Measurements of Urban Nanoparticles with the DMS500 and their Dispersion Modelling DR. PRASHANT KUMAR MANSA: NPL, TEDDINGTON 8 9 JUNE 2010

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1 Measurements of Urban Nanoparticles with the DMS5 and their Dispersion Modelling DR. PRASHANT KUMAR MANSA: NPL, TEDDINGTON 8 9 JUNE 21

2 OUTLINE BAKGROUND MEASUREMENTS MODELLING Street canyons (fixed point and at different heights pseudo simultaneously, rooftop of streets and vehicle wake) Key observations from field studies Effect of wind speed and direction on the various size ranges of nanoparticles in street canyons (i.e. testing of inverse wind speed law; cut off wind speed) Role of particle dynamics at street scale modelling Formulation of a simple dispersion model (a modified Box model) Comparison of measured and modelled concentrations of nanoparticles using OSPM, CFD (FLUENT) and the modified Box model Uncertainties in modelling due to particle number emission factors SUMMARY AND CONCLUSIONS KEY EMERGING SOURCES & QUESTIONS Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 2

3 BACKGROUND Urban street canyons are pollution hot spots because of limited dispersion due to surrounding built up environment exposure to high concentrations likely Numerous studies available on dispersion modelling of gaseous pollutants but very few on dispersion modelling of nanoparticles Number based Euro 5 & Euro 6 standards ambient air quality standards likely? Important to understand their dispersion and develop modelling tools Progress for developing a potential regulatory framework have been hampered by a number of technical challenges: A lack of standard instruments to measure number and size distributions Insufficient knowledge of the physicochemical characteristics ofemerging sources (manufactured and bio-fuel derived) and their appropriate treatment Limited understanding of nanoparticles dispersion in ambient environment A lack of scientifically validated modelling tools Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 3

4 MEASUREMENTS 1 of 4 Measurement Campaigns: Street canyon (Pembroke Street, Cambridge) Fen causeway (Cambridge) Taken separately within the street at fixed heights, pseudo-simultaneous at different heights, above rooftop and in vehicle wake Instrument:Differential Mobility Spectrometer (DMS5) Response: 1 Hz, real time continuous Sampling flow rate: 8. lpm at 25 mb for 5-1 nm 2.5 lpm at 16 mb for 5-25 nm Several objectives: Vertical variation what should be appropriate measurement height? Particle number emission factors inverse modelling technique Treatment of particle losses in sampling tubes during measurements Role of particle dynamics at street scale, rooftop and in vehicle wake Effect of wind speed and direction - whether follow inverse wind speed law like gaseous pollutants? Dispersion modelling how can these be modelled at various spatial scales? Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 4

5 MEASUREMENTS SAMPLING SITE 2 of 4 Measurements Measurements at fixed height 1.6 m Measurements at four heights z/h=.9,.19,.4 and.64 Lengths of sampling tubes: 5.17, 5.55, 8.9 and 13.4 m Switching time: 6 s; Sampling frequency:.5 (or 1) Hz Size range: 5 25 nm Sampling tunes i.d.: 7.85 mm Cross-canyon winds (NW and SE) Along canyon winds (SW and NE) 16.6 m Wind NW SW SE NE Chemical Engineering Department Site: Pembroke Street, Cambridge, UK Winds from NW H 11.6 m 66 m Measurement site 2.6 m 1.6 m Kerb Traffic flow (down-canyon) W = m 2.5 m Leeward side Windward side 3-cup vortex anemometer L 167 m Pembroke College Building (Figures not to scale) Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 5

6 MEASUREMENTS APPLICATION OF DMS5 3 of 4 Check the sensitivity level of the instrument Identify the suitable operating conditions (mainly sampling frequency) of the instrument which maximised its utility dn /dlogd p (# cm 3 ) s Av Noise (1 Hz) 1 s Av Noise (1 Hz) 1 s Av Noise (.1 Hz).1 s Av Roadside background (1 Hz).1 s Roadside (1 Hz) Smaller (1 Hz or lower) rather than maximal (1 Hz) sampling frequencies found appropriate, unless experiments rely critically upon fast response data Suggested sampling frequencies used in later experiments (Kumar et al., 28a d, 29a-c): D p (nm) measured PNDs well above instrument s noise level Sensitivity of the DMS5 (Source: Cambustion). Both typical roadside and background PNDs were measured at the fastest (1 Hz) sampling frequency. reduced size of data files to manageable proportions Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 6

