1
|
Tian X, Yin Y, He K, Qiu R, Cong J, Wang Z, Yu H, Chen Z, Chu Y, Ying H, Cui Z. Data-driven estimation of nitric oxide emissions from global soils based on dominant vegetation covers. Glob Chang Biol 2023; 29:5955-5967. [PMID: 37462298 DOI: 10.1111/gcb.16864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/16/2023] [Indexed: 09/20/2023]
Abstract
Soils are a major source of global nitric oxide (NO) emissions. However, estimates of soil NO emissions have large uncertainties due to limited observations and multifactorial impacts. Here, we mapped global soil NO emissions, integrating 1356 in-situ NO observations from globally distributed sites with high-resolution climate, soil, and management practice data. We then calculated global and national total NO budgets and revealed the contributions of cropland, grassland, and forest to global soil NO emissions at the national level. The results showed that soil NO emissions were explained mainly by N input, water input and soil pH. Total above-soil NO emissions of the three vegetation cover types were 9.4 Tg N year-1 in 2014, including 5.9 Tg N year-1 (1.04, 95% confidence interval [95% CI]: 0.09-1.99 kg N ha-1 year-1 ) emitted from forest, 1.7 Tg N year-1 (0.68, 95% CI: 0.10-1.26 kg N ha-1 year-1 ) from grassland, and 1.8 Tg N year-1 (0.98, 95% CI: 0.42-1.53 kg N ha-1 year-1 ) from cropland. Soil NO emissions in approximately 57% of 213 countries surveyed were dominated by forests. Our results provide updated inventories of global and national soil NO emissions based on robust data-driven models. These estimates are critical to guiding the mitigation of soil NO emissions and can be used in combination with biogeochemical models.
Collapse
Affiliation(s)
- Xingshuai Tian
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Yulong Yin
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Kai He
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Ruonan Qiu
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Jiahui Cong
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Zihan Wang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Huitong Yu
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Zhong Chen
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Yiyan Chu
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Hao Ying
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Zhenling Cui
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| |
Collapse
|