Atmospheric transport and dispersion modeling systems are often used in assessing human exposures to chemical hazards. Models validated through quantitative and qualitative evaluation can be applied to epidemiologic study. Here, we modeled the 2005 Graniteville, South Carolina, USA railcar release of chlorine using dense gas plume dispersion models including the Hazard Prediction and Assessment Capability (HPAC) and Areal Locations of Hazardous Atmospheres (ALOHA). The release volume (54,915 kg) and rate was estimated by an engineering analysis combining semi-quantitative observations and fundamental physical principles. The use of regional meteorological conditions was validated by statistically (correlation, mean bias, root mean square deviation) comparing 1,024 HPAC concentration and surface dosage point estimates generated by two source-location weather data sets. An improved HPAC model was then statistically (correlation, root mean square deviation) compared to the earlier HPAC model using up to 9,446 surface dosage sampling points paired in time and space. The older HPAC model consistently overpredicted compared to the newer, refined model. When compared to HPAC, the ALOHA model significantly overpredicted downwind, centerline concentrations (up to 55 times that of HPAC). The refined HPAC model was then evaluated against post-incident environmental indicators of exposure such as phytotoxicity, corrosion events, deposition benchmarks, casualty data and exposed animal health outcome. A further sub-analysis was performed by comparing observed dog health outcome-derived exposure estimations versus model-predicted exposure. This statistical sub-analysis showed good agreement between observed and estimated, particularly when a sub-cohort of indoor dogs was excluded to determine the impact of structural shielding. Although the model was favorably evaluated based on literature-established standards, further assessment should be performed before the model can be fully validated and applied in human epidemiologic study to estimate acute exposures. Language: English / 1 / Dev D. Jani

The evaluation of the potential hazards related with the operation of an LNG terminal includes possible release scenarios with the consequent flammable vapor dispersion within the facility; therefore, it is important to know the behavior of this phenomenon through the application of advanced simulation tools. Computational Fluid Dynamic (CFD) tools are often used to estimate the exclusion zones in an event of accidental LNG spill. In practice these releases are more likely to occur in the confines of complex geometries with solid obstacles such as LNG terminals, and LNG processing plants. The objective of this research is to study the effects that different obstacles have over the LNG vapor dispersion and the safety distance reduction caused by enhanced mixing. Through parametric analysis it is demonstrated that height, width and shape of the obstacles play an important role in the vapor concentration reduction. The findings of this research may be applied in the design stage of an LNG terminal, to improve the design of passive barriers, and for designing better layout configurations for storage tanks. Simulations results performed with FLACS (Flame Acceleration Simulator), a CFD solver, confirmed that these applications help to reduce safety distances.

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No description available.

Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for absence of solar radiation. Using AERMOD predictions of pollutant concentrations off property for regulatory purposes will likely affect a source?s ability to comply with limits set forth by State Air Pollution Regulatory Agencies (SAPRAs) and could lead to inappropriate regulation of the source.

Federal regulation 49 CFR 193 and standard NFPA 59A require the use of validated consequence models to determine the vapor cloud dispersion exclusion zones for accidental liquefied natural gas (LNG) releases. For modeling purposes, the physical process of dispersion of LNG release can be simply divided into two stages: source term and atmospheric dispersion. The former stage occurs immediately following the release where the behavior of fluids (LNG and its vapor) is mainly controlled by release conditions. After this initial stage, the atmosphere would increasingly dominate the vapor dispersion behavior until it completely dissipates. In this work, these two stages are modeled separately by a source term model and a dispersion model due to the different parameters used to describe the physical process at each stage. The principal focus of the source term study was on LNG underwater release, since there has been far less research conducted in developing and testing models for the source of LNG release underwater compared to that for LNG release onto land or water. An underwater LNG release test was carried out to understand the phenomena that occur when LNG is released underwater and to determine the characteristics of pool formation and the vapor cloud generated by the vaporization of LNG underwater. A mathematical model was used and validated against test data to calculate the temperature of the vapor emanating from the water surface. This work used the ANSYS CFX, a general-purpose computational fluid dynamics (CFD) package, to model LNG vapor dispersion in the atmosphere. The main advantages of CFD codes are that they have the capability of defining flow physics and allowing for the representation of complex geometry and its effects on vapor dispersion. Discussed are important parameters that are essential inputs to the ANSYS CFX simulations, including the mesh size and shape, atmospheric conditions, turbulence from the source term, ground surface roughness height, and effects of obstacles. A sensitivity analysis was conducted to illustrate the impact of key parameters on the accuracy of simulation results. In addition, a series of medium-scale LNG spill tests have been performed at the Brayton Fire Training Field (BFTF), College Station, TX. The objectives of these tests were to study key parameters of modeling the physical process of LNG vapor dispersion and collect data for validating the ANSYS CFX prediction results. A comparison of test data with simulation results demonstrated that CFX described the physical behavior of LNG vapor dispersion well, and its prediction results of distances to the half lower flammable limit were in good agreement with the test data.

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No description available.

No description available.

The purpose of this research is to investigate the dispersion of ammonia (NH3) from three Concentrated Animal Feeding Operations (CAFOs) in western Kentucky, as well as to investigate the Weather Research and Forecasting – Chemistry (WRF-Chem) model’s sensitivity response to initial NH3 concentrations under both wet conditions (significant precipitation) and dry conditions (no precipitation). As expected, pollutant concentrations generally were significantly higher near their points of origin and generally declined away from the sources. Contrary to expectations, ammonia tended to rise through the planetary boundary layer (PBL) regardless of atmospheric conditions. Results showed modeled NH3 pollution levels at the surface generally to be higher under wet conditions. A GIS-based analysis method was developed to investigate model sensitivity to initial NH3 concentrations. Using this method, it was found that WRF-Chem exhibits an exponential relation between initial NH3 concentration and the final amount of NH3 produced by the model.