Assessment of population exposure to air pollution by benzene

Acarbose protects from central and peripheral metabolic imbalance induced by benzene exposure

Benzene is a well-known human carcinogen that is one of the major components of air pollution. Sources of benzene in ambient air include cigarette smoke, e-cigarettes vaping, and evaporation of benzene containing petrol processes. While the carcinogenic effects of benzene exposure have been well studied, less is known about the metabolic effects of benzene exposure. We show that chronic exposure to benzene at low levels induces a severe metabolic imbalance in a sex-specific manner, and is associated with hypothalamic inflammation and endoplasmic reticulum (ER) stress. Benzene exposure rapidly activates hypothalamic ER stress and neuroinflammatory responses in male mice, while pharmacological inhibition of ER stress response by inhibiting IRE1α-XBP1 pathway significantly alleviates benzene-induced glial inflammatory responses. Additionally, feeding mice with Acarbose, a clinically available anti-diabetes drug, protected against benzene induced central and peripheral metabolic imbalance. Acarbose imitates the slowing of dietary carbohydrate digestion, suggesting that choosing a diet with a low glycemic index might be a potential strategy for reducing the negative metabolic effect of chronic exposure to benzene for smokers or people living/working in urban environments with high concentrations of exposure to automobile exhausts.

Validation of Armadillo officinalis Dumèril, 1816 (Crustacea, Isopoda, Oniscidea) as a bioindicator: in vivo study of air benzene exposure

This study tests the potential for using Armadillo officinalis as a bioindicator of exposure to and activation of benzene metabolic pathways using an in vivo model. A. officinalis specimens collected in a natural reserve were divided into a control and three test groups exposed to 2.00, 5.32 or 9.09 µg/m(3) benzene for 24h. Three independent tests were performed to assess model reproducibility. Animals were dissected to obtain three pooled tissue samples per group: hepatopancreas (HEP), other organs and tissues (OOT), and exoskeleton (EXO). Muconic acid (MA), S-phenylmercapturic acid (S-PMA), two human metabolites of benzene, and changes in mtDNA copy number, a human biomarker of benzene exposure, were determined in each sample; benzene was determined only in EXO. MA was measured by high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection, S-PMA by triple quadrupole mass spectrometer liquid chromatography with electro spray ionization (LC-MS-ESI-TQD), mtDNA by real-time quantitative PCR and end-point PCR, and benzene by quadrupole mass spectrometer head-space gas chromatography (HSGC-MS). MA and S-PMA levels rose both in HEP and OOT; EXO exhibited increasing benzene concentrations; and mtDNA copy number rose in HEP but not in OOT samples. Overall, our findings demonstrate that A. officinalis is a sensitive bioindicator of air benzene exposure and show for the first time its ability to reproduce human metabolic dynamics.

Modeling of human exposure to benzene in urban environments

Urban areas characterized by high spatial and temporal variability in air pollution levels require implementation of comprehensive approaches to address exposure of individuals. The main objective of this study was to implement a quantitative assessment of individual exposure to benzene in urban environments. For this purpose, ExPOSITION model based on a global positioning system (GPS) tracking approach was applied to estimate individual exposure in different microenvironments. The current investigation provides an application example and validation of the modeling approach against personal and biological exposure measurements collected during the measurements campaign. The probabilistic approach using the Johnson system of distributions was implemented to characterize variability of indoor concentrations. The results obtained for daily average individual exposure to benzene corresponded to mean levels of 1.6 and 0.8-2.7 μg/m(3) in terms of 5th-95th percentiles. Validation of the model results against several personal exposure samples collected for the selected individuals revealed a Pearson's correlation coefficient of .66. This modeling approach explicitly addressed the temporal and spatial variability in the exposure and established a source-receptor relationship.