Connecticut Regional Haze 

State Implementation Plan November 2009 

ATTACHMENT Q 

MANE-VU 2018 Visibility Projections 
i

2018 VISIBILITY PROJECTIONS

Brigantine-20% Worst

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5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Brigantine-20% Best

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2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Prepared by

NESCAUM 

For the 

Mid-Atlantic/Northeast Visibility Union Regional Planning Organization 

May 13, 2008 
2

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 
3

2018 VISIBILITY PROJECTIONS

Prepared by

NESCAUM 

for the 

Mid-Atlantic/Northeast Visibility Union Regional Planning Organization 

May 13, 2008 
4

2018 VISIBILITY PROJECTIONS 

Project Director 

 Gary Kleiman 

Editor

 Paul Miller 

Principal Contributors

 John Graham 

Shan He 

Iyad Kheirbek 

Gary Kleiman 

Emily Savelli 
5

Acknowledgments 

NESCAUM could not have completed this work without assistance from the MidAtlantic Regional Air Management Association (MARAMA) and the MANE-VU 

member states. Andy Bodnarik (NH DES) and Julie McDill (MARAMA) deserve 

special recognition for their assistance in gathering emission inventory information. The 

air quality modelers at the University of Maryland (MDE partner) and Rutgers (NJ DEP 

partner) shared equally in modeling the 2018 scenario detailed in this report. NESCAUM 

also acknowledges the funding for this work through U.S. EPA agreement number XA97318101-0 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. 
6

Executive Summary 

This report represents the most detailed effort to date to quantify the visibility 

impacts of those measures that are being actively considered by the MidAtlantic/Northeast Visibility Union (MANE-VU) states as a result of the regional haze 

consultation process. The visibility projections presented here will be useful to the 

MANE-VU states as they establish reasonable progress goals and develop their long-term 

emissions management strategies for Class I areas under the federal Regional Haze Rule. 

Over the past several years, NESCAUM – as a partner in the MANE-VU regional 

planning organization – has coordinated and conducted regional air quality modeling to 

better understand the visibility implications of a range of potential compliance options 

with the 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 documented in several prior reports that were 

intended to inform and encourage the decision making process leading up to this point in 

the SIP submission process. 

Results from prior analyses have shown 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. These are projected to continue in future years from all three of the eastern regional 

planning organizations (RPOs). This assessment of potential control measures that would 

address these future contributions includes a number of specific strategies and would 

yield significant visibility benefits at or beyond the uniform rate of progress. Perhaps 

more importantly, they reflect future visibility benefits corresponding to measures that 

the MANE-VU states are evaluating as being reasonable to implement.
7

INTRODUCTION

1.1. Background 

This report presents information intended to assist states in establishing 

reasonable progress goals and fulfilling their long-term emissions management strategies 

under the 1999 U.S. Environmental Protection Agency (USEPA) “Regional Haze Rule” 

[64 Fed. Reg. 35714 (July 1, 1999)] for MANE-VU Class I areas.1 NESCAUM has used 

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 (representative of the baseline period from 

2000 to 2004) and for the end of the first compliance period, 2018. 

In reviewing the results here, the reader should refer to prior reports prepared by 

NESCAUM that provide the foundation upon which these results are built. For example, 

dating back to the earliest overview of regional haze and visibility impairment in the 

Northeast and Mid-Atlantic U.S. (NESCAUM, 2001), NESCAUM presented a review of 

the available models along with their uses and limitations. This served to inform the 

choice of models and tools used to build the weight of evidence modeling approach taken 

by MANE-VU in conducting a contribution assessment and pollution apportionment 

(NESCAUM 2004, 2006). NESCAUM presented a review of the base year 2002 from a 

meteorological and chemical perspective in its report 2002, A Year in Review

(NESCAUM, 2004). NESCAUM has also separately published a performance evaluation 

of the MM5 meteorological model, the U.S. EPA Community Multi-scale Air Quality 

(CMAQ) chemical transport model, as well as a more complete description of the 

modeling platform used for prior control strategy analyses (NESCAUM, 2008). 

