AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1639-2017A DMA-train for precision measurement of sub-10 nm aerosol dynamicsStolzenburgDominikdominik.stolzenburg@univie.ac.athttps://orcid.org/0000-0003-1014-1360SteinerGerhardhttps://orcid.org/0000-0003-3008-1414WinklerPaul M.Faculty of Physics, University of Vienna, 1090 Vienna, AustriaInstitute for Ion and Applied Physics, University of Innsbruck, 6020 Innsbruck, AustriaDominik Stolzenburg (dominik.stolzenburg@univie.ac.at)2May20171041639165114October201614December201624March20179April2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1639/2017/amt-10-1639-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1639/2017/amt-10-1639-2017.pdf
Measurements of aerosol dynamics in the sub-10 nm size
range are crucially important for quantifying the impact of new particle
formation onto the global budget of cloud condensation nuclei. Here we
present the development and characterization of a differential mobility
analyzer train (DMA-train), operating six DMAs in parallel for high-time-resolution particle-size-distribution measurements below 10 nm.
The DMAs are operated at six different but fixed voltages and hence sizes,
together with six state-of-the-art condensation particle counters (CPCs). Two
Airmodus A10 particle size magnifiers (PSM) are used for channels below 2.5 nm
while sizes above 2.5 nm are detected by TSI 3776 butanol-based
or TSI 3788 water-based CPCs. We report the transfer functions and
characteristics of six identical Grimm S-DMAs as well as the calibration of a
butanol-based TSI model 3776 CPC, a water-based TSI model 3788 CPC and an
Airmodus A10 PSM. We find cutoff diameters similar to those reported in the
literature. The performance of the DMA-train is tested with a rapidly
changing aerosol of a tungsten oxide particle generator during warmup.
Additionally we report a measurement of new particle formation taken during a
nucleation event in the CLOUD chamber experiment at CERN. We find that the
DMA-train is able to bridge the gap between currently well-established
measurement techniques in the cluster–particle transition regime, providing
high time resolution and accurate size information of neutral and charged
particles even at atmospheric particle concentrations.
Introduction
Atmospheric aerosols still constitute the largest uncertainties in climate
models through their ambiguous effects on the climate system
. In addition to the direct effects of scattering and
absorption on incoming solar radiation, higher aerosol number concentrations
can increase the albedo of clouds and their lifetime
, leading to significant indirect radiative forcing.
New particle formation from gaseous precursor vapors is frequently observed
in the atmosphere . Model simulations show that up to 50 %
of the global budget of cloud condensation nuclei (CCN) might originate
from new particle formation . However,
these numbers strongly depend on the dynamics of the newly formed aerosol
. Nucleation occurring at the critical cluster size between
1 and 2 nm is often followed by growth up
to sizes of around 50–100 nm, where the particles can act as CCN
. In competition with the growth
by condensation is the probability of the newly formed particles to coagulate
with bigger pre-existing aerosol. The driving mechanisms responsible for the
aerosol growth are hence of big interest in current research
but still require detailed studies of aerosol
dynamics especially in the crucial sub-10 nm size range where
coagulation losses are the highest.
Electrical mobility analysis is widely used in order to infer aerosol size
information e.g.,. To this end differential mobility
analyzers (DMAs) are commonly used . Recent
improvements of DMAs made accurate particle sizing down to cluster sizes
around 1 nm possible
. Size-distribution
information is then obtained by applying either a stepwise varying
or continuously ramped voltage
to a DMA and measuring the downstream aerosol
concentration.
In a great number of applications condensation particle counters (CPCs) are
used for aerosol detection at single particle counting level. The generation
of the supersaturated vapor as well as the used working fluid itself varies
for different CPC types. All of them, either expansion type with various
working fluids , mixing type with diethylene
glycol (DEG) or the most common laminar-flow type with
butanol , water or
DEG , have reached particle detection at sizes as low as
∼ 1–2 nm, i.e., close to the critical cluster size.
Particle-size-distribution measurements in the sub-10 nm range done by
combining a DMA with a state-of-the-art CPC are reported for example by
and . Another approach was used by
with a particle size magnifier (PSM) operated in
scanning mode. There, the instrument's lower size-detection cutoff is varied
in order to infer size-distribution information of sub-3 nm particles
based on pre-(post-)calibration with the electrical mobility technique.
already pointed out the need for sufficient
time resolution, which depends on the actual aerosol system under
investigation. In atmospheric and especially in chamber studies, particle
growth rates between 10 and 100 nm h-1 can be observed and hence it might be
favorable to achieve scan times as low as 10 s, e.g., in order to
resolve the rise of the particle concentration in distinct size channels for
precision quantification of nanoparticle growth rates
e.g.,. However, while several fast
mobility scanning techniques are capable of measuring size distributions with
a time resolution in the order of seconds , the scanning cutoff method is still limited in that respect.
