Chen Jiabo, Li Fayun, Fan Zhiping, Wang Yanjie
National & Local United Engineering Laboratory of Petroleum Chemical Process Operation, Optimization and Energy Conservation Technology, Liaoning Shihua University, Fushun 113001, China.
Institute of Eco-Environmental Sciences, Liaoning Shihua University, Fushun 113001, China.
Int J Environ Res Public Health. 2016 Oct 21;13(10):1035. doi: 10.3390/ijerph13101035.
Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009-2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May-October, February-April and November-January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD) 5-day biochemical oxygen demand (BOD₅), NH₄⁺-N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.
河流水污染的源解析在水资源管理和水生生物保护中至关重要。运用了各种基于地理信息系统的多元统计方法对辽河系统（中国）2009 - 2011年的水质数据集进行综合分析。聚类分析（CA）根据水质特征的相似性将一年中的12个月分为三组（5月至10月、2月至4月和11月至1月），并将66个采样点分为三组（A组、B组和C组）。判别分析（DA）确定温度、溶解氧（DO）、pH值、化学需氧量（COD）、五日生化需氧量（BOD₅）、NH₄⁺-N、总磷（TP）和挥发酚是影响时间变化的显著变量，正确分类率达81.2%。主成分分析（PCA）和正定矩阵因子分解（PMF）确定了数据结构各部分的八个潜在污染因子，解释了总方差的61%以上。农田和林地径流中的耗氧有机物是A组的主要潜在污染因子。对于B组，主要污染物是耗氧有机物、石油、营养物质和粪便。对于C组，评估的污染物主要包括耗氧有机物、石油和有毒有机物。
