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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 inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I area. The pollutants inventoried by New York include volatile organic compounds, nitrogen oxides, fine particles (PM2.5), coarse particles (PM10), 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 emission 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 emission 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/eidocs/2002baseinven_102502new.pdf

which identifies the anticipated baseline emission inventory 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 inventories include emissions growth due to projected increases 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 first generated by individual states in the MANE-VU area. MARAMA then coordinated and quality-assured the 2002 inventory data, and projected it for the relevant control years. The 2002 emissions from non-MANE-VU areas within the modeling domain were obtained from other Regional Planning Organizations for their corresponding areas. These Regional Planning Organizations included the Visibility Improvement State and Tribal Association of the Southeast (VISTAS), the Midwest Regional Planning Organization (MRPO) and the Central Regional Air Planning 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 presented 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 for preparing this version of the 2002 base year criteria air pollutant and ammonia emissions inventory. Documentation for the future year estimations is presented in Appendix E, Development of Emission Projections for 2009, 2012 and 2018 for NonEGU Point, Area, and Nonroad Sources in the MANE-VU Region of this document. The inventory and supporting data prepared includes the following:

  1. Comprehensive, county-level, mass 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 spatial allocation profiles for the MANE-VU region inventories.
  3. Inventories for wildfires, prescribed 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 Kernel Emissions/Inventory Data Analyzer (SMOKE). The inventories include annual emissions for oxides of nitrogen (NOX), volatile organic compounds (VOC), carbon monoxide, ammonia, particles with an aerodynamic diameter less than or equal to a nominal 10 micrometers (PM10) and PM2.5. Temporal profiles 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 July 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 and/or data content issues. A contractor, E.H. Pechan & Associates, Inc. (Pechan), worked with the MANE-VU state/local agencies and the MARAMA 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.

Work on Version 2 (covering the period from April through September 2005) involved incorporating revisions requested by some MANE-VU state/local agencies on the point, area, and onroad inventories. 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 sources 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/visibility/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 Sources 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 District of Columbia. The following is a summary of the future year inventories that were developed:

The three projection years are 2009, 2012, and 2018;

  1. The five source sectors are Electric Generating Units (EGUs), non-electrical generating units (non EGUs), point sources, area sources, and nonroad mobile sources. MANE-VU prepared EGU 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:
    1. A combined "on-the-books/on-the-way" (OTB/OTW) control strategy accounting for emission control regulation already in place, as well as some emission control regulations that will be instituted as a result of this SIP.
    2. 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 that 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 SO2, NOX, VOCs, carbon monoxide, PM10 - Primary (sum of the filterable and condensable components), PM2.5 - 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 classifications in the emissions inventory as follows; Stationary point, Stationary area, Off-road mobile, On-road mobile, and Biogenic.

Stationary point sources are large sources that emit greater than a specified tonnage per year. Stationary 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 estimated 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 factors 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 City 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 industrial point sources, termed as non-EGU point sources.

7.2.1.1 Electric Generating Units

The base year inventory for EGU sources used 2002 continuous emissions monitoring (CEM) data reported to the EPA in compliance with the Acid Rain program or 2002 hourly emission data provided by stakeholders. These data provide hourly emissions profiles that can be used in the modeling of emissions of SO2 and NOx from these large sources. Emission profiles are used to estimate emissions of other pollutants (volatile organic compounds, carbon monoxide, ammonia, fine particles) based on measured emissions of SO2 and NOx.

Future year inventories of EGU emissions 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 power plants with newer, more efficient and cleaner units. While the output of the IPM model predicts that a certain number of older plants will be replaced by newer units to meet future electric growth and state-by-state NOx and SO2 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 reasonable 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 emissions as reported for the base year 2002 in MANE-VU Version 3. These emissions were temporally allocated to month, day, and source category code (SCC) based allocation factors. 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 site-specific growth factors as appropriate.

7.2.2 Stationary Area Sources

Stationary area sources include sources whose individual emissions are relatively small but due to the large number of these sources, the collective emissions are significant. Some examples include the combustion of fuels for heating, dry cleaners, and service stations. Emissions are estimated by multiplying an emission factor by some known 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 the roadways, such as construction equipment, aircraft, railroad locomotives, and lawn and garden equipment. For the majority of the off-road mobile sources, the emissions for base year 2002 were estimated using the EPA's nonroad model. The nonroad model assumes that a certain number of off-road sources will be replaced every year by newer, less polluting 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 received from lower sulfur gasoline in off-road vehicles. Aircraft engines, railroad 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 that a certain number of the vehicle fleet in each State will be replaced every year by newer, less polluting vehicles that meet the California Low Emission Vehicle 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 vehicles and the 2007 heavy-duty diesel standards. All new mobile source measures and standards, as well as any benefits from implementation of individual State Inspection and Maintenance programs, were used in developing the 2018 inventory.

