Repository logo
 

Faculty of Engineering and Built Environment

Permanent URI for this communityhttp://ir-dev.dut.ac.za/handle/10321/9

Browse

Filters

2015 - 201912020 - 20232

Settings

Author: search.filters.author.Dzwairo, BloodlessHas files: Yes

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Quantitative assessment of human health risks from chemical pollution in the uMsunduzi River, South Africa
    (Springer, 2023-10-24) Ngubane, Zesizwe; Dzwairo, Bloodless; Sokolova, Ekaterina; Moodley, Brenda; Stenstrom, Thor Axel
    A quantitative chemical risk assessment was performed using published data as well as data from the official monitoring programme for the uMsunduzi River in KwaZulu-Natal, South Africa. The chemicals assessed were organochlorinated pes- ticides (OCPs), pharmaceuticals and personal care products (PPCPs), heavy metals, and nitrates and phosphates. The water from uMsunduzi River is used locally without treatment. Consequently, the exposure routes investigated were via ingestion during domestic drinking and incidental ingestion during recreational activities, which were swimming and non-competitive canoeing, for both adults and children. For the individual chemicals, non-carcinogenic risks using the hazard quotient (HQ) and carcinogenic risks using the cancer risk (CR) were quantified. It was found that the exposed population is likely to experience non-carcinogenic effects from pesticides and phosphates, but not from PPCPs, heavy metals and nitrates. This study also found that the carcinogenic risks for OCPs were higher than the tolerable limit of 10-5, while for lead the risk was below the tolerable limit. Some of the activities that potentially contribute to chemicals onto the uMsunduzi River are sub- sistence farming, small plantations, illegal dumping, industries, and broken sewers. The findings of this study may act as the technical foundation for the introduction of pollution reduction measures within the catchment, including public education
  • Thumbnail Image
    Item
    Water quality modelling and quantitative microbial risk assessment for uMsunduzi River in South Africa
    (IWA Publishing, 2022-04) Ngubane, Zesizwe; Bergion, Viktor; Dzwairo, Bloodless; Troell, Karin; Amoah, Isaac Dennis; Stenstrom, Thor Axel; Sokolova, Ekaterina
    South African rivers generally receive waste from inadequate wastewater infrastructure, mines, and farming activities, among others. The uMsunduzi River in KwaZulu-Natal, South Africa, is among these recipients with recorded poor to very poor water quality. To identify parts of the uMsunduzi River that are polluted by Cryptosporidium and Escherichia coli (E. coli), this study mapped out pollutants emanating from point and non-point sources using the Soil and Water Assessment Tool (SWAT). Streamflow calibration in the upper and lower reaches of the catchment showed good performance with R2 of 0.64 and 0.58, respectively. SWAT water quality output data were combined with a Quantitative Microbial Risk Assessment (QMRA) to understand the microbial health implications for people using river water for drinking, recreational swimming, and non-competitive canoeing. QMRA results for Cryptosporidium and pathogenic E. coli showed that the probability of infection for most users exceeds the acceptable level for drinking and recreation as outlined in the South African water quality guidelines, and by the World Health Organization (WHO). The results of this study can be used as a baseline to assess the economic and health implications of different management plans, resulting in better-informed, cost-effective, and impactful decision-making.
  • Thumbnail Image
    Item
    Data pre-processing for process optimization at a drinking water treatment plant in Ugu District Municipality, South Africa
    (Business Perspectives, 2015) Magombo, James; Dzwairo, Bloodless; Moyo, Sibusiso; Dewa, Mendon
    When testing and recording water quality data from treatment plants, errors arise. The errors are in the form of re-cordings left blank (missing values), obvious errors in writing or typing, or they can be as a result of values being very small to detect and are therefore censored. The censored values are known to be below the limit of detection (LOD). In statistical analysis, the blank cells can be filled with a certain value. Censored values are often corrected by substituting with a constant value throughout. This value will be a fraction of the limit of detection and most commonly used frac-tions are, half the limit of detection, the limit of detection divided by the square root of 2, or multiplying the limit of detection by 0.75. The direct substitution method for handling missing and values below the limit of detection results in a uniform distribution for values below the limit of detection, and a true distribution for those above. As a result, treat-ment of the values below the limit of detection is dependent upon their percentage in the sample size. An alternative method used will mimic the characteristic of the distribution pattern of the values above the limit of detection to esti-mate the values below it. This can be done with an extrapolation technique or maximum likelihood estimation. In this study, data from the Umzinto Water Treatment Plant was used to develop a data pre-processing program using Visual Basics for Applications (VBA) and Microsoft Excel 2013. The procedure involved 4 stages: data preparation, data pre-processing for blanks and non-detects, data pre-processing for the censored values and finally the identifica-tion of the outliers. The developed program was then used to pre-process raw water quality data, which resulted in satisfactory process time and data conversion. The methodology used can be borrowed for the pre-processing of data driven environmental models and hence it has a great influence on sustainability of water treatment plants.