ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-12295-2019Relative humidity and O3 concentration as two prerequisites for sulfate formationRH and O3 concentrationFangYanhuaYeChunxianghttps://orcid.org/0000-0002-5417-2671WangJunxiaWuYushenghttps://orcid.org/0000-0001-7548-8272HuMinLinWeiliXuFanfanZhuTongtzhu@pku.edu.cnhttps://orcid.org/0000-0002-2752-7924BIC-ESAT and SKL-ESPC, College of Environmental Sciences and
Engineering, Peking University, Beijing, 100871, ChinaCollege of Life and Environmental Sciences, Minzu University of China,
Beijing 100081, China
Sulfate formation mechanisms have been discussed
extensively but are still disputed. In this work, a year-long particulate
matter (PM2.5) sampling campaign was conducted together with
measurements of gaseous pollutant concentrations and meteorological
parameters in Beijing, China, from March 2012 to February 2013. The sulfur
oxidation ratio (SOR), an indicator of secondary sulfate formation,
displayed a clear summer peak and winter valley, even though no obvious
seasonal variations in sulfate mass concentration were observed. A rapid
rise in the SOR was found at a relative humidity (RH) threshold of ∼45 % or an
O3 concentration threshold of ∼35 ppb, allowing us to
first introduce the idea that RH and O3 concentrations are two
prerequisites for rapid sulfate formation via multiphase reactions. In the
case of the RH threshold, this is consistent with current understanding of
the multiphase formation of sulfate, since it relates to the
semisolid-to-liquid phase transition of atmospheric aerosols. Correlation
analysis between SOR and aerosol water content (AWC) further backed this up. In the case of the
O3 concentration threshold, this is consistent with the consumption of
liquid oxidants in multiphase sulfate formation. The thresholds introduced
here lead us to a better understanding of the sulfate formation mechanisms and
sulfate formation variations. H2O2 might be the major oxidant of
sulfate formation, since another liquid-phase oxidant, O3, has
previously been shown to be unimportant. The seasonal variations in sulfate
formation could be accounted for by variations in the RH and O3
prerequisites. For example, over the year-long study, the fastest
SO2-to-sulfate conversion occurred in summer, which was associated with
the highest values of O3 (and also H2O2) concentration and
RH. The SOR also displayed variations with pollution levels; i.e. the SOR
increased with PM2.5 in all seasons. Such variations were primarily
associated with a transition from the slow gas-phase formation of sulfate to
rapid multiphase reactions, since RH increased higher than its prerequisite
value of around 45 % as pollution evolved. In addition, the self-catalytic
nature of sulfate formation (i.e. the formation of hydrophilic sulfate
aerosols under high RH conditions results in an increase in aerosol water
content, which results in greater particle volume for further multiphase
sulfate formation) also contributed to variations among the pollution
scenarios.
Introduction
Beijing, the capital of China, suffers from serious air pollution due to its
rapid economic growth and urbanisation (Hu et al., 2015). The
chemical composition and sources of fine particulate matter (PM2.5) in
Beijing have been studied extensively (Han et al., 2015; Lv et al., 2016;
Zhang et al., 2013; Zheng et al., 2005). Secondary components, especially
sulfate, nitrate, and ammonium (SNA), are the main contributors to
PM2.5 (Huang et al., 2014). On the most severely polluted days, SNA
accounts for more than half of total PM2.5 mass concentrations and plays
a more important role than on clean days (Quan et al., 2014; Y. S. Wang et al.,
2014; G. J. Zheng et al., 2015).
The kinetics and mechanisms of the formation of sulfate, a major component
of SNA, are complex and remain unclear (Ervens, 2015; Harris et al.,
2013; Warneck, 2018). For example, two key questions concerning sulfate
formation are (1) exactly how various parameters (oxidants, catalysts,
meteorological conditions, etc.) influence sulfate formation, and (2) how
multiple formation routes compete and contribute together to sulfate
formation under ambient conditions. In general, sulfate is produced from
SO2 via gas-phase oxidation reactions involving the hydroxide radical
(OH) and Criegee intermediates (Gleason et al., 1987; Sarwar et al.,
2014; Vereecken et al., 2012), heterogeneous reactions (mainly on dust
aerosols), and multiphase transformations with O3, H2O2, or
O2 (catalysed by transition metal ions (TMIs) (i.e. TMIs +O2)
and NO2 (NO2+O2)) as liquid-phase oxidants, which occur
mainly in clouds but also in aerosol droplets near the ground (Zhu
et al., 2011).
