Qualitative and quantitative analysis of the pollutant load of effluents discharged Northwestern of Lake Tanganyika, in the Democratic Republic of Congo

Kapepula, Lumami; Ndikumana, T.; Dieu-DonnÃ©, Musibono; Alconero, Patricia Luis; Tamungang, Njoyim Estella Buleng; Tarimo, Irene Aurelia; Bruggen, Bart Van der

doi:10.5897/ajest2020.2903

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Growing numbers of people living in the bordering cities of Lake Tanganyika, intensifying agriculture, and expanding industries will all lead to increased pollution and declining water quality [45]. The increased water quality index (WQI) in Lake Tanganyika is attributed to factors such as rising temperatures due to climate change, leading to thermal stratification, municipal wastewater, stormwater, improper waste disposal, oil spills and chemical spills, construction waste, atmospheric deposition, and decreased oxygen levels [46]. Additionally, increased anthropogenic activities, including industrial pollution, contribute to nutrient loading and eutrophication, further degrading water quality.…”

Section: Wqi =mentioning

confidence: 99%

“…Figure 9d demonstrates the high variation of WQI in the summer, especially in June, July, August, and September. Lake Tanganyika experiences higher pollution levels in the summer months due to increased human activities such as tourism, fishing, industries, agriculture, and construction waste, leading to greater runoff of pollutants into the lake [46]. The WQI is high from 2023 to 2024 due to the rising temperatures caused by climate change, leading to thermal stratification, municipal wastewater, stormwater, improper waste disposal, oil spills, chemical spills, atmospheric deposition, and decreased oxygen levels [44].…”

Section: Analysis Of the Water Quality Index Trained And Testedmentioning

confidence: 99%

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Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Niyongabo,

Zhang,

Guan

et al. 2024

Water

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Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

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Section: Wqi =mentioning

confidence: 99%

Section: Analysis Of the Water Quality Index Trained And Testedmentioning

confidence: 99%

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Niyongabo,

Zhang,

Guan

et al. 2024

Water

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“…This pressure makes water become increasingly scarce and expensive. In developing countries, poverty and ignorance of health risks make various wastewaters the first choice for watering in vegetable gardens [32]. These practices expose market gardening practitioners to health risks and also lead to the introduction of the various contaminants from these waters into the food chain [33].…”

Section: Exposure Of the Population To Hospital Effluentsmentioning

confidence: 99%

Ecotoxicity of hospital wastewaters and their impact on bacterial multi-drug resistance: a review

Ouedraogo¹,

Souleymane²,

Ouedraogo³

et al. 2021

jlsb

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“…The main aim of this work was to determine the toxicity caused by waste discharged into Lake Tanganyika, due to lack of proper sanitation, and the negative impacts of this situation of the inhabitants of Uvira. In a previous study [28], the threat of physico-chemical pollution on the lake coast was studied. The present work is geared towards the development of a master plan for the management of liquid and solid effluents in the countries bordering Lake Tanganyika as well as everywhere else, and to develop strategies for the elimination of wastewater pollutants.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Toxicological Risk of Urban Waste from the City of Uvira Dumped into the North-Western Coast in Lake Tanganyika (Democratic Republic of Congo)

Vercus¹,

Ndikumana²,

Tamungang³

et al. 2021

JEP

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This study focuses on determining the toxicological risks of urban waste from the city of Uvira, discharged into Lake Tanganyika, on the surrounding population. Volatile organic compounds were measured in a variety of solid waste matrices, including inorganic micropollutants in wastewater and fish. The concentrations of Hg and Pb in the lake were found to be 1.21 and 1.42 µg/L respectively and between 0.83 to 18.36 µg/L of Hg and 8.25 to 670 µg/L of Pb, at the collector outlet. The presence of trace metallic elements, such as Cr, Co, Ni, Cu, Zn, As, Sb, Hg and Pb, were detected at high concentrations compared to the WHO standard. An ecotoxicology experiment herein on wastewater samples showed lethal pollutant concentrations of the order of 0.0055 mL/mL which killed at least 50% of fish (LC50), confirming the toxicity of the wastewater. These potentially harmful effluents also contain volatile organic compounds originating in high concentration from the pharmaceutical discharges of the general Uvira hospital, in particular: toluene, ethylbenzene, mxylene/p-xylene, o-xylene and chloroform in higher concentrations compared

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Qualitative and quantitative analysis of the pollutant load of effluents discharged Northwestern of Lake Tanganyika, in the Democratic Republic of Congo

Cited by 5 publications

References 6 publications

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Ecotoxicity of hospital wastewaters and their impact on bacterial multi-drug resistance: a review

Determination of the Toxicological Risk of Urban Waste from the City of Uvira Dumped into the North-Western Coast in Lake Tanganyika (Democratic Republic of Congo)

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