Introduction
Severe and frequent haze pollution has become a crucial threat for the air
quality in the megacity of Beijing and the North China Plain in recent years. The
high concentrations of PM2.5 (particulate matter with an
aerodynamic diameter equal or less than 2.5 µm) during severe haze,
of which the hourly average can reach 1000 µg m-3 (B. Zheng
et al., 2015), are harmful to public health by contributing to
cardiovascular morbidity and mortality (Cheng et al., 2013; Brook et al.,
2010). Nitrate is an important component of PM2.5, accounting for
1–45 % of PM2.5 mass in Beijing and the North China Plain
(Wen et al., 2015; B. Zheng et al., 2015; G. Zheng et al., 2015). The main
formation pathways of atmospheric nitrate, defined herein as gas-phase
HNO3 plus particulate NO3-, in the urban area are
summarized in Fig. 1, which includes (i) NO2 oxidation by OH
radicals in the gas phase, (ii) heterogeneous uptake of NO2 on wet
aerosols, (iii) NO3 radicals reacting with hydrocarbon (HC), and
(iv) heterogeneous uptake of N2O5 on wet aerosols and
chlorine-containing aerosols. Since OH radicals are mainly present in the
daytime, while NO3 radicals and N2O5 are mainly present
in the nocturnal atmosphere (Brown and Stutz, 2012), NO2+OH is usually referred to as the daytime nitrate formation pathway, while
N2O5+H2O/Cl- and NO3+HC
are referred to as nocturnal formation pathways (Vicars et al., 2013; Sofen et
al., 2014). During haze in Beijing, the mixing ratio of daytime OH is
modeled to be low (G. Zheng et al., 2015; Rao et al., 2016), while a relatively
high mixing ratio of nocturnal N2O5 is observed in several
studies (Wang et al., 2017a, b; Li et al., 2018); therefore, nocturnal
pathways are suggested to be most responsible for the high concentrations of
atmospheric nitrate during haze (Su et al., 2017; Pathak et al., 2009, 2011).
In addition, the high PM2.5 concentration and relative humidity
during haze in Beijing favor heterogeneous reactions, which renders
NO2+H2O a potentially significant pathway for
nitrate production (J. Wang et al., 2017; Tong et al., 2015; B. Zheng et al.,
2015).
Simplified schematic of the main nitrate formation pathways in urban
air; “het.” refers to heterogeneous reactions on aerosols.
Nitrogen isotopic composition of nitrate (δ15N(NO3-),
wherein δ15N = (Rsample/Rreference-1), with R
representing isotope ratios of 15N/14N in the sample and the
reference atmospheric N2) is useful in tracing the source of its precursor
NOX (Xiao et al., 2015; Beyn et al., 2014; Fang et al., 2011; Hastings
et al., 2013). Anthropogenic sources of NOX such as coal combustion are
generally enriched in δ15N, while natural NOX sources such as
soil emissions or lighting typically have a negative or zero
δ15N
signature (Hoering, 1957; Yu and Elliott, 2017; Felix et al., 2012).
Therefore highly positive values of observed δ15N(NO3-)
can be considered as an indicator of anthropogenic combustion (Elliott et
al., 2009; Fang et al., 2011), although this judgment may be influenced by
isotopic exchange between NO and NO2 (Freyer et al., 1993; Walters et
al., 2016), isotopic fractionations associated with nitrate formation
pathways, and isotopic effects occurring during transport, such as deposition
of NO3- and HNO3 partitioning between the gas and particle phase
(Freyer, 1991; Geng et al., 2014). The oxygen-17 excess (Δ17O) of
nitrate, defined as Δ17O=δ17O-0.52δ18O, wherein δXO = (Rsample/Rreference-1), with R representing isotope ratios of XO/16O in the sample
and the reference Vienna Standard Mean Ocean Water and X=17 or 18, is
particularly useful in reflecting nitrate formation pathways (Michalski et
al., 2003). Atmospheric nitrate from nocturnal reaction pathways has higher
Δ17O than that from daytime OH oxidation at a given
Δ17O(NO2) (Table 1). And once formed, atmospheric
Δ17O(NO3-) cannot be altered by mass-dependent processes
such as deposition during transport (Brenninkmeijer et al., 2003). Previous
studies have shown the utility of atmospheric Δ17O(NO3-)
in quantifying the relative importance of various nitrate formation pathways
(Alexander et al., 2009; Michalski et al., 2003; Patris et al., 2007;
Savarino et al., 2013; Vicars et al., 2013). For example,
the Δ17O(NO3-)-constrained box modeling work of Michalski et
al. (2003) suggests that more than 90 % of atmospheric nitrate is from
nocturnal N2O5+H2O pathways in winter in La Jolla,
California, which is reflected by the highest Δ17O(NO3-)
values being observed in winter. In another study, Alexander et al. (2009)
use observed Δ17O(NO3-) to constrain a 3-D model and found
that the daytime NO2+OH pathway dominates global tropospheric
nitrate production, with an annual mean contribution of 76 %.
