摘要：Bioaugmentation with genetically engineered microorganisms(GEMs)in a membrane bioreactor(MBR)for enhanced removal of recalcitrant pollutants was *** atrazine-degrading genetically engineered microorganism(GEM)with green fluorescent protein was inoculated into an MBR and the effects of such a bioaugmentation strategy on atrazine removal were *** results show that atrazine removal was improved greatly in the bioaugmented MBR compared with a control *** a start-up period of 6 days,average 94.7%of atrazine was removed in bioaugmentedMBRwhen atrazine concentration of influent was 14.5 mg/*** volumetric removal rates increased linearly followed by atrazine loading increase and the maximum was 65.5 mg/(L?d).No negative effects were found on COD removal although carbon oxidation activity of bioaugmented sludge was lower than that of common *** inoculation,adsorption to sludge flocs was favorable for GEM *** GEM population size initially decreased shortly and then was kept constant at about 104–105 CFU/*** of micro-organisms played an important role in the decay of the GEM *** leakage from MBR was less than 102 CFU/mL initially and was then *** contrast,in a conventionally activated sludge bioreactor(CAS),sludge bulking occurred possibly due to atrazine exposure,resulting in bioaugmentation failure and serious GEM *** MBR was superior to CAS in atrazine bioaugmentation treatment using GEM.

摘要：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.