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

Introduction: 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 at a 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. Methods: 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. GIS-derived predictor variables, with varying buffer size, were evaluated to model spatial variation 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 by adding them in the stepwise procedure, or regressing them against residuals from the “base” model, and their capability to improve the model performance was evaluated. Results: Four LUR models were developed. Local variation in UFPs in the study area can be largely explained by the product of traffic intensity on the nearest major road and inverse of distance to the major road. The best model (adjusted R² = 0.71; root mean square error = ±1572 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, adding 3% more to the total variance explained. Urban green space and population in a 5000 m buffer were also relevant predictors. Conclusions: The spatial contrast in ambient PNC across the large conurbation of Rome, was successfully assessed. Results will be used to estimate the long-term health effects of ultrafine particle exposure of participants in the Rome Longitudinal Study.