Simulation of Air Quality in Underground Train Stations

Simulation of air quality in underground train stations

Benjamin Söderberg

Master of Science Thesis TRITA-ITM-EX 2020:604 KTH Industrial Engineering and Management Machine Design SE-100 44 STOCKHOLM

Examensarbete TRITA-ITM-EX 2020:604

Simulering av luftkvalité i underjordiska tågstationer

Benjamin Söderberg Godkänt Examinator Handledare Ulf Sellgren 2020-12-29 Ulf Olofsson Uppdragsgivare Kontaktperson KTH Ulf Olofsson Sammanfattning Partiklar är en utbredd luftföroreningar av mikroskopiska partiklar som finns i luften. Det finns höga halter av PM (particulate matter) i underjordiska tågstationer och tunnlar. Partikelhalten (PM10) som är uppmätt i Stockholms tunnelbana varierar betydligt mellan dag- och natttågtrafik. Emissionsfaktorn är ett representativt värde för mängden partiklar som släpps ut i atmosfären. Dessa faktorer kan uttryckas som massan av partikel per enhetsavstånd, volym eller vikt. I detta dokument uttrycks detta som den mängd energi som går åt för att producera enhetsviktspartiklar. Faktorn uttrycker en uppskattning av partikelemissioner från hjul-rälskontakt och broms.

Simulering har genomfördes i IDA tunnel för att utvärdera och förutsäga partikelhalten på tågplattformar. Tidigare uppmätta data under vinter och sommar från Mariatorgets plattform (utförd av SLB-analys) användes för validering av simuleringsmodellen. Detta användes sedan som grundläggande referens för att simulera och kalibrera emissionsfaktorer. Viktiga parametrar för tunneln, plattformen och tågen som användes i trafiken är inhämtade och evaluerade. Ventilationsschakt finns i varje ände av plattformen. Dessa ventilationsschakt är öppna under sommaren och stängda under vinterperioden. Således undersöktes två scenarier, vinter- och sommarfall. De erhållna resultaten utvärderades och analyserades senare. Känslighetsanalys gjordes för att testa effekten på emissionsfaktorerna av ventilationsschaktens öppningsgrad.

Resultaten från vinterfallstudien visade att emissionsfaktorerna är 0,57 g/kWh från hjul-rälkontakt och 0,03 g/kWh från bromsarna. Emissionsfaktorn från hjul-rälkontakten ger 70 % av den uppmätta PM10 koncentrationen, medan bromsarna ger 30 %. Resultaten från sommarfallstudien visade att emissionsfaktorerna är 0,61 g/kWh och 0,05 g/kWh från hjul-rälkontakten och bromsarna, respektive.

Nyckelord: PM10, luftkvalité, tåg, emissionsfaktorer, underjordiska tågstationer

Master of Science Thesis TRITA-ITM-EX 2020:604

Simulation of air quality in underground train stations

Benjamin Söderberg

Approved Examiner Supervisor 2020-12-29 Ulf Sellgren Ulf Olofsson Commissioner Contact person Ulf Olofsson Ulf Olofsson Abstract Particulate matter (PM) is a widespread air pollutant of microscopic particles that are suspended in the atmosphere. There is high concentration of PM in underground train stations and tunnels. The concentration of particulate matter (PM10) measured in Stockholm’s metro varies significantly between day and nighttime traffic of trains. Emission factors are a representative value of a pollutant released into the atmosphere. These factors can be expressed as the weight of pollutant divided by a unit distance, volume, or weight. In this document it is expressed as the amount of energy used to produce a unit weight. Such factor expresses estimation of emissions from train wheel-rail contact and brake. Simulation of particulate matter using IDA tunnel was conducted to evaluate and predict particulate matter (PM10) concentration levels. Previous measured data of PM10 from Mariatorget’s platform (performed by SLB analysis) was used for the validation of the simulation model. These data were collected during winter and summer periods. It was then used as fundamental reference to simulate and calibrate emission factors. Important parameters of the tunnel, platform and trains that were used in traffic were imported. There are ventilation shafts on each end of the platform. These ventilation shafts are open fully during summer and closed during winter periods. Thus, two case scenarios, winter and summer were investigated. The results obtained were later evaluated and analysed. Sensitivity analysis was made to test the effect of ventilation shaft’s openings on emission factors.

The results from winter case study showed that emission factors are 0.57 g/kWh from wheel-rail contact and 0.031 g/kWh from brakes. Emission factors from wheel-rail contact give 70% of the measured PM10 concentration whereas brakes give 30% of PM10 concentrations. Results obtained from summer case study showed that emission factors are 0.61 g/kWh and 0.05 g/kWh from wheel-rail contact as well as from brakes, respectively.

Keywords: PM10, aerosols, air pollution, particulate matter, emission factors, underground train station

3 4 FORAWORD

This master thesis project has been conducted at Royal Institute of Technology (KTH), Machine design department.

I would like to thank my supervisor Ulf Olofsson, for all the unreserved support in guiding and helping me carry out this project. It was a privilege to acquire extremely important expertise knowledge in the subject area.

In addition, I would like to thank Eirk Östbolm at Equa Solutions for providing me training in IDA tunnel simulation software as well as continuous support and advice throughout the project.

Finally, I would like to thank my Ulf Sellgren, examiner of the project. .

Benjamin Söderberg

Stockholm, December 2020

5 6 NOMENCLATURE

Notations Symbol Description

μg Micro gram kWh kilo watt hour

Abbreviations

PM Particulate matter EPA Environmental Protection Agency TEOM Tapered Element Oscillating Microbalance WBS Work Breakdown Structure SL Storstockholms lokaltrafik SLB-Analys Stockholms Luft- och Bulleranalys CFD Computational fluid dynamics RMSE Root mean square error R2 Correlation coefficient

7 LIST OF FIGURES

Figure 1.1 Classification of typical types of particles present in our environment and their common size 13

Figure 1.1 Particle sizes comparison relative to human hair 15

Figure 1.3 Work break down structure of the project 17

Figure 2. 1 Schematic representation of movement of a train in a tunnel and air flow patterns. Figure a shows train's movements in the direction of the ventilation shaft's position. Figure b shows right after the train past the ventilation shaft's position 20

Figure 2. 2 C14 train model used in Stockholm's metro Red line 21

Figure 2. 1 C20 train type used in Stockholm's metro Red line 21

Figure 2.4 A simplified view of bogie configuration. To the left is Cx model configuration

and to the right is C20 model configuration (Reference to picture could not be found) 22

Figure 2. 4 TEOM positioned in the middle of Mariatorget’s underground train station

platform 23

Figure 3. 1 Flowchart of the methodology in this thesis project. 25

Figure 3. 2 P-diagram for calibration process. 26

Figure 3. 3 Input and key components as well as parameters of the simulation model in IDA tunnel 27

Figure 3.4 Illustration of automatic speed control when there is a train or signal ahead

is red or when the train approaches end stations. Otherwise a maximum speed of 80 km/h applies between Slussen and Hornstull train stations in absence of any hinder. 28

Figure 3.5 Air flow through grill when damper is open. 30

Figure 3. 6 Position of grill when damper is partially open. 30

Figure 3. 7 Measured PM10 concentration daily mean value during winter period with closed ventilation shaft. 33

Figure 3. 8 Measured PM10 concentration daily mean value during summer period with ventilation shaft open 100%. 33

8 LIST OF TABLES

Table 1.1 WHO’s and European Union’s air quality guidelines and their rationale for PM10 concentration level. Annual and 24-hours mean, respectively. 14

Table 2.1 Summary of measured PM10 concentrations on platforms in Stockholm [13]. 19

Table 2. 2 Specification of Cx and C20 train types [17] 22

Tabell 2.3 1Braking mechanism of trains. 22

Table 3.1 Speed control mechanism using external inputs by Automatic train control system (ATC) 29

3.2 Size of ventilation shafts in stations 29

3. 3 Average real time weather data for winter and summer periods 30

3. 4 Filtered hourly mean PM10 concentration measured during winter period. Background level is concentration in ambient air 31

3. 5 Default apportionment of emission factors in IDA tunnel. 32

3. 6 Filtered hourly mean PM10 concentration measured during summer period. Background level is concentration level in ambient air 32

Table 4. 1 Results of simulated PM10 concentration daily mean values and emission factors for calibrated model for winter period 34

Table 4. 2 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on weekdays 35

Table 4. 3 Coefficients with 95 confidence bounds for the linear regression model of PM10 concentration on Saturdays 35

Table 4. 4 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on Sundays 35

Table 4. 5 Results of simulated PM10 hourly average values and emission factors for calibrated summer period on weekdays 35

Table 4. 6 Coefficients with 95%confidence bounds for the linear regression model of PM10 concentration on weekdays 36

Table 4. 7 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentrations on Saturdays 36

