Plain Language Summary
The extent of atmospheric chemical processing remains an uncertain
aspect of air mass characterization. Addressing this uncertainty is
important because chemical reactions in the atmosphere in the presence
of water (aqueous processing) produce a large fraction of global aerosol
mass. The oxalate-to-sulfate ratio has been proposed as an indicator of
aqueous processing, where higher values point to increased processing of
an air mass. In this study, we quantify a range in the
oxalate-to-sulfate mass ratio (0.0154 – 0.0296) using data from
multiple field campaigns encompassing a diverse set of environments.
This range is robust near the surface for particles below 1 micrometer
in diameter. Larger particles, especially dust, and biomass burning
particles significantly affect the oxalate-to-sulfate ratio and thus may
confound the interpretation of a high oxalate-to-sulfate ratio as a
signal of aqueous processing. In the absence of dust and biomass burning
particles, the oxalate-to-sulfate ratio range may be used to compare the
relative extent of aqueous processing between different air masses.
1 Introduction
Organic aerosol (OA) account for a major fraction of atmospheric aerosol
particles (Hallquist et al., 2009; Kanakidou et al., 2005; Q. Zhang et
al., 2007) comprising between 20 – 50% of fine aerosol mass in the
continental mid-latitudes (Putaud et al., 2004; Saxena & Hildemann,
1996), 30–80% in the free troposphere (Murphy et al., 2006), and over
80% in tropical forests (Andreae & Crutzen, 1997; Roberts et al.,
2001). Secondary organic aerosol (SOA) are derived from gas-to-particle
conversion processes, including aqueous processing (Blando & Turpin,
2000; Ervens et al., 2011; Warneck, 2003), wherein oxidized volatile
organic compounds (VOCs) partition into cloud droplets or wet aerosol
particles and undergo chemical reactions to form low-volatility products
that remain in the condensed phase (Ervens, 2015; Ervens et al., 2011;
McNeill, 2015). The formation of SOA through aqueous processing has been
estimated to serve as a global SOA source comparable to the gas-phase
pathway, with 90% of aqueous-phase SOA formed in-cloud (Fu et al.,
2008). Using the scheme from Fu et al. (2008), Heald et al. (2011) found
contributions of SOA to total OA ranging between 20 – 80% across
environments. Oxygenated organic species comprise 60 – 95% of total
organic aerosol mass across urban and remote sites (Q. Zhang et al.,
2007), while SOA from VOCs explains up to 70% of global organic carbon
mass (Hallquist et al., 2009). However, despite improvements in modeling
organic aerosol (Heald et al., 2005, 2011), atmospheric chemistry models
still underestimate SOA (Schroder et al., 2018) due partly to an
incomplete understanding and representation of aqueous processes,
resulting in poor model parameterization (Hallquist et al., 2009;
McNeill, 2015). The inclusion of SOA formation has been shown to
decrease model bias (–64% to –15%) and increase model correlations
with observations (R = 0.5 to 0.6) (Carlton et al., 2008); thus,
improving SOA estimates is a key area of development for models to more
accurately evaluate the impacts of atmospheric aerosol particles and
reduce uncertainties regarding the net effect of aerosol particles on
health and climate (IPCC, 2014).
Oxalic acid is the most abundant organic acid in tropospheric aerosol
particles across different regions (Cruz et al., 2019; Yang et al.,
2014; Ziemba et al., 2011). The dissociated ion oxalate (OXL) is a
well-established tracer of aqueous processing, contributing 1 – 10 %
of total particulate mass (Ervens, 2015; Myriokefalitakis et al., 2011)
and 3 – 4% of total organic mass over marine/continental areas
(Sorooshian et al., 2010). OXL is often used in combination with other
secondary tracers such as SO42- to
assess the extent of aqueous processing in a region (Crahan et al.,
2004; Hilario, Cruz, Bañaga, et al., 2020; Sorooshian, Varutbangkul, et
al., 2006; G. Wang et al., 2012; Yu et al., 2005). Direct sources are
thought to be minor relative to production via aqueous processing
(Ervens, 2015; Huang & Yu, 2007; Myriokefalitakis et al., 2011) and
photochemistry (C. Zhang et al., 2020). Sources of gaseous OXL
precursors include biomass burning (BB) (Narukawa et al., 1999; Yang et
al., 2014) and biogenic emissions (Boone et al., 2015). Early model
simulations overestimated OXL by an order of magnitude (Crahan et al.,
2004) while global simulations by Myriokefalitakis et al. (2011) showed
better agreement over marine/rural environments between observed and
modeled OXL (modeled:observed slope = 1.16 ± 0.14; R = 0.60) but could
not capture OXL over urban regions (weak correlation; r ≈ 0). More
recent work reports reduced discrepancy between models and observations
for SOA concentrations (Hodzic et al., 2016; Pai et al., 2020; Zhu &
Penner, 2019).
