Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

AERMOD and AUSPLUME: Understanding the Similarities and Differences

2,169 views

Published on

In this presentation, similarities and differences between AERMOD and AUSLPUME are discussed and analysed with the ultimate goal of easing the transition from AUSPLUME to AERMOD in Victoria, as well as Australia as a whole. Topics discussed include source types, treatment of terrain, plume rise algorithms, low wind speed conditions, and chemical transformations.

Published in: Environment
  • Login to see the comments

AERMOD and AUSPLUME: Understanding the Similarities and Differences

  1. 1. CASANZ2015 Conference, Melbourne, 20-23 September 2015 1 AERMOD AND AUSPLUME: UNDERSTANDING THE SIMILARITIES AND DIFFERENCES Tiffany Gardner, Qiguo Jing (PhD), Brian Holland, Weiping Dai (PhD, PE, CM) Trinity Consultants, Inc. Dallas, Texas, 75251 USA Abstract AUSPLUME has been the promulgated dispersion model in Australia since it was developed in 1985. The model is based on the United States Environmental Protection Agency (US EPA) Industrial Source Complex (ISC) Gaussian plume model, and includes strictly Gaussian calculations. The current version of AUSPLUME was released over one decade ago, in June 2004. Because of this, today the model lags behind newer Gaussian plume models, like AERMOD. AERMOD was developed by a collaborative working group of scientists from the American Meteorological Society (AMS) and the US EPA with the goal of incorporating the state of science planetary boundary layer (PBL) concepts into regulatory dispersion models. It has been the promulgated near-field dispersion model by the US EPA since 2006 for air quality impact assessments. The US EPA also continues to update and improve the model. The latest AERMOD executable was released in 2014 and another executable is expected to be released in summer 2015. Since 2006, more and more countries across the globe have started using AERMOD for these assessments including as of late Victoria, Australia, where the EPA (EPA Vic) has adopted AERMOD in place of AUSPLUME in January 2014. While AUSPLUME and AERMOD are both Gaussian plume models and assume steady-state conditions, AERMOD includes advanced algorithms to take into account impacts that cause a plume to act in a non-Gaussian manner. Due to these advanced algorithms in AERMOD, AUSPLUME and AERMOD calculate many key model parameters using differing approaches and therefore treat certain factors entirely different, including low winds, complex terrain, and plume rise. In this paper, these similarities and differences between AERMOD and AUSPLUME will be analysed in depth with the ultimate goal of easing the transition from AUSPLUME to AERMOD in Victoria, as well as Australia as a whole. The impact of the differing approaches in each model will also be analysed, so that modellers are aware of potential implications that using one model compared to the other may have on model results. Keywords: AERMOD; AUSPLUME; Victoria; Gaussian 1. Introduction With the promulgation of AERMOD in Victoria by the EPA (EPA Victoria 2013) and the potential for future promulgation of AERMOD in other Australian states and in New Zealand, it is crucial that modellers understand the similarities and differences between AERMOD and AUSPLUME. While both are Gaussian models, AERMOD includes additional algorithms to account for key non-Gaussian behaviours of a plume. The purpose of this paper is to provide modellers with an understanding of certain features and algorithms that compare and differ between these models to assist modellers with the transition from AUSPLUME to AERMOD. The topics covered in this paper are the similarities and differences in available source types, algorithms to account for the influence of terrain, plume rise calculations, low wind speed conditions and chemical transformation options. 2. Source Types In both AERMOD and AUSPLUME, modellers have the option of selecting from point, area, and volume source types. In addition to these, AERMOD also includes a line and open pit source type, as well as two beta source types (horizontal point source and rain-capped point source).
