To assess exposure level in a work environment, we consider the interval estimation and hypothesis testing problems of exposure level based on the skew-normal one-way classification random effect model. Firstly, the EM algorithm is used to give the maximum likelihood estimation of unknown parameters. Secondly, based on the Bootstrap approach, three types of Bootstrap confidence intervals for the individual average exposure level are constructed. The Monte Carlo simulation results indicate that the improved percentile Bootstrap confidence interval performs best in the sense of coverage probability, and the Bootstrap standard confidence interval performs best in the sense of upper confidence limit. Finally, the above approaches are applied to the real data example of styrene exposures to verify the reasonableness and effectiveness of the proposed approaches.