Uncertainties in modeling hazardous gas releases for emergency

Meteorologische Zeitschrift, Vol. 20, No. 1, 019-027 (February 2011) Open Access Articlec© by Gebruder Borntraeger 2011

Uncertainties in modeling hazardous gas releases foremergency response

KATHRIN BAUMANN-STANZER∗ and SIRMA STENZEL

Zentralanstalt fur Meteorologie und Geodynamik, Vienna, Austria

(Manuscript received October 16, 2009; in revised form October 19, 2010; accepted December 20, 2010)

Abstract

In case of an accidental release of toxic gases the emergency responders need fast information aboutthe affected area and the maximum impact. Hazard distances calculated with the models MET, ALOHA,BREEZE, TRACE and SAMS for scenarios with chlorine, ammoniac and butane releases are comparedin this study. The variations of the model results are measures for uncertainties in source estimation anddispersion calculation. Model runs for different wind speeds, atmospheric stability and roughness lengthsindicate the model sensitivity to these input parameters. In-situ measurements at two urban near-traffic sitesare compared to results of the Integrated Nowcasting through Comprehensive Analysis (INCA) in order toquantify uncertainties in the meteorological input. The hazard zone estimates from the models vary up to afactor of 4 due to different input requirements as well as due to different internal model assumptions. Noneof the models is found to be ‘more conservative’ than the others in all scenarios. INCA wind-speeds arecorrelated to in-situ observations at two urban sites in Vienna with a factor of 0.89. The standard deviationsof the normal error distribution are 0.8 ms−1 in wind speed, on the scale of 50 degrees in wind direction, upto 4◦C in air temperature and up to 10 % in relative humidity. The observed air temperature and humidityare well reproduced by INCA with correlation coefficients of 0.96 to 0.99. INCA is therefore found to give agood representation of the local meteorological conditions. Besides of real-time data, the INCA-short rangeforecast for the following hours may support the action planning of the first responders.

Zusammenfassung

Bei unfallbedingter Freisetzung eines toxischen Gases benotigen die Einsatzkrafte eine rasche Informationuber den betroffenen Bereich und die maximalen Auswirkungen. Berechnungen der Gefahrenzonen mitden Modellen MET, ALOHA, BREEZE, TRACE und SAMS und fur Szenarien mit Chlor-, Ammoniak-und Butanfreisetzung werden in dieser Untersuchung verglichen. Die Unterschiede in den Modellergebnis-sen liefern ein Maß fur die Unsicherheiten in der Quelltermabschatzung und der Ausbreitungsrechnung.Modellaufe fur verschiedene Windgeschwindigkeiten, atmospharische Stabilitaten und Rauigkeitslangenzeigen die Abhangigkeit der Modellergebnisse von diesen Eingangsdaten. Messungen an zwei stadtischenverkehrsnahen Standorten werden mit Ergebnissen der “Integrated Nowcasting through Comprehensive Ana-lysis” (INCA) verglichen, um Unsicherheiten in den meteorologischen Eingangsdaten zu quantifizieren. DieGefahrenzonenangaben der Modelle unterscheiden sich um bis zu einem Faktor 4 aufgrund verschiedenerErfordernissen hinsichtlich der Eingangsdaten sowie aufgrund unterschiedlicher interner Modellannahmen.Es zeigt sich, dass keines der Modelle bei allen Szenarien konservativere Ergebnisse als die anderen liefert.INCA Windgeschwindigkeiten sind mit den Stationsmessungen an zwei stadtischen Standorten in Wien miteinem Faktor 0.89 korreliert. Die Standardabweichungen der Normalverteilung des Fehlers betragt fur die

Windgeschwindigkeit 0.8 ms−1, fur die Windrichtung rund 50 Grad, bis zu 4◦C fur die Lufttemperatur undbis zu 10 % fur die relative Feuchte. Die Messwerte der Lufttemperatur und Luftfeuchte werden mit INCAmit Korrelationskoeffizienten zwischen 0.96 und 0.99 sehr gut wiedergegeben. INCA bietet demnach einegute Beschreibung der lokalen meteorologischen Verhaltnisse. Zusatzlich zu den Echt-Zeit-Daten kann dieINCA – Kurzfristvorhersage fur die nachsten Stunden die Einsatzplanung der Einsatzkrafte unterstutzen.

