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DEGREE PROJECT IN PROJECT MANAGEMENT AND OPERATIONAL DEVELOPMENT, SECOND LEVEL

STOCKHOLM, SWEDEN 2016

Sustainable Management of

Wastewater Transport Systems

- a case study in Trondheim, Norway

Maryam Beheshti Monfared

TMT 2016:67

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Abstract

Wastewater pipelines present a high value in water infrastructure assets and it is important to consider aging in them and manage them by specified rehabilitation plans. This study presents the results of a sustainability analyses from a Dynamic Metabolism Model (DMM) conducted on the wastewater transport system of Trondheim city in Norway. The goal is to examine and assess sustainability from environmental, physical, functional and economic points of view on a wastewater transport system in the period 2014-2040. For this purpose, four interventions ‘infiltration and inflow reduction’, ‘increasing rehab rate’, ‘extension of system regarding to population growth’, ‘energy management’ along with different combinations of them have been analyzed in two different conditions, which are a saturated network with no further extensions and a network, which is under further development until year 2040. From the analyses of different interventions, energy management and combination of all interventions showed good results from environmental and economic aspects in a saturated network. For the network under development, a combination of all interventions presented the best results.

The results of this study can give some arguments to decision-makers in wastewater section of Trondheim and other cities. In practice, in order to have a sustainable infrastructure asset management of wastewater network and making decisions, it is vital to consider different aspects of sustainability precisely and manage them in a comprehensive system. This can lead to long-term sustainable plans and management in wastewater transport system by having more focus on environmental feature of sustainable sewer asset management beside the economic, physical, functional and social aspects.

Key words: sustainability, dynamic metabolism model (DMM), wastewater transport system, rehabilitation plan

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FOREWORD

This thesis work was carried out at the department of water and environmental engineering at Norwegian University of Science and Technology (NTNU) as a master degree thesis in ‘Project Management and Operational Development’ at Royal Technical University of Sweden (KTH).

First, I would like to express my sincere gratitude to my primary supervisor Sveinung Sægrov, at Norwegian University of Science and Technology (NTNU), for his valuable help, support and encouragement.

I thank my co-supervisor Sven Å Antvik, at Royal Technical University of Sweden (KTH), for his help and advices during my study at KTH University and in writing this thesis.

I am grateful to Birgitte Johannessen in the wastewater department of Trondheim Municipality and the personnel of that department for their total support with data and information.

My thanks also go to G. Venkatesh, currently at Karlstad University, for his very valuable help.

Last but not least, I would like to thank my wonderful family for their total support and love. To my little princess, Nika, who cheers up my life, and my kind husband, Ali Tabeshian, for his understanding and love during my study period. His great support and encouragement always enlighten my heart to go ahead and achieve success. My parents receive my deepest gratitude and love for their dedication and many years of support during my undergraduate studies, which provided the foundation for this work.

Maryam Beheshti

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NOMENCLATURE

Here are the Notations and Abbreviations that are used in this Master thesis.

Notations

Symbol

Description

‘a’ Intervention of Reduction of Infiltration and inflow ‘b’ Intervention of Improvement of rehabilitation rate

‘c’ Intervention of Extension of wastewater transport network

‘d’ Intervention of Energy management

Abbreviations

UWCS Urban Water Cycle Services

DMM Dynamic Metabolism Model

WM2 WaterMet2 Model

WWTP Wastewater Treatment Plant

WW Wastewater

MFA Material Flow Analysis

LCA Life Cycle Assessment

ERA Environmental Risk Assessment

CIPP Cured-In-Place-Pipe

PE PolyEthylene

PVC PolyVinyl Chloride

I/I Infiltration and Inflow into the sewer system

DTS Distributed Temperature Sensing

GHG Greenhouse Gases

O & M Operation and Maintenance

CIPP Cured-In-Place-Pipe

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LIST OF FIGURES

Figure 1 - Dynamic metabolic model flow (G. Venkatesh et al. 2014) ... 8 Figure 2-The case study area (Slagstad & Brattebø, 2013) ... 15 Figure 3 - Water balance 2009-2011 (million m3/year), Trondheim, Norway (Trondheim Municipality 2013) ... 16 Figure 4 - Trondheim municipality sewer pipeline material at the end of 2013 ... 19 Figure 5 - Population growth forecast for Trondheim ... 22 Figure 6- Investment Plan for wastewater system Trondheim for 2013-2040 ... 24 Figure 7 - Investment Plan Wastewater transport system Trondheim for 2013-2040 ... 24 Figure 8– change in GHG emissions per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 30 Figure 9– change in GHG emissions per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 31 Figure 10– change in GHG emissions per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 31 Figure 11– change in GHG emissions per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 32 Figure 12– change in total energy consumption per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 32 Figure 13– change in total energy consumption per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 33 Figure 14– change in total energy consumption per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 33 Figure 15– change in total energy consumption per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 34 Figure 16- Changes in water supply and wastewater treatment volumes per capita in all interventions and both conditions ... 34 Figure 17- Changes in capital expenditure per capita in all interventions and both conditions ... 35

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Figure 18– change in operation and maintenance expenses per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 36 Figure 19– change in operation and maintenance expenses per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 36 Figure 20– change in operation and maintenance expenses per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) ... 37 Figure 21– change in operation and maintenance expenses per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) ... 37

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LIST OF TABLE

Table 1 - Unit greenhouse gas emission in kilograms of CO2 equivalent ... 10 Table 2- Risk factors and interventions of wastewater transport system ... 13 Table 3 - Average length and percentage of annual installed and registered pipelines

... 17 Table 4 - Assumption for unknown pipeline material ... 18 Table 5 - Material flow to the wastewater network ... 20 Table 6 - Detailed rehabilitation data length for sewer pipelines of Trondheim in

year 2013 ... 20 Table 7 - Detailed historical rehabilitation data of Trondheim ... 21 Table 8 - Detailed estimation for rehabilitation length in future ... 21 Table 9- detailed length and mass of rehabilitation by polyester liners in Trondheim

... 22 Table 10- population forecast for Trondheim ... 22 Table 11– energy conversion of diesel fuel ... 23 Table 12- Investment Plan sewage transport and water environment 2013-2040 . 25 Table 13– Selected indicators in 2040 for eight interventions for saturated

wastewater transport system ... 28 Table 14– Selected indicators in 2040 for eight interventions for under developed

wastewater transport system ... 28 Table 15– Percentage of changes of selected indicators in 2040 for saturated

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Table 16 – Percentage of changes of selected indicators in 2040 for under developed

wastewater transport system in comparison with status quo ... 29 Table 17- color codes for percentage of changes of indicators in comparison with

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TABLE OF CONTENTS

Abstract i

FOREWORD iii

NOMENCLATURE v

LIST OF FIGURES vii

LIST OF TABLES ix INTRODUCTION 1 1.1 Background 1 1.2 Purpose 2 1.3 Method 3 1.4 Research Question 3 2 LITERATURE REVIEW 5 3 METHODOLOGY 7 3.1 DMM 7 3.2 MFA 8

