Study of the Dilution of a Chemical Spill Through Tracer Experiments in the Käppala Association's Sewerage Network, Stockholm

DEGREE PROJECT IN ENVIRONMENTAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2021

Study of the dilution of a chemical spill through tracer experiments in The Käppala Association's sewerage network, Stockholm

JEROME SCULLIN

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT STUDY OF THE DILUTION OF A CHEMICAL SPILL THROUGH TRACER EXPERIMENTS IN THE KÄPPALA ASSOCIATION’S SEWERAGE NETWORK, STOCKHOLM

JEROME SCULLIN

Supervisor ELZBIETA PLAZA

Examiner ELZBIETA PLAZA

Supervisor at Käppalaförbundet MARCUS FRENZEL

Degree Project in Environmental Engineering and Sustainable Infrastructure KTH Royal Institute of Technology School of Architecture and Built Environment Department of Sustainable Development, Environmental Science and Engineering SE-100 44 Stockholm, Sweden

TRITA-ABE-MBT-21421

Summary in Swedish

Avloppsreningsverk spelar en viktig roll för att skydda miljön från mycket av det avfall som produceras av människor. Detta inkluderar inte bara mänskligt avfall utan allt som tar sig in i ett avloppssystem, till exempel gråvatten, dagvatten och potentiellt farliga kemikalier från bland annat industriutsläpp. Effekterna av ett kemiskt utsläpp kan vara katastrofala om det kommer in i ett avloppsreningsverk, vilket resulterar i ineffektiv behandling av inkommande vatten under längre perioder (Söhr, 2014). Detta är ett dilemma i urbana avloppssystem – ska man låta ett kemiskt utsläpp ledas förbi ett avloppsreningsverk, eller försöka behandla hela eller en del av utsläppet och riskera att skada mikroberna i den biologiska reningsprocessen (Schütze, 2002). För att beslutsfattare och processingenjörer vid avloppsreningsverk ska kunna fatta rätt beslut om vilka åtgärder som ska vidtas vid utsläpp måste egenskaperna för det specifika avloppsnätet definieras.

Syftet med detta projekt är att uppskatta transportparametrar och karakterisera utspädning i nätverket genom att utföra en serie spårningsförsök i Käppalaverkets upptagningsområde. För att nå syftet fanns det flera mål som genomförts: • Genomföra en litteraturstudie • Skapa en förutsägbar modell i Excel baserad på flödesdata längs Käppalaförbundets tunnelsystem • Genomföra en serie spårningsförsök vid flera punkter längs tunnelsystemet • Strukturerad datalagring av resultaten så att data är lätt att hitta för framtida projekt

Metoderna kan delas i två: modellering och försök. För att skapa en modell och simulera transport av ett ämne i nätet får man definiera relevanta ekvationer. För den hydrauliska delen av modellen användes Manning-Strickler-ekvationen. Resultaten från detta användes sedan i den förenklade formen av advektion-spridningsekvationen (ADE). Tunnelsystemet uppdelades i flera sektioner med samma egenskaper såsom form och geometri, och en anpassad form av ADE användes emellan sektionerna. För att nå framgång i försöken krävdes att rätt spårämne valdes. Uranin användes i försöken på grund av sina ogiftiga och stabila egenskaper och den låga detektionsgränsen. Injiceringspunkterna låg gradvis längre bort från inloppet; Försök 1 var 9km från verket till nästan 46km vid Arlanda flygplats för Försök 3.

Resultaten från simuleringarna användes för att planera injiceringstid, start- och stopptid för provtagningen och provtagningsfrekvens. Resultatet från första försöket användes för att kalibrera modellen inför de andra försöken. Resultaten från alla försök visade att en dispersionskoefficient på 1.55m2/s, som är ett mått på utspädning i nätet, verkar tillämpligt till hela tunnelsystemet. Koefficienten kan dock vara högre i de kommunala näten. En djupberoende metod för att härleda Mannings tal formulerades, men det kräver ytterligare validering.

Från alla tre försöken kan vi härleda ett förhållande mellan avstånd från inlopp och toppkoncentration samt avstånd från inlopp och varaktigheten av genombrottskurvan. Toppkoncentration visar ett linjärt eller kanske logaritmiskt förhållande med distans, och varaktigheten av genombrottskurvan visar ett starkt linjärt förhållande. Kunskaper om detta är viktigt när man vill genomföra en riskbedömning av ett kemiskt utsläpp i upptagningsområdet eftersom det ger en insikt om hur det kan påverka den biologiska reningen i ett avloppsreningsverk.

Sammanfattningsvis fungerar den enkla formen av ADE bra, men viss avvikelse ses i experiment 3. Detta beror kanske på möjliga övergående lagringsprocesser vid pumpstationerna längs tunnelsystemet. En enda dispersionskoefficient, som är ett mått på utspädning, är tillämplig i hela huvudtunnelsystemet, men spridningen i kommunala nätverk är sannolikt högre. Ytterligare arbete behövs inom dessa kommunala nätverk för att kvantifiera deras effekter.

På grundval av resultaten från detta projekt rekommenderas ytterligare forskningsundersökningar om vad som händer med föroreningar i avloppsreningsverket. Eftersom slammet vid Käppalaverket används för biogasproduktion och är Revaq-certifierat för användning på jordbruksmark är föroreningsnivån i slammet mycket viktigt både ur produktivitets- och hälso- och säkerhetsperspektiv.

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Abstract

Wastewater treatment plants (WWTPs) play a vital role in protecting the environment from much of the waste produced by humans. This includes not only human waste, but everything that makes its way into a sewerage system including greywater, stormwater, and potentially hazardous chemicals from, inter alia, chemical spills. The effects of a chemical spill if it enters a WWTP can be disastrous, resulting in the ineffective treatment of incoming water for prolonged periods of time (Söhr, 2014). This can lead to one of the dilemmas of urban wastewater systems, notably, whether it is more damaging to allow a chemical spill to bypass a WWTP, or to attempt to treat all or some of the spill and risk damaging the microbes working in the biological treatment processes (Schütze, 2002). In order to better inform policy makers and process engineers at WWTPs of which measures to take in the event of a spill, solute transport characteristics of a specific sewerage network must be defined.

A series of tracer tests were performed along The Käppala Association’s northern sewerage network to determine these solute transport characteristics, notably the dispersion coefficient which strongly affects the level of dilution that occurs between the injection point and the inlet. A simple solute transport model, carried out in Excel, was created using the Advection-Dispersion Equation (ADE) and the Manning-Strickler equation to relate flow measurements to flow velocity. Results from the experiments show that a dispersion coefficient of 1.55m2/s appears to be applicable throughout the whole of the tunnel network. A depth dependent Manning-Strickler coefficient seems to describe the flow-velocity relationship, however, this method has not been validated. The ADE begins to lose accuracy in describing solute transport as the distance from the inlet and the number of pumping stations the plume goes through increases.

Keywords

Sewerage network; Tracer tests; Solute transport; ADE; Breakthrough curve; Uranine

Acknowledgements

I would like to give thanks to Käppalaförbundet for offering the opportunity to carry out this master’s thesis. As well, I would like to thank all the staff at Käppala who have contributed in any way to this report and for welcoming me onboard for the duration. I extend a special thanks to Marcus Frenzel, my supervisor at Käppala for being suggesting the project, being available throughout, keeping the project on track, and brewing some fantastic beer, and to Pontus Nordgren for helping immensely in the sampling aspect of the project.

I would also like to extend a thank you to Viktor Plevrakis of Geosigma who helped with the planning and carrying out injections, and carried out the lab analyses, and to John Hader of Stockholm University for helping to develop my discussion and linking the findings of this thesis to the ECORISK2050 project.

I would like to give a sincere thanks to Elzbieta Plaza for the critical guidance given in structuring my thesis into a coherent text and the sage advice given throughout. Your experience was invaluable.

Finally, I would like to thank my mother for carrying out the thankless task of doing a final editorial check on this thesis. I am immensely grateful for this, and all the support she, and the rest of my family, have given me throughout the duration of my education.

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Table of Contents

1. Introduction ...... 1 1.1 The Dilemma of Urban Wastewater Systems in Relation to Chemical Spills ...... 1 1.2 Käppala WWTP’s Northern Sewerage network ...... 1 2. Aim and Objectives ...... 3 3. Solute Transport Theory in Relation to Sewerage ...... 4 3.1 Advection-Dispersion Equation...... 6 3.1.1 Advection ...... 6 3.1.2 Dispersion ...... 7 3.1.3 Advection + Dispersion ...... 7 3.1.4 Additional Processes ...... 8 3.2 Tracer Tests ...... 8 4. Methods ...... 10 4.1 Simulation ...... 10 4.1.1 Division of Network into Model Reaches ...... 10 4.1.2 Hydraulic and Solute Transport Properties ...... 11 4.1.3 Collection of Data – The SCADA System ...... 12 4.1.4 Extraction of Data ...... 13 4.1.5 Setting up the Excel Model ...... 13 4.1.6 Calibration of Results ...... 14 4.2 Experimental...... 15 4.2.1 Tracer Material, Injection Points, and Injected Mass ...... 17 4.2.2 Experimental Timing and Sampling Regime ...... 18 4.2.3 Lab technique for Determining Tracer Concentration ...... 21 5. Results ...... 22 5.1 Typical Flows at the inlet ...... 22 5.2 Comparison of Modelled and Measured Values ...... 23 5.2.1 Experiment 1: Spisen, Lidingö – Käppala WWTP ...... 23 5.2.2 Experiment 2: Sollentuna – Käppala WWTP ...... 25 5.2.3 Experiment 3: Arlanda airport – Käppala WWTP ...... 27 5.2.4 Results Summary...... 30 5.3 Application to ECORISK2050 ...... 31 5.4 Suggested Improvements ...... 32 6. Discussion ...... 33 6.1 Solute Transport in The Käppala Association’s Sewerage network ...... 33 6.2 Transferability of Results and Limitations...... 33 7. Conclusions ...... 34 8. Further Research ...... 35 9. References ...... 36 Appendices...... 38

Appendix 1 ...... 38 Appendix 2 ...... 39

Table of Figures

Figure 1 – The Käppala Association's tunnel network and the eleven member municipalities. The locations of the injection points are highlighted. Red points are location of pumping facilities. Source: Käppalaförbundet ...... 2 Figure 2 - Map showing the locations of the two pumping stations and inverted siphon along the network. Map generated using Envomap (Gemit solutions). Dyarledningen image courtesy of Käppalaförbundet ...... 3 Figure 3 - Solute transport due solely to advective forces. The plume at any given point remains the exact same in volume and concentration as at the injection point, X1...... 6 Figure 4 - Behaviour of a solute plume due solely to dispersive forces. The centre of mass of the solute plume remains the same, however, the affected water volume increases as the solute is diluted and the concentration decreases...... 7 Figure 5 - Behaviour of a solute plume under the influence of both advective and dispersive solute transport forces. The total mass of solute remains the same...... 7 Figure 8 - Left: From left to right, uranine control samples with 0.01, 0.1, and 1mg/l of uranine in deionized water. Right: Uranine concentration of 302.7mg/l in wastewater at the inlet. Image edited to remove glare...... 17 Figure 9 - Locations of injection point for a) Experiment 1, b) Experiment 2, and c) Experiment 3. Blue lines show approximate location of municipal networks not under The Käppala Association's control but connected to the tunnel network. Maps created using Envomap (Gemit Solutions) ...... 19 Figure 10 – a) Slug injection at the Spisen facility on Lidingö, Experiment 1. b) Injection at a manhole in Sollentuna municipality using the funnel pictured, Experiment 2. c&d) Injection at Arlanda airport, Experiment 3. Tracer sediment seen at the base of container in image d) was rinsed out using tap water...... 20 Figure 11 - Autosampler positioned at the inlet to the WWTP ...... 21 Figure 12 - Flow data from Käppala WWTP's inlet, with precipitation data for the time period the experiments were carried out. Although some double peaks are visible, for example in the last four periods, they are often not as pronounced and more regularly follow a regular wave form with a peak at around midnight and a trough at around 09:00. Precipitation values are the 24 hour total, from 06:00-06:00 (SMHI, 2021) ...... 22 Figure 13- Predicted breakthrough curves for Experiment 1, with experiment results in red. The yellow section shows the estimated visibility of uranine at a given concentration, with values above 1mg/l clearly visible...... 24 Figure 14- Fitted model curve for with results for Experiment 1. The small irregularity seen could be real, or could be due to a mix up in labelling sampling bottles as such a feature was not seen in any of the other experiments...... 25 Figure 15 - Predicted breakthrough curves for Experiment 2, with experiment results in red. The time difference between peaks is approximately 36 minutes, while the dispersion shows a strong resemblance...... 26 Figure 16 - Flow statistics for the three flows relevant to the Lidingö → WWTP model reach. Upstream values were consistently higher than downstream values. L-STR = Långängstrand. Statistics based on flow between 28/03/2021 - 01/05/2021. Graph created using aCurve (Gemit Solutions).Inflow from N. sector ...... 26 Figure 16 - Flow statistics for the three flows relevant to the Lidingö → WWTP model reach. Upstream values were consistently higher than downstream values. L-STR = Långängstrand. Statistics based on flow between 28/03/2021 - 01/05/2021. Graph created using aCurve (Gemit Solutions)...... 26 Figure 17 - Fitted model curve with results for Experiment 2. Overall, the model has very good agreement with the results due to calibrations to the model...... 27 Figure 19 - Inside the tunnel network. The base of the tunnel is cast in concrete and has a smoother surface than the walls, meaning the effective Manning-Strickler coefficient is depth dependant. Source: Käppalaförbundet ...... 28 Figure 18 - Predicted breakthrough curve prior to Experiment 3, with experiment results. Note a difference in peak concentration arrival time of almost two hours...... 28

