Significant contributions of combustion-related sources to ammonia emissions

Abstract

Atmospheric ammonia (NH₃) and ammonium (NH₄⁺) can substantially influence air quality, ecosystems, and climate. NH₃ volatilization from fertilizers and wastes (v-NH₃) has long been assumed to be the primary NH₃ source, but the contribution of combustion-related NH₃ (c-NH₃, mainly fossil fuels and biomass burning) remains unconstrained. Here, we collated nitrogen isotopes of atmospheric NH₃ and NH₄⁺ and established a robust method to differentiate v-NH₃ and c-NH₃. We found that the relative contribution of the c-NH₃ in the total NH₃ emissions reached up to 40 ± 21% (6.6 ± 3.4 Tg N yr⁻¹), 49 ± 16% (2.8 ± 0.9 Tg N yr⁻¹), and 44 ± 19% (2.8 ± 1.3 Tg N yr⁻¹) in East Asia, North America, and Europe, respectively, though its fractions and amounts in these regions generally decreased over the past decades. Given its importance, c-NH₃ emission should be considered in making emission inventories, dispersion modeling, mitigation strategies, budgeting deposition fluxes, and evaluating the ecological effects of atmospheric NH₃ loading.

By integrating nitrogen isotope systematics of ammonia emissions and transformations in the atmosphere, this study quantified the combustion-related ammonia emission and uncovered its importance for mitigating strategies of ammonia pollution.

Introduction

Ammonia (NH₃) is a highly water-soluble and reactive gas in the atmosphere¹. The uptake of NH₃ within clouds and rain and onto atmospheric aerosols causes the formation of ammonium (NH₄⁺) within atmospheric particulates and precipitation (denoted as p-NH₄⁺ and w-NH₄⁺, respectively)^1–3. Over the last century, urbanization and industrial and agricultural intensification have greatly increased NH₃ production and have led to a continuous increase in the NH₃ emission amounts and deposition fluxes^4–8. Globally, the NH₃ emission has increased from 20.6 Tg N yr⁻¹ in 1860 to 58.2 Tg N yr⁻¹ in 1993 and may double to 118.0 Tg N yr⁻¹ by 2050⁴. Since the 1950s, East Asia, North America, and Europe have been three regions of high NH₃ emissions^9,10, with total emission amounts of 10.7 ± 0.4, 3.7 ± 0.2, and 3.5 ± 0.3 Tg N yr⁻¹ during 2000–2015, respectively, according to emission inventory data (Supplementary Table 1). As a result, atmospheric NH₃ concentrations (averaging 2.9 ± 2.4, 1.4 ± 1.8, 1.2 ± 1.3 μg m⁻³, respectively¹¹) and NH_x deposition (the sum of NH₃, p-NH₄⁺, and w-NH₄⁺) (averaging 12.0, 4.7, and 6.9 Tg N yr⁻¹, respectively^12,13) in the above three regions also remain high. In human-disturbed areas, excessive NH₃ has promoted secondary aerosol production and air pollution^2–4. Elevated NH_x concentrations and deposition have caused negative impacts on ecosystem structure and function (e.g., biodiversity declines, soil acidification, water eutrophication^2,3,14) and huge economic loss^15–17.

There are two major groups of atmospheric NH₃ emission sources. One is NH₃ volatilization from NH₄⁺-containing substrates (mainly fertilized and natural soils, animal wastes, and natural and N-polluted water) (denoted as v-NH₃)^2,9,18,19. The dissolved NH₃ volatilizes from liquid-phase substrates containing NH₄⁺ at favorable pH, temperature, and pressure conditions²⁰. The other is NH₃ emission from combustion-related sources (mainly coal combustion, vehicle exhausts, and biomass burning) (denoted as c-NH₃)^21–24. NH₃ would be released from industrial coal combustion, heavy-duty and light-duty diesel vehicles equipped with selective catalytic or non-catalytic reduction systems because of excessive urea/NH₃ used for the catalytic degradation of nitrogen oxides (NO_x)^25,26. It is also produced via steam reforming from hydrocarbons²⁷ and or catalytic reaction of nitric oxide with molecular hydrogen²⁸ in light and medium-dust gasoline vehicles equipped with three-way catalytic converters, relating to the catalyst temperatures and air-to-fuel ratios²⁹. The biomass N, typically as amides (R-(C = O)-NH-R’) and amines (R-NH₂), can produce NH₃ under poor mixing conditions during biomass burning³⁰. So far, there have been direct observations on emission factors of various v-NH₃ sources to budget the v-NH₃ emission amount (Supplementary Table 1; Supplementary Fig. 1a). According to statistical emission inventories, the v-NH₃ is the dominant source of regional NH₃ emissions, accounting for 94 ± 1%, 90 ± 1%, and 95 ± 1% of the total emission in East Asia during 2001–2015, North America during 1970–2019, and Europe during 1970–2018, respectively (Supplementary Fig. 1b). In contrast, it has long been difficult to estimate the c-NH₃ emission because of limited data on emission factors of c-NH₃ sources and uncertainties associated with the amount of combusted materials, especially biomasses^22,23.

