Sustainable Wastewater Treatment - Technische Universiteit Eindhoven

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Sustainable Wastewater Treatment, developing a methodology and selecting promising systems

CIP-DATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN van der Vleuten-Balkema, Annelies J.

Sustainable wastewater treatment, developing a methodology and selecting promising systems / by Annelies J. van der Vleuten-Balkema. Eindhoven : Technische Univesiteit Eindhoven, 2003. Proefschrift. - ISBN 90-386-1805-0 NUR 950: Technische wetenschappen algemeen Trefwoorden: duurzaamheid / hergebruik / huishoudelijk afvalwater / model / optimalisering / regenwater / waterzuivering Subject headings: decision support systems / modelling / optimisation / sustainable development / recycling / wastewater / water treatment Printed on recycled paper by Eindhoven University Press. Cover by Frank, Anya and Sacha van der Vleuten. Thesis and software are available electronically at the internet site of the TU/e library.

Sustainable Wastewater Treatment, developing a methodology and selecting promising systems

PROEFSCHRIFT

ter verkrijging van de graad doctor aan de Technische Universiteit Eindhoven, op gezag van de Rector Magnificus, prof.dr. R.A. van Santen, voor een commissie aangewezen door het College voor Promoties in het openbaar te verdedigen op dinsdag 4 november 2003 om 16.00 uur

door

Annelise Juliana van der Vleuten-Balkema

geboren te Amersfoort

Dit proefschrift is goedgekeurd door de promotoren: prof.dr.dipl-ing. H.A. Preisig en prof.dr.-ing. R. Otterpohl

Copromotor: dr.ir. A.J.D. Lambert

voor FRANK

Contents SUMMARY THANKS TO .... 1

INTRODUCTION ................................................................ ................................................................................... ................... 1 1.1 SUSTAINABLE DEVELOPMENT ........................................................................1 1.2 SUSTAINABLE TECHNOLOGY ........................................................................2 1.3 THE CHALLENGES OF THE NEW MILLENNIUM.....................................................4 1.3.1 The global water crisis ...........................................................................4 1.3.2 Decentralised systems to close cycles .....................................................6 1.3.3 Small scale systems for source control to enable reuse ............................7 1.3.4 Optimising the existing systems or introducing to new systems?................7 1.4 RESEARCH OBJECTIVE .................................................................................9 1.5 OVERVIEW OF THE RESEARCH .......................................................................9 References ............................................................................................9

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ASSESSING THE SUSTAINABILITY SUSTAINABILITY OF DOMESTIC DOMESTIC WATER SYSTEMS ............ 11 2.1 EXPLORATION OF METHODOLOGIES (LITERATURE REVIEW) ..................................11 2.1.1 Exergy analysis....................................................................................11 2.1.2 Economic analysis ...............................................................................12 2.1.3 Life Cycle Assessment..........................................................................12 2.1.4 System analysis ...................................................................................14 2.2 EXPLORATION OF METHODOLOGY APPLICATION—A LITERATURE REVIEW ................16 2.2.1 System boundaries ..............................................................................16 2.2.2 Sustainability indicators........................................................................16 2.2.3 Interpretation of results ........................................................................17 2.3 METHODOLOGICAL OUTLINE FOR THIS RESEARCH ...........................................19 2.3.1 Methodology ......................................................................................19 2.3.2 Goal ..................................................................................................20 2.3.3 Scope.................................................................................................21 2.3.3.1 Chosen System boundaries...........................................................21 2.3.3.2 Selected Sustainability indicators ...................................................22 References ..........................................................................................25

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DEVELOPING A MODELMODEL-BASED DECISION SUPPORT SUPPORT TOOL...................... TOOL ...................... 27 3.1 DATA ..................................................................................................27 3.2 MODEL OF DOMESTIC WATER SYSTEMS .........................................................27 3.2.1 Superstructure .....................................................................................28 3.2.2 Model implementation.........................................................................30 3.3 SUSTAINABILITY INDICATORS ......................................................................31 3.4 OPTIMISATION .......................................................................................31 3.4.1 Integer optimisation.............................................................................31 3.4.2 Multi-objective optimisation..................................................................32 3.4.3 Normalization and Weighting ..............................................................32 3.4.4 Solvers used........................................................................................33 3.5 OUTPUT OF DECISION SUPPORT SYSTEM .......................................................36 References ..........................................................................................36

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STREAMS: QUANTIFYING INPUTS & CATEGORISING CATEGORISING OUTPUTS ................ 37 4.1 4.2

WATER SERVICES .....................................................................................37 WATER SUPPLY .......................................................................................37 4.2.1 Domestic water use: .....................................................................38 4.2.2 Economics of water supply:...........................................................39 4.2.3 Social and cultural aspects: Institutions for water supply ........................41 4.3 WASTEWATER ........................................................................................43 4.3.1 Economics of wastewater..............................................................45 4.3.2 Environmental aspects of wastewater treatment..............................45 4.3.3 Social and cultural aspects: Institutions for wastewater management46 4.4 THREATS TO WATER SERVICES .....................................................................46 4.4.1 Pathogenic organisms .........................................................................47 4.4.2 Heavy metals ......................................................................................49 4.5 RESOURCES IN WASTEWATER ......................................................................50 4.5.1 Nutrients.............................................................................................50 4.6 WATER SERVICES QUANTIFIED IN THE DECISION SUPPORT TOOL...........................56 References: .........................................................................................57 5

PROCESSING UNITS: TECHNOLOGY TECHNOLOGY CHARACTERISATION CHARACTERISATION ...................... 61 5.1 SELECTION AND CHARACTERISATION............................................................61 5.2 WATER SUPPLY .......................................................................................61 5.2.1 Water disinfection ...............................................................................61 5.2.2 Water conservation .............................................................................64 5.3 WATER REUSE ........................................................................................65 5.3.1 Dynamics of domestic water use ..........................................................65 5.3.2 Rainwater systems ...............................................................................68 5.3.3 Greywater systems ..............................................................................71 5.3.4 Closed water systems ..........................................................................72 5.4 SANITATION ..........................................................................................73 5.4.1 Different types of water closets .............................................................73 5.4.2 Composting toilets...............................................................................74 5.4.3 Dry toilets ...........................................................................................75 5.4.4 Urine separating toilets ........................................................................75 5.5 WASTEWATER TRANSPORT .........................................................................76 5.5.1 Conventional sewer.............................................................................76 5.5.2 Vacuum sewer ....................................................................................77 5.5.3 Truck ..................................................................................................78 5.5.4 Onsite disposal ...................................................................................78 5.6 WASTEWATER TREATMENT .........................................................................78 5.6.1 Activated sludge..................................................................................78 5.6.2 Anaerobic digestion.............................................................................81 5.6.3 Composting ........................................................................................81 5.6.4 Constructed wetlands ..........................................................................82 5.6.5 Fixed bed reactors...............................................................................84 5.6.6 Membranes ........................................................................................85 5.6.7 Rotation biological contactors ..............................................................88 5.6.8 Sedimentation .....................................................................................88 5.6.9 Septic tanks ........................................................................................89

5.6.10 Trickling filters .....................................................................................89 5.6.11 UV-disinfection....................................................................................91 5.7 TECHNOLOGY CHARACTERISATION IN THE DECISION SUPPORT TOOL ...................92 References ..........................................................................................93 6

SUSTAINABLE DOMESTIC WATER SYSTEMS ........................................... ........................................... 101 6.1 CLOSING NUTRIENT CYCLES ....................................................................101 6.1.1 Solution space ..................................................................................101 6.1.2 Selected systems ......................................................................................... 101 6.1.3 Visualising results...............................................................................1065 6.1.4 Sensitivity..........................................................................................106 6.2 CREATING LIFE SUPPORT SYSTEMS..............................................................109 6.2.1 Solution space ..................................................................................109 6.2.2 Selected systems ...............................................................................109 6.2.3 Visualising results ..............................................................................109 6.3 CONCLUSIONS ....................................................................................114 6.3.1 Sustainable systems...........................................................................114 6.3.2 Evaluating the decision support tool ...................................................114

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CONCLUSIONS & DISCUSSION DISCUSSION................................ .......................................................... .......................... 117 7.1 SUSTAINABLE WATER MANAGEMENT ...........................................................117 7.2 ASSESSING SUSTAINABILITY ......................................................................117 7.3 THE DECISION SUPPORT TOOL .................................................................118 7.3.1 Data.................................................................................................118 7.3.2 Indicators..........................................................................................118 7.3.3 Models .............................................................................................119 7.3.4 Optimisation .....................................................................................120 7.3.5 Using the decision support tool ..........................................................120 7.4 SUSTAINABLE DOMESTIC WATER SYSTEMS .....................................................121 7.4.1 Sustainable systems...........................................................................121 7.4.2 Trade-offs .........................................................................................121 7.4.3 Decisive variables .............................................................................122

GLOSSARY: Acronyms & symbols and Terminology APPENDIX 1: APPENDIX 2: APPENDIX 3: APPENDIX 4:

DATA IN SPREADSHEET (DATA2003) OPTIMISATION TECHNOLOGICAL DATA SHEETS RESULTS: SELECTED WATER SYSTEMS

CURICULUM VITAE SAMENVATTING

Summary A concrete challenge for this new millennium is to meet the UN target that aims at providing safe drinking water and hygienic sanitation to all people on earth by the year 2025. There is a wide variety of technologies that could fulfill these services, however many of the existing water systems do not provide an integral solution, such that we now face problems like eutrophication, heavy metals in sludge, water shortages. Even more complex problems are emerging, such as the loss of fertility due to traces of medicine and chemicals in the water, and scarcity of nutrients for food production due to the disruption of the nitrogen and phosphorus cycle. How can we try to meet those challenges? A major aspect in finding solutions maybe the fact that problems have become so complex that there is no single optimal solution. Furthermore, the solutions must be formulated with a long-term global view in mind, such that these do not trigger new problems. Or in other words: we are looking for sustainable solutions, carefully balancing the use of different resources such as environmental, economical, and social-cultural resources in such a way that the contribution to local and global problems is minimised or are at least known and accounted for. To gain insight into the sustainability of wastewater treatment systems we developed a sustainability assessment, which is a combination of existing tools, such as life cycle assessment, cost-benefit analysis, and social inventories. The main features that make the combination more than a sum of the parts are: the broad scope, the set of sustainability indicators, the process design oriented approach for modelling through the use of a superstructure, and selection of optimal structures by multi-objective integer optimisation. We implemented this sustainability assessment in a model-based decision support tool for the selection of sustainable domestic water systems. The three main components of this tool are: (1) Sustainability indicators: Based on the different dimensions of sustainability we defined a set of indicators including economic, environmental, and social-cultural aspects. In addition we included functional indicators that are used to account for characteristics of technologies such as robustness, adaptability, and maintenance. The total set consists of 27 sustainability indicators, some of these are quantified in the mass balance for instance rainwater use, some others are derived indirectly through a categorisation of the outgoing streams, for instance water with a quality suitable for domestic reuse. A third type of indicators provides a qualitative measure, for instance social acceptance and robustness. These are used to indicate a potential advantage or disadvantage of a certain technology. (2) Model: The quantification of the sustainability indicators is based on a model that represents the mass balances of the domestic water system. This model is constructed as a superstructure by superimposing a large number of known options for the supply of different water sources (drinkingwater, householdwater, and rainwater), in-house water disinfection, water conservation, and wastewater treatment ranging from smallscale onsite treatment to large-scale systems that serve complete urban areas. In the model structure, 37 simple static models of 13 different technologies are contained in the processing units. The optimisation selects a combination of technologies resulting in a complete model of a domestic water system.

(3) Optimisation: We defined the selection of optimal systems as a multi-objective integer optimisation problem. Important objectives in selecting sustainable domestic water systems are: minimise costs, the use of resources such as water, energy and space, maximise the production of clean water, nutrients, and biomass for reuse, minimise harmful waste products, maximise social-cultural embedding through acceptance, participation, and stimulation of sustainable behaviour. To combine these sometimes contradicting objectives, the different sustainability indicators quantifying these different objectives have to be normalised and weighted such that they can be integrated into a single final objective for optimisation. To select optimal domestic water systems for different cases, we experimented with different solvers. However, we found that due to the large number of possible combinations, about 7*1012 different water systems are contained in the decision support tool, and the discrete changes in sustainability it is difficult to find global optima within a reasonable calculation time. Therefore, we choose to reduce the problem size by defining a smaller solution space in the form of two scenarios of which the results are discussed below. We defined two scenarios, to select solutions that could help to fulfil the water challenge of this new millennium. These scenarios are: (1) systems for nutrient recycling, and (2) systems for water scarce condition aiming at minimizing drinking water use and maximising water reuse. In both scenarios we searched for affordable solutions being aware that poverty is a major issues still in many parts of the world. Based on the technology choices made by the decision support tool we can conclude that technologies not commonly used today, such as urine separation, membrane bioreactors, and rainwater systems, may become important in future domestic water systems that aim at the reuse of nutrients and water. We did not find the more conventional solutions due to the fact that we aim at recycling of nutrients and water while most studies aim at comparing and optimising existing treatment systems. Furthermore, the sludge treatment included in the decision support tool is limited. If clean sludge can be produced and/or reused in different ways then the treatment configurations based on conventional treatment may be promising too. Still, based on the selections made by the decision support tool and its present settings we conclude that, if future domestic water systems aim at nutrient and water reuse, it is likely that the conventional systems will have to be replaced by new systems that allow separation of wastewater streams at the source, a conclusion that is very much in line with what is reported in the recent literature. The reuse of water will probably trigger decentralised treatment of rainwater and greywater and may introduce low-flush or even dry toilets and disinfection of drinking water on household scale. For nutrient recycling decentralisation is not necessary although collection and reuse of urine and compost may trigger this as well. While affordable systems for nutrient recycling and minimising drinking water are available, water recycling seems to be expensive due to the choice for a membrane bioreactor. Further research is needed to find out whether the tool selects cheaper systems such as wetlands if realistic changes are made to data on removal rates, incoming streams, and restriction. Since no trade-off between domestic reuse and fertiliser was found, it is very well possible to combine the goals of our two scenarios and construct domestic water systems that recycle both nutrients and water, thereby approaching life support systems.

Sustainable wastewater treatment

Thanks to ....

Thanks to .... This project would never have been finalised if I had not had the support of my colleagues, many interested researchers, enthusiastic students, and dear friends and family. Therefore, I want to thank everybody who contributed to my project. First of all my promotor prof Heinz Preisig who despite difficulties at out faculty and his new job in Throndheim managed to stick with me throughout the project to provide creativity and critical reflection. Furthermore, my second promotor prof Ralf Otterpohl who is leading one of the most progressive wastewater research groups in Europe that creates new opportunities in wastewater treatment for the future. I am also proud to have both, prof Wim Rulkes and prof David Butler in my PhDcommission, both also lead inspiring research groups. Other people of the commission are prof Ruud van Heijningen and Fred Lambert, I owe them thanks for commenting on my work several times. Thanks also go out to prof Harry Lintsen who coached me through a challenging period in my research. Furthermore, I want to thank prof van Dongen and prof Schot in advanced for being my opponents at my PhD-defence. Stefan Weijers I want to thank for initiating the project, and prof Paul van den Bosch for giving me the opportunity to finish the project in his group. Thanks also to the Centre for Sustainable technology (TDO) for their financial support to the project and all their meetings. My colleagues in Eindhoven, all members of the System & Control group, and especially the PhD-students Georgo, Alexander, Roel, Uwe, Patrick, Gerwald, and Mathieu, I want to thank for the friendly atmosphere. Others that coloured my research period at TU/e were the MScstudents who all did interesting and sometimes amazing water projects, the design student Jean Arnaud with his socks-and-dish-washing machine, and Bart Verhoeven, who’s project made me measure our household water use real time and resulted in the implementation of our rainwater system, Elske van Doornum, and Gilbert Tychon. I also want to thank all supporting staff at TU/e, especially those who helped me out when having problems with my computer, the network, viruses, etc. Furthermore, I want to thank my Swedish colleagues Margareta Lundin and Daniel Hellström, keep up the good work! Thanks also to the Swedish researchers whom I never met but who’s work contributed to mine, namely those working ORWARE and those working on Tomlab. I also owe thanks to my Dutch colleagues at Wageningen University and TUDelft, especially Henri Spanjers for managing the COST Working group 5 in which I participated several times and Adriaan Mels. Also very inspiring were the European Junior Scientist Workshops, therefore thanks to all organisers and participants. Special thanks goes out to the ones I love, my husband Frank, our daughters Sacha and Anya, and the baby I am carrying now. Thanks for supporting me by making every day a special day, ;-) !!! Annelies van der Vleuten-Balkema, Eindhoven, September 2003.

Sustainable wastewater treatment

1 Introduction

1 Introduction Due to the complexity and the dynamic understanding of today’s problems there is a risk of introducing new problems when implementing solutions. To ensure that solutions have a positive overall effect, we use sustainability as point of departure in identifying solutions for the water sector. Therefore, we start this chapter with introducing sustainability and sustainable technology, followed by the problems in the water sector, and possible directions for solutions, leading to the research objective. 1.1 Sustainable development The concept of sustainable development is based on the observation that economy, environment and well-being can no longer be separated. The definition for sustainable development is often quoted from the World Commission on Environment and Development (WCED 1987): ‘development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs’. The fundamental thought behind this definition is to accept that all human individuals have equal rights, whether living now or in future. This means treasuring human life, independent of power and wealth. No newborn child should be doomed to a short or miserable life, merely because that child happens to be born in a certain class, country, or of particular sex. Sustainability defined as to future generations makes little sense if it means sustaining human deprivation. Nor should the less privileged today be denied the attention that we are willing to bestow on future generations. An important aspect of sustainable development is therefore equity in distributing development opportunities within present generations and between present and future generations. Another definition of sustainability formulated within the framework of the World Conservation Strategy (IUCN 1980, 1991) is ‘Improving the quality of human life while living in the carrying capacity of supporting ecosystems’. Alike the definition of the WCED, this sketches a concept rather than giving an unambiguous restrictive parameter that can be applied right away. Therefore, sustainability can be interpreted differently by different people, evoking the critique that the term sustainability could mean almost anything1 (Mitcham 1995). However, the room left for interpretation will prove to be valuable as ideas about sustainability are destined to be discussed over time and place, since different generations will have to deal with different problems and different cultures and local circumstances will give a different perspective on these problems. When looking closer at the different reflections on sustainable development, we see that scientists categorise different aspects of sustainability in a first step to make the concept of sustainability operational. For instance, Schumacher (1974) specified three categories of irreplaceable capital: fossil fuels, the tolerance margins of nature, and the human substance. Meadows (1972) defined sustainable development in terms of material well-being, social 1

In line with this criticism Tijmes (1995) argues that in traditional societies ‘desire’ was limited by institutions, such as religion, which were responsible for sharing wealth, while sustainability coupled with development is a Western concept to based on growth and competition unable to bring limitation of desire and leading to social and environmental destruction. However, if social and environmental aspects are valued as a ‘quality of life’ sustainable development will be able to bring this limitation in desire. Therefore, it is not the concept but the implementation that deserves this critic: if we rather invest abroad than changing or unsustainable behaviour we misuse the concept to sustain our Western way of life.

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Sustainable wastewater treatment

1 Introduction

security and ecological balance. The WCED (1987) distinguished three dimensions of the concept of sustainable development: environment, development and security. Security can be related to violent conflicts, but it also covers social security in terms of income distribution, and health care. All these quotations indicate a multidimensional character of sustainable development. The most important dimensions mentioned are: Economic: Economic sustainability implies paying for itself, with costs not exceeding benefits. Mainly focussing on increasing human well-being, through optimal allocation and distribution of scarce resources, to meet and satisfy human needs. This approach should in principle include all resources: even social and environmental values (e.g. in environmental economics). However, practical analysis include often only the financial costs and benefits. Environmental: The long-term viability of the natural environment should be maintained to support development by supplying resources and taking up emissions. This should result in protection and efficient utilisation of environmental resources. Environmental sustainability refers to the ability of the environment to sustain the human ways of life. The latter mainly depends upon the ethical basis: to what extent should policies be anthropocentric, and to what extent does nature have endogenous qualities. Although public opinion goes further, public policies mainly remain limited to so-called use-values, which can be incorporated in economic analysis relatively easily. Social-Cultural: Here the objective is to secure people’s social-cultural and spiritual needs in an equitable way, with stability in human morality, relationships, and institutions. This dimension builds upon human relations, the need for people to interact, to develop themselves, and to organise their society. Similar categorisation can be found in other publications as well (Ravetz 2000, Barbier 1987 in Bergh 1994). For instance, Barbier (1987) suggested that sustainable development is an interaction between three systems – biological, economical, and social, with the goal to optimise across these systems taking into account the trade-offs. The difficulty to express and weigh these trade-offs suggests that the optimisation is a political process rather than a scientific one. This is in line with the vision of the Scientific Council for Governmental Policies (WRR 1994). The central thought of this council is that when implementing the concept of sustainability, one cannot ignore the uncertainties and the mutual dependencies between the environment and the society. The forthcoming risks for the environment and for the economy will have to be balanced. The council concludes that due to the threatening, despite the uncertainty, sustainable development is seen as an important guideline for governmental policies. 1.2 Sustainable technology Implementing sustainability means seeking solutions that balances the costs with respect to the different resources (environmental, economical, and social-cultural) in such a way that the 2

Sustainable wastewater treatment

1 Introduction

contribution to local and global problems is minimised or are at least known and accounted for. These solutions should be based on a long and global view participating on possible future problems. Thereby avoiding export of problems over time or space. Realising that the solutions are embedded in a complex entirety, one should be looking for integrated solutions. The concept of sustainability leaves room for interpretation based on present knowledge, local circumstances and culture. This implies that a diversity of sustainable solutions must be available, preferably flexible to adapt to changing situations. In our case of the problems in the water sector, we are looking technological solutions. Taking sustainability as point of departure we have to analyse the sustainability of the different technological solutions. Note that sustainable technology is very similar to what used to be defined as appropriate technology, namely technology that is compatible with or readily adaptable to the natural, economic, technical, and social environment, and that offers a possibility for further development. Sustainability adds the long term and global view. This means that we should take into account the different dimensions of sustainability when analysing technology. Sustainable technology can either be high-tech or low-tech as long as it is appropriate for the particular circumstances. The sustainable technology solutions should be both effective, providing a real solution, and efficient, providing the solution against minimum costs. To analyse the sustainability of the technological solution we analyse the interaction of the technology with its environment, which is schematically represented in Figure 1. The first step is to translate the demands of the end user into functional criteria that must be fulfilled by the technology. This step is important as a choice on functional level is made. For instance, the demand for safe water supply maybe translated into drinking water standards. However, the actual problem Figure 1: Technology interaction with environment. may not be solved with drinking water supply alone, as lack of hygienic sanitation and hygiene in food preparation are also part of the problem. Therefore, it is important to get an overview of how the problem is embedded and interrelated with other problems and solutions. Furthermore, it is important to know what actors are involved. A clear problem definition and involvement of the actors should avoid choosing the most sustainable solution from a set of inferior solutions (ineffective solutions). In order to fulfil its function the technology uses resources out of its environment and will affect its environment through emissions. For instance different resources that can be used are: money from the economic environment, natural resources such as water and energy from the physical or ecological environment, and expertise from the social-cultural environment. Emissions that can be made affecting the environments are: decrease of economic value due to implementation of a technology or claim on scarce resources such as labour may affect the economic environment, environmental pollution may deteriorate the physical or ecological

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1 Introduction

environment, while inconvenience or participation may negatively or positively affect the social-cultural environment2. Sustainable technology is technology that does not threat the quantity and quality (including diversity) of the resources. The most sustainable solutions are those that have the lowest costs with respect to the different environments. As the quantity and quality of the resources and the resiliency of the environment to emissions differ over time and space so will the most sustainable technological solution. The balancing of the different costs, economic, environmental, and social cultural, depends on preferences of the actors. 1.3 The challenges of the new millennium 1.3.1

The global water crisis

The UN defined a major challenge for this millennium, namely to provide safe drinking water and hygienic sanitation to all people on earth by the year 2025. In spite of the efforts during the UNESCO’s International Water and Sanitation Decades in 1981-1990 and 1991-2000, about 18 % of the world’s population still has no access to safe drinking water and about 40% lacks hygienic sanitation (Mara and Feachem 2001). In many developing countries, rivers downstream of large cities are little cleaner than open sewers. For instance, the faecal coliform count in Asia’s rivers is 50 times higher than the WHO guidelines (UNEP 1999). Worldwide, polluted water is estimated to affect health of 1200 million people and to contribute to the death of about 15 million young children every year (UNEP 1999). That is comparable with about 70 to 100 jumbo jets crashing with total loss of passengers – every day of the year (IRC 1997). At the same time, we should realise that many water systems are unsustainable and give rise to other problems such as eutrophication and heavy metals in sludge. In addition some new water problems are emerging; the loss of fertility due to traces of medicine and chemicals in drinking water and the scarcity of nutrients for food production due to disruption of the nutrient cycles In their “Global Environmental Outlook”, UNEP states that the declining state of the world’s freshwater resources, in terms of quantity and quality, may prove to be the dominant issue on the environment and development agenda of the coming century (UNEP 1999). Also the World Health Organisation predicts straining conditions for the near future: two out of every three people on Earth will live in water-stressed3 conditions by the year 2025, if present consumption patterns continue (UNEP 1999). Similar, the World Water Vision (Cosgroce 2000) stresses that the today’s widespread water crisis will widen and deepen in the coming decades (see Box 1).

2

The 3 dimensions of sustainability we defined are not really distinct in the sense that for instance environmental pollution also affects the economic and social cultural environment. When turning it around we could express everything in Euros and making the physical or ecological and the social-cultural environment a part of the economic environment. However, using the definition of the 3 dimensions has the advantage that the trade off between the very different aspects of sustainability is expressed clearly. 3 Water stress is defined as the water consumption as percentage of the renewable freshwater supply, if between 10 and 20% water stress is said to be moderate, medium high if between 20 and 40%, and high if >40%.

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1 Introduction

Box 1: Main points of the world water crisis as defined in the World Water Vision (Cosgroce 2000). • An unacceptable large part of the world population – 1 in 5 – has no access to safe drinking water, and half of the world population has no access to hygienic sanitation. Each year 3 to 4 million people die of water related diseases, most of them are young children dying of diarrhoea. • Much economic progress has come at cost of severe impacts on natural ecosystems. Half of the world’s wetlands were destroyed in the 20th century, causing major loss of biodiversity. Many rivers and streams running through urban areas are dead or dying. Major rivers – from the Yellow River in China to the Colorado in North America – are drying up, barely reaching the sea. • Water services are often heavily subsidised, leading to unrealistic low prices. This undermines the incentives for water conservation and reuse. As a result water resources are often managed unsustainable leading to exhausting groundwater resources and pollution of all water resources. • In most countries water continues to be managed ineffectively by highly fragmented institutions.

The strange thing is that worldwide water is abundant. True, most water available on Earth is saline (97%), and most (75%) of the freshwater is stored in ice and snow, but still there is an estimated freshwater stock of about 10*1018 kg available as ground or surface water (Hoekstra 1998, p.34). Although, some aquifers recharge fairly quickly, the average recycle time for groundwater is 1,400 years, as opposed to only 20 days for river water (Sampat 2000). Still, groundwater is the water source for more than 1.5 billion people worldwide (up to 75% in Europe, Sampat 2000, p.12). Sustainable use of groundwater requires recharge and protection of the soil ecosystem. Unsustainable, excessive withdrawal resulted in dropping of the water table, with consequently loss of biodiversity, and sometimes followed by land subsidence, collapsing aquifers, and intrusion of saltwater. A more sustainable fresh water source is the water that moves in the hydrological cycle through evaporation, precipitation, runoff and river discharge. Even though, figures4 on the available amount of this fresh water resource differ, there seems to be enough to sustain the world population and the different ecosystems. However, local water scarcity occurs as water is distributed unevenly, 40% of the land on earth is classified as arid or semi-arid as it conceives only 2% of the continental run-off (WHO 2000). Maybe even more important than quantity is water quality, as nowadays pollution is a major threat to the water resources. Sewage pollution is a common problem threatening human health directly through the dissemination of pathogens. Furthermore, sewage leads to pollution of water resources with nutrients (N, P), heavy metals, and other toxic compounds such as traces of medicines (anti-depressia, heart medicines, hormones, etc). Industrial waste gives also rise to dissemination of heavy metals and persistent organic pollutants, while agriculture through intensive use of pesticides and fertilisers threatens water resources with chemical pollutants.

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The fresh water in hydrological cycle directly available to man is 43,000 km3 (Groot 1992), the amount world river flow available for human consumption is 12,500 km3 of which half is already taken up by human consumption according to WHO (WHO 2000). Vörösmarty 2000, quotes comparable figures, a long term available runoff of 40,000 km3/y, of which only 31% is accessible, while humans exploit more than half (10 to 15% of total available runoff), similar Hoekstra, estimates that the freshwater renewal rate or total continental run-off is 40 to 47*1015 kg/y of which mankind withdraws about 8 to 9% (Hoekstra 1998, p35) .

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Sustainable wastewater treatment

1 Introduction

Precipitation 109.5 * 109 m3/y Water use 2.1* 109 m3/y

Outflow surface water 86 * 109 m3/y

(Source RIVM/CBS 2000)

Evaporation 21.4* 109 m3/y

(domestic 0.8 * 109) m3/y)

Infiltration 80.4 * 109 m3/y

Inflow surface water 80.4 * 109 m3/y

(estimated: infiltration=precipitation+inflow-evaporation-outflow-use)

Figure 2: Water balance for the Netherlands (RIVM/CBS 2000).

Looking at Figure 2 makes one wonder how even in a water rich country such as the Netherlands problems like desiccation and eutrophication can occur. Of course it is true that water is distributed unevenly and that water quality is at least as important as quantity. On the other hand, a wide range of high and low-tech solutions is available for water treatment (including disinfection and desalination) and distribution. As such, we have to conclude that the water crisis is not a resource problem but to a large extent a management problem. Worldwide, there is a pressing need for sustainable solutions throughout the whole water cycle to assure freshwater needed to support hygienic living conditions, industrial development, irrigation, and conservation of ecosystems. 1.3.2

Decentralised systems to close cycles

Technically it is possible to close the water cycle on urban scale or even on household scale. However, when using flush toilets one mixes the water cycle with the nutrient cycle. This means often that nutrients such as carbon, nitrogen, phosphorus, potassium, calcium and magnesium, are not returned to agricultural land but end up in surface water or through treatment in sludge or gaseous emissions. This is in contradiction with nutrient management in natural ecosystems, where the flow of nutrients is conserved as much as possible and input and loss are usually small compared with the volume, which circulates within the system, notably in terrestrial systems (Groot 1992). Nutrient cycles are unbalance as some steps in the natural nutrient cycles are limiting while other steps are scaled-up by human activities. For instance the natural binding of atmospheric nitrogen by bacteria and plants is limiting. Nitrogen is abundant in the atmosphere and can be bound in industrialised processes to produce artificial fertilisers, although this is a very energy intensive process. Due to this human interference, the nitrogen cycle is the most disrupted element cycle, with related problems as eutrophication, greenhouse gas emission, and possibly a direct effect on human health through blue baby disease and cancer (Gijzen and Mulder 2001). Other nutrients, as phosphorus and potassium, are mined for artificial fertiliser production. The lack of gaseous compounds deprives these cycles of an atmospheric pathway linking land and sea, this means 6

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1 Introduction

that the closing of these cycles depends on very slow pace of sedimentation, uplift, and weathering (Groot 1992, Lange 1997, p.51-54). These nutrients can therefore be seen as unrenewable resources. The closing of both the water and the nutrient cycle are essential in finding sustainable solutions for the water sector. 1.3.3

Small scale systems for source control to enable reuse

Conventional solutions in the form of water flush toilets, combined sewerage, and centralised treatment did not bring an integral solution. On the contrary, the system dilutes the waste stream which makes it extremely difficult to separate the value components from the toxic components. This initiated a wide range of experiments with storage of rainwater in the sewerage system, rainwater infiltration, rainwater or household water for toilet flushing, low flush and vacuum toilets, the separate treatment of black and greywater and possibly even yellowwater (urine), anaerobic digestion, etc. One of the main questions is whether we can obtain sustainable urban water management through improving the existing systems or whether we will have to switch to completely new systems, for instance decentralised systems. Operating processes on a large scale may be preferable with respect to robustness and economy of scale. However, the end-of-pipe approach of centralised systems requires an increasingly sophisticated treatment as regulations become stricter, making the treatment increasingly expensive (for historical background see Box 2). An advantage of centralised treatment is the fact that monitoring and control of one single plant is easier than a large number of small ones. If not referring to robustness but solely to the number of treatment plants, one should realise that new techniques make it is possible to monitor and control at a distance, and that the effect of failure in a small-scale plant is less severe (see also Butler 1997). The main advantage of decentralised systems is that it offers the possibility to keep different wastewater streams separate, use different treatment techniques and close water and nutrients cycles locally. In addition the expensive transport of wastewater over large distances is eliminated. Furthermore, small-scale solutions are often experienced as very positive as it visualises sustainability in an understandable way. However, to make small-scale solutions successful the acceptation and participation of the end user and the different institutions involved is often needed. 1.3.4

Optimising the existing systems or introducing to new systems?

In the theory of large-scale technological system the growing in scale and complexity is sometimes described as growing autonomy of the system. The autonomy makes it difficult for alternative system to compete. At the same time, the growing complexity may cause internal defects in the system. Large-scale system created to control ‘old’ problems, may at the same time create ‘new’ uncontrolled and often invisible or distant problems. The urban water system, for instance, prevents spreading of diseases and flooding, however, uncontrolled by this system are combined sewer overflows in case of heavy rains, diluted toxic components are hard to remove and nutrients are often difficult to recycle. While some researches state that the autonomy off the large-scale systems is hard to break, others recognised that largescale systems go through different phases of stable growth, conflict, and reconfiguration (Vleuten 2000).

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Box 2: Some historical dynamics of the urban wastewater system. Traditional water services as drinking water supply and wastewater management were decentralised. Wells, surface water, and rainwater harvesting provided drinking water, while dry toilets such as pitlatrines or barrel latrines were used for sanitation, or else the excreta were dumped in the surface waters. The sludge gathered in latrines could be used as fertiliser on agricultural land. The rest of the domestic wastewater as well as surface runoff was either infiltrated or channelled to surface water. In densely populated urban areas these solutions were often not hygienic. With the increasing knowledge of spreading of diseases the importance of hygienic water supply and wastewater management became apparent. Around 1840 to 1900 improved drinking water supply and sewerage systems were introduced in the larger urban areas in Europe. The introduction of these systems was very expensive and therefore there was a lot of discussion, delay, and implementation of partial solutions (Lintsen 1993, p.60). Part of the discussion focussed on the recycling of nutrients to agriculture by collecting urine and faeces separate (Lintsen 1993, p.58). In the Netherlands two systems were implemented for this purpose: the barrel latrine and the Liernur system (Lintsen 1993, p.71-76 (barrel) p.62-71 (Liernur), Lange 1997, p.13-15). The shortage of fertiliser in agriculture made the human excreta valuable. Furthermore, the barrels system was simple and required low investments, however the transportation of the excreta was unhygienic (spilling, smelling). The Liernur system was an ingenious vacuum system transporting the excreta to collection tanks. These collection tanks were emptied by pumping wagons. This closed system was considered very hygienic and tested in Amsterdam. Leiden and Dordrecht. Technically the system functioned well, but costs were higher and revenues lower than expected (Lintsen 1993, p.65). In 1915 in Leiden the system was taken out when the iron pipes were corroded and needed replacement (45 years after installation) (Lintsen 1993, p.65). In Amsterdam where almost 40% of the population was connected, the introduction of the water closet system meant the end of the Liernur systems (Lintsen 1993, p.68). Large-scale introduction of combined sewerage systems followed, with the advantage that all wastewater was directly taken out of sight. The investment costs were high, but at that time one expected no exploitation costs, as the wastewater was discharged without treatment and maintenance was not considered necessary. Once the choice of a combined central sewerage system was made the implementation of end-of-pipe treatment to prevent pollution of the surface water was a logical next step.

