Introduction
Organic aerosol (OA) comprises a significant fraction of submicron
atmospheric particulate mass, ranging from 20 to 90 %
. OA has been shown to have negative
impacts on human health e.g., and remains highly
uncertain in its effect on radiative climate forcing
e.g.,. The physical and chemical characteristics of
OA can vary dramatically and depend strongly on source, location,
atmospheric age, and other factors. Despite the ubiquity and importance of
OA, real-time measurements are technically challenging due to the wide range
of chemical composition, particle size, and volatility represented by OA in
the atmosphere.
The aerosol mass spectrometer (AMS, Aerodyne Research, Inc.) is used widely
in both ambient and laboratory measurements of OA. It has enabled significant
advances in our understanding of how organic aerosols form
, age , and mix
by providing real-time measurements of size-resolved
composition and mass for submicron, non-refractory particulate matter
(NR-PM1). However, a lingering challenge with full quantification of
NR-PM1 in the AMS is the mass collection efficiency (CEm;
), which is the ratio of the measured AMS mass
signal to the actual NR-PM1 mass concentration. Another factor influencing
calibration of the AMS mass response is the species-specific relative
ionization efficiency (RIE) of analyte; this is relative to a calibrant,
typically ammonium nitrate. However, an average RIE value is often applicable
to most organic species ; there can
be some variability in RIE for specific organic species. RIE is not believed
to be subject to matrix effects.
To obtain quantitative agreement between the AMS and other collocated
instruments in field campaigns, CEm is usually applied to correct for the consistently
lower AMS-measured mass. CEm can be written as
CEm=SAMSSO,
where SAMS is the signal from the AMS and SO is the signal of another (perfectly calibrated) instrument. Importantly,
this calculation assumes ideal operating conditions for both instruments and
the application of all other appropriate correction factors. For example,
(), in a sulfate
aerosol intercomparison study, applied a scaling factor of 2.41
(CEm = 0.42) to the AMS-measured sulfate to achieve good
agreement with a collocated particle-into-liquid sampler (PILS) instrument.
In another example,
() recently reported parameterizations of
CEm for ambient sulfate-containing particles that could be
used to predict CEm based on particle acidity and mass
fraction of ammonium nitrate. High CEm values were
consistent with predicted liquid phase state at 298 K. Thus, CEm should be thought of as a sensitivity factor that varies for particle
types with different compositions and phase states.
() define
CEm as the product of three size-dependent terms:
CEm(dva)=EL(dva)×ES(dva)×EB(dva),
where EL(dva) is the lens transmission
efficiency as a function of vacuum aerodynamic diameter, (dva); ES(dva) is the striking
efficiency of particles on the AMS vaporizer transmitted through the lens to
the time-of-flight chamber; and EB(dva)
is the vaporization efficiency of particles that strike the vaporizer
surface, also known as “bounce” because particles can bounce away from the
vaporizer surface and escape detection. Any particle that enters the
instrument but is not detected by the mass spectrometer due to any of these
three loss terms contributes to the mass discrepancy between the AMS and
another (perfectly calibrated) mass measurement.
Previous measurements and models have characterized the loss of particles in
the lens region and orifice, and have shown near-unit transmission
efficiencies for particles in the size range of 60–600 nm. However, for
particle populations whose distribution is significantly outside of the
transmission window, especially for larger particles, EL
can contribute significantly to CEm .
The striking efficiency is a measurement of the divergence of the particle
beam upon expansion into the particle time-of-flight (PToF) chamber. While
spherical particles can be narrowly focused on the 3.8 mm wide vaporizer over
the distance of the PToF chamber, non-spherical particles can diverge from
the focused beam, causing sub-unit values of ES
. Studies show this term accounts for very little
particle loss for ambient aerosol , as well as
laboratory SOA and (NH4)2SO4 .
ES can be well characterized by the use of a beamwidth
probe .
In the majority of cases, the largest uncertainty and largest contributor to
sub-unit CEm is the particle bounce term, EB. Particle bounce has long been known to confound particle measurements,
such as impactors and surface-desorption mass spectrometers
e.g.,. A limited number of studies have
investigated the nature and root cause of particle bounce for laboratory
aerosols in the AMS. ()
identified particle phase state as a controller of particle bounce for a
selection of laboratory organics, where particle phase state was inferred
from the room temperature properties of the bulk materials. Liquid particles
had near-unit CEm, while solid particles had much lower
efficiencies (CEm= 0.2 to 0.5).
() found phase state to
govern particle bounce as well. Ammonium nitrate particles, thought to be
metastable liquids at their experimental conditions
, displayed high CEm, while dry
ammonium sulfate particles had CEm = 0.22, which increased
to 0.73 when the particles were hydrated and deliquesced.
() also found that
CEm for dry ammonium sulfate increased as the thickness of
a liquid dioctyl sebacate coating layer increased. In some chamber
experiments, () showed
this same increase in CEm for dry ammonium sulfate
particles with condensation of an SOA layer. However, for other SOA
experiments, CEm for ammonium sulfate seed particles
actually decreased with the condensation of SOA, implying that the SOA
phase state was highly variable in their experiments and/or that other
factors also govern particle bounce in the AMS, such as composition or
volatility. Similarly,
() showed that CE of liquid squalane (CE ∼ 1)
particles decreased following SOA condensation.
() report an inverse relationship between
CEm for chamber-generated SOA and the f44/f57 ratio
(where m/z 44 is comprised of CO2+, while m/z 57 is
comprised of the less oxidized marker fragments C4H9+ and
C3H5O+, and fi is the fraction of m/zi to the
total organic signal). This implicates oxidation state as either a factor
influencing CEm or a proxy variable for what makes a
particle bouncy.
