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Distinguishing Anthropogenic CO 2 Emissions From Different Energy Intensive Industrial Sources Using OCO‐2 Observations: A Case Study in Northern China
Author(s) -
Wang Songhan,
Zhang Yongguang,
Hakkarainen Janne,
Ju Weimin,
Liu Yongxue,
Jiang Fei,
He Wei
Publication year - 2018
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2018jd029005
Subject(s) - environmental science , greenhouse gas , emission inventory , coal , natural gas , carbon fibers , fossil fuel , fugitive emissions , atmospheric sciences , environmental engineering , meteorology , geography , geology , waste management , air quality index , engineering , materials science , oceanography , composite number , composite material
Abstract Energy intensive industries, such as iron and steel industry and coal‐processing industry, are one of the major anthropogenic fossil fuel carbon dioxide (CO 2 ) emission sources, especially in China. Monitoring the localized CO 2 emissions from these industrial point sources is valuable to improve emission estimates and inform on policy discussion. However, spatiotemporally explicit information on CO 2 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 CO 2 enhancements of carbon emission intensive industries, taking northern China as the case study area. CO 2 anomalies are derived from spaceborne column‐averaged CO 2 mixing ratio (XCO 2 ) data measured by the Orbiting Carbon Observatory 2 (OCO‐2). The Gaussian plume model is used to select the XCO 2 data when the CO 2 emissions from plants are localized, which allows us to distinguish CO 2 emissions from different industrial point sources. We demonstrate that high‐emission areas with industrial plants are detectable by CO 2 anomalies compared to natural background area and the average enhancement is about 1.8 ppm. The XCO 2 data also show a higher CO 2 emission from a cluster of iron and steel plants than that of coal‐processing plants, which is consistent with emission inventories. Furthermore, anthropogenic CO 2 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.