Determinants of key drivers for potable water treatment cost in uMngeni Basin

The study entailed the determination of key water quality parameters significantly influencing treatment cost in uMngeni Basin. Chemical dosage was used as a substitute for treatment cost as the study indicated that cost, in its monetary value, is influenced by market forces, demand and supply, which are both not directly linked to water quality. Chemical dosage is however, determined by the quality of water and thus provides a clear illustration of the effect of pollution on treatment cost. Three specific objectives were set in an effort to determine key water quality parameters influencing treatment costs in uMngeni Basin. The fourth objective was to develop a model for predicting chemical dosages. The first approach was analysis of temporal and spatial variability of water quality in relation to chemical dosage during production of potable water. The trends were explained in relation to river health status. For this purpose, time-series, box-plot, and the Seasonal-Kendal test were employed. The results showed that the quality of water significantly deteriorated from upstream to downstream in relation to algae, turbidity and Escherichia coli (E. coli). High mean range of E. coli (126-1319 colony count/100mL) and turbidity (2.7-38.7 NTU) observed indicate that the quality of water along the basin is not fit for human consumption as these parameters exceeded the target range stipulated in South Africa’s guidelines for domestic use. For water intended for drinking purpose, turbidity should be below 5 NTU, while zero E. coli count is expect in 100 mL. Among the six sampling stations considered along the uMngeni Basin, three dam outflows (Midmar, Nagle and Inanda) showed an improved quality compared with their respective inflow stations. This was expected and could be attributed to the retention and dilution effects. These natural processes help by providing a self-purification process, which ultimately reduces the treatment cost. While considering the importance of disseminating water quality information to the general public and non-technical stakeholders, the second objective of the study was to develop two water quality indices. These were; (1) Treatability Water Quality Index and (2) River Health Water Quality Index. The Treatability Water Quality Index was developed based on the Canadian Council Minister of Environment Water Quality Index (CCME-WQI). The technique is used to determine fitness of water against a set of assigned water quality resource objectives (guidelines). The calculated Harmonised Water Quality Resource Objectives (HWQRO) were used to compare the qualities of the raw water being abstracted at Nagle and Inanda Dam for the purpose of treatment. The results showed that Nagle Dam, which supplies Durban Heights, is significantly affected by E. coli (42% non-compliance), turbidity (20% non-compliance) and nitrate (18% non-compliance) levels. Wiggins Water Treatment Plant which abstracts from Inanda Dam has a problem of high algae (mean 4499 cell/mL), conductivity (mean 26.21 mS/m) and alkalinity (mean 62.66 mg/L) levels. The River Health Water Quality Index (RHWQI) was developed using the Weighted Geometric Mean (WQM) method. Eight parameters, namely, E. coli, dissolved oxygen, nitrate, ammonia, turbidity, alkalinity, electrical conductivity and pH were selected for indexing. Rating curves were drawn based on the target ranges as stipulated in South Africa’s guidelines for freshwater ecosystems. Five classes were used to describe the overall river health status. The results showed that the water is still acceptable for survival of freshwater animals. A comparison of the RHWQI scores (out of 100) depicted that dam inflow station (MDI(61.6), NDI(74.6) and IDI(63.8)) showed a relatively deteriorated quality as compared with their outflows (MDO(77.8), NDO(74.4) and IDO(80)). The third objective was to employ statistical analysis to determine key water quality parameters influencing chemical dosage at Durban Heights and Wiggins Water Treatment Plants. For each of the two treatment plants, treated water quality data-sets were analysed together with their respective raw water data-set. The rationale was to determine parameters showing concentration change due to treatment. The t-test was used to determine the significance of concentration change on each of the 23 parameters considered. Thereafter, the correlations between water quality parameters and the three chemicals used during treatment (polymer, chlorine and lime) were analysed. The results showed that the concentrations of physical parameters namely, algae, turbidity and total organic carbon at both treatment showed a significant statistical (p<0.05) reduction in concentration (R/Ro<0.95). This results implies that such parameters were key drivers for chemical dosage. From the results of the first three objectives, it is recommended that implementing measures to control physical parameter pollution sources, specifically sewage discharges and rainfall run-off from agricultural lands along the uMngeni Basin should assist in reducing the chemical dosage and ultimately cost. The fourth objective was to develop chemical dosage models for prediction purposes. This was achieved by employing a polynomial non-linear regression function on the XLStat 2014 program. The resultant models showed prediction power (R2) ranging from 0.18 (18%) up to 0.75 (75%). However, the study recommends a comparative study of the developed models with other modelling techniques.

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