The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa
The declining state of municipal wastewater treatment is one of the major contributors to the many pollution challenges faced in most parts of South Africa. <i>Escherichia coli</i> and Chemical Oxygen Demand are used as indicators for the performance of wastewater treatment plants. Waste...
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MDPI AG
2022-09-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/18/2802 |
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| author | Thabang Maphanga Benett S. Madonsela Boredi S. Chidi Karabo Shale Lawrence Munjonji Stanley Lekata |
| author_facet | Thabang Maphanga Benett S. Madonsela Boredi S. Chidi Karabo Shale Lawrence Munjonji Stanley Lekata |
| author_sort | Thabang Maphanga |
| collection | DOAJ |
| description | The declining state of municipal wastewater treatment is one of the major contributors to the many pollution challenges faced in most parts of South Africa. <i>Escherichia coli</i> and Chemical Oxygen Demand are used as indicators for the performance of wastewater treatment plants. Wastewater treatment plant (WWTP) efficiency challenges are associated with susceptibility to seasonal variations that alter microbial density in wastewater. This study sought to investigate the effect of rainfall on <i>E. coli</i> and COD in the effluent wastewater discharged from the Crocodile River, Mpumalanga Province, South Africa. To cover the spatial distribution of the pollutant in the Crocodile River, water samples were collected from 2016 to 2021 at three strategic sites. The rainfall data was acquired from the South African Weather Services from 2016 to 2021, which contains daily rainfall measurements for each sampling site. Data analysis was carried out using Microsoft Excel 2019, Seaborn package, and Python Spyder (version 3.8). The White River, which is located on the upper stream, recorded the highest COD levels of 97.941 mg/L and 120.588 mg/L in autumn and spring, respectively. Matsulu WWTP was found to have the highest E. coli concentration per milliliter (72.47 cfu/100 mL) in the spring compared to any other location or time of year. The results also indicated that each of the sampling sites recorded above 60 (cfu)/100 mL of <i>E. coli</i> in Kanyamazane (spring), Matsulu (summer), and White River (winter). It was noted that the rainfall is a significant predictor (<i>p</i> < 0.004) of <i>E. coli</i>. Additionally, it was discovered during the data analysis that the rainfall parameter did not significantly affect COD prediction (<i>p</i> > 0.634), implying that rain was not a reliable predictor of COD. |
| first_indexed | 2024-03-09T22:12:30Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T22:12:30Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-9767ea19cbed4393992d006752c22acf2023-11-23T19:30:25ZengMDPI AGWater2073-44412022-09-011418280210.3390/w14182802The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South AfricaThabang Maphanga0Benett S. Madonsela1Boredi S. Chidi2Karabo Shale3Lawrence Munjonji4Stanley Lekata5Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South AfricaDepartment of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South AfricaDepartment of Biotechnology and Consumer Science, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South AfricaDepartment of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South AfricaRisk and Vulnerability Science Center, University of Limpopo, Private Bag X1106, Sovenga 0727, South AfricaCentre for Postgraduate Studies (CPGS), Faculty of Applied Sciences, Cape Peninsula University of Technology, Bellville South Industrial, Cape Town 7530, South AfricaThe declining state of municipal wastewater treatment is one of the major contributors to the many pollution challenges faced in most parts of South Africa. <i>Escherichia coli</i> and Chemical Oxygen Demand are used as indicators for the performance of wastewater treatment plants. Wastewater treatment plant (WWTP) efficiency challenges are associated with susceptibility to seasonal variations that alter microbial density in wastewater. This study sought to investigate the effect of rainfall on <i>E. coli</i> and COD in the effluent wastewater discharged from the Crocodile River, Mpumalanga Province, South Africa. To cover the spatial distribution of the pollutant in the Crocodile River, water samples were collected from 2016 to 2021 at three strategic sites. The rainfall data was acquired from the South African Weather Services from 2016 to 2021, which contains daily rainfall measurements for each sampling site. Data analysis was carried out using Microsoft Excel 2019, Seaborn package, and Python Spyder (version 3.8). The White River, which is located on the upper stream, recorded the highest COD levels of 97.941 mg/L and 120.588 mg/L in autumn and spring, respectively. Matsulu WWTP was found to have the highest E. coli concentration per milliliter (72.47 cfu/100 mL) in the spring compared to any other location or time of year. The results also indicated that each of the sampling sites recorded above 60 (cfu)/100 mL of <i>E. coli</i> in Kanyamazane (spring), Matsulu (summer), and White River (winter). It was noted that the rainfall is a significant predictor (<i>p</i> < 0.004) of <i>E. coli</i>. Additionally, it was discovered during the data analysis that the rainfall parameter did not significantly affect COD prediction (<i>p</i> > 0.634), implying that rain was not a reliable predictor of COD.https://www.mdpi.com/2073-4441/14/18/2802spatio-temporal variationeffluentmicrobial quality<i>Escherichia coli</i>chemical oxygen demand |
| spellingShingle | Thabang Maphanga Benett S. Madonsela Boredi S. Chidi Karabo Shale Lawrence Munjonji Stanley Lekata The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa Water spatio-temporal variation effluent microbial quality <i>Escherichia coli</i> chemical oxygen demand |
| title | The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa |
| title_full | The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa |
| title_fullStr | The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa |
| title_full_unstemmed | The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa |
| title_short | The Effect of Rainfall on <i>Escherichia coli</i> and Chemical Oxygen Demand in the Effluent Discharge from the Crocodile River Wastewater Treatment; South Africa |
| title_sort | effect of rainfall on i escherichia coli i and chemical oxygen demand in the effluent discharge from the crocodile river wastewater treatment south africa |
| topic | spatio-temporal variation effluent microbial quality <i>Escherichia coli</i> chemical oxygen demand |
| url | https://www.mdpi.com/2073-4441/14/18/2802 |
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