1 Introduction

Whenever hazardous gases are released into the atmo-sphere (accidentally or due to a terrorist attack), theemergency responders need early information about themaximum outline of the area affected and the maxi-mum impact to be expected. Exposure estimates fromdispersion models are used to assess the possible conse-quences of the gas release and to support the planning ofcountermeasures (e.g. protective equipment for the fire

∗Corresponding author: Kathrin Baumann-Stanzer, Zentralanstalt fur Me-

teorologie und Geodynamik, Hohe Warte 38, 1190 Vienna, Austria

[email protected]

brigades, warning and information of the population tostay indoors).

Worldwide a variety of models is available (commer-cially or free of charge) which can be applied to estimatethe hazard zones caused by a toxic gas release. Manymodel packages include a chemical database, a graphi-cal user interface and an automated graphical output fora fast processing of the results. The models are designedfor the analysis of worst-case scenarios e.g. in the frameof emergency response planning as well as for real-timerisk assessment and management. The most models canbe coupled directly to automatic meteorological stationdata.

0941-2948/2011/0506 $ 4.05

DOI 10.1127/0941-2948/2011/0506 c© Gebruder Borntraeger, Stuttgart 2011

20 K. Baumann & S. Stenzel: Uncertainties in modeling hazardous gas releases Meteorol. Z., 20, 2011

Atmospheric dispersion is a stochastic phenomenonand, in general the concentration observed at a giventime and location downwind of a source cannot be pre-dicted precisely (RAO, 2005). As stated by Fox (1984),it is long recognized that the modeled concentrations ofhazardous contaminants in the atmosphere should be de-scribed in a probabilistic framework. In order to providea reliable basis for decisions in emergency response itis necessary to assess uncertainties in model input, insource estimation as well as in dispersion calculation.Uncertainty in meteorological input and model param-eters consist of two components, which are assumed tobe independent: the hour-to-hour variations at a singlesite and the inevitable variations between sites resultingfrom non-representativeness (HANNA et al., 2007). Aspointed out by IRWIN et al. (2007) estimates of the vari-ability of the plume trajectory and of the near-centerlineconcentration can provide useful information to supportthe emergency responders. According to IRWIN et al.(2007), the variability in the near-centerline concentra-tion values is on the scale of a factor 2.

In this paper, five emergency response models are ap-plied to estimate the hazardous areas for a selection ofscenarios. Model runs for different meteorological con-ditions indicate model sensitivity to meteorological in-put parameters. The variations of the model results areinterpreted as measure for uncertainties in source esti-mation and dispersion calculation. Comparisons of in-situ measurements at two near-traffic sites with resultsof the Integrated Nowcasting through ComprehensiveAnalysis (INCA, HAIDEN et al., 2009) are used to quan-tify uncertainties in the meteorological input.

2 Model approaches for hazardous gasreleases

The Gaussian model approach is still often applied whena simple, fast calculation of plume dispersion is re-quired. A major short-coming of Gaussian plume mod-els is that the emission rates as well as the meteorolog-ical conditions are assumed to remain constant duringthe release, although they may vary significantly withtime. Box models are often applied for instantaneouschemical releases, where the initial shape of the cloudis described by a sphere, ellipsoid or a cylinder. Thedistribution of the mass within the box is constant ora bell-shaped mass distribution is superimposed. Boxmodels are especially applicable for instantaneous re-leases of heavy-gas dispersion because physical effectsas the gravitational spreading and stability-modified topentrainment are well reproduced by this model type. Thetheoretical basis for Gaussian dispersion on the otherhand is eddy diffusivity, which requires close-to-neutralgas cloud stability. It has to be kept in mind that theserather simple model approaches, both Gaussian and boxmodels are subject to large uncertainties by prevailingvery low wind speeds, stable conditions, frequent wind

direction fluctuations and by flow deflection by terrainor obstacles.

Most of the problems above can be solved if a La-grangian model approach is used. In particle diffusionmodels, the gas cloud is represented by a large numberof “particles”. Each of them represents a fraction of thereleased quantity and is displaced with the mean windoverlaid with turbulent fluctuations according to the at-mospheric stability.

Compared to Gaussian models and Box models, par-ticle diffusion models are clearly more computation-intensive. Lagrangian models based on diagnostic orprognostic high-resolution meteorological fields are al-ready used for regional and large scale emergency re-sponse modeling (for nuclear incidents) and may in fu-ture replace the simpler model approaches in the localscale.