Due to the major role of multiphase transformations, sulfate production is
presumed to be self-catalysed; i.e. the formation of hydrophilic sulfate
aerosols under high relative humidity (RH) conditions results in an increase
in aerosol water content (AWC), which results in greater particle volume for
further multiphase sulfate formation (Cheng et al., 2016; Pan et al.,
2009; Xu et al., 2017). Analyses of the correlation of sulfate formation
with RH and AWC have been conducted to test this hypothesis, using the
concept of the sulfur oxidation ratio (SOR), defined as the molar ratio of
sulfate to total sulfur (the combination of sulfate and SO2). It is used to indicate
the magnitude of the secondary formation of sulfate and expressed as
(Wang et al., 2005)
SOR=nSO42-nSO42-+nSO2,
where nSO42- and nSO2 represent the molar concentrations of
sulfate and SO2, respectively. Even though regional transport or
intrusion of SO2 or sulfate (or local sulfate emissions) would modify
the SOR, it has still often been a relatively good proxy of secondary
sulfate formation (i.e. local SO2-to-sulfate conversion). For example,
Sun et al. (2014, 2013) found positive correlations between the SOR and
RH, and observed rapid increases in SORs at elevated RH levels. Xu et al. (2017) found positive correlations of the SOR with both RH and
AWC. Multiphase transformation routes, including O3 oxidation, TMIs +O2, and NO2+O2, are pH sensitive and suppressed at low pH
(Seinfeld and Pandis, 2006). Sulfate production raises the acidity of
aerosols, and therefore the multiphase transformations of sulfate are
presumed to be self-constrained (Cheng et al., 2016). For example, a
significant contribution from the O3 oxidation route can only be
expected under alkaline conditions (e.g. sea salt); otherwise, O3
oxidation is a minor pathway for sulfate formation (Alexander et al.,
2005; Sievering et al., 2004). How the self-constraining nature of sulfate
formation influences the relative significance of the TMIs +O2 and
NO2+O2 routes is still under debate. Cheng et al. (2016)
proposed that the NO2+O2 route is important during severe haze
events under neutral pH conditions (He et al., 2018; Wang et al., 2016).
Guo et al. (2017) suggested that aerosols are acidic in Beijing
(except for during the limited cases of dust or sea-salt events), casting
doubt on the importance of the NO2+O2 route in sulfate
formation (M. Liu et al., 2017). According to laboratory-based Raman
spectroscopy studies, sulfate can be produced via the aqueous oxidation of
SO2 by NO2+O2, with an SO2 reactive uptake
coefficient of 10-5, which represents an atmospherically relevant value
(Yu et al., 2018), whereas others have suggested that
this route is of minor importance in the atmosphere (Li et al., 2018;
Zhao et al., 2018). In addition, Xie et al. (2015) proposed that
NO2 could enhance the formation of sulfate in certain cases, for
example, in biomass burning plumes or dust storms (He et al., 2014).
Evaluation of the contribution of TMIs +O2 reactions appears to be
more complex since it depends on aerosol acidity, solubility, oxidation
state, and the synergistic effects of different TMIs (Deguillaume et al.,
2005; Warneck, 2018).
The compensating effects among AWC, aerosol acidity, and the concentrations
of precursors and catalysts show that the kinetics and mechanisms of sulfate
formation are highly complex. It can be inferred that there is competition
between the various routes, with dependence on atmospheric conditions
(e.g. seasonal and pollution-level variations) likely, but this has not
received much research attention previously. Here, daily PM2.5 samples
were collected in Beijing from March 2012 to February 2013 and their
chemical composition was analysed. The main parameters that influenced
sulfate formation (i.e. RH, O3 concentration, TMIs, etc.) were
determined. This valuable dataset enabled us to explore (1) the specific
role of each influencing factor in sulfate formation, and (2) how multiple
sulfate formation routes compete in different seasons and under various
pollution scenarios.
Measurements and methodologyMeasurementsMeasurement stations
The two measurement stations are shown in Fig. 1. The Peking University (PKU) station
(39.99∘ N, 116.30∘ E) is about 20 m above ground level
on the campus of Peking University, Beijing, China (Liang et al.,
2017). Daily PM2.5 samples were collected using a four-channel sampler
(TH-16A; Wuhan Tianhong Instruments, China) at a flow rate of 16.7 L min-1 from 1 March 2012 to 28 February 2013. The gaseous pollutants
SO2, NOx, and O3 were measured with a pulsed fluorescence
SO2 analyser (Model 43i TLE; Thermo Fisher Scientific, Waltham, MA,
USA), chemiluminescence NO–NO2–NOx analyser (Model 42i TL;
Thermo Fisher Scientific), and an ultraviolet photometric O3 analyser
(Model 49i; Thermo Fisher Scientific), respectively. Temperature and RH were
also monitored (MSO; Met One Instruments, Grants Pass, OR, USA). Solar
radiation data were obtained from the Beijing Meteorological Observatory
Station (39.81∘ N, 116.47∘ E). Daily averages were used
for all analyses conducted in this work.