Table 4. 8 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on Sundays 36

Table 4. 9 Results of simulated PM10 daylily mean values and calibrated emission factors for winter period. The results are from simulation of weekdays 36

9 Table 4. 10 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis weekdays 37

Table 4. 11Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis Saturdays 37

Table 4. 12 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis Sundays 37

Table 4. 13 Effect of calibrated emission factors in the summer model 37

Table 4. 14 effect of calibrated emission factors while the ventilation shaft is opened by 20% 38

10 TABLE OF CONTENTS

1 INTRODUCTION 13

1.1 Background 13 1.2 Purpose 15

1.3 Delimitations 15 1.4 Limitations 16

1.5 Risk analysis 16 1.6 Method 16

2 FRAME OF REFERENCE 18 2.1 Literature study 16

2.2 Regulations 18 2.3 Emission factors 19 2.4 Piston effect mechanism 20

2.5 Cx 20 2.6 C20 21

2.7 Ida tunnel 23 2.8 Tapered Element Oscillating Microbalance 23

3 Method 24 3.1 Research methodology 24

3.2 Data collection 26 3.3 Previous work 26

3.4 Model set up (preparation, simulation, evaluation) 26 3.5 Practical calibration methodology 31

3.5.1 Winter case scenario (ventilation shaft closed) 31 3.5.2 Summer case scenario (ventilation shaft opened) 32

11 3.5.3 Sensitivity analysis 32 4 RESULTS 34

4.1 Winter case study 34 4.2 Summer case study 35

4.3 Sensitivity analysis 36 5 DISCUSSION AND CONCLUSIONS 38

5.1 Discussion 38 5.1.1 Result analysis 38 5.2 Conclusions 41

6 RECOMMENDATIONS AND FUTURE WORK 42 6.1 Recommendation 42

6.2 Future work 42 7 REFERENCES 43

APPENDIX A: RISK ANALYSIS 45 APPENDIX B: SCHEMATIC VIEW OF IDA TUNNEL MODEL 46

APPENDIX C: ISHIKAWA DIAGRAM 47 APPENDIX D: TABLE OF LOSS COEFFICIENTS 48

APPENDIX E: TABLE OF TRAIN FREQUENCY 49 APPENDIX F: GRAPHS OF MEASURED PM10 51

APPENDIX G: REGRESSION ANALYSIS AND LINEARITY COMPARISON 55

12 1 INTRODUCTION

1.1 Background Small microscopic particles are generated and suspended in the atmosphere. The term PM (particulate matter) is a widespread air pollutant and stands for microscopic particles of solid or liquid matter that are suspended in the air or atmosphere. Some particles are large enough to be seen with naked eye such as dirt, dust, smoke. Others are very small and can only be detected using microscope [1]. They are made up of hundreds of types of chemicals. Figure1 below shows particulate matters in our environment and how these matters are classified according to their sizes. Particles that are 10 µm or less in diameter are designated as PM10.

Figure 1.1 Classification of typical types of particles present in our environment and their common size [2]

Figure 1.2 Particle sizes comparison relative to human hair [3].

Particulate matters have impacts that adversely affect human health in connection to direct inhalation [3]. Studies show that long term exposure to particles that are less than 2.5 micrometer in diameter can cause cardiovascular mortality and morbidity [4] and are even related to other health problems. World Health Organization [5] recommend the standards for PM10 air quality as 50 µg/m3 for 24-hour mean and 20 µg/m3 annual mean. European Union directive states that PM10 concentration can exceed 50 µg/m3 per day and annual average must not exceed 40 µg/m3 for outdoor air.

Table 1.1 WHO’s and European Union’s air quality guidelines and their rationale for PM10 concentration level. Annual and 24-hours mean, respectively. PM10 (µg/m3) WHO EU

Annual mean 40 20

24-hour mean 50 50

There is high concentration of PM in underground train stations and tunnels. The concentration and chemical composition of these particulate matters are generated from different sources such as rail track, wheel, brake pads, third rail & sliding shoes, ventilation system, passengers, and other operation systems. The size of the particles depends on the type of surface contact and

14 reaction that occurs. The content of particle amount measured in Stockholm’s metro varies. For PM10 70-400 휇푔/m3 is measured during winter and summer period. Nighttime traffic of trains with diesel engine release 10 times more particles than that of electric trains. But generally, more particles are generated in the daytime since there is much denser train traffic and activity [3]. More and more railway networks are built in the underground to mitigate environmental impact. Train movement and presence of large number of people increase particulate matter emission. A careful planning of ventilation system is vital to achieve the necessary air exchange and create comfortable condition for users. Therefore, to simulate, study and analyze particulate matter emission and concentration in tunnel railway systems provides important information for underground train station developments.

This study aims to validate particulate emission simulation capability in a simulation software for tunnel ventilation, IDA Tunnel. Existing air quality measurements from the Stockholm underground system at Mariatorget’s train station platform are used in the study.

1.2 Purpose Simulation modelling is important method to analyse problems efficiently. It plays important role in complex engineering projects to gain insights of a products performances during design stage. The results from simulation modelling can easily be verified and communicated via visualization. Simulation of particulate matter determines the cause of emission, concentration level and predicts the outcomes in multiple case scenarios. The main purpose of this thesis work is to calibrate and validate emission factors for particulate matter concentration levels in underground train stations using a simulation software called IDA tunnel. The results can be used to predict concentration of particulate matter emission factors for future underground train station developments. There are ventilation shafts built in train stations and these ventilation shafts are opened during summer period. The ventilation shafts are closed during the winter period to avoid ice problems in the tunnel. A central question for this scenario of opened and closed ventilation shafts is if the same emission factors can be used in the simulation of air quality at underground platforms in both these ventilation cases. Firstly – Calibration and validation of emission factors for air quality in IDA tunnel software. Secondly – Can emission factors from a winter ventilation case be moved to a summer ventilation case? There are several variables, controllable and uncontrollable, that contribute significantly to the simulation software. These variables are summer and winter case scenario and sensitivity analysis was performed alongside.

1.3 Delimitations No new PM10 measurement campaigns will be performed. Already existing data set for Mariatorget’s train station platform will be used. The thesis work is not taking into considerations the new train models known as C 30 that will be put into service on the Red line in the near future since there is no data for these train models. This thesis work is done during the COVID-19 pandemic season and from distance in agreement with the project’s immediate supervisor. Support and supervision over the program IDA and project are accommodated virtually throughout the time. This has affected the project’s timeline and performance significantly.

15 Few assumption are made during the project’s time span apart from delimitations mentioned above. Complexity of the model and simulation software are the main challenges in replicating real case scenarios.

1.4 Limitations Particulate matter emission factor apportionment cannot be made in IDA program due to limitations on the program. Limited time to learn and train about the simulation software. Limited information available on internet about IDA tunnel software. Limited information available regarding tunnel geometry parameters. Limited information available regarding exact apportionment of train types used in scheduled traffic during data measurement periods. Actual train traffic schedule obtained from published SL’s timetable was used to model number of trains that pass a station.

1.5 Risk analysis Risk identification was made through assumptions and reviewing older research project documents made around the subject matter. Risks were filtered using the two key matrix probability that it will occur and impact it has on the project if it does, see Appendix A. Basic preventive and contingency plan was made to tackle and manage risks during the project work.

1.6 Method A WBS (work breakdown structure) was used to outline the project work and break it down into manageable portions. Study of related former projects were made to understand the subject area. A literature study of particulate matter emissions in underground stations was made. Part of the study was also to collect relevant data about important parameters in problem analysis and solving process. The process of the project and results will be documented in a form of Master thesis. It will finally be presented at an event which will be open for the public.

Figure 1.3 Work break down structure of the project.

17 2 FRAME OF REFERENCE

The summary of the existing knowledge and former performed research about particulate matter is displayed in this chapter. This chapter presents the theoretical reference frame that is necessary for the performed research simulation of particulate matter. 2.1 Literature Study

Literature review of particulate matter as well as older analysis documents regarding the subject matter has been made. A few documents are listed below for further literature study.

SLB (Stockholms Luft- och Bulleranalys) has made several publications regarding particle emissions in train and road environments [6,7,8].

Ventilation System, Calibration of IDA Model for TNG project by Förvaltning för utbyggd tunnelbana is a well-documented report. It provides a short analysis of the simulation made on Stockholm’s metro blue line. Additionally, a short report ‘’particle emission, Akalla-Barkarby Systemhandling’’ had been made. Data set was collected from measurements of particulate matters at Akalla and Husby underground train stations to compare to computational results made using IDA tunnel.

An article’’Hälsoeffekter av lufföroreningar I stationsmiljöer till järnvägstunnlar’’ made by Umeå university has a comprehensive information about air pollution in underground train stations. It has analysis about concentration levels in road and rail tunnel environments and their effects on human health [9].