The expansion of the central sewerage systems in the Netherlands has come to an end. The few households (3%) that are not connected are allowed to treat their wastewater with a smallscale systems onsite. Throughout its history, the central treatment systems have been growing in scale and complexity. The growing of scale is limited through the costs of transporting wastewater. But the complexity is increasing, as the restrictions of discharging effluent and sludge are getting tighter. This could mean that system is in a phase of conflict that will lead to reconfiguration. Or do the advantages of small-scale solutions counter balance the disadvantages? Should we continue with improving the existing system? Or could one say that environmental problems have become the reverse salient5 of current large-scale systems such as conventional wastewater treatment systems (Nielsen 1999). A technological breakthrough can occur once the new decentralised approach is accepted as better than the centralised end-of-pipe treatment. Possibly a mixture of decentralised and centralised treatment can be created to 5

A key concept in understanding large technical systems is reverse salient, which is a part of the system that is behind in the development compared to other systems and therefore reduces the capability of the whole system. Reverse salient can lead to closure of a system if it weakens its competitive position in relation to alternatives (Nielsen 1999).

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combine the advantages of both systems. For instance decentralised treatment for water reuse inside the households, and centralised systems outside the urban area to regain the nutrients for use in agriculture. The changing perspective, induced by sustainability, triggers this process of change, however, before switching towards a more decentralised approach, smallscale systems with water and nutrient recycling have to prove themselves. Small scale systems are sometimes seen as intrinsic sustainable (Schumacher 1974), however in wastewater treatment the main advantage is to treat waste streams separate. This means that different treatment processes can be applied, enables the closing of the water and nutrient cycles. 1.4 Research objective The objective of the research is to gain insight into the sustainability of domestic water systems, including water supply, water use, and wastewater treatment. Thus we want to contribute to the development of a methodology for sustainability assessment, and to give an overview of promising combinations of wastewater treatment technologies and the trade-offs made in technology choices. We are specially interested in the selection of affordable systems and systems that close the water and nutrient cycles as we believe those are needed to fulfil the challenge of this millennium: providing safe drinking water and hygienic sanitation to all by 2025. 1.5 Overview of the research This chapter introduced the concept of sustainability and the present problems in water management. The next chapter gives an overview of methodologies applied to assess the sustainability of different parts of the urban water cycle and explicates the use of sustainability indicators in system analysis, the methodology we are applying. Chapter 3 describes the implementation of the sustainability assessment in the form of the decision support tool. Subsequently, Chapter 4 provides an overview of the water services and quantifies all inputs needed to quantify the streams and sustainability indicators in the decision support tool. In Chapter 5 the decision support tool is completed through the description of the technologies. Chapter 6 presents the results of the calculations executed with the tool, describing the use of the tool and the sustainable water systems selected. The last Chapter, number 7, gives an overview of the conclusions, discusses all findings and gives recommendations for further research. References Bergh, C.J.M. van den, and Straaten, J. van der (editors) (1994). Towards sustainable development, concepts, methods, and policy, International society for ecological economics, Island Press, Washington DC and Covelo California, ISBN 1559633492. Butler D. and Parkinson J. (1997). Towards sustainable urban drainage, Water Science and Technolgy, vol.35, no.9, p.53-63. Cosgrove W.J. and Rijsberman F.R. (2000). World water vision, making water everybody’s business, for the World Water Council, Earthscan Publications Ltd, London. (Internet http://www.watervision.org/ ). Dirkzwager A.H. (1997). Sustainable development: new ways of thinking about water in urban areas, European Water Pollution Control, Vol .7, No.1, Jan. 97, pp.28-40.

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Finnson A. (1996). Sustainable urban water systems, Platform for research submitted to MISTRA, Swedish Environmental Protection Agency. Gijzen H.J. and Mulder A. (2001). The nitrogen cycle out of balance, Water21, August 2001, pp.38-40. Groot, R.S. de (1992). Functions of nature, evaluation of nature in environmental planning, management and decision making, Wolters-Noordhoff, ISBN 900135594 3. Hoekstra A.Y. (1998). Perspectives on water, an integrated model-based exploration of the future, PhD thesis Delft University of Technology, Civil Engineering, Published by International Books, ISBN 9057270188. IRC (1997). Two billion and counting, Water newsletter, no.251, Oct. 1997, IRC , The Hague, The Netherlands. IUCN (1991). Caring for the earth: a strategy for sustainable living, Earthscan Publications Ltd, ISBN: 1853831263. IUCN (1980). World conservation strategy: living resource conservation for sustainable development, by the International Union for Conservation of Nature and Natural Resources (IUCN), with the advice, cooperation and financial assistance of the United Nations Environment Programme (UNEP) and the World Wildlife Fund, ISBN: 2880321018, 2880321042. Mara D.D., and Feachem R. (2001). Taps and toilets for all- two decades already, and now a quarter of a century more, Water 21, August 2001, pp.13-14. Lange J, Ottenpohl R (1997). Ökologie aktuell, Abwasser Handbuch zur einer zukunftfähigen Wasserwirtschaft (Wastewater handbook, for a future oriented water management), Mallbeton GmbH, DS Pfohren, ISBN 3980350215 (in German). Lintsen H.W. (editor) (1993). Geschiedenis van de techniek in Nederland, de wording van een moderne samenleving 1800-1890, Deel II Gezondheid en openbare hygiëne, Waterstaat en infrastructuur, Papier, druk en communicatie, (History of technology in the Netherlands, towards a modern society 1800-1890, part II, Health and public hygiene, water and infrastructure, Paper, press and communication),Stichting Historie der Techniek, Walburg Press, Zutphen, the Netherlands, ISNB 9060118367 (in Dutch). Meadows D.H., Meadows D.L., Randers J., and Behrens (1972). Limits to growth, a report for the Club of Rome’s project on the predicament of mankind, Universe Books, New York, ISBN 0856440086. Mitcham C. (1995). The concept of sustainable development: it’s origins and ambivalence, Technology in Society, Vol. 17, No. 3, pp. 311-326. Nielsen, S Balslev (1999). Urban ecology and transformation of technical infrastructure, Paper for IPS specials, February 1999, Department of Planning, building, Technical University Denmark, Lyngby. Otterpohl R., Grottker M., and Lange J. (1997). Sustainable water and waste management in urban areas, Water Science and Technology, vol.35, no.9, pp.121-134. Ravetz J. (2000). Integrated assessment for sustainability appraisal in cities and regions, Environmental Impact Assessment Review, 20 (2000), 31-64. Schumacher E.F. (1974). Small is beautiful, a study of economics as if people mattered, London, Abacus, ISBN 0349131325. SCC (1999). Water Supply and Sanitation Collaborative Council, Damme, Hans van, and Chatterjee, Ashoke, Vision 21- water for the people, (available on the Internet: http://www.wsscc.org/index.html Tijmes P. and Luijff R. (1995). The sustainability of ‘Our common future’: An inquiry into the foundations of an ideology, Technology in Society, Vol. 17, No 3, pp 327-336. UNDP (1998). Human development report 1998, New York: Oxford University Press, ISBN 0195124596. UNEP (1999). Global Environmental Outlook 2000 (GEO2000), UNEP’s millennium report on the environment, Earthscan publications Ltd London, ISBN 1 85383 588 9 (Internet: http://www.unep.org/Geo2000/ ). Vleuten, E. van der (2000). Twee decennia van onderzoek naar grote technische systemen: thema’s en afbakening en kritiek (Two decennia of research in the field of large scale technological systems: themes, boundaries, and critic), Reprint TM/675, also appeared in the NEHA-jaarboek voor economie, bedrijfs- en techniekgeschiedenis, dl.63, 2000, p.328-364, ISSN 13805717 (in Dutch). Vörösmarty C.J., and Sahagian D. (2000). Anthropogenic disturbance of the terrestrial water cycle, BioScience, September 2000, vol.50, no.9, pp.753-765) WCED (1987). (World Commission on Environment and Development so-called Brundtland report), Our common future, New York: Oxford University Press, ISBN 019282080X. WHO(2000). Global water supply and sanitation assessment, report 2000, World Health Organisation (WHO) & United Nations Children’s Fund (UNICEF) Joint programme for water supply and sanitation, ISBN 9241562021 (internet: http://www.who.int/water_sanitation_health/Globassessment/GlasspdfTOC.htm). WRR (1994). Report to the government by the Scientific board for governmental policies, Duurzame risico’s: een blijvend gegeven (Sustainability risks, a lasting fact), Sdu, ‘s Gravenhage, ISBN 90-399-0719-6 (in Dutch).

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2 Assessing the sustainability of domestic water systems Like development, sustainability is per definition impossible to measure in absolute terms. The subjectivity in terms such as ‘human needs’ and ‘quality of life’ make it impossible to define and measure sustainability unambiguously. To achieve our goal of providing insight into the sustainability of domestic water systems, indicators are defined to compare a wide variety of systems with respect to sustainability. This chapter describes the different methodologies that can be used for such a sustainability. 2.1 Exploration of methodologies (literature review) Our implementation of the methodology in a model-based decision support tool is described in Section 2.3, whilst the methodology of sustainability assessment itself is explicated in Section 2.2. This methodology utilises the results of previous research done in the field of sustainable or environmental friendly water systems. For instance, some researchers attempt to capture sustainability in a single indicator such as Exergy and Economic analysis. However, other, frequently used methodologies include multiple indicators as for instance Life Cycle Assessment or System analysis. This section, 2.1, analyses those four methodologies with respect to the scope of their application in assessing the sustainability of the domestic water cycle. 2.1.1

Exergy analysis

Exergy is defined as the part of energy that can perform mechanical work, as such it expresses the quality of a energy flow. Hellström, for example, compared different wastewater systems based on exergy (see Box 3). The advantage of the exergy analysis is that the whole comparison is based on a single unambiguously quantifiable indicator (exergy), no weighting of different indicators is involved. This advantage is at the same time a limitation, as insight is gained into the efficiency of the processes but not into the different environmental impacts the process has. Box 3: Conclusions based on exergy analysis (Hellström 1997, 1998). The analysis is based on a Swedish wastewater treatment plant focusing on the exergy contained in heat, organic matter, phosphorus, nitrogen, and electricity. The exergy of clean water and uncontaminated nutrients was not incorporated. • The exergy analysis shows that the largest exergy input is due to electric tap water heating. The calculated exergy is even underestimated because the energy losses to the surroundings are not included. However, the exergy reduces considerably when an energy source with a lower exergy than electricity is used. • Considerable flows of exergy are related to handling of organic matter. Possibilities to recover exergy as methane gives a strong influence on the exergy balance. • Comparing exergy flows related to nutrients we see that phosphorus is almost insignificant, whilst nitrogen is significant. When comparing the wastewater treatment plant with sources separation and on-site treatment the following conclusions are most interesting: • If nitrogen removal is considered important urine separation toilets and onsite treatment become an interesting alternative. However, efficient utilisation of organic matter is crucial, and the on-site treatment techniques are area-demanding (infiltration sites and wetlands). • The magnitude of the system, as well as the access to arable land and the degree of dilution, has a significant influence on the exergy consumption of the urine separation system.

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2.1.2

2 Assessing sustainability

Economic analysis

The economic theory also suggests a single indicator approach. A strong argument to express sustainability in terms of money is the general use of financial indicators in decision-making. Tools such as: Cost-Benefit Analysis, Life Cycle Costing, and Total Cost Assessment, all balance the expected costs and benefits, and are often the first step in a project. In theory, all kinds of costs and benefits can be included, however in practice these tools are mostly used as a one-dimensional techniques incorporating only financial costs and benefits. The obvious reason is that most social and environmental costs are difficult to quantify. De Groot (1992) combined Environmental Impact Assessment with Cost-Benefit Analysis to provide a decision support tool based on the concept of sustainability. He defined 47 functions of nature, categorized in four groups: (1) Regulation functions (for instance, regulation of climates, chemical composition of the atmosphere and oceans, storage and recycling of nutrients, etc.), (2) Carrier functions (for instance crop growing, settlements, tourism, etc.), (3) Production functions (for instance water, fuels, raw materials, etc.), and (4) Information functions (such aesthetic, spiritual, scientific information, etc.). In three case studies is shown that it is possible to estimate monetary values of some functions using: market prices, costs of environmental damage, maintenance costs, mitigation costs, willingness to pay, property pricing, and travel costs. Other functions are indicated qualitatively. In this way, a more refined feasibility study is presented to the decision-maker. Indicating sustainability in monetary values has the advantage that indicators are easier to handle in decision-making. However, translating environmental and social-cultural indicators into monetary values is a part of the decision-making process since it includes normative choices as determining values and weighing different indicators. In a perfect marketeconomy, prices would reflect the value of things as perceived by society. However, no perfect market-economy exists and especially in the water sector prices are often determined by governmental organisations, using price regulations such as taxes and subsidies. As such, an economic analysis of the sustainability of water supply and wastewater treatment could provide a valuable insight in the ‘real’ cost of water services. 2.1.3

Life Cycle Assessment

A methodology especially developed to assess different environmental impacts encountering during a product’s lifetime is Life Cycle Assessment (LCA). This is a structured methodology starting with defining the goal and scope of the study. Thereafter, a life cycle inventory of environmental aspects is made, based on mass and energy balances. Finally, these environmental aspects are categorised in environmental impact categories, such as depletion of resources, global warming potential, ozone depletion, acidification, eco-toxicity, desiccation, eutrophication, landscape degradation, etc. These categories can be normalised and weighted to come to a final decision whether to choose one technology or the other. The advantage of LCA is the well-described and standardised structure and the fact that it is applied to a wide range of products and services including the different parts of the urban water cycle (see Table 1) (ISO 14040 – 14043). However, LCA has some drawbacks, assessing the whole life cycle requires lots of data, and aggregating data into the standardized environmental impact categories means loss of insight in emissions specifically interesting for wastewater treatment. Furthermore, additional indicators are needed to indicate sustainability as LCA limits itself to environmental aspects. 12

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Table 1: Conclusions based on Life Cycle Assessment. Crettaz 1997

Hoek 1999

Dennison 1997

Emmerson 1995

Technologies compared: Rain water systems (10m3, 50m3, and a centralised system for 5 houses), and drinking water supply. Comparison of the supply of drinking water, rain water, treated greywater, and household water produced out of IJ-lake water, river Rhine water, polder seepage water, effluent, brackish groundwater, or local infiltrated rain water. 15 different treatment plants (activated sludge, percolating filters, bio-discs) and 5 sludge treatment & disposal strategies (land application, digestion, com-posting, thickening dewatering). Activated sludge and 2 biological filter plants.

Functional unit: Toilet flushing for one person during one day (54 litre or 22 litre per person per day depending on type). Assumed is a drinking water use of 144 litre per capita per day, household water will substitute 37% of this demand, greywater 21%, rainwater 16%. 9.8*108 kg raw sewage to be treated and sludge to be disposed.

Main conclusions: The rainwater systems use pumps and require more energy than conventional water supply. The 10m3 systems and the centralised system use the same amount of energy, while the 50m3 system uses 30 to 60 % more. A water saving toilet is a good way to safe water. Dual water supply with household water produced out of IJ-lake water is chosen as best option. The options to use treated effluent and infiltrated rainwater are rejected on public health aspects. The use of rainwater, greywater, Rhine water, and polder seepage water, is rejected based on costs. The environmental impact of the use of brackish groundwater is higher than the use of IJ-lake water and drinking water. Important improvement would be to decrease the sludge volume to be transported and disposed, dewatering or composting are good options for this purpose.

Population 1000, dry weather flow 200 m3/d.

The activated sludge plants used considerably less materials (30% land take, 11% raw materials, 10% solid waste), the biological filter plants used less energy (56%). For rural areas land use is no problem, therefore the more energy efficient biological filter plants are preferred, taking in account additional benefits as less control needed and being resilient to shock loads. Potential improvements lay in optimising energy use in aeration system, reducing the material used (PVC!) and use recycled materials. The process with chemical pre-treatment is favourable according to this analysis. Also when the plant is expanding to tertiary treatment. Furthermore, the biogas from anaerobic sludge digestion represents a valuable resource, thus the process maximising biogas production is favoured. Main environmental impacts associated with sludge treatment are: energy consumption, use of diesel in transportation, direct emissions such as NH3 and NH+4. Because of the process-inherent output of methane gas that can be used for energy production, the anaerobic variants score better on energy use and these score also better on in terrestrial eco-toxicity in zinc equivalents, but less good on eutrophication measured in PO3-4 equivalents per kg P. Urine sorting system is preferable to more conventional treatment.

Primary treatment (mechanical), secondary (chemical or chemical\biological), tertiary treatment (with and without N-removal). 6 different sludge recycle strategies: anaerobic or aerobic digested liquid sludge, or adding dewatering or adding composting.

100,000 population equivalent.

Bengtsson 1997

Urine sorting and conventional.

Bengtsson 1997

Liquid composting and wastewater mini-plant.

Bengtsson 1997

Local treatment and spreading and central treatment.

Treatment of the sewage from one person-equivalent during one year. Treatment of one person’s sewage and organic waste during one year. Handling of 1m3 raw sewage sludge.

Ødegaard 1995

Neumayr 1997

One kg phosphorus on the field.

13

Environmental impact from liquid composting is lower than impact from wastewater treatment in a mini-plant. Local treatment and spreading is more environmentally advantageous in comparison with transport and treatment in a central treatment plant.

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These disadvantages of LCA are reflected in the fact that most researchers adapt LCA for their research by looking at the operational phase only, by not categorising the emissions but using them straight away for their assessment, or by adding impact categories such as land use, costs, etc. (see Table 2 on page 18). If assessing the operational phase only, one should no longer speak of a LCA, but of a chain analysis or environmental impact analysis. When not using impact categories but making the comparison on the emissions and wastes produced, the analysis is a mass flow analysis rather than an environmental impact analysis. 2.1.4

System analysis

The general approach followed in a sustainability assessment of water services is a system analysis, based on mass and energy balances and design equations to indicate material use, emissions, costs and required land area. In principle LCA is a type of system analysis, also a structured method based on mass and energy balances that uses indicators (in LCA called impact categories). LCA is usually applied to compare a few technologies on environmental impacts only. While system theory as a rule assesses the process more generally and abstract by capturing the nature of the system in a mathematical description. In the case of domestic water systems, this means focussing on the comparison of whole systems including water supply, use and wastewater treatment, often incorporating larger numbers of alternatives, and using a multi-dimensional set of sustainability indicators. Both looking at whole systems and using a multi-dimensional set of indicators is essential for assessing sustainability. Looking at the whole system one can find integrated solutions that may not be visible when looking at smaller parts of the system. Similar, optimising in one dimension, for instance environmental, will improve this aspect of the system but may have unwanted effects in other dimensions, for instance the system may become unaffordable. Like in LCA, the different system analyses are difficult to compare because the goals and scopes as well as the assumptions differ per study. System analyses comparing a relatively small number of systems are done by, for instance, Mels and van Nieuwenhuizen (1999), Kärrman (2000), and Otterpohl (1997) (see Box 4). Ranking a large number of systems can also be done by comparing the systems pair wise on all selected indicators (for instance is system A more affordable than system B?, is system A more affordable than system C? etc.). In this way, a decision matrix is built for each indicator. These matrices are combined to reach a final decision. This approach is applied to wastewater treatment by Ellis and Tang (1990, 1994). They defined 20 parameters, including technical, economic, environmental and social-cultural factors, to rank 46 wastewater treatments systems. Based on several case studies, they conclude that their method is useful in selecting wastewater treatment systems. Ellis and Tang compare different configurations of wastewater unit operations, however, if one models the unit operations separately, the number of configurations is enormously enlarged. This is illustrated by the screening analysis by Chen and Beck (1997), which is used to classify and review over 120 unit operations for wastewater drainage and treatment. Chen and Beck constituted 50,000 candidate wastewater treatment systems. Comparison of the feasible candidate wastewater treatment systems led to the conclusions shown in Box 5. The screening analysis of Chen and Beck uses fixed processing units that do not provide decision makers the freedom to adapt to local conditions or to incorporate different assumptions. 14

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Box 4: Conclusions based on system analysis. Mels 1999: Three wastewater treatment scenarios are compared: (1) low-loaded activated sludge with biological P and N removal, (2) pre-precipitation before the low-loaded activated sludge followed by a rapid sand filtration, and (3) floatation before the low-loaded activated sludge followed by rapid sand filtration. • Scenario (1) generates more energy, less sludge, uses no chemicals, and is a bit cheaper, however, more suspended solids remain in the effluent and the system requires more space. • Scenario (2) and (3) do not differ much, (2) generates a bit less energy, uses some more space, and is a bit cheaper. Kärrman 2000:

Four systems are compared, conventional treatment, irrigation of energy forests, liquid composting, and urine separation. • All 4 alternative systems increase the nitrogen utilised by crops, and decrease the nitrogen emission to water. • All 4 systems remove more than 95% of the phosphorus. Urine separation is favourable in phosphorus recycling, as this does not increase the cadmium content in arable soil. • Looking at primary energy consumed, the difference between recycling of sewage sludge from conventional treatment to use of mineral fertiliser is marginal (recycling of sludge is 5% more efficient if transported 10 km – equal efficiency when transported over 1080 km). Using urine as fertiliser is 30% more energy efficient compared to mineral fertiliser if the urine is transported 1 km, and equally energy efficient if transported over 400km. • Energy consumption is considerably higher in liquid composting. • Liquid composting may be interesting when combined with treatment of organic wastes. Otterpohl 1997: The conclusions are based on a comparison between a conventional sanitation system and a vacuum toilet system in which the blackwater is digested together with organic household waste and the greywater is treated in constructed wetlands. • The traditional concept has the disadvantage of using too much water, diluting the faeces and rinsing out nutrients to the sea. • Separate treatment of black and greywater has the advantage of using less energy and materials; furthermore, emissions to receiving waters are reduced.

In contrast, a sanitation expert system such as SANEX (Loetscher 1999) uses information on local circumstances to screen out inappropriate sanitation systems. With SANEX the choice was made to provide a user-friendly interface thereby sacrificing on the transparency of the model by not providing insight into the equations and assumptions used. The ORWARE model (Dalemo 1999, Sonesson 1998) is again a more theoretically oriented decision support tool. The steady-state material flow model is developed for evaluating the environmental impact of organic waste management activities in different geographical areas, especially focussing on the return of nutrients to arable land. All the 43 indicators being are chemical compounds, such as BOD, metals, nutrients, etc. The main conclusions are summarised in Box 6. In general we can conclude that all of the above methodologies can lead to new insights when applied to the urban water system. Exergy analysis, economic analysis, and LCA provide specific insights into (energy) efficiency, ‘real’ costs, or environmental impacts respectively. The system analysis is a more general approach and can include a wide range of aspects through the use of self-defined sustainability, for instance exergy, costs, environmental impacts, or even social-cultural aspects such as acceptance, convenience, etc. 15

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Box 5: Conclusions based on screening analysis (Beck 1994, Chen 1997). • Powerful discriminants to screen out technologies that lack promise are capital costs, land area, the ability to remove heavy metals, N-bearing, and P-bearing materials. • Most of today’s commonplace technologies, including the activated sludge process, the trickling filter, and so on, are very rarely chosen as constituents of a satisfactory strand of technologies. • Processes exploiting a high biomass, such as improved reedbed system (with constructed infiltration facilities), pond systems, and all polishing technologies, including micro-filtration and reverse osmosis, emerge as being essential to success

Box 6: Main conclusions based the ORWARE model (Dalemo 1999, Sonesson 1998). • Land filling scenario has exceptionally high contribution to global warming even though 50% of the methane was collected. It was concluded that this was the least desirable scenario. • Environmental impact composting low for all categories, also low energy production. • Incineration results in strong acidification and a large amount of heat. • Anaerobic digestion has a large impact on the human health category and photooxidant formation, also recirculated large amount of nutrients. • The wastewater contained a major part of the plant nutrients although nitrogen is partly removed at the sewage plant there is a large eutrophication impact on surface waters.

2.2 Exploration of methodology application— application—a literature review 2.2.1

System boundaries

In general a sustainability assessment will not limit itself to a process but will rather be an integrated assessment over a whole chain of processes that provide a certain service. This wide view makes it possible to compare a large variety of solutions. For instance, comparison of large-scale and small-scale wastewater treatment systems requires inclusion of the household in order to enable separation of different wastewater streams, to apply different forms of sanitation, and use and reuse different water sources. However, a wide view often results in loosing details and possibly means a lot of extra work. A careful definition of the system boundaries is therefore required in the goal and scope definition of the research. Figure 3 visualises the system boundaries that different researches chose in their research. 2.2.2

Sustainability indicators

The definition of sustainability indicators is an important step, as the selection of sustainable solutions is based on these indicators. A sustainable solution means limited use and limited degradation of resources through harmful emissions, at the same time avoiding the export of the problem in time or space. As described in the section on sustainable technology, we distinguish three types of resources: economic, environmental and socio-cultural resources. Therefore, the same classification is used for the indicators, including one additional category, namely the functional indicators (see Figure 1 on page 3). While the economic, environmental, and social-cultural indicators give insight in the efficiency of the solution, the functional indicators determine the effectiveness of the solution. This last group of indicators can therefore be seen as constraints, as it is no use defining an efficient solution if in the perception of the end user this solution is not acceptable. 16

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Figure 3: System boundaries applied in different researches.

An overview of the different indicators used in literature is given in Table 2. Note that due to different goals and scopes as well as different terminology the different researches are not comparable, as shown in Figure 3. 2.2.3

Interpretation of results

In researches comparing a small number of systems, the results are mostly interpretate to select a single system for a particular situation. Whilst researches comparing a large number of systems are often done to gain inside in the factors determining the sustainability of water systems, for instance by listing decisive indicators, such as: •

Organic matter – methane recovery may be essential for sustainable wastewater treatment (Hellström 1997 and 1998, Neumayr et al. 1997, Otterpohl et al. 1997, Ødegaard 1995), composting seems to be a promising option for sludge handling as well (Dennison 1997).



Nutrients – urine separation may be essential for sustainable sanitation (Bengtsson 1997, Chen 1997, Dalemo 1999, Hellström 1997 and 1998, Kärrmann 2000, Sonnesson 1998).



Land area – land area is named in several studies for instance by Chen et al. 1997 and Hellström 1997 and 1998, there is however a trade-off with other indicators. Chen et al. for instance, mention land area as a decisive indicator to screen out technologies that lack promise. Nevertheless, they conclude that constructed wetlands and pond systems are among the technologies that emerge as essential to success. 17

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Table 2: An overview of indicators used in the literature to compare wastewater treatment systems. A Economical indicators: Costs Labour Environmental indicators: Accumulation Biodiversity / land fertility Dissication Export of problems in time & space Extraction Integration in natural cycles Land area required / space Odour / noise / insects / visual Optimal resource utilisation / reuse: Water Nutrients Energy Raw materials Pathogen removal / health

B *

Bu

P P

D

Em *

E

F

H

C

S

P

S S

100

S

P

I

J

L

E

M

N

O

S

Ø * E

T

T

P Cn P

S

P S

P

1

S

P V V V

S S S

S

Pollution prevention Emissions: BOD / COD Nutrients Heavy metals Others

V V

Sludge / waste production

V

100 0 100 10 100 0

V V

S

V

100 0 100 100 0 100 0 10

V

S S S S S

P P P P P

S S S S

S

P

S

S

P

V V

S S S S

V

S

S S t S t S t

S

P S S S

S

P S S S S

P Cn Cn Cn Cn

V

P

S S

S S

V V

S

S

V

Use of chemicals V S S S Technical indicators: Durability S S Ease of construction / low tech P Endure shock loads / seasonal S Cn effects Flexibility / adaptability S S S Maintenance Cn Reliability / security S P Small scale / onsite / local S T P solution e Social-cultural indicators: Awareness / participation S S S Competence / information S P requirements Cultural acceptance S S Institutional requirements S P Local development S Responsibility P Source: A = Azar, Holmberg 1996, B = Bengtsson 1997, Bu = Butler 1997, D = DTO 1994, Em = Emmerson 1995, E = ETC 1996, F = Finnson 1996, H = Helström 2000, I = Icke 1997, J = Jacobs 1996, L = Lundin, 1999, M = Mels et al. 1999, N = Niemcynowicz 1994, O = Otterpohl et al. 1997, Ø = Ødegaard 1995. Note: The numbers in the table indicate the used weighting factors, the abbreviations refer to the terms used in the publications; C=costs, Cn=concerns, E=environmental efficiency, P=principles for sustainability, S=sustainability indicator / factor / criterion, St=steering variables, T=target, Te=technical paradigm, V=variables in the LCA inputoutput table, * = LCA study.

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Although several researches name decisive indicators, none of them gives a clear analysis of the trade-offs made, as such there is still limited insight in what systems are the most sustainable in different situations. Through a sustainability assessment based on multiobjective optimisation that combine the results of the different tools into an integrated assessment we want to gain insights in the trade-offs and select the most sustainable domestic water systems for different cases. How this can be done is described in the remainder of this chapter. 2.3 Methodological outline for this re research 2.3.1 Methodology A suitable and often used methodology for sustainability assessment is a system analysis using a multi-disciplinary set of sustainability indicators. Similar to LCA, the methodology can be structured into three phases: goal and scope definition, inventory analysis, and optimisation. In the first phase, the goal and scope definition, the system boundaries and sustainability indicators are defined. To avoid ruling out sustainable solutions beforehand the system boundaries must be set to include whole systems rather than components and the sustainability indicators must reflect all dimensions of sustainability, including functional, economic, environmental, and social-cultural aspects. Some of these indicators are difficult to quantify, however, to assure the integrated and multidimensional character of the sustainability assessment it is better to include those indicators using crude quantification rather than not including those indicators at all. In the second phase of the analysis, the inventory analysis, the sustainability indicators are quantified through mass and energy balances, cost-benefit analysis, and actor analysis, or indicated qualitatively. In the third and last phase, the most sustainable systems are selected through multi-objective optimisation using the normalised and weighted sustainability indicators as the objective function. Literature analysis of LCA case studies reveals that this last phase, in LCA called the impact analysis, is often omitted due to the rather subjective character of this step. Scientists like to avoid the more political process of normalisation and weighting, however, this step is essential to the sustainability assessment. The results of the different assessment methods should be used in combination in order to obtain a balanced solution. The role of the scientist is to reveal the decisive indicators, trade-offs, and the sensitivity to the individual weighting factors.

All the methodologies described in the previous sections have their advantages and disadvantages. Achieving our objective, to provide insight in the achievable sustainability of domestic water systems, can in our case best be done with a system analysis, as we are mainly interested in the sustainability of the different technologies. Exergy analysis limits itself to efficiency of processes in energy terms, which could be used as a component of the system analysis, however goes into details beyond the scope of this research. The economic analysis includes subjectivity in the translation of sustainability aspects into terms of money, as costs often do not reflect ‘real costs’. For transparency we want to capture the subjectivity in weighting factors only, economic analysis in the form of a Cost Benefit Analysis is a component of our system analysis. LCA can also be seen as a component of our system analysis; however, instead of the standardised environmental impact categories we use a selfdefined multi-dimensional set of sustainability indicators. In our system analysis we try to combine the advantages of the different analysis methods. The freedom to adapt the comparisons to local conditions, different scales, etc., can be 19

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guaranteed by a model-based comparison, similar to ORWARE. The technology models applied are based on design equations incorporate scale and some incorporate temperature as indicator for the local climate. The freedom to incorporate local preferences can be expressed in weighting factors, by formulating the decision problem as a mathematical, optimisation problem with an objective defined as the weighted sum of the sustainability criteria. Furthermore, the comparison of a large number of systems is enabled by modelling all unit operations separately, similar to the approach of Chen and Beck (Chen 1997, Beck 1994). 2.3.2

Goal

We want to use the methodology to compare a large variety of different urban water systems. Our goal is to provide insight into the sustainability of wastewater treatment systems. Therefore, we aim at two outcomes for this research (1) to contribute to the development of a methodology for sustainability assessment, and (2) to give an overview of promising combinations of wastewater treatment technologies and the trade-off made in technology choices. Our assessment is based on literature data that we collected from a large number of references. Some data is defined in terms of ranges. This for several reasons, (1) removal rates will differ with circumstances such as climate and process control, (2) there are fluctuations in the influent concentrations (3) data are gathered from different literature sources that may not always be comparable, (4) the accuracy of the data is unknown. The data we use is therefore very rough. it would be good to collect the opinions of a number of experts in the field to compromise on the input data, however, this is beyond the scope of this project. Our models will therefore be simple and general rather than detailed and precise. The added value of the research is the combination of the wide system boundaries, the large number of different technologies, the wide range of sustainability indicators, and the application of the multi-objective optimisation. This means that the models have to quantify all sustainability indicators for all potential sustainable technologies that can be applied in water supply, domestic water use, and wastewater management. Indicators that cannot be quantified and are considered important are incorporated in the form of crudely quantified measures. According to the definition of sustainable technology in Sectionfprintf('\n\nThe size of the variable xtot indicates a optimisation with %4.0f evaluations',grx); 1.2, the technology has to be effective and efficient. The functional indicators express effectiveness, in other words the technology has to achieve a certain minimal performance. In the water sector this is for instance defined by drinking water quality and effluent standards. The efficiency is expressed in the form of economic, environmental, and social-cultural indicators. Meaning that the technology should use the different resources in a balanced process without threatening the quantity and quality of these resources. The resources in the domestic water system and potential options to improve sustainability are described in the next chapter. This inventory provides the quantification of the sustainability indicators that are the input data for the methodology. The implementation of the methodology as a model-based decision support system is described in Chapter 3. Resources are quantified in Chapter 4 and technologies in Chapter 5. The next sections of this chapter describe the scope of this research.

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2.3.3 Scope 2.3.3.1 Chosen System boundaries

We defined the domestic water system to include: water supply, domestic water use, wastewater transport and treatment including sludge treatment (see Figure 4). Drinking water treatment is excluded from the domestic water system, except for the water treated inside the household. This choice is made because we are mainly interested in comparing different wastewater systems. Rainwater use, water conservation, and water reuse, are more interesting to us than drinking water production. The comparison of large-scale and small-scale wastewater treatment systems requires inclusion of the household to enable separating different wastewater streams. We also chose to exclude agricultural and industrial water use and pollution. These sectors are major water consumers and polluters; however, the required water quality as well as the required wastewater treatment is very specific to the type of agriculture or industry. Industrial wastewater could have been added as an extra input stream to the wastewater treatment in the model however, water reuse and treatment of different water streams separately require a detailed analysis of industrial processes, which is beyond the scope of this research. Agricultural water use is essential different from industrial and domestic water use as irrigation, uptake by plants and soil is a diffuse process. Analysing this in a similar approach as the domestic water system would require the modelling of the whole agricultural ecosystem. Also, since the conditions in each of these later systems are very specific to the sectors, the assumption that the water treatment systems will have to be tailored to these conditions and thus require individual designs seems appropriate. Crucial aspects of the domestic water system are the control of pathogens and nutrient recycling. Within the domestic water system the food and water cycles are mixed as the waste in domestic wastewater originates largely from the nutrient cycle (see Figure 4). Therefore, recycling nutrients is one of the main focuses.

Figure 4: System Boundaries.

