Articles | Volume 22, issue 8
https://doi.org/10.5194/acp-22-5495-2022
https://doi.org/10.5194/acp-22-5495-2022
Research article
 | 
26 Apr 2022
Research article |  | 26 Apr 2022

Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach

Haoran Zhang, Nan Li, Keqin Tang, Hong Liao, Chong Shi, Cheng Huang, Hongli Wang, Song Guo, Min Hu, Xinlei Ge, Mindong Chen, Zhenxin Liu, Huan Yu, and Jianlin Hu

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Cited articles

An, Z., Huang, R. J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., and Ji, Y.: Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA, 116, 8657–8666, https://doi.org/10.1073/pnas.1900125116, 2019. 
Chang, Y., Huang, R. J., Ge, X., Huang, X., Hu, J., Duan, Y., Zou, Z., Liu, X., and Lehmann, M. F.: Puzzling Haze Events in China During the Coronavirus (COVID-19) Shutdown, Geophys. Res. Lett., 47, e2020GL088533, https://doi.org/10.1029/2020gl088533, 2020. 
Chen, W., Wang, X., Zhou, S., Cohen, J., Zhang, J., Wang, Y., Chang, M., Zeng, Y., Liu, Y., Lin, Z., Liang, G., and Qiu, X.: Chemical Composition of PM2.5 and its Impact on Visibility in Guangzhou, Southern China, Aerosol Air Qual. Res., 16, 2349–2361, https://doi.org/10.4209/aaqr.2016.02.0059, 2016. 
Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang, Q., He, K., Carmichael, G., Pöschl, U., and Su, H.: Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China, Sci. Adv., 2, e1601530, https://doi.org/10.1126/sciadv.1601530, 2016. 
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We developed a new algorithm with low economic/technique costs to identify primary and secondary components of PM2.5. Our model was shown to be reliable by comparison with different observation datasets. We systematically explored the patterns and changes in the secondary PM2.5 pollution in China at large spatial and time scales. We believe that this method is a promising tool for efficiently estimating primary and secondary PM2.5, and has huge potential for future PM mitigation.
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