REID pit latrine SI 3

Supporting information for:

Global Methane Emissions from Pit Latrines

Matthew C. Reid,∗,†,§ Kaiyu Guan,‡ Fabian Wagner,¶ and Denise L. Mauzerall†,‖

Department of Civil and Environmental Engineering, Princeton University, Princeton, New

Jersey 08544, USA, Department of Environmental Earth System Science, Stanford

University, Stanford, California 94305, USA, and International Institute for Applied

Systems Analysis, Laxenburg, Austria

E-mail: [email protected]

1

This Supporting Information (SI) contains 20 pages, with 3 tables and 12 figures. The2

tables contain sociodemographic data used in the geospatial modeling analysis, as well as3

complete pit latrine CH4 emissions estimates with a comparison to estimates in other green-4

house gas emissions inventories. The figures provide representative data used in the projec-5

tion of 2015 pit latrine utilization ratios, additional data on demographic factors related to6

pit latrine utilization, as well as spatial emissions maps for a subset of countries.7

8

∗To whom correspondence should be addressed†Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544,

USA‡Department of Environmental Earth System Science, Stanford University, Stanford, California 94305,

USA¶International Institute for Applied Systems Analysis, Laxenburg, Austria§Current address: Environmental Microbiology Laboratory, Ecole Polytechnique Federale de Lausanne,

Lausanne CH 1015, Switzerland‖Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey,

USA

S-1

Table S1 summarizes the latrine utilization data used in the CH4 emissions model and9

compares them to estimates in the GAINS integrated assessment modelS1 and in Graham10

and Polizzotto (2013),S2 which is focused on quantifying global pit latrine usage. There is11

good agreement between our estimates and the estimates in Graham and Polizzotto (2013).12

There is roughly good agreement between our estimates and GAINS for some Asian coun-13

tries, but in many other countries GAINS significantly underestimates the latrine usage14

estimates by the present study and by Graham and Pollizzotto (2013). This discrepancy is15

especially strong in many Southeast Asian countries – Vietnam, Burma, and Bangladesh.16

GAINS overestimates latrine utilization ratios in Pakistan, and does not include country-17

specific data for countries in sub-Saharan Africa other than South Africa.18

19

Table S2 presents the full results of the pit latrine CH4 emissions model from the present20

study, and compares the results to greenhouse gas inventory and other estimates of pit la-21

trine or general wastewater CH4 emissions. Doorn and Liles (1999)S3 is the only other study22

to quantify pit latrine CH4 emissions specifically. GAINSS1 quantified emissions from decen-23

tralized wastewater treatment (a category which combines pit latrines and septic systems),24

and USEPAS4 and EDGARS5 quantify CH4 from all wastewater sources. Table S3 summa-25

rizes population data for the 21 countries in our sample in 2000, 2015, and 2030.26

27

Figure S1 shows the sensitivity of modeled CH4 emissions to assumptions around the pit28

latrine depth. The uncertainty analysis described in the main text, and which contributes to29

the uncertainty ranges in Table S2, varied the pit latrine depth between 2 to 3, and here we30

assess how a broader range of pit latrine depths would affect the model results for a subset31

of countries. The results are sensitive to the assumed range of pit latrine depths, with a32

”true” pit latrine depth of 5 m leading to the model underestimating emissions by ∼10-15%33

(based on an assumed depth of 3 m). Likewise, a ”true” depth of 1 m leads to the model34

overestimating emissions by ∼10-20% (based on an assumed depth of 2 m). This analysis35

S-2

shows that latrine depths outside of the 2 to 3 m range used in the analysis will introduce36

some error to the estimated emissions, but these errors are on the same order as errors from37

uncertainty in BOD, MCF, or other model parameters.38

39

Figure S2 shows the fraction of pit latrine CH4 emissions as a fraction of total national40

CH4 emissions. Pit latrines are a major source of CH4 in Bangladesh and many African41

countries, but are less important in counties with major oil and gas sectors (e.g. Nigeria,42

Kazahkstan, and Bolivia) and countries with large agricultural sectors (e.g. Brazil). Figures43

S3 and S4 give the population relying on open defecation and the projected growth in ru-44

ral population from 2015 - 2030, both of which are key indicators of where pit latrine CH445

emissions are expected to grow most quickly.46

47

Figures S5 - S11 show spatial maps of population, groundwater level, and estimated pit48

latrine CH4 emissions for a subset of countries. Note the strong co-location of population49

density and shallow water tables in Bangladesh and India. In contrast, Ethiopia and South50

