● 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.

● 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.

● 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.

● 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.

● 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.

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.

● 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.

● Impact of WWTP effluent discharge on ARGs in downstream waterbodies is hotspot.

Antimicrobial resistance (AMR) has emerged as a significant challenge in human health. Wastewater treatment plants (WWTPs), acting as a link between human activities and the environment, create ideal conditions for the selection and spread of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). Unfortunately, current treatment processes are ineffective in removing ARGs, resulting in the release of large quantities of ARB and ARGs into the aquatic environment through WWTP effluents. This, in turn, leads to their dispersion and potential transmission to human through water and the food chain. To safeguard human and environmental health, it is crucial to comprehend the mechanisms by which WWTP effluent discharge influences the distribution and diffusion of ARGs in downstream waterbodies. In this study, we examine the latest researches on the antibiotic resistome in various waterbodies that have been exposed to WWTP effluent, highlighting the key influencing mechanisms. Furthermore, recommendations for future research and management strategies to control the dissemination of ARGs from WWTPs to the environment are provided, with the aim to achieve the “One Health” objective.

● 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.

● 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.

● Removal of cesium from radioactive wastewater is still a challenging.

Radiocesium is frequently present in radioactive wastewater, while its removal is still a challenge due to its small hydrated radius, high diffusion coefficient, and similar chemical behavior to other alkali metal elements with high background concentrations. This review summarized and analyzed the recent advances in the removal of Cs⁺ from aqueous solutions, with a particular focus on adsorption and membrane separation methods. Various inorganic, organic, and biological adsorbents have undergone assessments to determine their efficacy in the removal of cesium ions. Additionally, membrane-based separation techniques, including reverse osmosis, forward osmosis, and membrane distillation, have also shown promise in effectively separating cesium ions from radioactive wastewater. Additionally, this review summarized the main approaches, including Kurion/SARRY system + desalination system and advanced liquid processing system, implemented after the Fukushima Daiichi nuclear power plant accident in Japan to remove radionuclides from contaminated water. Adsorption technology and membrane separation technology play a vital role in treatment of contaminated water.

● 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.

● Screened 8862 metal-organic frameworks for I₂ capture via molecular simulation.

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture, as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing. Starting from the computation-ready experimental (CoRE) metal-organic frameworks (MOFs) database, grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs. A ranking list of MOFs based on their iodine uptake capabilities was generated, with the Top 10 candidates identified and their respective adsorption sites visualized. Subsequently, machine learning was used to establish structure-property relationships to correlate MOFs’ various structural and chemical features with their corresponding performances in iodine capture, yielding interpretable common features and design rules for viable MOF adsorbents. The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine, and moreover, other volatile environmentally hazardous species.

● Urban water systems are challenged by climate change.

Urban water systems are facing various challenges against climate change, impacting cities’ security and their sustainable development. Specifically, there are three major challenges: submersion risk of coastal cities as glaciers melt and sea level rises, more and severe urban flooding caused by extreme weather like intensified storm surge and heavy precipitation, and regional water resource patterns challenged by alteration of spatial distribution of precipitation. Regarding this, two strategies including proactive adaptation and positive mitigation were proposed in this article to realize the reconstruction and optimization of urban water systems, to enhance their resilience, and eventually increase their adaptability and coping ability to climate change. The proactive adaptation strategy consists of 1) construction of sponge cities to accommodate the increased regular rainfall and to balance the alterations of spatial redistribution of precipitation; 2) reconstruction of excess stormwater discharge and detention system to increase capability for extreme precipitation events based on flood risk assessment under future climate change; 3) deployment of forward-looking, ecological, and integrated measures to improve coastal protection capability against inundation risks caused by climate change and sea level rise. The positive mitigation strategy is to employ the systematic concept in planning and design and to adopt advanced applicable energy-saving technologies, processes, and management practices, aiming at reduction in flux of urban water systems, reinforcement in energy conservation and carbon reduction in both water supply systems and wastewater treatment systems, and thus a reduction of greenhouse gas emission from urban water systems.

● Spartina abundance decreases over time by chemical control.

