Connecticut Regional Haze 

State Implementation Plan November 2009 

ATTACHMENT G 

MANE-VU Modeling for Reasonable Progress Goals 
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 Planning 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

Anne Gobin, Bureau Chief

Connecticut Department of Environmental Protection, Bureau of Air Management 

James P. Brooks, Bureau Director 

Maine Department of Environmental Protection, Bureau of Air Quality 

Barbara Kwetz, Director 

Massachusetts Department of Environmental Protection, 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 

New York Department of Environmental Conservation, Division of Air Recourses 

Stephen Majkut, Chief 

Rhode Island Department of Environmental Management, Office of Air Resources 

Richard A. Valentinetti, Director 

Vermont Department of Environmental Conservation, Air Pollution Control Division 
ii

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 Planning Organization 

February 7, 2008 
iii

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, NESCAUM 
iv

Acknowledgments 

NESCAUM could not have completed this work without the preparation of emission 

inventories by the Mid-Atlantic Regional Air Management Association (MARAMA) and 

the MANE-VU member states. NESCAUM also acknowledges 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 Organization. NESCAUM is solely 

responsible for the content of this report and any errors it may contain. 

Printed: February 2008 
v

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) Emission Inventory ........ 1-14 

1.3.4. 2018 “Beyond on the Way” (BOTW) Emission Inventory ......................... 1-18 

1.3.5. 2018 Sulfate Tagging (BOTW) Emission Inventory................................... 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................................................................................................................ 7-1 
vi

FIGURES 

Figure 1-1. Modeling domains used in MANE-VU air quality 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 and 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 resolution domain......................... 1-5 

Figure 1-5. Examples of processed model-ready emissions: (a) SO2 from Point; (b) NO2

from Area; (c) NO2 from Onroad; (d) NO2 from Nonroad; (e) ISOP from Biogenic; 

(f) SO2 from all source categories......................................................................... 1-7 

Figure 2-1. 2002 seasonal average hourly bias of wind 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 Temperature................................... 2-7 

Figure 2-6. 2002 Seasonal Hourly Average Index of Agreement ................................... 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 humidity ........................................... 2-9 

Figure 2-9. 2002 seasonal hourly average index of agreement ....................................... 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 daily average PM2.5 species between 

CMAQ predictions and measurements from IMPROVE networks.................... 2-13 

Figure 2-12. Spatial distribution of correlation coefficient between PM2.5 Sulfate and 

measurement ....................................................................................................... 2-14 

Figure 2-13. Spatial distribution of correlation coefficient between PM2.5 and 

measurement ....................................................................................................... 2-14 

Figure 2-14. Mean Fractional Error of PM2.5 species within MANE-VU region......... 2-15 

Figure 2-15. Mean Fraction Bias of PM2.5 species within MANE-VU region............. 2-16 

Figure 2-16. Paired comparison of extinction coefficient between CMAQ prediction and 

IMPROVE measurement .................................................................................... 2-17 

Figure 2-17. Paired Comparison of Haze Index between 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 contributions in 2002 and 2018 at Acadia 

National Park on twenty percent worst visibility days.......................................... 4-2 

Figure 4-2. a. Measured and projected mass contributions in 2002 and 2018 at Brigantine 

Wildlife Refuge on twenty percent worst visibility days...................................... 4-3 
vii

Figure 4-3. a. Measured and projected mass contributions 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 contributions in 2002 and 2018 at Great 

Gulf Wilderness Area on twenty percent worst visibility days............................. 4-5 

Figure 4-5. a. Measured and projected mass contributions in 2002 and 2018 at 

Moosehorn National Wildlife Refuge on twenty percent worst visibility days.... 4-6 

Figure 5-1. Average change in 24-hr PM2.5 due to S1 emission reductions (μg/m3) ...... 5-2 

Figure 5-2. Average change in 24-hr PM2.5 due to S2 emission reductions, relative to S1 

(μg/m3) .................................................................................................................. 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 PM2.5 due to BART emission reductions (μg/m3

) 5-5 

Figure 5-5. Average change in 24-hr PM2.5 due to 167 EGU emission reductions (μg/m3

)

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

Figure 5-6. Visibility improvement relative to uniform rate of progress at Acadia 

National Park......................................................................................................... 5-7 

Figure 5-7. Visibility improvement relative to uniform rate of progress at Brigantine 

National Wildlife Refuge. ..................................................................................... 5-7 

Figure 5-8. Visibility improvement relative to uniform rate of progress at Great Gulf 

Wilderness Area.................................................................................................... 5-8 

Figure 5-9. Visibility improvement relative to uniform rate of progress at Lye Brook 

Wilderness Area.................................................................................................... 5-8 

Figure 5-10. Visibility improvement relative to uniform rate of progress at Moosehorn 

National Wildlife Refuge ...................................................................................... 5-9 

Figure 5-11. Visibility improvement relative to uniform rate of progress at Shenandoah 

National Park......................................................................................................... 5-9 

Figure 5-12. Visibility improvement relative to uniform rate of progress at Dolly Sods 

Wilderness Area.................................................................................................. 5-10 

Figure 5-13. Visibility improvement relative to uniform rate of progress at Presidential 

Range-Dry River Wilderness Area ..................................................................... 5-10 

Figure 5-14. Visibility improvement relative to uniform 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 .......................................................... 5-13 
viii

Executive Summary 

The main purpose of this report is to assist states in developing effective solutions 

to regional visibility and fine particle problems and comply with requirements under the 

Regional Haze Rule. NESCAUM has utilized in-house air quality modeling capabilities 

that include emission processing, 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 Plan (SIP), including a contribution assessment, a 

pollution apportionment for 2018, and the evaluation 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 include MM5, SMOKE, CMAQ 

and REMSAD, and incorporate tagging features that allow 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 contributor to visibility 

impairment in the Northeast’s Class I areas on the 20 percent worst visibility days – has 

significant contributions from states throughout the eastern U.S. that are projected to 

continue in future years from all three of the eastern regional planning organizations 

(RPOs). 

An assessment of potential control measures that would address this future 

contribution has identified a number of promising strategies that would yield significant 

visibility benefits beyond the uniform rate of progress and, in fact, significantly beyond 

the projected visibility conditions that would result 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 Available Retrofit Technology (BART) 

requirements, and additional electric generating unit (EGU) controls on select sources. 

The combined benefits of adopting all of these programs could lead to an additional 

benefit of between 0.38 and 1.1 deciviews at MANE-VU Class I areas on the 20 percent 

worst visibility days by 2018. 
MANE-VU Modeling for Reasonable Progress Goals Page 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 utilized in-house air quality 

modeling capabilities that include emission processing, 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 quality modeling platform using 

meteorological fields developed by the University of Maryland using the MM5 platform 

(Penn State, 2007), emission inventories developed by MANE-VU (MARAMA, 2007a) 

and processed through the SMOKE emissions processing tool (SMOKE, 2007), and air 

quality simulations conducted jointly by multiple modeling centers utilizing USEPA’s 

Community Multi-scale Air Quality (CMAQ) model (Byun and Ching, 1999). Sulfate 

apportionment was also carried out using the REMSAD model (SAI, 2005) with SO2

tagging capabilities and control strategy evaluation 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. After 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 conducted to serve as a basis for 

calculating final reasonable progress goals. 

This introduction provides a basic description of the modeling platform and the 

input data that we used for regional air quality simulations. 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 on the way” scenario that we take as a 

starting point for the haze program, pollution apportionment for 2018, and haze control 

strategy evaluation. 

