 id e nti fi e s a ll e xistin gc oa l- fi re d e le c tr ic g e ne ratin gun its in We st V ir g ini a and the ir curr ent and f

uture c ontrols. As shown in the table all coa l-fire d EGUs in West Virg inia are 2 x 2 curr ently controlled for

PM, S O and NO or ar e proje cted to either shut down or install SO and x 2 NO controls by

2018. As shown in the table SO controls ar e cur rently under c onstruction at 2 x five (5) units (4,007 MW ). I PM has predicte d the installation of SO and NO controls on an a d d it io n a l t

w e lv e ( 1 2 ) un it s ( 2 , 3 9 8 M W) b y 2 0 1 8 an d th e s hutd o w no f

f iv e ( 5 ) un it s ( 3 3 7 M W) by 2018.

2 4 F ina lly , F igur e s 7 .5 .d -3 thr ou g h 7 .5 d. 8 s ho w the SO dis tanc e we ighte d e mis sio ns * SO exti nction-we ighte d residenc e time plots (AoI maps) for

Br iga ntine (NJ), Great Smokey Mountains (NC/TN), James River Fa ce ( VA), L inville Gorg e (N C), Mammoth Cave (KY ), and Shenandoah ( VA). Ta

bles 7.5.d-4 through 7.5.d-9 show, in tabular form, the r elative 2 contributions of point source SO emissions withi n the AoI from nea rby states to Br iga

ntine (NJ), Great Smokey

Mountains (NC/TN), James River Fa ce ( VA), L inville Gorg e (N C), Mammoth Cave (KY ), and Shena ndoah (VA).W est V i rgi ni a R egi o nal H aze S IP Pag e 74 Tabl e 7. 5. d- 2. P oin t S our c e Uni ts Co nt r ibut ing atle as t 0 .5 % to S ulfat e atDo llySods

Sou rc e I denti f

i cat i on 2002 B ase Y ear 20 18 B ase C ase A oI a n d A ssoci ated M et ri cs Q / d *R T M ax S tat e/ S ou rce C ontr i bu t i on
S t a t e FI PS

CN TY P l an t I D Pl a n t Na me Poi nt ID 2

SI C SO

E m i ssi o ns ( t py) Q

2

SO E m i ssi o ns ( t py) CE

( % ) D i st an

ce ( k m) Q /d RT M ax Q / d *

R T M ax U n i t %

C o nt r i bu ti o n to T ot al Q / d* R T M ax S tat e %

C o n tr ib u ti o n to T ot al Q / d* R T M ax M D 001 001- 0011 N ew P age - L uke M i l l 1 2621 10, 160 9, 610 0 51. 59 186. 26 42. 35 7, 888. 1 5. 25% 11. 81% M D 001 001- 0011 N ew P age - L uke M i l l 2 2621 8, 923 8, 441 0 51. 59 163. 60 42. 35 6, 928. 5 4. 61% 11. 81% O H 167 0684000000 M usk i ng um R i v er Pow er Pl ant B 006 4911 0 5, 034 90 201. 89 24. 94 49. 41 1, 232. 3 0. 82% 7. 37% O H 053 0627010056 G av i n Pow er Pl ant B 003 4911 19, 222 6, 479 95 233. 68 27. 73 28. 16 780. 9 0. 52% 7. 37% O H 053 0627010056 G av i n Pow er Pl ant B 004 4911 12, 601 6, 465 90 233. 68 27. 66 28. 16 778. 9 0. 52% 7. 37% PA 059 4205900006 A l l eg heny Ener g y Suppl y C o / H at f i el ds Fe r r y 032 4911 53, 016 2, 220 90 93. 77 23. 68 36. 51 864. 6 0. 58% 5. 26% PA 059 4205900006 A l l eg heny Ener g y Suppl y C o / H at f i el ds Fe r r y 031 4911 55, 695 2, 215 90 93. 77 23. 62 36. 51 862. 4 0. 57% 5. 26% PA 059 4205900006 A l l eg heny Ener g y Suppl y C o / H at f i el ds Fe r r y 033 4911 50, 001 2, 213 90 93. 77 23. 60 36. 51 861. 6 0. 57% 5. 26% TN 163 0003 East m an Chem i ca l C o m pany 021520 2869 16, 855 16, 729 4 397. 20 42. 12 21. 07 887. 5 0. 59% 1. 25% V A 580 00003 M eadW es t V aco Pack agi ng R es ource G roup 25 2611 8, 552 9, 997 0 153. 90 64. 96 24. 77 1, 609. 1 1. 07% 5. 73% W V 023 0003 M oun t Stor m P ow er P l ant 001 4911 8, 817 3, 191 95 17. 36 183. 81 74. 90 13, 767. 4 9. 16% 66. 35% W V 023 0003 M oun t Stor m P ow er P l ant 002 4911 9, 572 3, 191 95 17. 36 183. 81 74. 90 13, 767. 4 9. 16% 66. 35% W V 023 0003 M oun t Stor m P ow er P l ant 003 4911 2, 779 2, 923 94. 98 17. 36 168. 39 74. 90 12, 612. 4 8. 39% 66. 35% W V 033 0015 M onon gahel a P ow er C o - H arr i son 003 4911 2, 582 5, 998 97. 98 82. 67 72. 56 86. 63 6, 285. 9 4. 18% 66. 35% W V 033 0015 M onon gahel a P ow er C o - H arr i son 002 4911 2, 731 5, 954 97. 98 82. 67 72. 02 86. 63 6, 239. 1 4. 15% 66. 35% W V 033 0015 M onon gahel a P ow er C o - H arr i son 001 4911 3, 355 5, 908 97. 98 82. 67 71. 47 86. 63 6, 191. 4 4. 12% 66. 35% W V 023 0014 N ort h B ranch P ow er Stat i on 002 4911 323 1, 018 94. 98 18. 73 54. 33 74. 90 4, 069. 3 2. 71% 66. 35% W V 023 0014 N ort h B ranch P ow er Stat i on 001 4911 324 1, 004 95 18. 73 53. 63 74. 90 4, 016. 9 2. 67% 66. 35%Tabl e 7. 5. d- 2. P oin t S our c e Uni ts Co nt r ibut ing atle as t 0 .5 % to S ulfat e atDo llySods

Sou rc e I denti f

i cat i on 2002 B ase Y ear 20 18 B ase C ase A oI a n d A ssoci ated M et ri cs Q / d *R T M ax S tat e/ S ou rce C ontr i bu t i on
S t a t e FI PS

CN TY P l an t I D Pl a n t Na me Poi nt ID 2

SI C SO

E m i ssi o ns ( t py) Q

2

SO E m i ssi o ns ( t py) CE

( % ) D i st an

ce ( k m) Q /d RT M ax Q / d *

R T M ax U n i t %

C o nt r i bu ti o n to T ot al Q / d* R T M ax S tat e %

C o n tr ib u ti o n to T ot al Q / d* R T M ax

W est V i rgi ni a R egi o nal H aze S IP Pag e 75 W V 073 0005 M onon gahel a P ow er C o - P l eas ants P ow er 001 4911 21, 507 6, 334 90 149. 65 42. 33 61. 20 2, 590. 6 1. 72% 66. 35% W V 073 0005 M onon gahel a P ow er C o - P l eas ants P ow er 002 4911 20, 090 6, 165 90 149. 65 41. 20 61. 20 2, 521. 4 1. 68% 66. 35% W V 061 0001 M onon gahel a P ow er C o - F ort M art i n P ow er 001 4911 46, 852 4, 922 90 78. 30 62. 86 36. 51 2, 295. 0 1. 53% 66. 35% W V 061 0001 M onon gahel a P ow er C o - F ort M art i n P ow er 002 4911 42, 467 4, 890 90 78. 30 62. 45 36. 51 2, 280. 0 1. 52% 66. 35% W V 079 0006 A pp al achi an P ow er - John E . A m os 003 4911 39, 570 10, 821 90 219. 63 49. 27 39. 08 1, 925. 5 1. 28% 66. 35% W V 053 0009 A pp al achi an P ow er - M oun t at i neer 001 4911 39, 064 11, 433 90 217. 38 52. 60 30. 89 1, 624. 8 1. 08% 66. 35% W V 077 0001 M o no ng ahe l a Po w er C o - A l br i g ht 003 4911 11, 273 660 90 46. 21 14. 29 87. 09 1, 244. 5 0. 83% 66. 35% W V 079 0006 A ppal ac hi an Po w er - Jo hn E. A m o s 002 4911 27, 651 6, 694 90 219. 63 30. 48 39. 08 1, 191. 2 0. 79% 66. 35% W V 079 0006 A ppal ac hi an Po w er - Jo hn E. A m o s 001 4911 30, 015 6, 613 90 219. 63 30. 11 39. 08 1, 176. 7 0. 78% 66. 35% W V 073 0006 C abo t C o r po r at i o n - O hi o R i v er Pl ant 002 2895 2, 025 2, 803 0 168. 40 16. 64 61. 20 1, 018. 4 0. 68% 66. 35% W V 073 0006 C abo t C o r po r at i o n - O hi o R i v er Pl ant 003 2895 1, 734 2, 400 0 168. 40 14. 25 61. 20 872. 1 0. 58% 66. 35% T O T A L A l l S ou rces 150297. 6 100. 00% 323 u n its C ontr i bu t i on fr om 0. 5% sou rces 1 0 8 ,3 8 3 .7 7 2 .1 1 % 2 9 u n its

C ontr i bu t i on fr om 1. 0% S ou rces 9 6 ,61 2 .8 6 4 .28 % 1 7 u n i ts N o te : U n its i d e n tif

ie d in b la c k tex t ar e E G U s, u n i t s i d e n t i f i e d i n b l u e t e x t a r e n o n - EG Us
. U ni t s i denti f

i ed w i t h bo l d t ext hav e great er t han 1. 0% contr i buti on to s ul f ate.West Virginia Regional Haze SIPPage 76Table 7.5.d-3. West Virginia Coal-Fired ElectricGenerating Units and Existing and Scheduled ControlsPlant ID Unit MW PMControlsSO2ControlsYr SO2ControlsInstalledNOxControlsYr NOxControlsInstalledCommentsAlbright 077-0001 1 73 ESP LNB 2018 IPM Predicted Shutdown2 73 ESP LNB 2018 IPM Predicted Shutdown3 137 ESP Scrubber 2018 LNB/SCR 2018 IPM Predicted ControlsFt. Martin 061-00001 1 552 ESP Scrubber 2010 SNCR Trim 2004 Controls currently underconstruction2 555 ESP Scrubber 2010 SNCR Trim 2004Grant Town 049-00026 84 FabricFilterLimestoneInjectionUponConstructionSNCR 2003Harrison 033-00015 1 640 ESP Scrubber 1994 SCR 20022 640 ESP Scrubber 1994 SCR 20033 640 ESP Scrubber 1994 SCR 2003John Amos 079-00006 1 800 ESP Scrubber 2009 SCR 2005 Controls currently underconstruction2 800 ESP Scrubber 2009 SCR 20043 1300 ESP Scrubber 2008 SCR 2003Kammer 051-00006 1 210 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls2 210 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls3 210 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted ControlsKanawhaRiver039-00006 1 200 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls2 200 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted ControlsMitchell 039-00006 1 800 ESP Scrubber 2007 SCR 20072 800 ESP Scrubber 2007 SCR 2007Table 7.5.d-3. West Virginia Coal-Fired ElectricGenerating Units and Existing and Scheduled ControlsPlant ID Unit MW PMControlsSO2ControlsYr SO2ControlsInstalledNOxControlsYr NOxControlsInstalledCommentsWest Virginia Regional Haze SIPPage 77MEA 061-00027 60 FabricFilterLimestoneInjectionUponConstructionLNB UponConstructionMountaineer 053-00009 1300 ESP Scrubber 2007 SCR 2003Mt. Storm 023-00003 1 533 ESP Scrubber 2002 SCR 20032 533 ESP Scrubber 2001 SCR 20033 521 ESP Scrubber 1994 SCR 2004North Branch 023-00014 84 FabricFilterLimestoneInjectionUponConstructionLNB UponConstructionPhilip Sporn 053-00001 1 150 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls2 150 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls3 150 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls4 150 ESP Scrubber 2018 OFA/SCR 2018 IPM Predicted Controls5 450 ESP Scrubber 2018 LNB/SCR 2018 IPM Predicted ControlsPleasants 073-00005 1 650 ESP Scrubber 1979 SCR 20032 650 ESP Scrubber 1980 SCR 2003Rivesville 049-00009 7 46 ESP 2018 IPM Predicted Shutdown8 91 ESP 2018 IPM Predicted ShutdownWillow Island 073-00004 1 54 ESP LNB 2018 IPM Predicted Shutdown2 181 ESP Scrubber 2018 LNB/SCR 2018 IPM Predicted ControlsW est V i rgi ni a R egi o nal H aze S IP Pag e 78

Br iga ntine, NJ

2 F igur e 7. 5. d. 3. B ri g anti ne , N J 200 2 ( le ft ) a nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * 4 SO e xtinc tio n w e ig hte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-4. 2018 SO P oint Sou rce Contribution to Brigan tine, NJ by State State Relative Contribution State Relative Contribution Connecticut 0.05% North Carolina 0.75% Washington, DC 0.05% Ohio 0.52% Delaw are 27.83% Pennsylvania 13.63% Maryland 7.67% Virg inia 7.90% New Jersey 40.11% West Virginia 0.94% New Y ork 0.56% There are 278 units identified within the Area of Influenc e which c ontribute to the sulfate at Br iga ntine, including 15 units in West Virg inia. Twenty -five ( 25) units have a relative c ontr ibutio n gre ate r t

ha n 0 .5 % a nd c ontr ibute 70 .7 5% to s ulf ate , n on e of

wh ic h a re loc ate d in Wes t V ir g ini a . T e n ( 10 ) u nit s h ave re lativ e c ontr ibutio n gre ate r t ha n 1 .0 % a nd c ontr ibute 59.92% to sulfate, none of which a re loc ated in West Virg inia. Capitol Cement, Kiln 7 (Emissi onW est V i rgi ni a R egi o nal H aze S IP Pag e 79

Poi nt 0 10 ), wi th a

re lativ e c ontr ibutio n o f 0 .1 0% , h a d th e larg e st r e lativ e c ontr ibutio n to su lf ate at B rig antine of a

ny West Virginia sour ce. T his source is addr essed in Section 7.7. Gre at Smokey

Mountains, NC and TN

2 Figure 7.5.d.4. Gre at Smokey Mountains, NC and TN 2002 (le ft) and 2018 ( rig ht) SO distance

4 we ig hte d e mis sio ns * SO e xtinc tio n w e ighte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-5. 2018 SO P oint Sou rce Contribution to Gre at Sm okey Mountains, NC and T N by State State Relative Contribution State Relative Contribution Alabama 3.12% South C arolina 2.44% Geor g ia 7.74% Tenne ssee 74.50% Kentucky 1.02% Virg inia 2.34% North Carolina 7.43% West Virginia 0.54% Ohio 0.87% There are 193 units identified within the Area of Influenc e which c ontribute to the sulfate at Gre at Smokey

Mountains, including f

ive (5) units in West Virg inia. Thirty -eight (38) units haveW est V i rgi ni a R egi o nal H aze S IP Pag e 80

a re lative contribution g reater than 0.5% and contr ibute 79.85% to sulfate, inc luding zero ( 0) un its in We st V ir g ini a . T e n ( 10 ) u nit s h ave re lativ e c ontr ibutio n gre ate r t ha n 1 .0 % a nd c ontr ibute 61.00% to sulfate, inc luding zero ( 0) units in W est Virg inia. J ames River F ace , VA

2 Figure 7.5.d.5. J ames River F ace , VA 2002 (le ft) and 2018 ( rig ht) SO distance w eig hted 4 e mis sio ns * SO e xtinc tio n w e ighte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-6. 2018 SO P oint Sou rce Contribution to Jame s River F ace, VA by State State Relative Contribution State Relative Contribution Washington, DC 0.02% Pennsylvania 1.59% Delaw are 0.59% South C arolina 0.89% Kentucky 2.02% Tenne ssee 1.69% Maryland 3.12% Virg inia 62.08% North Carolina 9.31% West Virginia 15.10% Ohio 3.60%W est V i rgi ni a R egi o nal H aze S IP Pag e 81

There are 336 units identified within the Area of Influenc e which c ontribute to the sulfate at J ames River F ace , including 66 units in West Virg inia. Thirty -nine (39) units have a r elative c ontr ibutio n gre ate r t

ha n 0 .5 % a nd c ontr ibute 49 .9 2% to s ulf ate , in c lud ingte n ( 10 ) u

nit s in Wes t Virg inia, nine (9) of

which ar e EGU s. Twelve ( 12) units have r elative c ontribution gre ater than 1.0% and c ontribute 67.91% to sulfate, inc luding thre e (3) units in W est Virg inia, all of which are EGUs. L inville Gorg e, NC

2 Figure 7.5.d.6. L inville Gorg e, NC 2002 (lef t) and 2018 (r ight) SO distance w eig hted emissions 4 * SO e xtinc tio n w e ig hte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-7. 2018 SO P oint Sou rce Contribution to Li nvill e G orge, N C by State State Relative Contribution State Relative Contribution Alabama 1.35% South C arolina 3.93% Geor g ia 1.98% Tenne ssee 36.29% Kentucky 0.90% Virg inia 16.53% North Carolina 34.76% West Virginia 2.49% Ohio 1.77% There are 207 units identified within the Area of Influenc e which c ontribute to the sulfate at L inville Gorg e, includingsevente en (17) units in W est Virg inia. Thirty -five ( 35) units have a re lativ e c ontr ibutio n gre ate r t ha n 0 .5 % a nd c ontr ibute 74 .6 9% to s ulf ate , in c lud ingon e (1 ) u

nitW est V i rgi ni a R egi o nal H aze S IP Pag e 82

in West Virginia, whic h is an EGU. Eighteen ( 18) units have r elative c ontribution gre ater than 1.0% and c ontribute 62.57% to sulfate, inc luding zero ( 0) units in W est Virg inia. Mammoth Cave, KY

