文章基本信息

标题：Accuracy, Conservatism and Parsimony of Three Vapour Intrusion Models Used in Sweden
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作者：Jeroen Provoost ; Stephanie Nouwen ; Jan Bronders 等
期刊名称：Modern Applied Science
印刷版ISSN：1913-1844
电子版ISSN：1913-1852
出版年度：2018
卷号：12
期号：10
页码：128-148
DOI：10.5539/mas.v12n10p128
语种：English
出版社：Canadian Center of Science and Education
摘要：This study presents an evaluation of three screening-level models, namely the Dilution Factor (DF) model from 1996, the update version from 2005, as well as the Johnson and Ettinger model (JEM) from 1997, that are applied within the frameworks for contaminated land management (CLM) in Sweden. This evaluation applies, besides a deterministic approach (point estimate), a probabilistic assessment plus sensitivity analysis. The latter approach allows the models to be ranked according to conservatism, accuracy and parsimony by contrasting predicted and observed soil and indoor air concentrations for two contaminants (benzene and ethylbenzene), as to determine their suitability for application within CLM. The results and conclusions from this study suggest that the most accurately model for predicting the soil and indoor concentration is the JEM followed by the DF 2005 and 1996. Predictions of the soil air concentration are primarily driven by variation in physico-chemical parameters. The variation in indoor air concentration by physico-chemical and/or soil parameters for the DF models, while for the JEM soil parameters dominate. The deterministic analysis showed that default parameter values could be revised as to increase the conservatism, and be closer to the probabilistic 95-percentile predicted indoor air concentration. The DF 1996 model includes a limited number of parameters, and this analysis shows that a model with more parameters is more accurate, and less conservative. The DF 2005 seems to be the most parsimonious model as it is accurate, sufficiently conservative, and has 14 parameters, whereas the DF 1996 with 9 parameters is the most conservative and the JEM with 27 parameters the most accurate with an increased probability to produce false negative predictions. For the latter some of the dominant parameters cannot easily be measured on site.