To ensure industrial workers are not overexposed to hazardous chemicals, it is necessary to evaluate the magnitude of occupational exposures. Small and medium sized companies usually do not employ industrial hygienists or other professionals trained in occupational exposure evaluations. Thus, traditional quantitative air sampling methods are usually not feasible due to resource and budgetary limitations.
Qualitative exposure assessment models have gained in popularity because they are simple, inexpensive, and less time consuming than quantitative methods. Models are available which predict exposure concentrations or categorize exposures as acceptable or unacceptable. Unfortunately, most models have not been validated or previous studies have shown models perform differently in different exposure conditions.
The primary purpose of this study was to validate the Control of Substances Hazardous to Health (COSHH) Essentials model and the Qualitative Exposure Assessment (QLEA) model in low, medium, and high occupational exposure conditions involving particulates and vapors.
COSHH Essentials was developed by the United Kingdom and predicts a range of possible concentrations based on the usage quantity and volatility (or dustiness) of the chemical substance. The QLEA Model was developed by a global manufacturing company. It classifies an exposures as acceptable, unacceptable, or uncertain using four predictor variables: duration of exposure, toxicity for inhalation, airborne risk, and controls present.
This study compared model predictions with retrospective exposure measurements obtained from National Institute of Occupational Safety and Health (NIOSH) Health Hazard Evaluation (HHE) Reports for 199 similar exposure groups (SEGs). SEGs were stratified based on the magnitude of the measured exposure (low, medium, high) and the physical state of the chemical substance (particulate or vapor).
This study illustrated that both models are vastly over-protective in low level exposure situations. Thus, prior to widespread adoption of these models, the rational concerning each model’s design should be critically evaluated and potentially modified to lessen the magnitude of over-prediction.