Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

State-of-the-art applications of machine learning in the life cycle of solid waste management

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented. ● Changes of research field over time, space, and hot topics were analyzed. ● Detailed application seniors of ML on the life cycle of SW were summarized. ● Perspectives towards future development of ML in the field of SW were discussed. Due to the superiority of machine learning (ML) data processing, it is widely used in research of solid waste (SW). This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW. Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress. Based on the articles considered, a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened. The three topics of ML, namely, SW categories, ML algorithms, and specific applications, as applied to the life cycle of SW were discussed. ML has been applied during the entire SW process, thereby affecting its life cycle. ML was used to predict the generation and characteristics of SW, optimize its collection and transportation, and model the processing of its energy utilization. Finally, the current challenges of applying ML to SW and future perspectives were discussed. The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW. ML plays an important role in the modernization and intellectualization of SW management. It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

The role of lipids in fermentative propionate production from the co-fermentation of lipid and food waste

● Lipid can promote PA production on a target from food waste. ● PA productivity reached 6.23 g/(L∙d) from co-fermentation of lipid and food waste. ● Lipid promoted the hydrolysis and utilization of protein in food waste. ● Prevotella , Veillonella and norank _f _Propioni bacteriaceae were enriched. ● Main pathway of PA production was the succinate pathway. Food waste (FW) is a promising renewable low-cost biomass substrate for enhancing the economic feasibility of fermentative propionate production. Although lipids, a common component of food waste, can be used as a carbon source to enhance the production of volatile fatty acids (VFAs) during co-fermentation, few studies have evaluated the potential for directional propionate production from the co-fermentation of lipids and FW. In this study, co-fermentation experiments were conducted using different combinations of lipids and FW for VFA production. The contributions of lipids and FW to propionate production, hydrolysis of substrates, and microbial composition during co-fermentation were evaluated. The results revealed that lipids shifted the fermentation type of FW from butyric to propionic acid fermentation. Based on the estimated propionate production kinetic parameters, the maximum propionate productivity increased significantly with an increase in lipid content, reaching 6.23 g propionate/(L∙d) at a lipid content of 50%. Propionate-producing bacteria Prevotella, Veillonella, and norank_f_Propionibacteriaceae were enriched in the presence of lipids, and the succinate pathway was identified as a prominent fermentation route for propionate production. Moreover, the Kyoto Encyclopedia of Genes and Genomes functional annotation revealed that the expression of functional genes associated with amino acid metabolism was enhanced by the presence of lipids. Collectively, these findings will contribute to gaining a better understanding of targeted propionate production from FW.     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

Effect of loading rate on shear strength parameters of mechanically and biologically treated waste

● Mechanical behavior of MBT waste affected by loading rate was investigated. ● Shear strength ratio of MBT waste increases with an increase in loading rate. ● Cohesion is inversely related to loading rate. ● Internal friction angles are positively related to loading rate. ● MBT waste from China shows smaller range of φ. Mechanical biological treatment (MBT) technology has attracted increasing attention because it can reduce the volume of waste produced. To deal with the current trend of increasing waste, MBT practices are being adopted to address waste generated in developing urban societies. In this study, a total of 20 specimens of consolidated undrained triaxial tests were conducted on waste obtained from the Hangzhou Tianziling landfill, China, to evaluate the effect of loading rate on the shear strength parameters of MBT waste. The MBT waste samples exhibited an evident strain-hardening behavior, and no peak was observed even when the axial strain exceeded 25%. Further, the shear strength increased with an increase in the loading rate; the effect of loading rate on shear strength under a low confining pressure was greater than that under a high confining pressure. Furthermore, the shear strength parameters of MBT waste were related to the loading rate. The relationship between the cohesion, internal friction angle, and logarithm of the loading rate could be fitted to a linear relationship, which was established in this study. Finally, the ranges of shear strength parameters cohesion c and effective cohesion c ´ were determined as 1.0–8.2 kPa and 2.1–14.9 kPa, respectively; the ranges of the internal friction angle φ and effective internal friction angle φ ´ were determined as 16.2°–29° and 19.8°–43.9°, respectively. These results could be used as a valuable reference for conducting stability analyses of MBT landfills.     