However, evidence from laboratory simulations, in-situ observations, satellite observations, and emission inventories points to an underestimation of c-NH₃ emissions relative to previous assessments (summarized in Supplementary Table 2). First, laboratory simulations found that biomass burning and light-duty diesel vehicles equipped with selective catalytic reduction are important NH₃ sources and have been overlooked (Supplementary Table 2). Second, ground observations found that ambient NH₃ concentrations at sites impacted by biomass burning, traffic pollution, industrial pollution, or urban pollution were enhanced by a factor of 1.4–20 compared to unpolluted sites (Supplementary Table 2). Third, satellite observations revealed that the NH₃ from biomass burning controlled seasonal variations of surface NH₃ concentrations in the southern and high-emission regions of China, the USA, and Europe in the northern hemisphere (Supplementary Table 2). Spatially, satellite observations also identified 13 hotspots of urban NH₃ pollution and 266 hotspots of industrial NH₃ pollution from coal combustion and coal-related industries (Supplementary Table 2). Fourth, according to emission inventories, the total c-NH₃ emissions from transportation (1.3 Tg N yr⁻¹)²³, biomass burning (8.2 Tg N yr⁻¹)³¹, and other combustion sources (6.3 Tg N yr⁻¹)¹⁰ has reached up to 15.9 Tg N yr⁻¹, which accounts for 30% of the global NH₃ emission (54.3 Tg N yr⁻¹). Additionally, isotopic evidence demonstrated that c-NH₃ had reached 29–62% in NH₃ of the ambient atmosphere^32–34 and 45–90% of the NH₄⁺ deposition in cities of China and the USA^35,36. All the above evidence suggests that the relative importance and amount of regional c-NH₃ emissions are still open questions and should be re-evaluated.

Here we collated observation data of natural N isotopes (expressed as δ¹⁵N, δ¹⁵N = (¹⁵N/¹⁴N)_sample/(¹⁵N/¹⁴N)_standard −1, where atmospheric N₂ is used as the standard) of primary v-NH₃ and c-NH₃ emission sources (Supplementary Fig. 2), NH₃ gas in the ambient atmosphere (a-NH₃), NH₄⁺ in atmospheric particulates (p-NH₄⁺) and precipitation (w-NH₄⁺) in East Asia, North America, and Europe (Fig. 1–3; Supplementary Figs. 3 & 4). In combination with the collected data of a-NH₃ and p-NH₄⁺ concentrations observed in the above regions, we evaluated δ¹⁵N differences of the initial NH₃ mixture of v-NH₃ and c-NH₃ (i-NH₃) to a-NH₃, p-NH₄⁺, or w-NH₄⁺, respectively (Supplementary Figs. 5–9; detailed in Methods). Then, based on the source δ¹⁵N, δ¹⁵N difference, δ¹⁵N observation of a-NH₃, p-NH₄⁺, and w-NH₄⁺, and the Stable Isotope Analysis in R model (i.e., the SIAR model; detailed in Methods), we established a set of isotopic methods to calculate relative contributions between v-NH₃ and c-NH₃ in above study regions (Fig. 4 & 5; Supplementary Figs. 10–12). Finally, using regional mean fraction values and emission amounts of v-NH₃, we recalculated the amounts of c-NH₃ and total NH₃ emissions in each region (Fig. 5; Supplementary Figs. 13 & 14).

Fig. 1. Conceptual framework of atmospheric NH₃ and NH₄⁺.

It shows the relationships among NH₃ emissions from combustion-related sources (c-NH₃) and volatilization sources (v-NH₃), the initial mixture of c-NH₃ and v-NH₃ (i-NH₃), ambient NH₃ (a-NH₃), particulate NH₄⁺ (p-NH₄⁺), and precipitation NH₄⁺ (w-NH₄⁺). The a-NH₃ potentially includes the NH₃ that has not been converted to p-NH₄⁺ in earlier stages and the fresh NH₃ emissions in relatively later periods.

Fig. 3. δ¹⁵N of ambient NH₃ (a-NH₃), particulate NH₄⁺ (p-NH₄⁺), and precipitation NH₄⁺ (w-NH₄⁺) (a) and the initial NH₃ mixture from different sources (δ¹⁵N_i-NH3) (b) in East Asia, North America, and Europe.

Each data point represents the site-based mean δ¹⁵N. Each box encompasses the 25^th−75^th percentiles, whiskers and red lines in boxes are the SD and mean values, respectively. The number below each box is that of observation sites. Note that the site with simultaneous a-NH₃, w-NH₄⁺, or p-NH₄⁺ observation is counted as one site in sub-figure b. In sub-figure a, different letters (a, b, and c) above the boxes indicate the significant differences (p < 0.05) among species in the same region; in sub-figure b, different letters (a and b) above the boxes indicate the significant differences among the three regions. The δ¹⁵N_a-NH3 based on the passive samplers have been calibrated by adding 15‰⁴¹. The δ¹⁵N_i-NH3 was calculated according to Eqs. (11–13) (detailed in Methods).

Fig. 4. Temporal variations of δ¹⁵N of the initial NH₃ mixture from different sources (δ¹⁵N_i-NH3) in East Asia, North America, and Europe.

The mean ± SD of replicate measurements at each site in each year is shown. We counted the same site with different years as different observations, given that δ¹⁵N observations at a few sites have been conducted in different sampling years.

Fig. 5. Relative contributions of volatilization NH₃ (v-NH₃) and combustion-related NH₃ (c-NH₃) sources (a) and their emission amounts (b).

The ‘Total’ is the sum of v-NH₃ and c-NH₃. Mean±SD is shown.

Results and Discussion

δ¹⁵N signatures of v-NH₃ and c-NH₃

δ¹⁵N for v-NH₃ (δ¹⁵N_v-NH3) average −18.9 ± 4.2‰, significantly lower than c-NH₃ (δ¹⁵N_c-NH3, averaging 8.0 ± 4.4‰) (Supplementary Fig. 2b, detailed in Methods). The volatilization of NH₃ includes three main steps, i.e., the equilibrium of NH₄⁺ ↔ NH₃, the diffusion of NH₃ to and away from volatilization sites³⁷. The overall isotope effects range from −60‰ to −30‰ (depending on temperature, pH, and cation-exchange capacities of substrates^20,38–40, causing the low δ¹⁵N_v-NH3. Differently, δ¹⁵N_c-NH3 would assemble or be slightly higher than δ¹⁵N of burning materials that are relatively ¹⁵N-enriched⁴¹. Distinct δ¹⁵N_v-NH3 and δ¹⁵N_c-NH3 provide a unique tool to differentiate the relative contributions between v-NH₃ and c-NH₃ in the SIAR isotope mass-balance model.