An even smaller number of studies have used the light-scattering
single-particle (LSSP) module of the AMS to investigate collection
efficiency, despite its ability to provide a real-time, particle-number-based
measurement of EB. When ES and
EL ∼ 1, collection efficiency is equal to the bounce
efficiency (CE ∼ EB). We denote this number-based
collection efficiency as CEp for “particle collection
efficiency”, which is defined as
CEp=Particles with ion
signal above thresholdAll particles detected by LS.
() first introduced LSSP as
a method to resolve real-time densities of externally mixed aerosols.
() later described the
ability of LSSP to measure CEp for ambient particles from
Mexico City and found that a significant fraction of the optically detected
particles were either undetected by the mass spectrometer due to bounce
(hereto referred to as “null”) or exhibited signal at a time much later than
would be expected based on their in situ measured velocity (referred to
as “delayed”). “Prompt” particles, those that gave an appreciable chemical
ion signal when they were expected to do so, made up only 23 % of the measured
aerosol, with the delayed fraction at 0.26 and the null fraction at 0.51.
() also report
CEp for ambient measurements taken in Bakersfield, CA
(Cal-Nex). They report a 0.46 prompt fraction, 0.06 delayed, and 0.48 null,
and found a slight size dependence in the campaign-average CEp, which exhibited a maximum around dva = 600 nm (0.52)
and a minimum (0.42) for large particles.
() compared CEm (density-corrected
scanning mobility particle sizer (SMPS)–AMS comparison) and CEp for an ambient biogenic SOA
event, and found them to be equal.
Here we further explore the use of LSSP to identify the nature of collection
efficiency for lab- and chamber-generated aerosols. We quantify particle
bounce for SOA from α-pinene ozonolysis, as well as ammonium nitrate,
ammonium sulfate, and d62-squalane. We illustrate the difference between
mass-based and number-based CE, which are not necessarily the same even for
monodisperse aerosol, due both to decreasing effective ionization
efficiencies for delayed particles (defined as ions per particle, or IPP) and
mass that registers at the detector on timescales much longer than the
chopper cycle. We show that IPP decreases with delay time, that
CEp is not a function of size for the SOA in this study,
and that low-volatility and/or high oxidation state decreases
CEp for SOA.
Experimental setup for SOA CE experiments.
Ensemble mass distributions from SMPS (black trace, adjusted for density) and AMS (green trace)
of size-selected α-pinene-derived SOA particles with 370 nm mobility diameter for an example SOA experiment.
Frequency of optically counted particles (from LSSP) as a function of size shown in blue. For this instance,
CEm = 0.39. The blue trace is normalized to 1 and plotted on the right axis so as to have the
same height as the SMPS trace, reflecting that optical detection in the AMS flight chamber is not affected by particle bounce.
Methods
Particle generation and sampling
We prepared inorganic aerosols (ammonium nitrate, ammonium sulfate) by
atomizing dilute solutions (1 g L-1) using a constant output nebulizer (aerosol
generator model 3076; TSI, Inc.). We sent these particles through a krypton
neutralizer (10 mCi) and then size-selected them using a differential mobility
analyzer (DMA; classifier model 3080; TSI, Inc.) before sampling.
We sampled size-selected SOA in this same manner, but with a different
preparation procedure. We injected a 1.2 µL aliquot of α-pinene
(Sigma Aldrich, > 99%) into a clean and dry (RH < 3 %) 100 L Tedlar
sample bag (SKC, Inc.) at an estimated mixing ratio of ∼ 2 ppm and
charged the bag with excess ozone. This SOA formed at a high concentration
(COA ≈ 1500 µg m-3). This allowed us to study
homogeneously nucleated SOA with the single-particle capability of the AMS,
as the scattering laser requires large (dva ≥ 180 nm)
particles. However, the composition of SOA is loading-dependent, as
demonstrated by (),
and so preparing SOA at this necessarily high concentration is a potential
limitation of this work, as is often the case for laboratory studies of SOA
systems. See Fig. for the general experimental
schematic.
We produced d62-squalane aerosols directly in the 12 m3 Carnegie
Mellon University smog chamber, described elsewhere in greater detail
e.g.,. In brief, we flushed the smog chamber
continuously for > 12 h with clean, dry air (cleaned with HEPA, silica-gel,
and activated-carbon filters in series) to ensure low background particle,
organic vapor, and water vapor concentrations. We prepared d62-squalane
particles by an evaporation–condensation process within the smog chamber,
using a small, resistive stainless-steel heater in situ. We placed a
small aliquot of d62-squalane (0.75 µL) on the heater surface, which
we then inserted into the smog chamber. Clean dispersion air flowed over the
the heater to carry and mix the d62-squalane vapor plume into the
chamber while we power-cycled the heater for 10 min. Pure
d62-squalane particles formed as the squalane vapor plume cooled.
We measured ensemble particle volume and number concentrations using a
SMPS (TSI, Inc.). We measured ensemble
composition and mass with the high-resolution time-of-flight aerosol mass spectrometer
(HR-ToF-AMS; Aerodyne, Inc,) operated in single-reflectron V-mode, fully
described by (). We
acquired single-particle mass spectra using the LSSP module coupled to the HR-ToF-AMS. We analyzed
single-particle AMS data using Sparrow 1.04D
and ensemble AMS composition data using SQUIRREL 1.51.
Thermodenuder experiments were conducted using the Aerodyne thermodenuder
(TD), based on the design of and fully detailed therein. Briefly, the TD
consists of a heating section followed by a cooling diffusion denuder section
to prevent recondensation. The TD temperature is controlled by
proportional–integral–derivative (PID)
controllers that adjust the power of three different strips of heating tape
according to temperature measurements from three thermocouples inserted into
the center of the TD tube. The ramp program increased temperature linearly
over 1 h from 27 to 130 ∘C, soaked at 130 ∘C for 10 min, and then returned back to 27 ∘C at the
same rate. Residence time in the heating section (calculated based on plug
flow through the TD tube at ambient temperature) was ∼ 30 s, which was
determined by the combined flow rates of the AMS and SMPS.