3.3 Scenarios and interventions 10

3.3.1 Interventions 11

4 CASE SYSTEM DESCRIPTION 15

4.1 Case Study 15

4.2 Data collection 17

4.2.1 Pipeline data 17

4.2.2 Pipe material 18

4.2.3 Renovation and replacement 20

4.2.4 Population change 22

4.2.5 Energy consumption 23

4.2.6 Economy 23

5 RESULTS AND DISCUSSION 27

6 CONCLUSIONS 39

6.1 Conclusions 39

6.2 Future work 40

6.3 Epilogue 40

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1. INTRODUCTION

1.1 Background

Urban water services are related to different aspects of sustainability such as social, economic, and environmental dimensions, which are ‘triple bottom line’ of the sustainability concept. Utilities usually have a robust effort on the quality of water, treatment efficiencies and cost-effectiveness; nevertheless, in the course of recent years more efforts are dedicated to broader sustainability characteristics, such as greenhouse gas emissions and life cycle environmental effects (Ashley et al., 2008; Slagstad & Brattebø, 2014). When it comes to decision-making regarding asset investments, the ‘triple bottom line’ approach of sustainability should be considered (Ashley et al., 2008). The aim of this work is to demonstrate a methodology for comparing different pathways toward a sustainable management of wastewater systems.

Urban water and wastewater infrastructure assets are undergoing aging and deterioration (Ana & Bauwens, 2010), which is based on lack of sufficient municipal investments on maintenance and rehabilitation (Rehan, Knight, Unger, & Haas, 2014). Functional efficiency and structural quality of sewer systems are the principal factors that ensure urban and industrial wastewater transport to treatment plants without infiltration and exfiltration (Ellis, Bertrand-Krajewski, Revitt, & Rieckermann, 2010). Management of infiltration and exfiltration in the urban sewer system are crucial in the long term to obtain good performance. This is a prerequisite for water infrastructural asset management and has significant environmental, social and economic impacts on cities (Beheshti, Sægrov, & Ugarelli, 2015). Moreover, the sustainable management of the water infrastructure should be considered in strategic long-term urban water cycle services (UWCS) plans, which leads to economic and environmental achievements for society. These infrastructures presents a high asset value and the next generations will inherit the consequences of today’s investments decisions (Marlow, Moglia, Cook, & Beale, 2013). Unfortunately, this important issue in general has not been considered seriously until now and there are only few studies which have been done on social and environmental aspects of sustainability on UWCS (Ludzia, Larsson, & Aguayo, 2014).

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Greenhouse gases are released continuously during the installation phase, operation and maintenance phase and rehabilitation phase. However, the main part of this emission happens during fabrication of pipelines, which is more than 80% of pipelines’ life cycle (Strutt, Wilson, Shorney-Darby, Shaw, & Byers, 2008; Venkatesh, Hammervold, & Brattebø, 2009). Therefore, to reduce the amount of greenhouse gases emission it is wise to keep current pipelines in the network by correct maintenance and rehabilitation plans, which are the main outcome of an efficient sustainable infrastructure management.

Urban water systems can be analyzed from sustainability point of view by metabolism-based models. The approach of these models is to assess and analyze the flow, conversion and process of sources in the concept of material and energy into the UWCS to accomplish the mandatory requirements of the system in the term of water supply and sanitary transport at the demanded quantity and quality stages (Venkatesh, Sægrov, Ugarelli, & Brattebø, 2014). WaterMet2 (WM2) developed at Exeter in the UK (Behzadian, Kapelan, Venkatesh, Brattebø, & Sægrov, 2014), and the Dynamic Metabolism Model (DMM) developed at Norwegian University of Science and Technology are two metabolism-based models for sustainability modeling and analysis of urban water systems. These models give the opportunity to quantitatively investigate and document metabolism of UWCS as a basis for sustainability analysis from physical, environmental and economic points of view based on different strategical scenarios and interventions for a long-term period. Both models have been applied for analysis of energy consumption, costs and environmental impact of urban water cycle services in Oslo (Venkatesh et al. 2014). Their study demonstrated that within the UWCS system, the effluent from wastewater treatment plants (WWTP), due to the use of chemicals and energy, has the highest environmental impact, most notably from acidification and eutrophication (G Venkatesh et al. 2014).

1.2 Purpose

The wastewater transport system has not been considered by itself in previous sustainability studies. In the current study, a sustainability analysis by DMM has been conducted on the wastewater transport system of Trondheim. The system consists of combined and separate sewer pipelines in addition to pumping stations.

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Other components of wastewater system such as wastewater treatment plants are excluded from this study. This will require detailed analysis of the wastewater system hydraulic performance and respective detailed information of components in WWTP. They are therefore subjected to separate studies.

1.3 Method

The wastewater transport system has not been considered by itself in the previous sustainability studies. In the current work, a sustainability analysis by DMM, which is a new model developed at NTNU, has been conducted on the wastewater transport system of Trondheim city in Norway.

1.4 Research Question

The aim of this study is to answer the following research question:

Is it possible to demonstrate a methodology for comparing different pathways toward a sustainable management of wastewater transport systems?

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2. LITERATURE REVIEW

Sustainable development is ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (World Council for Environment and Development 1987). The key word here is ‘to sustain’ – to maintain for long periods (Venkatesh 2011). Therefore, the ‘sustainability’ term is usually expressing a willingness to maintain some valuable assets in a long period.

Actually, introduction of sustainable development as a concept is an intellectual response to reconcile the conflicting goals of environmental protection and economic growth (Quental, Lourenco and da Silva 2009). The sustainable development has three dimensions, which should be considered simultaneously. This “triple bottom line” approach of society, the economy and the environment should be considered by decision-makers in urban wastewater systems on the value they add, or destroy, to these interconnected area (Ashley et al. 2008).

In the field of urban water systems, there is a need to integrate water supply system, and wastewater system for having a sustainable development. The sustainable management of urban water systems should be done in an integrated way with focusing on sustainability factors of environmental integrity, social equity, landscape aesthetics, economic efficiency in addition to integration of various professions and community engagements (van de Meene, Brown and Farrelly 2009; Venkatesh 2011).

There are different methodologies available for estimating the environmental impact of water and wastewater systems, such as Strategic Environmental Assessments, Cost-Benefit Analysis, Material Flow Analysis (MFA), Life Cycle Assessment (LCA), Environmental Risk Assessment (ERA), or Ecological Footprints (Chen, Ngo, & Guo, 2012; Finnveden et al., 2009). For illustration, MFA is an effective initial screening method and LCA is widely used in finding the optimal wastewater treatment technology and ERA mainly evaluates site-specific chemical hazards (Chen et al., 2012) . For example Slagstad and Brattebø made a LCA on the entire water and wastewater system in Trondheim in 2014. They assessed the environmental aspects of sustainability on UWCS of Trondheim city and they found that local fresh water recipients are potentially affected by

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wastewater effluents with corresponding local water quality problems (Slagstad & Brattebø, 2014).

