Predicting tropospheric cloud formation and subsequent nutrient deposition
relies on understanding the sources and processes affecting aerosol
constituents of the atmosphere that are preserved in cloud water. However,
this challenge is difficult to address quantitatively based on the sole use
of bulk chemical properties. Nitrogenous aerosols, mainly ammonium
(NH4+) and nitrate (NO3-), play a particularly important
role in tropospheric cloud formation. While dry and wet (mainly rainfall)
deposition of NH4+ and NO3- are regularly assessed,
cloud water deposition is often underappreciated. Here we collected
cloud water samples at the summit of Mt. Tai (1545 m above sea level) in
eastern China during a long-lasting biomass burning (BB) event and
simultaneously measured for the first time the isotopic compositions (mean
±1σ) of cloud water nitrogen species (δ15N-NH4+=-6.53 ‰ ± 4.96 ‰,
δ15N-NO3-=-2.35 ‰ ± 2.00 ‰, δ18O-NO3-= 57.80 ‰ ± 4.23 ‰), allowing insights into their sources and
potential transformation mechanism within the clouds. Large contributions of
BB to the cloud water NH4+ (32.9 % ± 4.6 %) and
NO3- (28.2 % ± 2.7 %) inventories were confirmed through a
Bayesian isotopic mixing model, coupled with our newly developed
computational quantum chemistry module. Despite an overall reduction in
total anthropogenic NOx emission due to effective emission control
actions and stricter emission standards for vehicles, the observed cloud
δ15N-NO3- values suggest that NOx emissions from
transportation may have exceeded emissions from coal combustion. δ18O-NO3- values imply that the reaction of OH with NO2
is the dominant pathway of NO3- formation (57 % ± 11 %), yet
the contribution of heterogeneous hydrolysis of dinitrogen pentoxide was
almost as important (43 % ± 11 %). Although the limited sample set
used here results in a relatively large uncertainty with regards to the
origin of cloud-associated nitrogen deposition, the high concentrations of
inorganic nitrogen imply that clouds represent an important source of
nitrogen, especially for nitrogen-limited ecosystems in remote areas.
Further simultaneous and long-term sampling of aerosol, rainfall, and
cloud water is vital for understanding the anthropogenic influence on
nitrogen deposition in the study region.
Introduction
Nitrogenous aerosols, mainly nitrate (NO3-) and ammonium
(NH4+), formed from the emissions of nitrogen oxides (NOx= NO +NO2) and ammonia (NH3) and are major chemical components of
aerosols, which serve as cloud condensation nuclei (CCN) and thus play an
important role during cloud formation in the troposphere (Gioda et al.,
2011; van Pinxteren et al., 2016). Cloud water nitrogenous
compounds also represent a vital contributor to nitrogen (N) budgets of
terrestrial (Li et al., 2016b; Liu et al., 2013; Weathers and Likens, 1997;
Vega et al., 2019) and marine ecosystems (Kim et al., 2014; Okin et al.,
2011). However, the sources and formation processes of cloud water N species
are only poorly understood.
NOx can be emitted from both anthropogenic and natural sources.
Globally, over 50 % of the NOx emissions derive from combustion of
fossil fuels (∼25 Tg N yr-1; Jaegle et al., 2005; Richter
et al., 2005; Duncan et al., 2016) with the remainder being primarily
soil-related emissions (∼9 Tg N yr-1; Lamsal et al.,
2011; Price et al., 1997; Yienger and Levy, 1995; Miyazaki et al., 2017) or
deriving from biomass burning (∼6 Tg N yr-1) and
lightning (2–6 Tg N yr-1) (Anenberg et al., 2017; Levy et al., 1996).
The atmospheric sinks of NOx include the production of
HNO3(g) and the formation of aerosol NO3- (Seinfeld
and Pandis, 2012), the partitioning of which can vary with time (Morino et
al., 2006). As for NH3, over 90 % of the NH3 emissions in
terrestrial ecosystems originate from agricultural production, such as
livestock breeding and NH3-based fertilizer application (Paulot and Jacobs,
2014; Kang et al., 2016; Reis et al., 2009; Bouwman et al., 1997; Heald et
al., 2012; Zhang et al., 2018; Balasubramanian et al., 2015; Huang et al.,
2011). In the urban atmosphere, recent studies suggest that nonagricultural
activities like wastewater discharge (Zhang et al., 2017), coal burning (Li
et al., 2016a), solid waste (Reche et al., 2012), on-road traffic
(Suarez-Bertoa et al., 2014), and green spaces (Teng et al., 2017) also
contribute to NH3 emissions. In reactions with H2SO4 and
HNO3, NH3 contributes to the formation of NH4+ salts,
which typically make up from 20 % to 80 % of fine particles (PM2.5) in
the atmosphere (Seinfeld and Pandis, 2012).
Biomass burning (BB) is an important source of N in the atmosphere (Lobert
et al., 1990; Souri et al., 2017). During the harvest or hot season of eastern
China, agricultural BB frequently occurs and modifies the concentration and
composition of aerosols in the atmosphere (Chen et al., 2017; Zhang and Cao,
2015). For example, about 50 % of the N derived from biomass combustion
can be released as NH3 and NOx to form particulate NH4+
and NO3-, which then account for over 80 % of total nitrogenous
species in BB smoke particles (Crutzen and Andreae, 1990). BB-induced
aerosols have not only been associated with poor air quality and the
detrimental effects on human health but have also shown to exert manifold
effects on tropospheric clouds, altering regional or even global radiation
budgets (Chen et al., 2014; Norris et al., 2016; Voigt and Shaw, 2015).
The optical and chemical properties of clouds (and thus their radiative
forcing) are directly related to the aerosol and precipitation chemistry
(Seinfeld et al., 2016). Moreover, clouds represent reactors of multiphase
chemistry, contributing to many chemical transformations that would
otherwise not take place or would proceed at much slower rates (Herrmann et
al., 2015; Lance et al., 2017; Ravishankara, 1997; Schurman et al., 2018;
Slade et al., 2017). Understanding the sources and fate of nitrogenous
species in BB-influenced clouds is particularly important to comprehensively
assess the environmental impacts of BB. But this challenge is difficult to
address based on the sole use of bulk chemical properties (as most often
done in previous studies).
Given that 15N can be preserved between the sources and sinks of
NOx and NH3, the N isotopic composition of NO3-
(δ15N-NO3-) and NH4+ (δ15N-NH4+) can be related to different sources of NOx
and NH3, and thus delivers useful information regarding the
partitioning of the origins of atmospheric cloud water NOx and NH3,
respectively (Hastings et al., 2013; Michalski et al., 2005; Morin et al.,
2008; Chang et al., 2018). This is different for the O isotopes.
NO3- production involves the oxidation of NO. The first step in
the overall process is the conversion of NO into NO2, e.g., through the
oxidation by either ozone (O3) or peroxy radicals (Michalski et al.,
2011). Significant 18O enrichments and excess 17O (i.e., clear
evidence for mass-independent fractionation) are observed in atmospheric
NO3- collected across the globe (e.g., Michalski et al., 2005;
Hastings et al., 2003). Such diagnostic isotope signatures, as well as their
variability in space and time, have been linked to the extent of O3
oxidation (Michalski et al., 2011). Put another way, the oxygen isotope
composition of NO3- (δ18O-NO3-) is largely
determined by chemical reactions rather than the source, and it is
primarily modulated by the O-atom exchange (Michalski et al., 2011) in the
atmosphere. Therefore, δ18O-NO3- has the potential to
indicate the relative importance of various NO3- formation
pathways (i.e., oxidation pathways during conversion of nitrogen oxides to
NO3) (Alexander et al., 2009; Elliott et al., 2009).
The O isotope fractionation during the conversion of NOx to
HNO3/NO3- (εONOx↔HNO3/εONOx↔NO3-) involves two oxidation pathways (Hastings et
al., 2003)
εONOx↔NO3-=εONOx↔HNO3=γ×εONOx↔NO3-OH+1-γ×εONOx↔pNO3-H2O=γ×εONOx↔HNO3OH+1-γ×εONOx↔HNO3H2O,
where γ/(1-γ) represents the contribution ratio of the
isotope fractionation associated with the formation of
HNO3/NO3- through the “OH+NO2” pathway
(εONOx↔NO3-OH) and the
hydrolysis of dinitrogen pentoxide (N2O5) (εONOx↔NO3-H2O),
respectively. The δ18O value of HNO3 produced by the
former process reflects the O atom partitioning of two-thirds O3 and one-third OH (Chang et al., 2018):
εONOx↔NO3-OH=εONOx↔HNO3OH=23εONO2↔HNO3OH+13εONO↔HNO3OH=23100018αNO2/NO-11-fNO21-fNO2+18αNO2/NO×fNO2+δ18O-NOx+13δ18O-H2O+100018αOH/H2O-1.
