The combustion of vinyl chloride (VC) after the train derailment accident in Ohio, USA in February, 2023 has caused widespread concern around the world. This paper tried to analyze several issues concerning the accident, including the appropriateness of the VC combustion in the emergency response in this accident, the meanings of so-called “controlled combustion”, the potential environmental risks caused by VC and combustion by-products, and follow-up work. In our view, this accident had surely caused environmental and health risks to some extent. Hence, a comprehensive environmental risk assessment is necessary, and then the site with risk should be comprehensively remediated, hazardous waste should be harmlessly treated as soon as possible. Finally, this accident suggests that further efforts should be taken to bridge the gap between chemical safety management and their environmental risk management.
● Recent advances in the photolysis of nitrate/HNO3 are reviewed. ● Mechanisms and key factors affecting the photolysis of nitrate/HNO3 are summarized. ● Atmospheric implications and future research recommendations are provided.
Nitrate is an important component of atmospheric particulate matter and affects air quality, climate, human health, and the ecosystem. Nitrate was previously considered a permanent sink for nitrogen oxides (NOx). However, this viewpoint has been challenged in recent years because growing research evidence has shown the transformation of nitrate into NOx (i.e., renoxification). The photolysis of nitrate/HNO3, especially in the particulate phase or adsorbed on particles, can be a significant renoxification process in the atmosphere. The formation and photolysis of nitrate in aerosol not only change the diurnal variation of NOx, but also provide long-distance transport of NOx in the form of nitrate, which affects local and regional atmospheric chemistry and air quality. This review summarizes recent advances in the fundamental understanding of the photolysis of nitrate/HNO3 under various atmospheric conditions, with a focus on mechanisms and key factors affecting the process. The atmospheric implications are discussed and future research is recommended.
● Abundance of MAGs carrying ARG-VF pairs unchanged in rivers after WWTP upgrade. ● Upgrade of WWTPs significantly reduced diversity of pathogenic genera in rivers. ● Upgrade of WWTPs reduced most VF (ARG) types carried by potential pathogens in rivers. ● Upgrade of WWTPs narrowed the pathogenic host ranges of ARGs and VFs in rivers.
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. ● 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.
● Pd-Cu modified CNT membranes were prepared successfully by electrodeposition method. ● The deposition voltage and deposition time were optimized for Pd-Cu co-deposition. ● NO3−-N was removed efficiently from water by Pd-Cu modified CNT membranes. ● The presence of dissolved oxygen did not affect the nitrate reduction performance. ● Mass transfer rate was promoted significantly with the increase in membrane flux.
Excessive nitrate in water is harmful to the ecological environment and human health. Electrocatalytic reduction is a promising technology for nitrate removal. Herein, a Pd-Cu modified carbon nanotube membrane was fabricated with an electrodeposition method and used to reduce nitrate in a flow-through electrochemical reactor. The optimal potential and duration for codeposition of Pd and Cu were −0.7 V and 5 min, respectively, according to linear scan voltammetry results. The membrane obtained with a Pd:Cu ratio of 1:1 exhibited a relatively high nitrate removal efficiency and N2 selectivity. Nitrate was almost completely reduced (~99 %) by the membrane at potentials lower than −1.2 V. However, −0.8 V was the optimal potential for nitrate reduction in terms of both nitrate removal efficiency and product selectivity. The nitrate removal efficiency was 56.2 %, and the N2 selectivity was 23.8 % for the Pd:Cu=1:1 membrane operated at −0.8 V. Nitrate removal was enhanced under acidic conditions, while N2 selectivity was decreased. The concentrations of Cl− ions and dissolved oxygen showed little effect on nitrate reduction. The mass transfer rate constant was greatly improved by 6.6 times from 1.14 × 10−3 m/h at a membrane flux of 1 L/(m2·h) to 8.71 × 10−3 m/h at a membrane flux of 15 L/(m2·h), which resulted in a significant increase in the nitrate removal rate from 13.6 to 133.5 mg/(m2·h). These findings show that the Pd-Cu modified CNT membrane is an efficient material for nitrate reduction.
● Definition of emerging contaminants in drinking water is introduced. ● SERS and standard methods for emerging contaminant analysis are compared. ● Enhancement factor and accessibility of SERS hot spots are equally important. ● SERS sensors should be tailored according to emerging contaminant properties. ● Challenges to meet drinking water regulatory guidelines are discussed.
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.
● The availability of PD-anammox was investigated with higher NO3––N concentration. ● NO3––N concentration affects NO3––N accumulation during denitrification. ● COD concentration is determinant for N removal pathways in PD-anammox process. ● The synergy/competition mechanisms between denitrifiers and anammox was explored.
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.
● Fundamentals of membrane fouling are comprehensively reviewed. ● Contribution of thermodynamics on revealing membrane fouling mechanism is summarized. ● Quantitative approaches toward thermodynamic fouling mechanisms are deeply analyzed. ● Inspirations of thermodynamics for membrane fouling mitigation are briefly discussed. ● Research prospects on thermodynamics and membrane fouling are forecasted.
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.
● Higher concentrations of PS, PS-NH2 and PS-SO3H inhibited seed germination. ● PS, PS-NH2 and PS-SO3H influenced seedling growth in a dose-dependent manner. ● PS, PS-NH2 and PS-SO3H reduced essential nutrients uptake and plant quality. ● PS, PS-NH2 and PS-SO3H increased antioxidant enzyme activities and MDA content. ● Nanoplastic toxicity was related to surface charges.
