Introduction
Nitrogen oxides (NOx = NO + NO2) are among the
most important molecules in tropospheric chemistry. They are involved in the
formation of secondary aerosols and atmospheric oxidants, such as ozone
(O3) and hydroxyl radicals (OH), which control the self-cleansing
capacity of the atmosphere (Galloway et al., 2003; Seinfeld and Pandis, 2012;
Solomon et al., 2007). The sources of NOx include both
anthropogenic and natural origins, with more than half of the global burden
(∼40 Tg N yr-1) currently attributed to fossil fuel burning
(22.4–26.1 Tg N yr-1) and the rest primarily derived from
nitrification/denitrification in soils (including wetlands; 8.9±1.9 Tg N yr-1), biomass burning (5.8±1.8 Tg N yr-1),
lightning (2–6 Tg N yr-1) and oxidation of N2O in the
stratosphere (0.1–0.6 Tg N yr-1) (Jaegle et al., 2005; Richter et
al., 2005; Lamsal et al., 2011; Price et al., 1997; Yienger and Levy, 1995;
Miyazaki et al., 2017; Duncan et al., 2016; Anenberg et al., 2017; Levy et
al., 1996). The main/ultimate sinks for NOx in the
troposphere are the oxidation to nitric acid (HNO3(g)) and the
formation of aerosol-phase particulate nitrate (pNO3-)
(Seinfeld and Pandis, 2012), the partitioning of which may vary on diurnal
and seasonal timescales (Morino et al., 2006).
Emissions of NOx occur mostly in the form of NO (Seinfeld
and Pandis, 2012; Leighton, 1961). During daytime, transformation from NO to
NO2 is rapid (few minutes) and proceeds in a photochemical steady
state, controlled by the oxidation of NO by O3 to NO2
and the photolysis of NO2 back to NO (Leighton, 1961):
NO+O3⟶NO2+O2,NO2+hv⟶NO+O,O+O2⟶MO3,
where M is any non-reactive species that can take up the energy released to
stabilize O. NOx oxidation to HNO3 is governed by
the following equations. During daytime,
NO2+OH⟶MHNO3.
During nighttime:
NO2+O3⟶NO3+O2,NO3+NO2⟶MN2O5,N2O5+H2O(surface)⟶aerosol2HNO3.
HNO3 then reacts with gas-phase NH3 to form ammonium
nitrate (NH4NO3) aerosols. If the ambient relative humidity (RH)
is lower than the efflorescence relative humidity (ERH) or crystallization
relative humidity (CRH), solid-phase NH4NO3(s) is formed (Smith
et al., 2012; Ling and Chan, 2007):
NH4NO3⇌HNO3g+NH3g.
If ambient RH exceeds the ERH or CRH, HNO3 and NH3
dissolve into the aqueous phase (aq) (Smith et al., 2012; Ling and Chan,
2007):
HNO3g+NH3g⇌NO3-(aq)+NH4+(aq).
While global NOx emissions are well constrained, individual
source attribution and their local or regional role in particulate nitrate
formation are difficult to assess due to the short lifetime of
NOx (typically less than 24 h) and the high degree of
spatiotemporal heterogeneity with regards to the ratio between gas-phase
HNO3 and particulate NO3-
(pNO3-) (Duncan et al., 2016; Lu et al., 2015; Zong et
al., 2017; Zhang et al., 2003). Given the conservation of the nitrogen (N)
atom between NOx sources and sinks, the N isotopic
composition of pNO3- can be related to the different
origins of the emitted NOx and thus provides valuable
information on the partitioning of the NOx sources (Morin et
al., 2008). Such a N isotope balance approach works best if the N isotopic
composition of various NOx sources display distinct
15N/14N ratios (reported as δ15N =
15N/14Nsample-(15N/14N)N215N/14NN2×1000). The
δ15N–NOx of coal-fired power plant
(+10 ‰ to +25 ‰) (Felix et al., 2012, 2013; Heaton, 1990), vehicle (+3.7 ‰ to +5.7 ‰)
(Heaton, 1990; Walters et al., 2015; Felix and Elliott, 2014; Felix et al.,
2013; Wojtal et al., 2016) and biomass burning (-7 ‰ to
+12 ‰) emissions (Fibiger and Hastings, 2016), for example, is
generally higher than that of lightning (-0.5 ‰ to
+1.4 ‰) (Hoering, 1957) and biogenic soil (-48.9 ‰ to
-19.9 ‰) emissions (Li and Wang, 2008; Felix and Elliott, 2014;
Felix et al., 2013), allowing the use of isotope mixing models to gain
insight on the NOx source apportionment for gases, aerosols
and the resulting nitrate deposition (-15 ‰ to
+15 ‰) (Elliott et al., 2007, 2009; Zong et al., 2017; Savarino et al.,
2007; Morin et al., 2008; Park et al., 2018; Altieri et
al., 2013; Gobel et al., 2013). In addition, because of mass-independent
fractionation during its formation (Thiemens, 1999; Thiemens and Heidenreich,
1983), ozone possesses a strong isotope anomaly
(Δ17O ≈ δ17O - 0.52×δ18O), which is propagated into the most short-lived
oxygen-bearing species, including NOx and nitrate.
