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长期PM_2.5度和希腊人口暴露的时空空间建模,使用机器学习和统计方法

Anastasia Kakouri¹, Themistoklis Kontos², Georgios Grivas³

¹Department of Environment, University of the Aegean, Greece; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece.

The Science of the total environment

|December 19, 2024

概括

这项研究为希腊开发了高分辨率的PM2.5数据集,揭示了广泛的暴露超过了世卫组织指导方针. 随机森林模型准确地绘制了空气污染,突出了需要针对公共卫生进行有针对性的干预的地区.

科学领域:

环境科学环境科学
大气科学大气科学
公共卫生公共卫生

背景情况:

希腊和东地中海地区缺乏高分辨率的长期PM2.5数据,这阻碍了对健康暴露的准确评估.
空间建模对于理解空气污染的变化及其对人口的影响至关重要.

研究的目的:

开发2015-2022年希腊PM2.5度的高分辨率 (1平方公里) 网格数据集.
评估PM2.5的空间变化和全国范围的人口暴露.
评估PM2.5预测的各种建模方法.

主要方法:

在现场观测,气象,排放和卫星气溶光学深度 (AOD) 数据的整合.
评估了七个统计,机器学习和混合模型,重点是随机森林 (RF).
使用表现最佳的射频模型开发国家级的PM2.5数据集.

主要成果:

射频模型获得了高精度 (R2 = 0.73,MAE = 2.2μg m-3).
冬季表现出PM2.5水平最高的月份 (平均每月) 16.8 μg m-3) 由于生物质燃烧和分散限制.
整个希腊人口超过了世卫组织的PM2.5指南 (5μg m-3),中部和北部地区的超值很大.

结论:

开发的高分辨率PM2.5数据集是希腊空气质量管理和健康研究的可靠工具.
迫切需要有针对性的干预措施,特别是在像伊奥尼纳这样的地区,以减轻严重的空气污染影响.
该研究强调了PM2.5暴露在希腊的普遍问题,需要持续监测和政策行动.

关键词:

空气质量空气质量人工智能的人工智能是人工智能.颗粒物 - 颗粒颗粒 (PM) 2.5)人口暴露的情况.随机的森林随机的森林空间模型的空间模型.

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