‘’Beräkning av källtermer för stoftemission in 1 D modeller’’ by Ramböll was made in 2005. Data set collection was made at Mariatorget’s underground train station and simulated in 1D model to predict emission factors in Citybanan.

Furthermore, a good sources for information and for further literature study are listed.

Åtgärder för luftkvalitet, 2015, Ramböll. Technical and economically viable measures are recommended in the report [10].

Airborne Particles in Railway Tunnels, Yingying Cha, 2018. Doctoral thesis made at KTH department of machine design [11].

Particles in road and railroad tunnel air Sources, properties and abatement measures, Mats Gustafsson Saeed Abbasi Göran Blomqvist Yingying Cha Anders Gudmundsson Sara Janhäll Christer Johansson Michael Norman Ulf Olofsson, 2016 [12]. 2.2 Regulations

A report by Ramböll ‘’Åtgärder för luftkvalitet ‘’ [12], suggests methods to improve air quality in Stockholm’s underground train stations. The study shows that there are no mechanical ventilation mechanisms in metro stations. Ventilation is done mainly via Piston effect mechanism. This mechanism is discussed later in this chapter. Currently there is no legislation that regulates PM emissions in Stockholm’s underground train stations. Most research has focused on comparing measured concentration levels with the limit values for outdoor air quality.

18 PM with size less than 10 µm annual average not to exceed 40 (µg/m3) is regulated for ambient air quality. However, these limits only apply to outside air and railway stations are not legally subject to these regulations. In a report published by KTH in 2018 shows PM10 concentration daily mean values at several train platforms, see table 2.1 below. A recommended PM10 hourly average value in underground train stations should not be above 240 (µg/m3).

Table 2.1 Summary of measured PM10 concentrations on platforms in Stockholm [13]. Anläggning Partikelhalt PM10 Antal tåg per Kommentar µg/m3 timme och (dygnsmedelnivå) riktning

Arlandabanan 90 5 Arlanda (Arlanda express) plattform Arlanda express

Arlandabanan 240 7 i högtrafik Arlanda (Fjärrtåg) plattform reguljär trafik

Citytunneln Malmö 120 Inriktningsmål för publika delar

Citytunneln Malmö 80 10 Mätt 2013

Stockholm Södra 150 24 högtrafik

Stockholm Central 25 Utomhus juni

Citybanan 120–70 Inriktningsmål för publika delar

Citybanan 250 10–15 Stockholm Odenplan, tre månader efter trafikstart (mätningar vid högtrafik)

Stockholms 240 Inriktningsmål för tunnelbana publika delar vid nybyggnation

2.3 Emission factors

EPA (Environmental Protection Agency, United States) defines emission factor as a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. These factors can be expressed as weight of pollutant divided by a unit weight, volume, and distance [3].

19 Emission factor in IDA tunnel is expressed as unit weight of particulate matter divided by unit kilowatt hours (g/kwh). This is an expression of the energy needed to release a certain amount or concentration of PM10 into the atmosphere.

2.4 Piston effect mechanism

[reader. Elsevier] studied vent shaft locations in subway tunnels [14]. Piston effect mechanism is demonstrated and computational analysis of a ventilation system in a subway tunnel is carried out. Trains that are running above ground push the air in front of them to the side of the trains. But when passing through a tunnel, the air is pushed by the front surface of the train since the tunnel wall constraints air to flow to the side or rear of the train. Therefore, some of the air pushed by the train is discharged through ventilation shafts, stations or tunnel exits. Particles are also pushed through the ventilation shaft in the process. The rear end of the train creates negative pressure vortex region and as a result air is sucked into the tunnel through openings forming the piston wind. New particulate matters are emitted by the passing train and they fall back to the ground or float in the air behind the passing train. This process and pattern of air flow is depicted visually in figure 2.1 below.

a b

Figure 2. 2 Schematic representation of movement of a train in a tunnel and air flow patterns. Figure a shows train's movements in the direction of the ventilation shaft's position. Figure b shows right after the train past the ventilation shaft's position.

2.5 CX

Cx is a collective name for train models of C5 – C15/16 that are in service in Stockholm’s Red and Blue metro lines. Out of these collective stocks only C6 and C14 stocks are used on the redline. These trains were manufactured and put into service between the years 1970-76 (C6 model) and 1985-89 (C14 model). The trains originally had only mechanical breaking mechanism [15]. They were later updated by SL (Storstockholms Lokaltrafik) to have electric braking but heat it away instead of generation the electricity back to the electrical system. Number of cars varies between 6 and 8. One full length train set has 8 cars and shorter train set has 6 cars. The term long- and short trains is used in this manner in this study. Today only a set of 8 cars is used in scheduled traffic operations.

Figure 2. 3 C14 train model used in Stockholm's metro Red line [15].

2.6 C20

C20 is the newer version of trains that were put in service to Stockholm’s metro lines between the years1997-2004 [16]. C20 train uses electrical engine brakes and applies mechanical brakes when the train is travelling below 25 km/h. One full length train has a set of 3 cars and shorter train set has 2 cars. Both sets were used in traffic operations during PM10 concentration data collection period.

Figure 2. 4 C20 train type used in Stockholm's metro Red line [16].

21 Specification of both train types is shown below in table 2.1 below.

Table 2. 2 Specification of Cx and C20 train types [17]. C20 Cx Number of seats 378 384 Length(m) 139.5 138.5 width(m) 2.9 2.8 Weight(tone) 201 232 Max speed (Red line) 80 80 (km/hr.)

capacity 3 car formation 8 car formation

The two train models types have fundamental differences in the configurations of their bogies set. There are two bogies in each car body of Cx train and are attached to the rigid frame of the train. These set of bogies are not controlled to move radially and does not move independent of each other. This causes consequently larger slippage and occur wear in wheel and rail. Whereas C20 train’s bogie sets can move radially and move independent of each other. Figure 2.4 illustrates bogie configurations on both Cx and C20 train models.

Figure 2.4 A simplified view of bogie configuration. To the left is Cx model configuration and to the right is C20 model configuration (Reference to picture could not be found).

Tabell 2.3 1Braking mechanism of trains. Speed C20 Cx >25 km/hr. Electrical engine brake Electrical engine brake <25 km/hr. Applies both electrical engine Applies both electrical engine and friction brake and friction brake 5-6 km/hr. Friction brake Friction brake

22 2.7 Ida tunnel

IDA tunnel is a comprehensive road and rail tunnel ventilation and fire simulation software. Train movement under normal traffic and emergency conditions can be simulated based on user defined acceleration, retardation and maximum power and speed parameters. Stochastic traffic patterns can be defined to avoid artificial train synchronization effects. In IDA’s one dimensional model, air movement driven by train piston effect, buoyancy and wind pressure can be modeled. Additional computational abilities are age of air (total time spent in underground), amount of carbon dioxide generated by occupants, particle concentrations generated by train movements, CO, NOX and HC generated by diesel engines. PM10 emission factor standards focus on mass of emitted PM in g per unit of power (kWh) [18].

2.8 Tapered Element Oscillating Microbalance (TEOM) TEOM was used to measure PM10 concentration at Mariatorget’s underground train station [6]. Data collection period was during winter period, when the ventilation shaft was closed, as well as during summer period, while the ventilation was opened. The instrument was stationed in the middle of platform part one, i.e., northbound track platform [7].

Figure 2. 5 TEOM positioned in the middle of Mariatorget’s underground train station platform [7].

Some of the platforms in Stockholm’s metro line are curvy. This curviness would contribute massively to increased amount of PM10 emissions. There is a lack of adequate information in modeling the relationship between track curvature degree and PM10 emission.

Therefore, this study will focus mainly on simulating emission factors in assumption to straight platforms.

Each reviews of the literatures mentioned above make a significant contribution to the understanding of subject matter in the project work ahead. Additional literature reviews were made throughout the thesis work. Emission factors are determined in gram per unit of power (kWh) in IDA tunnel. The project focuses on two case scenarios i.e., summer and winter case scenario. Clear

23 understanding is established why PM10 concentration is higher during the winter than summer period. Piston effect is directly related to the characterization of air flow in the tunnel and ventilation shafts. Train frequency is an important parameter for the results accuracy. These terms are explained further in this thesis. IDA tunnel simulation will give an understanding of factors that contribute to PM emission causes and thus in predicting emission factors.

24 3 METHOD

In this chapter the working process of the simulation model is described. The project’s process and case scenarios along with sensitivity analysis are presented.

This chapter provides the methodology used to prepare, calibrate, and validate the simulation data. The model created in this research will be simulated using a simulation software called IDA tunnel. IDA Tunnel is a comprehensive road and rail tunnel ventilation and fire simulation software, used by leading tunnel design companies’ worldwide [18]. This simulation program will be used to simulate particle emissions at Mariatorget’s underground train station. The program can also be used to view the computed results in animated 3D representations. 3.1 Research methodology

This phase is the vital part of the thesis. The methods used from data collection, model preparation and simulation as well as evaluation is documented. A flow chart of the process can be seen in Figure 3.1 below.