  2. 2. CASANZ2015 Conference, Melbourne, 20-23 September 2015 2 2.1. Additional Source Types in AERMOD 2.1.1. Line Source Type The line source type is typically used to represent roadways, and was first introduced in AERMOD in the 12345 EPA AERMOD executable (US EPA 2012). In AERMOD, the pollutants from line sources are modelled as a series of area sources and as such, the required input is similar to that of the area source. Users may still choose to model a roadway as an elongated volume or area source, but the line source type provides an alternative method of defining a rectangular area source that is much longer than it is wide. It is important to note that while a line source type is not available in AUSPLUME, EPA Vic developed a specific line source air quality model, AusRoads, for modelling the dispersion of emissions from roadways. AusRoads is based on algorithms utilized by Caline4, which was developed by the California Department of Transportation, and can predict concentrations of pollutants for receptors located close to roadways. 2.1.2. Open Pit Source Type The open pit source type is a specialized area source that may be used for surface coal mines or quarries when the total pollutant mass cannot escape (US EPA 2011). The algorithm in AERMOD employed for open pit source types uses an effective area for modelling pit emissions based on meteorological conditions. Then, the numerical integration area source algorithm is used to model the impact of emissions from the effective area sources. Typical source parameters include emission rate per area, coordinates and elevation, release height above ground, pit volume, and the pit dimensions, shape, and/or orientation. An important factor when using the open pit source type is that particle deposition parameters must be included if the much earlier 07026 AERMOD executable is selected even if the only result type selected is concentration. Additionally, when using the older 09292 or later AERMOD executable, open pit sources require particle deposition parameters if deposition is being modelled, or if at least one source in the model scenario specifies particle deposition parameters and the Control form option to explicitly turn off dry and wet depletion are not selected. 2.1.3. Beta Source Types AERMOD includes two additional source types that are currently considered beta types by the US EPA and EPA Vic: the horizontal point and capped point (US EPA 2011). Aside beta source types, these source types should not be used without specific approval from a regulatory agency (US EPA 2011; EPA Victoria 2013). If modelling for non-regulatory purposes, these source types may be used for stacks with non-vertical discharges (e.g., horizontal or downward) or have raincaps that change the outlet velocity from vertical to horizontal. If approved by the US EPA or EPA Vic for modelling for regulatory purposes though, the US EPA requires these source types to be modelled as a vertical point source with a vertical velocity of 0.001 m/s, which will eliminate the momentum component. Note that if the temperature for these stacks is greater than ambient, the stack diameter may need to be adjusted so that the volumetric flow rate is the same with the 0.001 m/s velocity as it is with the actual, non-vertical velocity. Maintaining the flow rate will also serve to maintain buoyancy of the plume in order to provide a more realistic estimate of plume rise. As is noted above though, in order to use these source types for regulatory modelling, approval must be received beforehand by US EPA or EPA Vic. Another option for calculating the vertical velocity for these source types is to use the following equation and then use the larger of Vvert or 0.001m/s as the exit velocity input to the model: Vvert = Vs cos (Q), Where: Vvert = Vertical Exit Velocity for AERMOD Vs = Exit Velocity as Reported Q = Angle of the Stack with the Vertical (degrees) 3. Terrain A main difference between AUSPLUME and AERMOD is the way the models handle terrain. In a majority of the older Gaussian models, like ISCST or AUSPLUME, a pollutant plume can either rise above a terrain feature or go around the terrain feature; not both. As such, the way a plume behaves when it encounters complex terrain is not accurately captured in these models. 3.1. AUSPLUME: ISC Horizontal Plume and Egan Half-Height Approaches In AUSPLUME, three options for terrain adjustment calculations are available (Ministry for the Environment 2004). 3.1.1. ISC Horizontal Plume Method First is the ISC method, or the horizontal plume approach. With this method, terrain is assumed to have no influence on the plume height above sea level and as such, the plume is not uplifted by the terrain below it (see Figure 1). This is a very simple approach.
