STATISTICAL ISSUES OF AIR POLLUTION AND DAILY MORTALITY
RICHARD L SMITH
Department of Statistics, University of North Carolina, Chapel Hill
For many years, the Environmental Protection Agency (EPA) has enforced
standards for airborne pollutants which are harmful to human health. New
regulations introduced in 1997 tightened the existing standards for ozone
and particulate matter, including a new standard for fine particles
(particulate matter of diameter 2.5 microns or less, known as PM2.5). These
new standards were introduced after extensive epidemiological research
claiming that airborne particulate matter is harmful at levels permitted by
previous standards. The standards have proved very controversial, with many
groups challenging their validity on scientific grounds as well as an
industry-sponsored lawsuit which has, for the time being, halted their
implementation. Nevertheless the EPA is proceeding with its year 2000
review of the standards in the expectation that a new standard will be
introduced and enforced in 2002.
This talk will discuss some of the statistical issues associated with the
epidemiological research into health effects of particulate matter. A
large class of studies is based on time series analysis in which the
essential hypothesis is that mortality tends to be higher on days
immediately following high air pollution events. Detailed analysis of such
data sets, however, often allows for alternative interpretations which make
the assertion of a direct causal relationship unclear. A recent study
using new air pollution data from Phoenix, Arizona, has suggested that the
direct effect may be stronger for coarse particles than for fine particles,
and also that the adverse effects of fine particles only occur above a
threshold in the region of 20-25 micrograms per cubic meter, which is above
the proposed EPA standard for the long-term mean. Finally, I shall also
discuss some of the methodological issues associated with regression
analysis with large numbers of covariates, suggesting that methods based on
ridge regression, principal components regression and various Bayesian
possibilities may perform better than the standard regression methods
currently employed in these studies.
Questions? Jiming Jiang