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2001-2019年全球表面氨度的演变:大小,模式和驱动因素

Jiageng Ma^1,2, Hao Shi¹, Yingjie Zhu²

¹State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS), Beijing 100085, PR China.

Environmental science & technology

|March 10, 2025

概括

全球氨 (NH3) 度因人类活动和气候变化而增加. 这项研究绘制了2001-2019年的这些变化,确定了热点和主要驱动因素,如肥料使用和温度.

科学领域:

大气化学和环境科学
全球变化和空气质量研究
循环和环境影响研究.

背景情况:

氨 (NH3) 是大气中主要的性气体,对空气质量,生态系统和人类健康产生重大影响.
预计NH3度的升高将在全球范围内增加,这是由于正在进行的全球变化和强化的人为排放.
了解NH3的空间和时间动态对于环境管理和政策制定至关重要.

研究的目的:

以高分辨率 (0.1°×0.1°) 从2001年到2019年估计表面氨 (NH3) 度的全球变化.
确定NH3度升高的热点,并分析这些变化背后的驱动因素.
开发和验证一种用于估计大气NH3度的新型规模适应型建模方法.

主要方法:

利用地面观测,对 (有机和无机) 肥料应用的网格数据,气象数据和辅助信息.
开发了一种新的规模适应方法:一个集成随机森林模型,包含旋转四树分区和盒子-Cox转换.
根据观察到的NH3模式验证了模型,实现了高精度 (R2 = 0.91,斜率 = 0.82整体;R2 = 0.79,斜率 = 0.70用于测试).

主要成果:

全球表面NH3度从2001年的1.44μg m−3 yr−1增加到2019年的1.51μg m−3 yr−1.
在南亚北部,中国北部,萨赫勒地区,南美东南部和美国中部发现了NH3升高的热点.
将NH3的增加归因于中国的SO2排放减少和肥料使用增加,以及南亚的投入. 温度影响了非洲,南美洲和大洋洲的NH3.

结论:

该研究提供了全球NH3度变化的高分辨率估计,并确定了关键的贡献因素.
这些发现支持对全球循环及其环境影响的研究,为有针对性的管理策略提供信息.
开发的建模算法有效地捕获异质数据中的复杂模式,解决大气研究中的升级挑战.

关键词:

盒子-Cox转换的变化全球表面氨度度机器学习是机器学习.化肥应用化肥应用适应规模的旋转四树木旋转四树木.

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