Abstract
A robust methodology was developed to compute population-weighted daily measures of ambient air pollution for use in time-series studies of acute health effects. Ambient data, including criteria pollutants and four fine particulate matter (PM) components, from monitors located in the 20-county metropolitan Atlanta area over the time period of 1999-2004 were normalized, spatially resolved using inverse distance-square weighting to Census tracts, denormalized using descriptive spatial models, and population-weighted. Error associated with applying this procedure with fewer than the maximum number of observations was also calculated. In addition to providing more representative measures of ambient air pollution for the health study population than provided by a central monitor alone and dampening effects of measurement error and local source impacts, results were used to evaluate spatial variability and to identify air pollutants for which ambient concentrations are poorly characterized. The decrease in correlation of daily monitor observations with daily population-weighted average values with increasing distance of the monitor from the urban center was much greater for primary pollutants than for secondary pollutants. Of the criteria pollutant gases, sulfur dioxide observations were least representative because of the failure of ambient networks to capture the spatial variability of this pollutant for which concentrations are dominated by point source impacts. Daily fluctuations in PM of particles less than 10 microm in aerodynamic diameter (PM10) mass were less well characterized than PM of particles less than 2.5 microm in aerodynamic diameter (PM2.5) mass because of a smaller number of PM10 monitors with daily observations. Of the PM2.5 components, the carbon fractions were less well spatially characterized than sulfate and nitrate both because of primary emissions of elemental and organic carbon and because of differences in measurement techniques used to assess these carbon fractions.