Small-scale domestic wastewater treatment and reuse

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					WATERSAVE Network
Second Meeting, 4 December 2001

Water consumption trends and domestic demand forecasting
F Memon & D Butler
Urban Water Research Group Department of Civil and Environmental Engineering Imperial College of Science, Technology & Medicine, London

Will the available freshwater resources be sufficient to meet the future demand if current water consumption trends remain unchanged?

To answer this
• Pace of population growth • Emerging socio-economic trends • Climate change

Water Stress
Low Moderate Medium-high High

Stress Indicator
(water consumption/Available water)

<0.1 0.1 to 0.2 0.2 to 0.4 >0.4

Water stress observed in 1985
0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0
Africa Asia North Ameriac South America Australia Europe Globe

Water stree

Continent

Expected change in water stress in 2025 (using 1985 as the reference year)
Change in water stress (%)
100 80 60 40 20 0 -20
Climate Population

Europe

Africa

Asia

North America

Continent

Australia

South America

Water consumption (l/hd.d)
900 800 700 600 500 400 300 200 100 0
Kuwait USA Spain Saudi Arabia Japan Germany UK Netherlands Afghanistan Nigeria Gambia

Per capita water consumption in some countries

Country

Frequency
160 140 120 100 80 60 40 20 0

5 to 14 45 to 54 85 to 94 125 to 134 165 to 174 205 to 214 245 to 254 285 to 294 325 to 334 365 to 374 405 to 414 445 to 454 485 to 494 525 to 534 565 to 574

Water consumption frequency distribution

Water consumption (l/hd.d)

Factors influencing domestic demand
• Household size (occupancy) • Household type (flat, detached, semi detached) • Age group (Retired, adult, children) • Seasonal variations

Per capita consumption (l/hd.d)
100 150 200 250 50 0
1 person 2 people 3 people 4 people 5 people 6 people 7 people 8 people

Impact of household size on per capita consumption

Household size

Water consumption by different micro-components
Outside supply Kitchen sink 4% 15% WC 31%

Washing machine 20%

Dishwasher 1%

Bath 15%

Basin 9%

Shower 5%

Micro-component use frequency
4

Use frequencey (per capita per day)

3.5 3 2.5 2 1.5 1 0.5 0 WC Sink Bath Shower Washing machine

Appliance

Approximate peak frequency of use and time
Peak frequency (uses/hour) Washing machine 0.03 Dishwasher Bath Shower Toilet 1.53 0.32 1.2 Micro-component Time of peak 10:45 03:15 18:45 07:30 8:00

Appliance daily discharge pattern

Water consumption by microcomponents
200 160

litres/use

120 80 40 0 Toilet Washing machine Dishwasher Finland Shower Bath Germany

England & Wales

France

Reduction in water consumed by washing machine in last 30 years
180 160 140 120 100 80 60 40 20 0 1970 1980 1985 1988 1992 1998

Water used (litres/cycle)

Year

Reduction in water consumed by dishwashers in last 30 years
60 50

Water used (litres/cycle)

40 30 20 10 0 1970 1980 1985 1992 1997 1999

Year

Water saved and potential for further saving (England and Wales)
9 8 7 6 5 4 3 2 1 0 Hospitals Leisure centers Nursing homes Retail stores

(Ml/d)

Savings made

Potential for further savings

Water saved and potential for further saving (England and Wales)
350 300 250

(Ml/d)

200 150 100 50
Factories Offices Schools Households Universities Hotels / Motels

0

Water saved

Potential for further saving

Demand Forecasting
• Purposes • Factors • Techniques

Demand Forecasting (Purposes)
• • • • Strategic planning; Investment appraisal; Operations planning; Appraisal of demand-management policies and innovations;

Demand Forecasting (Purposes)
• Demand management in “crisis” periods; • Calculation of future price trends as efficiency signals; and • Some supply forecasting

Demand Forecasting (Factors)
• Spatial and temporal variability; • Water conservation policies; • Characteristics associated with various appliances used (i.e. ownership, frequency and volume of water consumed per use);

Demand Forecasting (Factors)
• Lessons learnt from the forecasting techniques used in the past; • Past water consumption trends • Acceptability to the regulator; and • Feasibility w.r.t. cost and data collection and validation requirements.

Demand Forecasting (Techniques)
1.Techniques that build conceptually and require a limited amount of data to produce future projections in water demand. These techniques are usually used for long-term forecast

Demand Forecasting (Techniques)
2.Techniques that require extensive data collection. The data is used to extract the statistical relationships and infer the rules that will govern the extent of demand. These methods are used for short-term forecast

Demand Forecasting (Techniques)
• Micro-component analysis • Micro-component group analysis • Forecasting based on socioeconomic scenarios • Statistical methods • Neural Networks

Demand Forecasting Techniques (Micro-component analysis)

pcc   Oi  Fi Vi   pcr
i

Demand Forecasting Techniques (Micro-component group analysis)

pccg   Oi , g  Fi , g  Vi , g   pcrg
i

NMHH    pccg . popg 
g

Demand Forecasting Techniques (Socio-economic scenario based) • Alpha • Beta • Gamma • Delta

Demand Forecasting Techniques (Socio-economic scenario based)
• Scenario Alpha (Provincial Enterprise): Under this scenario, the preference to the environmental issues and social equity is low due to slow economic growth and lack of investment.

Demand Forecasting Techniques (Socio-economic scenario based)
• Scenario Beta (World market): This scenario assumes a high level of economic growth but little consideration is given to social equity. The concern for environment is low particularly in financially feeble sections of the community.

Demand Forecasting Techniques (Socio-economic scenario based)
• Scenario Gamma (Global sustainability): Sustained economic growth and social equity are the main feature of this scenario. There is a considerable investment in environmental research, which would produce clean technologies that help in resource conservation

Demand Forecasting Techniques (Socio-economic scenario based)
• Scenario Delta (Local stewardship): In this scenario, leadership at local level takes collective action to resolve environmental problems.

Expected change in water demand in 2025 for each scenario
40%

Change in water demand (%)

30% 20% 10% 0% -10% -20% -30%

Alpha

Beta

Gamma

Delta

Acknowledgements
• • • • Paul Jeffrey (Cranfield) David Howarth (Environment Agency) Gareth Rondel (Anglian Water) Paul Herrington (Water Economist)