State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China.
Key Laboratory of Coastal Zone Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China.
Sci Rep. 2017 Sep 6;7(1):10648. doi: 10.1038/s41598-017-10762-8.
To explore the utility of combining positive matrix factorization (PMF) with radiocarbon (C) measurements for source apportionment, we applied PM data collected for 14 months at a national background station in North China to PMF models. The solutions were compared to C results of four seasonally averaged samples and three outlier samples. Comparing the most readily interpretable PMF solutions and C results revealed that PMF modeling was well able to capture the source patterns of PM with two and three irrelevant source classifications for the seasonal and outlier samples. The contribution of sources that could not be classified as either fossil or non-fossil sources in the PMF solution, and the errors between the modeled and measured concentrations weakened the effectiveness of the comparison. Based on these two factors, we developed an index for selecting the most suitable C measurement samples for combining with the PMF model. Then we examined the potential for coupling PMF modeling and C data with a constrained PMF run using the C data as a priori information. The restricted run could provide a more reliable solution; however, the PMF model must provide a flexible dialog to input the priori restrictions for executing the constraint simulation.
为了探索结合正矩阵因子分解（PMF）和放射性碳（C）测量进行源解析的效用，我们将华北国家背景站收集的 14 个月的 PMF 数据应用于 PMF 模型。将解决方案与四个季节平均样本和三个异常值样本的 C 结果进行了比较。比较最易于解释的 PMF 解决方案和 C 结果表明，PMF 模型能够很好地捕捉 PM 的源模式，对于季节性和异常值样本，PMF 模型可以用两个和三个无关的源分类来捕捉 PM 的源模式。在 PMF 解决方案中无法归类为化石或非化石源的源的贡献，以及模型化浓度与实测浓度之间的误差，削弱了比较的有效性。基于这两个因素，我们开发了一个指数，用于选择最适合与 PMF 模型结合的 C 测量样本。然后，我们使用 C 数据作为先验信息，检查 PMF 模型与 C 数据相结合的潜力。受限运行可以提供更可靠的解决方案；然而，PMF 模型必须提供一个灵活的对话来输入执行约束模拟的先验限制。