Operation of light-scattering module
The LSSP module has been described in detail elsewhere in the literature
. Briefly, the LSSP module consists of a continuous-wave
laser (405 nm, 50 mW; LC BCL-050-405; CrystaLaser) that crosses the
collimated particle beam within the time-of-flight region of the AMS.
Scattered light from sampled particles is collected by an ellipsoidal mirror
that focuses the light onto a photomultiplier tube. This light-scattering
signal constrains the particle's velocity between the opening of the AMS
chopper and the laser, allowing for the calculation of the vacuum aerodynamic
diameter. It also prompts collection of individual mass spectra over the
entire chopper cycle (e.g., 200 spectra/chopper), allowing for the
identification of signals from individual particles within the full chopper
cycle. Saving at this data rate without the laser triggering (meaning all
chopper cycles, not just ones containing particles) is not practically
useful, as it results in an unmanageable data load. For example, when
() collected ToF-AMS
single-particle data without any triggering mechanism, of the 2.41 GB of data
they collected, only 4 MB represented meaningful single-particle spectra
after applying their thresholding algorithm. The LSSP enables continuous
single-particle detection at a high duty cycle for the long timescales of
chamber studies or ambient sampling.
For data processing, we used an operationally defined light-scattering
threshold of five (signal-to-noise ratio, SNR) to identify particle events and a
mass threshold of six ions to identify a detected particle to be further
considered for particle classification, similar to
(). For ammonium sulfate, ammonium nitrate, and SOA, we
used Sparrow's default ion list (m/z 15, 30, 35, 36, 41, 43, 46, 48,
55, 57, 64, 71, 73, 80, 81, 98) for identifying particle events in the mass
spectra of each chopper cycle. We used a different list of deuterated ions
(m/z 48, 50, 66, 82, 98) to identify MS events for d62-squalane
particles. We processed a subset of SOA experiments with an adjusted ion list
based on the 13 highest-signal ions for SOA that do not have significant
background interferences identified with MS mode spectra (m/z 15, 26,
27, 29, 41, 42, 43, 44, 53, 55, 65, 67, 69, 79), but our collection
efficiency results were not sensitive to this change.
At the number concentrations of the high-COA SOA experiments, coincident
particles – multiple particles sampled in a single chopper cycle – were
present (13 % of particles were coincident). We identified and filtered out
coincident particles (identified by multiple instances of threshold crossing,
where the threshold was equal to 5SNR of the scattered light trace) using the
Sparrow analysis program. Coincident particles were not considered in our
analysis or calculation of CEp. It should be noted that, in
the case where two or more particles enter the same chopper cycle and are
also the exact same size, the LS data analysis program fails at identifying
coincidence. However, even at the highest number concentrations used in these
experiments, we estimate the probability of failing to identify these
particles as coincident to be 0.4 %. Thus, we do not expect these false
negatives to effect the results stated in this paper. For low number
concentrations, e.g., those of typical smog chamber experiments, the chance of
failing to identify coincidence is essentially zero.
Calculation of collection efficiency
We classified individual particle events based on how they interacted with
the vaporizer, in terms of both their effective ionization efficiency and
vaporization quickness. Null particles are those that do not exceed the six-ion
mass threshold, despite being identified as a particle event by exceeding
the light-scattering threshold. As defined in
(), particles categorized as prompt arrive at the
mass detector within a narrow time range after they would be expected to
arrive based on their measured velocity in the PToF region and assuming
instantaneous vaporization/ionization. The operationally defined boundary
between the prompt and delayed particles is when the actual arrival of the
mass signal is greater than the expected arrival time by 20 % or more. In
other words, we compared the measured arrival time at the detector
(MS arrival) and the LS-estimated arrival time (LS arrival) based on the measured velocity between the chopper and laser to
draw the boundary between prompt particles (MS arrival/LS arrival < 1.2) and delayed particles (MS arrival/LS arrival > 1.2).
As we shall show, this particular
value for determining the boundary between prompt and delayed particles is
arbitrary.
LSSP provides an internal number-based measure of the AMS collection
efficiency . The wide laser beam (≈ 2 mm),
relative to the width of the particle beam (≈ 0.5 mm), allows for
near-complete optical detection of particles above the detection limit of the
laser (dva > 180 nm). The LSSP-based
CEp is the comparison between the optically detected
particles (i.e., all particles that enter the PToF region and that will hit
the vaporizer surface) and the number of particles that are
chemically detected (i.e., give signal in the mass spectrometer). For all
particles sampled here, ES and EL are
reasonably assumed to be 1. Thus, in terms of the categories prompt, delayed,
and null, the general definition of CEp from Eq. () can re-written as
CEp=Nprompt+NdelayedNprompt+Ndelayed+Nnull,
where, e.g., Nprompt is the number of prompt particles. In
this formulation, we consider both prompt and delayed particles as those that
give meaningful chemical signals at the detector, though it may be of
interest in other studies to look at the CEp from, e.g., only
prompt particles. We are equating CEp with EB, a reasonable assumption for the aerosols studied here as they all fall
within the lens transmission window (EL = 1) and are
spherical and therefore do not exhibit significant
divergence from the particle beam (ES=1). However, it is
important to note this collection efficiency accounts only for whether or not
a particle was observed in the mass spectrometer and does not account at all
for signal strength above the detection threshold. Additionally, while null
particles do not count as meaningful in the LSSP determination of collection
efficiency, the sum of many very weak signals from many null particles can
result in meaningful mass in the more typical bulk measurement mode (MS mode)
of the AMS.
Results and discussion
Delayed vaporization PToF artifact
It is standard practice to present comparisons between the mass-weighted size
distribution from the SMPS and the PToF mass
distribution from the AMS to compute density and collection efficiency
. The SMPS
size distribution is multiplied by the density to align the mode diameters
according to
dva=ρpρ0dveχ,
where ρp is particle density; ρ0 is standard density (1 g cm-3);
and χ is the dynamic shape factor, which is equal to 1 for
spherical particles and is assumed to be true in the case of SOA from
α-pinene ozonolysis . For spherical particles,
dve, the volume-equivalent diameter, is equal to mobility diameter.
