Assessment of Impacts of Acid Mine Drainage on Surface Water Quality of Tweelopiespruit Micro-Catchment, Limpopo Basin

This research aimed to contribute to current literature for Tweelopiespruit micro-catchment, Limpopo Basin, by trending SO42−, Cl−, Ca2+, Mg2+, Na+, K+, Fe, pH and EC, for points F1S1, F2S2, W1S3, F6S7, F8S9, F10S11 and F11S12, as identified by the Department of Water and Sanitation, South Africa, for years 2003 to 2008. Results showed that pollutant concentrations generally increased downstream, which questioned their possible sources since pollution generally attenuates towards downstream. A possible explanation was that groundwater (polluted with the effluent) could be decanting from various places, thus contributing to the increase in concentrations, in places. This could potentially add value to existing efforts, which aim to halt and reverse impacts of acid mine drainage (AMD) in the micro-catchment and possibly in the Goldfields (a highly negatively impacted environment), which incorporates the Cradle of Humankind. Conclusions reached could provide invaluable options for alternative technological or methodological approaches that could be adopted for the treatment of AMD. This is critical to South Africa?s water quality trending and sustainability of this ecosystem, especially because the Tweelopiespruit micro-catchment supports humans and a variety of wildlife like giraffe, within the preserve of the Krugersdorp Game Reserve (KGR) and also its outer boundaries

Interpretation of Water Quality Data in uMngeni Basin (South Africa) Using Multivariate Techniques

The major challenge with regular water quality monitoring programmes is making sense of the large and complex physico-chemical data-sets that are generated in a comparatively short period of time. Consequentially, this presents difficulties for water management practitioners who are expected to make informed decisions based on information extracted from the large data-sets. In addition, the nonlinear nature of water quality data-sets often makes it difficult to interpret the spatio-temporal variations. These reasons necessitated the need for effective methods of interpreting water quality results and drawing meaningful conclusions. Hence, this study applied multivariate techniques, namely Cluster Analysis and Principal Component Analysis, to interpret eight-year (2005–2012) water quality data that was generated from a monitoring exercise at six stations in uMngeni Basin, South Africa. The principal components extracted with eigenvalues of greater than 1 were interpreted while considering the pollution issues in the basin. These extracted components explain 67–76% of the water quality variation among the stations. The derived significant parameters suggest that uMngeni Basin was mainly affected by the catchment’s geological processes, surface runoff, domestic sewage effluent, seasonal variation and agricultural waste. Cluster Analysis grouped the sampling six stations into two clusters namely heavy (B) or low (A), based on the degree of pollution. Cluster A mainly consists of water sampling stations that were located in the outflow of the dam (NDO, IDO, MDO and NDI) and its water can be described as of fairly good quality due to dam retention and attenuation effects. Cluster B mainly consist of dam inflow water sampling stations (MDI and IDI), which can be described as polluted if compared to cluster A. The poor quality water observed at Cluster B sampling stations could be attributed to natural and anthropogenic activities through point source and runoff. The findings could assist in determining an appropriate set of water quality parameters that would indicate variation of water quality in the basin, with minimum loss of information. It is, therefore, recommended that this approach be used to assist decision-makers regarding strategies for minimising catchment pollution

Guide for Organising a Community Clean-up Campaign

While it is the government’s and municipality’s mandate to ensure that its citizens stay in a clean and safe environment, it is of concern that waste management remains a big challenge in urban areas especially in developing countries. Increased economic development, rapid population growth and improvement of living standards are among the factors attributed to increased quantity and complexity of solid waste being generated. On the other hand, while people generate wastes, they continue to be looked at as passive recipients of municipality services. Ultimately, citizens fail to recognise their role in waste management and become unwilling to either pay for service delivery or participate in clean-up campaigns. Waste dumps are prime breeding sites for communicable disease vectors such as rodents, mosquitoes and houseflies, which can exacerbate the prevalence of water, food and waterborne diseases such as cholera and typhoid. This chapter thus describes the methodology of successfully conducting a community-led cleanup campaign. It is based on experience gained during implementation of an urban water, sanitation and hygiene (WASH) project. Ward level clean-up campaigns were organised and conducted by community members and local leaders. Besides clearing illegal dumpsites, the activity was also used to raise awareness on the consequence of waste dumping. The experience showed that organising a clean-up campaign only requires careful timeous planning. Overall, it was concluded that not only does the activity serve the practical purpose of cleaning, but it also creates a greater sense of unity and friendship among community members. Additionally, the power of beautification in a clean-up campaign wold naturally motivate residents to believe that their problems could be solved, resulting in a shared responsibility for sustainable management of waste and commons at local level

uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness

One of the main challenges facing the potable water production industry is deterioration of the quality of raw water. Drinking water that does not meet quality standards is unfit for consumption. Yet, this quality is a function of various factors, key among them being quality of the raw water from which it is processed. This is because costs related to potable water treatment are related to the nature of raw water pollutants and the degree of pollution. Additionally, survival of aquatic species depends on self-purification of the water bodies through attenuation of pollutants, therefore, if this process is not efficient it might result in dwindling of the aquatic life. Hence, this chapter presents spatial and temporal water quality trends along uMngeni Basin, a critical raw water source for KwaZulu-Natal Province, in South Africa. As at 2014 the basin served about 3.8 million people with potable water. Results from this study are discussed in relation to uMngeni River’s health status and fitness for production of potable water treatment. Time-series and box plots of 11 water quality variables that were monitored at six stations over a period of eight years (2005 to 2012), were drawn and analysed. The Mann Kendall Trend Test and the Sen’s Slope Estimator were employed to test and quantify the magnitude of the quality trends, respectively. Findings showed that raw water (untreated) along uMngeni River was unfit for drinking purposes mainly because of high levels of Escherichia coli. However, the observed monthly average dissolved oxygen of 7 mg/L, that was observed on all stations, suggests that the raw water still met acceptable guidelines for freshwater ecosystems. It was noted that algae and turbidity levels peaked during the wet season (November to April), and these values directly relate to chlorine and polymer dosages during potable water treatment

Trend analysis and artificial neural networks forecasting for rainfall prediction

The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once this was accomplished, the ANNs’ accuracy was compared against a traditional forecasting method called multiple linear regression. The probability of an accurate forecast was calculated using conditional probabilities for the two models. Given the accuracy of the forecast, the benefits of the ANNs as a vital tool for decision makers in mitigating drought related concerns was enunciated. Keywords: artificial neural networks, drought, rainfall case forecast, multiple linear regression. JEL Classification: C53, C4

Sustainable leadership pre- and within the 21st century

This paper reviews literature on sustainable leadership pre- and within the 21st century, using the following nuances: (1) selected dynamics attributable to socio-demographics, (2) implications and barriers that skew gender and leadership bias, and (3) the role of cultural norms and values in leadership practices and processes within organizations. The global challenge for research on sustainable leadership is also discussed, as it must take into account the different cultures, needs and requirements of different organizations within specific environmental contexts. A challenge spoke to the use of international models and strategies in order to achieve semantic interoperability. It is concluded that from the established theoretical framework, a focus on culture and sustainable leadership is needed, as these impact on various aspects of leadership including sustainability. It is suggested that for the future, emerging issues should incorporate sustainability into businesses in order to align environmental and social objectives with business strategies