Introduction
Smog chamber experiments have been an important tool for the study of
atmospheric aerosol processes. One major challenge of smog chamber
experiments is the particle wall-loss processes. The aerosols inside the
chamber are lost to its walls due to Brownian diffusion, convection,
electrostatic effects (especially for Teflon chambers) and gravitational
sedimentation (Crump and Seinfeld, 1981). The particle wall-loss process is
first-order and the particle wall-loss rate constant, k, is defined as
∂N(Dp,t)∂t=-k(Dpt)N(Dpt),
where N(Dp,t) is the number concentration of particles with
diameter Dp at time t. For an aerosol population, k is in general a
function of particle size and time. Smaller sized particles (less than 50 nm)
have a higher loss rate due to the diffusion-dominated wall-loss process,
while particles larger than 1 µm are mainly lost due to sedimentation
for a reactor with low air motion inside. The electrostatic effect can play a
major role in intermediate sizes (McMurry and Rader, 1985). Charan et al. (2018)
studied the charge effect on the rate of particle wall deposition,
estimating both the eddy-diffusion coefficient and the average magnitude of
the electric field within their chamber.
Early studies of chamber simulations of secondary organic aerosol (SOA)
formation and growth assumed that particle wall loss is negligible in
fairly large chambers (∼30 m3) when determining SOA yields (Stern
et al., 1987). Several particle wall-loss correction methods have since been
developed and adopted in chamber studies. Pathak et al. (2007) proposed a
semi-empirical wall-loss correction method that involves determining the
first-order particle wall-loss rate constant, k, from the SOA
mass concentration measured by the scanning mobility particle sizer (SMPS) after chemical reactions have been completed. This
total-mass-concentration-based method is based on the assumption that k is
independent of particle size for the size range of particles present in the
experiment and remains constant during the course of an experiment. The
constant k is found as the slope of the linear regression:
lnCSOAsus(t)=-kt+Q,
where CSOAsus(t) is the measured SOA mass concentration at
time t and Q is an arbitrary constant. The values of
CSOAsus(t) used for the fit are taken after the SOA
production has finished (condensation and/or evaporation is minimal). The corrected SOA concentration can be found by
CSOAtot=CSOAsus(t)+k∫0tCSOAsus(t)dt-Cseed(0),
where Cseed(0) is the seed mass concentration when SOA formation
begins. This approach is relatively accurate when k remains more or less
constant over the size range of the aerosol population inside the chamber,
and accounts for the experiment-to-experiment variability of the particle
wall-loss rates. However, it requires a period during which no reactions are
taking place in the chamber and assumes that the rate constant does not vary
during the experiment.
The size-dependent correction method involves determining a first-order
k(Dp) through the aforementioned linear fitting of the number
concentration of the suspended particles, for each size, usually with the
help of an SMPS. Several studies that
adopted this method determined the k(Dp) profile for the
corresponding chamber through seed experiments in which inert (e.g., ammonium
sulfate) particles were used (McMurry and Grosjean, 1985; Keywood et al.,
2004; Ng et al., 2007; Fry et al., 2014; Nah et al., 2017). In these studies,
an average k(Dp) profile was applied to all experiments. This
method includes the size dependence of k but not its potential
variation from experiment to experiment. Ng et al. (2007) and Wang et
al. (2018) determined a k(Dp) profile using the initial seed
wall-loss period for each of their experiments, thus accounting for the
experiment-to-experiment variation.
The OA / sulfate correction method was proposed by Hildebrandt et
al. (2009) using the organic and the sulfate mass concentration measured by
the aerosol mass spectrometer (AMS). This approach assumes that the loss rate
constants of organic species and sulfate are the same during an experiment as
there are no processes affecting sulfate other than losses to the walls
(e.g., no added SO2 or other sulfate precursors). The corrected OA
mass concentration is then calculated as
COA(t)=COAsus(t)Cseed(t)Cseed(0),
where COAsus(t)/Cseed(t) is the AMS-derived
OA / sulfate ratio and Cseed(0) is the
seed concentration in the chamber when SOA formation starts. Several chamber
studies have adopted this method (Henry and Donahue, 2012; Loza et al.,
2012). Other variations of this method include the use of the ratio of OA to
other inert tracers like black carbon (BC), which are present in experiments
investigating the evolution of primary OA from combustion sources (Hennigan
et al., 2011). This method involving the use of OA / tracer
ratio is accurate when the OA and the tracer have the
same size distribution during the experiment or when the loss rate constant
is close to being size-independent. However, in experiments in which SOA
condenses more onto smaller sized particles, the size dependence of the loss
rate can introduce significant uncertainty in the corrected results,
especially for timescales of several hours (Wang et al., 2018).
