Measurement report: Soil reactive nitrogen gas emissions in the Tibetan Plateau

The Tibetan Plateau, highly sensitive to climate change, exerts strong atmospheric oxidation capacity partly through rapid cycling of atmospheric reactive nitrogen (Nr). Soil Nr emissions play a crucial role in atmospheric nitrogen cycling and oxidation capacity, yet their emissions on the Tibetan Plateau remain poorly quantified. Combining dynamic chamber measurements, laboratory analysis, and a parameterized model, we assessed the characteristics, driving factors, spatial distribution and annual emissions of soil Nr in the Tibetan Plateau. We found that the optimum soil fluxes of nitrous acid (HONO), nitric oxide (NO), nitrogen dioxide (NO₂), and ammonia (NH₃) were 21.6 ± 8.4, 43.7 ± 14.7, 15.8 ± 1.3, and 190.0 ± 116.3 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$, respectively. These emissions were mainly influenced by soil pH, nutrient content, and microbial community composition. After exposure to atmospheric NO_x and ozone (O₃), Nr emissions from forest soils were enhanced but those from croplands and grasslands were suppressed. The estimated annual emissions of soil HONO, NO, and NO_x from Tibetan Plateau to be 7.0 ± 3.4, 11.6 ± 7.8, and 20.3 ± 7.0 Gg N yr⁻¹, respectively. Soil HONO emissions contribute approximately 10.5 % of the external (NO_x-independent) daytime atmospheric HONO sources and modulate the regional atmospheric chemical balance by elevating the $\mathrm{HONO}/{\mathrm{NO}}_x$ ratio. Our results provide the first integrated quantification of soil Nr emissions on the Tibetan Plateau and emphasize their importance for regional nitrogen cycling and atmospheric oxidation capacity.

1 Introduction

Soil reactive nitrogen (Nr) gas emissions play a key role in the global nitrogen cycle, climate warming and atmospheric chemistry. Nr such as nitrous acid (HONO), nitrogen oxides (NO_x, including nitric oxide (NO) and nitrogen dioxide (NO₂)), and ammonia (NH₃), are capable of directly influencing key atmospheric processes related to climate and air quality (McCalley et al., 2011; Sutton et al., 2009). For example, HONO and NO_x regulate the formation of hydroxyl radicals (OH•) and accelerate gas-phase chemical reactions, leading to tropospheric O₃ formation and bursts of secondary aerosols (e.g., nitrate, sulphate, and secondary organic aerosols (SOA)) (Behrendt et al., 2014; Delaria and Cohen, 2023; Hereid and Monson, 2001; Liu et al., 2021; Sörgel et al., 2015). NH₃ promotes aerosol and cloud formation, affecting radiative attenuation and causing cooling of the Earth's surface (Behera et al., 2013; Bray et al., 2021; Erisman et al., 2011; McCalley et al., 2011). Given the diverse climatic and atmospheric effects of individual Nr species, (Liu et al., 2019; Ma et al., 2022; Song et al., 2023; Wu et al., 2022a), isolating the components of Nr and thoroughly understanding the specific contributions from soil sources is essential for accurately assessing their environmental impacts and informing effective mitigation strategies.

Soils release Nr via a series of biotic and abiotic processes, mainly through microbial nitrification and denitrification, and chemical acid-base equilibrium (Bhattarai et al., 2021; Ermel et al., 2018; Heil et al., 2016; Hong et al., 2025; Kumar et al., 2025; Medinets et al., 2015; Mushinski et al., 2019; Oswald et al., 2013; Su et al., 2011; VandenBoer et al., 2015; Wu et al., 2019). Upon comparing Nr emission from sterilized and natural soils, it was found that the emissions of HONO and NO from sterilized soils were significantly reduced (Oswald et al., 2013; Wu et al., 2019). It was also demonstrated that microbial processes contribute 8–42 times more to HONO emissions from agricultural soils than abiotic processes (Bhattarai et al., 2021). Therefore, the activity of soil N cycling microorganisms can dominate the rate and total amount of Nr emission (Maier et al., 2022). Furthermore, Nr is modulated by soil environmental factors, such as soil acidity, moisture, temperature, nitrogen fertilizer application, vegetation cover, inorganic nitrogen content (${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{NO}}_{\mathrm{2}}^{-}$, and ${\mathrm{NH}}_{\mathrm{4}}^{+}$), soil texture, $\text{carbon}/\text{nitrogen}$ ($\mathrm{C}/\mathrm{N}$) ratio, mineralization and (de)nitrification rates (Behrendt et al., 2014; Bhattarai et al., 2018; Delaria and Cohen, 2023; Donaldson et al., 2014; Kim and Or, 2019; Maljanen et al., 2013; Pilegaard, 2013; Su et al., 2013; VandenBoer et al., 2015). Agricultural activities, particularly the application of nitrogen fertilizers, have been shown to significantly enhance the emissions of HONO, NO, and NH₃ from soils and contribute to nitrate leaching to groundwater/aquifers, a major environmental concern exhibiting substantial spatial and crop-specific variability (Ren et al., 2025; Wang et al., 2025b). Results showed that NO and NH₃ emissions from agricultural fertilized soils can account for 15 % and 23 % of the total global agricultural Nr emissions, respectively (Lu et al., 2021; Ma et al., 2022; Tian et al., 2023). Agricultural soils are also a hotspot for HONO emissions (Wu et al., 2022b). However, the pattern of soil Nr emissions, particularly for HONO, remains unclear on the Tibetan Plateau.

The Tibetan Plateau promotes sustained atmospheric oxidation capacity due to strong ultraviolet radiation, high O₃ and water vapor concentrations, and rapid circulation of atmospheric Nr (Chen et al., 2022b; Liang et al., 2021; Tang et al., 2023). Several investigations have reported that O₃ concentrations at the surface of the Tibetan Plateau range from 25 to 100 ppb, and tend to be higher in spring and winter (Li et al., 2022; Škerlak et al., 2014; Xu et al., 2023; Xu et al., 2018; Yin et al., 2017). Moreover, external cycling (NO_x-independent) of Nr has been observed in the Tibetan Plateau region, triggering elevated HONO and NO_x concentrations and strengthening the local atmospheric oxidizing capacity (Wang et al., 2023a). Global warming and human activities also accelerated natural and anthropogenic emissions of NO_x in the Tibetan Plateau region, probably elevating O₃ concentrations further in the background region (Lyu et al., 2023; Xu et al., 2023).

In this work, we combined dynamic chamber flux measurement, laboratory analysis, and a parameterized model to assess the characteristics, driving factors, spatial distribution and annual emissions of soil Nr in Tibet. We hypothesized that: (1) Tibetan Plateau might be a hotspot for soil Nr emissions, driven by soil physicochemical properties and microbial nitrogen cycling taxa; (2) atmospheric NO_x and O₃ concentrations inhibit soil Nr emissions due to their effects on soil properties; (3) soil HONO emissions contribute significantly to atmospheric HONO concentrations and further influence atmospheric oxidation capacity in Tibetan Plateau.