For this example experiment, where 370 nm SOA particles were size-selected
using a DMA, shown in Fig. , we estimate the density to
be 1.1 g cm-3 from aligning the mode diameters of the SMPS-calculated
mass distribution with that from the AMS mass distribution measured in PToF
mode. The shaded blue area is the frequency of optically counted particles as
a function of size, as measured by light scattering in the AMS. Like the SMPS
distribution, this histogram is tight, as we expect it to be for
size-selected particles. However, even after shifting the SMPS distribution
by the density, the agreement between the SMPS and AMS PToF distributions
degrades considerably at large diameters.
Illustration of LS and MS signals for a typical delayed single particle in AMS. Scattered light signal (teal)
and mass spectrometer signal (orange) as a function of particle time of flight for an example delayed particle. The particle
velocity (vLS) is calculated by the measured time between the start of the chopper cycle (point A)
and detection of the scattered light peak (B). The velocity is used to estimate an expected arrival time of the chemical
ion signal at the mass spectrometer (C) assuming prompt evaporation and ionization of the particle at the vaporizer. The
difference between the expected (C) and actual (D) arrival times is denoted by δ and allows for the operational
definition of prompt and delayed particle events.
We explore the nature of the divergence between the AMS PToF mass
distribution and the SMPS-derived mass distribution at large apparent
diameters using data from LSSP mode. We show the flight path, and resulting
data, for a particle in the LSSP AMS in Fig. (similar to
Fig. 7 in ). The scattered light pulse (teal trace)
triggers acquisition of mass spectra over the entire chopper cycle.
Individual extractions from the mass spectrometer, which are averaged
together over tens of seconds to minutes in typical bulk mode operation, are
resolved at ∼ 30 µs (the ToF-MS pulser period) in single-particle
mode (orange trace). Using the distance between the chopper and the point of
intersection between the laser and particle beams, a flight velocity is
calculated and used to predict the arrival of the particle's ions at the mass
detector, assuming instantaneous vaporization and ionization. We show the
mass signal as a function of time of flight for the chopper cycle in orange.
For some particles, the arrival of the ions at the detector is significantly
offset (delayed) from the predicted arrival time. This offset (labeled
“δ” in Fig. ) is used to categorize particles into
prompt and delayed categories, further discussed in Sect. .
Figure shows total ion signals from individual
particles (gray circles) along with total summed signals of prompt (blue) and
delayed (red) particles as a function of time of flight. We see that the
large-diameter PToF tail (green) collected in AMS PToF mode matches the
delayed particle distribution. Additionally, none of the prompt particles
have measured times-of-flight greater than 4 ms. As described in
() for ambient OOA measured
in Mexico City, the physical basis for the broadened PToF distribution at
large diameters is particles with delayed vaporization, which comprise a
significant fraction of the measured single particles in this SOA experiment
(19 % of all particles).
However, the mechanism of the delayed vaporization has not yet been fully described for SOA from α-pinene + O3.
Size distribution comparison between bulk PToF and prompt and delayed single particles.
Particle signal versus particle time of flight from the chopper to the mass detector is shown for
ensemble mode (green trace) and for all detected single-particle events (gray circles) in a
representative SOA experiment (DMA size-selecting SOA particles with mobility diameter = 370 nm).
Particles are sorted into either prompt or delayed categories based on their delay time. The mass
signals for individual particles within each category are binned by flight time and summed to create
the prompt (blue trace) and delayed (red trace) distributions.
Collection efficiency
The average CEp across all SOA experiments was 0.30
(±0.04), while the average CEm was 0.49 (±0.07). We
calculated CEm using Eq. (), where
SAMS is the AMS-measured mass from MS mode and
SO is the density-corrected SMPS-measured mass. Like
(), we see that
CEm > CEp, which likely
reflects two differences between the mass-based and particle-based collection
efficiencies. First, by definition, null particles in LSSP mode, those which
do not register mass above the six-ion threshold, provide no chemical
information. LSSP can only tell us that these particles bounced away from the
vaporizer. However, there are examples (e.g., ) where
particulate mass is detected by the AMS on very long timescales (5 s)
compared to the length of the chopper cycle window (5 ms). While a particle
defined as null provides no chemical information whatsoever in LSSP mode,
it is likely that not all null particles are created equal: some bounce away
from the vaporization/ionization region altogether and are not measured at
all, while some bounce from the vaporizer cone but still do evaporate at very
long timescales relative to the chopper cycle. Evidently, the sum of some
number of these particles from the null category does result in detectable mass
on timescales longer than the chopper cycle, as evinced by CEm being significantly greater than CEp.
Secondly, some particles that would register mass above the LSSP threshold
may be delayed such that their mass signal registers at the detector just
beyond the chopper cycle. As depicted in Fig. , the
delay times for some particles are just beyond the chopper cycle window that
we used for these experiments, as there are still mass signals arriving at
the very right edge of the plot where the cycle ends. It should be noted that
the length of the chopper cycle used in these experiments was not optimal
and that a longer cycle would allow us to see the most-delayed particles. We
recommend a long chopper cycle for ambient measurements and/or any
experiments where delayed particles may be expected. For aerosol types with a
high delayed fraction like this SOA, a longer chopper cycle would better
accommodate these particles with long (2 ms) delay times. Thus, while LSSP
provides an in situ measurement of the AMS collection efficiency,
it is important to distinguish between the LSSP-based (Eq. )
and mass-based (Eq. ) calculations of collection efficiency.
