A detailed organic matter characterization of pretreated seawater using low pressure microfiltration hybrid systems

In this study, two different submerged membrane hybrid systems (SMHSs) namely (i) submerged membrane coagulation hybrid system (SMCHS) and (ii) submerged membrane coagulation-adsorption hybrid system (SMCAHS) were investigated as pretreatment options for seawater reverse osmosis. Organic matters in seawater before and after pretreatment were characterized in terms of XAD fractionation, molecular weight distribution (MWD) and fluorescence. A detailed study on the seawater organic matter (SWOM) structure was made through 1H-nuclear magnetic resonance (1H NMR), pyrolysis-gas chromatography–mass spectrometry (Py-GC/MS) and liquid chromatography–mass spectrometry-ion trap-time of fright (LC/MS-IT-TOF). The seawater investigated in this study is mainly composed of hydrophilic matter (57±3.2%). SMHSs removed a significant amount of organic matter. The EEM fluorescence showed a removal of humic-like materials by SMHSs. In addition, humic-like materials relative to protein-like compounds were reduced significantly but the aromaticity of humic-like materials increased. After pretreatment by SMHSs, humics and biopolymers of over 900 Da. were found to be reduced and their structure associated with element composition was also changed. The transformation of the SWOM structure after SMHSs pretreatment may have been due to hydrolyzation or oxidization of the organic compounds such as humics and biopolymers resulting in poly-conjugation to aromatic compounds. SMHSs were effective in improving the RO performance leading to higher RO permeate flux and lower permeate flux decline. The pretreatment reduced the amount of foulants on the RO membrane.

► Pretreatment of submerged membrane hybrid systems (SMHSs) with coagulation and/or adsorption.
► A detailed study on the structure of SWOM using 1H NMR and LC/MS-IT-TOF.
► The structure associated with element composition of biopolymer and humics altered.
► Organic characterization with 3D-EEM and MW distribution to understand pretreatment.