The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing
Open accessAbstract
Daily 24-hour PM2.5 samples were collected continuously from January 1 to December 31, 2010. Elemental concentrations from Al to Pb were obtained using particle induced X-ray emission (PIXE) method. This was the first full year continuous daily PM2.5 elemental composition dataset in Beijing. Source apportionment analysis was conducted on this dataset using the positive matrix factorization method. Seven sources and their contributions to the total PM2.5 mass were identified and quantified. These include secondary sulphur– 13.8 μg/m3, 26.5%; vehicle exhaust– 8.9 μg/m3, 17.1%; fossil fuel combustion– 8.3 μg/m3, 16%; road dust– 6.6 μg/m3, 12.7%; biomass burning– 5.8 μg/m3, 11.2%; soil dust– 5.4 μg/m3, 10.4%; and metal processing– 3.1 μg/m3, 6.0%. Fugitive dusts (including soil dust and road dust) showed the highest contribution of 20.7 μg/m3 in the spring, doubling those in other seasons. On the contrary, contributions of the combustion source types (including biomass burning and fossil fuel combustion) were significantly higher in the fall (14.2 μg/m3) and in the winter (24.5 μg/m3) compared to those in the spring and summer (9.6 and 8.0 μg/m3, respectively). Secondary sulphur contributed the most in the summer while vehicle exhaust and metal processing sources did not show any clear seasonal pattern. The different seasonal highs and lows from different sources compensated each other. This explains the very small seasonal variations (< 20%) in the total PM2.5.
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Atmospheric aerosol over Alaska: 2. Elemental composition and sources
Polissar, A. V., Hopke, P. K., Paatero, P., Malm, W. C., & Sisler, J. F. (1998) · Journal of Geophysical Research: Atmospheres · doi:10.1029/98jd01212 · Cited by 926
Emissions of Metals Associated with Motor Vehicle Roadways
Lough, G. C., Schauer, J. J., Park, J.-S., Shafer, M. M., DeMinter, J. T., & Weinstein, J. P. (2005) · Environmental Science & Technology · doi:10.1021/es048715f · Cited by 802
Reff, A., Eberly, S. I., & Bhave, P. V. (2007) · Journal of the Air & Waste Management Association · doi:10.1080/10473289.2007.10465319 · Cited by 725
Seasonal trends in PM2.5 source contributions in Beijing, China
Zheng, M., Salmon, L. G., Schauer, J. J., Zeng, L., Kiang, C. S., Zhang, Y., & Cass, G. R. (2005) · Atmospheric Environment · doi:10.1016/j.atmosenv.2005.03.036 · Cited by 553
Lee, E., Chan, C. K., & Paatero, P. (1999) · Atmospheric Environment · doi:10.1016/s1352-2310(99)00113-2 · Cited by 454
Morgan, J. W., & Anders, E. (1979) · Geochimica et Cosmochimica Acta · doi:10.1016/0016-7037(79)90180-7 · Cited by 323
Source apportionment of PM 2.5 in Seoul, Korea
Heo, J.-B., Hopke, P. K., & Yi, S.-M. (2009) · Atmospheric Chemistry and Physics · doi:10.5194/acp-9-4957-2009 · Cited by 322
Characterization of particulate matter sources in an urban environment
Mazzei, F., D'Alessandro, A., Lucarelli, F., Nava, S., Prati, P., Valli, G., & Vecchi, R. (2008) · Science of The Total Environment · doi:10.1016/j.scitotenv.2008.03.008 · Cited by 283
Concentration and chemical characteristics of PM2.5 in Beijing, China: 2001–2002
DUAN, F., HE, K., MA, Y., YANG, F., YU, X., CADLE, S., CHAN, T., & MULAWA, P. (2006) · Science of The Total Environment · doi:10.1016/j.scitotenv.2005.03.001 · Cited by 259
The impact of fireworks on airborne particles
Vecchi, R., Bernardoni, V., Cricchio, D., D’Alessandro, A., Fermo, P., Lucarelli, F., Nava, S., Piazzalunga, A., & Valli, G. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2007.10.047 · Cited by 245
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Chemical characterization and source apportionment of PM 2.5 in Beijing: seasonal perspective
Zhang, R., Jing, J., Tao, J., Hsu, S.-C., Wang, G., Cao, J., Lee, C. S. L., Zhu, L., Chen, Z., Zhao, Y., et al. (2013) · Atmospheric Chemistry and Physics · doi:10.5194/acp-13-7053-2013 · Cited by 1,206
Self-citationStatus and characteristics of ambient PM2.5 pollution in global megacities
Cheng, Z., Luo, L., Wang, S., Wang, Y., Sharma, S., Shimadera, H., Wang, X., Bressi, M., de Miranda, R. M., Jiang, J., et al. (2016) · Environment International · doi:10.1016/j.envint.2016.02.003 · Cited by 393
Liu, Q., Liu, Y., Yin, J., Zhang, M., & Zhang, T. (2014) · Atmospheric Environment · doi:10.1016/j.atmosenv.2014.03.057 · Cited by 102
Aguilera, J., & Whigham, L. D. (2018) · Isotopes in Environmental and Health Studies · doi:10.1080/10256016.2018.1531854 · Cited by 43
Metadata
- Authors
- Yu, L., Wang, G., Zhang, R., Zhang, L., Song, Y., Wu, B., Li, X., An, K., & Chu, J.
- Venue
- Aerosol and Air Quality Research
- Published
- 2013
- Volume
- 13
- Issue
- 2
- Pages
- 574-583
- ISSN
- 1680-8584 (ISSN-L)
- 2071-1409
- OpenAlex
- W2161113310
- Cited by
- 369
- References
- 31
- FWCI
- 17.52
- Type
- journal article
- Language
- en
- Open access
- gold
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- Yu, L., Wang, G., Zhang, R., Zhang, L., Song, Y., Wu, B., Li, X., An, K., & Chu, J. (2013). Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing. Aerosol and Air Quality Research. https://doi.org/10.4209/aaqr.2012.07.0192
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