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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. At the taxonomic level, there were 62 core species (58.48%) in water and 207 core species (44.56%) in sediment across urban and rural areas. Notably, significant differences were observed in the functional genetic composition of BOWs between urban and rural areas. Specifically, rural areas exhibited an enhanced abundance of genes involved in nitrogen fixation, Fe2+ transport, and sulfate reduction. Conversely, urban areas showed higher abundances of some genes associated with carbon fixation, nitrification and denitrification. A sulfur-centered ecological model of microbial communities was constructed by integrating data from the three levels of analysis, and 14 near-complete draft genomes were generated, representing a substantial portion of the microbial community (35.04% in rural BOWs and 29.97% in urban BOWs). This research provides significant insights into the sustainable management and preservation of aquatic ecosystems affected by BOWs. [Display omitted] ● The community assembly was dominated by stochastic factors in BOWs. ● 62 core species in water and 207 core species in sediment were detected. ● Metagenomics was used to establish an ecological model in BOWs. ● Sulfur metabolism was the core dynamic driving C, N, P and Fe metabolism. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building a multi-layer automated machine learning framework for AD systems. • GBM model was more suitable to learn AD data and to simulate and predict AD. • HRT was key factor with an optimal range of 33–45 d. • Methanocelleus was the key microorganisms for AD. • Biogas production is best predicted by archaea + environmental factors. Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus , within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems. [ABSTRACT FROM AUTHOR]

Due to the diversity of microbiota and the high complexity of their interactions that mediate biogas production, a detailed understanding of the microbiota is essential for the overall stability and performance of the anaerobic digestion (AD) process. This study evaluated the microbial taxonomy, metabolism, function, and genetic differences in 14 full-scale biogas reactors and laboratory reactors operating under various conditions in China. This is the first known study of the microbial ecology of AD at food waste (FW) at a regional scale based on multi-omics (16S rRNA gene amplicon sequencing, metagenomics, and proteomics). Temperature significantly affected the bacterial and archaeal community structure (R2 = 0.996, P = 0.001; R2 = 0.846, P < 0.002) and total inorganic carbon(TIC) slightly changed the microbial structure (R2 = 0.532, P = 0.005; R2 = 0.349, P = 0.016). The Wood-Ljungdahl coupled with hydrogenotrophic methanogenic pathways were dominant in the thermophilic reactors, where the acs , met F, coo A, mer, mch and ftr genes were 10.1-, 2.8-, 16.2-, 1.74-, 4.15-, 1.04-folds of the mesophilic reactors (P < 0.01). However, acetoclastic and methylotrophic methanogenesis was the primary pathway in the mesophilic reactors, where the ack A, pta , cdh and mta genes were 2.2-, 3.2-, 14.3-, 1.88-folds of the thermophilic group (P < 0.01). Finally, the Wilcoxon rank-sum test was applied to explain the cause of the temperature affecting AD microbial activities. The findings have deepened the understanding of the effect of temperature on AD microbial ecosystems and are expected to guide the construction and management of full-scale FW biogas plants. • Performance overview of full-scale AD reactors by various omics. • The mesophilic and thermophilic reactors are dominated by different methane metabolism pathways. • The genomics research test explains the cause of the temperature affecting AD microbial activities. [ABSTRACT FROM AUTHOR]

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. DOM was extracted from a river network under eco-agricultural rural (RUR), eco-residential urban (URB), eco-economical town (TOW), and eco-industrial park (IND) regions in Jiashan Plain of eastern China. The overlapping peaks observed in the SFS were successfully separated into four fractions using Gaussian band fitting, i.e., tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), and fulvic-like fluorescence (FLF) materials. Across all four regions, TRLF (44.79% ± 7.74%) and TYLF (48.09% ± 8.85%) were the dominant components. Based on 2D-COS, variations of TYLF and TRLF were extremely larger than those of FLF in RUR-TOW. However, in URB-IND, the former exhibited lower variations compared to the latter. These suggested that FLF be likely derived continuously from lignin and other residue of terrestrial plant origin along the river network, and TYLF and TRLF be originated discontinuously from domestic wastewater in RUR-TOW. By high-throughput sequenced OTUs, the number of organisms in RUR-TOW could be higher than those in URB-IND, while genes associated with carbohydrate metabolism were lower in former than those in the latter. According to co-occurrence networks, microbes could promote the production of TYLF and TRLF in RUR-TOW. In contrast, microbial communities in URB-IND might contribute to decompose FLF. The obtained results could not only reveal interactions between DOM fractions and bacterial communities in the river network, but this methodology may be applied to other water bodies from different landscapes. [Display omitted] • The overlapping peaks in SFS data were split into distinct fractions by Gaussian band fitting. • Tyrosine- and tryptophan-like predominated in riverine DOM, representing terrestrial sources. • Strong correlations of DOM with microbials were observed in river ecosystems. [ABSTRACT FROM AUTHOR]

