Overview of the delhi Air Quality forecasting system
1. Delhi 2010 and beyond
Demonstration of the Air Pollution
Modeling System
Dr. Sarath Guttikunda, Dr. Giuseppe Calori
& Dr. Benjamin Guinot
New Delhi, India
May 2010
ARIA Technologies SA LEOSPHERE
8/10, Rue de la Ferme, 92100 Boulogne Billancourt - FRANCE Bâtiment 503, Centre scientifique d'Orsay
Phone : +33 (0)1 46 08 68 60, –Fax : +33 (0)1 41 41 93 17 Plateau du Moulon, 91400 Orsay – FRANCE
e-mail : info@aria.fr, web: http:/ /www.aria.fr Phone: +33 (0)1 69 35 88 17 - Fax: + 33 (0)1 69 35 87 11
e-mail: info@leosphere.fr, web: http:/ /www.leosphere.com
2. Program Objectives
An Air Quality Forecasting System for Delhi
To be operational for Commonwealth Games, October, 2010
And beyond
A strategy to dessiminate information to national and local
agencies, media, and public
• Products customized for web
(AQ indexes, Google maps & forecasts)
• Time series for hot spots
• Customized spatial maps
2
3. Program Partners
Project is supported and financed by the French Ministry of Economy,
Industry & Employment of the Government of France under
Indo-French Technical Assistance Programme (FASEP)
Indian Counterpart French consortium
3
4. Program Components
• A 3D-nested air pollution
modeling system
• With support from the local
monitoring and a unique
aerosol LIDAR network
• Multi-pollutant analysis
including PM, SO2, NOx, CO,
HCs, & Ozone
• Dissemination of results
• Training
4
6. Air Quality Modeling System
Components
• A 3D chemical transport
eulerian air pollution
modeling system
• 4 Nests from National Scale
@ 64km to Urban Scale @
1 km (covering ~3000km
and 50km respectively)
• Multi-pollutant analysis
including PM, SO2, NOx, CO,
HCs, & Ozone
• Boundary conditions for
NEST 1 are from MOZART
6
15. LIDAR Configurations – Phase 2
Nest 4 During the Games
Tentative locations
DU
CWG village
Siri Fort SC
??
Seeking further
cooperation with
local institutions
to support LIDAR
50 km operations
17. Modeling Architecture
Synoptic meteorological
forecast (NCEP)
Gap
WRF SurfPRO
Geographic data
Meteorological input
4670000
4660000
Emission input
4650000
4640000
4630000
4620000
760000 780000 800000 820000
EMMA CHIMERE/
Emission inventories FARM
LIDAR Inputs
for calibration
pollution fields 17
18. Simulation Times
WRF: 8 processors
CHIMERE: 4 processors
FARM: 6 processors
54 hours of simulation from 12 UT (day 0) to 18 UT (day+2)
CHIMERE
CHIMERE FARM
FARM
NCEP download
NCEP download WRF
WRF (2 domains)
(2 domains) (2 domains)
(2 domains)
END
1hr 3hr 2hr 4hr
13:00 GMT 14:00 GMT 19:00 GMT 23:00 GMT
18:30hr local time 00h00 local time 04h30 local time
19. Input Data - Meteorology
WRF
Meteorological forecast for 48 hrs for 4 Nests
~ computational time 5 hrs
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21. 1. Multi-source & Multipollutant inventory
Meteorological outputs Meteorological Data
from WRF Processing
Normal Meteorological Variables:
Dust & Sea salt wind velocities, temperature, pressure,
Emissions relative humidity, precipitation, cloud
Biomass Emissions cover, etc
Biogenic Emissions
CHIMERE/FARM – Eulerian
Large point Emissions Chemical Transport Model – Post
sources Processing Analysis
coupled for aerosols
Volcanic Emissions
Anthropogenic Area Emissions
22. Input Data - Emissions
1. Global and Regional Inventories
Organization of Emissions Data
anthropogenic
RETRO
Point
National
(23 countries)
Regional
(94 regions)
Urban
(22 cities) (355 sources)
REAS
EDGAR
1° x 1° down to Lat/
1 km x 1 km long direct
Historical: Current: Projections:
75-95 90-00 95-30 BB
(5-yr) (1-yr) (5-yr)
Gridded Dust
emissions
Species: SO 2 BC NH 3
NO x PM 10 OC
SO 2, NO x CO PM 2.5
NMVOC
23. •
•
•
Road Network
Administrative
Point Sources
•
•
•
GIS Systems Surveys
24. • http://www.jamstec.go.jp/frsgc/research/d4/emission.htm
• Developped by the Frontier Research Center for
Global Change
• 0.5 degrees resolution
• NOx, SO2, CO, CO2, N2O, NH3, BC, OC, CH4, NMVOC
• Reference years 1995 - 2000
• Prospective inventories 2004-2009
• Past and future inventories (1980 - 2020)
26. Input Data - Emissions
1. New National Scale Inventories
4000000
3500000
3000000
2500000
21 Urban
2000000 20 Water and Land Mixtures
19 Forest/Field Mosaic
18 Mixed Forest
17 Deciduous Shrubs
16 Evergreen Shrubs
15 Ocean
14 Inland Water
13 Bogs and Marshes
1500000 12 Ice Caps and Glaciers
11 Semidesert
10 Irrigated Crops
9 Tundra
8 Desert
7 Tall Grass
6 Evergreen Broadleaf Trees
1000000 5 Deciduous Broadleaf Trees
4 Deciduous Needleleaf Trees
3 Evergreen Needleleaf Trees
2 Short Grass
1 Crops, Mixed Farming
500000
-1000000 -500000 0 500000 1000000 1500000
Power plants Landuse
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27. Input Data - Emissions
2. Emission Distribution Schemes
Population Urban Points
27
28. Input Data - Emissions
2. Emission Distribution Schemes
Non-urban Highways
Extents
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29. Input Data - Emissions
2. New National Scale Inventories
Domain 1
Domain 2
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30. Input Data - Emissions
3. Urban Scale Inventories
Industrial Population
layout by Wards
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31. Input Data - Emissions
3. Urban Scale Inventories
Wooded & Metro & rail
Green areas lines
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32. Input Data - Emissions
3. Urban Scale Inventories gridded
NH 1
NH 10
NH 24
Ghaziabad
CP
Outer Inner
Ring Ring
road road
India
Gate
Noida
NH 8
NH 2
Gurgaon
Faridabad
Dominant transport corridors
Traffic flow
survey data
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33. Input Data - Emissions
3. Urban Scale Inventories – Other sources
Summer Winter
Study results from PM2.5 source apportionment of measured samples
(2005, Chowdhary and Russell, et al.,)
Source apportionment & emissions inventory from CPCB, 2010
33
40. Next Steps
Finalizing inventories
Model configurations
LIDAR operations
Customizing outputs
Dissemination Strategy
Trainings
Seeking further cooperation with institutions to
support data collection 40
