Single-particle mass spectrometer (SPMS) instruments characterize the composition of individual aerosol particles in real time. We present a new method that combines SPMS composition with independently measured particle size distributions to determine absolute number, surface area, volume, and mass concentrations of mineral dust, biomass burning, sea salt, and other climate-relevant atmospheric particle types, with a fast time response applicable to aircraft sampling.
Single-particle mass spectrometer (SPMS) instruments characterize the composition of individual...
A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometry
A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometry
A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometryA new method to quantify mineral dust and other aerosol species from aircraft platforms using...Karl D. Froyd et al.
Karl D. Froyd1,2,Daniel M. Murphy1,Charles A. Brock1,Pedro Campuzano-Jost2,3,Jack E. Dibb4,Jose-Luis Jimenez2,3,Agnieszka Kupc1,5,Ann M. Middlebrook1,Gregory P. Schill1,2,Kenneth L. Thornhill6,Christina J. Williamson1,2,James C. Wilson7,and Luke D. Ziemba8Karl D. Froyd et al.Karl D. Froyd1,2,Daniel M. Murphy1,Charles A. Brock1,Pedro Campuzano-Jost2,3,Jack E. Dibb4,Jose-Luis Jimenez2,3,Agnieszka Kupc1,5,Ann M. Middlebrook1,Gregory P. Schill1,2,Kenneth L. Thornhill6,Christina J. Williamson1,2,James C. Wilson7,and Luke D. Ziemba8
Correspondence: Karl D. Froyd (karl.froyd@noaa.gov)
Received: 17 Apr 2019 – Discussion started: 25 Apr 2019 – Revised: 03 Oct 2019 – Accepted: 23 Oct 2019 – Published: 27 Nov 2019
Abstract
Single-particle mass spectrometry (SPMS) instruments
characterize the composition of individual aerosol particles in real time.
Their fundamental ability to differentiate the externally mixed particle
types that constitute the atmospheric aerosol population enables a unique
perspective into sources and transformation. However, quantitative
measurements by SPMS systems are inherently problematic. We introduce a new
technique that combines collocated measurements of aerosol composition by
SPMS and size-resolved absolute particle concentrations on aircraft
platforms. Quantitative number, surface area, volume, and mass
concentrations are derived for climate-relevant particle types such as
mineral dust, sea salt, and biomass burning smoke. Additionally, relative
ion signals are calibrated to derive mass concentrations of internally mixed
sulfate and organic material that are distributed across multiple particle
types.
The NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) instrument
measures size-resolved aerosol chemical composition from aircraft. We
describe the identification and quantification of nine major atmospheric
particle classes, including sulfate–organic–nitrate mixtures, biomass
burning, elemental carbon, sea salt, mineral dust, meteoric material, alkali
salts, heavy fuel oil combustion, and a remainder class. Classes can be
sub-divided as necessary based on chemical heterogeneity, accumulated
secondary material during aging, or other atmospheric processing.
Concentrations are derived for sizes that encompass the accumulation and
coarse size modes. A statistical error analysis indicates that particle
class concentrations can be determined within a few minutes for abundances
above ∼10 ng m−3. Rare particle types require longer
sampling times.
We explore the instrumentation requirements and the limitations of the
method for airborne measurements. Reducing the size resolution of the
particle data increases time resolution with only a modest increase in
uncertainty. The principal limiting factor to fast time response
concentration measurements is statistically relevant sampling across the
size range of interest, in particular, sizes D < 0.2 µm for
accumulation-mode studies and D > 2 µm for coarse-mode
analysis. Performance is compared to other airborne and ground-based
composition measurements, and examples of atmospheric mineral dust
concentrations are given. The wealth of information afforded by
composition-resolved size distributions for all major aerosol types
represents a new and powerful tool to characterize atmospheric aerosol
properties in a quantitative fashion.
Particle mass spectrometry is a valuable method for characterizing
atmospheric aerosol composition from airborne platforms. Instrumental
techniques can be broadly categorized into bulk methods, where all aerosol
within a size range are collected and characterized as a population
(Canagaratna et al., 2007; Pratt and Prather, 2012), and single-particle
methods that characterize individual particles as a subset of the aerosol
population, with a few hybrid methods also demonstrated (Cross et al., 2009;
Freutel et al., 2013). Single-particle mass spectrometry (SPMS) instruments (Hinz and Spengler, 2007; Murphy, 2007) have been used for over 25 years to
characterize the chemical composition of atmospheric aerosol from ground
sites and aircraft platforms. The NOAA Particle Analysis by Laser Mass
Spectrometry (PALMS) instrument first flew in 1998 (Thomson et al., 2000),
and several other SPMS instruments have successfully flown on airborne
platforms (Brands et al., 2011; Coggiola et al., 2000; Pratt et al., 2009a;
Trimborn et al., 2000; Zelenyuk et al., 2015). Their high sensitivity to a
wide variety of aerosol species, size-resolved capability, and ability to
characterize internally and externally mixed aerosol species make SPMS
instruments well suited to airborne studies of atmospheric aerosol
composition.
PALMS measures aerosol composition by evaporating individual particles and
ionizing their chemical constituents using a single pulse from a powerful
laser, then analyzes the ions with a time-of-flight mass spectrometer. PALMS
and other SPMS instruments that use single-step laser desorption–ionization
(LDI) are not inherently quantitative because ion formation is not a
well-controlled process and gives rise to considerable particle-to-particle
variability in both total and relative ion signals (Hinz and Spengler, 2007;
Murphy, 2007). Many bulk aerosol mass spectrometers (Canagaratna et al.,
2007; Tobias et al., 2000) and some SPMS instruments (Passig et al., 2017;
Simpson et al., 2009; Sykes et al., 2002) use a two-step particle desorption
and ionization process that can more readily quantify particle
sub-components. Very high laser irradiances generate plasmas that can also
improve consistency in ion signals but at the expense of losing all
molecular information (Wang and Johnston, 2006).
SPMS instruments have not typically calibrated the absolute ion signal
intensity to aerosol mass abundance due to ionization variability. Also,
SPMS particle detection usually relies on optical scattering, so that the
overall detection efficiency is a strong and variable function of particle
size. Nevertheless, a number of studies have used SPMS techniques to
quantify various aerosol species. Relative abundance measurements of
internally mixed aerosol sub-components have been reported for metals
(Cziczo et al., 2001; Healy et al., 2013; Murphy et al., 2007; Zawadowicz et
al., 2015), organosulfate species (Froyd et al., 2010; Liao et al., 2015),
elemental carbon (EC; Healy et al., 2012), and non-refractory material such as
ammonium and nitrate (Healy et al., 2013), or sulfate and organic material
(Healy et al., 2013; Jeong et al., 2011; Middlebrook et al., 1998; Murphy et
al., 2006; Zelenyuk et al., 2008; Zhou et al., 2016). Some groups have
scaled SPMS data rates to aerosol reference instruments, either under
controlled conditions prior to deployment (Shen et al., 2019) or more
commonly co-located in the field, to derive total number or mass
concentrations (Bein et al., 2006; Pratt et al., 2009a; Qin et al., 2006) or
concentrations for specific particle types and sub-components (Gemayel et
al., 2017; Healy et al., 2012, 2013; Jeong et al., 2011; Reinard et al.,
2007; Shen et al., 2019). Many of these scaling studies invoke potentially
large assumptions such as constant SPMS detection efficiencies or a single
density applied to all particles that can strongly affect derived
concentrations. Uncertainty estimates in these derived concentrations are
rarely reported (Shen et al., 2019), and a complete analysis of all
principal error sources has not been previously undertaken. To date these
methods have been restricted to ground-based sampling under relatively high
aerosol loadings (∼1–100 µg m−3) and have employed
long sample times ≥1 h. Few coarse-mode concentrations have been
reported (Gunsch et al., 2018; Qin et al., 2006), and in particular, studies
using SPMS to determine absolute concentrations of mineral dust are nearly
absent (Shen et al., 2019).
Mineral dust is one of the most abundant aerosol types in the atmosphere.
Dust contributes a substantial fraction to global aerosol optical depth by
scattering and absorbing radiation. Dust's role as a leading cirrus cloud
nucleating agent (Cziczo et al., 2013) further elevates its importance for
the climate system. However, dust measurement techniques for airborne
studies, particularly fast-response online methods, are lacking. Online bulk
mass spectrometry techniques are typically not sensitive to refractory
particles such as dust. Also, instruments and aircraft inlets must be
optimized to sample coarse-mode aerosol up to several microns in size.
Electron microscopy (EM) techniques with associated elemental analysis
remain valuable offline single-particle methods to detect and quantify
components associated with mineral dust (Kandler et al., 2009; Levin et al.,
2005; Lieke et al., 2011; Matsuki et al., 2010) and other less volatile
aerosol such as sulfate, sea salt, industrial metals, and some carbonaceous
particles (Pósfai et al., 2003; Sheridan et al., 1994).
Computer-controlled EM analysis can now characterize thousands of particles
and generate population statistics of size, morphology, and detailed
chemical composition (Ault and Axson, 2017; Craig et al., 2017). However,
continuous measurements at high time resolution remain impractical, the
derivation of dust mass concentrations under background aerosol levels is
challenging, and like most offline methods, volatile or reactive aerosol
species can change prior to analysis.
A measurement gap remains for fast-response detection of mineral dust and
other refractory or coarse-mode particles. Additionally, size-resolved
measurements and characterization of particle mixing state, i.e., the
distribution of chemical constituents within single particles or across
different particle types, are tractable by few methods. SPMS instruments are
uniquely capable of detecting both refractory and non-refractory particles
in real time. PALMS and other SPMS instruments with sufficient laser power
observe a chemical fingerprint for every type of aerosol particle in the
atmosphere, minimizing chemical bias. Lower laser power and/or a longer
ionization wavelength can result in biases against particles such as sulfate
(Wenzel et al., 2003). SPMS instruments are particularly adept at
characterizing some climate-relevant aerosol types, including mineral dust,
biomass burning smoke, sea salt, and biological particles with high
sensitivity and selectivity.
We present a new method that combines PALMS composition with independently
measured particle size distributions to determine absolute number, surface
area, volume, and mass concentrations of mineral dust, biomass burning, sea
salt, and other common atmospheric particle types, with fast time response
applicable to aircraft sampling where total mass concentrations are often
> 100 times lower than at ground level. Low detection limits on
the order of 10 ng m−3 for principal particle types are typical over a
few minutes of sampling time. A unique capability of this technique is the
derivation of number concentration for specific particle types, which is
particularly important for aerosol–cloud interaction studies. Size-resolved
aerosol composition is measured over a wide size range that spans the
accumulation and coarse modes under most atmospheric conditions.
Additionally, we determine bulk-like mass concentrations for sulfate and
organic material that are distributed across multiple particle types. The
quantification methods described here are developed specifically for the
PALMS instrument, but they are designed to act as a framework for
quantifying particle types using other well-characterized SPMS instruments.
We summarize the principal sampling considerations and measurement criteria
for deriving particle type concentrations, and we conclude with general
recommendations for implementing the method in airborne composition studies.
Estimations for principal sources of uncertainty are detailed in the
Appendix.
2 Measurement methods
Brief descriptions of the airborne aerosol sampling methods employed over
several aircraft campaigns are given below, followed by descriptions of
aerosol instrumentation for measuring composition and size distributions.
