UAVs for air quality monitoring and modeling

Air pollution remains a critical public health challenge across Europe and globally. Accurate air quality models are vital for identifying pollutant sources, understanding their dispersion and transformation, and informing mitigation policies. However, existing models rely on two primary data sources: ground-based stations, which provide precise but localized and sparse measurements, and satellites, which offer broad coverage but low resolution and depend on assumptions during data inversion. Crucially, data from the lower atmospheric boundary layer—where pollutant behavior is most dynamic—are notably scarce.

This research proposes the design and deployment of a drone-based aerial sensor network to address this gap. The study will develop advanced drone systems equipped with air quality sensors to capture pollutant profiles in the lower atmosphere. Data collected in diverse environments (urban, industrial, and rural) and across different seasons will enhance high-resolution emission modeling. A multi-scale fusion framework will integrate this drone-based data with satellite and ground-based observations, leveraging deep learning techniques to advance air quality assessment and policy development.