Abstract
We consider in this paper the problem of optimally sampling noise exposure at the workplace during a shift that can be partitioned into different working phases. We are faced with the problem of evaluating average exposure during an eight-hour shift from only a few samples of, say, one minute integration time. Independent information about the duration of the work phases is available and we assume that the variograms of the different phases are known. We present a criterion which allows to optimally split up a fixed number of samples over the different phases. This procedure reduces dramatically the estimation variance when estimating the mean sound exposure as compared to a blind sampling ignoring phase information, even with a small number of samples taken.
Though illustrated here with noise exposure data, the methodology is also applicable in industrial hygiene to exposure to chemicals or dust. From a geostatistical point of view this work can easily be extended to spatial problems in which a spatial domain is partitioned into zones for each of which the variogram is known.
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© 1999 Springer Science+Business Media Dordrecht
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Lajaunie, C., Wackernagel, H., Thiery, L., Grzebyk, M. (1999). Sampling Multiphase Noise Exposure Time Series. In: Gómez-Hernández, J., Soares, A., Froidevaux, R. (eds) geoENV II — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9297-0_9
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DOI: https://doi.org/10.1007/978-94-015-9297-0_9
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