As for the δ18O value of HNO3 formed during hydrolysis of
N2O5, five-sixths of the O atoms are derived from O3 and one-sixth from OH
(Hastings et al., 2003):
εONOx↔NO3-H2O=εONOx↔HNO3H2O=56δ18O-N2O5+16δ18O-H2O,
where fNO2 refers to the fraction of NO2 in the
total NOx pool. Values for fNO2 vary between
0.2 and 0.95 (Walters and Michalski, 2015). δ18O-X is the O
isotopic composition of X. The range of δ18O-H2O can be
approximated using an estimated tropospheric water vapor δ18O
range of -25 ‰ to 0 ‰ (Zong et al.,
2017). The δ18O of NO2 and N2O5 varies between
90 ‰ and 122 ‰ (Zong et al., 2017). 18αNO2/NO and 18αOH/H2O represent the equilibrium O isotope
fractionation factors between NO2 and NO and OH and H2O,
respectively, which is temperature dependent:
1000mαX/Y-1=AT4×1010+BT3×108+CT2×106+DT×104,
where A, B, C, and D are experimental constants over the temperature range
of 150–450 K. Based on Eqs. (1)–(4) and measured values for δ18O-NO3- of cloud water, a Monte Carlo simulation was
performed to generate 10 000 feasible solutions. The error between predicted
and measured δ18O was less than 0.5 ‰.
At the end of July 2015, a large-scale BB event occurred over eastern and
northern China. We took advantage of this special event to collect
cloud water samples at a high-altitude mountaintop site in the North China
Plain and to calibrate the isotopic signatures that BB events leave in the
N pool of clouds. Integrating cloud water nitrogenous-species isotopic data
(δ15N-NH4+, δ15N-NO3-, and
δ18O-NO3-) into a Bayesian isotopic mixing model
coupled with a newly developed computational quantum chemistry module (Chang
et al., 2018), and using an isotopic mass balance approach, the sources and
production pathways of inorganic nitrogen in cloud water were quantified.
Although numerous studies have been conducted that involved the chemical
characterization of fog water or cloud water, to our knowledge there are no
reports on the N (and O) isotopic composition of both NO3-andNH4+ in cloud water.
Materials and methodsCloud water sample collection
Mt. Tai (36∘18′ N, 117∘13′ E; 1545 m above sea level) is a
world-recognized geopark of key natural, historical, and cultural
significance, located in the eastern North China Plain (Fig. 1). It belongs
to China's most important agricultural and industrial production areas, and
the composition of the atmosphere near the mountain can be considered
representative with regards to the quality and levels of atmospheric
pollution in the region (Li et al., 2017; Liu et al., 2018). Given the
opportunistic nature of this study, cloud water sampling commenced at the
summit of Mt. Tai 3 d after the fire began (8 January 2015 19:12 LT (local time) to
8 March 2015 06:12 LT; Table 1). In total, six cloud water samples were collected
during the long-lasting cloud event using a single-stage Caltech active
strand cloud water collector (CASCC), as described by (Demoz et al., 1996).
The cloud collector was cleaned prior to each sampling using high-purity
deionized water. After sampling, cloud samples were filtered immediately
using disposable syringe filters (0.45 µm) to remove any suspended
particulate matter and then stored in a freezer at -80∘C until
further analysis. More details on the monitoring site and sampling
procedures can be found elsewhere (Li et al., 2017).
(a) Location of Mt. Tai (triangle) and
twenty-four 48 h back trajectories (black lines) of air masses simulated by
the HYSPLIT model (http://ready.arl.noaa.gov/HYSPLIT.php, last access: 10 August 2019) based on the
Global Data Assimilation System (GDAS) meteorological data set arriving at
Mt. Tai on 1 August 2015 (04:00 UTC) at an altitude 1500 m a.s.l (close to
the altitude of our sampling site). The base map of land use in China was
modified from Chang et al. (2018). The red dots represent the positions of
wildfires between 29 and 31 July 2015, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) (http://modis-fire.umd.edu, last access: 10 August 2019). (b) Field
photos of cloud-shrouded Mt. Tai and the cloud water collector.
Chemical and isotopic analysis
Inorganic ions (including SO42-, NO3-, Cl-,
NH4+, K+, Ca2+, Mg2+, and Na+), as well as
levoglucosan (a specific tracer of biomass burning), in cloud water samples
were analyzed using a Dionex™ ICS-5000+ system (Thermo Fisher
Scientific, Sunnyvale, USA). The IC (ion chromatograph) system was equipped with an automated
sampler (AS-DV). Cloud water samples were measured using an IonPac CG12A
guard column and a CS12A separation column with an aqueous methanesulfonic
acid (MSA, 30 mM L-1) eluent at a flow rate of 1 mL min-1.
Detailed information regarding sample processing, pretreatment, chemical
analyses, and analytical protocol adaption can be found elsewhere (Cao et
al., 2016, 2017). The detection limits for Na+, NH4+,
K+, Mg2+, Ca2+, Cl-, NO2-, NO3-,
SO42-, and levoglucosan are 0.06, 0.03, 0.12, 0.08, 0.13, 0.64,
1.11, 2.67, 1.41, and 1.29 ppb (parts per billion by weight in solution),
respectively. The analytical errors from duplicate analyses were within 5 %.
Analysis of the isotopic compositions of NH4+ (δ15N-NH4+) and NO3- (δ15N-NO3- and δ18O-NO3-) was based on
the isotopic analysis of nitrous oxides (N2O) after chemical conversion
of the respective target compound. More precisely, dissolved NH4+
in cloud water samples was oxidized to NO2- by alkaline hypobromite
(BrO-) and then reduced to N2O by hydroxylamine hydrochloride
(NH2OH⋅ HCl) (Liu et al., 2014). NO3- was initially
transformed to NO2- by cadmium and then further reduced to
N2O by sodium azide (NaN3) in an acetic acid buffer (McIlvin and
Altabet, 2005; Tu et al., 2016). The produced N2O was analyzed using a
purge and cryogenic trap system (Gilson GX-271, Isoprime Ltd., Cheadle
Hulme, UK), coupled to an isotope ratio mass spectrometer (PT-IRMS)
(Isoprime 100, Isoprime Ltd., Cheadle Hulme, UK). In order to correct for
any machine drift and procedural blank contribution, international
NH4+ (IAEA N1, USGS 25, and USGS 26) and NO3- (IAEA N3,
USGS 32, and USGS 34) standards were processed in the same way as samples.
Standard regressions were made based on the known isotopic values of
international standards and the measured standard δ15N values.
For the NO3-derived NO2- analysis, the slope of the plot of the sample versus the standard δ15N
(0.49) was very close to the expected slope (0.5), which can be predicted
based on the fact that half of the N atoms were derived from the azide
(McIlvin and Altabet, 2005). The r2 of the regression line was 0.999.
The analytical precision for both multiple N and O isotopic analyses was
better than 0.3 ‰ (n=5).
Bayesian mixing model analysis
By taking the uncertainty associated with the N isotopic signatures of
multiple sources and associated isotope fractionation during
(trans-)formations into account, the Bayesian method is more appropriate
than simple linear mixing modeling to yield estimates on the source
partitioning of a mixture like air pollutants (Chang et al., 2016, 2018). The relative contribution of each source in the Bayesian theorem is
expressed as
Pfqdata=θdatafq×Pfq/∑θdatafq×Pfq,
where θ (data|fq) and P(fq) represent the
likelihood of the given mixed isotope signature and the predetermined
probability of the given state of nature, based on prior information,
respectively. The denominator represents the numerical approximation of the
marginal probability of the data. Here the Bayesian mixing model MixSIR
(stable isotope mixing models using the sampling-importance-resampling method) was used
to disentangle the various potential NH3 and NOx sources
contributing to the cloud water NH4+ and NO3- pools,
respectively, by forming the true probability distributions through
generating 10 000 solutions of source apportionment. Details on the model
approach can be found in Appendix A.
