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走向更清洁的城市:通过混合EBM-CMA-ES建模估计车辆引起的PM_2.5

Saleh Alotaibi¹, Hamad Almujibah², Khalaf Alla Adam Mohamed³

¹Civil and Environmental Engineering Department, Faculty of Engineering-Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

|November 26, 2024

概括

车辆排放对发展中国家的城市空气质量产生重大影响. 这项研究使用混合模型来预测PM2.5污染,确定位置和湿度是关键因素.

科学领域:

环境科学环境科学
大气化学大气化学
数据科学数据科学数据科学

背景情况:

车辆排放是发展中国家城市空气污染的主要来源,老旧的车辆和较弱的法规加剧了这种情况.
高水平的颗粒物 (PM2.5) 会降低空气质量,并对公众健康构成风险.
了解PM2.5的驱动因素对于有效的空气质量管理至关重要.

研究的目的:

开发和验证一种混合模型,用于预测与车辆相关的PM2.5度.
确定和分析影响城市环境中PM2.5水平的关键因素.
为城市空气污染动态提供可解释的见解.

主要方法:

采用混合可解释提升机 (EBM) 框架进行预测和解释.
使用Covariance Matrix适应演化策略 (CMA-ES) 进行了EBM的超参数优化.
利用来自Open-Seneca传感器的空气质量数据,以及气象和交通数据.

主要成果:

调整为CMA-ES的EBM模型表现出高准确度,MAE为2.033和R2为0.843.
位置被确定为影响PM2.5的最重要因素,其次是湿度和温度.
湿度和交通量之间的相互作用被发现会影响PM2.5度.

结论:

混合CMA-ES-EBM模型为城市空气污染提供了卓越的预测性能和可解释性.
位置,湿度以及它们与交通量之间的相互作用是PM2.5.5的关键决定因素.
这些发现为城市地区的空气质量管理策略提供了可操作的见解.

关键词:

在PM2.5中,PM2.5是最常见的.空气质量空气质量是什么协差矩阵适应演变战略的演变策略.可以解释的提升机器.

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