ESTIMATING THE IMPACT OF URBAN VEGETATION ON AIR QUALITY IN A NEIGHBORHOOD: REAL CASE VS NEW VEGETATION SCENARIOS

Transcription

1 17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, 9-12 May 2016 Budapest, Hungary ESTIMATING THE IMPACT OF URBAN VEGETATION ON AIR QUALITY IN A NEIGHBORHOOD: REAL CASE VS NEW VEGETATION SCENARIOS E. Rivas, JL. Santiago, F. Martin, B. Sánchez, A. Martilli Atmospheric Pollution - Environmental Department, CIEMAT Avda. Complutense 40, 28040, Madrid, Spain esther.rivas@ciemat.es

2 Contents Introduction LIFE+RESPIRA project CFD Model Simulation set up Model set up Methodology Model Evaluation Experimental Data NOx_EXP vs NOx_ Effects of Vegetation on Air Quality: Real Case vs New Vegetation Scenarios Conclusions Vegetation effects on pollutant concentration: Deposition effects Vegetation effects on pollutant concentration: Dynamical effects 1

3 LIFE+RESPIRA project Nowadays, urban air quality is one of the most important environmental problems (in Europe, premature deaths due to respiratory diseases, with associated health care costs of M /y). LIFE+RESPIRA project goal: To demonstrate that it is possible to improve urban air quality and reduce exposure to air pollution by promoting healthy and sustainable mobility and a better urban planning and design. Our LIFE+RESPIRA project task: Development of a specific tool able to reproduce accurate pollutant maps and estimate the impact of urban vegetation. A CFD-RANS model has been used for simulating the urban air pollution. Introduction 2

4 Objective Main objective Determine the impact of vegetation on Air Quality in a real urban environment considering: The Dynamical effects The Pollutant deposition For this purpose, we analyze: The pollutant distributions in a real neighborhood with vegetation. The effects of including new vegetation in one of the street. 3

5 Urban Morphology Simulation set up PAMPLONA air quality station Pamplona s neighborhood (Source: Google Maps satellite image) Buildings that make up the neighborhood (AutoCAD 2016 file provided by Navarra University) 4

6 Urban Morphology Simulation set up PAMPLONA air quality station (*) CFD tool: STAR-CCM Pamplona s neighborhood (Source: Google Maps satellite image) Buildings Actual that heights make up of the buildings: neighborhood (AutoCAD 2016 file CFD provided 3D model by Navarra (*) University) 4

7 Geometry Model for Trees and Traffic emissions Simulation set up Trees at Pamplona s neighborhood (Source: Google Earth satellite image) CFD 3D model (*) (*) CFD tool: STAR-CCM

8 Simulation set up Geometry Model for Trees and Traffic emissions Trees at Roads at Pamplona s Pamplona s neighborhood neighborhood (Source: Google Earth satellite image) CFD 3D model (*) (*) CFD tool: STAR-CCM

9 Mesh Model Simulation set up 7Zmax (*) Plaza de la Cruz air quality station CV_1 Longitudinal plane section ZOOM 1.5Zmax 1.25Zmax Zmax+5m CV_2 5Zmax (*) CV_3 Zmax = 51m (*) CFD tool: STAR-CCM CFD Mesh model (*) Total number of cells: 7.4x10 6 (*) Franke et al. (2007) 6

10 Mesh Model Simulation set up 7Zmax (*) Plaza de la Cruz air quality station CV_1 Longitudinal plane section ZOOM 1.5Zmax 1.25Zmax Zmax+5m CV_2 5Zmax (*) CV_3 Zmax = 51m (*) CFD tool: STAR-CCM CFD Mesh model (*) Total number of cells: 7.4x10 6 (*) Franke et al. (2007) 6

11 Z (m) Simulation set up Meshing Test Plaza de la Cruz air quality station Sampling areas (*) 0 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5 Velocity (m/s) Mesh_coarse Mesh_fine Mesh_medium Velocity profiles in San Fermin (*) CFD tool: STAR-CCM

12 Z (m) Simulation set up Meshing Test Plaza de la Cruz air quality station Sampling areas (*) 0 0 0,5 0,11 1,5 0,22 2,5 0,3 3 3,50,4 4 4,5 0,5 5 5,5 0,6 Velocity TKE (J/kg) (m/s) Mesh_coarse Mesh_fine Mesh_medium Turbulent Kinetic Velocity Energy profiles (TKE) in San profiles Fermin San Fermin (*) CFD tool: STAR-CCM

