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.     

An evaluation of three empirical air-to-leaf models for polychlorinated dibenzo-p-dioxins and dibenzofurans

Three empirical air-to-leaf models for estimating grass concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans (abbreviated dioxins and furans) from air concentrations of these compounds are described and tested against two field data sets. All are empirical in that they are founded on simplistic bioconcentration and related approaches which rely on field data for their parameterization. One of the models, identified as the EPA Model, partitions the total air concentration into vapor and particle phases, and separately models the impact of both. A second model addresses only the vapor phase; grass concentrations are modeled as a function of vapor deposition. For the third model, it is assumed that the grass plants "scavenge" a fixed volume of air of dioxins, and hence grass concentrations are modeled as a simple product of total air concentration and a constant scavenging coefficient. Field data from two sites, a rural and an industrial site in the United Kingdom, included concurrent measurements of dioxins in air and field grass, and dioxin and furan depositions, for one 6-week sampling period. Principal findings include: (1) the EPA Model underpredicted grass concentrations at the rural field site by a factor of 2, while the Scavenging Model underpredicted grass concentrations by a factor of 3.8, and the Vapor Deposition Model significantly underpredicted grass concentrations (by a factor greater than 10), (2) the presence of high soil concentrations for some of the dioxins and furans at the industrial site appears to have caused higher grass concentrations and confounded the air-to-plant modeling exercise, (3) the Scavenging Model could be calibrated to the data set; however, a key premise of this model that vapor and particle phase dioxins equally impact the plants, is not supported by the field data, (4) measured depositions are highly correlated to but systematically lower than modeled depositions, which could be due to modeling assumptions or a systematic measurement bias.     

Characterizing perfluorooctanoate in ambient air near the fence line of a manufacturing facility: comparing modeled and monitored values

In order to improve our understanding of the nature, measurement and prediction of salts of perfluorooctanoic acid (PFOA) in air, two studies were performed along the fence line of a fluoropolymer manufacturing facility. First, a six-event, 24-hr monitoring series was performed around the fence line using the OSHA versatile sampler (OVS) system. Perfluorooctanoate concentrations were determined as perfluorooctanoic acid (PFOA) via liquid chromatography and mass spectrometry. Those data indicated that the majority of the PFOA was present as a particulate. No vapor-phase PFOA was detected above a detection limit of approximately 0.07 microg/m3. A follow-up study using a high-volume cascade impactor verified the range of concentrations observed in the OVS data. Both studies aligned with the major transport direction and range of concentrations predicted by an air dispersion model, demonstrating that model predictions agreed with monitoring results. Results from both monitoring methods and predictions from air dispersion modeling showed the primary direction of transport for PFOA was in the prevailing wind direction. The PFOA concentration measured at the site fence over the 10-week sampling period ranged from 0.12 to 0.9 microg/m3. Modeled predictions for the same time period ranged from 0.12 to 3.84 microg/m3. Less than 6% of the particles were larger than 4 microm in size, while almost 60% of the particles were below 0.3 microm. These studies are believed to be the first published ambient air data for PFOA in the environment surrounding a manufacturing facility.     

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.     

Transport of ammonium perfluorooctanoate in environmental media near a fluoropolymer manufacturing facility

In order to understand better the pathways for transport of ammonium perfluorooctanoate (APFO) from a point source, a focused investigation of environmental media (water and soil) near a fluoropolymer manufacturing facility (Site) was undertaken. Methods were developed and validated at 2 microg kg(-1) [the limit of quantitation (LOQ)] in soil, and at 50 ng l(-1) in water. Environmental media were sampled from a public water supply well field located north of the Site, across a river. The data suggest that APFO air emissions from the Site are transported to the well field, deposited onto the soil, and then migrate downward with precipitation into the underlying aquifer.     

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.     

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.     