2.1 Airborne aerosol sampling
Aerosol properties were measured aboard the NASA DC-8 aircraft during three
campaigns: DC3, SEAC4RS, and ATom. The Deep Convective Clouds and Chemistry
(DC3) campaign was based in Salina, Kansas, in April–May 2012 and targeted
convective outflow from isolated storm systems (Barth et al., 2015). The
Studies of Emissions and Atmospheric Composition, Clouds and Climate
Coupling by Regional Surveys (SEAC4RS) campaign was based in Houston, Texas, in
August–September 2013, and sampled a variety of continental environments including
regions with high biogenic activity, urban emissions, wildfires, and
convective outflow (Toon et al., 2016). The NASA Atmospheric Tomography
(ATom) campaign consisted of four seasonal deployments from 2016 to 2018 to map
the troposphere with near pole-to-pole coverage in north–south transects along the
Pacific and Atlantic basins (Wofsy et al., 2018). Measurements during the
New England Air Quality Study (NEAQS) campaign were taken aboard the NOAA
WP-3D aircraft based in Portsmouth, New Hampshire, in July–August 2004, with flights
targeting anthropogenic emissions from the eastern US (Fehsenfeld et al.,
2006). During the NASA Mid-latitude Airborne Cirrus Properties Experiment
(MACPEX) campaign the WB-57 aircraft was based in Houston, Texas, in March–April
2011 and sampled tropospheric continental and stratospheric background air
near cirrus cloud systems (https://espo.nasa.gov/macpex/, last access: 26 October 2019). Cloudy flight
segments are excluded from all aerosol data (Murphy et al., 2004b).
The airborne sampling methodology for ATom DC-8 deployments is detailed in
Brock et al. (2019). Instruments are described in Sect. 2.2. For all DC-8
deployments most instruments used the University of Hawaii aircraft inlet
operated at isokinetic conditions. This inlet was previously characterized
to transmit aerosol particles ≥5.0µm aerodynamic diameter at low
altitude and ≥3.2µm at 12 km altitude with 50 % efficiency
(McNaughton et al., 2007). PALMS and particle size spectrometers subsample a
minor flow from the main inlet flow. The PALMS instrument flow was 0.75 L min−1,
and particle spectrometer flows were 0.05–0.1 L min−1. Particle spectrometer flows were actively dried using Nafion driers (Perma Pure), typically to
< 40 % relative humidity. Residence times between the aircraft
inlet and instrumentation were 0.5–3.5 s. The AMS instrument used a
dedicated HIMIL aircraft inlet (Stith et al., 2009) with residence times
typically < 0.5 s and no active drying. The SAGA filter samplers
used the University of New Hampshire aircraft inlet that has similar
particle transmission characteristics to the University of Hawaii inlet
(McNaughton et al., 2007). Aboard the NOAA WP-3D during NEAQS aerosol
particles were sampled using a low-turbulence inlet operated isokinetically
(Wilson et al., 2004) and transmitted to size spectrometers inside the
cabin. In the WB-57 during MACPEX the FCAS instrument sampled particles
using an anisokinetic inlet (Jonsson et al., 1995). PALMS was located inside
a wing pod for NEAQS and inside the WB-57 nose for MACPEX, and for both
campaigns PALMS sampled aerosol using a forward-facing anisokinetic tube
that enhanced large particle concentrations.
A virtual impactor was added upstream of PALMS for the DC-8 ATom deployments
to enhance supermicron particle concentration. The design is based on Loo
and Cork (1988) and is scaled to achieve an enhancement of at least 50 %
of the flow ratio above ∼2.0µm. The virtual impactor
was operated at total-to-minor flow ratios of 5.6–11. Figure S1 in the Supplement shows the
virtual impactor design and its concentration enhancement for lab-generated
aerosol.
2.2 Aerosol size and composition measurements
The NOAA PALMS instrument
(Thomson et al., 2000) characterizes the size and chemical composition of
individual aerosol particles from about 0.15 to 5 µm in diameter.
Particles pass through an aerosol focusing lens (Schreiner et al., 2002) and
enter a vacuum where they pass through two continuous laser beams and
scatter light. The transit time between the beams provides the particle
velocity, which is used to determine particle aerodynamic diameters based on
laboratory calibrations using polystyrene latex sphere size standards (Duke
Scientific). A scatter signal triggers a 193 nm pulse from an excimer laser
that ablates and ionizes a single particle. Either positive or negative ions
are analyzed with a time-of-flight mass spectrometer, with the polarity
switched every few minutes during flight. Single-particle mass spectra are
post-processed to classify each particle into a compositional type and
calculate the relative abundance of particle sub-components (Froyd et al.,
2009; Hudson et al., 2004; Murphy et al., 2003).
An important consideration when measuring fractional and absolute abundance
from single-particle analysis is the estimation of potential sampling biases
due to particle composition or physical properties. In one example, if a
certain particle type does not produce detectable ions when hit by the
ionization laser, a systematic bias would occur that would underestimate
the concentration of that particle type. Additionally, particle mass
spectrometers can exhibit a low bias for non-spherical particles due to
diverging particle trajectories (Huffman et al., 2005; Vaden et al., 2011).
Many aspects of the PALMS instrument design reduce these potential biases
(Murphy, 2007). The very close proximity of detection and ionization beams
(center separation ∼100µm) enables PALMS to obtain
positive ion mass spectra for > 90 % of particle triggers. In
addition, the particle transit distance between the vacuum entrance and the
ionization region is shorter than most other SPMS systems. This high
targeting efficiency and compact detection region in PALMS minimizes
chemical biases from particles of different shape or density that could have
diverse trajectories. The PALMS ionization laser produces a mass spectrum
for all known particle types in the atmosphere. Even pure sulfuric-acid–water particles, which are notoriously difficult to ionize due to their
low absorption properties, generate sufficient ions in PALMS for particle
classification. For the data presented here, particles were exposed to
> 2500 MW cm−2 of laser fluence at 193 nm, which is above
the ion formation threshold for relatively pure sulfuric acid (Thomson et
al., 1997). For organics and sulfate–organic mixtures, ion production is
self-limiting due to space-charge repulsion, and total ion current in PALMS
is relatively insensitive to laser power. Lastly, particles in the
accumulation mode are nearly or completely ablated by the excimer laser
(Murphy, 2007). Consequently, PALMS mass spectra represent the composition
of the entire particle so that classification routines and relative
measurements of chemical sub-components are not biased to physical
inhomogeneities such as coatings, phase separation, or surface-enhanced
species. It should be noted that although coarse-mode sea salt mineral dust
particles do not fully ablate and therefore may give an incomplete picture
of the particle composition, the mass spectral signatures are still unique
to those particle types, and they are classified accurately. We therefore
assume that detection biases due to particle chemical or physical properties
yield systematic errors that are small compared to other sources of error
(see Appendix A).
Several optical particle spectrometer instruments were used for size
distribution measurements. During NASA DC-8 sampling campaigns, the Laser
Aerosol Spectrometer (LAS 3340, TSI, Inc) measured concentrations for
particles from 0.1 to about 5 µm, above which the aircraft inlet
transmission truncated the size distribution. Also on the DC-8, an
Ultra-High Sensitivity Aerosol Spectrometer (Droplet Measurement
Technologies) measured particles from 0.06 to either 0.5 or 1.0 µm,
above which the LAS data were used. On the NOAA P-3 aircraft, the
combination of a Lasair model 1001 (Particle Measurement Systems) and a
custom-built white light optical particle counter (WLOPC) measured particle
concentrations from 0.12 to about 8 µm. On the NASA WB-57 aircraft, a custom-built Focused Cavity Aerosol Spectrometer (FCAS II) measured
particles from about 0.07 to 1.5 µm dry diameter (Jonsson et al., 1995;
Wilson et al., 2008). All sizes are reported as ammonium sulfate diameters,
and all concentrations are reported at standard conditions (1013 hPa and
273.15 K).
During DC-8 sampling campaigns a high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS; Aerodyne Research; Canagaratna et al., 2007; Nault
et al., 2018) measured non-refractory, bulk aerosol mass composition at 1 Hz
resolution with 100 % transmission for vacuum aerodynamic diameter
100 < Dva < 500 nm (50 and 770 nm at 50 % efficiency;
see DeCarlo et al., 2004, for the definition of Dva). Raw mass spectra
were analyzed at 1 min intervals, yielding detection limits for organic and
sulfate aerosol mass concentrations of 75 and 10 ng m−3,
respectively, on average in the free troposphere. During DC-8
campaigns, soluble ions were also measured using the Soluble Acidic and Gases and
Aerosols (SAGA) offline ion chromatography from aerosol filters (Dibb et
al., 1999). Typical sampling times were 5–15 min with detection limits of
∼10 ng m−3. Filter data are excluded when cloudy periods
exceed 20 % of the sample time or over altitude ranges exceeding 3 km.
3 Deriving absolute concentrations
3.1 Composition-resolved size distributions from SPMS combined with particle size spectrometers
The general method of deriving quantitative abundance from single-particle
composition data is introduced here and outlined in Fig. 1, with details
described in the following sections. The approach combines size-resolved,
single-particle composition from the PALMS instrument with a concurrent
measurement of size-resolved absolute number concentration. Typically, an
optical particle spectrometer (OPS) is used to measure the aerosol size
distribution across the accumulation and coarse modes, although other sizing
techniques based on electric mobility or aerodynamic diameter, or a
combination of techniques, can be employed. Mass spectra of individual
particles acquired with PALMS are classified into one of several
compositional categories. Aerodynamic diameters, Da, for each particle
are converted to volume-equivalent (geometric) diameters, Dve, using
particle densities and dynamic shape factors to match the OPS data. The
particle classes are binned into size ranges that align with the particle
size spectrometer. Then the fraction of each particle class within each size
bin is multiplied by the average concentration within that bin. The
resulting composition-resolved size distribution is integrated to give
absolute number, surface area, or volume concentrations for each particle
class. Mass concentrations for each particle type are determined by applying
particle densities to the volume concentrations. Total sulfate and organic
mass concentrations were derived from the non-refractory particle types.
Figure 1Flow chart to derive particle type concentrations and
bulk sulfate and organic mass concentrations from SPMS and OPS data.
Figure 2Quantification of PALMS particle classes. (a, c) Raw PALMS
counts for different particle classes (colors) overlaid on the aerosol
volume size distribution (dashed black). (b, d) For each diameter bin, the
fractional contributions of each PALMS particle class are applied to the
total volume. Vertical dashed lines define four broad diameter bins that are
used to generate concentration products at higher time resolution (see
text). The upper panels are from 39 min of sampling at low altitude over the
Gulf of Mexico and contain influences from the marine boundary layer, long-range mineral dust transport, and lower tropospheric pollution. The bottom
panels represent the remote upper troposphere with minor influences from
continental and stratospheric sources, sampled over 27 min.
Figure 2 shows two composition-resolved volume distributions measured from
aircraft that are representative of two diverse atmospheric environments.
Panels a and c give raw spectra counts as a function of size for each PALMS
composition class, as well as the OPS volume distribution. The PALMS size
bins are then aligned to the OPS, and fractional abundances in each size bin
are applied to the OPS volume distribution to generate the panels b and d.
The composition-resolved size distributions in Fig. 2b and d contain a
wealth of information and represent a powerful set of tools to investigate
atmospheric aerosol properties. PALMS and other single-particle techniques
show that the atmosphere consists of an external mixture of particle types,
and in Fig. 2 the compositional size modes for these particle types are
clearly revealed. Three broad aerosol regions are apparent in the volume
distributions: the accumulation mode at Dve < 0.5 µm
consisting of mostly non-refractory particle types, a coarse mode at
Dve > 1 µm dominated by mineral dust and sea salt, and
the inter-mode minimum at 0.5 < Dve < 1 µm that is
a mixture of accumulation- and coarse-mode composition. Most particle classes
extend to sizes beyond their principal mode. For instance, sea salt and
mineral dust can also contribute significantly to submicron aerosol volume.
In many environments, an extension of the accumulation mode continues to
> 1 µm where non-refractory particle types contribute to
supermicron volume (Fig. 2d).
