measurementsof urban nanoparticles with the dms500 … · measurementsof urban nanoparticles with...

MANSA: NPL, TEDDINGTON 8–9 JUNE 2010

DR. PRASHANT KUMAR

Measurements of Urban Nanoparticles with the DMS500 and their Dispersion Modelling

OUTLINE

BAKGROUND

MEASUREMENTS

Street canyons (fixed point and at different heights pseudo–simultaneously,

rooftop of streets and vehicle wake)

Key observations from field studies

MODELLING

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 2010 2

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 of emerging

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



MEASUREMENTS

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 (DMS500)

Response: 10 Hz, real time continuous

Sampling flow rate: 8.0 lpm at 250 mb for 5-1000 nm

2.5 lpm at 160 mb for 5-2500 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?

1 of 4


SAMPLING SITEMEASUREMENTS 2 of 4

Site: Pembroke Street, Cambridge, UK

Kerb

Winds from NW

1.60 m Traffic flow (down-canyon) W = 11.75 m

66 m

Chemical Engineering Department

Measurement siteH ≈ 11.60 m

2.60 m

2.50 m

(Figures not to scale)3-cup vortex anemometer

Leeward side Windward

side

Pembroke College Building

L ≈ 167 m

NW

NE

SE

SWWind16.60 m

MeasurementsMeasurements at fixed height 1.60 m

Measurements at four heights z/H = 0.09, 0.19, 0.4 and 0.64

Lengths of sampling tubes: 5.17, 5.55, 8.9 and 13.4 m

Switching time: 60 s; Sampling frequency: 0.5 (or 1) Hz

Size range: 5–2500 nm

Sampling tunes i.d.: 7.85 mm

Cross-canyon winds (NW and SE)

Along canyon winds (SW and NE)


APPLICATION OF DMS500

Check the sensitivity level of the instrument

Identify the suitable operating conditions (mainly sampling frequency) of the

instrument which maximised its utility

3 of 4

1 10 100 1000D p (nm)

0.1 s Av Noise (10 Hz)

1 s Av Noise (1 Hz)

10 s Av Noise (0.1 Hz)

0.1 s Av Roadside background (10 Hz)

0.1 s Roadside (10 Hz)

dN

/dlo

gD

p (

# c

m–

3)

0.8

0.6

0.4

0.2

0.0

1.0× 105

Sensitivity of the DMS500 (Source: Cambustion). Both

typical roadside and background PNDs were measured

at the fastest (10 Hz) sampling frequency.

Smaller (1 Hz or lower) rather than

maximal (10 Hz) sampling frequencies

found appropriate, unless experiments

rely critically upon fast response data

Suggested sampling frequencies used

in later experiments (Kumar et al.,

2008a–d, 2009a-c):

measured PNDs well above

instrument’s noise level

reduced size of data files to

manageable proportions

MEASUREMENTS


OTHER KEY OBSERVATIONS

Such relatively fast response measurements can capture signature of individual

vehicles. Our experiments found 20s average 1000 times more than overall

averaged PNCs (104 to 107 # cm–3) because an idling lorry parked for few seconds

(Kumar et al., 2008c).

Important to consider particle losses in sampling tubes (Kumar et al., 2008d).

4 of 4MEASUREMENTS

0

20

40

60

80

100

1 10 100 1000 10000

Dp (nm)

Pen

etra

tio

n (

%) Experiments (L2)

0

20

40

60

80

100

1 10 100 1000 10000Dp (nm)

Pen

etra

tio

n (

%)

Experiments (L1)

Laminar model

Turbulent model

0

20

40

60

80

100

1 10 100 1000 10000

Pen

etra

tio

n (

%)

Experiments (L3)

1 10 100 1000 10000Dp (nm)

Experiments (L4)

(a) (b)

(c) (d)

Dp (nm)

Pen

etra

tio

n (

%)

Sampling tubes: 5.17 (L1), 5.55

(L2), 8.9 (L3) & 13.4 m (L4).

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.

Typical number weighted size distributions (Kumar et al., 2008c) and size dependent

deposition in alveolar and trancheo–bronchial regions (ICRP, 1994).

