● Abundance of MAGs carrying ARG-VF pairs unchanged in rivers after WWTP upgrade.

Wastewater treatment plants (WWTPs) with additional tertiary ultrafiltration membranes and ozonation treatment can improve water quality in receiving rivers. However, the impacts of WWTP upgrade (WWTP-UP) on pathogens carrying antibiotic resistance genes (ARGs) and virulence factors (VFs) in rivers remain poorly understood. In this study, ARGs, VFs, and their pathogenic hosts were investigated in three rivers impacted by large-scale WWTP-UP. A five-year sampling campaign covered the periods before and after WWTP-UP. Results showed that the abundance of total metagenome-assembled genomes (MAGs) containing both ARGs and VFs in receiving rivers did not decrease substantially after WWTP-UP, but the abundance of MAGs belonging to pathogenic genera that contain both ARGs and VFs (abbreviated as PAVs) declined markedly. Genome-resolved metagenomics further revealed that WWTP-UP not only reduced most types of VFs and ARGs in PAVs, but also effectively eliminated efflux pump and nutritional VFs carried by PAVs in receiving rivers. WWTP-UP narrowed the pathogenic host ranges of ARGs and VFs and mitigated the co-occurrence of ARGs and VFs in receiving rivers. These findings underline the importance of WWTP-UP for the alleviation of pathogens containing both ARGs and VFs in receiving rivers.

● A machine learning model was used to identify lake nutrient pollution sources.

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.

● Definition of emerging contaminants in drinking water is introduced.

Emerging contaminants (ECs) in drinking water pose threats to public health due to their environmental prevalence and potential toxicity. The occurrence of ECs in our drinking water supplies depends on their physicochemical properties, discharging rate, and susceptibility to removal by water treatment processes. Uncertain health effects of long-term exposure to ECs justify their regular monitoring in drinking water supplies. In this review article, we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy (SERS) for EC analysis in drinking water. Working principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost, time, sensitivity, and availability is made. Subsequently, we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per- and polyfluoroalkyl substances, novel pesticides, pharmaceuticals, endocrine-disrupting chemicals, and microplastics. In addition to maximizing the intrinsic enhancement factors of SERS substrates, strategies to improve hot spot accessibilities to the targeting ECs are equally important. This is a review article focusing on SERS analysis of ECs in drinking water. The discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.

● Fundamentals of membrane fouling are comprehensively reviewed.

Membrane technology is widely regarded as one of the most promising technologies for wastewater treatment and reclamation in the 21st century. However, membrane fouling significantly limits its applicability and productivity. In recent decades, research on the membrane fouling has been one of the hottest spots in the field of membrane technology. In particular, recent advances in thermodynamics have substantially widened people’s perspectives on the intrinsic mechanisms of membrane fouling. Formulation of fouling mitigation strategies and fabrication of anti-fouling membranes have both benefited substantially from those studies. In the present review, a summary of the recent results on the thermodynamic mechanisms associated with the critical adhesion and filtration processes during membrane fouling is provided. Firstly, the importance of thermodynamics in membrane fouling is comprehensively assessed. Secondly, the quantitative methods and general factors involved in thermodynamic fouling mechanisms are critically reviewed. Based on the aforementioned information, a brief discussion is presented on the potential applications of thermodynamic fouling mechanisms for membrane fouling control. Finally, prospects for further research on thermodynamic mechanisms underlying membrane fouling are presented. Overall, the present review offers comprehensive and in-depth information on the thermodynamic mechanisms associated with complex fouling behaviors, which will further facilitate research and development in membrane technology.

● The availability of PD-anammox was investigated with higher NO₃^––N concentration.