The measured δ15N-NO3- values of cloud water samples
depend on the δ15N signatures of the original NOx sources
(δ15N-NOx), the N isotope fractionation between nitrogen
oxides (i.e., NO and NO2; Walters et al., 2016), and the N isotope
enrichment factor (εN) associated with the kinetic
transformation of NOx to HNO3 (Walters and Michalski, 2015).
εN is considered a hybrid of two dominant processes: one
is the reaction of NO2 and OH radicals to form NO3- and the
other is the heterogeneous hydrolysis of dinitrogen pentoxide
(N2O5) with water to form NO3-. We recently developed a
quantum chemistry computation module to quantify the N fractionation during
nitrate formation, which had been validated by field measurements (Chang et
al., 2018). Here this module was adopted to calculate the N isotope
fractionation during NO3- formation, and in turn to correct the
raw δ15N-NO3- values of cloud water samples.
While the N isotopic source signatures of NOx are relatively well
constrained (Table B1 in Appendix B), this is not the case for NH3. We
recently established a pool of isotopic source signatures of NH3 in
eastern China, from which livestock breeding and fertilizer application were
identified to produce NH3 with a δ15N of -29.1 ‰ ± 1.7 ‰ and -50.0 ‰ ± 1.8 ‰,
respectively (Chang et al., 2016). Although fossil fuel combustion, urban
waste, and natural soils also represent potential sources of NH3, their
impacts are probably minor compared to those of agricultural and biomass
burning emissions, at least on a regional (or greater) scale (Kang et al.,
2016). For the N isotope signature of biomass-burning-derived NH3, we
assumed 12 ‰ (Kawashima and Kurahashi, 2011), a value
that has also been applied in other recent isotope-based source
apportionment studies (e.g., Chellman et al., 2016; Wang et al., 2017a).
Results and discussionChemical characterization of biomass-burning-influenced clouds
The Moderate Resolution Imaging Spectroradiometer (MODIS) wildfire map (Fig. 1) shows that there were intensive biomass burning events occurring over
mainland China, end of July 2015, just before the study period. Moreover,
analysis of the back trajectories of air masses at the study site revealed
the strong influence of atmospheric transport from regions that also
experienced intensive biomass burning events shortly before the sampling
campaign. It can thus be assumed that large amounts of BB-related pollutants
were transported from the southwest to the sampling site at Mt. Tai. Table 1
compiles sample information and results from the chemical and isotopic
analysis of cloud water samples in this study. The concentrations of
NO3- and NH4+ ranged from 4.9 to 19.9 mg L-1 (10.1 mg L-1 on average) and from 4.9 to 18.0 mg L-1 (9.1 mg L-1
on average), respectively, much higher than during non-BB seasons (Chen et
al., 2017; Desyaterik et al., 2013; Li et al., 2017, 2018; Lin et al.,
2017). Similarly, levoglucosan in our cloud water samples varied between 12.1
and 35.1 µg L-1 (19.9 µg L-1 on average), and
concentrations were thus 1 order of magnitude higher than those documented
during non-BB seasons (Boone et al., 2015; Fomba et al., 2015). Although
levoglucosan can be oxidized by OH radicals in the tropospheric aqueous
phase (Sang et al., 2016), it is nevertheless a reliable marker compound for
BB due to its high-emission factors and relatively high concentrations in
the ambient aerosols (Hoffmann et al., 2010). In our study, the
concentrations of NO3- (r2=0.55) and NH4+
(r2=0.66) are strongly correlated with that of levoglucosan,
suggesting that the pronounced increase in NO3- and NH4+
levels observed here can at least be partly attributed to BB activities
during the study period. Globally, BB accounts for around 10 % of NH3
and NOx emissions (Benkovitz et al., 1996; Bouwman et al., 1997;
Olivier et al., 1998; Schlesinger and Hartley, 1992).
Sampling details and results of chemical and isotopic
analysis for collected cloud water samples.
The N (and O) isotopic composition of cloud water nitrogenous species was
more (NH4+) or less (NO3-) variable (Table 1), with the
average δ15N values of 6.53 ‰ and
-2.35 ‰ for NH4+ and NO3-,
respectively. The average δ18O-NO3- value was
57.80 ‰. These values are generally different from gas,
rainwater, and aerosol values measured worldwide (Fig. 2). Various
atmospheric processes can influence the isotopic composition of atmospheric
nitrogenous species including the original emission source of NOx,
seasonality of oxidation pathways, isotope fractionation during transport,
partitioning between wet and dry components, and spatial gradients in
atmospheric chemistry (Elliott et al., 2007; Hastings et al., 2003). These
aspects may affect the δ15N and δ18O values
differentially. For example, the δ15N of atmospheric
NO3- retains spatial changes in the original NOx signature
quite well, in contrast to the δ18O. On the other hand, the
δ18O most strongly depends on the oxidation chemistry and
formation pathway in the atmosphere (see Eqs. 1–4).
At present, there are no other reports on the isotope ratios of both
NO3- and NH4+ in cloud water, and a comparison is
possible only with isotopic data from precipitation and aerosol N. Recently,
Vega et al. (2019) reported the δ15N (-8 ‰ ± 2 ‰) and δ18O
(71 ‰ ± 3 ‰) values of
NO3- in fog water at a forest site in Sweden. The relatively high
δ15N values in our study (-2.35 ‰) suggest
more NOx was emitted from combustion processes. In contrast, the much
higher δ18O values in Vega et al. (2019) indicate a much
greater contribution from O3 in sub-Arctic environments. In Fig. 2a, N
isotopic differences for NOx sources are greater
(35 ‰) than for δ18O-NOx. In fact,
the oxygen isotope signature of NOx is mainly driven by chemistry rather
than determined by the source (see discussion below), and thus δ18O measurements cannot be used to address the uncertainty in the
NOx sources that may remain when just looking at δ15N
values alone. As shown in Fig. 2b, the δ15N values of aerosol
NH4+ are systematically higher than that of NH3. Significant
εN during the conversion of gas to aerosol (up to
33 ‰) has been proposed to alter the δ15N
values during the transformation of the source (NH3) to the sink
(particulate NH4+). Indeed, our compilation of previous results
(Fig. 2b) reveals that particulate NH4+ (particularly in the
coarse aerosol fraction) is more enriched in 15N than NH3 (by
>23 ‰ on average), as well as NH4+
in precipitation (by 18 ‰ on average). This can most
likely be attributed to the preferential absorption of 14N-NH3 associated with washout processes during precipitation (Zheng et al., 2018).
We are aware of the fact that our sample and data used here are limited,
resulting in a relatively large uncertainty with regards to the N
isotope-based source apportionment. However, all δ15N-NH4+ values in cloud water samples fall within the
observed range of δ15N-NH4+ values for fine particles
(PM2.5), providing putative evidence that NH4+ in cloud water
is primarily derived from particulate NH4+ rather than NH3
absorption.
(a) Observed range of typical δ18O
and δ15N values of NO3- and NOx for different
sources (adapted from Fenech et al., 2012). BB and CC represent biomass
burning and coal combustion, respectively. The light purple squares represent nitrate
isotope data in cloud water (this study). (b) The 25th
percentiles, median, and 75th percentiles for the δ15N
values of the ambient NH3 (Chang et al., 2016; Felix et al., 2013;
Savard et al., 2018; Smirnoff et al., 2012) and NH4+ in
precipitation (Fang et al., 2011; Leng et al., 2018; Yang et al., 2014;
Zhang et al., 2008), cloud water (this study), PM2.5 (particulate matter
with aerodynamic diameter less than 2.5 µm; Lin et al., 2016; Park et
al., 2018; Proemse et al., 2012; Smirnoff et al., 2012), and TSPs (total suspended particles with aerodynamic diameter less than 100 µm (Kundu
et al., 2010; Savard et al., 2018; Yeatman et al., 2001) are shown.
