Qi Yanjie, He Zhuoshi, Huo Shouliang, Zhang Jingtian, Xi Beidou, Hu Shibin
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China.
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China.
J Environ Sci (China). 2017 Jul;57:321-328. doi: 10.1016/j.jes.2016.12.007. Epub 2016 Dec 28.
Receptor models have been proved as useful tools to identify source categories and quantitatively calculate the contributions of extracted sources. In this study, sixty surface sediment samples were collected from fourteen lakes in Jiangsu Province, China. The total concentrations of C-C-perfluoroalkyl carboxylic acids and perfluorooctane sulfonic acid (∑PFASs) in sediments ranged from 0.264 to 4.44ng/gdw (dry weight), with an average of 1.76ng/gdw. Three commonly-applied receptor models, namely principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and Unmix models, were employed to apportion PFAS sources in sediments. Overall, these three models all could well track the ∑PFASs concentrations as well as the concentrations explained in sediments. These three models identified consistently four PFAS sources: the textile treatment sources, the fluoropolymer processing aid/fluororesin coating sources, the textile treatment/metal plating sources and the precious metal sources, contributing 28.1%, 37.0%, 29.7% and 5.3% by PCA-MLR model, 30.60%, 39.3%, 22.4% and 7.7% by PMF model, and 20.6%, 52.4%, 20.2% and 6.8% by Unmix model to the ∑PFASs, respectively. Comparative statistics of multiple analytical methods could minimize individual-method weaknesses and provide convergent results to enhance the persuasiveness of the conclusions. The findings could give us a better knowledge of PFAS sources in aquatic environments.
受体模型已被证明是识别源类别并定量计算提取源贡献的有用工具。在本研究中，从中国江苏省的14个湖泊中采集了60个表层沉积物样品。沉积物中碳-碳全氟烷基羧酸和全氟辛烷磺酸（∑PFASs）的总浓度范围为0.264至4.44ng/gdw（干重），平均为1.76ng/gdw。采用了三种常用的受体模型，即主成分分析-多元线性回归（PCA-MLR）、正定矩阵因子分解（PMF）和非负矩阵分解（Unmix）模型来分配沉积物中的PFAS来源。总体而言，这三种模型都能很好地追踪∑PFASs浓度以及沉积物中解释的浓度。这三种模型一致识别出四种PFAS来源：纺织处理源、含氟聚合物加工助剂/氟树脂涂料源、纺织处理/金属电镀源和贵金属源，PCA-MLR模型对∑PFASs的贡献率分别为28.1%、37.0%、29.7%和5.3%，PMF模型分别为30.60%、39.3%、22.4%和7.7%，Unmix模型分别为20.6%、52.4%、20.2%和6.8%。多种分析方法的对比统计可以最大限度地减少单一方法的弱点，并提供趋同的结果以增强结论的说服力。这些发现可以让我们更好地了解水生环境中的PFAS来源。
