Holistic Decision Modeling for Solid Waste Technology Options in Developing Countries - Appendices

8/7/2019 Holistic Decision Modeling for Solid Waste Technology Options in Developing Countries - Appendices

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APPENDIX 2.3 Field Survey Result

(Presentation Summary)

1. Amman, Jordan

2. Buenos Aires, Argentina

3. Conakry, Guinea

4. Kathmandu, Nepal

5. Lahore, Pakistan

6. Sarajevo, Bosnia And Herzegovina

7. Shanghai, China

8. Kawasaki, Japan

9. Atlanta, Georgia, USA


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1

1

City 1: Amman (Jordan)

2

Schedule, Meetings and Site Visit

Schedule:Nov. 14 Nov. 26

Visited Organizations for Meeting:GAM (Greater Amman Municipality), Jordan Biogas Co, ArabPaper Converting & Trading Co, Jordan Paper and CardboardFactories Co, Friends of Environment Society, Ministry ofEnvironment, Ministry of Municipal Affairs, Ministry of Health,Ministry of Planning and International Cooperation, UNHABITAT

Visited SWM Facilities:Al Ghabawy Landfill, Al Rusaifah Landfill and Biogas facility, AlShaer TS, Al Yarmook TS, Ain Gazal TS, Slaughterhouse,

3

City & SWM Profile

Capital City, 688 km2

Temperature: 26, Precipitation: 230 mm GNI per capita: US$2,500 (2005, WDI)

Population: 2,125,000 Total Generation: 2,174 tonnes/day Generation rate: 1.02 kg /person/day

4

Waste Composition

2%

42%

11%9%

16%

2%

2% 14%

2%

Yard Trim m ings, Leaves Food W aste

C orrugated C ardboard M ixed Paper

Plastic G lass

M etal Textile and R ubber

O thers


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2

5

Waste Flow in Amman

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling

* Incineration can be with or without energy recovery.

Composting

Compost Product

End Use

6

Summary of SWM System

Discharge: 1100 litre metal containers, some220 litre plastic containers, street sweepers

Collection: High Rate, Municipality Collects, HighFrequency, Mixed Collection

Transfer: Three Transfer Stations

Disposal: Landfill with small scale MRF

Others: Gas recovery and Biogas digesters atold landfill with Electricity Generation

7

Collection

8

Transfer


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3

9

Disposal

10

Alternatives


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1

1

City 2: Buenos Aires (Argentina)

2


Schedule:Sep. 24 Oct. 6

Visited Organizations for Meeting:CBA, WB, ARS (Solid Waste Association), CEAMSE(Metropolitan Area Ecologic Coordination Society), AIDIS (Inter-americanAssociation of Sanitary Engineers), UBA, Cliba, IATASA, El

Ceibo Visited SWM Facilities:

Pompeya T/S, Relleno Sanitario Norte III,Relleno Sanitario Villa Dominico

3

City & SWM Profile

Capital City, 203 km2 (CBA), 4,758 km2 (GBA) Temperature: 17, Precipitation: 1,200 mm GNI per capita: US$4,470 (2005, WDI)

Population: 2,776,138 (2001), nearly 3 million inCBA in 2006, 12.4 million (2001) in GBA Total Generation: 4,300 tonnes/day (CBA),

13,617 tonnes/day Generation rate: 0.979 kg /person/day

4

Waste Composition

5% 1%

24%

14%1%35%

4%

3% 2% 4%1%6%

Yard Trim m ings, Leaves W oods

Paper Plastic

Rubber, Leather Food W aste

Diapers M etal

G lass Dem olition

Hazardous M isc.


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2

5

Waste Flow in Buenos Aires

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6


Discharge: Container, on-the-Ground, PlasticBags

Collection: High Rate, Private Contractor, HighFrequency, few Segregated Collection

Transfer: Three Transfer Stations Disposal: Landfill with small scale MRF &

Composting Others: Gas Recovery at Landfill, Beatification

Inspection, Cartonaros (Recycling)

7

Discharge

8

Collection


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3

9

Transfer Haul

10

Disposal

11

Landfill Gas Collection

12

Alternatives (compost)


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4

13

Alternatives (MRF)

14

Alternatives (Community Based Recycling)


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1

1

City 3: Conakry (Guinea)

2


Schedule:Dec. 6 Dec. 18

Visited Organizations for Meeting:SPTD (Service Public de Transfert des Dechets),PDU3 (3rd Urban development Project), Guinenned'Assainissement (SME), Electricit de Guine,Ministry of Public Health

Visited SWM Facilities:La Minire Landfill, Transfer points, Pilot compost,Slaughterhouse

3

City & SWM Profile

Capital City, Temperature: 27, Precipitation: 4,293 mm GNI per capita: US$ 370 (2005, WDI)

Population: 2,000,000 Total Generation: 800 tonnes/day Generation rate: 0.4 kg /person/day

4

Waste Composition

8%

40%

19%5%

3%

1%

12%5% 7%

Y ard W aste Food W astePaper PlasticsM etal G lass

Textile & Rubber Leather & Anim al M anureO thers


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2

5

Waste Flow in Conakry

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling(informal)


Composting(pilot)

Compost Product

End Use

6


Discharge: on-the-ground, in non standardcontainers within properties

Pre-Collection: Low Rate, SMEs (Small &Medium-sized Enterprises), High Frequency,Mixed Collection

Collection/Transfer: Low Rate, MunicipalityCollects, High Frequency, Mixed Collection

Transfer: 62 transfer points (Arm-roll containers) Disposal: Landfill with informal recycling & pilot

composting

7

Pre-collection

8

Collection


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3

9

Disposal

10

Alternatives


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1

1

City 4: Kathmandu (Nepal)

2


Schedule:

Dec. 11 Dec. 23

Visited Organizations for Meeting:

SWMRMC, KMC, LSMC, BKM, MTM, KRM,SchEMS, Watsan

Visited SWM Facilities:Teku T/S, Sisdol Landfill, BKMs SegregatedCollection and Compost

3

City & SWM Profile

Capital City, 580Km2 (Valley) Temperature: 13, Precipitation: 2,000 mm GNI per capita: US$ 270 (2005, WDI)

Population: 1,099,158 (5 municipalities total,estimated for 2004) Total Generation: 434.9 tonnes/day (5

municipality, 2004 estimate) Generation rate: 0.4 kg /person/day (5

municipality average, 2004)

4

Waste Composition

3% 12%8%

3%

0%

71%

0%

1%0%

2%

Yard Trim m ings, Leaves Paper

P lastic Textile

Rubber, Leather Food W aste

M etal G lass

C eram ics M isc.


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2

5

Waste Flow in Kathmandu

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6



Collection: Mid Rate, Door to Door (PrivateCompany, NGO by Tri-cycle), Bell Collection,few Segregated Collection

Transfer: One Transfer Station

Disposal: Landfill, Open Dumping, Composting(very small scale; windrow, vermi, home-composting)

Others: Long transportation haul

7

Discharge

8

Collection


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3

9

Transfer Haul

10

Disposal (Sisdol Landfill)

11

Disposal (Open Dump)

12

Alternatives (compost)


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4

13

Alternatives (Vermi Composting)

14

Alternatives (Recycling)


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1

1

City 5: Lahore (Pakistan)

2


Schedule:

Nov. 25 Dec. 10


The Urban Unit, CDGL, University ofEngineering and Technology

Visited SWM Facilities:Mahamood Booti LF, Saggian D/S, NashitarD/S, CDGLs workshops, Lahore Composting,Waste Busters, Children Hospital

3

City & SWM Profile

Capital City of Punjab, 1,772 Km2

Temperature: 20, Precipitation: 500 mm GNI per capita: US$ 690 (2005, WDI)

Population: 8,000,000 (2006, estimate) Total Generation: 5,200 tonnes/day Generation rate: 0.65 kg /person/day (2006,

estimate)

4

Waste Composition

28%

19%4%9%

1%

2%

1%

0%

25%0%

11%

F ood / K itchen L eaves & G rass, S traw & W ood

P aper P lastic & R ubber & P olyethylene B ags

C lothes/ Rags Bones

Anim al W aste G lass

M etal Dust, Dirt, Ashes, Stones, Bricks, etc

O ther


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2

5

Waste Flow in Lahore

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6



Collection: Mid Rate, Door to Door (PrivateCompany, NGO by Tri-cycle),

Transfer: No Transfer Station

Disposal: Landfill, Open Dumping, Composting Others: Hospital waste incinerator

7

Discharge

8

Collection


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3

9

Intermediate Treatment (Composting Plant)

10

Disposal


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1

1

City 6: Sarajevo (Bosnia & Herzegovina)

2


Schedule:Sep. 26 Oct. 9

Visited Organizations for Meeting:Cantonal Public Utility Rad, Cantonal PublicUtility Park, Papir Servis, Ministry ofEnvironment and urban development

Visited SWM Facilities:Smiljevii Landfill (& MRF), Papir Servis,Hospital, Slaughterhouse

3

City & SWM Profile

Capital City, 1,227 km2

Temperature: 17, Precipitation: 1,200 mm GNI per capita: US$2,440 (2005, WDI) Population: 410,000 Total Generation: 492 tonnes/day Generation rate: 1.2 kg /person/day

4

Waste Composition

1%

37%

17%13%

6%

8%

18%

Yard Trimmings, Leaves Food Waste

Paper Plastics

Metal Glass

Misc.


