AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

An evaluation of the steady-state dispersion model AERMOD was conducted to determine its accuracy at predicting hourly ground-level concentrations of sulfur dioxide (SO₂) by comparing model-predicted concentrations to a full year of monitored SO₂ data. The two study sites are comprised of three coal-fired electrical generating units (EGUs) located in southwest Indiana. The sites are characterized by tall, buoyant stacks, flat terrain, multiple SO₂ monitors, and relatively isolated locations. AERMOD v12060 and AERMOD v12345 with BETA options were evaluated at each study site. For the six monitor–receptor pairs evaluated, AERMOD showed generally good agreement with monitor values for the hourly 99th percentile SO₂ design value, with design value ratios that ranged from 0.92 to 1.99. AERMOD was within acceptable performance limits for the Robust Highest Concentration (RHC) statistic (RHC ratios ranged from 0.54 to 1.71) at all six monitors. Analysis of the top 5% of hourly concentrations at the six monitor–receptor sites, paired in time and space, indicated poor model performance in the upper concentration range. The amount of hourly model predicted data that was within a factor of 2 of observations at these higher concentrations ranged from 14 to 43% over the six sites. Analysis of subsets of data showed consistent overprediction during low wind speed and unstable meteorological conditions, and underprediction during stable, low wind conditions. Hourly paired comparisons represent a stringent measure of model performance; however, given the potential for application of hourly model predictions to the SO₂ NAAQS design value, this may be appropriate. At these two sites, AERMOD v12345 BETA options do not improve model performance.

Implications:

A regulatory evaluation of AERMOD utilizing quantile-quantile (Q–Q) plots, the RHC statistic, and 99th percentile design value concentrations indicates that model performance is acceptable according to widely accepted regulatory performance limits. However, a scientific evaluation examining hourly paired monitor and model values at concentrations of interest indicates overprediction and underprediction bias that is outside of acceptable model performance measures. Overprediction of 1-hr SO₂ concentrations by AERMOD presents major ramifications for state and local permitting authorities when establishing emission limits.     

Minimum turbulence assumptions and u* and L estimation for dispersion models during low-wind stable conditions

The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u _* , during low-wind stable conditions, thus increasing the calculated σ _v and σ _w and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant σ _v and σ _w over time periods of 15 to 60 min, while standard Monin–Obukhov Similarity Theory (MOST) predicts that σ _v and σ _w will approach zero. This paper focuses on the u _* estimation methods and the minimum turbulence (σ _v and σ _w ) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u _* and increases in the minimum σ _v parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM.

Implications:

Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u_*, L, and the minimum turbulence parameters (σ_v and σ_w) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.     

The Effectiveness of a Tall Stack for Reduction of Ambient Sulfur Dioxide Concentrations:A Field Investigation

Of many available methods for limiting ground level pollutant concentrations, tall stacks are many times the simplest, most effective, and least costly. Although this is theoretically explicit, field validation of the soundness of this approach is often hampered by lack of comparable "before" and "after" data. In this study at the Alma Power Plant, appropriate air quality and meteorological measurements were made for several years before and after conversion from short to tall stacks. Comparison of these data show that the tall stack has reduced ambient levels of SO₂ by from 50 to 95 % in the vicinity of the plant. This study also found that use of a Turner-Briggs dispersion model in a valley situation gave fairly accurate and reliable estimates of air quality. The model was useful in designing the tall stack, assessing its impact and locating air quality monitors.     

Experimental Characterization of Atmospheric Diffusion in Complex Terrain with Land-Sea Interactions

The body of information presented in this paper is directed to scientists working in atmospheric dispersion research and model development. Two years of field measurements in the coastal area of Bilbao in northern Spain show that the diffusion behavior in this complex terrain can be classified into several well defined patterns, which correspond to certain meteorological conditions. The approach taken has been the systematic use of SO₂ remote sensors (COSPEC) and ground level monitors in moving platforms which are used to follow and document the flow of the air mass. Results to date show that complex reentry cycles can occur and that synoptically different flows may be indistinguishable by wind sensors at ground level (affected by channeling), and yet result in totally different observed pollution levels by a fixed monitoring network (affected by topographical effects). These results are being used to parameterize the cause-effect relationships and guide the modeling efforts in this area of complex terrain.     

