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Biomonitoring of exposures to toxins is an important tool for monitoring public health and safety. Using this tool, exposures are typically measured by the collection of biological specimens such as blood and urine samples. Urine sampling represents a more convenient and less-invasive alternative to blood sampling; however, less work has been published on methodologies for characterizing the time course of excretion and the determination of the time of maximum excretion from urine samples. This paper compares two methods of characterizing the urine excretion profile and estimating the time of maximum excretion: Non-compartmental analysis versus a non-linear pharmacokinetic (PK) modeling. We examine these methodologies using both simulated data and observed data taken from a recent experiment examining a biomarker of diesel exhaust (DE), urinary 1-aminopyrene (1-AP). In the experiment, a series of spot urine samples were collected in a group of healthy volunteers for 24 h after a controlled DE exposure. Simulated data showed that the use of non-linear modeling techniques to estimate PK parameters was more likely to estimate the true time of maximum excretion compared with the non-compartmental approach. Our analysis of observed concentrations of 1-AP led to a hypothesis that there are two subgroups of subjects in terms of the timing of their 1-AP excretion. Results showed that approximately 63% of the subjects had a median time of maximum excretion of 5.37 h, whereas 30% of the subjects may have had maximum excretion times longer than 24 h.