Prediction models and major controlling factors of antibiotics bioavailability in hyporheic zone.

Recently, antibiotics have been frequently detected in the hyporheic zone (HZ) as a novel contaminant. Bioavailability assessment has gradually attracted more attention in order to provide a more realistic assessment of human health risks. In this study, two typical antibiotics, oxytetracycline (OTC) and sulfamethoxazole (SMZ), were used as target pollutants in the HZ of the Zaohe-Weihe River, and the polar organics integrated sampler was used to analyze the variation of antibiotics bioavailability. According to the characteristics of the HZ, the total concentration of pollutants, pH, and dissolved oxygen (DO) were selected as major predictive factors to analyze their correlation with the antibiotics bioavailability. Then the predictive antibiotic bioavailability models were constructed by stepwise multiple linear regression method. The results showed that there was a highly significant negative correlation between OTC bioavailability and DO (P < 0.001), while SMZ bioavailability showed a highly significant negative correlation with total concentration of pollutants (P < 0.001) and a significant negative correlation with DO (P < 0.01). The results of correlation analysis were further verified by Principal Component Analysis. Based on the experimental data, we constructed eight prediction models for the bioavailability of two antibiotics and verified them. The data points of the six prediction models were distributed in the 95% prediction band, indicating that the models were more reliable and accurate. The prediction models in this study provide reference for the accurate ecological risk assessment of the bioavailability of pollutants in the HZ, and also provide a new idea for predicting the bioavailability of pollutants in practical applications.

Temporal dynamic of antibiotic resistance genes in the Zaohe-Weihe hyporheic zone: driven by oxygen and bacterial community.

The widespread spread of antibiotic resistance genes (ARGs) in hyporheic zone (HZ) has become an emerging environmental problem due to their potentially harmful nature. In this research, three different oxygen treatment systems were set up to study the effects of oxygen changes on the abundance of ARGs in the HZ. In addition, the effects of temperature and salinity on ARGs were investigated under aerobic and anaerobic systems, respectively. The bacterial community composition of sediment samples and the relationship with ARGs were analyzed. The explanation ratio and causality of the driving factors affecting ARGs were analyzed using variation partitioning analysis (VPA) and structural equation model (SEM). The relative abundance of ARGs and mobile genetic elements (MGEs) in the anaerobic system increased significantly, which was higher than that in the aerobic system and the aerobic-anaerobic interaction system. The experiment of salinity and temperature also further proved this result. There were many bacterial communities that affected tetracycline and sulfonamide ARGs in sediments, and these host bacteria are mainly concentrated in Proteobacteria, Firmicutes and Bacteroidetes. VPA and SEM further revealed that the abundance of ARGs was mainly influenced by changes in bacterial communities and oxygen conditions, and horizontal gene transfer (HGT) of MGEs also had a positive effect on the spread of ARGs. Those findings suggest that complex oxygen conditions in the HZ alter bacterial communities and promote MGEs-mediated horizontal transfer, which together lead to the spread of ARGs. This study has value as a reference for formulating effective strategies to minimize the propagation of ARGs in underground environment.

Hydrolysis of norfloxacin in the hyporheic zone: kinetics and pathways.

Understanding the hydrolysis behavior and pathway of norfloxacin (NOR) in the hyporheic zone (HZ) is important for predicting its environmental persistence. Therefore, the effects of different environmental factors on NOR hydrolysis were investigated, and the hydrolysis pathway of NOR in the HZ was determined by DFT calculations and UPLC/TOF-MS. The hydrolysis process of NOR was consistent with the first-order kinetic. The experiment of environmental factors showed that DO was an important factor to affect NOR hydrolysis, and its hydrolysis rate was positively correlated with DO concentration. The superoxide radical (·O2-) was the main active species for NOR hydrolysis. The hydrolysis rates of NOR under neutral and alkaline conditions were higher than that under acidic conditions in both aerobic and anoxic environments. The ions of Ca2+, Mg2+, HCO3-, CO32-, and NO3- in simulated water samples inhibited the hydrolysis of NOR, while Cl- promoted its hydrolysis. In addition, the electronegativity of NOR was determined by DFT calculations, and it was speculated that the active sites of NOR hydrolysis were mainly located in the piperazine ring and quinolone ring. The main hydrolysis pathway of NOR in aerobic environment was piperazine ring cracking and quinolone ring decomposition, and that in anoxic environment was piperazine ring cracking. The results are of great significance to evaluate the environmental fate of NOR in the HZ and provide a theoretical basis for further understanding the degradation and governance of fluoroquinolones in water environment.

Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification.

Scene classification of high-resolution remote sensing images is a fundamental task of earth observation. And numerous methods have been proposed to achieve this. However, these models are inadequate as the number of labelled training data limits them. Most of the existing methods entirely rely on global information, while regions with class-specific ground objects determine the categories of high-resolution remote sensing images. An ensemble model with a cascade attention mechanism, which consists of two kinds of the convolutional neural network, is proposed to address these issues. To improve the generality of the feature extractor, each branch is trained on different large datasets to enrich the prior knowledge. Moreover, to force the model to focus on the most class-specific region in each high-resolution remote sensing image, a cascade attention mechanism is proposed to combine the branches and capture the most discriminative information. By experiments on four benchmark datasets, OPTIMAL-31, UC Merced Land-Use Dataset, Aerial Image Dataset and NWPU-RESISC45, the proposed end-to-end model cascade attention-based double branches model in this paper achieves state-of-the-art performance on each benchmark dataset.