In contrast, poor counting statistics oftentimes prevents quantitative
analysis of nanoparticle dynamics due to high diffusional sampling losses and
low particle charging probabilities for sub-10 nm particles
. Therefore a (fast) scanning over different sizes
with a DMA is usually not suitable to exploit the full counting statistics at
one size. The scanning cutoff technique, however, does not rely on particle
charging and not directly on mobility analysis. But it needs very careful and
broad calibration measurements, as the cutoff strongly depends on the
(unknown) aerosol chemical composition . Accordingly,
the sizing information might have high systematic uncertainties.
Here we present the development of a newly designed DMA-train setup featuring
six DMAs operated in parallel at six distinct but fixed voltages in
combination with six state-of-the-art CPCs to
obtain fast and precise size-distribution measurements. The idea was first
brought forward by , who showed that especially rapidly
changing aerosol can be tracked with this method. This approach has been used
recently to study nanoparticle formation in the NCAR aerosol chamber
. We now refined it for the application in the
cluster–particle transition regime in the sub-10 nm range.
The DMA-train setup
The DMA-train was planned and constructed as a fixed mounted setup providing
all necessary power supplies, flow pumps and controls, including a unified
data acquisition system. Furthermore, the inlets to all six channels are kept
identical in length and shape to assure equal flow patterns. Figure
shows the main design features. In order to keep the
instrument as compact as possible a symmetrical two-layer design was chosen,
with two identical layers consisting of three DMAs. The inlet is located in
between the layers with a common flow for all channels at a flow rate of 11
liter per minute (L min-1) maintained by the sample flow of the six
CPCs used. In order to reduce diffusional sampling
losses, core sampling provides an additional 9 L min-1 make-up flow in
the main sampling line controlled by a critical orifice.
Scheme of the DMA-train setup. The design follows a classical SMPS
design in each channel. After bringing the aerosol to a well-defined charging
state, it is split and classified in six identical Grimm S-DMAs and
subsequently detected in condensation particle counters. For optimal
detection of sub-2.5 nm particles a combination of a Airmodus A10
particle size magnifier (PSM) with a butanol-based TSI 3776 CPC is used.
Particles equal or larger than 2.5 nm are detected by either TSI model
3776 or TSI model 3788 CPCs to allow for maximum flexibility with respect to
particle activation properties. The whole setup measures 80 × 135 × 140 cm,
excluding the flow supply unit.
The flow is then split up at a 0.5 in. union T into two transport
flows for the two layers with 5.5 L min-1 each. The subsequent design
follows the classical approach of a differential/scanning mobility particle
sizer (DMPS/SMPS) . Each stream is passing through a
soft X-ray bipolar Advanced Aerosol Neutralizer (AAN) model 3088 from TSI
Inc. found that the AAN reproduces well the predicted
charging probability for negative particles predicted by
for flow rates up to 5 L min-1 and down to
sizes as small as 5 nm. Moreover, reported that
the size distribution of the negative aerosol charger ions does not exceed
mobility diameters of 1.6 nm for flow rates even as high as 16 L min-1.
This basically sets the lower sizing limit of the DMA-train.
After the aerosol has reached a defined charging state at the exit of the
AAN, the flow is split up three-fold in a custom-built three-way
flow splitter. Thereby the DMAs receive two 1.5 and one 2.5 L min-1
streams on each layer of the DMA-train, respectively. The
tubing between flow splitting and the DMA entrance is kept as short as
possible.
Afterwards the aerosol is classified in six stainless steel Grimm S-DMAs,
which follow the “Vienna-type” design . Some
characterization results can be found in . The closed-loop
sheath-air flow of the DMAs is regulated by temperature controlled critical
orifices connected to a single high throughput vacuum pump offering eight
separate pumping chambers. The critical orifices of six pumping chambers
provide a flow of 15 L min-1 while two closed loop circuits can be
connected to two additional pumping chambers with 10 L min-1 critical
orifices. Accordingly, we can operate four DMAs at a sheath flow rate of 15 L min-1
and two DMAs at 25 L min-1. The common flow unit designed
by Grimm Aerosol Technik GmbH & Co. KG is furthermore equipped with sensors
for temperature, pressure and relative humidity for the simultaneous
monitoring of all eight pumping circuits. The sheath air is dried by six 2.26 L
volume silica-gel dryers and filtered in active charcoal filters
from chemical impurities and in HEPA filters from remaining aerosol
particles. The six identical HV modules from Grimm Aerosol are able to
provide positive voltages up to +6 kV to the central electrode of the S-DMA.
