'Quasi Nonparametric' Upper Tolerance Limits for Occupational Exposure Evaluations
| Autor: | Paul F. Wambach, Charles B. Davis |
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| Rok vydání: | 2015 |
| Předmět: |
Percentile
Public Health Environmental and Occupational Health Nonparametric statistics Risk Assessment United States Occupational Exposure Statistics Econometrics Humans Occupational exposure limit Occupational exposure Occupational Health Exposure data Environmental Monitoring Probability Mathematics |
| DOI: | 10.6084/m9.figshare.1300245.v2 |
| Popis: | Upper tolerance limits (UTLs) are often used in comparing exposure data sets with an occupational exposure limit (OEL) or other regulatory criterion (RC): if the 95%-95% UTL does not exceed the OEL, one is 95% confident that at most 5% of exposures exceed the OEL, and the comparison "passes." The largest of 59 observations is a nonparametric (distribution-free) 95%-95% UTL (NPUTL); the chance that this largest value equals or exceeds the actual 95th percentile is at least 95%, regardless of the underlying data distribution. That many observations may seem excessive in clean environments or small studies, though, and one would like to "pass" using UTLs based on fewer observations sufficiently far below the OEL or RC. "Quasi-nonparametric" UTLs (QNP UTLs) accomplish this. QNP UTLs assign a "pass" so long as one has "59 [values] less than the RC" (the NPUTL itself), "30 less than 1/2 [of the RC]," "21 less than 1/3," and on down to "8 less than 1/10," the last matching a rule-of-thumb given in 2006 American Industrial Hygiene Association (AIHA) guidance. They are derived using the conservative, experience-based assumption that the data distribution is lognormal with log-scale standard deviation σ at most 2.0 (geometric standard deviation at most 7.39). Although based on this assumption, their statistical performance is reasonably unaffected or conservative when data come from other distributions often assumed for contaminant concentrations; moreover, their performance is insensitive to analytical variation. This conservative robustness merits the description "quasi-nonparametric." QNP UTLs are very easy to use. Reporting Limit (RL) issues do not arise. QNP UTLs reduce the numbers of observations needed to support conservative risk management decisions when sampling from compliant working conditions. |
| Databáze: | OpenAIRE |
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