Isotope-based assessment of the sources and formation of
nitrogenous species in clouds
Using the MixSIR model, the relative contribution of four NH3 sources
to NH4+ can be calculated, based on the isotope data of ambient
δ15N-NH4+ and considering the N fractionation and
prior information on the site. As upper limit for the N isotope enrichment
factor associated with the conversion of NH3 to NH4+
(εNH4+-NH3), we assumed
33 ‰ when using MixSIR but also considered lower values
for εNH4+-NH3 (Fig. 3a)
(given the conflicting evidence with regards to εNH4+-NH3; e.g., Deng et al., 2018; Li et al.,
2012). Dependent of the choice for εNH4+-NH3 (between 0 ‰ and
33 ‰ proposed by Heaton et al., 1997) the relative
contribution of biomass burning, fertilizer application, and livestock
breeding to NH4+ in cloud water ranges from 25.9 % to 85.4 %,
5.9 % to 37.0 %, and 8.7 % to 85.4 %, respectively. Irrespective of
the uncertainty related to εNH4+-NH3, the measurement of levoglucosan provides compelling
evidence that biomass burning represents an important NH3 source,
independently validating our isotope approach. Our sampling site is located
in the North China Plain, also known as the granary of China. Although
nonagricultural NH3 emissions like on-road traffic are important in
the urban atmosphere (Chang et al., 2016), their contribution must be
considered insignificant with respect to fertilizer application and
livestock breeding in this region (Kang et al., 2016). Besides, coal-based
heating in China is suspended during summertime, and coal combustion has
been demonstrated to be a minor contributor of total NH3 emissions (Li et al., 2016a). Hence the partitioning between the three main NH3
sources appears plausible. Moreover, existing emission inventory data
confirm that the ratio of NH3 emissions in the North China Plain from
livestock breeding (1658 kt) and fertilizer application (1413 kt) was 1.17
(Zhang et al., 2010), which is very close to our estimate (between 0.98 and
1.14) when εNH4+-NH3≥
25 ‰. For a εNH4+-NH3 range that we consider most plausible (i.e., between
25 ‰ and 33 ‰), the relative
cloud water NH4+ source partitioning between biomass burning,
fertilizer application, and livestock is 32.9 % ± 4.6 %, 32.9 % ± 3.0 %, and 34.2 % ± 1.6 %, respectively (indicated as red square in
Fig. 3a). It is important to note that a large uncertainty existed with regards to the assessment of biomass burning, which can partly be attributed to the lack of
localized isotopic source signatures in China. In addition, the isotopic
fractionation from the conversion of NH3 to NH4+ was
approximated in this study, and it was not possible to incorporate all of the
possible equilibrium and kinetic fractionation scenarios.
(a) Source partitioning estimates for
NH4+ in cloud water as a function of εNH4+-NH3. The red square highlights the
best-guess estimates based on εNH4+-NH3≥25 ‰. (b) Whisker
plot of the N fractionation for the conversation of NOx to
NO3- (εNH4+-NH3)
calculated by the computational quantum chemistry (CQC) module. The upper
line, dot, and bottom line indicate the 25th percentile, median, and
75th percentile, respectively. Refer to Table 1 for sample ID.
(c) Overall contribution of various NOx sources to
NO3- in cloud water as estimated by the MixSIR model. (d) Overall contribution of the two dominant pathways to NO3-
formation in cloud water, as estimated by the MixSIR model.
The computational quantum chemistry (CQC) module in MixSIR has proven to be a
robust tool to quantify the N isotope enrichment factor during
NOx-NO3- conversion (εNO3--NOx) (e.g., Zong et al., 2017; Chang et al., 2018).
Cloud water sample-based data from this study reveal that εNO3--NOx values fall into a small range
(5.21 ‰ to 5.98 ‰) (Fig. 3b),
suggesting robust isotope effects during the N isotopic exchange reactions.
Knowing εNO3--NOx, the
overall contribution of various NOx sources to NO3- in
cloud water can be estimated (Fig. 3c). As was expected, biomass burning was
the largest contributor (28.2 % ± 2.7 %), followed by on-road traffic
(27.1 % ± 2.2 %), coal combustion (26.8 % ± 3.4 %), and biogenic
soil (17.9 % ± 3.9 %). The fundamental importance of biomass-burning-emitted NOx to NO3- in cloud water is supported by the
observed correlation between the concentrations of levoglucosan and biomass-burning-derived NO3- (r2=0.66). The average contribution
ratio of coal combustion and on-road transportation to NOx emissions in
our study (0.99) is slightly lower than that calculated from regional
emission inventories (9.0 Tg/7.4 Tg = 1.22) (Zhao et al., 2013). The
apparent difference is likely real and reflects the fact that NOx
emissions by anthropogenic activities changed significantly since 2010: a
17 % total emission decrease between 2010 and 2017 can primarily be
attributed to upgraded emission standards and new “ultra-low emission”
techniques in the coal-fired power plant sector, and given that NOx emitted from traffic likely increased as a consequence of the continuous expansion of
the auto trade market during the last decade (Chang et al., 2018). In turn, our
source partitioning estimate probably reflects the most updated status of
NOx emissions in China, where transportation-related NOx emissions
have reached levels that are comparable to NOx emissions by coal
combustion. In this regard, our study demonstrates that Bayesian-based
isotopic mixing modeling can be an effective and timely approach to track
rapid emission changes in NOx in a fast-developing country like China.
Using the measured δ18O and Eqs. (1)–(4) (and the assumptions
above), we can calculate γ and the relative importance of the two
oxidation pathways of NO3- formation (Fig. 3d). On average, 57 %
NO3- formation can be attributed to the “NO2+ OH”
pathway and 43 % to the “N2O5+H2O” pathway. In the
low-latitude regions, where atmospheric OH concentrations are highest,
particulate NO3- production via the NO2+ OH pathway
predominates (up to 87 %) (Alexander et al., 2009). Sampling during
summertime, oxidation of NO2 through OH was expected to be the dominant
pathway of nitrate formation, in accordance with observations from the
subtropics (Hastings et al., 2003). However, our results highlight that
N2O5 hydrolysis can be an almost equally important process as the
oxidization of NO2 with OH with regards to the NO3- formation
in cloud water (Wang et al., 2017b).
Conclusions
In this study, we measured the isotopic composition of nitrogenous species
in cloud water at the summit of Mt. Tai during a long-lasting biomass burning
event in order to investigate the sources and processes involved in
cloud water NO3-/NH4+ formation, and in turn to test our
isotope-balance approach to constrain N source partitioning in cloud water.
Using a Bayesian isotope mixing model, the δ15N-based estimates
confirm that at least transiently biomass-burning-related NH3 and
NOx emissions are a major source of cloud water N. Moreover, our data are
in accordance with regional emission inventories for both NH3 and
NOx, validating the Bayesian isotope mixing model approach. Based on
cloud water nitrate δ18O measurements, the reaction of NO2
with OH turned out to be the dominant pathway to form cloud nitrate, yet the
contribution from the heterogeneous hydrolysis of N2O5 to
NO3- is almost equally important. Our study underscores the value
of cloud-water-dissolved inorganic nitrogen isotopes as carriers of
quantitative information on regional NOx emissions. It sheds light on
the origin and production pathways of nitrogenous species in clouds and
emphasizes the importance of BB-derived nitrogenous species as cloud
condensation nuclei in China's troposphere. Moreover, it highlights the
rapid evolution of NOx emissions in China. Despite an overall reduction
in total anthropogenic NOx emissions due to effective emission control
actions and stricter emission standards for vehicles, the relative
contribution of transportation to total NOx emissions has increased
over the last decade and may already have exceeded emissions from the power
sector.
Bayesian isotopic mixing model
The Bayesian mixing model makes use of stable isotope data to determine the
probability distribution of source contributions to a mixture, explicitly
accounting for uncertainties associated with multiple sources, their
isotopic signatures, and isotope fractionation during transformations. The
model has been widely used in ecological studies, such as food-web analyses.
In the Bayesian theorem, the contribution of each source is calculated based on
mixed data and prior information, such that
P(fq|data)=θ(data|fq)×p(fq)/∑θ(data|fq)×p(fq),
where θ(data|fq) and p(fq) refer to the likelihood
of the given mixed isotope signature and the predetermined probability of
the given state of nature, based on prior information, respectively. The
denominator represents the numerical approximation of the marginal
probability of the data. In a Bayesian model (stable isotope in R, SIAR),
isotope signatures from the mixed data pool are assumed to be normally
distributed. Uncertainty in the distribution of isotope sources and
associated isotope fractionation during transformations are factored into
the model by defining respective mean (μ) and standard deviation
(σ) parameters. Prior knowledge about proportional source
contributions (fq) is parameterized using the Dirichlet distribution,
with an interval of [0, 1]. To assess the likelihood of the given fq,
the proposed proportional contribution is combined with a user-specified
isotope distribution of sources and their associated isotope effects to
develop a proposed isotope distribution for the mixture. The probability of
fractional source contributions (fq) is calculated by the Hilborn
sampling-importance-resampling method.
