Jinsol Kim, Daniel H. Cusworth, Alana K. Ayasse, Katherine Howell, Kelly O’Neill, Riley M. Duren
{"title":"Performance of Airborne Imaging Spectrometers for Carbon Dioxide Detection and Emission Quantification","authors":"Jinsol Kim, Daniel H. Cusworth, Alana K. Ayasse, Katherine Howell, Kelly O’Neill, Riley M. Duren","doi":"10.1029/2024JD042755","DOIUrl":null,"url":null,"abstract":"<p>Carbon dioxide (CO<sub>2</sub>) emissions from strong point sources account for a significant proportion of the global greenhouse gas emissions, and their associated uncertainties in bottom-up estimates remain substantial. Imaging spectrometers provide a capability to monitor large point source CO<sub>2</sub> emissions and help reduce the uncertainties. In this study, we assess the capability of an airborne monitoring system with temporally sparse observations to constrain annual emissions at both facility and regional scales. We use observations of power plant emissions from 2022 to 2023 and compare the derived emission rates at facility scale to in stack emission observations across the United States. We show that CO<sub>2</sub> concentration enhancements retrieved using a lognormal matched filter are suitable for CO<sub>2</sub> quantification, achieving low bias and uncertainty in estimated emission rates. We find that annual emissions at the regional scale can be effectively constrained by offsetting errors identified at the facility scale, with a 30% uncertainty.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":"130 7","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024JD042755","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Atmospheres","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JD042755","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Carbon dioxide (CO2) emissions from strong point sources account for a significant proportion of the global greenhouse gas emissions, and their associated uncertainties in bottom-up estimates remain substantial. Imaging spectrometers provide a capability to monitor large point source CO2 emissions and help reduce the uncertainties. In this study, we assess the capability of an airborne monitoring system with temporally sparse observations to constrain annual emissions at both facility and regional scales. We use observations of power plant emissions from 2022 to 2023 and compare the derived emission rates at facility scale to in stack emission observations across the United States. We show that CO2 concentration enhancements retrieved using a lognormal matched filter are suitable for CO2 quantification, achieving low bias and uncertainty in estimated emission rates. We find that annual emissions at the regional scale can be effectively constrained by offsetting errors identified at the facility scale, with a 30% uncertainty.
期刊介绍:
JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.