4.0	Current Tools to Address/Implement Current State of Knowledge

Both regulatory air quality management and research analysis activities are based on measured data and analysis tools that are used to assess both the current state of air quality and the likely future response to proposed or promulgated air pollution control rules. In general, the databases involve measurements of ambient air quality, meteorological parameters, and to a lesser extent source emissions rates. The tools used to evaluate progress toward air quality improvement or future predicted conditions include techniques for estimating emissions from sources that are not measured directly, and air quality models that seek to predict future changes in air quality as a result of proposed changes in emissions characteristics due to control requirements and regulations.

The standard methods used to develop the primary databases and the tools used to assess future conditions are discussed in this chapter. The first section describes monitoring techniques for the primary pollutants of concern in ozone, PM and haze programs. This discussion on monitoring techniques addresses issues related to measurements of primary species and surrogate species that represent a broad class of related air pollutant species. The second section discusses current procedures for estimating both baseline and future year emissions conditions. The features of air quality models are addressed in the third section. Finally, some important issues that affect all air quality planning activities are discussed to lay a foundation for a policy that seeks to address air quality management in an integrated approach and recognizes the interrelation among ozone, PM and haze control programs.

4.A	Monitoring Technologies, Surrogates and Network Design

Monitoring Techniques

The discussion below describes methods and techniques currently in use for the measurement of ambient concentrations of importance air quality parameters and meteorological data to support analyses of the formation and transport of those pollutants. This section includes discussions of reference methods for the criteria pollutants, and accepted methods for other pollutant species of interest. There is also a brief discussion of techniques used to monitor routine meteorological parameters.

Ozone (O3)

Routine Techniques	Several techniques have been used to measure ozone and are described elsewhere (NRC, 1991; Pitts, 1986). Chemiluminescence is the EPA reference method used for most routine surface level ozone regulatory and NOx measurements. Chemiluminescence is produced from ozone reacting with excess nitric oxide gas, yielding electronically excited light-emitting nitrogen dioxide. Confidence in ozone measurements is quite high, relative to precursor measurements.

Nitric Oxide (NO)

Routine Techniques. Chemiluminescence is used for most NOx and NO2 measurements. Nitric oxide is reacted with excess ozone (reverse of ozone measurement), again forming light-emitting nitrogen dioxide. Chemiluminescence NO measurements are believed to be very reliable, although many outdated instruments currently being used lack satisfactory resolution to the single digit ppb range, which is important for rural studies, mid-afternoon urban measurements and associated observational based models. More modern chemiluminescence instruments are capable of measuring NO to sub ppb levels.

Nitrogen Dioxide (NO2)

Routine techniques. The same chemiluminescence technique used for NO is used as the reference method for NO2, except that the airstream is passed over a molybdenum catalyst to thermally convert NO2 to NO, resulting in a total NOx measurement with the difference (between NO) being reported as NO2. It has been shown that other NOy species (e.g., PAN and nitric acid) also are converted thus creating a positive bias in the reported NO2 measurement.

Non-routine techniques. Differential Optical Absorption Spectrometry (DOAS) is the only equivalent certified technique for NO2. DOAS produces high quality NO2 (and several other atmospheric gases) measurements, but at considerable initial capital costs. DOAS measures the absorption at a characteristic wavelength (for each gas of interest) compared to absorption at less absorbing nearby wavelengths. In addition, optical techniques like DOAS sample over specified path lengths and, depending on the instrument alignment, allows for varying degrees of greater volumetric and vertical profile representation. Other techniques include luminol chemiluminescence, photolytic (NO2 - NO) conversion, laser induced fluorescence (LIF), and tunable-diode laser absorption spectrometry (TDLAS). Photolytic conversion has

the potential for providing a low-cost, routine instrument. The air stream is irradiated with a light source (~ 4000 nm) photolyzing NO2 into NO and an oxygen atom; the NO is then measured through chemiluminescence to determine NO2 concentration. Difficulties associated with inefficient absorption efficiencies and production of a low-cost, intense light source

accompany this technology.

Total NOy

Total NOy is not required in routine measurement programs, although NOy essentially has become a routine measurement in nearly all modern field programs like SOS and NARSTO-Northeast. The basis for total NOy is the chemiluminescence/molybdenum (or gold converter) method modified to reduce the loss of nitric acid, PAN and organic-nitrates by placing the converter as close as possible to the intake. The technology for total NOy measurements is available and relatively inexpensive and is believed to be reliable. Remaining concerns for

routine total NOy operation are calibration/QA techniques, available expertise and associated operator difficulties.

Speciated NOy (nitric acid and PAN)

In addition to NO and NO2, the remaining major NOy components include nitric acid (HNO3), and peroxyacetylnitrate (PAN). Other NOy components include various organic nitrates and aerosol nitrate.

Nitric Acid -- Nitric acid is measured in the CASTNET/NDDN national monitoring network at roughly 35 sites, and in intensive field programs like SOS, SCAQS and SARMAP. Techniques include filter pack, annular denuder, denuder difference, TDLAS and several others. HNO3 is highly soluble, reactive with various surfaces and subject to temperature sensitive equilibrium with ammonium nitrate. These are all factors which result in difficult and less than reliable HNO3 measurements.

PAN -- PAN can be measured reliably with electron-capture gas chromatography. However, PAN is far from a routine measurement requiring substantial attention to calibration procedures and instrument operation.