  3. 3. CASANZ2015 Conference, Melbourne, 20-23 September 2015 3 3.1.2. Egan Half-Height Method The Egan half-height method, which is the second and preferred terrain correction method, assumes that in neutral or unstable conditions, a plume will tend to be uplifted by broad terrain features. Under stable conditions, this lifting will generally be less and the plume path will be closer to the face of the hill and may even impact the surface (see Figure 1). In situations when a plume passes into a valley though, the plume will tend to move further from the ground. In all of these situations, the plume centreline height above local terrain is more apparent as atmospheric stability increases. To simulate these reactions of the plume to terrain, the plume axis remains at the plume stabilization height above mean sea level in a stable atmosphere, while in an unstable or neutral atmosphere the half- height correction factor is used for changes in plume axis height above terrain (see Figure 1). It is also important to note that with this method, the plume axis is constrained to be at least 10 m above ground level. 3.1.3. Modified Egan Half-Height Method This third method, the Modified Egan Half-Height Method, allows the user to specify the constant of proportionality of approach for each of the Pasquill stability classes. This option is typically only used where observational data exist. Figure 1 below shows the ISC Horizontal Plume Method, where there is no influence of terrain taken into account; the Egan Half-Height Method, where the half-height factor is used to account for changes in the plume axis height to due terrain; and the Modified Egan Half-Height Method, where the user specifies the constant of proportionality of approach each Pasquill stability class. Figure 1. ISC Horizontal Plume Method and the Egan Half- Height Methods In each of these three methods, the plume can only rise above or move horizontally around the terrain feature; not both. 3.2. AERMOD: Dividing Streamline Height Approach Unlike AUSPLUME, AERMOD utilizes algorithms to enable a plume to both impinge and/or go around the hill, and also follow the terrain while maintaining a separation from ground level equal to the initial plume height. Using the concept of the dividing streamline height, which is calculated based on stability, wind speed, and plume height, AERMOD is able to account for this non-Gaussian behaviour of a plume. For the portion of the plume that is below the dividing streamline height, the plume goes around the terrain feature, and for the portion of the plume that is above the dividing streamline height, the plume rises up and over the terrain feature. Note that in neutral and unstable conditions the dividing streamline height is zero. 3.2.1. Terrain Data Selection for use in AERMOD The terrain files accepted by AERMAP, the terrain pre-processor of AERMOD, are Digital Elevation Model (.DEM) data and National Elevation Dataset (NED) GeoTIFF files and only the UTM coordinate system is supported by AERMAP. AERMAP imports model object elevations using these terrain data files into AERMOD. As such, if a new model object is added and AERMAP has already been run, it is important to remember to rerun AERMAP so the elevation for the new model object is also imported. 3.2.2. 10% Slope Rule AERMOD requires that the DEM or NED data files that are imported into the model encompass every model object and also satisfy the 10% slope rule. In other words, if a 10% slope is drawn from every receptor, then the DEM or NED terrain data files should include every terrain feature that rises above this slope. Estimating the number of DEM or NED files that are necessary to include in the terrain analysis performed by AERMAP is not straight forward because there is no standard distance for which terrain data should be provided; it varies case by case. Because of this, many modellers simply obtain terrain data that surrounds the extents of their receptors. In areas with significant topography, this will not be enough to compute the correct critical scale height required by AERMOD though, which is used to calculate the critical dividing streamline height. As a conservative estimate, it is good practice to estimate on the higher end to ensure the correct number are included instead of underestimating the number of DEM or NED data files required. 4. Plume Rise The final rise of a plume is essentially the sum of the stack height, plume rise due to buoyancy, and plume rise due to the initial momentum minus any stack-tip downwash. In both AUSPLUME and AERMOD, plume rise is taken into account but the methods and algorithms used to calculate the plume rise in each