1 10 100 1000 10000

D p (nm)

PM2.5

Dp ≤2.5 µm

PM10

Dp ≤10 µm

Ultrafine Particles

Dp ≤100 nm

Nanoparticles

Dp ≤300 nm

PM1

Dp ≤1 µm

0

0.2

0.4

0.6

0.8

1

Dep

osi

tion

Measured

Fitted modes

Deposition

0.0

0.2

0.4

0.6

0.8

1.0Nuclei mode Accumulation Coarse mode

modeN

orm

alis

ed d

istr

ibuti

ons

(1/C

tota

l) d

N/d

logD

p

Size range considered: 10–300 nm [N10–30: nucleation & N30–300: accumulation)

<10 nm considerable losses in sampling tubes

>300 nm negligible PNCs (<1% of total) as compared to ambient total

EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3001 of 6



Ur,crit is critical cut-off wind speed which divides zone of traffic-dependent and

wind-dependent concentrations

Ur,crit is generally considered as ≈ 1.2 m s-1 for gaseous pollutants (DePaul and

Sheih, 1986).

What about Ur,crit for N10-30, N30-300 and overall N10-300 during various wind

directions and speed?

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 N10-30 and N30-300; important

information for nanoparticle dispersion models

1 of 8EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3002 of 6


crit

m

ji

m

ji

T

N

T

N

=

−− For Ur <<Ur,crit (n = 0; m = 1)

Traffic dependent regime: Normalised PNCs are independent of Ur up to Ur,crit

n

rcritr

crit

m

ji

m

jiUU

T

N

T

N−−−

= , For Ur >> Ur,crit (n = 1; m = 1)

Ur dependent regime: Norm PNCs are inversely dependent of Ur after Ur,crit

A model with two distinct regimes, reflecting the role of both TPT and WPT,

was proposed and applied to the measured data:

Two limiting cases for PNCs dilution Ni-j = aTm Ur-n + Cb,i-j

Traffic dependent PNCs case (during smaller Ur; n=0 & m=1)

Wind dependent PNCs case (during larger Ur; n=1 & m=1)



0

1

1 0

1 0 0

1 0 0 0

Ni-

j/T

(# c

m-3

)/(v

eh h

/2-1

)

fit re sults (N 1 0 - 3 0 ) :

n = 0 and 1

n = 0 .4 0

1

1 0

1 0 0

1 0 0 0

fit re sults (N 3 0 - 3 0 0 ) :

n = 0 and 1

n = 0 .9 8

b c

0.1 1 10 0.1 1 10 0.1 1 10

Ur (m s-1)

0

1

1 0

1 0 0

1 0 0 0

N1

0-3

00/T

(# c

m-3

)/(v

eh p

er h

/2-1

)

fit re sults (N 1 0 - 3 0 0 ) :

n = 0 and 1

n = 0 .6 4

a

fit re sults (N 3 0 - 3 0 0 ) :

n = 0 a nd 1

n = 0 .9 9

c

0

1

1 0

1 0 0

1 0 0 0

Ni-

j/T

(#

cm

-3)/

(veh

h/2

-1)

fit re s ults (N 1 0 - 3 0 ) :

n = 0 a nd 1

n = 1 .0 4

b

0.1 1 10 0.1 1 10 0.1 1 10

Ur (m s-1)

0

1

1 0

1 0 0

1 0 0 0

0 1 1 0

N1

0-3

00

/T (

# c

m-3

)/(v

eh h

/2-1

)

fit re sults (N 1 0 - 3 0 0 ) :

n = 0 a nd 1

n = 0 .8 5

a

NW

SE

Norm PNCs against Ur (logarithmic plots) for cross-canyon wind direction

Different best-fit model tried; proposed model fitted data best which split data into

wind-independent (n=0) and wind-dependent (n=1) regions.

