GPH-International Journal of Applied Science

EVALUATION OF HEAVY METAL CONTENT IN SOIL AND SURFACE WATER AROUND E-WASTE BURNING SITES IN LAGOS STATE

2026-08-08T07:47:38+00:00

This study evaluated the heavy-metal contamination in soils and surface water at three informal e-waste burning sites in Lagos State: Alaba International Market, Ikeja Computer Village, and Abule-Egba scrapyard. The study objective was to evaluate the heavy metal pollution indices of soil and water and. A descriptive cross-sectional design was used. Soil samples were collected at two depths (0–15 cm and 15–30 cm), while surface-water samples were taken from nearby drainage channels. The heavy metal concentrations (Pb, Cd, Cr, Cu, Zn, and Ni) were determined using Atomic Absorption Spectrophotometry. Pollution indices (CF, PLI, NPI, WQI) and ANOVA was applied to interpret the results. The soils at the three sites were strongly acidic compared with the control soil, showing the long-term effect of e-waste burning and dismantling. Heavy-metal concentrations were extremely high in the surface soils, with lead reaching 432 mg/kg at Alaba, and remained elevated in deeper layers, indicating downward movement over time. Water samples also exceeded WHO limits, and the Water Quality Index rated the sites as poor to very poor. A notable finding was the unusually high zinc level at Abule-Egba, showing that even small scrapyards can become serious contamination hotspots. Overall, the study provides a clearer understanding of how heavy-metal pollution develops and spreads at informal e-waste sites and highlights the need for stricter controls, safer recycling practices, improved testing, and continuous monitoring to reduce long-term environmental risks.

Artificial Intelligence, Climate Governance, and Environmental Justice: Comparative Perspectives from the United Kingdom and Africa

2026-08-22T10:01:35+00:00

This paper examines the recent role of artificial intelligence (AI) in climate governance and its impacts on environmental justice with the United Kingdom and Africa as a comparative case study. As AI is increasingly incorporated into environmental monitoring and climate modelling, and into decision-making, it is often viewed as a means of making climate mitigation and adaptation more efficient. Nevertheless, it is implemented in uneven global conditions under the influence of variations in technological capacities, availability of data, and institutional coordination. This poses critical questions regarding the distribution of the benefits and impacts of AI-led climate governance across regions. The paper uses a qualitative and comparative approach to analyse the policy integration strategies, governance structures and capacity conditions that affect the application of AI in both situations. The findings suggest that the United Kingdom has a comparatively well-integrated policy frameworks and institutional settings, which can enable data-driven solutions to climate governance, but many African settings are limited by infrastructure, funding, and fragmentation of governance. These gaps characterize the extent to which AI can be optimally utilized in addressing climate problems. This article posits that AI is not a neutral technology, but it is an element of existing structural conditions, which impact access, use, and outcomes. Its inclusion within climate governance can, therefore, solidify existing inequalities in the environmental risk, adaptive capacity and participation. The paper concludes that the interaction of AI and climate governance cannot be analyzed without addressing larger issues of capacity and justice, particularly in the form of offering emerging technologies to improve more inclusive and equitable environmental outcomes.

EFFECT OF SAFETY TRAINING ON KNOWLEDGE OF WORKERS IN SELECTED INDIGENOUS OIL AND GAS COMPANIES IN PORT HARCOURT METROPOLIS

2026-08-26T10:04:35+00:00

This study investigated the effect of safety training on knowledge of workers in selected indigenous oil and gas companies in Port-Harcourt metropolitan. Quasi-experimental research design was adopted. The study population comprised of field workers in five selected indigenous oil and gas firms in Port-Harcourt. The sample size comprised of 137 workers from which 123 respondents completed the pre-test, training and post-test stages. The data collection was done using safety training manual and test-questions. The test question designed on “True or False: format was used to examine the knowledge of workers before and after the safety training. The reliability of the instrument was ascertained using Cronbach Alpha test. Data analysis was carried out with descriptive statistics and Analysis of Variance (ANOVA) using SPSS version 26. The revealed that; overall knowledge of the workers on fire safety training, emergency first aid response training and electrical safety training increased from 48.55% to 99.12% before and after the safety training. Z-test revealed that there was positive and significant difference between the overall levels of knowledge before and after the safety training (mean difference = 44.57%, p-value = 0.000<0.05). ANOVA results also revealed that there was significant difference in effect of safety training on knowledge among the sampled companies (p-value = 0.047 < 0.05). It was concluded that the safety training improved the knowledge of workers in the sampled indigenous oil and gas companies.

RESRAD‑BUILD SIMULATION OF INDOOR RADIATION EXPOSURE PATHWAYS IN NIGERIAN RESIDENTIAL BUILDINGS

2026-09-02T09:42:23+00:00

Indoor radiation exposure from naturally occurring radioactive materials (NORMs) in building materials poses potential long‑term health risks, yet comprehensive pathway‑specific dose assessments using advanced computational tools remain limited in Nigeria. This study evaluated activity concentrations and simulated indoor exposure pathways for 72 building material samples collected from Northern Delta State, Nigeria. Gamma spectrometry measured ⁴⁰K, ²³⁸U, and ²³²Th at 94.00 ± 15.52, 11.57 ± 2.32, and 9.05 ± 1.43 Bq/kg, respectively were all below UNSCEAR global averages. A RESRAD‑BUILD model (implemented in Python) simulated a typical Nigerian residential room with 5.0 m × 4.0 m × 3.0 m dimensions, 0.15 m wall thickness, 0.5 h^-1 air exchange rate, and 80 % indoor occupancy. The mean annual effective dose (AED) from all pathways was 0.0875 ± 0.0126 mSv/y, well below the ICRP public dose limit of 1 mSv/y. External gamma exposure was the dominant pathway, contributing 82.9 % of the total dose, followed by ingestion (17.1 %) and inhalation (0.04 %). Simulated doses agreed closely with empirical estimates (mean difference 3.1 %). Materials from South Africa and Italy showed the highest radionuclide concentrations while Chinese and local Nigerian materials showed the lowest. These results demonstrate that RESRAD‑BUILD simulation provides a reliable, pathway‑resolved assessment of indoor radiation exposure, and that external gamma radiation from building materials is the primary concern in typical Nigerian residential buildings.