δ¹⁵N differences between a-NH₃, p-NH₄⁺, or w-NH₄⁺ and i-NH₃

The δ¹⁵N of the initial NH₃ mixture of v-NH₃ and c-NH₃ emissions (δ¹⁵N_i-NH3) integrate their δ¹⁵N signatures and fractional contributions (F_v-NH3 and F_c-NH3, respectively) (Fig. 1, Eq. (1)).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{v}-\mathrm{NH}3}\times F_{\mathrm{v}-\mathrm{NH}3}+{\delta }^{15}{\mathrm{N}}_{\mathrm{c}-\mathrm{NH}3}\times F_{\mathrm{c}-\mathrm{NH}3}$$ 1

where F_v-NH3 + F_c-NH3 = 1. However, because NH₃ is very reactive and readily transformed into NH₄⁺, it is difficult in reality, if not impossible, to directly measureδ¹⁵N_i-NH3^42,43.

Practically, site-based δ¹⁵N values of a-NH₃, p-NH₄⁺, and w-NH₄⁺ have been widely measured (Fig. 2). However, their δ¹⁵N, namely δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+, cannot be directly used as δ¹⁵N_i-NH3 to calculate F_v-NH3 and F_c-NH3 (Eq. (1)). First, the i-NH₃ mixture of v-NH₃ and c-NH₃ emissions will be partially converted to p-NH₄⁺ and w-NH₄⁺ (Fig. 1). The conversion of NH₃ to p-NH₄⁺ has significant isotope effects as a result of either kinetic isotope fractionations (ε_k) during the unidirectional reaction of NH₃ → p-NH₄⁺ or equilibrium isotope fractionations (ε_eq) during reversible reactions of NH₃ ↔ p-NH₄⁺ (Supplementary Table 3). Accordingly, δ¹⁵N_a-NH3 and δ¹⁵N_p-NH4+ differ from δ¹⁵N_i-NH3 and thus cannot be directly used in Eq. (1)^34,35. Second, precipitation scavenges both a-NH₃ and p-NH₄⁺ via the rainout and washout processes (Fig. 1), but the preferential wet scavenge between a-NH₃ and p-NH₄⁺ can potentially cause differences between δ¹⁵N_w-NH4+ and δ¹⁵N_i-NH3⁴⁴. Consequently, δ¹⁵N_w-NH4+ cannot be directly used to calculate F_v-NH3 and F_c-NH3 in Eq. (1) either. According to both simultaneous observations at the same sites (Supplementary Table 4) and non-synchronous observations in the same regions (Fig. 3a), δ¹⁵N in NH₄⁺ (particularly p-NH₄⁺) are generally higher than δ¹⁵N_a-NH3, which is generally negative (Fig. 3a). The main reason is that large equilibrium isotope fractionations occur during the transformation of NH₃ to NH₄⁺ (Supplementary Table 3), leading to substantial differences (denoted as ¹⁵∆) of δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ to the corresponding δ¹⁵N_i-NH3 (¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+, respectively). In this work, we developed a new set of methods to constrain ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+, and then reconstruct δ¹⁵N_i-NH3 (detailed in Methods).

Fig. 2. Global and regional maps with the observation sites for δ¹⁵N of ambient NH₃ (a-NH₃), particulate NH₄⁺ (p-NH₄⁺), and precipitation NH₄⁺ (w-NH₄⁺) in East Asia, North America, and Europe.

Maps were created by using ArcGIS version 10.5 (Esri Inc., USA). The base map was download from https://hub.arcgis.com/datasets/esri::world-countries-generalized.

Generally, based on simultaneous observation data of seasonal mean C_a-NH3, C_p-NH4+, δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ at six sites (Supplementary Table 5), we estimated corresponding δ¹⁵N_i-NH3, ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ (Eqs. (7–10); detailed in Methods). Then, we established the relationships between ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, or ¹⁵∆_w-NH4+ and atmospheric NH₃ conversion ratios (expressed as f_p-NH4+, i.e., C_p-NH4+/(C_a-NH3 + C_p-NH4+)) (Supplementary Fig. 5). These relationships show that ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ decrease with an increase of f_p-NH4+ (Supplementary Fig. 5). ¹⁵∆_a-NH3 and ¹⁵∆_p-NH4+ vary linearly with the reaction degree of an open system (i.e., f_p-NH4+) (Supplementary Fig. 5a) consistent with the prediction of isotopic theory⁴⁵. The ¹⁵∆_w-NH4+ includes ¹⁵∆_a-NH3 and ¹⁵∆_p-NH4+ because precipitation scavenges a-NH₃ and p-NH₄⁺ via the rainout and washout processes (Supplementary Fig. 5b).

Meanwhile, we examined the impact of historical NO_x and sulfur dioxide (SO₂) reductions on mean annual f_p-NH4+ (Supplementary Fig. 6a–f). The f_p-NH4+ generally decreased from 1990 to 2017 in Europe and 2004 to 2018 in North America, but did not vary clearly from 2000 to 2018 in East Asia (Supplementary Fig. 7). Then, based on the relationships between ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, or ¹⁵∆_w-NH4+ and f_p-NH4+ (Supplementary Fig. 5) and mean annual f_p-NH4+ values (Supplementary Fig. 7), we calculated mean annual ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ in each region (Supplementary Fig. 8), which were further used to calculate the corresponding δ¹⁵N_i-NH3 (Fig. 3b) of site-based δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ (Fig. 3a) (detailed in Methods).