Software packages including a chemical data baseand dispersion models for accidental toxic gas releaseswere reviewed in respect to the usefulness for the firebrigades: a high usability and a short training time (intu-itive user interface, automatic, clear understandable out-come), reasonable demands on the required hardwareand input data (mobile use on a laptop) and the integra-tion of a chemical database of hazardous substances.

The following models were applied in the presentedstudy: MET, ALOHA, BREEZE HAZ, TRACE, SAMS.A short description of these models is given in the fol-lowing.

An implementation of the model MET is a Box modelprovided with the software MEMPLEX commerciallyby Keudel av-Technik GmbH (www.memplex.com)which contains a detailed substance data base (includinge.g. first aid advises) as well modules for the estimationof complex (time-developing) emission terms for acci-dental release scenarios and for the calculation of hazardzones. Two phase flow is considered in the dispersionmodel. The dispersion model MET has been validatedagainst data from several real accident cases. The soft-ware is easy to operate, robust and operationally widelyused by fire brigades in Austria, Germany and Switzer-land.

ALOHA (Areal Location of Hazardous Atmospheres)consists of an model for accidental release scenarios, aGaussian neutral gas and heavy gas dispersion model de-signed in cooperation by the US National Oceanic andAtmospheric Administration (NOAA) and the US En-vironmental Protection Agency (EPA, www.epa.gov) inorder to support first responders in operation as well asfor emergency planning and training. The model sim-ulates key hazards related to chemical releases: toxic-ity, flammability, thermal radiation (heat), overpressure(explosion blast force). Scenarios with direct source re-lease are simulated with a constant emission rate. In allother release scenarios (leakage, pool evaporation), theemission rate is time-developing. Two phase flow is con-sidered in the dispersion model. ALOHA as well as the

Meteorol. Z., 20, 2011 K. Baumann & S. Stenzel: Uncertainties in modeling hazardous gas releases 21

chemical data base CAMEO and the visualization soft-ware MARPLOT are provided by EPA free of charge.

Hazmat Responder is a commercial software productof the company Safer Systems (www.safersystem.com)comprising an emission model for accidental releasescenarios and a Lagrangian dispersion model. The pro-gram TRACE (also from Safer System) is a tool forchemical risk management including a dense gas and aGaussian dispersion model.

Emission rates in both model packages are calcu-lated time-developing. Two phase flow is considered inthe dispersion model. A suite of products are providedfor planning and for real-time response to a chemicalemergency. TRACE (Toxic Release Analysis of Chem-ical Emissions) is used for facility studies, emergencypreparedness planning, meeting regulatory requirementsand quantitative risk assessment. HAZMAT Responderis designed for emergency response dealing with haz-ard accidents (real time application) including the useof on-line meteorological data as well as chemical mea-surements (for source term recalculation) if available.

BREEZE HAZ is a commercial software pack-age distributed by Trinity Consulting (www.breeze-software.com). The products are used by environmen-tal professionals to analyze the effects of air pollu-tant emissions and explosions. This software packagecomprises the standard dispersion models developedor recommended by the U.S.EPA and other environ-mental authorities: the dense gas dispersion modelsDEGADIS and SLAB and the Gaussian puff modelsAFTOX and INPUFF. Emission rates, pool evaporationrates and other source-term parameters are calculatedtime-developing with the source term model EXPERT.

SAMS includes a Gaussian model based on VDI 3945regulation and is provided commercially by LohmeyerGmbH (www.lohmeyer.de) for the real time simulationof the dispersion of toxic gases released from a chemicalplant or in the course of a transport accident. The emis-sion rate is a user-defined fixed value for an instanta-neous or a continuous release (steady-state) or estimatedfrom tank or pipe dimensions. The calculation is updatedautomatically based on online meteorological measure-ments (if available). The software offers a direct inter-action with Google Earth for the determination of therelease site as well as for the graphical model output.If chemical concentration measurements are available,these can be used in the model for source term recalcu-lation.

