# MANE-VU Modeling for

Reasonable Progress Goals

Model performance evaluation, pollution apportionment, and control measure benefits

Prepared by NESCAUM For the Mid-Atlantic/Northeast Visibility Union Regional Pl anning Organization



## February 7, 2008 -200 -150 -100 -50 0 50 100 150 200

0 1 2 3 4 5 6 7 8 9 10 11 12

IMPROVE SO4 (ug/m3)Fractional Bias (%) Goal Criteria FB_2002B1_CMAQ FB_2002B1_CMAQ_20%W FB_2002A1_REMSAD FB_2002A1_REMSAD_20%W

0 5 10 15 20 25

B rigantine

Sulfate Nitrate EC O C Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRA P SE_BC W_BC N_BC VT RI ME DC MS CT NH MA WI AL NY IL SC MI TN KY NJ IN DE GA WV VA NC MD OH PA 0 5 10 15 20 25

Brigantine

S ulfate Nitrate E C OC Sea S alt S oil

### Members of Northeast States for Coordinated Air Use Management

Arthur Marin, Executive Director Northeast States for Coordinated Air Use Management

A nn e G ob i n , B u r ea u C h i e f Connecticut Department of Environmental Protection, Bureau of Air Management

James P. Brooks, Bureau Director Maine Department of Environmental Protection, Burea u of Air Quality

Barbara Kwetz, Director Massachusetts Department of Environmental Protectio n, Bureau of Waste Prevention
Robert Scott, Director New Hampshire Department of Environmental Services, Air Resources Division
William O’Sullivan, Director New Jersey Department of Environmental Protection, Office of Air Quality Management
David Shaw, Director N e w Y o r k D e p a r t m e n t o f E nv i r on m e n t a l C on s e r v a t i on , D i v i s i on o f A i r R e s ou r c e s Stephen Majkut, Chief Rhode Island Department of Environmental Management , Office of Air Resources

Richard A. Valentinetti, Director Vermont Department of Environmental Conservation, A ir Pollution Control Divisionii



# MANE-VU Modeling for Reasonable Progress Goals

Model performance evaluation, pollution apportionment, and control measure benefits

Prepared by NESCAUM for the Mid-Atlantic/Northeast Visibility Union Regional Pl anning Organization



## February 7, 2008iii



# MANE-VU MODELING FOR REASONABLE

PROGRESS GOALS MODEL PERFORMANCE EVALUATION, POLLUTION APPORTIONMENT, AND CONTROL

MEASURE BENEFITS

Project Director
Gary Kleiman, NESCAUM

Editor
Paul Miller, NESCAUM
Administrative Support
Emily Savelli, Iyad Kheirbek, NESCAUM

Principal Contributors
Shan He, Emily Savelli, Jung-Hun Woo, John Graham, NESCAUMiv



## Acknowledgments

NESCAUM could not have completed this work without the preparation of emission inventories by the Mid-Atlantic Regional Air Manage ment Association (MARAMA) and the MANE-VU member states. NESCAUM also acknowledge s the funding for this work through USEPA agreement number XA-97318101-0 to the Ozone Transport Commission in support of the MANE-VU Regional Planning Organiz ation. NESCAUM is solely responsible for the content of this report and any errors it may contain.

Printed: February 2008v TABLE OF CONTENTS
Acknowledgments....................................

................................................... ....................... iv TABLE OF CONTENTS

................................................... .............................................. v FIGURES

................................................... ................................................... .................... vi TABLES

................................................... ................................................... ..................... vii Executive Summary..................................

................................................... ....................viii 1. Introduction....................................

................................................... ....................... 1-1 1.1. Background....................................

................................................... ................... 1-1 1.2. Meteorology...................................

................................................... ................... 1-2 1.3. Emissions Preparations ........................

................................................... ............. 1-6 1.3.1. Emissions Processing Files..................

................................................... ....... 1-8 1.3.2. 2002 Emission Inventory .....................

................................................... ..... 1-10 1.3.3. 2018 “On the Books/On the Way” (OTB/OTW) Emi ssion Inventory ........ 1-14 1.3.4. 2018 “Beyond on the Way” (BOTW) Emission Inv entory ......................... 1-18 1.3.5. 2018 Sulfate Tagging (BOTW) Emission Invento ry................................... 1-19 1.4. Model Platforms...............................

................................................... ............... 1-20 1.4.1. CMAQ........................................

................................................... ............... 1-20 1.4.2. REMSAD......................................

................................................... ............ 1-21 2. Performance evaluation ..........................

................................................... .............. 2-1 2.1. Meteorological Evaluation.....................

................................................... ........... 2-1 2.2. Model Evaluation..............................

................................................... .............. 2-10 3. 2018 BOTW Projections...........................

................................................... ............ 3-1 4. 2018 Pollution Apportionment ....................

................................................... ......... 4-1 5. Control Strategy Evaluation.....................

................................................... ............. 5-1 5.1. Reduced sulfur fuel content (S1 and S2) .......

................................................... ... 5-1 5.2. Best Available Retrofit Program (BART) ........

................................................... 5-3 5.3. 167 EGU Strategy..............................

................................................... ............... 5-5 6. Conclusions.....................................

................................................... ...................... 6-1 7. References......................................

................................................... ....................... 7-1vi FIGURES Figure 1-1. Modeling domains used in MANE-VU air qu ality modeling studies with CMAQ. Outer (blue) domain grid is 36 km and inner (red) domain is 12 km grid. The gridlines are shown at 180 km intervals (5 × 5 36 km cells/15 × 15 12 km cells). ............................................

................................................... ...................... 1-2 Figure 1-2. Vertical Structure of Meteorological an d Air Quality Modeling Domains .. 1-4 Figure 1-3. MM5 modeled wind field map at 12:00 UTC on August 8, 2002 ................ 1-5 Figure 1-4. Observation Network sites within 12km r esolution domain......................... 1-5 Figure 1-5. Examples of processed model-ready emiss ions: (a) SO 2 from Point; (b) NO 2 from Area; (c) NO 2 from Onroad; (d) NO 2 from Nonroad; (e) ISOP from Biogenic; (f) SO 2 from all source categories........................ ................................................. 1- 7 Figure 2-1. 2002 seasonal average hourly bias of w ind speed and direction ................. 2-2 Figure 2-2. 2002 seasonal hourly average index of agreement for wind speed.............. 2-3 Figure 2-3. Quarterly correlation coefficient (r) of hourly wind speed between modeling and measurement for each observation site in 2002 .. ........................................... 2-4 Figure 2-4. Quarterly correlation coefficient (r) of hourly wind direction between modeling and measurement for each observation site in 2002 ............................. 2-5 Figure 2-5. 2002 Seasonal Hourly Average Bias of Te mperature................................... 2-7 Figure 2-6. 2002 Seasonal Hourly Average Index of A greement ................................... 2-7 Figure 2-7. Quarterly correlation coefficient (r) of hourly temperature between modeling and measurement for each observation site in 2002 .. ........................................... 2-8 Figure 2-8. 2002 Seasonal average hourly bias of hu midity ........................................... 2- 9 Figure 2-9. 2002 seasonal hourly average index of a greement ....................................... 2-9 Figure 2-10. Quarterly correlation coefficient (r) of hourly humidity between modeling and measurement for each observation site in 2002 .. ......................................... 2-10 Figure 2-11. Domain-wide paired comparison of dail y average PM 2.5 species between CMAQ predictions and measurements from IMPROVE netw orks.................... 2-13 Figure 2-12. Spatial distribution of correlation c oefficient between PM 2.5 Sulfate and measurement ........................................

................................................... ............ 2-14 Figure 2-13. Spatial distribution of correlation c oefficient between PM 2.5 and measurement ........................................

................................................... ............ 2-14 Figure 2-14. Mean Fractional Error of PM 2.5 species within MANE-VU region......... 2-15 Figure 2-15. Mean Fraction Bias of PM 2.5 species within MANE-VU region............. 2-16 Figure 2-16. Paired comparison of extinction coeffi cient between CMAQ prediction and IMPROVE measurement ................................

................................................... . 2-17 Figure 2-17. Paired Comparison of Haze Index betwe en CMAQ prediction and IMPROVE measurement at selected Class I sites...... ......................................... 2-17 Figure 3-1. Projected improvement in visibility at four Northeast sites based on 2009 and 2018 BOTW-1 projections............................

................................................... ..... 3-2 Figure 3-2. Projected improvement in visibility at three Mid-Atlantic sites based on 2009 and 2018 BOTW-1 projections. .......................

................................................... .. 3-2 Figure 4-1. a. Measured and projected mass contrib utions in 2002 and 2018 at Acadia National Park on twenty percent worst visibility da ys.......................................... 4-2 Figure 4-2. a. Measured and projected mass contrib utions in 2002 and 2018 at Brigantine Wildlife Refuge on twenty percent worst visibility days...................................... 4-3vii

Figure 4-3. a. Measured and projected mass contribu tions in 2002 and 2018 at Lye Brook Wilderness Area on twenty percent worst visibility days..................................... 4-4 Figure 4-4. a. Measured and projected mass contrib utions in 2002 and 2018 at Great Gulf Wilderness Area on twenty percent worst visibi lity days............................. 4-5 Figure 4-5. a. Measured and projected mass contrib utions in 2002 and 2018 at Moosehorn National Wildlife Refuge on twenty percen t worst visibility days.... 4-6 Figure 5-1. Average change in 24-hr PM 2.5 due to S1 emission reductions ( μ g/m 3 ) ...... 5-2 Figure 5-2. Average change in 24-hr PM 2.5 due to S2 emission reductions, relative to S1 ( μ g/m 3 ) ..................................................

................................................... ............. 5-3 Figure 5-3. Potential reductions from BART-eligible sources in the MANE-VU region (tons) .............................................

................................................... ..................... 5-4 Figure 5-4. Average change in 24-hr PM 2.5 due to BART emission reductions ( μ g/m 3 ) 5-5 Figure 5-5. Average change in 24-hr PM 2.5 due to 167 EGU emission reductions ( μ g/m 3 )

................................................... ................................................... ......................... 5-6 Figure 5-6. Visibility improvement relative to uni form rate of progress at Acadia National Park......................................

................................................... ................ 5-7 Figure 5-7. Visibility improvement relative to uni form rate of progress at Brigantine National Wildlife Refuge. ..........................

................................................... ........ 5-7 Figure 5-8. Visibility improvement relative to uni form rate of progress at Great Gulf Wilderness Area....................................

................................................... ............. 5-8 Figure 5-9. Visibility improvement relative to uni form rate of progress at Lye Brook Wilderness Area....................................

................................................... ............. 5-8 Figure 5-10. Visibility improvement relative to un iform rate of progress at Moosehorn National Wildlife Refuge ...........................

................................................... ........ 5-9 Figure 5-11. Visibility improvement relative to un iform rate of progress at Shenandoah National Park......................................

................................................... ................ 5-9 Figure 5-12. Visibility improvement relative to un iform rate of progress at Dolly Sods Wilderness Area....................................

................................................... ........... 5-10 Figure 5-13. Visibility improvement relative to un iform rate of progress at Presidential Range-Dry River Wilderness Area .................... ................................................. 5- 10 Figure 5-14. Visibility improvement relative to un iform rate of progress at RooseveltCampobello International Park ......................

................................................... .. 5-11

TABLES Table 3-1. 2018 twenty percent worst days relative reduction factors. .......................... 3-1 Table 5-1. Projected 2018 twenty percent worst day sulfate mass reduction at MANEVU Class I areas under various control assumptions. ......................................... 5-12 Table 5-2. Projected 2018 twenty percent worst day deciview goals for MANE-VU Class I areas under various control assumptions .......... ................................................ 5-1 3viii



## Executive Summary The main purpose of this report is to assist states in developing effective solutions to regional visibility and fine particle problems a nd comply with requirements under the Regional Haze Rule. NESCAUM has utilized in-house air quality modeling capabilities that include emission processing, meteorological in put analysis, and chemical transport modeling to conduct regional air quality simulation s for calendar year 2002 and several future periods. This work has been directed at sat isfying a number of compliance goals under the Haze State Implementation Plan (SIP), inc luding a contribution assessment, a pollution apportionment for 2018, and the evaluatio n of visibility benefits of control measures being considered for achieving reasonable progress goals and establishing a long-term emissions management strategy for MANE-VU Class I areas. The modeling tools utilized for these analyses incl ude MM5, SMOKE, CMAQ and REMSAD, and incorporate tagging features that a llow for the tracking of individual source regions or measures. These tools have been evaluated and found to perform adequately relative to USEPA modeling guidance. Results show that sulfate aerosol – the dominant co ntributor to visibility impairment in the Northeast’s Class I areas on the 20 percent worst visibility days – has significant contributions from states throughout th e eastern U.S. that are projected to continue in future years from all three of the east ern regional planning organizations (RPOs). An assessment of potential control measures that wo uld address this future contribution has identified a number of promising s trategies that would yield significant visibility benefits beyond the uniform rate of prog ress and, in fact, significantly beyond the projected visibility conditions that would resu lt from “on the books/on the way” air quality protection programs. These “beyond on the way” measures include the adoption of low sulfur heating oil, implementation of Best A vailable Retrofit Technology (BART) requirements, and additional electric generating un it (EGU) controls on select sources. The combined benefits of adopting all of these prog rams could lead to an additional benefit of between 0.38 and 1.1 deciviews at MANE-V U Class I areas on the 20 percent worst visibility days by 2018.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-1



## 1. INTRODUCTION

1.1. Background This report presents information intended to assist states in developing effective solutions to regional visibility and fine particle problems and comply with requirements under the 1999 U.S. Environmental Protection Agency (USEPA) “Regional Haze Rule” [64 Fed. Reg. 35714 (July 1, 1999)]. NESCAUM has u tilized in-house air quality modeling capabilities that include emission process ing, meteorological input analysis, and chemical transport modeling to conduct regional air quality simulations for calendar year 2002 and several future periods. This work has been directed at satisfying a number of compliance goals under the Haze State Implementation Plans (SIPs), including a contribution assessment ( see NESCAUM, 2006a), a pollution apportionment for 2018 , and the evaluation of benefits of control measures being considered for achieving reasonable progress establishing a long-term emissions management strategy for MANE-VU Class I areas. 1 NESCAUM has employed several tools to achieve all of these goals, but the primary tool described and detailed here consists of a regional air qualit y modeling platform using meteorological fields developed by the University o f Maryland using the MM5 platform (Penn State, 2007), emission inventories developed by MANE-VU (MARAMA, 2007a) and processed through the SMOKE emissions processin g tool (SMOKE, 2007), and air quality simulations conducted jointly by multiple m odeling centers utilizing USEPA’s Community Multi-scale Air Quality (CMAQ) model (Byu n and Ching, 1999). Sulfate apportionment was also carried out using the REMSAD model (SAI, 2005) with SO 2 tagging capabilities and control strategy evaluatio n was conducted utilizing a beta version of CMAQ-PPTM (ICF, 2006). This report describes these efforts that form the foundation upon which MANEVU states will base their haze SIP submissions. Af ter the MANE-VU RPO considers the results provided here and consults with neighboring states and federal land managers, we anticipate that a final model simulation will be co nducted to serve as a basis for calculating final reasonable progress goals. This introduction provides a basic description of t he modeling platform and the input data that we used for regional air quality si mulations. Chapter 2 provides a model performance evaluation for both the meteorological input data as well as the chemical transport model for the base year 2002. Chapters 3 through 5 present results from 2018 simulations with respect to the projected “beyond o n the way” scenario that we take as a starting point for the haze program, pollution appo rtionment for 2018, and haze control strategy evaluation.