Isotopic signatures of NOx emitted from various sources
Typical δ15N-NOx values for coal combustion,
transportation, biomass burning, and soils based on literature values.
Source typesMean (‰)Standard (‰)NumberReferenceCoal combustion13.724.5747Felix et al. (2012, 2015)Transportation-7.257.80151Walter et al. (2015a, b),Heaton et al. (1997)Biomass burning1.044.1324Fibiger and Hastings (2016),Felix and Elliott (2013)Biogenic soil-33.7712.166Hastings et al. (2009),Felix et al. (2012)Data availability
All data used to support the conclusion are presented in this paper.
Additional data are available upon request; please contact the corresponding
authors (Yanlin Zhang (dryanlinzhang@outlook.com) and Jianmin Chen
(jmchen@fudan.edu.cn)).
Author contributions
YZ and JC designed the study. YC and YL conceived the study. YC, JL, LS, XZ, WZ, TH and CZ carried out the field experiments and laboratory analysis. YC wrote the paper with ML and YZ. All the authors contributed to the interpretation of the results obtained with the new approach here described and revised the paper content, giving final approval of the version to be submitted. YC and ML addressed the reviewers' comments.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Financial support
This study was supported by the National Key R&D Program of China (grant
no. 2017YFC0212704), the National Natural Science Foundation of
China (grant nos. 41975166, 41705100, 91644103, 41761144056, 91644103), the Provincial Natural
Science Foundation of Jiangsu (grant nos. BK20180040, BK20170946),
the University Science Research Project of Jiangsu Province (17KJB170011), the
University of Basel research funds, the Joint Open Project of KLME & CIC-FEMD, NUIST (KLME201909), the opening project of Shanghai Key
Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), and the special fund of the State Key Joint Laboratory of Environment Simulation and Pollution Control (grant no. 19K01ESPCT).
Review statement
This paper was edited by Sergey A. Nizkorodov and reviewed by three anonymous referees.
ReferencesAlexander, B., Hastings, M. G., Allman, D. J., Dachs, J., Thornton, J. A.,
and Kunasek, S. A.: Quantifying atmospheric nitrate formation pathways based
on a global model of the oxygen isotopic composition (Δ17O) of
atmospheric nitrate, Atmos. Chem. Phys., 9, 5043–5056,
10.5194/acp-9-5043-2009, 2009.Anenberg, S. C., Miller, J., Minjares, R., Du, L., Henze, D. K., Lacey, F.,
Malley, C. S., Emberson, L., Franco, V., Klimont, Z., and Heyes, C.: Impacts and
mitigation of excess diesel-related NOx emissions in 11 major vehicle
markets, Nature, 545, 467–471, 2017.
Balasubramanian, S., Koloutsou-Vakakis, S., McFarland, D. M., and Rood, M. J.:
Reconsidering emissions of ammonia from chemical fertilizer usage in Midwest
USA, J. Geophys. Res., 120, 6232–6246, 2015.Benkovitz, C. M., Scholtz M. T., Pacyna J., Tarrasón L., Dignon J.,
Voldner E. C., Spiro P. A., Logan J. A., and Graedel T. E.: Global gridded
inventories of anthropogenic emissions of sulfur and nitrogen, J. Geophys.
Res., 101, 29239–29253, 10.1029/96JD00126, 1996.Boone, E. J., Laskin, A., Laskin, J., Wirth, C., Shepson, P. B., Stirm, B.
H., and Pratt, K. A.: Aqueous processing of atmospheric organic particles in
cloud water collected via aircraft sampling, Environ. Sci. Technol., 49,
8523–8530, 10.1021/acs.est.5b01639, 2015.Bouwman, A., Lee, D., Asman, W., Dentener, F., Van Der Hoek, K., and
Olivier, J.: A global high-resolution emission inventory for ammonia, Global
Biogeochem. Cy., 11, 561–587, 10.1029/97GB02266, 1997.Cao, F., Zhang, S. C., Kawamura, K., and Zhang, Y. L.: Inorganic markers,
carbonaceous components and stable carbon isotope from biomass burning
aerosols in Northeast China, Sci. Total Environ., 572, 1244–1251,
10.1016/j.scitotenv.2015.09.099, 2016, 2016.Cao, F., Zhang, S. C., Kawamura, K., Liu, X., Yang, C., Xu, Z., Fan, M.,
Zhang, W., Bao, M., Chang, Y., Song, W., Liu, S., Lee, X., Li, J., Zhang,
G., and Zhang, Y. L.: Chemical characteristics of dicarboxylic acids and
related organic compounds in PM2.5 during biomass-burning and
non-biomass-burning seasons at a rural site of Northeast China, Environ.
Pollut., 231, 654–662, 10.1016/j.envpol.2017.08.045, 2017.Chang, Y. H., Liu, X., Deng, C., Dore, A. J., and Zhuang, G.: Source
apportionment of atmospheric ammonia before, during, and after the 2014 APEC
summit in Beijing using stable nitrogen isotope signatures, Atmos. Chem.
Phys., 16, 11635–11647, 10.5194/acp-16-11635-2016, 2016.Chang, Y. H., Zhang, Y., Tian, C., Zhang, S., Ma, X., Cao, F., Liu, X.,
Zhang, W., Kuhn, T., and Lehmann, M. F.: Nitrogen isotope fractionation
during gas-to-particle conversion of NOx to NO3- in the
atmosphere - implications for isotope-based NOx source apportionment,
Atmos. Chem. Phys., 18, 11647–11661,
10.5194/acp-18-11647-2018, 2018.Chellman, N. J., Hastings, M. G., and McConnell, J. R.: Increased nitrate and decreased δ15N-NO3− in the Greenland Arctic after 1940 attributed to North American oil burning, The Cryosphere Discuss., 10.5194/tc-2016-163, 2016.Chen, J. M., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M. S., Wang,
S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L.,
Thai, P., Lam, Y. F., Pereira, G., Ding, A., Huang, X., and Dumka, U. C.: A
review of biomass burning: Emissions and impacts on air quality, health and
climate in China, Sci. Total Environ., 579, 1000–1034,
10.1016/j.scitotenv.2016.11.025, 2017.Chen, Y. C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.:
Satellite-based estimate of global aerosol-cloud radiative forcing by marine
warm clouds, Nat. Geosci., 7, 643, 10.1038/ngeo2214, 2014.Crutzen, P. J. and Andreae, M. O.: Biomass burning in the tropics: Impact
on atmospheric chemistry and biogeochemical cycles, Science, 250,
1669–1678, 10.1126/science.250.4988.1669, 1990.Demoz, B. B., Collett, J. L., and Daube, B. C.: On the Caltech active strand
cloudwater collectors, Atmos. Res., 41, 47–62,
10.1016/0169-8095(95)00044-5, 1996.Deng, Y., Li, Y., and Li, L.: Experimental investigation of nitrogen
isotopic effects associated with ammonia degassing at 0–70 ∘C,
Geochim. Cosmochim. Ac., 226, 182–191,
10.1016/j.gca.2018.02.007, 2018.Desyaterik, Y., Sun, Y., Shen, X., Lee, T., Wang, X., Wang, T., and Collett,
J. L.: Speciation of “brown” carbon in cloud water impacted by
agricultural biomass burning in eastern China, J. Geophys. Res., 118,
7389–7399, 10.1002/jgrd.50561, 2013.Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets,
D. G., Hurwitz, M. M., and Pickering, K. E.: A space-based, high-resolution view
of notable changes in urban NOx pollution around the world (2005–2014),
J. Geophys. Res., 121, 976–996, 2016.Elliott, E., Kendall, C., Wankel, S. D., Burns, D., Boyer, E., Harlin, K.,
Bain, D., and Butler, T.: Nitrogen isotopes as indicators of NOx source
contributions to atmospheric nitrate deposition across the Midwestern and
northeastern United States, Environ. Sci. Technol., 41, 7661–7667,
10.1021/es070898t, 2007.Elliott, E. M., Kendall, C., Boyer, E. W., Burns, D. A., Lear, G. G.,
Golden, H. E., Harlin, K., Bytnerowicz, A., Butler, T. J., and Glatz, R.:
Dual nitrate isotopes in dry deposition: Utility for partitioning NOx
source contributions to landscape nitrogen deposition, J. Geophys. Res.,
114, G04020, 10.1029/2008jg000889, 2009.Fang, Y. T., Koba, K., Wang, X. M., Wen, D. Z., Li, J., Takebayashi, Y.,
Liu, X. Y., and Yoh, M.: Anthropogenic imprints on nitrogen and oxygen
isotopic composition of precipitation nitrate in a nitrogen-polluted city in
southern China, Atmos. Chem. Phys., 11, 1313–1325,
10.5194/acp-11-1313-2011, 2011.Felix, J. D. and Elliott, E. M.: The agricultural history of human-nitrogen
interactions as recorded in ice core δ15N-NO3-,
Geophys. Res. Lett., 40, 1642–1646, 10.1002/grl.50209, 2013.Felix, J. D., Elliott, E. M., and Shaw, S. L.: Nitrogen isotopic composition
of coal-fired power plant NOx: Influence of emission controls and
implications for global emission inventories, Environ. Sci. Technol., 46,
3528–3535, 10.1021/es203355v, 2012.Felix, J. D., Elliott, E. M., Gish, T. J., McConnell, L. L., and Shaw, S.
