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Applying participatory mapping approaches to assess local communities’ perceptions of climate change and implications on their adaptation strategies: the case of communal rangeland community, Vulindlela, South Africa.
(2025) Nhlabathi, Nomaswazi Zamanhlabathi.; Mutanga, Onisimo.; Cho, Matilda Azong.
This study investigated the role of Participatory Geographic Information System (PGIS) and Participatory Rural Appraisal (PRA) in understanding local perceptions on the causes and impacts of climate change on communal rangeland communities and how local perception shapes communities’ responses. First, a systematic literature review was conducted to assess PGIS's contribution to elucidating local rangeland communities' vulnerability and adaptation in Africa. Analysis of 18 papers from ScienceDirect, Web of Science, and Scopus revealed a slow pace in the integration of PGIS in climate change research, thus indicating a knowledge gap. Despite this, PGIS has the potential to empower local communities in co-producing knowledge and creating adaptation solutions. The study then explored the effectiveness of integrating PGIS with PRA techniques in elucidating communal rangeland communities’ perceptions of and responses to the effects of climate change on rangeland resources and livelihoods using Vulindlela, South Africa, as a case study. Using focus group discussions, participatory mapping, key informant interviews, transect walks, and household questionnaires, the study uncovered diverse perceptions of climate change's drivers and impacts on livelihoods. It found that local perceptions are influenced by factors such as experience, age, education, and dependency on rangeland resources, which shape community responses to climatic risks. The PGIS mapping exercise highlighted areas most susceptible to events like floods and droughts. Overall, the study demonstrated PGIS as a valuable tool for capturing spatial insights and facilitating local participation. The integration with PRA and PGIS techniques provided a comprehensive understanding of climate change impacts and responses, offering both non-spatial and spatial perspectives. Participatory mapping has the
potential to enhance the co-design and formulation of inclusive adaptation plans
Influence of Bus Rapid Transit (BRI) on mixed-traffic capacity utilization and its time headway implications.
(2024) Modupe , Abayomi Emmanuel.; Ben-Edigbe , Johnnie Ebioye.
Bus Rapid Transit (BRT) systems are sustainable mobility interventions that provide commuters with fast, safe, and efficient mobility. Unfortunately, they have been characterized by fundamental traffic flow parameter anomalies, especially speed changes, with attendant consequences on capacity utilization, capacity differentials, and time headway implications. Consequently, this study was carried out to determine the influence of Bus Rapid Transit (BRT) on mixed traffic capacity utilization and implications for time headways. The overarching objective was to develop a capacity utilization criteria table for assessing roadway performance and determining the Level of Capacity Utilization (LCU) with and without the influence of BRT. Criteria tables serve as standards by which the performance of a roadways in terms of capacity utilization is decided. Traffic data at peak and off-peak periods were collected for a ‘with and without’ BRT impact study at four selected road segments along route R27, located on BRT major trunk route T02 which connects Atlantis, Table View, and Sunset City. Data were logged for 12 weeks continuously using an Automatic Traffic Counter (ATC).
The study assumed that density was a function of speed and flow and hence was not directly impacted by BRT infrastructure based on the conditions at the time of the survey. It suggests that capacity utilization is triggered by variations in speed, and the attendant variations in density, capacity, and time headways were used to estimate capacity utilization rates and determine the LCU. Traffic data on speed, vehicle classes, and volumes were collected, and the results were analyzed. The collected traffic volumes were converted to flow using the South African passenger car equivalent (PCE) values. The results showed speed reductions with attendant differentials in other parameters such as capacity, density, travel time, and time headways. From the developed criteria table, the estimated capacity utilization rates for the BRT dedicated lanes scenario showed poor LCU at E (9% and 36%) across the four sites SS001 to SS004, under steady flow conditions. The capacity utilization rates of the mixed traffic ‘without BRT’ scenario showed fair utilization within the range of 37% and 79%, with average LCU at C across the four sites, whilst the capacity utilization rates of mixed traffic ‘with BRT’ scenario also showed fair utilization (between 40% and 75%), with average LCU C. The time headway implications induced by the capacity utilization and its differentials were also modelled, and the empirical time headway data were fitted to continuous probability distribution models. The Burr continuous probability distribution model, which is known for its compatibility, flexibility and appropriateness in modelling headway data under different traffic conditions, provided the best fit, having emerged with the largest Log-likelihood, and the smallest Akaike Information Criterion (AIC) values at 95% confidence and 0.05 significance levels across the four sites. At sites SS002, SS003, and SS004, it ranked first with the lowest AIC values of 3623.33, 4002.73, 3857.44, and corresponding largest LLH values of -1807.64, -1997.35, and -1924.70, respectively, while the Gaussian distribution performed best at site SS001, with the lowest AIC value of 4356.01 and largest LLH value of -2176.00, closely followed by the Generalized Extreme Value (GEV) distribution with the lowest AIC and largest LLH values of 4368.09 and -2181.03, respectively at off-peak traffic period. The Burr distribution however performed second best at peak traffic with the lowest AIC and largest LLH values of 4352.49 and -2172.23. The P-values, which ranged between 0.65 and 0.81 across the four sites showed the likelihood of the occurrence of the data sets under the null hypothesis. Hence the null hypothesis was accepted. In conclusion, the study showed that mixed traffic operations ‘with BRT’ and its associated minimized time headways, could significantly enhance capacity utilization. In view of the mixed traffic scenarios considered with and without BRT, it is hereby recommended for future research consider analysing traffic capacity through the development and application of microscopic fundamental diagrams, and a simulation of the time headway distribution of the mixed traffic scenario with BRT should also be considered. In traffic engineering practice, the curbside and mixed traffic designs are therefore recommended for implementation in future BRT infrastructure as the way forward for the South African BRT system for enhanced capacity utilization and sustainable mobility.
The integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) for buildings in South Africa.
(2023) Osman, Razan Faisal.; Friedrich, Elena.
Anthropogenic growth has catapulted the effects of global warming and the building sector is at the forefront of emissions, responsible for approximately 37% of carbon dioxide emissions into the atmosphere (UNEP, 2022). This research addresses the need for South Africa to reduce carbon dioxide emissions in the building sector and investigates the integration of Building Information Modeling (BIM) and Life Cycle Assessment (LCA) as a framework for achieving lower carbon emissions for buildings. It also explores the adaptation of the Green Star SA rating tool to effectively incorporate LCA criteria. The research objectives include: 1) evaluating the use of BIM and LCA as a decision-making tool for improving the environmental performance of office buildings, 2) examining the interoperability of BIM and LCA through case studies, 3) identifying potential hotspots in buildings where improvements can be made and developing an improvement analysis, 4) establishing criteria for a BIM-LCA framework for Green Star SA, and 5) identifying motivations and barriers to adopting BIM-LCA in South Africa. To achieve these objectives, One Click LCA and Revit Software were used to evaluate a BIM-LCA framework of two case studies. The first case study used an existing building typical of BIM models prevalent in South Africa, with a low Level of Development (LOD), whereby the only material modelled was concrete of varying strength. The second case study was theoretically structured with a high LOD, and all major building materials included. Integrated BIM-LCA models were developed for both case studies.
The results showed a similar order of magnitude, with respect to the environmental burdens of lifecycle stages. The operational energy of both buildings had the most significant impact on the environment followed by the materials used. This is due to the concrete frame of both buildings and the dependency on non-renewable energy (i.e., coal) to generate electricity in South Africa. The findings indicate that the BIM-LCA framework provides valuable information by quantifying environmental contributions, helping with optimizing alternatives. A reduction of carbon emissions could be achieved for both case studies and a series of interventions were evaluated. The integration of LCA with BIM showed promising results even for the case study with a low LOD and it can enable designers to incorporate and quantify specific environmentally and socially responsible interventions. However, insufficient data can be a major barrier for implementation in South Africa and may affect the validity of results depending on the type of building and the data included. For the Green Star SA LCA criteria were developed and the award of 3 points for conducting an LCA with a BIM LOD of 300 is proposed. The acquisition of data will be improved if LCA criteria are incorporated into the Green Star SA rating tool, thereby providing motivation for material manufacturers and the building industry alike to release more specific LCA information. This research also suggests ways of improving the interoperability of BIM-LCA in the local context and expresses the importance of developing a local database for Environmental Product Declarations (EPDs) and the establishment of a BIM-LCA platform for knowledge sharing.
Spatiotemporal analysis of vegetation fires using satellite data.
(2024) Mupfiga, Upenyu Naume.; Mutanga, Onisimo.