 

1

 There are seven designated Class I areas in the Northeast and Mid-Atlantic States. They include Acadia 

National Park and Moosehorn Wilderness Area in Maine; 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 the Brigantine Wilderness Area in New Jersey. 
MANE-VU Modeling for Reasonable Progress Goals Page 1-2

1.2. Meteorology 

Professor Dalin Zhang’s group from University of Maryland (UMD) provided the 

2002 annual meteorological field for air quality modeling. Meteorological inputs for 

CMAQ are derived from the Fifth-Generation Pennsylvania State University/National 

Center for Atmospheric Research (NCAR) Mesoscale Model (MM5)2

 system 

meteorological fields. MM5 is a model with limited-area primitive equations of 

momentum, thermodynamics, and moisture with the option of hydrostatic and nonhydrostatic physics. It is designed to simulate mesoscale atmospheric circulation. 

Domains are uniform rectangular grids representing three-dimensional regions of the 

atmosphere. 

MANE-VU has adopted the Inter-RPO domain description for its modeling runs.3 

This 36-km domain covers the continental United States, 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 domain 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, and southeastern U.S., as well as 

southeastern Canada. It extends from 66oW~94oW in longitude and 29oN~50oN in 

latitude with 172 × 172 grid cells (Figure 1-1). 

Figure 1-1. Modeling domains used in MANE-VU air quality 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 radius of 6370km is used for all elements of the system 

(MM5/SMOKE/CMAQ). 
MANE-VU Modeling for Reasonable Progress Goals Page 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 as high as 1 km. Sigma (σ) is a 

terrain-following vertical coordinate that is a function of pressure at the point (for 

hydrostatic) or reference (non-hydrostatic) state pressure (P), the surface pressure (Ps0), 

and the pressure at the top (Ptop) of the model; σ = (P-Ptop) / (Ps0-Ptop). 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 (Figure 1-2). 

Based on test run results, the boundary layer processes were determined using the 

Blackadar high-resolution planetary boundary layer parameterization. Physics options 

also included explicit representations of cloud physics 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 boundary layer (PBL) schemes; (1) 

modified Blackadar [BL], (2) the Pleim-Xiu scheme with the soil module [P-X], and (3) 

modified Blackadar with soil module [SSIB].The model 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 Coast. 

 
MANE-VU Modeling for Reasonable Progress Goals Page 1-4

 Figure 1-2. Vertical Structure of Meteorological and 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 Level

Vertical Layer 

MM5 29 Layers

CMAQ 22 Layers

10

1133

622

2330

3976

5257

10796

Altitude (m)

185

68

The simulated meteorological fields were compared to the measurements from 

Techniques Development Laboratory of National Weather Service (TDL NWS) and 

Clean Air Status and Trends Network (CASTNET). The TDL data are reflective of 

urban/suburban settings, while the CASTNET sites are 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 captures 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 speed and temperature (Hao et al., 2004). 
MANE-VU Modeling for Reasonable Progress Goals Page 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 resolution domain 
MANE-VU Modeling for Reasonable Progress Goals Page 1-6

1.3. Emissions Preparations 

We simulated emission scenarios using the Sparse Matrix Operator Kernel 

Emissions (SMOKE) Modeling System. SMOKE is primarily an emissions processing 

system designed to create gridded, speciated, hourly 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 emissions modeling, SMOKE uses the 

Biogenic Emission Inventory System, version 2.3 (BEIS2) 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 permits rapid and flexible 

processing of emissions data. Flexible processing comes from splitting the processing 

steps of inventory growth, controls, chemical speciation, temporal allocation, and spatial 

allocation into independent steps whenever possible. The results from these steps are 

merged together in the final stage of processing using vector-matrix multiplication. It 

allows individual steps (such as adding a new control 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 national 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 Fortran compiler version 5.1. They use 

the 2002 static emission inventory, CEM data, and surrogates data based on the 2002 

RPO data. Biogenic emissions are calculated using BEIS3 with BELD3 data. Mobile 

source emissions are processed using MOBILE6. An updated 2000 inventory for Canada 

and a 1999 inventory for Mexico inventory were used for processing. 

The emissions processing was performed on a month-by-month and RPO-by-RPO 

basis, i.e., SMOKE processing was performed for each 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 was necessary for use with the 

36 km grid modeling only. For each month/RPO combination, a separate SMOKE 

ASSIGNS file was created, and the length of the episode 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 emissions outputs are shown in Figure 1-5. 
MANE-VU Modeling for Reasonable Progress Goals Page 1-7

Figure 1-5. Examples of processed model-ready emissions: 

(a) SO2 from Point; (b) NO2 from Area; (c) NO2 from Onroad; (d) NO2 from 

Nonroad; (e) ISOP from Biogenic; (f) SO2 from all source categories 

a b 

c d 

e f 
MANE-VU Modeling for Reasonable Progress Goals Page 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 MANE-VU area and nonroad 

temporal files listed above, but added the VISTAS-generated CEM-based 

2002 state-specific temporal profiles and cross-references for EGU sources for 

the MANE-VU states. No CEM, hour-specific, EGU emissions 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-specific emission files described 

in Sections 2.3.4 and 2.5.4 were utilized for the VISTAS 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-reference files 

(amptpro.m3.us+can.txt and amptref.m3.us+can.txt) were utilized. 

Chemical speciation 

The same speciation profiles (gspro.cmaq.cb4p25.txt) and cross-references 

(gsref.cmaq.cb4p25.txt) were utilized for all regions and all source categories. 

Different versions of these files were obtained (SMOKE2.1 default, USEPACAIR modeling, VISTAS, CENRAP and MANE-VU) and compared. After 

comparing the creation dates and header lines of these files, it was determined that 

the USEPA-CAIR and MANE-VU files had the most recent updates, and 

consequently the final speciation profile and cross-reference files used for all 

regions and source categories was based on the USEPA-CAIR files with the 

addition of MANE-VU specific updates. 
MANE-VU Modeling for Reasonable Progress Goals Page 1-9

Spatial Allocation 

U.S. 

The spatial surrogates for the 12 km and 36 km domains were extracted from 

the national grid 12 km and 36 km U.S. gridding surrogates posted at 

USEPA’s website at www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html.

The gridding cross-references were also obtained from this website, but for 

the processing of MANE-VU area source emissions, MANE-VU specific 

cross-reference entries posted on the MARAMA ftp site were added. 

Canada 

The spatial surrogates for Canadian emissions for the 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.html.

The gridding cross-references were also obtained from this website. 

Mexico 

The spatial surrogates for Mexican emissions the 36 km domain were 

extracted from the national 36 km gridding surrogates 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 Page 1-10

1.3.2. 2002 Emission Inventory 

A 2002 base year emission inventory was developed to assess model performance 

and to serve as a point of comparison for future year projections in terms of emissions 

reductions and air quality improvement. In order to assess model performance, actual 

2002 emissions (to the extent possible) are incorporated 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 emission reductions incorporated to 

see what degree of air quality improvement can be expected 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 Classification 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-to-date Canadian criteria 

emissions inventory for point sources. This is due largely to confidentiality 

rights afforded to Canadian facilities. Thus far, the 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 point source data and 

incorporate it into an inventory database. 