List of experiments and experimental conditions.
Exp.
Chamber
Type
Number of seed
Initial seed
Initial seed surface
Initial seed volume
Notes
volume
wall-loss periods
number concentration
concentration*
concentration*
(m3)
(cm-3)
(µm2 cm-3)
(µm3 cm-3)
1
1.5
Seed
1
13×104
19 500
1890
Regular seed wall-loss exp.
2
12
Seed
1
3.1×104
1630
53
Regular seed wall-loss exp.
3
12
Seed + SOA
2
1.8×104
1076
39
Aging exp. of α-pinene ozonolysis
4
12
Seed + SOA
2
4.0×104
1037
23
Aging exp. of α-pinene ozonolysis
5
12
Seed + SOA
2
2.8×104
1390
42
Aging exp. of α-pinene ozonolysis
6
12
Seed
2
2.3×104
1870
75
UV lights on for 3 h before final seed
7
12
Seed
2
5.2×104
4600
200
HONO addition (5 L min-1 for 20 min) before final seed
8
12
Seed
1
2.6×104
1270
40
Regular seed wall-loss exp. with overnight flushing after
9
12
Seed
1
3.4×104
1330
40
Regular seed wall-loss exp. ran on the day after Exp. 8
* Maximum concentration after initial seed injection (before
wall loss of these seed particles).
An alternative method for particle wall-loss correction is the use of models
of aerosol dynamics. Pierce et al. (2008) developed the Aerosol Parameter
Estimation (APE) model that simulates the processes of
condensation and/or evaporation, coagulation, and particle wall loss during a
chamber experiment. By constraining the unknown parameters with the
SMPS-measured particle size distribution, the model can predict SOA formation
for each experiment, accounting for wall losses. The predicted particle
wall-loss rates are both size- and time-dependent. The APE model predicts the
particle wall-loss rates by assuming specific functional forms of its
dependence on particle size (Crump and Seinfeld, 1981). The model has
performed well in experiments in which the reaction timescale was short, but
produced more uncertain results in experiments with slower reacting systems.
Nah et al. (2017) adopted a modified version of the APE model that calculates
the size-dependent wall-loss rate necessary to reproduce the observed size
distribution, assuming Brownian coagulation was the only other particle
process occurring in the chamber (i.e., no
condensation and/or evaporation occurred during the analyzed
portion of the experiment). The size-dependent, instantaneous particle loss
rates were calculated directly from the SMPS-measured seed number size
distribution at each time step. These instantaneous k(Dp) values
were then averaged over the initial seed loss period of the experiment (or a
separate experiment where k(Dp) was characterized). This determined
k(Dp) can then be applied to the SOA formation period of
experiments to correct for the size-dependent wall loss. This approach,
focusing on specific wall-loss characterization experiments, has the
advantage that the functional dependence of the wall-loss rate constant is
directly calculated from the measurements by simply removing the effect of
coagulation. Its disadvantage compared to APE is that it requires additional
time/experiments for seed measurements and can no longer address the
potential time dependence of k over the course of a complex experiment.
The aforementioned methods each have their own advantages and disadvantages,
and may perform well for specific experiments and chambers. However, for
long-lasting experiments such as SOA aging, whereby particle size
distribution may shift across a wide size range due to several generations of
condensation, it is important to address both the time and size dependence of
the particle loss rates for the purpose of SOA quantification. In this work,
we adopt the modified APE model following Nah et al. (2017) and derive the
size-dependent particle loss rate constants, k(Dp), based on seed
periods during the experiments. As an attempt to evaluate the time dependence
of the loss rates, we derive a second k(Dp) at the end of each
experiment with a second seed injection and loss characterization period. To
probe the effect of electrostatic forces on particle wall loss, we regularly
measured the k(Dp) during the time period when the chamber was
experiencing changes (e.g., changes in its surroundings, location or air
motion inside). We explore the coagulation effect on the estimated particle
wall-loss rates and particle number/volume concentration in both a
12 m3 Teflon chamber and a smaller 1.5 m3 Teflon reactor. We
evaluate the performance of the aforementioned particle wall-loss correction
methods for relatively complex aging experiments involving two or three
generations of condensation of the α-pinene ozonolysis
products.