3 Model comparison exercise

Reference scenarios for chlorine, ammoniac and butanereleases are defined for model comparison and sensi-tivity studies. Case 1 describes a train accident with arelease of 40 t chlorine from a railway car with a tankvolume of 30 m3 and a 90 % filling level. In case 1Aa wagon is accidently totally destroyed and the content

evaporates from a liquid pool. In case 1B a continuousrelease from a leakage in a tank with a diameter of 2.5cm takes place. A similar train accident scenario was se-lected for case 2 with 55 t ammoniac released from atank volume of 95 m3. The scenario for case 3 is a traf-fic accident with a truck with a tank volume of 17 m3

and 70 % filling level leading to a release of 7 t butane.The source models of the compared programs requiredifferent input parameters: in the MET model, only themass of gas or liquid contained in the tank is needed;detailed information about the tank diameter, length andvolume, tank content and storage temperature is used inALOHA for the estimation of a time developing emis-sion rate; averages of the emission rates calculated withALOHA are used in the presented model exercise to de-fine steady-state emission rates for the model runs withBREEZE, TRACE and SAMS. In respect to the inputparameters for the dispersion calculation, the modelsalso require a different amount of information: in con-trast to all other models, SAMS does not require the def-inition of a roughness length (but uses an internal prede-fined value).

The simulation of the source term and the chemicalcharacteristics of the toxic plume are very complex anda large number of significant parameters are often un-known. The models require various input parameters,some of them are relatively easy to identify (e.g. windspeed, stability, surface roughness), others are definedbased on assumptions and therefore highly uncertain(e.g. released mass or size of a leak). The total uncer-tainty in the model inputs can be represented as a com-bination of random biases and random errors as shownby IRWIN and HANNA (2008). Random biases have thegreatest impact on the long-term concentration values,while random errors mainly influence hourly concentra-tions values. When interpreting the model results theseuncertainties must be taken into account.

The comparison of the model results for the re-lease of chlorine from a pool in Figure 1a reveals that thedependence of the hazard distances on the atmosphericstability and wind speed is simulated by all models inthe expected way: the hazard zones are smaller due toincreased mixing under neutral and unstable conditionsand are decreasing with increasing wind speeds. Somemodels as SAMS do not discern between neutral and un-stable conditions explicitly. In the MET model, resultsdiffer for winter and summertime under unstable condi-tions and weak winds as can be seen from Figure 1a. Thereason for this is the different definition in the model forthe stability classes for summer and winter – clear skyand wind speeds < 3.5 m/s−1 imply in summer stabilityclass B and in winter stability class C. The results fromBREEZE for this scenario are not available, due to anerror in the heavy gas model (DEGADIS) in the demoversion.

It is to point out that the output distances fromALOHA are larger than 10 km. As expected, MET and


a) b)

Figure 1: Comparison of the maximum hazard distances for the ERPG-2 value (3 ppm chlorine) from different models for evaporation from

a pool of chlorine over grassland (scenario 1A) for different wind speeds: a) 1 ms−1 and b) 5 ms−1.

a) b)

Figure 2: Comparison of the maximum hazard distances for the ERPG-2 value (150 ppm ammoniac) from different models for a release of

ammoniac from a leakage (scenario 2B) with 1 ms−1 wind speed for different surface roughness lengths: a) release in a city (z0 = 1 m) b)

release over grassland (z0 = 0.1 m).

a) b)

Figure 3: Comparison of the maximum hazard distances for the ERPG-2 equivalent value (3154 ppm butane, as suggested in ALOHA)

from different models for a release of butane from a leakage (scenario 3B) over grassland for different wind speeds: a) 1 ms−1 and b) 5

ms−1.


SAMS yield lower toxic distances with higher windspeeds (5ms−1) than with lower wind speeds (1ms−1).On the other hand, the hazard zones with TRACE in-crease with wind speed as a higher evaporation rate iscalculated for the case with higher wind speed. Underunstable conditions, this effect is of minor relevance andall model results except SAMS agree closely.

Selected model results for the release of ammoniacfrom a leakage in case 2B with roughness lengths 1 m(release in a city) and 0.1 m (release over land) are pre-sented in Figure 2. For the continuous release of ammo-niac results from all the tested models are available. Thedependence on the atmospheric stability and roughnessis as expected – the toxic distances are smaller for the re-lease in a city than for the release over land. Ammoniacis stored liquefied (low temperature cooled) in a tankand behaves like a dense gas after being released un-til enough air is entrained that the plume is furthermoredispersed as neutral gas. This is taken into account by allmodels except by the neutral gas model AFTOX whichis used in BREEZE for this case. This becomes obvi-ous under stable conditions over land: BREEZE rendersa significantly larger hazard zone in this case than theother models. The model run for the higher roughnesslength on the other hand is in good agreement with allother model results.

