Sheng Huilin, Fan Liya, Chen Meifang, Wang Huanpeng, Huang Haomin, Ye Daiqi
School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; National Engineering Laboratory for Volatile Organic Compounds Pollution Control Technology and Equipment, Guangzhou 510006, China; Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, Guangzhou 510006, China; Guangdong Provincial Engineering and Technology Research Centre for Environmental Risk Prevention and Emergency Disposal, Guangzhou 510006, China.
Sci Total Environ. 2024 Jun 25;931:172779. doi: 10.1016/j.scitotenv.2024.172779. Epub 2024 Apr 26.
With the development of industries, air pollution in north-central Henan is becoming increasingly severe. The TROPOspheric Monitoring Instrument (TROPOMI) provides nitrogen dioxide (NO) column densities with high spatial resolution. Based on TROPOMI, in this study, the nitrogen oxides (NO) emissions in north-central Henan are derived and the emission hotspots are identified with the flux divergence method (FDM) from May to September 2021. The results indicate that Zhengzhou has the highest NO emissions in north-central Henan. The most prominent hotspots are in Guancheng Huizu District (Zhengzhou) and Yindu District (Anyang), with emissions of 448.4 g/s and 300.3 g/s, respectively. The Gaussian Mixture Model (GMM) is applied to quantify the characteristics of emission hotspots, including the diameter, eccentricity, and tilt angle, among which the tilt angle provides a novel metric for identifying the spatial distribution of pollution sources. Furthermore, the results are compared with the CAMS global anthropogenic emissions (CAMS-GLOB-ANT) and Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC), and they are generally in good agreement. However, some point sources, such as power plants, may be missed by both inventories. It is also found that for emission hotspots near transportation hubs, CAMS-GLOB-ANT may not have fully considered the actual traffic flow, leading to an underestimation of transportation emissions. These findings provide key information for the accurate implementation of pollution prevention and control measures, as well as references for future optimization of emission inventories. Consequently, deriving NO emissions from space, quantifying the characteristics of emission hotspots, and combining them with bottom-up inventories can provide valuable insights for targeted emission control.
随着工业的发展，豫北地区的空气污染日益严重。对流层监测仪器（TROPOMI）可提供高空间分辨率的二氧化氮（NO）柱浓度。基于TROPOMI，本研究推导了2021年5月至9月豫北地区的氮氧化物（NO）排放量，并采用通量散度法（FDM）识别了排放热点。结果表明，郑州是豫北地区NO排放量最高的城市。最突出的热点位于管城回族区（郑州）和殷都区（安阳），排放量分别为448.4克/秒和300.3克/秒。应用高斯混合模型（GMM）对排放热点的特征进行量化，包括直径、偏心率和倾斜角，其中倾斜角为识别污染源的空间分布提供了一种新的指标。此外，将结果与全球大气成分分析系统（CAMS）全球人为排放清单（CAMS-GLOB-ANT）以及用于气候和空气污染研究的多分辨率排放清单模型（MEIC）进行了比较，结果总体吻合较好。然而，这两个清单可能都遗漏了一些点源，如发电厂。研究还发现，对于交通枢纽附近的排放热点，CAMS-GLOB-ANT可能没有充分考虑实际交通流量，导致交通排放被低估。这些发现为精准实施污染防治措施提供了关键信息，也为未来排放清单的优化提供了参考。因此，从空间推导NO排放量、量化排放热点特征并将其与自下而上的清单相结合，可以为有针对性的排放控制提供有价值的见解。
