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Systematically selecting an alternative to remediate soil contaminating groundwater
Author(s) -
Elmore Andrew Curtis
Publication year - 1996
Publication title -
remediation journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.762
H-Index - 27
eISSN - 1520-6831
pISSN - 1051-5658
DOI - 10.1002/rem.3440060206
Subject(s) - groundwater , hazardous waste , environmental science , environmental remediation , monte carlo method , frame (networking) , computer science , decision tree , selection (genetic algorithm) , soil contamination , contamination , waste management , engineering , soil water , soil science , data mining , statistics , machine learning , mathematics , geotechnical engineering , ecology , telecommunications , biology
Abstract Contaminated soil is a continuing source of ground water contamination at some hazardous waste sites. Even if that soil does not pose a threat to human health or the environment, soil remediation may benefit ground‐water cleanup in terms of time, money, or protectiveness. A method has been developed to provide a systematic manner to select a soil cleanup alternative. Using commercially available Windows‐based software, the method consists of the development of a decision tree whose chance nodes are the restoration time frame probability distributions. Uncertainty associated with site data is quantitatively evaluated using Monte Carlo analysis to develop the probability distributions. The decision tree selects the alternative with the lowest cost. Data from an actual remedial investigation/feasibility study demonstrate the ease and practicality of the selection method.