Perhaps the most accurate and publicly accessible source of Canadian 

pollutant data is now available from the National Pollutant Release Inventory 

(NPRI) database. The NPRI data are available at Environment 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 file. The database contains a 

rather comprehensive list of information. Detailed information is available 

about each facility, including location, activity and 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 database for modeling 

purposes is that the data are only available at the facility level, so in order to 
MANE-VU Modeling for Reasonable Progress Goals Page 1-11

use this data, a few generalizations had to be made. Each facility has a 

Standard Industrial Classification (SIC) code associated with it; however, 

emissions models require SCCs. While no direct relationship exists between 

these two codes, a general albeit subjective association can be made, since 

SCCs are needed for SMOKE. In most cases, only a SCC3 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 obtained from USEPA’s CAIR 

NODA ftp site http://www.airmodelingftp.com (password protected); this 

adjustment was performed using the SMOKE programs cntlmat and grwinven to 

generate an adjusted IDA inventory file used for subsequent SMOKE processing 

for “other area” and point sources. 

Where data sets are month dependant, {MMM} represents JAN, FEB, MAR, etc. 

Note that for both area and nonroad sources, the annual and monthly inventories 

were processed in one step. Processed with SMK_AVEDAY_YN set to N such 

that seasonal profiles were used to apportion the inventories 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.ida, 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 Page 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 USEPA’s CAIR NODA ftp site 

http://www.airmodelingftp.com (password protected); this adjustment was performed 

using the SMOKE programs cntlmat and grwinven to generate 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 downloaded from 

http://bronze.nescaum.org/Private/junghun/MANE-VU/onroad_ver3_update/ 

Point

MANEVU_Point_SMOKE_INPUT_ANNUAL_SUMMERDAY_041006.txt 

MANEVU_Point_SMOKE_INPUT_ANNUAL_WINTERDAY_041006.txt 

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 obtained from USEPA’s CAIR 

NODA ftp site http://www.airmodelingftp.com (password protected); this 

adjustment was performed using the SMOKE programs cntlmat and grwinven to 

generate an adjusted IDA inventory file used for subsequent SMOKE processing 

for “other area” and point sources. 

Where data sets are month dependant, {MMM} represents jan, feb, mar, etc. and 

{MM} is 01, 02, 03, etc. 

 Area 

Agricultural Ammonia - arinv_nh3_2002_mrpok_{MMM}_3may2006.txt 

Wind Erosion Fug-Dust - dustinv_2002_mrpok_{MMM}_23may2006.txt 

- The month-specific files were processed separately from the annual runs and 

SMK_AVEDAY_YN was set to Y so that no seasonal profiles would be 

applied and the inventory numbers in the ‘average day’ column would be 

used. 

Other Area Sources - arinv_other_mrpok_2002_20jun2006_xfact.txt 
MANE-VU Modeling for Reasonable Progress Goals Page 1-13

- Adjusted for fugitive dust as described above 

- SMK_AVEDAY_YN was set to N, so seasonal profiles were used to 

apportion the annual inventory numbers by month. 

- To save SMOKE processing, the annual “marine” inventory was processed 

together with other area sources. 

Nonroad

NMIM Generated Sources - nrinv_2002_mrpok_{MMM}_3may2006.txt 

MAR (Marine/Air/Rail) - arinv_mar_mrpok_2002_27apr2006.txt 

- MAR inventory was SMOKE processed with annual other area sources. 

Onroad

M6-Input files & VMT – mobile_inventory_mrpobasek.tar.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 Stella (Alpine 

Geophysics) 

Point

EGU - ptinv_egu_2002_mrpok_1may2006.txt 

Non-EGU - ptinv_negu_2002_mrpok_1may2006.txt 

- Christian Hogrefe (NYSDEC) merged the two inventories and adjusted for 

fugitive dust, ptinv_egu_negu_2002_mrpok_1may2006_xfact.txt 

VISTAS: 

All VISTAS emission files were obtained from Greg Stella (Alpine Geophysics) 

via ftp.alpinegeophysics.com – username: vistasei They reflect version BaseG of 

the VISTAS inventory with the exception of fire emissions, which reflect BaseF 

for Lo-Fires and BaseD for Hi-Fires. Files were obtained between February and 

August, 2006. 

The header lines of these files indicate that the fugitive dust correction was 

already applied, so no further correction was performed. Where data sets are 

month dependant, {MMM} represents jan, feb, mar, etc. 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 fires 

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.tar 

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.txt 
MANE-VU Modeling for Reasonable Progress Goals Page 1-14

Annual Non-EGU - negu_ptinv_vistas_2002typ_baseg_2453909.txt 

Hour-specific - pthour_2002typ_baseg_{MMM}_28jun2006.ems 

Month Dependant Hi-Fire - ptinv_fires_{MM}_typ.vistas.ida (vr.BaseD) 

Hour-specific plume-rise - pthour_fires_{MM}_typ.vistas.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 similar manner for future 

projection years relative to the 2002 base year, but with the projected inventories. The 

future years “on the books/on the way” (OTB/OTW) emissions inventories account for 

emission control regulations already in place as well as emission control regulations that 

are final but have not yet been fully implemented and are likely to achieve additional 

reductions by 2009. Processing occurred during January 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” correction 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 #062 

- Original IPM parsed file (based on NEEDS 2.1.6) 

- Annualized emissions were calculated by combining summer and winter 

with FORTRAN to create and use ptinv_canada_2020_egu.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 obtained from USEPA’s CAIR 

NODA ftp site http://www.airmodelingftp.com (password protected); this 

adjustment was performed using the SMOKE programs cntlmat and grwinven to 

generate an adjusted IDA inventory file used for subsequent SMOKE processing. 
MANE-VU Modeling for Reasonable Progress Goals Page 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 inventories 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 inventories were processed 

with nonroad sources from the MWRPO and VISTAS. 

- “mod_w_mrpok” files include both MRPO and CENRAP sources 

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 inventories 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 fugitive dust as described 

- Obtained from Alpine Geophysics contracted by MARAMA 

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. (MARAMA, 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/transportfractions.xls; this 

adjustment was performed outside of SMOKE with FORTRAN for area and point 

sources. 
MANE-VU Modeling for Reasonable Progress Goals Page 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 revised their inventory for 

heating oil emissions due to two changes: (1) SO2 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 version from 2001. These 

two changes significantly altered the 2002 SO2 emissions for area source 

heating oil combustion. The revised version was used to do the 

projections. 

- The District of Columbia discovered a gross error in the 2002 residential, 

non-residential, and roadway construction sources. It requested that for 

PM10-PRIM and PM25-PRIM for SCCs 23110X0000, different values be 

used for the 2002 base year and as the basis for the 2009/2012/2018 

projections 

Nonroad

MANEVU_OTB2018_NR_IDAV3_1.txt (Oct 2006) 

ftp.marama.org/2009,12,18 OTB Version 3.1/NONROAD/NONROAD_IDA_Files_v3.1/

- MACTEC utilized the NMIM2005 model to develop projections for 

nonroad engines included in the NONROAD2005 model. Projected 

emission estimates were calculated using NMIM default data. Prior to 

starting the NMIM2005 runs, MACTEC confirmed with USEPA’s Office of 

Transportation and Air Quality (OTAQ) that the database used for fuel 

sulfur content, gas Reid Vapor Pressure (RVP) values, and reformulated fuel 

programs was current and up to date for the MANE-VU region. 

- Emission calculations were made at the monthly level and consolidated to 

provide annual values. This enabled monthly temperatures 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 2006) 

ftp.marama.org/2009,12,18 OTB Version 3.1/non-EGU Point/nonEGU IDA Files/

MRPO: 

Alpine Geophysics was contracted by MARAMA to convert 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 June 2006. 

