Health Risk Associated with Exposure to PM10 and Benzene in Three Italian Towns.
Antonella De DonnoMattia De GiorgiFrancesco BagordoTiziana GrassiAdele IdoloFrancesca SerioElisabetta CerettiDonatella FerettiMilena VillariniMassimo MorettiAnnalaura CarducciMarco VeraniSilvia BonettaCristina PignataSilvia BonizzoniAlberto BonettiUmberto Gelattinull nullPublished in: International journal of environmental research and public health (2018)
Air pollution in urban areas is a major concern as it negatively affects the health of a large number of people. The purpose of this study was to assess the inhalation health risk for exposure to PM10 and benzene of the populations living in three Italian cities. Data regarding PM10 and benzene daily measured by "traffic" stations and "background" stations in Torino, Perugia, and Lecce during 2014 and 2015 were compared to the limits indicated in the Directive 2008/50/EC. In addition, an inhalation risk analysis for exposure to benzene was performed for adults and children by applying the standard United States Environmental Protection Agency's (USEPA) methodology. The levels of PM10 detected in Torino exceeded the legal limits in both years with an increased mean concentration >10 µg/m³ comparing with background station. Benzene concentrations never exceeded the legislative target value. The increased cancer risk (ICR) for children exposed to benzene was greater than 1 × 10-6 only in the city of Torino, while for adults, the ICR was higher than 1 × 10-6 in all the cities. The results suggest the need for emission reduction policies to preserve human health from continuous and long exposure to air pollutants. A revision of legal limits would also be recommended.
Keyphrases
- air pollution
- health risk
- heavy metals
- particulate matter
- human health
- risk assessment
- lung function
- public health
- drinking water
- polycyclic aromatic hydrocarbons
- young adults
- healthcare
- climate change
- physical activity
- health information
- electronic health record
- big data
- life cycle
- chronic obstructive pulmonary disease
- deep learning
- solid state