资源环境科技发展态势分析平台

Energy intensive industries, such as iron and steel industry and coal-processing industry, are one of the major anthropogenic fossil fuel carbon dioxide (CO2) emission sources, especially in China. Monitoring the localized CO2 emissions from these industrial point sources is valuable to improve emission estimates and inform on policy discussion. However, spatiotemporally explicit information on CO2 emissions from different energy intensive industrial sources is still limited. In this study, we use remote sensing data sets with high spatial resolution to detect the patterns of CO2 enhancements of carbon emission intensive industries, taking northern China as the case study area. CO2 anomalies are derived from spaceborne column-averaged CO2 mixing ratio (XCO2) data measured by the Orbiting Carbon Observatory 2 (OCO-2). The Gaussian plume model is used to select the XCO2 data when the CO2 emissions from plants are localized, which allows us to distinguish CO2 emissions from different industrial point sources. We demonstrate that high-emission areas with industrial plants are detectable by CO2 anomalies compared to natural background area and the average enhancement is about 1.8ppm. The XCO2 data also show a higher CO2 emission from a cluster of iron and steel plants than that of coal-processing plants, which is consistent with emission inventories. Furthermore, anthropogenic CO2 emission hotspots are possible to be identified from surrounding natural background. Our results suggest the potential of satellite data for characterizing strong localized carbon emission from different industrial sources both in space and time.