13 Physical Models Model set up Steady State Simulations Segregated Flow Model RANS as turbulent approach: Realizable K-ε Two-Layer model All Y+ wall hybrid treatment Neutral atmospheric conditions Default values of STAR-CCM as free parameters of the turbulent model C M C μ Cε 1 Cε 2 σ K σ ε C t Re γ * Re

14 Physical Models: Dynamical and pollutant deposition effects of vegetation Model set up In cells where there are vegetation + a momentum sink + a TKE source/sink + a TDR source/sink + a mass sink S Su LAD c d u u, y, j j j x z S LAD c u uk 3 k d p d 3 S LAD cd C 4p u C 5du k Veg. LAD Vdep C (*) CFD c C C d p d 4 5 from Santiago et al. (2013) (*) Santiago et al. (2016) 9

15 Physical Model: NOx traffic emissions + an additional passive scalar transport equation Model set up t u C r C r S S j j CFD j CFD C Veg. Sc t jx, y, z i-th street + Pollutant source at traffic cells proportional to traffic intensity + Without atmospheric chemistry Daily average traffic intensity at Pamplona s neighborhood (Source: University of Navarra) 10

16 Physical Model: NOx traffic emissions + an additional passive scalar transport equation Model set up t u C r C r S S j j CFD j CFD C Veg. Sc t jx, y, z i-th street + Pollutant source at traffic cells proportional to traffic intensity + Without atmospheric chemistry Daily average traffic intensity at Pamplona s neighborhood (Source: University of Navarra) 10

17 Boundary Conditions Model set up Building: Solid boundary with surface specification: smooth Ground: Solid boundary with surface specification: roughness Inlet: Outlet: u z Pin out Top: Symmetry boundary condition 2 3 u z z u u 0 ln ; k ; z z z 0 C

18 Methodology (*) Time period March, 1 st 31 th Meteorological Data 2016 (**) : PAMPLONA GN Ref. Station ref CFD Simulations: LAD = 0.1 m 2 m Sectors v t Sector t m/ s dep 0.03 m/ s For v 3.2 m / s, v 0.01 m / s ref v v * * dep dep C C CFD CFD v v * v v * ref ref ref ref S13 S14 S12 S15 S11 S16 S10 March 2016 S1 0,20 S2 0,15 0,10 0,05 0,00 S9 48 s S8 S3 S7 S4 S6 S5 u > 7 6 < u < 7 5 < u < 6 4 < u < 5 3 < u < 4 2 < u < 3 1 < u < 2 v * 2 m / s v 0.03 m / s ref dep 2 / * m s v 4.5 m / s v 0.01 m / s ref dep 4.5 m / s v * v m / s ref dep u < 1 PAMPLONA GN Ref. station Model set up 0.75 Km (Source: Google Maps satellite image) (*) Parra et al. (2010) (**) Source: University of Navarra 12

19 Methodology (*) PAMPLONA GN Ref. station Model set up NOx Maps: CCFD ( r, Sector ( t)) TF( t) C r, t Ct v t / v ref in 0.75 Km If v 1 m/ s v 1 m/ s & C ( r, Sector ( t)) C ( r) ref ref CFD CFD 1 16 C ( r) C ( r,sec tor ) CFD 16 CFD i i 1 Ct is computed normalizing modelled concentration using time averaged concentration measured at air quality monitoring station (Source: Google Maps satellite image) (*) Parra et al. (2010) TF t Hourly traffic evolution 13

20 NOx [µg/m³] Experimental data Air Pollutants Data 2016 (*) : Pamplona - Air Quality Station (NG) Teledyne 200E NOx analyzer (*) Source: Gobierno de Navarra (GN) March 2016 (Source: Google Maps satellite image) 1 st week 2 nd week 3 rd week 4 th week 5 th week UTC NOx_EXP 14

21 NOx_ (LAD=0.1) [µg/m³] NOx_ (LAD=0.1) [µg/m³] NOx_EXP vs NOx_ Results I: Statistical Coefficient R-squared All Data 1 st and 2 nd weeks R FAC March 2016 March NOx_ (LAD=0.1) = 0,6907 NOx_EXP + 14,456 R = 0,62 All Data NOx_ (LAD=0.1) = 0,8851 NOx_EXP + 6,6554 R = 0,74 1 st and 2 nd weeks NOx_EXP [µg/m³] NOx_EXP [µg/m³] 15

