Important health relevance has been suggested for ultrafine particles (UFP) and ozone, but studies on long-term effects are scarce, mainly due to the lack of appropriate spatial exposure models. We designed a measurement campaign to develop land use regression (LUR) models to predict the spatial variability focusing on particle number concentration (PNC) as indicator for UFP, ozone and several other air pollutants in the Augsburg region, Southern Germany. Three bi-weekly measurements of PNC, ozone, particulate matter (PM10, PM2.5), soot (PM2.5abs) and nitrogen oxides (NOx, NO2) were performed at 20 sites in 2014/15. Annual average concentration were calculated and temporally adjusted by measurements from a continuous background station. As geographic predictors we offered several traffic and land use variables, altitude, population and building density. Models were validated using leave-one-out cross-validation. Adjusted model explained variance (R2) was high for PNC and ozone (0.89 and 0.88). Cross-validation adjusted R2 was slightly lower (0.82 and 0.81) but still indicated a very good fit. LUR models for other pollutants performed well with adjusted R2 between 0.68 (PMcoarse) and 0.94 (NO2). Contrary to previous studies, ozone showed a moderate correlation with NO2 (Pearson's r=-0.26). PNC was moderately correlated with ozone and PM2.5, but highly correlated with NOx (r=0.91). For PNC and NOx, LUR models comprised similar predictors and future epidemiological analyses evaluating health effects need to consider these similarities.