2 Figure 7.5.d.7. Mammoth Cave, KY 2002 ( left) a nd 2018 (right) SO distance w eig hted 4 e mis sio ns * SO e xtinc tio n w e ighte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-8. 2018 SO P oint Sou rce Contribution to Mam m oth Cave, K Y by State State Contribution State Contribution Alabama 4.33% Mississ ippi 0.04% Geor g ia 1.79% Missouri 0.53% I llinois 0.53% Ohio 3.95% Indiana 21.22% Tenne ssee 13.46% Kentucky 53.60% West Virginia 0.54% There are 261 units identified within the Area of Influenc e which c ontribute to the sulfate at Mammoth Cave, includingfive (5) units in W est Virg inia. For ty -one ( 41) units have a

relative c ontr ibutio n gre ate r t

ha n 0 .5 % a nd c ontr ibute 66 .6 0% to s ulf ate , in c lud ingzer o ( 0) un its in We st Virg inia. Ninetee n (19) units have relative contribution g reater than 1.0% and contr ibute 52.63% to sulfate, includingzero (0) units in West Virginia.W est V i rgi ni a R egi o nal H aze S IP Pag e 83

Shenandoah, VA

2 F igur e 7. 5. d. 8. She na

nd oa h, VA20 02 (l e ft ) a

nd 20 18 (r ight) SO dis tanc e we ighte d e mis sio ns * 4 SO e xtinc tio n w e ig hte d r e sid e nc e tim e plo ts

2 Tabl e 7.5-9. 2018 SO P oint Sou rce Contribution to S henand oah, VA by State State Relative Contribution State Relative Contribution Washington, DC 0.06% Ohio 4.63% Delaw are 1.46% Pennsylvania 6.11% Kentucky 1.10% Tenne ssee 0.36% Maryland 23.99% Virg inia 38.33% North Carolina 3.97% West Virginia 19.85% New Y ork 0.16% There are 300 units identified within the Area of Influenc e which c ontribute to the sulfate at Shenandoah, inc luding 69 units in West Virginia. Thir ty -nine (39) units have a r elative c ontr ibutio n gre ate r t

ha n 0 .5 % a nd c ontr ibute 57 .5 4% to s ulf ate , in c lud ingthi rt e e n ( 13 ) u nit s in West Virginia, twe lve (12) of which ar e EGU s. Ninetee n (19) units have relative contribution g reater than 1.0% and contr ibute 44.29% to sulfate, inc luding se ven (7) units in W est Virg inia, six (6) of which ar e EGU s.W est V i rgi ni a R egi o nal H aze S IP Pag e 84

7.5.e. Specific Sourc e Ty pes in the Are a of I

nfluenc e for

Dolly Sods and Otter Cree k Wil derne ss Area s The ne x t step in the analy sis was to review the emissions inventories to determine the source c ate g or ie s, a s w e ll a s sp e c if ic so ur c e s, fo un d to have the g re ate st i mpa c t on vis ibi lity at D oll y 2 Sods and Otter Cree k Wil derne ss Area s. L ists of SO point sources found w ithin the Area of Influenc e for

eac h Class Iare a we re de veloped usingthe most curre nt (Ba se G) V I STAS 2002 base year and 2018 future y ear

emissions. For this purpose the Area of Influenc e wa s defined a s the counties with maxim um sulfate extinction weighte d residenc e time g reater than f ive (5). F or 2 SO source s within each Area of Influenc e, the f

ollowing attributes were define d for e ach ind ivi du a l un it:

• State, county , source (plant) a nd industry

identification code s 2 • SO emissions for 2002 and 2018

• 2018 control ef

ficienc y
• Distance to Class Iare as (de fined by centr oid of the Class Iare a)

• Emissions di vided by distance ( Q/d), a me tric that ac counts for disper sion of emissions over distanc e
• M a xim

um s u lf ate e xti n c ti o nw e igh te d

r e s id e n c e ti me ( Rtmax) The re view wa s conducte d in a top down fa shion starting w ith an analy sis of the major sourc e 2 c ate g or ie s in

e a c h SO Ar e a

of Inf lue nc e to d e te rm ine wh ic h ma jor c ate g or ie s h a d th e highe st residual c ontribution to t he ar ea in 2018. It was also important to identify reduc tions that are projec ted to occur

betwee n 2002 and 2018 within eac h cate g ory or at spec ific units. This allowed VI STAS states to determine if c erta in source c ateg ories or units that had yet to be contr olled under the future year

base c ase ha d the potential for r eduction. Onc e the highest source ty pes wer e identified, subc ateg ories within those source s ty pes we re r eviewe d. The c ontributions from

2 ma jor so ur c e c ate g or ie s to the 20 18 B a se G2 inv e nto ry fo r t he SO Ar e a

of Inf lue nc e fo r D oll y Sods are listed in Table 7.5.e -1.W est V i rgi ni a R egi o nal H aze S IP Pag e 85

T a b l e 7 . 5 . e . - 1 . 2018 Emissions C ontributions from Major Source Categ ories in the Ar ea of

In f lu e n c e f o r D o ll y So d s , WV This table indicates that for Dolly Sods, Electric Utilities and Industrial Boiler s are the two 2 major sourc e ca

teg ories contr ibuting to 2018 SO emissions in t he Ar ea of Influenc e, eve n afte r implementation of CAI R. Toge ther these two source cate g ories contr ibute 80 perc ent of the 2018 2 SO emissions to t he Ar ea of Influenc e for

Dolly Sods. Other Fue l Combust ion and Other

2 Ind us tr ia l Pr oc e sse s c omp ri se ano the r 1 1% of the 20 18 SO e mis sio ns . This table ca

n also be used to e valuate the major sourc e categ ories contr ibuting to emissions of x 3 NO , NH and PM emissions in 2018. For instance, highway vehicles a

nd off- road ve hicles ar e

x major sourc es of NO emissions, in additi on to electr ic utiliti es and industrial boilers. The source cate g ory “miscellane ous” (whic h includes agricultural sourc es and f ires) is the major

contributor 3 to N H and

pr ima ry PM. Th e e mis sio ns se ns iti vit ie s d isc us se d in se c tio n 7 .4 ind ic ate d v e ry sma ll x 3 benef its to controlling NO , NH and primar y PM emissi ons at Dolly

Sods, but i f these e missions wer e of c oncer

n, differ ent source cate g ories would nee d to be addr essed.

2 Th e c ontr ibutio ns to S O e mis sio ns in 2 01 8 f ro m th e thr e e highe st s ourc e c ate g or ie s, El e c tr ic Ut ili tie s, Ind

us tr ia l B oil e rs and

Othe r F ue l Co mbu sti on

have be e n f

ur the r b ro ke n o ut i nto su bc ate g or ie s. Ta

ble 7. 5. e -2 ind ic ate s su

bc ate g or ie s f

or the Ar e a of Inf lue nc e fo r D oll y

Sod s. 2 With in E le c tr ic Ut ili tie s, a ll the SO e mis sio ns are attr ibuta ble to c oa l- fi re d p ow e r p lants . Within Industrial Boiler s, most emissi ons are attributable to coa l-fire d boilers with lesser c ontributions from oil and g as boilers. Commerc ial and instituti onal coa l- and oil-fire d boilers have smaller c ontr ibutio ns .W est V i rgi ni a R egi o nal H aze S IP Pag e 86 2 Table 7.5.e -2. 2018 SO Emissions contributi ons from Major Sourc e Categ ories in the Ar ea

o f In f lu e n c e f o r D o ll y So d s , WV
These a naly ses indicate that West Virginia should consider what additional contr ol measure s for e le c tr ic uti lit ie s and

ind us tr ia l bo ile rs are re a so na ble . T he lis ts o f i nd ivi du a l so ur c e s h ave a lso be e n u se d to de te rm ine if ind ivi du a l so ur c e s in

oth e r s ourc e s c ate g or ie s a re ma jor c ontr ibutor s to 2 SO emissions in t he Ar ea of Influenc e. As discussed in Appendix H - Reasona ble Prog ress Eva luation and L ongTerm Strate g y , Section 5, the WVD AQ e le c te d to fo c us on tho se un its that c ontr ibute d at le a st o ne pe rc e nt t o s ulf ate visibil ity impairment at a g iven Class Iare a. F irst, the units with t he lar g er c ontribution toward visibil ity impairment would likely show an e nvironmental bene fit under a control eva luation, and WVDAQ would be able to use that environmental be nefit to requir e contr ols on a g iven unit. Second, ther e ar e seve ral re gulatory programs that use a

highe r threshold than one perc ent for e va luatio n thre sh old s. 1. The B ART rule spec ifies that a maximum i mpact of 0.5 dv is an a cce ptable thre shold for e sta bli sh ingsignif

ic anc e . T his thr e sh old

e qu ate s to ro ughly

a 5 p e rc e nt c ha

ng e in v isi ble perc eption. This same sig nificanc e leve l is used in the Prevention of Signif icant Deter ioration/ New Sourc e Reviewprogram for the visibili ty

air qua lity

related value.W est V i rgi ni a R egi o nal H aze S IP Pag e 87

2. The NO x SI P Call l aid out a sig nificanc e leve l for Section 126 petitions of 4 par ts per milli on (ppm), that beingthe level to which a State' s contribution to another state' s ozone problem wa s considere d signific ant. Four ppm repre sents approximately 3.75 perc ent of th e 1

- h o ur

o z o n e s t an d ar d , w h i c hw a s i np l a c e atth e t i m e E P Ap r o mu l g at e d th e N O x S IP Ca ll. 3. For the CAI R rule, a PM contribution of 0.2 ug/m3 was used to demonstrate a sig nificant impact, which is 1.3% of

the annua l PM2.5 st andar d of 15 ug/m3. 4. L a stl y , whe n huma

n h e a lthsta

nd ard s a re pr op os e d (NAAQ S) signif ic ant i mpa c t le ve ls (S I L S) are a ssi g ne d whic h a llo w s ourc e s to de te rm ine the ir signif ic anc e on a ir qu a lity in the ar ea a round their f

acilities. Sources that demonstrate that their "c ontribution" fr om the ne w o r m od if

ie d s ourc e s is le ss t

ha n th e se signif

ic anc e le ve ls d o n ot h ave to c omp le te any fu rt he r m od e lin g . T he SI L S r e pr e se nt a pe rc e nta g e of

the NAAQ S. After revie wingall aver ag ing pe riods for the c riteria pollutants, WVDAQ de termined that the one per cent thre shold we we re pr oposing f or re asonable further

progress wa s as protec tive or more prote ctive than the significant impac t levels. The most restric tive threshold identified wa s for NO x , which has a

NAAQS of 100 ug/m3 and a sig nificanc e leve l of 1 ug/m3, which repr esents one pe rce nt of the total. Finally , WVDAQ consider ed ava ilable re source s to evaluate the source s in eac h Class Iare a’s su lf ate Ao I . WV DA Q r e c ognize d th at th e re wa s n e ith e r s uf fi c ie nt t ime or

re so ur c e s ava ila

ble to evaluate all units within a give n AoI . There fore , a thre shold was nee ded to deter mine which units would be evaluated. Table 7.5.e-3 shows that a one pe rce nt contribution threshold capture s 2 g reater than 64% of the total point source SO contribution to Dolly Sods, while requiringan e va luatio n o f o nly

17 un its . T he ne xt 27% of c umu lativ e c ontr ibutio n ma y be attr ibute d to un its with individual contribution between 0.1% - 1% and w ould require analy sis of 97 additional units. The WVDAQ believe s that the one pe rce nt threshold is appropriate, g iven the contr ibution to t he tot a l vi sib ili ty

imp a ir me nt at e a c h Cl a ss I

are a , a nd the lim ite d r e so ur c e s ava ila ble to c on du c t th e un it- by -u

nit e va luatio n f

or re a so na ble pr ogre ss.

2 Tabl e 7.5.e- 3. Num bers and Pe rce ntages of 2018 S O Em ission Units that Contribute

to Sulfat e Vi sibi lity I m pai r m e ntatDo llySods Dolly Sods # Units Contribut ing >1% 17 Perce ntag e of total Contribution 64.28% # Units Contribut ing >0.1%, but < 1% 97 Perce ntag e of total Contribution 27.33%2

Tabl e 7.5.e- 3. Num bers and Pe rce ntages of 2018 S O Em ission Units that Contribute

to Sulfat e Vi sibi lity I m pai r m e ntatDo llySods Dolly Sods

W est V i rgi ni a R egi o nal H aze S IP Pag e 88 # Units Contribut ing <0.1% 209 Perce ntag e of total Contribution 8.38%

2 7.6. Evalu ating Control Option s for Sp ecific SO Em ission s Sou rce s in E ach Are a of Inf luence The ne x t step was to identify emission reductions that have alre ady occur red w ithin each sourc e cate g ory and at spe cific units. Unit level tables of emission comparisons from 2002 to 2018 we re develope d, allowingVI STAS states to review e x isting emission reduc tions. These tables assig ned future y ear

control tec hnology

from I PM forec astingand state modifications for EGU and fr om c ontr ol e ff

ic ie nc y ta ble s f or no n- EGU p oint so ur c e s. Once emission control profiles for specific units were de fined, the ne x t step was to deter mine wh at, i f a ny , a dd iti on a l c ontr ol m e a sure s w ou ld f e a sib ly be ava ila ble , a nd to a ssi g n c os ts t o th os e c ontr ol m e a sure s. F or EG Us , th e 20 18 I PM f

ile s u se d b y

VI STA S f

or EGU s ourc e s w e re ob ta ine d a nd matc he d to the 2018 base case inventory of EGU sour ces. This step wa s conducte d to ensure that inc re me nta l c ontr ols a ssi g ne d to the se so ur c e ty pe s d id n ot d

up lic ate e xistin gba se c a se a ssumpt ion s. For non-EGUs, V I STAS used EPA’s AirControlNET databa se, modified for the VI STAS emission inventories. The c ore A irControlNET is a re lational databa se sy stem in which control te c hn olo g ie s are linke d to so ur c e s w ith in E PA e mis sio ns inv e nto ri e s. Th e sy ste m c onta ins a databa se of c ontrol measure applicability , eff iciency , and cost infor

mation for re ducing emissions. The control mea sure data file in AirControlNET include s not only the technology ’s c ontr ol e ff

ic ie nc y and c a lc ulate d e mis sio n r e du c tio ns fo r t hat so ur c e , b ut a lso e sti mate s th e c os ts (annua l and ca

pital) for a

pplication of the c ontrol measure
.
2 Us ingthe inv e nto ri e s id e nti fi e d a bo ve , V I STA S r an e ve ry

ava ila ble SO c ontr ol s tr ate g y

in Ai rC ontr olN e t a g a ins t th e EGU a nd no n- EGU p oint so ur c e inv e nto ri e s to de ve lop a ma ste r l ist of incre mental control strateg ies for e ach unit in the VI STAS 36-km domain. For source s within the Area

of Influenc e for

Dolly Sods, the master list of increme ntal control measure s was sorted to de termine the c osts of increme ntal control mea sures. The se data wer e c omb ine d in

a ma ste r s pr e a ds he e t w iththe dis tanc e fr om t he e mis sio n r e le a se po int to t he Cla ss I are a I MPROVE monit or (in km), the 2018 r esidual emissions and the distance (Q/d) or square d 2 distance ( Q/d^2), and the nor malized 2018 SO point source e missions times distance-w eig hted residenc e time (RTMAx) values for the county in which the emission rele ase point wa s located.W est V i rgi ni a R egi o nal H aze S IP Pag e 89

West Virginia use d these to eva luate contr ol measure s and costs as pa rt its statutory

fac tor ana ly sis fo r r e a so na

ble fu rt he r p ro g re ss. The re g ional haze rule r equire s that states consider the followingfac tors and demonstrate how the se fa c tor s w e re take n into c on sid e ra tio n in se le c tin gthe re a so na

ble pr ogre ss g oa l:

• the costs of c ompliance

• the time nece ssary

for c ompliance

• the ene rg y

and non-a ir quality

environmenta l impacts of compliance , and

• the re ma ini ngus e fu l li fe of any po te nti a lly

a ff e c te d s ourc e s. 7.6.a. Cost of Compl iance As define d in Section 7.5.e, coa l-fire d elec tric utiliti es and c oal-fir ed industrial boilers we re the 2 larg e st s ourc e c ate g or ie s c ontr ibutin gto S O in 2 01 8 in the Ar e a of Inf lue nc e de fi ne d f

or Do lly Sod s. Ind us tr ia l bo ile rs us ingoil and c omm e rc ia l and

ind us tr ia l bo ile rs us ingc oa l ha d s ma ll 2 contributions to SO in 2018. Sulfur diox ide emissions from utilit y , industrial, commerc ial or instituti onal boilers ca n be c ontr oll e d e ith e r b y

sw itc hin gthe fu e l so ur c e s to a low e r s ulf

ur fu e l c onte nt o r b y

ins ta lli ngpo stc omb us tio n c ontr ols . Co sts va ry by fu e l so ur c e and

bo ile r t y pe and re qu ir e so ur c e sp e c if

ic an a l ys e s f o r a c c ur a c y. B itumin ou s c oa l is c omm on ly burn e d in

bo ile rs in t

he e a ste rn

Un ite d St ate s. Swi tc hin gto 2 ano the r b itumin ou s c oa l w ithlow e r s ulf ur c onte nt o r b le nd ingbit

umi no us c oa ls c an r e du c e SO emissions with t he lea st impact to boiler perf ormanc e. While sub-bituminous coal ha s a lower sulfur conte nt than bituminous coal, it also has a lower he at content a

nd so more coa l has to be burned to g ener ate the sa

me ene rg y

output. For boilers that are desig ned for bituminous coal only a small fra ction of the fue l can be switched to low sulfur sub-bituminous coal without aff ectingboiler per formanc e. Costs of boiler modifications to ac commodate low sulfursubbituminous coal may

make switchingto sub-bituminous coal impra ctical. Contrac t initi ation and te rm inatio n c os ts, dif

fe re nti a l f ue l pr ic e s a

nd he at r ate s, tr ans po rt atio n c os ts, and mod if ic atio n to boiler oper ations, fuel handlingand wa ste handlingsy stems will have to be conside red on a case -by -ca se basis for

eac h source
. Flue g as desulfur ization i s the common post-combustion control for c oal-fir ed boilers. F lue g as is passed through an absor bent for sulfur

diox ide (g ener ally

limestone or lime) in either a wet scrubbe r, dry

scrubbe r or spra

y dryer. Acalc ium sulfate by -produc t is produced that may be further proce ssed to produce g y

psum as a commer cial by -produc t. Costs of flue g as de su lf ur izatio n in c lud e ini tia l c on str uc tio n c os ts a

nd on g oin gop e ra

tio na l and ma int e na nc e c os ts for the a bsorber tower, sor

bent handling , and wa ste product ha ndling f acilities. Costs per ton va ry wi th b oil e r s ize , ty

pe and fa c ili ty sit ingc on sid e ra

tio ns . For oil-fired boiler s, lower sulfur oil may be an option. Costs need to consider differ ential fue l pr ic e s and

he at r ate , b oil e r m od if

ic atio ns , f ue l ha nd lin gc os ts a

nd ma int e na nc e c os ts. Conceptually , post-combustion controls could be used f or oil boilers, but there is little prec edent fo r s uc h in sta llatio ns .W est V i rgi ni a R egi o nal H aze S IP Pag e 90

