Stephanie Cleland

Department of Health Sciences, Simon Fraser University

Title: Seize the Data: Using Data Fusion to Estimate Air Pollution Exposure for Health Studies
Date: Friday, October 18th, 2024
Time: 1:15PM (PDT)
Location: ASB 10900

Abstract:
Exposure to air pollution causes a wide range of adverse health outcomes, suggesting the importance of accurately estimating concentrations. While datasets such as air quality models, satellite observations, and monitoring station observations are often used independently to estimate population exposure, geostatistical methods can combine these datasets to produce more accurate and informed estimates. These more accurate estimates of exposure are necessary for evaluating the associated health impacts with greater certainty. In this seminar two different applications of the Bayesian Maximum Entropy (BME) framework are demonstrated for estimating concentrations of fine particulate matter (PM2.5). The first study uses BME data fusion to combine concentrations from monitoring stations, an air quality model, and satellite observations to estimate daily average PM2.5 during the 2017 California wildfires. These estimates are then used in a health impact assessment of the fires. The second study uses BME data fusion to combine observations from multiple air quality monitoring networks. These estimates are then used in an epidemiologic study to investigate the associations between daily and sub-daily PM2.5 exposure and cognitive performance in adults.