Although the anthropogenic emissions of SO2 have decreased
significantly in China, the decrease in SO42- in PM2.5 is
much smaller than that of SO2. This implies an enhanced formation rate
of SO42- in the ambient air, and the mechanism is still under
debate. This work investigated the formation mechanism of particulate
sulfate based on statistical analysis of long-term observations in
Shijiazhuang and Beijing supported with flow tube experiments. Our main
finding was that the sulfur oxidation ratio (SOR) was exponentially
correlated with ambient RH in Shijiazhuang (SOR =0.15+0.0032×exp(RH/16.2)) and Beijing (SOR =-0.045+0.12×exp(RH/37.8)). In
Shijiazhuang, the SOR is linearly correlated with the ratio of aerosol water
content (AWC) in PM2.5 (SOR =0.15+0.40×AWC/PM2.5).
Our results suggest that uptake of SO2 instead of oxidation of S(IV) in
the particle phase is the rate-determining step for sulfate formation.
NH4NO3 plays an important role in the AWC and the change of
particle state, which is a crucial factor determining the uptake kinetics of
SO2 and the enhanced SOR during haze days. Our results show that
NH3 significantly promoted the uptake of SO2 and subsequently the
SOR, while NO2 had little influence on SO2 uptake and SOR in the
presence of NH3.
Introduction
Atmospheric particulate matter (PM) is a worldwide concern due to its
adverse effect on human health, such as its association with respiratory and
cardiovascular diseases, lung cancer, and premature death (WHO, 2013;
Lelieveld et al., 2015). The Chinese government has made great efforts to
improve the air quality (Cheng et al., 2019). For example, the annual
PM2.5 concentration in Beijing decreased from 89.5 µg m-3 in 2013 to 58 µg m-3 in 2017 due to the stringent reduction of
local and regional emissions (Cheng et al., 2019; Ji et al., 2019). However,
the PM2.5 concentrations in most regions of China (Cheng et al., 2019;
Q. Chen et al., 2019; Huang et al., 2019; Tian et al., 2019) are still
significantly higher than the PM2.5 standard recommended by the World
Health Organization (WHO) (WHO, 2006). Haze events also occur with high
frequency, especially, in autumn and winter.
Secondary inorganic aerosol (SIA) including sulfate (SO42-),
nitrate (NO3-), ammonium (NH4+) and secondary organic
aerosol (SOA) usually contribute to ∼70 % of PM2.5
mass concentration in different regions (Huang et al., 2014; An et al.,
2019). SIA often accounts for more than a half of PM2.5 mass in severe
pollution events (Zheng et al., 2015; Wang et al., 2016). Even
SO42- exceeds more than 20 % of PM2.5 mass (Guo et al.,
2014; Wang et al., 2016; Xie et al., 2015; He et al., 2018). Interestingly,
the anthropogenic emissions of SO2 in 2017 reduced by ∼90 % when compared with 2000 in Beijing (Cheng et al., 2019; Lang et al.,
2017). However, the decrease rate of particulate SO42-
concentration (Lang et al., 2017; C. Li et al., 2017) is much smaller than
SO2 (Lang et al., 2017; Zhang et al., 2020; Liu et al., 2021). For
example, the annual mean concentration of SO42- decreased by 0.1 µg m-3 yr-1 from 2000 to 2013, followed by 1.9 µg m-3 yr-1 from 2013 to 2015 in Beijing, while it decreased by 3.8 µg m-3 yr-1 for SO2 (Lang et al., 2017). This implies
an enhanced oxidation rate of SO2 in the atmosphere (Lang et al.,
2017). However, the mechanisms and kinetics of particulate SO42-
formation in the real atmosphere are still open questions in many regions of
China although they have been extensively discussed (Ervens, 2015; Warneck,
2018).
Particulate SO42- can be formed through homogeneous oxidation of
SO2 by hydroxyl radicals (OH) and stabilized Criegee intermediates
(SCIs) in the gas phase and subsequent uptake onto particles, while the OH
pathway is the dominant gas-phase oxidation pathway (Seinfeld and Pandis,
2006; L. Liu et al., 2019). Modeling studies greatly underestimated
(∼54 %) SO42- concentration in serve pollution
events in Beijing if only considering gas-phase oxidation of SO2, while
the normalized mean bias (NMB) decreased significantly after heterogeneous
oxidation of SO2 being considered (Zheng et al., 2015). Several
heterogeneous and/or multiphase oxidation pathways, such as oxidation of
SO2 or sulfite by H2O2 (Huang et al., 2015; Maaß et al.,
1999; P. Liu et al., 2020; Ye et al., 2021; Liu et al., 2021), HONO (J. F. Wang et
al., 2020), and O3 (Maahs, 1983) or photochemical oxidation of SO2
(Yu et al., 2017; Xie et al., 2015), catalytic oxidation of SO2 by
transition metal ions (TMI) (Warneck, 2018; Martin and Good, 1991; Wang et
al., 2021) and oxidation of SO2 by NO2 (He et al., 2014; Clifton
et al., 1988; Wang et al., 2016; Cheng et al., 2016; Wu et al., 2019;
Spindler et al., 2003) in aqueous phase and heterogeneous oxidation of
SO2 on black carbon (Zhao et al., 2017; Zhang et al., 2020; Yao et al.,
2020), have been proposed based on field measurements and laboratory and
modeling studies. However, the relative
contribution of these pathways to the SO42- production is still controversial. For
example, the contribution of heterogeneous oxidation to SO42-
production had been evaluated to be (48 ± 5) % based on oxygen
isotopic measurements (He et al., 2018), while it was 31 % even in the
nighttime calculated by an observation-based modeling (OBM) (Xue et al.,
2016). Gas-phase oxidation by OH could explain 33 %–36 % of SO42-
production in the Beijing–Tianjin–Hebei region (L. Liu et al., 2019), while
it was negligible based on isotopic measurements (He et al., 2018) and OBM
simulations (Xue et al., 2016). As for the oxidation of S(IV) species, which
includes SO2, HSO3- and SO32-, in aqueous phase,
oxidation by H2O2 (T. Liu et al., 2020; P. Liu et al., 2020; Ye et
al., 2021), NO2 (J. F. Wang et al., 2020; Wang et al., 2016; Cheng et al.,
2016), O3 (Fang et al., 2019), or TMI (Mn2+) (Wang et al., 2021)
was proposed as the most important pathway by different researchers.
However, the relative importance of these oxidation paths varied greatly
among different researches. For instance, TMI-catalyzed oxidation could
explain ∼69 % of aqueous sulfate formation in North China Plain based on
isotopic measurements and modeling (Shao et al., 2019), while oxidation by
NO2 or O2 was the dominant oxidation path (66 %–73 %) based on
isotopic measurements in another study (He et al., 2018). It should be noted
that some reaction mechanisms mentioned above were proposed based on case
studies in short-term observations. Thus, long-term observations at
different environments are required to verify whether these mechanisms are
statistically important. In addition, the previous studies mainly focused on
oxidation process of SO2 in particle phase, while it is unclear what are the
controlling factors of the S(IV)-to-S(VI) conversion from the gas phase to
the particle phase. In particular, it has been found that the mass fraction
of NO3- and NH4+ is increasing gradually (Lang et al.,
2017; Li et al., 2018). This will modify its physical properties, such as
morphology, phase-state and so on. The
feedback between aerosol physics and aerosol chemistry is still poorly understood.