22 NOx [µg/m³] Results II: NOx time evolution NOx_EXP vs NOx_ March st week 2 nd week 3 rd week 4 th week 5 th week UTC NOx_EXP NOx_ (LAD = 0.1) 16

23 Vegetation effects on pollutant concentration Tafalla Street Real Case New Vegetation Scenario 17

24 C LAD0. 1 gm 3 Vegetation effects on pollutant concentration LAD 0. 1; Vdep m/ s C LAD 0. 1; Vdep 0 m/ s C LAD 0. 1; Vdep m/ s C LAD 0. 1; Vdep m/ s C Real Case New Vegetation Scenario 18

25 C LAD0. 5 gm 3 Vegetation effects on pollutant concentration LAD 0. 5; Vdep m/ s C LAD 0. 5; Vdep 0 m/ s C LAD 0. 5; Vdep m/ s C LAD 0. 5; Vdep m/ s C Real Case New Vegetation Scenario 19

26 Vegetation effects on pollutant concentration: Deposition effects C C LAD 0. 1; Vdep LAD 0. 1; Vdep 0 C C LAD 0. 1; Vdep LAD 0. 1; Vdep 0 C C LAD 0. 1; Vdep LAD 0. 1; Vdep 0 C LAD0. 1 gm 3 Real Case New Vegetation Scenario 20

27 Vegetation effects on pollutant concentration: Deposition effects C C LAD 0. 5; Vdep LAD 0. 5; Vdep 0 C C LAD 0. 5; Vdep LAD 0. 5; Vdep 0 C C LAD 0. 5; Vdep LAD 0. 5; Vdep 0 C LAD0. 5 gm 3 Real Case New Vegetation Scenario 21

28 Vegetation effects on pollutant concentration: Dynamical effects 22

29 Vegetation effects on pollutant concentration: Dynamical effects C LAD0. 1 gm 3 C C LAD0. 1; Vdep 0 LAD0. 1; Vdep 0 Real New Veg. C C LAD0. 1; Vdep 3 LAD0. 1; Vdep 3 Real New Veg. 23

30 Vegetation effects on pollutant concentration: Dynamical effects C LAD0. 1 gm 3 C C LAD0. 1; Vdep 0 LAD0. 1; Vdep 0 Real New Veg. C C LAD0. 1; Vdep 3 LAD0. 1; Vdep 3 Real New Veg. 23

31 Vegetation effects on pollutant concentration: Dynamical effects C LAD0. 5 gm 3 C C LAD0. 5; Vdep 0 LAD0. 5; Vdep 0 Real New Veg. C C LAD0. 5; Vdep 3 LAD0. 5; Vdep 3 Real New Veg. 24

32 Vegetation effects on pollutant concentration: Dynamical effects C LAD0. 5 gm 3 C C LAD0. 5; Vdep 0 LAD0. 5; Vdep 0 Real New Veg. C C LAD0. 5; Vdep 3 LAD0. 5; Vdep 3 Real New Veg. 24

33 Conclusions Maps of NOx concentration in a real neighbourhood are simulated including dynamical and pollutant deposition effects of vegetation by a RANS-CFD model. Modelled concentrations are in agreement with measurements from an air quality monitoring station for March, Different vegetation scenarios are analysed considering different LAD and deposition velocities. Influence of deposition can be important (10 µg/m³) for LAD = 0.1 and vd=0.03 and LAD 0.5. The dynamical effects of including new vegetation in one street are more important than deposition effects. New vegetation modifies wind flow patterns and concentration maps increasing, in general, the concentration in this street. Differences are also observed in other closer areas. In order to go deeper into the knowledge about the effect of trees on the urban air quality (further works): more experimental measurements are necessary (a field experimental campaign in RESPIRA project have been carried out and data will be analysed) other physical processes should be taken into account in the model: turbulence induced by traffic or thermal effects or chemical reactions. 25

34 The End Thank you for your attention! 34 26

35 Acknowledgements This study has been supported by the European Project LIFE+RESPIRA (LIFE13 ENV/ES/000417) funded by EU. Authors thank to Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT) by helping in using its computing facilities for the simulations. CETA-CIEMAT belongs to CIEMAT and the Government of Spain and is funded by the European Regional Development Fund (ERDF). This work has been done in collaboration with the project TECNAIRE (S2013/MAE-2972) funded by The Regional Government of Madrid and the Madrid City Council. 25