CUR - The Council on Undergraduate Research

The New Air Quality Measurement Method: Low-Cost Sensors on UAV’s

Jasper H. Bowles, Marwa M.H. El-Sayed, Department of Civil Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Bvld, Daytona Beach, FL, 32114 Avinash M. Krishnan, Marc D. Compere, Department of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Bvld, Daytona Beach, FL, 32114 Kevin A. Adkins, Department of Aeronautical Science, Embry-Riddle Aeronautical University, 1 Aerospace Bvld, Daytona Beach, FL, 32114

With the rapid industrialization and the current status of climate change, air pollution has become a global concern. However, detecting atmospheric pollutants is costly, time-consuming, and cumbersome. Currently, the Environmental Protection Agency (US EPA) utilizes filter-based techniques in their federal reference and federal equivalent methods (FRM and FEM, respectively) to measure ground-based particulate matter (PM) levels in the atmosphere. Recently, the development of low-cost sensors has helped in combatting the high cost associated with acquiring these measurements. These sensors allow for PM concentrations to be measured at high resolutions. Due to their surface mounted nature, the EPA’s methods are limited in measuring the concentrations of PM at the ground-level. Hence, they lack the ability of determining the concentrations at various altitudes, which is important in characterizing the origin and the formation pathway of such pollutants. To address these shortcomings, we propose placing multiple low-cost sensors on Unmanned Aerial Vehicles (UAVs) to measure the concentrations of PM in Daytona Beach, FL. Sampling will be conducted seasonally, and the PM concentrations will be compared to their counterpart observations obtained using the EPA’s methods. The findings of this study should aid in the development of low-cost air pollution sensors that can be hosted on UAVs. This work promises to be advantageous in detecting air pollutants in both congested and remote areas

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