从2000年到2019年在欧洲使用地理加权回归的月平均空气污染模型
Youchen Shen1, Kees de Hoogh2, Oliver Schmitz3
1Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
The Science of the total environment
|February 6, 2024
概括
新的土地利用回归模型每月提供25米分辨率的欧洲空气污染估计值. 这些模型有助于研究人员确定健康研究的关键暴露窗口.
科学领域:
- 环境科学 环境科学
- 流行病学 流行病学
- 空间建模 空间建模
背景情况:
- 季节性变化会影响整个欧洲的空气污染水平和空间分布.
- 中期空气污染暴露需要精细分辨率的空间和时间数据.
研究的目的:
- 开发欧洲范围的土地利用回归 (LUR) 模型,以25米分辨率计算每月空气污染物度 (NO2,O3,PM10,PM2.5).
- 评估空气污染模式的季节性变化和模型性能.
主要方法:
- 利用了2000-2019年的常规监测数据.
- 用于预测因素选择的受雇监督线性回归 (SLR) 和空间变化系数的地理加权回归 (GWR).
- 使用5倍交叉验证 (CV) 验证的模型,与每月调整的模型进行比较.
主要成果:
- 在空气污染估计和模型结构中观察到显著的月度变化,特别是在O3,PM10和PM2.5.5方面.
- 五倍CV R-平方值在所有污染物中从0.31-0.87不等.
- 月度GWR模型与月度调整模型相比,表现略高.
结论:
- 首次开发25米分辨率的强大,欧洲范围的每月LUR空气污染模型.
- 这些模型有助于对欧洲范围内的空气污染中期健康影响的描述.
- 促进出生队列研究中的关键暴露时间窗口调查.
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