UAV-Induced Measurement Bias in Air Pollution Sensing: A Physics-Informed Framework for Characterization, Sensor Placement, and Correction.

Unmanned aerial vehicles (UAVs) offer flexible, three-dimensional access for air pollution monitoring, but rotor-induced aerodynamic disturbance can bias onboard sensor readings, an effect not yet well characterized for multi-rotor platforms. This paper presents a physics-informed framework unifying rotor wake characterization, turbulence zone classification, pollutant sensitivity analysis, sensor placement strategy, and bias correction into one design tool. Grounded in actuator disk momentum theory and literature-constrained relationships, it predicts or bounds measurement bias across nine pollutants and multiple rotor configurations, quantitative for seven sensor classes and provisional for O3 and VOCs. It also introduces a four-zone contamination classification and a two-stage bias correction for motion-induced and environmental factors. Hover and constant-speed co-location flight tests on a 21-inch quadrotor, comparing a UAV-mounted sensor against a fixed reference under Zone 4 conditions, support these predictions: observed bias (3-10% in hover; 6-15% at 5 m/s, across NO2, CO, O3, PM10, and PM2.5) was directionally consistent with the predictions, though correlation dropped as low as r = 0.44 in forward flight, below typical validation thresholds. The tests thus support the framework's qualitative trends more than quantitative agreement, with wind-tunnel and multi-speed validation as natural next steps. The framework offers a reproducible foundation for UAV sensor integration design, uncertainty estimation, and campaign planning.