In this work, 1-year field observations have been performed in
Shijiazhuang and Beijing, synchronously. The formation mechanism of
particulate sulfate has been statistically investigated to identify the
controlling factors. The role of mass transfer of SO2 and the oxidation
of S(IV) in particle phase have been discussed based on flow tube
experiments and field measurements. The conversion ratio of SO2 to
sulfate is statistically and linearly correlated to the aerosol water
content (AWC), which is strongly modulated by particulate ammonium nitrate.
The reaction kinetics and other factors affecting sulfate production have
also been discussed.
Material and methodsField measurements
Field measurements were performed at Shijiazhuang
University (SJZ; 38.0281∘ N, 114.6070∘ E)
and the west campus of Beijing University of Chemical Technology (BUCT;
39.9428∘ N, 119.2966∘ E) from 15 March 2018 to 15 April 2019. The SJZ station is on a rooftop of the main teaching
building (5 floors, ∼23 m above the surface), which is around
250 m from the Zhujiang Road of Shijiazhuang. The BUCT station is on a
rooftop of the main building (5 floors, ∼18 m above the
surface), which is around 550 m from the 3rd Ring Road of Beijing. The
distance between the two stations, which are the representative cities of
Beijing-Tianjin-Hebei (BTH), is 260 km (Fig. S1). Both stations are surrounded by traffic and
residential emissions, thus, are typical urban observation sites. The
details about the observation stations have been described in our previous
work (Y. Liu et al., 2020c, b, a).
Ambient air was drawn from the roof of the corresponding building. At the
SJZ station, the mass concentration of PM2.5 was measured by a beta
attenuation mass monitor (BAM-1020, Met One Instruments, USA) with a smart
heater (model BX-830, Met One Instruments Inc., USA) to control the RH of
the incoming air to 35 % and a PM2.5 inlet (URG) to cut off the
particles with diameter larger than 2.5 µm. Particle-phase total
concentrations of iron and manganese were measured using a heavy-metal
analyzer (EHM-X100, Skyray Instrument). Water-soluble ions (Na+,
K+, Mg2+, Ca2+, NH4+, SO42-, Cl- and
NO3-) in PM2.5 and gas pollutants (HCl, HONO, HNO3,
SO2 and NH3) were measured using an analyzer for monitoring
aerosols and gases (MARGA, ADI 2080, Applikon Analytical B.V., the Netherlands)
with 1 h of time resolution. At the BUCT station, the mass concentration
of PM2.5 was the mean concentration obtained from four surrounding
monitoring stations (including Wanliu, Gucheng, Wanshouxigong and Guanyuan)
of China Environmental Monitoring Centre (http://www.cnemc.cn, last access: 31 December 2020). The chemical
composition of PM2.5 was measured using a time-of-flight aerosol
chemical speciation monitor (ToF-ACSM, Aerodyne) after the ambient air went
through a PM2.5 inlet (URG) and a Nafion dryer (MD-700-24, Perma Pure).
The configuration and the operation protocol of ToF-ACSM have been described in previous work (Fröhlich et al., 2013). The ionization efficiency
(IE) calibration for ACSM was performed using 300 nm dry NH4NO3
every month. Ambient air was drawn from the roof using a Teflon sampling
tube (BMET-S, Beijing Saak-Mar Environmental Instrument Ltd.) with the
residence time <10 s for gas-phase pollutant measurements. Trace
gases including NOx, SO2, CO and O3 were measured with the
corresponding analyzer (Thermo Scientific, 42i, 43i, 48i and 49i) at both
the SJZ and BUCT stations. Meteorological parameters including temperature,
pressure, relative humidity (RH), wind speed and wind direction were measured
using weather stations (WXT 520 at HAS/SJZ station and AWS 310 at AHL/BUCT
station, Vaisala).
Uptake kinetics of SO<inline-formula><mml:math id="M119" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> on dust internally mixed with
NH<inline-formula><mml:math id="M120" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula>NO<inline-formula><mml:math id="M121" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>
To understand the influence of RH on uptake kinetics
(γSO2), the γSO2 on dust internally mixed with
NH4NO3 was measured using a coated-wall flow tube reactor. The
configuration of the reactor and data process protocol have been described
in detail previously (Han et al., 2013; Liu et al., 2015). The γ parameter,
presenting the mass transfer kinetic of gas-phase SO2 to particle
phase, is defined by the net loss rate of SO2 per collision onto the
surface (Ravishankara, 1997; Usher et al., 2003), namely,
γobs=-dcdtω=2kobsrtube<c>,
where -dc/dt is the net loss rate of SO2 when the surface is exposed to
SO2 (molecules s-1); ω is the collision frequency
(s-1); kobs, rtube, and <c> are the
first-order rate constant of SO2, the flow tube radius, and the average
molecular velocity of SO2, respectively. A correction for gas-phase
diffusion limitations was considered for γobs calculations
using the Cooney–Kim–Davis (CKD) method (Cooney et al., 1974; Murphy and
Fahey, 1987). The Brunauer–Emmett–Teller (BET) uptake coefficient (γSO2,BET) was obtained from the mass dependence of γobs as
follows (Han et al., 2013; Liu et al., 2015):
γSO2,BET=slopeAgSBET,
where [slope] is the slope of the plot of γobs versus the
sample mass in the linear regime (mg-1), Ag is the inner surface
area of the sample tube (cm2) and SBET is the specific surface
area of the particle sample (cm2 mg-1).
Similar to a previous work (Zhang et al., 2019), dust internally mixed with
NH4NO3 was used in the kinetics study, because it was difficult to
deposit enough real ambient particles onto the inner surface of the sample
holder. Although the composition of the model particles is much simpler than
that of ambient particles, it is still meaningful, because we mainly focused
on the influence of RH or aerosol water content (AWC) on uptake kinetics of
SO2. The mixture (mass ratio =2:1) of A1 ultrafine test dust (Powder
Technology Inc.) and NH4NO3 (AR, Sinopharm Chemical Reagent Co.
Ltd, China) were suspended in the mixture of ethanol and water (v:v=1:3).
The inner surface of the Pyrex quartz tube (sample holder) was uniformly
coated by the above mixture and dried overnight in an oven at 393 K. The
sample mass was calculated according to the weighted mass of the dry tube
before and after coating. NH4NO3 in the mixture was further
confirmed using an ion chromatograph (Ω Metrohm 940, Applikon
Analytical B.V., the Netherlands). Around 50 % of the NH4NO3
remained in the mixture even after heating and potential evaporation. To
avoid the wall loss of SO2 on the sample holder, all the inner surface
of the sample holder was covered with particles. The wall loss of SO2
on the remained surface (the inner surface of the outside tube and the
outside surface of the sample holder) was subtracted at a steady-state condition at
the corresponding RH before the uptake experiment as done in our previous
work (Liu et al., 2015). The mean concentrations of SO2, NO2 and
NH3 were 8.3 ± 5.2 (0.4–49.1), 31.5 ± 13.2 (2.5–85.1) and
41.0 ± 18.4 (0.3–126.4) ppb, respectively, in polluted events (with the
PM2.5 concentration higher than 75 µg m-3 and the RH less
than 90 %) in Shijiazhuang. The initial concentrations of SO2,
NO2 and NH3 in the reactor were 190 ± 2.5, 100 ± 2.5
and 50 ± 2.5 ppb, respectively. The initial concentrations of NO2
and NH3 were close to their ambient concentrations, while a high
initial SO2 concentration was used here to obtain a good signal to
noise ratio for γSO2 measurements. In this work, we aimed to
understand the influence of AWC on the uptake kinetics of SO2.