Figure 3. 1 Flowchart of the methodology in this thesis project.

An Ishikawa diagram is created to identify potential causes of PM10 emissions railway systems, see Appendix D. Several reasons are stated in different literatures. Train types C20 and CX have different axel loads and this contributes significantly to PM10 emissions. Other important factors are length of train, traffic density as well as speed. Curvy track is one of the reasons for wear of railway as well as different contaminants laying on the track contribute to higher friction between wheel and railway [19] which in turn contribute to higher emission of PM10 pollutants. Airborne particles drop to the ground relatively fast in warmer air temperature compared to cold air. Humidity and temperature play a significant role; therefore, they are important parameters in simulating the model. Wind patterns impact PM10 concentrations in the tunnel. The variation of wind speed and pressure into the tunnel determines level of PM10 concentrations in the tunnel air.

Figure 3. 2 P-diagram for calibration process.

3.2 Data collection Particulate matter measurements and data collection was conducted independently by SLB analysis at Mariatorget’s underground train station. The measurement was performed for a period of one month throughout weekdays and weekends. Winter period PM10 data collection was made in the months of January to February. Ventilation shafts are closed during winter periods to stop warmer air escaping into the open atmosphere and prevent ice building in the underground stations. Summer period PM10 concentration measurement was made in the months of September to October and the ventilation shafts were completely open to allow air circulation within the station. Graphs of measured hourly average PM10 concentration for the periods of winter and summer is attached in Appendix G. The results from these collected data are used as a reference to calibrate the model [7].

3.3 Previous work Simulation of PM10 concentration project work was performed by Ramböll [11]. Measured data from Mariatorget’s underground station by SLB-analys was used to calibrate the model in earlier version of IDA tunnel simulation software. The simulation model was built with specific details of tunnel geometry as well as train parameters. Average number of trains per hour throughout the day was used to demonstrate train frequency. These models were used in this thesis as a base to further work on for calibration process. Furthermore, this was one of the verification methods of the model for correct model settings. This has reduced significant amount of time that would have been spent to construct the simulation model. Nevertheless, finding correct tunnel geometry coupled with limited knowledge of the software instrument would have been a big challenge.

3.4 Model set up (preparation, simulation, and evaluation) The simulation model in IDA tunnel was originally constructed in the earlier software version. Reconstruction of the model in the newer version of the software was necessary due to compatibility issues. Important components and parameter values were imported. A close examination and testing were required afterwards. Model components consists of tunnel sections,

26 platforms, ticket halls, escalators, ventilation shafts, grills, and dampers to demonstrate opening and closing of a ventilation shaft. Model simulation was performed to be able to assess the correctness of the generated new model. Few details of geometry parameters were corrected. New information about ventilation shaft geometry in all stations was acquired and applied in the new model. Global parameters of the trains were corrected separately to C20 and CX trains. New detailed timetables for all seasons published by SL were obtained from Kungliga biblioteket and inserted in the model. The last phase in this module was evaluation of the model by running it and make sure the output was as desired. The model’s correctness was verified and validated using expertise in IDA tunnel software. Furthermore, critical components and parameters were assessed in the process. See Appendix B for schematic view of the reconstructed model and components in IDA tunnel software.

Figure 3. 3 Input and key components as well as parameters of the simulation model in IDA tunnel In the schematic view of simulation model in Appendix B, double track underground stations between Slussen and Liljeholmen are represented. Each station has equal length of 150m. Distance between stations is important parameter since the trains in the simulation model travel a certain distance at a specific speed. Entrance speed before each station is modeled. Slope of the terrain in the tunnel between stations is modelled to represent ground elevation differences. Rout creation is to specify the direction in which the train travels through either way. Trains travel at a maximum speed of 80 km/h between each of these stations. The deceleration starts right before they enter the platform area. The maximum speed allowed along platforms is 50 km/h.

Train length Two types of trains that are used in Stockholm’s red metro line are modelled. Each train has different key parameter values such as weight and length. Total length of trains varies depending on time of operation. Long trains were used during daytime traffic and shorter trains were run in the evenings on weekday’s traffic. Time of scheduled traffic for long train was used between 11- 18 o’clock and short train was used the rest of the time on weekends. The term long train means 3 cars for c20 type and 8 cars for cx type trains. Coupling of trains is performed every day which is a regular routine. Short trains are used in the evenings by uncoupling one car from C20 trains, thus only two cars are used in the traffic. Coupling was performed in the mornings and uncoupling was done again during the evenings on weekends. Short trains were used approximately until eleven o’clock and additional car is coupled to C20 trains afterwards. Full train length consisting of three cars is used in the traffic until six pm. One car is uncoupled again from C20 trains thus short trains consisting of 2 cars is used in traffic the rest of the evening and until morning next day.

27 Dwell time Dwell time is the time the trains stays at a station to allow passenger exchange. It also includes the time of opening and closing of doors. Slussen and Liljeholmen stations are typical connection points for different train routes and bus lines. Flow of passengers is relatively high at these points. Therefore, trains usually have dwell time of around one minute. Dwell time in other stations in between these points is roughly thirty seconds. Taking these variations into consideration an average fixed dwell time of 40 seconds is applied to a train in each station. Each train does not run-in the specified minutes and seconds when it comes to reality. Therefore, entry delays are modelled by stochastic distributions to a train. Each passing train can enter a station few seconds late or early in the model. This was assigned randomly on trains by the simulation program.

Train frequency SL’s scheduled timetable was used to demonstrate train frequency during data collection period. Different timetables are used for weekdays, weekends as well as seasons. Number of scheduled trains that had run in each hour is attached in Appendix E. This timetable was put in simulating the model. It should be noted that SL uses different timetable during different seasons of the year. Wintertime table puts into effect in the month of August. Passenger traffic is dense therefore train operation traffic increases too. Passenger traffic is not as dense during the summer month June to August. Thus, train operation traffic is minimized resulting in less train frequency. Therefore, similar number of train frequency is used both for summer and winter season simulation for this reason.

Speed Speed of train between each station and right before it enters a station is modelled in the program. Particles emitted from brakes and rolling friction between wheels and rail are directly related to these parameters. The red line metro system in Stockholm has three speed limits which is 15, 50 or 80 km/h. The track is divided in three sections and each section is connected with impedance box. This impedance boxes send signals to the train about speed limit in their specific relevant section. 80 km/h is the maximum speed limit when there is no hinder in front of the train. The speed drops to automatically 15 km/h when there is a train ahead in the next section. This is communicated to a train along the track line that there is a train between the next section’s impedance boxes, see figure 3.4 below.

Figure 3.4 Illustration of automatic speed control when there is a train or signal ahead is red or when the train approaches end stations. Otherwise a maximum speed of 80 km/h applies between Slussen and Hornstull train stations in absence of any hinder.

28 Table 3.1 Speed control mechanism using external inputs by Automatic train control system (ATC)

Speed limit (km/h) Remarks 15 End stations, red signal ahead, train ahead 50 Curvy tracks, driving over track switches, train ahead in the next section or between impedance boxes, along platforms 80 Maximum speed free of hinder ahead

Ventilation shaft Ventilation shafts or pressure relief shaft as they are referred in other literatures, are in each end of underground stations. These shafts differ in size from one station to another. Positions and size of these ventilation shafts were obtained and used in simulating the model. Size of the shafts plays an important role since it serves as pressure relief outlet.

3.2 Size of ventilation shafts in stations.

Parameter Vent shaft cross sectional area(m2) Shaft 1 Shaft 2 Slussen 9 - Mariatorget 15 9 Zinkensdamm 9 9 Hornstull 9 9

The amount of air entering to the station is mainly through piston effect. The air current passes through ventilation shafts during summer where they are kept open. This helps to regulate the air temperature and particulate matters flow out. These ventilations shafts are closed during cold winter periods of October to March to keep the warm air and prevent ice buildup in the stations. This results in higher particulate matter concentration since it prevents particles from flowing out into the open air above the ground.

Dampers Dampers build in underground train stations control pressure and volumetric flow. Dampers connected to ventilation shaft in IDA tunnel model are to demonstrate the grill that is used to open or close the ventilation shaft. These are installed at the top end of the ventilation shaft. At partially or fully open position, flow through the damper is governed by a pressure loss coefficient.

Pressure loss coefficients Pressure loss coefficient is the change in pressure Δp caused by frictional resistance in the flow path, assuming the shaft is a long pipe. Air flow through a grill diverges or converges when it passes through the gill. Dynamic loss is the result of changes in direction and velocity of air flow. Loses for pipeline system with different geometry is attached in Appendix D.

