Toxic gas dispersion models: Can they predict protective action distances in case of a chemical spill?

2008 ◽  
Vol 6 (5) ◽  
pp. 23
Author(s):  
John S. Nordin, PhD
Keyword(s):  
Dispersion Model ◽  
Protective Action ◽  
Weather Conditions ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Gas Dispersion ◽  
The Public ◽  
Emergency Responders ◽  
Toxic Concentration ◽  
Stable Conditions

Emergency responders often use a gas dispersion model to estimate downwind airborne concentrations of a toxic chemical in case of a chemical spill accident. For protecting the public, a protective action distance from the spill source is established based on the distance where the toxic concentration drops below some level of concern. This distance is used as a basis for evacuation of the public from the area or for instructions to shelter-in-place. However, in real-world accidents, the responders neither know the amount of chemicals released into the air nor the duration of the release, and moreover, the concentrations of chemicals at any location will vary over time. Depending on what input information is put into the model, different results will be obtained. The problem of what input parameters to use for gas dispersion modeling is illustrated for a hypothetical 90-ton chlorine railcar accident, where the railcar is breached. Different answers for a protective action distance are obtained depending on whether the tables in the Emergency Response Guidebook or any of the popular gas dispersion models are used. Very different answers are obtained from any model depending on whether whole of the chemical is released at once as a gas or aerosol or whether the liquefied chlorine evaporates slowly inside a ruptured 90-ton railcar tank, and also the weather conditions. To avoid misunderstandings, people who use models to establish a protective action distance must also communicate the circumstances in which the models are used, eg, “worst possible what-if scenario,” etc, or “nighttime stable conditions,” or other situations.

scholarly journals Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

2018 ◽  
Vol 15 (8) ◽  
pp. 1640 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Sihang Qiu ◽  
Bin Chen ◽  
Rongxiao Wang ◽  
Xiaogang Qiu
Keyword(s):  
Particle Filtering ◽  
Error Propagation ◽  
Dispersion Model ◽  
Data Driven ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Concentration Data ◽  
Gas Dispersion ◽  
Computational Errors ◽  
Hazardous Gas

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

scholarly journals Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

2019 ◽  
Vol 19 (4) ◽  
pp. 2561-2576 ◽  
Author(s):  
Anna Karion ◽  
Thomas Lauvaux ◽  
Israel Lopez Coto ◽  
Colm Sweeney ◽  
Kimberly Mueller ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Methane Emissions ◽  
Gas Production ◽  
Estimation Methods ◽  
Trace Gas ◽  
Barnett Shale ◽  
Dispersion Models ◽  
Data Set ◽  
Gas Dispersion ◽  
Tracer Dispersion

Abstract. Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Use of Source Term and Air Dispersion Modeling in Planning Demolition of Highly Alpha-Contaminated Buildings

2011 ◽  
Author(s):  
James G. Droppo ◽  
Bruce A. Napier ◽  
Jeremy P. Rishel ◽  
Richard W. Bloom
Keyword(s):  
Source Term ◽  
Dispersion Model ◽  
Dispersion Modeling ◽  
Nuclear Materials ◽  
Emission Rates ◽  
The Public ◽  
Air Dispersion Modeling ◽  
Air Dispersion ◽  
Air Dispersion Model ◽  
War Production

The current cleanup of structures related to cold-war production of nuclear materials includes the need to demolish a number of highly alpha-contaminated structures. The process of planning for the demolition of such structures includes unique challenges related to ensuring the protection of both workers and the public. Pre-demolition modeling analyses were conducted to evaluate potential exposures resulting from the proposed demolition of a number of these structures. Estimated emission rates of transuranic materials during demolition are used as input to an air-dispersion model. The climatological frequencies of occurrence of peak air and surface exposures at locations of interest are estimated based on years of hourly meteorological records. The modeling results indicate that downwind deposition is the main operational limitation for demolition of a highly alpha-contaminated building. The pre-demolition modeling directed the need for better contamination characterization and/or different demolition methods—and in the end, provided a basis for proceeding with the planned demolition activities. Post-demolition modeling was also conducted for several contaminated structures, based on the actual demolition schedule and conditions. Comparisons of modeled and monitoring results are shown. Recent monitoring data from the demolition of a UO3 plant shows increments in concentrations that were previously identified in the pre-demolition modeling predictions; these comparisons confirm the validity and value of the pre-demolition source-term and air dispersion computations for planning demolition activities for other buildings with high levels of radioactive contamination.

scholarly journals Diagnosing the Surface Layer Parameters for Dispersion Models within the Meteorological-to-Dispersion Modeling Interface

2010 ◽  
Vol 49 (2) ◽  
pp. 221-233 ◽  
Author(s):  
M. Sofiev ◽  
E. Genikhovich ◽  
P. Keronen ◽  
T. Vesala
Keyword(s):  
Surface Layer ◽  
Weather Forecasting ◽  
Dispersion Model ◽  
Temporal Correlation ◽  
Complex Surface ◽  
Dispersion Modeling ◽  
Limited Area ◽  
Dispersion Models ◽  
Absolute Flux ◽  
The Absolute