Minimising the diff. between model and experimental results yielded Ur,crit



NE

SE

0

1

10

100

1000

Ni-

j/T

(# c

m-3

)/(v

eh h

/2-1

)

fit results (N 1 0 -3 0 ) :

n = 0 and 1

0

1

10

100

fit results (N 3 0 -3 0 0 ) :

n = 0 and 1

b c

0.1 1 10 0.1 1 10 0.1 1 10

Ur (m s-1)

0

1

10

100

1000

N1

0-3

00/T

(# c

m-3

)/(v

eh h

/2-1

)

fit results (N 1 0 -3 0 0 ) :

n = 0 and 1

a

fit results (N 30-300) :

n = 0 and 1

n = 0.690

1

10

100

1000

Ni-

j/T (

# c

m-3

)/(v

eh h

/2-1

)


n = 0 and 1

n = 1.35

b

0.1 1 10 0.1 1 10 0.1 1 10

Ur (m s-1)

0

1

10

100

1000

0 1 10

N1

0-3

00/T

(# c

m-3

)/(v

eh p

er h

/2-1

)


n = 0 and 1

n = 1.15

a c

Along-Canyon winds

Till Ur,crit- dilution independent of Ur; here TPT governs dilution

After Ur,crit - dilution independent of T; here WPT governs dilution



The values of n for:

N10-300 = 1.00±0.25

N10-30 = 0.98 ±0.36

N30-300 = 0.94±0.14

Irrespective of wind directions, results are consistent with unity exponent (i.e. follow

Inverse wind speed law) in wind-dependent PNC regions

The Ur,crit for:

N10-300 = 1.23±0.55 ms–1

N10-30 = 1.47±0.72 ms–1

N30-300 = 0.78±0.29 ms–1

Spanned often quoted 1.2 ms-1 for gaseous pollutants.

Dispersion models may predict better if consider both size ranges seperately.



THE MODIFIED BOX MODELMODELLING

C and Cb are the predicted and background PNCs (# cm-3)

Ur and Ur,crit are in cm s-1, k1 is exponential decay coefficient in cm-1

σ0 = 11 dimensionless parameter (Rajaratnam, 1976)

Ex,i-j (PNEF # veh-1cm-1 in any particle size range of any vehicle class x )

Tx = veh s-1 of a certain class

h0 (= 2 m) is assumed initial dispersion height close to road level

W (width in cm); z (vertical height in cm above road level)

( )zkCTEWU

C bx

n

x

jixn

r

1

1

,0 exp

4−

+= ∑

=

−

πσ

Vertical Concentration profile from

(pseudo–simultaneous measurements)

Constant for exchange velocity ≈ 1% of Ur

1 of 4

when z = max (z , h0), Ur = max (Ur, Ur,crit) and k1 = 0.11 m–1

For Ur << Ur,crit: n = 0 & Ur >> Ur,crit: n = 1

k1 = 0 when z ≤ 2 m & For & k1 = 0.11 m-1 when z > 2 m)


ROLE OF PARICLE DYNAMICSMODELLING2 of 4

Ignored for street scale modelling because:

Time scale analysis showed dilution was very quick (dilution ~40s; dry

deposition on road surface (30–130 s) and street walls (600–2600 s);

coagulation ~105 s and condensation ~104–105 s.

Vehicle wake study (Kumar et al., 2009c) 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

shape and the negligible shift in peak and geometric mean diameters of PNDs

in both modes at each height, as shown below.

0.E+00

2.E+04

4.E+04

6.E+04

8.E+04

1.E+05

1 10 100 1000 10000

Dp (nm)

dN

/dlo

gD

p (

cm

-3)

CorrectedMeasured

Fitted modes

(a) z/H = 0.09

0.E+00

2.E+04

4.E+04

6.E+04

8.E+04

1.E+05

1 10 100 1000 10000

dN

/dlo

gD

p (

cm-3

)

1 10 100 1000 10000

z/H = 0.19 (b)

z/H = 0.40 (c) z/H = 0.64 (d)

D p (nm)

dN

/dlo

gD

p (

cm-3

)

0

2

4

6

8

10

0

2

4

6

8

10

x 104

x 104

0.E+00

2.E+04

4.E+04

6.E+04

8.E+04

1.E+05

1 10 100 1000 10000

Dp (nm)

dN

/dlo

gD

p (

cm

-3)

CorrectedMeasured

Fitted modes

(a) z/H = 0.09 CorrectedMeasured

Fitted modesFitted modes

(a) z/H = 0.09

0.E+00

2.E+04

4.E+04

6.E+04

8.E+04

1.E+05

1 10 100 1000 10000

dN

/dlo

gD

p (

cm-3

)

