Exposure to Submicron Particles and Estimation of the Dose Received by Children in School and Non-School Environments.

Autor: Pacitto, Antonio, Stabile, Luca, Russo, Stefania, Buonanno, Giorgio
Předmět:
Zdroj: Atmosphere; May2020, Vol. 11 Issue 5, p485, 1p
Abstrakt: In the present study, the daily dose in terms of submicron particle surface area received by children attending schools located in three different areas (rural, suburban, and urban), characterized by different outdoor concentrations, was evaluated. For this purpose, the exposure to submicron particle concentration levels of the children were measured through a direct exposure assessment approach. In particular, measurements of particle number and lung-deposited surface area concentrations at "personal scale" of 60 children were performed through a handheld particle counter to obtain exposure data in the different microenvironments they resided. Such data were combined with the time–activity pattern data, characteristics of each child, and inhalation rates (related to the activity performed) to obtain the total daily dose in terms of particle surface area. The highest daily dose was estimated for children attending the schools located in the urban and suburban areas (>1000 mm2), whereas the lowest value was estimated for children attending the school located in a rural area (646 mm2). Non-school indoor environments were recognized as the most influential in terms of children's exposure and, thus, of received dose (>70%), whereas school environments contribute not significantly to the children daily dose, with dose fractions of 15–19% for schools located in urban and suburban areas and just 6% for the rural one. Therefore, the study clearly demonstrates that, whatever the school location, the children daily dose cannot be determined on the basis of the exposures in outdoor or school environments, but a direct assessment able to investigate the exposure of children during indoor environment is essential. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index