Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.

High potential in synergizing the reduction of dissolved organic carbon concentration and carbon dioxide emissions for submerged-vegetation-covered river networks.

Various technologies and projects have been explored and developed for the synergetic control of environmental pollution and carbon emissions in aquatic ecosystems. Planting submerged vegetation in shallow waters was also expected to achieve this purpose. However, the magnitude and mechanism of carbon dioxide (CO) emission affected by submerged vegetation is not clear enough in complex aquatic ecosystems.

Revealing microbial community assembly patterns and succession process in the blackening process of black-odor water.

Human-imported pollutants could induce water black, changing microbial community structure and function. Employed 16S rRNA high-throughput sequencing, field-scale investigations and laboratory-scale experiments were successively conducted to reveal mechanistic insights into microbial community assembly and succession of black-odor waters (BOWs). In the field-scale investigation, livestock breeding wastewater (56.

Mitigated NO emissions from submerged-plant-covered aquatic ecosystems on the Changjiang River Delta.

Submerged plants affect nitrogen cycling in aquatic ecosystems. However, whether and how submerged plants change nitrous oxide (NO) production mechanism and emissions flux remains controversial. Current research primarily focuses on the feedback from NO release to variation of substrate level and microbial communities.

The ecological role of microbiome at community-, taxonomic - and genome-levels in black-odorous waters.

Black-odorous waters (BOWs) are heavily polluted waters where microbial information remains elusive mechanistically. Based on gene amplicon and metagenomics sequencing, a comprehensive study was conducted to investigate the microbial communities in urban and rural BOWs. The results revealed that microbial communities' assembly in urban and rural BOWs was predominantly governed by stochastic factors at the community level.

Characterizing dissolved organic matter and bacterial community interactions in a river network under anthropogenic landcover.

Anthropogenic landcover could rise nutrient concentrations and impact the characteristics and bioavailability of dissolved organic matter (DOM) in a river network. Exploring the interactions between DOM and microbials might be conducive to revealing biogeochemistry behaviors of organic matter. In this study, synchronous fluorescence spectra (SFS) with Gaussian band fitting and two-dimensional correlation spectroscopy (2D-COS) were employed to identify DOM fractions and reveal their interactions with bacterial communities.

Mechanistic insights into dissolved organic matter-driven protistan and bacterial community dynamics influenced by vegetation restoration.

Vegetation restoration projects can not only improve water quality by absorbing and transferring pollutants and nutrients from non-vegetation sources, but also protect biodiversity by providing habitat for biological growth. However, the mechanism of the protistan and bacterial assembly processes in the vegetation restoration project were rarely explored. To address this, based on 18 S rRNA and 16 S rRNA high-throughput sequencing, we investigated the mechanism of protistan and bacterial community assembly processes, environmental conditions, and microbial interactions in the rivers with (out) vegetation restoration.

Bio-based carbon materials with multiple functional groups and graphene structure to boost methane production from ethanol anaerobic digestion.

This study evaluated the effects of bio-based carbon materials on methane production by anaerobic digestion. The results showed that biochar and hydrochar can promote cumulative methane yield by 15% to 29%. However, there was no statistical significance (p > 0.

Promoting potential direct interspecies electron transfer (DIET) and methanogenesis with nitrogen and zinc doped carbon quantum dots.

Although it has been demonstrated that one-dimensional, two-dimensional, and three-dimensional carbon nanomaterials can improve the CH production of anaerobic digestion (AD), the effect of zero-dimensional carbon nanomaterials on AD have not been reported. To expand the application of carbon nanomaterials in AD, the effect of zero-dimensional carbon nanomaterials-carbon quantum dots (CDs) on various feedstocks (c.a.