Patters of reactive nitrogen removal at the waters in the semi-constructed wetland.

Protection and rectification patters of urban wetlands have been considered in strategies to balance services to society and negative consequences of excess reactive nitrogen (Nr) loading. However, the knowledge about strategies of semi-constructed wetlands on nitrogen (N) cycling pathways and removal Nr from the overlying water is limited. This study aimed to reveal considerable differences among rectification patterns of the typical semi-constructed wetland (Xixi wetland), comprising rational exploitation area (REA), rehabilitation and reconstruction area (RRA), and conservation area (CA) by analyzing the N distribution and N protentional pathways among them. Results pointed out that both NH4+ and NO3- concentration were prominently higher in REA, as opposed to CA and RRA. Sediments in RRA had relatively higher NH4+ content, indicating the efficiency of dissimilatory nitrate reduction (DNRA) in RRA. Moreover, there was a significant shift in the microbial community structure across different sites and sediments. Metagenomic analysis distinguished the N cycling pathways, with nitrification (M00804), denitrification (M00529), and DNRA (M00530) being the crucial pathways in the semi-constructed wetland. The relative abundance of N metabolic pathways (ko00910) varied among different types of sediments, being more abundant in shore and rhizosphere areas and less abundant in bottom sediments. Methylobacter and Nitrospira were the predominant nitrifiers in shore sediments, while Methylocystis was enriched in the bottom sediments and rhizosphere soils. Furthermore, Anaeromyxobacter, Anaerolinea, Dechloromonas, Nocardioides, and Methylocystis were identified as the primary denitrifiers with N reductase genes (nirK, nirS, or nosZ). Among these, Anaeromyxobacter, Dechloromonas, and Methylocystis were the primary contributors containing the nosZ gene in semi-constructed wetlands, driving the conversion of N2O to N2. This study provides important insights into rectification-dependent Nr removal from the overlying water in terms of N distribution and N metabolic functional microbial communities in the semi-constructed wetlands.

Optimization of thermal-alkaline pretreatment for dewatering of excess sludge followed by thermal/persulfate oxidation for the elimination of extracellular ARGs in TAP-treated filtrate.

This study evaluated the dewatering of excess sludge and the removal of extracellular antibiotic-resistant genes (eARGs) from the treated filtrate by thermal-alkaline pretreatment (TAP) and thermal/persulfate (PS). The optimization of TAP and thermal/PS was investigated during excess sludge dewatering and removal of eARGs via response surface methodology (RSM). The results demonstrated that TAP could effectively decrease the water content of excess sludge (41%) at optimum operating conditions (such as temperature: 88 °C, operation time: 90 min, pH: 11.2). However, the increase in eARGs abundance in TAP-treated filtrate is probably due to the dissolved effluent of the intracellular matter during dewatering. Therefore, TAP-treated filtrate was subjected to thermal/PS, and the removal of eARGs after TAP was explored. The desirability function was used to optimize two kinds of removal efficiencies of eARGs, simultaneously. The optimal pH, persulfate concentration, and reaction temperature were 10.2, 0.039 M, and 75.12 °C, respectively. 6.28 log·copies/mL of tetA and 6.57 log·copies/mL of sulI were removed under the above-mentioned conditions. The process provided efficient dewatering of excess sludge and elimination of eARGs from the filtrate.

Predicting combined antibacterial activity of sulfapyridine and its transformation products during sulfapyridine degradation.

Antibiotics have strong antibacterial activity, even trace antibiotics can greatly inhibit the pollutant degradation efficiency. In order to effectively improve the pollutant degradation efficiency, it was hence of great significance to explore sulfapyridine (SPY) degradation and the mechanism of antibacterial activity. This study selected SPY as the research object, of which the trend of SPY concentration through hydrogen peroxide (H2O2), potassium peroxydisulfate (PDS) and sodium percarbonate (SPC) and resultant antibacterial activity at pre-oxidation was examined. The combined antibacterial activity (CAA) of SPY and its transformation products (TPs) was further analyzed. The SPY degradation efficiency reached more than 90 %. However, the degradation efficiency of antibacterial activity was between 40-60 %, and the mixture's antibacterial activity was difficult to be removed. The antibacterial activity of TP3, TP6 and TP7 was higher than that of SPY. TP1, and TP8 and TP10 were more prone to synergistic reaction with other TPs. The antibacterial activity of binary mixture gradually changed from synergism to antagonism as binary mixture concentration increased. The results provided a theoretical basis for the efficient degradation of antibacterial activity of the SPY mixture solution.

Water Quality Measurement and Modelling Based on Deep Learning Techniques: Case Study for the Parameter of Secchi Disk.

The Secchi disk is often used to monitor the transparency of water. However, the results of personal measurement are easily affected by subjective experience and objective environment, and it is time-consuming. With the rapid development of computer technology, using image processing technology is more objective and accurate than personal observation. A transparency measurement algorithm is proposed by combining deep learning, image processing technology, and Secchi disk measurement. The white part of the Secchi disk is cropped by image processing. The classification network based on resnet18 is applied to classify the segmentation results and determine the critical position of the Secchi disk. Then, the semantic segmentation network Deeplabv3+ is used to segment the corresponding water gauge at this position, and subsequently segment the characters on the water gauge. The segmentation results are classified by the classification network based on resnet18. Finally, the transparency value is calculated according to the segmentation and classification results. The results from this algorithm are more accurate and objective than that of personal observation. The experiments show the effectiveness of this algorithm.

Improved YOLO Based Detection Algorithm for Floating Debris in Waterway.

Various floating debris in the waterway can be used as one kind of visual index to measure the water quality. The traditional image processing method is difficult to meet the requirements of real-time monitoring of floating debris in the waterway due to the complexity of the environment, such as reflection of sunlight, obstacles of water plants, a large difference between the near and far target scale, and so on. To address these issues, an improved YOLOv5s (FMA-YOLOv5s) algorithm by adding a feature map attention (FMA) layer at the end of the backbone is proposed. The mosaic data augmentation is applied to enhance the detection effect of small targets in training. A data expansion method is introduced to expand the training dataset from 1920 to 4800, which fuses the labeled target objects extracted from the original training dataset and the background images of the clean river surface in the actual scene. The comparisons of accuracy and rapidity of six models of this algorithm are completed. The experiment proves that it meets the standards of real-time object detection.