Donghao Fan, Tianhai Cheng, Hao Zhu, Xiaotong Ye, Tao Tang, Haoran Tong, Xingyu Li, Lili Zhang
{"title":"污染-碳协同效应显著增强电厂空间CO2排放跟踪能力","authors":"Donghao Fan, Tianhai Cheng, Hao Zhu, Xiaotong Ye, Tao Tang, Haoran Tong, Xingyu Li, Lili Zhang","doi":"10.1021/acs.est.5c01100","DOIUrl":null,"url":null,"abstract":"The potential of satellite-based CO<sub>2</sub> emission estimation from power plants is gaining increasing attention. However, the limited spatiotemporal coverage of current satellite-derived XCO<sub>2</sub> data poses significant challenges to tracking CO<sub>2</sub> variations on a large scale and over extended periods. In view of this, this study uses satellite-derived NO<sub>2</sub> data as a suitable proxy and tracks CO<sub>2</sub> emissions from 38 selected power plants globally by integrating near-synchronously observed TROPOMI NO<sub>2</sub> data and OCO-2 XCO<sub>2</sub> data. The results show that our method significantly increases the effective observation frequency by almost 200 times compared to using OCO-2 data alone. Compared to the emissions reported by the power plants, the correlation coefficient of the method used in this study (0.78) is higher than that of the emission inventory estimates (0.43–0.62), resulting in an accuracy improvement of approximately 1.8–2.3 Mt/yr per power plant. The use of satellite-derived NO<sub>2</sub> data significantly enhances the ability to remotely estimate CO<sub>2</sub> emissions from power plants, which gives us confidence in studying anthropogenic point-source CO<sub>2</sub> emissions across different spatial and temporal scales. This enhances the understanding of their variability and mitigation potential, supporting the development of refined carbon inventories and advanced carbon cycle assimilation systems.","PeriodicalId":36,"journal":{"name":"环境科学与技术","volume":"1 1","pages":""},"PeriodicalIF":10.8000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pollution-Carbon Synergy Significantly Enhances the Capability of Tracking Power Plants’ CO2 Emissions from Space\",\"authors\":\"Donghao Fan, Tianhai Cheng, Hao Zhu, Xiaotong Ye, Tao Tang, Haoran Tong, Xingyu Li, Lili Zhang\",\"doi\":\"10.1021/acs.est.5c01100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The potential of satellite-based CO<sub>2</sub> emission estimation from power plants is gaining increasing attention. However, the limited spatiotemporal coverage of current satellite-derived XCO<sub>2</sub> data poses significant challenges to tracking CO<sub>2</sub> variations on a large scale and over extended periods. In view of this, this study uses satellite-derived NO<sub>2</sub> data as a suitable proxy and tracks CO<sub>2</sub> emissions from 38 selected power plants globally by integrating near-synchronously observed TROPOMI NO<sub>2</sub> data and OCO-2 XCO<sub>2</sub> data. The results show that our method significantly increases the effective observation frequency by almost 200 times compared to using OCO-2 data alone. Compared to the emissions reported by the power plants, the correlation coefficient of the method used in this study (0.78) is higher than that of the emission inventory estimates (0.43–0.62), resulting in an accuracy improvement of approximately 1.8–2.3 Mt/yr per power plant. The use of satellite-derived NO<sub>2</sub> data significantly enhances the ability to remotely estimate CO<sub>2</sub> emissions from power plants, which gives us confidence in studying anthropogenic point-source CO<sub>2</sub> emissions across different spatial and temporal scales. This enhances the understanding of their variability and mitigation potential, supporting the development of refined carbon inventories and advanced carbon cycle assimilation systems.\",\"PeriodicalId\":36,\"journal\":{\"name\":\"环境科学与技术\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":10.8000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"环境科学与技术\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.est.5c01100\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学与技术","FirstCategoryId":"1","ListUrlMain":"https://doi.org/10.1021/acs.est.5c01100","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
Pollution-Carbon Synergy Significantly Enhances the Capability of Tracking Power Plants’ CO2 Emissions from Space
The potential of satellite-based CO2 emission estimation from power plants is gaining increasing attention. However, the limited spatiotemporal coverage of current satellite-derived XCO2 data poses significant challenges to tracking CO2 variations on a large scale and over extended periods. In view of this, this study uses satellite-derived NO2 data as a suitable proxy and tracks CO2 emissions from 38 selected power plants globally by integrating near-synchronously observed TROPOMI NO2 data and OCO-2 XCO2 data. The results show that our method significantly increases the effective observation frequency by almost 200 times compared to using OCO-2 data alone. Compared to the emissions reported by the power plants, the correlation coefficient of the method used in this study (0.78) is higher than that of the emission inventory estimates (0.43–0.62), resulting in an accuracy improvement of approximately 1.8–2.3 Mt/yr per power plant. The use of satellite-derived NO2 data significantly enhances the ability to remotely estimate CO2 emissions from power plants, which gives us confidence in studying anthropogenic point-source CO2 emissions across different spatial and temporal scales. This enhances the understanding of their variability and mitigation potential, supporting the development of refined carbon inventories and advanced carbon cycle assimilation systems.
期刊介绍:
Environmental Science & Technology (ES&T) is a co-sponsored academic and technical magazine by the Hubei Provincial Environmental Protection Bureau and the Hubei Provincial Academy of Environmental Sciences.
Environmental Science & Technology (ES&T) holds the status of Chinese core journals, scientific papers source journals of China, Chinese Science Citation Database source journals, and Chinese Academic Journal Comprehensive Evaluation Database source journals. This publication focuses on the academic field of environmental protection, featuring articles related to environmental protection and technical advancements.