2 Materials and Methods

2.1 Soil sampling and property analysis

In this study, 25 soil samples were collected in April 2021 according to different land use types in Linzhi, Tibet (Grassland G1–G4, Cropland C1–C8, Forest F1–F8, Wetland W1– W3, and Bare land BL1–BL2, Fig. S1 in the Supplement). The sampling site is located in the southeastern part of the Tibetan Plateau (26°52^′ N–30°40^′ N, 92°09^′ E–98°47^′ E), where a variety of climatic types coexist, ranging from tropical to frigid. The altitude range was 150–7782 m, with an average elevation of ∼ 3100 m. The annual average temperature, precipitation and sunshine duration are 8.7 °C, 650 mm and 2022.2 h, respectively, and the frost-free period was 180 d (Linzhi Municipal People's Government, 2018). At each sampling site, a 10 m × 10 m sample square was delineated, and three sub-samples of topsoil (0–5 cm) were collected along the diagonal, subsequently homogenized to form a composite sample (total weight ∼ 1.5 kg). Subsequently, using a sterile sampling spoon, 1–2 g of soils were collected from the composite sample and placed into 2 mL sterilized centrifuge tubes for subsequent microbiological analysis. The soil samples were transported back to the laboratory within 48 h via SF Express's standard cold-chain service (maintained at a constant temperature of 4 °C throughout the entire process). Upon arrival at the laboratory, soil samples for microbiological analyses were stored at −80 °C, while soil samples for determining Nr gas emissions and physicochemical properties were stored at −20 °C.

Soil samples were passed through a 2 mm analytical sieve and used for the determination of physicochemical properties such as soil water content, pH, particle size and inorganic nitrogen. Soil water content (SWC) was calculated by drying at 105 °C for 24 h and using the change in soil mass before and after drying. Soil pH was determined by a pH meter (FE28, Mettler Toledo, Zurich, Switzerland) in a soil-water suspension (1:2.5, $w/v$). Particle size fractions were determined by a laser diffraction particle size analyzer (LA-960A, HORIBA, Kyoto, Japan) (Song et al., 2020). Soil inorganic nitrogen (${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{NO}}_{\mathrm{2}}^{-}$, ${\mathrm{NH}}_{\mathrm{4}}^{+}$) content was determined by mixing soil with deionized water (DIW) at a ratio of 1:5 ($w/v$) (Song et al., 2024), shaking and centrifuging, and then the extracted supernatant was analyzed by a continuous flow analyzer (Skalar San++ System, Skalar, Breda, Netherlands) (GB/T 42487-2023). At the same time, another identical sample was incubated in a thermostatic incubator at 25 °C for 7 d, and the soil inorganic N content was determined by the method described above, thus calculating the net mineralization rate and net nitrification rate (Lv et al., 2023). For the determination of soil dissolved organic carbon (DOC), 5 g of fresh soil was mixed with 25 mL of 0.5 mol L⁻¹ K₂SO₄ solution, shaken for 1 h at room temperature, centrifuged at 4000 r min⁻¹ for 5 min through a centrifuge (H1850, Xiangyi, Xiangtan, China). Then the supernatant was extracted by filtration with 0.45 µm aqueous filter membrane, and was analyzed by a total organic carbon analyzer (TOC-L CPN, Shimadzu, Kyoto, Japan). Air-dried soil samples passing a 0.15 mm analytical sieve were measured with an elemental carbon and nitrogen analyzer (Elementary Vario MAX CN, Frankfurt, Germany) to determine the total carbon (TC) and nitrogen (TN) content.

2.2 Soil Nr flux measurement

Dynamic chamber systems were typically used to study the release of Nr (HONO, NO, NO₂, and NH₃) from soils (Behrendt et al., 2014). Here, soil Nr gas fluxes were measured using a MUlti-gaS dynamIC measurement system (MUSIC, Fig. S2 in the Supplement). This system simulates soil Nr gas emissions during the wetting-drying cycle and the results are in good agreement with field fluxes and have been applied in several studies (Song et al., 2023; Wu et al., 2020, 2022a, 2019). Specifically, 40 g of fresh soil samples were added to petri dishes (inner diameter = 94 mm) and moistened with purified water until the maximum soil water holding capacity (WHC) was reached, which was measured by filtration (Whatman filter paper no. 42) (Behrendt et al., 2014). Subsequently, the petri dish containing soil was placed in a Teflon chamber (∼ 10 L) in the dark and at a constant temperature (25 ± 0.5 °C) and continuously flushed with dry purified air (free of HONO, NO, NO₂, NH₃, O₃, C_xH_y, H₂O, etc.) at a constant flow rate of 6 L min⁻¹ until the soil in the petri dish was completely dry. The system was set up with three Teflon chambers, and one of the chambers without soil samples was set up as the reference chamber for calculating the flux. Each chamber was measured for 2 min and then switched to the next chamber via a multipoint switching valve, resulting in a temporal resolution of 6 min per chamber. During the drying process, HONO and NO₂, NO, and NH₃ concentrations in the chamber headspace were measured continuously by incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS, AIOFM-CEAS-TY19-1), chemiluminescent NO_x analyzer (Thermo 42i-TL, Waltham, MA, USA), and NH₃ analyzer (Picarro G2103, USA), with instrumental time resolutions of about 10, 10, and 1 s, respectively. The lower limits of detection (LOD) for instrumental measurements of HONO, NO₂, NO, and NH₃ were approximately 150, 200, 100, and 100 ppt, respectively (Song et al., 2023). In addition to the concentration-based instrument LODs, we have now calculated the minimum detectable fluxes for each target gas. Specifically, the minimum detectable fluxes were approximately 0.8 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NO, 1.6 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NO₂, 0.8 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NH₃, and 1.2 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for HONO. To avoid uncertainties caused by switching chambers, only the respective mean value for the 30 s before chamber switching was calculated for each measurement. Furthermore, an infrared CO₂/H₂O analyzer (LI-COR 840A, Lincoln, NE, USA) was used to record soil desiccation and calculate the soil water content (SWC) of the samples.

The fluxes of soil Nr gases (HONO, NO, NO₂, and NH₃) were calculated as follows:

$$\begin{array}{}\text{(1)}& F_{\mathrm{N}}={\displaystyle \frac{Q}{A}}\times {\displaystyle \frac{\mathrm{1}}{V_{\mathrm{m}}}}\times \left(x_{\text{out}}\right(t)-x_{\text{in}})\end{array}$$

where F_N denotes the flux of Nr gases ($\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$); Q denotes the flow rate of the gases (m³ s⁻¹); A denotes the surface area of the soil (m²); V_m denotes the molar volume of air (m³ mol⁻¹); t represents the 30 s before chamber switching; and χ_out and χ_in denote the headspace mixing ratios at the outlet and inlet of the chamber (ppb). The soil sample preservation protocol used in this study (storage at −20 °C followed by thawing) may have amplified the potential for soil Nr emissions; therefore, we adopted the optimal flux (i.e., the highest value) under specific stimulating conditions, which represents the maximum potential emissions at the maximum WHC.

Meanwhile, based on the above flux data, the integrated soil Nr gases (HONO, NO, NO₂, and NH₃) emission during a single wet and dry alternation were calculated as follows:

$$\begin{array}{}\text{(2)}& E_{\mathrm{N}}={\int }_{t=\mathrm{0}}^{t=max}F\left(t\right)\times {\displaystyle \frac{\mathrm{d}t}{{\mathrm{10}}^{\mathrm{6}}}}\end{array}$$

where E_N denotes the integrated soil Nr gas emission (mg m⁻²) and F(t) denotes the soil Nr gas emission flux at moment t. The fluxes were assumed constant over the switch intervals.