From several methods and approaches, MFA and LCA are common methods, which are currently have a wide application (Venkatesh, Sægrov, & Brattebø, 2014). The efficiency of MFA in urban metabolism studies and urban planning is underlined by Brunner et al (2004). In 2009 a dynamic MFA model was presented by Brattebø et al. for built environment applications and this model was proven to be feasible for both historical analyses and future forecasts. A general review of urban metabolism studies, which is included urban water systems as well have been done by Kennedy et al. in 2011, and they supported such studies as a feasible utilization for decision-makers and urban planners.

Urban water systems integrated modelling such as Aquacycle (Mitchell et al. 2001), UWOT (Makropoulos et al. 2008), UVQ (Mitchell and Diaper 2010) and CWB (Mackay and Last 2010) has been introduced from the last decade. The DMM and the WM2 are integrated models which were developed to achieve particular end-goals (Venkatesh, Sægrov, & Brattebø, 2014).

Venkatesh et al (2011) developed the ‘Dynamic Metabolism Model’ (DMM) for urban water services and tested it for urban water system of Oslo (Venkatesh, Brattebø and Sveinung; 2014).

The WaterMet2 model has also been tested for Oslo as a model city (Behzadian et

al., 2014).

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3. METHODOLOGY

The wastewater transport system has not been considered by itself in previous sustainability studies. In this study, the metabolism of wastewater transport system of Trondheim city has been investigated and assessed by the DMM. For this purpose, different scenarios and interventions have been defined as possible actions for improving the current system. Each of these scenarios involves a broad range of changes in the economic and technological factors and the environmental, functional and economic aspects of sustainability have been analyzed and evaluated. The historical data of 2000-2013 has been used as the database of this study and the predictions have been made for the period 2014-2040.

3.1. DMM

Venkatesh et al. (2011) developed Dynamic Metabolism Model’ (DMM) for urban water systems at NTNU within the European research project TRUST (TRansitions to the Urban Water Services of Tomorrow, https://www.trust-i.net) (Brattebø et al., 2011). This model is based on the resource and material flow analysis (MFA) to the system and outflow of them in the context of energy and GHG and byproducts from the system (Venkatesh, 2011). The aim of the development was to implement a complete systematic outlook to the study of metabolism and environmental effects of resource flows in UWCS i.e. water flows, material and energy consumption, resource recovery, waste and emission flow. This model offers an instrument for the investigation of possible future services, strategies and intervention in UWCS (Venkatesh, Sægrov, & Brattebø, 2014). Moreover, the system can be quantitatively analyzed from physical, economic and environmental properties under different interventions toward future by this model (Venkatesh, Sægrov, Ugarelli, et al., 2014).

Some important characteristics of this model are flexibility and modifiable, nevertheless it has its own shortcomings. The model has some simplicities, which make it notable, such as receiving model inputs in a user-friendly Excel file, and allowing the end-user to analyze and predict the effects of variations and changes of different variables, which are important in future planning, on sustainability indicators. Simplicity of this model can be both an advantage and a limitation.

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However, the main limitation of the DMM model is that it is a concentrated and lumped model in this version, which is based on general data of the system. That is to say, this model needs less data in comparison with distributed models like WaterMet2 (Venkatesh, Sægrov, Ugarelli, et al., 2014).

Figure 1 illustrates a holistic view of the structure of this model.

Figure 1 - Dynamic metabolic model flow (Venkatesh, Sægrov, & Brattebø, 2014)

The main reason of applying this model in this study was that this model was quite new model, which was developed at NTNU. In addition, DMM was applied for UWSC of Oslo and it was so interesting to test it for UWSC of Trondheim as well.

3.2. MFA

MFA stands for material flow analyses and it refers to the inflows of material to the network due to additions of pipelines and rehabilitation rate. As a way of illustration, in Trondheim city it is planned to add a specified km pipelines to the system every year and rehabilitate existing pipelines by cured-in-place-pipe (CIPP) approach by a specific rate per year, which will be discussed later in details. In this study the flow of pipeline material to wastewater transport network has been considered and applied as inputs of DMM model.

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Some of the assumptions for transforming the pipeline length data to weights of the material are borrowed from the study that Venkatesh et al. have done on MFA-LCA analysis of wastewater pipeline networks in Oslo, Norway in 2009 (Appendix). In classifying the pipelines a mean diameter of 125 mm for small pipelines (less than 249 mm in diameter) and 375 mm for medium-size ones (between 250 mm and 499 mm in diameter) are considered. More than 500mm diameter considered as large pipelines in this study. Material of pipelines and rehabilitation is calculated by MFA. Moreover, data of 2013 was used as the database of this analysis.

In this study, we focused on wastewater transport system, including both combined and separated sewer pipelines and pumping stations. The materials of pipelines consists of concrete, gray cast iron, ductile iron, Polyvinyl Chloride (PVC), polyethylene (PE), and mild steel. For rehabilitation purposes, a polyester liner is utilized as CIPP. This rehabilitation technique focus on tightening and strengthening the existing pipelines on site and with no dig and trenchless solutions. Polyester liner is applied in Trondheim in 1/3 of wastewater rehabilitation projects. In general, installation, rehabilitation, and maintenance and operation of wastewater pipelines are energy-demanding in the case of requiring energy (diesel) for transportation of materials and using energy-demanding instruments in installation, rehabilitation and maintenance and operation.

In this study, retirement of pipes is also concluded in the analysis. However, normally this stage does not need energy for disconnecting the pipes from the network and lets them to remain in the ground.

The equivalent amount of CO2 emission for the pipe material, rehabilitation and diesel fuel is presented in table 1. This table has been exploited from the work that Venkatesh et al. (2009) have been done on combining the MFA-LCA of wastewater pipeline network.

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Table 1 - Unit greenhouse gas emission in kilograms of CO2 equivalent (Venkatesh et al., 2009)

Material Unit Kg CO2-eq

PVC pipe kg 2.36

PE Pipe Kg 2.33

Mild steel pipe Kg 1.51

Ductile iron pipe Kg 3.41

Gray cast iron pipe

Kg 3.34

Concrete pipe Kg 0.23

Epoxy resin Kg 6.70

Diesel fuel liter 3.19

3.3. Scenarios and interventions

A scenario is the combination of the action of risk factors which influence the system in a specific way (Venkatesh, Sægrov, & Brattebø, 2014). A risk factor is a specification, feature or aspect of something/somebody, which rises the probability of happening something undesirable. By identifying and understanding various risk factors, the related interventions and actions can be undertaken against them for preventing the undesirable consequences in the system. For assessing the impacts of different risk factors on the functionality of wastewater transport system, the simulation has been done under the impact of changes in different risk factors according to defined scenarios. Each of these scenarios involves a broad range of changes in the future population growth, as well as in the economic and technological factors like rehabilitation rate that may affect functionality of wastewater transport system from various aspects such as energy consumption, emissions of the greenhouse gases and aerosols and economy point of view. Studying these changes will affect decision-making regarding to future strategic and tactical sewer asset management.

The risk factors that are considered in this study are population growth; asset deterioration; increase in energy consumption; and climate change that affects infiltration and inflow (I/I) into the sewer pipelines. A scenario can be a combination of two or more risk factors, which affects the sewer system functionality.