AERCOARE: An overwater meteorological preprocessor for AERMOD

AERCOARE is a meteorological data preprocessor for the American Meteorological Society and U.S Environmental Protection Agency (EPA) Regulatory Model (AERMOD). AERCOARE includes algorithms developed during the Coupled-Ocean Atmosphere Response Experiment (COARE) to predict surface energy fluxes and stability from routine overwater measurements. The COARE algorithm is described and the implementation in AERCOARE is presented. Model performance for the combined AERCOARE-AERMOD modeling approach was evaluated against tracer measurements from four overwater field studies. Relatively better model performance was found when lateral turbulence measurements were available and when several key input variables to AERMOD were constrained. Namely, requiring the mixed layer height to be greater than 25 m and not allowing the Monin Obukhov length to be less than 5 m improved model performance in low wind speed stable conditions. Several options for low wind speed dispersion in AERMOD also affected the model performance results. Model performance for the combined AERCOARE-AERMOD modeling approach was found to be comparable to the current EPA regulatory Offshore Coastal Model (OCD) for the same tracer studies. AERCOARE-AERMOD predictions were also compared to simulations using the California Puff-Advection Model (CALPUFF) that also includes the COARE algorithm. Many model performance measures were found to be similar, but CALPUFF had significantly less scatter and better performance for one of the four field studies. For many offshore regulatory applications, the combined AERCOARE-AERMOD modeling approach was found to be a viable alternative to OCD the currently recommended model.

Implications: A new meteorological preprocessor called AERCOARE was developed for offshore source dispersion modeling using the U.S. Environmental Protection Agency (EPA) regulatory model AERMOD. The combined AERCOARE-AERMOD modeling approach allows stakeholders to use the same dispersion model for both offshore and onshore applications. This approach could replace current regulatory practices involving two completely different modeling systems. As improvements and features are added to the dispersion model component, AERMOD, such techniques can now also be applied to offshore air quality permitting.     

CALPUFF and AERMOD model validation study in the near field: Martins Creek revisited

This paper describes a near-field validation study involving the steady-state, U.S. Environmental Protection Agency (EPA) guideline model AERMOD and the nonsteady-state puff model CALPUFF. Relative model performance is compared with field measurements collected near Martins Creek, PA-a rural, hilly area along the Pennsylvania-New Jersey border. The principal emission sources in the study were two coal-fired power plants with tall stacks and buoyant plumes. Over 1 yr of sulfur dioxide measurements were collected at eight monitors located at or above the two power plants' stack tops. Concurrent meteorological data were available at two sites. Both sites collected data 10 m above the ground. One of the sites also collected sonic detection and ranging measurements up to 420 m above ground. The ability of the two models to predict monitored sulfur dioxide concentrations was assessed in a four-part model validation. Each part of the validation applied different criteria and statistics to provide a comprehensive evaluation of model performance. Because of their importance in regulatory applications, an emphasis was placed on statistics that demonstrate the model's ability to reproduce the upper end of the concentration distribution. On the basis of the combined results of the four-part validation (i.e., weight of evidence), the performance of CALPUFF was judged to be superior to that of AERMOD.     

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.     

