摘要
为提高工业集聚区土壤污染刻画模型的精度,以河北省某在产工业聚集区土壤重金属作为研究对象,对比研究距离权重法(IDW)、普通克里金(OK)、支持向量机(SVM)和梯度提升决策树(GBDT)等不同插值方法在处理浓度非平稳、有偏数据的刻画精度问题。结果显示:该场地土壤中主要污染物为As,GBDT在刻画土壤As污染时表现出最高的精度(R^(2)=0.9115);GBDT可视化结果发现,As的浓度分布格局为“斑块聚集”,并且表现出明显向深层迁移的趋势;相关性分析结果表明,As浓度在场地土壤中的分异行为主要与土壤岩性和水文地质条件有关;蒙特卡罗模拟风险评估结果显示,场地土壤成人和儿童的总致癌风险指数均超过指导值,并且儿童遭受的非致癌性和致癌性风险高于成人。
The heavy metals in the soil of an industrial agglomeration areas in Hebei Province were used to conduct a comparative study on the problem of characterization accuracy in handling non-stationary concentration and biased data with different interpolation methods including distance weighting(IDW),ordinary Kriging(OK),support vector machine(SVM),and gradient enhanced decision tree(GBDT)to improve the accuracy of models for characterizing the soil pollution in industrial agglomeration areas.The results showed that the main pollutant in the soil of this site was arsenic,and GBDT exhibited the highest accuracy in characterizing arsenic pollution in soil(R^(2)=0.9115).The results of GBDT visualization showed that the concentration distribution pattern of arsenic was"patchy aggregation"and had a good vertical migration capacity.The results of correlation analysis showed that the differentiation behavior of Arsenic concentration in the soil of this site was mainly related to the soil lithology and hydrogeological conditions.The results of Monte Carlo-based simulation showed that the total cancer risk index of both adults and children in the soil of this site exceeded the guidance value,and children suffered from higher non carcinogenic and carcinogenic risks than adults.
作者
卢合峰
阎秀兰
刘思言
苏艳超
杨潇
LU Hefeng;YAN Xiulan;LIU Siyan;SU Yanchao;YANG Xiao(Xingdong New Area Branch,Xingtai Ecological and Environmental Bureau,Xingtai 054001,China;Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences/Key Laboratory of Land Surface Patterns and Simulation,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shijiazhuang Pingshan Environment Monitoring Center,Shijiazhuang 050400,China)
出处
《华中农业大学学报》
CAS
CSCD
北大核心
2023年第6期185-195,共11页
Journal of Huazhong Agricultural University
基金
国家自然科学基金项目(42207456,U21A2023)。
关键词
土壤污染
空间分布
机器学习
梯度提升决策树
蒙特卡罗模拟
soil pollution
spatial distribution
machine learning
gradient boosting decision tree
Monte Carlo-based simulation
