Nitrate aerosol plays an increasingly important role in wintertime haze
pollution in China. Despite intensive research on wintertime nitrate
chemistry in recent years, quantitative constraints on the formation
mechanisms of nitrate aerosol in the Yangtze River Delta (YRD), one of the
most developed and densely populated regions in eastern China, remain
inadequate. In this study, we identify the major nitrate formation pathways
and their key controlling factors during the winter haze pollution period in
the eastern YRD using 2-year (2018–2019) field observations and detailed
observation-constrained model simulations. We find that the high atmospheric
oxidation capacity, coupled with high aerosol liquid water content (ALWC),
made both the heterogeneous hydrolysis of dinitrogen pentoxide
(N2O5) and the gas-phase OH oxidation of nitrogen dioxide
(NO2) important pathways for wintertime nitrate formation in this
region, with contribution percentages of 69 % and 29 % in urban areas
and 63 % and 35 % in suburban areas during the haze pollution episodes,
respectively. We further find that the gas-to-particle partitioning of
nitric acid (HNO3) was very efficient so that the rate-determining step
in the overall formation process of nitrate aerosol was the oxidation of
NOx to HNO3 through both heterogeneous and gas-phase processes.
The atmospheric oxidation capacity (i.e., the availability of O3 and OH
radicals) was the key factor controlling the production rate of HNO3
from both processes. During the COVID-19 lockdown (January–February 2020),
the enhanced atmospheric oxidation capacity greatly promoted the oxidation
of NOx to nitrate and hence weakened the response of nitrate aerosol to
the emission reductions in urban areas. Our study sheds light on the
detailed formation mechanisms of wintertime nitrate aerosol in the eastern
YRD and highlights the demand for the synergetic regulation of atmospheric
oxidation capacity and NOx emissions to mitigate wintertime nitrate and
haze pollution in eastern China.
Introduction
Atmospheric fine particulate matter (PM2.5) has profound impacts on air
quality, climate, and public health (Huang et al., 2014; Wang et al.,
2014; Lelieveld et al., 2015; von Schneidemesser et al., 2015). Over the
past decades, China has encountered severe PM2.5 pollution due to
rapid urbanization and industrialization (Huang et al., 2014; Zhang and
Cao, 2015; Tao et al., 2017; Peng et al., 2021). To tackle severe air
pollution, the Chinese government has implemented clean air policies such
as the “Action Plan for Air Pollution Prevention and Control” in recent
years. As a result, anthropogenic emissions of major air pollutants such as
sulfur dioxide (SO2), nitrogen oxides (NOx), and primary PM have
declined dramatically, and the nationwide PM2.5 air quality has
improved significantly (Shao et al., 2018; Zheng et al., 2018; Ding et
al., 2019; Zhang et al., 2019). In addition, with the emission reduction of
primary PM, secondary aerosol has become the most important component of
PM2.5 (Shao et al., 2018; Ding et al., 2019; Peng et al., 2021).
Secondary inorganic aerosol, consisting mainly of nitrate, sulfate, and
ammonium (SNA), contributed to 30 %–60 % of the PM2.5 mass in China
(Hua et al., 2015; Tao et al., 2017; Ye et al., 2017; Wang et al., 2018;
Fu et al., 2020; Lin et al., 2020). During the pollution episodes, the
proportion of SNA to PM2.5 could exceed 50 % (Tao et al., 2017;
Liu et al., 2020a; Peng et al., 2021). Before 2013, sulfate was often found
to be the most abundant component of PM2.5 in Chinese cities (Zhao
et al., 2013; Huang et al., 2014; Kong et al., 2014; Xie et al., 2015; Tao
et al., 2017). However, with the implementation of stringent clean air
policies, anthropogenic emissions of SO2 in China dropped by 59 %
from 2013 to 2017, while NOx emissions decreased only by 21 % during
the same period (Zheng et al., 2018). Consequently, sulfate aerosol
concentration has decreased dramatically nationwide since 2013, but
wintertime nitrate concentration has not decreased much (Ding et al.,
2019; Li et al., 2019a; Xu et al., 2019; Fu et al., 2020; Wang et al.,
2020b); nitrate has become an increasingly important component of PM2.5
in most regions of China during winter (Ye et al., 2017; Yun et al.,
2018; Li et al., 2019a; Xu et al., 2019; Chen et al., 2020; Fu et al., 2020;
Kong et al., 2020; Lin et al., 2020; Xie et al., 2020; Zhai et al., 2021;
Zhang et al., 2021). The high loading of nitrate has been considered to play
an important role in winter haze pollution (Wen et al., 2015; Sun et al.,
2018). Therefore, identifying the major nitrate formation pathways and their
controlling factors during haze events is of great importance for developing
effective particulate pollution mitigation policies in China.
In polluted regions, the nitrate aerosol arises mainly from two pathways:
(1) the gas-phase oxidation of nitrogen dioxide (NO2) by OH radicals
producing nitric acid (HNO3) (Calvert and Stockwell, 1983) and (2) the heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) that
was produced from the gas-phase reaction of NO2 with nitrate (NO3) radicals
on aqueous aerosols (Bertram and Thornton, 2009; Bertram et al., 2009;
Wagner et al., 2013; McDuffie et al., 2019). The gas-phase OH + NO2
pathway primarily occurs during the daytime and is mainly influenced by the
atmospheric oxidation capacity despite the NO2 concentration (Chen
et al., 2020; Fu et al., 2020). The heterogeneous formation of nitrate via
N2O5 hydrolysis is greatly affected by aerosol liquid water
content (ALWC) and the production of N2O5 (Alexander et al.,
2020; Lin et al., 2020; Wang et al., 2020b). As a result, this heterogeneous
pathway is generally weak during the daytime because of the fast photolysis
of NO3 radicals and titration by NO (Wayne et al., 1991; Brown
and Stutz, 2012), which inhibit N2O5 production. However, it could
be the dominant pathway for nitrate formation during the nighttime (Wang
et al., 2017; McDuffie et al., 2019), where N2O5 can be produced
more efficiently, and its hydrolysis is favored by the high relative humidity
(or ALWC).
