The emergence of low-cost sensing technologies is changing the way we think about and understand our personal air-pollutant exposures, and this has led to an explosion in new low-cost sensors and research studies involving these sensors. However, data quality remains a significant challenge. Our overarching research vision is to develop a well-validated, flexible, modular environmental sensing platform that assists communities in addressing their environmental challenges. We have been collaborating with several researchers who are helping us meet this challenge including Dr. Pierre Gaillardon, Dr. Ross Whitaker, and Dr. Gregory Madden.
This work has revealed previously unseen differences in air quality resulting from pollution sources, meteorology and topography (see recent publication in ES&T). We initially focused on light-scattering-based PM sensors through the NSF CPS sponsored AQ&U and a NSF CBET EAGER AirU projects. These projects have led to a better understanding of sensor performance and calibration strategies (See publications, Kelly et al. Sayahi et al. Sayahi et al. Becnel et al.). We have even studied how mounting sensors on drones affect their measurements (see pub). These networks are providing hyper-local estimates of particulate pollution (Becnel, Kelly) and even the effects of the COVID shutdown on air quality (see pub). We are also bringing complementary teaching modules to schools (AirU) that host sensors. See our very popular hands-on teaching module that allows you to build an air quality sensor from building blocks (link).
Moving forward, we are focusing on developing cost-effective carbon nanofiber sensors, complementary data analytics, and community-engaged measurements to solve critical air-quality issues. These solutions will be achieved by developing and deploying a high-resolution sensor network capable of geolocating air pollutant sources, and this is being funded through a new National Science Foundation CBET CAREER grant in Environmental Engineering.
We are also excited about a new NSF Smart and Connected Communities grant that leverages dynamic air quality feedback to study personal choices related to vehicle idling. Learn more about the project here.