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Nitrous oxide sources, mechanisms and mitigation

  • Guibing Zhu*
  • , Hao Shi
  • , Lei Zhong
  • , Guang He
  • , Baozhan Wang
  • , Jun Shan
  • , Ping Han
  • , Tongxu Liu
  • , Shanyun Wang
  • , Chunlei Liu
  • , Nan Zhang
  • , Liping Jiang
  • , Longbin Yu
  • , Chunhui Zhan
  • , Ziyang Tang
  • , Teng Wen
  • , Bin Ma
  • , Xiaoxuan Su
  • , Shujun Zhang
  • , Jinbo Zhang
  • Hongjie Di, Lijun Hou, Alexander H. Krichels, Mark Trimmer, Mike S. M. Jetten, Yongzhen Peng, Frank E. Löffler, Hanqin Tian, Yong-Guan Zhu*
*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Atmospheric nitrous oxide (N2O) is a potent greenhouse gas and ozone-depleting substance. In this Review, we outline global N2O sources, with a focus on hotspots and hot moments, and discuss strategies to mitigate N2O emissions. N2O can be released by natural sources such as bedrock weathering, but anthropogenic sources such as agriculture account for 40% of total emissions. Hotspots are localized regions of high emissions and include cropland soils (2.1 Tg N yr−1), tropical forests (1.55 Tg N yr−1), pasture soils with animal waste return (1.7 Tg N yr−1), and streams and small lakes (0.4 Tg N yr−1). Brief periods of intense emissions, known as hot moments, include post-deforestation, upland soils after fertilizer application, and desert and grasslands after precipitation. N2O production from terrestrial and aquatic environments is mainly driven by two microbial processes: nitrification and denitrification. Bioaugmentation and biogeoengineering technologies hold potential for reducing N2O emissions; for example, nature-based anammox hotspot geoengineering in Jiaxing, China, reduces N2O emissions by 27.1%. However, the spatiotemporal heterogeneities and different production pathways of N2O emissions are poorly represented in existing models, hindering the quantification and mitigation of emissions. A global N2O database is needed to address this limitation. Additionally, artificial intelligence technology could enable real-time agricultural management to align nitrogen supply with crop demand. © Springer Nature Limited 2025.
Original languageEnglish
Pages (from-to)574-592
Number of pages19
JournalNature Reviews Earth and Environment
Volume6
Issue number9
Online published12 Aug 2025
DOIs
Publication statusPublished - Sept 2025

Funding

The authors acknowledge financial support from the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB0750400), the National Natural Science Foundation of China (92251304) and the Special Project on eco-environmental technology for peak carbon dioxide emissions and carbon neutrality (RCEES-TDZ2021-20). G.B.Z., S.Y.W. and C.L.L. acknowledge the Program of the Youth Innovation Promotion Association of the Chinese Academy of Sciences. A.H.K. is supported in part by the USDA Forest Service Rocky Mountain Research Station. M.S.M.J. is supported by ERC Synergy MARIX 854088.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

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