Document Type
Peer-Reviewed Article
Publication Date
5-23-2016
Abstract
The 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.
DOI
10.4172/2157-7587.1000241
Recommended Citation
Alkhatib, E. A., Grunzke, D., &, Chabot, T. (2016). Multi-regression prediction of metal partition coefficients under various physical/chemical conditions “Design of experiments As, Cr, Cu, Ni and Zn”. Hydrology: Current Research, 7(2). Doi:10.4172/2157-7587.1000241
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Comments
When this article was researched and written Danielle Grunzke and Trey Chabot were Master of Science students in the Environmental Science and Management program in the Department of Biology at Sacred Heart University.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.