同位素分析
同位素
电感耦合等离子体质谱法
化学
示踪剂
环境化学
锶
富集因子
主成分分析
锶同位素
感应耦合等离子体
污染
放射性核素
环境科学
重金属
质谱法
地质学
数学
生物
生态学
色谱法
海洋学
物理
统计
有机化学
等离子体
量子力学
核物理学
作者
Yan Cong,Ruilian Yu,Yan Yu,Bo-sen Weng,Gongren Hu,Jing-wei Sun,Jianyong Cui,Yanyan Yanyan,Yaoyi Huang
出处
期刊:Catena
[Elsevier]
日期:2023-08-01
卷期号:229: 107194-107194
被引量:7
标识
DOI:10.1016/j.catena.2023.107194
摘要
Many studies have focused on the concentrations of heavy metals (HMs) in Tieguanyin tea plants and surrounding soil; however, limited information is available on the specific sources of HMs in tea plantation trees. In this study, HMs in tea plants (roots, stems, old leaves, and young leaves) from Anxi County of the Fujian Province were determined by inductively coupled plasma mass spectrometry (ICP-MS). Additionally, correlation analysis, principal component analysis (PCA), and isotope tracers were used to analyze the sources of HMs pollution. The contribution rate of HMs in tea from different specific sources was further quantified by establishing a relative isotope mixing model. Our results showed that the highest Pb content in the roots was 23.89 μg·g−1 and was positively correlated with Sr and Ni in the roots, indicating that these three metals in the roots are from the same source, mainly from the soil. PCA analysis showed that Sr and Pb in factor 1 have higher positive charges in the young leaves, and their contribution rate was 27.16%. Sr is commonly used in the cement industry, indicating that factor 1 was mainly affected by industrial activities. The Pb and Sr isotope ratio showed that the average values of 208Pb/206Pb and 206Pb/207Pb in young leaves were 2.1124 and 1.1619, respectively, while the average values of 87Sr/86Sr in young leaves was 0.709774. The contribution rate of each potential source was quantitatively analyzed using the Pb-Sr multiple isotope mixture model, and agricultural activities were found to be the biggest contributors of HMs in tea. For example, agricultural sources of HMs found in old leaves and young leaves were 62% and 31%, respectively. Using multivariate statistical analysis methods combined with isotope tracing techniques, this study was able to identify sources of multiple heavy metals in Tieguanyin tea plants.
科研通智能强力驱动
Strongly Powered by AbleSci AI