7.6.b. Time Nec essar

y for Compliance For fuel switching , the time nece ssary to terminate e x isting fue l contrac ts and initiate new contra cts nee ds to be consider ed. Ge nera lly two to three year s may

be re quired. Installation of post-combustion controls will require 3 or more year s dependingon marke t availability of labor and mater ials and utility

sy stem-wide pr iorities. Time nece ssary for c ompliance will nee d to be re fi ne d f or sp e c if ic so ur c e s. 7. 6. c . En e rgy

and No n- Ai r Q ua lity

Envir onme nta l Impa c ts Swi tc hin gto l ow e r s ulf

ur fu e l or ins ta lli ngpo st- c omb us tio n c ontr ols may re du c e bo ile r h e at r ate and ene rg y

output. Scrubbers a

nd spray dryers will re quire a dditional safeguards f or fue l ha nd lin gand

wa ste ha nd lin gsy ste ms t o avo id a dd iti on a l no n- a ir qu a lity e nv ir onme nta l im

pa c ts su c h a s in c re a se d e ff

lue nts in w a ste wate r d isc ha rg e s a nd sto rm wate r r un of f. Th e se fa c tor s w ill n e e d t o b e c o n s i d e r e d

s p e c i f i c a l l y f o r i n d i v i d u a l s o ur c e s . C ar

b o nd i o x i d e i s e m i tt e d

a s a b ypr od uc t of fl ue g a s d e su lf

ur izatio n, the re fo re , im

pa c ts o f i nc re a se d c arb on dio xide e mis sio ns wi ll ne e d to be c on sid e re d, pa rt ic ula rl y if c arb on e mis sio ns are lim ite d in

the futur e un de r c lim ate c ha ng e mit ig atio n s tr ate g ie s. 7.6.d. RemainingUseful L ife Remaininguseful life is spec ific to the unit for whic h controls ar e conside red a nd needs to be a dd re sse d o n a

c a se -b y -c a se ba sis
. 7. 7. Whi c h Co nt r ol M e as ur e s R e pr e se ntRe as ona ble P r og r e ss fo rIndi vid ualSour c e s? Th e fo llo wi ngsumma

ri zes the pr oc e ss f

or de te rm ini ngre a so na

ble pr ogre ss f

or Wes t V ir g ini a source s. For a

detailed discussion of the r easona ble progress a ssessments for a ll unit’s contribution g reater than one perc ent (1%) to any Class Iare a in West Virg inia or in neighboring states, please see A

ppendix H. Step 1: Determine pollutants of c oncer n. VI STAS evaluated the specie s contribution on the 20 perc ent worst day s and conc luded that sulfate a ccounte d for greater than 70% of the visibility impairingpollution. The VI STAS S tates 2 c on c lud e d th at c ontr oll ingSO e mis sio ns wa s th e a pp ro pr iate ste p in

a dd re ssi ngthe re a so na

ble p r o g r e s s a s s e s s m e n tf

o r 2 0 1 8
. T h e V IS T A S f

i n d i n g s w e r e c o n s i s t e n tw i thth e f i n d i n g s o f S A M I. As you may

rec all, SAMIconfirme d that sulfate pa rticles ac count for the g reatest portion of the haze af fec ting Class I

are as in the Southern Appa lachian r eg ion and that these sulfates wer e

2 produce d in larg e par

t from SO emissions from coal c ombustion. Step 2: Determine which source sectors should be e valuated f or re asonable progress.

2 Be cause SO point source e missions in 2018 are projec ted to repr esent greater than 95% of the

2 total SO emissions inventory , the VI STAS S tates conc luded that ana ly ses should focus on EGU 2 and no n- EG U p oint so ur c e s o f S O e mis sio ns .W est V i rgi ni a R egi o nal H aze S IP Pag e 91

Ste p 3 : D e te rm ine if

CA I R is su ff

ic ie nt f or re a so na ble pr ogre ss r e g ard ingEGUs
.
2 The WVDAQ c alculate d the fra ctional contribution from a ll emission uni ts within t he SO AoI for a g iven Class Iare a and ide ntified the EGUs w ith a contribution of one pe rce nt (1%) or more to the sulfate visibility

impairment at that Class Iare a. Ag ain, it should be noted that the AoI analy ses wer e not source apportionment modelinganaly ses, but wer e base d on the magnitude of emissions from a sourc e, its distance to the Class I are a(s) of conc ern, a

nd the sulfate e x tinction weighted re sidence time plots, developed usingback tr ajec tories. [Note: No units were ide ntified with a contribution of $ 1% to sulfate visibility impairment within the AoIfor B rig antine, NJ.]

2 The West Virg inia Division of Air Quality (WVDAQ) e valuated the amount of SO reduc tion fr om t he EG Us pr oje c te d d

ue to C AIR. T he EGUs in We st V ir g ini a are pr oje c te d to re du c e the ir 2 2 20 02 SO e mis sio ns by

47 pe rc e nt b y

20 09 and a tot a l of 78 pe rc e nt b y

20 18 . T he pr oje c te d SO emission reductions ar e the r esult of CAI R which relies on unc hecke d tradingand the use of banked T itle I V allowanc es. Ther efor e the r eductions ar e not direc tly enfor cea ble. Howe ver, WVDAQ has issued pe rmits for the installation of controls, and c onfirmed the c ommenceme nt of the installation of controls at the three plants (6 units) projec ted to reduc e emissions by 2009, and one of

the plants (2 units) projec ted to control by 2018. In fac t, the controls on two of the plants (3 units) wer e oper ational by the summer of 2007. The additional controls projec ted by I PM on three plants ar e not as c erta in due to the rule’ s relianc e on the ba nking a nd tradingof 2 allowanc es. The WVDAQ intends to re-e valuate the I PM predictions of SO reduc tions for CAI R at the time of our fir st periodic re port in 2012 to ensure that the predic ted re ductions are ind e e d ta kin gpla c e wh e re the y

are e xpe c te d a

nd ne e de d. B a se d o n th e c on fi rm e d CA I R c ontr ols an d th e C A IR

c o n tr o l s p r e d i c t e d

b y IP M , W e s tV i r g i n i a

h a s c o n c l u d e d

th at , atth i s t i m e , C A IR constitutes rea sonable mea sures for West Virginia’ s EGUs duringthis first assessment period (betwe en base line and 2018). The WVDAQ notes that due to the c lose proxim ity of Mt. Storm and Nor th Bra nch Power

Stations t o Dolly

Sods and Otter Cree k, these plants ar e of pa rticular conce rn. Tabl e 7. 7- 1. EG U So ur c e s wit h Co nt r ibut ion $ 1% to any Class I Area P lan t I D P lan t P oin t ID Class I areas where Co nt r ibut ion $ 1 % 023-0003 Mount St orm Power Station 001 Dolly Sods, S henandoa

h 1 023-0003 Mount St orm Power Station 002 Dolly Sods, S henandoa

h 1 023-0003 Mount St orm Power Station 003 Dolly Sods, S henandoa

h 1 033-0015 Har

rison Power Station 001 Dolly Sods, S henandoa

h 1 033-0015 Har

rison Power Station 002 Dolly Sods, S henandoa

h 1 033-0015 Har

rison Power Station 003 Dolly Sods, S henandoa

h 1 023-0014 North B ranc h Power Station 001 Dolly Sods 2 023-0014 North B ranc h Power Station 002 Dolly Sods 2Tabl e 7. 7- 1. EG U So ur c e s wit h Co nt r ibut ion $ 1% to any Class I Area

P lan t I D P lan t P oin t ID Class I areas where Co nt r ibut ion $ 1 %

W est V i rgi ni a R egi o nal H aze S IP Pag e 92 073-0005 Pleasants Power Station 001 Dolly Sods, S henandoa

h 1 073-0005 Pleasants Power Station 002 Dolly Sods, S henandoa

h 1 061-0001 Ft. Mar

tin Power Station 001 Dolly Sods 3 061-0001 Ft. Mar

tin Power Station 002 Dolly Sods 3 079-0006 J ohn Amos Power Station 001 Dolly Sods, J ames River F ace 3 079-0006 J ohn Amos Power Station 002 J ames River F ace 3 079-0006 J ohn Amos Power Station 003 J ames River F ace , Shenandoah 3 053-0009 Mountaineer

Power Station 001 Dolly Sods, S henandoa

h 1

2 Cur r en t l y e qu i pped we t s cr u bber s w i t h SO c on t r ol e f f i ci en cy >90% 1
2 Ci r cu l at i n g Fl u i di ze d B ed ( CFB ) boi l er w i t h SO c on t r ol e f f i ci en cy >90% 2
2 Cons t r u ct i on h as c ommen ce d on w et s cr u bber s w i t h SO c on t r ol e f f i ci en cy >90% , t o be c ompl et ed 2008- 10 3 Ste p 4 : D e te rm ine wh ic h e mis sio n unit s w ou ld b e e va luate d b a se d o n im pa c t.

2 The WVDAQ c alculate d the fra ctional contribution from a ll emission uni ts within t he SO AoI for a g iven Class Iare a and ide ntified the non-EGUs with a contribution of one perc ent (1%) or mor e to t he su lf ate vis ibi lity

imp a ir me nt at th at Cla ss I

are a . [N ote : N o unit s w e re ide nti fi e d w ith a contr ibution of $ 1% to sulfate visibility impairment within the AoIfor B rig antine, NJ.] Tabl e 7. 7- 2. No n- EG U So ur c e s t o be Eva lua te d forRe as ona ble F ur th e rP r og r e ss P lan t I D P lan t P oin t ID Class I areas where C o nt r ib ut io n > 1 % 003-006 Capitol Cement 010 Shenandoah
Ste p 5 : E va luate the fo ur fa c tor s. Eac h emission unit i dentified in Step 4 above was e valuated usingthe statutory and re gulatory fa c tor s o f: 1) c os t of

c omp lia nc e , 2 ) t ime ne c e ssa

ry fo r c omp lia

nc e , 3 ) t he e ne rgy and no n- a ir quality environmenta l impacts of compliance , and 4) the rema ining use ful life of the emissions unit. Whil e all four

(4) f actor s should be considere d, any one of the m may

provide a compelling rea son to ex clude a

control mea sure. This is particula rly true of c ontrol cost. I f no cost ef fec tive c ontr ol e xists , th e n it

is n ot r e a so na

ble to r e qu ir e c ontr ols . F or the purp os e of

e va luatin gthe c os tW est V i rgi ni a R egi o nal H aze S IP Pag e 93

for the r easona

ble progress a ssessment in this first reg ional haze SI P, W VDAQ be lieves it is not equitable to re quire non-E GUs to bea r a greater e conomic burde n than EGUs f or a g iven control str ate g y . T hat is , th e a g e nc y do e s n ot b e lie ve it i s r e a so na ble to p ote nti a lly

su bje c t no n- EGUs to control mea sures that re quire a highe r cost per

ton than that which has be en found r easona ble for 2 EGUs. In order

to determine the cost eff

ectivene ss of SO controls for E GUs, the WVDAQ r e v ie w e d E PA ’ s CA I R a

n a ly s is
. [S e e E PACA I R a n a ly s is at 7 0 F R 2 5 2 0 1

- 2 5 2 0 8 , 1 2 M A Y 2 0 0 5 ]. 2 Th e CA I R ana ly sis is a

re c e nt a nd c omp re he ns ive re vie w o f t he c os t- e ff

e c tiv e ne ss o f E GUSO c ontr ols , whic h is a

pp ro pr iate to c on sid e r a s a be nc hma rk in d e te rm ini nga

re a so na ble c os t 2 e ff e c tiv e ne ss f

or SO c ontr ol f

or purp os e s o f t

he re a so na

ble pr ogre ss a sse ssm e nt. In th e CA I R pr e ambl e EPAno te s th at:

2 “The SO annual co n t ro l cost s that were prese nted in the CAI R NPR [ Notice of Proposed Rulemaking ] wer e avera g e costs of $700 per ton and $800 per ton for y ear s 2010 and 2015, respectively , and marg inal costs of $700 per ton and $1,000 per ton fo r y e a r s 2 0 1 0 an d

2 0 1 5
. Ina d d i t i o n , th e N P R i n c l u d e d th e r e s u l t s o f

s e n s i t i v ity 2 an al y s es th atex am i n edco s t s o f t h e p ro p o s edS O c o n t r o l s b as edo nth e E n er gy Information Administration’s pr ojections for elec tricity g rowth and natura l g as 2 price s. These se nsitivi ty analy ses showed ma rg inal SO control costs of $900 pe r ton and $1,100 per

ton for year s 21010 and 2015, re spectively .”

2 EPA g oes on to addre ss the analy sis of SO emission reduction re quirements for the CAI R final rule.

2 “ (I ) R e fe re nc e L ist s o f C os t- Ef fe c tiv e SO Contr ols

“ F or tod ay ’s a c tio n, EPAup date d th e re fe re nc e lis t of c ontr ols inc lud e d in the NPR 2 of the ave ra g e and

marg ina l c ots pe r ton of re c e nt S O c ontr ol a c tio ns . T he EPA sy stematically d evelope d a list of cost information fr om both rece nt actions and proposed a ctions. The EPA compiled c ost information for a ctions t ak en by the Ag e nc y , a nd e xami ne d pub lic c omm e nts su bmi tte d a ft e r t

he NPR wa s p ub lis he d, to ide nti fy a ll ava ila ble c ontr ol c os t in fo rm atio n to pr ov ide th e update d r e fe re nc e lis t for today ’s pre amble. The updated ref ere nce list includes both ave rag e and ma rg inal c os ts o f c ontr ol, to whic h E PA c omp are s th e CA I R c ontr ol c os ts, and the l ist repr esents what regulatory decision maker s and/or the public believe s are the control c os ts.

2 “ Ta ble IV- 3 p ro vid e s ave ra g e c os ts of SO c ontr ols . T his ta ble inc lud e s ave ra g e c os ts 2 for r ece nt BACT pe rmitting dec isions for SO . Under EPA’s NewSource Revie w (NSR) program, if a company is pla nning to build a new plant or modify an existi ng pla nt s uc h th at a

signif ic ant n e t in c re a se in e mis sio ns wi ll o c c ur , th e c omp any mus t obtain a NSR permit that addre sses controls for air emissions. B ACT is the ty pe of control re quired by

the NS R p rogram for existi ngsource s in attainment are as. The BA CT decisions are deter mined on a case-by-ca se basis, usually by State and loca l pe rm itt inga g e nc ie s, and re fl e c t c on sid e ratio n o f a ve ra g e and

inc re me nta l c os t e ff e c tiv e ne ss. Th e se de c isi on s ar e re le va nt f or EPA ’s re fe re nc e lis t of

ave ra g e c os tsW est V i rgi ni a R egi o nal H aze S IP Pag e 94 2 of SO controls, bec ause the y repres ent cost-e ffe ctive contr ols that have be en demonstrated.

2 TA B L E IV- 3. – A ve ra g e Cos ts p e r T on of Annu a l SO Contr ols

2 SO c ontr ol a c tio n Av e ra g e c os t per ton Be st Available Control Tec hnology (B ACT) De terminations $400-$2,100 1 Nonroa d Diesel Eng ines and F uel $800 2 Proposed B est Available Retrofit Tec hnology (B ART) for Electric Power Sec tor $2,600-$3,400 3

T h e se nu m b e rs re flec t a r a n g e o f co st-effe c tive n e ss d a ta e n te re d into E P A ’s 1
2 R A C T / B A C T / L A E R C l ea ringho use (R B L C ) f o r ad d

- o n S O co ntro l s ( www . ep e. go v/ t t n/ cat c/ ). W e i dent i f

i ed act i ons i n t he dat a bas e f or l ar ge, ut i l i t y s cal e, coal

- f i r ed bo i l er un i t s f

or w hi ch cos t e f f ec t i v en es s da t a we r e r epor t ed. T h e r an g e of c os t s s h own h er e i s f

or boi l er s r an g i n g f

r om 30M W t o a n e s t i mat ed 790 M W ( we u s ed a con v er s i on f ac t or of 10 mmB t u / h r = 1 M W f or u n i t s f or w h i ch s i ze w as r epor t ed i n mmB t u / h r )
. Emi s s i on l i mi t s f or t h es e ac t i on s r an g ed f r om 0
. 1 m m B t u t o 0.27 l b / m m B t u. A d d

- o n co nt rols repo rt ed f o r t hese uni t s are dry o r wet s crubb ers ( i n on e ca s e wi t h a dded al k al i a n d i n on e ca s e wi t h a ba g h ou s e)
. W h er e t h e dol l ar - y ea r w as n ot r epor t ed we a s s u med 1999 dol l ar s . T h e cos t r an g e pr es en t ed i n t h e N PR w as $500- $2, 100- t o d ay’ s range i ncl ud es add i t i o nal B A C T co st s t hat were ent ered i nt o t he cl eari ngho use af t er t he N PR w as pu bl i s h ed. Cont r ol of

Emi s s i on s of

Ai r Pol l u t i on f

r om N on r oad D i es el Eng i n es a n d Fu el ; Fi n al Rul e ( 69 2 FR 39131; J u n e 29, 2004)
. T h e v al u e i n t h i s t abl e r epr es en t s t h e l on g - t er m c os t pe r t on of e m issio n s re d u c e d fro m the to ta l f u e l an d e n g ine s p ro g ra m (c o st p e r to n o f em issio n s re d u c e d in t he y ear 20 3 0 ). 19 9 9 $ pe r t o n. T he E P A I P M mo del i ng 200 4, av ai l abl e i n t he do cke t
. T he E P A mo del ed t he R egi onal H aze 3 Requ i r eme n t s a s s ou r ce s pec i f i c l i mi t s ( 90 pe r ce n t SO 2 r edu ct i on of 0. 1 l b/ mmB t u r at e; e x ce pt t h e f i v e s t at e W RAP r eg i on f or w h i ch w e di d n ot mode l SO 2 c on t r ol s be y on d w h at i s don e f or t he W R A P cap i n t he b ase case m o d el i ng). E st i m at ed average co st s b ased o n t hi s m o d el i ng are $ 2 , 6 0 0 pe r t o n i n 20 1 5 and $3 , 4 0 0 pe r t o n i n 20 2 0
. 19 9 9 $ pe r t o n. “Ta ble IV-4 provide s the marg inal cost per ton of rec ent State and re g ional decisions

2 fo r a nnua l SO c ontr ols
.

