Tropospheric NO2 columns over East Central China: Comparisons between SCIAMACHY measurements and nested CMAQ simulations

被引:27
|
作者
Shi, Chune [1 ]
Fernando, H. J. S. [2 ]
Wang, Zifa [3 ]
An, Xingqin [4 ]
Wu, Qizhong [3 ]
机构
[1] Anhui Inst Meteorol Sci, Key Lab Atmospher Sci & Satellite Remote Sensing, Hefei 230031, Peoples R China
[2] Arizona State Univ, Dept Mech & Aerosp Engn, Environm Fluid Dynam Program, Tempe, AZ 85287 USA
[3] Chinese Acad Sci, LAPC NZC, Inst Atmospher Phys, Beijing 100029, Peoples R China
[4] CAWAS, CMA, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Tropospheric NO2 columns; SCIAMACHY; East central China; Models-3/CMAQ;
D O I
10.1016/j.atmosenv.2008.05.046
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Tropospheric NO2 vertical column densities over East Central China (ECC) simulated with a regional air quality model are compared with those measured by the remote sensor SCIAMACHY (SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY). A 3D Eulerian air quality model (Models-3/CMAQ) and a best available emission inventory are employed in the simulations. The objectives are to delve into (i) the suitability of the emission inventory employed, (ii) the reliability of SCIAMACHY observations over ECC, and (iii) the role of model resolution on predictions. The predicted NO2 concentrations are integrated from the bottom to the model top and converted from the model grid to satellite pixel bases. The model reproduces the spatial distribution of SCIAMACHY-observed NO2 vertical column densities satisfactorily with a correlation coefficient of about 0.76, but with a large normalized mean bias similar to-60%. The latter bias is ascribed to the sharp increase of emissions that have occurred in ECC owing to rapid industrialization ever since the compilation of the emission inventory. When the model grid size is larger than the size of a satellite pixel, a decrease of grid size improves the CMAQ predictions when compared with SCIAMACHY, although higher resolutions in general do not necessarily improve CMAQ predictions. A critical cloud fraction of 0.2 is found to give the best comparisons between SCIAMACHY data and simulations. (c) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:7165 / 7173
页数:9
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