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.     

Operational air pollution modelling in the UK—Street canyon applications and challenges

Local air quality management requires the use of screening and advanced modelling tools that are able to predict roadside pollution levels under a variety of meteorological and traffic conditions. So far, more than 200 air pollution hotspots have been identified by local authorities in the UK, many of them associated with NO₂ and/or PM₁₀ exceedences in heavily trafficked urban streets that may be classified as street canyons or canyon intersections. This is due to the increased traffic-related emissions and reduced natural ventilation in such streets. Specialised dispersion models and empirical adjustment factors have been commonly used to account for the entrapment of pollutants in street canyons. However, most of the available operational tools have been validated using experimental datasets from relatively deep canyons (H/W⩾1) from continental Europe. The particular characteristics of low-rise street canyons (H/W<1), which are a typical feature of urban/sub-urban areas in the UK, have been rarely taken into account.The main objective of this study is to review current practice and evaluate three widely used regulatory dispersion models, WinOSPM, ADMS-Urban 2.0 and AEOLIUS Full. The model evaluation relied on two comprehensive datasets, which included CO, PM₁₀ and NO_x measurements, traffic information and relevant meteorological data from two busy street canyons in Birmingham and London for a 1-year period. The performance of the selected models was tested for different times of the day/days of the week and varying wind conditions. Furthermore, the ability of the models to reproduce roadside NO₂/NO_x concentration ratios using simplified chemistry schemes was evaluated for one of the sites. Finally, advantages and limitations of the current regulatory street canyon modelling practice in the UK, as well as needs for future research, have been identified and discussed.     

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.     

Fuel reformulation in the Metropolitan Area of Mexico City

Two models frequently used to simulate the dispersion of pollutants in the atmosphere have been compared. This is necessary because only a well-tested and well-calibrated simulation model can be a good representation of the reality of the dispersion of pollutants. The models evaluated (HYSPLIT_4 with its four variants and MEDIA) were run using as input parameters the same meteorological dataset (for 23-26 October 1994) from the French model ARPEGE. The following statistical criteria were compared: the space and time evolution of the pollutant cloud; the variation of statistical parameters in time and space; and the differences between the simulated and measured values of concentration in time for six different stations. The results emphasise the characteristics of the two models and their abilities in the framework of the air quality monitoring.     

A simple model for the dispersion of pollutants from a road tunnel portal

The dispersion of pollutants from a roadway tunnel portal is mainly determined by the interaction between the ambient wind and the jet stream from the tunnel portal. In principal, Eulerian microscale models by solving the conservation equations for mass, momentum, and energy, are thus able to simulate effects such as buoyancy etc. properly. However, for engineering applications such models need too much CPU time, and are not easy to handle by non-scientific personnel. Only a few dispersion models, applicable for regulatory purposes, have so far appeared in the literature. These models are either empirical models not always applicable for different sites, or they do not capture important physical effects like buoyancy phenomena. Here, a rather simple model is presented, which takes into account most of the important processes considered to govern the dispersion of a jet stream from portals. These are the exit velocity, the buoyancy, the influence of ambient wind direction fluctuations on the position of the jet stream, and traffic induced turbulence. Although the model contains some heuristic elements, it was successfully tested against tracer experiments taken near a motorway tunnel portal in Austria. The model requires relatively little CPU time. Current limitations of the model include the neglect of terrain, building, and vehicle effects on the dispersion, and the neglect of the horizontal dispersion arising from entrainment of ambient air in the jet stream. The latter could lead to an underestimation of plume spreads for higher wind speeds. The validation of the model will be the focus of future research. The experimental data set is also available for the scientific community.     

A suggested method for dispersion model evaluation

Too often operational atmospheric dispersion models are evaluated in their ability to replicate short-term concentration maxima, when in fact a valid model evaluation procedure would evaluate a model's ability to replicate ensemble-average patterns in hourly concentration values. A valid model evaluation includes two basic tasks: In Step 1 we should analyze the observations to provide average patterns for comparison with modeled patterns, and in Step 2 we should account for the uncertainties inherent in Step 1 so we can tell whether differences seen in a comparison of performance of several models are statistically significant. Using comparisons of model simulation results from AERMOD and ISCST3 with tracer concentration values collected during the EPRI Kincaid experiment, a candidate model evaluation procedure is demonstrated that assesses whether a model has the correct total mass at the receptor level (crosswind integrated concentration values) and whether a model is correctly spreading the mass laterally (lateral dispersion), and assesses the uncertainty in characterizing the transport. The use of the BOOT software (preferably using the ASTM D 6589 resampling procedure) is suggested to provide an objective assessment of whether differences in model performance between models are significant.

