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

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

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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-6

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2

4

6

8

-100 0 100

XC

O2urb

, ppm

Lattitude (o)

r = 0.44 / r = 0.56

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0 5 10 15 20 25 30

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

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atasets –D

emo

graph

iarep

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Urb

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eat Island

Temp

erature –

Socio

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Datasets, N

ASA

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P –

Natio

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stitute o

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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