1. ^aDepartment of Epidemiology, Rome E Local Health Authority, Rome, Italy
2. ^bInstitute of Environmental Medicine, Karolinska Institutet, Sweden
3. ^cDepartment of Occupational and Environmental Health, Stockholm County Council, Sweden
4. ^dEnvironmental Epidemiology Unit, Regional Environmental Protection Agency, Emilia Romagna, Italy
5. ^eATAC, Mobility Agency for the City of Rome, Rome, Italy

Correspondence: Dr. F Forastiere, Department of Epidemiology, Rome E Local Health Authority, Via di S Costanza, 53, 00198 Rome, Italy. Tel.: +39 06/83060484; Fax: +39 06/83060463; E-mail: forastiere@asplazio.it

Received 10 August 2006; Accepted 4 February 2007; Published online 11 April 2007.

Abstract

Spatial modeling of traffic-related air pollution typically involves either regression modeling of land-use and traffic data or dispersion modeling of emissions data, but little is known to what extent land-use regression models might be improved by incorporating emissions data. The aim of this study was to develop a land-use regression model to predict nitrogen dioxide (NO₂) concentrations and compare its performance with a model including emissions data. The association between each land-use variable and NO₂ concentrations at 68 locations in Rome in 1995 and 1996 was assessed by univariate linear regression and a multiple linear regression model that was constructed based on the importance of each variable. Traffic emissions (particulate matter, carbon monoxide, nitrogen oxides, and benzene) were estimated for 164 areas of the city based on vehicle type, traffic counts and driving patterns. Mean NO₂ concentration across the 68 sites was 46.8 g/m³ (SD 9.8 g/m³; inter-quartile range 11.5 g/m³; min 24 g/m³; max 73 g/m³). The most important predicting variables were the circular traffic zones (main ring road, green strip, inner ring road, traffic-limited zone), distance from busy streets, size of the census block, the inverse population density, and altitude. A multiple regression model including these variables resulted in an R² of 0.686. The best-fitting model adding an emission term of benzene resulted in an R² of 0.690, but was not significantly different from the model without emissions (P=0.147). In conclusion, these results suggest that a land-use regression model explains the traffic-related air pollution levels with reasonable accuracy and that emissions data do not significantly improve the model.