Figure 20 - Fitted model curve for with results for Experiment 3. The shape of the main breakthrough curve shows an adequate resemblance; however, the peak is significantly higher (~9%) in the model, and the tail is noticeably more pronounced in the experimental results...... 29 Figure 21 – Peak concentration at the inlet using an equalized injection mass of 1kg. Both logarithmic (orange) and linear trendlines approximate the relationship between dilution and distance from the WWTP...... 31 Figure 22 – Breakthrough curve durations, bounded by the 5%-of- peak concentrations before and after the peak arrival...... 32

Table of Tables

Table 1 - Ideal conservative tracer properties, in approximate order of importance for this project. Table amended from Leibungut, et al., 2009 ...... 9 Table 2 - Summary of empirical equations used to calculate the dispersion coefficients for Experiment 1 ...... 23 Table 3 - Parameter values used after the final calibration of the model ...... 30

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1. Introduction

1.1 The Dilemma of Urban Wastewater Systems in Relation to Chemical Spills

Although often cited as a source of contaminants to the natural environment, wastewater treatment plants (WWTPs) are a crucial aspect of protecting the environment from the increasing amount of wastewater our cities produce as they become more populated, and our way of life becomes ever more industrialised. The occurrence of a chemical spill that has the potential to kill off a microbial population in a WWTP’s biological reactors could have dire consequences for the receiving waterbody. Given Sweden’s cool climate, repopulating a biological reactor could take a significant amount of time (Söhr, 2014), after which the environmental and economic costs are likely to be unacceptably high.

This leads to a common dilemma of urban wastewater systems; in the event of an upstream spill event, is it environmentally less damaging to let the polluted incoming wastewater completely bypass any treatment, thereby saving the microbial populations of the WWTP, and allowing the treatment of sewage to resume once the plume has passed, or do you attempt to treat the polluted wastewater and risk killing of the delicate microbial communities in the WWTP? The first option, which would protect the WWTP enough so that high quality effluent would continue to be released to the receiving water body, could lead to a short lived, acute toxicity in the area surrounding the outlet or overflow, whereby wildlife quickly recovers. Likewise, the pollutant could be persistent, or pollute sediments, and remain in the system over a larger timescale, having a chronic negative impact on the surrounding area. In the second option, trying to treat the polluted water may not have any effect other than to kill off carefully cultivated microbial communities, and the pollution is released largely unaffected leading to a lose-lose scenario. An interim, likely costly, solution would have to be put in place until the microbial populations have recovered. Alternatively, there may be a short-term reduction in efficiency in the treatment process, but the isolation of the polluting chemical within the WWTP reduces the impact on the receiving waterbody.

This dilemma has warranted the issue to be investigated by the ECORISK2050 project, whose overarching aim is to investigate the environmental risks chemicals pose in the future (https://ecorisk2050.eu/). The ECORISK2050 project can be broken down into four main parts that investigate the fate of chemicals in the environment. These include creating scenarios, quantifying exposure, determining the effects, and calculating risks & proposing mitigation measures (ECORISK2050, 2021). This project will provide data that will aid in quantifying exposure that a WWTP could expect from a chemical spill by carrying out a series of tracer tests along the length of The Käppala Association’s northern sewerage network. Using the results from these experiments, in tandem with previous research on the industries and chemicals used within Käppala WWTP’s catchment, the project hopes to offer a screening tool to enable proactive upstream management to reduce the likelihood of a spill within the catchment (J. Hader, pers. comms, 04/06/21). A longer- term goal could be to produce a set of tools that may be used by policy makers or process engineers at the WWTP to assess the chemical risk within the catchment, and to take appropriate actions should a spill occur (J. Hader, pers. comms, 04/06/21).

1.2 Käppala WWTP’s Northern Sewerage network

The Käppala Association’s (Käppalaförbundet) wastewater treatment plant (WWTP) is situated on the southeast corner of Lidingö outside Stockholm. Completed in 1969 and extended in 2000, the facility has a capacity of up to 700,000 PE (persons equivalent) and treats water from its eleven member municipalities with a population of over 540,000 (The Käppala Association, n.d.). Both the WWTP and main tunnel system are maintained by the Käppala Association, with smaller municipal networks and connections along its length maintained by the individual municipalities. The tunnels, for the most part, were constructed by blasting the bedrock and have a constant slope of 1‰ throughout the whole network. The base of the tunnels are cast in concrete and the blasted rock walls are sprayed with concrete (see Figure 19). Small stretches of the network after each of the pumping stations and at the dykarledningen are composed of circular concrete pipes, each less than two

kilometres in length. The northern tunnel system stretches from the WWTP to Sigtuna to the northwest of the works, with branches reaching Vallentuna and Upplands-Bro municipalities, and is comprised of a total length of approximately 65km (Figure 1) (The Käppala Association, n.d.).

The red line shows Käppala’s sewerage network that is made up of tunnels and pipes. Red dots are pumping stations. . Injection Point 1 . Injection Point 2 . Injection Point 3

In terms of facilities and control systems along the northern sector of the network, where the experiments were carried out, there are three major flow altering sections. These are the Antuna pumping station, Edsberg pumping station, and the dykarledningen which is an inverted siphon structure which transports the water from Danderyd to Lidingö by travelling under the waterbody, Lilla Värtan, which separates them. The pumping stations are always engaged, however, the pumping rate is dependent on the level measurements in the sumps of the pumping station. As the depth, and therefore amount of water flowing in, increases, so too does the pumping rate. There are no controls in place at the dykarledningen, and the water flows based on pressure head differences at the inlet and at the outlet of the structure, which is two metres below the inlet’s level. The locations of these facilities are shown in Figure 2.

The sewerage system is a separated system which receives both household and industrial wastewaters. During storm events, however, significant quantities water makes its way into the system due to infiltration, broken pipes, and misconnections. There are no separate storm water basins in the system, where water is redirected for storage during a storm event. Instead, excess water is stored

within the tunnel itself, which acts as a storage basin. This gives the WWTP the same control over the amount of incoming water as a stormwater storage basin, and prevents the washout of the biological reactors. Given the significant backwater effects which could occur in such a scenario, as well as the heterogeneity and uniqueness of storm events, the experiments were carried out in dry weather conditions.

Figure 2 - Map showing the locations of the two pumping stations and inverted siphon along the network. Map generated using Envomap (Gemit solutions). Dyarledningen image courtesy of Käppalaförbundet

2. Aim and Objectives

The primary aim of this project is to estimate solute transport parameters and characterise dilution in the network by carrying out a series of tracer test in Käppala WWTP’s northern sewerage network. The data collected will be further used as part of the ECORISK 2050 project. This is an EU wide project which aims to investigate chemical spills and pollution events upstream of a WWTP to be able to assess the probable damage to a WWTP’s biological treatment process in the event of a spill. Through this, upstream management measures will be suggested to mitigate the risk (J. Hader, pers. comms, 04/06/21). The potential to build on this further include creating a tool that could be utilised by process engineers to aid in deciding whether to let polluted water bypass the work in order to save the microorganisms responsible for treating sewerage, or to treat the incoming sewerage. A key aspect of this tool would be understanding solute transport in the sewerage network, in this case Käppala WWTP’s northern sewerage network. Of particular interest is the dilution of the contaminant between point-of-entry and the inlet of the WWTP.

Chemical spill events can, and have, caused substantial damage to biological wastewater treatment processes both abroad and within Stockholm in the past (Söhr, 2014; Xiao, et al., 2015). Sewerage networks are unique to the catchment they were built to serve, and as such, the same chemical spill will respond differently in each network. Tracer experiments performed in a sewerage network will give an insight into the unique hydraulic characteristics of the tunnel and pipe system and give an indicative value for typical dispersion coefficients along its length. By performing this series of tracer tests, it is hoped to find out if these characteristics remain more or less constant along the length of the network, given method of construction and material used in construction is the same, or if they change significantly between different tunnel sections. In the case of the latter, more small-scale targeted tracer tests would need to be performed.

The results from this project will be built upon further by a collaboration between The Käppala Association and Stockholm University within the ECORISK2050 project to assess zones of high/low risk in relation to distance from the WWTP and industrial activities carried out in the serviced area. To obtain this knowledge a number of objectives are outlined below:

• Perform a literature review to investigate sewer specific hydraulics and solute transport • Create an Excel based predictive model based on flow values along the sewerage network provided by The Käppala Association • Perform a series of tracer experiments along the length of Käppala WWTP’s sewerage network, and beyond, in the case of the Arlanda injection (Experiment 3) • Structured storage of data so results can be easily found and extracted for future projects

System Borders and Delimitations As a major part of this thesis is based on modelling, clear and concrete system borders and delimitations must be set out.

• The system boundaries for each experiment are defined by the injection point, the inlet to the WWTP, and all contributing connections within the sewerage network between. This means the system boundaries will vary from one injection point to the next based on location. • The mathematical boundary conditions are set out in Section 4.1.2 • The experiments were carried out during dry weather to negate the effects of inflow due to precipitation. The results are therefore only applicable during dry weather flow conditions. • As a result, overflow and storm water storage structures are not considered. • The main focus of this work is on solute transport. Although the hydraulic behaviour and processes in the network is paramount to accurate prediction, it has nonetheless been simplified – uniform flow conditions are assumed along the length of the network, except for at the dykarledning where water flows under pressure (see section 4.1.2) • Experiments will use Uranine, a fluorescent conservative tracer. In reality, few polluting chemicals can be defined as conservative, so the real behaviour of chemicals will vary, some significantly.

Conservative tracers are still useful as it gives a better indication of the hydraulic and solute transport properties of the system, such as the Manning-Strickler coefficient for the tunnels, and the dispersion coefficient. Additionally, the results could be described as “neutral” in that they are easier for other chemicals to be compared to. The tracer is assumed to undergo no biological, chemical, or physical degradation throughout the experiment.

3. Solute Transport Theory in Relation to Sewerage

A solute is defined as a component dissolved in a solvent, in this case wastewater. Therefore, to understand how a solute is transported through a system, a good understanding of how the solvent behaves in the system is necessary. Many of the equations used in the design of sewerage systems are

similar, or the same, as those used in natural hydrological settings (Schütze, 2002). Compared to a natural river system, there are comparatively few unknowns in a sewerage system. For example, the tunnel geometry is well defined, fixed, and remains constant over a known stretch, with the locations of changes in geometry known. The average slope has been built to exact specifications and is as accurate as the tools available at the time of construction allowed, and there is often a network of flowmeters spread across the system allowing for a real-time relay of data. Despite this, modelling the hydraulic behaviour within a sewerage network is nonetheless, complex.

The complexity of the modelling is a function of the complexity of the sewerage network. Facilities such as pumping stations and stormwater storage tanks, or characteristics such as the number of bends or flow transition zones within a network all add varying degrees of complexity to the systems flow regime, however, can be modelled if enough data is available. These complex models solve the full set of Saint-Venant equations, which are based on the continuity of momentum (Eq.1) and momentum (Eq. 2) equations. The Saint-Venant equations make a number of assumptions, which can be found in Halvik, 1998. Further assumptions can simplify the application of these equations, such as assuming backwater effects to be negligible (Schütze, 2002), but may result in a less accurate model. The assumptions made are largely a factor of the requirements of the model, the complexity of the system, and the availability of reliable data. Models which utilise the full set of Saint-Venant equations are commercially available, with the MOUSE package used in MIKE+ (formerly MIKE- Urban) developed by DHI, probably the most well known in Sweden.