1 There are seven designated Class I areas in the No rtheast and Mid-Atlantic States. They include Acadi a National Park and Moosehorn Wilderness Area in Main e; Roosevelt Campobello International Park in New Brunswick and Maine; the Lye Brook Wilderness Area in Vermont; the Great Gulf and Presidential RangeDry River Wilderness Areas in New Hampshire; and th e Brigantine Wilderness Area in New Jersey.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-2



### 1.2. Meteorology Professor Dalin Zhang’s group from University of Ma ryland (UMD) provided the 2002 annual meteorological field for air quality mo deling. Meteorological inputs for CMAQ are derived from the Fifth-Generation Pennsylv ania State University/National Center for Atmospheric Research (NCAR) Mesoscale Mo del (MM5) 2 system meteorological fields. MM5 is a model with limited -area primitive equations of momentum, thermodynamics, and moisture with the opt ion of hydrostatic and nonhydrostatic physics. It is designed to simulate me soscale atmospheric circulation. Domains are uniform rectangular grids representing three-dimensional regions of the atmosphere. MANE-VU has adopted the Inter-RPO domain descriptio n for its modeling runs. 3 This 36-km domain covers the continental United Sta tes, southern Canada and northern Mexico. The dimensions of this domain are 145 and 102 cells in the east-west and northsouth directions, respectively. A 12-km inner doma in was selected to better characterize air quality in MANE-VU and surrounding RPO regions. This domain covers the eastern region, which includes the northeastern, central, a nd southeastern U.S., as well as southeastern Canada. It extends from 66 o W~94 o W in longitude and 29 o N~50 o N in latitude with 172 × 172 grid cells (Figure 1-1). Figure 1-1. Modeling domains used in MANE-VU air qu ality modeling studies with CMAQ. Outer (blue) domain grid is 36 km and inner (red) domain is 12 km grid. The gridlines are shown at 180 km intervals (5 × 5 36 km cells/15 × 15 12 km cells).

2 http://www.mmm.ucar.edu/mm5/

3 The modeling system for 2002 annual simulation is applied with a Lambert Conformal Conic projection with parallels at 33N and 45N. A spherical earth r adius of 6370km is used for all elements of the sys tem (MM5/SMOKE/CMAQ).MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-3

The UMD MM5 model runs are made on these two nested domains with the inner (12 km) domain using finer resolution terrain data. Initially, we conducted a set of test runs for the period of August 6 to 16, 2002. The horizontal coordinated system is equally spaced geographically and uses the Arakawa-B gridding scheme. The resolution can be a s high as 1 km. Sigma ( σ

 ) is a terrain-following vertical coordinate that is a fun ction of pressure at the point (for hydrostatic) or reference (non-hydrostatic) state p ressure (P), the surface pressure (P s0 ), and the pressure at the top (P top ) of the model; σ = (P-P top ) / (P s0 -P top ). The model utilizes a terrain-following sigma coordinate with 29 layers . The first level is at 10 m and a radiative upper-boundary condition is at 50 hPa (Fi gure 1-2). Based on test run results, the boundary layer proce sses were determined using the Blackadar high-resolution planetary boundary layer parameterization. Physics options also included explicit representations of cloud phy sics with simple ice microphysics (no mixed-phase processes) and the Kain-Fritsch cumulus parameterization. UMD ran the non-hydrostatic MM5 v3.5.3 with three planetary bou ndary layer (PBL) schemes; (1) modified Blackadar [BL], (2) the Pleim-Xiu scheme w ith the soil module [P-X], and (3) modified Blackadar with soil module [SSIB].The mode l was initialized with the analyses of the National Center for Environmental Prediction (Eta Model). TDL data are used for MM5 nudging. A modeled wind field map (Figure 1-3) shows typical prevailing mesoscale flows from the midwest U.S. to the East C oast.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-4

Figure 1-2. Vertical Structure of Meteorological a nd Air Quality Modeling Domains

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1



### Sigma LevelVertical Layer



### MM5 29 Layers
CMAQ 22 Layers

10 1133

622 2330 3976 5257 10796Altitude (m)

185

68

The simulated meteorological fields were compared t o the measurements from Techniques Development Laboratory of National Weath er Service (TDL NWS) and Clean Air Status and Trends Network (CASTNET). The TDL data are reflective of urban/suburban settings, while the CASTNET sites ar e more representative of rural areas. There are 48 CASTNET sites and about 800 TDL sites within Domain 2 (as shown in Figure 1-4). Overall, the BL scheme shows a better correspondence to the measured data than the other two schemes, although it poorly capt ures the diurnal pattern of humidity. While the P-X scheme shows a better correspondence with the observed diurnal pattern for humidity, it fails to perform well for wind spe ed and temperature (Hao et al., 2004).MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-5

Figure 1-3. MM5 modeled wind field map at 12:00 UTC on August 8, 2002

Figure 1-4. Observation Network sites within 12km r esolution domainMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-6



### 1.3. Emissions Preparations We simulated emission scenarios using the Sparse Ma trix Operator Kernel Emissions (SMOKE) Modeling System. SMOKE is primar ily an emissions processing system designed to create gridded, speciated, hourl y emissions for input into a variety of air quality models, such as CMAQ and REMSAD. SMOKE supports area, biogenic, mobile (both onroad and nonroad), and point source emissions processing for criteria, particulate, and toxic pollutants. For biogenic em issions modeling, SMOKE uses the Biogenic Emission Inventory System, version 2.3 (BE IS2) and version 3.09 and 3.12 (BEIS3). SMOKE is also integrated with the onroad emissions model MOBILE6. The sparse matrix approach used throughout SMOKE pe rmits rapid and flexible processing of emissions data. Flexible processing comes from splitting the processing steps of inventory growth, controls, chemical speci ation, temporal allocation, and spatial allocation into independent steps whenever possible . The results from these steps are merged together in the final stage of processing us ing vector-matrix multiplication. It allows individual steps (such as adding a new contr ol strategy, or processing for a different grid) to be performed and merged without having to redo all of the other processing steps ( http://cf.unc.edu/cep/empd/products/smoke/version2. 1/html/ ). The emission processing for CMAQ for the 36 km nati onal domain and 12 km eastern domain (Domain 2) has been performed by the New York State Department of Environmental Conservation (NYS DEC) (for base year 2002 and future year 2009) and by NESCAUM (for future year 2018) using SMOKE v2.1 compiled on a Red Hat 9.0 Linux operating system with the Portland Group Fort ran compiler version 5.1. They use the 2002 static emission inventory, CEM data, and s urrogates data based on the 2002 RPO data. Biogenic emissions are calculated using BEIS3 with BELD3 data. Mobile source emissions are processed using MOBILE6. An u pdated 2000 inventory for Canada and a 1999 inventory for Mexico inventory were used for processing. The emissions processing was performed on a month-b y-month and RPO-by-RPO basis, i.e., SMOKE processing was performed for eac h of the RPOs (MANE-VU, VISTAS, CENRAP, MRPO, WRAP) individually as well as for Canada and Mexico. Note the processing of WRAP and Mexican emissions w as necessary for use with the 36 km grid modeling only. For each month/RPO combi nation, a separate SMOKE ASSIGNS file was created, and the length of the epi sode in each of these ASSIGNS files was set to the entire month. Specific data sources for individual source categories are listed below and the examples of processed emission s outputs are shown in Figure 1-5.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-7

Figure 1-5. Examples of processed model-ready emiss ions: (a) SO 2 from Point; (b) NO 2 from Area; (c) NO 2 from Onroad; (d) NO 2 from Nonroad; (e) ISOP from Biogenic; (f) SO 2 from all source categories

a b

c d

e fMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-8



### 1.3.1. Emissions Processing Files The profile and cross reference files listed below are held constant for all modeling years unless stated otherwise. 

### Temporal Allocation MANE-VU: Area and Nonroad sources: amptpro.m3.us+can.manevu.030205.txt and amptref.m3. manevu.012405.txt Mobile source: MANEVU_2002_mtpro_02022006_addCT.txt MANEVU_2002_mtref_02022006_addCT.txt Point sources: Based on the same files as for the M ANE-VU area and nonroad temporal files listed above, but added the VISTAS-g enerated CEM-based 2002 state-specific temporal profiles and cross-ref erences for EGU sources for the MANE-VU states. No CEM, hour-specific, EGU emi ssions were used. CENRAP: The following temporal profiles and cross-reference files were used for all source categories: amptpro.m3.us_can.cenrap.010605. txt, amptref.m3.cenrap.010605.txt These files were downloaded from the CENRAP website www.cenrap.org/emission_document.asp For point sources, the CEM-based hour-specific EGU emissions described in Section 2.2.4 were utilized to override the annual- total based emissions whenever a match could be established by SMOKE VISTAS, WRAP and MRPO: The following month-specific temporal profiles and cross-reference files were used for all source categories: amptpro_typ_us_can_{MMM}_vistas_27nov04.txt where { MMM} is jan, feb, mar, etc., amptref_2002_us_can_vistas_17dec04.txt These files were obtained from Greg Stella (Alpine Geophysics) For point sources (EGU and fires), the hour-specifi c emission files described in Sections 2.3.4 and 2.5.4 were utilized for the V ISTAS and WRAP states to override the annual-total based emissions whenever a match could be established by SMOKE Canada and Mexico: The SMOKE2.1 default temporal profiles and cross-re ference files (amptpro.m3.us+can.txt and amptref.m3.us+can.txt) w ere utilized. 

### Chemical speciation The same speciation profiles (gspro.cmaq.cb4p25.txt ) and cross-references (gsref.cmaq.cb4p25.txt) were utilized for all regio ns and all source categories. Different versions of these files were obtained (SM OKE2.1 default, USEPACAIR modeling, VISTAS, CENRAP and MANE-VU) and comp ared. After comparing the creation dates and header lines of th ese files, it was determined that the USEPA-CAIR and MANE-VU files had the most recen t updates, and consequently the final speciation profile and cross -reference files used for all regions and source categories was based on the USEP A-CAIR files with the addition of MANE-VU specific updates.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-9



### Spatial Allocation U.S. The spatial surrogates for the 12 km and 36 km doma ins were extracted from the national grid 12 km and 36 km U.S. gridding sur rogates posted at USEPA’s website at www.epa.gov/ttn/chief/emch/spatial/newsurrogate.htm l. The gridding cross-references were also obtained fr om this website, but for the processing of MANE-VU area source emissions, MA NE-VU specific cross-reference entries posted on the MARAMA ftp si te were added. Canada The spatial surrogates for Canadian emissions for t he 12 km and 36 km domains were extracted from the national grid 12 km and 36 km Canadian gridding surrogates posted at USEPA’s website at www.epa.gov/ttn/chief/emch/spatial/newsurrogate.htm l. The gridding cross-references were also obtained fr om this website. Mexico The spatial surrogates for Mexican emissions the 36 km domain were extracted from the national 36 km gridding surrogat es used by USEPA in the CAIR modeling. These files were obtained from USEPA ’s CAIR NODA ftp site www.airmodelingftp.com . The gridding cross-references were also obtained from this ftp site.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-10



### 1.3.2. 2002 Emission Inventory A 2002 base year emission inventory was developed t o assess model performance and to serve as a point of comparison for future ye ar projections in terms of emissions reductions and air quality improvement. In order t o assess model performance, actual 2002 emissions (to the extent possible) are incorpo rated into the inventory and simulated in CMAQ in order to compare with observations. In addition, 2002 simulated values are compared to 2009 or 2018 projections with various e mission reductions incorporated to see what degree of air quality improvement can be e xpected as a result of those reductions. 

### CANADA: All source categories except that of point sources where were obtained from USEPA’s ftp site ftp.epa.gov/EmisInventory/canada_2000inventory

. No county/province-specific correction factors were available for Canada. Hence, a “divide-by-four” correction for Source Classifica tion Codes (SCCs) listed at www.epa.gov/ttn/chief/emch/invent/index.html#dust were adjusted with FORTRAN prior to running SMOKE.

Area AS2000_SMOKEready.txt Nonroad NONROAD2000_SMOKEready.txt Onroad MOBILE2000_SMOKEready.txt Point There has long been difficulty in obtaining an up-t o-date Canadian criteria emissions inventory for point sources. This is due largely to confidentiality rights afforded to Canadian facilities. Thus far, t he most recent inventory of Canadian point sources is rooted in the 1985 NAPAP data. Toward this end, an effort was made to obtain more recent Canadian p oint source data and incorporate it into an inventory database.
Perhaps the most accurate and publicly accessible s ource of Canadian pollutant data is now available from the National P ollutant Release Inventory (NPRI) database. The NPRI data are available at Env ironment Canada’s website, www.ec.gc.ca/pdb/npri/npri_home_e.cfm . The page hosts a database available for download as an MS Access or Excel fil e. The database contains a rather comprehensive list of information. Detailed information is available about each facility, including location, activity a nd annual emissions. In addition, facilities having stacks with a height of 50 meters or more are required to report stack parameters.
Unfortunately, one of the limitations of the NPRI d atabase for modeling purposes is that the data are only available at the facility level, so in order toMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-11

use this data, a few generalizations had to be made . Each facility has a Standard Industrial Classification (SIC) code assoc iated with it; however, emissions models require SCCs. While no direct rel ationship exists between these two codes, a general albeit subjective associ ation can be made, since SCCs are needed for SMOKE. In most cases, only a SC C3 level code was assigned with confidence.



