Distinguishing Petroleum (Crude Oil and Fuel) From Smoke Exposure within Populations Based on the Relative Blood Levels of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX), Styrene and 2,5-Dimethylfuran by Pattern Recognition Using Artificial Neural Networks

Distinguishing Petroleum (Crude Oil and Fuel) From Smoke Exposure within Populations Based on the Relative Blood Levels of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX), Styrene and 2,5-Dimethylfuran by Pattern Recognition Using Artificial Neural Networks

Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene, ethylbenzene, xylenes (BTEX), and styrene (BTEXS) because of their toxicity and gas-phase prevalence, where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are prima...

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Bibliographic Details
Published in:Environmental science & technology Vol. 52; no. 1; pp. 308 - 316
Main Authors: Chambers, D. M, Reese, C. M, Thornburg, L. G, Sanchez, E, Rafson, J. P, Blount, B. C, Ruhl, J. R. E, De Jesús, V. R
Format: Journal Article
Language:English
Published: United States American Chemical Society 02-01-2018
Subjects:
Artificial neural networks
Benzene
Blood
Blood levels
Cigarettes
Crude oil
Ethylbenzene
Exposure
Fuels
Human exposure
Hydrocarbons
Inhalation
Natural gas
Neural networks
Nutrition
Pattern recognition
Petroleum
Respiration
Signatures
Smoke
Smoking
Styrene
Tobacco
Tobacco smoke
Toluene
Toxicity
VOCs
Volatile organic compounds
United States > US
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Summary:Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene, ethylbenzene, xylenes (BTEX), and styrene (BTEXS) because of their toxicity and gas-phase prevalence, where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are primarily from exposure to tobacco smoke, where smokers have blood levels on average up to eight times higher than nonsmokers. This work describes a method using partition theory and artificial neural network (ANN) pattern recognition to classify exposure source based on relative BTEXS and 2,5-dimethylfuran blood levels. A method using surrogate signatures to train the ANN was validated by comparing blood levels among cigarette smokers from the National Health and Nutrition Examination Survey (NHANES) with BTEXS and 2,5-dimethylfuran signatures derived from the smoke of machine-smoked cigarettes. Classification agreement for an ANN model trained with relative VOC levels was up to 99.8% for nonsmokers and 100.0% for smokers. As such, because there is limited blood level data on individuals exposed to crude oil/fuel, only surrogate signatures derived from crude oil and fuel were used for training the ANN. For the 2007–2008 NHANES data, the ANN model assigned 7 out of 1998 specimens (0.35%) and for the 2013–2014 NHANES data 12 out of 2906 specimens (0.41%) to the crude oil/fuel signature category.
ISSN:0013-936X
1520-5851
DOI:10.1021/acs.est.7b05128