Spatiotemporal patterns and driving forces of NO2 concentrations from different emission sources in the energy golden triangle of China
SHEN Yong-lin1,2, LUO Ji-hao1, Ma Yu-yang2, YAO Ling3, HU Chu-li2
1. National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China; 2. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; 3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Abstract:In order to understand the air pollution caused by the utilization of EGT resources and development, the air monitoring data were derived from OMI (ozone monitoring instrument) sensor, combined with data of industrial structure, vehicle ownership, national policies and measures, etc. The spatial and temporal characteristics of tropospheric NO2 vertical column concentration in the EGT region from 2005 to 2019 were extracted by using the emission source analysis method of urban and rural NO2 concentration differences, and the driving factors affecting the regional atmospheric NO2 concentration were analyzed and discussed. Finally, comparing the spatial characteristics of TROPOMI (tropospheric monitoring instrument) with OMI NO2 products, the results show that The correlation coefficient between NO2 concentration of EGT coal chemical source and output value growth of the secondary industry was 0.71 (P<0.05), indicating that the NO2 concentration of long-term urban source and coal chemical source extracted by this method can effectively reflect the adjustment of regional industrial structure and changes in national policies and measures. The NO2 concentration increased from 90.56molec/m2 in 2005~2011 to 720.77molec/m2 in 2012~2015, and then decreased to 247.36molec/m2 in 2016~2019. The spatial-temporal variations of NO2 illustrate that the economic development model in the EGT has gradually evolved from a small-scale and moderate-polluting scattered model to a large-scale, heavy-polluting extensive model, and then to a large-scale, light-polluting fine model. Compared with the Beijing-Tianjin-Hebei Urban Agglomeration, the Central of China, and the Yangtze River Delta region, the changing characteristics of the contribution of traffic and industrial emissions to urban source NO2 pollution in the EGT further reflect the development of urbanization and the optimization of industrial structure. In comparison with OMI, it can be found that TROPOMI provides rich and detailed image information in a short time series, and as the observation time increases, it can provide long time series and precise atmospheric NO2 pollution monitoring.
沈永林, 骆济豪, 马雨阳, 姚凌, 胡楚丽. 中国能源金三角NO2时空格局及其驱动因子[J]. 中国环境科学, 2022, 42(4): 1585-1593.
SHEN Yong-lin, LUO Ji-hao, Ma Yu-yang, YAO Ling, HU Chu-li. Spatiotemporal patterns and driving forces of NO2 concentrations from different emission sources in the energy golden triangle of China. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(4): 1585-1593.
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