A Comparison of Model Performance between AERMOD and AUSTAL2000

ABSTRACT

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäβ der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

IMPLICATIONS This study contributes to the understanding of dispersion modeling and demonstrates the advantage of detailed meteorological data. It also helps air quality regulators and planners to identify the most appropriate model to use. It is indicated that AERMOD is more suitable for air quality planning and regulation, when all required meteorological information is available, because its predictions are mostly closer to field observations. Furthermore AUSTAL2000 predicted concentrations that showed a narrow range and triggered far less impacts from the source.     

A comparison of model performance between AERMOD and AUSTAL2000

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gem?beta der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.     

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.     

A complex terrain dispersion model for regulatory applications

This paper demonstrates the development of a model designed to estimate concentrations associated with a source situated in complex terrain. The model is designed to provide estimates of concentration distributions and is thus primarily suitable for regulatory applications. The model assumes that the concentration at a receptor is a combination of concentrations caused by two asymptotic states: the plume remains horizontal, and the plume climbs over the hill. The factor that weights the two states is a function of the fractional mass of the plume above the dividing streamline height. The model has been evaluated against data from four complex terrain sites. The evaluation shows that the model performs at least as well as CTDMPLUS (Perry, S.G., 1992. CTDMPLUS, a dispersion model for sources near complex topography. Part I: technical formations. Journal of Applied Meteorology 31, 633–645), a more comprehensive model designed for complex terrain applications.     

Evaluation of low wind modeling approaches for two tall-stack databases

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO₂ monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO₂ monitors within 10 km of the Dakota Gasification Company’s plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO₂ monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the overprediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions.

Implications: AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the overprediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.     

Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources

Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105–8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean dispersion are shown to produce results several orders of magnitude more efficiently with a loss of accuracy small compared to the absolute accuracy of advanced dispersion models near sources. The method can be readily incorporated into existing dispersion models, and may allow for additional computation time to be expended on modelling dispersion processes more accurately in future, rather than on accounting for source geometry.     

Validation of ADMS against wind tunnel data of dispersion from chemical warehouse fires

Comparisons are presented of the predictions of the atmospheric dispersion modelling system (ADMS) and wind tunnel data for plume dispersion from chemical warehouse fires. The focus of the comparisons is dispersion from structurally intact buildings with open roofs and dispersion of plumes flush with the ground without obstacles, however, dispersion from building shells and doors is also considered. Both buoyancy driven and momentum driven flows are treated, although emphasis is on buoyancy driven flows as these are generally more likely to occur in warehouse fires. The study shows that the ADMS building module is able to reproduce many of the features of dispersion observed in the wind tunnel. These include a recirculating region behind the building in which material may be trapped, a main wake which brings material down towards the surface, and appropriate sensitivity to the buoyancy and momentum of the emitted material, and the location of sources on the building roof. The comparisons suggest that the ADMS building model can be used to predict dispersion from the stages of fire development studied. The precise level of agreement depends (but not in a systematic way) on the buoyancy flux parameter F_B, the momentum flux parameter F_M and the number of roof lights. There are some significant differences between the wind tunnel boundary layer and the simulated atmospheric boundary layer in ADMS which have to be considered when making wind tunnel model comparisons. These relate mainly to the near surface where the wind tunnel underestimates turbulent velocities, the boundary layer height which in the wind tunnel corresponds to an atmospheric boundary layer depth of 82.5 m (atmospheric boundary layers are frequently an order of magnitude deeper), and the boundary layer top where the ADMS boundary layer is capped by an inversion and has low turbulence levels whereas the wind tunnel boundary layer has higher levels of turbulence and no capping inversion.     

Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune,India

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13–17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF–AERMOD modeling system.     

