Abstract
Assessing the changing parameters of water quality at different points in the river–reservoir system can help prevent river pollution and implement remedial policies. It is also crucial in modeling water resources. Multivariate statistical analysis is useful for the analysis of changes in surface water quality. It helps to identify indicators that may be responsible for the eutrophication process of a reservoir. Additionally, the analysis of the water quality profile and the water quality index (WQI) is useful in assessing water pollution. These tools can support and verify the results of a multivariate statistical analysis. In this study, changes in water quality parameters of the Turawa reservoir (TR), and the Mała Panew river at the point below the Turawa reservoir (bTR) and above the Turawa reservoir (aTR), were analyzed. The analyzed period was from 2019 to 2020 (360 samples were analyzed). It was found that TN, NO2-N, and NO3-N decreased after passing through the Turawa reservoir. Nevertheless, principal component analysis (PCA) and redundancy analysis (RDA) showed that NO2-N and NO3-N contribute to the observed variability of the water quality in the river-reservoir system. PCA showed that pH and PO4-P had a lower impact on the water quality in the reservoir than nitrogen compounds. Additionally, RDA proved that the values of the NO3-N and NO2-N indicators obtained the highest values at the aTR point, PO4-P at the bTR, and pH at the TR. This allows the conclusion that the Turawa reservoir reduced the concentration of NO2-N and NO3-N in comparison with the concentration of these compounds flowing into the reservoir. PCA and RDA showed that both parameters (NO2-N and NO3-N) may be responsible for the eutrophication process of the Turawa reservoir. The analysis of short-term changes in water quality data may reveal additional sources of water pollution. High temperatures and alkaline reaction may cause the release of nitrogen and phosphorus compounds from sediments, which indicates an increased concentration of TP, PO4-P, and Norg in the waters at the TR point, and TP, PO4-P, and NH4-N concentrations at the bTR point. The water quality profile combined with PCA and RDA allows more effective monitoring for the needs of water management in the reservoir catchment area. The analyzed WQI for water below the reservoir (bTR) was lower than that of the reservoir water (TR), which indicates an improvement in water after passing through the reservoir.
Highlights
The water quality profile showed that total nitrogen (TN), NO2 -N, and NO3 -N decreased after passing through the reservoir, whereas the values of phosphorus compounds (TP and PO4 -P)
The use of principal component analysis (PCA) and redundancy analysis (RDA) showed that only NO2 -N and NO3 N contributed to the observed variability in water quality in the river–reservoir system
These analyzes showed that pH and PO4 -P did not have the same influence on the water quality in the reservoir as the nitrogen compounds
Summary
Many scientists have researched water quality changes in river–reservoir systems using multivariate statistical methods [2,3,32,33] In this case, the research at points below the reservoir, in the reservoir, and above the reservoir are relevant. According to Ling et al [5], multivariate statistical analysis can determine important factors responsible for most of the variance in the water quality of a river. It can be usefully combined with other kinds of analyses. PCA, RDA, and analysis of water quality profile was undertaken The combination of these analyses will allow us to determine the factors responsible for the eutrophication process of the Turawa reservoir. The WQI will assess how the water quality changes in the analyzed river–reservoir system
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