7-1 7.0 Emission Inventory

Section 51.308(d)(4)(v) of the EPA’s Regional Haze Rule, 40 CFR 51.308 requires the establishment of a statewide emission inv entory of pollutants that are reasonably anticipated to cause or cont ribute to visibility impairment in any mandatory Class I area. The po llutants inventoried by Ne w York include volatile organic compounds, nitrogen oxides, fine particles (PM 2.5 ), coarse particles (PM 10 ), ammonia, carbon monoxide and sulfur dioxides. The information for New York was provided to MANE-VU, which conducted the modeling of visibility impacts for the MANE-VU region. This section provides information on the development of baseline and future emi ssion inventories that were used in modeling visibility for the purposes of this SIP.

7.1 Baseline and Future Year Emission Inventories for Modeling

Section 51.308(d)(3)(iii) of the EPA’s Regional Haze Rule requires the State of
New York as well as other states to identify a baseline em ission inventory upon which future emission projections will be based and from which the necessary emission reductions for meeting reasonable progress goals can be determined.

Based on EPA guidance entitled, 2002 Base Year Emission Inventory SIP Planning: 8-hour Ozone, PM 2.5 , and Regional Haze Programs , found at the following link:

http://www.epa.gov/ttnchie1/ei docs/2002baseinven _102502new.pdf

which identifies the antici pated baseline emission inve ntory year for regional haze, MANE-VU and the State of New York are using 2002 as the baseline year. From this, future year inventories were developed for 2009, 2012 and 2018 based on this base year. These future year emission in ventories include emissions growth due to projected incr eases in population and economic activity
as well as the emissions reductions due to the implementation of control

measures.

7.1.1 Baseline Inventory

The 2002 emissions inventory data were fi rst generated by individual states in
the MANE-VU area. MARAMA then coordinated and quality-assured the 2002 inventory data, and projected it for t he relevant control years. The 2002
emissions from non-MANE-VU areas withi n the modeling domain were obtained from other Regional Planning Organizati ons for their corresponding areas. These Regional Planning Organizations incl uded the Visibility Improvement State
and Tribal Association of the Southeas t (VISTAS), the Midwest Regional Planning Organization (MRPO) and the Central Regional Air Planning7-2 Association (CENRAP).

Version 3 of the 2002 base year emission inventory was used in the regional modeling exercise. A technical support document for the MANE-VU 2002 base inventory is present ed in Appendix H, Technical Support Document (TSD) for 2002 MANE-VU SIP Modeling Inventories, Version 3 . This document explains the data sources, methods, and results fo r preparing this version of the 2002 base year criteria air pollutant and ammonia emissions inventory. Documentation for the future year es timations is presented in Appendix E, Development of Emission Projecti ons for 2009, 2012 and 2018 for NonEGU Point, Area, and Nonroad Source s in the MANE-VU Region of this document. The inventory and supporting data prepared includes the following:

1. Comprehensive, county-level, ma ss emissions and modeling inventories
for 2002 emissions for criteria air pollutants and ammonia for the State and Local agencies included in the MANE-VU region. 2. The temporal, speciation, and spatia l allocation profiles for the MANE-VU region inventories. 3. Inventories for wildfires, prescr ibed burning and agricultural field burning for the southeastern provinces of Canada. 4. Inventories for other Regional Planning Organizations, Canada, and
Mexico.

The mass emissions inventory files were converted to the National Emissions Inventory Input Format Version 3.0. The modeling inventory files were processed in Sparse Matrix Operator Ke rnel Emissions/Inventory Data Analyzer (SMOKE). The inventories include annual emissions for oxides of nitrogen (NO X ), volatile organic compounds (VOC), car bon monoxide, ammonia, particles with an aerodynamic diameter less than or equal to a nominal 10 micrometers (PM10) and PM2.5. Temporal prof iles prepared for MANE-VU were used to calculate daily emissions for all MANE-VU states.

Work on Version 1 of the 2002 MANE-VU inventory began in April 2004. The consolidated inventory for point , area, onroad, and nonroad sources was prepared starting with the inventories that MANE-VU state and local agencies submitted to the EPA from May through Ju ly of 2004 as a requirement of the Consolidated Emissions Reporting rule . The EPA’s format and content quality assurance (QA) programs (and other QA checks not included in the EPA’s QA software) were run on each inventory to identify format a nd/or data content
issues. A contractor, E.H. Pechan & Asso ciates, Inc. (Pechan), worked with the MANE-VU state/local agencies and the MARA MA staff to resolve QA issues and augment the inventories to fill data gaps in accordance with the Quality Assurance Project Plan prepared for MANE -VU. The final inventory and SMOKE and input files were finalized during January 2005.7-3 Work on Version 2 (covering the peri od from April through September 2005) involved incorporating revisions reques ted by some MANE-VU state/local agencies on the point, area, and onroad in ventories. Work on Version 3 (completed on November 20, 2006) included additional revisions to the point, area, and onroad inventories as requested by some states. Thus, the Version 3 inventory for point, area, and onroad source s was built upon Versions 1 and 2. This work also included development of the biogenics inventory. In Version 3, the nonroad inventory was completely redone because of changes that the EPA
made to the NONROAD2005 model. Emissions inventory data files are available
on the MARAMA website at:

http://www.marama.org/visibi lity/EI_Projects/index.html

7.1.2 Future Year Emission Control Inventories

An inventory technical support document for these future inventories is included in Appendix E, Development of Emission Projections for 2009, 2012 and 2018 for NonEGU Point, Area, and Nonroad S ources in the MANE-VU Region of this document and explains the data sources, methods, and results for future year emission forecasts for three years; four emission sectors; two emission control scenarios; seven pollutants; and eleven states plus the Dis trict of Columbia. The following is a summary of the future y ear inventories that were developed:

The three projection y ears are 2009, 2012, and 2018;

1. The five source sectors are Electr ic Generating Units (EGUs), non-electrical generating units (non EGUs), point s ources, area sources, and nonroad mobile sources. MANE-VU prepared EG U projections using the Integrated Planning Model (IPM) and onroad mobile source projections using the SMOKE emission modeling system.

2. The two emission control scenarios are:

a. A combined “on-the-books/on-the- way” (OTB/OTW) control strategy accounting for emission control regulat ion already in place, as well as some emission control regulations that will be instituted as a result of this
SIP.

b. A beyond on the way (BOTW) scenario to account for controls from potential new regulations that may be necessary to meet visibility and other regional air quality goals.

(Note that these measures are described in detail in Section 10, and that7-4 emission reductions based on currently expected measures to which New York is committing are presented at the end of this section).

3. The inventories were developed for seven pollutants, which are SO 2 , NO X , VOCs, carbon monoxide, PM 10 – Primary (sum of the filterable and condensable components), PM 2.5 – Primary (sum of the filterable and condensable components), and ammonia.

4. The states are those that comprise the MANE-VU region. In addition to the District of Columbia, the other 11 MANE-VU states are Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont.

7.2 Inventories for Specific Source Types

There are five emission source classifi cations in the emissions inventory as follows; Stationary point, Stationary ar ea, Off-road mobile, On-road mobile, and Biogenic.

Stationary point sources are large source s that emit greater than a specified tonnage per year. Stationar y area sources are those sources whose emissions are relatively small but due to the large number of these sources, the collective emissions could be significant (i.e., dry cleaners, service stations, agricultural sources, fire emissions, etc.). Off -road mobile sources are equipment that can move but do not generally use roadways, (i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, etc.). On-road mobile sources are automobiles, trucks, and motorcycles that use the roadway system. The
emissions from these sources are esti mated by vehicle type and road type. Biogenic sources are natural sources like trees, crops, grasses and natural decay of plants. Stationary point sources emission data is tracked at the facility, point and process level. For all other source types, emissions are summed on the county level.

7.2.1 Stationary Point Sources

Point source emissions are emissions from large individual sources. Generally, point sources have permits to operate and their emissions are individually calculated based on source specific fact ors on a regular schedule. The largest point sources are inventoried annually . These are considered to be major sources having emissions of 100 tons per year (TPY) of a criteria pollutant, 25 tpy of NOx and VOC in the New York Cit y Metropolitan Area, 10 tpy of a single hazardous air pollutant (HAP), or 25 tpy total HAPs. Emissions from smaller sources are also calculated individually but less frequently. Point sources are grouped into EGU sources and other indus trial point sources, termed as nonEGU point sources.7-5 7.2.1.1 Electric Generating Units

The base year inventory for EGU s ources used 2002 continuous emissions monitoring (CEM) data reported to the EPA in compliance with the Acid Rain program or 2002 hourly emission data prov ided by stakeholders. These data provide hourly emissions pr ofiles that can be used in the modeling of emissions
of SO 2 and NO
x from these large sources. Emi ssion profiles are used to estimate emissions of other pollutants (volat ile organic compounds, carbon monoxide, ammonia, fine particles) based on measured emissions of SO 2 and NO
x .

Future year inventories of EGU em issions for 2009 and 2018 were developed using the IPM model to forecast growth in electric demand and replacement of older, less efficient and more polluting pow er plants with newer, more efficient and cleaner units. While the output of t he IPM model predicts that a certain number of older plants will be replaced by ne wer units to meet future electric growth and state-by-state NO
x and SO 2 caps, the State of New York did not directly rely upon the closure of any particular plant in establishing the 2018 inventory upon which the reas onable progress goals were set. This results in a conservative (higher) future year emission estimate.

7.2.1.2 Non-EGU Point Sources

The non-EGU category used annual emission s as reported for the base year
2002 in MANE-VU Version 3. These emi ssions were temporally allocated to month, day, and source category code (S CC) based allocation factors. The general approach for estimating future y ear emissions was to use growth and
control data consistent with EPA’s CAIR analyses. This data was supplemented with site-specific growth factors as appropriate.

7.2.2 Stationary Area Sources

Stationary area sources include source s whose individual emissions are relatively small but due to the large num ber of these source s, the collective emissions are significant. Some examples include the combustion of fuels for heating, dry cleaners, and service stati ons. Emissions are estimated by multiplying an emission factor by some k nown indicator of collective activity, such as fuel usage, or number of households or population. The general approach for estimating future year emissions was to use growth and control data consistent with EPA’s CAIR analyses. This data was supplemented with state-specific growth factors as appropriate.

