The use of biomonitoring data in exposure and human health risk assessment: benzene case study

Figures & data

Table 1. Biomonitoring common criteria.

Exposure	Toxicology/toxicokinetics	Epidemiology	Analytical methodology/biomarker of exposure	Risk assessment/risk management
Source(s) identified?	Are there sufficient data including longer duration studies?	Are reasonable cause–effect inferences supported?*	Were standard reference materials used in the biological matrix of interest?	Are there sufficient and relevant toxicology data
Pathway(s)/route(s) understood?	Do routes used in toxicology studies compare to anticipated human exposure?	Has an adverse health effect been observed in humans?	Have specificity and sensitivity of methods been described?	Known relationship between biomarker of exposure and human health effect?
Human exposure relationship to existing toxicology data	Are toxicokinetic data in animals available?	Has the pathogenesis of the health effect been described?	Is biomarker of exposure valid for intended use?†	Applicable toxicokinetic data?
Exposure–dose relationship understood?	Is/are the critical effect(s) known?	Is there a health effect in the exposed population	Does sampling strategy consider potential sources of error?	If applicable – evidence that remediation efforts are working?
Temporality/duration understood‡	Is the mode/mechanism of action understood?	Have toxicokinetic and/or toxicodynamic genetic polymorphisms been described which may impact risk?	Does sampling strategy consider stability of biomarker of exposure?
			Did sampling strategy incorporate toxicokinetics?

*Are the Bradford-Hill criteria fulfilled?

†Does the biomarker of exposure accurately reflect the intended use?

‡Temporality refers to the relationship of when exposure occurred relative to when the sample was collected. Duration refers to how long the exposure occurred relative to when the sample was collected.

Figure 1. (A) Benzene ambient air concentrations in the USA, HEI (2007). Reprinted with permission from the Health Effects Institute, Boston, MA. (B) Benzene ambient air concentrations in European metropolitan areas. Adapted from Bruinen de Bruin et al. (2008) with kind permission from Springer Science + Business Media.

Figure 2. A schematic of liver metabolism of benzene. From Boogaard (2009); reproduced with permission of John Wiley & Sons Ltd. EH, epoxide hydrolase; GSH, glutathione; GST, glutathione-S-transferase; DHDH, dihydrodiol dehydrogenase; MPO, myeloperoxidase; NQO1, NADPH quinone oxidoreductase 1.

Table 2. Human PBPK model parameters for benzene (reproduced from Brown et al. (1998) with permission from John Wiley & Sons).

Parameter	Male	Female
Body weight (kg)	70	60
Alveolar ventilation (L/h)	450	363
Cardiac output (L/h)	336	288
Blood flow fractions (%)
Liver	25	25
Fat	8	8
Slow perfused (muscle and skin)	28.5	28.5
Rich perfused (brain, kidney and heart)	38.5	38.5
Tissue volume fractions (%)
Liver	2.6	2.3
Fat	20	30
Slowly perfused	64	55
Richly perfused	6	5
Partition coefficients
Blood/air	7.8	8.2
Liver/blood	2.95	2.8
Fat/blood	54.5	51.8
Slow perfused/blood	2.05	2
Rich perfused/blood	1.92	1.8
Metabolic constants
Km – Michaelis–Menten (mg/L)	0.35	0.35
Vmax – maximum velocity (mg/h)	13.89	19.47

Table 3. Suitability of benzene biomarkers of exposure.

