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A novel approach with a combination of trypsin, nano-zero-valent iron (NZVI) and activated carbon (AC) was conducted to promote the methane production of waste activated sludge (WAS) during the anaerobic digestion (AD) processes. Results showed that the combined addition of trypsin-NZVI-AC exhibited the synergistic effect during different AD stages. Trypsin mainly facilitated the hydrolysis process and the acetic acid conversion, while NZVI-AC enhanced the substrate metabolism and the electronic transfer to subsequently produce methane. A dose of 1000 mg/L trypsin was optimal to maximize this synergistic effect. Metagenomic analysis showed that trypsin-NZVI-AC addition effectively improved the relative abundance of acetyl-CoA carboxylase, and then strengthened both acetoclastic methanogenesis (M00357) and hydrogenotrophic methanogenesis (M00567). Hydrogenotrophic methanogens such as Methanobacterium, Methanoculleus, and Methanosarcina were greatly enriched with trypsin-NZVI-AC compared with trypsin or NZVI-AC addition. Moreover, electroactive bacteria G. sulfurreducens and G. metallireducens were also enriched by this method to conduct direct interspecies electron transfer among methanogens, leading to the better improvement of methane production. These findings supply a promising way to optimize the enzyme pretreatment technology and elevate the methanogenic efficiency of WAS. [Display omitted] • CH 4 production of WAS was effectively enhanced with trypsin-NZVI-AC combination • A synergistic effect of trypsin and NZVI-AC was achieved in different AD stages • High dose of trypsin inhibited CH 4 production and 1000 mg/L was the optimal dose • Both acetoclastic and hydrogenotrophic methanogenesis were strengthened • Electroactive bacteria were greatly enriched to conduct DIET with methanogens [ABSTRACT FROM AUTHOR]

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O 3 and PM 2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O 3 , CO is the most important factor for PM 2.5 , and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs. [Display omitted] • CNN coupled with LSTM and attention mechanism for atmospheric predictions. • Proposal outperforms other models, with R2 > 0.9 between predicted and observed data. • Sensitivity analysis identifies factors contributing most to pollutant levels. • Removing variables that contribute little to output improves modeling efficiency. • Key factors remain similar during the whole period and before COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The metabolic pathways of protein during anaerobic digestion (AD) of waste activated sludge (WAS) were comprehensively investigated. Results showed that 100 kinds of peptidases were involved in the hydrolysis and acidogenesis processes. Serine endopeptidases (EC 3.4.21.53) and serine-type carboxypeptidases (EC 3.4.16.4) were the key enzymes of endopeptidases and exopeptidases, respectively. The pathways of ko00250 (alanine, aspartate and glutamate metabolism), ko00260 (glycine, serine and threonine metabolism), ko00270 (cysteine and methionine metabolism), ko00280 (valine, leucine and isoleucine degradation), ko00360 (phenylalanine metabolism) and ko00310 (lysine degradation) were the critical metabolic pathways of amino acids during AD of WAS, since they have complete pathways from amino acids to vital intermediates (pyruvate or acetyl-CoA). l -aspartate, l -alanine, threonine, glycine, serine, l -cysteine were the intermediate products in the conversion of protein to pyruvate, while l -leucine, l -isoleucine, phenylalanine, lysine could be directly metabolized to acetyl-CoA. Dechloromonas and Thauera played major roles in the crucial metabolic pathways of amino acids (ko00250, ko00260, ko00280 and ko00270). These important discoveries could provide a new biological perspective for improving AD performance. [Display omitted] • Serine endopeptidases and serine-type carboxypeptidases were the key peptidases. • Ko00250, ko00260, ko00270, ko00280, ko00360, ko00310 were the critical pathways. • Six kinds of amino acids were the intermediate products from protein to pyruvate. • l -leucine, l -isoleucine, phenylalanine, lysine directly metabolized to acetyl-CoA. • The contribution of microbes to the crucial protein metabolic pathways was revealed. [ABSTRACT FROM AUTHOR]

