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Atmosphere
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Remote Sensing Measurement of Greenhouse Gases Emission
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Remote Sensing Measurement of Greenhouse Gases Emission

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 6641

Special Issue Editors

Dr. Yusheng Shi

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: greenhouse gas observation; human CO2 emission; CH4 emissions; satellite retrieval of CO2 emissions; forest fires; power plant CO2 emissions
Dr. Meng Fan

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing of atmospheric chemical components
Dr. Li Sun

E-Mail Website
Guest Editor
Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Interests: atmospheric pollution monitoring; pollution source apportionment; anthropogenic pollutant emissions

Special Issue Information

Dear Colleagues,

Anthropogenic activity, terrestrial ecosystems, land use, forest fire, biomass burning, power plants and traffic transportation emissions are all the main sources of global CO2 emissions. A precise and reliable estimation of emission sources from different sections is urgently required for a better understanding of greenhouse gas effects. The observation, retrieval, and modeling of carbon/methane fluxes and exchange among different pools help to understand the carbon/methane cycle and budget at local, regional, and global scales.

This Special Issue focuses on the methodology and application of remotely sensed datasets to estimate CO2 emissions from human activity, power plants, biomass burning, forest fires, land use, etc. Satellite observation, aircraft-based monitoring, and ground measurements of emissions and concentrations of CO2 and CH4 are greatly encouraged. Additionally, the transport of greenhouse gases (GHGs) using GEOS-Chem or satellite-retrieved source emissions are also within our scope. The aim of this Special Issue is to provide a consistent source of information concerning past and present activities regarding different aspects of atmospheric GHGs studies, as well as to allow for more efficient knowledge exchange concerning GHGs research.

Original results, review papers, and model studies related to the following aspects are all welcome contributions:

  • Anthropogenic carbon emissions;
  • Power plant carbon emissions;
  • Traffic transportation carbon emissions;
  • Urban carbon emissions;
  • Forest fire emissions;
  • Biomass burning emissions;
  • Methane emission;
  • Coal mining CO2 emissions;
  • Oil and gas CO2 emissions.

Dr. Yusheng Shi
Dr. Meng Fan
Dr. Li Sun
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CO2 and CH4 emission observation
  • anthropogenic emissions
  • greenhouse gas retrieval
  • biomass burning emissions
  • land-use emissions

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Published Papers (3 papers)

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Research

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18 pages, 8125 KiB  
Article
Comparison of PBL Heights from Ceilometer Measurements and Greenhouse Gases Concentrations in São Paulo
by Amanda Vieira dos Santos, Elaine Cristina Araújo, Izabel da Silva Andrade, Thais Corrêa, Márcia Talita Amorim Marques, Carlos Eduardo Souto-Oliveira, Noele Franchi Leonardo, Fernanda de Mendonça Macedo, Giovanni Souza, Pérola Pereira de Queiroz Lopes, Gregori de Arruda Moreira, Maria de Fátima Andrade and Eduardo Landulfo
Atmosphere 2023, 14(12), 1830; https://doi.org/10.3390/atmos14121830 - 16 Dec 2023
Viewed by 1344
Abstract
This paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO2 and CH [...] Read more.
This paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO2 and CH4 measured at three distinct experimental sites in the city. The period of study was July 2021. This study also included a comparison between ceilometer data and lidar data, which demonstrated the favorable applicability of the ceilometer data for PBLH estimation. An examination of the correlation between changes in average CO2 concentrations and PBLH values revealed stronger correlations for the IAG and UNICID stations, with correlation coefficients (ρ) of approximately −0.86 and −0.85, respectively, in contrast to the Pico do Jaraguá station, which exhibited a lower correlation coefficient of −0.42. When assessing changes in CH4 concentrations against variations in PBL height, the retrieved correlation coefficients were approximately −0.78 for IAG, −0.66 for UNICID, and −0.38 for Pico do Jaraguá. The results indicated that CO2/CH4 concentrations are negatively correlated with PBL heights, with CO2 concentrations showing more significant correlation than CH4. Additionally, among the three measurement stations, IAG measurements displayed the most substantial correlation. The results from this study contribute to the understanding of the relationship between PBLH and greenhouse gas concentrations, emphasizing the potential of remote sensing systems like ceilometers in monitoring and studying atmospheric processes. Full article
(This article belongs to the Special Issue Remote Sensing Measurement of Greenhouse Gases Emission)
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15 pages, 3389 KiB  
Article
Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product
by Yang Liu and Yusheng Shi
Atmosphere 2023, 14(10), 1575; https://doi.org/10.3390/atmos14101575 - 18 Oct 2023
Cited by 5 | Viewed by 1768
Abstract
Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different [...] Read more.
Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different forests and carbon emissions from forest fires. This study developed a high-resolution (0.05° × 0.05°) daily global inventory of carbon emissions from biomass burning during 2016–2022. The inventory estimates of carbon emissions from biomass burning are based on the newly released FY-3 data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Forest fire carbon emissions were assessed using active fire data from FY-3 series satellites from 2016 to 2022, and it was linearly compared with GFED, FEER, and GFAS data on time and spatial scales with R2 of 0.7, 0.73, and 0.69, respectively. The results show spatial patterns of forest cover and carbon emissions, with South America, Africa, South-East Asia, and northern Asia as high-emission zones. The analysis shows an overall upward trend in global forest fire carbon emissions over the study period. Different types of forests exhibited specific emission patterns and temporal variations. For example, most needleleaf forest fires occur in areas with low tree cover, while broadleaf forest fires tend to occur in areas with high tree cover. The study showed that there was a relationship between inter-annual trends in forest fire carbon emissions and land cover, with biomass burning occurring mainly in the range of 60–70% tree cover. However, there were also differences between evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest indicating the importance of considering differences in forest types when estimating emissions. This study identifies the main sources of carbon emissions from forest fires globally, which will help policymakers to take more targeted measures to reduce carbon emissions and provide a reliable basis for appropriate measures and directions in future carbon mitigation actions. Full article
(This article belongs to the Special Issue Remote Sensing Measurement of Greenhouse Gases Emission)
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Review

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32 pages, 2054 KiB  
Review
Methane Retrieval Algorithms Based on Satellite: A Review
by Yuhan Jiang, Lu Zhang, Xingying Zhang and Xifeng Cao
Atmosphere 2024, 15(4), 449; https://doi.org/10.3390/atmos15040449 - 3 Apr 2024
Cited by 1 | Viewed by 3091
Abstract
As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview [...] Read more.
As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. Full article
(This article belongs to the Special Issue Remote Sensing Measurement of Greenhouse Gases Emission)
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