Show simple item record

dc.contributor.advisorFerrer, Stuart Richard Douglas.
dc.contributor.advisorGraham, Mark.
dc.creatorGebremedhin, Samuel Kahsai.
dc.date.accessioned2010-09-20T09:30:00Z
dc.date.available2010-09-20T09:30:00Z
dc.date.created2009
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10413/1223
dc.descriptionThesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.
dc.description.abstractThis study is a component of a research project on the economic costs of eutrophication in the Vaal River system. Its objective is to investigate the relationship between raw water quality and the chemical costs of producing potable water at two water treatment plants: Zuikerbosch Station #2 (owned by Rand Water) in the Upper Vaal Water Management Area (UVWMA), and Balkfontein (owned by Sedibeng Water) in the Middle Vaal Water Management Area (MVWMA). Time series data on raw water quality and chemical dosages used to treat raw water were obtained for Zuikerbosch Station #2 (hereafter referred to as Zuikerbosch) for the period November 2004 – October 2006 and for Balkfontein for the period January 2004 to December 2006. Descriptive statistics reveal that raw water in the Vaal River is of a poorer quality at Balkfontein compared to that at Zuikerbosch. Furthermore, the actual real chemical water treatment costs (measured in 2006 ZAR) averaged R89.90 per megalitre at Zuikerbosch and R126.31 at Balkfontein, indicating that the chemical water treatment costs of producing potable water tend to increase as raw water quality declines. Collinearity among water quality (WQ) variables at both water treatment plants was analysed using Principal Component Analysis (PCA). The dimensions of water quality identified in the analysis are similar to those reported in Pieterse and van Vuuren’s (1997) study of the Vaal River. For both water treatment plants, Ordinary Least Squares (OLS) regression was used to identify the relationship between real chemical costs of water treatment and the dimensions of water quality identified through the respective Principal Components Analyses. The estimated regression models account for over 50.2% and 34.7% of variation in real chemical water treatment costs at Zuikerbosch and Balkfontein, respectively. The coefficient estimated for PC1 at Zuikerbosch is statistically significant at the 1% level of probability with high negative loadings of total alkalinity and turbidity. Increases in the levels of total alkalinity and turbidity in raw water treated at Zuikerbosch is negatively related to the chemical costs of water treatment. An increased total alkalinity level was found to reduce the chemical costs of treating potable water. PC2 is statistically the most important variable in the estimated explanatory model for Balkfontein. The estimated regression coefficient for PC2 is statistically significant at the 5% level of probability. The estimated relationship between chemical water treatment costs and PC2 shows that there is a positive relationship between the raw water temperature and chemical water treatment costs. However, increases in the levels of chlorophyll and pH in raw water treated at Balkfontein is negatively related to the chemical costs of water treatment. Total hardness, magnesium, calcium, sulphate, conductivity, and chloride, being the highest positive loadings in PC1, relate negatively to the chemical cost of treating water. For predictive rather than explanatory purposes, a partial adjustment regression model was estimated for each of the two water treatment plants. Using this model, real chemical water treatment costs were specified as a function of real chemical water treatment costs in the previous time period, and of raw water quality variables in the current period. The R2 statistics for the two regression models were 61.4% using the data for Zuikerbosch and 59.9% using the data for Balkfontein, suggesting that both models have reasonable levels of predictive power. The chemical cost of water treatment for Zuikerbosch and Balkfontein are predicted at R96.25 and R90.74 per megalitre per day respectively. If raw water nitrate in the UVWMA increases by 1% per megalitre a day while other factors remain constant, chemical water treatment costs at Zuikerbosch can be expected to increase by 0.297% per megalitre and the cost accompanied this change is (R0.285*1998ML*365days) R207,841.95 provided that Zuikerbosch treats an average of 1998 megalitres per day. Likewise, if Zuikerbosch maintains its daily average operating capacity and is able to maintain an optimal level of total alkalinity in UVWMA, the estimated saving on chemical water treatment cost will be R150.063.78 per annum. At Balkfontein, chemical water treatment cost is expected to increase on average by 0.346% per megalitre per day for a 1% per megalitre per day increase in the level of chlorophyll-a, and the cost accompanied this change is R41,128.20 per annum. The prediction also shows a 2.077% per megalitre per day increase chemical water treatment cost for a 1% increase in turbidity and this accompanied with a chemical water treatment cost of R 249,003 per annum, provided that Balkfontein operates at its full capacity (i.e., 360 megalitres per day).
dc.language.isoenen_US
dc.subjectEutrophication--Control--South Africa--Vaal River.en_US
dc.subjectDrinking water--Purification--South Africa.en_US
dc.subjectWater--Purification--South Africa.en_US
dc.subjectWater--Purification--Costs.en_US
dc.subjectWater quality--Economic aspects--South Africa--Vaal River.en_US
dc.subjectWater-supply--Economic aspects--South Africa.en_US
dc.subjectWater--Pollution--Economic aspects--South Africa--Vaal River.en_US
dc.subjectWater treatment plants--South Africa.en_US
dc.subjectTheses--Agricultural economics.en_US
dc.titleAnalysis and prediction of chemical treatment cost of potable water in the Upper and Middle Vaal water management areas.en_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record