在2003-2020年使用机器学习模拟全欧洲精细分辨率的每日环境温度
Alonso Bussalleu1, Gerard Hoek2, Itai Kloog3
1Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
The Science of the total environment
|April 18, 2024
概括
欧洲各地的高分辨率温度数据现在可用于健康研究. 新模型提供了日常环境温度 (Ta) 表面,改善了对温度相关健康影响的流行病学研究的暴露评估.
科学领域:
- 环境流行病学环境流行病学
- 气候科学是气候科学.
- 地理空间分析是什么?
背景情况:
- 调查极端温度对健康的影响的流行病学研究需要准确的,高分辨率的环境温度 (Ta) 数据.
- 现有的数据往往缺乏大规模欧洲多队列研究所需的时空分辨率和协调.
研究的目的:
- 为欧洲开发1x1公里分辨率的日常平均,最小和最大环境温度 (Ta) 表面.
- 涵盖2003年至2020年期间,为欧洲多队列研究提供协调的暴露评估.
主要方法:
- 采用了两阶段的随机森林建模方法.
- 阶段1:使用植被和天气数据计算缺失的卫星衍生陆地表面温度 (LST).
- 第二阶段:采用空隙填充的LST,土地使用和气象数据进行模拟的Ta变化,并使用随机和块法验证.
主要成果:
- 模型解释了区块验证集中超过81% (LST) 和93% (Ta) 的变化.
- 平均根平均平方误差 (RMSE) 为1.3°C (平均Ta),1.9°C (最小Ta) 和1.7°C (最大Ta).
- 模型性能一般稳定,但在气象站数据稀少的地区,准确性较低.
结论:
- 开发的高分辨率温度表面为欧洲流行病学研究提供了宝贵的资源.
- 这些数据集可以在研究环境温度对健康影响的研究中改进暴露评估.
- 该方法提供了一种可靠的方法,用于在整个大陆范围内生成协调的环境暴露数据.
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