Carbon Monoxide (CO)

[Nothing Provided]

Nonmethane Hydrocarbons (NMHC)

Until recently, speciated NMHC were not included in routine air quality measurement programs and sampling and analysis for speciated hydrocarbons was limited to various special field studies. The PAMS program (which is discussed in the following section on monitoring networks) requires near-continuous speciated NMHC measurements of roughly 56 target species. Gas chromatography with flame ionization detector GC-FID is the standard analysis system in use. Samples are collected either with canisters or through continuous GC operation. The more prevalent continuous GCs are subject to water management/sample preparation difficulties which can result in losses of the more polar-behaving compounds, including biogenic alkenes and alcohols (technically not a NMHC). Operation of continuous GCs is not trivial, requiring substantial operator attention, calibration and QA procedures.

Carbonyls

Carbonyls are more difficult to measure than NMHC. In general, GC-FID is not very sensitive to carbonyls and other methods must be applied. Common methods use cartridges filled with various solid sorbents coated with 2,4-dinitrophenylhydrazine (DNPH) to collect a sample, and analyses are performed with high pressure liquid chromatography (HPLC). The most common sorbents are silica gel (PAMS standard) and a modified silica gel, C18, which is used more frequently in research studies. Both sorbents are subject to various interferences, although very recent studies suggest that C18 may be more a reliable sorbent. Carbonyl measurements remain difficult, are subject of substantial debate in the research and monitoring communities, and pose significant challenges to routine networks like PAMS. The PAMS program requires reporting of three carbonyls: acetone, acetaldehyde and formaldehyde. Several additional higher molecular weight carbonyls are also captured with cartridge techniques and their cumulative mass, as a fraction of total VOC, can be significant.

Peroxides and Radicals

Measurements of intermediate, fast-reacting radicals (OH, HO2, RO2) and more stable peroxides currently are restricted to research grade studies like the Southern Oxidant Study (SOS). However, these measurements provide valuable information as indicators of NOx or VOC-limiting environments, and support 'in-situ' evaluation of chemical mechanism behavior.

[Nothing Provided for PM, PM-10, PM-2.5 or Haze]

Meteorology

The following is a breif discussion of what types of meteorological data are currently collected across the United States. This section is followed by more detailed assessment of how these data are used in air quality management/control studies.

Surface Observations: A substantial network of surface meteorological instruments provides hourly wind velocities and temperatures. Many of the surface meteorological sites are associated with air quality monitoring stations, PAMS being a typical example.

Upper Air Observations: Historically, there has been a shortage of wind and temperature vertical profile data, which are critical inputs for air quality models. Until the last 3 or 4 years, the twice daily NWS/FAA rawindsondes, limited to major airport locations, were the primary sources of such data. Recent advances in remote sensing technology, however, combined with several field study efforts and PAMS have resulted in near routine operation of sodar and radar/RASS systems to profile winds and temperatures on a near continuous basis. Additionally, the NOAA/NWS NEXRAD system is operational, providing an enormous national network of vertical profile data. Overall, the implementation of upper meteorological monitoring devices over the last 2 or 3 years is probably the most substantial relative improvement across all disciplines supporting air quality management.

Meteorological Data Requirements

While great strides have been made with profilers, in conjunction with radiosonde measurements to sample the vertical profiles of meteorological variables needed in air pollution assessment and forecasting, there are still frequently large gaps in data which complicate the process of determining when and where transport of air pollutants occur. More data is needed to define the spatial continuity of air pollution masses, their chemical changes with time, and particularly in Southern California, the spatial (both horizontal and vertical) and temporal variations in wind direction and speed. Each Basin and valley has its own mesoscale wind and thermal influence on local conditions which are strongly tied to the diurnal heating cycle; and these variations are superimposed on the larger scale synoptic flow pattern. Where mountain/hilly terrain are prevalent, these slopes should be used to sample wind, temperature, humidity, ozone and precursors to fill some of the voids in data. Along coastlines, the data voids are particularly severe because very little data is available over the ocean, yet the water has a profound impact on wind circulations in all adjacent areas. In addition to the use of automatic weather stations and pollution monitoring stations over hilly terrain, ozonesondes are a potentially very useful technique for determining the vertical profile of ozone. Launched on a balloon (with conventional radiosondes in tandem), these soundings can be used to detect rivers of pollution which remain trapped within inversion layers aloft, and flow to neighboring areas by winds which can be dramatically different than the flow at the surface. To help fill in some of the voids at sea, aircraft measurements and island stations are needed to help define the seaward transport of air pollutants and their subsequent transport to other regions. Because of the large amount of horizontal and vertical variability, the largest possible spatial domain must be used in modeling studies in order to get a clear picture of transport.

Diurnal Changes

In regions characterized by land/sea breeze regimes, there is sometimes the tendency to simplify the meteorology by assuming the wind is offshore at night and onshore in day, and using wind statistics at two times a day to represent these changes. However, to understand transport, it is essential to know how (and through what directions) the wind changes in going from landbreeze to sea breeze. For instance, in Southern California, there is very frequently a period of southeast winds between the nighttime northerly land breeze and the afternoon westerly sea breeze. The nighttime land breeze advects varying amounts of polluted air out to sea, and then the southeast winds are responsible for transporting some (or in some cases, much) of the polluted air mass along the coast to adjacent Basins. At certain times of day and year, these intervening winds may actually be the predominant wind. These climatological features need to be detected (i.e. monitored), and incorporated into modeling studies. Simplification of realistic wind conditions can lead to grave misconceptions about where polluted air is coming from (e.g., confusion between 'local episodes' and 'transport days').

Vertical Shear

In some regions, surface winds may be completely unrepresentative of flow patterns that exist even 500 or 1000 feet above the surface. For example, in the southeasterly flow discussed previously, ozone and/or precursors may be transported tens of miles or more to a neighboring area. However, a shallow westerly sea breeze may cut in underneath at the downwind site, thus making it appear that observed pollutants were coming from a different direction, including the ocean. Some initial studies are underway in Southern California to determine in what regions and how much of the time, the inversion layer may be embedded within an air stream that is markedly different than the surface flow. Since it has been found that inversions can store ozone and transport it to downwind areas where it may later be fumigated back down to the surface, detecting and understanding the local variability of the wind in the vertical is essential for understanding and predicting transport.