2 Ta ble IV- 4. – M arg ina l Co sts pe r T on of Annu a l SO Contr ols

2 SO c ontr ol a c tio n Ma rg ina l c os t per ton New H ampshire Rule $600 1

2 WRAP Reg ional SO TradingRule $1,100-$2,200 22 Ta ble IV- 4. – M arg ina l Co sts pe r T on of Annu a l SO Contr ols

2 SO c ontr ol a c tio n Ma rg ina l c os t per ton

W est V i rgi ni a R egi o nal H aze S IP Pag e 95

T h e E P A IP M b a se c a se m o d e ling A u g u st 2 0 0 4 , av a ilab le in the d o c k e t. (1 9 9 9 $ p e r to n ). W e 1
2 m o d el ed N ew H am p shi re’ s Stat e B i l l E N V
- A 2 9 0 0 , whi ch cap s SO em i ss i o ns o f al l exi st i ng f o ssi l st ea m unit s.

2 " A n A ss ess m ent of C ri t i cal M ass f o r t he R egi o nal SO T rading P rogram , ” p repa red f o r t he 2 W es t er n Regi onal Ai r P ar t ner s hi p M ar ket T r adi ng Foru m b y I C F C ons ul t i ng G r oup, Sept emb er 27 , 200 2, av ai l abl e i n t he do cke t
. T hi s an al y s i s l oo ked a t t he i mp l i cat i ons of one or mo r e St at es 2 cho o si ng t o op t
- o ut of t he W R A P regi o nal SO t rading p rogram . (19 9 9 $ pe r t o n).

2 “(I I ) Cost Effec tiveness of the CAI R Annual SO Reductions

2 “. . .Ta ble IV-5 provide s the avera ge an dmarg inal costs of annua l SO reduc tions un de r the CA I R for 20 10 and 20 15 .( Whe n pre se nti ng e sti mate d CA I R c ontr ol c os ts i ns e c t i o nIVo f

th i s p r e amb l e , E P A u s e s “ M a i nC a s e ” t o i n d i c at e th e p r i m ar y C A IR I PM analy sis, as di ffe rentiate d from other I PM analy ses such a s sensitivit y runs used to examin e the imp a c ts of va ry inga ssumpt ion s a

bo ut n atur a l g a s p ri c e and e le c tr ic g rowth.)

2 Table IV-5. – Estimated Costs per Tons of SO Controlled Under CAI R, Cap

L evels B eg inning in 2010 and 2015 1 Ty pe of c ost effe ctiveness 2010 2015 Aver ag e Cost – Main Case $500 $700 Marg inal Cost – Main Case 700 1,000 Sensitivi ty

Analy sis: Marg inal Cost Using EIA Growth and Natura l Gas Prices 800 1,200

T he E P A IP M m o d el i ng 20 0 4 , avai l ab l e i n t he d o cket
. 19 9 9 $ pe r t o n. 1

2 2 “The se estimated SO control costs under the CAI R ref

lect annual EGU S O caps of 3.6 milli on tons in 2010 and 2.5 mill ion tons in 2015 wit hin the CAI R reg ion. . . .

2 “ (IV) Cos t E ff

e c tiv e ne ss: Marg ina l Co st C ur ve s f or SO Contr ol “. . . F igur e IV-1 shows that the “knee ” in the 2010 mar g inal cost-ef fec tiveness cur

ve 2 – th e po int

wh e re the c os t of c ontr oll ing a ton of SO fr om EGUs is i nc re a sin gat a noticeably

highe r rate – appe ars to occ ur at about $2,000 per ton of S O2. Figure IV2 shows that the “kne e” in the 2015 ma rg inal cost-ef

fec tiveness cur

ve also a ppear s 2 to occur at about $2,000 per

ton of SO . . . . ”W est V i rgi ni a R egi o nal H aze S IP Pag e 96W est V i rgi ni a R egi o nal H aze S IP Pag e 97 2 After revie w of EPA’s CAI R cost analy ses the WVDAQ de termined that the CAI R SO control 2 c os ts v ary by y e ar o f a na ly sis (2 01 0 v s. 20 15 ) a

nd may ra ng e fr om $ 40 0 to $3 ,4 00 pe r t on SO remove d. Ultimately , EPA found a c onsistent marg inal cost for both year s at $2000 per ton, wh ic h WV DA Q b e lie ve s e sta bli sh e s an a

pp ro pr iate be nc hma

rk a g a ins t whic h c os t e ff

e c tiv e ne ss may be eva luated for

rea sonable progress. Dur ing the curr ent re asonable progress a ssessment, no non-EGU units in West Virg inia wer e identified f or additional control be cause no measure s were found to be c ost effe ctive. Th e on ly no n- EG U i de nti fi e d a s c ontr ibutin gg re ate r t ha n 1 % t o s ulf

ate vis ibi lity

imp a ir me nt at a Class Iare a wa s Capitol C ement, with a 1.27% contribution to sulfate visibilit y impairment at Shenandoah. Capitol Cement C a p i t o l C e m e n ti s a

p o rt l an d c e m e n tm anu f

a c tur i n gf a c i l i ty l o c at e d i nM art i n s b ur g , W V
. In 20 02 , E SSRO C a c qu ir e d Ri ve rt on Inv e stm e nt C or

po ratio n whic h o pe rate d Ca pit ol C e me nt. Capitol Cement then applied for a permit to modernize operations at the fac ility by repla cing cer tain exis ting ope rations and incr easingovera ll plant production capa city . The moder nization projec t included the re place ment of two existi nglong w et proc ess ce ment kilns (Kilns 8 and 9) and assoc iated clinker

cooler s with a modern lar g e pre calc iner kiln sy stem and re lated equipment. Kiln 7 (point I D 010, identified a bove), a long w et proc ess kiln, remaine d at the fac ility and is used to make a

limit ed amount of c linker, includingcer tain specia lty

(colore d) clinkers. The moder

nization project at Capitol Cement was subject to both minor source and major sour ce permitting r equire ments, and wa s require d to underg o PSD permitti ng . Permit R14-026 was 2 issued for the pr oject on June 2, 2005. Under the c onditions of permit R14-026 the SO limitfor 2 Kiln 7 was re duced to 900 tpy ; actual SO emissions in 2002 from Kiln 7 were 1,247 tons a nd 2 projec ted SO emissions for 2018 wer e 1,717 tons. [S ee A ppendix H for a copy

of the pe rmit and fac t sheet.] Although only

Kiln 7 was identified a s requiringa Rea sonable Progress a naly sis, the WVDAQ de te rm ine d th at th e ne w p re he ate r k iln sh ou ld a lso be re vie we d w ithre sp e c t to re a so na ble 2 p r o g r e s s , s i n c e th e a l l o w a b l e S O e m i s s i o n s f

r o mth e n e wl ar g e p r e h e at e r k i l nar e 4 , 4 2 5
. 8 t

p y. The fa cility

underw ent PSD permitting for CO and VOC, and B est Available Control 2 Tec hnology

(B ACT) wa s require d for CO a nd VOC. Howe ver, B ACT for SO was not re quired un de r P SD n e tti nga llo wa nc e s. Cos t of Com pli anc e - We t K iln VI STAS contracte d with Alpine Geophy sics to evaluate control options and costs for sour ces wi thi n th e Ao Ifo r t

he Cla ss I are a s o f c on c e rn . A lpi ne us e d E PA’ s A ir Contr olN e t so ft wa re to evaluate control options and costs for controls on Kiln 7 at Capitol Cement. The control option identified wa s flue g as desulfur ization (FGD) w ith a cost eff ectivene ss of $25,266 per ton. [See Appendix H.] Sin c e the c os t of

c omp lia nc e fo r a n F GD on Ki ln 7 is m or e tha

n 1 2 ti me s gre ate r t

ha n th e c os t e ff e c tiv e ne ss b e nc hma

rk , a nd the fa c ili ty

ha s ta ke n p e rm it l imi ts t o r e du c e e mis sio ns and lim itW est V i rgi ni a R egi o nal H aze S IP Pag e 98 2 op e ratio ns of

Ki ln 7 , th e WVD AQ de te rm ine d th at th e re a re no c os t e ff

e c tiv e SO c ontr ols available
. Cost of Compl iance - Prec alciner Sy stem The Mid-Atlantic/Nor

theast Visibility

Union (MANE- VU) RPO contra cted with MACTEC, Inc. to e va luate c ontr ol o pti on s and the fo ur statut or y fa c tor s f or a sse ssi ngRe a so na

ble Pr ogre ss Go a ls ( RPG s) fo r r e g ion a l haze i n th e MA NE -VU r e g ion . T he pr oje c t r e po rt [ Assessment of Reasonable Progre

ss for Regional Haze In MANE- VU Class I Areas , dated July 9, 2007] 2 summarizes the control measur es ava ilable for SO emissions from elec tric utiliti es, industrial boilers, kilns, residential heating oil and wood c ombustion. For kilns, the repor t identifies four 2 c ontr ol o pti on s f

or SO fo r c e me nt k iln s:

• L imi tin gsu lf

ur c onte nt i n r aw m ate ri a ls a nd fu e l,

• Dry Flue G as De sulfurization - Spray Dryer Absorption (Dry

FGD ),

• Wet Flue Ga s Desulfurization (Wet FGD ), and

• Advanc ed F lue Gas D esulfurization (Advanc ed F GD).

2 Emissions of S O from ce ment kilns are pr imarily deter mined by the content of

sulfur in the ra

w mate ri a ls a

nd no t f ro m th e su lf

ur c onte nt o f t

he fu e l, a s Ca

pit ol C e me nt n ote d in

the ir pe rm it application. Capitol Cement’s fa cility is situated on about 800 acr es nea r Mar tinsburg, WV, alongwith an open pit quar ry from which the limestone (a r aw mate rial) is obtained. Since the 2 lim e sto ne is m ine d o n s ite , a n a lte rn ativ e wa s n ot c on sid e re d to be a via ble op tio n f

or SO c ontr ol. MACTEC, in their report, pr ovided cost estimates for the other thre e contr ol options i dentified above.

2 Tabl e 7. 7- 3. SO Co nt r ol C os ts forLar ge P r e he at e rKilns (2 00 6 D oll ar s)

Ca pit ol C os t (10 $) 6 Annu a l O pe ra tin gCos ts (10 $) 6 2

SO Cos t E ff

e c tiv e ne ss 2 ($/ton SO reduc tion) Dry FGD $3.6 - $105.2 $0.9 - $21.0 $10,000 - $72,800 Wet FGD $6.2 - $92.5 $0.9 - $18.9 $9,700 - $64,600 Advanc ed F GD $17.8 - $58.0 $1.2 - $11.8 $13,600 - $38,000 Since the cost of c ompliance f or all three control options rang e fr om 5 to 36 tim es greater than 2 the c os t e ff

e c tiv e ne ss b e nc hma

rk , th e WVD AQ de te rm ine d th at th e re are no c os t e ff e c tiv e SO c ontr ols ava ila

ble fo r t he ne w p re c a lc ine r s y ste m at Ca pit ol C e me nt. Time Nec essar

y for Compliance , Energy

and Non- Air Impacts, and Rema ining Use ful L ife The thre e re maining statutory fac tors: 1) time nece ssary for c ompliance, 2) the ene rgyand nonair qua lity

environmenta l impacts of compliance , and 3) the rema ining use ful life of the emissions unit , while re quired to be c onsidered, w ere deeme d not applicable, sinc e ther e we re no cost eff ective c ontrols to evaluate.W est V i rgi ni a R egi o nal H aze S IP Pag e 99

7.8. What Control Det erm ination s Repre sent Best Available Ret rofit Technology (B AR T) forIndi vid ualSour c e s? Section 169A of the CAA dir ects States to assess c erta in larg e, older emission sources for a dd iti on a l c ontr ols in o rd e r t o a dd re ss v isi bil ity

imp a c ts. State s a

re dir e c te d to c on du c t B AR T de te rm inatio ns fo r s uc h s ourc e s in sp e c if ic so ur c e c ate g or ie s, wh ic h c ontr ibute to v isi bil ity impairment in Class Iare as. The 1999 r eg ional haze rule inc ludes the B ART require ment, and direc ts St ates to include B ART in their reg ional haze SI Ps. On J uly 6, 2005, U.S. EPA published a re vised final rule , including A ppendix Y to 40 C FR par t 51, the Guidelines for BART De te rm ina tio ns Un de r th eRe gio na l H az eRu le(h e re ina ft e r r e fe rr e d to a s th e “ B AR T Guidelines”) that provides dire ction to states on deter mining which of

these sourc es should be subject to BA RT, and how to deter mine BA RT for ea ch sourc e. AB A R T
- e l i g i b l e s o ur c e i s o n e w h i c hh a s th e p o t e n t i a l t o e m i t2 5 0 t o n s o r m o r e o f a v i s i b i l i tyimp a ir inga ir

po llutant, wa s p ut i n p la c e be tw e e n Augus t 7, 19 62 and Au gus t 7, 19 77 , a

nd wh os e operations fall within one or more of 26 specific ally

listed source c ateg ories. Unde r the CAA, BA RT is required f

or any BA RT-elig ible source that a State dete rmines ‘‘e mits any air pollutant which may

rea sonably

be anticipated to cause or contribute to a ny impairment of visibility

in any such ar ea.’ ’ F or tho se so ur c e s su

bje c t to B AR T, Se c tio n 1 69 A( g )( 7) of the CA A r e qu ir e s th at State s mu st c on sid e r t he fo llo wi ngfa c tor s in

makin gB AR T d e te rm inatio ns : (1) the c osts of compliance , (2) the e nergy

and non-a ir quality

environmenta l impacts of compliance , (3) a ny exis ting pollution control technology in use at the sourc e, (4) the r emaininguseful life of the source , and (5 ) t he de g re e of

imp ro ve me nt i n v isi bil ity wh ic h ma

y re a so na bly be anti c ipate d to re su lt f r o mth e u s e o f

s u c ht e c h n o l o g y. 7. 8. a . B AR T- El ig ibl e Sourc e s in Wes t V ir g ini a Th e fo llo wi ngis a

lis t of fa c ili tie s w ithB AR T- e lig ibl e so ur c e s in Wes t V ir g ini a . Se e Ap pe nd ix H f or a de ta ile d d e sc ri pti on

of e a c h B AR T- e lig ibl e e mis sio n unit :
Tabl e 7. 8. a- 1. BA RT -E lig ible Sour c e s in W e stVi r gin ia F acility Nam e F acility ID Location Al le g he ny

En e rgy - F t. M art in 06 1- 00 00 1 Ma ids vil le Alleg heny

Energy - Ha rrison 033-00015 Hay wood Alleg heny

Energy - Pleasants 073-00005 Wil low I sland AEP - Appala chian Power - John Amos 079-00006 St. Albans AE P - Oh io P ow e r - Mit c he ll 05 1- 00 00 5 Mo un ds vil leTabl e 7. 8. a- 1. BA RT -E lig ible Sour c e s in W e stVi r gin ia

F acility Nam e F acility ID Location

W est V i rgi ni a R egi o nal H aze S IP P ag e 100 AEP - Appala chian Power - Mountainee r 053-00009 New H aven B ay e r M ate ri a l Sc ie nc e 05 1- 00 00 9 Ne w M art ins vil le Ca bo t Co rp 07 3- 00 00 6 Wav e rl y Capitol Cement Corporation 003-00006 Martinsburg Century Aluminum 035-00002 Ravenswood Clearon Corpor ation 039-00011 South C harle ston Col umb ian Ch e mic a ls 05 1- 00 01 9 Mo un ds vil le Dominion - Mt. St orm 023-00003 Mt. St orm Du po nt 03 9- 00 00 1 B e lle Ke ple r P ro c e ssi ng 10 9- 00 01 3 Pin e vil le ERG ONCor

p - Wes t V ir g ini a , Inc . 02 9- 00 00 8 Ne we ll GE Adva nced Materials 107-00010 Washington Mittal S teel USA - Weirton, Inc. (f ormerly Weirton St eel) 029-00001 Weirton Pinna c le Min ingCo. - N o. 50 Coa l Pr e p Pl ant 10 9- 00 00 6 Pin e vil le Poc aho nta s Co a l Co . - Ea st Gulf Pr e p Pl ant 08 1- 00 01 2 Rho de ll PPG I ndustries 051-00002 Natrium Mountain State Carbon ( f ormer l y W hee l i ng P i t t s bur gh St eel ) 009-00002 Wheeling The B ART-elig ible source s were identified usingthe methodolog y in the BA RT Guidelines. For an e mis sio n unit

so ur c e to b e ide nti fi e d a s B AR T- e lig ibl e , w e us e d th e c ri te ri a fr om t he B AR T Gu ide lin e s:

