Metamorphic rocks are useful in many heat-based geotechnical facilities since they are believed to be strong and durable due to their crystalline nature. However, rocks get degraded under repeated thermal stress. This work evaluates the effect of repeated heating and cooling on the geochemistry, mineralogy, microstructure and physical properties of some metamorphic rocks from the Nigerian Basement Complex. Effect of cooling condition was also investigated. Marble, granite gneiss and banded gneiss were subjected to fifty cycles of heating and cooling in air and water using heating temperature between 100 oC and 700 oC. Marble samples used in the study completely disintegrates at the 35th cycle of heating and air-cooling at 500 oC. They could not withstand more than 26 cycles of heating and air-cooling at higher temperature of 700 oC. The porosity, water absorption and degradation degree are highest in marble and least in granite gneiss. The crushing strength of both samples of granite gneiss is higher than that of banded gneiss and marble. Similar trend was observed in the residual values of these physical properties after fifty thermal cycles. Microstructural images showed micro-cracks in the thermally stressed rocks. However, no changes in mineralogy and geochemistry were recorded in the rocks. The water – cooled samples exhibited greater degradation than the air-cooled ones. Granite gneiss is more resilient than banded gneiss while both gneisses are more durable than marble. Metamorphic grade appears to play a major role in the resilience of the rocks to degradation.

This study examines the performance of predictive machine learning models implemented with an integrated tool of geospatial technology (GST) and machine learning techniques (MLT). Historical data of daily volume of water consumed in dry and wet seasons was gathered through questionnaires, and integrated with socioeconomic data, weather data, property data and geospatial data. The datasets were passed through Principal Component Analysis algorithm to select few features that explain the variability in all the original variables. The selected features were inputted into four predictive models – Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). Three error metrics, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R squared (R2) score were used to measure the model performances. All four models performed very well in predicting volume of water consumed by poor urban households as they produced RMSE of 57 litres, 46 litres, 63 litres and 52 litres respectively during training, and R2 score of 88%, 92%, 88% and 90% respectively. Significance test performed at 95% confidence level shows that there is significant difference between volume of water consumed during dry and wet seasons.

This research evaluates groundwater quality in Effurun, southern Nigeria, particularly focusing on contamination from solid waste sources such as dumpsites and abattoirs in Osubi, Ugbomro, and Agbarho. The geophysical assessments to identify contamination levels in the study area were employed by integrating Wenner-Werner arrays and vertical electrical sounding (VES) data. Using the WINRESIST and RES 2D inversion software, resistivity models revealed significant contamination at values below 50 Ohm-meters, with lateral extents ranging from 49 to 85 meters and depths up to 6.39 meters. Notably, the Osubi abattoir exhibited the highest contamination level, while some areas in Ugbomro dumpsite and Agbarho dumpsite remained less affected. The findings highlight the vulnerability of unconfined aquifers characterized by weak protective layers. The subsurface pollution in the study areas necessitates urgent improvements in waste management and groundwater protection strategies. Additionally, the results from Dan Zarrouk parameters indicated low aquifer protective capacity in key areas, correlating with observed contamination. This study underscores the critical need for targeted interventions to safeguard groundwater resources amid rising industrialization and waste generation in the region.