The practical limitations of this method mostly originate from the need in
airborne studies to derive statistically significant composition
measurements across the atmospherically relevant sizes within a reasonable
sampling time (∼1–5 min). The Fig. 2 examples required tens
of minutes of sampling to populate nearly the entire accumulation- and coarse-mode size ranges with particle mass spectra. Although PALMS size range
encompasses most of the accumulation and coarse volume modes, Fig. 2a and c
show how the outer ranges of each mode are not efficiently characterized.
The white areas in Fig. 2b and d represent aerosol volume that is not
allocated to any particle class. In most cases the unallocated volume is a
minor fraction of the total, and the composition can be extrapolated to fill
the entire mode, assuming composition remains constant. The following
sections describe simplifying assumptions that allow faster measurements of
particle type concentration while maintaining reasonable uncertainties.
Uncertainties and limits of detection for particle type concentrations are
detailed in Appendix A.
Table 1PALMS principal atmospheric particle types.
a Density at measurement conditions.
bχv, within or near free molecular flow.
c Mass fraction within the dominant size mode for background
tropospheric air.; strat = lower stratosphere, MBL = marine boundary layer.
d Calculated based on organic-to-sulfate mass ratio.
e Derived from optical–aerodynamic analysis.
Each PALMS particle mass spectrum is classified as one composition type,
e.g., mineral dust, sea salt, or sulfate–organic–nitrate (SO) mixtures, according
to dominant spectral signatures. PALMS particle classification has been
described previously (Cziczo et al., 2001, 2004; Froyd et al., 2009; Hudson
et al., 2004), and definitions for nine principal atmospheric particle types
are updated here. Chemical signatures, sizes, and other properties are
listed in Table 1. These particle classes are defined so as to broadly
capture the main chemical components or identify a distinct aerosol source.
It is important to note that PALMS and other SPMS particle type definitions
are flexible and can be tailored to a particular environment or objective.
Only positive ion mass spectra are used to categorize particles into these
classes. The classification method uses empirical criteria based on relative
peak intensities, and a spectra clustering algorithm (Murphy et al., 2003)
is then used to refine particle sorting. Figure S2 shows representative
particle mass spectra for all classes in Table 1.
The most abundant classes under most tropospheric environments are the sulfate–organic–nitrate internal mixtures and biomass burning (BB)
particles. Particles classified as SO can be composed of primary or
secondary material from a wide variety of sources but contain no biomass
burning or other clear chemical markers that denote a particular source.
Biomass burning particles are identified by a distinct potassium signature,
abundant organic signatures, and a lack of crustal, marine, or industrial
metals, based on the method of Hudson et al. (2004). The crucial potassium
signature is stable over weeks of aging, and due to PALMS extreme
sensitivity to alkali metals, the potassium signal is observed above organic
background peaks even when potassium constitutes ≪0.1 %
of particle mass (Cziczo et al., 2001). The identification of these primary
biomass burning particles is both highly sensitive and selective and does
not deteriorate with particle aging. Single-particle information is critical
to differentiating biomass burning potassium from other potassium sources
that can confound bulk measurements (Legrand et al., 2016; Sullivan et al.,
2019). Although secondary aerosol material is by definition distributed
across many composition classes, the SO and BB classes contain the vast
majority of sulfate, organic, ammonium, and nitrate aerosol mass.
A variety of minor but important particle types contributes to the external
aerosol mixture of the lower atmosphere. The elemental carbon (EC) class
include particles dominated by C mass spectral signatures and
are interpreted as mostly EC by mass (presumably black carbon) since small
amounts of internally mixed organics will obscure the EC signatures.
Particles with minor EC content are therefore not distinguishable by PALMS
and are instead classified as SO. Some EC particles contain potassium, which
suggests a biomass burning source, and when accompanied by organic
signatures these particles are instead classified as BB. Sea salt is easily
distinguished by a dominant sodium signal, often with calcium, strontium,
other alkalis, and sodium chloride ion clusters, but without crustal metals.
Mineral dust (MD) spectra are identified from multiple crustal metal
signatures such as silicon, aluminum, iron, and calcium and often contain
trace amounts of alkalis, barium, tin, antimony, or lanthanides. This
category is more heterogeneous than other classes and contains many
different sub-types of spectra, representing a wide variety of mineralogies.
Meteoric material (MT) is identified by iron, nickel, and magnesium within
particular intensity ratios (Cziczo et al., 2001) and without other crustal
material, and it is usually accompanied by strong sulfate signatures. The
alkali salt (KS) category is reported here for the first time. The spectra
for this class contain potassium and other alkali metals but no crustal
material, and very low organic signatures distinguish them from biomass
burning particles. Despite the similarity to biomass burning particles they
are not enhanced in smoke plumes. The spatial and vertical patterns of these
particles suggest primary continental emissions, but their exact source is
still uncertain. Their size is exclusively submicron, which suggests they
are not a type of mineral dust. The KS class constitutes 0.1 %–0.5 % of
accumulation-mode particles over the US and < 0.1 % in the remote
troposphere. Heavy fuel oil combustion particles (FO) are readily identified
by strong vanadium signatures mixed with sulfate, organics, and sometimes
iron or nickel (Ault et al., 2010; Divita et al., 1996). Spectra not
identified as any of the above composition types are compiled into a class
labeled as “Other” (OT), which contains a variety of minor particle types.
By far the most abundant subtype in OT is a sulfate–organic mixture with
possible alkali or metallic signatures that are small and difficult to
distinguish from organic peaks. Other examples include spectra with the
pyridinium ion and other amine signatures, industrial metals without obvious
crustal components, and several types of organic-rich particles with
distinct signatures that suggest unique but unknown sources. Primary
biological particles are currently identified from negative ion spectra only
(Zawadowicz et al., 2017), and a separate particle class is not implemented.
In continental air they account for ∼1 % of supermicron
particles and < 0.1 % of all detected particles (Zawadowicz et
al., 2019).
All particle types acquire secondary material such as sulfate, ammonium,
organics, and nitrate during atmospheric transport and aging. This secondary
accumulation does not change particle assignments, except that heavy
coatings may partially obscure unique signatures, resulting in a particle
classified as “Other”. For example, a mineral dust particle that contains
secondary sulfate, nitrate, and organic material will still be classified as
mineral dust, and the derived dust mass includes the secondary material.
Similarly, BB particles may contain secondary material sourced from biomass
burning and non-biomass burning emissions. Laboratory calibrations of
secondary mass spectral signatures could be used to subtract secondary mass
from primary particle types. In some cases, the chemical component that
identifies a particle's source is a minor constituent. For example,
particles in the meteoric class are mostly sulfuric acid by mass, and the
metals from ablated meteorites only account for a few percent of mass.
Similarly, particles from heavy fuel oil combustion are composed of mostly
sulfate and organic material but also contain traces of vanadium and other
metals that denote their unique emission source.
3.3 Simplifying the size distribution
It is infeasible to retain the raw size resolution of the OPS for the integrated concentration analysis, since some
common commercial instruments report up to 50 size bins per decade of
diameter. For example, to achieve a minimal compositional representation
with > 5 particle spectra in each size bin would require
> 5000 spectra if acquired evenly across the instrument's size
range. Accounting for inefficient acquisition at the extreme size limits of
the instrument and with typical single-particle mass spectra data rates of a
few hertz, this would require sampling times > 1 h to display
composition at the native resolution of the OPS. Therefore, raw size bins of
the optical spectrometer must be combined into fewer bins to improve time
resolution but at some expense in accuracy of the derived concentrations.
Ideally, size bins are defined such that the composition is homogeneous
within each combined bin, in which case this simplification is rigorous and
introduces no error to the derived number, surface area, and volume
concentrations. Induced error should be minimized by defining size bins such
that neither the concentration nor particle type fractions have strong
gradients across a bin limit. Concentration products cannot be determined if
zero PALMS spectra are acquired within any one size bin that contributes
significantly to the integrated concentration from the OPS. In practice,
composition gradients across size bins and statistical noise at the size
range extremes generate systematic error that increases as size bins are
combined and the size distribution simplified.
To estimate the systematic errors associated with this approach, integrated
volumes were calculated for a number of cases where composition was constant
over an extended flight period. For each case PALMS particle class volumes
were first determined at a high size resolution of ∼20 bins
per decade of diameter. Nearest-neighbor diameter bins were then combined,
and integrated particle volumes were recalculated for each particle class at
the lower size resolution. When the total number of bins was reduced to
three or four, the diameter limits were empirically defined based on volume
modes and composition gradients, e.g., one or two bins across the
accumulation mode from about 0.06 to 0.5 µm, one across the inter-mode
minimum at 0.5–1.1 µm, and one coarse-mode bin at 1.1–5 µm
(vertical grey lines in Fig. 2b and d). The high-resolution analysis is
treated as a reference value, and the average deviation of derived volumes
as a function of final bin count is plotted in Fig. 3a for diverse cases
across several flight campaigns. Typical deviations are 5 %–25 % when the
size distribution is represented by three or four bins.
For sampling times of a few minutes, reducing the size resolution improves
the data coverage (Fig. 3b). For each flight campaign, the number of sampling
periods with sufficient statistics to generate concentration products are
plotted relative to the two-bin case. To generate concentration products,
every diameter bin that contributes significantly to the total volume must
include ≥5 PALMS spectra. The three- or four-bin approach offers a good
trade-off between reasonable time resolution and data coverage (65 %–85 % for
three bins) while still yielding particle volume and mass concentrations whose
systematic errors are less than or equal to typical volume uncertainties of
a particle spectrometer (Kupc et al., 2018). Small particle detection
efficiency (see Sect. 3.6) was worse for DC3 than for SEAC4RS, resulting in
a steeper reduction in data coverage between three and four diameter bins. For DC3
the lower sensitivity to small particles results in more sampling periods
with less than five particles in the smallest size bin, so that relative data
coverage with four diameter bins is 25 %–50 % compared to SEAC4RS with
65 %–75 %.
Figure 3(a) Error in integrated volume introduced by reducing the
size resolution of the analysis. Integrated volume was calculated for every
PALMS particle class over several long flight segments with externally mixed
but constant composition. Volumes computed at full diameter resolution
(25–29 bins) provide a reference, and the average deviation for populous
particle classes (contributing > 5 % of volume) is plotted as
bins are combined. (b) Lines show the relative data coverage, defined as the
number of time periods with > 5 particles in every diameter bin,
using three different raw sampling times for two flight campaigns.
Figure 3a suggests that induced errors are not a simple function of a
particular atmospheric environment. Instead, most of the variability for any
given bin count is due to sparse data within a single size bin or
composition inhomogeneity across a size bin limit. The choice of size
binning and time resolution when deriving integrated products can be altered
based on both these conditions. Three size bins, where one bin encompasses
the entire accumulation mode, are adequate for many tropospheric sampling
environments (Fig. 2b). When certain external mixtures are apparent, it is
recommended to split the accumulation mode into two size bins. For instance,
biomass burning particles and meteoric or other stratospheric particles
occupy the larger end of the accumulation mode, D > 200 nm,
whereas secondary sulfate–organic particles typical of the upper troposphere can be
smaller, ∼60–150 nm (Fig. 2d).
The PALMS size range encompasses the majority of accumulation- and coarse-mode size ranges under most atmospheric conditions (Fig. 2). Exceptions
include the marine boundary layer and strong mineral dust plumes that often
contain particles larger than ∼4µm, and very clean
upper tropospheric conditions, where number and volume contributions can be
significant for sizes below the PALMS range, e.g., Dve < 150 nm. In the latter case for example, the lower size bin limits for the
concentration analysis can be set to fully include the lower end of the
accumulation mode, e.g., Dve(bin1) =60–250 nm. The PALMS composition
averages applied to that bin will be biased to the larger end of the bin
(Dve∼150–250 nm), but PALMS fractions are applied to
the entire bin as usual. The total concentration is still accurately
measured by the particle spectrometer, but the PALMS composition is
effectively extrapolated to sizes outside of the PALMS size range. The
inherent assumption is that the composition across the lower half of the
accumulation mode is homogeneous. While this extrapolation can be
appropriate for many tropospheric and stratospheric environments, care
should be taken in cases where the accumulation mode is weak and shifted to
small diameters, such as very clean upper tropospheric conditions, or in
heterogeneous environments, such as active aerosol emission sources mixing
with background air.