Although vegetation fires are key in maintaining the savanna ecosystem, their uncontrolled occurrence profoundly threatens ecosystem stability, economies, and human safety. The increased risk of climate change requires robust spatiotemporal analysis methods to understand the impact of fire on ecosystems. Additionally, the accurate prediction of vegetation fire and the associated key drivers are critical in understanding fire regimes and the implementation of effective fire management strategies. This research utilised Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to analyse the spatiotemporal dynamics of vegetation fires. The first objective focused on systematically reviewing literature on the effects of burning on various ecosystem services. The reviewed articles were extracted from Elsevier’s Scopus, Web of Science and PubMed databases and analysed based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) method. The findings from the review highlighted that there has been an increase in publications since 2010 and most studies were carried out in Asia and the United States of America. The most common satellite data used for analysing the effects of burning on ecosystem services was Landsat, whilst information on fire occurrence was extracted from the MODIS satellite data. Very few studies utilised AVIRIS, PlanetScope, and ASTER satellite data. Moreso, findings from the review revealed fire as a threat to grassland, forest, soil and wetland ecosystems with the forest landscapes being widely studied. The atmosphere is also affected by vegetation fires through particulate matter and carbon emissions. The second objective focused on detecting fire intensity hotspots and cold spots in Zimbabwe by utilising spatial statistics and MODIS-derived fire radiative power (FRP), a proxy for fire intensity. The variability of fire intensity clusters within various topographic and vegetation conditions was also analysed. The results indicated that most (44%) of the vegetation fires remotely sensed in Zimbabwe by the MODIS satellite sensor were of low intensity, mostly occurring in the shrublands. On the other hand, high intensity fires (22%) were generally distributed within Zimbabwe’s eastern and western regions. The third objective focused on detecting long-term spatiotemporal fire patterns in Zimbabwe using MODIS fire location data and a spatially explicit method (Emerging Hot Spot Analysis). The study also statistically analysed how the spatiotemporal distribution of vegetation fires is related to environmental factors. The research findings show that the occurrence of vegetation fire varies with seasons with the highest number of fires occurring in September. New information unveiled from the third objective indicated that fire activity tends to be high in June, July, and November despite these months being excluded from the official fire season in Zimbabwe, generally observed from August to October. Persistent, diminishing, oscillating, and historical spatiotemporal fire hotspots were observed in the northern regions of Zimbabwe. The final objective assessed the various topographic, bioclimatic, topographic, vegetation and anthropogenic factors that influence the occurrence of fires in Zimbabwe. The fire hazard levels were also predicted using the Maxent model based on the analysis of MODIS fire location data combined with topographic, bioclimatic, topographic, vegetation and anthropogenic factors. The jack-knife test evaluated the contribution of each variable towards the performance of the model, while the AUC (receiver operating characteristic curve) was used to estimate the model's accuracy. The research findings identified temperature annual range, precipitation seasonality, human influence and elevation as contributing highly to the occurrence of vegetation fires across Zimbabwe’s landscapes. The average AUC of 0.77 demonstrated good model accuracy. Conclusively, results from this thesis reveal the utility of spatial statistics and machine learning methods based on satellite fire data to understand spatiotemporal patterns of fire in Zimbabwe. Specifically, the detection of spatiotemporal patterns of vegetation fires, fire intensity clusters and their predicted hazard levels were successfully mapped. The information derived from this study is valuable in improving fire management in Zimbabwe and other regions. The detection of spatiotemporal patterns of fire, fire intensity and fire hazard levels result in new valuable information important for the mplementation of key fire management policies and strategies in Savanna ecosystems.
A systematic review of the literature on the preparation of preservice teachers for english first additional language teaching.
(2023) Nkala, Nomfundo Philile.; Amin, Nyna.; Dhunpath, Rabikanth.
Pre-service preparation programmes for English First Additional Language (FAL) teachers are designed to ensure they graduate with the skills and expertise to become competent teachers. So, when teachers in service feel inadequate, one must investigate the character of preparation programmes to gain insight into what they entail and solutions to ensure that teachers are
adequately prepared when they enter the world of work. This study investigates how pre-service English teachers are prepared for English FAL teaching. A systematic review of the literature was conducted to discover studies from 2010 to 2020 pertinent to pre-service English teachers’ preparation programmes for English FAL teaching. Fifty studies from Australia, Cyprus,
Germany, Indonesia, Japan, Malaysia, Myanmar, Philippines, Saudi Arabia, Turkey, United Kingdom, United States, and Vietnam met the inclusion criteria of this study. These studies were
qualitative, quantitative, and mixed methods (qualitative and quantitative, concurred, convergent parallel). Data extraction sheets were used as a quality assessment tool to extract information later presented in tables and flowcharts, and thematic synthesis was used to analyse data. Results indicate that preparation programmes for English FAL teachers train pre-service teachers for classroom management, assessment practices, teaching and learning approaches and provide them with pedagogical content knowledge. Findings also indicate that pre-service English preparation programme objectives were rarely recognised in some institutions, which brought challenges to pre-service teachers during their practicum. The study concludes that pre-service teacher preparation programmes provide extensive theoretical training on coping when in service but do not adequately prepare them practically due to insufficient time to complete teacher practicum. Also, different qualifications are required to become a teacher in different countries. The study recommends that the practicum period be extended for greater exposure to natural classroom environments and that the qualification requirements be uniform so that teachers are adequately prepared to teach in different school contexts. Pre-service teachers must be exposed to speaking and presenting (approaches that also emphasise orals), so they do not get overwhelmed in crowded classrooms.