Implications:

Regulatory agencies can choose to treat modeling results as “pseudo-monitors,” but air quality models actually only predict what they are constructed to predict, which certainly does not include the stochastic variations that result in observed short-term maxima (e.g., arc-maxima). Models predict the average concentration pattern of a collection of hours having very similar dispersive conditions. An easy-to-implement evaluation procedure is presented that challenges a model to properly estimate ensemble average concentration values, reveals where to look in a model to remove bias, and provides statistical tests to assess the significance of skill differences seen between competing models.     

Three long-range transport models compared to the ETEX experiment: a performance study

For operational or research purposes (dispersion computations of radioactive effluents during nuclear emergency situations, simulations of chemical pollution in the vicinity of thermal power plants), different models of passive dispersion in the atmosphere have been developed at the Environment Department of EDF’s R and D Division. This report presents the comparison of the performances of three such models: DIFTRA (lagrangian puff model, with operational goal), DIFEUL (three dimensional eulerian) and DIFPAR (Monte Carlo particle model) for the simulation of the first ETEX release, an international tracer campaign during which a passive tracer cloud has been followed over Europe. The results obtained in this study give model vs. experience differences of the same order as the model vs. experience differences observed during an international model comparison experiment using data of the Chernobyl release, the ATMES exercise. In addition to the standard statistical scores used in the evaluation of the performances of the transport models two asymmetric scores (in contradistinction with the Figure of Merit in Space) are proposed: “efficiency” and “power”. Their aim is to separate the two manners in which a model may be wrong: by predicting presence of pollutant while none is measured or conversely predicting absence when pollutant is actually detected.     

Derivation of exemption formulas for air quality regulatory applications

The regulatory agencies and the industries have the responsibility for assessing the environmental impact from the release of air pollutants, and for protecting environment and public health. The simple exemption formula is often used as a criterion for the purpose of screening air pollutants. That is, the exemption formula is used for air quality review and to determine whether a facility applying for and described in a new, modified, or revised air quality plan is exempted from further air quality review. The Bureau of Ocean Energy Management’s (BOEM) air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. If a facility is not exempt after completing the air quality review, a refined air quality modeling will be required to regulate the air pollutants. However, at present, the scientific basis for BOEM’s exemption formula is not available to the author. Therefore, the purpose of this paper is to provide the theoretical framework and justification for the use of BOEM’s exemption formula. In this paper, several exemption formulas have been derived from the Gaussian and non-Gaussian dispersion models; the Gaussian dispersion model is a special case of non-Gaussian dispersion model. The dispersion parameters obtained from the tracer experiments in the Gulf of Mexico are used in the dispersion models. In this paper, the dispersion parameters used in the dispersion models are also derived from the Monin-Obukhov similarity theory. In particular, it has been shown that the total amount of emissions from the facility for each air pollutant calculated using BOEM’s exemption formula is conservative.

Implications:?The operation of offshore oil and gas facilities under BOEM’s jurisdiction is required to comply with the BOEM’s regulations. BOEM’s air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. The exemption formulas have been used by BOEM and other regulatory agencies as a screening tool to regulate air emissions emitted from the oil and gas and other industries. Because of the BOEM’s regulatory responsibility, it is important to establish the scientific basis and provide the justification for the exemption formulas. The methodology developed here could also be adopted and used by other regulatory agencies.     

Determination of Setback Distances for Livestock Operations Using a New Livestock Odor Dispersion Model (LODM)

ABSTRACT

Setback distance has been used as an effective tool to avoid odor nuisance from livestock operations. Many setback distances were guidelines that were determined by empirical methods that are considered to be lack of science base. Air dispersion models have been used to determine setback distances; however, these models do not consider the short-time fluctuations of odor. A livestock odor dispersion model (LODM) was developed to consider the short-time variations of odor and predict occurrence frequency for certain levels of odor. In this study, this model was used to predict the occurrence frequency for various levels of odor in the vicinity (10 km) of a swine farm. Using selected odor criteria, setback distances between the swine farm and nearby communities were defined. Results indicate that the LODM can be used as an effective tool to determine setback distances.

IMPLICATIONS One of the more important applications of odor dispersion models is to determine setback distances for major odor sources, such as intensive livestock operations, from nearby communities. This study provided a case study in determining directional setback distances from a typical swine farm using a newly developed livestock odor dispersion model (LODM). It is also the first study in using hourly odor frequency to determine setback distances.     

