通过在深度学习中包含地理物理a Priori信息来提高细颗粒物质度的全球估计
Siyuan Shen1, Chi Li1, Aaron van Donkelaar1
1Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
概括
改进全球细颗粒物 (PM2.5) 评估,本研究使用了一种新的深度学习模型. 它提高了PM2.5度估计,即使使用有限的地面监测器,也可以更好地监测空气质量.
科学领域:
- 环境科学环境科学
- 大气中的化学成分
- 数据科学是数据科学.
背景情况:
- 对细颗粒物 (PM2.5) 的全球评估受到稀疏监测网络的限制.
- 准确的PM2.5数据对于公共卫生和环境政策至关重要.
研究的目的:
- 改进估计全球PM2.5度分布的方法.
- 开发一个强大的深度学习模型用于PM2.5预测,使用多种数据源.
主要方法:
- 开发和优化了一个卷积神经网络,集成卫星,模拟和监控数据.
- 引入了一个新的损失函数和空气质量的空间交叉验证.
- 整合了先验的地质物理PM2.5估计,以提高数据稀疏地区的模型性能.
主要成果:
- 该模型准确地预测了1998-2019年每月PM2.5度的局部偏差.
- 估计显示与全球和区域地面监测数据的高度一致.
- 该模型表现出强度,保持高精度 (R2 = 0.73),即使只有1%的监视器用于培训.
结论:
- 开发的深度学习方法显著改善了全球PM2.5估计.
- 地理学先验数据集成提高了模型性能,特别是在监视器很少的地区.
- 该方法为全球全面的空气质量评估提供了可靠的解决方案.
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