Delayed particle signal strengths
Histogram of ions per particle for null, delayed, and prompt SOA particles. Histogram of ions per particle
for null (gray), delayed (red), and prompt (blue) particle categories for 370 nm SOA particles. The y axis is the
frequency of single-particle events within each category (in other words, all data for each category sum to 1).
The histogram bars for the delayed category are offset (by five ions) on the x axis for clarity.
Despite nearly equal numbers of prompt (17 % of all particles) and delayed
particles (19 % of all particles) for this SOA, these two particle categories
do not contribute equal mass signal to the detector. As shown in Fig. , prompt particles produce significantly more signal
per particle than delayed particles even though they are all the same nominal
size. We plot in Fig. a histogram of IPP, normalized so that the sum of the bins for each category is
1. This figure shows that the effective ionization efficiency for prompt
particles is higher than that of delayed particles. Note that this
“effective” ionization efficiency is not only a function of the ionization
efficiency of the molecules being ionized by the 70 eV source (a molecular
property), but it also convolves the instrument sensitivity to particles that
may be vaporized in a sub-optimal location (for ion formation and
extraction). If delayed and prompt particles had the same IPP, the delayed
vaporization tail in the AMS mass distribution for SOA shown in Fig. would be even more pronounced.
The single-particle mass signal (IPP) is a smooth function within both the
prompt and delayed categories, possibly providing reason to redefine what it
means to be prompt vs. delayed. Figure shows a
steady decrease in the average IPP as a function of delay time for delays
shorter than 1 ms. For delay times longer than 1 ms, the IPP is constant with
delay time. The error bars represent the standard error of the mean within
each bin, while the gray shadow shows the standard deviation for each bin
reflecting the inherent spread of single-particle mass signals. For
comparison, we include on the plot the average IPP value across all prompt
and delayed particles, which is very similar to its calculated value based on
the calibration IE and a RIE value for organics of 1.4.
Average ions per particle as a function of delay time for SOA. Ions per particle as a function of the delay time
between the expected time of arrival for the chemical ion signal and the actual time of arrival for 370 nm SOA particles.
The data points are the arithmetic mean IPP value for a given delay time bin. Error bars are the standard error of the
mean for each bin, which represent the precision of the average IPP values. The gray shadow behind is the standard
deviation of ions per particle within each bin, which reflects the inherent spread of single-particle signals at a
given delay time for monodisperse SOA. Dotted line shows the average IPP for the entire ensemble, while the solid
line shows the calculated IPP based on an ionization efficiency (IE) of 5e-7 and an RIE of 1.4 for organics
compared to ammonium nitrate. We also estimate the nominal distance bounced (top x axis) for these particles,
assuming completely elastic collisions with the vaporizer and surrounding surfaces and the average velocity
of the size-selected particles measured between the chopper and laser.
Cumulative particle counts as a function of delay time for SOA, NH4NO3, (NH4)2SO4, d62-squalane, and SOA.
(a) Cumulative probability distributions of particle counts as a function of delay time for ammonium nitrate (blue), d62-squalane (purple),
ammonium sulfate (red), and SOA (green).
All traces are normalized by the respective CEp values, which are the average value across all experiments for that particle type.
(b) Cumulative probability distributions for single-particle counts (dark green) and single-particle mass (light green) for an individual SOA
experiment. The dark green trace is scaled by CEp.
The light green trace, up to 2.5 ms delay time, is scaled by the mass collection efficiency as determined by comparing the AMS-PToF-determined
mass to the SMPS mass (denoted CEm'), according to Eq. ().
The broken axis represents additional mass seen beyond the window of the chopper cycle, and that mass is scaled according to the mass collection
efficiency determined by the AMS mass seen in MS mode compared to the SMPS mass (CEm).
Mass spectral signal profiles for two (prompt and delayed) PToF arrival times for SOA.
Profiles of single-particle mass arrival for SOA (a) and ammonium nitrate particles (b) under different
vaporization scenarios. (a) Plot shows total chemical ion detection as a function of time from arrival of
maximum signal for SOA. The traces represent the average signal for all particles with the same MS arrival
time. The two arrival time bins shown correspond to either all prompt (PToF bin = 3.21 ms, blue trace)
or delayed (PToF bin = 4.05 ms,
red trace) particles. N is the number of particles used to make the
average trace. (b) Similarly, average chemical signals as a function of arrival time are shown for
ammonium nitrate particles at two different vaporizer temperatures. The arrival of mass at the
detector (event length) is significantly longer for ammonium nitrate at 200 compared to 600 ∘C.
It should be noted here that, as is done in most analysis of AMS data,
converting from the nitrate-equivalent mass to the absolute mass measurement
for a given non-refractory species (e.g., organics, sulfate, chloride)
requires the application of species-specific values of both CE and
RIE (see, e.g., Eqs. 3.8 and 3.9 in ,
). Thus, any measurement of CE also has inherent
value regarding understanding RIE for a given species. Figure
illustrates this, as the measured average IPP for all particles matches the
calculated value. However, clearly the least and most delayed particles have
IPPs much different than the average, and thus particle bounce and the
associated loss of signal significantly affect IPP for a given particle.
Measurements of RIE for various species using the AMS – as have been reported
by, e.g., , , and
– are only possible when CE for the sampled aerosol particles is well known.
Given that LSSP measures CE inherently, easier and more routine measurements
of species-specific RIE values, especially in ambient datasets, should be
made possible with application of the LSSP module.
Plotting the accumulated particle counts as a function of delay time shows
how single-particle information from LSSP mode can be used to best understand
the response of the AMS to different particle types, each with its own
sensitivity in the instrument (Fig. ). We scale the
traces in Fig. a by their measured CEp values (from Eq. ). The effect of delay time on IPP
is absent for ammonium nitrate, the standard mass calibrant for the AMS,
because all particles arrive within the first few delay time bins.
