Contributions of meteorology and anthropogenic emissions to the trends in winter PM_2.5 in eastern China 2013–2018

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), the Yangtze River Delta (YRD), and the 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. 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 previous 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 in which 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 processes (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, and emission <88 % and non-emission >12 % for PRD.