Paper
30 October 2009 Analysis on the spatio-temporal gradients of carbon monoxide in China by SCIAMACHY remotely sensed data
Ke Wang, Hong Jiang
Author Affiliations +
Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74982V (2009) https://doi.org/10.1117/12.832873
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
Carbon monoxide is an important atmospheric pollutant whose emissions and atmospheric concentrations need to be monitored. This paper presents the spatio-temporal gradients of carbon monoxide in China based on the vertical columns of carbon monoxide measured by the SCIAMACHY sensor on board ENVISAT during 2003-2005. The annual average of carbon monoxide vertical columns is up to 2.3×1018molecule•cm-2 more than the background 1.6×1018molecule•cm-2. The seasonal variation of carbon monoxide vertical columns is obvious. The peak centers of monthly averaged carbon monoxide occurred in winter and spring, whereas the troughs in summer. The high concentrations of carbon monoxide are distributed on the Yangtze Delta, around Sichuan Basin, around Beijing regions and the Pearl Delta. The carbon monoxide concentration has a strong correlation with government-generated provincial-level statistical data on environment and energy. The regional pollution as well as biomass burning plays an important role for carbon monoxide levels over China and that SCIAMACHY is a promising tool for comprehensive understanding of carbon monoxide gradients and emissions.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ke Wang and Hong Jiang "Analysis on the spatio-temporal gradients of carbon monoxide in China by SCIAMACHY remotely sensed data", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74982V (30 October 2009); https://doi.org/10.1117/12.832873
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KEYWORDS
Carbon monoxide

Satellites

Data modeling

Pollution

Combustion

Atmospheric sensing

Atmospheric modeling

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