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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 2,5dimethylfuran by pattern recognition using artificial neural networks David Michael Chambers, Christopher Mark Reese, Lydia Grace Thornburg, Eduardo Sanchez, Jessica Patricia Rafson, Benjamin C. Blount, John Russell Erskine Ruhl, and Victor Raul De Jesus Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05128 • Publication Date (Web): 07 Dec 2017 Downloaded from http://pubs.acs.org on December 13, 2017

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Environmental Science & Technology

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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 2,5dimethylfuran by pattern recognition using artificial neural networks

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Chambers, D.M.*; Reese, C.M.; Thornburg, L.G.; Sanchez, E.; Rafson, J.P.; Blount, B.C.; Ruhl III, J.R.E.; De Jesús, V.R.

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Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center for Environmental Health, US Centers for Disease Control and Prevention, Atlanta, GA 30341

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*Corresponding author: 4770 Buford Hwy., NE, Mail Stop F-47, Atlanta, GA 30341

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E-mail address: [email protected]

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Abstract Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene,

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ethylbenzene, xylenes (BTEX) and styrene (BTEXS) because of their toxicity and gas-phase prevalence,

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where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are

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primarily from exposure to tobacco smoke, where smokers have blood levels on average up to eight

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times higher than nonsmokers. This work describes a method using partition theory and artificial neural

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network (ANN) pattern recognition to classify exposure source based on relative BTEXS and 2,5-

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dimethylfuran blood levels. A method using surrogate signatures to train the ANN was validated by

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comparing blood levels among cigarette smokers from the National Health and Nutrition Examination

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Survey (NHANES) with BTEXS and 2,5-dimethylfuran signatures derived from the smoke of machine-

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smoked cigarettes. Classification agreement for an ANN model trained with relative VOC levels was up

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to 99.8 % for nonsmokers and 100.0% for smokers. As such, because there is limited blood level data on

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individuals exposed to crude oil/fuel, only surrogate signatures derived from crude oil and fuel were

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used for training the ANN. For the 2007-2008 NHANES data, the ANN model assigned 7 out of 1998

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specimens (0.35%) and for the 2013-2014 NHANES data 12 out of 2906 specimens (0.41%) to the

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crude oil/fuel signature category.

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1. Introduction As oil and natural gas extraction capacity grows to meet increased demand for petroleum-based

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products, the potential for exposure to volatile organic compounds (VOCs) emanating from drilling1,

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industrial2, and consumer product3 sources increases. Exposure to VOCs from these sources is important

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to identify and minimize because these VOCs include toxicants such as benzene, toluene, ethylbenzene,

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and xylenes (BTEX) that can cause cancer, neurological damage and impairment, and pulmonary and

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cardiovascular disease4-8. BTEX concentrations are commonly measured in environmental and

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biomonitoring samples to assess exposure levels associated with petroleum sources because they are

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prominent volatile components in petroleum9 and are chemically stable10 allowing them to be measured

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as intact compounds in the body11.

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Absorption of VOCs occurs through three major routes—inhalation, dermal absorption and

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ingestion. Of these inhalation is generally the primary absorption route for nonpolar VOCs because

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100% of the blood circulates through the lungs. Only approximately 20% of the blood passes through

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the digestive tract and the dermal stratum corneum barrier generally inhibits VOC adsorption12.

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Furthermore, relative gas-phase levels of BTEX that have similarly low molecular weights are

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conserved as they evaporate from crude oil or fuel, a nonpolar matrix with relatively low-

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intermolecular-forces13. For these reasons, relative BTEX blood levels in this study are associated with

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inhalation absorption.

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Nevertheless, a common impediment to petroleum exposure assessment using biomarkers such as

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BTEX has mainly been confounded by exposure to BTEX in cigarette smoke. In fact, tobacco smoke

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contains ppm levels14 of these and other petroleum biomarkers as both are derived from the biomass.

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Among cigarette smokers, blood BTEX levels can reach the low ng/mL range, whereas nonsmokers are

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typically in the pg/mL range11. In the same vein, confounding from other types of smoke exist such as

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from marijuana smoke, which is increasing in prevalence15,16. Therefore, if including individuals


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exposed to smoke in biomonitoring studies that investigates petroleum exposure, smoke exposed

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individuals need to be accurately classified because of their potential to confound the assessment.