A comparison of the model results for a release ofbutane from a leakage in scenario 3B is shown in Fig-ure 3. The dependence on the atmospheric stability andwind speed is found to be as expected in all model re-sults. The hazard zones under neutral and unstable con-ditions calculated with BREEZE and SAMS are 50 to100 m smaller than those of the other models. On theother hand, TRACE provides 50 to 100 m higher dis-tances than the other models in the case with a windspeed of 5 ms−1. The relative differences between themodel results in this case seem to be rather large, but theabsolute value is within the general model uncertaintydue to differences in model assumptions.

The estimated hazard distances may differ to some or-ders of magnitude, in the presented case studies, the haz-ard zone estimates from the models vary up to a factorof 4. These differences are caused by different input re-quirements as well as due to different internal model as-sumptions. Based on these case studies it cannot be gen-eralized that a particular model is more or less “conser-vative” than the other models. The results of this studyfurthermore illustrate, that it is not possible to assess the“correctness” of the model outputs based on pure com-parison of the model results, without appropriate com-parison with measurements.

4 Influence of the meteorological input

Sensitivity studies are conducted with series of modelruns for the reference scenarios in order to assess theimpact of uncertainties and the variability in the main

input parameters (wind, stability, ambient temperatureand roughness length) on the predicted hazard zones.

The maximum hazard distances typically decreasewith increasing the wind speed. The wind speed deter-mines the transport speed of the toxic cloud, the disper-sion of the cloud, the evaporation from a puddle and in-fluences indirectly the exposure period of the affectedpersons in the area of concern (dosis).

According to the findings of RAO (2005), meteoro-logical input data uncertainty accounts for more thanhalf of the total uncertainty in predicting the observed1-hr ground level concentrations with a complex plumemodel. For any given application, there is an optimummodel complexity (defined by the number of the mete-orological input parameters required for a model run)that minimizes the uncertainty. This optimum of modelcomplexity cannot be defined a priori, but has to be de-termined individually for each application.

As stated by RAO (2005), large uncertainties can bein the meteorological input data e.g. due to unrepresen-tative siting even if the instrument itself is accurate. Fur-thermore, the natural variability of the atmosphere undercircumstances results in a large stochastic uncertainty inthe prediction of short-term concentrations.

Under very light wind conditions heavy gasesas chlorine tend to form a compact, shallow plume(“pancake-shaped”) around the source. In this case theresult is independent from wind direction. The width ofthe plume (not shown) as well as the maximum hazarddistance (Figure 1 and Figure 3) decrease with increas-ing wind speeds for heavy gases as well as neutral gases,since the rate of air mixing is gradually increasing. Inemergency response applications of dispersion model-ing, the wind speed and direction are usually derivedfrom nearby measurements or from representative mete-orological model data. IRWIN and HANNA (2008) con-clude based on the comparison of observations with pre-dictions from mesoscale meteorological modeling thatthe random bias in wind speed is following a normal er-ror distribution with a standard deviation of order 0.5ms−1 and that the random bias in wind direction followsa standard deviation of order 10 degrees. In section 5, asimilar investigation is conducted based on a compar-ison of meteorological analyses with independent ob-servations. The stability categories are usually derivedwithin the models from cloud cover and wind speed. Inthe heavy gas scenario, the maximum plume width (notshown) decreases and the maximum horizontal range ofthe hazard zones increases with increasing stability (Fig-ure 1). For near field impacts, dense gases are foundto be only weakly sensitive to the stability. For neutralgases, the hazard zones are largest under very stable con-ditions and shortest under unstable conditions (as seen inFigure 2 and Figure 3).

The ambient temperature with connection to the boil-ing point of the substance determines whether the liq-uid flashes, boils or evaporates. The maximum hazard


a) b)

c) d)

Figure 4: Frequency distribution of the wind directions and mean wind speeds per wind direction a) observed at Inzersdorf b) analyzed for

Inzersdorf c) observed at Erdberger Brucke d) analyzed for Erdberger Brucke.

distance for chlorine (heavy gas) is almost not sensitiveto the temperature changes below 20 ◦C and increasesrapidly with higher temperatures. The impact of the tem-perature on the ammoniac spread is more complex asshown by BUBBICO and MAZZAROTTA (2008). The va-por mass fraction of a release is decreasing with an am-bient air temperature below 10 ◦C and increasing above.A maximum vapor mass fraction of 70 % is reached atan ambient air temperature of 25 ◦C.

