(7d) The Effectiveness of Drones in Measuring Particulate Matter

Obtaining highly resolved spatial and temporal measurements of pollutants in the air is difficult due to the cost of high-quality monitoring stations and equipment. Low-cost sensors and sensor networks are an innovative and affordable way to collect measurements between monitoring stations, and are being studied and evaluated in many locations. Mounting these low-cost sensors on unmanned aerial vehicles (UAVs), often referred to as drones, provides a versatile platform for collecting air quality measurements, especially in locations where placing a sensor would be difficult or dangerous. However, the flow created by many drones is complex, unsteady, and directly interacts with pollutants, such as fine particulate matter — a main contributor to adverse health effects from poor air quality. It is critical, therefore, to understand and quantify the effect of the drone-induced flow on low-cost sensor measurements so strategies can be established to maximize data quality. Our work aims to quantify the effect from the flow of a quad-rotor drone on low-cost, light-scattering particulate matter sensors through experiments performed in a wind tunnel and an open-air environment. We also employ an array of high-fidelity sensors to compare with the low-cost sensors and to measure the particle size distribution. Our results show negligible impact on the particle size distribution, but a significant impact on the concentration measurements at various locations around the drone. For a given location around the drone, we also observe significant variations in the concentration change across tests even though the experimental conditions remained the same. This is consistent with the turbulent nature of the flow and illustrates the difficulty of using a drone for measurements. A potential solution to improve the consistency of the results includes changing the design of the sensor inlet and housing. Future work will investigate adjustments to the sensor design and seek to better understand the flow and vortices generated by the rotors of the drone through simulation.