A dataset for ¹⁵N natural abundance of soil ammonia volatilization.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-18 DOI:10.1038/s41597-024-04216-w

Lingyun Peng, Chaopu Ti, Bin Yin, Xiao Bai, Miao Li, Limin Tao, Xiaoyuan Yan

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引用次数: 0

Abstract

Investigating the sources of ammonia (NH₃) in the atmosphere and the contribution of each source is essential for environmental pollution control. The presented dataset aims to provide ¹⁵N natural abundance (δ¹⁵N) data collected from different controlled treatments to fill the knowledge gap between insufficient data of soil δ¹⁵N-NH₃ and accurately identifying atmospheric NH₃ source apportionments. Our results showed that the overall δ¹⁵N-NH₃ values emitted from soil ranged from -46.09 to 10.22‰, with an average of -26.81 ± 11.17‰. The mean δ¹⁵N-NH₃ values under different nitrogen (N) application rates, N fertilizer types, air temperatures, soil moisture, soil pH, soil types, and land use types were -29.41 ± 10.91, -32.43 ± 6.86, -29.10 ± 10.04, -30.31 ± 6.18, -24.84 ± 13.76, -23.53 ± 7.66, and -14.57 ± 12.54‰, respectively. Significant correlations were observed between δ¹⁵N-NH₃ values and soil pH, soil NO₃^--N concentration, and NH₃ volatilization. This unique database provides basic data and evidence for the qualification of atmospheric NH₃ source apportionments under different study area conditions.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.