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Scaling Down Air Pollution Modeling Tools for Urban Stakeholders
Dr. Sarath GuttikundaNew Delhi, India
NILUOslo, Norway; June 5th 2008
Contact: [email protected]: www.urbanemissions.info
Why Bother?
While air quality is important for health, it is also important for the economy
High proportion of cities in developing countries, especially Asian cities, have significant urban air quality problems
Why Bother?
Yet, the information base is often poor, fragmented, unorganized, and inaccessible
Managers of these cities often face a bewildering array of management options to analyze
Everyone has a better modeling mousetrap!
Places & People
• Cities are growing at the rate of 1 million people/week• Megacities (>10 m pop)
-Currently, 23 Megacities (on 2% of land)-36 Megacities expected by 2015(300 cities >1 m pop by 2025 + thousands of Secondary Cities)
From: BBCNews, June 1, 2006
732 m3,800 m
Urbanization is on the rise…
Visualization of Global GIS cities on Google Earth (Download Google Earth at http://earth.google.com and city information from http://geographynetwork.com )
Global Urban “Pin Cushion”
Source: Harvard University
Cities are growing…(e.g. Delhi)
Urban PM10 vs. Population
0
20
40
60
80
100
120
140
160
180
200
Beijing (China)
Chengdu (China)
Chongqing (China)
Jinan (China)
Shanghai (China)
Shenyang (China)
Taiyuan (China)
Tianjin (China)
Wuhan (China)
Jakarta (Indonesia)
Manila (Philippines)
Bangkok (Thailand)
Ahm
edabad (India)
Bangalore (India)
Kolkata (India)
Chennai (India)
Delhi (India)
Hyderabad (India)
Mum
bai (India)
Pune (India)
Tehran (Iran)
Cairo (Egypt)
Accra (G
hana)
Nairobi (K
enya)
Cordoba (Argentina)
Rio de Janeiro (Brazil)
Sao Paulo (Brazil)
Santiago (Chile)
Bogota (Colombia)
Mexico City (M
exico)
Caracas (Venezuela)
PM10
( g/
m3 ) i
n 20
02
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
Popu
latio
n (m
illio
ns) i
n 20
05
Source: PMSA Handbook, Guttikunda et al., 2008; data from World Development Indicators, 2006
Cities: A part of the problem
Urban Energy Demand Energy Production
Energy Demand and Pollution
Rising GHG levels
Waste Management
Urban transportation
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
No. o
f Veh
icle
s
MotorCycle
Car
Van
TukTuk
Truck
Taxi
HeavyDuty
Bus
Motorization is increasing…e.g. Bangkok
Source: Road Transport Statistics, Department of Land Transport
Bangkok Visibility Index
Source: Climatology Division, meteorology department, Thailand
0
2
4
6
8
10
12
14
16
18
20
Jan
-60
May
-61
Oct
-62
Feb
-64
Jul-
65N
ov-6
6A
pr-
68A
ug
-69
Dec
-70
May
-72
Sep
-73
Feb
-75
Jun
-76
Nov
-77
Mar
-79
Jul-
80D
ec-8
1A
pr-
83Se
p-8
4Ja
n-8
6Ju
n-8
7O
ct-8
8F
eb-9
0Ju
l-91
Nov
-92
Ap
r-94
Au
g-9
5D
ec-9
6M
ay-9
8Se
p-9
9F
eb-0
1
Vis
bili
ty (
km)
Cities: A part of the solution
urban air pollution
vector-borne disease
agricultural pesticides
industrial wastes
groundwater contamination
surface water pollution
indoor air pollution
Tropospheric ozone, aerosols, greenhouse
gases
Toxics, chemical pollutants
agricultural runoff
Concept of Integration
acid rain
soil erosion
A. A
char
ya
Impacts are Felt on Scales from Local to Global
Integrated Assessment of AirIntegration among
People, Places, Programs, Problems, & Pollution
Air we breathe
Pollutants, Clean air, Limits, Regulations
Quality
Ambient levels, Health, Visibility
Management of
Options - Policy, Technical, Economic, & Institutional
Polluted AirPolluted AirCleaner AirCleaner Air
Integrated Air Quality Management
Array of Management Options
Policy
Monitoring
Industrial Zoning
Residential Zoning
Compliance
Traffic Management
Public Transport
NMT
Landuse
Technical
Cleaner Technologies
Fuel Improvements
End of Pipe Control Devices
Cleaner Production
Economic
Taxes
Subsidies
Pricing
Charges
Fines
Tradable Permits
Institutional
Emission Standards
Fuel Standards
Energy Efficiency
Maintenance
Capacity Building
Compliance
Awareness
Waste Management
InfrastructureTransportation
Buildings & OperationsRenewable Energy
Examples of Management Options
Decision Making
Let the “Blame Games” begin..Sources are Many
Why not making a decision is a decision...
