Release scenarios

Throughout this section, calculated events are rounded to the five year date. 

---

When considering release scenarios, the most hazardous unit in a plant should be chosen, based on inventoiy and process conditions. The idea is to imagine the release of material in the fastest way that is reasonably possible. The worst realistic scenario should be considered. This can be based on the outcome of a review, from a HAZOP study or a hazard analysis. The time a scenario will take is almost always considered to be continuous, because after a few minutes a stable dispersion distance exists. Making the time longer will not necessarily change the hazard distance. 

Allows site-specific inputs to the calculation of vulnerability zones and provides release scenarios Calculations are based on site-specific planning factors such as wind speed, stabihty class, and chemical toxicity. 

Assembles quantitative facility information concerning possible release scenarios ... 

It can simulate a wide variety of release scenarios but is particularly well suited to assessing health consequence impacts and risk. 

The Mark-I analysis (NUREG/CR-6025) was controversial and manifested prior polarisation, Follow-up work was carried out at RPI (on melt release scenarios), ANL (on spreading), SNL (on coriumconcrete interactions), and ANATECH (on liner structural failure by creep) resulted in a consensus (NUREG/CR-6025). 

FIGURE 7.19 Decision Flow Chart for Manual Blowdown in Gas Release Scenario. 

An estimate of the likelihood of a range of release scenarios, e.g. flange weep to whole tank events. 

Take advantage of ongoing research aimed at the development of models of the airflow within aircraft, terminals, and so forth, based on empirical studies of specific facilities, and explore the dispersal of chemical/biological simulants under various release scenarios ... 

An off-site consequence analysis that evaluates specific potential release scenarios, including worst-case and alternative scenarios... 

The selection of the appropriate dispersion model in an accidental release scenario requires the behavior of dispersing gas to be known since each model is specialized on one kind of release (buoyancy or gravity driven). 

Hazard assessment is a consequence analysis for a range of potential hazardous chemical releases, including the history of such releases at the facility. The releases must include the worst-case scenario and the more likely but significant accident release scenarios. A risk matrix can be used to characterize the worst-case and more likely scenarios. 

The EPA requires the following consequence analyses (1) A single worst-case release scenario is analyzed for all covered flammable materials on the site, and only one flammable substance is analyzed for other more likely scenarios and (2) a single worst-case release scenario is analyzed for all toxic substances on the site, and more likely releases are analyzed for each toxic substance covered by the rule. 

Alternative release cases for toxic substances cover scenarios with toxic concentrations beyond the fenceline. Alternative cases for flammable substances cover scenarios that may cause substantial damage off site and on site. The release scenarios that have a potential to reach the public are of the greatest concern. Those with no off-site potential damage are not required to be reported. 

Dispersion model calculations are normally used to estimate downwind concentrations these concentrations are the basis for determining the consequences resulting from toxicity, fires, and/or explosions. For those not interested in using dispersion models, the standard includes lookup tables for all the listed substances to help a facility determine the impact distances for specific release scenarios. 

An 800-lb tank of chlorine is stored at a water treatment plant. A study of the release scenarios indicates that the entire tank contents could be released as vapor in a period of 10 min. For chlorine gas, evacuation of the population must occur for areas where the vapor concentration exceeds the ERPG-1. Without any additional information, estimate the distance downwind that must be evacuated. 

Empirical modek Empirical models rely on the correlation of atmospheric dispersion data for characteristic release types. Two examples of empirically based models are the Pasquill-Ginord model (for passive contaminants) and the Britter-McQuaid model (for denser-than-air contaminants) both of which are described below. Empirical models can be useful for the validation of other mathematical models but are limited to the characteristic release scenarios considered in the correlation. Selected empirical models are discussed in greater detail below because they can provide a reasonable first approximation of the hazard extent for many release scenarios and can be used as screening tools to indicate which release scenarios are most important to consider. 

Simplified mathematical models These models typically begin with the basic conservation equations of the first principle models but make simplifying assumptions (typically related to similarity theory) to reduce the problem to the solution of (simultaneous) ordinary differential equations. In the verification process, such models must also address the relevant physical phenomenon as well as be validated for the application being considered. Such models are typically easily solved on a computer with typically less user interaction than required for the solution of PDEs. Simplified mathematical models may also be used as screening tools to identify the most important release scenarios however, other modeling approaches should be considered only if they address and have been validated for the important aspects of the scenario under consideration. 

For a given release scenario, estimate the state of the released contaminant after it has depressurized and become airborne (including any initial dilution). The initial mole fraction of hazardous components will be applied to the final reported concentrations and hazardous endpoint concentrations throughout the process. If source momentum is important (as in a jet release or for plume rise), other models are available that can address these considerations. Disregarding the dilution due to source momentum will likely result in higher concentrations downwind, but not always. 

The best avenue is to use input data that would be considered the WCCE for the incident under evaluation. One should then question if the output data provided is realistic or corresponds to historical records of similar incidents for the industry and location. In other cases where additional analysis is needed, several release scenarios (small, medium and large) can be examined and probabilities can be assigned to each outcome. This would then essentially be an Event Tree exercise normally conducted during a quantitative risk analysis. Certain releases may also be considered so rare an event they may be outside the realm of accepted industry practical protective requirements. 

Haslbeck and Holm, 2005), and the potential deposition of inorganic copper salts (Arias, 1999). Standardised methods have also been demonstrated to overpredict in situ release scenarios (Valkirs et al., 2003). Quoting Haslbeck and Holm with the current methods it will be difficult to interpret and predict release rate results and to estimate how reformulation of coatings or limits placed on release rates would impact the environment . Thus, it seems that further work is needed in order to optimise these methods. 

Hazard assessment. A hazard assessment is required to assess the potential effects of an accidental (or intentional) release of a covered chemical/material. This RMP element generally includes performing an off-site consequence analysis (OCA) and the compilation of a five-year accident history. The OCA must include analysis of a least one worst-case scenario. It must also include one alternative release scenario for the flammables class as a whole also each covered toxic substance must have an alternative release scenario. USEPA has summarized some simplified consequence modeling... 

---