Introduction
Frequent occurrence of haze events in Beijing and throughout
the North China Plain (NCP) during cold seasons is a health threat for around
400 million people living there. High concentrations of PM2.5
(particulate matter with an aerodynamic diameter less than
2.5 µm), of which the daily average can exceed
300 µgm-3 during severe haze (He et al., 2014; Jiang et
al., 2015), contribute to cardiovascular morbidity and mortality (Brook et
al., 2010; Cheng et al., 2013). As one of the major components of PM2.5,
sulfate is of particular concern due to its high concentrations in haze days
(Zheng et al., 2015b, a) and its key role in the climate system (Seinfeld and
Pandis, 2006). Hourly sulfate concentrations can exceed
100 µgm-3 and account for up to one-quarter of PM2.5
mass during severe haze (Zheng et al., 2015a). However, due to the generally
low solar radiation and cloud liquid water content during haze (Zheng et
al., 2015a; Wang et al., 2014), conventional sulfate formation via OH
oxidation in the gas phase and from aqueous-phase SO2 (referred to as
S(IV) = SO2⚫ H2O + HSO3-+SO32-)
oxidation by H2O2 (McArdle and Hoffmann, 1983), O3 (Hoffmann
and Calvert, 1985), and O2 via a radical chain mechanism initiated by
transition metal ions (TMIs) in clouds (Ibusuki and Takeuchi, 1987; Alexander
et al., 2009; Harris et al., 2013) cannot explain the observed high sulfate
concentrations (Zheng et al., 2015a). To explain the observed high sulfate
concentrations during haze in Beijing and the NCP, recent studies have
suggested that heterogeneous reactions on/in aerosols/aerosol water are
potentially important (He et al., 2014; Hung and Hoffmann, 2015; Cheng et
al., 2016; Wang et al., 2016, 2014; Zheng et al., 2015a, b). In particular,
Zheng et al. (2015a) largely improved the underestimate of modeled sulfate
concentrations in 2013 Beijing haze by using a relative-humidity-dependent
uptake coefficient (γ) of SO2 on aerosols, without knowing the
specific mechanisms of heterogeneous oxidation of SO2. Calculations by
Guo et al. (2017) suggest heterogeneous oxidation of SO2 in Beijing may
be dominated by O2 via a radical chain mechanism initiated by TMIs.
Laboratory work has suggested SO2 oxidation by O3 on mineral dust
is a significant pathway for sulfate production (Li et al., 2006), but its
role in Beijing haze has not been determined. More recently, Hung and
Hoffmann (2015) proposed that rapid S(IV) oxidation by O2 via a radical
chain mechanism on acidic microdroplets (pH ≤ 3) could be responsible
for heterogeneous sulfate production in Beijing haze, while Cheng et
al. (2016) suggested that S(IV) oxidation by NO2 (Lee and Schwartz,
1983; Clifton et al., 1988) in aerosol water could be important due to the
high relative humidity and NO2 mole fraction during severe haze in the
NCP. Due to the strong pH dependence of SO2 oxidation and the large
variability in model-calculated aerosol pH in Beijing haze (Cheng et
al., 2016; Wang et al., 2016; Liu et al., 2017), the relative importance of
heterogeneous SO2 oxidation is difficult to constrain.
Sulfate isotope assumptions.
Sulfate formation
Δ17O(SO42-)
pathways
(‰)
SO2+ OH
0
S(IV) + H2O2
0.7
S(IV) + O3
6.5
S(IV) + NO2
0
S(IV) + O2
0
Primary sulfate
0
The oxygen-17 excess (Δ17O) of sulfate, defined as Δ17O =δ17O - 0.52δ18O, wherein δ=(Rsample/Rreference-1) with R representing the
isotope ratios of 17O / 16O or 18O / 16O in the
sample and the reference Vienna Standard Mean Ocean Water, respectively
(Matsuhisa et al., 1978), is a useful tool for estimating the relative
importance of different sulfate formation pathways because each oxidant
transfers its Δ17O signature to the product (Table 1) through
SO2 oxidation (Savarino et al., 2000). SO2 has
Δ17O = 0 ‰ due to the rapid isotopic exchange with
abundant vapor water whose Δ17O is near 0 ‰ (Holt et
al., 1981). S(IV) oxidation by H2O2 and O3 leads to Δ17O(SO42-) = 0.7 and 6.5 ‰, respectively, on the
basis of Δ17O(H2O2) = 1.4 ‰ (Savarino and
Thiemens, 1999) and assuming Δ17O(O3) = 26 ‰
(Vicars and Savarino, 2014; Ishino et al., 2017). Other sources of sulfate
exhibit Δ17O(SO42-) at or near 0 ‰. Specifically,
sulfate directly emitted from natural and anthropogenic sources or formed by
OH and O2 oxidation has Δ17O(SO42-) values at or near
0 ‰ (Dubey et al., 1997; Luz and Barkan, 2005; Lee et al., 2002; Bao
et al., 2000). Sulfate produced by NO2 oxidation is suggested to occur
either via a radical chain mechanism (Shen and Rochelle, 1998), via
oxygen–atom transfer from OH- (Clifton et al., 1988), or from O2
based on experimental results of He et al. (2014), resulting in Δ17O(SO42-) = 0 ‰. Once formed, atmospheric sulfate
does not undergo further isotopic exchange, and Δ17O(SO42-)
will not be altered by mass-dependent processes such as deposition.
In this work, characteristics of PM2.5Δ17O(SO42-)
during haze events from October 2014 to January 2015 in Beijing are reported,
contributions of O3 and H2O2 oxidation in heterogeneous
sulfate formation are quantified, and the roles of NO2 and O2
oxidation are explored.