Therefore, the oxygen isotopic composition of nitrate (δ18O,
Δ17O) can provide information on the oxidants involved in the
conversion of NOx to nitrate (Michalski et al., 2003; Geng
et al., 2017). Knopf et al. (2006, 2011) and Shiraiwa et al. (2012) have
shown that NO3 can be taken up efficiently by organic (e.g.,
levoglucosan) aerosol and may dominate oxidation of aerosol in the polluted
urban nighttime (Kaiser et al., 2011). Globally, theoretical modeling results
show that nearly 76 %, 18 % and 4 % of annual inorganic nitrate are
formed via pathways/reactions involving OH, N2O5, and
dimethyl sulfide or hydrocarbons, respectively (e.g., Alexander et
al., 2009). The stable O isotopic composition of atmospheric nitrate is a
powerful proxy for assessing which oxidation pathways are important for
converting NOx into nitrate under changing environmental
conditions (e.g., polluted, volcanic events, climate change). Along the same
lines, in this study, the average δ18O value of
pNO3- in Nanjing was 83.0±11.2 ‰
(see Discussion section), suggesting that pNO3- formation
is dominated by the pathways of “OH + NO2” and the
heterogeneous hydrolysis of N2O5.
δ15N-based source apportionment of NOx
requires knowledge of how kinetic and equilibrium isotope fractionation may
impact δ15N values during the conversion of
NOx to nitrate (Freyer, 1978; Walters et al., 2016). If
these isotope effects are considerable, they may greatly limit the use of
δ15N values of pNO3- for
NOx source partition (Walters et al., 2016). Previous
studies did not take into account the potentially biasing effect of N isotope
fractionation, because they assumed that changes in the δ15N
values during the conversion of NOx to nitrate are minor
(without detailed explanation) (Kendall et al., 2007; Morin et al., 2008;
Elliott et al., 2007) or relatively small (e.g., +3 ‰) (Felix and
Elliott, 2014; Freyer, 2017). However, a field study by Freyer et al. (1993)
has indicated that N isotope exchange may have a strong influence on the
observed δ15N values in atmospheric NO and NO2,
implying that isotope equilibrium fractionation may play a significant role
in shaping the δ15N of NOy species (the
family of oxidized nitrogen molecules in the atmosphere, including
NOx, NO3, NO3-, peroxyacetyl nitrate,
etc.). The transformation of NOx to nitrate is a complex
process that involves several different reaction pathways (Walters et al.,
2016). To date, few fractionation factors for this conversion have been
determined. Recently, Walters and Michalski (2015) and Walters et al. (2016)
used computational quantum chemistry methods to calculate N isotope
equilibrium fractionation factors for the exchange between major
NOy molecules and confirmed theoretical predictions that
15N isotopes get enriched in the more oxidized form of
NOy and that the transformation of NOx to
atmospheric nitrate (HNO3, NO3(aq), NO3(g))
continuously increases the δ15N in the residual
NOx pool.
As a consequence of its severe atmospheric particle pollution during the cold
season, China has made great efforts toward reducing NOx
emissions from on-road traffic (e.g., improving emission standards, higher
gasoline quality, vehicle travel restrictions) (Li et al., 2017). Moreover,
China has continuously implemented denitrogenation technologies (e.g.,
selective catalytic reduction) in the coal-fired power plants sector
since the mid-2000s and has been phasing out small inefficient units (Liu et
al., 2015). Monitoring and assessing the efficiency of such mitigation
measures, and optimizing policy efforts to further reduce
NOx emissions, require knowledge of the vehicle- and
power-plant-emitted NOx to particulate nitrate in urban China (Ji
et al., 2015; Fu et al., 2013; Zong et al., 2017). In this study, the
chemical components of ambient fine particles (PM2.5) were quantified,
and the isotopic composition of particulate nitrate
(δ15N–NO3-,
δ18O–NO3-) was assessed in order to elucidate
ambient NOx sources in the city of Nanjing in eastern China. We
also investigated the potential isotope effect during the formation of
nitrate aerosols from NOx and evaluated how disregard of
such N isotope fractionation can bias N-isotope-mixing-model-based estimates
on the NOx source apportionment for nitrate deposition.
Methods
Field sampling
In this study, PM2.5 aerosol samples were collected on precombusted
(450 ∘C for 6 h) quartz filters (25×20 cm) on a
day–night basis, using high-volume air samplers at a flow rate of
1.05 m3 min-1 in Sanjiang and Nanjing (Fig. 1). After sampling,
the filters were wrapped in aluminum foil, packed in air-tight polyethylene
bags and stored at -20 ∘C prior to further processing and
analysis. Four blank filters were also collected. They were exposed for
10 min to ambient air (i.e., without active sampling). PM2.5 mass
concentration was analyzed gravimetrically (Sartorius MC5 electronic
microbalance) with a ±1 µg precision before and after sampling
(at 25 ∘C and 45%±5 % during weighing).