Results and discussion
Role of coagulation in particle wall-loss processes
Figure 1 shows the apparent (ka) and coagulation-corrected
(kc) particle wall-loss rate constants as a function of particle
size for the 1.5 m3 Teflon reactor (Exp. 1) and the 12 m3 chamber
(Exp. 2) after both systems have remained “undisturbed” in the lab for
weeks. The particle loss rate constants for a given size can only be measured
reliably when there are enough particles of this size available in the
system. In the experiments shown, the produced ammonium sulfate particle size
distribution included few larger particles. As a result, the k values at
bigger particle sizes were quite uncertain and are not shown. Since the
differences in ka and kc are attributed to coagulation
according to the aerosol dynamics model, coagulation was a significant loss
process in Exp. 1 for particles with diameters smaller than 250 nm and for
particles smaller than 150 nm in Exp. 2. In Exp. 1, the apparent loss rate
constant for 100 nm particles was 0.5 h-1, while the actual rate
constant after correcting for coagulation was only 0.2 h-1. For 200 nm
particles, the corresponding values were 0.3 and 0.2 h-1 respectively.
Note that the initial particle number concentration in Exp. 1 was 1 order of
magnitude higher than that in Exp. 2 (Table 1), and thus coagulation played a
more prominent role in Exp. 1. The coagulation effects were minor for
particles larger than 250 nm in both cases. Once corrected for coagulation,
the particle wall-loss rate constants indicated little size dependence for
particles larger than 100 nm in both experiments. The corresponding values
were 0.25 h-1 for the small reactor and 0.1 h-1 for the
12 m3 chamber. The uncertainty of the aerosol dynamics model for the
larger particles is significantly higher than that of the direct calculation
based on Eq. (5) (for example at Dp=790 nm in Exp. 1 for the
1.5 m3 reactor, the uncertainty of kc is 50 %, while that
of ka is only 7 %). This is due to the reliance of the dynamics
model on observed small changes of small number concentrations versus the
linear regression fit of the logarithm of the measured particle concentration
in each size bin. We suggest using twice the uncertainty of the linear
regression as representative of the uncertainty of the rate loss constants of
particles larger than 300 nm given the small impact of coagulation on
particle concentrations and sizes in this range.
The SMPS-measured (black symbols) and the particle-loss-corrected
(a) number and (b) volume concentration using the
ka(Dp) profile (red symbols) and the
kc(Dp) profile (blue symbols) for Exp. 1.
To evaluate the coagulation effect on particle number and volume
concentrations, we corrected them for wall loss with both
ka(Dp) and kc(Dp). The results for the
1.5 m3 reactor (Exp. 1) are shown in Fig. 2 as an example. The measured
ka values were extremely uncertain due to a lack of particles at small
sizes, and thus they could not be used. To estimate the ka(Dp) values
at Dp<50 nm (and the kc(Dp) values at Dp<70 nm), we used a linear fit of the ka(Dp) values from 50 to
70 nm (and of the kc(Dp) values from 70 to 100 nm) to
back-extrapolate the ka values at smaller sizes; kc, being the
coagulation-corrected particle wall-loss rate constant, intrinsically
excludes the impact of coagulation on particle number concentration. Since
coagulation reduces particle number (but conserves mass),
kc-corrected particle number concentration is lower than the
ka-corrected one, with the difference attributed to the coagulation
rate. Coagulation caused the particle number concentration to decrease by
27 % over a 5 h period in this case, but had, as expected, a negligible
effect on particle volume concentration. Please note that even if the overall
effect of coagulation on total particle number is moderate, it is mostly
concentrated in the lower end of the size distribution. As a result, the
difference between ka and kc due to coagulation for
particles smaller than 100 nm is almost an order of magnitude higher than
the average difference. In this case there is little difference in the
calculated total volume concentration, which is the most important quantity
for SOA studies. However, this difference depends in general on the particle
size distribution. If a significant part of the volume (or mass) is in
particles with diameters less than 200 nm or so, the effect of coagulation
will be significant for the corrected particle volume too. Nah et al. (2017)
also studied the effect of coagulation on corrected SOA volume for the
α-pinene ozonolysis system, and found that coagulation plays a minor
role in experiments with an initial seed surface area of
<3000 µm2 cm-3, while in experiments with a high seed
surface area (>8000 µm2 cm-3), the SOA can be
substantially overestimated if one ignores coagulation.
The kc(Dp) profiles for the 12 m3 CMU Teflon chamber over a span of 3 years. The particle wall-loss rate constants were
derived based on SMPS measurements from experiments with only ammonium
sulfate particles.
