To investigate the cloud water chemistry and the effects
of cloud processing on aerosol properties, comprehensive field observations
of cloud water, aerosols, and gas-phase species were conducted at a
mountaintop site in Hong Kong SAR in October and November 2016. The chemical
composition of cloud water including water-soluble ions, dissolved organic
matter (DOM), carbonyl compounds (refer to aldehydes and acetone),
carboxylic acids, and trace metals was quantified. The measured cloud water
was very acidic with a mean pH of 3.63, as the ammonium (174 µeq L-1) was insufficient for neutralizing the dominant sulfate (231 µeq L-1) and nitrate (160 µeq L-1). Substantial DOM
(9.3 mgC L-1) was found in cloud water, with carbonyl compounds and carboxylic
acids accounting for 18 % and 6 % in carbon molar concentrations,
respectively. Different from previous observations, concentrations of
methylglyoxal (19.1 µM; µM is equal to µmol L-1) and glyoxal (6.72 µM) were higher than
that of formaldehyde (1.59 µM). The partitioning of carbonyls between
cloud water and the gas phase was also investigated. The measured aqueous
fractions of dicarbonyls were comparable to the theoretical estimations,
while significant aqueous-phase supersaturation was found for less soluble
monocarbonyls. Both organics and sulfate were significantly produced in
cloud water, and the aqueous formation of organics was more enhanced by
photochemistry and under less acidic conditions. Moreover, elevated sulfate
and organics were measured in the cloud-processed aerosols, and they were
expected to contribute largely to the increase in droplet-mode aerosol mass
fraction. This study demonstrates the significant role of clouds in altering
the chemical compositions and physical properties of aerosols via scavenging
and aqueous chemical processing, providing valuable information about
gas–cloud–aerosol interactions in subtropical and coastal regions.
Introduction
Clouds in the troposphere play a key role in atmospheric aqueous-phase
chemistry by acting as efficient media for the in-cloud formation of sulfate
and secondary organic aerosol (SOA) (Harris et al., 2013; Ervens, 2015).
Numerous studies on cloud and fog chemistry have been conducted in Europe
and North America since the 1990s (Collett et al., 2002; Ervens, 2015;
van Pinxteren et al., 2016). During the past decade, studies of the
compositions of cloud and fog water, cloud scavenging, and aqueous-phase
reactions have also been carried out in Asia, particularly in China and
Japan (Aikawa et al., 2007; Guo et al., 2012). In-cloud sulfate
production, which causes acid rain, has been extensively characterized
(Lelieveld and Heintzenberg, 1992; Harris et al., 2013; Guo et al.,
2012). Recently, more attention has been given to organic materials, which
are present in comparable amounts as sulfate and nitrate in cloud and fog
water (Collett et al., 2008; Herckes et al., 2013),
because of the significant contribution of chemical cloud processing to the formation of
aqueous SOA (aqSOA) in high-humidity environments (Ervens et
al., 2011; Huang et al., 2011; Tomaz et al., 2018).
Many field observations and laboratory studies have reported direct evidence
for the in-cloud formation of low-volatility products and aqSOA.
Kaul et al. (2011) observed enhanced SOA production
and increased ratios of organic to elemental carbon (OC/EC) upon fog
evaporation due to aqueous-phase chemistry. Comparison of the mass spectra
of ambient aerosols and cloud organics suggests that functionalization of
dissolved organics possibly dominates the formation of SOA through oxidative
cloud processing (Lee et al., 2012). A chamber study
showed faster SOA formation (by a factor of 2) from isoprene photo-oxidation
under cloud conditions than dry conditions
(Brégonzio-Rozier et al., 2016), highlighting the
importance of aqueous-phase reactions. Aircraft measurements by
Sorooshian et al. (2007) found ubiquitous layers of enhanced
organic acids levels above clouds, implying that the in-cloud formation of
organic acids contributes significantly to emerging organic aerosol layers
after droplet evaporation. Oxalate, an aqueous-phase oxidation product, has
been considered as a good tracer for aqSOA formation, given the common
in-cloud formation pathway of oxalate and sulfate (Yu et al., 2005;
Sorooshian et al., 2006). Single-particle mass spectrometry analysis
confirmed that oxalate in cloud droplet residuals and cloud interstitial
particles was 3 times as abundant as that in ambient (cloud-free)
particles (Zhang et al., 2017), demonstrating the in-cloud formation of
oxalate. At present, soluble dicarbonyls are recognized as the primary
precursors of carboxylic acids and oligomers in the aqueous phase
(Lim et al., 2010; Ervens et al., 2011). The
irreversible uptake and aqueous oxidation of glyoxal, the simplest
dicarbonyl compound, is suggested to be the primary formation pathway of
oxalic acid and aqSOA (Warneck, 2003; Carlton et al., 2007).
In general, water-soluble organic compounds (e.g., carbonyls) can partition
into cloud droplets and form low-volatility products such as carboxylic
acids and oligomers, which stay in the particle phase after cloud
evaporation and form aqSOA (Blando and Turpin, 2000; Lim et al., 2005, 2010;
van Pinxteren et al., 2005; Carlton et al., 2007; Galloway
et al., 2014; Brégonzio-Rozier et al., 2016).
Chemical cloud processing not only contributes to aerosol mass production
but also alters the chemical composition of aerosols. Highly oxidized aqSOA
usually exhibits higher O/C ratios (1–2) compared to SOA formed in the gas
phase (0.3–0.5) (Ervens et al., 2011), as indicated by model
predictions that glyoxal SOA formed in cloud water and wet aerosols are
predominantly oxalic acid and oligomers, respectively (Lim
et al., 2010). Even with similar O/C ratios, the molecular compositions of
organics in aerosols and cloud water could be quite different; for example,
the organosulfate hydrolysis and nitrogen-containing compound formation
were observed more often in cloud water compared to atmospheric particles, suggesting
the significant role of cloud processing in changing the chemical properties
of aerosols (Boone et al., 2015). In addition to the in-cloud sulfate
formation (Meng and Seinfeld, 1994), the in-cloud organic formation is
also likely to add substantial mass to droplet-mode particles
(Ervens et al., 2011). For example, maximum droplet-mode
organics and a shift in particle mass size distribution were observed in
simulated cloud events (Brégonzio-Rozier et al.,
2016). However, our current knowledge of aqSOA formation mechanisms and how
aerosol properties change during real cloud processing remains limited.