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In this work, we describe a biomonitoring approach that distinguishes exposure specific to petroleum

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through pattern recognition of relative biomarker levels of BTEX and 2,5-dimethylfuran using artificial

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neural network (ANN) modeling. As a way to minimize confounding seen by other significant exposure

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sources such as tobacco smoke, we investigated the use of relative combustion biomarker levels of

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BTEX + styrene (BTEXS) and 2,5-dimethylfuran. Styrene levels, although not typically measured with

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BTEX and not always available, are included in this work where possible because styrene is a

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monoaromatic similar to BTEX and a BTEX concomitant at appreciable levels in smoke and

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petroleum11,17. 2,5-Dimethylfuran is included because it is a smoke biomarker that exists at levels

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comparable to BTEX and styrene18,19. This application of ANN, novel to exposure assessment, arose out

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of the need to objectively distinguish exposure between different VOC sources involving thousands of

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specimens. As such, we demonstrate that relative blood BTEXS levels among smokers and cigarette

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tobacco smoke remain consistent over a range of cigarette brand varieties and smoking techniques. This

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approach takes advantage of the similarities of the physical properties of BTEXS and partition theory.

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Next, this approach is used for the identification of individuals having relative levels of these

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compounds in their blood that are relatable to relative concentrations in petroleum (e.g., crude oil and

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fuel). Although BTEXS blood levels vary with source concentration, relative proportions can remain

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consistent and unique to a particular source, especially for nonpolar VOCs with similar chemical

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properties in nonpolar matrices, as in the case of BTEXS in petroleum.

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2. Experimental

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2.1.Population Data

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Population data were taken from the 2007-2008 and 2013-2014 National Health and Nutrition

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Examination Survey (NHANES) provided by the National Center for Health Statistics (NCHS)20.

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Survey data for the 2007-2008 NHANES cycle on recent VOC exposure was included in SAS export ACS Paragon Plus Environment


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file, VOCWB_E.XPT, and recent tobacco use and drug use were collected at the mobile examination

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center (SAS export file: SMQRTU_E.XPT and DUQ_E.XPT, respectively) on the day of the health

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exam. Corresponding survey data for the 2013-2014 NHANES cycle on recent VOC exposure was

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reported in SAS export file VTQ_H.XPT and recent tobacco and drug use in file SMQRTU_H.XPT and

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DUQ_H.XPT, respectively. Specific questionnaire data regarding recent VOC exposure and tobacco use

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for evaluating ANN model results are specified in the Discussion section.

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Whole blood specimens are collected at NHANES mobile examination centers (MEC) by certified

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phlebotomists and are on average collected approximately 40 min. after check-in. Analyses of these

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hermetically collected blood specimens were performed by CDC’s Division of Laboratory Sciences

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(DLS) at the National Center for Environmental Health (NCEH). The specific analysis method used by

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the DLS for determining VOC blood levels by equilibrium headspace solid phase microextraction

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(SPME)/gas chromatography (GC)/mass spectrometry (MS) is described in detail elsewhere17,21,22. For

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accurate quantification this laboratory used stable isotopically labeled analogs for each VOC for internal

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standardization to offset any matrix and competition effects in sample preparation and analysis23.

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Whole blood from 3415 participants for the 2007-2008 NHANES (SAS export file:

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VOCWB_E.XPT) and 3489 for the 2013-2014 NHANES (SAS export file: VOCWB_H.XPT) was

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analyzed, where both cycles consisted of a half subsample of ≥12 years of age. These whole blood levels

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for benzene, toluene, ethylbenzene, m/p-xylene, o-xylene, styrene, and 2,5-dimethylfuran were used in

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two different pattern recognition analyses. One analysis involved classifying specimens only between

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other/nonsmoker and smoker categories for proof of concept, and the other for classifying specimens

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among other/nonsmoker, smoker and crude oil/fuel categories. Because styrene was only available in the

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2007-2008 NHANES data set, only the 2007-2008 NHANES data set was used in pattern recognition

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proof of concept. Participants were excluded from the analyses if they were missing blood levels for any

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of the six VOCs. Therefore, for the 2007-2008 NHANES data, 1858 out of 3415 were used where

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styrene was included (other/nonsmoker vs. smoker classification model) and 1998 out of 3415 where ACS Paragon Plus Environment

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styrene was not used (other/nonsmoker, smoker, crude oil/fuel classification model). For the 2013-2014

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NHANES data, which did not have styrene data, 2906 specimens of the 3489 were used

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(other/nonsmoker, smoker, crude oil/fuel classification model).