Partial denitrification-anammox (PD-anammox) is an innovative process to remove nitrate (NO₃^––N) and ammonia (NH₄⁺–N) simultaneously from wastewater. Stable operation of the PD-anammox process relies on the synergy and competition between anammox bacteria and denitrifiers. However, the mechanism of metabolic between the functional bacteria in the PD-anammox system remains unclear, especially in the treatment of high-strength wastewater. The kinetics of nitrite (NO₂^––N) accumulation during denitrification was investigated using the Michaelis-Menten equation, and it was found that low concentrations of NO₃^––N had a more significant effect on the accumulation of NO₂^––N during denitrification. Organic matter was a key factor to regulate the synergy of anammox and denitrification, and altered the nitrogen removal pathways. The competition for NO₂^––N caused by high COD concentration was a crucial factor that affecting the system stability. Illumina sequencing techniques demonstrated that excess organic matter promoted the relative abundance of the Denitratesoma genus and the nitrite reductase gene nirS, causing the denitrifying bacteria Denitratisoma to compete with Cadidatus Kuenenia for NO₂^––N, thereby affecting the stability of the system. Synergistic carbon and nitrogen removal between partial denitrifiers and anammox bacteria can be effectively achieved by controlling the COD and COD/NO₃^––N.

● A review of machine learning (ML) for spatial prediction of soil contamination.

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.

● This study systematically examined the relationship between groundwater Cd and UCL.

Cadmium (Cd) has received widespread attention owing to its persistent toxicity and non-degradability. Cd in the human body is mainly absorbed from the external environment and is usually assessed using urinary Cd. Hunan Province is the heartland of the Chinese non-ferrous mining area, where several serious Cd pollution events have occurred, including high levels of Cd in the urine of residents. However, the environmental factors influencing high urinary Cd levels (UCLs) in nearby residents remain unclear. Therefore, 211 nearby residents’ UCLs and the corresponding sociological characteristics from nine groundwater samples in this area were analyzed using statistical analysis models. Groundwater Cd concentration ranged from 0.02 to 1.15 μg/L, aligning with class III of the national standard; the range of UCL of nearby residents was 0.37–36.60 μg/L, exceeding the national guideline of 0–2.5 μg/L. Groundwater Cd levels were positively correlated with the UCL (P < 0.001, correlation coefficient 95 % CI = 9.68, R² = 0.06). In addition, sociological characteristics, such as smoking status and education level, also affect UCL. All results indicate that local governments should strengthen the prevention and abatement of groundwater Cd pollution. This study is the first to systematically evaluate the relationship between groundwater Cd and UCL using internal and external environmental exposure data. These findings provide essential bases for relevant departments to reduce Cd exposure in regions where the heavy metal industry is globally prevalent.

● Microplastics (MPs) undergo photoaging in natural water under light irradiation.

Plastic products widespread in natural water can be broken into smaller-sized microplastics (MPs, < 5 mm) under light irradiation, thermal degradation and biodegradation, posing a serious threat to aquatic ecosystems and human health. This perspective concludes that MPs can generate reactive oxygen species (ROS) through initiation, propagation and termination steps, which can attack the polymer resulting in the photoaging and breakdown of C–C and C–H bonds under ultraviolet (UV) irradiation. Free radical generation and weathering degree of MPs depend on their physicochemical properties and environmental conditions. In general, UV irradiation and co-existed MPs can significantly accelerate MP photoaging. With plentiful chromophores (carbonyl, carboxyl and benzene rings, Dissolved organic matter (DOM) mainly absorbs photons (300–500 nm) and generates hydrated electrons, ³DOM* and ROS, which may affect MP photoaging. However, whether DOM may transfer the electron and energy to MPs under UV irradiation, affect ROS generation of MPs and their photoaging pathway are inadequately studied. More studies are needed to elucidate MP photoaging pathways and mechanisms, consider the influence of stabilization capacity, photosensitization and photoionization of DOM as well as their competitive light absorption with MPs, which provides valuable insights into the environmental behavior and ecological risk of MPs in natural water.

● Online learning models accurately predict influent flow rate at wastewater plants.

Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants (WWTPs), as it is closely related to wastewater characteristics such as biochemical oxygen demand (BOD), total suspend solids (TSS), and pH. Previous studies have been conducted to predict influent flow rate, and it was proved that data-driven models are effective tools. However, most of these studies have focused on batch learning, which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly. Online learning, which has distinct advantages of dealing with stream data, large data set, and changing data pattern, has a potential to address this issue. In this study, the performance of conventional batch learning models Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), and their respective online learning models Adaptive Random Forest (aRF), Adaptive K-Nearest Neighbors (aKNN), and Adaptive Multi-Layer Perceptron (aMLP), were compared for predicting influent flow rate at two Canadian WWTPs. Online learning models achieved the highest R², the lowest MAPE, and the lowest RMSE compared to conventional batch learning models in all scenarios. The R² values on testing data set for 24-h ahead prediction of the aRF, aKNN, and aMLP at Plant A were 0.90, 0.73, and 0.87, respectively; these values at Plant B were 0.75, 0.78, and 0.56, respectively. The proposed online learning models are effective in making reliable predictions under changing data patterns, and they are efficient in dealing with continuous and large influent data streams. They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.

● Simultaneous NH₄⁺/NO₃^– removal was achieved in the FeS denitrification system

An autotrophic denitrifying bioreactor with iron sulfide (FeS) as the electron donor was operated to remove ammonium (NH₄⁺) and nitrate (NO₃⁻) synergistically from wastewater for more than 298 d. The concentration of FeS greatly affected the removal of NH₄⁺/NO₃⁻. Additionally, a low hydraulic retention time worsened the removal efficiency of NH₄⁺/NO₃⁻. When the hydraulic retention time was 12 h, the optimal removal was achieved with NH₄⁺ and NO₃⁻ removal percentages both above 88%, and the corresponding nitrogen removal loading rates of NH₄⁺ and NO₃⁻ were 49.1 and 44.0 mg/(L·d), respectively. The removal of NH₄⁺ mainly occurred in the bottom section of the bioreactor through sulfate/ferric reducing anaerobic ammonium oxidation (Sulfammox/Feammox), nitrification, and anaerobic ammonium oxidation (Anammox) by functional microbes such as Nitrospira, Nitrosomonas, and Candidatus Kuenenia. Meanwhile, NO₃⁻ was mainly removed in the middle and upper sections of the bioreactor through autotrophic denitrification by Ferritrophicum, Thiobacillus, Rhodanobacter, and Pseudomonas, which possessed complete denitrification-related genes with high relative abundances.

● A better risk assessment can combine the improved non-target analysis method.

The continuous input of various emerging contaminants (ECs) has inevitably introduced large amounts of transformation products (TPs) in natural and engineering water scenarios. Structurally similar to the precursor species, the TPs are expected to possess comparative, if not more serious, environmental properties and risks. This review summarizes the state-of-the-art knowledge regarding the integrated risk assessment frameworks of TPs of ECs, mainly involving the exposure- and effect-driven analysis. The inadequate information within existing frameworks that was essential and critical for developing a better risk assessment framework was discussed. The main strategic improvements include (1) non-targeted product analysis in both laboratory and field samples, (2) omics-based high-throughput toxicity assessment, (3) multichannel-driven mode of action in conjugation with effect-directed analysis, and (4) machine learning technology. Overall, this review provides a concise but comprehensive insight into the optimized strategy for evaluating the environmental risks and screening the key toxic products from the cocktail mixtures of ECs and their TPs in the global water cycle. This facilitates deciphering the mode of toxicity in complex chemical mixtures and prioritizing the regulated TPs among the unknown products, which have the potential to be considered a class of novel “ECs” of great concern.

● The fouling is summarized based on ceramic membrane performance and pollutants.

