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This study demonstrates novel measurements of in situ ozone (O 3 ) concentrations and thermodynamics sampled on-board an instrumented Skywalker Unmanned Aerial Vehicle (UAV). Small spatial and temporal gradients were observed over a localized region, which nearby ground-based in situ measurements lack the ability to resolve. It was found that the UAV-measured O 3 concentrations provided a useful additional indicator of O 3 variability at the sub-urban scale. The ability to sample subtle variability over a localized area highlights the important and novel capabilities of UAVs to rapidly characterize local area micrometeorology and chemistry.

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This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, and heading rates and speeds.