Remote sensing of environment and disaster laboratory	

Institute of Industrial Science, The University of Tokyo, Japan	

CHARACTERIZATION OF AIR QUALITYIN GLOBAL MEGA-CITIESBY REMOTE SENSING AND INVENTORY DATAAya Fujikawa and Wataru Takeuchi
IIS, the University of Tokyo, Japan
Abstract: According to previous study, aerosol is classified into two types –natural or artificial. However, it isn’t so clear that what rate does aerosol contain in the air and how much two types aerosol affects. The objectiveof this study is to clear the relation between air pollution and human activity, so we use indexes as angstrom exponent (ANG), Aerosol Optical Depth (AOD) by MODIS. Firstly, aerosol condition was classified by ANGAOD. A scatter plot was made about 61 mega cities. Air conditions of cities were revealed three characteristics- the environmentally friendly cities(European cities, Tokyo, New York, etc)・the cities influenced artificialaerosol(Shanghai, Lagos, etc)・the cities influenced natural aerosol (Kabul, Baghdad, etc). Secondly, the value of ANG-AOD was transformed to the value of RGB color space. Not only chosen cities but also cities whereare not chosen and the other areas are categorized. Thirdly, the value of RGB was found trend. The RGB data of monthly average were compared in Tokyo, Kabul and Shanghai. The superiority of the effect of natural or
artificial aerosol was different with seasons and the combination of them was different with cities. In the future, the result of this research could be an index of the choice to invest for the air pollution improvement.	

2.Methodology	

1.Background	
Today, it is considerably revealed that what is the aerosol itself. However, it isn’t so clear that how
much range natural or artificial aerosol affects and that how different it affects by each seasons.
Especially, it isn’t so clear how much degree aerosol in the field of transportation affects atmosphere.
So, I focus on Angstrom exponent (ANG) and optical thickness of aerosols (AOD) to analyze the stateof the atmosphere. 	
Recently there face severe air pollution in China, India, Mexico and so on. In past, Japan had the healthdamage caused by the air pollution, and at present developing countries have it. Fu Zhe (2011) showeda relation between air pollution and health damage in Shanghai. Also, Ewing et al. (2010) developed amethod for tracing fine airborne particulate pollution with origins in East Asia and found the medianproportion of Asian lead in the PM2.5 was 29% in the San Francisco Bay Area. Air pollution is a severe
problem in the world. It is necessary to grasp a situation of the atmosphere not only in the part area but
also in the whole area of the world.

Choose target cities	
MODIS	ANG	Use the data from2001～2010(data size 100km×100km)	

①AOD-ANG
scatter plot

Use the data from2004/01～2013/09(data size10km×10km)	

②the data by RGB 	AOD	

Comparison between
cities or regions③Analyze secularchange of RGB	The objective of this research is to find the characteristic of each cities or the regionality by researching
Angstrom exponent (ANG) and optical thickness of aerosols (AOD) and comparing the data of eachyear or monthly average.	

Figure 1 flow chart of this research	3.Result
ANG	The reference points are determined
R（1.3,1.6) G(0.1,1.9) B(0.3,0.5)↓value=255×(1−​𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒  𝑓𝑟𝑜𝑚  𝑎  𝑝𝑜𝑖𝑛𝑡  𝑡𝑜  2

𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒  𝑝𝑜𝑖𝑛𝑡/𝑡ℎ𝑒  𝑙𝑒𝑛𝑔𝑡ℎ  𝑜𝑓  𝑝𝑒𝑟𝑝𝑒𝑛𝑑𝑖𝑐𝑢𝑙𝑎𝑟  𝑙𝑖𝑛𝑒 )	  ANG

1.51(When a value≤0, it is 0.)

0.5

AOD	

2013/09 average
10km×10km size of data 	

00

0.4 0.8 1.2 1.62

AOD

Figure 2 Scatter plot(used data 2001-2010)	

Figure 4 Secular change around Moscow (LAT:55°45N,LON:37°34E)	

2013/07-2013/04-

2013/01-

2012/10-

2012/07-2012/04-

2012/01-

2011/10-

2011/07-

2011/04-

2011/01-

2010/10-2010/07-2010/04-

2010/01-

2009/10-

2009/07-

2009/04-

2009/01-

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2008/07-

2008/04-

2008/01-

2007/10-

2007/07-

2007/04-

2007/01-

2006/10-

2006/07-

Linear (RΔR)

Figure 5 Secular change around New Deli (LAT:28°40N,LON:77°9E)	

2013/07-2013/04-

2013/01-

2012/10-

2012/07-2012/04-

2012/01-

2011/10-

2011/07-

2011/04-

2011/01-

2010/10-2010/07-2010/04-

2010/01-

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2008/01-

2007/10-

2007/07-

2007/04-

2007/01-

2006/10-

2006/07-

2006/04-

2006/01-

100

R-ΔR

Tokyo-RGB	

G-ΔG

50

B-ΔB

0

Linear (RΔR)

