College of Atmospheric Sciences, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou University, Lanzhou 730000, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Environ Pollut. 2016 Sep;216:350-360. doi: 10.1016/j.envpol.2016.05.085. Epub 2016 Jun 10.
We observed PM2.5, PM10 concentration, aerosol optical depth (AOD), and Ångström exponents (α) in three typical stations, the Beijing city, the Xianghe suburban and the Xinglong background station in the Beijing metropolitan region, from 2009 to 2010, synchronously. The annual means of PM2.5 (PM10) were 62 ± 45 (130 ± 88) μg m(-3) and 79 ± 61 (142 ± 96) μg m(-3) in the city and suburban region, which were much higher than the regional background (PM2.5: 36 ± 29 μg m(-3)). The annual means of AOD were 0.53 ± 0.47 and 0.54 ± 0.46 and 0.24 ± 0.22 in the city, suburban and the background region, respectively. The annual means of Ångström exponents were 1.11 ± 0.31, 1.09 ± 0.31 and 1.02 ± 0.31 in three typical stations. Meanwhile, the rates of PM2.5 accounting for PM10 were 44%-54% and 46%-70% in the city and suburban region during four seasons. The pollution of fine particulate was more serious in winter than other seasons. The linear regression functions of PM2.5 (y) and ground-observed AOD (x) were similarly with high correlation coefficient in the three typical areas, which were y = 74x + 18 (R(2) = 0.58, N = 337, in the City), y = 80x + 25 (R(2) = 0.55, N = 306, in the suburban) and y = 87x + 9 (R(2) = 0.64, N = 350, in the background). The functions of PM10 (y) and ground-observed AOD (x) were y = 112x + 57 (R(2) = 0.54, N = 337, in the city) and y = 114x + 68 (R(2) = 0.47, N = 304, in the suburban). But the functions had large differences in four seasons. The correlations between PM2.5, PM10 and MODIS AOD were similar with the correlations between PM2.5, PM10 and the ground-observed AOD. With MODIS C6 AOD, the distributions of PM2.5 and PM10 concentration were retrieved by the seasonal functions. The absolute retrieval errors of seasonal PM2.5 distribution were less than 5 μg m(-3) in the pollutant city and suburb, and less than 7 μg m(-3) in the clean background.
我们在北京都市地区的三个典型站点(北京市区、香河县郊区和兴隆背景站)同步观测了 2009 年至 2010 年的 PM2.5、PM10 浓度、气溶胶光学深度(AOD)和 Ångström 指数(α)。市区和郊区的 PM2.5(PM10)年平均值分别为 62±45(130±88)μg m(-3)和 79±61(142±96)μg m(-3),明显高于区域背景值(PM2.5:36±29μg m(-3))。市区、郊区和背景区的 AOD 年平均值分别为 0.53±0.47、0.54±0.46 和 0.24±0.22。三个典型站点的 Ångström 指数年平均值分别为 1.11±0.31、1.09±0.31 和 1.02±0.31。同时,市区和郊区四个季节的 PM2.5 占 PM10 的比例分别为 44%-54%和 46%-70%。冬季细颗粒物污染比其他季节更为严重。三个典型区域的 PM2.5(y)与地面观测 AOD(x)的线性回归函数具有高度相关性,其关系式分别为 y=74x+18(R(2)=0.58,N=337,在城市)、y=80x+25(R(2)=0.55,N=306,在郊区)和 y=87x+9(R(2)=0.64,N=350,在背景)。PM10(y)与地面观测 AOD(x)的关系式分别为 y=112x+57(R(2)=0.54,N=337,在城市)和 y=114x+68(R(2)=0.47,N=304,在郊区)。但是,四个季节的函数差异较大。PM2.5、PM10 和 MODIS AOD 之间的相关性与 PM2.5、PM10 和地面观测 AOD 之间的相关性相似。利用 MODIS C6 AOD,通过季节性函数反演了 PM2.5 和 PM10 浓度的分布。在污染城市和郊区,季节性 PM2.5 分布的绝对反演误差小于 5μg m(-3),在清洁背景下,绝对反演误差小于 7μg m(-3)。