D62-squalane, a liquid at room temperatures with a near-unit
CEp, largely accumulates its signal at small delay times as
well but is slightly slower to do so than ammonium nitrate. We speculate
that this difference may be attributable both to the lower volatility of
d62-squalane and to the larger molecular weight of d62-squalane
(423 g mol-1) compared to ammonium nitrate (80 g mol-1). We estimate the
d62-squalane vapor pressure using SIMPOL and use
the ammonium nitrate vapor pressure reported by
(): ammonium
nitrate is more volatile than d62-squalane (∼ 30
and ∼ 0.1 µg m-3, respectively).
() calculated the differences in evaporation
timescales in the AMS vaporizer for species of different volatility, while
() discussed the molecular
weight dependence on the movement of ions from the ion source to the ion
optics region in a free molecular regime. Unlike both ammonium nitrate and
d62-squalane, which accumulate signal at very short delay times despite
their differences, SOA exhibits substantial delayed vaporization and low
CEp. The SOA in this study behaves similarly to crystalline
ammonium sulfate, a possible indication of a solid or semi-solid phase state,
extremely low-volatility material, or both.
Figure b shows how the total mass signal from single SOA
particles accumulates faster than the particle counts as a function of delay
time, as particles with low delay times contribute relatively more mass
signal on average. The accumulation of single-particle counts is scaled by
CEp, while the single-particle mass accumulation trace is
scaled by CEm. We use CE'm to
denote the mass collection efficiency calculated by comparing the AMS PToF
vs. SMPS mass, and CEm to denote the mass collection efficiency
calculated according to Eq. (). The difference between
CE'm and CEm is the amount of
mass measured at timescales longer than the chopper cycle.
Nature of particle–vaporizer interactions
These results seem to indicate that, when an aerosol type exhibits bounce, it
also exhibits delayed vaporization and thus lower effective ionization
efficiency for some fraction of particles. In investigating the offset
between expected and actual arrival times, we tested two ideas about how the
signal at the mass detector would arrive for SOA within the LSSP chopper
cycle. If an SOA particle strikes and adheres to the vaporizer surface but
does not promptly vaporize, it should show an accumulation of mass at the
detector over time, beginning at the expected arrival time. However, if the
particle bounces off the vaporizer without any significant evaporation and
somehow returns to a hot surface at a later time, then the time-resolved
arrival of ions should look similar to a prompt particle that vaporizes upon
impact, albeit after some time associated with its bouncy journey.
Indeed, when the mass arrival signals for an ensemble of single-particle
events are averaged together, we see that prompt and delayed SOA particles
have the same peak shape (Fig. a). Here, we display
the average single-particle mass signal for particles with the same arrival
time. We chose two arrival-time bins with times-of-flight equal to 3.21
and 4.05 ms. All particles in each bin are categorized as prompt and
delayed, respectively. The similar, sharp peak shape suggests that delayed
particles are truly delayed in starting their vaporization process and not
simply evaporating at a slower rate.
() present the vaporization “event length”
quantity, which is the full width at half maximum (FWHM) of mass arrival
signals from individual particles. In our study, the time resolution of the
mass arrival trace (determined by the pulser period, 30 µs) is on the
same order as the event length, which does not allow us to quantify the event
length with any precision. It should be noted that the pulser period used in
this work is a limitation; a faster pulser
period should be used for future
similar work, as it would allow for proper quantification of the event
length. However, qualitatively we can say that prompt and delayed particles
for the SOA presented here have similar event lengths and are on the order
of ∼ 30 µs, similar to those measured by
() for ammonium sulfate aerosol. We found nearly
identical event lengths for prompt versus delayed ammonium sulfate as well,
indicating that ammonium sulfate exhibits the same behavior of “flash
vaporizing” even when the particles are delayed. The event length for
ammonium nitrate aerosol at low vaporizer temperatures, however, is
fundamentally different (see Fig. b); mass arrives
over a much longer timescale (1 ms), indicating that particles are sticking
to the vaporizer and slowly losing mass. Thus we conclude that delayed SOA,
as well as ammonium sulfate, particles are bouncing around the ionization
cage after initially striking the front of the vaporizer before they finally
land and flash-vaporize on one of the hot surfaces in the vaporization region
(e.g., side of the vaporizer, ionization cage). Our conclusion is the same as
that of (), who identified
this mechanism acting on delayed particles in ambient measurements in Mexico
City.
The AMS vaporizer is a cylindrical tube furnace (r = 3.81; l = 20 mm)
with a concave beveled cone (60∘ included angle) serving as the stop
for the particle beam. It is centered within an ionization cage, a
rectangular stainless-steel housing (h = 6; w = 8; l = 15 mm)
which is open on each end. The front end of the vaporizer is set back
∼ 10 mm from the front opening of the ion cage and ∼ 2 mm from the
ion extraction volume, maximizing the intersection of the vaporized particle
plume, the electron beam from the filament, and the axis of ion extraction.
Because of the long hot surface of the vaporizer, which is housed inside a
sheet metal cage, this mechanistic picture of particles bouncing around this
region before finally landing on a hot surface is plausible. Importantly, for
this SOA, the actual vaporization of the particle still can be thought of as
rapid – when the particle finally does stick, it is vaporized and ionized on
the same timescale as a prompt particle. Thus, the “PToF broadening”
shown in Fig. can be attributed to SOA particles
bouncing around before vaporizing, not slowly boiling off adsorbed material
over time, as discussed in
() for lead salts (e.g., PbCl+) and in
() for sea salt and
other semi-refractory components (e.g., ZnI2). Furthermore, this
explanation is consistent with the decrease in IPP as a function of delay
time: when particles vaporize on, e.g., the side of the vaporizer, they are in
a sub-optimal position for ionization of the resulting vapor plume and thus
detection of the full single-particle mass . From
Fig. , the decrease in IPP with delay times up to 1 ms
indicates an increasingly sub-optimal average vaporization location for the
particle with respect to the ionization region. For long delay times
(> 1 ms), the likelihood of the particle landing near the
ionization region becomes very low, but further delay does not influence the
effective ionization efficiency. As indicated by a wide spread of IPP values
for a given delay time in Fig. , it is very unlikely
that a long-delayed particle can provide as many ions to the mass detector
as the average prompt particle can.
