揭示卫星观测陆地表面温度对地面层臭氧高分辨率绘图的影响,使用可解释机器学习
Qingqing He1,2, Jingru Cao1, Pablo E Saide2,3
1School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
Environmental science & technology
|August 28, 2024
概括
本研究介绍了一种使用陆地表面温度 (LST) 和XGBoost.的高分辨率臭氧测绘方法. 该模型准确预测地面层臭氧,有助于公共卫生评估和暴露评估.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 公共卫生 公共卫生
背景情况:
- 准确地绘制地面层臭氧的地图对于公共卫生评估至关重要.
- 需要高的时空分辨率 (每天,1公里) 来进行有效的暴露评估.
- 确定臭氧变化的可靠代理是必不可少的.
研究的目的:
- 开发一种使用机器学习的高分辨率臭氧建模方法.
- 利用卫星衍生的陆地表面温度 (LST) 作为臭氧的主要预测指标.
- 为了在2019年以1公里分辨率绘制中国地面层臭氧度的地图.
主要方法:
- 使用XGBoost算法进行臭氧建模.
- 利用卫星衍生的陆地表面温度 (LST) 作为关键预测因素.
- 在不同的空间分辨率中验证模型性能.
主要成果:
- 在1公里分辨率模型中,实现了0.91的交叉验证R2和13.51μg/m3的RMSE.
- 生成了地下臭氧的详细地图,揭示了以前未解决的空间变化.
- 陆地表面温度 (LST) 被确定为最有影响力的预测因素.
结论:
- 带有LST的XGBoost模型提供了精确的高分辨率地面层臭氧映射.
- 模型性能随着更粗的空间分辨率而显著降低.
- 该方法和数据为暴露评估和流行病学研究提供了宝贵的资源.
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