Where data sets are month dependant, {MMM} represents jan, feb, mar, etc. and 

{MM} is 01, 02, 03, etc. 
MANE-VU Modeling for Reasonable Progress Goals Page 1-17

Area 

Other Area Sources – arinv_other_mrpok_2018_22aug2006.txt 

Agricultural Ammonia – arinv_nh3_2018_mrpok_{MMM}_22aug2006.txt 

Wind Erosion Fug-Dust Base F – dustinv_mrpo_basef_2018_29jul05.ida 

- In order to save time, all area source categories were 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 inventories where processed 

with nonroad sources from the MWRPO and VISTAS. 

On-road 

M6LIST – .in files can be found in the sub-directory input 

VMT - mbinv_vistas+mrpo_18g_vmt_12jun06.ida 

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

- To save SMOKE processing all mobile source inventories 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 fugitive dust as described 

- Used IPM2.1.9 includes post-IPM adjustments 

VISTAS: 

The header lines of these files indicate that the fugitive dust correction was 

already applied, so no further correction was performed. Where data sets are 

month dependant {MMM} is jan, feb, mar, etc. and {MM} is 1, 2, 3, etc. 

Area 

arinv_vistas_2018g_2453922_w_pmfac.txt 

- To save SMOKE processing, area source inventories where 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 inventories were processed 

with nonroad sources from the MWRPO and VISTAS. 

Onroad 

M6LIST – .in files can be found in the sub-directory 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.tar and 

Baseg_2018_mv_vmt.tar 
MANE-VU Modeling for Reasonable Progress Goals Page 1-18

- To save SMOKE processing all mobile source inventories 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.vistasbaseg18.{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. These inventories were based on 

additional control measures that the MANE-VU states are considering for attaining 

various regional haze, ozone, and PM2.5 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 the long-term benefits of regional 

ozone control programs and was conducted on the Integrated 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 projections for 2018 

under different scenarios (MARAMA, 2007b). 

The EGU inventories were developed by ICF Consulting for the RPOS using the 

Integrated Planning Model (IPM version 2.1.9). Alpine Geophysics processed the 

results into IDA inventory format for MANE-VU. 

Fugitive dust correction was applied as county-specific 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/transportfractions.xls; this adjustment 

was performed outside of SMOKE with FORTRAN. 

Area 

manevu_botw2018_area_IDAV3_2_xfact.txt 
MANE-VU Modeling for Reasonable Progress Goals Page 1-19

- “_xfact” version is the adjusted version for fugitive 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_B4

Non-Fossil 2009: manevu_nonfossil_2009_19sept2006.txt 

- Alpines ftp – marama -- Work_Order_1/Task_4_2009_Nonfossil/ 

Non-EGU: MANEVU_BOTW2018_nonegu_IDAV3_1_xfact.txt 

- “_xfact” version is the adjusted version for fugitive 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 specifically 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 for the regular BOTW scenario; 

however, in order to process this inventory for use with the tagging methodology, various 

components of the inventory were processed separately and identified as a specific “type” 

of sulfur dioxide so that it could be tracked through the system. 

The state-by-state tagging used the identical inventory to the 2018 BOTW 

inventory described in the previous section. It was processed such that each state’s SO2

emissions were separately tagged requiring three separate REMSAD simulations to 

accommodate 29 eastern states, Canada, and the boundaries. 

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 include: 

1. OTB/OTW 

2. S-1 fuel oil strategy (500 ppm distillate; 0.5% fuel-sulfur content by weight 

for No. 6 residual oil; 0.25% fuel-sulfur content by 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 weight for No. 4 residual oil.) 

4. BART (approximately 35,000 tons of SO2 reductions at specific facilities 

identified by state survey of permitting staff) 
MANE-VU Modeling for Reasonable Progress Goals Page 1-20

5. “167 Stack” Strategy; (90% control on all EGUs in the 167 stacks identified 

as having the most significant impact on MANE-VU Class I areas) 

Two additional tags were required to account for corrections to the assumed 

baseline fuel sulfur content of distillate and to add 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 Chapter 4. 

1.4. Model Platforms 

Currently two regional-scale air quality models have 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 ICF Consulting/Systems 

Applications International (ICF/SAI) with USEPA support. CMAQ has undergone 

extensive community development and peer review (Amar et al., 2005) and has been 

successfully used in a number of regional air quality 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 (Seigneur 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 Eulerian model that 

incorporates output fields from emissions and meteorological modeling systems and 

several other data sources through special interface processors into the CMAQ Chemical 

Transport Model (CCTM). The CCTM then performs chemical transport modeling for 

multiple pollutants on multiple scales. With this structure, CMAQ retains the flexibility 

to substitute other emissions processing systems and meteorological models. CMAQ is 

designed to provide an air quality modeling system with a “one atmosphere” capability 

containing state-of-science parameterizations of atmospheric processes affecting 

transport, transformation, and deposition of such pollutants 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. The 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 mechanism due to better computing 

efficiency with no significant model performance differences for ozone and PM as 

compared to observations. 

NY DEC has completed annual 2002 CMAQ modeling on the 36 km domain to 

provide dynamic boundary conditions for all simulations performed on the 12 km 

domain. Three-hourly boundary conditions for the outer domain were derived from an 

annual model run performed by researchers at Harvard University using the GEOS-
MANE-VU Modeling for Reasonable Progress Goals Page 1-21

CHEM global chemistry transport model (Park et al., 2004). Model resolution was 

species dependent at either 4° latitude by 5° longitude or 2° by 2.5°. 

Five modeling centers are working collectively to maximize efficiency of 

computing resources in MANE-VU for SIP modeling. These centers include NY DEC, 

NJ DEP/Rutgers, VA DEQ, UMD, and NESCAUM. Annual CMAQ modeling on the 

12 km domain is divided into five periods. UMD is responsible for the period from 

January 1 to February 28; NJ DEP/Rutgers are responsible for the period from March 1 to 

May 14; NY DEC is responsible for the period from May 15 to September 30; VA DEQ 

is responsible for the period from October 1 to October 31; and NESCAUM is 

responsible for the period from November 1 to December 31. Each period uses a 15 day 

spin up run to minimize the impact of the default initial concentration fields. Each group 

performs CMAQ simulations on its period for a series 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 benchmark test was conducted by each 

modeling group. 

In addition to the annual simulations conducted with CMAQ by the five modeling 

centers, NESCAUM has conducted limited sensitivity analysis of several control 

measures using the beta version of CMAQ with the particle and precursor tagging 

methodology (CMAQ-PPTM) (ICF, 2006). These runs and their results are described 

separately in Chapter 5. 

1.4.2. REMSAD 

The Regional Modeling System for Aerosols and Deposition (REMSAD) is a 

three-dimensional Eulerian model designed to support a better understanding of the 

distributions, sources, and removal processes relevant to fine particles and other airborne 

pollutants. It calculates the concentrations of both 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 relevant emissions, transport, diffusion, 

chemical reactions, and removal processes are expressed in mathematical terms. The 

REMSAD model performs a four-step solution procedure: emissions, horizontal 

advection/diffusion, vertical advection/diffusion and deposition, and chemical 

transformations during one half of each advective time step, and then reverses the order 

for the following half time step. The maximum advective time step for stability is a 

function of the grid size and the maximum wind velocity or horizontal diffusion 

coefficient. Vertical diffusion is solved on fractions of the advective time step to keep 

their individual numerical schemes stable. 

