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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.

, authors=Wen-hui ZHU¹, Liang CHENG^{1, *}, Gong-wei WANG², Ke ZHAO², Ya TAO¹, Ran LU¹, authorsList=Wen-hui ZHU, Liang CHENG, Gong-wei WANG, Ke ZHAO, Ya TAO, Ran LU, authorCompany=null, correspAuthors=Liang CHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314922055431192829, articleId=1314922053661196538, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=危险废物填埋污染地块地下水长期监测指标优化研究, columnId=1240689598737731645, journalTitle=中国环境科学, columnName=固体废物, runingTitle=null, highlight=null, articleAbstract=

针对地块尺度地下水风险管控项目长期监测指标缺乏优化调整方法的短板，以某大型危险废物填埋污染地块为研究对象，通过收集施工期、效果评估期和长期监测期已有地下水监测数据，构建地下水监测井超标决策的特征数据集，并采用卡方自动交叉检验、穷举卡方自动交叉检验、分类与回归树三种决策树模型，识别影响地下水监测井超标的关键监测指标．结果表明，决策树应用于地下水监测井超标情况预测是可行的．分类与回归树模型在准确率、精度、召回率、精度和召回率的调和平均方面的性能均显著优于卡方自动交叉检验和穷举卡方自动交叉检验模型，分类与回归树模型的总体优化算法可能更适合地下水监测井超标情况预测．1，2，4-三氯苯和镍对分类与回归树模型地下水监测井超标情况预测的能力有非常重要影响，氟化物、石油烃、三氯甲烷、二氯甲烷、镉、顺-1，2-二氯乙烯也对该模型的预测能力有重要影响，建议在后续的地下水长期监测工作中着重关注这8项监测指标的污染变化．

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朱文会（1988-），男，江苏泰州人，高级工程师，硕士，主要从事土壤和地下水风险管控与修复研究．zhuwh@caep.org.cn.

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朱文会（1988-），男，江苏泰州人，高级工程师，硕士，主要从事土壤和地下水风险管控与修复研究．zhuwh@caep.org.cn.

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