The relation between subjective dust exposure estimates and quantitative dust exposure measurements in California agriculture

Am J Ind Med. 1997 Oct;32(4):355-63. doi: 10.1002/(sici)1097-0274(199710)32:4<355::aid-ajim6>;2-t.


Measuring exposure levels for epidemiologic research is time consuming and expensive and therefore subjective exposure estimates are sometimes used instead. In this study we related the subjective dust exposure estimates of workers in California agriculture to personal dust exposure measurements. One hundred and twenty-four observations were available for comparison of subjective dust estimates and inhalable dust measurements and 129 observations for comparison of subjective dust estimates and respirable dust measurements. Individual subjective dust estimates showed weak to moderate correlations with measured dust concentrations for both the inhalable (Rs = 0.67) and respirable dust fraction (Rs = 0.36). The within-worker reliability coefficients were low (0.2 and 0.1, respectively). Grouped subjective dust estimates performed better and showed a consistent increase with average measured dust levels, in particular for the inhalable dust fraction (R2 = 0.81). Age, the number of years working in agriculture, education level, the presence of any respiratory symptoms, and the language of the questionnaire did not have a significant independent effect on the relationship between measured dust levels and subjective dust estimates. California agricultural workers appear to be reasonably good at estimating inhalable dust levels, in particular if an average of many different workers is taken, but they are unable to provide good estimates of respirable dust levels. Measuring dust levels remains the preferred option.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Agriculture*
  • Air Pollutants, Occupational
  • Analysis of Variance
  • California
  • Cohort Studies
  • Dust*
  • Environmental Monitoring / instrumentation*
  • Humans
  • Occupational Exposure*
  • Regression Analysis
  • Statistics, Nonparametric
  • Surveys and Questionnaires


  • Air Pollutants, Occupational
  • Dust