Particle wall-loss rate constants in the CMU chamber over 3 years
Figure 3 shows the coagulation-corrected kc(Dp) profiles in
the 12 m3 CMU smog chamber over a span of 3 years (Fig. S1 shows the
uncertainty). All these measurements were performed during periods in which
the chamber was undisturbed. These four curves are representative of the
variability range of the size-dependent particle wall-loss rates in the CMU
chamber under undisturbed conditions. The rate constants show a monotonic
decreasing trend with sharp decrease initially until 100 nm due to diffusion
dominating the wall-loss processes. Then the loss rate constants gradually
decrease until 300 nm, after which they stay almost constant until the end
of the measured size range. Using the kc(Dp) determined in
2017 as an example, kc decreased from 0.3 h-1 at 50 nm, to
0.14 h-1 at 100 nm, then gradually to 0.05 h-1 at 300 nm, and
stayed constant until approximately 500 nm. The kc(Dp)
profiles over the past 3 years stayed fairly consistent, with values equal to
0.32±0.03 h-1 at 50 nm, 0.16±0.03 h-1 at 100 nm,
0.10±0.02 h-1 at 200 nm, and 0.07±0.01 h-1 at 300 nm.
The behavior of the chamber after disturbances (e.g., repairs, upgrades) will
be discussed in a subsequent section.
Applying different particle wall-loss correction methods to SOA aging
experiments
The measured particle volume concentration time series of a typical aging
experiment (Exp. 3) of α-pinene ozonolysis products in the
12 m3 CMU Teflon chamber is shown in Fig. 4. In this experiment there
were three separate stages. We injected ammonium sulfate seeds both at the
beginning (t=-4.5 h) of the experiment and at t=3.5 h. At t=0, ozone
was introduced into the chamber to react with α-pinene producing SOA
in the dark. HONO was bubbled into the chamber twice at t=0.5 and 1.2 h to
produce OH radicals under UV illumination, leading to a second round of
reactions in the system. The size-dependent ka(Dp) and
kc(Dp) derived from the initial 4.5 h seed loss period
differed by up to 0.2 h-1 for particles smaller than 100 nm and were
practically the same for particles
larger than 100 nm (Fig. 4c). The size-independent loss rate constants
k1–k3 were derived during the three periods when
condensation and/or evaporation was minimal (based on both SMPS and AMS
measurements). A value of k1=0.05 h-1 (R2=1) was derived from
volume concentration measurements from t=-4.5 to 0 h according to Eq. (2),
k2=0.04 h-1 (R2=0.8) from t=2 to 3.4 h, and
k3=0.03 h-1 (R2=0.9) from t=4.7 to 8.4 h. One major
contributor to the difference in these three k values is the size
dependence of the particle wall-loss rate constants; k2 was calculated
from the period after three rounds of condensation (α-pinene
ozonolysis and two doses of HONO). The particle size distribution shifted to
larger sizes (Fig. 4b) and thus resulted in a smaller value compared to
k1; k3 was derived from the final seed loss period when relatively
large seed particles were present due to the higher concentration of the
atomized ammonium sulfate solution.
(a) The SMPS-measured particle volume concentration time
series for an aging experiment (Exp. 3) with three colored periods used to derive
the corresponding size-independent particle wall-loss rate constants,
k1–k3 (Eq. 2). The grey area indicates that the chamber was dark.
The dashed lines mark the beginning and the end of bubbling HONO into the
chamber twice; (b) the averaged particle volume size distribution
over the three periods used to develop k1–k3 based on the SMPS
measurement for Exp. 3; (c) the size-dependent particle wall-loss
rate constants determined from SMPS-measured particle number concentration
from Period 1 for Exp. 3. Only ka values (red symbols) with an R2>0.5 are shown. The error bars correspond to ± 1 standard deviation.
The grey area is the uncertainty associated with kc (black
symbols).
The particle-wall-loss-corrected SOA mass concentration (ρ=1.4 µg m-3) time series based on SMPS measurements using
both the size-independent k1–k3 and the size-dependent
ka(Dp) and kc(Dp) for Exp. 3 are shown
in Fig. 5. Applying k1–k3 to Eq. (3) resulted in corrected SOA
mass concentration differing up to 20 %. To estimate the
k(Dp) values at Dp<50 nm and Dp>300 nm,
we used a linear fit of the k(Dp) values from 50 to 70 nm to
back-extrapolate the k values at smaller sizes and assumed a constant k
value equal to that at 300 nm for particles larger than 300 nm (Fig. S2).
