研究组在高耗能工业CO2排放遥感监测方面取得重要进展,博士生王松寒研究论文被地学权威期刊Journal of Geophysical Research: Atmosphere接受!祝贺!论文连接:https://doi.org/10.1029/2018JD029005 

A paper has been accepted by Journal of Geophysical Research: Atmosphere by one of our PhD student, Songhan Wang. Congratulations to Songhan!

Figure 1. Location of the study area. (a): Mean ODIAC CO2 emissions over China between 2015 and 2016. (b): Location of the study area, including Shanxi province, Hebei province, Tianjin and Beijing. The dots with different color represent different types of industrial facilities.


Figure 2. An example of the selecting procedure based on Gaussian plume model. (a): High-resolution image of an iron & steel plant  located in Hebei province, with the plume labeled in red. (b)- (e): Normalized CO2 vertical column of this plume based on Gaussian plume model during the four OCO-2 overpasses time. The red arrows represent the wind direction and the dashed lines represent the boundary of a pixel. Results show that the influence distance during June 14, 2015 is beyond the boundary (c). Thus the original OCO-2 footprint overpassing this plant June 14, 2015 is excluded from our analysis.




Figure 3. 
(a): Results of the mean anomalies calculated in the study area. The round dots with different colors represent different types of industrial facilities and city centers. The box with red color represents the highest XCO2 anomaly area in each stripe, while the box with cyan color represents the lowest one. (b): Mean ODIAC CO2 emissions of the study area between 2015 and 2016.


Figure 4. Classification result of the 73 plants. Spatial pattern of the classification result is shown in (a). Three different sizes of dots represent three CO2 emission levels, bigger means higher. Statistic results of the three CO2 emission levels are shown in (b). For each level, the left Y-axis is the XCO2 anomaly and the right Y-axis is the ODIAC inventory-based CO2 emission.