Ozone versus Precursors and Particulates

In detecting and predicting when transport takes place, it is also critical that enough variables to chemically describe the polluted mixture be simultaneously tracked. Visibility studies have been conducted in the past which document the diurnal and large scale transport of particulates associated with "smoggy" air masses responsible for high ozone levels inland. However, in areas with proximity to the ocean, ozone levels within the polluted air may not be significant due to various influences such as reduced sun light, ozone destruction and other processes. If only ozone is measured or recorded, erroneous conclusions can be obtained regarding the presence of air pollution, but even more importantly, about the source of ozone in downwind areas where the same polluted air mass may move back into heated, inland valleys to again produce exceedances.Some of the confusion also leads to erroneous attributing of the offshore polluted air transported to far away coastal locations as consisting of natural 'marine haze'.

Network Design

Considerations of Network Design for Exposure Assessment

Ambient air quality monitoring networks are designed primarily to collect a sufficient base line of information to identify population areas that are exposed to concentrations of pollutants that are in excess of the National Ambient Air Quality Standards (NAAQS) and to measure progress toward reducing those population exposures. EPA requirements and guidance for criteria pollutant monitoring, are codified in 40 CFR 58. These requirements have focused predominantly on the single objective of determining compliance with the NAAQS. While this is an important and desirable objective, other important information on the environmental state and ambient pollution exposures, sources, formation and transport processes, and effects is neglected and/or precluded by the "traditional" approach.

Measurement of an exceedance of any NAAQS, under this traditional approach, results in locally unpleasant consequences for sources and regulatory agencies in the proximity of measured exceedances. Conversely, there are no consequences for economically competitive sources which may contribute substantially to exceedances at many sites, but are located in upwind, "attainment" political jurisdictions. This situation produces strong disincentives to expand measurement networks and to locate additional monitoring sites to characterize maximum human exposures, or otherwise provide useful information on pollution sources, processes or effects.

NAAQS are typically established for the most easily quantified individual chemicals (e.g. ozone) or broad categories of complex pollutant mixtures (e.g. particulate matter), which are taken as surrogates for larger groups of related chemicals (e.g. photochemical oxidants), or for more specific chemical components of a complex pollutant mixture (e.g. the sulfate, nitrate, acidic, organic, or trace metal components of particulate matter). This approach precludes the collection of information that could more accurately define actual pollutant exposures to injurious chemicals, and/or could support future development of revised standards to achieve more efficient progress toward national health, welfare and environmental goals.

Ambient concentrations and effects of ozone, fine particles and regional haze result from emissions from many different source types over broad regions, as modified by complex intervening atmospheric chemical transformation and meteorological transport processes. Measurement of the resultant end products of these complex emissions and processes provides limited information to support development of effective emission reduction strategies to attain standards or to advance the scientific basis for development of such strategies. Notable exceptions to this approach include the systematic measurements of some oxidant precursor concentrations in the PAMS program, and comprehensive measurements of atmospheric optics, scenes, and detailed aerosol composition in the Interagency IMPROVE program

Discussion of Existing Networks

National networks: SLAMS/NAMS/PAMS. State and Local Air Monitoring Stations (SLAMS) networks are the primary source for ozone ambient air quality data used by the U.S. EPA and State and local air quality management agencies. Of the 981 total ozone monitoring sites reported to the U.S. EPA's AIRS database, 780 of these, or approximately 80%, are SLAMS ozone sites. Most of the SLAMS are located near or within urban areas. However, roughly 10% are located in more rural settings, particularly those with transport assessment or

background concentration determination objectives.

National Air Monitoring Stations (NAMS) are subsets of the SLAMS. The NAMS were established to provide consistent air quality data that could be used to assess national ozone trends and to make national ozone policy decisions. Of the total number of SLAMS ozone monitoring sites, there are approximately 229 ozone NAMS sites in operation at this time. The 40 CFR 58 regulation requires that two NAMS sites be established within any urban area with a population of 200,000 or more. One of these sites is located to assess population exposure and the second is located to determine the maximum concentrations for that urban area. In addition, all PAMS monitoring sites (see below) require ozone measurements, and the Clean Air Status and Trends Network (CASTNET) includes rural ozone measurements.

Photochemical Assessment Monitoring Stations (PAMS) networks, the most recent addition to the ozone monitoring requirements in 40 CFR 58, are a subsets of SLAMS and include routine measurements of ozone precursors as required by Section 182 (c)(1) of the 1990 Clean Air Act Amendments. PAMS networks are currently required in 22 ozone nonattainment areas designated as serious, severe, and extreme with respect to the current 1-hour ozone NAAQS.

These monitoring stations collect ambient air measurements for a target list of approximately 60 volatile organic compounds (VOC) including several carbonyls, as well as oxides of nitrogen, ozone, and both surface (10-m) and upper air meteorological measurements. PAMS is the first national program to require near-continuous (hourly, or 3-hour averages) speciated VOC and NOx measurements.

PAMS requirements are designed to provide as much information as practicable on the roles of ozone precursors, pollutant transport, and local meteorology in the photochemical process as well as to establish a feedback loop as a "reality check" on proposed ozone control (State Implementation Plan or SIP) strategies. Eventually, PAMS will provide a data base for evaluating the success of the control programs and developing mid-course strategy corrections. Specific provisions of the Rule require the establishment and operation of up to five PAMS in each affected area, depending on the population. The stations are designed to sample representative air parcels upwind, within the central business district, and downwind of the urban core of nonattainment areas.

