ABSTRACT
Modeling time series data and the temporal processes that generate them is of paramount importance in environmental epidemiology, where we find several areas of application. This chapter discusses a background to the study of temporal processes, which replaces space as the domain of interest whilst drawing on many of the concepts. One of the areas in which time series methods are most extensively used in environmental epidemiology is in assessing the short-term effects of changes in air pollution on health outcomes. Confounders may include meteorological conditions such as temperature, humidity, wind speed and rainfall. Unknown risk factors may result in long-term trends and seasonal variation in time series studies and large-scale spatial trends, for example north to south gradients, in spatial studies. Ozone is a colorless gas produced through a combination of photochemistry, sunlight, high temperatures, oxides of nitrogen emitted by motor vehicles.
