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Impact assessment of
socio-economic development
on urban air quality in
Indian mega cities
Prakhar MISRA(D3 PhD Candidate),
Dr. Wataru TAKEUCHI
(Supervisor)
7/17, 2017
LCLUC SARI INTERNATIONAL REGIONAL SCIENCE MEETING IN S./SE. ASIA
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Contents
Previous results
STIRPAT model
AirRGB decomposition
Urban morphology
Future course
2
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3
Clear Day
Hazy Day
Traffic congestion,
New Delhi
Municipal waste
Varanasi
Roadside dust,
Kanpur
DNA, 2017
Industrial emission,
Lucknow Brick factories,
Ghaziabad
WSJ, 2016
HT, 2016
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4
20 Dec, 2015
21 Dec, 2015
Traffic congestion,
New Delhi
Municipal waste
Varanasi
Roadside dust,
Kanpur
DNA, 2017
Industrial emission,
Lucknow Brick factories,
Ghaziabad
WSJ, 2016
HT, 2016
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ACRS, 2016
Industrial, construction GDP growth Granger causes pollution
5
GDP causes AOD(MODIS)
Pro
vin
ce
GDP causes AE(MODIS) GDP causes NO2(OMI)
Economic
sector
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Nightlight characteristics
can differentiate emission
causes
6
AQ-NL Classification (Misra&Takeuchi, IGRSM, 2016)
AOD & NL
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High correlation of built-area, populationwith urban pollutants growth
Urban
area
Urban area
growth
(‘02-’15)
Populatio
n (‘11)
Populatio
n growth
(‘01-’11)
ANG
growth
(‘10-
’15)
AOD
growth
(‘10-
’15)
SO2
growth
(‘09-
’14)
NO2
growth
(‘09-
’14)
Urban area 1.00
Urban area
growth 0.08 1.00
Population 0.01 -0.28 1.00
Population growth 0.50 -0.23 -0.04 1.00
ANG growth 0.59 0.08 -0.17 0.64 1.00
AOD growth 0.90 0.08 0.19 0.37 0.52 1.00
SO2 growth 0.10 -0.24 -0.53 0.31 0.12 -0.22 1.00
NO2 growth -0.34 0.35 -0.05 -0.30 -0.07 -0.39 0.01 1.00ACRS, 2015
7
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Hypothesis
Socio-economic development (from Remote
Sensing) impacts air urban air quality
8
ObjectiveAssess impact of socio-economic development
and land-use land-cover change on urban air
quality
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Incorporating Socio-economic development9
AOD, ANG
(MODIS)
SO2, NO2(OMI)
AW3DSM (ALOS)
GDEMv2 (ASTER)
LANDSAT 7,8
ALOS PALSAR2
Nightlight(DMSP)
Population(census)
Sector GDP
(CSO)
Decompose 3 scenarios
RGB componen
t
Seasonal growth
pattern &Annual
composite
AQ
FRP(MODIS)
Urban morphology
maps
Area growth
functions Fi:f1(Residential),f2(Commercial), f3(Industry), f4(Brick Kiln), f5(Agrofire)
Residential area
Commercial area
Industrial area
Brick factory
Agro areas
Nightlight(VIIRS)
Spatial weight
Wi:IDW, Patchiness, built-up density
AQ&LULC socio-economic growth model
AQ= f(ECi . Fi . Wi)
Granger causality, EKC
Emission coefficient
ECi
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Incorporating Socio-economic development10
AOD, ANG
(MODIS)
SO2, NO2(OMI)
AW3DSM (ALOS)
GDEMv2 (ASTER)
LANDSAT 7,8
ALOS PALSAR2
Nightlight(DMSP)
Population(census)
Sector GDP
(CSO)
Decompose 3 scenarios
RGB componen
t
Seasonal growth
pattern &Annual
composite
AQ
FRP(MODIS)
Urban morphology
maps
Area growth
functions Fi:f1(Residential),f2(Commercial), f3(Industry), f4(Brick Kiln), f5(Agrofire)
Residential area
Commercial area
Industrial area
Brick factory
Agro areas
Nightlight(VIIRS)
Spatial weight
Wi:IDW, Patchiness, built-up density
AQ&LULC socio-economic growth model
AQ= f(ECi . Fi . Wi)
Granger causality, EKC
Emission coefficient
ECi
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• Environmental Kuznet's Curve
11
• IPAT Equation I = PATI – Impact
P – Population
A - Affluence
T - Technology
(Ehrlich & Holdren, 1976)
(Kuznet, 1955; Panayotou, 1993)
• STIRPAT modification
: I = PaAbTc
(Dietz&Rosa, 1997)
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AirRGB decomposition 12
AOD, ANG(MODIS)
SO2, NO2(OMI)
AW3DSM (ALOS)
GDEMv2 (ASTER)
LANDSAT 7,8
ALOS PALSAR2
Nightlight(DMSP)
Population(census)
Sector GDP(CSO)
Decompose to 3 scenarios
RGB component
Seasonal growth
pattern &Annual
composite
AQ
FRP(MODIS)
Urban morphology
maps
Area growth
functions Fi:f1(Residential),f2(Commercial), f3(Industry), f4(Brick Kiln), f5(Agrofire)
Residential area
Commercial area
Industrial area
Brick factory
Agro areas
Nightlight(VIIRS)
Spatial weight Wi:IDW, Patchiness, built-up density
AQ&LULC socio-economic growth model
AQ= f(ECi . Fi . Wi)
Granger causality, EKC
Emission
coefficient ECi
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1. AirRGB – analyze anthropogenic
urban pollution Consider AOD, ANG from MODIS across 60 cities globally (2001- 2016)
13(ACRS 2013; Misra et al, Under
review)
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AirRGB scale for scenario estimation
Sc
14
RGB
Decomposition scheme
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Rising pollution across North India
C
15
2001 2005 2010 2015
New Delhi
Beijing
AirRGB map
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Validation with US Embassy monitor,
Beijing. More required.16
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Rising pollution across North India
C
17
2001 2005 2010 2015
New Delhi
AirRGB map
What is causing rise in emissions?