Spatial and temporal patterns of δ¹⁵N_i-NH3 variations

Spatially, δ¹⁵N_i-NH3 is higher in North America (−5.7 ± 4.6‰) than in Europe (−7.7 ± 6.3‰) and East Asia (−8.0 ± 6.0‰) (Fig. 3b). Because the δ¹⁵N_c-NH3 is distinctly higher than the δ¹⁵N_v-NH3 (Supplementary Fig. 2), the emission strength of c-NH₃ relative to v-NH₃ in North America is higher than that in East Asia and Europe. On the one hand, the emission inventories also show that the proportion of v-NH₃ in North America is lower than that in East Asia and Europe (Supplementary Fig. 1b). On the other hand, because energy consumption is the primary source of c-NH₃ and fertilizer consumption and animal manure are the source of v-NH₃ (Supplementary Fig. 15a, b), the energy consumption ratio to fertilizer consumption and animal manure is higher in North America than in East Asia and Europe (Supplementary Fig. 15c).

δ¹⁵N_i-NH3 decreased significantly between 1972 and 2018 in North America (p < 0.01) and decreased slightly from 2001 to 2018 in East Asia (p = 0.13) and 1974–2017 in Europe (p = 0.39) (Fig. 4). These results suggest that the emission strength of v-NH₃ relative to c-NH₃ generally increased during the past decades in our three study regions, especially in North America. This finding coincided with increasing fertilizer consumption and animal manure in East Asia and North America over the past decades (Supplementary Fig. 16).

Relative contributions and amounts of v-NH₃ and c-NH₃ emissions

We considered ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ (detailed in Methods) in Eq. (1) to establish a set of new isotope mass-balance equations to calculate F_v-NH3 and F_c-NH3 by using δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ in each region (Eqs. (2–4), respectively).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{a}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{v}-\mathrm{NH}3}\times F_{\mathrm{v}-\mathrm{NH}3}+{\delta }^{15}{\mathrm{N}}_{\mathrm{c}-\mathrm{NH}3}\times F_{\mathrm{c}-\mathrm{NH}3}{+}^{15}{\Delta }_{\mathrm{a}-\mathrm{NH}3}$$ 2

$${\delta }^{15}{\mathrm{N}}_{\mathrm{p}-\mathrm{NH4}+}={\delta }^{15}{\mathrm{N}}_{\mathrm{v}-\mathrm{NH}3}\times F_{\mathrm{v}-\mathrm{NH}3}+{\delta }^{15}{\mathrm{N}}_{\mathrm{c}-\mathrm{NH}3}\times F_{\mathrm{c}-\mathrm{NH}3}{+}^{15}{\Delta }_{\mathrm{p}-\mathrm{NH}4+}$$ 3

$${\delta }^{15}{\mathrm{N}}_{\mathrm{w}-\mathrm{NH4}+}={\delta }^{15}{\mathrm{N}}_{\mathrm{v}-\mathrm{NH}3}\times F_{\mathrm{v}-\mathrm{NH}3}+{\delta }^{15}{\mathrm{N}}_{\mathrm{c}-\mathrm{NH}3}\times F_{\mathrm{c}-\mathrm{NH}3}{+}^{15}{\Delta }_{\mathrm{w}-\mathrm{NH}4+}$$ 4

F_v-NH3 and F_c-NH3 values were calculated by the SIAR model (detailed in Methods).

F_c-NH3 averages 40 ± 21% in East Asia, 49 ± 16% in North America, and 44 ± 19% in Europe (Fig. 5a), which confirms higher emission strength of c-NH₃ relative to v-NH₃ in North America than in East Asia and Europe. These estimations based on isotope methods are generally higher than the fractions of c-NH₃ emissions in corresponding regions (5–10%; Supplementary Fig. 1b) or the globe based on emission inventories (30%^10,23,31). One possible explanation is that the study regions are hotspots of globally high c-NH₃ emissions²⁴. Supportively, the total consumption of fossil fuels in these regions accounts for more than 60% of the global energy consumption (Supplementary Fig. 15a), though their areas account for only 24% of the worldwide land area⁴⁶. Further, the spatial pattern of F_c-NH3 (North America > Europe > East Asia; Fig. 5a) assembles that of the energy consumption ratio to fertilizer consumption and animal manure (Supplementary Fig. 15c). Based on F_v-NH3 in our study and explicit amounts of v-NH₃ (A_v-NH3) in emission inventories (Fig. 5), we further estimated the amounts of c-NH₃ (A_c-NH3) and total NH₃ emissions (detailed in Methods), which average 6.6 ± 3.4 Tg N yr⁻¹ and 16.6 ± 3.5 Tg N yr⁻¹ in East Asia, 2.8 ± 0.9 Tg N yr⁻¹ and 5.8 ± 1.1 Tg N yr⁻¹ in North America, and 2.8 ± 1.3 Tg N yr⁻¹ and 7.0 ± 1.6 Tg N yr⁻¹ in Europe, respectively (Fig. 5b). The highest energy consumption in East Asia supports its highest A_c-NH3 among the three study regions (Fig. 5b & Supplementary Fig. 15a).

Our new estimates of total NH₃ emission in Europe are very close to its total NH_x deposition (Supplementary Fig. 13). In China and the United States, the NH_x deposition fluxes could be explained by our updated total NH₃ emissions (Supplementary Fig. 13). Lower deposition than the emission was observed in China and the United States because part of the NH₃ emissions was diffused or deposited out of these polluted areas^24,47. Before this work, total NH₃ emissions based on statistical inventories in China, the United States, and Europe were all distinctly lower than the corresponding NH_x deposition (Supplementary Fig. 13). Accordingly, our results provided new estimates on c-NH₃ and total NH₃ emissions. However, because the v-NH₃ underestimation may still exist⁴⁸, the contribution of the c-NH₃ underestimation to the underestimation of total NH₃ emissions and the mismatches between regional NH₃ emissions and NH_x deposition (Supplementary Fig. 13) remains uncertain.

