[Nonlinear Variations in PM2. 5 Concentration in the Three Major Urban Agglomerations in China].

ITA and Beast methods were used to quantitatively analyze the nonlinear process of a PM2.5 concentration time series based on the PM2.5 concentration data of the three major urban agglomerations in China. The results showed that： ① the degree of the PM2.5 pollution in the three major urban agglomerations had decreased， and the high-concentration areas had noticeably shrunk. The degree of spatial polarization of PM2.5 concentration was reduced， and the spatial difference was narrowed. The PM2.5 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 PM2.5 in the BTH was still at a relatively high level. ② The concentration of PM2.5 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 PM2.5 concentration between winter and summer， and the convergence of PM2.5 concentration in summer was greater than that in winter. Areas with high PM2.5 concentration also had obvious downward trends， but the downward trends of PM2.5 concentration in the PRD were not obvious compared with those in the YRD and BTH. ③ The PM2.5 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 PM2.5 concentration had the largest downward trends in winter. The higher the PM2.5 pollution level， the greater the downward trends. ④ The trend component of the PM2.5 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 PM2.5 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 PM2.5 concentration time series in the PRD. These results can provide scientific references for regional air pollution control.

[Scale Dependence Between PM2.5 and Meteorological Factors and Its Influencing Factors in "2+26" Cities].

Based on the PM2.5 concentration and meteorological data of "2+26" cities, the variations in PM2.5 time series were analyzed by the continuous wavelet transform(CWT) and discrete wavelet transform(DWT). Wavelet coherence(WTC) and multiple wavelet coherence(MWC) were used to quantify the response relationship between PM2.5 and single/multiple meteorological factors in the time-frequency domain. Partial wavelet coherence(PWC) was used to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationship. The results showed that:① the concentration of PM2.5 in the "2+26" cities had the spatial distribution characteristics of high in the middle area and low in the peripheral area. The PM2.5 mutation events were mainly concentrated before 2018 and mostly occurred in winter when the meteorological conditions were stable. The annual scale period of 256-512 d was relatively stable, and it was also the dominant period of the PM2.5 time series. ② The coherences between PM2.5 and meteorological factors depended on the time-frequency scale and variable combination. At all time-frequency scales, PM2.5 had strong coherences with relative humidity and temperature. At small and medium time-frequency scales, PM2.5 had strong coherences with wind speed. At large scales, PM2.5 had strong coherences with temperature. The combination of precipitation, temperature, and relative humidity could explain the variation in PM2.5 at all time-frequency scales. ③ At different time-frequency scales, the enhancement/weakening effects of atmospheric teleconnection factors on the response relationship were not the same. At all time-frequency scales, the El Niño-Southern Oscillation(ENSO) had a greater impact on the response relationship between PM2.5 and precipitation/temperature, and the Pacific decadal oscillation(PDO) had a greater impact on the response relationship between PM2.5 and relative humidity/wind speed. These results provide reference for regional air pollution control.