In this project, a simplified hydraulic model is used based on the Manning-Strickler equation (Eq. 3). This equation is applicable in open channel uniform flow scenarios, and is often used in the design of gravity driven sewerage systems (Bizier, 2007). Its versatility is shown in its ability to simultaneously calculate the water velocity at a given flow, required pipe diameters or dimensions for expected flows, and its capability to simulate different construction materials through different Manning-Strickler coefficients (Hager, 2010). An important aspect of designing a sewerage network is to factor in a self- cleansing effect which can be done by ensuring a constantly high velocity so that sediment doesn’t deposit, or by ensuring that a sufficiently high velocity is reached daily to remove any deposited sediments or detritus. More recently, an approach based on a minimum tractive force or shear stress approach has been suggested (Nalluri & Ab Ghani, 1996; Bizier, 2007). As this network was constructed in 1960s, the minimum velocity approach was likely used. Knowledge of this is useful when testing the validity of estimated or calculated Manning-Strickler coefficients in the model. The results from the Manning-Strickler equation gave the required velocity parameter from which the Advection-Dispersion Equation (ADE) could be solved.

Box 1:

Continuity of 휕퐴 휕푄 Eq. 1 + = 0 momentum: 휕푡 휕푥

Momentum 풒 푽 휕푦 1 휕 푄2 1 휕푄 Eq. 2 푺 − 푺 = − ퟏ ퟏ + + ( ) + 풐 풇 품푨 휕푥 𝑔퐴 휕푥 퐴 𝑔퐴 휕푡

A Cross-sectional area of channel [m2] So Bottom slope [- or L/L] Q Flow rate [m3/s] Sf Friction slope [- or L/L] x Longitudinal distance [m] y Channel depth [m] t Time [s] g Gravity [m/s2] q1 Lateral inflow per unit length [m2/(s∙m)] V1 Longitudinal component of the velocity of the water inflow represented by q1 above [m/s]

Bold – Kinematic wave approximation (requires extra assumptions) Red + Bold – Diffusive wave approximation (requires extra assumptions) Green + Red + Bold – Full Saint-Venant equation

Manning-Strickler Equation 1 2 1 Eq. 3 푉 = 푅3푆2 푛

V Velocity [m/s] R Hydraulic radius [m] n Manning-Strickler coefficient [m1/3s-1] S Slope [- or L/L]

3.1 Advection-Dispersion Equation

The advection-dispersion equation is almost exclusively applied in 1D form when calculating solute transport within a sewerage system (Schütze, 2002). Doing so assumes a uniform distribution of solute across the waterbody’s cross-section (Runkel & Bencala, 1995) This is due to the longitudinal distance being many orders of magnitude larger than the distance in the y-axis and z-axis, rendering a 3D computation unnecessary. This greatly simplifies calculation, both conceptually and computationally. The ADE was developed using the conservation of mass concept, which states that the mass of a substance accumulated in a given volume of water is equal to the incoming mass of substance into the volume minus the outgoing mass from the given volume.

The simple form of the ADE is made up of two main processes for transporting solutes. The first is advection, which is describes the mean longitudinal velocity of water. The second is dispersion, which describes how a solute plume spreads as it moves downstream and is a combination of dispersion due to velocity difference within the water column, and molecular diffusion. These processes will be explained in greater detail in the following sections.

3.1.1 Advection

The advective component of the ADE is the most important for the timing of the arrival of the peak concentration of the plume. This process is responsible for the movement of the plume according to the mean longitudinal velocity. If just this process was present, the concentration at the sampling point would be equal to the concentration at the injection point as no dispersion or diffusion would have taken place. This is visualised in the Figure 3 below.

Figure 3 - Solute transport due solely to advective forces. The plume at any given point remains the exact same in volume and concentration as at the injection point, X1

3.1.2 Dispersion

This process includes both molecular diffusion and dispersion due to velocity differences within a volume of water. This process is responsible for the dilution of a solute over time. Even in a body of water with no movement, molecular diffusion would still play a role and dilute the solute, increasing the volume of water with solute in it. In moving water, molecular diffusion plays a minor role, with longitudinal dispersion orders of magnitude larger than the diffusive component alone. As dispersion is linked to the velocity profile across a waterbody’s cross-section, it is logical then that a rougher tunnel surface, or a higher Manning-Strickler coefficient, and higher mean velocities should increase the dispersion exhibited along a tunnel reach, leading to a variable parameter, rather than a fixed coefficient. In this model, a single dispersion coefficient will be used at all flows in order to simplify it, and its suitably discussed in later sections. Figure 4 shows how the dispersive forces effect a solute plume over time in a waterbody with no longitudinal velocity.

No flow, stagnant water

Figure 4 - Behaviour of a solute plume due solely to dispersive forces. The centre of mass of the solute plume remains the same, however, the affected water volume increases as the solute is diluted and the concentration decreases.

3.1.3 Advection + Dispersion

The combination of the above processes, illustrated in Figure 5, results in a more realistic, yet still simplistic, representation of solute transport in a waterbody. In this representation, the centre of mass of the plume moves in accordance with the mean longitudinal velocity. Dilution of the solute occurs due to dispersive forces, and is represented by the spread of solute or the increase in affected water volume. This project used this model of solute transport when attempting to represent the reality of how a tracer element is transported through the Käppala WWTP’s northern sewerage network.

Figure 5 - Behaviour of a solute plume under the influence of both advective and dispersive solute transport forces. The total mass of solute remains the same.

3.1.4 Additional Processes

The decay of a solute or retardation caused by adsorption to sediment, can be appended to this equation if such processes are relevant and the decay order or adsorption coefficient, KD value, are known. The effect of applying a decay constant is that solute mass is removed from the system. If such a process is relevant in a system, it may be necessary to consider decay by-products. The effect of retardation is that it delays the arrival of a solute, however, no solute mass is lost from the system. A more complicated process to incorporate into the ADE is that of transient storage caused by eddy and ripple effects, and hyporheic zone mixing, however, several representations do exist, such as Bencala & Walters (1983) and Wörman, et al. (2002). With the aim of a sewerage network to transport wastewater from the greatest number of customers over the least distance, both of these processes are actively designed out of a sewerage network as eddy and ripple pools would represent an inefficiency in transport, and the presences of a hyporheic zone would indicate an unacceptable level of sedimentation in the network. Temporary storage, such as in the sump of a pumping facility, could manifest itself in a similar way to these processes, however, and produce the associated elongated tail.

Box 2:

Advection- 휕퐶 휕퐶 휕2퐶 Eq. 4 = −푉 + 퐷 Dispersion Equation 휕푡 휕푥 퐿 휕푥2

C Concentration of a given solute at time t and position x in the longitudinal axis [g/m3] V Advective velocity in the x direction averaged over the CSA [m/s] DL Longitudinal dispersion coefficient [m2/s]

휕퐶 Advection component −푉 휕푥 휕2퐶 Dispersion component +퐷 퐿 휕푥2

A decay constant, λ, and retardation constant KD, can be added if the solute decays over time or adsorbs to sediment The full derivation of the ADE from the conservation of mass equation can be found in Chapter 5 of Runkel & Bencala (1995)

3.2 Tracer Tests

Tracer tests make use of natural or artificial tracer elements to gain a better understanding into how an individual waterbody behaves. The behaviour of the tracer element, which can be measured at a selected point or points downstream of injection, is used as a proxy for water and can give a unique insight into the dynamics of the waterbody under study, as well as providing data from which key parameters, such as dispersion and mean longitudinal velocity, can be calculated more reliably (Leibundgut, et al., 2009). Additionally, tracer tests are used to calibrate and validate models of a particular system and can be vital in identifying unaccounted for processes (Leibundgut, et al., 2009).

In order to make the most of a tracer test, an in-depth pre-planning stage must be carried out. Of primary importance is defining the problem the tracer test aims to help solve, having a clear idea of the results you hope to attain, and how these results will be used in resolving the defined problem. After this, comes the pre-planning and preparation stage. This includes getting acquainted with the waterbody under study, acquiring any available data on the waterbody, and determining the likely values for the main processes that determine the fate of a solute, such as an estimation of the mean

velocity, flow rate, and dispersion in a system. This can be done through data acquisition of present or past flows, and dispersion can be estimated either using empirical calculations, or by using a reference value taken from literature (Field, 2003; Leibundgut, et al., 2009; Lepot, et al., 2014). Within a sewerage network, additional considerations have to be made for the presence man-made structures, such as pumping stations and valves which could significantly alter flow along the network (Schütze, 2002).

Determination of the tracer material is done with reference to the aims of the project and the physical and chemical properties of the water. Additionally, biological effects must be considered given the high organic content and microbiology of wastewater. Tracers, broadly speaking, fall into two main categories: natural and artificial. Artificial tracers, one of which was used in this project, can be further categorised into four main categories: fluorescent dyes (Uranine, Rhodamine WT), salt tracers (Sodium Chloride), radioactive nuclides (3H), and floating particles (Lycopodium plant spores) (Leibundgut, et al., 2009). It is beneficial to have an idea of the types of tracers commercially available and their relative costs prior to the planning stage, and to carry out a more in-depth selection once the characteristics of the waterbody and resources available for the project are confirmed. The ideal tracer characteristics are given in Table 1.

Table 1 - Ideal conservative tracer properties, in approximate order of importance for this project. Table amended from Leibungut, et al., 2009 Tracer Property Required to be: 1 Toxicity/Ecotoxicity None - minimal 2 Solubility High 3 Fluorescence intensity High 4 Detection limit Low 5 Chemical and biological stability High 6 Sorption processes None - negligible 7 Costs in material and analysis Low 8 pH dependence Low 9 Photolytic stability High 10 Temperature dependence Low

Once a tracer has been selected, in this case uranine, planning for injection and sample collection can begin. The type of tracer must be known before planning for the injection as different chemicals will have different limits of detection, which influence the required mass to be injected, as will the flow rate, distance between injection point and sampling point, and dispersion. There are several ways in which the mass of tracer can be injected ranging from inverse modelling to empirical estimations (Kilkpatrick, 1989; Field, 2003; Leibundgut, et al., 2009; Lepot, et al., 2014). An additional aspect when deciding on the injected mass in this project was the lack of quick, onsite method of indicating an indicative rather than absolute measurement of tracer levels, so that only relevant samples were sent for analysis. The results being that the incoming peak had to be detectable by the naked eye, increasing the limit of detection several orders of magnitude. To aid this task, an inverse modelling method was used that ensured the peak concentration of uranine reached these elevated levels. This required much more tracer to be injected, however, helped ensure the success of the experiments.

The sampling regime decided upon is a product of tracer used which has an impact on the analysis technique, and the estimated breakthrough curves. These determine the effort required in setting up a sampling regime, with logging or online devices significantly less labour intensive, both in planning and analysis, than wet sampling methods. Some tracers have to be analysed in a laboratory. Others can be measured using instruments, but the instruments are expensive and samples are therefore often analysed in the lab, while other, such as salt tracers which are generally only used in small scale

experiments (Leibundgut, et al., 2009) have to use instruments as wet sampling may not be feasible. Kilpatrick & Wilson (1989) suggests that around 30 samples between the two 10%-of-peak- concentration values will give enough data from which a well-defined breakthrough curve can be obtained. As Kilpatrick & Wilson (1989) was giving recommendations for a natural river system, some flexibility was taken to this approach when creating the sampling regime, most notably taking a 5%- of-peak-value concentration as the boundary values.

Assuming the previous steps regarding pre-planning investigation, experimental planning, and sampling regime have resulted in successful experiments, the results are processed, analysed, and interpreted in relation to the initial problem definition.

4. Methods

This project involved work in both simulation of solute transport through a simple Excel model and carrying out tracer experiments using the simulation results. The simulations were used to calculate the required mass to be injected for each experiment and to plan a sampling regime. In turn, the experiments provided necessary data with which to calibrate and improve the model. Given the number of unknowns when carrying out the initial experiment, a flexible approach was taken to how the experiments and calibrations were carried out.