3.4 Response of optical particle spectrometers (OPSs) to composition
The optical scattering response of an aerosol particle depends on its size,
shape, refractive index, and the light collection geometry of the
spectrometer instrument. Size distributions derived from optical particle
spectrometers are based on the assumption of a fixed refractive index and
spherical shape to translate the optical response of the measured particle
population into volume-equivalent diameter, Dve≡Dopt. A
typical atmospheric air mass contains an external mixture of several diverse
particle types. Fortunately, the real refractive indices for background
tropospheric environments have been observed to fall within a narrow range
(n≈1.50–1.56 at mid-visible wavelengths) such that ambient
particle size measurements are not strongly affected by this assumption
(Hand and Kreidenweis, 2002; Liu et al., 2008; Müller, 2002;
Espinosa et al., 2017; Shingler et al., 2016; Yamasoe et al., 1998).
Ammonium sulfate and ammonium nitrate (n=1.53 and 1.56 at λ=532 nm, respectively) are common inorganic constituents. Visible refractive
indices (n-ki) have been derived for mineral dust aerosol from a variety of
field measurements and typically range from about n=1.52–1.58 and
k∼0.001–0.01 (Balkanski et al., 2007; Dubovik et al., 2002;
Kandler et al., 2011; McConnell et al., 2010; Müller et al., 2010;
Petzold et al., 2009; Schladitz et al., 2009), with absorption increasing at
shorter visible wavelengths. Pure mineral samples can exhibit higher
variability. Many retrievals for ambient organic aerosol material fall
within typical inorganic refractive indices (Aldhaif et al., 2018; Kassianov
et al., 2014; Espinosa et al., 2017; Shingler et al., 2016), although
some laboratory surrogate species and a few atmospheric organics can have a
wider range, n≈1.47–1.65 (Dinar et al., 2007; Dubovik et al., 2002;
Hoffer et al., 2006; Rizzo et al., 2013; Schkolnik et al., 2007). Sulfuric
acid, which is abundant in the stratosphere and sporadically in the
troposphere, has a much lower refractive index of n=1.44 at 532 nm (Luo et
al., 1996). Sulfuric acid aerosol also retain water (n=1.33 at 532 nm;
Daimon and Masumura, 2007) even at low RH, making it a predominant outlier
to typical refractive indices. Kupc et al. (2018) investigated the potential
systematic error in prescribing the wrong refractive index to a
representative upper tropospheric aerosol population. The difference in aerosol volume
assuming pure ammonium sulfate (n=1.54) versus sulfuric acid (n=1.44)
was only 12 %, which is lower than a typical aggregate volume uncertainty.
Few atmospheric particle types are strongly absorbing, and the complex index
of refraction (k) for an ambient population is assumed to be zero. Several
exceptions follow. Particles containing elemental carbon (EC), presumably in
the form of black carbon (BC), typically account for < 1 % of
accumulation-mode mass in the background atmosphere but up to
∼10 % inside wildfire plumes (Andreae and Merlet, 2001).
The refractive index of pure black carbon has high variability, e.g., with
n-ki =1.74–0.44i (Hess et al., 1998), 1.95–0.79i (Bond and Bergstrom,
2006), and 2.26–1.26i (Moteki et al., 2010). Although their measured
diameters can be erroneous by > 10 % (Kupc et al., 2018), the
net error on total aerosol number and mass is typically much lower due to
their small relative population. Hematite mineral also has a unique
refractive index (2.5–1.0i at 405 nm; Sokolik and Toon, 1999), but PALMS data
indicate that pure hematite particles are extremely rare in the atmosphere.
Ammonium sulfate and ammonium nitrate are convenient materials to calibrate
optical particle spectrometers for use in atmospheric sampling. If
generating monodisperse aerosol at coarse-mode sizes is impractical,
polystyrene latex sphere (PSL) size standards can be used, but their
refractive index (n=1.59 at 532 nm; Ma et al., 2003) is not representative
of typical atmospheric aerosol. Therefore, the scattering response of the
sizing instrument to PSL particles must be converted to atmospherically
relevant particles using Mie theory. Figure 4a shows Mie scattering
intensities calculated for the LAS instrument at λ=663 nm for PSL
and ammonium sulfate particles. At each diameter, the scatter intensity for
a PSL particle is located on the ammonium sulfate intensity curve, and the
associated ammonium sulfate diameter is determined. For a given scatter
response the calibrated PSL diameter can shift up to 20 % due to the
different refractive index of ammonium sulfate. The inset in Fig. 4a shows
an expanded region from D=0.8 to 2.4 µm, where oscillations render
optical particle sizing more uncertain. These oscillations introduce
additional sizing uncertainty that is inherent to all monochromatic particle
spectrometers in the range where, depending on the collection geometry,
particle diameters are about 1–2 times the laser wavelength. Scattering
intensity curves were smoothed so that each PSL diameter yielded one unique
ammonium sulfate diameter. A raw PSL calibration curve is shown in Fig. 4b
along with an ammonium sulfate-equivalent calibration curve derived using
the diameter ratio curve. Also plotted in Fig. 4b are points from laboratory
measurements of monodisperse ammonium sulfate particles generated with a
differential mobility analyzer for D < 1 µm. Closure between the
PSL-derived and measured ammonium sulfate response was not fully achieved.
However, rapidly effloresced ammonium sulfate particles are slightly
non-spherical, and as a result the volume-equivalent diameter of the
monodisperse ammonium sulfate aerosol is smaller than the mobility diameter,
Dve < Dmob. Agreement between measured and derived
ammonium sulfate calibration curves improved after correcting the
calibration diameters using shape factors of χt=1.03 to 1.09,
increasing with size (Huffman et al., 2005; Zelenyuk et al., 2006b). We
confirmed that the ammonium sulfate particles had fully effloresced in the
calibration system by observing an abrupt increase in apparent optical size
due to a change in the refractive index when the relative humidity was reduced
below the efflorescence point.
Figure 4Using Mie theory to translate the response of an optical
particle spectrometer between particles of different refractive index. (a) Calculated scatter intensities at λ=663 nm for PSL and
effloresced ammonium sulfate (AS) particles in the LAS instrument. PSL
diameters are translated into ammonium sulfate diameters of the same scatter
intensity using the Mie response curves, shown conceptually with grey
arrows. The diameter ratio is plotted on the right axis. Smoothing must be
applied to the Mie curves (inset in a, dashed line) in order to yield unique
diameter translations. (b) A calibration curve derived from PSL particle
standards (dashed black) is translated into an ammonium sulfate calibration
curve (solid black) using Mie theory. Size-selected AS particles yield a
directly measured AS calibration curve (solid red) to compare with the
PSL-derived AS calibration curve.
Highly non-spherical particles such as some mineral dusts and black carbon
aggregates have different scattering intensities and phase functions
compared to their volume-equivalent spheres. The effect on the angular
scattering pattern is not consistent across different shapes and cannot be
described by any simple measure of asphericity (Curtis et al., 2008;
Mishchenko et al., 1997; Moteki et al., 2010; Peter and Michael, 1988). In
general, the wide variety of atmospheric mineral dust morphologies will
produce a diverse scattering response for particles with the same Dve,
with the principal effect of increasing the uncertainty in optically
measured diameters. The degree to which sizing accuracy is affected depends
on the degree and distribution of particle asphericity, surface roughness,
the local steepness of the scattering intensity curve, and the angular
collection geometry of the spectrometer. For particles that are freely
rotating and not aligned with the instrument sample flow, as is the case
near atmospheric pressure, optical mis-sizing may be minimized because the
scattering response is an average of multiple particle orientations. In the
current treatment all particles are assumed to be spheres for the purposes
of optical sizing.
3.5 Particle densities and dynamic shape factors
PALMS measures the aerodynamic diameter, Da, for > 90 % of
the chemically analyzed particles. For each particle Da is converted to
a volume-equivalent diameter, Dve, using the particle density ρ,
dynamic shape factor χ, and the Cunningham slip correction factor,
Cc.
In the limit of continuum flow β=0.5, and for free molecular flow
β=1 (DeCarlo et al., 2004). Particles exit the aerodynamic focusing
lens at approximately 35 hPa and accelerate into a vacuum region at
∼0.25 hPa where their aerodynamic size is measured by the
time difference in scattering events from two laser beams spaced 33.1 mm
apart. During acceleration, particles have Knudsen numbers of 1–20 and
therefore experience flow that is near the free molecular regime. A
comprehensive model that considers transitional flow is used to convert
aerodynamic diameter measured by PALMS to Dve (Murphy et al., 2004a).
For submicron particles the model predicts β > 0.89, such
that the measured aerodynamic diameter is near the free molecular limit
(Da∼Dva). As size increases beyond ∼1µm, particle motion in the PALMS inlet becomes more transitional,
e.g., β=0.75 for 3 µm. The dynamic shape factor under these
conditions for most particles is close to the vacuum shape factor χv, which can deviate significantly from shape factors measured at
atmospheric pressure (Alexander et al., 2016; Dahneke, 1973a, b). We
further discuss free molecular shape factors for mineral dust below. Dry
particle densities and shape factors for each composition class are listed
in Table 1. Particle density for each measured particle is determined using
one of three methods: (1) prescribed based on literature values for the
observed particle type, (2) calculated using prescribed values for pure
particle sub-components and their relative component abundance, or (3) estimated here using simultaneous optical and aerodynamic measurements.
Optical scattering intensities are measured in PALMS as particles pass
through two detection lasers. Scatter intensities are not directly used for
individual particle sizing for several reasons: (1) the particle stream is
wider than the laser cross section so that particles experience inconsistent
laser intensities, (2) Mie oscillations produce a relatively flat scattering
intensity from 0.5 to 1.0 µm, and (3) the photomultiplier signals begin
to saturate at Dve > 0.6 µm. However, when averaged
over hundreds of particles, the scattering response yields information about
particle density, shape, and refractive index for submicron sizes (Moffet
and Prather, 2005; Murphy et al., 2004a). Figure 5 shows simultaneous PALMS
measurements of optical scattering intensity (Iscat) and aerodynamic
diameter from one scattering laser for several different particle classes.
For a given Dve, a higher particle density translates into larger
Da values, yielding an Iscat–Da curve further toward the right
side of the graph. A larger shape factor will shift curves further to the
left.
Figure 5PALMS simultaneous optical and aerodynamic diameter
measurements during the SEAC4RS airborne campaign. (a) Raw scatter
intensities versus measured aerodynamic diameters for populations of
different particle classes. Curves toward the right side represent particle
with higher density ∕ shape factor ratios, and a large real refractive index
shifts curves upwards for these sizes. Sub-populations of the
sulfate–organic–nitrate particle class are plotted for nearly pure (mass
fraction > 0.9) organic (green) and sulfate (red) particles. Lines
are the average of 100–72000 particle measurements. (b) Aerodynamic
diameters are converted to volume-equivalent diameters by prescribing
density and shape factors to each particle. Divergence at D > 0.5 µm is due to Mie resonances, which are highly sensitive to refractive
index. (c) Calculated Mie scattering intensities at λ=405 nm are
plotted for different refractive indices (n-ki) that correspond to
composition classes.
After converting each particle's Da to Dve, the scattering curves
converge for Dve < 0.5 µm (Fig. 5b). A single
relationship is expected between physical diameter and optical scattering
intensity for all spherical particle types with similar refractive index.
