Identification of groundwater pollution sources based on multivariate statistical approach
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摘要: 为实现地下水水源地的科学保护，识别水源地可能的污染源，通过采集吴忠市金积水源地保护区及周边16个水井地下水样品，依据采样测试分析结果，遵循连续性和代表性原则，选择了电导率、溶解性总固体、总硬度、浊度、溶解氧、氟化物、亚硝酸盐和细菌总数等8个指标进行多元统计分析。采用因子分析法，提取出4个公因子，利用因子得分进行多元线性回归，实现对金积水源地可能的地下水污染源的识别。结果表明，因子分析提取出的4个公因子，解释了所选样本总方差的96.979%，其中第一公因子代表蒸发浓缩作用，第二公因子代表粪便污染，第三公因子代表含氟矿物的溶解和工业污染，第四公因子代表自然作用，且各污染因子对不同采样点的污染程度不同，四个公因子对地下水水质的贡献率分别为：42.63%，29.23%，22.40%和5.74%。Abstract: Comprehensive and joint applications of factor analysis and multivariate linear regression were carried out for identification of groundwater pollution sources. In the factor analysis, four factors were extracted based on eigenvalue (larger than 1), which represented four potential pollution sources, such as mineralization effect, fecal pollution, mineral dissolution and natural pollution. The factor scores of each sampling site were analyzed using the inverse distance weighting method, and the results show that the influencing degree by various pollution sources differed among the sampling sites. The results of multivariate linear regression based on factor scores show that the contribution rates of the 4 factors on water quality were 42.63%, 29.23%, 22.40% and 5.74%. This study indicates that the multivariate approach is useful and effective for identification of groundwater pollution sources.
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