update on asian studies related to emissions and modeling
Post on 02-Feb-2016
41 Views
Preview:
DESCRIPTION
TRANSCRIPT
Update on Asian Studies Related to Emissions and Modeling
Gregory R. Carmichael
Department of Chemical & Biochemical Engineering
Center for Global & Regional Environmental Research and the
University of Iowa
Topics Reported
• Recent advances in emissions estimates
• Long-term sulfur deposition calculation
• Update on model intercomparison study
• Completed passive sampler pilot project
Sources of airborne pollution in Asia are many: home cooking, power generation, industry, traffic, and biomass burning
Methodology for Asian Emission Estimates
Methodology for Asian Emission Estimates
Energy Use
RAINS-AsiaModel
EmissionControls
Activitydata
Other human activities
Biomassburning
Natural emissions
Biogenic, Volcanic...Emission
factors, Regulations
Anthropogenic emissions
“Total” emissions
Organization of emissions dataOrganization of emissions data
National (23 countries)
Regional (94 regions)
Urban (22 cities)
Point (355 sources)
Historical:75-95(5-yr)
Current:90-00(1-yr)
Projections:95-30(5-yr)
TRACE-P Species for year 2000:
SO2, NOx, CO2, CO, CH4, NMVOC (19 classes), BC, OC, NH3
1° x 1° down to1 km x 1 km
Lat/long
Griddedemissions
The TRACE-P/Ace-Asia emission inventory shows the important sources of each type of
air pollutant in Asia
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
SO2 NOx CO2 CO CH4 NMVOC BC OC NH3
BiomassburningOther
Agriculture
PowergenerationTransportation
Residential
Industry
Seasonality of emissions of the different species has been studied for China
Seasonality by Species
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
AugSep
tOct
NovDec Ja
nFeb M
arApril
May
June Ju
ly
Fra
ctio
n o
f A
nn
ual
Em
issi
on
s BC
CO
NMVOC
SO2
NOx
Seasonality by Species
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
Fra
ctio
n o
f A
nn
ual
Em
issi
on
s
NH3
CH4
Mainly combustion-related species
Mainly evaporation-related species
This is an example of TRACE-P SO2 emissions at 30 sec. resolution for the Pearl River Delta
Guangzhou
Macau/Zhuhai
Shenzhen
Hong Kong
Zhongshan
Boxed area ~ 150 km x 150 km
30 sec = ~ 0.7 km
Biomass burning treatment combines country survey data with AVHRR analysis to give long-term average or
“typical” burning amounts
Comparison of country surveys with various AVHRR fire-count adjustments reveals problem areas for further investigation
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
sig
ma
Xinjiang
Mongolia
IndonesiaVietnam
fire count > country surveys
fire count < country surveys
India
It remains difficult to make the linkbetween satellite observations
of fire and atmospheric emissions
The Importance of Fossil, Biofuels and Open Burning Varies by Region
Uncertainty analysis has revealed wide differencesin our knowledge of the emissions of particular
species in particular parts of Asia …
0%
100%
200%
300%
400%
500%
600%
700%
800%
900%
China Japan Other EastAsia
SoutheastAsia
India OtherSouth Asia
Ships All Asia
(95%
Con
fiden
ce In
terva
l,
? )SO2
NOx
CO2
CO
CH4
VOC
BC
OC
NH3
Distribution of Percent Primary Non-carbonaceous PM2.5 Emissions --How to distinguish from dust?
Pink – DUST; Brown-Blue BC
R o n B r o w no b s e r v e d h i g h A O D
i n J a p a n S e a
1 4
1 2
1 0
8
6
4
2
0
El Al
& C
a (u
g/m
3)
1 0 51 0 09 59 08 58 0
J d a y ( UTC)
4 0 0
3 0 0
2 0 0
1 0 0
0
DUST_CFORS (ug/m3)
t o t El Al 'DUST- f i n e _ CFORS' 'DUST- a l l _ CFORS ' t o t El Ca
T a k l a m a k a n
G o b i
L o g 1 0 ( D u s t )
A O D
O b s e r v e d A l a n d m o d e l d u s t
L i d a r E x t .O f R o n B r o w n= 0 . 2 / k m
R e c e n t E x p e r i m e n t s ( T r a c e - P , A c e - A s i a ) a r e P r o v i d i n g D a t a t o T e s t / I m p r o v e D u s t M o d e l i n g / P r o c e s s e s
E.Q.