The Iscat–Da analysis in Fig. 5b helps validate prescribed densities
and shape factors for known particles and also provides guidance for unknown
particle types. Particle types with known density and shape factors, such as
pure sulfuric acid, ammonium sulfate, and known organic species, act as
internal standards.
All particles in the non-refractory classes (SO, BB, MT, FO) are internal
mixtures of sulfate, organic material, and other minor components. Density
is calculated for each particle in these classes as a weighted average of
the pure component densities using the measured organic-to-sulfate mass
fraction (see Sect. 3.7). Nitrate content is not considered in deriving
density because nitrate is difficult to differentiate from ammonium and
other nitrogen species in PALMS positive ion spectra. In regions where
ammonium nitrate is the dominant aerosol constituent, particle density and
water content can be similarly calculated (Clegg et al., 1998) using an
estimated aerosol nitrate or total nitrogen calibration.
Density values for ammonium-sulfate–water and sulfuric-acid–water solutions
are calculated at the temperature and RH of the instrument inlet (Clegg et
al., 1998; Vehkamäki et al., 2002; Wexler, 2002). Sulfuric acid can
retain ∼10 %–20 % water even under very dry (RH ≪ 1 %) sampling conditions, although additional water evaporation
will take place in the PALMS low pressure inlet and vacuum region (Murphy,
2007; Zelenyuk et al., 2006a). The density for pure organic material is
prescribed as 1.30 g cm−3 for SEAC4RS (Fig. 5) and other continental
sampling campaigns, approximately the middle of a range of typical values
observed in continental air, 1.2–1.45 g cm−3 (Cross et al., 2007;
Turpin and Lim, 2001; Vaden et al., 2011; Zelenyuk et al., 2010, 2015).
Organic density increases with oxidation level (Kuwata et al., 2012) as
particles age in the atmosphere. Consequently, for the ATom remote
troposphere an Iscat–Da analysis like Fig. 5 indicates higher
average organic densities of 1.35–1.45 g cm−3. The density of organic
material in biomass burning has a similar range, 1.2–1.45 g cm−3 (Reid
et al., 2005; Zelenyuk et al., 2015; Zhai et al., 2017), and 1.25 g cm−3
is prescribed here for continental US sampling and 1.35 g cm−3 for the
ATom campaigns.
Refractive index differences affect the vertical position of the curves and
shift the size where the optical response flattens out. Mie scattering
intensity curves are plotted to demonstrate the effect of refractive index
in Fig. 5c. For the PALMS wavelength and collection geometry, increasing
the real and decreasing the imaginary refractive index gives increased scatter
intensity for D < 0.5 µm. Large deviations beyond the typical
range of 1.44–1.54 for atmospheric constituents are required to shift the
response curve beyond typical variability. Elemental carbon stands out due
to its large real refractive index and strong absorption. For most particle
types, the Iscat–Da curves are far more sensitive to density and
shape parameters than the refractive index.
Elemental carbon (EC) particles are assumed to be composed of black carbon
(BC), and particle density is prescribed at 1.8 g cm−3 based on the
density for pure BC of 1.8–2.1 g cm−3 (Bond and Bergstrom, 2006; Lide,
2016; Park et al., 2004). Shape factors for EC particles in this size range
will vary widely depending on the morphology of aggregates. Uncoated BC
particles have χt values that range from 1.0 to > 3.0
depending on size (Khalizov et al., 2012; Slowik et al., 2007). χv=2.0 is prescribed here, based on the assumption that relatively
pure EC may exist as chain aggregates that have not fully collapsed into a
quasi-spherical shape (Schnitzler et al., 2014). The density and shape
factors for EC are less well constrained than other particle classes since
the unique refractive index renders the Iscat–Da analysis
ineffective (Fig. 5b), and uncertainties in derived concentrations are
accordingly higher.
Sea salt aerosol, when fully dehydrated to anhydrous inorganic salts, has a
density of 2.1–2.2 g cm−3 (Lewis and Schwartz, 2004; Zelenyuk et al.,
2005). However, dried ambient sea salt particles have lower densities due to
retention of water even after efflorescence (Cziczo et al., 1997; Shinozuka
et al., 2004; Tang et al., 1997; Weis and Ewing, 1999) and to internally
mixed organic material. In the marine boundary layer where most sea salt is
sampled, particles exist as solution droplets since the ambient RH is
greater than the efflorescence RH (ERH) of 40 %–45 % (Cziczo et al., 1997;
Tang et al., 1997). Upon sampling, particles are dried to RH ≪ 45 % in the sampling lines, yet water does not fully evaporate.
The Iscat–Da analysis yields a sea salt aerosol density ∕ shape factor
ratio of 1.45, which is close to the 1.41 g cm−3 density of a spherical
sea-salt–water particle at the ERH of ∼45 % (Tang et al.,
1997; Zhang et al., 2005). This suggests that efflorescence did not occur
during the 0.5 s residence time between sampling and analysis and that most
sea salt in PALMS is analyzed as a metastable solution. Sea salt density is
prescribed at 1.45 g cm−3 with a shape factor of χv=1.
Occasionally during research flights, particles were sent through a 300 ∘C thermal denuder for 3.3 s prior to analysis. The
Iscat–Da analysis indicates that the heated sea salt fully
effloresced and, assuming a dynamic shape factor of χv=1.08
(Beranek et al., 2012), had a density of 1.8 (see Fig. S3). We presume that
efflorescence was complete during the 2–3 s residence time in the actively
dried transfer tubing prior to sampling with optical particle spectrometers.
Mineral dust particle densities for the large majority of crustal minerals
are typically 2.5–2.65 g cm−3 (Davies, 1979; Kandler et al., 2009;
Linke et al., 2006), with a few subtypes such as hematite having higher
values. Shape factors are invariably χt > 1 with a
typical range of 1.3–1.5 (Davies, 1979; Kulkarni et al., 2011; Linke et al.,
2006). χt denotes the dynamic shape factor measured at
atmospheric pressure, which is in transitional flow but near the continuum
flow limit, i.e., χt (1 atm) ∼χc. Most
ambient mineral dust is coated by secondary organic and inorganic material,
which reduces both particle density and shape factor. Preliminary PALMS
laboratory studies indicate that typical organic coatings add 5 %–10 % to
the mass of ambient dust particles, which reduces the density of a 2.65 g cm−3 dust particle to 2.4–2.5 g cm−3 and will also reduce the
shape factor slightly. However, prescribing a density of 2.5 and shape
factor of 1.4 yields Iscat–Da curves for dust that are clearly not
consistent with other particle types (Fig. S3). To achieve internal
consistency, dust must either be prescribed an implausibly low particle
density of ∼2.0 g cm−3 or a larger shape factor. Based
on the Iscat–Da analysis a density of 2.5 g cm−3 and χv of 1.6–1.8 is prescribed to all ambient dust particles. Large shape
factors for ambient mineral dust are discussed in Appendix B.
Scattering intensities and sizes for additional particle types from Table 1
are shown in Fig. S3. Biomass burning particles are ∼80 %–90 % organic material by mass soon after emission (Cubison et al.,
2011; Levin et al., 2010; May et al., 2014), and they acquire sulfate,
ammonium, and nitrate upon further aging, whereby density increases to
1.4–1.5 g cm−3. In the stratosphere meteoric particles are nearly pure
sulfuric acid (∼1.7 g cm−3) with small meteoritic
inclusions, and they acquire organic material upon mixing into the
troposphere, whereby their density decreases. Particles from heavy fuel oil
combustion are mostly composed of mixed sulfate and organic material with
trace industrial metals and typically have a density of 1.3–1.6 g cm−3.
As stated above, for the concentration analysis, individual particle
densities for these three classes (BB, MT, FO), as well as
sulfate–organic–nitrate particles (SO) are calculated from relative sulfate
and organic mass (see Sect. 3.7). Alkali salts have a density ∕ shape factor
ratio of approximately 1.5, which, assuming spherical shape, is well below
the 2.0–2.6 g cm−3 density typical of crystalline alkali sulfates,
chlorides, carbonates, or oxides. It is possible that like sea salt, these
alkali salt mixtures have not fully effloresced during sampling, and the
retained water lowers their particle density.
3.6 Detection efficiency
The efficiency of acquiring single-particle mass spectra from aircraft
platforms depends on many factors including sampling biases of the aircraft
inlet, losses in sample tubing, transmission through critical orifices and
focusing lenses, particle beam dispersion upon entering a vacuum, sensitivity
of the optical detection system, and targeting accuracy with the
desorption–ionization laser. In practice the detection efficiency at small
sizes (D < ∼0.3µm) is limited by the optical
scattering signal-to-noise ratio and is a strong function of size, and large
particle (D > ∼1.5µm) detection is limited by
impaction losses in tubing and instrument inlets. Detection efficiency is
not used directly in deriving particle type concentrations presented here,
yet it is an important diagnostic to assess SPMS performance over the
dominant size modes in the aerosol distribution.
PALMS detection efficiencies are calculated for flight segments with fairly
constant and low concentration (about < 0.1 µm3 cm−3), when the particle rate is not limited by hardware or software
and therefore dead time is minimal. Measured Da is converted to
Dve for all particles, and the total counts in each size bin are
converted to an apparent concentration using the PALMS flow rate and
sampling time. The PALMS observed concentrations are divided by OPS
concentrations to determine detection efficiency. Figure 6 shows detection
efficiencies for two airborne campaigns. Although the curves show that
average instrument performance is similar across different campaigns,
variability within and between individual flights can be large. Within any
given flight, the detection efficiency at a particular size routinely varies
by ×2–×5, and variations of > ×10 are not uncommon between
flights. This variability is due to a variety of the factors listed above,
many of which change with ambient pressure. In particular, changes in the
overlap between the particle beam and laser beams will dramatically affect
the detection efficiency for all sizes or a range of sizes. This variability
in detection efficiency affects all particle classes nearly equally for a
given particle size. A reduced detection efficiency does not directly impact
particle type concentrations, but it can increase the statistical
uncertainties and sampling time required to generate concentration products.
A tempting alternative to the method presented here (combining SPMS data
with coincident size distribution measurements) is to determine the SPMS
particle detection efficiency as a function of size under controlled
conditions, and then multiply this curve by the airborne size-dependent data
rate to yield a quantitative particle concentration, similar to SPMS scaling
methods used at ground sites (Bein et al., 2006; Jeong et al., 2011; Pratt
et al., 2009b; Shen et al., 2019). However, this approach is not recommended
due to many possible pitfalls and large, unquantifiable errors. The key
drawback is that the detection efficiency curve for PALMS and other SPMS
instruments is extremely steep as it ascends several orders of magnitude
across the accumulation mode from D∼ 0.1 to 0.5 µm, where
particle number concentrations are also changing by orders of magnitude. The
multiplication of these two strong functions, combined with the inherent
variability over different ambient conditions and instrument alignment, will
produce large and intractable uncertainties, e.g., > ×10 in mass.
Other problems include the following.
Both the measurement of detection efficiency and its application to derive
concentrations are only valid when the particle data rate is not
artificially limited by software or hardware or when instrument dead time
can be accurately determined. In practice, aerosol concentrations in many
lower tropospheric conditions and particularly inside plumes are high enough
that SPMS systems will far exceed their maximum acquisition rate and
generate erroneously low concentrations.
Small changes to the alignment of the particle beam with the detection
lasers, ionization laser, and ion extraction optics, which are not uncommon
on airborne platforms, have a large effect on particle detection efficiency.
Detection efficiency must also be re-determined after any routine alignment
adjustments. For PALMS the second detection laser beam is only
∼150µm wide.