Afterwards the classification the detection is performed with six modern
CPCs. Three channels are equipped with a TSI 3776
butanol-based ultrafine CPC with a cutoff diameter as low as ∼ 2.5 nm. For one channel a TSI
3788 water-based ultrafine CPC is used . The last two
channels are operated with a combination of an Airmodus A10 PSM together with
a TSI 3776 butanol CPC for the activation of sub-2 nm aerosol
. An additional hyco-membrane pump provides the necessary
operating vacuum for the Airmodus PSMs. The usage of particle counters with
different working fluids introduces a possible feature to the DMA-train:
sampling aerosol of the same size in two or three channels with different CPC
types can therefore provide information on aerosol composition
, as the activation efficiencies of the CPCs depend on the
chemical composition of the seed particle .
The particle counters are directly connected to the Grimm S-DMA outlet, such
that further transport losses are minimized. As the PSMs require a higher
sample flow of 2.5 L min-1, the two DMAs upstream are operated at a
sheath-air flow rate of 25 L min-1 to keep the DMA resolution similar
to the channels with only 1.5 L min-1 sample flow and
15 L min-1
sheath flow.
The complete setup is controlled by a National Instruments LabView (Version
2014F) data acquisition software. It provides the control of the HV modules,
the readout of the CPCs and important sheath-air parameters, such that stable
operating conditions of all devices can be verified during operation.
Calibration setup for the retrieval of the Grimm S-DMA transfer
function and the gauging of the voltage–mobility relation. After aerosol
generation particles are classified in a high-resolution DMA (UDMA) and then
led into the Grimm S-DMA. Two Faraday cup electrometers (FCEs) are measuring
the concentration upstream and downstream of the Grimm S-DMA.
Laboratory characterizationDMA calibration
All six DMAs used in the DMA-train are of identical type and purchased as
Grimm S-DMAs from Grimm Aerosol Technik GmbH & Co. KG. To test their
performance we used a high-resolution DMA (UDMA) operated at sheath-air flow
rates of ∼ 450 L min-1, in detail described by
.
For the calibration setup the UDMA classified nanoparticles from either
a tungsten oxide generator or an electrospray source to create well-defined mobility standards. The calibration setup is shown in Fig.
and follows the classical tandem-DMA
configuration, which is described in detail for example by
. A brief description of the procedure is given
in Sect. .
Calibration of the UDMA
Due to the high sheath flow rates in the UDMA, which are not measurable
during operation, a calibration of the voltage–mobility relation is
necessary. This was done by using clusters of positively electrosprayed
tetraheptylammonium bromide (THABr) . The spectrum recorded
by a Faraday cup electrometer (FCE) downstream of the UDMA shows clear peaks
at electrical mobilities of 0.97, 0.65 and 0.53 cm2 V-1 s-1,
each associated with clusters of the form A+ABn of the electrosprayed salt.
A fit to the well-identified monomer allows to calibrate the voltage–mobility
relation of the UDMA shown in Eq. ()
,
Z=1V⋅lnRi/Ro⋅Qsh2⋅π⋅L,
by identifying the peak voltage of the monomer with a lognormal fit and then
calculating the corresponding sheath-air flow rate Qsh. All other
parameters of Eq. (), the outer and inner electrode
radii Ro and Ri as well as the length of the classification region L
are geometric factors which correspond to the construction values.
Calibration of the voltage–mobility relation for six Grimm S-DMAs
In order to calibrate the Grimm S-DMA voltage–mobility relation a tungsten
oxide nanoparticle generator from Grimm Aerosol was connected to the UDMA.
The calibration of the UDMA done with the positively charged THABr monomer is
assumed to be valid as long as the flow conditions are not changed. Tungsten
oxide particles of mean diameters 2.0, 2.5, 3.0, 4.0 and 5.0 nm were
classified. The Grimm S-DMA was connected downstream of the UDMA as was the
reference FCE (FCE1) measuring the concentration N1 immediately after the
UDMA. As shown in Fig. a second electrometer
(FCE2) measured the concentration N2 downstream of the Grimm S-DMA
while the DMA voltage was scanned in logarithmic equidistant intervals. For
each classified particle size, a reference measurement was performed with the
Grimm S-DMA removed and the FCE2 directly connected to the UDMA, thereby
correcting the relative concentration ratio N2/N1 for nonidealities in
flow splitting, possible different diffusional losses or electrometer
offsets. A least-squares lognormal fit is used again for identifying the peak
voltage. The fitted mean voltages for all selected mobilities and DMAs are
plotted in Fig. .
