Abstract
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 $\mu$g/m$^3$ for NO$_2$, 4.49 $\mu$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.
Abstract (translated)
准确评估大气中二氧化氮（NO₂）和二氧化硫（SO₂）的浓度，对于理解气候与空气质量的相互作用、支持环境政策制定以及保护公众健康至关重要。传统监测方法存在局限：卫星观测虽能提供广阔的空间覆盖，但存在数据缺失问题；而地面传感器虽具备高时间分辨率，却受限于空间覆盖范围。为应对这些挑战，本研究提出一种基于视觉Transformer的框架——PollutionNet，该框架整合了Sentinel-5P卫星TROPOMI传感器的垂直柱浓度（VCD）数据与地面观测数据。通过利用自注意力机制，PollutionNet能够捕捉到传统卷积神经网络（CNN）和循环神经网络（RNN）模型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，PollutionNet展现出卓越性能（NO₂预测均方根误差：6.89微克/立方米，SO₂：4.49微克/立方米），相较于基线模型预测误差最高降低14%。除精度提升外，PollutionNet还为应用气候学提供了可扩展且数据高效的解决方案，能够在监测网络稀疏的地区实现稳健的污染评估。这些结果凸显了先进机器学习方法在提升气候相关空气质量研究、指导环境管理及支持可持续政策决策方面的巨大潜力。
URL
https://arxiv.org/abs/2604.03311
