Air pollution emissions have significantly decreased over the last couple of decades. Have emissions decreased equitably throughout the United States? Are there counties that have seen disproportional reductions or increases? What are some demographic and social factors that influence such differences? This project aims to answer these and other similar questions by evaluating temporal and geographical patterns of air pollution emissions across the contiguous United States from 1970 to the present.
The effects of air pollution on the central nervous system and its potential contribution to neurodegenerative diseases is an emerging environmental health issue. This project examines the relationship between long-term exposure to fine particulate matter (PM2.5) and disease aggravation in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In addition to characterizing exposure to total PM2.5 mass, we evaluate exposure to specific chemical components of PM2.5—which can help identify relevant emission sources. Our research leverages data from New York State and Denmark medical registries and various spatio-temporal air pollution prediction models.
More epidemiology studies now focus on evaluating exposure to environmental mixtures, and new statistical methods are being adapted and developed for this task. This study employed methods geared toward distinct research questions to illustrate differences across mixture methods and highlight the importance of a carefully devised research question. Through this work, we aimed to help epidemiologists better understand complex statistical mixture methods, such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, principal component analysis (PCA), and others. Below is a table with helpful information on some methods used to evaluate exposure to environmental mixtures. For more info, check out our related publications:: Gibson, E., et al.; Nunez, Y., et al..