L.: Characterizing the isotopic composition of atmospheric ammonia emission
sources using passive samplers and a combined oxidation-bacterial
denitrifier approach, Rapid Commun. Mass Sp., 27, 2239–2246,
10.1002/rcm.6679, 2013.Felix, J. D., Elliott, E. M., Avery, G. B., Kieber, R. J., Mead, R. N.,
Willey, J. D., and Mullaugh, K. M.: Isotopic composition of nitrate in
sequential Hurricane Irene precipitation samples: Implications for changing
NOx sources, Atmos. Environ., 106, 191–195,
10.1016/j.atmosenv.2015.01.075, 2015.Fenech, C., Rock, L., Nolan, K., Tobin, J., and Morrissey, A.: The potential
for a suite of isotope and chemical markers to differentiate sources of
nitrate contamination: a review, Water Res., 46, 2023–2041,
10.1016/j.watres.2012.01.044, 2012.Fibiger, D. L. and Hastings, M. G.: First measurements of the nitrogen
isotopic composition of NOx from biomass burning, Environ. Sci.
Technol., 50, 11569–11574, 10.1021/acs.est.6b03510, 2016.Fomba, K. W., van Pinxteren, D., Müller, K., Iinuma, Y., Lee, T., Collett Jr., J. L., and Herrmann, H.: Trace metal characterization of aerosol particles and cloud water during HCCT 2010, Atmos. Chem. Phys., 15, 8751–8765, 10.5194/acp-15-8751-2015, 2015.Gioda, A., Reyes-Rodríguez, G. J., Santos-Figueroa, G., Collett, J. L.,
Decesari, S., Ramos, M. D. C. K. V., Bezerra Netto, H. J. C., de Aquino
Neto, F. R., and Mayol-Bracero, O. L.: Speciation of water-soluble
inorganic, organic, and total nitrogen in a background marine environment:
Cloud water, rainwater, and aerosol particles, J. Geophys. Res., 116, D05203,
10.1029/2010JD015010, 2011.Hastings, M., Sigman, D. M., and Lipschultz, F.: Isotopic evidence for
source changes of nitrate in rain at Bermuda, J. Geophys. Res., 108, 4790,
10.1029/2003JD003789, 2003.Hastings, M., Jarvis, J., and Steig, E.: Anthropogenic impacts on nitrogen
isotopes of ice-core nitrate, Science, 324, 1288,
10.1126/science.1170510, 2009.Hastings, M., Casciotti, K. L., and Elliott, E. M.: Stable isotopes as
tracers of anthropogenic nitrogen sources, deposition, and impacts,
Elements, 9, 339–344, 10.2113/gselements.9.5.339, 2013.Heald, C. L., Collett Jr., J. L., Lee, T., Benedict, K. B., Schwandner, F. M., Li, Y., Clarisse, L., Hurtmans, D. R., Van Damme, M., Clerbaux, C., Coheur, P.-F., Philip, S., Martin, R. V., and Pye, H. O. T.: Atmospheric ammonia and particulate inorganic nitrogen over the United States, Atmos. Chem. Phys., 12, 10295–10312, 10.5194/acp-12-10295-2012, 2012.Heaton, T. H. E., Spiro, B., and Robertson, S. M. C.: Potential canopy
influences on the isotopic composition of nitrogen and sulphur in
atmospheric deposition, Oecologia, 109, 600–607,
10.1007/s004420050122, 1997.Herrmann, H., Schaefer, T., Tilgner, A., Styler, S. A., Weller, C., Teich,
M., and Otto, T.: Tropospheric aqueous-phase chemistry: Kinetics,
mechanisms, and its coupling to a changing gas phase, Chem. Rev., 115,
4259–4334, 10.1021/cr500447k, 2015.Hoffmann, D., Tilgner, A., Iinuma, Y., and Herrmann, H.: Atmospheric
stability of levoglucosan: A detailed laboratory and modeling study,
Environ. Sci. Technol., 44, 694–699, 10.1021/es902476f,
2010.Huang, C., Chen, C. H., Li, L., Cheng, Z., Wang, H. L., Huang, H. Y., Streets, D. G., Wang, Y. J., Zhang, G. F., and Chen, Y. R.: Emission inventory of anthropogenic air pollutants and VOC species in the Yangtze River Delta region, China, Atmos. Chem. Phys., 11, 4105–4120, 10.5194/acp-11-4105-2011, 2011.Jaegle, L., Steinberger, L., Martin, R. V., and Chance, K.: Global partitioning
of NOx sources using satellite observations: Relative roles of fossil
fuel combustion, biomass burning and soil emissions, Faraday Discus., 130,
407–423, 2005.Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang, Q., Shao, M., and Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058, 10.5194/acp-16-2043-2016, 2016.Kawashima, H. and Kurahashi, T.: Inorganic ion and nitrogen isotopic
compositions of atmospheric aerosols at Yurihonjo, Japan: Implications for
nitrogen sources, Atmos. Environ., 45, 6309–6316,
10.1016/j.atmosenv.2011.08.057, 2011.Kim, I. N., Lee, K., Gruber, N., Karl, D. M., Bullister, J. L., Yang, S.,
and Kim, T. W.: Increasing anthropogenic nitrogen in the North Pacific
Ocean, Science, 346, 1102–1106, 10.1126/science.1258396,
2014.Kundu, S., Kawamura, K., and Lee, M.: Seasonal variation of the
concentrations of nitrogenous species and their nitrogen isotopic ratios in
aerosols at Gosan, Jeju Island: Implications for atmospheric processing and
source changes of aerosols, J. Geophys. Res., 115, D20305,
10.1029/2009JD013323, 2010.Lamsal, L. N., Martin, R. V., Padmanabhan, A., van Donkelaar, A., Zhang, Q.,
Sioris, C. E., Chance, K., Kurosu, T. P., and Newchurch, M. J.: Application of
satellite observations for timely updates to global anthropogenic NOx
emission inventories, Geophys. Res. Lett., 38, L05810,
10.1029/2010GL046476, 2011.Lance, S., Barth, M., and Carlton, A.: Multiphase chemistry: Experimental
design for coordinated measurement and modeling studies of cloud processing
at a mountaintop, B. Am. Meteorol. Soc., 98, ES163–ES167,
10.1175/bams-d-17-0015.1, 2017.Leng, Q., Cui, J., Zhou, F., Du, K., Zhang, L., Fu, C., Liu, Y., Wang, H.,
Shi, G., Gao, M., Yang, F., and He, D.: Wet-only deposition of atmospheric
inorganic nitrogen and associated isotopic characteristics in a typical
mountain area, southwestern China, Sci. Total Environ., 616, 55–63,
10.1016/j.scitotenv.2017.10.240, 2018.Levy, H., Moxim, W. J., and Kasibhatla, P. S.: A global three-dimensional
time-dependent lightning source of tropospheric NOx, J. Geophys. Res.,
101, 22911–22922, 1996.Li, J., Wang, X., Chen, J., Zhu, C., Li, W., Li, C., Liu, L., Xu, C., Wen, L., Xue, L., Wang, W., Ding, A., and Herrmann, H.: Chemical composition and droplet size distribution of cloud at the summit of Mount Tai, China, Atmos. Chem. Phys., 17, 9885–9896, 10.5194/acp-17-9885-2017, 2017.Li, L., Lollar, B. S., Li, H., Wortmann, U. G., and Lacrampe-Couloume, G.:
Ammonium stability and nitrogen isotope fractionations for
NH3(aq)-NH3(gas) systems at 20–70 ∘C and pH of 2-13:
Applications to habitability and nitrogen cycling in low-temperature
hydrothermal systems, Geochim. Cosmochim. Ac., 84, 280–296,
10.1016/j.gca.2012.01.040, 2012.Li, Q., Jiang, J., Cai, S., Zhou, W., Wang, S., Duan, L., and Hao, J.:
Gaseous ammonia emissions from coal and biomass combustion in household
stoves with different combustion efficiencies, Environ. Sci. Technol. Lett.,
3, 98–103, 10.1021/acs.estlett.6b00013, 2016a.Li, T., Wang, Y., Mao, H., Wang, S., Talbot, R. W., Zhou, Y., Wang, Z., Nie,
X., and Qie, G.: Insights on chemistry of mercury species in clouds over
Northern China: Complexation and adsorption, Environ. Sci. Technol., 52,
5125–5134, 10.1021/acs.est.7b06669, 2018.Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann,
C. M. B., Puchalski, M. A., Gay, D. A., and Collett, J. L.: Increasing
importance of deposition of reduced nitrogen in the United States, P. Natl.