21

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2.3.3.2

2 Assessing sustainability

Selected Sustainability indicators

The definition of sustainability indicators is essential, as the selection of sustainable solutions is based on these indicators. A sustainable solution aims at limited use and limited degradation of resources through harmful emissions, at the same time avoiding the export of the problem in time or space. As described in Chapter 1, we distinguish three types of resources: economic, environmental and socio-cultural resources. As mentioned above, we use the same classification but add the class of the functional indicators (see Figure 1 on page 3), which can be seen as constraints. Literature describes the use of different sets of sustainability criteria (Azar 1996, Foxon 2000, Hellstrom 2000, Holmberg 1995, Kärrman 2000, Larsen 1997, Lundin 1999, Mels 1999, Otterpohl 1997, Parkinson 1997). However, there seems to be a consensus as functional indicators measuring performance are chosen for the effluent and sludge quality. Economics is mostly expressed in terms of monetary costs and benefits, while environmental indicators often incorporate both resource use and emissions. Literature describes the use of different sets of sustainability criteria (Azar 1996, Foxon 2000, Hellstrom 2000, Holmberg 1995, Kärrman 2000, Larsen 1997, Lundin 1999, Mels 1999, Otterpohl 1997, Parkinson 1997). However, there seems to be a consensus as functional indicators measuring performance are chosen for the effluent and sludge quality. Economics is mostly expressed in terms of monetary costs and benefits, while environmental indicators often incorporate both resource use and emissions. It is widely recognised that other aspects are also important for the functioning of the system, for instance the ability to cope with shock loads, the maintenance required, cultural acceptance, etc. However, these aspects are more difficult to quantify and therefore rarely used. In developing a decision support tool we aim at including a comprehensive list of sustainability indicators thereby providing the decision maker with a complete view on the various aspects of the technology. Realising that a decision maker has the option to ignore individual indicators, we define a “super-set” of indicators by including, qualitatively, the following hard-to-quantify-indicators: acceptance, adaptability, expertise, quality of space, institutional requirements, maintenance, participation, reliability, robustness, and stimulation of sustainable behaviour. A complete overview of the indicators is given in Table 3. As stated in the beginning of this chapter, sustainability is by definition impossible to measure quantitatively. As knowledge proceeds, ideas changes, situations and priorities, there is no guarantee that the solutions we identify as sustainable today will prove to be sustainable in the far future. Although, one may expect that a system with 100% reuse and powered by solar energy could stand the test of time. Still, the set of sustainability indicators defined below should be seen as a first step. Over time new indicators will be added and others, proven less effective will be removed. Functional indicators: Functional indicators define the minimal technical requirements of the solution. In our model there is one crucial indicator namely the functional indicator waste. Waste is defined as those outgoing streams that have potentially no reuse and cannot be discharged based on restrictions in legislation and health guidelines. This means that if one allows waste to be produced and for instance optimise costs the selected domestic water system will consist of water use only as one defined the quality of outgoing streams as not important. Thus it is the 22

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amount of waste produced that indicates the ineffectiveness of the domestic water system. Additional indicators are adaptability (possibility to extend the system in capacity, or with additional treatment), robustness (ability to cope with fluctuations in the influent), maintenance required, and reliability (sensitivity of the system to malfunctioning of equipment and instrumentation). Compared to waste these indicators are optional, like all other indicators defined below. Economic indicators: We include the commonly used indicators: estimated investment costs and operation and maintenance costs accounting for the estimated lifetime. Based on these one can derive secondary indicators such as affordability and cost effectiveness. Environmental indicators:

Our environmental indicators are split into 2 categories, namely (1) optimal resources and (2) emissions. The optimal resource use is expressed in indicators such as water use, energy use, bio-gas production, space requirements, the quality of the utilised space, and the possibility to reuse nutrients in the form of fertiliser or soil conditioner. For water use several indicators are available, namely total water use, drinking water use, household water use, rainwater use, and possibility to reuse treated wastewater in the household or for irrigation or infiltration. Emissions are expressed in the indicators discharge, waste, and CSO. We would have liked to include emissions to air and the use of chemicals in processes such as precipitation but due to the scarcity of available data we left those indicators out for the time being. Social-cultural indicators: Both social and cultural indicators are difficult to quantify and are therefore often not addressed. However, these indicators play an important role in the implementation of technology. This is especially the case when the end-user is directly involved, like in water use, sanitation, and on-site treatment. The social-cultural indicators included are: • Acceptance: In different cultures, people will have a different perception of waste and sanitation, resulting in different habits. New sanitation concepts in particular different toilet systems may encounter social-cultural difficulties in the implementation. For instance: the need to explain visitors how to use the separation toilet was one of the reasons to remove these toilets from the houses of an ecological village (Fittschen & Niemczynowicz 1997). • Expertise: The selected technological solution requires a certain level of expertise for installation and operation. If the expertise is not locally available it may be gained through import or training. • Institutional requirements: Different wastewater treatment systems will require different regulations and control mechanisms. These requirements should fit in the existing institutional infrastructure of the region. • Participation: Participation of different actors, including the end-users, creates ownership, responsibility, and awareness. • Stimulation of sustainable behaviour: Sustainable behaviour can be stimulated by tailoring the technological design such that sustainable behaviour is the most convenient option. Sustainable behaviour can also be stimulated through increased awareness.

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The quantification of the sustainability indicators for the inputs to the model is given in Chapter 4, and for the different technologies in Chapter 5. The next chapter describes the setup of the decision support tool in more detail. Table 3: Sustainability indicators for the domestic water system. Indicator: Functional: - Adaptability - Maintenance - Reliability - Robustness - Waste Economic: - Costs Environmental: Emissions: - CSO - Discharge Resource Utilisation: Energy: - Energy use – Gas Space: - Land area - Quality of space Nutrients: - Fertiliser - Soil conditioner Water: - Total water use - Discharge - Domestic reuse - Drinking water - Household water - Infiltration - Irrigation - Rainwater use Social-Cultural: - Acceptance - Expertise - Institutional requirements - Participation - Sustain. behaviour 6

Description:

Expressed:

- Indication of flexibility of the process with respect to the implementation on different scales, increasing / decreasing of capacity, and anticipate on changes in legislation etc. - Indication of maintenance required. - Indication of sensitivity of the process concerning malfunctioning equipment and instrumentation. - Indication of sensitivity of the process concerning toxic substances, shock loads, seasonal effects etc. - The effectiveness of the treatment is expressed in the sustainability indicators that define reuse and waste. If reuse or discharge is not allowed the stream is categorised as waste.

Qualitative

- Investment and operation and maintenance costs.

Euro

- Untreated wastewater discharged combined sewer overflow. - Treated water that can be discharged (TSS, BOD, N, P)6.

m3 m3

- Energy used by treatment. - Bio-gas produced in anaerobic treatment.

kWh m3

- The total land area required. - Indication of the possibilities to integrate the wastewater treatments system (partly) in green areas.

m2 Qualitative

- Nutrients suitable for reuse, (P,Cu,Zn). - Stabilised unpolluted organic matter (TSS, Cu, Zn).

kg kg

- Sum of different water uses - Treated water that can be discharged (TSS, BOD, N, P) - Treated water suitable for domestic reuse (TSS, Cu, FC). - Amount of drinking water used. - Amount of household water used. - Treated water suitable for infiltration (TSS, P, Cu, Zn). - Treated water for irrigation (TSS, BOD, Cu, Zn, FC). - Amount of rainwater used.

m3 m3 m3 m3 m3 m3 m3 m3

- Indication of the cultural changes and impacts: convenience and correspondence with local ethics. - Indication whether a system can be designed and built or can be repaired, replicated and improved locally / in the country / or only by specialised manufacturers. - Indication of efforts needed to control and enforce the existing regulations and of embedding of technology in policymaking. - Indication of the possibilities for end user participation.. - Indication of stimulance in the design to behave sustainable.

Qualitative

See for details on the categorisation of outgoing streams as waste or reuse Table 13 on page 56

24

Qualitative Qualitative Qualitative m3

Qualitative Qualitative Qualitative Qualitative

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References Azar C., Holmberg J., and Lidgren K. (1996). Socio-ecological indicators for sustainability, Ecological economics 18 (1996), 89-112. Beck M.B., Chen J., Saul A.J., and Butler D. (1994). Urban Drainage in the 21st century: assessment of new technology on the basis of global material flows, Wat.Sci.Tech., vol 30, no2, pp1-12. Bengtsson M., Lundin M., and Molander S. (1997). Life Cycle Assessment of Wastewater systems, case studies on conventional treatment, urine sorting and liquid composting in three Swedish municipalities, Technical Environmental Planning, report 1997:9, Göteborg, Sweden Chen J., and Beck M.B. (1997). Towards designing sustainable urban wastewater infrastructures: a screening analysis, Water Science and Technology, Vol. 35, no 9, pp 99-112, 1997. Crettaz P., Jolliet O., Cuanillon J.M., Orlando S. (1997). Life Cycle Assessment of drinking water management and domestic use of rainwater, 5th LCA case study symposium, held in Brussels on 2 December 1997, organised by SETAC Europe, pp. 99-103. Dalemo M. (1999). Environmental Systems Analysis of Organic Waste Management, The ORWARE model and the sewage plant and anaerobic digestion submodels, Doctoral thesis, Swedish university of Agricultural Sciences, Uppsala, Sweden, ISBN 91-576-5453-0. Das I. (1997). Muti-objective optimisation, Das Indraneel formerly worked for Rice University, downloaded on 24 December 1999 from http://www-fp.mcs.anl.gov/otc/Guide/OptWeb/multiobj/, site last updated 22 January 1997. Dennison F.J., Azapagic A., Clift R., and Colbourne J.S. (1997). Assessing management options for sewage treatment works in the context of life cycle assessment, 5th LCA case study symposium, held in Brussels on 2 December 1997, organised by SETAC Europe, pp. 169-177. Ellis K.V. and Tang S.L. (1990). Wastewater treatment optimisation model for developing world, I model development, ASCE journal of Environmental Engineering Division, 1990, vol. 117 pp 501-518. Emmerson R.H.C., Morse G.K., Lester J.N., and Edge D.R. (1995). The life-cycle analysis of small-scale sewage-treatment processes, J.CIWEM, 9, June, 317-325, 1995. Foxon T.J. (2000). A multi-criteria analysis approach to the assessment of sustainability of urban water systems, proceedings of the 15th European Junior Scientist Workshop, held May 11-14 2000, in Stavoren, The Netherlands, pp. 119-128. Groot, R.S. de (1992). Functions of nature, evaluation of nature in environmental planning, management and decision making, Wolters-Noordhoff, ISBN 9001 35594 3. Hellström D., Jeppsson U., and Kärrman E. (2000). A framework for systems analysis of sustainable urban water management, Environmental Impact Assessment Review, vol.20 (2000), no.3, p.311-321. Hellström D. (1998), Nutrient management in sewerage systems: Investigation of components and exergy analysis, PhD thesis, Lulea university of technology, Department of environmental engineering, Division of sanitary engineering, Sweden. Hellström D. (1997). An exergy analysis for a wastewater treatment plant - an estimation of the consumption of physical resources, Water Environment Research, January/February 1997, Vol. 69, No.1, p.44-51. Hoek J.P. van der, Dijkman B.J., Terpstra G.J., Uitzinger M.J., and Dillen M.R.B. van (1999). Selection and evaluation od a new concept of water supply for “IJburg” Amsterdam, Wat. Sci. Tech., vol.39, No.5, pp.3340. Holmberg J. (1995). Socio-ecological principals and indicators for sustainability, Dissertation, Institute of Physical Resource Theory, Göteborg, Sweden. ISO 14040:1997 Environmental management - Life cycle assessment - Principles and framework. ISO14041:1998 Environmental management - Life cycle assessment - Goal and scope definition and Inventory analysis. ISO 14042:2000 Environmental management - Life cycle assessment - Life cycle impact assessment. ISO 14043:2000 Environmental management - Life cycle assessment – Life cycle interpretation. Kärrman E. (2000), Environmental systems analysis of wastewater management, thesis for the degree of doctor in philosophy, Department of Water, Environment, and Transport, Chalmers University of Technology, Göteborg, Sweden. Larsen T.A., and Gujer W. (1997). The concept of sustainable water management, Water Science and Technology, No. 9, pp 3-10. Loetscher T. (1999). Appropriate sanitation in developing countries: the development of a computerised decision aid, PhD Thesis, Department of Chemical Engineering at the University of Queensland, Brisbane, Australia, ISBN 186499097X. Lundin M., Molander S., and Morrison G.M. (1999). A set of indicators for the assessment of temporal variations in sustainability of sanitary systems, Wat. Sci Tech. vol. 39 no. 5 pp 235-242.

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Mels A.R., Nieuwenhuijzen A.F., Graaf J.H.J.M. van der, Klapwijk B., Koning J. de, Rulkens W.H., (1999). Sustainability criteria as a tool in the development of new sewage treatment methods, Wat. Sci Tech. vol.39, no. 5, pp.243-250. Neumayr R., Dietrich R., and Steinmüller H. (1997). Life cycle assessment of sewage sludge treatment, 5th LCA case study symposium, held in Brussels on 2 December 1997, organised by SETAC Europe, pp. 155-160. Otterpohl R., Grottker M., and Lange J. (1997). Sustainable water and waste management in urban areas, Water Science and Technology, vol.35, no.9, pp.121-134. Parkinson, J. and Butler, D. (1997). Assessing the sustainability of urban wastewater systems, Sixth IRNES Conference, ‘Technology, the environment, and us’, held at the Imperial College London, 22-23 September 1997. Sonesson U. (1998). Systems analysis of waste management, the ORWARE model, transport and compost sub models, Doctoral Thesis, Swedish University of Agricultural Sciences, Uppsala 1998, ISBN 91-576-5470-0. Schweiger C.A. and Floudas C.A. (1998). Process synthesis, design, and optimal control: a mixed-integer optimal control framework, in the Proceedings of DYCOPS-5 on Dynamics and control of process systems, pp 189-194 (downloaded from internet http://titan.princeton.edu/bib.html ) Tang S.L., Wong C.L., and Ellis K.V. (1997). An optimisation model for the selection of wastewater and sludge treatment alternatives, J CIWEM 1997, 11, February, pp.14-23. Tang S.L. and Ellis K.V. (1994). Wastewater treatment optimisation model for developing world, II model testing, Journal of environmental eng. Division, 1994, vol. 120, pp. 610-624 Ødegaard H. (1995). An evaluation of cost efficiency and sustainability of different wastewater treatment processes, Vatten 51:291-299, Lund.

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3 Model based decision support tool

3 Developing a modelmodel-based decision support tool The proposed methodology, as described in Chapter 2, is implemented in a model-based decision support tool. The structure of this tool is visualised in Figure 5. It consists of four parts: (1) data storage, (2) model of the domestic water system, (3) the quantification of the sustainability indicators, and (4) optimisation. These four parts are described in the following paragraphs

Figure 5: Outline of the model-based decision support tool. 3.1 Data Input data is obtained through an extensive literature review (Balkema 2001). This spreadsheet, named “Data2003”, summarises all data that is used in the calculations, including inputs such as water use, contaminants added to the water, prices of commodities and technologies, performance of technologies, normalisation factors, etc. The spreadsheet provides decision makers with a complete overview and enables adaptation of the data (see Appendix 1). The data can be transferred to Matlab-Simulink and are the input for the calculations. 3.2 Model of domestic water systems All calculations are performed in the Matlab-Simulink model, beginning with water use inside the household all the way through wastewater treatment and discharge. The model is a combination of streams and technologies, in which one can chose to split or combine streams or change the composition of the stream by inserting technologies. The choices are either made by hand to calculate the sustainability indicators for a single system or through the optimisation procedure to select optimal systems for a given set of weighting factors. Note that the software implementing this model can be downloaded from the TU/e internet site (www.tue.nl/bib under Publications TU/e, Full-text Dissertations TU/e, look for my name “van der Vleuten-Balkema”) For instance, a particular domestic water system, including water use and wastewater treatment is specified by choosing the water source one wants to use, whether or not to reuse 27

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wastewater, to combine or separate wastewater streams, which transport option one wants to use, and which treatment technologies are to be used. The models of the unit operations chosen are coupled to calculate the mass balance, as well as energy use, costs, land use, etc., for the total domestic water system.

Figure 6: Superstructure. 3.2.1

Superstructure

To enable the comparison of a large variety of wastewater treatment systems, we want to keep the number of possibly limiting preconceptions as small as possible. Therefore, we based the model on a superstructure comprising most relevant water use and wastewater treatment possibilities (see Figure 6). This structure fulfils the following requirements: • • • • • •

enable comparison of small- and large-scale systems; enable separate treatment of different wastewater streams; enable use and reuse different water streams, such as drinking water, greywater (water lightly polluted by household use), household water (a secondary water quality), rainwater, and treated wastewater; include the different products that can be made, such as biogas, fertiliser, and water for irrigation; include the toilet and the sewer system for comparison of systems such as vacuum toilets and compost toilets with conventional water based systems; include bio-waste that can be treated in the dry sanitation systems such as composting.

The superstructure is constructed by superimposing a large number of known options for domestic water use, water conservation and reuse, as well as wastewater treatment. As such, the superstructure is an aggregated structure of a large variety of domestic water systems. In 28

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the structure, we distinguish different types of domestic water use based on the water quality required and the characteristics and destination of the wastewater (toilet flushing, kitchen water use, personal hygiene, washing clothes, and outdoor water use). Based on the characteristics of the different wastewater flows we included the option to treat three flows separately; (1) water from the toilet, so-called blackwater, (2) yellowwater (= urine), and (3) greywater, more diluted wastewater from the other household activities. The superstructure shows the possible mass streams connecting different units. Two different types of units are defined, namely (1) stream combiners & splitters (triangles), and (3) processing units (squares). 3.2.1.1 Stream combiners and stream splitters

In our static model, most stream combiners in the superstructure are virtual ones that do not physically exist but indicate a choice for a water source or a sequence of different sources used over time. It is assumed that in combiners all incoming streams are added up to one homogeneous outgoing stream (ideally mixed, no reactions). Like the combiners, splitters indicate that different choices lead to different directions of flows. For instance directing drinking water to toilet flushing and to the washing machine (two options of splitter d in Figure 6). However, it can also mean that some of the water is consumed while the rest is poured into the sink (splitter k2 in Figure 6). The outgoing streams of stream splitters have always the same composition as the incoming stream (no separation of solids or chemical reactions take place). 3.2.1.2 Stream definition

Streams are defined by their respective volumetric and mass flow rate (l/d and g/d) of the most relevant components indicating possible harmful emissions or possible products. Selected components are TS, TSS, BOD, Total N, Total P, heavy metals (Cu, Zn), faecal coliforms (FC), and H2O. Due to lack of data it was not possible to quantify all components we selected in the first stage, namely NH3-N, technology choice Total K, Ca, Mg, Cd, Pb, N2 / NOx, and the Gas gases CO2, CH4, H2O, and NH3, had to be left Membrane out (see paragraph 5.7). Influent

3.2.1.3 Processing units

Precipitation

Effluent

Processing units contain technological unit Sedimentation operations obtained through segregation of wastewater treatment systems. In processing size, costs, units separation and/or material energy use, Recycle Sludge transformation takes place. These blocks are products, stream indicators therefore defined by state information Figure 7: Processing unit (bw1). (temperature, components, etc.) and by information about the nature of the block (chemical reactions, physical separation, etc.). The technological unit operations are grouped in the processing units. As such, the processing units are super-blocks containing models of different unit operations that can be applied to fulfil the function of the processing unit. Processing units can be found in the household, for instance the toilet, the rainwater system, and the disinfection unit. Outside the household there are processing unit for wastewater transport and treatment. There is an on-site treatment unit to allow small-scale wastewater treatment before transport. Then, for blackwater treatment there are 4 treatment units and a 29

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sludge treatment unit, while for greywater there are 2 treatment units and for yellowwater only 1. For instance, the processing ‘blackwater treatment 1’ (bw1) contains three models, namely membrane filtration, precipitation, and sedimentation. One of these technologies is chosen to treat the incoming wastewater. It is also possible to choose a blank option meaning that no treatment takes place in bw1 (see Figure 7). Note that technologies can have a constraint with respect to the incoming wastewater, for instance the concentration of solids in the influent of constructed wetland should be limited to prevent clogging. If the concentration in the incoming stream is too high another type of wetland is chosen (wwtconstraint(2,10)=360 mgTS/l see Appendix 1). To indicate that the model overruled a choice made by the user or the optimisation procedure the model sets a flag. When showing the results, the original choices and the choices corrected for these flags7 are shown. The models used for the unit operations contained in the processing units are static black box models based on data from literature (see Chapter 5). The models do not scale the technologies, but different models are inserted for the same technology on different scales. For instance, if the population is 100 persons, one can chose either the technology model for 100 persons or 20 times the 5 persons model for the same technology. However, transport and treatment are coupled, if for instance the option “no transport” is chosen, the treatment is restricted to small scale. If one chooses no transport in combination with large scale treatment the model will overrule this by changing the transport into combined or separated sewer (the rule is: if scale 0,η > 0,i = {1,2,...,27}

C = {x _ L < x < x _U; b _ L < A(x) < b _U}

x∈C

minF(x) = ∑αi *ηi *SIi (x)

Weighted27sum of sustainability factors.

Objective function:

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The search strategy of this solver is based on the “Direct algorithm” described by Jones 1993. This strategy is based on dividing the solution space into rectangles and defining a set op potential optimum rectangles in each evaluation. However, it seems that due to the discrete changes in sustainability it loses itself into extensive search as this is the only way to be sure that the global optimum is found. Due to the large number of domestic water systems that is contained in the super structure, namely 7*1012, the calculation time is quite extensive, namely 1 to 2 seconds for a single evaluation on a regular personal computer (662MHz) thus about 3.6*105 years for the whole set of domestic water systems. To limit the calculation time, we explored 2 options, (1) decreasing the number of possible solutions, and (2) using another solver. To decrease the number of evaluations, a procedure is written to chose technologies that one wants to consider in optimisation, thus creating a smaller solution space from which optimal systems can be selected by gclSolve (see Appendix 2 – files gcl2003small.m and in2003.m). (2) Mixed-integer solver: mipSolve A solver with another search strategy we used is the mixed-integer11 solver, mipSolve that runs under Tomlab (Tomlab 2000). This solver uses the standard techniques: Branch-and-Bound and Cutplane (see Adfjiman 1998, Biegler 1997, and Drakos 1996). To check whether the found optimum is a local one, multiple runs have to be made starting from different points in the solution space. Therefore, the calculation time depends on the number of runs one decides to make. Note that one is in principle never sure that the optimum found with this solver is the global as the starting points are generated randomly without checking earlier starting points. Similar as with the global solver, we included the option to use the solver with a smaller solution space (see Appendix 2 – files mip2003small.m and in2003.m). (3) Genetic algorithm: gaminI The search strategy of genetic algorithms is based on the concept of the evolution of species. The population of the species is in our case a set different domestic water systems and the score on the objective defines the individual fitness. The fittest individuals mate to produce the next generations. Or in other words, the solver combines the inputs that generate the best solutions into new inputs to calculate the objective values for a new generation. From time to time mutations are inserted and the best sofar is reinserted. The search for an optimum stops when the maximum number of generations is reached or when no improvement is made during a defined number of generations. As such there is no guarantee that the optimum found is the global optimum, the optimum is “the best so far”. The GA-solver used for the selection of optimal domestic water systems is an adapted version of gamin.m, which is available on the Internet as freeware (Gordy 2001). The algorithm is described by Dorsey R.E. and Mayer W.J. (1995). The most important adaptation to the solver we made is the use of integers as input variables. To reduce calculation time, we also made a problem definition that 11

At first we defined our problem as mixed-integer optimisation and selected this solver. The problem is now defined as integer problem but the same this solver can be used.

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makes it possible to use a smaller set of decision variables (define your set of decision variables with in2003.m and use ga2003small.m for the optimisation – see Appendix 2). All solvers save the results automatically an overview of the results can be generated by running the following m-file: dws2003out, SortResults, and moreplots. For more detailed information on the different files used to run the optimisation see Appendix 2. 3.5 Output of decision support system The optimisation selects the choices for combiners, splitters, and processing units that minimize the objective function. In this way, optimal combinations of technologies forming a complete domestic water system can be selected for a particular situation. The output provided lists the five best systems in the dataset set defined, and gives a detailed overview of the sustainability indicators and the mass balance. Furthermore, one can save and plot the data to get an overview of how the value of the objective changes with the different choices made. For details on the output see Appendix 4. A sensitivity analysis is preformed by varying the inputs, the solution space, and the weighting factors. This provides insight in the sensitivity of the solutions and the trade-offs made by choosing different combinations of technology characteristics, different technologies, or different weights on the sustainability indicators.

References Adjiman C.S., Schweiger C.A., and Floudas C.A. (1998). Mixed-integer Nonlinear Optimisation in Process synthesis, in Handbook of Combinatorial Optimisation, Edited by DU, D-Z and Pardalos PM, 1998, Kluwer Academic Publishers (downloaded from internet http://titan.princeton.edu/bib.html ). Balkema A.J. (2001). Characterising wastewater treatment technologies, exploring the essence of technologies to quantify sustainability indicators, Internal report, Technical University Eindhoven, April 2001 (unpublished but digital version available from the author). Biegler L.T., Grossmann I.E., Westerberg A.W., (1997). Systematic methods of chemical process design, Prentice Hall PTR, New Jersey, ISBN 0-13-49422-3. Dorsey R.E. and Mayer W.J. (1995). Genetic Algorithms for estimation problems with multiple optima, nondifferentiability and other irregularities, Journal of business & economic statistics, vol. 13, no.1, pp.5366, ISSN 07350015. Drakos N. (1996). Tutorial Optimisation, version 96.1, Computer Based Learning Unit, university of Leeds, version 96.1, downloaded 19-4-99 from Internet site: http://matgsia.cmu.edu/orclass/integer/node13.html . Gordy M. (2001), adapted by A.J. Balkema (2002), a genetic algorithm implemented in Matlab available on the Internet: http://econpapers.hhs.se/software/codmatlab/ga.htm. Holström K. (1999). TOMLAB v2 Usersguide, September 20th 1999, on Internet at www.ima.mdh.se/tom . Jones D.R., Perttunen C.D., and Stuckman B.E. (1993). Lipschitzian optimisation without the Lipschitz constant, Journal of Optimization Theory and Applications, Vol.79, No.1, October 1993. Schweiger C.A. and Floudas C.A. (1998). Process synthesis, design, and optimal control: a mixed-integer optimal control framework, in the Proceedings of DYCOPS-5 on Dynamics and control of process systems, pp 189-194, downloaded from internet http://titan.princeton.edu/bib.html Tomlab (2000), developed at Mälardalen University in Sweden, Tomlab versions 1 to 2.2 were available as freeware, Tomlab 3 became a commercial product, see http://www.ima.mdh.se/tom/tom-software/tomlab.htm or www.tomlab.biz.

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4 Streams: quantifying inputs & categorising outputs Data required as input for the decision support tool is described in two parts, stream definition described in this chapter and technology characterisation described in Chapter 5. In this chapter on stream definition we quantify the incoming streams (drinking water, household water, rainwater, bio-waste, faeces, urine, and contaminants) and the associated sustainability indicators such as costs, energy use, space requirements, etc. Furthermore, we determine restrictions to indicate the reuse potential of the outgoing streams. Hereto, we first define the objectives of water services in paragraph 4.1, and then describe in 4.2 water supply and in 4.3 wastewater, and in 4.4 the major threats defining the constrains for water services and in 4.5 the resources in wastewater that define the opportunities for the water services. 4.1 Water services Urban water systems are to maintain a healthy living environment through supplying safe drinking water, providing hygienic sanitation, and protecting against flooding and surface water pollution. Focussing on domestic water systems, we find that water is essential in many of our daily activities. It is the major constituent in our drinks, important in food preparation, and essential in personal hygiene. It fulfils many functions; in the morning, you may use cold water to wake up, it may refresh you after sporting, warm you up when feeling cold, and relax you when being stressed. Furthermore, water is not only used for washing oneself, but also for clothes, cars, and when flushing the toilet. In our country, water is so plentiful, so common, that it is almost used unnoticed. Some people may even not be aware of its origin, the effort in treatment, supply, use, collection, treatment, and the effects of wastewater discharge. While in other societies water is very scarce, it may take hours walking to fetch 10 or 20 litres water. Still, the functions drinking, cooking, washing and cleaning, as well as sanitation are fulfilled one way or the other. These functions are the essence of the domestic water systems in the decision support tool and therefore described and quantified in this chapter. 4.2 Water supply Water can be drawn from the following different sources: groundwater, surface water, rainwater, and wastewater. Groundwater has the advantage to be in most cases naturally purified by the soil’s ecosystem and is therefore often of good quality. However, in some cases there are groundwater quality problems due to high natural concentrations of arsenic12, iron, fluoride13, nitrate, or extreme salinity. Furthermore, groundwater quality is threatened through contamination with pathogens from domestic sanitation and life stock, and chemicals from industry and agriculture such as fertiliser, pesticides, and heavy metals. The fact that withdrawal often exceeds recharge is another problem with groundwater as source for water supply. Surface water, if available, is often recharged quicker than groundwater. However, the quality is usually less, thus requiring better treatment. Rainwater, if available in sufficient quantities, can also be a sustainable water source. The quality of rainwater depends on the

12

The natural contamination of groundwater with arsenic in Bangladesh is the largest poisoning of a population in history with millions of people exposed (Smith 2000). 13 Fluoride in groundwater affected nearly 100,000 villagers in the remote Karbi Anglong district in the north-eastern state of Assam, many have been crippled for life. Fluoride levels in the area vary from 5-23 mg/l while the permissible limit is 1.2 mg/l [Times of India / UNI, 2 June 2000].

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local air pollution as well as on the method of collection and storage. In cases where surface water and rainwater are not available treated wastewater may serve as a water source. The treatment of the water depends on the source as well as the intended use. There are a number of standard treatment techniques such as micro-filtration, precipitation, floatation, active carbon, and sand filtration. Additional disinfection can be carried out with chlorine, ozone, UV, or membrane filtration, or through heating. Some constituents, for instance certain pathogens (as for instance Cryptosporidium and Giardia) and pesticides, are difficult to remove. Water standards are set to safeguard drinking water quality. People need approximately 2 litres drinking water per capita for drinking and cooking per day. For other usage, such as bathing and cleaning, the water quality can be slightly lower, for instance as indicated by the European swimming water standards. If the contact with the water is limited, as for instance in washing clothes with the washing machine or flushing the toilet, the water quality may even be less. For these purposes alternative water sources as for instance lightly treated surface water, treated greywater, or stored rainwater can be used. The main question is whether this implies taking some health risks. Research by the governmental institute for health and environment (Medema et al. 1994, 1996, and Versteegh 1997), indicate that there is a potential risk especially when using alternative water sources for the shower, bath, garden tap, and even for wash machine and toilet flushing, as there is a risk of aerosol infection. The cited reports are based on limited literature resources, thus additional research is needed in order to come to the definition of an appropriate water quality standard. At the same time, rainwater use is accepted in Germany, for instance, while greywater reuse is gaining acceptance in countries such as Japan, Australia, USA, Canada, UK, Germany, and Sweden (see paragraph 5.3). 4.2.1

Domestic water use:

In the Netherlands domestic water use is on average 130 litres drinking water per person per day (47 m3 per person per year, see Figure 9). This is about average in Europe, but low compared to the USA were people use on average 265 (l/(cap*d))(Cites et al. 1998). On the other hand, 130 litres is high compared to many developing countries, in Tanzania for instance domestic water use in on average 10 l/cap/d (WRI 2000, p.277). According to the United Nations, 50 l/cap/d is minimum required for consumption, and personal hygiene, independent of climate, technology, and culture (see also footnote 15 on page 42).

Figure 9: Domestic water use in the Netherlands categorized by function (NIPO 1999).

In the Netherlands, domestic water use slightly decreased in 1992 the average was 135 (l/(cap*d), in 1995 this was 134 (l/(cap*d)), and in 1998 128 (l/(cap*d)) (NIPO 1992, 1995, 1999). This decrease is due to the introduction of water saving measures as water saving showerheads, smaller flush water reservoirs with flush interrupters, and water saving washing 38

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machines. Several studies analyse domestic water use in the Netherlands and identify options for water conservation. The general conclusions are that water conservation measures, such as water saving toilets and showers, can reduce water consumption by 20% (Ewijk et al. 1998, Geldof et al. 1997, 1997b, Haffmans et al. 1995, Kilian et al. 1996, Langeveld 1997). When rainwater or treated greywater is used in addition, the total reduction in drinking water consumption is about 60 to 80% (Ewijk et al. 1998, Geldof et al. 1997, 1997b, Herrmann et al. 1997, Kilian et al. 1996, Langeveld 1997). Water conservation, rainwater and greywater systems are discussed in detail in Chapter 5. 4.2.2

Economics of water supply:

Although, there is a trend in privatisation, most water companies are still governmental institutions, primarily because health is a public responsibility but also due to the nature of investments, namely the high cost of the necessary infrastructure. This is also reflected in the public investments dominating in the water Figure 10: Investment in water world-wide (Additional sector (see Figure 10). As result of a investment is an estimated amount required to counter number of reasons, the prices for water the water crisis, source: Cosgrove et al. 2000). supply and wastewater management may not reflect the actual costs, or the actual costs are not transparent to the users. Reasons for this may be: subsidizing water, no metering of water use, no inclusion of environmental costs, billing together with electricity, many of illegal connections, loss through leakage, etc. Table 4: Costs of piped water supply. Costs for treatment and supply: Household water IJburg, Treatment 0.58 (Euro/m3) GWA 1997, p.50, and p.53) Amsterdam Supply 0.59 Drinking water Source: groundwater 0.17 (Euro/m3), excl. groundwater tax of 0.15, Source: surface water 0.30 Dufour 1998, p.146 Variable rate paid by Dutch consumers*: Water Eindhoven and Drinking water 1.11 (Euro/m3), incl. 0.20 ecotax and 0.13 Meerhoven Household water 0.88 Euro/m3 groundwater tax, WOB 2001 Water supply IJburg Drinking water 1.17** (Euro/m3), GWA 1997, p.VII Amsterdam Household water 1.03** Estimated price elasticity Drinking water 0.1 – 0.2 Noorman 1998 p193, Haffmans 1995 p8 Average costs (with fixed rate incorporated) paid by consumer: Brussels Drinking water 1.4 (Euro/m3), Dufour 1998, p.148, based on Amsterdam 0.9 200 m3/year. Washington 0.4 Rome 0.3 * Note: In the Netherlands the drinking water prices vary per region (0.77 to 1.57 Euro/m3), similar the fixed rate that is charged in addition (12 to 66 Euro/(household*year)). An average Dutch household (2.5 person, 125 m3/year), pays between 111 to 319 Euro/year for drinking water supply. Charges for wastewater treatment are often based on population equivalent, although the system is changing to couple wastewater fees to water use. These charges also vary per region, in Eindhoven a household with 2 or more inhabitants, pays 218 Euro/year (WOB 1998). ** Note that it was apolitical decision to make household water cheaper and drinking water more expensive, these prices are totally reflecting the production cost of the product. Household water has to be cheaper as it is a lesser product, and drinking water becomes more expensive to make up for the loss and as less is sold while production capacity is available. Original data in Dutch Guilders; 1 Euro = 2.20317 NLG.

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The World Bank estimated that governments recover about one third of the cost of providing drinking water services in developing countries. Often the relatively rich part of the population living in the better neighbourhoods of the cities, are connected to the subsidized piped water supply and sewerage. Whilst as the poorer part of the population mostly depends on commercially sold water, typically 2 to 3$ per m3, that is 12 times higher on average than the price of the subsidized municipal water (Moor et al. 1997, p.16). Table 4 gives an overview of the costs of water supply. Environmental impact assessment: Besides water withdrawal, energy use is seen as one of the major environmental impacts of drinking water supply. Reported figures on energy use14 are 0.7 kWh/m3 (electricity use mainly pumping, Tarantini and Ferri 1998, Italy), 0.77 kWh/m3 (Bengtsson et al. 1997, p.20, Sweden), and 0.13 to 0.22 kWh/m3 for treatment and 0.06 kWh/(m3*km) for transport (VROM 1992, the Netherlands), and 2.2 ± 0.4 kWh/m3 for drinking water production and supply using groundwater and 2.8 ± 1.4 kWh/m3 when using surface water (Ewijk et al. 1998, p.25, the Netherlands). Land requirement for drinking water production from surface water is estimated to be 0.01 to 0.03 m2/litre for the storage basins, 0.45 m2/litre for transport, and 0.0025 m2/litre for drinking water treatment (VROM 1992). The land area for storage basins obviously depends on the depth of the basins. For transport, the space occupation is mostly underground, meaning that the land area can be used for other purposes with some limitations. Table 5: Comparison of different supply systems for IJburg in Amsterdam (Hoek 1999, GWA 1997) Scenario:

Description:

Reference Rainwater

Drinking water supply. Rainwater for the toilet substitutes 16% of drinking water, 1 system per 4 households. 1 wetland per 24 houses for greywater treatment supplying to toilet flushing, substituting 21% of the drinking water. Water from the IJ-lake treated by inline coagulation, pellet softening, and UV disinfection is used to flush the toilet and supply the washing machine, substituting 37% of the drinking water. Brackish groundwater is treated with reverse osmosis and also substitutes 37% of the drinking water.

Greywater IJ-lake water

Brackish groundwater

Costs: (Euro/m3) 1.3 7.1 6.7 1.2

1. 1

Environmental impact*: Only better score than reference scenario on ozone depletion. Only better score than reference scenario on ozone depletion. Better than reference scenario on exhaustion of fuel, greenhouse effect, acidification, eutrophication, smog forming, human toxicity, and energy consumption. Worse than reference scenario on all points, especially high on aquatic toxicity**.

* According to LCA methodology the following categories of environmental impacts are incorporated in the analysis: depletion of resources, depletion of fuels, global warming, acidification, eutrophication, ozone layer depletion, aquatic eco-toxicity, photochemical air pollution, and humane toxicity. ** The report gives little insight into the results of the LCA. The fact that brackish water scores so low is surprising. Two treatment schemes for brackish water are proposed one based on ultra-filtration the other on flocculation using FeCl3 followed by filtration. May be the addition of this flocculant is debit to the bad score on aquatic toxicity. Aquatic toxicity is a difficult category in LCA as it is difficult to weight different toxic effects, not all chemicals are included in the data base, and in water treatment is not always clear what of the chemical remains in the water and in what form.

