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一种新而灵活的方式来推导铜的水质标准

Yang Li¹, Di Mu¹, Hong-Qing Wu¹

¹Engineering Research Center of Seawater Utilization Technology of Ministry of Education, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300401, China; Hebei Collaborative Innovation Center of Modern Marine Chemical Technology, Tianjin 300401, China.

The Science of the total environment

|February 28, 2025

概括

制定准确的水质标准 (WQC) 对于保护水生生物至关重要. 机器学习模型,特别是人工神经网络,通过考虑环境因素和物种数据,有效预测铜的毒性.

科学领域:

环境科学环境科学
生态毒理学生态毒理学
计算化学计算化学

背景情况:

建立准确的水质标准 (WQC) 保护水生生物是复杂的,因为不同的物种和环境变量.
机器学习 (ML) 提供了一种有希望的方法来解决WQC开发中的复杂性.
人工神经网络 (ANN) 在探索它们在创建全面,多种类的WQC模型中的潜力.

研究的目的:

评估混合物种人工神经网络 (ANN) 模型的适用性,以推导受十个水化学因素影响的水质标准 (WQC).
通过敏感性分析,确定影响铜毒性的关键环境因素.
评估中国黄河和长江铜矿的生态风险.

主要方法:

采用人工神经网络 (ANN) 来构建一个全面的混合物种模型,包括分类信息和十个水化学因素.
使用Sobol方法进行灵敏度分析,以确定环境因素对铜毒性的影响.
从使用所有因素,简化关键因素和原始数据的模型中得出的WQC开发和比较.

主要成果:

混合物种ANN模型表现出高的预测准确性,特别是在编码分类信息后.
敏感性分析确定温度,溶解有机碳 (DOC),溶解无机碳 (DIC) 和电导率/硬度 (EC/硬度) 是影响铜毒性的关键因素.
专注于这些关键因素的简化模型产生了WQC结果,与来自综合模型的结果相比,表明了稳定性.
生态风险评估显示,黄河的风险高于长江的风险,干旱季节的风险比雨季的风险更大.

结论:

编码分类信息并关注关键的环境因素,如温度,DOC,DIC和EC/硬度,可以导致准确和实用的WQC导出.
季节性变化和特定的河流特征显著影响生态风险,突出了WQC环境背景的重要性.
该研究为改进水质保证方法和评估水生生态系统中的生态风险提供了宝贵的见解.

关键词:

铜铜铜铜铜铜环境因素环境因素机器学习是机器学习.风险评估风险评估风险评估水质标准水质标准

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