Environmental, Economic, and Social Benefits
Timing is important !!
Why not making a decision is a decision...
5001,0001,5002,0002,5003,0003,5004,0004,500
1985 1990
Annual Emissions (tons per year)
1995 2005 2010 2015 2020 2025 20302000 2035
Current ScenarioActing sooner
Timing is important !!
Why not making a decision is a decision...
5001,0001,5002,0002,5003,0003,5004,0004,500
1985 1990
Annual Emissions (tons per year)
1995 2005 2010 2015 2020 2025 20302000 2035
Current Scenario
Environmental, Economic, and Social Benefits
Acting sooner
Timing is important !!
Why not making a decision is a decision...
5001,0001,5002,0002,5003,0003,5004,0004,500
1985 1990
Annual Emissions (tons per year)
1995 2005 2010 2015 2020 2025 20302000 2035
Acting sooner
Environmental, Economic, and Social Benefits
Current Scenario
Air Pollution Modeling
Results are only as good as the databases – coverage, quality, consistency and access problems
Consensus on types of options to be considered
Evolving methodologies and tools for assessment
Involvement of multiple stakeholders and disciplines
Adequate reflection of political economy
Actual USE and constant updating of the systems developed
Limitations
Knowledge Base
Target Audience
Types of ModelsKnowledge Base
What can we do with what’s available?
What you have – Collate Data
Modeling Tools
Software and Modeling tools available Spreadsheets GIS Fortran or Visual basic based programs
Modeling Toolsfor Emissions and Dispersion
Energy and Emissions Analysis Tools RAINS, GAINS, MOBILE 6, IPIECA Toolkit, HEAT,
IVE, MARKAL, COPERT Dispersion Models
ADMS, ATMOS, ISC3, UAM-V, CMAQ, WRF-Chem Health Impacts Analysis
Ben Map, APHEBA Integrated Models
SIM-air, IDEAS, GAINS, DSS/IPC, Air-QUIS
Road Ahead..
Simple equations or complicated models – plenty available to use
Example Applications
Hyderabad, India
Top-Down Vs. Bottom-UpQualitative Comparison
Location Vehicles Veh + Road Dust Industries OWB+Dom SA M SA M SA M SA M
Punjagutta 54 ± 10 40 - 45 81 ± 10 66 - 70 13 ± 10 15 - 20 5 ± 10 4-6 Chikkadpally 45 ± 10 40 - 45 80 ± 10 60 - 66 15 ± 10 20 - 30 4 ± 10 4-6 HCU 43 ± 10 30 - 35 80 ± 10 50 - 60 16 ± 10 10 - 15 5 ± 10 8-10 Note: Top-Down is source apportionment (SA) and Bottom-Up is modeled (M)
Limitations to interpretations
• No distinction is made between diesel and fuel oil utilized by vehicles and industries.• Coal combustion between industries and domestic sector are not distinguished.• All types of dust – road and soil – are clubbed together. • Two of the sampling points for the source apportionment study are urban with most
vehicular activity.• Modeling results are annual average values and the source apportionment results are
averaged over the three sampling months.
Estimated Health Impacts in 2006
Estimated total cost of health impacts is US $241 million or Rs. 917 crores
Health Endpoint Number of Incurred Cases
Mortality 2,143 Adult Chronic Bronchitis 5,621 Child Acute Bronchitis 33,308 Respiratory Hospital .Admission 1,837 Cardiac Hospital Admission 765 Emergency Room Visit 36,032 Asthma Attacks 4,990,049 Restricted Activity Days 8,724,933 Respiratory Symptom Days 28,011,627
Proposed Action Plan
Improve road maintenance, dust control, and traffic management
Conversion of buses and auto rickshaws from diesel to alternate fuels (natural gas, biodiesel, LPG)
Increase public transport use from 40% to 60% (including adding new buses)
Introduction of new emissions standards for cars Phasing out old vehicles Improve dust collection facilities at industries and
energy efficiency Enforce laws against waste burning.