7.2.3 Off-Road Mobile Sources

Off-road mobile sources are equipment that can move but do not use the7-6 roadways, such as construction equipment , aircraft, railroad locomotives, and lawn and garden equipment. For the majority of the off-road m obile sources, the emissions for base year 2002 were estimated using the EPA’s nonroad model.
The nonroad model assumes that a certai n number of off-road sources will be replaced every year by newer, less pollu ting vehicles that meet the new EPA
standards for off-road sources. These lower emissions have been built into the 2018 inventory as well as the benefits receiv ed from lower sulfur gasoline in offroad vehicles. Aircraft engines, railr oad locomotives and commercial marine
vessels are not included in the nonroad model.

7.2.4 Highway Mobile Sources

For on-road vehicles, EPA’s MOBILE6.2 was used to estimate emissions. For future year emissions the MOBILE6.2 model considers t hat a certain number of
the vehicle fleet in each St ate will be replaced every year by newer, less polluting
vehicles that meet the California Low Emission Vehi cle standards promulgated by New York State as 6NYCRR Part 218. These lower emissions have been built into the 2018 inventory as well as the benefits received from lower sulfur gasoline in on-road diesel and gasoline v ehicles and the 2007 heavy-duty diesel
standards. All new mobile source m easures and standards, as well as any benefits from implementati on of individual State Inspection and Maintenance programs, were used in dev eloping the 2018 inventory.

7.2.5 Biogenic Emission Sources

Biogenic emissions were estimated us ing SMOKE-BEIS3 (Biogenic Emission Inventory System 3 version 0.9) preproc essor. Further information on Biogenic
emissions estimation is contained in t he modeling section of this document.

7.3 Emission Processor Select ion and Configuration (SMOKE)

The mass emissions inventory files were converted to the National Emissions Inventory Input Format Version 3.0. The modeling inventory files were processed in Sparse Matrix Operator Ke rnel Emissions/Inventory Data Analyzer (SMOKE). The SMOKE Processing Syst em was selected for the modeling analysis. SMOKE is principally an emi ssions processing system, as opposed to a true emissions inventory preparation syst em, in which emissions estimates are simulated from “first principles.” This means that, with the exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for converting emissions inventory data into the formatted emissions files required for a photochemical air quality model. Insi de the MANE-VU region, the modeling inventories were processed by the D epartment using the SMOKE (Version 2.1) processor to provide inputs for the CMAQ model. A detailed description of all SMOKE input files such as area, mobile , fire, point and biogenic emissions files and the SMOKE model configur ation are provided in the Technical Support7-7 Document on Agricultural and Forestry Smoke Management in the MANE-VU Region , Appendix I. The MANE-VU member states selected several control strategies for inclusion in the mode ling. Emission reduction requirements mandated by the Clean Air Act were also included in projecting future year emissions. In addition, Section 51.308(d )(3)(v)(D) requires the State of New York to consider source retirement and replacement schedules in developing the future inventories and long-term strategy.

7.4 Sources of Visibility Impairing Pollutants in MANE-VU

This section explores the origin and quant ity of haze-forming pollutants emitted in
the eastern and the mid-Atlantic United St ates. Section 51.308(d)(4)(v) of EPA’s Regional Haze Rule requires a statewide emission inventory of pollutants that are reasonably anticipated to cause or cont ribute to visibility impairment in any mandatory Class I area. The pollutants that affect fi ne particle formation, and thus contribute to regional haz e, are sulfur oxides (SO X ), nitrogen oxides (NO X ), volatile organic compounds (VOC), and ammonia (NH 3 ). Particles with an aerodynamic diameter less than or equal to 10 and 2.5 μm (i.e., primary PM 10 and PM 2.5 ) can be directly emitted from various sources.

The emissions dataset illustrated below is the 2002 MANE-VU Version 2 regional haze emissions inventory. The emissi on inventories include carbon monoxide (CO), but it is not considered here as it does not contribute to regional haze. The MANE-VU regional haze emissi ons inventory version 3.0, released in April 2006, has superseded version 2.0 for modeling purposes. This inventory update was developed through the Mid-At lantic Regional Air Management Association (MARAMA) for the MANE-VU RPO. T he trends among recent emission inventories presented her e use the 1996 EPA NET and 1999 NEI and Version 2 of the MANE-VU inventory. This section describes emission characteristics by pollutant and source type (e.g., point, area, and mobile).

7.5 Emission Inventory Characteristics

This section reviews trends in emissions of SO 2 , VOC, NO
x , PM and ammonia. The trends among recent emission inv entories presented here use the 1996 EPA
NET and 1999 NEI and Version 3.0 of the 2002 MANE-VU inventory. 1 1 This

1 EPA's Emission Factor and Inventory Group (EFIG) (EPA/OAR (Office of Air and Radiation)/OAQPS (Office of Air Quality Planning and Standards)/EMAD (Emissions, Monitoring and Analysis Division) prepares a national database of ai r emissions information with input from numerous state and local air agencies, from tribes, and from industry. This database contains information on stationary and mobile sources that emit criteria ai r pollutants and their precur sors, as well as hazardous air pollutants (HAPs). The database includes estimates of annual emissions, by source, of air pollutants in each area of the country on an annual basis. The NE I includes emission estimates for all 50 states, the District of Columbia, Puerto Rico, and the Virgin Is lands. Emission estimates for individual point or major7-8 section describes emission characteristi cs by pollutant and source type (e.g., point, area, and mobile). As described la ter, this data was superseded by more up-to-date data for modeling pur poses, but this data shows trends in emissions.