7.2.5 Biogenic Emission Sources

Biogenic emissions were estimated using SMOKE-BEIS3 (Biogenic Emission Inventory System 3 version 0.9) preprocessor. Further information on Biogenic emissions estimation is contained in the modeling section of this document.

7.3 Emission Processor Selection 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 Kernel Emissions/Inventory Data Analyzer (SMOKE). The SMOKE Processing System was selected for the modeling analysis. SMOKE is principally an emissions processing system, as opposed to a true emissions inventory preparation system, 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. Inside the MANE-VU region, the modeling inventories were processed by the Department 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 configuration are provided in the Technical Support 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 modeling. 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 quantity of haze-forming pollutants emitted in the eastern and the mid-Atlantic United States. 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 contribute to visibility impairment in any mandatory Class I area. The pollutants that affect fine particle formation, and thus contribute to regional haze, are sulfur oxides (SOX), nitrogen oxides (NOX), volatile organic compounds (VOC), and ammonia (NH3). Particles with an aerodynamic diameter less than or equal to 10 and 2.5 µm (i.e., primary PM10 and PM2.5) can be directly emitted from various sources.

The emissions dataset illustrated below is the 2002 MANE-VU Version 2 regional haze emissions inventory. The emission inventories include carbon monoxide (CO), but it is not considered here as it does not contribute to regional haze. The MANE-VU regional haze emissions inventory version 3.0, released in April 2006, has superseded version 2.0 for modeling purposes. This inventory update was developed through the Mid-Atlantic Regional Air Management Association (MARAMA) for the MANE-VU RPO. The trends among recent emission inventories presented here 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 SO2, VOC, NOx, PM and ammonia. The trends among recent emission inventories presented here use the 1996 EPA NET and 1999 NEI and Version 3.0 of the 2002 MANE-VU inventory.1 This section describes emission characteristics by pollutant and source type (e.g., point, area, and mobile). As described later, this data was superseded by more up-to-date data for modeling purposes, but this data shows trends in emissions.

7.5.1 Sulfur Dioxide (SO2)

SO2 is the primary precursor pollutant for sulfate particles. Sulfate particles commonly account for more than 50 percent of particle-related light extinction at northeastern Class I areas on the clearest days and for as much as or more than 80 percent on the haziest days. Hence, SO2 emissions are an obvious target of opportunity for reducing regional haze in the eastern United States. Combustion of coal and, to a lesser extent, of certain petroleum products accounts for most anthropogenic SO2 emissions. In fact, in 1998 a single source category, coal-burning power plants, was responsible for two-thirds of total SO2 emissions nationwide (NESCAUM, 2001a). Figure 7-1 shows SO2 emissions trends in the MANE-VU states extracted from the NEI for the years 1996, 1999, and the 2002 MANE-VU inventory (EPA 2005 and MARAMA, 2004).

Most of the states (with the exception of Maryland) show declines in year 2002 annual SO2 emissions as compared to 1996 emissions. 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 upward trend in emissions after 1996 probably reflects electricity demand growth during the late 1990s combined with the availability of banked emissions allowances from initial over-compliance with control requirements in Phase 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 implement new controls as electricity output expanded.

The observed decline in the 2002 SO2 emissions inventory reflects implementation of the second phase of the EPA Acid Rain Program, which in 2000 further reduced allowable emissions and extended emissions limits to more power plants. Figure 7-2 shows the percent contribution from different source categories to overall, annual 2002 SO2 emissions in the MANE-VU states. The chart shows that point sources dominate SO2 emissions, which primarily consist of stationary combustion sources for generating electricity, industrial energy, and heat. Smaller stationary combustion sources called "area sources" (primarily commercial and residential heating, and smaller industrial facilities) are another important source category in the MANE-VU states. By contrast, on-road and non-road mobile sources make only a relatively small contribution to overall SO2 emissions in the region (NESCAUM, 2001).

