Water Quality Assessment Using Optimal Multiobjective Waste-load Allocation Approach The Case Of Little Akaki River

In Addis Ababa, indiscriminant waste disposal from domestic, industrial and commercialrnsources affect the water quality of the Little Akaki River. Moreover, this situation is limiting thernusability of the River. Consequently, in many studies, the River is regarded as one of the mostrnpolluted Rivers in Ethiopia. On the other hand, there are inadequate comprehensive studies onrnthe river principally due to insufficient research fund. In addition, existing studies on the Riverrnmainly focus on concentration measurement of certain constituents and their comparison againstrnlocal and international standards. This approach has limited the public and policy makers fromrnknowing the exact pollution status of the River. Furthermore, a number of studies conducted onrnthe pollution problem and ongoing efforts to enforce existing environmental regulations have notrneffectively restored the river water quality. As a result, its pollution problem has increasinglyrnworsened.rnTherefore, in this study, the application of statistical multivariate analysis for regular andrneconomical water quality assessment, water quality index analysis for summarizing the waterrnquality situation and optimal waste-load allocation modeling as a tool for decision-making arernsought.rnFor statistical multivariate analysis, twenty-seven locations from the River andrntributaries were sampled and analyzed in October/November 2015. Afterwards, multivariaternstatistical tools were used to investigate data from measurements and laboratory analysis.rnAccordingly, the cluster analysis divided the sampled sites into three according to level of theirrnpollution. This result indicates that water quality variation was caused because of the differencernin land-use conditions. In addition, for the spatial analysis of the three pollution groups,rnbackward stepwise approach of discriminant analysis was identified to provide data reductionrn(87.5%) to two parameters resulting in 85.2% correct assignment. The principal component analysis/factor analysis identified ten parameters accounting for 81.9% of total variation.rnHowever, data reduction was not significant. The factors that were latent and identified from thernprincipal components’ varimax rotation suggest that variation in water quality was caused mainlyrnby domestic sewage. The outcomes show that the methods can be applied to evaluate the riverrnwater quality variation using three monitoring sites and ten parameters: total nitrogen, totalrnsuspended solids, total ammonia, chemical oxygen demand, nitrite, total phosphorus, phosphate,rnnitrate, biological oxygen demand and electrical conductivity. This, in consequence, requiresrnlesser cost and effort and hence paves way for more affordable, regular water quality evaluationrnof Little Akaki River.rnFor index analysis, twelve water quality parameters from twenty-seven sampling sites inrnthe Dry season (January/February, 2017) and Wet season (October/November, 2015) were usedrnfor index determination. Results show that, all sampling sites except one site in the upstreamrnwere under poor water quality category. Afterwards, the neural network model was trained andrnvalidated, for twelve inputs and one output, using several combinations of hidden layers (2-20),rnnumber of neurons in the hidden layers (5, 10, 15, 20, 25), transfer, training and learningrnfunctions. The most optimal model architecture was obtained with eight hidden layers, fifteenrnhidden neurons that resulted in R2 value of 0.93. This shows a good agreement between therncalculated and predicted index values suggesting that artificial neural network can bernsuccessfully applied for modeling Little Akaki River’s water quality index.rnOne of the ongoing efforts by the Addis Ababa Water and Sewerage Authority to controlrnand minimize impact from pollution source is channeling a portion of domestic discharges torncentral treatment plants. However, the nature of these discharges is variable in time and space.rnAccordingly, impact on the river varies. Under limited treatment plant capacity, the river’s healthrncan be maximized either by putting in place strict environmental control or by preferentiallyrnchanneling the streams having significant impact on the river. Identification of these streams canrnbe done through routine field sampling and laboratory analysis and decision making afterwards.rnHowever, this requires high financial, time and human resource. In this regard, water qualityrnsimulation can help to understand the interaction between pollution sources and the river. Forrnthis, monitoring data can be used to predict pollution contribution of the various sources on thernriver and agencies can apply this approach for environmental decision-making. In this study,QUAL2Kw was used to predict the river water quality. The model was calibrated and validatedrnusing data collected during dry (January/February, 2017) and wet seasons (October/November,rn2015), respectively. The results from the calibrated model indicate that the model was able tornreasonably predict the pollution of the river with R2 values of 0.91, 0.90, 0.81 and 0.89,rnrespectively for dissolved oxygen, biological oxygen demand, total nitrogen and totalrnphosphorus. Moreover, sensitivity analysis showed that dissolved oxygen, biological oxygenrndemand, total nitrogen and total nitrogen predictions are highly sensitive to point source flowrnand Manning’s n. Therefore, this model may be applied as an option for water qualityrnmanagement of the Little Akaki River.rnFor optimal waste-load allocation, a simulation-optimization model was developedrnthrough integration of a water quality model - QUAL2Kw and genetic algorithm - PIKAIA.rnAfterwards, cost-performance and cost-performance-equity models were applied on waterrnquality data set. The model resulted in pareto-optimal curves for conflicting objectives such asrntreatment cost and equity versus water quality performance. These curves offer convenientrnmeans for informed decision-making during environmental planning and implementation.rnEspecially, the strategy is helpful in finding compromised solutions for pollution problems withrnconflicting objectives. In general, the study results indicate that significant waste load reductionrnis required for an improved water quality condition of the Little Akaki River.

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