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Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models
被引:1
|作者:
Cui, Wenwen
[1
]
Dong, Xiaoqiang
[1
,2
]
Liu, Jiajiang
[1
]
Yang, Fan
[1
]
Duan, Wei
[1
]
Xie, Mingxing
[1
]
机构:
[1] Taiyuan Univ Technol, Dept Civil Engn, Located 79 West Yingze St, Taiyuan 030024, Shanxi, Peoples R China
[2] Civil Engn Disaster Prevent & Control Key Lab Shan, Situated 79 West Yingze St, Taiyuan 030024, Shanxi, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Red mud yard;
Heavy metals;
Agricultural soil;
Risk analysis;
Source apportionment;
SPATIAL-DISTRIBUTION;
FARMLAND SOIL;
ACCUMULATION;
PROVINCE;
CHINA;
RISK;
CITY;
PARK;
D O I:
10.1007/s10653-024-02267-x
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
In recent years, industrial waste and agrochemicals have reduced soil fertility and productivity, significantly impacting food security and ecosystems. In China, areas near red mud deposits from the aluminum industry show severe heavy metal contamination. This study examines agricultural soil near a red mud site in Shanxi Province, analyzing Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn levels and distribution. Geostatistical methods and GIS are utilized to assess heavy metal pollution using the single factor index, the Nemerow integrated index, and the Hakanson potential ecological risk index. Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models are used for quantitative analysis of pollution sources. Research indicates that the average concentrations of eight heavy metals exceed the natural background values of Shanxi, placing them at a severe pollution level with moderate ecological risk. Specifically, indices for As, Pb, and Cr are 3.79, 3.38, and 3.26, indicating severe pollution; Cd, Cu, and Hg at 2.36, 2.62, and 3.00 suggest moderate pollution; Ni at 1.87 shows mild pollution, while Zn at 0.97 is not polluted. Hg presents the highest ecological risk with a coefficient of 120.00, followed by Cd (70.69) and As (37.92). Spatial analysis shows significant correlations among Pb, Zn, Cu, and Ni, while Cr, Cd, Hg, and As show greater variability and weaker correlations. Both models identify five main sources: industrial activities, agricultural fertilizers, red mud leachate, energy combustion, and natural geological backgrounds, with respective contribution rates in the APCS-MLR model at 27.7%, 24.6%, 18.1%, 15.2%, and 14.4%, and in the PMF model at 29.2%, 21.5%, 16.9%, 16.7%, and 15.7%. This study offers a scientific basis for controlling soil pollution in the region, filling a literature gap.
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页数:19
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