2.3 Atmospheric NO_x and O₃ fumigation experiment

Three typical soil types, forest (F3), grassland (G5) and cropland (C2), were selected for the fumigation experiment to simulate the effects of atmospheric NO_x and O₃ concentrations on soil Nr gas emissions. Four fumigation treatments were installed, i.e., 10 ppb  NO, 50 ppb  NO, 10 ppb  NO_x + 30 ppb  O₃, and 50 ppb  NO_x + 100 ppb  O₃. The first two treatments (solely NO) were designed to isolate the direct effect of NO gas on soil nitrogen cycling, independent of its rapid oxidation process, the other two treatments (NO_x + O₃) were designed to simulate the synergistic effects after photochemical aging, where NO is rapidly converted to NO₂ in the presence of O₃, thereby assessing the combined impacts of NO, NO₂, and strong oxidants on the soil system. Although NO rapidly reacts with O₃, and O₃ is concurrently consumed by reactions with soil organic matter, the physicochemical kinetic conditions governing gas conversion and consumption are consistent across all treatment groups under identical flow rates and fumigation regimes. Thus, the relative ranking of actual exposure intensities among the different treatment groups is still considered valid. Three replicates were provided for each treatment, with a total of 36 samples. Specifically, ∼ 100 g of fresh soil samples were weighed and placed into 500 mL brown incubation flasks (Bottom diameter: 7.5 cm; Bottle height: 17.5 cm; Soil thickness: 3–5 cm). The NO standard gas (5.2 ppm, Air Liquide, Shanghai) was subsequently diluted to a pre-set $\mathrm{NO}/{\mathrm{NO}}_x+{\mathrm{O}}_{\mathrm{3}}$ mixing ratio concentration by the Sonimix ozone gas master standard (SONIMIX 4001, LNI, Italy) and piped into an incubation bottle containing soil (flow rate ∼ 3 L min⁻¹) (Fig. S3 in the Supplement). The lid of the incubation bottle was equipped with two connecting pipes and loaded with valves that enable controlled on/off switching. The second pipe of the incubation bottle was connected to the NO_x analyzer to allow real-time observation of changes in NO_x concentration in the bottle. When the fumigant gas in the bottle reaches the pre-set concentration, promptly close the valve of the pipeline connected to the instrument. The whole process will take about 5 min. Then, the soil was fumigated for another 10–20 s before closing the valve of the fumigant gas input pipe. The tube junction was sealed with a sealing film and then placed in a thermostatic incubator (25 ± 0.5 °C). Based on the results from preliminary tests, we selected a fumigation period of 5 d (Table S1 in the Supplement), during which the fumigant gas input was repeated every 24 h to ensure a stable concentration of fumigant gas in the incubation bottles. After fumigation incubation was completed, ∼ 2 g of soil samples were collected within 3 min into 2 mL sterilized centrifuge tubes, and then placed in a refrigerator at −80 °C for storage for subsequent soil microbial analyses. In addition, ∼ 40 g of soil samples were removed from the incubation bottles for the determination of Nr flux. The remaining samples were used for the determination of soil pH, inorganic nitrogen content, and DOC content.

2.4 Soil DNA extraction and sequencing

DNA was extracted from 25 natural soil samples using a DNA extraction kit (MOBIO Laboratories, Carlsbad, CA, USA). The concentration and quality of DNA were determined by NanoDrop2000 (Thermo Fisher Scientific, Waltham, Massachusetts USA) and 1 % agarose gel electrophoresis. The primer pairs 338F (5^′-ACTCCTACGGGAGGCAGCAG-3^′) and 806R (5^′-GGACTACHVGGGTWTCTAAT-3^′), 524F10extF (5^′-TGYCAGCCGCCGCGGTAA-3^′) and Arch958RmodR (5^′-YCCGGCGTTGAVTCCAATT-3^′), ITS1F (5^′-CTTGGTCATTTAGAGGAAGTAA-3^′) and ITS2R (5^′-GCTGCGTTCTTCATCGATGC-3^′) for bacterial V3-V4 region, archaeal V4-V5 region, and fungal ITS1-ITS2 region by PCR amplification (GeneAmp^®9700 PCR Thermal Cycler, ABI, CA, USA), respectively. The volume of the PCR reaction system was 20 µL, see Table S2 in the Supplement for specific reagents and volumes. The PCR amplification cycling conditions were: pre-denaturation at 95 °C for 3 min, followed by denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 45 s for a total of 35 cycles, and finally extension at 72 °C for 10 min until 10 °C. In 3 replicates per sample, PCR products from the same sample were mixed and detected by 2 % agarose gel electrophoresis and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer's instructions, and Quantus™ fluorometer (Promega, USA) was used to quantify. Library construction of purified PCR products was performed using the NEXTFLEX^® Rapid DNA-Seq Kit. Finally, the library was subjected to paired-end sequencing on the Illumina MiSeq PE300 platform (Meiji Biomedical Technology Co., Ltd., Shanghai, China). Raw sequencing data were uploaded to the Scientific Data Centre of Beijing Institute of Genomics, Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/gsub/submit/gsa/subCRA019428/finishedOverview, last access: 22 August 2023) under the accession number CRA012324.

The raw sequencing sequence of the double-end was quality controlled using Fastp software (V0.19.6, https://github.com/OpenGene/fastp, last access: 14 July 2022) and spliced using FLASH software (V1.2.11, https://ccb.jhu.edu/software/FLASH/index.shtml, last access: 14 July 2022). Using UPARSE software (V11, http://www.drive5.com/uparse/, last access: 14 July 2022), the quality-controlled spliced sequences were clustered by operational taxonomic unit (OTU) and chimeras were excluded based on 97 % similarity. The RDP classifier (V2.13, https://sourceforge.net/projects/rdp-classifier/, last access: 14 July 2022) was used to compare the Silva 16S rRNA gene database (V138, https://www.arb-silva.de/, last access: 14 July 2022) as well as the Unite fungal ITS database (V8, https://unite.ut.ee/, last access: 14 July 2022) for OTU species taxonomic annotation with a confidence threshold of 70 %, and the community composition of each sample was counted at different species classification levels.

2.5 Quantification of functional genes involved in nitrogen cycling

Natural soil DNA was extracted from 0.25 g of frozen soil using the MoBio PowerSoil DNA Kit (MOBIO Laboratories) and fumigated soil DNA was extracted from 0.25 g of frozen soil using the DNeasy^® PowerSoil^® Pro Kit (QIAGEN, USA). Quantitative real-time PCR (qPCR) (7300 Real-Time PCR System, Applied Biosystems, USA) was performed on natural soil DNA samples using SYBR fluorescent dye quantitative PCR to determine the copies of amoA, narG, nirK, nosZ and napA genes. The qPCR reaction mixture for natural soil consisted of 2 µL Template DNA, 0.8 µL forward and reverse primers, 10 µL 2×ChamQ SYBR Color qPCR Master Mix (Vazyme), 0.4 µL 50×ROX Reference Dye 1 and 6 µL ddH₂O. Fumigated soil DNA samples were also used by SYBR fluorescent dye method for qPCR (7500 Real-Time PCR System, Applied Biosystems, USA) to determine the copies of amoA_AOA, amoA_AOB, comammox Clade A, nirK, nirS, nxrA, nxrB, napA, and nosZ genes. The qPCR reaction mixture for fumigated soil consisted of 1 µL Template DNA, 0.4 µL forward and reverse primers (0.8 µL for comammox Clade A and napA genes), 10 µL 2×TB Green Premix Ex Taq (Takara), 0.4 µL 50×ROX Reference Dye II and 7.8 µL ddH₂O (7 µL for comammox Clade A and napA genes). Primer sets and qPCR conditions for each gene were listed in Table S3 in the Supplement. A qPCR standard curve based on threshold cycling (CT) values was constructed using a 10-fold dilution of the linearized plasmid. The gene copies of each soil sample were then determined by correlating the CT value of each sample with the known copies in the standard curve using a known linear logarithmic relationship. Plasmid copies conversion formula (copies µL⁻¹) = plasmid concentration (ng µL⁻¹) $\times {\mathrm{10}}^{-\mathrm{9}}\times \mathrm{6.02}\times {\mathrm{10}}^{\mathrm{23}}/(\text{molecular mass}\times \mathrm{660})$. The amplification efficiencies of all qPCRs in this study were in the range of 80 %–110 % and R² > 0.99 (Table S3).