In this report, we do not repeat this information, but rather rely upon the prior 

documentation. The following sections describe the control scenario being considered 

and present the resulting visibility projections in the context of the uniform rate of 

progress determined by baseline conditions and estimated natural visibility conditions for 

each Class I area. 

1.2. Meteorology 

MANE-VU has adopted the Inter-RPO domain description for its modeling runs.2 

This 36-km grid cell 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 north-south directions, respectively. A 12-km grid cell 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., 

 

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. 

2

 The modeling system for the 2002/2018 annual simulation is applied with a Lambert Conformal Conic 

projection with parallels at 33°N and 45°N. A spherical earth radius of 6,370 km is used for all elements of 

the system (MM5/SMOKE/CMAQ). 
8

as well as southeastern Canada. It extends from 66oW~94oW in longitude and 

29oN~50oN in latitude with 172 × 172 grid cells (Figure 2-1). 

Figure 2-1. Modeling domains used with CMAQ for MANE-VU studies. 

Figure note: Outer (blue) domain is a 36 km grid and inner (red) domain is a 12 km grid. 

The gridlines are shown at 180 km intervals (5 × 5 36 km cells/15 × 15 12 km cells). 

Meteorological inputs for CMAQ, provided by Dalin Zhang’s group at the 

University of Maryland (UMD), are derived from the Fifth-Generation Pennsylvania 

State University/National Center for Atmospheric Research (NCAR) Mesoscale Model 

(MM5).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 6–16, 2002. A detailed description of the 

meteorological inputs can be found in the report MANE-VU Modeling for Reasonable 

Progress Goals (NESCAUM 2008). 

1.3. Emissions Preparations 

NESCAUM simulated emission scenarios using the Sparse Matrix Operator 

Kernel Emissions (SMOKE) Modeling System, an emissions processing system designed 

to create gridded, speciated, hourly emissions for input into a variety of air quality 

models such as CMAQ. SMOKE supports area, biogenic, mobile (onroad and nonroad), 

 

3

http://www.mmm.ucar.edu/mm5/ 
9

and point source emissions processing for criteria, particulate, and toxic pollutants. The 

MANE-VU Modeling for Reasonable Progress Goals report describes the SMOKE 

emissions processing methods in detail (NESCAUM 2008). 
2. 2018 EMISSIONS INVENTORY 

Descriptions of the 2002, 2018 On the Books/On the Way (OTB/OTW), and 2018 

Beyond on the Way (BOTW) inventories are included in the report on reasonable 

progress modeling (NESCAUM 2008). Based on this previous modeling, contribution 

assessments, and analyses of the four statutorily required factors, MANE-VU selected a 

number of control measures on which to base the modeling that would be used to develop 

proposed reasonable progress goals. These measures include additional SO2 emissions 

reductions at electric generating units (EGUs), the use of low-sulfur fuels in MANE-VU, 

and reductions in non-EGU SO2 emissions outside of MANE-VU. Revisions due to 

implementation of BART and anticipated changes in Canadian emissions are also 

included in the projected 2018 emissions inventory used for this modeling. 

MANE-VU received comments from several stakeholders and another RPO 

related to the fact that the modeling described in this report included control measures 

and emission reductions that went beyond currently existing regulations. Commenters 

suggested that since the CAIR program and other “on the books” or “on the way” 

measures are projected to achieve uniform rates of progress as previously modeled, 

additional reductions to both EGU and non-EGU sectors were unnecessary. As described 

below, there are two reasons why MANE-VU has chosen to include these measures in 

this modeling analysis. 

First, while the results of the modeling described in this report suggest 

individual MANE-VU Class I areas will be able to meet or exceed uniform rates of 

progress by 2018, our current analysis also suggests that this would be difficult without 

including additional measures beyond implementation of CAIR. This result is due, in 

part, to our assumptions about the effectiveness of CAIR. We believe that it is 

appropriate for MANE-VU to take a conservative approach to estimating the potential for 

emissions reductions under the CAIR program. Therefore MANE-VU added EGU 

emissions to estimate the impact of banking and trading under CAIR. Additional EGU 

reductions would be feasible with additional federal action to control EGU emissions 

(e.g., a third phase of CAIR), but MANE-VU does not believe that these reductions are 

likely to occur absent additional regulation. 