Materials and methods
Sampling and atmospheric observations
A high-volume air sampler (model TH-1000C II, Tianhong Instruments Co., Ltd.,
China) with a quartz microfiber filter (Whatman Inc., UK, pre-combusted at
450 ∘C for 4 h) was used to collect PM2.5 samples at a flow
rate of 1.05 m3 min-1 from October 2014 to January 2015. The
collections lasted for 12 h (08:00–20:00 or 20:00–08:00 LT) for each
sample. The sample site is located on the rooftop of the First Teaching
Building at the campus of the University of the Chinese Academy of Sciences
(40.41∘ N, 116.68∘ E, around 20 m from the ground) in
Beijing, around 60 km northeast of downtown. Hourly PM2.5 concentration
and SO2, NO2, and O3 mole fractions were observed at Huairou
station (40.33∘ N, 116.63∘ E) by the Beijing Municipal
Environmental Monitoring Center, which is about 10 km from our aerosol
sampling site. The mole fraction of atmospheric H2O2 was not
observed in our campaign, but long-term observations from March to November
in Beijing show a good correlation between H2O2 mole fraction and
air temperature (T in ∘C) according to
[H2O2] / (nmol mol-1) =0.1155e0.0846T/∘C (Fu, 2014). In the present study,
H2O2 mole fraction was estimated from our measured T with the
empirical equation above. Our calculated H2O2 mole fraction based
on this formula in October and November 2014 is, respectively
(0.32±0.08) and (0.17±0.04) nmol mol-1, comparable to
the observed values of (0.44±0.18) and
(0.38±0.11) nmol mol-1, respectively, in October and November
2013 (Fu, 2014). Meteorological data including temperature, pressure, and
relative humidity were recorded by an automatic weather station (model MetPak
with integrated wind sonic, Gill Instruments Limited, UK). Time reported in
this paper is local time (LT = UTC + 8).
Measurements of ions and isotope ratios
The measurements of ions were conducted in Anhui Province Key Laboratory of
Polar Environment and Global Change in the University of Science and
Technology of China. A detailed description of the method for chemical
analysis of NH4+, K+, Ca2+, Na+, Mg2+,
SO42-, NO3-, and Cl- can be found in the literature (Ye
et al., 2015). Briefly, ions were extracted from a part
(2 cm × 2 cm) of each filter with 20 mL of Millipore water
(≥ 18 MΩ) by sonication for 80 min in an ice water bath.
Insoluble substances in the extract were filtered with 0.45 µm
filters before analysis. The pH of filtrates was measured by an ion activity
meter (model PXS-215, Shanghai INESA Scientific Instrument Co., Ltd., China).
And the ion concentrations were analyzed using a Dionex ICS-2100 ion
chromatograph system (Thermo Fisher Scientific Inc., USA). Typical analytical
precision of our instrument is better than 10 % RSD (relative standard
deviation) for all ions (Chen et al., 2016). The preparation and measurements
of Δ17O(SO42-) were conducted in the stable isotope
laboratory (IsoLab) (https://isolab.ess.washington.edu/isolab/, last
access: 12 December 2017) at the University of Washington, USA. A detailed
description of the method can be found in the literature (Savarino et
al., 2001; Geng et al., 2013; Chen et al., 2016; Alexander et al., 2012).
Briefly, PM2.5 sample filters were dissolved in Millipore water
(≥ 18 MΩ) and the insoluble substances were filtered. Prepacked
ion capture cartridges (Alltech Maxi-Clean IC-RP SPE) were used for the first
step of removal of organics. Cations in the samples were replaced with sodium
using a cation exchange resin and 30 % H2O2 solution was added
as the second step of removal of organics. Excess H2O2 was removed
via evaporation and SO42- was separated from other anions (e.g.,
NO3-) by ion chromatography. After ion separation, SO42- was
converted to Ag2SO4, dried, and then pyrolyzed at 1000 ∘C
in an elemental analyzer to form Ag(s), SO2(g), and O2(g). The
produced gases were carried by He gas to pass through a liquid nitrogen trap
to remove SO2, and then a gas chromatographer to further purify the
O2 gas, which was finally moved to a mass spectrometer
(Thermo Scientific MAT 253). Masses of 32, 33 and 34 of O2 were measured
to determine δ17O and δ18O and then Δ17O was
calculated. The typical amount of O2 for each run is
0.4–0.8 µmol. The precision of Δ17O measurements in
our method is ±0.3 ‰ based on replicate analysis of standards,
which is consistent with previous studies (Alexander et al., 2005; Sofen et
al., 2014; Chen et al., 2016). To quantify the uncertainty in each sample, 30
samples were measured in triplicate, two samples in quadruplicate, and two
samples in duplicate depending on the limitation of sample size. In total, 10
filters sampled on non-polluted days (NPDs,
PM2.5<75 µgm-3) and 24 filters sampled on polluted
days (PDs, PM2.5≥75 µgm-3) were analyzed.
Estimate of the overall rate of heterogeneous sulfate production
Heterogeneous sulfate production (Phet) is commonly parameterized
in models according to Eq. (1) (Jacob, 2000; Zheng et al., 2015a):
Phet=3600sh-1⋅96gmol-1⋅pRTRpDg+4νγ-1SpSO2(g),
where Phet is in units of µgm-3h-1,
3600 s h-1 is a time conversion factor, 96 g mol-1 is the molar
mass of SO42-, p is atmospheric pressure in kPa, R is the gas
constant (8.31 Pa m3 mol-1 K-1), and T is temperature in
K. Rp is the radius of aerosol particles (m), Dg is
the gas-phase molecular diffusion coefficient of SO2 (m2 s-1),
ν is the mean molecular speed of SO2(g) (m s-1), γ is
the uptake coefficient of SO2 on aerosols with the unit of 1,
[SO2(g)] is the gas-phase mole fraction of SO2 (nmol mol-1),
and Sp is the aerosol surface area per unit volume of air
(m2 m-3). The typical tropospheric value of Dg and
ν is 2×10-5 and 300 m2 s-1, respectively
(Jacob, 2000). Observations of PM2.5 mass concentrations
(c(PM2.5), µgm-3) and PM2.5 mean radius (m)
during Beijing haze roughly follow an empirical formula:
Rp/m = (0.254c(PM2.5)/(µgm-3) +10.259)×10-9
(Guo et al., 2014). By using the volume and surface area formulas of a sphere
and the mean density of particles ρ=1.5×106 g m-3 (Guo
et al., 2014), Sp can be estimated from Eq. (2). A relative
humidity-dependent γ (=(2-5)×10-5, Eq. 3) derived from
Zheng et al. (2015a) during 2013 Beijing haze was used. This range of
γ is also consistent with the estimated values of γ from
(1.6±0.7) ×10-5 to (4.5±1.1) ×10-5
by Wang et al. (2016).
