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Optimization of long-term groundwater monitoring indicators for contaminated sites in hazardous waste landfills
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Wen-hui ZHU1, Liang CHENG1, *, Gong-wei WANG2, Ke ZHAO2, Ya TAO1, Ran LU1
China Environmental Science | 2026, 46(1) : 258 - 268
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China Environmental Science | 2026, 46(1): 258-268
• Solid Waste •
Optimization of long-term groundwater monitoring indicators for contaminated sites in hazardous waste landfills
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Wen-hui ZHU1, Liang CHENG1, *, Gong-wei WANG2, Ke ZHAO2, Ya TAO1, Ran LU1
Affiliations
  • 1.Center for Innovation of Zero-waste Society, Center of Heavy Metal Environment Risk Prevention and Control, Chinese Academy for Environmental Planning, Beijing 100012, China
  • 2.Jiangxi Environmental Co., Ltd, Nanchang 330077, China
Published: 2026-01-20
Outline
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To address the shortcoming of lacking optimization and adjustment methods for long-term monitoring indicators of the groundwater risk control project at contaminated sites, a typical large-scale contaminated site impacted by hazardous waste landfill was focused in the present study. By collecting the existing groundwater monitoring data during the construction, effect evaluation and long-term monitoring periods, a feature dataset regarding exceedances of monitoring wells was constructed for decision-making optimization exploration. Further, the three decision tree models, including Chi-squared Automatic Interaction Detector (CHAID), Exhaustive CHAID(E-CHAID), and Classification and Regression Trees (CART), were employed to identify key monitoring indicators influencing groundwater monitoring well exceedances. The results indicated that it was feasible to use decision tree models to predict groundwater monitoring well exceedances. Based on the all evaluated metrics, including accuracy, precision, recall, and the F1-score (harmonic mean of precision and recall), the CART model significantly outperformed both the CHAID and E-CHAID models, suggesting that the overall optimization algorithm of the CART model was much suitable for predicting groundwater pollution exceedance events. 1,2,4-Trichlorobenzene and Nickel were identified as the most significant impact factors on the CART model's predictions. Moreover, the Fluoride, Petroleum Hydrocarbons, Trichloromethane, Dichloromethane, Cadmium, and cis-1,2-Dichloroethylene also demonstrated obvious influences. Consequently, it was recommended that the particular attention should be paid to the pollution dynamics of these 8key monitoring indicators during subsequent long-term monitoring of site groundwater.

hazardous waste landfills  /  contaminated sites  /  long-term groundwater monitoring  /  decision tree
朱文会, 程亮, 王恭伟, 赵珂, 陶亚, 卢然. 危险废物填埋污染地块地下水长期监测指标优化研究. 中国环境科学, 2026 , 46 (1) : 258 -268 .
Wen-hui ZHU, Liang CHENG, Gong-wei WANG, Ke ZHAO, Ya TAO, Ran LU. Optimization of long-term groundwater monitoring indicators for contaminated sites in hazardous waste landfills[J]. China Environmental Science, 2026 , 46 (1) : 258 -268 .
Year 2026 volume 46 Issue 1
85
11
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Article Info
  • Receive Date:2025-06-23
  • Online Date:2026-10-08
  • Published:2026-01-20
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  • Received:2025-06-23
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Affiliations
    1.Center for Innovation of Zero-waste Society, Center of Heavy Metal Environment Risk Prevention and Control, Chinese Academy for Environmental Planning, Beijing 100012, China
    2.Jiangxi Environmental Co., Ltd, Nanchang 330077, China
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表12种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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