Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in south China – constraining analysis uncertainties by using multiple analysis tools

Abstract

Abstract. In this study, three methods including the random forest (RF) algorithm, boosted regression trees (BRTs) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were adopted for investigating emission-driven interannual variations in concentrations of air pollutants including PM2.5, PM10, O3, NO2, CO, SO2 and (NO2+O3) monitored in six cities in south China from May 2014 to April 2021. The first two methods were used to calculate the deweathered hourly concentrations, and the third one was used to calculate decomposed hourly residuals. To constrain the uncertainties in the calculated deweathered or decomposed hourly values, a self-developed method was applied to calculate the range of the deweathered percentage changes (DePCs) of air pollutant concentrations in annual scale. Emission-driven trends and emission-driven percentage changes (PCs) during the whole seven-year period were generated with the four methods being applied to analyzing the data. The consistency in the trends between the RF-deweathered and BRTs-deweathered concentrations and the ICEEMDAN-decomposed residuals of an air pollutant in a city reaches approximately 70 % of all the studied cases, but that in the PCs reaches only approximately 30 % of all the cases. The remaining cases with inconsistent trends and/or PCs indicated large uncertainties produced by one or more of the three methods. The calculated PCs from the deweathered concentrations and decomposed residuals were thus combined with the corresponding range of DePCs calculated from the self-developed method to gain the robust range of DePCs where applicable. Building on the robust ranges, the mitigation effects were discussed.