Land Use Regression Approach to Model NO2–Concentrations in a Swedish Medium-City
Journal article, 2018


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 NO2 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 NO2 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 NO2 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.

exposure modeling

Air polluti dioxide

geographic information system

LUR model.

Author

Mateus Habermann

Monica Billger

Chalmers, Architecture and Civil Engineering, Architectural theory and methods

Marie Haeger-Eugensson

University of Gothenburg

Environmental Pollution and Protection

2519-1055 (ISSN) 2519-1063 (eISSN)

Vol. 3 3 71-89

Driving Forces

Sustainable development

Areas of Advance

Building Futures (2010-2018)

Subject Categories

Meteorology and Atmospheric Sciences

Physical Geography

Environmental Sciences

DOI

10.22606/epp.2018.33001

More information

Latest update

1/23/2019