Distributed Sensing of Aersol Particle counts using small unmanned aircraft

PI: Mike Sama, UK

RESEARCH OBJECTIVES, SPECIFIC AIMS: Exposure to small aerosols that may enter the respiratory systems, and particularly those that may reach the alveolar region increase risks for both acute and chronic respiratory diseases such as asthma and bronchitis. They are also associated with long-term chronic irritation and inflammation, which can potentially lead to cancer. If the source of aerosols can be accurately determined, interventions, regulations, and policies can be implemented to reduce exposures and prevent disease. Sparse ground-based measurements of aerosol particles may not be adequate for distinguishing sources. The combination of ground-based measuring techniques with distributed aerial measurements may lead to much greater characterization of both the source and concentrations of exposures.
Our long-term goal is to develop the ability to quantify the spatiotemporal distribution in local air quality to better understand the impacts of airborne contaminants on disease rates in agricultural communities. Aerosol particle counters deployed on small unmanned aircraft systems (UAS) will enable distributed sensing over the short time scales needed to map the spatial variability in local air quality. This in turn will help differentiate particle sources associated with agricultural activities (e.g., field equipment, grain handling, animal facilities) from external sources (e.g., roadways). Recent advances in UAS and miniature particle counters make this vision feasible. However, research is needed to understand how UAS deployment affects the accuracy of aerosol particle count measurements and how the distribution of aerosol particles sensed in the lower atmospheric boundary layer correspond to conditions at the surface where humans are exposed. We hypothesize that the spatial distribution of aerosol particle counts is heterogenous at local scale based on geography and proximity to aerosol sources. The rationale for completing this work is that better understanding of the spatiotemporal distribution of outdoor air quality may elucidate observed differences in health outcomes for farmers and their surrounding communities. The preliminary results obtained from our field experiment will be used as part of an investigator-initiated R01 application to NIOSH.