Sp=c(PM2.5)×10-6gµg-14/3⋅πRp3⋅ρ⋅4πRp2,γ=2×10-5,Ψ≤50%2×10-5+5×10-5-2×10-5100-50%⋅Ψ-50%,50%≤Ψ≤100%
where Ψ refers to relative
humidity with the unit of %.
Estimate of primary sulfate
The primary sulfate, which is directly emitted into air, includes the sea
salt source, terrigenous source, and anthropogenic source (Li et al., 2013;
Faloona, 2009). The concentration of sea salt sulfate was calculated by using
the observed concentrations of Na+ and the mass ratio of
c(SO42-) / c(Na+) = 0.252 in seawater (Calhoun et
al., 1991). The terrigenous sulfate was estimated using the observed
concentrations of Ca2+ and the mass ratio of
c(SO42-) / c(Ca2+) = 0.18 in soil (Legrand et
al., 1997), where c(Ca2+) / c(Na+) = 0.038 in seawater
was used to calculate the fraction of observed Ca2+ from soil (Legrand
and Mayewski, 1997). The anthropogenic primary sulfate is estimated as
3 % of anthropogenic SO2 emissions in models (Faloona, 2009;
Alexander et al., 2009). Supposing all the observed mole fractions of SO2
and precursors of secondary sulfate are anthropogenic, we have
c(ap) / 96 = 0.03(c(SO2) / 64 + c(sas) / 96),
where c(sas) = c(tos) -c(ss) -c(ts) -c(ap) and c(ap),
c(sas), c(tos), c(ss), and c(ts) are the mass concentrations of
anthropogenic primary sulfate (ap), secondary sulfate (sas), total sulfate
(tos), sea salt sulfate (ss), and terrigenous sulfate (ts). The estimated
concentration of total primary sulfate (p-SO42-) is the sum of
primary sulfate from all these sources.
Estimate of sulfate production rate from OH oxidation in the gas phase
The sulfate production rate from OH oxidation in the gas phase
(PSO2+OH) can be expressed as
PSO2+OH=3600sh-1⋅96gmol-1⋅p⋅RSO2+OHRT,
where PSO2+OH is in units of µgm-3h-1
and
3600 s h-1, 96 g mol-1, p, R, and T are the same as in
Eq. (1). RSO2+OH is the chemical reaction rate
(nmol mol-1 s-1), calculated as shown in Tables S1 and S2 in the
Supplement.
Estimate of in-cloud sulfate production rate
The main in-cloud sulfate formation pathways considered here include S(IV)
oxidation by H2O2, O3, NO2 (Wang et al., 2016), and
O2 via a radical chain mechanism initiated by TMIs (Alexander et
al., 2009). Their chemical reaction rate expressions
(RS(IV)+oxi) and rate constants (k) are summarized in
Table S3. The rate of in-cloud sulfate production by a certain oxidant
(Pcloud,S(IV)+oxi) can be expressed as (Seinfeld and
Pandis, 2006)
Pcloud,S(IV)+oxi=3600sh-1⋅96gmol-1⋅RS(IV)+oxi⋅Lcρw,
where Pcloud,S(IV)+oxi is in units of
µgm-3h-1, 3600 s h-1 and 96 g mol-1 are the
same as in Eq. (1), and RS(IV)+oxi is in units of M s-1.
Cloud liquid water content (Lc, in unit of mg m-3) was
derived from a global reanalysis, GEOS-FP
(https://gmao.gsfc.nasa.gov/products/). ρw is the
density of water (1 kg L-1). By summing up in-cloud S(IV) oxidation by
H2O2, O3, NO2, and O2 initiated by TMIs, we can
get the total rate of in-cloud sulfate production (Pcloud).
Isotopic constraints on sulfate formation pathways
Since S(IV) oxidation by O3 and H2O2 are the sole sources of
nonzero Δ17O(SO42-) (Table 1) (Savarino et al., 2000), the
relative importance of different sulfate formation pathways can be calculated
as follows (Alexander et al., 2012):
Δ17Oobs=6.5‰⋅fS(IV)+O3+0.7‰⋅fS(IV)+H2O2+0⋅fzero-Δ17O,
where fS(IV)+O3 and fS(IV)+H2O2
are fractional contributions of S(IV) oxidation by O3 and H2O2
to the observed sulfate, respectively, and fzero-Δ17O represents the fractional contribution of sulfate with
zero-Δ17O processes such as primary sulfate, and secondary sulfate
formed via OH oxidation, NO2 oxidation, and O2 oxidation. By using
Eq. (6) and the definition fS(IV)+O3+fS(IV)+H2O2+fzero-Δ17O=1, we have fS(IV)+O3=(Δ17Oobs-0.7‰⋅fS(IV)+H2O2)/6.5‰ and
fzero-Δ17O=(6.5‰-Δ17Oobs-5.8‰⋅fS(IV)+H2O2)/6.5‰. Since
fS(IV)+O3, fS(IV)+H2O2, and
fzero-Δ17O should be in the range of 0 to 1
at the same time, fS(IV)+H2O2 is further limited to
meet fS(IV)+H2O2<min {Δ17Oobs/0.7‰, (6.5‰-Δ17Oobs)/5.8‰}. Therefore, the
possible range of fS(IV)+O3 and fzero-Δ17O can be obtained at differentfS(IV)+H2O2 assumptions.
In addition, as sulfate with nonzero Δ17O(SO42-) is
produced either via in-cloud reactions or via heterogeneous reactions or
both, Eq. (6) can also be written as follows:
Δ17Oobs=fhet⋅Δ17Ohet+fcloud⋅Δ17Ocloud+fSO2+OHΔ17OSO2+OH+fp⋅Δ17Op,
where fhet, fcloud, fSO2+OH, and
fp respectively represent the fractional contribution of
heterogeneous sulfate production, in-cloud sulfate production, gas-phase
sulfate production, and primary sulfate to the observed sulfate. fp=c(p-SO42-)/c(SO42-), fhet=Phet/Phet+Pcloud+PSO2+OH⋅(1-fp),
fcloud=Pcloud/Phet+Pcloud+PSO2+OH⋅(1-fp) and fSO2+OH={PSO2+OH/Phet+Pcloud+PSO2+OH}⋅(1-fp).
