School of Geography, Archaeology and Environmental Studies (ETDs)

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    Modelling and analysis of COVID-19 outspread at micro-levels using spatial autocorrelation: Case of eThekwini
    (University of the Witwatersrand, Johannesburg, 2024-09) Ngubane, Samukelisiwe; Chimhamhiwa, Dorman; Adam, Elhadi
    The alarming effects of the COVID-19 pandemic on different socio-economic spheres have been felt across the globe. These destructive effects have prompted plenty of research to understand and control the coronavirus pandemic. Notably, one strategic method of mitigating the effects of the coronavirus epidemic has been the utilisation of spatial and geostatistical models to gain insights into the potential predictors of the prevalence of the coronavirus. Considering the above, it was the aim of this study to explore the use of advanced geospatial modelling and analysis techniques, including Moran’s I, spatial error models, spatial lag models, MGWR, and GWR for analysing and modelling the settlement level determining factors of COVID-19 incidence within the eThekwini Metro to inform effectual micro-level planning. Notably, the lack of micro-level modelling of COVID-19 prevalence predictors also motivated the undertaking of this study. To the above aim, the objectives of the research were to utilise spatial autocorrelation to map the granular level COVID-19 spatial distribution over the 3rd wave in the eThekwini Metro, compare the applicability of global and local models in analysing and modelling micro-level COVID-19 incidence, analyse the spatial dependence of the occurrence of COVID-19 on local level variables through Moran’s I and to spatially model the effects of significant local-level determinants on COVID-19. The incidence of COVID-19 cases for the 3rd wave, which was from the 2nd of May 2021 to the 11th of September 2021, was analysed and modelled. The Moran’s I result illustrated that COVID-19 incidence within the eThekwini settlement places had a positive spatial autocorrelation, with a Moran’s I value of 0.14 and a p-value of 0.00. Also, the MGWR model's local R2 value was greater (72.5%) as compared to the other models. Moreover, economic wellness score, the sum of TB cases and population density came out as the significant determining factors of settlement level incidence of COVID-19. This research report offers a great foundation for gaining insights into the applicability of advanced geospatial models in guiding targeted COVID-19 interventions at lower levels.
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    Investigating the impact of the land reform policy on land use and land cover changes, in Ngaka Modiri Molema district of the North West province
    (University of the Witwatersrand, Johannesburg, 2024) Mmangoedi, Molebogeng Precious; Adam, Elhadi
    The purpose of this study was to assess how land reform policies affected changes in land use and land cover in the province of North West's Ngaka Modiri Molema district municipality. The study employed remote sensing technologies to analyse changes in land use and land cover (LULC) resulting from the implementation of land reform programs between 1985 and 2015. The primary objective of the research was to systematically map Land Use and Land Cover types across five-year intervals spanning from 1985 to 2024, leveraging Landsat earth observation data in conjunction with a random forest classifier. These methodologies were employed to facilitate the identification of spatial patterns and trends associated with the implementation of land reform policies within the study area. Furthermore, the study utilized Landsat data and advanced change detection algorithms to quantitatively assess LULC changes over the specified timeframes. Through the application of spatial analysis techniques, the research aimed to elucidate the relationship between the implementation of land reform measures and corresponding shifts in LULC patterns across the research study area. The findings of the investigation indicated a noticeable expansion in built-up areas between the years 1985 and 2024 which was approximately 10.86%. This expansion was primarily attributed to the growth experienced by the municipality during this period. Additionally, more opportunities might have risen from the agricultural farming activities and also from the land reform policy being implemented. However, as the ownership changed due to land redistribution and more land was being acquired by black people through the land reform policy, agricultural farming decreased slightly throughout the years. The reduction was due to the factors that arose from inefficient policy implementation. The study also recommends that remote sensing techniques should be utilised to carry out studies to determine LULC changes that derive from land policies aiming at dealing with socio-economic factors and urbanisation. An incorporated agrarian reform sustainable programme has vast potential in cultivating the production of the projects, particularly if it involves packages in rural infrastructure, support services, and co-operatives. The major role of such an approach should be in the trainings conducted for the farmers, obtaining, and distributing agricultural resources and equipment to agrarian reform or beneficiaries of the land reform projects. Additionally, there should be an allowance for special grants which will be useful in supporting the government’s efforts.
