Abstract

Modelling applications in trends of environmental sciences are currently very much sought after in identifying the determinants affecting the ecosystem. This study thus aspires to demonstrate the modelling procedures in the study of the relationship between the concentration of heavy metals in the soft tissues and the physical properties of Geloina similis by using the exponential regression modelling approach. Data collected for this study were obtained from a mangrove lagoon in Salut, in the state of Sabah, East Malaysia. Experimental analyses were carried out in the laboratory of the Environmental Science Program of the Faculty of Science and Natural Resources, Universiti Malaysia Sabah. Physical properties of molusc (Geloina similis) considered were differences in length, height, width, wet weight, and dry weight, besides the length and width of the soft tissues. Mathematical modelling procedures were then employed, involving listing out all the possible models, model transformation of non-linear to linear models, multicollinearity test, coefficient test, followed by the Runs test and residuals normality test. The best model obtained was tested for its robustness and accuracy for prediction via the Mean Absolute Percentage Error (MAPE). Findings showed that the physical properties of Geloina similis involving height (X₂), wet weight (X₄), and interaction between height and length tissue (X₂A), had significantly contributed to the concentration of heavy metals accumulation (HMA) as given by equation. Due to the absorption habit of Geloina similis, and the presence of heavy metals in the soils, it can be concluded that the presence of heavy metals concentration in the soft tissues of G.similis are thus found to have significant relationship with the molusc physical properties. 

Keywords

Environmental sciences, Physical properties, Heavy metal accumulation, Model transformation, Exponential models, Mean absolute percentage error, Goodness-of-fit, Dependent variables, Variance inflation factor, Eight selection criteria