Browsing by Author "Uzoigwe, Luke"

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    Modeling lateral distribution of heavy metal and bio-accumulation in earthworm in the varying acidic surface horizon of waste-polluted soil
    (U. P., 2013) Atulegwu, Patrick Uzoije; Uzoigwe, Luke; Otuonye, Erick; Kamalu, Clifford O.; Onunkwo-Akunne, Austine
    Heavy metal concentrations and its distributions in the soil have been a source of concern to soil usage, particularly to agriculture as concentration and distribution of heavy metal determine to a large extent, the soil quality and consequently that of the crops. The ability to quantify the amount of heavy metal in the soil is of immense importance to soil management. The use of accurate model is essential to estimate the actual soil heavy metal values and its distribution for efficient management. In this study, soil and earthworm samples of the battery-waste-polluted site and that of the background site were collected from five different locations(A,B,C,D and E) along the gradient of decreasing pollution. With five replicates each form one sampling location, twenty–five soil and fifty earthworms samples (two earthworms from each replicate) were collected using stratified random sampling technique. Lead(pb), Cadmium(Cd), Chromium(Cr), Nickel(Ni), Manganese(Mn), Arsenic(As), pH and Mercury(Hg) were analyzed using standard methods. The same process was replicated for the background site. Values of the heavy metals in soil and earthworms were natural and typical of Ameki-Nanka-soil formation. The average range of heavy metals concentrations in soil and earthworm samples from the polluted site were; pb(1025-695 mg/kg), Cd(11.34-6.3 mg/kg), Mn(290-81 mg/kg), pH(2.3-6.9mg/kg),Cr(185-3.7 mg/kg), Ni(12.87-1.7 mg/kg), As(72-4.5 mg/kg), Hg(1.7-0.002mg/kg) and pb(193-37.98 mg/kg), Cd(14.04-0.01 mg/kg), Mn(17.34-1.10mg/kg), pH(6.9-2.3mg/kg), Cr(8.45-0.01 mg/kg), Ni(1.41-0.03 mg/kg), As(0.75-0.01 mg/kg), Hg(0.4-0.009mg/kg) respectively. Concentrations of heavy metals for soil and earthworm samples decreased along the gradient of decreasing pollution of the polluted site. Three models(Linear , Logarithmic and quadratic models) were developed to test their suitability to the data in which Ph was correlated with heavy metals. Inverse correlation was observed with coefficient R2 of between 0.77-0.95 and lowest percentage deviation of the field from the predicted values.
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    ItemOpen Access
    Oil and grease removal from vegetable oil polluted wastewater; advanced oxidation process approach (Fenton Process )
    (Elixir International Journal, 2015) Uzoije, Atulegwu Patrick; Kamalu, Clifford. I. O.; Uzoigwe, Luke
    Oil and grease removal process, through the use of fenton oxidation reaction on an oil polluted wastewater from a vegetable oil plant has been studied. The study was designed to assess the effectiveness of fenton oxidation reaction process in eliminating oil and grease contaminant in the wastewater. The raw wastewater was subjected to analysis through standard methods to determine BOD, Oil and grease, phenol, salinity and sodium batch oxidation process was adopted to remove the oil and grease in which four input parameters ; ph, Fe2+, temperature and hydrogen peroxide(H2O2) were considered. Four runs of experiment were performed where each parameter was varied while the other three remained constant. In each run, oil and grease removal was determined at ten minutes interval for 60 minutes through analysis. The results showed that the highest oil and grease removal efficiencies of 96.28% 98.74%, 99.02 and 93.03%were achieved at the optimum conditions of ph=3, fe2+=3.2 g/l, temp=450C and H2O2=4.5moles respectively and the oil and grease removal was progressive with time until at the point of inflection at 50 minutes where the removal appeared steady. Oil and grease removal efficiency was highly sensitive to the operating conditions. At excess values of the operating conditions, that is, at values beyond the optimum values, the rate of formation of the hydroxyl radical (OH•) became sluggish, impairing removal process. This also explained why the removal efficiency progressed to the plateau at the optimum condition values, and then declined as the values of the operating conditions increased.