Africa are examples of arid countries where only a very small fraction of the population re-51

sides in areas with shallow water tables. Nigeria, Brazil, and Ghana represent intermediate52

cases. Differences in the co-location of population and shallow water tables leads to the vari-53

ation in EF described in Fig. 3 in the main text. Note as well that the intensity of emissions54

in Northeast India are relatively low despite the co-location of high population density and55

shallow water tables. This is due to the relatively limited utilization of pit latrines in India56

(Table S2).57

58

Figure S12 shows a representative set of WHO/UNICEF Joint Monitoring Program pit59

latrine utilization data for six countries. The figure also shows the regression analyses used60

to predict utilization ratios in 2015 based on trends observed from 1998 through approxi-61

mately 2010.62

S-3

63

S-4

Table S1: Pit Latrine Utilization (%)Country Present Study Ref. S2 GAINS†

2000 2015§ ca. 2010 2000 2015

Urban Rural Total Urban Rural Total

Cameroon 82±1 87±2 85 80±1 (n=6) 84±2 (n=6) 82 82.6 - -D.R. Congo? 92±5 84±5 86 82±5 (n=4) 82±5 (n=4) 82 80 - -Ethiopia 71±1 20±1 29 72±1 (n=5) 60±1 (n=5) 62 56 - -Ghana 63±5 67±3 65 63±5 (n=10) 66±3 (n=10) 64 56.6 - -Nigeria 58±6 60±6 59 41±6 (n=12) 55±6 (n=12) 48 58.9 - -Kenya 60±5 77±1 74 56±5 (n=8) 82±1 (n=8) 75 67.3 - -South Africa 16±1 63±2 36 15±1 (n=10) 72±2 (n=10) 36 36.7 22.3 19.0Tanzania 93±5 89±5 90 89±5 (n=10) 82±5 (n=10) 84 78.8 - -Uganda 91±5 83±3 84 92±5 (n=12) 93±3 (n=12) 93 66.4 - -

Bangladesh 78±17 74±6 75 49±17 (n=13) 79±6 (n=13) 70 60.1 2.2 2.2Burma 82±11 83±11 83 78±11 (n=5) 82±11 (n=5) 81 74.9 2.2 1.9China 25±1 80±6 60 0±1 (n=7) 73±6 (n=9) 32 49.9 27.3 24.9India 23±9 11±2 14 1±1 (n=11) 13±2 (n=11) 9 12.9 20.3 19.4Indonesia 15±3 31±1 24 9±3 (n=13) 31±1 (n=13) 19 3.8 1.7 1.4Pakistan 5±1 13±3 10 1±1 (n=8) 8±3 (n=8) 5 13.7 61.2 56.9Phillipines 7±4 23±3 15 5±4 (n=7) 16±3 (n=7) 11 11.7 1.3 1.2Vietnam 25±17 64±12 55 10±10 (n=10) 33±12 (n=10) 25 18.2 2.2 2.1Kazahkstan 20±1 85±9 48 28±1 (n=3) 86±9 (n=3) 51 62.3 34.1 31.8Turkey 8±3 65±8 28 6±3 (n=3) 62±8 (n=3) 22 22.8 32.5 28.1

Bolivia 24±6 32±2 27 9±6 (n=15) 45±2 (n=15) 20 25.7 - -Brazil 23±1 62±10 30 23±1 (n=16) 65±10 (n=15) 29 24.2 13.4 10.1§n indicates the number of surveys used in linear model for 2015 urban and rural utilizationrates. †Amann and et al. S1 , decentralized wastewater treatment, including septic systemsand pit latrines. GAINS does not have country-specific data for countries in sub-Saharanother than South Africa. ?A representative regional uncertainty of ±5% was used

S-5

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

La

trin

e U

sa

ge

2015 Urban Projection = 9.32%; p=0.02

2015 Rural Projection = 45.09%; p=0.00

Bolivia

Urban

Rural

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

La

trin

e U

sa

ge



EthiopiaUrban

Rural

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

La

trin

e U

sa

ge



South AfricaUrban

Rural

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

La

trin

e U

sa

ge



Uganda

Urban

Rural

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

La

trin

e U

sa

ge



IndiaUrban

Rural

1998 2000 2002 2004 2006 2008 2010 2012 2014 20160

10

20

30

40

50

60

70

80

90

100

Year

Pe

rce

nt

Pit L

atr

ine

Usa

ge



IndonesiaUrban

Rural

Figure S.1: Representative regression analyses used to project country-level pit latrine uti-lization ratios through 2015. p gives the significance level of the slope parameter, and the2015 prediction from the regression analysis was only used if p<0.1. When p>0.1, the meanof the two most recent latrine utilization ratios was used for the 2015 ratio. If the regressionanalysis yielded a negative value for a 2015 projection, 0 was used.

