ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

ARIMA Time Series Analysis of Nano Exposure Measurements

4 September 2018, 11:40 – 12:00

Abstract

Submitted by
Wouter Fransman
Authors
Wouter Fransman (TNO)
Abstract
Real-time measurement strategies of nano-exposure at the workplace result in large amounts of data, typically with sampling intervals of 1-30 seconds. These data are, for instance, of interest to determine the effect of a specific task on a worker’s exposure to nano particles. Such data have generally been analyzed by comparing means using summary statistics like the (geometric) mean and standard deviation, t-tests, or standard regression techniques. However, in this paper we argue that such methods neglect important aspects of exposure measurement sequences like autocorrelation and the dynamics in the data and therefore can lead to erroneous conclusions. To overcome those problems, we propose the use of time-series methods using standard ARIMA models and extensions of those models to include explanatory variables to statistically test for task effects on exposure. After illustrating the advantages of this methodology with a simulation study, we present the results for some real-data examples. In conclusion, we suggest a step-wise approach for the analysis of real-time exposure measurements that does account for the dynamic nature of the data. We argue that the recording of process information during exposure measurement can substantially improve the conclusions that can be drawn from such a measurement series.

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