A dynamic approach for the impact of a toxic gas dispersion hazard considering human behaviour and dispersion modelling
Introduction
Toxic gas dispersion can be the result of natural or industrial accidents or terrorist attacks [1], [2]. These gases can be released in the atmosphere from three prime sources: dispersion of toxic gases; dispersion following an explosion; and dispersion of combustion products following a fire of harmful chemicals [3]. Toxic gas dispersion could be hazardous for humans since, depending on the atmospheric conditions, it can affect people located at large distances from the point of the initial source [4].
The intent of risk management is to reduce the frequency of accidental releases of toxic gases and to minimize the consequences of releases that do occur [5]. According to Pietersen [6], the evaluation of the consequences of toxic gas dispersions includes three steps: (1) source term estimation; (2) transport/dispersion modelling of the toxic gas; (3) toxic gas concentration estimation; and (4) evaluation of the impact of the toxic gas concentrations on the people.
To predict the gas dispersion and gas concentrations, many different models have been developed in the past decades [7], [8]. These models can be classified in accordance with the mathematical approach in use [1]. It is possible to distinguish different categories: (1) Gaussian models; (2) Box models; (3) Lagrangian particle and puff models; (4) analytical models; and (5) computational fluid dynamics (CFD) models [9]. In the past decades, the increment in computational power has allowed the spread of the use of CFD models to investigate a number of indoor [8] and outdoor accident scenarios such as railway accidents [10], gas dispersion in urban areas [11], [12], [13], [14] or railcar releases [15]. This type of models has been in some instances more effective in representing scenarios with complex geometries, such as urban areas [14]. However, the selection of the gas dispersion model can be affected by several other factors, such as the scale of the problem.
The evaluation of the impact of the toxic gas concentrations on humans is the final step of risk assessment. To address this issue, several approaches and indices have been introduced in the literature, such as lethal dose, lethal concentration, level of concern, etc. [1], [5], [16]. The toxic absorbed dose is the amount of toxic substances that got into the body through the eyes, skin, stomach, intestines, or lungs. The absorbed dose by inhalation is generally calculated by multiplying the concentration of the gases (to the power of an empirical constant) and the exposure time [17]. This dose is often used to calculate the probability of death by the exposure to a toxic substance for a given time using probit models [18], [19], [20], [21]. Another approach available to evaluate the health impact rate of a person is the one adopted in fire safety engineering for the case of fire enclosures. In this case, the health impact rate for each person is calculated using the Fractional Effective Doses (FED) [22]. The FED is calculated by integrating the area under the concentration/time curves for the toxic products inhaled by a person and dividing this amount by the effective dose to cause incapacitation or death [23]. Therefore, incapacity or death occurs whenever the FED exceeds one.
Despite the existence of different methodologies to perform risk analysis in case of toxic gas dispersion, the main issue of the existing methods is that while sophisticated indices which may consider people behavioural activities are often used for hazard in enclosures (e.g. FED calculations) [24], [25], [26], the health impact indices in open spaces are generally calculated assuming a ‘stationary observer’, i.e. the cumulative absorbed dose over time is calculated for static points or areas of a threatened environment. In other words, this approach assumes that a person stays in a fixed point for the whole duration of the release without taking into account people attempts to escape from these areas [1], [14], [27]. For this reason, existing methods do not fully take into account human behaviour during emergencies. By contrast, risk assessment should take into account the evacuation of people from threatened areas to a safe place and calculate the health impact rate of people accordingly (considering their position over the time, i.e. their trajectories). To date, several pedestrian and traffic evacuation models have been developed to predict human behaviour and movement for both indoor and outdoor scenarios [28], [29], [30], [31]. Those models may work on different scales, ranging from small to larger city scales [32], [33]. However, despite the availability of these evacuation models, an approach for risk assessment in open spaces considering gas dispersion modelling and human behaviour modelling has not been fully investigated in the literature. To date, human factors have been investigated using modelling approaches based on assumptions on people human behaviour (i.e. the evacuees move on a predefined trajectory in a network with pre-defined constant speed without interact with each other) [21] or considering agent-based models for vehicle representation which have still not applied for human movement [34]. Furthermore, to date, no systematic comparison of different approaches is available.