This assumption is justified because kc remains relatively constant
throughout the 300–500 nm range based on our measurements (Fig. 3) when
the chamber is undisturbed and most of the SOA mass in this experiment was
distributed in particles smaller than 300 nm (Fig. 4b). We corrected for
total particle number concentration, applying the size-dependent loss rates
to Eq. (6), and then calculated the corrected SOA mass concentration using
Eq. (7). Note that the ka(Dp)- and the
kc(Dp)-corrected SOA mass concentration time series were
practically the same for this experiment because the majority of the formed
SOA mass condensed on particles with diameters exceeding 100 nm. If one is
interested in the total produced SOA after 3.5 h, the differences among the
results of the different corrections are 20 % or less. If one is
interested in the SOA produced during the aging phases the estimates vary by
25 %–30 %.
Depending on which wall-loss rate constants are used, the corrected SOA mass
concentration can vary by 20 %–30%. We recommend using the
size-dependent wall-loss rate constants for the correction. However, when the
chamber is undisturbed and the experiment only lasts a couple of hours, using
the size-independent wall-loss rate constant derived from the initial 4 h
seed wall-loss period can give relatively accurate results (errors of 5 %
or less).
Particle-loss-corrected SOA mass concentration
(ρSOA=1.4 µg m-3) time series based on SMPS
measurements using the size-independent k values (open symbols) and the
size-dependent k(Dp) values (solid symbols) for Exp. 3; k1–k3
were derived from the total mass concentration-based method (Eq. 2) when wall
loss was the only process (t1=-4.5–0 h; t2=2–3.4 h;
t3=4.7–8.4 h). The ka(Dp) and the
kc(Dp) profiles were derived from the two models based on
the SMPS-measured number concentration of the seed wall-loss periods. The
shaded area indicates that the chamber was dark. The dashed lines mark the
beginning and the end of bubbling HONO into the chamber.
Effect of size-dependent wall loss on organic to sulfate ratio
Figure 6 shows the AMS-measured organic to sulfate ratio (OA / sulfate)
for Exp. 4. In the beginning, the ratio increased to 0.8 at t=0.6 h due to
the first generation of SOA formation. It then stayed practically constant
until OH was introduced into the chamber at t=1.7 h. The second generation
of SOA formation led to an increase of the ratio to 1.0 at t=2.0 h. The
ratio decreased gradually to 0.9 at t=3.5 h. This decrease could be
explained as a loss of SOA due to photodegradation or other chemical
processes such as SOA evaporation driven by organic-vapor uptake by the walls
(Bian et al., 2015). Another explanation for the decreasing trend of
OA / sulfate during this period is the size dependence of the particle
wall-loss rates (Loza et al., 2012). Particles of smaller sizes with higher
organic to sulfate ratios can be lost to the walls at a faster rate, thus
causing the OA / sulfate to decrease during periods when wall loss is the
dominant process in the chamber. The strong size dependence of the
OA / sulfate ratio in this experiment is
indicated in Fig. 7. The organic mass distribution peaked at an aerodynamic
vacuum diameter equal to around 150 nm, while that of sulfate peaked at
210 nm. This indicates that the majority of the organic vapors condensed
onto smaller particles with a higher surface to volume ratio. Figure 7b shows
the OA / sulfate derived from the AMS-measured mass distribution
(averaged from t=2.1 to 3.5 h) as a function of the particle vacuum
aerodynamic diameter. For particles with Dva from 75 to 150 nm,
the ratio dropped dramatically from 7 to 1. It then decreased gradually and
stabilized at 0.4 from Dva=150 to 600 nm.
The organic to sulfate ratio time series derived from AMS
measurements for Exp. 4 (data after the second HONO introduction are not
shown). The inset is a blow-up of the OA / sulfate
ratio from its maximum until the second HONO
introduction. The black symbols are the size-dependence-corrected
OA / sulfate during that 1.5 h. The shaded area
indicates that the chamber was dark. The dashed lines mark the beginning and
the end of the first HONO injection into the chamber.
