Statistical Analysis for Water Quality Assessment: A Case Study of Al Wasit Nature Reserve

This study presents a comprehensive data analysis using univariate and multivariate statistical techniques as a tool to establish a baseline for the assessment of water quality parameters in environmental compartments. The Al Wasit Nature Reserve is a hypersaline wetland in the UAE with a spatial fluctuation in water parameters as water flows above ground as well as ponds forming in deeper areas and over the year due to the arid climate and seasonality. Water samples were collected at fifteen sites along the hypersaline wetland over three periods during the months of February to March 2021 as temperatures started to rise with the oncoming summer. Water quality parameters, including the temperature, pH, turbidity, dissolved oxygen (DO), oxidation-reduction potential (ORP), electrical conductivity (EC), chemical oxygen demand (COD), chloride, ammonia, and nitrates, were measured. The results of the data analysis were used to group the sites, which were divided into three groups with similar water quality characteristics. Correlation assessments between all studied parameters revealed significant differences in the values of eight of the evaluated parameters between the three identified clusters, with only the nitrate concentrations and dissolved oxygen parameters not being significant. It was found that one of the three clusters (cluster 1) performed better than the other two for most of the studied parameters. The results of this study demonstrate the applicability and the potential time and cost savings of the usage of data analysis tools for long-term data monitoring in the wetland and other environmental systems worldwide.

Automated summary

This study presents a comprehensive data analysis to establish a baseline for water quality assessment in a hypersaline wetland. The results show three distinct water quality clusters, with one cluster performing better in most parameters. The study demonstrates the applicability and potential time and cost savings of data analysis tools in long-term data monitoring in wetlands and other environmental systems.