How to statistically analyze nano exposure measurement results: Using an ARIMA time series approach
article
Measurement strategies for exposure to nano-sized particles differ from traditional integrated sampling methods for exposure assessment by the use of real-time instruments. The resulting measurement series is a time series, where typically the sequential measurements are not independent from each other but show a pattern of autocorrelation. This article addresses the statistical difficulties when analyzing real-time measurements for exposure assessment to manufactured nano objects. To account for autocorrelation patterns, Autoregressive Integrated Moving Average (ARIMA) models are proposed. A simulation study shows the pitfalls of using a standard t-test and the application of ARIMA models is illustrated with three real-data examples. Some practical suggestions for the data analysis of real-time exposure measurements conclude this article. © 2011 Springer Science+Business Media B.V.
TNO Identifier
446780
ISSN
13880764
Source
Journal of Nanoparticle Research, 13(12), pp. 6991-7004.
Pages
6991-7004
Files
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