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驱动因素在中国表面土壤中区分PAH异质,使用深度学习与地缘统计方法相结合

Weiwei Wang¹, Songchao Chen², Lu Chen³

¹College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; Key Laboratory of Environmental Pollution and Ecological Health of Ministry of Education, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China.

Journal of hazardous materials

|February 23, 2024

概括

中国表面土壤中的多环芳 (PAH) 主要是由PAH排放和土壤特性 (如粘土和沙子含量) 驱动的. 深度学习可以准确地预测土壤的PAH水平,从而提高对环境命运的理解.

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:

了解地表土壤中的多环芳 (PAH) 不同质性仍然是一个挑战.
现有的研究重点关注源,过程或土壤因素,缺乏全面的机制.
影响土壤PAH度的因素的复杂相互作用需要先进的分析方法.

研究的目的:

探索17个"源-过程-沉没"因素对中国表面土壤中的16种PAH的影响.
通过深度学习识别PAH异质性的关键驱动因素.
开发和验证土壤PAH度的预测模型.

主要方法:

在1公里分辨率的660个土壤样本 (2003-2020) 的数据集上利用深度学习 (eXtreme Gradient Boosting).
优化模型参数,包括学习率,最大深度和子样本比率.
集成的深度学习与地缘统计模型用于对283个测试样本的预测验证.

主要成果:

前五个因素解释了土壤PAH度变化的84.8%至98.1%.
PAH排放是主要的驱动因素,其影响力随着logK_ow的增加而增加.
土壤特性,特别是粘土和沙子含量,显著影响了基于logK_ow的PAH分区.

结论:

这项研究阐明了土壤PAH异质性背后的关键驱动因素和机制.
深度学习与地缘统计学相结合,为预测土壤污染物度提供了一种新且准确的方法.
这样可以更好地了解土壤PAHs的环境命运和空间变异性.

关键词:

数据挖掘是一种数据挖掘.深度学习是一种深度学习.机理机制机理机制多环芳香碳化合物多环芳香碳化合物表面土壤的表面土壤

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