Sample sites in this study (red stars): (a) Peking University and
(b) Beijing Meteorological Observatory.
Filter sampling and analysis
Each PM2.5 sample set consisted of one quartz filter (47 mm; Whatman
QM/A, Maidstone, England) and three Teflon filters (47 mm; pore size 2 µm; Whatman PTFE). The quartz filters were baked for 5.5 h at 550 ∘C before use. The Teflon filters were weighed in a weighing room
before and after sampling using a delta range balance (0.01 mg/0.1 mg
precision; AX105; Mettler Toledo, Switzerland). To minimise contamination,
all Teflon filters were placed in a super-clean room (temperature of 22±1∘C; RH of 40±2 %) for 24 h before being
weighed. After sampling, all filters were stored at -20∘C
prior to analysis.
Water-soluble cations (Na+, NH4+, K+, Mg2+, and
Ca2+) and anions (SO42-, NO3-, Cl-, and
F-) were measured using ion chromatography (ICS-2500 and ICS-2000;
DIONEX, USA). Trace elements (Na, Mg, Al, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu,
Zn, Se, Mo, Cd, Ba, Tl, Pb, Th, and U) were analysed by inductively coupled
plasma–mass spectrometry (ICP–MS, X-Series; Thermo Fisher Scientific).
Organic carbon (OC) and elemental carbon (EC) were measured using a
thermal/optical carbon analyser (RT-4; Sunset Laboratory Inc., Tigard, OR,
USA). The procedure for the measurement of water-soluble Fe has been
described in detail in a previous study (Xu et al., 2018).
Estimation of the mass concentrations of PM2.5 components
The chemical components of PM2.5 were divided into eight categories:
sulfate, nitrate, ammonium, organic matter (OM), EC, minerals, trace element
oxides (TEOs), and others. The mass concentrations of OM, minerals, and TEOs
were calculated from OC, Al, and trace element concentrations, respectively.
The details of this method are provided in the Supplement. For minerals, validation of the method using only Al to represent all
minerals is shown in Fig. S1 in the Supplement. TEOs mostly originated from anthropogenic
sources (Fig. S2).
Quality assurance and quality control
The PM2.5 sampling instruments were cleaned and calibrated every 2–3 months. Before the daily filter replacement, filter plates were scrubbed
with degreasing cotton that had been immersed in dichloromethane. For water-soluble ions, OC/EC, and trace element measurements, standard solutions were
analysed before each series of measurements. The R2 values of the
calibration curves were all >0.999. For water-soluble ion
measurements, beakers, tweezers, and vials were cleaned with deionised water
(18.2 MΩ; Milli-Q, USA) three times before use. Certified reference
standards (National Institute of Metrology, China) were used for
calibration. For OC/EC measurements, tweezers and scissors were scrubbed
with degreasing cotton immersed in dichloromethane for every filter. Total
organic carbon (TOC) was calculated based on calibration with external
standard solutions. For trace element measurements, containers and tweezers
were cleaned three times with nitric acid before use, and the analysis of a
certified reference standard (NIST SRM-2783) was used to verify accuracy.
The recovery of all measured trace elements fell within ±20 % of
their certified values. For gaseous pollutants and meteorological
parameters, all instruments were maintained and calibrated weekly based on
manufacturers' protocols.
Results and discussionGeneral description
The annual and seasonal mean (± 1 standard deviation; SD)
concentrations of PM2.5 and its seven major known components are
summarised in Table 1. The annual mean PM2.5 concentration was 84.1
(±63.1) µgm-3, which is more than 2 times greater than
the Chinese National Ambient Air Standard annual mean concentration of 35 µgm-3. On 145 of the 318 (46 %) measurement days, daily mean
PM2.5 concentrations were above the Chinese National Ambient Air
Standard 24 h mean concentration of 75 µgm-3. Time series of
PM2.5 concentrations and its seven major known components are shown in
Fig. 2. Seasonal variations in PM2.5 loading are obvious, with spring
and winter peaks and summer and autumn valleys. OM and EC concentrations
displayed common seasonal variations, with a plateau from mid-October to
mid-February and a valley in summer (Fig. 2), which resembles the variations
in PM2.5, K+, Cl-, and F- (Figs. 2 and 3). The seasonal
variations in minerals also indicate an important contribution of dust
events to PM2.5 loading during spring, which is a well-known phenomenon
(Zhang et al., 2003; Zhuang et al., 2001). TEOs displayed no obvious
seasonal variations (Fig. 2). SNA accounted for more than one-third of
PM2.5 annually and showed similar seasonal variations to that of
PM2.5 (Fig. 2), with the notable exception that sulfate became the
highest contributor to PM2.5 (∼25 %) in summer (Fig. 4). The summer peak in sulfate could be accounted for by fast secondary
formation, as will be discussed later.