Co-Benefits for 2010
Largest benefits for Air Quality Sweeping of paved roads Increasing moisture content on paved roads 3-Wheelers to LPG Improving industrial efficiency at PM capture
Largest reductions of GHG emissions Gross polluters – goods vehicles and buses Inspection and maintenance Domestic cooking to LPG
Co-Benefits for 2010Estimated Reductions
2,119
888
847
236
PM10 (tons)
Promoting wet sweeping resulting in 20% reduction in silt loading on paved roads and increasing moisture content on unpaved roads by 5%
800,293Promoting public transport with an expected VKT reductions of 10% in cars, 20% in 2Ws, and 20% in 3 Ws
105,847Conversion of all Petrol based 3-Wheelers to LPG
109,494Conversion of 50% of in-use diesel public transport bus fleet to CNG
CO2 (tons)Intervention
Co-Benefits for 2010Estimated Reductions
3,283
1,457
125
70
PM10 (tons)
Improving dust collection efficiency at industrial sites by 10%
883,001Doubling the emission regulations of in-use diesel goods vehicles – light and heavy duty
92,897Inspection and maintenance of in-use vehicles –5% improvement in the deterioration rates
55,851Replacement of 50% of in-use diesel public transport bus fleet with newer diesel buses
CO2 (tons)Intervention
Co-Benefits for 2010Estimated Overall Percent Reductions
5.8Wet & vacuum sweeping8.52.4Public transport1.12.33Ws Petrol to LPG1.20.650% buses old diesel to CNG
8.94.00.40.2
PM10 (%)
Dust collection efficiency at industries9.4Emission regulations for GVs1.0I & M0.650% buses old diesel to new diesel
CO2 (%)Intervention
Co-Benefits for 2010Estimated Reductions
Combined Reductions of all interventions PM10 = 9,025 tons (25%) CO2 = 2,047,348 tons (22%)
Combined reductions from vehicles (direct) PM10 = 3,622 tons (33%) CO2 = 2,047,348 tons (25%)
Combined reductions in industrial sector PM10 = 3,283 tons (23%)
Handbook on Particulate Pollution
Source Apportionment
#S#S
#S#S #S#S
#S#S#S #S
#S#S
#S#S
#S#S
#S
#S
#S
#SLima
Cairo
DhakaMumbai
ManilaBogota Bangkok
Cordoba
Sao Paulo Qalabotjha
Mexico City
BeijingTianjin
Chengdu
Kolkata
Shanghai
Santiago
New DelhiChandigarh
Buenos Aires
Guttikunda and Johnson, The World Bank, 2008
Source Apportionment of Urban Air Pollution
EstimatedSourceImpacts
EstimatedSourceImpacts
AmbientCharacteristics
AmbientCharacteristics
SourceCharacteristics
SourceCharacteristics
Receptor ModelReceptor Model
Locates monitoring sites with critical pollutant levels
Identifies chemical composition of the PM
Describes source impact estimates
Documents primary and secondary PM
Identifies sources would be most effective to control
Top Down
Receptor Modeling Framework
Transport Contribution =
Assumed
Gasoline + Diesel
Chan
diga
rhN
ew D
elhi
Mum
bai
Kolk
ata
*-24-*-*
35-69-64-24
28-*-22-26
22-24-19-23
Spr-Sum-Aut-Win
Geogia Tech (USA), 2004
India Case Studies (2001 PM2.5)
Summary of PMSA Studies
Guttikunda and Johnson, The World Bank, 2008
Urban Emissions Inventories
Mexico City, 1998
Industries15%
Commer5%Biogenic
43%
Transport37%
Santiago, 2000 (PM2.5)
Point3%
Area9%
Transport16%
Fugitive Dust72%
Sao Paulo, 2002
Transport40%
Industry47%
Road Dust13%
Lima, 2000
Transport30%
Stationary20%
Fugitive50%
Bangkok, 1998
Industries34%PP
12%
Resuspension33%
Building Const
3%
Transport18%
Shanghai, 2005
PP33%
Industry40%
Commer6%
Residen2%
Transport12% Agri
7%
Kathmandu, 2001
Domestic28%
Industry46%
Transport9%
Road Dust17%
Greater Mumbai, 2001
