Cite this article: Lang, J., Cheng, S., Wen, W., Liu, C. and Wang, G. (2017). Development and Application of a New PM2.5 Source Apportionment Approach.
Aerosol Air Qual. Res.
17: 340-350. https://doi.org/10.4209/aaqr.2015.10.0588
A new PM2.5 source apportionment approach was proposed.
The method combined receptor models, source-oriented models and emission inventory.
It could identify the source contributions of secondary components.
Target sources were optional.
The main sources and their contributions in Beijing and Tangshan were identified.
Due to the similarity of PM2.5 chemical species profiles of different sources, time synchronization of source contributions and the uncertainties of source-oriented models, it is difficult to get a well-separated and relatively accurate PM2.5 source apportionment result, especially for the secondary components, when only one method was applied. A new PM2.5 source apportionment approach, combining the receptor models, source-oriented models and emission inventory, was developed in this study. The proposed method had following strengths: (1) it could identify the source contributions to secondary components; (2) target (or expected) sources were optional; (3) mixed sources could be avoided. The new approach was then applied in two typical cities in North China – Beijing and Tangshan, based on intensive PM2.5 observation results from 2011 to 2013. The source apportionment result indicated that the annual average contribution to PM2.5 in Tangshan was 7.4%, 21.5%, 7.6%, 18.0%, 14.5%, 10.9% and 20.0% for power, metallurgy, cement, coal combustion, vehicle, dust and other sources, respectively; the annual average contribution ratio for vehicle, industry and industrial coal combustion, residential coal combustion, dust and other sources in Beijing was 31.5%, 22.9%, 10.6%, 14.5% and 20.4%, respectively. Seasonal variation of the source contributions was also analyzed. The demonstration results showed that the combined method was feasible. In addition, the detailed source contribution results could also provide scientific support for making effective PM2.5 mitigation strategy.