  4. 4. CASANZ2015 Conference, Melbourne, 20-23 September 2015 4 model differ. The sections below discuss how plume rise is calculated in these models, highlighting the similarities and differences. 4.1. Plume Rise in AUSPLUME In AUSPLUME, there are three types of plume rise options that can be selected; a) gradual rise of a buoyant plume, b) partial penetration of elevated inversions, and c) stack-tip downwash (Ministry for the Environment 2004). Out of these options, while a) and c) or b) and c) may be selected together, a) and b) may not be. When gradual plume rise is selected, the plume gradually rises to its final height as it moves downwind. If this option is not selected, then AUSPLUME assumes the plume is at the final plume height everywhere when calculating ground-level concentrations. As such, it is typically recommended to select this option. Unlike the gradual plume rise option though, the patrial penetration of elevated inversions option in AUSPLUME assumes the plume reaches its maximum height instantaneously when it exits a stack. This option does not simulate the gradual rise of a plume to its final plume rise height and as such is used in cases when a tall buoyant sources and low mixing heights are present and thus, the partial penetration of inversions is important. As is mentioned above, in AUSPLUME the user may only pick one of these two options; the model will not calculate both at the same time. In other words, if the partial penetration of plumes through an elevated inversion level is required, then in AUSPLUME the gradual plume rise option cannot be selected. Due to this limitation, it is necessary in certain scenarios to run the ASUPLUME with one and then the other selected to see which yields the highest ground-level concentration. Lastly, the stack-tip downwash calculations in AUSPLUME are the same as those used in AERMOD. As such, in both models the maximum reduction to plume height due to stack-tip downwash is three times the stack diameter. 4.2. Plume Rise in AERMOD In AERMOD, the plume rise calculations are different under stable and convective (unstable) boundary layers and account for rise due to momentum as well as buoyancy. Additionally, unlike AUSPLUME where users may either select gradual plume rise (e.g., plume gradually rises to final height) or partial penetration of elevated inversions (e.g., plume is assumed to reach final height instantaneously), AERMOD takes both into account. 4.2.1. Plume Rise in a Stable Boundary Layer In a stable boundary layer, AERMOD uses the minimum of the following four equations to determine the plume rise (US EPA 2004):  Transitional Rise:  Final Rise:  Neutral Limit: where  Near-Calm Conditions: The transitional rise equation, which is taken from Weil (1988b), applies when the plume is still rising, and once the stable plume reaches its final height, the final rise equation is used. In the final rise equation in a stable boundary layer, notice that the force due to momentum (Fm) is not present; only the force due to buoyancy results in the rise of the plume out of these two forces. As such, if there is no buoyancy in a stable boundary layer, then there is no plume rise calculated. When the atmosphere is close to neutral, the Brunt Vaisala frequency (N) is close to zero and as such, the transitional rise equation can calculate an unrealistically large plume rise. For this reason, in neutral conditions the Neutral Limit equation is used. Lastly, when the wind speed is near zero (e.g., calm conditions), an unrealistically large plume rise would be calculated from the transitional rise equation so the Near-Calm Conditions equation is used. 4.2.2. Plume Rise in a Convective Boundary Layer In a convective boundary layer, the plume rise is calculated using a combination of three equations (US EPA 2004):  Direct Plume:  Indirect Plume:  Penetrated Plume:
  5. 5. CASANZ2015 Conference, Melbourne, 20-23 September 2015 5 The direct plume is the plume within the mixed layer that initially does not interact with the mixing height whereas the indirect plume is the portion of the plume that is within the mixed layer but rises up and tends to loft near the mixing height. The penetrated plume is the portion of the plume that is released in the mixed layer but due to its buoyancy, penetrates into the elevated stable layer aloft. By combining these equations together to obtain the plume rise in a convective boundary layer, the force due to buoyancy and momentum are taken into account. Similarly, by combining these equations the calculated plume rise takes into account the transition from the stack height to the final rise of the plume as it does in the stable boundary layer. 