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THE CONTRIBUTION OF LARGE URBAN AREAS TO ENHANCEMENTS IN LOCAL
CARBON DIOXIDE CONCENTRATIONS BASED ON OCO-2 AND GOSAT OBSERVATIONSLev Labzovskii1, Su-Jong Jeong1
1 – Global Change Laboratory, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen (China)
O B J E C T I V E S A N D M O T I V A T I O N
M E T H O D O L O G Y
U R B A N X C O 2 : O C O - 2 / G O S A T
U R B A N X C O 2 V S S I D E FA C T O R S
A C K N O W L E D G E M E N T S
C O N C L U S I O N S
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Shanghai/Suzhou
Tianjin
San-Diego
Karachi
Tokyo/Yokohama
Los Angeles
Nagoya/Gifu
Seoul
Delhi
Guangzhou
Shenzhen
Beijing
Tehran
Cairo
Madrid
Chicago
Johannesburg
Bangalore
Rio De Janeiro
Melbourne
Buenos Aires
XCO2 urban enhancement (ppm)
OCO-2
GOSAT
MAIN SCOPE
SEPA
RA
TE A
NA
LYSIS
URBAN AREAS ARE RESPONSIBLE FOR 70% OF GLOBAL CO2
EMISSIONS• To delineate urban areas for the study based on objective numerical criteria
• To quantify local CO2 anomalies based on OCO-2 and GOSAT observations across urban areas worldwide to show which of them are responsible for highest local CO2 anomalies
• To identify the potential relationship of CO2 urban anomalies and sidefactors such as city population, city GDP, urban heat island temperature
THESE 70% ARE UNEVENLYDISTRIBUTED ACROSS URBAN AREAS
WORLDWIDE -> QUANTIFICATION REQUIRED
70%СO2
• 21 urban areas are suitable for intercomparison between OCO-2 and GOSAT (enough comparable averaged measurements)
• High agreement between OCO-2 and GOSAT XCO2 acquired (correlation coefficient = 0.9)
• Median bias is reasonable (1.2 ppm) considering instrumental uncertainties taken into account
• Only 1 urban area shows instrumental disagreement probably due to temporal differences of XCO2 soundings in certain urban area from these instruments (Johannesburg)
DMSP-OLS (Night Lights Observations)
XCO2 QUANTIFICATIONURBAN AREA DELINEATION
GOSAT (ACOS 3.3. v)OCO-2
• Only urban areas with more than 1 million population are considered here• In total we isolated 461 urban areas worldwide• 64 urban areas are available for OCO-2 measurements, 74 urban areas
are available for GOSAT-based analysis• 21 urban areas are eligible for intercomparison between instruments
for the considered period of study -> See results on the right side of the poster
The approach is based on the threshold of digital number (DN) for night-lightsobservations from DMSP-OLS (Defensive MeteoSatellite Program-Operational Line Scan System)system from 2013 (last available dataset). Threshold of 60 DN is most suitable for urban area isolation (according to comparison with independent sources such as populationfrom Socioeconomics Data from NASA and Demographiareport 2016). To exclude potential inclusion of gas flaring, biomass burning zones we overlapDN > 60 zones in urban areas (red color in central panel below) with MODIS-retrieved datasets (left)over the land to obtain numerically retrieved urban areas worldwide (right panel)
~ FIRST TWO YEARS OF
OCO-2 OBSERVATIONS
October 2014 – January 2017
Warn Level < 9Warn Level < 15
XCO2urb = XCO2ind – XCO2hem
XCO2urb – Urban XCO2 enhancement in comparison with hemispheric medianXCO2ind – XCO2 averaged over a month period of measurements XCO2hem – Hemispheric median value of XCO2, monthly averaged
Hemispheric values are calculated for each instrument (OCO-2, GOSAT) in each urban area predetermined by the method that is described on the left, hemispheric results from the instruments agree quite well: absolute median bias between instruments equals to 0.36 ppm. CO2urb are calculated based on monthly averaged values over whole period of study. Two filtering approaches to minimize seasonality are applied: only urban areas with enough months from different seasons are used (1 month from each season), amount of months to be averaged must exceed 4. We understand that by applying hemispheric values we cannot fully exclude biogenic signal from urban CO2 anomalies. However, we assume that we can minimize this signal applying above-mentioned filtering
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0 10 20 30 40
XC
O2urb
, ppm
Population, mln. peop.
