Abstract. Source contributions to ambient PM10 (particles with an aerodynamic diameter of 10 μm or less) in Beijing, China were determined with positive matrix factorization (PMF) based on ambient PM10 composition data including concentrations of organic carbon (OC), elemental carbon (EC), ions and metal elements, which were simultaneously obtained at six sites through January, April, July and October in 2004. Results from PMF indicated that seven major sources of ambient PM10 were urban fugitive dust, crustal soil, coal combustion, secondary sulfate, secondary nitrate, biomass burning with municipal incineration, and vehicle emission, respectively. In paticular, urban fugitive dust and crustal soil as two types of dust sources with similar chemical characteristics were differentiated by PMF. Urban fugitive dust contributed the most, accounting for 34.4% of total PM10 mass on an annual basis, with relatively high contributions in all four months, and even covered 50% in April. It also showed higher contributions in southwestern and southeastern areas than in central urban areas. Coal combustion was found to be the primary contributor in January, showing higher contributions in urban areas than in suburban areas with seasonal variation peaking in winter, which accounted for 15.5% of the annual average PM10 concentration. Secondary sulfate and secondary nitrate combined as the largest contributor to PM10 in July and October, with strong seasonal variation peaking in summer, accounting for 38.8% and 31.5% of the total PM10 mass in July and October, respectively. Biomass burning with municipal incineration contributions were found in all four months and accounted for 9.8% of the annual average PM10 mass concentration, with obviously higher contribution in October than in other months. Incineration sources were probably located in southwestern Beijing. Contribution from vehicle emission accounted for 5.0% and exhibited no significant seasonal variation. In sum, PM10 source contributions in Beijing showed not only significant seasonal variations but also spatial differences.