4.B	Emission Estimates and Inventories

The development of an emissions inventory is a complex process that often involves a variety of specific procedures to prepare a complete comprehensive data base for specific applications. This section outlines the fundamentals of the emissions inventory development process and is not meant to describe all aspects of emissions inventories. Emissions inventory data are prepared to support regulatory efforts and to serve as input for air quality simulation models. This section also addresses the types of emissions inventories currently in use for these regulatory support and research activities. The role of emissions inventory data for modeling exercises is increasing in importance and emissions inventories are directly related to many of the issues being discussed in the overall implementation development process to support the new NAAQS and Regional Haze programs.

Considerations for Use of Emissions Inventories in Modeling Analyses

A regional air quality modeling effort begins with an emissions inventory. An existing regulatory emissions inventory is often the starting point for development of the modeling emissions inventory, which must satisfy the specific needs of the selected air quality model. Therefore, modeling emissions inventory tools are a superset of regulatory emissions inventory development tools.

The purpose of a regulatory emissions inventory is to estimate and report emissions of specified primary pollutants by county. The regulatory emissions inventory tools include emissions factor estimation software such as MOBILE for on-road mobile sources, FAAED for aircraft, TANKS for storage tanks, BEIS for biogenic sources, and other specialized models. Emissions factors for many sources are simply looked up in AP-42 or EPA's FIRE database. These emissions factors are multiplied by estimated activity levels to produce estimated emissions. The emissions may be reported for an average day, an average weekday of a specific season, or a worst-case scenario. The report includes emissions by source category for each reported county.

The purpose of a modeling emissions inventory is to provide the emissions data at the level of detail required by a selected air quality model. In terms of the top down and bottom up inventories (which are later in this document) a modeling inventory inherently has additional uncertainties relative to any common regulatory inventory. Some of the causes and consequences of these uncertainties are summarized below.

The purposes of the additional tools used in modeling inventory development are:

convert primary pollutant emissions into emissions of the pollutant species used by the air quality model's chemical mechanism;

estimate emission factors for physical conditions of the modeling scenario (e.g., use actual temperatures instead of average temperatures for evaporative emissions);

forecast activity levels from the year of the base emission inventory to the year of the modeling scenario;

estimate emission factors for the year of the modeling scenario (e.g., run MOBILE for fleet and RVP characteristics of the modeling scenario);

estimate activity levels for the time of the modeling scenario (e.g., weekday of season in modeling scenario instead of average);

allocate daily emissions to each hour of the day; and,

spatially allocate county emissions to the three-dimensional grid cell structure in the air quality model.

Development of a modeling emissions inventory adds to model generalization by using one speciation or temporal profile to represent multiple emissions source categories, and by assuming all emissions are constant within each hour. The development of a modeling emissions inventory also adds to model distortion by speciating emissions to fit the chemical mechanism in the model, by using spatial surrogates to locate emission sources, and by spatially smearing emissions into a large grid cell volume under an assumption of complete and instantaneous mixing. Preparation of modeling emissions inventories adds to model deletion by ignoring those sources that are not represented in the inventory (i.e., each ignored source may be small, but the cumulative effect of all ignored sources is unknown), and often by ignoring other chemical species such as methane.

Uncertainties in the modeling emissions inventory include all uncertainties in the base regulatory emissions inventory (i.e., methods, measurements, and missing sources), as well as all uncertainties associated with converting the base to the modeling emissions inventory. These conversion uncertainties include:

measurements of speciation;

development of speciation profiles;

representativeness of speciated samples to unsampled sources in the same source category, in a different source category, in a different geographic region, or in a different year;

growth factor estimates;

spatial surrogate errors (i.e., original data errors, calculation errors);

applicability of spatial surrogates (i.e., validity of population surrogates for both 1990 and 2010, and the adequacy of a surrogate for a specific source category); and,

applicability of temporal profiles (i.e., validity of EPA default temporal profiles for this modeling domain, representativeness of one profile for all sources of one source category throughout all urban and rural areas in the modeling domain).

Existing Emissions Inventories

Emissions inventories are used in several ways as tools in air quality management. The inventory on its own provides useful information on the magnitude and distribution of emissions across all anthropogenic and natural sources of emissions and the inventory can be tracked over time to present useful information on the growth or reduction of emissions from the various anthropogenic and natural sources. It is also a major input to atmospheric computer models that are used to predict ground-level pollutant concentrations and to analyze various air pollution control strategy options.

Two basic types of emission inventories (e.g. top down and bottom up) are used in air pollution management studies. The top down inventory is the simpler, and the less expensive of the two. When combined to form national or regional inventories, inventory method consistency is a concern especially when comparing emissions from one inventory area to another. Top down inventories are prepared from existing data sources and use estimation techniques that are comprehensive but less specific than the bottom up method. The geographical extent is usually large, often being national in scope. Top down inventories have the advantages of being consistent in methodology, can cover very large geographical areas, are relatively inexpensive and can be produced and modified quickly. The U.S. Environmental Protection Agency (EPA) uses top down inventories for regional scale modeling and the tracking of emission trends over time.

Bottom up inventories are prepared using specific locally derived data and may cover an area as small as a single point source or as large as a state. Bottom up inventories are characterized by developing specific emissions data from as many specific sources as possible. This entails direct contact with emission sources, particularly large point sources. Bottom up inventories are usually produced by State and local environmental agencies, and are regarded as having greater accuracy than top down inventories. These inventories are costly and time consuming to produce.