Temporally, F_c-NH3 has decreased significantly (p < 0.01) over the past decades in North America (Supplementary Fig. 12a), leading to increasing ratios of F_v-NH3 to F_c-NH3 (p < 0.01) and also A_v-NH3 to A_c-NH3 (p < 0.05) generally from lower than 1.0 to higher than 1.0 (Supplementary Figs. 12b & 14b). F_c-NH3 in East Asia and Europe has decreased slightly (p = 0.12 and p = 0.26, respectively) (Supplementary Fig. 12a), leading to slightly increasing ratios of F_v-NH3 to F_c-NH3 (p = 0.12 and p = 0.51, respectively) and also A_v-NH3 to A_c-NH3 over the past decades (p = 0.25 and p = 0.61, respectively) (Supplementary Figs. 12b & 14b). In East Asia and North America, the temporally increasing v-NH₃ emissions relative to c-NH₃ are supported by the increasing fertilizer consumption and animal manure production (Supplementary Fig. 16). Emission inventories also showed the rapid increase of the relative contribution of v-NH₃ in North America (Supplementary Fig. 1b). These temporal variations in North America revealed that the more dominant NH₃ emission has shifted from c-NH₃ to v-NH₃ sources over the past decades. Rapidly increasing v-NH₃ emissions may be one of the reasons for the shift from nitrate-dominated to NH₄⁺-dominated N deposition in the United States⁷.

In this study, the non-urban F_c-NH3 does not change with the corresponding distance between the sampling site and the nearest urban area (Supplementary Fig. 11). Meanwhile, there are no significant differences in F_c-NH3 between urban (73 sites) and non-urban (65 sites) or between agricultural (32 sites) and non-agricultural sites (33 sites) (Supplementary Fig. 11). Similar numbers of replicate sites between the above surface environments reduce the risks of over- or under-estimating c-NH₃ or v-NH₃ contributions. Accordingly, the F_c-NH3 at urban or non-urban sites is not substantially influenced by local NH₃ emissions but reflects the source diversity and transporting/mixing complexity of regional NH₃ emissions. There has been much evidence from ground monitoring, satellite observations, and emission inventories to show the co-occurrence of c-NH₃ and v-NH₃ emissions and extensive NH₃ or NH₄⁺ transportation and mixing among landscapes. Firstly, v-NH₃ sources (mainly from solid wastes and sewages) have comparable emission strengths with c-NH₃ sources (mainly from fossil fuel combustion) in urban areas. Human excreta contributed 11.4% to the total NH₃ emissions in Shanghai urban of eastern China⁴⁹. The v-NH₃ from urban green space contributed up to 60% to ambient NH₃ in Qingdao in northern China⁵⁰. The urban NH₃ concentrations influenced by the v-NH₃ from urban waste containers, sewage systems, humans, and open markets were 2.5 times higher than that of traffic-influenced urban areas in Spain⁵¹. Ambient NH₃ concentrations in the Beijing urban peaked when fertilizer was intensively applied on the North China Plain⁵². Secondly, the c-NH₃ emission (mainly wildfire, fossil fuel and crop residue combustion) is undoubtedly as significant as the v-NH₃ (mainly fertilizer application and live stocks) in non-urban areas. For example, the c-NH₃ from biomass burning control seasonal variations of surface NH₃ concentrations in major disturbed regions of the Northern Hemisphere, which is even stronger in the Southern Hemisphere with frequent wildfires (Supplementary Table 2). Based on a data synthesis (Supplementary Table 2), ambient NH₃ concentrations during wildfire smoke-impacted periods could be enhanced by a factor of 2–20 compared to periods with no wildfires. Besides, our results, as well as previous studies, show that v-NH₃ contributed 58% (31–87%) for sites in Beijing urban^34,35,53, while the c-NH₃ accounted for 33% (25–69%) in the total NH₄⁺ deposition of the cropland 370 km far from the Beijing urban¹¹. Collectively, site-based δ¹⁵N_a-NH3 and δ¹⁵N_NH4+ represent mixing signatures and can be feasibly used to evaluate regional c-NH₃ and v-NH₃ emissions.

Implications and uncertainties

This study demonstrates that c-NH₃ emissions have been considerably underestimated in regions with significant anthropogenic sources. In the process, the new estimates of c-NH₃ emissions have brought measured NH_x deposition data into closer agreement with emissions. Our conclusion about the underestimation of c-NH₃ emission has important implications for control measures since the current efforts have been focused almost exclusively on agricultural sources. Although the marginal abatement cost of NH₃ emissions is only 10% of the global NO_x emission, with 162 billion US dollars net benefit, the reduction can effectively improve air pollution and its negative impact^54,55. For example, measures to reduce c-NH₃ emissions should be considered in the methods for mitigating v-NH₃ emissions by improved farm management practices with N use reductions, deep machine placement of fertilizer, enhanced-efficiency fertilizer use, and enhanced manure management in the agriculture sector^56,57. The revelation of high c-NH₃ emissions also implies that the potential, costs, and impacts of NH₃ emissions reduction need to be re-assessed. Steps to reduce c-NH₃ emissions may alleviate the pressure of reducing agricultural NH₃ emissions and achieve ‘win-win’ outcomes for agricultural production and food supply, human and environmental health^2,56.