• One or

more e missions units at the fac ility fit within one of the 26 ca teg ories listed in the B AR T Guid e lin e s;
• Th e e mis sio n unit (s ) w e re in e xiste nc e on Au gus t 7, 19 77 and

be g an o pe ratio n at so me point on or after

August 7, 1962; and

• The limited potential emissions from all emission unit s identified in the pre vious two bu lle ts e mis sio n unit s w e re g re ate r t ha n 2 50 ton s o r m or e pe r ye ar o f a ny of the se 2 x 1 0 vis ibi lity -i mpa ir ingpo llutants : SO , N O , a nd PM
. The B ART Guidelines re commend addr essingthese visibility -impairingpollutants: sul fur

2 x dio xide (S O ), nit

ro g e n o xide s (NO ), and

pa rt ic ulate matte r, and

su g g e sts that State s u se d th e irW est V i rgi ni a R egi o nal H aze S IP P ag e 101

best judg ment in determiningwhether to addre ss VOC or ammonia emissions. WVDAQ 2 x 1 0 addre ssed SO and NO , and used pa rticulate matter less than 10 microns in diame ter (PM ) as an indicator

for pa rticulate matter to identify BA RT-elig ible units, as the BA RT Guidelines re c omm e nd s. As dis c us se d in de ta il i n App e nd ix H, VI STA S mo de lin gde mon str ate d th at V OC s and ammo nia fr om p oint so ur c e s are no t si g nif ic ant v isi bil ity

imp a ir ingpo llutants . F or thi s rea son, WVDAQ did not evaluate emissions of Volatile Org anic Compounds (VOCs) a nd ammo nia in B AR T d e te rm inatio ns . 7. 8. b. De te rm inatio n o f S ourc e s Su bje c t to B AR T i n We st V ir g ini a Un de r t he B AR T Guid e lin e s, WVD AQ may c on sid e r e xemp tin gso me so ur c e s f ro m B AR T i f i t i s f o un d th atth e y d o n o tc a u s e o r c o n tr i b ut e t o v i s i b i l i ty

i mp a i r m e n ti na C l a s s I ar e a . In a c c or da nc e wi th t he B AR T guid e lin e s, WVD AQ c ho se to p e rf or m so ur c e -s pe c if

ic ana ly se s to det erm in e wh ich so urce s cau se o r con tr ib ut e to vi si bi li ty impai rm entus in g the C AL P UFF mod e l. T he CA L PUF F mod e lin gpr oto c ol u se d f or de te rm ini ngwh ic h f

a c ili tie s a re su bje c t to B AR T i s in c lud e d in

Ap pe nd ix H. In a c c or da nc e wi th t he Gu ide lin e s, a

c ontr ibutio n thre sh old of less than 0.5 de civiews wa s employ ed for deter mining which sour ces w ere exempt from BA RT. Twenty -one ( 21) of West Virg inia’s twenty -two (22) BA RT-elig ible source s submitt ed e xemp tio n mo de lin gde mon str atio ns . N ine te e n ( 19 ) o f t he tw e nty-o ne (2 1) so ur c e s w e re a ble to demonstrate e x emption. Results of these demonstrations ar e summarized in Tables 7.8.b- 1 and 7.8 b-2 below. A dditional details are a vailable in Appe ndix H. Capitol Cement (003-00006) w as the only BA RT-elig ible source which chose not to submi t exempti on modeling.

Tabl e 7.8.b-1. BART Exem ption Modelin g Results 12 k m M ode l i ng - M ax i m um Pre di c t e d V i s i bi l i t y I m pac t ( de l t a- de c i vi e w ) pe r C l as s I A r e as Compa ny Yea r Dolly Sod s, WV O t t e r C r e e k,

WV Shenandoah,

VA Jam es River

F ac e, VA Li n v ill e

Go rge, NC G r e a tS m o ky

M ou nta ins, TN Mountain State Carbon (009- 00002)

20 01 0. 05 7 0. 05 9 0. 09 1 n/a n/a n/a 20 02 0. 05 5 0. 05 1 0. 06 1 n/a n/a n/a 20 03 0. 04 8 0. 05 1 0. 06 5 n/a n/a n/a Mittal S teel USA - Weirton, Inc.

20 01 0. 13 1 0. 14 8 0. 17 0 n/a n/a n/a 20 02 0. 18 2 0. 15 5 0. 08 1 n/a n/a n/a 20 03 0. 18 5 0. 29 9 0. 10 2 n/a n/a n/aTabl e 7.8.b-1. BART Exem ption Modelin g Results

12 k m M ode l i ng - M ax i m um Pre di c t e d V i s i bi l i t y I m pac t ( de l t a- de c i vi e w ) pe r C l as s I A r e as Compa ny Yea r Dolly Sod s, WV O t t e r C r e e k,

WV Shenandoah,

VA Jam es River

F ac e, VA Li n v ill e

Go rge, NC G r e a tS m o ky

M ou nta ins, TN

W est V i rgi ni a R egi o nal H aze S IP P ag e 102 ERGON Corp. - West Virg inia, Inc.

20 01 0. 03 1 0. 02 2 0. 05 5 n/a n/a n/a 20 02 0. 03 2 0. 03 6 0. 02 6 n/a n/a n/a 20 03 0. 03 5 0. 05 4 0. 02 0 n/a n/a n/a Century Aluminum (035-00002) 20 01 0. 07 7 0. 08 1 0. 09 2 0. 05 2 n/a n/a 20 02 0. 06 7 0. 07 5 0. 07 1 0. 05 4 n/a n/a 20 03 0. 03 7 0. 04 9 0. 04 0 0. 03 0 n/a n/a DuPont - Be lle (039-00001)

2001 0.091 0.087 0.129 0.10 0.107 0.076 2002 0.070 0.078 0.069 0.125 0.096 0.056 2003 0.124 0.124 0.157 0.126 0.206 0.081 Clearon ( 039-00011)

20 01 0. 01 7 0. 02 6 0. 01 9 0. 01 7 0. 01 0 n/a 20 02 0. 01 0 0. 01 5 0. 01 2 0. 02 2 0. 00 9 n/a 20 03 0. 01 1 0. 01 5 0. 02 0 0. 01 7 0. 00 9 n/a Pocahontas Coal Co - Ea stern Gulf Pre p Plant ((081-00012)

2001 0.033 0.038 0.073 0.098 0.062 0.054 2002 0.033 0.037 0.065 0.117 0.052 0.034 2003 0.068 0.065 0.064 0.136 0.044 0.050 GE Woodmark (107-00010)

20 01 0. 06 0 0. 07 2 0. 06 1 0. 04 0 n/a n/a 20 02 0. 03 6 0. 04 0 0. 06 1 0. 03 3 n/a n/a 20 03 0. 04 4 0. 05 5 0. 03 4 0. 03 5 n/a n/aTabl e 7.8.b-1. BART Exem ption Modelin g Results

12 k m M ode l i ng - M ax i m um Pre di c t e d V i s i bi l i t y I m pac t ( de l t a- de c i vi e w ) pe r C l as s I A r e as Compa ny Yea r Dolly Sod s, WV O t t e r C r e e k,

WV Shenandoah,

VA Jam es River

F ac e, VA Li n v ill e

Go rge, NC G r e a tS m o ky

M ou nta ins, TN

W est V i rgi ni a R egi o nal H aze S IP P ag e 103 Pinnacle Mining- No. 50 Coal Prep Plant (109- 00006)

2001 0.022 0.022 0.066 0.075 0.034 0.049 2002 0.019 0.019 0.036 0.069 0.050 0.027 2003 0.039 0.047 0.055 0.120 0.032 0.046 Kepler Processing(109-00013) 2001 0.018 0.013 0.039 0.029 0.023 0.029 2002 0.012 0.010 0.021 0.029 0.026 0.013 2003 0.024 0.031 0.032 0.056 0.022 0.032W est V i rgi ni a R egi o nal H aze S IP P ag e 104 Tabl e 7.8.b-2. BART Exem ption Modelin g Results 4km Modeling - 98 P erc entile Visibili ty Im pact (delta- deciview) per Class I Are a th Compa ny Yea r Dolly Sod s, WV O t t e r C r e e k,

WV Shenandoah,

VA Jam es River

F ac e, VA Li n v ill e

Go rge, NC G r e a tS m o ky

M ou nta ins, TN AEP - Appala chian Power Co. - J ohn Amos (079-00006) 20 01 0. 05 4 0. 07 3 0. 06 2 0. 07 5 0. 02 9 n/a 20 02 0. 05 4 0. 08 1 0. 05 5 0. 05 2 0. 01 4 n/a 20 03 0. 05 9 0. 08 2 0. 07 4 0. 06 2 0. 01 8 n/a AEP - Ohio Power Co. - Mitche ll (051-00005)

20 01 0. 09 4 0. 09 7 0. 07 6 0. 03 1 n/a n/a 20 02 0. 04 5 0. 05 8 0. 05 3 0. 02 2 n/a n/a 20 03 0. 07 6 0. 07 7 0. 06 6 0. 02 9 n/a n/a AEP - Appala chian Power Co. - Mountaineer

((053- 00009) 20 01 0. 25 9 0. 34 4 0. 25 3 0. 16 4 n/a n/a 20 02 0. 17 2 0. 26 6 0. 28 1 0. 14 2 n/a n/a 20 03 0. 20 1 0. 25 0 0. 21 7 0. 14 2 n/a n/a Alleg heny

Energy - F t. Martin (061-00001) 20 01 0. 10 3 0. 12 8 0. 10 0. 02 8 n/a n/a 20 02 0. 09 8 0. 14 2 0. 06 8 0. 02 7 n/a n/a 20 03 0. 12 1 0. 11 3 0. 07 8 0. 02 1 n/a n/a Alleg heny

Energy - Ha rrison (033- 00015) 20 01 0. 21 6 0. 27 8 0. 18 5 0. 06 7 n/a n/a 20 02 0. 17 3 0. 25 3 0. 14 7 0. 04 9 n/a n/a 20 03 0. 19 6 0. 23 3 0. 18 1 0. 05 9 n/a n/a Alleg heny

Energy - Pleasants (073- 00005) 20 01 0. 05 9 0. 06 2 0. 04 6 0. 01 9 n/a n/a 20 02 0. 03 7 0. 05 3 0. 03 7 0. 01 8 n/a n/a 20 03 0. 03 9 0. 04 9 0. 03 6 0. 01 6 n/a n/aTabl e 7.8.b-2. BART Exem ption Modelin g Results

4km Modeling - 98 P erc entile Visibili ty Im pact (delta- deciview) per Class I Are a th Compa ny Yea r Dolly Sod s, WV O t t e r C r e e k,

WV Shenandoah,

VA Jam es River

F ac e, VA Li n v ill e

Go rge, NC G r e a tS m o ky

M ou nta ins, TN

W est V i rgi ni a R egi o nal H aze S IP P ag e 105 Ba y er ( 051-0009) 20 01 0. 12 5 0. 01 39 0. 09 3 0. 05 6 n/a n/a 20 02 0. 07 5 0. 12 1 0. 08 0 0. 05 8 n/a n/a 20 03 0. 08 0 0. 10 0 0. 07 3 0. 04 2 n/a n/a Columbi an Chemica ls (051-00019)

20 01 0. 26 1 0. 32 8 0. 37 8 0. 24 7 n/a n/a 20 02 0. 29 5 0. 29 8 0. 40 7 0. 22 8 n/a n/a 20 03 0. 25 7 0. 29 6 0. 27 2 0. 17 6 n/a n/a Cabot Corporation (073-00006)

20 01 0. 38 0 0. 39 1 0. 42 5 0. 24 2 n/a n/a 20 02 0. 40 5 0. 44 1 0. 37 2 0. 37 0 n/a n/a 20 03 0. 33 3 0. 39 6 0. 40 1 0. 22 0 n/a n/a

The e x emption modeling r esults for the B ART-elig ible fac iliti es which ha d an impact of g reater tha n 0 .5 de c ivi e ws of

vis ibi lity imp a c t at any

Cla ss I are a are sh own in

Ta ble 7. 8. b- 3. Ap pe nd ix H contains more detailed r esults of the modelinganaly ses for e ach B ART-elig ible source
.

Tabl e 7.8.b-3. Exem ption Modelin g Results for F acilities with > 0.5 deciview of Im pact 4km Modeling - 98 P erc entile Visibili ty Im pact (delta- deciview) per Class I Are a th C om pan y Y ea r D ol l y S ods , WV Ot t er Cr ee k, WV S he nan doa h, VA J am es Ri ve r F ac e, VA Dominion - Mt. St orm (023-00003)

2001 0.770 0.205 0.538 0.124 2002 0.636 0.286 0.381 0.097 2003 0.416 0.297 0.401 0.098Tabl e 7.8.b-3. Exem ption Modelin g Results for F acilities with > 0.5 deciview of Im pact 4km Modeling - 98 P erc entile Visibili ty Im pact (delta- deciview) per Class I Are a th C om pan y Y ea r D ol l y S ods , WV Ot t er Cr ee k, WV S he nan doa h, VA J am es Ri ve r F ac e, VA

W est V i rgi ni a R egi o nal H aze S IP P ag e 106 PPG I ndustries ((051- 00002)

2001 0.557 0.669 0.786 0.513 2002 0.635 0.615 0.816 0.507 2003 0.578 0.596 0.587 0.364 Capitol Cement (003-00006)

2001 Not available . Capitol Cement elected not to do exemption modeling. T he B ART unit is scheduled to be re place d. 2002 2003
Al tho ugh PPGini tia lly

mod e le d a

vis ibi lity imp a c t gre ate r t

ha n 0 .5 de c ivi e ws on

mul tip le Cla ss Iare a s, PPGe le c te d to a c c e pt a pe rm it l imi t on

its B AR T e lig ibl e un it whic h r e du c e s it s visibil ity impact to below the e x emption threshold of 0.5 dec iviews of impact at any Class Iare a. 2 Permit, R14-027B, re quires 4690.56 tpy of SO emission reductions from B oiler #5 by May 1, 20 08 . T he re fo re , PPGis c on sid e re d B AR T e xemp t.

Tabl e 7.8.b-4. Summ ary of BART Exem ption Requ irem ents for P P G ( 54-051-00002) Unit: # 5 B o i l er Unit ID: 003 P ollutan t:
2 SO Control

M e th od: M u lti p le Overall Control Ef fi ciency: 58% BART Limit:

1478. 8 l bs /

hr MRR Re qu ir e me nts Com pl i ance wi t h t he Boi l er 5 e m i s s i on l i m i ts s hal lbe de t er m i ned by t he us e of a CEM S. The CE MS s hal lbe i ns t al l ed, ope r at ed, m ai nt ai ned and c er t i f i ed as ac cur at e under t he appl i cabl e r equi r ement s of 40CF R60. The dat e, t i m e and dur at i on of al lnonc o m p lia n c e sh a ll b e re c o rd e d a n d su b m itt e d to th e D ire c to r in c o m p lia n c e w ith 4 5 C S R 1 0

. Othe r P e rm it Re qu ir e me nts 2 Tot al SO em i s s i on s f

r om B oi l ers 3, 4 an d 5 ( Uni tI D 002 , 001 and 003 , r es pect i vel y) sh a l ln o tex ce ed 3 7 6 6 .8 lb / h r. Com pli anc e Date M ay 1, 2008 Seven of West Virg inia’s B ART-elig ible source s are EGUs, with a total of 16 units and a g ener atingcapa city

of 11,714 MW, or approximately 80% of West Virg inia’s coa l-fire d g ener atingcapa city . Six of the seve n fac iliti es (14 units) have alre ady installed SelectiveW es t V i r gi ni a s ubmi t t ed a CA I R SI P t o t he E P A i n J un e 200 6. T he s ubmi t t al was bel i eve d t o be app r ovabl e a t t hat 1 t i m e. E P A s ub sequentl y revi sed vari o us aspec t s o f t he t he C A IR pro gram t hrough o t her r ul e- m aki ngs, i ncl ud i ng pr omu l g at i on of

a Fe der al I mpl eme n t at i on Pl an ( FI P). W es t V i r g i n i a t h en s u bmi t t ed an “ abbr ev i at ed SI P” t o be co nsi st ent wi t h t he FIP and rel at ed E P A gui d ance. W est V i rgi ni a i s subject t o t he C A IR FIP (as m o d i f i ed by t he abbr ev i at ed SI P) u n t i l a f u l l CAI R SI P i s s u bmi t t ed an d a ppr ov ed. W est V i rgi ni a R egi o nal H aze S IP P ag e 107 x Cataly tic Reduction (SCRs) to reduce NO , and the r emainingfac ility (2 units) has installed

x SNCR, t o meet either

the re quirements of the NO SI P Call or CAI R. Five of the seven f acilities 2 (11 units) curr ently

have sc rubber s installed to reduce SO emissions (scrubber s were installed on 3 of the units in 2007 to meet CAI R). Construction has commence d at the two re maining fac iliti es (5 units) with startup sche duled in 2008 for one unit, 2009 for two units and 2010 for the re maining two units. Th e EPAha s d e te rm ine d th at, a s a wh ole , th e CA I R c a

p- and -t ra de pr ogra

m im pr ov e s v isi bil ity more than implementingBA RT for individual source s in states aff ecte d by CAI R. A state that opts to participate in the CAI R prog ram unde r 40 CFR Part 96 AAA -EEE ne ed not re quire 2 x aff ecte d BA RT-elig ible EGUs to install, operate and maintain B ART controls for SO or NO emissions. Given that all BART- elig ible units have alre ady installed or ar e installing sc rubber s

x and NO c ontr ols , a

nd sin c e Wes t V ir g ini a

is p art ic ipatin gin C AIR , a nd a c c e pts EPA ’s ov e ra ll 1 x 2 findingthat CAI R “substitut es” f or B ART for NO and SO , West Virginia’ s EGUs we re allowed to submit BART exemption modeling demonstrations for PM emissions only . Only one EG U f a c ili ty (3 un its ) d e mon str ate d th at th e y c ontr ibute d s ignif

ic antl y to v isi bil ity

imp a ir me nt i n any Class Iare a.
7.8.c. Deter

mination of BART Require ments for Subject-to- BA RT Sources Only Dominion’s Mt. S torm Power Station in Mt. S torm was unable to demonstrate a c ontr ibutio n o f l e ss t

ha n 0 .5 de c ivi e w at a ll Cl a ss I are a wi thi n 3 00 km f

ro m th e ir B AR T e lig ibl e source s. There fore , Mt. St orm was the only source considere d to be “subjec t to BART” a nd require d to submita per

mit application, containingtheir eva luation of potential BA RT options and a pr oposed B ART deter mination. Mt. Sto rm

su bmi tte d a B AR T d e te rm inatio n o n N ov e mbe r 3 , 2 00 6. A c op y of the B AR T deter mination was sent to EPA and the F eder al L and Mana g ers f or re view on January