Invasions by Spartina species pose serious threats to global coastal ecosystems. Although many studies have examined the effectiveness and ecological impacts of invasive Spartina management, no comprehensive global synthesis has been conducted to assess the effects of management on Spartina per se and on wider non-targets. Here, we conducted a global meta-analysis of 3,459 observations from 102 studies to quantify the effects of different management interventions (physical, chemical, biological, and integrated control) on Spartina per se and native biodiversity and environments. We found that physical measures quickly suppressed Spartina but that their effectiveness declined over time. By contrast, chemical measures decreased the abundance and growth of Spartina to a lesser degree in the early stage, but the effectiveness increased over time. Different management measures did not significantly decrease the diversity of native biota on the whole, but native-plant diversity significantly decreased with time after physical control. Different management measures did not affect abiotic factors differently. These results support the use of chemical measures to control invasive Spartina, although their effectiveness would depend on the time since the management intervention. Addressing the problem of Spartina regrowth following physical control requires improved techniques. We hold that initial states of invaders and subsequent environmental changes after management interventions should be weighed in evaluating control efficacy.

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.

● The application of ML in groundwater quality assessment and prediction is reviewed.

Groundwater quality assessment and prediction (GQAP) is vital for protecting groundwater resources. Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding. Recently, the application of machine learning (ML) in GAQP (GQAPxML) has been widely studied due to ML’s reliability and efficiency. While many GQAPxML publications exist, a thorough review is missing. This review provides a comprehensive summary of the development of ML applications in the field of GQAP. First, the workflow of ML modeling is briefly introduced, as are data preparation, model development, model evaluation, and model application. Second, 299 publications related to the topic are filtered, mainly through ML modeling. Subsequently, many aspects of GQAPxML, such as publication trends, the spatial distribution of study areas, the size of data sets, and ML algorithms, are discussed from a bibliometric perspective. In addition, we review in detail the well-established applications and recent findings for several subtopics, including groundwater quality assessment, groundwater quality modeling using groundwater quality parameters, groundwater quality spatial mapping, probability estimation of exceeding the groundwater quality threshold, groundwater quality temporal prediction, and the hybrid use of ML and physics-based models. Finally, the development of GQAPxML is explored from three perspectives: data collection and preprocessing, model building and evaluation, and the broadening of model applications. This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.

● Factor analysis of ammonium nitrate formation based on thermodynamic theory.

High levels of fine particulate matter (PM_2.5) is linked to poor air quality and premature deaths, so haze pollution deserves the attention of the world. As abundant inorganic components in PM_2.5, ammonium nitrate (NH₄NO₃) formation includes two processes, the diffusion process (molecule of ammonia and nitric acid move from gas phase to liquid phase) and the ionization process (subsequent dissociation to form ions). In this study, we discuss the impact of meteorological factors, emission sources, and gaseous precursors on NH₄NO₃ formation based on thermodynamic theory, and identify the dominant factors during clean periods and haze periods. Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution. The dust source is dominant emission source in clean periods; while a combination of coal combustion and vehicle exhaust sources is more important in haze periods. And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate. The findings of this work inform the design of effective strategies to control particulate matter pollution.

● Microplastics (MPs) decreased the protein/amino sugars and increased the lipids.

Chemodiversity of dissolved organic matter (DOM) is a crucial factor controlling soil nutrient availability, greenhouse gas emissions, and pollutant migration. Microplastics (MPs) are widespread pollutants in terrestrial ecosystems in many regions. However, the effects of MPs on DOM chemodiversity are not sufficiently understood, particularly under different types of polymers. Using UV–Vis spectroscopy, 3D fluorescence spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry, the effects of three prevalent MPs [polyethylene, polystyrene, and polyvinyl chloride (PVC)] on the chemical properties and composition of soil DOM were investigated via a 310-d soil incubation experiment. The results showed that MPs reduced the aromatic and hydrophobic soil DOM components by more than 20%, with PVC MPs having the greatest effect. Furthermore, as MP contents increase, the humification level of soil DOM significantly decreases. MPs increased DOM molecules with no heteroatom by 8.3%–14.0%, but decreased DOM molecules with nitrogen content by 17.0%–47.8%. This may be because MPs cause positive “priming effect,” resulting in the breakdown of bioavailable components in soil DOM. This is also related to MPs changing microbial richness and diversity and enriching microbial communities involved in lignin compositions degradation. In the presence of MPs, soil DOM chemodiversity depended on soil pH, electrical conductivity, dissolved organic carbon, soil organic matter, bacterial Shannon, and fungal Chao index. Specifically, DOM in MP-contaminated soils featured more lipids and less condensed aromatics and proteins/amino sugars, thereby conferring a lower DOM aromaticity and higher lability.

