Modeling Ambient Air Pollution using Optimal Sensor Placement and Multiscale Spatiotemporal Data Fusion

Research on the adverse effects of air pollution on human health and environment has benefited from monitoring stations that routinely collect data on air quality. However, monitoring networks are sparsely located and preferentially placed.

Relying only on data from these fixed stations restricts study regions and also leads to uncertainty in estimates of human exposure to air pollution. Our interdisciplinary team proposed a supplementary data collection strategy and a geospatial data fusion approach that will improve our estimates of air quality and increase their resolutions. To achieve this goal the project will: (1) develop low-cost air quality sensors using recent technological advancements in sensor developments; (2) determine optimal monitoring locations for low-cost environmental sensor placement; and (3) investigate a novel geospatial data fusion approach to combine air quality information obtained from various sources at multiple spatial scales.

The project’s principal investigator is Eun-Hye Yoo, PhD, associate professor in the Department of Geography. Co-principal investigators are Tarun Singh, PhD, Professor of Mechanical and Aerospace Engineering at UB, Wenyao Xu, PhD, assistant professor in the Department of Computer Science and Engineering, and Lina Mu, PhD, associate professor in Epidemiology and Environmental Health.