7 MEASUREMENTS OTHER KEY OBSERVATIONS 4 of 4 Such relatively fast response measurements can capture signature of individual vehicles. Our experiments found 2s average 1 times more than overall averaged PNCs (1 4 to 1 7 # cm 3 ) because an idling lorry parked for few seconds (Kumar et al., 28c). Penetration (%) Penetration (%) Penetration (%) Important to consider particle losses in sampling tubes (Kumar et al., 28d). 1 (a) 8 Exp eriments (L1) Laminar model Turbulent model 6 Penetration (%) 4 2 Experiments (L2) Dp (nm) (c) Dp (nm) (d) Experiments (L3) Experiments (L4) Dp (nm) D p (nm) (b) Sampling tubes: 5.17 (L 1 ), 5.55 (L 2 ), 8.9 (L 3 ) & 13.4 m (L 4 ). Sampling from a diesel-engined car. Though the flow is laminar through the tube, the losses in the tube are higher than would be predicted by models available (Hinds) in the literature. Confirmed / confirms independent experiments (Cambustion) using both DMS and CPC-SMPS. Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 7

8 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 6 Size range considered: 1 3 nm [N 1 3 : nucleation & N 3 3 : accumulation) <1 nm considerable losses in sampling tubes >3 nm negligible PNCs (<1% of total) as compared to ambient total Normalised distributions (1/C total ) dn/dlogd p Nuclei mode Accumulation Coarse mode mode Ultrafine Particles D p 1 nm Nanoparticles D p 3 nm D p PM 1 1 µm PM 2.5 Measured Fitted modes Deposition D p 2.5 µm PM1 D p 1 µm D p (nm) Deposition Typical number weighted size distributions (Kumar et al., 28c)and size dependent deposition in alveolar and trancheo bronchial regions (ICRP, 1994). Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 8

9 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 68 Measurements taken for 17 days continuously; sampling rate 1 Hz Measurements at 1.6 m with intention that effect of TPT s can be observed Period covered smaller and larger Ur s covering effect of TPT and WPT Objective was to test inverse-wind speed law on N 1-3 and N 3-3 ; important information for nanoparticle dispersion models U r,crit is critical cut-off wind speed which divides zone of traffic-dependent and wind-dependent concentrations U r,crit is generally considered as 1.2 m s -1 for gaseous pollutants (DePaul and Sheih, 1986). What about U r,crit for N 1-3, N 3-3 and overall N 1-3 during various wind directions and speed? Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 9

10 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 6 Two limiting cases for PNCs dilution N i-j = at m U r -n + C b,i-j Traffic dependent PNCs case (during smaller U r ; n= & m=1) Wind dependent PNCs case (during larger U r ; n=1 & m=1) A model with two distinct regimes, reflecting the role of both TPT and WPT, was proposed and applied to the measured data: N T i j m N = T i j m For U r <<U r,crit (n= ; m= 1) crit Traffic dependent regime: Normalised PNCs are independent of U r up to U r,crit N T i j m = N T i j m crit U n r, critu r For U r >> U r,crit (n= 1; m= 1) U r dependent regime: Norm PNCs are inversely dependent of U r after U r,crit Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 1

11 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 6 NW SE N 1-3 /T (# cm -3 )/(veh per h/2-1 ) 1 N1-3 /T (# cm -3 )/(veh h/2-1 ) fit results (N 1-3 ) : n = and 1 n =.6 4 a Ni-j/T (# cm -3 )/(veh h/2-1 ) fit results (N 1-3 ) : n = and 1 n =.4 1 b fit results (N 3-3 ) : n = and 1 n = Ni-j/T (# cm -3 )/(veh h/2-1 ) fit re sults (N 1-3 ) : 1 n = a nd 1 n = a 1 fit re s ults (N 1-3 ) : n = a nd 1 n = 1. 4 U r (m s -1 ) b fit re sults (N 3-3 ) : n = a nd 1 n = U r (m s -1 ) Norm PNCs against U r (logarithmic plots)for cross-canyon wind direction Different best-fit model tried; proposed model fitted data best which split data into wind-independent (n=) and wind-dependent (n=1) regions. c c Minimising the diff. between model and experimental results yielded U r,crit Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 11

12 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 6 NE N 1-3 /T (# cm -3 )/(veh h/2-1 ) fit results (N 1-3 ) : n = and 1 a Ni-j/T (# cm -3 )/(veh h/2-1 ) fit results (N 1-3 ) : n = and 1 b fit results (N 3-3 ) : n = and 1 c U r (m s -1 ) SE N1-3/T (# cm -3 )/(veh per h/2-1 ) Ni-j/T (# cm -3 )/(veh h/2-1 ) fit results (N 1-3 ) : 1 n = and 1 n = fit results (N 1-3 ) : n = and 1 n = 1.35 b fit results (N 3-3 ) : n = and 1 n = Along-Canyon winds a U r (m s -1 ) Till U r,crit -dilution independent of U r ; here TPT governs dilution After U r,crit -dilution independent of T; here WPT governs dilution c Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 12