Therefore, we fixed the initial concentrations of pollutants and the
temperature at 300 K. SO2 and NO2 were measured using the
corresponding analyzer (Thermo 43i and 42i), and NH3 was measured by an
ammonia analyzer (EAA-22, LGR, USA). The specific surface area of the
mixture of A1 dust and NH4NO3 was 0.813 m2 g-1, measured by a nitrogen BET physisorption analyzer (Quantachrome
Autosorb-1-C). RH from 0 to 80 % was adjusted by varying the ratio of dry
to wet zero air (water bubbler) and measured by a RH sensor (HMP110,
Humicap). Control experiments demonstrate that adsorption of SO2 on the
quartz tube is negligible. It should be noted that the wall loss of SO2
in the presence of NH3 and/or NO2 would be larger in the absence
of seed aerosols. Additional control experiments in the presence of NO2
and NH3 demonstrate that the contribution of wall loss of SO2
should be less than 3 % to the measured γ.
Calculations of AWC, aerosol pH and production rates of sulfate in aerosol liquid water
The AWC and aerosol pH in Shijiazhuang were calculated
using the ISORROPIA II model using the measured concentrations of
SO42-, NH4+, NH3, NO3-, HNO3,
Cl-, HCl, Na+, Ca2+, K+ and Mg2+, as well as RH and
temperature as input. The particles were assumed in metastable phase using a
forward method (Song and Osada, 2020; Shi et al., 2019). The dataset with RH
lower than 35 % was excluded (Pye et al., 2020) due to large
uncertainties of aerosol pH (Ding et al., 2019; Guo et al., 2016; Pye et
al., 2020). The pH was then calculated according to Pye et al. (2020) and Ding et
al. (2019):
pH=-log10(γH+mH+)=-log101000γH+cH+AWC,
where γH+ is the activity coefficient of H+, and mH+ is
the molality of H+. The deliquescence curves of inorganic salts were
calculated at 298.5 K using the E-AIM model (Clegg et al., 1998). Then, the
AWC attributed to individual salt was calculated with the mass of the salt
and the mass-based growth factor at the corresponding RH. The AWC of model
particles for laboratory studies was also calculated with the known
composition, while the aerosol pH values in Beijing were not calculated, because the
concentrations of Na+, Ca2+, K+ and Mg2+ were
unavailable.
Similar to previous studies (P. Liu et al., 2020; Cheng et al., 2016), four
oxidation pathways of S(IV) in aqueous-phase were accounted for, i.e.,
oxidation by O3, H2O2, NO2 and TMI (Fe3+ and
Mn2+), according to the following equations (Seinfeld and Pandis, 2006;
Cheng et al., 2016; P. Liu et al., 2020):
4-dSIVdtO3=k0SO2,aq+k1HSO3-+k2SO32-O3,aq,5-dSIVdtH2O2=k3H+HSO3-H2O2,aq1+KH+,6-dSIVdtTMI=k4H+αMn2+Fe3+SIV,7-dSIVdtNO2=k5NO2,aqSIV,
where k0=2.4×104 M-1 s-1, k1=3.7×105 M-1 s-1, k2=1.5×109 M-1 s-1, k3=7.45×107 M-1 s-1, K=13 M-1, k4=3.72×107 M-1 s-1, and α=-0.74 (for pH ≤ 4.2)
or k4=2.51×1013 M-1 s-1, α=0.67 (for pH > 4.2), and k5= (1.24–1.67) ×107 M-1 s-1 (for 5.3 ≤ pH ≤ 8.7; the linear interpolated
values were used for pH between 5.3 and 8.7) at 298 K (Clifton et al., 1988;
P. Liu et al., 2020; Tilgner et al., 2021; Liu et al., 2021). [O3, aq],
[H2O2, aq] and [NO2, aq] were calculated according to the
Henry's constants, which are 1.1×10-2, 1.0×105
and 1.2×10-2 M atm-1 at 298 K for O3,
H2O2 and NO2, respectively (Seinfeld and Pandis, 2006).
H2O2 concentrations were unavailable during our observations. It
was fitted based on temperature like in previous work (Fang et al., 2019).
Figure S2 shows the derived H2O2 concentrations and the diurnal
curves of H2O2 in winter in Shijiazhuang. The H2O2
concentrations varied from 0.05 to 3.7 ppbv, with a mean value of
0.62 ± 0.52 ppbv. Overall, the wintertime H2O2 concentrations
derived in this work are comparable with those reported in the literature
(Ye et al., 2018). The concentrations of Fe3+ and Mn2+ were
calculated according to the measured total Fe and Mn concentrations assuming
18 % of total Fe and 30 % of total Mn were soluble (Wang et al.,
2014; Cui et al., 2008) and the precipitation equilibriums of Fe(OH)3
and Mn(OH)2 depending on pH. The concentrations of Fe and Mn before
December 2018 were estimated according to their mean ratios to PM2.5
mass concentration (Wang et al., 2014), because the instrument was
unavailable.
Results and discussionVariation of sulfate in PM<inline-formula><mml:math id="M288" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
Figure 1a shows the hourly mean mass
concentration of PM2.5 measured at SJZ and BUCT stations from 15 March 2018 to 15 April 2019. The mass concentration of PM2.5 in Shijiazhuang
generally coincided with that in Beijing. This highlights the regional
characteristic of air pollution in BJH. However, Shijiazhuang usually showed
significantly higher PM2.5 concentration than that in Beijing. The
hourly mean PM2.5 concentration varied in the range of 0–650 µg m-3 with an annual mean concentration of 86.4 ± 77.8 µg m-3. The corresponding values in Beijing were 1.5–556 and 55.0 ± 51.0 µg m-3. Particularly, the wintertime mass
concentration of PM2.5 in Shijiazhuang was around 2.4 times as that
in Beijing. This is consistent with previous results that Shijiazhuang is
suffering from more serious air pollution (D. Chen et al., 2019b) because of
its larger density of heavy industries and more intensive emissions than in
Beijing (D. Chen et al., 2019a).
The hourly mean (a) mass concentration of PM2.5, (b) sulfate
concentration, (c) sulfate fraction in PM2.5, (d) molar ratio of
nitrate to sulfate, (e) sulfur oxidation ratio (SOR) and (e) Ox
(= NO2+O3) concentration in Shijiazhuang and Beijing from 15 March 2018 to 15 April 2019.