Combined analysis of modeled and monitored SO2 concentrations at a complex smelting facility

Vale Canada Limited owns and operates a large nickel smelting facility located in Sudbury, Ontario. This is a complex facility with many sources of SO₂ emissions, including a mix of source types ranging from passive building roof vents to North America's tallest stack. In addition, as this facility performs batch operations, there is significant variability in the emission rates depending on the operations that are occurring. Although SO₂ emission rates for many of the sources have been measured by source testing, the reliability of these emission rates has not been tested from a dispersion modeling perspective. This facility is a significant source of SO₂ in the local region, making it critical that when modeling the emissions from this facility for regulatory or other purposes, that the resulting concentrations are representative of what would actually be measured or otherwise observed. To assess the accuracy of the modeling, a detailed analysis of modeled and monitored data for SO₂ at the facility was performed. A mobile SO₂ monitor sampled at five locations downwind of different source groups for different wind directions resulting in a total of 168 hr of valid data that could be used for the modeled to monitored results comparison. The facility was modeled in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model) using site-specific meteorological data such that the modeled periods coincided with the same times as the monitored events. In addition, great effort was invested into estimating the actual SO₂ emission rates that would likely be occurring during each of the monitoring events. SO₂ concentrations were modeled for receptors around each monitoring location so that the modeled data could be directly compared with the monitored data. The modeled and monitored concentrations were compared and showed that there were no systematic biases in the modeled concentrations.

Implications:

This paper is a case study of a Combined Analysis of Modelled and Monitored Data (CAMM), which is an approach promulgated within air quality regulations in the Province of Ontario, Canada. Although combining dispersion models and monitoring data to estimate or refine estimates of source emission rates is not a new technique, this study shows how, with a high degree of rigor in the design of the monitoring and filtering of the data, it can be applied to a large industrial facility, with a variety of emission sources. The comparison of modeled and monitored SO₂ concentrations in this case study also provides an illustration of the AERMOD model performance for a large industrial complex with many sources, at short time scales in comparison with monitored data. Overall, this analysis demonstrated that the AERMOD model performed well.     

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.     

Improving ozone modeling in complex terrain at a fine grid resolution: Part I – examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model

Meteorological variables such as temperature, wind speed, wind directions, and Planetary Boundary Layer (PBL) heights have critical implications for air quality simulations. Sensitivity simulations with five different PBL schemes associated with three different Land Surface Models (LSMs) were conducted to examine the impact of meteorological variables on the predicted ozone concentrations using the Community Multiscale Air Quality (CMAQ) version 4.5 with local perspective. Additionally, the nudging analysis for winds was adopted with three different coefficients to improve the wind fields in the complex terrain at 4-km grid resolution. The simulations focus on complex terrain having valley and mountain areas at 4-km grid resolution. The ETA M–Y (Mellor–Yamada) and G–S (Gayno–Seaman) PBL schemes are identified as favorite options and promote O₃ formation causing the higher temperature, slower winds, and lower mixing height among sensitivity simulations in the area of study. It is found that PX (Pleim–Xiu) simulation does not always give optimal meteorological model performance. We also note that the PBL scheme plays a more important role in predicting daily maximum 8-h O₃ than land surface models. The results of nudging analysis for winds with three different increased coefficients' values (2.5, 4.5, and 6.0 × 10^?4 s^?1) over seven sensitivity simulations show that the meteorological model performance was enhanced due to improved wind fields, indicating the FDDA nudging analysis can improve model performance considerably at 4-km grid resolution. Specifically, the sensitivity simulations with the coefficient value (6.0 × 10^?4) yielded more substantial improvements than with the other values (2.5 and 4.5 × 10^?4). Hence, choosing the nudging coefficient of 6.0 × 10^?4 s^?1 for winds in MM5 may be the best choice to improve wind fields as an input, as well as, better model performance of CMAQ in the complex terrain area. As a result, a finer grid resolution is necessary to evaluate and access of CMAQ results for giving a detailed representation of meteorological and chemical processes in the regulatory modeling. A recommendation of optimal scheme options for simulating meteorological variables in the complex terrain area is made.     