Миллионы
r = 0.32 / r = 0.21
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2
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XC
O2urb
, ppm
Lattitude (o)
r = 0.44 / r = 0.56
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XC
O2urb
, ppm
UHI T(Co)
r = 0.20 / r = 0.20r = 0.31 / r = 0.21
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-100 100 300 500
XC
O2urb
, ppm
GDP
• As expected linear relationship between XCO2urb and population amount has been found. Correlation coefficientof OCO-2-retrieved XCO2urb vs populations size is 0.32. We tested the same comparison for GDP expecting similar relationship between XCO2
and GDP of the cities of interest, but much weaker relationship has been found for both instruments in that case (r = 0.20)• Strong latitude-dependent relationship is evidenced for XCO2urb from both instruments. The highest correlation is observed from GOSAT-retrieved
values in this case (r = 0.56). This relationship is probably related to several factors including dominance of anthropogenic sources in northern hemisphere, frequent use of heating systems in northern hemispheric cities and nearby power plant activities in crucial regions such as East Asia,
Northern America and Europe.• UHI (Urban heat island) temperature here is defined as difference in average summer nighttime minimum land surface temperature between urban
and buffer (10 km distance) zone of city. Datasets are taken from Socioeconomic Archive of NASA. We can see that there is weak positive relationship between UHI temperature and XCO2 especially when XCO2urb are retrieved from OCO-2 (r = 0.31), this effect is especially pronounced in Asian cities and have to be investigated closely in Asian regions in future
Pop
ulatio
n D
atasets –D
emo
graph
iarep
ort
Urb
an H
eat Island
Temp
erature –
Socio
eco
no
mic
Datasets, N
ASA
GD
P –
Natio
nal In
stitute o
f En
viron
men
tal Science (Jap
an)
OCO-2:
• Based on OCO-2, highest XCO2urb anomalies (> 5 ppm) are observed in Shanghai/Suzhou (7.11 ppm), Asansol (5.99 ppm), Linyi (5.89 ppm), Nantong (5.58 ppm), Tianjin (5.25 ppm)
• Several geographical groups of urban areas are seen from OCO-2 top list such as China, South Korea + Japan, Pakistan + India, California
• Unexpected examples of cities include Barcelona (probably recent raise in 7% of coal consumption in Spain is the reason), Irbil (Middle East citieshave been marked in previous studies as Hakkarainen et al., 2016 byexerting CO2 anomalies above inventory-based expectations)
• There is numerical consistency for urban areas that are closely located to each other in results such as Shenzhen – Guangzhou (XCO2urb absolute difference is 0.13 ppm), Shanghai/Suzhou –Nantong (1.51 ppm), San Diego – Los Angeles (0.77 ppm), Nagoya/Gifu – Tokyo/Yokohama (0.20 ppm).
1 2 3 4 5 6 7 8
Shanghai/Suzhou
Asansol
Linyi
Nantong
Tianjin
San-Diego
Barcelona
Karachi
Tokyo/Yokohama
Los Angeles
Rajkot
Nagoya/Gifu
Seoul
Irbil
Charlotte
Dehli
Guangzhou
Hyderabad(PK)
Shijianzhuang
Shenzhen
XCO2 urban enhancement (ppm)
OCO-2
EX
AM
PLE
S O
F U
RB
AN
AR
EA
S
1 2 3 4 5 6 7 8
Jinan
Chengdu
Wuhan
Hangzhou
Shanghai/Suzhou
Hiroshima
Tianjin
Beijing
Changchun
Seoul
Los Angeles
New-York
Shenyeng
San-Diego
Almaty
Tokyo/Yokohama
Dehli
Shenzhen
XCO2 urban enhancement (ppm)
GOSAT
• Highest GOSAT-retrieved XCO2urb anomalies (> 4 ppm) are observed mainly in Chinese cities: Jinan
(6.10 ppm), Chengdu (4.69 ppm), Wuhan (4.55 ppm), Hangzhou (4.34 ppm), Shanghai/Suzhou (4.10 ppm)
and one Japanese city (Hiroshima, 4.09 ppm)• Geographical groups of cities are very clear based on
GOSAT since only Asian and U.S. cities compose top-20 of XCOurb emitting list
• We have high agreement with one of fundamental studies on urban CO2 based on GOSAT observations for Los Angeles from Kort et al., 2012 (3.21 ppm in
that study vs our 3.41 ppm)• There is also reasonable agreement with previous
GOSAT-based study from Janardanan et al., 2016 for Los Angeles (2.75 ppm vs our 3.41 +\- 2 ppm in this
study)
• Successfully quantified XCO2 urban anomalies in comparison with median hemispheric values of XCO2 in more than 100 urban areas where both XCO2 and urban areaboundaries are determined based on numerical criteria from spaceborne observations between October 2014 and January 2017
• 461 urban areas with population > 1 million are extracted where 10 urban areas represent agglomerations of two or more administrative units• OCO-2 observations revealed highest XCO2urb in such urban areas as Shanghai/Suzhou, Asansol, Linyi, Nantong and Tianjin. Several geographical regions are seen from top-
emitting group such as China, South Korea + Japan, India + Pakistan, California.• GOSAT observations showed that highest XCO2urb are evidenced in Chinese cities of Jinan, Chengdu, Wuhan, Hangzhou and Shangai/Suzhou + Japanese city of Hiroshima. All
highly-emitting urban areas are located either in USA or in Asia according to GOSAT analysis• Side factor analysis showed that XCO2urb has weak positive relationship with population amount according to OCO2- observations. Strong latitude gradient of XCO2urb is
evidenced based on both instruments where Northern Hemispheric cities dominate in high CO2 emissions. Urban heat island seems to be positively related to XCO2urb based on OCO-2 observations. This relationship is especially remarkable in Asia.
• GDP did not show any reasonable relationship with XCO2urb from OCO-2 and GOSAT
INTE
RC
OM
PA
RIS
ON
Investigate CO2 urban anomalies
worldwide solely based on satellite
remote sensing
ASSU
MPTI
ON
S
STUDY PERIOD
OBJECTIVES
This study is entirely based on open-access datasets from different sources, to this end we acknowledge OCO-2 and GOSAT teams for providing the georeferenced datasets on CO2 concentration with appropriate instrumental uncertainties. We acknowledge the research team that have been working on ACOS 3.3 version datasets as well. Moreover, we would like to mention the team from National Institute for Environmental Studies (Japan) for providing GDP gridded datasets in open source. Socioeconomic Database from NASA has been used to obtain urban heat island temperature datasets and we acknowledge the appropriate team has been working to compile these datasets. We would like to underline that during the preparation of the manuscript we received much help and assistance from Sergey Victorov, Janne Hakkarainen and Sam Silva. Their efforts are sincerely acknowledged as well.
Which factors
are related to increased
urban
CO2 concentration ?
ADDITIONAL QUESTION