electronic supplementary information ESI -revised

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a Singapore Membrane Technology Centre, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798

* Corresponding author, email: [email protected], Tel: +65-67904773

Electronic Supplementary Information

(13 pages, including 1 Scheme, 6 Tables, 1 Figure)

Environmental Life Cycle Assessment of Brackish Water Reverse Osmosis Desalination

for Different Electricity Production Models

Jin Zhou a, Victor W.-C. Chang a,*, Anthony G. Fane a

Supplementary Material (ESI) for Energy & Environmental ScienceThis journal is © Royal Society of Chemistry 2011

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Contents

Scheme S1. Procedure for establishing SG LCI for electricity production .............................................................................................................................. 3

Table S1. Assumptions for establishing SG LCI for electricity production .............................................................................................................................. 4

Table S2. Assumptions for electricity from heavy fuel oil in SG ............................................................................................................................................... 5

Table S3. Assumptions for electricity from natural gas in SG .................................................................................................................................................. 6

Table S4. Characterization models used in Life cycle impact assessment ............................................................................................................................ 7

Table S5. Contribution analysis of US, SG, and ES scenarios a,b ........................................................................................................................................... 8

Table S5. Contribution analysis of US, SG, and ES cases a,b (continued) .............................................................................................................................. 9

Table S6. The impact break down of crude oil production in ODP ........................................................................................................................................ 10

Figure S1. The contribution of individual processes under different salinity levels .............................................................................................................. 11

Reference ................................................................................................................................................................................................................................... 12


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Scheme S1. Procedure for establishing SG LCI for electricity production


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Table S1. Assumptions for establishing SG LCI for electricity production

Processes Comments

Singapore electricity production mix

(1) The assumptions for developing datasets of electricity from heavy fuel oil are summarized in Table S1-S1

(2) The assumptions for developing datasets of electricity from natural gas are summarized in Table S2-S1. The Wobbe Index of natural gas is assumed to be 45.2 MJ/m

3 1.The natural gas composition is assumed to be the same with Chan’s study

2 .

(3) The overall performance of Orimulsion is about 9% lower than heavy fuel oil 3. Therefore, the data used to represent electricity from Orimulsion is

modified from the heavy fuel oil dataset in French scenario.

(4)The photovoltaic electricity generation in Singapore is modelled as German scenario

(5) The datasets of electricity from waste incineration and diesel combustion are modelled as Switzerland scenario, which is the only available data in Ecoinvent database.

↑ Electricity transmission

and distribution (1) ABB is a major contractor for the management and operation of Singapore’s electricity transmission and distribution network

4. Therefore, all the

construction of transmission and distribution network in Singapore is assumed to use the same technology as Switzerland. However, some changes were made to simulate local context. Firstly, wood poles and wood preservative process are removed because the transmission and distribution network in Singapore are all underground cables; secondly, extra excavation process was added for digging trenches for cables.

(2) The overall electricity power transmission and distribution loss in Singapore is about 5% 5. Since the transmission distance in Singapore is limited,

the loss of transmitting electricity at high voltage is assumed as 2%, while the electricity loss during medium-voltage is 3%


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Table S2. Assumptions for electricity from heavy fuel oil in SG

Processes Comments

Electricity, oil, at power plant It is calculated based on power plant efficiency (33%) 6

↑ Heavy fuel oil, burned in power plant Senoko Power, the largest oil-fired power generation company in SG, employs the technology of Hitachi steam

7. It is appropriate to use

Japanese (JP) dataset as a proxy for SG. In addition, the dataset for JP oil-fired power plant is the same as that in French (FR) scenario in Ecoinvent database. The FR dataset is modified to fit SG context. The release of NOx and SOx is reduced to 30% with the help of retaining technology

↑ Heavy fuel oil, at regional storage This process mainly accounts for the impacts imposed by transporting heavy fuel oil from refinery to end user. Ecoinvent provide datasets

for Europe (RER) and Switzerland (CH) scenario. Since the impacts of transportation is proportional to distance, the CH scenario is used as the proxy for SG scenario

