Tool 1 (Mirror)
Tool 1, simplified version (Mirror)

This tool interprets a dataset of exposure measurements (including non detects) with regards to an OEL. In additional to multiple illustrative graphs, it includes 5 parts:

  • Goodness of fit to the lognormal model (graphical evaluation)
  • Descriptives statistics
  • Risk assessment based on exceedance of the OEL
  • Risk assessment based on the 95th percentile
  • Risk assessment based on the arithmetic mean

Calculations are performed using a bayesian model fit using a Monte Carlo Markov Chain (MCMC) engine. It is assumed that the underlying exposure distribution is lognormal.

Tool 2 (Mirror)

This tool interprets a dataset of exposure measurements including an identifier for workers. It requires that at least some workers have several measurements. In addition to assessing group compliance, this tool evaluates to what extent individual risk should be considered. The following quantities are estimated:

  • Group risk assessment based on exceedance, 95th percentile, arithmetic mean.
  • Probability that a random worker might be overexposed (several metrics)
  • Between-worker differences (several metrics)
  • Risk assessment for individual workers based on exceedance, 95th percentile, arithmetic mean.

Calculations are performed using a bayesian hierarchical model fit using a Monte Carlo Markov Chain (MCMC) engine. It is assumed that the underlying exposure distribution is lognormal.

Tool 3 (Mirror)

This tool compares the underlying distributions corresponding to several categories of a variable of interest (e.g. presence / absence of ventilation, season of sampling). Data is entered as an EXCEL file with a column containing exposure levels, and columns containing variables of interest.The following calculations, accompanied by illustrative graphs, are shown :

  • Group risk assessment based on exceedance, 95th percentile, arithmetic mean.
  • Individual category risk assessment based on exceedance, 95th percentile, arithmetic mean.
  • Comparison of two selected categories (e.g. before/after intervention)
    • Change in the geometric mean
    • Change in the geometric standard deviation
    • Change in risk (according to 95th percentile, arithmetic mean)
    • Probability that a prespecified difference exists

Calculations are performed using a bayesian model fit using a Monte Carlo Markov Chain (MCMC) engine. It is assumed that the underlying exposure distribution is lognormal.

▸ UPDATED DECEMBER 2018 ◂

Windows users   Access the complete set of tools in a single package for off-line use → Download page

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