Assessing the Impact of Quarrying Activities on Soil and Surface Water Quality in Mpape, Abuja, Nigeria: An Ai-Driven Approach

Abstract

Quarrying, a critical industry in Nigeria driven by construction demands, poses significant environmental challenges, particularly in areas like Mpape, Abuja, where quarry sites are densely concentrated. This research utilizes advanced AI techniques to assess the impacts of quarrying activities on soil and surface water quality. A total of 50 samples were systematically collected from five quarry and rock crushing sites and analyzed for various physicochemical parameters and heavy metals through standard analytical methods. The results revealed that soil pH levels ranged from 5.89 ± 0.15 to 6.82 ± 1.07, indicating a trend towards acidity, while water samples maintained pH levels between 6.24 ± 0.11 and 7.95 ± 2.00, with most falling within WHO acceptable limits, except at site QW3. However, physicochemical parameters such as total organic carbon (TOC) %, electrical conductivity (EC) µs/cm, and nitrate (NO3-) mg/kg were found to be below WHO guidelines for soil quality. Analysis of water samples indicated elevated mean hardness and calcium (Ca2+) levels at 162.43 mg/l and 95.10 mg/l, respectively, exceeding WHO thresholds, while other parameters like chloride (Cl-), magnesium (Mg2+), sulphate (SO42-), and nitrate(NO3-) remained within acceptable limits. The contamination of soil near quarry sites was evident, with significantly high concentrations of metals, including Cu, Mn, Fe, Pb, Cd, and Ni, detected. Notably, the mean concentration of heavy metals in soil samples ranged from 6.68 ± 0.15 mg/kg (Ni) at site QS4 to an alarming 1875.03 ± 1.12 mg/kg (Fe) at site QS2, while surface water samples exhibited metal concentrations ranging from 0.012 ± 0.00 mg/l (Cd) at site QW2 to 0.520 ± 0.01 mg/l (Mn) at site QW5. Pearson correlation analysis, enhanced through AI methodologies, revealed strong positive relationships among heavy metals and other parameters (Fe/Mn, Fe/Ni, TOC/Pb, hardness/Ca, and pH/Pb), indicating a common source of pollution and strong negative relationships (Mn/Cu, SO42-/NO3-, Cd/TOC) for both samples. This study underscores the potential of AI-driven analysis in environmental science, offering vital insights into the detrimental effects of quarrying in Nigeria and advocating for informed decision-making to mitigate environmental degradation in the African context.