Isotope assumptions of different nitrate formation pathways.
No.
Reaction
Δ17O of product
Reference
Expression
Value (‰)a
R1
NO+O3→NO2+O2
Δ17O(NO2)=1.18×Δ17O(O3)+6.6 ‰
37
Savarino et al. (2008)
R2
NO+HO2/RO2→NO2+OH/RO
Δ17O(NO2)=0.0
0.0
Sofen et al. (2014)
R4
NO2+O3→NO3+O2
Δ17O(NO3)=23Δ17O(NO2)+13(1.23×Δ17O(O3)+9.0 ‰ )
25α+14
Berhanu et al. (2012)
R5
NO2+NO3→N2O5
Δ17O(N2O5)=25Δ17O(NO2)+35Δ17O(NO3)
30α+8
Sofen et al. (2014)
R6
NO2+OH→HNO3
Δ17O(NO3-)=23Δ17O(NO2)
25α
Sofen et al. (2014)
R7
2NO2+H2O→HNO3+HNO2
Δ17O(NO3-)=23Δ17O(NO2)
25α
b
R8
NO3+HC→HNO3+products
Δ17O(NO3-)=Δ17O(NO3)
25α+14
Sofen et al. (2014)
R9
N2O5+H2O→2HNO3
Δ17O(NO3-)=56Δ17O(N2O5)
25α+7
Sofen et al. (2014)
R10
N2O5+Cl-→HNO3+ClNO2
Δ17O(NO3-)=Δ17O(NO3)
25α+14
c
a The values are calculated on assumptions that bulk
Δ17O(O3)=26 ‰ (Vicars and Savarino, 2014;
Ishino et al., 2017) and Δ17O(HO2/RO2) = 0 ‰. Δ17O(RO2) is equal to
0 ‰ in the troposphere (Morin et al., 2011); in contrast,
observations suggest Δ17O(HO2) = 1–2 ‰ (Savarino and Thiemens, 1999). However, the difference
in calculated Δ17O(NO3-) between assuming Δ17O(HO2) = 0 ‰ and
Δ17O(HO2) = 2 ‰ is negligible in this study
(<0.1 ‰). And the assumption that Δ17O(HO2) = 0 ‰ simplifies calculations and
is also consistent with previous studies (Michalski et al., 2003; Alexander
et al., 2009; Morin et al., 2008; Kunasek et al., 2008; Sofen et al., 2014).
α is the proportion of O3 oxidation in NO2 production
rate, calculated by Eq. (3).
b Previous studies suggest that in R7 one oxygen atom of NO3-
is from H2O and the other two are from NO2 (Li et al., 2010;
Cheung et al., 2000; Goodman et al., 1999), which will result in Δ17O(NO3-) = 2/3Δ17O(NO2).
c R4 and R5 suggest that the central oxygen atom of N2O5
(O2N–O–NO2) is from NO3 radicals (O–NO2), with
Δ17O (‰) =1.23×Δ17O(O3)+9.0 ‰. R10 is suggested to occur
via O2N–O–NO2 (aq.) →NO2++NO3- and the following
NO2++Cl-→ClNO2 (Bertram and Thornton, 2009), so
Δ17O(NO3-) =1/3(1.23×Δ17O(O3)+9.0 ‰) +2/3Δ17O(NO2)=Δ17O(NO3).
Until now, however, field observations of atmospheric Δ17O(NO3-) have not been conducted in north China to constrain the
relative importance of different nitrate formation pathways during haze. In
this work, we present the first observations of atmospheric
Δ17O(NO3-) during Beijing haze from October 2014 to
January 2015, and use this observation to examine the importance of nocturnal
formation pathways. We also present the signature of simultaneously observed
δ15N(NO3-).