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The status quo shows the current condition of the network without considering any risk factors. According to the policies of the wastewater section of Trondheim municipality, the current plan is to improve the performance of wastewater transport system and improving the storm-water management in addition to have an energy management consideration. By analyzing various risk factors new interventions also rise.

In the status quo situation, the population growth was considered non-uniform according to the Trondheim municipality plan for the future until 2040 according to figure 5 and table 10.

3.3.1. Interventions

Various interventions can be implemented for the wastewater transport system. Some possible interventions which can be considered in this study are listed as below:

a) Reduction of Infiltration and inflow (I/I)

Reduction in infiltration and inflow of non-sewer water to the system, at a rate of 20 % by the year 2040. Removing this water may add economic, environmental, and social benefits to the entire urban water and wastewater system. Spot-repair is a solution for reduction of I/I to wastewater network. The realistic number of repair in this study is assumed 100 repairs per year. Spot-repair can be supported by some high-tech tools like fiber-optic Distributed Temperature Sensing (DTS) cables for localizing the exact I/I location in the sewer pipelines. The cost assumption for this purpose is about 10000 NOK for each repair, which sums up to 1 million NOK per year for 100 spot-repairs. This value adds to the operational expenses for each year. b) Improvement of wastewater transport system rehabilitation rate

According to the master plan of Trondheim municipality, the rehabilitation rate for wastewater transport system in status quo situation is around 5 km (0.41%) now and it will increase to 8 km (0.67%) by the year 2040. In this part, the rehabilitation rate of 1.6 % of the whole is considered as a standard rate on the system until the year 2040. For having detailed information regarding to the size and length of pipelines, the corresponding ratio of year 2013 will be used for whole of the study period. The detailed data related to

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rehabilitation was described in rehabilitation section. The cost assumption for rehabilitation of each kilometer pipeline is about 6000 NOK/km. This value is considered in simulation, for the rest of rehabilitation, which until now is not considered in the rehabilitation plan of Trondheim.

c) Extension of wastewater transport network

In the first approach it is assumed that the wastewater network is almost saturated and does not need to improve and in the other approach it is assumed a constant development as average of 2000-2013 for period 2014-2040. However, the population growth may require extension of the wastewater and stormwater networks. The cost of installing new pipelines and extending the network is assumed to 10000 NOK/km.

d) Energy management

From energy point of view, it is assumed to have reduction in diesel energy consumption by 20% due to automation/optimization of pumping stations, using more no-dig technology in rehabilitation of network and using more electric vehicles and instruments by 2040. For obtaining this purpose, the investment of 1 million NOK per year is assumed and considered as the model inputs.

The combination of a+b, a+b+c, a+b+d, a+b+c+d are also considered in this study. Table 2 demonstrates the risk factors and corresponding interventions, which are considered in this study for the wastewater transport system. The black cells present the risk factors which are related to each intervention.

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Table 2- Risk factors and interventions of wastewater transport system

Interventions Risk factors

Population growth Sewer asset deterioration Energy consumption Climate change a: Reduction of Infiltration and

inflow

b: Improvement of rehabilitation rate c: Extension of WW transport network d: Energy management a+b a+b+c a+b+d a+b+c+d

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4. CASE SYSTEM DESCRIPTION

4.1. Case Study

Trondheim city with the population of 181,000 residents is the third largest town in Norway by population. The main water source of Trondheim is Lake Jonsvatnet, which is a big lake in east part of Trondheim city (Figure 1). Vikelvdalen or VIVA is the water treatment plant of Trondheim and the surface water from Jonsvatnet is purified in this treatment plant, and then the drinkable water is distributed to households and consumers with almost 100% coverage. The urban water cycle path in Trondheim city is completed in downstream by transporting the stormwater and sewage from consumers such as households and industries through the wastewater transport system to two wastewater treatment plants Høvringen (HØRA) and Ladehammeren (LARA) in north parts of Trondheim. Around 50% of the wastewater is from industries in Trondheim (Slagstad & Brattebø, 2013). These two wastewater treatment plants release the purified wastewater to Trondheim fjord. HØRA with the catchment area of 95 km2 treated 22.6 million cubic meter wastewater in 2011, while LARA treated 11.9 million cubic meter wastewater in 2011 with a catchment of 18.7 km2. Figure 2 gives an overview of Trondheim city and its treatment plants.

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The wastewater transport system of Trondheim is about 1190 km in public and municipal section. The average age of the network is around 30 year. However, the oldest pipelines, which are still in use, are more than 150 years old. The wastewater network consists of pumping stations and combined and separate sewer pipelines for transporting foul sewer and stormwater to treatment plant before releasing to Trondheim fjord. The total length of foul sewer network in Trondheim is 719489.5 meter, which 51.7% of this value is separate foul sewer, and 48.3% of them are combined sewers. The length of separate stormwater network in Trondheim is 471489 meter at the end of year 2013, which is 40% of the whole wastewater transport network in Trondheim. Moreover, today there are 54 pumping stations in the wastewater transport system of Trondheim, which 3 of them are for stormwater, 24 for foul sewer, and 27 for combined system (Trondheim Municipality 2013). The water balance of wastewater and stormwater system in Trondheim is shown in figure 3, which is developed for the years 2009-2011. We assume these values are the same for our period of study and used these values as model inputs for period 2013-2040.

Figure 3 - Water balance 2009-2011 (million m3/year), Trondheim, Norway (Trondheim Municipality 2013)

The figure shows that the amount of infiltration and inflow of extraneous water in to the sewer system in dry weather condition is about 48% of the water, which is delivered to the wastewater treatment plant. This amount is very high and it is a

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high priority in the municipality to reduce the number. Furthermore, Trondheim municipality has another focus on changing the combined systems gradually to separate systems for reducing the costs for pumping and treatment and decreasing overflows, which lead the spread of pollution to recipient.

The possible actions for handling these problems can be:

i) increasing treatment capacity by developing the treatment plants, ii) increasing the capacity of wastewater transport system by for example

installing some detention basins, and

iii) decreasing the amount of Inflow and infiltration to wastewater transport system by spot repair and complete rehabilitation of sewer network.

In this study, some possible actions inside the wastewater transport system are considered by defining different scenarios and interventions in order to decrease the flaws and deficiencies and improving the current system.

4.2. Data collection

4.2.1. Pipeline data

It is necessary to acquire detailed information on characteristics of the pipeline network in Trondheim for modelling sustainability in DMM. However, some of these data were not available and some assumptions have been made.

Based on available pipeline data the average percentage of pipelines with different sizes have been calculated for each year. Afterwards, according to the total length of installed pipelines in the system in each year, detailed length of each size was calculated (Table 3).

Table 3 - Average length and percentage of annual installed and registered pipelines (2000-2013)

size small medium large Average percentage (%) 52% 34% 13,8%

Average length (m) 8189,2 5156,3 2162,2

The recorded data of 2013 has been used as the database for this study and the predictions have been made for the period 2014-2040. In this study analyzes has

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been conducted for two different approaches in Trondheim city. In the first approach in the status quo, it is assumed that the network is saturated and no new pipelines will be installed in the system in the period 2014-2040. From the other point of view, the network is under developed and the average pipeline installation values were assumed as input values for the whole of prediction period 2014-2040 in status quo situation.

