ABSTRACT
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:â 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. â¡ 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. ⢠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.
ABSTRACT
Based on the MAIAC AOD and PM2.5 mass concentration data for the Beijing-Tianjin-Hebei region and surrounding areas from 2014 to 2018, the temporal and spatial differences in Aerosol Optical Depth (AOD) and PM2.5 mass concentrations were explored and their correlation was analyzed by linear regression. The results show that the daily average concentration of PM2.5 exceeds the standard for 33% and 57% of measurements based on the daily average standard values of the World Health Organization IT.1 and IT.2, respectively, indicating serious pollution levels. The annual average concentrations of PM2.5 and Terra and Aqua MAIAC AOD all show downward trends. The PM2.5 concentrations are high in winter and spring and low in summer and autumn; Terra and Aqua AOD values are high in spring and summer and low in autumn and winter. The seasonal and annual average concentrations of PM2.5 and AOD all show the regional pattern of "low in the north and high in the south". High-value areas are mainly located in southern Hebei, southwestern Shanxi, western Shandong, and northern Henan, while low-value areas are mainly located in northwestern Shanxi, northern Hebei, and eastern Shandong. The annual average concentration of PM2.5 is between 27 and 99µg·m-3, and the annual average AOD is between 0.20 and 0.69. The correlation between Aqua AOD and PM2.5 concentration is strong whereas and the correlations between Terra AOD, Aqua AOD, and PM2.5vary significantly in different seasons; overall, correlations are strongest in spring and winter and weakest in summer and autumn. After vertical-humidity correction, the correlation between satellite AOD and PM2.5 data is significantly improved.