Materials and methods
Sampling and atmospheric observations
PM2.5 filter samples were collected at a flow rate of 1.05 m3 min-1 using a high volume air sampler (model TH-1000C II, Tianhong
Instruments Co., Ltd, China). The quartz microfiber filter
(Whatman Inc., UK) is pre-combusted at 450 ∘C for 4 h before
sampling. Our sampling period lasted from October 2014 to January 2015, with
the collection interval being 12 h (08:00–20:00 LT or 20:00–08:00 LT)
for each sample. Blank control samples were also collected. The blank was
sampled identically to the real sample except that the collection interval
was 1 min. Due to the fact that gaseous HNO3 is likely to adsorb onto particulate
matter already trapped by the filter material (Vicars et al., 2013), the
nitrate species collected is likely to include both particulate nitrate and
gaseous HNO3, which is referred to as atmospheric nitrate in previous
studies (Vicars et al., 2013; Morin et al., 2009; Michalski et al., 2003)
and in this study. The sampling site is at the campus of University of the
Chinese Academy of Sciences (40.41∘ N, 116.68∘ E;
∼20 m high) in suburban Beijing, about 60 km northeast of
downtown Beijing (Fig. 2), which is a supersite set up by HOPE-J3A (Haze
Observation Project Especially for Jing–Jin–Ji Area), with various
observations being reported (Zhang et al., 2017; Xu et al., 2016; Chen et
al., 2015; Tong et al., 2015; He et al., 2018). Hourly concentrations of
surface PM2.5, CO, SO2, NO2, and O3 were observed at
Huairou station (40.33∘ N, 116.63∘ E) by Beijing
Municipal Environmental Monitoring Center, about 10 km from our sampling site.
Meteorological data including relative humidity (RH) and air temperature
(T) were measured by an automatic weather station (model MetPak, Gill
Instruments Limited, UK). Time used in the present study is local time (LT = UTC +8).
A brief map of the sampling site in Beijing. The map scale of base map
is 1:1250000. Huairou station is set up by the Beijing Municipal Environmental
Monitoring Center, where hourly PM2.5, SO2, CO,
NO2, and O3 were observed.
Measurements of ions and isotopic ratios
Ion concentrations of NO3- and Cl- were measured in Anhui
Province Key Laboratory of Polar Environment and Global Change in the
University of Science and Technology of China. A detailed description of
this method can be found in the literature (Ye et al., 2015). Briefly, ions
in the PM2.5 filter sample were extracted with Millipore water (≥18 MΩ) and insoluble substances in the extract were filtered. Then
the ion concentrations were analyzed using an ion chromatograph system (model
Dionex ICS-2100, Thermo Fisher Scientific Inc., USA). The measured ion
concentrations of blank samples were subtracted when determining the ion
concentrations of real samples. Typical analytical precision by our method
is better than 10 % relative standard deviation (RSD) (Chen et al.,
2016).
δ15N(NO3-) and Δ17O(NO3-) were
measured with a bacterial denitrifier method (Kaiser et al., 2007) in IsoLab
at the University of Washington, USA. Briefly, ions in the filter sample were
extracted with Millipore water (≥18 MΩ) and the insoluble
substances were filtered. NO3- in each sample was converted to
N2O by the denitrifying bacteria, Pseudomonas aureofaciens. Then N2
and O2, which were decomposed from N2O in a gold tube at
800 ∘C, were separated using a gas chromatograph. The isotopic ratios
of each gas were then measured by a Finnigan Delta-Plus Advantage isotope
ratio mass spectrometer. Masses of 28 and 29 from N2 were measured to
determine δ15N. Masses of 32, 33, and 34 from O2 were measured
to determine δ17O and δ18O, and Δ17O was then
calculated. We use international nitrate reference materials, USGS34, USGS35,
and IAEANO3, for data calibration. The uncertainty (1σ) of
δ15N and Δ17O measurements in our method is 0.4 ‰ and 0.2 ‰, respectively, based on
replicate analysis of the international reference materials. All the samples
including blank samples were measured in triplicate to quantify the
uncertainty in each sample. The blank was subtracted for each sample by using
an isotopic mass balance on the basis of isotopic ratios and concentrations
of the blank. To minimize the blank effect, samples with blank concentrations
being >10 % of their concentrations were not analyzed for
isotopic ratios. This ruled out 3 of the total 34 samples, all of which are
from non-polluted days (NPD, PM2.5 < 75 µg m-3).