Annual and seasonal mean concentrations (µgm-3, ±1 standard deviation) of PM2.5 and its seven major known components.
Time series of fine particulate matter (PM2.5) concentrations
and its seven major known components from March 2012 to 28 February 2013
(open black circles). The boxes represent, from top to bottom, the
75th, 50th, and 25th percentiles for each season. The
whiskers, solid red squares, and open red circles represent 1.5 times the
interquartile range (IQR), seasonal mean values, and outlier data points,
respectively.
Time series of Cl-, K+, and F- from 1 March 2012 to 28 February 2013.
On an annual basis, the seven major known components accounted for over 80 % of PM2.5 (Fig. 4). The diversity of the seasonal variations in
PM2.5 and its major components found in our study imply that there were
seasonal variations in both the primary sources and secondary formation of
PM2.5.
Seasonal variations in PM2.5 and its eight major components
from 1 March 2012 to 28 February 2013.
Influence of various parameters on sulfate formation
To further explore the parameters that influenced sulfate formation, SORs
were plotted against RH and the concentrations of O3, NO2, and Fe
(total Fe, including both water-soluble and water-insoluble Fe), which is a
major tracer of transition metals (Figs. 5 and 6).
(a) Plot of the SOR against RH grouped by O3 concentration. The solid blue circles
represent O3>35 ppb and the solid black circles represent
O3<35 ppb. (b) Plot of the SOR against O3 grouped by
RH. The solid blue circles represent RH >45 % and the solid
black circles represent RH <45 %. The boxes represent,
from top to bottom, the 75th, 50th, and 25th percentiles in
each bin (ΔRH =5 %; ΔO3=5 ppb). The whiskers,
solid red squares, and open red circles represent 1.5 times the IQR, mean
values, and outlier data points, respectively. The red lines are best fits
to mean values based on a sigmoid function. Data for days with rain or snow
were excluded from these plots.
Plot of SOR against RH grouped by O3 concentration in four
seasons. The solid blue circles represent O3>35 ppb and
the solid black circles represent O3<35 ppb. The boxes
represent, from top to bottom, the 75th, 50th, and 25th
percentiles in each bin (ΔRH =5 %). The whiskers, solid red
squares, and open red circles represent 1.5 times the IQR, mean values, and
outlier data points, respectively. The red lines are best fits to mean
values based on a sigmoid function. Data for days with rain or snow were
excluded from these plots.
As shown in Fig. 5a, an RH threshold of ∼45 % was critical
for efficient SO2 oxidation (i.e. a high SOR). Such a threshold effect
was thought to be reasonable given that AWC increases sharply when RH was
above a threshold of 45 %, at which the aerosol undergoes a phase
transition from a (semi-)solid particle to a droplet (Pan et al., 2009;
Russell and Ming, 2002). Further correlation analysis between SOR and AWC
further supports that the multiphase reactions are responsible for sulfate
formation (Fig. S3). Our observation of a daily average RH threshold of
∼45 % is in line with previous reports of 40 %–50 %
(Liu et al., 2015; Quan et al., 2015; Xu et al., 2017; Yang et al., 2015;
G. J. Zheng et al., 2015) but is slightly lower than the in situ phase transition
threshold RH of 50 %–60 % previously observed in Beijing (Y. C. Liu et al.,
2017). Correlation analysis of SOR and RH (or AWC) has often been conducted
in previous studies. For example, Wang et al. (2005) found a
weak positive correlation of SORs with RH (R=0.38), while Sun et al. (2006) found a strong positive correlation (R=0.96).
However, the analysis in the present work and those of a few previous
studies revealed that the relationship between the SOR and RH is nonlinear
(Sun et al., 2013, 2014; G. J. Zheng et al., 2015). In fact, the
RH threshold suggests that high RH (or AWC) is a prerequisite for fast
sulfate formation via multiphase reactions, which are known to account for
the majority of sulfate accumulation.