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

● Established a quantification method of pollutant emission standard. ● Predicted the SO₂ emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy. ● Analyzed the SO₂ emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.     

Passive convergence-permeable reactive barrier (PC-PRB): An effective configuration to enhance hydraulic performance

● A novel PRB configuration based on passive convergent flow effect was proposed. ● A 2D finite-difference hydrodynamic model, PRB-Flow, was developed. ● PC-PRB can significantly enhance the hydraulic capture capacity of PRB. ● The PRB geometric dimensions and materials cost are effectively reduced. ● The dominant influential factor of the PC-PRB capture width is pipe length, L_p. A novel permeable reactive barrier (PRB) configuration, the so-called passive convergence-permeable reactive barrier (PC-PRB), is proposed to overcome several shortcomings of traditional PRB configurations, such as high dependency to site hydrogeological characteristics and plume size. The PC-PRB is designed to make the plume converge towards the PRB due to the passive hydraulic decompression-convergent flow effect. The corresponding passive groundwater convergence (PC) system is deployed upstream of the PRB system, which consists of passive wells, water pipes, and a buffer layer. A two-dimensional (2D) finite-difference hydrodynamic code, entitled PRB-Flow, is developed to examine the hydraulic performance parameters (i.e., capture width (W) and residence time (t)) of PC-PRB. It is proved that the horizontal 2D capture width (W_h) and vertical 2D capture depth (W_v) of the PC-PRB remarkably increase compared to that of the continuous reactive barrier (C-PRB). The aforementioned relative growth values in order are greater than 50% and 25% in this case study. Therefore, the PRB geometric dimensions as well as the materials cost required for the same plume treatment lessens. The sensitivity analysis reveals that the dominant factors influencing the hydraulic performance of the PC-PRB are the water pipe length (L_p), PRB length (L_PRB), passive well height (H_w), and PRB height (H_PRB). The discrepancy between the W_h of PC-PRB and that of the C-PRB (i.e., ΔW_h) has a low correlation with PRB parameters and mainly depends on L_p, which could dramatically simplify the PC-PRB design procedure. Generally, the proposed PC-PRB exhibits an effective PRB configuration to enhance hydraulic performance.     

Evaluating the impact of sulfamethoxazole on hydrogen production during dark anaerobic sludge fermentation

● SMX promotes hydrogen production from dark anaerobic sludge fermentation. ● SMX significantly enhances the hydrolysis and acidification processes. ● SMX suppresses the methanogenesis process in order to reduce hydrogen consumption. ● SMX enhances the relative abundance of hydrogen-VFAs producers. ● SMX brings possible environmental risks due to the enrichment of ARGs. The impact of antibiotics on the environmental protection and sludge treatment fields has been widely studied. The recovery of hydrogen from waste activated sludge (WAS) has become an issue of great interest. Nevertheless, few studies have focused on the impact of antibiotics present in WAS on hydrogen production during dark anaerobic fermentation. To explore the mechanisms, sulfamethoxazole (SMX) was chosen as a representative antibiotic to evaluate how SMX influenced hydrogen production during dark anaerobic fermentation of WAS. The results demonstrated SMX promoted hydrogen production. With increasing additions of SMX from 0 to 500 mg/kg TSS, the cumulative hydrogen production elevated from 8.07 ± 0.37 to 11.89 ± 0.19 mL/g VSS. A modified Gompertz model further verified that both the maximum potential of hydrogen production (P_m) and the maximum rate of hydrogen production (R_m) were promoted. SMX did not affected sludge solubilization, but promoted hydrolysis and acidification processes to produce more hydrogen. Moreover, the methanogenesis process was inhibited so that hydrogen consumption was reduced. Microbial community analysis further demonstrated that the introduction of SMX improved the abundance of hydrolysis bacteria and hydrogen-volatile fatty acids (VFAs) producers. SMX synergistically influenced hydrolysis, acidification and acetogenesis to facilitate the hydrogen production.     

