Kumasi Technical University

This study reports the fabrication and characterization of polyvinyl alcohol (PVA)-based composite membranes incorporating activated clay (AC) and magnetite Fe₃O₄ nanoparticles (NPs) for enhanced water treatment. The membranes were synthesized using a solution casting method and evaluated for structural, morphological, and functional properties using FTIR, ¹H-NMR, SEM-EDX, XRD, and zeta potential analyses. Performance testing involved the removal of methylene blue (MB) dye and divalent ions (Ca²⁺ and Mg²⁺), as well as assessments of porosity, pure water flux, fouling resistance, and membrane reusability. Among the various formulations, the PVA/2AC:1Fe₃O₄ membrane demonstrated the highest porosity (84.8

Abstract Copula models offer a flexible statistical framework for describing complex dependence structures between variables by linking marginal distributions to a joint distribution. In recent years, they have attracted growing interest in air pollution research due to their ability to capture nonlinear relationships and extreme dependencies among pollutants. This study presents a bibliometric and content analysis of research applying copula models in air pollution studies using data retrieved from the Scopus database for the period 2005–2026. The review examines publication trends, leading contributors, and emerging research directions. A synthesis matrix for the relevant publications, allowed the identification of methodological developments and key research themes. The results showed increasing scholarly attention to modelling pollutant interdependence and environmental risk. Three main thematic areas were identified: dependence structure modelling of air pollutants, copula-based forecasting and hybrid machine learning approaches, and risk assessment of extreme pollution events. Recent studies show a methodological shift toward more advanced techniques like vine copulas, hybrid AI-copula frameworks, and spatiotemporal dependence models, which enhance predictive performance and interpretability. Overall, the review highlights the expanding role of copula models in air pollution analysis and emphasizes the need for methodological consistency, and stronger integration with public health and climate resilience research.

Urban waste management interventions have prioritised generic urban areas, with limited focus on the unique dynamics of Central Business Districts. This study draws on a qualitative exploratory design, empirical observations and literature reviews to explore the critical challenges of solid waste management (SWM), demonstrate the uniqueness of solid waste in Central Business Districts (CBDs), and propose a Sustainable Attitudinal Change (SAC) framework to aid SWM in urban Africa. The study found that CBDs have complex waste generation dynamics driven by diverse user groups with fluid behaviours that generate high volumes of inadequately managed waste. The results indicate widespread and indiscriminate disposal practices. Although waste management interventions, ranging from public-private partnerships, policy-driven solutions, technology, and circularisation, are evident, the behavioural approach to waste management is under-reported. Therefore, this comment proposes a Sustainable Attitudinal Change (SAC) framework as a complementary mitigating intervention for Africa’s urban waste crisis. This framework repositions waste as a socio-economic resource rather than a nuisance, emphasising the need for households, businesses, and institutions to foster sustainably conscious practices through attitudinal change. The operationalisation of the framework advocates for the prioritisation of antecedent and consequential behavioural strategies as complementary to infrastructure, accessibility, coordination, policies, and law with a centralised and decentralised system to drive long-term attitudinal shifts in SWM using CBDs as the strategic entry points. Thus, SAC moves SWM discourse beyond disjointed interventions to more comprehensive initiatives which centre attitudinal change.

PurposeThe transitioning to cleaner energy sources, such as renewables and improving energy efficiency, has been identified as an important objective of the UN Sustainable Development Goals (SDGs). This study aims to examine whether the imposition of environmental taxes can be a useful mechanism for countries to achieve the sustainable energy transition agenda.Design/methodology/approachA panel data of 88 countries covering the period of 1996-2021 was used for the empirical analysis. The method of moments quantile regression estimation technique was used to examine the non-linear and asymmetric relationships among the variables.FindingsThe empirical results demonstrate that environmental taxes significantly drive sustainable energy use within the sampled countries. Further analysis demonstrates that the observed relationship between environmental taxes and sustainable energy transition is more pronounced among high-, lower-middle- and low-income countries. However, environmental taxes do not seem to have a significant impact on sustainable energy use in upper-middle-income countries.Practical implicationsThe findings of this study provide policymakers with important insight to rethink environmental taxation strategies and have implications, particularly for achieving the SDGs.Social implicationsThe findings of this study provide an indication that the imposition of environmental taxes has important implications on the extent to which countries rely on cleaner energy sources to meet their energy demands.Originality/valueThis study offers valuable insights into the role of environmental taxes in promoting environmental sustainability, particularly through the lens of sustainable energy transition. By examining the global impact and conducting a breakdown by income level, this research provides a comprehensive understanding of the effectiveness of environmental taxes in achieving environmental goals.

The justification for the need for continuous assessment and monitoring of groundwater (GW) quality lies in the global demand for safe, clean, accessible water for all. Earlier works in parts of the Wassa West District of Ghana, where the study area belongs, reported groundwater quality issues, justifying the need for further groundwater pollution investigation within the catchment. Indexical proxies and multivariate statistics have been used in pollution studies, however, a combination of multiple proxies, multivariate statistics, prediction modeling, and spatial mapping will elucidate comprehensively the pollution patterns of groundwater in the catchment. Hence, this study used multi-indexical proxies of water quality index (WQI), groundwater pollution index (GPI), water pollution index (WPI), percentage of pollution index (PPI), compositional pollution index (CPI), water nutrients pollution index (WNPI), and nitrogen pollution index (NPI) together with multivariate statistics, and multilinear regression modeling (MLM) to characterize and predict the groundwater pollution in the area. The physicochemical parameters, except for NO3−, K+, and HCO3−, in 8