14

The figures of Ewijk et al 1998 do include all primary energy used for drinking water supply and distribution excluding energy use for the production of piping materials used. Bengtsson et al 1997 give separate figures for electricity and fossil energy and include the production of treatment chemicals as well, the other sources give no further details.

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Figure 11: Institutions and their responsibility in the water sector (Note: arrows mean those institutions fall under the ministry or that ‘upper’ institutions have shares in the ‘lower’ one or are a branch organisation).

An example of an environmental impact assessment for water supply is given in Table 5. This example considers the water supply for a new housing area in Amsterdam called IJburg. The dual water supply system, supplying drinking water and locally treated surface water as household water, was selected on costs and environmental impacts. Although selected, the system is never implemented. When it came out that in Leidsche Rijn, a Dutch pilot project with household water near Utrecht, a family drank household water for more than a year due to a wrong connection, all other Dutch projects were stopped. Despite the fact that, luckily, none of the family members' health was affected (NBMZ 2003). 4.2.3

Social- cultural aspects: Institutions for water resource management and water supply

Water demand depends on climate, available technology, culture, and also on personal behaviour. Therefore, water supply and especially domestic water use have a strong socialcultural aspect. Dutch water companies, responsible for production and supply of drinking water, are mostly (partly) owned by provinces and/or municipalities. The provinces issue the licences for groundwater withdrawal, while water boards issue licences for surface water intake. The national government is responsible for public health and the environment. Waterworks are inspected by the Ministry of Welfare, Public Health and Sports. Whilst, the Ministry of Housing, Spatial Planning and the Environment (VROM) decrees policies and legislation regarding drinking water supply and wastewater discharge. The Commission Integral Water 41

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management (CIW) coordinates the water management tasks of the different governmental institutions. Private companies play a role as suppliers of products and equipment and are restricted and controlled by the standards and certificates issued by governmental institute for certification and inspection of water appliances (KIWA). Figure 11 shows the institutional set-up in the Dutch water sector. Legislation relevant in drinking water supply is; ‘Waterleidingwet’ (Water supply law), ‘Wet Bodembescherming’ (Soil protection law), and ‘Bouwbesluit’ (Building directive). The Dutch legislation has to comply with the legislation of the European Union. For instance, the EU Bathing water quality directive is often used as an interim quality standard for household water (Directive 76/160/EEC 1975 to be revised in 2001). The responsibility of the water companies ends at the main tap inside the household. The water installation inside the house is under the responsibility of the house owner. Projects of water companies aiming at more sustainable solutions mainly focus on water conservation and alternative water sources such as surface water and rainwater. The IJburg project in Amsterdam a good example. Within this project, an inquiry was made to explore the level of acceptance of a lesser water quality by the end users. It revealed that 97% would accept the water for toilet flushing, and 80% for the washing machine if clean laundry is guaranteed. When the lesser quality is cheaper 90% is willing to use it in the washing machine. Surprisingly, 70% was willing to pay as much or even more for the water than for drinking water (GWA 1997, p.V). The experiences with more environmentally friendly solutions in households in the Netherlands are not always positive. In some cases, the systems are not used as intended or even replaced. In the enquiry, however, people indicated to accept extra effort for a more environmentally friendly solution (GWA 1997, p.13). Water supply in the model based decision support tool: The minimum qualities, as set by the different water standards, are constraints that must be met by any water supply. This means that water supplied to the kitchen has to be of drinking water quality, and water supplied for personal hygiene has to be of bathing water quality. Another restriction is the minimum amount of water15 needed to maintain a healthy living environment. However, it is hard to determine what the minimum amount of water is for personal hygiene because this also depends on the climate, technology available, culture, etc. In the model, the water demand calculated on the basis of average Dutch water use. The water demand has to be met completely. The only way to lower water demand is to insert waterconserving technologies (see paragraph 5.2.2 Water conservation). Other parameters such as costs, space requirements, acceptance, etc., are negotiable (see paragraph 4.5 Resources in wastewater). Which solution to chose depends on the local situation and the preferences of the actors.

15

One can also chose to use an absolute minimum supply for instance 50 l/(cap*d), of which 2 l/(cap*d) has to be of drinking water quality. However, in the Netherlands water is plenty, which does not justify forcing a change in behaviour by limiting water supply. Furthermore, setting a minimum is not evident, UNHCR and WHO guidelines were in 1960 30 litres per person a day, in 1970 this was reduced to 20 litres. Regarded, as absolute minimum for survival is 7 litres. However, experiences in refugee camp showed that 2.5 more diarrhoea occurred when supplying 10 to 15 litres instead of 30 litres (Roberts 1998). The new standard of 50 litres per capita per day was set as a basic water requirements for drinking, sanitation, bathing and cooking – in dependent of climate, technology and culture (Gleick 1998, p.44).

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Different choices that can be made in the decision support tool concerning water supply are: • • • • •

practise water conservation, supply drinking water or household water to all intended water uses, supply rainwater to the toilet, washing machine and uses outdoor and/or supply rainwater to the kitchen and personal hygiene, disinfection of household water and/or rainwater for use in the kitchen and personal hygiene reuse treated wastewater

4.3 Wastewater Domestic wastewater is a mixture of the wastewater streams resulting from the different water uses inside the household, with the quality varying accordingly. Generally, the following streams are distinguished: blackwater (wastewater from the toilet), greywater (all domestic wastewater excluding toilet wastewater), and rainwater (see also Figure 12). Blackwater contains the human excreta, brownwater (faeces) and yellowwater (urine). Most pathogens are contained in brownwater, while most nutrients are contained in yellowwater (see Table 6). In conventional water-flush toilets these streams are mixed and diluted with flush water. This results in a relatively large stream contaminated with pathogens (see Figure 12), while in source sanitation the blackwater is separated and low or undiluted, keeping the pathogens in a relatively small volume. Urine separation toilets collect urine separately, which leaves the nutrients uncontaminated. In addition, blackwater may contain toxic components originating from cleaning agents, traces of medicines, and non-biodegradable solid sanitary waste such as condoms, sanitary towels, tampons, etc.

Figure 12: Different wastewater streams and their approximate amounts for the Netherlands (based on Winblad et al. 1980).

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Table 6: Fertiliser value of faeces and urine (Polprasert 1989, in Bitton 1999, p.173). Component: Quantity wet Quantity dry BOD5 Moisture content Total solids (TS) Organic matter Nitrogen (N) Phosphorus (P2O5) Potassium (K2O) Carbon (C) Calcium (CaO) C/N ratio

(g/(cap*d)) (g/(cap*d)) (g/(cap*d)) (%) (%) (% of TS) (% of TS) (% of TS) (% of TS) (% of TS) (% of TS) -

Faeces 100 - 400 30 - 60 15 – 20 70 -85 15 - 30 88 - 97 5.0 – 7.0 3.0 – 5.4 1.0 – 2.5 44 - 55 4.5 6 - 10

Urine 1,000 – 1,310 50 – 70 10 93 –96 4 –7 68 – 85 15 – 19 2.5 – 5.0 3.0 – 4.5 11 – 17 4.5 – 6.0 1

Table 7: Costs of wastewater treatment. Costs of sewers and wastewater treatment: Average expenditure in the Public treatment: 35 (Euro/(cap*y)) CBS 1998, p.51 Netherlands Exploitation: 63 % Investment treatment: 33 % Investment sewer: 4 % Average cost Europe Capital costs 11 to 33 (Euro/(cap*y)) Bode et al. 2000 Operation costs 32 to 57 (Euro/(cap*y)) Paid by consumer in the Netherlands: Levy for treatment Municipality Nuenen 117 (Euro/(Household*y)) Verhoeven 2000, Levy sewers 0.62 (Euro/m3) p.10 Cost municipal wastewater treatment, nutrient removal, anaerobic digestion (50,000- 200,000 pe) Energy 10 kWh/(pe*y) 1.2 (Euro/(pe*y)) Nowak 2000 Chemicals 18 moles /(pe*y) 1.1 (Euro/(pe*y)) Sludge disposal 60 kg/(pe*y) 2.4 (Euro/(pe*y)) Maintenance 0.6% of construction 2.0 (Euro/(pe*y)) Personnel 4.2 (Euro/(pe*y)) Others 15% 1.7 (Euro/(pe*y)) Total 12.5 (Euro/(pe*y)) Economy of scale of centralised treatment: 100,000 to 300,000 p.e. German data 23 (Euro/(cap*y)) Bode et al. 2000 50,000 to 100,000 p.e. 27 (Euro/(cap*y)) 30,000 to 50,000 p.e. 31 (Euro/(cap*y)) Costs of nutrient removal:

Organic matter Nitrogen Phosphorus

German data

0.5 to 1 5 to 8 13 to 20

(Euro/kg COD) (Euro/kg N) (Euro/kg P)

Bode et al. 2000

German data

160-1,600 (Euro/ton dry solids) 1,050 (Euro/ton dry solids) 1,050 – 2,100 (Euro/ton ds)

Rüdiger 1998

209 – 523 314 – 418

Rüdiger 1998

Cost of sludge disposal:

Landfill Dewatered landfill Incineration

sludge

Costs of nutrient reuse in agriculture:

Liquid sludge Dewatered sludge

German data

(Euro / ton ds) (Euro / ton ds)

Note: p.e. = population equivalent, ds = dry solids, all data reported by Bode et al. and Rüdiger were in German Marks, 1 Euro = 0.95583 DEM.

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Greywater is the dilute wastewater stream originating from domestic activities such as showering, bathing, washing hands, tooth brushing, dishwashing, washing clothes, cleaning, food preparation, etc. The water contains some organic matter, for instance, food remains, hairs, and salvia, with pathogens, and inorganic material, such as detergents, sand, and salt. Besides this, there is the runoff from roofs and other non-porous surfaces. This rainwater may be contaminated with dirt accumulated on the surfaces originating from human activities (car repairing), animals, trees, and soluble contaminants from air pollution, and roofing materials. Heavy metals are the main problem, these originate from roofing materials such as zinc coated corrugated iron sheets, lead slabs, zinc gutters, copper roofing tiles on monumental buildings, etc. If the runoff is not drained the stagnant water, especially in tropical countries, animals may be attracted and it can become a source of diseases (for instance malaria mosquitoes, rodents, and snails). 4.3.1

Economics of wastewater

In 1996, the exploitation costs of wastewater treatment and discharge in the Netherlands was approximately 0.7*103 million Euro, of which 28% was used for sludge treatment, 18% for transport, and 55% for wastewater treatment (CBS 1998, p.49). Income is generated through levies for the service ‘wastewater transport and treatment’. Little or no income is generated by selling the products treated water and or sludge. Table 7 gives an overview of the different cost aspects of wastewater treatment. 4.3.2

Environmental aspects of wastewater treatment

A Life Cycle Assessment comparing small-scale sewage treatment plants by Emmerson et al. (1995) shows that, depending on the treatment process, environmental impact during construction and demolition can be significant. While, for the activated sludge plant included in the research, 95% of the energy was used during operation, for the biological filter systems studied, using two circular filters with slag media, the energy use during construction and operation was of the same order of magnitude (Emmerson et al. 1995). The research by Bengtsson et al. (1997, p.59) indicates that the energy use is dominated by the operational phase, however, for emissions both construction and operation are significant. The inclusion of the construction and demolition phase in the decision support tool is difficult. One problem is that there are different alternatives for construction and demolition. Other problems are that little is reported in the open literature on material use, and that it is different to quantify impacts of material use, as small amounts of certain materials may have a large impact. Therefore, the construction and demolition phase of is not included in the decision support tool. General data about energy use16 of wastewater treatment plants are 35 kWh/(cap*y) (mean Austria 1997, Nowak 2000), and 29 kWh/(cap*y) (on-site electricity use, mean Italy, Tarantini et al. 1998). Bengtsson et al 1997 (p.20) report energy use for a specific treatment plant: for conventional treatment: 44.5 kWh/cap/y and for a urine sorting system: 34.1 kWh/cap/y. Space requirements for centralised wastewater treatment in the Netherlands are estimated by Mels et al. (1999) to be 0.1 m2 per population equivalent.

16

Figures by Bengtson et al 1997 include electricity as well as fossil energy used and take into account wastewater treatment and transport and the energy used for the production of and transport of the chemicals used for precipitation.

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Table 8: Urban wastewater treatment directive (91/271/EEC in Gray 1999, p.304).

BOD5 COD SS Total P Total N

Minimum concentration: (mg/l) 25 125 35 1* 2** 10* 15**

Minimum reduction: (%) 70 – 90 75 90 80 80 70 - 80 70 – 80

* 10,000 to 100,000 pe, ** > 100,000 pe

4.3.3

Social and cultural aspects: Institutions for wastewater management

In the Netherlands, the municipality is responsible for the sewerage system, while the Water Board is responsible for wastewater treatment and water management in general. Both institutions have a democratically elected management. However, especially the turnout at the elections of Water Boards can be low as the civilians may not always be aware of and/or concerned with the functioning of the Water Boards (turnout: 10 to 50 %, Boogers 1998). Policies and legislation are defined by the Ministry of Transport, Public Works and Water Management (V&W) and the Ministry of Housing, Spatial Planning, and the Environment (VROM) (see Figure 11 on page 41). Important legislation concerning wastewater management in the Netherlands incorporates: ‘Wet Verontreiniging Oppervlaktewater’ (surface water pollution), ‘Wet Bodembescherming’ (soil protection), and ‘Meststoffenwet’ (nutrients). The Dutch laws comply with the directives of the European Union, for instance, the directive on wastewater treatment effluent (see Table 8). Wastewater in the model based decision support tool The constraint on the wastewater treatment and discharge in the model is the ability to meet the effluent standards as prescribed by the European Union and the health guidelines of the World Health Organisation (see Table 8 and Table 10). The possible choices in the model are: • • • • • • •

dry sanitation or the use of flush water, separation or mixing of yellowwater, brownwater, greywater, and rainwater, on-site treatment or transport of wastewater, scale of treatment ranging between 5 persons and 100,000 persons. combined or separate of organic waste and wastewater, reuse treated wastewater for domestic reuse, irrigation, infiltration, fertiliser, and/or reuse sludge as soil conditioner, discharge wastewater and/or use sludge as landfill.

4.4 Threats to water services There are in fact many threats to water services, for instance toxic compound naturally present such as arsenic or fluoride, or toxic compounds discharged by industries. Flooding can be threat as well, and salinity can form a threat especially to land fertility. As the main focus of this research is domestic wastewater treatment, we limit ourselves to the main threats in domestic wastewater these are (4.4.1) pathogens that can be a threat to heath, and (4.4.2) heavy metals being a restriction in nutrient recycling. 46

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Pathogenic organisms

More than 10 million people die each year from cholera, typhoid, dysentery and other diarrhoeal diseases caused by poor water supply and unhygienic sanitation. This is equivalent to 70 jumbo jets crashing with total loss of all passengers every day of the year (IRC 1997). Most of the pathogens causing these diseases originate from human and animal faeces. There are many different routes that lead from excretion of pathogens to infection. The contact with the pathogens can be through ingestion, contact with the skin, or in some cases inhalation of aerosols. To prevent diseases a wide scale of hygiene measures must be taken; supply of safe drinking water as well as hygienic sanitation, hygienic food storage and preparation, hygiene in animal keeping, hygienic waste management, and flood protection through storm water and surface water management. General information on pathogens is summarised in Table 9. Removal mechanisms for pathogens are: adsorption, chlorination, filtration, ‘natural’ die-off, ozonation, predation, sedimentation, and solar radiation. Up to 99.9 % reduction of enteric bacteria can be obtained by storage, depending on temperature (higher temperature results in higher natural die-off), retention time, availability of food, pH, excretion of antibiotics from roots of plants, and the other toxics present in the water (Gray 1999, p.272). Helminths and protozoa are the largest pathogens, and can therefore be filtered out effectively by the soil (Fourie et al. 1995, Mara 1996). Clay sized particles are small enough to filter out most of the bacteria. Viruses are too small for even the finest grained clays to be filtered out by the soil. The main removal process for viruses in soil is adsorption. In raw sewage 50 – 70 % of the coliforms are associated with particles with settling velocity > 0.05 cm/s. This implies that in primary sedimentation in treatment plants significant removal of enteric bacteria is achieved. Sedimentation transfers the pathogens to the solid phase, therefore, sludge must be handled with care. Stabilisation, desiccation and solar radiation can be used to reduce the pathogens in the sludge (Reed et al. 1995, p.72, 75, 199). Conventional treatment removes up to 80-90 % of bacterial pathogens, tertiary treatment can increase this to > 98 %, and additional disinfection results in > 99.99 % removal. Still there will be significant numbers of pathogens present in the final effluent (Gray 1999, p.271). Advanced treatment technologies such as micro- and ultra filtration may achieve >6-log reduction (99.9999% removal) in comparison with 3-log (99.9 % removal) achieved by welloperated conventional water treatment plants (Butler et al. 1996). 4.4.1.1 Pathogens in the decision support model

Although technologies are available, pathogens are the major threat to water supply. Since pathogens are mostly invisible, consumers cannot easily distinguish safe water from unsafe water. In the decision support tool, European and WHO water standards (Mara et al. 1989) are used as a constraint to control water quality, although in practice it is difficult to protect people with these standards as many people world-wide still depend on informal water supply. Furthermore, knowledge and behaviour are important factors in hygiene. Microbiological guidelines for reuse of wastewater in agriculture are presented in Table 10.

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Table 9: Pathogens.

Bacteria

Protozoa

Sizeb:

Multiplyb

Small; 0.3 to 2 µm

multiply outside host

Realtively large

form cysts that are very resistant

Helminths

Typically > 25 µm

Viruses

Very small colloidal particles; 25-350nm

Unable to multiply outside host

Surivald: (in days at 20 to 30°C) Water Soil 10-2) for all purposes of rainwater use (toilet flushing, washing machine, and outdoor tap). However, this conclusion is based on one single scientific article. Holländer et al. (1996), analysed about 1,600 samples from 102 water cisterns, and found that more than 95% of all samples met the quality standards for bathing water set by the European Community. Therefore, they concluded that the use of rainwater for toilet flushing, garden irrigation, and laundry washing presents no unacceptable risk to public health. Similar, the conclusion of Haffmans et al. (1995, p.54) is that the use of rainwater is safe with respect to pathogens, however, problems may be accumulation of heavy metals in the sludge at the bottom of the tank and in traffic intensive neighbourhoods PAH’s may become a problem. Haffmans et al. (1995, p52-53), based on German and Austrian literature, state that washing with rainwater is safe as most pathogens in the washing process originate from the laundry itself. Furthermore, most micro-organisms in the rainwater storage tank are non-coliforms such as fungi, which are less threatening to human health. These micro-organisms are mainly in the sludge that forms at the bottom of the storage tank. Therefore, one should avoid mixing the tank's contents and have an outflow above the bottom of the tank. Generally, the growth of bacteria is limited below 15°C and above 55°C (SEV 1999, p.54). While the growth rate often increases at higher temperatures, the persistence of bacteria is higher at lower temperatures. However, the main restricting factor for growth of pathogens is probably the nutrient availability (Holländer et al.1996). For instance, infective bacteria from animal faeces, such as Salmonella, Clampylobacter, and Yersinia are unable to multiply in rainwater due to its low nutrient level (5 mg COD / l, Krampitz and Holländer 1998/1999). Therefore, temperature of the rainwater storage may be less important than the roof and rainwater filter, as these determine the nutrient inlet in the storage (Nolde 1999, p.68). An advantage of larger storage facilities is that the longer residence time of the water may result in higher die-off (Nolde 1999, p.68). The main point of concern is to prevent mixing of rainwater and drinking water. Precautions have to be taken, for instance through the use of different pipes, jackwalves or intermediate decoupling tanks, to avoid interconnections of the two water systems. 5.3.2.2

Economic aspects of rainwater systems

In this research, we include two types of rainwater systems, namely (1) a small gravity system and (2) a larger and more advanced pumped system. The small gravity system consists of a 350-litre storage tank, a simple plastic rainwater filter, taps, a second water level controller in the flush water storage tank, and some pipes or hoses. This kind of system can be made from components available at the do-it-yourself-stores for approximately 120 Euro. While these systems are commercially available for 500 Euro (excluding installation costs of approximately 1,000 euro/system, Haffmans 1995, p34, and HEWA 2001). The more advanced system consists of a large underground storage tank (2 to 3 m3), a stainless steal rainwater filter, pumps, level controllers, switches, pipes, and possibly a display. This system is commercial available at the costs of 2,000 to 5,500 Euro with expected maintenance costs of 15 to 40 Euro/year (Bie 2000, Herrmann 1997, Wouterse 1999, p.47). In projects (20 to 600 houses), prices are typically lower, between 600 and 2000 Euro (Jacobs 1997). 69

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Communal systems for 6 houses are estimated to cost 1300 and 1700 Euro/household (Wouterse 1999, p.45). Operation costs can be very low especially when no pumps are involved. Which system to chose depends on rainwater availability, the size of the household, and the end user’s preferences. Simulations show that with the favourable weather conditions in the Netherlands small rainwater systems can achieve relative large drinking water savings. For instance a system with a 1.4 m3 storage volume can achieve maximum water conservation. While smaller system (0.2 m3 storage) can already conserve 20% of the total drinking water used (see Table 16, 19 and 20). In cases where a significant amount of the personal water demand is fulfilled outside the house, for instance at work, sports facilities, restaurants, etc., small systems (0.4 m3) can cover the total flush water need in the household (see Table 20). 5.3.2.3

Social and cultural aspects

Although, there is a widespread idea that it is a waste to flush toilets with drinking water, penetration of rainwater systems is low. A reason for this may be a lack of institutional backing, for drinking water companies decentralised systems are probably difficult to manage. As the house owner is by Dutch law responsible for the in-house water distribution. Rainwater is, according to the Dutch law, a free commodity; rainwater falling on your property can be collected and becomes your property. A more workable approach could be promoting rainwater systems through the Dutch building directive (Bouwbesluit, article 69).

Figure 17: Fluctuating water level in rainwater storage tank (350 l, for a 2 persons household, both working 40 hr/week, see also Table 18).

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That now prescribes a drinking water connection for the toilets and washing machines, but permits equally good solutions. There is a Dutch norm on safety, prohibiting direct connections between alternative water systems and drinking water supply and prescribing clearly marked pipes (NEN 100629). Another Dutch safety regulation exists, namely the test and certificate for technical safety of rainwater systems by the government (KIWA)(SEV 1999, p.64). Dutch housing associations, municipalities, and project developers, expressed a positive attitude towards rainwater systems in a telephonic enquiry. Rainwater utilisation in households was acceptable for 84% of the respondents (333 respondents = 100%, Wouterse 1999). Utilisation for garden use scored highest (98%), next was toilet flushing (93%), washing machine (42%), and shower (26%). Remarkable is that 34% indicated that it would be acceptable to them to use rainwater for in-house drinking water production with additional purification step (Wouterse 1999, p.93). Furthermore, 75% of the respondents indicated that a 30% savings of drinking water would already justify the installation of a rainwater system. If the rainwater system costs less than 1,000 euro, this would be acceptable to 44% of the respondents. On the question what secondary water quality systems one would prefer, the answers were as follow; a greywater system 11%, a collective rainwater system 23%, a dual piped supply 28%, and an individual rainwater system 38% (Wouterse 1999, p.94). 5.3.2.4

Rainwater systems in the decision support tool

In the decision support tool, two types of rainwater systems are included; (1) a small-scale gravity system with a 0.35 m3 storage tank, and (2) a more advanced pumped system with a 3m3 underground storage tank. The small-scale system substitutes approximately 65 to 90% of the drinking water used for toilet flushing and or washing machine (assumptions 50m2 roof, household 2 to 4 people, all water used at home, water saving toilet with on average 4.5 litres flush, and washing machine using 39 l/wash). This implies that approximately 35 to 55% of the total rainwater that can be collected by the roof is utilised. For the more advanced system, the total rainwater volume collected can be utilised. Therefore, the rainwater available and the water demand are limiting for the volume used, not the storage tank volume as in the case of the small-scale rainwater system. 5.3.3 Greywater systems When rainwater is not available, for instance in dry climates or densely populated areas, one can consider a greywater system to reuse the lightly polluted wastewater inside the household. The advantage of reusing greywater is that the supply is regular. The disadvantage is, however, that the water quality is less than rainwater and treatment is needed to remove pathogens, soap, and dirt. Simulations show that in this case storage volumes can even be smaller than water used for the shower and washing machine are supplied regularly in quantities similar to the flush water volumes needed for the toilet (see Table 16, 17, and 18). In some cases wastewater from the kitchen is not recycled as it contains more contaminants than the rest of the greywater. Also wastewater from the washing machine may be excluded from reuse as pathogen concentrations can be high, for instance in the case were baby diapers are washed (Nolde 2000, p.279). The social-cultural acceptance differs per culture. While greywater systems are applied by governmental directive rule in Japan (Ogshi et al. 2000), other governments are reserved, 29

In Germany, rainwater systems have to comply with the DIN-standards 1988 and 1986.

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arguing that public health is at stake (Dutch (VROM) and German in Nolde 1999, p.275). In the meantime, two thirds of the experts questioned on “water technology in the year 2010”, considered greywater systems technically feasible without public health risks (Delphi 1999, in Nolde 2000). Greywater treatment technologies that are included in the decision support tool are: membrane bioreactor, rotating bio-discs, trickling filters, constructed wetlands, UVdisinfection, and infiltration systems. These technologies are described in paragraph 5.6 on Wastewater treatment. 5.3.4

Closed water systems

When water is extremely scarce one may consider a ‘closed water’ system, approaching the life-support system of a spaceship. All domestic wastewater is collected and treated to meet drinking water quality enabling reuse for all purposes. Technically, it is possible to recycle also the water contained in urine and faeces, however, if it is possible to make up for small losses30 this maybe preferred, as the value of urine and faeces is not in water content but in nutrient content. Evaporation losses may be recovered through air conditioning. In extreme cases, as for instance in a spaceship, personal hygiene can be almost dry using dry pads for body hygiene31. Technologies that can be applied in a domestic closed water system are similar to those used for greywater recycling (for instance constructed wetlands, membrane-bioreactors), wastewater treatment (anaerobic digestion and aerobic treatment) and drinking water production (membrane filtration, disinfection, etc.). For space missions, high tech solutions such as vapour compression distillation and reverse osmosis are considered for the recovery of water from urine and faeces (Saulmon 1996). In short-term missions, however, human wastes are usually stored for post flight treatment or disposed using an overboard-vacuumsystem. On long term space missions it will be necessary to close not only the water cycle but also the food cycle, for this purpose bio-regenerative life support systems that are being designed (Blüm 1995, Nelson 1994, Parker 1973, Saulmon 1996). The only32 example of full-scale implementation is Biosphere 2 (Nelson 1994). In Biosphere 2, the complete regeneration of human and animal waste products is accomplished by an in-vessel composting system for inedible crop residues and animal manure, and by a created wetlands system for handling of human wastes. The aquatic waste treatment system consists of anaerobic holding tanks followed by aerobic mash lagoons. The plants used in these lagoon systems are fast growing and are periodically cut for fodder and used in composting. After passing through the marsh waste treatment system, the water is added to irrigation supply for agricultural crops, thus utilizing the remaining nutrients (Nelson 1994). It is of course nearly impossible to engineer a stable closed ecosystem. The Biosphere 2 experiment, where 8 people lived in the closed ecosystem for 2 years, fell short in food production and oxygen. During the experiment 19 of the 25 animal species had become extinct and many plant species were dying (Scholten 1993).

30

Normally, approximately 2 to 3 litres water per capita per day is consumed and approximately 10% of the water used in washing and cleaning evaporates (Kilian 1996). 31 Even showering in space could be done with extremely little water (approximately 1.5 litre) as the water forms a thin film around the body due to lack of gravity (Parker 1973). 32 In the Russian experiment, Bios-3, relative large amounts of food were imported and some waste was exported.

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To close the domestic water and nutrients cycles it is not necessary to create a closed ecosystem, this is for instance demonstrated by integrated farming systems and living machines. In these artificial aquatic ecosystems, wastewater treatment and food production is combined by growing fish and plants in anaerobic pre-treated wastewater (Blüm 1995). The technologies that can be applied in closed water system are discussed in paragraph 5.6 on Wastewater treatment. 5.4 Sanitation In this section, we discuss different toilet system and the consequences for wastewater treatment.

Picture 3: Different types of toilets; Lady P the women’s urinal by Sphinx (4 litres/flush), double flush by Gustavsberg (2 or 4 litres/flush), and the urine separation toilet by Dubletten (0.2 litres flush for urine, 4 litres for faeces). 5.4.1

Different types of water closets

In many cultures water is associated with hygiene and there is no doubt that water flush toilets are comfortable and hygienic for the user. However, the technology has its draw back as well, the pathogens are diluted and may, if not treated well, spread in the environment and become a threat to drinking water supply. Furthermore, valuable resources in the excreta such as nutrients are also diluted and probably lost for reuse. Conventional flush toilets use large amounts of water. For more information on the water flush toilets, see Table 15 on page 64, and Picture 3. The following sections discuss composting toilets, dry toilets, and urine separation toilets. 5.4.1.1

Toilets in the decision support tool:

Water flush toilets that are included in the tool are: (1) an old toilet (9 to 12 l/flush), (2) a new toilet (6 to 9 l/flush), (3) a toilet with flush interrupter (6 to 9 for faeces and 3 l/flush for urine), (4) low flush toilet (0.8 to 1.5 l/flush). Details on the different types of toilets are included in the decision support system are given in the Technology Data Sheets 4 to 9 in Appendix 3. 73

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Composting toilets

Composting toilets are either low flush or dry toilets. A wide range of composting toilets is commercially available. One of the main differences between composting toilets is the handling of urine and flush water. The urine is sometimes separated in the toilet (for instance in the Dubletten system), or collected with the flush water before entering the composting chamber (as done in the Aquatron systems). In other systems the moisture leaches through the composting chamber (sometimes referred to as tea, see for instance Ecolet), the moisture can be actively evaporated (see Sun-Mar), or in the case of no flush toilets the urine can be kept in the composting chamber (Clivus Multrum). Separating the urine improves conditions for composting by decreasing the moisture content and increasing the C:N ratio (see also paragraph 5.6.3). This can also be obtained by adding more bulking agent and/or evaporation of the moisture. Most composting toilets have different compartments or are built with a slope to move the compost in a plug flow to make sure that first in gets first out. Simple composting toilets can be built by the user him or herself (see Winblad 1985). More complex toilets include ventilation, heating, and sometimes a rotating drum system for mixing (Sun-Mar Centrex three chamber system), or an UV-disinfection unit for the separated flush water (Aquatron). Depending on climate, height of water table, frequency of use, availability of water, electricity, and budget, an appropriate system can be chosen. Composting toilets provide hygienic sanitation, are low in resource use, and enabling safe reuse of the valuable nutrients in the excreta. In the composting process, micro-organisms and heat break down the waste to 10 to 30% of its original volume (NSFC 1998). According to a limited number of measurements faecal coliforms range from undetectable to 35 per gram compost of (CM 2000, NSF’s standard is < 200 /g, Ekolet 2000). To conserve nutrients there are two strategies (1) separate urine and use it as fertiliser, and (2) keep the leachate with the compost and control the C:N ratio optimising the composting process and consequently minimising the nitrogen losses (see paragraph 5.6.3). Prices of commercial composting toilets range from 1,000 to 6,000 Euro for household systems (NSFC 1998, Burkhard et al. 2000, and product information manufacturers). Depending on the system, size, and usage the compost compartment has to be emptied every month up to once every 2 years (Naturum product info, NSFC 1998). Energy use for electric ventilation is about 22 to 330 kWh/y33, and for heating about 880 to 1750 kWh/y34, or even higher when all excess liquid is evaporated. Space requirement varies with system, for instance the Naturum toilet is relatively compact, merely a big toilet, while systems such as the Clivus Multrum has a relatively large composting chamber of 2 to 3 m2. This composting chamber may be placed in ‘low quality’ space such as a basement, as long as it is placed straight underneath the toilet. This makes it unattractive to place a larger number of toilets, for instance in a high rise building as each toilet needs it own straight down pipe to the composting chamber. Low-flush systems offer greater freedom with respect to placing the composting chamber as the waste is flushed and therefore, turns in pipes are no problem. Low flush systems do however require another

33

For instance: 2.6 Watt Naturum, 166 kWh/y for Separett Villa 6000, 21 to 37 Watt in Fittschen et al. 1997. For instance: 100 to 200 Watt in Fittschen 1997, 1000 kWh/y Lange 1997 (fan + heating), Sun-Mar Excel 150 Watt fan + heating. 34

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solution for moisture control, for instance urine separation, leaching, or drying through heating (see 5.4.4 Urine separating toilets). Social-culturally composting toilets are often disputed, especially in regions where water is plenty and the sewerage system is present. Some see the composting toilet as a low-tech uncomfortable solution for remote areas, while other perceive the toilet as the most environmental friendly solution for sanitation. In cultures, where water is strongly associated with hygiene, or where seeing the faeces is a taboo, the conventional no flush composting toilet may be unacceptable. Furthermore, problems such as flies, smell, and disturbances of the composting process resulting in too wet compost strengthened the public perception that this technology is still experimental and alternative (Huizinga 1993, GD 2001). In the Swedish eco-village Toarp, urine separating composting toilets were removed because the energy use for heating was perceived un-ecological, furthermore people were uncomfortable in handling the waste and about informing their guest how to use the toilet (Fittschen et al. 1997). 5.4.2.1

Composting toilets in the decision support tool:

Two composting toilets are incorporate in the decision support model, (1) a low flush urine separating composting toilet and (2) a conventional dry composting toilet that does not require electricity for mixing or heating. Details on these systems are summarised in the Technology Data Sheets 5 and 6 (see Appendix 3). 5.4.3

Dry toilets

There is a variety of dry toilets on the market, for instance pit latrines, chemical toilets, composting toilets, and toilets that burn the waste. However, in this paragraph we focus on toilets that use the sun to dry the excreta. In this type of toilet urine is separated and possibly stored or dried by mixing it with dust, or diluted with treated greywater and directly used for irrigation. Faeces are stored in a closed compartment of the toilet, where a solar heater is used to dry the faeces completely. Due to the heat, retention time and dry conditions most pathogens will be eliminated, although worm eggs and spores may remain. The dried faeces can be used as soil conditioner. 5.4.3.1

Dry toilet in the decision support tool:

In the decision support tool a solar drying toilet can be implemented for single households, though in hot dry climates only. The cost of this type of toilet is estimated 600 US$ per household, which is twice the cost of a VIP latrine as reported by Loetscher (1999, p.147). 5.4.4

Urine separating toilets

About 80% of the nitrogen, 50% of the phosphorus, and 60% of the potassium in domestic wastewater originates from human urine (Czemiel 2000, Fittschen et al. 1998, Hellström et al. 1999, Larsen et al. 1996). Therefore, an effective way to recycle nutrients is the separate collection of urine for use as fertiliser in agriculture. An additional advantage is that the nutrients are provided suitable for uptake by crops, nitrogen as urea, phosphorus as orthophosphate, and potassium as ionic potassium. Furthermore, urine is almost free from pathogens and heavy metals (Drangert 1997, Jönsson et al. 1998). To utilise these nutrients in agriculture urine has to be collected, stored, and transported to the field. Special toilets are designed for separate urine collection as shown in Picture 3. This 75

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toilet may have some social-cultural implications, as it is different in use than ‘normal’ toilets (Fittschen et al. 1997). Another option for urine separation is the use of urinals, or to collect the urine less pure though by separating it just after the toilet. Local or centralised storage are both feasible options, both require transport of urine. For centralised storage separate sewers for urine are required, or in some cases flushing the urine through nearly empty sewers overnight maybe an option (Larsen et al. 1996). There are two main reasons for storage of urine; (1) to wait for the growing season when fertilisers are demanded in agriculture, (2) to facilitate natural die-off of pathogens and possibly break down of hormones and traces of medicines that are present in urine. In temperate regions, this implies that urine has to be stored during winter season (0.5 – 1 m3 per person) (Adamsson 1996). The disadvantage of long-term storage is that urea35, the dominant nitrogen source in urine, decomposes into ammonia (Jönsson et al. 1998). Ammonia is more toxic and may as a gas ‘escape’ resulting in a nitrogen loss. Besides storage, treatment of urine maybe considered, options described in literature are: precipitation of MgNH4PO4 (struvite), stripping of NH3 gas, concentration of urine by freezing (Lind et al. 2001), or drying of urine (Hellström et al. 1999, Larsen et al. 1996, Otterpohl 2000). 5.4.4.1

Urine separation in the decision support tool:

The options for urine separation in the decision support model consist of a separating toilet or a normal toilet and a urinal, and long-term storage of urine as treatment before utilisation of the nutrients in agriculture (Technology Data Sheets 8 and 27 in Appendix 3). 5.5 Wastewater transport 5.5.1

Conventional sewer

Sewerage is in many cases perceived as indispensable as it hygienically and conveniently transports our wastewater and also prevents flooding. However, sewerage is expensive, Otterpohl (2000, p.3) estimates the costs for sewerage systems to be approximately 70% of the total wastewater transport and treatment costs in more densely populated rural and periurban areas in Germany. Furthermore, during heavy rains the sewerage systems cannot cope with the large amounts of water and diluted wastewater is discharged directly into surface water, the so-called combined sewer overflows (CSO). To reduce pollution from combined storm water overflows, storm water and wastewater can be transported separately. In rural and peri-urban it may possible to infiltrate storm water. Where surface water is nearby, the storm water may be discharged directly into this surface water. It is also possible to use a so-called improved separated system, here the first flush of the storm water carrying most of the pollutants is directed to the wastewater treatments system while the rest of the storm water is infiltrated or directed to surface water. Another option to reduce combined storm water overflows is to include extra storage capacity in the sewers and/or at the treatment plant to store the water during heavy rains. Costs of sewerage systems listed in Dutch literature range between 104 and 390 Euro/m, see Table 19.

