Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Enhanced electrochemical performance by nickel-iron layered double hydroxides (LDH) coated on Fe3O4 as a cathode catalyst for single-chamber microbial fuel cells.

The improvement of cathode performance has always been the bottleneck and research hot spot for microbial fuel cells (MFCs). An Fe3O4@NiFe-LDH composite with a nanoscale core-shell structure containing an Fe3O4 magnetic core and a layered double hydroxide (LDH) shell was prepared by the hydrothermal method. The Fe3O4@NiFe-LDH was characterized by FT-IR, XRD, SEM and EDS. The characterization results showed that the composite had a unique cauliflower-like nanoflake structure and special pore size distribution, which greatly improved the ORR performance. Moreover, the use of the synthesized Fe3O4@NiFe-LDH core-shell structure as an electrode in an MFC was characterized by CV and LSV, which showed that the Fe3O4@NiFe-LDH exhibited excellent ORR catalytic properties. The voltage output of the Fe3O4@NiFe-LDH MFC was maintained at approximately 0.39 V, with insignificant variations over 110 h. The maximum power density was 211.40 ± 2.27 mW/m2, which was 34 times that of the blank control group MFC and was caused by the many electroactive sites, good rate capability and remarkable cycling stability of LDH. This study provides the possibility for using Fe3O4@NiFe-LDH in cathodes to operate continuously and at low cost in fuel cells.

[Co-existence of resistance genes and their association with the genetic marker of integrons among multi-resistant Escherichia coli isolates].

OBJECTIVE: To investigate co-existence of resistance genes (beta-lactamases, BLs, and aminoglycoside-modifying enzymes, AMEs) and their association with the genetic marker genes of Class I, II, III integrons carried by multiresistant Escherichia coli isolates. METHODS: We used VITEK-GNS to determine the susceptibility of 136 isolates to 14 antibiotics, disc agar diffusion test to confirm ESBL-producing isolates, PCR to analyze BLs, AMEs and integrons genes, conjugation and plasmids extraction to locate the methylase genes. RESULTS: We found that 70.59% of the isolates produced ESBLs. They showed stronger resistance against 9 antibiotics than isolates without ESBLs in 14 antibiotics. PCR amplification showed that the positive rate of BLs, AMEs and qacEdelta1-sul1 was 96.32% , 100% and 94.12%, respectively, but Class II, III integrons genes were negative. Only one strain was oprD2 gene negative. 90.44% of the isolates were both positive for BLs and qacEdelta1-sul1 genes, and 94.12% for AMEs and qacEdelta1-sul1 genes, but there was no statistical significance. 90.44% of the isolates were all positive for the 3 genes. 12 strains carried 16S rRNA methylase genes including armA (2.21%), rmtB (7.35%) while rmtA, rmtC, rmtD were negative. The conjugation assay and plasmids mapping results showed that the methylase genes were located on the 23 kb plasmid, and the efficiency of transformation was 83.3%. CONCLUSIONS: The results suggested that there was a tight correlation between the 3 genes (BLs, AMEs and qacEdelta1-sul1) and the incidences of multi-resistance of Escherichia coli, but there was no correlation of the incidence of multi-resistance with Class II, III integrons. 16S rRNA methylase genes harboured plasmids of -23 kb which transformed other isolates within the same strains efficiently.