Ambient air pollutant measurement error: characterization and impacts in a time-series epidemiologic study in Atlanta

Abstract

In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.

FIGURE 1

Map of 20-county metropolitan Atlanta study area. Census tracts, interstate highways, and ambient air pollutant monitoring sites are shown.

FIGURE 2

Time-series simulation flow chart. For each error type and each pollutant, the procedure was repeated 20 times to obtain average of a and b and then repeated without optimization (dashed lines) to generate 1000 simulated time-series.

FIGURE 3

Semivariograms for the log normalized pollutant concentrations. Exponential curves are fitted to the data. Similar semivariograms were constructed for normalized pollutant concentrations.

FIGURE 4

Percent reduction in risk ratio due to instrument precision error and spatial variability error versus semivariogram nugget (γ_o) and integrated population-weighted semivariance ($\overline{{\gamma }^{\prime }}$), respectively, on concentration basis with one-sided error bars indicating the standard deviation of the 1000 simulations. Spatial variability error points are labeled in order of increasing $\overline{{\gamma }^{\prime }}$. For reference, a one-to-one line is shown.