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Background and purpose: Monitoring quality of rivers water and awareness of its health status over time and identifying vulnerable zones are considered as essential steps towards sustainable development and efficient management of the environment. This study used multivariate statistical methods for analyzing water quality and awareness of rivers state in the Mazandaran province, Iran. Materials and methods: The monthly data of the rivers water quality parameters including electrical conductivity (EC), pH, NO3-, PO43-, CO32-, HCO3-, SO42-, Ca2+, Mg2+, K+, Na+, sodium absorption rate (SAR), Na%, permanent hardness (Per.H) and temporary hardness (Tem.H) during the 2011-2012 was collected. The principal components analysis, cluster analysis and spatial analysis were used to provide better results. Results: There were overlapped between many of the measured parameters. TDS, pH, SO42-, NO3-, and PO43- were optimization variables that could describe the water quality with 94.96% of variance. Results of grouped stations showed the relative quality of rivers water was lower in the western areas of the Mazandaran province. Relative situation of some stations including Baliran, Doab, Pol Mergen, Pol Sefid and Kercha was critical. Conclusion: Multivariate statistical analysis is able to process vast amounts of data, simultaneously. Determining important rivers water quality parameters can reduce costs and identification of homogeneous regions can play a significant role in integrated environmental management.

Keywords: River water quality, principal component analysis (PCA), cluster analysis (CA), Mazandaran province, Iran

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