Second, EPA’s Regional Haze Rule requires that states must identify and consider all

potential measures that could improve visibility and the preamble contains language 

indicating that states should adopt the amount of progress required to achieve the uniform 

rate as its target “unless it determines that additional progress beyond this amount is also 

reasonable. If the State determines that additional progress is reasonable based on the 

statutory factors, the State should adopt that amount of progress as its goal for the first 

long-term strategy.” [40 CFR part 51, July 1, 1999, pg. 35,732 ].

 MANE-VU Class I states have concluded based on review of four factor analyses 

that the control assumptions described below for all three RPOs represent reasonable 

ways to achieve the goals MANE-VU set forth in consultations. MANE-VU understands 

that states will document in their Regional Haze SIPs any difference of opinion as to 

whether reasonable measures exist beyond CAIR for EGUs and as to what measures are 

reasonable in the non-EGU sector. 
11

The following sections describe the adjustments made to the BOTW inventory to 

develop the visibility projections documented in this report. These results are available 

for the MANE-VU states with Class I areas to consider in proposing reasonable progress 

goals. 

 

2.1. Implementation of Top 167 EGU SO2 Control Scenario 

The Vermont Department of Environmental Conservation (DEC) and 

Environmental Resources Management, on behalf of the Maryland Department of the 

Environment/Maryland Department of Natural Resources (MDE/MDNR), simulated 

sulfate at MANE-VU Class I areas using CALPUFF to identify the major contributors to 

ambient pollution. The effort identified 167 EGU emission sources as contributing a 

substantial visibility degradation at northeast Class I sites. As part of the MANE-VU 

strategy to meet its reasonable progress goals, MANE-VU asked for a 90 percent 

reduction relative to 2002 emission levels from these stacks. This request did, however, 

provide flexibility to pursue equivalent reductions by region in lieu of reductions at these 

specific facilities. The resulting emission levels from the EGU sector for this version of 

the 2018 MANE-VU inventory reflect the SO2 control request on the top 167 EGUs over 

three RPOs: MANE-VU, VISTAS, and MWRPO; while maintaining the SO2 emission 

level under the CAIR cap for all states subject to the CAIR cap-and-trade program. A 

more complete description of the EGU emissions inventory preparation is provided 

elsewhere (Alpine Geophysics, March 2008). 

First, NESCAUM determined the desired emissions levels for the 167 stacks 

based on continuous emissions monitoring data from 2002 (representing a 90 percent 

reduction). Table 2-1 displays the target levels summarized by RPO. For the same 

stacks, states provided their best estimate of emissions in 2018, with IPM results as a 

starting point and specific knowledge of anticipated activity for each stack (e.g., 

installation of controls). These future emissions are summed by RPO and shown in the 

second row of Table 2-1. A comparison of these emissions levels shows that no RPO 

achieves the desired reductions at these 167 stacks. Therefore, reductions at other stacks 

at the same facilities as the 167 stacks or from other EGUs are required to meet the target 

emissions level. 

Table 2-1. SO2 Emissions Summary (TPY) for 167 Top EGU stacks 

MANE-VU MRPO VISTAS 

10% of 2002 CEMS 117,217 170,454, 169,816 

Projected 2018 193,026 436,138 299,090 

Shortfall 75,809 265,683 129,275 

NESCAUM next reviewed anticipated 2018 emissions by RPO at all stacks other 

than the 167. For MANE-VU, an emissions reduction exactly matching the shortfall 

(75,809 tons) was recorded at one hypothetical stack in the region.4

 The VISTAS G2 

 

4

 This hypothetical reduction was not assigned to any specific source since the subsequent “add back” of 

emissions reductions not backed up by enforceable regulations led to no net reduction. 
12

inventory with some Virginia adjustments estimated reductions relative to IPM 2.1.95 of 

over 180,000 tons for the EGUs not included in the 167 stacks. These reductions exceed 

the shortfall from the 167 stacks and no further adjustments were required. For MRPO, 

IPM 3.0 results (based on RPO communication) were used to guide the location of 

reductions to meet the shortfall. Emissions from 65 units where IPM 3.0 predicted 

emissions lower than IPM 2.1.9 were adjusted downward to be 10 percent of 2002 

emissions, resulting in 290,551 tons per year of additional reductions. 

