جستجوی مقالات مرتبط با کلیدواژه "روش های چند متغیره" در نشریات گروه "آب و خاک"

Pollution and declining quality of water resources reduce usable water resources, weaken economic development and threaten human health. In this study, groundwater quality was studied and analyzed in Ardabil aquifer. For this purpose, 32 samples of groundwater sources in the Ardabil plain were collected and the concentrations of major ions, minor ions and some heavy metals were measured. Then, hydrogeochemical factors affecting the groundwater quality of the study area were analyzed using multivariate statistical methods, including correlation coefficients and factor analysis. According to the correlation matrix, the concentrations of metals such as arsenic, barium, scandium, silicon, zinc and vanadium indicated a little correlation with the main ions. Therefore, it can be concluded that the entry of these elements into groundwater is due to human activities. In factor analysis, the groundwater quality changes were affected by four main factors with 77.31% of the total variance of data in Ardabil plain. The results of factor analysis showed that the first factor was lithogenic, the second and third factors were lithogenic and anthropogenic, and the fourth factor was just anthropogenic. The results showed that the concentrations of heavy metals such as arsenic, barium, scandium, silicon, vanadium and zinc did not have a high correlation with the main ions, so the presence of these elements could be due to human activity. Also, the greatest effect on groundwater hydrochemistry in the study area was related to magnesium, sodium, calcium ions and electrical conductivity.

Lake Urmia, as one of the most important lakes in the country, has an inappropriate environmental condition due to excessive consumption of water and climate change. The study of climate change and rainfall distribution in this area can improve water management in this basin. In this study, the information of 65 weather stations in the period of 1997-2016 were used for precipitation zoning of Urmia lake basin. For this purpose, the data of each month were standardized and arranged in a matrix with dimensions of (n*m) in which n is the number of stations (65) and m is the number of months (12). Principal Component Analysis (PCA) was performed on data matrix and the main components were determined according to their Eigen values greater than one. Then the principal component score (PCS) values were calculated for the selected components. These values were used as inputs in the Ward cluster analysis method. Then, the Procrustes method was used to determine the index stations. The results showed that the first two main components incorporated more than 87% of the all data variances. Based on the selected components, six distinct precipitation regions were identified throughout the basin. Moreover, it was found that four stations located in different points of the Urmia lake basin namely Mehmandar, Sarab, Babaroud and Santeh can be considered as indicator stations. These stations incorporated more than 84% of the all data variances of basin stations. The Mann-Kendall trend test showed that the rainfall in the autumn season has a significant increase trend, while annual precipitation has only a significant increase trend in one of the clusters.

In the present study, the information of 31 synoptic weather stations in the period of 1987-2010 were used to delineate Iran’s precipitation regions. For this purpose, each month’s data were standardized and written in the (n*m) matrix, where n is the number of stations (31) and m is the number of months (12). Principal component analysis was conducted on the mentioned matrix. The main components were determined according to the criterion of having an eigen value greater than one. Then principal components scores were calculated for the selected components. These valeus were used as an input for Ward method of cluster analysis. Then according to the cluster diagram, precipitation climates of the country were identified. The Procrustes analysis (PA) was used to answer the question of which stations could be considered as the indicator of rainfall climates? Furthermore, from this method those months, which their precipitation can be recognized as an indicator of annual rainfall, were selected. Results showed that the first three components incorporated more than 97% of total variance. Based on the selected components the six distinct precipitation regions were distinguished across the country. Furthermore, PA results indicated that the precipitation of May, August and December approximately had whole of the annual precipitation information. Moreover, it was found that seven stations located in different points of the country namely Zahedan, Tehran, Urmia, Ilam, Yasooj, Gorgan and Rasht could be considered as the indicator stations. These stations incorporated over 87 percent of total variance of data of all selected stations in the country.