There have been a number of field studies on the pollution characteristics
and formation mechanisms of nitrate aerosol during haze events in China over
the past decades (Tao et al., 2016; Li et al., 2018; Sun et al., 2018;
Wen et al., 2018; Ding et al., 2019; Ye et al., 2019; Chen et al., 2020; Fu
et al., 2020; Lin et al., 2020; Wang et al., 2020b; Zhao et al., 2020a; Chan
et al., 2021). However, most of these studies were carried out in the North
China Plain (NCP) (Li et al., 2018; Wen et al., 2018; Chen et al., 2020;
Fu et al., 2020; Wang et al., 2020b; Chan et al., 2021). Earlier studies
suggested that the nitrate formation during the pollution episodes in this
region was mainly attributed to the heterogeneous hydrolysis of
N2O5 (Su et al., 2017; Wang et al., 2017; He et al., 2018; Li
et al., 2018). However, recent studies showed that the gas-phase OH + NO2 process has become more important, and sometimes this process was
even the dominant pathway for nitrate formation (Chen et al., 2020; Fu et
al., 2020). The Yangtze River Delta (YRD) in eastern China is one of the
most developed regions in China (Ding et al., 2013). The wintertime
O3 concentration is relatively high in this region, with an average of
∼20 ppb, and sometimes could even reach 75 ppb (Li et al.,
2019c; Ye et al., 2019; Zhao et al., 2020a), which is significantly higher
than that (average: 6–16 ppb) in the NCP region (Li et al., 2019a; Duan
et al., 2020; Liu et al., 2020a). Furthermore, the relative humidity (RH) in
this region is also high, with the average winter RH ranging from 63 % to
71 % (Tao et al., 2016; Shen et al., 2020; Yu et al., 2020b), which was
also significantly higher than the average RH (20 %–40 %) in the NCP region
(Fang et al., 2019; Li et al., 2019a; Huang et al., 2020; Xie et al.,
2020). The high atmospheric oxidation capacity, coupled with the high RH
that led to high ALWC, would favor the production of secondary aerosol (Peng
et al., 2021).
Haze pollution events frequently occurred in the YRD during winter (Hua
et al., 2015; Sun et al., 2018; Ding et al., 2019). Although there have been
many studies on the pollution characteristics of nitrate and PM2.5 in
this region (Tao et al., 2016; Sun et al., 2018; Chen et al., 2019; Ding
et al., 2019; Ye et al., 2019; Lin et al., 2020; Shen et al., 2020), only a
few studies have focused on nitrate formation mechanisms. It has been
reported that the heterogeneous hydrolysis of N2O5 contributed
dominantly to nitrate formation in the western YRD (Sun et al., 2018),
and its production rate could be 5 times higher than that of the gas-phase
OH + NO2 process during severe haze pollution events (Lin et
al., 2020). In contrast, some other studies have qualitatively pointed out
that the gas-phase OH + NO2 reaction was an important formation
pathway of nitrate in the eastern YRD, though the heterogeneous hydrolysis
of N2O5 during the nighttime also contributed (Ye et al., 2019;
Zhao et al., 2020a). Overall, quantitative constraints on the detailed
formation mechanisms of wintertime nitrate aerosol in the YRD region remain
limited. The relative contribution of different nitrate formation pathways
and their controlling factors are still unclear.
In this study, we conducted hourly measurements of nitrate and associated
particulate and gaseous air pollutants at an urban site and a regional site
in the eastern YRD during winter in 2018 and 2019, aiming to clarify the
nitrate formation mechanisms during winter. An observation-constrained box
model using the detailed Master Chemical Mechanism (MCM v3.3.1) updated with
the state-of-the-art heterogeneous chemistry of N2O5, NO2,
and particulate nitrate was employed to quantitatively identify the major
reaction pathways and key controlling factors for wintertime nitrate aerosol
formation in this region. This study will help to understand the nitrate
aerosol chemistry in the eastern YRD and develop effective strategies to
mitigate secondary aerosol pollution in this densely populated region.
Materials and methodsObservation sites and instrumentation
PM2.5 and its chemical composition, inorganic gases, volatile organic
compounds (VOCs), and meteorological parameters were continuously measured
at a regional site (Qingpu) and an urban site (Pudong) in Shanghai from
1 December to 12 February in both 2018 and 2019. The Qingpu site (120.989∘ E, 31.097∘ W) is a suburban site (see Fig. 1),
located near Dianshan Lake and surrounded by the residential areas and
vegetation, about 46 km away from the urban Shanghai. In addition, the
Qingpu site is located at the junction of Shanghai, Jiangsu, and Zhejiang
provinces and is a typical regional site in the eastern YRD. The instruments
at this site were on the rooftop of a 10 m tall building. The Pudong site
(121.533∘ E, 31.228∘ W) is an urban site located near
Century Avenue with heavy traffic, and it is only ∼3 km
from the business center Lujiazui. The instruments at this site were located
on the roof of a 20 m tall building. The eastern YRD region is affected by
the subtropical monsoon climate, dominated by the northwest and northeast
winds in winter.
Map of the eastern YRD region and the two observation sites, i.e.,
Qingpu (suburban and regional) and Pudong (urban).
The measurements at the two sites were conducted hourly. The PM2.5 mass
concentration was measured by a tapered element oscillating microbalance
combined with a filter dynamic measurement system (TEOM-FDMS; TEOM 1405-F,
Thermo Fisher Scientific, USA.). Water-soluble ions including NO3-, SO42-,
NH4+, Cl-, Na+, Ca2+, and Mg2+ were measured using an
online Monitor for Aerosol and Gases (MARGA; ADI 2080, Applikon Analytical
B.B.Corp., Netherlands). Organic carbon (OC) and elemental carbon (EC) were
measured by a semi-continuous OC/EC analyzer (Model 4, Sunset Laboratory
Inc., USA), and a denuder was installed before the analyzer to avoid the
disturbance of organic vapors. The surface area and volume concentrations of
aerosol particles were measured using a scanning mobility particle sizer
(SMPS, TSI, USA, which consists of a 3080 electrostatic classifier, a 3081A
different mobility analyzer, and a 3787 condensation particle counter) and
an aerodynamic particle sizer (APS 3321, TSI, USA). The combination of SMPS
and APS was able to cover the particle size range from 13.6 nm to 10 µm. Considering that the Pudong sampling site lacks the data of aerosol surface area and volume concentrations, we performed a linear fit between
the aerosol surface area and volume concentrations and PM2.5 mass concentration at the Qingpu
site (see Fig. S1 in the Supplement) and predicted the values for the
Pudong site based on such a linear fit and the measured PM2.5 mass
concentration. The surface area and volume concentrations of dry aerosol particles
measured by SMPS and APS were corrected to the ambient RH based on an
empirical composition kappa function and the kappa-Köhler function (see
details in Sect. S1 in the Supplement). The O3, NOx, and
SO2 were measured by an ozone, NOx, and SO2 analyzer (Model
49i, 42i, and 43i, Thermo Fisher Scientific, USA), respectively. A total of
56 VOCs were measured using gas chromatography equipped with a flame
ionization detector (GC-FID; Chromatotec A11000/A21022 at the Qingpu site
and PerkinElmer Clarus 580 at the Pudong site). Meteorological parameters
including temperature, RH, pressure, and wind speed and direction were measured
by a meteorological transducer (WXT520, Vaisala Ltd., Finland).
