Trajectory clustering, potential source contribution function （PSCF） and concentration-weighted trajectory （CWT） methods were applied to investigate the transport pathways and identify potential sources of PM2.s a...Trajectory clustering, potential source contribution function （PSCF） and concentration-weighted trajectory （CWT） methods were applied to investigate the transport pathways and identify potential sources of PM2.s and PMIo in different seasons from June 2014 to May 2015 in Beijing. The cluster analyses showed that Beijing was affected by trajectories from the south and southeast in summer and autumn. In winter and spring, Beijing was not only affected by the trajectories from the south and southeast, but was also affected by trajectories from the north and northwest. In addition, the analyses of the pressure profile of backward trajectories showed that backward trajectories, which have important influence on Beijing, were mainly distributed above 970 hPa in summer and autumn and below 950 hPa in spring and winter. This indicates that PM2.s and PMIo were strongly affected by the near surface air masses in summer and autumn and by high altitude air masses in winter and spring. Results of PSCF and CV/T analyses showed that the largest potential source areas were identified in spring, followed by winter and autumn, then summer. In addition, potential source regions of PMIo were similar to those of PM2.5. There were a clear seasonal and spatial variation of the potential source areas of Beijing and the airflow in the horizontal and vertical directions. Therefore, more effective regional emission reduction measures in Beijing＇s surrounding provinces should be implemented to reduce emissions of regional sources in different seasons.展开更多
The variations of mass concentrations of PM2.5, PMl0, SO2, NO2, CO, and 03 in 31 Chinese provincial capital cities were analyzed based on data from 286 monitoring sites obtained between March 22, 2013 and March 31,201...The variations of mass concentrations of PM2.5, PMl0, SO2, NO2, CO, and 03 in 31 Chinese provincial capital cities were analyzed based on data from 286 monitoring sites obtained between March 22, 2013 and March 31,2014. By comparing the pollutant concentrations over this length of time, the characteristics of the monthly variations of mass concentrations of air pollutants were determined. We used the Pearson correlation coefficient to establish the relationship between PM2.5, PM10, and the gas pollutants. The results revealed significant differences in the concentration levels of air pollutants and in the variations between the different cities. The Pearson correlation coefficients between PMs and NO2 and SO2 were either high or moderate （PM2.s with NO2： r = 0.256-0.688, mean r = 0,498：PM10 with NO2： r = 0.169-0.713, mean r=0.493; PM2.5 with SO2： r=0.232-0.693, mean r=0.449; PM10 with SO2： r=0.131-0.669, mean r = 0.403）. The correlation between PMs and CO was diverse （PM2.5： r = 0.156-0.721, mean r = 0.437; PMl0： r= 0.06-0.67, mean r= 0.380）. The correlation between PMs and 03 was either weak or uncorrelated （PM2.s： r= -0,35 to 0.089, mean r= -0.164; PM10： r= -0.279 to 0.078, mean r= -0.127）, except in Haikou （PM2.5： r=0.500; PM10： r=0,509）.展开更多
基金supported by the National Key Foundation for Exploring Scientific Instrument(No.2012YQ060147)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB05040402)the Key Program of the Chinese 473 Academy of Sciences(No.KJZD-EW-TZ-G06-01)
文摘Trajectory clustering, potential source contribution function （PSCF） and concentration-weighted trajectory （CWT） methods were applied to investigate the transport pathways and identify potential sources of PM2.s and PMIo in different seasons from June 2014 to May 2015 in Beijing. The cluster analyses showed that Beijing was affected by trajectories from the south and southeast in summer and autumn. In winter and spring, Beijing was not only affected by the trajectories from the south and southeast, but was also affected by trajectories from the north and northwest. In addition, the analyses of the pressure profile of backward trajectories showed that backward trajectories, which have important influence on Beijing, were mainly distributed above 970 hPa in summer and autumn and below 950 hPa in spring and winter. This indicates that PM2.s and PMIo were strongly affected by the near surface air masses in summer and autumn and by high altitude air masses in winter and spring. Results of PSCF and CV/T analyses showed that the largest potential source areas were identified in spring, followed by winter and autumn, then summer. In addition, potential source regions of PMIo were similar to those of PM2.5. There were a clear seasonal and spatial variation of the potential source areas of Beijing and the airflow in the horizontal and vertical directions. Therefore, more effective regional emission reduction measures in Beijing＇s surrounding provinces should be implemented to reduce emissions of regional sources in different seasons.
基金funded by the Tsinghua National Laboratory for Information Science and Technology(TNList) Cross-discipline Foundationthe special fund of the Key Laboratory of Eco Planning & Green Building,Ministry of Education(Tsinghua University), China
文摘The variations of mass concentrations of PM2.5, PMl0, SO2, NO2, CO, and 03 in 31 Chinese provincial capital cities were analyzed based on data from 286 monitoring sites obtained between March 22, 2013 and March 31,2014. By comparing the pollutant concentrations over this length of time, the characteristics of the monthly variations of mass concentrations of air pollutants were determined. We used the Pearson correlation coefficient to establish the relationship between PM2.5, PM10, and the gas pollutants. The results revealed significant differences in the concentration levels of air pollutants and in the variations between the different cities. The Pearson correlation coefficients between PMs and NO2 and SO2 were either high or moderate （PM2.s with NO2： r = 0.256-0.688, mean r = 0,498：PM10 with NO2： r = 0.169-0.713, mean r=0.493; PM2.5 with SO2： r=0.232-0.693, mean r=0.449; PM10 with SO2： r=0.131-0.669, mean r = 0.403）. The correlation between PMs and CO was diverse （PM2.5： r = 0.156-0.721, mean r = 0.437; PMl0： r= 0.06-0.67, mean r= 0.380）. The correlation between PMs and 03 was either weak or uncorrelated （PM2.s： r= -0,35 to 0.089, mean r= -0.164; PM10： r= -0.279 to 0.078, mean r= -0.127）, except in Haikou （PM2.5： r=0.500; PM10： r=0,509）.
