University of Mines and Technology

It has now been established that the chemical composition of Particulate Matter (PM) is essential for predicting its associated health endpoints. This paper presents a comprehensive study of the chemical composition and associated health risk of atmospheric particulate matter in the mining city of Tarkwa, Ghana. Total suspended particles (TSP) and PM10 were monitored biweekly for one year using miniVol air samplers for three communities within Tarkwa. The exposed filter samples were divided into two parts and chemically characterised using Scanning Electron Microscope – Energy Dispersive X-ray (SEM–EDX) for elemental particle analysis, and ICP-MS for metal analysis. The risk of non-carcinogenic health impact in the exposed population was computed using the Target Hazard Quotient (THQ), while Positive Matrix Factorisation (PMF) was applied for source identification. The recorded mean TSP mass concentrations for monitoring sites Abekoase (AK), Nsuta (NV), and Tarkwa Banso (TN) were 27.74, 157, and 163 µg/m3, respectively. PM10 mean concentrations were 23.8, 70.85 and 78.64 µg/m3 for AK, NV and TN, respectively. The SEM–EDX analysis revealed particles rich in Si, Al, Na, Zn, Fe, Ca, K, F, O, Mn, and C, with varied mass percentage abundance pointing to Aluminosilicates, Aluminosilicates-soot and likely psilomelane for both TSP and PM10. Varied Metal concentrations in decreasing order of Al > Zn > Mg > Fe > Si > Mn were recorded for TN and NV, while a decreasing order of Mg > Al > Zn > Si > Fe > Mn was recorded for AK. The dry season recorded higher PM-bound metal concentration than the wet season. The metallic concentrations of Fe, Si, Mg and Zn corresponded to a THQ of below unity for both children and adults. However, the estimated THQs for Al and Mn ranged from 2.22 to 19.44 and from 2.64 to 21.78, indicating an adverse health impact on the exposed population. The major PM emission sources were identified as crustal dust, traffic sources and industrial/mining activities. This study establishes a baseline for the study area by providing critical insight into PM pollution, its sources, associated health impacts and priority areas for management intervention. Future investigations of PM chemical composition should extend beyond elemental analysis to include water-soluble fractions, mineralogical characteristics and mechanism of action, particularly in relation to human health, to achieve a more comprehensive understanding of PM properties and behaviour.

Fluvial flooding predominantly occurs in areas near large water bodies. This study utilized an integrated approach combining Geographic Information System (GIS), Remote Sensing and the Analytic Hierarchy Process (AHP) to assess fluvial flood vulnerability in the Lower Manya Krobo Municipality, Ghana. Ten causative factors of flooding were derived from remotely sensed data and weighted using AHP. The model revealed that 9.19% of the municipality is in a very high-risk zone, and 26.76% is in a high-risk zone, with slope and elevation identified as the primary contributors to flooding. Notable towns and landmarks within the very high- and high-risk zones include parts of Kpong and Akuse, along with Obele M.A. Primary School, Modern Senior High School, and Presby and New Covenant Apostolic Churches. These findings highlight vulnerable areas and provide valuable data for disaster management agencies, such as the National Disaster Management Organization and the Volta River Authority, to enhance flood preparedness and mitigation efforts in the region.