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2.2 Estimation of Surrogate Signatures of BTEXS and 2,5-Dimethylfuran from Source Concentration

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Cigarette tobacco smoke BTEXS and 2,5-dimethylfuran levels used in this study were quantified

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using Tedlar bag collection of machine smoked cigarettes followed by equilibrium headspace

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SPME/GC/MS analysis as described elsewhere24. In summary, Tedlar bags fitted with butyl o-rings

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were attached to an ASM500 smoking machine (Cerulean, Milton Keynes, U.K.) and then analyzed

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directly by SPME with a specially configured CTC Combi-PAL autosampler (Leap Technologies,

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Carrboro, NC). SPME collection was by 75-µm carboxen-PDMS SPME fiber (Supelco, Bellefonte, PA)

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for 1 min at room temperature. As similar to the blood VOC method, isotopically labeled internal

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standard analogs were used to offset analyte losses and competition effects. These cigarette smoke VOC

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levels were generated using the International Organization for Standardization (ISO) (3308:2012) and

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Canadian Intense (CI) protocols from 50 U.S. brand varieties of cigarettes from brand families that

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comprise 78% of the market share at the time of the sampling14. Surrogate estimates of blood BTEXS

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and 2,5-dimethylfuran levels were produced by multiplying these smoke levels by their respective

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blood/air partition constant, Kblood/air, and normalizing the levels relative to toluene, a high detection rate

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analyte in blood. Although, m/p-xylene blood levels had a slightly higher detection rate than toluene,

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toluene was used because available crude oil and fuel BTEX data combined m/p-xylene with o-xylene

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and o-xylene had a lower detection rate than toluene. The corresponding Kblood/air values used for this

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adjustment were taken from Meulenberg and Vijverberg25, where benzene is 7.37, toluene is 15.11,

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ethylbenzene is 28.2, styrene is 55.6, total xylene is 36.13 (the average of 33.2 for m-xylene, 38.9 for p-

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xylene, and 36.3 for o-xylene), and m/p-xylene is 36.05 (the average Kblood/air for m-xylene and p-

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xylene). A measured value of Kblood/air for 2,5-dimethylfuran was not available, but was estimated to be

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8.5 using previous published regression data26. The use of these surrogate VOC signatures (i.e., relative ACS Paragon Plus Environment


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BTEXS and 2,5-dimethylfuran levels derived from the source material) is compared with the use of

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measured blood VOC signatures (i.e., relative BTEXS and 2,5-dimethylfuran levels in blood) for

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training the ANN.

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The surrogate blood BTEX (styrene not available) and 2,5-dimethylfuran signatures for crude oil and

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fuel exposure used for training the ANN were estimated from crude oil and fuel characterizations from

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the Environment Canada Environmental Technology Centre Oil Properties Database27. Reported in the

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database are ppm concentrations (by weight) of benzene, toluene, ethylbenzene and total xylene, but not

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styrene, in a number of the 450 different petroleums. Unweathered crude oil selected were representative

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of different regions of the world and fuels were representative of different types of fuels. The petroleums

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as listed in the database with location of the oil field in parentheses that were used include Alaska North

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Slope (2002) (Alaska), Amauligak (Canada), Arabian Light (2000) (Saudi Arabia), Arabian Medium

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(Saudi Arabia), BCF 24 (Venezuela), Boscan (Venezuela), Cano Limon (Columbia), Carpinteria

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(California), Chayvo #6 (Sakhalin), Cold Lake Blend (Alaska), Cusiana (Columbia), Dos Cuadras

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(California), Ekofisk (Norway), Eugene Island Block 32 ( Louisiana), Isthmus (Mexico), Maya

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(Mexico), Maya (1997) (Mexico), Mississippi Canyon Block 194 (Mississippi), South Louisiana (2001)

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(Louisiana), Terra Nova (Canada), West Detlat Block 97 (Louisiana), West Texas (2000) (Texas), West

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Texas Intermediate (Texas), West Texas Sour (Texas), and Vasconia (Columbia). Fuels included

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Aviation Gasoline 100LL, Diesel Fuel Oil (2002), Diesel Fuel Oil (Alaska), Diesel Fuel Oil (Southern

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U.S.A., 1994), Diesel Fuel Oil (Southern U.S.A., 1997), Fuel Oil No. 5 (2000), High Viscosity Fuel Oil,

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Jet A/Jet A-1, Jet B (Alaska). The automobile gasoline BTEX signature used was from the Total

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Petroleum Hydrocarbon Criteria Working Group9 because a gasoline signature was not available in

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Environment Canada’s database. These surrogate blood signatures were created using the same

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procedure as described for producing the surrogate blood BTEXS signatures for cigarette smoke

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exposure.


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2.3 Statistical Analysis

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JMP 12.0 was used for all the statistical analyses. Central tendency for blood VOC levels is

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expressed as geometric mean because the data followed a log normal distribution. For the artificial

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neural network (ANN) analyses, model parameters were fit via penalized maximum likelihood

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maximization28. Separate log-likelihoods were computed for each response, and the overall log-

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likelihood for all the responses was taken as the sum of the log-likelihoods of the individual responses.