Estimation of aerosol liquid water content and pH
The ISORROPIA-II thermodynamic model was used to calculate aerosol pH and
ALWC (Fountoukis and Nenes, 2007). The water-soluble inorganic ion
concentrations, along with RH and temperature, were used as the model input.
The model was run in the forward mode, which would give a more accurate
estimation of aerosol pH than using the reverse mode with only particulate
inorganic ions as the model input (Guo et al., 2015; Hennigan et al.,
2015). Further, considering the relatively high RH in eastern YRD, we
selected the metastable state for aerosol in this study. ISORROPIA-II
calculated the equilibrium concentrations of particle hydronium ions (Hair+, µg m-3) and ALWC (µg m-3) per air volume. Then
the aerosol pH can be derived by the following equation:
pH=-log10Haq+=-log101000Hair+ALWC,
where Haq+ is the concentration of hydronium ions in aqueous aerosol (mol L-1). It should be mentioned that when the RH was extremely high
(>95 %), a slight deviation in measured RH would cause
significant uncertainty in the estimation of ALWC. Therefore, we only
considered the data with the RH below 95 % in the further analysis.
Observation-constrained model simulation
The Framework for 0-D Atmospheric Modeling (F0AM v3.1) (Wolfe et al.,
2016) employing the MCM v3.3.1 (Jenkin et al., 2015) was used to
simulate the formation of nitrate in the pollution events during the whole
observation period. Figure 2 summarizes the formation pathways of HNO3
in the atmosphere (Alexander et al., 2020; Chan et al., 2021). In the
model, we considered the reaction pathways including heterogeneous
hydrolysis of N2O5 (Reaction R3) and NO2 (Reaction R8), gas-phase OH + NO2 (Reaction R7), NO3 radical oxidation of VOCs (Reaction R5), and reaction of NO
with hydroperoxy (HO2) radicals (Reaction R2), which together contributed to
88 % of HNO3 formation in the global troposphere (Alexander
et al., 2020). The model did not include the hydrolysis of NO3 radicals
and organic nitrate (Reactions R1, R4, and R6), as well as the reaction of NO2
with halogen oxide species (Reaction R9). However, these pathways only had a small
contribution to the production of HNO3 (Alexander et al.,
2020). Therefore, they would not significantly affect the model results in
this study.
Simplified HNO3 formation mechanisms in the troposphere. X
represents Cl, Br, and I.
The default MCMv3.3.1 does not consider the heterogeneous hydrolysis of
N2O5 in detail and the heterogeneous production of nitrous acid
(HONO), an important precursor of OH radicals in the polluted atmosphere.
Therefore, we parameterized these processes in the model based on recent
advances in these processes. For the heterogeneous hydrolysis of
N2O5, the N2O5 molecules accommodated on aqueous
aerosols can undergo reversible hydrolysis to form NO3- and H2ONO2+ (Reaction R1), followed by the reaction of H2ONO2+ with H2O or Cl-
to form HNO3 or nitryl chloride (ClNO2) (Reactions R2 and R3)
(Finlayson-Pitts et al., 1989; Schweitzer et al., 1998; Thornton and
Abbatt, 2005):
R1N2O5(aq)+H2O⇌k1fk1bH2ONO2+(aq)+NO3-(aq)R2H2ONO2+(aq)+H2O(l)⟶k2H3O+(aq)+HNO3(aq)R3H2ONO2+(aq)+Cl-(aq)⟶k3ClNO2(g)+H2O(l).
The rate of the heterogeneous hydrolysis of N2O5 on aqueous
aerosols (k4) could be calculated by Eq. (2) when ignoring the gas-phase
diffusion limitation:
k4=γN2O5⋅c⋅Sa4,
where γN2O5 is the uptake coefficient of N2O5,
defined as the probability of removal of N2O5 per collision with
the wet aerosol surface; c is the mean molecular speed of N2O5; and
Sa is the measured aerosol surface area concentration. In this study,
we employed an observation-based empirical parameterization of γN2O5, which was first developed by Bertram and Thornton (2009) and recently modified by Yu et al. (2020a) to provide a reasonable representation of the
PM2.5 reactivity toward N2O5 at different Chinese sites:
γN2O5=4cVaSaKH×k1f′×1-1k2k1b×H2ONO3-+1+k3k1b×Cl-NO3-,
where Va is the measured aerosol volume concentration; KH is the dimensionless Henry's law coefficient of N2O5, with a value of 51 (Bertram and Thornton, 2009); k1f′ is the reaction
rate constant of N2O5 with water, which was calculated using a
linear function with [H2O], as 3.0×104× [H2O] (Yu et al., 2020a); k2k1b and
k3k1b are the relative rates of reactions of H2ONO2+ (aq) with H2O or Cl- (Reactions R2 and R3) versus that with NO3- (the
reverse reaction of R1), with values determined to be 0.033 and 3.4,
respectively, for different Chinese sites (Yu et al., 2020a); and [H2O], [NO3-], and [Cl-] are
the molarity of water, nitrate, and chloride in aerosol, respectively.
The yields (Φ, ranging between 0 and 1) of HNO3 and ClNO2 from the heterogeneous hydrolysis of N2O5 depend on the H2O
and Cl- content in the aerosol (Bertram and Thornton, 2009; Yu et
al., 2020a). In this study, the yield of HNO3 (ΦHNO3) was
estimated from Eq. 4 (Bertram and Thornton, 2009; Yu et al., 2020a):
N2O5+Aerosol→2ΦHNO3+2(1-Φ)ClNO2ΦHNO3=1-1/1+H2Ok3k2×Cl-,
where k3k2 is the ratio of reaction rates of Reaction (R3) versus Reaction (R2),
which has been determined to be 105 (Yu et
al., 2020a).