Figure 3.6 Air flow through grill when damper is open.

Figure 3. 7 Position of grill when damper is partially open.

Temperature in the surrounding bedrock should be estimated to compute conditions in tunnel system after a long time [20]. This was done In IDA tunnel by time rescaling, which means temperature developments which cover years shortened to simulations to hours or days. The purpose of time rescaling is to speed up the process. Five years of winter and summer season simulations were done in the beginning. Real time weather data for each day was imported into the system and mean values of weather data is displayed in table 2 below.

3. 3 Average real time weather data for winter and summer periods.

Design day Temperature (ͦ c) Winter Summer Dry-bulb min -2,8 6,3 Dry- bulb max 2 12,6 Wet-bulb max -0,5 7,8 3. 2 Emission sources apportionment

SLB analys had conducted chemical analysis of measured PM10 at Mariatorgets train station. Sources of metal particles were indicated to come from brakes, third rail contact, rail wheel contact and ballast. Results of the chemical analysis from the study shows a combination of different metallic particles constitute PM10 concentrations. According to this study sources of these particles is from materials that are used to manufacture C20 and CX train’s brakes as well as the rail itself. Thus 30 % of PM10 concentration is a contribution from brakes. The rest 70 % of PM10 concentration is from rail-wheel contact [8].

30 Uncertainty

Both models of train were used in traffic on the red line during data collection period. But there is no documented data indicating how many of these trains Cx or C20 models were. The timetable was studied to assess and minimize this uncertainty and it was clearly indicated that most of the scheduled trains in traffic during summer period was C20. Winter period timetable could not be assessed since there is lack of detailed information on the published timetables used during data collection period.

3.5 Practical calibration methodology The technique is using only one type of train model at a time during summer and winter period separately. The objective is to calibrate emission factors and study their effect during different seasons of the year. The calibration was performed in stages as explained below.

3.5.1 Winter case study (ventilation shaft closed) Winter design day simulation model was used to calibrate when the ventilation shaft is closed. Important parameters were tuned in such as, mean temperature during data collection periods of January and February months, wintertime table as well as important global train parameters for CX trains. It is important to note that only CX trains were used in traffic most of the time. Fewer number of C20 trains were used in traffic and this will not affect the results significantly. This loss coefficient of the grill to 2.5, see Appendix D for loss coefficient values. This value has significant impact on the outcome of the result since the air from the tunnel through the ventilation shaft barely goes out into the open atmosphere. Number of passing trains in both directions was tuned in the model according to published wintertime table, see appendix F for train frequency tables.

3. 4 Filtered hourly mean PM10 concentration measured during winter period. Background level is concentration in ambient air.

Weekdays Saturdays Sundays PM10 (μg/m3) 359,84 293,2 240,12 Background level 63,5 13 13 PM10 (μg/m3)

Calibration with respect to closed ventilation shaft was done until the desired emission factors that contribute to 30% of PM10 concentration is achieved. The reference PM10 concentration value is hourly mean from weekdays. Furthermore, simulation was made repeatedly until emission factors that contribute 70% of PM10 emission is achieve. Simulation was run repeatedly until the results show minimal discrepancy.

Originally IDA simulation program’s default parameter value apportionments are seen in table 3.5 below.

31 3. 5 Default apportionment of emission factors in IDA tunnel. Emission factor from brakes (g/kWh) Emission factor from wheel-rail contact (g/kWh) 0.0234 0.756

3.5.2 Summer case study (ventilation shaft open) Long term simulation was performed in the beginning. IDA tunnel automatically saves two models after afterwards. These two models are named summer and winter design day simulations. To run summer simulation, summer design day model has to be used. Important parameters are set and saved afterwards.

During the summer season ventilations shafts in Stockholm’s underground metro stations is opened fully. This was represented in the model by setting loss coefficient of the grill to 1 which is negligible, see Appendix D for loss coefficient values. This value has no significant impact on the outcome of the result since the air from the tunnel through the ventilation shaft goes out into the open atmosphere without obstacle.

Number of passing trains in both directions was inserted in the model according to published timetable for the public, see Appendix E.

3. 6 Filtered hourly mean PM10 concentration measured during summer period. Background level is concentration level in ambient air.

Weekdays Saturdays Sundays PM10 μg/m3 228.2 163.6 107.39 Background level 63.5 13 13 PM10 (μg/m3)

Global parameters of C20 vehicles were tuned in. Average weather data values during data collection period for the months of September and October was used. The calibration is dependent on measurement granularity. Lots of trial-and-error cycle was made to study the effect of parameters. Simulation was made repeatedly until the results show minimal discrepancy.

3.5.3 Sensitivity analysis Sensitivity analysis was conducted to study the effects of opening the ventilation shafts. Emission factors obtained from winter case scenario was used. The ventilation shaft grill was opened fully to 100% but all other parameters were kept the same. Additionally, the effects of opening the ventilation shaft on PM10 concentration levels by only 20% was studied.

The obtained emission factors and PM10 concentrations for different case scenario was compared and evaluated eventually. The significance of the model simulation results was studied. Regression models of the results were drawn and studied. These results were later used, and linearity comparison was made.

Figure 3. 8 Measured PM10 concentration daily mean value during winter period with closed ventilation shaft.

Figure 3. 9 Measured PM10 concentration daily mean value during summer period with ventilation shaft open 100%.

33 4 RESULTS

Results of calibration for winter and summer case scenarios are presented in this chapter. In addition, sensitivity analysis results are included to compare and analyze the effects of opening the ventilation shaft to a certain degree or fully. 4.1 Winter case study Graphical representation of measured PM10 concentration with standard deviations for a period of one month is attached in Appendix F. The graph is a representation of hourly mean values. Observe that the graphs shows the relation between PM10 concentrations and hourly average number of scheduled traffic trains. Results from winter case scenario is presented below in Table 4.1. This result is calibration based on daily avarage values of PM10 for the weekdays. Emission factors from wheel-rail contact contribute for seventy percent of PM10 concentraion. Thirty percent of PM10 concentration contributions come from brakes. These emission factor results from the weekdays were used to simulate PM10 concentrations for weekend trafic. Results are shown together in table 1 below.

Table 4. 1 Results of simulated PM10 concentration daily mean values and emission factors for calibrated model for winter period.

PM10 PM10 (µg/m3) Emission factors(g/kWh) parameter from -

Measured Simulated Wheel-rail Brakes contact

Weekdays 359.8 360.4 0.57 0.03

Saturday 293.2 223.9 0.57 0.03

Sunday 107.4 214.4 0.57 0.03

Figure 1 in Appendix G shows linear regression model for calibrated as well as simulated models. Coefficients with 95% confidence bounds for the linear regression model is given in equation 1. 푦 = 푘푥 + 푐

Comparison of the measured and simulated linear regression models is presented in Appendix H for three traffic variation i.e., weekdays, Saturday, and Sunday. Table 4.2 – 4.4 below show the correlation between simulated and measured values. The results indicate greater proportion of variance is accounted for by the model.

34 Table 4. 2 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on weekdays.

R2 RMSE k c

Simulated 0.87 44.8 6.74 180 Measured 0.85 66.7 9.2 114

Table 4. 3 Coefficients with 95 confidence bounds for the linear regression model of PM10 concentration on Saturdays.

R2 RMSE k c Simulated 0.87 36 11 84.13 Measured 0.89 53.83 6.8 94

Table 4. 4 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on Sundays.

R2 RMSE k c Simulated 0.86 33.2 10.7 53.9 Measured 0.93 32.6 14.7 21.1

4.2 Summer case study Calibration results using summer case scenario is presented below. Linear regression model for calibrated as well as simulated models is shown in Appendix G. Similarly, coefficients with 95% confidence bounds for the linear regression model is shown in table 4.6 - 4.8 below. Table 4. 5 Results of simulated PM10 hourly average values and emission factors for calibrated summer period on weekdays.

PM10 parameter PM10 (µg/m3) Emission factors(g/kWh) Measured Simulated Wheel-rail Brakes contact Weekdays 228.2 228.67 0.63 0.05

Saturday 136.56 232.24 0.63 0.05

Sunday 107.39 175.53 0.63 0.05

35 Table 4. 6 Coefficients with 95%confidence bounds for the linear regression model of PM10 concentration on weekdays.

R2 RMSE k c Simulated 0.91 33.4 6.1 67 Measured 0.91 38.7 7 40

Table 4. 7 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentrations on Saturdays.

R2 RMSE k c Simulated 0.93 38.87 11 27 Measured 0.88 36.47 7 31

Table 4. 8 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration on Sundays.

