受体模型是识别特定污染物来源和定量估计每种污染源贡献的有效工具.为了分析广州市主要城区土壤中铅(Pb)、镉(Cd)、锌(Zn)、汞(Hg)、砷(As)、铜(Cu)、铬(Cr)、镍(Ni)8种重金属的含量、分布及来源,采集了广州市主要城区208个表层土壤样本.通过APCS-MLR、PMF和UNMIX 3种受体模型对土壤中重金属的潜在源进行了研究.结果表明:8种重金属元素的平均浓度均高于相应的背景值浓度,其中Cd的浓度几乎是其背景值的5倍.三种受体模型均识别出三种污染源,即自然源、交通源和工业源.但PMF和UNMIX模型还能识别出一个农业源,可以更好地区分不同类型的污染源.此外,将三种受体模型的结果进行比较,发现各潜在污染源的源识别类型和源贡献均存在差异.最终可得知:PMF方法更适合本研究.由此一般也建议采用多种受体模型来确定来源分配.研究结果可为地方政府控制和管理污染,更好地保护重要土壤质量提供参考.“,”Receptor models are useful tools to identify the types of pollution source and estimate the contributions of each source of the observed samples. To analyze the concentrations, distributions and sources of eight heavy metals including lead ( Pb) , cadmium ( Cd) , zinc ( Zn) , mercury ( Hg) , arsenic ( As) , copper ( Cu) , chromium ( Cr) , and nickel ( Ni) in soils, 208 topsoil samples were collected in the main urban area of Guangzhou, China. Three receptor models ( Multi-Linear Regression of the Absolute Principal Component Scores ( APCS-MLR ) method, Positive Matrix Factorization ( PMF ) method and UNMIX method) were employed to identify the potential pollution sources of heavy metals and to apportion the pollution sources. Results show that the mean concentrations of eight heavy metal elements are higher than the corresponding background values, with the mean concentration of Cd being almost five times its background value. The three receptor models all identify three potential pollution sources, which are nature source, traffic source and industry source. Moreover, PMF and UNMIX can identify an agricultural source besides the three pollution sources, which better distinguishes the different types of pollution sources. Comparison among the results of APCS-MLR, PMF and UNMIX shows that there are some significant differences in the estimated contributions for each potential pollution source. It is also found that PMF appears to be more plausible for this investigation. It is advisable to use multiple receptor models to perform source identification and source apportionment, and the results could be very useful to local administrations for the control and management of pollution and better protection of important soil quality.