S-6

Tab

leS2:

Est

imat

esof

Was

tew

ater

Met

han

eE

mis

sion

s(T

gC

H4

y−1)

Cou

ntr

yP

rese

nt

Stu

dy

GA

INS†

USE

PA‡

Door

nan

dL

iles§

ED

GA

R?

2000

2015

2000

2015

2000

2015

ca.

1999

2000

Cam

eroon

0.03

3(0

.026

-0.

060)

0.04

4(0

.035

-0.

082)

--

0.01

00.

014

-0.

071

D.R

.C

ongo

0.08

2(0

.058

-0.

160)

0.12

3(0

.087

-0.

238)

--

00

-0.

201

Eth

iopia

0.02

3(0

.014

-0.

043)

0.07

4(0

.048

-0.

138)

--

0.01

90.

033

-0.

259

Ghan

a0.

024

(0.0

17-

0.04

7)0.

033

(0.0

23-

0.06

6)-

-0.

019

0.02

3-

0.08

6N

iger

ia0.

176

(0.1

26-

0.35

4)0.

220

(0.1

53-

0.45

2)-

-2.

172.

940.

3(0

.1-

0.6)

0.50

3K

enya

0.04

0(0

.029

-0.

075)

0.06

2(0

.044

-0.

118)

--

0.01

00.

014

0.1

(0-

0.1)

0.13

9Sou

thA

fric

a0.

016

(0.0

01-

0.03

1)0.

020

(0.0

12-

0.03

8)0.

024

0.02

40.

019

0.03

30.

1(0

-0.

2)0.

299

Tan

zania

0.05

7(0

.040

-0.

110)

0.08

3(0

.059

-0.

162)

--

0.03

80.

052

-0.

152

Uga

nda

0.03

4(0

.024

-0.

065)

0.06

2(0

.044

-0.

118)

--

00.

005

-0.

088

Ban

glad

esh

0.53

4(0

.398

-0.

973)

0.61

2(0

.443

-1.1

40)

0.00

60.

008

0.61

90.

762

0.5

(0.2

-0.

8)0.

581

Burm

a0.

147

(0.1

01-

0.27

6)0.

157

(0.1

08-0

.295

)0.

002

0.00

20.

229

0.27

1-

0.22

7C

hin

a2.

500

(1.6

70-

4.75

)1.

100

(0.7

41-

1.98

3)0.

770

0.76

5.97

06.

430

4.4

(2.1

-7.

6)6.

100

India

0.44

0(0

.220

-1.

000)

0.32

4(0

.179

-0.

700)

0.48

00.

571.

110

2.13

03.

3(1

.6-

5.7)

3.87

Indon

esia

0.20

3(0

.150

-0.

362)

0.13

5(0

.090

-0.2

62)

0.00

80.

008

1.00

51.

200

0.6

(0.3

-1.

2)1.

200

Pak

ista

n0.

057

(0.0

32-

0.11

5)0.

041

(0.0

18-

0.09

8)0.

201

0.26

01.

110

2.13

00.

4(0

.2-

0.8)

0.58

8P

hillipin

es0.

041

(0.0

25-

0.08

2)0.

030

(0.0

14-

0.06

9)0.

002

0.00

30.

105

0.13

8-

0.39

5V

ietn

am0.

193

(0.1

21-

0.39

2)0.

097

(0.0

47-

0.24

4)0.

004

0.00

40.

067

0.07

6-

0.37

8K

azah

kst

an0.

027

(0.0

18-

0.05

0)0.

026

(0.0

18-

0.04

8)0.

020

0.02

00.

019

0.01

9-

0.04

3T

urk

ey0.

032

(0.0

14-

0.07

3)0.

0247

(0.0

10-

0.05

9)0.

201

0.26

00.

100

0.10

5-

0.31

2

Bol

ivia

0.00

5(0

.003

-0.

010)

0.00

5(0

.003

-0.

011)

--

0.01

90.

043

-0.

038

Bra

zil

0.32

0(0

.173

-0.

321)

0.16

1(0

.108

-0.

288)

0.07

60.

067

0.6

0.71

90.

6(0

.3-

1)1.

820

Tab

legi

ves

mea

nva

lues

,w

ith

range

inpar

enth

eses

.† R

ef.,S1

Dec

entr

aliz

edW

aste

wat

er;‡ R

ef.,S4

All

Was

tew

ater

Sou

rces

;§ R

ef.,S3

Pit

Lat

rines

;?R

ef.,S5

All

Was

tew

ater

Sou

rces

S-7

Table S3: Demographic Data†

Country Population (millions) Urban Fraction (%)

2000 2015 2030 2000 2015 2030

Cameroon 16 22 29 50 62 71D.R. Congo 51 77 109 30 39 49Ethiopia 66 92 119 18 18 24Ghana 20 27 35 44 55 65Nigeria 124 180 258 42 52 61Kenya 31 46 66 20 26 33South Africa 45 51 55 57 64 70Tanzania 34 52 82 22 29 37Uganda 24 39 60 12 17 25

