Yara Abu Awad, Horizon Postdoctoral Fellow, Psychology (Concordia) and Visiting Scientist (Harvard) will give a presentation based on the publication of A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States.
Yara Abu Awad, Petros Koutrakis, Brent Coull and Joel Schwartz. In Environ Res. 2017 Nov;159:427-434
Journal club attendees are encouraged to read the motivating publication
Refreshments will be served
Hosted by The Montreal Health Statistics Centre and the Department of Mathematics and Statistics
Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects. But due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data (for a total of 24,301 measurements over a 12 year period) and used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. Our model can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in several north-eastern states in the US.
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