Modeling studies of ammonia dispersion and dry deposition at some hog farms in North Carolina

A modeling study was conducted on dispersion and dry deposition of ammonia taking one hog farm as a unit. The ammonia emissions used in this study were measured under our OPEN (Odor, Pathogens, and Emissions of Nitrogen) project over a waste lagoon and from hog barns. Meteorological data were also collected at the farm site. The actual layout of barns and lagoons on the farms was used to simulate dry deposition downwind of the farm. Dry deposition velocity, dispersion, and dry deposition of ammonia were studied over different seasons and under different stability conditions using the short-range dispersion/air quality model, AERMOD. Dry deposition velocities were highest under near-neutral conditions and lowest under stable conditions. The highest deposition at short range occurred under nighttime stable conditions and the lowest occurred during daytime unstable conditions. Significant differences in deposition over crop and grass surfaces were observed under stable conditions.     

Soil Suspension/Dispersion Modeling Methods for Estimating Health-Based Soil Cleanup Levels of Hexavalent Chromium at Chromite Ore Processing Residue Sites

Abstract

The primary health concern associated with exposures to chromite ore processing residue (COPR)-affected soils is inhalation of hexavalent chromium [Cr(VI)] particulates. Site-specific soil alternative remediation standards (ARSs) are set using soil suspension and dispersion models to be protective of the theoretical excess cancer risk associated with inhalation of soil suspended by vehicle traffic and wind. The purpose of this study was to update a previous model comparison study that identified the 1995 AP-42 particulate emission model for vehicle traffic over un-paved roads and the Fugitive Dust Model (FDM) as the most appropriate model combination for estimating site-specific ARSs. Because the AP-42 model has been revised, we have updated our past evaluation. Specifically, the 2006 AP-42 particulate emissions model; the Industrial Source Complex–Short Term model, version 3 (ISCST3); and the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) air dispersion models were evaluated, and the results were compared with those from the previously used modeling approaches. Two sites with and two sites without vehicle traffic were evaluated to determine if wind erosion is a significant source of emissions. For the two sites with vehicle traffic, both FDM and ISCST3 produced total suspended particulate (TSP) estimates that were, on average, within a factor of 2 of measured; whereas AERMOD produced estimates that were as much as 5-fold higher than measured. In general, the estimated TSP concentrations for FDM were higher than those for ISCST3. For airborne Cr(VI), the ISCST3 model produced estimates that were only 2- to 8-fold of the measured concentrations, and both FDM and AERMOD estimated airborne Cr(VI) concentrations that were approximately 4- to 14-fold higher than measured. Results using the 1995 AP-42 model were closer to measured than those from the 2006 AP-42 model. Wind erosion was an insignificant contributor to particulate emissions at COPR sites.     

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.     

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO₂ concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models—CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)—are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations.

Implications: The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Validation of modeling approach to evaluate congener-specific concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in air and soil near a solid waste incinerator

A concise modeling approach using long-term averaged meteorological data was developed to estimate site-specific concentrations of congeners of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) near a solid waste incinerator. This approach consists of calculation of atmospheric dispersion, dry and wet deposition of gaseous and particle-bound congeners, and non-steady-state concentrations in soil. The predictability of this approach was evaluated by comparison of calculated concentrations of congeners in soil with those measured at eight locations near a municipal solid waste incinerator (MSWI). The variation of these concentrations due to variability of meteorological parameters is small. A considerable number of mean values show good agreement with measured concentrations within a factor of three. The reasonable agreement between calculated and measured concentrations indicates that algorithms for the calculation of vapor-phase deposition and non-steady-state concentrations in soil must be included in the modeling approach for an accurate estimation of the concentrations of congeners of PCDD/Fs emitted from MSWIs to the atmosphere. For a detailed estimation of site-specific concentrations, it is important to specify the bulk density of soil in the evaluated area, together with meteorological parameters.     

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.     

