用卫星与地面数据融合，提升氮氧化物和二氧化硫的空气质量评估精度。
PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion
- 基于视觉变压器融合卫星与地面观测数据，捕捉复杂时空关系。
- 在爱尔兰2020-2021年数据上，NO₂和SO₂预测误差分别降至6.89和4.49 μg/m³。
- 适合关注空气污染监测、气候研究及政策制定的科研与管理部门。
准确评估大气中氮氧化物（NO₂）和二氧化硫（SO₂）对理解气候-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。传统监测方法存在局限：卫星观测覆盖范围广但有数据缺失，地面传感器时间分辨率高但空间覆盖有限。为此，我们提出 PollutionNet，一种基于视觉变压器的框架，融合哨兵-5P TROPOMI 垂直柱浓度（VCD）数据与地面观测数据。通过自注意力机制，PollutionNet 捕获了传统卷积神经网络和循环神经网络常忽略的复杂时空依赖关系。在爱尔兰（2020–2021年）的案例研究中，该模型达到领先性能（NO₂ RMSE: 6.89 μg/m³，SO₂ RMSE: 4.49 μg/m³），相比基线模型误差降低最高达14%。除精度提升外，PollutionNet 还提供了一种可扩展、数据高效的方法，适用于监测网络稀疏区域的稳健污染评估。结果表明，先进机器学习方法能有效提升气候相关空气质量研究能力，支持环境管理与可持续政策决策。
原文摘要 · Abstract (English)
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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