机器学习预测甲聚集同位素 (CH2D2) 分布约束生物地化学过程,并估计潜在预算
Xinchu Wang1, Cong-Qiang Liu1, Yuanbi Yi2
1Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China.
Environmental science & technology
|July 6, 2023
概括
全球甲同位素学数据有限. 这项研究汇编了全球的甲凝聚同位素数据库,并使用随机森林建模来预测新的分布,改善了我们对甲来源和下沉点的理解.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 同位素地球化学 同位素地球化学
背景情况:
- 甲 (CH4) 是一个重要的温室气体,但由于技术和样本量方面的挑战,全球同位素学数据不足.
- 了解甲聚集同位素对于追踪生态化学过程和量化排放至关重要.
研究的目的:
- 编制一个全面的全球甲凝聚同位素数据库.
- 开发和验证用于预测甲同位素分布的机器学习模型.
- 改进对甲来源,沉积点和大气组成的量化.
主要方法:
- 汇编了465个全球甲凝聚同位素数据集.
- 机器学习模型的比较和应用,特别是随机森林 (RF).
- 预测 Δ12CH2D2 分布和大气中甲凝聚同位素组成.
主要成果:
- 创建了一个可靠和连续的射频预测数据库,涵盖了各种甲来源 (反动物,乙类) 和实验条件.
- 该模型准确地预测了稳定状态大气甲凝聚同位素组成 (Δ13CH3D: +2.26 ± 0.71‰, Δ12CH2D2: +62.06 ± 4.42‰),表明了重要的生物贡献.
- 水中排放的气体的季节性变化揭示了与大气凝聚同位素变化相关的温度驱动的微生物进化.
结论:
- 机器学习有效地预测甲聚集的同位素,增强地质化学理解.
- 开发的数据集和模型改善了对甲循环过程和大气组成的量化.
- 调查结果为气候模型提供了可量化的变量,可能为温室气体排放和减缓政策提供信息.
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