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Simulation of Air Quality in Underground Train Stations

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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 1 2 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
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