[Nonlinear Variations in PM_{2. 5} Concentration in the Three Major Urban Agglomerations in China]

ITA and Beast methods were used to quantitatively analyze the nonlinear process of a PM_2.5 concentration time series based on the PM_2.5 concentration data of the three major urban agglomerations in China. The results showed that： ① the degree of the PM_2.5 pollution in the three major urban agglomerations had decreased， and the high-concentration areas had noticeably shrunk. The degree of spatial polarization of PM_2.5 concentration was reduced， and the spatial difference was narrowed. The PM_2.5 concentration in most areas showed downward trends， but the degree of change was not the same. Compared with the YRD and PRD， the concentration of PM_2.5 in the BTH was still at a relatively high level. ② The concentration of PM_2.5 in the three major urban agglomerations had seasonal variation characteristics that were high in winter and spring and low in summer and autumn. There were obvious differences in PM_2.5 concentration between winter and summer， and the convergence of PM_2.5 concentration in summer was greater than that in winter. Areas with high PM_2.5 concentration also had obvious downward trends， but the downward trends of PM_2.5 concentration in the PRD were not obvious compared with those in the YRD and BTH. ③ The PM_2.5 concentration time series of the three major urban agglomerations all had significant downward trends： Beijing-Tianjin-Hebei （BTH） > the Yangtze River Delta （YRD） > the Pearl River Delta （PRD）. The PM_2.5 concentration had the largest downward trends in winter. The higher the PM_2.5 pollution level， the greater the downward trends. ④ The trend component of the PM_2.5 concentration time series in the BTH had two change points， and there was one change point in the seasonal component. The trend and seasonal components of the PM_2.5 concentration time series in the YRD had no change point. There was no change point in the seasonal component but one change point in the trend component of the PM_2.5 concentration time series in the PRD. These results can provide scientific references for regional air pollution control.