Journal
ENVIRONMENTAL RESEARCH
Volume 212, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2022.113181
Keywords
Groundwater monitoring; Network optimization; Information entropy; Principal component analysis
Funding
- Enterprise and Enter-prise Entrustment Project [40004016202001]
- Beijing Advanced Innovation Program for Land Surface Science
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The purpose of this study is to find a quantitative method to screen ideal monitoring locations and evaluate the efficiency of the monitoring network. Hydrogeological information, monitoring density, and monitoring location were used to select suitable sites for monitoring groundwater quality and identifying pollution sources. The efficiency of the monitoring network was evaluated using information entropy and principal component analysis, and the optimized network consisted of 10 monitoring wells.
The arbitrary distribution of groundwater monitoring sites and the redundancy of observation data restrict the ability of monitoring network to provide reliable and effective data information. The purpose of this study is aimed at finding a quantitative method to screen ideal monitoring locations and evaluate the efficiency of the monitoring network. In terms of site selection, we use hydrogeological information, monitoring density and monitoring location to select the suitable site to monitor groundwater quality, understand the temporal trends and identify the abnormal signals of pollution sources. To evaluate the efficiency of monitoring network we used the groundwater quality data for consecutive years to evaluate the groundwater monitoring network based on information entropy and principal component analysis (PCA). The results show that the optimized groundwater monitoring network is comprised of 10 monitoring wells. The efficiency evaluation results of information entropy and PCA are basically consistent. The maximum mutual information (T) and comprehensive index of monitoring site (Laiguangying) were 1.29 and 3.25 respectively, while the minimum T and comprehensive index of monitoring site (Jinzhan) were 1.05 and-0.36 respectively, and the data efficiency was low. This study provides a good example for optimizing a groundwater pollution monitoring network.
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