Ozone is a pollutant that is formed in the atmosphere from precursors that are emitted from various sources. The precursors that are important in ozone formation are volatile organic compounds (VOC), oxides of nitrogen (NOx) and carbon monoxide (CO). These precursors have been inventoried for a number of years and inventoried intensively since 1990. A national top down inventory for these precursors has been developed by the EPA and is publicly available via anonymous FTP at earth1.epa.gov under the directory pub/gopher/EmisInventory. The file names are: US90ARV2, US90MBV2 and US90PTV2. In addition to the ozone precursors, this inventory also contains sulfur dioxide (SO2), ammonia (NH3), particulate matter less than 10 m (PM-10), particulate matter less than 2.5 m (PM-2.5) and secondary organic aerosols (SOA). This top down inventory is comprehensive, with emission estimates presented for all source categories for each county in the United States.

Emission Factors

As stated above, emission factors are a primary tool in the development of emission inventories. Table 4.2-1 is a presentation, by pollutant, of the emission factors currently contained in the EPA's Factor Information Retrieval Data Base (FIRE).

TABLE 4.2-1. FIRE EMISSION FACTORS

POLLUTANT

NUMBER OF EMISSION FACTORS

PERCENT OF EMISSION FACTORS RATED A OR B CO 912 24 NOX 1232 17 VOC 2487 10 SO2 1358 27 PM 2008 27 PM-10 1212 23 PM-2.5 0 - NH3 0 -

In addition to the number of emission factors for each pollutant, the table contains information on the percentage of these factors that have the higher A or B data quality rating. This table also reveals that there is good coverage for the ozone pollutants although the quality of VOC is low relative to NOx and CO. The particulate matter and haze pollutants are less well covered. Good coverage exists for NOx, VOC and SO2. The coverage for PM-10 would also appear to be good. However, the great majority of the PM-10 emission factors were derived from the PM emission factors shortly after the EPA promulgated the current PM-10 NAAQS. This effort involved applying existing particle size distribution data to the PM emission factors to derive the new PM-10 factors. The data quality ratings for the new PM-10 emission factors were not lowered as they should have been. Therefore, the coverage for PM-10 is still good but the confidence in the factors is lower.

No emission factors for PM-2.5 are in this data base. The factors that were used in the EPA top down inventory were derived from the PM emission factors using a calculator program that included particle size distribution data for the various source categories. Confidence in these size distribution data is low, especially for the PM-2.5 size interval. Subsequently, EPA is currently collecting field test data for PM-10 and PM-2.5 emissions from paved and unpaved roads, since both of these fugitive particulate source categories are major emission sources. When these data are available, confidence in the fine particle emission factors will improve. Emission factors for NH3 are not in the EPA FIRE data base. The NH3 factors that were used in the EPA top down inventory are a compilation of NH3 factors based on recent studies primarily from Europe. Subsequently, confidence in these factors is also low.

Data Sources and Tools

A list of the data sources and tools used for emissions inventory development that have been discussed in this Section and in Chapter 3 is provided below:

Department of Energy utility fuel data;

1985 National Acid Precipitation Assessment Program (NAPAP) emission inventory;

Bureau of Economic Analysis (BEA) growth factors;

Quantity of solvents consumed;

County Business Patterns; County employment for specific industry groups;

EPA wood burning model;

Federal Highway Administration's Highway Performance Monitoring System (HPMS) database;

County population;

EPA MOBILE mobile sources emission factor model;

EPA/OMS detailed 1990 nonroad mobile inventory for 27 nonattainment areas;

Biogenic Emissions Inventory System - Version 2 (BEIS2);

Land use data;

Meteorology data;

Point source surveys;

AP-42;

EPA Factor Information Retrieval Data Base (FIRE);

EPA/OMS PART5 mobile sources PM emission factor model;

Department of Agriculture crop land area, area planted, crop type, soil parameters, times tilled per year;

Department of Agriculture census of agriculture;

Wind erosion model;

Cost of construction;

Average duration of construction activity; and,

Recent European NH3 studies.

4.C	Air Quality Models

This section addresses some of the issues related to the application of modeling analyses to regulatory decision making. The discussion focusses on two principal modeling approaches that are referred to here as air quality simulation models and receptor models. Although air quality simulation models are used for the analysis of PM issues, they are frequently applied to studies of urban and regional ozone episodes. Receptor models are used primarily to assess urban scale or smaller scale PM issues. The section begins with a discussion of the basic issues related to model formulation, inputs and performance. That is followed by a brief discussion of some of the problems encountered in modeling analyses and the section ends with a summary of some of the features of existing air quality models.

Model Formulation and Applications

Basics This section addresses urban, regional and super-regional scale modeling systems, most of which use gridded Eulerian frameworks. Gridded models utilize a fixed-frame reference with respect to moving air masses and can accommodate a physically realistic treatment of atmospheric mixing processes. In contrast, trajectory or Lagrangian systems utilize a moving reference frame perspective tracking the movement (and retaining the identity) of individual plumes. Trajectory models estimate particular plume concentration distributions along a path; whereas Eulerian systems provide simultaneous calculations at every grid cell, essentially capturing the fully coupled calculations of all interacting plumes (actually, air volumes). The formulation of Lagrangian systems restricts the realistic treatment of atmospheric mixing, since plumes are subject to degradation through wind shear and other physical phenomena and thus lose individual identity. The Lagrangian treatment of individual plumes requires substantially fewer computational resources, whereas the solution of fully coupled simultaneous systems (Eulerian) is far more demanding. The chances for plume degradation are enhanced when trajectory models are applied on the regional scale, that is of interest in so many

air quality issues and, therefore, Eulerian systems have become the most common modeling approach for urban and regional studies.

