Temperature, air pollution and total mortality during summers in Sydney, 1994-2004

Int J Biometeorol. 2008 Sep;52(7):689-96. doi: 10.1007/s00484-008-0161-8. Epub 2008 May 28.


This study investigated the effect of temperature and air pollutants on total mortality in summers in Sydney, Australia. Daily data on weather variables, mortality and air pollution for the Sydney metropolitan area from 1 January 1994 to 31 December 2004 were supplied by Australian Bureau of Meteorology, Australian Bureau of Statistics, and Environment Protection Agency of New South Wales, respectively. We examined the association of total mortality with weather indicators and air pollution using generalised additive models (GAMs). A time-series classification and regression tree (CART) model was developed to explore the interaction effects of temperature and air pollution that impacted on mortality. Our results show that the average increase in total daily mortality was 0.9% [95% confidence interval (CI): 0.6-1.3%] and 22% (95% CI: 6.4-40.5%) for a 1 degrees C increase in daily maximum temperature and 1 part per hundred million (pphm) increase in daily average concentration of sulphur dioxide (SO(2)), respectively. Time-series CART results show that maximum temperature and SO(2) on the current day had significant interaction effects on total mortality. There were 7.3% and 12.1% increases in daily average mortality when maximum temperature was over 32 degrees C and mean SO(2) exceeded 0.315 pphm, respectively. Daily maximum temperature was statistically significantly associated with daily deaths in Sydney during summers between 1994 and 2004. Elevated daily maximum temperature combined with high SO(2) concentrations appeared to have contributed to the increased mortality observed in Sydney during this period.

MeSH terms

  • Air Pollution / statistics & numerical data*
  • Australia / epidemiology
  • Computer Simulation
  • Humans
  • Incidence
  • Models, Statistical*
  • Mortality*
  • Proportional Hazards Models
  • Risk Assessment / methods*
  • Risk Factors
  • Survival Analysis*
  • Temperature*
  • Urban Population / statistics & numerical data*