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Volume 18, issue 8
Atmos. Chem. Phys., 18, 5343–5358, 2018
https://doi.org/10.5194/acp-18-5343-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 18, 5343–5358, 2018
https://doi.org/10.5194/acp-18-5343-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Apr 2018

Research article | 19 Apr 2018

Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective

Ziyue Chen et al.

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Revised manuscript accepted for ACP
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Cited articles

Cao, C., Jiang, W., Wang, B., Fang, J., Lang, J., Tian, G., Jiang, J., and Zhu, T.: Inhalable Microorganisms in Beijing's PM2.5 and PM10 Pollutants during a Severe Smog Event, Environ. Sci. Technol., 48, 1499–1507, 2014.
Cao, J., Shen, Z., Chow, J., Watson, J. G., Leed, S., Tie, X., Ho, K., Wang, G., and Han, Y.: Winter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities, J. Air Waste Manage., 62, 1214–1226, 2012.
Chen, T., He, J., Lu, X., She, J., and Guan, Z.: Spatial and temporal variations of PM2. 5 and its relation to meteorological factors in the urban area of Nanjing, China, Int. J. Env. Res. Pub. He., 13, 921, https://doi.org/10.3390/ijerph13090921, 2016.
Chen, W., Zhang, H. T., and Zhao, H. M.: Diurnal, weekly and monthly spatial variations of air pollutants and air quality of Beijing, Atmos. Environ., 119, 21–34, 2015.
Chen, Y., Schleicher, N., Fricker, M., Cen, K., Liu, X. L., Kaminski, U., Yu, Y., Wu, X. F., and Norra, S.: Long-term variation of black carbon and PM2.5 in Beijing, China with respect to meteorological conditions and governmental measures, Environ. Pollut., 212, 269–278, 2016.
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With an advanced causality analysis method CCM, we quantified the influence of individual meteorological factors on PM2.5 concentrations and revealed notable spatial and temporal variations of meteorological influences on PM2.5 concentrations across China. Temperature, humidity and wind exert the strongest influences on PM2.5 concentrations in most cities across China. Given the notable spatial variations, meteorological means for reducing PM2.5 concentrations should be designed accordingly.
With an advanced causality analysis method CCM, we quantified the influence of individual...
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