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The model used one hidden layer with 15 hidden nodes each using a hyperbolic tangent (TanH)

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activation function and was validated using a holdback fraction of 0.33. The optimal number of hidden

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nodes was determined as the minimal number of nodes that yielded, on average, the best classification

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accuracy in the training set with regard to the surrogate crude oil/fuel signatures. The output layer had

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three output nodes, corresponding to smoker, to crude oil/fuel, and to neither smoker nor crude oil/fuel,

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which is referred to as other/smoker. The input layer variables for the characterization of

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other/nonsmoker (not exposed to either crude oil/fuel or smoke) and smoker included blood levels for

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benzene, toluene, ethylbenzene, o-xylene, m/p-xylene, styrene and 2,5-dimethylfuran where smoking

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status was designated using the 2,5-dimethylfuran cutpoint of 0.014 ng/mL rather than questionnaire

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data for convenience, as 2,5-dimethylfuran blood levels are available with the BTEXS levels. The

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surrogate blood levels used as input variables for the classification of crude oil/fuel signatures were

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calculated from petroleum and fuel source concentrations using the same approach as for creating the

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surrogate blood levels from cigarette smoke levels described above. Specifically, benzene, toluene,

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ethylbenzene, total xylene and 2,5-dimethylfuran concentrations were multiplied by their respective

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Kblood/air and normalized relative to the toluene concentration. 2,5-Dimethylfuran was imputed as below

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LOD (i.e., LOD/√2) and styrene levels were not used because they were was not available. Models that

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involved classifying only between smokers and other/nonsmokers used the entire 2007-2008 NHANES

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data set where there were approximately 4 times more other/nonsmokers than smokers. However, for

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models created with the other/nonsmoker, smoker and crude oil/fuel categories, the size of the three ACS Paragon Plus Environment


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input groups were adjusted so that each group had similar numbers of results of approximately 500,

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where 333 of these were set aside for training and 167 for validating the ANN. In this case, the training

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set for the other/nonsmoker group was randomly sampled to decrease its size to 525 participants, all

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smokers were used (N=494) and the surrogate crude oil/fuels signatures were oversampled29 by

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duplicating their results (14 replicates of the 35 petroleums, N=490). The random selection was

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performed by creating a new column and making the initial data values randomly equal to 1 for 25% or

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0 for 75% of the data. Rows where the column result equals 1 were used. Output value (probability

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ranging from 0 to 1) cutpoint for assignment to a category is set to 0.5. Blood levels were normalized to

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toluene because the surrogate signature used for crude oil and fuel exposure expresses relative level and

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not absolute concentration. Rows with missing values were ignored.

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The robustness and appropriateness of using measured vs. surrogate signatures were assessed in an

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experiment involving classification of smokers and other/nonsmokers using the 2007-2008 NHANES

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data. The percentage of correct classification assignments was based on 2,5-dimethylfuran cut-point.

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Note that the 2007-2008 NHANES data had only two categories, other/nonsmoker and smoker, any

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individuals that may have had a signature corresponding to the surrogate crude oil/fuel signature were

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initially assigned to either other/nonsmoker or smoker in the training. Following this experiment, an

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ANN model used for classification of all three groups—other/nonsmoker, smoker, and crude oil/fuel—

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was constructed by combining the surrogate crude oil/fuel signatures with the other/nonsmoker and

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smoker signatures from the 2007-2008 NHANES data. Once this final model with the three categories

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was created, it was applied to the 2013-2014 NHANES data.

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3. Results

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Without a sufficient number of known crude oil or fuel blood signatures with which to train the

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ANN, it was necessary to generate surrogate a BTEX and 2,5-dimethylfuran signature from petroleum

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concentrations that could be compared with specimen blood levels. For this surrogate signature, blood


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BTEX and 2,5-dimethylfuran signatures needed to be estimated from relative levels of these chemicals

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in different crude oils and fuels by adjusting the relative crude oil or fuel BTEX concentrations based on

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blood-air partition constant, Kblood/air. This approach is validated in the following data where relative

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blood BTEXS levels of cigarette smokers are estimated from the relative BTEXS levels measured in

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cigarette smoke from different cigarettes.