The main objective of this work is to introduce a methodology considering both physical and human behaviour modelling to investigate the impact of the toxic gas concentrations on people in open spaces and compare results of static and dynamic approaches adopting different levels of behavioural complexity in the modelling assumptions. Compared with previous research on the topic [21], the proposed method uses an advanced modelling representation of human behaviour, i.e., microscopic agent-based modelling and it directly addresses people movement rather than vehicle movement [26]. Agent-based modelling is considered a high-level modelling approach in evacuation modelling if compared with simpler pedestrian flow calculations since it allows complex interactions among pedestrians (including the occurrence of emergent behaviours). This approach aims at improving the accuracy of risk assessment for disasters involving toxic gas dispersion. The proposed methodology is presented through a hypothetical exemplary scenario, which includes a ship releasing Nitrogen dioxide (NO2), affecting a crowd attending a music festival [35], [36]. In the case study, the gas dispersion is simulated using the Fire Dynamics Simulator model (FDS 6.1.2) [37]. The crowd evacuation is modelled using Pathfinder [38], and the evacuees’ health impact rate is calculated using the modified Haber’s law [17]. The results of this study are analysed using a probabilistic spatial-temporal analysis based on probit models for NO2 [27], [39], which allows identifying the areas having the highest probabilities of mortality. It is important to stress that the goal of the present study is to provide a proof-of-concept of the approach. In other words, it is not the intention to obtain the highest level of accuracy possible for CFD, nor for the evacuation modelling. In contrast, this study wants to show the feasibility and application of a dynamic approach and the differences in the results obtained in comparison with a static approach.
Section snippets
Method
This section introduces a methodology for toxic gas dispersion risk assessment. The proposed method allows performing risk analysis, including the simulation of behaviour of individuals threatened by the toxic gases, as illustrated in Fig. 1.
The methodology starts with the identification of a scenario. This scenario could either be hypothetical or represent a real accident. In case of hypothetical scenarios, different criteria can be used to identify the relevant hypothetical scenarios for risk
Case study
The approach introduced in Section 2 is applied to a hypothetical evacuation scenario, based on the case study developed in [35], [36]. The scenario concerns an outdoor music festival in an area restricted by fences. The festival area is close to a residential area, a river and major road transport infrastructure (highway and secondary roads). Only a portion of the festival is considered in the present case study. The number of attendees is calculated considering an average initial density of 3
Results
The concentrations of NO2 released from the ship have been calculated in a horizontal plane, set at 1.6 m height, as illustrated in Fig. 4b. This plane represents the closest one to the position of the nose and mouth of the evacuees [78]. Output concentrations have been recorded every second. Nitrogen dioxide (NO2) is the main harmful chemical specie based on the decomposition reaction of ammonium nitrate considered in this study hence it is considered here. As it has been previously reported,
Discussion
Including behavioural modelling in hazard risk assessment allows investigating the impact of different human factors and evacuation strategies, such as way-finding, dynamic availability of exits, flow through exits, the impact of density conditions at the beginning and during the emergency, etc. Considering human factors in a risk assessment in case of gas dispersion can help disaster managers to reduce the risk of mortality and injury and improve the people resilience in case of emergencies.
Conclusion
This work introduced an approach to perform risk assessment that considers human behaviour and gas dispersion modelling. The proposed method expands previous attempts to model human factors [21] by using an advanced modelling approach for human behaviour (i.e. a microscopic agent-based approach). The main advantage of the approach is that the absorbed dose of a toxic gas is calculated considering that people threatened by a toxic gas are not ‘stationary observers’, but they move towards a safe
Acknowledgements
This work is part of the Seventh Framework Programme EU project CascEff − Modelling of dependencies and cascading effects for emergency management in crisis situations, Grant agreement No: 607665. The CascEff project aims to improve the emergency response in incidents that involve cascading effects. The authors wish to acknowledge the members of the project consortium. The authors also thank Bart Bruelemans, Wim Van de Vijver and Kris de Troch for their help in the definition of the model case
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