The OXL:SO42- ratio has been suggested
in past work to be an indicator of aqueous processing (Ervens et al.,
2014; Wonaschuetz et al., 2012; Yu et al., 2005). The usage of the ratio
as an aqueous processing marker implies that OXL and
SO42- are entirely sourced from
aqueous-phase oxidation, whether it be in cloud droplets or wet aerosol
particles, and does not account for gas-phase oxidation in cloud-free
air (Ervens, 2015; D. D. Huang et al., 2020; Zhan et al., 2021). This is
a good assumption for OXL as there is thought to be no gas-phase
reaction that would produce OXL (i.e., an aqueous medium is required for
OXL production) (Warneck, 2003); however, OXL is influenced by
gas-particle partitioning equilibrium and can exist in the gas-phase as
oxalic acid (Nah et al., 2018; Tao & Murphy, 2019). For
SO42-, gas-phase oxidation is an
important source of uncertainty as it can dominate over aqueous
processing at times (D. D. Huang et al., 2020). Though we also note that
oxidation in the gas-phase is much slower than in the aqueous-phase
(Cautenet & Lefeivre, 1994) and aqueous-phase oxidation explains 60 –
90% of SO42- in global models (Barth
et al., 2000; Faloona, 2009; Fu et al., 2008).
The OXL:SO42- ratio can serve as an
aqueous processing marker because aqueous media (including clouds and
wet aerosol particles) facilitate the production of both OXL and
SO42- at rates dependent on liquid
water content for SO42- formation and
droplet surface area for OXL (Ervens et al., 2011). These two cloud
parameters correlate within growing clouds (Kim et al., 2003), which
connects in-cloud OXL and SO42-production such that their ratio may lie within some range. The
OXL:SO42- ratio has been observed to
correlate well with cloud fraction and fall within 0.01 – 0.03 between
0 – 4 km above ground level (AGL) when cloud fractions are high
(Wonaschuetz et al., 2012). Therefore, identifying a range in the
OXL:SO42- ratio across different
environments can be useful for comparing relative extents of aqueous
processing with higher ratios suggesting more processed air. This comes
with the assumptions outlined above that
SO42- is mainly sourced from aqueous
processing, which may not hold for certain environments. However, this
ratio is expected to be particularly applicable near clouds. It is
important to note that this ratio likely exhibits a seasonal cycle as
observed in Tao & Murphy (2019). Work by Yao et al. (2004) also
demonstrated seasonal shifts in the relative contributions of primary
and secondary OXL.
Laboratory experiments are often relied on for mechanistic details of
aerosol particles (e.g., Hennigan et al., 2010; Pang et al., 2019) but
sometimes disagree with aircraft measurements (May et al., 2014). Thus,
aircraft campaigns provide a valuable opportunity to study aerosol
particles influenced by cloud processes in their most natural
environment (Sorooshian et al., 2020). This study leverages composition
data from multiple field campaigns, predominantly based on airborne
measurements, to investigate the following questions: (1) Is there a
generally consistent range of
OXL:SO42- across different regions?;
(2) does this ratio depend on particle size?; and (3) what conditions
can significantly affect OXL:SO42-values?
2 Methods
This work relies mostly on airborne field datasets, with the focus
predominantly being on particle-into-liquid sampler (PILS) data. The
PILS converts sampled aerosol particles into droplets sufficiently large
to be collected via inertial impaction, with the resultant liquid
transported to vials on a rotating carousel for post-collection chemical
analysis via ion chromatography (IC) (Sorooshian, Brechtel, et al.,
2006; Weber et al., 2001).