4.1 Simulation

As a sewerage network could be described as a relatively closed, controlled, and predictable environment compared to natural river systems, an Excel based model was set up for the multiple purposes of predicting the mass of uranine to inject prior to experiment, determining the sample regime, as well as to have a more user-friendly way of changing parameters when calibrating the model and fitting the results. To build the model, certain assumptions and simplifications had to be made, which are outlined in the following sections.

4.1.1 Division of Network into Model Reaches

The flow from the most northern injection point varies significantly from the flow at the inlet on the scale of two orders of magnitude. The addition of water along the network is not uniform, with significant additions at municipal connection points and branch intersections, such as the intersection between the Täby branch and the main tunnel. As a result, the tunnels at each section have been built to different specifications, with the tunnels closer to the inlet of the works having a larger cross-sectional area than those upstream.

For a section of the network to be considered the same reach, the geometry had to be the same. This simplified converting flow data into velocity values by means of the Manning-Strickler equation. Additionally, major intersections and pumping facilities delineated model reaches. A major intersection is defined in this model as a point where the main tunnel intersects either with the Stäket (Upplands-Bro) or Täby stretches of tunnel (see Figure 1). For example, despite the geometry of the stretch of tunnel from Antuna pumping station to the Täby branch intersection being the same, this stretch is divided into three reaches. The first break is at the intersection with the Stäket connection, and another at Edsberg pumping station. In order to simplify the model, the reaches were kept as large as possible. The reasoning behind delineating at Edsberg pumping station is due to the flow data likely being more reliable. As well as this, should it be identified that there is a significant residence time in the pumping station sump, which would lead to an elongation in the tail of the breakthrough curve and a lower peak, then this can more easily be inserted into the model at this point.

One reach that was significantly different than the others was the “dykarledningen”. In this short section (880m), the tunnel goes down vertically from 8m below sea level to a maximum depth of 88m below sea level, before coming back up to a height of 10m below sea level. Between these points are two shafts connected by a 2000mm circular pipe where the water flows under pressure, as opposed to free-surface-flow as in the rest of the network.

The procedure for assigning reaches can be summarised as:

i. Ensuring each reach has continuity in cross-section geometry ii. Identifying facilities in the network where flow data may be more reliable, such as pumping stations iii. Identifying major connection points in the network iv. Delineating between stretches of pressurised flow and free-surface flow

4.1.2 Hydraulic and Solute Transport Properties

The hydraulic behaviour of sewage plays an integral role in how solutes are transported within it. For example, getting an accurate estimation of the average flow velocity ensures an accurate estimation in the time of peak concentration. In free-surface flow, the key unknown for determining the velocity in the sewer is the Manning-Strickler coefficient if the tunnel geometry and average slope are known. For the sake of simplifying the model, only a simplified hydraulic model based on the Manning- Strickler equation has been used. This means that potential backwater effects, which could be the case on the approach to the dykarledningen under high flows, have been ignored. Other areas which may have complex hydraulic behaviours, such as pumping stations, have been simplified greatly. Simply put, the flow along the entirety of the network is assumed to be under uniform flow conditions, except at the dykarledning where the flow and cross-sectional area of the full pipe was used to calculate average velocity. As turbulent flows and transition water profiles generally only occur over relatively short lengths compared to the total length of the system, this was seen as a fair assumption.

To sufficiently model complex hydraulic behaviours such as backwater effects and pressurised flow, the full set of Saint-Venant equations must be used. Such models are commercially available; however, they can be computationally intensive and often require specialist training and a high level of hydraulic engineering expertise if to be used effectively (Schütze, 2002).

For the first experiment, the average velocity was calculated using a Manning-Strickler coefficient estimated from the literature. The geometry data for different sections of the network were available to calculate the area and hydraulic radius at a given depth. After the first experiment, this coefficient was adjusted with the flow data measured at the time of arrival at Käppala WWTP’s inlet to get a more accurate number. This was carried out using the trial-and-error method. The exact methodology for deriving the reach flow, from which the average reach velocity is derived, was continuously improved upon throughout the experiment. These will be more thoroughly explained in the section 4.1.6.

The dispersion coefficient was estimated using a number of empirical equations from Riekerman, et al., 2005 and Lepot, et al., 2014 for the first experiment. It was then recalculated after the first experiment by fitting the model to the results using the trial-and-error method. This figure was used in the following experiments. Both decay and retardation coefficients were not used in this model as a conservative hydrophilic tracer was used.

The initial and boundary conditions of these slug injection experiments are defined mathematically in the following:

Initial condition: 푀 Eq. 5 퐶(푥, 푡 = 0) = 훿(푥) 퐴

Boundary conditions: 퐶(푥, 푡 = 0) = 0 Eq. 6

lim 퐶(푥, 푡) = 0 Eq. 7 푥→∞

Where M is the mass (M), A is the cross-sectional area (L2), and 훿 is the Dirac delta function (L-1) which can be described as the unit impulse and serves to represent instantaneous nature of the slug injection (Jayawardena, 2013).

With reference to the ADE (Eq. 4) and the above initial and boundary conditions, the analytical solution is given by Eq. 8.

푀 (푥 − 푣푡)2 Eq. 8 퐶(푥, 푡) = 푒푥푝 {− } 2퐴√휋퐷푡 4퐷푡

Convolution was carried out on the results of Eq. 8 on the reach following injection, with each subsequent reach convoluting the results from the reach before. Convolution is a well-known superposition method used in surface water hydrology as well as a spectrum of other scientific branches (Olsthoorn, 2008). The reaches following carried out a convoluted form of this equation of the form:

푡 Eq. 9 퐶(푥푛, 푡) = ∫ 퐶(푥푛,휏) ∙ 𝑔푛(푡 − 휏)푑휏 0

Where:

푥 − 푥 [(푥 − 푥 ) − 푣 (푡 − 휏)]2 Eq. 10 𝑔 (푡 − 휏) = 푛 푛−1 ∙ 푒푥푝 {− 푛 푛−1 푛 } 푛 3 √4휋퐷푛(푡 − 휏) 4퐷푛(푡 − 휏)

τ Time of input value (s)

As the flow of water changed from one reach to the next, the results from the previous reach’s calculations, given in concentration (mg/l), had to be converted into a transferrable unit from which the convolution could be carried out. This meant finding the integral between two points in time, τ, giving a type of loading rate in mg/l·s. This was multiplied by the previous reach’s flow to give an injection mass equivalent between the two time points. Verification was carried out by summing the mass totals for each time period and ensuring it equalled the injected mass amount. Further explanation on how the Excel model was set up is given in section 4.1.4.

4.1.3 Collection of Data – The SCADA System

Monitoring of the sewerage network is carried out through a distributed network of monitors connected to a central SCADA (Supervisory Control And Data Acquisition) system. These monitors measure parameters such as flow, water pressure, depth at a pumping station sump, among other relevant parameters. For this project, we were interested in the network of flowmeters, which have a specified accuracy of ±10% (Rosenholm, 2020). The data reported to the SCADA system is uploaded onto Käppala WWTP’s web-based data management tool, aCurve. This tool allowed for the simple extraction of real-time and historical data and displays the data in a graphical format.

Flow measurements are communicated to the SCADA system either as a directly measured value from a single flowmeter instrument, or a calculated value which sums together different values from different flow instruments. For example, a pump station may have several pumps to cope with the

daily fluctuations between high and low flows. An individual meter will give limited information, unless you are looking at the performance of an individual pump. Having a calculated value makes extracting data a more user-friendly experience, however, complications arise when these values calculate the cumulative flow for stations that have a significant distance between them. Obtaining an accurate calculated value at these points must take into consideration the travel time of a volume of water from one point, and the interacting effects of additional water at downstream points. Such considerations increase the uncertainty of the calculated values. Nevertheless, these were used as the basis for model calculations.

4.1.4 Extraction of Data

The data used in calculations were obtained primarily from Käppala WWTP, however, for sections not under Käppala WWTP’s responsibility, data was obtained from the relevant bodies. This included flow data from Arlanda airport, as well as tunnel and pipe dimensions, slope, and construction material. Flow data was available at the beginning and end of each reach length on Käppala WWTP’s network; however, flow values were not known for tunnel sections outside of Käppala WWTP’s control, except for at the injection point at Arlanda airport.

The length of each reach was calculated digitally using Käppala WWTP’s online maps of its tunnel network, and the geometries were available from internal sources. For reaches outside of this network, the geometries and locations of the reaches were provided by external partners, and distances were measured digitally up to the connection point with the main tunnel system. The external partners provided images on a map overlay of their network. Although the connection point to the main network and injection point locations were known, some inaccuracy may be present due to the manual translation from image to map of the bend points when measuring the distance for these stretches. This adds a level of uncertainty which is difficult to quantify, but is related to the total length of the stretch and the number of bends within that stretch.

Prior to each experiment, data from the previous week was collected and the average flow values were taken ±1 hour of the expected arrival time. These values were averaged to give a prediction of flow during the experiment. Flow values given along the network are derived in two main ways: directly from a single instrument, or a calculated value which adds values from several instruments. The calculated values are, in general, less robust. For example, if an instrument within the calculation is malfunctioning, the whole value is negatively affected and may not be immediately evident. On the other hand, if a reading is taken from a single instrument, it is often straightforward to identify a malfunction, for example by an output of a series of zero values.

4.1.5 Setting up the Excel Model

The Excel model was set up with one workbook per experiment. A separate Manning-Strickler equation workbook was set-up to calculate the velocity of water based on flow data. This was linked to the workbook of the experiment being worked on. Having a separate Manning-Strickler workbook ensured that all changes carried out to these parameters, for example during calibration, were carried out on all three experiments. In this way, any changes made had to satisfy the results of all three experiments simultaneously.

The Manning-Strickler equation was applied to each tunnel geometry. Applying this equation meant splitting the geometry into sections made up of simple shapes to calculate the area and hydraulic radius. All tunnel sections maintained by The Käppala Association were of a similar shape as seen in Figure 6, and so were split into a triangle, rectangle, and circle. The only other geometry encountered were circular pipes of varying diameters in the municipal sections, and at the dykarledningen. Short circular pipe sections after the pumping stations were ignored, and their length incorporated into the total reach length for simplicity. This workbook acted as a look-up table from which values of flow were input into the model workbooks, and the corresponding velocity and cross-sectional area was taken from the Manning-Strickler workbook and input into the model workbook. The control parameter for calculating the sewerage velocities at different flow values was depth, meaning that the flow value obtained from the flowmeters could only be approximated. To counter this, depth intervals were reduced to a suitable level where such approximations would have negligible effect compared to the uncertainty in instrument readings.

For each experiment workbook, the first page was the site of injection, and the simple form of the ADE was carried out for a selection of times based on the length and estimated travel time of the plume. On consequent pages, a convolution of the results from the previous reach is carried out using a modified form of the ADE (Eq. 9). Such a model allowed for a simple way to view and change parameters, and to visualise results. The strengths of such a model include its simplicity in these aspects, however, it does have shortcomings, one being that a new workbook would have to be created to simulate spills from different points or municipal connections along the network. Figure 7 shows a page of the excel model used for Experiment 3, including how Figure 6 - Typical tunnel geometry the convolution equation was applied.

4.1.6 Calibration of Results

After the completion of each experiment, the results were analysed, and the model calibrated. The newly calibrated model was used in predicting the breakthrough curve for the following experiment. This process was caried out after each experiment, until the final calibrated model was created after the final experiment. The uncertainty in flow meter values is ±10%, so flow values were adjusted within this range when fitting the model to the experiment results.

After the first experiment, the Manning-Strickler coefficient was altered so that the peak arrival of the model would coincide with that of the experimental results at the flow during the experiment. The dispersion coefficient was altered to reflect the spread of the experimental results. As this experiment only included one reach, the calibration was basic and carried out by trial-and-error.

For the second experiment, the model needed further calibration. The Manning-Strickler coefficient and dispersion coefficient remained the same as in the first experiment, however, the measured length of the municipal section was revised, as was the method for calculating the average flow. The new method for determining the average flow of a reach was calculated by taking an average of the flows at the beginning and the end of a given reach at the time at the approximate arrival time of the peak concentration at each of the respective points on that reach.

