African Institute of Science and Technology

Nigeria's significant role in regional and global climate dynamics underscores the importance of understanding its emission trends across key sectors. Despite the country’s commitment to climate action, uncertainties remain regarding the actual effectiveness of its mitigation efforts, particularly following the adoption of its Intended Nationally Determined Contributions (INDCs). Therefore, this study evaluates the effectiveness of Nigeria's climate policies by analyzing sectoral greenhouse gas (GHG) emissions trends before (2011-2015) and after (2016-2020) the implementation of its Intended Nationally Determined Contributions (INDCs). National-level emissions data sourced from the Emissions Database for Global Atmospheric Research (EDGAR), sector-specific emissions were analyzed using descriptive statistics and paired t-tests to assess changes pre- and post-INDCs. Results indicate varied sectoral responses: while emissions from the buildings sector decreased post-INDCs, reflecting potential policy impacts on energy efficiency, the transport sector exhibited fluctuating patterns. Statistical analyses revealed significant emission reductions in the buildings and power industry sectors post-INDCs, highlighting policy effectiveness in these areas. However, other sectors showed stable emissions, indicating persistent challenges in achieving comprehensive reductions. These findings underscore the necessity for tailored climate policies addressing sector-specific dynamics to enhance Nigeria's contributions to global climate goals under the Paris Agreement. It is advocated that continuous monitoring and evaluation of sectoral emissions are essential for informing targeted policy interventions and sustainable development strategies. Keywords: Environmental footprints, greenhouse gas emissions, Intended Nationally Determined Contributions, Nigeria

This study presents an integrated framework combining computational simulations and machine learning (ML) to design and optimize supercritical carbon dioxide (CO₂) pipeline networks for large-scale carbon capture and storage (CCS) applications. Using ANSYS fluent, the fluid dynamics of supercritical CO₂ transport were analyzed under various operational conditions to generate synthetic insights and complement real-world datasets collected from CO₂ sequestration activities and industrial sources. Key variables such as pressure, temperature, and pipeline diameter were extracted, engineered into time-lagged and rolling features, and used to train a random forest regressor model. Feature importance analysis revealed that short-term flow trends, volatility, and geographic segmentation are dominant predictors of CO₂ flow behavior. The ML model achieved high performance, with an R² score of 0.9897 and a low RMSE of 0.0335, indicating strong predictive reliability. Hyperparameter tuning further optimized model accuracy, while visual analyses including actual vs. predicted comparisons, residual distributions, and feature interaction heatmaps validated the model’s robustness. The results demonstrate that data-driven approaches can complement simulation-based methods to improve the design, monitoring, and operational efficiency of CO₂ transport systems. This work provides a scalable, intelligent solution for CCS pipeline optimization, supporting the global transition toward low-carbon infrastructure.

ABSTRACT Mounting climate and environmental crises remain a key challenge for West African countries. More vulnerabilities among West African populations seem to have been created due to increased uncertainty and lack of confidence to invest in the business environment. In view of this, the study explores the impact of climate change vulnerability on per capita GDP/income in the West African context over the period 2000–2023. Using pooled mean group (PMG) estimator and panel non‐causality test, climate change vulnerability index has an adverse impact on both per capita GDP and gross national income (GNI) per capita in the long run. This suggests that high susceptibility to climate risks is likely to reduce the availability of economic resources, exacerbating poor development outcomes in the long term. Findings further stress that increased climate vulnerability could amplify existing inequalities and other socioeconomic problems. The study also recognizes that the associated challenges of the levels of climate vulnerability and socioeconomic conditions are interconnected, as these indicators could reinforce each other based on the evidence of bidirectional causality between them. It is emphasized that the prevailing socioeconomic conditions, particularly in West Africa, can be strongly linked to climate phenomena. Growth and income gains are unlikely to be sufficiently high in the face of rising susceptibility to changing weather patterns, generating renewed socio‐economic concerns. Hence, it is recommended that more equitable and effective responses to climate change in terms of mitigation and adaptation measures are imperative and be fully motivated towards desired socioeconomic outcomes across the West African sub‐region.

<title>Abstract</title> Urethral catheterization is a frequently performed procedure that can sometimes result in complications such as failure to deflate a Foley catheter balloon. This can present significant challenges in managing a patient. We describe a case involving a 37-year-old woman diagnosed with severe cerebral malaria, eclampsia, and HELLP syndrome whose Foley catheter balloon failed to deflate despite the use of standard techniques. An easy bedside technique was used by cutting the catheter to a short length and using a spinal needle stylet to unblock the existing inflation passage, thus successfully deflating the balloon without complications or the need for anesthesia. This method is safe, minimally invasive, cost-effective, and particularly valuable in low-resource environments.

This study presents a comprehensive analysis of global methane (CH&lt;sub&gt;4&lt;/sub&gt;) emissions using advanced data exploration and machine learning techniques, with an emphasis on identifying key sectoral contributors, geographic emission hotspots, and the performance of mitigation technologies. Employing methods such as random forest regression, geospatial mapping, and multi-dimensional visual analytics, the research highlights the energy sector’s dominant role in methane output and reveals detailed emission patterns across U.S. states. The analytical framework includes time-series feature engineering, synthetic data augmentation for localized insights, and 3D surface modeling to examine the relationships between energy production levels, temporal trends, and emission intensities. The results provide actionable insights for policymakers by identifying critical points of intervention and advocating for the integration of artificial intelligence-driven exhaust after-treatment systems to reduce methane emissions. This work offers a scalable, reproducible approach for environmental monitoring and supports global decarbonization efforts in line with U.S. clean energy objectives. The random forest model used in this study achieved a mean absolute error of 2.71 and an R² score of 0.81, demonstrating strong predictive accuracy for methane emissions trends based on regional and sectoral data.

Blast-induced air over-pressure (AOP) is an unavoidable and potentially annoying problem that can lead to conflicts between mine management and living communities surrounding the blasting area. Controlling and monitoring AOP is crucial to mitigate its side effects. Various techniques have been employed to monitor the blast AOP. This study explores the use of the finite element method with the commercial software LS-DYNA to simulate blast-induced AOP in tunnel excavation. The numerical results were then compared to real field data to validate the model. The rock material and air were modelled using the MAT¬-MOHR-COULOMB and MAT-NULL keywords, respectively. The explosive was defined using the HIG-EXPLOSIVE-BURN model and Jones-Wilkins-Lee equation of state. Stemming material was represented by the MAT-SOIL-AND-FOAM material model. Based on the blast geometry and the average explosive charge per hole, the numerical modelling results were consistent with the field experiments. The maximum absolute error was 6.7 dB, with a percent error of less than 5% in two different models monitored at 100 m and 50 m distances. These findings indicate that blast-induced AOP can be estimated through numerical simulation as cost-effective and alternative to extensive field experiments.