2.6 Upscaling soil emissions of Nr gases to the Tibetan Plateau

We compiled a dataset of soil Nr fluxes in Tibet, including measured data in this study and published data in literature (Table S4 in the Supplement). Stepwise regression analysis was performed to establish quantitative relationships between soil Nr fluxes and key soil properties, including soil pH, inorganic nitrogen contents, and the abundance of nitrogen-cycling functional genes. Based on these relationships and the flux dataset, we estimated the annual soil emissions of HONO, NO, and NO_x in Tibet using parameterized soil Nr emissions model (Wu et al., 2022b). This model takes into account factors such as soil temperature, precipitation, and nitrogen fertilizer application (on farmland). It should be clarified that the current annual emission model does not directly incorporate the effects observed in the fumigation experiments. The upscaling model relies on statistical relationships established between soil Nr fluxes and key in-situ soil properties. While these long-term in-situ properties inherently reflect historical deposition, capturing the short-term chemical feedback of ambient NO_x/O₃ into a regional-scale model is complex. Thus, these effects are qualitatively discussed in relation to the fumigation results (Figs. S5–S7 in the Supplement).

2.7 Quantification the contribution of soil HONO emissions to atmospheric external HONO sources

We quantified the contribution of soil HONO emissions to NO_x-independent (external) atmospheric HONO sources by integrating the external source framework of Wang et al. (2023a) with the soil-atmosphere exchange model of Meusel et al. (2018), based on field data collected at the Nam Co Multisphere Observation and Research Station (30°46.44^′ N, 90°59.31^′ E, 4730 $\mathrm{m}\mathrm{a}.\mathrm{s}.\mathrm{l}.$) (Wang et al., 2023a). Here, community-weighted maximum HONO flux ($F_{\text{comm},max}$) was calculated by weighting land-use-specific maximum HONO fluxes by their areal coverage (bare soil: 12 %; grassland: 74 %; cropland: 0.1 %; forest: 9 %; wetland: 5.6 %) (Yang and Huang, 2025):

$$\begin{array}{}\text{(3)}& F_{\text{comm},max}={\sum }_{i=\mathrm{1}}^n{F}_{max,i}\times {\displaystyle \frac{p_i}{\mathrm{100}}}\end{array}$$

where $F_{max,i}$ = maximum HONO flux for land-use type i ($\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$); p_i = areal percentage of land-use type i. Land-use-specific $F_{max,i}$ values were: bare soil (21.6 ± 8.4); grassland (18.3 ± 6.2); cropland (42.1 ± 28.1); forest (15.7 ± 5.3); wetland (9.8 ± 3.7) $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$.

Soil HONO molecular flux normalized to boundary layer volume ($S_{\text{ground},{\mathrm{cm}}^{-\mathrm{3}}}$) was calculated as:

$$\begin{array}{}\text{(4)}& S_{\text{ground},{\mathrm{cm}}^{-\mathrm{3}}}={\displaystyle \frac{F_{\text{comm},max}\times a}{\text{BLH}\times {\mathrm{10}}^{\mathrm{6}}}}\end{array}$$

where a = unit conversion factor; 10⁶ = scaling to cm³; BLH (boundary layer height) of 182 m was used derived from ERA5 reanalysis data for Nam Co station. A constant BLH is a simplification. The 182 m daily-average from ERA5 for Nam Co was adopted to focus on daytime oxidation capacity driven by HONO photolysis.

The volume mixing ratio production rate S_mix (pptv h⁻¹) representing soil HONO contributions to atmospheric external sources was derived as:

$$\begin{array}{}\text{(5)}& S_{\text{mix}}=S_{\text{ground},{\mathrm{cm}}^{-\mathrm{3}}}\times k\end{array}$$

where k = 3.11 × 10⁻⁴ pptv h⁻¹ per $\mathrm{molec}.{\mathrm{cm}}^{-\mathrm{3}}{\mathrm{s}}^{-\mathrm{1}}$, calculated using the ideal gas law, calibrated based on the air molecular number density (1.159 × 10¹⁹ cm⁻³) and Nam Co's in-situ meteorology (mean daytime temperature: 278.15 K; average atmospheric pressure: 4.45 × 10⁴ Pa) to account for high-altitude thermodynamic properties.

Figure 1 Soil archaeal, bacterial and fungal communities of α-diversity (a) and β-diversity (b). The error bars show the standard errors (Forest: N=8; Grassland: N=4; Cropland: N=8; Wetland: N=3; Bare land: N=2). Kruskal–Wallis test was used for one-way ANOVA between groups, and different lowercase letters show differences between groups. Richness is actual observations; evenness is based on Simpson's index measure; diversity is Simpson's diversity index.

2.8 Statistical analysis

Kruskal–Wallis test, and one-way analysis of variance (ANOVA) and Duncan's multiple comparisons (SPSS Version 26.0., IBM Corp., Armonk, NY, USA) were used to distinguish the differences of soil physicochemical properties, functional genes, microbial community diversity and Nr fluxes for different soil use types and fumigated soils, respectively. Non-metric multidimensional scaling (NMDS), analysis of similarity (ANOSIM) and non-parametric multivariate analysis of variance using distance matrices (ADONIS) were used to reveal differences in the composition of archaeal, bacterial and fungal communities across soil use types (R V3.6.3, “vegan” package, https://www.r-project.org/, last access: 26 October 2022). Spearman correlation analysis resolved the relationship between physicochemical properties, functional genes, microbial communities and Nr fluxes in Tibetan soils, while Pearson correlation analysis was applied to resolve the relationships in fumigated soils. Both methods were performed using Origin 2023.