Calibration of the voltage–mobility relation for all six Grimm
S-DMAs used. The mean voltage of least-squares fits to the relative FCE response
downstream of the six Grimm S-DMAs is plotted against the classified inverse
mobility of the UDMA. The linear least-squares fit has only one free
parameter: the effective classification length of the Grimm S-DMAs.
To extend the calibration size range below 2 nm, a negative mobility
standard from the ionic liquid methyltrioctylammonium bis(trifluoromethylsulfonyl)imide (short MTOA-B3FI) was used due to the positive high
voltages applied to the Grimm S-DMAs. A typical recorded spectrum downstream
of the UDMA while electrospraying MTOA-B3FI is illustrated in Fig. .
The peaks of the MTOA-B3FI monomer (A-) and
dimer (A-(AB)1) are well separated at mobilities of
1.80 and 0.77 cm2 V-1 s-1, respectively. These mobilities
correspond to mobility diameters of 1.06 nm for the monomer and 1.62 nm
for the dimer. The trimer (A-(AB)2) and the tetramer
(A-(AB)3) can be identified in Fig. as well.
However, they were not used for calibration measurements as they overlap with
the background from multiply charged bigger clusters.
In a second set of experiments the same procedure as for the size-classified
tungsten oxide particles was repeated with the UDMA classifying monomer or
dimer of the MTOA-B3FI and therefore providing a calibration measurement
using a strictly monodisperse aerosol. The peak mobilities retrieved from the
lognormal fit are added to Fig. . For all
calibration runs, the sheath-air flow rate Qsh is measured with a
Gilian Gilibrator-2 low-pressure drop bubble flow meter from Sensidyne, LP.
For the individual sheath-air circuits of the flow unit the sheath-air flow
rate is very stable over time due to the temperature control of the critical
orifices. Therefore, the classification length L is used as the free
parameter of the least-squares fit to the data in Fig. , according to the inverse of Eq. ().
For the other parameters, the design values of
Ri=0.013 and Ro=0.020m are used.
The results summarized in Table are all
slightly higher than the specified classification length by the manufacturer
of 13 mm but are still in reasonable agreement. Moreover, all six
DMAs seem to be identical within the uncertainties of the measurements.
Voltage–mobility calibration and transfer function characteristics
of all Grimm S-DMAs used. L is the calibrated classification length of the
DMA, fσ accounts for additional transfer function broadening and
Lpene represents an effective diffusional length for inlet and
outlet losses of the DMA.
Typical MTOA-B3FI spectrum as recorded by a FCE downstream of the
UDMA. The peaks of the monomer and dimer are clearly visible and well
separated. The trimer and tetramer can be identified as well but are
overlapped by a background of multiply charged larger clusters.
Retrieval of the transfer function of the Grimm S-DMA
Additionally, the well-defined mobility of the classified MTOA-B3FI monomer
and dimer allows us to infer information about the DMA transfer function and its
penetration characteristics. According to , the response
downstream of the test DMA can be written as
N2N1=ηdmadp⋅ΩV,Zdp,
because only the monodisperse MTOA monomer or dimer particles were sent into
the Grimm S-DMAs.
Here N2 and N1 correspond to the FCE concentration readings downstream
and upstream of the test DMA, ηdma represents a loss parameter
for losses in the inlet and outlet regions of the Grimm S-DMAs and
ΩV,Zdp is the DMA transfer function. The
notation we use here and details about DMA transfer functions can be found in
. In general the transfer function yields the
fraction of particles with mobility Z passing the DMA operated at a fixed
voltage V. However, in the setup presented in Fig.
the DMA voltage was not fixed to one value and
monodisperse particles of different mobilities were sent into the DMA. In the
measurements presented here, the size of the particles was fixed with the
UDMA and the voltage of the tested DMA was scanned. Hence, the measured
response cannot be directly projected back into the transfer function as the
transfer function of the DMA becomes a function of the DMA voltage due to the
significant diffusional broadening below 2 nm.
The transfer function properties are therefore retrieved by a least-squares
fit of Eq. () to the measured ratio N2/N1 by
assuming Stolzenburg's diffusive transfer function
, including a voltage dependence of the transfer
function width σtheoV. As suggested by
, two fit parameters are used: the penetration loss
parameter ηdma, which just reduces the transfer function
height due to losses in the inlet and outlet region of the DMA, and an
additional broadening parameter for the transfer function fσ. This
is a multiplicative factor to the theoretical width σtheo,
which accounts for instrument nonidealities like electrode misalignment and
distortions of the flow pattern.
Voltage scan of a Grimm S-DMA while the UDMA is classifying either
MTOA-B3FI monomer or dimer. The measurements are fitted by the expected
response based on Stolzenburg's diffusive transfer function and two
additional free parameters: a penetration efficiency ηdma and
an additional width parameter fσ. Reported results correspond to
DMA serial number 5.