From the large scattering of data points around the fit line in Fig. 5a, it
might be inferred that RH was not the only prerequisite for fast
SO2-to-sulfate conversion. As shown in Fig. 5b, a significant increase
in the SOR was also observed at an O3 concentration threshold of
∼35 ppb. High O3 concentrations (i.e. >35 ppb) were accompanied by high SOR values of ∼0.4 (right-hand
side of Fig. 5b). Correlation analyses of SORs with O3 have been
conducted but inconsistent results were reported. Wang et al. (2005) found a weak positive correlation between SORs and
O3 (R=0.47) for continuous observations in Beijing during
2001–2003. However, Liu et al. (2015) found a weak negative
correlation between SORs and O3 (R=-0.53, p=0.01) during a haze
episode in September 2011. Zhang et al. (2018) found no correlation
between SORs and O3 during winter haze days in 2015. Quan et al. (2015) found that the SOR decreased with O3 when O3
concentrations were lower than 15 ppb but increased with O3 when
O3 concentrations were higher than 15 ppb, for observations made during
autumn and winter 2012. In the present study, our observations revealed that
the relationship between the SOR and O3 concentration, like RH, was
nonlinear and that a high O3 concentration was another prerequisite for
fast sulfate formation. Such a conclusion was a surprise first, since
O3 oxidation was not thought to be a major route for
SO2-to-sulfate conversion (He et al., 2018; Sievering et al., 2004).
However, as a primary precursor to OH radicals and H2O2 (via
HO2) (Lelieveld et al., 2016; Lu et al., 2017), high O3
concentrations (e.g. >35 ppb) correspond to a high
concentration of oxidants, which favours multiphase sulfate formation and
thus a high SOR, whereas low O3 concentrations suggest a lack of
available oxidants for multiphase SO2-to-sulfate conversion and thus a
low SOR. In addition, the simultaneous occurrence of low SORs and low
O3 concentrations had a secondary cause. Low O3 concentrations in
the Beijing urban area were often due to the titration of O3 by NO
(Li et al., 2016), which accumulated together with SO2 (Fig. S4).
The accumulation of SO2, which “diluted” the SOR (Eq. 1), was thus
naturally accompanied by the titration of O3. The L-shaped dependence
of the SOR on several other primary pollutants, such as EC, NO, and Se (Fig. S5), further confirmed this secondary cause. Therefore, the accumulation of
primary pollutants might also help to explain the low SOR values of
∼0.1 on the left-hand side of Fig. 5b, in addition to the
lack of available oxidants for multiphase SO2-to-sulfate conversion.
The large scattering of data points around the fit line in Fig. 5b suggests
that O3 concentration, like RH, was not the only prerequisite for fast
SO2-to-sulfate conversion. The dependence of the SOR on RH was
separated into low (<35 ppb) and high (>35 ppb) O3
groups (solid black circles and solid blue circles, respectively, in Fig. 5a). SOR values above the fit line are found mostly for the high O3
group. After the dependence of the SOR on O3 concentration was
separated into low (<45 %) and high (>45 %) RH
groups (solid black circles and solid blue circles, respectively, in Fig. 5b), a similar pattern was found for the high RH group. In other words, fast
multiphase SO2-to-sulfate conversion could only occur when both O3
and RH exceeded their respective thresholds simultaneously.
The seasonal variations of such thresholds of RH and O3 were further
discussed. As show in Fig. 6, the RH threshold was roughly around 45 % during
all four seasons in Beijing, while the threshold of O3 varied among
seasons (Fig. 7). A turning point of 25–40 ppb was observed for fast SOR
increase in spring, summer, and autumn, while the turning point is not clear
due to lack of high O3 data in winter. The variation of O3
threshold value might be due to the shift of O3–H2O2
relationship which might be modified by temperature, etc. in different
seasons. Despite of the variation of thresholds of RH and O3 in
different seasons or even in different sampling locations (not discussed
here), the thresholds of RH and O3 for fast sulfate formation further
found in our study have its implications on the sulfate formation mechanism (see
below).
Plot of the SOR against O3 grouped by RH. The solid blue
circles represent RH >45 % and the solid black circles
represent RH <45 %. The boxes represent, from top to
bottom, the 75th, 50th, and 25th percentiles in each bin. The
bin widths were set such that there was an approximately equal number of
data points in each bin. The whiskers, solid red squares, and open red
circles represent 1.5 times the IQR, mean values, and outlier data points,
respectively. The red lines are best fits to mean values based on polynomial
functions. Data for days with rain or snow were excluded from these plots.
The SOR was further plotted against Fe and NO2. No clear dependence of
the SOR on concentrations of Fe or NO2 was found (Fig. 8a and b).
