An assessment of the purification performance and resilience of sponge-based aerobic biofilm reactors for treating polluted urban surface waters

Simultaneously enhancement in the assimilation of microalgal nitrogen and the accumulation of carbohydrate by Debaryomyces hansenii

Microalgae-based techniques are considered an alternative to traditional activated sludge processes for removing nitrogen from wastewater. Bacteria consortia have been broadly conducted as one of the most important partners. However, fungal effects on the removal of nutrients and changes in physiological properties of microalgae, and their impact mechanisms remain unclear. The current work demonstrates that, adding fungi increased the nitrogen assimilation of microalgae and the generation of carbohydrates compared to pure microalgal cultivation. The NH₄⁺-N removal efficiency was 95.0% within 48 h using the microalgae-fungi system. At 48 h, total sugars (glucose, xylose, and arabinose) accounted for 24.2 ± 4.2% per dry weight in the microalgae-fungi group. Gene ontology (GO) enrichment analysis revealed that, among various processes, phosphorylation and carbohydrate metabolic processes were more prominent. Gene encoding the key enzymes of glycolysis, pyruvate kinase, and phosphofructokinase were significantly up-regulated. Overall, for the first time, this study provides new insights into the art of microalgae-fungi consortia for producing value-added metabolites.

Fluorescein diacetate hydrolytic activity as a sensitive tool to quantify nitrogen/sulfur gene content in urban river sediments in China

The relative abundance of functional genes used to quantify the abundance of functional genes in communities is controversial. Quantitative PCR (qPCR) technology offers a powerful tool for quantifying functional gene abundance. However, humic substances can inhibit qPCR in sediment/soil samples. Therefore, finding a convenient and effective quantitative analysis method for sediment/soil samples is necessary. The functional genes and physicochemical properties in sediments with different-level pollutions were analyzed in this study. Correlations between physicochemical properties and the relative abundance of functional genes were used to test whether relative abundance in gene prediction quantifies the abundance of functional genes. The abundance of functional genes could be corrected by multiplying the fluorescein diacetate (FDA) hydrolytic rates by the relative abundance of functional genes since the FDA assay has been widely used as a rapid and sensitive method for quantifying microbial activity in sediments. Redundancy analysis showed significant interrelations between the functional genes and the physicochemical properties of sediments. The relative abundance of functional genes is unreliable for quantifying the abundance of functional genes because of the weak correlation (R < 0.5, P < 0.05) between different pollutants and the relative abundance of functional genes. However, a significant positive correlation between concentrations of different pollutants and the activities of associated enzymes was obtained (R > 0.933, P < 0.05), which revealed that the abundance of functional genes could be reliably quantified by the relative abundance and FDA hydrolytic rate. This study proposed an alternative method besides qPCR to quantify the absolute abundance of functional genes, which overcomes the problem of humic interference in the quantitative analysis of sediment/soil samples.

Grade evaluation of black-odorous urban rivers in the Greater Bay Area of China using an improved back propagation (BP) neural network

With the rapid development of urbanization, the urban water environment is receiving continuous attention. It is necessary to understand water quality in a timely manner and make a reasonable comprehensive evaluation. However, existing black-odorous water grade evaluation guidelines are not sufficient. Understanding the changing situation of black-odorous water in urban rivers is a growing concern, especially in real-world scenarios. In this study, a BP neural network combined with the fuzzy membership degree was applied to evaluate the black-odorous grade of urban rivers in Foshan City, which is within the Greater Bay Area of China. The optimal 4 × 11 × 1 topology structure of the BP model was constructed by taking the dissolved oxygen (DO), ammonia nitrogen (NH₃-N), chemical oxygen demand (COD), and total phosphorus (TP) concentrations as input water quality indicators. There was almost no occurrence of black-odorous water in the two public rivers outside the region in 2021. Black-odorous water was most significant in 10 urban rivers, with grade IV and grade V occurring over 50% of the time in 2021. These rivers had three features, i.e., parallel with a public river, beheaded, and close proximity to Guangzhou City, the provincial capital of Guangdong. The results of the grade evaluation of the black-odorous water found basically matched those of the water quality assessment. The existence of some inconsistencies between the two systems justified the necessity to expand and extend the number of employed indicators and grades in the present guidelines. The results confirm the capability of the BP neural network combined with the fuzzy-based membership degree in the quantitative grade evaluation of black-odorous water in urban rivers. This study makes a step forward in understanding the grading of black-odorous urban rivers. The findings can provide a reference for local policy-makers regarding the priority of practical engineering projects in prevailing water environment treatment programs.