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2

5

Waste Flow in Sarajevo

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6

Summary of SWM System Discharge: 1100 litre metal containers,

occasional collection of large items (furniture,white goods) some dedicated yard waste pick-up, street sweepers

Collection: High Rate, Municipality Collects,Medium Frequency, Pilot Segregated Collection

Transfer: Direct to Landfill Disposal: Landfill & MRF Others: Gas Recovery and Electricity Generation

at Landfill

7

Collection

8

Disposal


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3

9

Alternatives


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1

1

City 7: Shanghai (China)

2


Schedule:

Oct. 26 Nov. 13, Dec. 18 22


SCEID, SIDREE, SCAESAB, Shanghai ElectricPower Design Institute, Tongji University

Visited SWM Facilities:Huangpu Transfer Station, Jiangqiao incinerator,Yangpu Transfer Station, Huling Dock, Gacu dumping

site, Minghan dumping site, Laogang Landfill Site

3

City & SWM Profile

Most urbanized city in China, 6,340 Km2

Temperature: 15, Precipitation: 1,440 mm GNI per capita: US$ 1,740 (2005, WDI), Local

GDP per capita: >US$ 7,000 (2006, investment

Shanghai) Population: 17,800,000 Total Generation: 17,000 tonnes/day Generation rate: 0.96 kg /person/day

4

Waste Composition

63%

19%

2%

8%

3%0%

3%2% 0%

Kitchen and Fruits Plastic

Papers Cloth

Bam boo & wood M etal

Glass Slag & stones

Hazardous and others


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2

5

Waste Flow in Shanghai

Collection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6



Collection: High Rate, Compactor Truck

Transfer: 5 Transfer Stations, 8 Canal Docks

Disposal: 1 Landfill, Many Open Dumping sites,

1 Composting Plant, 2 Incinerators Others: Gas recovery at landfill

7

Discharge and Pre-collection

8

Collection


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3

9

Transfer Station and Dock

10

Intermediate Treatment

11

Disposal


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1

1

City 8: Kawasaki (Japan)

2


Schedule:

October 2006 February 2007


Environmental Dept. of Kawasaki City

Visited SWM Facilities:

Kase Transfer Station, Sikine Incinerator,Ukisima Final Disposal Site (sea reclamation),Ukisima Incinerator, Nanbu MRF, ShikineMRF

3

City & SWM Profile

Metropolitan City between Tokyo and Yokohama,144 Km2

Temperature: 17, Precipitation: 1,932 mm GNI per capita: US$ 38,980 (2005, WDI) Population: 1,327,000 Total Generation: 1,399 tonnes/day Generation rate: 1.01 kg /person/day

4

Waste Composition

3%

33%

14%4%5%

36%

1% 4%

W ood and leaves Paper Plastic

M etals G lass Food W aste

Fabric M isc.


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2

5

Waste Flow in KawasakiCollection

Incineration*Ash

LandfillMSW

Landfill

Remanufacturing

Facility

Recycling


Composting

Compost Product

End Use

6

Summary of SWM System Discharge: Plastic bags, Community collection

points Collection: High Rate, Segregated collection,

Small compactor truck, Group recyclablecollection

Transfer: 1 Transfer Station, Railwaytransportation

Disposal: 4 Incinerators, 5 MRF, 1 Ash Landfill(Sea Reclamation)

Others: Power generation at 3 Incinerators

7

Collection and Transport

8

Transfer Station


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3

9

Material Recovery Facility

10

Incinerator

11

Disposal


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1

City 9: Atlanta (United States)

City Profile

Atlanta was selected as a representative US city

Area: 343 km

Atlanta has a population of roughly 442,000

GNI per capita is US$37,750

Annual precipitation is about 1,270 mm

The average temperature is 16.

City Waste Data

Total Generation: 2,899 tonnes/day

Population: 442,000

Generation rate: 1.72 kg /person/day

Collection: 2,174 tonnes/day

Collection efficiency: 75%

Waste landfilled: 2,038 tonnes/day

7%

41%

15%6%

4%

12%

15%

Yard Trim m ings Paper Plastic M etals

Glass Food W aste M iscellaneous

Waste Composition


29/272


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APPENDIX 2.4.1 Composition Rate for Compostablematerials vs. Economic Indicator (GDP or GNI)


31/272

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Tan

za

nia/Da

rE

sS al a

am(

39

wa

rds)(

19

96

)

Ni g

er/Ni a

mey

(20

01

)

Lao

s/V

ien

tia

n(1

99

1)

Ke

nya

/Nai r

ob

i(19

97

)

Nep

al/K

athm

an

du

(***)

Egy

pt/

Ale

xa

nd

ria(1

98

4)

G uine

a/C

onak

ry(*

** )

Vietn

am/H

anoi

(20

00

)

Nicar a

gua

/Mana

gu

a(1

995)

Pak

ista

n/Raw

alpi n

di(19

95

)

Paki s

tan/Q

uetta

(199

6)

I nd

on

es

ia/Ja

ka

rta

(1986

)

Ind

on

esi a

/S ur a

ba

ya

(199

2)

Eg

yp

t/A

lexa

nd

ria

(1994

)

Phil

ipp

ine

s/M

etr

op

olita

nM

an

ila(1

99

7)

Paki s

tan/L

ah

ore

(***)

S ryL

an

ka

/Mo

ratu

wa

(19

97

)

Indo

nes

ia/U

jung

pa

nd

an

g(19

94

)

S r y

Lan

ka/B

adu

lla(2

00

2)

S ry

Lan

ka/C

hi l

aw

(2002

)

S ry

La

nk

a/G

am

pa

ha(2

002

)

S ryL

anka

/Ka

nd

y(2

002)

S r y

Lan

ka

/Ma

tal e

(200

2)

S ry

La

nk

a/N

eg

om

bo(2

002

)

S ryL

ank

a/Nu

wara

Eliya

(20

02

)

G u

ate

ma

la/M

etro

pol i

tan(1

99

2)

Bul g

aria

/S of i

a(1

993

)

S yri

a/L a

tak

iaan

do

ther

thr e

eci ti e

s(2

001

)

S yr i

a/A

lepp

o(1

997

)

Per u

/Lima

(7

distr

icts

in

no

rtha

na

reas

)(1

98

4)

S y

ria/D

ama

sc

us(1

99

5)

Mo

rocc

o/S

afi(

Thre

eu

r ban

co

mmu

nes

)(1

99

6)

Kaza

khs

tan/A

lmaty

(19

99

)

Po

lan

d/P

oz

na

n(1

99

2)

Do

mi n

ica/S

an

toD

om

ingo

(200

6)

Ro

mania

/Buc

har e

st(1

99

4)

Pe

ru/K

aja

yo(

19

95)

Par ag

uay/A

sun

cin

Metr

opolita

n(19

94

)

Ma

laysia

/Pen

an

(198

8)

Bosn

ia/S

ara

jevo

(** *

)

Jo

rdan/A

mm

an(*

**)

Pa

na

ma/P

an

am

a(2

00

2)

Tu

rke

y/A

da

na

metr

opo

lita

n(1

99

8)

Tu

rkey/M

ersi n

Metr

op

oli

tan(1

998

)

Hu

nga

ry/B

uda

pes

t(1

99

2)

Me

xic

o/M

exico

city(

1998

)

Ar g

en

tine

/Buen

os

Aire

s(**

*)

C h

ina/S

han

gh

ai(**

*)0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Kitchen waste(%) Wood, Grass(%) GDP or GNI(dollors/person/year)% dollors/person/year

GDP, GNI or RGDPLow High

A2.4-2


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APPENDIX 2.4.2 Compostable: kitchen waste and yard waste(wood and grass), by regional areas


33/272

0

10

20

30

40

50

60

70

80

90

100

Tanzania/DarEsSalaam

(39wards)(1996)

Niger/Niamey(2001)

Kenya/Nairobi(1997)

Guinea/Conakry(***)

LaoPDR/Vientian(1991)

Indonesia/Jakarta(1986)

Indonesia/Surabaya(1992)

Philippines/Metropolitan

Manila(1997)

Indo

nesia/Ujungpandang(1994)

Malaysia/Penan(1988)

China/Shanghai(***)

Bulgaria/Sofia(1993)

Kazakhstan/Almaty(1999)

Poland/Poznan(1992)

Romania/Bucharest(1994)

Bosnia/Sarajevo(***)

Turkey/Adana

metropolitan(1998)

Turkey/Mersin

Metropolitan(1998)

Hungary/Budapest(1992)

Nicaragua/Managua(1995)

Guatemala/Metropolitan(1992)

Peru/Lima(7districtsin

northanareas)(1984)

Dominica/Santo

Domingo(2006)

Peru/Kajayo(1995)

Paraguay/Asuncin

Metropolitan(1994)

Panama/Panama(2002)

Mexico/Mexicocity(1998)

Argentine/BuenosAires(***)

Egypt/Alexandria(1984)


S

yria/Latakiaandotherthree

cities(2001)

Syria/Aleppo(1997)

Syria/Damascus(1995)

Morocco/Safi(Threeurban

communes)(1996)

Jordan/Amman(***)

Nepal/Kathmandu(***)

Pakistan/Rawalpindi(1995)

Pakistan/Quetta(1996)

Pakistan/Lahore(***)

SriLanka/Moratuwa(1997)

SriLanka/Badulla(2002)

SriLanka/Chilaw(2002)

SriLanka/Gampaha(2002)

SriLanka/Kandy(2002)

SriLanka/Matale(2002)

SriLanka/Negombo(2002)

SriLanka/NuwaraEliya(2002)

Africa East Asia & Pacific Europe and Central Asia Latin America and Caribbean Middle East and North

Africa

South Asia

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Kitchen waste Wood, Grass GDP or GNI(dollors/person/year)% dollors/person/year

A2.4-4


34/272

APPENDIX 2.4.3 Composition Rate for Combustiblematerials vs. Economic Indicator (GDP or GNI)