The uncertainty of this study lies in the technical difficulty in measuring δ¹⁵N for all NH₃ emission sources in each region. It is even more challenging to conduct simultaneous observations on C_a-NH3, C_p-NH4+, δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ among landscapes. As a result, a limited number of data is available in terms of observation sites and their spatial distribution in the three regions, and the corresponding data comparison among the three regions is preliminary in the current stage. Moreover, δ¹⁵N observations focus largely on the middle- and low-latitudes and low altitudes of the Northern Hemisphere but rare on the high mountains and high latitudes of the Northern Hemisphere and the Southern Hemisphere. More observations on the concentration and δ¹⁵N parameters of NH₃ and NH₄⁺ in both source emissions and deposition are necessary to improve the isotope source apportionment, especially in remote areas. Finally, despite the updated regional c-NH₃ and total NH₃ emissions, future in-depth cooperation among different observational and modeling methods should be encouraged.

Methods

δ¹⁵N of major NH₃ emission sources

We collected the δ¹⁵N data of major NH₃ emission sources (Supplementary Fig. 2a) from 17 relevant publications (by December 2020). For c-NH₃, the proportional contributions of global NH₃ emissions from vehicle exhausts (ve-NH₃), coal combustion (cc-NH₃), and biomass burning (bb-NH₃) (1.3, 6.3, and 8.2 Tg N yr⁻¹, respectively^10,23,31) in total c-NH₃ emissions (15.9 Tg N yr⁻¹) are 8%, 40%, and 52%, respectively (Eq. (5)). Accordingly, we calculated the δ¹⁵N_c-NH3 by a mass-balance method (Eq. (5)).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{c}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{ve}-\mathrm{NH}3}\times 8\%+{\delta }^{15}{\mathrm{N}}_{\mathrm{cc}-\mathrm{NH}3}\times 40\%+{\delta }^{15}{\mathrm{N}}_{\mathrm{bb}-\mathrm{NH}3}\times 52\%$$ 5

For v-NH₃, global NH₃ emissions from fertilizer application (fa-NH₃) and waste materials (wm-NH₃) account for 56% and 44% of the total v-NH₃ emission, respectively¹⁰. Similarly, we calculated the δ¹⁵N_v-NH3 by Eq. (6).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{v}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{fa}-\mathrm{NH}3}\times 56\%+{\delta }^{15}{\mathrm{N}}_{\mathrm{wm}-\mathrm{NH}3}\times 44\%$$ 6

The standard deviation (SD) of δ¹⁵N_c-NH3 and δ¹⁵N_v-NH3 is propagated errors estimated using the Monte Carlo method (MCM). Briefly, we ran 10000 trials for the MCM in the software of Microsoft Excel-Add-In and calibrated the SD to match the corresponding true values.

Atmospheric δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ observations

Keywords used for the search are ‘nitrogen isotope’, ‘ammonia/NH₃’, ‘ammonium/NH₄⁺’, ‘rainfall’, ‘rain’, ‘rain water’, ‘precipitation’, ‘aerosol’, and ‘particulate’. The databases include the Web of Science (http://isiknowledge.com), Google Scholar (http://scholar.google.com.hk), and Baidu Scholar (http://xueshu.baidu.com). By December 2020, there are 18 publications on δ¹⁵N_a-NH3 (listed in Supplementary Text 1), 43 publications on δ¹⁵N_w-NH4+ (listed in Supplementary Text 2), and 28 publications on δ¹⁵N_p-NH4+ (listed in Supplementary Text 3). Data in the figures of these publications were extracted using the software of Web Plot Digitizer (Version 4.2, San Francisco, California, USA).

Spatial distributions of the sites with δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ observations are shown in Fig. 2. When counting the same site with observations in different years as one site only, there are 387 measurements of δ¹⁵N_a-NH3 at 32 sites (including 12 sites in East Asia, 19 sites in North America, and one site in Europe), 857 measurements of δ¹⁵N_p-NH4+ at 33 sites (including 22 sites in East Asia, seven sites in North America, two sites in Europe, one site in Africa, and one site in Atlantic), and 1540 measurements of δ¹⁵N_w-NH4+ at 80 sites (including 42 sites in East Asia, 25 sites in North America, ten sites in Europe, one site in South America, one site in Africa, and one site in Atlantic) (Fig. 2 & Supplementary Fig. 3). The surface land types of observation sites were identified according to descriptions in original publications. There are 73 urban sites (mainly constructed lands) and 65 non-urban sites (mainly including 32 agricultural sites and 33 non-agricultural sites) with δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ observations in the study areas of East Asia, North America, and Europe (Supplementary Fig. 4a, b).

We only analyzed the data in the major areas of East Asia during 2001–2018, North America during 1972–2018, and Europe during 1974–2017 due to the sparsity of available data out of these areas (Fig. 2 & 4). It should be noted that analytical methods of ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ differ among studies, including converting to 1) N₂ as an end product using the Elemental Analyzer combustion method or 2) N₂O as an end product using the bromate oxidation and azide or hydroxylamine reduction, or using the persulfate oxidation and denitrifier method⁵⁸, but such difference would not change the spatiotemporal patterns of δ¹⁵N_i-NH3 (Fig. 3 & 4) because the analytical accuracy is generally better than ±0.7‰. In addition, 9%, 16%, and 75% of the δ¹⁵N observations were conducted across cooler seasons, warmer seasons, and the whole year, respectively. The seasonal differences in NH₃ emissions would not substantially influence the spatiotemporal patterns of δ¹⁵N_i-NH3 (Fig. 3 & 4). Moreover, all observation sites in this study are more than 1 km away from obvious local emission sources, excluding the influence of a single source.

