Novel insights into the co-metabolism of pyridine with different carbon substrates: Performance, metabolism pathway and microbial community.

Pyridine is a widely employed nitrogen-containing heterocyclic organic, and the discharge of pyridine wastewater poses substantial environmental challenges due to its recalcitrance and toxicity. Co-metabolic degradation emerged as a promising solution. In this study, readily degradable glucose and the structurally analogous phenol were used as co-metabolic substrates respectively, and the corresponding mechanisms were thoroughly explored. To treat 400 mg/L pyridine, all reactors achieved remarkably high removal efficiencies, surpassing 98.5%. And the co-metabolism reactors had much better pyridine-N removal performance. Batch experiments revealed that glucose supplementation bolstered nitrogen assimilation, thereby promoting the breakdown of pyridine, and resulting in the highest pyridine removal rate and pyridine-N removal efficiency. The high abundance of Saccharibacteria (15.54%) and the enrichment of GLU and glnA substantiated this finding. On the contrary, phenol delayed pyridine oxidation, potentially due to its higher affinity for phenol hydroxylase. Nevertheless, phenol proved valuable as a carbon source for denitrification, augmenting the elimination of pyridine-N. This was underscored by the abundant Thauera (30.77%) and Parcubacteria (7.21%) and the enriched denitrification enzymes (narH, narG, norB, norC, and nosZ, etc.). This study demonstrated that co-metabolic degradation can bolster the simultaneous conversion of pyridine and pyridine-N, and shed light on the underling mechanism.

[Microbial Community Structure of Activated Sludge for Total Nitrogen Upgrading Project].

The activated sludge of a biochemical unit (WLK_OD) and an advanced denitrification unit (WLK_AD) were collected from a municipal wastewater treatment plant (WWTP), in which the TN concentration of effluent was less than 1.5 mg·L-1, and their microbial community structure and function profiles were analyzed using 16S rRNA gene high-throughput sequencing. The microorganisms in WLK_AD had lower evenness compared with that in WLK_OD, which was attributed to environmental selection. Furthermore, PCoA revealed that different incoming wastewaters had an impact on microbial community structure. At the phylum level, Proteobacteria (70.11%) was enriched in WLK_AD. At the genus level, Thauera, Flavobacterium, Hydrogenophaga, and Zoogloea served as distinct-dominant denitrifying bacteria in WLK_AD; however, Trichococcus (3.50%) and Terrimonas (1.10%) were enriched in WLK_OD. Through the comparison between groups (P<0.05), the biomarkers detected in each WWTP were different. Furthermore, the results of the co-occurrence network showed that the bacteria from module I had a higher proportion in WLK_AD; the bacteria from module II had a higher proportion in WLK_OD, and they were common microorganisms in WWTPs, implying that wastewater environments drpve the differences in the microbial community structure. Among the types of environmental parameters, the removal efficiency of COD and TN had the greatest impact on the microbial community by the RDA. The removal efficiency of COD was positively correlated with the dominant bacteria from WLK_OD, such as Saccharibacteria, Thermomarinilinea, Terrimonas, and Comamonas; the removal efficiency of TN was positively correlated with the denitrifying bacteria from WLK_AD, such as Dokdonella, Thauera, Flavobacterium, and Zoogloea. WLK_AD was enriched with Novosphingobium, Dokdonella, Thauera, and Sphingomonas, which synergistically removed TN, leading to the TN of the effluent being less than 1.5 mg·L-1. Moreover, based on the results of function prediction, WLK_AD had a higher proportion of genes that could code the denitrification enzymes.

Petrochemical and municipal wastewater treatment plants activated sludge each own distinct core bacteria driven by their specific incoming wastewater.

Microorganisms in activated sludge from wastewater treatment plants (WWTPs) form complex networks to convert a wide variety of pollutants, thus ensuring water purification and environmental protection. In this study, activated sludge samples were collected from three full-scale WWTPs: a petrochemical WWTP (PWWTP), a municipal WWTP treating domestic wastewater (MWWTP_D), and a municipal WWTP treating a mixture of domestic wastewater and multiple industrial effluents (MWWTP_I+D). These samples were analyzed by high-throughput sequencing of 16S rRNA gene PCoA and CPCoA indicated that the samples from three WWTPs were separated, suggesting that each WWTP had unique microbiome characters (P < 0.05). This was also evidenced by the different predominant bacteria (PDB), biomarkers, and key nodes of co-occurrence network in the three WWTPs. Microorganisms with all three above mentioned characteristics were defined the core bacteria, specifically: Georgfuchsia, Thauera and GP4 in PWWTP, Phaeodactylibacter and Hyphomicrobiuml in MWWTP_D, and Otheakwangia, Terrimonas, Phenylobacterium, etc. in MWWTP_I + D. Furthermore, in accordance with the functional profile prediction, the functional groups in PWWTP metabolized aromatic compound, sulfur compounds and heavy metal typically present in petrochemical wastewater. In contrast, the microbiome in MWWTP_D was represented by the population breaking down macromolecular biodegradable organic matter and the nitrogen nutrients that constitute the vast majority of domestic wastewater pollutants. Both functional groups coexist in MWWTP_I + D. These results revealed that the specific composition of incoming wastewaters produced distinct ecological niches and modulated the ecological structure of activated sludge microbial communities in real-world WWTPs. However, the generalization of the results of this study will require further research.