COMPLEX I and II Model Performance Evaluation in Nevada and New Mexico

The COMPLEX I and COMPLEX II Gaussian dispersion models for complex terrain applications have been made available by EPA. Various terrain treatment options under IOPT(25) can be selected for a particular application, one of which [IOPT(25) = 1] is an algorithm similar to that of the VALLEY model. A model performance evaluation exercise involving three of the available options with both COMPLEX models was carried out using SF⁶ tracer measurements taken during worst-case stable impaction conditions in complex terrain at the Harry Allen Plant site in southern Nevada. The models did not reproduce observed concentrations on an event by event basis, as correlation coefficients for 1-h concentrations of 0-0.3 were exhibited. When observed and calculated cumulative frequency distributions for 1-h and 3-h concentrations were compared, a close correspondence between observations and concentrations calculated with COMPLEX I, IOPT(25) = 2 or 3 was noted; both options consistently overestimated observed concentrations. With IOPT(25) = 1, upper percentile (maximum) values in the calculated frequency distribution exceeded the corresponding IOPT(25) = 2 or 3 value by roughly a factor of 2, and observed values by 2.5-5. COMPLEX II typically produced maximum values 2-4 times as great as COMPLEX I for the same terrain treatment option. From these results it is concluded that: 1) the physically unrealistic sector-spread approach used in VALLEY and COMPLEX I under stable impaction conditions is a surrogate for wind direction variation, and 2) the doubling of the plume centerline concentration due to ground reflection under terrain impingement conditions that is included in IOPT(25) = 1 is inappropriate.

These findings were found to be consistent with an analysis of noncurrent observed and calculated SO₂ χ/Q frequency distributions for 1, 3, and 24 hours near the Four Corners Plant in New Mexico. The comparison involved a four-year calculated χ/Q data set and a two-year observed χ/Q data set at the worst-case high terrain impact location near the plant.     

Pseudo-source parameters for flares: Derivation,implementation, and comparison

The United States Environmental Protection Agency (US EPA) flare pseudo-source parameters are over 30 years old and few dispersion modellers understand their basis and underlying assumptions. The calculation of plume rise from the user inputs of pseudo-stack diameter, temperature and velocity have the most influence on air dispersion model predictions of ground-level concentrations. Regulatory jurisdictions across Canada, the United States and around the world have adopted their own approach to pseudo-source parameters for flares; all relate buoyancy flux to the heat release rate, none consider momentum flux and flare tip downwash as adopted by the Alberta Energy Regulator (AER). This paper derives the plume buoyancy flux for flares burning a gas in terms of combustion variables readily known or calculated without simplifying assumptions. Dispersion model prediction sensitivity to flared gas composition, temperature and velocity, and ambient conditions are now correctly handled by the AER approach. The AER flare pseudo-source parameters are based on both the buoyancy and momentum flux, thus conserving energy and momentum. The AER approach to calculate the effective source height for flares during varying wind speeds is compared to the US EPA approach. Instead of a constant source for all meteorological conditions, multiple co-located sources with varying effective stack height and diameter are used. AERMOD is run with the no stack tip downwash option as flare stack tip downwash is accounted for in the effective stack height rather than the AERMOD model calculating the downwash incorrectly using the pseudo-source parameters. The modelling approaches are compared for an example flare. Maximum ground level predictions change, generally increasing near the source and decreasing further away, with the AER flare pseudo-source parameters. It's time to update how we model flares.

Implications: What are the implications of continuing to model flare source parameters using the overly simplified US EPA approach? First, the regulators perpetuate the myths that the flare source height, temperature, diameter and velocity are constant for all wind speeds and ambient temperatures. Second, that it is acceptable to make simplifying assumptions that violate the conservation of momentum and energy principles for the sake of convenience. Finally, regulatory decisions based on simplified source modelling result in predictions that are not conservative (or realistic). The AER regulatory approach for flare source parameters overcomes all of these shortcomings. AERflare is a publicly available spreadsheet that provides the “correct” inputs to AERMOD.     