Possible reasons and implications of this result will be discussed in the
following section.
Plots of SORs against (a) Fe and (b)NO2. Plots of Fe against
(c) RH and (d)O3. Data for days with rain or snow were excluded from
these plots.
Implications for sulfate formation mechanisms
Our observations of the factors that influence sulfate formation have
implications for sulfate formation routes and its variations among seasons
and pollution conditions.
In retrospect, thresholds in RH and O3 concentrations were found to be
critical to the SOR, suggesting that AWC and liquid-phase oxidant were two
prerequisites for fast multiphase SO2-to-sulfate conversion.
H2O2 and O3 are the two liquid-phase oxidants which are
responsible for sulfate formation. The O3 oxidation route was proposed
to not be important in high aerosol acidity areas, such as Beijing (Guo et al.,
2017; Sievering et al., 2004). A recent study on aerosol pH in Beijing
showed that the PM2.5 was acidic (RH >30 %)
(Ding et al., 2019), confirming a minor contribution
from O3 oxidation. H2O2 was then the only possible oxidant
responsible for sulfate formation. Although direct measurements of aqueous
H2O2 were not performed in this study, the H2O2
concentrations in Beijing reported by Fu (2014) were found to be
positively correlated with temperature. By assuming the reported
H2O2–temperature relationship is applicable to our measurements, a
proxy H2O2 concentration was then estimated. As shown in Fig. S6,
maximum concentration of H2O2 in summer is expected and
confirmed, which is in line with the fastest sulfate formation in summer all
over the measurement year. SOR was further plotted against H2O2
and positive correlation was found between them (Fig. S7). In addition,
coincident increases in the concentration of H2O2 and PM2.5
in winter of Beijing also led to an important role of the H2O2
oxidation route in sulfate formation (Ye et al., 2018). Based on
the above discussions, we propose that H2O2 might be the major
oxidant for sulfate formation in Beijing.
The plot of SORs against Fe, the dominant transition metal species, shows no
clear dependence (Figs. 8a and S8). Similarly, the plot of SORs against
NO2 shows no clear dependence either (Fig. 8b). If Fe acted as a
catalyst, its concentration might not be directly proportional to
SORs. Therefore, such a pattern does not safely exclude TMIs +O2 as
a major route for sulfate formation. Several laboratory studies excluded
NO2 as a direct oxidant in SO2-to-sulfate conversion. For example,
Zhao et al. (2018) tested the oxidation of SO2 by NO2 in
an N2 atmosphere and concluded that NO2 is not an important
oxidant, since NO2 was more likely to undergo disproportionation
(Li et al., 2018). However, Yu et al. (2018) further explored this reaction, and found that
the reaction rate was 2–3 orders of magnitude greater in an O2+N2 atmosphere, indicating potentially important roles of NO2+O2 oxidation in sulfate formation (He et al., 2014; Ma et al., 2018). As with Fe, if NO2 acted as a catalyst, its concentration might
not be directly proportional to that of sulfate. Therefore, such a pattern
does not safely exclude NO2+O2 as a major route for sulfate
formation either. Although direct aerosol pH measurement is not available
here, previous studies have reported a mean aerosol pH value of 4.2 with a
low limit of 3.0 in Beijing (Ding et al., 2019; M. Liu et al., 2017), which
suggests that several routes of sulfate formation, including NO2+O2, TMIs +O2, O3 oxidation, etc., are suppressed. Hence, we
carefully propose here that neither TMIs +O2 nor NO2+O2 seem
to be a major route for sulfate formation.
On one hand, a direct measurement of aerosol pH is also urgently needed in
the future to examine our proposal here; on the other hand, our proposal here
has further implication on the understanding of sulfate formation.
Previously, aerosol surface area and concentrations of Fe, Mn, and NO2
were used in model evaluations of catalytic sulfate formation in the
boundary layer (Y. Wang et al., 2014; B. Zheng et al., 2015). However, our
proposals here suggest that a careful reassessment of such calculations is
required. In addition, model calculations have often suggested important
contributions of in-cloud processes to sulfate accumulation near the ground
(Barth et al., 2000), although few observational
constraints are available for confirmation of these model results (Harris
et al., 2014; Shen et al., 2012). The O3 concentration and RH
prerequisites found in the present work might indicate a major role of in situ sulfate formation in the boundary layer, via multiphase reactions with
H2O2 as the main oxidant, rather than in-cloud processes and
intrusion from the free troposphere.
Time series of (a) SORs, (b) RH, (c)O3, (d)SO2, (e) aerosol water content (AWC), and (f) solar radiation from 1 March 2012 to
28 February 2013 (open black circles). The boxes represent, from top to
bottom, the 75th, 50th, and 25th percentiles for each season.
