Comparative Study of Trace Level of Extracted Mercury in Different Water Samples with Aided Multivariate Statistical Analysis

Abstract

Comparative study of trace level of extracted mercury in different types of water was successfully carried out. Data sets of batch samples were grouped in two clusters (C I; 4, C II; 4) to represent similarity of data structure under optimized and direct extraction procedure respectively. Similarity level for inter batch samples (optimized procedure) was obtained in the range of 96.7-99.2 %; which was better than by direct extraction (67.2-92.5 %) with mean distance from centroid was calculated at 0.462. The first two components (PC1 and PC2) on score plot explained about 86.2 % (ultrapure) and 73.9 % (salt water) of the total variance in signal data sets. In discriminant analysis, latent variables namely pH, extraction time and temperature were able to enhance the correctness of inter batch sample variations accounted to be 80 and 91.7 %. A fitted model expressed by multiple linear regressions obtained with two organomercury species (methyl and ethyl) were recognized as independent variables explained about 90.04 % (ultrapure) and 90.85 % (salt water) traceability from sum of peak areas. Analysis of real samples gave relative standard deviation value of less than < 0.33 % indicating that good performance in terms of repeatability. Recovery was found to range from 75.62-95.46% (river water) and 73.44-91.14 % (sea water).

Colorado River (Argentina) Water Crisis Scenarios and Influence on Irrigation Water Quality Conditions

“…The temporal and spatial differences in the water and soil quality of an area are the base of distinguishing the groups from one another. This analysis was developed based on electrical conductivity, since the identification and development of salt-tolerant forage crops can help address the scarcity of good-quality water in many arid regions of the world where there are large reserves of saline and brackish water [28]. The resulting HCA dendrogram, statistically significant (p < 0.05) for the water quality data, is shown in Figure 4.…”

“…As they operate with a large volume of spatial and temporal data, they are used to carry out studies on water quality and ecological status. Further different statistical techniques, such as a class hierarchy analysis (HCA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), have been used to perform this type of study because they have the capacity to assess temporal and spatial variations in river water quality, identify the possible sources of water pollution and cluster monitoring stations into groups with similar characteristics [27][28][29][30][31].…”