Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling

The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dp<100 nm) and particle number size distributions was studied by two statistical regression approaches. The models were applied to a 1 km2 study area in Braunschweig, Germany. A land use regression model (LUR) using different urban morphology parameters as input is compared to a multiple regression type model driven by pollutant and meteorological parameters (PDR). While the LUR model was trained with UFP concentration the PDR model was trained with measured particle number size distribution data. The UFP concentration was then calculated from the modelled size distributions. Both statistical approaches include explanatory variables that try to address the 'process chain' of particle emission, dilution and deposition. LUR explained 74% and 85% of the variance of UFP for the full data set with a root mean square error (RMSE) of 668 cm(-3) and 1639 cm(-3) in summer and winter, respectively. PDR explained 56% and 74% of the variance with RMSE of 4066 cm(-3) and 6030 cm(-3) in summer and winter, respectively. Both models are capable to depict the spatial variation of UFP across the study area and in different outdoor microenvironments. The deviation from measured UFP concentrations is smaller in the LUR model than in PDR. The PDR model is well suited to predict urban particle number size distributions from the explanatory variables (total particle number concentration, black carbon and wind speed). The urban morphology parameters in the LUR model are able to resolve size dependent concentration variations but not as adequately as PDR.