Earth Sciences Pakistan (ESP)

This study deals with taxonomic consideration of one new genus: Ornatodella Anan (2023) from Pakistan, and also thirteen benthic and two planktic foraminiferal species were erected from two countries in the Southern Tethys: Pakistan and India. The recorded foraminiferal species belong to thirteen benthic genera and two planktic genera (one from Pakistan and the other from India). Fifteen species of the recorded assemblage are from Pakistan (⁓94%), while only one species from India (⁓6%). One of the recorded species belongs to Suborder Textulariina, 2 to Miliolina, 1 to Lagenina, 12 to Rotaliina, while 2 species to Globigerinina. Some of the recorded species present an evolutionary foraminiferal lineages.

Coal consists of mainly organic materials and minor inorganic constituents. These components determine coal’s possible value in the metallurgical, power generation, and other sectors. Coal is one of the most affordable and dependable energy sources used to produce electricity globally. Geological mapping of Okobo coal in the Anambra Basin of Nigeria was carried out and coal samples taken and subjected to proximate analysis, ultimate analysis, calorific value determination, and free swelling index test in order to source coking coals for metallurgical industries and thermal coals for power generation in the nation. The average composition of the coal, according to the results of the proximate analysis, is 11.94% moisture, 5.66% ash, 36.73% volatile matter, and 45.67% fixed carbon. The ultimate analysis also showed that the average composition of coal is as follows: 60.20% carbon, 4.46% hydrogen, 1.59% nitrogen, 15.52% oxygen, 0.65% sulphur, and 0.027% phosphorus. The coal samples have an average heating (calorific) value of 10452 Btu/1b (24311 kj/kg) and a free swelling index (FSI) of zero (0). These traits imply that coal is non-coking and unsuitable for use in the production of iron and steel in the metallurgical sector. The coal, however, is suitable for boilers, industrial heating ovens, and the production of power.

This research compares ordinary kriging (OK) and inverse distance weighting (IDW) techniques in the estimation of volume, tonnage, average grade, and ounces of gold within the alluvial gold deposit located in the northern part of the Kibi-Winneba gold belt, Ghana, to ascertain the technique that yields results that align more closely with actual production values. The deposit contains fine nuggets of gold in gravel profiles covered by sharply contrasted overburden materials. Overburden and gravel layers from 219 hand-dug pits were logged, while the gravel portions were sampled and assayed by washing to determine the gold grade of each pit. Implicit modelling of the deposit using wireframes was carried out in Datamine Studio RM software. The block model and resource estimates were compared and accurately reflected the input sample grade. At a cutoff grade of 0.4 g/m3, the resource has 12.453 million cubic metres of auriferous gravel at an average grade of 0.51 g/m3. This is expected to produce about 204238 ounces of gold after mining and extraction. Different block models and resource estimates using the inverse distance weighting (IDW) approach were used to compare the OK estimate. The IDW2 correlated well with the OK model. Hence, a resource estimate was generated using the IDW2 and the difference in the expected ounces of gold was calculated to be 2.02% lower than that of the OK estimate.

The source parameter imaging technique was used for the composite Ekiti State’s aeromagnetic data to determine the depth of the magnetic basement at different locations in the research area. Determining basement depth in this type of terrain is essential for groundwater exploration which is crucial for sustenance of life. The aeromagnetic data covering the entire state were processed using the oasis montaj 8.4 software. Firstly, the first order polynomial fitting method was used for the regional – residual separation of the total magnetic field intensity data before the application of the reduction to equator transformation filter on the residual component. The data were further enhanced by using upward continuation smoothing filter to remove the effect of the short-wavelength noise from the data. The depth of the magnetic basement, derived from the source parameter imaging technique, ranges from 116 m to 658 m, which indicate the depths to the top of the magnetic sources within the basement rocks in the study area.