35/272

0

10

20

30

40

50

60

70

80

90

100

Tan

zan i

a/Dar

EsS

ala

am

(3

9w

ard

s)(

199

6)

Nig

er /

Ni a

me

y(2

001)

Lao

s/V

ien

tia

n(19

91

)

Ke

ny

a/N

air

ob

i(1

997

)

Ne

pal /

Ka

thma

ndu

(** *

)

Egy

pt/

Alex

an

dr i

a(1

98

4)

G uin

ea/C

ona

kry(*

**)

Vie

tna

m/Ha

no

i(2

000

)

Nica

rag

ua

/Mana

gu

a(1

99

5)

Pa

ki s

tan/R

aw

alp

ind

i(19

95

)

Pa

kista

n/Q

uetta

(199

6)

Indo

ne

sia

/Ja

ka

rta

(198

6)

Indon

es

ia/S

ur a

ba

ya

(19

92

)

Eg

yp

t/Ale

xa

nd

ria(1

99

4)

P h

i lipp

ine

s/M

etrop

olita

nMa

nila

(1997

)

Pa

kista

n/La

hor e

(***

)

Sry

La

nk

a/M

ora

tuwa

(199

7)

Indo

nes

ia/U

jung

pan

da

ng

(19

94

)

Sry

Lank

a/B

ad

ulla

(20

02

)

Sry

La

nka

/C hila

w(2

002)

SryL

ank

a/G

am

pah

a(2

00

2)

Sr y

La

nk

a/K

an

dy

(20

02)

Sry

La

nka

/Mata

le(2

00

2)

Sr y

Lan

ka/N

ego

mbo

(20

02

)

Sry

Lank

a/N

uw

ara

Eliya

(20

02

)

G ua

tem

ala

/Metr

opo

lita

n(1

99

2)

Bu

lga

ria

/So

fia(1

993)

Syr i

a/La

takia

and

oth

er

thr e

ec

itie

s(2

00

1)

Sy

ria

/Ale

pp

o(1

99

7)

Pe

ru/L

ima(7

distr

ictsin

no

rth

ana

reas

)(1

98

4)

Syr i

a/D

am

as

cus

(19

95

)

Moroc

co

/Sa

fi(

Th

ree

urb

an

comm

un

es

)(19

96

)

Kaz

akh

sta

n/A

lmaty

(19

99

)

Po

lan

d/P

oz

nan

(199

2)

Do

min

ica

/Santo

Dom

ing

o(2

00

6)

Rom

an

ia/B

uch

ar e

st(19

94

)

Pe

ru/K

aja

yo

(1995

)

Pa

ra

guay

/Asu

nci

nM

etrop

olita

n(199

4)

Malay

si a

/Pena

n(1

98

8)

Bos

ni a

/Sara

jev

o(*

**)

Jord

an

/Am

ma

n(*

**)

Pa

na

ma/Pa

na

ma(20

02

)

Tu

rke

y/A

da

na

metr

opo

litan

(19

98

)

Turk

ey

/Me

rsi n

Metr

op

olita

n(1

99

8)

Hu

ng

ary

/Bu

da

pe

st(1

99

2)

Me

xico

/Mex

icoc

ity

(1

998

)

Ar g

entin

e/B

ue

nos

Aire

s(**

*)

C hi n

a/Sh

ang

ha

i(*

**)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Combustible(%) GDP or GNI(dollors/person/year)% dollors/person/yearGDP, GNI or RGDPLow High

A2.4-6


36/272

APPENDIX 2.4.4 Composition Rate for Combustiblematerials vs. Economic Indicator (GDP or GNI), by regionalareas


37/272

0

10

20

30

40

50

60

70

80

90

100


(39wards)(1996)

Niger/Niamey(2001)

Kenya/Nairobi(1997)

Guinea/Conakry(***)

Laos/Vientian(1991)

Vietnam/Hanoi(2000)




Manila(1997)

Indo



China/Shanghai(***)



Poland/Poznan(1992)



Turkey/Adana

metropolitan(1998)

Turkey/Mersin

Metropolitan(1998)



Gu

atemala/Metropolitan(1992)


northanareas)(1984)

Dominica/Santo

Domingo(2006)

Peru/Kajayo(1995)

Paraguay/Asuncin

Metropolitan(1994)

Panama/Panama(2002)


Argentine/BuenosAires(***)



S

yria/Latakiaandotherthree

cities(2001)

Syria/Aleppo(1997)



communes)(1996)

Jordan/Amman(***)





S

ryLanka/Moratuwa(1997)

SryLanka/Badulla(2002)

SryLanka/Chilaw(2002)

SryLanka/Gampaha(2002)

SryLanka/Kandy(2002)

SryLanka/Matale(2002)

SryLanka/Negombo(2002)

SryLanka/NuwaraEliya(2002)


Africa

South Asia

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Combustible(%) GDP or GNI(dollors/person/year)% dollors/person/year

A2.4-8


38/272

APPENDIX 2.4.5 Composition Rate for Recyclable materialsvs. Economic Indicator (GDP or GNI)


39/272

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Tan

zan

ia/Da

rE

sS

al a

am

(3

9w

ards)(

19

96

)

Nige

r/Ni a

mey

(20

01)

Lao

s/V

ienti

an(1

99

1)

Ken

ya

/Na

irob

i(1

997

)

Nepa

l/K

ath

ma

nd

u(*

**)

Eg

ypt/

Alexa

nd

ria

(198

4)

G ui n

ea

/C o

na

kry

(** *

)

Vi e

tnam

/Ha

no

i(2

00

0)

Ni c

ar a

gu

a/M

anag

ua

(19

95)

Pak

ista

n/R

awal p

indi(

199

5)

Pa

ki s

tan/Q

ue

tta

(199

6)

Indon

esi a

/Jaka

rta

(1986)

Indo

ne

sia

/Su

rabay

a(1

99

2)

Egyp

t/A

lex

an

dria

(19

94

)

Ph

ili p

pin

es

/Metro

po

lita

nM

an

ila

(199

7)

Paki s

tan/L

aho

re(** *

)

Sr y

Lan

ka/M

oratu

wa

(1

997)

Ind

on

es

ia/U

jun

gp

and

an

g(1

99

4)

SryL

anka

/Ba

du

lla(2

00

2)

SryLa

nk

a/C

hi l

aw

(2002

)

Sry

La

nk

a/G

am

pa

ha(2

002

)

Sry

Lan

ka

/Ka

nd

y(2

00

2)

Sr y

Lan

ka/M

atale

(2002

)

Sry

Lank

a/N

eg

om

bo

(20

02)

SryL

an

ka

/Nu

wa

raEliy

a(2

002)

G u

ate

ma

la/M

etro

po

lita

n(1

992

)

Bul g

ar i

a/S

of i

a(1

99

3)

Syri

a/L

ata

kia

an

do

the

rth

ree

ci t

ies(2

001

)

Sy

ria/A

leppo

(199

7)

Per u

/Lim

a(7

di s

tricts

inn

orth

ana

reas)

(19

84

)

Sy

ria

/Da

ma

sc

us(1

99

5)

Mo

rocco

/Sa

fi(

Th

reeu

rban

co

mm

unes)(

199

6)

Kaz

akh

sta

n/A

lmaty

(19

99

)

Po

lan

d/P

ozn

an

(1992

)

Dom

inica

/Sa

nto

Dom

ing

o(2

006

)

Rom

an

ia/B

uch

ar e

st(19

94

)

Pe

ru/K

aja

yo

(1

99

5)

Para

gu

ay

/Asunc

inM

etrop

olita

n(199

4)

Mala

ysia

/Pen

an(1

98

8)

Bos

ni a

/Sar a

jev

o(*

**)

Jord

an

/Am

man

(***)

Pan

ama

/Pan

ama

(200

2)

Tu

rke

y/A

da

nam

etro

pol i

tan

(1998

)

Tur k

ey/Me

rsinM

etr

op

ol i

tan(1

998

)

Hu

ng

ary

/Bu

da

pes

t(1

99

2)

Me

xic

o/M

ex

ico

cit

y(

199

8)

Arge

nti

ne/B

uen

osA

ire

s(*

**)

C hin

a/S

ha

ngh

ai(*

**)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Paper Plastic Metal Glass GDP or GNI(dollors/person/year)% dollors/person/year

GDP, GNI or RGDPLow High

A2.4-10


40/272

APPENDIX 2.4.6 Composition Rate for Recyclable materialsvs. Economic Indicator (GDP or GNI), by regional areas


41/272

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0


Niger/Niamey(2001)

Kenya/Nairobi(1997)

Guinea/Conakry(***)

Laos/Vientian(1991)

Vietnam/Hanoi(2000)




Indo



China/Shanghai(***)



Poland/Poznan(1992)



Turkey/Adana

Turkey/Mersin



Guatemala/Metropolitan(1992)


Dominica/Santo

Peru/Kajayo(1995)

Paraguay/Asuncin

Panama/Panama(2002)


A

rgentine/BuenosAires(***)



Syria/Latakiaandotherthree

Syria/Aleppo(1997)



Jordan/Amman(***)





S

ryLanka/Moratuwa(1997)

SryLanka/Badulla(2002)

SryLanka/Chilaw(2002)

SryLanka/Gampaha(2002)

SryLanka/Kandy(2002)

SryLanka/Matale(2002)

SryLanka/Negombo(2002)

Sry

Lanka/NuwaraEliya(2002)


Africa

South Asia

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Paper Plastic Metal Glass GDP or GNI(dollors/person/year)% dollors/person/year

A2.4-12


42/272

APPENDIX 2.5 ANALYSIS ASSUMPTIONS


43/272

Waste:

For scenario analysis, each city has a large enough waste flow to get economiesof scale in each treatment option considered.

