基于APCS-MLR模型解析广西岩溶区风化型锰矿区周边农田土壤重金属污染源

王金龙; 郑国东; 覃建勋; 王磊; 李杰; 欧阳鑫东; 顾文博; 黄小霞; 覃艳引

doi:10.11932/karst20250409

基于APCS-MLR模型解析广西岩溶区风化型锰矿区周边农田土壤重金属污染源

doi: 10.11932/karst20250409

广西壮族自治区地质调查院, 广西南宁 530023

基金项目: 广西重点研发计划项目（桂科AB24010136，桂科AB24010219)；广西壮族自治区地质矿产开发局部门前期科研项目（桂地矿综研（2022）15号，桂地矿综研（2023）8号，桂地矿函（2024）51号）

详细信息

作者简介:
王金龙（1992－），男，工程师，硕士研究生，研究方向：环境地球化学。E-mail：531950310@qq.com

通讯作者:
郑国东（1983－），男，高级工程师，硕士研究生，研究方向：环境地球化学。E-mail：156001601@qq.com。

中图分类号: X53
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出版历程
- 收稿日期: 2024-12-09
- 录用日期: 2025-04-23
- 修回日期: 2025-03-27
- 网络出版日期: 2025-11-07
- 刊出日期: 2025-08-25

Analysis of heavy metal pollution sources in farmland soil surrounding weathered manganese ore mining areas in the Guangxi karst region based on APCS-MLR model

Guangxi Institute of Geological Survey, Nanning, Guangxi 530023, China

摘要

摘要: 为了探究锰矿开采对周边农田带来的生态风险，对锰矿区周边农田表层土壤进行采样分析，运用潜在污染风险指数法与地累积指数法对研究区农田土壤进行生态风险评价，并采用APCS-MLR模型与PMF模型对土壤重金属来源定量解析与验证。结果表明，研究区农田土壤潜在污染风险为中等（RI=222.28），主要表现为Cd（$ {E}_{i} $=90.05）与Hg（$ {E}_{i} $=89.6）；地累积污染程度排序为Cd＞Hg＞Zn＞Pb＞Ni＞Cr＞Cu＞As；主成分分析表明研究区农田土壤主要受两种污染源影响，推测为人为矿业开采及排放源与自然源，贡献率分别为61.8%与26.2%；APCS-MLR模型与PMF模型判定Cd、Cr、Hg、Mn、Ni，主要受人为矿业源影响，As、Pb、Zn主要受自然源影响，Cu主要受人为矿业源与自然源两种因素叠加影响。研究结果可为广西风化型锰矿区周边农田土壤重金属修复及生态风险防控提供理论基础。
- 广西岩溶区 /
- 风化型锰矿区 /
- 农田土壤 /
- 重金属 /
- 生态风险评价 /
- APCS-MLR模型 /
- PMF模型
Abstract: Guangxi boasts an abundant supply of manganese ore resources, with its annual output constituting approximately 30% of the national total. The distribution is extensive, encompassing manganese ore deposits in all 14 prefecture-level cities. Nevertheless, while endowed with rich manganese ore resources, the ecological and environmental issues resulting from manganese mining have grown increasingly severe, particularly the presence of excessive heavy metals in the surrounding farmland soil of manganese mining areas. According to relevant statistics, the area of farmland influenced by manganese mining within Guangxi amounts to approximately 13.32% of the total farmland area in the region. The contents of heavy metal elements, such as Cd and As, in the soil significantly exceed the average levels of soil in Guangxi. Relevant research indicates that in most edible parts of crops grown in farmland surrounding manganese mining areas, five types of heavy metals have surpassed the national food safety thresholds. These crops, containing excessive heavy metals, will ultimately accumulate in the human body through the food chain, causing irreversible harm to human health with long-term consumption. However, to date, no research has been so far conducted on heavy metal pollution in farmland soil associated with manganese mining in Guangxi. Hence, to investigate the ecological risks posed by manganese mining to the surrounding farmland, the soil from farmland adjacent to the weathered manganese mining area in a typical karst region of Guangxi was selected as the research subject. Soil samples were collected from the 0 to 20 cm soil layer of farmland surrounding the manganese mining area, and the contents of eight heavy metal elements were analyzed. The potential pollution index method and the geoaccumulation index method were employed to assess the ecological risk of the farmland soil in the study area. Principal Component Analysis (PCA) and the Absolute Factor Score-Multiple Linear Regression model (ACPS-MLR) were utilized to qualitatively and quantitatively analyze the sources of heavy metals in the soil, while the Positive Matrix Factorization (PMF) model was applied to verify the results. The findings reveal that the potential pollution risk of the farmland soil in the study area is moderate (RI = 222.28), among which the pollution indices of single indicators Cd ( Ei= 90.05) and Hg ( Ei= 89.6) reach the medium to severe level. The degree of geoaccumulation pollution follows the order: Cd > Hg > Zn > Pb > Ni > Cr > Cu > As. The evaluation results disclose that there exists a certain degree of pollution in the soil of the study area, mainly manifested by heavy metal elements such as Cd, Hg, As, Pb and Zn. The PCA results suggest that the farmland soil in the study area is primarily influenced by two pollution sources, which are likely human mining and emission sources and natural sources, contributing 61.8% and 26.2%, respectively. The quantitative analysis results of the ACPS-MLR demonstrate that Cd, Cr, Hg, Mn and Ni are mainly affected by human mining activities, contributing 97.22%, 81.79%, 92.47%, 97.28% and 82.81%, respectively. In contrast, As, Pb, and Zn are mainly influenced by natural sources, contributing 60.71%, 88.97% and 72.14%, respectively. Cu is mainly affected by the superposition of human mining activities and natural sources, contributing 47.75% and 52.25%, respectively. The verification results of the PMF indicate that the two models exhibit a high degree of similarity in identifying pollution sources. However, due to the significant differences in their mechanisms, discrepancies arise in the analysis of the contribution rates of pollution sources. Nevertheless, the two models together form a robust method verification system. A comprehensive consideration of both models can provide a more scientifically sound theoretical basis for pollution source tracing. Based on this study, the findings can serve as a foundation for the remediation of heavy metals in farmland soil and for the prevention and control of ecological risks in the study area.
- Guangxi karst area /
- weathered manganese mining area /
- farmland soil /
- heavy metal /
- ecological risk assessment /
- APCS-MLR model /
- PMF model

