Generalized Additive Models (GAMs) with natural cubic splines (NS) as smoothing functions have become a standard analytical tool in time series studies of health effects of air pollution. However, standard model selection procedures ignore the model uncertainty which may lead to biased estimates, in particular those of the lagged effects. On the other hand, the degrees of smoothing to adjust for time-varying confounders are often determined by data-driven methods such as penalized likelihood. This presentation addressed these two issues with using a Bayesian model averaging (BMA) approach to account for model uncertainty in GAMs with NS and a generalized linear mixed modeling (GLMM) approach to adjust for time-varying confounders. Firstly, we conducted a sensitivity analysis with simulation studies for Bayesian model averaging with different calibrated hyperparameters contained in the posterior model probabilities. Our results indicated the importance of selecting the optimum degree of lagging for variables, based not only on maximizing the likelihood, but also by considering the possible effects of concurvity, consistency of degree of lagging, and biological plausibility. Simulation studies suggested that GLMM produces less biased estimates than GLM+NS. These methods were illustrated by analyses of the Allegheny County Air Pollution Study (ACAPS) where the quantity of interest was the relative risk of cardiopulmonary hospital admissions for a 20% increase in PM10 values for the current day and five previous days.