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生态系统削减与截留效应及土壤潜在毒性元素空间分布的相关性分析

Correlation Analysis of Ecosystem Reduction and Retention Effects and Spatial Distribution of Soil Potential Toxicity Elements.

作者信息

Wang Junlei, Sun Sijing, Mu Liyuan, Zhang Naiming, Bao Li

机构信息

College of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China.

Yunnan Soil Fertilization and Pollution Remediation Engineering Research Center, Kunming 650201, China.

出版信息

ACS Omega. 2024 Dec 3;9(50):49214-49222. doi: 10.1021/acsomega.4c05994. eCollection 2024 Dec 17.

DOI:10.1021/acsomega.4c05994
PMID:39713666
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11656599/
Abstract

Soil contamination by potentially toxic elements (PTEs) poses a significant threat to crop quality and human health, making it a global concern. However, the distribution patterns of PTEs across different land-use types are not well understood. To investigate the relationship between the reduction and retention effects of various ecosystem types on soil PTEs, we analyzed five categories of target elements in 299 soil samples from the southeastern Yunnan Province. Using the intelligent urban ecosystem management system's surface source control (runoff) model, descriptive statistical methods, spatial interpolation analyses, and GIS, we simulated the effects of different ecosystem types on soil heavy metals. This approach allowed us to examine the spatial correlations among ecosystem reduction, retention, and PTE distribution in soils. Our results indicate that soil PTE concentrations were indicative of a high-background value area, with concentrations of arsenic, cadmium, copper, lead, and zinc exceeding risk screening values. The coefficients of variation for arsenic, cadmium, and lead were extremely high and attributable to high external anthropogenic interference. Soil heavy metal reduction and spatial distribution were affected by the ecosystem's control function, and different ecosystems had different reduction effects. The reduction simulations for As and Pb were concentrated in building areas, while those for Cd and Zn were primarily focused on water bodies. The reduction simulations for Cu were concentrated in the forested areas. In conclusion, ecosystem reduction and retention influence heavy metal distribution, which is essential when planning green ecological development and construction.

摘要

潜在有毒元素(PTEs)对土壤的污染对作物质量和人类健康构成了重大威胁,成为一个全球关注的问题。然而,人们对PTEs在不同土地利用类型中的分布模式了解不足。为了研究各种生态系统类型对土壤PTEs的削减和截留效应之间的关系,我们分析了来自云南省东南部的299个土壤样本中的五类目标元素。利用智能城市生态系统管理系统的面源控制(径流)模型、描述性统计方法、空间插值分析和地理信息系统(GIS),我们模拟了不同生态系统类型对土壤重金属的影响。这种方法使我们能够研究生态系统削减、截留与土壤中PTEs分布之间的空间相关性。我们的结果表明,土壤PTEs浓度表明该区域具有高背景值,其中砷、镉、铜、铅和锌的浓度超过了风险筛选值。砷、镉和铅的变异系数极高,这归因于高强度的外部人为干扰。土壤重金属的削减和空间分布受生态系统控制功能的影响,不同的生态系统具有不同的削减效果。对砷和铅的削减模拟集中在建筑区域,而对镉和锌的削减模拟主要集中在水体。对铜的削减模拟集中在森林区域。总之,生态系统的削减和截留影响重金属分布,这在规划绿色生态发展和建设时至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/19aff5cc3cd4/ao4c05994_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/5b6ed6542b84/ao4c05994_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/8e4c549a2cce/ao4c05994_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/0a8ef86d0f81/ao4c05994_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/19aff5cc3cd4/ao4c05994_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/5b6ed6542b84/ao4c05994_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/8e4c549a2cce/ao4c05994_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/0a8ef86d0f81/ao4c05994_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4705/11656599/19aff5cc3cd4/ao4c05994_0004.jpg

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本文引用的文献

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2
Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models.运用组合受体模型对宝山土壤中潜在有毒元素进行定量源解析
Toxics. 2023 Mar 14;11(3):268. doi: 10.3390/toxics11030268.
3
Characteristics, Health Risk Assessment, and Transfer Model of Heavy Metals in the Soil-Food Chain in Cultivated Land in Karst.
喀斯特地区耕地土壤-食物链中重金属的特征、健康风险评估及迁移模型
Foods. 2022 Sep 11;11(18):2802. doi: 10.3390/foods11182802.
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Quantitative source apportionment and associated driving factor identification for soil potential toxicity elements via combining receptor models, SOM, and geo-detector method.基于受体模型、自组织映射(SOM)和地理探测器方法的土壤潜在毒性元素定量源解析及相关驱动因素识别。
Sci Total Environ. 2022 Jul 15;830:154721. doi: 10.1016/j.scitotenv.2022.154721. Epub 2022 Mar 24.
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