Δ17Ohet, Δ17Ocloud,
Δ17OSO2+OH, and Δ17Op
respectively represent Δ17O of corresponding sulfate produced via
the pathways
above. Both Δ17OSO2+OH and
Δ17Op are equal to 0 ‰.
Δ17Ocloud can be calculated as shown in Eq. (8) as the
lifetime of sulfate produced in clouds will not depend on the specific S(IV)
oxidant.
Δ17Ocloud=6.5‰⋅Pcloud,S(IV)+O3+0.7‰⋅Pcloud,S(IV)+H2O2Pcloud
Calculation of aerosol liquid water content, aerosol pH, and ionic strength (Is)
Aerosol liquid water content, aerosol pH, and Is were calculated by
the ISORROPIA II model, which is a thermodynamic equilibrium model for
NH4+-K+-Ca2+-Na+-Mg2+-SO42--NO3--Cl--H2O
aerosols (Fountoukis and Nenes, 2007). The ISORROPIA II model can solve
forward problems in which T, relative humidity, and the concentrations of
gas + aerosols are known (e.g., NH3+NH4+), and reverse
problems in which T, relative humidity, and the concentrations of aerosol
(but not gas) species are known. We used the forward method to calculate
aerosol liquid water content, aerosol pH, and Is as this method
has been shown to best predict aerosol pH (Hennigan et al., 2015). The
aerosol liquid water content, pH, and Is were first calculated in
metastable mode (assuming that bulk aerosol solution is supersaturated),
which is consistent with previous studies about Beijing haze (Liu et
al., 2017; Guo et al., 2017). However, the work of Rood et al. (1989) in
California, USA, suggested that not all aerosols are in a metastable state, even
though the fractional occurrence of metastable aerosols increases with
increasing relative humidity at urban sites (roughly following Eq. 9). We
also calculated the aerosol liquid water content, pH, and Is
assuming stable mode (assuming that bulk aerosols crystallize once saturation
is exceeded), which is consistent with Wang et al. (2016). The input of
observed inorganic ion concentrations and meteorological parameters are
summarized in Table S4. Since gaseous NH3 was not measured in our
campaign, we used the empirical equation [NH3] / (nmol mol-1)
= 0.34[NOx]/(nmol mol-1) +0.63, derived from observations
of Meng et al. (2011) in Beijing winter, to estimate the NH3 mole
fraction. We used NO2 mole fraction instead of NOx as input due to
the lack of NOx observations in our study, which would give a lower end
of NH3 mole fraction. Given the importance of aerosol liquid water
content for reaction rates and the fact that ISORROPIA II underestimates
aerosol liquid water content at low relative humidity (Bian et al., 2014),
samples with relative humidity < 40 % are excluded from analysis
(Hennigan et al., 2015). This excludes 8 out of the total 34 samples
(24 %), with six of them on NPDs. A total of four samples on NPDs and 22 samples
on PDs were analyzed for aerosol liquid water content, aerosol pH, and
Is. Due to the fact that the predicted Is is high
(Is>10 M, Table S4), which suggests aerosol water is nonideal,
the influence of Is on reaction rate constants (Table S3) and
effective Henry's law constants (Table S5) is taken into consideration when
the influence is known.
x(metastable)=0,Ψ<30%-0.024Ψ/%2+4.18Ψ/%-89.13,30%≤Ψ≤80%100%,80%<Ψ≤100%,
where x(metastable) is the fraction of metastable aerosols to total
aerosols in the unit of %.
Estimate of aqueous concentrations of trace species
The aqueous concentrations of SO2, O3, H2O2, and NO2
were calculated as described in Table S5. The determination of in-cloud
concentrations of TMIs (here only Fe(III) and Mn(II); Alexander et al., 2009)
is described below.
The concentration of soluble Fe(III) follows Eqs. (10)–(13) (Liu and
Millero, 1999):
log10[Fe(III)]/c⊖=log10KFe(OH)3∗c⊖2+3log10H+/c⊖+log101+β1∗H+/c⊖-1+β2∗H+/c⊖-2,
where
log10KFe(OH)3∗(c⊖)2=-13.486-0.1856Is/c⊖0.5+0.3073Is/c⊖+5254K/T,log10β1∗/(c⊖)2=2.517-0.8885Is/c⊖0.5+0.2139Is/c⊖-1320K/T,log10β2∗/(c⊖)2=0.4511-0.3305Is/c⊖0.5-1996K/T,
and [Fe(III)] is the aqueous concentration of Fe(III) in units of M, T is
temperature in units of K, and Is is ionic strength in units of
M.
KFe(OH)3∗ is the solubility product constant of
Fe(OH)3 in units of (mol L-1)-2, and β1∗
and β2∗ are respectively first-order and second-order
cumulative hydrolysis constants of Fe3+ in units of
(mol L-1)2.
Our calculation suggested in-cloud [Fe(III)] was in the range of 0.6 to
6.1 µM with a mean of (2.6±1.8) µM, which is
similar to the observed values in the NCP (Guo et al., 2012; Shen et al., 2012).
The concentration of soluble Mn(II) in cloud water was set to be
1 µM in the present study, which is the general value observed in
cloud water in the NCP (Guo et al., 2012; Shen et al., 2012).
