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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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 3
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 4
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 5
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)
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 6
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 7
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 8
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 9
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 10
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)
EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3003 of 6
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 11
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
EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3004 of 6
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 12
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
)
fit results (N 10-30) :
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
)
fit results (N 10-300) :
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
EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3005 of 6
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 13
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.
EFECT OF WIND DIRECTIONS & SPEED ON N10-30 and N30-3006 of 6
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 14
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)
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 15
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 16
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 17
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 18
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 19
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 20
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 21
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 22
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
Dr. Prashant Kumar MANSA, NPL (UK), 8–9 JUNE 2010 23
THANK YOU
CONTACT
DR. PRASHANT KUMAR
Email: [email protected]
Webpage: http://www2.surrey.ac.uk/cce/people/prashant_kumar/