Once EGU SO2 emissions levels were lowered to meet the desired reductions, 

NESCAUM compared the adjusted emissions (including adjustments to IPM 2.1.9 made 

by states directly and those from changes made by NESCAUM to meet the 167 stack 

reduction targets) with IPM 2.1.9 emissions by each of the three RPOs. The analyses 

looked at three groupings of EGU stacks: the 167 stacks, other units at the same facilities 

as the 167 stacks, and all other EGUs. Table 2-2 gives these differences by category. 

Since the total IPM 2.1.9 EGU emissions sums to the CAIR cap, the sum of the 

differences in the table represents reductions beyond the CAIR level. Because MANEVU Class I states made the decision to maintain the CAIR level of emissions in this 2018 

modeling, the 516,350 tons of emissions were added back. 

Table 2-2. Emissions difference between IPM 2.1.9 and adjusted emissions based on 

state-specific comments and MANE-VU effort to meet 167 stack reduction levels. 

MANE-VU MRPO VISTAS 

167 stacks 39,465 -37,913 -14,673 

Other stacks at 167 

facilities 21,433 24,098 -2,244 

Other EGUs -75,809 -290,551 -180,155 

Sum -14,912 -304,367 -197,071 

Note: negative values indicate emissions below IPM 2.1.9 

Next, NESCAUM increased the emissions from states subject to the CAIR capand-trade program. For MANE-VU, 75,809 tons were added back to the hypothetical 

facility controlled to meet the “167 stack” reduction request. The remaining 440,188 tons 

were allocated to VISTAS and MRPO at EGUs that were not among the “167 Stack” 

facilities based on the fraction of their contribution to the total SO2 emission. The 

additional emissions correspond to an increase of 20.5 percent at each of these facilities, 

with a total of 216,685 tons added to MRPO and 223,504 tons added to VISTAS. 

 

5

 To predict future emissions from EGUs, the Mid-Atlantic/Northeast Visibility Union (MANE-VU) and 

other Regional Planning Organizations have followed the example of the US Environmental Protection 

Agency (EPA) in using the Integrated Planning Model®

 (IPM), an integrated economic and emissions 

model. IPM projects electricity supply based on various assumptions and develops a least-cost solution to 

generating needed electricity within specified emissions targets. IPM runs are defined by numerous 

economic and engineering assumptions. EPA developed Base Case v.2.1.9 using IPM to evaluate the 

impacts of CAIR and the Clean Air Mercury Rule (CAMR). Recently, EPA updated their input data and 

developed Base Case v.3.0. All of the IPM results used in MANE-VU modeling were based on EPA Base 

Case v.2.1.9 with some updates and corrections. 
13

The intent of the EGU emissions adjustments was to retain the same overall level 

of emissions as predicted by the VISTAS/Inter-RPO run of IPM 2.1.9 overall. The 

locations of the emissions, however, were modified to better reflect the states’ estimates 

of where emissions would be reduced and to implement the MANE-VU “ask” to achieve 

reductions at the 167 stacks identified as contributors to visibility reduction at MANEVU Class I areas. 

2.2. Implementation of Low Sulfur Fuel Strategy in MANE-VU 

This strategy reduces SO2 emissions by 2018 from all MANE-VU (non-EGU) 

sources combusting #1, #2, #4, #5, and #6 oil. Reductions were achieved by lowering 

sulfur content in fuel from their original levels to 0.0015 percent (equivalent to fuel sulfur 

content of 15 ppm by volume) for #1 and #2 oil; to 0.25 percent for #4 oil; and to 

0.5 percent for #5 and #6 oil. Emissions were reduced from 2002 levels by 168,222 for 

light distillates (#1 and #2) and 42,875 tons per year for the other fuels. These reductions 

– when applied within MANE-VU – result in a 35% reduction of our projected 2018 nonEGU SO2 inventory. 

