A Comparison of Multiple Combined Models for Source Apportionment, Including the PCA/MLR-CMB, Unmix-CMB and PMF-CMB Models

TLDR: In this paper, multiple combined models, including the PCA/MLR-CMB, Unmix-cMB, and PMF-cmb models, were developed and employed to analyze the synthetic datasets, in order to understand 1) the accuracies of the predictions by multiple combined model; 2) the effect of Fpeak-rotation on the predictions of the PMF CMB model; and 3) the relationship between the extracted mixed source profiles (in the first stage) and the final predictions.

Abstract: A combined models was developed and applied to synthetic and ambient PM datasets in our prior works. In this study, multiple combined models, including the PCA/MLR-CMB, Unmix-CMB and PMF-CMB models, were developed and employed to analyzed the synthetic datasets, in order to understand 1) the accuracies of the predictions by multiple combined models; 2) the effect of Fpeak-rotation on the predictions of the PMF-CMB model; and 3) the relationship between the extracted mixed source profiles (in the first stage) and the final predictions. 50 predictions based on different combined model solutions were obtained and compared with the synthetic datasets. The average absolute errors (AAE), cluster analysis (CA), and PCA plots were applied to evaluate the precision of the predictions. These statistical methods showed that the predictions of the PCA/MLR-CMB and PMF-CMB model (with Fpeaks from 0 to 1.0) were satisfactory, those of the Unmix-CMB model were instable (some of them closely approached the synthetic values, while other them deviated from them). Additionally, it was found that the final source contributions had good correlation with their marker concentrations (obtained in the first stage), suggesting that the extracted profiles of the mixed sources can determine the final predictions of combined models.