Location of the sampling sites Sanjiang and Nanjing. The black dots
indicate the location of sampling sites (sites are located in the area of
mainland China and the Yellow, East China and South China seas) with
δ15N–NO3- data from the literature (see also
Table S4).
The Sanjiang campaign was performed during a period of intensive burning of
agricultural residues between 8 and 18 October 2013, to examine if there is
any significant difference between the δ15N values of
pNO3- and NOx emitted from biomass burning. The Sanjiang site
(in the following abbreviated as SJ; 47.35∘ N, 133.31∘ E)
is located at an ecological experimental station affiliated with the Chinese
Academy of Sciences located in the Sanjiang Plain, a major agricultural area
predominantly run by state farms in northeastern China (Fig. 1). Surrounded
by vast farm fields and bordering far-eastern Russia, SJ is situated in a
remote and sparsely populated region, with a harsh climate and rather poorly
industrialized economy. The annual mean temperature at SJ is close to the
freezing point, with daily minima ranging between -31 and -15 ∘C
in the coldest month, January. As a consequence of the relatively low
temperatures (also during summer), biogenic production of
NOx through soil microbial processes is rather weak. SJ is
therefore an excellent environment in which to collect biomass-burning-emitted
aerosols with only minor influence from other sources.
The Nanjing campaign was conducted between 17 December 2014 and
8 January 2015 with the main objective to examine whether N isotope
measurements can be used as a tool to elucidate NOx source
contributions to ambient pNO3- during times of severe haze.
Situated in the lower Yangtze River region, Nanjing is, after Shanghai, the
second-largest city in eastern China. The aerosol sampler was placed at the
rooftop of a building on the Nanjing University of Information Science and
Technology campus (in the following abbreviated as NJ; 18 m a.g.l.;
32.21∘ N, 118.72∘ E; Fig. 1), where NOx
emissions derive from both industrial and transportation sources.
Laboratory analysis
The mass concentrations of inorganic ions (including SO42-,
NO3-, Cl-, NH4+, K+,
Ca2+, Mg2+ and Na+), carbonaceous components
(organic carbon, or OC; elemental carbon, or EC) and water-soluble organic
carbon were determined using an ion chromatograph (761 Compact IC,
Metrohm, Switzerland), a thermal-optical OC–EC analyzer (RT-4 model, Sunset
Laboratory Inc., USA) and a total organic carbon analyzer (Shimadzu, TOC-VCSH, Japan),
respectively. Importantly, levoglucosan, a molecular marker for the biomass
combustion aerosols, was detected using a
Dionex™ ICS-5000+ system (Thermo Fisher
Scientific, Sunnyvale, USA). Chemical aerosol analyses, including sample
pre-treatment, analytical procedures, protocol adaption, detection limits
and experimental uncertainty were described in detail in our previous work
(Cao et al., 2016, 2017).
For isotopic analyses of aerosol nitrate, aerosol subsamples were generated
by punching 1.4 cm disks out of the filters. In order to extract the
NO3-, sample discs were placed in acid-washed glass vials with
10 ml deionized water and placed in an ultra-sonic water bath for 30 min.
Between one and four disks were used for NOx extraction,
dependent on the aerosol NO3- content of the filters, which was
determined independently. The extracts were then filtered (0.22 µm)
and analyzed the next day. N and O isotope analyses of the
extracted/dissolved aerosol nitrate (15N/14N,
18O/16O) were performed using the denitrifier method (Sigman et
al., 2001; Casciotti et al., 2002). Briefly, sample NO3- is
converted to nitrous oxide (N2O) by denitrifying bacteria that lack
N2O reductase activity (Pseudomonas chlororaphis ATCC
13985; formerly Pseudomonas aureofaciens, referred to below as
such). N2O is extracted, purified and analyzed for its N and O
isotopic composition using a continuous-flow isotope ratio mass spectrometer
(Thermo Finnigan Delta+, Bremen, German). Nitrate N and O isotope ratios
are reported in the conventional δ notation with respect to
atmospheric N2 and Vienna standard mean ocean water, respectively.
Analyses are calibrated using the international nitrate isotope standard
IAEA-N3, with a δ15N value of 4.7 ‰ and a
δ18O value of 25.6 ‰ (Böhlke et al., 2003). The
blank contribution was generally lower than 0.2 nmol (as compared to
20 nmol of sample N). Based on replicate measurements of standards and
samples, the analytical precision for δ15N and
δ18O was generally better than ±0.2 ‰ and ±0.3 ‰ (1σ), respectively.
The denitrifier method generates δ15N and δ18O
values of the combined pool of NO3- and NO2-. The
presence of substantial amounts of NO2- in NO3-
samples may lead to errors with regards to the analysis of
δ18O (Wankel et al., 2010). We refrained from including a
nitrite-removal step, because nitrite concentrations in our samples were
always <1 % of the NO3- concentrations. In the following
δ15NNOx and δ18ONOx
are thus referred to as nitrate δ15N and δ18O
(or δ15NNO3 and δ18ONO3).
