Impact on Water Quality of Nandoni Water Reservoir Downstream of Municipal Sewage Plants in Vhembe District, South Africa

Abstract

Abstract:The deterioration of water quality in our freshwater sources is on the increase worldwide and, in South Africa, mostly due to the discharge of municipal sewage effluent. Here we report on the use of principal component analysis, coupled with factor and cluster analysis, to study the similarities and differences between upstream and downstream sampling sites that are downstream of municipal sewage plants. The contribution of climatic variables, air temperature, humidity, and rainfall were also evaluated with respect to variations in water quality at the sampling sites. The physicochemical and microbial values were higher than the Department of Water Affairs and Forestry (DWAF) and World Health Organization (WHO) guidelines. The cluster analysis showed the presence of two clusters for each of the Mvudi, Dzindi, and Luvuvhu Rivers and Nandoni reservoir sampling sites. The principal component analysis (PCA) accounted for 40% of the water quality variation and was associated strongly with pH, electrical conductivity, calcium, magnesium, chloride, bromide, nitrate, and total coliform, and negatively with rainfall, which represented Mvudi downstream and was attributed to the Thohoyandou sewage plant. The PCA accounted for 54% of the variation and was associated strongly with electrical conductivity, sulfate; total dissolved solids, fluoride, turbidity, nitrate, manganese, alkalinity, magnesium, and total coliform represented Dzindi downstream, with inflows from the Vuwani sewage plant and agriculture. The PCA accounted for 30% of the variation and was associated strongly with total dissolved solids, electrical conductivity, magnesium, fluoride, nitrate, sulfate, total coliform average air temperature, and total rainfall, and negatively associated with manganese and bromide represented Luvuvhu upstream and was associated with commercial agriculture. The PCA accounted for 21% of the variation and was associated strongly with turbidity, alkalinity, magnesium, chloride, fluoride, nitrate, and strongly negatively associated with rainfall, which represented Luvuvhu downstream, associated with inflows from Vuwani oxidation ponds, Elim and Waterval sewage plants, and agriculture. The PCA accounted for 14% of the variation and was moderately associated with rainfall and weakly associated with chloride and bromide and negatively associated with nitrate, which represented the natural Nandoni reservoir system. The continued discharge of effluent may render the raw water supply unsuitable for human consumption and lead to eutrophication due to nitrate enrichment and proliferation of harmful algal blooms and schistomiasis infections in the long term.

Water quality, pollutant loads, and multivariate analysis of the effects of sewage discharges into urban streams of Southeast Brazil

“…Application of the KMO test resulted in a coefficient of 0.71, demonstrating that the sample size of the water quality dataset was adequate for PCA, since there was sufficient redundancy in the data to perform the factor analysis. Similar values, using similar sample sizes, were obtained in other water quality studies in watersheds (Gumbo et al 2016;Mao et al 2013). Table 3 provides the correlation matrix, while Table 4 shows the significances of the correlations.…”

“…It is not appropriate to apply factor analysis when the correlations among the variables are near zero or very weak, because it is not possible to summarize the information. The KMO value ranges from 0 to 1, with values above 0.6 indicating that the variables and samples evaluated are suitable for factor analysis (Gumbo et al 2016;Gholizadeh et al 2016;Mao et al 2013).…”

“…Principal component analysis (PCA) was used to understand the contribution of each water quality parameter to the observed variability (Gumbo et al 2016;Walker et al 2015;Jabeen et al 2014;Legendre and Legendre 2012;Dalal et al 2010;Ouyang et al 2006).…”

“…The results for the surface waters were then compared to the limits provided in the World Health Organization guidelines for drinking water (WHO 2011), following the approach adopted elsewhere (Jabeen et al 2014;Gumbo et al 2016). The results were also compared with those obtained in previous studies of streams in the Piracicaba river basin (Machado et al 2018;Medeiros et al 2013;Silva et al 2012;Medeiros et al 2009;Filoso et al 2003;Daniel et al 2002;Krusche et al 2002;Martinelli et al 1999;Ometo et al 2000).…”

“…These results indicated the importance of ecosystem services provided by grasslands and urban green spaces in contributing to better water quality (Martinico et al 2014), because the Recanto microbasin has a lower degree of urbanization (21%), compared to the Santa Angélica (47%) and Pylles (81%) areas, leading to a lower specific TDS load. Other studies have also described the influence of urbanization and industrialization on the degradation of water resources in catchments of developed and developing countries including Brazil (Medeiros et al 2013;Daniel et al 2002;Silva et al 2013), Colombia (Sandoval et al 2014), the USA (O 'Neill et al 2013;Gholizadeh et al 2016), South Africa (Gumbo et al 2016), Portugal (Gomes et al 2014), India (Jabeen et al 2014;Jamwal et al 2008), Australia (Hatt et al 2004), Turkey (Bilgin 2015), Sweden (Galfi et al 2016;Berndtsson and Bengtsson 2006) and Finland (Metsäranta et al 2005). …”