Comparison of Features and Data Requirements among the CALPUFF, AERMOD, and ADMS Models

Transcription

1 Comparison of Features and Data Requirements among the CALPUFF, AERMOD, and ADMS Models Prepared By: Russell F. Lee BREEZE SOFTWARE Park Central Drive Suite 2100 Dallas, TX (972) breeze-software.com Modeling Software for EHS Professionals

2 Comparison of Features and Data Requirements among the CALPUFF, AERMOD, and ADMS Models Introduction to plume and puff models CALPUFF, AERMOD, and ADMS are new advanced air quality models that are either being used or are being proposed for use for air pollution regulatory applications. The physical processes in the atmosphere that determine the transport and dispersion of air pollutants are very complex, and are not all well understood. Furthermore, simplifying the physical processes in a model that is sufficiently accurate for regulatory purpose, but not excessively costly and difficult to use, is not an insignificant endeavor. Consequently, the development of air quality models is a continuing process. The Interagency Workgroup on Air Quality Modeling (IWAQM) has recently recommended the use of the CALPUFF modeling system for use in assessing air quality associated with prevention of significant deterioration of air quality in Federal Class 1 and Wilderness areas. For such purposes, the ability of the model to adequately represent long-range transport of pollutants, effect of pollution on visibility, and certain chemical reactions is critical. Once CALPUFF was selected for their recommendation, IWAQM was involved in enhancing that modeling system to better suit these purposes. In 1991, the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) cosponsored an initiative to bring plume models up to date. The committee formed to accomplish this is the AMS/EPA Regulatory Model Improvement Committee (AERMIC), and the resulting model is AERMOD (AERMIC Model). AERMOD is intended to fill the niche currently occupied by the ISCST3 model. It is intended primarily to be used for modeling non-reactive pollutants such as sulfur dioxide, carbon monoxide, and particulate matter. These pollutants are characteristically emitted by local sources, resulting in the highest concentrations occurring within a few kilometers of the contributing sources. After reviewing existing models, AERMIC elected to develop a new model that was sufficiently similar to ISCST3 that users would not have excessive difficulty in learning to apply it. Concurrently with the development of AERMOD in the United States, a new plume model was being developed in United Kingdom. ADMS (more accurately, UK-ADMS UK-Atmospheric Dispersion Modelling System) was being developed by the CERC (Cambridge Environmental Research Consultants, Ltd.) with from industry and government organizations. An overview of plume and puff models Plume models have been the mainstay of regulatory near-field modeling of nonreactive pollutants. The current regulatory model in the U.S., ISCST3, is based on work by Frank Pasquill in the 1950 s, with modifications by Frank Gifford and adaptations for computer modeling by D. Bruce Turner. In that model, atmospheric states are divided into six stability categories, and plume growth is treated as a function of stability category and downwind distance. The concentration distribution of pollutants through the plume is assumed to be Gaussian in all cases. This is a reasonable assumption in the horizontal direction. For neutral and stable flow, when there is no local boundary (such as the ground) affecting the turbulence profile, this is also a reasonable assumption in the vertical direction. However, the presence of a ground surface skews the shape of the pollutant distribution by suppressing turbulence (and, therefore, dispersion) on

3 the low side of a near-ground plume. During convective (i.e., unstable) conditions, things are even more complex. Because there are very large turbulent eddies for this case, the presence of the ground skews even elevated plumes. In addition, since the turbulent eddies are much larger than the plume itself, parts of the plume get caught in updrafts while other parts get caught in downdrafts. This results in a very non-gaussian plume. These and many other problems with the older models have been addressed in the newer plume models AERMOD and ADMS. These newer models no longer characterize turbulence in terms of six discrete stability categories. They recognize that turbulence is continuously variable, depending on height above ground, amount of heating in the daytime or heat loss at night, and wind shear. Concerning the height above ground, virtually all airflow near the ground is neutral, becoming more turbulent (daytime) or less turbulent (nighttime) with height. All plume models are, however, steady state by nature. This means that the models assume steady or constant conditions between the time of emission and the time it reaches any receptor. For downwind distances of a few kilometers, this is a reasonable assumption. However, consider this. A plume model applied to a pollutant release during stable conditions at six o clock in the morning will make concentration calculations at a distance of 50 kilometers based on that one wind direction under stable conditions. It will ignore the fact that the plume may have traveled for six hours or more, changing direction due to changes in wind speed, and growing at varying rates as it encounters a whole range of stabilities. It is clear that plume models cannot, in general, be expected to give reasonable results for long range transport of pollutants. This is where puff models are valuable. Puff models do not require the steady state assumption. The emission that occurs from a source during the first time step (which may be a few minutes or an hour) will be followed downwind. As the wind direction changes, the direction of motion of the puff changes. It can even account for different winds at different locations. Unfortunately, to give an accurate picture of the concentration distribution in a plume, these puffs must be sufficiently close together to overlap. Close to the source, where little dispersion has occurred and, thus, the puffs are small, it may be necessary to model a puff a second to avoid gaps between the puffs. This would lead to extremely large runtimes for the model. For this reason, a puff model is generally not a practical way to model concentrations near a point source, although CALPUFF gets around the problem by some techniques described below. At large travel distances, where the puffs have grown to considerable size, model runtimes are less severe. In addition, the puff model can account for changes in wind and stability as the puff moves downwind. Thus, a puff model can provide good results when long range transport is involved. Discussion of the CALPUFF model While many of the entries in Tables 1 3 are self-evident, several also require some discussion. Under item 3b of Table 1, both puff and slug are indicated as options for CALPUFF. As noted above, puff models are generally unable to represent a continuous plume near a point source, due to the small sizes of the puffs. The plume ends up being represented by a sequence of puffs with space between them, with resultant errors in calculated concentrations. CALPUFF solves this problem with the optional use of a slug for such cases. A slug is an elongated puff. This allows two separated puffs to be connected, to better represent the plume without having to generate an excessive number of puffs. This may make it possible for CALPUFF to be used to estimate concentrations nearer the source than is normally possible for puff models. CALPUFF also allows an option to use a non-gaussian probability density function for convective (unstable)