2.3. Implementation of BART Strategy in MANE-VU 

SO2 emissions at BART-eligible sources that were not controlled for any other 

reason (e.g., NOx RACT, CAIR, multi-P state regulations, etc.) have been set to levels as 

determined by the states. 

2.4. Implementation of Gas-Turbine EGU in Canada 

SO2 emissions were removed entirely from six coal-burning EGUs in Ontario, 

Canada (6500 MW of total capacity) that are scheduled to be shut down (Ontario Power 

Authority 2006) and replaced with nine natural gas turbine units with Selective Catalytic 

Reduction (SCR). Emission rates for modeled pollutants from the ‘new’ gas facilities 

were based on a combination of factors: recommendation from NH DES (Andy 

Bodnarik, personal communication), a NYSERDA study (Wien et al. 2003) and AP42 

ratios among pollutants. Ontario EGU emissions were reduced by more than 144,000 

tons per year as a result of this measure. 

2.5. Implementation of 28 percent non-EGU SO2 emission reduction 

Given MANE-VU’s low sulfur fuel strategy, MANE-VU requested a comparable 

reduction in SO2 emissions from MRPO and VISTAS. The 28 percent value derives 

from a preliminary estimate of emissions reductions reasonably achievable from nonEGUs sources in MANE-VU. Based on 2002 emissions, this level reduction would 

amount to 131,600 TPY in MRPO and 308,000 TPY in VISTAS. A number of emission 

reductions were made to reach these levels, including: reducing emissions from coal-fired 

ICI boilers by 60 percent, reducing emissions from oil-fired ICI boilers by 75 percent, 

and reducing emissions from ICI Boilers lacking fuel specification by 50 percent. An 

additional control was required in VISTAS that reduced emissions from other area oilcombustion sources by 75 percent. These sources were identified by SCCs, matching the 

source types identified in the list of oil combustion SCCs developed by Alpine 

Geophysics for the sensitivity runs described previously (NESCAUM, 2008). 
14

3. 2018 MODELING PROJECTIONS 

The modeling results based on adjustments to the 2018 emissions inventory 

detailed in the previous section are given here. All results were developed using the 

CMAQ modeling platform described previously (NESCAUM, 2008). Table 3-1 provides 

species-specific relative reduction factors (RRFs) at each Class I area for the 20 percent 

worst and 20 percent best days. The factors are developed from the 2002 baseline 

modeling and 2018 modeling results. Ambient measurements identify which days to use 

in the calculations. The model concentrations for these days are averaged to create the 

RRF, which is the ratio of the future year to base year average concentration. 

Based on the tabulated data, modeled sulfate is reduced by about one-third on 

worst days, and range from a 6 percent to 31 percent reduction on best days. Nitrate and 

elemental carbon also show substantial reductions across all sites for both best and worst 

days. Reductions in organic carbon levels are generally small, while increases are 

predicted for the fine soil component. The increase may be due to differences in the fire 

inventory used in VISTAS, as the base year relied on an earlier version of fire emissions 

than did the 2018 inventory. No changes occur for sea salt since the model does not track 

that component. 

To determine visibility levels in 2018, the measured baseline average 

concentrations are multiplied by their corresponding RRF for each worst and best day. 

The projected concentrations are then used to derive daily visibility in deciviews and are 

averaged across all best and worst days to create the projected future visibility. The 

results of this procedure are plotted along with the uniform progress glide slope in Figure 

3-1 through Figure 3-7. In addition, annual observed 20 percent best and 20 percent 

worst visibility are plotted as well as a line representing no degradation from current 

baseline best 20 percent visibility. 

All MANE-VU sites are projected to meet or exceed the uniform rate of progress 

goal for 2018 on the 20 percent worst days. In addition, no site anticipates increases in 

20 percent best day visibility impairment relative to the baseline. The nearby sites of 

Shenandoah and Dolly Sods also show improvement relative to baseline conditions on 

the 20 percent best days. At Dolly Sods, however, projected visibility impairment on the 

20 percent worst days in 2018 exceeds the level determined by the uniform rate. 