A representative measurement of the MTOA-B3FI monomer and dimer is shown in
Fig. and the results for all DMAs are reported in Table .
Generally we find an additional broadening,
fσ, between 1.01 and 1.22 for all DMAs and both mobility standards. The
measurements of the dimer and monomer for the individual DMAs are very
consistent for all DMAs, confirming the deviation from the Stolzenburg theory.
Moreover, it supports the results of the voltage–mobility calibration:
all six DMAs perform similarly but differ slightly from the manufacturer
values.
The penetrated fractions ηdma of the inlet and outlet regions
of the Grimm S-DMA derived from the monomer and dimer measurement can be
described by considering them as diffusional losses. As suggested by various
authors , the
modified Gormley and Kennedy equation ,
ηpenedp=0.819e-3.66μ+0.0975e-22.3μ+0.0325e-57.0μ+0.0154e-107.6μforμ>0.02ηpenedp=1.0-2.56μ2/3+1.2μ+0.1767μ4/3forμ≤0.02,
reproduces well the diameter dependence of such losses with μ=DLtubeπQ and D the particle diffusivity, Q the volume flow
and Ltube the length of the tube (all in SI units).
As the flow through the entrance and exit region of the DMA Qae
is known, it is appropriate to report an effective diffusional length
Lpene. It can be used in Eq. () in
order to extrapolate the DMA penetration to different sizes by setting
ηdma=ηpeneQ=Qae,Ltube=Lpene; i.e., the DMA losses are represented by
diffusional losses through a straight tube.
As summarized in Table , we find effective
penetration lengths close to 1.6 m for all DMAs. The very good
transmission characteristics of the Grimm S-DMA are underlined by the fact
that the highly diffusive MTOA-B3FI monomer can still be detected downstream
of the DMA and the transmission of the MTOA-B3FI dimer (1.62 nm) is
even as high as 8–10 %.
The measurements of the transfer function therefore confirm that the Grimm
S-DMA is well suited even for measurements in the sub-2 nm regime,
where resolution and transmission normally drop significantly.
Characterization of the CPCs
The performance of the three different types of CPCs used in the DMA-train was tested with the calibration setup shown in
Fig. . Silver nanoparticles were generated in a
tube furnace and then sent into one of the six calibrated Grimm S-DMAs for
size classification. The DMA was operated at 1.5 L min-1 aerosol flow,
controlled by the throughput of the silver furnace and at 15 L min-1
sheath-air flow. An additional make-up flow was supplied after the DMA exit
to account for the flow rates of the detectors. The total flow was then split
up at a four-way flow splitter similar to TSI model 3708 with two exits
closed.
The counting efficiency is calculated as the ratio of the measured
concentrations of the particle counter under investigation and the reference
FCE. The results of three typical efficiency measurements are shown in Fig. . In order to represent the functional
form of the activation curve best, we use the Gompertz function
,
ηcpcdp=A⋅e-e-k⋅dp-dp0,
and fit it within a least-squares routine to the observed data. Free
parameters are the plateau height A, dp0 and k.
Results of the cutoff diameter measurements with Ag seed particles
for the three types of used particle counters and the corresponding settings
for the measurements. For the TSI instruments we used the standard
manufacturer settings.
From the fit a dp50⋍0.3665k+dp0
cutoff diameter is inferred, where the counting efficiency reaches
50 % of the plateau height A. The temperature settings we used are
reported in Table together with the
determined cutoff diameters. We find that the DEG-based PSM together with
the TSI 3776 for detection achieve the lowest cutoff, well below
2 nm. Rather moderate temperature settings with a ΔT= 75 ∘C
between saturator and growth tube and the usage of
different test aerosol compared to the manufacturer explain why the PSM does
not reach the instrument's cutoff specifications as low as 1 nm.
However, these settings allow for an operation with very low background from
homogeneous nucleation occurring inside the PSM, guaranteeing a high
signal-to-noise ratio and still sufficient activation above 1.7 nm.
Calibration setup for the determination of the counting efficiency
curve of the three types of condensation particle counters used. Silver
aerosol particles are generated in a tube furnace and classified in one of
the Grimm S-DMAs. Afterwards the flow is split into the CPC under
investigation and a reference FCE.
Characterization of the counting efficiency with silver
nanoparticles for the three types of condensation particle counters used.
Green represents the DEG-based Airmodus PSM A10, red the butanol-based TSI
3776 and blue the water-based TSI 3788.
