Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements

NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating...

Full description

Saved in:
Bibliographic Details
Published inGeophysical research letters Vol. 51; no. 20
Main Authors Hamilton, Sofia D., Wu, Dien, Johnson, Matthew S., Turner, Alexander J., Fischer, Marc L., Dadheech, Nikhil, Jeong, Seongeun
Format Journal Article
LanguageEnglish
Published Washington John Wiley & Sons, Inc 28.10.2024
Wiley
Subjects
Online AccessGet full text

Cover

Loading…
Abstract NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires. Plain Language Summary Satellites can measure variations in carbon dioxide concentrations over urban areas. These measurements can be combined with models of the atmosphere to validate estimates of carbon dioxide emissions. We use this approach to better understand emissions in and around San Francisco and Los Angeles, the two major cities in California. We study the year 2020 to see how emissions changed in response to COVID‐19 lockdowns and to observe emissions from wildfires. The satellite measurements combined with the atmospheric model provide us with updated emission estimates that more closely match the measurements and provide greater certainty than our initial estimate of emissions. We also observe reduced emissions during lockdowns in both cities, updated emissions at the scale of individual wildfires, and large emissions from wildfires during peak wildfire season in the San Francisco Bay Area. Key Points Satellite measurements can be used to validate emission inventories of CO2 within urban areas of California Changes in CO2 emissions due to COVID‐19 lockdowns and wildfires in 2020 are captured by satellites During August 2020, wildfires were responsible for 72% of CO2 emissions in the Bay Area
AbstractList Abstract NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires.
NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires.
Abstract NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO 2 ) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO 2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO 2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO 2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO 2 emissions during August 2020 to wildfires. Plain Language Summary Satellites can measure variations in carbon dioxide concentrations over urban areas. These measurements can be combined with models of the atmosphere to validate estimates of carbon dioxide emissions. We use this approach to better understand emissions in and around San Francisco and Los Angeles, the two major cities in California. We study the year 2020 to see how emissions changed in response to COVID‐19 lockdowns and to observe emissions from wildfires. The satellite measurements combined with the atmospheric model provide us with updated emission estimates that more closely match the measurements and provide greater certainty than our initial estimate of emissions. We also observe reduced emissions during lockdowns in both cities, updated emissions at the scale of individual wildfires, and large emissions from wildfires during peak wildfire season in the San Francisco Bay Area. Key Points Satellite measurements can be used to validate emission inventories of CO 2 within urban areas of California Changes in CO 2 emissions due to COVID‐19 lockdowns and wildfires in 2020 are captured by satellites During August 2020, wildfires were responsible for 72% of CO 2 emissions in the Bay Area
NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires. Plain Language Summary Satellites can measure variations in carbon dioxide concentrations over urban areas. These measurements can be combined with models of the atmosphere to validate estimates of carbon dioxide emissions. We use this approach to better understand emissions in and around San Francisco and Los Angeles, the two major cities in California. We study the year 2020 to see how emissions changed in response to COVID‐19 lockdowns and to observe emissions from wildfires. The satellite measurements combined with the atmospheric model provide us with updated emission estimates that more closely match the measurements and provide greater certainty than our initial estimate of emissions. We also observe reduced emissions during lockdowns in both cities, updated emissions at the scale of individual wildfires, and large emissions from wildfires during peak wildfire season in the San Francisco Bay Area. Key Points Satellite measurements can be used to validate emission inventories of CO2 within urban areas of California Changes in CO2 emissions due to COVID‐19 lockdowns and wildfires in 2020 are captured by satellites During August 2020, wildfires were responsible for 72% of CO2 emissions in the Bay Area
Author Johnson, Matthew S.
Turner, Alexander J.
Dadheech, Nikhil
Hamilton, Sofia D.
Wu, Dien
Jeong, Seongeun
Fischer, Marc L.