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    Spatiotemporal characteristics of surface water in Sua Pan, Botswana, using Earth Observation data: 1992–2022
    (University of the Witwatersrand, Johannesburg, 2024-10) Peplouw, Muchelene Tiara; Adam, Elhadi; Grab, Stefan
    Surface water is a critical resource for sustaining both human and ecological health. However, climate change and human actions threaten its availability in semi-arid regions like Botswana. In addition, current research on monitoring and understanding surface water dynamics in Botswana lacks the application of remote sensing and machine learning. This highlights a crucial gap in knowledge that this study aims to address. This study investigates the spatiotemporal dynamics of land use/land cover (LULC) and surface water extent changes in Sua Pan, Botswana, from 1992 to 2022. Employing remote sensing, machine learning, and statistical techniques, the research offers valuable insights into the intricate relationships between land cover modifications, surface water variations, and climatic variables. Google Earth Engine (GEE) facilitated efficient analysis of Landsat imagery for LULC mapping. Random Forest (RF) effectively classified several land cover types within Sua Pan. To address the challenges of saline environments, a novel water index, the Saline Water Index (SWI), was developed specifically for Sua Pan. The McNemar statistical test compared the performance of SWI to established indices like the Modified Normalised Difference Water Index (MNDWI) and the Normalised Difference Salinity Index (NDSI). Surface water variations were analysed using homogeneity tests and the Mann-Kendall trend test. The relationships between hydro climatic data (rainfall, evapotranspiration, land surface temperature) retrieved from GEE and surface water area for both wet and dry seasons were evaluated using Pearson correlation coefficients and visualised by line and area graphs. Additionally, the influence of the El Niño Southern Oscillation (ENSO) on rainfall and surface water area was assessed using Analysis of Variance (ANOVA) to identify the specific ENSO phases that exert an influence. The findings demonstrate the effectiveness of GEE for LULC mapping with the RF algorithm, achieving moderate to high classification accuracy (65.2% - 90.69%) and Kappa coefficients (0.54 - 0.85). Surface water and bare area exhibited increasing trends (coefficients: 13.017 and 9.0609, respectively), whereas vegetation and salt hard pan showed decreasing trends (-16.786 and -5.3081, respectively). The newly developed SWI outperformed MNDWI and NDSI in detecting surface water, achieving the highest overall accuracy (94%) compared to MNDWI (64%) and NDSI (59%). The McNemar test confirmed no significant statistical difference between the SWI map and the validation dataset (p = 0.2673), while both MNDWI and NDSI maps showed significant differences (p < 0.0001). Utilising SWI, the study revealed that surface water was most prevalent in central and northeastern regions, with an average coverage of 33%. Seasonal homogeneity tests indicated a non-homogenous distribution of surface water area in wet seasons, with abrupt changes in 1994 and 2003. Conversely, dry seasons exhibited a homogenous distribution. The Mann-Kendall trend test identified a statistically significant (p-value = 0.01) but weak positive trend (tau = 0.329) for surface water areas in wet seasons. In contrast, the dry seasons displayed a non-significant (p-value = 0.734) and a very weak positive trend (tau = 0.043). Surface water area, rainfall, evapotranspiration, and temperature consistently increase during the wet seasons compared to the dry seasons. Notably, increased evapotranspiration significantly impacted surface water presence. ENSO exhibited no significant influence on either rainfall or surface water extent (p-value > 0.05 for both). These findings highlight the potential of earth observation data for real-time surface water monitoring in salt pans. The developed techniques offer valuable insights for policy decisions regarding environmental management and conservation efforts in Sua Pan. In addition, the study emphasises the importance of cost-effective approaches for water change assessment, particularly appropriate for under-resourced regions.