13 EFECT OF WIND DIRECTIONS & SPEED ON N 1-3 and N of 6 The values of nfor: N 1-3 = 1.±.25 N 1-3 =.98 ±.36 N =.94± Irrespective of wind directions, results are consistent with unity exponent (i.e. follow Inverse wind speed law) in wind-dependent PNC regions The U r,crit for: N 1-3 = 1.23±.55 ms 1 N 1-3 = 1.47±.72 ms 1 N 3-3 =.78±.29 ms 1 Spanned often quoted 1.2 ms -1 for gaseous pollutants. Dispersion models may predict better if consider both size ranges seperately. Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 13

14 MODELLING THE MODIFIED BOX MODEL 1 of 4 Constant for exchange velocity 1% of U r n 4σ π C = Ex i jtx C n, + b exp U r W x= 1 ( k z) when z= max (z, h ), U r = max (U r, U r,crit ) and k 1 =.11 m 1 1 Vertical Concentration profile from (pseudo simultaneous measurements) For U r << U r,crit : n= & Ur >>Ur,crit: n= 1 k 1 = when z 2 m & For & k 1 =.11 m -1 when z> 2 m) Cand C b are the predicted and background PNCs (# cm -3 ) U r and U r,crit are in cm s -1, k 1 is exponential decay coefficient in cm -1 σ = 11 dimensionless parameter (Rajaratnam, 1976) E x,i-j (PNEF# veh -1 cm -1 in any particle size range of any vehicle class x) T x = veh s -1 of a certain class h (= 2 m) is assumed initial dispersion height close to road level W (width in cm); z(vertical height in cm above road level) Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 14

15 MODELLING ROLE OF PARICLE DYNAMICS Ignored for street scale modelling because: Time scale analysis showed dilution was very quick (dilution ~4s; dry deposition on road surface (3 13 s) and street walls (6 26 s); coagulation ~1 5 s and condensation ~ s. Vehicle wake study (Kumar et al., 29c) indicated that the competing influences of transformation processes was nearly over by the time particles reach from the tailpipe to the road side. Pseudo-simultaneous measurements at different heights found similarity in shapeand the negligible shift in peak and geometric mean diametersof PNDs in both modes at each height, as shown below. 2 of 4 dn/dlogdp (cm -3 ) x E+5 (a) z/h =.9 Corrected 8 Measured 8.E+4 Fitted modes 6 6.E x dn/dlogdp (cm -3 ) 4.E+4 2.E+4.E+ 1.E z/h =.4 (c) D z/h p (nm) =.64 (d) 8.E+4 dn/dlogdp (cm -3 ) 6.E+4 4.E+4 2.E+4 z/h =.19 (b) Before correction for losses in sampling tube After correction for losses in sampling tube.e Dr. Prashant Kumar D p (nm) MANSA, NPL (UK), 8 9 JUNE 21 15

16 MODELLING CFD SIMULATIONS 3 of 4 CFD code: FLUENT Standard k-ε model 2D domain; Ht. = 6H Inlet U r profile: constant grid cells, expansion factor 1.1 near walls TKE profile k = IU in2 (I =.1) Turbulent dissipation profile ( z) = C k κ z ε µ with C μ =.9 and κ=.4 Constant discharge emission sources of 4 various sizes used 24 set of simulations were made for 24 h selected data ρand T a changed every hour Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 16

17 MODELLING CFD SIMULATIONS 4 of OSPM z/h =.9 z/h =.19 CFD_Sc 1.2 Box.8 Modelled N1-3 (# cm -3 ) z/h =.4 z/h =.64 (a) (b).8.4 (c) (d) Measured N 1-3 (# cm -3 ) 1 5 The measured PNCs at different heights compared well within a factor of 2 3 to those modelled using OSPM, Box model and CFD simulations, suggesting that if model inputs are given carefully, even the simplified approach can predict the concentrations as well as more complex models. See Kumar et al. (29b) for details Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 17

