### i 

# 2018 VISIBILITY PROJECTIONS

Brigantine-20% Worst

0 5 10 15 20 25

2000-2004
Baseline 2018 CMAQ Natural Background ConditionsMass (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 ConditionsMass (ug/m3)

Sulfate Nitrate EC OC Sea Salt Soil



### Prepared by NESCAUM For the Mid-Atlantic/Northeast Visibility Union Regional Pl anning 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, Burea u of Air Quality

Barbara Kwetz, Director Massachusetts Department of Environmental Protectio n, Bureau of Waste Prevention

Robert Scott, Director New Hampshire Department of Environmental Services, Air Resources Division

William O’Sullivan, Director New Jersey Department of Environmental Protection, Office of Air Quality Management

David Shaw, Director N e w Y o r k D e p a r t m e n t o f E n v i r o n m e n t a l C o n s e r v a t i o n , D i v i s i o n o f A i r R e s o u r c e s

Stephen Majkut, Chief Rhode Island Department of Environmental Management , Office of Air Resources

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

### 3 

# 2018 VISIBILITY PROJECTIONS

Prepared by NESCAUM for the Mid-Atlantic/Northeast Visibility Union Regional Pl anning 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 (MARAM A) and the MANE-VU member states. Andy Bodnarik (NH DES) and Julie Mc Dill (MARAMA) deserve special recognition for their assistance in gatheri ng 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 scenar io 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 responsib le 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 c onsidered by the MidAtlantic/Northeast Visibility Union (MANE-VU) state s as a result of the regional haze consultation process. The visibility projections p resented here will be useful to the MANE-VU states as they establish reasonable progres s goals and develop their long-term emissions management strategies for Class I areas u nder the federal Regional Haze Rule.
Over the past several years, NESCAUM – as a partner in the MANE-VU regional planning organization – has coordinated and conduct ed 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 a ir quality modeling capabilities that include emission processing, meteorological in put analysis, and chemical transport modeling to conduct regional air quality simulation s for calendar year 2002 and several future periods. This work has been documented in s everal prior reports that were intended to inform and encourage the decision makin g 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 Northea st’s Class I areas on the 20 percent worst visibility days – has significant contributio ns from states throughout the eastern U.S. These are projected to continue in future yea rs from all three of the eastern regional planning organizations (RPOs). This assessment of potential control measures that would address these future contributions includes a numbe r of specific strategies and would yield significant visibility benefits at or beyond the uniform rate of progress. Perhaps more importantly, they reflect future visibility be nefits corresponding to measures that the MANE-VU states are evaluating as being reasonab le 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 Cla ss I areas. 1 

### NESCAUM has used in-house air quality modeling capabilities that inc lude emission processing, meteorological input analysis, and chemical transpo rt modeling to conduct regional air quality simulations for calendar year 2002 (represe ntative of the baseline period from 2000 to 2004) and for the end of the first complian ce period, 2018.
In reviewing the results here, the reader should re fer to prior reports prepared by NESCAUM that provide the foundation upon which thes e results are built. For example, dating back to the earliest overview of regional ha ze and visibility impairment in the Northeast and Mid-Atlantic U.S. (NESCAUM, 2001), NE SCAUM presented a review of the available models along with their uses and limi tations. 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 o f the base year 2002 from a meteorological and chemical perspective in its repo rt 2002, A Year in Review (NESCAUM, 2004). NESCAUM has also separately publi shed a performance evaluation of the MM5 meteorological model, the U.S. EPA Commu nity Multi-scale Air Quality (CMAQ) chemical transport model, as well as a more complete description of the modeling platform used for prior control strategy a nalyses (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 esti mated natural visibility conditions for each Class I area.



## 1.2. Meteorology MANE-VU has adopted the Inter-RPO domain descriptio n 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 surr ounding RPO regions. This domain covers the eastern region, which includes the north eastern, central, and southeastern U.S.,

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

2 The modeling system for the 2002/2018 annual simul ation is applied with a Lambert Conformal Conic projection with parallels at 33°N and 45°N. A sphe rical earth radius of 6,370 km is used for all elem ents of the system (MM5/SMOKE/CMAQ).

