The objective of this study was to demonstrate the methodological shortcomings of currently available analytical methods for single-city time series data. We analyzed daily Chronic Obstructive Pulmonary Disease (COPD) and daily asthma hospital admissions in Melbourne, Australia from July 1989 to December 1992. Air pollution data comprised nitrogen dioxide, ozone and sulphur dioxide and air particles index consistent with particulates between 0.1 and 1 microm in aerodynamic diameter. Statistical analyses were performed using generalized linear models, generalized additive models, Poisson autoregressive models and transitional regression models. The estimated effect of nitrogen dioxide on COPD hospital admissions was similar across the different statistical models, RR = 1.06 (95% CI 1.01-1.11). Similarly the estimated effect of nitrogen dioxide on asthma hospital admissions was also consistent, RR = 1.05 (95% CI 1.01-1.09). However, the effects of ozone, air particles index and sulphur dioxide were highly sensitive to model specification for both COPD and asthma hospital admissions. In single-city studies of air pollution and respiratory disease, very different conclusions can be drawn from competing models. Furthermore, real time series data have greater complexity than any of the commonly-used existing models allow. Consequently, single-city studies should use several statistical models to demonstrate the stability of estimated effects.