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Shown in Fig. 1 is a comparison of blood levels measured among smokers with different smoking

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frequencies. The BTEXS and 2,5-dimethylfuran blood VOC levels were from the 2007-2008 NHANES

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and are baseline adjusted mean levels for participants who reported smoking only 5, 10, 15, 20, 30, or 40

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cigarettes per day (CPD). The number of individuals (N) in these categories ranged from 14 to 91 as

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noted in the figure caption. Baseline adjustment of cigarette smoker blood levels was performed by

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subtracting the mean blood level of individuals that were classified as other/nonsmoker (N=4876). This

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adjustment was only performed on Fig. 1 and 2 data for the sake of comparing BTEXS and 2,5-

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dimethylfuran levels among smokers and with levels in cigarette smoke in Fig. 2. It is important to note

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that the data used to produce the crude oil/fuel ANN classification models described later are not

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baseline adjusted. Other/nonsmoker were identified as having blood 2,5-dimethylfuran levels < 0.014

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ng/mL. Levels in Fig. 1 were not normalized so that magnitude of BTEXS exposure could be compared.

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Error bars were not added to this figure because they complicated the graphics, however they are

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included in the composite signature shown in Fig. 2. Upon normalization of each CPD category to

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toluene, the percent relative standard deviation (RSD) that existed across these selected CPD categories

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were 9.0% for m/p-xylene, 6.3% for benzene, 9.3% for ethylbenzene, 13.3% for styrene, 14.7% for o-

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xylene, and 5.5% for 2,5-dimethylfuran.

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Surrogate blood BTEXS signatures estimated from the cigarette smoke of 50 U.S. brand varieties

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smoked using two different regimens are compared with a normalized blood BTEXS and 2,5-

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dimethylfuran composite from smokers in Fig. 2. These surrogate signatures were created by first

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multiplying the amount of the compounds per smoked cigarette (generated using either the Canadian ACS Paragon Plus Environment


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Intense and ISO protocols) by the respective Kblood/air, and then normalizing these levels with respect to

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toluene. The BTEXS and 2,5-dimethylfuran blood signature is a composite for all 2007-2008 NHANES

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smokers (N=456) normalized to toluene and background subtracted, where smokers are classified using

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a 2,5-dimethylfuran cutpoint level above 0.014 ng/mL. Error bars for Fig. 2 are represented as 1

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standard error.

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The same procedure used to estimate blood VOC level signatures for smokers from cigarette smoke

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was used to generate surrogate blood signatures consistent with those deduced from crude oil or fuel.

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Specifically, crude oil or fuel benzene, toluene, ethylbenzene, total xylene and 2,5-dimethylfuran ppm

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(by weight) concentrations were multiplied by their respective Kblood/air and normalized relative to

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toluene. In this application, BTEX levels among different crude oil and fuels were obtained primarily

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from Environment Canada’s Oil Properties Database. Petroleums selected included 25 crude oils from

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the United States and other large oil producing countries, and nine fuels27. Because a signature for

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automobile gasoline was not available in this database, the one from the Total Petroleum Hydrocarbon

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Working Group was used9. In the Environment Canada’s database, levels of xylene’s structural isomers

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(m-xylene, p-xylene, and o-xylene) were combined and there were no data for styrene. Levels for 2,5-

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dimethylfuran, which is not a substantial compound in petroleum, were added to the signature and set to

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below the LOD (imputed as LOD/√2). Correlation between analyte pairs were high to moderate where

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Pearson correlation coefficients were 0.82 (toluene vs. xylenes), 0.80 (benzene vs. toluene), 0.74

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(benzene vs. ethylbenzene), 0.71 (benzene vs. xylenes), and 0.59 (toluene vs. ethylbenzene), and 0.42

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(ethylbenzene vs. xylenes). Accordingly, the BTEX levels for different petroleums were adjusted by

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multiplying them by their corresponding Kblood/air, and then normalizing relative to toluene for

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comparison with the NHANES blood signatures. Shown in Fig. 3 is a comparison of the raw composite

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petroleum signature, the surrogate blood level composite signature derived from the petroleum

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signatures (adjusted based on blood/air partition constants and normalized relative to toluene), and an

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individual blood signature from a known fuel exposure22. ACS Paragon Plus Environment

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Shown in Fig. 4 are density plots characterizing ANN predictions for the 2013-2014 NHANES data

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using a model trained and validated with 2007-2008 NHANES data. Toluene, total xylenes (m/p-xylene

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+ o-xylene), benzene, ethylbenzene, and 2,5-dimethylfuran levels were used as input variables in

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modeling. For the ANN model the prediction variables were other/nonsmoker, smoker, and crude

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oil/fuel groups, where training signatures were from individuals identified as other/nonsmoker and

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smokers (established using blood 2,5-methylfuran cutpoint of 0.014 ng/mL11) and surrogate crude

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oil/fuel signatures. The training set for the other/nonsmoker signature potentially included crude oil/fuel

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signatures that had not yet been identified as so. Fitting the NHANES data repeatedly ten times resulted

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in varying assignments where the number of predicted crude oil/fuel signatures ranged from 7 to 24 for

