Several recent studies have reported associations between common levels of particulate air pollution and small increases in daily mortality. This study examined whether a similar association could be found in the southern United States, with different weather patterns than the previous studies, and examined the sensitivity of the results to different methods of analysis and covariate control. Data were available in Birmingham, Alabama, from August 1985 through 1988. Regression analyses controlled for weather, time trends, day of the week, and year of study and removed any long-term patterns (such as seasonal and monthly fluctuations) from the data by trigonometric filtering. A significant association was found between inhalable particles and daily mortality in Poisson regression analysis (relative risk = 1.11, 95% confidence interval 1.02-1.20). The relative risk was estimated for a 100-micrograms/m3 increase in inhalable particles. Results were unchanged when least squares regression was used, when robust regression was used, and under an alternative filtering scheme. Diagnostic plots showed that the filtering successfully removed long wavelength patterns from the data. The generalized additive model, which models the expected number of deaths as nonparametric smoothed functions of the covariates, was then used to ensure adequate control for any nonlinearities in the weather dependence. Essentially identical results for inhalable particles were seen, with no evidence of a threshold down to the lowest observed exposure levels. The association also was unchanged when all days with particulate air pollution levels in excess of the National Ambient Air Quality Standards were deleted. The magnitude of the effect is consistent with recent estimates from Philadelphia, Steubenville, Detroit, Minneapolis, St. Louis, and Utah Valley.