Photolysis of HONO was shown to contribute 20 %–92 % of the production of OH
radicals during winter haze pollution events in China (Tan et al., 2017;
Slater et al., 2020; Xue et al., 2020). Here, on the basis of previous
studies (Lee and Schwartz, 1983; Kleffmann et al., 1998; Kurtenbach et
al., 2001; Wong et al., 2011; Wong et al., 2013; Han et al., 2016; Ye et
al., 2016; Liu et al., 2017; Trinh et al., 2017; Romer et al., 2018; Zare et
al., 2018; Liu et al., 2019; Wang et al., 2020a; Xue et al., 2020), we
parameterized the major heterogeneous production pathways of HONO and its
dry deposition to estimate the HONO budget during the pollution episodes. We
also considered the direct emissions of HONO from vehicles based on a 4 km ×4 km emission inventory of NOx and an empirical emission
ratio (0.8 %) of HONO to NOx (Kurtenbach et al., 2001; An et al.,
2021). The added mechanisms are summarized in Table 1. A detailed
description of the parameterization is provided in the Supplement (Sect. S2). Considering that there remain significant uncertainties in the key
parameters (i.e., the uptake coefficient of NO2 on aerosol or ground
surfaces, the enhancement factor of the photolysis rate of particulate nitrate (jNO3-) vs. that of gas-phase nitric acid (jHNO3), and HONO emission ratios) of the heterogeneous HONO formation
pathways and its direct emissions as listed in Table 1, we performed the
sensitivity analyses for these parameters to evaluate their influences on
the model results.
Parameterization of the formation and removal pathways of HONO added
to the model.
* The value of jNO2noon used in the model was 0.005 s-1.
References are given in detail here. a: Xue et al. (2020); b: Liu et al. (2019);
c: Wong et al. (2011); d: Kleffmann et al. (1998);
e: Wong et al. (2013); f: Zare et al. (2018);
g: Han et al. (2016); h: Romer et al. (2018); i: Ye et al. (2016); j: Kurtenbach et al. (2001); k: Liu et al. (2017), l: Trinh et al. (2017); m: Lee and Schwartz (1983); n: Wang et al. (2020a). PBL is the planetary boundary layer.
In addition, we considered the dilution of species via deposition and
entrainment, etc., using a highly simplified parameterization:
d[X]dt=-kdilX-Xbkg,
where kdil is the first-order dilution rate constant, and [X]bkg is a
fixed background concentration of pollutants. Here, a typical dilution
lifetime of 1 d was assumed; i.e., kdil=1/24 h-1. As
the species background concentration was unknown, [X]bkg was set to 0
for simplicity. Considering the uncertainties in the parameterization of
dilution process using a constant rate constant, we also conducted a
sensitivity test for kdil with its value ranging between 0.028
and 0.2 h-1, which covers the typical values used in box model
simulations (Romer et al., 2018; McDuffie et al., 2019; Liu et al.,
2020b), to evaluate its influence on the model results.
In the model, the j values of various gaseous species were calculated using
the default MCMv3.3.1 parameterization with input of the solar zenith angle
at the observation sites and scaled by the ratio of measured to calculated
jNO2 values. The observed pollutant concentrations and meteorological
parameters were used as the model input, which were updated hourly (one
model step) using the observation data and held constant during each model
step, except for the observed concentrations of NO and NO2 (the sum of
NO and NO2 concentrations was constrained by the observation,
but their specific ratios were simulated by the model).
Results and discussionOverview of pollution characteristics during winter
Time series of temperature, relative humidity (RH), aerosol liquid
water content (ALWC), NOx, O3, Ox, and nitrogen oxidation ratio
(NOR), as well as PM2.5 and major particulate compositions at the
Pudong site in the winter of 2019.
Table 2 shows the overall pollution conditions of the two observation sites
in winter 2018 and 2019. The average PM2.5 concentration increased by
17 %–21 % in 2019 compared to that in 2018. Accordingly, nitrate
concentration also increased by 11 %–14 % in 2019. The O3 concentration
was slightly higher in 2019 than in 2018, consistent with increased
atmospheric oxidation capacity in recent years (Lu et al., 2018; Li et
al., 2019b; Liu and Wang, 2020; Yang et al., 2020). In the 2 years, both
of the PM2.5 and nitrate concentrations at the Qingpu site were higher
than those at the Pudong site. As mentioned above, the Qingpu site is at the
junction of Shanghai, Jiangsu, and Zhejiang, so it is more easily influenced
by the transport of air pollutants from Jiangsu, which is usually more
polluted than Shanghai. Additionally, the average temperature at the Qingpu site
was also slightly lower than that at the Pudong site, which might to some
extent favor the gas-to-particle partitioning of HNO3. Notably, the
average RH was as high as 80 % during the observation period, which was
significantly higher than that (63 %) recorded in 2016 (Tao et
al., 2016). In particular, the RH exceeded 90 % for more than one-third of
the days during the observation period.
Concentrations (average ± standard deviation) of PM2.5,
particulate nitrate, NOx, and O3, as well as temperature and RH at
Qingpu and Pudong sites in the winter of 2018 and 2019.
Taking the Pudong site in 2019 as an example, we analyzed the time series of
PM2.5, nitrate, and other related parameters and presented the results
in Fig. 3 (time series of the pollutants at the Qingpu site can be seen in
Sect. S3 and Fig. S2). PM2.5 pollution events occurred frequently
in the eastern YRD during winter. During the observation period, the
PM2.5 concentration exceeded 75 µg m-3 for 34 d and 150 µg m-3 for 6 d. During the pollution episodes (PM2.5>75µg m-3), nitrate had become the most important
component of PM2.5, and its concentration was a factor of 2.2 higher
than that of sulfate. In winter, the emission of NOx was obviously
high. During the periods with high nitrate concentration, the NOx
concentration always exceeded 100 ppb. The O3 concentration was also at
a relatively high level, with a maximum value of 60 ppb and an average of 22 ppb, which was much higher than the wintertime average O3 concentration
(6–16 ppb) in the NCP (Li et al., 2019a; Duan et al., 2020; Liu et al.,
2020a). The concentration of odd oxygen (Ox= O3+ NO2)
ranged between 20–83 ppb with an average of 44 ppb, indicating a relatively
high atmospheric oxidation capacity in the eastern YRD during winter.
Consistently, the nitrogen oxidation ratio (NOR; NOR =NO3-/(NO3- + NO2)) was up to 0.51, suggesting a high degree of atmospheric
oxidation. Meanwhile, the high atmospheric RH in the eastern YRD led to a
high ALWC. During the high nitrate periods, the ALWC was often at its peak
and could exceed 200 µg m-3 on rainy or hazy–foggy days. Such a
high ALWC level would have an important impact on the nitrate formation.
Notably, the NOx concentration dropped sharply on 23 January and kept
at a low level until the end of the observation (12 February 2020). This is
mainly a result of marked emission reductions during the COVID-19 lockdown.
Such an emission reduction had a complicated influence on the nitrate
formation chemistry, which will be discussed in detail in Sect. 3.5.