Although many current efforts are dedicated to large scale reactive modeling systems, receptor and non-reactive dispersion modeling tools perform important roles. For example, the demand for observational based analyses has and will expand the use of receptor modeling techniques for interpreting ambient data and evaluating emissions. The strong probability of a fine particle standard requires attention to regional scale reactive modeling systems. This is because the fraction of mass due to secondary formation increases with decreasing size cutoffs, and the atmospheric residence times for small particles approach those of gases. Nevertheless, primary particles are important components of particle mass regardless of the size cutoff. Furthermore, the current PM-10 standards are not disappearing. Consequently, the application of receptor and nonreactive, local-scale modeling techniques remains an important component of a comprehensive air quality management effort. The lack of attention given to more classical techniques in this document reflects the current interest in large-scale regional issues and the feasibility of market- based trading across pollutants and broad regions.

Eulerian Models All modeling systems invoke many approximations both in the description of physical and chemical processes as well as in the solution of the system of mathematical equations used to represent the physics and chemistry. The spatial extent, or "domains", of model applications often are characterized as being of urban (100-500 km), regional (500-2000 km) or super-regional (> 2000 km) scales. Gridding refers to the horizontal resolution used to delineate simulated air quality concentrations and provide detail on the emissions distribution and meteorological variables (e.g., winds and temperatures). Typical grid resolution ranges from 2-5 km and 20- 80 km for urban and regional applications, respectively. Most models produce hourly outputs, which can be aggregated for other averaging times of interest (e.g., 8-hr, 24-hr, seasonal, annual). Models are resolved vertically (typically 5 -15 levels or more) to account for varying meteorology and emissions and to approximate vertical mixing phenomena. Some modern systems accommodate nested or variable scale gridding schemes which allow for detailed spatial treatment in urban areas (2- 8 km) and less dense resolution in peripheral/rural portions (20-80 km) of the domain in order to optimize computational resources and apparent precision. Over the past decade there has been a trend toward increasing regional- scale (or mixed regional/urban) Eulerian modeling in recognition of the interaction between regional/rural and urban areas and associated "transport" issues.

Models should be viewed as a "system" (see Figure 4.3.1), including the meteorological and emission preprocessing models and the air quality simulation model (AQSM). Preprocessors assemble raw data (i.e., emissions inventories and meteorological measurements) into the spatial and temporal fields required by the AQSM. The AQSM calculates concentration fields of air quality species (i.e., compound, element, free radical, or surrogate group) which are determined by the combined interactive effects of source emissions, mixing processes (advection and dispersion), deposition and chemical transformations.

So-called "transport" (e.g. via advection and dispersion) into a modeling domain is quantified through boundary conditions which are user-specified. The AQSM includes the chemical mechanism which performs the chemical transformation calculations through highly condensed approximations. Typically, chemical mechanisms will include 20-50 species and the order of 100 reactions to represent the thousands of actual chemical reactions occurring in the atmosphere. Chemical mechanisms have been developed and tested through combinations of fundamental reaction chemistry and smog chambers.

All gridded modeling systems share the general attributes discussed above. Differentiation among modeling systems is dictated by the required species and averaging times of interest and the desired balance between physical reality and computational resource demands. For example, UAM and ROM ozone models are generally applied for episodic ozone events occurring over periods of 2 to 14 days where processes/events such as precipitation, cloud interactions and particle formation may not be critically important to determining peak ozone concentrations. Hence, unlike the Regional Acid Deposition Model (RADM), the UAM and ROM do not include cloud and precipitation processes nor a characterization of sulfur and nitrogen chemistry (gas and aqueous phases) which are required for precipitation and particle formation processes. Conversely, the REMSAD does not include the detailed oxidant chemistry of the UAM or ROM, which would exact enormous computational burdens on performing "screening" types of analyses over annual time scales.

Modern model applications utilizing gridded systems are extremely resource and data intensive exercises. A series of sequential steps applicable to regulatory model applications include:

Establishing model domain, and characterization/selection of modeling episodes;

Raw data gathering and processing of model inputs;

Model testing, including component testing of emissions and meteorological preprocessors;

Development of emission control strategies and model application and interpretation of results; and,

Corroboration and evaluation of strategy results.

While guidance documents exist for ozone modeling (EPA, 1991), the application process is a mix of art, science and engineering, and the relative attention to any particular step varies strongly among applications. The potential quality of a modeling application often is constrained by available data. The use of national emissions, meteorological and air quality data bases as sole data sources for modeling is a much debated topic, and is often viewed as more critical than the internal differences among the AQSMs. Consequently, most historical modeling applications have been associated with special intensive field programs designed to augment national data bases. Equally perplexing are issues related to "appropriate" use of models. For example, the current CAA and EPA guidance require modeled ozone attainment demonstrations;

whereas, the scientific community has questioned how a deterministic tool can be applied to a probabilistic standard under highly stochastic meteorological and emissions phenomena. All of these problems are compounded with secondary particulate modeling, given the need to characterize additional physical/chemical processes (aqueous and gaseous phases, organic aerosols, size distributions and growth; the shortage of ambient data for model evaluation, especially time-varying speciated data), and related incommensurabilities between models and measurements.

Particulate Air Quality Modeling

Currently, there are no routine methods available that can be used to assess the formation of secondary PM-10 or PM-2.5 for either the 24-hour or annual standards. Conceptually, the most attractive approach for the 24-hour standard is to build upon the framework of the Eulerian models now used primarily for ozone assessment. This can be done by having the model produce hourly concentrations for ozone, other oxidants, the nitrogen oxide species, gaseous organic species, and sulfur oxide species. These chemical species can then be converted into the corresponding secondary aerosols by appropriate additions to the chemical mechanisms, coupled with a physical module to describe the conversion and growth of gaseous species to aerosols. At the present time, there is insufficient data to even evaluate these models to determine if they are capable of reproducing the aerosol concentrations, much less determining the accuracy and precision of the models. Furthermore, the data needs for such a model have not been described specifically, therefore it is not clear what data would be required to use this type of model in a regulatory effort. Until this type of model, or some other approaches are developed, it will be difficult to determine source receptor relationships for pollutants such as ammonium nitrate, secondary organic aerosols, or ammonium sulfate.