Responses of microbial interactions to elevated salinity in activated sludge microbial community

● Salinity led to the elevation of NAR over 99.72%. ● Elevated salinity resulted in a small, complex, and more competitive network. ● Various AOB or denitrifiers responded differently to elevated salinity. ● Putative keystone taxa were dynamic and less abundant among various networks. Biological treatment processes are critical for sewage purification, wherein microbial interactions are tightly associated with treatment performance. Previous studies have focused on assessing how environmental factors (such as salinity) affect the diversity and composition of the microbial community but ignore the connections among microorganisms. Here, we described the microbial interactions in response to elevated salinity in an activated sludge system by performing an association network analysis. It was found that higher salinity resulted in low microbial diversity, and small, complex, more competitive overall networks, leading to poor performance of the treatment process. Subnetworks of major phyla (Proteobacteria, Bacteroidetes, and Chloroflexi) and functional bacteria (such as AOB, NOB and denitrifiers) differed substantially under elevated salinity process. Compared with subnetworks of Nitrosomonadaceae, Nitrosomonas (AOB) made a greater contribution to nitrification under higher salinity (especially 3%) in the activated sludge system. Denitrifiers established more proportion of cooperative relationships with other bacteria to resist 3% salinity stress. Furthermore, identified keystone species playing crucial roles in maintaining process stability were dynamics and less abundant under salinity disturbance. Knowledge gleaned from this study deepened our understanding of microbial interaction in response to elevated salinity in activated sludge systems.     

Ozonation of aromatic monomer compounds in water: factors determining reaction outcomes

● p- CNB and IBP were selected, to explore factors determining ozonation outcomes. ● •OH contributed only 50 % to IBP removal, compared to the 90 % for p -CNB removal. ● IBP achieved fewer TOC removal and more by-product types and quantities. ● A longer ring-opening distance existed during the degradation of IBP. ● Multiple positions on both branches of IBP were attacked, consuming more oxidants. For aromatic monomer compounds (AMCs), ozonation outcomes were usually predicted by the substituents of the benzene ring based on the electron inductive effect. However, the predicted results were occasionally unreliable for complex substituents, and other factors caused concern. In this study, p-chloronitrobenzene (p-CNB) and ibuprofen (IBP) were selected for ozonation. According to the electron inductive theory, p-CNB should be less oxidizable, but the opposite was true. The higher rates of p-CNB were due to various sources of assistance. First, the hydroxyl radical (•OH) contributed 90 % to p-CNB removal at pH 7.0, while its contribution to IBP removal was 50 %. Other contributions came from molecular O₃ oxidation. Second, p-CNB achieved 40 % of the total organic carbon (TOC) removal and fewer by-product types and quantities, when compared to the results for IBP. Third, the oxidation of p-CNB started with hydroxyl substitution reactions on the benzene ring; then, the ring opened. However, IBP was initially oxidized mainly on the butane branched chain, with a chain-shortening process occurring before the ring opened. Finally, the degradation pathway of p-CNB was single and consumed fewer oxidants. However, both branches of IBP were attacked simultaneously, and three degradation pathways that relied on more oxidants were proposed. All of these factors were determinants of the rapid removal of p-CNB.     