5. Low Wind Speed Conditions In Gaussian models, stable conditions with low wind speeds is typically a combination that will produce worst-case concentrations. The reason for this is that in stable conditions, there is not much atmospheric turbulence and as a result, the plume stays more concentrated instead of mixing with the ambient air. Additionally, in these models the pollutant concentration and wind speed have an inverse relationship, so if the wind speeds are low, the concentrations are high. Because of this, the ability of Gaussian models to handle low wind speeds breaks down when wind speeds are very low (e.g., calm conditions). As the wind speed gets lower and lower, the concentration can rise to unrealistic values. To try to account for this limitation though, AERMOD and its meteorological pre-processor, AERMET, have a few beta options available. 5.1. Beta u* Option in AERMET The friction velocity (u*) computed by AERMET is used to calculate the mixing height, as well as the initial horizontal and vertical dispersion dimensions. In 2007, the US EPA noted issues with high concentrations in AERMOD due to the treatment of low winds at the EPA Regional/State/Local Modelers Workshop, and AERMOD users began to see that the highest impacts are typically associated with low wind speeds during night time hours when the atmosphere is stable. After looking into the matter and conducting a number of analyses, the US EPA realized that AERMET underestimates the friction velocity for low wind speeds in stable conditions and as such, adjustments for this u* value were tested. In the US EPA AERMET 12345 executable, a new beta option to adjust the surface friction velocity (u*) value for stable low wind speed conditions was introduced (ADJ_U*) based on Qian and Venkatram (2011). This AERMET release was followed by 13350 which included a modification for ADJ_U* to incorporate the Bulk Richardson Number approach based on Luhar and Rayner (2009), which again was followed by further modifications in the 14134 executable related to the use of the Bulk Richardson Number. This ADJ_U* beta option is currently considered non-default by the US EPA as it is still undergoing testing and as such, approval is required before using it in regulatory applications. 5.2. LOWWIND1 and LOWWIND2 Options in AERMOD At the same time that AERMET 12345 was released with the ADJ_U* beta option, the US EPA AERMOD executable 12345 also included two new beta options related to low wind speeds; LOWWIND1 and LOWWIND2 (US EPA 2012). When LOWWIND1 is selected, the minimum value of sigma-v (the standard deviation of the horizontal wind speed) is increased from 0.2 to 0.5 m/s and the horizontal meander algorithm is disabled. When LOWWIND2 is enabled, the minimum value of sigma-v is increased to 0.3 m/s and adjustments to the horizontal meander algorithm are applied such as an upper meander factor limit of 0.95. While the ADJ_U* beta option is based on a peer- reviewed study lead by Qian and Venkatram (2011), these LOWWIND options in AERMOD have not been peer reviewed. These options are also still currently being reviewed and tested so they are non- default options and specific approval from local regulatory agencies is required in order to use them in modelling scenarios for regulatory purposes. 6. Chemical Transformations Chemical transformations in Gaussian models, including AERMOD, are limited, however, they can use a decay coefficient or half-life for pollutants of interest (US EPA 2004). AERMOD in particular has certain algorithms built in for specific pollutants that AUSPLUME does not. As such, it is important to understand the purpose of these options when setting up a modelling scenario in AERMOD. Note that these options are currently not to be used if modelling for regulatory purposes in Victoria and in order to use these options, specific approval by EPA Vic must be received (EPA Victoria 2013). 6.1. Ambient Ratio Method Beginning with AERMOD version 13350, two Ambient Ratio Method (ARM) options, namely, the default ARM and the non-default/Beta ARM2, are incorporated into AERMOD (US EPA 2013). When using the default ARM, the model predicted NOx concentrations are multiplied by the empirically- derived NO2/NOx ratio, generally based on a ratio
  6. 6. CASANZ2015 Conference, Melbourne, 20-23 September 2015 6 derived from ambient monitoring data. An annual US national default ratio of 0.75 is recommended by US EPA (US EPA 2005). In response to the fact that estimated hourly concentrations using the current three-tier levels were predicted much higher than concentrations that were observed (Jing and Schewe 2014), the American Petroleum Institute (API) developed ARM2 based on an empirical polynomial equation for the calculation of the ambient ratio and derived it by fitting all 2001-2010 monitoring data. 6.2. Ozone Limiting Method The Ozone Limiting Method, or OLM, involves an initial comparison of the estimated maximum NOX concentration and the ambient ozone concentration to determine which is the limiting factor to NO2 formation (Jing and Schewe 2014). When this OLM option is activated in AERMOD, if the ozone concentration is greater than the NOX concentration, then total NOX to NO2 conversion is assumed. If the maximum NOX concentration is greater than the ozone concentration though, then the formation of NO2 is limited by the amount of ozone available in the ambient. A limitation of the OLM is that fresh ozone is assumed to be uniformly mixed across the cross section of the plume. The molar ratio of NOX to ozone mixed into the plume is not taken into account, now if the gradual entrainment and mixing of ambient ozone in the plume. 