Similarly, there is no recommended regulatory approach for modeling secondary aerosols relative to the annual standard. Any of the conceptual models to perform this kind of assessment would necessarily require a significant number of simplifying assumptions about the chemistry of secondary aerosol formation and the role of meteorology. There have been suggestions that a suite of models could be used to provide information that could be used to identify those emission sources which need to be controlled. For example, one can use a speciated linear rollback approach which assumes that changes in particulate (including secondary particulate) will be proportional to the relative contribution of the different sources of that particulate or its precursor. Another member of the model suite could be a trajectory model or a particle in cell model. These models need both gaseous and particulate emissions inventories, but they can use simplifying assumptions of rates of formation of secondary sulfate and nitrate aerosols in place of a sophisticated chemical mechanism. They can also use simple treatment of meteorology for advection and dispersion in place of sophisticated meteorological models. This hybrid approach could be applied for either the 24-hour or annual standards. However, such a hybrid approach contains numerous assumptions and approximations, and needs to be tested against any and all available data to corroborate the findings.

Receptor Models

This section will describe issues surrounding the availability of air quality models for assessment of both 24-hour and annual fine primary and secondary particle assessment. It will also address the need for and availability of emissions, air quality and meteorological data for evaluation of model performance, tests of sensitivity of emissions reductions relative to changes in ambient fine particle levels, and use for assessment of alternative control. None of the described modeling approaches can be used to demonstrate attainment.

Present methodology for assessment of PM-10 strategies are primarily based on the use of statistical models which attempt to allocate the concentration of ambient monitored species to sources which emit some combination of these species. This approach, referred to as receptor modeling, is used for directly emitted compounds that, in the aggregate, make up primary PM-10 and PM-2.5. The model assumes that no chemical transformation occurs once the particles are emitted. EPA has endorsed the use of Chemical Mass Balance (CMB), and related models to develop PM strategies. CMB is viable for sources such as road dust, construction, wood stoves and primary mobile to name a few. CMB and related receptor models are not viable for secondary particle components-those particles which arise due to chemical transformation in the atmosphere, such as ammonium nitrate, ammonium sulfate and secondary organic aerosols.

Neither meteorology nor emissions are required for receptor models; consequently, CMB can not give any indication about the spatial distribution of the potential culpable sources. CMB can be used to assess strategies for both the 24 hour and annual standards, given the above restrictions. In terms of being able to carry out an assessment using receptor modeling techniques, it is critical that there be source profiles available for each primary source type that contributes to the primary ambient PM. These profiles must be speciated to allow separation of similar types of sources. There needs to be a unique combination of speciated components which allow the source type to be well characterized. For example, nickel and vanadium are unique tracers for crude oil. Hoptanes (steroidal type compounds) may prove to be unique tracers for light duty mobile sources now that lead is no longer in fuels. In addition, the profile should have been developed through a robust set of measurements. At the present time, many profiles are not statistically robust, and do not provide a sufficient characterization of the source. This is especially true for the family of mobile sources and different sources of fugitive dust.

Summary of Uncertainties Affecting Modeling Analyses

The performance characteristics of any model generally depend on three factors: (1) model assumptions; (2) input data; and, (3) numerical solutions. This discussion also addresses a fourth factor that is often overlooked, the evaluation and purpose of the model. Many of the issues related to input data, primarily emissions estimates, have been discussed in previous sections of this document, and will not be repeated here. These issues will be discussed in more detail below.

Model Assumptions

Chemistry: Use of the CBM-IV mechanism, the mechanism most commonly applied in urban modeling studies, makes it difficult to predict the concentrations of speciated VOC except for the explicit species like isoprene. Formaldehyde concentrations are frequently over predicted because the mechanism treats some reactive olefins as formaldehyde. Aromatics chemistry is still not well known and as a result CBM-IV tends to be less reactive than SAPRC-90. SAPRC-90, however, has problems defining the rate constants of the different lumped species, because these rate constants depend on the constituents represented by the lumped species. Currently, this problem is treated on an ad hoc basis.

Existing mechanisms have been evaluated against chamber results where initial NOx concentrations are typically several times higher than ambient. These mechanisms must still be checked against experiments for conditions of low NOx concentrations and low VOC/NOx ratios. These experiments are very difficult to do.

Meteorology: Currently, there is a large uncertainty in predictions of mixing height based on limited measurement data. Furthermore, our knowledge of the mixing coefficients is still based on the homogeneous surface layer assumptions which have never been shown to be valid in an urban environment. The nocturnal jet is ignored by most regional models, and wind field predictions in the lower troposphere over complex terrain are not good.

Scale Problems: Nonlinearity in chemistry substantially affects model results over the range of temporal and spatial scales involved in the chemical and physical processes. Most modeling applications use rather large grid sizes and time steps. As a result, the model's ability to predict pollutant concentrations deteriorates. For example, the dissimilar distributions frequently observed in VOC and NOx emissions and concentrations, can affect the size of the predicted NOx-control region depending on the grid size of the model used.

Numerical solutions

This issue is often taken for granted, even in cases when serious problems can result, especially in the solution of the advection equations. The impact on the model predictions can be significant (10 -20% can be expected for the predicted peak concentrations.) All present air quality models have such difficulties, however, there are efforts underway to resolve these problems.

Evaluation and purpose of the model

Even if the model were perfect, with no serious problems with the above three factors, it may still perform poorly for a given application, and the results may be evaluated incorrectly. First, it is always more difficult to predict the upper extreme values than the means. This is true for the daily max as well as for the annual extremes of the daily max. Extreme values result from combinations of unusual events due to fluctuations. The time scale of these fluctuations leading to the daily max may cause the effects to be partially averaged out in the model. For annual extremes, however, these effects are not averaged out. Therefore these fluctuations must be modeled faithfully, unless the model is based on a longer time scale (e.g., daily). Such a model, however, may not be too useful for control planning.