35

Approximately 50 to 90% of the nitrogen in human urine is contained in urea, depending on the proteins in the diet, (Adamsson 1996).

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Sewerage systems in the decision support tool

In the decision support tool it is possible to chose for combined, separate, or vacuum sewerage systems. More details on sewerage systems are summarised on the technology data sheet 12 (see Appendix 3). 5.5.2

Vacuum sewer

Vacuum sewers are commonly used in boats, airplanes, and trains. These are cases where the reduced water use and the smaller diameter of the pipe are decisive advantages. Other advantages are that wastewater can relatively easily be transported upwards and that in case of leakage water is sucked into the pipe rather than wastewater flowing into the environment. Potential disadvantages are energy use, and the need for separate systems for large flows such as rainwater. Vacuum system can be operated in any size. Small systems of 4 to 5 toilets can be feasible in special situations, for instance when the toilet are situated under the water level, when water saving is a priority issue, or when weight and space occupation are major issues. The most frequently used system however is for approximately 100 people. The pump for this system uses about 4 kW, and is only running to make up for vacuum ‘losses’ occurring during flushing. Estimated energy use ranges 25 and 117 kWh per person per year (QUA-VAC 2000, Otterpohl 2000, p.13, and Otterpohl 1997). 5.5.2.1

Vacuum sewers in the decision support tool

In the decision support tool vacuum sewerage is one of the transport options (Technology Data Sheet 12 in Appendix 3).

Table 19: Costs indication of different sewerage systems in the Netherlands. System Gravity sewer mixed Gravity sewer separated Gravity sewer improved separated Pressure sewer Vacuum sewer Storage settling sewer

Investmenta (Euro/m) 153 – 213

Depreciation and maintenancea (Euro/m/y) 3.7 – 4.5

60 –93 60 – 110

1.8 – 2.4 3.3 – 4.5

Storage settling basin

Investmentb

Maintenanceb

(Euro/m) 270c 365c 390c

(Euro/m/y) 3.4 4.5 5.0

(Euro/m3) 800 – 1,900 (Euro/m3) 400 – 2,300

approx. 5% of investment

(a) Assuming: 150 m sewer per plot, 170 plots, no interest is taken into account, original figures per plot, source: RIONED 1998. (b) Maintenance is approximately 25 to 30% of depreciation and maintenance, average sewer length per household 10 m (note original costs given per household), very few data available, costs separated 1.35*costs mixed, improved separated costs are 1.24* costs mixed, source RIONED 1996. (c) In Geldof et al. 1997b, pages 23-27 investments: mixed 2,700, separated 3,600 and improved separated 4,100 Euro/household, 50 years lifetime and for improves separated system still 70 to 80% of rainwater to wastewater treatment. Note: original figures in Dutch Guilders, 1 Euro = 2.20371 NLG.

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5 Processing units: technology characterisation

Truck

In cases where sewerage is not available, wastewater can be treated onsite and transport of products such as sludge or urine to centralised treatment or reuse can be done by truck. The amount of fossil fuels used for sludge transport is estimated to be: 0.8*10-3 kWh/kg/km and for urine spreading: 0.46 kWh/m3/km (Bengtsson et al. 1997, pp.17+20). 5.5.3.1

Truck transport in the decision support tool

In the decision support tool truck transport is a possibility for sludge and urine that are stored on-site (Technology Data Sheet 13 in Appendix 3). Table 20: Overview infiltration systems (RIONED 1998).

Size Land are Investment Energy COD/BOD TN TP

(pe) (m2) (Euro/house) (Euro/ houses for clusters) (kWh/y) (%) (%) (%)

Filtration System 5, 10. 20 or 50 4-5, 8-10, 16-20, 4050 3,600-7,300 for 50 pe: < 21,300 350 - 1325 85-90 / 90-95 60 – 90

Infiltration field 5, 10. 20 or 50

350 - 1325 90 / 90 10 – 15

Constructed wetlanda 5, 10. 20 or 50 15-25, 30-50, 60-100, or 150-200 3,600 – 7,300 for 50 pe: < 21,300 350 - 1325 90-99 / 80-95 70 – 90 85 – 90

(a) Vertical flow, note that according to this study, horizontal flow wetlands are larger and more expensive. Investment costs include installation and septic tank. Note: original cost data in Dutch guilders, 1 Euro = 2.20371 NGL.

5.5.4

Onsite disposal

Onsite disposal is only permitted if wastewater is treated such that it can be infiltrated, reused or discharged. Options for treatment and reuse are discussed in paragraph 5.6 and 5.3, in this paragraph we shortly address infiltration. Technologies that can be used for infiltration are swales (in Dutch wadi’s), infiltration ditches or trenches, soak away pits, and infiltration fields. Information on these systems is summarised in Table 20 and technology data sheet 14 (see Appendix 3). 5.5.4.1

Infiltration in the decision support tool

In the decision support system infiltration is an option for rainwater and/or treated wastewater. It is obvious that local soil conditions and ground water level are crucial for implementing infiltration systems. 5.6 Wastewater treatment 5.6.1

Activated sludge

Activated sludge is an aerobic treatment process in which the organic substances in the wastewater are degraded by micro-organisms in the presence of oxygen. Naturally occurring micro-organisms convert soluble and colloidal material into a dense microbial mass that can readily be separated from the purified liquid using conventional sedimentation. The breakdown of organic matter relies on two processes, (1) oxidation or respiration forming a mineral end product, and (2) syntheses producing new biomass. 78

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Generally, two types of processes can be distinguished: suspended growth and attached growth. In suspended growth processes the micro-organisms are present in suspension, for instance in activated sludge, aerated lagoons, sequencing batch reactors, and the aerobic digestion process. In attached growth processes, the micro-organisms grow on an inert medium such as rock, ceramics, or plastic, for instance in trickling or percolating filters, roughing filters, rotating biological contactors, and fixed film nitrification reactors. In this paragraph we discuss activated sludge, an aerobic suspended growth process. Attached growth processes such as fixed bed reactors, rotating biological contactors, and trickling filters are discussed in the paragraphs 5.6.5, 5.6.7 and 5.6.10. Important in activated sludge is the adequate mixing and aeration, to sustain the high concentration of suspended sludge, which is a mixture of micro-organisms and the organic matter or substrate that is serving as food. The degradation of the organic matter occurs through adsorption on the micro-flocs, assimilation (conversion into new microbial cell material), and mineralisation (complete oxidation). The recycling of a large proportion of the biomass is an important characteristic of the activated sludge process. This makes the mean cell residence time (i.e. sludge age) much larger than the hydraulic retention time, maintaining a large number of micro-organisms to effectively oxidise organic compounds in a relatively short time (detention time aeration tank: 4 to 8 h, Bitton 1999, p.182). Most virus particles (>90%) are solids-associated and ultimately transferred to sludge; however, viruses are also inactivated in the process (Bitton 1999, p.203). Removal rates measured in field studies are: 90 to 99% removal of enteric viruses (Bitton 1999, p.203). The removal rates for bacteria are about 80 to 99% (Bitton 1999, p.202). Removal of parasitic protozoa such as Giardia cysts is > 98%, and for Cryptosporidium Parvum occyst 80 to 84% for the activated sludge process, up to 96.8% for the total plant (Bitton 1999, p.204). Helminth eggs are removed during sedimentation, although generally parasite eggs are not detectable in the effluent, some investigators reported Ascaris and Toxocare eggs in effluents of activated sludge plants (Bitton 1999, pp.206). Nitrification in activated sludge processes depends on the growth rate of nitrifiers, which is lower than that of heterotrophs in sewerage, therefore, a high sludge age (> 4 days) is necessary for the conversion of ammonia to nitrate (Hawkes 1983 in Bitton 1999, p.197). Nitrification proceeds well in a two-stage activated sludge system where BOD is removed in the first stage, while nitrifiers are active in the second stage (Bitton 1999, p.197). The next step, denitrification, the removal of nitrogen by conversion to nitrogen gas, is accomplished under anoxic conditions. Denitrification, is often restricted by the concentration of biomass and electro-donor (methanol or ammonium) in the wastewater, as the nitrate concentration in mostly abundant. Due to the required anoxic conditions denitrification is accomplished in another reactor or another reactor zone than nitrification. For the aerobic processes, oxygen is supplied mostly through active aeration by blowing or mixing air into the wastewater. Such aeration processes are the major energy consumers of the activated sludge process. Typical power requirements for maintaining a completely mixed flow regime with mechanical aerators vary from 19 to 39 kWh/103 m3, depending on the design of the aerator and the geometry of the tank (Crites 1998, p.463, Nowak 1999).

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Land requirements depend on the scale of the activated sludge systems. Van Nieuwenhuijzen (2000), for instance, reports a land use of 0.02 m2/pe for a 100,000 pe system while a 5 pe system may require 0.4 m2/pe (see Table 28 on page 87, and Table 32 on page 91). Investment costs for large-scale activated sludge systems are estimated to be 21 Euro/pe/y, and maintenance 15 Euro/pe/y for operation (Nowak 1999). While smaller scale systems for instance a 10 pe system costs approximately 460 Euro/pe, see also Table 21 (Alexandre 2000, Geenens and Thoeye 2000). Table 21: Approximate costs of small-scale treatment systems in Belgium (Geenens 2000).

Activated sludge* Rotating biological contactor Submerged aerated filter Trickling filter

5 pe Euro 4000 5800 3400 5800

10 pe Euro 4600 7400 4100 -

20 pe Euro 8300 8600 6000 -

O&M Euro/pe/y 97 108

* for 1,000 to 2,000 pe systems, investments costs are approximately 200 to 350 Euro/pe.

Table 22: Summary of different process conditions in anaerobic treatment (Oldenburg 1997). Process condition: Process set-up

Variations:

Advantages & disadvantages:

§ Standards rate or single state

+ simple process, can handle slow degradable substrates, lower investment costs - larger digester volume, instable if C/N10 days) during the composting process. Composting can reduce virus concentration to below their detection limit (0.25PFU/g ds, Haug 1993, p.192). Also pathogenic bacteria, Ascaris eggs, and other helminths eggs can be eliminated effectively in composting. However, regrowth of bacteria such as faecal coliforms and Salmonella may occur. The energy use of composting ranges between 7.5 and 93 kWh/(cap*y) depending on size and type of process (Bengtsson et al. 1997, p.42, 44, Kärrman 2000 p.89, 151). Due to the long retention times (7 to 14 weeks and up to two years for composting toilets) relatively large amounts of space are required. Social acceptance can play an important role in the decision to apply of composting. If the waste to be composted is biodegradable kitchen and garden waste, people have to participate by separating waste inside the household. This is mostly no problem except for densely populated urban areas were people live in high-rise building with no possibility to store the bio-waste outside. When sewage sludge is composted the acceptance lays at the other side of the chain, namely the farmers to whom the compost is offered as a soil conditioner. The farmer has to be convinced that the compost is safe with respect to pathogens, heavy metals, etc. 5.6.3.1

Composting in the decision support tool

In the decision support tool we include two composting processes (1) small-scale composting in composting toilets and (2) large-scale composting as sludge treatment, see Technological Data Sheets 5, 6, and 17 in Appendix 3. 5.6.4

Constructed wetlands

Generally, two types of wetlands are distinguished: free-water surface (FWS) wetlands and subsurface flow wetlands (SF), see Figure 18. For the subsurface flow wetland there are 2 possible feeding orientations: from one side (horizontal flow) or from just below the soil surface (vertical flow). Horizontal flow beds are fed continuously, while vertical flow is fed intermittently, such that the bed dries for short periods allowing oxygen to penetrate into the bed. Wetland systems can effectively treat high levels of biodegradable compounds (BOD), suspended solids (SS), and nitrogen, as well as significant levels of metals, trace organics and pathogens. Phosphorus removal is minimal due to the limited contact opportunities with the soil. Basic treatment mechanisms are sedimentation, chemical precipitation, and adsorption, and microbial interactions (BOD and Nitrogen), as well as some uptake by the vegetation (Reed 1995, p186). Performance of wetlands is summarised in Table 23. Removal of pathogens in wetland systems is due to natural die-off, predation, filtration, sedimentation, adsorption, excretion of antibiotics from roots of plants, and solar radiation (FWS type only). Removal of pathogens in a FWS-wetland was measured to be 95% of the faecal coliforms and 92% of the viruses, for a SF-wetland the removal was >98 and >99% 82

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respectively. There does not appear to be any consistent seasonal effect on removal performance, since the initial removal is due to physical separation even though the actual die-off is temperature dependent. A long hydraulic detention time and effective settling and minimise the risk of parasitic infection from effluents, some risk may occur when removing the sludge for disposal. Sludge stabilisation, desiccation, and solar radiation can be used to reduce the pathogens in the sludge (Reed 1995, p.72, 75, 199). Nitrogen removal in wetlands can range up to 79%, of which probably less than 16% is removed by plant uptake (Reed 1995, p.91). Although, research findings are not conclusive about the significance of nutrient removal through plant up take, some researchers mark it as insignificant while others conclude that it is the dominant pathway for nitrogen removal (Billore et al. 1999, p.168). The organic nitrogen entering a wetland is typically associated with particulate matter such as organic wastewater solids and/or algae. The initial removal of this material as suspended solids is usually quite rapid. Much of this organic nitrogen then undergoes decomposition or mineralisation and releases ammonia to the water. Plant detritus and other naturally occurring organic materials in the wetlands can also be a source of organic nitrogen, resulting in a seasonal release of ammonia as decomposition occurs (Reed 1995, p 191). Geldof et al. 1997b, p.54 estimate the space requirements of SF-wetlands at 0.5 to 5 m2 per person. In the Flintenbreite project, vertically fed constructed wetlands for greywater treatment are constructed with sizes of 2 m2 per inhabitant, these could have been smaller 1 m2/capita but space was available (Otterpohl 2000, p.7 and 11). Although, wetlands do require a relatively large surface area, the area is not completely lost as an aesthetic green space is constructed that enhances biodiversity (plants and small animals) and the grown reeds can be harvested. Costs for constructed wetlands vary with size and the nature of the wastewater. Some data are summarised in Table 24. The social acceptance of wetland is generally good; as wetlands are natural systems that can be integrated in the landscape and when carefully designed there will be no nuisance by smell and insects. However, depending on the system and the wastewater treatment one should be more or less careful not to perceive the wetland as an area for recreation as the systems contain pathogens.

Figure 18: Constructed wetlands (left: subsurface flow, right: free water surface).

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Table 23: Purification efficiency of wetlands for the treatment of domestic wastewater. Horizontal flow wetland removal efficiency (%) a b 80 65

source:

Vertical flow wetland removal efficiency (%) c d 96 99

Wetland general removal efficiency (%) e f > 95 90-99

BOD 66 73 40 34

COD TSS TN NH4-N NO3-N TP TC Zn, Pb

32 99.7g

78 79 2 58

87 90 38 66

97 99 58 96

> 95 98 75

80-95

58 99-99.9h

40 >99%

90

80 – 95

80

(a) Based on 71 installations in Denmark, Scierup 1990 in Haberl 1995, (b) Billlore 1999, P is ortho, (c) Average over 4 installations (Haberl 1995), (d) Hartjes and Geurts van Kessel 1996, (e) Geldof et al. 1997b, p.54, (f) RIONED 1998b, p.22, (g) Ecoli removal at residence time of 48 h, Green 1997, (h) FC estimation based on filters without plants, Buuren 1998

Table 24: Size, energy use, and investment costs for small wetlands (RIONED 1998b, p.32, 39, *note investment includes septic tank as pre-treatment). Type: Vertical flow

Horizontal flow

5.6.4.1

Size: (pe) 5 10 20 50 5 10 20 50

Size: (m2) 15 – 20 30 – 50 60 – 100 150 – 250 15 – 30 30 – 60 60 – 120 150 – 300

Energy use: (kWh/y) 30 60 120 300 same as above

Costs*: (Euro/system) 3,600 – 7,300 5,400 – 10,000 < 12,000 < 21,000 same as above

Constructed wetlands in the decision support tool

In the decision support tool subsurface flow wetlands are included for greywater and blackwater treatment on varying scales, namely 5, 200, and > 1,000 pe (see Technological Data Sheet 18 in Appendix 3). 5.6.5

Fixed bed reactors

Fixed bed reactors are an attached growth aerobic process, in which the micro-organisms grow on a porous filter median that is submerged in wastewater. Conceptually and operationally the process is similar to the trickling filter process, although oxygen is supplied actively by blowing air through the water (see paragraph 5.6.10). Data on size, performance, costs, land and energy use can be found in Table 21 on page 80 (costs), Table 28 on page 87, and Table 32 on page 91. 5.6.5.1

Fixed bed reactors in the decision support tool

In the decision support system we include fixed bed reactors at the scale of a single household and a block of houses (at 5 and 50 pe). For more detail see Technology Data Sheet 19 in Appendix 3. 84

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5 Processing units: technology characterisation

Membranes

Membrane filtration is used to physically filter out minute particles including viruses and some ions. Pore size is the most critical parameter is removal of particles and pathogens, however, it is not the only mechanism, sieve retention and adsorption sequestration also play a role. It has been shown that pore size can be greater than particle size for significant retention and also that bacteria may pass through membranes with smaller pore size than the bacteria diameter (Madaeni 1999). Different membrane types and configurations are used for different purposes. Membranes are often used in combination with other treatment technologies, as for instance in the membrane bioreactor where the membrane replaces the secondary clarifier, however, it is also possible to use a combination of membranes, for instance micro-filtration (MF) and reverse osmosis (RO), to configure a complete wastewater treatment and water reuse systems. One could even go a step further and use more specialised membrane to separate valuable substances from wastewater water as certain enzymes, hormones, etc. for application in medicines or biotechnology. In the next two paragraphs we discuss membranes applications that are included in the decision support system, namely membrane filtration for disinfection of wastewater and membrane bioreactors for the treatment of mixed wastewater or greywater. 5.6.6.1

Membranes for wastewater disinfection

Membranes are finding increasing application in disinfection processes for raw water and municipal effluent reuse. Membranes are capable of removing viruses completely (UF/NF/RO) or significantly (MF). Ben Aim et al. (1993) report the following removal efficiencies for a MF-membrane to treat secondary treated sewage effluent; l00% removal of total coliforms, faecal streptococci, and entroviruses, furthermore, a 83% removal of BOD, a 92% removal of turbidity, and a 22% removal of total phosphorus. More details are summarised in Table 25, and Technology Data Sheet 20 in Appendix 3.

Table 25: Membranes after activated sludge (STOWA 1998b, p.18). Type Pressure DS max. in effluent Concentration max. Energy use Flux Exploitation cost Membrane cost

(bar) (g/l) (%) (kWh/m3)* (l/(m2*h)) (Euro/m3)* (Euro/m2)

Life-time

(y)

Clogging

-

Dead-end MF/UF 0.4 – 1.0 0.1 – 0.2 90 - 95 0.05 – 0.2 50 - 100 0.2 – 0.4 110 capillary 160 tubular 4 capillary 5 tubular Sensitive

Cross-flow MF/UF 1-6 30 90 - 95 2-4 100 - 150 0.2 – 0.4 110 capillary 160 tubular 4 capillary 5 tubular Non-sensitive

* per m3 permeate, note: original figures in Dutch guilders, 1 Euro=2.20371 NLG.

85

Hybride MF/UF 0.4 – 1.0 0.2 90 – 95 0.1 – 0.3 70 – 120 0.2 – 0.4 160 tubular 4 capillary 5 tubular Sensitive

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5.6.6.2

5 Processing units: technology characterisation

Membrane Bioreactor Aerobic

There are different configurations of membrane bioreactors possible, in these configurations the membrane establishes the liquid-solids separation, for instance replacing the secondary clarifier in a conventional activated sludge processes. The advantages of the membrane bioreactor is that the complete uncoupling of the hydraulic retention time (HRT) and sludge retention time (SRT), the absolute retention of all micro-organisms insures an increase in sludge concentration and longer contact time enhancing the treatment of low biodegradable pollutants. Furthermore, the membrane produces an effluent almost free of pathogens suitable for reuse. Due to the absence of a secondary clarifier and the high sludge concentration the overall size of the treatment plant is reduced significantly. For details on performance see Table 26 and 27. 5.6.6.3

Membrane Bioreactor Anaerobic

The option of a membrane bioreactor is simple and straightforward. The influent is pumped into the reactor where it gets in contact with the micro-organisms. The mixed liquor is then pumped through the membrane filtration unit where the biomass and the treated effluent are separated. The treated effluent (permeate) leaves the process and the biomass (concentrate) is returned back to the reactor. More than 95% of the applied COD load can be removed by anaerobic membrane bioreactor for mass loading rates up to 0.8-0.9 kg COD/kgVSS/day (Beaudien 1996).

Table 26: Performance of an aerobic membrane bioreactor (Parameshwaran 1999). Influent TS TSS BOD COD TN TKN TP TC FC

(mg/l) (mg/l) (mg/l)

295-375 530-625 26-165 26-165 2.2-9.0 >107 >105

(mg/l) (mg/l) (mg/l) (count/ml) (count/ml)

Effluent 0 0 60 –80a > 60 –80a

91 – 225 32 – 113 23 – 36 80 > 90 50 < 50 50 50 > 60 –80a > 60 –80a

Fixed bed reactor

Trickling filter

1

< 16 3 to 4

4,500 – 6,800

2,300

340 ?b ?b 90c 90c > 70 50 50 50 > 80 > 80

23 – 36b Low ?b 80 90 50 50 50 50 > 60 – 80a > 60 – 80a

(a) The technology is used for the treatment of domestic wastewater, domestic wastewater with some rain , greywater, or rainwater runoff, for the last to application Oil and PAC removal is estimated to be > 80% for the first 2 > 60%, (b) Costs for energy and/or sludge disposal included in maintenance, (c) For rainwater runoff treatment somewhat lower approximately 80%, (d) For a 100,000 pe plant, (e) For smaller plants, 100 pe and larger. Note: original cost data in Dutch guilders, 1 Euro = 2.20371 NGL.

Table 29: Performance of sedimentation and precipitation based literature. Retention time Surface-loading Sludge production Removal of: TS TSS VS BOD COD TN NH4+-N TP Cd Cu Pb Zn

(h) (m3/m2/h) (kg DS/m3) (kg DS /(cap*y)) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%)

Pre-Sedimentation: 1.5 to 2.5 1.5 to 3 or < 1.6e or < 2.0e

50 – 64e 30 – 40 or 50 -70c 85d a

Precipitation:

0.27 ± 0.2b 34.5 ± 16b 60 – 80a or 91b 75d

20 – 30a or 24 – 40c or 25 –33e 20 – 30a or 25 – 33e 5 – 10a or 9e

20 – 30a or 81b

10 – 20a or 11e

60 – 80a or 94b 57b 81b 64b 50b

20 – 30a or 73b 20 – 30a or 28b

(a) STOWA 1998, p.26, Table 13, refferring to STOWA 1996 – 20; (b) Ødegaard 1992, p.261 and p.263, (c) Crites et al. 1998, p.300, (d) Kärrman 2000 p.83, (e) German design standards ATV-DVWK A131 in COST WG5 (2000), note lower values at retention time 0.5 to 1 h, higher values retention time1.5 to 2 h, lower value is horizontal throughflow, higher is vertical throughflow, TN in this case TKN.

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Membrane bioreactors seem expensive, however, on small-scale they can be more economical than activated sludge or anaerobic digestion. For instance, Davies et al. (1998 in Stephenson et al. 2000 p.172) compared the economics of a membrane bioreactor and an activated sludge process for design flows of 2,350 and 37,500 population equivalents. The capital costs for the lower flow membrane bioreactor are reported to be approximately 880,000 Euro, of which 78% was attributed to the membrane system. The activated sludge systems costs for this flow were 160% of the membrane bioreactor, but is cheaper at higher flows, costs approximately 54% of the membrane bioreactor. 5.6.6.4

Membranes in the decision support tool

In the decision support tool, we include membranes in drinking water production (see paragraph 5.2.1), effluent disinfection, and aerobic membrane bioreactors for both domestic wastewater treatment and greywater treatment (see Technology Data Sheets 20 and 21 in Appendix 3). 5.6.7

Rotation biological contactors

Rotating biological contactors are an attached growth aerobic process, in which the microorganisms grow on disks that rotate such that the micro-organisms are in alternating contact with wastewater and air. Conceptually and operationally the process is similar to the trickling filter process with high rate of circulation (see paragraph 5.6.10). Data on size, performance, costs, land and energy use can be found in Table 21 on page 80 (costs), Table 28 on page 87, and Table 32 on page 91. 5.6.7.1

Rotating biological contactors in the decision support tool

In the decision support system we include rotating biological contactors at household and neighbourhood scale. 5.6.8

Sedimentation

Sedimentation is commonly used as preliminary treatment and for solid-liquid separation after biological treatment. Efficient designed and operated primary sedimentation tanks should remove 50 to 70 % of the suspended solids and 24 to 40% of the BOD (Crites 1998, p.300). Normally, primary sedimentation tanks are designed to provide 1.5 to 2.5 hours of detention (Crites 1998, p.309). Primary sedimentation tanks are loaded with 1.5 to 3.0 m3/(m2*h), while sedimentation tanks used after biological treatment have a lower surface loading, ranging from 0.7 to 1.5 m3/(m2*h) (STOWA 1998. p.25). Sludge from sedimentation generally has a dry matter content of 0.5 to 2.5% (STOWA 1998, p.25, Kärrman 2000 p.83). Bacteria and viruses are removed significantly by sedimentation, as these are mostly solidsassociated (Bitton 1999, p.203). For ova (eggs) and cysts of parasites sedimentation and filtration are the only significantly removal processes in wastewater treatment (Gray 1999, p.274). 5.6.8.1

Sedimentation in the decision support tool

In the decision support tool sedimentation is implemented as pre-treatment and as post treatment and as pre-treatment with the addition of chemicals (precipitation) (see Technological Data Sheet 23 in Appendix 3).

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5 Processing units: technology characterisation

Septic tanks

Septic tanks are small rectangular chambers in which household wastewater is retained for 1 to 3 days. The waste inside the tank stratifies into four layers, (1) an upper layer of scum, (2) a sedimentation zone, (3) a sludge digestion zone, and (4) on the bottom a sludge storage zone. Septic tanks are commonly used as onsite primary treatment for domestic wastewater. Strengths and weaknesses of septic tanks are presented in Table 30. The removal mechanisms in a septic tank are sedimentation, digestion, and floatation. The digestion is single-phase wet digestion, at lower temperatures. Performance and data on size and costs are presented in Table 31 on page 90. Costs for septic tanks depend on size, type, soil etc, but can roughly be estimated to be 1,500 to 8,000 US$ (average 4,000) (EPA 1999, Loetscher 1999, p.154). Maintenance costs for a proper functioning septic tank system is on average 100US$, or on average 9% of investment per year (EPA 1999, Loetscher 1999, p.156). For details on the septic tank that is included in the decision support tool see Technological Data Sheet 24 in Appendix 3. 5.6.10 Trickling filters

In trickling filters, the biofilm growing on the porous filter media degrades the organic matter in the wastewater that trickles on top of the filter and percolates through. The depth of the filter is approximately 1 to 2.5 m (Bitton 1999, p.234). For optimal degradation there has to be a large surface area for biofilm growth but also enough inner space to provide aeration and to prevent ponding due to filter clogging through biomass or freezing. In the filter a stratification of micro-organisms takes place, in the upper part where the BOD concentration in the wastewater is higher, heterotrophic bacteria dominate, while in the lower parts of the filter nitrifying bacteria dominate. Removal of pathogens by trickling filters is generally low and erratic, for bacteria varying from 20% to >90% depending on operation (Bitton 1999, p.242). Removal of Salmonella 75 to 95%, total coliforms 92 to 95%, enteroviruses 59 to 95%, and bacterial phages 40 to 90% (Bitton 1999, p.242). Nitrification is virtually eliminated by hydraulic loadings of domestic wastewater > 2.5 m3 * m-3 * d-1, due to enhanced heterotrophic growth extending to the depth of the filter overgrowing the slower growing nitrifing bacteria (Grey 1999, p.364). Other limitations for nitrification are too little oxygen supply and low temperature ( compost toilet)' '5 vOST (wwtconstraint(2,18) or (2,19))' '6 vBWT (scale > minpphh no tranport not allowed)' '7 vGWT (scale > minpphh no tranport not allowed)' '8 BW1 (wwtconstraint(2,18) or (1,24) or (1,26))' '9 vBW2 (wwtconstraint(2,16),(2,17),(2,18), or (2,19))_' '10 vBW4 (tp or wwtconstraints)' '11 vGW2(tp or wwtconstraints)' '12 vRW1 (tpconstraints)' '13 vBS1 (wwwtconstraint(1,24))' x-vector resulting in the optimum f – corrected for flags – there are 24 x only mipsolve uses 25 but this extra x is set to1 and does not go through the model. matrix containing all x-vectors that resulted in f as saved in ftot – corrected for flags. x0 is the x-vector uncorrected for flags that results in f when running through the model. is the matrix of all x0 vectors that result in the ftot.

The direction structure used is:

Running optimisation can be done by calling the solvers ga2003small, gcl2003small or mip2003small. The datafile they use is data2003.mat, the solution space is solspace, and the model is dws2003.mdl. Solvers settings can be changed in the solvers’ m-files. These solvers run with the 24 decision variables and all technology options available (see Figure 20 page 105 or the spreadsheet data2003.xls). The idea is to optimise over a smaller solution space that can be created with in2003.m, when including all options one can use the full solution space however the calculation time may then be outrages. When running the global solver (gclSolve.m) there is a risk of running out of memory during optimisation. Therefore, gcl-solver has the possibility to restart with input of data from a previous run saved in gcl_restart by gclsmallr_restart.m when saved by gcl2003smallrestart.m. However, I did not use this option as I adapted gcl_small to avoid memory problems by saving the data of every 7000 evaluations in a new results file. To make a full run of the optimisations by mip2003small.m,

Sustainable Wastewater Treatment

Appendix 2: Explaining m-files for optimisation

ga2003small.m, and gcl2003small.m, after each other run optot.m. Mat-files that contain the results of the optimisation are saved automatically at the end but also during optimisation. The name of this mat-file is the name of the solver followed by _best or _interim depending on the time of saving. To generate a complete overview of the output run dws2003out.m, for sorting and plotting of the results use SortResults.m. More details are given in the table below.

Table explaining all m-files, mdl-files, mat-files, and xls-files used: file-name: CalcTime.m

directory: \AAWater\2003

function of file and settings to be changed: This m-file estimates the calculation time for a smaller set of decision variables.

ChangeRR

\AAWater\2003

With this m-file you can change the removal rates of all technologies.

ChangeWeight.m

\AAWater\2003

With this m-file you can change the weights and make a new ordering of the results to find the new optimum. Input needed is the mat-file you want to use – this must be one of the interim.mat files saved during optimisation or the changeweight_best.mat file that is automatically saved by a previous run of this m-file. Note: only when using an gclsmall_interim.mat file saved at the end of the run makes ensures you that you have all possible systems – thus the new optimum is the optimum of the solution space you defined. If using mipsmall_interim or gasmall_interim you can say something about the sensitivity of the optimum for weighting factors but you may find an other optimum that when making a new run with the solver and the new weighting factors.

data2003.mat data2003_nutrients.mat data2003_nutrients_RR1 05 data2003_water.mat

\AAWater\2003 \x-matfiles

Date-file containing all data needed for the model calculation except the decision variables. This file is generated by transferring the date from the Excel spreadsheet (data2003.xls) to Matlab. Change the filename of your data file into this name such that you do not have to make changes in the different m-files of the solvers. I used data2003_nutrients (with weighting factors aiming at fertiliser and soil conditioner production), data2003_nutrients_RR105 (with removal rates * 1.05) and data2003_water (with different weighting factors set to promote water reuse and lowered copper and zinc in the rainwater and drinking water assuming plastic pipes to allow domestic reuse).

data2003.xls

\AAWater\2003

This is a data file in form of a Excel spread sheet that gives an overview of all input data used by the model. The spreadsheet contains a macro to send the data to Matlab if the link is established by the add-in excllink.xla.

dws2003.mdl

\AAWater\2003

This is the model of the domestic water system in Simulink, which is called by the optimisation routines to calculate the objective (=f).

dws2003in.mat

This data-file is automatically save by in2003.m and defines the solution space with selected choices and technologies contains the variables: xL3 xU3 mask masktxt pHW pRWa pRWb pCONS pRWS pDis pToilet pOST pBWT pYWT pGWT pBST pBW1 pBW2 pBW3 pBW4 pGW1 pGW2 pBS1 pRW1 pSplitGW1 pSplitR pSplitR7 pSplitR8 pSplitYW2

dws2003in_refresh.mat

This data-file is automatically save by in2003refresh.m it is an

Sustainable Wastewater Treatment

Appendix 2: Explaining m-files for optimisation

update dws2003in.mat with the same variables. dws2003_manual.mdl

\AAWater\2003

This is a manual version of the model similar to dws2003.mdl but in this file the decision variables and the normalisation and weighting factors can be set by hand

dws2003out.m

\AAWater\2003

This m-file gives a 2-page overview of the output of the optimisation. It shows the x-vector with explanation added, the objective, the 5 best solutions found and from the best the sustainability indicators for the different processing units, the normalisation and weighting factors, the mass balance, and the solution space used. Running dws2003out includes running a simulation with the model, thus to see all details open the model (dws2003.mdl) prior to running dws2003out, one can place additional displays in the model to see streams of variables.

filedefaultfields.m

\AAWater\2003 \ga \AAWater\2003 \ga

Defines settings for the solver gaminI.m

ga_CWeightsF.m

\AAWater\2003 \ga

This m-file the ga-solver several times each time with different weighting factors (same solspace and dataset).

gadafaultoptions.m

\AAWater\2003 \ga \AAWater\2003 \ga

Defines settings for the solver gaminI.m

\AAWater\2003 \ga

Solver Genetic Algorithm adapted to handle integer problems see appendix 6

GA2003small.m

GAF2003small.m

gaminI.m

gaminI_interim.mat

Defines the optimisation problem – the following settings can be changed in this file, the values shown are what I used for my research and [default]: optu.GenerationSize=20; % [20] must be even integer optu.CrossoverRate=-1; % [-1] If -1, use Booker's VCO, must be in (0,1). optu.MutationRate=0.2; % [0.02] If -1 adaptive mutation rate, must be in (0,1). optu.DisplayIterations=1; % [0] Display status once every DisplayIterations optu.MaximumIterations=2000; % [20000] optu.Epsilon=1; %[1e-4] Smallest gain worth recognizing. optu.StopIterations=20; % [2000] Min. number of gains < Epsilon before stop. optu.ReplaceBest=10; % [50] re-insert best into population after ? generations optu.VectorizedFunction='off’'; % [0] Logical indicator set to 1 if the function can simultaneously evaluate many parameter vectors. optu.ReproductionSelection='tournament'; % [ roulette (gives error AB2003) | {tournament} | elitist ]; This file calls the solver gaminI.m and loads the solutionspace (solspace.m) and the dataset (data2003.m). Results are saved in gasmall_best.mat and gasmall_interim.mat and by the solver in gaminI_interim.mat.

