scaling down air pollution modeling tools for urban

Scaling Down Air Pollution Modeling Tools for Urban Stakeholders

Dr. Sarath GuttikundaNew Delhi, India

NILUOslo, Norway; June 5th 2008

Contact: sguttikunda@gmail.comWebsite: 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