Estimate of sulfate production rate in aerosol water
The reaction rate expressions, rate constants (k), and influence of
Is on k for sulfate production in aerosol water are summarized
in Table S3. The overall rates for S(IV) oxidation in aerosol water depend
not only on chemical reaction rates (Table S3) but also on mass transport
limitations. A standard resistance model was used to estimate effects of mass
transport following the work of Cheng et al. (2016):
1RH,S(IV)+oxi=1RS(IV)+oxi+1Jaq,lim,
where RH,S(IV)+oxi is the overall reaction rate for S(IV)
oxidation by a certain oxidant (oxi) such as O3, H2O2,
NO2, and O2 on acidic microdroplets (M s-1),
RS(IV)+oxi is the chemical reaction rate (M s-1), and
Jaq,lim is the rate limited by mass transfer from the gas to
the aqueous phase (M s-1). Due to the large decrease in the
aqueous-phase reaction rate constant for TMI-initiated S(IV) oxidation by
O2 with increasing Is (Martin and Hill, 1967) and the high
Is of aerosols (Table S4), combined with the fact that the rate
constant for the S(IV) + O2 mechanism on acidic microdroplets
proposed by Hung and Hoffman (2015) likely includes the effect of TMIs, we do
not directly consider TMI-initiated S(IV) oxidation by O2 in aerosol
water. RS(IV)+oxi was calculated as described in Table S3.
The limiting mass transfer Jaq,lim was calculated using Eqs. (15)
and (16).
Jaq,lim=minJaqSO2,Jaqoxi,JaqX=kMTX⋅X(aq),
where X= SO2, O3, H2O2, or NO2 and
kMT (s-1) is the mass transfer rate coefficient and was
calculated as Eq. (17) (Cheng et al., 2016; Seinfeld and Pandis, 2006):
kMTX=Rp23Dg+4Rp3αν-1,
where Rp, Dg, and ν are the same as in Eq. (1). The
α used in our calculation is 0.11 for SO2, 0.23 for
H2O2, 2.0×10-3 for O3, and 2.0×10-4 for
NO2 (Seinfeld and Pandis, 2006; Jacob, 2000). The term on the left-hand
side of Eq. (17) is the gas-phase diffusion limitation while the term on the
right-hand side of Eq. (17) is the interfacial mass transport limitation.
kMT was limited by interfacial mass transport in our study.
Characteristics of haze events in Beijing (October 2014–January
2015). (a) Temporal evolution of PM2.5 and SO42-
concentrations. (b) Temporal evolution of sulfur oxidation ratio
(SOR, which equals SO42- molar concentration divided by the sum of
SO42- and SO2 molar concentration) and observed
Δ17O(SO42-) (Δ17Oobs).
(c) Temporal evolution of observed O3 and calculated
H2O2. The error bar of Δ17Oobs in
(b) is ±1σ of replicate measurements (n= 2–4) of
each sample. The light yellow shaded area indicates polluted days (PDs,
PM2.5≥ 75 µgm-3). Data used here are 12-hourly averaged
values, corresponding with filter samples.
The rate of heterogeneous sulfate production by a certain oxidant
(Phet,S(IV)+oxi) in aerosol water can be expressed as
Phet,S(IV)+oxi=3600sh-1⋅96gmol-1⋅RH,S(IV)+oxi⋅Laρw,
where Phet,S(IV)+oxi is in units of
µgm-3h-1 and 3600 s h-1 and 96 g mol-1 are the
same as in Eq. (1). RH,S(IV)+oxi is in units of
M s-1, La is aerosol liquid water content in units of
mg m-3, and ρw is the density of water
(1 kg L-1).
Results and discussion
Characteristics of haze events in Beijing
Figure 1a shows the temporal evolution of concentrations of PM2.5 and
SO42- during our sampling period. The 12-hourly averaged PM2.5
concentrations ranged from 16 to 323 µgm-3 with a mean of
(141±88 (1σ)) µgm-3. In comparison, the
grade II of the Chinese national ambient air quality standard of daily
PM2.5 is 75 µgm-3. The SO42- concentrations
varied from 1.5 to 56.4 µgm-3 with a mean of
(21.2±15.4) µgm-3. As shown in Fig. 1a,
SO42- concentrations presented a similar temporal trend as PM2.5
concentrations, i.e., increased from a mean of
(3.9±1.8) µgm-3 on NPDs (PM2.5<75 µgm-3) to (28.4±12.5) µgm-3 on
PDs (PM2.5≥75 µgm-3). The fraction of
SO42- to PM2.5 mass concentration ranged from 8 to 25 % and
increased from a mean of (11±2) % on NPDs to (15±5) %
on PDs. The sulfur oxidation ratio (SOR, which equals SO42- molar
concentration divided by the sum of SO42- and SO2 molar
concentration), a proxy for secondary sulfate formation (Sun et al., 2006),
also increased rapidly with PM2.5 levels, from a mean of
0.12±0.04 on NPDs to 0.41±0.17 on PDs (Fig. 1b).
Observed Δ17O(SO42-) (Δ17Oobs) ranged
from 0.1 to 1.6 ‰ with a mean of (0.9±0.3) ‰
(Fig. 1b). The highest Δ17Oobs= 1.6 ‰
occurred during PDs of Case II in October 2014, while the lowest
Δ17Oobs=0.1 ‰ occurred during PDs of Case IV
in December 2014. Δ17Oobs reported here is similar in
magnitude to previous observations of Δ17O(SO42-) in
aerosols and rainwater collected in China (Lin et al., 2017; Li et al., 2013)
and other midlatitude sites (Table S6). The overall
Δ17Oobs levels during our entire sampling time are
similar for NPDs and PDs, being (0.9±0.1) and
(0.9±0.4) ‰, respectively. However, the NPD-to-PD difference
of Δ17Oobs can be case dependent. For Case I and II in
October 2014, Δ17Oobs increased from NPDs to PDs, while
the opposite trend was observed for Cases III to V in November 2014 to
January 2015 (Fig. 1b). These Δ17Oobs variations are
generally similar to variability in mole fractions of observed O3 and
calculated H2O2 (Fig. 1c), which is consistent with the fact that
O3 and H2O2 are the sole sources of nonzero Δ17O(SO42-) (Table 1).
Direct estimate of sulfate formation pathways based on Δ17Oobs
Figure 2 shows the calculated possible fractional contributions of each
formation pathway (fS(IV)+H2O2,
fS(IV)+O3, and
fzero-Δ17O) for each sample using Eq. (6).