The Hong Kong SAR and Pearl River Delta (PRD) region is one of the most
industrialized areas in Asia, and it experiences serious particulate and
photochemical air pollution. High cloudiness and abundant water vapor lead
to significant gas–cloud–aerosol interactions in this region, and half of
all surface SOA is estimated to be contributed by the aqueous chemistry of
dicarbonyls (Li et al., 2013). To better understand the role of
chemical cloud processing in aerosol production and the associated changes
in its physicochemical properties, we conducted a comprehensive field
campaign with simultaneous measurements of trace gases, aerosols, and cloud
water at a mountaintop site in Hong Kong. In this paper, we first present an
analysis of the chemical composition of cloud water and then discuss the
partitioning of individual carbonyl compounds between gaseous and aqueous
phases. Finally, cloud water organic formation and the effects of cloud
processing on aerosol physicochemical properties are investigated.
MethodologyObservation site and sampling
The field campaign was carried out at the summit of Tai Mo Shan (TMS; 22∘24′ N, 114∘16′ E, 957 m a.s.l.), the highest
point of Hong Kong in the southeastern PRD region (Wang et al., 2016),
where the coastal and subtropical climate leads to frequent occurrence of
cloud and fog events. The site is influenced by both urban and regional pollution
from the PRD region and cleaner marine air masses from the western Pacific
Ocean. Cloud water, aerosols, and gas-phase carbonyl compounds were
simultaneously sampled from 9 October to 22 November 2016.
Cloud water samples were collected in a 500 mL acid-cleaned high-density polyethylene (HDPE) bottle by
using a single-stage Caltech Active Strand Cloudwater Collector (CASCC) with
a flow rate of 24.5 m3 min-1. A detailed description of the
collector can be found in our previous work (Guo et al., 2012).
The sampling duration was set to 1–3 h to obtain enough sample volume.
Cloudwater pH and electrical conductivity were measured on site using a
portable pH meter (model 6350M, JENCO). After filtration through 0.45 µm microfilters (ANPEL Laboratory Technologies (Shanghai) Inc.), aliquots of
the cloud water samples for dissolved organic carbon (DOC; 30 mL), carbonyl
compounds (20 mL), water-soluble ions (15 mL), organic acids (15 mL; 5 % volume concentration (fraction; v/v) chloroform added), and trace metals (15 mL; 1 % v/v hydrochloric
acid added) were properly prepared and stored at 4 ∘C in
the dark until laboratory analysis. Derivatization of carbonyl compounds
(refer to aldehydes and acetone) with 2,4-dinitrophenylhydrazine (DNPH) was
performed on site after adjusting the pH to 3.0 using a buffer solution of
citric acid and sodium citrate. Interstitial gas-phase carbonyl compounds
were sampled with acidified DNPH-coated silica cartridges (Waters Sep-Pak
DNPH-Silica) at a flow rate of 0.5 L min-1 for 2–4 h using a
semi-continuous cartridge sampler (model 8000, ATEC). A Teflon filter
assembly and an ozone scrubber were installed before the cartridge to remove
large droplets and particles and prevent the influence of ozone. All
cartridges were refrigerated at -20∘C after sampling.
Daily fine aerosol samples were collected on quartz filters (47 mm diameter,
Pall Inc.) using a four-channel sampler (RAAS-400, Thermo Anderson, USA)
with a size-selective inlet removing particles and droplets larger than 2.5 µ m, with a flow rate of 16.7 L min-1 and sampling duration of
23 h. The sample filters were then refrigerated at -20∘C
before laboratory analysis. An ambient ion monitor (URG 9000) with a 2.5 µm cut-size cyclone inlet was used to measure the hourly
concentrations of water-soluble ions in PM2.5. During the cloud event,
the collected fine aerosols were mostly interstitial aerosols because
almost all activated cloud droplets were larger than 3 µm and were
removed by the cyclone in the sampling inlet. A NanoScan scanning mobility particle sizer (SMPS) spectrometer (model 3910, TSI Inc.) and an optical particle sizer (OPS)
spectrometer (model 3330, TSI Inc.) were used to measure particle mass size
distributions in the range of 0.01 to 10 µm with 29 size bins at
1 min scan intervals. Because of the instrument test and failure, the
valid data for ambient ions and particle size distribution were only
available from 2 to 11 November and 3 to 21 November, respectively. Trace
gases including SO2, NOx, and O3 were measured with a pulsed
ultraviolet (UV) fluorescence analyzer (model 43c, Thermo), a chemiluminescence analyzer
(model 42i, Thermo), and a UV photometric analyzer (model 49i, Thermo),
respectively. Hourly PM2.5 mass concentration data were provided by
the Hong Kong Environmental Protection Department. Ambient temperature and
relative humidity (RH) were measured using a MetPak weather station (Gill, UK),
and the solar radiation was monitored using a spectral radiometer
(meteorologieconsult gmbh, Germany).
Laboratory chemical analysis
Water-soluble organic carbon (WSOC) in PM2.5 sample filters was
extracted with 20 mL Milli-Q water (18.25 MΩ cm, Millipore) via
sonication for 30 min followed by filtration. The DOC in cloud water and WSOC
in PM2.5 were quantified by nondispersive infrared detection of
CO2 after thermocatalytic oxidation at 650 ∘C using a TOC (total organic carbon) analyzer (Shimadzu TOC-L, Japan). Sucrose standards were used for
calibration, with a method detection limit of 0.112 mg L-1. In this
study, the dissolved organic matter (DOM) in cloud water and water-soluble
organic matter (WSOM) in fine aerosols were estimated to be 1.8 times
of DOC and WSOC, respectively (van Pinxteren et al., 2016).
The DNPH derivatives of carbonyl compounds in the cloud water samples were
extracted into 20 mL dichloromethane three times. The extract was then
concentrated to dry yellow powder by reduced-pressure distillation at 38 ∘C and transferred into a volumetric flask using 2 mL
high-pressure liquid chromatography (HPLC) grade acetonitrile. The sampled
cartridge of gas-phase carbonyl compounds was similarly eluted with 2 mL
HPLC grade acetonitrile to a volumetric flask. The cloud water and cartridge
extracts were analyzed using an HPLC system (PerkinElmer 200 Series) equipped
with a UV detector. The method detection limits were determined to be 0.22 µM (µM is equal to µmol L-1) for formaldehyde, 0.02 µM for acetaldehyde, 0.13 µM for
acetone, 0.13 µM for propanal, 0.05 µM for butanal, 0.09 µM
for iso-pentanal, 0.06 µM for p-tolualdehyde, 0.07 µM for glyoxal,
and 0.15 µM for methylglyoxal. The recovery rate ranged from 81 % to
98 % for individual carbonyls.
