Abstract. Airfield areas are a source of significant aviation emissions, negatively impacting air quality in nearby residential areas. This study examines the development and experimental validation of a UAV-based digital airspace smoke monitoring platform. The primary focus of the study is the concentration of soot and aerosol particles formed during the combustion of kerosene aviation fuel, which negatively impacts human health and the environment. The developed platform uses a capacitive sensor that records changes in the dielectric properties of the airspace, converting them into an electrical signal processed by Arduino and Raspberry Pi microcontrollers capable of digital recording and data transmission. Field experiments were conducted with UAV flights along a three-dimensional cylindrical trajectory at altitudes of 20-100 m. The platform was also calibrated with a Meta-01MP-0.1 smoke meter. A graphical calibration curve for the sensor output voltage versus the smoke coefficient was constructed, and the results were processed using a mathematical approach of correlation and regression analysis. A 3D model in Python and the Plotly library were created to visualize the distribution of aerosol particles and gas pollutants. The measured data confirm the effectiveness of the presented platform for air monitoring and predictive assessment of smoke levels in real time, making it suitable for use in environmental monitoring systems in airport areas and other urbanized areas.

Keywords: air pollution, digital platform, smoke, 3D air distribution model, UAV, environmental impact assessment, environmental monitoring.