PollutionNet：一种用于利用卫星-地面数据融合的氮氧化物和硫氧化物气候评估的视觉Transformer框架
PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion
AI总结：
本文提出PollutionNet框架，结合卫星与地面数据融合，利用自注意力机制提升对NO₂和SO₂的时空依赖性建模，实现高精度污染评估，减少预测误差14%，为气候研究提供高效工具。
摘要：
准确评估大气氮氧化物（NO₂）和硫氧化物（SO₂）对于理解气候-空气质量相互作用、支持环境政策和保护公众健康至关重要。传统监测方法面临局限：卫星观测提供广泛的空间覆盖但存在数据缺口，而地面传感器提供高时间分辨率但空间范围有限。为解决这些挑战，我们提出PollutionNet，一种基于视觉Transformer的框架，整合Sentinel-5P TROPOMI垂直柱密度（VCD）数据与地面观测数据。通过利用自注意力机制，PollutionNet捕捉到传统CNN和RNN模型常忽略的复杂时空依赖性。应用于爱尔兰（2020-2021），我们的案例研究显示，PollutionNet在NO₂（RMSE：6.89 μg/m³）和SO₂（RMSE：4.49 μg/m³）的评估中达到最先进的性能，与基线模型相比，预测误差减少高达14%。除了精度提升外，PollutionNet还提供了一种可扩展且数据高效的工具，用于应用气候学，使在监测网络稀疏的地区能够实现稳健的污染评估。这些结果突显了先进机器学习方法在增强与气候相关的空气质量研究、指导环境管理以及支持可持续政策决策方面的潜力。
英文摘要：
Accurate assessment of atmospheric nitrogen dioxide (NO$_2$) and sulfur dioxide (SO$_2$) is essential for understanding climate-air quality interactions, supporting environmental policy, and protecting public health. Traditional monitoring approaches face limitations: satellite observations provide broad spatial coverage but suffer from data gaps, while ground-based sensors offer high temporal resolution but limited spatial extent. To address these challenges, we propose PollutionNet, a Vision Transformer-based framework that integrates Sentinel-5P TROPOMI vertical column density (VCD) data with ground-level observations. By leveraging self-attention mechanisms, PollutionNet captures complex spatiotemporal dependencies that are often missed by conventional CNN and RNN models. Applied to Ireland (2020-2021), our case study demonstrates that PollutionNet achieves state-of-the-art performance (RMSE: 6.89 $μ$g/m$^3$ for NO$_2$, 4.49 $μ$g/m$^3$ for SO$_2$), reducing prediction errors by up to 14% compared to baseline models. Beyond accuracy gains, PollutionNet provides a scalable and data-efficient tool for applied climatology, enabling robust pollution assessments in regions with sparse monitoring networks. These results highlight the potential of advanced machine learning approaches to enhance climate-related air quality research, inform environmental management, and support sustainable policy decisions.
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