Lungs of the city of Eindhoven

Transcription

1 Lungs of the city of Eindhoven ir. Rob Vervoort Eindhoven University of Technology, PDEng-trainee Smart Buildings & Cities, Department of the Built Environment, Building Physics and Services Supervisory team: prof. dr. ir. Bert Blocken (TU/e, KU Leuven), dr. ir. Twan van Hooff (KU Leuven, TU/e)

2 Introduction Particulate matter (PM) Health effects PM exposure Local PM removal (ENS Technology) Research question/methodology Case study city center of Eindhoven Computational setup Results Presentation content Summary of results (conclusions and further work) Parking garage case studies Examples Summary of results (conclusions and further work) 2

3 Introduction: particulate matter (PM) Solid and liquid particles in air (organic/inorganic) Possibly hazardous Various sizes Fine dust (PM 10 ): diameter 10 μm Fine(r) dust (PM 2.5 ): diameter 2.5 μm Ultra-fine dust (PM 0.1 ): diameter 0.1 μm PM between can remain in the atmosphere for days/weeks Major contribution road traffic to PM pollution urban environments (± 30-50%) [1] Parking garages are locations where emissions are high and PM accumulates [e.g. 2] No part of this presentation can be reproduced without written consent of the first author, ir. Rob Vervoort: 3

4 Source: US Environmental Protection Agency (EPA). 4

5 Introduction: health effects PM exposure Linked to respiratory and cardiovascular morbidity and mortality [1] Linked with Alzheimer s, Parkinson s, Dementia, multiple sclerosis and stroke incidences [e.g. 2] Threshold for which PM concentrations have effect on human health unknown [3] By 2050, world s top environmental cause of premature mortality [4] Numbers on health effects for the Netherlands in 2013 [5] 11,530 premature deaths PM 2.5 exposure 152,200 years of life lost PM 2.5 exposure [1] World Health Organization (WHO) (2013). Health effects of particulate matter. [2] Chen H., Kwong J.C., Copes R., Tu K., Villeneuve P.J., van Donkelaar A., Hystad P., Martin R.V., Murray B.J., Jessiman B., Wilton A.S., Kopp A., Burnett R.T. (2017). Living near major roads and the incidece of dementia, Parkinson s disease, and multiple sclerosis: a population-based cohort study, Lancet 389, [3] World Health Organization (WHO) (2005). Air quality guidelines, global update Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. ISBN [4] Organization for Economic Cooperation and Development - OECD (2012). OECD Environmental Outlook to 2050: the Consequences of Inaction. [5] European Environmental Agency (2016). Air quality in Europe 2016 report. 5

6 Source: EEA (2015). World premature deaths due to urban pollution from particulate matter and ground-level ozone. 6

7 200 Annual mean PM 2.5 (µg/m 3 ) worst cities overall WHO limit = 10 µg/m 3 Source: World Health Organization (WHO) (2016). WHO Global Urban Ambient Air Pollution Database, retrieved from the guardian. PM 2.5 concentration [µg/m 3 ] WHO limit 7

8 200 Annual mean PM 2.5 (µg/m 3 ) global cities Asia WHO limit = 10 µg/m 3 Source: World Health Organization (WHO) (2016). WHO Global Urban Ambient Air Pollution Database, retrieved from the guardian. PM 2.5 concentration [µg/m 3 ] WHO limit 8

9 200 Annual mean PM 2.5 (µg/m 3 ) global cities Europe WHO limit = 10 µg/m 3 Source: World Health Organization (WHO) (2016). WHO Global Urban Ambient Air Pollution Database, retrieved from the guardian. PM 2.5 concentration [µg/m 3 ] Summary procedure (Rechtbank Den Haag 2017): Dutch state is condemned to establish an air quality plan that complies with the air quality regulations. Amsterdam: 60% above limit 0 WHO limit 9

10 Introduction: local PM removal Power: 0.34 kw (incl. the fan) - Volume flow rate: 10,000 m 3 /h Efficiency: PM 10 70% - PM % (σ = 14%) Source: Environmental Nano Solutions (ENS) Europe. 10

11 Introduction: research question/methodology Research question What are the effects of ionization units, positioned in semi-enclosed garages, on the particulate matter concentrations in the city center of Eindhoven? Research methodology Computational Fluid Dynamics (CFD) analysis Sub-configuration validation study (ANSYS Fluent) Creation of the computational grid Determination of boundary conditions Calculations and results analysis (ANSYS Fluent) 11

12 Case study city center of Eindhoven: computational setup Sub-configuration validation study [1] Representation of main flow features Determination of computational parameters and settings for the case study of Eindhoven Computational grid for the Eindhoven city center Grid quality and grid resolution in accordance to BPG [2] 65,735,787 (hexahedral) cells Geometrical simplifications built environment [1] Blocken B., Vervoort R., van Hooff T. (2016). Reduction of outdoor particulate matter concentrations by local removal in semi-enclosed parking garages: a preliminary case study for Eindhoven city center, Journal of Wind Engineering and Industrial Aerodynamics 159, pp [2] Franke, J., Hellsten, A., Schlünzen, H., Carissimo, B. (2007). Best practice guideline for the CFD simulation of flows in the urban environment. COST Action 732: Quality assurance and improvement of microscale meteorological models. 12

13 Case study city center of Eindhoven: computational setup [1] Blocken B., Vervoort R., van Hooff T. (2016). Reduction of outdoor particulate matter concentrations by local removal in semi-enclosed parking garages: a preliminary case study for Eindhoven city center, Journal of Wind Engineering and Industrial Aerodynamics 159, pp

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16 65,735,787 (hexahedral) cells Lungs of the city of Eindhoven