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图 1 研究区采样点分布图

Figure 1. Distribution of sampling sites in the study area

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图 2 研究区表层土壤元素含量频数分布图

Figure 2. Frequency distributions of element contents in the surface soil of the study area

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图 3 pH区间土壤采样点个数

Figure 3. Number of soil sampling sites at pH interval

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图 4 土壤重金属含量空间分布特征

Figure 4. Characteristics of spatial distributions of heavy metal contents in soil

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图 5 潜在污染风险评价结果

Figure 5. Evaluation results of potential pollution risks

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图 6 地累积污染指数评价结果

Figure 6. Evaluation results of land cumulative pollution indices

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图 7 土壤重金属含量Pearson相关系数

Figure 7. Pearson correlation coefficients of heavy metal contents

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图 8 PMF模型解析的土壤重金属污染源贡献率

Figure 8. Contribution of heavy metal pollution sources in soil analyzed by APCS-MLR model

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图 9 APCS-MLR模型解析的土壤重金属污染源贡献率

Figure 9. Contribution of heavy metal pollution sources in soil analyzed by PMF model

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表 1 土壤潜在污染风险等级标准

Table 1. Criteria for ranking potential soil pollution risks

单指标污染风险指数（$ {E}_{i} $）^[14]		综合污染风险指数（RI）^[15]		地累积污染指数（$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $）^[19]
指数	等级	指数	等级	指数	等级
$ {E}_{i} $≤40	轻微污染	RI＜150	轻微污染风险	$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $≤0	无污染
40＜$ {E}_{i} $≤80	中度污染	150≤RI＜300	中等污染风险	0＜$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $≤1	轻度污染
80＜$ {E}_{i} $≤160	中重度污染	300≤RI＜600	强污染风险	1＜$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $≤2	中度污染
160＜$ {E}_{i} $≤320	重度污染	RI≥600	超强污染风险	2＜$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $≤3	中重度污染
$ {E}_{i} $＞320	超重度污染			3＜$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $≤4	重度污染
				$ {I}_{\mathrm{g}\mathrm{e}\mathrm{o}} $＞4	超重度污染

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表 2 土壤重金属含量特征

Table 2. Characteristics of heavy metal contents in soil

指标/μg·g⁻¹	As	Cd	Cr	Cu	Hg	Ni	Pb	Zn	Mn
最小值	4.76	0.19	48.90	13.80	0.07	14.30	13.90	58.60	115.00
最大值	60.10	4.37	182.00	57.30	0.56	145.00	65.00	281.00	15700.00
算数平均值	16.89	0.80	91.58	29.39	0.27	38.25	34.56	116.58	1723.71
标准差	11.42	0.81	31.51	10.13	0.11	22.76	10.75	41.44	2848.85
变异系数	0.67	1.01	0.34	0.34	0.41	0.59	0.31	0.35	1.65
广西土壤背景值^[18]	20.50	0.267	82.10	27.80	0.152	26.60	24.00	75.60	669.60
全国表层土壤平均值^[26]	10.00	0.09	65.00	24.00	0.04	26.00.	23.00	68.00	600.00

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表 3 研究区土壤重金属潜在污染风险评价

Table 3. Evaluation of the risk of potential heavy metal pollution in soil within the study area

指标	$ {E}_{i} $（As）	$ {E}_{i} $（Cd）	$ {E}_{i} $（Cr）	$ {E}_{i} $（Cu）	$ {E}_{i} $（Hg）	$ {E}_{i} $（Ni）	$ {E}_{i} $（Pb）	$ {E}_{i} $（Zn）	RI
最小值	3.19	21.35	1.19	2.48	24.00	2.69	2.90	0.78	72.28
最大值	127.43	491.01	4.43	21.94	186.67	27.26	245.63	11.71	750.67
平均值	13.52	90.50	2.25	5.60	89.60	7.37	11.70	1.73	222.28

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表 4 土壤重金属含量主成分分析

Table 4. Principal component analysis of soil heavy metal content

指标	第一主成分			第二主成分
指标	旋转后载荷系数	特征值	贡献率/%	旋转后载荷系数	特征值	贡献率/%
Mn	0.939			0.055
Cd	0.914			0.055
Ni	0.885			0.384
Hg	0.850			0.143
Cr	0.740	5.558	61.8	0.345	2.360	26.2
Cu	0.451			0.862
As	0.289			0.932
Zn	0.180			0.974
Pb	−0.058			0.985

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