In total, isotopic compositions of 7 samples from NPD and 24 samples from polluted
days (PD, PM2.5≥75 µg m-3) are reported here.
Estimate of different nitrate formation pathways based on Δ17O(NO3-)
The observed Δ17O(NO3-) is determined by the relative
importance of different nitrate formation pathways and the relative
importance of O3 oxidation in NOX cycling as shown in Eq. (1):
Δ17O(NO3-)=Δ17OR6×fR6+Δ17OR7×fR7+Δ17OR8×fR8+Δ17OR9×fR9+Δ17OR10×fR10,
where Δ17OR6, Δ17OR7, Δ17OR8, Δ17OR9, and Δ17OR10
are, respectively, Δ17O(NO3-) resulting from NO2+OH, NO2+H2O, NO3+HC, N2O5+H2O, and
N2O5+Cl- pathways (Table 1); fR6, fR7, fR8,
fR9, and fR10 are, respectively, corresponding fractional
contributions
of the above pathways to nitrate production. Using the Δ17O
assumptions for different pathways in Table 1 and the definition fR6+fR7+fR8+fR9+fR10=1, Eq. (1) is further
expressed as
Δ17O(NO3-)/‰=25αfR6+25αfR7+25α+14×fR8+25α+7×fR9+25α+14×fR10=25α+14×fR8+fR10+7fR9,
where α is the proportion of O3 oxidation in NO2
production rate, calculated by Eq. (3):
α=kR1NO[O3]kR1NO[O3]+kR2aNO[HO2]+kR2bNO[RO2].
In Eq. (3), kR1, kR2a, and kR2b are, respectively, the reaction rate
constants listed in Table 2. To evaluate α, we estimated HO2
mixing ratios on the basis of empirical formulas between HO2 and
O3 mixing ratios derived from observations in winter (Kanaya et al.,
2007); i.e., [HO2]/(pmolmol-1)=exp(5.7747×10-2×[O3]/(nmolmol-1)-1.7227) during the daytime and [HO2]/(pmolmol-1)=exp(7.7234×10-2×[O3]/(nmolmol-1)-1.6363) at night. Then
the RO2 mixing ratio was calculated as 70 % of HO2 mixing ratios
based on previous studies (Liu et al., 2012; Elshorbany et al., 2012;
Mihelcic et al., 2003). As the NO mixing ratio was not observed in our study, we
estimated NO mixing ratios following the empirical formulas between NOX
and CO mixing ratios derived from observations in winter Beijing (Lin et
al., 2011); i.e., [NO]/(nmolmol-1)=([CO]/(nmolmol-1)-196)/27.3-[NO2]/(nmolmol-1) during daytime and
[NO]/(nmolmol-1)=([CO]/(nmolmol-1)-105)/30.9-[NO2]/(nmolmol-1) at night.
Reaction expressions for different NO2 production pathways.
No.
Reaction
Rate expression
Rate constant
Reference
(cm3 molecule-1 s-1)
R1
NO+O3→NO2+O2
kR1[NO][O3]
kR1=3.0×10-12×e(-1500/T)
Burkholder et al. (2015)
R2a
NO+HO2→NO2+OH
k2Ra[NO][HO2]
k2Ra=3.3×10-12×e(270/T)
Burkholder et al. (2015)
R2b
NO+RO2→NO2+RO
k2Rb[NO][RO2]
k2Rb=k2Ra
Burkholder et al. (2015); Kunasek et al. (2008)
General characteristics of haze events in Beijing
(October 2014–January 2015). (a) Time series of PM2.5 and
NO3- concentrations. (b) Time series of nitrogen oxidation ratio
(NOR, which is equal to the NO3- molar concentration divided by the
sum of NO3- and NO2 molar concentration) and
Cl- concentrations. (c) Time series of
Δ17O(NO3-) and visibility. (d) Time series of
δ15N(NO3-) and relative humidity (RH). The error bars in
(c, d) are ±1σ of replicate measurements (n=3) of each
sample. The khaki shaded area indicates polluted days (PD, PM2.5≥75 µg m-3).
By using Eq. (2), the relative importance of nocturnal formation pathways
(fR8+fR9+fR10) can be written as Eq. (4):
fR8+fR9+fR10=fR92+Δ17O(NO3-)14‰-1.8α.