2013/07-y = -0.0977x + 5.8995
y = 0.0282x - 1.7029
y = -0.2049x + 12.375

2013/04-

2013/01-

2012/10-

2012/07-2012/04-

2012/01-

2011/10-

2011/07-

2011/04-

2011/01-

2010/10-2010/07-2010/04-

2010/01-

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2006/07-

2006/04-

2006/01-

2005/10-

2005/07-

2005/04-

2005/01-

2004/10--100

2004/07--50

2004/04-

・AOD and ANG can be understood visually by making scatter plot and convert RGB.
・The 61 cities in the world are divided three atmosphere characteristics –the environmentally friendly
city, the city influenced artificial aerosol, the city influenced natural aerosol-.
・Secular change may be characteristic each city or region. It is clear some value of RGB is changing
year by year.

y = -0.0334x + 1.9588
y = -0.0789x + 4.631
y = -0.3948x + 23.182

Figure 7 Secular change around Shanghai (LAT:31°10N,LON:121°29E)	

2004/01-

4.Conclusion

2005/10--100

2005/07--50

2005/04-

2013/07-2013/04-

y = 0.0578x - 3.3644
y = -0.0842x + 4.9031
y = -0.1961x + 11.417

2013/01-

2012/10-

2012/07-2012/04-

2012/01-

2011/10-

2011/07-

2011/04-

2011/01-

2010/10-2010/07-2010/04-

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2006/01-

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2005/07-

2005/04-

2005/01-

2004/10-

2004/07-

2004/04-

2004/01-

-50

B-ΔB

0

2005/01-

Linear (RΔR)

G-ΔG

50

2004/10-

B-ΔB

0R-ΔR

Shanghai-RGB	

2004/07-

G-ΔG

100

2004/04-

R-ΔR

NewDeli-RGB	

50

-100

y = -0.2676x + 15.601
y = -0.1941x + 11.315
y = -0.1974x + 11.509

Figure 6 Secular change around Beijing (LAT:39°55N,LON:116°19E)	

2004/01-100

2006/04--1002006/01--50

2005/10-2013/07-2013/04-

y = 0.073x - 4.3395
y = -0.1036x + 6.1568

2013/01-

2012/10-

2012/07-2012/04-

2012/01-

2011/10-

2011/07-

2011/04-

2011/01-

2010/10-2010/07-2010/04-

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2004/07-

2004/04-

2004/01--100

y = -0.0494x + 2.9381

2005/07--50

Linear (RΔR)

2005/04-

Linear (RΔR)

B-ΔB

0

2005/01-

0G-ΔG502004/10-

B-ΔB

R-ΔR

Beijing-RGB	

2004/07-

G-ΔG

50100

2004/04-

R-ΔR

Moscow-RGB	

2004/01-

100

Figure 3 RGBmap (for example:2013/09 monthly average)	

Figure 8 Secular change around Tokyo (LAT:35°45N,LON:139°39E)	

5.Future works
・add NO2 data to analyze the value of red means the effect of artificial origin including natural fireand exhaust gas
・inventory data coupled with these satellite-derived and in-situ results are carried out to investigate a
relationship between GDP, population density, environmental regulation of vehicles and so on

Reference	[1] ITAHASHI ET AT AL, 2012: INTERANNUAL VARIATION IN THE FINE-MODE MODIS AEROSOL OPTICAL DEPTH AND ITS RELATIONSHIP TO THE CHANGES INSULFUR DIOXIDE EMISSIONS IN CHINA BETWEEN 2000 AND 2010[2]FU ZHE, 2011, AIR POLLUTION AND HEALTH DAMAGE IN SHANGHAI THE ISSUE FROM EPIDEMIOLOGICAL STUDY[3]STEPHANIE A. EWING ET AT AL, 2010, PB ISOTOPES AS AN INDICATOR OF THE ASIAN CONTRIBUTION TO PARTICULATE AIR POLLUTION IN URBANCALIFORNIA[4]KITAJIMA ET AT AL, 2002, PHOTOPOLARIMETORY OF ATMOSPHERIC AEROSOLS[5]HARA ET AT AL, 2012, RECENT INTER-ANNUAL TREND OF SPHERICAL AEROSOL IN EAST ASIA
[6]YAMAGATA ET AT AL, 1997, DEVELOPMENT OF VEGETATION-SOIL-WATER INDEX ALGORITHMS AND APPLICATIONS[7]TSUTSUMI ET AT AL, 2003, A DIFFERENCE BETWEEN AEROSOL OPTICAL THICKNESS AND ANGSTROM EXPONENT BY YELLOW SAND IN JAPAN IN SPRING 2002

For further details, contact: Aya Fujikawa, Ce-505, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/E-mail: ayafuji@iis.u-tokyo.ac.jp)