Abstract
The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.
Highlights
Improving air quality requires new technologies to better identify and characterize distributed pollutant sources and assess human exposure
particulate matter (PM) data were presented as the PM sensor was not affected by the unmanned aerial vehicle (UAV) operation
Our UAV measurements showed that average (±standard deviation) PM2.5 concentrations were 13.2 ± 2.0, 8.8 ± 3.7, and 10.5 ±
Summary
Improving air quality requires new technologies to better identify and characterize distributed pollutant sources and assess human exposure. Air pollution has been identified as a leading risk factor for the global disease burden [1,2]. The current networks are often considered insufficient to cover large areas, account for new source regimes, or implement effective pollution control strategies. The Internet of Things (IoT), defined as the network of connected air pollution sensors, has been commercialized to gather air quality data by consumers worldwide [3,4]. They are only able to capture data with low spatial resolutions and cannot track the change of air pollution in the spatial dimension. Dedicated vehicle-based sampling systems have improved spatial coverage and demonstrated source-characterization capabilities; these applications can be limited by site access, proper orientation to the source (e.g., upwind vs. downwind), complex topography, or public road networks, and require extremely sensitive and expensive instrumentation
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