For the third experiment, the previous model was unsatisfactory, even with revised distances and updated flow values. A revision of how the velocity was calculated from flow values was carried out. A new, depth dependant, form of the Manning-Strickler equation was used, where the Manning- Strickler coefficient was variable based on the depth of the water flowing. The hypothesis behind this was that the base of the tunnel was much smoother than the walls, and as such, low flows with little

contact with the walls would have a lower effective resistance to flow. Confirmation of this hypothesis is shown by from the photograph seen in Figure 19, which shows the inside of the tunnel. The base is cast in relatively smooth concrete, however, due to the construction process, the walls are jagged with a layer of concrete over the blasted bedrock. The effective Manning-Strickler coefficient was determined by the equation:

(0.018 ± 0.002)푃 + (0.044 ± 0.001)푃 Eq. 11 푛 = 푤푏 푤푤 푃푤

Pwb Wetted perimeter in contact with the base of the tunnel at a given flow Pww Wetted perimeter in contact with the walls of the tunnel at a given flow Pw Total wetted perimeter at a given flow, Pwb + Pww

4.2 Experimental

In order to maximise the transferability and relevancy of the results from the tracer tests, they were carried out in dry weather conditions. As the effects of precipitation events on the flow are variable, even if the amount of precipitation is the same, the modelling of results becomes increasingly complex. Even with the best models, the amount of input data needed, such as the soil saturation, rainfall intensity, the spatial distribution of precipitation, etc., mean getting accurate results is incredibly difficult (Schütze, 2002). As a result, it is common practice to carry out tracer experiments in dry weather conditions when investigating the general characteristics of a sewerage network (Schütze, 2002; Riekermann, et al., 2005)

The method for planning and carrying out the experimental side of this project is intrinsically linked to the simulations used. As the model was calibrated after each experiment, the calculations used in planning the experiments changed. The success of the experiments was based largely on the results of the model simulations, which were used for estimating the necessary injected mass, projected breakthrough curves, and sampling regimes.

Several considerations had to be taken into account when deciding on a tracer to use for carrying out this experiment. Uranine was selected as the tracer material for this experiment due to the low concentrations needed for detection, its low toxicity, its resistance against degradation from microorganisms (Blom, et al., 2016) and affordability. The drawbacks to this tracer, such as its photochemical instability, pH sensitivity, and heat sensitivity (Blom, et al., 2016) were all aspects that were deemed manageable within the setting of a WWTP. The tracer used dictates the handling and processing of samples, as well as the lab analysis method.

Figure 7 - Screenshot of the convolution carried out in the Excel model. Model parameters from the previous reach are given at the top of the page for comparison and calculation of injected mass equivalent for the convolution. The cumulative distance travelled, and the Time Of Peak Arrival (TOPA) is calculated for each reach. The input at the beginning of the reach is given in the horizontal, “tau”, axis, and the arrival at the end of the reach is calculated according to the time in the vertical, “Time”, axis. The summation of the horizontal row values (e.g. H14+H15+…Hn) at a given time gives the concentration at a given time, t.

4.2.1 Tracer Material, Injection Points, and Injected Mass

The requirements of the tracer element to be used, and their relative importance within this project are outlined in Table 1 (section 3.2). Of paramount importance is the toxicity/ecotoxicity of the substance used. As the solute will be passing through the biological processes of a WWTP, the potential negative side-effects on the receiving waterbody’s ecology go beyond the isolated ecotoxicity of the substance. The use of a potentially toxic tracer could have precisely the effect we are trying to investigate with regards to the aim of the project. Of secondary importance is the solubility, fluorescent intensity, and detection limit of the tracer. The nature of a sewerage network means that the incoming water is not directly visible, and samples must be pumped from the inlet to be collected. The lack of any method of quickly determining the presence of the tracer by means of an instrument, meant the incoming peak concentration had to be visible to the naked eye. The high microbial, organic, and chemical content, compared to natural river systems meant extra consideration had to be given to these tracer characteristics. Given the tracer would have to be at a level visible to the naked eye at the inlet, the amount of tracer used would have to be considerably larger than otherwise necessary. For this reason, the tracer used would have to be economically viable, while satisfying all of the above requirements to ensure the experiments could be carried out. The tracer requirements below this level of importance were deemed to be manageable with certain precautionary measures.

The tracer material used in this experiment was uranine. Uranine has a low level of interference with turbidity and is largely unaffected by microbes (Ellis & Revitt, 2010) which are at an elevated level in wastewater. The tracer is so safe that it is used in biomedical research and healthcare applications (Blom, et al. 2016). With reference to Table 1, uranine fulfilled the majority of these requirements. For the requirements where uranine showed weaknesses, such as temperature dependency, pH sensitivity, and photolytic stability (Blom, et al., 2016), suitable precautionary measurements could be taken. These included processing the samples as quickly as reasonably possible, and avoiding prolonged exposure to light.

Uranine is a relatively inexpensive tracer, and controlled concentration experiments carried out prior to the injections showed it is clearly visible in wastewater at levels of 1mg/l Subsequent sample collection from the experiments showed levels easily detectable down to 0.3mg/l with the naked eye. These can be seen in Figure 8. Using the method of inverse modelling and with these target peak concentrations in mind, the mass of injected tracer material was calculated. The nature of the model made simulating different injection amounts and seeing the associated breakthrough curves incredibly simple and intuitive. The injection mass was calculated using the trial-and-error method with the model simulation until a suitable concentration profile was obtained. After each experiment, model calibration was carried out which ensured the most Figure 8 - Left: From left to right, uranine control samples with 0.01, 0.1, and relevant estimation of 1mg/l of uranine in deionized water. Right: Uranine concentration of 302.7mg/l in injected mass was calculated. wastewater at the inlet. Image edited to remove glare.

The locations of injection were ordered so that data could be collected in a systematic way, ranging from the most simplistic flow model which included a single reach, to the most complex which was comprised of nine reaches, two pumping stations, and an inverted siphon structure (dykarledningen). At the beginning of the experiments there was very little prior knowledge of solute

transport within this system. As the nature of a sewerage system means it is difficult to directly observe flow, coming up with adequate estimations to base a preliminary model off was difficult. Starting from a location close to the inlet meant reducing the duration of sampling, meaning a greater margin of error could be applied with less effort. Incorporating greater lengths and more structures in subsequent experiments allowed us to test the efficacy of simplistic calibration and add complexity where required. Additionally, we were able to see the effects that the large structures in the system had on the breakthrough curve at the inlet. The locations of each injection are shown in Figure 9.

The method of injection varied slightly from one experiment to the next. In all instances, the mass was injected as suddenly as possible to replicate a slug injection, with care taken not to lose any tracer material in the process, for example from splash back or spillage. Figure 10 shows the methods used for each injection, and the type of tunnel or pipe that was injected into. In Experiment 1, there was ample room to carry out a typical slug injection by pouring the tracer directly into the sewer. The bucket and container used to inject the tracer were not rinsed once the majority of the tracer had been released from them in as it was thought that this could impact the tracer levels in the tail of the breakthrough curve given the proximity between the injection point and sample point. As a result, some mass losses were incurred this way.

In Experiment 2, the injection point was a manhole with a depth of over three meters to the waterbed. In order to minimise losses from splashback onto the manhole and sewer pipe walls, and to increase the safety of the operation, a long funnel was used to inject the tracer mass. Using this meant no-one had to enter the manhole and all work could be carried out above ground. The drawback with this method was that the injection was slower than a typical slug injection, however, all of the tracer material was able to be injected within three minutes. Again, the large container was not rinsed out to reduce any effect doing so could have had on the concentration levels in the tail.

Experiment 3 was carried out at a sampling station just outside Arlanda airport, approximately 46km from the inlet. Access to the sewer was good, and there was enough room for one person to inject the tracer into the sewer, and another to aid in passing the buckets of tracer to be injected. The injection was carried out in much the same way as in Experiment 1, however, the large container was rinsed out with tap water so that undissolved tracer material stuck at the bottom was also injected. As this injection point was so far from the effect of doing so was assumed to have a negligible effect on concentrations in the tail of the breakthrough curve. In total, the injection was carried out in two minutes, one minute shorted than the injection for Experiment 2.

4.2.2 Experimental Timing and Sampling Regime

The experiments were planned to be carried out in dry-weather conditions to improve the transferability of results. de Bénédittis & Bertrand-Krajewski (2005) suggest a dry weather period of eight days as optimal, however, a period of four days reduces uncertainties in infiltration/exfiltration estimates to a suitably low level. The framework of four dry days prior to the experiment was kept in mind, however, was not always able to be adhered to. Given the dry weather during April, it was assumed that precipitation under 0.5mm/day would have a negligible effect on wastewater flow, and were considered dry days. As can be seen in Figure 12, only Experiment 1 was within four days of a precipitation event ≥1mm/day.

The experiments and sample collection were carried out with consideration to the expected travel time to the inlet, and that the samples had to be collected during working hours between 07:00- 17:00, as access is restricted outside of these times. The time of injection, injection masses, as well as sampling regime can be found in Appendix 1.

The analysis technique for assessing the concentration of uranine in incoming wastewater and the instrumentation made available at Käppala WWTP determined the sampling regime for the experiments. Samples were decided to be taken at the inlet only, and analysed by a spectrophotometer by Geosigma AB as an online or logging fluorometer was not available. Samples would be collected

a ) b )

Figure) 9 - Locations of injection point for a) Experiment 1, b) Experiment 2, and c) Experiment 3. Blue lines show approximate location of municipal networks not under The Käppala Association's control but connected to the tunnel network. Maps created using Envomap (Gemit Solutions)

using an automatic sampler pre-set to take samples at a specified time interval, determined from the results of the modelling exercise carried out before each experiment. Kilpatrick & Wilson (1989) suggest that a breakthrough curve can be well defined by around 30 samples within the curve which influenced the time interval between samples. The minimum sampling frequency of the autosampler was one minute which set the lower sampling frequency limit. A picture of the autosampler instrument is shown in Figure 11.

The autosampler can be set up for different sizes and numbers of receptacles, however, cartridges of 24 bottles were used throughout this project. The sample volume was set to approximately 60ml. This volume was selected as it allowed for several samples to be run in the spectrophotometer, which required 4ml of sample, while reducing the weight for ease of transport. This allowed a greater number of samples to be sent for analysis. The total number of cartridges available was 10, giving a maximum of 240 samples collected during a single experiment. The full set of cartridges did not have to be used for any of the experiments.

a) b)

c) d)

Figure 10 – a) Slug injection at the Spisen facility on Lidingö, Experiment 1. b) Injection at a manhole in Sollentuna municipality using the funnel pictured, Experiment 2. c&d) Injection at Arlanda airport, Experiment 3. Tracer sediment seen at the base of container in image d) was rinsed out using tap water.

The most important decisions to be made when deciding on the sampling regime were the start time and the sampling interval. Starting too early and having too frequent of a sample interval would lead to an increased workload, and could mean that sample bottles run out before the breakthrough curve has been adequately captured. Starting too late would mean risking incomplete data in a best-case scenario, or having to completely re-do the experiment in the worst-case. As the latter has more serious consequences in terms of experiment success, sampling was started well before the predicted arrival of the rising limb. The sample regime for each experiment is laid out in Appendix 1.

Approximately 50 samples were sent for analysis from each experiment, however, up to 80 samples were collected for each experiment. As the mass of injected uranine was calculated to ensure the peak concentration was visible to the naked eye, a rough estimate of the time of peak concentration could be observed when transferring the samples into smaller bottles for transport. This made selecting only the most relevant samples easier and reduced the Figure 11 - Autosampler positioned at the inlet to workload on the analysis team. the WWTP

Once the samples had been collected and the most relevant selected, they were bottled up and transported to the lab. The lab analysis for each experiment was carried out within 12 hours of sample collection in order to minimise any degradation effects that could occur.

4.2.3 Lab technique for Determining Tracer Concentration

Lab analysis of the samples was carried out by Geosigma AB. A brief outline of the procedure is given, however, a more in-depth procedure can be found in Appendix 2, along with the raw data from the analysis.

The concentration of uranine was determined using a Jasco FP-777 spectrophotometer. The limit of detection for uranine using this spectrophotometer is 0.002 ppb, or 2.0x10-9 g/l in deionised water. In this analysis, the excitation and emission wavelength of uranine was set to 493nm and 515nm respectively. Preparation for analysis included taking the sample from the sample bottles and putting them into 4ml cuvettes. A small space is left at the top of the cuvette so an appropriate volume of buffer solution can be added (1-2 drops). As the measured concentrations of uranine are pH sensitive, the buffer solution is added to bring the solution within the range of maximum fluorescence (Leibungut, et al., 2009). The buffer solution used in this analysis was Titrisol (pH9). Testing of the pH on random cuvettes was done to ensure the pH was above pH7. If it was below this pH, an additional drop of Titrisol was added. Before being put into the spectrophotometer, the cuvette was wiped with a fine cloth to remove any sample droplets, dust, or fingerprints which could interfere with the analysis. The analysis is run on the spectrophotometer and the reading is printed out from the spectrophotometer’s printer.

