Liu Lianhua, Li You, Gu Xiang, Tulcan Roberto Xavier Supe, Yan Lingling, Lin Chunye, Pan Junting
Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
J Environ Sci (China). 2025 Jan;147:153-164. doi: 10.1016/j.jes.2023.11.007. Epub 2023 Nov 18.
Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.
农业土壤中的重金属（类金属）污染已成为锑（Sb）矿区的一个环境问题。然而，由于多种复杂污染源并存，优先污染源识别以及对重金属环境风险的深入理解面临巨大挑战。在此，采用综合方法对中国南方一个典型锑矿流域的污染源进行区分，并评估人类健康风险（HHR）和生态风险（ER）。该方法将绝对主成分得分-多元线性回归（APCS-MLR）和正定矩阵因子分解（PMF）模型与ER和HHR评估相结合。两种模型都区分出了四个污染源，且APCS-MLR模型更准确、更合理。主要的重金属浓度来源是自然源（39.1%），其次是工农业活动（23.0%）、未知源（21.5%）和锑矿开采与冶炼活动（16.4%）。虽然自然源对重金属浓度的贡献最大，但并未构成显著的生态风险。工农业活动主要导致了生态风险，应关注镉和锑。锑矿开采与冶炼活动是人类健康风险的主要人为来源，尤其是锑和砷污染。综合考虑生态风险和人类健康风险评估，锑矿开采与冶炼以及工农业活动是关键污染源，对生态和健康造成严重威胁。本研究展示了多受体模型在获得可靠的源识别以及提供更好的基于源的风险评估方面的优势。强烈建议进行重金属污染管理，如规范采矿和冶炼以及对污染的农业土壤实施土壤修复，以保护生态系统和人类。