Apparently, the net result of adding back SO2 emissions across the domain in order to 

maintain the CAIR cap and reducing emissions in the MidWest RPO and VISTAS in 

order to comply with the MANE-VU non-EGU ask has been to increase the anticipated 

visibility impairment relative to previous modeled scenarios. This result is most evident 

at southern and western sites where more emissions (on an absolute basis) were added 

back to EGUs. 
15

Table 3-1. 2018 20% best and worst days relative reduction factors at seven sites. 

 20% Worst Days Relative Reduction Factors 

 Acadia Lye Brook Brigantine Moosehorn Dolly Sods Shenandoah Great Gulf 

SO4 0.65 0.65 0.63 0.69 0.77 0.65 0.63

NO3 0.79 0.91 0.93 0.73 0.55 0.47 0.85

EC 0.75 0.67 0.62 0.77 0.73 0.58 0.74

OC 0.95 0.93 0.98 0.95 0.93 0.88 0.86

Sea Salt* 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Soil 1.10 1.13 1.26 1.09 1.21 1.16 1.15

20% Best Days Relative Reduction Factors 

 Acadia Lye Brook Brigantine Moosehorn Dolly Sods Shenandoah Great Gulf 

SO4 0.90 0.81 0.69 0.95 0.94 0.91 0.94

NO3 0.75 0.67 0.62 0.77 0.73 0.58 0.74

EC 0.74 0.75 0.64 0.78 0.71 0.52 0.83

OC 0.94 0.93 0.97 0.92 0.91 0.72 0.99

Sea Salt* 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Soil 1.06 1.04 1.17 1.03 1.14 1.08 1.08

* RRFs for Sea Salt are not calculated from CMAQ. We assume no changes in observed values between 

2002 and future time periods. 

Figure 3-1. Projected improvement in visibility at Acadia National Park based on 

2018 Best and Final Projections 

0

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19851990199520002005201020152020202520302035204020452050205520602065

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Decivews

20% Worst Observed

20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'
16

Figure 3-2. Projected improvement in visibility at Brigantine National Wildlife 

Refuge based on 2018 Best and Final Projections 

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20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'

Figure 3-3. Projected improvement in visibility at Great Gulf Wilderness based on 

2018 Best and Final Projections 

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20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'
17

Figure 3-4. Projected improvement in visibility at Lye Brook Wilderness based on 

2018 Best and Final Projections 

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20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'

Figure 3-5. Projected improvement in visibility at Moosehorn National Wildlife 

Refuge based on 2018 Best and Final Projections 

0

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20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'
18

Figure 3-6. Projected improvement in visibility at Dolly Sods Wilderness based on 

2018 Best and Final Projections 

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20% Best Observed

20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'

Figure 3-7. Projected improvement in visibility at Shenandoah National Park based 

on 2018 Best and Final Projections 

0

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19851990199520002005201020152020202520302035204020452050205520602065

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20% Worst Baseline

20% Best Baseline

Uniform Progress

No Degradation

CMAQ 20% Worst

CMAQ 20% Best

20% Best 'Natural'
19

4. 2018 VISIBILITY RESULTS 

Figure 4-1A through G show the absolute magnitude of measured and projected 

sulfate, nitrate, elemental carbon (EC), organic carbon (OC), sea salt, and soil at each 

MANE-VU Class I monitor and two nearby Class 1 sites, Shenandoah and Dolly Sods. 

Current and projected vibility conditions are shown for both the twenty percent best 

visibility days (right) and the twenty percent worst visibility days (left; note that the range 

of the y-axes are five times greater than for the best days!) These figures show that 

despite large reductions in sulfate relative to the baseline, substantially greater reductions 

are required to reach natural background conditions. Reductions in nitrate will also be 

needed. Similarly, the carbonaceous species warrant attention moving forward, although 

a substantial fraction of the organic carbon will remain as natural background. 