The flow characteristics of pressure reduction orifices change with upstream
(ambient) pressure, changing particle trajectories downstream of the orifice
in ways that are very sensitive to physical alignment and may not vary
smoothly with pressure. One example is that pressure-controlled inlets can
act as virtual impactors that enhance concentrations above a certain
particle size that is both difficult to define and changes with pressure.
Upstream pressure reduction orifices routinely accumulate small amounts of
aerosol material that subtly change particle trajectories with large impacts
to detection efficiency (Fig. 6).
The approach described in Sect. 3.1, mapping the PALMS composition
measurements to independently measured size distributions without the need
to determine size-dependent detection efficiencies, circumvents these
complications.
Figure 6Detection efficiency curves for the PALMS instrument in
flight. Detection efficiency was calculated for several cases, identified as
clean flight segments when the particle data rate was not actively limited
by software or hardware. Thick lines and shading are geometric means and
standard deviations over all cases. Higher efficiencies for
Dve > 3 µm during ATom are partly due to addition of a
virtual impactor upstream of PALMS. Thin black lines are five individual
cases during one ATom-1 flight. Variations in altitude, particle
composition, inlet performance, and unknown factors result in detection
efficiencies that vary by > ×10 within and between flights. The
lowest thin line was a case where a buildup of aerosol material on the
pressure reduction orifice altered particle trajectories inside the inlet.
In addition to deriving concentrations for individual particle classes, the
subcomponents of internally mixed particles can also be quantified. Signal
intensity ratios in PALMS mass spectra for components of interest are
calibrated to known mass fractions in laboratory-generated aerosol
standards. By combining these calibrations with the particle class
concentrations described here, absolute mass fractions for aerosol
subcomponents such as sulfate and organic material can be determined. For
instance, the average sulfate mass fraction is first determined for all
non-refractory particle classes using mass spectral signal ratios, and then this
mass fraction is multiplied by total mass concentration of those particle
classes to yield an aerosol sulfate mass concentration. The resulting mass
concentrations for sulfate, organic material, metals, or other components
can be compared directly to bulk composition measurements from instruments
such as the AMS, SAGA, or a variety of offline analytical methods (see Sect. 4.1).
Murphy et al. (2006) derived sulfate and organic mass fractions from PALMS
negative polarity spectra by calibrating airborne data to a quadrupole AMS.
New calibrations for sulfate and organic mass fraction were performed on
positive mass spectra for the current study using realistic atmospheric
surrogate particles, shown in Fig. 7. Aqueous solutions were nebulized to
generate a submicron aerosol population that was dried to RH < 40 %
and sampled with PALMS. Solutions were composed of ammonium sulfate mixed
with varying amounts of sulfuric acid, sucrose, and mixed dicarboxylic acids
(see Table S1 in the Supplement). Positive spectra were very similar to free tropospheric
aerosol spectra in the SO particle class, confirming that the ionization
environment for calibration particles is representative. Another set of
calibration solutions contained ∼1 % potassium and
generated particle spectra that were very similar to biomass burning aerosol
in the atmosphere. Relative intensities were summed for C+, CH+,
CO+, and ions to represent the total organic signal. In
PALMS, these ions comprise > 90 % of the total organic ion
intensity in positive spectra for nearly all non-refractory particles in the
atmosphere. This efficient conversion of organic material into a few simple
ion species demonstrates a weak dependence of the principal mass spectra
signatures on organic aerosol species and facilitates calibration across
different particle types. The ion was excluded here due to
interference with Mg+ in the meteoric class. The sulfate signal is the
sum of S+, SO+, and ion intensities, and it is
similarly consistent across non-refractory particles.
Figure 7PALMS calibration of organic mass fraction for
atmospheric aerosol surrogates composed of neutralized and acidic sulfate
mixed with a variety of organic compounds (see Table S1). Blue points are
ammonium sulfate–sucrose–adipic-acid solutions, and red points are ammonium-sulfate–sulfuric-acid–dicarboxylic-acid solutions. (a) Raw signal fractions
(dashes) are fit to the solution organic mass fraction data (dotted line).
Symbols are averages of 130–1900 spectra. (b) Calibrated organic mass
fractions from PALMS (red and blue) confirm a linear response, with averages
that exhibit similar deviations to a quadrupole AMS. The dotted line is a
linear fit to PALMS data forced through zero. (c) Relative error (standard
deviation ∕ mean) for all calibration points as a function of the population
size used to calculate the average.
Figure 7a shows the PALMS response as a function of aerosol organic mass
fraction. The organic signal fraction, sforg, defined as the intensity
ratio of organic peaks ∕ (organic + sulfate peaks), is fit to the organic
mass fraction mforg using the following functional form.
The single parameter α represents the relative ionization efficiency
(RIE) of organic material to sulfate. No systematic differences were found
in the PALMS response to organic and sulfate mixtures with and without
potassium, indicating that organic and sulfate ionization is fairly
consistent across different ionization environments in non-refractory
particles. The calibration fit is applied to the signal fractions in Fig. 7b. The organic mass fraction for individual particles can have large errors
due to the inherent particle-to-particle variability in SPMS spectra.
However, errors are quickly reduced when averaging over a population of
particles. To estimate precision uncertainty, particles were arranged into
groups of increasing size, and the average organic mass fraction for each
population was compared to the solution mass fraction, Fig. 7c. The relative
standard deviation converged to 8 % when averaging ≥15 spectra, and
errors exhibited no trend with mass fraction (not shown). Coincident with
PALMS, an Aerodyne AMS using a quadrupole mass analyzer (Jayne et al., 2000)
measured sulfate and organic mass of the generated aerosol. In Fig. 7b,
average mass fractions from the AMS show similar deviations from the 1:1
line to the PALMS averages.
The calibration is applied to positive spectra for all non-refractory
particle classes, including SO, BB, MT, and FO. The organic or sulfate mass
concentration for an individual particle class can be calculated by
multiplying the mass concentration by the organic or sulfate mass fraction.
Similarly, total organic and sulfate mass concentrations are calculated as
the sum of organic and sulfate mass concentrations from all non-refractory
particle classes. These mass concentrations are conceptually comparable to
bulk aerosol analysis of organic and sulfate made by common online (e.g.,
aerosol mass spectrometry) and offline (e.g., ion chromatography)
techniques. By truncating the PALMS size range accordingly (Hu et al.,
2017), direct comparisons between PALMS and these other measurements can be
made. Total uncertainties in PALMS sulfate and organic mass concentrations
are estimated from uncertainties in the simplified size distribution (Sect. 3.3) combined with errors in mass fraction, OPS volume, particle
classification, and particle density (see Appendix A). For ATom campaigns
using 3 min sample periods, estimates of total relative uncertainties
(1σ) are 40 %–50 % for mass concentrations ≤0.01µg m−3 and 20 %–35 % at higher concentrations. Any potential biases due to
extrapolation of accumulation-mode composition to sizes below the PALMS size
range (Sect. 3.3) are not included.
Primary sulfate on sea salt or mineral dust, as well as secondary sulfate
accumulated on other particle types, is not included in the calibration
because sulfate ionization on refractory particles is complicated by
interferences and stronger matrix effects. The analysis assumes that dry
aerosol mass is composed entirely of organic and sulfate material.
Constituents like ammonium, nitrate, chloride, and alkali metals are
disregarded. In most free tropospheric environments these components account
for a small fraction of the aerosol mass. In the continental boundary layer
or under polluted conditions, higher levels of ammonium and nitrate will
introduce a high bias to the derived organic and sulfate mass
concentrations. Future calibration studies can address the aerosol total
nitrogen content from positive spectra or nitrate content using negative
spectra.
Figure 8Comparison of PALMS with other speciated aerosol mass
measurements. (a) PALMS derived sulfate mass at 3 min time resolution
compared to co-located AMS and SAGA filter samples during the ATom-1
airborne campaign. Sea salt sulfate () is subtracted from SAGA
data. SAGA filters taken over an altitude range > 3 km are
excluded. Small points are 3 min averages (blue) or represent one SAGA
filter measurement (orange). Large symbols are medians with interquartile
error bars. The solid black line is 1:1, and grey dashed lines are 1:1.5 and
1:2. The inset graph is a histogram of PALMS ratio to AMS or SAGA for all
individual samples. PALMS sulfate (b) and mineral dust (c) mass are compared
to nearby IMPROVE ground station data for three airborne campaigns. Each
point is the average of airborne data for non-targeted flight segments in
the continental boundary layer within 0.5∘ latitude and
1∘ longitude of an IMPROVE site that reported data that day.
IMPROVE data are 24 h averages, and airborne segments are typically
∼3–30 min duration during daytime. Error bars are estimated
statistical uncertainty calculated as described in Appendix A. Dashed lines
are weighted linear fits.
4.1 Comparison to other aerosol composition measurements
Figure 8 compares PALMS mass concentrations for sulfate and mineral dust
with other online and offline techniques during routine airborne
measurements. PALMS sulfate mass is calculated as the sum of all
non-refractory particle types (SO, BB, MT, and FO), accounting for the
sulfate mass fraction of each particle type (Sect. 3.7). PALMS sulfate mass
concentration shows excellent agreement with other airborne sulfate
measurements over several orders of magnitude (Fig. 8a). For the AMS
comparison the PALMS and LAS size ranges are truncated using the AMS
size-dependent lens transmission efficiency, which is similar to that
reported in Hu et al. (2017).
Few standard methods exist to derive accurate mineral dust aerosol
concentrations. The Interagency Monitoring of Protected Visual Environments
(IMPROVE) program performs routine aerosol composition measurements at over
200 ground sites throughout the US. Particles are collected on filters over
a 24 h period every few days. Bulk concentrations of aerosol components
are measured using particle-induced X-ray emission (PIXE), X-ray
fluorescence (XRF), and other offline analytical techniques. Sulfate is
derived from sulfur measurements assuming partial neutralization by
ammonium. Soil dust concentration is derived from crustal metal
concentrations and by applying a basic mineralogy. In Fig. 8b and c, PALMS
sulfate and mineral dust mass concentrations are compared to IMPROVE data
for airborne sampling in the continental boundary layer near an active
IMPROVE site. PALMS airborne data are truncated to Da < 2.5 µm
to match the IMPROVE size range. Spatial and temporal variability render
this an indirect comparison. Nevertheless, the sulfate measurements are
strongly correlated, suggesting that measurement colocation is reasonable
and that for sulfate, the snapshot obtained during short airborne segments
can often be representative of the daily average. Mineral dust is also
positively correlated but exhibits higher variability than sulfate. The
variability is large compared to estimated uncertainties, suggesting that
real atmospheric variability rather than measurement error is the cause.
This is not surprising given the different sources of sulfate and mineral
dust. Dust resuspension from land surfaces is a strong function of wind
speed, and localized wind patterns give ground-level dust a high variability
at small spatial scales, whereas secondary sulfate sources are more regional
in scope. Additionally, the different size ranges for sulfate and dust can
lead to different loss rates due to precipitation scavenging or
gravitational deposition. Despite the imperfect sampling overlap, the
qualitative agreement and positive correlation in Fig. 8c furthers
confidence in the ability of PALMS to measure absolute mineral dust mass
concentrations.
Figure 9Vertical profiles of PALMS mineral dust mass (a) and mass
fraction (b) over the continental US for Dve > 0.1 µm.
Lines are campaign average concentrations binned at 1 km intervals from
products generated at native resolutions of 3 min (SEAC4RS, DC3, NEAQS) or 5 min (MACPEX). Biomass burning plumes and clouds are excluded. By truncating
the size range using a typical cyclone impactor transmission curve with
Da (50 %) =2.5µm (http://www.urgcorp.com/, last access: 26 October 2019), these mass
concentrations are equivalent to a PM2.5 measurement. The MACPEX size range
is limited by the optical particle spectrometer to Dve < 1.5 µm. Positive error bars are one standard deviation.
