The Influence of Traffic Flow and Winds Factors on Particle Number Concentration (PNC) around an Urban Intersection

Institute for Transport Studies FACULTY OF ENVIRONMENT The Influence of Traffic Flow and Winds Factors on Particle Number Concentration (PNC) around an Urban Intersection Noor.Z. Yahaya 1, J.E. Tate 2, M.R. Tight 2 1 University Malaysia Terengganu MALAYSIA. 2 Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK QUEEN'S ANNIVERSARY PRIZE WINNERS - 'sustained transport excellence' www.its.leeds.ac.uk/queensprize/

INTRODUCTION Traffic emissions in urban streets have been intensively studied: Monitoring and Modelling tools Presence of street canyons will effect levels of Ultrafine particle(ufp) Few studies have explored the spatial variation of UFP in particular Particle Number Concentration[PNC in urban streets University of Leeds has established a semi-permanent research site, the INSTRUMENTED JUNCTION that monitors: Traffic flow and congestion Dispersing air-flows Air pollution (NO x, O 3, PNC) at the kerbside (3 sites) & local background site PRESENTATION: Research objectives & background PART A: Methods and Materials PART B: Results Conclusions & Further work

Background The dominant source of ultra-fine particles (UFP) in street canyons is traffic-related emissions (Longley et al., 23; Harrison & Jones, 25); UFP are well correlated with other traffic-related pollutants (oxides of nitrogen, carbon monoxide) (Wahlin et al., 26); Epidemiological research has shown that particles are detrimental to human health (Bayer-Oglesby et al., 26, Penttinen, 21, Seaton et al., 1995); Comprehensive UK urban experimental sites is available: the ITS Instrumented Junction Facility Marylebone Road super-site, London Other UK cities

Objectives The objectives of this study are as follow: i. To collect simultaneous measurements. (Particle number concentration [PNC], traffic flow and congestion, dispersing air-flows at a congested intersection and in adjoining street canyon geometries) ii.to determine the relationships between [PNC], meteorological factors and traffic parameters. iii.to develop a new approach or method, to predict and forecast [PNC] in urban areas.

Street canyon Theory Nicholson (1975) An ideal street canyon is a relatively narrow street with buildings continuously lining both sides of the street. Vardoulakis et al (23) Defined the dimensions of a street canyon by the aspect ratio (H/w), which is the height (H) of the canyon divided by the width of the canyon (w). A street canyon may be termed: regular if its aspect ratio is approximately equal to 1 an avenue canyon when its aspect ratio is less than.5; a deep canyon if the ratio value greater than 2. WINDWARD SIDE MEAN WIND LEEWARD SIDE BUILDING PRIMARY BUILDING VORTEX W Q

PART A: Method and Materials

STUDY SITE - Headingley, Leeds, UNITED KINGDOM

INSTRUMENTED JUNCTION - Materials DATA COLLECTION: Reported Sampling period 1 March 29 22 March 21 TRAFFIC MONITORING (at Entry/ Exits): Classified flow and speed Journey times/ congestion IN-STREET AIR-FLOWS: Wind speed and direction (horizontal and vertical) Turbulence parameters AIR QUALITY: [PNC] at 4 sites (CPC, TSI 3775) [NO 2 ] at 5 sites [O 3 ] at 2 sites {Including Local Background}

INSTRUMENTED JUNCTION - Study area topography [1] NORTH ENV2: Intersection ENV1: Asymmetric canyon

INSTRUMENTED JUNCTION - Study area topography [2] NORTH ENV3: ENV2: Canyon Intersection

PART B : Results

DISTRIBUTION BACKGROUND WINDS - Leeds City Council Meteorological Mast (12m) 1 January 29 to 31 March 21 2 9 8 15 7 1 6 5 5 4 3 2 1 POLAR FREQUENCY PLOT: Background wind speed (U ref, ms -1 ) and direction (from North, θ ref )

Result 1 :Wind Speed and Direction Analysis (ENV1) A66 Street Axis 18 9 A66 Street Axis Perp. A66 θenv1 27 36 Figure A(ii) 9 Perp.A66 18 27 36 θref Figure A(i) Figure A(iii)

Result 1 :Wind Speed and Direction Analysis (ENV2) 36 1.2 Figure B(ii) A66 Axis B6157 Axis A66 Axis 1. B6157 Axis.4.6.8 Ti UEnv2 Uref 18 Result 1 :Wind Speed and Direction Analysis (ENV1) 9 18 A66 Axis 27 36 9 Perpendicular A66 18 θref 27 36 B6157 Axis A66 Axis 1 2 A66 Axis -1 Perpendicular A66 B6157 Axis -2 B6157 Axis Vertical Angle: Env2 (deg) 9 θref θennv2.2 B6157 Axis Ti & UEnv2 Uref 27 A66 Axis 9 18 θref Figure B(iii) 27 36

Result 1 :Wind Speed and Direction Analysis (ENV3) 1.2 36 Figure C(ii).6.8 Perpendicular.2 18 B6157 Axis Perpendicular 9 18 9 36 θref 36 2 1 27 Perpendicular -1 18 B6157 Axis -2 9 Perpendicular B6157 Vertical Angle: Env3 (deg) Perpendicular B6157 27 θref B6157 Axis θeenv3 B6157 Axis B6157 Axis.4 27 Ti & UEnv3 Uref 1. Ti UEnv3 Uref B6157 Axis Perpendicular Figure C(iii) 9 18 θref 27 36

RESULTS 2: Bivariate Polar plot for ENV1 instreet polar plot of ENV1 station A66 Street Axis N 2 15 (m/s) 4 1 5 3 W E 2 1 S Highest concentrations when background winds from north-easterly direction with wind speed in the range 2-7 ms -1.