7.5.1 Sulfur Dioxide (SO 2 )

SO 2 is the primary precursor pollutant fo r sulfate particles. Sulfate particles commonly account for more than 50 percent of particle-related light extinction at
northeastern Class I areas on the clearest da ys and for as much as or more than 80 percent on the haziest days. Hence, SO 2 emissions are an obvious target of opportunity for reducing regional haze in the eastern United States. Combustion of coal and, to a lesser ex tent, of certain petroleum products accounts for most anthropogenic SO 2 emissions. In fact, in 1998 a single source category, coalburning power plants, was responsi ble for two-thirds of total SO 2 emissions nationwide (NESCAUM, 2001a) . Figure 7-1 shows SO 2 emissions trends in the
MANE-VU states extracted from the NEI for the year s 1996, 1999, and the 2002 MANE-VU inventory (EPA 2005 and MARAMA, 2004).

Most of the states (with t he exception of Maryland) s how declines in year 2002
annual SO 2 emissions as compared to 1996 em issions. Some of the states show an increase in 1999 followed by a decline in 2002 and others show consistent declines throughout the entire period. The up ward trend in emissions after 1996 probably reflects electricit y demand growth during the late 1990s combined with the availability of banked em issions allowances from initial overcompliance with control requirements in P hase 1 of the EPA Acid Rain Program. This led to relatively low market prices for allowances later in the decade, which encouraged utilities to purchase allowances rather than implem ent new controls as electricity output expanded.

The observed decline in the 2002 SO 2 emissions inventory reflects implementation of the second phase of t he EPA Acid Rain Program, which in
2000 further reduced allowable emissions a nd extended emissions limits to more power plants. Figure 7-2 shows the percent contribution from different source categories to overall, annual 2002 SO 2 emissions in the MANE-VU states. The chart shows that point sources dominate SO 2 emissions, which primarily consist of stationary combustion sources for generat ing electricity, industrial energy, and

sources (facilities), as well as county level estimate s for area, mobile and othe r sources, are available currently for years 1985 through 1999 for criteria pol lutants, and for years 1996 and 1999 for HAPs. Data from the NEI help support air dispersion modeling, regional strategy development, setting regulation, air toxics risk assessment, and tracking trends in emissions over time. For emission inventories prior to 1999, the National Emission Trends (NET) database main tained criteria pollutant emission estimates and the National Toxics Inventory (NTI) database maintai ned HAP emission estimates. Beginning with 1999, the NEI began preparing criteria and HAP emissions data in a more integrated fashion to take the place
of the NET and the NTI.7-9 heat. Smaller stationary combustion source s called “area sources” (primarily commercial and residential heating, and sma ller industrial facilities) are another important source category in the MANE-VU states. By contrast, on-road and non-road mobile sources make only a relati vely small contribution to overall SO 2 emissions in the region (NESCAUM, 2001).

Figure 7-1 - State Level Su lfur Dioxide Emissions

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AREA NONROAD ONROAD POINT Emission7-11 7.5.2 Volatile Organic Compounds (VOCs)

Existing emission inventories generally refer to “volatile organic compounds” (VOCs) as hydrocarbons whose volati lity in the atmosphere makes them particularly important from the standpoint of ozone fo rmation. From a regional haze perspective, there is less concern with the volatile organic gases emitted directly to the atmosphere and more with the secondary organic aerosol (SOA)
that the VOCs form after condensation and oxidation processes. Thus, the VOC inventory category is of inte rest primarily from the org anic carbon perspective of

PM 2.5 . After sulfate, organic carbon generally accounts for the next largest share of fine particle mass and particle-related light extinction at northeastern Class I sites. The term organic carbon encompa sses a large number and variety of chemical compounds that may come directly from emission sources as a part of primary PM or may form in the atmospher e as secondary pollutants. The organic carbon present at Class I sites includes a mix of species, including pollutants originating from anthropogenic (i.e., m anmade) sources as well as biogenic hydrocarbons emitted by vegetation. Recent effort s to reduce manmade organic carbon emissions have been undertaken prim arily to address summertime ozone
formation in urban centers. Future effo rts to further r educe organic carbon emissions may be driven by programs that address fine particles and visibility.
These efforts are discussed in Section 10 of this document.

Understanding the transport dynamics and sour ce regions for organic carbon in northeastern Class I areas is likely to be more complex than for sulfate. This is partly because of the large number and vari ety of organic carbon (OC) species, the fact that their transport characteristics vary widely, and the fact that a given species may undergo numerous complex chem ical reactions in the atmosphere. Thus, the organic carbon contribution to vi sibility impairment at most Class I sites in the East is likely to include manmade pollution transported from a distance, manmade pollution from nearby sources, and biogenic emissions, especially terpenes from coniferous forests.