Table 1 summarizes relevant details across campaigns, namely: the
International Consortium for Atmospheric Research on Transport and
Transformation (ICARTT), the Marine Stratus/Stratocumulus Experiment
(MASE-I), the Gulf of Mexico Atmospheric Composition and Climate Study
(GoMACCS), the Marine Stratus/Stratocumulus Experiment II (MASE-II), the
Nucleation in California Experiment (NiCE), the Atmospheric Tomography
Mission (AToM), the Cloud, Aerosol, and Monsoon Processes-Philippines
Experiment (CAMP2Ex), the ground-based
CAMP2Ex weatHEr and CompoSition Monitoring (CHECSM)
study, and the Aerosol Cloud meTeorology Interactions oVer the western
ATlantic Experiment (ACTIVATE). Note that species from AToM were
collected by Soluble Acidic Gases and Aerosol (SAGA) filters (Dibb et
al., 2002, 2003). We use ground-based data from Metro Manila,
Philippines during CHECSM collected by a micro-orifice uniform deposit
impactor (MOUDI) and analyzed via IC to assess how the
OXL:SO42- ratio may vary within the
mixed layer and across particle sizes. We examined a total of 53 MOUDI
sets that were collected on a weekly basis with a sample duration of
~48 hours per set. Six of those sets were impacted by BB
based on the criteria presented by Gonzalez et al. (2021) for the same
dataset.
As we analyze a CAMP2Ex case study in Section 3.2, we
summarize the campaign here with more details provided in Hilario et al.
(2021). CAMP2Ex took place over the Western Pacific
and aimed to study the influence of radiation, convection, and
meteorology on aerosol and gas species. With 19 research flights between
August to October 2019, CAMP2Ex provided a rich
composition, radiation, and convection dataset spanning 0 – 9 km AGL.
In this study, we analyze submicrometer non-refractory aerosol from the
aerosol mass spectrometer (AMS; Aerodyne) (Canagaratna et al., 2007;
DeCarlo et al., 2006) and size distributions collected by a laser
aerosol spectrometer (LAS; TSI Model 3340) for particle diameters 50 –
3162 nm. A comparison of SO42- from
the PILS and AMS during CAMP2Ex shows good agreement
(AMS:PILS slope = 0.81; R = 0.88), suggesting that
SO42- was predominantly in the
submicrometer size range given the size ranges of the AMS
(PM1) and PILS (PM~4).
Based on sea salt SO42- calculations,
less than 5% of SO42- during
CAMP2Ex originated from sea salt.
During
AToM, CAMP2Ex,
and ACTIVATE, the aerosol sampling inlet likely limited the upper size
to approximately 4 μm (McNaughton et al., 2007) although there may have
been additional impaction losses in the sampling lines internal to the
aircraft that further smoothed the particle transmission curve near this
upper bound. This higher cutoff size allowed for sampling of sea salt
and dust.
To meaningfully compare OXL:SO42-ratios between campaigns, we separated out samples impacted by strong
point sources (e.g., ship plumes, cattle feedlots, smoke) as identified
using flight scientist notes and clear enhancements in particle
concentration data. When calculating species ratios, we excluded
instances when the denominator value was below its 5thpercentile to reduce the uncertainty caused by low denominator values.
Ratios of species in this study refer to mass ratios unless otherwise
indicated (e.g., molar ratios). To quantify an all-campaign statistic
and uncertainty, median and 95% confidence intervals of the
OXL:SO42- ratio were derived via
bootstrapping of all campaigns using different combinations of sample
size and number of iterations, excluding samples with confounding
influence (further details are provided in Table S1).
Table 1 . Details of aircraft campaigns analyzed. Statistics of
oxalate (OXL) and sulfate (SO42-)
collected by PILS from Figure 1 are listed (R = Pearson correlation, N =
number of samples). Median absolute deviation (MAD) is provided as a
measure of slope uncertainty (e.g., slope ± MAD; r, N). Statistics are
presented separately for biomass burning (BB) and non-biomass burning
(non-BB) samples.