Atmospheric C_a-NH3 and C_p-NH4+ observations

‘Atmospheric ammonia/NH₃’, ‘particulate ammonium/NH₄⁺’, and ‘aerosol ammonium/NH₄⁺’ were used as keywords to search publications for the concentration of a-NH₃ and p-NH₄⁺ (C_a-NH3 and C_p-NH4+, respectively) in the same databases as described above. There are 107 publications published by July 2021 (listed in Supplementary Text 4) with simultaneous observations on C_a-NH3 and C_p-NH4+. To describe temporal variations of C_p-NH4+/(C_a-NH3 + C_p-NH4+) values (i.e., f_p-NH4+ values) in each region (Supplementary Fig. 7), we counted the same site with different years as different observations because few sites observed C_a-NH3 and C_p-NH4+ for many years. We only used data with an observation period exceeding six months to improve the estimation of annual f_p-NH4+ values. According to this criterion, there are 262 sites in East Asia during 1993–2018, 459 sites in North America during 1986–2018, and 1018 sites in Europe during 1981–2017 (Supplementary Fig. 7).

Differences of δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ from i-NH₃

Based on simultaneous observations of seasonal C_a-NH3, C_p-NH4+, δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ values at the same sites (Supplementary Table 5), we calculated the δ¹⁵N_i-NH3 by the following isotope mass-balance equation (Eq. (7)).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{a}-\mathrm{NH}3}\times f_{\mathrm{a}-\mathrm{NH}3}+{\delta }^{15}{\mathrm{N}}_{\mathrm{p}-\mathrm{NH}4+}\times f_{\mathrm{p}-\mathrm{NH}4+}$$ 7

where f_a-NH3 = C_a-NH3 / (C_a-NH3 + C_p-NH4+) and f_p-NH4+ = C_p-NH4+ / (C_a-NH3 + C_p-NH4+).

Then, we calculated the differences between δ¹⁵N_i-NH3 and δ¹⁵N_a-NH3 (¹⁵∆_a-NH3, Eq. (8)), between δ¹⁵N_i-NH3 and δ¹⁵N_p-NH4+ (¹⁵∆_p-NH4+, Eq. (9)), between δ¹⁵N_i-NH3 and δ¹⁵N_w-NH4+ (¹⁵∆_w-NH4+, Eq. (10)) for the same sites with simultaneous observations of seasonal C_a-NH3, C_p-NH4+, δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, and δ¹⁵N_w-NH4+ values (Supplementary Table 5).

$${}^{15}{\Delta }_{\mathrm{a}-\mathrm{NH}3}={\delta }^{15}{\mathrm{N}}_{\mathrm{a}-\mathrm{NH}3}-{\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3}$$ 8

$${}^{15}{\Delta }_{\mathrm{p}-\mathrm{NH}4+}={\delta }^{15}{\mathrm{N}}_{\mathrm{p}-\mathrm{NH}4+}-{\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3}$$ 9

$${}^{15}{\Delta }_{\mathrm{w}-\mathrm{NH}4+}={\delta }^{15}{\mathrm{N}}_{\mathrm{w}-\mathrm{NH}4+}-{\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3}$$ 10

Based on the relationships in Supplementary Fig. 5 and mean annual f_p-NH4+ values in Supplementary Fig. 7, we calculated the mean annual ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ in each region (Supplementary Fig. 8). Because no clear trends were observed in f_p-NH4+ between 2001–2018 in East Asia and before implementing emission reduction measures in Europe (i.e., 1971–1989 in this study) and in North America (i.e., 1971–2004 in this study)^59–61 (Supplementary Fig. 7), we calculated isotope effect values by using the mean f_p-NH4+ during above-mentioned years in the corresponding region (Supplementary Fig. 8). In other words, the same values are assumed for ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, or ¹⁵∆_w-NH4+ during these years in the region (Supplementary Fig. 8).

To examine the applicability of our method for estimating and calibrating ¹⁵∆ values, we used the mean annual ¹⁵∆ values and simultaneous δ¹⁵N_a-NH3 and δ¹⁵N_p-NH4+ observations, δ¹⁵N_a-NH3 and δ¹⁵N_w-NH4+ observations, or δ¹⁵N_p-NH4+ and δ¹⁵N_w-NH4+ observations at the same sites (Supplementary Table 4) to calculate their corresponding δ¹⁵N_i-NH3 (denoted as δ¹⁵N_i-NH3(a-NH3), δ¹⁵N_{i-NH3(p-NH4+)}, or δ¹⁵N_{i-NH3(w-NH4+)}; Eq. (11 − 13), respectively).

$${\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3\left(\mathrm{a}-\mathrm{NH}3\right)}={\delta }^{15}{\mathrm{N}}_{\mathrm{a}-\mathrm{NH}3}{-}^{15}{\Delta }_{\mathrm{a}-\mathrm{NH}3}$$ 11

$${\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3\left(\mathrm{p}-\mathrm{NH}4+\right)}={\delta }^{15}{\mathrm{N}}_{\mathrm{p}-\mathrm{NH}4+}{-}^{15}{\Delta }_{\mathrm{p}-\mathrm{NH}4+}$$ 12

$${\delta }^{15}{\mathrm{N}}_{\mathrm{i}-\mathrm{NH}3\left(\mathrm{w}-\mathrm{NH}4+\right)}={\delta }^{15}{\mathrm{N}}_{\mathrm{w}-\mathrm{NH}4+}{-}^{15}{\Delta }_{\mathrm{w}-\mathrm{NH}4+}$$ 13

We found that differences between δ¹⁵N_i-NH3(a-NH3) and δ¹⁵N_{i-NH3(p-NH4+)}, δ¹⁵N_i-NH3(a-NH3) and δ¹⁵N_{i-NH3(w-NH4+)}, δ¹⁵N_{i-NH3(p-NH4+)} and δ¹⁵N_{i-NH3(w-NH4+)} are negligible, averaging 0.4 ± 2.3‰, −0.2 ± 1.3‰, and −0.4 ± 2.5‰, respectively (Supplementary Fig. 9). This demonstrates that the mean annual ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ values (Supplementary Fig. 8) estimated by the mean annual f_p-NH4+ values (Supplementary Fig. 7) can be used to calculate corresponding δ¹⁵N_i-NH3 (Fig. 3b) of site-based δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ (Fig. 3a) and to estimate the source contributions using the SIAR model.