Figure 4 shows the mass ratio of nitrate to PM2.5 as a function of the
PM2.5 concentration and ALWC at Qingpu and Pudong sites in 2018 and
2019. The ratio of nitrate to PM2.5 increased with increasing
PM2.5 concentration. When the PM2.5 concentration was above 75 µg m-3, the average mass fraction of nitrate was
more than 30 %. In addition, the nitrate formation rate was much higher
than that of sulfate and ammonium during PM2.5 pollution episodes, as
indicated by the slope of nitrate vs. PM2.5 that was twice that of the
other two ions (see Fig. S3). These results indicate that the formation of
nitrate played a driving role in the formation of PM2.5 pollution. In
general, when the ALWC was high, the nitrate concentration was also at a
high level. On the one hand, ALWC could promote the nitrate formation by
favoring the heterogeneous hydrolysis of N2O5 and the
gas-to-particle partitioning of HNO3. On the other hand, the increase
in nitrate concentration could enhance the hygroscopicity of PM2.5,
leading to an increase in ALWC, which would further promote the nitrate
formation (Wang et al., 2020b). It is worth noting that, when
PM2.5<100µg m-3, the mass
ratio of NO3- to PM2.5 increased rapidly with rising PM2.5
concentration, but when the PM2.5 concentration exceeded 100 µg m-3, the ratio reached a plateau. This might be due
to the fact that when the PM2.5 concentration increased to a certain
level, the formation process of other components may also speed up, causing
the nitrate proportion to stay basically constant.
Mass ratio of nitrate to PM2.5 as a function of PM2.5
concentration at (a, c) Qingpu and (b, d) Pudong sites in the winter of 2018
and 2019. The circles represent the measured ratio of
NO3-/PM2.5, and their area is linearly scaled with the square
root of ALWC.
Gas-to-particle partitioning of nitrate
The gas-to-particle partitioning of nitrate determines the sensitivity of
particulate nitrate formation to the production of HNO3. Figure 5 shows
the particulate nitrate concentration (measured) and its fraction to total
nitrate (εHNO3, εHNO3=NO3-/(NO3-+ HNO3), predicted by ISORROPIA-II) as a function of ALWC and
aerosol pH. In order to avoid the influence of rainy and foggy days during
the observation period, which could lead to the abnormal high ALWC, we only
used the data with RH below 95 % for analysis. Obviously, ALWC promoted
the formation of particulate nitrate, but such a promoting effect varied
greatly under different aerosol pH (top panel in Figs. 5a–d). As the pH
increased, the slope of nitrate vs. ALWC also increased significantly,
indicating a stronger promoting effect. ALWC plays a dual role in the
formation of nitrate aerosol: it can promote the heterogeneous formation of
nitrate, e.g., via N2O5 hydrolysis, by providing more reaction
medium and decreasing the kinetic limitation (Mozurkewich and Calvert,
1988; Bertram and Thornton, 2009; Wang et al., 2020b); the ALWC can also
promote the gas-to-particle partitioning of HNO3. The different
promoting effect of ALWC under different aerosol pH is mainly due to the
fact that pH can significantly influence the gas-to-particle partitioning of
HNO3. As shown in Figs. 5a–d (bottom part of each panel), when the aerosol pH was
low, the gas-to-particle partitioning of HNO3 was inhibited, with the
value of εHNO3 basically below 0.6 at pH <2.
Under these conditions, the increase of particulate nitrate concentration
would require more ALWC. When the pH increased, the inhibition effect of pH
on the gas-to-particle partitioning of HNO3 was weakened. When the pH
was higher than 2.5, the nitrate was almost in the particle phase
(εHNO3=1). As a result, the increase of ALWC would
rapidly promote the nitrate formation, particularly when ALWC was at a low
level. It is important to point out that during the whole observation
period, the values of εHNO3 were larger than 0.9 for
90 % of time when the PM2.5 concentration was higher than 75 µg m-3 (see Fig. S4). This indicates that the
gas-to-particle partitioning of HNO3 was very efficient and not a
limiting factor for particulate nitrate formation during the pollution
episodes. The gas-to-particle partitioning of HNO3 was also efficient
in the NCP region, and its average εHNO3 could reach
100 % during the haze pollution period (Guo et al., 2018; Li et al.,
2019a). However, the average εHNO3 in the northeastern
United States during winter was only 39 % (Guo et al., 2018); this
might be due to the relatively lower pH in this region (0.8±1.0)
(Guo et al., 2016), which inhibited the gas-to-particle partitioning.
Particulate nitrate concentration and its fraction to total nitrate
(εHNO3) as a function of ALWC and aerosol pH at (a, c) Qingpu and (b, d) Pudong sites in the winter of 2018 and 2019. The circles
are colored according to aerosol pH.
Observational constraints on the nitrate formation mechanism
The dominant nitrate formation pathway is different at different
times of the day. The heterogeneous hydrolysis of N2O5 was often
found to be an important pathway for nighttime nitrate formation. Here, we
evaluated the role of this pathway to nitrate formation in the eastern YRD
using the nighttime averages' correlation between particulate nitrate
concentration and the production of N2O5. Due to the lack of
direct observational data of N2O5 in this study, we used the value
of NO2 squared multiplied by O3 ([NO2]2× O3) to indicate the N2O5 level (Liu et al., 2020a).
Figure 6 shows the nighttime average nitrate concentration as a function of
[NO2]2× O3 in winter. The average particulate
nitrate concentration showed a strong positive correlation with
[NO2]2× O3. In particular in 2019, as the value of
[NO2]2× O3 increased to ∼15000 ppb3, the nitrate concentration increased from 15–20 µg m-3 to 40–45 µg m-3,
suggesting that the heterogeneous hydrolysis of N2O5 was an
important pathway for wintertime nitrate formation in the eastern YRD.
Notably, there are some data points with low values of
[NO2]2× O3 but high nitrate concentrations. This
might be partly due to their relatively high aerosol pH (>3),
which could promote the gas-to-particle partitioning of HNO3.
Nighttime average particulate nitrate concentration (empty circles)
as a function of [NO2]2× [O3] at (a, c) Qingpu and
(b, d) Pudong sites in 2018 and 2019. The circles are colored according to
aerosol pH, and their size is linearly scaled with the square root of ALWC. The
blue filled circles represent the average of nitrate concentration within a
certain [NO2]2× [O3] interval. To reduce the
influences of the daytime remainder on the analysis of nighttime nitrate
formation, only the data with an obvious peak or increasing trend during
nighttime were included in the plots.