Author_xml – sequence: 1
  givenname: Sofia D.
  orcidid: 0000-0002-3662-5737
  surname: Hamilton
  fullname: Hamilton, Sofia D.
  email: sdhamilton@lbl.gov
  organization: Lawrence Berkeley National Laboratory
– sequence: 2
  givenname: Dien
  surname: Wu
  fullname: Wu, Dien
  organization: California Institute of Technology
– sequence: 3
  givenname: Matthew S.
  surname: Johnson
  fullname: Johnson, Matthew S.
  organization: NASA Ames Research Center
– sequence: 4
  givenname: Alexander J.
  orcidid: 0000-0003-1406-7372
  surname: Turner
  fullname: Turner, Alexander J.
  organization: University of Washington
– sequence: 5
  givenname: Marc L.
  orcidid: 0000-0001-7956-2361
  surname: Fischer
  fullname: Fischer, Marc L.
  organization: Lawrence Berkeley National Laboratory
– sequence: 6
  givenname: Nikhil
  orcidid: 0000-0001-6324-5337
  surname: Dadheech
  fullname: Dadheech, Nikhil
  organization: University of Washington
– sequence: 7
  givenname: Seongeun
  orcidid: 0000-0003-2032-0127
  surname: Jeong
  fullname: Jeong, Seongeun
  organization: Lawrence Berkeley National Laboratory
BookMark eNp9kU1vEzEQhi3USqQtN36AJa4Exh_r3T1CCGmkoEqlPVuzu-PK0cYO9oaSf4_TIMSJkaUZeZ739VhzxS5CDMTYWwEfBMj2owSpVxtRooJXbCZarecNQH3BZgBtqWVtXrOrnLcAoECJGdsv8-R3OPnwxBeYuhj4Fx9_-YH4cudz9jFk7gN_eI6lP3oXU_DIF37ylPljPuk-45Gyx8DX4Selk4RjGPh3nGgc_UT8G2E-JNpRmPINu3Q4ZnrzJ1-zx6_Lh8XtfHO3Wi8-bea9FFLMB-Vk23ethA6HRmlqkFrTKdCVrqGvOyNqXdqqb7CSg-qhcSCgb5yjGo1T12x99h0ibu0-lU-mo43o7ctFTE8W0-T7kWwtnUGlKuNEp-vBtB1WpLGcVpvBUPF6d_bap_jjQHmy23hIoYxvVRm2amrZVoV6f6b6FHNO5P6-KsCe9mP_3U_B5Rl_9iMd_8va1f3GNKoR6jdblJMI
Cites_doi 10.1029/2023GL103834
10.1002/2017JD027359
10.1002/2016GL070885
10.1016/j.rse.2021.112314
10.1029/2021GL092744
10.1007/s10546‐005‐9030‐8
10.5194/gmd‐15‐8411‐2022
10.5281/zenodo.4018123
10.5194/amt‐16‐581‐2023
10.1175/MWR3199.1
10.1016/j.envpol.2022.119888
10.1111/cdev.12169
10.1029/2018JD029933
10.5281/zenodo.3931948
10.5194/gmd‐11‐4843‐2018
10.1021/acs.est.3c09642
10.5281/zenodo.7229674
10.5194/acp‐19‐2991‐2019
10.5194/acp‐16‐9591‐2016
10.1016/B978-0-12-814952-2.00007-1
10.1111/geb.13498
10.1038/s41467‐020‐20871‐0
10.1029/2010JD015139
10.1175/BAMS‐87‐3‐343
10.1088/1748‐9326/ad3cf7
10.3390/rs15204904
10.5194/acp‐19‐9797‐2019
10.5194/acp‐17‐8313‐2017
10.1002/2016GL067843
10.1525/elementa.2023.00102
10.5194/gmd‐14‐3633‐2021
10.1117/1.2898457
10.1088/1748‐9326/ac0660
10.1088/1748‐9326/ad0f74
10.5194/acp‐16‐13449‐2016
10.5194/amt‐5‐99‐2012
10.1002/eap.2763
10.1016/j.rse.2021.112625
10.1029/2023GL104376
10.3334/ORNLDAAC/1810
10.1029/2020JD032974
10.5194/gmd‐6‐583‐2013
10.5194/amt‐16‐109‐2023
10.1029/2022AV000732
10.1098/rsta.2010.0240
10.5194/essd‐11‐1291‐2019
10.5194/essd‐12‐699‐2020
10.5194/amt‐10‐2759‐2017
10.5194/essd‐13‐357‐2021
10.1029/2020GL090037
10.1088/1748‐9326/ab9cfe
10.1525/elementa.188
10.1029/2012GL052738
ContentType Journal Article
Copyright 2024. The Author(s).
2024. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2024. The Author(s).
– notice: 2024. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 24P
WIN
AAYXX
CITATION
7TG
7TN
8FD
F1W
FR3
H8D
H96
KL.