Abstract The problem of providing dispersion models with meteorological information from general atmospheric models used, for example, for weather forecasting is considered. As part of a generalized meteorological-to-dispersion model interface, a noniterative scheme diagnosing the surface layer characteristics from wind, temperature, and humidity profiles was developed. The scheme verification included long-term comparison with data of meteorological masts at Cabauw, the Netherlands, and Hyytiälä, Finland. The algorithm compatibility and consistency with the High-Resolution Limited-Area Model (HIRLAM) was also checked, as this model is routinely used as a meteorological driver for the Air Quality and Emergency Modeling System (SILAM). The comparison with Cabauw mast data showed a good quantitative agreement between observed and diagnosed heat and momentum fluxes: the temporal correlation coefficient was ∼0.8, bias was less than 10% of the absolute flux levels, regression slope deviated from unity for less than 20% with the intercept being less than 10% of the absolute flux values, and so on. In the case of complex surface features (Hyytiälä mast in forest) the scheme proved to be robust with large deviations appearing only if the input profile data were taken outside the constant-flux layer. Comparison with the HIRLAM model showed qualitatively good agreement but also highlighted several differences between the goals, standards, and methodologies of meteorological and dispersion models. The scheme was implemented in SILAM, which served as the development platform.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

scholarly journals Inter-comparison of Atmospheric Trace Gas Dispersion Models: Barnett Shale Case Study

2018 ◽  
Author(s):  
Anna Karion ◽  
Thomas Lauvaux ◽  
Israel Lopez Coto ◽  
Colm Sweeney ◽  
Kimberly Mueller ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Methane Emissions ◽  
Gas Production ◽  
Estimation Methods ◽  
Trace Gas ◽  
Barnett Shale ◽  
Dispersion Models ◽  
Data Set ◽  
Gas Dispersion ◽  
Tracer Dispersion

Abstract. Greenhouse gas emissions mitigation requires understanding dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emissions sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, U.S.A. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often under-predicted the observed methane enhancements with significant variability between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

scholarly journals Dense gas dispersion model development and testing for the Jack Rabbit II phase 1 chlorine release experiments

2018 ◽  
Vol 192 ◽  
pp. 218-240 ◽  
Author(s):  
Simon Gant ◽  
Jeffrey Weil ◽  
Luca Delle Monache ◽  
Bryan McKenna ◽  
Maria M. Garcia ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Phase 1 ◽  
Model Development ◽  
Dense Gas ◽  
Gas Dispersion

scholarly journals Radiation Dose Calculations for a Hypothetical Accident in Xianning Nuclear Power Plant

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Bo Cao ◽  
Junxiao Zheng ◽  
Yixue Chen
Keyword(s):  
Radiation Dose ◽  
Nuclear Power ◽  
Atmospheric Dispersion ◽  
Harmful Effect ◽  
Atmospheric Stability ◽  
Dispersion Modeling ◽  
Dose Calculations ◽  
Air Concentration ◽  
The Public ◽  
Ground Deposition

Atmospheric dispersion modeling and radiation dose calculations have been performed for a hypothetical AP1000 SGTR accident by HotSpot code 3.03. TEDE, the respiratory time-integrated air concentration, and the ground deposition are calculated for various atmospheric stability classes, Pasquill stability categories A–F with site-specific averaged meteorological conditions. The results indicate that the maximum plume centerline ground deposition value of1.2E+2 kBq/m2occurred at about 1.4 km and the maximum TEDE value of1.41E-05 Sv occurred at 1.4 km from the reactor. It is still far below the annual regulatory limits of 1 mSv for the public as set in IAEA Safety Report Series number 115. The released radionuclides might be transported to long distances but will not have any harmful effect on the public.

scholarly journals Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: the 2010 Eyjafjallajökull eruption

2011 ◽  
Vol 11 (9) ◽  
pp. 4333-4351 ◽  
Author(s):  
A. Stohl ◽  
A. J. Prata ◽  
S. Eckhardt ◽  
L. Clarisse ◽  
A. Durant ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
A Priori ◽  
Volcanic Eruptions ◽  
General Nature ◽  
Aviation Industry ◽  
Dispersion Modeling ◽  
Source Information ◽  
European Area ◽  
Ash Dispersion

Abstract. The April–May, 2010 volcanic eruptions of Eyjafjallajökull, Iceland caused significant economic and social disruption in Europe whilst state of the art measurements and ash dispersion forecasts were heavily criticized by the aviation industry. Here we demonstrate for the first time that large improvements can be made in quantitative predictions of the fate of volcanic ash emissions, by using an inversion scheme that couples a priori source information and the output of a Lagrangian dispersion model with satellite data to estimate the volcanic ash source strength as a function of altitude and time. From the inversion, we obtain a total fine ash emission of the eruption of 8.3 ± 4.2 Tg for particles in the size range of 2.8–28 μm diameter. We evaluate the results of our model results with a posteriori ash emissions using independent ground-based, airborne and space-borne measurements both in case studies and statistically. Subsequently, we estimate the area over Europe affected by volcanic ash above certain concentration thresholds relevant for the aviation industry. We find that during three episodes in April and May, volcanic ash concentrations at some altitude in the atmosphere exceeded the limits for the "Normal" flying zone in up to 14 % (6–16 %), 2 % (1–3 %) and 7 % (4–11 %), respectively, of the European area. For a limit of 2 mg m−3 only two episodes with fractions of 1.5 % (0.2–2.8 %) and 0.9 % (0.1–1.6 %) occurred, while the current "No-Fly" zone criterion of 4 mg m−3 was rarely exceeded. Our results have important ramifications for determining air space closures and for real-time quantitative estimations of ash concentrations. Furthermore, the general nature of our method yields better constraints on the distribution and fate of volcanic ash in the Earth system.

Sign in / Sign up

Export Citation Format

Share Document