↑ Heavy fuel oil, at refinery The technology used in SG is assumed the same with FR. Therefore, this datasets is established based on the modification of RER

scenario. ↑

Crude oil production Share of oil imported in Singapore: Middle East (RME) - 88%, Malaysia (MY) – 5%, Indonesia (ID) – 2%, China(CN) – 1%, Vietnam (VN) – 1%, Russia (RU) – 1%, others – 2%

8. The transportation of crude oil from exploration site to refinery is modelled as Europe scenario.


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Table S3. Assumptions for electricity from natural gas in SG

Process Comments

Electricity, natural gas, at

power plant

It is calculated based on power plant efficiency (50%) 9

↑

Natural gas, burning in

power plant

Singapore’s gas-fired power plant is modelled as German (DE) scenario since two of largest power generation company in Singapore, Senoko Power 10

and Power Seraya 7, are identified as using Siemens Combined-Cycle Plants.

↑

Natural gas, high pressure,

at consumer

Both SG and JP are the Asian countries with highest population density 11

, so the SG dataset of natural gas distribution network is calculated based

on that in JP scenario, assuming the length of pipelines is proportional to the territorial area.

↑

Natural gas, production MY

and ID, at long-distance

pipeline

About 75% of natural gas is imported from ID, while 25% is from MY 12

. It is assumed that half of the natural gas pipelines transported from Malaysia /Indonesia to Singapore are onshore pipeline, while another half are offshore pipelines. The leakage of Singapore’s natural gas transportation is assume to be similar to reference

13.

↑

Natural gas, at production

MY and ID

The composition of natural gas is relatively constant and carries similar impacts, so the developed datasets using the similar technology is adopted to mimic the upstream process of natural gas in SG scenario. it was Royal Dutch Shell that began the gas exploration and production there

14, the

production of natural gas in MY and ID is modelled as Netherlands (NL) scenario.

↑

Natural gas, production NL It is assumed that 70% of natural gas is produced offshore, while another 30% is onshore.


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Table S4. Characterization models used in Life cycle impact assessment

Impact category Indicator Characterization model

Abiotic depletion Abiotic Depletion Potential (ADP) Guinee and Heijungs, 1995 15

Acidification Acidification Potential (AP) Huijbregts, 1999 16

Eutrophication Eutrophication Potential (EP) Heijungs et al., 1992 17

Global warming Global Warming Potential (GWP100) Houghton et al., 1994, 1997 18, 19

Ozone layer depletion) Ozone layer Depletion Potential (ODP) WMO, 1992, 1994, 1999 20-22

Human Toxicity Human Toxicity Potential (HTP) Huijbregts, 1999 23

Fresh water aquatic ecotoxicity Fresh water Aquatic EcoToxicity Potential (FAETP) Huijbregts, 1999 23

Marine Aquatic ecotoxicity Marine Aquatic EcoToxicity Potential (MAETP) Huijbregts, 1999 23

Terrestrial ecotoxicity Terrestrial EcoToxicity Potential (TETP) Huijbregts, 1999 23

Photochemical oxidation Photochemical Ozone Creation Potential (POCP) Derwent et al., 1996, 1998 24, 25


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Table S5. Contribution analysis of US, SG, and ES scenarios a,b

Process contribution (%) ADP AP EP GWP100 ODP HTP FAETP MAETP TETP POCP

country code US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES

ELECTRICITY 98 97 97 96 84 96 72 61 74 97 96 96 94 97 96 94 91 87 80 41 70 91 89 85 92 92 93 96 83 95 Electricity from coal

