Preferential sorption of polysaccharides on mackinawite: A chemometrics approach

Abstract

Marine sediments represent the most important sink for natural organic matter (NOM) across geological time spans. Strong associations between minerals, including iron oxides, and organic matter reaching the seafloor play a fundamental role in this preservation and have been known for some decades. Despite the importance of this protective mechanism in the balances of the global carbon budget, very little is known about the affinity of NOM for reduced iron species such as mackinawite (FeS) in the anoxic layers of sediment. In this study, equilibrium partition coefficients (Kd) for three types of NOM (soil extract, corn leaf extract and plankton extract) on FeS were determined through batch sorption experiments. Models were then built via multiple linear regression (MLR) and partial least squares regression (PLSR) in order to predict sorption Kd’s based on the chemical characteristics of the NOM. Investigation of the PLSR regression coefficients indicates that functional groups characteristic of polysaccharides are the greatest positive predictors of NOM sorption onto FeS at marine sediment porewater pH. This conclusion was independently verified by analyses of NOM FTIR spectra pre- and post-sorption. PLSR outperformed MLR with a lower root mean square error of prediction (RMSEP) of 53.4 L/kg compared to 64.0 L/kg. To our knowledge, this research presents a novel machine-learning approach to the quantitative modelling of NOM sorption to minerals found in anoxic marine environments.