### CENRAP: All CENRAP BaseB files were downloaded from its ftp site ftp.cenrap.org

. County-specific correction factors were applied to take into account fugitive dust for SCCs listed at: www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obta ined from USEPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected); this adjustment was performed using the SMOKE programs c ntlmat and grwinven to generate an adjusted IDA inventory file used for su bsequent SMOKE processing for “other area” and point sources.
Where data sets are month dependant, {MMM} represen ts JAN, FEB, MAR, etc. Note that for both area and nonroad sources, the an nual and monthly inventories were processed in one step. Processed with SMK_AVE DAY_YN set to N such that seasonal profiles were used to apportion the i nventories into monthly values.

Area CENRAP_AREA_MISC_SMOKE_INPUT_ANN_STATE_071905.txt CENRAP_AREA_BURNING_SMOKE_INPUT_ANN_TX_NELI_071905. txt CENRAP_AREA_MISC_SMOKE_INPUT_NH3_MONTH_{MMM}_072805 .txt CENRAP_AREA_SMOKE_INPUT_NH3_MONTH_{MMM}_071905.txt CENRAP_AREA_SMOKE_INPUT_ANN_STATE_081705_xfact.txt

- “_xfact” is the adjusted version for fugitive dust as described above Nonroad CENRAP_NONROAD_SMOKE_INPUT_ANN_071305.txt CENRAP_NONROAD_SMOKE_INPUT_MONTH_{MMM}_071305.txt Onroad M6-Input files + VMT - MOBILSMOKE_Inputs.zip (Mar06 ) VMT/Speed files: mbinv02_vmt_cenrap_ce.ida, mbinv02_vmt_cenrap_no.ida, mbinv02_vmt_cenrap_so.id a, and mbinv02_vmt_cenrap_we.ida Point CENRAP_POINT_SMOKE_INPUT_ANNUAL_DAILY_072505_xfact. txt

- “_xfact” is the adjusted version for fugitive dust as described above



### MANE-VU: PECHAN prepared all of the MANE-VUv3.0 inventories for SMOKEv2.1 located at ftp://ftp.marama.org/2002 Version 3/ (username: mane-vu, password: exchange).MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-12

County-specific correction factors were applied to take into account fugitive dust for SCCs listed at: www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obtained from U SEPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected); this adjustment was performe d using the SMOKE programs cntlmat and grwinven to ge nerate an adjusted IDA inventory file used for subsequent SMOKE processing for area and point sources.

Area MANEVU_AREA_SMOKE_INPUT_ANNUAL_SUMMERDAY_040606.txt MANEVU_AREA_SMOKE_INPUT_ANNUAL_WINTERDAY_040606.txt Nonroad MANEVU_NRD2002_SMOKE_030306.ida Onroad VMT/Speed: MANEVU_2002_mbinv_02022006_addCT.txt was prepared by PECHAN and NESCAUM; MANEVU_V3_update.tar can be dow nloaded from http://bronze.nescaum.org/Private/junghun/MANE-VU/o nroad_ver3_update/ Point MANEVU_Point_SMOKE_INPUT_ANNUAL_SUMMERDAY_041006.tx t MANEVU_Point_SMOKE_INPUT_ANNUAL_WINTERDAY_041006.tx t


### MRPO: MARAMA contracted Alpine Geophysics to convert MRPO BaseK NIF formatted inventory to IDA, a SMOKE ready inventory format. Files can be found at ftp.alpinegeophysics.com – username: marama or on MARAMA’s ftp site ftp.marama.org – username: mane-vu, password: exchange. Obtained by NESCAUM between April and June 2006.
County-specific correction factors were applied to take into account fugitive dust for SCCs listed at: www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obta ined from USEPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected); this adjustment was performed using the SMOKE programs c ntlmat and grwinven to generate an adjusted IDA inventory file used for su bsequent SMOKE processing for “other area” and point sources.
Where data sets are month dependant, {MMM} represen ts jan, feb, mar, etc. and {MM} is 01, 02, 03, etc.

Area Agricultural Ammonia - arinv_nh3_2002_mrpok_{MMM}_3 may2006.txt Wind Erosion Fug-Dust - dustinv_2002_mrpok_{MMM}_23 may2006.txt

- The month-specific files were processed separately from the annual runs and SMK_AVEDAY_YN was set to Y so that no seasonal prof iles would be applied and the inventory numbers in the ‘average d ay’ column would be used. Other Area Sources - arinv_other_mrpok_2002_20jun20 06_xfact.txtMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-13

- Adjusted for fugitive dust as described above

- SMK_AVEDAY_YN was set to N, so seasonal profiles we re used to apportion the annual inventory numbers by month.

- To save SMOKE processing, the annual “marine” inven tory was processed together with other area sources. Nonroad NMIM Generated Sources - nrinv_2002_mrpok_{MMM}_3ma y2006.txt MAR (Marine/Air/Rail) - arinv_mar_mrpok_2002_27apr2 006.txt

- MAR inventory was SMOKE processed with annual other area sources. Onroad M6-Input files & VMT – mobile_inventory_mrpobasek.t ar.gz M6-Ancillary – mobile_m6files_mrpobasek.tar.gz VMT/Speed file: mbinv_mrpo_02f_vmt_02may06.txt

- VMT is based on VISTAS Phase II modeling which was verified and updated for MRPOs BaseK May 2006 provided by Greg S tella (Alpine Geophysics) Point EGU - ptinv_egu_2002_mrpok_1may2006.txt Non-EGU - ptinv_negu_2002_mrpok_1may2006.txt

- Christian Hogrefe (NYSDEC) merged the two inventori es and adjusted for fugitive dust, ptinv_egu_negu_2002_mrpok_1may2006_x fact.txt



### VISTAS: All VISTAS emission files were obtained from Greg S tella (Alpine Geophysics) via ftp.alpinegeophysics.com – username: vistasei They reflect version BaseG o f the VISTAS inventory with the exception of fire emi ssions, which reflect BaseF for Lo-Fires and BaseD for Hi-Fires. Files were obt ained between February and August, 2006.

The header lines of these files indicate that the f ugitive dust correction was already applied, so no further correction was perfo rmed. Where data sets are month dependant, {MMM} represents jan, feb, mar, et c. and {MM} is 01, 02, 03, etc.

Area arinv_vistas_2002g_2453922_w_pmfac.txt – Base G ida_ar_fire_2002_vistaonly_basef.ida – Base F low f ires Nonroad NMIM Generated Sources - nrinv_vistas_2002g_2453908 .txt MAR (Marine/Air/Rail) - marinv_vistas_2002g_2453908 .txt Onroad M6-Input files – vistas_baseg02_m6_inputs_20Jul06.t ar VMT/Speed – mbinv_vistas_02g_vmt_12jun06.txt Base G generated by C. Loomis (Alpine Geophysics) July 2006 for VISTAS states Point Annual EGU - egu_ptinv_vistas_2002typ_baseg_2453909 .txtMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-14

Annual Non-EGU - negu_ptinv_vistas_2002typ_baseg_24 53909.txt Hour-specific - pthour_2002typ_baseg_{MMM}_28jun200 6.ems Month Dependant Hi-Fire - ptinv_fires_{MM}_typ.vist as.ida (vr.BaseD) Hour-specific plume-rise - pthour_fires_{MM}_typ.vi stas.ida (vr.Jan05)



### 1.3.3. 2018 “On the Books/On the Way” (OTB/OTW) Emission Inventory The emissions processing was conducted in a very si milar manner for future projection years relative to the 2002 base year, bu t with the projected inventories. The future years “on the books/on the way” (OTB/OTW) em issions inventories account for emission control regulations already in place as we ll as emission control regulations that are final but have not yet been fully implemented a nd are likely to achieve additional reductions by 2009. Processing occurred during Janu ary of 2007. 

### CANADA: All source categories except that of point sources were obtained from USEPA’s ftp site ftp.epa.gov/EmisInventory/canada_2000inventory

. No county/province-specific correction factors were available for Canada. Hence, for Area, Onroad, and Nonroad, a “divide-by-four” c orrection for SCCs listed at www.epa.gov/ttn/chief/emch/invent/index.html#dust were adjusted with FORTRAN prior to running SMOKE.

Area AS2020_SMOKEready.txt Nonroad NONROAD2020_SMOKEready.txt Onroad MOBILE2020_SMOKEready.txt Point Non-EGUs -- ptinv_canada_2002_negu.ida same as 2002 BaseB4 EGUs -- egu062idasum_cp.txt and egu062idawin_cp.txt

- U.S.-Canada 2020 Canadian Base Case -- Scenario #06 2

- Original IPM parsed file (based on NEEDS 2.1.6)

- Annualized emissions were calculated by combining s ummer and winter with FORTRAN to create and use ptinv_canada_2020_eg u.ida



### CENRAP County-specific correction factors were applied to take into account fugitive dust for SCCs listed at: www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obta ined from USEPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected); this adjustment was performed using the SMOKE programs c ntlmat and grwinven to generate an adjusted IDA inventory file used for su bsequent SMOKE processing.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-15

Area arinv_nodust_ref_cenrap2002-2018_081705.ida fdinv.cnrap2002_2018_wfac.ida nh3inv.annual.cenrap2002_2018.ida nh3inv.cenrap2002_2018.ann.ida nh3inv.misc_annual.cenrap2002_2018.ida nh3inv.misc.cenrap2002_2018.ann.ida rdinv.cnrap2002_2018.wfac.ida

- To save SMOKE processing, all area source inventori es were processed with area sources from the MWRPO and VISTAS. Nonroad cenrap_2018_fnl_nrd_emissions091506.txt nrinv_cenrap_2018_mod_w_mrpok_15sep2006.txt nrinv_cenrap_2018_mod_w_mrpok_14sep2006.txt

- To save SMOKE processing, all nonroad source invent ories were processed with nonroad sources from the MWRPO and VISTAS.

- “mod_w_mrpok” files include both MRPO and CENRAP so urces Onroad M6List – BaseG_2018_mobile_m6.tar.gz or in the sub- directory input VMT – cenrap2018_vmt_072005.ida

- bronze.nescaum.org/Private/junghun/CMV_mobile/

- To save SMOKE processing all mobile source inventor ies where processed with mobile sources from the MWRPO and VISTAS. Point EGU – ptinv_egu_2018_cenrap_11sep2006.txt Non-EGU – ptinv_negu_cenrap2018_25aug2006_xfact.ida

- “_xfact” version is the adjusted version for fugiti ve dust as described

- Obtained from Alpine Geophysics contracted by MARAM A ftp.alpinegeophysics.com/Work_Order_1/Task_2_BaseK_ 2018\ (12-Sep06) – username: marama, password: emisdata

- Used IPM2.1.9 without adjustments



### MANE-VU: MARAMA developed the future year OTB/OTW emissions inventories for nonEGU point, area, and nonroad sources accounting for the OTB/OTW inventories, based on the MANE-VU 2002 Version 3 inventory. (MAR AMA, 2007b).
County-specific correction factors were applied to take into account fugitive dust for SCCs listed at: www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the factors were obtained from www.epa.gov/ttn/chief/emch/invent/transportfraction s.xls ; this adjustment was performed outside of SMOKE with FORT RAN for area and point

sources.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-16

Area MANEVU_OTB2018_Area_IDA3V_2.txt (Nov 2006) ftp.marama.org/2009,12,18 OTB Version 3.1/AREA/Area IDA files/ Inventory Development Notes:

- After the release of version 3, Massachusetts revis ed their inventory for heating oil emissions due to two changes: (1) SO 2 emission factors were adjusted for the sulfur content from 1.0 to 0.03; ( 2) use of the latest DOEEIA 2002 fuel use data instead of the previous vers ion from 2001. These two changes significantly altered the 2002 SO 2 emissions for area source heating oil combustion. The revised version was use d to do the projections.

- The District of Columbia discovered a gross error i n the 2002 residential, non-residential, and roadway construction sources. It requested that for PM10-PRIM and PM25-PRIM for SCCs 23110X0000, differ ent values be used for the 2002 base year and as the basis for th e 2009/2012/2018 projections Nonroad MANEVU_OTB2018_NR_IDAV3_1.txt (Oct 2006) ftp.marama.org/2009,12,18 OTB Version 3.1/NONROAD/N ONROAD_IDA_Files_v3.1/

- MACTEC utilized the NMIM2005 model to develop proje ctions for nonroad engines included in the NONROAD2005 model. Projected emission estimates were calculated using NMIM defau lt data. Prior to starting the NMIM2005 runs, MACTEC confirmed with U SEPA’s Office of Transportation and Air Quality (OTAQ) that the data base used for fuel sulfur content, gas Reid Vapor Pressure (RVP) value s, and reformulated fuel programs was current and up to date for the MANE-VU region.

- Emission calculations were made at the monthly leve l and consolidated to provide annual values. This enabled monthly temper atures and changes in reformulated gas to be captured by the program. Onroad ManevuFutureM6_v2_20051103_wjh.tar.gz

- bronze.nescaum.org/Private/junghun/CMV_mobile/ Point Non-EGU: MANEVU2018NonEGUV3_0_Point_IDA.txt (Jun 20 06) ftp.marama.org/2009,12,18 OTB Version 3.1/non-EGU P oint/nonEGU IDA Files/



### MRPO: Alpine Geophysics was contracted by MARAMA to conve rt MRPO BaseK NIF formatted inventory to IDA a SMOKE ready inventory format. Files can be found at ftp.alpinegeophysics.com/Work_Order_1/Task_2_BaseK_ 2018/ – username: marama or on MARAMA’s ftp site ftp.marama.org – username: mane-vu, password: exchange. Obtained between April and Jun e 2006.
Where data sets are month dependant, {MMM} represen ts jan, feb, mar, etc. and {MM} is 01, 02, 03, etc.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-17

Area Other Area Sources – arinv_other_mrpok_2018_22aug20 06.txt Agricultural Ammonia – arinv_nh3_2018_mrpok_{MMM}_2 2aug2006.txt Wind Erosion Fug-Dust Base F – dustinv_mrpo_basef_2 018_29jul05.ida

- In order to save time, all area source categories w ere processed simultaneously for CENRAP, MRPO and VISTAS. Nonroad arinv_mar_mrpok_2018_22aug2006.txt nrinv_2018_mrpok_apr_22aug2006.txt

- To save SMOKE processing all nonroad source invento ries where processed with nonroad sources from the MWRPO and VISTAS. On-road M6LIST – .in files can be found in the sub-director y input VMT - mbinv_vistas+mrpo_18g_vmt_12jun06.ida

- bronze.nescaum.org/Private/junghun/CMV_mobile/

- To save SMOKE processing all mobile source inventor ies where processed with mobile sources from the CENRAP and VISTAS. Point EGU: ptinv_egu_2018_mrpok_11sep006.txt Non-EGU: ptinv_negu_2018_mrpok_23aug2006_xfact.txt

- “_xfact” version is the adjusted version for fugiti ve dust as described

- Used IPM2.1.9 includes post-IPM adjustments



### VISTAS: The header lines of these files indicate that the f ugitive dust correction was already applied, so no further correction was perfo rmed. Where data sets are month dependant {MMM} is jan, feb, mar, etc. and {M M} is 1, 2, 3, etc.