3 Results and Discussion

3.1 Soil physicochemical properties, microbial communities and functional genes abundance

Tibetan region has remarkable differences in physicochemical properties among different soil types, such as pH, TN, TC, $\mathrm{C}/\mathrm{N}$ ratio, ${\mathrm{NH}}_{\mathrm{4}}^{+}\text{-}\mathrm{N}$, particle size, and moisture (p<0.05, Table S5 in the Supplement). In this study, we detected that pH was alkaline in bare soil (8.14 ± 0.18) and wetland (7.84 ± 0.19), acidic in grassland (5.10 ± 0.19) and cropland (6.36 ± 0.64), whereas in forest pH (4.76–8.16) had a wide range of distributions in the Tibetan region, generally in accordance with the results of other studies (Ma et al., 2020; Wang et al., 2023b; Yang et al., 2021). Soil clay content was low (≤ 1 %), with essentially consisting of silt and sand in all soils (Table S5). It has been reported that carbon and nitrogen storage is substantial in surface soils of the Tibetan Plateau, particularly in grasslands (Chen et al., 2022a). Similar results were also obtained in this study, with the TN and TC contents of grassland being 3–26 times more than other soil types (Table S5). Agricultural soils showed comparatively higher ${\mathrm{NO}}_{\mathrm{3}}^{-}\text{-}\mathrm{N}$ content and N transformation rate (net mineralization and nitrification rate) than other soil types, indicating fast N turnover effects caused by fertilization. In addition, Tibetan soils had elevated ${\mathrm{NO}}_{\mathrm{3}}^{-}\text{-}\mathrm{N}$ content compared with ${\mathrm{NO}}_{\mathrm{2}}^{-}\text{-}\mathrm{N}$ and ${\mathrm{NH}}_{\mathrm{4}}^{+}\text{-}\mathrm{N}$ content, which was similar to the results of other studies (Gao et al., 2016; Lin et al., 2019; Yao et al., 2019).

Overall, 1 202 964, 1 079 854 and 1 589 093 high-quality bacterial, archaeal and fungal gene sequences were detected from 25 soil samples, respectively. The sequences were clustered with 97 % similarity to obtain 8355, 531 and 5806 OTUs (Table S6 in the Supplement). The Shannon–Wiener dilution curve was flattened, indicating that the sequencing volume was able to reflect most of the microbial diversity information in the soil samples (Fig. S4 in the Supplement) (Wang et al., 2012; Wu et al., 2022a). The number of shared species of bacteria, archaea, and fungi across all soil types was 878, 46, and 75, respectively. However, archaea (average ∼ 71) and fungi (average ∼ 778) had more unique species than shared species in different soil types (Table S7 in the Supplement). In general, microbial community alpha-diversity was assessed in terms of richness, evenness, diversity and coverage, while community beta-diversity reflected differences in species composition between populations via comparisons of species composition structure between samples (He et al., 2013). In this study, the Good's coverage indices of bacterial, archaeal and fungal communities all ranged from 0.97 to 0.99 (Table S8 in the Supplement), indicating that the results of the current sequencing were highly plausible. It was found that the diversity and community structure of soil archaea, bacteria and fungi were moderately heterogeneous among the different soil types (Kang et al., 2021). This study revealed that archaeal community richness and fungal community richness, evenness and diversity were statistically different between soil types, whereas bacterial richness, evenness and diversity did not differ significantly (Fig. 1a). Meanwhile, based on the results of NMDS ordination (Fig. 1b) and statistical analyses (Table S9 in the Supplement, p<0.05), it was shown that beta-diversity of bacteria, archaea, and fungi varied significantly across soil types, in particular between cropland soils and other soil types. The results demonstrated that soil habitat specificity shaped the diversity of soil microbial communities on the Tibetan Plateau.

Figure 2 Soil Archaea, Bacteria, and Fungi Community Composition (a) and Functional Genes (b). The error bars show the standard errors (Forest: N=8; Grassland: N=4; Cropland: N=8; Wetland: N=3; Bare land: N=2). The figure shows the top 10 phyla in relative abundance of soil archaea, bacteria and fungi. Kruskal–Wallis test was used for one-way ANOVA between groups, and different lowercase letters show differences between groups.

Through the sequencing results, soil bacteria, archaea and fungi were affiliated to 50, 12 and 17 phyla, respectively (Table S6). Figure 2a showed that the soil archaeal community was dominated by Crenarchaeota (10 %–99 %), Thermoplasmatota (0 %–59 %), Euryarchaeota (0 % –62 %), and Halobacterota (0 %–47 %) in the soil archaeal community. The dominant phyla of the bacterial community were Actinobacteriota (6 %–65 %), Proteobacteria (13 %–46 %), Acidobacteriota (3 %–25 %), Chloroflexi (3 %–21 %), and Firmicutes (1 %–23 %). The dominant phyla of the fungal community were Ascomycota (1 %–90 %), Basidiomycota (0 %–69 %), Mortierellomycota (0 %–84 %), and Chytridiomycota (0 %–62 %). It was similar with other studies reporting the composition of soil microbial communities on the Tibetan Plateau (Cao et al., 2022, 2021; Yang et al., 2022; Zou et al., 2023). Crenarchaeota predominated in the Tibetan soil archaeal community, especially in cropland soils (average relative abundance ∼ 98 %), far above other soil types. Additional studies confirmed that Crenarchaeota extensively exist in marine, hot spring and soil environments, which constitute the most numerous and widespread ammonia-oxidising archaeal taxa in nature, with the ability to oxidize ammonia to nitrite for chemoenergetic autotrophic growth, thus being critical for nitrogen fertiliser utilization (Baker et al., 2020; Brochier-Armanet et al., 2008; Dang et al., 2008). Further clarification was provided to explain the low ${\mathrm{NH}}_{\mathrm{4}}^{+}\text{-}\mathrm{N}$ content and high net mineralization/nitrification rates in croplands in Tibet (Table S5). It was found that Actinobacteriota genomes harboured numerous functional genes related to plant residue degradation, and that Ascomycota fungi exhibited the ability to degrade multiple types of plant cellulose and hemicellulose, which increased nitrogen availability during microbial-driven decomposition of plant residues (Bao et al., 2021; Swarnalakshmi et al., 2016; Zhang et al., 2013). Thus, those in cropland soils with high abundance of Actinobacteriota (average relative abundance ∼ 36 %) and Ascomycota (average relative abundance ∼ 54 %) presumably provide additional nitrogen for themselves and other microorganisms. Meanwhile, the abundance of key functional genes for N cycling in grassland and cropland soils was higher than that in other soil types, with napA, narG, nirK, and nosZ gene abundance higher in grassland soils, and amoA gene abundance higher in cropland soils, whereas all of the above genes had lower abundance in bare soils (Fig. 2b). This indicated that grassland and cropland soils in the Tibetan Plateau region possessed high nitrification and denitrification potentials.

Figure 3 Optimum flux (a) and integrated emissions (b) of soil Nr gases (HONO, NO, NO₂, NH₃). The error bars show the standard errors (Forest: N=8; Grassland: N=4; Cropland: N=8; Wetland: N=3; Bare land: N=2). Kruskal–Wallis test was used for one-way ANOVA between groups, and different lowercase letters show differences between groups.

