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Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data

Journal of Exposure Science & Environmental Epidemiology volume 14, pages 154–163 (2004)Cite this article

Abstract

Current air pollution levels have been shown to affect human health. Probabilistic modeling can be used to assess exposure distributions in selected target populations. Modeling can and should be used to compare exposures in alternative future scenarios to guide society development. Such models, however, must first be validated using existing data for a past situation. This study applied probabilistic modeling to carbon monoxide (CO) exposures using EXPOLIS-Milan data. In the current work, the model performance was evaluated by comparing modeled exposure distributions to observed ones. Model performance was studied in detail in two dimensions; (i) for different averaging times (1, 8 and 24 h) and (ii) using different detail in defining the microenvironments in the model (two, five and 11 microenvironments). (iii) The number of exposure events leading to 8-h guideline exceedance was estimated. Population time activity was modeled using a fractions-of-time approach assuming that some time is spent in each microenvironment used in the model. This approach is best suited for averaging times from 24 h upwards. In this study, we tested how this approach affects results when used for shorter averaging times, 1 and 8 h. Models for each averaging time were run with two, five and 11 microenvironments. The two-microenvironment models underestimated the means and standard deviations (SDs) slightly for all averaging times. The five- and 11-microenvironment models matched the means quite well but underestimated SDs in several cases. For 1- and 24-h averaging times the simulated SDs are slightly smaller than the corresponding observed values. The 8-h model matched the observed exposure levels best. The results show that for CO (i) the modeling approach can be applied for averaging times from 8 to 24 h and as a screening model even to an averaging time of 1 h; (ii) the number of microenvironments affects only weakly the results and in the studied cases only exposure levels below the 80th percentile; (iii) this kind of model can be used to estimate the number of high-exposure events related to adverse health effects. By extrapolation beyond the observed data, it was shown that Milanese office workers may experience adverse health effects caused by CO.

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Notes

  1. In Latin hypercube sampling, the parametric probability distribution is divided into slices of equal probability according to the selected number of iterations. Then one sample is drawn randomly from each slice. Compared to the standard Monte Carlo sampling, the generated random number sequence better represents the full range of the probability distribution.

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Acknowledgements

This work has been supported by EC Environment and Climate 1994–1998 Programme Contract No. ENV4-CT96-0202 (DG 12-DTEE) and C.E. Ispra No. 18161-2001-07 F1ED ISP IT.

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Authors and Affiliations

  1. Department of Occupational Health, University of Milan, Via San Barnaba 8, Milan, 20122, Italy

    Yuri Bruinen de Bruin, Paolo Carrer & Marco Maroni

  2. Department of Environmental Health, KTL, P.O. Box 95, Kuopio, FIN-70701, Finland

    Yuri Bruinen de Bruin, Otto Hänninen & Matti Jantunen

  3. EC Joint Research Centre, Institute for Health and Consumer Protection, Via E. Fermi, Ispra, 21020 (VA), Italy

    Yuri Bruinen de Bruin, Stylianos Kephalopoulos & Greta Scotto di Marco

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  1. Yuri Bruinen de Bruin
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  2. Otto Hänninen
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  5. Stylianos Kephalopoulos
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  7. Matti Jantunen
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Correspondence to Yuri Bruinen de Bruin.

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Bruinen de Bruin, Y., Hänninen, O., Carrer, P. et al. Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data. J Expo Sci Environ Epidemiol 14, 154–163 (2004). https://doi.org/10.1038/sj.jea.7500308

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