估计加泰罗尼亚的每日空气温度和污染:多次暴露的综合时空建模
Carles Milà1, Joan Ballester2, Xavier Basagaña3
1ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Environmental pollution (Barking, Essex : 1987)
|September 10, 2023
概括
这项研究开发了加泰罗尼亚每日空气温度和污染 (PM2.5,PM10,NO2,O3) 的高分辨率模型. 这些模型利用TROPOMI数据和机器学习,为环境流行病学研究提供关键的暴露估计.
科学领域:
- 环境流行病学环境流行病学
- 大气科学 大气科学
- 地理空间分析是什么?
背景情况:
- 环境流行病学需要准确的多次暴露模型来评估共同暴露和相互作用.
- 空气温度和污染物的时空建模对于了解健康影响至关重要.
研究的目的:
- 开发高分辨率 (每天,250米) 时空暴露模型,用于2018-2020年加泰罗尼亚的表面空气温度和主要空气污染物 (PM2.5,PM10,NO2,O3) 的空气污染.
- 纳入包括TROPOMI卫星数据在内的新方法,先进的填空技术,预测不确定性估计和可解释的机器学习 (SHAP值).
- 为环境流行病学研究提供可靠的暴露数据,考虑共暴露和潜在的相互作用.
主要方法:
- 收集了各种数据:气象和空气质量站测量,再分析数据和TROPOMI卫星产品.
- 采用随机森林 (RF) 和量子射频 (QRF) 来填补空白的远程传感数据和曝光建模,并采用地缘统计插值.
- 使用结构化数据分割验证的填空模型和通过嵌套交叉验证评估模型性能;使用SHAP值进行变量重要性和关系分析.
主要成果:
- 温度模型实现了高性能 (R2=0.98),其次是气态污染物 (NO2 R2=0.81,O3 R2=0.86),而颗粒物模型显示中等性能 (PM2.5 R2=0.57,PM10 R2=0.63).
- 热带 NO2 柱被确定为 PM 和 NO2 模型的非常重要的预测因素.
- 预测的暴露模式成功捕获了城市热岛,尘埃引流和NO2热点等现象.
结论:
- 准确验证缺口填补模型至关重要,因为袋外 (OOB) 估计可以低估错误.
- 卫星TROPOMI数据为改善空气污染暴露模型提供了显著的潜力.
- 对于PM模型的中等性能可能归因于有限的地面站覆盖率;开发的估计可以支持带有不确定性考虑的流行病学研究.
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