جستجوی مقالات مرتبط با کلیدواژه "multivariate statistics" در نشریات گروه "آب و خاک"

In recent years, soil contamination with potentially toxic elements (PTEs) has become a major problem in most parts of the world. PTEs are naturally generated from the pedogenesis in the soil and are formed mainly by rock weathering. Nevertheless, the natural content of metals, i.e., Cr, Zn, Ni, Pb, Cd, used to be low in the soil, but due to anthropogenic activities such as industrial emissions, atmospheric transportation, sewage irrigation, and application of pesticides and fertilizers, there is an increase in the content of PTEs. PTEs in soil are one of the most important environmental pollutants due to their toxicity, durability, easy absorption by plants and long half-life. Therefore, the assessment of soil health is very important for the sustainable development of agriculture and the rehabilitation of soils contaminated with PTEs. The present study was conducted to quantify PTEs pollution for soil environmental assessment using a flexible approach based on multivariate analysis and using pollution indicators in a part of the central lands of Khuzestan province.
 
For this purpose, in February 2021, 200 surface soil samples (0-10 cm) were taken using stratified random sampling. The collected soil samples were cleaned by removing plant materials and other pebbles, and air dried, powdered, and sieved by using a 2 mm sieve size. The interest in soil's physical and chemical properties i.e., pH was determined with a digital pH meter. Soil textural particles were measured by the hydrometer method, soil organic carbon (SOC) content was estimated by following Walkley and Black method, bulk density (BD) was measured by the Clod method, and total metal content was determined using the aqua-regia solution digestion method and analyzed using Inductively Coupled Plasma-Optical Emission spectrometry (ICP-OEC). The level of Pb, Ni, Zn, Cr pollution was estimated based on environmental indicators including contamination factor (CF), enrichment factor (EF), geo-accumulation index (Igeo), pollution index of individual metals (PI), and modified pollution index of individual metals (MPI). Multivariate statistical methods including correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to find the source of metals in the soil. All statistical methods were performed using SPSS (26 version) software.
 
Measurement of soil pH showed that the soil of the studied area tends to alkalinity. Also, the soil texture in this area is loam. The results showed that the SOC in these soil samples is 0.71%, and the range of EC (between 0.18 and 60.5 dS/m) indicates the distribution of saline and non-saline soils in the studied area. The total average concentration of Zn, Ni, Cr, and Pb were 60.26, 50.96, 50.38, and 12.67 mg/kg, respectively. The order of average for heavy metals was Zn> Ni> Cr> Pb. The highest amount of standard deviation and concentration changes were observed in Zn and Pb elements. These two elements also showed a high degree of variation coefficient in the studied area, which can indicate the high impact of human activities on the content of these elements. The results obtained from the application of multivariate statistics showed that there is a positive correlation between the elements such as Zn, Ni, and Pb in the study area, indicating that these metals probably have the same source. Whereas the absence of correlation of Cr with these elements indicates a separate source for this element compared to Pb, Zn, and Ni. There was also a strong relationship among these elements based on the PCA and CA classification. Based on the multivariate statistical analysis the source of pollution for the metals studied was mainly from both anthropogenic and geogenic activities. The results showed that the soil samples taken from the study area are in the low pollution category based on the individual element indices of CF and Igeo, but in the moderate pollution class based on the EF index. In addition, the evaluation based on the cumulative and multi-element indices of PI and MPI showed that 100% of samples have high pollution.
 
The present study concludes that the average values of Zn, Ni, Cr, and Pb were found to be below the guidelines set by the IEPA (Iran Environmental Protection Agency) as well as the Earth's crust values. The results indicate existing relationships among the studied variables, revealing that the heavy metals Zn, Ni, and Zn share the same source in the study area. Additionally, it was observed that the source of Cr is primarily geogenic in nature. These findings highlight the significance of utilizing multivariate statistical methods and pollution indicators in tandem, as they prove to be valuable tools for evaluating and quantitatively determining the potential pollution risk.

Multi-variate statistical methods such as principal component analysis (PCA) and regressions could be used to facilitate the interpretation of complex relationships. The objective of this study was to determine the most important soil fertility properties affecting rice yield in the paddy fields. For this purpose, soil samples were taken from the plow layers of 119 points with suitable distribution in the paddy fileds located in Shaft and Fouman cities of Guilan province. Then after, physical and chemical properties of the soil fertility were measured and analysed using descriptive statistics, principal component analysis and regression methods. Results showed that three PCs with eigen values greater than one named as “k and it’s preservation factors”, ”Total N and it’s provider factors” and ”P and Thickness of plow layer” are respectively explained more than 67.4% of the variability in the soil physical and chemical properties and 55% of the yield variability. In addition, the corresponded properties to the PCs explained 80% of the yield variability. Consequently, in order to increase the yield, management practices such as proper fertilizer applications of nitrogen, potassium and phosphorous and proper tillage for creating suitable plow layer are recommended.

In order to monitor the changing water table in the field, determination of the main sampling points is very important to reduce sites and save time and cost. Principal Component Analysis (PCA) is one of the data reduction techniques used to extract the important components that explain the variance of a system. In this paper, the PCA was used to identify the effective wells of Qheidar Aqufer, Zanjan, to determine the groundwater level and remove the less important ones. From the study region which an area of about 920 km2, 48 wells (sites) were investigated. Using PCA, the relative importance of each well was calculated between 0 (for completely ineffective well) to 1 (for the very effective wells). The study showed the elimination of wells whose relative importance was less than 0.5 (i.e. half the total number of wells), coefficient of variation of groundwater level relative to the use of all wells did not greatly increase, and the error to determine the level of groundwater was less than 13 percent.