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the 2013-2014 NHANES and from 1 to 7 for the 2007-2008 NHANES. Each of these 10 models

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properly identified a positive control blood signature from an individual with known fuel exposure

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presumed to be from inhalation22 with probabilities ranging from 0.9800 to 0.9999. Visual inspection of

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each sample identified as having the crude oil/fuel signature by any of these 10 models were generally

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similar to the surrogate and known exposure blood sample signatures. The first model fit of the 2007-

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2008 NHANES data classified 7 individuals as crude oil/fuel (0.35%), 1591 as other/nonsmoker

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(79.63%) and 400 as smoker (20.02%). Prediction results using the 2013-2014 NHANES blood VOC

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data from the first model fit are graphed in Fig. 4a and consisted of 12 crude oil/fuel (0.41%), 2290

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other/nonsmoker (78.80%) and 604 smoker (20.79%). Note that the crude oil/fuel probability cutpoint is

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0.5, however because of smoothing by JMP, the graphical boundary for the crude oil/fuel distribution

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extends below 0.5. Most smokers and other/nonsmoker had a probability below 0.25 with 5

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other/nonsmoker and 1 smoker between 0.25 and 0.5. Shown in Fig. 4b are corresponding probabilities

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for other/nonsmoker and smoker with the petroleum/fuel group identified in Fig. 4a. Among the samples

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classified as either other/nonsmoker or smoker, most (i.e., 2791) had probabilities less than 0.25 or

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greater than 0.75, where 103 samples fell between these limits.



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Compared in Fig. 5 are BTEXS and 2,5-dimethylfuran composite signatures based on assignments

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for the three exposure categories (crude oil/fuel, other/nonsmoker, and smoker) for the 2007-2008 and

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2013-2014 NHANES data with m/p-xylene and o-xylene separated and with styrene included for the

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2007-2008 data. Blood concentrations are expressed as geometric means because the data are

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lognormally distributed. Error bars associated with geometric mean are not included because the

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geometric means are presented on a linear scale. Individuals classified in the crude oil/fuel group

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generally had levels of combined xylene that were higher than toluene, but overall high toluene and

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ethylbenzene as well. Specimens classified as other/nonsmoker typically had lowest levels of BTEXS

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and 2,5-dimethylfuran, where toluene and total xylenes were typically comparable. The signature for the

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smoker category had higher levels of toluene and comparable levels of xylenes and benzene. Benzene

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and styrene levels were comparable between smoker and crude oil/fuel groups.

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4. Discussion

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Elimination half-life for BTEXS, which have similar blood stabilities, polarities and solubilities, fall

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within a narrow range from 8-30 hrs and are generally an order of magnitude longer than α-phase half-

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lives30-33. Therefore, blood levels can persist above detectable levels even if blood samples are collected

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beyond the α-phase half-life, where α-phase half-life is the half-life of the VOC if it is only in the blood

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(compartment) and elimination half-life incorporates equilibration of the VOCs between the blood and

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the body tissues (muscle, adipose, organ compartments). Because we are evaluating relative BTEXS and

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2,5-dimethylfuran blood levels rather than absolute blood concentrations, exposure intensity is

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unimportant as long as relative BTEXS and 2,5-dimethylfuran levels remain conserved. Studies on

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weathering of petroleums demonstrate fast evaporation of BTEX because they exist as volatile

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compounds with similarly low molecular weights in a relatively nonpolar matrix, causing relative gas

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phase levels to be generally conserved13. Furthermore, BTEXS signatures resulting from crude oil/fuel

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exposure can be confounded by exposure to tobacco smoke, especially from cigarette use, as smoking


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prevalence in the United States is 16.4%34 and tobacco smoke has high levels of BTEXS

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(µg/cigarette)14. Individuals who are not exposed to smoke often have BTEXS blood levels near or

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below detection as this VOC source substantially drives BTEXS levels in the general U.S. population11.

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Based on findings shown in Figs. 1 and 2, blood BTEXS signature among smokers is conserved (based

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on relative level) despite demographic and smoking technique differences, although absolute levels vary

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substantially. This consistency in relative blood BTEXS level is apparent from the moderate to high

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correlation among the BTEXS compounds for smokers who report smoking between 5 and 40 cigarettes

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per day (Fig. 1), where Pearson r values range from 0.55 (o-xylene vs. benzene) to 0.96 (m/p-xylene vs.

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ethylbenzene) and are consistent with those previously reported11. The basis for this consistency is

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attributed to the conservation of relative BTEXS and 2,5-dimethylfuran levels from mainstream

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cigarette smoke where these relative levels have been shown to exist within a narrow range.

