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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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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