Acad. Sci. USA, 113, 5874–5879, 10.1073/pnas.1525736113, 2016b.Lin, C. T., Jickells, T. D., Baker, A. R., Marca, A., and Johnson, M. T.:
Aerosol isotopic ammonium signatures over the remote Atlantic Ocean, Atmos.
Environ., 133, 165–169, 10.1016/j.atmosenv.2016.03.020,
2016.Lin, Q., Zhang, G., Peng, L., Bi, X., Wang, X., Brechtel, F. J., Li, M., Chen, D., Peng, P., Sheng, G., and Zhou, Z.: In situ chemical composition measurement of individual cloud residue particles at a mountain site, southern China, Atmos. Chem. Phys., 17, 8473–8488, 10.5194/acp-17-8473-2017, 2017.Liu, D., Fang, Y., Tu, Y., and Pan, Y.: Chemical method for nitrogen
isotopic analysis of ammonium at natural abundance, Anal. Chem., 86,
3787–3792, 10.1021/ac403756u, 2014.Liu, X., Zhang, Y., Han, W., Tang, A., Shen, J., Cui, Z., Vitousek, P.,
Erisman, J. W., Goulding, K., Christie, P., Fangmeier, A., and Zhang, F.:
Enhanced nitrogen deposition over China, Nature, 494, 459–462,
10.1038/nature11917, 2013.Liu, L., Zhang, J., Xu, L., Yuan, Q., Huang, D., Chen, J., Shi, Z., Sun, Y., Fu, P., Wang, Z., Zhang, D., and Li, W.: Cloud scavenging of anthropogenic refractory particles at a mountain site in North China, Atmos. Chem. Phys., 18, 14681–14693, 10.5194/acp-18-14681-2018, 2018.Lobert, J. M., Scharffe, D. H., Hao, W. M., and Crutzen, P. J.: Importance
of biomass burning in the atmospheric budgets of nitrogen-containing gases.
Nature, 346, 552, 10.1038/346552a0, 1990.McIlvin, M. R. and Altabet, M. A.: Chemical conversion of nitrate and
nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in
freshwater and seawater, Anal. Chem., 77, 5589–5595,
10.1021/ac050528s, 2005.Michalski, G., Bockheim, J. G., Kendall, C., and Thiemens, M.: Isotopic
composition of Antarctic Dry Valley nitrate: Implications for NOy
sources and cycling in Antarctica, Geophys. Res. Lett., 32, L13817,
10.1029/2004GL022121, 2005.Michalski, G., Bhattacharya, S., and Mase, F.: Oxygen isotope dynamics of
atmospheric nitrate and its precursor molecules (Chapter 30), in: Handbook of Environmental Isotope Geochemistry, edited by: Baskaran, M., Advances in Isotope
Geochemistry, 10.1007/978-3-642-10637-8_30,
Springer-Verlag, Berlin Heidelberg, 2011.Miyazaki, K., Eskes, H., Sudo, K., Boersma, K. F., Bowman, K., and Kanaya, Y.: Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation, Atmos. Chem. Phys., 17, 807–837, 10.5194/acp-17-807-2017, 2017.Morin, S., Savarino, J., Frey, M. M., Yan, N., Bekki, S., Bottenheim, J. W.,
and Martins, J. M.: Tracing the origin and fate of NOx in the Arctic
atmosphere using stable isotopes in nitrate. Science, 322, 730–732,
10.1126/science.1161910, 2008.Morino, Y., Kondo, Y., Takegawa, N., Miyazaki, Y., Kita, K., Komazaki, Y.,
Fukuda, M., Miyakawa, T., Moteki, N., and Worsnop, D. R.: Partitioning of
HNO3 and particulate nitrate over Tokyo: Effect of vertical mixing, J.
Geophys. Res., 111, D15215, 10.1029/2005JD006887, 2006.Norris, J. R., Allen, R. J., Evan, A. T., Zelinka, M. D., O'Dell, C. W., and
Klein, S. A.: Evidence for climate change in the satellite cloud record.
Nature, 536, 72, 10.1038/nature18273, 2016.Okin, G. S., Baker, A. R., Tegen, I., Mahowald, N. M., Dentener, F. J.,
Duce, R. A., Galloway, J. N., Hunter, K., Kanakidou, M., Kubilay, N.,
Prospero, J. M., Sarin, M., Surapipith, V., Uematsu, M., and Zhu, T.:
Impacts of atmospheric nutrient deposition on marine productivity: Roles of
nitrogen, phosphorus, and iron. Global Biogeochem. Cy., 25, GB2022,
10.1029/2010GB003858, 2011.Olivier, J., Bouwman, A., Van der Hoek, K., and Berdowski, J.: Global air
emission inventories for anthropogenic sources of NOx, NH3 and
N2O in 1990, Environ. Pollut., 102, 135–148,
10.1016/B978-0-08-043201-4.50024-1, 1998.Park, Y., Park, K., Kim, H., Yu, S., Noh, S., Kim, M., Kim, J., Ahn, J.,
Lee, M., Seok, K., and Kim, Y.: Characterizing isotopic compositions of
TC-C, NO3--N, and NH4+-N in PM2.5 in South Korea:
Impact of China's winter heating, Environ. Pollut., 233, 735–744,
10.1016/j.envpol.2017.10.072, 2018.
Paulot, F. and Jacob, D. J.: Hidden cost of US agricultural exports:
particulate matter from ammonia emissions, Environ. Sci. Technol., 48,
903–908, 2014.Price, C., Penner, J., and Prather, M.: NOx from lightning: 1. Global
distribution based on lightning physics, J. Geophys. Res., 102, 5929–5941,
1997.Proemse, B. C., Mayer, B., Chow, J. C., and Watson, J. G.: Isotopic
characterization of nitrate, ammonium and sulfate in stack PM2.5
emissions in the Athabasca Oil Sands Region, Alberta, Canada, Atmos.
Environ., 60, 555–563, 10.1016/j.atmosenv.2012.06.046, 2012.Ravishankara, A. R.: Heterogeneous and multiphase chemistry in the
troposphere, Science, 276, 1058–1065,
10.1126/science.276.5315.1058, 1997.Reche, C., Viana, M., Pandolfi, M., Alastuey, A., Moreno, T., Amato, F.,
Ripoll, A., and Querol, X.: Urban NH3 levels and sources in a Mediterranean
environment, Atmos. Environ., 57, 153–164, 2012.Reis, S., Pinder, R. W., Zhang, M., Lijie, G., and Sutton, M. A.: Reactive nitrogen in atmospheric emission inventories, Atmos. Chem. Phys., 9, 7657–7677, 10.5194/acp-9-7657-2009, 2009.
Richter, A., Burrows, J. P., Nuss, H., Granier, C., and Niemeier, U.: Increase
in tropospheric nitrogen dioxide over China observed from space, Nature,
437, 129–132, 2005.Sang, X. F., Gensch, I., Kammer, B., Khan, A., Kleist, E., Laumer, W.,
Schlag, P., Schmitt, S. H., Wildt, J., Zhao, R., Mungall, E. L., Abbatt, J.