The whiskers, solid red squares, and open red circles represent 1.5 times
the IQR, seasonal mean values, and outlier data points, respectively. The
horizontal dashed lines in panels (b) and (c) represent thresholds of RH =45 % and O3=35 ppb, respectively.
As the two prerequisites showed strong seasonal and pollution-level
variations over the measurement year, the SOR exhibited corresponding
variations. As shown in Fig. 9, SORs displayed clear seasonal variations,
with the highest value (±1 SD) of 0.46 (±0.22) in summer,
followed by spring (0.23±0.14), autumn (0.18±0.15), and
winter (0.09±0.05). The highest SOR (i.e. fastest
SO2-to-sulfate conversion rate) was found in summer, which is not
surprising because the ambient conditions in summer were conducive
SO2-to-sulfate conversion (Wang et al., 2005). RH and
O3 concentrations in summer were not only the highest in the year but
on average were also both higher than their thresholds of 45 % and 35 ppb, respectively, which was unique among the four seasons. In summer, the
median and mean (±1 SD) RH levels were 57.4 % and 57.6 (±13.6) %, respectively, and the median and mean O3 concentrations
were 46.9 ppb and 46.0 (±18.3) ppb. It should be noted that the
median and mean SO2 concentrations were 2.6 and 4.0 (±3.7) ppb,
respectively, which were the lowest in the year. Despite the low
concentrations of SO2, there were considerable sulfate concentrations
(Figs. 2 and 9), which can be accounted for by fast SO2-to-sulfate
conversion. Although the rapid accumulation of secondary sulfate during
winter haze days in Beijing has been widely reported (Y. S. Wang et al., 2014;
G. J. Zheng et al., 2015), the lowest SOR was observed during winter in the
present study (Fig. 9a), which is consistent with previous observations
(Wang et al., 2005). On winter haze days, RH values of up to
73.6 % and PM2.5 mass loadings of up to 375.3 µgm-3 were
observed. Therefore, AWC was not the limiting factor in SO2-to-sulfate
conversion (Fig. 9b and e). The SO2-to-sulfate conversion rate in
winter could have been limited by the reduced concentration of oxidants
(Fig. 9c) as a result of both high emissions of the primary pollutant NO
(Fig. S9) and low solar radiation levels (Fig. 9f). Sulfate concentrations
in winter were comparable to those in summer, which might have been driven
by high SO2 concentrations in winter (Fig. 9d), despite slow
SO2-to-sulfate conversion. The lower boundary layer height in winter
relative to other seasons would also have encouraged the accumulation of
both PM2.5 and its components, including sulfate (Gao et al., 2015;
Zhang et al., 2015). The SORs in spring and autumn were comparable and
moderate, possibly representing a transition in conditions between summer
and winter.
Variations in the mean concentrations (upper panels) and
contributions (lower panels) of the seven major known components of
PM2.5 with pollution levels in each season. C, clean; M, moderate
pollution; H, heavy pollution; S, severe pollution.
Variations in (a) SORs, (b)SO2, (c)O3, (d) RH, and
(e) AWC with pollution levels in each season. C, clean; M, moderate
pollution; H, heavy pollution; S, severe pollution. The boxes represent,
from top to bottom, the 75th, 50th, and 25th percentiles for
each pollution level. The whiskers, solid red squares, and open red circles
represent 1.5 times the IQR, mean values, and outlier data points,
respectively. The horizontal dashed lines in panels (c) and (d) represent
thresholds of O3=35 ppb and RH =45 %, respectively.
For each season, four pollution scenarios were classified according to
PM2.5 level. The lowest 25 %, 25 %–50 %, 50 %–75 %, and highest
25 % of pollution levels were defined as “clean”, “moderate
pollution”, “heavy pollution”, and “severe pollution”, respectively.
The relative contributions of the seven major known components of PM2.5
among the four pollution scenarios are shown in Fig. 10. In all four
seasons, the relative contribution of SNA increased with PM2.5 loading.
