Land Use Regression Approach to Model NO 2 –Concentrations in a Swedish Medium-City Mateus Habermann 1 , Monica Billger 1 , Marie Haeger-Eugensson 2,3 1 Department of Architecture, Chalmers University of Technology, Gothenburg, Sweden. 2 Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden. 3 COWI AB, Gothenburg, Sweden. E-mail: mathab@gmail.com Abstract. In order to visualize the geographical distribution of air pollution concentration realistically, we applied the Land Use Regression (LUR) model in the urban area of Gothenburg, Sweden. The concentration of NO 2 was obtained by 25 passive air samplers during 7-20 May, 2001. Explanatory variables were estimated by GIS in buffers ranging from 50 to 500 m-radii. Linear regression was calculated, and the most robust were attained to the multiple linear regression. Additionally, the LUR model was compared with a dispersion model. The final model explained 81.7% of the variance of NO 2 concentration with presence of sum of traffic within 150 m and altitude as predictor variables. Mann-Whitney Test did not exhibit significant difference between yearly concentrations of NO 2 measured by regulatory measurement sites and measurements from passive samplers, thus LUR model was extrapolated for later years and mapped. The extrapolation indicated more elevated levels of pollution for the years 2003, 2006 and 2010. The results highlight the contribution of traffic on air quality and suggest that LUR modelling may explain the variations of atmospheric pollution with good accuracy. In addition, the model puts focus on spatial and temporal variability needed to describe retrospective exposure to air pollution in studies that evaluate health effects. Keywords: Air pollution; nitrogen dioxide; exposure modeling; geographic information system; LUR model. 1 Introduction Exposure assessment can be used to evaluate, at various levels of detail, the degree and linkage between contaminant sources and concentration of hazards and receptors (e.g. humans) in the environment by studying different exposure pathways (e.g. air, water, and soil) and routes (e.g. inhalation, ingestion, and dermal contact) between them. As one kind of exposure assessment air pollution exposure assessment indicates human exposure to air pollutants [1]. The most exposed receptors to air pollution include either individuals whose residences, study or work places offices are located near to heavy traffic roads or individuals who remain long time on roads (bus drivers, traffic guards, street vendors etc.). Therefore the environment may influence the exposure to pollutants and thereby trigger various outcomes [2]. Although the literature has documented significant variation of outdoor air pollution at small scales within urban areas for important pollutants such as NO 2 and black smoke [3-5], many studies assessed exposure based only on the proximity to polluted source, e.g. proximity to busy traffic [6]. This approach is limited because it disregards other parameters that may influence the dispersion of pollutants such as altitude, land use, population, road type, traffic intensity, temperature and atmospheric stagnation [7]. Therefore, recent models became more refined including some of those parameters [1]. Ordinary dispersion modelling requires good databases which are updated frequently (at least every five years); however, measurements are expensive if they are conducted at numerous places [1] and when dispersion modelling (DM) does not include measurements the computational analysis can be labour intensive. In many of urban areas levels of air pollution frequently exceed environmental standards, it is Environmental Pollution and Protection, Vol. 3, No. 3, September 2018 https://dx.doi.org/10.22606/epp.2018.33001 71 Copyright © 2018 Isaac Scientific Publishing EPP