KR7
L.G
L7M
DOA
DOI 10.1029/2024GL111150
DatabaseName Wiley-Blackwell Open Access Collection
Wiley Online Library Journals
CrossRef
Meteorological & Geoastrophysical Abstracts
Oceanic Abstracts
Technology Research Database
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Aerospace Database
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
Meteorological & Geoastrophysical Abstracts - Academic
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Advanced Technologies Database with Aerospace
Directory of Open Access Journals
DatabaseTitle CrossRef
Aerospace Database
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Meteorological & Geoastrophysical Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
Oceanic Abstracts
Technology Research Database
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Meteorological & Geoastrophysical Abstracts - Academic
DatabaseTitleList
Aerospace Database
CrossRef

Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: 24P
  name: Wiley-Blackwell Open Access Collection
  url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Geology
Physics
EISSN 1944-8007
EndPage n/a
ExternalDocumentID oai_doaj_org_article_72f6a3356f1b47d69ba5e4ae4a946d6e
10_1029_2024GL111150
GRL68381
Genre article
GeographicLocations Los Angeles California
San Francisco Bay
California
United States--US
GeographicLocations_xml – name: California
– name: Los Angeles California
– name: San Francisco Bay
– name: United States--US
GrantInformation_xml – fundername: NASA
  funderid: 80HQTR21T010
GroupedDBID -DZ
-~X
05W
0R~
1OB
1OC
24P
33P
50Y
5GY
5VS
702
8-1
A00
AAESR
AAHHS
AAIHA
AAXRX
AAZKR
ABCUV
ABPPZ
ACAHQ
ACCFJ
ACCZN
ACGFO
ACGFS
ACGOD
ACIWK
ACNCT
ACPOU
ACXBN
ACXQS
ADBBV
ADEOM
ADKYN
ADMGS
ADOZA
ADXAS
ADZMN
ADZOD
AEEZP
AEFZC
AENEX
AEQDE
AEUQT
AFBPY
AFGKR
AFPWT
AFRAH
AIURR
AIWBW
AJBDE
ALMA_UNASSIGNED_HOLDINGS
ALUQN
ALXUD
AMYDB
AVUZU
AZFZN
AZVAB
BENPR
BMXJE
BRXPI
CS3
DCZOG
DPXWK
DRFUL
DRSTM
DU5
EBS
F5P
G-S
GODZA
GROUPED_DOAJ
HZ~
LATKE
LEEKS
LITHE
LOXES
LUTES
LYRES
MEWTI
MSFUL
MSSTM
MXFUL
MXSTM
MY~
O9-
OK1
P-X
P2P
P2W
R.K
RNS
ROL
SUPJJ
TN5
TWZ
UPT
WBKPD
WH7
WIH
WIN
WXSBR
WYJ
XSW
ZZTAW
~02
~OA
~~A
AAYXX
CITATION
7TG
7TN
8FD
F1W
FR3
H8D
H96
KL.
KR7
L.G
L7M
ID FETCH-LOGICAL-c2121-d3f29cb920bad834e8ae96b3045470c7b6174b923c8a52d3c08f010c8ffe7a6f3
IEDL.DBID DOA
ISSN 0094-8276
IngestDate Mon Nov 11 19:42:54 EST 2024
Thu Nov 07 07:20:28 EST 2024
Wed Nov 13 12:44:39 EST 2024
Wed Oct 30 09:55:58 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 20
Language English
License Attribution-NonCommercial-NoDerivs
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2121-d3f29cb920bad834e8ae96b3045470c7b6174b923c8a52d3c08f010c8ffe7a6f3
ORCID 0000-0002-3662-5737
0000-0003-1406-7372
0000-0001-7956-2361
0000-0001-6324-5337
0000-0003-2032-0127
OpenAccessLink https://doaj.org/article/72f6a3356f1b47d69ba5e4ae4a946d6e
PQID 3121587295
PQPubID 54723
PageCount 11
ParticipantIDs doaj_primary_oai_doaj_org_article_72f6a3356f1b47d69ba5e4ae4a946d6e
proquest_journals_3121587295
crossref_primary_10_1029_2024GL111150
wiley_primary_10_1029_2024GL111150_GRL68381