RESULTS 2: [PNC] at ENV3 a regular Canyon instreet polar plot of ENV3 station N B6157 Street Axis 15 (m/s) 2 25 1 2 5 W E 15 1 5 S Highest concentrations when background winds from south-easterly direction with wind speed in the range 3-7 ms -1 which concentrated at the leeward side of the street canyon.

RESULTS 2: [PNC] at ENV4 a local background site Instreet PNC Conc polar.plot for background station ENV4 29 N 2 18 16 15 (m/s) 1 14 5 12 W E 1 8 6 4 S 2 BIVARIATE POLAR PLOT: Considering background wind direction (θ ref ) and speed (U ref, -1ms -1 ), and PNC concentrations (#/cm 3 ))

RESULTS 2: Bivariate Polarplots [PNC - background] for all kerbside sites Instreet PNC Conc polar.plot for ENV2 A66 street axis 29 N 15 (m/s) 2 25 A66 street axis Instreet PNC Conc polar.plot for ENV1 29 N 15 (m/s) 2 4 1 1 5 2 5 3 W E 15 W E 2 B6157 street axis S 1 5 S 1 Instreet PNC Conc polar.plot for ENV3 29 N 2 15 (m/s) B6157 street 1 axis 25 2 5 W E 15 1 5 S

Results 3 : Average daily mean plots (all stations) Total Traffic Flow 2 6 Total Traffic ENV1 and ENV2 Total traffic ENV3 2 4 6 8 1 12 14 16 18 2 22 24 Time (hourly) Mean Vehicles Speed (km/hr) 2 4 6 Vehicles Speed at ENV1 and ENV2 Vehicles Speed at ENV3 2 4 6 8 1 12 14 16 18 2 22 24 Time (hourly) Diurnal mean plots(left plot) - Diurnal traffic flow and the vehicle speeds resulted with an increasing in [PNC]; Diurnal [PNC] and Normalised Plot(right) - Highest level of [PNC] at morning peak, remain stable during day time and decreased at night; [PNC] follow the similar trends with the traffic and wind speed; - Wind speed and traffic flow, significantly influence the [PNC] which associated with the turbulence and dispersion processes of UFP in street canyon.

Result 3 : Normality plots for ENV1 Normalised..2.4.6.8 1. 1.2 N o r m a lit y in s t r e e t w in d f lo w N o r m a lit y t r a f f ic a t E N V 1 N o r m a lit y P N C C o n c e n t r a t io n [PNC] peak [PNC] decrease 2 4 6 8 1 1 2 1 4 1 6 1 8 2 2 2 2 4 T i m e ( h o u r ly ) 1. Normality plots- Traffic flow,[pnc] and wind speed by their maximum values of each dataset; 2. In-street airflows has influence the [PNC] which associated with the turbulence and dispersion processes of UFP in restricted geometries.

Results 3: Regression Analysis Instreet PNC ENV2 (cm 3 ) 1 2 3 4 5 y = 144 x + 761.32, R2 =.921 PNC AT ENV3 STATION(cm 3 ) 5 1 15 2 25 y = 215 x + 288.5, R2 =.93 5 1 15 2 25 3 2 4 6 8 1 Normalised Traffic (TMU2) flow by wind speed Normalised Traffic (TMU6) flow by wind speed i. Correlation,R between the [PNC] and the normalised traffic flow by the wind speed are high, with R values more than.959 and R 2 values more than.92; ii. [PNC] are strongly and significantly influenced by the wind speed and the traffic in a street canyon; iii. Further analysis will determine other parameters(humidity, temperature, solar intensity) that affects the [PNC] levels.

Conclusions bivariate polar plots are useful analysis tools to understand the temporal and spatial variation in PNC at street canyon and junction sites; across-canyon vortices developed in irregular street canyon, elevating concentration of traffic-related pollutants on the leeward side of the canyon evidence of along canyon flow channelling at this site; by subtracting the local background concentration of PNC: clearer understanding of the traffic induced emissions and dispersion processes in complex street environments can be developed; strong relationships (R and R 2 values) between [PNC], traffic flow follows and wind speeds : analysis suggests the adopt method to predict the [PNC] in urban environment are accepted.

Further Work To focus on the sources and dispersion mechanisms of PNC in the street canyons and at the junction; To determine other parameters that influence the PNC concentrations in street canyon; To study complex variable interactions between traffic (source) and dispersing air-flows (non-linear) using a Boosted Regression Tree (BRT) approach.

ACKNOWLEDGEMENTS Funding Bodies: Ministry of Higher Education of Malaysia and the University Malaysia Terengganu for funding my study EPSRC, HEFCE and The Instrumented City Facilities, University of Leeds for Infrastructure Awards The Royal Academy of Engineering (United Kingdom) and The Aerosol Society United Kingdom for Travel Awards Open-source software and analysis tools: www.r-project.org Supported by: www.openair-project.org Leeds City Council, Golden River Traffic Ltd, Enviro Technology Services

Thank you Q & A