Indoor radon distribution in Switzerland: lognormality and Extreme Value Theory

Tuia, Devis; Kanevski, Mikhaïl

doi:10.1016/j.jenvrad.2007.09.004

Cited by 18 publications

(12 citation statements)

References 12 publications

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“…Additional environmental applications include the likelihood of adverse meteorological conditions (Hüsler, 1983; Sneyer, 1983), exceedances of thresholds relevant to dietary intake of pesticides and heavy metals (Tressou et al, 2004; Paulo et al, 2006), concentrations of metals Mn and Pb in blood (Batterman et al, 2011), deposition of pollutants in surface soils (Huang and Batterman, 2003), and risks of leakage due to pipe corrosion (HSE, 2002). Air quality applications of EVT include the exceedance of air quality standards (Surman et al, 1987; Hopke and Paatero, 1994), exposure to ambient air pollutants (Kassomenos et al, 2010), indoor concentrations of radon (Tuia and Kanevski, 2008), and VOC exposures in the NHANES mentioned earlier (Jia et al, 2008). …”

Section: Introductionmentioning

confidence: 99%

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Jia

Batterman

2012

Atmospheric Environment

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Extreme value theory, which characterizes the behavior of tails of distributions, is potentially well-suited to model exposures and risks of pollutants. In this application, it emphasizes the highest exposures, particularly those that may be high enough to present acute or chronic health risks. The present study examines extreme value distributions of exposures and risks to volatile organic compounds (VOCs). Exposures of 15 different VOCs were measured in the Relationship between Indoor, Outdoor and Personal Air (RIOPA) study, and ten of the same VOCs were measured in the nationally representative National Health and Nutrition Examination Survey (NHANES). Both studies used similar sampling methods and study periods. Using the highest 5 and 10% of measurements, generalized extreme value (GEV), Gumbel and lognormal distributions were fit to each VOC in these two large studies. Health risks were estimated for individual VOCs and three VOC mixtures. Simulated data that matched the three types of distributions were generated and compared to observations to evaluate goodness-of-fit. The tail behavior of exposures, which clearly neither fit normal nor lognormal distributions for most VOCs in RIOPA, was usually best fit by the 3-parameter GEV distribution, and often by the 2-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extrema. Among the RIOPA VOCs, 1,4-dichlorobenzene (1,4-DCB) caused the greatest risks, e.g., for the top 10% extrema, all individuals had risk levels above 10−4, and 13% of them exceeded 10−2. NHANES had considerably higher concentrations of all VOCs with two exceptions, methyl tertiary-butyl ether and 1,4-DCB. Differences between these studies can be explained by sampling design, staging, sample demographics, smoking and occupation. This analysis shows that extreme value distributions can represent peak exposures of VOCs, which clearly are neither normally nor lognormally distributed. These exposures have the greatest health significance, and require accurate modeling.

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Section: Introductionmentioning

confidence: 99%

Extreme value analyses of VOC exposures and risks: A comparison of RIOPA and NHANES datasets

Jia

Batterman

2012

Atmospheric Environment

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“…A range of statistical and geostatistical methods have been employed in some countries to map the spatial variation in indoor radon concentrations ( (Bossew, 2009;Bossew et al, 2008;Dubois et al, 2007;Dubois et al, 2010;Friedmann and Gröller, 2010;Raspa et al, 2010;Tuia and Kanevski, 2008;). …”

Section: A Non-geological Radon Potential Mappingmentioning

confidence: 99%

Radon in Air and Water

Appleton

2012

Essentials of Medical Geology

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“…Figure 2 shows that this model, estimated via maximum likelihood, fits the histogram reasonably well in the case of the Bergamo data too. In addition, models for radon concentration other than the log-normal have been recently shown to be adequate (Murphy and Organo 2008;Tuia and Kanevski 2008).…”

Section: The Datamentioning

confidence: 99%