In recent years, a vast number of epidemiological evidences for the effects of a wide range of environmental contaminants on child health outcomes were revealed. Common chemical exposures include: outdoor air pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), particulate matter (PM – including PM 2.5 and PM 10), polycyclic aromatic hydrocarbons (PAHs), and sulfur dioxide (SO₂); toxic heavy metals (lead, mercury, cadmium, arsenic), organochlorine compounds (PCBs, DDT/DDE, HCB, dioxins), perfluoroalkyl substances, polybrominated diphenyl ethers, pesticides, phthalates, and bisphenols. Exposure to these chemicals is widespread globally, through air, water, soil, dust and food contamination, and many consumer goods including plastics and cosmetics. Pollutant exposure may disrupt normal development and thus result in adverse cognitive outcomes with global intelligence quotient as most studied outcome, and behavioral disorders including autism and autism spectrum disorder and attention deficit hyperactivity disorder.
This research aims at combining the information from environmental contamination data with HBM data and personal sensors data aiming at calculation of robust human exposure model. This was achieved by collecting biological samples from 550 participants residing in Thessaloniki, Greece; data from 120 participants using personal silicone samplers from Thessaloniki, Athens, Ljubljana, Madrid, Basel and Milan. Identification of exposure levels for phthalates was carried with both biological samples and personal samplers, PAH’s exposure levels were identified with silicone wristbands. Thus, the use of individual HBM data, accompanied by ancillary information will shed light on the mechanistic link between exposure dynamics and observed HBM data.