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Neural network regression for modelling the effects of selected soil physico-chemical properties on adsorption
Heavy metals in soils have been known as soil pollutants, to constitute serious economic importance as their accumulation has led to reduced agricultural production and quality of life. In the present paper, we studied the adsorption behaviour of selected heavy metals in soils, due to some physico-c...
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| Format: | Conference Proceeding |
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| Published: |
2017
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/5318 | ||
| 042 | |a dc | ||
| 720 | |a Udomboso, C. G. |e author | ||
| 720 | |a Nzelu, N. |e author | ||
| 720 | |a Olu-Owolabi, B. I. |e author | ||
| 260 | |c 2017 | ||
| 520 | |a Heavy metals in soils have been known as soil pollutants, to constitute serious economic importance as their accumulation has led to reduced agricultural production and quality of life. In the present paper, we studied the adsorption behaviour of selected heavy metals in soils, due to some physico-chemical properties. The soil under study was obtained from the River Benue Basin in the middle belt region of Nigeria. The heavy metals considered included lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd), while the physico-chemical properties included hydrogen ion concentration (pH), percentage goethite, percentage humic acid, time, and sorbate concentration. Estimation of the effects was carried out using the statistical neural network at α = 0.05, while the cubic spline was used to interpolate within values and extrapolate forecasted values. Results show that rates of adsorption differ across properties. In all physical properties, except humic acid, Cd is most adsorped at AIC of 0.067, 0.079, 0.002, and 21.137 (all at p<0.05). For humic acid, most adsorped is Zn at AIC of 5.692 (p<0.05). These call for effective soil management system in Nigeria, which is expected to yield reliable data on soil behaviour, as well as concerted effort in eradicating (or reducing) the presence of these pollutants. | ||
| 024 | 8 | |a ui_inpro_udomboso_neural_2017 | |
| 024 | 8 | |a Nigeria Statistical Society 1, pp. 252-256 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/5318 | |
| 653 | |a Soil | ||
| 653 | |a Heavy metals | ||
| 653 | |a Physico-chemical properties | ||
| 653 | |a Adsorption | ||
| 653 | |a Neural network | ||
| 245 | 0 | 0 | |a Neural network regression for modelling the effects of selected soil physico-chemical properties on adsorption |