To further analyze the effect of size-dependent wall loss on
OA / sulfate, we adopted the method suggested in Wang et al. (2018). This
approach allows the estimation of mass-weighted wall-loss rate constants for
both species, k‾SO4 and k‾OA, by
discretizing the AMS-measured mass distribution in the diameter space and
assigning the corresponding kc(Dp). (An SOA density of
1.4 g cm-3 was used to convert the AMS-measured Dva to
Dp.) For periods during the experiment when particle wall loss is
the only process, the loss-corrected OA / sulfate can be estimated as
OA/sulfatem(t)expk‾SO4-k‾OAt, where OA/sulfatem(t) is the measured
OA / sulfate ratio. For Exp. 4 in this work, we
discretized the AMS-measured mass distribution (averaged from t=2.1 to
3.5 h) into 10 diameter bins and found
k‾OA=0.13 h-1 and
k‾SO4=0.11 h-1. The particle-wall-loss-corrected
OA / sulfate for the chosen time period is shown in the inset of Fig. 6.
The correction explains more than 70 % of the decrease in the
OA / sulfate (over 1.4 h) in this experiment, indicating that the
size-dependent particle wall-loss process coupled with the different size
distributions of the organics and sulfate played a major role in the observed
decrease in OA / sulfate.
(a) The AMS-measured organic (green) and sulfate (red) mass
distribution (Dva from 75 to 600 nm) for Exp. 4; (b) the
dependence of the AMS-derived organic to sulfate ratio on particle vacuum
aerodynamic diameter (75–600 nm). The results are based on particle
time-of-flight data averaged over ∼1.4 h (t=2.1–3.5 h).
Time dependence of particle wall-loss rates during an experiment
When the CMU chamber is undisturbed, the wall-loss rate constant is around
0.1 h-1 for particles larger than 100 nm. However, friction with the
Teflon walls induced by small repairs (addition of a sampling line,
replacement of lights, etc.) around the chamber can increase the loss rates
dramatically and the effects can last for weeks. During these periods, the
size-dependent coagulation-corrected particle wall-loss rate constants,
kc(Dp), can change significantly during the course of an
experiment. The results of such an experiment in a
“disturbed” smog chamber are
described below.
(a) The SMPS-measured (black symbols), the initial
kc(Dp)-corrected (red symbols), and the final
kc(Dp)-corrected (blue symbols) particle volume
concentration time series for an aging experiment (Exp. 5), together with
(b) the initial (red symbols) and the final (blue symbols)
kc(Dp) profiles. The colored areas are the uncertainties
associated with the corresponding kc(Dp). The grey area
indicates that the chamber was dark. The two dashed lines mark the beginning
and the end of HONO addition into the chamber. Ammonium sulfate seed
particles were injected into the chamber at t=4 h.
The comparison of the two kc(Dp) profiles derived from the
initial and the final seed periods for Exp. 5 is shown in Fig. 8, together
with the raw and the corrected aerosol volume concentration time series. This
is a similar aging experiment of α-pinene ozonolysis products to
Exp. 3, but with only one HONO injection. Before t=0, ammonium sulfate seed
particles were lost to the chamber walls. At t=0, ozone
was added into the chamber to react with α-pinene. The aerosol volume
increased due to condensation of the first-generation products. At
t=2.5 h, HONO was introduced into the chamber and OH radicals were
produced at t=3 h under UV illumination. The aerosol volume increased
again due to additional SOA formation from the second-generation oxidation.
At t=4 h, we injected ammonium sulfate particles into the chamber to
characterize the particle wall-loss rates for a second time. The final
kc values were statistically higher than their initial counterparts
at every size, and both sets of kc values were higher than their
usual values in the chamber (Fig. 3). Comparing the initial kc
values with the averaged usual values under undisturbed chamber conditions,
the initial kc was 0.33 h-1 as compared to the usual
0.16 h-1 at 100 nm, 0.21 h-1 compared to 0.10 h-1 at
200 nm, and 0.15 h-1 compared to 0.07 h-1 at 300 nm. The
particle wall-loss correction was applied when the ammonium sulfate seed was
first injected into the chamber (as depicted in Fig. 8a). The final
kc(Dp)-corrected volume concentration was higher than the
one corrected using the initial kc(Dp) by 37 % at
t=4 h. In this case, both kc profiles were representative of the
chamber condition, but during the corresponding time periods. The time
dependence of kc(Dp) during the course of this experiment
introduced an uncertainty of 40 % or so in the corrected aerosol mass or
volume concentration. The change in the loss rates suggests a change in the
electric field in the chamber during this experiment. This could be due to
additional charge buildup or redistribution of the charges as the experiment
progresses (lights are turned on and off, the chamber walls move due to the
air motion from the temperature control system, etc.).