On average, over all samples collected,
fS(IV)+O3= 4–13 %,
fS(IV)+H2O2= 0–88 %, and
fzero-Δ17O= 8–87 %. For samples
during PDs of Case IV in December 2014 with the three lowest
Δ17Oobs values (Fig. 1b), fzero-Δ17O was in the range of 57–95, 86–98, and 57–95 %,
corresponding to fS(IV)+H2O2 being in the range of
0–43, 0–14, and 0–43 %, respectively, which clearly suggests
zero-Δ17O pathways dominated sulfate formation during PDs of
Case IV. However, for other samples, the maximum possible
fS(IV)+H2O2 ranged from 71 to 100 % with a mean
of (93±7) % while the maximum possible
fzero-Δ17O was 75 to 92 % with a mean
of (86±4) %, implying that sulfate formation during these
sampling periods was dominated by H2O2 oxidation and/or
zero-Δ17O pathways.
Ternary diagram of possible fractional contribution of different
pathways to total sulfate production directly estimated from
Δ17Oobs. The colored lines are contour lines of
Δ17Oobs, representing possible fractional contribution
of sulfate formation via O3 (fS(IV)+O3) and
H2O2 (fS(IV)+H2O2) oxidation or
zero-Δ17O processes (fzero-Δ17O) such as
primary sulfate, secondary sulfate formed via OH oxidation, NO2
oxidation, and O2 oxidation. fS(IV)+H2O2 is in
the range of 0 to minΔ17Oobs/0.7‰,(6.5‰-Δ17Oobs)/5.8‰,
fS(IV)+O3=(Δ17Oobs-0.7‰×fS(IV)+H2O2)/6.5‰,
and fzero-Δ17O=(6.5‰-Δ17Oobs-5.8‰×fS(IV)+H2O2)/6.5‰. See Eq. (6) and its
caption in Sect. 2.7 for details.
The relationship between relative humidity (RH) and SOR (a)
and time series of overall heterogeneous sulfate production
(Phet) along with SO42- concentrations (b). The
black line in (a) is the linear least-squares fitting line.
Estimate of different sulfate production pathways. Time series of
estimated sulfate production rate via OH oxidation in the gas phase
(PSO2+OH), overall heterogeneous reactions on aerosols
(Phet) and in-cloud reactions (Pcloud), and
concentrations of primary sulfate (p-SO42-) and observed sulfate.
fhet represents the fraction of overall heterogeneous sulfate
production to total sulfate production during PDs of each case. The light
yellow shaded area indicates polluted days (PDs,
PM2.5≥ 75 µgm-3). Data used here are
12-hourly averaged values, corresponding with filter samples.
Estimated fractional contribution of different sulfate production
pathways during Beijing haze.
PD of
fp ∗
fhet
fcloud
fSO2+OH
case
(%)
(%)
(%)
(%)
I
9
54
29
8
II
6
23
68
3
III
11
41
47
1
IV
15
47
37
1
V
9
49
41
1
∗ fp, fhet, fcloud,
and fSO2+OH respectively represent fractional
contribution from primary sulfate, heterogeneous reactions, in-cloud
reactions, and the gas-phase pathway.
Temporal evolution of cloud liquid water content (LWC, a),
in-cloud sulfate production rate via S(IV) oxidation by H2O2,
O3, NO2, and O2 initiated by TMIs (denoted as
Pcloud,S(IV)+H2O2,
Pcloud,S(IV)+O3,
Pcloud,S(IV)+NO2, and
Pcloud,S(IV)+O2, respectively; b) and
estimated Δ17O of sulfate produced in clouds (Δ17Ocloud, c). The light yellow shaded area indicates
polluted days (PDs, PM2.5≥ 75 µgm-3). Data used
here are 12-hourly averaged values, corresponding with filter samples.
Aerosol parameters during Beijing haze. The aerosol liquid water
content (AWC, a), ionic strength (Is, b), and
aerosol pH (c) was predicted by ISORROPIA II assuming stable aerosol
state and metastable aerosol state. The pH of filtrate was measured using an ion
activity meter.
Estimate of heterogeneous sulfate production pathways. Time series
of overall heterogeneous sulfate production rate (Phet) and
heterogeneous sulfate production rate in aerosol water via H2O2
(Phet,S(IV)+H2O2) and NO2
(Phet,S(IV)+NO2) under stable (a) and
metastable (b) aerosol assumption.
Phet,S(IV)+O2 in (b) represents heterogeneous
sulfate production rate via SO2 oxidation by O2 via a radical chain
mechanism on acidic microdroplets. fhet,zero-Δ17O
represents the fraction of heterogeneous reactions that result in sulfate
with zero-Δ17O, such as S(IV) oxidation by NO2 and O2,
to the overall heterogeneous sulfate production during PDs of each case with
the constraint of Δ17O(SO42-) (see the main text for
details). In calculating Phet,S(IV)+H2O2, the
influence of Is was considered. In calculating
Phet,S(IV)+NO2 and
Phet,S(IV)+O2 the influence of Is was not
considered due to the lack of experimental data about the influence of
Is. Phet,S(IV)+O2 was calculated using the
aqueous-phase rate constant for pH ≤ 3 due to the lack of rate constant
information at pH > 3. The light yellow shaded area indicates polluted
days (PDs, PM2.5≥ 75 µgm-3). Data used here are
12-hourly averaged values, corresponding with filter samples.
The estimated fraction of metastable aerosol to total aerosol
(x(metastable), a) using Eq. (9) and heterogeneous sulfate
production rate from S(IV) oxidation by NO2 assuming a combination of
metastable and stable states
(Phet,S(IV)+NO2, b) as
Phet,S(IV)+NO2=x(metastable) ×Phet,S(IV)+NO2,metastable + (100 % -x(metastable)) ×Phet,S(IV)+NO2,stable.