Lf not providedassume percent office paper in waste is 5 percent of paper

waste; remaining paper waste is then assumed split equally between cardboard

and newsprint.

Collection Assumptions;

For the purpose of focusing analysis on comparison of treatment technologies,

Collection is being made similar across the cities. There are TWO sets of

relevant assumptions one for the Cost of Collection the other related to

emissions and energy consumption for collection. In this manner we can

capture the relevant differences between collection of Non-segregated waste

and collection of Segregated wastes that will be appropriate for some of the

technologies considered. The following are those assumptions identified to

date:

A. Cost of Collection:

Low income $35

Middle income $45

Shanghai, Buenos Aires $55

US $90

Japan $120

*for extra 20 km to landfill, add $10 per metric ton

B. Emissions and Energy Consumption of Collection

1) Assume 100 percent collection

2) Daily collection is 6 days per week"

3) Collection vehicle travel speeds:

I it 30 k / h


44/272

4) Travel distances

Collection vehicle to city boundary 20 km

City boundary to treatment 10 km

Treatment to landfill 20 km

Composting to end user 20 km

MRF to remanufacturing 10 km

5) Waste segregation at the households

We may find it useful to include this in the list of sensitivity analyses rather than

building into the Collection assumptions for the scenario runs. However it is

provided here for clarity

High cooperatjon 60 percent

Low cooperation 20 percent

6) For the purposes of collection assumptions, we defined low, middle and

higherincome cities. They are assumed as follows:

Low: Kathmandu, ConakryMiddle: Lahore, Amman, Sarajevo

Higher: Shanghai, Buenos Aires

7) Split of city waste:

% of waste Families per stop

Laid out areas" 10 percent 2

Mixed Commercial and Multi-family 90 percentLow income cities 30

Middle income cities 30

Shanghai & Buenos Aires 60

8) Household size:

Low income 6 persons per family

Middle income 6 persons per familyShanghai & Buenos Aires 4 persons per family

AII Laid out areas 6 persons per family

9) Collection vehicle mix:


45/272

10) Equipment Prices

Compactor $85,000

Open Tipper $60,000 assumes construction type vehicles rather than

lighter duty waste truck design a common practice in

developing countries.

11) Annual O&M on vehicles:

20 percent of capital cost

12) Economic life of collection vehicles: 7 years throughout

13) Waste density in collection vehicles in developing country cities:

Compacted 500 kg per cubic meter

Un-compacted 300 kg per cubic meter

14) Labor requirements assumptions:

Always driver and 3 collectors on compactors

Always driver and 4 collectors on open trucks

1 supervisor per 5 trucks

1 mechanic per5trucks

1 inspector per5trucks

Collectors paid 10 percent less than driver. others above paid drivers rate.

15) Labor rates VERY city specific; use data collected in field visits

16) Benefits above salary [since govt employees] are typically 30%

Economic Scale for Treatment Technologies in metric tons perday

Landfill 300

Incineration 300 with energy recovery and meeting EU standards

Composting 150

MRF 150*below 100 metric tons per day, we assume the volume is too small to conduct

incineration


46/272

Landfill

1. Open Dump Scenario has 30% open burning; bums in pockets; remaining isas open dump landfill. The burning results in issues with lead, volatilized

organics, etc. Need to deal with resulting pollutants.Ozge is checking

whether the open barrel burning work included lead. on the remaining open

dump, Thornloe-provided emission factors will be used.

2. Assuming ALL remaining landfills described in the scenarios [i.e. beyond

number l above on open dump"] will be FLARED LANDFILLS. This issomewhat different from what some team members remember Sandra

suggesting so we will check this before proceeding. Some recall this being

energy recovery landfills". Flared seems like a better assumptions since

this is little increase on carbon avoided between these two and energy

recovery are more difficult.

3. Landfills are 15 meters deep. 3:l slope except will add note in report thatindicates that 5:1 may be required in areas of seismic activity. Seventy (70)

percent of acreage is landfill cells. Remainder is buffer, lagoons for

treatment, etc

4. Gas capture 70 percent

Will use 50 and 80 percent on sensitivity analysis

5. The World Bank acknowledges that municipal landfills in developing

countries are okay to accept 10 percent of waste volume as sanitary

sludges and animal wastes. Sanitation sludges are expected to be

dewatered to 70 percent moisture. Animal wastes are typically 60 percent

moisture.

6. The following ranges were assumed for composition of these wastes inlandfill as mentioned above:

Percent of Total Waste

Lo Base Hi

S it ti l d 3% 6% 10%


47/272

MRF and IncinerationRequire skilled labor

EU standards apply

Vehicle and Other Equipment FuelsAll vehicles use diesel fuel which contains NOLEAD

Vermicomposting

l. Vermicomposting is always performed on waste that has already been

treated [at least partially] in a Composting treatment step. (1)

2. Assume 10 percent of Composting always goes to vermicomposting.

3. Assume price multiplier from India study for differential of vermicompost

market price over regular compost.

Incineration

NK/RTI will check for anomalies using the following that are typical values of

wastes are as follows:

Low income 800900 kcal/kg

Middle 900 1000 kcal / kgHigher income 1000 1 300 kcal / kg

The above were provided by Sandra considering her former field work that

included the following: Singapore 1500; Seoul 1300; India 800

Assume oil is bought to bring the heating value up high enough to sustain burning

in scenarios requiring the use of incineration.

Per Capita Income for Cities Studied


48/272

Carbon Finance

1. World Bank assumes l metric ton methane = 21 metric ton CO22. Carbon credit is currently $10/ metric ton Carbon. Since this has been as low

as $3.50 as recently as 3 years ago, Bank Carbon Finance staff should be

asked to provide a forecast so team can do sensitivity analysis.

3. Carbon Finance is based on metric tons of methane reduced or avoided.

4. Always include Carbon Finance as Revenue in relevant scenarios. Need to

discuss whether there is a minimum economic floor for level of carbon

credit.5. Use UN requirements for determining most likely payment. This requires the

use of the landfill decay model. UN does not presently allow recycling

offsets.

6. Consider also analyzing for what the World Bank would LIKE to see as the level

of payment.

7. Check new lPPC report.

8. Charles Peterson, World Bank, is a resource on this.


49/272

APPENDIX 2.5.1 FIELD DATA FOR GENERAL INPUT BY CITY

Appendix 2.5.1 Field Data for General Input by City


50/272

Katmandu Conakry Lahore Sarajevo Amman Buenos Aires Shanghai Kawasaki Atlanta

270 370 690 2,440 2,500 4,470 1,740 (7,000) 38,980 43,740

0.08 0.09 0.49 1.78 0.70 6.90 2.10 18.49 11.00

16 27 23 10 26 17 15 17 16

Mixed waste MRF-Manual sorting

yes yes yes yes

Mixed waste MRF-Automated sorting

yes yes yes

Commingled MRF-Manual sorting

Commingled MRF-Automated sorting

yes

Windrow composting yes yes yes yesAerated Pile composting yes yes

stemanagementunits

GNI/capita ($US)

Collector Wage ($US/person-hour)

Temperature (Deg C)

WTE yes yesIncineration yes

Anaerobic bioreactor yesAsh Landfill yes yes

Landfill vented yes yes yes

Landfill flared yes yes yesLandfill energy recovery yes yes yes

Notes :The value in the colum of GNI is based on World Development Indicators on database (Atlas methodology), World Bank, 1 July in 2005.The data in parenthesis is GDP data from "http://www.investment.gov.cn/2007-02-09/1169371958133.html".Katmandu & BsAs Temperature: http://www.bbc.co.uk/weather/world/city_guides/Lahore Temperature: http://worldweather.wmo.int/047/c00891.htmKawasaki Collector Wage: JP\440,000/25(d/m)/8(hr/d)/119(\/$)

Existingwa


51/272

APPENDIX 2.5.2 FIELD DATA FOR GENERATION INPUTSBY CITY -METRIC


52/272

Appendix 2.5.2 Field Data for Generation Inputs by City - Metric

Input to the

model

Katmandu

(collected)

Katmandu

(modeled)

Conakry

(collected)

Conakry

(modeled)

Lahore

(collected)

Lahore

(modeled)

Sarajevo

(collected)

Sarajevo

(modeled)

Amman

(collected)

Amman

(modeled)

Buenos Aires

(collected)

Buenos Aires

(modeled)

Shanghai

(collected)

Shanghai

(modeled)

Kawasaki

(collected)

Kawasaki (for

adjustment)

Kawasaki

(modeled)

Atlanta

(collected)

Atlanta

(modeled)

Plastic - Non-Recyclable yes 12.8%bles

DST

ories


53/272

Misc. Combustibles yes 2.2% 11.6% 10.2% 10.2% 7.0% 6.2% 13.6% 12.0% 13.6% 12.6% 2.6% 0 .1% 4.6% 4.6% 5.2%

C&D Wood 1.7%

Other C&D 0.8%Inerts 0.2%

Drywall 0.5%

Carpet 1.7%

rubber/leather 0.5% 0.7% 0.3%Tires 0.3%

Ferrous - Non-recyclable yes 6.0% 5.3%

Al - Non-recyclable yes

Glass - Non-recyclable yes 8.0% 7.0% 0.0% 0.0% 0.4%

Misc. Non-combustibles yes 0.8% 7.4% 1 0.5% 22.7% 18.0% 15.8% 1.3% 4.1% 1.6% 1.5% 3.0% 0 .1% 4.2% 4.2% 2.3%

Ceramics 0.2%Ceramic, soil, rock 0.2%

Dust, Dirt, Ashes,

Stones, Brickes, slag, etc24.8% 0.3%

Other Inorganics 0.6%Supplement moisture and other wastes 3.9%

Computers 0.1%

Other Electronics 1.6%

6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 6.0%

Bones 0.8%

Animal Waste 1.8%

1.3% 0.1% 0.5%

0.7% 0.9% 3.2%

4.5% 1.1%

100.0% 100.0% 100.0% 100.0% 100.5% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