1 10 100 1000 10000

z/H = 0.19 (b)

z/H = 0.40 (c) z/H = 0.64 (d)

D p (nm)

dN

/dlo

gD

p (

cm-3

)

0

2

4

6

8

10

0

2

4

6

8

10

x 104

x 104

After correction for

losses in sampling tube

Before correction for

losses in sampling tube


CFD SIMULATIONS

CFD code: FLUENT

Standard k-ε model

2D domain; Ht. = 6H

Inlet Ur profile: constant

53824 grid cells, expansion factor

1.10 near walls

TKE profile k = IUin2 (I = 0.1)

Turbulent dissipation profile

( ) 115.175.0 −−= zkCz κε µ

Constant discharge emission sources

of 4 various sizes used

24 set of simulations were made for 24

h selected data

ρ and Ta changed every hour

with Cμ = 0.09 and κ = 0.40

3 of 4MODELLING


CFD SIMULATIONSMODELLING 4 of 4

OSPM

CFD_Sc

Box

0 0.4 0.8 1.2 1.60 0.4 0.8 1.2 1.6

0

0.4

0.8

1.2

1.6

0

0.4

0.8

1.2

1.6 × 105

× 105

(a) (b)

(d)(c)

z/H = 0.40 z/H = 0.64

z/H = 0.09 z/H = 0.19

Measured N 10-300 (# cm-3

)

Model

led N

10-3

00

(# c

m-3

)

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. (2009b) for details


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 (Ur<<Ur,crit), concentrations in each size

range were approximately constant and independent of wind speed and

direction.

In wind-dependent PNC region (Ur>>Ur,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.

1 of 1SUMMARY & CONCLUSIONS 1 of 2


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. (2009a).

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.

1 of 1SUMMARY & CONCLUSIONS 2 of 2


Manufactured nanoparticles (Kumar et al., 2010a)

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., 2010b)

Will they raise concern as their use does not seem to be reducing PNCs?

How should these be treated in regulatory framework?

KEY EMERGING SOURCES & QUESTIONS


1 of 1

• Kumar, P., Robins, A., Vardoulakis, S., Britter, R., 2010b. 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., 2010a. Comparsion of the behaviour of manufactured and other airborne

nanoparticles and the consequences for prioritising research and regulation activities. Journal of Nanoparticle

Research 12, 1523-1530.

• Kumar, P., Robins, A., Britter, R., 2009c. Fast response measurements of the dispersion of nanoparticles in a vehicle

wake and a street canyon. Atmospheric Environment 43, 6110-6118.

• Kumar, P., Garmory, A., Ketzel, M., Berkowicz, R., 2009b. Comparative study of measured and modelled number

concentration of nanoparticles in an urban street canyon. Atmospheric Environment 43, 949-958.

• Kumar, P., Fennell, P., Hayhurst, A., Britter, R., 2009a. Street versus rooftop level concentrations of fine particles in a

Cambridge Street Canyon. Boundary–Layer Meteorology 131, 3-18.

• Kumar, P., Fennell, P., Symonds, J., Britter, R., 2008d. Treatment for the losses of ultrafine aerosol particles in long

sampling tubes during ambient measurements. Atmospheric Environment 42, 8831-8838.

• Kumar, P., Fennell, P., Britter, R., 2008c. 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 402, 82-94.

• Kumar, P., Fennell, P., Britter, R., 2008b. Pseudo-simultaneous measurements for the vertical variation of coarse,

fine and ultrafine particles in an urban street canyon. Atmospheric Environment 42, 4304-4319.

• Kumar, P., Fennell, P., Britter, R., 2008a. Measurements of the Particles in the 5-1000 nm range close to the road

level in an urban street canyon. Science of the Total Environment 390, 437-447.

RELATED ARTICLES FOR DETIALED INFORMATION


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)

Dr. Jonathon Symonds (Cambustion Instruments, Cambridge)

Dr. John Dennis and Prof. Alan Hayhurst (Cambridge University, UK) –lending of the DMS500

1 of 1ACKNOWLEDGEMENTS

Helping in

experiments, data

analysis, discussions

and publishing


THANK YOU

CONTACT

DR. PRASHANT KUMAR

Email: [email protected]

Webpage: http://www2.surrey.ac.uk/cce/people/prashant_kumar/

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