18 SUMMARY & CONCLUSIONS 1 of 21 An advanced particle spectrometer was successfully applied to measure PNDs and PNCs in street canyons and was found to be useful for fast response measurements. Nanoparticle number concentrations in each size range during all wind directions were better described a proposed two regime model (wind- and traffic-dependent mixing), rather than by simply assuming that the PNCs are inversely proportion to the wind speed. In the traffic dependent PNC region (U r <<U r,crit ), concentrations in each size range were approximately constant and independent of wind speed and direction. In wind-dependent PNC region (U r >>U r,crit ), concentrations were inversely proportional to wind speed, irrespective of any particle size range and wind direction following a best-fit power law (or inverse wind speed law). Use of critical-cut off wind speed for nanoparticles can help dispersion models to avoid over-prediction of concentrations. Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 18

19 SUMMARY & CONCLUSIONS 12 of 21 Particle dynamics at street-scale modelling can be neglected as competing influences of transformation processes seems to be over by the time particles are measured at road side. However, it is important to consider it at above-rooftop and city scale modelling; not discussed here but details cane be seen in Kumar et al. (29a). Model comparison suggested that If model inputs are given carefully, a simplified approach can predict the PNCs to accuracy comparable with that obtained using more complex models. The particle number emission factor is one of the most important model input parameter which is not abundantly available for routine application. This can result in large uncertainties (i.e. up to an order of magnitude), meaning that modelled results are likely to be affected by the similar degree irrespective of the accuracy of a model. Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 19

20 KEY EMERGING SOURCES & QUESTIONS Manufactured nanoparticles (Kumar et al., 21a) Do the characteristics of MNPs differ from those of other ANPs? Should MNPs be regarded as an emerging class of MNPs? What is the appropriate measurement metric? Can the same instruments be applied to measure air dispersed MNPs & ANPs? Are the dispersion characteristics of MNPs and ANPs similar? Is exposure to MNPs a concern? How can these be included in potential regulatory framework? Bio-fuelled vehicle derived nanoparticles (Kumar et al., 21b) Will they raise concern as their use does not seem to be reducing PNCs? How should these be treated in regulatory framework? Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 2

21 RELATED ARTICLES FOR DETIALED INFORMATION 1 of 1 Kumar, P., Robins, A., Vardoulakis, S., Britter, R., 21b. A review of the characterstics of nanoparticles in the urban atmosphere and the prospects for developing regulatoy control. Atmospheric Environment (under review). Kumar, P., Fennell, P., Robins, A., 21a. Comparsion of the behaviour of manufactured and other airborne nanoparticles and the consequences for prioritising research and regulation activities. Journal of Nanoparticle Research 12, Kumar, P., Robins, A., Britter, R., 29c. Fast response measurements of the dispersion of nanoparticles in a vehicle wake and a street canyon. Atmospheric Environment 43, Kumar, P., Garmory, A., Ketzel, M., Berkowicz, R., 29b. Comparative study of measured and modelled number concentration of nanoparticles in an urban street canyon. Atmospheric Environment 43, Kumar, P.,Fennell, P., Hayhurst, A., Britter, R., 29a. Street versus rooftop level concentrations of fine particles in a Cambridge Street Canyon.Boundary Layer Meteorology131, Kumar, P., Fennell, P., Symonds, J., Britter, R., 28d. Treatment for the losses of ultrafine aerosol particles in long sampling tubes during ambient measurements. Atmospheric Environment 42, Kumar, P., Fennell, P., Britter, R., 28c. Effect of wind direction and speed of the dispersion of nucleation and accumulation mode particles in an urban street canyon. Science of the Total Environment 42, Kumar, P., Fennell, P., Britter, R., 28b. Pseudo-simultaneous measurements for the vertical variation of coarse, fine and ultrafine particles in an urban street canyon. Atmospheric Environment 42, Kumar, P., Fennell, P., Britter, R., 28a. Measurements of the Particles in the 5-1 nm range close to the road level in an urban street canyon. Science of the Total Environment 39, Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 21

22 ACKNOWLEDGEMENTS 1 of 1 Prof. Alan Robins (University of Surrey, UK) Prof. Rex Britter (MIT, USA) Dr. Paul Fennell (Imperial College, London) Dr. Matthias Ketzel & Dr. Ruwim Berkowicz (NERI, Denmark) Helping in experiments, data analysis, discussions and publishing Dr. Jonathon Symonds (Cambustion Instruments, Cambridge) Dr. John Dennis and Prof. Alan Hayhurst (Cambridge University, UK) lending of the DMS5 Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 22

23 THANK YOU CONTACT DR. PRASHANT KUMAR Webpage: Dr. Prashant Kumar MANSA, NPL (UK), 8 9 JUNE 21 23