MSWDST

categories

WasteComposition

M

iscellaneousCombustib

MSWD

catego

Others

Others

Animal

waste

Sanitary Sludge

Hazardous

Others

MiscellaneousNon-Combustibles

Others

Unclassified


54/272

APPENDIX 2.5.3 FIELD DATA FOR COLLECTION INPUTS BY

CITY -METRIC

Appendix 2.5.3 Field Data for Collection Inputs by City - Metric

Collection costFamilies per stop

Household size in

the multifamilyNumber of Fraction of

Fraction of openUsable capacity of

Waste density in

compactor

Waste density in

compactor


55/272

Collection cost

($US/metric ton)*in the multifamily

sector**

the multifamily

sector

(people/house)**

multifamily

collection locations

compactor

vehicles***

Fraction of open

trucks***compactor vehicles

(m3)

compactor

vehicles-

compacted (kg/m3)

vehicles-

uncompacted

(kg/m3)***

Katmandu 35 30 6 5,490 0.1 0.9 12 NF NFConakry 35 30 6 3,215 0.1 0.9 12 NF NF

Lahore 35 30 6 28,000 0.1 0.9 12 NF NF

Sarajevo 45 30 6 1,976 0.7 0.3 12 NF NF

Amman 45 30 6 10,520 0.7 0.3 12 NF NF

Buenos Aires 55 60 4 39,270 0.9 0.1 12 NF NF

Shanghai 55 60 4 64,413 0.9 0.1 12 NF NF

Kawasaki 120 50 2 12,784 1 1 6 500 300

Atlanta 90 50 2 11,352 1 1 12 500 300

Notes :

*Used to adjust the final results.**Used to estimate the number of multifamily collection locations

***MSW DST tool does not have a corresponding input.

NF: Not found. Set to be Kawasaki and Atlanta's value.


56/272

APPENDIX 2.5.4 FIELD DATA FOR MRF INPUTS BY CITY

-METRIC


57/272


58/272

APPENDIX 2.5.5 FIELD DATA FOR COMPOST INPUTS BY

CITY -METRIC

Appendix 2.5.5 Field Data for Compost Inputs by City - Metric

Number of

operating hours

(hours/day)

Number of

days per week

Operating days

per year

Wage for operator

($US/hour)

Wage for manager

($US/hour)

Compost

residence time

(days)

Curing stage

residence time

(days)

Compost pile

turning frequency

(times/week)

Value of compost

product ($US/metric

ton)


59/272

(hours/day) (days) (days) (times/week) ton)Katmandu 7 7 365 0.08 0.18 57 0 0.75 15.00

Conakry 9 6 312 0.07 0.00 90 0 0.75 15.00

Lahore 0 0 0 0.39 0.00 90 0 0.00 30.00

Sarajevo 0 0 0 0.00 0.00 0 0 0.00 40.00

Amman 0 0 0 0.00 0.00 0 0 0.00 40.00

Buenos Aires 0 0 0 3.47 5.20 60 45 NA 50.00

Shanghai 0 0 0 0.00 0.00 0 0 0.00 50.00

Kawasaki 0 0 0 0.00 0.00 0 0 0.00 60.00

Atlanta 8 5 262 8.00 15.00 168 90 1.00 25.00

Notes :

NF means Not Found

Zero values mean that the default values are used (see Atlanta values)


60/272

APPENDIX 2.5.6 FIELD DATA FOR INCINERATION INPUTS

BY CITY -METRIC

Appendix 2.5.6 Field Data for Incineration Inputs by City - Metric

Waste heating values-

typical ranges (kcal/kg)*

Waste heating values

(kcal/kg)**

Unit WTE capital cost

($US/metric ton/year)

Unit WTE O & M cost

($US/metric ton/year)


61/272

Katmandu 800- 900 2,212

Conakry 800- 900 2,147

Lahore 800- 900 2,161

Sarajevo 900- 1000 1,911

Amman 900- 1000 3,199

Buenos Aires 1000- 1300 3,134

Shanghai 1000- 1300 3,133 220.27 65.33

Kawasaki 3,185 1381.50 127.82

Atlanta 3,841 311.62 65.33

Notes :*Values provided as typical values.

**Values presented for comparison with typical values

Blank cells mean default values are used (see Atlanta values)

e au t va ue s use or t e un t & cost n ang a see t anta va ue


62/272

APPENDIX 2.5.7 FIELD DATA FOR LANDFILL INPUTS BY

CITY - METRIC

Appendix 2.5.7 Field Data for Landfill Inputs by City - Metric

Active life of facility

(years)Number of cells

Post closure period

(years)Liner?

Depth of soil in

primary liner (cm)

Liner is single or

double?

Depth of soil in

secondary liner (cm)

Gas collection

efficiency (collected)

Gas collection

efficiency (modeled)

Katmandu 2 0 2 yes 24.99 single 0.00 0.00 0.70


63/272

Conakry 30 2 10 no NA NA NA 0.00 0.70

Lahore 0 0 0 no NA NA NA 0.00 0.70

Sarajevo 25 7 10 yes 29.87 double 60.05 0.90 0.70

Amman 23 9 30 yes 20.12 double 0.61 0.75 0.70Buenos Aires 15 0 30 yes 91.44 single NA 0.70 0.70

Shanghai 20 12 0 yes 0.00 double 0.00 0.65 0.70

Kawasaki* 21 0 0 yes 0.00 0.00 0.00 0.00 0.00

Atlanta 20 5 30 yes 60.96 single 60.96 0.75 0.70

CO2 quality CH4 qualitySludge CH4 yield

(m3/metric tons)Precipitation (mm/yr)

Engineering rate

(capital)

Engineering rate

(operations)

Minimum labor cost

($US/year)

Maximum daily

waste handled by

minimum labor costs

(metric ton/day)

Utility rate as (as

fraction of labor

costs)

Katmandu 0.00 0.00 82.59 1995 0.00 0.00 5,991 384 0.00

Conakry 0.74 0.26 82.59 4293 0.05 0.05 1,827 298 0.00

Lahore 0.00 0.00 50.83 800 0.00 0.00 0 0 0.00Sarajevo 0.45 0.55 27.53 940 0.10 0.10 0 0 0.06

Amman 0.52 0.48 32.39 228 0.10 0.10 0 0 0.00

Buenos Aires 0.475 0.50 33.74 1194 0.00 0.00 0 0 0.00

Shanghai 0.3223 0.5487 34.41 1437 0.00 0.00 0 0 0.04

Kawasaki* 0 00 0 00 NA 1659 0 00 0 00 0 0 0 00. . . . .

Atlanta 0.45 0.55 NA 889 0.10 0.10 327,000 363 0.01

Overhead costs

(overhead cost $US/

wage $US)

Equipment and

maintenance

($US/year)

Capital cost of

turbine ($US)

Capital cost of

internal combustion

engine ($US)

Land prices

($US/m2)

Revenue from

electric buyback

rates ($US/Kwh)

Katmandu 0.18 0 0 0 2.0 0.00

Conakry 0.00 0 0 0 6.7 0.00

Lahore 0.00 0 0 0 29.3 0.00

Sarajevo 0.37 421 389,188 259,459 8.4 0.13

Amman 0.00 0 0 600,000 108.6 0.09

Buenos Aires 0.00 0 0 0 0.6 0.02

Shanghai 0.00 580 0 0 62.1 0.06

Kawasaki* 0.00 0 0 0 855.3 0.00

Atlanta 0.46 2,000 4,725,245 1,417,573 4.3 0.04

Notes :

NF: Not Found

NA: Not Applicable

*Kawasaki only has an ash landfill*Cost for equipment and maintenance in Conakry is set zero instead of the data collected in the field.

Zero values mean default values are used (see Atlanta values)


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APPENDIX 2.5.8 FIELD DATA FOR ENERGY INPUTS BY CITY

Appendix 2.5.8 Field Data for Energy Inputs by City

Katmandu Conakry Lahore Sarajevo Amman Buenos Aires Shanghai Kawasaki Atlanta


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Coal 0.076 0.47 0.011 0.2470 0.5645

Natural Gas 0.503 0.947 0.458 0.3327 0.2570 0.0975Residual Oil 0.745 0.02 0.046 0.3327 0.0262

Distillate Oil 0.046 0.005 0.3327 0.0820 0.0023

Nuclear 0.012 0.090 0.2910 0.2213

Hydro 0.9 0.225 0.110 0.51 0.006 0.390 0.1000 0.0859

Wood 0.0024

Other* 0.1 0.030 0.005 0.001 0.0018 0.0002

Coal yes yes yes yes yesNatural Gas yes yes yes yes

Residual Oil yes yes yes yes yes

Distillate Oil yes yes yes yes yes

Nuclear yes yes yes

Hydro yes yes yes

Wood yes yes yes

Other (diesel oil) yes yes yes

NF 0.07 NF 0.13 0.09 0.021 0.08 0.16 0.04

NF 0.0114 NF 0.05 0.05 0.018 0.06 0.09 0.04

3.26 3.46 2.39 4.62 1.67 1.76 2.24 3.60 2.40

Notes :

* In Lahore the others category correspond to diesel oil.

0.294

EnergyBreakdown

Electricity Cost- purchase

($US/kWh)

Electricity Price- sale

($US/kWh)

Diesel Fuel ($US/gal)

LFGTEFuelDisplacement


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APPENDIX 2.5.9 FIELD DATA FOR CONSTANS DATA

-METRIC

Appendix 2.5.9 Constants Data - Metric

Parameter Value Units Comments


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Fract ion o f res ident ial waste 0 .1 unit less Used to est imate the tota l generated was te for the res ident ia l sector .