Area arinv_vistas_2018g_2453922_w_pmfac.txt

- To save SMOKE processing, area source inventories w here processed with area sources from the MWRPO and CENRAP. Lo-Fire: area_level_fires_vistas2018_baseg.ida Nonroad marinv_vistas_2018g_2453972.txt nrinv_vistas_2018g_2453908.txt

- To save SMOKE processing, all nonroad source invent ories were processed with nonroad sources from the MWRPO and VISTAS. Onroad M6LIST – .in files can be found in the sub-director y input VMT - mbinv_vistas+mrpo_18g_vmt_12jun06.ida

- bronze.nescaum.org/Private/junghun/CMV_mobile/

- Based off Base G inventory BaseG_2018_mobile_m6.ta r and Baseg_2018_mv_vmt.tarMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-18

- To save SMOKE processing all mobile source inventor ies where processed with mobile sources from the MWRPO and CENRAP. Point EGU: egu_18_vistas_g_2453993.txt Non-EGU: negu_ptinv_vistas_2018_baseg_2453957_xfact .txt Hourly: pthour_2018_baseg_{MMM}_2453993.ems Hi-Fire: ptinv.plume.vistasbaseg18.{MM}.ida ptday.plume.vistasbaseg18.{MM}.ida Hi-Fire hourly plume-rise: pthour.plume.vistasbaseg 18.{MM}.ida

- Used IPM2.1.9 includes post-IPM adjustments



### 1.3.4. 2018 “Beyond on the Way” (BOTW) Emission Inventory The emissions processing for a “beyond on the way” (BOTW) inventory was conducted in a very similar manner to other future projection scenarios relative to the 2002 base year, but with different inventories. Th ese inventories were based on additional control measures that the MANE-VU states are considering for attaining various regional haze, ozone, and PM 2.5 National Ambient Air Quality Standards (NAAQS) goals. The resulting CMAQ simulation (BOTW ) is the same run that has been used by the OTC Modeling Committee for projecting t he long-term benefits of regional ozone control programs and was conducted on the Int egrated SIP Modeling Platform by the five regional modeling centers.



### CANADA: Same as 2018OTB/OTW



### CENRAP: Same as 2018OTB/OTW



### MANE-VU: MARAMA produced the Nonroad, Area and Non-EGU proje ctions for 2018 under different scenarios (MARAMA, 2007b).
The EGU inventories were developed by ICF Consultin g for the RPOS using the Integrated Planning Model (IPM version 2.1.9). Alp ine Geophysics processed the results into IDA inventory format for MANE-VU.
Fugitive dust correction was applied as county-spec ific correction factors for SCCs listed at http://www.epa.gov/ttn/chief/emch/invent/index.html #dust ; the correction factors were obtained from http://www.epa.gov/ttn/chief/emch/invent/transportf ractions.xls ; this adjustment was performed outside of SMOKE with FORTRAN.

Area manevu_botw2018_area_IDAV3_2_xfact.txtMANE-VU Modeling for Reasonable Progress Goals Pa ge 1-19

- “_xfact” version is the adjusted version for fugiti ve dust as described Nonroad nrinv_manevu_18_19oct05.txt Onroad Same as 2018 OTB/OTW Point EGU: ptinv_egu_2018_manevu_11sep2006.txt

- bronze.nescaum.org/Private/junghun/POINT_2018BOTW_B 4 Non-Fossil 2009: manevu_nonfossil_2009_19sept2006.t xt

- Alpines ftp – marama -- Work_Order_1/Task_4_2009_No nfossil/ Non-EGU: MANEVU_BOTW2018_nonegu_IDAV3_1_xfact.txt

- “_xfact” version is the adjusted version for fugiti ve dust as described



### MRPO: Same as 2018OTB/OTW



### VISTAS: Same as 2018OTB/OTW



### 1.3.5. 2018 Sulfate Tagging (BOTW ) Emission Inventory An additional BOTW inventory was prepared specifica lly to allow for a state-bystate tagging run with REMSAD and a sensitivity run with the CMAQ Particle and Precursor Tagging Methodology (CMAQ-PPTM) system. The inventory used for these runs was essentially the same inventory described f or the regular BOTW scenario; however, in order to process this inventory for use with the tagging methodology, various components of the inventory were processed separate ly and identified as a specific “type” of sulfur dioxide so that it could be tracked throu gh the system. The state-by-state tagging used the identical inven tory to the 2018 BOTW inventory described in the previous section. It wa s processed such that each state’s SO 2 emissions were separately tagged requiring three se parate REMSAD simulations to accommodate 29 eastern states, Canada, and the boun daries. A separate CMAQ-PPTM simulation was conducted using the same inventory, but modified to reflect additional controls due to a number of strategies to be tested. The specific scenarios that were tracked by this run in clude:
1. OTB/OTW 2. S-1 fuel oil strategy (500 ppm distillate; 0.5% fue l-sulfur content by weight for No. 6 residual oil; 0.25% fuel-sulfur content b y weight for No. 4 residual oil.) 3. S-2 fuel oil strategy (15 ppm distillate; 0.5% fuel -sulfur content by weight for No. 6 residual oil; 0.25% fuel-sulfur content by we ight for No. 4 residual oil.)
4. BART (approximately 35,000 tons of SO 2 reductions at specific facilities identified by state survey of permitting staff)MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-20

5. “167 Stack” Strategy; (90% control on all EGUs in t he 167 stacks identified as having the most significant impact on MANE-VU Cl ass I areas) Two additional tags were required to account for co rrections to the assumed baseline fuel sulfur content of distillate and to a dd EGU emissions reductions back into the system as a result of potential permit trading in response to the 167 stack strategy. These strategies are described in more detail in Ch apter 4.



### 1.4. Model Platforms Currently two regional-scale air quality models hav e been evaluated and used by NESCAUM to perform air quality simulations. These are the Community Multi-scale Air Quality modeling system (CMAQ; Byun and Ching, 1999) and the Regional Modeling System for Aerosols and Deposition (REMSAD ; SAI, 2002). CMAQ was developed by USEPA, while REMSAD was developed by I CF Consulting/Systems Applications International (ICF/SAI) with USEPA sup port. CMAQ has undergone extensive community development and peer review (Am ar et al., 2005) and has been successfully used in a number of regional air quali ty studies (Bell and Ellis, 2003; Hogrefe et al., 2004; Jimenez and Baldasano, 2004; Mao and Talbot, 2003; Mebust et al., 2003). REMSAD has also has been peer reviewed (Sei gneur et al., 1999) and used by USEPA for regulatory applications ( www.epa.gov/otaq/regs/hd2007/frm/r00028.pdf and www.epa.gov/clearskies/air_quality_tech.html ) to study ambient concentrations and deposition of sulfate and other PM species.


### 1.4.1. CMAQ The CMAQ modeling system is a three-dimensional Eul erian model that incorporates output fields from emissions and meteo rological modeling systems and several other data sources through special interfac e processors into the CMAQ Chemical Transport Model (CCTM). The CCTM then performs che mical transport modeling for multiple pollutants on multiple scales. With this structure, CMAQ retains the flexibility to substitute other emissions processing systems an d meteorological models. CMAQ is designed to provide an air quality modeling system with a “one atmosphere” capability containing state-of-science parameterizations of at mospheric processes affecting transport, transformation, and deposition of such p ollutants as ozone, particulate matter, airborne toxics, and acidic and nutrient pollutant species (Byun and Ching, 1999). To date, MANE-VU SIP modeling on both 36 km and 12 km domains used CMAQv4.5.1, IOAPI V2.2 and NETCDF V3.5 libraries. T he CMAQ model is configured with the Carbon Bond IV mechanism (Gery et al., 1989) using the EBI solver for gas phase chemistry rather than the SAPRC-99 me chanism due to better computing efficiency with no significant model performance di fferences for ozone and PM as compared to observations. NY DEC has completed annual 2002 CMAQ modeling on t he 36 km domain to provide dynamic boundary conditions for all simulat ions performed on the 12 km domain. Three-hourly boundary conditions for the o uter domain were derived from an annual model run performed by researchers at Harvar d University using the GEOS-MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-21

CHEM global chemistry transport model (Park et al., 2004). Model resolution was species dependent at either 4° latitude by 5° longi tude or 2° by 2.5°. Five modeling centers are working collectively to m aximize efficiency of computing resources in MANE-VU for SIP modeling. Th ese centers include NY DEC, NJ DEP/Rutgers, VA DEQ, UMD, and NESCAUM. Annual CM AQ modeling on the 12 km domain is divided into five periods. UMD is r esponsible for the period from January 1 to February 28; NJ DEP/Rutgers are respon sible for the period from March 1 to May 14; NY DEC is responsible for the period from M ay 15 to September 30; VA DEQ is responsible for the period from October 1 to Oct ober 31; and NESCAUM is responsible for the period from November 1 to Decem ber 31. Each period uses a 15 day spin up run to minimize the impact of the default i nitial concentration fields. Each group performs CMAQ simulations on its period for a serie s of scenarios including 2002 Base Case, 2009 Base Case, 2018 Base Case, 2009 Control Case, and 2018 Control Case. All scenarios adopt the same meteorological field (2002 ) and boundary conditions, varying only emission inputs. To ensure consistency, a benc hmark test was conducted by each modeling group. In addition to the annual simulations conducted wit h CMAQ by the five modeling centers, NESCAUM has conducted limited sensitivity analysis of several control measures using the beta version of CMAQ with the pa rticle and precursor tagging methodology (CMAQ-PPTM) (ICF, 2006). These runs an d their results are described separately in Chapter 5.


### 1.4.2. REMSAD The Regional Modeling System for Aerosols and Depos ition (REMSAD) is a three-dimensional Eulerian model designed to suppor t a better understanding of the distributions, sources, and removal processes relev ant to fine particles and other airborne pollutants. It calculates the concentrations of bo th inert and chemically reactive pollutants by simulating the physical and chemical processes in the atmosphere that affect pollutant concentrations. The basis for the model is the atmospheric diffusion equation representing a mass balance in which all of the rel evant emissions, transport, diffusion, chemical reactions, and removal processes are expre ssed in mathematical terms. The REMSAD model performs a four-step solution procedur e: emissions, horizontal advection/diffusion, vertical advection/diffusion a nd deposition, and chemical transformations during one half of each advective t ime step, and then reverses the order for the following half time step. The maximum adve ctive time step for stability is a function of the grid size and the maximum wind velo city or horizontal diffusion coefficient. Vertical diffusion is solved on fract ions of the advective time step to keep their individual numerical schemes stable. REMSAD uses a flexible horizontal and vertical coor dinate system with nestedgrid capabilities and user-defined vertical layers. It accepts a geodetic (latitude/longitude) horizontal coordinate system o r a Cartesian horizontal coordinate system measured in kilometers. REMSAD uses a simpl ified version of CB-IV chemistry mechanism that is based on a reduction in the numbe r of different organic compound species and also includes radical-radical terminati on reactions. The organic portion of the chemistry is based on three primary organic compoun d species and one carbonyl species.MANE-VU Modeling for Reasonable Progress Goals Pa ge 1-22

The model parameterizes aerosol chemistry and dynam ics for PM and calculates secondary organic aerosol (SOA) yields from emitted hydrocarbons. REMSAD V7.12 and newer versions have capabilities that allow mod el tags of sulfur species (up to 11 tags), nitrogen (4 tags), mercury (up to 24 tags), and cadmium (up to 10 tags) to identify the impact of specific tagged species. Unlike CMAQ, REMSAD provides no choice of chemical and physical mechanisms. The modeling configuration for future work with REMSAD will be similar to the CMAQ modeling setup. The initial concentrat ions and boundary conditions will be generated using the same concentration profile used by CMAQ. The approach is to use similar model inputs to allow comparison of REMSAD with CMAQ to better understand differences between the two models. Due to the sim plified chemistry mechanism, REMSAD may not simulate atmospheric processes as we ll as CMAQ. However, advantages such as the tagging feature for sulfur, more efficient modeling, and reasonable correspondence with measurements for many species, make REMSAD an important source apportionment tool for MANE-VU. In our present REMSAD modeling, we use the same 12 km domain (i.e., domain2) presented in the previous section f or three full annual runs for the base year (2002). Multiple runs are necessary to permit tagging of sulfur emissions for all of the states in the domain, Canada, and the boundary conditions.MANE-VU Modeling for Reasonable Progress Goals Page 2-1



## 2. PERFORMANCE EVALUATION

2.1. Meteorological Evaluation The 2002 annual 12 km resolution meteorological fie lds generated by MM5 have been evaluated by NESCAUM using ENVIRON's METSTAT p rogram. Model results of surface wind speed, wind direction, temperature, an d humidity are paired with measurements from EPA’s Clean Air Status and Trends Network (CASTNET) and National Center for Atmospheric Research’s Techniqu es Data Laboratory (TDL) network by hour and by location and then statistically comp ared. Figure 2-1 presents domainwide average hourly bias of wind speed (left panel) and wind direction (right panel) between the MM5 results and two sets of measurement for every season in 2002 (winter includes Jan., Feb., and Dec.; spring includes Mar. , Apr., and May; summer includes Jun., Jul., and Aug.; fall includes Sep., Oct., and Nov.). It shows that MM5 capably predicts wind speed with reasonably small bias and equal consistency. Within the domain, MM5 tends to overestimate wind speed (hourl y bias up to 1.7 m/s) at CASTNET sites, and underestimate wind speed (hourly bias up to -1.85 m/s) at TDL sites. Seasonal mean bias of MM5 wind speed to CASTNET wind speed i s ~0.3 to 0.4 m/s, while seasonal mean bias of MM5 wind speed to TDL wind sp eed is about ~-0.5 to -0.6 m/s. No significant seasonal variation on this wind spee d bias is observed. MM5 prediction of wind direction shows a larger variation with CASTNE T wind direction (hourly bias from ~-30 degree to ~30 degree) than with TDL wind directi on (hourly bias from ~-5 degree to ~10 degree). However, seasonal mean bias of MM5 win d direction to CASTNET wind direction (~2 degree) is smaller than seasonal mean bias of MM5 wind direction to TDL wind direction (~3 degree) because the large variati on of positive and negative bias offset each other.MANE-VU Modeling for Reasonable Progress Goals Page 2-2