Membrane separation, as an important drinking water treatment technology, has wide applications. The remarkable advantages of ceramic membranes, such as chemical stability, thermal stability, and high mechanical strength, endow them with broader prospects for development. Despite the importance and advantages of membrane separation in water treatment, the technique has a limitation: membrane fouling, which greatly lowers its effectiveness. This is caused by organics, inorganic substances, and microorganisms clogging the pore and polluting the membrane surface. The increase in membrane pollution greatly lowers purification effectiveness. Controlling membrane fouling is critical in ensuring the efficient and stable operation of ceramic membranes for water treatment. This review analyzes four mechanisms of ceramic membrane fouling, namely complete blocking, standard blocking, intermediate blocking, and cake filtration blocking. It evaluates the mechanisms underlying ceramic membrane fouling and summarizes the progress in approaches aimed at controlling it. These include ceramic membrane pretreatment, ceramic membrane surface modification, membrane cleaning, magnetization, ultrasonics, and nanobubbles. This review highlights the importance of optimizing ceramic membrane preparation through further research on membrane fouling and pre-membrane pretreatment mechanisms. In addition, combining process regulations with ceramic membranes as the core is an important research direction for ceramic membrane-based water treatment.

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.

● Data quality assessment criteria for MP/NPs in food products were developed.

Data on the occurrence of microplastics and nanoplastics (MP/NPs) in foods have been used to assess the human health risk caused by the consumption of MP/NPs. The reliability of the data, however, remains unclear because of the lack of international standards for the analysis of MP/NPs in foods. Therefore, the data quality needs to be assessed for accurate health risk assessment. This study developed 10 criteria applicable to the quality assessment of data on MP/NPs in foods. Accordingly, the reliability of 71 data records (69 of them only focused on MPs) was assessed by assigning a score of 2 (reliable without restrictions), 1 (reliable but with restrictions), or 0 (unreliable) on each criterion. The results showed that only three data records scored 2 or 1 on all criteria, and six data records scored 0 on as many as six criteria. A total of 58 data records did not include information on positive controls, and 12 data records did not conduct the polymer identification, which could result in the overestimation or underestimation of MP/NPs. Our results also indicated that the data quality of unprocessed foods was more reliable than that of processed foods. Furthermore, we proposed a quality assurance and quality control protocol to investigate MP/NPs in foods. Notably, the characteristics of MP/NPs used in toxicological studies and those existing in foods showed a remarkable discrepancy, causing the uncertainty of health risk assessment. Therefore, both the estimated exposure of MP/NPs and the claimed potential health risks should be treated with caution.

● Application of the MOF-composite membranes in adsorption was discussed.

Presence of emerging organic contaminants (EOCs) in water is one of the major threats to water safety. In recent decades, an increasing number of studies have investigated new approaches for their effective removal. Among them, metal-organic frameworks (MOFs) have attracted increasing attention since their first development thanks to their tunable metal nodes and versatile, functional linkers. However, whether or not MOFs have a promising future for practical application in emerging contaminants-containing wastewater is debatable. This review summarizes recent studies about the removal of EOCs using MOFs-related material. The synthesis strategies of both MOF particles and composites, including thin-film nanocomposite and mixed matrix membranes, are critically reviewed, as well as various characterization technologies. The application of the MOF-based composite membranes in adsorption, separation (nanofiltration and ultrafiltration), and catalytic degradation are discussed. Overall, literature survey shows that MOFs-based composite could play a crucial role in eliminating EOCs in the future. In particular, modified membranes that realize separation and degradation might be the most promising materials for such application.

● Data-driven approach was used to simulate VFA production from WAS fermentation.

Riboflavin is a redox mediator that promotes volatile fatty acids (VFAs) production from waste activated sludge (WAS) and is a promising method for WAS reuse. However, time- and labor-consuming experiments challenge obtaining optimal operating conditions for maximal VFA production. In this study, three machine learning (ML) models were developed to predict the VFAs production from riboflavin-mediated WAS fermentation systems. Among the three tested ML algorithms, eXtreme Gradient Boosting (XGBoost) presented the best prediction performance and excellent generalization ability, with the highest testing coefficient of determination (R² of 0.93) and lowest root mean square error (RMSE of 0.070). Feature importance analysis and their interactions using the Shepley Additive Explanations (SHAP) method indicated that pH and soluble protein were the top two input features for the modeling. The intrinsic correlations between input features and microbial communities corroborated this deduction. On the optimized ML model, genetic algorithm (GA) and particle swarm optimization (PSO) solved the optimal solution of VFA output, predicting the maximum VFA output as 650 mg COD/g VSS. This study provided a data-driven approach to predict and optimize VFA production from riboflavin-mediated WAS fermentation.