4.2.2. Pipe material

According to the data collection from Trondheim municipality database we found that the total length of combined and separated sewer system and overflow pipelines in Trondheim City is 1196817 meter which 3523 meter of it, which is less than 1% of the whole network, is unknown material. Therefore, some assumptions for filling data deficiency gap have been done, based on the year of installation and their size and application (Table 4).

Table 4 - Assumption for unknown pipeline material

Pipeline size Small < 250 250≤ Medium <500 Large≥ 500 Separated Wastewater PVC with great certainty PVC with great certainty Concrete Combined Wastewater PVC PVC Concrete

Overflow Concrete Concrete

with great

certainty

Concrete

with great

certainty

Figure 4 illustrates pipeline materials of Trondheim city by the end of the year 2013. Pipes are divided based on their material in this figure. As it is illustrated in this figure, the main part of the system is concrete pipeline with 81% of the total pipelines. PVC and PP pipelines are in the second score by 10% ratio.

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Figure 4 - Trondheim municipality sewer pipeline material at the end of 2013

For material flow into the system, in the status quo situation we are faced with two different approaches. In the first approach, the network is saturated and it will not develop by installing any new pipeline. In the second approach, it is assumed the average values of 2000-2013 for the rest of the prediction period 2014-2040. Table 5 gives a brief view of material flow to the system. For the concrete, PVC and PPP flow to the system, the values are assumed as the average value of period 2000-2013. However, the influx of PE and the other material flow to the system according to recorded data and their trend are stopped in the system and they are not considered in the calculations.

81 % 10 %

3 % 1 % 5 %

Trondheim sewer network material BET PVC/PP PE Glassfiber Others 72 % 18 % 1 %5 % 4 % Wastewater BET PVC PPP PE Other 90 % 8 % 0 % 1 % 1 % Stormwater BET PVC PPP PE Other 78 % 4 % 0 % 3 % 15 % CSO BET PVC PPP PE Other

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Table 5 - Material flow to the wastewater network

Year Concrete (kg/year) PVC (kg/year) PP (kg/year)

2013 659460,93 94912,8 945,6158

Average 2000-2013 1917811,42 88131,37 2872,36

4.2.3. Renovation and replacement

The general rehabilitation objectives for Trondheim municipality are: - minimum 5 km sewer pipelines per year in period 2013-2014 - minimum 6 km sewer pipelines per year in period 2015-2019 - minimum 7 km sewer pipelines per year in period 2020-2024 - Around 8 km sewer pipelines per year in 20-30 years

The percentage of rehabilitation in 2013-2014, according to this plan is about 0.41% of total pipelines in the network.

According to Trondheim municipality master plan, renovation proportion by CIPP with polyester liners is today about 1/3 of all sewer network rehabilitation in Trondheim municipality and the other 2/3 is replacement. According to Trondheim municipality master plan, where it is possible and appropriate, renovation is preferred rather than digging and replacement methods. However, in the case of replacement of combined pipelines by digging methods, it is important to change it to separate pipelines.

Renovation is applied:

- When combined system should be retained

- When combined system assumed to lie minimum 20-30 years ahead. - When the renovated combined pipeline can serve as wastewater pipe in a

future separate system

Table 6 - Detailed rehabilitation data length for sewer pipelines of Trondheim in year 2013

Sewer pipelines Size Sum

Small Medium Large

Renovation 958,65 626,5 252,9 1838

Replacement 1610 1052 425 3087

Rehabilitation 2568,7 1678,5 677,9 4925

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Detailed historical rehabilitation data related to each year is also presented in table 7 for period 2005-2013.

Table 7 - Detailed historical rehabilitation data of Trondheim

Wastewater 2005 2006 2007 2008 2009 2010 2011 2012 2013 Changing 5560 4063 2771 3612 5762 5716 4230 3379 3332

Renovating 2144 1724 2105 1205 2819 4061 1263 2388 1838

Rehabilitation 7704 5787 4876 4817 8581 9777 5493 5767 5170

Based on the ratios of pipeline size and length in table 6 and objectives for pipeline rehabilitation lengths in future, according to Trondheim municipality master plan, detailed estimations for rehabilitation length for different sizes of pipelines in each period has been made (Table 8).

Table 8 - Detailed estimation for rehabilitation length in future

Length of wastewater pipes rehabilitated (meter) Year Small Medium Large Total length

2013 2568,7 1678,6 677,7 4925

2014 2607,9 1704,2 688 5000

2015-2019 3129,4 2045 825,6 6000

2020-2024 3651 2385,8 963,2 7000

2025-2040 4172,6 2726,7 1100,8 8000

Trondheim municipality uses polyester liners for rehabilitation of these pipelines. According to detailed rehabilitation data, the influx of polyester into wastewater pipeline networks was calculated. Table 9 shows the length and calculations of rehabilitated pipelines by polyester liners for the assumption that around 1/3 of rehabilitation proportion in Trondheim is renovated by CIPP with polyester liners.

For finding the influx of polyester to sewer pipelines by renovation methods, it is assumed a nominal diameter for each group of small, medium and large pipelines (small: 150, medium; 300, and large: 550 mm). According to this, the influx of polyester to sewer system was calculated. Chosen Thickness of Polyester coating in a CIPP – 7.6 millimeters with a specific density of 1380 kilograms per cubic meter.

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Table 9- detailed length and mass of rehabilitation by polyester liners in Trondheim

Influx of Polyester to sewer system (Kg)

Year Small Medium Large Total Polyester mass

2013 4229,7 5528 4091,4 13849,2 2014 4294,2 5612,2 4153,7 14060,1 2015-2019 5152,98 6734,6 4984,5 16872,1 2020-2024 6011,8 7857,08 5815,2 19684,1 2025-2040 6870,6 8979,5 6645,9 22496,1

4.2.4. Population change

Net migration to Trondheim is rising and is expected to continue to rise. Prognosis for Trondheim city shows that Trondheim municipality will grow with 34,000 new residents until 2024. By 2040 there will be nearly 60,000 new residents in the municipality, which is 31.5% increase in population in comparison with year 2013 (Trondheim Municipality 2013).

Table 10- population forecast for Trondheim (Trondheim Municipality 2013)

Year 2012 2013 2014 2015 2020 2025 2030 2035 2040 Population 176348 179385 181880 184723 198437 210291 220088 228535 235944

Figure 5 - Population growth forecast for Trondheim

y = 2181.6x - 4E+06 R² = 0.9894 170000 180000 190000 200000 210000 220000 230000 240000 2012 2016 2020 2024 2028 2032 2036 2040 Po p u lat ion Year

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4.2.5. Energy consumption

The energy consumption data in wastewater transport system was extracted from Trondheim municipality reports. Furthermore, for converting the values to suitable unit some calculation have been done to convert kWh to liter of Diesel. The assumption for this conversion is each liter of diesel contents around 10 kWh energy (Wikipedia, April 2015). ‘Energy density is the amount of energy stored in a given system or region of space per unit volume or mass, though the latter is more accurately termed specific energy. ’ (Wikipedia, April 2015). Detailed data regarding to this conversion is presented in table 11.

