Sources apportionment and spatial prediction of soil heavy metal pollution using UNMIX model and multivariate statistical simulation

被引:0
|
作者
Yang Q. [1 ]
Wang L. [2 ]
Li P. [2 ]
Lyu L. [1 ]
Fan Y. [1 ,3 ]
Zhu G. [1 ]
Wang Y. [2 ]
机构
[1] School of Public Administration, Nanjing University of Finance & Economics, Nanjing
[2] Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing
[3] The Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing
关键词
heavy metal; soils; source apportionment; spatial prediction; Taicang City; UNMIX model;
D O I
10.11975/j.issn.1002-6819.202310005
中图分类号
学科分类号
摘要
This study aims to understand the sources and pollution of soil heavy metals. Taking Taicang City, Jiangsu Province as an example, the soil surface samples were collected to conduct the heavy metal content analysis. A novel receptor (UNMIX) model was selected to analyze the source and contribution of soil heavy metals. Geoaccumulation index and multivariate geostatistical techniques were used to quantitatively evaluate for the spatial prediction of heavy metal pollution. The results showed that: (1) The contents of As, Cd, Cr, Cu, Hg, Ni, and Zn were relatively high, exceeding the background values of soil heavy metals in the study area. The enrichment degree of Cd, Cu, Hg, Pb, and Zn was relatively high, while there were the normal enrichment coefficients of As, Cr, and Ni similar to the natural basic level. Except for Cr, the average content of the rest heavy metal elements exceeded the background values of soil heavy metals. The coefficients of variation for Cu and Zn were 0.60 and 0.64, respectively, indicating a high level of variation and a high skewness state. (2) The distribution trend of As and Pb gradually increased from west to east, with the highest content in Chengxiang and Ludu towns. The soil was mainly developed from basalt and metamorphic rocks, containing a certain amount of Ni element. There was the similar pattern of Cd, Cr, Cu, and Zn spatial distribution, where the high value areas were located in Shuangfeng Town and Chengxiang Town in the southwest. Overall, the high and low value areas of the eight heavy metal elements were distributed alternately with the island-like spatial feature, indicating the negative effects of human activities on the soil environment. (3) Soli heavy metals were comprehensively affected by transportation-industrial, natural-agricultural, industrial-natural, and agricultural-industrial sources, with the contribution rates of 35.18%, 27.32%, 20.26%, and 17.24%, respectively. Among them, Hg, Pb, Ni, Cu, and Zn were mainly from transportation-industrial sources, Cr, Zn, and Cu were from natural-agricultural sources, As and Ni were from industrial-natural sources, and Cd was from agricultural-industrial sources. (4) The ground accumulation index of Hg was ranked first among all elements with a mean of 1.12. A high degree of artificial accumulation of Hg was greatly influenced by non-natural factors. The elements with a ground accumulation index greater than 0 were Ni and Cd, with values of 0.68 and 0.42, respectively. The pollution levels of As, Cu, and Zn were in the middle. The soil condition was less affected by Cr and Pb. (5) The pollution areas of Ni, Cu, and Hg were the largest, reaching 107.42, 75.56, and 55.02 km2 respectively. The area of Cd pollution was 342 km2 (accounting for 8.6% of the total area) that distributed in industrial, tailings surrounding, and urban suburbs. The potential pollution space was distributed in patches, which should be given more attention. At the same time, it was also necessary to strictly prevent the expansion of Cr and Pb pollution space. This soil investigation can provide the scientific basis for the soil environmental management and heavy metal pollution remediation. © 2024 Chinese Society of Agricultural Engineering. All rights reserved.
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页码:224 / 234
页数:10
相关论文
共 51 条
  • [1] WANG Y, GUO G, ZHANG D, Et al., An integrated method for source apportionment of heavy metal(loid)s in agricultural soils and model uncertainty analysis, Environmental Pollution, 276, (2021)
  • [2] YANG S, HE M, ZHI Y, Et al., An integrated analysis on source-exposure risk of heavy metals in agricultural soils near intense electronic waste recycling activities, Environment International, 133, (2019)
  • [3] ZAINI N M, LEE H, MOHAMED K N, Et al., Datasets on spatial and temporal distribution of heavy metals concentration in recent sediment at merang river system, Terengganu, Malaysia, Data in Brief, 31, (2020)
  • [4] LYU Jianshu, Source apportionment and spatial prediction of heavy metals in soils of Yantai coastal zone, Acta Geographica Sinica, 76, 3, pp. 713-725, (2021)
  • [5] ZHANG Yi, ZHOU Xinquan, ZENG Xiaomin, Et al., Characteristics and assessment of heavy metal contamination in soils of industrial regions in the Yangtze River Economic Belt, Environmental Science, 43, 4, pp. 2062-2070, (2022)
  • [6] XU Xibo, LYU Jianshu, XU Ruru, Source spatial distribution and risk assessment of heavy metals in Yiyuan county of Shandong province, Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE), 34, 9, pp. 216-223, (2018)
  • [7] XIE Y, HU C, QIN Z, Et al., Ecological-health risks assessment and characteristic pollutants identification of heavy metals in the soils of a coking plant in production in Guangxi, China, Ecological Indicators, 154, (2023)
  • [8] MAZUMDER P, DASH S, KHWAIRAKPAM M, Et al., Ecological and health risk assessment associated with translocation of heavy metals in Lycopersicum esculentum from farmland soil treated with different composts, Journal of Environmental Management, 344, (2023)
  • [9] ZENG W, WAN X, WANG L, Et al., Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, geodetector, and multiple linear regression of distance, Journal of Hazardous Materials, 438, (2022)
  • [10] HENRY R C, LEWIS C W, HOPKE P K, Et al., Review of receptor model fundamentals, Atmospheric Environment, 18, 8, pp. 1507-1515, (1984)