通过深度学习和因果建模,解读土壤重金属污染的驱动因素和机制
Anqi He1, Qi Wang2, Gang Zhao3
1School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082, PR China.
Journal of hazardous materials
|March 4, 2026
概括
一个新的深度学习模型HMNet准确地绘制了土壤重金属 (HM) 污染的地图. 人类活动是主要的驱动因素,但土壤特性和环境因素也在HM分配和管理中发挥着关键作用.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 土壤重金属化物 (HM) 构成重大环境和健康风险.
- 了解HM形成和发展机制对于有效的土壤管理和污染控制至关重要.
- 由于复杂的污染输入和环境相互作用,目前存在知识差距.
研究的目的:
- 开发一个深度学习模型 (HMNet),用于精细分辨率地图土壤HM污染.
- 识别和归因影响HM分布和积累的关键驱动因素.
- 为控制土壤HM污染制定基于科学的管理策略.
主要方法:
- 开发和应用一个深度学习模型 (HMNet),集成多源开放访问数据集.
- 使用DeepSHAP和相关性分析来归因HM污染的驾驶员.
- 采用结构方程建模来量化驾驶员之间的交互路径.
主要成果:
- 在监测和概括土壤HM污染模式方面,HMNet表现强.
- 人类活动被确定为主要的外部输入,影响HM积累.
- 发现土壤的物理化学特性,植被,地形和气候调节了HM保留,移动性和空间异质性.
结论:
- 该研究提供了一个综合框架,将监测,机制分析和对土壤HM污染的管理指南联系起来.
- 管理策略应优先考虑源控制,精密营养管理,侵蚀减缓和特定地点的恢复.
- 建立了一个基于过程的范式,以更精细地了解和控制土壤HM污染.
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