In several studies, investigators have reported associations among air pollution, weather, and daily deaths, usually from all causes. In the current study, we focused on the difference in lag time between exposure to total suspended particulates or extreme weather and cause-specific mortality in an effort to understand the potential underlying mechanism. We used a robust Poisson regression in a generalized additive model to investigate the association between air pollution and daily mortality. We used a loess smooth function to model season, weather, and humidity; indicator variables for hot days were also used. To examine the relationship in a currently meaningful range, we excluded all days with a total suspended particulate concentration higher than 200 µg/m3. We found a significant association on the concurrent day, both for respiratory infection deaths (11% increase/100 µg/m3 increase in total suspended particulate; 95% confidence interval = 5, 17) and for heart-failure deaths (7% increase; 95% confidence interval 3, 11). The associations with myocardial infarction (i.e., 10% increase; 95% confidence interval = 3, 18) and chronic obstructive pulmonary disease (12% increase, 95% confidence interval = 6, 17) were found for the means of 3 and 4 d prior to death. We observed an effect of cold weather at lag 1 for respiratory infections and an effect of hot weather at lag 0 for heart failure and myocardial infarctions. The association for all causes and cause-specific deaths was almost identical to that noted previously in Philadelphia, Pennsylvania. Smoothed functions of total suspended particulates suggested a higher slope at lower concentrations, and this finding may account for differences noted between European and U.S. studies. Given that both the dependence between weather and daily mortality and the lag between exposure and death varies by cause of death, analyses by specific causes of death would be very useful in the future.