REMSAD uses a flexible horizontal and vertical coordinate system with nestedgrid capabilities and user-defined vertical layers. It accepts a geodetic 

(latitude/longitude) horizontal coordinate system or a Cartesian horizontal coordinate 

system measured in kilometers. REMSAD uses a simplified version of CB-IV chemistry 

mechanism that is based on a reduction in the number of different organic compound 

species and also includes radical-radical termination reactions. The organic portion of the 

chemistry is based on three primary organic compound species and one carbonyl species. 
MANE-VU Modeling for Reasonable Progress Goals Page 1-22

The model parameterizes aerosol chemistry and dynamics for PM and calculates 

secondary organic aerosol (SOA) yields from emitted hydrocarbons. REMSAD V7.12 

and newer versions have capabilities that allow model 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 concentrations 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 simplified chemistry mechanism, 

REMSAD may not simulate atmospheric processes as well 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 for 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 fields generated by MM5 have 

been evaluated by NESCAUM using ENVIRON's METSTAT program. Model results of 

surface wind speed, wind direction, temperature, and humidity are paired with 

measurements from EPA’s Clean Air Status and Trends Network (CASTNET) and 

National Center for Atmospheric Research’s Techniques Data Laboratory (TDL) network 

by hour and by location and then statistically compared. 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 (hourly 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 is ~0.3 to 0.4 m/s, while 

seasonal mean bias of MM5 wind speed to TDL wind speed is about ~-0.5 to -0.6 m/s. 

No significant seasonal variation on this wind speed bias is observed. MM5 prediction of 

wind direction shows a larger variation with CASTNET wind direction (hourly bias from 

~-30 degree to ~30 degree) than with TDL wind direction (hourly bias from ~-5 degree to 

~10 degree). However, seasonal mean bias of MM5 wind 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 variation 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 wind 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

123456789

1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859

335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365

Julian Day

m/s

CASTNet TDL

 

Bias Wind Direction

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

123456789

1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859

335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365

Julian Day

Degree

CASTNet TDL

 

 

 

Index of Agreement (IOA) is a statistical measure of difference between 

prediction and measurement, calculated as a ratio of Root Mean Square Error to the sum 

of the difference between prediction and mean observation and difference between 

observation and mean observation. IOA varies from 0 to 1, with a value of 1 indicating 

winter spring summer fall
MANE-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. Domain-wide average hourly IOAs 

of wind speed are presented in Figure 2-2. MM5 predictions of wind speed values are in 

good agreement (IOA from ~0.5 to ~0.9) to both CASTNET 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 show good MM5 performance on 

hourly wind speed for each observation site. MM5 predictions exhibit similar spatial 

patterns of correlation with CASTNET (left panel) and TDL (right panel) measurements 

– stronger correlation in north than in south. Over the year, the model has stronger 

correlation in the 1st quarter (Jan., Feb., Mar., top 1st row), 2nd quarter (Apr., May, Jun., 

2

nd row) and 4th quarter (Oct., Nov., Dec., bottom row) than it does in the 3rd quarter 

(Jun., Jul., Aug., 3rd row), with an average of 0.1 correlation coefficient difference. 

Generally, MM5 predictions and measurements have strongest 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 predictions consistently show very similar 

spatial patterns and temporal variations for wind direction (as shown in Figure 2-4) and 
MANE-VU Modeling for Reasonable Progress Goals Page 2-4

wind speed. There is strong correlation (>0.7) between 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 2002 
MANE-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 2002 
MANE-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 season. MM5 tends to 

underestimate temperature at TDL sites throughout the year and at CASTNET sites for 

non-ozone season months. The seasonal mean temperature 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 temperature are shown in Figure 2-6. 

Model predicted temperatures have significantly better agreement with TDL data 

(average IOA as ~0.95) than with CASTNET data (average IOA as ~0.85), although both 

indicate accurate MM5 performance on temperature. 

Figure 2-7 shows the spatial distribution of quarterly correlation coefficients 

between MM5 prediction and measurement of surface temperature. 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 patterns or quarterly variations are 

apparent. MM5 performs consistently well throughout the year and the domain. 

The TDL network also provides humidity measurements. Comparison between 

MM5 prediction of hourly surface humidity and TDL measurement are presented in 

Figure 2-8. MM5 captures the general trend of humidity change. It tends to 

underestimate humidity during the ozone season (seasonal mean bias as ~0.35g/kg), and 

overestimate it during the rest of year (seasonal mean bias range from ~0.17 to ~0.4). 

Domain-wide average hourly humidity bias shows a large 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 correlation coefficient in Figure 2-10 shows a 

distinctive spatial pattern and temporal trend. MM5 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, temperature, and humidity, MM5 performs 

well. An acceptable small bias, high index of agreement 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 than with CASTNET data. MM5 

performs better in Midwest and Northeast than Southeastern 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 Agreement 

Winter Spring Summer Fall

Winter Spring Summer Fall
MANE-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 2002 
MANE-VU Modeling for Reasonable Progress Goals Page 2-9

Figure 2-8. 2002 Seasonal average 

hourly bias of humidity 

Winter Spring Summer Fall

Figure 2-9. 2002 seasonal hourly 

average index of agreement 

Winter Spring FallWinter Spring Summer FallWinter Spring FallWinter Spring Summer Fall
MANE-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 described in Chapter 1. CMAQ 

performance for PM2.5 species and visibility is examined based on this CMAQ run on a 

12 km resolution domain. Measurements from IMPROVE and STN networks are paired 

with model predictions by location and time for evaluation. Figure 2-11 presents the 

domain-wide paired comparison of PM2.5 species (sulfate, nitrate, OC, EC, fine soil, and 

PM2.5) daily average concentration from the CMAQ simulation and two sets of 

observations (STN and IMPROVE). It shows that predicted PM2.5 sulfate (top row left 

panel) and measured sulfate are in a good 1:1 linear relationship with r2 varying from 0.6 

to 0.7. PM2.5 nitrate (top row right panel) also has close to a 1:1 linear relationship 

between the model and observations, although the r2

 values are much lower (from ~0.2 to 

~0.5) than for sulfate. Paired OC (middle row left panel) concentrations have a scattered 

distribution with over- and under-estimation and a very weak linear relationship (r2 of 

~0.1). CMAQ tends to overestimate EC (middle row right panel) and fine soil (bottom 

row left panel) concentrations. 

EC and soil are inert species not involved in chemical transformation. Poor 

emission inventory data may be the main cause for the weak linear relationships between 
MANE-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 that are not predicted by CMAQ. 

Because sulfate is the dominant PM2.5 species, modeled PM2.5 (bottom row right 

panel) shows a relatively strong near 1:1 linear relationship (slope between 0.7–0.8 with 

r

2

 of 0.4–0.5). Figure 2-12 describes the spatial distribution 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 stronger correlations than does 

the southern region. Correlation coefficients within the MANE-VU region are highest 

(~0.9 on average) compared to other RPO regions. The spatial distribution of correlation 

coefficient for PM2.5 is presented in Figure 2-13. The PM2.5 correlation coefficient spatial 

pattern follows PM2.5 sulfate correlation coefficient, although at the same observation site 

coefficient values are ~0.1 lower than the sulfate coefficient value. Like PM2.5 sulfate, 

CMAQ also performs the best for PM2.5 in the MANE-VU region with a ~0.7 annual 

average for the correlation coefficient. 

The goal and the criteria for PM2.5 evaluation suggested by Boylan and Baker 

(2004) have been adopted by every RPO for SIP modeling. 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 PM2.5 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 PM2.5 sulfate, nitrate, OC, 

EC, fine soil, and PM2.5, 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 PM2.5 all have the majority of data points within the goal 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 outside the criteria curve, but there are 

some sites still within the goal. For the MANE-VU region, CMAQ performs best for 

PM2.5 sulfate, followed by PM2.5, EC, nitrate, OC, and then fine soil. 

