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通过使用机器学习与空间相互作用效应相结合,对油田土壤进行生态风险评估

Shihan Wang¹, Mingxia Zheng¹, Yushan Tian¹

¹State Key Laboratory of Environmental Criteria and Risk Assessment, and State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Eiknvironmental Sciences, Beijing 100012, China.

Ecotoxicology and environmental safety

|June 22, 2025

概括

这项研究引入了一种新的机器学习和遥感方法,用于监测石油开采造成的土壤污染. 该方法准确预测石油总碳化合物和有毒元素,识别环境管理的高风险区域.

科学领域:

环境科学环境科学
地理空间分析是什么
机器学习机器学习

背景情况:

石油开采加剧了土壤与总石油碳化合物 (TPHs) 和有毒元素的污染,这给环境带来了严重的挑战.
有效的污染调查和监测对于减轻油田活动对生态的影响至关重要.

研究的目的:

开发和验证一种结合机器学习 (ML) 和遥感 (RS) 的新方法,用于预测油井周围的土壤污染物度.
评估与土壤污染相关的生态风险,并确定修复的优先领域.
了解油田地区土壤污染的驱动机制.

主要方法:

遥感 (RS) 数据被用来提取环境变量,如部分植被覆盖 (FVC),土壤类型和地形因素.
机器学习 (ML) 模型,包括渐变增强机 (GBM),用于预测TPH和有毒元素 (As,Pb,Ni,Cd,Hg) 的度.
潜在的生态风险指数 (RI) 和空间自相关性分析 (双变量LISA) 用于风险评估和空间分布分析.

主要成果:

GBM模型对TPHs (R2=0.7730) 和几个有毒元素,包括As (R2=0.8614) 和Pb (R2=0.8678) 的高预测准确度.
石油开采活动,土地利用和土壤特性被确定为影响污染物积累的主要因素.
生态风险评估确定了没有风险的区域的特定百分比,TPH和有毒元素风险的组合,有毒元素风险,TPH风险和不确定的风险.

结论:

综合的RS和ML方法为油田地区的土壤污染调查和监测提供了强大的框架.
该研究成功确定了需要风险控制措施的优先领域,支持有针对性的环境管理.
这些发现为土壤风险管理和在油田及其周围的工业地点规划提供了宝贵的技术支持.

关键词:

自动机器学习自动化机器学习生态风险评估生态风险评估油田油田是一个油田.空间双变的相关性分析.TPHs 是一个 TPH.有毒元素有毒元素

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