Abstract
A harmonised in-stream water quality guideline was constructed to develop a water quality index for the Upper and Middle Vaal Water Management Areas, in the Vaal basin of South Africa. The study area consisted of 12 water quality monitoring points; V1, S1, B1, S4, K9, T1, R2, L1, V7, V9, V12, and V17. These points are part of a Water Board’s extensive catchment monitoring network but were re-labelled for this paper. The harmonised guideline was made up of 5 classes for NH4+, Cl-, EC, DO, pH, F-, NO3-, PO43- and SO42- against in-stream water quality objectives for ideal catchment background limits. Ideal catchment background values for Vaal Dam sub-catchment represented Class 1 (best quality water), while those for Vaal Barrage, Blesbok/Suikerbosrand Rivers and Klip River represented Classes 2, 3 and 4, respectively. Values above those of Klip River ideal catchment background represented Class 5. For each monitoring point, secondary raw data for the 9 parameters were cubic-interpolated to 2 526 days from 1 January 2003 to 30 November 2009 (7 years). The IF-THEN-ELSE function then sub-classified the data from 1 to 5 while the daily index was calculated as a median of that day’s sub-classes. Histograms were constructed in order to distribute the indices among the 5 classes of the harmonised guideline. Points V1 and S1 were ranked as best quality water (Class 1), with percentage class frequencies of 91% and 60%, respectively. L1 ranked Class 3 (34%) while V7 (54%), V9 (53%), V12 (66%) and V17 (46%) ranked poorly as Class 4. B1 (76%), S4 (53%), K9 (41%), T1 (53%) and R2 (61%) ranked as worst quality (Class 5). The harmonised in-stream water quality guideline resulted in class frequency distributions. The surface raw water quality index system managed to compare quality variation among the 12 points which were located in different sub-catchments of the study area. These results provided a basis to trade pollution among upstream-downstream users, over a timeframe of 7 years. Models could consequently be developed to reflect, for example, quality-sensitive differential tariffs, among other index uses. The indices could also be incorporated into potable water treatment cost models in order for the costs to reflect raw water quality variability.Keywords: class frequency distribution; cubic interpolation; harmonised in-stream water quality guideline; ideal catchment background; Vaal basin; water quality index
Highlights
Understanding complex systems involves constructing models, comparing their predictions with observations and improving them by using feedback mechanisms from continuous assessments (Even et al, 2007)
While salinity (DWAF, 1996) and conductivity (Dzwairo et al, 2011b) provide an overall indication of the pollution status of sites along the Vaal River and its tributaries, the water quality indicators and rule-sets described in this paper offer more detail and allow for the identification of problem parameters
The general pollution trend evident in DWAF (2007) indicates good quality water for Vaal Dam, which is in agreement with the results reported in this paper
Summary
Understanding complex systems involves constructing models, comparing their predictions with observations and improving them by using feedback mechanisms from continuous assessments (Even et al, 2007). A WQI that is based on parameters which represent the broader pollution sources is a vital tool for assessing a water body’s spatial and temporal quality trends within a boundary system which spans different sub-catchments. The focus of this study was to model and compare pollution trends in the Upper and Middle Vaal Water Management Areas (WMAs) of South Africa (Fig. 1). This sinking of pollution exerts tremendous pressure on the water resource and its various treatment processes aiming to meet receiving water quality objectives. Class frequency distribution and basin-specific WQI are such tools which will be determined
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