To evaluate the role of the gas-phase OH + NO2 process in nitrate
formation during the daytime, we use the Ox to indicate the atmospheric
oxidation capacity due to the lack of direct observational data of OH
radicals. Figure 7 shows the daytime average particulate nitrate
concentration as a function of Ox. Notably, as the Ox
concentration increased, the nitrate concentration also increased
significantly. However, the increase in ALWC seemed to have a relatively
small impact on the nitrate concentration during the daytime, indicating
that the reaction of NO2 with OH radicals to form HNO3 (rather
than the gas-to-particle partitioning) was a rate-limiting step in daytime
nitrate formation. We also note that there are some data points with low
Ox values but high ALWC and nitrate concentrations (Fig. 7c). This
phenomenon might be owing to a certain degree of heterogeneous process in
the hazy–foggy days, when the photochemical reactions were relatively weak.
Overall, the high atmospheric oxidation capacity made the gas-phase OH + NO2 reaction an important pathway for nitrate formation during the
daytime in the eastern YRD.
Daytime average particulate nitrate concentration as a function of
Ox at (a, c) Qingpu and (b, d) Pudong sites in 2018 and 2019. The
circles are colored according to aerosol pH, and their size is linearly
scaled with the square root of ALWC. The blue filled circles represent the
average of nitrate concentration within a certain Ox interval. The data
points inside the black circle in (c) correspond to low Ox levels but
high ALWC and nitrate concentrations. Only the data with an obvious peak or
increasing trend during daytime were included in the plots.
Model constraints on the nitrate formation mechanism
To quantify the contribution of different formation mechanisms to wintertime
nitrate formation in the eastern YRD, we used an observation-constrained
model (F0AM v3.1) updated with the heterogeneous chemistry of N2O5 and NO2 (see Sect. 2.3 for details) to simulate the formation rate
of HNO3 from different pathways during the observation period. During
the winter of 2019, six haze pollution episodes (PM2.5>75µg m-3) occurred at both sites (there was an additional episode
during the outbreak of the COVID-19 pandemic, which was discussed separately in
Sect. 3.5). We conducted simulations for all the six pollution episodes
and took two representative ones at the Pudong site for detailed
analysis. Considering the large uncertainties in ALWC estimation and aerosol
surface area and volume corrections at high RH levels (>95 %),
which could significantly affect the simulation results, we excluded the
simulated data above 95 % RH from the further analysis. Figure 8 shows the
time series of various particulate (measured) and gaseous (measured and
simulated) air pollutants, as well as the formation rate of HNO3
(simulated) from different pathways during these two episodes (the case
studies of the same episodes at the Qingpu site are given in Sect. S4 and
Fig. S5).
Time series of particulate nitrate, NO2, Ox, ALWC, OH, and N2O5, as well as the formation rate of HNO3 from different
processes during the two selected cases during the pollution episodes at the
Pudong site in 2019. The simulated data with RH >95 % were not
included in the figure (see main text).
In episode 1 (Fig. 8a), the nitrate concentration increased rapidly from
15.2 µg m-3 at 22:00 LT on 29 December to 39.0 µg m-3 at
10:00 LT on 30 December, with an average growth rate of 2.0 µg m-3 h-1. The simulated NO2 concentration was in good agreement with
the observation, expect for a short period around the midnight of 30
December, during which the NO emissions led to an overprediction of the
NO2 level. During the high nitrate periods, the nighttime
N2O5 concentration could reach 0.5–1 ppb and contributed
noticeably to HNO3 formation via the heterogeneous hydrolysis. However,
the high daytime OH concentration (up to 2.5×106 molecules cm-3) facilitated a relatively more rapid nitrate formation from the
gas-phase OH + NO2 pathway. The average production rate of HNO3
from the gas-phase OH + NO2 reaction during the daytime was 2.9 µg m-3 h-1, which was twice the average production rate of
HNO3 from the heterogeneous hydrolysis of N2O5 during the
nighttime.
We note that the overestimation of NO2 during the night of 30 December
(case 1) could lead to an overestimation of nighttime HONO, but it did not
significantly affect the overall production rate of HONO and thereby OH
radicals in this case, which was dominated by daytime heterogeneous
photochemical processes (see Fig. S7, HONO production rate in the base
scenario). In addition, as the O3 concentration in the model was
constrained by the measured value, which was very low (<5 ppb)
during this time, the overestimation of NO2 would also not
significantly affect the prediction of N2O5. As a result, the
overprediction of NO2 would not have a large influence on the major
formation pathways of nitrate.
There were two cases in episode 2 (Fig. 8b). In case 2, the
concentration of nitrate increased from 26.8 µg m-3 at 05:00 LT to
46.0 µg m-3 at 13:00 LT on 12 January 2020, with an average growth
rate of 2.4 µg m-3 h-1. Then, the nitrate
concentration achieved a fast growth from 40.2 to 70.5 µg m-3 within only 6 h during the night of 12 January, with an average rate
of 5.1 µg m-3 h-1. During the nitrate increasing
period, the maximum OH concentration was ∼1.0×106 molecules cm-3. As a result, the gas-phase OH + NO2
reaction led to a slow increase of nitrate concentration in the daytime of
12 January. During the nighttime, the N2O5 concentration quickly
increased to 0.83 ppb. The high N2O5 level, in combination with
the high ALWC, made the heterogeneous hydrolysis of N2O5 a more
important pathway for nitrate formation. The simulated average production
rate of HNO3 from the heterogeneous hydrolysis of N2O5 during
this case was 4.0 µg m-3 h-1, which was 3.6 times that of the
formation rate from the gas-phase OH + NO2 reaction (1.1 µg m-3 h-1). In case 3, the nitrate concentration increased from 22.5 µg m-3 at 0:00 LT to 53.8 µg m-3 at 11:00 LT on 14 January,
with an average growth rate of 2.8 µg m-3 h-1. The
N2O5 concentration was at a high level (∼1 ppb)
during the nighttime, and its hydrolysis contributed significantly to nitrate
formation at the beginning of the nitrate-increasing period. In the morning
of 14 January, the OH concentration rapidly increased to 1.3×106 molecules cm-3, resulting in considerable nitrate formation
from the gas-phase process. The average production rates of HNO3 from
the heterogeneous and gas-phase processes in this case were 3.9 and 2.4 µg m-3 h-1, respectively, suggesting that both processes were
important nitrate formation pathways.