Figure 2-1. 2002 seasonal average hourly bias of w ind speed and direction Wind Speed Wind Direction

Bias Wind Speed

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

1 2 3 4 5 6 7 8 9 1 0 11 1 2 13 14 15 1 6 17 1 8 19 2 0 21 2 2 23 24 25 2 6 27 28 29 3 0 31 3 2 33 34 35 3 6 37 38 39 4 0 41 42 43 44 45 4 6 47 48 49 5 0 51 52 53 54 55 56 57 58 59 3 35 336 3 37 3 3 8 3 39 3 4 0 3 41 3 4 2 3 43 3 4 4 3 45 3 4 6 3 47 3 4 8 349 3 5 0 3 51 3 5 2 3 53 3 5 4 3 55 3 5 6 3 57 3 5 8 359 3 6 0 3 61 3 6 2 363 3 6 4 3 65 Julian Daym/s CASTNet TDL Bias Wind Direction

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

1 2 3

4 5 6 7 8 9 1 0 1 1 1 2 13 1 4 15 1 6 1 7 18 1 9 2 0 2 1 22 2 3 24 2 5 2 6 27 2 8 2 9 3 0 3 1 3 2 33 3 4 3 5 36 3 7 3 8 39 4 0 4 1 42 4 3 4 4 45 4 6 4 7 48 4 9 5 0 5 1 5 2 5 3 54 5 5 5 6 57 5 8 5 9 335 3 3 6 337 338 33 9 340 341 34 2 3 4 3 344 3 4 5 346 34 7 3 4 8 349 350 35 1 3 5 2 353 3 5 4 355 35 6 3 5 7 358 359 36 0 361 362 36 3 364 36 5 Julian DayDegree CASTNet TDL

Index of Agreement (IOA) is a statistical measure o f difference between prediction and measurement, calculated as a ratio o f Root Mean Square Error to the sum of the difference between prediction and mean obser vation and difference between observation and mean observation. IOA varies from 0 to 1, with a value of 1 indicatingwinterspringsummerfallMANE-VU Modeling for Reasonable Progress Goals Page 2-3

the perfect agreement between model prediction and observation, and a value larger than 0.5 IOA indicating acceptable model performance. D omain-wide average hourly IOAs of wind speed are presented in Figure 2-2. MM5 pre dictions of wind speed values are in good agreement (IOA from ~0.5 to ~0.9) to both CASTNE T data and TDL data with similar IOA variation. Seasonal mean values of IOA are ~ 0.7. No particular season of the year stands out in terms of its agreement with measurement. Figure 2-2. 2002 seasonal hourly average index of agreement for wind speed a) winter b) spring

c) summer d) fall

Quarterly correlation coefficients in Figure 2-3 sh ow good MM5 performance on hourly wind speed for each observation site. MM5 p redictions exhibit similar spatial patterns of correlation with CASTNET (left panel) a nd TDL (right panel) measurements – stronger correlation in north than in south. Ove r the year, the model has stronger correlation in the 1 st quarter (Jan., Feb., Mar., top 1 st row), 2 nd quarter (Apr., May, Jun., 2 nd row) and 4 th quarter (Oct., Nov., Dec., bottom row) than it doe s in the 3 rd quarter (Jun., Jul., Aug., 3 rd row), with an average of 0.1 correlation coefficie nt difference. Generally, MM5 predictions and measurements have st rongest correlation (0.8~0.9) within the midwestern U.S., strong correlation (0.7 ~0.8) within the northeastern U.S. and along the coastline, and acceptable correlation (0. 5~0.7) within the southern U.S. and interior portions of the U.S. East Coast. MM5 pred ictions consistently show very similar spatial patterns and temporal variations for wind d irection (as shown in Figure 2-4) andMANE-VU Modeling for Reasonable Progress Goals Page 2-4

wind speed. There is strong correlation (>0.7) bet ween prediction and measurement for wind direction at most of sites. Figure 2-3. Quarterly correlation coefficient (r) of hourly wind speed between modeling and measurement for each observation site in 2002MANE-VU Modeling for Reasonable Progress Goals Page 2-5

Figure 2-4. Quarterly correlation coefficient (r) of hourly wind direction between

modeling and measurement for each observation site in 2002MANE-VU Modeling for Reasonable Progress Goals Page 2-6

Figure 2-5 presents domain-wide average hourly bias of surface temperature between MM5 results and CASTNET and TDL for every s eason. MM5 tends to underestimate temperature at TDL sites throughout t he year and at CASTNET sites for non-ozone season months. The seasonal mean tempera ture bias values are from ~-1 K (winter) to ~-0.3 K (summer) for TDL sites and ~-1 K (winter) to ~0.5 K (summer) for CASTNET sites. MM5 predictions show significantly larger variations of temperature bias at CASTNET sites (-4 K~9 K) than at TDL sites ( -3 K~1 K). Domain-wide average hourly IOA values of temperatur e are shown in Figure 2-6. Model predicted temperatures have significantly bet ter agreement with TDL data (average IOA as ~0.95) than with CASTNET data (avera ge IOA as ~0.85), although both indicate accurate MM5 performance on temperature. Figure 2-7 shows the spatial distribution of quarte rly correlation coefficients between MM5 prediction and measurement of surface t emperature. It reveals very strong correlation (>0.95) over most of the domain for TDL data, with strong correlation (>0.8) for the majority of CASTNET sites. No spatial patt erns or quarterly variations are apparent. MM5 performs consistently well throughou t the year and the domain. The TDL network also provides humidity measurements . Comparison between MM5 prediction of hourly surface humidity and TDL m easurement are presented in Figure 2-8. MM5 captures the general trend of humi dity change. It tends to underestimate humidity during the ozone season (sea sonal mean bias as ~0.35g/kg), and overestimate it during the rest of year (seasonal m ean bias range from ~0.17 to ~0.4). Domain-wide average hourly humidity bias shows a la rge diurnal variation, as much as 2g/kg. Domain-wide average hourly IOA in Figure 2- 9 shows that MM5 predicted humidity values are in good agreement with TDL data (average IOA as ~0.9) throughout year. Spatial distribution of quarterly correlatio n coefficient in Figure 2-10 shows a distinctive spatial pattern and temporal trend. MM 5 results have stronger correlation to TDL data in the northern US than in the Southern US . Through the year, the strongest correlation between MM5 prediction and measurement occurs in the 4th Quarter (>0.95), followed by the 1st and 2nd Quarters, and finally, the 3rd Quarter, which shows the weakest correlation (0.5~0.9). Based on this statistical comparison between model prediction and data from two networks for wind speed, wind direction, temperatur e, and humidity, MM5 performs well. An acceptable small bias, high index of agree ment and strong correlation with CASTNET and TDL data are shown. Since MM5 uses TDL data for nudging, the model predictions are in better agreement with TDL data t han with CASTNET data. MM5 performs better in Midwest and Northeast than South eastern US.MANE-VU Modeling for Reasonable Progress Goals Page 2-7

Figure 2-5. 2002 Seasonal Hourly Average Bias of Temperature Figure 2-6. 2002 Seasonal Hourly Average Index of AgreementWinter SpringSummerFallWinter SpringSummerFallMANE-VU Modeling for Reasonable Progress Goals Page 2-8

Figure 2-7. Quarterly correlation coefficient (r) of hourly temperature between modeling and measurement for each observation site in 2002MANE-VU Modeling for Reasonable Progress Goals Page 2-9

Figure 2-8. 2002 Seasonal average hourly bias of humidityWinter SpringSummerFall Figure 2-9. 2002 seasonal hourly average index of agreementWinterSpringFallWinter SpringSummerFallWinterSpringFallWinter SpringSummerFallMANE-VU Modeling for Reasonable Progress Goals Page 2-10

Figure 2-10. Quarterly correlation coefficient (r) of hourly humidity between modeling and measurement for each observation site in 2002



### 2.2. Model Evaluation CMAQ modeling has been conducted for the year 2002 (completed by cooperative modeling efforts from NYDEC, UMD, NJDEP , Rutgers, VADEP, and NESCAUM) under the Base B4 emission scenario descri bed in Chapter 1. CMAQ performance for PM 2.5 species and visibility is examined based on this C MAQ run on a 12 km resolution domain. Measurements from IMPROVE and STN networks are paired with model predictions by location and time for eva luation. Figure 2-11 presents the domain-wide paired comparison of PM 2.5 species (sulfate, nitrate, OC, EC, fine soil, and PM 2.5 ) daily average concentration from the CMAQ simulat ion and two sets of observations (STN and IMPROVE). It shows that pred icted PM 2.5 sulfate (top row left panel) and measured sulfate are in a good 1:1 linea r relationship with r 2 varying from 0.6 to 0.7. PM 2.5 nitrate (top row right panel) also has close to a 1:1 linear relationship between the model and observations, although the r 2 values are much lower (from ~0.2 to ~0.5) than for sulfate. Paired OC (middle row left p anel) concentrations have a scattered distribution with over- and under-estimation and a very weak linear relationship (r 2 of ~0.1). CMAQ tends to overestimate EC (middle row ri ght panel) and fine soil (bottom row left panel) concentrations. EC and soil are inert species not involved in chemi cal transformation. Poor emission inventory data may be the main cause for t he weak linear relationships betweenMANE-VU Modeling for Reasonable Progress Goals Page 2-11

prediction and measurement. In addition, there are no fire emissions considered in CMAQ modeling. The wild fire in Quebec, Canada in early July of 2002 led to high concentrations of observed OC, EC, and fine soil th at are not predicted by CMAQ. Because sulfate is the dominant PM 2.5 species, modeled PM 2.5 (bottom row right panel) shows a relatively strong near 1:1 linear re lationship (slope between 0.7–0.8 with

r 2 of 0.4–0.5). Figure 2-12 describes the spatial di stribution of the correlation coefficient of sulfate between CMAQ prediction and observations (STN data on the top row and IMPROVE data on the bottom row) at network sites. CMAQ predictions show a similar spatial pattern of correlation with both networks. Generally, the northern region of the domain has st ronger correlations than does the southern region. Correlation coefficients with in the MANE-VU region are highest (~0.9 on average) compared to other RPO regions. Th e spatial distribution of correlation coefficient for PM 2.5 is presented in Figure 2-13. The PM 2.5 correlation coefficient spatial pattern follows PM 2.5 sulfate correlation coefficient, although at the s ame observation site coefficient values are ~0.1 lower than the sulfate c oefficient value. Like PM 2.5 sulfate, CMAQ also performs the best for PM 2.5 in the MANE-VU region with a ~0.7 annual average for the correlation coefficient. The goal and the criteria for PM 2.5 evaluation suggested by Boylan and Baker (2004) have been adopted by every RPO for SIP model ing. The proposed performance goals are: Mean Fractional Error (MFE) ≤ +50%, and Mean Fraction Bias (MFB) ≤ ±30%; while the criteria are proposed as: MFE ≤ +75%, and MFB ≤ ±60%. CMAQ prediction of PM 2.5 species from 40 STN sites and 17 IMPROVE sites within MANE-VU region are paired with measurements and statistically analyzed to generate MFE and MFB values. Figure 2-14 presents MFE of PM 2.5 sulfate, nitrate, OC, EC, fine soil, and PM 2.5 , and curves of the goal and criteria. MFB values are shown in Figure 2-15. Considering CMAQ performance in terms of MFE and MFB goals, sulfate, nitrate, OC, EC, and PM 2.5 all have the majority of data points within the go al curve, some are between the goal and acceptable criteria, and only a few are outside the criteria curve. Only fine soil has the majority of points o utside the criteria curve, but there are some sites still within the goal. For the MANE-VU region, CMAQ performs best for PM 2.5 sulfate, followed by PM 2.5 , EC, nitrate, OC, and then fine soil. Regional haze modeling also requires a CMAQ perform ance evaluation for aerosol extinction coefficient (B ext ) and the haze index. Modeled daily aerosol extinc tion at each IMPROVE site is calculated following the IM PROVE formula with modeled daily PM 2.5 species concentration and relative humidity factor s from IMPROVE. The approaches used here and throughout this analysis, have used natural background visibility estimates and the haze index following E PA Guidance. Figure 2-16 shows the paired comparison between pre diction and measurement of daily B ext from seven sites for 2002. The modeled B ext shows a near 1:1 linear relationship (slope of 0.78 and r 2 of 0.46) with IMPROVE observed B ext . The regression excluded three points from July 7, 2002; the monito rs were directly impacted by Canadian fires whose emissions were not modeled.MANE-VU Modeling for Reasonable Progress Goals Page 2-12

CMAQ prediction of the B ext agrees well with IMPROVE observation because CMAQ performs well on sulfate, which dominates aero sol extinction. Further, the modeled haze index (HI) is calculated based on mode led B ext . Figure 2-17 presents the paired comparison between CMAQ prediction and IMPRO VE measurement for 2002 of HI values at seven Class I sites in the eastern U.S .. Acadia and Moosehorn show the best model performance with regression slopes of 0.97 an d r 2 of ~0.6., The poorest model performance occurs at Lye Brook and Shenandoah, wit h regression slopes less than 0.6 and r 2 of ~0.3. Note the regression equations and best fi t lines are not plotted.MANE-VU Modeling for Reasonable Progress Goals Page 2-13

Figure 2-11. Domain-wide paired comparison of dail y average PM 2.5 species between CMAQ predictions and measurements from IMPR OVE networks PM 2.5 

### Sulfate PM 2.5 

### Nitrate

PM 2.5 

### Organic Carbon PM 2.5 

### Elemental Carbon

Daily Average EC (ug/m3) 2002

y = 0.8813x + 0.0404 R 2 = 0.2933

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 IMPROVEMODEL

CMAQ_2002B1 1:1

PM 2.5 

### Soil PM 2.5 Daily Average Fine Soil (ug/m3) 2002

y = 0.0216x + 1.3139 R 2 = 0.0003

0 2 4 6 8 10 12 14 16 18 20

0 2 4 6 8 10 12 14 16 18 20 IMPROVEMODEL CMAQ_2002B1 1:1 Daily Average PM2.5 (ug/m3) 2002

y = 0.6654x + 3.6324 R 2 = 0.3404

0 10 20 30 40 50 60

0 10 20 30 40 50 60 IMPROVEMODEL CMAQ_2002B1 1:1 Daily Average OC (ug/m3) 2002

y = 0.2707x + 1.1677 R 2 = 0.147

0 5 10 15 20 25 30

0 5 10 15 20 25 30 IMPROVEMODEL CMAQ_2002B1 1:1 Daily Average NO3 (ug/m3) 2002

y = 1.7303x + 0.6609 R 2 = 0.4499

0 5 10 15 20 25 30

0 5 10 15 20 25 30 IMPROVEMODEL

CMAQ_2002B1 1:1 Daily Average SO4 (ug/m3) 2002

y = 0.8572x - 0.0598 R 2 = 0.6795 y = 0.7776x + 0.4006 R 2 = 0.6035

0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40 IMPROVEMODEL CMAQ_2002B1 REMSAD_2002A1 1:1MANE-VU Modeling for Reasonable Progress Goals Page 2-14