Chemical kinetic calculations with the constraint of Δ17Oobs
The good correlation between relative humidity and SOR in Fig. 3a (r=0.76, p<0.01) suggests heterogeneous reactions played an important role
in sulfate formation. Our local atmospheric-conditions-based calculations
show that overall heterogeneous sulfate production (Phet; see
Sect. 2.3) presented similar trends to SO42- concentrations except
for Case II (Fig. 3b) and increased from a mean of
(0.6±0.3) µgm-3h-1 on NPDs to
(2.0±1.1) µgm-3h-1 on PDs during our entire
sampling period. In comparison, Cheng et al. (2016) reported that the missing
sulfate production rate required explanation of the observed sulfate
concentration being around 0.07 µgm-3h-1 when PM2.5<50 µgm-3 and around 4 µgm-3h-1 when
PM2.5>400 µgm-3 during 2013 Beijing haze. We also
calculated the contribution from primary sulfate and performed chemical
kinetic calculations including SO2 oxidation by OH in the gas phase and
in-cloud sulfate production (Fig. 4 and Table 2; see Sect. 2.4–2.6) to
estimate the relative importance of heterogeneous sulfate production in our
sampling period. Heterogeneous reactions were found to contribute
41–54 % to total sulfate formation during PDs of Cases I and III–V,
with a mean of (48±5) % (Fig. 4). This is consistent with Zheng
et al. (2015a), who modeled that about half of the observed sulfate was from
heterogeneous reactions during 2013 Beijing haze. In contrast, we found that
during PDs of Case II in October 2014, heterogeneous sulfate production only
accounted for 23 % of total sulfate production while in-cloud sulfate
production predominated total sulfate production with an estimated fraction
of 68 %. The predominant role of in-cloud sulfate production on PDs of
Case II was supported by the relatively high cloud liquid water content
during this time period (Fig. 5a). Our local atmospheric-conditions-based
calculations also suggest the in-cloud sulfate production was dominated by
H2O2 oxidation throughout our sampling period (Fig. 5b), which is
consistent with previous findings that H2O2 oxidation is the most
important in-cloud sulfate production pathway globally (Alexander et
al., 2012) and in the NCP (Shen et al., 2012). In addition, the
Δ17O of sulfate produced in clouds (Δ17Ocloud) was estimated to range from 0.5 to 0.8 ‰
with a mean of (0.6±0.1) ‰ during our sampling period and
showed similar variations as Δ17Oobs (Fig. 5c). The
mean value of Δ17Ocloud calculated here is close to
Δ17O(SO42-) in rainwater observed in central China
(0.53±0.19 ‰) (Li et al., 2013) and at Baton Rouge, USA
(0.62±0.32 ‰) (Jenkins and Bao, 2006). In addition, by using
Eq. (7), the Δ17O of sulfate produced via heterogeneous reactions
(Δ17Ohet) was calculated to be respectively 1.8, 3.1,
1.4, 0.1, and 0.8 ‰ for PDs of Cases I–V. Since
Δ17O(SO42-) produced via H2O2 oxidation is
0.7 ‰, smaller than Δ17Ohet in Cases I–III
and V, O3 oxidation must contribute to heterogeneous sulfate production.
To explore the specific mechanisms of heterogeneous oxidation of SO2, we
calculated aerosol parameters such as aerosol liquid water content, pH, and
ionic strength (Is) by using the ISORROPIA II thermodynamic model
(Fountoukis and Nenes, 2007) (Fig. 6; see Sect. 2.8). It was found that the
assumptions about aerosol thermodynamic state (salts crystallize once
saturation is exceeded, termed as stable state or aerosol solution is
supersaturated, termed as metastable state) significantly influence the
calculated aerosol pH but have little impact on the calculated aerosol liquid
water content and Is (Fig. 6). Calculated aerosol liquid water
content increased with PM2.5 concentrations, from
(5.3±7.4) µgm-3 on NPDs to
(63.5±54.6) µgm-3 on PDs when assuming a stable
state and from (9.6±6.0) µgm-3 on NPDs to
(84.2±49.2) µgm-3 on PDs when assuming a metastable
state (Fig. 6a). Calculated Is was similar for stable and
metastable assumptions, ranging from 11.3 to 51.6 M (Fig. 6b). The high
Is suggested aerosol water was nonideal and thus the influence of
Is on reaction rate constants (Table S3) and effective Henry's
law constants (Table S5) was taken into consideration when the influence was
known. The bulk aerosol pH predicted in stable state was in the range of 7.5
to 7.8 with a mean of 7.6±0.1, consistent with bulk aerosol
pH =7.63±0.03 calculations during a haze event in Beijing 2015
predicted by Wang et al. (2016). The bulk aerosol pH calculated assuming
metastable state was in the range of 3.4 to 7.6 with a mean of
4.7±1.1, consistent with the mean value of 4.2 calculated in
metastable aerosol assumption during severe haze in Beijing 2015–2016 by Liu
et al. (2017). The calculated aerosol pH assuming metastable state decreased
with increasing PM2.5 levels, from a mean of 6.5±1.3 on NPDs to
4.4±0.6 on PDs, while that assuming stable state shows no
relationship with PM2.5 concentrations (Fig. 6c). Our measured pH of
filtrate ranged from 4.6 to 8.2 with a mean of 5.7±1.0, similar to pH
of filtrate from PM2.5 in Beijing reported by Wang et al. (2005). The
measured pH of filtrate shows similar trends with bulk aerosol pH calculated
assuming metastable state (Fig. 6c), with a mean value 6.9±0.7 on
NPDs and 5.1±0.6 on PDs, which suggests that bulk aerosols are in
metastable state with moderate acidity on PDs. This is also consistent with
our estimate that most aerosols are in a metastable state with a fraction of
(74±17) % on PDs by using Eq. (9) and our cognition that the
mixture of major acidic aerosols with minor neutral aerosols would lead to
the bulk being acidic. However, as the effective Henry's law constant of
SO2 at pH = 7.6 (stable state) can be 3 orders of magnitude higher
than that at pH = 4.4 (metastable state on PDs), even a small fraction of
aerosol at this high pH value could be potentially significant active sites
for heterogeneous sulfate production during PDs.
The main heterogeneous sulfate formation pathways considered include S(IV)
oxidation by H2O2, O3, NO2, and O2 on acidic
microdroplets as proposed by Hung and Hoffmann (2015). Other sulfate
formation pathways such as S(IV) oxidation by NO3 radical,
methyl-hydrogen peroxide (MHP), peroxyacetic acid (PAA), and hypohalous acids
in aerosol water (Feingold et al., 2002; Walcek and Taylor, 1986; Chen et
al., 2017) are thought to be negligible during haze in the NCP (Cheng et
al., 2016), and thus are not considered here. We estimate the relative
importance of these heterogeneous sulfate formation pathways as follows.
