Estimated contributions and uncertainties of PCA/MLR–CMB results: Source apportionment for synthetic and ambient datasets

Self Cite

In this work, PM 2.5 and PM 10 samples were collected from four functional regions in Chengdu city from Jan. to Feb. in 2010 and 2011. The chemical concentrations of nineteen elements, two water-soluble ions and TC were measured. The results showed that the levels of particulates and their chemical components were relatively higher in 2010 than those in 2011: for PM 10 , PM (39%), Si (27%), TC (76%), NO 3 -(16%), SO 4 2-(19%); for PM 2.5 , PM (33%), Si (11%), TC (67%), NO 3 -(32%), SO 4 2-(17%). Ternary diagram analysis, enrichment and other statistical methods were employed to study the spatial and temporal variations of the concentrations of PM and their compositions. Moreover, relative changes (%) of concentrations combined with the relative reported data from statistical yearbook and environmental quality reports are discussed to evaluate the effects of pollution control strategies for different functional regions. Finally, back-trajectory and factor-cluster analysis are proposed as an aid for the discussion of the meteorological influences and the possible source categories. The reduction of crustal dust may be attributed from the influence of long-range air transport. All the methods used above resulted in an overall agreement that showed the pollution control strategies were effective in reducing particulate air pollution: the total carbon (TC) reduction, accounting for 40-49% (PM 10 ) and 35-48% (PM 2.5 ), was mostly attributed to the strategy of banning high-emission yellow-tag vehicles, especially in commercial regions; the reduction of SO 2 and NO x emissions had an impact on the sulphate (17.03% for PM 10 and 15.37% for PM 2.5 ) and nitrate (12.48% for PM 10 and 21.58% for PM 2.5 ) decrease. The findings of this study can provide useful information for developing effective control strategies.

Section: Levels Of Major Constituents In Pmmentioning

confidence: 99%

Evaluation of the Changes of the Concentration, Composition and Possible Sources of Size-Resolved Particulate Matter between 2010 and 2011 in a Western Chinese Mega City

Zhang

Liu

et al. 2014

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“…As described in our prior studies (Shi et al, 2009;Zeng et al, 2010;Shi et al, 2011), the combined model mainly contains two stages.…”

Section: Principle Of Combined Modelmentioning

confidence: 99%

“…The combined model was tested, and acceptable results were obtained, using synthetic datasets with collinearity (Shi et al, 2009. The methodology has been quoted in a review paper on source apportionment methods by Pant and Harrison (Pant and Harrison, 2012) and involved in source apportionment works for air pollution in some cities (Shi et al, 2009;Zeng et al, 2010;Shi et al, 2011). In our prior works (Shi et al, 2009;Zeng et al, 2010;Shi et al, 2011), the factor analysis (PCA/MLR and PMF) and CMB model were combined together for source apportionment.…”

Section: Introductionmentioning

confidence: 99%

“…The methodology has been quoted in a review paper on source apportionment methods by Pant and Harrison (Pant and Harrison, 2012) and involved in source apportionment works for air pollution in some cities (Shi et al, 2009;Zeng et al, 2010;Shi et al, 2011). In our prior works (Shi et al, 2009;Zeng et al, 2010;Shi et al, 2011), the factor analysis (PCA/MLR and PMF) and CMB model were combined together for source apportionment. On the first stage, the mixed source (containing near collinear sources) was extracted by the PMF or PCA/MLR models; and on the second stage the mixed source is treated as a new receptor and be apportioned by CMB model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shi

Liu

Peng

et al. 2014

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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.

“…It is thus difficult to use real ambient datasets to explore the performance of models. Therefore, artificial dataset and simulation studies have typically been conducted by researchers (Shi et al, 2011;Tian et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Advanced and Traditional Three-Way Factor Analysis Models for Determining Source Contributions to Particulate Matter

Tian

Huangfu

Shi

et al. 2016

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Although three-way factor analysis models can take more information into account, their degree of accuracy must be investigated further. We simulated numerous synthetic datasets to evaluate traditional (PMF3) and advanced three-way models (AAB or ABB). On one hand, scenarios whereby sources share the same profiles but different emission patterns were constructed and introduced into PMF3 and advanced AAB models that can estimate the same source profile matrixes B but different emission pattern matrixes A. On the other hand, datasets with the same emission patterns but different profiles were set to simulate the variability of source profiles and were used to evaluate an advanced ABB model that can estimate the same emission pattern matrix A but different source profile matrixes B. The AAEs of PMF3 under two different conditions ranged from 2.95% to 90.22% and from 2.98% to 90.11%, respectively, while the results of the advanced three-way models were 2.88%-27.51% and 2.89%-29.89%, respectively. We observed that the PMF3 performed well when all sources showed strong emission patterns and source profiles were similar. The application of advanced AAB model was stable under various emission patterns; and ABB model was stable under different variability of source profiles. At the same time, several ambient datasets were estimated by the advanced three-way models. Our findings suggest that the performance of advanced three-way models make full use of spatial or size distribution information to enhance capacities to identify source categories.