Figure 2-12. Spatial distribution of correlation coefficient between PM 2.5 Sulfate and measurement

Figure 2-13. Spatial distribution of correlation c oefficient

between PM 2.5 and measurement -90 -80 -70 30 35 40 45 50

-90 -80 -70 30 35 40 45 50

0.4 to 0.5 0.5 to 0.6 0.6 to 0.7 0.7 to 0.8 0.8 to 0.9 0.9 to 1 IMPROVE vs. 2002BaseB1 CMAQ Annual IMPROVE vs. 2002BaseA1 REMSAD AnnualMANE-VU Modeling for Reasonable Progress Goals Page 2-15

Figure 2-14. Mean Fractional Error of PM 2.5 species within MANE-VU region PM 2.5 

### Sulfate PM 2.5 

### Nitrate

PM 2.5 

### Organic Carbon PM 2.5 

### Elemental Carbon

0 20 40 60 80 100 120 140 160 180 200

0 0.5 1 1.5 2 2.5 3 IMPROVE PM2.5 (ug/m3)Fractional Error (%) Goal

Criteria FE_2002B1_CMAQ_MANEVU FE_2002B1_CMAQ_other3RPOs

PM 2.5 

### Soil PM 2.5

0 20 40 60 80 100 120 140 160 180 200

0 0.5 1 1.5 2 2.5 3 IMPROVE PM2.5 (ug/m3)Fractional Error (%) Goal

Criteria FE_2002B1_CMAQ_MANEVU FE_2002B1_CMAQ_other3RPOs

0 20 40 60 80 100 120 140 160 180 200

0 2 4 6 8 10 12 14 16 IMPROVE PM2.5 (ug/m3)Fractional Error (%) Goal

Criteria FE_2002B1_CMAQ_MANEVU FE_2002B1_CMAQ_other3RPOs 0 20 40 60 80 100 120 140 160 180 200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 IMPROVE OC (ug/m3)Fractional Error (%) Goal Criteria FE_2002B1_CMAQ_MANEVU FE_2002B1_CMAQ_other3RPOs 0 20 40 60 80 100 120 140 160 180 200

0 0.5 1 1.5 2 2.5 3 IMPROVE NO3 (ug/m3)Fractional Error (%) Goal Criteria FE_2002B1_CMAQ_MANEVU FE_2002B1_CMAQ_other3RPOs

0 20 40 60 80 100 120 140 160 180 200

0 2 4 6 8 10 12 IMPROVE SO4 (ug/m3)Fractional Error (%) Goal Criteria FE_2002B1_CMAQ FE_2002B1_CMAQ_20%W FE_2002A1_REMSAD FE_2002A1_REMSAD_20%WMANE-VU Modeling for Reasonable Progress Goals Page 2-16

Figure 2-15. Mean Fraction Bias of PM 2.5 species within MANE-VU region PM 2.5 

### Sulfate PM 2.5 

### Nitrate

PM 2.5 

### Organic Carbon PM 2.5 

### Elemental Carbon

-200 -150 -100 -50 0 50 100 150 200

0 0.5 1 1.5 2 2.5 3

IMPROVE PM2.5 (ug/m3)Fractional Bias (%) Goal

Criteria FB_2002B1_CMAQ_MANEVU FB_2002B1_CMAQ_other3RPOs

PM 2.5 

### Soil PM 2.5

-200 -150 -100 -50 0 50 100 150 200

0 0.5 1 1.5 2 2.5 3

IMPROVE PM2.5 (ug/m3)Fractional Bias (%) Goal

Criteria FB_2002B1_CMAQ_MANEVU FB_2002B1_CMAQ_other3RPOs

-200 -150 -100 -50 0 50 100 150 200

0 2 4 6 8 10 12 14 16

IMPROVE PM2.5 (ug/m3)Fractional Bias (%) Goal Criteria FB_2002B1_CMAQ_MANEVU FB_2002B1_CMAQ_other3RPOs -200 -150 -100 -50 0 50 100 150 200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

IMPROVE OC (ug/m3)Fractional Bias (%) Goal
Criteria FB_2002B1_CMAQ_MANEVU FB_2002B1_CMAQ_other3RPOs -200 -150 -100 -50 0 50 100 150 200

0 0.5 1 1.5 2 2.5 3

IMPROVE NO3 (ug/m3)Fractional Bias (%)

Goal

Criteria FB_2002B1_CMAQ_MANEVU FB_2002B1_CMAQ_other3RPOs -200 -150 -100 -50 0 50 100 150 200

0 1 2 3 4 5 6 7 8 9 10 11 12

IMPROVE SO4 (ug/m3)Fractional Bias (%) Goal Criteria FB_2002B1_CMAQ FB_2002B1_CMAQ_20%W FB_2002A1_REMSAD FB_2002A1_REMSAD_20%WMANE-VU Modeling for Reasonable Progress Goals Page 2-17

Figure 2-16. Paired comparison of extinction coeffi cient between CMAQ prediction and IMPROVE measurement

y = 0.78x + 26.88 R 2 = 0.46

0 100 200 300 400 500 600

0 200 400 600 800

IMPROVE Aerosol Extinction (Mm-1)CMAQ Aerosol Extinction (Mm-1)

Outliers not included in regression (July 7 Fire)

Figure 2-17. Paired Comparison of Haze Index betwe en CMAQ prediction and

IMPROVE measurement at selected Class I sites

0 20 40

0 20 40 IMPROVE (dv)CMAQ (dv) Acadia Brigantime Lye Brook Camp Dodge Moosehorn Dolly Sods ShenandoahMANE-VU Modeling for Reasonable Progress Goals Page 3-1



## 3. 2018 BOTW PROJECTIONS In order to assess the projected visibility improve ment at MANE-VU Class I areas prior to consideration of potential reasonable meas ures for adoption in a long-term emissions management strategy, a simulation of the MANE-VU “Beyond on the Way” (BOTW-1) inventory was conducted. As indicated in Chapter 2, this inventory/scenario combination represents additional measures beyond e xisting regulations that have been accepted by the OTC Modeling Committee for attainme nt of the 8-hour ozone and PM 2.5 NAAQSs. These measures include regulations on port able fuel containers, architectural and maintenance (AIM) coatings, and some consumer p roducts. In addition, at the point that this inventory was “closed” for further change s, most states had indicated a willingness to adopt regulations limiting fuel sulf ur content of distillate fuel oil to 500 ppm or lower. 4 While all states have subsequently agreed that th ey will pursue regulation of distillate AND residual fuel oil and that these regulations would cap distillate at 15 ppm fuel sulfur content by 2018, t his additional level of reduction is not reflected in the BOTW-1 simulation discussed below. The BOTW-1 scenario was processed through SMOKE for 2009 by NYDEC and for 2018 by NESCAUM and distributed to the other mo deling centers in a manner similar to the 2002 base year scenario that was SMOKE proce ssed by NYDEC. After each center had completed its portion of the processing, NESCAUM obtained the results for all projection years for analysis of haze metrics. The results of this run are shown in Table 3-1 and Figures 3-1 and 3-2, which show relative reduction factors at each Class I are a by species and the overall projected improvement in visibility in deciviews based on the 2009 (NYDEC) and 2018 (NESCAUM) BOTW-1 projections, respectively.
Table 3-1. 2018 twenty percent worst days relative reduction factors. Shenandoah Dolly Sods Brigantine Great Gulf Lye Brook Moosehorn Acadia Sulfate 0.49 0.51 0.53 0.59 0.58 0.63 0.60 Nitrate 0.46 0.63 0.95 0.87 0.91 0.73 0.80 EC 0.58 0.71 0.62 0.73 0.67 0.77 0.75 OC 0.88 0.92 0.98 0.86 0.93 0.95 0.95 Sea Salt 1 1 1 1 1 1 1 Soil 1.27 1.26 1.28 1.16 1.13 1.09 1.10

4 Delaware and Vermont had not given an indication b y the time the inventory was closed.MANE-VU Modeling for Reasonable Progress Goals Page 3-2

Figure 3-1. Projected improvement in visibility at four Northeast sites based on 2009 and 2018 BOTW -1 projections.

18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020Deciview Acadia Camp Dodge Lye Brook Moosehorn Acadia BOTW_BaseB4 Camp D. BOTW_BaseB4 Lye B BOTW_BaseB4 Moos BOTW_BaseB4

Figure 3-2. Projected improvement in visibility at three Mid-Atlantic sites based on 2009 and 2018 BOTW -1 projections.

22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020Deciview Brigantine Dolly Sods Shenandoah Brig BOTW_BaseB4 Dolly Sods BOTW_BaseB4 Shenandoa BOTW_Base4

The projections for the BOTW-1 scenario indicate th at the adoption of 500 ppm distillate regulations by all MANE-VU states is suf ficient to achieve visibility improvements beyond the uniform rate of progress de fined by the 2064 natural conditionsMANE-VU Modeling for Reasonable Progress Goals Page 3-3

visibility goal. However, it should be noted that USEPA guidance for setting reasonable progress goals asks states to consider reviewing al l measures identified through the fourfactor analysis process and to adopt each measure t hat is determined to be reasonable. While the interpretation of USEPA guidance on this subject continues to be debated by various stakeholders and some states out side the MANE-VU region, MANEVU believes that the four-factor analysis provision s in the Clean Air Act requires states to analyze additional measures and adopt those that ar e reasonable. We have identified and analyzed several additional measures for considerat ion in determining regional haze reasonable progress goals and these options are exp lored in Chapter 5.MANE-VU Modeling for Reasonable Progress Goals Page 4-1



## 4. 2018 POLLUTION APPORTIONMENT One requirement of the regional haze rule is a “pol lution apportionment” that provides an assessment of the major contributors to MANE-VU visibility impairment by geographical region or by sector. MANE-VU had con ducted an extensive apportionment of 2002 visibility impairment from su lfate in the prior Contribution Assessment

report (NESCAUM, 2006a) and conceptual description (NESCAUM, 2006b). In order to update this work to reflect changes in the contributions by various states to visibility impairment projected for 2018, we have u tilized the 2018 BOTW emission inventory and tagged all SO 2 emissions from each of 29 states in the eastern U. S. This required three separate runs with 11 tags per run. In addition, three tags for baseline (2002) boundary conditions ( N orth, S outh_ E ast, and W est) provide an estimate for sulfate contributions external to the model domain. Note their contribution includes emissions that originated within the domain, but we re advected out of the modeling domain only to recirculate back into the domain (i. e. the state-specific tagged contributions represent, in this sense, a lower-bou nd). This tagging scheme provides a comprehensive report ing of the influence of most of these states to visibility impairment within the model domain. It also provides a partial accounting of the influence of several states along the western and southern edge of the model domain where only a portion of the states’ em issions were tracked. Results indicate that the relative contribution of states within the domain will decrease significantly due, in large part, to the a nticipated SO 2 emissions reductions from the CAIR program. As a result, we see large increases in the relative contribution from Canada and the boundaries. This apparent increase is simply due to the fact that we are showing relative contributions and as a share of th e total, these fixed contributions contribute a larger share after CAIR has reduced th e contribution within the domain. Figures 4-1 through 4-5 show the absolute magnitude of measured and projected sulfate at each MANE-VU class I monitor as well as the relative contributions of each state to that sulfate as contrasted against their 2 002 contributions.MANE-VU Modeling for Reasonable Progress Goals Page 4-2

Figure 4-1. a. Measured and projected mass contrib utions in 2002 and 2018 at Acadia National Park on twenty percent worst visibi lity days.μg/m3

0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC MS VT RI AL DE WI CT TN SC GA NJ VA IL KY MD MI NC NH IN WV ME NY PA OH MA 0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Massμg/m3

0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC MS VT RI AL DE WI CT TN SC GA NJ VA IL KY MD MI NC NH IN WV ME NY PA OH MA 0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass 0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC MS VT RI AL DE WI CT TN SC GA NJ VA IL KY MD MI NC NH IN WV ME NY PA OH MA 0 2 4 6 8 10 12 14

Acadia

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass

b. 2002 and 2018 sulfate mass from at Acadia Nation al Park, twenty percent worst days apportioned by REMSAD

0 1 2 3 4 5 6 7 8

2002 2018ug/m3 MA OH PA NY ME WV IN NH NC MI MD KY IL VA NJ GA SC TN CT WI DE AL RI VT MS DC N_BC W_BC SE_BC CENRAP CANADAMANE-VU Modeling for Reasonable Progress Goals Page 4-3

Figure 4-2. a. Measured and projected mass contrib utions in 2002 and 2018 at Brigantine Wildlife Refuge on twenty percent worst visibility days.μg/m3

0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC VT RI ME DC MS CT NH MA WI AL NY IL SC MI TN KY NJ IN DE GA WV VA NC MD OH PA

IMPROVE mass 00-04 REMSAD Contribution to Sulfate 0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil CMAQ RRF-Based 2018 Massμg/m3

0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC VT RI ME DC MS CT NH MA WI AL NY IL SC MI TN KY NJ IN DE GA WV VA NC MD OH PA

IMPROVE mass 00-04 REMSAD Contribution to Sulfate 0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil CMAQ RRF-Based 2018 Mass 0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC VT RI ME DC MS CT NH MA WI AL NY IL SC MI TN KY NJ IN DE GA WV VA NC MD OH PA

IMPROVE mass 00-04 REMSAD Contribution to Sulfate 0 5 10 15 20 25

Brigantine

Sulfate Nitrate EC OC Sea Salt Soil CMAQ RRF-Based 2018 Mass

b. 2002 and 2018 sulfate mass from Brigantine Wildl ife Refuge, twenty percent worst days from REMSAD

0 1 2 3 4 5 6 7 8 9 10

2002 2018ug/m3 PA OH MD NC VA WV GA DE IN NJ KY TN MI SC IL NY AL WI MA NH CT MS DC ME RI VT N_BC W_BC SE_BC CENRAP CANADAMANE-VU Modeling for Reasonable Progress Goals Page 4-4

Figure 4-3. a. Measured and projected mass contribu tions in 2002 and 2018 at Lye

Brook Wilderness Area on twenty percent worst visib ility days.

