University of the Western Cape

Tail risk assessment is crucial in financial markets, especially for commodities such as Brent crude oil, where extreme price fluctuations pose a risk for investors and policymakers. Risk models such as generalised autoregressive conditional heteroscedasticity (GARCH) often struggle to capture these extreme movements accurately, leading to potential underestimation of risk exposure. To solve this problem, we combine an alpha-recurrent neural network with a generalised Pareto distribution to better predict extreme price changes and improve tail risk estimation. Our findings demonstrate that this approach effectively captures downside risk, with backtesting results yielding high p-values, confirming its statistical reliability. The results from the shape parameter reveal that losses in crude oil markets are significantly riskier than gains, highlighting the asymmetric nature of price movements. Risk estimates indicate that the model provides robust assessments for both long- and short-term trading positions, making it a valuable tool for risk management. Nevertheless, these results have broader implications for financial risk modelling, particularly in commodity markets, where macroeconomic and geopolitical factors influence price volatility. Future work should focus on expanding the data set, enhancing computational efficiency and adding external risk factors such as liquidity constraints and regulatory shifts. Better calibration methods and the ability to adjust in real-time can make predictions more accurate, helping risk assessment models stay useful in changing market conditions.

Abstract Line-intensity mapping (LIM) is an emerging cosmological technique that traces large-scale structure through the integrated spectral-line emission of unresolved sources. Reconstructing unbiased sky maps requires careful joint treatment of instrumental calibration and map-making, a task made challenging by time-varying receiver gains, thermal drifts, and correlated 1/f noise intrinsic to single-dish radio telescopes. We present a Bayesian framework for joint calibration and map-making using Gibbs sampling, giving access to the full joint posterior of calibration and sky map parameters. Our data model is grounded in the radiometer equation, capturing the coupling between noise level and system temperature without assuming a fixed noise amplitude. Gain and system temperature are estimated via an iterative generalised least squares (GLS) scheme, while absolute flux calibration is achieved either with external calibrators or via known signal injections such as noise diodes. We further introduce a 1/f noise model that avoids spurious periodic correlations arising from the common assumption of a diagonally structured noise covariance in the frequency domain. The workflow is implemented in an efficient software package using the Levinson algorithm and a polynomial emulator to reduce computational cost. Demonstrated on simulations representative of MeerKLASS single-dish observations, the framework generalises to other single-dish surveys and to cross-correlation and interferometric data.

Background: The growing global population, expected to reach 9.7 billion by 2050, is increasing the demand for sustainable food system practices and resilient food systems. The food system contributes to nearly one-third of global emissions, while smallholder farmers, who survive on farming, face challenges related to climate change and inefficient resource use. Existing research suggests a lack of innovative approaches to reduce food system emissions and waste while improving sustainability in the face of climate change. Objectives: The study's primary objective is to propose a conceptual climate finance framework to enable small-scale farmers to reduce pollution and generate verifiable environmental pollution credits. The study addresses a significant gap in the literature by proposing a machine learning-based conceptual climate finance framework for an environmental pollution credit system, aimed at small-scale farmers in the Western Cape, South Africa. Method: The study adopted an organisational cybernetics systems approach to propose a conceptual climate finance framework. The climate finance framework will use machine learning (ML) techniques such as supervised learning, which can accurately predict and classify new and previously unseen data, learning from labelled datasets collected from various datasets in food systems. Conclusion: The study findings suggest that the proposed climate finance framework will not only help optimize farm practices but also allow farmers to earn pollution credits, offering new revenue streams. The study supports the COP29 agenda and drives advancements toward the Sustainable Development Goals (SDGs). The proposed framework contributes to advancing the Sustainable Development Goals (SDGs) and driving meaningful environmental change in the region. Keywords: Artificial Intelligence, Machine Learning, Climate Finance, Sustainable Development Goals, Organisational Cybernetics, Systems Approach. Citation: Jokonya, O. and Moravčík, O. (2026): A Machine Learning Climate Finance Framework for Environmental Pollution Credits among Smallholder Farmers in the Western Cape, South Africa. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, Nos. 1/2/3, pp. 243-259. WASD: London, United Kingdom.