Table 11– energy conversion of diesel fuel (Wikipedia, April 2015)

Storage material Specific energy (kWh/kg) Energy density (kWh /L)

Diesel 13,44 10,024

In the energy scenario, it is assumed to lower the amount of energy consumption by 20% by 2040 due to automation of instrument and using more electricity instead of diesel and fossil fuels in addition to optimization and automation of stations and using more no-dig rehabilitation techniques in wastewater network.

4.2.6. Economy

The plan for annual investment into wastewater distribution system of Trondheim municipality is like the figure 6. This figure is obtained from Wastewater department of Trondheim municipality. According to their experts, it is assumed that 60% of the values is for wastewater transport system.

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Figure 6- Investment Plan for wastewater system Trondheim for 2013-2040

Figure 7 - Investment Plan Wastewater transport system Trondheim for 2013-2040

As it is obvious from the graphs above, the costs are expected to decrease from 2023 to 2032. In fact, it reflects high earlier investment until year 2023, which comes back gradually to normal condition after it by a decrease in depreciation and interest values. According to figure 7, the annual investment on wastewater distribution system is presented in table 12. The values for 2032-2040 are assumed to be constant. 0 50000000 100000000 150000000 200000000 250000000 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 N OK Kostnadsfordeling-Avløp

Lønn Varer og tjenster kjøp fra andre/Overføringer Depreciation interests indirekte kostnader

0 50000000 100000000 150000000 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 N OK Year Kostnadsfordeling-Avløpsnett Lønn Varer og tjenster

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Table 12- Investment Plan sewage transport and water environment 2013-2040 Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Total costs (mill NOK) 99,6 105 107, 4 114, 6 117, 9 120, 6 124, 2 127, 5 128, 7 132 Year 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032-2040 Total costs (mill NOK) 135, 3 135 135 133, 2 131, 4 129, 9 128, 7 126, 6 124, 8 124,5

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5. RESULTS AND DISCUSSION

The per capita indicators, which are investigated in this study, are listed as below:

- GHG emissions per capita

- Total energy consumption per capita - Length and mass of pipeline per capita - Wastewater treated per capita

- Operation and maintenance expenses per capita - Capital expenditure per capita

Intervention ‘a’ did not affect the result of model and the result was the same as status quo. Therefore, infiltration factor cannot make a big change in the result of the model and the combination of it with rehabilitation of pipelines should be considered.

Intervention ‘b’ affected the results of model significantly. Intervention ‘a+b’ also presented the same results as Intervention ‘b’. Again, we can see the infiltration factor does not make a change in the results by itself.

The climate change effect was tested by increasing the precipitation. Changing this factor does not affect the result of DMM in wastewater transport system. Therefore, we omitted it from the analyses.

Tables13-14 list the results of indicators for year 2040, which are modelled in DMM for wastewater transport system Trondheim in two different status quo conditions (saturated and under developed system), for the eight cases defined in the intervention section. Moreover, the status quo situation presented the network in two conditions: first, the network is saturated and there will not be installed any new pipeline, and second, it is under developed by a constant average value in the period 2014-2040.

Tables 15-16 demonstrate the percentages of changes of selected indicators in 2040 for two different assumptions of wastewater transport system, saturated and developing, in comparison with status quo in the same year. In all the changes, the negative value is desirable. Table 17 describes the color-coding, which is used in tables 15 and 16 for describing the desirability of results.

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Table 13– Selected indicators in 2040 for eight interventions for saturated wastewater transport system

Interventions in 2040

Indicators Status quo a b c d a+b a+b+c a+b+d a+b+c+d

Environmental GHG emissions per capita (kg CO2-eq

per cap/y) 5,07 5,07 5,82 7,74 4,28 5,82 8,45 5,04 7,67 Total energy consumption per capita

(kWh per cap/y) 15,06 15,06 15,60 21,66 12,69 15,62 21,6 13,2 19,26 GHG emissions per cubic meter

demand (kg CO2-eq per m3/y) 0,05 0,05 0,05 0,07 0,04 0,05 0,08 0,04 0,07

Total energy consumption per cubic

meter water demand (kWh per m3/y) 0,13 0,13 0,14 0,19 0,11 0,14 0,19 0,12 0,17

Physical &

Functional

Pipeline material mass per capita (kg per

cap) 539,87 539,87 539,87 591,10 539,87 539,87 591,10 539,87 591,10 Length of pipelines per capita (km per

cap) 5,05 5,05 5,05 5,86 5,05 5,05 5,86 5,05 5,86 Water supplied per cap per year (m3 per

cap) 112,49 112,49 112,49 112,49 112,49 112,49 112,49 112,49 112,49 Wastewater treated per cap per year (m3

per cap) 157,13 157,13 157,13 157,13 157,13 157,13 157,13 157,13 157,13

Economic O&M expenses per capita (Euros per

cap/y) 29,50 29,50 29,60 30,66 29,09 29,60 30,65 29,19 30,24 Capital expenditure per capita (Euros

per cap/y) 27,13 27,13 27,13 27,13 27,13 27,13 27,13 27,13 27,13 O&M expenses per cubic meter water

demand (Euros per m3 / y) 0,26 0,26 0,26 0,27 0,26 0,26 0,27 0,26 0,27

Capital expenditure per cubic meter

water demand (Euros per m3 / y) 0,24 0,24 0,24 0,24 0,24 0,24 0,24 0,24 0,24

Table 14– Selected indicators in 2040 for eight interventions for under developed

wastewater transport system

Interventions in 2040

Indicators Status quo a b c d a+b a+b+c a+b+d a+b+c+d

Environmental GHG emissions per capita (kg CO2-eq

per cap/y) 14,96 14,96 15,72 7,74 14,18 15,72 8,45 14,93 7,67 Total energy consumption per capita

(kWh per cap/y) 39,07 39,07 39,63 21,66 36,71 39,63 21,63 37,26 19,26 GHG emissions per cubic meter

demand (kg CO2-eq per m3/y) 0,13 0,13 0,14 0,07 0,13 0,14 0,08 0,13 0,07

Total energy consumption per cubic

meter water demand (kWh m3/y) 0,35 0,35 0,35 0,19 0,33 0,35 0,19 0,33 0,17

Physical &

Functional

Pipeline material mass per capita (kg

per cap) 769,75 769,75 769,75 591,10 769,75 769,75 591,10 769,75 591,10 Length of pipelines per capita (km per

cap) 6,83 6,83 6,83 5,86 6,83 6,83 5,86 6,83 5,86 Water supplied per cap per year (m3 per

cap) 112,49 112,49 112,49 112,49 112,49 112,49 112,49 112,49 112,49 Wastewater treated per cap per year

