C1: Research Roundup Noise

Occupational hygienists are constantly challenged to justify the allocation of resources towards implementing advanced control measures (e.g., substitution or engineered controls). A systems engineering approach is proposed to describe and implement higher level controls through causal loop diagrams. The case study from an engineered control solution to an occupational noise issue it presented. 

Bayesian models have gained significant traction in the field of industrial hygiene due to their inherent strengths in handling situations with limited exposure measurement data. The models play a crucial role in aiding professional judgments regarding whether the exposure profiles for similar exposure groups (SEGs) warrant placement into exposure control categories (ECCs) in relation to established OELs. ECCs are pivotal in managing occupational hazards. ECCS have been derived from the Exposure Assessment Strategies Committee-American Industrial Hygiene Association (EASC-AIHA) Exposure Control Strategy Model. The ECCs are categorized as follows: a) ECC 0 signifies negligible and trivial exposures, where the true X0.95 point estimate is < or = 1% of the OEL; b) ECC 1 designates a highly controlled work environment or cases where the true X0.95 point estimate is < or = 10% of the OEL; c) ECC 2 is intended for well-controlled work environments, with the X0.95 point estimate being < or = 50% of the OEL; d) ECC 3 pertains to a controlled work environment, with the X0.95 point estimate < or = 100% of the OEL; and e) ECC 4 characterizes poorly controlled work environments or situations where the datasets suggest a X0.95 point estimate > or = 1% of the OEL for exposures. The objective of this paper is to apply Bayesian statistical models to analyze limited noise exposure data within SEGs of groundskeepers.