5. Results

5.1 Typical Flows at the inlet

Due to the routined lives of modern city dwelling humans, WWTPs typically have an associated diurnal flow pattern that doesn’t vary greatly during dry weather periods. How this pattern manifests itself is a function of many inputs such as the distance to the works from major residential areas, the number of different residential hubs connected to a WWTP, and the proportion of wastewater made up of industrial and residential customers.

From residential areas, there is usually an increase in water consuming activities before the typical workday begins, at around 09:00, and another increase after the workday, at around 17:00. Käppala WWTP’s diurnal flow pattern displayed a peak flow at around 23:00-00:00, and a minimum flow at around 09:00-10:00. The pattern has a weak double peak pattern, but more often has a simpler sinusoidal pattern. The inflow, along with associated precipitation data (SMHI, 2021), for the period the experiments were carried out, are shown in Figure 12. A particular artefact of the ongoing corona pandemic is that flows coming from Arlanda airport were substantially reduced compared to previous years. The average flow in March 2020, which was the latest available data at the time of analysis, was down 49.5% on the preceding six-year average for same period, with a range of between 35-57% compared to each of the preceding six years. Such significant changes were not immediately noticeable in the flow data in the rest of the network, however, an in-depth analysis may show some subtle changes in the network’s flow.

On an annual timescale, the lowest flows are in the summer months of July and August, and 1m3/s is the minimum flow that can be expected. Between autumn to spring, the flow is a lot more variable and dependent on that year’s seasonal conditions. Maximum annual flows can happen anywhere between November and May, with maximum flows typically around 3.5-4.5m3/s. In May 2021, an all- time high flow of 8.35m3/s was registered. During these high flows, a high flow treatment and purification process is utilised which has the capacity to treat 2m3/s. It is worth noting these high flows are short lived, event-based flows, and generally recover quickly to levels below 2m3/s.

Experiment 1 Experiment 2 Experiment 3

Figure 12 - Flow data from Käppala WWTP's inlet, with precipitation data for the time period the experiments were carried out. Although some double peaks are visible, for example in the last four periods, they are often not as pronounced and more regularly follow a regular wave form with a peak at around midnight and a trough at around 09:00. Precipitation values are the 24 hour total, from 06:00-06:00 (SMHI, 2021)

5.2 Comparison of Modelled and Measured Values

Each experiment resulted in a breakthrough curve which described the concentration profile of the tracer over time at the inlet to the WWTP. The spectrophotometer analysis used in these experiments returns uranine concentration in the units of ppb (parts per billion), however, the values given in the graphs in the results section are in mg/l. This is done to make the results more comparable to the model results. Although wastewater will have a greater density than pure water, this varies over time and is a related to the total dissolved solids (TDS) present in the wastewater at any given time. As a TDS analysis was not carried out on any of the samples, the exact deviation in units is unknown. Routine suspended solids analysis carried out by The Käppala Association showed an average value of 304mg/l in the incoming water over the past year, so the effects of such a conversion are assumed to be minimal to negligible.

5.2.1 Experiment 1: Spisen, Lidingö – Käppala WWTP

The dispersion coefficients and Manning-Strickler coefficient were obtained from empirical equations obtained from the literature (Riekermann, et al., 2005; Lepot, et al., 2014) and literature reference values for concrete. The values for the dispersion coefficient equations were flow dependent, and the values calculated ranged between 0.26 – 1.37m2/s for flows between 1.03- 1.78m3/s. the equation taken from Lepot, et al. (2014) gave consistently the lowest values, and equation 3 from Riekermann, et al. (2014) gave consistently the highest values. The equations are outlined in Table 2.

Table 2 - Summary of empirical equations used to calculate the dispersion coefficients for Experiment 1

Taken from Original Source Equation Comments Lepot, et al., Best used for simple cross de Bénédittis & 퐾푥 = 6ℎ√𝑔ℎ퐼 2014 Bertrand-Krajewski, section with regular cross- 2005b sections and few bends 3 Riekermann, et Iwasa & Aya, 1991 푤 2 Tends to underestimate al., 2005 퐾 = 2 ( ) ℎ ∙ 푣∗ dispersion in streams, so 푥 ℎ (Equation 1) perhaps more applicable in sewer setting Riekermann, et Koussis & 푤 2 Tends to underestimate 퐾 = 0.6 ( ) ℎ ∙ 푣∗ al., 2005 Rodríguez-Mirasol , 푥 ℎ dispersion in streams, so (Equation 2) 1998 perhaps more applicable in sewer setting Riekermann, et Huisman, et al., 푣2푤2 Assumes regular channels, as is 퐾 = 0.003 ( ) al., 2005 2000 푥 ℎ ∙ 푣∗ the case in a sewer reach (Equation 3)

The Manning-Strickler coefficient was estimated by taking a value above the higher limit for concrete (0.015), but below that of a rock cut channel (lower limit 0.025) (Akan, 2006). The final value used in pre-experimental simulations was 0.017. The reasoning for this value was the assumption that the base of the tunnel, where the flow would have most contact, would be smoother than the irregular sides of the tunnel. Additionally, it was thought best to underestimate the value as the simulations would be used in determining the sampling regime. If too high a value was taken, there would have been a chance that sampling would have started too late which could have resulted in failing to collect any useful data.

Using values taken from the literature and empirically derived values introduces a significant margin of error, as can be seen from the range of values given for the dispersion coefficient and Manning-Strickler coefficient. Relevant measures were taken to ensure success of the experiment such as erring on the side of caution when selecting values, and using an intensive sampling regime.

The mass was injected at 09:15, and sampling started at 11:30, or 8100s after injection, and would continue as long as required.

The results of the simulation can be seen in Figure 13, which show simulations for flows between 1.38-1.56m3/s using the largest dispersion coefficient (~1.25m2/s), along with the experimental results. The simulations using the smaller dispersion coefficients resulted in the same peak arrival time, however, the peak increased up to 4mg/l, and the duration of the breakthrough curve decreased. As can be seen, the breakthrough curve was predicted to occur over an incredibly short period of less than 20 minutes. As a result, the sampling interval was set to one minute, which was the minimum interval possible on the sampling equipment.

Simulation parameters 1/3 Manning-Strickler Coefficient - 0.017 s/m 2 Dispersion coefficients range - 1.15-1.25 m /s Dilution factor - Not applied Loss - 0% (Not applied)

Figure 13- Predicted breakthrough curves for Experiment 1, with experiment results in red. The yellow section shows the estimated visibility of uranine at a given concentration, with values above 1mg/l clearly visible. The results from the experiment showed a significant underestimation in the Manning-Strickler coefficient, however, the estimation for the dispersion coefficient was relatively close and deviated from the fitted dispersion by approximately 0.3m2/s, with a fitted value of 1.55m2/s. The Manning- Strickler coefficient was much closer to the lower value given for a rock cut channel than to the upper limit given for concrete, with a fitted value of 0.0237. The loss of uranine in the experiment was calculated using the trapezium method to find the area under the curve of experimental results. The calculated loss was just under 26%.

During analysis, an issue was identified with the values used for the inflow, which were used to calculate the velocity along the reach. The values used in the simulations was the total inflow to the works, including flow from the southern network. As the connection between the northern and southern parts of the network is located at the works, the southern network flow has no effect on the flow characteristics upstream of the connection point, however, will have an impact in terms of diluting the concentration of tracer. To rectify this, the flow from the northern section of the network was used to calculate the hydraulic behaviour of the wastewater, and the flow from the southern section was considered by applying a dilution factor, which was simply the ratio of the flow from the northern sector of the network to total inflow from the whole network. This ratio typically varies between 0.75-0.90, and, at the time of this experiment, was 0.781.

The results from fitting the curve and applying the loss and dilution factors resulted in the curve shown in Figure 14. The results from this experiment were used to update the Manning-Strickler look- up tables used in the following simulations and replaced the empirical value used for the dispersion coefficient used in the main tunnel reaches. A loss factor and dilution factor were also considered in subsequent simulations.

Figure 14- Fitted model curve for with results for Experiment 1. The small irregularity seen could be real, or could be due to a mix up in labelling sampling bottles as such a feature was not seen in any of the other experiments.

5.2.2 Experiment 2: Sollentuna – Käppala WWTP

The values obtained from Experiment 1, which were used in the simulation for Experiment 2, achieved a much better simulation result compared to the pre-experimental simulation for Experiment 1, despite the increased complexity of being composed of five modelled reaches. The pre-experimental simulation had a peak concentration approximately 36 minutes before the actual peak arrival (Figure 15), and was mostly due to a lower flow on the day of the experiment compared to the preceding week (see Figure 12). The dispersion coefficient was the same in all reaches, apart from the municipal section, where it was set to 3m2/s. The dispersion was increased in this section due to the increased number of bends and connections over a relatively short distance, as well as the low flow and increased pipe wall contact.

The sample resolution was set to two minutes. Although this would give less than the ideal 30 samples suggested by Kilpatrick & Wilson (1989) for ideal breakthrough curve definition, the simulations suggested it would still give good definition while allowing spare sampling capacity in the event that the model was significantly out, as was the case in Experiment 1.

Figure 15 - Predicted breakthrough curves for Experiment 2, with experiment results in red. The time difference between peaks is approximately 36 minutes, while the dispersion shows a strong resemblance.

The loss calculated in this experiment was just under 29%, an increase of approximately 3% on the last experiment. This increase in loss was seen as quite low given the greater distance between the injection point and the sampling point, and that the method of injection was more likely to result in losses on the pipe walls than the first experiment. The initial dispersion coefficients appeared to give a good fit with the experimental results, so these were kept the same.

After correcting flows to the measured values, there was still some disparity between the model and the experimental results, with the modelled results having a shorter time of travel. This was corrected by two changes in the model. The first was done by recalculating the length of the municipal section. Recalculation rendered an increase in length of from 700m to 800m. The second change reconsidered the flow for each reach by taking the flow at the beginning of the reach, and averaging it with the flow at the end of the reach at the approximate time of travel along the reach. The result of this was a lower effective flow rate from which the velocity was calculated as the flow at the beginning of the reach was lower than the flow at the end of the reach. The average flows were adjusted to within ±5% of the calculated value, well within the uncertainty limits of the flowmeters.

One reach where this was not the case Flow Statistics for Lidingö → WWTP model reach was on the final Lidingö → WWTP reach. In this section, the flows at the beginning of the reach were consistently higher than the northern sewerage network’s flow into the WWTP. This can be seen in Figure 16, where both the mean flow and the integral for the flow calculated at the dykarledningen were higher than the total flow from the northern sector of the sewerage network. The integral is just over 10% larger at the dykarledningen than it is at the inlet, which is notably higher than L-STR Total Inflow Inflow from N. sector the uncertainty level in the flowmeters given that the flow should actually be Figure 16 - Flow statistics for the three flows relevant to the Lidingö → WWTP model reach. Upstream values were consistently larger downstream. As a result, only the higher than downstream values. L-STR = Långängstrand. flow at the inlet was used for calculation Statistics based on flow between 28/03/2021 - 01/05/2021. Graph of flow velocity. created using aCurve (Gemit Solutions).

Figure 17 shows the calibrated model results along with the experimental results. The results from the experiment are in good agreement with the model, however, there are some small differences in the leading limb of the curve, and a notable tail in the results curve. This new method of calculating the effective reach flow was used when planning the final experiment.

Figure 17 - Fitted model curve with results for Experiment 2. Overall, the model has very good agreement with the results due to calibrations to the model.

5.2.3 Experiment 3: Arlanda airport – Käppala WWTP

Experiment 3 included more unknowns than the previous two experiments, most notably the effects of passing through two pumping stations and scant flow data on the stretch between the injection point at Arlanda airport and Löwenströmska, Upplands Väsby, where responsibility of the sewerage network returns to The Käppala Association (refer to Figure 1). The distance between these two points is approximately 11.3km, or almost 25% of the total distance between injection point and sample point. As a result, this 11.3km section was split up into only two reaches; one reach for the concrete pipe flowing from Arlanda to the connection with the Märsta tunnel section (1400m), and the other from this connection point all the way to Löwenströmska (10900m). In reality, it may have been more applicable to create more reaches, however, a lack of flow data and a consistency in tunnel geometry meant there was no evidence to suggest this was necessary.