Gas diffusion over an isolated hill under neutral,stable and unstable conditions

Wind tunnel experiments of gas diffusion were performed over flat terrain and over an isolated three-dimensional hill under neutral, stable and unstable (sea breeze) conditions. Conditions of airflow in the wind tunnel were determined so as to satisfy the similarity rule for the bulk Richardson number, by controlling temperature profiles and wind velocity of the thermally stratified wind tunnel. Typical characteristics were observed under each condition of atmospheric stability; reversed vortex behind the hill in neutral condition, downward slope wind in stable one and convective motion in unstable one.We compared these experiments with the results of a Direct Numerical Simulation (DNS) model for the wind velocity over the hill under neutral conditions. The numerical results showed good agreement with the experimental results.     

Urban air pollution modelling and measurements of boundary layer height

An urban field trial has been undertaken with the aim of assessing the performance of the boundary layer height (BLH) determination of two models: the Met Office Unified Model (UM) and a Gaussian-type plume model, ADMS. Pulsed Doppler lidar data were used to measure mixing layer height and cloud base heights for a variety of meteorological conditions over a 3 week period in July 2003. In this work, the daily growth and decay of the BLH from the lidar data and model simulations for 5 days are compared. The results show that although the UM can do a good job of reproducing the boundary layer growth, there are occasions where the BLH is overestimated by 30–100%. Within dispersion models it is the BLH that effectively limits the height to which pollution disperses, so these results have very important implications for pollution dispersion modelling. The results show that correct development of the boundary layer in the UM is critically dependant on morning cloud cover. The ADMS model is used routinely by local authorities in the UK for local air-quality forecasting. The ADMS model was run under three settings; an ‘urban’ roughness, a ‘rural’ roughness and a ‘transition’ roughness. In all cases, the ‘urban’ setting over estimated the BLH and is clearly a poor predictor of urban BLH. The ‘transition’ setting, which distinguishes between the meteorological data input site and the dispersion modelling site, gave the best results under the well mixed conditions of the trial.     

Dispersion of Traffic and Space Heating Emissions In Urban Settings

A long-term dispersion model is presented for traffic and space heating emissions in urban areas, allowing fast assessment of the spatial-averaged and center-maximum pollutant concentrations.

The assumption of study areas with circular shape and normal emissions density profiles is made for the purpose of streamlining model inputs with the inventory data normally available. In addition, the rather typical assumptions of Gaussian dispersion, narrow plume, flat or gently rolling terrain, homogeneous wind field and nonreactive pollutants are made. Values of σz from Briggs correlation are used with an initial value of 30 to account for building effects.

Meterological data inputs are reduced to six parameters, inventory data inputs to two, while computations are simplified to a degree that use of a digital computer is not required.

The model is well suited to yield separate assessments for individual types of sources and control measures, as well as to reveal sensitivities from parameters such as city size, or emission density levels and distribution patterns. Its predictions are virtually identical to those of the CDM-2 UNAPMAR model for study areas with circular shape and normal emissions density profiles, and as results do not appear overly sensitive to shape and distribution patterns, the model is believed to be valid for most urban areas.     

An Atmospheric Dispersion Model for the Environmental Impact Assessment of Thermal Power Plants in Japan—A Method for Evaluating Topographical Effects

Abstract

An atmospheric dispersion model was developed for the environmental impact assessment of thermal power plants in Japan, and a method for evaluating topographical effects using this model was proposed. The atmospheric dispersion model consists of an airflow model with a turbulence closure model based on the algebraic Reynolds stress model and a Lagrangian particle dispersion model (LPDM). The evaluation of the maximum concentration of air pollutants such as SO₂, NO_x, and suspended particulate matter is usually considered of primary importance for environmental impact assessment. Three indices were therefore estimated by the atmospheric dispersion model: the ratios (α and β, respectively) of the maximum concentration and the distance of the point of the maximum concentration from the source over topography to the respective values over a flat plane, and the relative concentration distribution [γ(x)] along the ground surface projection of the plume axis normalized by the maximum concentration over a flat plane. The atmospheric dispersion model was applied to the topography around a power plant with a maximum elevation of more than 1000 m. The values of α and β evaluated by the atmospheric dispersion model varied between 1 and 3 and between 1 and 0.4, respectively, depending on the topographical features. These results and the calculated distributions of γ(x) were highly similar to the results of the wind tunnel experiment. Therefore, when the slope of a hill or mountain is similar to the topography considered in this study, it is possible to evaluate topographical effects on exhaust gas dispersion with reasonable accuracy using the atmospheric dispersion model as well as wind tunnel experiments.     