The coagulation-corrected particle wall-loss rate constant,
kc, at each diameter derived from experiments with only ammonium
sulfate particles in the 12 m3 CMU Teflon chamber before and after some
major maintenance in the room where the chamber is suspended. The chamber was
partially deflated and its walls subjected to friction repeatedly during the
maintenance.
We define the chamber conditions under which these abnormally high loss rates
and exacerbated time dependence of kc(Dp) were observed as
disturbed. The kc(Dp) profiles shown in Fig. 3 were
under undisturbed chamber conditions. Since electrostatic forces start to
dominate the wall-loss process when particles are usually larger than 100 nm
(McMurry and Rader, 1985), we postulate that excess electrostatic forces
within the chamber are most likely the cause of the disturbed conditions.
Friction created with the Teflon walls was found to be a major contributor to
the exacerbated electrostatic forces and the disturbed chamber
conditions.
Figure 9 shows the kc(Dp) profiles measured over a span of
5 months after some major maintenance work (January 2016) in the room
where the chamber is suspended (Fig. S3 shows the uncertainty). During the
1-week maintenance, friction with Teflon walls was created by partially
deflating the chamber, moving, and touching it repeatedly. The measured
kc(Dp) profile changed drastically in shape for days after.
The 16-day post-maintenance kc(Dp) profile presented an
increasing trend from 75 to 300 nm, with particles bigger than 200 nm
getting lost at a rate 3–4 times faster than before. Once we noticed the
abnormally high particle loss rates in the chamber, we refrained from being
in any form of contact with the chamber walls. The chamber was left suspended
and full during those 5 months. Records of experiments performed in the
chamber during that time support the previous statements. About a month
later, the kc values recovered to the decreasing trend, but were in
general high as compared to their pre-maintenance counterparts with values >0.2 h-1 at 300 nm. A period of 3 months after the maintenance, particles
smaller than 100 nm recovered to their pre-maintenance values, while particles
bigger than 150 nm still had loss rate constants up to 0.1 h-1 higher
than before. A period of 5 months after, the kc(Dp) values made a full
recovery, with values decreased further to 0.13 h-1 at 100 nm,
0.09 h-1 at 200 nm, and 0.06 h-1 at 300 nm. During the 5 months,
the chamber was left fully inflated, stationary, and suspended in the room.
Only steps necessary for an experiment (overnight flushing, injection flow
etc.) were taken. Experiment 5 was performed a month after the major
maintenance when kc(Dp) was still in recovery, and thus the
exacerbated electrostatic forces within the chamber likely played a major
role in the extra time sensitivity of the kc(Dp) values in
Exp. 5.
To test whether certain steps during an experiment cause changes in the
particle loss rates, we explored potential impact on kc(Dp)
of turning on the UV lights, injecting HONO, and overnight flushing
individually in separate seed experiments. Experiments 5–8 were designed to
test each of these factors individually. These experiments were performed
about 3 months after the maintenance when particle wall-loss rates had almost
recovered to their pre-maintenance values, indicating the chamber had mostly
recovered to undisturbed conditions. Turning on the UV lights inside the room
where the chamber is suspended can cause changes in the air circulation
around the chamber walls, thus affecting the turbulence. Carrying HONO into
the chamber with a clean air flow at a rate of ∼5 L min-1 for
20 min may potentially impact the turbulence within the chamber. Though
cleaning the chamber with overnight flushing may not have a direct impact
on kc during the day of an experiment, flow
rates higher than 100 L min-1 into the chamber may well have an
effect. The results of Exp. 5–8 are shown in Fig. 10 and none of the
aforementioned processes had an evident impact on kc(Dp).
The t test results indicated that the kc(Dp) profiles
derived before and after each factor were statistically the same. We thus
conclude that the usual steps taken during a typical SOA aging experiment do
not have a significant impact on kc if the chamber is in its
undisturbed state. However, when the chamber has been disturbed and the
losses are already high, they also become sensitive to routine changes in the
experimental conditions.
The coagulation-corrected particle wall-loss rate constant,
kc, at each diameter for (a) Exp. 6, (b) Exp. 7,
and (c) Exp. 8 and 9. The uncertainties associated with the
corresponding kc(Dp) values are either expressed as the grey area
or the red error bars.