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Specifically, in a study of 50 different U.S. brand varieties generated with two different smoking

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machine protocols, the Pearson r values among the BTEXS ranged from 0.83 (o-xylene vs. benzene) to

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0.98 (m/p-xylene vs. o-xylene)14. This high degree of consistency and correlation persists despite

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differences in influential cigarette parameters such as percent tip ventilation and number of puffs per

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cigarette14 and confirms that there is a characteristic BTEXS signature (based on relative level) among

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the different brand varieties.

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This consistency in relative BTEXS levels in mainstream smoke makes possible a proportional

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relationship between tobacco smoke and blood levels that is attributed to quick equilibration of inhaled

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VOCs. Blood VOC levels dependent on blood-air partitioning, Kblood/air, equilibrate quickly because of

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relatively small pulmonary venous blood volume and high flowrate where the entire body blood volume

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completely circulates through the lungs in approximately 1 min35,36. This relationship is confirmed by

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the similarity of smoker blood levels and VOC levels in machine-generated smoke that has been

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adjusted with Kblood/air by multiplying smoke BTEXS and 2,5-dimethylfuran levels by their respective

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Kblood/air. During exposure to the BTEXS and 2,5-dimethylfuran in cigarette smoke, the proportions of ACS Paragon Plus Environment


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VOC levels in the blood are based on their affinity that corresponds to Kblood/air. Because magnitude of

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BTEXS and 2,5-dimethylfuran levels can vary depending on smoking topography, that is, the manner in

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which a smoker smokes a cigarette, two machine smoking protocols with different smoking intensities

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were evaluated—the ISO and CI37,38. Shown in Fig. 2 is a comparison of the normalized adjusted

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BTEXS and 2,5-dimethylfuran signatures produced by the ISO and CI protocols from a U.S. market

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study of 50 brand varieties. Although the signatures associated with these two protocols are similar,

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there are some minor differences that are statistically significant. Specifically, m/p-xylene and styrene

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levels were substantially lower relative to toluene for the ISO protocol than with the CI protocol.

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Because the blood BTEXS and 2,5-dimethylfuran signature of smokers more closely resembles that

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produced by the ISO protocol than the CI protocol (Fig. 2), the ISO signature was used in ANN training

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comparison experiments discussed below. The similarity between BTEXS and 2,5-dimethylfuran levels

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in smokers and adjusted BTEXS and 2,5-dimethylfuran levels in smoke generated by the ISO method is

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a noteworthy finding, and underscores the consistency of relative BTEXS and 2,5-dimethylfuran levels

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in cigarette smoke and among smokers.

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Because of the consistency of relative blood levels of BTEXS and 2,5-dimethylfuran among smokers

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given vastly different exposure magnitudes, we investigated whether a BTEXS signature associated with

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crude oil or fuel could be identified in the general population using ANN pattern recognition. To train

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the ANN, best practice is to use relative levels of blood BTEXS of petroleum exposed individuals,

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however because such data is not available we used surrogates signatures derived from different crude

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oils and fuel. The limitation in using surrogate signatures produced from source concentrations to train

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the ANN is that they lack magnitude information, which can be used to adjust the contribution of any

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baseline levels that might exist. For example, in the comparison shown in Fig. 2, the mean baseline level

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of nonsmokers was subtracted from the smoker composite signature. In reality, the smoker composite

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includes baseline levels as shown in Fig. 5. In models using surrogate signatures constructed from

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cigarette smoke concentrations, this baseline level was absent. The resulting ANN models (10 model ACS Paragon Plus Environment

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iterations) made with these surrogate signatures agreed with classifications defined by the 0.014 ng/mL

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2,5-dimethylfuran cutpoint (discussed below) for 99.5–99.9 % of nonsmokers (N=1502), but only 85.7–

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90.7 % of smokers (N=356). On the contrary, ANN models (10 model iterations) created using blood

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BTEXS signatures from smokers where classification was defined by the 2,5-dimethylfuran cutpoint,

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classification agreement of 99.0–99.8 % for nonsmokers (N-1502) and 97.5–100.0% for smokers

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(N=356) were achieved. The lower level of classification agreement using surrogate signatures is

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attributed to not being able to add a baseline level to surrogate signatures causing the model to classify

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individuals with low-level smoke BTEXS exposure as other/nonsmoker as the baseline level becomes a

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more prominent component of the signature. In conclusion, using the surrogate signature slightly under

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estimated exposed individuals.

