To examine factors influencing long-term ozone (O3) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O3 concentrations, as well as related housing and other questionnaire information collected in the one-year-long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O3 concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance-covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best-linear unbiased prediction technique. The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O3 concentration, central ambient O3 concentration, outdoor O3 concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O3 concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O3 concentrations plus questionnaire variables, central and indoor O3 concentrations plus questionnaire variables, indoor O3 concentrations plus questionnaire variables, central O3 concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O3 for children and offer insights into how to reliably and cost-effectively predict personal O3 exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost-effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.