Tracking and Predicting the Atmospheric Dispersion of Hazardous Material Releases Implications for Homeland Security (2003) / Chapter Skim
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4. Dispersion Modeling: Application to C/B/N Releases
4. Dispersion Modeling: Application to C/B/N Releases
Pages 33-54
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From page 33... ...
Such computational advances have led to a proliferation of the number and types of atmospheric dispersion models. ~ Gaussian-plume models assume that the concentration of the agent downwind of the source (averaged over a large number of realizations of the given dispersion problem)
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. CATEGORIES OF DISPERSION MODELS Atmospheric dispersion models can be broadly placed into categories using three distinguishing characteristics: (1)
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the output of a spatial-average dispersion model is properly interpreted not as a 2 Large-eddy simulation is the term used for the numerical calculation of three-dimensional, timedependent turbulent flows using spatial resolution sufficient to resolve the largest turbulent eddies.
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could change this situation. Such data used in a spatial-average dispersion model could substantially reduce the realizationto-realization variability that now accompanies the prediction of the atmospheric dispersion of a short-term release.
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In general, the predicted and observed maxima occur at different points on the line. If a dispersion model used in an air quality application yields a 1:1 line on a Q-Q plot, the probability distribution of its predictions over a downwind region agrees with that of the observations in that region.
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10 10~ 1,.~:2 £~ ;590:) FIGURE 4.2 Observed versus predicted ground-level sulfur hexaflouride (SF6)
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* ~~ ~7 .' 1 '~1 -a 10 16130 1000 10000 1~00000 Ott 39 FIGURE 4.5 A dosage scatter plot for three-hour releases of sulfur hexaflouride (SF~.
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An additional challenge is that atmospheric dispersion models must be capable of assimilating measurements that come from an assortment of data collection networks, with information of uneven quality and quantity, collected over irregular time periods. For a model to be useful in the response stage of C/B/N events, input data must be available in real time and the model must have a short execution time.
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However, a probabilistic description of the concentration field can be estimated via dispersion modeling, even with an incomplete wind field input. Hazard Source Characterization The C/B/N source characteristics (Iocation, release rate, timing, buoyancy, momentum, toxicity, persistence, etc.)
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These observational wind data are processed in some dispersion models with local surface characteristics (topography, vegetation, structures) to form an estimate of the spatial and temporal wind field over the domain of C/B/N events.
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The typical spatial ranges of several dispersion modeling methods are depicted in Figure 4.~. The geographic extent of the wind data used for dispersion modeling should be several times greater than the anticipated maximum extent of the hazard (i.e., if the level of concern will stay within an urban area, then wind data in the surrounding area .f,,;~,..~..'::::::::: ^,,~ fin / ~~n ~~ Hi Rem.~:~e ~ g]
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. REVIEW OF SELECTED C/B/N DISPERSION MODELING SYSTEMS During the workshop proceedings, several presentations discussed the current status of selected dispersion modeling systems that were applicable to C/B/N events.
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system has chemical, biological, and nuclear databases for source identification purposes, and it accesses weather data from in-house, NWS, and military providers. HPAC uses the SCIPUFF dispersion model, which uses a collection of Gaussian puffs to predict both the ensemble average concentration and the concentration variance out to regional scales.
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a, o a, a, cd O ~ ~ .5 .
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In particular, given a dosage level of concern (LOC) for a toxin and a prediction confidence level, the dispersion model should provide a spatial contour defining the three-dimensional hazard zone.
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Laboratory simulations are important tools for creating site-specific databases of C/B/N event scenarios and for the development and evaluation of both fast-response urban dispersion models and CFD-based dispersion models. Laboratory simulations provide better resolution of turbulent motions than current CFD models in the urban setting.
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preparedness and recovery models need further development and evaluation. Once a viable set of dispersion models capable of C/B/N event predictions is established, an independent quantitative review of these models should be initiated, and the results should be used to improve model performance.
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Different dispersion modeling methodologies are required in the preparedness, response, and recovery stages of C/B/N events. For the preparedness stage, an accurate model capable of providing confidence-level estimates is desired, but model execution time is not important.
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Model operators and developers would benefit from broader interaction with the meteorological community, to take advantage of leading-edge research in data assimilation, quantitative precipitation forecasting, shortrange numerical weather prediction, and high-resolution forecasting initialized with radar data. Likewise, observational research programs studying issues such as weather prediction, properties of boundary layer turbulence, and air pollution transport should be viewed as targets of opportunity for testing and evaluating dispersion models.
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From page 53... ...
These observational and modeling tools could have multiple applications, which would help justify costs and ensure that the systems are frequently used, maintained, evaluated, and quality controlled. There is a wealth of knowledge about meteorological and dispersion models residing in universities, NWS Weather Forecast Offices, and private sector facilities throughout the nation.
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