0 2 4 6 8 10 12 14 16

Lye Brook

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC RI MS ME CT VT NH WI DE SC MA AL IL NJ TN GA MI KY VA IN NC MD WV NY OH PAμg/m3

0 2 4 6 8 10 12 14 16

Lye Brook

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass 0 2 4 6 8 10 12 14 16

Lye Brook

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC RI MS ME CT VT NH WI DE SC MA AL IL NJ TN GA MI KY VA IN NC MD WV NY OH PAμg/m3

0 2 4 6 8 10 12 14 16

Lye Brook

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass b. 2002 and 2018 sulfate mass from Lye Brook Wilder ness Area, twenty percent worst days from REMSAD

0 1 2 3 4 5 6 7 8

2002 2018ug/m3 PA OH NY WV MD NC IN VA KY MI GA TN NJ IL AL MA SC DE WI NH VT CT ME MS RI DC N_BC W_BC SE_BC CENRAP CANADAMANE-VU Modeling for Reasonable Progress Goals Page 4-5

Figure 4-4. a. Measured and projected mass contrib utions in 2002 and 2018 at Great Gulf Wilderness Area on twenty percent worst visibility days.

0 2 4 6 8 10 12 14

Great Gulf

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC MS VT RI ME CT DE AL NJ WI SC TN VA IL MD GA KY NH NC MI WV MA IN NY PA OHμg/m3

0 2 4 6 8 10 12 14

Great Gulf

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass 0 2 4 6 8 10 12 14

Great Gulf

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC DC MS VT RI ME CT DE AL NJ WI SC TN VA IL MD GA KY NH NC MI WV MA IN NY PA OHμg/m3

0 2 4 6 8 10 12 14

Great Gulf

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass

b. 2002 and 2018 sulfate mass from Great Gulf Wilde rness Area, twenty percent worst days from REMSAD

0 1 2 3 4 5 6 7 8

2002 2018ug/m3 OH PA NY IN MA WV MI NC NH KY GA MD IL VA TN SC WI NJ AL DE CT ME RI VT MS DC N_BC W_BC SE_BC CENRAP CANADAMANE-VU Modeling for Reasonable Progress Goals Page 4-6

Figure 4-5. a. Measured and projected mass contrib utions in 2002 and 2018 at Moosehorn National Wildlife Refuge on twenty percen t worst visibility days.

0 2 4 6 8 10 12

Moosehorn

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC MS DC VT RI AL CT WI DE TN SC NJ GA IL MI VA KY NH MD NC IN WV NY ME OH PA MAμg/m3

0 2 4 6 8 10 12

Moosehorn

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass 0 2 4 6 8 10 12

Moosehorn

Sulfate Nitrate EC OC Sea Salt Soil 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2002 2018 CANADA CENRAP SE_BC W_BC N_BC MS DC VT RI AL CT WI DE TN SC NJ GA IL MI VA KY NH MD NC IN WV NY ME OH PA MAμg/m3

0 2 4 6 8 10 12

Moosehorn

Sulfate Nitrate EC OC Sea Salt Soil
IMPROVE mass 00-04 REMSAD Contribution to Sulfate CMAQ RRF-Based 2018 Mass b. 2002 and 2018 sulfate mass from Moosehorn Nation al Wildlife Refuge, twenty

percent worst days from REMSAD

0 1 2 3 4 5 6 7 8

2002 2018ug/m3 MA PA OH ME NY WV IN NC MD NH KY VA MI IL GA NJ SC TN DE WI CT AL RI VT DC MS N_BC W_BC SE_BC CENRAP CANADAMANE-VU Modeling for Reasonable Progress Goals Page 5-1



## 5. CONTROL STRATEGY EVALUATION We evaluated the visibility benefits of four potent ial control strategies aimed at reducing regional haze at Class I areas in the MANE -VU region beyond what has been included in the “OTB/OTW” scenario described earlie r. These programs include two separate but linked low-sulfur content fuel initiat ives (the S1 and S2 strategies), the BART provisions of the Regional Haze Rule, and cont rols on EGUs at the 167 stacks most likely to affect MANE-VU Class I areas (“167 E GU strategy”). This chapter reviews the control strategies in more detail, desc ribes the potential emissions reductions, and evaluates the potential visibility benefits of each strategy in combination with the others.


### 5.1. Reduced sulfur fuel content (S1 and S2) The MANE-VU states have agreed through consultation s to pursue a low sulfur fuel strategy within the region. This phased strat egy would be implemented in two steps; however, both components of the strategy are to be fully implemented by 2018. We have analyzed both steps of the program as separate stra tegies, but it is the combined benefit of implementing the program that is relevant to the qu estion of program benefits in 2018. The S1 strategy involves the lowering of fuel-sulfu r content in distillate (No. 2 oil) from current levels that range between 2,000 a nd 2,300 ppm down to 500 ppm by weight. It also restricts the sale of heavier blen ds of residual oil (No. 4 fuel oil and No. 6 bunker fuels) that have sulfur content greater than 0.25 percent sulfur and 0.5 percent sulfur by weight, respectively. The S2 strategy fur ther reduces the fuel-sulfur content of the distillate fraction to 15 ppm sulfur by weight. The residual oil is maintained at the same S1 level for this strategy. The S1 strategy and S2 strategy are to be implement ed in sequence with slightly different timing for an “inner zone” 5 and the remainder of MANE-VU. All states, however, have agreed to pursue the adoption and imp lementation of an “emission management” strategy, as appropriate and necessary, to reduce the sulfur content of distillate oil and residual fuel oil as specified i n the MANE-VU statements adopted June 20, 2007 by the MANE-VU Board. Thus for the purpos es of this analysis, we have examined the benefits of the S1 and S2 strategies s eparately below. Based on the fuel sulfur limits within the S1 strat egy, we estimated a decrease of 140,000 tons of SO 2 emitted from distillate combustion and 40,000 tons of SO 2 from residual combustion in MANE-VU. Figure 5-1 display s the resulting average change in 24-hr average PM 2.5 between the baseline case (OTB/OTW) and the contro l case where the S1 fuel strategy has been implemented.

5 The inner zone includes New Jersey, Delaware, New York City, and potentially portions of eastern Pennsylvania.MANE-VU Modeling for Reasonable Progress Goals Page 5-2

Figure 5-1. Average change in 24-hr PM 2.5 due to S1 emission reductions ( μ g/m 3 )

We used the concentration changes in Figure 5-1 abo ve to derive visibility benefits. Because the S1 fuel sulfur program only affects sources within MANE-VU, that region sees the largest PM 2.5 reduction and the greatest visibility benefits. The S2 fuel strategy further reduces the sulfur con tent of distillate from 500 ppm to 15 ppm while keeping the sulfur limits on residu al oils to 0.25 percent and 0.5 percent for No. 4 and No. 6 oils, respectively. By lowerin g the distillate fuel sulfur limit from 500 ppm to 15 ppm, we estimate an additional reduct ion of 27,000 tons of SO 2 emissions in MANE-VU from distillate combustion in 2018. Fig ure 5-2 displays the average change in 24-hr PM 2.5 calculated from CMAQ modeled concentrations betwee n the S1 scenario and the S2 scenario. It reflects the pred icted change in PM 2.5 due solely to the change from 500 ppm to 15 ppm distillate. Due to a high baseline fuel sulfur level, the incremental change in PM 2.5 concentration is much smaller between 500 ppm and 15 ppm than the baseline to 500 ppm levels observed in the S1 scenario.MANE-VU Modeling for Reasonable Progress Goals Page 5-3

Figure 5-2. Average change in 24-hr PM 2.5 due to S2 emission reductions, relative to S1 ( μ g/m 3 )

To determine the full benefit of the fuel strategie s being considered relative to the OTB/OTW baseline, we can look at the combined benef its from the S1 (500 ppm distillate and 0.25/0.5 percent residual oil) strat egy and the S2 (15 ppm distillate) strategy. The combined benefits can be gauged in F igures 5-6 through 5-14 and are shown in the results presented in Table 5-2 at the end of this section.



### 5.2. Best Available Retrofit Program (BART) To assess the impacts of the implementation of the BART provisions of the Regional Haze Rule, we included estimated reduction s anticipated for BART-eligible facilities in the MANE-VU region in the 2018 CMAQ m odeling analysis. An inital survey of state staff indicated that these 14 units would likely be controlled under BART alone and were modeled in this analysis. These stat es provided potential control technologies and levels of control, which were in t urn incorporated into the 2018 emission inventory projections. NESCAUM (2007) pro vides the survey approach. Updates to this preliminary assessment (including t he removal of six Pennsylvania sources with combined emissions reductions of 6600 tons of SO 2 ) will be incorporated into the Best and Final modeling run scheduled to b e completed in March, 2008. Figure 5-3 displays the locations of the BART sources and estimated SO 2 reductions expected inMANE-VU Modeling for Reasonable Progress Goals Page 5-4

2018. Additional visibility benefits are likely to result from installation of controls at BART-eligible facilities that are located in adjace nt RPOs. These benefits are not accounted for in the present analysis.

Figure 5-3. Potential reductions from BART-eligible sources in the MANE-VU region (tons)

We applied the SO 2 reductions at the initial 14 facilities relative t o the 2018 OTB/OTW emissions inventory. Figure 5-4 shows the average change in 24-hr PM 2.5 concentrations within the modeling domain used to c alculate the visibility benefits.MANE-VU Modeling for Reasonable Progress Goals Page 5-5

Figure 5-4. Average change in 24-hr PM 2.5 due to BART emission reductions ( μ g/m 3 )



### 5.3. 167 EGU Strategy The MANE-VU states have recognized that SO 2 emissions from power plants are the single largest contributing sector to the visib ility impairment experienced in the Northeast’s Class I areas. The SO 2 emissions from power plants continue to dominate the inventory. Sulfate formed through atmospheric processes from SO 2 emissions are responsible for over half the mass and approximatel y 70-80 percent of the extinction on the worst visibility days (NESCAUM, 2006a,b). In o rder to ensure that EGU controls are targeted at those EGUs with the greatest impact on visibility in MANE-VU, a modeling analysis was conducted to determine which sources t hose were. A list of 167 EGU stacks was developed (MANE-VU, 2007) that includes the 100 largest impacts at each MANEVU Class I site during 2002. MANE-VU is currently asking for 90 percent control on all units emitting from those stacks by 2018 as part of consultations within MANE-VU and with other RPOs. MANE-VU recognizes that this leve l of control may not be feasible in all cases. The Best and Final modeling run current ly underway will incorporate State comments gathered during the inter-RPO consultation process. The “167 EGU strategy,” if implemented as defined h ere, could lead to large reductions in SO 2 emissions due to installation of stack control tec hnologies such as SO 2 scrubbers. To determine the possible health benefi ts of this EGU control program, we modeled 2018 emissions for the 167 EGUs in the Nort heast, Southeast, and Midwest at levels equal to 10 percent of their 2002 emissions. We used CMAQ to model sulfate concentrations in 2018 after implementation of this control program and convertedMANE-VU Modeling for Reasonable Progress Goals Page 5-6

sulfate concentrations to PM 2.5 concentrations. Figure 5-5 displays the average c hange in 24-hr PM 2.5 seen between the OTB/OTW baseline and the EGU stac k control program.

Figure 5-5. Average change in 24-hr PM 2.5 due to 167 EGU emission reductions ( μ g/m 3 )

Figure 5-5 shows that significant reductions of PM 2.5 are predicted for the MANE-VU region as well as for portions of the VISTA S and Midwest RPO regions as a result of the targeted EGU strategy. Figures 5-6 through 5-14 show the visibility benefi ts – relative to the uniform rate of progress determined our national visibility goal of natural conditions in 2064 – of the OTB/OTW scenario as well as for the four potential measures analyzed here. In addition to these measures, MANE-VU has asked neighboring RP Os to consider non-EGU emissions reductions comparable to our low sulfur f uel strategies, which are expected to achieve a greater than 28 percent reduction in non- EGU SO 2 emissions in 2018. The figures indicate that additional progress could be achieved depending upon what strategies are identified by VISTAS and the Midwest RPO in response to this request.MANE-VU Modeling for Reasonable Progress Goals Page 5-7

Figure 5-6. Visibility improvement relative to uni form rate of progress at Acadia National Park

18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm) S-2 (15 ppm) 167 EGUs 18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm) S-2 (15 ppm) 167 EGUs

Figure 5-7. Visibility improvement relative to uni form rate of progress at

Brigantine National Wildlife Refuge.

22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)
S-2 (15 ppm) 167 EGUs

22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)
S-2 (15 ppm) 167 EGUsMANE-VU Modeling for Reasonable Progress Goals Page 5-8

Figure 5-8. Visibility improvement relative to uni form rate of progress at Great Gulf Wilderness Area

18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm) S-2 (15 ppm) 167 EGUs 18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm) S-2 (15 ppm) 167 EGUs

Figure 5-9. Visibility improvement relative to uni form rate of progress at Lye Brook Wilderness Area

18 19 20 21 22 23 24 25

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

18 19 20 21 22 23 24 25

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUsMANE-VU Modeling for Reasonable Progress Goals Page 5-9

Figure 5-10. Visibility improvement relative to un iform rate of progress at

Moosehorn National Wildlife Refuge

17 18 19 20 21 22

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

17 18 19 20 21 22

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Figure 5-11. Visibility improvement relative to un iform rate of progress at Shenandoah National Park

OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs 21 22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs 21 22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025DeciviewMANE-VU Modeling for Reasonable Progress Goals Page 5-10

Figure 5-12. Visibility improvement relative to un iform rate of progress at Dolly Sods Wilderness Area

21 22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs 21 22 23 24 25 26 27 28 29 30

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Figure 5-13. Visibility improvement relative to un iform rate of progress at

Presidential Range-Dry River Wilderness Area

18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

18 19 20 21 22 23 24

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART S-1 (500ppm)

S-2 (15 ppm) 167 EGUsMANE-VU Modeling for Reasonable Progress Goals Page 5-11

Figure 5-14. Visibility improvement relative to un iform rate of progress at

Roosevelt-Campobello International Park

17 18 19 20 21 22

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

17 18 19 20 21 22

2000 2005 2010 2015 2020 2025Deciview OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Tables 5-1 and 5-2 summarize the sulfate mass reduc tions and the deciview targets that represent the progress shown in the pr ior figures.MANE-VU Modeling for Reasonable Progress Goals Page 5-12

Table 5-1. Projected 2018 twenty percent worst day sulfate mass reduction at MANE-VU Class I areas under various control assumpt ions.