Comparison of the complex terrain algorithms incorporated into two commonly used local-scale air pollution dispersion models (ADMS and AERMOD) using a hybrid model

ADMS and AERMOD are the two most widely used dispersion models for regulatory purposes. It is, therefore, important to understand the differences in the predictions of the models and the causes of these differences. The treatment by the models of flat terrain has been discussed previously; in this paper the focus is on their treatment of complex terrain. The paper includes a discussion of the impacts of complex terrain on airflow and dispersion and how these are treated in ADMS and AERMOD, followed by calculations for two distinct cases: (i) sources above a deep valley within a relatively flat plateau area (Clifty Creek power station, USA); (ii) sources in a valley in hilly terrain where the terrain rises well above the stack tops (Ribblesdale cement works, England). In both cases the model predictions are markedly different. At Clifty Creek, ADMS suggests that the terrain markedly increases maximum surface concentrations, whereas the AERMOD complex terrain module has little impact. At Ribblesdale, AERMOD predicts very large increases (a factor of 18) in the maximum hourly average surface concentrations due to plume impaction onto the neighboring hill; although plume impaction is predicted by ADMS, the increases in concentration are much less marked as the airflow model in ADMS predicts some lateral deviation of the streamlines around the hill.     

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.     

Dependence of the Wind Profile Power Law on Stability for Various Locations

Recent environmental regulations have increased the need for construction of meteorological towers at power generation facilities. Due to practical and economic considerations, tower heights are usually lower than effluent release heights. At heights where wind speed data are not available, the wind speed is usually estimated from the measured wind speed using the %th wind profile power law and assuming neutral stability conditions. This study examines published data for many locations and shows that the %th wind profile power law is often unrepresentative of actual conditions because the degree of variation of wind speed with height depends greatly on atmospheric stability. The frequency of neutral stability conditions also varies appreciably by site. These two considerations are especially important in dispersion models which extrapolate wind speed at stack height from low level wind speed data.     

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.     

CALPUFF and AERMOD Model Validation Study in the Near Field: Martins Creek Revisited

ABSTRACT

This paper describes a near-field validation study involving the steady-state, U.S. Environmental Protection Agency (EPA) guideline model AERMOD and the nonsteady-state puff model CALPUFF. Relative model performance is compared with field measurements collected near Martins Creek, PA—a rural, hilly area along the Pennsylvania-New Jersey border. The principal emission sources in the study were two coal-fired power plants with tall stacks and buoyant plumes. Over 1 yr of sulfur dioxide measurements were collected at eight monitors located at or above the two power plants' stack tops. Concurrent meteorological data were available at two sites. Both sites collected data 10 m above the ground. One of the sites also collected sonic detection and ranging measurements up to 420 m above ground. The ability of the two models to predict monitored sulfur dioxide concentrations was assessed in a four-part model validation. Each part of the validation applied different criteria and statistics to provide a comprehensive evaluation of model performance. Because of their importance in regulatory applications, an emphasis was placed on statistics that demonstrate the model's ability to reproduce the upper end of the concentration distribution. On the basis of the combined results of the four-part validation (i.e., weight of evidence), the performance of CALPUFF was judged to be superior to that of AERMOD.

IMPLICATIONS Use of the nonsteady-state CALPUFF model in the near field (<50 km) for regulatory applications has been limited because of the lack of appropriate model validation studies. Considered an alternative model by EPA, use of CALPUFF for regulatory purposes in the near field must be supported by a relevant performance evaluation using measured air quality data. This validation study should help address the lack of information on the performance of CALPUFF in near-field applications. The potential problem with the use of the robust high concentration as a metric in model validations is also examined.     

