| 地下水污染源识别的数学方法研究进展 |
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| 引用本文： | 王景瑞,胡立堂.地下水污染源识别的数学方法研究进展[J].水科学进展,2017,28(6):943-952. |
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| 作者姓名： | 王景瑞 胡立堂 |
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| 作者单位： | 1.北京师范大学水科学研究院, 北京 100875; |
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| 基金项目： | 国家自然科学基金资助项目（41572220）；北京市自然科学基金资助项目（J150002） |
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| 摘 要： | 地下水污染源识别模型可利用有限的观测资料估计污染源位置、污染物泄露强度及其泄露过程，是制定地下水污染修复方案的依据。在阐明地下水污染源识别基本问题基础上，综述了污染源识别研究的两大类数学方法，一类为直接方法，包括反向追踪法和基于正则化的方法；另一类为间接方法，包括基于优化的方法和基于概率统计的方法。同时指出了当前污染源识别数学方法应用中存在的主要问题:地下水污染源识别问题的复杂性、地下水有机污染问题和模型求解效率的低下性。对土壤-地下水的联合管理、基于物联网的地下水污染监测、对非水相流体（Non-aqueous Phase Liquid，NAPL）类污染源识别以及基于图形处理器（GPU）的异构并行计算将是未来研究的重点方向。
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| 关 键 词： | 地下水污染源识别 非适定性 优化算法 贝叶斯推理 非水相流体 |
| 收稿时间： | 2017-04-10 |
Advances in mathematical methods of groundwater pollution source identification |
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| WANG Jingrui,HU Litang.Advances in mathematical methods of groundwater pollution source identification[J].Advances in Water Science,2017,28(6):943-952. |
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| Authors: | WANG Jingrui HU Litang |
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| Affiliation: | 1.College of Water Sciences, Beijing Normal University, Beijing 100875, China;2.Engineering Research Center of Groundwater Pollution Control and Remediation of Ministry of Education, Beijing 100875, China |
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| Abstract: | Using limited observation data of groundwater quality, models of groundwater pollution source identification can be used to estimate the locations, leakage rate, and the dominant processes of the pollution sources, which thus can provide a reference for formulating remedial schemes for groundwater pollution. Based on the principles and theories of pollutant movement and source identification, this paper presents an overview of existing mathematical methods, including direct methods (inverse particle tracing methods and regularization-based methods) and indirect methods (optimization-based and probability-based methods). The main problems in the application of these methods are ① the complexity of groundwater pollution source identification, ② groundwater organic contamination, and ③ the low efficiency of model calculation. Integrated research of soil-groundwater systems, instrumentation-based groundwater pollution monitoring, identification of NAPL pollutants, and GPU-based heterogeneous parallel computing will be the keys to groundwater pollution source identification in the future. |
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| Keywords: | groundwater pollution source identification Ill-posedness optimization Beyesian inference non-aqueous phase liquid |
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