First, the heterogeneous sulfate production rate via S(IV) oxidation by
H2O2 (Phet,S(IV)+H2O2) was calculated
with the influence of Is being considered, which has been
determined at high Is in laboratories (Tables S3 and S5). Then,
the fractional contribution of H2O2 oxidation
(fhet,S(IV)+H2O2) to overall heterogeneous sulfate
production (Phet) calculated using apparent γ (see
Sect. 2.3) was estimated. Large uncertainties exist in the influence of
Is on the reaction rate constant of S(IV) oxidation by O3 in
aerosol water (Table S3), rendering the estimate of its fractional
contribution (fhet,S(IV)+O3) to Phet from
purely chemical kinetic calculations uncertain. Instead,
fhet,S(IV)+O3 was estimated using our calculated
fhet,S(IV)+H2O2 and Δ17Ohet
values, on the basis that Δ17O(SO42-) > 0 ‰
originates solely from H2O2 and O3 oxidation. Then
zero-Δ17O pathways such as S(IV) oxidation by NO2 and by
O2 were estimated to be the remaining part
(fhet,zero-Δ17O). At last, the potential
importance of S(IV) oxidation by NO2 and by O2 is discussed.
Calculations show that fhet,S(IV)+H2O2 was
4–6 % with a mean of (5±1) % under stable aerosol
assumptions and 8–19 % with a mean of (13±4) % under
metastable-state assumptions for PDs of all the cases.
fhet,S(IV)+O3 was calculated to be 2–47 % with a
mean of (22±17) % in stable-state assumption and 0–47 % with
a mean of (21±18) % in metastable-state assumption.
Correspondingly, fhet,zero-Δ17O was the remaining
73 % (47–94 %) in stable assumption, or 66 % (42–81 %) in
metastable assumption for PDs of all the cases (Fig. 7). Excluding PD of
Case II, in which sulfate formation was predominated by in-cloud reactions,
our local atmospheric-conditions-based calculations suggest
zero-Δ17O pathways such as S(IV) oxidation by NO2 and/or by
O2 are important for sulfate formation during Beijing haze.
Cheng et al. (2016) suggested that S(IV) oxidation by NO2 in aerosol
water could largely account for the missing sulfate source in 2013 Beijing
haze. In their study, the calculated mean aerosol pH is 5.8, while influence
of Is was not taken into account due to the lack of relevant
experimental data. The calculated Phet,S(IV)+NO2 is
highly sensitive to aerosol pH. In our study, when aerosol pH decreased from
7.6±0.1 assuming a stable state to 4.7±1.1 assuming a
metastable state, mean Phet,S(IV)+NO2 decreased from
(6.5±7.7) µgm-3h-1 to
(0.01±0.02) µgm-3h-1 for PDs of all the cases
(Fig. 7). The former is much larger than our estimate of overall
heterogeneous production rate, Phet=(2.0±1.1) µgm-3h-1, while the latter is too
small. Moreover, the influence of Is was not considered, which is
expected to increase the reaction rate constant of S(IV) oxidation by
NO2 (Cheng et al., 2016). The treatment of aerosols as a bulk quantity,
assuming that all aerosols are either in stable or metastable state, or that
all aerosol particles have the same pH, may lead to errors in calculating
heterogeneous sulfate production rates. As stated in Sect. 2.8, not all
aerosols are in a metastable state, even though the fractional occurrence of
metastable aerosols increases with increasing relative humidity (Rood et
al., 1989). Figure 8a shows that the fraction of metastable aerosols to total
aerosols, estimated by using Eq. (9), increases with PM2.5 levels.
However, when assuming a combination of stable- and metastable-state aerosol
as shown in Eq. (9), Phet,S(IV)+NO2 increases with
PM2.5 levels and reaches
(0.9±0.7) µgm-3h-1 during PDs of all the cases
(Fig. 8b), much higher than Phet,S(IV)+NO2=(0.01±0.02) µgm-3h-1 under solely metastable
aerosol assumption. This estimate suggests that even though the majority of
aerosols may be in a metastable state during PDs (74±17 % in our
calculation), the high pH of the minority of aerosols in a stable state could
render S(IV) oxidation by NO2 a potentially significant pathway for
heterogeneous sulfate production.
Since Phet,S(IV)+NO2 using calculated aerosol pH
assuming metastable state was 2 orders of magnitude lower than
Phet during PDs, we further examined S(IV) oxidation by O2
on acidic microdroplets under the metastable-state assumption. A laboratory
study suggested that SO2 oxidation by O2 on acidic microdroplets
has a large aqueous-phase reaction rate constant of
1.5×106[S(IV)] (M s-1) at pH ≤ 3, a pH range much
lower than our calculated pH values. The rate constant was shown to decrease
with increasing pH; however, no values of the rate constant at pH > 3
were reported (Hung and Hoffmann, 2015). Figure 7b shows heterogenous sulfate
production rate via S(IV) oxidation by O2 on acidic microdroplets
(Phet,S(IV)+O2) with aerosol liquid water content
calculated assuming metastable state and the aqueous-phase rate constant for
pH ≤ 3 being used, even though the calculated aerosol pH is > 3. The
estimated Phet,S(IV)+O2 is 1.5×103 to
1.3×105 µgm-3h-1 with a mean of 2.5×104 µgm-3h-1 during PDs of all cases, which is 4
orders of magnitude larger than Phet. This value should be an
overestimate due to our calculated bulk aerosol pH predicted in a metastable
state being 4.4±0.6 during PDs and the experimental results of He et
al. (2014) and Wang et al. (2016) suggest the O2 oxidation pathway is
negligible at higher pH conditions (e.g., on CaO and in NH4+
solution). However, some fraction of aerosols may have a pH ≤ 3 due to
the Kelvin effect (Hung and Hoffmann, 2015), rendering S(IV) oxidation by
O2 on acidic microdroplets a potentially important pathway for
heterogeneous sulfate production even if it may occur on only a small
fraction of the ambient aerosol.