Fract ion o f mult ifamily was te 0 .9 unit less Used to est imate the tota l generated was te for the mult ifami ly sec tor.

Fraction of residential waste- Kawasaki and Atlanta 0.5 unitlessUsed to estimate the total generated waste for the residential sector. Obtained from the Comprehensive Solid WasteManagement Plan- Introduction, City of Atlanta, December 2005.

Fraction of multifamily waste- Kawasaki and Atlanta 0.5 unitlessUsed to estimate the total generated waste for the residential sector. Obtained from the Comprehensive Solid Waste

Management Plan- Introduction, City of Atlanta, December 2005.

Fraction of sanitary sludges 0.06 unitless Used to estimate the modeled waste composition.

Fraction of animal waste 0.06 unitless Used to estimate the modeled waste composition.

Sanitary sludges moisture content 0.7 unitless

Animal waste moisture content 0.6 unitless This value was not used since animal waste was modeled as foodwaste per Keith Weitz et al.'s e-mail, May 23, 2007

Addit ional cos t per ext ra 20 km 10 $US/metri c ton MSW DST tool does not have a corresponding input .

Capture rate 1 unitless

Collection frequency 6 days/week

Families per stop in the residential sector 2 unitless

Household size in the residential sector 6 people/house

Usable capacity in open trucks 5 m3 MSW DST tool does not have a corresponding input.

Travel speeds:

*To city boundary 30 km/hr

*City boundary to treatment 45 km/hr

Travel distances:

*To city boundary 20 km

Generation

Collection

*City boundary to treatment 10 km

Fraction of waste to open burning 0.3 unitless Used to define the modeled scenarios.

Fraction of waste to open dumping 0.7 unitless Used to define the modeled scenarios.

MRF economic scale 150 metric tons/day Used to make decisions.

Travel speeds:

*MRF to remanufacturing 55 km/hr MSW DST tool does not have a corresponding input.

Travel distances:

*MRF to remanufacturing 10 km

Composting economic scale 150 metric tons/day Used to make decisions.

Fraction of composted waste to vermicompost 0.1 uni tless Used to adjust f inal resul ts.

Travel speeds:

*Composting to end user 55 km/hr MSW DST tool does not have a corresponding input.

Travel distances:

*C i d k MSW DST l d h di i

Open dump

Composting

MRF

Appendix 2.5.9 Constants Data - Metric



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Animal waste heating value 1797 Kcal/wet KgSet to be the foodwaste heating value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23,

2007.

Sanitary s ludge ult imate analysis data- Carbon 0.546 uni tless

Sanitary sludge ultimate analysis data- Hydrogen 0.079 unitless

Sanitary s ludge ult imate analysis data- Oxygen 0.32 uni tless

Sanitary sludge ultimate analysis data- Nitrogen 0.045 unitless

Sanitary s ludge ul timate analys is data- Chlorine 0 unit less

Sanitary s ludge ult imate analysis data- Sul fur 0.01 uni tless

Animal waste ult imate analysis data- Carbon 0.1790 uni tless

Animal waste ultimate analysis data- Hydrogen 0.0260 unitless

Animal waste ult imate analysis data- Oxygen 0.1290 uni tless

Animal waste ult imate analysis data- Nitrogen 0.0110 uni tless

Animal waste ult imate analysis data- Chlorine 0.0040 uni tless

Animal waste ult imate analysis data- Sul fur 0.0010 uni tless

Animal waste ult imate analysis data- Water 0.6000 uni tless

Animal waste ult imate analysis data- Ash 0.0510 un it less

Sanitary sludge ash content 0.05weight fraction (dry

basis)Set to be the foodwaste's value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Animal waste ash content 0.05 weight fraction (drybasis)

Set to be the foodwaste's value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Sanitary sludge combustion efficiency 0.95weight fraction

(volatile solidsSet to be the foodwaste's value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Animal waste combustion efficiency 0.95weight fraction(volatile solids

Set to be the foodwaste's value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Set to be the foodwaste's values as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Landfill

From Partial Oxidation of Sewage Sludge document, as suggested in Keith's e-mail, 5/29/07

Landfill Economic scale 300 metric tons/day Used to make decisions.

Travel speeds:

*Treatment to landfill 55 km/hr MSW DST tool does not have a corresponding input.

Travel distances:

*Treatment to landfill 20 km

Landfill depth 15 m

Landfill slope 33 percent

Landfill usable capacity 0.7 unitless MSW DST tool does not have a corresponding input.

Fraction of LF gas capture 0.7 unitless

Animal waste Lo (methane yield) 300.7 L/kg Set to be the foodwaste Lo value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Sanitary sludge K (degradation rate) 0.11 yr-1 Set to be the foodwaste K value as suggested by Dr. Barlaz and documented in Ozge et al.'s e-mail, April 18, 2007.

Animal waste K (degradation rate) 0.11 yr-1 Set to be the foodwaste K value as suggested by Keith Weitz and documented in Keith et al.'s e-mail, May 23, 2007.

Methane Carbon dioxide equiv alent 21 unitless Used to adjust final results.

Carbon credit 12 $US/metric ton Used to adjust final results.

Others


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APPENDIX 3.3 OPTIMIZATION SCENARIOS MASS FLOWS BYCITY AND MANAGEMENT PROCESS

Table A3.3.1 Group 3- Maximizing Materials Recovery (Via Recycling and Composting) Mass Flow


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Mass Flow (Ton/year)

CollectionCity Scenario

R-Commingled

R-Residuals MF- YardWaste

MF- Commingled

Commingled

Recycling

Mixed Waste

Composting

Landfill

Disposal

Group 3 DHC 1,982 15,652 17,841 140,864 19,824 156,515 33,149

Group 3 BHC 1,982 15,652 17,841 140,864 19,824 156,515 33,149

Group 3 DLC 881 16,636 7,929 150,775 8,811 167,412 37,333Katmandu

Group 3 BLC 881 16,753 7,929 150,775 8,811 167,528 37,358

Group 3 DHC 1,437 8,890 12,931 80,008 14,368 65,112 36,143

Group 3 BHC 1,437 8,890 12,931 80,008 14,368 65,112 36,143

Group 3 DLC 639 9,688 5,747 87,192 6,386 87,192 27,796Conakry

Group 3 BLC 639 9,688 5,747 87,192 6,386 87,192 27,796

Group 3 DHC 11,518 134,628 103,663 1,211,651 115,181 734,284 730,730

Group 3 BHC 11,518 134,628 103,663 1,211,651 115,181 734,284 730,730

Group 3 DLC 5,119 141,027 46,072 1,269,242 51,192 820,727 730,730Lahore

Group 3 BLC 5,119 141,027 46,072 1,269,242 51,192 820,727 730,730

Group 3 DHC 2,176 16,939 19,587 152,454 21,763 169,393 27,578

Group 3 BHC 2,176 16,939 19,587 152,454 21,763 169,393 27,578

Group 3 DLC 967 18,149 8,705 163,336 9,673 181,484 32,656Sarajevo

Group 3 BLC 967 18,149 8,705 163,336 9,673 181,484 32,656

Group 3 DHC 14,941 71,451 134,473 643,058 149,414 714,508 147,520

Group 3 BHC 14,941 71,451 134,473 643,058 149,414 714,508 147,520

Group 3 DLC 7,159 79,233 64,431 713,099 71,590 687,998 259,177Amman

Group 3 BLC 7,159 79,233 64,431 713,099 71,590 687,998 259,177

Group 3 DHC 92,070 320,742 828,628 2,886,673 920,698 2,488,742 1,238,434

Group 3 BHC 92,070 320,742 828,628 2,886,673 920,698 2,488,742 1,238,434

Group 3 DLC 40,920 371,891 368,279 3,347,022 409,199 3,228,039 1,238,434

B

uenosAires

Group 3 BLC 40,920 371,891 368,279 3,347,022 409,199 3,228,039 1,238,434

Group 3 DHC 90,568 574,002 811,267 5,141,653 901,835 5,715,655 1,259,013

Group 3 BHC 90,568 574,002 811,267 5,141,653 901,835 5,715,655 1,259,013

Group 3 DLC 42,562 622,009 366,358 5,586,562 408,919 6,208,571 1,455,848Shanghai

Group 3 BLC 42,562 622,009 381,250 5,571,670 423,812 6,185,513 1,455,848

Group 3 DHC 67,688 220,056 84,278 203,466 151,967 423,521 108,399

Group 3 BHC 67,688 220,056 84,278 203,466 151,967 423,521 108,399

Group 3 DLC 30,084 257,660 37,457 250,287 67,541 507,947 140,768Kawasaki

Group 3 BLC 30,084 257,660 37,457 250,287 67,541 507,947 140,768

Group 3 DHC 103,499 333,853 102,137 329,460 205,636 663,312 182,673

Group 3 BHC 103,499 333,853 102,137 329,460 205,636 663,312 182,673

Group 3 DLC 46,000 391,352 45,394 386,203 91,394 777,555 227,180Atlanta

Group 3 BLC 46,000 391,352 45,394 386,203 91,394 777,555 227,180

Table A3.3.2 Group 4- Maximizing Energy Recovery Mass Flow


Collection Separation


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City

ScenarioR-Mixed

Waste

R-

Commingled

R-

Residuals

MF-

Commingle

d

MF-

Residuals

Mixed

WasteCommingled

Combustion Disposal

Group 4 DHC 17,634 0 0 15,940 142,765 160,399 15,940 150,131 16,628

Group 4 BHC 17,634 0 0 15,940 142,765 160,399 15,940 150,131 16,628

Group 4 DLC 17,634 0 0 7,084 151,621 169,255 7,084 157,435 11,998Katmandu

Group 4 BLC 17,634 0 0 7,084 151,621 169,255 7,084 157,435 11,998

Group 4 DHC 10,327 0 0 8,602 84,337 94,663 8,602 88,910 12,845

Group 4 BHC 10,327 0 0 8,602 84,337 94,663 8,602 88,910 12,845

Group 4 DLC 10,327 0 0 3,823 89,116 99,443 3,823 92,820 13,552Conakry

Group 4 BLC 10,327 0 0 3,823 89,116 99,443 3,823 92,820 13,552

Group 4 DHC 146,146 0 0 82,473 1,232,841 1,378,987 82,473 1,306,119 241,879

Group 4 BHC 0 9,164 136,982 82,473 1,232,841 1,369,824 91,636 1,303,322 241,317

Group 4 DLC 0 4,639 141,507 41,751 1,273,563 1,415,070 46,390 1,343,481 249,850Lahore