Regional haze modeling also requires a CMAQ performance evaluation for 

aerosol extinction coefficient (Bext) and the haze index. Modeled daily aerosol extinction 

at each IMPROVE site is calculated following the IMPROVE formula with modeled 

daily PM2.5 species concentration and relative humidity factors from IMPROVE. The 

approaches used here and throughout this analysis, have used natural background 

visibility estimates and the haze index following EPA Guidance. 

Figure 2-16 shows the paired comparison between prediction and measurement of 

daily Bext from seven sites for 2002. The modeled Bext shows a near 1:1 linear 

relationship (slope of 0.78 and r2 of 0.46) with IMPROVE observed Bext. The regression 

excluded three points from July 7, 2002; the monitors 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 Bext agrees well with IMPROVE observation because 

CMAQ performs well on sulfate, which dominates aerosol extinction. Further, the 

modeled haze index (HI) is calculated based on modeled Bext. Figure 2-17 presents the 

paired comparison between CMAQ prediction and IMPROVE 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 and r2

 of ~0.6., The poorest model 

performance occurs at Lye Brook and Shenandoah, with regression slopes less than 0.6 

and r2

 of ~0.3. Note the regression equations and best fit lines are not plotted. 
MANE-VU Modeling for Reasonable Progress Goals Page 2-13

Figure 2-11. Domain-wide paired comparison of daily average PM2.5 species 

between CMAQ predictions and measurements from IMPROVE networks 

PM2.5 Sulfate PM2.5 Nitrate 

PM2.5 Organic Carbon PM2.5 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

IMPROVE

MODEL

CMAQ_2002B1

1:1

PM2.5 Soil PM2.5

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

IMPROVE

MODEL

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

IMPROVE

MODEL

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

IMPROVE

MODEL

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

IMPROVE

MODEL

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

IMPROVE

MODEL

CMAQ_2002B1

REMSAD_2002A1

1:1
MANE-VU Modeling for Reasonable Progress Goals Page 2-14

 Figure 2-12. Spatial distribution of correlation coefficient 

between PM2.5 Sulfate and measurement 

Figure 2-13. Spatial distribution of correlation coefficient 

between PM2.5 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 Annual
MANE-VU Modeling for Reasonable Progress Goals Page 2-15

Figure 2-14. Mean Fractional Error of PM2.5 species within MANE-VU region 

PM2.5 Sulfate PM2.5 Nitrate 

PM2.5 Organic Carbon PM2.5 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

PM2.5 Soil PM2.5

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%W
MANE-VU Modeling for Reasonable Progress Goals Page 2-16

Figure 2-15. Mean Fraction Bias of PM2.5 species within MANE-VU region 

PM2.5 Sulfate PM2.5 Nitrate 

 

PM2.5 Organic Carbon PM2.5 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

PM2.5 Soil PM2.5

-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%W
MANE-VU Modeling for Reasonable Progress Goals Page 2-17

Figure 2-16. Paired comparison of extinction coefficient 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 between 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

Shenandoah
MANE-VU Modeling for Reasonable Progress Goals Page 3-1

3. 2018 BOTW PROJECTIONS 

In order to assess the projected visibility improvement at MANE-VU Class I areas 

prior to consideration of potential reasonable measures 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 existing regulations that have been 

accepted by the OTC Modeling Committee for attainment of the 8-hour ozone and PM2.5

NAAQSs. These measures include regulations on portable fuel containers, architectural 

and maintenance (AIM) coatings, and some consumer products. In addition, at the point 

that this inventory was “closed” for further changes, most states had indicated a 

willingness to adopt regulations limiting fuel sulfur content of distillate fuel oil to 

500 ppm or lower.4 While all states have subsequently agreed that they will pursue 

regulation of distillate AND residual fuel oil and that these regulations would cap 

distillate at 15 ppm fuel sulfur content by 2018, this 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 modeling centers in a manner similar 

to the 2002 base year scenario that was SMOKE processed 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 area 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 by 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 2020

Deciview

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 2020

Deciview

Brigantine

Dolly Sods

Shenandoah

Brig BOTW_BaseB4

Dolly Sods BOTW_BaseB4

Shenandoa BOTW_Base4

The projections for the BOTW-1 scenario indicate that the adoption of 500 ppm 

distillate regulations by all MANE-VU states is sufficient to achieve visibility 

improvements beyond the uniform rate of progress defined by the 2064 natural conditions 
MANE-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 all measures identified through the fourfactor analysis process and to adopt each measure that is determined to be reasonable. 

While the interpretation of USEPA guidance on this subject continues to be 

debated by various stakeholders and some states outside the MANE-VU region, MANEVU believes that the four-factor analysis provisions in the Clean Air Act requires states to 

analyze additional measures and adopt those that are reasonable. We have identified and 

analyzed several additional measures for consideration in determining regional haze 

reasonable progress goals and these options are explored 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 “pollution apportionment” that 

provides an assessment of the major contributors to MANE-VU visibility impairment by 

geographical region or by sector. MANE-VU had conducted an extensive 

apportionment of 2002 visibility impairment from sulfate 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 utilized the 2018 BOTW emission 

inventory and tagged all SO2 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 (North, South_East, and West) provide an estimate for 

sulfate contributions external to the model domain. Note their contribution includes 

emissions that originated within the domain, but were 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-bound). 

This tagging scheme provides a comprehensive reporting 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’ emissions were tracked. 

Results indicate that the relative contribution of states within the domain will 

decrease significantly due, in large part, to the anticipated SO2 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 the total, these fixed contributions 

contribute a larger share after CAIR has reduced the 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 2002 contributions. 
MANE-VU Modeling for Reasonable Progress Goals Page 4-2

Figure 4-1. a. Measured and projected mass contributions in 2002 and 2018 at 

Acadia National Park on twenty percent worst visibility 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 Mass

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 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 National Park, twenty percent worst 

days apportioned by REMSAD 

0

1

2

3

4

5

6

7

8

2002 2018

ug/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

CANADA
MANE-VU Modeling for Reasonable Progress Goals Page 4-3

Figure 4-2. a. Measured and projected mass contributions 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 Mass

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 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 Wildlife Refuge, twenty percent 

worst days from REMSAD 

0

1

2

3

4

5

6

7

8

9

10

2002 2018

ug/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

CANADA
MANE-VU Modeling for Reasonable Progress Goals Page 4-4

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

Brook Wilderness Area on twenty percent worst visibility 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

PA

μg/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

PA

μg/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 Wilderness Area, twenty percent 

worst days from REMSAD 

0

1

2

3

4

5

6

7

8

2002 2018

ug/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

CANADA
MANE-VU Modeling for Reasonable Progress Goals Page 4-5

Figure 4-4. a. Measured and projected mass contributions 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

OH

μg/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

OH

μg/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 Wilderness Area, twenty percent 

worst days from REMSAD 

0

1

2

3

4

5

6

7

8

2002 2018

ug/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

CANADA
MANE-VU Modeling for Reasonable Progress Goals Page 4-6

Figure 4-5. a. Measured and projected mass contributions in 2002 and 2018 at 

Moosehorn National Wildlife Refuge on twenty percent 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

MA

μg/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

MA

μg/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 National Wildlife Refuge, twenty 

percent worst days from REMSAD 

0

1

2

3

4

5

6

7

8

2002 2018

ug/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

CANADA
MANE-VU Modeling for Reasonable Progress Goals Page 5-1

5. CONTROL STRATEGY EVALUATION 

We evaluated the visibility benefits of four potential 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 earlier. These programs include two 

separate but linked low-sulfur content fuel initiatives (the S1 and S2 strategies), the 

BART provisions of the Regional Haze Rule, and controls on EGUs at the 167 stacks 

most likely to affect MANE-VU Class I areas (“167 EGU strategy”). This chapter 

reviews the control strategies in more detail, describes 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 consultations to pursue a low sulfur 

fuel strategy within the region. This phased strategy 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 strategies, but it is the combined benefit of 

implementing the program that is relevant to the question of program benefits in 2018. 