Sea salt shows interesting behavior. At coastal sites, the worst day sea salt mass 

is shown to increase when going from baseline and 2018 time periods to natural 

background conditions. Presumably this observation is a result of the EPA/IMPROVE 

program choice to base future estimates of worst day visibility conditions on the current 

distribution of worst day visibility. We note that for sea salt, this may not be the best 

method to estimate future worst day conditions as the greatest concentration of sea salt is 

observed in the Northeast U.S. on the best visibility days, not the worst visibility days. 
20

Figure 4-1A-G. Observed Baseline, CMAQ-projected∗, and Estimated Natural 

Speciated PM2.5 Mass Values for MANE-VU Class I Sites. 

A. Acadia National Park 

Acadia-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Acadia-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 6.29 4.11 0.53 

Nitrate 0.82 0.65 0.21 

EC 0.43 0.33 0.04 

OC 3.17 3.00 3.32 

Sea Salt 0.19 0.19 0.32 

Mass (

μg/m

3) 

Soil 0.52 0.58 0.52 

Visibility dv 22.9 19.4 12.4 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 0.77 0.69 0.09 

Nitrate 0.11 0.09 0.03 

EC 0.09 0.06 0.01 

OC 0.76 0.71 0.68 

Sea Salt 0.06 0.06 0.03 

Mass (

μg/m

3) 

Soil 0.11 0.12 0.10 

Visibility dv 8.8 8.3 4.7 

 

∗

 CMAQ projected values are calculated by applying CMAQ-based RRFs by the observed baseline values. 
21

B. Brigantine National Wildlife Refuge 

Brigantine-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Brigantine-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 11.58 7.35 0.39 

Nitrate 1.73 1.60 0.13 

EC 0.70 0.43 0.03 

OC 5.83 5.72 3.40 

Sea Salt 0.06 0.06 0.57 

Mass (

μg/m

3) 

Soil 0.97 1.23 0.85 

Visibility dv 29.0 25.1 12.2 

 20% Best Days 

 Species 

2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 1.85 1.28 0.12 

Nitrate 0.46 0.29 0.04 

EC 0.24 0.15 0.01 

OC 1.47 1.43 0.86 

Sea Salt 0.22 0.22 0.04 

Mass (

μg/m

3) 

Soil 0.23 0.28 0.24 

Visibility dv 14.3 12.2 5.5 
22

C. Great Gulf Wilderness Area 

Great Gulf-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Great Gulf-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 7.28 4.61 0.54 

Nitrate 0.36 0.30 0.13 

EC 0.39 0.29 0.04 

OC 3.84 3.31 3.76 

Sea Salt 0.02 0.02 0.02 

Mass (

μg/m

3) 

Soil 0.57 0.66 0.53 

Visibility dv 22.8 19.1 12.0 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 0.74 0.70 0.09 

Nitrate 0.12 0.09 0.04 

EC 0.08 0.07 0.01 

OC 0.68 0.67 0.56 

Sea Salt 0.03 0.03 0.02 

Mass (

μg/m

3) 

Soil 0.10 0.11 0.10 

Visibility dv 7.7 7.2 3.7 
23

D. Lye Brook Wilderness Area 

Lye Brook-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Lye Brook-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 8.46 5.52 0.61 

Nitrate 1.07 0.98 0.18 

EC 0.48 0.32 0.04 

OC 3.94 3.67 3.91 

Sea Salt 0.01 0.01 0.01 

Mass (

μg/m

3) 

Soil 0.64 0.73 0.66 

Visibility dv 24.4 20.9 11.7 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 0.59 0.48 0.05 

Nitrate 0.14 0.10 0.03 

EC 0.06 0.04 0.01 

OC 0.44 0.41 0.36 

Sea Salt 0.01 0.01 0.01 

Mass (

μg/m

3) 

Soil 0.09 0.10 0.09 

Visibility dv 6.4 5.5 2.8 
24

E. Moosehorn National Wildlife Refuge 

Moosehorn-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Moosehorn-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 5.67 3.90 0.48 

Nitrate 0.71 0.52 0.20 

EC 0.44 0.34 0.04 

OC 3.38 3.20 3.34 

Sea Salt 0.03 0.03 0.24 

Mass (

μg/m

3) 

Soil 0.76 0.83 0.40 

Visibility dv 21.7 19.0 12.0 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 0.80 0.77 0.11 