### 8

as well as southeastern Canada. It extends from 66 o 

### W~94 o 

### W in longitude and 29 o 

### N~50 o 

### N in latitude with 172 × 172 grid cells (Figure 2-1).

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

Figure note: Outer (blue) domain is a 36 km grid an d 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 Z hang’s group at the University of Maryland (UMD), are derived from the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Re search (NCAR) Mesoscale Model (MM5). 3 

### The UMD MM5 model runs are made on these two nest ed 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 d etailed 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 Spar se Matrix Operator Kernel Emissions (SMOKE) Modeling System, an emissi ons 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 200 8).

# 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 i n the report on reasonable progress modeling (NESCAUM 2008). Based on this pr evious modeling, contribution assessments, and analyses of the four statutorily r equired factors, MANE-VU selected a number of control measures on which to base the mod eling that would be used to develop proposed reasonable progress goals. These measures include additional SO 2 

### emissions reductions at electric generating units (EGUs), the use of low-sulfur fuels in MANE-VU, and reductions in non-EGU SO 2 

### emissions outside of MANE-VU. Revisions due to implementation of BART and anticipated changes in C anadian 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 secto rs 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 me et or exceed uniform rates of progress by 2018, our current analysis also suggest s 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 appr oach to estimating the potential for emissions reductions under the CAIR program. Theref ore MANE-VU added EGU emissions to estimate the impact of banking and tra ding under CAIR. Additional EGU reductions would be feasible with additional federa l 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 stat es must identify and consider all potential measures that could improve visibility an d the preamble contains language indicating that states should adopt the amount of p rogress required to achieve the uniform rate as its target “unless it determines that addit ional 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 amou nt 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 rev iew of four factor analyses that the control assumptions described below for al l three RPOs represent reasonable ways to achieve the goals MANE-VU set forth in cons ultations. MANE-VU understands that states will document in their Regional Haze SI Ps any difference of opinion as to whether reasonable measures exist beyond CAIR for E GUs and as to what measures are reasonable in the non-EGU sector.

### 11 The following sections describe the adjustments mad e to the BOTW inventory to develop the visibility projections documented in th is report. These results are available for the MANE-VU states with Class I areas to consid er in proposing reasonable progress goals.



## 2.1. Implementation of Top 167 EGU SO 2 

## Control Scenario The Vermont Department of Environmental Conservatio n (DEC) and Environmental Resources Management, on behalf of th e Maryland Department of the Environment/Maryland Department of Natural Resource s (MDE/MDNR), simulated sulfate at MANE-VU Class I areas using CALPUFF to i dentify the major contributors to ambient pollution. The effort identified 167 EGU e mission sources as contributing a substantial visibility degradation at northeast Cla ss I sites. As part of the MANE-VU strategy to meet its reasonable progress goals, MAN E-VU asked for a 90 percent reduction relative to 2002 emission levels from the se 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 SO 2 

### control request on the top 167 EGUs over three RPOs: MANE-VU, VISTAS, and MWRPO; while maint aining the SO 2 

### 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 inve ntory preparation is provided elsewhere (Alpine Geophysics, March 2008).
First, NESCAUM determined the desired emissions lev els for the 167 stacks based on continuous emissions monitoring data from 2002 (representing a 90 percent reduction). Table 2-1 displays the target levels s ummarized by RPO. For the same stacks, states provided their best estimate of emis sions in 2018, with IPM results as a starting point and specific knowledge of anticipate d 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 emi ssions 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 ot her EGUs are required to meet the target emissions level.

Table 2-1. SO 2 

### 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 stac k in the region. 4 

### The VISTAS G2

4 This hypothetical reduction was not assigned to an y specific source since the subsequent “add back” o f emissions reductions not backed up by enforceable r egulations led to no net reduction.

### 12

inventory with some Virginia adjustments estimated reductions relative to IPM 2.1.9 5 

### 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 ad justments were required. For MRPO, IPM 3.0 results (based on RPO communication) were u sed to guide the location of reductions to meet the shortfall. Emissions from 6 5 units where IPM 3.0 predicted emissions lower than IPM 2.1.9 were adjusted downwa rd to be 10 percent of 2002 emissions, resulting in 290,551 tons per year of ad ditional reductions.