The S1 strategy involves the lowering of fuel-sulfur content in distillate (No. 2 

oil) from current levels that range between 2,000 and 2,300 ppm down to 500 ppm by 

weight. It also restricts the sale of heavier blends 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 further 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 implemented 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 implementation of an “emission 

management” strategy, as appropriate and necessary, to reduce the sulfur content of 

distillate oil and residual fuel oil as specified in the MANE-VU statements adopted June 

20, 2007 by the MANE-VU Board. Thus for the purposes of this analysis, we have 

examined the benefits of the S1 and S2 strategies separately below. 

Based on the fuel sulfur limits within the S1 strategy, we estimated a decrease of 

140,000 tons of SO2 emitted from distillate combustion and 40,000 tons of SO2 from 

residual combustion in MANE-VU. Figure 5-1 displays the resulting average change in 

24-hr average PM2.5 between the baseline case (OTB/OTW) and the control 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 PM2.5 due to S1 emission reductions (μg/m3) 

We used the concentration changes in Figure 5-1 above to derive visibility 

benefits. Because the S1 fuel sulfur program only affects sources within MANE-VU, 

that region sees the largest PM2.5 reduction and the greatest visibility benefits. 

The S2 fuel strategy further reduces the sulfur content of distillate from 500 ppm 

to 15 ppm while keeping the sulfur limits on residual oils to 0.25 percent and 0.5 percent 

for No. 4 and No. 6 oils, respectively. By lowering the distillate fuel sulfur limit from 

500 ppm to 15 ppm, we estimate an additional reduction of 27,000 tons of SO2 emissions 

in MANE-VU from distillate combustion in 2018. Figure 5-2 displays the average 

change in 24-hr PM2.5 calculated from CMAQ modeled concentrations between the S1 

scenario and the S2 scenario. It reflects the predicted change in PM2.5 due solely to the 

change from 500 ppm to 15 ppm distillate. Due to a high baseline fuel sulfur level, the 

incremental change in PM2.5 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 PM2.5 due to S2 emission reductions, relative to 

S1 (μg/m3) 

To determine the full benefit of the fuel strategies being considered relative to the 

OTB/OTW baseline, we can look at the combined benefits from the S1 (500 ppm 

distillate and 0.25/0.5 percent residual oil) strategy and the S2 (15 ppm distillate) 

strategy. The combined benefits can be gauged in Figures 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 reductions anticipated for BART-eligible 

facilities in the MANE-VU region in the 2018 CMAQ modeling 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 states provided potential control 

technologies and levels of control, which were in turn incorporated into the 2018 

emission inventory projections. NESCAUM (2007) provides the survey approach. 

Updates to this preliminary assessment (including the removal of six Pennsylvania 

sources with combined emissions reductions of 6600 tons of SO2) will be incorporated 

into the Best and Final modeling run scheduled to be completed in March, 2008. Figure 

5-3 displays the locations of the BART sources and estimated SO2 reductions expected in 
MANE-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 adjacent 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 SO2 reductions at the initial 14 facilities relative to the 2018 

OTB/OTW emissions inventory. Figure 5-4 shows the average change in 24-hr PM2.5

concentrations within the modeling domain used to calculate the visibility benefits. 
MANE-VU Modeling for Reasonable Progress Goals Page 5-5

Figure 5-4. Average change in 24-hr PM2.5 due to BART emission reductions 

(μg/m3

) 

5.3. 167 EGU Strategy 

The MANE-VU states have recognized that SO2 emissions from power plants are 

the single largest contributing sector to the visibility impairment experienced in the 

Northeast’s Class I areas. The SO2 emissions from power plants continue to dominate 

the inventory. Sulfate formed through atmospheric processes from SO2 emissions are 

responsible for over half the mass and approximately 70-80 percent of the extinction on 

the worst visibility days (NESCAUM, 2006a,b). In order 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 those 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 level of control may not be feasible in 

all cases. The Best and Final modeling run currently underway will incorporate State 

comments gathered during the inter-RPO consultation process. 

The “167 EGU strategy,” if implemented as defined here, could lead to large 

reductions in SO2 emissions due to installation of stack control technologies such as SO2

scrubbers. To determine the possible health benefits of this EGU control program, we 

modeled 2018 emissions for the 167 EGUs in the Northeast, 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 converted 
MANE-VU Modeling for Reasonable Progress Goals Page 5-6

sulfate concentrations to PM2.5 concentrations. Figure 5-5 displays the average change in 

24-hr PM2.5 seen between the OTB/OTW baseline and the EGU stack control program. 

Figure 5-5. Average change in 24-hr PM2.5 due to 167 EGU emission reductions 

(μg/m3) 

Figure 5-5 shows that significant reductions of PM2.5 are predicted for the 

MANE-VU region as well as for portions of the VISTAS and Midwest RPO regions as a 

result of the targeted EGU strategy. 

Figures 5-6 through 5-14 show the visibility benefits – 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 RPOs to consider non-EGU 

emissions reductions comparable to our low sulfur fuel strategies, which are expected to 

achieve a greater than 28 percent reduction in non-EGU SO2 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 uniform rate of progress at Acadia 

National Park 

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm)

167 EGUs

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm)

167 EGUs

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

Brigantine National Wildlife Refuge. 

22

23

24

25

26

27

28

29

30

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

167 EGUs S-2 (15 ppm)

22

23

24

25

26

27

28

29

30

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

167 EGUs S-2 (15 ppm)
MANE-VU Modeling for Reasonable Progress Goals Page 5-8

Figure 5-8. Visibility improvement relative to uniform rate of progress at Great 

Gulf Wilderness Area 

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Figure 5-9. Visibility improvement relative to uniform rate of progress at Lye 

Brook Wilderness Area 

18

19

20

21

22

23

24

25

2000 2005 2010 2015 2020 2025

Deciview

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 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

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

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

Moosehorn National Wildlife Refuge 

17

18

19

20

21

22

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

17

18

19

20

21

22

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Figure 5-11. Visibility improvement relative to uniform 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 2025

Deciview

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 2025

Deciview
MANE-VU Modeling for Reasonable Progress Goals Page 5-10

Figure 5-12. Visibility improvement relative to uniform rate of progress at Dolly 

Sods Wilderness Area 

21

22

23

24

25

26

27

28

29

30

2000 2005 2010 2015 2020 2025

Deciview

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 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm)

167 EGUs

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

Presidential Range-Dry River Wilderness Area 

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

18

19

20

21

22

23

24

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

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

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

Roosevelt-Campobello International Park 

17

18

19

20

21

22

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

17

18

19

20

21

22

2000 2005 2010 2015 2020 2025

Deciview

OTB/OTW

BART

S-1 (500ppm)

S-2 (15 ppm) 167 EGUs

Tables 5-1 and 5-2 summarize the sulfate mass reductions and the deciview 

targets that represent the progress shown in the prior 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 assumptions. 