Differences in distributions,assembly mechanisms,and putative interactions of AOB and NOB at a large spatial scale

● Nitrifiers in WWTP were investigated at large spatial scale. ● AOB populations varied greatly but NOB populations were similar among cities. ● Drift dominated both AOB and NOB assembling processes. ● DO did not show a significant effect on NOB. ● NOB tended to cooperate with AOB and non-nitrifying microorganisms. Ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) play crucial roles in removing nitrogen from sewage in wastewater treatment plants (WWTPs) to protect water resources. However, the differences in ecological properties and putative interactions of AOB and NOB in WWTPs at a large spatial scale remain unclear. Hence, 132 activated sludge (AS) samples collected from 11 cities across China were studied by utilizing 16S rRNA gene sequencing technology. Results indicated that Nitrosomonas and Nitrosospira accounted for similar ratios of the AOB community and might play nearly equal roles in ammonia oxidation in AS. However, Nitrospira greatly outnumbered other NOB genera, with proportions varying from 94.7% to 99.9% of the NOB community in all WWTPs. Similar compositions and, hence, a low distance–decay turnover rate of NOB (0.035) across China were observed. This scenario might have partly resulted from the high proportions of homogenizing dispersal (~13%). Additionally, drift presented dominant roles in AOB and NOB assembling mechanisms (85.2% and 81.6% for AOB and NOB, respectively). The partial Mantel test illustrated that sludge retention time and temperature were the primary environmental factors affecting AOB and NOB communities. Network results showed that NOB played a leading role in maintaining module structures and node connections in AS. Moreover, most links between NOB and other microorganisms were positive, indicating that NOB were involved in complex symbioses with bacteria in AS.     

Mechanism of ball milled activated carbon in improving the desalination performance of flow- and fixed-electrode in capacitive deionization desalination

● BACs were used in electrode material for both fixed and flowing electrodes. ● ASAR of FCDI and MCDI was improved by 134% and 17%, respectively. ● ENRS of FCDI and MCDI was improved by 21% and 53%. ● The mechanism of improving desalination performance was analyzed in detail. Capacitive deionization (CDI) is a novel electrochemical water-treatment technology. The electrode material is an important factor in determining the ion separation efficiency. Activated carbon (AC) is extensively used as an electrode material; however, there are still many deficiencies in commercial AC. We adopted a simple processing method, ball milling, to produce ball milled AC (BAC) to improve the physical and electrochemical properties of the original AC and desalination efficiency. The BAC was characterized in detail and used for membrane capacitive deionization (MCDI) and flow-electrode capacitive deionization (FCDI) electrode materials. After ball milling, the BAC obtained excellent pore structures and favorable surfaces for ion adsorption, which reduced electron transfer resistance and ion migration resistance in the electrodes. The optimal ball-milling time was 10 h. However, the improved effects of BAC as fixed electrodes and flow electrodes are different and the related mechanisms are discussed in detail. The average salt adsorption rates (ASAR) of FCDI and MCDI were improved by 134% and 17%, respectively, and the energy-normalized removal salt (ENRS) were enhanced by 21% and 53%, respectively. We believe that simple, low-cost, and environmentally friendly BAC has great potential for practical engineering applications of FCDI and MCDI.     

Characterization and variation of dissolved organic matter in composting: a critical review

● Effect of composting approaches on dissolved organic matter (DOM). ● Effect of composting conditions on the properties of DOM. ● Character indexes of DOM varied in composting. ● The size, hydrophobicity, humification, and electron transfer capacity increased. ● The hydrophilicity, protein-like materials, and aliphatic components reduced. As the most motive organic fraction in composting, dissolved organic matter (DOM) can contribute to the transfer and dispersal of pollutants and facilitate the global carbon cycle in aquatic ecosystems. However, it is still unclear how composting approaches and conditions influence the properties of compost-derived DOM. Further details on the shift of DOM character indexes are required. In this study, the change in properties of compost-derived DOM at different composting approaches and the effect of composting conditions on the DOM characteristics are summarized. Thereafter, the change in DOM character indexes’ in composting was comprehensively reviewed. Along with composting, the elements and spectral properties (chromophoric DOM (CDOM) and fluorescent DOM (FDOM)) were altered, size and hydrophobicity increased, and aromatic-C and electron transfer capacity were promoted. Finally, some prospects to improve this study were put forward. This paper should facilitate the people who have an interest in tracing the fate of DOM in composting.     