In the case of atmospheric or aerosol nitrate samples with comparatively high
δ18O values, δ15N values tend to be
overestimated by 1–2 ‰ (Hastings et al., 2003) if the contribution
of 14N14N17O to the N2O mass 45 signal is not
accounted for during isotope ratio analysis. For most natural samples, the
mass-dependent relationship can be approximated as
δ17O ≈ 0.52×δ18O, and the
δ18O can be used for the 17O correction. Atmospheric
NO3- does not follow this relationship but inhabits a
mass-independent component. Thus, we adopted a correction factor of 0.8
instead of 0.52 for the 17O-to-18O linearity (Hastings et
al., 2003).
Calculation of N isotope fractionation value
(εN)
As we described above, the transformation process of NOx to
HNO3 or NO3- involves multiple reaction pathways (see
also Fig. S1 in the Supplement) and is likely to undergo isotope equilibrium
exchange reactions. The measured δ15N–NO3- values
of aerosol samples are thus reflective of the combined N isotope signatures
of various NOx sources
(δ15N–NOx) plus any given N isotope
fractionation. Recently, Walter and Michalski (2015) used a computational
quantum chemistry approach to calculate isotope exchange fractionation
factors for atmospherically relevant NOy molecules;
based on this approach, Zong et al. (2017) estimated the N isotope
fractionation during the transformation of NOx to
pNO3- at a regional background site in China. Here we
adopted, and slightly modify, the approach by Walter and Michalski (2015) and
Zong et al. (2017), and assumed that the net N isotope effect
εN (for equilibrium processes A↔B:
εA↔B=heavy
isotope/light isotopeAheavy isotope/light
isotopeB-1⋅1000‰;
εN refers to εN(NOx↔pNO3-) in this study unless otherwise
specified) during the gas-to-particle conversion from NOx to
pNO3- formation (Δδ15NpNO3--NOx=δ15N–pNO3--δ15N–NOx≈εN) can be considered a hybrid of the isotope effects of
two dominant N isotopic exchange reactions:
εN=γ×εNNOx↔pNO3-OH+1-γ×εNNOx↔pNO3-H2O=γ×εNNOx↔HNO3OH+1-γ×εNNOx↔HNO3H2O,
where γ represents the contribution from isotope fractionation by the
reaction of NOx and photochemically produced OH to form
HNO3 (and pNO3-), as shown by
εNNOx↔HNO3OH(εNNOx↔pNO3-OH). The remainder is formed by the
hydrolysis of N2O5 with aerosol water to generate HNO3
(and pNO3-), namely, εNNOx↔HNO3H2O
(εNNOx↔pNO3-H2O). Assuming that kinetic N isotope
fractionation associated with the reaction between NOx and
OH is negligible, εNNOx↔pNO3-OH can be calculated based on
mass-balance considerations:
εNNOx↔pNO3-OH=εNNOx↔HNO3OH=εNNO2↔HNO3OH=1000×15αNO2/NO-11-fNO21-fNO2+15αNO2/NO×fNO2,
where 15αNO2/NO is the temperature-dependent (see
Eq. 7 and Table S1 in the Supplement) equilibrium N isotope fractionation
factor between NO2 and NO, and fNO2 is the
fraction of NO2 in the total NOx.
fNO2 ranges from 0.2 to 0.95 (Walters and Michalski,
2015). Similarly, assuming a negligible kinetic isotope fractionation
associated with the reaction N2O5 + H2O + aerosol
→ 2HNO3, εNNOx↔pNO3-H2O can be computed
from the following equation:
εNNOx↔pNO3-H2O=εNNOx↔HNO3H2O=εNNOx↔N2O5H2O=1000×15αN2O5/NO2-1,
where 15αN2O5/NO2 is the equilibrium isotope
fractionation factor between N2O5 and NO2, which also
is temperature-dependent (see Eq. 7 and Table S1).
Following Walter and Michalski (2015) and Zhong et al. (2017), γ can
then be approximated based on the O isotope fractionation during the
conversion of NOx to pNO3-:
εONOx↔pNO3-=γ×εONOx↔pNO3-OH+1-γ×εONOx↔pNO3-H2O=γ×εONOx↔HNO3OH+1-γ×εONOx↔HNO3H2O,
where εONOx↔pNO3-OH and εONOx↔pNO3-H2O represent the O isotope effects associated with
pNO3- generation through the reaction of
NOx and OH to form HNO3, and the hydrolysis of
N2O5 on a wetted surface to form HNO3, respectively.