Vehicles11%
Area Sources
24%
Building Const
3%
Road Const
2%
Industries60%
Guttikunda and Johnson, The World Bank, 2008
Ulaanbaatar, Mongolia
Challenges
Harmonization of scattered and conflicting data
Evolution of tools and methodologies Problems in adequately reflecting political
economy of decision-making
Dispersion in Winter…
Traffic
0
35,000
70,000
105,000
140,000
175,000
210,000
245,000
280,000
315,000
350,000
385,000
1930
19351940
19441950
19561960
19651970
19751980
19851990
19952000
20012002
20032004
20052006
20102015
2020
Total Number of Householdsin Ulaanbaatar
4% Growth Rate
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1960
1966
1970
1975
1980
1985
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
-10%
0%
10%
20%
30%
40%
50%Total VehiclesGrowth Rate
Local Statistics
Traffic Congestion
0
10,000
20,000
30,000
40,000
50,000
60,000
192519301933193619571960196619701975198019851990199119921993199419951996199719981999200020012002200320042005
0
50
100
150
200
250
300
350
400
450Total Passenger VehiclesImproved Road, km
Introduction of Public Transport
Vehicular Population in Ulaanbaatar
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1980
1985
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
OtherPublic TransportPrivate Vehicles
Air Quality Management
Bureau
National AQ Council
(with MNE)
Secretary of AQMB
Specialized Organizations
AQ Division of UB
AQ Divisions for
Provinces
CLEM
Ozone
Monitoring
ICC
Inst. Of Meteorology and Hydrology
Bangkok, Thailand
Guttikunda, IDEAS Coordinator, The World Bank, Thailand, 2006
Used by heavy duty traffic Significant emitter of fine particulates, SOx, NOx (secondary PM) associated health impacts
Diesel vehicles have a long life time delays introduction of latest technology
Black smoke major visible nuisance
Summary of BKK DIESEL Results
10 20 30 10 20 30 10 20 30
HC 0.52 0.32 0.25 2.35 1.31 0.93 1.46 0.93 0.71
CO 1.88 1.32 1.07 10.17 6.59 5.11 13.12 10.35 9.02
NOx 2.97 2.34 2.03 19.68 12.00 8.98 15.02 10.44 8.44
CO2 414.74 317.95 272.17 1299.06 843.09 654.71 1163.51 921.26 803.67
PM 216.18 187.54 172.59 1319.11 962.30 800.18 2445.90 1859.44 1583.94
HC 0.36 0.24 0.20 1.81 1.10 0.82 1.65 1.18 0.96
CO 1.51 1.09 0.90 17.40 16.02 15.26 4.24 3.46 3.08
NOx 3.37 2.60 2.24 22.45 13.30 9.80 14.24 10.88 9.30
CO2 409.56 322.43 280.32 1317.69 999.87 850.79 1185.70 980.67 877.59
PM 153.14 155.50 156.90 1928.59 1759.84 1668.06 933.68 880.38 850.62
HC 0.34 0.21 0.16 0.85 0.46 0.32 1.83 1.22 0.97
CO 1.83 1.08 0.79 18.21 15.42 13.99 4.24 3.46 3.08
NOx 2.87 2.23 1.93 19.68 12.00 8.98 15.02 10.44 8.44
CO2 437.58 342.34 296.56 1789.18 1154.76 893.83 1401.33 1127.40 992.71
PM 169.94 166.20 164.05 835.09 620.84 522.00 1283.03 925.60 764.66
HC 0.27 0.19 0.16 1.83 1.05 0.76 0.83 0.55 0.43
CO 1.70 1.37 1.21 6.36 3.72 2.72 5.40 3.61 2.85
NOx 1.45 1.14 0.98 13.50 9.47 7.70 15.07 10.03 7.91
CO2 420.66 342.25 303.35 1474.90 1038.44 845.75 1438.36 1009.98 821.27
PM 138.24 145.44 149.83 1116.67 982.14 911.09 447.67 410.40 390.06
1997
-99
1997
-200
0
1997
-200
0
afte
r 20
00
afte
r 20
01
afte
r 20
01
Pre-
1995
Pre-
1995
1994
-96
1996
-97
1996
-97
Average Speed
Heavy Duty Buses
Average Speed
Heavy Duty TrucksLight Duty Vehicles
Average SpeedPr
e-19
94
Program details @ http://www.pcd.go.th/info_serv/en_air_diesel.html
Program final report (April’08)@ http://www.cleanairnet.org/caiasia/1412/article-72628.html