Like the mass concentration of PM2.5, both the mass concentration (Fig. 1b) and the fraction of sulfate in PM2.5 (Fig. 1c) in Shijiazhuang were
usually higher than those in Beijing. The annual mean sulfate concentrations
in Shijiazhuang and Beijing were 11.7 ± 12.7 and 5.4 ± 6.9 µg m-3, which annually contributed 15.3 ± 8.7 % and 10.7 ± 7.3 % to the PM2.5 mass concentrations, respectively. However, the
molar ratio of NO3- to SO42- (3.37 ± 3.05),
corresponding to the mass ratio 2.17 ± 1.97 in Beijing was
significantly higher than that in Shijiazhuang (2.69 ± 1.80,
corresponding to mass ratio of 1.77 ± 1.72) at the 0.05 level. This is
consistent with the emission inventories of air pollutants, in which
Shijiazhuang had larger SO2 emissions than Beijing, and vice versa for
NOx emissions (Yang et al., 2019; F. Liu et al., 2017; D. Chen et al.,
2019a). A decrease of sulfate concentration (5.4 ± 6.9 µg m-3) in Beijing was significant even when compared with that in
PM1.0 (8.1 ± 8.3 µg m-3) measured from July 2011 to June 2012 (Sun et al., 2015), while the mass ratio of
NO3-/SO42- (2.17 ± 1.97) in Beijing showed an
obvious increase compared with those in 2011–2012 (1.3–1.8) (Sun et al.,
2015) and 2008 (0.8–1.5) (Zhang et al., 2013). This can be ascribed to the
effective reduction of SO2 emissions but less effective reduction of
traffic emissions in Beijing.
The ground surface concentrations of pollutants are prone to be affected by
variation of mixing layer height (MLH) (Zhong et al., 2018; Tang et al.,
2016). Sulfur oxidation ratio (SOR), which is defined as the molar ratio of
sulfate to total sulfur (Wang et al., 2005; Fang et al., 2019),
SOR=nSO42-nSO42-+nSO2,
was calculated and should be less affected by the MLH variation. As shown in
Fig. 1e, the SOR in Beijing was overall higher than that in Shijiazhuang.
Thus, the annual mean SOR in Beijing (0.42 ± 0.29) was comparable with
that reported in literature (Fang et al., 2019), while it was significantly
higher than that in Shijiazhuang (0.31 ± 0.19) at the 0.05 level. The high
primary emissions of SO2 in Shijiazhuang should lead to a lower SOR
than that in Beijing. On the other hand, secondary transformation of
SO2 to sulfate should also have an influence on the SOR. The Ox
(Ox= NO2+O3) concentration in Shijiazhuang was usually
higher than that in Beijing (Fig. 1f). The annual mean Ox concentration
in Shijiazhuang was 55.2 ± 22.3 ppb, which was significantly higher
than that in Beijing (50.7 ± 21.5 ppb) at the 0.05 level. This is
inconsistent with the observed higher SOR in Beijing if gas-phase oxidation
mainly contributed to sulfate formation. These results suggest that
heterogeneous and/or multiphase reactions may also play important roles in
particulate sulfate formation during transport (Zheng et al., 2015; Martin
and Good, 1991; Wu et al., 2019).
Figure 2a–c show the mass concentration of PM2.5 colored according to
the mass concentration of sulfate, the fraction of sulfate in the soluble PM
and the SOR in Shijiazhuang. In most serve pollution events, high PM2.5
mass concentration coincided with the high sulfate concentration, the
fraction of sulfate and the SOR (colored in grey color). For example, the
mean PM2.5 concentration was 411.7 ± 98.1 µg m-3 during
the pollution event that occurred from 08:00 on 12 January 2019 to 00:00 on
15 January 2019 (All times in this paper are given in local time unless stated otherwise.). The corresponding sulfate concentration, fraction of
sulfate in soluble PM and SOR were 80.6 ± 24.0 µg m-3, 39.4 ± 3.6 % and 0.79 ± 0.09, respectively. Other pollution
episodes, which were highlighted in grey color in Fig. 2, showed a similar
trend. The variations of the sulfate concentration, the fraction of sulfate
in nonrefractory PM2.5 and the SOR with PM2.5 mass concentration
in Beijing were similar to Shijiazhuang and shown in Fig. S3. These results
confirm that the conversion rate of SO2 to sulfate is promoted in
pollution days when compared with that in clean days.
Mass concentration of PM2.5 colored according to (a) sulfate
concentration, (b) sulfate fraction in soluble PM, (c) SOR, (d) RH and (e)
AWC in Shijiazhuang. The shade areas in grey indicate the pollution events
with high concentration of sulfate at high RH, while the purple ones the
mean pollution events with low sulfate fraction at high RH.
Role of aerosol water content in sulfate formation
Previous studies
have found that severe pollution events are frequently accompanied with high
RH (Zhang et al., 2018; Tang et al., 2016; Wu et al., 2018; Y. Liu et al.,
2019; Clifton et al., 1988; Maahs, 1983; Martin and Good, 1991). As shown
in Fig. 2d, the high concentration of sulfate positively correlated with
high RH in most cases, which are shaded in grey columns. However, some
pollution events (shaded in purple columns) also occurred under high RH, but
the sulfate concentration or sulfate fraction in soluble PM was not so high.
This means that high RH is a necessary but not a sufficient condition for
sulfate conversion in severe haze pollution events. Thus, it is difficult to
fully understand the general regularity behind the dataset or overemphasize
the importance of a specific process in the atmosphere based on case
studies. This might be the reason why contrary conclusions about the
formation path of sulfate were drawn by different researchers. We
statistically analyzed the relationship between the SOR and the RH. All the
hourly mean data of the SOR and RH have been binned into 100 × 100
boxes. Then, the density of data points, which statistically indicates the
occurrence of the events at given values of RH and SOR, was calculated using
a bivariate kernel density estimator (Wand and Jones, 1993).
Figure 3a and b show the 2D kernel density graphs between the SOR and the RH
in Shijiazhuang and Beijing. The color bar shows the density of data points.
Although the SOR varied obviously at a certain RH, the most probable
distribution of SOR could be exponentially fitted as a function of RH in
Shijiazhuang (Fig. 3a); i.e., SOR =0.15+0.0032×exp(RH/16.2)
(R=0.79). This is consistent with the dependence of SOR on RH based on
previous studies (Tian et al., 2019; Wu et al., 2019). It should be noted
that both SOR and RH showed obvious diurnal variation (Fig. S4). Their
diurnal variations were somewhat similar, but a 4 h time lag was
observed between their minimum values. This means that the diurnal
variations of SOR and RH might also contribute to the strong dependency of
SOR on RH (Fig. 3a and b). However, the exponential dependency of SOR on RH
was still observable in the night or in the day (Fig. S5a and b). It did so
in winter or summer (Fig. S5c and d). This means that aqueous reactions are
important for sulfate formation even if the influence of diurnal and
seasonal variations are ruled out (Wang et al., 2016; Cheng et al., 2016).
