Sustainable management of riverine N2O emission baselines
The riverine N<sub>2</sub>O fluxes are assumed to linearly increase with nitrate loading. However, this linear relationship with a uniform EF<sub>5r</sub> is poorly constrained, which impedes the N<sub>2</sub>O estimation and mitigation. Our meta-analysis discovered a universal N<sub>2</sub>O emission baseline (EF<sub>5r</sub> = k/[NO<sub>3</sub> <sup>-</sup>], k = 0.02) for natural rivers. Anthropogenic impacts caused an overall increase in baselines and the emergence of hotspots, which constitute two typical patterns of anthropogenic sources. The k values of agricultural and urban rivers increased to 0.09 and 0.05, respectively, with 11% and 14% of points becoming N<sub>2</sub>O hotspots. Priority control of organic and NH<sub>4</sub> <sup>+</sup> pollution could eliminate hotspots and reduce emissions by 51.6% and 63.7%, respectively. Further restoration of baseline emissions on nitrate removal is a long-term challenge considering population growth and declining unit benefits (ΔN-N<sub>2</sub>O/N-NO<sub>3</sub> <sup>-</sup>). The discovery of EF lines emphasized the importance of targeting hotspots and managing baseline emissions sustainably to balance social and environmental benefits.
Authors
- Guodong Ji (ORCID: https://orcid.org/0000-0002-0195-2367)
- Wei Zhi (ORCID: https://orcid.org/0000-0001-5485-1095)
- Tao Lyu (ORCID: https://orcid.org/0000-0001-5162-8103)
- Shuo Wang (ORCID: https://orcid.org/0000-0002-7907-9676)
- Shengjie Li (ORCID: https://orcid.org/0000-0002-2371-5143)
Institutions
- Hohai University (CN)
- Peking University (CN)
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (CN)
- Max Planck Institute for Marine Microbiology (DE)
- Ministry of Water Resources of the People's Republic of China (CN)
- Cranfield University (GB)
Publication Details
- Journal
- National Science Review
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1093/nsr/nwae458
- Citations
- 6
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 1.23
Funders
- National Natural Science Foundation of China