Shuaishuai Guo¹ and Jonathan Sahagun², ¹USA, ²California State Polytechnic
University, USA

This paper addresses the critical environmental challenge of air quality degradation, exacerbated by industrial emissions, vehicular pollutants, and agricultural activities [1]. Our proposed solution, a Real-Time and FineGranularity Air Quality Monitoring and Analytical System, leverages machine learning and drone technology to dynamically monitor and analyze air quality across diverse locations and altitudes. By integrating drone-mounted sensors, advanced machine learning algorithms, and a user-friendly interface, the system offers unprecedented spatial and temporal resolution in air quality assessment. The study navigated through limitations such as data transmission reliability and the complexity of real-time data analysis, employing robust communication protocols and enhanced analytical models for improved accuracy [2]. Experimentation across various urban and rural settings demonstrated the system's effectiveness in identifying pollution hotspots and predicting air quality trends, with significant improvements over traditional stationary monitoring methods. Our findings highlight the potential of combining drone mobility with machine learning efficiency to revolutionize air quality monitoring, making it an indispensable tool for environmental management and public health protection [3].

Air Quality Monitoring, Drone Technology, Machine Learning, Real-time Data Analysis