Road construction is a critical infrastructure vital for human civilization and infrastructure. However, Nigeria is currently experiencing significant road failures across all levels of government, from federal to municipal. Among the various factors contributing to road failure are corruption and poor maintenance or supervision, but the primary reason for road failure is the quality of materials used in road construction. This research presents a laboratory study of four soil samples as material used as base and subbase in road construction. All analyses were conducted following British Standards. The liquid limit of the samples ranged from 27.1% to 29.3%, while the plastic limit ranged from 19% to 19.7%, and the plasticity index ranged from 8% to 9.65%. Linear shrinkage values were between 13.0% and 13.4%, and moisture content ranged from 10.2% to 11.2%. Maximum dry densities (MDD) varied from 2101 kg/m³ to 2144 kg/m³, with optimum moisture contents (OMC) ranging from 10.9% to 12.4%. The California Bearing Ratio (CBR) for unsoaked samples ranged from 35% to 70%, while soaked samples ranged from 27% to 56%. Due to their lateritic composition and compliance with Nigerian regulatory standards for geotechnical properties, the soils in the study area are deemed suitable as subbase materials. However, soil improvement is necessary to make them suitable for base course applications.

Climate change has been identified as a primary driver of changes in land use and cover, resulting in negative impacts on the environment, biodiversity, and socioeconomic factors. This study looked at the spatiotemporal changes in land use and land cover in Dugda Dawa Woreda, Southern Ethiopia, as a result of climate change. The study used a combination of remote sensing, GIS analysis, and field surveys to analyze alterations in land use patterns across time. Agriculture, settlements, wetlands and grasslands, barren terrain, and forest regions were discovered and quantified using satellite imagery from various time periods. Climate data analysis indicated that from 1991 to 2021, average monthly temperatures ranged from 16.23°C to 24.43°C, with an average yearly temperature ranging from 19.18°C to 21.19°C. Rainfall varied by season, with larger quantities recorded in months such as August, July, September, June, May, and April. The study found that land cover percentages changed over time, with wetlands and grasslands accounting for 41.19% in 1990-2000 and agriculture accounting for 73.23% by 2000-2010. Climate, economic, and demographic variables influenced land use and land cover changes, resulting in challenges such as bare land expansion, increased runoff, and soil erosion. The study’s recommendations underlined the need of sustainable land management methods in improving ecosystem resilience and combating the detrimental effects of climate change and land use changes.

This study focuses on assessing the water quality and heavy metal contamination in surface and groundwater in the Owo metropolis. Water, a vital resource for human existence and ecosystem maintenance, plays a crucial role in various activities. The research emphasizes the significance of water quality in preventing waterborne illnesses, particularly in areas lacking proper sanitation. The physicochemical characteristics of surface and groundwater were analyzed, revealing parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and concentrations of various metals. The results indicate that the studied parameters generally fall below the World Health Organization (WHO) and Nigerian guidelines, suggesting a lower risk to public health. Cluster analysis revealed associations between water samples, linking certain clusters to anthropogenic activities like dumpsites, farming, and households. Correlation analysis demonstrated relationships between different hydrochemical parameters, highlighting potential sources of major ions. Geochemical assessments, illustrated through Piper and Gibbs diagrams, indicated the dominance of Ca-Cl water types and the influence of rock weathering on water composition. Bivariate plots and ion exchange processes provided insights into water-rock interactions. The study also evaluated ecological risks and water quality indices, revealing potential risks from heavy metals. Hazard and cancer risk assessments suggested the cumulative potential of heavy metals to pose health risks, particularly for children. The research emphasizes the need for urgent interventions to address water quality issues in the Owo metropolis.