Hard coal

mining 70 53 2 2 2 2

burning 59 52 37 35 68 45 4 43 16 6 6 18 18 53 47

ash disposal 5 2 10 12 48 40 41 40

Lignite

mining 4 7

burning 1 26 4 2 7 1 2 1 27

ash disposal 4 4 15 5 16

Electricity from heavy fuel oil

extraction 2 29 14

production 1 1 2 26 84 38

transportation 9 3 4 4 1 1 3 7 2

refinery 2 11 1

burning 3 52 9 3 36 14 2 29 14 2 33 12 24 3 2 27 8 11 84 17 3 33 7

Wastewater discharge 3 2 4 1

Electricity from natural gas

well for exploration and production

2 3 31 4 1 56 7 1

extraction 18 64 21 1 13 16 26 1

transportation 2 1 11 41 3 4

production 20 2 1 20 2 1

burning 9 2 12 3 12 60 16 4 15 6 2 17 1


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Table S5. Contribution analysis of US, SG, and ES cases a,b (continued)

Process contribution (%) ADP AP EP GWP100 ODP HTP FAETP MAETP TETP POCP

country code US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES US SG ES

ELECTRICITY 98 97 97 96 84 96 72 61 74 97 96 96 94 97 96 94 91 87 80 41 70 91 89 85 92 92 93 96 83 95

Electricity from nuclear

blasting 2 9 2

enrichment 58 14

Electricity from wood

wood burning 5 2

wood -5

Transmission network

wood preservatives 2 52 48

Other processes 4 4 2 9 8 5 15 5 8 9 7 11 6 2 3 19 8 23 12 13 12 10 5 8 11 8 8 10 12 7

ANTISCALING 1 2 2 3 14 4 26 36 24 2 2 2 3 2 3 4 5 8 15 44 23 7 9 11 3 3 3 3 14 4

INFRASTRUCTURE PRODUCTION 1 1 1 0 1 0 1 1 1 1 1 1 2 1 1 2 3 4 3 8 4 1 1 2 5 5 5 0 2 0

MEMBRANE PRODUCTION 1 1 1 1 1 1

INFRASTRUCTURE DISMANTLING 1 2 5 3 1 1 1

OTHER PROCESSES

REMAINING PROCESSES 1 1 1 1 1 1 2 1 1 1 a abbreviations:

ADP, Abiotic Depletion Potential;

AP, Acidification Potential;

EP, Eutrophication Potential;

GWP100, Global Warming Potential in 100 years;

ODP, Ozone Depletion Potential; HTP, Human Toxicity Potential;

FEATP, Fresh-water Aquatic Eco-Toxicity Potential;

MAETP Marine Aquatic Eco-Toxicity Potential;

TETP, Terrestrial Eco-Toxicity Potential;

POCP, Photochemical Ozone Creation Potential

b the blank cells stand for contribution is less than 1%


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Table S6. The impact break down of crude oil production in ODP

Countries Crude oil exploration site

Shares of oil imported

Major ODP substances

Amount ( kg / kg crude oil produced)

ODP emission factor (in kg CFC-11 eq./kg)

SG RME onshore 88% Halon 1301 5.82E-8 12 Others 12% ES RME onshore 24% Halon 1301 5.82E-8 12 NO offshore 22% Halon 1211 7.30E-10 5.1 RU onshore 18% Halon 1301 5.82E-8 12 GB offshore 17% Halon 1301 4.68E-10 12 RAF onshore 10% Halon 1301 5.82E-8 12 Others 9%


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(a) The energy demand of desalination has dominant contribution to environmental impacts (feed water is low-salinity

brackish water with TDS of 1500 mg/L; it consumes 1 kWh of electricity to produce 1 m3 of fresh water from feed water27)

(b) The energy demand of desalination has dominant contribution to environmental impacts (feed water is sea water with TDS

of 35000 mg/L; it consumes 4 kWh of electricity to produce 1 m3 of fresh water from feed water28)

Figure S1. The contribution of individual process under different salinity levels


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23. M. A. J. Huijbregts, Priority assessment of toxic substances in LCA. , IVAM environmental research, University of Amsterdam,

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