Methodological aspects for modeling the environmental risk of transporting hazardous materials by road
Introduction
Over the last few years there has been growing concern regarding the transportation of Hazardous Materials (HM), specially due to rise in the consumption of HM, which results in an increase in road transportation of these goods. Additionally, there is the issue raised by the dominance of transportation of hazardous materials by road (THMR) over other transport modes. For instance, 63% of hazardous materials are transported by road in Brazil (CETESB, 2005), and more than 90%, in the USA (Pedro, 2006, Panwhar et al., 2000). Other issues concerning the THMR in Brazil include the amount of built up areas along roads and vehicle fleet age (Nardocci and Leal, 2006). This increases the risk of the THMR for society and environment. Owing to this, there is a demand for environmental research to assess the risk involved in transporting hazardous goods, in order to reduce the risk and minimize damage caused by accidents involving the transportation of this kind of material.
Two kinds of models to assess risks involving hazardous material can be found in the literature: static and dynamic models. Static models analyze the risk at a fixed place, for example in an industrial plant (Planas et al., 2006). Considering that THMR is a mobile risk, since the material is constantly being moved through the environment, dynamic models are the most appropriate ones.
Additionally, dynamic models offer two kinds of analysis: those that include only the social risk, without considering the environmental variable, represent the most frequent analyses with risk and are determined by the road characteristics and the exposed population; those that involve both the social and the environmental risk, and therefore have become an important tool for management and decision making as they are more comprehensive.
In the dynamic models involving only the social risk, in order to characterize the road, various factors can be considered. Fabiano et al. (2002) considered the inherent factors such as: tunnels, railway bridges, high gradient, downward slopes, neighborhood characteristics and traffic conditions. According to the method used by Carotenuto et al., 2008, Bonvicini and Spadoni, 2008, the characterization of the road can be carried out by the probability of accidents happening in the road segment studied while hazardous goods are being transported. Verter and Kara (2001) point out that in order to determine the risk involved in transporting hazardous goods by road, the probability of a traffic accident varies according to the road’s features, such as the number of lanes.
There are also more complex systems which take into consideration the road’s features, the exposed population, the characteristics of the goods being transported, as well as weather conditions. In order to determine the risk of THMR, Bubbico et al. (2004a) included the accident rate related to the road’s features, the weather conditions and the population density related to the area’s characteristics. Afterward, Bubbico et al. (2004b) developed a risk analysis for road and rail transportation of hazardous goods using a GIS to manage land information in conjunction with a data bank of the goods. Nathanail et al. (2010) determined the social risk for transporting hazardous materials through tunnels, a method applied to Attica Tollway, an urban road in Athens, Greece. For the development of the model, the authors considered: the tunnels’ features; the type of hazardous material; the traffic characteristics; the probability of accidents involving THMR; and the population at risk (the number of people living within a certain distance along the route).
In dynamic models that include the environmental variable, models by Lepofsky et al., 1993, Martínez-Alegría et al., 2003, Pedro, 2006, Tixier et al., 2006 were found. Lepofsky et al. (1993) carried out the management of accidents and risk assessment in THMR using GIS in various case studies on Californian roads. The developed model related the probability of accident occurrence, the probability of leakage of hazardous goods being transported and the consequences of such leakage, measured by the exposed population and financial damage to environmentally sensitive areas.
Martínez-Alegría et al. (2003) carried out a comprehensive analysis of the road network of Valladolid, Spain defining the areas exposed to higher risk. To determine the environmental risk of THMR, the authors related the environmental and population vulnerability, as well as the probability of occurrence. To determine the environmental vulnerability, the authors created a hierarchical list of elements to be considered. They proposed to use a weighing scale ranging from 0 to 4 to determine the vulnerability, without considering the characteristics of each place where the model would be applied. Afterward, Pedro (2006) applied the methodology by Martínez-Alegría et al. (2003) in Campinas-SP, Brazil based on the probability of accident occurrence and the severity of environmental damage.
Tixier et al. (2006) proposed a method to map the environmental vulnerability around an industrial plant in France. For this, a study was carried out in order to use the criteria according to the characteristics of the region and the following numbers were obtained: 75% human vulnerability; 20% environmental vulnerability and 5% material damage. The 75% for human vulnerability is clear, mainly because the study was applied in a densely built-up area. Notwithstanding, those percentages criteria may vary depending on the area where the THMR will be.
However, no consensus was found in the researched literature regarding the use of the criteria and factors that would compose the environmental vulnerability in these dynamic models. The main goal of this study is to establish procedures for modeling environmental risk of traffic accidents involving hazardous materials. Also, this model can be used to create a map of environmental risk by using a GIS, which could be applied to different types of locations.
Section snippets
Development of methodological procedures
The following paragraphs define the fundamental concepts used as the theoretical basis for the development of the methodological procedures of this work. In the next section, the developed risk model is presented.
Development of the risk model
Eq. (1) expresses risk as the product of the probability that an accident will to occur (P) by the severity of the damage (G). Firstly, the variables were chosen through literature review. Then, the most significant of these variables were selected by an expert panel.
Step-by-step to implement the developed risk model
To apply the developed risk model, a methodological approach was proposed which is divided into four different steps as shown in Fig. 1. Step 1, step 2 and step 3 have to be done to adapt the model to the needs of the place where the risk assessment is intended, whereas during step 4 the maps are generated using the GIS tool. These steps are described in the following paragraphs.
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Step 1: Characterization of the area under study
First of all, it is essential to define the segment of road to be
Example of application
This section presents an example of application of the four steps in three locations of the same road. The study was carried out in a segment of Raposo Tavares road that connects the cities of Santa Cruz do Rio Pardo and Ourinhos, in São Paulo State. The simulation considered the characteristics of this segment between the kilometers 0–32.
Conclusions
This research proposed a methodological framework for mapping the environmental risk of THMR. The multicriteria analysis employed proved to be feasible with regards to the selection and weighing of the criteria by an expert panel, which can be applied for different locations. A simple approach was taken through the elimination of the main limitations usually found in complex models such as, specialized personnel and difficulties in collecting and organizing large amounts of data. Such complex
Acknowledgements
The authors would like to thank CAPES/Brazil and FAPESP/Brazil (No. 2012/16886-5) for the grant received to develop this work.
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