Relationship between SOR and γSO2,BET on dust
internally mixed with NH4NO3 (2:1) and RH in (a) Shijiazhuang and
(b) Beijing and the correlation of (c) SOR in Shijiazhuang and (d)γSO2,BET with AWC/PM2.5. The initial concentrations of SO2,
NO2 and/or NH3 in the flow tube reactor were 190 ± 2.5,
100 ± 2.5 and/or 50 ± 2.5 ppb, respectively. The grey lines are
the fitting curves for the most probable SOR, and the white lines are the
fitting curves for the γSO2,BET.
In Fig. 3a, 72.5 % of the data points of Shijiazhuang (6509 over 8980
effective points) were in the domain with
the RH range of 10 %–70 % and the SOR range of 0.05–0.42, while
10.1 % of data points were in the region with the RH greater than 70 %
and the SOR greater than 0.42. The first region corresponded to a lower mean
PM2.5 concentration, sulfate concentration and SOR (76.1 ± 62.78 µg m-3, of 8.1 ± 6.3 µg m-3, and 0.21 ± 0.09,
respectively) compared with the second one (115.7 ± 96.7 µg m-3, 22.4 ± 20.4 µg m-3 and 0.62 ± 0.14,
respectively). As shown in Fig. 3b, the SOR also exponentially increased as
a function of RH in Beijing. A percentage of 74.6 % of 8169 data points were in the first
region. The mean PM2.5 concentration, sulfate concentration and SOR
were 48.2 ± 44.8 µg m-3, 2.9 ± 3.0 µg m-3
and 0.21 ± 0.10, respectively, in the low RH region, while they were 69.9 ± 50.9 µg m-3, 9.4 ± 8.5 µg m-3 and 0.83 ± 0.15 in the high RH region. The most probable distribution of SOR in Beijing
could also be exponentially fitted as a function of RH
(SOR =-0.045+0.12×exp(RH/37.8), R=0.92). However, the SOR was
more sensitive to RH in Beijing than that in Shijiazhuang. This might be
explained by the increased importance of sulfate formation via gas-phase
reactions in Beijing (Fang et al., 2019; Hollaway et al., 2019), because the
PM2.5 mass concentrations in Beijing were significantly lower than that
in Shijiazhuang (Fig. 1).
Formation of particle-phase sulfate through heterogeneous or multiphase
oxidations includes the uptake of SO2 and the following oxidation in
particle phase. Thus, it is meaningful to identify the rate-determining step
(RDS) for understanding the evolution of the SOR. As shown in Fig. 3, the
initial γSO2,BET increased exponentially from 0 to (1.13 ± 0.21) × 10-5 when the RH increases from 2 % to 80 % in the presence of 50 ± 2.5 ppb NH3 with or without 100 ± 2.5 ppb NO2. The dependence of γSO2,BET on RH
was γSO2,BET=2.44E-7+6.69E-8×exp(RH/17.4) with a correlation coefficient of 0.96. A transition region of
the γSO2,BET versus the RH was observable when the RH ranged
from 60 % to 80 %. When the RH was higher than 70 %, the γSO2,BET increased quickly as a function of the RH. The similar
dependency on RH for the γSO2,BET and the SOR suggests that
the uptake kinetic of SO2 might determine sulfate formation.
In a previous work (Zhang et al., 2019), it has been found that all the
uptake of SO2 on dust or nitrate-coated dust can be transformed into
sulfate over the timescale of the uptake experiment using the similar
coated-wall flow tube reactor. Another study also observed a quick formation
of sulfate on the surface of aqueous microdroplets under acidic conditions
(pH < 3.5) without the addition of other oxidants, which was
explained by the direct interfacial electron transfer from SO2 to
O2 on the aqueous microdroplets (Hung et al., 2018). The pH of
deliquesced NH4NO3 is 4.2 as calculated using the ISORROPIA II
model. This means that oxidation of S(IV) might not be a RDS of sulfate
formation. The oxidation processes can be ascribed to catalytic oxidation by
O2 in the presence of transition metals, oxidation by O2 and
nitric acid promoted by protons in the presence of nitrate (Zhang et al.,
2019) and the oxidation by other dissolved oxidants in liquid phase (T. Chen
et al., 2019; Cheng et al., 2016; Wang et al., 2016). To further validate
this assumption, the formation rates of SO42-
(d[SO42-]/dt) in aerosol liquid phase were calculated according to
the method used in previous work (P. Liu et al., 2020; Cheng et al., 2016). If
oxidation of S(IV) is the rate-determining step, the formation rate should
show a similar dependence on RH like the SOR.
As shown in Fig. 4a, the relative contributions of different oxidation paths
of S(IV) varied obviously case by case. In summer and autumn, oxidation by
H2O2 was the most important path followed by TMI. In winter,
however, NO2, O3 or H2O2 could contribute to the
major oxidation path. This might be the reason why these oxidation paths
showed inconsistent relative importance of among different studies even
using the same method, such as isotopic measurements (Shao et al., 2019; He
et al., 2018). Figure 4b and c show the dependence of the formation rates of
sulfate on RH in the range of 35 %–100 % in Shijiazhuang. The dataset
for RH below 35 % was omitted due to the large uncertainty in aerosol pH
calculations (Ding et al., 2019; Guo et al., 2016; Pye et al., 2020). The
relative contributions of different oxidation paths of S(IV) also varied
obviously as a function of RH. NO2 and O3 played important role in
aqueous S(IV) oxidation when RH was from 35 % to 45 %, while TMI became
the dominator when RH ranged from 45 % to 70 %. Above 70 % RH, the
contribution of H2O2 was dominant, which is consistent with
several recent studies (P. Liu et al., 2020; T. Liu et al., 2020). However, the
total formation rate of sulfate in aerosol liquid phase slightly decreased
as RH increased. A weak downward trend of the d[SO42-]/dt rate with RH
was also observable in the 2D kernel density graphs as shown in Fig. 4c.
This is opposite to the dependencies of the SOR and the γSO2 on
RH as discussed above, which means the RDS for sulfate formation should be
the uptake of SO2 instead of oxidation of S(IV) in aqueous phase. We
further calculated the production rate of sulfate through uptake of SO2
(mass transfer to aerosol particles) according to
dSO42-dt=3600⋅9664⋅γSO2AsωcSO24,
where As is the surface area concentration of PM2.5, ω is
the mean molecular velocity of SO2 and cSO2 is the mass
concentration of SO2. As shown in Fig. 4c, the probability-weighted
production rate of sulfate through uptake of SO2 (the grey line) is
lower than that through aqueous oxidation of S(IV), in particular, when RH
is lower than 70 %. It should be noted that the mass transfer of SO2
was not assumed to be the RDS using a large mass accommodation coefficient
of SO2 (α=0.11) (Cheng et al., 2016). According to the
relationship between the mass accommodation coefficient (α) and the
uptake coefficient (γ) of SO2 (Kulmala and Wagner, 2001), the
αSO2 on particles is on the same order of the γSO2. This means that mass transfer rate might have been
overestimated by Cheng et al. (2016).
(a) The relative importance of oxidation paths of S(IV) in aqueous
phase, the dependence of (b) sulfate formation rates and (c) the probability-weighted sulfate formation rates on RH in Shijiazhuang.