Figure 7-1 - State Level Sulfur Dioxide Emissions

Graph comparing levels of sulfur dioxide emissions for various states

Figure 7-2 - 2002 SO2 Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in million (106) tons per year)

Graph comparing 2002 SO2 emissions by state

7.5.2 Volatile Organic Compounds (VOCs)

Existing emission inventories generally refer to "volatile organic compounds" (VOCs) as hydrocarbons whose volatility in the atmosphere makes them particularly important from the standpoint of ozone formation. 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 interest primarily from the organic carbon perspective of PM2.5. 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 encompasses 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 atmosphere as secondary pollutants. The organic carbon present at Class I sites includes a mix of species, including pollutants originating from anthropogenic (i.e., manmade) sources as well as biogenic hydrocarbons emitted by vegetation. Recent efforts to reduce manmade organic carbon emissions have been undertaken primarily to address summertime ozone formation in urban centers. Future efforts to further reduce 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 source 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 variety of organic carbon (OC) species, the fact that their transport characteristics vary widely, and the fact that a given species may undergo numerous complex chemical reactions in the atmosphere. Thus, the organic carbon contribution to visibility 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 inventory 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 transportation fuels. VOC emissions may also originate from a variety of area sources (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 hydrocarbon molecules containing seven or more carbon atoms is generally the most significant pathway for the formation of light-scattering organic aerosol particles (Odum et al., 1997). Smaller reactive hydrocarbons that may contribute significantly to urban smog (ozone) are less 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 promoting the oxidation of other available hydrocarbons, including biogenic emissions (NESCAUM, January 2001). In short, further work is needed to characterize the organic carbon contribution to regional haze in the Northeast 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 106 tons per year)

Comparison of 2002 VOC emissions by state

7.5.3 Oxides of Nitrogen (NOX)

NOX emissions contribute to visibility impairment in the eastern U.S. by forming light-scattering nitrate particles. Nitrates generally account for a substantially smaller fraction of fine particle mass and related 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, NOX 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 NOX emissions in the MANE-VU region at the state level. Since 1980, nationwide emissions of NOX from all sources have 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 NOX emissions from 1996 through 2002, except Massachusetts, Maryland, New York, and Rhode Island, which show an increase in NOX emissions in 1999 before declining to levels below 1996 emissions in 2002. Power plants and mobile sources generally dominate state and national NOX emissions inventories. Nationally, power plants account for more than one-quarter of all NOX 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 NOX emissions have a particularly significant power plant contribution. By contrast, mobile sources dominate the NOX inventories for more urbanized Mid-Atlantic and New England 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 NOX source category. Emissions from non-road (i.e., off-highway) mobile sources, primarily diesel-fired engines, also represent a substantial fraction of the inventory. While there are fewer uncertainties associated with available NOX estimates than in the case of other key haze-related pollutants - 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 the contribution of area and non-highway mobile sources may be of most interest in the context of regional haze planning. First, available emission estimation methodologies are weaker for these types of sources than for the large stationary combustion sources. Moreover, because SO2 and NOX emissions must mix with ammonia to participate in secondary particle formation, emissions that occur over large areas at the surface may be more efficient in secondary fine particulate formation than concentrated emissions from isolated tall stacks (Duyzer, 1994).

Figure 7-4 - State Level Nitrogen Oxides Emissions

Comparison of nitrogen oxides emissions by state

Figure 7-5 - 2002 NOX Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in 106 tons per year)

Comparison of 2002 NOx emissions by state

7.5.4 Primary Particulate Matter (PM10 and PM2.5)

Directly-emitted or "primary" particles (as distinct from secondary particles that form in the atmosphere through chemical reactions involving precursor pollutants like SO2 and NOX) can also contribute to regional haze. For regulatory purposes, a distinction is made between particles with an aerodynamic diameter less than or equal to 10 micrometers and smaller particles with an aerodynamic diameter less than or equal to 2.5 micrometers (i.e., primary PM10 and PM2.5, respectively). Figure 7-6 and Figure 7-7 show PM10 and PM2.5 emissions for the MANE-VU states for the years 1996, 1999, and 2002. Note that for PM10 the inventory values are drawn from the 2002 NEI. Most states show a steady decline in annual PM10 emissions over this time period.

By contrast, emission trends for primary PM2.5 are more variable. Crustal sources are significant contributors of primary PM emissions. This category includes fugitive dust emissions from construction activities, paved and unpaved roads, and agricultural tilling. Typically, monitors estimate PM10 emissions from these types of sources by measuring the horizontal 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 observed 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 concentrations, modelers typically reduce estimates of total PM2.5 emissions from all crustal sources by applying a factor of 0.15 to 0.25 to the total PM2.5 emissions before including it in modeling analyses. From a regional haze perspective, 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 MANE-VU Class 1 sites. On the 20 percent worst-visibility days, however, crustal material generally plays a much smaller role relative to other haze-forming pollutants, ranging from two to three percent. Moreover, the crustal fraction includes material of natural origin (such as soil or sea salt) that is not targeted under the Regional Haze Rule.

Of course, the crustal fraction can be influenced by certain human activities, such as construction, agricultural practices, and road maintenance (including wintertime salting) - thus, to the extent that these types of activities are found to affect visibility at northeastern Class I sites, control measures targeted at crustal material may prove beneficial. Experience from the western 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, Connecticut and Presque Isle, Maine, have some experience with the control of dust and road-salt as a result of regulatory obligations stemming from their past non-attainment status with respect to the NAAQS for PM10.