The results from the pre-experimental simulation and the experimental results are shown in Figure 18. The peak concentration of the experiment occurred approximately two hours before the simulated peak. Thanks to a robust sampling regime the actual leading limb was able to, just about, be captured and the breakthrough curve defined. Revision of the distances between Arlanda and Löwenströmska was carried out, reducing original the distance by 1500m. Such a large miscalculation was done by initially determining the total length between the injection point and Löwenströmska. This led to a double count in the Arlanda → Märsta reach. Despite these significant changes, the model results continued to show huge shortcomings and a more in-depth rethink was required.

Figure 19 - Predicted breakthrough curve prior to Experiment 3, with experiment results. Note a difference in peak concentration arrival time of almost two hours.

Figure 19 shows how the inside of the tunnel system looks when no water is flowing. It is clear that the base of the tunnel is significantly smoother than the walls. In the upper reaches of the sewerage network, where flow was lower, a larger proportion of the flow is in contact with this base section, and therefore the resistance to flow will be lower. The results obtained from Experiment 3 and Figure 19 backed up the requirement for a depth-based application of the Manning-Strickler equation that could be applied to all three experiments. With reference to the typical values given in Akan (2006) and Chow (1959), and the results from the previous two experiments, two separate Manning-Strickler coefficients were used for the base of the tunnel and the walls of the tunnel. The effective Manning- Strickler coefficient was calculated using Eq. 11. The value used for the cast concrete base is above the upper limit from Akan (2006) of 0.015, however, within the range of a number of different concrete values given by Chow (1959). As the tunnel system was built in the 1960s, these values are perhaps more relevant due to their age. The walls of the tunnel are irregular and sprayed with concrete, and at the upper limit of the values given by Akan (2006) for rock- cut channels.

Applying these changes resulted in the results shown in Figure 20. The newly applied method for applying the Manning-Strickler coefficient based on flow shows a great improvement on the simulations prior to the experiment, and Figure 19 shows that it is a more accurate representation of reality. The arrival time is accurately portrayed Figure 18 - Inside the tunnel network. The base of the tunnel is giving credence to the methods used cast in concrete and has a smoother surface than the walls, for calculating the velocity from flow, meaning the effective Manning-Strickler coefficient is depth and the shape of the breakthrough dependant. Source: Käppalaförbundet

curve shows a good resemblance if the tail is ignored. The “additional processes” outlined in Section 3.1.4 appears to have exerted a much greater impact compared to the previous two experiments. As we can assume there is no hyporheic zone interaction within well-functioning sewer, we can assume that temporary storage processes account for the bulk of the tail.

The results of these changes are shown in Figure 20, where there is a much better agreement between the modelled and experiment results. The timing of the peak and the rising and falling limbs are described quite well with the model, however, there is a disparity of around 9% in the measured and simulated peak concentration. This could be due to the shifting of the centre of mass of the plume due to some retardation effects along the network, giving rise to the observed longer tail. It could also indicate that the flow values used in the model were too low, which would give a higher solute concentration at the inlet as the solute mass is dissolved in a smaller volume of water.

5.2.4 Results Summary

All three experiments were carried out successfully, and experimental data was able to be collected from all three locations. Taken together, the results give a good insight into the behaviour of a solute as it travels through this sewerage network, and also suggest that further work may be needed to quantify the effects of processes not included in the simple ADE used in this project’s model. The experiments resulted in a dispersion coefficient of 1.55m2/s that appears to be applicable throughout the whole tunnel network under The Käppala Association’s control, and a flow-based Manning- Strickler coefficient due to the differences in roughness in the base of the tunnel compared to the walls. The parameter values used in each of the models is given in Table 3.

Table 3 - Parameter values used after the final calibration of the model

Reach Reach Average Manning- Dispersion Injected Nacka Calculated Length Flow Strickler coefficient mass dilution loss (%) (m) (m3/s) Coefficient (m2/s) (kg) factor (m1/3s-1) (-) Experiment 1 Lidingö 9100 1.19 0.0232 1.55 1.045 0.78 25.7 Experiment 2 Sollentuna 800 0.013 0.0170 3 2.021 - - Rinkeby 4700 0.735 0.0234 1.55 - - - Långängstrand 6300 0.937 0.0227 1.55 - - - Dykarledningen 880 0.966 NA * 1.55 - - - Lidingö 9100 1.15 0.0230 1.55 - 0.83 28.6 Total 21680 ------Experiment 3 Arlanda - 1400 0.029 0.0100 2 4.002 - - Märsta Märsta - 10900 0.190 0.0182 1.55 - - - Löwenströmska Antuna PS 6200 0.263 0.0194 1.55 - - - Stäket jct. 2900 0.382 0.0209 1.55 - - - Edsberg PS 3500 0.545 0.0222 1.55 - - - Rinkeby 4700 0.757 0.0235 1.55 - - - Långängstrand 6300 1.05 0.0232 1.55 - - - Dykarledningen 880 1.10 NA * 1.55 - - - Lidingö 9100 1.23 0.0232 1.55 - 0.88 33.2 Total 45880 - - - - - * Manning-Strickler equation not used to calculate flow velocity - Q = Velocity x Area relationship used

Overall, the application of the simple ADE seems to be applicable if the arrival time of a pollutant plume and approximate maximum concentration is required. In the first two experiments, the simple ADE also gave a good approximation of the falling limb. This was not the case in the third experiment, where the tail of the breakthrough curve was significant. This may explain the subdued peak concentration in the experimental results. The extended tail suggests that intermittent storage processes may play a more significant role, with the pumping station likely contributing greatest to this due to their sumps and varying pumping rates.

Due to the significant change in calculating the velocity related to flow carried out after the final experiment, an extra experiment validating the model would have been ideal. The results do, however, provide a solid foundation from which further experiments can be carried out and the model built upon.

5.3 Application to ECORISK2050

The results from this project will be used to aid in determining the risk of exposure a WWTP has to a chemical spill, which is one of the aims of the ECORISK2050 project. By knowing the characteristics of the breakthrough curve of a solute at the inlet, certain assumptions can be made about the concentrations and the total mass to be expected at different process steps and in the WWTP at any one time. From this, and with reference to known toxicity and PNEC values for different chemicals, predictions of the effects on the microbial populations within the WWTP can be made.

In terms of dilution, the results show that there is an approximately linear relationship with distance from the inlet, or more accurately a logarithmic relationship. In Figure 21, the peak concentration of an equalised injection mass of 1kg is shown. As a small portion of Experiment 2 is composed of the municipal network with a higher dispersion coefficient, we could assume a slightly higher peak of around 0.4mg/l using the equalised injection if we were to consider only The Käppala Association’s tunnel network. As only three data points were used to derive the trendlines, the exact relationship should be viewed with caution. It is nonetheless useful in indicating the severity of outcome with distance from the WWTP.

Figure 21 – Peak concentration at the inlet using an equalized injection mass of 1kg. Both logarithmic (orange) and linear trendlines approximate the relationship between dilution and distance from the WWTP.

The duration of each breakthrough curve follows an almost perfectly linear relationship as seen in Figure 22. The duration of the curve was taken by bounding the curve at 5% of the peak concentration value for each experiment. Depending on the quantity and type of chemical spilled, concentrations of pollutant below this 5% level could still pose a risk, however, the bulk of the mass of the spill will likely be contained within these limits. The biological treatment at Käppala WWTP takes 32 hours at the design flowrate, QDim (Johansson, 2000). The duration of the curves, regardless of the distance from the inlet, suggest that the majority of the spilled mass will make its way into the biological step before any reaches the effluent. We can assume that this would be the case, even for spills released over a

period of several hours. Additional assumptions can then be made on the levels of pollutant in the resulting sludge and the effects on biogas production and the suitability of the sludge for agricultural applications.

Figure 22 – Breakthrough curve durations, bounded by the 5%-of- peak concentrations before and after the peak arrival.

5.4 Suggested Improvements

In order to improve the results from this series of tests, some improvements could have been made. Each of these improvements comes at a price, mostly in terms of economy and manpower, rather than quality of results. The first improvement would have been to have more sample points along the network, particularly at points of interest such as pumping stations and the dykarledningen. The current results suggest that these facilities could be responsible for the presence of the large tail seen in the results from Experiment 3. However, without data to back up this hypothesis, it remains just that, and any attempt at quantifying the effect would be speculative at best.

Sampling at these points would give real data for the concentration present at the pumping station and the associated time stamp. These results could then be related to the pumping rate at the time of measurement, giving in to the possibility of modelling processes at play. The requirement to carry out this would be additional manpower and instrumentation for sample collection, and additional lab analysis resources. An alternative to this would be to carry out smaller scale experiment close to the facilities under investigation. This would reduce the amount of tracer needed and opens up the possibility of using other tracers that can be measured using relatively inexpensive instruments, such as salt tracing. This has the obvious benefit of reducing the need for lab analysis, and makes repeating experiments under different flow conditions much easier as the instrumentation could be left in place between injections.

Additional experiments investigating other sectors of the network, such as the Täby branch, would be beneficial to get an overview of the whole system, however, comes with the costs of carrying out an extra experiment. As the stretches not investigated in this experiments flow under gravity systems, with no major flow altering infrastructure, these would provide ideal locations for validating the final calibration carried out after Experiment 3.

6. Discussion

6.1 Solute Transport in The Käppala Association’s Sewerage network

Solute transport in The Käppala Association’s northern sewerage network can be represented quite accurately using simple hydraulic engineering equations and the simple form of the ADE. The applicability of the ADE decreases with increased distance from the WWTP and with increased number of pumping stations. It is difficult to say which has the greater effect, as no samples were taken anywhere other than at the inlet. The rationalist view would suggest the number of facilities being the dominating factor affecting the deviation from the ADE due to the presence of sumps and varying pumping rates, which would have the effect of inducing a transient storage effect. Hyporheic interactions are likely to be minimal due to the construction of the tunnel network in bedrock, and the low hydraulic conductivity of said bedrock. The increased distance between the locations in Experiment 1 and Experiment 2 did not result in major deviations from the ADE, suggesting that distance plays only a minor role, if any, on the applicability of the ADE.

The dispersion coefficient obtained from the experiments appears to be applicable along the length of the main tunnel, and didn’t have to be changed once a value was obtained from the first experiment. We can therefore assume that a dispersion value of 1.55m2/s to be a validated value, and applicable throughout the whole tunnel network. Compared to a typical river system, dispersion is low within this sewerage network (Kashefipour & Falconer, 2002), and the result is a well-defined breakthrough curve that has only a small degree of spreading. This finding in itself could have huge implications in what measures are taken in the event of a spill.

The final method for determining the velocity of wastewater based on the depth of flow, or more accurately, the relative amount of contact the flowing water has with the base of the tunnel and the tunnel walls, worked well for all three experiments. However, as the method could not be validated with further experiments, it cannot be confirmed as an appropriate technique. It does, however, offer a logical calculation method that represents the reality of the tunnel better than a single value used at all depths. In this way it shows promise.

6.2 Transferability of Results and Limitations

The results can be tied in with numerous aspects of incoming wastewater quality management and control. The model shows that sources of pollutants close to the WWTP will generally follow simple solute transport mechanisms, with solutes originating from further sources undergoing more complex solute transport mechanisms less accurately defined by the ADE. In general, the breakthrough curves last a relatively short period of time, with dispersion low in relation to natural rivers. Such findings could be applied in general to other sewerage networks that are similar in character to The Käppala Association’s. Additionally, the results could be applied in regards to the type of pollution the WWTP process engineers can come to expect. For pollutant sources close to the works, acute toxicity may be more of a threat due to the higher peak concentrations. Sources originating further away may pose more of a chronic threat to microbial wellbeing, with the peak likely to be less than that calculated, however, the tail concentrations significantly higher than those calculated, and lasting a much longer time. For two spills with the exact same characteristics, but at different locations along the network, vastly different management techniques could be employed.

The use of a conservative tracer enhances the transferability of results, as it will give a general view of residence time and dilution of a solute within the system. The high affinity to remain in solution and the non-reactive nature of uranine mean that the peak concentration is likely to be a maximum, or worst-case scenario. Certain assumptions and translations have to be made to apply the results to a real-world scenario. One positive is that the incoming peak is most likely to be less than that calculated using the model, unless an erroneous decay constant is applied in the model equations. This gives a

certain margin of error when considering acute toxicity levels related to the peak concentrations, however, could present a considerable level of uncertainty when considering the duration of low-level toxicity in the tail of the breakthrough curve. The level of uncertainty is likely to be related to knowledge on the behaviour (biological, physical, chemical) of the pollutant with wastewater.

