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欧洲的每日高分辨率表面PM_2.5估计通过基于ML的CAMS区域预测的缩小

Shobitha Shetty¹, Paul D Hamer², Kerstin Stebel²

¹NILU, Kjeller, Norway; Department of Geosciences, University of Oslo, Oslo, Norway.

Environmental research

|November 15, 2024

概括

一个新的基于卫星和机器学习的高分辨率地表空气质量估计 (S-MESH) 模型提供了准确的,高分辨率的每日细颗粒物 (PM2.5) 估计. 在捕捉污染事件和减少偏差方面,S-MESH优于现有模型,提供更快,更有效的解决方案.

科学领域:

环境科学环境科学
大气化学大气化学
数据科学数据科学数据科学

背景情况:

细颗粒物 (PM2.5) 对健康构成重大风险,需要准确,高分辨率的每日空气质量数据.
目前用于PM2.5评估的方法难以平衡准确性,空间覆盖和时间分辨率.
像CAMS这样的化学运输模型提供连续的数据,但往往缺乏所需的空间细节.

研究的目的:

开发和验证一种基于机器学习 (ML) 的新方法,S-MESH,用于在欧洲范围内以1公里分辨率估计每日表面PM2.5.
通过使用卫星数据和气象变量来增强和缩小现有的粗分辨率模型输出.
用地面观测来评估S-MESH的性能,并与已建立的模型进行比较.

主要方法:

开发了S-MESH,这是一个集成卫星数据,ML (堆叠XGBoost) 和气象变量的协同方法.
缩小了科珀尼克斯大气监测服务 (CAMS) 区域组合的24小时PM2.5预测.
在2021-2022年期间在欧洲进行校准和验证,使用车站观测训练了ML模型.

主要成果:

S-MESH实现了3.54μg/m3的平均绝对误差 (MAE),超过了CAMS预测 (MAE 4.18μg/m3) 并接近CAMS重新分析的准确性 (MAE 3.21μg/m3).
与再分析 (-1.5μg/m3) 相比,S-MESH显示平均偏差 (MB) 显著降低,为-0.3μg/m3.
该模型在捕捉高PM2.5污染事件 (>20μg/m3) 和日复一日的变化 (时间相对绝对误差为5%) 中表现出色,特别是在重新分析低估的地区.

结论:

S-MESH提供了高精度和高分辨率的每日PM2.5估计方法,在关键指标上表现优于当前的模型.
这种方法在计算上是高效的,比传统方法需要更少的资源和处理时间.
对于空气质量评估来说,S-MESH是一个有价值的工具,其中空间分辨率和及时性都至关重要,特别是在识别高污染事件时.

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