The two TSI particle counters, types 3776 and 3788, reach the cutoff values
specified by the manufacturer. In contrast to the PSM, where the turbulent
mixing prevents the activation of all particles , the two
counters clearly reach 100 % counting efficiency above
∼ 4 nm (see Fig. ). Thus the
parameter A of Eq. () is set to 1 in the reported
fits. The water-based TSI 3788 CPC shows the higher dp50 cutoff
due to the unfavorable conditions for nucleation of water on silver. Much
better activation properties for the water-based 3788 are reported when
sampling sodium chloride particles .
The composition dependence of the dp50 cutoff values of the
various CPCs should be kept in mind, when
investigating undefined aerosol. In this first approach we can, however,
already verify that the particle counters we used operate as specified by the
manufacturers. Moreover, most channels in the DMA-train can be set such that
the classified particle diameter lies in the plateau region, well above the
cutoff. Thus, inferred particle number concentrations should not depend too
much on the pattern of the size-dependent counting efficiency curves. For sub-2.5 nm channels, as well as for the potential feature of comparing activation
properties of the examined aerosol with different counters, more detailed
calibration measurements would be beneficial because in these cases the
classified diameters will be close to the cutoff values.
Instrument transmission
With the DMA-train fully set up, a calibration of the instrument's sampling
efficiency was done. Nanoparticles were produced either in the silver tube
furnace (> 5 nm) or in the tungsten oxide particle generator
(< 5 nm) and subsequently classified with one of the Grimm S-DMAs
removed from the rack. The aerosol flow from the generators was diluted in
order to achieve the 20 L min-1 sample flow required by the DMA-train.
Directly at the main inlet of the DMA-train, an additional core-sampling
probe was used for measuring the inlet concentration with an FCE. With all
instruments in operation inside the DMA-train a second FCE was installed at
the position of the removed DMA used for aerosol size classification and
hence measuring the concentration occurring at the DMA inlet in standard
operation. In order to account for FCE measurement offsets, a
cross-calibration of the two FCEs was done over the full possible FCE-current
measurement range. The concentration ratios of the two FCEs corrected for the
offset are shown in Fig. .
We present two different types of transmission measurements, in order to
account for the different sample flow rates. As expected, the channels with a
higher sample flow rate of 2.5 L min-1 achieve a higher transmission
compared to the channels with a 1.5 L min-1 sample flow rate, which
can be seen in Fig. . Among channels with the
same volume flow rates no significant differences were found.
Results of the transmission calibration of the DMA-train. The two
free fit parameters for the threefold Gormley and Kennedy Eq. ()
are reported for the two sample flow rates.
Final sample flowLeff1 (m)Leff2 (m)2.5 L min-1(1.6 ± 0.1)(0.75 ± 0.04)1.5 L min-1(1.6 ± 0.1)(0.88 ± 0.04)
The functional dependence of the sampling losses is fitted with a threefold
Gormley and Kennedy equation. This accounts for the fact that the flow within
the sampling procedure is reduced in two steps. After the 20 L min-1
main sample flow, the chargers are passed with a flow of 5.5 L min-1,
followed by the final sampling flow after the three-fold flow splitter. The
fitted function is reported in Eq. ():
ηsam=ηpeneQ=20Lmin-1,L=0.85m⋅ηpeneQ=5.5Lmin-1,L=Leff1⋅ηpeneQ=1.5/2.5Lmin-1,L=Leff2.
As the main sampling line upstream of the core-sampling probe is just a
straight tube of 0.85 m length, this part of the fitting function is
fixed. When the long sampling probe is not used, it can just be neglected.
The fit is therefore left with two free parameters, Leff1 and
Leff2, summarized in Table .
Size-distribution measurements
The fully characterized DMA-train can be used to infer size-distribution
information of sub-10 nm aerosol. For data inversion it is preferable to
combine the DMA-train data with other instruments covering the size range above
10 nm. This reduces possible systematic errors from multiply charged bigger particles.
Raw data are inverted according to the procedure of
. We use
dNdlndpdp*=N⋅a*β⋅fc(dp*)⋅ηsam(dp*)⋅ηcpc(dp*)⋅ηdma(dp*),
assuming symmetrical flow conditions at the DMAs . Here N
represents the raw counts of the particle counter used in the DMA-train
channel (PSM channels are corrected for their internal instrument dilution)
operated at classifying diameter dp*. β is the ratio of aerosol to sheath
flow used in the DMAs, fc corresponds to the Fuchs charging
efficiency according to Wiedensohler's approximation
and a*=-dlnZ/dlndpdp*. The different diameter-dependent efficiencies η are
already explained in detail in the previous section and summarized in Fig. .
It shows the individual contributions and
the total detection efficiency of the instrument for a channel with a TSI
3776 butanol CPC. For comparison the total detection efficiency of a channel
using an Airmodus A10 PSM is illustrated as well.
