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在韩国使用机器学习与地面观测数据对环境PM_2.5中的硫酸盐和酸盐度进行了全国范围的映射

Sang-Jin Lee¹, Jeong-Tae Ju¹, Jong-Jae Lee²

¹Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.

The Science of the total environment

|March 25, 2024

概括

机器学习模型映射了韩国的二次无机气溶. 硫酸盐度与工业相关,酸盐与车辆相关,在高PM2.5事件期间具有跨境影响.

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:

颗粒物 (PM) 在东北亚带来了重大的空气质量挑战.
高PM事件需要PM2.5及其组件的详细空间分布映射.

研究的目的:

开发预测模型,用于在韩国PM2.5范围内绘制硫酸盐 (SO4^2-) 和酸盐 (NO3^-) 度.
调查这些二次无机气溶的全国空间分布.

主要方法:

利用机器学习,特别是随机森林算法.
利用来自548个空气质量监测站的地面观测数据.
专注于预测PM2.5.5中的SO4^2和NO3^-度.

主要成果:

全国平均度:PM2.5 (17.2 ± 2.8 μg/m3),SO4^2- (3.0 ± 0.6 μg/m3),NO3^- (3.4 ± 1.2 μg/m3) 在全国范围内.
在韩国西部和中部观察到较高的SO4^2-和NO3^-度.
SO4^2-与工业活动有关,NO3^-与车辆排放有关.
在高PM2.5事件期间,SO4^2-受到中国SOx的影响,NO3^-受到中国和韩国NOx的影响.

结论:

开发的方法提供PM2.5化学特征的高时空分辨率.
为针对PM2.5二次污染的全国性减缓战略提供了宝贵的见解.
在PM2.5中区分SO4^2-和NO3^-的排放源,有助于有针对性的空气质量管理.

关键词:

机器学习是机器学习.亚酸盐是一种酸盐.在PM中{2.5)二次无机离子二次无机离子硫酸盐是一种硫酸盐.

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