Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Abstract

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

Inter-Annual and Seasonal Variations of Water Quality and Trophic Status of a Reservoir with Fluctuating Monsoon Precipitation

“…As reported previously [7,68], TP appeared to be a better indicator of Chl-a than TN, the R 2 values of which were less than 0.1, except for the monsoon period of the drought years (Figure 6). Chl-a and TP showed a noticeable correlation in the post-monsoon periods of the normal and the flood groups, with R 2 values of 0.30 and 0.37, respectively.…”

Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea

“…Previous studies have reported that high COD levels are caused by high levels of non-biodegradable organic wastewater, increased inputs of several non-point source pollutants such as humic substances during rainfall, and internal production by algae and aquatic plants [29,42,76]. As with the Yeongsan River, the concentrations of COD are increasing in the Nakong, Han, and Geum Rivers, while BOD is declining [12,42,77]. The CHL-a concentration in the river has been growing, and more frequent algal blooms have occurred in recent years.…”

“…The highest mean values of COD and BOD were found at sites S4 and S5, which receive wastewater discharge from two STPs. TP and TN are indicators of nutrient pollution [12]. As with BOD and COD, the highest mean TP and TN values were observed at sites S4 and S5, which receive wastewater discharge from the STPs serving Gwangju City.…”

“…As rivers provide the leading inland water resources for domestic, industrial, and irrigation purposes, it is vital to have reliable information about river water quality. Despite the dramatic development of water quality monitoring programs over the past few decades, representative and reliable water quality assessment remains difficult; therefore, gathering reliable information on river water quality, assessing spatial and temporal changes, performing source apportionment of pollution, and determining and controlling water pollution in rivers are indispensable tasks [9,[12][13][14].…”

Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process

“…It will be shown how to reduce the imposing multi-index dataset to a single clustered nominal response. This is a new concept and it furnishes an alternative to extracting water quality classification information from small data [49][50][51][52][53][54][55][56]. This would quickly refashion the problem to the naiver non-linear saturated but 'unreplicated' form.…”