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    Optically stimulated luminescence dating of Kalundu and Urewe tradition ceramics
    (University of the Witwatersrand, Johannesburg, 2024-03) Haupt, Rachel Xenia; Schoeman, Maria; Evans, Mary
    Optically stimulated luminescence (OSL) dating is a method of providing the direct age of artefacts. While radiocarbon and seriation dating provide indispensable insight into archaeological sites, the direct dating of artefacts is beneficial in entangled contexts. The Lydenburg Heads Site is significant to the beginning of the Early Farming Communities (EFCs) sequence within the Mpumalanga province. The site has been occupied multiple times, as can be seen from the presence of the two major ceramic traditions of the age, Urewe and Kalundu. The site was originally excavated and analysed by Evers (1982) in the 1970s, with a reanalysis of the ceramic assemblage by Whitelaw (1996) and organic residue analysis on the ceramics by Becher (2021). The use of OSL dating on twelve ceramic sherds allowed for new insights into the chronological intricacies within the study site. To determine the age of the ceramics, the OSL quartz dating technique was used. The adjustments to the technique involved the use of a less destructive means of sample extraction. A slightly altered version of the standard means of sample extraction was used to create a comparison and allow the dating of the ceramics to be reliable. The minimal destruction technique (MET) combined with the bulk sampling proved useful to the dating of the ceramics. The use of previously excavated ceramics meant that some aspects of age determination required estimation and analysis. The major obstacles from such were the water content, the depth of burial, and the lack of in situ soil samples. In light of the elements of ambiguity for the site, the OSL dating considered these variations and how they affected the age. The Urewe tradition ceramics were determined to be in 6th and 8th century AD. The finding creates the alignment with the range of the radiocarbon ages done within previous work and the assumptions made by Evers (1982) and Whitelaw (1996). The Kalundu tradition ceramics ages were determined to be between the 7th and 10th century AD, conflicting with previous assumptions on the occupation. The result is the possibility the ceramic assemblages could be considered to be contemporaneous. The work in this thesis has, in part, been presented at the Luminescence and Electron Spin Resonance Dating conference in Copenhagen (LED2023) and at the Association of Southern African Professional Archaeologists 2024 Biennial Meeting (ASAPA 2024).
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    The Influence of Climate Change on the Speed of Movement of Tropical Cyclones in the South Indian Ocean
    (University of the Witwatersrand, Johannesburg, 2024-07) Mahomed, Aaliyah; Fitchett, Jennifer
    Recent studies on the speed of movement of tropical cyclones indicate that anthropogenic warming has resulted in a 10% global decrease of tropical cyclone translation speeds over the period 1949-2016. The recent increase in high intensity storms could severely impact Southern Hemisphere regions which are considerably more vulnerable than their Northern Hemisphere counterparts. High intensity storms occurring at a lower speed would worsen the impacts of tropical cyclones resulting in prolonged periods of flooding, storm surges, and winds. This would subsequently lead to a loss of lives, economic loss and infrastructural and agricultural damage. However, studies have challenged this slowdown, suggesting that the transition to the geo-stationary era, introduces heterogeneity to tropical cyclone data. Additionally, imprecise estimates of tropical cyclone frequency influences the average speed of tropical cyclones, thereby impacting trend analysis. Using tropical cyclone data from National Oceanic and Atmospheric Administration (NOAA) International Best Track Archive for Climate Stewardship (IBTrACS), this study explores the current translation speed debate for the South Indian Ocean, over the period 1991-2021. The results of this study indicate that the translation speed of tropical cyclones has increased at a rate of 0.06km/h/yr over the 30-year period (r = 0.06 p = 0.19). Whilst the translation speed debate remains at an aggregated global scale, a comprehensive understanding of the influence of climate change on tropical cyclones is crucial for generating forecasts as this enables vulnerable regions to plan and adjust to evolving tropical cyclones.