To demonstrate the utility of the new quantification method, we calculate
the average mineral dust mass over the continental US. Figure 9 compares
PALMS mineral dust mass concentrations for four airborne campaigns that span
different regions and seasons. In general, the large majority of dust mass
was present in the coarse mode, Dve > 1 µm. For the
summertime campaigns, concentrations decrease steadily with increasing
altitude, suggesting that dust was emitted from regional sources and removed
during vertical transport. For the DC3 campaign the dust maximum occurred in
the mid-troposphere. This profile is consistent with Asian dust sources
contributing significantly to springtime US dust loadings (Chin et al.,
2007) and also to convective lofting of dust. The MACPEX campaign targeted
large-scale convective systems. The dust mass increase at 11–12 km is
consistent with deep convective outflow. For the SEAC4RS, DC3, and NEAQS
campaigns, the aircraft inlets and aerosol instrumentation measured sizes up
to 4 µm and captured nearly the entire coarse mode. An exception was a
weak Saharan dust plume encountered over the Gulf of Mexico, where external
cloud–aerosol probes showed particles up to 20 µm. Future publications
will explore mineral dust abundance and geographic distributions over
continental and remote regions.
5 Summary and recommendations
We present a new method to measure composition-resolved aerosol size
distributions and quantitative concentrations using single-particle mass
spectrometry (SPMS) combined with absolute particle concentration
measurements. Novel approaches include simplifying the size distribution
into a few size bins guided by compositional transitions, applying density
and shape factors that are validated using optical–aerodynamic sizing
consistency to each particle, and avoiding the reliance on SPMS detection
efficiency to generate concentration products. Using this method PALMS can
determine particle type concentrations for sample periods that are
> 10 times shorter than previous studies, and under ambient
aerosol loadings that are > 100 times lower. This method is
successfully applied to background free tropospheric conditions with typical
time resolutions of a few minutes, thereby introducing a critical new
capability for fast-response measurement of mineral dust aerosol
concentration from aircraft platforms. Other common refractory and
non-refractory particle concentrations are also determined, including sea
salt, sulfate–organic internal mixtures, biomass burning, heavy fuel oil
products, and aerosol chemical components distributed across many
particle types such as sulfate and organic material.
The principal strengths of this approach are summarized below.
PALMS and many other SPMS instruments fundamentally classify individual
aerosol particles into distinct composition types. PALMS detects all major
particle types in the atmosphere, including refractory particles.
Particle types and sub-types can be defined flexibly to suit a particular
science objective, e.g., particles of stratospheric versus tropospheric
origin. Definitions can be refined to characterize newly discovered particle
types.
There is a fast time response on the order of 1–5 min for concentrations of
∼10 ng m−3 (see Appendix A).
Intermittent clouds, plumes, or other events within a sample period can be
excluded at high time resolution, e.g., 1 s.
Concentration products can be derived for many historical PALMS and other
SPMS datasets that have coincident particle size distribution measurements.
The size distribution measurement can employ any of several standard sizing
techniques (Dopt, Dmob, Da), for which many commercial units
are available.
Stable SPMS detection efficiency is not a prerequisite. Variations in
size-dependent detection efficiency due to different sampling conditions or
instrument configurations only affect the product time resolution and
uncertainty, not the derived concentrations. The alternative method that
scales observed SPMS detection rates by fixed detection efficiencies results
in large, unquantifiable uncertainties in integrated number, surface, and
volume.
PALMS differentiates externally mixed particle types based on mass spectral
signatures. Climate-relevant particle types such as mineral dust, biomass
burning, and sea salt are readily distinguished, giving PALMS and other SPMS
instruments the unique capability to measure these important primary aerosol
species with high time and size resolution. Quantification of rare types
from unique emission sources is also possible, such as industrial metallic
particles and bioaerosol. In addition to concentrations for individual
particle classes, calibration of mass spectral signal ratios allows for
quantification of sub-components within a particle class or across several
classes. Aggregate sulfate and organic masses are determined here by summing
the contributions over the non-refractory particle classes.
Time resolution and statistical accuracy for aircraft studies are primarily
limited by the data acquisition rate across the entire accumulation and coarse
modes. Reducing native size resolution to 3 or 4 bins improves sampling
statistics and allows for faster time resolution, while introducing modest
systematic errors (typically 5 %–25 % in volume).
Particle densities and dynamic shape factors for each particle class are
prescribed from literature or determined by simultaneous optical and
aerodynamic size measurement in PALMS. Densities for particle types that
are predominantly sulfate and organic material by mass are determined from
their mass fraction. Density and shape values affect the size bin alignment
between PALMS and the particle size spectrometer. Particle type
concentration products are more sensitive to prescribed density and shape
than to assumptions of particle refractive index, with the exception of
highly absorbing species like BC. In agreement with limited literature
studies, we find that the dynamic shape factor for irregular particles such
as natural and synthetic mineral dust can be significantly higher under
near-vacuum conditions than at atmospheric pressure.
We conclude with several recommended guidelines for adopting this method for
airborne SPMS measurements.
SPMS users should at least recreate Figs. 3, 4, 5, and A1, and also an OPS
counting efficiency and sizing accuracy assessment (Kupc et al., 2018), to
help estimate the principal components of concentration uncertainty in their
respective systems.
User-selectable parameters for particle type concentration products include
definition of particle types, the sample averaging time, number and range of
size bins, minimum number of mass spectra per size bin, and densities and
shape factors for all particle types.
SPMS users should estimate systematic biases due to particle composition,
which may include a lack of detectable ions for certain particle types
(Hatch et al., 2014; Marsden et al., 2018; Wenzel et al., 2003) or diverging
particle trajectories inside the vacuum (Huffman et al., 2005; Vaden et al.,
2011) that can lead to different detection efficiencies between spherical
and non-spherical particles, particularly for instruments for which particles
transit a long distance.
Increasing the size range and improving the detection efficiency of
single-particle instruments across the size mode(s) of interest are the most
important parameters for reducing biases, systematic errors, and statistical
uncertainties that translate directly into faster time resolution.
Aircraft inlets and size distribution instruments must demonstrate effective
transmission and detection efficiency through the coarse mode, which in the
background continental troposphere extends to Dve≥4µm and
can be larger in plumes or at low altitudes. In the presence of dust events
or in the marine boundary layer, nearly all aircraft inlets will sample only
a minor fraction of the coarse-mode mass (Brock et al., 2019).
Desired attributes for airborne optical particle spectrometers include a
size range that covers the full accumulation mode (D∼0.06–1 µm) and a large fraction of the coarse mode (D∼1–10 µm), a sample flow rate of ∼ > 1 L min−1 to allow
reasonable statistical sampling times for coarse aerosol but while limiting
coincidence errors for small particles, and the ability to operate with a
large pressure difference between instrument interior and a pressurized
aircraft cabin. The combination of a dedicated accumulation-mode instrument
with a separate coarse-mode instrument operating at a higher sample flow is
advantageous.
Sampling lines can be actively dried to remove aerosol water. Otherwise,
residual water must be accounted for in prescribing particle density and for
OPS sizing when deriving dry volume and mass concentrations. Inlet RH should
be kept below 40 % for sea salt, and preferably lower for sulfuric acid or
sulfate internally mixed with organics. Operators should consider trade-offs
between active drying and possible loss of volatile aerosol material.
Airborne sampling inside water and ice clouds produces a variety of artifact
particles. Even brief cloud segments can perturb average concentrations by
large factors, particularly for coarse-mode and refractory aerosol, although
submicron non-refractory measurements are also affected (Cziczo and Froyd,
2014; Murphy et al., 2004b; Weber et al., 1998). Artifact contributions to
measured aerosol properties are difficult to predict, and measurements
inside clouds using typical aerosol inlets should be considered suspect
unless they have been thoroughly validated under specific cloud conditions.
Appendix A: Uncertainty sources and limits of detection
The principal uncertainty sources in deriving SPMS particle type
concentrations are OPS counting and sizing errors, simplification of the
size distribution (Sect. 3.3), and the statistical noise for detecting
individual particle types within each size bin. Minor contributors to
uncertainty include density and shape factor errors, provided they can be
validated or constrained in the SPMS system, and particle classification
errors. Estimation of total measurement uncertainty is not always a simple
propagation of individual error sources because compensating factors can
buffer some types of error. For example, an error in Dve due to
inaccurate density or dynamic shape factor that does not shift the
particle to a different size bin would not contribute to any additional
uncertainty in number, surface, and volume concentrations (Sect. 3.5).
Another example is that poor SPMS particle statistics within a size bin will
contribute a variable amount of uncertainty to total concentration,
depending on the relative concentration within that bin (Sect. 3.3). Error
sources vary for different SPMS instruments, the chosen methodology
parameters (Sect. 4), and the sampling environment.
A1 Size distribution
Overall OPS uncertainty is dominated by systematic uncertainties from the
sample volume measurement, the error in prescribed refractive index, and
counting statistics. Given reasonable constraints on refractive index,
typical overall number, surface area, and volume uncertainties for the
accumulation mode for 3 min samples are ∼2 %, ∼10 %, and ∼15 % for aerosol loadings of ≥0.1µm3 cm−3. In very clean environments (N < 10 cm−3) or for
supermicron sizes where concentrations are often very low (N ≪ 1 cm−3), statistical sampling limitations are higher, and
longer sampling times or regional averages are recommended to reduce
statistical error. See relevant error analyses in Brock et al. (2011, 2019)
and Kupc et al. (2018).
A2 Particle classification
Particle classification error can contribute to particle type concentration
uncertainty, but is typically lower than other error sources. Classification
error is difficult to determine for all principal atmospheric particle types
(Table 1) due to a lack of accurate reference measurements under realistic
atmospheric conditions, e.g., mineral dust and biomass burning. Laboratory
experiments for surrogate particles can help estimate typical classification
error. For a mixed sample of laboratory air and resuspended Arizona Test
Dust (Power Technology, Inc), a compositionally diverse dust surrogate, a
manual verification of 2500 particle spectra classified as dust showed
< 3 % classification error. Errors can sometimes be estimated by
sampling in atmospheric environments overwhelmingly dominated by one
particle type, such as the remote marine boundary layer for coarse sea salt,
thick dust plumes for coarse mineral dust, and thick smoke plumes for
biomass burning particles. A manual inspection was performed of 1255
particle spectra with sizes Dve > 1 µm sampled during
ATom-1 pristine MBL periods. PALMS classification routines identified 1094
sea salt spectra, for which 2 (0.2 %) were false positives, and 9 sea salt
spectra (0.8 %) were mis-classified as other particle types. Typical PALMS
classification errors are < 5 % for all classes in Table 1 and
represent a minor contribution to particle type concentration uncertainty.
However, mis-classification of a common particle type as a rare particle
type can contribute a larger relative uncertainty. For instance, if 10 %
of biomass burning particles were mis-classified as mineral dust during the
SEAC4RS campaign, dust volume concentration would be anomalously high by
30 %. SPMS users should consider possible biases that systematically
suppress the identification of a particular particle type due to low ion
signal (e.g., sulfuric acid) or poor-quality spectra (e.g., mineral dust).
A3 Density and shape factor
Density and shape factor errors affect conversion to Dve and also the
conversion of volume to mass concentration. Density uncertainties should be
determined for each SPMS particle type and possibly for particle
subcomponents such as sulfate and organic material. Simultaneous optical and
aerodynamic particle sizing (Sect. 3.5) or similar methods help constrain
prescribed values. Estimated PALMS uncertainties are ±0.1 g cm−3 for
non-refractory particle type densities, ±0.15 g cm−3 for dust and sea
salt densities, and ±0.15 for the dynamic shape factor of mineral
dust. The EC particle type is less well constrained in both density and
shape. For common particle types, such as sulfate–organic–nitrate, when
hundreds of spectra are observed during a sample period, statistical sampling
uncertainties are reduced to levels where density and dynamic shape factor
errors can begin to compete. For rare particle types, statistical
uncertainties dominate (see below), and dynamic shape factor errors are
typically minor contributions.
