Monitoring modern air pollution requires flexible but portable systems that can be used to gather real-time data in various settings. This paper describes a drone-based air quality monitoring platform with IoT-enabled sensors integrated into it. The system carries multiple gas sensors CO, NO 2 , O 3 , SO 2 , and PM 2.5 , all managed by a Raspberry Pi 4 (Model B). The platform operates at a 1 Hz sampling rate performing onboard data filtering, dynamic caching and wireless synchronization with a cloud platform (ThingSpeak®) for real-time visualization. The performance of the system was validated in three different campus micro-environments up to 50 m Above Ground Level (AGL). The overall calculated Air Quality Index (AQI) was in the range of 21.7 to 42.1 (which falls in the “Good” category in the US EPA/CPCB criteria). Importantly, the platform was able to capture spatial micro-gradients, such as those of O 3 driven by photochemistry, which are not detected by static surface networks, including the presence of micro-peaks (maximum 0.1571 ppm at 50 m AGL) and transient localized shifts (0.0125 ppm to 0.0182 ppm) in NO 2 . Further, the platform provides a scalable proof-of-concept foundation that can be adapted for mobile micro-climate air quality monitoring across diverse urban and industrial topographies.
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Bongale et al. (2026) studied this question.
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