机器学习与因果路径框架集成 揭示生物炭驱动土壤有机碳动力学在应力下的差异化机制
Xuan Sun1,2, Zhaolin Du1,2, Jian Ding1,2
1Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, P. R. China.
Environmental science & technology
|January 20, 2026
概括
机器学习揭示了土壤特性,特别是和pH值,是污染土壤中土壤有机碳动态的关键驱动因素. 这为碳捕获和重金属管理的生物炭应用提供了信息.
科学领域:
- 环境科学 环境科学
- 土壤科学 土壤科学
- 生物地质化学生物地质化学
背景情况:
- 在 (Cd) 污染的土壤中,土壤有机碳 (SOC) 动态是复杂的.
- 了解关键驱动因素对于有效的生物炭策略在碳封存中至关重要.
- 目前的知识差距阻碍了优化生物炭应用.
研究的目的:
- 为生物炭介导的SOC动态建立一个可解释的因果框架.
- 确定影响Cd污染土壤中SOC封存的关键驱动因素和机制.
- 提高生物炭设计,以优化SOC封存和Cd管理.
主要方法:
- 集成机器学习 (ML) 和部分最小平方路径建模 (PLS-PM).
- 利用全球数据集与高精度随机森林模型.
- 使用独立数据集验证因果关系框架.
主要成果:
- 土壤属性是主要预测因素 (60.27%), (P) 和pH是关键因素.
- 非线性值确定了Cd (5.8 mg/kg) 和P (<0.7 g/kg) 的临界水平.
- PLS-PM量化了包括物理化学相互作用,气候级联和生物炭衰老在内的因果途径.
结论:
- 特定地点的生物炭设计应优先考虑土壤固有的特性 (pH,P),Cd水平和生物炭特性.
- 开发的框架改进了生物炭应用,以加强SOC封存.
- 该研究提供了一种协同方法,与碳封存一起进行Cd管理.
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