Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13131-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13131-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2021
Research article |
| 06 Sep 2021
| 06 Sep 2021
Assessing urban methane emissions using column-observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework
Taylor S. Jones
CORRESPONDING AUTHOR
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Department of Earth and Environment, Boston University, Boston, MA USA
Jonathan E. Franklin
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Florian Dietrich
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Kristian D. Hajny
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA
Johannes C. Paetzold
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Adrian Wenzel
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Conor Gately
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Department of Earth and Environment, Boston University, Boston, MA USA
Elaine Gottlieb
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Harrison Parker
Los Alamos National Laboratory, Los Alamos, NM, USA
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
Manvendra Dubey
Los Alamos National Laboratory, Los Alamos, NM, USA
Frank Hase
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Paul B. Shepson
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA
Levi H. Mielke
Department of Chemistry, University of Indianapolis, Indianapolis, IN, USA
Steven C. Wofsy
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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- Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter S. Wang et al. 10.1016/j.snb.2024.136371
- Improved calibration procedures for the EM27/SUN spectrometers of the COllaborative Carbon Column Observing Network (COCCON) C. Alberti et al. 10.5194/amt-15-2433-2022
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- Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements K. Che et al. 10.1088/1748-9326/ac7477
- Comparison of OCO-2 target observations to MUCCnet – is it possible to capture urban XCO2 gradients from space? M. Rißmann et al. 10.5194/amt-15-6605-2022
- Climate Impact Comparison of Electric and Gas‐Powered End‐User Appliances F. Dietrich et al. 10.1029/2022EF002877
- Pyra: Automated EM27/SUN Greenhouse Gas Measurement Software P. Aigner et al. 10.21105/joss.05131
- Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network A. Luther et al. 10.5194/acp-22-5859-2022
- Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach A. Forstmaier et al. 10.5194/acp-23-6897-2023
- Critical method needs in measuring greenhouse gas fluxes D. Bastviken et al. 10.1088/1748-9326/ac8fa9
- Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model X. Zhao et al. 10.5194/acp-23-14325-2023
- Greenhouse gas column observations from a portable spectrometer in Uganda N. Humpage et al. 10.5194/amt-17-5679-2024
- Highly sensitive mid-infrared methane remote sensor using a deep neural network filter S. Wang et al. 10.1364/OE.520245
- Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter S. Heerah et al. 10.1029/2021JD034785
- MUCCnet: Munich Urban Carbon Column network F. Dietrich et al. 10.5194/amt-14-1111-2021
18 citations as recorded by crossref.
- Investigation of spaceborne trace gas products over St Petersburg and Yekaterinburg, Russia, by using COllaborative Column Carbon Observing Network (COCCON) observations C. Alberti et al. 10.5194/amt-15-2199-2022
- Using Multiscale Ethane/Methane Observations to Attribute Coal Mine Vent Emissions in the San Juan Basin From 2013 to 2021 A. Meyer et al. 10.1029/2022JD037092
- Recovery of sparse urban greenhouse gas emissions B. Zanger et al. 10.5194/gmd-15-7533-2022
- Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter S. Wang et al. 10.1016/j.snb.2024.136371
- Improved calibration procedures for the EM27/SUN spectrometers of the COllaborative Carbon Column Observing Network (COCCON) C. Alberti et al. 10.5194/amt-15-2433-2022
- Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations H. Ohyama et al. 10.5194/acp-23-15097-2023
- Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities A. Karion et al. 10.1021/acs.est.3c05050
- The CU Airborne Solar Occultation Flux Instrument: Performance Evaluation during BB-FLUX N. Kille et al. 10.1021/acsearthspacechem.1c00281
- Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements K. Che et al. 10.1088/1748-9326/ac7477
- Comparison of OCO-2 target observations to MUCCnet – is it possible to capture urban XCO2 gradients from space? M. Rißmann et al. 10.5194/amt-15-6605-2022
- Climate Impact Comparison of Electric and Gas‐Powered End‐User Appliances F. Dietrich et al. 10.1029/2022EF002877
- Pyra: Automated EM27/SUN Greenhouse Gas Measurement Software P. Aigner et al. 10.21105/joss.05131
- Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network A. Luther et al. 10.5194/acp-22-5859-2022
- Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach A. Forstmaier et al. 10.5194/acp-23-6897-2023
- Critical method needs in measuring greenhouse gas fluxes D. Bastviken et al. 10.1088/1748-9326/ac8fa9
- Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model X. Zhao et al. 10.5194/acp-23-14325-2023
- Greenhouse gas column observations from a portable spectrometer in Uganda N. Humpage et al. 10.5194/amt-17-5679-2024
- Highly sensitive mid-infrared methane remote sensor using a deep neural network filter S. Wang et al. 10.1364/OE.520245
2 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
Methane emissions from leaks in natural gas pipes are often a large source in urban areas, but they are difficult to measure on a city-wide scale. Here we use an array of innovative methane sensors distributed around the city of Indianapolis and a new method of combining their data with an atmospheric model to accurately determine the magnitude of these emissions, which are about 70 % larger than predicted. This method can serve as a framework for cities trying to account for their emissions.
Methane emissions from leaks in natural gas pipes are often a large source in urban areas, but...
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