Simple fabrication of carboxymethyl cellulose and κ-carrageenan composite aerogel with efficient performance in removal of fluoroquinolone antibiotics from water

● A composite aerogel was simply obtained to remove various fluoroquinolones (FQs). ● The structural and textural properties of this composite aerogel are improved. ● Its adsorption capacity was improved at a low content of coexisting Cu²⁺ or Fe³⁺ ion. ● Two substructural analogs of FQs are compared to explore the adsorption mechanisms. ● This aerogel after saturated adsorption can be reused directly for Cu²⁺ adsorption. 3D composite aerogels (CMC-CG) composed of carboxymethyl cellulose and κ-carrageenan were designed and fabricated using the one-pot synthesis technique. The optimized CMC-CG showed a good mechanical property and a high swelling ratio due to its superior textural properties with a proper chemically cross-linked interpenetrating network structure. CMC-CG was utilized for the removal of various fluoroquinolones (FQs) from water and exhibited high adsorption performance because of effective electrostatic attraction and hydrogen bonding interactions. Ciprofloxacin (CIP), a popular FQ, was used as the representative. The optimized CMC-CG had a theoretically maximal CIP uptake of approximately 1.271 mmol/g at the pH of 5.0. The adsorption capacity of CMC-CG was improved in the presence of some cations, Cu²⁺ and Fe³⁺ ions, at a low concentration through the bridging effect but was reduced at a high concentration. The investigation of adsorption mechanisms, based on the adsorption kinetics, isotherms and thermodynamic study, Fourier transform infrared spectrometry and X-ray photoelectron spectroscopy analyses before and after adsorption, and changes in the adsorption performance of CMC-CG toward two molecular probes, further indicated that electrostatic attraction was the dominant interaction rather than hydrogen bonding in this adsorption. CMC-CG after saturated adsorption of CIP could be easily regenerated using a dilute NaCl aqueous solution and reused efficiently. Moreover, the disused aerogel could still be reused as a new adsorbent for effective adsorption of Cu²⁺ ion. Overall, this study suggested the promising applications of this composite aerogel as an eco-friendly, cost-effective, and recyclable adsorbent for the efficient removal of FQs from water.     

Low-temperature caproate production,microbial diversity,and metabolic pathway in xylose anaerobic fermentation

● Converting xylose to caproate under a low temperature of 20 °C by MCF was verified. ● Final concentration of caproate from xylose in a batch reactor reached 1.6 g/L. ● Changing the substrate to ethanol did not notably increase the caproate production. ● Four genera, including Bifidobacterium , were revealed as caproate producers. ● The FAB pathway and incomplete RBO pathway were revealed via metagenomic analysis. Mixed culture fermentation (MCF) is challenged by the unqualified activity of enriched bacteria and unwanted methane dissolution under low temperatures. In this work, caproate production from xylose was investigated by MCF at a low temperature (20 °C). The results showed that a 9 d long hydraulic retention time (HRT) in a continuously stirred tank reactor was necessary for caproate production (~0.3 g/L, equal to 0.6 g COD/L) from xylose (10 g/L). The caproate concentration in the batch mode was further increased to 1.6 g/L. However, changing the substrate to ethanol did not promote caproate production, resulting in ~1.0 g/L after 45 d of operation. Four genera, Bifidobacterium, Caproiciproducens, Actinomyces, and Clostridium_sensu_stricto_12, were identified as the enriched caproate-producing bacteria. The enzymes in the fatty acid biosynthesis (FAB) pathway for caproate production were identified via metagenomic analysis. The enzymes for the conversion of (C_n+2)-2,3-Dehydroxyacyl-CoA to (C_n+2)-Acyl-CoA (i.e., EC 1.3.1.8 and EC 1.3.1.38) in the reverse β-oxidation (RBO) pathway were not identified. These results could extend the understanding of low-temperature caproate production.