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109 Carrier, 3.9 Air Quality Origin, Number of departures performed, Air quality refers to the level of contaminants in ambient Tonnage of freight, tonnage of mail, air. It is either determined through measurement techniques Number of passengers, and/or estimated through applications of models--numerical Seating capacity, and techniques to predict ambient levels of pollutants from atmos- Payload. pheric releases. Most air quality impacts from goods move- ment activities are assessed either by modeling studies alone Using this database, it is possible to estimate the number of or coupled with measurements. This section discusses air qual- aircraft departures attributable to freight transport and the ity modeling assessments and associated uncertainties. number attributable to passenger transport, then to use the freight fraction to split each airport's aircraft emission total 3.9.1 Summary of Methods and Models into a freight and non-freight component. This allocation process can be summarized as follows: To characterize ambient concentrations, all air quality mod- els require some level of input information for meteorology Air cargo aircraft (e.g., winds, stability, atmospheric structure) and source infor- Aircraft departures that do not have any passengers or mation (e.g., emission rate, stack height, initial plume size, seats but have payload capacity of 18,000 lbs or larger temporal profile). Numerous other inputs may be required, are assumed to be air cargo commercial aircraft. This depending on the complexity of the model and application. definition is consistent with that used by FAA for cate- Most commonly applied air quality model formulations gorizing the aircraft type when reporting emissions. are deterministic and include Gaussian plume, puff, and box Aircraft departures for which no freight tonnage and no models. These models approximate the physical (e.g., trans- passengers are reported are assumed to be non-freight port, dispersion, and removal) and chemical (e.g., scavenging, (passenger) movements if departure was reported as secondary formation) processes that operate on pollutants re- having a seating capacity greater than zero, otherwise it leased into the atmosphere. These models work by parameter- was assumed to be a freight movement. izing the controlling processes that occur at emission source(s) Passenger aircraft and between the source(s) and receptor(s) at discrete time For flights with passengers, it is assumed the flight is a steps. Other special modeling cases include approaches based commercial flight if the plane has 60 or more seats. This on computational fluid dynamics (CFD)--used particularly to definition is consistent with that used by FAA for report- characterize source-induced and downwind turbulence effects ing aircraft emissions. on the flow, and stochastic approaches that approximate air For those aircraft that are commercial and that carry both quality distributions from data sets of controlling variables-- freight and passengers, the number of departures is allo- including regression, Monte-Carlo, and extreme-value theory- cated to freight activity and non-freight activity based on based approaches, (173) as well as those that incorporate sto- weight fractions. The freight weight fraction is the com- chastic properties in a deterministic setting such as combined bined weight of the freight plus mail divided by the sum puff-particle models (e.g., Puff-Particle Model inclusions in of all weight--passengers, mail, and freight (average pas- CALPUFF). (174) senger weight of 240 lbs was used based on a March 21, EPA (175) distinguishes air quality models into the follow- 2003 FAA-sponsored weight survey of more than 6,000 ing three categories: passengers that included an average adult passenger weight of 196 lbs, 16 lbs of carry-on items, and 29 lbs of Dispersion models typically are used for small spatial scales checked baggage). Similarly, the passenger weight frac- and to estimate impacts from individual source(s). These tion is the weight of all passengers divided by the sum of models contain either no or limited chemistry and may be all weight. These fractions are then multiplied by the plume or puff formulations. EPA recommended/guideline number of departures for each record. dispersion models include the following: The weighted freight and non-freight departures are AERMOD and summed for all flights departing from the airport in 2002, CALPUFF. using the ratio of freight departures to total departures to Other specialized preferred/recommended models in apportion the airport's emission total to a freight com- this category include ponent. This approach assumes that all departures have BLP, a corresponding arrival, so the freight departure frac- CALINE3, tion is equivalent to the freight LTO fraction. CAL3QHC(R),