As mentioned above, there were six haze pollution episodes during the
observation period. At the Qingpu site, the heterogeneous hydrolysis of
N2O5 was the major formation pathway (65 %–80 %) of nitrate
aerosol for four episodes, while the gas-phase OH + NO2 reaction had
a major contribution (54 %–60 %) for the other two episodes. At the Pudong
site, the heterogeneous process also contributed dominantly (67 %–89 %) to
nitrate formation during four episodes, and for the other two episodes, the
contributions of the heterogeneous and gas-phase processes were comparable
(51 %–53 % vs. 45 %–47 %). Figure S6 shows the average diurnal variation of
the production rates of HNO3 from different pathways during the
observation period in 2019. The gas-phase process produced HNO3 mainly
from 7:00 to 16:00 LT, while the HNO3 production from the heterogeneous
process occurred mainly from 17:00 to 6:00 LT. The average production rates of
HNO3 from the heterogeneous and gas-phase processes are given in Fig. 9. At the Qingpu site, the average production rate of HNO3 from the
two processes was 3.79 µg m-3 h-1 for the heterogeneous
process during the nighttime (14 h) vs. 2.94 µg m-3 h-1
for the gas-phase reaction during the daytime (10 h). The production
rate from other processes such as NO2 hydrolysis and NO3 radical
oxidation of VOCs was only 0.08 µg m-3 h-1. Therefore, the
heterogeneous and gas-phase processes contributed to 63 % and 35 % of
nitrate formation at this site, respectively. At the Pudong site, the
average formation rate of HNO3 from the hydrolysis of N2O5
was 3.83 µg m-3 h-1, significantly higher than that from the
gas-phase reaction (2.27 µg m-3 h-1). As a result, the
contributions of heterogeneous and gas-phase processes to nitrate formation
were 69 % and 29 %, respectively.
Simulated average formation rates of HNO3 at (a) Qingpu and
(b) Pudong sites during the haze pollution periods in 2019.
As mentioned in Sect. 2.3, significant uncertainties remain in the key
parameters of the heterogeneous HONO formation pathways and the dilution
process in the model, which could affect the prediction of OH radicals and
N2O5 and thereby the production of HNO3. However, sensitive
analyses for various parameters show that the current parameterization of
the heterogeneous HONO formation and dilution process in the model allows
for robust quantitative constraints on the relative contributions of the
gas-phase and heterogeneous processes to nitrate formation during haze
pollution episodes (see Sect. S5 and Figs. S7, S8 for more details). In
addition, monoterpenes that are very reactive to NO3 radicals
(Atkinson and Arey, 2003) were not included in the model because
their measurements are not available in this study. However, a case study
considering the monoterpene chemistry in the model shows that the low
monoterpene emissions during the winter did not significantly affect the
budget of NO3 radical and N2O5 and thereby the nighttime
HNO3 production (see Sect. S6 and Fig. S9 for more details).
As discussed in Sect. 3.2, the gas-to-particle partitioning of HNO3
was rather efficient, with the value of εHNO3 larger than
0.9 for 90 % of the time during the haze pollution periods. Therefore, the
overall formation rate of particulate nitrate would be determined by the
production rate of HNO3 from the heterogeneous hydrolysis of
N2O5 and gas-phase OH + NO2 reaction. To identify the key
chemical factors that controlled the production rates of HNO3 from
these two major reaction pathways, the relationships between the HNO3 production rate and concentrations of NO2 and oxidants (i.e., O3
or OH radicals) are examined and plotted in Fig. 10.
Production rates of HNO3 from the (a) heterogeneous and (b) gas-phase processes as a function of NO2 concentration at the Pudong
site during the nighttime and daytime, respectively. The circles are colored
according to the O3 concentration in (a) and OH radical concentration
in (b).
As shown in Fig. 10a, the slopes of the HNO3 production rate from the
heterogeneous process vs. NO2 during the nighttime were different under
different O3 concentrations. When O3 concentrations were higher
than 10 ppb, the increase in NO2 led to a significant increase in
HNO3 production, with the production rate exceeding 5 µg m-3 h-1 when the NO2 was higher than 30 ppb. However, when the O3
level was low (<10 ppb), the heterogeneous process was relatively
slow, even with NO2 concentration exceeding 60 ppb. These results
suggest that the atmospheric oxidation capacity (or the availability of
O3), which affected the production of N2O5, played a vital
role in controlling the nitrate formation rate from the heterogeneous
process. Furthermore, the reactive uptake of N2O5 by aerosols was
found to be very efficient (see Fig. S10), so that it was not the
rate-limiting step of the heterogeneous nitrate formation during the haze
pollution periods. Similarly, the slope of the HNO3 production rate
from the gas-phase process vs. NO2 during the daytime also varied
dramatically under different OH radical concentrations (Fig. 10b). As the
OH radical concentration was higher than 7×105 molecules cm-3, this rate increased markedly with the increase in NO2. This
phenomenon proved again that the atmospheric oxidation capacity played a
driving role in the production of HNO3 from the gas-phase process.
The results in Fig. 10 also suggest that solely reducing the NOx
emissions might result in an increase of O3 and OH concentrations
(Lu et al., 2019; Zhao et al., 2020b), which could enhance the oxidation
of NOx and thereby offset the effect of NOx emission reductions on
HNO3 production. Therefore, a synergistic control of atmospheric
oxidants and NOx emissions would be of great importance for mitigating
wintertime particulate nitrate pollution in the eastern YRD.
Nitrate aerosol formation during the COVID-19
Average concentrations of NOx, SO2, O3, OH
radicals, PM2.5, nitrate, and sulfate, as well as the nitrogen and sulfur
oxidation ratio (NOR and SOR) at (a–c) Pudong and (d–f) Qingpu sites before
(1–22 January 2020) and during (23 January–12 February 2020) the COVID-19
pandemic.
The city lockdowns during the COVID-19 pandemic resulted in substantial
emission reductions from vehicular and industrial sources, which provided an
opportunity to investigate the response of secondary aerosols to primary
emission reductions. Here, we selected the 23 January 2019 as a demarcation
point (since then many cities in China started to implement lockdown
measures) and analyzed the characteristics of particulate nitrate pollution
before and during the COVID-19 pandemic.
Figure 11 shows the concentrations of major gaseous and particulate air
pollutants, NOR, and sulfur oxidation ratio (SOR) in the eastern YRD before
(1–22 January, 2020) and during (23 January–12 February, 2020) the COVID-19
pandemic. At the Pudong site (Fig. 11a, b, c), the average NOx
concentration decreased by 57 % due to marked reductions in vehicular
emissions during the pandemic. In contrast, the SO2 concentration only
had a small decrease (16 %) during the pandemic, since it mainly comes
from coal-combustion sources and is less affected by vehicular emissions.
However, the O3 concentration increased by 66 % during the pandemic.