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For training the ANN using smokers, smokers were initially classified using the smoke biomarker

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2,5-dimethylfuran with a cutpoint of 0.014 ng/mL. It was convenient to use 2,5-dimethylfuran levels

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versus questionnaire data because 2,5-dimethylfuran is measured simultaneously with the other VOCs

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and is a well-established smoking biomarker19,39. Training and validation of the ANN involved using the

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the 2007-2008 NHANES data and did not include specimens with missing data. The ANN model

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classified participants who reported recent use of other combustible tobacco products [e.g., pipe

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(SMQ755 = 1, N = 1), cigar (SMQ785 = 1, N = 6)] as cigarette smokers, with the exception of three

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cigar smokers. Two of these cigar smokers (SEQN# 46111 and 47799) had 2,5-dimethylfuran levels

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below the established 0.014 ng/mL cutpoint and were identified as nonsmokers in all 10 iterations of the

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model. BTEXS levels for these two samples are more consistent with those of the other/nonsmoker

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category having m/p-xylene below 0.1 ng/mL. The third cigar smoker (SEQN# 51320) had a blood 2,5-

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dimethylfuran level of 0.055 ng/mL and m/p-xylene = 0.457 ng/mL, but was classified as an

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other/nonsmoker in 5 of the 10 ANN models, where the smoker classification probability average was

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0.52 ± 0.25 and other/nonsmoker classification probability average was 0.48 ± 0.25. This average result,

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which assigns this third cigar smoker as a smoker, suggests that averaging probabilities over multiple ACS Paragon Plus Environment


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ANN model instances may better predict borderline signatures. Furthermore, recent marijuana use did

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not interfere with proper classification of tobacco smokers. Specifically, for the 2 tobacco smokers

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(SEQN#41623 and 47126) who reported recent marijuana use (DUQ220Q = 0/DUQ220U = 1), both

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signatures were assigned as smoker and all had blood 2,5-dimethylfuran level above the smoker

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biomarker cutpoint. Complete BTEXS data was not available for exclusive marijuana smokers (N=1) for

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the 2007-2008 NHANES data, but was for the 2013-2014 NAHNES data (N=8) and is discussed below.

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Although marijuana is most commonly smoked, information regarding the method of use was not

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collected and, therefore, it is possible that these marijuana users may have exclusively used ingestion.

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The petroleum blood signature was defined using a surrogate approach because of a lack of VOC

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data for petroleum exposed individuals. To create crude oil and fuel signatures with which to train the

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ANN, crude oil and fuel BTEX levels from the Oil Properties Database27 and a literature source for

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automobile gasoline9 were multiplied by the corresponding Kblood/air and normalized relative to toluene

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as demonstrated with the cigarette smoke signatures. Despite the fact that fuel composition varies across

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crude oil sources, manufacturers, and the time of year9,13, especially in terms of absolute levels, relative

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BTEX levels among low-end (e.g., gasoline) and middle distillates (diesel, jet, and home heating fuel)

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were similar to those seen in crude oil. This consistency is apparent in Fig. 3 for the adjusted and

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normalized BTEX signature where standard deviation error bars do not overlap with the exception of

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toluene and ethylbenzene. Relative total xylene levels were highest, toluene and ethylbenzene levels

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were moderate and benzene levels were the lowest. These adjusted crude oil and fuel signatures are

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consistent with previously published blood VOC data resulting from a fuel inhalation exposure subject

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included in Fig. 3, which was used as a blinded positive control22.

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Using the ANN model to evaluate the 2013-2014 NHANES data, 12 individuals out of 2906 had

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exposure patterns that placed them in the crude oil/fuel exposure category with a probability that ranged

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from 0.52 to 0.98 (Fig. 4a). Of the 12 crude oil/fuel classified individuals, all were nonsmokers based on

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the 2,5-dimethylfuran cutpoint and questionnaire responses. There was no consistent trend with recent ACS Paragon Plus Environment

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use of gas for cooking (VTQ241A, 4 out of 12), pumping gas or diesel (VTQ244A and VTQ281C, 7 out

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of 12), or time spent near smoke in the last 10 hours or less (VTQ265B, 1 out of 12), however all had

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reported having an attached garage (VTQ210, 12 out of 12). Association between attached garage and

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increased blood BTEX levels has been previously reported40,41. This association is attributed to

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evaporative fuel emissions from vehicles, which have been identified as an important sources of VOC

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emissions in the U.S. and other industrialized nations3,42,43.

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Of the 32 individuals that reported pumping gas in the last hour, one (SEQN# 80105) was classified

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as crude oil/fuel and had a high m/p-xylene level of 1.140 ng/mL. It is important to note that this

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identification was made based on relative levels of VOCs and not magnitude because data fed into the

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model were normalized relative to toluene. In this subset of 32 individuals, the 19 individuals were

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classified as other/nonsmoker with m/p-xylene blood levels ranging from

Distinguishing petroleum (crude oil and fuel) from ... - ACS Publications

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