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110 CTDMPLUS, and The focus here is only on the emissions-relevant model OCD. parameters and processes and does not include other neces- Other models in this category include sary model inputs (such as meteorological data, surface and ADAM, terrain characteristics, biogenic or coincidental emissions ADMS-3, data, chemical schema, etc.). AFTOX, Generally, one of two methods will be employed in air qual- ASPEN, ity modeling, depending on the domain size, the physical and Canyon-Plume-Box Model (not a regulatory model chemical processes to be included, and the desired output res- but a research-grade FHWA model to demonstrate olution. Note that there is significant overlap between these nonlinear effects of vortex separation and resulting criteria. The two methods we consider here are grid modeling dispersion from roads within cut sections), for national and regional scales (typically applied to citywide EDMS, and larger analyses) and dispersion modeling for local/project HOTMAC/RAPTAD, scales (facility to citywide analyses). Note that at some scales, HYROAD, either method could be appropriate. Exhibit 3-66 shows these ISC3 (ISC-PRIME), methods. Panache, PLUVUEII, 3.9.2 Evaluation of National and SCIPUFF, and Regional Methods and Models SDM. Photochemical grid models typically are used to assess National or regional simulations of air quality will most cumulative impacts or interactions of a range of sources likely be made with a photochemical grid model. In many over large spatial scales. These are box models but typically cases, these are limited in time to episodic simulations, also contain plume or puff formulations. This group of although annual or even multi-annual simulations are capa- models includes the following: ble in some models. In all cases, input preparation and model Community Multiscale Air Quality (CMAQ), executions are resource intensive. Comprehensive Air quality Model with extensions (CAMx), and Photochemical Grid Model Methodology Regional Modeling System for Aerosols and Deposition (REMSAD). Photochemical grid models (PGMs) rely on gridded model Receptor Models that relate observed concentrations to domains and simulate all processes that influence concentra- source types and contributions. tion (chemistry, diffusion, advection) in each grid cell during a time step. However, this approach is physically limited for The focus of this analysis is not a review of the models small spatial scale applications due to artificial dilution of emis- commonly used to estimate the ambient concentrations sions, unrealistic near-source concentrations, and spatially un- associated with goods movement, but rather the methodolo- resolved receptors for sizes smaller than an individual grid cell. gies and inputs used by these models. That is, how the emission Most current models allow for plume in grid (PiG) or other outputs discussed in Sections 3.2 to 3.8 are used to predict subgrid scale treatment of gas, aqueous, and aerosol chemistry, downwind pollutant concentrations. As such, this section at least for major or elevated point sources. Other parameters does not review the uncertainties in any given model or the are (horizontally) resolved only at the grid-cell level (typically uncertainties in any other parameter input to these models. 2 km to 36 km), including emissions and meteorology. Some Exhibit 3-66. Air quality modeling methods. Method/Model Type Geographic Scale Pollutants Freight/Passenger Grid modeling Method National and regional Primary and All* methodology secondary criteria, toxics Dispersion modeling Method Local/project Primary criteria and Both methodology toxics** * Typically requires simulation of all sources, but specialized techniques may be used to identify impacts from individual elements. ** Some limited chemistry may be included for primary reactions and secondary species.

