Important contributions of non-fossil fuel nitrogen oxides emissions

Global δ¹⁵N_w-NO3− observations

Publications of δ¹⁵N_w-NO3− studies were obtained through the databases of the Web of Science (http://isiknowledge.com), Google Scholar (http://scholar.google.com.hk), and Baidu Scholar (http://xueshu.baidu.com) by searching keywords of “nitrogen isotope”, “nitrate”, “rainfall”, and “precipitation”. By the end of December 2018, a total of 128 publications were available (Supplementary Text 1), spanning the sampling time of 1956–2017 (Supplementary Fig. 11). We extracted δ¹⁵N_w-NO3− values of individual precipitation samples by using the software of Web Plot Digitizer³⁷.

There are totally 3483 individual δ¹⁵N_w-NO3− data and 222 sampling sites when multiple observations in different sampling years at the same site were counted once only (Fig. 1). There are 56 urban sites, 158 non-urban sites, and eight arctic sites (Fig. 1), in which non-urban sites are mainly situated in rural, mountain, forest, and lake areas. Due to the sparsity of available data before 2000 (Supplementary Fig. 11), we analyzed δ¹⁵N_w-NO3− data at major urban and non-urban sites in East Asia, Europe, and North America during 2000–2017 to ensure a better site representation and to reduce the uncertainty caused by inconsistency in sampling time (Fig. 1). To describe spatial differences in δ¹⁵N_w-NO3− values between urban and non-urban sites among three regions (totally 214 sites), only site-based mean values during the period of 2000–2017 (totally 169 sites) were used (detailed in Fig. 2). To describe temporal variations of δ¹⁵N_w-NO3− values in urban and non-urban areas of each region, respectively (Fig. 3), we counted observation sites by different sampling years, given that δ¹⁵N_w-NO3− observations at few sites have been conducted in different sampling years. In this way, there were a total of 206 sites during 2000–2017 (detailed in Fig. 3). In addition, 35%, 29%, and 36% of the δ¹⁵N_w-NO3− observations were conducted in warmer, cooler, and the whole year, respectively. The seasonal effects of NO_x emissions may not substantially influence the patterns of regional δ¹⁵N_w-NO3− variations.

Differences between δ¹⁵N_w-NO3− and δ¹⁵N_i-NOx values

NO is normally insoluble in water, and w-NO₃⁻ is scavenged only from the ambient NO₂ and the oxidized NO_x (i.e., HNO₃ and p-NO₃⁻) (Supplementary Fig. 1)^32,38,39. Moreover, isotopic effects during the NO_x cycles lead to differences between δ¹⁵N_NOx and δ¹⁵N_NO2. Therefore, substantial differences exist between the δ¹⁵N_w-NO3− and δ¹⁵N_i-NOx values in the atmosphere (hereafter denoted as ¹⁵∆_{i-NOx→w-NO3−}). In this study, we calculated ¹⁵∆_{i-NOx→w-NO3−} values by using the following equation (Eq. (2)):

Combined Eq. (1) with Eq. (2), we get Eq. (3) to calculate the ¹⁵∆_{i-NOx→w-NO3−} values.

To obtain more accurate ¹⁵∆_{i-NOx→w-NO3−} values, we estimated the ¹⁵∆_{i-NOx→w-NO3}− values in two independent scenarios. In Scenario 1, mean values of global δ¹⁵N_NOx and f_NO2 values, simultaneously observed values of ambient C_NO2, C_HNO3, C_p-NO3−, δ¹⁵N_HNO3, δ¹⁵N_p-NO3−, and δ¹⁵N_w-NO3− were used for the calculation in Eq. (3). In Scenario 2, non-synchronously observed values of ambient f_NO2, C_NO2, C_HNO3, C_p-NO3−, δ¹⁵N_NOx, δ¹⁵N_HNO3, δ¹⁵N_p-NO3−, and δ¹⁵N_w-NO3− were used for the calculation in Eq. (3). The values and data sources of parameters used for estimating ambient ¹⁵∆_{i-NOx→w-NO3−} values are included in Supplementary Table 1. Because data of f_NO2 and δ¹⁵N_NOx are very sparse globally, we used global mean values and considered their SD values into the uncertainty analysis by the Monte Carlo method. Furthermore, because of no significant difference between ¹⁵∆_{i-NOx→w-NO3−} values obtained in Scenario 1 (2.1 ± 1.7‰) and Scenario 2 (5.7 ± 3.2‰) (Supplementary Fig. 2), we used a mean value of them (3.9 ± 1.8‰; Supplementary Fig. 2) in the calculations of source contributions (Eqs. (4) and (5)).

Contributions of dominant fossil fuel and non-fossil fuel NO_x sources

Based on δ¹⁵N_w-NO3−, ¹⁵∆_{i-NOx→w-NO3−}, and δ¹⁵N values of NO_x sources, we estimated relative contributions of dominant fossil fuel and non-fossil fuel NO_x sources to total NO_x emissions by using the isotope mass-balance method. We considered coal combustion (denoted as S1) and vehicle exhausts (S2) as dominant fossil fuel NO_x sources, and biomass burning (S3), and microbial N cycles (S4) as dominant non-fossil fuel NO_x sources. The major reasons include: (1) these four sources have been considered as dominant sources of total NO_x emissions in studies of both emission inventory and deposition modeling^{2,9,11,13,14,15,19,20,21}; (2) they are also the dominant sources influencing δ¹⁵N variations of NO_x and NO₃⁻ in the atmosphere;^26,27 (3) their mean δ¹⁵N values of NO_x emission sources differ significantly (P < 0.05, Supplementary Fig. 3) and therefore can be used to differentiate their relative contributions.

