1 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
2 Chengdu Research Academy of Environmental Sciences, Chengdu 610041, China
3 Nanjing Institute of Environmental Sciences, MEP, (unll), China
4 Georgia Institute of Technology, Atlanta, Georgia, USA
Cite this article: Shi, G.L., Chen, H., Tian, Y.Z., Song, D.L., Zhou, L.D., Chen, F., Yu, H.F. and Feng, Y.C. (2016). Effect of Uncertainty on Source Contributions from the Positive Matrix Factorization Model for a Source Apportionment Study.
Aerosol Air Qual. Res.
16: 1665-1674. https://doi.org/10.4209/aaqr.2015.12.0678
HIGHLIGHTS
The effects of uncertainties estimation on PMF were studied by synthetic and ambient datasets.
Uncertainties should be estimated according to errors of datasets.
Subjective emphasis on unsuitable species may disturb PMF results.
ABSTRACT
Uncertainty estimation plays an important role in source apportionment models such as the positive matrix factorization (PMF) model. In this study, synthetic datasets were generated and analyzed using PMF with specified uncertainties at different levels to investigate the impact of uncertainty inputs on the results of PMF model, as well as the benefits and risks of emphasizing on certain species. The results showed that: (1) uncertainties for the PMF model should be estimated based on characteristics of the dataset being analyzed; (2) emphasizing on correct tracers will improved model performance; and (3) emphasizing on unsuitable tracers may lead to disruptive consequences that might not be captured by the Q metric. Tests were also performed on collected ambient PM2.5 samples and similar conclusions were drawn: emphasizing on correct tracers was shown to improve the separation of important source categories from mixed sources. When emphasizing on incorrect tracers, a counterfeit factor of Fe industrial source was extracted, which are inconsistent with field observations. Results from this study provide insights on how uncertainties should be estimated for the PMF model.
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