Relative contributions and amounts of v-NH₃ and c-NH₃ emissions

Site-based F_v-NH3 and F_c-NH3 values of each region (Eqs. (2−4)) were calculated using the SIAR model⁶². Because each of our calculations has only two end members, the SIAR model can determine the F_v-NH3 and F_c-NH3 values. Moreover, this model allows us to incorporate isotope effects (by inputting mean annual ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, and ¹⁵∆_w-NH4+ in our calculations; Supplementary Fig. 8), the variabilities in δ¹⁵N of both sources (by inputting mean ± SD of δ¹⁵N_v-NH3 and δ¹⁵N_c-NH3; Supplementary Fig. 2b) and the mixture (by inputting all replicate measurements of δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ at each site) into the source contributions. In each run, the percentage data (n = 10000) output from the SIAR model was used to calculate the mean ± SD values of corresponding F_v-NH3 and F_c-NH3 at each site, then site-based mean ± SD values of F_v-NH3 and F_c-NH3 (Supplementary Figs. 10 & 11) were used to calculate the mean ± SD values of each region (Fig. 5a). The mean annual F_v-NH3 and F_c-NH3 were calculated by inputting all replicate measurements of δ¹⁵N_a-NH3, δ¹⁵N_p-NH4+, or δ¹⁵N_w-NH4+ in each year at each site (Fig. 4; Supplementary Fig. 12).

Based on the amount of the v-NH₃ emission (A_v-NH3), we calculated corresponding amounts of total NH₃ emissions (A_total, Eq. (14)) and the c-NH₃ emission (A_c-NH3, Eq. (15)).

$$A_{\mathrm{total}}=A_{\mathrm{v}-\mathrm{NH}3}/F_{\mathrm{v}-\mathrm{NH}3}$$ 14

$$A_{\mathrm{c}-\mathrm{NH}3}=A_{\mathrm{total}}-A_{\mathrm{v}-\mathrm{NH}3}$$ 15

Regional mean ± SD of A_v-NH3 and F_v-NH3 were used to calculate regional mean ± SD of A_total and A_c-NH3 (Fig. 5). The annual A_c-NH3 (Supplementary Fig. 14a) was calculated using the mean A_v-NH3 (Supplementary Fig. 1a) and mean ± SD of site-based F_v-NH3 values in each year (Supplementary Fig. 12). The SDs of A_total and A_c-NH3 values were propagated errors estimated by using the same MCM described above.

Statistical analyses

The SPSS 18.0 software package (SPSS Science, Chicago, USA) and Origin 2016 statistical package (OriginLab Corporation, USA) for Windows were used for data analyses in this study. The Tukey honest significant difference (Tukey HSD) and the least significant difference (LSD) tests of the one-way analysis of variance (ANOVA) were used to identify significant differences in δ¹⁵N among a-NH₃, w-NH₄⁺, and p-NH₄⁺ (Fig. 3a), East Asia, North America, and Europe (Fig. 3b), major sources (Supplementary Fig. 2), and urban and non-urban sites or agricultural and non-agricultural sites (Supplementary Fig. 4), in F_c-NH3 values among East Asia, North America, and Europe (Supplementary Fig. 10), and urban and non-urban sites or agricultural and non-agricultural sites (Supplementary Fig. 11a). Linear regressions were used to examine correlations between sampling years and δ¹⁵N_i-NH3 (Fig. 4), ¹⁵∆_a-NH3, ¹⁵∆_p-NH4+, or ¹⁵∆_w-NH4+ and f_p-NH4+ values (Supplementary Fig. 5), f_p-NH4+ values and NO_x and SO₂ emissions (Supplementary Fig. 6), F_c-NH3 values at non-urban sites with the corresponding distances from the edge of the nearest urban area (Supplementary Fig. 11b), sampling years and F_c-NH3 or F_v-NH3/F_c-NH3 ratio (Supplementary Fig. 12), and sampling years and A_c-NH3 or A_v-NH3/A_c-NH3 ratio (Supplementary Fig. 14). Statistically significant differences were set at p < 0.05 or as otherwise stated. The maps with the observation sites for δ¹⁵N (Fig. 2) were plotted with ArcGIS 10.5 software (Esri Inc., USA).

Source data

Source Data ^{(58.5KB, xls)}

Author contributions

X.Y.L. designed the research. Z.L.C., W.S. and X.Y.L. conducted the research (data collections and analyses) and co-wrote the manuscript. C.C.H., X.J.L., G.Y.C., W.W.W., G.M., C.Q.L. and D.F. commented on the manuscript.

Peer review

Peer review information

Nature Communications thanks Shaoneng He, Jun Li and Yanan Shen for their contribution to the peer review of this work.

Data availability

The data underlying the findings of this study are provided in the Source Data file. Source data are provided with this paper.

Code availability

The SPSS package can be downloaded from https://www.ibm.com/products/spss-statistics. The Origin 2016 statistical package can be downloaded from https://www.originlab.com. The ArcGIS package can be downloaded from https://www.esri.com/en-us/arcgis/about-arcgis/overview. The source code for SIAR used in this paper is openly available from https://rdrr.io/cran/siar.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-022-35381-4.