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    A geographical analysis of the impacts of construction and demolition waste on wetland functionality in South Africa: a study of Gauteng province
    (University of the Witwatersrand, Johannesburg, 2024-09) Mangoro, Ngonidzashe; Kubanza, Nzalalemba Serge; Mulala, Danny Simatele
    The purpose of this study was to investigate construction and demolition waste management processes in sub-Saharan Africa and how they affect wetland ecosystems, using South Africa as a case study. Construction and demolition (CDW) waste has become a massive urban environmental challenge on a global scale, but more so in developing countries found in sub-Saharan Africa. In the context of South Africa, construction and demolition waste is not a waste stream taken seriously by local and national authorities because it is ‘general waste that does not pose an immediate threat to the environment. This position is premised on the idea that construction and demolition waste is generally inert (chemically inactive) and therefore cannot cause an immediate environmental risk. In this study, it is argued that the environmental risk of waste goes beyond the embedded chemical constituencies because some waste streams can cause immediate environmental risk through their physical properties depending on the location of disposal. It is further argued that although CDW is generally inert, disposal in wetlands immediately disrupts the way wetland ecosystem’s function, causing several environmental risks. To mitigate the environmental threats posed by construction and demolition waste, this study proposes a change in the methodological approaches and strategies deployed to manage the waste stream, such as by introducing a hybrid of circular economy and industrial ecology to minimize or eliminate waste production. This study involved several data collection and analysis methods. Using a combination of qualitative and quantitative studies methods, data was collected with the goal to understand the perceptions of experts on how construction and demolition waste management in South Africa affects wetland ecosystems and what can be done to effectively manage the waste stream in the context of a developing country. Data informing this study were collected through semi-structured interviews and surveys in the province of Gauteng, specifically in the City of Johannesburg and City of Ekurhuleni Municipalities, where there is massive illegal dumping in wetlands for various reasons. Furthermore, apart from the use of semi-structured interviews and surveys, a digital elevation model was generated in ArcGIS Pro 10.1 software to measure the effects of construction and demolition waste on wetlands in the study area. The approach to this study using both qualitative and quantitative methods was crucial because it provided human perceptions which were accurately corroborated by GIS software. The study found that construction and demolition waste management in South Africa is affected by several challenges that lead to massive illegal dumping in critical ecological ecosystems such as wetlands. In a broad sense, the major challenge to sustainable construction and demolition waste management in South Africa is institutional failure at both the local and national levels. Local authorities such as municipalities are characterized by massive corruption, poor funding, and lack of strategic technologies among other things, while at the national level, there is massive interference with municipal affairs through bureaucratic delays in the disbursement of municipal funds. A combination of these and other factors leads to illegal dumping of construction and demolition waste across the Gauteng Province, particularly in wetlands in low-income areas. The data informing this study reveals that dumping construction and demolition waste in wetlands causes an immediate threat to the existence of wetlands through massive sedimentation with insoluble materials. It is ultimately found that construction and demolition waste destroy the ability of wetlands to offer ecosystem services such as flood attenuation, carbon sequestration, water filtration, and habitat provision, among other functions, leading to environmental events such as flooding. A combination of circular economy and industrial ecology can be one of the ways that can be deployed to effectively and sustainably manage construction and demolition waste in South Africa. The circular economy and its three principles of ‘reduce’, ‘recycle’, and ‘reuse’ has been successfully deployed in developed countries in the European Union, where recycling has topped 70% of the total construction waste generated. Industrial ecology with its analogy of industrial ecoparks has been deployed in the European Union with immense success, until more attention was directed to circular economy. With an increase in municipal funding and introduction of a construction waste information system, a combination of ‘circular economy’ and ‘industrial ecology’ can significantly help to reduce pressure on wetlands and the environment at large. Even though the methodological improvements suggested above could significantly reduce pressure on wetlands, the implementation could be faced with institutional challenges. Therefore, it is argued that urgent institutional transformation is required to make tangible changes in the field of construction and demolition waste management. It is recommended that there should be increased law enforcement to curb widespread illegal dumping in South Africa’s major cities. It is also recommended that, like in Europe, South Africa must introduce tailor-made legislation of policies for construction and demolition waste alone. Promulgation of dedicated legislation provides clear direction on how the waste stream is managed and who is responsible for specific roles. Furthermore, dedicated legislation can be a crucial tool to deliver sustainable construction and demolition waste management in South Africa because it can be used to encourage the use of recycled aggregates and limit the amount of illegal dumping or extraction of materials from the environment. Finally, dedicated construction and demolition waste legislation can be used to shift from the traditional view of pollution or contamination through toxicity, and so the value of this study is immediately apparent.