Notes on Table 5-1: 1. Baseline values represent the average sulfate mass (μg/m 3 ) over the 5 year baseline period on the 20 percent worst days. 2. OTB/OTW represents the combined estimated mass redu ction (μg/m 3 ) due to all “on the books”

measures. 3. BART mass reduction reflects preliminary estimates of emission reductions resulting from BART determinations. These determinations are still in the process of being conducted, however, and thus are subject to change. 4. S-1 oil strategy assumes the adoption of 500 ppm di stillate, 0.25 percent S for all No. 4 oil and 0.5 percent S for all No. 6 residual oil. 5. S-2 oil strategy assumes the adoption of 15 ppm di stillate, 0.25 percent S for all No. 4 oil and 0.5 percent S for all No. 6 residual oil. 6. 167 EGU strategy benefits are based on net reductio ns after each of the 167 stacks is controlled to at least the 90 percent level and after the identif ied emissions reductions (beyond 2018 projections contained in the Base B emissions files) are redist ributed among all other CAIR-eligible EGUs in the modeling domain. 

### 0.24 0.03 0.21 0.07 2.03 5.67 Roosevelt-Campobello International Park, NB 

### 0.13 0.01 0.09 0.06 2.96 7.28 Presidential Range – Dry River Wilderness, NH 

### 0.24 0.03 0.21 0.07 2.03 5.67 Moosehorn Wilderness, ME 

### 0.18 0.01 0.13 0.09 3.49 8.46 Lye Brook Wilderness, VT 

### 0.13 0.01 0.09 0.06 2.96 7.28 Great Gulf Wilderness, NH 

### 0.51 0.02 0.20 0.07 5.35 11.58 Brigantine Wilderness, NJ 

### 0.37 0.03 0.29 0.08 2.40 6.32 Acadia National Park, ME 167 EGUs S-2 S-1 BART OTB/OTW [2018] Baseline [2000-2004] MANE-VU Class I Area



### 0.24 0.03 0.21 0.07 2.03 5.67 Roosevelt-Campobello International Park, NB 

### 0.13 0.01 0.09 0.06 2.96 7.28 Presidential Range – Dry River Wilderness, NH 

### 0.24 0.03 0.21 0.07 2.03 5.67 Moosehorn Wilderness, ME 

### 0.18 0.01 0.13 0.09 3.49 8.46 Lye Brook Wilderness, VT 

### 0.13 0.01 0.09 0.06 2.96 7.28 Great Gulf Wilderness, NH 

### 0.51 0.02 0.20 0.07 5.35 11.58 Brigantine Wilderness, NJ 

### 0.37 0.03 0.29 0.08 2.40 6.32 Acadia National Park, ME 167 EGUs S-2 S-1 BART OTB/OTW [2018] Baseline [2000-2004] MANE-VU Class I AreaMANE-VU Modeling for Reasonable Progress Goals Page 5-13

Table 5-2. Projected 2018 twenty percent worst day deciview goals for MANE-VU

Class I areas under various control assumptions

Notes on Table 5-2: 1. Baseline values represent the 5-year average baseli ne conditions (dv) on the 20 percent worst days. 2. OTB/OTW represents the projected deciview goal due to all OTB/OTW measures. 3. Pluses indicate that the deciview goals assume impl ementation of all measures to the left of and including the column indicated. 4. BART reflects preliminary estimates of emissions re ductions due to BART determinations. These determinations are still in the process of being co nducted and thus are subject to change. 5. S-1 oil strategy assumes the adoption of 500 ppm di stillate, 0.25 percent S for all No. 4 oil and 0.5 percent S for all No. 6 residual oil. 6. S-2 oil strategy assumes the adoption of 15 ppm di stillate, 0.25 percent S for all No. 4 oil and 0.5 percent S for all No. 6 residual oil. 7. 167 EGU strategy benefits are based on net reductio ns after each of the 167 stacks is controlled to at least the 90 percent level and after the identif ied emissions reductions (beyond 2018 projections contained in the Base B emissions files) are redist ributed among all other CAIR-eligible EGUs in the modeling domain. 17.79 18.15 18.19 18.49 18.58 21.72 Roosevelt-Campobello International Park, NB 18.59 18.77 18.78 18.90 18.98 22.82 Presidential Range – Dry River Wilderness, NH 17.80 18.16 18.20 18.50 18.59 21.72 Moosehorn Wilderness, ME 19.90 20.12 20.13 20.29 20.40 24.45 Lye Brook Wilderness, VT 18.43 18.61 18.62 18.74 18.81 22.82 Great Gulf Wilderness, NH 23.47 23.98 24.00 24.19 24.26 29.01 Brigantine Wilderness, NJ 18.50 19.05 19.10 19.51 19.62 22.89 Acadia National Park, ME +167 EGUs +S-2 +S-1 +BART OTB/OTW [2018] Baseline [2000-2004] MANE-VU Class I Area

17.79 18.15 18.19 18.49 18.58 21.72 Roosevelt-Campobello International Park, NB 18.59 18.77 18.78 18.90 18.98 22.82 Presidential Range – Dry River Wilderness, NH 17.80 18.16 18.20 18.50 18.59 21.72 Moosehorn Wilderness, ME 19.90 20.12 20.13 20.29 20.40 24.45 Lye Brook Wilderness, VT 18.43 18.61 18.62 18.74 18.81 22.82 Great Gulf Wilderness, NH 23.47 23.98 24.00 24.19 24.26 29.01 Brigantine Wilderness, NJ 18.50 19.05 19.10 19.51 19.62 22.89 Acadia National Park, ME +167 EGUs +S-2 +S-1 +BART OTB/OTW [2018] Baseline [2000-2004] MANE-VU Class I AreaMANE-VU Modeling for Reasonable Progress Goals Page 6-1



## 6. CONCLUSIONS This report provides details on modeling platforms and input data as well as a description of the processing steps that were under taken to prepare inputs for use in simulating future air quality on an eastern U.S. do main that includes MANE-VU Class I areas. The findings are consistent with previous wo rk documenting the role of SO 2 emissions in the formation of visibility impairing fine particulate in the eastern U.S. (NESCAUM, 2006a, b). This report goes further, how ever, in terms of providing detailed simulations of (1) projected visibility im pairment in 2018 under a “beyond on the way” scenario that represents a starting point for the regional haze program; (2) stateby-state apportionment of 2018 emissions for that 2 018 “beyond on the way” scenario; and (3) sensitivity analysis of the projected benef its of several additional measures that are being considered by the MANE-VU states for incl usion in reasonable progress goals. The findings of these simulations suggest that:

• The “beyond on the way” scenario – defined by CAIR with other “on the books” measures and the limitation of fuel sulfur c ontent to 500 ppm for all No. 2 “distillate” fuel oil sold in the MANE-VU region – is sufficient to achieve visibility improvement beyond the so-cal led “uniform rate of progress” defined by uniform visibility improvement between now and 2064, the planning horizon for the regional haze pr ogram.

• The 2018 pollution apportionment suggests that this improvement is due to significant reductions in the relative contribut ions of almost all eastern U.S. states, resulting in a relative increase (though not an absolute increase) in the projected contribution from areas outside the modeling domain (e.g., Canada and the model domain boundary conditions).

• Potential additional emissions reduction strategies (including the reduction of fuel sulfur content of No. 2 distillat e to 15 ppm, limits on sulfur content of residual oil, control of BART-eli gible sources, and additional EGU controls beyond CAIR) could yield si gnificant further reductions of sulfate and corresponding significant visibility improvements at MANE-VU Class I areas and should be considered with respect to the four statutory factors in setting re asonable progress goals. As MANE-VU states consider these results and conduc t consultations with each other and neighboring RPOs, NESCAUM will prepare a “best and final” modeling scenario for 2018 that may assist the Class I state s in setting reasonable progress goals based on their assessment of which measures are rea sonable to implement. This final model run is anticipated to be complete in March 20 08.MANE-VU Modeling for Reasonable Progress Goals Page 7-1



## 7. REFERENCES

Amar, P., D. Chock, A. Hansen, M. Moran, A. Russell , D. Steyn, and W. Stockwell. 2005: Final Report: Second Peer Review of the CMAQ Model, Report submitted to the Community Modeling and Analysis System Center, Univ ersity of North Carolina at Chapel Hill, July 2005.
K.W. Appel, A. Gilliland, and B. Eder. An Operational Evaluation of the 2005 Release of Models-3 CMAQ , 2005 CMAQ Workshop, Chapel Hill, NC.

Bell, M., and H. Ellis. Comparison of the 1-hr and 8-hr National Ambient Ai r Quality Standards for ozone using Models-3, J. Air Waste Manage. Assoc. 53, 1531-1540, 2003.
Boylan, J., and K. Baker. Photochemical Model Performance and Consistency. National RPO Modeling Meeting, Denver, CO, May 26, 2004.
Byun D.W., and J.K.S. Ching. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , EPA/600/R-99/030, March 1999.
Gery M.W., G.Z. Whitten, J.P. Killus, and M.C. Dodg e. A photochemical kinetics mechanism for urban and regional scale computer mod eling , J. Geophys. Res. 94, 12925-12956, 1989.
Hao, W., M. Ku, and G. Sistla. Analysis of MM5 Simulations based on three PBL schemes over the eastern US for August 6 to 16, 200 2 , NYSDEC-DAR. Albany, NY, March 4, 2004.
Hogrefe, C., J. Biswas, B. Lynn, K. Civerolo, J.-Y. Ku, J. Rosenthal, C. Rosenzweig, C. Goldberg, and P.L. Kinney. Simulating regional-scale ozone climatology over th e eastern United States: Model evaluation results, Atmos. Environ. 38, 2627-2638, 2004.
ICF, Implementation of Mercury Tagging in the Commu nity Multi-Scale Air Quality (CMAQ) Model, Draft Project Report, prepared by Sha ron Douglas, Tom Meyer, Yihua Wei for USEPA Office of Air Quality Planning and St andards, ICF Consulting, San Rafael, CA, July 7, 2006.
Jimenez P., and J. M. Baldasano. Ozone response to precursor controls in very comple x terrains: Use of photochemical indicators to assess O3-NOx-VOC sensitivity in the northeastern Iberian Peninsula , J. Geophys. Res. V109, D20309, 2004.
Ku, K. et al. Comparison of RPO model results, National RPO Modeling Meeting, Denver, CO, Dec. 2005.MANE-VU Modeling for Reasonable Progress Goals Page 7-2

MARAMA, MANE-VU emission inventories were developed by the Mid-Atlantic Regional Air Management Agency (MARAMA) and can be accessed at: http://www.marama.org/visibility/EI_Projects/index. html , 2007a.

MARAMA. Development of Emission Projections for 2009, 2012, and 2018 for NonEGU Point, Area, and Nonroad Source in the MANE-VU Regi on, http://www.marama.org/visibility/Inventory%20Summar y/MANEVU_Emission_Projecti ons_TSD_022807.pdf , 2007b.
Mao H., and R. Talbot. Role of meteorological processes in two New England ozone episodes during summer 2001 , J. Geophys. Res. V109, D20305, 2004.
Mebust, M.R., B.K. Eder, F.S. Binkowski, S.J. Rosel le. Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 2. Model evaluation , J. Geophys. Res. V108, D6
, 4184, 2003.

NESCAUM. Contributions to Regional Haze in the Northeast and Mid-Atlantic U.S. , available at: http://www.nescaum.org/documents/contributions-to-r egional-haze-in-thenortheast-and-mid-atlantic--united-states/ , Northeast States for Coordinated Air Use Management, Boston, MA, 2006a.

NESCAUM. The Nature of the Fine Particle and Regional Haze Air Quality Problems in the MANE-VU Region: A Conceptua l Description , Available at: http://www.nescaum.org/documents/2006-1102-pm-conce ptual-model.pdf/ , Northeast States for Coordinated Air Use Management, Boston, MA, 2006b.

NESCAUM. Five-Factor Analysis of BART-Eligible Sources; Surv ey of Options for Conducting BART Determinations , available at: http://www.nescaum.org/documents/bart-final-memo-06 -28-07.pdf , Northeast States for Coordinated Air Use Management, Boston, MA, 2007.
Park, R. J., D. J. Jacob, B. D. Field, R. M. Yantos ca, and M. Chin. Natural and transboundary pollution influences on sulfate-nitra te-ammonium aerosols in the United States: implications for policy , J. Geophys. Res. D15204, 10.1029/2003JD004473, 20 04.
Ray, J.D., R.L. Heavner, M. Flores, and C.W. Michae lsen. Surface-level measurements of ozone and precursors at coastal and offshore locati ons in the Gulf of Maine

. J. Geophys. Res

. 101, 29005-29011, 1996.
SAI, User’s Guide to the Regional Modeling System f or Aerosols and Deposition (REMSAD), Version 8, ICF Consulting/SAI, San Franc isco, CA., 2005. Available at www.remsad.com

. Seigneur C., G. Hidy, I. Tombach, J. Vimont, and P. Amar. Scientific peer-review of the Regulatory Modeling System for Aerosols and Deposit ion (REMSAD), September 1999.MANE-VU Modeling for Reasonable Progress Goals Page 7-3

Timin, B., C. Jang, P. Dolwick, N. Possiel, T. Brav erman. Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation . CMAS Annual Workshop, RTP, NC. October 22, 2002.
Tonnesen, G., Z. Wang, C.J. Chien, M. Omary, B. Wang, R. Morris, Z. Adelman, T. Tesche, D. Olerud. Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP . CMAS Annual Meeting, RTP, NC. October 27, 2003.
CMAQ Peer Review Report. Final Report Summary: December 2003 Peer Review of the CMAQ Model August 2004 http://www.cmascenter.org/html/CMAQ%20peer%20review %20final_CMAS-web.pdf

. CMAQ website http://www.cmascenter.org/ , 2007.
CMAQ Manual http://www.epa.gov/asmdnerl/models3/doc/science/sci ence.html , 2007.

SMOKE website http://cf.unc.edu/cep/empd/products/smoke/index.cfm , 2007.

SMOKE Manual http://cf.unc.edu/cep/empd/products/smoke/version2. 1/html/ , 2007.

MM5 website http://www.mmm.ucar.edu/mm5/ , 2007.

REMSAD website http://www.remsad.com

.