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111 current models extend subgrid cell treatment to nonpoint Total uncertainty in predicted concentrations from PGMs sources, such as resolution of individual roads and receptors is due to uncertainty in the emission inputs as well as the un- within a grid cell, such as the use of PiG for near roadway con- certainties in all other inputs (meteorology, chemistry, model centrations of mobile source air toxics (MSATs). (176177) formulation, etc.) and model formulations. This value is gen- This formulation highly parallels that of the dispersion mod- erally unquantifiable. It is possible, however, to characterize eling methodology discussed in the following section, but of- the sensitivity of predicted concentrations to the representa- fers the additional ability of full chemistry simulations. It is, tion of emissions, particularly emission strength. Because however, highly computationally expensive. PGMs involve nonlinear processes, this is typically done nu- Inputs to PGMs are typically in the form of detailed input merically by performing multiple PGM simulations of vary- files describing the emissions, meteorology, initial and bound- ing emission levels while other parameters are kept fixed to ary conditions, underlying surface geographical and topolog- estimate the relative change about a default state (linear error ical characteristics, appropriate chemical reactions and rates, term). However, this sensitivity would be context specific as well as domain and simulation period. Each of these must and, in general, could not be generalized to overall model be derived from other sources by a series of typically complex sensitivity (or uncertainty). processes and carries inherent uncertainty. For example, meteorological inputs are commonly derived from a diagnostic Summary of Strengths and Weaknesses. Strengths and application of a different model that simulates the meteorolog- weaknesses of the photochemical grid modeling methodol- ical environment during the period. As discussed, however, the ogy are summarized in Exhibit 3-67. only uncertainty in a photochemical grid modeling method- ology related to goods movement activities is that of the emis- 3.9.3 Evaluation of Local/Project-Level sions parameters. Methods and Models Emissions Parameters. Emission parameters are typically Evaluation of the impacts of emissions from project-specific detailed for the PGMs using emissions input files. These scale applications, such as individual ports, intramodal yards, describe both low-level and elevated emissions. Low-level freeways, or intersections, are typically done with a dispersion emissions are those released within the lowest atmospheric modeling method. layer (surface layer--typically tens of meters), and are com- prised of area, mobile, low-level point, and biogenic sources. Area sources are representations of groups of point sources Dispersion Modeling Methodology that are either spatially distributed or poorly spatially charac- Dispersion models simulate the effects of atmospheric tur- terized, but collectively important. They include, for example, bulence, mixing depth, and wind flow that drives the advec- various industrial and agricultural processes, dry cleaners, etc. tion and diffusion of pollutants following their release into Elevated emissions include releases from tall point sources, the atmosphere. Dispersion models simulate these processes such as power plant stacks. Emission inputs are generally pre- as either a straight line Gaussian plume or as an advecting pared for PGMs using external tools, such as SMOKE or EPS. puff. Both formulations have advantages and disadvantages. Goods movement activities are included in PGMs either Most Gaussian plume models--including AERMOD, as mobile (e.g., trucking) or area (e.g., cargo handling equip- which is the EPA's preferred model for near-field regulatory ment) sources. In air quality modeling, the strength, location, applications--have either no or highly simplified chemistry. and profile of emissions are all influential. Because goods Furthermore, guideline models such as AERMOD were movement emission inventories, such as those described pre- designed to predict peak concentration distributions, not to viously, are typically annual totals, temporal profiles must be accurately assess temporally and spatially varying concentra- assigned. This may be an additional source of uncertainty. The tions. (178) Although skill is being improved, the limitations spatial distribution of mobile and area sources is not typically of these model formulations (i.e., assessing source contribu- critical within a given grid cell, since that is the minimum res- tions to all receptors at each simulated hour) must be consid- olution of PGMs in most contexts. However, uncertainty in lo- ered. These models are relatively straightforward, however, cations that lead to source placement across cell borders may and have shown reasonable predictive skill in their operating lead to biased predictions of concentration. This, too, is an ad- range (50 km for AERMOD). (179) They also have several ditional source of uncertainty for emissions not characterized advanced or specialized treatments that make their appli- previously. Also, PGMs require simulation of all sources in the cation for specific projects advantageous. For example, model domain for correct chemical analysis, not just those of AERMOD has state-of-the-science boundary layer physics, a given project or those from freight transport. This additional plume rise, deposition, and building downwash methods. burden may introduce uncertainties or lead to bias. (180) The CALINE series of models is designed to characterize