4 cases, as is done in AERMOD. There are also options allowing CALPUFF to emulate ISCST3 and CTDM in the near field. These should be used with caution at the present time, since there is limited experience using CALPUFF to estimate near-field concentrations. It will be interesting to learn how well it emulates plume models such as AERMOD and ADMS for these cases. Since the main purpose of CALPUFF is to provide a model for long range transport of pollutants, the meteorological data requirements can be substantial (see Tables 2 and 3). Fortunately, for regulatory purposes, it will probably not be required to use hourly gridded wind fields from the MM4, MM5, or CSUMM models. Nevertheless, it is a much more difficult model to use than the traditional Gaussian models. In general, CALPUFF is a technically good approach for long-range transport modeling, and can account for some chemical reactions. Because of the introduction of the use of the concept of the slug, as well as the non-gaussian pdf, CALPUFF has potential for near-field analyses as well, though this needs to be tested. Discussion of the AERMOD model AERMOD represents an improvement over traditional Gaussian models. The theory is much more solid, and evaluation studies so far show substantial improvement over the traditional models as well. AERMOD uses a non-gaussian pdf during convective conditions, and accounts for variations in wind, turbulence, and temperature gradient with height. The effect of wind shear on transport is accounted for by estimating a characteristic wind speed and direction through the thickness of the plume. AERMOD does not, however, deform the plume in response to the shear as CALPUFF does. AERMOD is a steady state plume model, and as such is not appropriate for long-range transport. Although it will probably be approved for use for distances up to 50 kilometers, it is doubtful that any plume model will retain its accuracy much beyond kilometers. At the present time, AERMOD does not include the capability of accounting for deposition, though there are plans for adding this.

5 Table 1. Comparison of Model Features Category Feature CALPUFF AERMOD ADMS 1. Graphical User Interface Point & click model setup and data input Trinity version: EPA version: NO Enhanced error checking Trinity version: EPA version: NO Online help files Trinity version: EPA version: NO 2. Source Types 2a. Point sources 50 1 Variable emissions & stack parameters Plume rise Building downwash Other Huber-Snyder NO Schulman-Scire NO Selection method User option Automatic N/A 2b. Line sources NO 1 1 Variable emissions N/A Plume rise N/A

6 Category Feature CALPUFF AERMOD ADMS 2c. Volume sources 5 1 Variable emissions Plume rise NO NO NO 2d. Area sources 5 1 Variable emissions Uses Emissions Production Model (U.S. Forest Service) output for controlled burns and wildfires. NO NO Plume rise NO 2e. Jet sources (non-buoyant, ejected at any angle) NO NO 2f. Treatment of variable emissions (all source types): Hourly file of emissions and stack parameters User option User option User option Factors by hour of day User option User option User option Factors by season NO User option User option Factors by month User option User option User option Factors by hour & season Factors by wind speed & stability User option User option User option User option Factors by temperature User option NO NO User option User option

7 Category Feature CALPUFF AERMOD ADMS 3. Plume/Puff Characteristics 3a. Plume Rise Buoyant rise Momentum rise (?) Stack tip effects Partial penetration (?) Vertical wind shear (limited does not deform plume) (limited does not deform plume) (?) 3b. Plume/puff form Steady-state plume NO Puff User option NO Optional puff for short releases Slug User option NO NO Non-Gaussian pdf User option 3 3c. Dispersion coefficients y, z based on: Direct measurements of v, w Estimated v, w using similarity theory User option 2 User option (?) User option 2 User option PG (ISC3-Rural) User option NO NO McElroy-Pooler (ISC3- Urban) CTDM coefficients (neutral/stable) User option NO NO User option NO NO

8 Category Feature CALPUFF AERMOD ADMS 3d. Overwater and coastal effects Overwater boundary layer parameters Change from overland to over water conditions modeled NO Overland coastal only NO NO NO NO Plume fumigation NO (over land) TIBL included in subgrid scale modeling User option NO (onshore wind) 3e. Spatial variability of meteorology affecting plume or puff Gridded 3-D winds and temperature 2-D fields of zi, u*, w*, L, precipitation rate Vertical variations in turbulence NO NO NO NO Individual puffs split User option N/A N/A Horizontal variation in turbulence NO NO 3f. Chemical transformation Exponential decay NO Pseudo first-order reaction for SO2, SO4 =, NOX, HNO3 & NO3 Diurnal cycle of transformation rate User option 2 (MESO- PUFF II method) NO User option NO NO (?)