P. D., and Kiendler-Scharr, A.: Chemical stability of levoglucosan: an
isotopic perspective, Geophys. Res. Lett., 43, 5419–5424,
10.1002/2016GL069179, 2016.Savard, M. M., Cole, A. S., Vet, R., and Smirnoff, A.: The Δ17O and δ18O values of atmospheric nitrates simultaneously collected downwind of anthropogenic sources – implications for polluted air masses, Atmos. Chem. Phys., 18, 10373–10389, 10.5194/acp-18-10373-2018, 2018.Schlesinger, W. and Hartley, A.: A global budget for atmospheric NH3,
Biogeochem., 15, 191–211, 10.1007/BF00002936, 1992.Schurman, M. I., Boris, A., Desyaterik, Y., and Collett, J. L.: Aqueous
secondary organic aerosol formation in ambient cloud water photo-oxidations,
Aerosol Air Qual. Res., 18, 15–25,
10.4209/aaqr.2017.01.0029, 2018.
Seinfeld, J. and Pandis, S. N.: Atmospheric Chemistry and Physics: From air
pollution to climate change, John Wiley and Sons, 2012.Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J.,
Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas,
I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K.
A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R.,
Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental
understanding of the role of aerosol-cloud interactions in the climate
system, P. Natl. Acad. Sci. USA, 113, 5781–5790,
10.1073/pnas.1514043113, 2016.Slade, J. H., Shiraiwa, M., Arangio, A., Su, H., Pöschl, U., Wang, J.,
and Knopf, D. A.: Cloud droplet activation through oxidation of organic
aerosol influenced by temperature and particle phase state, Geophys. Res.
Lett., 44, 1583–1591, 10.1002/2016GL072424, 2017.Smirnoff, A., Savard, M. M., Vet, R., and Simard, M. C.: Nitrogen and triple
oxygen isotopes in near-road air samples using chemical conversion and
thermal decomposition, Rapid Commun. Mass Sp., 26, 2791–2804,
10.1002/rcm.6406, 2012.Souri, A. H., Choi, Y., Jeon, W., Kochanski, A. K., Diao, L., Mandel, J.,
Bhave, P. V., and Pan, S.: Quantifying the impact of biomass burning
emissions on major inorganic aerosols and their precursors in the U.S, J.
Geophys. Res., 122, 12, 10.1002/2017JD026788, 2017.
Suarez-Bertoa, R., Zardini, A. A., and Astorga, C.: Ammonia exhaust emissions
from spark ignition vehicles over the New European Driving Cycle, Atmos.
Environ., 97, 43–53, 2014.Teng, X., Hu, Q., Zhang, L., Qi, J., Shi, J., Xie, H., Gao, H., and Yao, X.:
Identification of major sources of atmospheric NH3 in an urban
environment in Northern China during wintertime, Environ. Sci. Technol., 51,
6839–6848, 2017.Tu, Y., Fang, Y., Liu, D., and Pan, Y.: Modifications to the azide method
for nitrate isotope analysis, Rapid Commun. Mass Sp., 30, 1213–1222,
10.1002/rcm.7551, 2016.van Pinxteren, D., Fomba, K. W., Mertes, S., Müller, K., Spindler, G., Schneider, J., Lee, T., Collett, J. L., and Herrmann, H.: Cloud water composition during HCCT-2010: Scavenging efficiencies, solute concentrations, and droplet size dependence of inorganic ions and dissolved organic carbon, Atmos. Chem. Phys., 16, 3185–3205, 10.5194/acp-16-3185-2016, 2016.Vega, C., Mårtensson, M., Wideqvist, U., Kaiser, J., Zieger, P., and
StrÖm, J.: Composition, isotopic fingerprint and source attribution of
nitrate deposition from rain and fog at a Sub-Arctic Mountain site in
Central Sweden (Mt Åreskutan), Tellus B, 71, 1445379,
10.1080/16000889.2018.1559398, 2019.Voigt, A. and Shaw, T. A.: Circulation response to warming shaped by
radiative changes of clouds and water vapour, Nat. Geosci., 8, 102–106,
10.1038/ngeo2345, 2015.Walters, W. W. and Michalski, G.: Theoretical calculation of nitrogen
isotope equilibrium exchange fractionation factors for various NOy
molecules, Geochim. Cosmochim. Ac., 164, 284–297,
10.1016/j.gca.2015.05.029, 2015.Walters, W. W., Simonini, D. S., and Michalski, G.: Nitrogen isotope
exchange between NO and NO2 and its implications for δ15N
variations in tropospheric NOx and atmospheric nitrate, Geophys. Res.
Lett., 43, 440–448, 10.1002/2015gl066438, 2016.Wang, Y. L., Liu, X. Y., Song, W., Yang, W., Han, B., Dou, X. Y., Zhao, X.
D., Song, Z. L., Liu, C. Q., and Bai, Z. P.: Source appointment of nitrogen
in PM2.5 based on bulk δ15N signatures and a Bayesian
isotope mixing model, Tellus B, 69, 1299672,
10.1080/16000889.2017.1299672, 2017a.Wang, Z., Wang, W., Tham, Y. J., Li, Q., Wang, H., Wen, L., Wang, X., and Wang, T.: Fast heterogeneous N2O5 uptake and ClNO2 production in power plant and industrial plumes observed in the nocturnal residual layer over the North China Plain, Atmos. Chem. Phys., 17, 12361–12378, 10.5194/acp-17-12361-2017, 2017b.Weathers, K. C. and Likens, G. E.: Clouds in Southern Chile: An important
source of nitrogen to nitrogen-limited ecosystems?, Environ. Sci. Technol.,
31, 210–213, 10.1021/es9603416, 1997.Yang, J.-Y. T., Hsu, S.-C., Dai, M. H., Hsiao, S. S.-Y., and Kao, S.-J.: Isotopic composition of water-soluble nitrate in bulk atmospheric deposition at Dongsha Island: sources and implications of external N supply to the northern South China Sea, Biogeosciences, 11, 1833–1846, 10.5194/bg-11-1833-2014, 2014.Yeatman, S., Spokes, L., Dennis, P., and Jickells, T.: Comparisons of
aerosol nitrogen isotopic composition at two polluted coastal sites, Atmos.
Environ., 35, 1307–1320, 10.1016/S1352-2310(00)00408-8,
2001.Yienger, J. J. and Levy, H.: Empirical model of global soil-biogenic
NOx emissions, J. Geophys. Res., 100, 11447–11464, 1995.Zhang, C., Geng, X., Wang, H., Zhou, L., and Wang, B.: Emission factor for
atmospheric ammonia from a typical municipal wastewater treatment plant in
South China, Environ. Pollut., 220, 963–970, 2017.
Zhang, L., Chen, Y., Zhao, Y., Henze, D. K., Zhu, L., Song, Y., Paulot, F., Liu, X., Pan, Y., Lin, Y., and Huang, B.: Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates, Atmos. Chem. Phys., 18, 339–355, 10.5194/acp-18-339-2018, 2018.Zhang, Y. L., and Cao, F.: Is it time to tackle PM2.5 air pollutions in
China from biomass-burning emissions?, Environ. Pollut., 202, 217–219,
10.1016/j.envpol.2015.02.005, 2015.Zhang, Y., Zheng, L. X., Liu, X. J., Jickells, T., Cape, J. N., Goulding,
K., Fangmeier, A., and Zhang, F. S.: Evidence for organic N deposition and
its anthropogenic sources in China, Atmos. Environ., 42, 1035–1041,
10.1016/j.atmosenv.2007.12.015, 2008.Zhang, Y., Dore, A. J., Ma, L., Liu, X. J., Ma, W. Q., Cape, J. N., and
Zhang, F. S.: Agricultural ammonia emissions inventory and spatial
distribution in the North China Plain, Environ. Pollut., 158, 490–501,
10.1016/j.atmosenv.2007.12.015, 2010.Zhao, Y., Zhang, J., and Nielsen, C. P.: The effects of recent control policies on trends in emissions of anthropogenic atmospheric pollutants and CO2 in China, Atmos. Chem. Phys., 13, 487–508, 10.5194/acp-13-487-2013, 2013.Zheng, X. D., Liu, X. Y., Song, W., Sun, X. C., and Liu, C. Q.: Nitrogen
isotope variations of ammonium across rain events: Implications for
different scavenging between ammonia and particulate ammonium, Environ.
Pollut., 239, 392–398, 10.1016/j.envpol.2018.04.015, 2018.Zong, Z., Wang, X., Tian, C., Chen, Y., Fang, Y., Zhang, F., Li, C., Sun,
J., Li, J., and Zhang, G.: First assessment of NOx sources at a
regional background site in North China using isotopic analysis linked with
modeling, Environ. Sci. Technol., 51, 5923–5931,
10.1021/acs.est.6b06316, 2017.