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112 Exhibit 3-67. Summary of strengths and weaknesses--photochemical grid modeling methodology. Criteria Strengths Weaknesses Representation of physical Complex physical and chemical processes Limited to model spatial (and temporal) processes parameterized resolution Sensitivity to input parameters A number of parameters may affect model Highly susceptible to uncertain, complex results inputs Flexibility Ability to incorporate effects of Yields most realistic air quality impacts since Indirect: incorporated via emission emission reduction strategies model explicitly treats nearly all of the important characterization chemical and dispersion processes Representation of future Incorporated via emission characterization. emissions Consideration of alternative Indirect: incorporated via emission vehicle/fuel technologies characterization Data quality Models are typically verified against observed Relies on numerous inputs of varying quality data for some air pollutants, lending confidence and uncertainty to other air concentration predictions Spatial variability Generally limited to grid cell resolution (typically 2 km or more) Temporal variability Current concentration is a function of all of the Generally limited to hourly time steps previous hour's emissions Review process Models and methods have undergone continuous revisions since the 1970s. Endorsements EPA and other federal, state, and local agencies enhanced turbulence from vehicle motions and hot-exhaust dispersion, and Equation 21 provides the general equation for rise near the emission sources on roadways. (181) The OCD Gaussian dispersion. model is designed to simulate pollutants on-shore after being dispersed in the over-water boundary layer. (182) Gaussian Q ( z - h )2 C ( x , y, z ) = exp - plume models are relatively straightforward to apply, how- 2 u y z 2 z 2 ever, these models cannot predict more complex impacts from air circulation, stagnation, or other non-steady-state condi- ( z + h )2 y2 + exp - exp - (Equation 21) tions. Exhibit 3-68 shows a schematic illustration of Gaussian 2 z 2 2 z 2 Where Exhibit 3-68. Gaussian dispersion. C is the concentration at point (x, y, z), Q is the emission rate, u is the wind speed, y and z are the horizontal and vertical dispersion coeffi- cients (at a downwind distance), and h is the effective stack height. Advecting puff models, such as CALPUFF, simulate non- continuous plumes. CALPUFF is a non-steady-state La- grangian puff model that can include the effects of a three- dimensional wind field on the puff as it migrates through complex terrain. CALPUFF is EPA's preferred model for long- range transport applications (greater than 50 km, and prima- rily for Class I increment studies) or for near field applications involving complex winds, although complete verification of Source: http://upload.wikimedia.org/wikipedia/commons/1/10/Gaussian_Plume.png current versions is still being undertaken by EPA. (183) In

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113 these models, a "puff" of pollutants is followed from emission ature must also be included. As for point sources, uncertainty source, through the atmosphere, and to a receptor. During in any of these parameters will lead to uncertainty in output this transport, simple chemical changes, effects of wind shear, concentrations. effects of terrain, and wind circulations are simulated. This Here, too, the relationships between most of these param- allows the models to more completely parameterize atmo- eters and concentrations are complex, except for the linear re- spheric effects than simple straight-line, steady-state Gaus- lationship between concentration and emission rate. If uncer- sian models. However, the setup, execution, and model form- tainty in any input parameter is known, standard propagation ulation are all more complex. In many circumstances, their of uncertainty can be used to show uncertainty in concentra- performance is not sufficiently enhanced over Gaussian tion. Commonly, uncertainty is not known, especially for plume models to justify their use. methodological or choice issues to fit model requirements. For example, when modeling freight emissions from HDVs, Emission Parameters. Although the atmospheric effects some line source models (e.g., CALINE) may only require a on pollutants are parameterized differently using the two single release height for both light- and heavy-duty vehicles. types of dispersion models discussed in this section, the para- Selection of an appropriate value is sometimes discussed in meterization of emissions is similar. Most dispersion models modeling guidance documents. treat emission sources as either point, area, volume, or line Total uncertainty in predicted concentrations from goods sources. In all cases, the locations of the emission releases do movement represented using a dispersion methodology is