● Data acquisition and pre-processing for wastewater treatment were summarized.

The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

● The main direct seal up carbon options and challenges are reviewed.

Carbon capture and storage (CCS) technology is an imperative, strategic, and constitutive method to considerably reduce anthropogenic CO₂ emissions and alleviate climate change issues. The ocean is the largest active carbon bank and an essential energy source on the Earth’s surface. Compared to oceanic nature-based carbon dioxide removal (CDR), carbon capture from point sources with ocean storage is more appropriate for solving short-term climate change problems. This review focuses on the recent state-of-the-art developments in offshore carbon storage. It first discusses the current status and development prospects of CCS, associated with the challenges and uncertainties of oceanic nature-based CDR. The second section outlines the mechanisms, sites, advantages, and ecologic hazards of direct offshore CO₂ injection. The third section emphasizes the mechanisms, schemes, influencing factors, and recovery efficiency of ocean-based CO₂-CH₄ replacement and CO₂-enhanced oil recovery are reviewed. In addition, this review discusses the economic aspects of offshore CCS and the preponderance of offshore CCS hubs. Finally, the upsides, limitations, and prospects for further investigation of offshore CO₂ storage are presented.

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking.

Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.

● A controlled-release fertilizer was developed based on chitosan biopolymer scaffold.

Fertilizer consumption is increasing drastically along with the rapid expansion of farming in response to the ever-growing population. However, a significant portion of the nutrients in traditional fertilizers is lost during leaching and runoff causing economic loss and environmental threats. Polymer-modified controlled-release fertilizers provide an opportunity for mitigating adverse environmental effects and increasing the profitability of crop production. Here, we present a cheap and easy-to-fabricate controlled-release fertilizer excipient based on hydrogels scaffolded by safe and biodegradable chitosan and montmorillonite (MMT) nanoclays. By introducing elastic and flexible physical crosslinking induced by 2-dimensional (2D) MMT nanoflakes into the chitosan hydrogel, highly swellable and degradable chitosan-MMT nanocomposites were fabricated. The addition of MMT into the chitosan hydrogels enhanced the total release of phosphorous (P) and potassium (K), from 22.0 % to 94.9 % and 9.6% to 31.4 %, respectively, compared to the pure chitosan gel. The chitosan-MMT nanocomposite hydrogel achieved a well-controlled overall fertilizer release in soil. A total of 55.3 % of loaded fertilizer was released over 15 d with a daily release of 2.8 %. For the traditional fertilizer podwer, 89.2 % of the fertilizer was washed out during the first irrigation under the same setup. In the meantime, the nanocomposites improved the water retention of the soil, thanks to its excellent water absorbency. Moreover, the chitosan-MMT nanocomposite hydrogels exhibited high degradation of 57 % after swelling in water for 20 d. Such highly degradable fertilizer excipient poses minimal threat to the long-term fertility of the soil. The engineered Chitosan-MMT biopolymer scaffold as a controlled-release fertilizer excipient provides a promising opportunity for advancing sustainable agriculture.

Converting biomass materials to humic acid is a sustainable method for humic acid production and achieve biomass valorization. A two-step hydrothermal treatment method was adopted in this study to produce humic acid from corn stalks. In the first step of the process, hydrochar was prepared at different hydrothermal temperatures and pH values. Their chemical properties were then analyzed, and the hydrochar-derived humic acids were produced under alkaline hydrothermal conditions (denoted as HHA_alk). The hydrochar, prepared under high temperature (200 °C) and strong acidic (pH 0) conditions, achieved high HHA_alk yields (i.e., 67.9 wt% and 68.8 wt% calculated based on weight of hydrochar). The sources of HHA_alk formation were as follows: 1) production in the hydrochar preparation stage, and 2) increment under the alkaline hydrothermal treatment of hydrochar. The degree of hydrochar unsaturation was suggested as an indicator for evaluating the hydrochar humification potential under alkaline hydrothermal conditions. This study provides an important reference for the preparation of suitable hydrochar with high hydrothermal humification potential.