Development of a black carbon-inclusive multi-media model: application for PAHs in Stockholm

A multi-media model was developed for predicting the fate of organic chemicals in the Greater Stockholm Area, Sweden, and applied to selected polycyclic aromatic hydrocarbons (PAHs). Although urban models have been previously developed, this model is novel in that it includes sorption to pyrogenically-derived particles, commonly termed "black carbon" (BC), within the model structure. To examine the influence of BC sorption on environmental fate of PAHs, two versions of the model were generated and run: one in which sorption to BC was included and one in which BC sorption was excluded. The inclusion of BC sorption did not cause any significant variations to air levels, but it did cause an average 20-30% increase in sediment concentrations related to increased sediment solids partitioning. The model also predicted reduced advective losses out of the model domain, as well as chemical potential to diffuse from sediments, whilst total chemical inventory increased. In all cases, the lighter PAHs were more affected by BC inclusion than their heavier counterparts. We advocate the addition of sorption to BC in future multi-media fate and exposure models, which as well as influencing fate will also alter (lower) chemical availability and, thus, wildlife exposure to hydrophobic chemicals. A quantification of the latter was derived with the help of the soot-inclusive model version, which estimated a lowering of dissolved water concentrations between five and >200 times for the different PAHs of this study.     

A trajectory plume model for simulating air pollution transients

An air quality simulation model that is simple, yet capable of accurately estimating concentrations under unsteady meteorological conditions, has been developed. This trajectory plume model uses the Gaussian plume equation, but has an applicability that is approximately as wide as the Lagrangian puff model. The plume axis is represented by a series of straight-line plume segments. The performance of this model was evaluated by comparing it with other diffusion models. A comparison between simulation results using the present model and those using integrated puff and Eulerian diffusion models for three different metropolitan areas (one in Japan and two in the U.S.) has indicated that a simple trajectory plume model performs as well as the two other more complex models in simulating pollutant dispersion under complicated meteorological conditions such as those which occur during the transition period from a sea breeze to a land breeze.     

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.     

Determination of setback distances for livestock operations using a new livestock odor dispersion model (LODM)

Setback distance has been used as an effective tool to avoid odor nuisance from livestock operations. Many setback distances were guidelines that were determined by empirical methods that are considered to be lack of science base. Air dispersion models have been used to determine setback distances; however, these models do not consider the short-time fluctuations of odor. A livestock odor dispersion model (LODM) was developed to consider the short-time variations of odor and predict occurrence frequency for certain levels of odor. In this study, this model was used to predict the occurrence frequency for various levels of odor in the vicinity (10 km) of a swine farm. Using selected odor criteria, setback distances between the swine farm and nearby communities were defined. Results indicate that the LODM can be used as an effective tool to determine setback distances.     

Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.     

The ozone source–receptor model – A tool for UK ozone policy

The Ozone Source–Receptor Model (OSRM) is a Lagrangian trajectory model developed to describe photochemical ozone production in the UK. The OSRM builds on existing boundary layer trajectory models used previously for assisting the development of UK ozone policy, but has a number of notable differences. A novel feature of the OSRM is a surface conversion module to represent the vertical gradient in ozone arising from chemical loss and deposition to the surface. This has significantly improved the performance of the model, especially in urban areas. In this paper, the modelling system is described and its performance against measured ozone concentrations and metrics and other UK ozone models is discussed. The model has been used to calculate future ozone concentrations in the UK and thus to assess a number of possible control measures developed for the UK Air Quality Strategy.     

Sulfur dioxide dispersion and source contribution to receptors of downtown Patras, Greece

Background The development of the city of Patras, including harbour relocation, in conjunction with the protection of the regional ecosystems, requires air quality assessment and management. For this reason, a model applicable in the Patras area is necessary and valuable. The goal of this study was to validate a model suitable for predicting the dispersion of sulfur dioxide (SO₂), based on particular activity, topography and weather conditions. Methods We used the US-EPA ISCLT3 integral dispersion model to predict SO₂ concentrations for Patras, Greece. We assumed that the major contribution to Patras air pollution came from central heating, harbour and traffic. We calculated traffic emissions using COPERTIII. Results and Discussion Assigning suitable values of the mixing height, the model predicted the local and spatial distribution of the mean monthly SO₂ concentrations in downtown Patras, as well computed the contribution of the SO₂ emissions originating from each particular source at each receptor location on a seasonal and annual basis. The comparison between predictions and measurements shows that the model performance for estimating the SO₂ concentrations and period pattern is satisfactory. Conclusion The mixing height was the critical parameter for calibrating the model. Model validation promises satisfactory predictions for SO₂ pollution levels on monthly basis. Recommendations and Outlook The model could be used in predicting SO₂ concentrations and source contribution for several downtown Patras receptors using pertinent meteorological and emission information. It could be also extended to predict the dispersion of other primary air pollutants. The calibrated model predictions could be used to fill gaps in monitoring data, saving money and time, and help in assess and manage air quality as Patras develops.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.