Multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA),factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal and seasonalvariations and interpretation of a complex water quality data set at Yangling Section of Weihe River. Hierarchicalcluster analysis grouped 12 months into three clusters, i.e., C1 (relatively highly polluted months), C2 (moderatepolluted months) and C3 (less polluted months), based on the similarity of water quality characteristics.Factor analysis/principal component analysis, tested to the data sets of the three groups obtained fromcluster analysis, identified 9, 6 and 7 latent factors explaining more than 76, 69 and 62% of the total variancein the data sets of C1, C2 and C3, respectively. The varifactors obtained indicate that parameters responsiblefor variation are mainly related to temperature and DO (natural), CODMn, turbidity, NH4+, TN, pH and TOC(point source: domestic wastewater) in C1; temperature, DO and EC (natural), CODMn, TN, pH, and TOC inC2; and temperature, DO and EC (natural), CODMn, pH and TOC (point source: domestic wastewater andindustrial effluents), turbidity and TN (non-point source: agriculture and soil erosion) in C3. However,discriminant analysis showed no significant data reduction, as it used 8 parameters (turbidity, EC,NH4+, DO, TN, pH, temperature and TOC) affording more than 81% correct assignations in temporalanalysis, while 8 parameters (CODMn, turbidity, EC, DO, TN, pH, temperature, TOC) affording more than88% correct assignations in seasonal analysis. Thus, this research illustrated the necessity andusefulness of multivariate statistical techniques for analysis and interpretation of large complex waterquality data sets, identification of possible pollution sources/factors and information about variation inwater quality for effective river water quality management.
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