Multi-Regression Prediction of Metal Partition Coefficients under Various Physical/Chemical Conditions Design of Experiments As, Cr, Cu, Ni and ZnChemistry & Physics Faculty Publications
AbstractThe behavior of metals in surface water is complex and their partition coefficients can be impacted by many factors. Organic matter (OM) content in sediments, pH and salinity, are factors that may influence speciation and partitioning of metals. The difficulty in describing the impacts and relationships are that these processes are interconnected with no dominant associations among all. In this study, the partitioning of five metals (As, Cr, Cu, Ni and Zn) under different levels of salinity, pH, and OM content were investigated. A series of factorial design experiments are evaluated in which three levels of OM are tested each time against five levels each of salinity and pH; the design of experiments was generated by the statistical software program MiniTab16®. All metals tested showed a trend of increasing Kd with the increase of OM 0.36% to 4.32%. Higher Kd were the result of the increase in pH from 3-10.5 and lower Kd values resulted after an increase in salinity 0-3%. However, within that lower range of salinity, a positive linear correlation between Kd and salinity was observed which is attributed to potential formation of insoluble metal species with the increase of salinity. Multiple regression equations with the variables pH, OM and salinity were generated to predict Kd of each metal. The study showed no interaction between salinity/OM and pH/OM for all five metals.
Citation InformationAlkhatib EA, Danielle G, Chabot T (2016) Multi-Regression Prediction of Metal Partition Coefficients under Various Physical/Chemical Conditions “Design of Experiments As, Cr, Cu, Ni and Zn”. Hydrol Current Res 7:241. doi:10.4172/2157-7587.1000241