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首页> 外文期刊>Journal of Civil Engineering and Science >Risk-Based Characterization for Vapour Intrusion at a Conceptual Brownfields Site: Part 1. Data Worth and Prediction Uncertainty
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Risk-Based Characterization for Vapour Intrusion at a Conceptual Brownfields Site: Part 1. Data Worth and Prediction Uncertainty

机译：基于概念的Brownfields站点的基于风险的蒸汽入侵表征：第1部分。数据价值和预测不确定性

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摘要

The focus of this paper is to present a methodology to assimilate soil core permeability and trichloroethylene (TCE) soil gas concentration data, and then to assess their worth in reducing prediction uncertainty with a numerical model. The specific problem involves a residential development impacted by indoor air exposure of TCE contamination originating from a groundwater plume. Three metrics are used to quantify the prediction uncertainty, namely: the ability to accurately predict the indoor air concentration within the houses at any point in time; the ability to reduce the standard deviation of predicted indoor air concentration within these houses; and, the ability to accurately forecast the probability of indoor air concentrations exceeding a regulatory limit. The data assimilation methodology involves generating multiple realizations of heterogeneous permeability fields conditioned upon a geostatistical analysis of the borehole data, combined with a discrete static Kalman filter to assimilate actual soil gas concentration data, to estimate soil gas and indoor air concentrations at those locations where the developer does not have any data but liability. The worth of using progressively more permeability and soil gas concentration data is quantified on the basis that it provides a statistically significant improvement in the three metrics used to measure prediction uncertainty.
机译：本文的重点是提出一种吸收土壤核心渗透率和三氯乙烯（TCE）土壤气体浓度数据的方法，然后通过数值模型评估它们在减少预测不确定性方面的价值。具体问题涉及一个住宅开发项目，该项目受到室内空气暴露于源自地下水羽流的TCE污染的影响。使用三个指标来量化预测不确定性，即：在任何时间点准确预测房屋内室内空气浓度的能力;减少这些房屋中预测室内空气浓度的标准偏差的能力;以及能够准确预测室内空气浓度超过规定限值的可能性。数据同化方法涉及根据井眼数据的地统计学分析生成多种非均质渗透率场，并结合离散静态卡尔曼滤波器来吸收实际的土壤气体浓度数据，从而估算出土壤气体和室内空气浓度。开发人员没有任何数据，但有责任。使用逐步增加的渗透率和土壤气体浓度数据的价值被量化为基础，因为它在用于测量预测不确定性的三个指标上提供了统计上显着的改进。

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