Determination of threshold values and heavy metal pollution assessment of soils in an industrial area in Ghana.

Industrial activities have the potential to pollute soils with a wide variety of heavy metals (HMs). In Ghana, however, assessment of HM pollution of soils in industrial areas remains limited. Accordingly, HM soil pollution in one of the industrial areas in Accra, Ghana was assessed. Soil samples were taken and analysed for HMs, including Fe, Zr, Zn, Ti, Sr, Rb, Mn, Pb, Cu, and Co, using X-Ray Fluorescence (XRF). HM geochemical threshold values (GTVs) were determined to establish soil HM pollution levels and identify areas needing remediation. Furthermore, risk assessments were conducted to evaluate the potential ecological and human health risks associated with these metals. The mean concentrations of Fe, Zn, Rb, Sr, Zr, Ti, Mn, Co, Cu, and Pb in the soils were: 27133.83, 147.72, 16.30, 95.95, 307.11, 4663.66, 289.85, 418.54, 44.97, and 112.88 mg/kg, respectively. Generally, the concentrations of HMs decreased with depth, although some lower layers exhibited elevated HM levels. Soil pollution levels were categorized as low for Fe, Rb, Zr, Ti, Mn, Co, and Cu; moderate for Sr and Zn; and considerable for Pb. Notably, the northwestern part of the study area displayed a considerable to very high degree of HM contamination. While HMs in the soils posed low ecological risk, the human health risk assessment indicated potential health effects from Co, particularly in children. The presence of HMs in the soils was noted to originate from both natural geological phenomena and human activities, including industrial operations, agricultural practices, landfill activities, and vehicular emissions.

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana.

The main objective of this study was to map the quality of groundwater for domestic use in the Nabogo Basin, a sub-catchment of the White Volta Basin in Ghana, by applying machine learning techniques. The study was conducted by applying the Random Forest (RF) machine learning algorithm to predict groundwater quality, by utilizing factors that influence groundwater occurrence and quality such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation Index (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile Curvature (PRC), Lineament density, Distance to faults, and Drainage density. The groundwater quality of the area was predicted by building a Random Forest model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) of existing boreholes, to serve as an indicator of the groundwater quality. The predicted WQI of groundwater in the study area shows that it ranges from 9.51 to 69.99%. This implied that 21.97 %, 74.40 %, and 3.63 % of the study area had respectively the likelihood of excellent. The models were found to perform much better with an RMSE of 23.03 and an R2 value of 0.82. The study conducted highlighted an essential understanding of the groundwater quality in the study area, paving the way for further studies and policy development for groundwater management.

Hydrogeological conditions of a crystalline aquifer: simulation of optimal abstraction rates under scenarios of reduced recharge.

A steady state numerical groundwater flow model has been calibrated to characterize the spatial distribution of a key hydraulic parameter in a crystalline aquifer in southwestern Ghana. This was to provide an initial basis for characterizing the hydrogeology of the terrain with a view to assisting in the large scale development of groundwater resources for various uses. The results suggest that the structural entities that control groundwater occurrence in the area are quite heterogeneous in their nature and orientation, ascribing hydraulic conductivity values in the range of 4.5 m/d to over 70 m/d to the simulated aquifer. Aquifer heterogeneities, coupled possibly with topographical trends, have led to the development of five prominent groundwater flowpaths in the area. Estimated groundwater recharge at calibration ranges between 0.25% and 9.13% of the total annual rainfall and appears to hold significant promise for large-scale groundwater development to support irrigation schemes. However, the model suggests that with reduced recharge by up to 30% of the current rates, the system can only sustain increased groundwater abstraction by up to 150% of the current abstraction rates. Prudent management of the resource will require a much more detailed hydrogeological study that identifies all the aquifers in the basin for the assessment of sustainable basin yield.