3.2 Soil Nr emissions characteristic and driving factors

Under natural conditions, soil–atmosphere exchange of reactive nitrogen is typically bidirectional (Bao et al., 2022). Here, we quantify net soil emissions and eliminate the masking effect of atmospheric Nr background by using dry Nr-free air as the carrier gas. For example, the observed HONO flux represents the net apparent flux, which integrates direct soil biogenic emission and additional HONO formation via heterogeneous uptake and conversion of atmospheric NO₂ on soil particle surfaces (Fan et al., 2025). This dual-source characteristic helps explain the co-occurrence of HONO release and NO₂ consumption observed in Fig. 3. It is important to acknowledge the methodological limitations regarding the interpretation of our flux results. In this study, all soils were found to emit of HONO, NO, NO₂ and NH₃ during wetting and drying cycles (Fig. 3). The optimum fluxes ranged from 2.5 to 235.2 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for HONO, 1.2 to 268.4 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NO, 7.6 to 43.7 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NO₂, and 6.4 to 547.8 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ for NH₃ (Fig. 3a). Integrating the single wetting and drying cycle, the release of HONO was 0.02–0.30 mg N m⁻², NO was 0.01–0.51 mg N m⁻², NH₃ was 0.47–3.07 mg N m⁻², and NO₂ was < 0 (Fig. 3b). When compared to studies with similar conditions or methods, the emissions in this study were consistent with those reporting soil HONO, NO, or NO₂ emissions ranging from 1 to 3000 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ (Mamtimin et al., 2016; Meusel et al., 2018; Oswald et al., 2013; Song et al., 2023; Su et al., 2011; Weber et al., 2015; Wu et al., 2022a, 2019). Optimal fluxes and integrated emissions of HONO, NO and NO₂ showed high consistency and convergence across cropland soils. Cropland soils consistently remained the primary source of HONO, NO and NO₂ emissions (42.1 ± 28.1, 49.2 ± 31.8 and 19.9 ± 3.6 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$, respectively), whether in terms of instantaneous peaks or cumulative emissions. The findings suggest that the emission mechanisms in cropland ecosystems for HONO, NO, and NO₂ are relatively continuous over time and highly stable (Fig. 3). Studies have shown that high Nr gas emissions from cropland soils depend strongly on fertiliser application factors (Luo et al., 2022). Nitrogen fertiliser application promoted strong HONO emissions, the flux of HONO from fertilised agricultural soils can exceed 1000 $\mathrm{ng}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ (Tang et al., 2019; Xue et al., 2021, 2019). In addition, it has been shown that fertilization or grazing increased soil HONO emissions, which were expressed as farmland ≥ grassland ≥ forest and peatland for different soil types (Bhattarai et al., 2021). It is worth noting that the substantial NO₂ emissions observed in this study cannot be fully explained by classical microbial nitrification and denitrification mechanisms alone. Apart from biological pathways, direct NO₂ emissions are likely driven by several abiotic or non-classical biotic mechanisms. Chemodenitrification is recognized as a significant source of NO₂ in soils; specifically, under acidic conditions, the accumulated ${\mathrm{NO}}_{\mathrm{2}}^{-}$ in soil undergoes chemical decomposition to produce NO and NO₂ (Cai et al., 2012).

However, it should be noted that extrapolating the results of this study to actual field conditions has inherent limitations. Xue et al. (2024) showed that surface water exchange plays a critical role in mediating soil Nr emissions. Under the dry-air conditions employed in our experimental setup, the evaporation of surface water is likely maximized. This enhanced evaporation could potentially facilitate the escape of dissolved Nr gases from the soil matrix, thereby leading to an overestimation of the net fluxes compared to ambient conditions with higher relative humidity. Therefore, our findings provide theoretical maximum emission potentials, and caution is advised when directly applying the absolute flux magnitudes obtained here to field assessments without considering local in situ environmental data (e.g., soil moisture, temperature). Future studies integrating in situ field measurements or environmentally controlled microcosm systems are essential to delineate the dynamic impacts of wet-dry cycles on Nr emissions.

Figure 4 Correlation matrix between soil physicochemical properties, functional genes, microbial communities and Nr fluxes based on Spearman correlation analysis. SWC: Soil water content; TN: total nitrogen; TC: total carbon; $\mathrm{C}/\mathrm{N}$ ratio: $\mathrm{TC}/\mathrm{TN}$; Net. N: net nitrification rate; Net. M: net mineralization rate; Richness is actual observations; evenness is based on Simpson's index measure; diversity is Simpson's diversity index. ^∗ P < 0.05; ${}^{\ast \ast }$ P < 0.01; ${}^{\ast \ast \ast }$ P < 0.001.

Spearman's correlation analysis revealed that the potential of soil Nr gas emission in Tibet was remarkably correlated with soil physicochemical properties, microbial communities and key functional genes of the nitrogen cycle (Fig. 4, p<0.05). Generally, soil HONO and NO emissions were significantly correlated with soil pH and nitrogen content. Su et al. (2011) found that HONO emissions increased with higher nitrogen content and acidity in the soil. In contrast, Oswald et al. (2013) measured soil HONO emissions under different pH conditions globally and found that the fluxes did not decrease with increasing pH. Furthermore, another study demonstrated that the pH at the surface of soil mineral particles, rather than the pH of the soil solution, influences soil HONO emissions (Donaldson et al., 2014). Meusel et al. (2018) reported that HONO and NO emissions were significantly correlated with soil ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NO}}_{\mathrm{2}}^{-}$. Wu et al. (2022b) found that HONO and NO emissions from different types of soils in Shanghai had been correlated remarkably with the concentrations of ${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NO}}_{\mathrm{2}}^{-}$ concentrations. Nevertheless, in this study, soil HONO was significantly correlated with amoA gene abundance and archaeal evenness, while soil NO was significantly correlated with amoA, narG, nirK, and Actinobacteriota abundance (Fig. 4, p<0.05). Wu et al. (2022a) found that amoA_AOA abundance directly affected the wetland soil HONO emission, while NO emission were mainly controlled by amoA_AOA, nirK and nirS genes. In this instance, soil nitrification rate was dictated by AOA and negatively correlated with the $\mathrm{C}/\mathrm{N}$ ratio, particularly when the $\mathrm{C}/\mathrm{N}$ ratio exceeds 25 (Maljanen et al., 2013; Mushinski et al., 2019). At this threshold, soil HONO and NO emissions decreased rapidly. Additionally, the fluxes of HONO and NO were affected by AOB, which influenced amo gene abundance through soil pH, subsequently impacting soil HONO emissions (Mushinski et al., 2019; Scharko et al., 2015).

Actinobacteria belong to Gram-positive bacteria, which can degrade soil organic matter, provide nutrients to the soil, as well as maintain high activity and DNA repair ability even at low temperatures, acting as a dominant group of bacteria in low-temperature environments (Johnson et al., 2007). In our study, we also found Actinobacteriota (6 %–65 %) to be the dominant phylum of the bacterial community in Tibet, especially in cropland soils (Fig. 2a). High NO emissions from cropland soils showed a strong consistency with Actinobacteriota abundances. Therefore, it is suggested that Actinobacteriota may act as a significant microbial regulator for NO emissions in Tibetan cropland soils. Overall, soil reactive N emission potential in Tibet was explained by soil pH, carbon and nitrogen content, (de)nitrification functional genes, and key microflora.

Figure 5 Conceptual diagram linking the effects of fumigation with high and low concentrations of NO and NO_x + O₃ on soil microbial functional genes, physicochemical properties, and Nr fluxes across forest, grassland, and cropland soils. Red bold indicate statistically significant increases, while blue bold indicate statistically significant decreases.