(m3 per cap) 157,13 157,13 157,13 157,13 157,13 157,13 157,13 157,13 157,13

Economic O&M expenses per capita (Euros per

cap/y) 33,71 33,71 33,80 30,66 33,29 33,80 30,65 33,39 30,24 Capital expenditure per capita (Euros

per cap/y) 27,13 27,13 27,13 27,13 27,13 27,13 27,13 27,13 27,13 O&M expenses per cubic meter water

demand (Euros per m3 / y) 0,30 0,30 0,30 0,27 0,30 0,30 0,27 0,30 0,27

Capital expenditure per cubic meter

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Table 15– Percentage of changes of selected indicators in 2040 for saturated wastewater transport system in comparison with status quo

Increase desirable? (Y/N)

Interventions in 2040

Indicators a b c d a+b a+b+c a+b+d a+b+c+d

Environme ntal

GHG emissions per capita N 0,00 14,7 52,76 -15,5 14,9 66,82 -0,63 51,31 Total energy consumption per

capita

N

0,00 3,59 43,84 -15,7 3,72 43,63 -11,99 27,92 GHG emissions per m3 demand N

0,00 14,7 52,76 -15,5 14,9 66,82 -0,63 51,31 Total energy consumption per

m3 water demand

N

0,00 3,59 43,84 -15,7 3,72 43,63 -11,99 27,92

Physical & Functional

Pipeline material mass per capita N 0,00 0,00 9,49 0,00 0,00 9,49 0,00 9,49 Length of pipelines per capita N 0,00 0,00 15,87 0,00 0,00 15,87 0,00 15,87 Water supplied per cap per year N 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Wastewater treated per cap per

year

Y-N

0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Economic O&M expenses per capita N 0,00 0,32 3,92 -1,4 0,33 3,90 -1,07 2,49

Capital expenditure per capita Y-N 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 O&M expenses per cubic meter

water demand

N

0,00 0,32 3,92 -1,4 0,33 3,90 -1,07 2,49 Capital expenditure per cubic

meter water demand

Y-N

0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Table 16 – Percentage of changes of selected indicators in 2040 for under developed wastewater transport system in comparison with status quo

Increase desirable? (Y/N)

Interventions in 2040

Indicators a b c d a+b a+b+c a+b+d a+b+c+d

Environme ntal

GHG emissions per capita N 0,0 5,04 -48,27 -5,25 5,04 -43,51 -0,21 -48,76 Total energy consumption per

capita

N 0,0 1,43 -44,56 -6,05 1,43 -44,65 -4,62 -50,70 GHG emissions per m3 demand N 0,0 5,04 -48,27 -5,25 5,04 -43,51 -0,21 -48,76

Total energy consumption per m3 water demand

N 0,0 1,43 -44,56 -6,05 1,43 -44,65 -4,62 -50,70

Physical & Functional

Pipeline material mass per capita N 0,0 0,00 -23,21 0,00 0,00 -23,21 0,00 -23,21 Length of pipelines per capita N 0,0 0,00 -14,24 0,00 0,00 -14,24 0,00 -14,24 Water supplied per cap per year N 0,0 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Wastewater treated per cap per

year

Y-N 0,0 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Economic O&M expenses per capita N 0,0 0,29 -9,04 -1,23 0,29 -9,06 -0,94 -10,28

Capital expenditure per capita Y-N 0,0 0,00 0,00 0,00 0,00 0,00 0,00 0,00 O&M expenses per cubic meter

water demand

N 0,0 0,29 -9,04 -1,23 0,29 -9,06 -0,94 -10,28 Capital expenditure per cubic

meter water demand

Y-N 0,0 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Table 17- color codes for percentage of changes of indicators in comparison with status quo

Percentage of decrease in comparison with status quo

Color of desirable changes Color of undesirable changes 0- 1 % 1-10% 10-20% 20-30% 30-40% 40-50% 50-60% >50%

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From the environmental point of view, as it is clear in from the tables 13 and 14, interventions ‘d’ and ‘a+b+d’ present good results in decreasing GHG emission and energy consumption per capita in comparison with status quo in the saturated network. In intervention ‘d’ GHG emissions decreases by 15.5% in 2040 in comparison with the status quo. The decrease in intervention ‘a+b+d’ is around 1% which is not significant. Energy consumption per capita will decrease by 15.7% in intervention ‘d’ and 12% in ‘a+b+d’.

In under developed network condition ‘c’, ‘d’, ‘a+b+c’, ‘a+b+d’ and ‘a+b+c+d’ present better results in decreasing GHG emission and energy consumption per capita. In interventions ‘c’ and ‘d’ GHG emissions decreases by 48.3% and 5.3% respectively, while in ‘a+b+c’, ‘a+b+d’ and ‘a+b+c+d’ decreases by 43.5%, 0.2% and 48.8% respectively. From energy point of view, there is a drop by 44.6% and 6% in interventions ‘c’ and ‘d’ respectively. Moreover, in ‘a+b+c’, ‘a+b+d’ and ‘a+b+c+d’ energy consumption decreases by 44.7%, 4.6% and 50.7% in year 2040 comparison with status quo in the same year.

Figures 8-11 demonstrate the changes in GHG emission for different interventions and different conditions during the modeling period until 2040. In addition, figures 12-15 present the plots of total energy consumption per capita in modeling period 2014-2040 under different interventions and conditions.

Figure 8– change in GHG emissions per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’) 4.00 6.00 8.00 10.00 12.00 14.00 16.00 2012 2016 2020 2024 2028 2032 2036 2040 G H G e m iss io ns p er c ap it a (k g CO 2 -eq u iv al en t/c ap /y ear ) year

GHG emission in saturated network

status quo 1 a

b c d

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Figure 9– change in GHG emissions per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’)

Figure 10– change in GHG emissions per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’)

4.00 6.00 8.00 10.00 12.00 14.00 16.00 2012 2016 2020 2024 2028 2032 2036 2040 G H G e m iss io ns p er c ap it a (k g CO 2 -equ iv al ent /c ap /y ear ) year

GHG emission in saturated network

status quo 1 a+b a+b+c a+b+d a+b+c+d 7.00 9.00 11.00 13.00 15.00 17.00 19.00 21.00 2012 2016 2020 2024 2028 2032 2036 2040 G H G e m iss io ns p er c ap it a (k g CO 2 -equ iv al ent /c ap /y ear ) year

GHG emission in developing network

status quo 2 a

b c d

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Figure 11– change in GHG emissions per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’)

Figure 12– change in total energy consumption per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’)

7.00 9.00 11.00 13.00 15.00 17.00 19.00 21.00 2012 2016 2020 2024 2028 2032 2036 2040 G H G e m iss io ns p er c ap it a (k g CO 2 -equ iv al ent /c ap /y ear ) year

GHG emission in developing network

status quo 2 a+b a+b+c a+b+d a+b+c+d 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 2014 2019 2024 2029 2034 2039 To tal e ne rg y co ns um pt io n pe r cap it a (k W h/ cap /y ear ) year

Total energy consumption in saturated network

status quo 1 a

b c d

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Figure 13– change in total energy consumption per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’)

Figure 14– change in total energy consumption per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’)

10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 2014 2019 2024 2029 2034 2039 To tal e ne rg y co ns um pt io n pe r cap it a (k W h/ c ap /y ear ) year

Total energy consumption in saturated network

status quo 1 a+b a+b+c a+b+d a+b+c+d 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 2014 2024 2034 To tal e ne rg y co ns um pt io n pe r cap it a (k Wh/ cap it a/ ye ar ) year

Total energy consumption in developing network

status quo 2 a

b c d

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Figure 15– change in total energy consumption per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’)

From a physical point of view, with comparing the pipeline material masses and length we can see in the under developed network situation, in the interventions ‘c’, ’a+b+c’ and ‘a+b+c+d’ the network develops less than other scenarios and status quo situation, while the population increase is the same for all the scenarios over time. This is vice versa for saturated network condition. In the saturated network, interventions ‘c’, ’a+b+c’ and ‘a+b+c+d’ have more development in comparison with status quo and other scenarios.

