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在海洋环境中揭示有机酸盐的来源,使用多因素多模态高维聚合算法

Nan Hu¹, Xing Liu², Muhammad Zeshan¹

¹Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.

|May 29, 2025

概括

在海洋环境中识别有机酸 (OPE) 源是具有挑战性的. 一个新的机器学习算法成功追踪了波海海域的OPE污染,并确定了主要的工业和城市贡献者.

科学领域:

环境化学环境化学
海洋污染海洋污染
计算毒理学计算毒理学

背景情况:

在海洋生态系统中,有机酸 (OPE) 的来源,特别是新出现的OPE (eOPE),人们对其了解甚少,这阻碍了有效的污染控制.
精确的来源追踪对于管理OPE污染和减轻生态风险至关重要.

研究的目的:

开发和验证一个先进的机器学习框架,用于追踪海洋环境中OPE的来源.
应用这个框架来确定波海海域主要的OPE污染贡献者.

主要方法:

开发了一种多因素多模式高维集群 (MFM-clustering) 算法,集成物理化学性质 (log Kow,log BCF) 和地理数据.
应用了一个系统的框架,包括现场调查,目标选,风险评估和MFM集群分析.
在波海沉积物样本中量化29个OPE,包括15个eOPE.

主要成果:

与传统的统计方法相比,MFM集群算法显示出更高的污染物分类准确性.
在博海海域确定了OPE的主要来源,包括生产工业,污水处理厂,工业排放,城市排水,大气运输和石油运营.
根据集群分析,详细的生态风险评估得到了改进.

结论:

在复杂的海洋环境中,MFM集群方法为归因OPE污染源提供了有效的工具.
缓解策略应侧重于追踪上游生产和开发更安全的替代方案,以减少OPE排放.
了解各种污染途径对于保护海洋生态系统免受OPE污染至关重要.

关键词:

生态风险评估生态风险评估在MFM-clustering中使用MFM集群.海洋沉积物的海洋沉积物有机酸盐 Ester 是一种有机酸盐.来源追踪追踪来源的追踪没有监督的学习学习.

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