Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Water quality modelling and management of nitrogenous compounds in a data scarce environment
Mahlathi, C. D. 2025. Water Quality Modelling and Management of Nitrogenous Compounds in a Data Scarce Environment. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/635024cd-320f-4760-add2-588c94fd6932
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Stellenbosch : Stellenbosch University
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613902419787776 |
|---|---|
| access_status_str | Open Access |
| author | Mahlathi, Christopher Dumisani |
| author2 | Du Plessis, J. A. |
| author_browse | Du Plessis, J. A. Mahlathi, Christopher Dumisani |
| author_facet | Du Plessis, J. A. Mahlathi, Christopher Dumisani |
| author_sort | Mahlathi, Christopher Dumisani |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Mahlathi, C. D. 2025. Water Quality Modelling and Management of Nitrogenous Compounds in a Data Scarce Environment. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/635024cd-320f-4760-add2-588c94fd6932 |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132237 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:43:31.605Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132237 Water quality modelling and management of nitrogenous compounds in a data scarce environment Mahlathi, Christopher Dumisani Du Plessis, J. A. Wilms, J. M. Brink, I. C. Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering. Water quality management -- South Africa Nitrogen compounds -- Environmental aspects -- South Africa Hydrology -- Mathematical models -- South Africa Watersheds -- Mathematical models UCTD Mahlathi, C. D. 2025. Water Quality Modelling and Management of Nitrogenous Compounds in a Data Scarce Environment. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/635024cd-320f-4760-add2-588c94fd6932 Thesis (PhD)--Stellenbosch University, 2025. ENGLISH ABSTRACT: This dissertation addresses the critical challenge of effectively modelling and managing nitrogenous compounds in South African rivers, particularly when combined with wastewater treatment plant (WWTP) effluents under conditions of data scarcity. Nitrogenous compounds such as nitrates, nitrites, ammonia, nitrogen oxides, and organic nitrogen compounds are vital to aquatic ecosystems, but their excessive presence can lead to nutrient overload and eutrophication, severely impacting water quality. Despite advancements in water quality modelling tools like QUALKw and WASP, their effective application in South Africa is hindered by the limited availability of reliable input data, compounded by declining water quality monitoring efforts due to inadequate funding, equipment failures, and deteriorating infrastructure. The primary outcome of this research is the development of systematized guiding principles for water quality modelling under data-scarce conditions. These principles encompass critical aspects such as data sourcing, the impact of data augmentation techniques, and the effects of variable hydrodynamic input data sources on model performance. These principles are then practically applied to address the real-world challenge of controlling nitrogenous compounds in river systems using Model Predictive Control (MPC) strategies. The research begins by evaluating the current state of hydrodynamic and water quality data in South African river systems, identifying general data quality issues, and offering initial considerations for modelers working under data-scarce conditions. Various data augmentation techniques, including interpolation and machine learning, are explored to address data gaps and enhance model inputs. The findings reveal that while there were no statistically significant differences between the outcomes of each interpolation method applied to complete datasets, introducing artificial gaps resulted in significant differences, especially when 25% of gaps were introduced to the original dataset. The models simulated with augmented data with artificial gaps performed better when gaps were not located at data extrema. Machine learning approaches proved reasonably accurate, though upsampling was necessary to meet minimum data size requirements. In examining the impact of variable hydrodynamic input data sources, the research compares the performance of two water quality models, the Basic Model (BM) and WASP, across multiple segments of the Natal Spruit River. The results show that both models performed well closer to the model boundary but experienced a significant drop in performance downstream. This variation in performance underscores the importance of segment-specific assessments, as model accuracy is closely linked to the nature and accuracy of hydrodynamic data inputs. The study then applies the developed modelling principles to the practical problem of controlling ammonia concentrations in the Klip river using MPC. Despite some limitations in predicting extreme values and temporal variations, the Basic Model, calibrated using gradient descent optimization, exhibited reasonable accuracy in capturing overall trends. The study demonstrates the feasibility and effectiveness of MPC strategies in optimizing WWTP operations and safeguarding environmental integrity by reducing Mean Squared Error (MSE), minimizing deviations from setpoint concentrations, and eliminating constraint violations. Overall, this research highlights the dynamic nature of water quality modelling, emphasizing the intricate relationship between hydrodynamics, data augmentation techniques, and model performance. The systematized guiding principles developed in this study contribute to advancing water quality modelling practices by providing a framework for data sourcing and the integration of diverse data sources to ensure robust model construction. Additionally, the practical application of these principles in engineering design, particularly in wastewater treatment and nitrogenous compound control, illustrates their potential to enhance water resource management strategies in South Africa, even in data-scarce environments. By addressing significant gaps and limitations in available hydrodynamic and water quality data, and offering innovative approaches for managing scarce data environments, this dissertation makes a valuable contribution to the sustainable management and preservation of South Africa's highly limited and vulnerable water resources. Future research should focus on expanding monitoring networks, exploring advanced modelling techniques, and assessing the long-term sustainability of water resource management practices amidst climate change and human activities. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Doctoral 2025-05-30T11:32:20Z 2025-05-30T11:32:20Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132237 en Stellenbosch University xviii, 206 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Water quality management -- South Africa Nitrogen compounds -- Environmental aspects -- South Africa Hydrology -- Mathematical models -- South Africa Watersheds -- Mathematical models UCTD Mahlathi, Christopher Dumisani Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title | Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title_full | Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title_fullStr | Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title_full_unstemmed | Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title_short | Water quality modelling and management of nitrogenous compounds in a data scarce environment |
| title_sort | water quality modelling and management of nitrogenous compounds in a data scarce environment |
| topic | Water quality management -- South Africa Nitrogen compounds -- Environmental aspects -- South Africa Hydrology -- Mathematical models -- South Africa Watersheds -- Mathematical models UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/132237 |
| work_keys_str_mv | AT mahlathichristopherdumisani waterqualitymodellingandmanagementofnitrogenouscompoundsinadatascarceenvironment |