R2 RMSE k c Simulated 0.86 24.17 7.8 59 Measured 0.59 20 3 63

4.3 Sensitivity analysis The results shown in this section is results of sensitivity analysis using winter model. Calibrated emission factors from winter case study were used and the ventilation shaft was opened in the model to study the effect it has on PM10 concentration level. The results are displayed below in comparison with simulated PM10 concentration values for winter case scenario. Results of linearity comparison are presented in table 4.11 – 4.12 below.

Table 4. 9 Results of simulated PM10 daylily mean values and calibrated emission factors for winter period. The results are from simulation of weekdays.

PM10 parameter PM10 (µg/m3) Emission factors(g/kWh)

Measured Simulated Wheel-rail Brakes contact Weekdays 359,84 255,2 0,57 0,03

Saturday 293,2 181,3 0,57 0,03

Sunday 107,39 172,5 0,57 0,03

36 Table 4. 10 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis weekdays.

R2 RMSE k c Simulated 0,89 35,49 6 95,2 Measured 0,85 66,72 9,2 114

Table 4. 11Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis Saturdays.

R2 RMSE k c Simulated 0,89 29,87 6,2 64,14 Measured 0,89 53,84 11 84

Table 4. 12 Coefficients with 95% confidence bounds for the linear regression model of PM10 concentration sensitivity analysis Sundays.

R2 RMSE k c Simulated 0,87 27,9 9,27 34,28 Measured 0,92 32,58 14,68 21,11

Calibrated emission factor values from winter model were tuned in the summer model of IDA tunnel. Results are displayed in table 4.13 below.

Table 4. 13 Effect of calibrated emission factors in the summer model.

PM10 parameter PM10 (µg/m3) Emission factors(g/kWh)

Measured Simulated Wheel-rail Brakes contact

Weekdays 228.2 281.3 0,57 0,031

Saturday 136.56 193.6 0,57 0,031

Sunday 107.39 146.6 0,57 0,03

37 Results from opening the ventilation shaft by 20% using calibrated emission factors in winter case scenario.

Table 4. 14 effect of calibrated emission factors while the ventilation shaft is opened by 20%.

PM10 parameter PM10 (µg/m3) Emission factors(g/kWh)

Measured Simulated Wheel- Brakes rail contact Weekdays 359,84 306 0,57 0,03

Saturday 293,2 223.1 0,57 0,03

Sunday 107,39 213.2 0,57 0,03

38 5 DISCUSSION AND CONCLUSIONS All studied case scenario results are discussed and evaluated in this chapter. Conclusions are drawn based on the simulation model and obtained results.

5.1 Discussion 5.1.1 Result analysis A regression analysis was performed to determine the effect of important variables such as train frequency on PM10 concentrations by assessing the interaction terms. The results obtained from this study gives an insight of PM10 concentration character on the platform during winter and summer case scenario. Emission factors are relatively high when the ventilation shaft is closed during winter period. Calibration is made using weekdays measurements of PM10 concentration hourly mean value. The first reason for selecting weekdays is that traffic intensity is considerably high compared to weekends and PM10 concentration measurement is made at least 5 consecutive weekdays in each week period. The second reason is that the ventilation shaft is closed during winter period, thus emitted particulate matters do not disperse into the open atmosphere.

As seen in the regression analysis graph in Appendix G PM10 concentration level is at highest in the morning and afternoon rush hour traffic which is directly related to train frequency in the station. PM10 concentration drops quite sharply after evening rush hours and its lowest level during night times when there is no scheduled traffic. After fitting the linear regression model of simulated PM10 concentrations, the values obtained are close to the measured value.

The calibrated emission factors that were used for weekdays deviate somehow from measured values. But curve fitting in the linear regression model shows a strong correlation with 95% confidence interval and coefficients 0.85 and 0.87 for simulated and measured respectively. A few reasons could have contributed to this. The reason for deviation of the curves proportionality could be, first and for most data obtained from measurements were not complete. Few hours of measured data, even days are missing from the data set. Therefore, it was necessary to filter out those incomplete parts. Filtering out these incomplete dataset influences daily mean values. Similarly incomplete measured data for Saturdays and Sundays were filtered out. This affects hourly mean values significantly since each weekend daily mean values vary depending on previous day’s PM10 concentration level on the platform.

Second reason can be irregularities in traffic intensity. Graphical representation of each measured weekdays for both winter and summer period are presented in Appendix F. These variations can be related to irregular train traffic frequency for various reasons, accuracy of measurement instrument, wind speed, temperature, humidity, type of train, varying dwell time and more. Note that the model is a simplified model of a greatly complex tunnel system. Actual train traffic data during PM10 data measurement was not tuned in the simulation model due to lack of available information.

It was learned that few C20 trains were used in scheduled traffic during winter measurement period but only Cx trains were used in the model for simplicity reasons and limitations on the software to model and run both train types in the same simulation model.

Calibration results of the model in summer case scenario shows emission factors are much higher. The ventilation shaft is open during this period and its effects show that emission factors from wheel-rail contact are 10% higher than winter case scenario calibration results. Similarly, emission factors from brakes are 40% higher that winter calibration results.

39 However, the model for summer case scenario has few draw backs. The ventilation shaft is open during this time and emitted particles are blown out through the shaft openings. Thus the significance of the results is not high.

Overall, the calibration results show that the model is calibrated using measured winter PM10 daily average concentration values for weekdays. Emission factors are 0.57 g/kWh from wheel- rail contact and 0.031 g/kWh from brakes.

Sensitivity analysis was performed to evaluate the effect of opening the ventilation shaft. PM10 concentration dropped close to summer concentration value. Note that Cx train parameter was used for this study which emits relatively higher PM10 than by C20 train types.

A short case study was performed on IDA tunnel using summer case scenario model after new scheduled traffic information was obtained. Varying number of Cx trains are set in traffic every day during morning and afternoon rush hours which is about 6 to 25 % of train passage in Mariatorget’s station. This corresponds to approximate average of 12% scheduled train traffic. The model was simulated based on this new information in order to study the effect of Cx trains in scheduled traffic. The result shows that average PM10 emission is 7% higher than the result obtained by using only C20 train models.

Similarly, separate case study was conducted by opening the ventilation shaft by 20% in the winter model. This was done by changing the values of pressure lose coefficients in the model. Results show simulated values for weekdays and Saturdays are lower by 15% and 25% respectively compared to measured values. Simulated values for Sundays almost doubles in contrary.

Calibrated emission factors from winter case scenario were tuned in the summer model to study the effect of emission factors. As seen in table 4.13 simulated results for the weekdays are higher by approximately 30% for weekdays and by 20% for weekends compared to measured values.

But it is important to compare these results against simulated values of winter case scenario. Thus, simulated daily mean values are lower by approximately 22% for weekdays and 30 % on average for weekends. This result leads to a conclusion that shaft opening degree is an important factor in PM10 concentration reduction on platforms. In addition to the above mentioned point, the effect of C20 train models in reduction of PM10 emission is significant.

In summary of the result analysis, IDA tunnel is a complex tool and requires several important parameters to be evaluated carefully. Critical view of the model and results are displayed below. An accurate parameter value would contribute to obtain better emission factor result. Geometry of a ventilation shaft and grill is an important factor as different pipe geometry forms require separate pressure loss calculations. Additionally, loss coefficient values depend on shape of the tube and outlet.

It should be noted that the results in this study is using a measured PM10 concentration data that was collected for 30 days in each case for winter and summer period. Therefore, the lowest and highest values vary significantly. Only long trains were used for winter period in the beginning of this study. But the results showed a huge discrepancy. Much fewer trains per hour than the scheduled traffic produced equal PM10 concentration as measured data. Therefore, it was assumed that short trains were used in low traffic hours of evening in the weekdays and similar train frequency as per the timetable. Results are displayed for comparison and analysis reason.

40 It is also learned that few C20 trains were used during winter data measurement period. Important points to summarize the factors that influence the results should be stressed. Due to uncertainty of the model geometry parameters, calibrated simulation results should be viewed critically. Some of the points listed in this summary are important factors that could contribute significantly to PM10 emissions.

 Bogie configuration is different in both train types as mentioned in previous chapter. This is not modeled in IDA tunnel software. Only longitudinal and perpendicular train movements could be modeled.  All but Slussen platforms in this study are straight. Tunnel sections have curvy geometry. All these geometries were modeled as straight in IDA software. Transvers movement of train to demonstrated movements in curvy sections of the tunnel are not modelled. Perpendicular movements are modelled by setting elevation parameter values in IDA software.  Both train types used this study have different number of rail-wheel contact areas. Full length C20 train has 12 bogies and full length Cx train has 16 bogies. As mentioned in previous chapter, rail-wheel contact emits 70% of total PM10 concentration. These wheel configurations could not be modelled in IDA software to study their effects on PM10 emission.  Load factor is an important parameter. Significance of passenger load during various traffic times could not be studied in IDA model.  It is learned that operational speed of trains on red line metro has changed by the operator through times for various reasons. Actual operational speed of service trains is set based on current one.  Characteristics and average deceleration speed of trains along the platform during data measurement period was not obtained. For this reason the maximum allowed speed of 50 km/h along each platform was used in the model. In reality actual speed of the trains would vary 15 – 60 km/h on the red line metro system.  Graphical representation of measured daylily PM10 concentrations in appendix G shows significant discrepancy in hourly and daily mean values. The probable cause of varying daily mean values are mentioned earlier in this chapter. But it requires careful study and analysis.