Current emissions inventories for the entire 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 important role in addition to the contribution from the region's many highly populated urban areas. An important consideration in this regard is that residential wood combustion occurs primarily in the winter months, while managed or prescribed burning activities occur largely in other seasons. The latter category includes agricultural field-burning activities, prescribed burning of forested areas and other burning activities such as construction waste burning. Limiting burning to times when favorable meteorological conditions can 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 categorizes residential wood combustion and some other combustion sources as area sources.) The relative contribution of point sources is larger in the primary PM2.5 inventory than in the primary PM10 inventory since the crustal component (which consists mainly of larger or "coarse-mode" particles) contributes mostly to overall PM10 levels. At the same time, pollution control equipment commonly installed at large point sources is usually more efficient at capturing coarse-mode particles.

Figure 7-6 - State Level Primary PM10 Emissions

Comparison of primary PM10 emissions by state

Figure 7-7 - State Level Primary PM2.5 Emissions

Comparison of primary PM2.5 emissions by state

Figure 7-8 - 2002 Primary PM10 Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in 106 tons per year)

Comparison of 2002 primary PM10 emissions by state

Figure 7-9 - 2002 Primary PM2.5 Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in 106 tons per year)

Comparison of 2002 primary PM2.5 emissions by state

7.5.5 Ammonia Emissions (NH3)

Knowledge of ammonia emission sources will be necessary in developing effective regional haze reduction strategies because of the importance of ammonium sulfate and ammonium nitrate in determining overall fine particle mass and light scattering. Identifying emissions from ammonia sources is necessary to help develop regional haze reduction strategies. According to 1998 estimates, livestock agriculture and fertilizer use accounted for approximately 86 percent of all ammonia emissions to the atmosphere (EPA, 2000b). However, better ammonia inventory data is needed for the photochemical models used to simulate fine particle formation and transport in the eastern United States. States were not required to include ammonia in their air emissions data collection efforts until fairly recently (see consolidated emissions reporting 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 other criteria pollutants.

Ammonium ions (formed from ammonia emissions to the atmosphere) are an important constituent of airborne particulate matter, typically accounting for 10-20 percent of total fine particle mass. Reductions in ammonium ion concentrations can be extremely beneficial because a more-than-proportional reduction in fine particle mass can result. Ansari and Pandis (1998) showed that a one mg/m3 reduction in ammonium ion could result in up to a four mg/m3 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 ammonia 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 values, (2) inadequate temporal and spatial resolution of ammonia emissions estimates, and (3) a lack of standardized ammonia source categories.

The CMU project established an inventory framework with source categories, emissions factors, and activity data that are readily accessible to the user. With this framework, users can obtain data in a variety of formats3 and can make updates easily, allowing additional 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 according to Figure 7-11. Specifically, emissions from agricultural sources and livestock production account for the largest share of estimated ammonia emissions in the MANE-VU region, except in the District of Columbia. The two remaining sources with a significant emissions contribution are wastewater treatment systems and gasoline exhaust from highway vehicles.

Figure 7-10 - State Level Ammonia Emissions

Comparison of various states' ammonia emissions levels

Figure 7-11 - 2002 NH3 Emissions (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in 106 tons per year)

Comparison of 2002 NH3 emissions by state

7.5.6 Further Discussion

Figures 7-1, 7-4, 7-6, 7-7, and 7-10 show 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 each 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 period. 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 emissions allowances from initial 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 projections 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 PM10 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, contribuing 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-VU emissions as a whole, VOC, NOx and SO2 emissions are expected to decrease significantly as well.

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/2002EmissionsInventory.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/FutureEmissionsInventory.htm

EGU Point Emissions: VISTAS_PC_If IPM Run (Alpine Geophysics, 2008)

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 1,861 686,426 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 Proposed 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 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/FutureEmissionsInventory.htm

7.6.3 Summary of New York 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 2,767 118,936 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/Inventory%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

__________

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 air 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 air pollutants and their precursors, 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 NEI includes emission estimates for all 50 states, the District of Columbia, Puerto Rico, and the Virgin Islands. Emission estimates for individual point or major sources (facilities), as well as county level estimates for area, mobile and other sources, are available currently for years 1985 through 1999 for criteria pollutants, 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 maintained criteria pollutant emission estimates and the National Toxics Inventory (NTI) database maintained 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.
2 SO2 reacts in the atmosphere to form sulfuric acid (H2SO4). Ammonia can partially or fully neutralize this strong acid to form ammonium bisulfate or ammonium sulfate. If planners focus future control strategies on ammonia and do not achieve corresponding SO2 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 attribute emissions based on land-use data.