Concentrations of water-soluble ions (Na+, NH4+, K+,
Mg2+, Ca2+, Cl-, NO3-, and SO42-) in the
cloud water samples were measured using an ion chromatograph (ICS
1000, Dionex). Four carboxylic acids (acetic, formic, pyruvic, and oxalic acids) were
analyzed using an ion chromatograph (ICS 2500, Dionex), with an IonPac
AS11-HC separator column under NaOH gradient elution. Trace metals including
Al, V, Cr, Mn, Fe, Ni, Cu, As, Se, Cd, Ba, and Pb were measured by
inductively coupled plasma mass spectrometry (ICP-MS; Agilent 7500a) based
on the EPA 200.8 method. More details on the ions and trace metal analyses
were described in our previous works (Guo et al., 2012; Li et al., 2015).
Aqueous-phase fraction of carbonyl compounds
The measured fraction of carbonyl compounds partitioning in the aqueous
phase (Fme) is calculated by Eq. (1).
Fme=CcwCcw+Cint,
where Ccw is the air equivalent concentration of carbonyl compounds in
cloud water (µg m-3) and Cint is the interstitial gas-phase
carbonyl compounds concentration (µg m-3).
Assuming equilibrium, the theoretical aqueous-phase fraction (Ftheo)
can be calculated from the following equation (van
Pinxteren et al., 2005):
Ftheo=KH⋅R⋅T⋅LWC⋅10-61+KH⋅R⋅T⋅LWC⋅10-6,
where KH is the Henry's law constant (M atm-1), R is the gas constant
of 0.08205 L atm mol-1 K-1, T is the mean temperature in K, and LWC
is the cloud liquid water content (g m-3).
Results and discussionCharacterization of cloud water chemistry
A total of 32 cloud water samples in six cloud events were collected at TMS
in Hong Kong during the campaign (Fig. S1 in the Supplement). The average LWC was 0.26 g m-3 with a range of 0.08–0.53 g m-3. Cloud water pH ranged
between 2.96 and 5.94 with a volume-weighted mean (VWM) value of 3.63, lower
than the cloud and fog pH observed in most other areas (e.g., Tai Shan: 3.86 in Guo et al., 2012; Baengnyeong Island: 3.94 in Boris
et al., 2016; Lulin mountain, Taiwan: 3.91 in Simon, 2016;
southeastern Pacific: 4.3 in Benedict et al., 2012; and Schmücke, Germany: 4.30 in van Pinxteren et al., 2016), indicating
the severe acidification of cloud water in this region.
Overview of chemical composition of cloud water
Table 1 summarizes the VWM concentrations of water-soluble ions, DOC,
carboxylic acids, carbonyl compounds, and trace metals in the cloud water
samples. The concentrations of sulfate, nitrate, and ammonium ions were 231,
160, and 174 µeq L-1, respectively, accounting for 81 % of the
total measured ions. The sulfate and nitrate concentrations were much lower
than those in clouds in northern China (Guo et al., 2012) and in
fogs at Baengnyeong Island (Boris et al., 2016), but they were higher
than those at many sites in the United States, Europe, and Taiwan (Straub et al.,
2012; van Pinxteren et al., 2016; Simon, 2016). Meanwhile, there was
insufficient ammonium to neutralize the acid ions, as indicated by the low
slope (0.46) of charge balance between [NH4+] and [NO3-+SO42-]. The elevated Cl- (109 µeq L-1) and
Na+ (69 µeq L-1) indicated the considerable influence of
maritime air from the western Pacific Ocean. In contrast to the commonly
observed chloride depletion in coastal cloud water (Benedict et
al., 2012), the molar ratio of Cl-/Na+ (1.86) at TMS was
obviously higher than the sea-salt ratio (1.16). The abundant Cl- in
cloud water can be ascribed to potential anthropogenic chloride sources
(e.g., coal-fired power plants and biomass burning) in the PRD region
(Wang et al., 2016). Non-sea-salt sulfate (nss-SO42-) was
determined to be 96±3 % of total sulfate based on the
SO42-/Na+ molar ratio in seawater (0.06), demonstrating that
SO42- was mainly derived from in-cloud oxidation of SO2
(Harris et al., 2013; Guo et al., 2012) rather than a marine source.
Concentrations of inorganic and organic species in cloud water
samples measured at TMS during November 2016.
DOC concentrations varied from 2.0 to 108.6 mgC L-1 with a VWM value of
9.3 mgC L-1, lower than those in polluted urban fogs but much higher
than most remote and marine clouds (Herckes et al., 2013; van Pinxteren
et al., 2016; Ervens et al., 2013; Benedict et al., 2012). The VWM
concentrations of formic, acetic, pyruvic, and oxalic acids were measured to
be 10.8, 7.2, 1.5, and 8.3 µM, respectively, accounting for 6±2 % (molar ratio of carbon) of the DOC in total. Carbonyl compounds (Table 1) comprised 18±10 % of DOC in cloud water. Methylglyoxal (19.1 µM) was the predominant carbonyl species, followed by glyoxal (6.72 µM), iso-pentanal (5.90 µM), and glycolaldehyde (3.56 µM),
while formaldehyde (1.59 µM) and acetaldehyde (0.03 µM) were much
lower. The abundance of methylglyoxal, nearly triple that of glyoxal, at TMS differed from the previous observations at Puy de Dôme, France
(Deguillaume et al., 2014), Schmücke,
Germany (van Pinxteren et al., 2005), and Davis, California, USA
(Ervens et al., 2013), where glyoxal concentrations were 2
to 10 times higher than methylglyoxal, but it was similar to the results
observed at Whistler, Canada (Ervens et al., 2013), where
the methylglyoxal / glyoxal ratio was much higher. These different patterns
could partially be attributed to the large differences in precursors at
various locations (Table S1 in the Supplement) and also the availability of oxidants.
Generally, the overall yields of these aldehydes from isoprene are much
lower than those from the oxidation of aromatics (Ervens et
al., 2013), and the latter also contributes to higher yields of methylglyoxal
than glyoxal (Ervens et al., 2011). For example, the glyoxal and
methylglyoxal yields from toluene are approximately equal (0.14 and 0.12,
respectively, at high NOx conditions), and methylglyoxal yields from
xylene exceed the ones of glyoxal by a factor of 6 (0.08 and 0.47,
respectively) (Nishino et al., 2010; Ervens et al., 2011). The higher
aromatics concentrations (toluene of 2.3 ppb, xylene of 0.9 ppb) than the
biogenic isoprene (0.16 ppb) measured at TMS are expected to be the
important precursors of these aldehydes and lead to the different ratio
observed in the cloud water. The lower cloud water formaldehyde amount is likely
associated with the deficient partitioning of formaldehyde in the aqueous
phase as discussed in Sect. 3.2.
Aluminum (131.9 µg L-1) dominated the cloud water trace metals,
of which the concentration was comparable to that measured at other mountain
sites in China (99.7 to 157.3 µg L-1) (Li et al., 2017).
