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基于机器学习的中国海内有机酸盐的分布,运输和生态风险预测

Rui Chen¹, Wenyang Huang¹, Linlin Sun¹

¹Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China.

The Science of the total environment

|November 15, 2024

概括

通过先进的机器学习,分析了中国海的有机酸盐 (OPEs) 污染. 在城市地区发现了更高的OPE度,大多数地点具有较低的生态风险.

科学领域:

环境化学环境化学
生态毒理学生态毒理学
机器学习应用机器学习应用

背景情况:

有机酸盐 (OPEs) 是全球使用的化学物质,在环境中被广泛检测到.
传统的OPE分析方法繁且耗时,阻碍了全面的风险评估.
OPE的环境持久性,生物积累和生态风险需要先进的监测技术.

研究的目的:

通过机器学习研究中国海OPE的分布和生态风险.
为了比较XGBoost和LGBM模型在预测OPE度方面的表现.
确定影响OPE分布的关键因素,并评估相关的生态风险.

主要方法:

使用了极端梯度增强 (XGBoost) 和轻梯度增强机 (LGBM) 模型.
采用了12个特征变量和463个数据点用于OPE度分析.
应用模型来预测总OPEs (T-OPEs),化基OPEs (Cl-OPEs) 和烯-OPEs度和生态风险.

主要成果:

在预测T-OPEs和Cl-OPEs方面,LGBM模型表现出色 (RMSE 0.48,0.46;R2 0.79,0.76).
对于aryl-OPEs (RMSE 0.82,R2 0.87),XGBoost模型的表现更为优异.
在城市聚集地和河口中观察到较高的OPE污染,特别是在夏季;大多数地点显示生态风险较低.

结论:

机器学习模型有效预测海洋环境中的OPE分布和生态风险.
在沿海城市地区和河口确定了OPE污染热点.
这些发现为OPE污染管理和中国海的生态风险减轻提供了关键数据.

关键词:

中国海是中国海,中国海就是中国海.生态风险生态风险机器学习机器学习有机酸盐 (OPEs) 是一种有机酸盐.

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