摘要
为了准确评价强异质性区域农田重金属的潜在生态风险,本研究选取河北省张家口市宣化区工矿周边农田土壤为研究对象,基于土壤中Cd、Cr、Cu、Hg、Ni、Pb和Zn元素含量数据,分别采用传统确定性方法(TCM)中的Hakanson生态风险模型,以及该方法与蒙特卡洛-纠偏模型(MCS-COR)、蒙特卡洛-三角模糊数模型(MCS-TFN)的耦合模型对该区域重金属潜在生态风险进行评价,并综合结果不确定性、模型稳定性分析模型结果差异,确定最优评价方法。与TCM相比,MCS-COR和MCS-TFN模型均能有效降低评价结果的不确定性,不确定性分别从281%降至209%和118%,其中MCS-COR模型在模型稳定性上表现更佳,训练集和验证集的相对偏差小于20%。研究表明,MCS-COR更适合偏正态分布数据的风险评价。该区域生态风险综合等级为中等风险,其中Cd潜在生态风险最为突出,主要分布情况为中等风险占46.03%、较强风险占24.98%;其次是Hg,为中等风险(29.87%)和较强风险(21.95%);而Pb、Cu、Zn的生态风险较小,99.99%为轻微风险。
In order to accurately assess the potential ecological risk of heavy metals in farmland in a strongly heterogeneous area.This study selected farmland soils near industrial and mining sites in Xuanhua District,Zhangjiakou City,Hebei Province,as the research object.Using measured concentrations of Cd,Cr,Cu,Hg,Ni,Pb,and Zn in soil samples,the potential ecological risk posed by these heavy metals was evaluated with three approaches:the traditional deterministic Hakanson ecological risk model(TCM),and two coupled models:TCM integrated with Monte Carlo simulation and bias correction(MCS-COR),and TCM integrated with Monte Carlo simulation and triangular fuzzy numbers(MCS-TFN).The differences in model results were analyzed from the perspectives of result uncertainty and model stability to determine the optimal assessment method.Compared with TCM,both MCS-COR and MCS-TFN models can effectively reduce the uncertainty of the assessment results from 281%to 209%and 118%,respectively,and the MCS-COR model performs better in terms of model stability,with the relative bias between the training set and validation set less than 20%.The study shows that MCS-COR is more suitable for risk evaluation of skewed normal distribution data.The comprehensive ecological risk level of the region was medium risk,in which the potential ecological risk of Cd was the strongest,which was mainly distributed in medium risk(46.03%)and stronger risk(24.98%);followed by Hg,which was medium risk(29.87%)and stronger risk(21.95%);while the ecological risks of Pb,Cu and Zn were small,with 99.99%probability of slight risk.
作者
卢合峰
王子祥
张有军
阎秀兰
杨潇
师碧玲
王德宁
从辉
LU Hefeng;WANG Zixiang;ZHANG Youjun;YAN Xiulan;YANG Xiao;SHI Biling;WANG Dening;CONG Hui(Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Resources,Chinese Academy of Sciences,Beijing 100101,China;Xingtai Ecological Environment Comprehensive Law Enforcement Detachment,Xingtai 054001,China;College of Resources and Environment,Huazhong Agricultural University,Wuhan 430070,China;Tianjin North China Geological Exploration Institute,Tianjin 300170,China;China Oil&Gas Pipeline Network Corporation,Beijing 100028,China)
出处
《农业环境科学学报》
北大核心
2026年第2期411-423,共13页
Journal of Agro-Environment Science
基金
国家重点研发计划项目(2023YFD1702700)
国家留学基金委员会项目(202304910160)
天津市科技计划项目(22YFYSHZ00310)。
关键词
农田土壤
重金属污染
生态风险评价
模型改进
不确定性分析
agricultural soil
heavy metal pollution
ecological risk assessment
model improvement
uncertainty analysis