Group 4 BLC 0 4,639 141,507 41,751 1,273,563 1,415,070 46,390 1,343,481 249,850

Group 4 DHC 19,116 0 0 11,584 160,457 179,573 11,584 168,601 52,518

Group 4 BHC 0 1,287 17,829 11,584 160,457 178,286 12,871 167,917 52,448

Group 4 DLC 19,116 0 0 5,148 166,893 186,008 5,148 175,750 53,159Sarajevo

Group 4 BLC 19,116 0 0 5,148 166,893 186,008 5,148 175,750 53,159

Group 4 DHC 86,392 0 0 15,466 762,064 0 15,466 848,457 85,309

Group 4 BHC 86,392 0 0 15,466 762,064 0 15,466 848,457 85,309

Group 4 DLC 86,392 0 0 6,874 770,657 0 6,874 857,049 90,179Amman

Group 4 BLC 86,392 0 0 6,874 770,657 0 6,874 857,049 90,179

Group 4 DHC 0 46,047 366,765 414,419 3,300,882 3,667,647 460,465 3,281,803 267,131

Group 4 BHC 0 46,047 366,765 414,419 3,300,882 3,667,647 460,465 3,281,803 267,131

Group 4 DLC 412,811 0 0 184,186 3,531,115 3,943,926 184,186 3,515,909 342,197

B

uenosAires

Group 4 BLC 0 20,465 392,346 184,186 3,531,115 3,923,461 204,651 3,506,190 338,732

Group 4 DHC 664,570 0 0 151,605 5,801,315 6,465,886 151,605 6,189,352 449,778

Group 4 BHC 0 16,925 647,646 151,605 5,801,315 6,448,961 168,530 6,181,379 447,630

Group 4 DLC 664,570 0 0 108,753 5,844,167 6,508,737 108,753 6,280,649 479,837Shanghai

Group 4 BLC 664,570 0 0 108,753 5,844,167 6,508,737 108,753 6,280,649 479,837

Group 4 DHC 0 46,404 241,340 46,404 241,340 482,680 92,808 447,299 54,688

Group 4 BHC 0 46,404 241,340 46,404 241,340 482,680 92,808 447,299 54,688

Group 4 DLC 0 20,624 267,120 20,624 267,120 534,240 41,248 473,587 61,754Kawasaki

Group 4 BLC 0 20,624 267,120 20,624 267,120 534,240 41,248 473,587 61,754

Group 4 DHC 0 83,134 354,217 82,040 349,557 703,774 165,175 602,327 86,779

Group 4 BHC 0 83,134 354,217 82,040 349,557 703,774 165,175 602,327 86,779

Group 4 DLC 0 36,949 400,403 37,897 393,700 794,103 74,846 675,852 104,377Atlanta

Group 4 BLC 0 36 949 400 403 37 897 393 700 794 103 74 846 675 852 104 377

Table A3.3.3 Group 5- Minimize Carbon (Global Warming) Emissions Mass Flow




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City

ScenarioR-Yard

waste

R-

MixedWaste

R-CommingledR-

Residuals

MF-

Commingled

MF-

Residuals

Mixed

Waste Commingled

CombustionAsh

Landfill

Group 5 DHC 0 0 1,349 15,285 12,137 145,558 152,853 13,485 155,220 17,203

Group 5 BHC 0 0 1,349 15,285 12,137 145,558 152,853 13,485 155,220 17,203

Group 5 DLC 0 0 505 17,128 4,549 154,155 171,284 5,055 152,944 12,531Katmandu

Group 5 BLC 0 0 505 17,128 4,549 154,155 171,284 5,055 152,944 12,531

Group 5 DHC 0 10,327 0 0 4,420 88,519 98,845 4,420 93,978 13,525

Group 5 BHC 0 0 491 9,835 4,420 88,519 98,355 4,911 93,855 13,497

Group 5 DLC 0 10,327 0 0 1,955 90,975 101,301 1,955 95,381 14,035Conakry

Group 5 BLC 0 0 218 10,108 1,955 90,975 101,083 2,183 95,283 14,011

Group 5 DHC 0 0 14,974 131,172 134,755 1,180,548 1,311,720 149,740 1,224,371 237,472

Group 5 BHC 14,131 0 14,974 117,041 134,755 1,180,548 1,297,588 149,740 1,224,371 237,472

Group 5 DLC 0 0 5,555 139,491 59,895 1,255,418 1,394,909 55,551 1,285,099 247,010Lahore

Group 5 BLC 0 0 5,555 139,491 59,895 1,255,418 1,394,909 55,551 1,285,099 247,010

Group 5 DHC 0 0 558 18,548 5,110 155,931 185,479 5,577 177,881 53,500

Group 5 BHC 0 0 558 18,548 5,110 155,931 185,479 5,577 177,881 53,500

Group 5 DLC 0 0 252 18,853 2,271 159,770 188,533 2,523 181,325 53,820Sarajevo

Group 5 BLC 0 0 252 18,853 2,271 159,770 188,533 2,523 181,325 53,820

Group 5 DHC 0 0 7,701 78,592 59,305 708,225 785,917 77,005 731,530 72,220

Group 5 BHC 0 0 7,701 78,592 59,305 708,225 785,917 77,005 731,530 72,220

Group 5 DLC 0 0 3,422 82,970 30,802 745,728 829,598 34,225 755,525 77,825Amman

Group 5 BLC 0 0 3,422 82,970 30,802 745,728 829,598 34,225 755,525 77,825

Group 5 DHC 0 0 53,580 349,231 572,224 3,143,077 3,492,308 535,804 3,121,422 253,575

Group 5 BHC 0 0 53,580 349,231 572,224 3,143,077 3,492,308 535,804 3,121,422 253,575

Group 5 DLC 0 0 28,258 384,553 254,322 3,450,979 3,845,533 282,580 3,379,275 335,395

Bu

enosAires

Group 5 BLC 0 0 28,258 384,553 254,322 3,450,979 3,845,533 282,580 3,379,275 335,395

Group 5 DHC 0 0 59,783 594,787 525,084 5,327,835 5,922,523 594,857 5,517,283 415,474

Group 5 BHC 0 0 59,783 594,787 525,084 5,327,835 5,922,523 594,857 5,517,283 415,474

Group 5 DLC 0 0 31,015 533,555 277,815 5,575,105 5,308,550 308,830 5,799,091 455,385Shanghai

Group 5 BLC 0 0 31,015 533,555 277,815 5,575,105 5,308,550 308,830 5,799,091 455,385

Group 5 DHC 0 0 45,595 241,049 45,595 241,049 482,097 93,391 442,147 55,272

Group 5 BHC 0 0 45,595 241,049 45,595 241,049 482,097 93,391 442,147 55,272

Group 5 DLC 0 0 20,754 255,990 20,754 255,990 533,981 41,507 455,494 52,541Kawasaki

Group 5 BLC 0 0 20,754 255,990 20,754 255,990 533,981 41,507 455,494 52,541

Group 5 DHC 0 0 42,257 395,085 41,711 389,885 784,971 83,978 735,984 100,575

Group 5 BHC 0 0 42,257 395,085 41,711 389,885 784,971 83,978 735,984 100,575

Group 5 DLC 0 0 18,785 418,555 18,538 413,059 831,525 37,324 770,041 114,045Atlanta

Table A3.3.4 Group5- Minimize PM (Global Dimming) Emissions Mass Flow


C ll i S i Di l


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Collection Separation DisposalCity Scenario

R-Commingled R-Residuals MF-Commingled MF-Residuals MixedWaste Commingled

Combustion

Landfill Ash-landfill

Group 5 DHC 89,255 348,097 88,081 343,515 591,513 177,335 599,287 0 85,592

Group 5 BHC 89,255 348,097 88,081 343,515 591,513 177,335 599,287 0 85,592

Group 5 DLC 39,559 397,583 39,147 392,450 790,133 78,815 574,055 0 104,258Atlanta

Group 5 BLC 39,559 397,583 39,147 392,450 790,133 78,815 574,055 0 104,258

Group 5 DHC 95,754 558,805 857,810 5,095,110 5,553,915 953,574 5,158,031 0 381,953

Group 5 BHC 95,754 558,805 857,810 5,095,110 5,553,915 953,574 5,158,031 0 381,953

Group 5 DLC 42,552 522,009 381,249 5,571,571 5,193,579 423,811 5,570,281 0 433,833Shanghai

Group 5 BLC 42,552 522,009 381,249 5,571,571 5,193,579 423,811 5,570,281 0 433,833

Group 5 DHC 92,070 320,742 828,528 2,885,573 3,207,414 920,598 2,785,581 0 228,221

Group 5 BHC 92,070 320,742 828,528 2,885,573 3,207,414 920,598 2,785,581 0 228,221