The influence of the meteorological input on the re-sults of the hazard distance computations strongly de-pend on the chemical characteristics of the toxic re-lease. Worst case weather conditions, respective scenar-ios, are not necessarily identical for the different toxicsubstances. If the boiling point of the toxic substance islower than the air temperature (e.g. chlorine –33.9◦C,ammoniac –33.4◦C, butane –0,5◦C), the emitted liquidevaporates immediately and hazard distances are largestunder stable conditions and low wind speeds.

Substances with a high boiling point (e.g. petrol 39◦C)are mainly dispersed as liquid. As shown by BUBBICO

and MAZZAROTTA (2008), wind speeds are less im-portant in these cases and the size of hazard distances

mainly depends on the release temperature and poolsize. Under stable conditions impact areas increase withwind speed in these cases. Worst case weather scenariosfor these substances are characterized by increased tem-perature and wind speeds under neutral conditions.

Roughness length is required by most models and in-fluences the hazard distances: e.g increasing the surfaceroughness by a factor of 10 may result in concentra-tion reductions by factor of 2 (in the case of the modelBreeze). For planning purposes, whenever worst case es-timates are needed, it is advisable to use a rather smallroughness height.

Only few models consider the impact of rain and fog:In the MET model, hygroscopic gases are modeled asdense gases. Under foggy conditions, the vertical extentof the dense gas plume is limited in the MET model. Inthe dense gas models in BREEZE, the plume is trappedbelow a user-defined inversion base. The maximum haz-ard distance in the MET model decreases with increas-ing amount of precipitation.


a) b)

c) d)

Figure 5: Comparison of observed and analyzed air temperatures at a) Inzersdorf and b) Erdberger Brucke. Comparison of observed and

analyzed relative humidities at c) Inzersdorf and d) Erdberger Brucke.

5 Use of INCA data for model input

INCA (Integrated Nowcasting through ComprehensiveAnalysis, HAIDEN et al. 2009) is an observation-basedanalysis and forecasting system, created to complementclassical NWP forecasts by providing improved fore-casts at short lead times (very-short-range up to +12hours). INCA is computed operationally at the CentralInstitute for Meteorology and Geodynamics (ZAMG)with 1 km spatial and 15 min (precipitation) to 1hour temporal resolution. INCA takes into account allavailable data sources as model results of the limitedarea weather forecast model ALADIN (operated op-erationally at ZAMG), station data, radar and satellitedata, radio-soundings and compiles a physically consis-tent analyses of atmospheric fields. The applicability ofINCA wind analysis, as meteorological input for emer-

gency response calculations in case of a traffic accidentwith hazardous goods in the area of Vienna is investi-gated in the presented study. For this purpose, INCAwind, temperature and humidity data are compared tomeasurements conducted at two near-traffic sites in Vi-enna. The measurements were conducted for six monthsin 2008 (April to September). In this exemplarily com-parison, INCA analysis is found to be an advanced pos-sibility to provide real-time meteorological input foremergency response calculations in the local scale.

The main wind directions at Inzersdorf (16◦20’15”48◦08’51”) are well reproduced by INCA (Figure 4aand b). Less agreement is found at Erdberger Brucke(16◦24’54” 48◦11’35”, Figure 4c and d). The observedflow is in this case obviously influenced by about 45 mhigh buildings situated north-west to south-west of thesite, leading to a high frequency of north-north-west


winds instead of the winds from west to north-west inINCA. Southwest winds are also deflected by a station-near building. INCA wind-speeds and measurements arecorrelated with a factor of 0.89 at both sites. The randombias in wind speed is following a normal error distribu-tion with a standard deviation of order 0.8 ms−1. Thisvalue is somewhat higher than the random bias of 0.5ms−1 found by IRWIN and HANNA (2008). The windanalysis overestimates the observed wind speeds with anaverage bias of 0.8 ms−1 at both sites which is explainedby an underestimation of the urban roughness length inthe analysis. An improved INCA version using a spatialdistribution of roughness length is under development.

The standard deviations of the normal error distribu-tion in wind direction at the two urban sites are 40 de-grees and 50 degrees which is a factor of 4 to 5 higherthan found by IRWIN and HANNA (2008).