εONOx↔pNO3-OH can be further expressed as
εONOx↔pNO3-OH=εONOx↔HNO3OH=23εONO2↔HNO3OH+13εONO↔HNO3OH=23100018αNO2/NO-11-fNO21-fNO2+18αNO2/NO×fNO2+δ18O-NOx+13δ18O-H2O+100018αOH/H2O-1,
and εONOx↔pNO3-H2O can be determined as follows:
εONOx↔pNO3-H2O=εONOx↔HNO3H2O=56δ18O-N2O5+16δ18O-H2O,
where 18αNO2/NO and 18αOH/H2O
represent the equilibrium O isotope fractionation factors between
NO2 and NO between and OH and H2O, respectively. The range of
δ18O–H2O can be approximated using an estimated
tropospheric water vapor δ18O range of
-25 ‰ to 0 ‰. The δ18O values for
NO2 and N2O5 range from 90 ‰ to
122 ‰ (Zong et al., 2017).
15αNO2/NO, 15αN2O5/NO2, 18αNO2/NO and 18αOH/H2O in
these equations are dependent on the temperature, which can be expressed
as
1000mαX/Y-1=AT4×1010+BT3×108+CT2×106+DT×104,
where A, B, C and D are experimental constants (Table S1 in the
Supplement) over the temperature range of 150–450 K (Walters and Michalski,
2015; Walters et al., 2016; Walters and Michalski, 2016; Zong et al., 2017).
Based on Eqs. (4)–(7) and measured values for δ18O–pNO3- of ambient PM2.5, a Monte Carlo
simulation was performed to generate 10 000 feasible solutions. The error
between predicted and measured δ18O was less than
0.5 ‰. The range (maximum and minimum) of computed contribution
ratios (γ) was then integrated in Eq. (1) to generate an estimate
range for the nitrogen isotope effect εN (using
Eqs. 2–3). δ15N–pNO3- values can be
calculated based on εN and the estimated
δ15N range for atmospheric NOx (see
Sect. 2.4).
Bayesian isotope mixing model
Isotopic mixing models allow estimating the relative contribution of multiple
sources (e.g., emission sources of NOx) within a mixed pool
(e.g., ambient pNO3-). By explicitly considering the
uncertainty associated with the isotopic signatures of any given source, as
well as isotope fractionation during the formation of various components of a
mixture, the application of Bayesian methods to stable isotope mixing models
generates robust probability estimates of source proportions and is often
more appropriate when targeting natural systems than simple linear mixing
models (Chang et al., 2016a). Here the Bayesian model MixSIR (a stable
isotope mixing model using sampling, importance and resampling) was used to
disentangle multiple NOx sources by generating potential
solutions of source apportionment as true probability distributions, which
has been widely applied in a number of fields (e.g., Parnell et al., 2013;
Phillips et al., 2014; Zong et al., 2017). Details on the model frame and
computing methods are given in Sect. S1 in the Supplement.
Here, coal combustion (13.72±4.57 ‰), transportation (-3.71±10.40 ‰), biomass burning (1.04±4.13 ‰) and
biogenic emissions from soils (-33.77±12.16 ‰) were considered
to be the most relevant contributors of NOx (Table S2 and
Sect. S2). The δ15N of atmospheric NOx is
unknown. However, it can be assumed that its range in the atmosphere is
constrained by the δ15N of the NOx sources
and the δ15N of pNO3- after equilibrium
fractionation conditions have been reached. Following Zong et al. (2017),
δ15N–NOx in the atmosphere was determined
by performing iterative model simulations, with a simulation step of 0.01 times
the equilibrium fractionation value based on the
δ15N–NOx values of the emission sources (mean
and standard deviation) and the measured
δ15N–pNO3- of ambient PM2.5
(Fig. S2).
Discussion
Sanjiang campaign: theoretical calculation and field validation of
N isotope fractionation during pNO3- formation
To be used as a quantitative tracer of biomass-combustion-generated aerosols,
levoglucosan must be conserved during its transport from its source, without
partial removal by reactions in the atmosphere (Hennigan et al., 2010). The
mass concentrations of non-sea-salt potassium (nss-K+ =
K+ - 0.0355×Na+) is considered as an
independent/additional indicator of biomass burning (Fig. 2b). The
association of elevated levels of levoglucosan with high nss-K+
concentrations underscores that the two compounds derived from the same
proximate sources and thus that aerosol levoglucosan in Sanjiang was indeed
pristine and represented a reliable source indicator that is unbiased by
altering processes in the atmosphere. Moreover, in our previous work (Cao et
al., 2017), we observed that there was a much greater enhancement of
atmospheric NO3- compared to SO42- (a typical
coal-related pollutant). This additionally points to biomass burning, and not
coal-combustion, as the dominant pNO3- source in the
study area, making SJ an ideal “quasi-single-source” environment for
calibrating the N isotope effect during
pNO3- formation.
Original δ15N values
(δ15Nini) for pNO3-,
calculated values for the N isotope fractionation (εN)
associated with the conversion of gaseous NOx to
pNO3- and corrected δ15N values
(δ15Ncorr; 15Nini minus
εN) of pNO3- for each sample
collected during the Sanjiang sampling campaign. The colored bands represent
the variation range of δ15N values for different
NOx sources based on reports from the literature (Table S2).
See Table S3 for the information regarding sample ID.