This study is a seismic hazard assessment done with a high degree of detail over a central metropolitan area across Nigeria to understand the risks, vulnerabilities, and mitigative strategies. Probabilistic seismic hazard analysis shows high seismic hazard levels in the cities, for example, in Lagos (Peak Ground Acceleration – 0.432g) against lower risk areas such as Maiduguri (0.143g). A study on susceptibility analysis discovered that steel frame (77.74%) and precast concrete (71.13%) are most likely affected by seismic disturbances among the common construction materials. A spatial model comprising population density overlaid with the seismic exposure index identified the cities with high risks in the densely populated mega-cities like Lagos (population density 7231 people/square km; index of seismic exposure 0.823). Critical infrastructure comes up with significant dangers within, which include hospitals (0.8761), airports (0.771), and dams (0.63). Hazard exposure indexes concerning socioeconomic vulnerability indices map emergency hotspots in Lagos (hazard index = 0.823; socioeconomic index = 0.754), which asks for special attention or explicit planning for resilience. The study’s results call for implementing seismic-resistant building codes, retrofits of aging facilities, strengthening disaster preparedness, and a holistic approach toward dealing with the socioeconomic fragility of earthquake-prone urban locations. Multihazard speedy modeling empowers cities with quantitative information to apply uniquely customized urban policies based on the evaluation of risk factors. The main recommendations reflect the issues of resilient cities like Lagos and the mandatory seismic safety standards for essential buildings such as hospitals worldwide. The suggestions also highlight the importance of integrating joint resistance and socio-economic vulnerability reduction strategies. With data-driven risk mitigation, Nigeria can strengthen the resilience of its fast-growing urbanscapes in terms of standing against earthquakes.

Extensive growth in development, urbanization, and population has exacted more pressure on the availability and quality of groundwater resources. Human effort has been directed at solving groundwater scarcity in a crystalline basement rock environment through the identification of joints, cracks, fractures, faults, and weathered materials that may exhibit favorable disposition to groundwater accumulation for water sustainability. This research applied Multi-Criteria Decision Analysis (MCDA) in the context of Analytical Hierarchical Process (AHP) to geoelectric parameters to model Groundwater Potential Zones (GWPZ) in the Federal Polytechnic, Ado Ekiti, Ekiti State, Nigeria. The Electrical Resistivity method was adopted using 2D Resistivity Tomography and Vertical Electrical Sounding (VES) utilizing Schlumberger configuration. 2D Resistivity Tomography was delineated to determine vertical and lateral ranges in apparent resistivity of the subsurface geological properties favorable to groundwater accumulation and development. Eighteen (18) VES were acquired across the study area. The iterated VES results were used to generate geoelectric sections, maps, and second-order parameters. The MCDA in the context of the AHP technique was used to assign scores to various contributing parameters based on their relative contribution to groundwater potential. The GWPZ was generated by incorporating the selected and weighting seven important defined variables (Coefficient of anisotropy, overburden thickness, aquifer resistivity, aquifer thickness, storativity, transmissivity, and hydraulic conductivity) in the Surfer 12 environment in reflection to their groundwater availability. The groundwater potential was categorized into high, moderate, low, and very low. Very low to low groundwater potential characterized the entire study area, occupying 75%, with moderate to high occupying 25%. The findings revealed that the study area was characterized by very low to low groundwater potential. This research will assist in the development and monitoring of groundwater occurrences by decision policymakers to improve recharge techniques, especially in very low and low groundwater recharge zones.

Earthquake early warning systems have become vital for minimizing damage from seismic events; however, their automated detection capabilities need strengthening to provide real-time alerts. Current algorithms have limitations in identification of P-waves and magnitude estimation, impacting warning lead times. Additionally, existing single-algorithm dependent systems are prone to errors. There is a need for standardized practices to optimally select and combine algorithms. Machine learning and artificial intelligence show promise to make detection more robust. Models trained on diverse seismological data can learn complex patterns to detect emergent P-waves earlier and refine magnitude assessment. However, research exploring such data-driven approaches within early warning systems is limited. This study aims to address this research gap and strengthen automated detection capabilities. It proposes a machine learning model integrating multiple existing algorithms using a novel prioritization framework. Performance is evaluated on real earthquake datasets through simulations vis-à-vis single algorithms. By developing an optimized multi-algorithm framework, this study seeks to improve warning lead times and reliability. The model is designed considering operational requirements of early warning systems. Comparison of results with past methods helps evaluate contributions to the field. Overall, the research strives to enhance seismic hazard mitigation through more efficient automated detection in early warning networks.