Giuseppe Maschio

Abstract: Following recent severe natural events, attention h as been focused on industrial installations located in areas prone to natural hazards. This work concerns the st udy of volcanic Na-Tech events (i.e. technological risks triggered by natural causes) and aims at defining a procedure for the representation of the vulnerabil ity of industrial facilities in areas with the potential v olcanic ash fallout by means a Geographical Informa tion System (GIS). Here, we focused on the construction of a se mi-automatic procedure for the vulnerability mappin g for cases where input data is very limited; it is based on the use of a specific tool named ModelBuilder o f the ArcGIS software. Ancione Giuseppa, Milazzo Maria Francesca, Salzano Ernesto, Maschio Giuseppe Semi-automatic geo-processing procedure for the vulnerability mapping of industrial facilities in areas with the potential volcanic ash fallout 2 This paper is focused on the implementation on a Geographical Information System (GIS) of the procedure developed by [8] for the vulnerability assessment of industrial facilities. The easier management of geographical data (georeferenced) and other related information, through a GIS software, allows to calculate the vulnerability associated with each point of the territory and, th en, the cartographical representation. The use of a GIS , in this case, also allowed the development of semiautomatic procedures for the vulnerability mapping. In section 2 the implementation of the semiautomatic procedure is described. A case-study, related to the area surrounding the volcano Etna (Italy) and describing the application of the whole methodology, is given in the third part of the pape r. 2. Methodology for vulnerability mapping The methodology described in this section is a generic and simplified approach for estimating the vulnerability of industrial facilities to volcanic NaTech events. As mentioned above, it has recently been proposed by [8]. The whole approach is summarized in the flow-chart of Figure 1. Figure 1. Flow-chart for the representation of vulnerability The first step concerns the choice of a specific volcanic phenomenon (characterised by a given occurrence and magnitude) and the selection of a vulnerable facility located in the surrounding of t he volcano. Hence, it is necessary to define a potenti al damage with respect to the physical parameter, associated with the magnitude of the selected volcanic phenomenon, which causes the failure of the facility. Then, a threshold value of the parame ter and the probability of exceeding this limit must be estimated. Finally, by using both these estimates, the vulnerability mapping is possible. In order to apply the procedure of Figure 1, a general knowledge of the territory is necessary, as well as a careful collection of meteorological statistics and historical information about the volcanic eruptions . This information is important to derive a number of simulation maps from which the exceedance probability curves of physical parameters are derived. This contribution focuses on the last step of the method of Figure 1. Quite clearly, in order to achieve the vulnerability mapping, it is necessary to estimate the probabilities also for the points wher e these are not known. This operation can be made through a spatial interpolation method. To this regard, it is worth mentioning that there are sever al interpolation procedures, each of them characterize d by a different time for the data elaboration, accur a y, sensitivity to parameters variation and degree of smoothness of the interpolated surface. These procedures can be grouped in two main classes: deterministic and stochastic methods [6]. Deterministic approaches are based on a correlation among neighbouring points whose parameters have an explicit physical meaning; stochastic methods correlate neighbouring points through a statistical relation. The class of deterministic procedures includes geometric methods (area interpolation method), Inverse Distance Weighting (IDW) method (named also Point Interpolation Method); finally th e class of stochastic methods procedures includes the Kriging and Cokriging methods [6]. The spatial interpolation assumes that the data is a continuous spatial function [16]. Waters [18] state s that using data related to a series of points, as i n our case, the choice of interpolation method is crucial because it approximates a representation in the spa ce of the physical phenomenon (in this case volcanic ash fallout). Both the quality of the original data nd the interpolation method are essential to give a reliable estimate. Some causes of a non-optimal estimate are: a) few available points; b) limited spatial coverage of the points; c) uncertainty about the location and the value of the measured physical quantity. Volcanic phenomenon selection

TL;DR: In this paper, the authors analyzed the influence of the volcanic ash fallout on the potential damage of tanks located in the area surrounding Mt. Etna and found that the amount of the overhead on the roof is much greater when the ash is saturated with rain because of the formation of aggregates.

TL;DR: In this paper, a large eddy simulation approach is applied to fuel matrixes in form of piles of different sizes, made of polyolefins and polyvinylchloride burned in an open field.

TL;DR: The significant correlation between the two components suggests that in renal disease a simultaneous aggregation of the two proteins in factor VIII takes place, and might suggest incipient atherosclerotic damage.