Biomarker	Analytical methodology	Sampling issues potentially impacting biomarker interpretation	Specific for benzene exposure	Endogenous background sources	Exogenous background sources (confounders)*	Suitability for industrial exposure	Suitability for general population
Benzene in urine	Head-space GC; SPE–GC–MS	Potential volatilization of benzene from sample; potential contamination from smoking, gasoline, mobile sources, industrial activities	Yes	None	None	Yes	Yes
Benzene in blood	Head-space GC; SPE–GC–MS	Potential volatilization of benzene from sample; potential contamination from smoking, gasoline, mobile sources, industrial activities	Yes	None	None	Yes	Yes/no†
Benzene in expired air	Head-space GC; SPE–GC–MS	Potential contamination from smoking, gasoline, mobile sources, industrial activities	Yes	None	None	Yes	Yes
Urinary metabolites
Phenol	GC–FID, GC–MS, HPLC–UV, LC–MS–MS	None	No	Production by gut flora	Diet, medicine, smoking	No	No
Catechol	GC–FID, GC–MS, HPLC–UV, LC–MS–MS	None	No	Production by gut flora	Diet, smoking	No	No
Hydroquinone	GC–FID, GC–MS, HPLC–UV, LC–MS–MS	None	No	None	Diet, arbutin, smoking	No	No
SPMA	LC–MS–MS, GC–MS, HPLC–fluorescence, immunoassay, needs sophisticated GC–MS	Mercapturates are unstable in alkaline urine (freezing or acidification is needed)	Yes	None	None	Yes	Yes
ttMA	SPE–HPLC, GC–MS	None	No	None	Diet (sorbitol)	Yes	No

*Individual smoking habits should be recorded as benzene and its urinary metabolites are derived from tobacco smoke.

†Blood is the standard matrix for the CDC NHANES program; however, the EU prefers not to use invasive sampling if possible.

Table 4. Non-benzene sources of benzene urinary metabolites.

Chemical	Source	Amount	Reference
SPMA	No known endogenous or exogenous sources
ttMA	Diet (sorbitol) Europe	6–30 mg/d	Ruppert et al. (1997)
	Diet (sorbitol) USA	25 mg/d	Yu & Weisel (1996b)
	Percentage of ttMA in smokers attributed dietary sorbic acid	10–50%	Ruppert et al. (1997)
	Percentage of ttMA in non-smokers attributed to dietary sorbic acid	5–25%	Ruppert et al. (1997)
Phenol	Diet and endogenous sources	0.2 mg/kg-bw/d	McDonald et al. (2001)
	Mainstream cigarette smoke	60–140 µg/cig	Hoffmann & Wynder, (1986)
	Sidestream cigarette smoke	1.6–3.0 × mainstream smoke	Hoffmann & Wynder, (1986)
	Over the counter medicines	Not quantified	McDonald et al. (2001)
Catechol	Diet and endogenous sources	0.3 mg/kg-bw/d	McDonald et al. (2001)
	Mainstream cigarette smoke	100–350 µg/cig	Hoffmann & Wynder (1986)
	Sidestream cigarette smoke	0.6–0.9 × mainstream smoke	Hoffmann & Wynder (1986)
Hydroquinone	Diet and endogenous sources	0.1 mg/kg-bw/d	McDonald et al. (2001)
	Black and white photographic processing	Not quantified	McDonald et al. (2001)
	Mainstream cigarette smoke	110–300 µg/cig	Hoffmann & Wynder (1986)
	Sidestream cigarette smoke	0.7–0.9 × mainstream smoke	Hoffmann & Wynder (1986)

Table 5. Air exposure levels corresponding to toxicological endpoints for the general public.

Agency/guideline	Critical effect	Endpoint	Uncertainty factor	Guideline value	Reference
Non-cancer
USEPA RfC	Decreased lymphocyte count in workers	BMCL = 8.2 mg/m^3	300	3 × 10^−2 mg/m^3	USEPA (2003)
EU risk assessment report	Decreased lymphocyte count in workers	NOAEC = 3.2 mg/m^3	None	3.2 mg/m^3	European Chemicals Bureau (2008)
Cancer
USEPA	Leukemia in workers	Low-dose linearity utilizing maximum likelihood estimates	Not applicable	1 × 10^−5 risk (1.3–4.5 µg/m^3)	USEPA (2003)
European Commission Air Quality Standards				5 µg/m^3	http://ec.europa.eu/environment/air/quality/standards.htm

BMCL = the 95% lower bound on the benchmark concentration. NOAEC = no observed adverse effect concentration.

Table 6. Published regression models comparing airborne benzene concentrations with urinary benzene and SPMA concentrations.

