Abstract

Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollution. The main basis for determining the characteristics of pollution sources is the pollutant concentration data of each observation well. However, due to the layout of the monitoring wells, the noise intensity of observation well concentration and unknown aquifer parameters, inversion results of groundwater pollution sources will be influenced significantly. Therefore, this article focuses on how to reduce its influence on the inversion results. In this article, a stochastic programming optimization model is introduced to explore its ability to control errors in complex situations. Results show that: in homogeneous aquifer, the normalized error (NE%) produced by simulation-optimization method is stable at about 2%, and the NE generated by stochastic programming method in confidence interval of 60%∼95% is between 0.20% and 5.42%. Moreover, stochastic programming model can effectively control the influence of noise. In the simulation-optimization model, when the noise intensity is 0.1 ∼ 0.5, the NE value is 2.01%∼12.68%, and the corresponding NE of stochastic programming model is 0.12%∼11.87%. Finally, this article considers the case that aquifer parameters are unknown (simultaneous identification of aquifer parameters and groundwater pollution sources). The results show that with the increase of the number of unknown aquifer parameters, the NE of the simulation-optimization model gradually increases from 1.16% to 8.75%. The NE value of the stochastic programming model decreases by 30% compared with the simulation-optimization model when the confidence level is 80%.

摘要

地下水污染严重威胁着生态环境和人类生活。要在污染发生后准确、高效地确定污染源的特征。确定污染源特征的主要依据是各观测井的污染物浓度数据。但受监测井布局、观测井浓度噪声强度及含水层参数未知等因素影响，地下水污染源反演结果将受到较大影响。因此，本文重点讨论如何降低其对反演结果的影响。在本文中，引入了一个随机规划优化模型来探索其在复杂情况下控制错误的能力。结果表明：在均质含水层中，模拟优化法产生的归一化误差（NE%）稳定在2%左右，随机规划法在60%~95%的置信区间内产生的NE在0.20%~5.42%之间。此外，随机规划模型可以有效控制噪声的影响。在仿真优化模型中，当噪声强度为0.1～0.5时，NE值为2.01%～12.68%，随机规划模型对应的NE值为0.12%～11.87%。最后，本文考虑了含水层参数未知的情况（同时识别含水层参数和地下水污染源）。结果表明，随着未知含水层参数数量的增加，模拟优化模型的NE从1.16%逐渐增加到8.75%。