Application of stochastic programming in groundwater pollution source identification. Issue 1 (4th March 2022)

• Record Type: Journal Article
• Authors: Shi, Chenxuan; Zhao, Ying; Lu, Wenxi
• Is Part Of: Environmental forensics. Volume 23:Issue 1/2(2022)
• Page Start: 170
• Page End: 178
• Publication Date: 2022-03-04
• Subjects: Groundwater pollution -- pollution source identification -- simulation-optimization method -- stochastic programming method; Environmental forensics -- Periodicals; Pollution -- Measurement -- Periodicals; Environmental law -- Periodicals; Enquêtes environnementales -- Périodiques; 363.25945
• DOI: 10.1080/15275922.2021.1892879
• Languages: English
• ISSNs: 1527-5922

Abstract

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%.