A4 Statistical uncertainties in volume concentrations
In this section we consider the uncertainty in PALMS particle type volume
concentrations due to statistical sampling limitations. Statistical
uncertainties for each particle type can be estimated at every time point by
assuming Poisson statistical behavior. Uncertainties for the particle number
fractions for each class are determined in each size bin and are propagated
through the multiplication of number fractions by optical particle
spectrometer concentrations. The total uncertainty in volume concentration,
δVi, for particle class i across all diameter bins d in a
sampling period with Nd total particles is determined as follows. The
uncertainty for each particle class and size bin is based on the assumption
of a Poisson probability distribution. To treat particle counts as
independent variables we define Nj,d as the number of counts for all
non-i classes.
Particle classification errors would add to the count uncertainties in Eqs. (A2) and (A3). The number fraction of particles in class i and size bin d is
fi,d with an uncertainty δfi,d that is determined from error
propagation formulae.
The uncertainty in the volume for each class and bin is δVi,d, which is determined from the number fraction uncertainty and
volume measured by the particle spectrometer, Vd. In order to limit this
investigation to statistical uncertainties in PALMS particle class
concentrations, the uncertainty in the particle spectrometer measurement is
ignored here, i.e., δVd≡0. In practice, users should
include δVd in Eq. (A8) when estimating total concentration
error.
To then calculate the mass concentration for a particle sub-component such
as sulfate, Msulf (see Sect. 3.7), the mass concentration Mi for
class i consisting of all non-refractory particles is determined using
particle densities, ρi, and the average sulfate mass fraction,
mfsulf,i, is applied.
Note that particle classification error between non-refractory particle
types does not contribute any uncertainty to sulfate mass concentration if
the particle classes all use the same calibration function (Fig. 7).
It should be noted that Poisson statistical errors for zero- and low-count
samples can be problematic. The estimated standard deviation of 0 % for
zero-count samples is not realistic. Likewise, the 100 % standard
deviation for one-count samples is often unsuitably large for rare particle
types. This extreme variability of sample-to-sample error for rare particle
types can render single-sample error estimates of little practical value.
This problem becomes more prevalent with the increasing number and specificity
of user-defined particle classes. A possible alternative to deriving
reasonable single-sample error estimates for rare particle types is to
determine a true mean and true standard deviation across multiple samples
within a similar atmospheric environment. Despite these limitations, the
Poisson model is self-consistent such that estimated errors propagated
across multiple samples converge properly to the true overall error, and the
standard Poisson model is presented here.
Figure A1(a) Estimates of statistical uncertainties from the
12 August 2016 ATom flight for common particle types (sulf/org/nitrate, biomass
burning, and MBL sea salt) and rare particle types (dust, meteoric, sea salt
outside the MBL). Each point represents one 3 min measurement. (b) Relative
errors for two flights are plotted versus volume concentration of each
particle type. Errors for sulfate mass concentrations in (a) and (b) also
include errors propagated from particle density and sulfate mass fraction.
(c) The variation of statistical error with volume contribution and the
number of analyzed spectra for 3 min time periods during the two flights in (b). (d) Analysis of the airborne sampling time necessary to acquire 50
particle spectra. Points are from three diverse flights in the free
troposphere (> 3 km), and the line is the median.
A5 Statistical analysis applied to PALMS airborne measurements
Figure A1a shows how statistical uncertainties for five particle classes
vary with their volume concentration for one aircraft flight. The flight was
chosen to represent a variety of tropospheric environments and includes both
very clean conditions and an MBL with a substantial coarse mode. Figure A1b
summarizes statistical errors for coarse particle concentrations of mineral
dust and sea salt, and for sulfate mass determined from non-refractory
accumulation-mode particles. Figure A1b also includes data from a second
flight with elevated concentrations of upper tropospheric mineral dust.
Minor particle types with few particles per sampling period have lower
derived concentrations and higher relative uncertainty. At low volume
concentrations of ∼0.01µm3 cm−3 typical
(interquartile) errors for a 3 min sample are 50 %–80 % for rare particle
types such as dust and sea salt, and 10 %–30 % for common particles such as
sulfate–organic–nitrate. Relative errors decrease to 20 %–50 % and 5 %–10 %,
respectively, for concentrations ≥0.01µm3 cm−3. Propagation
of density and sulfate mass fraction errors result in typical sulfate mass
concentration errors of 15 %–40 % at 0.01 µg m−3 and 10 %–25 % for higher concentrations. The convergence of PALMS
concentration standard deviations toward 100 % below ∼0.01µm3 cm−3 is a consequence of low-count samples for rare classes,
whereby many samples should be averaged to reduce statistical noise. Figure A1c illustrates how statistical noise depends both on sample population and
absolute abundance and is also a complex function of how particles observed
by PALMS are distributed across the dominant size modes. For common particle
types that account for > 30 % of the total volume, the relative
statistical error in a 3 min time period is reduced to < 30 % when
> 50 particles are sampled in the dominant volume mode.
PALMS and other SPMS instruments have zero background at zero concentration.
Since background subtraction is not required, the lower limit of detection
(LLOD) depends only on the measurement uncertainty and can be estimated as
1.645 times the standard deviation of a low concentration sample (Armbruster
and Pry, 2008). Since the detection efficiency at the time of measurement
can vary over an order of magnitude and is most sensitive to instrument
inlet and laser alignment but also to ambient pressure, the actual LLOD
throughout a measurement period can only be estimated.
A simple estimate of number concentration LLOD can be made from sampling
counts and an average detection efficiency. The minimum detectable
concentration depends on particle size since the PALMS detection efficiency
increases from for particles 150 nm in diameter to
∼0.05 for 500 nm particles to ∼0.1 for
supermicron sizes (see Fig. 6). The peak of the PALMS size distribution is
typically about Dve=400 nm. In a 1 min sample period, detection
of one 400 nm particle corresponds to a concentration of approximately 0.07 cm−3, with an LLOD of ∼0.1 cm−3. For Dve= 150 nm the LLOD is ∼20 cm−3.
Estimation of LLOD for the derived concentration products is more complex.
The particle volume LLOD depends on both the PALMS size response and the
shape of the volume size distribution. The examples in Fig. A1 show that
typical uncertainties in particle volume are still quantifiable at very low
atmospheric concentrations. Although LLODs can be estimated from the
uncertainties in Fig. A1, the statistical analysis does not provide an
obvious volume below which quantification is not feasible, nor can a single
LLOD value be derived. The statistical analysis (Fig. A1c) does, however,
provide a good rule of thumb that a minimum of 50 total spectra are needed
to generate volume concentrations for the dominant particle types within
acceptable statistical noise of around < 30 % using three size bins.
Figure A1d shows the sampling time required to measure 50 particles in the
free troposphere. Under typical lower tropospheric conditions with an
accumulation-mode volume of ∼1µm3 cm−3, PALMS
can quantify the dominant particle types in < 1 min of sampling. In
very clean conditions of 0.01 µm3 cm−3 occasionally observed
in the upper troposphere, PALMS needs a median sampling time of 1 min.
Fig. A1a and b show that at concentrations below ∼0.01µm3 cm−3 statistical uncertainties increase toward 100 %. These
considerations suggest a reasonable estimate of LLOD as 0.01 µm3 cm−3 in 1 min of sampling for dominant particle types. On the
other hand, particle types with low relative abundance may require thousands
of sampled spectra to determine their concentration to an uncertainty of
< 30 %. Since the derived concentration is also based on size
distribution measurements, the optical particle spectrometer sample flow and
detection efficiency could potentially contribute to the detection lower
limit. However, most particle sizing instruments have a LLOD that is similar
to or better than PALMS.
Appendix B: Shape factors for mineral dust
We briefly discuss evidence to support large mineral dust shape factors
observed by PALMS. Experimental studies of irregular particles including PSL
aggregates, soot aggregates, and quartz particles demonstrate that χv > χt, and χv can often approach
(Alexander et al., 2016; Zelenyuk et
al., 2006b). This higher relative drag force for irregular particles under
free molecular flow is consistent with theoretical treatments by Dahneke (1973a, b) for idealized shapes such as cubes and spheroids. However, no
simple general relationship exists between χt and χv
for realistic particles because the increase in particle drag in free
molecular conditions is a complex function of particle physical shape and
surface morphology. For example, oblate and prolate spheroids behave
differently, and concave surface features were not considered by Dahneke.
Furthermore, when particle Reynolds numbers exceed ∼0.1,
particles begin to partially align with accelerating flows, increasing their
apparent shape factor (Dahneke, 1973a; Kulkarni et al., 2011).
Following previous SPMS studies (Alexander et al., 2016; Zelenyuk et al.,
2006b), a laboratory experiment was performed to investigate the large
apparent χv for dust. A differential mobility analyzer (DMA) was used
to select commercial illite NX particles (Arginotec) with a fixed mobility
size, Dmob, and the distribution of aerodynamic sizes was measured by
PALMS. Given the material density of 2.65 g cm−3 and shape factor at
conditions of the DMA of χt (∼1 atm) (Hiranuma et al., 2015), one can derive the free molecular
shape factor χv (DeCarlo et al., 2004; Zelenyuk et
al., 2006b),
where Cc is the Cunningham slip correction factor under DMA conditions.
For particles with Dmob=0.580µm the PALMS most probable
aerodynamic diameter was Da=0.485µm, which when adjusted
slightly to free molecular flow gives Dva=0.500µm. This
yields an average shape factor of χv=2.27, which is
significantly higher than χt. A second method to derive χv uses a parameterization of χtχv for a variety
of irregularly shaped particles (Zelenyuk et al., 2006b). Using equation 11
of that reference gives an even larger value, χv=2.58. A
third independent method uses the internal consistency of PALMS optical and
aerodynamic diameters (Fig. 5b), which gives approximately χv=2.3 for illite NX. Ambient mineral dust particles sampled during the lab
study had lower derived shape factors of χv=1.7, which is
comparable to values derived from dust in airborne studies but is still
higher than typical transitional shape factors for dust (χt∼1.4; Davies, 1979; Kulkarni et al., 2011; Linke et al.,
2006). These experiments confirm that for dust particles, the shape factor
near free molecular flow can be significantly greater than in other flow
regimes, i.e., χv > χt∼χc.
KDF designed the study and performed airborne aerosol composition and
microphysical measurements. DMM, PCJ, JED, JLJ, and GPS performed airborne
aerosol composition measurements. CAB, AK, KLT, CJW, JCW, and LDZ performed
airborne aerosol microphysical measurements. GPS and AMM performed
laboratory aerosol composition calibrations.
Competing interests
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Acknowledgements
The authors
gratefully acknowledge the following people for their valuable input: Jin Liao, Daniel Cziczo, Troy Thornberry, Paula Hudson, and David Thomson.
Financial support
Financial support
PALMS is supported by NOAA internal climate funding and also in part by NASA awards NNH12AT29I and NNH15AB12I. Pedro Campuzano-Jost and Jose-Luis Jimenez were supported by NASA awards NNX15AH33A, NNX15AT96G, 80NSSC19K0124, and 80NSSC18K0630.
Review statement
Review statement
This paper was edited by Mingjin Tang and reviewed by Nicholas Marsden and Robert Healy.
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Single-particle mass spectrometer (SPMS) instruments characterize the composition of individual aerosol particles in real time. We present a new method that combines SPMS composition with independently measured particle size distributions to determine absolute number, surface area, volume, and mass concentrations of mineral dust, biomass burning, sea salt, and other climate-relevant atmospheric particle types, with a fast time response applicable to aircraft sampling.
Single-particle mass spectrometer (SPMS) instruments characterize the composition of individual...