Transition metals Fe, Cu, and Mn, which play important roles in the
heterogeneous catalytical formation of sulfate
(Harris et al., 2013), were also found to
be abundant in the cloud water, with mean concentrations of 50.6, 10.0, and
5.9 µg L-1, respectively. The toxic Pb concentration in cloud
water (18.7 µg L-1) was around 10 times higher than that observed
at sites in Europe (1.4 µg L-1) (Fomba et al., 2015)
and the United States (0.6 µg L-1) (Straub et al., 2012),
probably due to traffic emissions from the surrounding city cluster.
Relatively high concentrations of V (7.9 µg L-1) and Ni (7.1 µg L-1) implied notable impacts of residual oil combustion from
shipping emissions (Viana et al., 2009; Wang et al., 2014). Clearly, the
cloud water at TMS was significantly influenced by anthropogenic
emissions.
Comparisons among different air masses
Three-day back trajectories were reconstructed using the HYbrid
Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to
investigate the origins of air masses arriving at TMS, which were
influenced by both continental and marine air masses. Three types of air
mass plumes for the six cloud events (E.1–6) are identified and displayed
in Fig. 1: continental (E.1–2), mixed (E.3–4), and marine (E.5–6).
Detailed descriptions are given in Table S2.
Air mass plumes arriving at TMS (black cross) in Hong Kong for
six cloud events (E.1–6) simulated using the HYbrid Single-Particle
Lagrangian Integrated Trajectory model. The model was driven by
archival meteorological data from the Global Data Assimilation System (GDAS;
1∘×1∘ resolution).
The concentration and distributions of major components in cloud water
during six cloud events are compared in Figs. 2a and S2. In general,
continental air masses brought more abundant major components including DOM,
SO42-, NO3-, and Ca2+ compared with marine ones,
which had lower total concentrations but higher proportions of Cl- and
Na+. For example, continental E.1, which was heavily polluted by
anthropogenic emissions within the passage of a cold front (Table S2 and
Fig. S2), exhibited the largest amount of major components (393.9 mg L-1), whereas marine E.6 had the least (15.7 mg L-1). For each
event, DOM dominated the major components (29 %–53 %), followed by
SO42- (17–28 %) and NO3- (17 %–30 %).
Nss-SO42-/NO3- ratios in E.1 (1.03) and E.3 (0.91) were
lower than in other events (1.38–1.69), indicating the strong influence of
regional air masses from the PRD region. The elevated NOx from traffic
emissions in the PRD region (Zheng et al., 2009) is likely to be
responsible for the higher nitrate proportions and lower
nss-SO42-/NO3- ratios in these two events. Ca2+
mainly existed in continental cloud water, and the 3 % of Ca2+ in E.1
likely contributed to the higher pH (5.50). Influenced by marine air masses,
the concentration (and proportions) of Cl- and Na+ notably
increased from 0.2 mg L-1 (0.4 %) and 1.0 mg L-1 (2 %)
in continental cloud water (E.2) to 2.5 mg L-1 (5 %) and 5.9 mg L-1 (11 %) in the marine one (E.5), respectively. Meanwhile, the
equivalent molar ratios of Cl-/Na+ and Ca2+/Na+
decreased from 3.11 and 5.06 to 1.50 and 0.04, respectively, close to their
ratios in seawater (Table S2). Fig. 2a shows elevated proportions of V
were observed in marine-influenced E.4–6, which is consistent with plumes
passing over the busy international shipping routes (Fig. S3), suggesting
the contribution of residual oil combustion by shipping to coastal cloud
water chemistry (Gao et al., 2016).
Concentration distributions of (a) major components and trace
metals and (b) carbonyl compounds and carboxylic acids in cloud water for
each cloud event (E.1–6). The volume-weighted mean concentrations of
individual species are used. Percentages of carbonyl compounds and
carboxylic acids in DOC are determined by carbon molar concentration. Trace
metals are absent from E.1 due to limited sample volume.
Similar trends for the carbonyl compounds and carboxylic acids with major
components can also be seen in Fig. 2b, but the distribution patterns are
obviously distinct. Methylglyoxal dominated the carbonyl compounds in
E.2–5, accounting for 54 %–87 % of total carbonyls. In contrast, glyoxal
(65 %) became the major species in E.1, followed by iso-pentanal (19 %)
and methylglyoxal (16 %); meanwhile, iso-pentanal (59 %) was dominant in
E.6, which had more glyoxal (15 %) than methylglyoxal (5 %). The
concentration ratios of formaldehyde / acetaldehyde (C1/C2) and
acetaldehyde / propanal (C2/C3) in the gas phase during E.3–6 were calculated
(Table S3), to diagnose the possible sources of carbonyls in cloud events.
The C1/C2 ratios in the range of 2.8–4.5 suggest the combined contributions
of both anthropogenic emissions and biogenic sources to the measured
carbonyls. This is because C1/C2 ratios are normally 1 to 2 for urban areas but
close to 10 for the rural forests, due to more photochemical production of
formaldehyde than acetaldehyde from natural hydrocarbons (Servant et al.,
1991; Possanzini et al., 1996; Ho et al., 2002). As propanal is believed to
be associated only with anthropogenic emissions, the C2/C3 ratio, which is
high in the rural atmosphere and low in polluted urban air, can be used as an
indicator of the anthropogenic origin of carbonyl compounds
(Possanzini et al., 1996). The average C2/C3 ratios recorded
for TMS were 4.7±2.7, similar to those measured in roadside and
urban environments in Hong Kong (5.0±0.8)
(Cheng et al., 2014), indicating the considerable
anthropogenic sources (e.g., vehicle emissions) of carbonyls at TMS. The
higher concentrations and proportion of iso-pentanal in E.1 (14.02 µM,
19 %) and E.6 (11.37 µM, 59 %) than in other events were also
noted, possibly resulting from unconfirmed direct sources.
The ratio of formic to acetic acid (F/A) has been suggested to be a useful
indicator of sources of carboxylic acids from direct emissions (e.g.,
anthropogenic sources and biomass burning) or secondary photochemical formation
in the gas phase (Talbot et al., 1988), rainwater
(Fornaro and Gutz, 2003), and cloud water (Wang et al., 2011b).
The direct anthropogenic emission of acetic acid from vehicle-related sources is
higher than of formic acid, resulting in F/A ratios much less than 1.0,
whereas photochemical oxidation of natural hydrocarbons leads to higher
concentrations of formic acid than acetic acid and therefore the increase
in F/A ratios (> 1.0) (Talbot et al., 1988;
Fornaro and Gutz, 2003). The F/A ratio in the liquid phase (rainwater or
cloud water) is expected to be higher than in the gas phase at equilibrium
conditions, which is dictated by Henry's law constants, dissociation
constants of formic and acetic acids, and pH. So the corresponding gas-phase
F/A ratio can be calculated from the aqueous concentrations to evaluate the
dominant sources. In this study, a remarkable correlation between formic and
acetic acid (r=0.97, p < 0.01) suggests their similar sources or
formation pathways. The high F/A ratios (1.2–1.9) for E.2–5 (Table S2)
indicate the more important secondary formation for carboxylic acids in
cloud water. In contrast, the F/A ratios in E.1 and E.6 were 0.4 and 0.5,
respectively, suggesting the significant contributions from direct emissions
during these two events. In addition, despite the decrease of total
concentrations, the proportion of oxalic acid notably increased from 5 %
to 58 % under more influence of marine air masses.
