Source apportionment of fine particulate matter (PM_2.5) were conducted at the Lin'an Regional Atmospheric Background Station (LA) in the Yangtze River Delta (YRD) region in China from July 2014 to April 2015 with three receptor models including principal component analysis combining multiple linear regression (PCA-MLR), UNMIX and Positive Matrix Factorization (PMF). The model performance, source identification and source contribution of the three models were analyzed and inter-compared. Source apportionment of PM_2.5 was also conducted with the receptor models. Good correlations between the reconstructed and measured concentrations of PM_2.5 and its major chemical species were obtained for all models. PMF resolved almost all masses of PM_2.5, while PCA-MLR and UNMIX explained about 80%. Five, four and seven sources were identified by PCA-MLR, UNMIX and PMF, respectively. Combustion, secondary source, marine source, dust and industrial activities were identified by all the three receptor models. Combustion source and secondary source were the major sources, and totally contributed over 60% to PM_2.5. The PMF model had a better performance on separating the different combustion sources. These findings improve the understanding of PM_2.5 sources in background region.