6.3. Plume Volume Molar Ratio Method Similar to the OLM, the Plume Volume Molar Ratio Method (PVMRM) is an option that may be activated in AERMOD for modelling the conversion of NOX to NO2. However, unlike the OLM, a key concept in the PVMRM is that the conversion of NOX to NO2 is determined by the ratio of the number of fresh ozone moles entrained into a plume to the number of NOX moles in the plume as it reaches a receptor (Jing and Schewe 2014). Basically, this method calculates the ratio of ozone moles to NOX moles in an effluent plume segment volume at downwind receptor locations, and then multiples this molar ratio by the NOX concentrations estimated by AERMOD in order to calculate the NO2 concentrations in the plume. Similar to the OLM, the PVMRM does not account for the gradual entrainment and mixing of ambient ozone in the plume which is a limitation of this method. 6.4. SO2 in an Urban Mode While the OLM and PVMRM options in AERMOD must be selected in order to be used in a model run and are non-default options, another option that relates to chemical transformation is default and occurs automatically when a specific pollutant and mode are selected. In AERMOD, if the pollutant being modelled is SO2 and the urban mode is selected, then by default a half-life of four hours will automatically be applied (US EPA 2004). While this automatically occurs by default in AERMOD, in order to use the urban mode, specific approval from a local regulatory agency like the US EPA and EPA Vic must be received. 7. Conclusion While AERMOD and AUSPLUME are both Gaussian models and include similar options and features, the way certain options and features are accounted for within the models differ. From differences in available source types modellers can choose from, to the way terrain is accounted for in each model, there are similarities and differences between these two models that are important to understand. The choice to use features and options that are included in AERMOD but are not accounted for in AUSPLUME will mainly be dependent upon local regulations so it is important to keep in mind that some of the AERMOD features and options discussed in this paper are not considered regulatory and require special approval to use in regulatory modelling analyses. By understanding how certain features are accounted for in AERMOD compared to AUSPLUME, as well as how the algorithms that are built into the models differ, modellers have a better understanding of model input and output and as such, are more prepared to make the transition to using AERMOD for regulatory dispersion modelling analyses. References Environment Protection Authority (EPA) Victoria 2013, Guidance Notes for using the Regulatory Air Pollution Model AERMOD in Victoria, EPA Victoria, 200 Victoria Street, Carlton. Jing, Q., Schewe, G. J. 2014, ‘Hourly NO to NO2 Conversion Methods in AERMOD’, Trinity Consultants, Inc. May 2014 e-News. Luhar, A. K., Rayner, K. N. 2009, ‘Methods to Estimate Surface Fluxes of Momentum and Heat from Routine Weather Observations for Dispersion Applications under Stable Stratification’, Boundary-Layer Meteorology, 132:437-454. Ministry for the Environment, 2004, Manatu Mo Te Taiao, New Zealand, 2004, Good Practice for Atmospheric Dispersion Modelling. Pitts, O. 2009, ‘Preliminary Evaluation of Ausplume for Low Wind Speed (LWS) Dispersion from Surface Sources’ CASANZ Workshop, Perth 6th September 2009.
  7. 7. CASANZ2015 Conference, Melbourne, 20-23 September 2015 7 Qian, W., Venkatram, A. 2011, ‘Performance of Steady-State Dispersion Models Under Low Wind- Speed Conditions’, Boundary-Layer Meteorology, 138: 3: 475-491. USUS Environmental Protection Agency, 2004, ‘AERMOD: Description of Model Formulation’. US Environmental Protection Agency, 2005, ‘Revision to the Guidance on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule’ 40 CFR Part 51. US Environmental Protection Agency, 2009, ‘AERMOD Implementation Guide’. US Environmental Protection Agency, 2011, ‘User’s Guide for the AMS/EPA Regulatory Model – AERMOD’. US Environmental Protection Agency, 2012, ‘Model Change Bulletin MCB#8 AERMOD (dated 12345)’. US Environmental Protection Agency, 2013, ‘Model Change Bulletin MCB#9 AERMOD (dated 13350)’. Weil, J.C. 1988b, ‘Plume Rise’, Lectures in Air Pollution Modeling, A. Venkatram and J.C. Wyngaard, Eds., American Meteorological Society, 119-162.

×