Li Yingsong, Jiang Fei, Jia Mengwei, Feng Shuzhuang, Lai Yong, Ding Junnan, He Wei, Wang Hengmao, Wu Mousong, Wang Jun, Shen Fanhui, Zhang Lingyu
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China.
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing 210023, China.
Sci Total Environ. 2024 Feb 25;913:169586. doi: 10.1016/j.scitotenv.2023.169586. Epub 2023 Dec 29.
CO emissions from power plants are the dominant source of global CO emissions, thus in the context of global warming, accurate estimation of CO emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.
来自发电厂的一氧化碳排放是全球一氧化碳排放的主要来源，因此在全球变暖的背景下，准确估算发电厂的一氧化碳排放对于有效控制碳排放至关重要。基于轨道碳观测站2（OCO-2）的一氧化碳反演数据和高斯烟羽模型（GPM），已经开展了一系列研究来估算发电厂的一氧化碳排放。然而，GPM是一个理想模型，在使用该模型时需要做出一些假设，这导致反演排放存在很大的不确定性。在此，基于OCO-2卫星在中国长江三角洲观测到的6个发电厂烟羽案例，我们使用耦合在社区多尺度空气质量模型（CMAQ）中的在线烟羽上升模块来模拟烟羽并反演排放，并将模拟烟羽和反演排放与GPM模型进行比较。我们发现，基于GPM模拟，一氧化碳排放可能被显著高估或低估，并且CMAQ在线烟羽模拟可以显著改善估算结果。然而，风速的模拟偏差会显著影响反演结果。这些结果表明，准确的气象场和烟羽模拟对于未来点源排放的反演至关重要。
