Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 21
Articles | Volume 23, issue 21
https://doi.org/10.5194/acp-23-14065-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-14065-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 21
Research article
 | 
13 Nov 2023
Research article |  | 13 Nov 2023

The role of temporal scales in extracting dominant meteorological drivers of major airborne pollutants

Miaoqing Xu, Jing Yang, Manchun Li, Xiao Chen, Qiancheng Lv, Qi Yao, Bingbo Gao, and Ziyue Chen

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Cited articles

Chen, Z., Xu, B., Cai, J., and Gao, B.: Understanding temporal patterns and characteristics of air quality in Beijing: A local and regional perspective, Atmos. Environ., 127, 303–315, 2016. 
Chen, Z., Cai, J., Gao, B., Xu, B., Dai, S., and He, B.: Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the jing-jin-ji region, Sci. Rep.-UK, 7, 40735, https://doi.org/10.1038/srep40735, 2017. 
Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N., and Xu, B.: Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective, Atmos. Chem. Phys., 18, 5343–5358, https://doi.org/10.5194/acp-18-5343-2018, 2018. 
Chen, Z., Chen, D., Xie, X., Cai, J., Zhuang, Y., Cheng, N., He, B., and Gao, B.: Spatial self-aggregation effects and national division of city-level PM2.5 concentrations in china based on spatio-temporal clustering, J. Clean. Prod., 207, 875–881, https://doi.org/10.1016/j.jclepro.2018.10.080, 2019a. 
Chen, Z., Zhuang, Y., Xie, X., Chen, D., Cheng, N., and Yang, L.: Understanding long-term variations of meteorological influences on ground ozone concentrations in beijing during 2006–2016, Environ. Pollut., 245, 29–37, https://doi.org/10.1016/j.envpol.2018.10.117, 2019b. 
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Although the temporal-scale effects on PM2.5–meteorology associations have been discussed, no quantitative evidence has proved this before. Based on rare 3 h meteorology data, we revealed that the dominant meteorological factor for PM2.5 concentrations across China extracted at the 3 h and 24 h scales presented large variations. This research suggests that data sources of different temporal scales should be comprehensively considered for better attribution and prevention of airborne pollution.
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