This work gives an overview of design and analysis of temporal studies using aggregated data in air pollution epidemiology. In the last years time series are often used to study the short-term association between ambient air pollution levels and aggregated health data. Health endpoints are usually daily mortality and/or daily hospital admission data from routine health registers. Air quality data are commonly obtained from one (or a few) fixed site monitoring stations. To detect the temporal association between the time-pattern in air pollution and the time-pattern in health data particular attention needs to be given to the autocorrelation structure, to the seasonality and long term trend in the data, and to the weather variables. Poisson regression with autocorrelated residuals is the suitable statistical method to analyze time studies. Furthermore, the pollutant variable can be analyzed at different lag-times to account for short latency periods in the manifestation of diseases. Studies with temporal aggregated data show the same disadvantages of the ecologic studies, although, in this case, confounding is less of a problem. Temporal studies usually are based on a large database, so that sufficient power can be achieved to detect even weak associations. Finally, the exposure information on subjects is often better characterized by short-term fluctuations in ambient air quality than is the case in geographic aggregations.