中文摘要：

控制泥沙迁移一直是流域管理的重点,而泥沙携带污染物与养分（磷）对下游水体的影响愈发引起关注。研究泥沙来源的位置、特征及各来源对泥沙输出的贡献,有助于针对重点源区实施水土流失以及水污染治理措施。农业小流域中磷的输出以泥沙吸附的颗粒态磷为主,研究泥沙来源可为探讨颗粒态磷的来源提供重要基础。复合指纹技术是一种可靠的泥沙源解析方法,但在一些地表物质相对均一、输沙量较小、受人为因素影响较多的东部小流域,能否应用指纹识别法解析泥沙来源并探讨颗粒态磷来源还需要验证。该文以南京市九乡河上游小流域为研究区,尝试以指纹识别技术分析流域泥沙来源为基础,进而研究不同来源对颗粒态磷输出的相对贡献。研究结果表明,农田对泥沙输出的贡献为25.3%-65.2%,对颗粒态磷输出的贡献达52.2%-85.8%;矿山及道路施工用地对泥沙输出的贡献为34.8%-74.7%,但是对颗粒态磷输出的贡献仅为14.2%-47.7%;而来源于林地的泥沙与颗粒态磷总体上均不到0.1%。复合指纹技术不但能够有效识别泥沙来源,且以泥沙源解析来研究颗粒态磷来源,能够为基础资料缺乏地区提高颗粒态磷来源识别的合理性以及流域非点源磷污染控制提供一种思路和方法。

英文摘要：

Sediment control has attracted more attention in watershed management planning, especially for the transport of contaminants and nutrients（such as phosphorus（P）） by fine sediment. Implementation of specific management solutions for a watershed requires understanding the location and nature of the major sediment sources. Composite fingerprinting techniques have been proved as an effective mean of sediment source apportionment in other countries. However, research using this method is not reported for small watersheds that are relative homogeneity in underlying surface, or have lower sediment load output and more human disturbance. As for nutrients, particulate P adsorbed by fine sediment is the major part of P output in an agricultural watershed. Therefore, successful sediment source apportionment would give an access to P source identification and quantitative apportionment study. The objectives of this paper were to identify the sources of particulate P in a small catchment of the upper Jiuxiang River in Nanjing, and to estimate the relative contributions of the potential sources based on sediment source fingerprinting. A total of 56 surface samples from potential sediment sources were collected, including 20 from woodland, 21 from cultivated land, and 15 from mine areas and roads of transportation. In addition to suspended sediment collection, 17 deposited sediment samples were collected in the mainstream near catchment outlet. Twenty three potential fingerprint properties were selected for laboratory analysis with all samples screened to 〈63 μm. Composite fingerprints were acquired by a two-stage statistical fingerprinting procedure and a multivariate mixing model was used to estimate the contributions of potential sediment sources. A goodness of fit model was used to test the performance of the multivariate mixing model. The particulate P source apportionment was then proceeded based on the sediment fingerprinting results and P content information from all potential sources. The apportionment resul