摘要：A large fraction of fossil fuel CO2 emissions emanate from“hotspots”, such as cities (where direct CO2 emissions related tofossil fuel combustion in transport, residential, commercial sectors, etc.,excluding emissions from electricity-producing power plants, occur), isolatedpower plants, and manufacturing facilities, which cover a small fraction ofthe land surface. The coverage of all high-emitting cities and point sourcesacross the globe by bottom-up inventories is far from complete, and for mostof those covered, the uncertainties in CO2 emission estimates inbottom-up inventories are too large to allow continuous and rigorousassessment of emission changes (Gurney et al., 2019). Space-borne imagery ofatmospheric CO2 has the potential to provide independent estimates ofCO2 emissions from hotspots. But first, what a hotspot is needs to bedefined for the purpose of satellite observations. The proposed space-borneimagers with global coverage planned for the coming decade have a pixel sizeon the order of a few square kilometers and a XCO2 accuracy andprecision of <1ppm for individual measurements of verticallyintegrated columns of dry-air mole fractions of CO2 (XCO2). Thisresolution and precision is insufficient to provide a cartography ofemissions for each individual pixel. Rather, the integrated emission ofdiffuse emitting areas and intense point sources is sought. In this study,we characterize area and point fossil fuel CO2 emitting sourceswhich generate coherent XCO2 plumes that may be observed from space. Wecharacterize these emitting sources around the globe and they are referred toas “emission clumps” hereafter. An algorithm is proposed to identifyemission clumps worldwide, based on the ODIAC global high-resolution 1kmfossil fuel emission data product. The clump algorithm selects the majorurban areas from a GIS (geographic information system) file and two emissionthresholds. The selected urban areas and a high emission threshold are usedto identify clump cores such as inner city areas or large power plants. A lowthreshold and a random walker (RW) scheme are then used to aggregate all gridcells contiguous to cores in order to define a single clump. With ourdefinition of the thresholds, which are appropriate for a space imagery with0.5ppm precision for a single XCO2 measurement, a total of 11314individual clumps, with 5088 area clumps, and 6226 point-source clumps(power plants) are identified. These clumps contribute 72% of the globalfossil fuel CO2 emissions according to the ODIAC inventory. The emissionclumps is a new tool for comparing fossil fuel CO2 emissions fromdifferent inventories and objectively identifying emitting areas that have apotential to be detected by future global satellite imagery of XCO2. Theemission clump data product is distributed fromhttps://doi.org/10.6084/m9.figshare.7217726.v1.