Water supply and wastewater treatment values per capita also remains constant for all the interventions and do not change in comparing to status quo in year 2040, as well as it decreases significantly in comparison with year 2013 (Figure 16).

Figure 16- Changes in water supply and wastewater treatment volumes per capita in all interventions and both conditions

15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 2014 2024 2034 To tal e ne rg y co ns um pt io n pe r cap it a (k W h/c ap it a/y ear ) year

Total energy consumption in developing network

status quo 2 a+b a+b+c a+b+d a+b+c+d 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 2014 2019 2024 2029 2034 2039 wat er s up pl ie d an d wast ewat er t re at ed pe r cap it a (m 3/c ap ) year

Water and wastewater volume

Water Wastewater

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When we have considered the leakage percentage from water pipelines is 21% in the year 2013, and decreases to 18% in 2016 and remains constant until year 2040. Therefore, it changes the results of water supply a bit to compensate this loss! However, it does not affect the result of wastewater because it is a loss of water and does not included in the wastewater, which goes to treatment plant.

When it comes to economic analyses, the capital expenditure per capita and per cubic meter water demand is the same in all interventions and with status quo in both situations because of the assumption of same annual investment on wastewater distribution system in all interventions. However, operation and maintenance (O&M) expenses per capita will drop in interventions ‘d’ and ‘a+b+d’ in saturated situation. It also drops in interventions ‘c’, ‘d’, ‘a+b+c’, ‘a+b+d’ and ‘a+b+c+d’ in the under developed situation. The O&M expenses per cubic meter water also changes the same as operation and maintenance (O&M) expenses per capita in both conditions. Figures 17 - 21 illustrate the changes in capital expenditure and operation and maintenance expenses per capita under different interventions and conditions.

Figure 17- Changes in capital expenditure per capita in all interventions and both conditions 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 2012 2016 2020 2024 2028 2032 2036 2040 C ap it al e xpe nd it ur e pe r c ap it a (E ur o /C ap it a/Y ear ) year

Capital expenditure per capita

Capital expenditure per capita

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Figure 18– change in operation and maintenance expenses per capita in saturated condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’)

Figure 19– change in operation and maintenance expenses per capita in saturated condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’)

27.00 29.00 31.00 33.00 35.00 37.00 39.00 41.00 43.00 2014 2019 2024 2029 2034 2039 O pe rat io n & M ai nt ena ce e xpe ns es pe r c ap it a (E ur o /C ap /Y ear ) year

Operation and Maintenace expences in saturated network

status quo 1 a b c d 27.00 29.00 31.00 33.00 35.00 37.00 39.00 41.00 43.00 2014 2019 2024 2029 2034 2039 O pe rat io n & M ai nt ena ce e xpe ns es pe r c ap it a (E ur o /C ap /Y ear ) year

Operation and Maintenace expences in saturarted network

status quo 1 a+b a+b+c a+b+d a+b+c+d

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Figure 20– change in operation and maintenance expenses per capita in under developed condition (status quo, ‘a’, ‘b’, ‘c’, ‘d’)

Figure 21– change in operation and maintenance expenses per capita in under developed condition (status quo, ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’) It can be concluded from the tables and figures above, in the saturated network condition, interventions ‘d’ and ‘a+b+d’ present good results from environmental and economic points of view and can be adopted in the long-term plans in the saturated network condition. It is noteworthy that intervention ‘d’ presents greater reduction in GHG emission.

On the other hand, in the under developed network condition, which is an assumption that is more realistic for Trondheim city, interventions ‘c’, ‘d’, ‘a+b+c’ and ‘a+b+c+d’ present good and desirable results from environmental and economic point of view. However, the results, which are driven from interventions

30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00 2014 2024 2034 O pe rat io n an d m ai nt ena nc e expe ns es pe r cap it a (E ur o /C ap /Y ear ) year

Operation & Maintence expenses in developing network

status quo 2 a b c d 30.00 35.00 40.00 45.00 2014 2024 2034 O pe rat io n an d m ai nt ena nc e expe ns es pe r cap it a (E ur o /C ap /Y ear ) year

Operation & Maintence expenses in developing network

status quo 2 a+b a+b+c a+b+d a+b+c+d

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‘c’ and ‘a+b+c’ and ‘a+b+c+d’ are better results and ‘a+b+c+d’ gives the best result in comparison with other interventions. These interventions can be considered in long-term strategic plans for wastewater transport system of Trondheim city.

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6. CONCLUSIONS

6.1. Conclusions

The wastewater transport system has not been considered by itself in previous sustainability studies. In the present study, wastewater transport system in Trondheim city is analyzed from the sustainability point of view by dynamic metabolism model (DMM). The focus of this research is on environmental, energy, physical, and economic aspects of sustainable wastewater infrastructure management in the period 2014-2040, based on database 2000-2013. The aim of this study is to answer the research question: ‘Is it possible to demonstrate a methodology for comparing different pathways toward a sustainable management of wastewater transport systems?’

For answering the research question some possible actions inside the wastewater transport system are considered by defining different scenarios and intervention, and the impacts of different risk factors on the wastewater transport system have been analyzed and compared with ‘status quo’ with a constant development of the wastewater network. Two different conditions, which are investigated in this study are: the saturated wastewater transport network, which does not need to improve, and an under developed network with a constant development as average of 2000-2013 for period 2014-2040. To obtain this aim four interventions ‘a’ (infiltration and inflow reduction), ‘b’ (increasing rehab rate), ‘c’ (extension of system by new pipes), ‘d’ (energy management) along with four combinations of these interventions ‘a+b’, ‘a+b+c’, ‘a+b+d’, ‘a+b+c+d’ have been tested.

Based on the obtained results, the overall conclusions are as follows:

- In the saturated network condition, interventions ‘d’ and ‘a+b+d’ present good results in decreasing the GHG emission, energy consumption, and economic aspects. By automation/optimization of pumping stations, using more no-dig technology in rehabilitation of network and using more electric vehicles and instrument, cities can have energy management and significant reduction in diesel energy consumption, which is assumed 20% by year 2040 in this study. The other intervention is a combination of energy

References

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