Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

TitoloDevelopment of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2017
AutoriCattani, G., Gaeta A., A. di Bucchianico Di Menno, De Santis A., Gaddi R., Cusano M., Ancona C., Badaloni C., Forastiere F., Gariazzo C., Sozzi R., Inglessis M., Silibello C., Salvatori E., Manes F., and Cesaroni G.
RivistaAtmospheric Environment
Volume156
Paginazione52-60
Parole chiaveAir pollution, article, atmospheric pollution, building, cohort analysis, cold climate, concentration (composition), environmental exposure, exhaust gas, Exposure assessment, fines, geographic information system, GIS, Italy, Land use, Land use regression, land use regression model, Lazio, Mean square error, model, Particle number concentration, particle size, particulate matter, pollution exposure, population density, Population exposure, Population statistics, priority journal, Regression analysis, Roads and streets, Roma [Lazio], Rome, seasonal variation, spatial variation, Spatial variations, traffic emission, Ultrafine particle
Abstract

The health effects of long-term exposure to ultrafine particles (UFPs) are poorly understood. Data on spatial contrasts in ambient ultrafine particles (UFPs) concentrations are needed with fine resolution. This study aimed to assess the spatial variability of total particle number concentrations (PNC, a proxy for UFPs) in the city of Rome, Italy, using land use regression (LUR) models, and the correspondent exposure of population here living. PNC were measured using condensation particle counters at the building facade of 28 homes throughout the city. Three 7-day monitoring periods were carried out during cold, warm and intermediate seasons. Geographic Information System predictor variables, with buffers of varying size, were evaluated to model spatial variations of PNC. A stepwise forward selection procedure was used to develop a “base” linear regression model according to the European Study of Cohorts for Air Pollution Effects project methodology. Other variables were then included in more enhanced models and their capability of improving model performance was evaluated. Four LUR models were developed. Local variation in UFPs in the study area can be largely explained by the ratio of traffic intensity and distance to the nearest major road. The best model (adjusted R2= 0.71; root mean square error = ±1,572 particles/cm³, leave one out cross validated R2= 0.68) was achieved by regressing building and street configuration variables against residual from the “base” model, which added 3% more to the total variance explained. Urban green and population density in a 5,000 m buffer around each home were also relevant predictors. The spatial contrast in ambient PNC across the large conurbation of Rome, was successfully assessed. The average exposure of subjects living in the study area was 16,006 particles/cm³ (SD 2165 particles/cm³, range: 11,075–28,632 particles/cm³). A total of 203,886 subjects (16%) lives in Rome within 50 m from a high traffic road and they experience the highest exposure levels (18,229 particles/cm³). The results will be used to estimate the long-term health effects of ultrafine particle exposure of participants in Rome. © 2017 Elsevier Ltd

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013629534&doi=10.1016%2fj.atmosenv.2017.02.028&partnerID=40&md5=2f626371fefff310e8a7e6565ea73e8d
DOI10.1016/j.atmosenv.2017.02.028