5.2 Conclusions In this master’s thesis a calibration of PM10 concentration using IDA tunnel software was carried out. Important parameters close to real case scenario were tuned in IDA simulation software model. The models were calibrated using measured PM10 concentration data at Mariatorget underground metro station. The comparison between measured and simulated daily average concentration shows the obtained emission factors can predict PM10 concentrations. Therefore, the model could be considered calibrated against PM10 concentration during closed ventilation shaft. Sensitivity analysis shows that the concentration drops considerably when the ventilation shaft is open fully. The results highlight the link between train frequency and PM10 concentration.

Moreover, the study lies a basis to understand important parameters in the model that affect the emission factors and PM10 concentration. After performing simulation with various train frequency, length and weight of train, speed as well as dwell time, the results show these parameters play important role in PM10 emission factor values.

41 6 RECOMMENDATIONS AND FUTURE WORKS

6.1 Recommendations Emission factor results validation using measurement data in other location would contribute to the quality of the simulation instrument. Collecting actual train traffic data combined with relevant study of influential factors as mentioned in previous chapter, and comparing the results with scheduled traffic according to timetable would reduce the deviations in PM10 concentration simulation analysis.

There are different design and geometry of ventilation shafts. It is an area of interest to use exact ventilation shaft geometry model in the train station to obtain more accurate pressure lose coefficients. Grill shape and openings are important components and the right parameter value of pressure loss coefficient should be used. A complete CFD analysis of different grill design would be critical and impacts the results since PM10 concentration measurement is directly related airflow in the tunnel and ventilation shafts.

Other areas of importance are the curvature of tunnels and platforms. Underground metro station platforms and tunnel sections are curved to some extent. It is an area of interest how these curvatures contribute to PM10 concentrations and change emission factor values.

6.2 Future work Below is a list of potential future work  Analysis of ventilation shaft geometry as well as analysis of airflow through a grill to obtain pressure loss coefficients for different designs and geometry forms.  Study of PM10 emission contribution from C20 and Cx trains types separately. Both trains have different bogie configuration set.  Effect of number of passengers on a train during various scheduled traffic times. Total mass of a train is one of important parameters in the simulation software.  Effect of track curvature radius on PM10 emission both in the tunnel and platforms.  Study of effect of operational speed limit on PM10 emission.  Characteristic study of ventilation shaft opening degree as well as their position in the tunnel or platform and their effect on PM10 concentration level.

42 7 REFERENCES

[1] Ramböll, ‘’Åtgärder för luftkvalitet’’, 2015 https://www.nyatunnelbanan.sll.se/sites/tunnelbanan/files/Underlagsrapport%20Inomhusluft%20 %C3%85tg%C3%A4rder.pdf, accessed 2020-02-20 [2] Ramböll, ‘’Luftkvalitet i järnvägs anläggningar under mark’’, https://www.energi-miljo.se/sites/default/files/hstenlund.pdf, accessed 2020-02-26. [3] Institutionen för hälsa och klinisk medicin, Umeå universitet, ‘’Hälsoeffekter av luftföroreningar I stationsmiljöer till järnvägstunnlar’’, https://www.trafikverket.se/contentassets/48964205064e4ee489530881e152942e/halsoeffekter_j arnvagspartiklar_jarvholm.pdf, accessed 2020-02-20,

[4] Yingying Cha, ‘’Airborne Particles in Railway Tunnels’’, KTH department of machine design 2018. https://www.diva-portal.org/smash/get/diva2:1245358/FULLTEXT01.pdf, accessed 2020-03-12.

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[6] Wikipedia, https://en.wikipedia.org/wiki/Particulates, accessed 2020-03-10. [7] EPA, ‘’Particulate matter basics’’, https://www.epa.gov/pm-pollution/particulate-matter-pm- basics, accessed 2020-02-20. [8] WHO guidelines, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air- quality-and-health, accessed, 2020-02-20. [9] EQUA Solutions, https://www.equa.se/en/tunnel, accessed 2020-02-20. [10] WHO–World Health Organization. Air quality guidelines for particulate matter, ozone, nitrogen dioxide and Sulphur dioxide. In Global Update 2005, Summary of Risk Assessment; WHO–World Health Organization: Geneva, The Swiss, 2006. https://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf;j sessionid=846AF5CBD48C52C1B7949CE84077DDA9?sequence=1. Accessed 2020-02-23 [11] EPA, https://www.epa.gov/air-emissions-factors-and-quantification/basic-information-air- emissions-factors-and-quantification, accessed 2020-03-12 [12] C20 and CX train specification table, https://en.wikipedia.org/wiki/SL_C20, accessed 2020- 03-20 [13] Ulf Olofsson och Minghui Tu, Metodutveckling av partikelutredning I tunnelbanan, KTH deparment of of machine design 2018, accessed 2020-05-05 [14] Jung-Yup Kim,Kwang-Young Kim. Effects of vent shaft location on ventilation performance in a subway tunnel, August 2009, accessed 2020-05-06. [15] Wikipedia, https://sv.wikipedia.org/wiki/C14_ (tunnelbanevagn), accessed 2020-11-12 [16] Wikipedia, https://en.wikipedia.org/wiki/SL_C20, accessed 2020-11-12 [17] Christer Johansson, Per-Åke Johansson, Particulate matter in the underground of Stockholm, Environment and health protection administration, Stockholm 2002, accessed 2020-04-16

43 [18] Yi Zhu, Adhesion in Wheel-rail contact under contaminated conditions, KTH department of machine design, 2011, accessed 2020-04-18 https://www.diva-portal.org/smash/get/diva2:457625/FULLTEXT02.pdf [19] Christer Johansson, Partikelhalter I Stockholms Tunnelbana, Miljöförvaltningen I Stockhom, http://slb.nu/slb/rapporter/pdf8/slb2001_002.pdf, accessed 2020-03-10 [20] IDA tunnel training manual [21] Christer Johansson, Källor till partiklar i Stockholms tunnelbana, SLB analys, Stockholm 2005, accessed 2020-04-03

44 APPENDIX A: RISK ANALYSIS

# Risk Probability Impact Action 1 Insufficient Low Miss the objective of the project. Adequate and open information about Results will be irrelevant to the communication customer demand scope of the project with customer 2 Unknown or High Wrong outcome Use experienced inaccurate parameters and knowledgeable expertise in the field with in the industry and KTH. 3 Access to IDA tunnel Low Calibration and validation Secure access software process will be complicated. through negotiations and meetings. 4 Lose of files Low Project unexcitable or take Back up files longer time to recreate it. through different means 5 Insufficient data set low Hard to execute the project Use available experimental data set 6 Not finish in time Medium Publication date for final results Create a clear plan of the project will be extended and milestone. Follow the plan strictly. 7 Wrong model setup High Inaccurate outcome Use expertise help with in the industry and KTH.

45 APPENDIX B: SCHEMATIC VIEW OF MODEL

46 APPENDIX C: ISHIKAWA DIAGRAM

47 APPENDIX D: TABLE OF LOSS COEFFICIENTS

48 APPENDIX E: TABLE OF TRAIN FREQUENCY

T-banan Mariatorget, antal tågpassager per timme Linje 13 Linje 14 måndag-fredag varav Summa varav kortaSumma tåg kl Norsborg-RopstenRopsten-Norsborgkorta tåg Antal tåg kl Fruängen-MörbyMörby centrumcentrum-Fruängen Totalt totalla kortatåg summa 00-01 2 2 1 3 00-01 3 2 4 4 00-01 4 9 9 6 01-02 0 0 0 01-02 0 0 0 01-02 0 0 0 02-03 0 0 0 02-03 0 0 0 02-03 0 0 0 03-04 0 0 0 03-04 0 0 0 03-04 0 0 0 04-05 0 0 0 04-05 0 0 0 04-05 0 0 0 05-06 4 4 8 05-06 5 3 8 05-06 16 17 17 06-07 6 7 13 06-07 10 6 16 06-07 29 27 27 07-08 11 12 23 07-08 12 12 24 07-08 47 46 46 08-09 11 12 23 08-09 12 12 24 08-09 47 46 46 09-10 6 7 13 09-10 12 12 24 09-10 37 36 36 10-11 6 6 12 10-11 12 12 24 10-11 36 35 35 11-12 6 7 13 11-12 12 12 24 11-12 37 36 36 12-13 6 6 12 12-13 12 12 24 12-13 36 36 36 13-14 6 6 12 13-14 12 12 24 13-14 36 36 36 14-15 6 6 12 14-15 15 12 27 14-15 39 38 38 15-16 6 12 18 15-16 16 12 28 15-16 46 47 47 16-17 11 12 23 16-17 13 12 25 16-17 48 48 48 17-18 12 12 24 17-18 12 12 24 17-18 48 48 48 18-19 9 10 19 18-19 10 11 21 18-19 44 43 43 19-20 6 7 13 19-20 6 6 12 19-20 25 26 26 20-21 6 6 12 20-21 6 6 12 20-21 24 25 25 21-22 5 6 3 8 21-22 5 6 6 9 21-22 17 24 9 19 22-23 4 5 9 6 22-23 4 4 6 7 22-23 13 19 15 14 23-24 3 4 5 5 23-24 3 4 7 4 23-24 9 16 12 12

