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).

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.

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).

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).

The δ15N–pNO3- and δ18O–pNO3- values of the eight samples collected from the Sanjiang biomass burning field experiment ranged from 9.54 ‰ to 13.77 ‰ (mean: 12.17 ‰) and 57.17 ‰ to 75.09 ‰ (mean: 63.57 ‰), respectively. In this study, atmospheric concentrations of levoglucosan quantified from PM2.5 samples collected near the sites of biomass burning in Sanjiang vary between 4.0 and 20.5 µg m-3, 2 to 5 orders of magnitude higher than those measured during non-biomass-burning season (Cao et al., 2017, 2016). Levoglucosan is an anhydrosugar formed during pyrolysis of cellulose at temperatures above 300 ∘C (Simoneit, 2002). Due to its specificity for cellulose combustion, it has been widely used as a molecular tracer for biomass burning (Simoneit et al., 1999; D. Liu et al., 2013; Jedynska et al., 2014; Liu et al., 2014). Indeed, the concentrations of levoglucosan and aerosol nitrate in Sanjiang were highly correlated (R2=0.64; Fig. 2a), providing compelling evidence that particulate nitrate measured during our study period was predominately derived from biomass burning emissions.

The mass concentrations meanmin⁡max⁡±1σ,n=43 of PM2.5 and pNO3- measured in Nanjing were 122.139.0227.8±47.9 and 17.84.045.2±10.3 µg m-3, respectively. All PM2.5 concentrations exceeded the Chinese Air Quality Standards for daily PM2.5 (35 µg m-3), suggesting severe haze pollution during the sampling period. The corresponding δ15N–pNO3- values (raw data without correction) ranged between 5.39 ‰ and 17.99 ‰, indicating significant enrichment in 15N relative to rural and coastal marine atmospheric NO3- sources (Table S4). This may be due to the prominent contribution of fossil-fuel-related NOx emissions with higher δ15N values in urban areas (Elliott et al., 2007; Park et al., 2018).

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.

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 εNmeanmin⁡max⁡±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.

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.

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.

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.

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).