In Fig. we also show a secondary x axis of distance
bounced based on the nominal particle velocity. This is the distance traveled
after the initial particle impact on the vaporizer, assuming elastic
scattering as the particle bounces. Work by
() and
() shows that there are velocity losses for
submicron particle collisions with surfaces, though the range of velocity
loss appears to be a complex function of particle composition, incident angle
of the collision, and other factors. Thus, while our assumption of completely
elastic collisions is flawed, it does provide a back-of-the-envelope measure
of the upper limit of distance bounced by these particles. This inferred
distance is much longer than the length scales of the vaporizer and
ionization cage. We thus conclude that the particles are probably literally
bouncing randomly around the ionization region, impelled by surfaces that are
rough at the length scale of the particles. The top x axis of Fig. shows our upper-limit estimate for the nominal distance
bounced for these 370 nm particles. We used the average measured velocity of
the prompt particles, as measured between the chopper and laser. When comparing
the length scales (∼ 1 cm) of the ionization cage and vaporizer with our
estimated distance bounced based on delay times, the most delayed particles
experience many collisions with ionizer/vaporizer surfaces before
finally vaporizing.
As a further check that the SOA particles measured in LSSP mode are rapidly
vaporizing – just simply doing so at a time later than would be expected
based on their measured size and expected time of flight – we increased the
temperature of the vaporizer from 600 to 800 ∘C. Were
the particles sitting on the vaporizer surface and slowly boiling, we would
expect this temperature increase to decrease the broadened PToF tail (Fig. a). We do not see this effect (note: the degradation in
the organic PToF signal at 800 ∘C is due to low particle numbers at
the end of our experiment due to wall loss). However, when we coated SOA
particles with d62-squalane, a liquid at STP and a material that
exhibits essentially no particle bounce in the AMS (CEp
∼ 1), the broadened tail of the SOA mass distribution diminished, as shown in Fig. 9c. When
we heated the chamber, causing the d62-squalane to evaporate, the
broadened tail reappeared. This further supports this idea that delayed SOA
particles are bouncing around the vaporizer–ionizer region before finally
flash-vaporizing (Fig. b).
On the other hand, the PToF distribution for ammonium nitrate can be
broadened by decreasing the vaporizer temperature from 600 to 200 ∘C. Figure b shows the mass distribution of
m/z 46 (NO2+) for both vaporizer temperatures. The increase in PToF
arrival times (which translates to the broadened mass distribution for the
dominant mode of particles) with decreased vaporizer temperature indicates
that these particles do stick to the surface and have a reduced mass flux at
lower temperatures, thus spreading the signal arrival out over time of flight
(Fig. b). The secondary mode of ammonium nitrate
particles shown in Fig. b is multiply charged, larger
particles. (),
operating their vaporizer temperature on a programmed cycle between 200 and
600 ∘C, also see PToF broadening for nitrate in ambient data. Mass
arrival signals from individual ammonium nitrate particles at these low
vaporizer temperatures (shown in Fig. b) are much
longer (event lengths ∼ 200 µs, consistent with those measured by
, ) than those shown
for prompt and delayed SOA particles in Fig. a.
There seem to be different mechanisms for particle delay both for different
operating conditions of the AMS and for different particle types.
Ensemble mass distributions of different particle types. (a) Organic mass distributions
for α-pinene-derived SOA particles at two different vaporizer temperatures with DMA-selected mobility
diameter of 370 nm: 600 (green) and 800 ∘C (brown). Note: the degraded signal at
800 ∘C is due to low particle numbers due to smog chamber wall loss, as these data were taken
at the end of an experiment where particle number was relatively low. (b) m/z 46 PToF mass
distributions for DMA-selected mobility diameter of 300 nm at the standard vaporizer temperature
(600 ∘C, dark blue) and low temperature (200 ∘C, sky blue). (c) m/z 43 mass
distributions from SOA particles at three stages of a mixed-particle experiment: homogeneously nucleated
SOA (teal), SOA particles coated with d62-squalane (red), and SOA/d62-squalane particles after
an increase in chamber temperature (purple). Note the disappearance of the delayed tail with the condensation
of d62-squalane and the reappearance of the tail with heating despite the decrease in mode diameter.
Difference plots between prompt and delayed average mass spectra for (a) SOA and (b) ammonium sulfate.
Plot is prepared by first normalizing each spectrum by the total signal and then subtracting the normalized
delayed MS from the normalized prompt MS. Thus, mass fragments with positive values (blue) are enriched in
the prompt MS, and those with negative values (red) are enriched in the delayed MS. Error bars are the
propagated standard errors of the mean for each population.
Collection efficiency of SOA as a function of Dva and thermodenuder temperature.
(a) Particle collection efficiency as a function of Dva for all SOA experiments.
Data are from both size-selected experiments (crosses) and polydisperse SOA from a smog chamber
(circles), with each color representing a separate experiment. (b) Particle collection efficiency
for 370 nm size-selected particles (colored markers) as a function of thermodenuder temperature
for an example SOA experiment, colored by the fraction of m/z 44 (f44)
to the total organic mass measured in MS mode. Confidence intervals (95 % CI) for a linear fit are
shown (slope: -0.0020 ∘C-1).
Consistent with this proposed mechanism – that delayed SOA particles are
bouncing around and vaporizing on surfaces away from the vaporizer
cone – there are differences in mass spectra between prompt and delayed
particles. Figure shows the difference mass
spectrum between prompt and delayed particles for both SOA and ammonium
sulfate, both of which exhibit a high delayed fraction. We created average
mass spectra for prompt and delayed particles by summing the single-particle
spectra for each category and dividing by the number of particles. We then
normalized these average spectra by the sum of ions across all m/z, and
the difference mass spectra are the normalized prompt MS minus normalized
delayed MS. Error bars indicate the propagated standard error of the mean at
each m/z.