Atmospheric Nr emissions and deposition rates have been highly elevated in China during recent years, and studies have shown that a shift towards approximately equal wet and dry nitrogen deposition occurred in 2011–2015, accompanied by increasing dry nitrogen deposition (Liu et al., 2020; Yu et al., 2019; Zhu et al., 2022). Total N emissions from terrestrial ecosystems to be 100 Tg N yr⁻¹, with an eventual 70 Tg N yr⁻¹ being deposited back into terrestrial ecosystems (Fowler et al., 2013). Total annual input of atmospheric nitrogen dry deposition in China was 7.78 kg N ha⁻¹, of which the annual deposition flux of NO₂ was 0.67 kg N ha⁻¹ (Jia et al., 2016). In this study, we found that exposure to simulated atmospheric NO_x/O₃ concentrations altered soil Nr emissions, while HONO and NO emissions from different soil types responded differently to NO_x/O₃ fumigation with different concentration gradients (Figs. 5 and S5–S7). Specifically, the HONO and NO emissions from forest soils were enhanced after NO and NO_x/O₃ mixture fumigation, which resulted in peak emissions that were 2–8 times higher than the previous levels (Fig. S5). However, HONO and NO emissions from cropland and grassland soils were suppressed by fumigation, with HONO and NO peaks in cropland soils decreasing by an average of approximately 31 % and 31 % (Fig. S7), while HONO and NO peaks in grassland soils were decreased by an average of approximately 68 % and 84 % (Fig. S6 in the Supplement). Interestingly, while cropland and grassland soils typically serve as sources of HONO and NO under natural conditions, their fluxes were significantly suppressed under fumigation with elevated NO and NO_x + O₃. These results indicate that high concentrations of NO and NO_x + O₃ may lead to a decrease in the soil production rates of HONO and NO, thereby causing a reversal of the source-sink balance. Previous study showed that the net atmospheric Nr budgets for paddy fields, forests, and tea plantations in a sub-tropical catchment to be +3.7, −36.1, and +23.8 $\mathrm{kg}\mathrm{N}{\mathrm{ha}}^{-\mathrm{1}}{\mathrm{yr}}^{-\mathrm{1}}$, respectively, suggesting that forests served as sinks of atmospheric Nr, whereas paddy fields and tea plantations served as sources of atmospheric Nr (Zhu et al., 2021). In addition, forests have a higher efficiency of atmospheric nitrogen capture than other land-use types (Du et al., 2019). Therefore, forest soils under NO_x/O₃ exposure might absorb more inputs from external nitrogen sources, accelerating the rate of nitrogen transformation under the dry-wet cycle, which led to increased emissions of HONO and NO.

Fumigation with different concentration gradients of NO and NO_x/O₃ also altered soil physicochemical and biological properties (Fu et al., 2020; Ning et al., 2015). Forest (pH ∼ 5.59 ± 0.16), cropland (pH ∼ 5.44 ± 0.18) and grassland (pH ∼ 5.10 ± 0.05) soils were acidic before fumigation in the study. It was found that soil pH tended to increase after fumigation, especially in forest soils, with an average increase of ∼ 0.53 (Fig. S8 in the Supplement). The increase in pH is primarily attributed to changes in the redox state, H⁺ consumption during the reduction of nitrogen intermediates, and shifts in microbial functional communities (Chen et al., 2023; Slessarev et al., 2016; Wu et al., 2026). Under strong oxidative fumigation conditions, the abundance of denitrification genes increased (Fig. S8), which likely led to greater H⁺ consumption, thereby raising the soil pH. Furthermore, the differential responses among soil types, rather than a uniform shift across all treatments, suggest that the pH increase was predominantly driven by the fumigation gases rather than merely by the duration of laboratory incubation. Fig. S9 in the Supplement results showed that soil pH was remarkably positively correlated with HONO and NO emissions (p<0.001), in agreement with the findings of (Scharko et al., 2015). Therefore, soil pH increased probably one reason for the increased emissions of HONO and NO from forested soils under fumigation. Moreover, it has also been shown that soils with pH near neutrality were capable of more HONO emissions (Oswald et al., 2013). While atmospheric nitrogen deposition, as a new nitrogen input to terrestrial ecosystems, possibly causes an increased soil carbon storage (Bala et al., 2013; Magnani et al., 2007), consistent with the results of this study on increased soil DOC content under fumigation. It was shown that atmospheric nitrogen deposition decreased the soil $\mathrm{C}/\mathrm{N}$ ratio, facilitated the mineralization of organic matter in the soil, increased the accumulation of inorganic nitrogen in the soil (Fu et al., 2020; Lu et al., 2011). This phenomenon was seen in cropland soils, where increased ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ content was found at elevated fumigation concentrations (Fig. S8). The results showed that under NO and NO_x/O₃ fumigation, cropland soils possibly preferred to undergo mineralization and nitrification processes, which resulted in higher nitrogen accumulation and lower gaseous losses, consistent with the results of the Nr emissions we measured (Fig. S7). Grassland soils under high concentration of NO fumigation increased pH and decreased ${\mathrm{NO}}_{\mathrm{2}}^{-}$ content, which was not beneficial to soil HONO emissions in this condition, as well as raised ${\mathrm{NO}}_{\mathrm{3}}^{-}$ content indicating that grassland soil tended to undergo the process of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ accumulation more than ${\mathrm{NO}}_{\mathrm{3}}^{-}$ consumption under high concentration of fumigation (Figs. 5 and S8). Therefore, HONO and NO emissions from grassland soils under NO and NO_x/O₃ fumigation tended to decrease (Figs. 5 and S6). Functional microbial genes associated with nitrogen cycling in forest soils were sensitive to nitrogen addition (Levy-Booth et al., 2014). In this study, the abundance of nxrB, nirK and nirS genes was altered in forest soils under different concentration gradients of NO and NO_x/O₃, which increased significantly under high NO_x/O₃ fumigation, resulting in the decomposition of more ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NO}}_{\mathrm{2}}^{-}$, thereby releasing more HONO and NO (Figs. S5 and S8). It should be noted that the effects observed in the fumigation experiments (Figs. S5–S7) are discussed qualitatively but are not incorporated into the annual emission parameterization. The annual model relies on long-term in-situ soil properties, which makes it challenging to extrapolate short-term chemical feedback to a regional scale. Therefore, this qualitative discussion highlights the complex interplay between atmospheric chemistry and soil biogeochemistry. Furthermore, exposure to elevated NO_x/O₃ also affected the diversity and composition of soil microorganisms, as well as the key flora involved in the nitrogen cycle, all of which were also related to soil Nr gas emissions (Berthrong et al., 2014; Fierer et al., 2012; Leff et al., 2015; Treseder, 2008; Wang et al., 2018; Zhang et al., 2018b). However, this study has not obtained the relevant data, which leads to the limitation of the study modeling the effects of atmospheric NO_x/O₃ exposure on soil Nr gas emission.

Figure 6 Spatial distribution of annual emissions of HONO (a), NO (b), and NO_x (c) from soils in Tibet. The values in bracket show the minimum and maximum emissions.

3.3 Regional soil HONO and NO emissions and atmospheric implications

The annual emissions of soil Nr gases in Tibet were estimated as 7.0 ± 3.4 Gg N yr⁻¹ for HONO, 11.6 ± 7.8 Gg N yr⁻¹ for NO, and 20.3 ± 7.0 Gg N yr⁻¹ for NO_x in 2021, respectively (Fig. 6 and Table S10 in the Supplement). These emissions were lower with the recently reported HONO value of 12.3 Gg N yr⁻¹ but higher than the NO_x value of 8.9 Gg N yr⁻¹ for 2015 (Li et al., 2026). This discrepancy arises mainly from improved model inputs in this study, including in-situ soil moisture and microbial activity data, in contrast to statistical interpolation with sparse field observations in previous work.

