Quantitative level of protection offered to workers by ACGIH threshold limit values occupational exposure limits.
The details of the example or modeling methodologies used herein are not critical to the general point of this article, which advises the estimation of residual risk at the OEL by using some quantitative modeling structure. Specifically, the authors believe that an explicit attempt to gauge the level of residual risk at the OEL based on conceptual stochastic models with transparent and testable assumptions could be seen as an important enhancement to the process. This is especially true in sharing the OEL deliberations and explaining OEL decisions to the stakeholders. Indeed, if this approach is used, it is critically important to understand and continually communicate that this "cloud of uncertainty" represents model estimates in which the true risk would most likely be less than worst case estimates and could possibly be zero. It is also possible but highly unlikely that it could be higher than the worst case upper-bound estimate. The above quantitative estimation scheme represents a possible improvement that could provide a reasoned attempt on the part of the risk assessors to use rational science (i.e., conceptual models with transparent and testable assumptions) to inform all of the OEL users and stakeholders of their meaning.
Rohm and Haas Co., Spring House, PA, USA.
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D000395:Air Pollutants, Occupational / Q000032:analysis*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D019055:No-Observed-Adverse-Effect Level; D016273:Occupational Exposure / Q000032:analysis* / Q000331:legislation & jurisprudence / Q000517:prevention & control; D016272:Occupational Health; D018570:Risk Assessment; D020711:Threshold Limit Values; D014481:United States
D000395:Air Pollutants, Occupational