This study investigated the hydrogeochemistry, spatial distribution, health risks, and underlying mechanisms of elevated fluoride (F-) concentrations in groundwater across communities situated within the Voltaian Supergroup of the semi-arid Volta Basin in Ghana. This was achieved through a medical geology framework that integrates hydrogeochemistry, GIS-based spatial analysis, geochemical modelling, unsupervised machine learning, petrography, PXRD analysis, Monte Carlo Simulation, Sobol sensitivity analysis, probabilistic health risk assessment, and community-based health surveillance to investigate the geogenic sources, spatial distribution, and health implications of F- in groundwater. Thirty-one borehole water samples were collected from Mion District, Karaga District, and Gushegu Municipality in the Northern Region of Ghana. Petrographic and PXRD analyses identified minerals such as quartz, albite, biotite, zeolite, illite, and opaque minerals in the reservoir rocks. Lithological evaluation of boreholes confirmed the reservoir rocks at depth. These minerals dissolve in groundwater, enriching it with various ions. The major cations in groundwater were found in the order: Mg2+ > Ca2+ > K+ > Na+, and the major anions in the order: HCO3- > SO42- > Cl-. The groundwater is alkaline, with Mg2+ - HCO3- as the common water type. Fluoride concentrations ranged from 0.23 to 19.5 mg/L, with an average of 4.71 mg/L, exceeding the World Health Organization's guideline value of 1.5 mg/L in about 87 % of the boreholes around the northeastern fringe of the study area. The high F- levels are primarily due to natural sources and are influenced by water-rock interaction, weathering, ion exchange, and mineral dissolution and precipitation. Health risks associated with fluoridated drinking water were assessed using the US Environmental Protection Agency Model, Monte Carlo Simulation, and Sobol sensitivity analysis revealing non-carcinogenic risks such as dental and skeletal fluorosis, particularly affecting children, followed by adults, teenagers, and infants. Sobol sensitivity analysis reveals that body weight is the primary driver of the non-carcinogenic risk variability across the population, followed by water consumption and F- concentration. However, age-specific patterns show that for infants and children, F- levels and body weight are key contributors, while in teenagers and adults, F- concentration alone dominates risk variability. Community-based health surveillance confirmed these findings. Due to the high F- levels, the people now use polluted surface water for drinking and domestic purposes, leading to severe gastrointestinal infections. Therefore, urgent attention is needed by the stakeholders to curb the threat of groundwater contamination and the associated human health risks.

Regulators in the banking industry in the Sub-Saharan Africa (SSA) region are progressively concentrating on corporate innovation and bank social, health and environmental disclosures (BSHED) as crucial corporate governance (CG) structures to improve bank financial performance (BFP). Nonetheless, it is unclear whether corporate innovation and BSHED can lead to an improvement in BFP. Understanding these crucial relationships can help regulators and banking practitioners to design and implement sustainable strategies that can enhance BFP. This paper seeks to bridge this gap by investigating the effects of corporate innovation and BSHED on BFP and examining whether CG mechanisms moderate these relationships. Through the lens of multi-theoretical perspectives, the study conducts fixed effects and dynamic two-step system generalised method of moments models over a dataset of 2785 observations (220 banks) from 16 SSA countries between 2007 and 2022. The findings show that corporate innovation has a positive effect on BSHED and BFP. The study detects that corporate innovation has a positive association with BFP. We find that BSHED is positively related to BFP. The study identifies and tests possible moderators of these associations. We observe that CG mechanisms positively moderate these relationships. Finally, we establish that the predicted associations vary across different operating periods. The findings help stakeholders including corporate boards, executives and regulators to understand how innovation investments and internal governance structures such as CG mechanisms are associated with BSHED and BFP.

Accurate lithology classification is essential for optimizing drilling operations and improving reservoir understanding. Conventional well logging methods often suffer from delays, limiting real-time decision-making. This study introduces drilling acoustic signals as a high-frequency alternative and presents a novel hybrid MLP-XGBoost framework for lithology prediction. Acoustic features, including dominant frequency, amplitude, spectral centroid, spectral bandwidth, and zero-crossing rate, were extracted from drilling sounds recorded at the Johan Sverdrup 16/2 6 well. Three machine learning models were trained: Multi-Layer Perceptron (MLP), XGBoost, and the hybrid MLP-XGBoost model. The hybrid model, unique in its design, leverages the MLP as an adaptive feature engineer to learn complex lithology-specific patterns, while XGBoost performs robust classification, even with noisy data. This two-stage approach achieved the most balanced performance, with 86% accuracy on the training set and 85% on the testing set, outperforming the standalone MLP (78% and 76%) and XGBoost (98% and 82%). Feature importance and SHAP analyses revealed that dominant frequency, amplitude, and spectral centroid were key contributors, demonstrating how the model integrates both dynamic and steady acoustic characteristics to distinguish lithologies. Validation across eight North Sea wells yielded accuracies from 81.8% to 89.5%, confirming the model’s generalization potential. Overall, this study establishes, for the first time, a robust and scalable machine learning framework for real-time lithology classification using exclusively acoustic signals, offering a practical complement to traditional formation evaluation techniques and opening new opportunities for integration with operational drilling parameters.