Faculty Bibliography

Recent epidemiological studies have indicated that ambient air pollution, including PM-10, is associated with excess mortality and morbidity. These studies have included both cross-sectional comparisons across communities and rime-series analyses over time in single communities. Time-series analysis offers certain advantages, primarily in that the study population is the same over time, so that it acts as its own ''control.'' However, modeling such data is complicated by the fact that other environmental factors and other causes of illness can confound the results unless they are adequately addressed. For example, wintertime influenza epidemics cause long-wave peaks in respiratory mortality, and variations in emissions, dispersion, and atmospheric chemistry can cause seasonal cycles in pollution. Such superimposed long-wave variations in both health outcomes and pollutant concentrations can undermine the statistical validity of time-series models by inducing autocorrelation, and can create long-wave ''noise'' signals that can overwhelm a short-term ''signal'' of interest. Also, model specification can strongly affect the results of a time-series model. For example, analyses focusing on only one routinely collected pollution metric, to the exclusion of other possibly more influential pollution components, can cause the effects of the overlooked pollutants to be ascribed to the studied pollutant. In addition, the potential effects of nonnormal (e.g., Poisson) data distributions on time-series results need to be considered. It is concluded that how these various time-series modeling factors are, or are not, addressed can have a large influence on the study conclusions, or the ''message'' resulting from such analyses. Sensitivity analyses incorporating multiple modeling methods and model specifications are therefore recommended as part of such an analysis. Moreover, in this article exploratory and diagnostic procedures are recommended that may aid the modeler in assessing and avoiding the noted problems and that will allow the validity of such studies to be more easily documented and intercompared

A study of acidic sulfate aerosol was conducted at two sites (8 km apart) along Chestnut Ridge in western Pennsylvania during November 1987. Fine (< 2.5-mu-mad) aerosol composition was measured using dichotomous samplers with Teflon membrane filters. Three 8-h samples per day were collected for 10 days. The major species were SO4(2-) and NH4+, which averaged about 95 and 70 neq m-3, respectively, at both sites. The particulate acidity was less than 15 per cent of sulfate equivalents; the averages were 6-14 neq m-3 (< 1.0-mu-g m-3 as H2SO4). Acidity exceeded this level only 30% of the sampling intervals at one site, with the peak value almost-equal-to 50 neq m-3. This site received a higher frequency of upper level winds from the direction of several nearby coal-fired power plants (the nearest 5 km away), and the period of highest acidity was observed concurrently with elevated SO2. The 3 x daily data suggest that higher acidity occurred in the overnight period (midnight to 8 a.m.) in the late fall, while sulfate had its highest levels in the morning to afternoon period

Several past studies of the historical London air pollution record have reported an association between daily mortality and British Smoke levels. However, this pollution index does not give direct information on particulate mass or its chemical composition. A more specific particulate matter index, aerosol acidity, was measured at a site in central London, and daily data are available for the period 1963-1972. British Smoke and SO2 were also measured at the same site. Also, meteorological parameters were routinely measured at a nearby British Meteorological Office. Thus, daily fluctuation of the acidic aerosols was characterized in terms of other environmental parameters. Each of the other parameters analyzed seems necessary, but not sufficient to explain a high level of acidic aerosol. Overall, about half of the variance of log-transformed daily fluctuations of acidic aerosols can be explained by a combination of parameters including SO2 and British Smoke concentrations, temperature, ventilation by wind, and humidity. The rest of the variance cannot be explained by the parameters included in this analysis. Potential factors responsible for this unique variance would be variations in the availability of basic gases to cause neutralization and variation in the availability of catalytic metal salts. Because the acidic aerosol has a unique component of variation, it may be possible to distinguish health effects due to this specific pollutant from other available pollution indices or environmental factors