Chen Zhifan, Ding Yongfeng, Jiang Xingyuan, Duan Haijing, Ruan Xinling, Li Zhihong, Li Yipeng
College of Geography and Environmental Science, Henan University, Ministry of Education, Kaifeng 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Henan Engineering Research Center for Control & Remediation of Soil Heavy Metal Pollution, Henan University, Kaifeng 475004, China; Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China.
College of Geography and Environmental Science, Henan University, Ministry of Education, Kaifeng 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Henan Engineering Research Center for Control & Remediation of Soil Heavy Metal Pollution, Henan University, Kaifeng 475004, China.
Ecotoxicol Environ Saf. 2022 Apr 1;234:113369. doi: 10.1016/j.ecoenv.2022.113369. Epub 2022 Mar 9.
Quantitative identification of heavy metals (HM) sources in soils is key to prevention and control of heavy metal pollution. In this study, UNMIX, PMF (Positive matrix factorization) model and Pb-Zn-Cu isotopic compositions were combined to quantitatively identify heavy metal sources in a suburban agricultural area of Kaifeng, China. Using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) and ICP-MS, we measured Pb, Zn and Cu stable isotopic compositions, HM concentrations and HM chemical fractions in studied soils, as well as potential sources around the highly polluted site, including total suspended particle, compound fertilizer, irrigated river water and sediments. The results showed that total contents and chemical fractions of heavy metals, as well as Pb-Zn-Cu isotopic compositions presented great variation in different sites, which implied that heavy metal accumulation was obviously affected by local anthropogenic pollution source. UNMIX and PMF presented good agreement on source apportionment that industrial and agricultural activities (61.74% and 60.75% for UNMIX and PMF, respectively) were the major contributors to heavy metal accumulation in the study area. Especially, sewage irrigation and atmosphere deposition accounted for a large proportion (28.14% and 41.03% for UNMIX and PMF, respectively). Moreover, isotopic compositions of Pb, Zn and Cu in highly polluted soils and environment media gave further confirmation that sewage irrigation and atmosphere deposition were primary anthropogenic source. Therefore, combination of UNMIX, PMF model and Pb-Zn-Cu isotopic compositions showed good coordination in quantitative and specific source identification of heavy metals in agricultural soils.
土壤中重金属（HM）来源的定量识别是预防和控制重金属污染的关键。本研究将UNMIX、PMF（正定矩阵因子分解）模型与Pb-Zn-Cu同位素组成相结合，对中国开封市郊区农业区的重金属来源进行了定量识别。利用多接收电感耦合等离子体质谱仪（MC-ICP-MS）和电感耦合等离子体质谱仪（ICP-MS），我们测量了研究土壤中Pb、Zn和Cu的稳定同位素组成、重金属浓度和重金属化学形态，以及高污染场地周围的潜在来源，包括总悬浮颗粒物、复合肥、灌溉河水和沉积物。结果表明，不同点位重金属的总量、化学形态以及Pb-Zn-Cu同位素组成存在很大差异，这表明重金属积累明显受到当地人为污染源的影响。UNMIX和PMF在源解析方面表现出良好的一致性，即工业和农业活动（UNMIX和PMF分别为61.74%和60.75%）是研究区域重金属积累的主要贡献者。特别是，污水灌溉和大气沉降占比很大（UNMIX和PMF分别为28.14%和41.03%）。此外，高污染土壤和环境介质中Pb、Zn和Cu的同位素组成进一步证实，污水灌溉和大气沉降是主要的人为来源。因此，UNMIX、PMF模型与Pb-Zn-Cu同位素组成相结合，在农业土壤重金属定量和具体来源识别方面表现出良好的协同作用。