Linje 13 Linje 14 frånkopplingsumma efter efterkl 19 till- frånkoppling lördag varav kortaSumma tåg varav kortatågSumma fullständigt tåg kl Norsborg-RopstenRopsten-Norsborg Antal tåg kl Fruängen-MörbyMörby centrumcentrum-Fruängenkorttåg Totalt 00-01 4 4 8 5 00-01 4 4 8 5 00-01 10 01-02 2 2 4 2 01-02 2 3 5 3 01-02 5 02-03 2 2 4 2 02-03 2 2 4 3 02-03 5 03-04 1 1 2 1 03-04 2 1 3 2 03-04 3 04-05 0 0 0 0 04-05 0 0 0 0 04-05 0 05-06 1 0 1 1 05-06 0 0 0 0 05-06 1 06-07 4 4 8 5 06-07 4 3 7 5 06-07 10 07-08 4 4 8 5 07-08 4 4 8 5 07-08 10 08-09 4 4 8 5 08-09 5 4 9 6 08-09 11 09-10 5 5 10 korta tåg 6 09-10 6 6 8 korta tåg 9 09-10 15 10-11 6 6 3 korta tåg 11 10-11 6 6 3 korta tåg 11 10-11 22 11-12 6 6 5 korta tåg 9 11-12 6 6 3 korta tåg 11 11-12 20 12-13 6 6 12 12-13 6 6 0 12 12-13 39 13-14 6 6 12 13-14 6 6 0 12 13-14 39 14-15 6 6 12 14-15 6 6 0 12 14-15 39 15-16 6 6 12 15-16 6 6 0 12 15-16 39 16-17 6 6 12 16-17 6 6 0 12 16-17 39 17-18 6 6 12 17-18 6 6 0 12 17-18 39 18-19 6 6 3 11 18-19 6 6 2 10 18-19 24 19-20 6 6 11 8 19-20 6 6 9 6 19-20 24 20-21 6 6 12 8 20-21 6 6 10 7 20-21 23 21-22 6 6 12 8 21-22 5 5 11 7 21-22 18 22-23 4 4 8 5 22-23 4 4 8 5 22-23 10 23-24 4 4 8 5 23-24 4 4 8 5 23-24 10

49 Linje 13 Linje 14 frånkopplingsumma efter efterkl 19 till-frånkoppling Söndag varav kortaSumma tåg varav kortatågSumma fullständigt tåg kl Norsborg-RopstenRopsten-Norsborg Antal tåg kl Fruängen-MörbyMörby centrumcentrum-Fruängenkorttåg Totalt 00-01 4 4 8 5 00-01 4 4 8 5 00-01 10 01-02 3 2 4 3 01-02 2 3 5 3 01-02 6 02-03 2 2 4 2 02-03 2 2 4 2 02-03 4 03-04 1 1 2 2 03-04 1 1 3 1 03-04 3 04-05 0 0 0 0 04-05 0 0 0 0 04-05 0 05-06 4 4 1 2 05-06 4 0 0 2 05-06 4 06-07 4 4 8 6 06-07 4 3 8 4 06-07 10 07-08 4 4 8 5 07-08 4 4 8 5 07-08 10 08-09 4 6 8 5 08-09 6 4 9 6 08-09 11 09-10 5 7 10 korta tåg 7 09-10 7 6 8 korta tåg 9 09-10 16 10-11 6 6 3 korta tåg 11 10-11 6 6 3 korta tåg 9 10-11 20 11-12 6 5 5 korta tåg 9 11-12 5 6 3 korta tåg 10 11-12 19 12-13 6 6 11 12-13 7 6 0 12 12-13 23 13-14 6 6 12 13-14 6 6 0 12 13-14 24 14-15 6 6 12 14-15 6 6 0 12 14-15 24 15-16 6 6 12 15-16 6 6 0 12 15-16 24 16-17 6 6 12 16-17 6 6 0 12 16-17 24 17-18 6 6 korta tåg 12 17-18 6 6 kårta tåg 12 17-18 24 18-19 6 6 2 11 18-19 6 6 1 12 18-19 23 19-20 6 6 7 9 19-20 6 7 9 10 19-20 19 20-21 6 6 11 8 20-21 6 7 11 9 20-21 17 21-22 6 7 9 10 21-22 5 5 8 7 21-22 17 22-23 5 5 8 7 22-23 4 4 7 8 22-23 15 23-24 4 4 8 5 23-24 4 4 5 6 23-24 11

50 APPENDIX F:MEASURED DATA PM10

PM10 concentration winter period mean hourly average Measured 600

500

] 400

3 g/m

μ 300

200 PM10 [ PM10

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration winter saturdays hourly average

600 Measured

500

] 400

3 g/m

μ 300

PM10 [ PM10 200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

51 PM10 concentration winter sundays hourly average

450 Measured 400

350 ]

3 300

250

g/m μ 200

150 PM10 [ PM10

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration winter period week days hourly average

800

700

600 ]

3 500

/ g

μ 400

300 PM10 [ PM10

200

100

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration summer saturday hourly average mean value 450 Measured 400

350

300

] 3

250

g/m μ

200 PM10 [ PM10 150

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration summer saturday hourly average

400 Measured 350

300 ]

3 250 g/m

μ 200

PM10 [ PM10 150

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration summer sundays hourly average 300 Measured

250

200

]

3 g/m

μ 150

PM10 [ PM10 100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

PM10 concentration summer weekdays hourly average 600

500

] 3

g/m 400 μ

300 PM10 [ PM10

200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time[hr]

54 APPENDIX G: REGRESSION ANALYSIS

Figure 1. 1 Simulated PM10 concentration for weekday’s winter model.

Figure 1. 2 Simulated PM10 concentration for Saturday winter model.

Figure 1. 3 Simulated PM10 concentration for Sunday winter model.

Figure 1. 4 Linearity comparison measured vs. simulated for weekday’s winter model.

Figure 1. 5 Linearity comparison measured vs. simulated for Saturdays winter model.

Figure 1. 6 Linearity comparison measured vs. simulated for Sundays winter model.

Figure 1. 7 Simulated PM10 concentration for weekday’s summer model.

Figure 1. 8 Simulated PM10 concentration for Saturday’s summer model.

Figure 1. 9 Simulated PM10 concentration for Sunday’s summer model.

Figure 1. 10 Linearity comparison measured vs. simulated for weekday’s summer model.

Figure 1. 11 Linearity comparison measured vs. simulated for Saturday’s summer model.

Figure 1. 12 Linearity comparison measured vs. simulated for Sunday’s model.

Figure 1. 13 Simulated PM10 concentration for winter weekday’s model ventilation shaft open 100%.

Figure 1. 14 Simulated PM10 concentration for winter Saturday’s model ventilation shaft 100%.

Figure 1. 15 Simulated PM10 concentration for winter Sunday’s model ventilation shaft open 100%.

Figure 1. 16 Linearity comparison measured vs. simulated for weekday’s model while ventilation shaft open 100%.

Figure 1. 17 Linearity comparison measured vs. simulated for Saturday’s winter model while ventilation shaft open 100%.

Figure 1. 18 Linearity comparison measured vs. simulated for Sunday’s winter model while ventilation shaft open 100%.

Figure 1. 19 PM10 regression analysis for calibrated emission factors tuned in weekday’s summer model.

Figure 1. 20 Linear comparison measured vs. simulated for weekday’s summer model.

Figure 1. 21 PM10 regression analysis for calibrated emission factors tuned in Saturday’s summer model.

Figure 1. 22 Linear comparison measured vs. simulated for Saturday’s summer model.

Figure 1. 23 PM10 regression analysis for calibrated emission factors tuned in Sunday’s summer model.

Figure 1. 24 Linear comparison measured vs. simulated for Sunday’s summer model.