Studies of human exposure to petroleum (crude oil and fuel) often involve monitoring volatile monoaromatic compounds because of their toxicity and prevalence. Monoaromatic compounds such as benzene, toluene, ethylbenzene, and xylenes (BTEX) associated with these sources have been well studied and have established reference concentrations (RfC) and reference doses (RfD). However, BTEX exposure levels for the general population are primarily from tobacco smoke, where smokers have blood levels up to 8 times higher on average than nonsmokers. Therefore, in assessing petroleum exposure, it is essential to identify exposure to tobacco smoke as well as other types of smoke exposure (e.g., cannabis, wood) because many smoke volatile organic compounds are also found in petroleum products such as crude oil, and fuel. This work describes a method using partition theory and artificial neural network (ANN) pattern recognition to accurately categorize exposure source based on BTEX and 2,5-dimethylfuran blood levels. For this evaluation three categories were created and include crude oil/fuel, other/nonsmoker, and smoker. A method for using surrogate signatures (i.e., relative VOC levels derived from the source material) to train the ANN was investigated where blood levels among cigarette smokers from the National Health and Nutrition Examination Survey (NHANES) were compared with signatures derived from machine-generated cigarette smoke. Use of surrogate signatures derived from machine-generated cigarette smoke did provide a sufficient means with which to train the ANN. As a result, surrogate signatures were used for assessing crude oil/fuel exposure because there is limited blood level data on individuals exposed to either crude oil or fuel. Classification agreement between using an ANN model trained with relative VOC levels and using the 2,5-dimethylfuran smoking biomarker cutpoint blood level of 0.014 ng/mL was up to 99.8 % for nonsmokers and 100.0% for smokers. For the NHANES 2007–08 data, the ANN model using a probability cutpoint above 0.5 assigned 7 samples out of 1998 (0.35%) to the crude oil/fuel signature category. For the NHANES 2013–14 data, 12 out of 2906 samples (0.41%) were assigned to the crude oil/fuel signature category. This approach using ANN makes it possible to quickly identify individuals with blood levels consistent with a crude oil/fuel surrogate among thousands of results while minimizing confounding from smoke. Use of an ANN fixed algorithm makes it possible to objectively compare across populations eliminating classification inconsistency that can result from relying on visual evaluation.

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A method to achieve accurate measurement of unmetabolized volatile organic compounds (VOCs) in urine was developed and characterized. The method incorporates a novel preanalytical approach of adding isotopically labeled internal standard (ISTD) analogues directly to the collection container at the point of collection to compensate for analyte loss to the headspace and the collection container surfaces. Using this approach, 45 toxic VOCs ranging in water solubility and boiling point were evaluated and analyzed by headspace solid-phase microextraction/gas chromatography–mass spectrometry. Results show that urine VOCs could be equally lost to the container headspace as to the container surface suggesting similarity of these two regions as partition phases. Surface adsorption loss was found to trend with compound water solubility. In particular, with no headspace, more nonpolar VOCs experienced substantial losses (e.g., 48% for hexane) in a standard 120 mL urine cup at concentrations in the low- and sub-ppb range. The most polar VOCs evaluated (e.g., tetrahydrofuran) showed no significant loss. Other commonly practiced methods for urine sample collection and analysis such as aliquoting, specimen freezing, and use of surrogate ISTD were found to significantly bias results. With this method, we achieved errors ranging from −8.0 to 4.8% of spiked urine specimens. Paired urine and blood specimens from cigarette smokers were compared to assess this method.