As shown in Figure 7-3, the VOC inv entory is dominated by mobile and area
sources. On-road mobile sources of VOCs include exhaust emissions from gasoline passenger vehicles and diesel- powered heavy-duty vehicles as well as evaporative emissions from transportati on fuels. VOC emissions may also originate from a variety of area sour ces (including solvents, architectural coatings, and dry cleaners) as well as from some point sources (e.g., industrial facilities and petroleum refineries).

Biogenic VOCs may play an important role within the rural settings typical of Class I sites. The oxidation of hydrocar bon molecules containing seven or more carbon atoms is generally the most signific ant pathway for the formation of lightscattering organic aerosol particles (O dum et al., 1997). Smaller reactive hydrocarbons that may contribute signifi cantly to urban smog (ozone) are less7-12 likely to play a role in organic aerosol formation, though it was noted that high
ozone levels can have an indirect effect on visibility by promoti ng the oxidation of other available hydrocarbons, including biogenic emissions (NESCAUM, January 2001). In short, further wo rk is needed to characterize the organic carbon contribution to regional haze in the Nort heast and Mid-Atlantic states and to develop emissions inventories that will be of greater value for visibility planning

purposes.

Figure 7-3 - 2002 VOC Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions in 10 6 tons per year)

7.5.3 Oxides of Nitrogen (NO X )

NO X emissions contribute to visibility impairm ent in the eastern U.S. by forming light-scattering nitrate particles. Nitrate s generally account for a substantially smaller fraction of fine particle mass and rela ted light extinction than sulfates and organic carbon at northeastern Class I sites. Notably, nitrates may play a more important role at urban sites and in the wintertime. In addition, NO X may have an indirect effect on summertime visibility by virtue of its role in the formation of ozone, which in turn promotes the formation of secondary organic aerosols (NESCAUM 2001a). Figure 7-4 shows NO X emissions in the MANE-VU region at the state level. Since 1980, nationwide emissions of NO X from all sources have 0% 25% 50% 75% 100%

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AREA NONROAD ONROAD POINT Emission7-13 shown little change. In fact, emissions increased by 2 percent between 1989 and 1998 (EPA, 2000a). This increase is most likely due to industrial sources and the transportation sector, as power plant combustion sources have implemented modest emissions reductions during the same time period. Most states in the MANE-VU region experienced declining NO X emissions from 1996 through 2002, except Massachusetts, Maryland, New Yo rk, and Rhode Island, which show an
increase in NO X emissions in 1999 before dec lining to levels below 1996 emissions in 2002. Power plants and mob ile sources generally dominate state
and national NO X emissions inventories. Nati onally, power plants account for more than one-quarter of all NO X emissions, amounting to more than six million tons. The electric sector plays an even larger role, however, in parts of the industrial Midwest where high NO X emissions have a particularly significant power plant contribution. By contras t, mobile sources dominate the NO X inventories for more urbanized Mid-Atlantic and New Engla nd states to a far greater extent, as shown in Figure 7-5. In these states, on-road mobile sources - a category that mainly includes highway vehicles - represent the most significant NO X source category. Emissions from non-road (i.e., off-highway) mobile sources, primarily diesel-fired engines, also represent a substa ntial fraction of the inventory. While
there are fewer uncertainties associated with available NO X estimates than in the case of other key haze-related pollutant s - including primary fine particle and
ammonia emissions - further efforts could improve current inventories in a number of areas (NESCAUM, 2001a).

In particular, better information on t he contribution of area and non-highway mobile sources may be of most interest in the context of re gional haze planning. First, available emission estimation met hodologies are weaker for these types of sources than for the large stationary co mbustion sources. Moreover, because
SO 2 and NO X emissions must mix with amm onia to participate in secondary particle formation, emissions that occur ov er large areas at the surface may be more efficient in secondary fine particula te formation than concentrated emissions from isolated tall stacks (Duyzer, 1994).7-14 Figure 7-4 - State Level Nitrogen Oxides Emissions

Figure 7-5 - 2002 NO X Emissions (Bar graph: Percen tage fraction of four source categories, Circle: Annual emissions amount in 10 6 tons per year)

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AREA NONROAD ONROAD POINT Emission7-15 7.5.4 Primary Pa rticulate Matter (PM 10 and PM 2.5 )

Directly-emitted or “primary ” particles (as distinct from secondary particles that form in the atmosphere through chemical re actions involving precursor pollutants
like SO 2 and NO X ) can also contribute to regional haze. For regulatory purposes, a distinction is made between particles wit h an aerodynamic diameter less than or equal to 10 micrometers and smaller parti cles with an aerodynamic diameter less than or equal to 2.5 micr ometers (i.e., primary PM 10 and PM 2.5 , respectively). Figure 7-6 and Figure 7-7 show PM 10 and PM 2.5 emissions for the MANE-VU states for the years 1996, 1999, and 2002. Note that for PM 10 the inventory
values are drawn from the 2002 NEI. Most states show a steady decline in
annual PM 10 emissions over this time period.