PublicationCentury 2000
PublicationDate 28 October 2024
PublicationDateYYYYMMDD 2024-10-28
PublicationDate_xml – month: 10
  year: 2024
  text: 28 October 2024
  day: 28
PublicationDecade 2020
PublicationPlace Washington
PublicationPlace_xml – name: Washington
PublicationTitle Geophysical research letters
PublicationYear 2024
Publisher John Wiley & Sons, Inc
Wiley
Publisher_xml – name: John Wiley & Sons, Inc
– name: Wiley
References e_1_2_7_1_55_1
e_1_2_7_1_11_1
e_1_2_7_1_32_1
e_1_2_7_1_57_1
e_1_2_7_1_13_1
e_1_2_7_1_34_1
National Oceanic and Atmospheric Administration Global Monitoring Laboratory (e_1_2_7_1_27_1) 2023
e_1_2_7_1_51_1
e_1_2_7_1_30_1
e_1_2_7_1_53_1
e_1_2_7_2_3_1
e_1_2_7_2_7_1
e_1_2_7_2_5_1
e_1_2_7_1_5_1
e_1_2_7_1_29_1
e_1_2_7_1_7_1
e_1_2_7_1_25_1
e_1_2_7_1_48_1
e_1_2_7_1_3_1
e_1_2_7_1_21_1
e_1_2_7_1_44_1
e_1_2_7_1_23_1
e_1_2_7_1_46_1
e_1_2_7_1_9_1
e_1_2_7_1_40_1
e_1_2_7_1_63_1
e_1_2_7_1_42_1
e_1_2_7_1_61_1
e_1_2_7_1_18_1
e_1_2_7_1_14_1
e_1_2_7_1_37_1
e_1_2_7_1_16_1
e_1_2_7_1_39_1
e_1_2_7_1_58_1
e_1_2_7_1_10_1
e_1_2_7_1_33_1
e_1_2_7_1_54_1
e_1_2_7_1_12_1
e_1_2_7_1_35_1
e_1_2_7_1_56_1
e_1_2_7_1_50_1
e_1_2_7_1_31_1
e_1_2_7_1_52_1
e_1_2_7_2_2_1
e_1_2_7_2_6_1
e_1_2_7_2_4_1
e_1_2_7_1_6_1
e_1_2_7_1_8_1
e_1_2_7_1_2_1
e_1_2_7_1_26_1
e_1_2_7_1_47_1
e_1_2_7_1_4_1
e_1_2_7_1_28_1
e_1_2_7_1_49_1
e_1_2_7_1_22_1
e_1_2_7_1_43_1
e_1_2_7_1_24_1
e_1_2_7_1_45_1
e_1_2_7_1_20_1
e_1_2_7_1_41_1
e_1_2_7_1_62_1
e_1_2_7_1_60_1
e_1_2_7_1_19_1
e_1_2_7_1_15_1
e_1_2_7_1_36_1
e_1_2_7_1_17_1
Seto K. C. (e_1_2_7_1_38_1) 2014
e_1_2_7_1_59_1
References_xml – ident: e_1_2_7_1_63_1
  doi: 10.1029/2023GL103834
– ident: e_1_2_7_1_7_1
– ident: e_1_2_7_1_13_1
  doi: 10.1002/2017JD027359
– ident: e_1_2_7_1_17_1
  doi: 10.1002/2016GL070885
– ident: e_1_2_7_1_22_1
  doi: 10.1016/j.rse.2021.112314
– ident: e_1_2_7_1_60_1
  doi: 10.1029/2021GL092744
– ident: e_1_2_7_2_4_1
  doi: 10.1007/s10546‐005‐9030‐8
– ident: e_1_2_7_1_50_1
  doi: 10.5194/gmd‐15‐8411‐2022
– ident: e_1_2_7_1_28_1
– ident: e_1_2_7_1_55_1
  doi: 10.5281/zenodo.4018123
– ident: e_1_2_7_1_57_1
  doi: 10.5194/amt‐16‐581‐2023
– ident: e_1_2_7_2_2_1
  doi: 10.1175/MWR3199.1
– ident: e_1_2_7_1_20_1
  doi: 10.1016/j.envpol.2022.119888
– ident: e_1_2_7_1_49_1
  doi: 10.1111/cdev.12169
– ident: e_1_2_7_1_45_1
  doi: 10.1029/2018JD029933
– ident: e_1_2_7_2_6_1
– ident: e_1_2_7_1_32_1
  doi: 10.5281/zenodo.3931948
– ident: e_1_2_7_1_56_1
  doi: 10.5194/gmd‐11‐4843‐2018
– ident: e_1_2_7_1_2_1
  doi: 10.1021/acs.est.3c09642
– ident: e_1_2_7_1_30_1
– ident: e_1_2_7_1_6_1
– ident: e_1_2_7_1_51_1
  doi: 10.5281/zenodo.7229674
– ident: e_1_2_7_1_5_1
  doi: 10.5194/acp‐19‐2991‐2019
– ident: e_1_2_7_1_33_1
  doi: 10.5194/acp‐16‐9591‐2016
– ident: e_1_2_7_1_12_1
  doi: 10.1016/B978-0-12-814952-2.00007-1
– ident: e_1_2_7_1_37_1
  doi: 10.1111/geb.13498
– ident: e_1_2_7_1_16_1
  doi: 10.1038/s41467‐020‐20871‐0
– ident: e_1_2_7_2_7_1
  doi: 10.1029/2010JD015139
– volume-title: Climate change 2014: Mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change
  year: 2014
  ident: e_1_2_7_1_38_1
  contributor:
    fullname: Seto K. C.
