Human biomarker measurements in tissues including blood, breath, and urine can serve as efficient surrogates for environmental monitoring because a single biological sample integrates personal exposure across all environmental media and uptake pathways. However, biomarkers represent a "snapshot" in time, and risk assessment is generally based on long-term averages. In this study, a statistical approach is proposed for estimating long-term average exposures from distributions of spot biomarker measurements using intraclass correlations based upon measurement variance components from the literature. This methodology was developed and demonstrated using a log-normally distributed data set of urinary OH-pyrene taken from our own studies. The calculations are generalized for any biomarker data set of spot measures such as those from the National Health and Nutrition Evaluation Studies (NHANES) requiring only spreadsheet calculations. A three-tiered approach depending on the availability of metadata was developed for converting any collection of spot biomarkers into an estimated distribution of individual means that can then be compared to a biologically relevant risk level. Examples from a Microsoft Excel-based spreadsheet for calculating estimates of the proportion of the population exceeding a given biomonitoring equivalent level are provided as an appendix.