17 Jun 2022
17 Jun 2022
Status: this preprint is currently under review for the journal ACP.

Contributions of meteorology and anthropogenic emissions to the trends in winter PM2.5 in eastern China 2013–2018

Yanxing Wu1, Run Liu1,2, Yanzi Li3, Junjie Dong1, Zhijiong Huang1,2, Junyu Zheng1,2, and Shaw Chen Liu1,2 Yanxing Wu et al.
  • 1Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
  • 2Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, 511443, China
  • 3Guangzhou Huayue Technology Co., Ltd., Guangzhou, 510630, China

Abstract. Multiple linear regression (MLR) models are used to assess the contributions of meteorology/climate and anthropogenic emission control to linear trends of PM2.5 concentration during the period 2013–2018 in three regions in eastern China, namely Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). We find that quantitative contributions to the linear trend of PM2.5 derived based on MLR results alone are not credible because a good correlation in the MLR analysis does not imply any causal relationship, let alone a quantitative relationship. As an alternative, we propose that the correlation coefficient should be interpreted as the maximum possible contribution of the independent variable to the dependent variable, and the residual should be interpreted as the minimum contribution of all other independent variables. Under the new interpretation, the MLR results become self-consistent. We also find that the results of a short-term (2013–2018) analysis are significantly different from those of a long-term (1985–2018) analysis for the period 2013–2018 they overlap, indicating that MLR results depend critically on the length of time analyzed. The long-term analysis renders a more precise assessment, because of additional constraints provided by the long-term data. We therefore suggest that the best estimates of the contributions of emissions and non-emission (including meteorology/climate) to the linear trend in PM2.5 during 2013–2018 are those from the long-term analyses: i.e., emission <51 % and non-emission >49 % for BTH, emission <44 % and non-emission >56 % for YRD, emission <88 % and non-emission >12 % for PRD.

Yanxing Wu et al.

Status: open (until 29 Jul 2022)

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Yanxing Wu et al.


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Short summary
Multiple linear regression (MLR) analyses often interpret the correlation coefficient (CC) as the contribution of an independent variable to the dependent variable. Since a good correlation does not imply a causal relationship, we propose that CC 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.