| 土壤重金属含量变化的影响因素多目标识别方法 |
| 摘要点击 1878 全文点击 594 投稿时间：2023-08-22 修订日期：2023-11-12 |
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| 中文关键词 重金属 空间分析 影响因素 地理探测器 主成分分析（PCA） 地块尺度 |
| 英文关键词 heavy metal spatial analysis influencing factors geographical detector principal component analysis（PCA） patch scale |
| DOI 10.13227/j.hjkx.20240839 |
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| 中文摘要 |
| 识别土壤重金属含量变化影响因素是减少或防治土壤重金属污染的基础.以北京市昌平区农业试验田为例,首先分析2012和2022年重金属As、 Cr、 Cu、 Ni、 Pb和Zn的含量变化；其次基于地理探测器分别从单目标和多目标水平探测重金属含量变化的影响因素；最后设置与相关性分析方法和已有研究的对比试验,评估影响因素识别方法的有效性.结果表明人类活动因素加剧了研究区域内土壤重金属含量的变化：①在单目标水平探测上,土地利用类型是Cr、 Cu和Zn含量变化的主要影响因素,而年沉降通量影响As的含量变化；交互探测结果显示各个因子间均具有增强效应,人类活动因素的交互作用占主导；②多目标水平探测结果涵盖单目标水平探测结果且能够识别出更多的影响因素,土地利用类型影响Cu、 Zn、 Cr、 Ni和As的含量变化,As和Zn含量变化受到年沉降通量影响；③耦合地理探测器和主成分分析的多目标识别方法可以更加有效地识别出土壤重金属含量变化的影响因素,比单个土壤重金属相关性分析方法更为有效.研发的重金属含量变化影响因素多目标识别方法可为区域土壤重金属污染监测与宏观管理提供技术支撑. |
| 英文摘要 |
| Identifying the influencing factors of soil heavy metal content changes is the basis for reducing or preventing soil heavy metal pollution. Taking an agricultural experimental field in Changping District of Beijing as an example, the heavy metal content changes in As, Cr, Cu, Ni, Pb, and Zn from 2012 to 2022 were firstly analyzed. Secondly, the influencing factors of the heavy metal content changes were detected based on the geographical detector at the single-target and multi-target levels, respectively. Finally, comparative experiments with the correlation analysis method and existing studies were set up to evaluate the effectiveness of the identification method of influencing factors developed in this study. The results showed that human activity factors have exacerbated the changes in soil heavy metal content in the study area as follows： ① At the single-target level, the land use type was the main influencing factor on the changes in Cr, Cu, and Zn contents, and the annual deposition flux influenced the changes in As. The results of the interaction detection showed that there was an enhancement effect among the factors, and the interaction of the human activity factors dominated for the factor identification. ② The results of the multi-target level detection covered the results of the single-target level detection, which could identify more influencing factors. The land use type affected the changes in Cu, Zn, Cr, Ni, and As, and the changes in As and Zn were influenced by the annual deposition fluxes. ③ The multi-target identification method coupled with geographical detector and principal component analysis could effectively identify the influencing factors of soil heavy metal content changes, which was much more effective than the single soil heavy metal correlation method. The developed multi-target identification method for influencing factors of heavy metal content changes can provide technical support for the regional pollution monitoring and macro-management of soil heavy metals. |
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