Defines function of x that must be minimised, which is the model dws2003.mdl. In this m-file, the masks are used to translate the x-value chosen by the solver back to the original x-value used by the model (see also mask and in2003.m).

Intermediate

results

of

a

run

with

ga2003small.m,

Sustainable Wastewater Treatment

Appendix 2: Explaining m-files for optimisation

automatically saved by gaminI.m. The variables saved are: beta, iter, funstr , and options. ga_Normalisation.m

\AAWater\2003 \ga

gasmall_interim.mat

This file runs the ga-solver for all sustainability indicators setting all weighting factors to zero expect the one of the sustainability indicator chosen to determine the highest or lowest values this indicator can have given the solution space and dataset defined. These values are used as normalisation factors and saves in NewNorm.mat and in a ready to use data set for instance Data2003_nr1_NN.mat. Intermediate results automatically saved by gaF2003small.m, containing the variables: vlagtot xtot ftot suOSTtot suBW1tot suBW2tot suBW3tot suBW4tot suGW1tot suGW2tot suBS1tot suRW1tot CostsTot EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. This file is cleaned at very new run of ga2003small.m

gcl2003small.m

\AAWater\2003 \gcl

Defines the optimisation problem for gclSolve. Setting that can be changed here are: % Number of evaluations (default = 200) GLOBAL.MaxEval=25921; % Global/local weight parameter (default =1e-4) GLOBAL.epsilon=1e-4; % Error tolerance (default=0.01) GLOBAL.tolerance=10; % Printing Level % PriLev >= 0 warnings, PriLev > 0 each iteration info PriLev=1; To avoid running out of memory the data-files are split up – see gclF2003small.m gclsmall_interim.mat, and gclsmall_teller.mat

gcl2003smallrestart.m

\AAWater\2003 \gcl

To avoid running out of memory during optimisation keep de number of evaluations low and restart optimisation later. Note that it is not possible to make a restart after the error message out of memory.

gcl2003small_teller.mat

\AAWater\2003 \gcl

The number of evaluations is indicated by the variable teller that is saved in gclsmall_teller.mat. This variable is reset to 1 at every new run of gcl2003small.m

gclF2003small.m

\AAWater\2003 \gcl

Defines the function of x that must be minimised by gcl2003.m – this means running the simulation with dws2003.mdl. Including translating the x-vector back to the original x-values for the simulation and saving info for gcl2003small. To avoid running out of memory this file saves the result of every 7000 evaluations in another mat-file: gclsmall_interim, gclsmall_interim_2, gclsmall_interim_3, gclsmall_interim_4 and gclsmall_interim_5. Furthermore, the number of evaluations stored in the variable teller is saved to gclsmall_teller.mat.

gclF2003smallrestart.m

\AAWater\2003 \gcl

Similar to gclF2003small.m but and saving info for gcl2003smallrestart variable saved is GLOBAL.

gclSolve.m

\AAWater\2003 \gcl

Stand-alone solver for global optimisation, see appendix 2.

gclsmall_interim.mat

Intermediate results automatically saved by gclF2003small.m,

Sustainable Wastewater Treatment

Appendix 2: Explaining m-files for optimisation

gclsmall_interim_2.mat gclsmall_interim_3.mat gclsmall_interim_4.mat gclsmall_interim_5.mat

containing the variables: vlagtot xtot ftot suOSTtot CostsTot EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. If large number of evaluatiomns are done one risks running out of memory, to avoid this very 7000 evaluations a new mat-file is initiated to store the results. These results are glued back together by gclsmall.m, above 21000 evaluations gclsmall.m makes to result files and gives2 outputs. The number of evaluations is counted by gclsmall_teller.mat. These files are cleaned at very new run of gcl2003small.m

gclsmallr_interim.mat

Intermediate results automatically saved by gclF2003smallrestart.m, containing the variables: vlagtot xtot ftot suOSTtot CostsTot EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. This file is cleaned at very new run of gcl2003smallrestart.m

gclsmallr_restart.mat

Saved by gcl2003smallrestart.m at the end of the run to enable restart with results from previous run – variable saved is GLOBAL.

in2003.m

\AAWater\2003

This m-file is used to create a small set of decision variables such that the optimisation is limited to a smaller solution space requiring less calculation time. The result of the m-file is a new set of lower and upper boundaries and masks to translate the x back to the original x for the simulation. These variables xL3, xU3 and the vector mask are automatically saved in dws2003in.mat.

in2003refresh.m

\AAWater\2003

This file is related to in2003.m. It is used to give an overview of the new solution space after having this created with in2003.m or having made some changes by hand to the definition of the solution space.

makeset.m

\AAWater\2003

This m-file makes a new solution set with given ranges of results from the gcl-interim-mat-files. In this way one can select all best ranges and glue them together in one results file.

MIP2003small.m

\AAWater\2003 \mip

Definig the problem for mipSolve.m, note that mipSolve only runs under Tomlab. Settings that can be changed here are: maxoptim=1500; % maximal number of searches starting with new random selected staring point % Printing Level % Output every iteration >2 Output each step in simplex alg Prob2.optParam.PriLevOpt=0; %PriLev Print level in mipSolve PriLev=0; % Number of iteratios Max.Iter=500; Note that mip2003small uses an x-vector with 25 rows but the last x is set to 1 and only for the purpose to not leave the amatrix empty, this x plays no role in the calculation of the objective.

MIPF2003small.m

\AAWater\2003 \mip

Defining the function of x that has to be minimised – this means running the simulation with dws2003.mdl. Including translating x-values back with masks as a smaller solution space is defined in solspace.mat.

Sustainable Wastewater Treatment

mipsmall_interim.mat

Appendix 2: Explaining m-files for optimisation

Intermediate results automatically saved by MIPF2003small.m contains the variablesvlagtot xtot ftot suOSTtot suBW1tot suBW2tot suBW3tot suBW4tot CostsTot EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. This file is cleaned at very new run of mip2003small.m

moreplots

\AAWater\2003

This m-file was used to make the plots for Chapter 6.

optot.m

\AAWater\2003 \ga_gcl_mip

This m-files runs the 3 solvers mip3002small, ga2003small, and gcl2003small after each other. The solver setting can be changed in this m-file. This m-file uses data2003.mat, solspace.mat, and mipF2003small.m, gaF2003small.m and gclF2003small.m. All results are saved in the regular ..._best and ..._interim.mat files.

solspace.mat solspace_ls1.mat solspace_nr1.mat

\AAWater\2003 \x-matfiles

This mat-file contains the upper and lower bounds (xL3, xU3) as well as the masks (mask, masktxt, maskinfo) that define a limited solution space. This file is needed to run optimisations with ga2003small.m, gcl2003small.m, and mip2003small.m. I defined solspace_nr1 including technologies interesting for nutrient recycling and solspace_ls1 including technologies interesting for water and nutrient reuse (ls stands for life support). Solution spaces nr2 and ls2 were not used research.

SortDiff.m

\AAWater\2003

This file sorts the results on different variables than f the objective value, for instance on Costs, Water use, Fertiliser, Acceptance, Toilet type, etc.

SortResults.m

\AAWater\2003

This file sorts and plots the results. One can chose to plot the f and x-values for all data points or a range or one can plot f versus x. The sorted results are automatically saved in Sorted.mat.

SortQuick.m

\AAWater\2003

Sorts results of different runs of gclSolve after each other without plotting.

Sortx.m

\AAWater|2003

Selects from results these solutions that contain a given value of specified decision variable (is a row in the xtot-vector).

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Appendix 3: Technology data sheets No:

Configurations:

1 2 3

RO disinfection unit Sunlight for disinfection Small-scale UV-disinfection unit

4

Water flush toilet

5 6

15

Compost toilet (dry) Compost toilet (low flush, urine separation) Solar dry toilet (urine separating) Two compartments Urine separating toilet Toilet + urinal Vacuum toilet Small gravity Rainwater system (basic) system > 3m3 Rainwater system (advanced) Mixed Sewerage system Separated Vacuum Transport by truck Soak away Infiltration Swales & ditches Infiltration fields Activated sludge

16

Anaerobic digestion

17 18

Composting Constructed wetlands

19 20 21

Fixed bed reactor Membrane Membrane bioreactor

22

Rotating biological contactor

23

Sedimentation

24 25

Septic tank Trickling filter

26 27

UV-disinfection wastewater Yellowwater storage

7 8 9 10 11 12 13 14

37

Technology:

Scale:

min. 1/hh37

Processing units: ds ds ds, bw4, gw2 t

5 pe > 5 pe 5 pe 5, 50, 400, and 1,000 pe > 100 pe /m3

rws bwt, gwt, ywt

265 (l/d) 10 (l/d) 10 (l/d) Old New Flush interrupter Low flush

Mixed wastewater Greywater

Sedimentation Precipitation

1/hh means that we assume 1 system per household, pe is population equivalent.

bst, ywt bw4, gw2, rw1 ost, bw2

ost, bw2, bs1 bs1 ost, bw3, gw2 ost, bw2 bw1, bw4 bw2, gw1 bw2, gw1 bw1, bw4 bw1 ost ost, bw3, gw1 bw4, gw2 yw1

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 1: Small-scale reverse osmosis (RO) unit for disinfection. Description: Scale: Constraints: Processing unit Incoming streams: Outgoing streams: Assumptions:

Pre-filtration and reverse osmosis for drinking water production in side the household. Maximal 265 litres/day/system. Influent low in solids to prevent clogging of the pre-filters (TDS max = 2 g/l, in model TS 600 W/m2, exposure time > 5 h, thus temperature rises above 75°C, (8) effectively removes bacteria and viruses, and most protozoa cyst, (9) no or low costs either using bottles (SODIS) or solar heaters (see Jørgensen et al. 1998).

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC

average 0 0 0 0 0 0 0 99.5

H2O General info: Temperature (°C) Retention time (h)

0 average -

Costs: - investment (euro/l)

average 0

- O & M (euro/l/y) - life time (y) Emissions: CO2 CH4 NH3 Resources: - energy: - nutrients - space - water Qualitative indicators: - acceptance

min

max

99

99.999

min

max > 75 >5

Remarks:

min 0

Max 0.008

Remarks: (euro/l) see Jørgensen et al. 1998 Danish prototype thus high costs. (euro/(household*y)) (y)

min

max

Remarks:

min

max

Remarks: Solar energy only.

0 10 average 0 0 0 average 0 1 -

0

- adaptability - expertise - quality of space - institutional requirements - maintenance - participation - reliability - robustness

0 0 0 -1

- sustainable behaviour

1

0 1 0 -1

Remarks:

Medema et al. 1996, 2 to 5-log reduction for UVdisinfection

very limited in case of bottles, more for solar water heaters. Probably some lost water for cleaning – this is neglected. Remarks: Relatively easy to operate, but needs discipline, maybe seen as too low tech, works only if enough sunlight is available, and how safe is storage of the treated water? Some knowledge required Decentralised approach may be difficult for institutions to control and inspect

In house water purification by owner Low in required commodities – sunshine has to be abundant though. End-users has only an indication that water is safe, influent has to be of relatively high quality. Does not stimulate to treat large amounts of water therefore people maybe conservative with drinking water, this may also be a limitation

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 3: Small-scale UV-disinfection unit. Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Activated carbon filter followed by UV-light (system described by Serpieri et al. 2000). 10 l/system/d, therefore small scale. Influent relatively low in solids to prevent clogging of filters (TS 95a)

Ntot-N

20

Ptot-P

80 (> 80a)

Cu Zn FC H2O General info: Temperature

80 (> 80a) 90 (> 90a) 99 100

Costs: - investment (Euro/household) - o & m. - life time Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/y) - nutrients - space (m2/household) - water

80 (> 80a, 30 – 99c) 80 (> 80a) 90 (> 90a) 99 (> 99c) 100

90 90 95 (90c, 8598d) 20 (10-15c, 10-80c) 80 (20-95c) 80 90 99 (90- >99c) 100

Remarks: Remarks: 136 (136a, 5,000b) 20

42 (25-60c) 20

20

-

-

-

42 (25-60c)

0

0c

0.5a

17 (5 –30d)

0 (350– 1,325c, 0d) 17 (5 –30d)

-

-

(b) Burkhard et al. 2001 in US$/hh

Remarks:

Remarks:

General Qualitative indicators: - acceptance 0 - adaptability 0 - expertise 0 - quality of space 1 - institut. requir. 0 - maintenance 0 - participation 0 - reliability 1 - robustness 0 - sustain. behav. 0 39

90 90 (> 90a) 95 (> 95a, 86 – 98c) 20

Remarks: amount removed remains in topsoil, rest to groundwater and soil. Rough estimate based on TSS. (a) Geldof et al. 1997b, p.29-35 (c) RIONED 1998 (d) Sperling 1996, p.66-67 Rough estimate.

Assuming 5 pe per household

Remarks: If designed correctly – readily accepted. Depends on design, and local situation Swales, ditches and infiltration fields offer esthetical green areas.

Requires no electricity, or control.

Estimated to be somewhat stricter than European guidelines for discharge (BOD < 25 and TSS < 35 mg/l, 91/271/EEC). Note BOD and TSS are not mentioned in bathing water or irrigation guidelines. The FC restriction is based on the bathing water standard (2,000 FC/100ml, 76/160/EEC) for swales and ditches and public accessible infiltration fields this is reasonable. For un-accessible infiltration facilities the restriction on FC is non applicable. Note that guideline for irrigation in public parks and sports facilities is lower 1,000 FC / 100ml (Mara et al. 1989).

40

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 15: Activated sludge

Description: Scale: Constraints: Processing unit: Incoming: Outgoing: Assumptions:

Size (pe) Removal (%): TS

Biological degradation though micro-organisms that are held in suspension in an aerated tank. 5, 50, 400, 1000, 100,000 pe. Onsite treatment (ost, 5 pe), blackwater treatment (bw2, > 5 pe). Black or mixed domestic wastewater (with or without rainwater) (BW1, GW1, B2=0, BW4). Effluent and sludge stream (BW2, BS1, BW5, BS3, BGout=neglected=0). (1) Activated sludge here refers to a unit operation not including pre and post treatment, (2) In large systems nitrification and denitrification is included, (3) Treatment on larger scale more efficient, (4) Assumed is that all what is removed goes to sludge except 50% of the removed TS, TSS, BOD, N-total, and FC (rough estimate !!). 5 50 400 1,000 >100,000 50

55

60

60

70

TSS

70

75

80

80

90 80-95

BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time Size (pe) Costs: - investment (Euro/pe)

80 50 30 30 30 80 30

85 60 50 50 40 90 35

90 70 60 50 50 95 20

90 70 60 50 50 95 20

90 70 70 60 60 99 15

-O&M (Euro/pe/y) - life time (y) Size (pe) Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/pe/y) - nutrients - space (m2/pe)

Remark: Rough estimate based on data from literature and assumption 3, note TSS approximately 30% of TS in medium strength wastewater (Tchobanoglous 1991, p.56). Literature: Alexandre 2000, RIONED 1998, 10.2, Henze 1997, p.58, bosman 1997, Helmer 1997, p.58, STOWA 1998, p.39, 156.

Remark:

5

50

400

1,000

>100,000

800

500

400

200

20

100

70

40

30

7

10 5

10 50

20 400

20 1,000

40 >100,000

Remark: Estimated based on literature: Alexandre 2000, Geenens 2000, Geldof 1997b, 45-53, Nieuwenhuijzen 2000, Nowak 1999. See above.

Remark: Neglected. Neglected. Neglected.

75

75

40

40

15

0.4

0.2

0.1

0.1

0.02

Estimated, RIONED 1998 50-54, Nieuwenhuijzen 2000 Estimated see: RIONED 1998 50-54, Nieuwenhijzen 2000, Khurana 1997

- water Qualitative indicators: - acceptance 1 - adaptability 0 - expertise -1 - quality of space -1 - institutional -1 / 0 /1 requirement - maintenance 0 - participation 0 - reliability 0 - robustness -1 / 0 - sustainable 0 behaviour

Remarks: Commonly used. High expertise level required in design and operation. All space is lost. Decentralised hard to control / Centralised fits in existing institutional set-up.

Depends on electricity and control equipment. Small-scale system less robust (5 pe).

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 16: Anaerobic digestion

Description: Scale: Constraints: Proces. units: Incoming streams: Outgoing: Assumptions:

Wastewater treatment in absence of molecular oxygen. Household (5 pe), neighbourhood (400 pe) and centralised (50,000 pe).

Removal rate (%): TS

5 pe

400 pe

50,000 pe

40

45

55

TSS

60

70

97

BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature HRT (days)

60 10 20 40 40 95 20

70 20 40 80 80 99.9997b 10

95 40 50 95 95 99.9997b 10

35-55e 15

35-55e 15

On-site treatment (ost), blackwater (bw2) and black sludge treatment (bs1). Blackwater, maybe mixed with urine and possibly with low amounts of flush water, and biowaste (BW1+B2+GW2, or BW4, or BS1, BS2, BS3, BS4).. Sludge and biogas (BS1, BG1 or BG2, BS3, or BG2out, BSout). (1) of gaseous emissions 65% CH4 and the rest CO2, (2) inhibitors not present, (3) Large scale treatment is more efficient than small scale, (4) Assumed is that all what is removed goes to sludge except 50% of the removed TS, TSS, BOD, N-total, and 20% FC (Grey 1999, p.446, rough estimate!! assumed: higher temperatures gives higher FC reduction).

Costs: - investment (Euro/pe) -O&M (Euro/pe/y) - life time

5 pe 460

400 pe 400

50,000 pe 280

25

25

60

10

20

20

Emissions: CO2 (g/pe/d)

5 pe 8

400 pe 10

50,000 pe 20

CH4 (g/pe/d) Gas (l/pe/d) % - methane CH4 (kWh/pe/y) NH3 Resources: - energy (kWh/pe/y) - nutrients - space (m2/pe) - water

6 12 65a 18 5 pe

13 28 65a 42 50,000 pe

-15 0.4 -

8 16 65 24 400 pe -110i -20 0.1 -

-36=0.85*42 0.04 -

Qualitative: - acceptance - adaptability - expertise - quality space - institut. req. - maintenance - participation - reliability - robustness - sustain. behav.

5 pe 1 0 -1 -1 -1 0 1 -1 -1 1

400 pe 1 0 -1 -1 0 0 1 -1 0 1

50,000 pe 1 0 -1 -1 1 0 0 -1 0 0

Remarks: Estimated based on assumption 4 and literature and spreadsheet. Baymoumy 1999, Gray 1999, p.446, Geldof et al. 1997b, Huyard et al. 2000. Rough estimate no data except 10 for 5 pe.

(b) Remarks: (e) Oldenburg 1997, p.5 (c) Tchobanoglous 1979, p.820, 825, 10-20 thermo, 30-60 meso. Remarks: Beek 1998, p.29 and Loetscher 1999, p.154, note 50,000 pe may be cheaper reported not full capacity.

Remarks: CO2=gas production*0.35*density CO2 (1.98g/l), assumption 1. Estimated on: Lettinga 1993, 12-15 l CH4/cap/d, density methane 0.72 kg/m3, Gray 1999, p.454, 545, % methane ranges between 46-80, average 65%. Methane: 23 MJ/m3 (Gray 1999, p.454). Remarks: Estimated Otterpohl (1997), Energy use 5% of prod. gas, los 10% Dalemo 1996, p5 Estimated, only 5pe in Geldof 1997b, Loetscher 1999 Remarks: Used around the world, positive due to biogas. Once constructed configuration fixed Quite some expertise required in design and operation. Space lost, maybe built underground though. Decentralised is difficult to control. Possibly in providing waste and using produced energy. Depends on energy for heating and electricity. Sensitive to temperature, influent and certain compounds.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 17: Composting

Description: Scale: Constraints: Processing unit: Incoming : Outgoing : Assumptions:

Removal (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O

Larger scale composting (aerobic stabilisation) of sludge and or biowaste. > 100 pe (note: small scale composting in included in toilet). Dry solids content > 40 and < 80%. Sludge treatment (bs1). Sludge and/or biowaste (BS5 sum of BS1=BW1+B2, BS2, BS3, BS4). Compost and gaseous emissions (BG2out, BSout). (1) C:N ratio favourable (20 to 30) or corrected through addition of woodchips, addition neglected. (2) No adding or recycling of bulking agents. (3) No leachate. (4) Emissions to air are assumed to be mainly H2O, CO2, and NH3, reduced by use of condenser and filter. average 18 15 95 4 0 0 0 99.99

min 13

max 24

6

73

In final product 0.5 and 20%, 6 to 73%.

Remarks: Valuable product, natural process, low tech imago though. Some expertise needed in operation (too wet, aeration, C/N-ratio, too cold, worms). Space is lost, covers maybe necessary to educe smell.

Some participation possible in waste separation and use of final product. Does not need electricity or water supply, possibly heated or aerated. Sensitive to temperature and moisture content. Waste separation may stimulate of visualise sustainable behaviour.

For the qualitative indicators, 1 indicates a potential advantage, 0 neutral, and -1 a potential disadvantage.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Estimating the removal of Water, Carbon and Nitrogen: From the mass balance in Haug 1993 (see Figure 3.2) we find removal of water of 86% and a reduction of total solids of 21%. Based on the measurements of Lasardi 2000 (see Table 3.3) we can calculate the total solids removal based on the assumption that the P is inert (P-total unchanged), this gives us a s decrease of solids of 13% while an increase of 23% is measured (%ww). If we assume this difference due to water evaporation, the water removal is 35%. Similar calculations can be done based on the measurements of Trubetskaya 2001 (see also Table 3.3), giving a TS removal of 24% and a water removal of about 52%. In the vent gas carbon dioxide was measured to be 0.2%, humidity was 95%, hydrogen sulphide was 0.5 ppm, ammonia 3 ppm, and methane 4 ppm (not detectable were sulphur dioxide, carbon monoxide, and methyl mercaptan) (CM 2000). From these measurements, it becomes plausible that the main loss in TS is due to carbon losses. In the ORWARE model it is assumed that 50 to 70% of the carbon is transformed into CO2, to trace amounts of methane (0.35% of CO2) and to humus (Björklund et al. 2000, p.47). Assuming, as done in the ORWARE model, that the major part of nitrogen in compost is bound to humus, while the remainder is mineralised as NO3- and NH4+. Gaseous losses are estimated according the following equation; N loss = 0.55903 − 0.01108 * (C / N ) in which Nloss is the lost fraction of the incoming nitrogen, C is the incoming carbon, and N is the incoming nitrogen. The nitrogen loss is mainly NH3 (96% while 2% is N2O, and 2% is N2) (Kärrman 2000, p.88, and Sonesson 1998, p.28). According to the formula, the nitrogen loss at a C:N ratio of 25 is about 28%. According to Gotaas (in Jenkins 1994, on p.29) this would be much lower, namely 14.8% for a C:N ratio of 22 and 0.5% at a C:N ratio of 30. According to the measurements of Lasardi the Total N as % of the dry weight is the same in the sludge straw mixture and final compost, however, as TS the TS contents increases, the nitrogen loss can be estimated at 13%. Similar for the Trubetskaya measurements an estimate of 54% N-total reduction can be estimated. Bengtsson et al (1996, p.44) estimates a nitrogen loss for kitchen waste composting of 20%, however, in kitchen waste the nitrogen contents is significantly lower than in mixed faeces, urine and biowaste. Through a biofilter and a condenser, 90% of the nitrogen loss is returned to the compost, and 50% of the CH4 is oxidised to CO2 (Kärrman 2000, p.88, and Sonesson 1998, p.28). Energy: Bengtsson et al. (1997, pp.42), estimate the energy use of liquid composter for the wastewater and kitchen waste of 57 households, to be 84 kWh/(cap*y) for a continuous and 57kWh/(cap*y) for a batch process, including grinding of the waste excluding 27 kWh/(cap*y) for vacuum transport of the waste. According to Kärrman 2000 (p.89), 0.015 MJ oil and 0.025 MJ electricity is consumed per kg wet weight compost in windrow composting. Kärrman (2000, p.151) also mentions 335 MJ/(cap*y) for a liquid composting reactor and no oil use for windrow composting. Björklund et al. (2000, p.47) report an energy use of 97 MJ/103 kg electricity and 5 MJ/103 kg diesel (compost reactor with biofilter, without heat recovery, an ORWARE sub-model). The electricity demand for the drum composter for kitchen waste used in the ORWARE model is 7.5 kWh/(cap*y) (Bengtsson et al. 1997, p.44).

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 18: Constructed wetlands. Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Treatment of wastewater in sub-surface flow wetland, which is a soil filter with reeds. Different scales: single household (5 pe), neighbourhood (200 pe), and large scale (> 1000pe). Blackwater has to be pre-treated to lower the solid content, (TS < 360 mg/l42). On-site treatment (ost), blackwater treatment (bw3), and greywater treatment (gw2). Pre-treated wastewater or greywater (BW1, BW4, GW3). Treated water and accumulation of solids in and on the wetland (BW2, BW6, GWout). (a) gaseous emissions neglected, (b) accumulated solids neglected. 5 pe 75 75

200 pe 90 90

>1,000 pe 99 99

70 40 30 80 80 98 10 average -

90 50 40 90 90 99 10 min

98 80 60 95 99 99.9 10 max

0.3b, 3a

10b, 14a

5 pe blackwater: 850 greywater: 200 17

200 pe blackwater: 370 greywater: 225 5

> 1,000 pe blackwater: 14 greywater: 1.5 0.00004

10

20

20

5 pe 5 pe 6

200 pe

> 1,000 pe

200 pe 1.5

> 1,000 pe 0.15

blackwater: 6 greywater: 2 -

blackwater: 3 greywater: 1 -

blackwater: 1 greywater: 0.5 -

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time (days) Costs: - investment (Euro/pe)

-O&M (Euro/pe/y) - life time (y) Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/pe/y) - nutrients - space (m2/pe)

- water

Qualitative indicators: - acceptance - adaptability - expertise - quality of space - institut. requir. - maintenance - participation - reliability - robustness - sustain. behaviour 42

1 0 0 1 -1 / 0 0 0 1 0/1 1/0

Remarks: all vertical flow. Rough estimate Scierup 1990 in Haberl 1995, Billore 1999, Haberl 1995, Hartjes 1996 Reed et al. 1995, p.191, of which 16 % by plants Jenssen et al. 1996 using porous media Rough estimate no data. Rough estimate only data for > 1,000 pe. Buuren 1998, Green in Buuren 1998 Rough estimate, depends on climate. Remarks: (a) Reed et al. 1995, p.5, (b) Kampf et al. 1998 effluent polishing Remarks: Kampf et al. 1998 in US$, Rietland Brochure 2000, Jenssen 1996, Burkhard et al. 2001, Geldof et al. 1997b, p.54, RIONED 1998b, p.32, 39. Estimated, Burkhard 2001, Kampf et al. 1998 in US$/m3

Remarks: Neglected Neglected Neglected Remarks: Rough estimate, only data for 5 pe from RIONED 1998b, p.32, 39 Kampf et al. 1998 polishing, Cooper 1996, 1 m2/cap for BOD removal, 2 m2 additional for nitrification, Geldof et al. 1997b, p.54, Otterpohl 2000, p.7, 11, RIONED 1998b, p.32, 39, Buuren 1998.

Remarks: Natural treatment systems, widely used. Once designed little to adapt. Nice green area created. Depends on scale. Low: cutting reeds and checking on performance one in a while. No energy or water supply required. Larger systems resistant to shock loads. Visualise treatment when onsite, encourage use of biological degradable products.

Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as influent to the wetland.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 19: Fixed bed reactor

Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions: Size (pe) Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time Size (pe) Costs: - investment (Euro/pe) - O. & M (Euro/pe/y) - life time (y) Size (pe) Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/pe/y) - nutrients - space (m2/pe) - water Qualitative indicators: - acceptance - adaptability - expertise - quality of space - institutional requirements - maintenance - participation - reliability - robustness - sustainable behaviour

43

Attached growth systems of micro-organisms on filter bed. 5, 50 pe. Medium strength settled wastewater as influent (TS < 360 mg/l43). On-site treatment (ost) and blackwater treatment (bw2). Mixed wastewater (BW1 or BW4) Treated wastewater (BW2 or BW5), solids accumulate in the filter bed these are neglected. (5) 50 pe system is more effective than 5 pe, (6) Waste such as filter material and settled solids are neglected. 5 50 Remarks: 90 95 Estimated based on Geldof 1997b, 45-53 and RIONED 1998. 90 95 90 98 80 90 35 70 50 60 50 60 90 95 0 0

5

50

1,245

420

70

23

10 5

10 50

-

-

110

51

RIONED 1998

0.4

0.16

RIONED 1998

Remarks: Estimated based on Geldof 1997b, 45-53 and RIONED 1998 Geldof 1997b, 45-53 only data on 5 pe, rough estimate for 50 pe (3:1 same as investment costs). Remarks:

1 0 0 0 -1 0 0 0 0 0

Remarks: Combination of filter and biological treatment, regularly used, commonly accepted.

Decentralised difficult to control.

Mostly closed system, no real feed back on user, although it may stimulate use of biodegradable cleaning agents.

Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as influent to the wetland.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 20: Membrane

44

Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Membrane filtration for disinfection and solid liquid separation as tertiary treatment. Any scale. Medium strength settled wastewater as influent (TS < 360 mg/l44). Blackwater treatment (bw1 and bw4) Mixed domestic wastewater (BW3, BW6). Treated wastewater and concentrate (BW4, BS2 or BWout, BS4). (1) Only separation no reactions occur (everything removed to sludge), (2) No gaseous emissions.

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time

Disinfection + L/S 95 90b 83a 30 22a 50 50 100a 15

Remarks:(removed means lost in concentrate) Estimated on TSS. (b) STOWA 1998b, p.18 – MF/UF. (a) Ban Aim 1993 MF-membrane Roughly estimated.

Costs: - investment (Euro/m3) - O & M (Euro/m3) - life time (years)

Disinfection + L/S 135b

Remarks:

Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/m3) - nutrients - space (m2/pe) - water

Disinfection + L/S 0 0 0

Rough estimate – no data available. Rough estimate – no data available. Rough estimate, depends on influent, membrane, pressure. Remarks:

0.3b 4.5

0.15c 0.02h -

Qualitative indicators: - acceptance

1

- adaptability

1

- expertise

0

- quality of space - institutional requirements - maintenance - participation - reliability - robustness - sustainable behaviour

-1 0 0 0 -1 1 0

Remarks:

Remarks: (c) Broens 2001, p.14 dead end, cross flow several kWh/m3 for RO-units. (h) Fane 1996, one tenth of conventional processes

Remarks: High tech and simple to operate – may not be accepted as only barrier against pathogens though. Modular set-up allows adding or taking out of modules to adapt to changing flows. Expertise in needed in design and installation, operation can be selfcontrolled. Space is lost. Depends on scale. Requires replacement of membranes once in while. In greywater recycling reuse of water involves end-users. Depend on energy and control equipment, high tech spare parts. Effluent quality independent of influent.

Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as influent to the wetland.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 21: Membrane bioreactor Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Integration of aerobic treatment with membrane filtration for solid-liquid separation. Modular can go from household scale up to centralised treatment. Medium strength settled wastewater as influent (TS < 360 mg/l45). On-site treatment (ost), blackwater and greywater treatment (bw2, gw1). Greywater or mixed wastewater (BW1, BW4, GW2). Treated wastewater and sludge (BW2, BS1, BWout, BS4, GWout). (3) Only separation no reactions occur (everything removed to sludge). (4) No gaseous emissions. Mixed ww 99 100

Greywater 99 100

97.5 90 70 50 50 100

98.5 85 72 50 50 100

H2O General info: Temperature HRT (days) SRT (days)

15 Mixed ww 0.21a, 8b 5a, 60-70b

5 Greywater

Costs: - investment (Euro/m3) - O & M (Euro/m3) - life time (y)

Mixed ww 140

Greywater 110

0.6 2

0.5 3

Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/m3)

Mixed ww -

Greywater -

1.5f (0.15 – 2.4) 0.04h -

0.2

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC

- nutrients - space (m2/pe) - water

1

- adaptability - expertise - quality of space - institut. requir. - maintenance - participation - reliability - robustness

1 0 -1 0 0 0 -1 1

- sustainable behaviour

0

45

Qualitative indicators: - acceptance

0.08d, 1c

0.002h -

Remarks: Rough estimate based on TSS. (a) Xing 2000 – activated sludge and UF, (g) Stephensons 2000, p.162 (b) Kishino 1996, (d) Parameshwaran 1999 (e) Chiemchaisri 1992 in (d). Rough estimate – no data available. Rough estimate – no data available. (a) Xing 2000, below detection limit, (c) Jefferson 2000 (7log), (d) > 6log Rough estimate, depends on influent, membrane, pressure. Remarks: (c) Jefferson 2000, Remarks: (f) STOWA 1998b, p.24, note only data for blackwater, greywater estimated by taking lower values of blackwater.

Remarks:

Remarks: note only data for blackwater, greywater estimated by taking lower values of blackwater. (h) Fane 1996, one tenth of conventional processes

Remarks: High tech and simple to operate – may not be accepted as only barrier against pathogens though. Modular set-up allows adding or taking out of modules to adapt to changing flows. Expertise in needed in design and installation, operation can be self-controlled. Space is lost. Requires replacement of membranes once in while. In greywater recycling reuse of water involves end-users. Highly dependent on electricity and control equipment. Effluent quality independent of influent (higher rejection if influent of lower quality)

Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as influent to the wetland.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 22: Rotating biological contactors Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions: Size (pe) Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time Size (pe) Costs: - investment (Euro/pe) - O. & M. (Euro/pe/y) - life time (y) Size (pe) Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/pe/y) - nutrients - space (m2/pe) - water Qualitative indicators: - acceptance - adaptability - expertise - quality of space - institutional requirements - maintenance - participation - reliability - robustness - sustainable behaviour

Rotating disk with micro organisms go through the wastewater and the air to obtain an aerated biological treatment of the wastewater. 5, 50, and 400 pe. On-site treatment (ost), black and greywater treatment (bw2, gw1). Wastewater (BW1, BW4, GW2). Treated wastewater (BW2, BW5, GW3). (7) Gaseous emissions neglected, (8) Accumulation of solids and biomass on the disks neglected. 5 50 400 Remarks: 75 80 90 Rough estimate no data. 80 (80a) 85 (80a) 95 (95-96b) (a) Geldof 1997b, 45-53, (b) Bosman 1997 90 (>90a) 93 (>90a) 96 (95-97b) 40 (50a) 50 (50a) 60 (28-89b) a a 20 (< 50 ) 20 (< 50 ) 24 (13-24) 50 50 50 Geldof 1997b, 45-53 50 50 50 Geldof 1997b, 45-53 80 85 90 No data. 0 0 0 No data.

5

50

400

1000 (6001360) 18.2 10 5

415 (300-626)

300

4.5 10 50

0.03 20 400

-

-

-

70

26.5

10

0.7

0.3

0.1

-

-

-

1 0 0 0 -1 0 0 0 0 0

Remarks: Alexandre 2000, Geenens 2000, RIONED 1998, Geldof 1997b, 45-53 Alexandre 2000, Geldof 1997b,45-53 Remarks: Neglected Neglected Neglected RIONED 1998, no data 400 pe thus estimated. RIONED 1998, no data 400 pe thus estimated.

Remarks: Generally used. Some expertise needed in design and operation. Decentralised and therefore probably difficult to control.

Requires energy supply. Depends on size.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 23:Sedimentation

Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Separation of liquid and solid phase through sedimentation. Large scale (on household scale a septic tank can be used for sedimentation). Blackwater treatment (bw1 and bw4). Mixed wastewater (BW3, BW6). Treated wastewater and sludge (BW4, BS2, BWout, BS4). (1) In precipitation chemicals are used to form flocs to enhance sedimentation, this results in a larger sludge volume 34.5 ± 16 (Ødegaard 1992) or 15 to 23 % (STOWA 1998, p.40) and higher costs. The amount of chemicals added ranges between 9 and 15 g/m3 (STOWA 1998, p.40, 50) with additional costs of 0.006 Euro/g (STOWA 1998, p.205). (2) Size on surface loading: 1.5 to 3.0 m3/(m2*h) (STOWA 1998). (3) Assumed is that 50% of FC removed is inactivated through natural die-off rest to sludge.

