机器学习预测的叶片中的全球分布
Long Chen1,2, Jun Zhou3, Long Guo4
1Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, 200241, China.
Environmental science & technology
|June 11, 2024
概括
这项研究首次绘制了全球叶状 (Hg) 度和池的地图. 它揭示了热带地区的热点,改善了对全球Hg循环的理解.
科学领域:
- 环境科学 环境科学
- 生物地质化学生物地质化学
- 大气化学 大气化学
背景情况:
- 基本 (Hg) 的叶片同化对全球生物地球化学循环至关重要.
- 之前对叶子Hg吸收的全球估计存在重大不确定性,原因是叶子中的Hg度数据有限.
- 准确的Hg预算对于了解环境污染至关重要.
研究的目的:
- 开发了第一张叶子Hg度的全球空间分布图.
- 为了估计植物叶片中的全球池.
- 为了精确估计全球叶子Hg同化.
主要方法:
- 整合了观察到的叶子Hg度的综合数据库.
- 利用机器学习算法来预测空间Hg度.
- 基于预测分布的量化全球Hg池和同化率.
主要成果:
- 估计的全球平均叶子Hg度在24.0 ngg-1 (范围:7.5-56.5 ngg-1).
- 计算出全球叶子Hg池为4561.3 Mg (范围:1455.2-9062.8 Mg).
- 在亚马逊,中非和东南亚等热带地区确定了叶子度的热点.
- 估计每年的叶子Hg同化在2268.5-2727.0Mgyr-1之间,考虑到常青植被.
结论:
- 开发的空间地图为了解全球Hg生物地球化学循环提供了重大进展.
- 这些发现对于评估气候变化和全球植被变化的背景下Hg动态至关重要.
- 这项研究有助于减少全球预算的不确定性.
相关概念视频
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The...
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01:07End Point Prediction: Gran Plot
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For potentiometric titration, the Gran plot is created by plotting the...
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