Bangladesh 130 158 182 24 30 39Burma 45 53 59 29 37 48China 1,270 1,370 1,390 36 56 69India 1,050 1,310 1,520 28 33 40Indonesia 213 252 280 42 54 63Pakistan 148 206 266 33 38 46Phillipines 77 101 126 48 50 56Vietnam 79 92 101 24 34 43Kazahkstan 15 16 17 56 60 67Turkey 66 80 90 65 72 78

Bolivia 8 11 13 62 68 74Brazil 174 203 220 81 86 88†UN FiguresS6

S-8

1 2 3 4 50.7

0.8

0.9

1

1.1

1.2

Latrine Depth [m]

No

rmaliz

ed C

H4 E

mis

sio

ns

Nigeria

China

India

Figure S.2: Sensitivity of emissions in China, India, and Nigeria to varying the pit latrinedepth used in the model calculations. The uncertainty ranges described in Table S.2 arebased on a latrine depth of 2.5±0.5m.

S-9

0 5 10 15 20 25 30

BoliviaPakistan

South AfricaBrazil

TurkeyKazahkstan

IndonesiaIndia

PhillipinesEthiopiaNigeria

ChinaVietnam

BurmaD.R. Congo

TanzaniaKenyaGhana

CameroonUganda

Bangladesh

Fraction of Total CH4 Emissions [%]

Figure S.3: Pit Latrine Emissions as a Fraction of Total National CH4 Emissions in 2015

S-10

0 10 20 30 40 50 60

ChinaTurkeyBrazil

BangladeshSouth Africa

VietnamBurma

UgandaPhillipinesTanzania

KenyaD.R. Congo

NigeriaBolivia

IndonesiaGhana

EthiopiaIndia

Population Using Open Defecation [%]

3 million0 million

6 million7 million3 million6 million4 million3 million

10 million7 million6 million11 million

35 million2 million

60 million7 million

35 million652 million

Figure S.4: Open Defecation Rates circa 2010, with the number of people relying on opendefecation next to each bar

S-11

−30 −20 −10 0 10 20 30 40 50

ChinaBrazil

IndonesiaKazahkstanSouth Africa

TurkeyBurma

VietnamCameroon

BangladeshGhanaBolivia

IndiaPhillipines

PakistanD.R. Congo

NigeriaEthiopia

KenyaTanzaniaUganda

435 million25 million103 million6 million16 million20 million31 million58 million8 million111 million12 million






Projected Change in Rural Population: 2015 − 2030 [%]

Figure S.5: Rural population change from 2015 - 2030, with the projected rural populationin 2030 next to each bar

S-12

Figure S.6: Ethiopia: Population, Water Table, and CH4 Emissions Maps in 2000

S-13

Figure S.7: Nigeria: Population, Water Table, and CH4 Emissions Maps in 2000

S-14

Figure S.8: Ghana: Population, Water Table, and CH4 Emissions Maps in 2000

S-15

Figure S.9: South Africa: Population, Water Table, and CH4 Emissions Maps in 2000

S-16

Figure S.10: India: Population, Water Table, and CH4 Emissions Maps in 2000

S-17

Figure S.11: Bangladesh: Population, Water Table, and CH4 Emissions Maps in 2000

S-18

Figure S.12: Brazil: Population, Water Table, and CH4 Emissions Maps in 2000

S-19

References64

(S1) Amann, M.; et al., Cost-effective control of air quality and greenhouse gases in Europe:65

Modeling and policy applications. Environ Modell Softw 2011, 26, 1489 – 1501.66

(S2) Graham, J.; Polizzotto, M. Pit Latrines and Their Impacts on Groundwater Quality:67

A Systematic Review. Environmental Health Perspectives 2013, 121, 521–530.68

(S3) Quantification of Methane Emissions and Discussion of Nitrous Oxide, and Ammo-69

nia Emissions from Septic Tanks, Latrines, and Stagnant Open Sewers in the World ;70

United States Environmental Protection Agency: Research Triangle Park, NC, 1999.71

(S4) Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990 - 2030 ; United States72

Environmental Protection Agency (USEPA): Washington, DC, 2012.73

(S5) European Commission Joint Research Centre, Netherlands Environmental Assessment,74

Emission Database for Global Atmospheric Research (EDGAR), Release Version 4.2.75

2010; http://edgar.jrc.ec.europa.eu.76

(S6) United Nations Department of Economic and Social Affairs. World Urbanization77

Prospects: The 2009 Revision Population Database. 2013; http://esa.un.org/78

wup2009/unup/index.asp?panel=1.79

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