Equation (4) suggests that the relative importance of nocturnal pathways is
solely a function of the assumption of fR9 at given Δ17O(NO3-) and α. SincefR9, fR8+fR10, and fR8+fR9+fR10 should be in the range of 0–1
all the time, fR9 is further limited to meet Eq. (5):
fR9>0<min1,Δ17O(NO3-)7‰-3.6α,2+3.6α-Δ17O(NO3-)7‰.
We estimated the relative importance of nocturnal pathways (fR8+fR9+fR10) by using concentration-weighted Δ17O(NO3-) observations and production-rate-weighted
α from PD of each haze event rather than each sample due to the
lifetime of atmospheric nitrate is typically on the order of day (Liang et
al., 1998), larger than our sampling collection interval.
Simulation of surface N2O5 and NO3 radicals
To see whether the relative importance of nocturnal pathways constrained by
Δ17O(NO3-) can be reproduced by models, we use the
standard Master Chemical Mechanism (MCM, version 3.3; http://mcm.leeds.ac.uk/, last access: 3 September 2018) to simulate the mixing ratios of surface
N2O5 and NO3 radicals during our sampling period. The input
for this modeling work includes (i) 1 h averaged mixing ratios of observed
surface CO, NO2, SO2, and O3 and estimated NO (see Sect. 2.3),
(ii) observed RH and T, and (iii) the mixing ratios of organic compounds from
the literature (Table S1) (Wang et al., 2001; Wu et al., 2016; Rao et al.,
2016).
Results and discussion
Overview of observations in Beijing haze
Figure 3 describes general characteristics of haze events during our
observations. The 12 h averaged PM2.5 concentrations, corresponding with
filter samples, varied from 16 to 323 µg m-3 with a mean of
(141±88 (1 SD)) µg m-3. In comparison, the Grade II of
NAAQS (National Ambient Air Quality Standard) in China is 75 µg m-3 for daily PM2.5. The NO3- concentrations present
similar trends with PM2.5 levels (Fig. 3a), ranging from 0.3 to
106.7 µg m-3 with a mean of (6.1±5.3) µg m-3
on NPD (PM2.5 < 75 µg m-3) and
(48.4±24.7) µg m-3 on PD (PM2.5≥75 µg m-3). Correspondingly, the nitrogen
oxidation ratio (NOR, which is equal to the NO3- molar concentration
divided by the sum of NO3- and NO2 molar concentration), a proxy
for secondary transformation of nitrate (Sun et al., 2006), increased from a
mean of 0.09±0.05 on NPD to 0.31±0.10 on PD (Fig. 3b). In
the residential heating season (Case III–V in November 2014–January 2015,
Fig. 3b), Cl- concentrations present similar trends to NO3-
levels, increasing from (0.6±1.0) µg m-3 on NPD to
(7.9±4.8) µg m-3 on PD. However, during Case I–II in
October 2014, Cl- concentrations were (3.5±1.6) µg m-3 on NPD and (3.5±1.9) µg m-3 on PD, showing no
significant difference at the 0.01 level (t test). Throughout our observational
period, the visibility decreased from (11.4±6.7) km on NPD to
(3.1±1.8) km on PD (Fig. 3c), while RH increased
from (37±12) % on NPD to (62±12) % on PD (Fig. 3d).
Atmospheric Δ17O(NO3-) in aerosols obtained
from the literature and this study.