Observed and analyzed hourly values for air tempera-ture and relative humidity are compared in Figure 5. Ingeneral, air temperatures in 2 m above ground are verywell reproduced by INCA at both sites in Vienna (Fig-ure 5a and b) with a correlation coefficient of 0.98 and0.99 and an average bias of 0.6◦C at the site Inzersdorfand 0.3◦C at the site Erdberger Brucke. The differencesbetween observed and analyzed air temperatures followa normal distribution with a standard deviation of 4◦Cand 2.6◦C respectively. In some cases with wind speedsbelow 4 ms−1 and air temperatures above 15◦C, INCAtends to underestimate the air temperature at Inzersdorf(Figure 5a). A larger scatter is found in the compari-son between the observed and analyzed relative humid-ity values in Figure 5c and d. At both sites, INCA tendsto slightly overestimate air humidity under dry condi-tions (below 50 %). The analyzed values are system-atically slightly higher than the observed air humiditywith wind speeds of more than 4 ms−1. In general, therelative humidity values are correlated with a factor of0.97 and 0.96 and the average bias between observedand analyzed relative humidity is found between 2 %and 4 %. The differences between the relative humidityvalues follow a normal distribution with a standard de-viation of 4.5 % at Inzersdorf and of 10 % at ErdbergerBrucke.

6 Conclusions

Modeling the release and dispersion of toxic gases re-quires a large number of input parameters. Some of theseparameters are easy to identify, others, as source termparameters, are subject to large uncertainties in the caseof an accidental release. Assumptions often have to bemade, which tend to over-estimate the hazard zones -as worst case scenarios are usually preferred in emer-gency response. Since the input requirements differ frommodel to model, and the outputs are based on unequalcriteria for the determination of hazard zones, a high de-gree of caution is required in the interpretation of the

model results. Five models are tested and compared. Allthese models are available with a user interface, but dif-fer significantly in the demands on the user expertise andin the amount and content of model output. With highercomplexity of the model, uncertainties in the model esti-mates can be reduced to some extent. For this reason, theuse of more complex model systems is recommendedfor stationary emergency response applications (indus-trial plants) as environmental effects (e.g. the impactof buildings and topography on the flow field) shouldbe considered in these applications. For mobile applica-tions, however, complex calculations that take into ac-count terrain, buildings, etc. are not possible in the cur-rent state of technology.

The use of chemical sensors for source-to back calcu-lations is – according to experiences of the fire-brigades– feasible for chemical plants but not in the case of anaccident somewhere in a city like Vienna. The use of arelatively simple and robust program, which deliberatelyrequires only few input parameters, in this case appearsto be preferable at the present state.

The comparison of the hazard distances estimatedwith the different models for selected reference scenar-ios shows a relatively big variability. The differences inthe model results are mainly due to different require-ments on the input parameters but also on different inter-nal model assumptions. The model comparison revealsthat none of the models tends in general to be ’more con-servative’ than the others.

Besides of the choice of an appropriate model, thequestion of the optimum input data – e.g. concerning themeteorological input – needs to be considered. It must benoticed that the required meteorological parameters forthe calculation results are differently relevant: for mostsubstances, such as chlorine and ammoniac, wind speedsare most relevant as well as the atmospheric stability.Stable, low wind conditions result in largest hazard ar-eas from toxic gas releases. Depending on the chemicalcharacteristics of the toxic gas, the ambient air tempera-ture may also be important in respect to the release gastemperature.

For mobile applications, e.g. in case of a traffic acci-dent with a hazardous material transportation, the avail-ability of a near-by representative meteorological stationcannot be assumed. In this case meteorological analysisand forecast data offer a spatial coverage if the horizon-tal resolution is sufficient for dispersion calculations inthe local range. The analysis and forecast system INCAis found to give a good representation of wind directionand wind speed as well as air temperature and humid-ity for near-traffic sites in Vienna. Besides of real-timedata, the INCA-short range forecast (for the followinghours) may support the action planning of the first re-sponders e.g. the change of wind direction or the on-setprecipitation may be crucial for a planned evacuation,fire fighting or other countermeasures.


Acknowledgments

This work was funded by the KIRAS safety researchprogram of the Austrian Ministry of Transport, Innova-tion and Technology (www.kiras.at) under the researchcontract for the project RETOMOD (reference scenarioscalculations for toxic gas releases – model systems andtheir utility for the fire brigade) which was conductedby ZAMG in cooperation with the Vienna fire brigade,OMV Refining & Marketing GmbH and SyneX GmbH.

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Documents

Uncertainties in modeling hazardous gas releases for emergency