用深度学习修正空气质量模型偏差，提升臭氧预测精度。
Leveraging Deep Learning for Physical Model Bias of Global Air Quality Estimates
- 用2D卷积神经网络学习化学模型的残差偏差。
- 在北美和欧洲，预测精度优于传统机器学习方法。
- 结合高分辨率卫星土地利用数据，可优化城市尺度臭氧估算。
空气污染是全球最严重的环境健康风险因素，2019年导致超过600万过早死亡。当前对地表臭氧这一关键污染物的建模仍面临挑战，尤其在与人类健康相关的尺度上，全球臭氧变化的驱动因素尚不明确，限制了物理模型的实际应用。本文采用基于2D卷积神经网络的架构，估计地表臭氧模型MOMO-Chem的残差（即模型偏差）。在北美和欧洲区域验证中，该方法显著优于传统机器学习方法，能更准确捕捉物理模型的系统性偏差。研究还评估了融合高分辨率卫星遥感土地利用信息对模型改进的效果。结果有助于深化对城市尺度臭氧偏差成因的理解，为环境政策制定提供科学支持。
原文摘要 · Abstract (English)
Air pollution is the world's largest environmental risk factor for human disease and premature death, resulting in more than 6 million permature deaths in 2019. Currently, there is still a challenge to model one of the most important air pollutants, surface ozone, particularly at scales relevant for human health impacts, with the drivers of global ozone trends at these scales largely unknown, limiting the practical use of physics-based models. We employ a 2D Convolutional Neural Network based architecture that estimate surface ozone MOMO-Chem model residuals, referred to as model bias. We demonstrate the potential of this technique in North America and Europe, highlighting its ability better to capture physical model residuals compared to a traditional machine learning method. We assess the impact of incorporating land use information from high-resolution satellite imagery to improve model estimates. Importantly, we discuss how our results can improve our scientific understanding of the factors impacting ozone bias at urban scales that can be used to improve environmental policy.
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