22, 2007. On September

5, 2007 the company

submitt ed a pe rmit application to incorporate the re sults of the B ART deter

mination. The WVDAQ published a leg al ad, whic h commence d the thirty day public comment per iod, on September 18, 2007. A c opy of the a pplication and dra ft permit wer e sent to the Fe dera l L and Mana g ers f or re view, via e mail, on September 17, 2007. Permit R13- 2735 was issued De cembe r 13, 2007. [See Appendix Lfor c opies of application, draft per mit and final per mit.] The c urre nt PM controls at Mt. S torm were deter mined to satisfy

BA RT, however , 10 the allowable PM emission rate wa s lowere d from 0.05 lb/mmBtu to 0.03 lb/mm Btu, re sulting in a re duction of up to 508 tpy per unit, or a

maxi mum reduction of 1524 tpy . A summary of the permit requir ements is contained in Ta ble 7.8.c- 1 below.W est V i rgi ni a R egi o nal H aze S IP P ag e 108

Ca pit ol C e me nt i n M art ins burgc ho se no t to su bmi t e xemp tio n mo de lin g , b ut s imp ly to acknow ledg e that Kiln 9 was B ART-subject. Capitol Cement had pr eviously applied for

and been grante d a PSD (Prevention of Sig nificant De terioration) permit (R14-026, issued June 2, 2005) for the re place ment of two existi nglong w et proc ess ce ment kilns and associate d clinker c oo le rs wi th a

mod e rn pr e c a lc ine r s y ste m and

a sso c iate d e qu ipm e nt. Th e on ly B AR T- e lig ibl e unit at the fac ility

(Kiln 9) wa s one of the two kilns beingrepla ced, a nd the per mit included a require ment for the pe rmane nt shutdown of the exist ing kilns whe n full-produc tion was ac hieved with the repla ceme nt kilns, or no later than 180 da y s after startup. Ther efor e, the c ompany reque sted that the per

mit be modified to require the per manent shutdown of the Kiln 9 sy stem by the B ART compliance deadline or when f ull-production was a chieve d with the repla ceme nt kilns, or no later than 180 da y s after startup, whiche ver c ame f irst. The WVDAQ issued a n Administrative Update to pe rmit R14-026B on Oc tober 11, 2006, re quiringthat “Ope ration of the exist ing kiln 9 sy stem shall permane ntly cea se af ter the pr e-he ater -pre calc iner kiln sy stem a c hie ve s f ull pr od uc tio n o r w ith in 1 28 0 d ay s a ft e r t he pr e -h e ate r- pr e c a lc ine r k iln sy ste m f ir st become s operational or befor e the B ART compliance deadline ( approximately 2013) whiche ver comes fir st”. [S ee A

ppendix Lfor c opies of letter and per mit.] The modifications at Capitol 2 x Ce me nt a re e xpe c te d to re su lt i n 1 ,7 41 .5 1 tp y of SO re du c tio ns , 1 ,3 74 .8 1 tp y of NO re du c tio ns , 10 and 66 .0 1 tp y of PM re du c tio ns . Asumma ry of the B AR T r e qu ir e me nts is c onta ine d in Ta ble 7.8.c-3 be low. In a c c or da

nc e wi th 4 0 CF R 51 .3 08 (e )( 1) (i v) , We st V ir g ini a ha s r e qu ir e d e a c h s ourc e “ su bje c t to BA RT” to install and operate B ART, as expeditiously as pra cticable , but no later than f ive y ear s a ft e r a pp ro va l of

thi s SI P. A dd iti on a lly , in a c c or da nc e wi th 4 0 CF R 51 .3 08 (e )( 1) (v ), Wes t Virg inia has re quired e ach B ART source to maintain the require d control equipment a nd establish proce dures to ensur e such e quipment is properly operated and maintaine d.W est V i rgi ni a R egi o nal H aze S IP P ag e 109

Tabl e 7.8.c- 1. Summ ary of BART Requi rem ents for Mt. Stor m (54-023- 00003) Unit: Un it 1 Unit ID: 001 P ollutan t:

PM 10 ( f i l t er ab l e) Control

M e th od: ES P & F GD Ove r all

Control Ef fi ciency: 99. 50% BART Limit:

0 .0 3 lb /m m B tu ( f i l t er ab l e)
MRR Re qu ir e me nts T h e p e rm itt e e sh a ll c a lc u la te th e p o te n tia l p a rticu la te m a tter e m issio n s fro m Un it 1 , U n it 2, and U ni t3 on a dai l

y bas i s us i ng t he m oni t or i ng pr oce dur es and c al cul at i on m e th o d o lo g

y o u tli n e d in th e 4 5 C S

R 2 m o n ito rin g p la n . T h e p e rm itt e e sh a ll re c o rd a n y in sta n c e o f ca lc u la te d e m issio n s in e x c e ss o f t h e li m its g iv e n u n d e r S e c tio n 4 .1 o f t h is p e rm it a n d a n y c o rre c tive a c tio n s th e re fo re ta k e n . The pe r m i t t ee s hal lm ai nt ai n and ope r at e, atal lr eas onabl e t i m es , appr opr i at e equi pm ent o n th e E S

P a n d F G D to c o n tin u o u sly m o n ito r th e p e rfo rm a n c e o f ea c h c o n tro l d e v ic e . 10 P M tes tin g o n 4 5 C S

R 2 sc h e d u le

. Othe r P e rm it Re qu ir e me nts 10

Agg r e g a t e 9 9

. 5 0 % PM r e mo v a le f f i c i e n c y o n E S P & F GD Compli ance Date D ec ember 13, 2007

Unit: U n it 2 Unit ID:

001 2 P ollutan t:
10 PM ( f i l t er ab l e) Control

M e th od: ES P & F GD Ove r all

Control Ef fi ciency: 99. 50% BART Limit:

0 .0 3 lb /m m B tu ( f i l t er ab l e)
MRR Re qu ir e me nts S a m e a s Un it 1 Othe r P e rm it Re qu ir e me nts 10

Agg r e g a t e 9 9

. 5 0 % PM r e mo v a le f f i c i e n c y o n E S P & F GD Compli ance Date D ec ember 13, 2007

Unit: Un it 2 Unit ID:

001 3 P ollutan t:
10 PM ( f i l t er ab l e) Control

M e th od: ES P & F GD Ove r all

Control Ef fi ciency: 99. 50% BART Limit:

0 .0 3 lb /m m B tu ( f i l t er ab l e)
MRR Re qu ir e me nts S a m e a s Un it 1 Othe r P e rm it Re qu ir e me nts 10

Agg r e g a t e 9 9

. 5 0 % PM r e mo v a le f f i c i e n c y o n E S P & F GD Compli ance Date D ec ember 13, 2007W est V i rgi ni a R egi o nal H aze S IP P ag e 110

Tabl e 7.8.c- 2. Summ ary of BART Requi rem ents for Capitol Cem ent (54- 003-00006) Unit: # 9 Ki l n Unit ID: 012 P ollutan t:
2 x SO , NO , 10 PM Control

M e th od: Sh utDown Ove r all

Control Ef fi ciency: N

/A BART Limit: N /A
MRR Re qu ir e me nts C o n firm sh u td o w n o

f kiln Othe r P e rm it Re qu ir e me nts N /A Compli ance Date S h u t d o wn #9 Ki l n wi t h i n 1 8 0 d a y s o f s t a r t u p o f t h e n e w p h

-

p c k i l n , o r b e

f o r e BA RT C o m p l i a n ce d ea d l i n e, w h i ch ev er c o m es fi rstW est V i rgi ni a R egi o nal H aze S IP P ag e 111

7.9. What Addi tional Emissions Controls Were C onsid ere d as p art of the Long-Term Strate gy for Visib ility Im provem ent by 2018? 40 CF R 51 .3 08 (d )( 3) (v ) o f t he re g ion a l haze r

ule lis ts s e ve ra l f

a c tor s th at mu st b e a dd

re sse d in eac h SI P. These fa ctors include the role of f ire at the Class Iare as and status of state planningfor smo ke mana g e me nt, the ro le of du st a nd fi ne so il at th e Cla ss I

are a s a nd status of pla ns to mit ig ate e mis sio ns fr om c on str uc tio n a c tiv iti e s, and

the ro le of

ammo nia and po te nti a l be ne fi ts i f emissions from ag ricultural sourc es we re mitig ated. As discussed in Section 2.4 and shown in F igur es 2.4-1 a nd 2.4-2, ca

rbon sourc es (suc h as a g ri c ult ur a l f ir e s, pr e sc ri be d f

ir e s and wi ldf ir e s) area re lativ e ly min or c ontr ibutor

to v isi bil ity imp a ir me nt at D oll y

Sod s. Sin c e c arb on

is a min or c ontr ibutor

to v isi bil ity

imp a ir me nt, WVDAQ is not proposing to a dopt additional require ments for c ontrols on these sourc es during the first re view per iod. Never theless, West Virg inia has a r ule in place to regulate emissions from these source s, 45CSR 6 [effec tive date July 1, 2001] – To P reve nt and Control Air Pollut ion fr om C omb us tio n o f R e fu se – whic h in c lud e s a pr oh ibi tio n o f o pe n b ur

nin g , unle ss s pe c if

ic c on dit ion s are me t. We st V ir g ini a do e s n ot h ave a

re qu ir e me nt f

or a Smo ke Ma

na g e me nt P lan to re gulate e mis sio ns fr om p re sc ri be d f ir e s, ho we ve r, Wes t V ir g ini a ha s p ro po se d r e vis ion s to 45 CSR6 , whic h in c lud e a pr ov isi on

fo r p re sc ri be d b ur

nin g– th e c ontr oll e d a

pp lic atio n o f f

ir e to veg etation under

specifie d environmenta l conditions and prec autionary measure s which allows the fi re to b e c on fi ne d to a pr e de te rm ine d a

re a fo r t he purp os e of

a c c omp lis hin gsp e c if

ic a lly planned wildlife a

nd fore st manag ement objec tives – in acc ordanc e with a wr itten prescr ibed fire pla n a pp ro ve d b y

the Wes t V ir g ini a

Di vis ion of F or e str y , o r i n th e c a se of

fe de ra l la nd s, approve d by

the appr opriate A g ency

Administrator and e ndorsed by the West Virg inia Division of F orestry . The propose d revisions to 45CSR 6 are schedule d to go be fore the 2008 L eg islature for a pproval a nd are expected to bec ome ef fec tive in the J une 2008 timefra me. WVDAQ has worke d with the WV Divisi on of F orestry and the F eder al L and Mana g ers to deve lop the langua g e a llo wi ngpr e sc ri be d b ur ns . Stra te g ic pr e sc ri be d f

ir e s, wh e n p ro pe rl y

c on du c te d a c tua lly r e d u c e th e i n c i d e n c e o f

w i l d f i r e s an d o v e r a l l s h o u l d

yi e l d b e n e f i t s w i thr e s p e c tt o v i s i b i l i ty. Copies of the cur

rent rule and the pr oposed rule a re inc luded in Appendix H. Also as discussed in Section 2.4 and de monstrated in Figures 2.4- 1 and 2.4-2, f ine soils are a re lativ e ly min or c ontr ibutor

to v isi bil ity

imp a ir me nt at D oll y

Sod s. Ne ve rt

he le ss, in r e g ard

to construction ac tiviti es, the WVDAQ ha s rule 45CSR17 [ eff ective date August 31, 2000] – To Prevent a nd Control Particulate Matter Air Polluti on from Mate rials Handling , Prepar ation, S t o ra ge an d

Ot h er S o urc es o f F u gi t i v e P art i cu l at e M at t er – wh i ch p ro h i b i t s th e d i s ch ar ge “beyond the boundar

y lines of the prope rty

lines on which the discha rg e orig inates or at any public or re sidential location, which c auses or contributes to statutory

air pollution”. Copies of this rule are included in Appendix H. In a dd iti on , th e WVD AQ ha s a numbe r o f r

ule s in pla c e that r e qu ir e the c ontr ol o f f ug iti ve du st wi thi n p lant b ound ari e s, the se inc lud e 45 CSR2 – T o Pr e ve nt a nd Contr ol P a

rt ic ulate Ai r Pollut ion form Combustion of Fuel in Indirec t Heat Exchang ers; 45CSR3 – To P reve nt and Control Air Poll ution from the Oper ation of Hot Mix Asphalt Plants; 45C SR5 – To P reve nt and Control Air Poll ution from the Oper ation of Coal Prepa ration Plants, Coal HandlingOper ations and Coa l Re fu se Di sp os a l A re a s; a nd 45 CSR7 – T o Pr e ve nt a

nd Contr ol P art ic ulate Matte r A irW est V i rgi ni a R egi o nal H aze S IP P ag e 112

Pollut ion from Manufa cturingProcesse s and Associated Oper ations. Copies of these rule s are included in Appendix H. West Virginia a lso has rules in place to regulate emissions from newsource s, including 45CSR 13 – Permits for Construction, Modification, Relocation And Ope ration of Stationary Sources of

Air Pollutants, Notification Requirements, Administrative Update s, Tempora ry Permits, Gener al Permits, and Proce dures f or Evaluation (minor source N SR); 45C SR14 – Permits for Construction and Major Modific ation of Major Stationary Sources of

Air Polluti on for the Pre vention of Signific ant Dete rioration (PSD); and 45CSR19 – Permits for Construction and Ma jor Mo dif ic atio n o f M a

jor Statio na ry Sourc e s o f A ir

Pol lut ion wh ic h Ca

us e or Contr ibute to Nonattainment (nonattainment NSR). The rules identified a bove have all been a

pproved by EPA as par

t of the State Implementation Plan. The West Virg inia PSD and nonattainment NSR regulations will address emissions from new major sourc es that may

locate ne ar a

Class Iare a, or inc rea sed emissions from major modifications to exi sting sourc es located nea r a Class I are a. B oth the PSD and nonattainment NSR pr ogra ms r e qu ir e c on su ltatio n w iththe F L Ms a nd re qu ir e that e mis sio ns wi ll n ot a dv e rs e ly i mp a c tany i d e n t i f

i e d a i r qu a l i ty r e l at e d

v a l u e , s u c ha s v i s i b i l i ty. In r e g ard to a g ri c ult

ur a l ammo nia , We st V ir g ini a do e s n ot h ave a

larg e a g ri c ult

ur a l in du str

y wi th corr esponding ly

larg e ammonia e missions . West Virginia a ccounts for 1.9 perc ent of the ammo nia e mis sio ns in t

he VI STA S r e g ion , whic h a s sh own in Ta ble 7. 9- 1 is the sma lle st c ontr ibutio n in

the re g ion . N or

th C aro lin a , w ithits larg e a g ri c ult ur a l in du str

y , h a s ammo nia emissions which totaled 173,083 tons in 2002 (26.1%), or 13.9 times West Virg inia’s total. Tabl e 7. 9- 1. VI STA S Am m oni a Em iss ion s
3 State 2002 NH Em ission s (tons) State % Contribu tion to Total Alabama 66,519 10.0 Florida 58,602 8.3 Geor g ia 95,936 14.5 Kentucky 57,563 8.7 Mississ ippi 63,929 9.6 North Carolina 173,084 26.1 South C arolina 36,509 5.5 Tenne ssee 43,258 6.5 Virg inia 55,570 8.4 West Virginia 12,461 1.9 Total 663,431 100W est V i rgi ni a R egi o nal H aze S IP P ag e 113

Neve rtheless, a s shown in Table 7.9-2, the majority (~80%) of the ammonia emissions are attributable to the a

rea source s sector, w ith ~90% of those emissions attributable to Agricultural L ivestock Production. Due to the sma ll siz e of the agricultural industry in West Virginia a

nd the relatively small amount of ammonia emitted in the state, the WVDAQ has dete rmined that controls ar e not war

rante d on agricultural sourc es. F or a more detailed a rea source emissions inventory see A ppendix H.

3 Tabl e 7. 9- 2. We stVi r gin ia N H Em iss ion s Sector 2002

(t ons ) 2002

( %) 2009

(t ons ) 2009

( %) 2018

(t ons ) 2018

( %) Point 453 3.64 671 4.93 593 4.12 Are a 9,963 79.95 10,625 78.14 11,504 80.02 NonRoad 9 0.07 11 0.08 13 0.09 OnROad 2,036 16.34 2,292 16.86 2,268 15.76 B iog e nic s N/A N/A N/A N/A N/A N/A Total 12,461 100 13,598 100 14,377 100W est V i rgi ni a R egi o nal H aze S IP P ag e 114

8. 0. RE ASO NA BLE P RO G RE SS G OA LS Th e re g ion a l haze r

ule at 40 CF R 51 .3 08 (d )( 1) re qu ir e s State s to e sta bli shre a so na

ble pr ogre ss g oals (RPG) for e ach Class I are a within the state (e x presse d in deciviews) that provide for rea sonable progress towa rds ac hievingnatura l visibi lity . In addition, EPA relea sed guidance on J une 7, 2007 to use in settingrea sonable progress g oals. The g oals must provide improvement in v isi bil ity

fo r t he mos t im

pa ir e d d ay s, and

e ns ur e no de g ra datio n in vis ibi lity fo r t he le a st impaired da

y s over the State Implementation Plan (SI P) period. The state must also provide an a sse ssm e nt o f t

he numbe r o f ye ars it w ou ld t ake to atta in n atur a l vi sib ili ty c on dit ion s if imp ro ve me nt c ontin

ue s at th e rate re pr e se nte d b y the re a so na

ble pr ogre ss g oa l. Ina c c o r d an c e w it

hth e r e qu ir e me n ts o f

4 0 CF R § 5 1
. 3 0 8 ( d ) ( 1 ) , th is Re g io n a l H a ze Implementation Plan establishes rea sonable progress g oals for D olly Sods and Otter Cree k Wild e rn e ss a

re a s. To c a lc ulate the rate of pr ogre ss r e pr e se nte d b y

e a c h r e a so na

ble pr ogre ss g oa l, WVD AQ c omp are d b a se lin e vis ibi lity c on dit ion s to natur a l vi sib ili ty c on dit ion s in

e a c h c la ss I are a and de te rm ine d th e un if

or m r ate of vis ibi lity imp ro ve me nt ( in d e c ivi e ws ) t hat w ou ld n e e d to be maintained dur ing e ach implementation period in order to attain natura l visibi lity conditions by 20 64 . T he Re a so na

ble Pr ogre ss G oa ls a

re ba se d o n th e ava ila

ble B a se G2 mod e lin gre su lts , and re prese nt the best data a vailable at this t ime.

