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Updated: Feb 23, 2026

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一个集成的框架合过程驱动的模型和机器学习来模拟土壤重金属的积累和预测区域风险

Shiyi Yi¹, Xiaonuo Li², Weiping Chen¹

¹State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of hazardous materials

|February 21, 2026

概括

这项研究引入了一种新的模型来追踪土壤重金属 (HM) 的积累,揭示了大气沉积量的增加和土壤承载能力的下降,特别是和铜.

科学领域:

环境科学环境科学
地质化学地质化学
计算机建模计算建模

背景情况:

区域土壤重金属 (HM) 的评估受到挑战,因为在没有大量数据的情况下,很难将时空过程和量化流量联系起来.
现有的方法缺乏准确模拟长期土壤HM动态和预测环境承载能力的能力.

研究的目的:

开发和应用一个综合建模框架来模拟土壤HM动态和评估区域范围内的环境风险.
量化HM输入和输出流,预测土壤承载能力的变化,并确定高风险区域.

主要方法:

结合多源排放清单,AERMOD大气分散模型和基于HYDRUS的水溶液运输模块.
结合对输出过程的实证方法和用于校准净HM流量的机器学习模块.
应用在中国江省的一个工业聚集区,用于长期模拟.

主要成果:

大气中HM的沉积随着工业化而增加,而农业投入减少,在点源附近的高沉积区域.
泄漏和土壤侵蚀是HM损失的主要途径;随着不透水面的出现,下水流量增加.
预计50年内土壤环境承载能力将大幅下降,As,Cu,Cd,Pb和Hg的过载区将扩大.

结论:

开发的框架有效地模拟了土壤HM动态,确定了关键的输入/输出路径,并预测了未来的环境风险.
和铜在林地构成显著的生态毒性风险;在耕地和住宅用地,承载能力急剧下降.
该框架作为追踪排放源,风险区分和区域环境管理的强有力的工具.

关键词:

重金属的化物 (loid) 类型输入-输出流是指输入-输出流.长期的时间空间动态.机器学习机器学习溶于水的运输模型.

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