● A machine learning approach was applied to predict free chlorine residuals.

Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R², of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.

● Titanium-based flow-through electrode achieved high Cr(VI) reduction efficiency.

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI), but shortcomings are tedious preparation and short lifetimes. Herein, porous titanium available in the market was studied as a flow-through electrode for Cr(VI) electroreduction. In addition, the intelligent prediction of electrolytic performance based on a back propagation neural network (BPNN) was developed. Voltametric studies revealed that Cr(VI) electroreduction was a diffusion-controlled process. Use of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable kinetics. Electroreduction of Cr(VI) in the flow-through system was 1.95 times higher than in a parallel-plate electrode system. When the influent (initial pH 2.0 and 106 mg/L Cr(VI)) was treated at 5.0 V and a flux of 51 L/(h·m²), a reduction efficiency of ~99.9% was obtained without cyclic electrolysis process. Sulfate served as the supporting electrolyte and pH regulator, as reactive CrSO₇²⁻ species were formed as a result of feeding HSO₄⁻. Cr(III) was confirmed as the final product due to the sequential three-electron transport or disproportionation of the intermediate. The developed BPNN model achieved good prediction accuracy with respect to Cr(VI) electroreduction with a high correlation coefficient (R² = 0.943). Additionally, the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model, which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.

● Methods for estimating the aging of environmental micro-plastics were highlighted.

With the increasing production of petroleum-based plastics, the problem of environmental pollution caused by plastics has aroused widespread concern. Microplastics, which are formed by the fragmentation of macro plastics, are bio-accumulate easily due to their small size and slow degradation under natural conditions. The aging of plastics is an inevitable process for their degradation and enhancement of adsorption performance toward pollutants due to a series of changes in their physiochemical properties, which significantly increase the toxicity and harm of plastics. Therefore, studies should focus on the aging process of microplastics through reasonable characterization methods to promote the aging process and prevent white pollution. This review summarizes the latest progress in natural aging process and characterization methods to determine the natural aging mechanism of microplastics. In addition, recent advances in the artificial aging of microplastic pollutants are reviewed. The degradation status and by-products of biodegradable plastics in the natural environment and whether they can truly solve the plastic pollution problem have been discussed. Findings from the literature pointed out that the aging process of microplastics lacks professional and exclusive characterization methods, which include qualitative and quantitative analyses. To lessen the toxicity of microplastics in the environment, future research directions have been suggested based on existing problems in the current research. This review could provide a systematic reference for in-depth exploration of the aging mechanism and behavior of microplastics in natural and artificial systems.

● A new adsorption-membrane separation strategy is used for phosphate removal.

Enhanced phosphorus treatment and recovery has been continuously pursued due to the stringent wastewater discharge regulations and a phosphate supply shortage. Here, a new adsorption-membrane separation strategy was developed for rational reutilization of phosphate from sea cucumber aquaculture wastewater using a Zr-modified-bentonite filled polyvinyl chloride membrane. The as-obtained polyvinyl chloride/Zr-modified-bentonite membrane was highly permeability (940 L/(m²·h)), 1–2 times higher than those reported in other studies, and its adsorption capacity was high (20.6 mg/g) when the phosphate concentration in water was low (5 mg/L). It remained stable under various conditions, such as different pH, initial phosphate concentrations, and the presence of different ions after 24 h of adsorption in a cross-flow filtration system. The total phosphorus and phosphate removal rate reached 91.5% and 95.9%, respectively, after the membrane was used to treat sea cucumber aquaculture wastewater for 24 h and no other water quality parameters had been changed. After the purification process, the utilization of the membrane as a new source of phosphorus in the phosphorus-free f/2 medium experiments indicated the high cultivability of economic microalgae Phaeodactylum tricornutum FACHB-863 and 1.2 times more chlorophyll a was present than in f/2 medium. The biomass and lipid content of the microalgae in the two different media were similar. The innovative polyvinyl chloride/Zr-modified-bentonite membrane used for phosphorus removal and recovery is an important instrument to establish the groundwork for both the treatment of low concentration phosphate from wastewater as well as the reuse of enriched phosphorus in required fields.

● A protocol is proposed for simultaneous oil/water separation and electricity generation.

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m² is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.