融合污染源解析的矿区土壤重金属空间建模  

Spatial modeling of soil heavy metals in mining areas incorporating pollution source analysis

作　　者：李世杰 张晖 冯徽徽 王珍 LI Shi-jie;ZHANG Hui;FENG Hui-hui;WANG Zhen(Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen 518000,China;School of Earth Science and Information Physics,Central South University,Changsha 410083,China;Central South Survey and Planning Institute,National Forestry and Grassland Administration,Changsha 410083,China;Development Research Center for Natural Resource and Real Estate Assessment,Shenzhen 518000,China)

机构地区：[1]自然资源部城市国土资源监测与仿真重点实验室,广东深圳518000 [2]中南大学地球科学与信息物理学院,湖南长沙410083 [3]国家林业和草原局中南调查规划院,湖南长沙410083 [4]深圳市自然资源和不动产评估发展研究中心,广东深圳518000

出　　处：《中国环境科学》2025年第3期1444-1455,共12页China Environmental Science

基　　金：国家重点研发计划(2022YFD1700100);自然资源部城市国土资源监测与仿真重点实验室开放基金资助项目(KF-2022-07-021);湖南省自然科学基金杰出青年项目(2024JJ2071)。

摘　　要：以某典型矿区为例,结合数理统计分析以及PMF源解析等手段,定性定量识别区域重点污染源及其贡献特征.在此基础上,筛选主导环境变量和最佳空间尺度,研究构建顾及污染源空间特征和主导环境因子的矿区土壤重金属空间模型.研究结果表明,研究区的重金属污染来源包括自然源、废气排放源、废渣排放源、废水排放源和交通源,综合贡献率分别为8.40%、9.55%、1.73%、55.37%、24.99%,大气沉降(q=0.113)和土壤淋溶(q=0.097)是主要的重金属输入和输出路径.不同建模策略的土壤重金属模拟结果差异较大,综合考虑污染源空间特征与环境变量的模型预测精度最高,其次为基于主导环境因素的模型,基于污染源空间特征的模型预测效果相对较差;在建模方法上,地理加权回归克里格(GWRK)模型在不同数据聚合下展现了较高的预测精度(mRadius=0.2916).本研究结果为扩展矿区土壤污染风险区识别的新思路提供了科学依据,强化了对土壤重金属污染影响因素与含量之间生态环境效应的认识,并为分区防治工作提供了有效的参考.Taking a typical mining area as an example,statistical methods and Positive Matrix Factorization(PMF) were integrated to qualitatively and quantitatively identify key regional pollution sources and their contributors.A spatial model was further constructed,considering the spatial heterogeneity of soil heavy metal pollution and its dominant environmental drivers,with the best environmental variables and spatial scale being selected.The results revealed that the sources of soil heavy metal pollution were natural sources,exhaust gas emission sources,slag emission sources,wastewater emission sources,and transportation sources,with contributions of 8.40%,9.55%,1.73%,55.37%,and 24.99% of the total pollution,respectively.Notably,atmospheric deposition(q =0.113) and soil leaching(q=0.097) were identified as the primary input and output pathways for heavy metals.Among various spatial modeling strategies,the model that integrated both spatial pollution source characteristics and environmental variables demonstrated the highest predictive accuracy,outperforming the model based solely on dominant environmental factors or pollution source characteristics.The importance of incorporating spatial information to enhance model performance was highlighted by this finding.In particular,the Geographically Weighted Regression Kriging(GWRK) model was found to achieve superior predictive accuracy(mRadius=0.2916) when multiple data sources were integrated.Overall,a scientific foundation was provided for identifying high-risk soil pollution zones in mining regions,the understanding of ecological and environmental interactions between influencing factors and heavy metal contamination was enhanced,and valuable insights were offered for spatially targeted pollution control strategies.

关 键 词：土壤重金属 重点污染源 地理探测器 空间预测 

分 类 号：X53[环境科学与工程—环境工程]