Sample location
Sample period
Collection interval
Δ17O (‰) range
Reference
Huairou, Beijing
October 2014–January 2015
12 h
27.5–33.9
This study
(40.41∘ N, 116.68∘ E)
(30.6±1.8)
Trinidad Head, California
April–May 2002
1–4 days
20.1–27.5
Patris et al. (2007)
(41.0∘ N, 124.2∘ W)
La Jolla, California
March 1997–April 1998
3 days
20–30.8
Michalski et al. (2003)
(32.7∘ N, 117.2∘ W)
Mt. Lulin, Taiwan
January–December 2010
1 day
2.7–31.4
Guha et al. (2017)
(23.5∘ N, 120.9∘ E)
(17±7)
Cabo Verde islands
July 2007–May 2008
2–3 days
25.5–31.3
Savarino et al. (2013)
(16.9∘ N, 24.9∘ W)
Cruise in coastal California
May–June 2010
2–22 h
19.0–29.2
Vicars et al. (2013)
(32.8–38.6∘ N)
(24.1±2.2)
Cruise from 65∘ S to 79∘ N
September–October 2006
1–4 days
Nonpolar:
Morin et al. (2009)
April–May 2007
24–33
February–April 2006
Polar: 35±2
Alert, Nunavut
March–May 2004
3–4 days
29–35
Morin et al. (2007b)
(82.5∘ N, 62.3∘ W)
(32.7±1.8)
Utqiaġvik (formerly Barrow), Alaska
March 2005
1 day
26–36
Morin et al. (2007a)
(71.3∘ N, 156.9∘ W)
Dumont d'Urville, Antarctic
January–December 2001
10–15 days
20.0–43.1
Savarino et al. (2007)
(66.7∘ S, 140.0∘ E)
Dumont d'Urville, Antarctic
January 2011–January 2012
7 days
23.0–41.9
Ishino et al. (2017)
(66.7∘ S, 140.0∘ E)
Δ17O(NO3-) ranged from 27.5 ‰ to
33.9 ‰ with a mean of (29.1±1.3) ‰ on NPD and (31.0±1.7) ‰ on PD (Fig. 3c). Our observed Δ17O(NO3-) is in the range
of aerosol Δ17O(NO3-) reported in the literature (Table 3) and wet deposition Δ17O(NO3-) observed
in East Asia (Nelson et al., 2018; Tsunogai et al., 2010, 2016). All our observed Δ17O(NO3-) values,
whether from the daytime sample (08:00–20:00) or the nighttime sample (20:00–08:00), are
larger than 25 ‰, the maximum of Δ17O(NO3-) that can be produced via NO2+OH and
NO2+H2O (Table 1) with the assumption of bulk Δ17O(O3)=26 ‰ (Ishino et al., 2017; Vicars
and Savarino, 2014). This directly suggests that nocturnal formation pathways
(N2O5+H2O/Cl- and NO3+HC) must contribute
to all the sampled nitrate. Given that the lifetime of atmospheric nitrate is
typically larger than our sampling collection interval (Vicars et al.,
2013), each of our samples is expected to reflect both daytime and nocturnal
nitrate production. Not surprisingly, the Δ17O(NO3-) mean
of daytime and nighttime samples is (30.3±1.5) ‰
and (30.9±2.1) ‰, respectively, showing no
significant difference at the 0.01 level (t test).
Relationships between Δ17O(NO3-) and other
parameters. The relationship between Δ17O(NO3-) and
NO3-concentrations (a), PM2.5 concentrations (b),
nitrogen oxidation ratio (NOR, c), visibility (d), relative humidity (RH,
e), and δ15N(NO3-) (f). The dark red dots are samples with
NO3- < 50 µg m-3 and the orange dots are
samples with NO3- > 50 µg m-3. The
black dashed lines are linear least-squares fitting lines for all samples, the
dark red solid lines are linear least-squares fitting lines for samples with
NO3- < 50 µg m-3, and the orange solid
lines are linear least-squares fitting lines for samples with
NO3- > 50 µg m-3. The error bars are
±1σ of replicate measurements of each sample.
δ15N(NO3-) in our observation varied from -2.5 ‰ to 19.2 ‰ with a mean of
(7.4±6.8) ‰, which is in the range of δ15N(NO3-) observed from rainwater in Beijing, China (Zhang et
al., 2008), and similar to δ15N(NO3-) values observed
from aerosols in Germany (Freyer, 1991). Figure 3d shows that
δ15N(NO3-) varies largely in October 2014. The mean
δ15N(NO3-) varied from (0.4±1.5) ‰ in the period 08:00 on 18 October–08:00 on 21 October to
(10.7±1.4) ‰ in the period 08:00 on 21 October–08:00 on 23 October and then decreased to
(-0.9±2.1) ‰ in the period 08:00 on 23 October–08:00 on 26 October,
which corresponds to PM2.5 concentrations being 155±63, 57±19, and (188±51) µg m-3 respectively. However, during
the residential heating season, relatively high δ15N(NO3-)
(7.6 ‰–19.2 ‰) levels were always observed, both on NPD and PD.
This may be related to the high NOX emission from coal combustion in
north China (Wang et al., 2012; Lin, 2012; Zhang et al., 2007).
Relationships between Δ17O(NO3-) and other
data
Figure 4 presents the relationships between Δ17O(NO3-)
and NO3- concentrations, PM2.5 concentrations, NOR,
visibility, RH, and δ15N(NO3-).
Δ17O(NO3-) shows a positive correlation with
NO3- concentrations when
NO3- < 50 µg m-3 (r=0.81, p<0.01).