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    Assessing the Validity of the Exclusion of Night-time Thermal Comfort in Tourism Climate Indices
    (University of the Witwatersrand, Johannesburg, 2024-09) Mnguni, Zandizoloyiso; Fitchett, Jennifer
    Biometeorological indices are instruments that can be used to streamline complex climatic information for economic and other decision-making. Indices hold inherent assumptions where the use of an index is only reliable and valuable if those assumptions are true. The Holiday Climate Index (HCI) is presented as the improved version of the TCI, with a key difference being the removal of night-time thermal comfort due to the assumption that air conditioning is ubiquitous throughout Europe. This study investigated the validity of this exclusion of night-time thermal comfort in tourism climate indices, particularly for the HCI using the six European cities for which the index was developed – Barcelona, Stockholm, London, Istanbul, Paris and Rome. The assumption of ubiquitous air conditioning was investigated using Booking.com accommodation listings, the night-time economy and prevalence of night-time activities outside of each accommodation establishment, and whether tourists experienced adverse thermal comfort during the night through posted reviews. Without the air conditioning filter applied, the proportion of listings categorized as offering air conditioning ranged from 28.8% for Stockholm to 98.9% for Rome. With the filter applied, the proportions ranged from 96.4% for Stockholm and 99.0% for Paris. A total of 24,252 TripAdvisor reviews were also examined for both accommodation establishments and night-time tourist activities. The reviews were manually examined for the mention of weather, climate, night-time temperature and air conditioning. The findings of this study exhibit a range of night-time activities, many of which are outdoors, where tourists did comment on night-time thermal comfort. The research disproves the claim of the original authors, and it was found that air conditioning is not ubiquitous. Therefore, the assumption that the HCI is based on is problematic, and the index should be used with caution. Moreover, a similar approach in index validity testing should be performed prior to future studies seeking to apply indices.
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    Modelling for Rainwater Harvesting Structures Using Geospatial Techniques
    (University of the Witwatersrand, Johannesburg, 2024-10) Makaringe, Precious Nkhensani; Atif, Iqra
    Climate change poses a significant threat, leading to droughts, floods, and hindering sustainable development. Water scarcity is a growing concern, particularly in developing countries like South Africa, where limited freshwater resources are further strained by climate variability. This research explores the potential of rainwater harvesting (RWH) as a strategy to address water scarcity in such regions. This study aims to model potential rainwater harvesting sites in Lynwood Park, Pretoria, South Africa, utilising geospatial techniques. Object-Based Image Classification (OBIC) was employed to extract building footprints from high-resolution satellite imagery. Microsoft and Google building footprints were utilised to determine the suitable automated building footprints for Lynnwood Park. ArcGIS Pro software served as the primary platform for spatial data analysis and mapping potential RWH sites. Data integration included high-resolution satellite imagery, a Digital Elevation Model (DEM), building footprints, and rainfall data. Additionally, questionnaires were distributed to estimate population and water demand within the study area. The research demonstrates the efficacy of geospatial tools in identifying suitable locations for RWH systems. Indicating that steeper slopes in the southern region of Lynnwood Park have limited collection from large rooftops, while the flatter north offered greater potential. Rainfall graphs and PRWH results suggest that over half of Lynwood Park's annual water demand could be met through rooftop rainwater collection. However, factors such as system losses due to evaporation, inefficiencies in collection and storage, and variability in rooftop sizes across different buildings would need to be incorporated into more detailed models, as well as water quality analysis for rooftop harvested water in future studies. This study highlights the potential of RWH as a viable water security strategy in water-scarce regions. The findings contribute to the development of geospatial approaches for RWH implementation, promoting water security and sustainability in a changing climate.