Several fragments are more prominent in either the prompt or delayed mass
spectra, colored by blue and red sticks, respectively. For instance,
m/z 43 (mostly C2H3O+) is higher and m/z 44 (CO2+) is
lower for delayed SOA particles; the acidic fragments m/z 81
(HSO3+) and 98 (H2SO4+) are higher in the delayed MS for
ammonium sulfate particles, while m/z 48 (SO+) and 64 (SO2+)
are higher in the prompt MS for ammonium sulfate. The water ion (H2O+)
is enhanced in the prompt MS, while ammonia (NH3+) is enhanced in the
delayed MS for ammonium sulfate particles. We attribute these differences in
mass spectra between prompt and delayed particles to the wide range of
possible temperatures experienced by delayed particles that have bounced
away from the center of the AMS vaporizer. The lower temperatures at these
sub-optimal vaporization positions (e.g., side of the vaporizer, on the ion
cage) can lead to different thermal decomposition pathways, which could
be important for interpreting ambient single-particle spectra.
To support this hypothesis, we look at previous work conducted by
(). They show that
acidic fragments from ambient ammonium sulfate measured during the Study of
Organic Aerosols at Riverside (SOAR-2005) are enhanced when they lower the
AMS vaporizer temperature from 600 to 200 ∘C, which is
consistent with our hypothesis that delayed ammonium sulfate particles were
vaporizing on cooler surfaces.
() also show that ambient OA in SOAR-2005 appeared
more oxidized at lower vaporizer temperatures, as indicated by increased
f44 and increased O : C. While f44 is slightly higher in our prompt
SOA MS, perhaps indicating that the prompt particles appear more oxidized,
the rest of the mass spectrum shows that the delayed particles are enhanced
in oxidized fragments, while the prompt particles are enhanced in reduced
fragments. We see an enhancement in the delayed MS of CxHyO fragments,
such as m/z 71 (C4H7O+), m/z 83 (C5H7O+), and
m/z 97 (C6H9O+). Other studies have found that f44 does
not change or even decreases with lower vaporizer temperatures than
the standard 600 ∘C; for example,
() showed that f44 decreases in the MS of
cis-pinonic acid at 200 ∘C compared to the standard vaporizer
temperature. Thus, the enhancement of these CxHyO+ fragments in the
delayed MS is a more robust indicator than f44 that our delayed SOA
particles appear more oxidized than the prompt ones. Excluding f44, our
data are consistent with
() and the hypothesis that our delayed particles
are bouncing around the vaporization/ionization region before landing on
cooler surfaces and finally evaporating. Importantly, these data show that
particles delayed due to particle bounce, like ammonium sulfate and the SOA
studied here, can have differences in their mass spectra that need to be
considered when analyzing ambient single-particle data.
Collection efficiency as a function of size and thermodenuder temperature
As reported previously in the literature, some studies have shown collection
efficiency for OA to be size- and composition-dependent
. To investigate any size-dependent collection
efficiency that our SOA might have, we selected particles at different
mobility diameters with a DMA upstream of the AMS. Figure a
shows CEp as a function of selected mobility diameter. LSSP
can also provide a size-resolved CEp for polydisperse
aerosol (as in , ), as each
optically counted particle has an estimated dva, estimated
from the time of flight between the chopper and the laser. Importantly, this
measure of size is unaffected by any vaporization delays and can be compared
across LS particle categories (e.g., prompt, delayed, null). Figure a also shows CEp for polydisperse SOA from
multiple smog chamber experiments, which agree well with the size-selected
data. The CEp for SOA studied here was not a strong
function of size between in diameter range 170–460 nm. The mean
CEp across all experiments for SOA was 0.3 (±0.04
SD) and ranged from as low as 0.25 to as high as 0.4. Mobility diameters
for the monodisperse experiments were adjusted according to Eq. (), so CEp vs. size data could be on the
same scale.
While CEp for this SOA is independent of size, we do
observe a decreasing trend in CEp by passing the SOA
through a thermodenuder. We sampled SOA alternately through a
thermally denuded line, or through a bypass line of the same length held at
the same temperature as the chamber. Figure shows
CEp plotted against thermodenuder temperature for an
experiment where SOA particles passed through a thermal denuder operating on
a temperature ramp profile. Above 110 ∘C, almost all SOA evaporated
in the thermodenuder, making the CEp measurement impossible
due to small particle size. The CEp values in Fig. are calculated for particles with 200 nm >
dva > 300 nm to isolate the effects of volatility
and/or oxidation state on CEp, instead of measuring smaller
particles less likely to provide enough detectable mass above the threshold.
We use temperature as a proxy variable for the volatility of the aerosol,
because SOA particles that have passed through the denuder will have had some
fraction of their more-volatile components removed, the amount of which
increases with increasing temperature. We color data points in Fig. by f44 as measured from MS mode bulk mass spectra,
which is used in AMS analysis as both a direct measurement of oxidation state
and a proxy for OA volatility . These data show that
CEp is inversely related to either the SOA oxidation state,
volatility, or both. These results are consistent with the trend shown by
(), who saw decreasing
CEm with increasing oxidation state, though they are within the
range of scatter shown in Fig. a for all SOA experiments.
It should be noted that this SOA is similarly oxidized (f44/f57 ≈ 6)
and has similar CEm values (CEm∼ 0.2–0.4) to much of the SOA in their study. SOA sampled through the
bypass line during this same time period did not have any decrease in
CEp. It is not possible to determine whether the decrease
in CEp is attributable to changes in volatility or
oxidation state, as the two are coupled in our measurements. However, this
example shows that LSSP can be used to verify whether this trend in
CEp with these compositional changes exists for other types
of NR-PM1.