As a volume of water passes through a sewerage system, it undergoes a series of biological, chemical, and physical reactions due to interactions with microbes, biofilms, and the atmosphere. This results in a small self-purifying effect (Schütze, 2002). To try and quantify this, one solution may be to consider using a decay constant in the ADE, however, this is likely to change seasonally if the decay is biologically driven. Additionally, reaction by-products may have to be considered if they pose a risk, which adds an extra element of complexity to assessing the risk.

The model shows that by using software that is familiar to most, a well performing solute transport model can be created for indicative or simulation purposes. The use of such software has its drawbacks, notably in the requirement for the user to input flow values for each reach. In order for the model to be of more use, a new platform where real-time data links could be used to provide the most up to date predictions, whilst reducing the requirement for user inputs. This would likely lead to a greater uptake in model use by process engineers at the plant. An additional benefit to automating the input of flow values is that the number of reaches could be increased dramatically. Dividing the network into reaches based on the location of flowmeters and connections would likely result in a more robust model while simplifying the process for calculating flows within a reach. The flow values used would simply be the value measured at the beginning of the reach.

The model used in this project considers only a slug injection at the origin point. Given that an actual chemical spill event is unlikely to be accurately described as a slug injection, this particular model may be of limited use. This, however, is easily rectified. By simply changing the boundary conditions at the point of injection, the relevant equations could be applied. One method could be to approximate the spill as a breakthrough curve, or apply a Heaviside step function between two time points, and then convolute this profile much in the same way already done in reaches subsequent to the injection point in the model.

7. Conclusions

In conclusion, the application of the ADE to describe solute behaviour seems to hold up well, however, results from Experiment 3 suggest that certain additional processes, such as transient storage, need to be considered if the plume passes through flow altering facilities, such as pumping stations. The experiments resulted in a robust dispersion coefficient of 1.55m2/s that appears to applicable across the whole sewerage network under The Käppala Association’s control. The dispersion will give a good idea of the level of dilution one can expect from a spill. The caveat to this is that the behaviour of a solute in the municipal sections, particularly with regards to dispersion, is still relatively unknown. The results from Experiment 2 suggest that dispersion in these sections could be substantially higher. The method of determining the velocity of the water from flow data using a depth dependent Manning-Strickler coefficient makes logical sense, and worked for these three experiments, however, a fourth experiment validating the method and coefficient values used would have been ideal.

The experiments made use of slug injections to obtain transport characteristics, however, it is unlikely that real-world chemical spills will take this form. Given the short time-span of the breakthrough curves, even at significant distances from the inlet, certain minor adjustments should be applied to the model so that chemical spills are more realistically defined, such as estimating the length of time over which a spill occurs. This would give better results in determining peak and tail concentrations at the inlet and give a better indication into the type of toxicity (acute vs chronic) to mitigate against. The final model provides a solid base from which a more user-friendly risk assessment and prediction tool can be created, aiding in policy making and helping process engineers in deciding on mitigation measures if a spill were to occur.

8. Further Research

To build on this experiment, tracer tests could be carried out in the Täby stretch of the sewerage network using the newly calibrated model in order to validate the model. This would validate the current model, could suggest areas where improvement is needed, and improve the overall robustness of the model. Smaller tracer tests could be carried out in the reaches before the pumping stations to investigate any transient storage effects, and attempt to quantify these.

Additionally, the southern sector of the sewerage network, which includes the Värmdö and Nacka municipalities, is constructed in a completely different way to the northern sector. This part of the network has comparatively more pumping stations and is composed of pipes rather than large tunnels. Both the hydraulic and solute transport properties likely vary significantly from the figures obtained in the above experiments.

The results of this experiment lend themselves to the study of investigating the fate of pollutants within the WWTP. The sewage sludge from this plant is Revaq accredited, meaning that it may be used in agriculture. The sludge is also used to produce biogas. If a considerable portion of the pollutant ends up in the sludge it could have implications on both biogas production efficiency and present environmental, health, and safety concerns for the sludge used in agriculture.

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Appendices

Appendix 1

Location, time, and mass of injection for each experiment. Approximate locations can be seen on the map in Figure 9. Experiment Location of Time of Duration of Injection Mass (kg) Injection Injection Injection (min Requested Actual (SWEREF99 TM) Lidingö (1) 675787,6586730 09:15 <1 1.0 1.045 Sollentuna (2) 666805,6592300 23:30 3 2.0 2.021 Arlanda (3) 663410,6613898 11:00 2 4.0 4.002

Sampling regime for each experiment

Experime Sample Predicted Actual Sampling Est. finish Actual No. nt frequency peak peak start time time finish samples (mins) arrival arrival time sent for analysis Lidingö 1 ~12:00 12:49 11:45 12:30 13:15 51 (1) Sollentun 2 08:13 08:49 07:15 10:15 10:45 52 a (2) Arlanda 4 09:45- 07:53 07:17 12:30 10:45 50 (3) 10:00

Lab analysis for Experiment 1 was carried out 29-03-2021 15:00-17:00 Lab analysis for Experiment 2 was carried out 16-04-2021 17:00-19:00 Lab analysis for Experiment 3 was carried out 28-04-2021 16:00-18:00

Appendix 2

Käppalaförbundet’s archive number (diarienummer) for all documents related to this project is KF2021-037.

Dye tracing – Lab methodology

Författare: Anna Svensson

1 Dye-Tracing Lab Methodology

1.1 Sample preparation

During the dye tracing experiment performed, several samples of sewage water was collected. Each sample of sewage water need preparation before the analysis.

To prepare samples for the analysis, a proper number of cuvettes (4 ml) is organized on a cuvette tray, one cuvette for each sample of sewage water collected. 1-2 drops of a buffer solution (Titrisol pH 9) are then added to each cuvette. This is done to ensure that the pH is higher than 7, a too low pH would lead to incorrect results.

The collected sewage water is then added to the cuvettes, one cuvette per collected sample of sewage water, and filled up to approximately 0,5 cm from the top of each cuvette. pH test strips are used on randomly selected cuvettes, to verify the pH of the solution in the cuvettes before performing the analysis. If the pH is too low, below 7, an additional drop of the buffer solution is added to the cuvette.

Before each cuvette is placed in the spectrophotometer, it is wiped clean with a fine cloth to avoid droplets, dust or fingerprints to interfere with the beams of the instrument.

1.2 Analysis

Before running the analysis, the spectrophotometer is switched on, and the right settings for the Uranine used during the experiment is selected. The wavelength of excitation is set to 493 nm, and the wavelength of emission is set to 515 nm during this analysis.

The cuvette to be analyzed is carefully placed in the cuvette holder inside of the spectrophotometer, the cover lid closes, and the analysis is performed. Only one cuvette is analyzed at a time.

For each sample, intensity and concentration is obtain from the instrument, printed on a paper sheet. The analysis takes just a few seconds per sample.

The analyzed cuvette is removed from the cuvette holder before the next cuvette to be analyzed is inserted.

When the analysis is finished the used cuvettes are discharged, not to be reused for future analysis.

1.3 Equipment

• Spectrophotometer: Jasco FP-777. Detection limit: 2,0*10-9 g/l (0,002 ppb) • Buffer solution: Titrisol pH 9 • Cuvettes: 4 ml • pH test strips

1.3.1 Safety precautions

• Lab coats • Gloves

2 Results

2.1 First injection: Spisen 2021-03-29 Diluted amount of Uranine: 1045 gr Injection: 2021-03-30. Lab analysis was made the same day.

Concentration (ppb) Sample time analysis 29-03-2021 Concentration (ppb) analysis (hh:mm) 15:00-17:00 Comment 14-04-2021, 16:00 12:45 455,9 11:48 -0,819 12:24 -0,758 12:28 -0,869 12:32 0,52 12:35 2,469 12:36 5,913 5,007 12:38 23,89 21,41 12:40 76,02 12:42 163,01 138,1 12:44 324,5 12:46 563,7 505,9 12:47 619,4 12:48 659,7 12:49 696,3 12:50 656,3 587,1 12:51 640,6 12:52 599 12:53 458,6 12:54 490,8 I analyzed again the cuvette because it did not fit to the 12:54 489,1 curve 12:55 372,5 310,2 12:56 321,4 12:57 220,9 12:58 189,9 166,8 12:59 127,8 13:00 109,9 13:01 85,32 13:02 55,92 13:03 44,19 13:04 28,43 13:05 16,31 13:06 15,42 14,3 13:07 18,7 13:09 6,36

13:11 5,476 13:13 3,282 2,51 13:15 2,703 From this point and below I chose to analyze more samples to have a better resolution in the breakthrough 13:07 16,14 curve. I poured a sample from 12:54 in a new cuvette to analyze again in the spectrometer because it did not fit 12:54 503,4 the curve. 12:30 4,359 12:30 3,414 Analyzed the same cuvette a second time 12:31 -0,362 Poured in a new couvette and analyzed in the spectrometer. It was high turbidity in the sample. 12:30 5,771 Maybe it affected the result.

2.2 Second injection: Sollentuna 2021-04-45 Diluted amount of Uranine: 2021 gr Injection: 2021-04-15 23:30. Lab analysis was made the day after (2021-04-16).

Concentration Concentration (ppb) (ppb) analysis analysis 16-04-2021 20-04-2021 Sample time (hh:mm) Intensity 17:00-19:00 Comment 19:00 07:15 5,724 0,936 07:39 4,978 0,693 07:51 4,075 0,398 07:55 5,382 0,825 07:59 3,734 0,287 08:03 5,29 0,795 08:07 6,162 1,079 08:13 4,916 0,672 08:17 5,134 0,744 08:21 4,854 0,652 08:25 14,37 3,759 2,297 08:29 67,21 21,06 08:31 122,5 39,27 08:33 225,5 73,5 08:35 392,3 129,7 111,1 08:39 887,7 302,7 08:41 1099 379,6 08:43 1501 529,5 08:45 1777 636,1 08:49 2077 754,8 08:53 1942 701 08:55 1752 626,3

08:57 1588 562,8 469,1 08:59 1228 427 09:01 1012 347,6 09:03 716,7 242 196,6 09:05 543,5 182,6 09:07 106,6 134,6 09:09 301,2 98,92 09:13 17,7 55,92 09:17 114,5 36,65 29,88 09:21 82,64 26,13 09:25 73,06 22,98 09:29 67,99 21,31 09:35 46,02 14,11 09:37 10,89 12,43 09:41 31,52 9,362 14,58 09:45 29,34 8,651 09:49 26,2 7,622 09:55 26,42 7,694 4,765 09:59 20,38 5,72 10:03 22,96 6,565 3,982 08:47 1974 713,5 08:51 2013 729 08:51 1999 723,7 08:23 10,33 2,439 08:27 32,58 9,709

2.3 Third injection: Märsta 2021-04-27 Diluted amount of Uranine: 4002 gr Injection: 2021-04-27. Lab analysis was made the day after (2021-04-28). Concentration (ppb) analysis 28-04-2021 16:00- Sample time (hh:mm) Intensity 18:00 Comment 07:17 20,28 5,69 07:21 59,36 18,48 07:25 146,6 47,26 07:29 306,5 100,7 07:33 582,3 195 07:37 977,2 335 07:41 1390 487,3 07:45 1778 636,4 07:49 2123 773,4 07:53 2232 817,6 07:57 2180 796,4 08:01 1982 716,8 08:05 1751 625,8 08:09 1423 499,9

08:13 1133 391,7 08:17 783,7 265,7 08:21 521,2 173,9 08:25 398,3 131,7 08:29 303,8 99,79 08:33 239,3 78,13 08:37 220 71,68 08:41 174 56,36 08:45 153,7 49,59 08:49 130,1 41,8 08:53 116,1 37,15 08:57 109,5 34,99 09:01 99,85 31,79 09:05 89,77 28,47 09:09 79,23 25,01 09:17 69,95 21,95 09:21 64,32 20,11 09:29 66,37 20,78 09:37 61,39 19,14 09:45 62,48 19,5 09:53 48,45 14,9 09:57 49,85 15,36 10:09 46,51 14,27 10:17 43,87 13,4 10:41 37,71 11,38 10:45 40,92 12,43

TRITA TRITA-ABE-MBT-21421