In risk assessment of risk chemicals, variability in susceptibility in the population is an important aspect. The health hazard of a pollutant is related to the internal exposure to the chemical, i.e. the target dose, rather than the external exposure. The target dose may be calculated by physiologically based pharmacokinetic (PBPK) modeling. Furthermore, variability in target dose may be estimated by introducing variability in the physiological, anatomical, and biochemical parameters of the model. Data on these toxicokinetic model parameters may be found in the scientific literature. Since the early seventies, a large number of experimental inhalation studies of the kinetics of several volatiles in human volunteers have been performed at the National Institute for Working Life in Solna. To this day, only very limited analyses of these extensive data have been performed. A Bayesian analysis makes it possible to merge a priori knowledge from the literature with the information in experimental data. If combined with population PBPK modeling, the Bayesian approach may yield posterior estimates of the toxicokinetic parameters for each subject, as well as for the population. One way of producing these estimates is by so-called Markov-chain Monte Carlo (MCMC) simulation. The aim of the thesis was to apply the MCMC technique on previously published experimental data. Another objective was to assess the reliability of PBPK models in general by the combination of the extensive data and Bayesian population techniques. The population kinetics of methyl chloride, dichloromethane, toluene and styrene were assessed. The calibrated model for dichloromethane was used to predict cancer risk in a simulated Swedish population. In some cases, the respiratory uptake of volatiles was found to be lower than predicted from reference values on alveolar ventilation. The perfusion of fat tissue was found to be a complex process that needs special attention in PBPK modeling. These results provide a significant contribution to the field of PBPK modeling of risk chemicals. Appropriate statistical treatment of uncertainty and variability may increase confidence in model results and ultimately contribute to an improved scientific basis for the estimation of occupational health risks.