This phenomenon has been reported in previous studies, but data availability
was limited in autumn (Xu et al., 2017) and winter (G. J. Zheng et
al., 2015). The SOR increased consistently in all four seasons as pollution
accumulated, where both the highest value and strongest variability were
observed in summer (Fig. 11a). Although SO2 should have reduced the SOR
(Eq. 1), concurrent increases in primary SO2 and SORs were observed
(Fig. 11a and b), indicating a significant increase in the
SO2-to-sulfate conversion rate with PM2.5 loading, which offset
the “dilution” effect (Eq. 1). Such variations in sulfate formation with
pollution levels can be accounted for by the corresponding variations in
both O3 concentrations and RH (Fig. 11c and d). In all four seasons,
RH increased consistently as pollution accumulated (Fig. 11d). O3
concentrations decreased consistently as pollution evolved in all of the
seasons except for summer (Fig. 11c). The distinct variations in O3
during summer, imply strong photochemistry and high concentrations of OH,
which might result in a non-negligible contribution of gas-phase reactions
to the formation of sulfate. However, gas-phase reactions alone could not
account for the rate of sulfate formation either in Beijing or globally
(Finlayson-Pitts and Pitts Jr., 2000; He et al., 2018), due to the relatively
slow reaction of SO2 with OH. For example, the lifetime of SO2
with respect to OH oxidation is about 3–4 d, assuming a 24 h average OH
concentration of 1×106 molecules cm-3 and a
pseudo-secondary-order rate constant of 10-12 cm3 molecule-1 s-1 (Brothers et al., 2010). However, the overall oxidation lifetime
of SO2 is on the order of hours (Berglen et al., 2004; He et al.,
2018). Overall, the increase in SO2-to-sulfate conversion with
PM2.5 loading can be attributed to the self-catalytic nature of the
multiphase formation of sulfate; i.e. both RH and PM2.5 increased
continuously with the accumulation of PM2.5, resulting in a rapid rise
in AWC and providing greater reaction volume for further sulfate formation.
Therefore, the increases in RH and PM2.5 could have compensated for the
low concentration of oxidants, resulting in fast sulfate formation as
pollution evolved. Particularly in summer, not only did both RH and O3
concentrations increase as pollution evolved, but both RH and O3
concentrations were generally above their respective thresholds at all
pollution levels (dashed lines in Fig. 11c and d). This explains our
observations of both the highest values and strongest dependence on
pollution level for SORs in summer.
Conclusions
In this study, the annual mean concentration of PM2.5 in Beijing during
2012–2013 was 84.1 (±63.1) µgm-3, with clear seasonal and
pollution-level variations in its chemical components, highlighting the
contribution of SNA formation to the accumulation of PM2.5 in all
seasons. RH and O3 concentrations were identified as two prerequisites
for fast SO2-to-sulfate conversion. RH above a threshold of
∼45 % greatly accelerated the conversion rate. A similar
effect was also found for O3 at a concentration threshold of
∼35 ppb. Such dependence has interesting implications.
First, they indicate a major role of the H2O2 route in sulfate
formation, which might further indicate a major role of in situ sulfate production
in the boundary layer, rather than in-cloud processes and intrusion from the
free troposphere. Second, the observed dependence was also able to account
for the seasonal and pollution-level variations in SO2-to-sulfate
conversion rates. Both the highest value and strongest variability of SOR
were observed in summer, which might be attributed to the highest values of
O3 concentrations and RH in summer. SO2-to-sulfate conversion
accelerated as pollution accumulated, which was primarily attributed to a
shift from gas-phase oxidation to the multiphase oxidation route, which is
self-catalytic in nature. The increase in RH was able to offset the low
concentration of oxidants under heavily polluted conditions and resulted in
increasingly fast SO2-to-sulfate conversion as pollution accumulated.
While our simultaneous observations of the SOR and concentrations of Fe and
NO2 could not exclude TMIs +O2 and NO2-based reactions, a
reassessment of the relationships between sulfate formation, aerosol surface
area, and the concentrations of Fe and NO2 is necessary. Future
quantitative studies of the relative contributions of different sulfate
formation routes should include additional measurements, namely NH3 for
the proxy calculation of pH values, and H2O2 to confirm its
contribution under different conditions.
Data availability
The data of stationary measurements are available upon request. Radiation data can be applied through the National
Meteorological Information Center (http://data.cma.cn/, last access: 2 October 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-12295-2019-supplement.
Author contributions
TZ designed the study. YF, CY, and TZ prepared the manuscript with input
from all co-authors. YF and JW collected and weighed the PM2.5 filter
samples and carried out the analysis of the components of PM2.5. FX
carried out the measurement of water-soluble Fe. YW and MH provided the
data for gaseous pollutants, temperature, and RH. WL provided the solar
radiation data.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
We also thank Robert Woodward-
Massey for his kind help in English writing.
Financial support
This research has been supported by the National Natural Science Foundation Committee of China (grant nos. 91544000, 41121004, and 91744206).
Review statement
This paper was edited by Jianmin Chen and reviewed by three anonymous referees.
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