中国能源金三角NO2时空格局及其驱动因子

利用臭氧监测仪(OMI)提供的大气污染监测数据,结合产业结构、汽车保有量、国家政策措施等,通过城乡NO₂浓度差异的排放源分析方法提取能源金三角(EGT)地区2005~2019年对流层NO₂垂直柱浓度时空变化特征并探讨影响区域大气NO₂浓度驱动因素.结果表明,EGT煤炭化工源NO₂浓度与第二产业产值增速的相关系数为0.71(P<0.05),说明本文方法所提取的长时序煤炭化工源NO₂浓度能有效地指示产业结构调整和政策措施变化.NO₂浓度从2005~2011年的90.56molc/m²增加至2012~2015年的720.77molc/m²,再下降至2016~2019年的247.36molc/m²,反映EGT经济发展模式经历了从小规模、中污染的点模式逐步发展成大范围、重污染的粗放模式,再到大范围、低污染的精工模式.与京津冀、华中、长三角等地区相比,EGT交通和工业排放对城市源NO₂污染贡献的变化特征进一步反映城镇化水平的发展和产业结构的优化.与OMI相比,高分辨率对流层观测仪(TROPOMI)能在短时序上提供丰富的影像细节信息,且随着观测时长的增加,有望增强长时序大气NO₂污染的精准监测.

Spatiotemporal patterns and driving forces of NO2 concentrations from different emission sources in the energy golden triangle of China

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.