Similarly, Δ17O(NO3-) shows a positive correlation with
PM2.5 concentration in Fig. 4b and NOR in Fig. 4c when
NO3- < 50 µg m-3 (r=0.71 and r=0.80,
p<0.01, respectively). Figure 4d shows that
Δ17O(NO3-) is negatively correlated with visibility in
general (r=-0.66, p<0.01). The significant decrease of visibility
will largely reduce surface radiation and thereby OH mixing ratios (G. Zheng
et al., 2015), which is unfavorable for nitrate production via the NO2+OH pathway. Since the NO2+OH pathway produces low
Δ17O(NO3-) (Table 1), the decreased importance of
the NO2+OH pathway will conversely increase
Δ17O(NO3-). While the rise of RH accompanying the large
increase of PM2.5 favors nitrate production via the heterogeneous
uptake of gases, e.g., N2O5 (G. Zheng et al., 2015; B. Zheng et
al., 2015), and the heterogeneous uptake of N2O5 produces relative
high Δ17O(NO3-) (Table 1), the enhanced heterogeneous
uptake of N2O5 will increase Δ17O(NO3-)
too. Therefore, the decrease of the importance of NO2+OH and
the increase of the importance of the heterogeneous uptake of N2O5
should be responsible for the positive correlation between
Δ17O(NO3-) and NO3- concentrations. In
addition, for samples with NO3- > 50 µg m-3,
visibility was always low with narrow variations (2.3±1.0 km), and RH
was always high with a narrow range (67±7 %), which may be the
reason for the relatively high Δ17O(NO3-) being observed
(31.2±1.7 ‰). Figure 4f shows that
Δ17O(NO3-) is not correlated with
δ15N(NO3-).
Estimate of the proportion of O3 oxidation in
NOX cycling, α. The gray column represents the possible
α range determined by Δ17O(NO3-). The blue dot
represents a specific α value calculated by Eq. (3).
The possible range of fractional contribution of different nitrate
formation pathways during PD of each case estimated on the basis of observed
Δ17O(NO3-)a.
PD of case
fR9 assumption (%)
fR8+fR9+fR10 (%)
fR8+fR10 (%)
fR6+fR7 (%)
I
0–97
49–97
0–49
3–51
II
0–83
58–100
17–58
0–42
III
0–80
60–100
20–60
0–40
IV
0–90
45–90
0–45
10–55
V
0–59
70–100
41–70
0–30
Average
0–82
56–97
16–56
3–44
a R6, R7, R8, R9, and R10 are, respectively,
NO2+OH, NO2+H2O, NO3+HC, N2O5+H2O, and N2O5+Cl- pathways.
Estimate of the nocturnal formation pathways. The estimated
relative importance of nocturnal formation pathways (fR8+fR9+fR10) during PD of each case on the
basis of observed Δ17O(NO3-) (see Sect. 2.3, a) and the
simulated mixing ratios of N2O5 and NO3 radicals by
MCM (b). R8, R9, and R10 in (a) represent NO3+HC,
N2O5+H2O, and N2O5+Cl-
pathways, respectively.
Estimate of nocturnal formation pathways
Before estimating the relative importance of different nitrate formation
pathways, we estimate the proportion of O3 oxidation in the NO2
production rate, α. The possible α range can be calculated
based on observed Δ17O(NO3-). It can be obtained from
Table 1 that
25α ‰ < Δ17O(NO3-) < (25α+14) ‰,
so the lower limit of possible α is
(Δ17O(NO3-) -14 ‰) /25 ‰. And since
Δ17O(NO3-)≥27.5 ‰ in our observation, the higher limit of α
is always 1 for all the samples. Figure 5 presents the possible range of
calculated α based on Δ17O(NO3-). The
calculated lower limit of α ranged from 0.56 to 0.81 with a mean of
0.68±0.07, which directly suggests that O3 oxidation played a
dominated role in NOX cycling during Beijing haze. To estimate the
specific α value, chemical kinetics in Table 2 and Eq. (3) were
used. Specific α is estimated to range from 0.86 to 0.97 with a mean
of (0.94±0.03), which is in the possible range of α value
calculated directly based on Δ17O(NO3-) (Fig. 5) and
close to the range of 0.85–1 determined in other midlatitude areas
(Michalski et al., 2003; Patris et al., 2007).