Group 5 DLC 40,920 371,891 358,279 3,347,022 3,718,913 409,199 3,189,992 0 315,539

BuenosAires

Group 5 BLC 40,920 371,891 358,279 3,347,022 3,718,913 409,199 3,189,992 0 315,539

Group 5 DHC 1,982 15,552 17,841 140,854 155,515 19,824 0 147,415 0

Group 5 BHC 1,982 15,390 17,841 140,854 155,253 19,824 0 147,155 0

Group 5 DLC 787 15,847 7,084 151,520 158,457 7,872 0 157,029 0Katmandu

Group 5 BLC 787 15,847 7,084 151,520 158,457 7,872 0 157,029 0

Group 5 DHC 54,208 223,535 54,208 223,535 447,071 128,417 395,458 0 51,559

Group 5 BHC 54,208 223,535 54,208 223,535 447,071 128,417 395,458 0 51,559

Group 5 DLC 28,537 259,207 28,537 259,207 518,414 57,074 431,548 0 59,181Kawasaki

Group 5 BLC 28,537 259,207 28,537 259,207 518,414 57,074 431,548 0 59,181

Group 5 DHC 1,287 17,829 11,584 150,457 178,285 12,871 173,594 0 52,795

Group 5 BHC 1,287 17,829 11,584 150,457 178,285 12,871 173,594 0 52,795

Group 5 DLC 572 18,544 5,148 155,893 185,435 5,720 179,538 0 53,377Sarajevo

Group 5 BLC 572 18,544 5,148 155,893 185,435 5,720 179,538 0 53,377

Group 5 DHC 17,500 128,545 158,398 1,155,915 1,285,452 175,998 1,190,581 0 233,955

Group 5 BHC 17,500 128,545 158,398 1,155,915 1,285,452 175,998 1,190,581 0 233,955

Group 5 DLC 7,822 138,324 70,399 1,244,915 1,383,239 78,221 1,254,085 0 244,835Lahore

Group 5 BLC 7,822 138,324 70,399 1,244,915 1,383,239 78,221 1,254,085 0 244,835

Group 5 DHC 15,108 70,285 144,959 532,551 702,845 151,077 0 520,224 0

Group 5 BHC 15,108 59,341 144,959 532,551 701,903 151,077 0 519,281 0

Group 5 DLC 7,159 79,233 54,431 713,100 792,333 71,590 0 588,455 0Amman

Group 5 BLC 7,159 79,233 54,431 713,100 792,333 71,590 0 588,455 0

Group 5 DHC 1,437 8,890 12,931 80,008 88,898 14,358 82,187 0 12,303

Group 5 BHC 1,437 8,890 12,931 80,008 88,898 14,358 82,187 0 12,303

Group 5 DLC 539 9,588 5,747 87,192 95,880 5,385 88,444 0 13,215Conakry

G 5 BLC 539 9 588 5 747 87 192 95 880 5 385 88 444 0 13 215

APPENDIX 3.4 Simulation and Optimization Scenario

Results by City


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Results by City

1. Amman, Jordan

2. Atlanta, Georgia, USA

3. Buenos Aires, Argentina

4. Conakry, Guinea

5. Kathmandu, Nepal

6. Kawasaki, Japan

7. Lahore, Pakistan

8. Sarajevo, Bosnia And Herzegovina

9. Shanghai, China


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APPENDIX 3.4.1 Amman, Jordan

ParameterUnits (per

year)Recycling- manual sort Recycling- Mechanical sort

Group 2- All MSW to One Option


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Col lect ion Transfer Separation Treatment Disposal Transportat ion Remanufacturing Col lect ion Transfer Separation Treatment Disposal Transportat ion Remanufacturing

Cost with land price US$ 24,210,508 0 16,965,986 0 33,658,185 1,439,922 -17,578,557 24,210,508 0 14,691,851 0 33,658,185 1,439,922 -17,578,557

Cost without land price 17,449,346 17,449,346

Energy Consumption MJ 123,688 0 138,644 0 295,602 14,043 -2,385,737 123,688 0 247,385 0 295,602 14,043 -2,385,737

Air Emissions

Mercury (Air) kg n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Total Particulate Matter kg 1,390 0 3,301 0 23,890 1,200 -729,841 1,390 0 4,338 0 23,890 1,200 -729,841

Nitrogen Oxides kg 116,164 0 97,904 0 75,686 8,334 -870,462 116,164 0 190,881 0 75,686 8,334 -870,462

Sulfur Oxides kg 8,826 0 296,607 0 23,765 2,365 -891,091 8,826 0 654,358 0 23,765 2,365 -891,091

Carbon Monoxide kg 18,904 0 52,682 0 815,908 8,215 -1,025,072 18,904 0 99,831 0 815,908 8,215 -1,025,072

Carbon Dioxide Biomass kg 2,048 0 5,116 0 185,553,987 233 75,316,646 2,048 0 10,378 0 185,553,987 233 75,316,646

Carbon Dioxide Fossil kg 2,701,454 0 24,858,205 0 2,553,137 971,367 -93,910,984 2,701,454 0 50,510,514 0 2,553,137 971,367 -93,910,984

Carbon Equivalents MTCE 821 0 7,821 0 53,352 293 -28,305 821 0 15,956 0 53,352 293 -28,305

Hydrocarbons (non CH4) kg 4,601 0 88,524 0 8,937 3,353 -1,316,886 4,601 0 184,722 0 8,937 3,353 -1,316,886

ea r g 0 0 0 0 0 0 -49 0 0 1 0 0 0 -49

Ammonia (Air) kg 0 0 438 0 14 2 -3,593 0 0 969 0 14 2 -3,593

Methane (CH4) kg 1,391 0 55,110 0 8,329,242 154 -11,502 1,391 0 122,084 0 8,329,242 154 -11,502

Hydrochloric Acid kg 8 0 19 0 10,720 1 -3,194 8 0 37 0 10,720 1 -3,194

Ancillary Solid Waste kg 45,099 0 855,443 0 342,158 5,073 4,546,340 45,099 0 1,883,322 0 342,158 5,073 4,546,340

Water Emissions

Dissolved Solids kg 11,934 0 447,941 0 13,926 1,327 -44,979 11,934 0 991,991 0 13,926 1,327 -44,979

Suspended Solids kg 270 0 8,152 0 414 30 24,481 270 0 18,027 0 414 30 24,481

BOD kg 44 0 450 0 73,173 5 55,798 44 0 981 0 73,173 5 55,798

COD kg 295 0 6,310 0 203,835 33 -275,591 295 0 13,910 0 203,835 33 -275,591

Oil kg 276 0 7,872 0 65,729 31 7,228 276 0 17,401 0 65,729 31 7,228

Sulfuric Acid kg 2 0 18 0 1 0 58 2 0 39 0 1 0 58

Iron kg 6 0 14 0 2 1 9,485 6 0 28 0 2 1 9,485

Ammonia (Water) kg 5 0 11 0 2,337 1 -1,517 5 0 22 0 2,337 1 -1,517

Copper kg 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Cadmium kg 0 0 20 0 1 0 -1 0 0 45 0 1 0 -1

Arsenic kg 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Mercury (Water) kg 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Phosphate kg 1 0 9 0 16 0 -1,555,421 1 0 20 0 16 0 -1,555,421

Selenium kg 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Chromium kg 0 0 20 0 1 0 -2 0 0 45 0 1 0 -2

Lead (Water) kg 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Zinc kg 0 0 7 0 0 0 -51,720 0 0 15 0 0 0 -51,720

PPENDIX3.4_AmmanSimulationResults Group 2 Page 1

ParameterUnits (per

year)

C ll t i T f S ti T t t Di l T t t i R f t i C l l t i T f S ti T t t Di l T t t i R f t i

Composting- manual turning

Group 2- All MSW to One Option

Composting- windrow turner


77/272

Cost with land price US$

Cost without land price

Energy Consumption MJ

Air Emissions

Mercury (Air) kg

Total Part iculate Matter kg

Nitrogen Oxides kg

Sulfur Oxides kg

Carbon Monoxide kg

Carbon Dioxide Biomass kg

Carbon Dioxide Fossil kg

Carbon Equivalents MTCE

Hydrocarbons (non CH4) kg

Col lect ion Transfer Separation Treatment Disposal Transportat ion Remanufacturing Col lect ion Transfer Separation Treatment Disposal Transportat ion Remanufacturing

24,210,508 0 0 15,743,952 15,215,292 439,372 0 24,210,508 0 0 40,244,386 15,215,366 439,368 0

7,888,033 7,888,072

123,688 0 0 255,009 152,918 8,065 0 123,688 0 0 301,541 152,918 8,065 0

n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

1,390 0 0 550 3,055 689 0 1,390 0 0 7,675 3,055 689 0

116,164 0 0 47,386 20,437 4,786 0 116,164 0 0 140,052 20,437 4,786 0

8,826 0 0 182,237 3,891 1,358 0 8,826 0 0 202,250 3,892 1,358 0

18,904 0 0 24,057 81,691 4,718 0 18,904 0 0 56,047 81,694 4,718 0

2,048 0 0 309,765,661 14,833,766 134 0 2,048 0 0 309,765,413 14,834,486 134 0

2,701,454 0 0 13,077,715 1,094,935 557,851 0 2,701,454 0 0 16,694,338 1,094,941 557,847 0

821 0 0 4,147 5,305 168 0 821 0 0 5,250 5,305 168 0

4,601 0 0 49,586 3,762 1,926 0 4,601 0 0 64,755 3,762 1,926 0

ea r g

Ammonia (Air) kg

Methane (CH4) kg

Hydrochloric

Holistic Decision Modeling for Solid Waste Technology Options in Developing Countries - Appendices

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