Ammonium-derived nitrous oxide is a global source in streams

Shanyun Wang, Bangrui Lan, Longbin Yu, Manyi Xiao, Liping Jiang, Yu Qin, Yucheng Jin, Yuting Zhou, Gawhar Armanbek, Jingchen Ma, Manting Wang, Mike S. M. Jetten, Hanqin Tian, Guibing Zhu () and Yong-Guan Zhu
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Shanyun Wang: Chinese Academy of Sciences
Bangrui Lan: Chinese Academy of Sciences
Longbin Yu: Chinese Academy of Sciences
Manyi Xiao: Chinese Academy of Sciences
Liping Jiang: Chinese Academy of Sciences
Yu Qin: Chinese Academy of Sciences
Yucheng Jin: Chinese Academy of Sciences
Yuting Zhou: Chinese Academy of Sciences
Gawhar Armanbek: Chinese Academy of Sciences
Jingchen Ma: Chinese Academy of Sciences
Manting Wang: Chinese Academy of Sciences
Mike S. M. Jetten: Radboud University Nijmegen
Hanqin Tian: Schiller Institute for Integrated Science and Society, Boston College
Guibing Zhu: Chinese Academy of Sciences
Yong-Guan Zhu: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Date: 2024
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DOI: 10.1038/s41467-024-48343-9

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