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    Assessing and comparing the performance of different machine learning regression algorithms in predicting Chlorophyll-a concentration in the Vaal Dam, Gauteng
    (University of the Witwatersrand, Johannesburg, 2024-03) Mahamuza, Phemelo Hope; Adam, Elhadi
    The state of Vaal Dam is influenced by various land uses surrounding the Dam, including agricultural activities, mining operations, industrial enterprises, urban settlements, and nature reserves. Mining activities, farming practices, and sewage outflows from nearby villages led to access contamination within the Dam, increasing algal bloom levels. Sentinel-2 MSI data were utilized to forecast and comprehend the spatial pattern of Chlorophyll-a concentration, indicating algal bloom occurrence in the Vaal Dam. Targeting Sentinel-2 Level-1C, the image was preprocessed on the Google Earth Engine (GEE) with acquisition dates from 25 – 26 October 30, 2016, corresponding to the on-site data collection between October 26 and October 28, 2016. Due to limited resources, up-to-date data on the Vaal Dam could not be collected. However, since this study focuses on applying various machine learning regression models to predict chlorophyll-a levels in waterbodies, the dataset is used to test the models rather than reflect the current state of the Vaal Dam. The dataset, comprising 23 samples, was divided into 70% training and 30% test sets, allowing for comprehensive model evaluation. Band ratio reflectance values were extracted from the satellite image and correlated with in-field Chlorophyll-a values. The highest correlation coefficient values were utilized to train five machine-learning models employed in this study: Random Forest (RF), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression, and Multilinear Regression (MLR). Each model underwent training with ten iterations each; the best learning iteration was then used to generate the final Chlorophyll-a predictive model. The predictive models were validated using the Sentinel-2 MSI satellite data and in-situ measurements using R2, RMSE, and MAPE. Among the five machine learning algorithms trained, RF performed the best, with an R2 of 0.86 and 0.95, an RMSE of 1.38 and 0.8, and MAPE of 15.09% and 10.92% for the training and testing sets, respectively, indicating its ability to handle small, non-linear datasets. SVR also demonstrated a fair performance, particularly in handling multicollinearity in the data points with an R2 of 0.68 and 0.87, an RMSE of 2.37 and 1.56, and MAPE of 18.13% and 19.28% for the training and testing sets, respectively. The spatial pattern of Chlorophyll-a concentrations, mapped from the RF model, indicated that high concentrations of Chlorophyll-a are along the Dam shorelines, suggesting a significant impact of land use activities on pollution levels. This study emphasizes the importance of selecting suitable machine learning algorithms tailored to the dataset's characteristics. RF and SVR demonstrated proficiency in handling nonlinearity, with RF displaying enhanced generalization and resistance to overfitting. Limited field data evenly distributed across the Dam and satellite overpass dates may affect result accuracy. Future research should align satellite pass dates with fieldwork dates and ensure an even distribution of in-field samples across the Dam to represent all land uses and concentration levels.
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    GIS-Based Location-Allocation Modelling of School Accessibility in the Bojanala Platinum District Municipality, South Africa
    (University of the Witwatersrand, Johannesburg, 2024-09) Molefe, Kebarileng Christinah; Atif, Iqra
    School accessibility modelling performs a crucial part in guaranteeing that educational institutions are physically and practically reachable by every student, irrespective of their abilities, disabilities, or socioeconomic status. Neglecting school accessibility perpetuates inequality, reinforces negative stereotypes, and isolates affected students. Therefore, the principal goal of this research was to evaluate the distribution of schools across the Bojanala Platinum District Municipality, focusing on their accessibility to local communities. The study employed an integrated approach, combining geostatistical techniques, location-allocation modelling, and multicriteria decision analysis. By considering both quantitative data and spatial relationships, these methodologies contributed to robust decision-making and informed policy recommendations. The study utilized population data and school-related information sourced from the Department of Education and the HUMDATA websites, both dated to the year 2020. The study examined the distribution of schools in the Bojanala Platinum District Municipality. It was discovered that the schools were clustered, with a concentration in the Rustenburg local municipality, followed by Madibeng. However, a significant inequality in school access was evident. Secondary school students faced the greatest vulnerability, as most accessible schools primarily served primary students. To address this, potential school sites were proposed across the district. The study emphasizes the need for effective interventions by educational administrators and policymakers to eliminate this inequality. This study recommends the establishment of new schools in underserved regions, strategically enhance existing schools, and maximize school accessibility for all residents. Adequate school provision promotes equity, reduces travel burdens, and strengthens community bonds.