Keywords: dispersion modelling, receptor modelling, multiple linear regression, particulate matter, inverse modelling, urban air quality, emissions, source apportionment, air pollution, road dust, wood burning
Source apportionment of PM2.5 in urban areas using multiple linear regression as an inverse modelling technique
One method to assess the source contribution of particulate matter to urban air quality is through inverse modelling where emissions are estimated using dispersion models and monitoring data. In this paper, a straightforward inverse modelling method, using Multiple Linear Regression, is described and applied to the urban area of Oslo for PM2.5. The results of the inverse modelling method are compared with independent receptor modelling. The method shows that the model underestimates the source contribution from suspended road dust by a factor of 7–10 and overestimates the source contribution from wood burning by a factor of 2–3.