The S1–S4 are considered as dominant NO_x sources at urban sites but S2 cannot be considered as a dominant NO_x source at non-urban sites. First of all, studies of roadside NO_x emissions have evidenced that vehicle exhausts contribute little to atmospheric NO_x at non-urban sites due to limited amounts of long-range transport^40,41,42. Statistical data also show 76%, 82%, and 78% of vehicles distributed in urban areas of East Asia, North America, and Europe, respectively while their urban areas account for only 1.7%, 1.4%, and 16.6% of total land area, respectively (Supplementary Tables 2, 3, Supplementary Fig. 12). Secondly, 76% and 91% of δ¹⁵N_w-NO3− values at urban and non-urban sites fall in the δ¹⁵N range of NO_x from vehicle exhausts (Supplementary Figs. 3, 13). Consequently, when the NO_x from vehicle exhausts is considered into the calculations of relative contributions of different NO_x sources at non-urban sites, its contributions at non-urban sites (25 ± 12%) are similar to urban sites (28 ± 8%), which is unlikely. Besides, because mutual NO_x transportations always occur between urban and non-urban areas, δ¹⁵N values of NO₃⁻ in precipitation at a given urban or non-urban site integrate δ¹⁵N values of NO_x from both local emissions and regional transportations. However, physical NO_x transportation might have no substantial isotope effects, and thus likely will not influence the site-specific evaluations of fossil and non-fossil fuel NO_x contributions.

According to isotope mass-balance theory, we calculated relative contributions of S1–S4 (f_S1, f_S2, f_S3, and f_S4, respectively) at urban sites by using Eq. (4):

where we assumed that f_S1 + f_S2 + f_S3 + f_S4 = 1.

Then, we calculated their relative contributions at non-urban sites by Eq. (5):

where we assumed that f_S1 + f_S3 + f_S4 = 1. δ¹⁵N_S1, δ¹⁵N_S2, δ¹⁵N_S3, and δ¹⁵N_S4 represent δ¹⁵N values of NO_x from coal combustion (S1), vehicle exhausts (S2), biomass burning (S3), and microbial N cycles (S4), respectively (Supplementary Fig. 3).

The f_S1, f_S2, f_S3, and f_S4 values were calculated by using a Bayesian isotope-mixing model (named Stable Isotope Analysis in R, SIAR). The SIAR model⁴³ uses a Bayesian framework to establish a logical prior distribution based on Dirichlet distribution⁴⁴ for estimating source contributions (f_S1–f_S4). It has the potential to provide reliable estimations of source contributions because the isotope effect (i.e., ¹⁵∆_{i-NOx→w-NO3−} values in this study), the variability in δ¹⁵N values of both sources (i.e., δ¹⁵N values of NO_x from S1–S4 in this study), and the mixture (i.e., δ¹⁵N_w-NO3− values in this study)^45,46 are considered. The SIAR model has been widely used to quantify the relative contributions of multiple NO_x emission sources to p-NO₃⁻ and w-NO₃^{−26,27,31,47}. In each run of the SIAR model, the mean ± SD of δ¹⁵N_NOx values (Supplementary Fig. 3), the mean ± SD of ¹⁵∆ _{w-NO3−→i-NOx} values (Supplementary Fig. 2), and replicate δ¹⁵N_w-NO3− values at each urban or non-urban site in each sampling year (Fig. 3) were input into the model. In addition, the percentage data of each source (n = 10,000) output from each run of the SIAR model were used to calculate mean ± SD values of corresponding source contributions (Supplementary Figs. 5–8).

We calculated the total contribution of each NO_x source in each region (F; Eq. (6)) by using its annual mean relative contributions at urban and non-urban sites during 2000–2017 (n = 28, 9, 13 for urban sites and n = 47, 21, 88 for non-urban sites in East Asia, Europe, and North America, respectively) (f_urban and f_non-urban, respectively; Supplementary Fig. 9) and annual mean proportions of urban and non-urban populations in the total population of each region during 2000–2017 (P_urban and P_non-urban, respectively; Supplementary Fig. 14).

Then, we calculated annual mean relative contributions of dominant fossil fuel and non-fossil fuel NO_x sources in each region (F_fossil and F_non-fossil, respectively) by using Eq. (7) and Eq. (8), respectively.

Finally, based on the annual mean amounts of fossil fuel NO_x emissions (A_fossil) in East Asia during 2000–2010, in Europe during 2000–2015, and in North America during 2000–2016, respectively (Fig. 4b, Supplementary Fig. 10), the annual mean amounts of total NO_x emissions (A_total) and non-fossil fuel NO_x emissions (A_non-fossil) in each region during 2000–2017 were calculated by using Eq. (9) and Eq. (10), respectively:

We estimated the SD values of calculated values in Eqs. (6)–(10) and finally propagated into the uncertainties of the A_non-fossil values by using the Monte Carlo method.

Statistical analyses

The one-way analyses of variance (Fig. 2) and Pearson correlation analyses (Fig. 3) were performed by using the Origin 2016 statistical package (OriginLab Corporation, USA) and SPSS 16.0 statistical package (SPSS Inc., Chicago, IL). Because of regionally limiting observation sites and inherently high variability of δ¹⁵N_w-NO3−, spatial differences are significant only at the level of P < 0.1 (Fig. 2). Mean values and standard deviation (SD) were reported.

Source: Ecology - nature.com