CyberSEES: Type 2: Collaborative Research: Connecting Next-generation Air Pollution Exposure Measurements to Environmentally Sustainable Communities

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Ambient exposure to ground-level air pollution is linked to adverse health effects in many populated areas of the world. However, advances in relating air pollution exposure to sustainable communities are hindered by limited direct observations of exposure and the coarseness of regional and global air quality models used for decision making. As a result, existing models do not resolve the scales of variability in either pollutant concentrations or population distributions necessary to accurately assess exposure nor provide the type of probabilistic uncertainty bounds required for policy. This project aims to assimilate comprehensive cyber information for use in air quality management. It advances the interdisciplinary field of cyber-environmental research through investigation into (1) cyber-scale data analysis to harness and distill valuable cyber information to support community-scale air pollution modeling; (2) micro-environment targeted sensing to augment community-scale studies with accurate, on-demand, and in situ sensing capabilities; and (3) scalable exposure modeling and analysis by solving a complex, spatiotemporally varying problem with high-dimensional data containing cyber, sensing, and model outputs. Advances in cyber-environmental research have the potential to improve government policy making, regulations, and personal choices with regard to environmentally sustainable community development. Results of this project can apply across a wide spectrum of sectors including energy, transportation, and healthcare. This project broadens vertical research and education integration across information technologies and environmental science and engineering, to both graduate and undergraduate students. Through in-field trials, this project offers unique real-world education and research opportunities to attract students from underrepresented groups and industrial professionals.