Tabl e 8. 0- 1. We stVi r gin ia R e as ona ble P r og r e ss G oa ls Class IAre a Ba seline Vi sib ili ty (D e c ivi e ws ) Re a so na

ble Prog ress Goa l (De ciview Improvement Ex pecte d by 2018, 20% Wor st D ay s) Re a so na ble Prog ress Goa l (De ciview Improvement Ex pecte d by 2018, 20% B e st D ay s) Dec iview

Improvement Nee ded 2018 - 2064 (20% Wor st D ay s)
Dolly Sods W ilderness Ar ea 29.0 7.3 1.2 11.3 Otter Cre ek Wilderness Are a 29.0 7.3 1.2 11.3W est V i rgi ni a R egi o nal H aze S IP P ag e 115



### 9 . 0 . M ONI T ORI NG S T RAT E GY T h e St ate Imp le me n tati o nPl anis to b e a c c o mp an ie d b y a s tr ate g y f o r mo n it o r in gr e g io n a l h a ze vis ibi lity imp a ir me nt. Spe c if

ic a lly , th e Re g ion a l H a ze R ule at 40 CF R 51 .3 08 (d )( 4) state s: (4) Monitoring strat egy and othe r implementation plan requireme nts. Th e State mus t su bmi t with the implementation plan a monitoringstrategy for mea suring , char acte rizing, and re po rt ingof

re g ion a l haze v isi bil ity

imp a ir me nt t hat is re pr e se ntativ e of

a ll m and ator

y Cla ss IFe dera l are as within the State. This monitoring strate g y must be coordinate d with the mon ito ri ngstr ate g y

re qu ir e d in

§5 1. 30 5 f

or re a so na bly attributa ble vis ibi lity

imp a ir me nt. Compli ance with this requirement may be met through participation in the I MPROVE n et wo rk
. T h e i mp l em en t at i o np l anmu s tal s o p ro v i d e f o r t h e f o l l o wi n g: (i ) T he e sta

bli shme nt o f a

ny a dd iti on a l mo nit or ingsit e s o r e qu ipm e nt n e e de d to a sse ss wh e the r r e a so na

ble pr ogre ss g oa ls t o a dd

re ss r e g ion a l haze f

or a ll m and ator

y Cla ss I Fe dera l are as within the State are beingachie ved. (ii) - (iv) [Other implementation plan require ments that pertain to re portingand use of monitoring data and a

n emission inventory .] Suc h mo nit or ingis i nte nd e d to pr ov ide the data ne e de d to satis fy

fo ur ob je c tiv e s: 1. Trac k the expected visibility improvements re sulting fr om emissions reductions ide nti fi e d in

thi s SI P. 2. Be tter under stand the atmospher ic proc esses of importa nce to ha ze. 3. I dentify

chemica l species in the ambient particulate matter a nd relate them to emissions fr om s ourc e s. 4. Evaluate reg ional air qua lity models for ha ze and construc t relative r esponse f actor s (RRF s) fo r u sin gtho se mod e ls. The primar

y monitoring ne twork for

reg ional haze, both nationwide and in West Virg inia, is the I MPROVE network. Given that I MPROVE monit oringdata f rom 2002-2004 ser

ve as the ba se lin e fo r t

he re g ion a l haze p ro g ra

m, t he futur e re g ion a l haze m on ito ri ngstr ate g y mus t ne c e ssa ri ly be ba se d o n, or

dir e c tly c omp ara ble to, I MPRO VE . T he I MPRO VE me a sure me nts provide the only

long- term re cord a

vailable f or trac king visibility

improvement or de g radation, there fore West Virginia intends to re ly on the I MPROVE network for complying w ith the reg ional haze monitoring r equire ment in the Reg ional Haze Rule. There is curre ntly

one I MPROVE site in W est Virg inia located at Dolly Sods, which also serve s a s th e mon ito ri ngsit e fo r O tte r C re e k. Th e I MPRO VE me a sure me nts are c e ntr a l to Wes t Virg inia’s re g ional haze monitoring strateg y , and it is difficult to imagine how the objec tives listed above c ould be met without the monitoring provide d by I MPROVE. Any

reduc tion in the sc op e of the I MPRO VE ne tw or

k in Wes t V ir g ini a

wo uld je op ard ize the State ’s a

bil ity to demonstrate r easona

ble progress towa

rd visibilit y

improvement at Dolly

Sods and Otter Cree k. In p art ic ula

r, Wes t V ir g ini a ’s re g ion a l haze s tr ate g y re lie s o n e mis sio n r e du c tio ns that w ill re su lt from CAI R which occ ur at diff ere nt times and will mostlikely not be spatially

uniform. Of c ours e , mo nit or ingat e ve ry Cla ss I are a may imp or tant, but it

is e sp e c ia lly

imp or tant t oW est V i rgi ni a R egi o nal H aze S IP P ag e 116

document the diff

ere nt air quality

response s to the emissions reductions at sites like Dolly Sods that are located in c omplex terra in. The I MPROVE network is periodica lly assessed to optimize the data ac quisiti on versus the require d resour ces. B eca

use the c urre nt I MPROVE monit or in West Virg inia has an e x tensive data r ecor d that repr esents a unique airshed, r educingthe I MPROVE network by shutting down the monitoring site would sig nificantly impede tra ckingprogress at Dolly Sods and Otter Cree k. I t is c ri tic a l to the mon ito ri ngstr ate g y that I MPRO VE wi ll c ontin ue to o pe rate thi s si te , th e on ly on e in We st V ir g ini a . A lte rn ativ e ly , E PA s ho uld e ns ur e that f

e de ra l f un din gis a

va ila ble to operate an e quivalent monitor. Data pr oduced by

the I MPROVE monit oringnetwork w ill be used nea rly continuously

for prepa ringthe 5-year

prog ress re ports and the 10- y ear SI P revisions, eac h of which r elies on analy sis of the prec edingfive year s of data. Conseque ntly , the monitoring data from the I MPROVE sites needs to be re adily acc essible and to be ke pt up to date. Presumably , I MPROVE wi ll c ontin

ue to p ro c e ss i nf

or matio n f ro m it s o wn

me a sure me nts at a

bo ut t he same pa c e and

wi th the same attention to quality as it has shown in the re cent pa st. The VI EWS website ha s been ma int a ine d b y

VI STA S and

the oth e r R e g ion a l Pla

nn ingOr g anizatio ns to p ro vid e re a dy a c c e ss to the I MPROVE data and data ana ly sis tools . West Virginia is e ncoura g ing V I STAS and other RPOs to m aintain VI EWS or a similar data manag ement sy stem to facilitate a naly sis of the I MPROVE data. In addition to the I MPROVE measure ments, some ong oing long -term limited monitoring supported by

Fe dera l L and Mana g ers pr ovides additional insight into progress towa rd re g ional ha ze g oa ls. Wes t V ir g ini a be ne fi ts f ro m th e data

fr om t he se me a sure me nts , b ut i s n ot r e sp on sib le for the f unding de cisions to maintain these measur ements into the future . Such long- term mon ito ri nginc lud e s a

Web c ame ra

op e ra te d b y the USD A F or e st Se rv ic e at D oll y Sod s. Moreove r, the WVDAQ ope rate s a PM2.5 network of the filter-ba sed F eder al re fer ence method mon ito rs and

fi le r- ba se d s pe c iatio n mo nit or s. A m a p o f t he va ri ou s lo c atio ns aro un d th e State is inc lud e d in

F igur e 9. 0- 1. Th e se PM2 .5 me a sure me nts he lp t he WVD AQ c ha

ra c te ri ze a ir po llutio n le ve ls i n a

re a s a c ro ss t

he state , a nd the re fo re a id i n th e ana ly sis of vis ibi lity imp ro ve me nt i n a

nd ne ar t he Cla ss I are a s.W est V i rgi ni a R egi o nal H aze S IP P ag e 117 F igure 9.0-1. PM2.5 Monit oringNetwor k in West Virginia.W est V i rgi ni a R egi o nal H aze S IP P ag e 118



### 1 0 . 0 . IN T E R S T A T E C O N S U L T A T IO N R E G A R D IN G W E S T V IR G IN IA C O N TR I BU TI O N TO VI SI BI LI TY I M PA I R M EN T I N C LA SS I A R EA S I N N EI G H BO R I N G STA TES The VI STAS states have jointly develope d the technic al ana ly ses to define the expected visibil ity improvement by

2018 under e x isting fe dera l and state re gulations compare d to the 2 un if o r m r ate o f p r o g r e s s , SO A r e a s o f

In f lu e n c e f o r e a c hCl a s s Iar e a , an d

me th o d s to p r io r it iz e c ontr ibutio ns fr om i nd ivi du a l so ur c e s w ith in t he Ar e a s o f I

nf lue nc e . T he state s c oll e c tiv e ly a c c e pt t he c on c lus ion s o f t

he se ana ly se s. In Dec ember

2006, the VI STAS S tate Air D irec tors held their fir st formal consultation meeting 2 to reviewBa se G modelingresults and the SO Are as of Influenc e ana ly ses. The A ir Direc tors 2 agree d to look at rea sonable c ontrol measure s for sourc es on the lists for the SO Are as of Inf lue nc e . E a c h s tate wo uld

c on sid e r s ourc e s w ith in t he ir state and wo uld ide nti fy so ur c e s in neig hboringstates that they would like to have that state consider. States a cknowledg ed the revie w proc ess would diffe r amongstates since some Class I are as ar e proje cted to ac hieve visibil ity improvements nea

r the unifor

m rate of progress while most Class Iare as ar e proje cted to h ave g re ate r i mpr ov e me nts tha n unif or m r ate of pr ogre ss. In M ay 20 07 , th e VI STA S State Ai r D ir e c tor s me t f or the ir se c on d f or ma l inte rs tate consultation. States shared the ir lists of sources in their state and neighboringstates for e ach Class Iare a. The y

also share d their cr iteria for listi ngsource s and their plans f or fur ther interstate c onsultation. West Virginia ha s evaluated the impact of West Virginia sour ces on Class I are as in neighboring states and de termined that ther e ar e no additional re asonable control mea sures that should be implemented to mitigate impac ts in Cl ass I are as in neighboringstates. West Virg inia has c on su lte d w iththe re sp on sib le state s r e g ard ingWV’ s d e c isi on no t to re qu ir e a dd iti on a l c ontr ols fo r t ho se Cla ss I

are a s. An a ly se s o f i mpa c ts f ro m WVso ur c e s a

re dis c us se d in

g re ate r d e ta il i n Ap pe nd ix H. Ne ig hb or ingstate s h ave sh are d th e c ontr ols that are int e nd e d f or so ur c e s th at are in the Are as of Influenc e for Dolly Sods. For the f

irst control period ( five year s), West Virg inia has not asked ne ighbor ing states to do more than a lrea dy planned by

that state. Th e MA NE -VU s tate s o f N e w Je rs e y , N e w H amps hir e , a nd Ve rm ont se nt l e tte rs to We st Vi rg ini a in t he sp ri ngof

20 07 statin gthat ba se d o n 2 00 2 e mis sio ns , WVc ontr ibute d to vis ibi lity imp a ir me nt at Cla ss I

are a s lo c ate d in

tho se state s. MA

NE -VU s tate s a sk e d We st V ir g ini a to participate in further

consultation with these states, and the WVDAQ par ticipated in a consultation meetingbetwee n VI STAS and MANE-VU in Atlanta, Geor g ia, on August 20. The MANE -VU state s identified 167 EGU stac ks, thirteen (13) of which w ere in West Virginia, as contributingsignific antly to visibi lity

impairment at MANE- VU Class Iare as in 2002. The 13 sta c ks ide nti fi e d b y

MA NE -VU v e nt t

he e mis sio ns fr om t

we nty-t hr e e (2 3) EGUs (i de nti fi e d in

2 Table 10.0- 1) in West Virg inia. MANE-VU has a sked states to control the SO emissions from these units with a 90% e fficie ncy . MANE-VU also re quested a control strate g y

to provide a 28% 2 re du c tio n in

SO e mis sio ns fr om s ourc e s, oth e r t ha n E GU s, that w ou ld b e e qu iva le nt t o th e irW est V i rgi ni a R egi o nal H aze S IP P ag e 119

pr op os e d lo w s ulf

ur re sid e nti a l f

ue l oi l strate g y . We st V ir g ini a ha s c ontr oll e d o r i s e xpe c tin gto control all of the E GUs identified by MANE-VU, with at least a 90% control e fficie ncy

by 2018. As shown in Table 10.0- 1, all of the units identified by MANE-VU in their “EGU Ask” a re expected to be c ontrolled by 2018, and thirtee n (13) of the units will be controlled with a 95% 2 eff iciency , resultingin an additional 73,015 tons of SO reduc tions beyond those reque sted in the “EGUAsk”. In addition, I PM has predicte d the shutdown of five (5) EGUs (337 MW of 2 c a pa c ity ) n ot i de nti fi e d b y

MA NE -VU, the se un its a c c ounte d f or a tot a l of 18 ,8 49 .5 ton s o f S O in 2 00 2. As sh own in

Ta ble 7. 5. d- 3, by

20 18 a ll the EGUs in We st V ir g ini a are e xpe c te d to 2 either be controlled for

SO or shutdown.

2 Wes t V ir g ini a ’s no n- EG Us are pr e dic te d to e mit 61 ,7 04 ton s o f S O in 2 01 8. MA NE -VU i n th e ir

2 “ No n- EG U A sk ” ha s r e qu e ste d a 28 % r e du c tio n in

the se e mis sio ns , o r 1 7, 27 7 to ns of SO 2 reduc tions. The additional 91,864 tons of SO reduc tions achieve d by the installation and op e ratio n o f m or e e ff

ic ie nt c ontr ols on

the EGUs and

the shutd own o f a dd iti on a l E GU s, wi ll achie ve greater re ductions than the 28% r eduction in the MANE- VU “N on-EGU A sk”. The WVD AQ be lie ve s th at th e se re du c tio ns satis fy MA NE -VU’ s r e qu e st. Th e le tte rs fr om t

he se state s, and

re sp on se s f

ro m WV DA Q, are inc lud e d a s atta c hme nts in Ap pe nd ix J. Wes t V ir g ini a su bs tanti ve ly

dis a g re e s w iththe sc op e and signif ic anc e of vis ibi lity impairment that MANE-VU has attributed to emissions from within our state. Past MANE-VU ana ly se s h ave fo c us e d o n 2 00 2 b a se y e ar e mis sio ns and of te n f a il t o a c c ount f

or the dr amatic 2 SO re du c tio ns e xpe c te d f ro m E GU s in

Wes t V ir g ini a

un de r t he CA I R. Wes t V ir g ini a

be lie ve s th at th e me a sure s w e have a do pte d to pr ote c t vi sib ili ty in o ur own Cl a ss I are as ar e suff

icient to addre ss any

out of state impacts on other Class Iare as. B ased on the 2018 VI STAS analy ses, it can be shown that the entire state of West Virg inia contributes less than 1% to sulfate in the nea

rest MANE- VU Class Iare a, B rig antine. Table 10.0- 2. below illustrates this fac t. The asse ssment is based on sulfate e missions divided by the dis tanc e to t

he are a , mu lti pli e d b y

the maximumre sid e nc e tim e . T his me tr ic is t he same as that used by

West Virginia to e valuate impac ts from ourselve s and other V I STAS states, and a lso to h e lp a sse ss r e a so na ble pr ogre ss. Mo re ov e r, the ta ble de mon str ate s th at no ind ivi du a l un it in West Virginia c ontributes more than 0.10% to Q/d * RTMax for B rig antine.W est V i rgi ni a R egi o nal H aze S IP Pag e 120 Ta b le 1 0. 0-1 . 2 3 W V E lec tri c Gene rati n g Un it s I d en ti f ied in MANE -VU “ EGU A S K”

2 Pl ant I D U ni t M W 2002 SO Emi s s i ons (to n s) 2

SO C o n tr o ls 2

Y r SO C o n tr o ls I ns t al l ed 2

SO C ont rol E f f i ci ency 2018
2

Proj ec t ed SO Emi s s i ons (to n s) C o m m en ts

A lb rig h t 0 7 7 -0 0 0 0 1 3 1 3 7 1 0 ,1 3 6 .5 S c ru b b e r 2 0 1 8 9 0 % 6 6 0 .1 IP M P re d ic te d C o n tro ls Ft
. M ar t i n 061- 00001 1 552 45, 228. 5 Sc r u bber 2010 95% 4, 922. 1 Cont r ol s u n der c on s t r u ct i on 2 9, 812. 1 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc y 2 5 5 5 4 5 ,8 9 0 .7 S c ru b b e r 2 0 1 0 9 5 % 4 ,8 9 0 .0 Joh n Amos 079- 00006 1 800 31, 938. 0 Sc r u bber 2009 95% 6, 612. 7 Cont r ol s u n der c on s t r u ct i on 2 24, 127. 6 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc y 2 8 0 0 3 1 ,4 6 5 .0 S c ru b b e r 2 0 0 9 9 5 % 6 ,6 9 3 .9 3 1 3 0 0 4 4 ,0 3 0 .0 S c ru b b e r 2 0 0 8 9 5 % 1 0 ,8 2 1 .0 K am m er 0 5 1

- 0 0 0 0 6 1 2 1 0 1 4 ,89 6 .0 S cru b b er 2 0 1 8 9 0 % 9 5 1 .1 IP M P red i cted C o ntro l s

