Data for Publication
Description
This dataset offers a comprehensive analysis of the water quality of the River Jhelum, encompassing Water Quality Index (WQI) values that reveal significant spatial variations across different sections of the river. Detailed statistical analyses of water samples from upstream, midstream, and downstream sections are provided, offering insights into the water quality dynamics. The dataset also includes eigenvalues and detailed descriptions of the components extracted from the correlation matrix, facilitating a deeper understanding of the underlying factors influencing water quality. Additionally, a three-dimensional biplot illustrates the relationships between the first three principal components and the highly correlated variables, supported by hierarchical clustering using Ward's Linkage and Euclidean Distance metrics, which further elucidate the complex interactions within the water quality data.
Files
Steps to reproduce
The data was gathered by collecting 60 water samples along the River Jhelum, spanning from upstream to downstream domains. These samples were analyzed for various physicochemical parameters, such as pH, dissolved oxygen, turbidity, and conductivity. The Water Quality Index (WQI) was then calculated using the weighted arithmetic index method. Statistical analysis, including descriptive statistics and comparative analysis between the different sections of the river, was performed on the collected data. Software like IBM SPSS was utilized to generate summary statistics and visualizations. For deeper insights, Principal Component Analysis (PCA) was conducted to identify the key components influencing water quality, using software such as Origin and SPSS to extract eigenvalues and visualize the relationships between the first three principal components and highly correlated variables via a three-dimensional biplot. Hierarchical clustering was also applied, utilizing Ward's Linkage and Euclidean Distance metrics to group similar water quality variables. This was performed using R or Python, with the appropriate libraries to construct the dendrogram and interpret the clustering results. Following these steps, while adhering to standardized protocols and using the specified instruments and software, allows for the accurate replication of this research and the generation of similar data.
Institutions
Categories
Funding
King Saud University
RSPD2024R666
United Arab Emirates University
12S158