Relationships of cloud water composition with LWC and pH
LWC and pH are important factors influencing the phase partitioning,
chemical reactions, and solute concentrations in cloud water (Tilgner et
al., 2005; Li et al., 2017). Figures 3 and S4 show the relationships
of individual chemical species with LWC and pH. The non- and semi-volatile
species in cloud water at TMS, including water-soluble ions, DOC,
carboxylic acids, and trace metals, were inversely related to LWC in empirical
power functions due to dilution effects, which have been widely observed in
previous studies (Herckes et al., 2013; Li et al., 2017).
Similar inverse-power relationships of water-soluble ions, DOC, and
carboxylic acids with pH were also found (Figs. 3 and S4). Increased
air pollution and the formation of secondary acid ions likely made the cloud water
more acidic, in turn promoting the dissolution of trace metals (Li et
al., 2017). Similarly, glyoxal concentrations decreased in a power function
as LWC and pH increased. However, methylglyoxal showed different
relationships with LWC and pH. Unexpectedly, methylglyoxal tended to
increase linearly with LWC and pH for most of the samples except three
low-concentration samples (Figs. 3d and S4b), which show the
opposite pattern to the dilution effect and suggest more methylglyoxal
dissolved in diluted and less acidic cloud waters. The different
relationships (increase or decrease) of other carbonyls concentrations with
increased LWC and pH are also shown in Fig. S4. In addition to
aqueous-phase reactions, the gas–aqueous phase partitioning of each carbonyl
compound influenced by LWC and pH is another possible reason for the
observed relationships (Lim et al., 2010; Ervens et al., 2011, 2013).
Relationships of water-soluble ions, dissolved organic carbon, glyoxal, and methylglyoxal with liquid water content. Color
scale represents the pH range. Solid lines are empirical inverse-power fits
to the data. Methylglyoxal has a good linear relationship with LWC for
samples in the blue dashed circle.
(a) Mass concentration fractions of measured carbonyl compounds in the
gas phase and cloud water and (b)Fme/Ftheo ratios as a function
of Henry's law constant (KH). Colored squares represent the mean
Fme/Ftheo ratios, and whiskers indicate the standard deviation. For
comparison, Fme/Ftheo ratios measured at Schmücke
(van Pinxteren et al., 2005) are indicated by open
circles. The gray fitted line shows the decreasing trend of
Fme/Ftheo ratios with an increasing Henry's law constant for all
species. The dashed line indicates the Fme/Ftheo ratio of 1.
Gas–aqueous phase partitioning of carbonyl compounds
The simultaneous measurement of carbonyl compounds in both gas and aqueous
phases enables the investigation of their partitioning between different
phases (Fig. 4a and Table S4). Acetone, formaldehyde, and acetaldehyde
were the dominant carbonyl species (92 %) measured in the gas phase during
cloud events, while methylglyoxal (4 %) and glyoxal (1 %) were the
minor species. Due to a high KH value (and solubility) (Table S4), the dicarbonyls were
found to be much more abundant in cloud water, with methylglyoxal and glyoxal
accounting for 63 % and 29 % of total carbonyl species, respectively,
despite their low gas-phase mixing ratios. However, diverse discrepancies
were observed between the measured (Fme) and theoretical (Ftheo)
aqueous-phase fraction of the individual carbonyl compounds. The
Fme/Ftheo ratios for each carbonyl are plotted as a function of
KH in Fig. 4b. The Fme values for carbonyls with a small KH value
were about 1–3 orders of magnitude higher than Ftheo, while for highly
soluble dicarbonyls the Fme/Ftheo ratios approached unity, similar
to the result found at Schmücke, Germany (van Pinxteren et al., 2005).
The cloud water sulfate molality (∼0.1 mol kg-1 LWC on
average) at TMS should be far from high enough to cause a significant
salting-in or salting-out effect (i.e., an increased or decreased solubility of organics by
higher salt concentrations) to remarkably alter the solubility of carbonyls
in the dilute cloud water, although the salting-in or salting-out effect is of
particular importance in the effective uptake of carbonyl compounds by
ambient particles (Waxman et al., 2015; Shen et
al., 2018) where solute concentrations are high. Oligomerization on the droplet
surface layer induced by chemical production and adsorption has been
suggested to be able to enhance the supersaturation of less soluble
carbonyls in the aqueous phase (van Pinxteren et al., 2005; Li et al.,
2008). Djikaev and Tabazadeh (2003) had proposed an uptake model to
account for the gas adsorption at the droplet surface, in which some
adsorption parameters and the adsorption isotherm need to be known. The lack of
these parameters and the measurement of droplet surface area or a
surface-to-volume value in the present work did not allow us to quantify the
effects of the adsorption and oligomerization. According to the simulation
with some organic species (e.g., acetic acid, methanol, and butanol) by
Djikaev and Tabazadeh (2003), the “overall” Henry's law constant
considering both volume and surface partitioning was only < 4 %
higher than the experimental effective Henry's law constant. Thus the
adsorption and oligomerization effects may contribute to but cannot explain
the observed aqueous supersaturation phenomenon here. In contrast, using the
effective Henry's law constants considering hydration for glyoxal
(4.2×105 M atm-1) (Ip et al., 2009) and
methylglyoxal (3.2×104 M atm-1) (Zhou and Mopper,
1990), the calculated equilibrium partitioning of these dicarbonyls in the
aqueous phase is comparable to the measured fraction. Formaldehyde was
deficient in the cloud water with a Fme/Ftheo value of 0.12,
similar to the lower measured formaldehyde in the aqueous phase than that
expected at equilibrium reported by Li et al. (2008). The reaction of
formaldehyde with S(IV) can readily form hydroxymethanesulfonate (HMS)
(Rao and Collett, 1995; Shen et al., 2012). Based on the
average SO2 concentration (∼1 ppb) and cloud water pH
(3.63) at TMS, the upper limit of in-cloud HMS formation was estimated
to be 0.07 µM, which only accounts for 4.2 % of total formaldehyde
and thus is insufficient to explain the formaldehyde deficit.
Theoretical (Ftheo; gray circle) and measured (Fme;
colored diamond) aqueous-phase fraction (Fp) of carbonyl compounds as a
function of LWC and pH.