Phase state is a crucial factor determining the mass transfer of pollutants
from gas phase to particle phase (Davis et al., 2015; Marshall et al., 2018;
Shiraiwa et al., 2011; Liu et al., 2014), while the AWC or RH greatly
affects the phase state of aerosol particles (Duan et al., 2019; Y. Liu et al.,
2019; Shiraiwa et al., 2017). For example, ambient particles were found to
change from semisolid to liquid state when the RH was above ∼60 % (Y. Liu et al., 2019, 2017), corresponding to the AWC
higher than ∼15µg m-3 (Y. Liu et al., 2017) under
the typical urban environment in Beijing based on rebound fractions
measurements. It was also confirmed that haze particles displayed a
solid–aqueous equilibrium state when the RH was around 60 %–80 % using an
individual particle hygroscopicity system (Sun et al., 2018). As shown in
Fig. S6, the most probable distribution of the AWC exponentially increased
with the RH (AWC =-5.76+5.15×exp(RH/36.1), R=0.98) in
Shijiazhuang. An obvious transition region of the RH between 60 % and 80 % was also observed. These results indicate that the liquid-phase aerosol
should appear when the RH is higher than ∼60 % (Y. Liu et
al., 2019, 2017) and subsequently promote the conversion of
SO2 to sulfate. The SOR increased as a power function of AWC (SOR =0.072+0.043×AWC0.53, R=0.78), while it was linearly
correlated with the ratio of AWC/PM2.5 (SOR =0.15+0.40×AWC/PM2.5, R=0.78) as shown in Fig. 3c. Similarly, the AWC
of dust internally mixed with NH4NO3 was also calculated using the
ISORROPIA II model. The γSO2,BET also showed a similar trend as
a function of AWC/PM2.5 (γSO2,BET=3.08E-5×AWC/PM2.5, R=0.95) (Fig. 3d), although the ranges of AWC/PM2.5
were different due to the difference in aerosol composition. This means that
the fraction of aerosol liquid water governs both the conversion of SO2
to sulfate and uptake kinetics of SO2.
It should be noted that although the SOR showed a similar RH dependence as
the SO2, a deviation was observed in both Shijiazhuang and Beijing
(Fig. 3). The γSO2 was measured at a fixed temperature and
initial SO2 concentration. In the atmosphere, both of them varied
obviously. This might lead to the observed deviation. On the other hand, the
coexisted components such as organic aerosol and black carbon in atmospheric
particles should have complicated the influence on the hygroscopicity and the
phase change of particles. The difference between the model particles and
the real ambient aerosol particles might also partially lead to the
deviations of the RH dependence between the SOR and the γSO2,BET. In addition, it also implies that besides the reaction in
aerosol liquid phase, other reaction paths such as oxidation of SO2 by
gas-phase oxidants should also play an important role in sulfate formation
(Duan et al., 2019).
Influence of particle composition on AWC and sulfate formation
Besides RH, particle composition is another important factor to affect the AWC.
According to the ions balance (Fig. S7a), ammonia was adequate to neutralize
the anions in PM2.5, which is consistent with the results in the
literature (Y. Wang et al., 2020b). In addition, (81.5 ± 15.9) % (with
the median of 87.1 %) of ionic anions (nitrate, chloride and sulfate)
were neutralized by ammonium (Fig. S7b). This means NH4NO3,
(NH4)2SO4 and NH4Cl should be the dominant form of
nitrate, sulfate and chloride in PM2.5, respectively. We further reconstructed the
molecular composition from the ions based on the principles of aerosol
neutralization and molecular thermodynamics (Kortelainen et al., 2017). The
molecular concentrations were estimated according to the molar ratio of
NH4+ to SO42- (RNH4+/SO42-) according to the
following rules: (i) if 0<RNH4+/SO42-<1,
NH4+ existed as the chemical forms of H2SO4 and
NH4HSO4; (ii) 1<RNH4+/SO42-<2, NH4+
existed as (NH4)2SO4 and NH4HSO4; (iii) if
RNH4+/SO42->2, then the fraction NH4+
corresponding to twice the amount of SO42- existed as
(NH4)2SO4, and the remaining fraction of NH4+ was
associated with NO3- and Cl-; and (iv) the rest of NO3-,
which was not neutralized by NH4+, was from NaNO3. Figure 5a
and b show the variation of the molecular composition as a function of RH in
Shijiazhuang. Obviously, NH4NO3 and (NH4)2SO4 were
the major molecular components. Both of them showed upward trend as the RH
increased. In particular, the fraction of NH4NO3 increased
gradually from ∼10 % to ∼50 % when the RH
increased from ∼30 % to 90 %. Correspondingly, the
fraction of (NH4)2SO4 decreased as the RH increased.
Variations of (a) the mass concentrations and (b) the mass fractions
of molecular composition in PM2.5, (c) the estimated AWC attributed to
different composition and (d) the corresponding AWC fraction as a function
of RH in Shijiazhuang.
It should be noted that the deliquescence RH (DRH) of NH4NO3 (61.8 %) (Onasch et al., 1999) is lower than those values of NH4Cl (78 %) (Hu
et al., 2011) and (NH4)2SO4 (80 %) (Lightstone et al.,
2000). We further calculated the AWC attributed to the individual molecular
component based on the growth factors and mass concentrations. As shown in
Fig. 5c, the sum of the AWC of individual salts is underestimated by around
13 % compared to that calculated using the ISORROPIA II model (the grey
line), because the mixing state was not considered in the former method.
However, we can still draw a conclusion that NH4NO3 and
(NH4)2SO4 are the major contributors to the AWC. Especially
NH4NO3 dominated the AWC when the RH ranged from 60 % to 80 %,
in which the SOR and the γSO2 were very sensitive to RH. These
results suggest that NH4NO3 should be the most important mediator
to AWC and subsequently the uptake of SO2 in the transition regime of RH
in Fig. 3a. It should be noted that (NH4)HSO4 has a lower DRH than
NH4NO3 (Y. J. Li et al., 2017). However, 98.4 % of the data points
showed an RNH4+/SO42- higher than 2.0 in Shijiazhuang. This means that
the contribution of (NH4)HSO4 to PM2.5 should be negligible
because of the abundance of atmospheric NH3 in northern China. In previous
work, it has been found that SO2 oxidation can be promoted by
particulate nitrate through the accumulation of proton (Zhang et al., 2019)
and the formation of NO+NO3- (Kong et al., 2014). Our results
further showed the importance of NH4NO3 in the AWC, which possibly
determines the phase state of particles and subsequently the uptake kinetics
of SO2 and the SOR as discussed above. To further confirm the role of
NH4NO3 in the uptake of SO2, uptake experiment of SO2 on
pure dust has been carried out at 2 % and 80 % RH. The
corresponding γSO2,BET was 1.10 ± 1.05 × 10-7
and 1.66 ± 0.28 × 10-7 on pure dust sample in the presence
of NH3 and NO2. However, as discussed above, it was 0 and
1.12 ± 0.15 × 10-5 on dust internally mixed with 33 %
NH4NO3. This directly confirmed the role of NH4NO3 in
SO2 uptake via aerosol liquid water.