N30
E120E90Rishiri
OkinawaFukuoka
Beijing
Nagasaki& Fukue
E150
Harbin
Amami
TsukubaSado
Shanghai
Hachijo
Ogasawara
Tarukawa
Qingdao
6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0
- 1 0
0
1 0
2 0
3 0
4 0
5 0
Area sources(ton/yr)
18 to 500
500 to 1000
1000 to 5000
5000 to 10000
10000 to 15000
15000 to 30000
30000 to 100000
100000 to 833600
SO2 em issions - RAINS-ASIA 1995 & FSU 1990
1 0
6 0 0 0 0 0
LPS (ton/yr)
-100%
0%
100%
200%
300%
400%
500%
1975 1980 1985 1990 1995 2000
Year
Emis
sion
% c
hang
e
P.R. China
India
Japan
Malaysia
Pakistan
Republic of Korea
Singapore
Sea lanes
SULFUR EMISSIONS IN ASIA ARE CHANGING,
REGION BY REGION, IN DIFFERENT WAYS
We have analyzed sulfur deposition in Asia over last 25 yrs
Uncertainty in the Source-Receptor Relationships
0
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90 100
Contribution (%)
Variability
(%
)
INDI -> LAOS
VOLC -> INDO
KORN -> KORS
CHIN -> J APA
THAI -> CAMB
INDI -> BANG
INDI -> MYAN
FSU -> MONGCHIN -> CAMB
INDI -> BHUT
INDI -> INDI
CHIN -> CAMB
Interannual Variability (10-yrs)
Effect of Interannual Meteorological Variability on Sulfur Deposition can be Much Larger than
Changes in Emissions!
China
-30%-20%-10%
0%10%20%30%
85 90 91 92 93 94 95 96 97 98 99
Year
Japan
-30%-20%-10%
0%10%20%30%
85 90 91 92 93 94 95 96 97 98 99
Year
Is there climate feedback link?
•Need to assess inter-model variability.•The MICS-ASIA Study: Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia
How Robust Are The Source-Receptor Relationships?
Many different models with important similarities and differences:Lagrangian, Eulerian, Hybrid, etc.
Participant(s) Organization Model name Model type
Main model focus
S.-B. Kim,T.-Y. Lee, K-Y. Ma
Dept of Atmospheric Sciences, Yonsei University, Seoul (Korea)
YU-SADM (Yonsei Univ.-
Sulfur Acid Dep. Model)
3D Eulerian
Long-term period or episodic estimation of sulfur sources contributions
H. Hayami, Y. Ichikawa
CRIEPI (Japan) CRIEPI trajectory model
Lagrangian
1-layer
Long-term evaluation
H. Hayami, O. Hertel,Y. Ichikawa
CRIEPI (Japan) and National Env. Research Institute (Denmark)
ACDEP ASIA Lagrangian
1-layer
Long-term evaluation
I. Uno,E.S. Jang
Research Institute for Applied Mech., Kyushu Univ, Fukuoka (Japan)
RIAM version of RAMS on-line tracer model
3D Eulerian
Episodic and long-term simulation for chemical climate studies
Y. Ikeda, R. Yasuda, H. Nakaminami
Osaka Prefecture University (Japan)
OPU-Model (Osaka
Prefecture Univ.)
3D Eulerian
Long-term deposition
S.Y. Cho, G. Carmichael
CGRER, University of Iowa
STEM 3D Eulerian
Episodic and long-term studies
G. Calori, G. Carmichael
CGRER, University of Iowa
ATMOS-2 Lagrangian
multi-layer
Long-term conc. and depositions of sulfur in Asia. Source-receptor relationships.
M. Engardt Swedish Meteorological and Hydrological Institute
MATCH 3D Eulerian
Long-term concentrations and depositions of ozone and acidifying substances
Uncertainty in the Source-Receptor Relationships
Receptor 4 - Komae (Japan)
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8
NW ASIA
SE ASIA
TAIWAN
S CHINA
C-E CHINA
NE CHINA
N & S KOREA
JAPAN
Inter-model Variability
Receptor 12 - Yangyang (Korea)
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8
Receptor 16 - Nanjing (China)
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8
Passive Sampler Network – one way to increase coverage and participation
SO2 Results – Annual Averages
NH3 Results – Annual Averages
Dha
ngad
iB
aniz
oum
bou
Kal
iman
tan
Buk
it K
otot
aban
gT
aman
rass
etL
amto
Mt.
