Semiquantitative Estimates of Exposure to Methylene Chloride and Styrene: The Influence of Quantitative Exposure Data

Abstract

Nine occupational hygienists semiquantitatively estimated the exposure to methylene chloride and styrene in a small polyester factory. They ranked the jobs from low to high exposure, and subsequently classified them into three exposure categories (0–1/2TLV, 1/2TLV-TLV, and > TLV). The influence of quantitative exposure data on the results of the estimations was studied. Therefore, three estimations were performed. The first estimation was made after a visit to the workplace; the second and third were made after limited exposure data were presented. The results showed that presenting quantitative exposure data hardly improved the ranking of jobs from low to high exposure. The ranking of styrene exposure was, in general, poor compared to the ranking of methylene chloride exposure. Physical properties such as perception of smell, application in the process, and level of exposure might be the reasons for this striking difference. Classification of exposure into quantitative exposure categories was poor without knowledge of actual exposure data. The classification of both exposures improved after exposure data became available. No differences in the performance of the occupational hygienists between the two solvents were present.

The results suggest that the success of an exposure estimation method depends on the type of exposure (kind of chemical, use, appearance), the available information on jobs and process, and the kind of estimate (ranking or classification). Semiquantitative classification of exposure by occupational hygienists appears to be better if they have a limited set of air sampling data at their disposal. Ranking of jobs can be performed successfully without exposure data, but a detailed description of the workplace and tasks performed is needed. More insight is needed concerning the influence of the chemical type, exposure pattern(s), and raters' experience on the results of semiquantitative ranking methods.