The model may be tuned to predict a specific episode well, but the conditions leading to that episode might not be repeated again. In that case, if the necessary controls predicted for that episode are not substantially valid for other episodes the modeling analyses may not provide accurate control strategies. Similarly, predictions for the average conditions are not always valid for the extreme conditions. The regulatory requirement of predicted extreme values has added a tremendous and almost unreasonable demand on air quality models, given the uncertainties inherent in modeling analyses. Comparison of model outputs with observations can also be problematic, because measurements are often representative of only a limited spatial scale relative to the model grid size. Longer averaging times of multiple hours could limit spatial averaging problems except in areas near major NOx sources or major ozone sinks where is ozone is not uniform locally. The scale of nonuniformity can be dramatically worsened by the wind shifts and fluctuations.

Emitted species concentrations are not always uniform in the early morning because of poor mixing. If the dominant mixing scale and the model grid resolution do not match, comparison with observations could be rather meaningless. Even if they match, comparison may still be meaningless if the position of measurements does not correspond to the relevant spatial scale. Third, it is often quite difficult to compare the predicted peak concentrations with observations because the latter are restricted by the availability of the monitors which may not be located near the predicted peak concentrations.

Despite numerous issues related to models and the model application process, the modeling system remains an extremely powerful tool for:

1.	Expanding/complementing the time and space constraints of ambient monitoring which often is limited to a small number of surface observations;

2.	Further expanding time by predicting future conditions; the very basis of control strategy analysis; and,

3.	Integrating emissions, meteorological and air quality data for explaining physical and chemical phenomena that can not be described by data alone (or a single data category).

Description of Common Eulerian AQSMs

The following summaries of Eulerian AQSMs provides basic information regarding pertinent references, applicability (pollutants and spatial scales), and chemical mechanisms. Comments regarding any unintended mischaracterizations or omissions are greatly appreciated. One of the most comprehensive modeling reviews was written by Seigneur (1990) for the Electric Power and Research Institute (EPRI), and along with assistance from numerous model developers provides the basis for the modeling summary. The American Petroleum Institute (API) recently issued a Request for Proposals (RFP) for a review of particle models. PM and ozone modeling are treated together, reflecting the commonality in process treatment among the available Eulerian models. Visibility is a direct function of particle levels, particle size distributions and particle composition; all properties that particle models strive to simulate. Consequently, visibility is (can be) treated as a postprocessing step in any particle model under discussion. A summary of Eulerian Air Quality Simulation Models (AQSMs) is presented in Table 1.

Table 1: Summary of Eulerian Air Quality Simulation Models Model Pollutants/ scales/comments Chemical Mechanism Major Applications Principal references UAMIV O3; urban; EPA guideline model; public domain CB-IV(full gas-phase oxidant chemistry) State SIPs; 5-City; SCAQS; NE U.S. Corridor Roth et al., 1983; EPA, 1990; EPA (1991); Morris et al (1990); Scheffe and Morris (1992); EPA (1992) UAMV O3; variable scale; PIG; urban and rural; proprietary CB-IV(full gas-phase oxidant chemistry) LMOS; GMAQS; OTAG SAI (1995) ROM O3; regional; public domain CB-IV(full gas-phase oxidant chemistry) ROMNET; MATRIX study; State UAM SIPs Lamb (1982); Schere (1986); Possiel et al., 1991??; Chu et al. (1992) RADM-II Acid dep; O3; regional; public/research Lumped molecular (full gas- phase oxidant chemistry with S); aqueous chemistry; aerosols (sulfate only) NAPAP; acid dep. stnd. feasibility; Ches. Bay N-dep; EMEFS Chang et al., (1990); EPA (1995); Spicer et al. (19??) RPM secondary PM; RADM physical attributes; research (not evaluated) reads full RADM fields; adds nitrate-sulfate- ammonium; highly parameterized org-aerosol treatment; bi-modal PM base-1990 and 2010 CAA strategies Binkowski and Shankar (1995); SAQM O3 CB-IV; SAPRC-90 Aug. 3-6, 1990 Episode, S. Calif. Venkatram et al. (1990); DeMassa et al. (1996) DAQM secondary aerosol; PM, O3 Rocky Mtn Front Range; Colorado Middleton et al. (1993); Middleton (1996) CIT O3 Los Angeles McRae et al. (1982);Russell and Cass (1984); Harley et al. (1993) URM O3; aerosols; multi- scales SAPRC-90 NE U.S.; Mexico/U.S. border Kumar et al. (1993); Kumar and Russell (1996) STEM-II O3; acid dep; regional lumped molecular for VOC; sulfur-nitrogen- ammonia/ammonium chemistry for acid precip Philadelphia; central Japan; Kentucky, NW U.S. Carmichael et al. (1986); Carmichael et al. (1987) CALGRID O3; regional and urban scale CB-IV; SAPRC-90 LMOS, MOCA Yamartino et al. (1992); Yamartino et al. (1996) ADOM O3; acid dep; PM; regional scale oxidant chemistry with wet deposition Eastern North America; Northern Europe Venkatram et al. (1988) REMSAD PM; deposition; national, regional and urban scales empirical reduced form oxidant chemistry - O3 is required input Contiguous U.S. SAI (1996); Guthrie et al. (1995); Emery et al. (1995) MODELS3 multipollutant; under development RADM-II/RPM SESAUM/SOS prototype ??

4.D	Integrated/Cross-Cutting Analyses

(To Be Added)