Figure S8 shows the dependencies of the AWC/PM2.5 and SOR on the
fraction of the individual molecular component. Both the AWC/PM2.5 and
SOR statistically increased as the fraction of NH4NO3 increased
(Fig. S8a and d). A weak increase followed by a decrease was observed for
the AWC/PM2.5 as the fraction of (NH4)2SO4 increased,
while a negative correlation between the AWC/PM2.5 and the fraction of
NH4Cl was observed. It did so for the SOR and the fraction of
NH4Cl. These phenomena were overall consistent with the sequence of
their hygroscopicity. In addition, chloride is a primary pollutant mainly
from coal combustion and biomass burning (Bi et al., 2019). Besides
chloride, other primary particles from combustion such as soot, which were
not accounted for in this work, might also decrease the uptake capability of
water and subsequently be unfavorable for SO2 uptake.
To assess the relative importance of sulfate and nitrate (the major SIA
component) to AWC, the sensitivity of their fraction to AWC in Shijiazhuang
was tested using the ISORROPIA II model and shown in Fig. S9. The base case
means the AWC was calculated using the measured concentration of the ions.
Then, we reduced the fraction of NH4NO3 or
(NH4)2SO4 from 0 to 80 % individually compared with the
base case. Figure S9a shows the time series of the calculated AWC after
reducing 50 % of NH4NO3 or (NH4)2SO4. Reduction
of either NH4NO3 or (NH4)2SO4 resulted into obvious
decrease of AWC during pollution events. In most cases, the reduction
amplitude of AWC was larger when reducing 50 % of NH4NO3 than
(NH4)2SO4. Figure S9b shows the mean ratio of AWC at a
certain reduction fraction of NH4NO3 or (NH4)2SO4
to that under the base case. When NH4NO3 was reduced from 0 %
to 80 %, the AWC linearly reduced from 0 % to 61.1 ± 0.1 %
with a slope of 0.48 %. It varied from 0 % to 66.0 ± 0.2 % for
(NH4)2SO4 (with a slope of 0.41 %). This means that the AWC
is more sensitive to the fraction of NH4NO3 than
(NH4)2SO4 in Shijiazhuang. This also implies the importance
of NH4NO3 in the observed high AWC in haze days. On the other
hand, reducing 10 % of NH4NO3 can lead to a reduction of
5.2 ± 1.0 % AWC during haze days. Subsequently, we can roughly
estimate that the SOR might be reduced by ∼4 % through a
linear interpolation according to the equation of the SOR and the
AWC/PM2.5 (SOR =0.15+0.40×AWC/PM2.5) fitted in
Fig. 3c. This means reduction of NOx and NH3 should lead to additional
reduction of particulate sulfate.
Influence of other factors on sulfate formation
Several studies have
proposed out that NO2 can promote the oxidation of SO2 on particle
surfaces and in aqueous phase. For example, laboratory studies have found
that parts per million (ppm) levels of NO2 can promote sulfate formation on the surface of
dust through NO+NO3- which is disproportionated from
N2O4 intermediate (He et al., 2014; Liu et al., 2012; Ma et al.,
2008) or parts per million levels of NO2 can promote the oxidation of SO2 in the
deliquesced oxalic acid (Wang et al., 2016). This is supported by the
evidence that a high fraction of sulfate in PM2.5 is positively
correlated with NO2 concentration (Xie et al., 2015) and that a high
PM2.5 concentration is accompanied with high ratio of NO2/SO2
in several case studies (He et al., 2014). The importance of the SO2 oxidation by NO2 in aqueous phase has also been confirmed in modeling
studies (Cheng et al., 2016; Xue et al., 2016). However, this reaction path
is still under debate because of the following reasons. (1) The concentration
of NO2 in laboratory studies was about 2 orders of magnitude higher
than that in ambient air. This will affect the surface concentration of the
intermediate (N2O4) and the concentration of solved NO2 in
aqueous phase. (2) The dissolved NO2 concentration is highly sensitive
to pH. The pH value in aerosol was 5.6–6.2 estimated in modeling study
(Cheng et al., 2016). However, a recent work found that it varied from 3.8
to 4.5 at RH > 30 % and showed a moderate acidity because of
the thermodynamic equilibrium between NH4+ and NH3 (Ding et
al., 2019). (3) The previous calculations were conducted using a high
reaction rate constant of the NO2 reaction with dissolved S(IV)
(Clifton et al., 1988; Cheng et al., 2016), while a smaller value was
reported in the more recent study (Spindler et al., 2003; Tilgner et al.,
2021). (4) The relative importance of each path depends on the concentration
of the relevant pollutants including H2O2 and TMI (P. Liu et al.,
2020). Therefore, it is necessary to verify the importance of this process
by long-term observation at different environments.
Figure 6 shows the 2D kernel density graph of the sulfate fraction in
soluble PM and the SOR in Shijiazhuang as a function of the concentration of
different gas-phase pollutants. It should be pointed out that the SOR or the
γSO2 should be positively correlated to NO2 concentration
if it can promote the conversion of SO2 to sulfate or the uptake of
SO2. However, both sulfate fraction and SOR were negatively correlated
with the concentration of NO2 from the point of view of statistics. A same
trend was observed in Beijing (Fig. S10). This is similar to recent studies
that observed the opposite correlation between SOR and NOx concentration in
the Sichuan Basin (Tian et al., 2019) and in Beijing (Fang et al., 2019). This
means that NO2 concentration is statistically not a determining factor
for sulfate formation in the atmosphere. This is well supported by the
uptake kinetics of SO2 measured using a flow tube reactor. As shown in
Fig. 3a and b, when 50 ± 2.5 ppb of NH3 presenting in the reactant
gases, no difference was observable for the γSO2,BET between the presence (red squares) and absence of 100 ± 2.5 ppb
of NO2 (white triangles). This is consistent with these previous
studies that found NO2 to be having no influence on SO2 uptake when
NH3 was abundant in the atmosphere (Wu et al., 2019; Wang et al.,
2021). In addition, it is consistent with the fact that H2O2
dominated the oxidation of S(IV) in aerosol liquid water when RH was higher
than 60 % (Fig. 4a). It should be pointed out that the γSO2
at 80 % RH was 1.7 ± 0.3 × 10-6 on the mixture of dust
and NH4NO3 in the absence of NH3 and NO2 (Fig. 3). It
increased to 3.7 ± 0.2 × 10-6 in the presence of NO2.
This is consistent with the promotion effect of NO2 for converting
SO2 to sulfate in the absence of NH3 as observed in both a smog
chamber (Wang et al., 2016) and a bubbling reactor (T. Chen et al., 2019).
However, the enhanced uptake of SO2 induced by NO2 might be too
low to be measured in the presence of NH3. Therefore, the weak
promotion effect by NO2 alone cannot explain the negative correlation
between the SOR and the concentration of NO2 in Fig. 6f.
Dependence of the sulfate fraction in soluble PM and the SOR on
gaseous pollutant concentration in Shijiazhuang.