Figure 5 depicts the dependence of Fme/Ftheo ratios of carbonyl
compounds on LWC and pH. In general, the Ftheo of measured carbonyls
increased to different degrees with enhanced LWC because larger water
content has a greater capacity to retain organic species. The Fme also
increased remarkably as the LWC increased, but it deviated to different degrees
from Ftheo. For example, the Fme values for methylglyoxal and
acetone surpassed their Ftheo values when LWC exceeded ∼0.2 g m-3, whereas the Fme values of formaldehyde and acetaldehyde
were approximately parallel to their Ftheo throughout the LWC range,
being 1 order of magnitude lower and higher, respectively. It should be
noted that pH value was positively related to LWC but not involved in the
Ftheo calculation, so the elevated Ftheo and increase in pH were
not necessarily correlated. In contrast, the Fme seemed to be more
close to Ftheo at lower pH values, but it increased more rapidly than Ftheo
at higher pH values for the monocarbonyls except formaldehyde. For dicarbonyls, the
Fme of glyoxal slightly decreased at higher pH values and showed a larger
departure from Ftheo, while the Fme of methylglyoxal far exceeded
the theoretical values around pH values of 3.0–3.5. Previous studies have found
that the solution acidity can largely affect the reactive uptake of
dicarbonyls (Gomez et al., 2015; Zhao et al., 2006). The cloud water
acidity may also influence the partitioning of carbonyls and to some extent
contribute to the different gaps between Fme and Ftheo in the
present study. The complicated partitioning behaviors could be affected by
both physical (e.g., interface adsorption effect) and chemical processes
(e.g., chemical production from precursors) (van Pinxteren et al., 2005, and
references therein). It is currently impossible to account for the results
in detail. Further laboratory and theoretical studies are critically
warranted.
Pairwise scatter plot of selected organic species in cloud water.
The confidence ellipses were plotted (using OriginPro 2015 software) to
indicate the strength of bivariate correlations at a 99 % confidence level.
The confidence ellipse collapses diagonally as the correlation between two
variables approaches 1 or -1, and it becomes more circular when two variables
are uncorrelated.
Correlations between carbonyls and carboxylic acids
To investigate the potential precursors of carboxylic acids and DOM in cloud
water, the correlations among all detected organic compounds and
water-soluble ions were examined. Significant correlations were found for
secondary water-soluble ions (SO42-, NO3-, and
NH4+) with glyoxal (0.76 < r < 0.88, p < 0.01) and carboxylic acids (0.72 < r < 0.94, p < 0.01). As sulfate is primarily produced by in-cloud S(IV) oxidation
(Harris et al., 2013), a strong correlation
(r=0.75, p < 0.01) between oxalic acid and sulfate suggests
significant in-cloud formation of oxalic acid. Likewise, the chemical cloud
processing might have contributed to the secondary formation of other
organic matters in the aqueous phase, such as DOM with a significant
correlation with sulfate (r=0.83, p < 0.01)
(Ervens et al., 2011; Yu et al., 2005).
Figure 6 shows the pairwise correlations (p < 0.01) among the
selected organic species. Aqueous-phase glyoxal was positively correlated
with all carboxylic acids (0.57 < r < 0.95) and DOM (r=0.86) in both daytime and nighttime. Moreover, the gas-phase glyoxal
exhibits positive relationships with aqueous-phase glyoxal and carboxylic
acids, particularly oxalic acid (Fig. S5). Many laboratory experiments
have demonstrated that radical (mainly ⋅ OH) (Lee et al., 2011;
Schaefer et al., 2015) and non-radical aqueous oxidation of glyoxal (Lim
et al., 2010; Lee et al., 2011; Schaefer et al., 2015; Gomez et al., 2015)
can produce abundant small carboxylic acids (e.g., oxalic and formic acids),
oligomers, and highly oxidized organics, which subsequently lead to mass
increase in SOA upon droplet evaporation (Galloway et al., 2014). In this
study, the abundant methylglyoxal showed no significant correlations with
glyoxal, carboxylic acids, or DOM in both daytime and nighttime. Given the
high solubility of glyoxal and its potential yield of carboxylic acids
(Carlton et al., 2007; Lim et al., 2005, 2010; Blando and
Turpin, 2000), glyoxal should be of great importance in the formation of secondary
organic matters in cloud water at TMS. In addition, as oxalic
acid is predominantly formed in clouds (Myriokefalitakis et al., 2011;
Ervens et al., 2011), the good interrelationship among carboxylic acids and
DOM (Fig. 6) indicates that carboxylic acids can contribute to DOM
formation directly and/or indirectly via oxidizing to oligomers (Carlton
et al., 2006; Tan et al., 2012).
Formation of aqueous organics and cloud effects on aerosol propertiesFormation of cloud water organics
Cloud processing can efficiently alter the aerosol concentration and
composition by nucleation and impaction scavenging (Ervens,
2015), especially at the initial stage of cloud events (Wang et al.,
2011a; Li et al., 2017). On the other hand, chemical cloud processing
greatly favors the in-cloud formation of sulfate
(Harris et al., 2013) and SOA
(Brégonzio-Rozier et al., 2016), which can remain
in the particle phase upon droplet evaporation. To investigate the
scavenging and changes of aerosols during cloud events, temporal variations
of glyoxal, carboxylic acids, DOM and sulfate in cloud water, and ambient
PM2.5 during three cloud events (E.2, E.4, and E.5) were examined
(Fig. 7). Based on hourly PM2.5 and water-soluble ion data (not
shown here), the average scavenging ratios were determined to be 0.72 for
PM2.5, 0.85 for aerosol sulfate, 0.69 for nitrate, and 0.68 for ammonium
within the first 1–2 h of cloud processing, which were ascribed to the high
cloud density, long cloud duration, and little external aerosol invasion.
Figure 7 illustrates the variations of cloud water organics, sulfate, and
ambient PM2.5 along with cloud evolution. Positive change rates were
found during the daytime with enhanced solar radiation, while negative
change rates appeared with reduced solar radiation at sunset and nighttime.
This result agreed with the simulation by Huang et al. (2011), in
which increasing solar radiation enhanced organic acids and SOA production
through photochemical reactions. During the clean continental case E.2, the
total carboxylic acids in cloud water increased by a factor of 1.9, and DOM
was elevated from 3.7 to 5.7 µg m-3 as solar radiation intensified
to ∼300 W m-2, corresponding to the dramatic growth in
aqueous glyoxal from 69 to 216 ng m-3, while the increment of sulfate
was relatively small, with only 0.15 µg m-3 (i.e., 10 %).