– ident: e_1_2_7_1_41_1
– ident: e_1_2_7_1_44_1
– ident: e_1_2_7_2_3_1
  doi: 10.1175/BAMS‐87‐3‐343
– ident: e_1_2_7_1_8_1
  doi: 10.1088/1748‐9326/ad3cf7
– ident: e_1_2_7_1_47_1
– ident: e_1_2_7_2_5_1
– ident: e_1_2_7_1_58_1
  doi: 10.3390/rs15204904
– ident: e_1_2_7_1_10_1
  doi: 10.5194/acp‐19‐9797‐2019
– ident: e_1_2_7_1_52_1
  doi: 10.5194/acp‐17‐8313‐2017
– ident: e_1_2_7_1_19_1
  doi: 10.1002/2016GL067843
– ident: e_1_2_7_1_25_1
  doi: 10.1525/elementa.2023.00102
– ident: e_1_2_7_1_54_1
  doi: 10.5194/gmd‐14‐3633‐2021
– ident: e_1_2_7_1_9_1
  doi: 10.1117/1.2898457
– ident: e_1_2_7_1_18_1
  doi: 10.1088/1748‐9326/ac0660
– ident: e_1_2_7_1_29_1
  doi: 10.1088/1748‐9326/ad0f74
– ident: e_1_2_7_1_40_1
  doi: 10.5194/acp‐16‐13449‐2016
– ident: e_1_2_7_1_31_1
  doi: 10.5194/amt‐5‐99‐2012
– ident: e_1_2_7_1_42_1
  doi: 10.1002/eap.2763
– ident: e_1_2_7_1_24_1
  doi: 10.1016/j.rse.2021.112625
– ident: e_1_2_7_1_36_1
  doi: 10.1029/2023GL104376
– ident: e_1_2_7_1_14_1
  doi: 10.3334/ORNLDAAC/1810
– ident: e_1_2_7_1_15_1
  doi: 10.1029/2020JD032974
– ident: e_1_2_7_1_61_1
  doi: 10.5194/gmd‐6‐583‐2013
– ident: e_1_2_7_1_26_1
– ident: e_1_2_7_1_34_1
– ident: e_1_2_7_1_4_1
  doi: 10.5194/amt‐16‐109‐2023
– ident: e_1_2_7_1_48_1
– year: 2023
  ident: e_1_2_7_1_27_1
  article-title: Earth system research Laboratories
  publication-title: CarbonTracker CT2022
  contributor:
    fullname: National Oceanic and Atmospheric Administration Global Monitoring Laboratory
– ident: e_1_2_7_1_35_1
– ident: e_1_2_7_1_62_1
  doi: 10.1029/2022AV000732
– ident: e_1_2_7_1_59_1
  doi: 10.1098/rsta.2010.0240
– ident: e_1_2_7_1_3_1
  doi: 10.5194/essd‐11‐1291‐2019
– ident: e_1_2_7_1_21_1
  doi: 10.5194/essd‐12‐699‐2020
– ident: e_1_2_7_1_53_1
  doi: 10.5194/amt‐10‐2759‐2017
– ident: e_1_2_7_1_43_1
  doi: 10.5194/essd‐13‐357‐2021
– ident: e_1_2_7_1_46_1
  doi: 10.1029/2020GL090037
– ident: e_1_2_7_1_39_1
  doi: 10.1088/1748‐9326/ab9cfe
– ident: e_1_2_7_1_11_1
  doi: 10.1525/elementa.188
– ident: e_1_2_7_1_23_1
  doi: 10.1029/2012GL052738
SSID ssj0003031
Score 2.5067124
Snippet NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial...