Ken
yaC
hian
g M
aiM
t.Sto
. Tom
asL
uang
Pra
bang
Ber
ham
pur
Wal
igua
n M
ount
ain
Nag
arko
tN
akho
n S
ri T
ham
mar
atS
avan
nake
thB
hube
nesw
arS
hui-
Li
Cap
e po
int
Coc
hin
Cap
e D
'Aeq
uier
Sha
ng D
ian
Zhi
Mar
capo
mac
ocha
Lin
An
Ela
ndsf
onte
in
0
2 0
4 0
6 0
8 0
1 0 0
NH
3/S
O2
ratio
0.01
0.1
1
10
100
0.01 0.1 1 10 100
SO2(ppb)
NH3(ppb)
NH3/SO2 Ratios – Annual Averages
O3 Results – Annual Averages
http://www.cgrer.uiowa.edu/people/carmichael/GURME/GURME.html
On-going Activities• Continued needs to improve emission estimates• Follow-on activities related to model inter-
comparison• Interest in expanding base-line measurements –
e.g., ABC study• Closer linkages between urban/regional/global
scales and health,air quality, climate communities• WMO-GURME
T he U n iversity o f Iow a, U S A
A ir Q u ality
ControlStrategies
ControlStrategies
EmissionsDistribution
EmissionsDistribution
Air Q ualityModel
Air Q ualityModel
Pollutant Distribution
Pollutant Distribution
MeteorologyMeteorology
AtmosphericChemistry
AtmosphericChemistry
Air Q uality I mpacts• health and welf are• secondary impacts• population exposure
Air Q uality I mpacts• health and welf are• secondary impacts• population exposure
Air Q uality Goals• technical f easibility• economic issues• robustness
Air Q uality Goals• technical f easibility• economic issues• robustness
Climate : Air Quality
Analysis Framework
The University of Iowa, USA
Characterization of Urban Signals
Science Support to Policy
UnderstandingUnderstandingUnderstanding
Field Experiments
Field Field ExperimentsExperiments
Long-termMonitoring
LongLong-- termtermMonitoringMonitoring
Satellites &Data Systems
Satellites &Satellites &Data Systems Data Systems
Regional and Global Simulations
Regional and Global Regional and Global SimulationsSimulations
PollutionPrediction
PollutionPollutionPredictionPrediction
PollutionDetection
PollutionPollutionDetectionDetection
Enhanced Enhanced Quality Quality of Lifeof Life
InformedInformedPolicyPolicy
DecisionsDecisions
ProcessProcessStudiesStudies UnderstandingUnderstandingUnderstanding
Field Experiments
Field Field ExperimentsExperiments
Long-termMonitoring
LongLong-- termtermMonitoringMonitoring
Satellites &Data Systems
Satellites &Satellites &Data Systems Data Systems
Regional and Global Simulations
Regional and Global Regional and Global SimulationsSimulations
PollutionPrediction
PollutionPollutionPredictionPrediction
PollutionDetection
PollutionPollutionDetectionDetection
Enhanced Enhanced Quality Quality of Lifeof Life
InformedInformedPolicyPolicy
DecisionsDecisions
ProcessProcessStudiesStudies
46
33
26
1915
35
3 2
17
0
5
10
15
20
25
30
35
40
45
50
5-6 6-7 7-8 8-9 9-10 10-12 12-15 15-20 20-30 30-40
Population in millions
No
of
gri
d c
ells
Urban Environments are Key Drivers of Change
5
1 0
2 0
3 0
4 0
5 0
6 0
6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0- 2 0
- 1 0
0
1 0
2 0
3 0
4 0
5 0
Percentage contribution to total sulfur deposition due to SO2
emissions from megacities and major urban centers in Asia,
averaged over the period of 1975-2000
Estimated Source-Receptor Relations for Nitrate
Variation in Aerosols in Asian Outflow
has Strong Latitudinal Gradient
Propane data from Blake et al.
Biomass burning treatment combines country survey data with AVHRR analysis to give long-term
average or “typical” burning amounts
Original two-year daily fire count data for 1999-2000
Nitrate Deposition is of Growing Importance
% of Total Deposition as Nitrate
Speciation of NMVOC reveals great differencesamong countries with different levels of
economic developmentNMVOC Fractions
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Species
Fracti
on
of
tota
l
China India
Laos Japan
SO2 concentrations - 11-20 January
0
5
10
15
20
ppb
SO4 concentrations - 11-20 January
0
5
10
15
20
ug/m
3
SO4 wet depositions - 11-20 January
1
10
100
1000
ug/m
2
Comparison with monitoring data for the 11-20 January period.
Hourly profile
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
1 3 5 7 9 11 13 15 17 19 21 23
Layer 3
Layer 2
Layer 1
Weekly profile
0.06
0.08
0.10
0.12
0.14
0.16
0.18
MON TUE WED THU FRI SAT SUN
Layer 3
Layer 2
Layer 1
… and converted them into appropriate profiles for China for the three different emission layers (both
January and July)
Transboundary Pollution Issues are of Growing Importance
Japan
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1975 1980 1985 1990 1995 2000
Year
De
po
sitio
n c
on
trib
utio
ns
0
50
100
150
200
250
300
350
400
450
To
tal d
ep
. (G
g S
/yr)
JAPA
CHIN
VOLC
KORS
SEAL
Tot
S-Deposition
RAINS-Asia –Carmichael et al., (2001)
Transboundary Pollution Issues are of Growing Importance
Japan
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1975 1980 1985 1990 1995 2000
Year
De
po
sitio
n c
on
trib
utio
ns
0
50
100
150
200
250
300
350
400
450
To
tal d
ep
. (G
g S
/yr)
JAPA
CHIN
VOLC
KORS
SEAL
Tot
S-Deposition
RAINS-Asia –Carmichael et al., (2001)
The Emissions Vary Greatly by Region – Reflecting Many Social/Economic Factors
Hourly Profile
0
100
200
300
400
500
600
700
800
900
1000
HR1 HR5 HR9 HR13 HR17 HR21
Industry(point)
Resident/Agriculture/Industry(Area)Off-road vehicle
Powerplant
Urban local vehicles
Series6
Series7
Series8
Series9
Series10
Series11
Series12
Series13
Series14
Series15
Series16
We studied hourly profiles of emissions for different source types
…
Daily Profile
0
20
40
60
80
100
120
140
160
180
MON TUE WED THU FRI SAT SUN
Area
PP
Off-road Veh
Urban Veh
High way
Ind
… also daily profiles …
top related