Removal rate (%): TS TSS

Sedimentation 57 (50-64c) 55 (30-40b, 50 – 70a, 85d) 30 (24-40a, 20-30b, 25-33c) 10 (-10b, 9c) 10 (0-20b, 11e) 40 30 60 10

Precipitation 75 77 (60 – 80b, 75d, 91e) 44 (20-30b, 81e)

Sedimentation 1.5-2.5a-

Precipitation

Sedimentation pre: 340b, {0.9} post; 170, {0.45}

Precipitation 1,134b {3.15}

pre: 2b post: 1

11b + 0.006/m3,b

20

20

Sedimentation Sedimentation pre: 1.9b, post: 1 Pre: 0.014, post: 0.007 -

Precipitation Precipitation

BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature HRT (hours) Costs: - investment (Euro/m2) {Euro/pe} -o&m (Euro/m2/y) - life time Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/y/pe) - nutrients - space (m2/pe) - water

Qualitative indicators: - acceptance - adaptability - expertise - quality of space - instit requirements - maintenance - participation - reliability - robustness - sustai. behaviour

1 -1 0 0 0 0 0 1 1 0

25 (20-30b, 28e) 78 (60-80b, 94e) 81e 50e 80 10

1.9b

0.014

Remarks: (c) COST WG5 2000, no data precipitation. (a) Crites 1998, p.300, (b) STOWA 1998 p.25, (d) Kärrman 2000 p.83, (e) Ødegaard 1992, p.261, 263

No data pre sedimentation – rough estimate. No data pre sedimentation – rough estimate. Rough estimate, No data. Rough estimate, (d) Kärrman 2000 p.83, 2.5% d.s in sludge, (b) 0.5 – 1% Remarks: Process retards at lower temperatures. (a) Crites 1998, p.309 Remarks: (b) for tanks > 700m2, approx. 2.25 m3/m2/h, 0.15 m3/d/pe --> 252.000 pe (b) precipitation in addition flocculator p.155

Remarks:

Remarks: (b) rectangular tank 500 m2, approx. 180.000 pe

(b) pre 1.5 –3 after biological treatment: 0.7-1.5 m3/m2/h, 0.00625 m3/h/pe.

Remarks: Simple and general practice treatment. Once designed flexibility is limited. Some in design. Lost, although some ducks may make use of the tank. Low in labour and maintenance. Only requires electricity for sludge take out. As long as not over loaded – little can disturb the process.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets Technology Data Sheet 25: Trickling filter

Description: Scale: Constraints: Processing unit: Incoming: Outgoing: Assumptions: Size (pe) Removal (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time Size (pe) Costs: - investment (Euro/pe) - O. & M. (Euro/pe/y) - life time Size (pe) Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/pe/y) - nutrients - space (m2/pe) - water

Treatment through micro-organisms in a biofilm formed on a porous filter on which wastewater is trickled. Aeration is passive. 5, 50, 400, and 1000 pe. For high rate trickling filters pre-treated wastewater (TS < 360 mg/l46), else low rate filter. On-site treatment (ost), black and greywater treatment (bw3, gw1). Wastewater (BW1, BW5, or GW2). Treated wastewater (BW2, BW6, GW3). (1) The removal rates listed by Geldof et al. (1997b, p.45-53) for < 16 pe are used for the 5, 10 and 20 pe systems, (2) All literature data not specific for small-scale systems is used for systems of 50 pe and larger, (3) Depth filter 1.8 m. 5 50 400 1,000 Remarks: 45 / 75 45 / 75 45 / 75 45 / 75 Rough estimate based on TSS, no data. 50 / 80 50 / 80 50 / 80 50 / 80 Geldof 1997b, p.45-53, Helmer 1997, p.58. 55 / 85 55 / 85 55 / 85 55 / 85 Crites 1998 p.485 low rate 40-70%, high rate 8090%. 30 / 50 30 / 50 40 / 60 40 / 60 Rough estimate, only data 50 for 5 pe system. 30 / 50 30 / 50 40 / 60 40 / 60 Rough estimate, only data 50 for 5 pe system. 30 / 50 30 / 50 40 / 60 40 / 60 Rough estimate, only data 50 for 5 pe system, and > 50 larger systems. 30 / 50 30 / 50 40 / 60 40 / 60 Rough estimate, only data 50 for 5 pe system, and > 50 for larger systems. 90 / 95 90 / 95 90 / 95 90 / 95 5 5 5 5 Rough estimate, some evaporation. Remarks: 5

50

400

1,000

1,100

425

300

150

4.6

0.72

0.03

0.07

10 5

10 50

20 400

30 1,000

75

40

10

3.75

0.8 -

0.08 -

0.01 -

0.005 -

Qualitative indicators: - acceptance 0 - adaptability 0 - expertise 0 - quality of space 0 - institution. req. 0 - maintenance 1 - participation 0 - reliability 1 - robustness 0 - sustain. behav. 0 46

Remarks: RIONED 1998, p50-54, Geldof 1997b, p.45-53, Geenens 2000, Alexandre 2000. (a) Geldof 1997b, (d) Alexandre 2000.

Remarks: Neglected. Neglected. Neglected. Remarks: RIONED 1998, p50-54, only 5 and 1,000 pe, rest estimated. RIONED 1998, p50-54, space= BODin* Qin/1.08.

Remarks: Problems with smell and or insects may occur. Depends on design Low in expertise Space is lost although can be built partly underground. Depends on design – small decentralised or large scale centralised systems Low in maintenance Can operate without power. Can cope with shock loads, low temperatures slows down the process.

Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as influent to the wetland.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 26:UV-disinfection of wastewater Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Disinfection of treated wastewater with UV. Large scale (>100 pe, for small scale UV-disinfection see TDS3). Turbidity of the water has to be low (TSS < 30 mg/l). Black and greywater treatment (bw4, gw2). Treated wastewater (BW6, GW3). Disinfected wastewater (BWout, GWout). (1) No other changes to the water than die-off of pathogens.

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC

average 0 0 0 0 0 0 0 99.9

H2O General info: Temperature Retention time

0 average -

Costs: - investment (Euro/m3/d) -O&M (Euro/(m3/d)/y) - life time

average 0.14

Emissions: CO2 CH4 NH3 Resources: - energy: (kWh/m3/y) - nutrients - space (m2/system) - water

average average 1,600

Qualitative indicators: - acceptance - adaptability - expertise - quality of space - institutional requirements - maintenance - participation - reliability - robustness - sustainable behaviour

min

max

99

99.999

min

max

Remarks:

min 0.03

max 0.23

Remarks: EPA 1999 (original data in US$, in 1999 US$

0.004

Remarks:

Serpieri et al. 2000 p.18 down to 1-2 CFU/100ml, carbon filter adds bacteria to the water, Medema et al. 1996 2 to 5-log reduction for UV-disinfection.

Euro).

EPA 1999, 2.5 % of investment.

10

5 -

1 0 0 0 0 0 0 -1 0 0

min

max

Remarks:

min

max

Remarks: Calculated based on Valenti 1997.

Rough estimate, Minimal – lamps in water flow. Remarks: Relatively easy to operate. Some expertise is required – but can be a self controlling unit. Decentralised approach may be difficult for institutions to control and inspect. Little maintenance, cleaning and replacing bulbs and filter. When reuse participation may be possible. Depends on availability of bulbs and electricity. Cannot handle turbidity, measures light intensity and stops if turbidity of influent is too high.

Sustainable Wastewater Treatment

Appendix 3: Technology data sheets

Technology Data Sheet 27: Yellowwater storage. Description: Scale: Constraints: Processing unit: Incoming streams: Outgoing streams: Assumptions:

Storage of yellowwater (urine) before using it as fertiliser (production). Any scale Yellowwater treatment (yw1). Yellowwater (YW5). Yellowwater (YWout) (1) Storage no reactions or nitrogen loss. (2) Assume storage period 6 months.

Removal rate (%): TS TSS BOD Ntot-N Ptot-P Cu Zn FC H2O General info: Temperature Retention time

average 0 0 0 0 0 0 0 99 0 average -

min

max

Remarks:

min

max

Remarks:

Costs: - investment (Euro/m3) -O&M - life time (y)

average 100

min 150

max 50

Remarks: Rough estimate, no data.

Emissions: CO2 CH4 NH3 Resources: - energy: - nutrients - space (m2)

average min max 0 0 0 average min max 0 (H2Oin/1000*182.5/1)

- water Qualitative indicators: - acceptance - adaptability - expertise - quality of space - institutional requirements - maintenance - participation - reliability - robustness - sustainable behaviour

0 20 Remarks:

Remarks:

Half year (182.5 days), height tank 1 m, Maybe underground.

0

0 0 0 0 0

Remarks: Storage can be out of sight, with out smell. Depends on design. Little required – maybe NH3 losses, salt formation. Space lost, possibly underground. Depends on size.

0 0 1 0 0

Limited. None. Depends on design – no electricity required. Only size is essential. No feed back to end users in this stage.

Sustainable Wastewater Treatment

Appendix 4: Results

Appendix 4: Results Table 1 gives an overview of the results by different solvers for the same solution space and data set. The following pages give an overview of results for the two scenarios, nutrient recycling and water recycling, for the first 6999 evaluations with the global solver. The overview is created using the m-files dws2003out and SortResults.

Table 1: Systems for nutrient recycling selected by the different solvers within the 2 solution spaces defined, weighting fertiliser (100), soil conditioner (50), and waste (-100).

Solution space: Data-set: Evaluations: min(f)=min(-objective) max(f)=max(-objective) number of systems with min(f) place first min(f) in unsorted

solspace_nr1 data2003_nutrients gcl 25920# -82.59 99.98 120 1177

ga 584$ -82.59 97.18 124 215

mip 1500& -82.59 99.98 11 18

choices contained in

The best domestic water systems found: solspace_nr1 Household water {0} {0} {0} 0 Rainwater for toilet, {0} {0} {0} 0 washing & outdoor x3 Rainwater for kitchen {0} {0} {0} 0 & personal hygiene x4 Water saving shower & {0} {0} {0} 0 washing machine x5 Rainwater system {3} {3} {3} 3 x6 Disinfection unit {4} {4} {4} 4 x7 Toilet 6 6 6 5,6,8 x8 On-site treatment {6} {6} {6} 6 x9 Blackwater transport 1,2,3 1,2,3 1,2,3 2,3,4* x10 Yellowwater transport {3} {3} {3} 3 x11 Greywater transport {2} {2} {2} 2* x12 Black sludge transport {2} {2} {2} 2 x13 Blackwater treatment 1 4 4 4 1,2,3,4 x14 Blackwater treatment 2 6 6 6 2,4,5,6,13,14 x15 Blackwater treatment 3 3 3 3 3,7,8 x16 Blackwater treatment 4 {7} {7} {7} 7 x17 Greywater treatment 1 9 9 9 1,2,4,8,9 x18 Greywater treatment 2 8 8 8 5,8 x19 Rainwater treatment {3} {3} {3} 38 x20 Sludge treatment {3} {3} {3} 3* x21 Greywater splitter 1 1 1 0,1 x22 Rainwater splitter (R7) {0} {0} {0} 0 x23 Rainwater splitter (R8) {1} {1} {1} 1 x24 Yellowwater splitter 0 0 0 0,1 # 25920 is the number for combinations contained in this solution space the number of domestic water systems is lower due to overruled choices in de model which the solver cannot see but the data are corrected and this leads to some doubles in the solution set, $ the ga-solvers stopped after 20 generations without improvement, & the mip-solver is set to do 1500 evaluations, {0} the brackets indicate that the lower and upper boundaries fix the x on this value, * the model can overrule the value. x1 x2

9 4 4 0

0 4 4 1

1 1 1 -

2 0 0 -

3 1 1 -

4 0 (original choices) 0 (corrected for flags) -

9 4 4 0

9 4 4 0

Of the 5 best domestic water systems found, this is nr.: 3: f-value: -82.592 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 x0-vector: 0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8 x-vector: 0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8 flags: 0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 0

Of the 5 best domestic water systems found, this is nr.: 4: f-value: -82.592 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 x0-vector: 0 0 0 0 3 4 6 6 2 3 1 2 4 6 3 7 9 8 x-vector: 0 0 0 0 3 4 6 6 2 3 2 2 4 6 3 7 9 8 flags: 0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 1 0 4 4 1

0 4 4 1

1 1 1 -

1 1 1 -

2 0 0 -

2 0 0 -

3 1 1 -

3 1 1 -

4 0 (original choices) 0 (corrected for flags) -

4 0 (original choices) 0 (corrected for flags) -

Of the 5 best domestic water systems found, this is nr.: 2: f-value: -82.592 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 x0-vector: 0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8 4 4 1 0 1 0 (original choices) x-vector: 0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8 4 4 1 0 1 0 (corrected for flags) flags: 0 - 0 - - - 0 0 0 - 1 - 0 0 - 0 - 1 0 1 - - - -

Of the 5 best domestic water systems found, this is nr.: 1: f-value: -82.592 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 x0-vector: 0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8 x-vector: 0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8 flags: 0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 1

Number of EVALUATIONS: 6999 (based on size xtot)

Nutrient Recycling: Output of dws2003out.m for sorted results of global solver (first 6999 evaluations): >> clear all >> load data2003_nutrients >> save data2003 >> clear all >> load solspace_ls1 >> save solspace >> clear all >> load Sorted_1_nr1 >> dws2003out

Sustainable Wastewater Treatment

9 4 4 0

0 1 2 3 4 4 1 0 1 0 (original choices) 4 1 0 1 0 (corrected for flags) 1 - - - -

1. SELECTED DOMESTIC WATER SYSTEM: '1 cHW = 0 - no household water use' '2 cRWa = 0 - no rainwater for toilet, wash.machine & outdoor' '3 cRWb = 0 - no rainwater use in kitchen and for personal hygiene' '4 cCON = 0 - no use of water conserving shower and washing machine' '5 cRWS = 3 - no rainwater available or chosen not to use rainwater' '6 cDis = 4 - no disinfection of rainwater and/or householdwater' '7 cToilet = 6 - urine separation toilet' '8 cOST = 6 - no on-site treatment' '9 cBWT = 3 - vacuum sewerage' '10 cYWT = 3 - no separate transport of yellowwater' '11 cGWT = 2 - greyswater to blackwater, no transport' '12 cBST = 2 - no transport of blacksludge' '13 cBW1 = 4 - no treatment in bw1' '14 cBW2 = 6 - anaerobic digestion (50,000 pe)' '15 cBW3 = 3 - constructed wetland (1,000 pe)' '16 cBW4 = 7 - no treatment in bw4' '17 cGW1 = 9, SplitGW1=1 - greywater to blackwater' '18 cGW2 = 8, SplitGW1=1 - greywater to blackwater' '19 cRW1 = 4 - no treatment in rw1' '20 cBS1 = 4, vBS1=1 - no treamt. (in 0 or wwtConstraint(1,24))' '21 SplitGW1= 1 - all greywater to on-site treatment' '22 SplitR7 = 0 - no rainwater to greywater (to infiltr. or backw)' '23 SplitR8 = 1 - rainwater to infiltration' '24 SplitYW2= 0 - yellowwater to greywater'

There are 5 optima selected (or the same optimum is found several times). Enter the column number of the optima you want to see the details off: 1 Note that the details shown below are generated by loading data2003.mat and running dws2003.mdl - if you used a different data-file during optimisation the results will differ from the ones shown above!! To see the details of the other optima - start new run later (clear all and run dws2003out)

Of the 5 best domestic water systems found, this is nr.: 5: f-value: -82.592 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 x0-vector: 0 0 0 0 3 4 6 6 2 3 1 2 4 6 3 7 9 8 x-vector: 0 0 0 0 3 4 6 6 2 3 2 2 4 6 3 7 9 8 flags: 0 - 0 - - - 0 0 0 - 1 - 0 0 - 0 - 1

Appendix 4: Results (Nutrient Recycling)

Costs: (euro/cap/y) Energy: (kWh/cap/y) Space: (m2/cap)

Total: W.Cons: 1.03e+003 0.00e+000 4.74e+005 0.00e+000 1.76e+004

2. SUSTAINABILITY INDICATORS: Population: 200000 people Total water use: 36.5 (m3/cap/y) Drinking water: 36.5 (m3/cap/y) Householdwater: 0 (m3/cap/y) Rainwater: 0 (m3/cap/y) Water: 7.47e+001 8.04e+000 1.76e+004

Toilet: 4.10e+001 0.00e+000 5.00e-001

Chosen x in the x-vector: '5 cRWS = 3 - no rainwater available or chosen not to use rainwater' '6 cDis = 4 - no disinfection of rainwater and/or householdwater' '7 cToilet = 6 - urine separation toilet' '8 cOST = 6 - no on-site treatment' '9 cBWT = 3 - vacuum sewerage' '14 cBW2 = 6 - anaerobic digestion (50,000 pe)' '15 cBW3 = 3 - constructed wetland (1,000 pe)' '16 cBW4 = 7 - no treatment in bw4' '17 cGW1 = 9, SplitGW1=1 - greywater to blackwater' '18 cGW2 = 8, SplitGW1=1 - greywater to blackwater'

Disinf: 0.00e+000 0.00e+000 0.00e+000

Rainwsys: 0.00e+000 0.00e+000 0.00e+000

Based on the lower and upper boundaries found in solspace.mat the following x variables are fixed: x1 is always 0 x2 is always 1 x3 is always 1 x4 is always 1 x10 is always 3 x11 is always 2 x12 is always 2 x13 is always 4 x19 is always 3 x20 is always 3 x21 is always 0 x22 is always 1 x23 is always 0 x24 is always 1

Sustainable Wastewater Treatment

Transport: 8.40e+002 4.74e+005 0.00e+000

Treatment: 7.47e+001 -3.59e+001 1.40e+000

Appendix 4: Results (Nutrient Recycling)

Treatment total: 7.47e+001 -3.59e+001 1.40e+000

Costs: (euro/cap/y) Energy: (kWh/cap/y) Space: (m2/cap)

0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 1 0 0

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable behaviour:

2 1 0 0 0 0 1 1 1 1

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 1 0

Water:

OST: 0.00e+000 0.00e+000 0.00e+000

W.Cons:

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Total:

Sustainable Wastewater Treatment

0 0 0 0 0 0 0 0 0 0

BW1: 0.00e+000 0.00e+000 0.00e+000

1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 1 0 1

Toilet:

1 0 0 0 1 0 0 0 0 0

BW2: 7.40e+001 -3.60e+001 4.00e-002

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

Disinf:

1 0 0 1 0 0 0 1 1 0

BW3: 7.00e-001 1.50e-001 1.00e+000

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0

BW4: 0.00e+000 0.00e+000 0.00e+000

1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

GW1: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

Rainwsys: Transport: Treatment:

0 0 0 0 0 0 0 0 0 0

GW2: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

BS1: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

RW1: 0.00e+000 0.00e+000 0.00e+000

Appendix 4: Results (Nutrient Recycling)

0 0 0 0 0 0 0 0 0 0

Treatment total:

OST:

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

BW1: 0 0 1 1 0 0 0 1 0 0

BW2: 0 0 0 0 0 0 0 0 0 0

BW3: 0 0 0 0 0 0 0 0 0 0

BW4: 0 0 0 0 0 0 0 0 0 0

GW1:

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space:

2 1 0 0

7 7 7 7

0 0 0 0

0 0 0 0

3. NORMALISATION and WEIGHTING factors (note: weight can be + or -, in optimisation -1*objective is minimised) Value: Norm: Weight: In objective: Drinking water: 3.65e+001 3.76e+001 0.00e+000 0.00e+000 Householdwater: 0.00e+000 3.76e+001 0.00e+000 0.00e+000 Rainwater: 0.00e+000 1.00e+000 0.00e+000 0.00e+000 Total water use: 3.65e+001 1.09e+000 0.00e+000 0.00e+000 Costs: 1.03e+003 3.31e+006 0.00e+000 0.00e+000 Energy: 4.74e+005 4.74e+005 0.00e+000 0.00e+000 Space: 1.76e+004 2.01e+004 0.00e+000 0.00e+000

REUSE OR WASTE?: RESTRICTIONS for categorisation: Landfill: 0.00e+000 (kg/cap/y) TS: 1e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS) Soil condit.r: 2.86e+003 (kg/cap/y) TS: 5e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS) Fertiliser: 2.24e+004 (kg/cap/y) Cu: 70 Zn: 500 Ptot: 15 (mg/kg P and mg/kg total weight) Irrigation: 0.00e+000 (kg/cap/y) TSS: 2e+001 Cu: 0.2 Zn: 2 BOD: 10 FC: 1e+004 (mg/l and no/l) Domestic reuse: 0.00e+000 (kg/cap/y) TSS: 1e+001 Cu: 2 FC: 2e+004 (mg/l and no/l) Discharge: 0.00e+000 (kg/cap/y) TSS: 35 BOD: 25 Ptot: 1 Ntot: 10 (mg/l) Infiltration: 0.00e+000 (kg/cap/y) TSS: 0.5 Cu: 0.01 Zn: 0.07 Ptot: 0.4 (mg/l) CSO: 0.00e+000 (kg/cap/y) (calculated as percentage of water through combined sewer) Gas: 2.92e+000 (kg/cap/y) (calculated in models for anaerobic treatment) Waste: 2.16e+004 (kg/cap/y) (not categorised as one above)

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Sustainable Wastewater Treatment

0 0 0 0 0 0 0 0 0 0

GW2: 0 0 0 0 0 0 0 0 0 0

BS1: 0 0 0 0 0 0 0 0 0 0

RW1:

Appendix 4: Results (Nutrient Recycling)

1 0 1 0 0 0 0 1 0 0

Value: 0 0 1 1 1 1

1.00e+000 1.20e+003 2.24e+004 2.36e+004 2.48e+004 1.00e+000 2.38e+004 4.94e+004 1.00e+000 2.92e+000

7 7 7 7 7 7 7 7 7 7

Norm: 7 7 7 7 7 7

0.00e+000 5.00e+001 1.00e+002 0.00e+000 0.00e+000 0.00e+000 0.00e+000 -1.00e+002 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

Weight: 0 0 0 0 0 0

0.00e+000 1.19e+002 9.99e+001 0.00e+000 0.00e+000 0.00e+000 0.00e+000 -4.37e+001 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

In objective: 0 0 0 0 0 0

OUT-IN: TS (g/d) TSS (g/d) BOD (g/d)

0.00e+000 0.00e+000 0.00e+000

5. MASS BALANCE:

4. OBJECTIVE: Objective: 1.756e+002 (normalised and weighted sum of sus. indicators - in optimisation -1*objective is minimised)

REUSE OR WASTE?: Landfill: 0.00e+000 Soil conditioner: 2.86e+003 Fertiliser: 2.24e+004 Irrigation: 0.00e+000 Domestic reuse: 0.00e+000 Discharge: 0.00e+000 Infiltration: 0.00e+000 Waste: 2.16e+004 CSO: 0.00e+000 Gas: 2.92e+000

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Sustainable Wastewater Treatment

Appendix 4: Results (Nutrient Recycling)

Biowaste: 1.54e+007 6.17e+006 3.86e+006 6.49e+005 1.35e+005 1.89e+003 5.59e+003 0.00e+000 2.85e+007

0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

Faeces: 9.79e+006 7.83e+006 2.63e+006 4.06e+005 1.08e+005 4.70e+002 2.15e+003 3.80e+013 3.16e+007

Urine: 1.08e+007 1.43e+005 1.20e+006 2.33e+006 2.12e+005 1.31e+001 6.14e+001 0.00e+000 2.31e+008

Contaminants: DrinkW: 4.53e+007 1.50e+002 9.51e+006 0.00e+000 8.85e+006 0.00e+000 2.94e+005 1.32e-001 3.90e+005 2.70e-002 5.68e+003 0.00e+000 2.64e+004 0.00e+000 6.33e+010 0.00e+000 0.00e+000 9.98e+004

HouseHW: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

RainW: 6.12e+007 1.02e+005 0.00e+000 4.90e+003 1.02e+002 0.00e+000 0.00e+000 1.02e+011 1.01e+010 Evap.+Cons.+Infil.: 9.90e+006 1.11e+006 1.08e+006 3.61e+004 4.78e+004 5.04e+002 2.40e+001 7.00e+009 2.91e+009

Appendix 4: Results (Nutrient Recycling)

6. SOLUTION SPACE Note: solspace is loaded to print this info. DECISION VARIABLES and their BOUNDARIES: Explanation: Name: Choices: (no) (boundaries) (explanation on choices) 1 householdwater pHW =0 (0,0) (0=optimise, 1=off, 2=on) 2 rainwater (t,w,o) pRWa =2 (1,1) (0=optimise, 1=off, 2=on) 3 rainwater (k,ph) pRWb =2 (1,1) (0=optimise, 1=off, 2=on) 4 water conservation pCONS =2 (1,1) (0=optimise, 1=off, 2=on) 5 rainwater system pRWS =1,2,0 (2) (1,2) (1=small, 2=large, 3=no) 6 disinfection pDis =1,0,3,0 (2) (2,3) (1=RO, 2=sun, 3=UV, 4=no) 7 toilet pToilet =0,0,0,0,0,6,0,8,0,10 (3) (8,10) (1=old,2=new,3=int.,4=low,5=vac.,6=u.sep.,7=urinal,8=c.lf,9=c.dry,10=sol)

OUT: Biowaste: BlackW: YellowW: GreyW: RainW*: CSO: Sludge: Compost: Accuml.: TS (g/d) 0.00e+000 4.26e+005 0.00e+000 0.00e+000 6.45e+007 0.00e+000 5.20e+007 0.00e+000 4.57e+007 TSS (g/d) 0.00e+000 6.31e+003 0.00e+000 0.00e+000 6.98e+005 0.00e+000 2.04e+007 0.00e+000 1.54e+006 BOD (g/d) 0.00e+000 1.20e+004 0.00e+000 0.00e+000 1.74e+005 0.00e+000 1.14e+007 0.00e+000 3.90e+006 TN (g/d) 0.00e+000 3.92e+005 0.00e+000 0.00e+000 4.11e+004 0.00e+000 1.31e+006 0.00e+000 1.93e+006 TP (g/d) 0.00e+000 1.58e+005 0.00e+000 0.00e+000 1.25e+004 0.00e+000 3.95e+005 0.00e+000 2.37e+005 Cu (g/d) 0.00e+000 1.10e+001 0.00e+000 0.00e+000 3.16e+003 0.00e+000 4.17e+003 0.00e+000 2.09e+002 Zn (g/d) 0.00e+000 3.95e+000 0.00e+000 0.00e+000 2.63e+004 0.00e+000 7.50e+003 0.00e+000 3.91e+002 FC (g/d) 0.00e+000 0.00e+000 0.00e+000 0.00e+000 1.02e+011 0.00e+000 3.84e+012 0.00e+000 3.42e+013 H2O (g/d) 0.00e+000 1.23e+010 0.00e+000 0.00e+000 1.18e+010 0.00e+000 1.51e+009 0.00e+000 1.91e+009 Note: to the outgoing stream rainwater can be larger than the incomming rainwater stream,as added are contaminants from the roof and water with contaminants used outdoor (for garden irrigation, carwashing, etc.) is added.

IN: TS (g/d) TSS (g/d) BOD (g/d) TN (g/d) TP (g/d) Cu (g/d) Zn (g/d) FC (g/d) H2O (g/d)

TN (g/d) TP (g/d) Cu (g/d) Zn (g/d) FC (g/d) H2O (g/d)

Sustainable Wastewater Treatment

Explanation: 8 on-site treatment 9 bw transport 10 yw transport 11 gw transport 12 sludge transport 13 bw treatment 1 14 bw treatment 2 15 bw treatment 3 16 bw treatment 4 17 gw treatment 1 18 gw treatment 2 19 sludge treatment 20 storm/rainwater 21 gw direction rw direction 22 rw direction 23 rw direction 24 yw direction

Name: Choices: pOST =1,2,3,4,0,0 pBWT =0,2,0,4 pYWT =0,0,3 pGWT =0,2 pBST =0,2 pBW1 =0,0,0,4 pBW2 =1,0,0,0,5,0,0,0,0,0,11,0,0,14 pBW3 =1,0,0,4,0,0,0,8 pBW4 =0,0,0,4,0,6,7 pGW1 =1,0,0,0,0,0,0,0,9 pGW2 =0,0,0,4,0,0,7,8 pBS1 =0,0,3,0 pRW1 =0,0,3,0 pSplitGW1 =0 pSplitR =2 pSplitR7 =1 pSplitR8 =2 pSplitYW2 =1

Sustainable Wastewater Treatment (no) (boundaries) (explanation on choices) (4) (1,4) (1=AS5, 2=AD5, 3=membrane, 4=mbr, 5=septic tank, 6=no) (2) (3,4) (1=combined sewer, 2=separate, 3=vacuum, 4=no) (1) (3,3) (1=separate sewer, 2=truck, 3=none) (1) (2,2) (1=separate sewer, 2=none) (1) (2,2) (1=truck, 2=none) (1) (4,4) (1=membrane, 2=pre-sedimentation, 3=precipitation, 4=no) (4) (11,14) (1=AS50,400,1E3,1E6,5=AD400,5E5,7=FB5,50,9=mem,mbr,11=RBC5,50,400,14=no) (3) (6,8) (1=CW5, 2=CW200, 3=CW1,000, 4=TF5, 5=TF50, 6=TF400, 7=TF1000, 8=no) (3) (5,7) (1=soak away, 2=swales, 3=infil.field, 4=membrane, 5=post sed., 6=UV, 7=no) (2) (8,9) (1=MBR, 2=RBC5, 3=RBC50, 4=RBC500, 5=TF5, 6=TF50, 7=TF400, 8=TF1000, 9=no) (3) (6,8) (1=soak away, 2=swales, 3=infil.field, 4=CW5, 5=CW200, 6=CW>1000, 7=UV, 8=no) (1) (3,3) (1=AD400, 2=AD50,000, 3=composting>5, 4=no) (1) (3,3) (1=soak away, 2=swales & ditches, 3=infiltration field, 4=no) (0,0) (0=optimise, 1=to greywater treatment, 2=to on-site treatment) (0=optimise, 1=to infiltration, 2=to greywater treatment, 3=to blackwater) (1,1) (0=optimise, 1=to greywater treatment, 2=other direction,) (0,0) (0=optimise, 1=to infiltration, 2=other direction) (1,1) (0=optimise, 1=to yellowwater treatment 2=to greywater treatment)

Appendix 4: Results (Nutrient Recycling)

Number of EVALUATIONS: 6999 (based on size xtot) Finding levels in objective values... 1 The objective value f=Inf is found 5 times (column 1 to 5) 2 The objective value f=-82.59 is found 115 times (column 6 to 120) 3 The objective value f=-81.51 is found 240 times (column 121 to 360) 4 The objective value f=-7.50 is found 120 times (column 361 to 480) 5 The objective value f=-6.17 is found 120 times (column 481 to 600) 6 The objective value f=-5.70 is found 240 times (column 601 to 840) 7 The objective value f=2.39 is found 432 times (column 841 to 1272) 8 The objective value f=9.20 is found 432 times (column 1273 to 1704) 9 The objective value f=13.23 is found 120 times (column 1705 to 1824) 10 The objective value f=14.99 is found 120 times (column 1825 to 1944) 11 The objective value f=15.28 is found 168 times (column 1945 to 2112) 12 The objective value f=15.28 is found 240 times (column 2113 to 2352) 13 The objective value f=15.34 is found 24 times (column 2353 to 2376) 14 The objective value f=17.94 is found 120 times (column 2377 to 2496) 15 The objective value f=18.18 is found 240 times (column 2497 to 2736) 16 The objective value f=36.31 is found 29 times (column 2737 to 2765) 17 The objective value f=36.42 is found 240 times (column 2766 to 3005) 18 The objective value f=37.06 is found 120 times (column 3006 to 3125) 19 The objective value f=38.66 is found 60 times (column 3126 to 3185) 20 The objective value f=39.38 is found 120 times (column 3186 to 3305) 21 The objective value f=39.47 is found 240 times (column 3306 to 3545) 22 The objective value f=39.49 is found 6 times (column 3546 to 3551) 23 The objective value f=39.55 is found 6 times (column 3552 to 3557) 24 The objective value f=39.62 is found 6 times (column 3558 to 3563) 25 The objective value f=39.99 is found 12 times (column 3564 to 3575) 26 The objective value f=40.03 is found 12 times (column 3576 to 3587) 27 The objective value f=40.05 is found 12 times (column 3588 to 3599) 28 The objective value f=40.11 is found 12 times (column 3600 to 3611) 29 The objective value f=40.35 is found 12 times (column 3612 to 3623) 30 The objective value f=41.32 is found 12 times (column 3624 to 3635) 31 The objective value f=41.39 is found 12 times (column 3636 to 3647) 32 The objective value f=41.99 is found 102 times (column 3648 to 3749) 33 The objective value f=42.03 is found 12 times (column 3750 to 3761) 34 The objective value f=42.04 is found 6 times (column 3762 to 3767) 35 The objective value f=42.13 is found 12 times (column 3768 to 3779) 36 The objective value f=42.14 is found 6 times (column 3780 to 3785)

Nutrient Recycling: Output of SortResults.m for sorted results of global solver (first 6999 evaluations):

Sustainable Wastewater Treatment 37 The objective value f=42.16 is found 6 times (column 3786 to 3791) 38 The objective value f=42.23 is found 6 times (column 3792 to 3797) 39 The objective value f=42.23 is found 12 times (column 3798 to 3809) 40 The objective value f=42.26 is found 6 times (column 3810 to 3815) 41 The objective value f=42.29 is found 12 times (column 3816 to 3827) 42 The objective value f=42.35 is found 6 times (column 3828 to 3833) 43 The objective value f=42.37 is found 24 times (column 3834 to 3857) 44 The objective value f=42.37 is found 6 times (column 3858 to 3863) 45 The objective value f=42.40 is found 12 times (column 3864 to 3875) 46 The objective value f=42.44 is found 24 times (column 3876 to 3899) 47 The objective value f=42.44 is found 6 times (column 3900 to 3905) 48 The objective value f=42.47 is found 24 times (column 3906 to 3929) 49 The objective value f=42.51 is found 6 times (column 3930 to 3935) 50 The objective value f=42.56 is found 24 times (column 3936 to 3959) 51 The objective value f=42.88 is found 12 times (column 3960 to 3971) 52 The objective value f=42.92 is found 12 times (column 3972 to 3983) 53 The objective value f=42.94 is found 12 times (column 3984 to 3995) 54 The objective value f=43.00 is found 12 times (column 3996 to 4007) 55 The objective value f=44.21 is found 12 times (column 4008 to 4019) 56 The objective value f=44.28 is found 12 times (column 4020 to 4031) 57 The objective value f=44.49 is found 18 times (column 4032 to 4049) 58 The objective value f=44.61 is found 18 times (column 4050 to 4067) 59 The objective value f=44.92 is found 9 times (column 4068 to 4076) 60 The objective value f=45.02 is found 9 times (column 4077 to 4085) 61 The objective value f=45.05 is found 6 times (column 4086 to 4091) 62 The objective value f=45.12 is found 9 times (column 4092 to 4100) 63 The objective value f=45.15 is found 6 times (column 4101 to 4106) 64 The objective value f=45.24 is found 6 times (column 4107 to 4112) 65 The objective value f=45.60 is found 18 times (column 4113 to 4130) 66 The objective value f=45.75 is found 18 times (column 4131 to 4148) 67 The objective value f=45.77 is found 12 times (column 4149 to 4160) 68 The objective value f=45.88 is found 18 times (column 4161 to 4178) 69 The objective value f=50.30 is found 162 times (column 4179 to 4340) 70 The objective value f=50.32 is found 162 times (column 4341 to 4502) 71 The objective value f=52.97 is found 162 times (column 4503 to 4664) 72 The objective value f=55.64 is found 162 times (column 4665 to 4826) 73 The objective value f=55.82 is found 162 times (column 4827 to 4988) 74 The objective value f=59.84 is found 162 times (column 4989 to 5150) 75 The objective value f=59.87 is found 162 times (column 5151 to 5312) 76 The objective value f=62.66 is found 162 times (column 5313 to 5474) 77 The objective value f=65.24 is found 36 times (column 5475 to 5510)

Appendix 4: Results (Nutrient Recycling)