Abstract NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO 2 ) with sufficient...
Abstract NASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient...
SourceID doaj
proquest
crossref
wiley
SourceType Open Website
Aggregation Database
Publisher
SubjectTerms Atmospheric models
Bayesian analysis
Bayesian atmospheric inversion
Bayesian theory
California
Carbon dioxide
Carbon dioxide concentration
Carbon dioxide emissions
Carbon dioxide flux
Cities
CO2 emissions
COVID-19
Emission measurements
Emissions
Emissions control
Estimates
Fluxes
Mathematical models
Observatories
OCO satellite
Probability theory
Satellite observation
Satellites
Spatial discrimination
Spatial resolution
Urban areas
Wildfires
XCO2
SummonAdditionalLinks – databaseName: Wiley-Blackwell Open Access Collection
  dbid: 24P
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fa9swEBajY9CX0nUtTdsNPbRPxTSRZFl6XLI0pbSj9Af0zZxkqeTFDnHG1v9-d4qbpC-DgR-MLIG48-m-O-k-MXaKbidKr1XmjHeZ0ipmkMcqEyZHfzggzE61w7c_9dWTun7On7uEG9XCLPkhVgk3soy0XpOBg2s7sgHiyMSoXU1uyOIpZP9IpDHEnS_U3WolxuV5eWOeVZkRhe4OvuP4i83R71xSYu5_Bzc3QWvyOpe7bKeDi_z7Ur-f2YdQ77FPk3Qd7yu-pQOcvv3CZmO0VUKf9Qsfwdw1Nf8xbf5Mq8DHqErKibV8WvPH3w1f12PxUSJU5engAB_Ca6CaSk7kGymNxqGu-AMk1s5F4LfrhGK7z54ux4-jq6y7TSHz6J4GWSWjsN5Z0XdQGamCgWC1o51SVfR94RDLKPwsvYFcVNL3TcRgzZsYQwE6ygO2VTd1OGQcQ0BwAwjGRKOouBVBULTaGA9QAPgeO3sTaDlbkmaUabNb2HJT8D02JGmv-hDVdWpo5i9lZzllIaIGKXMdB04VlbYO8qAAH6t0pUOPnbzpquzsry0lkWbgtGzeY-dJf_-cSDm5v9EGwcvRf_U-ZtvUTq5MmBO2tZj_Cl8Royzct_Qj_gWfbtxj
  priority: 102
  providerName: Wiley-Blackwell
Title Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements
URI https://onlinelibrary.wiley.com/doi/abs/10.1029%2F2024GL111150
https://www.proquest.com/docview/3121587295
https://doaj.org/article/72f6a3356f1b47d69ba5e4ae4a946d6e
Volume 51
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BERIXxKdYKCsf4IQisrbj2Md22W6FWoSgiyou0dix0V6yVXdR23_PjJOlywUuSDlESRSNZpKZN_bMG4A3FHaSCkYX3gZfaKNTgVVqC2kriocTxuzcO3z6yRwv9Mfz6nxn1BfXhPX0wL3i3tcyGVSqMmnidd0a57GKGulw2rQmZu9bum0yNfhgcsz9rDynCytrM5S8l9Jxtq_nJ-wpuNd-Jxhlzv4_gOYuXM3x5ugRPByAojjoBXwMd2L3BO7P8yDeGzrLpZth_RQuZvSXMu7sfogpXvpVJz4sV9fLNooZGZFXw9Zi2Ymzq5W47cQS00ylKnLJgDjEm8jdlIJpN_ICmsCuFV8x83Vuoji9XUpcP4PF0exselwMcxSKQIFpUrQqSRe8k6XH1iodLUZnPO-R6roMtScUo-m2ChYr2apQ2kRpWrApxRpNUs9hr1t18QUISv7QTzBam6zmtlaCP8kZawNijRhG8Har0Oaip8to8ja3dM2u4kdwyNr-_QyTXOcLZPpmMH3zL9OPYH9rq2b489aNYroMEstVI3iX7fdXQZr5lxNjCba8_B8SvYIH_HKObdLuw97m8md8TaBl48dwV-rPY7h38G3xfTHOX-svqGPn2Q
