Topic Areas: Health Effects of Air Pollution
Parametric and non-parametric time-series analyses of ten years of daily mortality, pollution, and weather data from Los Angeles County were performed. State-space modeling and time and frequency domain regressions were used to modify the database and to isolate significant weather factors and pollutants associated with increased daily mortality.
Mortality was significantly related to pollution and temperature. The relationship with temperature was parabolic, with minimum mortality at about 75 degrees. Mortality had a significant and increasing relationship with concentrations of three highly correlated pollutants carbon monoxide, total hydrocarbons, and particulate whose individual contributions to the increased pollution associated with increased mortality could not be distinguished. Nitrogen dioxide and sulfur dioxide were much less strangely related to mortality, and models relating mortality to both ozone and temperature could not distinguish their respective contributions. A parametric nonlinear time series model involving linear and squared terms in temperature and the logarithm of pollution provides a reasonable predictive model. Minimum and maximum predicted mortalities differed by approximately ten percent over the ranges of temperature and pollution in the data set. The parametric and non-parametric regressions yielded similar relationships.
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