Water recreation in contaminated environments poses serious health hazards, especially for vulnerable groups such as children. This study examines the environmental risk of heavy metal exposure for children who participate in water recreation activities in the Manyera River, Niger State, Nigeria. The Manyera River, which has been affected by different human activities, has been identified as a potential source of toxic heavy metals such as lead, mercury, and cadmium. The study employed both questionnaire and interview methods to source for its data. The results revealed an increased artisanal mining and unsustainable methods posed substantial threats, particularly to community members and children who frequently utilized the river for domestic and recreational activities. Children were more exposed to heavy metals, such as mercury, which is found in concentrations higher than the W.H.O. safe limit. This exposure offers substantial insecurity to children, potentially affecting their respiratory, cognitive, and cardiovascular systems and increasing their chances of developing cancer in the future. The study emphasizes the urgent need for public health interventions, environmental remediation, and legislative changes to safeguard children from the long-term effects of exposure. Addressing these concerns is critical for protecting children's health and creating safer recreational areas.

BackgroundIt is an established toxicological principle that the inhalation pathogenicity of respirable and biodurable fibres is caused by excessive fibre length as alveolar macrophages fail to uptake and remove such fibres. However, studies on carbon nanotubes showed that this principle needs revision, as thin, flexible variants showed reduced fibre-specific toxicity. One potential explanation is that the low flexural rigidity of thin fibres enables macrophages to bend and internalize even those that are long relative to the cell size. To evaluate this proposed “rigidity hypothesis,” the mechanisms governing the uptake of flexible long fibres that determine a critical threshold value for flexural rigidity require clarification.MethodsWe exposed NR8383 rat alveolar macrophages to three silver nanowire variants differing in diameter and length. Time-lapse microscopy captured fibre uptake processes. Successful internalization of long fibres was found to require fibre bending during uptake. A mechanical model was developed by combining established cytoskeletal biophysics with the observed fibre deformation dynamics. As flexural rigidity describes fibre behaviour under load, our model estimated rigidity by reproducing the observed bent fibre shape. By defining limit cases for physically ‘weak’ and ‘strong’ NR8383 macrophages, i.e., assuming upper bounds on the forces generated by their cytoskeletal nanomachinery, our model enabled us to derive a range for the critical fibre rigidity threshold.Results and conclusionA macrophage was observed bending an exceptionally long fibre (~ 140 μm) first into an arc and then a spiral for full internalization, initiated by a pseudopod extending along the fibre and buckling the internalized segment. Our model can reproduce such behaviour. It yielded a flexural rigidity of 20 mN·nm² for this fibre. Predicted critical rigidity limits for fibres that just fit into NR8383 macrophages range from 3 to 62 mN·nm². Using the conservative lower bound, long and biodurable fibres with a rigidity lower than 3 mN·nm² are expected to be readily phagocytized by this cell line. Although this rigidity scale may not be directly translatable to human alveolar macrophages, our experimental findings and their modeling emphasize the key role of rigidity in fibre–cell interactions. Fibre rigidity is therefore central for material safety aspects and sustainable product design.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12989-026-00666-9.

South Africa faces environmental and public health risks due to pollution of various environmental systems by pesticide and pharmaceutical residues resulting from anthropogenic activities. However, regulatory and monitoring mechanisms remain inadequate. This review aimed to assess occurrences, sources, regulatory frameworks, and policy responses related to pesticide and pharmaceutical pollution in South Africa. The review of published (peer-reviewed) articles and government and policy documents found that pharmaceuticals such as acetaminophen and diclofenac, and pesticides such as atrazine, endosulfan, and chlorpyrifos, are commonly reported micropollutants, even though they are banned. The spatial distribution of the reviews shows that Western Cape, Gauteng, and KwaZulu-Natal appear to have more research conducted on these pollutants. Related laws and policies managed by the Department of Agriculture, Land Reform and Rural Development (DALRRD) and the South African Health Products Regulatory Authority (SAHPRA) are insufficient and lack thorough environmental risk assessments, regular monitoring, and strict enforcement. Comparison with the EU, USA, Switzerland, Australia, Japan, and South Korea reveals that these countries have stronger regulatory systems, including obligatory risk assessments, national take-back schemes, and integrated monitoring, which are mostly absent in South Africa. The informal sale of pesticides, misuse, improper disposal of pharmaceutical waste, and the slow implementation of the Integrated Pest Management (IPM) approach further exacerbate the problem. To prevent future risks to ecosystems and public health, the review recommends regulatory adjustments, improved interagency coordination, and enhanced environmental monitoring systems to align South Africa's regulatory framework with world best practices.