due not change during simulations. to uncertainty in the emission input parameters, uncertainties Point sources typically represent emissions from station- in all other input parameters (e.g., meteorology), as well as ary stacks and are generally buoyant. They could be used to uncertainties in methodology (e.g., model formulation and represent exhaust stacks of hotelling vessels, for example. choice). This value is generally unquantifiable without Input parameters required include location, instantaneous or comparison to observed concentrations. Those uncertainties average emission rate, release height, exit temperature, exit due to calculated emission rate, however, may be characterized velocity, and stack inside diameter (or flow rate). For stacks directly from the input uncertainty. Other emission param- where building downwash is important, additional parame- eters due to the methodology by which the emitting process ters also must be included to simulate these effects. Uncer- is represented--such as spatial scale of activity--generally tainty in any of these parameters will lead to uncertainty in can not be characterized, but could be assessed for any par- output concentrations. The relationship between most of ticular scenario. these parameters and concentrations may be complex, due to interactions with input meteorology as formulated in the Summary of Strengths and Weaknesses. Strengths and model. Concentration is linearly proportional to emission weaknesses of the dispersion modeling methodology are rate in all cases; standard propagation of uncertainty can be summarized in Exhibit 3-69. used to show uncertainty in concentration from a known emission uncertainty. Uncertainty due to other (nonlinear) parameters may be derived between a specific source and re- 3.9.4 Evaluation of Parameters ceptor due to propagation of uncertainty and Equation 21. Exhibit 3-70 summarizes all emissions-related parameters Line, area, and volume sources are one-, two-, and three- relevant for calculating concentrations from goods movement dimensional source types commonly used to describe emissions activities. Each of these has been discussed. Other parameters, where the spatial distribution of emissions within a particular such as initial and boundary chemical conditions, meteorol- boundary is not fully known (e.g., an industrial complex) or ogy, and selection of appropriate models and methods, are not within which the emissions occur more or less uniformly included here. (e.g., a freeway link). Their governing equations are a varia- tion on the equation for point sources found in Equation 21. Pedigree Matrix. Exhibit 3-71 shows the pedigree matrix Area and volume sources may be non-buoyant (AERMOD) for the seven general parameters that relate goods movement or buoyant (CALPUFF, area sources only). Further, some emissions to pollutant concentration through the use of air models do not contain the ability to model line sources ex- quality modeling methodologies. Criteria to assign scores in plicitly (e.g. AERMOD); instead, modeling sources such as the pedigree matrix are included in Appendix A. Note that all roads, rail lines, or shipping channels may be achieved by as- entries here are ranked as "5" for "Range of Variation." This sembling adjacent groups of volume or area sources. Emission is because the variation in the variation of values between inputs for these source types include emission rate, location, methods, models, and applications is wide, which is also con- orientation, release height, and initial plume size (lateral and sidered a "5." See documentation in Appendix A for further vertical dimensions). If buoyancy is considered, exit temper- explanation.

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114 Exhibit 3-69. Summary of strengths and weaknesses--dispersion modeling methodology. Criteria Strengths Weaknesses Representation of physical Dominant processes generally parameterized, Model formulations are generally simplistic processes as long as operated within model limitations (e.g., spatial scale) Sensitivity to input Generally rely on readily available inputs Susceptible to uncertain inputs parameters Flexibility Generally adaptable to a variety of scenarios Gaussian plume models operate on an and available information underlying assumption of a steady-state Ability to incorporate effects Indirect: incorporated via emission of emission reduction characterization strategies Representation of future Indirect: incorporated via emission emissions characterization Consideration of alternative Indirect: incorporated via emission vehicle/fuel technologies characterization Data quality Varies: relies on input data quality and model formulations; is particularly susceptible to inappropriate model choice or input variables Spatial variability Can model concentrations in close proximity to Gaussian plume model formulations may not source and with arbitrarily high spatial represent variability well in complex terrain or resolution wind flow regimes Temporal variability Limited only by input data resolution Review process Models and methods continuously updated Result is model-specific and expanded. Endorsements EPA and other federal, state, and local Result is model-specific agencies Emission Rate. Concentrations are always directly pro- accurate emission