Zhejiang University-the University of Western Australia Joint Centre in Integrated Water Management and Protection, and College of Environment and Natural Resources, Zhejiang University, 310029 Hangzhou, PR China.
Water Res. 2010 Mar;44(5):1562-72. doi: 10.1016/j.watres.2009.11.003. Epub 2009 Nov 11.
Understanding the spatial distribution and apportioning the sources of water pollution are important in the study and efficient management of water resources. In this work, we considered data for 13 water quality variables collected during the year 2004 at 46 monitoring sites along the Qiantang River (China). Fuzzy comprehensive analysis categorized the data into three major pollution zones (low, moderate, and high) based on national quality standards for surface waters, China. Most sites classified as "low pollution zones" (LP) occurred in the main river channel, whereas those classified as "moderate and high pollution zones" (MP and HP, respectively) occurred in the tributaries. Factor analysis identified two potential pollution sources that explained 67% of the total variance in LP, two potential pollution sources that explained 73% of the total variance in MP, and three potential pollution sources that explained 80% of the total variance in HP. UNMIX was used to estimate contributions from identified pollution sources to each water quality variable and each monitoring site. Most water quality variables were influenced primarily by pollution due to industrial wastewater, agricultural activities and urban runoff. In LP, non-point source pollution such as agricultural runoff and urban runoff dominated; in MP and HP, mixed source pollution dominated. The pollution in the small tributaries was more serious than that in the main channel. These results provide information for developing better pollution control strategies for the Qiantang River.
了解水污染的空间分布并分配其污染源对于水资源的研究和有效管理非常重要。在这项工作中,我们考虑了 2004 年在钱塘江沿 46 个监测点收集的 13 个水质变量的数据。模糊综合分析根据中国地表水国家质量标准将数据分为三个主要污染区(低、中和高)。大多数被归类为“低污染区”(LP)的地点位于主河道,而被归类为“中高污染区”(MP 和 HP)的地点则位于支流。因子分析确定了两个潜在的污染源,它们分别解释了 LP 总方差的 67%、MP 总方差的 73%和 HP 总方差的 80%。UNMIX 用于估计每个水质变量和每个监测点的已识别污染源的贡献。大多数水质变量主要受到工业废水、农业活动和城市径流造成的污染的影响。在 LP 中,以农业径流和城市径流为主的非点源污染占主导地位;在 MP 和 HP 中,混合源污染占主导地位。小支流的污染比主河道严重。这些结果为钱塘江制定更好的污染控制策略提供了信息。