Notes on Table 5-1: 

1. Baseline values represent the average sulfate mass (μg/m3

) over the 5 year baseline period on the 

20 percent worst days. 

2. OTB/OTW represents the combined estimated mass reduction (μg/m3

) 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 distillate, 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 distillate, 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 reductions after each of the 167 stacks is controlled to 

at least the 90 percent level and after the identified emissions reductions (beyond 2018 projections 

contained in the Base B emissions files) are redistributed among all other CAIR-eligible EGUs in 

the modeling domain. 

5.67 2.03 0.07 0.21 0.03 0.24

Roosevelt-Campobello 

International Park, NB

7.28 2.96 0.06 0.09 0.01 0.13

Presidential Range – Dry 

River Wilderness, NH 

5.67 2.03 0.07 0.21 0.03 0.24

Moosehorn Wilderness, ME

8.46 3.49 0.09 0.13 0.01 0.18

Lye Brook Wilderness, VT

7.28 2.96 0.06 0.09 0.01 0.13

Great Gulf Wilderness, NH

11.58 5.35 0.07 0.20 0.02 0.51

Brigantine Wilderness, NJ

6.32 2.40 0.08 0.29 0.03 0.37

Acadia National Park, ME

167 

EGUs BART S-1 S-2 OTB/OTW

[2018]

Baseline

[2000-2004] MANE-VU Class I Area

5.67 2.03 0.07 0.21 0.03 0.24

Roosevelt-Campobello 

International Park, NB

7.28 2.96 0.06 0.09 0.01 0.13

Presidential Range – Dry 

River Wilderness, NH 

5.67 2.03 0.07 0.21 0.03 0.24

Moosehorn Wilderness, ME

8.46 3.49 0.09 0.13 0.01 0.18

Lye Brook Wilderness, VT

7.28 2.96 0.06 0.09 0.01 0.13

Great Gulf Wilderness, NH

11.58 5.35 0.07 0.20 0.02 0.51

Brigantine Wilderness, NJ

6.32 2.40 0.08 0.29 0.03 0.37

Acadia National Park, ME

167 

EGUs BART S-1 S-2 OTB/OTW

[2018]

Baseline

[2000-2004] MANE-VU Class I Area
MANE-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 baseline 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 implementation of all measures to the left of and 

including the column indicated. 

4. BART reflects preliminary estimates of emissions reductions due to BART determinations. These 

determinations are still in the process of being conducted and thus are subject to change. 

5. S-1 oil strategy assumes the adoption of 500 ppm distillate, 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 distillate, 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 reductions after each of the 167 stacks is controlled to 

at least the 90 percent level and after the identified emissions reductions (beyond 2018 projections 

contained in the Base B emissions files) are redistributed among all other CAIR-eligible EGUs in 

the modeling domain. 

21.72 18.58 18.49 18.19 18.15 17.79

Roosevelt-Campobello 

International Park, NB

22.82 18.98 18.90 18.78 18.77 18.59

Presidential Range – Dry 

River Wilderness, NH 

21.72 18.59 18.50 18.20 18.16 17.80

Moosehorn Wilderness, ME

24.45 20.40 20.29 20.13 20.12 19.90

Lye Brook Wilderness, VT

22.82 18.81 18.74 18.62 18.61 18.43

Great Gulf Wilderness, NH

29.01 24.26 24.19 24.00 23.98 23.47

Brigantine Wilderness, NJ

22.89 19.62 19.51 19.10 19.05 18.50

Acadia National Park, ME

+167 

EGUs +BART +S-1 +S-2 OTB/OTW

[2018]

Baseline

[2000-2004] MANE-VU Class I Area

21.72 18.58 18.49 18.19 18.15 17.79

Roosevelt-Campobello 

International Park, NB

22.82 18.98 18.90 18.78 18.77 18.59

Presidential Range – Dry 

River Wilderness, NH 

21.72 18.59 18.50 18.20 18.16 17.80

Moosehorn Wilderness, ME

24.45 20.40 20.29 20.13 20.12 19.90

Lye Brook Wilderness, VT

22.82 18.81 18.74 18.62 18.61 18.43

Great Gulf Wilderness, NH

29.01 24.26 24.19 24.00 23.98 23.47

Brigantine Wilderness, NJ

22.89 19.62 19.51 19.10 19.05 18.50

Acadia National Park, ME

+167 

EGUs +BART +S-1 +S-2 OTB/OTW

[2018]

Baseline

[2000-2004] MANE-VU Class I Area
MANE-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 undertaken to prepare inputs for use in 

simulating future air quality on an eastern U.S. domain that includes MANE-VU Class I 

areas. The findings are consistent with previous work documenting the role of SO2

emissions in the formation of visibility impairing fine particulate in the eastern U.S. 

(NESCAUM, 2006a, b). This report goes further, however, in terms of providing 

detailed simulations of (1) projected visibility impairment 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 2018 “beyond on the way” scenario; 

and (3) sensitivity analysis of the projected benefits of several additional measures that 

are being considered by the MANE-VU states for inclusion 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 content 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-called “uniform rate of 

progress” defined by uniform visibility improvement between now and 

2064, the planning horizon for the regional haze program. 

• The 2018 pollution apportionment suggests that this improvement is due 

to significant reductions in the relative contributions 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 distillate to 15 ppm, limits on 

sulfur content of residual oil, control of BART-eligible sources, and 

additional EGU controls beyond CAIR) could yield significant 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 reasonable progress goals. 

As MANE-VU states consider these results and conduct consultations with each 

other and neighboring RPOs, NESCAUM will prepare a “best and final” modeling 

scenario for 2018 that may assist the Class I states in setting reasonable progress goals 

based on their assessment of which measures are reasonable to implement. This final 

model run is anticipated to be complete in March 2008. 
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, University 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 Air 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. Dodge. A photochemical kinetics 

mechanism for urban and regional scale computer modeling, 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, 2002, 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 the eastern 

United States: Model evaluation results, Atmos. Environ. 38, 2627-2638, 2004. 

ICF, Implementation of Mercury Tagging in the Community Multi-Scale Air Quality 

(CMAQ) Model, Draft Project Report, prepared by Sharon Douglas, Tom Meyer, Yihua 

Wei for USEPA Office of Air Quality Planning and Standards, ICF Consulting, San 

Rafael, CA, July 7, 2006. 

Jimenez P., and J. M. Baldasano. Ozone response to precursor controls in very complex 

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 Region,

http://www.marama.org/visibility/Inventory%20Summary/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. Roselle. 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-regional-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 Conceptual Description, Available at: 

http://www.nescaum.org/documents/2006-1102-pm-conceptual-model.pdf/, Northeast 

States for Coordinated Air Use Management, Boston, MA, 2006b.

NESCAUM. Five-Factor Analysis of BART-Eligible Sources; Survey 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. Yantosca, and M. Chin. Natural and 

transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United 

States: implications for policy, J. Geophys. Res. D15204, 10.1029/2003JD004473, 2004.

Ray, J.D., R.L. Heavner, M. Flores, and C.W. Michaelsen. Surface-level measurements of 

ozone and precursors at coastal and offshore locations in the Gulf of Maine. J. Geophys. 

Res. 101, 29005-29011, 1996. 

SAI, User’s Guide to the Regional Modeling System for Aerosols and Deposition 

(REMSAD), Version 8, ICF Consulting/SAI, San Francisco, 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 Deposition (REMSAD), September 1999.
MANE-VU Modeling for Reasonable Progress Goals Page 7-3

Timin, B., C. Jang, P. Dolwick, N. Possiel, T. Braverman. 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/science.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.