1 2 2 1 0 1 2 ,7 1 1 .0 S c ru b b e r 2 0 1 8 9 0 % 9 4 9 .2 IP M P re d ic te d C o n tro ls 3 2 1 0 1 4 ,4 5 2 .0 S c ru b b e r 2 0 1 8 9 0 % 9 4 8 .7 IP M P re d ic te d C o n tro ls K anawha R i ver 0 3 9 -0 0 0 0 6 1 2 0 0 7 ,9 4 4 .0 S c ru b b e r 2 0 1 8 9 0 % 9 0 2 .7 IP M P re d ic te d C o n tro ls 2 2 0 0 7 ,7 4 1 .0 S c ru b b e r 2 0 1 8 9 0 % 9 0 0 .3 IP M P re d ic te d C o n tro ls

2 M i t ch el l 039- 00006 1 800 29, 568. 0 Sc r u bber 2007 95% 7, 646. 2 15, 228. 1 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc y 2 8 0 0 2 9 ,5 3 2 .0 S c ru b b e r 2 0 0 7 9 5 % 7 ,5 8 1 .9

2 M ou n t ai n ee r 053- 00009 1 1300 42, 992. 0 Sc r u bber 2007 95% 11, 433. 5 11, 433. 5 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc yTa b le 1 0. 0-1 . 2 3 W V E lec tri c Gene rati n g Un it s I d en ti f ied in MANE -VU “ EGU A S K”

2 Pl ant I D U ni t M W 2002 SO Emi s s i ons (to n s) 2

SO C o n tr o ls 2

Y r SO C o n tr o ls I ns t al l ed 2

SO C ont rol E f f i ci ency 2018
2

Proj ec t ed SO Emi s s i ons (to n s) C o m m en ts

W est V i rgi ni a R egi o nal H aze S IP Pag e 121 2 M t
. St or m 023- 00003 1 533 9, 958. 5 Sc r u bber 2002 95% 3, 191. 0 9305. 3 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc y 2 5 3 3 1 0 ,4 6 7 .4 S c ru b b e r 2 0 0 1 9 5 % 3 ,1 9 1 .0 3 5 2 1 2 ,9 4 4 .4 S c ru b b e r 1 9 9 4 9 5 % 2 ,9 2 3 .3 P hil i p S p o rn 0 5 3

- 0 0 0 0 1 1 1 5 0 6 ,63 8 .0 S cru b b er 2 0 1 8 9 0 % 6 6 8 .1 IP M P red i cted C o ntro l s 1 , 2 2 1 5 0 6 ,8 2 2 .0 S c ru b b e r 2 0 1 8 9 0 % 6 4 7 .6 IP M P re d ic te d C o n tro ls

3 1 5 0 6 ,9 3 9 .0 S c ru b b e r 2 0 1 8 9 0 % 6 4 3 .0 IP M P re d ic te d C o n tro ls 4 1 5 0 6 ,8 7 6 .0 S c ru b b e r 2 0 1 8 9 0 % 6 3 3 .2 IP M P re d ic te d C o n tro ls 5 4 5 0 1 3 ,8 6 3 .0 S c ru b b e r 2 0 1 8 9 0 % 1 ,9 4 2 .6 IP M P re d ic te d C o n tro ls

2 Pl ea s an t s 073- 00005 1 650 21, 667. 2 Sc r u bber 1979 95% 1, 575. 6 3, 109. 1 a ddi t i on al t on s of SO r emo ved due t o gr eat er t han 90 % Co nt r ol E f

f i ci enc y 2 6 5 0 2 0 ,2 4 1 .7 S c ru b b e r 1 9 8 0 9 5 % 1 ,5 3 3 .5
Tot al 476, 943. 90 84, 880. 30 Expe ct ed Redu ct i on s of
2 3 9 2 ,0 6 3 .6 to n s o f S O , 7 3 ,0 1 5 .7 t on s g r ea t er t h an t h e “ As k ” of 9 0 % C o n tro l o n the se un its A E P C o nsent D ecree [U S D i st ri ct C o urt f o r t he So ut hern D i st ri ct of O hi o , E ast ern D i vi si o n], P aragraph 8 7 requi res t hat a t o t al of at l east 60 0 M W f rom t he 1 f ol l owi ng l i s t of

U ni t s : Spo r n U ni t s 1- 4, Cl i nch Ri ver U ni t s 1- 3, T ann er s Cree k 1- 3, an d/ or K amm er U ni t s 1- 3 be R et i r ed, Ret r of i t , or R e- po wer ed by D ece mb er 31, 2018. A E P Co ns ent D ecr ee [ U S D i s t r i ct Co ur t f or t he Sout her n D i s t r i ct of O hi o, E as t er n D i vi s i on] , P ar agr aph 87 re qui r es t hat Spo r n U ni t 5 be R et i r ed, Ret r of i t , or R e- 2 powe r ed by D ec embe r 31, 2018.W est V i rgi ni a R egi o nal H aze S IP Pag e 122 Tabl e 10 .0 -2 . C ont r ibut ion of We stVi r gin ia 2 01 8 P oin t S our c e Sulfat e Em iss ion s t o Br iga nt ine

Sou rc e I denti f

i cat i on 2002 B ase Y ear 20 18 B ase C ase A oI a n d A ssoci ated M et ri cs Q / d *R T M ax S tat e/ S ou rce C ontr i bu t i on
S t a t e FI PS

CN TY Pl a nt

I D Pl a n t Na me Poi nt ID 2

SI C SO

E m i ssi o ns ( t py) Q

2

SO E m i ssi o ns ( t py) CE

( % ) D i st an

ce ( k m) Q /d RT M ax Q / d *

R T M ax U n i t %

C o nt r i bu ti o n to T ot al Q / d* R T M ax S tat e %

C o n tr ib u ti o n to T ot al Q / d* R T M ax W V 003 0006 C api t o l C em ent C o r po r at i o n 010 3241 1, 247 1, 717 0 303. 36 5. 66 15. 22 86. 1 0. 10% 0. 94% W V 051 0005 O hi o Pow er - M i t che l l Pl ant 001 4911 29, 158 7, 646 90 546. 93 13. 98 5. 50 76. 9 0. 09% 0. 94% W V 051 0005 O hi o Pow er - M i t che l l Pl ant 002 4911 29, 575 7, 582 90 546. 93 13. 86 5. 50 76. 2 0. 09% 0. 94% W V 051 0002 PP G I ndust r i es , I nc. 003 2812 7, 071 7, 016 4 549. 04 12. 78 5. 50 70. 3 0. 08% 0. 94% W V 061 0001 M o no ng ahe l a Po w er C o . - Fo r t M ar t i n Pow er 001 4911 46, 852 4, 922 90 469. 75 10. 48 5. 81 60. 9 0. 07% 0. 94% W V 061 0001 M o no ng ahe l a Po w er C o . - Fo r t M ar t i n Pow er 002 4911 42, 467 4, 890 90 469. 75 10. 41 5. 81 60. 5 0. 07% 0. 94% W V 023 0003 M o unt St o r m Pow er Pl ant 001 4911 8, 817 3, 191 95 415. 55 7. 68 7. 68 59. 0 0. 07% 0. 94% W V 023 0003 M o unt St o r m Pow er Pl ant 002 4911 9, 572 3, 191 95 415. 55 7. 68 7. 68 59. 0 0. 07% 0. 94% W V 051 0011 V enc o M o undsv i l l e Cal ci ni ng Pl ant K 02 2999 4, 350 5, 638 0 547. 45 10. 3 5. 50 56. 7 0. 07% 0. 94% W V 023 0003 M o unt St o r m Pow er Pl ant 003 4911 2, 779 2, 923 94. 98 415. 55 7. 03 7. 68 54. 0 0. 06% 0. 94% W V 051 0011 V enc o M o undsv i l l e Cal ci ni ng Pl ant K 01 2999 3, 200 4, 147 0 547. 45 7. 58 5. 50 41. 7 0. 05% 0. 94% W V 051 0002 PP G I ndust r i es , I nc. 001 2812 3, 538 3, 510 4 549. 04 6. 39 5. 50 35. 1 0. 04% 0. 94% W V 051 0002 PP G I ndust r i es , I nc. 002 2812 2, 069 2, 053 4 549. 04 3. 74 5. 50 20. 6 0. 02% 0. 94% W V 003 0006 C api t o l C em ent C o r po r at i o n 012 3241 211 289 0 303. 36 0. 95 15. 22 14. 5 0. 02% 0. 94% W V 037 0007 H al l t o w n Paper bo ar d C o m pany 001 2631 231 260 4 288. 58 0. 90 15. 22 13. 7 0. 02% 0. 94% N o te : U n its i d e n tif

ie d in b la c k tex t ar e E G U s, u n i t s i d e n t i f i e d i n b l u e t e x t a r e n o n - EG Us
.W est V i rgi ni a R egi o nal H aze S IP P ag e 123



### 1 1 . 0 . CO MP RE HE NS I V E P E RI OD I C I MP L E ME NT AT I ON P L AN RE V I S I ON S 40 CFR §51.308(f) require s West Virginia to re vise its reg ional haze implementation plan and submit a plan revision to EPA by J uly 31, 2018 and eve ry

ten year s therea fter. I

n acc ordanc e with the require ments listed in 40 C FR §51.308(f ), West Virg inia has an oblig ation to revise a nd submit t his reg ional haze implementation plan by J uly 31, 2018 and eve ry

ten year s therea fter. In addition, 40 CFR §51.308(g ) re quires per iodic repor ts evaluatingprogress towa rds the rea sonable progress g oals established for eac h mandatory

Class Iare a. In acc ordanc e with the re qu ir e me nts lis te d in

40 CF R §5 1. 30 8( g ) o f t

he fe de ra l r

ule fo r r e g ion a l haze, Wes t V ir g ini a has an oblig ation to submita re port on rea sonable progress to EPA eve ry five year s following the ini tia l su

bmi tta l of the SI P. T he re po rt

wi ll b e in t

he fo rm of a SI P r e vis ion . T he re a so na ble prog ress re port will evaluate the progress made towards the r easona ble progress g oal for e ach mandatory

Class Iare a located within West Virginia a nd in eac h mandatory

Class Iare a located outside West Virginia which may be af fec ted by emissions from within W est Virg inia. T h e r equ i re m en t s l i s t edi n4 0 C FR § 5 1
. 3 0 8 (g) i n cl u d e t h e f

o l l o wi n g: 1. Desc ription of the status of implementation; 2. Summary

of emission reduc tions achieve d thus far, inc luding e specia lly the status of i mp l em en t at i o no f C AIR f o r E GU s co mp ar edt o th e c o n tro l s as s um edi nth e m o d el i n g. [The WVDAQ rec og nizes that the 2018 projections of EGUcontrols from the IPM runs repr esent one solution as to how the CAI R require ments may be met. B y

the time of the first periodic r eport, the WVDAQ anticipates that the actua l compliance strateg y for the various utility

companies w ill be much more de fined. An a ssessment of those ac tual compliance plans will be done for the first per iodic repor

t.] 3. Assessment of c hang es in visibili ty conditions at eac h Class Iare a (c urre nt vs. baseline), e xpre sse d a s 5 -ye ar a

ve ra g e s o f a

nnua l va lue s f

or the 20 pe rc e nt b e st a

nd wo rs t da y s; 4
. A n a l ys i s o f

e m i s s i o n s c h an g e s o v e r

th e 5
- ye ar

p e r i o d , i d e n t i f

i e d b y s o ur c e o r

a c t i v i ty; 5. An a ly sis of

any signif ic ant c ha ng e s in or out of the State wh ic h h ave imp e de d p ro g re ss; 6. As se ssm e nt o f t

he su ff ic ie nc y of the imp le me ntatio n p lan to me e t RPG s; 7. Review a

nd any modification to our visibili ty monitoring plan. All require ments listed in 40 C FR §51.308(g ) shall be a ddresse d in the SI P revision for rea sonable progress. The WVDAQ r ecognizes that the 2018 projections of EGUcontrols from the IPM runs repre sent a so lut ion a s to ho w the CA I R r e qu ir e me nts may be met, not nec essar ily

the solution as to how the CA I R r e qu ir e me nts will be met. B y

the time the of the f

irst periodic re port, the WVDAQ anticipates that the actua l compliance strateg y for the va

rious elec tric utility

companies w ill be muc h mo re de fi ne d. An

a sse ssm e nt o f t

he a c tua l c omp lia

nc e pla ns wi ll b e c omp le te d f

or the fi rs t pe ri od ic re po rt
.W est V i rgi ni a R egi o nal H aze S IP P ag e 124

The WVDAQ a cknowledg es that continuingemissions reductions will be requir ed to ac hieve natur a l ba c kgro un d c on dit ion s b y 20 64 . A lth ou g h th e fi rs t r e vie w p e ri od fo c us e d o n s ulf

ate reduc tions, future re view per iods may find that additional sulfate r eductions ar e re quired a nd a c hie va ble a s c ontr ol t e c hn olo g ie s im pr ov e . F ur

the rm or e , a s e mis sio ns are re du c e d f ro m sp e c if

ic so ur c e se c tor s o ve r t ime , e mis sio ns fr om o the r s ourc e se c tor s ma y be c ome inc re a sin g ly signif ic ant. The WVDAQ a lso commits t o ong oing c onsultation with t he F L Ms throug hout the imp le me ntatio n p ro c e ss, inc lud ingannua l di sc us sio n o f t he imp le me ntatio n p ro c e ss a nd the mos t rec ent I MPROVE monit oringdata a nd VI EWS data. Consultation betwee n WVDAQ and the F L Ms w ill inc lud e e arl y

inv olv e me nt o f t

he F L Ms i n th e pe ri od ic re vie w p ro c e ss a nd F L Ms w ill rec eive c opies of re vised reg ional haze SI Ps for comment prior to fina liz ation. There are sever al technic al improvements that ar e re commended in the e missions inventory and a ir qu a lity mod e ls t hat are us e d to su

pp or t r e gulator

y de c isi on s f

or re g ion a l haze. Th e se i mp r o v e m e n t s ar e r e c o m m e n d e d , a s f un d i n gi s av a i l a

b l e , t o s u p p o rtth e n e x tl o n gt e r

ms tr at e g y. Th e fo llo wi ngis a

n o ve ra ll s umm ary ; App e nd ix K c onta ins a fu lle r d isc us sio n o f p os sib le te c hn ic a l im

pr ov e me nts
. F ir st a nd fo re mos t, c ontin

ue d im pr ov e me nts are ne e de d in the int e g ra

te d o ne -atmo sp he re a ir quality models that are used to projec t air quality response s to emissions reductions. As our understandingof par

titioni ngbetwee n g aseous a nd aer osol phases improves, this understanding ne e ds to b e re fl e c te d in

the mod e ls. Impr ov e me nts c an a lso be ma de in h owthe mod e ls h and le individual pollut ants. Sulfate per formanc e for the CMAQ re g ional air qua lity model is good overa ll. However , sulfate de position i s fre quently overe stimated in the models, particular ly in the summer months. At the coastal sites, whe n winds are blowing f rom the Gulf of

Mexi co or Atlantic Oce an, CMAQ under estimates measur ed sulfate at the monitors. CMAQ’s proc esses should also be re viewed f or sulfate formation over

water . Nitrate is over estimated by the model in t he wi nte r a

nd un de re sti mate d in

the summe r, a lth ou g h s umm e r m on ito re d v a lue s o f n itr ate are ve ry low . A dd iti on a l im

pr ov e me nts in s e a so na l a llo c atio n o f a mmo nia e mis sio ns wo uld imp ro ve mod e l e sti mate s o f a mmo nium ni tr ate fo rm atio n. Or g anic c arb on is g e ne ra lly un de re sti mate d in

the summe r m onths . Impr ov e me nts are ne e de d in the c ha ra c te ri zat ion

of bo th primary car bon emissions and formation of se condar y org anic c arbon. Other improve ments neede d include better tools for org anic c arbon sour ce a pportionment, and more c onsistent measureme nt techniques be tween r ural a nd urban monitoringnetworks. To improve our unde rstandingof the c ontribution of fire f rom natura l forest fir es, pre scribed burning , land clea

ringand agricultural bur

ning, states nee d improved re cord ke eping . Additional improvements to international e missions inventory are also nee ded, to improve our understandingof boundar y conditions for our modelingdomain and of the contributions from int e rn atio na l e mis sio ns to p oll uta nt c on c e ntr atio ns at th e VI STA S Cla ss I are a s.W est V i rgi ni a R egi o nal H aze S IP P ag e 125



### 1 2 . 0 . DE T E RMI NA T I ON OF T HE AD E QU AC Y O F T HE E X I S T I NG P L AN Depe nding on the finding s of the five -year progress report, West Virg inia will take one of the a c tio ns lis te d in

40 CF R se c tio n 5 1. 30 8( h) . T he fi nd ing s o f t he fi ve -ye ar p ro g re ss r e po rt wi ll d e t e r m i n e w h i c ha c t i o ni s a

p p r o p r i at e an d

n e c e s s ar y. L ist of Possi ble Actions – 40 CFR sec tion 51.308(h) 1. Wes t V ir g ini a

de te rm ine s th at th e e xistin gSI P r e qu ir e s n o f ur the r s ub sta

nti ve re vis ion

in order to achieve established g oals. West Virg inia provides to the Administrator a neg ative dec laration that further r evision of the SI P is notneede d at the time. 2. Wes t V ir g ini a

de te rm ine s th at th e e xistin gSI P ma y be ina de qu ate to e ns ur e re a so na ble prog ress due to emissions from other states which pa rticipated in the r eg ional planning pr oc e ss. Wes t V ir g ini a

pr ov ide s n oti fi c atio n to the Ad min ist rator and tho se state s. Wes t Virg inia collaborates with states throug h the re g ional planningproce ss to address the SI P’ s d e fi c ie nc ie s. 3. Wes t V ir g ini a

de te rm ine s th at th e c ur

re nt S I P ma

y be ina de qu ate to e ns ur e re a so na ble prog ress due to emissions from another c ountry . West Virginia pr ovides notification, alongwith available inf

ormation, to the Administrator. 4. Wes t V ir g ini a

de te rm ine s th at th e e xistin gSI P is i na de qu ate to e ns ur e re a so na ble pr ogre ss d

ue to e mis sio ns wi thi n th e state . We st V ir g ini a wi ll r e vis e its SI P to a dd re ss the plan’s de ficienc ies within one y ear .