inventories for given sources are the key- portional to emissions. Uncertainty in characterizing the total stone of reasonable model predictions. Uncertainty in emis- emissions from any given source (and from all modeled sions has been discussed in all previous sections. sources, if chemistry is included) leads directly to uncertainty in concentrations. In this case, emission rate refers to the emis- Source Location. Uncertainty in geographic placement sions, usually in grams/second, emitted by a given source at a of sources leads to uncertainty in concentration at a given re- given time, which is usually determined from the (annual) ceptor site due to the uncertainty in transit distance between emission inventory and the emission temporal profile. The the two locations. In plume or puff modeling, this distance al- relationship of concentration to emissions becomes more lows the pollutants to be more (less) diffuse and have greater complicated as the modeling becomes more complex, but (less) time for chemical transformation reactions, settling, Exhibit 3-70. Parameters. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Emission rate All All Source location All All Emission temporal profile All All Release height All All Initial plume size and shape All All Source orientation, size, and All All shape Exhaust temperature and other All (if plume rise is All buoyancy parameters considered)

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115 Exhibit 3-71. Pedigree matrix--harbor craft equipment parameters. Technological Correlation Geographic Correlation Temporal Correlation Representativeness Acquisition Method Range of Variation Impact on Result Independence Parameter Emission rate 4 Varies Varies Varies N/A N/A Varies 5 Source location 3 Varies Varies Varies N/A N/A Varies 5 Emission temporal profile 3 Varies Varies Varies N/A N/A Varies 5 Release height 3 Varies Varies Varies N/A N/A Varies 5 Initial plume size and shape 3 Varies Varies Varies N/A N/A Varies 5 Source orientation, size, and shape 2 Varies Varies Varies N/A N/A Varies 5 Exhaust temperature and other 3 Varies Varies Varies N/A N/A Varies 5 buoyancy parameters and other removal processes to act if the source is placed fur- complex relationships, concentration uncertainty caused by ther (closer) to the receptor. In a grid modeling application, uncertainty in release height can not generally be quantified. the locations of the sources (within the model resolution) are less important, as long as they are assigned to the correct grid Initial Plume Size and Shape. Dispersion generally in- cell. The general relationship between location and concen- creases plume/puff size, and therefore dilutes concentrations. tration is unquantifiable, but uncertainties in location will Thus, the concentration observed at a particular receptor lo- impact simulated concentrations and are likely to change the cation will be due to both the processes acting on the pollu- spatial distribution of concentrations. tants after emission and on the initial state of the emissions. As the size and shape of the initial plume influences the Emission Temporal Profile. Concentration estimates are downwind concentration at a given location, uncertainty in highly sensitive to the temporal profile imposed on the total, initial shape will lead to uncertainty in resulting concentra- annual emission rate determined from an inventory of goods tions. This uncertainty can be mitigated by following pub- movement activities. The temporal profile assigns emissions lished modeling guidance and characterizing the sources in as to specific hours of the year where the model pairs them with realistic a method as possible. corresponding meteorological and other parameters. If the di- urnal, weekly, or other cycles are mischaracterized, the dis- Source Orientation, Size, and Shape. Particularly for persion will be, too. Values of the profiles are often taken nonpoint sources, the initial size, shape, and orientation of from published studies of activity of specific equipment types the source can dictate the dispersion characteristics. Orienta- (184) based on SCCs. More accurate representation would tion can change the size of the source relative to a given wind require knowledge of activity profiles throughout the inven- direction, and therefore influence the downwind concentra- tory period, which are often unavailable. The impact of emis- tion. Generally, the initial plume size is related to the source sion temporal profile on total concentration uncertainty is size; thus, the uncertainties discussed for initial plume size not generally quantifiable, but may be determined for specific relate here, too. scenarios. Buoyancy Parameters. Buoyancy and rise of the emitted Release Height. Release height is the vertical component pollutants is related to the initial exhaust temperature relative of source location. The relationship between release and re- to the ambient temperature and exhaust flow rate. This has ceptor height, in combination with terrain, stability, initial an effect similar to raising the release height. Thus, uncertain- dispersion, building downwash, and other parameters can ties here propagate to concentration in a method similar to greatly influence modeled concentrations. Because of these that discussed for release height.