Equation	Exposure group	n	Exposure range (ppm)	p Value, r	Reference
Urinary benzene (uB)
log uB (ng/L) = 1.4 + 0.65 × log [benzene (µg/m^3)]	Chemical and service stations	110	0.02–4.1	p < 0.0001, r = 0.559	Ghittori et al. (1993)
log uB (ng/L) = 0.645 × log [benzene (ppm)] + 3.974	Chemical industry	124	0.01–0.5	p < 0.0001, r = 0.54	Ghittori et al. (1995)
log uB (nmol/L) = 0.64 log [benzene (ppm)] + 1.49	Shoe factory workers	42	0.12–68	p < 0.01, r = 0.5	Ong et al. (1995)
ln uB (ng/L) = 0.42 × ln [benzene (ppm)] + 7.72	Service stations	9	0.03–0.11	Not available	Lagorio et al. (1998)
ln uB (µg/L) = 0.2 + 0.71 × ln [benzene (ppm)]	Benzene used as a solvent	37	0.85–332	p < 0.0001, r = 0.72	Waidyanatha et al. (2001)
ln uB (nmol/L) = 5.42 + 0.886 × ln [benzene (ppm)]	Shoe factory workers	228	0.15–88.9	r = 0.65	Kim et al. (2006a)
Urinary SPMA
ln SPMA (mg/L) = −1.85 + 0.604 ln [benzene (ppm)]	Benzene used as a solvent	86	0.016–329	Not available	Waidyanatha et al. (2004)
log SPMA (µg/g cr.) = 1.644 + 0.712 × log [benzene (ppm)]	Chemical industry	145	0.01–21.1	p < 0.0001, r = 0.74	Ghittori et al. (1999)
log SPMA (µg/g cr.) = 0.657 × log [benzene (ppm)] + 1.592	Chemical industry	123	0.01–0.5	p < 0.0001, r = 0.63	Ghittori et al. (1995)
log [benzene (mg/m^3)] = 0.782 × log SPMA (µmol/mol cr.) − 0.518	Chemical manufacturing and oil refineries	58	0.01–100	p < 0.0001, r = 0.885	Boogaard & van Sittert (1996)
SPMA (µg/g cr.) = 0.05 × [benzene (µg/m^3)] + 1.0387	Traffic policemen	206	0.00003–0.02	0.0001, r = 0.35	Bono et al. (2005)
SPMA (µg/g cr.) = −0.15 + 0.067 × [benzene (ppb)]	Gas station attendants	70	up to 0.107	<0.01, r = 0.673	Inoue et al. (2001)

Table 7. CDC biomonitoring data for benzene in blood (ng/L).

	Sample size	50th percentile
Ages 20–59 years*	1345	27
Smokers†	316	110
Non-smokers†	874	<LOD (24)
Blood benzene concentration corresponding to self-reported exposure in NHANES database‡
Second-hand smoke	721	50
No second-hand smoke	740	17
Pumped gasoline	715	29
Did not pump gasoline	747	25
Breathe diesel exhaust	133	39
Did not breathe diesel exhaust	1327	27

*CDC (2009).

†Kirman et al. (2012).

‡http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm/

Figure 3. Reported blood benzene concentrations (central tendency) for the general population compared to the benzene biomonitoring equivalent value based upon USEPA non-cancer and cancer benchmarks. Each bar represents a separate exposure population.

Figure 4. Reported urinary benzene concentrations (central tendency) for the general population compared to the urinary concentration related to USEPA non-cancer and cancer benchmarks. Each bar represents a separate exposure population.

Figure 5. Reported urinary SPMA concentrations (central tendency) for the general population compared to the urinary concentration related to USEPA non-cancer and cancer benchmarks. Each bar represents a separate exposure population. NA = North America and ND = not defined.

Figure 6. Reported urinary SPMA concentrations (central tendency) for urban workers compared to the ACGIH BEI. Each bar represents a separate exposure population. ND = not definded.

Figure 7. Reported urinary ttMA concentrations (central tendency) for urban workers compared to the ACGIH BEI. Each bar represents a separate exposure population. ND = not definded.

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