UAV integrated IoT sensing platform for sustainable air quality monitoring and real time AQI estimation

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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Publication Details

Journal
Discover Mechanical Engineering
Published
2026-10-06
DOI
https://doi.org/10.1007/s44245-026-00370-0
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
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article

UAV integrated IoT sensing platform for sustainable air quality monitoring and real time AQI estimation

Arunkumar M. Bongale, Prajwal Birwadkar, Harish Kumar M, Satish Kumar et al.
Discover Mechanical Engineering
Air Quality Monitoring and Forecasting
article

UAV integrated IoT sensing platform for sustainable air quality monitoring and real time AQI estimation

Arunkumar M. Bongale, Prajwal Birwadkar, Harish Kumar M, Satish Kumar, C. Hemanth Kumar
article en

Abstract

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.

Discover Mechanical EngineeringVol. 5(1)
Symbiosis International University (IN), Christ University (IN)
Openalex Percentile: Top 20%
Air Quality Monitoring and Forecasting
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