17 Case study city center of Eindhoven: computational setup Boundary conditions Inlet conditions Wind direction south-east, highest PM 10 concentrations are measured for this wind direction in Eindhoven [1]. Neutral atmospheric boundary layer (ABL) flow Profiles for mean velocity (U), turbulent kinetic energy (k) and turbulent dissipation rate (ε), as provided by Richards and Hoxey [2] Reference inlet velocity at 10 m height (U ref,10m ) of 1 m/s PM 10 background concentrations µg/m 3 [3] Terrain roughness as specified in Davenport [4] classification [1] AiREAS. Meetdata Eindhoven, [2] Richards, P.J., Hoxey, R.P. (1993) Appropriate boundary conditions for computational wind engineering models using the k- ε turbulence model, Wind Eng. Ind. Aerodyn., 46, pp [3] Measurement station Veldhoven Europalaan data. [4]. Wieringa, J. (1993). Representative roughness parameters for homogeneous terrain, Bound. Meteorol., 63 (4), pp

18 Case study city center of Eindhoven: computational setup Boundary conditions SRE 3.0 traffic model (municipality of Eindhoven) Relating driven kilometers to PM 10 emissions 18

19 Case study city center of Eindhoven: computational setup Boundary conditions SRE 3.0 traffic model (municipality of Eindhoven) Relating driven kilometers to PM 10 emissions Garage ventilation NEN m 3 /h ventilation per square meter floor area of the garage Ionization units garages 1 unit / 65 parking spaces (99 total) 6 units / 65 parking spaces (594 total) 19

20 Case study city center of Eindhoven: computational setup Solver settings 3D steady Reynolds-averaged Navier-Stokes (RANS) Realizable k-ε turbulence model [1] Standard wall functions [2] with sand grain roughness modification [3] Standard interpolation scheme for pressure Second-order discretization for all flow variables PM dispersion modeled in a simplified way: Eulerian advection-diffusion equation and standard gradient-diffusion hypothesis (particles are treated as a gas excluding aerosol dynamic) Turbulent Schmidt number (Sc t ) [1] Shih, T.H., Liou, W.M., Shabbir, A., Zhu, J. (1995). A new k-ε turbulence model for high Reynolds number turbulent flows model development and validation, Comput. Fluids, 24 (3), pp [2] Launder, B.E., Spalding, D.B. (1974). The numerical computation of turbulent flows. Comput. Methods Appl. Mech. Eng. 3, [3] Cebeci, T., Bradshaw, P. (1977). Momentum Transfer in Boundary Layers. Hemisphere Publishing Corporation, New York. 20

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23 594 ionization units 0 ionization units Lungs of the city of Eindhoven

24 0 ionization units 24

25 594 ionization units 25

26 594 ionization units 26

27 594 ionization units 27

28 594 ionization units 28

29 594 ionization units 29

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31 Sc t = 0.7 Canyon 1: No ionization Canyon 1: Ionization (594 units total) 31

32 Sc t = 0.7 Canyon 2: No ionization Canyon 2: Ionization (594 units total) 32

33 Case study city center of Eindhoven: summary of results Conclusions 99 units: Up to 10% PM 10 reduction near outlets, further away indiscernible 594 units: Up to 50% PM 10 reduction near outlets, further away up to 10% Preliminary results show that local removal in semi-enclosed parking garages can be an effective strategy towards improved outdoor air quality. Further work Validation of the model using full-scale measurements (pilot Stadhuisplein, Eindhoven) Other atmospheric conditions (e.g. wind direction, wind velocity, thermal effects) Unequal spreading of traffic intensity over the domain and over time (unsteady RANS) More accurate modeling of parking garages (accurate modeling ionization unit positions) More accurate modeling techniques (large eddy simulation) 33

34 OPEN ACCESS: Blocken B., Vervoort R., van Hooff T. (2016). Reduction of outdoor particulate matter concentrations by local removal in semi-enclosed parking garages: a preliminary case study for Eindhoven city center, Journal of Wind Engineering and Industrial Aerodynamics 159, pp

35 Parking garage case studies: parking garage Weert 35

36 cut-cell grid constructed out of approximately 9.1 million hexahedral cells 36

37 Total garage ventilation: 7,973 m3/h Total garage ventilation: 18,000 m3/h Lungs of the city of Eindhoven

38 Parking garage case studies: parking garage Weert Garage volume: PM10 concentration: -53% PM2.5 concentration: -46% Garage volume: PM10 concentration: -32% PM2.5 concentration: -28% Garage emission: PM10: -43% PM2.5: -29% Garage emission: PM10: -23% PM2.5: -14% Total garage ventilation: 7,973 m3/h Total garage ventilation: 18,000 m3/h Lungs of the city of Eindhoven

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40 Parking garage case studies: summary of results Conclusions PM reduction potential highly sensitive to: Positioning of ionization unit (and amount) Parking garage geometry and ventilation design Detailed analysis parking garages (including optimized positioning ionization units) shows higher PM reductions than obtained in the Eindhoven case study Discussion / further work PM modeling based on several assumptions Obstacles such as cars not taken into account Wind effects in the urban surrounding not taken into account Flow in parking garages can be highly transient (consider the use of LES) Movement inside the parking garage Turbulent nature of flow and low velocities 40

41 Lungs of the city of Eindhoven ir. Rob Vervoort Eindhoven University of Technology, PDEng-trainee Smart Buildings & Cities, Department of the Built Environment, Building Physics and Services Supervisory team: prof. dr. ir. Bert Blocken (TU/e, KU Leuven), dr. ir. Twan van Hooff (KU Leuven, TU/e)