This is mainly due to the significant reduction in NOx emissions,
though the changes in meteorological conditions could also contribute
(Zhao et al., 2020b). Accordingly, the model simulations show that the
atmospheric OH concentration (median) increased by 14 % during the
pandemic, though the average value only increased slightly. The increase in
O3 and OH concentrations could significantly promote the oxidation of
NOx to nitrate and SO2 to sulfate through both gas-phase and
heterogeneous processes. As shown in Fig. 11c, the average values of NOR
and SOR increased from 0.15 and 0.46 before the pandemic to 0.21 and 0.50
during the pandemic, respectively. The enhanced oxidation of NOx and
SO2 would weaken the response of particulate nitrate and sulfate to the
emission reductions. As can be seen in Fig. 11b and c, the simulated
HNO3 production rate and measured particulate nitrate concentration
dropped by 42 % and 40 % during the pandemic, respectively, which were
both significantly smaller than the decrease in NOx concentration
(57 %), while the particulate sulfate concentration only decreased by
2 %, also substantially smaller than the reduction in SO2
concentration (16 %).
Similarly, at the Qingpu site, the NOx concentration decreased by
58 % during the pandemic, while the concentrations of O3 and OH
radicals (median) increased by 90 % and 17 %, respectively. The
significantly enhanced atmospheric oxidation capacity made the simulated
HNO3 production rate only decrease by 17 % during the pandemic.
However, the measured particulate nitrate concentration at this site
decreased by 60 %, comparable to the decrease in NOx concentration.
The inconsistency between the decrease in measured nitrate concentration and
simulated HNO3 production rate at the Qingpu site was different from
the situation observed at the Pudong site, which is likely due to the fact
that the Qingpu site was more easily to be influenced by the regional
transport. We note that the average wind speed at the Qingpu site (1.8 m s-1) was higher than that at the Pudong site (1.1 m s-1). In addition,
the haze pollution was more serious at the Qingpu site than at the Pudong
site before the pandemic: both PM2.5 and nitrate concentrations were
significantly higher at the Qingpu site (see Fig. 11). Therefore, the
marked emission reductions on a regional scale during the pandemic would
decrease both the local formation and transport of particulate nitrate from
the upwind regions, resulting in a more pronounced reduction in observed
nitrate concentration at the Qingpu site. In addition, before the pandemic,
the transport of aged air plume with relatively high nitrate and sulfate
concentrations from upwind regions resulted in relatively high NOR and SOR
values at the Qingpu site. However, during the pandemic, the significant
decrease in nitrate and sulfate concentrations in the aged air plume due to
regional emission reductions led to lower NOR and SOR at this site.
The results at the Pudong site clearly show that the enhanced atmospheric
oxidation capacity during the COVID-19 pandemic promoted the formation of
secondary aerosols and offset the effects of primary emission reductions in
the eastern YRD. Such a phenomenon was also observed in many other
regions in China during the COVID-19 lockdown (Le et al., 2020; Zheng et
al., 2020; Huang et al., 2021; Liu et al., 2021; Tian et al., 2021; Zhong et
al., 2021). These results suggest an important role of atmospheric oxidation
capacity in regulating secondary aerosol formation. They also highlight the
importance of the synergetic regulation of atmospheric oxidants and other
air pollutants in the mitigation of particulate pollution in China. However,
the Qingpu site also provided us a special case that in severely polluted
regions with a stronger influence from the regional transport, the offset
effects of enhanced atmospheric oxidation capacity on emission reductions
could be more complicated and less significant.
Conclusions
In this study, the chemical mechanisms and key controlling factors of
wintertime nitrate formation in the eastern YRD of China were investigated
using a combination of online field observations and detailed model
simulations. During the observation period (winter 2018 and 2019), the haze
pollution events (PM2.5>75µg m-3) occurred
frequently in this region. The mass fraction of nitrate in PM2.5
increased dramatically with PM2.5 concentration and exceeded 30 %
throughout the pollution periods. The measured nitrate concentration was
well correlated with [NO2]2× [O3] (an indicator of
N2O5) at night and the level of Ox (an indicator of
atmospheric oxidation capacity) during the daytime, indicating that both the
heterogeneous hydrolysis of N2O5 and gas-phase OH + NO2 process played important roles in wintertime nitrate formation in the
eastern YRD. Observation-constrained model simulations further show that the
average production rates of HNO3 from the heterogeneous hydrolysis of
N2O5 during the nighttime and gas-phase OH + NO2 reaction
during the daytime were 3.81 and 2.61 µg m-3 h-1, respectively, during the haze pollution periods; these
two pathways accounted for 66 % and 32 % of wintertime nitrate formation
in the eastern YRD, respectively.
The ALWC significantly promoted the formation of nitrate by facilitating the
hydrolysis of N2O5 and the gas-to-particle partitioning of
HNO3. However, the promoting effect of ALWC on nitrate formation varied
with aerosol pH due to its significant influence on the gas-to-particle
partitioning of HNO3. During the pollution periods, the gas-to-particle
partitioning of HNO3 was very efficient, with the partitioning
coefficients, εHNO3, larger than 0.9 for 90 % of the
time. Therefore, the overall formation processes of wintertime particulate
nitrate were not limited by the gas-to-particle partitioning of HNO3
but rather by its production from both heterogeneous and gas-phase
processes. Further analyses of the response of HNO3 formation to the
variation in the concentrations of NO2, O3, and OH radicals
suggest that the atmospheric oxidation capacity (i.e., the availability of
O3 and OH radicals) played a key role in controlling the formation of
nitrate from both processes.
During the COVID-19 lockdown (January–February 2020), the enhanced
atmospheric oxidation capacity promoted the oxidation of NOx to nitrate
and weakened the effects of primary emission reductions on particulate
pollution in typical urban areas in the eastern YRD, though such an offset
effect was less significant in regions with a stronger influence from the
regional transport. This phenomenon again suggests that the atmospheric
oxidation capacity played an important role in driving the formation of
secondary aerosols and highlights the importance of the synergetic
regulation of atmospheric oxidation capacity and other air pollutants in the
mitigation of particulate pollution in eastern China.
Data availability
The data presented in this work are available upon request from the
corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-4355-2022-supplement.
Author contributions
YZ designed the study, JH, QZ, QF, and YD performed field measurements, JS
conducted ISORROPIA-II model calculation, JA and CH provided the NOx
emission inventory, and YZ and HZ analyzed the data, conducted model
simulations, and wrote the paper. LX, ZL, CL, and HX contributed to
discussion and writing.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Yue Zhao acknowledges the Program for Professor of Special Appointment
(Eastern Scholar) at Shanghai Institutions of Higher Learning. The authors
are grateful to Hongli Wang and Yaqin Gao for kindly sharing their
monoterpene observation data for a sensitivity test.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 22022607) and the Science and Technology Commission of Shanghai Municipality (grant no. 19DZ1205004).
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by two anonymous referees.
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