Collocated comparisons of continuous and filter-based PM2.5 measurements at Fort McMurray,Alberta, Canada

Collocated comparisons for three PM_2.5 monitors were conducted from June 2011 to May 2013 at an air monitoring station in the residential area of Fort McMurray, Alberta, Canada, a city located in the Athabasca Oil Sands Region. Extremely cold winters (down to approximately ?40°C) coupled with low PM_2.5 concentrations present a challenge for continuous measurements. Both the tapered element oscillating microbalance (TEOM), operated at 40°C (i.e., TEOM₄₀), and Synchronized Hybrid Ambient Real-time Particulate (SHARP, a Federal Equivalent Method [FEM]), were compared with a Partisol PM_2.5 U.S. Federal Reference Method (FRM) sampler. While hourly TEOM₄₀ PM_2.5 were consistently ~20–50% lower than that of SHARP, no statistically significant differences were found between the 24-hr averages for FRM and SHARP. Orthogonal regression (OR) equations derived from FRM and TEOM₄₀ were used to adjust the TEOM₄₀ (i.e., TEOM_adj) and improve its agreement with FRM, particularly for the cold season. The 12-year-long hourly TEOM_adj measurements from 1999 to 2011 based on the OR equations between SHARP and TEOM₄₀ were derived from the 2-year (2011–2013) collocated measurements. The trend analysis combining both TEOM_adj and SHARP measurements showed a statistically significant decrease in PM_2.5 concentrations with a seasonal slope of ?0.15 μg m^?3 yr^?1 from 1999 to 2014.Implications: Consistency in PM_2.5 measurements are needed for trend analysis. Collocated comparison among the three PM_2.5 monitors demonstrated the difference between FRM and TEOM, as well as between SHARP and TEOM. The orthogonal regressions equations can be applied to correct historical TEOM data to examine long-term trends within the network.     

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.     

High-resolution simulation of volcanic sulfur dioxide dispersion over the Miyake Island

After severe eruptions of the volcano at Miyake Island in August 2000, a large amount of volcanic gas was released into the atmosphere. To simulate flows and dispersion of sulfur dioxide (SO₂) over Miyake Island, a set of numerical models was developed. The multi-nesting method was adopted to reflect a realistic meteorological field and to sufficiently resolve the flow over the island with a diameter of 8 km. The outermost model was the Regional Spectral Model (RSM) of the Japan Meteorological Agency (JMA) with a horizontal grid size of 10 km. Finer atmospheric structure was simulated with the nonhydrostatic model jointly developed by the Meteorological Research Institute and the Numerical Prediction Division of JMA (MRI/NPD-NHM) with grid intervals of 2 km, 400 m and 100 m. Realistic topography of the island was represented in the innermost model. The Lagrangian particle method was applied to the dispersion model, which is driven by the meteorological field of the 100 m grid MRI/NPD-NHM. The random walk procedure was used to represent the turbulent diffusion. The model was verified in four cases. Simulated SO₂ concentrations agreed well with observed concentrations at a monitoring station including temporal variation. Under a large synoptic change, however, accurate prediction became difficult. Further numerical experiments have been done to investigate characteristics of the flow and the distribution of SO₂. Steady inflows, classified according to the surface wind speed and direction, were assumed. Simulated SO₂ distribution on the ground apparently depends on the surface wind. Under relatively weak inflow, there is a large diurnal change in SO₂ distribution, affected by the thermally induced flow. SO₂ gas is widely spread downstream in the nighttime but hardly reaches the coastal area in the daytime. On the other hand, SO₂ gas steadily reached the downstream coast with little diurnal variation under the stronger inflow. Ground temperature, as well as the static stability of the inflow, also influences downstream wind, turbulent diffusivity and SO₂ distribution.     

The Limitations of Air Quality Standards

This paper describes a diffusion model designed to permit calculation of seasonal average concentrations of an air pollutant, in particular, sulfur dioxide. The calculations can encompass multiple sources and multiple receptors. For each receptor location the model sums the effect of all sources over a wide range of meteorological conditions. Input data include source pollutant emissions, source configuration and location, receptor location, and meteorological data expressed as a joint frequency distribution of wind direction, wind speed, stability. To determine the model’s accuracy, concentration estimates for St. Louis, Mo., are compared with measured SO₂ concentrations. The overall correlation with observed data is satisfactory. A computer program to handle the numerous calculations was written in Fortran IV language for use on an IBM 1130 computer.