1.广东省佛山生态环境监测站,广东 佛山 528000
2.广东省地质局佛山地质调查中心,广东 佛山 528000
3.中山大学 环境科学与工程学院,广东 广州 510275
4.中国地质大学(北京)水资源与环境学院,北京100083
邓思欣(1988年生),女;研究方向:环境化学;E-mail:xiahao@sthj.foshan.gov.cn
徐明宇(1989年生),男;研究方向:水文地质;E-mail:315142886@qq.com
收稿:2025-10-10,
修回:2025-12-12,
录用:2026-01-29,
网络首发:2026-04-03,
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邓思欣, 张元昌, 曹英杰, 等. 地下水污染多手段联合溯源——以珠三角典型污染场地为例[J/OL]. 中山大学学报(自然科学版)(中英文), 2026,1-11.
DENG Sixin, ZHANG Yuanchang, CAO Yingjie, et al. Multi-method approach for groundwater pollution source apportionment: A case study in the Pearl River Delta[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-11.
邓思欣, 张元昌, 曹英杰, 等. 地下水污染多手段联合溯源——以珠三角典型污染场地为例[J/OL]. 中山大学学报(自然科学版)(中英文), 2026,1-11. DOI: 10.11714/acta.snus.ZR20250217.
DENG Sixin, ZHANG Yuanchang, CAO Yingjie, et al. Multi-method approach for groundwater pollution source apportionment: A case study in the Pearl River Delta[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-11. DOI: 10.11714/acta.snus.ZR20250217.
为识别地下水污染的主控因子,厘清其来源。选取佛山市产业重镇狮山镇某工业区作为研究区,采集135组地下水样品,运用水化学分类法和正定矩阵因子分解模型(PMF),对地下水主控因子和潜在污染源进行初步识别;基于自组织神经网络(SOM)开展污染区分类与源解析,划分为3个水化学分区:Ⅰ区为研究区中部及长虹岭工业园周边区域,Ⅱ区为北部、西部农田与低强度工业区交错带,Ⅲ区为东部原铅酸电池拆解工业区。结果表明:1)研究区地下水的Mn、Fe、Al和Ni质量浓度均值超过国家地下水质量标准Ⅳ类水标准限值。区域重金属浓度差异显著,受人类活动影响较大,PMF模型初步揭示了与工业活动相关的污染源占比为78.6%。2)Ⅰ区水化学类型为Cl·HCO
3
-Ca·Na和Cl-Na型,Ⅱ区水化学类型为HCO
3
-Ca和HCO
3
·Cl-Ca型,Ⅲ区水化学类型为SO
4
型。3)基于SOM聚类分析方法和水化学分析法的地下水水化学分区研究结果一致。Ⅰ区和Ⅲ区的地下水水化学类型差异最明显,Ⅰ区和Ⅱ区的差异相对较小。Ⅰ区和Ⅲ区的Ni浓度均超标,但相互间无必然水化学联系;Ⅰ区的地下水Ni浓度超标可能与历史填埋有关,属于点源污染类型。
To identify the dominant factors governing groundwater pollution and to apportion their sources, a representative industrial area in Shishan Town, one of the major manufacturing hubs in Foshan City, was selected as the study area. A total of 135 groundwater samples were collected. Hydrochemical classification combined with a Positive Matrix Factorization (PMF) model was employed to preliminarily identify the dominant controlling factors and potential pollution sources in groundwater. Pollution zone classification and source apportionment were then performed based on a Self-Organizing Map (SOM) neural network. The results showed that the study area can be divided into three hydrochemical zones: Zone Ⅰ encompasses the area around the historical landfill and Changhongling Industrial Park in the central part of the study area; Zone Ⅱ is the interlaced zone of farmland and low-intensity industrial areas in the northern and western parts; and Zone Ⅲ is the former lead-acid battery dismantling industrial area in the eastern part. The results indicate that: 1) Average concentrations of Mn, Fe, Al, and Ni in groundwater of the study area exceed the Class Ⅳ limit values of the Chinese National Groundwater Quality Standard. Heavy metal distributions exhibit pronounced spatial heterogeneity strongly influenced by anthropogenic activities. The PMF model suggests that pollution sources contribute 78.6% of the observed pollution. 2) Hydrochemical patterns in Zone Ⅰ are Cl·HCO₃-Ca·Na and Cl-Na; those in Zone Ⅱ are HCO₃-Ca and HCO₃·Cl-Ca; and the hydrochemical type in Zone Ⅲ is SO₄-type. 3) Hydrochemical zoning results derived from SOM cluster analysis and hydrochemical analysis are consistent. The hydrochemical patterns of groundwater in Zone Ⅰ and Zone Ⅲ exhibit the most significant differences, while the differences between Zone Ⅰ and Zone Ⅱ are relatively minor.No direct hydrochemical correlation was found between the excessive Ni concentrations in Zone Ⅲ and Zone Ⅰ. The excessive Ni concentration in the groundwater of Zone Ⅰ may be associated with historical landfilling, reflecting a point source pollution.
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