Once EGU SO 2 

### 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 N ESCAUM 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 st acks, other units at the same facilities as the 167 stacks, and all other EGUs. Table 2-2 g ives 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 beyo nd 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 s ubject to the CAIR capand-trade program. For MANE-VU, 75,809 tons were a dded back to the hypothetical facility controlled to meet the “167 stack” reducti on 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 contribut ion to the total SO 2 

### emission. The additional emissions correspond to an increase of 2 0.5 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-Atl antic/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 va rious assumptions and develops a least-cost solutio n to generating needed electricity within specified emis sions targets. IPM runs are defined by numerous economic and engineering assumptions. EPA develope d Base Case v.2.1.9 using IPM to evaluate the impacts of CAIR and the Clean Air Mercury Rule (CAM R). 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 r un 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 implemen t the MANE-VU “ask” to achieve reductions at the 167 stacks identified as contribu tors to visibility reduction at MANEVU Class I areas.



## 2.2. Implementation of Low Sulfur Fuel Strategy in MANE- VU This strategy reduces SO 2 

### emissions by 2018 from all MANE-VU (non-EGU) sources combusting #1, #2, #4, #5, and #6 oil. Red uctions were achieved by lowering sulfur content in fuel from their original levels t o 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 redu ced from 2002 levels by 168,222 for light distillates (#1 and #2) and 42,875 tons per y ear for the other fuels. These reductions – when applied within MANE-VU – result in a 35% red uction of our projected 2018 nonEGU SO2 inventory.



## 2.3. Implementation of BART Strategy in MANE-VU

SO 2 

### emissions at BART-eligible sources that were not c ontrolled for any other reason (e.g., NOx RACT, CAIR, multi-P state regulat ions, etc.) have been set to levels as determined by the states.



## 2.4. Implementation of Gas-Turbine EGU in Canada

SO 2 

### emissions were removed entirely from six coal-burn ing EGUs in Ontario, Canada (6500 MW of total capacity) that are schedul ed 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 pollut ants from the ‘new’ gas facilities were based on a combination of factors: recommendat ion from NH DES (Andy Bodnarik, personal communication), a NYSERDA study (Wien et al. 2003) and AP42 ratios among pollutants. Ontario EGU emissions wer e reduced by more than 144,000 tons per year as a result of this measure.



## 2.5. Implementation of 28 percent non-EGU SO 2 

## emission reduction Given MANE-VU’s low sulfur fuel strategy, MANE-VU r equested a comparable reduction in SO 2 

### emissions from MRPO and VISTAS. The 28 percent va lue 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 VI STAS. A number of emission reductions were made to reach these levels, includi ng: 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 fue l specification by 50 percent. An additional control was required in VISTAS that redu ced emissions from other area oilcombustion sources by 75 percent. These sources we re identified by SCCs, matching the source types identified in the list of oil combusti on SCCs developed by Alpine Geophysics for the sensitivity runs described previ ously (NESCAUM, 2008).

### 14



# 3. 2018 MODELING PROJECTIONS
The modeling results based on adjustments to the 20 18 emissions inventory detailed in the previous section are given here. A ll results were developed using the CMAQ modeling platform described previously (NESCAU M, 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 de veloped from the 2002 baseline modeling and 2018 modeling results. Ambient measur ements 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 red uced by about one-third on worst days, and range from a 6 percent to 31 percen t reduction on best days. Nitrate and elemental carbon also show substantial reductions a cross all sites for both best and worst days. Reductions in organic carbon levels are gene rally small, while increases are predicted for the fine soil component. The increas e may be due to differences in the fire inventory used in VISTAS, as the base year relied o n 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 measure d baseline average concentrations are multiplied by their correspondin g RRF for each worst and best day. The projected concentrations are then used to deriv e daily visibility in deciviews and are averaged across all best and worst days to create t he projected future visibility. The results of this procedure are plotted along with th e uniform progress glide slope in Figure 3-1 through Figure 3-7. In addition, annual observ ed 20 percent best and 20 percent worst visibility are plotted as well as a line repr esenting no degradation from current baseline best 20 percent visibility.
All MANE-VU sites are projected to meet or exceed t he uniform rate of progress goal for 2018 on the 20 percent worst days. In add ition, 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 rel ative 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 det ermined by the uniform rate. Apparently, the net result of adding back SO 2 

### emissions across the domain in order to maintain the CAIR cap and reducing emissions in the MidWest RPO and VISTAS i n order to comply with the MANE-VU non-EGU ask has be en 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 5 10 15 20 25 3019851990199520002005201020152020202520302035204020452050205520602065



### YearDecivews 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

0 5 10 15 20 25 30 3519851990199520002005201020152020202520302035204020452050205520602065