Our δ18O–pNO3- values are well within
the broad range of values in previous reports (Zong et al., 2017; Geng et
al., 2017; Walters and Michalski, 2016). However, as depicted in Fig. 3, the
δ15N values of biomass-burning-emitted NO3- fall
within the range of δ15N–NOx values typically
reported for emissions from coal combustion, whereas they are significantly
higher than the well-established values for
δ15N–NOx emitted from the burning of various
types of biomass (mean: 1.04±4.13 ‰; range: -7 to
+12 ‰) (Fibiger and Hastings, 2016). Turekian et al. (1998)
conducted laboratory tests involving the burning of eucalyptus and African
grasses, and determined that the δ15N of
pNO3- (around 23 ‰) was
6.6 ‰ higher than the δ15N of the burned biomass. This
implies significant N isotope partitioning during biomass burning. In the
case of complete biomass combustion, by mass balance, the first gaseous
products (i.e., NOx) have the same δ15N as
the biomass. Hence any discrepancy between the pNO3-
and the δ15N of the biomass can be attributed to the N isotope
fractionation associated with the partial conversion of gaseous
NOx to aerosol NO3-. Based on the computational
quantum chemistry (CQC) module calculations, the N isotope fractionation
εNmeanminmax±1σ determined from the Sanjiang data was 10.9910.3012.54±0.74 ‰. After correcting the primary
δ15N–pNO3- values under the
consideration of εN, the resulting mean
δ15N of 1.17-1.892.98±1.95 ‰ is very
close to the N isotopic signature expected for biomass-burning-emitted
NOx (1.04±4.13 ‰) (Fig. 3) (Fibiger and
Hastings, 2016). The much higher
δ15N–pNO3- values in our study compared
to reported δ15N–NOx values for biomass
burning can easily be reconciled when including N isotope fractionation
during the conversion of NOx to NO3-. Put
another way, given that Sanjiang is an environment where we can essentially
exclude NOx sources other than biomass burning at the time
of sampling, the data nicely validate our CQC-module-based approach to
estimating εN.
Source apportionment of NOx in an urban
setting using a Bayesian isotopic mixing model
Due to its high population density and intensive industrial production, the
Nanjing atmosphere was expected to have high NOx
concentrations derived from road traffic and coal combustion (Zhao et al.,
2015). However, the raw δ15N–pNO3-
values (10.93±3.32 ‰) fell well within the variation range of
coal-emitted δ15N–NOx (Fig. 3). It is
tempting to conclude that coal combustion is the main, or even sole,
pNO3- source (given the equivalent δ15N
values), yet this is very unlikely. The data rather confirm that significant
isotope fractionation occurred during the conversion of NOx
to NO3- and that, without consideration of the N isotope effect,
traffic-related NOx emissions will be markedly
underestimated.
In the atmosphere, the oxygen atoms of NOx rapidly exchanged
with O3 in the “NO–NO2” cycle (see Reactions R1–R3)
(Hastings et al., 2003), and the
δ18O–pNO3- values are determined by its
production pathways (Reactions R4–R7), rather than the sources of
NOx (Hastings et al., 2003). Thus,
δ18O–pNO3- can be used to gain
information on the pathway of conversion of NOx to nitrate
in the atmosphere (Fang et al., 2011). In the computational quantum chemistry
module used here to calculate isotope fractionation, we assumed that
two-thirds of the oxygen atoms in NO3- derive from O3
and one-third from ⚫OH in the ⚫OH generation pathway (Reaction R4) (Hastings et
al., 2003); correspondingly, five-sixths of the oxygen atoms then derived
from O3 and one-sixth from ⚫OH in the
“O3–H2O” pathway (Reactions R5–R7). The assumed range for
δ18O–O3 and δ18O–H2O values
were 90 ‰–122 ‰ and -25 ‰–0 ‰,
respectively (Zong et al., 2017). The partitioning between the two possible
pathways was then assessed through Monte Carlo simulation (Zong et al.,
2017). The estimated range was rather broad, given the wide range of
δ18O–O3 and δ18O–H2O values
used. Nevertheless, the theoretical calculation of the average contribution
ratio (γ) for nitrate formation in Nanjing via the reaction of
NO2 and ⚫OH is consistent with the results from
simulations using the Weather Research and Forecasting model coupled with
Chemistry (WRF-Chem) (Fig. 4; see Sect. S3 for details). A clear diurnal
cycle of the mass concentration of nitrate formed through ⚫OH
oxidation of NO2 can be observed (Fig. S3), with much higher
concentrations between 12:00 and 18:00. This indicates the importance of
photochemically produced ⚫OH during daytime. Yet, throughout
our sampling period in Nanjing, the average pNO3-
formation by the heterogeneous hydrolysis of N2O5
(12.6 µg mm-3) exceeded pNO3- formation
by the reaction of NO2 and ⚫OH
(4.8 µg mm-3), even during daytime, consistent with recent
observations during peak pollution periods in Beijing (Wang et al., 2017).
Given the production rates of N2O5 in the atmosphere are
governed by ambient O3 concentrations, reducing atmospheric
O3 levels appears to be one of the most important measures to
take for mitigating
pNO3- pollution in China's urban atmospheres.