By contrast, emission trends for primary PM 2.5 are more variable. Crustal sources are significant contributors of primar y PM emissions. This category includes fugitive dust emissions from construc tion activities, paved and unpaved roads, and agricultural tilling. Typically, monitors estimate PM 10 emissions from these types of sources by measuring the horizont al flux of particulate mass at a fixed downwind sampling location within perhaps 10 meters of a road or field. Comparisons between estimated emission rates for fine particles using these types of measurement techniques and obser ved concentrations of crustal matter in the ambient air at downwind receptor sites suggest that physical or chemical processes remove a significant fraction of crustal material relatively quickly. As a result, it rarely entrains into layers of the atmosphere where it can transport to downwind receptor locations. Because of this discrepancy between estimated
emissions and observed ambient concent rations, modelers typically reduce
estimates of total PM 2.5 emissions from all crustal sources by applying a factor of
0.15 to 0.25 to the total PM 2.5 emissions before includi ng it in modeling analyses. From a regional haze perspecti ve, crustal material generally does not play a major role. On the 20 percent best-visibility days during the baseline period (2000-2004), it accounted for six to eleven percent of particle-related light extinction at MANEVU Class 1 sites. On the 20 percent wo rst-visibility days, however, crustal material generally plays a much smaller role relative to other haze-forming pollutants, ranging from tw o to three percent. Moreov er, the crustal fraction includes material of natural origin (such as soil or sea salt) t hat is not targeted under the Regional Haze Rule.

Of course, the crustal fraction can be influe nced by certain human activities, such as construction, agricultural practices, and road maintenance (including wintertime salting) — thus, to the ext ent that these types of activities are found to affect visibility at northeastern Class I sites, control measures ta rgeted at crustal material may prove beneficial. Experience from the we stern United States, where the crustal component has generally played a more significant role in driving overall particulate levels, may be helpful to the extent that it is relevant in the eastern context. In addition, a few areas in the Northeast, such as New Haven,7-16 Connecticut and Presque Isle, Maine, have some experience with the control of dust and road-salt as a result of regulator y obligations stemming from their past
non-attainment status with re spect to the NAAQS for PM 10

.

Current emissions inventories for the ent ire MANE-VU area indicate residential wood combustion represents 25 percent of primary fine particulate emissions in the region. This implies that rural sources can play an impor tant role in addition to
the contribution from t he region’s many highly populated urban areas. An important consideration in this regard is that reside ntial wood combustion occurs primarily in the winter months, while m anaged or prescribed burning activities occur largely in other seasons. The latte r category includes agricultural fieldburning activities, prescribed burning of fore sted areas and other burning activities such as construction waste burning. Li miting burning to times when favorable meteorological conditions c an efficiently disperse resulting emissions can manage many of these types of sources.

Figure 7-8 and Figure 7-9 show that area and mobile sources dominate primary PM emissions. (The NEI inventory categor izes residential wood combustion and
some other combustion sources as area s ources.) The relative contribution of point sources is larger in the primary PM 2.5 inventory than in the primary PM 10 inventory since the crustal co mponent (which consists main ly of larger or “coarsemode” particles) contributes mostly to overall PM 10 levels. At the same time, pollution control equipment commonly install ed at large point sources is usually more efficient at capturing coarse-mode particles.

Figure 7-6 - State Level Primary PM 10 Emissions

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NEI 1999

NEI 20027-17 Figure 7-7 - State Level Primary PM 2.5 Emissions

Figure 7-8 - 2002 Primary PM 10 Emissions (Bar graph: Pe rcentage fraction of four source categories, Circle: Annual emissions amount in 10 6 tons per year) 0.0 0.1 0.2 0.3

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AREA NONROAD ONROAD POINT Emission7-18 Figure 7-9 - 2002 Primary PM 2.5 Emissions (Bar graph: Pe rcentage fraction of four source categories, Circle: Annual emissions amount in 10 6 tons per year)

7.5.5 Ammonia Emissions (NH 3 )

Knowledge of ammonia emission sour ces will be necessary in developing effective regional haze reduction strat egies because of the importance of
ammonium sulfate and ammonium nitrate in determining overall fine particle mass and light scattering. Identifyi ng emissions from ammonia sources is necessary to help develop regional haze reduction strate gies. According to 1998 estimates, livestock agriculture and fertilizer use acc ounted for approximately 86 percent of
all ammonia emissions to the atmos phere (EPA, 2000b). However, better ammonia inventory data is needed for the p hotochemical models used to simulate fine particle formation and transport in the ea stern United States. States were not required to include ammonia in their air emi ssions data collection efforts until fairly recently (see consolidated emissions r eporting rule, 67 FR 39602; June 10, 2002), and so it will take time for the quality of ammonia inventory data to match the quality of the data for the ot her criteria pollutants.

