Membrane separation of antibiotics predicted with the back propagation neural network.

Antibiotics and antibiotic resistance genes (ARGs) have been frequently detected in the aquatic environment and are regarded as emerging pollutants. The prediction models for the removal effect of four target antibiotics by membrane separation technology were constructed based on back propagation neural network (BPNN) through training the input and output. The membrane separation tests of antibiotics showed that the removal effect of microfiltration on azithromycin and ciprofloxacin was better, basically above 80%. For sulfamethoxazole (SMZ) and tetracycline (TC), ultrafiltration and nanofiltration had better removal effects. There was a strong correlation between the concentrations of SMZ and TC in the permeate, and the R2 of the training and validation processes exceeded 0.9. The stronger the correlation between the input layer variables and the prediction target was, the better the prediction performances of the BPNN model than the nonlinear model and the unscented Kalman filter model were. These results showed that the established BPNN prediction model could better simulate the removal of target antibiotics by membrane separation technology. The model could be used to predict and explore the influence of external conditions on membrane separation technology and provide a certain basis for the application of the BPNN model in environmental protection.

Remediation of Cd-contaminated soil by electrokinetics coupled with the permeable reactive barrier from immobilized yeast.

Yeast was used to prepare permeable reactive barrier (PRB) with immobilized microbial technology, and the electrokinetics coupled with the immobilized yeast PRB (IMEK-PRB) was established to remediate Cd-contaminated soil. The effect of the different PRBs prepared by immobilized microbial technology on Cd removal was explored. The voltage gradient had influence on the removal of Cd, and the removal reached as high as 53.70 % at a voltage gradient of 2.5 V/cm. The lowest removal about 34.12 % was obtained with yeast pellets prepared by the embedding method used as PRB. The yeast in PRB was partially broken and adhered, and the intensity of the absorption peak of the group analyzed with infrared spectra and the crystal diffraction peak from X-ray diffraction changed, leading to a decrease in its activity. The average removal of Cd increased by >10 % when fly ash-based yeast pellets prepared with the adsorption-embedding method, and fly ash-adsorbed yeast prepared by the adsorption method were used as PRB. IMEK-PRB remediation would greatly reduce the toxicity of Cd-contaminated soil, weaken harmful effects on the soil environment and reduce environmental risks. The fly ash-based yeast pellets used in IMEK-PRB have great application prospects for the remediation of Cd-contaminated soil.

Degradation of sulfamethoxazole and antibiotic resistance genes from surface water in the photocatalyst-loading bionic ecosystems.

The behavior and removal of sulfamethoxazole (SMX) and 3 typical corresponding antibiotic resistance genes (ARGs) including sul1, sul2, sul3, and 16S rDNA in surface water were investigated in the photocatalyst-loading bionic ecosystems (PCBEs). Synthesized composite photocatalyst g-C3N4/TiO2 showing higher catalytic activity than Fe/g-C3N4/TiO2 was selected in the PCBEs. Five PCBEs, i.e., A-the control (without bionic grass or photocatalyst), B-bionic grass loaded with 4.12 g/m2 g-C3N4/TiO2, C-bionic grass loaded with 8.25 g/m2 g-C3N4/TiO2, D-bionic grass loaded with 12.37 g/m2 g-C3N4/TiO2, and E-bionic grass loaded with 16.5 g/m2 g-C3N4/TiO2 were constructed and operated in a medium-scale running cyclical flume. SMX could be photolyzed efficiently by g-C3N4/TiO2 with an optimal unit load on the bionic grass of 12.37 g/m2. 3-amino-5-methylisooxazole and p-aminobenzene sulfonamide were selected as main intermediates through the analyses of SMX degradation mechanisms and pathways, and detected in the aqueous phase and bionic grass. The intermediates were higher in the underwater part of the bionic grass than the above-water part. The overall removal of SMX ranged from 31.7 % to 82.3 % in 5 PCBEs, and the removal of sul1and sul2 were 0.2 %- 62.9 % in the aqueous phase and 8.4 %-63.2 % in the sediment. PCBE D might be the best construction when SMX and ARGs' removal was considered comprehensively. Moreover, the microbial structures showed Proteobacteria as the most dominant bacterial species had a relative abundance of 22.2 %-26.6 % and 33.4 %-68.2 % in the aquatic phase and sediment respectively, illustrating that the removal of the antibiotic and ARGs was bound up with the variations of dominant bacteria in the ecosystems. The findings illustrated that ecosystems with bionic grass and photocatalysts could be a promising technology for the removal of typical antibiotics and ARGs from surface water.

Enzymatic integrated in-situ advanced anaerobic digestion of sewage sludge for the removal of antibiotics and antibiotic resistance genes.

Antibiotics and antibiotic resistance genes (ARGs) in sewage sludge can cause high ecotoxicological risks in the environment and public health concerns. The aims of this study were to establish enzymatic integrated in-situ advanced anaerobic digestion (AAD) by adding cellulase and papain as well as the two enzymes combined with zero valent iron (ZVI) directly into the anaerobic digesters to explore the removal of antibiotics and ARGs under the mesophilic condition (35 °C). The methane production potential during in-situ AAD was effectively improved. Papain and cellulase at 30 mg/gTSS were most effective in improving antibiotic removal. The removal of sulfamerazine (SMZ) and sulfadiazine (SMR) could reach 89.10 % and 71.75 %. Combined enzymes with ZVI also enhanced the removal of all target antibiotics, especially roxithromycin (ROX), SMZ and SMR most significantly. Except for sul1, tetA and tetB, the removal of ARGs by papain reached 6.33 %-82.15 %. The addition of cellulase effectively improved tetA removal. The combination of biological enzymes further enhanced the removal of qnrS and ermX. The tetG, tetB, sul3, ermX, ermT, qnrS, and aac(6')-IB-CR by combined enzymes with ZVI could even not be detected after digestion. Addition of papain, cellulase, and ZVI caused variations in the dominant bacteria. All target antibiotics presented significant positive correlations with the genera norank_f__Bacteroidetes_vadinHA17, norank_f__norank_o__SJA-15, norank_f__norank_o__Aminicenantales. Redundancy analysis showed archaea Methanosaeta and Candidatus_ Methanoacidiosum genera greatly contributed to antibiotics removal with the combination of enzymes and ZVI. Co-occurrence network analysis indicated the removal of ARGs was mainly based on the changes of existence of host bacteria.