title: Use of a nonnegative constrained principal component regression chemical mass balance model to study the contributions of nearly collinear sources.
abstract: 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.
authors: State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China.
authors: Guo-Liang
authors: GL
authors: Shi
authors: Yin-Chang
authors: YC
authors: Feng
authors: Fang
authors: F
authors: Zeng
authors: Xiang
authors: X
authors: Li
authors: Yu-Fen
authors: YF
authors: Zhang
authors: Yu-Qiu
authors: YQ
authors: Wang
authors: Tan
authors: T
authors: Zhu
mesh_terms: D000397:Air Pollution / Q000032:analysis*; D002681:China; D008956:Models, Chemical; D025341:Principal Component Analysis; D012044:Regression Analysis