Temporal variation of air equivalent concentrations of glyoxal,
carboxylic acids, dissolved organic matter, the SO42- and
DOM/SO42- ratio in cloud water, and ambient conditions during three cloud
events (E.2, E.4, and E.5). Meteorological parameters (relative humidity and
solar radiation) and air pollutants (NOx, SO2, and PM2.5) are
also displayed. Mass change rates of cloud water components are indicated by
dashed lines and slopes.
The oxalate / sulfate ratio can be indicative of the in-cloud oxalate
formation relative to sulfate. For example, aircraft observations
(Sorooshian et al., 2007; Wonaschuetz et al., 2012) have shown an
increasing aerosol oxalate / sulfate ratio throughout the mixed cloud layer
from 0.01 for below-cloud aerosols to 0.09 for above-cloud aerosols,
suggesting more aqueous production of aerosol oxalate relative to sulfate by
chemical cloud processing. Similarly, the observed oxalate / sulfate and
DOM / sulfate ratios in cloud water for case E.2 increased from 0.04 to 0.09
and from 2.7 to 3.3 after sunrise, respectively, also demonstrating the
increased formation of cloud water organics as contributed by cloud processing.
A chamber study demonstrated that the faster photochemical uptake of glyoxal
under irradiation than that in dark conditions remarkably enhanced the aqSOA
formation rate by several orders of magnitude (Volkamer et al.,
2009), and the radical-initiated photo-production of aqSOA mass in the
daytime was predicted to be an order of magnitude higher than at nighttime
(Ervens and Volkamer, 2010). Therefore, photochemical reactions are
expected to enhance the production of cloud water organics (0.51 µg m-3 h-1) compared to sulfate (0.10 µg m-3 h-1) in
case E.2. During the mixed E.4 event, the approximate growth rates of DOM (0.52 µg m-3 h-1) and sulfate (0.45 µg m-3 h-1) were
comparably fast. By contrast, the growth of daytime DOM (0.05 µg m-3 h-1) and sulfate (0.03 µg m-3 h-1) during the
marine E.5 event was observed to be much slower, which could result from the relatively
fewer precursors in the cleaner marine air masses. It was noted that aqueous
glyoxal gradually increased after sunrise (6–37 ng m-3 h-1),
which likely produced carboxylic acids such as oxalic acid rapidly via
photo-oxidation and contributed to the formation of aqueous organics
(Carlton et al., 2007; Warneck, 2003). But at nighttime, the
decrease of aqueous glyoxal (-6.8 ng m-3 h-1) was observed to be
concurrent with the apparent reduction of DOM (-0.47µg m-3 h-1) in E.5. Although aqueous-phase oligomers can be formed at
nighttime, the oligomer formation is most likely not important in clouds
(Lim et al., 2010) and thus not relevant here.
Figure 7 also shows that DOM/SO42- ratios during E.4 and E.5
remained nearly constant at ∼1.0 with stronger cloud water
acidity (pH of 2.96–3.68), whereas the ratios during other cloud events
varied from 1.6 to 6.5 under higher pH conditions (3.62–5.94). Figure 8
shows the DOM/SO42- ratios as a function of pH values and the
significantly positive relationship between DOM and sulfate, which indicates
their common source of in-cloud aqueous production. The increased sulfate
leads to more acidic conditions (i.e., lower pH), except in the most polluted
case, E.1. Although the DOM also showed higher concentration in lower pH
conditions, the DOM/SO42- ratios clearly decreased at a lower pH
range. It is well known that in-cloud oxidation of S(IV) by H2O2
is the predominant pathway for sulfate formation at pH < 5, within
which the oxidation rate is independent of pH (Seinfeld and
Pandis, 2006; Shen et al., 2012). The reduced DOM/SO42- ratios
with pH suggest that DOM production was reduced compared to the sulfate in
the more acidic condition. It is consistent with a previous study which
found that oxalic acid production was more efficient relative to sulfate in
the larger size and less acidic droplets (Sorooshian et al.,
2007). Laboratory studies also found that the uptake of both glyoxal and
methylglyoxal by acidic solutions increased with decreasing acid
concentration, contributing to the formation of organic aerosols more
efficiently (Gomez et al., 2015; Zhao et al., 2006). Additionally, there
is a lack of significant competition for H2O2 by carbonyl
compounds versus S(IV) since the reactions of carbonyls with H2O2 are likely negligible under cloud conditions due to their very small
reactivities (Schöne and Herrmann, 2014). Although the influence
mechanism of cloud water acidity on organics production remained unclear,
the observed DOM/SO42- dependent trend on pH suggests that the
in-cloud formation of DOM is likely more efficient under less acidic
conditions.
DOM/SO42- ratio as a function of cloud water pH. The
embedded graph shows the relationship between DOM and SO42-.
SO42- concentrations and pH values are both indicated by color
scales.
Mass concentration distributions of major water-soluble components
in the pre-cloud and post-cloud aerosols for cloud events E.1 and E.3.
Average mass size distributions of aerosol particles during the
pre-cloud and dissipation periods for cloud events E.1 and E.4. Embedded
graphs show the droplet-mode fraction of the total mass. A particle density
of 1.65 µg m-3 was used in this study. dM represents the mass concentration of the particles in each size bin, and Dp is mobility diameter.
Impacts of cloud processing on aerosols composition and size
distribution
To evaluate the impacts of cloud processing on aerosol chemistry, the major
water-soluble components in pre-cloud and post-cloud aerosols (size
< 2.5 µm) are compared in Figs. 9 and S6. For the
highly polluted case E.1, the total mass concentration of water-soluble
components increased from 11.5 µg m-3 in pre-cloud aerosols to
16.7 µg m-3 in post-cloud aerosols. A slightly elevated mass
concentration of post-cloud aerosol components was also observed for the mixed
case E.3. Those increases in the cloud-processed aerosols were mainly
contributed by sulfate and WSOM (Fig. S6), suggesting the contribution of
in-cloud aqueous production. In the polluted case E.1, although the mass
concentration of sulfate was increased after cloud processing, the fraction
slightly decreased from 57 % to 51 % in the post-cloud aerosols, which
was mostly due to the large increases of WSOM from 19 % (pre-cloud
aerosols) to 28 % (post-cloud aerosols). In the mixed case E.3, the
fractions of sulfate and WSOM were both enhanced in the post-cloud aerosols
from 18 % and 36 % to 30 % and 47 %, respectively, with a notable
increase in sodium fraction due to the influence of marine air masses. The
results demonstrated the important role of chemical cloud processing in
altering aerosol composition through the formation of aqueous sulfate and organics. It has been suggested that highly oxidized cloud water organics
readily remain in evaporating cloud droplets and contribute to aqSOA mass
production, whereas volatile products are prone to escape into the gas phase
(Schurman et al., 2018). Thus we hypothesize that the large
fraction of unidentified organics in cloud water (76 % in this study)
could be oxidized species with low volatility, which possibly contribute to
the increased WSOM in cloud-processed aerosols. In addition, abundant
interstitial particles were observed during the cloud events, similar to the
previous measurements in northern and southern China which also observed large
amounts of interstitial particles with a median diameter of 422 nm and
enhanced oxalate formation in interstitial particles (Liu et al., 2018;
Zhang et al., 2017). The aqueous reactions on the wetted but unactivated
particles could also play an important role in altering the particle
composition and producing secondary species, like sulfate and carboxylic
acids, which require more investigations in the future.