### YearDecivews 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'



### Figure 3-3. Projected improvement in visibility at Great Gulf Wilderness based on 2018 Best and Final Projections

0 5 10 15 20 25 3019851990199520002005201020152020202520302035204020452050205520602065

YearDecivews 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'

### 17

Figure 3-4. Projected improvement in visibility at Lye Brook Wilderness based on 2018 Best and Final Projections

0 5 10 15 20 25 3019851990199520002005201020152020202520302035204020452050205520602065

YearDecivews 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'



### Figure 3-5. Projected improvement in visibility at Moosehorn National Wildlife Refuge based on 2018 Best and Final Projections

0 5 10 15 20 2519851990199520002005201020152020202520302035204020452050205520602065

YearDecivews 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'

### 18

Figure 3-6. Projected improvement in visibility at Dolly Sods Wilderness based on 2018 Best and Final Projections

0 5 10 15 20 25 30 35 4019851990199520002005201020152020202520302035204020452050205520602065

YearDecivews 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'



### Figure 3-7. Projected improvement in visibility at Shenandoah National Park based on 2018 Best and Final Projections

0 5 10 15 20 25 30 35 4019851990199520002005201020152020202520302035204020452050205520602065
YearDecivews 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'

### 19



# 4. 2018 VISIBILITY RESULTS
Figure 4-1A through G show the absolute magnitude o f measured and projected sulfate, nitrate, elemental carbon (EC), organic ca rbon (OC), sea salt, and soil at each MANE-VU Class I monitor and two nearby Class 1 site s, Shenandoah and Dolly Sods. Current and projected vibility conditions are shown for both the twenty percent best visibility days (right) and the twenty percent wors t visibility days (left; note that the range of the y-axes are five times greater than for the b est 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 warran t attention moving forward, although a substantial fraction of the organic carbon will r emain as natural background.
Sea salt shows interesting behavior. At coastal si tes, the worst day sea salt mass is shown to increase when going from baseline and 2 018 time periods to natural background conditions. Presumably this observation is a result of the EPA/IMPROVE program choice to base future estimates of worst da y 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 t he greatest concentration of sea salt is observed in the Northeast U.S. on the best visibili ty days, not the worst visibility days.

### 20

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

### , and Estimated Natural

Speciated PM 2.5 

### 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 ConditionsMass (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 ConditionsMass (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 0.32Mass (μg/m3)

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 0.03Mass (μg/m3)

Soil 0.11 0.12 0.10 Visibility dv 8.8 8.3 4.7

∗ CMAQ projected values are calculated by applying C MAQ-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 ConditionsMass (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 ConditionsMass (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 0.57Mass (μg/m3)

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 0.04Mass (μg/m3)

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 ConditionsMass (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 ConditionsMass (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 0.02Mass (μg/m3)

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 0.02Mass (μg/m3)

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 ConditionsMass (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 ConditionsMass (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 0.01Mass (μg/m3)

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 0.01Mass (μg/m3)

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 ConditionsMass (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 ConditionsMass (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 0.24Mass (μg/m3)

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 0.02Mass (μg/m3)

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 ConditionsMass (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 ConditionsMass (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 0.05Mass (μg/m3)

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 0.01Mass (μg/m3)

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 ConditionsMass (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 ConditionsMass (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 0.03Mass (μg/m3)

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 0.01Mass (μg/m3)

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 Gener ation Units in the Eastern U.S. for MANE-VU’s Regional Haze Mod eling, prepared by Alpine Geophysics for Mid-Atlantic Regional Air Managers A ssociation, Baltimore, MD, March, 2008.

NESCAUM. Regional Haze and Visibility in the Northeast and M id-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-r egional-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-reaso nable-progress-final-021208.pdf Northeast States for Coordinated Air Use Management , Boston, MA, 2008.

Ontario Power Authority. Ontario’s Integrated Powe r System Plan Discussion Paper 7: Integrating the Elements—A Preliminary Plan. Novem ber 2006. Available at: http://www.powerauthority.on.ca/ipsp/Storage/32/273 4_DP7_IntegratingTheElements.pdf



### Wien, S., England, G.C. and Chang, O.M.C., “Develop ment of Fine Particulate Emission Factors and Speciation Profiles for Oil and Gas-fir ed Combustion Systems, Topical Report: Test Results for a Gas-Fired Process Heater (Site Alpha), 2003.”