● MSWNet was proposed to classify municipal solid waste.

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.

● Haze formation in China is highly correlated with iron and steel industry.

Recent years have witnessed significant improvement in China’s air quality. Strict environmental protection measures have led to significant decreases in sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter (PM) emissions since 2013. But there is no denying that the air quality in 135 cities is inferior to reaching the Ambient Air Quality Standards (GB 3095–2012) in 2020. In terms of temporal, geographic, and historical aspects, we have analyzed the potential connections between China’s air quality and the iron and steel industry. The non-target volatile organic compounds (VOCs) emissions from iron and steel industry, especially from the iron ore sinter process, may be an underappreciated index imposing a negative effect on the surrounding areas of China. Therefore, we appeal the authorities to pay more attention on VOCs emission from the iron and steel industry and establish new environmental standards. And different iron steel flue gas pollutants will be eliminated concurrently with the promotion and application of new technology.

● Effect of composting approaches on dissolved organic matter (DOM).

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.

● A CNT filter enabled effective KMnO₄ activation via facilitated electron transfer.

Numerous reagents have been proposed as electron sacrificers to induce the decomposition of permanganate (KMnO₄) by producing highly reactive Mn species for micropollutants degradation. However, this strategy can lead to low KMnO₄ utilization efficiency due to limitations associated with poor mass transport and high energy consumption. In the present study, we rationally designed a catalytic carbon nanotube (CNT) membrane for KMnO₄ activation toward enhanced degradation of micropollutants. The proposed flow-through system outperformed conventional batch reactor owing to the improved mass transfer via convection. Under optimal conditionals, a > 70% removal (equivalent to an oxidation flux of 2.43 mmol/(h·m²)) of 80 μmol/L sulfamethoxazole (SMX) solution can be achieved at single-pass mode. The experimental analysis and DFT studies verified that CNT could mediate direct electron transfer from organic molecules to KMnO₄, resulting in a high utilization efficiency of KMnO₄. Furthermore, the KMnO₄/CNT system had outstanding reusability and CNT could maintain a long-lasting reactivity, which served as a green strategy for the remediation of micropollutants in a sustainable manner. This study provides new insights into the electron transfer mechanisms and unveils the advantages of effective KMnO₄ utilization in the KMnO₄/CNT system for environmental remediation.

● BACs were used in electrode material for both fixed and flowing electrodes.

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.

● Medium poly Al salts dominated the PAC residual salts with a rational dosage.

With the widespread introduction of pre-coagulation prior to the biological unit in various industrial wastewater treatments, it is noteworthy that long-term accumulation of residual coagulants has certains effect on both micro and macro characteristics of activated sludge (AS). In this study, the morphology distributions of residual aluminum salts (RAS) and their effects on the removal efficiency of AS were investigated under different PAC concentrations. The results showed that the dominance of medium polymeric RAS, formed under an appropriate PAC dose of 20 mg/L enhanced the hydrophobicity, flocculation, and sedimentation performances of AS, as well as the enzymatic activity in cells in the sludge system, improving the main pollutants removal efficiency of the treatment system. Comparatively the species composition with monomer and dimer / high polymer RAS as the overwhelming parts under an over-dosed PAC concentration of 55 mg/L resulted in excessive secretion of EPS with loose flocs structure and conspicuous inhibition of cellular activity, leading to the deterioration of physico-chemical and biological properties of AS. Based on these findings, this study can shed light on the role of the RAS hydrolyzed species distributions, closely relevant to Al dosage, in affecting the comprehensive properties of AS and provide a theoretical reference for coagulants dosage precise control in the pretreatment of industrial wastewater.