Impact of source collinearity in simulated PM2.5 data on the PMF receptor model solution

被引:29
作者
Habre, Rima [1 ]
Coull, Brent [1 ]
Koutrakis, Petros
机构
[1] Harvard Univ, Sch Publ Hlth, Dept Environm Hlth, Dept Biostat, Boston, MA 02115 USA
关键词
PMF; Receptor model; Source collinearity; Simulation; PM2.5; Source apportionment; POSITIVE MATRIX FACTORIZATION; SOURCE APPORTIONMENT; PARTICULATE MATTER; DAILY MORTALITY; UNCERTAINTY; EXPOSURE; ASSOCIATION; POLLUTION; MASS; FIT;
D O I
10.1016/j.atmosenv.2011.09.034
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Positive Matrix Factorization (PMF) is a factor analytic model used to identify particle sources and to estimate their contributions to PM2.5 concentrations observed at receptor sites. Collinearity in source contributions due to meteorological conditions introduces uncertainty in the PMF solution. We simulated datasets of speciated PM2.5 concentrations associated with three ambient particle sources: "Motor Vehicle" (MV), "Sodium Chloride" (NaCl), and "Sulfur" (S), and we varied the correlation structure between their mass contributions to simulate collinearity. We analyzed the datasets in PMF using the ME-2 multilinear engine. The Pearson correlation coefficients between the simulated and PMF-predicted source contributions and profiles are denoted by "G correlation" and "F correlation", respectively. In sensitivity analyses, we examined how the means or variances of the source contributions affected the stability of the PMF solution with collinearity. The % errors in predicting the average source contributions were 23, 80 and 23% for MV, NaCl, and S, respectively. On average, the NaCl contribution was overestimated, while MV and S contributions were underestimated. The ability of PMF to predict the contributions and profiles of the three sources deteriorated significantly as collinearity in their contributions increased. When the mean of NaCl or variance of NaCl and MV source contributions was increased, the deterioration in G correlation with increasing collinearity became less significant, and the ability of PMF to predict the NaCl and MV loading profiles improved. When the three factor profiles were simulated to share more elements, the decrease in G and F correlations became non-significant. Our findings agree with previous simulation studies reporting that correlated sources are predicted with higher error and bias. Consequently, the power to detect significant concentration-response estimates in health effect analyses weakens. (C) 2011 Elsevier Ltd. All rights reserved,
引用
收藏
页码:6938 / 6946
页数:9
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