In Fig. 10, the average mass size distributions of aerosol particles
during pre-cloud and dissipation periods for cases E.1 and E.4 are compared.
Multimodal distribution is apparent, with the dominant accumulation mode
peaking at ∼0.4µm and a second coarse mode at
∼2.0µm. Accumulation-mode aerosols (0.1–1.0 µm)
usually consist of two subgroups, with the condensation and droplet modes peaking
typically at 0.2–0.3 and 0.5–0.8 µm, respectively (Hinds, 2012).
In this study, the overlapping of the two subgroups likely made the
accumulation mode peak. For the polluted E.1 event, both accumulation- and coarse-mode aerosols decreased during the cloud dissipation period compared to the
pre-cloud aerosols, probably due to the cloud scavenging effect. In
contrast, the concentrations of aerosols with a diameter of ∼0.6–1.1 µm exceeded the pre-cloud ones, and the droplet-mode (0.5–1.0 µm) mass fraction increased notably after the cloud processing, from
the pre-cloud value of 9 % to 18 % (dissipation period). For the mixed event E.4,
though aerosols were scavenged in all modes, the cloud-processed aerosols
still showed an increased droplet-mode mass fraction (19 %) compared to
the pre-cloud aerosols (15 %). As droplet-mode aerosols are mainly
produced from aqueous reactions, the increase in droplet-mode mass fraction
after cloud dissipation may be associated with the in-cloud formation of
sulfate and aqSOA (Blando and Turpin, 2000; Ervens et al., 2011). Model
simulations reveal that the relative mass increase of droplet-mode aerosols
after cloud processing can be up to ∼100 % for marine air
masses with significantly accumulated sulfate and oxalate in the 0.56 µm
range (Ervens et al., 2018). Hence we can expect that sulfate (air
equivalent concentration of 4.9 µg m-3) and the low-volatility
fraction of DOM (15.0 µg m-3) measured in cloud water could be
mostly retained in droplet-mode aerosols upon cloud evaporation. Although
the mass size distributions of particle compositions were not measured in
this study, the abundant droplet-mode oxalate, organic carbon, and sulfate
aerosols reported in Hong Kong (Bian et al., 2014; Gao et al., 2016),
together with the clearly increased bulk sulfate and WSOM in the post-cloud
aerosols (Fig. S6), seem to support our hypothesis.
Conclusions
Gas–cloud–aerosol interactions can determine the fate of trace gases and
the physicochemical properties of aerosols, but the multiphase processes in
the subtropical PRD region are still poorly understood. This study
presents the results from a field campaign with concurrent measurements of
gases, particles, and cloud waters conducted at a mountain site in Hong Kong
for the first time. The chemical compositions of the acidic cloud water (pH
ranges of 2.96–5.94) during different cloud events were dominated by DOM
and secondary inorganic ions, which were heavily influenced by anthropogenic
emissions from continental air masses. Continental air masses generally
contributed more pollutants to cloud water than the marine air masses did.
The distinct relationships of carbonyl compounds with LWC and pH were likely
controlled by their partitioning between cloud water and the gas phase.
Simultaneous measurements in the two phases enabled the investigation of
their partitioning behaviors. The Fme values of dicarbonyls considering
hydration reactions agreed well with their theoretical values, whereas large
discrepancies were found between Fme and Ftheo of monocarbonyls.
The complicated partitioning behaviors of carbonyls possibly result from the
combined physical and chemical effects, which require further investigation.
The good correlation between DOM and sulfate indicated the in-cloud
formation of aqueous organics, for which abundant glyoxal likely played an
important role given its significant correlations with carboxylic acids and
DOM. During cloud processing, growth of cloud water DOM (0.05–0.52 µg m-3 h-1) and sulfate (0.03–0.45 µg m-3 h-1) was
observed as solar radiation increased, with a simultaneous increase of glyoxal
(5.9–37 ng m-3 h-1). The cloud water DOM production seemed to be
more efficient under less acidic conditions relative to sulfate. Apart from the
cloud scavenging of aerosol particles, cloud processing played crucial roles
in changing the chemical composition and mass size distribution of
particles. Remarkably, an increase in absolute concentrations and mass fractions
of sulfate and WSOM was observed in the cloud-processed aerosols compared to
the pre-cloud ones. It is expected that large amounts of sulfate and
organics produced in cloud water can remain in the particle phase after
cloud dissipation and lead to the mass increase in droplet-mode particles.
The observations provide direct evidence for the modification of aerosols by
cloud processing, promoting our understanding of the gas–cloud–aerosol
interactions and multiphase chemistry of polluted coastal environments.
Data availability
The measurement data of cloud water chemistry that support the findings of
this study are openly available in DataSpace@HKUST at
10.14711/dataset/KURDVK (Li and Wang, 2019). The trace gas and PM2.5 data used
in this study are available upon request from the Hong Kong Environmental
Protection Department or the corresponding author (z.wang@ust.hk).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-391-2020-supplement.
Author contributions
ZW, TW, and YW designed the research. TL, YW, CW, and YL performed the field
measurements of cloud water and the sample analysis. MX, CY, HY, and WW conducted the
measurement of trace gases and aerosols. TL, ZW, JG, and HH performed data
analysis and wrote the paper. All authors contributed to the discussion and
commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Marine organic matter: from biological production in the ocean to organic aerosol particles and marine clouds (ACP/OS inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The authors would like to thank Steven Poon and Bobo Wong for their support
during the campaign and the Hong Kong Environmental Protection
Department for sharing the trace gas and PM2.5 data from the Tai Mo
Shan AQM station. The authors also thank Guyline (Asia) Limited for providing NanoScan SMPS and OPS spectrometers for the measurement of particle size distribution. The authors greatly acknowledge the editor and anonymous reviewers for their
constructive suggestions which largely improved the quality of the work.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2016YFC0200503), the Research Grant Council of the Hong Kong Special Administrative Region, China (grant nos. 25221215, 15265516, and T24-504/17-N), and the National Natural Science Foundation of China (grant nos. 41605093, 41475115, and 91744204).
Review statement
This paper was edited by Paul Zieger and reviewed by two anonymous referees.
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