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A Geostatistically Based Probabilistic Risk Assessment Approach

A Geostatistically Based Probabilistic Risk Assessment Approach

Claudia Cherubini
Copyright: © 2010 |Pages: 27
ISBN13: 9781605668161|ISBN10: 1605668168|ISBN13 Softcover: 9781616924485|EISBN13: 9781605668178
DOI: 10.4018/978-1-60566-816-1.ch013
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MLA

Cherubini, Claudia. "A Geostatistically Based Probabilistic Risk Assessment Approach." Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions, edited by Pedro Nuno San-Banto Furtado, IGI Global, 2010, pp. 277-303. https://doi.org/10.4018/978-1-60566-816-1.ch013

APA

Cherubini, C. (2010). A Geostatistically Based Probabilistic Risk Assessment Approach. In P. Furtado (Ed.), Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions (pp. 277-303). IGI Global. https://doi.org/10.4018/978-1-60566-816-1.ch013

Chicago

Cherubini, Claudia. "A Geostatistically Based Probabilistic Risk Assessment Approach." In Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions, edited by Pedro Nuno San-Banto Furtado, 277-303. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-816-1.ch013

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Abstract

Most data required for cleanup risk assessment are intrinsically characterized by a high degree of variability and uncertainty. Moreover, typical features of environmental datasets are the occurrence of extreme values like a few random ‘hot spots’ of large concentrations within a background of data below the detection limit. In the field of environmental pollution risk assessment constitutes a support method for decisions inherent the necessity to carry out a procedure of remediation of an area. Therefore it would be adequate to provide the analysis with elements that allow to take into account the nature of the data themselves, particularly their uncertainty. In this context, this chapter focuses on the application of an uncertainty modeling approach based on geostatistics for the parameters which enter as input in the probabilistic procedure of risk assessment. Compared with a traditional approach, the applied method provides the possibility to quantify and integrate the uncertainty and variability of input parameters in the determination of risk. Moreover, it has proved to be successful in catching and describing in a synthetic way the relations and tendencies that are intrinsic in the data set, characteristics that are neglected by a traditional classical approach.

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