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走向整体的河流污染源分配:跨场景,媒体,污染物和来源的多面框架
Qifan Zhang1, Xuefeng Guo2, Weijun Sun2
1Key Laboratory of Water and Sediment Sciences, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China; China South-to-North Water Diversion Middle Route Corporation Limited, Beijing, 100038, China.
Environmental research
|October 6, 2025
概括
识别河流污染源对于有效控制至关重要. 本综述综合了包括机器学习 (ML) 在内的先进方法,以精确追踪污染来源和传输,改善流域管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 分析化学 分析化学
背景情况:
- 河流面临着来自各种污染物的重大生态和健康威胁.
- 准确识别污染源及其运输对于有效的环境管理至关重要.
研究的目的:
- 系统地审查和批判性地评估确定河流污染源的方法.
- 为污染物识别提供标准,并探索复杂场景的复合框架.
主要方法:
- 审查已建立的方法:源库存,传输-扩散模型,受体模型,同位素追踪.
- 机器学习 (ML) 应用在污染源分配中的评估.
- 复合框架的分析:基于受体模型和基于机器学习的技术.
主要成果:
- 单一的方法在复杂的河流环境中具有局限性,具有时空空间异质性.
- 定制的复合框架,集成特定站点的数据,增强定量跟踪和预测准确性.
- 先进的方法提高了动态源贡献的解释性.
结论:
- 河流污染源的分配是复杂的,因为河流的双重来源/沉角色.
- 综合,定制的方法提供了理论和技术途径,用于精确控制流域污染.
- 未来的战略应该解决局限性问题,提高复杂河流系统的准确性.
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