en Environmental Health, Sciences, and Engineering Environmental Health, Sciences, and Engineering پژوهشي Original Article <div style="text-align: justify;"> <div class="WordSection1"><span style="line-height:1;"><span style="font-size:14px;"><span style="font-family:Times New Roman;"><span style="page:WordSection1"><b>Background:</b> The present study aimed to classify the water quality of the Shafarood River in Gilan Province, Iran, using multivariate analysis methods.<br> <b>Methods:</b> This research employed Cluster Analysis (CA), Discriminant Analysis (DA), Principal Component Analysis (PCA), and Factor Analysis (FA) as effective methodologies for decision-making in river water quality management. The analysis was based on selected data from physicochemical parameters and heavy metal concentrations measured at five water sampling stations over a six-year period (2017-2022).<br> <b>Results:</b> The CA results classified the sampling stations into two clusters:&nbsp; medium pollution (Stations 1 and 2) and high pollution (Stations 3, 4, and 5). PCA results confirmed the quality clustering of CA data. The findings of CA and FA methods facilitated the reduction of parameters identified in the first cluster including arsenic (As), lead (Pb), cadmium (Ca), chromium (Cr), nitrate (NO^3-<span style="background:white"><span style="color:#1f1f1f">)</span></span>, and phosphate (PO₄^3-). In contrast, the second cluster was characterized by total suspended solids, turbidity, hardness, ammonia, fecal coliform, electrical conductivity, biological oxygen demand, and chemical oxygen demand. PCA analysis revealed that the first principal component, accounting for 69.5% of the total variance, identified Pb, As, Cd, and Cr as the most important factors influencing changes in water quality. The second principal component, explaining 15.8% of the total variance, identified ammonia, nitrate, turbidity, and total suspended solids as the main parameters affecting the water quality of the Shafarood River.<br> <b>Conclusion:</b> The findings suggest that multivariate statistical techniques are valuable for interpreting large data sets, assessing water quality, and elucidating relationships between parameters and pollutant sources. These methodologies provide essential information regarding water quality and represent an effective approach to decision-making in the management of the Shafarood River<span lang="EN">&rsquo;</span>s water quality.</span></span></span></span></div> <div aria-label="Page Break" class="cke_pagebreak" contenteditable="false" data-cke-display-name="pagebreak" data-cke-pagebreak="1" style="page-break-after:always" title="Page Break"></div></div> Water quality, Statistical analysis, Cluster analysis, Discriminant analysis, Principal component analysis, Factor analysis, 129 137 http://jhehp.zums.ac.ir/browse.php?a_code=A-10-539-2&slc_lang=en&sid=1 Elham Gholami Deljomanesh del.lilium@yahoo.com 100319475328460015619 100319475328460015619 No Department of Environment, Lahijan Branch, Islamic Azad University, Lahijan, Iran. Mahsa Hakimi Abed hakimimah@gmail.com 100319475328460015620 100319475328460015620 Yes Department of Environment, Lahijan Branch, Islamic Azad University, Lahijan, Iran. Ebrahim Fataei eafataei@gmail.com 100319475328460015621 100319475328460015621 No Department of Environment, Ardabil Branch, Islamic Azad University, Ardabil, Iran. Fatemeh‏ Shariati Feyzabadi‏ fshfeyzabadi@gmail.com 100319475328460015622 100319475328460015622 No Department of Environment, Lahijan Branch, Islamic Azad University, Lahijan, Iran. Ali Akbar Imani aa.imani@gmail.com 100319475328460015623 100319475328460015623 No Department of Agriculture, Ardabil Branch, Islamic Azad University, Ardabil, Iran.