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Underestimation of particulate dry nitrogen deposition in China

Nature Geoscience volume 19, pages 137–144 (2026)Cite this article

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Abstract

Nitrogen is indispensable for global food production and ecosystem carbon sequestration, but excess nitrogen leads to water eutrophication, soil acidification and air pollution. Atmospheric nitrogen deposition is a key yet uncertain component of the biogeochemical cycle. Currently, global networks monitoring particulate nitrogen dry deposition rely mainly on measured concentrations and modelled dry deposition velocities, which remain poorly constrained. Here we develop a spatially explicit dataset by integrating observation-constrained size distribution and dry deposition mechanisms to re-evaluate atmospheric nitrogen deposition across China. We reveal that atmospheric chemistry models underestimate the particle size of fine-mode nitrogen-containing aerosols in China by more than twofold. Additionally, dry particle deposition velocity estimates with different mechanisms diverge by up to two orders of magnitude. Our corrections indicate that atmospheric chemistry models and China’s observation network underestimate particulate nitrogen dry deposition by 2–5 times. Furthermore, most Earth system models underestimate particulate dry deposition of ammonium, a major nitrogen species, by 31%–98%. By integrating these corrections into the Community Land Model, we demonstrate that the effect of nitrogen deposition on China’s terrestrial net ecosystem productivity may have been underestimated by 9%–13%. As China contributes nearly 20% of global nitrogen deposition, its impact on terrestrial carbon sinks and ecosystem health could be greater than previously recognized.

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Fig. 1: Key factors affecting particulate nitrogen dry deposition.
Fig. 2: Particulate nitrogen dry deposition flux in China.
Fig. 3: The impact of nitrogen deposition variation on the terrestrial carbon sink in China.

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Data availability

The underlying data employed in this study are available from sources cited in the main text and Supplementary Information or are provided in Supplementary Data. The CMIP6 data are available at https://aims2.llnl.gov/search/cmip6/. The revised map database is available via GitHub at https://github.com/huangynj/NCL-Chinamap.

Code availability

The default WRF-Chem model source code is freely available at https://www2.mmm.ucar.edu/wrf/users/download/get_sources_new.php. The CLM5 model code is available via Github at https://github.com/ESCOMP/CTSM/releases.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (grant numbers 42476127, 42577530 and 42521004); China Postdoctoral Science Foundation (2022M720005); Beijing Natural Science Foundation (8244068) and Science and Technology Projects of Xizang Autonomous Region, China (XZ202501ZY0091), and was supported by the High-Performance Computing Platform of Peking University and in part through research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at the Georgia Institute of Technology, Atlanta, Georgia, USA. We also thank the public instrument platform of the College of Urban and Environmental Sciences at Peking University. Maodian Liu is also supported by the Fundamental Research Funds for the Central Universities, Peking University (7100604874). Y.-H.R. was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (RS-2023-00274625). H.L. was funded by the National Natural Science Foundation of China (42275166). We thank Y. Huang (IAP/CAS) for providing the map database (https://github.com/huangynj/NCL-Chinamap.git).

Author information

Authors and Affiliations

  1. Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Qianru Zhang, Maodian Liu, Junfeng Liu, Shu Tao & Xuejun Wang

  2. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Qianru Zhang & Si-Yi Wei

  3. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Qianru Zhang & Yuhang Wang

  4. School of the Environment, Yale University, New Haven, CT, USA

    Maodian Liu

  5. Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea

    Young-Hee Ryu

  6. State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Mingxu Liu

  7. Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China

    Huoqing Li

  8. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China

    Si-Yi Wei

  9. School of Management, Beijing Institute of Technology, Beijing, China

    Si-Yi Wei

  10. Institute of Carbon Neutrality, Peking University, Beijing, China

    Xuejun Wang

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Contributions

Y.W., X.W., Maodian Liu and Q.Z. designed and led the research; X.W., Maodian Liu and Q.Z. acquired the funding needed to complete the study; Q.Z. and Maodian Liu performed the research and data collection; Y.-H.R. and Mingxu Liu contributed to the model configuration; H.L. contributed the updated land-use data; Y.W., Q.Z. and Maodian Liu made the data analysis and interpreted the results; Q.Z. and Maodian Liu wrote the original manuscript in close discussion with Y.W., S.W., J.L., S.T. and X.W. and all authors contributed to manuscript revision and completion.

Corresponding authors

Correspondence to Yuhang Wang, Maodian Liu or Xuejun Wang.

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Nature Geoscience thanks Cheng Gong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Camilla Brunello, Xujia Jiang and Carolina Ortiz Guerrero, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Table 1 Aerosol dry deposition schemes
Full size table

Extended Data Fig. 1 Observed particle size distributions of NO3−, NH4+, and SO42− aerosols under different air pollution conditions in China.

The blue curves, ranging from light to dark, represent simulated results with pollution levels increasing from low to high, corresponding to the left y-axis. Similarly, the red curves, ranging from light to dark, depict observed results with increasing pollution levels from low to high, corresponding to the right y-axis. ‘SIM’ means simulations. ‘OBS’ means observations. ‘N’ means normal days. ‘LP’ means lightly polluted days. ‘HP’ means heavily polluted days. Daily air quality levels were classified based on average daily PM2.5 values. Some cities did not have heavily polluted days during the simulation period, resulting in the absence of some locations in SIM_HP figures. The observations here are obtained from previous publications102,103.

Extended Data Fig. 2 Changes in particulate nitrogen dry deposition over China constrained by observed particle size and new deposition mechanisms.

The spatial distribution of total particulate nitrogen dry deposition over China from the eight simulation experiments (a) and change in total particulate nitrogen dry deposition of the observation-derived experiment (Osize_E2020) compared with each other simulation experiment (b). Maps based on the original NCAR Command Language (NCL) map framework with updated boundary information derived from the National Catalogue Service for Geographic Information of China (http://www.webmap.cn/commres.do?method=result100W).

Extended Data Fig. 3 Changes in total nitrogen deposition over China constrained by observed particle size and new deposition mechanisms.

The spatial distribution of total nitrogen deposition over China from the eight simulation experiments (a) and change in total nitrogen deposition of the observation-derived experiment (Osize_E2020) compared with each other simulation experiment (b). Maps based on the original NCAR Command Language (NCL) map framework with updated boundary information derived from the National Catalogue Service for Geographic Information of China (http://www.webmap.cn/commres.do?method=result100W).

Extended Data Fig. 4 The comparison of net primary productivity (NPP) between simulations with the NASA NPP observations across China.

The gray line indicates the linear regression fit (mean estimate), and the gray shaded area denotes the 95% confidence interval for that regression line. Statistical metrics including R-squared (R2 = 0.74), root-mean-square deviation (RMSE = 155 gC m−2 yr−1) and fraction of simulation within a factor of two (FAC2 = 67%) are presented.

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Supplementary Texts 1–5, Figs. 1–6, Tables 1–9 and references.

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The datasets include: (1) additional observed NO3− and NH4+ concentrations in 2015, obtained from published literature used in this study. (2) Additional observed NO3− and NH4+ concentrations in 2014, obtained from published literature used in this study. (3) Simulated particulate nitrogen dry deposition in China by WRF-Chem under eight different model experiments in 2015. (4) Simulated total nitrogen deposition in China by WRF-Chem under Osize_E2020 in 2015.

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Zhang, Q., Wang, Y., Liu, M. et al. Underestimation of particulate dry nitrogen deposition in China. Nat. Geosci. 19, 137–144 (2026). https://doi.org/10.1038/s41561-025-01873-3

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