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Urban Morphology 18
AOD, ANG(MODIS)
SO2, NO2(OMI)
AW3DSM (ALOS)
GDEMv2 (ASTER)
LANDSAT 7,8
ALOS PALSAR2
Nightlight(DMSP)
Population(census)
Sector GDP(CSO)
Decompose to 3 scenarios
RGB component
Seasonal growth
pattern &Annual
composite
AQ
FRP(MODIS)
Urban morphology
maps
Area growth
functions Fi:f1(Residential),f2(Commercial), f3(Industry), f4(Brick Kiln), f5(Agrofire)
Residential area
Commercial area
Industrial area
Brick factory
Agro areas
Nightlight(VIIRS)
Spatial weight Wi:IDW, Patchiness, built-up density
AQ&LULC socio-economic growth model
AQ= f(ECi . Fi . Wi)
Granger causality, EKC
Emission
coefficient ECi
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2. Urban morphology affects air quality
Livemnt, 2011
19
Residential area (Source:Panoramio) Commerial area(Source:Flickr)
Industrial area(Source: TopCem)
Clark et al2011, Marquez & Smith,1999, Bertaud, 2009;
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Processing step
Flowchart20
nDSM
DEM (ALOS AW3D)
DTM
Built nDSM
LANDSAT8 VIIRS DNB (NL)
Urban Morphology
MSD Filtering(Perko, 2015)
Select Built pixels
Rule-based classifier:1. Residential2. Commercial3. Industrial
Classification
Study Location
Morphological erosion
ISRS, 2017
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Datasets Used
Landsat8 OLI
30m
Oct’13-Oct’14
21
DSM
Resolution
Acquisition
Platform
Accuracy
AW3D
1’’ (30m)
~2010-11
ALOS PRISM
5m (Tadono2015)
Other data
Resolution
Acquisition
VIIRS DNB
15’’ (450m)
Jan’14-Dec’14
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‘urban core’ of Kanpur city22
VIIRS DNB upsampled to
30m form 450m imageVIIRS DNB Eroded image
and binary (>=2
Watt/cm2/sr)
Assumption: Commercial building only within ‘core’
Morphological
erosion
(Kernel = 50)
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h and NL rule classifier
Height Nightlight Core Class
0 0 0 Residence
1 0 0 Industry
1 1 0 Industry
0 0 1 Residence
0 1 1 Unsure
1 0 1 Industry
1 1 1 Commercial
23NL’ = f(h) = 3.16ln(h) + 16.62
Thresholds:
height > = Municipal limit
NL >= NL’
Core = 1
Commercial
Industrial
Un
sure
AW3D
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24
Commercial
Industrial
Residential
Behera et al, 2011
Urban morphological
map – Kanpur city
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25
Commercial
Industrial
Residential
FALSE
TRUE
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Next Step-
Identifying
Brick Factories:
26
LANDSAT-8
PALSAR2 Google Earth
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Future direction27
• Urban morphology ~2000 (ASTER)
• Agricultural biomass burning (FRP), roadside dust
• Estimate emission factors and inventory
• City compactness:
• CDR and mobility (Call data records)
• Citizen Science + Crowd Sourcing
Borrego, 2004
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Conclusion28
• Incorporate LCLUC in socio-economic model
• AirRGB decomposition can indicate anthropogenic
aerosol from MODIS
• Urban morphology can be identified from AW3D and
Nightlight
• Open and free data
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Thank you for your kind attention
29
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Brick factory
identification30
LANDSAT-8
PALSAR2 Google Earth
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Worsening air quality in urban regions
AOD ANG NO2 SO2
Summer Rain Winter Misra & Takeuchi, 2015
312001-2016 2004-2016
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Conclusions To manage, measure. Remote sensing datasets.
Rise in urban air pollution
Statistically, industries and construction are causes
Strong correlations with built-up urban area
Next steps:
Technology dependent emission coefficients
Use as a policy variable for future emission
Estimate future scenarios
32
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Correlation and Causality TestEconomic sectors
• Agriculture and allied activities
• Industry
– Manufacturing
– Construction,
– Public utilities, Others
• Services
– Transport, Railways, Trade and hotels, Banking/Insurance, Communication, Real estate , Public administration
Misra & Takeuchi, 2016
33