Comparison between the theoretical calculation and WRF-Chem
simulation of the average contribution ratio (γ) for nitrate
formation in Nanjing via the reaction of NO2 and photochemically
produced ⚫OH.
(a) Time-series variation of coal combustion and road
traffic contribution to the mass concentrations of ambient
pNO3- in Nanjing, as estimated through MixSIR; (b) correlation
analysis between the mass concentrations of coal-combustion-related pNO3- and SO2;
(c) correlation analysis between the mass concentrations of
road-traffic-related pNO3- and CO.
In Nanjing, dependent on the time-dependent, dominant
pNO3- formation pathway, the average N isotope
fractionation value calculated using the computational quantum chemistry
module varied between 10.77 ‰ and 19.34 ‰ (15.33 ‰
on average). Using the Bayesian model MixSIR, the contribution of each source
can be estimated, based on the mixed-source isotope data under the
consideration of prior information at the site (see Sect. S1 for detailed
information regarding model frame and computing method). As described above,
theoretically, there are four major sources potentially contributing to
ambient NOx: road traffic, coal
combustion, biomass burning and biogenic soil. As a start, we tentatively integrated all four
sources into MixSIR (data not shown). The relative contribution of biomass
burning to the ambient NOx (median value) ranged from 28 %
to 70 % (average 42 %), representing the most important source. The
primary reason for such apparently high contribution by biomass burning is
that the corrected δ15N–pNO3- values of
-4.29-10.320.42±3.66 ‰ are relatively close to the N
isotopic signature of biomass-burning-emitted NOx (1.04±4.13 ‰) compared to the other possible sources. Based on
δ15N alone, the isotope approach can be ambiguous if there are
more than two sources. The N isotope signature of NOx from
biomass burning falls right in between the spectrum of plausible values, with
highest δ15N for emissions from coal combustion on the one
end and much lower values for automotive and soil emissions on the other,
and will be similar to a mixed signature from coal combustion and
NOx emissions from traffic.
We can make several evidence-based presumptions to better constrain the
emission sources in the mixing model analysis: (1) when sampling at a typical
urban site in a major industrial city in China, we can assume that the
sources of road traffic and coal combustion are dominant, while the
contribution of biogenic soil to ambient NOx should have
minimal impact or can be largely neglected (Zhao et al., 2015); (2) there is
no crop harvest activity in eastern China during the winter season.
Furthermore, deforestation and combustion of fuelwood have been discontinued
in China's major cities (Chang et al., 2016a). Therefore, the contribution of
biomass-burning-emitted NOx during the sampling period
should also be minor. Indeed, Fig. S4 shows that the mass concentration of
biomass-burning-related pNO3- is not correlated with
the fraction of levoglucosan that contributes to OC, confirming a weak impact
of biomass burning on the variation of pNO3-
concentration during our study period.
Estimates of the relative importance of single NOx
sources (mean ±1σ) throughout China based on the original
δ15N–NO3- values extracted from the literature
(εN=0 ‰) and under consideration of
significant N isotope fractionation during NOx
transformation (εN=5 ‰, 10 ‰,
15 ‰ or 20 ‰).
In a second, alternative and more realistic scenario, we excluded biomass
burning and soil as a potential source of NOx in MixSIR (see
above). As illustrated in Fig. 5a, assuming that NOx
emissions in urban Nanjing during our study period originated solely from
road traffic and coal combustion, their relative contribution to the mass
concentration of pNO3- is 12.5±9.1 µg m-3 (or 68±11 %) and 4.9±2.5 µg m-3 (or 32±11 %), respectively. These numbers
agree well with a city-scale NOx emission inventory
established for Nanjing recently (Zhao et al., 2015). Nevertheless, on a
nation-wide level, relatively large uncertainties with regards to the overall
fossil fuel consumption and fuel types propagate into large uncertainties of
NOx concentration estimates and predictions of longer-term
emission trends (Li et al., 2017). Current emission-inventory estimates
(Jaegle et al., 2005; Zhang et al., 2012; Liu et al., 2015; Zhao et al.,
2013) suggest that in 2010 NOx emissions from coal-fired
power plants in China were about 30 % higher than those from
transportation. However, our isotope-based source apportionment of
NOx clearly shows that in 2014 the contribution from road
traffic to NOx emissions, at least in Nanjing (a city that
can be considered representative for most densely populated areas in China),
is twice that of coal combustion. In fact, due to changing economic
activities, emission sources of air pollutants in China are changing rapidly.
