K2b: Assessing Exposure Judgment Errors Arising from Traditional IH Training

Ernest N. Morial New Orleans Convention Center 

In IH Data Analyst software, championed by AIHA, workplace chemical exposures are analyzed using a Bayesian approach incorporating the prior knowledge that exposures usually follow a lognormal pattern. Studies with IHDA have led to the understanding that many IH practitioners who rely on traditional professional judgment (intuition), expect data to follow a Gaussian (normal) distribution which induces them to underestimate exposures. While AIHA has addressed this problem with free training programs, practitioners who do not embrace this new information continue to commit the fundamental errors arising from traditional IH education and training.

In much traditional IH training, since air sampling and analysis were seen as very expensive, a premium has been placed on making professional exposure judgments from a small number of samples; sometimes one or zero. This tradition was supported by OSHA's placing great emphasis on minimizing the error in sampling methods, while ignoring the large variation in workplace exposures, which led OSHA to believe they could detect worker overexposures by collecting small numbers of samples during rare workplace visits.

Recently, AIHA has urged employers to adopt a higher "Standard of Care" (beyond OSHA compliance) by utilizing the IH Data Analyst framework which allows one to ensure that the 95th percentile or worker exposures does not exceed the Occupational Exposure Limit (OEL).

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Inspired by John Mulhausen and Paul Hewett 

Exposure Assessment

Gas and vapor detection

Labs – Health & Safety, Testing

Regulatory compliance

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