Both the fraction of sulfate and the SOR in Shijiazhuang statistically
decreased as a function of SO2 and CO concentration, respectively (Fig. 6b, c, g and h). This might be explained by the high concentration of
primary aerosol components when pollution events occurred with high
concentrations of primary gas-phase pollutants. However, the fraction of
sulfate increased as a function of O3 (Fig. 6d). When the O3
concentration was greater than 50 ppb, the SOR slightly increased with the
O3 concentration (Fig. 6i). A more obvious positive dependence of
sulfate fraction on O3 concentration was observed in Beijing (Fig. S10e). This means oxidation capacity also plays an important role in sulfate
formation, especially in Beijing. This is consistent with the recent finding
that O3 plays an important role in SO2 oxidation at different
locations (Fang et al., 2019; Tian et al., 2019; Duan et al., 2019). As
shown in Fig. 6j, the SOR positively correlated with the concentration of
NH3 in Shijiazhuang. This means that NH3 can promote the
conversion of SO2 to sulfate. This is well in agreement with laboratory
studies that observed the promotion effect by NH3 to the heterogeneous
reaction of SO2 on different mineral oxides (Yang et al., 2016). In
addition, flow tube experiments were also carried out by exposing the
internal mixing sample (2:1 dust and NH4NO3) to 200 ± 2.5 ppb SO2 in the absence of NH3 and NO2 at 2 % and 80 %
RH, respectively. As shown in Fig. 3a and b, the γSO2,BET
was zero regardless of the reactants under dry condition (2 % RH), while
it increased to (1.66 ± 0.28) × 10-6 at 80 % RH.
However, it was significantly smaller than the γSO2,BET
((1.13 ± 0.21) × 10-5) in the presence of 50 ± 2.5 ppb NH3 with or without 100 ± 2.5 ppb NO2. These results
further confirm that NH3 can promote the uptake of SO2 at high RH,
possible through enhancing the solubility of SO2 in water (T. Chen et al.,
2019; Cheng et al., 2016; Wang et al., 2016), because the effective
solubility of SO2 can be enhanced due to the increase of the aerosol
pH.
Aerosol acidity is one of the important factors affecting the sulfate
formation and the partitioning of semivolatile gases in the atmosphere (Liu
et al., 2021). As shown in Fig. S11, when aerosol pH is lower than 4.5, the
oxidation rate of S(IV) in aerosol liquid phase decreases with decreasing pH,
because the oxidation of S(IV) by transition metals is the dominant path and
is decreasing with aerosol pH. However, the oxidation rate of S(IV)
increases when the aerosol pH is higher than 4.5. This can be explained by
the fact that the solubility and effective Henry's law constant of SO2
are positively dependent on pH (Cheng et al., 2016; Liu et al., 2021; P. Liu et
al., 2020), which is consistent with the promotion effect of sulfate
formation by NH3.
Conclusions and atmospheric implications
Based on 1 year of observations, we confirmed that high PM2.5 mass
concentration in pollution events usually coincided with the high sulfate
concentration, the fraction of sulfate, and the SOR in both Beijing and
Shijiazhuang. In Shijiazhuang, the SOR exponentially increased as a function
of RH from the point of view of statistics, which was similar to the RH
dependence of the γSO2 on the model particles containing 33 %
NH4NO3 in the presence of NH3. The SOR and γSO2
linearly increased as a function of the fraction of aerosol water content in
PM2.5. The enhanced uptake coefficient of SO2 at high RH after the
liquid-phase aerosol appeared might explain the increased SOR, because uptake
of SO2 was the rate-determining step for the conversion of SO2 to
particulate sulfate. NH4NO3 played an important role in the AWC,
the phase state of aerosol particles and subsequently the uptake kinetics of
SO2 in haze days under high RH conditions.
The contribution of nitrate to PM2.5 is increasing in China (Li et al.,
2018; Tian et al., 2019) due to the intensive emissions of NOx from
steel production and cement manufacturing (Wu et al., 2018; Qi et al., 2017)
as well as the increasing NOx emissions from traffic (Liu et al., 2007; Wang
et al., 2011). The mean fraction of nitrate in PM2.5 was 21.4 ± 12.4 % in Shijiazhuang and 15.8 ± 13.4 % in Beijing. They were close to the reported values in PM1.0 during
the summer of Beijing (24 %) (Li et al., 2018) and in PM2.5 during
the winter of Chengdu (23.3 %) and Chongqing (17.5 %) (Tian et al.,
2019). It has been found that the fraction of nitrate and ammonium usually
increases as a function of PM2.5 mass concentration (Li et al., 2018).
Therefore, NOx should be an urgent air pollutant in the future in China
even from the point view of its contribution to PM2.5 mass.
As observed in this work, NH4NO3 has an important contribution to
PM2.5 mass concentration, the aerosol water content and subsequently
the phase state of particles in the RH range of 60 %–80 %. Reduction of
NOx emissions should lead to a decrease in NH4NO3
concentration and subsequently the AWC during serve pollution events. This
will lead to an additional reduction of SO2 uptake and the formation of
particulate sulfate through aqueous reactions. Based on our rough
estimation, 4 % of sulfate might be reduced due to aqueous reaction in
Shijiazhuang if the mass concentration of NH4NO3 was reduced by 10 %.
More work is required to quantitatively assess the contribution of nitrate
to sulfate formation from aqueous reactions in the future. It should be
noted that ozone pollution becomes more and more important in China (Z. Chen et
al., 2019; Ziemke et al., 2019). This requires us to harmoniously reduce
NOx and volatile organic compounds in the near future. It is also
important to take actions on NH3 emission control in the future as
NH3 can significantly promote the uptake of SO2 in liquid-phase
aerosol.
Data availability
The experimental data are available upon request to the corresponding
authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-13269-2021-supplement.
Author contributions
YoL and XB designed the experiments. YoL and YuL wrote the paper. ZF, FZ,
YZ, XF, CY, BC, YW, WD and JC carried out measurements at BUCT. XB and TJ
carried out measurements at SJZ. YG, YZ and YoL carried out flow tube
experiments. PL, YM and YoL performed sulfate formation calculations. YuL,
FB, TP, YM, HH and MK revised the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The research was financially supported by the National Natural Science
Foundation of China (92044301), the Ministry of Science and Technology of
the People's Republic of China (2019YFC0214701), Academy of Finland via
the Center of Excellence in Atmospheric Sciences (272041, 316114, 315203 and
1307537), and European Research Council via ATM-GTP 266 (742206) and via
ERA-NET-Cofund through SMart URBan Solutions for air quality, disasters and
city growth (SMURBS/ERA-PLANET), the Strategic Priority Research Program of
Chinese Academy of Sciences and Beijing University of Chemical Technology.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 92044301) and the Ministry of Science and Technology of the People's Republic of China (grant no. 2019YFC0214701), Academy of Finland via the Center of Excellence in Atmospheric Sciences (272041, 316114, 315203 and 1307537), and European Research Council via ATM-GTP 266 (742206) and via ERA-NET-Cofund through SMart URBan Solutions for air quality, disasters and city growth (SMURBS/ERA PLANET), the Strategic Priority Research Program of Chinese Academy of Sciences and Beijing University of Chemical Technology.
Review statement
This paper was edited by Harald Saathoff and reviewed by two anonymous referees.
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