70: Land Use Regression Models for Predicting PM2.5: A Comparative Analysis from the Accra Birth Cohort

Music City Center 

PM2.5 as an aggregate air pollutant has been widely studied for its potential health impacts. Existing prediction approaches using linear mixed models do not work well during the Harmattan periods. We propose to expand the models by integrating multiple types of predictors, including geo-spatial and satellite, and expand to consider non-linear models. Additionally, we expand the library of modeling approaches to consider machine learning and Bayesian methods (e.g., Bayesian maximum entropy, artificial neural networks, support vector machines, etc.), as well as other complex spatiotemporal methods such as the two-step local regression. Models will be trained using data from Accra collected over a 52-week period and validated using comparable independent data collected in Kigali, Rwanda.

land use regression

PM2.5

air pollution

machine learning

Bayesian methods 

Section on Statistics and the Environment