Modeling-based prediction methods enable rapid, reagent-free air pollution detection based on inexpensive multi-source data than traditional chemical reaction-based detection methods in order to quickly understand the air pollution situation. In this study, a convolutional neural network (CNN) and long and short-term memory (LSTM) neural networks are integrated to create a CNN-LSTM time series prediction model to predict the concentration of PM 2.5 and its chemical components (i.e., heavy metals, carbon component, and water-soluble ions) using meteorological data and air pollutants (PM 2.5 , SO 2 , NO 2 , CO, and O 3). In the integrated CNN-LSTM model, the CNN uses convolutional and pooling layers to extract features from the data, whereas the powerful nonlinear mapping and learning capabilities of LSTM enable the time series prediction of air pollution. The experimental results showed that the CNN-LSTM exhibited good generalization ability in the prediction of As, Cd, Cr, Cu, Ni, and Zn, with a mean R2 above 0.9. Mean R2 predicted for PM 2.5 , Pb, Ti, EC, OC, SO 4 2−, and NO 3 − ranged from 0.85 to 0.9. Shapley value showed that PM 2.5 , NO 2 , SO 2 , and CO had a greater influence on the predicted heavy metal results of the model. Regarding water-soluble ions, the predicted results were dominantly influenced by PM 2.5 , CO, and humidity. The prediction of the carbon fraction was affected mainly by the PM 2.5 concentration. Additionally, several input variables for various components were eliminated without affecting the prediction accuracy of the model, with R2 between 0.70 and 0.84, thereby maximizing modeling efficiency and lowering operational costs. The fully trained model prediction results showed that most predicted components of PM 2.5 were lower during January to March 2020 than those in 2018 and 2019. This study provides insight into improving the accuracy of modeling-based detection methods and promotes the development of integrated air pollution monitoring toward a more sustainable direction. [Display omitted] • CNN coupled with LSTM for atmospheric time series predictions. • Many elements had better simulation effects when atmospheric pollutants as inputs. • CNN-LSTM has superior prediction performance with more than 90% accuracy. • The Shapely value method identifies the key indicators for predicting pollutant. • Predicted pollutant contents were lower during COVID-19 outbreak than in 2019. [ABSTRACT FROM AUTHOR]

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R2 of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14–27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments. [Display omitted] • Heavy metals and magnetic parameters in Taihu sediment have risen since the 1970s. • Ecological risk lagged behind heavy metal pollution by 10 years in Taihu sediment. • The test-R of Cd, Cr, Hg, Sb, As, Co and Pb were better than those of Cu and Ni. • RF was better than MIC in improving SVM models in most heavy metal simulations. [ABSTRACT FROM AUTHOR]

Liquid nitrogen was employed as a low-temperature medium to activate zero-valent iron (ZVI) powder in an attempt to strengthen its enhancement effect on anaerobic digestion (AD) of swine manure (SM). Surprisingly, it was found that both pristine ZVI and liquid nitrogen-pretreated ZVI (LZVI) did not significantly improve the AD performance or change the archaeal community structure. It was hypothesized that ZVI might not be effective at stress-free environment like in these digesters. To confirm this, an additional set of AD experiments were performed at high ammonia stress (about 4000 mg/L), results showed that ZVI and LZVI greatly alleviated ammonia inhibition and increased the CH 4 yield by 11.6% and 28.2%, respectively. Apparently, ZVI mainly affected AD systems by changing the metabolism pathways and enhancing the microbial activity to overcome process inhibition, and pretreatment of liquid nitrogen could significantly accelerate the dissolution of ZVI and improve its utilization efficiency, contributing to a greater extend of process recovery and improvement. [Display omitted] • Liquid nitrogen was applied to improve ZVI utilization efficiency during AD of SM. • ZVI/LZVI did not affect CH 4 yield and archaeal distribution in healthy digesters. • ZVI could alter metabolisms and activate microbes to overcome AD inhibition. • Liquid nitrogen soaking strengthened ZVI's enhancement effect at high TAN stress. • ZVI showed potentials in process revival. [ABSTRACT FROM AUTHOR]