PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion

2026-03-31

AI Summary

This study addresses the longstanding trade-off between spatial coverage and temporal resolution in traditional monitoring of atmospheric NO₂ and SO₂, which often compromises accuracy or geographic comprehensiveness. To overcome this limitation, the work proposes a novel end-to-end framework that, for the first time, integrates Vision Transformer architecture with multi-source pollution data—specifically fusing Sentinel-5P TROPOMI satellite vertical column densities (VCDs) and ground-based observations. By leveraging self-attention mechanisms to capture complex spatiotemporal dependencies, the model surpasses conventional CNN- or RNN-based approaches in modeling capacity and significantly enhances prediction accuracy, particularly in regions with sparse monitoring stations. Evaluated on Ireland’s 2020–2021 dataset, the method achieves RMSEs of 6.89 and 4.49 μg/m³ for NO₂ and SO₂, respectively, reducing prediction errors by up to 14% compared to baseline models.

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 $μ$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.

Problem

• NO2
• SO2
• satellite-ground data fusion
• air quality assessment
• climatological monitoring

Innovation

• Vision Transformer
• satellite-ground data fusion
• spatiotemporal modeling
• air quality assessment
• NO2 and SO2 estimation