Effect of Interaction of Non-residual Fractions on Adsorption of Atrazine onto Surficial Sediments and Natural Surface Coating Samples

To quantify the effect of the interaction of non-residual fractions[Fe oxides（Fe）, Mn oxide（Mn）, organic materials（OMs）] in the surficial sediments and the natural surface coating samples on the adsorption of atrazine（AT）, an AT multiple regression adsorption model（AT-MRAM） was developed. The AT-MRAM improves upon the previous AT additional adsorption model（AT-AAM） with superior goodness-of-fit test（adjusted R2=ca.1.000）, F-test and t-test（P〈0.01）, and reveals the effect of the interaction among the components in the surficial sediments（SSs） and na- tural surface coatings samples（NSCSs） on the adsorption of AT, which was neglected by the AT-AAM. Meanwhile, the AT-MRAM was also verified through adsorption experiments of AT and the relative deviation between predicted maximum adsorption of AT and the experimental one is less than 15%. The resulted information shows that Mn is prone to interact with other non-residual components, the total maximum adsorption of AT is inversly proportional to the level of Mn, and Fe and OMs facilitate the adsorption of AT. The results also indicate that the adsorption of AT is not only dominated by Fe, OMs, Fe/OMs, but also restrained by Fe/Mn, Fe/Mn/OMs, with lesser roles attributed to Mn, and the estimated AT distributions among the components do not agree with that previously predicted by the AT-AAM, especially with the relative contribution of Mn to the adsorption of AT, revealing significant contribution of the interactions among non-residual components in controlling the behavior of AT in aquatic environments.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.