link.rule.ids 315,783,787,867,2109,11574,27936,27937,46064,46488,50826,50935
linkProvider Directory of Open Access Journals
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwELbQEIIXxE9RtoEf4AlFtLbj2I-s61qgnRB00t6ss2NPfUmmttO2_547N-u6FySkPESJrVi-nO-7891nxj6h2UkyaFV4E3yhtEoFlKkuhCnRHg4Is1Pt8OxUT87Uj_PyvDvnlGphNvwQ24AbaUZer0nBKSDdsQ0QSSa67Wo8JZUnn_0xfsdQTpdQv7ZLMa7PmyPzrCqMqHSX-Y79v-72fmCTMnX_A7y5i1qz2Tl5wZ53eJF_2wj4JXsUm1fsyTifx3uLdzmDM6xes8sRKivBz-aCD2Hp24YfL9qbRR35CGVJQbEVXzR8ft3y-4IsPsyMqjxnDvAjuI1UVMmJfSPH0Tg0Nf8DmbZzHfnsPqK4esPOTkbz4aTojlMoAtqnQVHLJGzwVvQ91EaqaCBa7WmrVFX9UHkEMwpfy2CgFLUMfZPQWwsmpViBTvIt22vaJr5jHH1A8AOIxiSjqLoVUVCy2pgAUAGEHvt8N6HucsOa4fJut7Bud-J77Ihme9uGuK7zg3Z54TrVcZVIGqQsdRp4VdXaeiijArys0rWOPXZwJyvXKeDKSWLNwGHZsse-ZPn9cyBu_HuqDaKX9__V-iN7OpnPpm76_fTnPntGbciuCXPA9tbLq3iIgGXtP-Sf8i-qOt_X
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LbxMxELZQKxAXxFOkFPABTmhFsvZ67WObJimQVggaVHGxxq8ql90oCYL-e2acbZpekJD2sNr1riyPx_PNeOYzY-_Q7CThlSyc9q6QSqYCqhSKUldoDweE2al2-Oxcnc7k58vqsgu4US3Mhh9iG3AjzcjrNSn4IqSObIA4MtFrl5MpaTy57PsINEqc4ftHP2Y_Z9u1GBfozZl5Rha6rFWX-o5_-Lj7_R2jlLn77wDOXdia7c74MXvUAUZ-tJHwE3YvNk_Z_Uk-kPca73IKp189Y4sRaivhz-aKD2Hp2oafzNs_8xD5CIVJUbEVnzf84nfLbyuy-DBTqvKcOsCP4TpSVSUn-o0cSOPQBP4dMm_nOvKz25Di6jmbjUcXw9OiO0-h8GigBkUQqTTembLvIGgho4ZolKO9Uln3fe0QzUh8LbyGqgzC93VCd83rlGINKokXbK9pm_iScXQCwQ0gap20pPJWhEHJKK09QA3ge-z9zYDaxYY2w-bt7tLY3YHvsWMa7W0bIrvOD9rlle10x9ZlUiBEpdLAyToo46CKEvAyUgUVe-zwRla208CVFUSbgd0yVY99yPL7Z0fs5NtUaYQvB__V-i178PVkbKefzr-8Yg-pCdm1Uh-yvfXyV3yNgGXt3nSz8i-f6ODG
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+Carbon+Dioxide+Emissions+in+Two+California+Cities+Using+Bayesian+Inversion+and+Satellite+Measurements&rft.jtitle=Geophysical+research+letters&rft.au=Sofia+D.+Hamilton&rft.au=Dien+Wu&rft.au=Matthew+S.+Johnson&rft.au=Alexander+J.+Turner&rft.date=2024-10-28&rft.pub=Wiley&rft.issn=0094-8276&rft.eissn=1944-8007&rft.volume=51&rft.issue=20&rft.epage=n%2Fa&rft_id=info:doi/10.1029%2F2024GL111150&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_72f6a3356f1b47d69ba5e4ae4a946d6e
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0094-8276&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0094-8276&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0094-8276&client=summon