Teflon chamber maintenance and operating procedure of chamber
experiments
Routine seed experiments appear to be necessary for the quantification of the
particle loss rates in Teflon chambers. Any deviation in the particle
wall-loss rate constants from the usual values can be a sign of disturbed
chamber conditions, which may result in higher particle loss rates and time
sensitivity of kc(Dp) during an experiment. As discussed
above, friction with the chamber walls can introduce excess electrostatic
forces within the chamber and thus introduce significant uncertainty in the
particle loss rates. In order to maintain minimum particle loss in Teflon chambers, one should refrain from creating any type of friction with the
chamber walls such as touching the walls or having the walls rubbing against
each other. When transporting the chambers such as the dual-chamber system, it
is ideal to leave them full or at least half-filled with air and fixed onto a
rigid structure that can be packed during the transportation. This can
minimize potential friction and shorten the recovery time for the particle
loss rates. Other practices like using metal gloves when it is absolutely
necessary to touch the chamber can help reduce the buildup of static
electricity on the chamber walls.
When the chamber is in a disturbed state, the kc(Dp) values
can vary with time during the course of an experiment. It is thus vital in
these cases to include two seed loss periods, one at the beginning and the
other at the end, for each SOA experiment to characterize the
kc(Dp), especially if the chambers have recently been subjected
to friction. When performing SOA experiments in a Teflon chamber, we
recommend the following operating procedure:
Inject seeds and initially characterize kc(Dp)
for 3–4 h.
Perform necessary steps for the SOA experiment and wait until the mass
loadings in the chamber become low.
If the losses in step 1 are high, a second injection and another 3–4 h
of measurements for final kc(Dp) characterization are
necessary.
Conclusions
Particle number losses in chamber experiments due to coagulation can be
significant for small particles (<150 nm under conditions in this work).
It is thus important to correct for this coagulation effect when calculating
the particle wall-loss rate constants, especially for experiments in which the
behavior of the nanoparticles is important (e.g., when they carry a
significant fraction of the total particle mass).
The Teflon chamber used in this study appeared to operate in two different
states: an undisturbed and a disturbed one. The chamber entered the second
state after either major repairs or even after smaller changes (e.g.,
addition of a sampling line or replacement of a few lights), probably because
it was touched by the researchers or because friction was created during the
repairs, causing charge buildup. The disturbed state could last for several
weeks or even months. In this state the particle loss rates increased by more
than a factor of 3–4 and their size dependence became more pronounced. There
was significant variation of the losses from experiment to experiment and
even within the same experiment. In the undisturbed state, the loss rate
constant was less than 0.1 h-1 for particles larger than approximately
200 nm and was constant from experiment to experiment. Under these
conditions the cleaning of the chamber, turning on the chamber lights,
and injection of reactants, etc., did not have a statistically significant impact
on the loss rate constants.
The accuracy of the use of size-independent loss rate constants for the
correction of the experimental results depends on the state of the chamber
and the size distribution of the aerosol during the experiment. If the
aerosol volume is dominated by particles larger than 200 nm and the chamber
is undisturbed, the corresponding results can be quite accurate under
conditions in this work. However, if the chamber has been disturbed or if the
size distribution during some phase of the experiment includes a lot of
ultrafine particles, significant errors can be introduced.
The correction based on the OA / sulfate ratio
can also introduce uncertainties under at least some conditions. The SOA mass
distribution is usually shifted towards the smaller particles compared to
that of the sulfate seeds. As a result, the losses of sulfate can be
different than those of the organics. The sign and the magnitude of the error
depend on both the differences between the two size distributions and also
the size dependence of the losses in this specific experiment. A method to
correct the OA / sulfate ratio for these effects
has been developed. In one of the experiments, this explains most of the
apparent decreases of the ratio. The errors appear to be of the order of
20 % or less, but may lead to problematic conclusions about potential
processes taking place at longer timescales (e.g., photolysis and loss of
SOA). Corrections similar to the one used in this work (taking into account
the size-dependent losses and the size distributions of OA and sulfate)
should be performed in the case of a disturbed chamber as a safeguard against
higher errors.
Due to the above complexities, seed experiments for testing the particle loss
rates in Teflon chambers should be performed regularly, probably before each
experiment. If the rates are high, a second measurement of the losses should
be performed after the end of the experiment to constrain any potential
changes. The use of size-dependent corrections accounting for coagulation
effects is the preferred approach, even if in a number of experiments when the
chamber is undisturbed the errors introduced by neglecting the size
dependence or the role of coagulation could be small. However, this depends a
lot on the evolution of the aerosol volume distribution during the experiment
and especially on the importance of the particles smaller than 200 nm or so
for the objectives of the experiment.