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

TL;DR

PollutionNet is a Vision Transformer framework that fuses satellite and ground data to improve atmospheric NO$_2$ and SO$_2$ assessment, outperforming traditional models in accuracy and scalability.

Contribution

This paper introduces PollutionNet, a novel Vision Transformer-based model that effectively integrates satellite and ground data for enhanced atmospheric pollution prediction.

Findings

PollutionNet achieves RMSE of 6.89 μg/m³ for NO₂ and 4.49 μg/m³ for SO₂.

It reduces prediction errors by up to 14% compared to baseline models.

The framework demonstrates high scalability and data efficiency in pollution assessment.

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.