If the channels of the DMA-train are then set to different diameters, an
approximation of the measured aerosol size distribution can be obtained by
linearly interpolating between the six channels. It should be kept in mind
that if the spacing between the different size channels gets bigger, local
structures of the size distribution cannot be resolved any longer. However,
for a wide range of measurement applications the inferred size-distribution
information will be precise enough to infer the aerosol dynamics in the range
below 10 nm. This is demonstrated in the following with two example
measurements.
Transmission calibration of the DMA-train. The green points show a
measurement for the channels operated with a 1.5 L min-1 final sample
flow (the CPC channels) and the black points for the channels with a
2.5 L min-1 final sample flow (the PSM channels). Open symbols are
measured with tungsten oxide aerosol particles and filled symbols are
measured with silver aerosol particles. The solid lines represent a threefold
Gormley and Kennedy equation fit with two free parameters.
Efficiency in % of the several individual contributions and the total
value are plotted against the set diameter at the corresponding DMA. This is
similar for all three channels using a TSI 3776 CPC as particle counter
downstream of the DMA. For the two channels using a Airmodus A10 PSM, the
total efficiency is shown as well.
Evolution of the size distribution measured by a conventional DMPS
(a) and the DMA-train (b) of a tungsten oxide generator
during warmup. The generator is switched on a time 0. The DMA-train uses its
maximum resolution of 1 s while the DMPS bins are placed between start
and stop of the scanning cycle.
Time evolution of the signal in the different DMA-train channels
during an α-pinene ozonolysis event in the CLOUD chamber. In order to
cover a wider size range, some DMAs are switched to bigger sizes as soon as a
steady state is reached. The clear subsequent appearance of the signal in the
different channels verifies the size calibration and allows for the
determination of particle growth rates.
Fast-changing aerosol of a tungsten oxide generator at warmup
In order to test the performance of the DMA-train with an aerosol which
undergoes very rapid changes, we connected the tungsten oxide generator
directly to the inlet of the DMA-train. For comparison, a standard
DMPS system
was connected to the core-sampling probe at the
entrance of the DMA-train. The DMPS system uses a FCE as particle detector,
which sampled the aerosol at a flow rate of 2.9 L min-1. The tungsten
oxide generator was then switched on and we followed the evolution of the
size distribution with both systems in parallel, which is shown in Fig. .
The DMPS system only achieves five scanning cycles during the 8 min of
warmup, although its particle scanning range is already reduced from
standard operation mode. As the DMPS scanning cycle starts at the highest
voltages, i.e., at the biggest particle sizes, it already detects the first
small particles at the end of the second scanning cycle. In
Fig. ,
however, the scanning information is binned into the
time window between start and end of the scan. During the third scan the
generator almost achieved its full performance.
The DMA-train, in contrast, produces concentration signals in 1 s
time intervals, but the actual time resolution will vary among the different
size channels. Based on transmission time and CPC response times specified by
the manufacturers a conservative estimate of the overall time resolution is
on the order of 5 s. Still the resolution is high enough to
provide detailed information about the warmup between 1.7 and 7.26 nm
with the DMAs are set to 1.7, 2.14, 2.82, 3.63, 5.18 and 7.26 nm. It can be clearly
seen that as the tungsten oxide coil warms up, first particles of smaller
diameter are produced and then, subsequently, the size distribution reaches
bigger sizes. The same trend can be observed with the DMPS system, but much
less details, e.g., the precise onset of particles at a certain size, can be
resolved. The absolute concentrations, however, agree reasonably well within
a factor of 2.
DMA-train operated at the CLOUD experiment
The DMA-train was operated at the CLOUD experiment during a technical run,
where instruments could be tested intensively. CLOUD is described in detail
elsewhere and recently published detailed
results about pure biogenic nucleation and subsequent
growth .
Figure shows the time evolution of the different
size channels of the DMA-train during a typical nucleation event from pure
α-pinene ozonolysis in the CLOUD chamber. After establishing stable
gas concentrations, switching off the electrical field cage inside the
chamber at time 0 starts ion-induced nucleation . The
subsequent growth of the particles could be tracked by the DMA-train. The
signal consecutively appears at the different particle size bins, clearly
showing the growth of the particles from smaller to bigger sizes. In
Fig. ,
in total nine different sizes are covered as some DMAs
are automatically set to higher voltages and thus bigger particles, as soon
as a steady-state concentration is reached in the smaller size channels. This
opens an opportunity for covering wider size ranges with the DMA-train during
particle growth events and allows for the determination of size-dependent
growth rates by relating the signal appearance times to their classified
diameters e.g,. In this model case of growth driven by
organic vapors, the signal rises from 0 to its plateau value in one channel
within 5 min. This clearly points out the need for sufficient time
resolution in order to precisely capture the particle rise and hence
improve growth rate evaluation.