Figure 6a shows the estimated relative importance of nocturnal formation
pathways (N2O5+H2O/Cl- and NO3+HC)
during PD of each case on the basis of observed
Δ17O(NO3-). Possible fractional contributions of
nocturnal formation pathways range from 49 to 97 %, 58 to 100 %, 60 to 100 %,
45–90 % and 70–100 % on PD of Case I to V,
respectively, with a mean of 56–97 %. This directly implies that
nocturnal chemistry dominates atmospheric nitrate production in Beijing haze.
This finding is consistent with the suggested importance of the heterogeneous
uptake of N2O5 during Beijing haze by previous studies (Su et
al., 2017; Wang et al., 2017b). The other pathways (NO2+OH and NO2+H2O) account for the remaining
fraction with a mean possible range of 3–44 %. Since
NO2+OH and NO2+H2O produce the
same Δ17O(NO3-) signature in our assumptions (Table 1),
we cannot distinguish their fractional contributions purely from the observed
Δ17O(NO3-) in the present study. However, the overall
positive correlation between Δ17O(NO3-) and RH (r=0.55, p<0.01; Fig. 4e) suggests that the heterogeneous uptake of NO2
should be less important than the heterogeneous uptake of N2O5;
otherwise, a negative relationship between Δ17O(NO3-)
and RH is expected. Our calculations also suggest that the sum of possible
fractional contributions of N2O5+Cl- and
NO3+HC is in the range of 0–49 %,
17–58 %, 20–60 %,
0–45 %, and 41–70 % on PD of Case I to V, respectively, with a mean of 16–56 % (Table 4),
which emphasizes that N2O5+Cl- and NO3+HC played a unignorable role in nitrate production during Beijing
haze. Due to the fact that N2O5+Cl- and NO3+HC produce the same Δ17O(NO3-) in our
assumptions (Table 1), we cannot distinguish their fractional contributions
purely from the observed Δ17O(NO3-) in this study
either. However, NO3+HC should be minor for nitrate
production. For example, the 3-D modeling work of Alexander et al. (2009)
suggests that the NO3+HC pathway only accounts for 4 % of
global tropospheric nitrate production annually on average, and Michalski et
al. (2003) found that the NO3+HC pathway contributes
1–10 % to nitrate production on the basis of an annual observation at La
Jolla, California, with low values in winter. Therefore, in addition to
NO3+HC, N2O5+Cl- is likely to
also contribute to nitrate production during haze in Beijing. In support of this,
the concentrations of Cl- are as high as (5.5±4.1) µg m-3 during PD of all the cases in our observation
and the mixing ratios of ClNO2, an indicator of the N2O5+Cl- pathway, reached up to 2.9 nmol mol-1 during a summer
observation in suburban Beijing (Wang et al., 2018b) and reached up to
5.0 nmol mol-1 in another modeling work in summer rural Beijing (Wang et
al., 2017c).
Figure 6b presents the simulated mixing ratios of surface N2O5
and NO3 radicals during our observational period by using the box
model MCM. The 12 h averaged mixing ratios of simulated N2O5
ranged from 3 to 649 pmol mol-1, while simulated NO3 radicals
ranged from 0 to 27 pmol mol-1. In comparison, previous observations
in Beijing suggest that 5 s averaged N2O5 can be as high as
1.3 nmol mol-1 and 30 min averaged NO3 radicals can be as
high as 38 pmol mol-1, with large day-to-day variability (Wang et al.,
2015, 2017b). During Case I and II in October, simulated N2O5 and
NO3 radicals present similar trends with the observed
NO3- and remain relatively high during PD (346±128 and
9±7 pmol mol-1, respectively, Fig. 6b), which supports the
dominant role of nocturnal formation pathways suggested by
Δ17O(NO3-). However, during Case III–V in the residential
heating season, the simulated surface mixing ratios of N2O5 and
NO3 radicals remain relatively low during PD (63±80 and <1 pmol mol-1, respectively, Fig. 6b), which seems to be inconsistent
with Δ17O(NO3-) observations. We note that a recent
study suggests that the heterogeneous uptake of N2O5 is negligible
at the surface but larger at higher altitudes (e.g., >150 m) during winter
haze in Beijing (Wang et al., 2018a). So during PD of Case III–V in our
observational period, large nitrate production via heterogeneous uptake of
N2O5 may occur aloft rather than at the surface, which leads to the
dominant role of nocturnal formation pathways as suggested by
Δ17O(NO3-).