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AREA NONROAD ONROAD POINT Emission7-19 Ammonium ions (formed from ammonia emissions to the atmosphere) are an important constituent of ai rborne particulate matter, ty pically accounting for 10–20 percent of total fine particle mass. Reduc tions in ammonium ion concentrations can be extremely beneficial because a mo re-than-proportional reduction in fine particle mass can result. Ansari and Pandis (1998) showed that a one mg/m 3

reduction in ammonium ion could result in up to a four mg/m 3 reduction in fine particulate matter. Decision makers, however, must weigh the benefits of ammonia reduction against the significant role it plays in neutralizing acidic
aerosols. 2

To address the need for improved ammoni a inventories, MARAMA, NESCAUM and EPA funded researchers at Carnegie Mellon University (CMU) in Pittsburgh to develop a regional ammonia inventory (Davidson et al., 1999). This study focused on three issues with respect to current emissions estimates: (1) a wide range of ammonia emission factor val ues, (2) inadequate temporal and spatial
resolution of ammonia emissions esti mates, and (3) a lack of standardized ammonia source categories.

The CMU project established an inventor y framework with source categories, emissions factors, and activity data that are readily accessible to the user. With this framework, users can obtai n data in a variety of formats 3 and can make updates easily, allowing addi tional ammonia sources to be added or emissions factors to be replaced as better information becomes available (Strader et al., 2000; NESCAUM, 2001b). Figure 7-10 shows that estimated ammonia emissions were fairly stable in the 1996, 1999, and 2002 NEI for MANE-VU states, with some increases observed for Massachusetts, New Jersey and New York. Area
and on-road mobile sources dominate accord ing to Figure 7-11. Specifically, emissions from agricultural sources and livestock production account for the largest share of estimated ammonia emissi ons in the MANE-VU region, except in
the District of Columbia. The two remain ing sources with a significant emissions
contribution are wastewat er treatment systems and gasoline exhaust from highway vehicles.

2 SO 2 reacts in the atmosphere to form sulfuric acid (H 2 SO 4 ). Ammonia can partially or fully neutralize this strong acid to form ammonium bisulf ate or ammonium sulfate. If planners focus future control strategies on ammonia and do not achieve corresponding SO 2 reductions, fine particles formed in the atmosphere will be substantially more acidic than those presently observed.

3 For example, the user will have the flexibility to choose the temporal resolution of the output emissions data or to spatially attri bute emissions based on land-use data.7-20 Figure 7-10 - State Level Ammonia Emissions

Figure 7-11 - 2002 NH 3 Emissions (Bar graph: Percentage fraction of four source categories, Circle : Annual emissions amount in 10 6 tons per year) 0.000 0.025 0.050 0.075 0.100

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AREA NONROAD ONROAD POINT Emission7-21 7.5.6 Further Discussion

Figures 7-1, 7-4, 7-6, 7-7, and 7-10 s how SO2, NOx, PM10, PM2.5 and ammonia
emissions trends in the MANE-VU states extracted from the NEI for the years 1996, 1999, and the 2002 MANE-VU inventory. Comparing emissions from each year, these figures provide an indication of whether there is an identifiable trend in emissions prior to the base year, as well as the ability to show the relative emissions on a state-specific basis for these three years. It is thus possible to compare the relative emissions from eac h state as well as to assess whether a
trend in emissions is evident over this period. This information is useful in determining what air program-related changes might have been effective in influencing the levels of these pollutants in recent years, and is suggestive of what trends might be seen in the first planning pe riod. For example, the figures related to SO2 suggests that most states show declines in year 2002 as compared tn 1996 emissions. Where it occurred. the upward trend in emissions after 1996 likely reflects electricity demand growth during the late 1990s combined with the availability of banked emissi ons allowances from in itial overcompliance with control requirements in Phase 1 of the EPA Acid Rain Program. Understanding the material presented in these graphs if useful in determining how to project emissions and judging whether projecti ons are reasonable. The specific interpretation of each graph is discussed in detail in the adjacent portions of
section 7.

7.6 Summary of Emission Inventories

The tables below summarize the 2002 and 2018 emissions as developed by the methods described above, and used in the modeling for haze impacts through the period of this SIP. Tables 7-1 and 7-2 present the MANE-VU-wide figures, with the percent changes over the period for the entire MANE-VU region shown in Table 7-3. Likewise, Tables 7-4 and 7- 5 summarize the emissions for New York State, with Table 7- 6 showing the percent changes over time.

For MANE-VU, while both PM2.5 and PM 10 emissions are shown to increase slightly, the emission of the precursors for particulate matter VOC, NOx and SO2 decrease significantly. With sulfate being the predominate contributor to regional haze, the reductions are dramatic, cont ribuing to the expected meeting of the reasonable progress goals for the Class I areas within MANE-VU.