Co-digestion of lignocellulosic biomass and animal manure is a common approach to increase the efficiency of methane production, but the niche differentiation and microbial metabolism of the anaerobic digestion (AD) process remain to be determined. To further explore the effect of the interaction between species and their compositional niches, the methane yield and resulting microbial community were determined by machine learning (ML) and 16S rRNA gene sequencing in mixed high-solid anaerobic digestion (HS-AD) with spray-enhanced conditions to explore the internal relationship between physical and chemical parameters and microorganisms and to speculate on the enhancement mechanism of co-digestion. In this study, three ML models (extreme learning machine (ELM), artificial neural network (ANN), and random forest (RF)) were applied to analyse and model AD of dry fermentation. The results showed that the best prediction model, based on ELM, could best predict the material biogas production in this experiment with a mean absolute error (MAE/10) of 0.678 and a coefficient of determination (R2) of 0.9574, whereas the characteristic percentage analysis of the RF model showed that butyric acid, acetic acid, and pH had three important influences on the biogas production values. Meanwhile, the results of high-throughput 16S rRNA gene sequencing and PICRUSt showed that the addition of manure containing ammonia nitrogen improved the metabolism of amino acids, enriched species capable of Clostridiales and Methanosarcinales , promoted the electronic transfer of nutrient metabolism, and thus enhanced AD. Moreover, the co-occurrence network showed that seven niches were differentiated within the HS-AD system to reduce the shock of ammonia nitrogen for methanogens. Overall, microbial analysis and ML can help understand the dynamic processes of methanogenic microorganisms and predict biogas production for the efficient operation of AD. [Display omitted] • PICRUSt showed that the addition of manure containing ammonia nitrogen enhanced archaeal methanogenesis. • ELM is the most accurate model for predicting biogas production. • The characteristic factors influencing biogas production can be derived from RF. • Network analysis shows seven niches have differentiated within the HS-AD system. [ABSTRACT FROM AUTHOR]

Exploring the complex mechanism of anaerobic digestion with hydrothermal pretreatment (HTAD) for biomass efficiently and optimising the reaction conditions are critical to improving the performance of methane production. This study used H 2 O automated machine learning (AutoML) for comprehensive prediction, analysis, and targeted optimization of the HTAD system. An IterativeImputer system for data filling was constructed. The comparison of three basic regressors showed that random forest performed optimally for filling (R2 > 0.95). The gradient boosting machine (GBM) model was searched by H 2 O AutoML to show optimal performance in prediction (R2 > 0.96). The software was developed based on the GBM model, and two prediction schemes were devised. The generalization error of the software was less than 10%. The Shapley Additive exPlanations value showed that solid to liquid ratio, hydrothermal pretreatment (HT) temperature, and particle size have greater potential for improving cumulative methane production (CMP). A Bayesian-HTAD optimization strategy was devised, using the Bayesian optimization to directionally optimize the reaction conditions, and performing experiments to validate the results. The experimental results showed that the CMP was significantly improved by 51.63%. Compared to the response surface methodology, the Bayesian optimization relatively achieved a 2.21–2.50 times greater effect. Mechanism analyses targeting the experiments showed that HT was conducive to improving the relative abundance of Sphaerochaeta, Methanosaeta, and Methanosarcina. This research achieved accurate prediction and targeted optimization for the HTAD system and proposed multiple filling, prediction, and optimization strategies, which are expected to provide an AutoML optimization paradigm for anaerobic digestion in the future. [Display omitted] • Built a machine learning framework for data filling and automated search prediction. • Developed software based on gradient boosting machine with prediction errors of 6.33%. • The solid to liquid ratio influences cumulative methane production most critically. • Bayesian optimization enhanced methane production by 51.63% over the control group. • The Methanosarcina and high energy production pathways increased after optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Changing properties to explore biochar on methane production of anaerobic digestion. • Surface area and ordered graphitization do not influence methane production. • Aromatic functional groups have a major effect on electron transfer. • Uniform dispersion provides stable environment for microorganisms. 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 > 0.05) between hydrochar and biochar produced at different temperature on methane production. 16S rRNA gene sequencing and bioinformatics analysis showed that biochar and hydrochar enriched microorganism that might participate in direct interspecies electron transfer (DIET) such as Pseudomonadaceae , Bacillaceae , and Clostridiaceae. The the surface properties of the modified biochar were characterized with BET, Raman, FTIR and XPS. Bio-based carbon materials with uniform dispersion provided a stable environment for the DIET of microorganisms and electrons are transferred through aromatic functional groups on the surface of materials. This study reveals bio-based carbon materials surface properties on methane production in anaerobic digestion and provides a new approach to recycling spent coffee grounds. [ABSTRACT FROM AUTHOR]

Although it has been demonstrated that one-dimensional, two-dimensional, and three-dimensional carbon nanomaterials can improve the CH 4 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. cellulose, glucose, ethanol, and vinegar residue) were investigated in this study. Results have shown that CH 4 yield from ethanol was increased by 24.59% (p = 0.396) after adding 5 g/L zinc doped carbon quantum dots (Zn-doped CDs) while that from vinegar residue was dramatically increased by 230% (p = 0.000) using 5 g/L nitrogen doped carbon quantum dots (N-doped CDs). In addition, photoluminescence demonstrated that CDs acted as a capacitor for transmitting and receiving electrons. Furthermore, co-occurrence network analysis revealed that Clostridiales might be used as a signal source to communicate with other species. This study firstly shifted the application of CDs from fluorescence to AD and manifested its positive impact on AD. In short, these findings provided a better understanding on the effects of CDs on different feedstocks of methanogenesis and revealed new evidence of stimulating methanogenesis via CDs. [Display omitted] • The influence of carbon quantum dots on anaerobic digestion was first explored. • Ethanol after adding 5 g/L Zn-doped carbon quantum dots increases methane yield by 24.59%. • Clostridiales may be used as a signal source to transmit signals. [ABSTRACT FROM AUTHOR]