Autocorrelation in real-time continuous monitoring of microenvironments

Interpretation of continuous measurements in microenvironmental studies and exposure assessments can be complicated by autocorrelation, the implications of which are often not fully addressed. We discuss some statistical issues that arose in the analysis of microenvironmental particulate matter concentration data collected in 1998 by the Harvard School of Public Health. We present a simulation study that suggests that Generalized Estimating Equations, a technique often used to adjust for autocorrelation, may produce inflated Type I errors when applied to microenvironmental studies of small or moderate sample size, and that Linear Mixed Effects models may be more appropriate in small-sample settings. Environmental scientists often appeal to longer averaging times to reduce autocorrelation. We explore the functional relationship between averaging time, autocorrelation, and standard errors of both mean and variance, showing that longer averaging times impair statistical inferences about main effects. We conclude that, given widely available techniques that adjust for autocorrelation, longer averaging times may be inappropriate in microenvironmental studies.