However, CLOUD is typically operated close to atmospheric
conditions, and particle number concentrations are low. Hence, the expected
signal, especially below 2.5 nm, is only in the range of a few counts
per minute. As there is no scanning cycle involved, data are acquired
constantly at each size, allowing for averaging over longer time periods in
order to even identify very low counts. This can be seen in Fig. ,
where a 2 min average was applied to the larger
size channels, but a 5 min average to the 1.7 and 2.0 nm channels
in order to account for the lower count rates at smaller sizes. Although
sampling losses are highest and charging and activation efficiencies are
lowest below 2 nm, respectively, the DMA-train is still sensitive to
the low count rates and can therefore resolve even that early growth, which
was not possible by a fast-scanning nano-SMPS , connected
to the chamber at the same time.
In this context it should be noted that for such low concentrations the DMA-train signal relies on a stable and negligible background. Especially the
temperature settings of the PSMs need to be chosen carefully to allow
detection of sub-2 nm particles while avoiding homogeneous nucleation at the
same time. It is another important feature of the DMA-train that the
background can easily be tested at any point in time by setting the DMA
voltages to 0 V and measuring particle-free sheath air. For the settings used
in this study we determined the background to be less than 1 count per
minute.
Conclusions
The DMA-train concept has been presented and its performance was demonstrated
by calibration experiments of DMAs, CPCs and sampling losses. These
measurements showed that our particle counters operate as specified by the
manufacturers. The usage of different CPC types might allow estimates of the
chemical composition of the sampled aerosol at different sizes due to the
different activation probabilities of the counters with respect to the seed
particle composition. All six Grimm S-DMAs reach accurate size classification
with a transmission as high as 8–10 % at diameters as low as
1.6 nm. The sampling losses are minimized as much as possible by
using a compact setup, which only needs short sampling tubes and provides
high flow rates during the sampling procedure.
Fast-changing aerosol can be measured with a time resolution on the order of
seconds. A measurement of a tungsten oxide generator during warmup and a
comparison to a DMPS system measuring in parallel showed the advantageous use
of the DMA-train when analyzing fast-changing aerosol. Furthermore the
comparison could also verify the absolute concentrations inferred from the
DMA-train.
The DMA-train is fully mobile and therefore adjusted for measurements at
atmospheric conditions as demonstrated during an intensive measurement
campaign at the CLOUD experiment. Measuring at several fixed sizes in
parallel offers the possibility of using the full counting statistics at the
distinct sizes. At low concentrations and small sizes an averaging of the
signal allows us to increase the sensitivity significantly compared to
other state-of-the-art instruments which infer size-distribution information
through scanning procedures.
Thus, the DMA-train allows us to bridge the gap between measurements in the
cluster size range obtained by mass-spectrometry or scanning PSMs and results
from conventional SMPS systems above 10 nm. Furthermore it provides a
high time resolution to observe very fast aerosol growth, especially in
the critical sub-10 nm range, where diffusional losses are very high
and the survival of freshly nucleated particles is crucial for growing them
to atmospherically relevant sizes.
If combined with other measurement devices such as conventional SMPS systems,
the full particle size range can be measured, beginning at cluster sizes as
low as 1.6 nm, which is the lower size limit for the DMA-train due to
the charger ions. This might allow us to perform detailed comparisons to
aerosol growth models or apply inverse modeling
procedures in order to obtain precise size- and time-dependent growth rates
. Thus the DMA-train opens a new field for
the analysis of aerosol growth processes.
The data sets presented in this study are available upon request from the corresponding author.
Dominik Stolzenburg and Paul M. Winkler designed the setup, Dominik Stolzenburg and Gerhard Steiner performed
the calibration experiments, Dominik Stolzenburg performed the size-distribution measurements,
Dominik Stolzenburg, Gerhard Steiner and Paul M. Winkler were involved in the
scientific interpretation and discussion, and Dominik Stolzenburg, Gerhard Steiner and Paul M. Winkler wrote the
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank Andrea Ojdanic for her help with the CPC characterizations and Iris
Brodacz for her support in operating the UDMA. We gratefully acknowledge the
CLOUD experiment for providing us the opportunity for instrument testing.
This work was supported by the European Research Council under the European
Community's Seventh Framework Programme (FP7/2007/2013)/ERC grant agreement
no. 616075 and the Austrian Science Fund, FWF, project number
P27295-N20. Edited by: P. Herckes
Reviewed by: two anonymous referees
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