Aquanty, Inc., 564 Weber Street North, Unit 2, Waterloo, ON, N2L 5C6, Canada; Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309-0428, USA.
J Contam Hydrol. 2022 Jan;244:103909. doi: 10.1016/j.jconhyd.2021.103909. Epub 2021 Oct 22.
Contaminant source identification improves the understanding of contaminant source characteristics including location and release time, which can lead to more effective remediation and water resources management plans. The backward probability model can provide probabilities of source locations and release times under various contaminant properties and hydrogeologic conditions. The backward probability model has been applied to numerous synthetic and real contamination sites for locating possible contaminant sources, but it is also important to evaluate the reliability of the backward probability model through rigorous verification analyses. Here, we present a model verification framework for the backward probability model using a stepwise approach from simple to complex model settings: comparison with previous studies, transient saturated flow under various hydrogeologic conditions, and transient variably-saturated flow conditions. As a simple condition, one-dimensional homogeneous problems under steady-state and transient flow conditions were verified by comparing with previous studies. Model verifications with complex conditions were conducted by comparing forward and backward probability simulation results. The verification results demonstrate that the backward probability model performs well for homogeneous problems. For heterogeneous problems, the backward probability model results in slightly different backward travel times due to differences in solute decay and boundary conditions assigned for both forward and backward probability simulations, but the backward travel time at the maximum probability can be reproduced well.
污染物溯源可以提高对污染物来源特征的认识，包括位置和释放时间，从而制定更有效的修复和水资源管理计划。后向概率模型可以提供在各种污染物特性和水文地质条件下的污染源位置和释放时间的概率。该后向概率模型已应用于许多合成和实际污染场地，用于定位可能的污染源，但通过严格的验证分析评估后向概率模型的可靠性也很重要。在这里，我们提出了一种后向概率模型的模型验证框架，采用从简单到复杂模型设置的逐步方法：与先前研究的比较、各种水文地质条件下的瞬态饱和流以及瞬态非饱和流条件。作为一个简单的条件，通过与先前的研究进行比较，验证了稳态和瞬态流动条件下的一维均匀问题。通过比较正演和反演概率模拟结果，对复杂条件下的模型进行了验证。验证结果表明，后向概率模型在均匀问题上表现良好。对于非均匀问题，由于正演和反演概率模拟中溶质衰减和边界条件的差异，后向概率模型会导致略微不同的反向迁移时间，但最大概率处的反向迁移时间可以很好地再现。