In the case of New York’s emissions, PM2.5 emissions are predicted to decrease slightly but, as is the case with MANE-V U emissions as a whole, VOC, NOx and SO2 emissions are expected to decrease significantly as well.7-22 7.6.1 Summary of MANE-VU Emissions Inventories

Table 7-1 - MANE-VU 2002 Emissions Inventory Summary (TPY)

Sector VOC NOx PM2.5 PM10 NH3 SO2

Point 97,300 673,660 55,447 89, 150 6,194 1,907,634

Area 1,528,141 262,477 332, 729 1,455,311 249,795 316,357

On-Road

Mobile 789,560 1,308,233 22,107 31,561 52,984 40,091

Non-Road

Mobile 572,751 431,631 36,084 40,114 287 57,257

Biogenics 2,575,232 28,396 - - - -

TOTAL 5,562,984 2,704,397 446,367 1,616,136 309,260 2,321,339

Source: Pechan, 2006. "Technical Support Document for 2002 MANE -VU SIP Modeling Inventories, Version 3."
November 2006. Available online: http://www.marama.org/visibility /Inventory%20Summary/20 02EmissionsInventory.htm

Table 7-2 - MANE-VU 2018 Emissions Inventory Summary (TPY)

Sector VOC NOx PM2.5 PM10 NH3 SO2

Point 115,052 413,021 93,580 129, 315 11,134 657,018

Area 1,387,882 284,535 345,419 1,614,476 341,746 305,437

On-Road

Mobile 269,981 303,955 9,189 9,852 66,476 8,757

Non-Road

Mobile 380,080 271,185 23,938 27,059 369 8,643

Biogenics 2,575,232 28,396 - - - -

TOTAL 4,728,227 1,301,092 472,126 1,780,702 419,725 979,855

Source: MACTEC, 2007. "Development of Emission Projections for 2009, 2012, and 2018 for non-EGU Point, Area, and Nonroad Sources in the MANE-VU Region." February 28, 2007.
Available online: http://www.marama.org/visibility/Inventory% 20Summary/FutureEmi ssionsInventory.htm EGU Point Emissions: VISTAS_PC_If IPM Run (Alpine Geophysics, 2008)7-23 Table 7-3 - Change in MANE-VU Emissions 2002 to 2018 (*Percent)

Sector VOC NOx PM2.5 PM10 NH3 SO2

Point 18.2 -38.7 68.8 45.1 79.8 -65.6

Area -9.2 8.4 3.8 10.9 36.8 -3.4

On-Road

Mobile -65.8 -76.8 -58.4 -68.8 25.5 -78.2

Non-Road

Mobile -33.6 -37.2 -33.7 -32.5 28.6 -84.9

Biogenics 0.0 0.0 -- -- -- --

TOTAL -15.0 -51.9 5.8 10.2 35.7 -57.8

*Negative percent indicates a decrease in emissions

7.6.2 Summary of New York 2002 Emissions Inventories

Table 7-4 - New York 2002 Emissions Inventory Summary (TPY)

Sector CO NOx VOC NH3 SO2 PM10 PM25

Area 356,287 98,804 514,425 67,422 113,978 369,595 85,841

Point 53,563 584,450 134,363 1,86 1 686,426 10,326 25,075

Nonroad 1,205,509 119,808 158,121 79 13,288 9,605 9,000

Onroad 2,942,730 313,888 179,731 14, 439 10,229 7,599 5,402

Biogenic 63,436 8,313 492,483 - - - -

Totals 4,621,525 1,125,263 1,479,123 83,801 823,921 397,125 125,318

Source: NOx, SO2 and PM2.5: NYSDEC’s Propos ed PM2.5 Attainment Demonstration (May 2008) CO and VOC :NYSDEC’s Proposed 8-Hour Ozone Attainment Demonstration (Feb 2008) Others: MACTEC, 2007. "Development of Emission Projecti ons for 2009, 2012, and 2018 for non-EGU Point, Area, and Nonroad Sources in the MANE-VU Region." February 28, 2007. Available online: http://www.marama.org/visibility/In ventory%20Summary/FutureEmissionsInventory.htm7-24 7.6.3 Summary of New Yo rk 2018 Emissions Inventories

Table 7-5 - New York 2018 Emissions Inventory Summary (TPY)

Sector CO NOx VOC NH3 SO2 PM10 PM25

Area 307,659 108,444 457,421 96,078 141,408 392,027 86,422

Point 101,118 55,681 13,091 2,76 7 118,936 17,062 13,460

Nonroad 1,474,727 72,400 104,562 103 1,686 5,830 5,349

Onroad 1,694,820 78,365 68,104 19, 167 1,794 2,775 2,542

Biogenic 63,436 8,313 492,483 -- -- -- --

Totals 3,641,760 323,203 1,135,571 118, 115 263,824 417,694 107,773

Source: MACTEC, 2007. "Development of Emission Projections for 2009, 2012, and 2018 for non-EGU Point, Area, and Nonroad Sources in the MANE-VU Region." February 28, 2007. Available online: http://www.marama.org/visibility/In ventory%20Summary/FutureEmissionsInventory.htm

Table 7-6 - Change in New York Emissions 2002 to 2018 (*Percent)

Sector CO NOx VOC NH3 SO2 PM10 PM25

Area -13.6 9.8 -11.1 42.5 24.1 6.1 0.7

Point 88.8 -90.5 -90.3 48. 7 -82.7 65.2 -46.3

Nonroad 22.3 -39.6 -33.9 30.4 -87.3 -39.3 -40.6

Onroad -42.4 -75.0 -62.2 32. 7 -82.5 -63.5 -52.9

Biogenic 0.0 0.0 0.0 -- -- -- --

Totals -21.2 -71.3 -23.2 40. 9 -68.0 5.2 -14.0

*Negative percent indicates a decrease in emissions