Computational Fluid Dynamics (CFD) Simulations on the Effect of Rough Surface on Atmospheric Turbulence Flow Above Hilly Terrain Shapes

The behavioral distribution of the atmospheric turbulence flow over the terrain with changes in a rough surface has become one of the most important topics of air pollution research, among such other topics as transportation and dispersion pollutants. In this study, a computational model on atmospheric turbulence flow over a terrain hill shaped with rough surface was investigated under neutral atmospheric conditions. The flow was assumed to be 2D and modeled using computational fluid dynamics (CFD) models, which were numerically solved using Reynolds-averaged Navier-Stokes equations. Rough surface conditions were modeled using a number of windbreak fences regularly spaced on the hill. The mean velocity and turbulent structures such as turbulence intensity and turbulent kinetic energy were investigated in the upwind and downwind regions over the hill, and the numerical models were validated against the wind-tunnel results to optimize the turbulence model. The computational results agreed well with the results obtained from the wind tunnel experiments. The computational results indicate that the mean velocity was observed to increase dramatically around the crest of the upwind slope of the hill. A thick internal boundary layer was observed with a fence on the crest and downwind region of the hill. The reversed flow and recirculation zone were formed in the wake region behind the hill. It was thus determined that turbulent kinetic energy decreases as the mean velocity increases.     

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.     

Improving Plume Rise Prediction Accuracy for Stable Atmospheres with Complex Vertical Structure

Accurately predicting the rise of a buoyant exhaust plume is difficult when there are large vertical variations in atmospheric stability or wind velocity. Such conditions are particularly common near shoreline power plants. Simple plume rise formulas, which employ only a mean temperature gradient and a mean wind speed, cannot be expected to adequately treat an atmosphere whose lapse rate and wind velocity vary markedly with height. This paper tests the accuracy of a plume rise model which is capable of treating complex atmospheric structure because it integrates along the plume trajectory. The model consists of a set of ordinary differential equations, derived from the fluid equations of motion, with an entralnment parameterization to specify the mixing of ambient air into the plume. Comparing model predictions of final plume rise to field observations yields a root mean square difference of 24 m, which is 9 % of the average plume rise of 267 m. These predictions are more accurate than predictions given by three simpler models which utilize variants of a standard plume rise formula, the most accurate of the simpler models having a 12% error.     

An atmospheric dispersion model for the environmental impact assessment of thermal power plants in Japan--a method for evaluating topographical effects

An atmospheric dispersion model was developed for the environmental impact assessment of thermal power plants in Japan, and a method for evaluating topographical effects using this model was proposed. The atmospheric dispersion model consists of an airflow model with a turbulence closure model based on the algebraic Reynolds stress model and a Lagrangian particle dispersion model (LPDM). The evaluation of the maximum concentration of air pollutants such as SO2, NOx, and suspended particulate matter is usually considered of primary importance for environmental impact assessment. Three indices were therefore estimated by the atmospheric dispersion model: the ratios (alpha and beta, respectively) of the maximum concentration and the distance of the point of the maximum concentration from the source over topography to the respective values over a flat plane, and the relative concentration distribution [gamma(x)] along the ground surface projection of the plume axis normalized by the maximum concentration over a flat plane. The atmospheric dispersion model was applied to the topography around a power plant with a maximum elevation of more than 1,000 m. The values of alpha and beta evaluated by the atmospheric dispersion model varied between 1 and 3 and between 1 and 0.4, respectively, depending on the topographical features. These results and the calculated distributions of y(x) were highly similar to the results of the wind tunnel experiment. Therefore, when the slope of a hill or mountain is similar to the topography considered in this study, it is possible to evaluate topographical effects on exhaust gas dispersion with reasonable accuracy using the atmospheric dispersion model as well as wind tunnel experiments.