A training exercise in subjectively estimating inhalation exposures

Sean Semple, L. A. Proud, M. E. Tindall, S. N. Tannahill, John Cherrie

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Objectives This study examined whether it is possible to train occupational hygienists to estimate inhalation exposures reliably from limited occupational information using a new method and assessed improvements in the quality of the estimate using the aggregate from multiple assessors.

Methods Five occupational hygienists estimated inhalation exposure for 40 tasks covering a range of chemical hazards using a recently developed subjective modeling technique supplemented by detailed guidance notes. The measured exposure levels were used to determine the validity of the method. The correlation coefficients of the log-transformed data were used to assess the discriminative power of the method, and the ratio of the mean estimate to measured values was used to measure accuracy.

Results There was good-to-excellent agreement between the assessors' estimates and the measured data, the correlation coefficients ranging from 0.73 to 0.85. There was a tendency for assessors to overestimate the exposure levels by, on the average, two- to fourfold. Aggregating the assessors' estimates helped to improve the correlation coefficient to 0.88, the overestimation being 2.6-fold. Using more than three assessors for aggregate estimates did not improve the reliability of the method.

Conclusions Overall, the assessors found the method to be useful in generating exposure estimates that correlate well with measured levels. The provision of high-quality guidance information is likely to be important in the generation of reliable exposure estimates. The method is likely to be of use in epidemiologic studies in which limited exposure data are available.

Original languageEnglish
Pages (from-to)395-401
Number of pages6
JournalScandinavian Journal of Work, Environment & Health
Issue number6
Publication statusPublished - 2001


  • epidemiology
  • exposure assessment
  • modeling


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