Nitrate 0.12 0.09 0.04 

EC 0.10 0.08 0.01 

OC 1.02 0.94 0.76 

Sea Salt 0.04 0.04 0.02 

Mass (

μg/m

3) 

Soil 0.11 0.12 0.12 

Visibility dv 9.2 8.6 5.0 
25

F. Dolly Sods Wilderness Area 

Dolly Sods-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Dolly Sods-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 13.35 10.30 0.38 

Nitrate 0.37 0.20 0.14 

EC 0.47 0.34 0.03 

OC 3.75 3.51 3.11 

Sea Salt 0.02 0.02 0.05 

Mass (

μg/m

3) 

Soil 0.77 0.94 0.75 

Visibility dv 29.0 26.3 10.4 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 1.79 1.69 0.10 

Nitrate 0.38 0.27 0.05 

EC 0.21 0.15 0.01 

OC 1.56 1.41 0.80 

Sea Salt 0.02 0.02 0.01 

Mass (

μg/m

3) 

Soil 0.18 0.20 0.17 

Visibility dv 12.3 11.4 3.6 
26

G. Shenandoah National Park 

Shenandoah-20% Worst

0

5

10

15

20

25

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

Shenandoah-20% Best

0

1

2

3

4

5

2000-2004

Baseline

2018 CMAQ Natural

Background

Conditions

Mass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil

 20% Worst Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 13.19 8.54 0.43 

Nitrate 0.65 0.31 0.07 

EC 0.57 0.33 0.03 

OC 4.21 3.69 3.78 

Sea Salt 0.01 0.01 0.03 

Mass (

μg/m

3) 

Soil 0.72 0.84 0.83 

Visibility dv 29.3 24.7 11.4 

 20% Best Days 

 

Species 2000-2004 

Baseline 2018 CMAQ Natural Background Conditions 

Sulfate 1.45 1.31 0.08 

Nitrate 0.52 0.30 0.07 

EC 0.16 0.08 0.01 

OC 0.95 0.69 0.56 

Sea Salt 0.02 0.02 0.01 

Mass (

μg/m

3) 

Soil 0.16 0.17 0.14 

Visibility dv 10.9 9.4 3.1 
27

5. REFERENCES 

Alpine Geophysics, Documentation of 2018 Emissions from Electric Generation Units 

in the Eastern U.S. for MANE-VU’s Regional Haze Modeling, prepared by Alpine 

Geophysics for Mid-Atlantic Regional Air Managers Association, Baltimore, MD, 

March, 2008. 

NESCAUM. Regional Haze and Visibility in the Northeast and Mid-Atlantic States.

Available at: http://www.nescaum.org/documents/regional-haze-and-visibility-in-thenortheast-and-mid-atlantic-states/ Northeast States for Coordinated Air Use 

Management, Boston, MA, 2001. 

NESCAUM. 2002 A Year in Review. Available at: 

http://www.nescaum.org/documents/2002_report.pdf/ Northeast States for Coordinated 

Air Use Management, Boston, MA, 2004. 

NESCAUM. Contributions to Regional Haze in the Northeast and Mid-Atlantic United 

States. Available at: http://www.nescaum.org/documents/contributions-to-regional-hazein-the-northeast-and-mid-atlantic--united-states/, Northeast States for Coordinated Air 

Use Management, Boston, MA, 2006. 

NESCAUM. MANE-VU Modeling for Reasonable Progress Goals. Available at: 

http://www.nescaum.org/documents/modeling-for-reasonable-progress-final-021208.pdf 

Northeast States for Coordinated Air Use Management, Boston, MA, 2008. 

Ontario Power Authority. Ontario’s Integrated Power System Plan Discussion Paper 7: 

Integrating the Elements—A Preliminary Plan. November 2006. Available at: 

http://www.powerauthority.on.ca/ipsp/Storage/32/2734_DP7_IntegratingTheElements.pdf

Wien, S., England, G.C. and Chang, O.M.C., “Development of Fine Particulate Emission 

Factors and Speciation Profiles for Oil and Gas-fired Combustion Systems, Topical 

Report: Test Results for a Gas-Fired Process Heater (Site Alpha), 2003.”