Use of a Nonnegative Constrained Principal Component Regression Chemical Mass Balance Model to Study the Contributions of Nearly Collinear Sources

共线性 主成分分析 分摊 宇宙微波背景 主成分回归 组分(热力学) 回归 数学 合成数据 回归分析 计量经济学 统计 生物系统 计算机科学 应用数学 物理 热力学 生物 各向异性 法学 量子力学 政治学
作者
Guoliang Shi,Yinchang Feng,Fang Zeng,Xiang Li,Yufen Zhang,Yuqiu Wang,Tan Zhu
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:43 (23): 8867-8873 被引量:52
标识
DOI:10.1021/es902785c
摘要

In this study, a nonnegative constrained principal component regression chemical mass balance (NCPCRCMB) model was used to solve the near collinearity problem among source profiles for source apportionment. The NCPCRCMB model added the principle component regression route into the CMB model iteration. The model was tested with the synthetic data sets, which involved contributions from eleven actual sources, with a serious near collinearity problem among them. The actual source profiles were randomly perturbed and then applied to create the synthetic receptor. The resulting synthetic receptor concentrations were also randomly perturbed to simulate measurement errors. The synthetic receptors were separately apportioned by CMB and NCPCRCMB model. The result showed that source contributions estimated by the NCPCRCMB model were much closer to the true values than those estimated by the CMB model. Next, five real ambient data sets from five cities in China were analyzed using the NCPCRCMB model to test the model practicability. Reasonable results were obtained in all cases. It is shown that the NCPCRCMB model has an advantage over the traditional CMB model when dealing with near collinearity problems in source apportionment studies.
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