Air-vegetation transfer of PCDD/PCDFs: an assessment of field data and implications for modeling

This study was designed to evaluate soil and air (gas and particle) transfer of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) to vegetation in residential and industrial areas. In a first part, soil-vegetation transfer was assessed. The levels of PCDD/Fs in 120 soil and 120 herbage samples collected from 1996 to 2002 in an industrial area of Montcada (Barcelona, Spain), near a municipal solid waste incinerator (MSWI), were determined. Some additional individual samples were also evaluated. It was concluded that high soil concentrations, which are not at steady state with the air layer above it, show a tendency for PCDD/Fs to escape via volatilization. In a second part of the study, air-vegetation transfer was examined. PCDD/F concentrations from 24 herbage samples were used, while PCDD/F concentrations were also measured in seven high-volume air samples and seven passive air-vapor samples. Scavenging coefficients (m3 air "sampled"/g grass d.m.) ranged from 1.9 to 11.3 m3/g. A good trend with K(OA) was observed for PCDDs (R=0.82), while it was lower for PCDFs (R=0.55). The current results corroborate that PCDD/F concentrations in vegetation are associated with atmospheric deposition. For the highest substituted PCDD/F congeners, the air-particle uptake from plants is the principal pathway. In regions impacted by combustion emission sources, PCDD/F gas-particle partitioning is influenced by a higher concentration of particles in the air. Particles and associated particle-bound PCDD/Fs would sorb to leaf surfaces, and are subject to removal via wash off. However, in areas where emissions to air are not very notable, vapor absorption would be the principal source of vegetation pollution. The results of this investigation can have a potential interest in risk assessment studies and environmental fate models.     

Soil suspension/dispersion modeling methods for estimating health-based soil cleanup levels of hexavalent chromium at chromite ore processing residue sites

The primary health concern associated with exposures to chromite ore processing residue (COPR)-affected soils is inhalation of hexavalent chromium [Cr(VI)] particulates. Site-specific soil alternative remediation standards (ARSs) are set using soil suspension and dispersion models to be protective of the theoretical excess cancer risk associated with inhalation of soil suspended by vehicle traffic and wind. The purpose of this study was to update a previous model comparison study that identified the 1995 AP-42 particulate emission model for vehicle traffic over unpaved roads and the Fugitive Dust Model (FDM) as the most appropriate model combination for estimating site-specific ARSs. Because the AP-42 model has been revised, we have updated our past evaluation. Specifically, the 2006 AP-42 particulate emissions model; the Industrial Source Complex-Short Term model, version 3 (ISCST3); and the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) air dispersion models were evaluated, and the results were compared with those from the previously used modeling approaches. Two sites with and two sites without vehicle traffic were evaluated to determine if wind erosion is a significant source of emissions. For the two sites with vehicle traffic, both FDM and ISCST3 produced total suspended particulate (TSP) estimates that were, on average, within a factor of 2 of measured; whereas AERMOD produced estimates that were as much as 5-fold higher than measured. In general, the estimated TSP concentrations for FDM were higher than those for ISCST3. For airborne Cr(VI), the ISCST3 model produced estimates that were only 2- to 8-fold of the measured concentrations, and both FDM and AERMOD estimated airborne Cr(VI) concentrations that were approximately 4- to 14-fold higher than measured. Results using the 1995 AP-42 model were closer to measured than those from the 2006 AP-42 model. Wind erosion was an insignificant contributor to particulate emissions at COPR sites.     

Validation of modeling approach to evaluate congener-specific concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in air and soil near a solid waste incinerator.

A concise modeling approach using long-term averaged meteorological data was developed to estimate site-specific concentrations of congeners of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) near a solid waste incinerator. This approach consists of calculation of atmospheric dispersion, dry and wet deposition of gaseous and particle-bound congeners, and non-steady-state concentrations in soil. The predictability of this approach was evaluated by comparison of calculated concentrations of congeners in soil with those measured at eight locations near a municipal solid waste incinerator (MSWI). The variation of these concentrations due to variability of meteorological parameters is small. A considerable number of mean values show good agreement with measured concentrations within a factor of three. The reasonable agreement between calculated and measured concentrations indicates that algorithms for the calculation of vapor-phase deposition and non-steady-state concentrations in soil must be included in the modeling approach for an accurate estimation of the concentrations of congeners of PCDD/Fs emitted from MSWIs to the atmosphere. For a detailed estimation of site-specific concentrations, it is important to specify the bulk density of soil in the evaluated area, together with meteorological parameters.     

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.