Articles | Volume 22, issue 18
https://doi.org/10.5194/acp-22-11945-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-11945-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2022
Research article |
| 16 Sep 2022
| 16 Sep 2022
Contributions of meteorology and anthropogenic emissions to the trends in winter PM2.5 in eastern China 2013–2018
Yanxing Wu
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Guangdong–Hong Kong–Macau Joint Laboratory of Collaborative Innovation
for Environmental Quality, Jinan University, Guangzhou, 511443, China
Yanzi Li
Guangzhou Huayue Technology Co., Ltd., Guangzhou, 510630, China
Junjie Dong
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Zhijiong Huang
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Guangdong–Hong Kong–Macau Joint Laboratory of Collaborative Innovation
for Environmental Quality, Jinan University, Guangzhou, 511443, China
Junyu Zheng
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Guangdong–Hong Kong–Macau Joint Laboratory of Collaborative Innovation
for Environmental Quality, Jinan University, Guangzhou, 511443, China
Shaw Chen Liu
CORRESPONDING AUTHOR
Institute for Environmental and Climate Research, Jinan University,
Guangzhou, 511443, China
Guangdong–Hong Kong–Macau Joint Laboratory of Collaborative Innovation
for Environmental Quality, Jinan University, Guangzhou, 511443, China
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Cited
12 citations as recorded by crossref.
- What caused large ozone variabilities in three megacity clusters in eastern China during 2015–2020? T. Hu et al. 10.5194/acp-24-1607-2024
- A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
- Key Role of Arctic Sea‐Ice in Subseasonal Reversal of Early and Late Winter PM2.5 Concentration Anomalies Over the North China Plain X. An et al. 10.1029/2022GL101841
- Identifying the impacts of warming anomalies in the Arctic region and the Tibetan Plateau on PM2.5 pollution and regional transport over China X. Sun et al. 10.1016/j.atmosres.2023.106966
- Anomalous high ozone in the Pearl River Delta, China in 2019: A cause attribution analysis Y. Wu et al. 10.1016/j.atmosenv.2023.120278
- Processes conducive to high ozone formation in Pearl River Delta in the presence of Pacific tropical cyclones W. Hu et al. 10.1016/j.atmosenv.2023.119859
- 降雨非绝热加热对2023年2月上旬中国东部“南雨-北霾”事件的影响 霞. 安 et al. 10.1360/SSTe-2023-0121
- Influence of rainfall-induced diabatic heating on southern rainfall-northern haze over eastern China in early February 2023 X. An et al. 10.1007/s11430-023-1181-3
- A comprehensive attribution analysis of PM2.5 in typical industrial cities during the winter of 2016–2018: Effect of meteorology and emission reduction A. Huang et al. 10.1016/j.atmosres.2023.107181
- A Newly Established Air Pollution Data Center in China M. Zheng et al. 10.1007/s00376-024-4055-4
- A comprehensive investigation of PM2.5 in the Huaihe River Basin, China: Separating the contributions from meteorology and emission reductions X. Liu et al. 10.1016/j.apr.2023.101647
- Variations in VOCs Emissions and Their O3 and SOA Formation Potential among Different Ages of Plant Foliage B. Zhang et al. 10.3390/toxics11080645
12 citations as recorded by crossref.
- What caused large ozone variabilities in three megacity clusters in eastern China during 2015–2020? T. Hu et al. 10.5194/acp-24-1607-2024
- A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
- Key Role of Arctic Sea‐Ice in Subseasonal Reversal of Early and Late Winter PM2.5 Concentration Anomalies Over the North China Plain X. An et al. 10.1029/2022GL101841
- Identifying the impacts of warming anomalies in the Arctic region and the Tibetan Plateau on PM2.5 pollution and regional transport over China X. Sun et al. 10.1016/j.atmosres.2023.106966
- Anomalous high ozone in the Pearl River Delta, China in 2019: A cause attribution analysis Y. Wu et al. 10.1016/j.atmosenv.2023.120278
- Processes conducive to high ozone formation in Pearl River Delta in the presence of Pacific tropical cyclones W. Hu et al. 10.1016/j.atmosenv.2023.119859
- 降雨非绝热加热对2023年2月上旬中国东部“南雨-北霾”事件的影响 霞. 安 et al. 10.1360/SSTe-2023-0121
- Influence of rainfall-induced diabatic heating on southern rainfall-northern haze over eastern China in early February 2023 X. An et al. 10.1007/s11430-023-1181-3
- A comprehensive attribution analysis of PM2.5 in typical industrial cities during the winter of 2016–2018: Effect of meteorology and emission reduction A. Huang et al. 10.1016/j.atmosres.2023.107181
- A Newly Established Air Pollution Data Center in China M. Zheng et al. 10.1007/s00376-024-4055-4
- A comprehensive investigation of PM2.5 in the Huaihe River Basin, China: Separating the contributions from meteorology and emission reductions X. Liu et al. 10.1016/j.apr.2023.101647
- Variations in VOCs Emissions and Their O3 and SOA Formation Potential among Different Ages of Plant Foliage B. Zhang et al. 10.3390/toxics11080645
Latest update: 20 Nov 2024
Short summary
Multiple linear regression (MLR) analyses often interpret the correlation coefficient (r2) as the contribution of an independent variable to the dependent variable. Since a good correlation does not imply a causal relationship, we propose that r2 should be interpreted as the maximum possible contribution. Moreover, MLR results are sensitive to the length of time analyzed; long-term analysis gives a more accurate assessment because of its additional constraints.
Multiple linear regression (MLR) analyses often interpret the correlation coefficient (r2) as...
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