I-Corps: Mobile air quality sensor module that can be attached to unmanned aerial vehicles (UAVs)

West Virginia University Research Corporation, Morgantown WV

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project will be a device to enable air quality and environmental engineers to collect mobile air quality data via a drone (unmanned aerial vehicle: UAV). This will have a direct impact on a wide range of industries that are required to gather air quality data including but not limited to energy production, natural gas, and oil. This device allows engineers to create air quality models from real geospatial data rather than simulated data leading to more accurate emission values. These accurate values will allow both companies and government agencies to better track the direct effect air pollution is having on the environment and communities. The device also may be used in identifying the environmental effects and spread of air pollution from natural disasters such as wildfires on local communities. In addition, the device may be used in military applications to determine criteria and hazardous pollutant concentrations for special use cases or battlefield scenarios. During the I-Corps program, the goal is to speak with a variety of potential customers that include air quality engineers, environmental engineers, and leak detection and repair (LDAR) supervisors as well as government agencies such as the Environmental Protection Agency (EPA) to make the device adaptable across a variety of industries and use cases. This I-Corps project is based on a previously developed mobile air quality sensor module that can be attached to UAVs to perform advanced air sampling techniques that enable geospatial data logging of pollutant concentrations in real time. The device is comprised of low-cost, high quality sensors that are interchangeable within the printable circuit board (PCB). The device has the ability to simultaneously collect the following air pollutants: Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Particulate Matter (PM1.0, PM2.5, PM10), Carbon Monoxide (CO), Ozone (O3), and Lead (Pb). Combining the mobile air quality data collected by the device with unique machine learning models, new micro and macro environmental insights may be seen across the country. The proposed solution has been successfully evaluated with an alpha prototype. The device that has been developed is unique to the air quality monitoring industry and is currently not offered on the market. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.