78 The objective value f=65.49 is found 162 times (column 5511 to 5672) 79 The objective value f=65.71 is found 162 times (column 5673 to 5834) 80 The objective value f=65.73 is found 108 times (column 5835 to 5942) 81 The objective value f=65.90 is found 54 times (column 5943 to 5996) 82 The objective value f=68.31 is found 94 times (column 5997 to 6090) 83 The objective value f=68.50 is found 54 times (column 6091 to 6144) 84 The objective value f=88.84 is found 54 times (column 6145 to 6198) 85 The objective value f=89.95 is found 9 times (column 6199 to 6207) 86 The objective value f=89.96 is found 9 times (column 6208 to 6216) 87 The objective value f=90.05 is found 9 times (column 6217 to 6225) 88 The objective value f=90.06 is found 9 times (column 6226 to 6234) 89 The objective value f=90.07 is found 84 times (column 6235 to 6318) 90 The objective value f=90.14 is found 9 times (column 6319 to 6327) 91 The objective value f=90.16 is found 9 times (column 6328 to 6336) 92 The objective value f=90.28 is found 2 times (column 6337 to 6338) 93 The objective value f=90.35 is found 2 times (column 6339 to 6340) 94 The objective value f=90.38 is found 2 times (column 6341 to 6342) 95 The objective value f=90.47 is found 2 times (column 6343 to 6344) 96 The objective value f=90.63 is found 1 times (column 6345 to 6345) 97 The objective value f=91.38 is found 54 times (column 6346 to 6399) 98 The objective value f=91.89 is found 1 times (column 6400 to 6400) 99 The objective value f=91.97 is found 1 times (column 6401 to 6401) 100 The objective value f=92.10 is found 94 times (column 6402 to 6495) 101 The objective value f=92.40 is found 2 times (column 6496 to 6497) 102 The objective value f=92.46 is found 28 times (column 6498 to 6525) 103 The objective value f=92.52 is found 2 times (column 6526 to 6527) 104 The objective value f=92.61 is found 7 times (column 6528 to 6534) 105 The objective value f=92.71 is found 7 times (column 6535 to 6541) 106 The objective value f=92.80 is found 6 times (column 6542 to 6547) 107 The objective value f=93.51 is found 14 times (column 6548 to 6561) 108 The objective value f=93.53 is found 18 times (column 6562 to 6579) 109 The objective value f=93.66 is found 10 times (column 6580 to 6589) 110 The objective value f=93.67 is found 6 times (column 6590 to 6595) 111 The objective value f=93.68 is found 168 times (column 6596 to 6763) 112 The objective value f=93.80 is found 10 times (column 6764 to 6773) 113 The objective value f=93.81 is found 6 times (column 6774 to 6779) 114 The objective value f=93.98 is found 3 times (column 6780 to 6782) 115 The objective value f=94.00 is found 3 times (column 6783 to 6785) 116 The objective value f=94.08 is found 3 times (column 6786 to 6788) 117 The objective value f=94.10 is found 3 times (column 6789 to 6791) 118 The objective value f=94.10 is found 84 times (column 6792 to 6875)

Sustainable Wastewater Treatment 119 The objective value f=94.17 is found 3 times (column 6876 to 6878) 120 The objective value f=94.19 is found 3 times (column 6879 to 6881) 121 The objective value f=94.66 is found 1 times (column 6882 to 6882) 122 The objective value f=94.70 is found 1 times (column 6883 to 6883) 123 The objective value f=94.72 is found 1 times (column 6884 to 6884) 124 The objective value f=94.78 is found 1 times (column 6885 to 6885) 125 The objective value f=94.89 is found 5 times (column 6886 to 6890) 126 The objective value f=95.29 is found 3 times (column 6891 to 6893) 127 The objective value f=95.39 is found 3 times (column 6894 to 6896) 128 The objective value f=95.46 is found 3 times (column 6897 to 6899) 129 The objective value f=95.48 is found 3 times (column 6900 to 6902) 130 The objective value f=95.56 is found 3 times (column 6903 to 6905) 131 The objective value f=95.62 is found 2 times (column 6906 to 6907) 132 The objective value f=95.66 is found 3 times (column 6908 to 6910) 133 The objective value f=95.69 is found 2 times (column 6911 to 6912) 134 The objective value f=95.72 is found 2 times (column 6913 to 6914) 135 The objective value f=95.81 is found 2 times (column 6915 to 6916) 136 The objective value f=95.99 is found 1 times (column 6917 to 6917) 137 The objective value f=96.06 is found 1 times (column 6918 to 6918) 138 The objective value f=96.17 is found 6 times (column 6919 to 6924) 139 The objective value f=96.32 is found 6 times (column 6925 to 6930) 140 The objective value f=96.46 is found 6 times (column 6931 to 6936) 141 The objective value f=96.70 is found 3 times (column 6937 to 6939) 142 The objective value f=96.80 is found 3 times (column 6940 to 6942) 143 The objective value f=96.90 is found 3 times (column 6943 to 6945) 144 The objective value f=97.75 is found 2 times (column 6946 to 6947) 145 The objective value f=97.86 is found 2 times (column 6948 to 6949) 146 The objective value f=98.85 is found 10 times (column 6950 to 6959) 147 The objective value f=99.00 is found 2 times (column 6960 to 6961) 148 The objective value f=99.03 is found 6 times (column 6962 to 6967) 149 The objective value f=99.14 is found 2 times (column 6968 to 6969) 150 The objective value f=99.17 is found 6 times (column 6970 to 6975) 151 The objective value f=99.31 is found 6 times (column 6976 to 6981) 152 The objective value f=99.45 is found 3 times (column 6982 to 6984) 153 The objective value f=99.55 is found 3 times (column 6985 to 6987) 154 The objective value f=99.64 is found 3 times (column 6988 to 6990) 155 The objective value f=99.66 is found 3 times (column 6991 to 6993) 156 The objective value f=99.76 is found 3 times (column 6994 to 6996) 157 The objective value f=99.85 is found 3 times (column 6997 to 6999)

Appendix 4: Results (Nutrient Recycling)

3 4 6 6 2 3

This range contains 431 different combination of the x variables. The values of the x-variables within this best level are: x1 is always 0 x2 is always 1 x3 is always 1 x4 is always 1 x5 within the range (841 to 1272 datapoints) has the values: 3 x6 within the range (841 to 1272 datapoints) has the values: 4 x7 within the range (841 to 1272 datapoints) has the values: 8 x8 within the range (841 to 1272 datapoints) has the values: 6 x9 within the range (841 to 1272 datapoints) has the values: 4 x10 is always 3 x11 is always 2

Do you want to see x-plots of a range? (type 0 for yes) 0 Give the starting point: 841 Give the ending point: 1272

This m-file offers 4 types of plots, namely: (1) full plot or smaller plot of best data (2) plot of best levels (3) plot of certain range of data (4) plot of f versus x You can see all these plots if you want to. When choosing plot range, the values of the x-variables and the min-max-values of the sustainability indicators in this range are also shown.

The values of the x-variables within this best level are: x2 is always 1 x3 is always 1 x4 is always 1 x5 within the best level (1 to 5 datapoints) has the values: x6 within the best level (1 to 5 datapoints) has the values: x7 within the best level (1 to 5 datapoints) has the values: x8 within the best level (1 to 5 datapoints) has the values: x9 within the best level (1 to 5 datapoints) has the values: x10 is always 3 x11 is always 2 x12 is always 2 x13 is always 4

Sustainable Wastewater Treatment

ADVANTAGES: Acceptance: Adaptability: Expertise:

1 2 1

1 2 1

The values of the sustainability indicators within this range: Min: Max: Drinking water: 3.76e+001 3.76e+001 Householdwater: 0.00e+000 0.00e+000 Rainwater: 0.00e+000 0.00e+000 Total water use: 3.76e+001 3.76e+001 Costs: 9.91e+005 9.93e+005 Energy: 6.00e+003 6.00e+003 Space: 1.81e+004 1.81e+004

x1 is always 0 x14 within the best level (1 to 5 datapoints) has the values: x15 within the best level (1 to 5 datapoints) has the values: x16 within the best level (1 to 5 datapoints) has the values: x17 within the best level (1 to 5 datapoints) has the values: x18 within the best level (1 to 5 datapoints) has the values: x19 is always 3 x20 is always 3 x21 is always 0 x22 is always 1 x23 is always 0 x24 is always 1 The sorted results are saved in Sorted.mat x12 is always 2 x13 is always 4 x14 within the range (841 to 1272 datapoints) has the values: x15 within the range (841 to 1272 datapoints) has the values: x16 within the range (841 to 1272 datapoints) has the values: x17 within the range (841 to 1272 datapoints) has the values: x18 within the range (841 to 1272 datapoints) has the values: x19 is always 3 x20 is always 3 x21 is always 0 x22 is always 1 x23 is always 0 x24 is always 1 14 8 7 1 5

6 3 7 9 8

Appendix 4: Results (Nutrient Recycling)

1 0 0 0 0 0 0 0 0 0

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

0.00e+000 8.26e+002 1.52e+003 0.00e+000 0.00e+000 0.00e+000 2.25e+004 2.16e+004 0.00e+000 0.00e+000

1 0 0 0 0 0 0 0 0 0

Max: 1 1 1 1 1 2 1

The sorted range of the results and information on the levels of the objective value are saved in SortedRange.mat

REUSE OR WASTE?: Landfill: 0.00e+000 Soil conditioner: 8.26e+002 Fertiliser: 1.52e+003 Irrigation: 0.00e+000 Domestic reuse: 0.00e+000 Discharge: 0.00e+000 Infiltration: 2.25e+004 Waste: 2.16e+004 CSO: 0.00e+000 Gas: 0.00e+000

Min: 1 1 1 1 1 2 1

Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Sustainable Wastewater Treatment

Appendix 4: Results (Nutrient Recycling)

3 0 0 -

3 0 0 -

Of the 5 best domestic water systems found, this is nr.: 3: f-value: 47.217 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1 x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1 flags: 0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1 1 - -

Of the 5 best domestic water systems found, this is nr.: 4: f-value: 47.217 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 flags: 0 - 0 - - - 1 1 0 - 0 - 0 1 - 0 - 0 1 0 4 4 1

1 2 0 1 0 1 - -

3 0 0 -

1 2 0 1 0 1 - -

Of the 5 best domestic water systems found, this is nr.: 2: f-value: 47.217 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 flags: 0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1 0 4 4 1

3 0 0 -

Number of EVALUATIONS: 6999 (based on size xtot) Of the 5 best domestic water systems found, this is nr.: 1: f-value: 47.217 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1 x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1 flags: 0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1 1 - -

>> clear all >> load data2003_water >> save data2003 >> clear all >> load solspace_ls1 >> save solspace >> clear all >> load Sorted_gcl_ls1 >> dws2003out

4 1 (original choices) 1 (corrected for flags) -

4 1 (original choices) 1 (corrected for flags) -

4 1 (original choices) 1 (corrected for flags) -

4 1 (original choices) 1 (corrected for flags) -

Life support Systems Output of dws2003out.m for sorted results of global solver (first 6999 evaluations):

Sustainable Wastewater Treatment

0 4 4 1

1 2 0 1 0 1 - -

3 0 0 -

4 1 (original choices) 1 (corrected for flags) -

1. SELECTED DOMESTIC WATER SYSTEM: Explaining x-vector: '1 cHW = 0 - no household water use' '2 cRWa = 1 - rainwater use for toilet, wash.machine & outdoor' '3 cRWb = 1 - rainwater use in kitchen and for personal hygiene' '4 cCON = 1 - use of water conserving shower and washing machine' '5 cRWS = 2 - large advanced rainwatersystem in use' '6 cDis = 3 - disinfection by small scale UV-disinfection (k only)' '7 cToilet = 9 - composting toilet dry (urine sepration possible)' '8 cOST = 6 - no on-site treatment' '9 cBWT = 4 - no blackwater no transport' '10 cYWT = 3 - no separate transport of yellowwater' '11 cGWT = 2 - no separate transport of greywater' '12 cBST = 2 - no transport of blacksludge' '13 cBW1 = 4 - vBW1=1 - no blackwater no treat.' '14 cBW2 = 14, vBW2=1 - no blackwater no treat.' '15 cBW3 = 8, vBW3=1 - no blackwater no transport' '16 cBW4 = 7, vBW4=1 - no blackwater no transport' '17 cGW1 = 1 - membrane bioreactor' '18 cGW2 = 8 - no treatment in gw2' '19 cRW1 = 4 - no treatment in rw1' '20 cBS1 = 4, vBS1=1 - no treamt. (in 0 or wwtConstraint(1,24))' '21 SplitGW1= 0 - dry toilet thus no blackwater line' '22 SplitR7 = 1 - rainwater to greywater transport' '23 SplitR8 = 0 - no rainwater to infiltration (to greyw or blackw)' '24 SplitYW2= 1 - yellowwater to yellowwater treatment'

There are 5 optima selected (or the same optimum is found several times). Enter the column number of the optima you want to see the details off: 1 Note that the details shown below are generated by loading data2003.mat and running dws2003.mdl if you used a different data-file during optimisation the results will differ from the ones shown above!! To see the details of the other optima - start new run later (clear all and run dws2003out)

Of the 5 best domestic water systems found, this is nr.: 5: f-value: 47.217 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 flags: 0 - 0 - - - 1 1 0 - 0 - 0 0 - 0 - 0 1

Appendix 4: Results (Life Support Systems)

Costs: (euro/cap/y) Energy: (kWh/cap/y) Space: (m2/cap)

Total: W.Cons: Water: 4.20e+005 0.00e+000 3.80e+001 3.83e+007 -2.00e+001 1.13e+000 2.49e+003 2.48e+003

2. SUSTAINABILITY INDICATORS: Population: 200000 people Total water use: 23.8 (m3/cap/y) Drinking water: 5.15 (m3/cap/y) Householdwater: 0 (m3/cap/y) Rainwater: 18.6 (m3/cap/y) Toilet: 1.75e+002 2.50e+001 3.00e-000

Chosen x in the x-vector: '5 cRWS = 2 - large advanced rainwatersystem in use' '6 cDis = 3 - disinfection by small scale UV-disinfection (k only)' '7 cToilet = 9 - composting toilet dry (urine sepration possible)' '8 cOST = 6 - no on-site treatment' '9 cBWT = 4 - no blackwater no transport' '14 cBW2 = 14, vBW2=1 - no blackwater no treat.' '15 cBW3 = 8, vBW3=1 - no blackwater no transport' '16 cBW4 = 7, vBW4=1 - no blackwater no transport' '17 cGW1 = 1 - membrane bioreactor' '18 cGW2 = 8 - no treatment in gw2'

Disinf: 1.77e+005 2.28e+001 0.00e+000

Rainwsys: 1.48e+002 3.83e+004 1.50e+000

Based on the lower and upper boundaries found in solspace.mat the following x variables are fixed: x1 is always 0 x2 is always 1 x3 is always 1 x4 is always 1 x10 is always 3 x11 is always 2 x12 is always 2 x13 is always 4 x19 is always 3 x20 is always 3 x21 is always 0 x22 is always 1 x23 is always 0 x24 is always 1

Sustainable Wastewater Treatment

Transport: 0.00e+000 0.00e+000 0.00e+000

Treatment: 2.43e+005 1.30e+003 3.67e+001

Appendix 4: Results (Life Support Systems)

Treatment total: 2.43e+005 1.30e+003 3.67e+001

Costs: (euro/cap/y) Energy: (kWh/cap/y) Space: (m2/cap)

0 0 0 0 0 0 0 0 0 0

1 0 0 0 2 1 0 0 0 0

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable behaviour:

3 1 0 0 0 0 3 2 2 3

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 1 0

Water:

OST: 0.00e+000 0.00e+000 0.00e+000

W.Cons:

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Total:

Sustainable Wastewater Treatment

0 0 0 0 0 0 0 0 0 0

BW1: 0.00e+000 0.00e+000 0.00e+000

1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 1 1 0 1

Toilet:

0 0 0 0 0 0 0 0 0 0

BW2: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 1 0 0 0 0 0

1 0 0 0 0 0 1 1 0 1

Disinf:

0 0 0 0 0 0 0 0 0 0

BW3: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

BW4: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 1 0

GW1: 2.41e+005 1.30e+003 2.00e-003

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

Rainwsys: Transport: Treatment:

0 0 0 0 0 0 0 0 0 0

GW2: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

BS1: 0.00e+000 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

RW1: 0.00e+000 0.00e+000 0.00e+000

Appendix 4: Results (Life Support Systems)

0 0 0 0 0 0 0 0 0 0

Treatment total:

0 0 0 0 0 0 0 0 0 0

OST: 0 0 0 0 0 0 0 0 0 0

BW1: 0 0 0 0 0 0 0 0 0 0

BW2: 0 0 0 0 0 0 0 0 0 0

BW3: 0 0 0 0 0 0 0 0 0 0

BW4: 0 0 0 1 0 0 0 1 0 0

GW1:

3. NORMALISATION and WEIGHTING factors (note: weight can be + or -, in optimisation -1*objective is minimised) Value: Norm: Weight: In objective: Drinking water: 5.15e+000 8.07e+000 -1.00e+002 -6.38e+001 Householdwater: 0.00e+000 1.00e+000 0.00e+000 0.00e+000 Rainwater: 1.86e+001 1.86e+001 0.00e+000 0.00e+000 Total water use: 2.38e+001 2.92e+000 0.00e+000 0.00e+000 Costs: 4.20e+005 2.94e+006 0.00e+000 0.00e+000 Energy: 3.96e+004 4.86e+007 0.00e+000 0.00e+000 Space: 2.49e+003 2.35e+004 0.00e+000 0.00e+000

REUSE OR WASTE?: RESTRICTIONS for categorisation: Landfill: 0.00e+000 (kg/cap/y) TS: 1e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS) Soil condit.: 6.94e+001 (kg/cap/y) TS: 5e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS) Fertiliser: 3.53e+002 (kg/cap/y) Cu: 70 Zn: 500 Ptot: 15 (mg/kg P and mg/kg total weight) Irrigation: 0.00e+000 (kg/cap/y) TSS: 2e+001 Cu: 0.2 Zn: 2 BOD: 10 FC: 1e+004 (mg/l and no/l) Domestic reuse 6.06e+003 (kg/cap/y) TSS: 1e+001 Cu: 2 FC: 2e+004 (mg/l and no/l) Discharge: 0.00e+000 (kg/cap/y) TSS: 35 BOD: 25 Ptot: 1 Ntot: 10 (mg/l) Infiltration: 0.00e+000 (kg/cap/y) TSS: 0.5 Cu: 0.01 Zn: 0.07 Ptot: 0.4 (mg/l) CSO: 0.00e+000 (kg/cap/y) (calculated as percentage of water through combined sewer) Gas: 0.00e+000 (kg/cap/y) (calculated in models for anaerobic treatment) Waste: 2.99e+003 (kg/cap/y) (not categorised as one above)

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Sustainable Wastewater Treatment

0 0 0 0 0 0 0 0 0 0

GW2: 0 0 0 0 0 0 0 0 0 0

BS1: 0 0 0 0 0 0 0 0 0 0

RW1:

Appendix 4: Results (Life Support Systems)

1 0 0 0 2 1 0 0 0 0

DISADVANTAGES Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

1.00e+000 2.68e+003 6.47e+002 1.00e+000 2.22e+004 1.00e+000 1.00e+000 2.81e+004 7.02e+002 4.38e+000

5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5

Norm:

0.00e+000 0.00e+000 0.00e+000 1.00e+002 1.00e+002 -1.00e+002 0.00e+000 -1.00e+002 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

Weight:

0.00e+000 0.00e+000 0.00e+000 0.00e+000 2.72e+001 0.00e+000 0.00e+000 -1.06e+001 0.00e+000 0.00e+000

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

In objective:

4. OBJECTIVE: Objective: -4.722e+001 (normalised and weighted sum of sus. indicators - in optimisation -1*objective is minimised)

REUSE OR WASTE?: Landfill: 0.00e+000 Soil conditioner: 1.19e+002 Fertiliser: 3.53e+002 Irrigation: 0.00e+000 Domestic reuse: 6.06e+003 Discharge: 0.00e+000 Infiltration: 0.00e+000 Waste: 2.99e+003 CSO: 0.00e+000 Gas: 0.00e+000

3 1 0 0 0 0 3 2 2 3

ADVANTAGES: Acceptance: Adaptability: Expertise: Quality of space: Institutional req.: Maintenance: Participation: Reliability: Robustness: Sustainable beh.:

Value:

Sustainable Wastewater Treatment

Appendix 4: Results (Life Support Systems)

Biowaste: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

OUT: TS (g/d) TSS (g/d) BOD (g/d) TN (g/d) TP (g/d) Cu (g/d) Zn (g/d) FC (g/d) H2O (g/d)

BlackW: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

Faeces: 9.79e+006 7.83e+006 2.63e+006 4.06e+005 1.08e+005 4.70e+002 Faeces: 2.15e+003 3.80e+013 3.16e+007

YellowW: 8.66e+006 1.14e+005 9.63e+005 1.87e+006 1.69e+005 1.05e+001 4.91e+001 0.00e+000 1.85e+008

Urine: 1.08e+007 1.43e+005 1.20e+006 2.33e+006 2.12e+005 1.31e+001 Urine: 6.14e+001 0.00e+000 2.31e+008

GreyW: 5.21e+005 0.00e+000 9.13e+004 2.87e+004 7.46e+004 0.00e+000 0.00e+000 0.00e+000 3.32e+009

RainW*: 3.34e+006 1.78e+005 1.72e+005 8.16e+003 8.44e+003 0.00e+000 0.00e+000 0.00e+000 1.63e+009

Contaminants: DrinkW: 4.53e+007 1.50e+002 9.51e+006 0.00e+000 8.85e+006 0.00e+000 2.94e+005 1.32e-001 3.90e+005 2.70e-002 5.68e+003 0.00e+000 Contaminants: DrinkW: 2.64e+004 0.00e+000 6.33e+010 0.00e+000 0.00e+000 9.98e+004

CSO: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

HouseHW: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 HouseHW: 0.00e+000 0.00e+000 0.00e+000

Sludge: 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

RainW: 6.12e+007 1.02e+005 0.00e+000 4.90e+003 1.02e+002 0.00e+000 RainW: 0.00e+000 1.02e+011 1.01e+010

Compost: 2.47e+007 1.40e+007 6.73e+006 1.52e+006 2.85e+005 2.36e+003 7.75e+003 1.90e+012 1.06e+007

Accuml.: 5.16e+007 6.38e+006 6.00e+006 1.62e+005 1.92e+005 0.00e+000 0.00e+000 3.62e+013 2.70e+008

Note: to the outgoing stream rainwater can be larger than the incomming rainwater stream,as added are contaminants from the roof and water with ontaminants used outdoor (for garden irrigation, carwashing, etc.) is added.

Biowaste: 1.54e+007 6.17e+006 3.86e+006 6.49e+005 1.35e+005 1.89e+003 Biowaste: 5.59e+003 0.00e+000 2.85e+007

0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000 0.00e+000

IN: TS (g/d) TSS (g/d) BOD (g/d) TN (g/d) TP (g/d) Cu (g/d) IN: Zn (g/d) FC (g/d) H2O (g/d)

OUT-IN: TS (g/d) TSS (g/d) BOD (g/d) TN (g/d) TP (g/d) Cu (g/d) Zn (g/d) FC (g/d) H2O (g/d)

5. MASS BALANCE:

Sustainable Wastewater Treatment

Evap.+Cons.+Infil.: 8.10e+006 1.13e+006 1.08e+006 3.38e+004 4.72e+004 0.00e+000 0.00e+000 8.35e+009 5.60e+008

Appendix 4: Results (Life Support Systems)

Explanation: 10 yw transport 11 gw transport 12 sludge transport 13 bw treatment 1 14 bw treatment 2 15 bw treatment 3 16 bw treatment 4 17 gw treatment 1 18 gw treatment 2 19 sludge treatment 20 storm/rainwater Explanation: 21 gw direction rw direction 22 rw direction 23 rw direction 24 yw direction

Name: Choices: pYWT =0,0,3 pGWT =0,2 pBST =0,2 pBW1 =0,0,0,4 pBW2 =1,0,0,0,5,0,0,0,0,0,11,0,0,14 pBW3 =1,0,0,4,0,0,0,8 pBW4 =0,0,0,4,0,6,7 pGW1 =1,0,0,0,0,0,0,0,9 pGW2 =0,0,0,4,0,0,7,8 pBS1 =0,0,3,0 pRW1 =0,0,3,0 Name: Choices: pSplitGW1 =0 pSplitR =2 pSplitR7 =1 pSplitR8 =2 pSplitYW2 =1

DECISION VARIABLES and their BOUNDARIES: Explanation: Name: Choices: 1 householdwater pHW =0 2 rainwater (t,w,o) pRWa =2 3 rainwater (k,ph) pRWb =2 4 water conservation pCONS =2 5 rainwater system pRWS =1,2,0 6 disinfection pDis =1,0,3,0 7 toilet pToilet =0,0,0,0,0,6,0,8,0,10 8 on-site treatment pOST =1,2,3,4,0,0 9 bw transport pBWT =0,2,0,4 (no) (1) (1) (1) (1) (4) (3) (3) (2) (3) (1) (1) (no)

(2) (2) (3) (4) (2)

(no)

6. SOLUTION SPACE Note: solspace is loaded to print this info.

Sustainable Wastewater Treatment

(boundaries) (explanation on choices) (3,3) (1=separate sewer, 2=truck, 3=none) (2,2) (1=separate sewer, 2=none) (2,2) (1=truck, 2=none) (4,4) (1=membrane, 2=pre-sedimentation, 3=precipitation, 4=no) (11,14) (1=AS50,400,1E3,1E6,5=AD400,5E5,7=FB5,50,9=mem,mbr,11=RBC5,50,400,14=no) (6,8) (1=CW5, 2=CW200, 3=CW1,000, 4=TF5, 5=TF50, 6=TF400, 7=TF1000, 8=no) (5,7) (1=soak away, 2=swales, 3=infil.field, 4=membrane, 5=post sed., 6=UV, 7=no) (8,9) (1=MBR, 2=RBC5, 3=RBC50, 4=RBC500, 5=TF5, 6=TF50, 7=TF400, 8=TF1000, 9=no) (6,8) (1=soak away, 2=swales, 3=infil.field, 4=CW5, 5=CW200, 6=CW>1000, 7=UV, 8=no) (3,3) (1=AD400, 2=AD50,000, 3=composting>5, 4=no) (3,3) (1=soak away, 2=swales & ditches, 3=infiltration field, 4=no) (boundaries) (explanation on choices) (0,0) (0=optimise, 1=to greywater treatment, 2=to on-site treatment) (0=optimise, 1=to infiltration, 2=to greywater treatment, 3=to blackwater) (1,1) (0=optimise, 1=to greywater treatment, 2=other direction,) (0,0) (0=optimise, 1=to infiltration, 2=other direction) (1,1) (0=optimise, 1=to yellowwater treatment 2=to greywater treatment)

(boundaries) (explanation on choices) (0,0) (0=optimise, 1=off, 2=on) (1,1) (0=optimise, 1=off, 2=on) (1,1) (0=optimise, 1=off, 2=on) (1,1) (0=optimise, 1=off, 2=on) (1,2) (1=small, 2=large, 3=no) (2,3) (1=RO, 2=sun, 3=UV, 4=no) (8,10) (1=old,2=new,3=int.,4=low,5=vac.,6=u.sep.,7=urinal,8=c.lf,9=c.dry,10=sol) (1,4) (1=AS5, 2=AD5, 3=membrane, 4=mbr, 5=septic tank, 6=no) (3,4) (1=combined sewer, 2=separate, 3=vacuum, 4=no)

Appendix 4: Results (Life Support Systems)

Sustainable Wastewater Treatment

Curriculum Vitae

Curriculum Vitae After graduating from high school and I chose to study Environmental Technology, at the section Chemical Engineering of the Higher Technical School in Eindhoven (1987-1992). While studying environmental engineering I got more and more interested in sustainable development. Therefore, I decided to continue my studies at the Technology Management Faculty of the Technical University Eindhoven where I chose International Technological Development Studies (1992-1996). My MSc-project, "Industrial wastewater in Dar es Salaam, the Vingunguti Waste Stabilisation Ponds", gave me the opportunity to live and work in Tanzania. The different dynamics of this society and the different culture gave me a broader view on life and sustainable development. After my study, I participated in the inspiring workshop “Sustainable municipal wastewater treatment" which was organised by the consultancies ETC and Waste. One of the conclusions of the workshop was that it would be interesting to make a comparison on treatment technologies on many different aspects of sustainability. I got the opportunity to make such a comparison within the framework of this PhD-project, which was original titled "Small scale sustainable wastewater treatment" and was a joint project of research groups Systems and Control at the faculty of Applied Physics and Environment and Energy at the faculty of Technology Management (1997-2003). Besides the research I enjoyed teaching, supervising MSc-students, and participating in international conferences and the working groups. In a next occupation, I would like to contribute to the implementation of sustainable water systems. Annelies van der Vleuten-Balkma Eindhoven, September 2003

Sustainable Wastewater Treatment

Samenvatting

Samenvatting De schatting van de Wereld Gezondheidsorganisatie is dat anno 2003 ongeveer 18% van de wereldbevolking geen toegang heeft tot veilig drinkwater en dat ongeveer 40% niet is voorzien van hygiënische sanitatie. Dit heeft grote gevolgen voor de gezondheid en de kwaliteit van het leven van deze mensen en is levensbedreigend voor kleine kinderen. Het streven, vastgelegd in een doelstelling van de Verenigde Naties, is dan ook om in 2025 de totale wereld bevolking te voorzien van veilig water en hygiënische sanitatie. Voor het vervullen van deze services kan gekozen worden uit een grote variatie aan technologieën. Toch blijkt dat de systemen die nu in de Westerse landen in gebruik zijn geen integrale oplossing bieden, zo worden we nu geconfronteerd met problemen als eutrofiëring, verdroging, en zware metalen in het zuiveringsslib. En dreigen er meer complexe problemen als verminderde vruchtbaarheid door sporen van chemische stoffen en medicijnen in het afvalwater en tekorten aan nutriënten in de landbouw door het ontregelen van de stikstof en fosfaat kringlopen. Hoe kunnen deze problemen het hoofd geboden worden? Cruciaal voor het vinden van een oplossing is wellicht dat we ons moeten realiseren dat de problemen zo complex zijn dat er niet één beste oplossing is maar er gezocht moet worden naar een verzameling van oplossingen. Deze oplossingen moeten worden geformuleerd met een mondiale en lange termijn visie zodat de oplossingen geen nieuwe problemen initiëren. In andere woorden, het is van belang duurzame oplossingen te vinden die een weloverwogen balans bieden met betrekking tot economische, ecologische, en sociaal-culturele kosten, zodat de bijdrage aan lokale en mondiale problemen wordt geminimaliseerd of in ieder geval bekend is en kan worden opgevangen. Om inzicht te krijgen in de duurzaamheid van de verschillende huishoudelijke water systemen, watergebruik en afvalwaterzuivering omvattend, hebben wij een duurzaamheidanalyse ontwikkeld die is samengesteld uit bestaande methoden als kostenbatenanalyse, levenscyclusanalyse, en sociale analyses. De belangrijkste kenmerken die deze analyse als geheel meerwaarde geven zijn; de brede visie, de set van indicatoren, de ontwerpgerichte methode van modelleren die is gebaseerd op een superstructuur, en de multiobjective optimalisatie. Deze duurzaamheidanalyse hebben we geïmplementeerd in een beslissingsondersteunend computermodel voor de selectie van duurzame huishoudelijke water systemen. De drie belangrijkste onderdelen van de analyse zijn: (1) Duurzaamheidindicatoren: Gebaseerd op de verschillende dimensies van duurzaamheid is een set van indicatoren samengesteld die bestaat uit economische, ecologische en sociaalculturele indicatoren. Daarbij zijn functionele indicatoren opgenomen die technologie eigenschappen zoals bijvoorbeeld robuustheid en onderhoud omvatten. De totale set bestaat uit 27 indicatoren waarvan sommige directe uitkomsten van de massabalans zijn, bijvoorbeeld regenwatergebruik, terwijl andere worden gecategoriseerd op basis van restricties, bijvoorbeeld gezuiverd afvalwater met een kwaliteit die zo goed is dat het water in het huishouden kan worden hergebruikt. Weer andere indicatoren worden niet berekend maar worden meegenomen als kwalitatieve indicatoren die potentiële voor en nadelen van een technologie aangeven, bijvoorbeeld de sociaal-culturele acceptatie van verschillende toiletten.

Sustainable Wastewater Treatment

Samenvatting

(2) Model: De duurzaamheidindicatoren worden berekend in een model dat de massabalans van huishoudelijke watersystemen weergeeft. Dit model is geconstrueerd aan de hand van een superstructuur die is opgebouwd uit de massabalansen van vele verschillende systemen voor watervoorziening (drinkwater, huishoudwater, en regenwater), water disinfectie, watergebruik, en verschillende stappen van afvalwaterbehandeling variërend in schaalgrote van kleinschalige systemen per huishouden tot grootschalige systemen waarmee hele stadsdelen kunnen worden voorzien. De model structuur omvat 37 eenvoudige modellen van 13 verschillende technologieën. De optimalisatie selecteert een aantal technologieën die samen een compleet huishoudelijk watersysteem vormen. (3) Optimalisatie De selectie van duurzame systemen is gedefinieerd als een multi-objective integer optimalisatie probleem. Verschillende objectives voor duurzame watersystemen zijn bijvoorbeeld: het minimaliseren van kosten, watergebruik, energiegebruik, en ruimtegebruik, en het maximaliseren van waardevolle producten als schoon water, nutriënten en biogas en tevens het maximaliseren van de sociaal-culturele inpassing door passende organisatie en wetgeving en groot gebruikersgemak. Om deze soms tegenstrijdige doelstellingen te combineren in één doelstelling voor de optimalisatie moeten de indicatoren die de verschillende doelstellingen kwantificeren worden genormaliseerd en gewogen zodat deze kunnen worden opgeteld. Voor de optimalisering van huishoudelijke water systemen voor verschillende scenario's hebben we verschillende oplossingsmethoden toegepast. Het was moeilijk een oplossingsmethode te vinden die in een redelijk rekentijd het globale optimum kan bepalen dit komt door het grote aantal mogelijkheden in het model, er kunnen ongeveer 7*1012 verschillende systemen worden doorgerekend, en door de discrete verandering van de duurzaamheidindicatoren. Daarom is er voor gekozen voor de definitie van een kleinere oplossingsruimte waarin technologieën worden geanalyseerd die speciaal voor het gekozen scenario interessant zijn. Om duurzame huishoudelijke watersystemen te selecteren die kunnen bijdragen aan het oplossen van de eerder genoemde waterproblematiek, zijn twee scenario's doorgerekend met de beslissingsondersteunende software, namelijk: (1) het recyclen van nutriënten naar de landbouw en het voorkomen van eutrofiering en (2) het minimaliseren van drinkwatergebruik en het maximaliseren van waterrecycling. Voor beide scenario’s is vervolgens gezocht naar betaalbare systemen omdat armoede een belangrijke rol speelt in de water problematiek. Gebaseerd op de keuzes die worden gemaakt door de beslissingsondersteunende software kunnen we concluderen dat technologieën die vandaag de dag niet veelvuldig worden toegepast, zoals urine separatie en compost toiletten, membraan bioreactoren en regenwater systemen belangrijke componenten kunnen zijn in toekomstige water systemen die zich richten op de recycling van water en nutriënten. De meer conventionele systemen worden door ons niet gevonden omdat wij ons richten op recycling waar veel andere onderzoekers zich richten op de optimalisatie van bestaande systemen. Wat ook een rol kan spelen is dat technologieën voor slibverwerking in het model beperkt zijn. Daarom moeten we stellen dat als schoon slib geproduceerd kan worden of als slib op alternatieve wijze kan worden hergebruikt dat dan meer conventionele oplossingen wellicht ook veelbelovend kunnen zijn. Maar afgaande op de resultaten van de huidige berekeningen, komen wij tot de conclusie dat,

Sustainable Wastewater Treatment

Samenvatting

als toekomstige huishoudelijke watersystemen zich gaan richten op de recycling van water en nutriënten, conventionele systemen zullen moeten worden vervangen door alternatieven die in staat zijn afvalwaterstromen aan de bron te scheiden en geschikt te maken voor hergebruik. Hergebruik van water zal kleinschalige decentrale systemen introduceren voor regenwatergebruik en grijswater-hergebuik en wellicht ook waterzuinige of zelfs droge toiletten en de disinfectie van drinkwater in het huishouden. Voor het recyclen van nutriënten is decentralisatie minder noodzakelijk maar systemen als urine separatie en waterzuinige toiletten zullen dit wel bevorderen. Betaalbare systemen voor drinkwaterbesparing en nutriënten recycling zijn voor handen maar waterhergebruik is duur door de keuze van een membraan bioreactor. Verder onderzoek moet uitwijzen of het realistisch is data als ingaande stromen, zuiveringsrendementen of restricties aan te passen zodat de beslissingsondersteunende software kiest voor goedkopere technologieën als bijvoorbeeld een helofyten filter in plaats van de membraan bioreactor. Omdat waterbesparing en recycling gecombineerd kunnen worden met het recyclen van nutriënten is het mogelijk kleinschalige systemen te ontwikkelen die beide kringlopen op kleine schaal sluiten, de zogenoemde "life support systems".