Introduction: The European Environment Agency has identified Northern Italy as one of the most polluted areas in Europe. Among air contaminants, black carbon (BC) has been identified as a sensitive marker of traffic related air pollution. This study aims to investigate the spatial distribution of BC in the catchment area of an elementary school of Milan, the biggest city in Northern Italy, using Land Use Regression (LUR) models and focusing especially on Morning Rush Hour (MRH). Methods: Two recruitment campaigns were performed asking schoolchildren's parents and residents of the study area to host a monitoring site in their own dwellings. Finally, 34 monitoring sites and 1 reference site were sampled. BC was measured in two seasonal campaigns using eight micro-aethalometers. Six seasonal and annual LUR models were developed, 3 focused on MRH. Results: Overall, median BC was 3247 and 1309 ng/m3 in the cold and warm season, respectively. In both seasons, there was a significant spatial variation between the monitoring sites. MRH values were higher than the daily values with median concentrations of 4227 and 2331 ng/m3, respectively. Developed LUR models showed that BC variability is well explained only by traffic variables; R2 ranged from 0.52 to 0.79 and from 0.65 to 0.81, for seasonal/annual and MRH LUR models respectively. Discussion.: LUR models based on traffic variables explain most of the measured BC distribution variability for both warm and cold season. MRH represents a critical moment for BC during all the year, with an increase of 1000 ng/m3 respective to the daily median value and differences in magnitude according to location. Our results highlight that the mobility issue is one of the most important challenges to reduce air pollution in the city of Milan and this is of particular concern for elementary schoolchildren that commute to school during MRH.
|Titolo:||Annual, seasonal, and morning rush hour Land Use Regression models for black carbon in a school catchment area of Milan, Italy|
FUSTINONI, SILVIA (Corresponding)
|Parole Chiave:||Air pollution; Black carbon (BC); Environmental monitoring; Land use regression (LUR); Schoolchildren; Traffic pollution|
|Settore Scientifico Disciplinare:||Settore MED/44 - Medicina del Lavoro|
|Data di pubblicazione:||3-giu-2019|
|Digital Object Identifier (DOI):||10.1016/j.envres.2019.06.001|
|Appare nelle tipologie:||01 - Articolo su periodico|