9 Category Feature CALPUFF AERMOD ADMS 3g. Dry deposition User option NO Gas and/or particle Full space/time variations of deposition resistance values Simpler diurnal cycle of deposition resistance values Both optional NO Both optional User option NO NO (?) User option NO NO (?) 3h. Wet deposition User option NO Scavenging coefficient approach Rate is function of precip. intensity & type NO NO 4. Outputs Concentration (Max, 2 nd high, etc.) Averaging times < 1 hour NO Running averages Percentiles Requires external processing Requires external processing Requires external processing Requires external processing Deposition NO Visibility NO Radioactive dose NO NO 1. Maximum number of sources or receptors permitted in the model. 2. A (or the) preferred option for this model. 3. For convective cases, CALPUFF has non-gaussian pdf for near-source concentrations to approximately emulate AERMOD

10 NOTE: CALPUFF features referenced above do not include those in the CALGRID Photochemical Model and the KSP Particle Model. CALPUFF and KSP are part of the CALPUFF modeling system, and allow additional model features, including complete photochemical modeling and lagrangian particle modeling. These models, though included in the CALPUFF system, are documented separately.

11 Table 2. Comparison of Data Requirements among the Meteorological Preprocessors Category Feature CALMET 1 Surface data Hourly observations of: wind speed, wind direction temperature cloud cover ceiling height surface pressure relative humidity precipitation type precipitation rate (CALPUFF) multiple sites (Precipitation type required only for wet deposition) AERMET (AERMOD) single site (Precipitation type, relative humidity, surface pressure not needed) ADMS (Meteorological input module) single site (Precipitation type, relative humidity, surface pressure not needed)

12 Category Feature CALMET 1 Upper air data Daily maximum mixing heights RAWINSONDES: observed vertical profiles of: wind speed wind direction temperature pressure elevation Hourly gridded wind fields from MM4/MM5 Hourly gridded wind fields from CSUMM Overwater observations air-sea temperature difference air temperature relative humidity overwater mixing height wind speed wind direction overwater temperature gradients above and below mixing height (CALPUFF) AERMET (AERMOD) NO NO twice daily soundings or more one early morning sounding User option NO NO User option NO NO User option NO NO ADMS Requires hourly mixing heights

13 Category Feature CALMET 1 Geophysical data (CALPUFF) AERMET (AERMOD) Terrain elevations (gridded) ADMS Land use categories -gridded NO (?) Surface roughness Albedo Bowen ratio Soil heat flux constant anthropogenic heat flux User optiongridded User optiongridded User optiongridded User optiongridded User optiongridded -by direction -by direction -by direction (?) NO NO (?) Derived from city size NO (?) leaf area indices NO NO 1. CALPUFF can optionally be run, with degradation of accuracy, using meteorological data preprocessed for ISCST3. It requires the addition of hourly (not gridded) friction velocity, Monin-Obukhov length, surface roughness, precipitation code and rate, potential temperature gradient, wind speed profile power-law exponent, short-wave solar radiation, and relative humidity. An additional option allows the use of the surface and profile meteorological data files which are used in CTDMPLUS and AERMOD, with the addition of precipitation code and rate, short-wave solar radiation, and relative humidity to the surface file. The accuracy is expected to be degraded from the normal CALPUFF mode, but should be better than that obtained using ISCST3 meteorological data.

14 Table 3. Comparison of Other Input Data Requirements among the Models Category Feature CALPUFF AERMOD ADMS Emissions data Point source, constant or diurnal emission pattern Point source, arbitrarily varying emission pattern (i.e., a file of hour by hour emission parameters) Line source, constant or diurnal emission pattern Line source, arbitrarily varying emission pattern Area source, constant or diurnal emission pattern Area source, arbitrarily varying emission pattern Volume source, constant or diurnal emission pattern Volume source, arbitrarily varying emission pattern (no (no NO NO (no (no (no (no (no (no Other data Deposition velocity data NO (?) Ozone monitoring data NO Chemical transformation data NO (?) Terrain data USGS DEM data, or equivalent Hill shape and height parameters Receptor locations with associated hill ID (UK data) (as in CTDM+) Derived in model from terrain data Derived in model from terrain data Coastal boundary data NO Derived in model from terrain data Derived in model from terrain data

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