For example, over the past several years, China has implemented an extended
portfolio of plans to phase out its old-fashioned and small power plants, and
to raise the standards for reducing industrial pollutant emissions (Chang,
2012). On the other hand, China continuously experienced double-digit annual
growth in terms of auto sales during the 2000s, and in 2009 it became the
world's largest automobile market (X. Liu et al., 2013; Chang et al., 2017,
2016b). Recent satellite-based studies have successfully analyzed the
NOx vertical column concentration ratios for megacities in
eastern China and highlighted the importance of transportation-related
NOx emissions (Reuter et al., 2014; Gu et al., 2014; Duncan
et al., 2016; Jin et al., 2017). Moreover, long-term measurements of the
ratio of NO3- to non-sea-salt SO42- in
precipitation and aerosol jointly revealed a continuously increasing trend in
eastern China throughout the latest decade, suggesting decreasing emissions
from coal combustion (X. Liu et al., 2013; Itahashi et al., 2018). Both coal-combustion-
and road-traffic-related pNO3-
concentrations are highly correlated with their corresponding tracers (i.e.,
SO2 and CO, respectively), confirming the validity of our MixSIR
modeling results. With justified confidence in our Bayesian isotopic model
results, we conclude that previous estimates of NOx
emissions from automotive/transportation sources in China based on bottom-up
emission inventories may be too low.
Previous δ15N–NO3--based
estimates on NOx sources
Stable nitrogen isotope ratios of nitrate have been used to identify nitrogen
sources in various environments in China, often without large differences in
δ15N between rainwater and aerosol NO3- (Kojima
et al., 2011). In previous work, no consideration was given to potential N
isotope fractionation during atmospheric pNO3-
formation. Here, we reevaluated 700 data points of
δ15N–NO3- in aerosol (-0.77±4.52 ‰; n=308) and rainwater (3.79±6.14 ‰; n=392) from 13 sites that are located in the area of mainland China and the
Yellow, East China and South China seas (Fig. 1), extracted from the literature
(see Table S4 for details). To verify the potentially biasing effects of
neglecting N isotope fractionation (i.e., testing the sensitivity of ambient
NOx source contribution estimates to the effect of N isotope
fractionation), the Bayesian isotopic mixing model was applied (a) to the
original NO3- isotope data set and (b) to the corrected nitrate
isotope data set, accounting for the N isotope fractionation during
NOx transformation. All 13 sampling sites are located in
non-urban areas; therefore, apart from coal combustion and on-road traffic,
the contributions of biomass burning and biogenic soil to nitrate need to be
taken into account.
Although most of the sites are located in rural and coastal environments,
when the original data set is used without the consideration of N isotope
fractionation in the Bayesian isotopic mixing model, fossil-fuel-related
NOx emissions (coal combustion and on-road traffic) appear
to be the largest contributor at all the sites (data are not shown). This is
particularly true for coal combustion: everywhere except for the sites of
Dongshan islands and Mt. Lumin, NOx emissions seem to be
dominated by coal combustion. Very high contribution from coal combustion (on
the order of 40 %–60 %) particularly in northern China may be plausible and
can be attributed to a much larger consumption of coal. Yet, rather unlikely,
the highest estimated contribution of coal combustion (83 %) was calculated
for Beihuang Island (a full-year sampling on a costal island that is 65 km
north of Shandong Peninsula and 185 km east of the Beijing–Tianjin–Hebei
region) and not for mainland China. While Beihuang may be an extreme example,
we argue that, collectively, the contribution of coal combustion to ambient
NOx in China as calculated on the basis of isotopic analyses
in previous studies without the consideration of N isotope fractionation
represents overestimates.
As a first step towards a more realistic assessment of the actual
partitioning of NOx sources in China in general (and
coal-combustion-emitted NOx in particular), it is imperative to
determine the location-specific values for εN.
Unfortunately, without δ18O–NO3- data on hand,
or data on meteorological parameters that correspond to the 700
δ15N–NO3- values used in our meta-analysis, it is
not possible to estimate the εN values through the
abovementioned CQC module. As a viable alternative, we adopted the
approximate values for εN as estimated in Sanjiang
(10.99 ‰) and Nanjing (15.33±4.90 ‰). As indicated in
Fig. 6, the estimates of the source partitioning are sensitive to the choice
of εN. Whereas, with increasing
εN, estimates on the relative contribution of on-road
traffic and biomass burning remained relatively stable, estimates for coal
combustion and biogenic soil changed significantly, in opposite directions.
More precisely, depending on εN, the average estimate
of the fractional contribution of coal combustion decreased drastically from
43 % (εN=0 ‰) to 5 %
(εN=20 ‰) (Fig. 6), while the contribution
from biogenic soil to NOx emissions increased in a
complementary way. Given the lack of better constraints on
εN for the 13 sampling sites, it cannot be our goal
here to provide a robust revised estimate on the partitioning of
NOx sources throughout China and its neighboring areas. But
we have very good reasons to assume that disregard of N isotope fractionation
during pNO3- formation in previous isotope-based source
apportionment studies has likely led to overestimates of the relative
contribution of coal combustion to total NOx emissions in
China. For what we would consider the most conservative estimate, i.e., lowest
calculated value for the N isotope fractionation during the transformation of
NOx to pNO3- (εN=5 ‰), the approximate contribution from coal combustion to the
NOx pool would be 28 %, more than 30 % less than
N-isotope-mixing-model-based estimates would yield without consideration of the
N isotope fractionation (i.e., εN=0 ‰)
(Fig. 6).