By land-use type, bare soils are the largest contributor due to their vast coverage (43 % of land area), accounting for 43 % of HONO (3.0 ± 2.0 Gg N yr⁻¹), 46 % of NO (5.3 ± 4.2 Gg N yr⁻¹), and 67 % of NO_x (13.6 ± 3.6 Gg N yr⁻¹) emissions. This aligns with studies highlighting high Nr emissions from desert soils and biological soil crusts (Bhattarai et al., 2021; Meusel et al., 2018). Grasslands contribute 29 % to HONO and 44 % to NO emissions. Our estimated grassland NO emission of 0.2 (0–1.7) $\mathrm{kg}\mathrm{N}{\mathrm{ha}}^{-\mathrm{1}}{\mathrm{yr}}^{-\mathrm{1}}$ is comparable with previous plateau studies (Gao et al., 2016; Holst et al., 2007; Lin et al., 2019; Yao et al., 2019; Zhang et al., 2018a). Croplands, despite having the highest fluxes per unit area (Fig. 3), contribute only 8 % to HONO emissions (0.5 ± 0.1 Gg N yr⁻¹) due to their limited spatial extent (∼ 0.3 %). This contrasts with global patterns where croplands are often the dominant source (Wu et al., 2022b), highlighting the unique emission profile of the Tibetan Plateau where natural ecosystems prevail. To validate our estimates, the upscaling model integrated both measured and published field data from the Tibetan Plateau (Table S4). Our HONO and NO_x estimates are consistent with recent regional bottom-up inventories (Li et al., 2026; Wu et al., 2022b) and provide useful constraints for GEOS-Chem simulations over the Third Pole (Wang et al., 2025a).

To quantify the contribution of soil HONO emissions to the atmospheric budget, we integrated our flux data with observations from the Nam Co Multisphere Observation and Research Station. Our optimized calculation framework, combining the external source definition and the soil-atmosphere exchange model (Meusel et al., 2018), reveals that soil HONO emissions on the Tibetan Plateau produce a volume mixing ratio of 10.5 pptv h⁻¹. This accounts for approximately 10.5 % of the 100 pptv h⁻¹ external daytime HONO source identified in the plateau's background atmosphere (Wang et al., 2023a). When combined with the estimated surface nitrate photolysis contribution of ∼ 35 pptv h⁻¹ from the same study, these two pathways jointly explain 45.5 % of the missing HONO source. These findings highlight that surface processes, particularly soil emissions, play a crucial role in regulating atmospheric HONO and consequently influence the oxidative capacity of the plateau's atmosphere.

For NO_x, our estimated soil emissions of 20.3 ± 7.0 Gg N yr⁻¹ dwarfs the weak anthropogenic emissions of less than 5 Gg N yr⁻¹ reported by Wang et al. (2023a). This establishes soils as the dominant natural NO_x source on the plateau. The high HONO to NO_x ($\mathrm{HONO}/{\mathrm{NO}}_x$) emission ratio, we observed for bare soil (0.49) is consistent with the elevated daytime $\mathrm{HONO}/{\mathrm{NO}}_x$ concentration ratio of 0.31 ± 0.06 observed at Nam Co. This observed concentration ratio significantly exceeds the photo-stationary state ratio (0.02–0.05) for internal cycling, which confirms that soil emissions are a primary driver of this chemical disequilibrium. Under intense solar radiation on the Tibetan Plateau, HONO emitted from soils undergoes rapid photolysis to generate OH radicals, while NO_x emissions from soils provide key precursors for tropospheric O₃ production. Continuous warming and wetting of the Tibetan Plateau (Yan et al., 2026) likely further enhance soil Nr emissions, forming positive feedback that strengthens atmospheric oxidation capacity. This climate–chemistry feedback requires accurate representation in Earth system models to support reliable projections of air quality, ecosystem stability, and the lifetime of greenhouse gases over this climatically sensitive region.

Our study further identifies that soil Nr emissions uniquely modulate the regional atmospheric chemical balance by elevating the $\mathrm{HONO}/{\mathrm{NO}}_x$ ratio beyond the atmospheric internal cycling equilibrium. Our field-constrained soil NO_x and HONO emission estimates provide key constraints to reduce the underestimation of O₃ production in GEOS-Chem simulations (Wang et al., 2025a) and narrow the gap between modelled and observed atmospheric chemistry over the Third Pole. The distinctiveness of this effect stems from the plateau's extreme environment: intense solar radiation (${\mathrm{j}}_{{\mathrm{O}}^{\mathrm{1}}\mathrm{D}}$ up to 5.5 × 10⁻⁵ s⁻¹, double the sea-level value) accelerates HONO photolysis to OH radicals, while the high background O₃ concentrations (Yin et al., 2017) mean soil NO_x emissions sustain O₃ accumulation without significant anthropogenic interference. In contrast to global studies that emphasize the role of soil HONO emissions in particulate matter pollution (Li et al., 2026), our regional observations demonstrate that soil Nr emissions on the Tibetan Plateau directly and strongly regulate atmospheric oxidation capacity and O₃ cycling, with profound cascading effects on regional air quality and climate systems.

4 Conclusions

The Tibetan Plateau's soil Nr emissions exhibit distinct patterns shaped by land-use characteristics and environmental factors. Cropland soils emitted the highest fluxes of HONO, NO, and NO₂, driven by soil pH and inorganic nitrogen content, along with abundant nitrogen-cycling functional genes such as amoA and microbial communities dominated by Actinobacteriota and Crenarchaeota. These findings confirm that agricultural soils serve as significant sources of HONO and NO_x due to their unique biogeochemical properties. However, despite higher per-area emissions from croplands, the extensive grassland and bare land areas contributed substantially more to total annual Nr emissions at the regional scale, demonstrating how land-use distribution fundamentally influences nitrogen budgets.

Atmospheric interactions further modulate these emission patterns in complex ways. Exposure to NO_x and O₃ produced divergent responses across ecosystems-forest soils increased emissions, likely through pH elevation and enhanced activity of nitrification genes including nxrB, nirK, and nirS that accelerate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NO}}_{\mathrm{2}}^{-}$ decomposition. Conversely, cropland and grassland soils became emission sinks as altered soil properties, such as accumulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in croplands and reduced ${\mathrm{NO}}_{\mathrm{2}}^{-}$ in grasslands favored nitrogen retention over release. These contrasting responses highlight the intricate relationships between atmospheric chemistry and soil biogeochemistry mediated by microbial communities.

The regional atmospheric impacts of these emissions prove significant despite modest global contributions. Estimated annual soil emissions reached 7.0 ± 3.4 Gg N yr⁻¹ for HONO and 20.3 ± 7.0 Gg N yr⁻¹ for NO_x across Tibet. Soil HONO emissions account for approximately 10.5 % of the external daytime HONO source identified in the plateau's background atmosphere, modulating the regional atmospheric chemical balance by elevating the $\mathrm{HONO}/{\mathrm{NO}}_x$ ratio beyond the atmospheric internal cycling equilibrium. Under the plateau's intense solar radiation, rapid HONO photolysis likely boosted OH• radical production, thereby enhancing atmospheric oxidation capacity. Such processes may exacerbate NO_x and O₃ pollution with cascading effects on regional climate and air quality. These findings underscore soil Nr emissions as crucial components of the Tibetan Plateau's nitrogen cycle and atmospheric chemistry, with implications for understanding climate change impacts and developing mitigation strategies in this ecologically vulnerable region.