Nanoplastic pollution has become a significant problem in farmland systems worldwide. However, research on the effects of nanoplastics (NPs) with different charges on field crops is still limited. In our study, NPs with different charges, including unmodified polystyrene nanoplastics (PS), positively charged polystyrene nanoplastics (PS-NH2), and negatively charged polystyrene nanoplastics (PS-SO3H), were investigated for their impacts on seed germination and seedling growth of rape. The results showed that seed water uptake (after 12 h), seed germination, seed vigour, and relative root elongation were all significantly reduced under exposure to NPs (200 mg/L). Similarly, remarkable decreases in plant biomass (root weight, shoot weight), growth (root length, plant height), photosynthesis ability (chlorophyll a, chlorophyll b, carotenoids), essential nutrient uptake (Fe, Mn, Zn, Cu), and plant quality (soluble protein, soluble sugar, crude fibre content) of rape seedlings were also observed after exposure to NPs. Among the three kinds of NPs, PS-NH2 showed stronger effects. Moreover, superoxide dismutase, peroxidase, and catalase activities of rape seedlings were changed, and the content of malondialdehyde was significantly increased under exposure to NPs. Furthermore, positively charged PS-NH2 showed stronger effects on the phenotype, physiology, biochemistry, nutrient uptake, and plant quality of rape. Notably, a comprehensive toxicity evaluation revealed that PS-NH2 had the strongest toxicity to rape. The present study provides important implications for the interaction and risk assessment of NPs and crops in soil-plant systems.
● This study systematically examined the relationship between groundwater Cd and UCL. ● The study covered 211 UCL and sociological characteristic from nine groundwater samples. ● We found a significant positive correlation between groundwater Cd and UCL. ● Smoking status and education level also significantly affected 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.
● 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.
● 38 PhACs and 2 pesticides were detected in the three rivers of the Pearl River basin. ● Anti-inflammatory/analgesics drugs were the predominant PhACs. ● The concentrations of PhACs showed seasonal and spatial variation. ● Diazepam and ibuprofen were the two PhACs with a moderate environmental risk.
The occurrence, fate, and environmental risk of 40 pharmaceutically active compounds (PhACs) from surface waters and sediments were comprehensively investigated in the Beijiang River, Xijiang River, and Maozhou River of the Pearl River basin, South China. Salicylic acid and diclofenac (anti-inflammatory drugs), gemfibrozil (a lipid regulator), carbamazepine (an antiepileptic drug), diazepam (a psychoactive drug), and 2-methyl-4-chloro-phenoxyacetic acid (MCPA, a pesticide) were the most ubiquitous compounds in the studied region. The average concentrations of detected PhACs in surface waters and sediments ranged from 0.17 to 19.1 ng/L and 0.10 to 10.4 ng/g, respectively. Meanwhile, PhACs concentration in surface waters and sediments varied greatly among and within the Beijiang River, Xijiang River, and Maozhou River. The largest annual flux of PhACs of the Xijiang River and Beijiang River was more than 11 000 kg per annum, whereas only 25.7 kg/a in the Maozhou River. In addition, the estimated emissions of PhACs in the Beijiang River, Xijiang River, and Maozhou River ranged respectively from 0.28 to 4.22 kg/a, 0.12 to 6.72 kg/a, and 6.66 to 91.0 kg/a, and the back-estimated usage varied with a range from 12.0 to 293 kg/a, 6.79 to 944 kg/a, 368 to 17 459 kg/a. Moreover, the emissions of PhACs showed a close relationship with the gross domestic product (GDP) of each city along the Pearl River. The environmental risk assessment suggested that diazepam and ibuprofen had a moderate risk in this region.
● Simultaneous NH4+/NO3– removal was achieved in the FeS denitrification system ● Anammox coupled FeS denitrification was responsible for NH4+/NO3– removal ● Sulfammox, Feammox and Anammox occurred for NH4+ removal ● Thiobacillus, Nitrospira , and Ca. Kuenenia were key functional microorganisms
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.
● Microplastics (MPs) undergo photoaging in natural water under light irradiation. ● ROS generation plays an important role in the photoaging pathway of MPs. ● Dissolved organic matter (DOM) ubiquitous in natural water affects MP photoaging. ● Future works are suggested to study the effect mechanism of DOM on MP photoaging.
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.
● A better risk assessment can combine the improved non-target analysis method. ● Multi-evidence is advised in molecular determination and risk-based prioritization. ● Combining omics, multi-endpoint EDA, and machine learning to assess product risks.
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.
● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved.
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 quality of 71 data records (69 of them only focused on MPs) was assessed. ● About 96% of the data records were considered unreliable in at least one criterion. ● Improvements need to be made regarding positive controls and polymer identification. ● A mismatch between MP/NPs used in toxicity studies and those in foods was recorded.
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.
● The fouling is summarized based on ceramic membrane performance and pollutants. ● The current research methods and theoretical models are summarized. ● The membrane fouling control methods and collaborative technology are reviewed.
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.
● Application of the MOF-composite membranes in adsorption was discussed. ● Recent application of MOFs-membranes for separation was summarized. ● Separation and degradation for emerging organic contaminants were described.
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. ● Three machine learning models were developed and evaluated. ● XGBoost showed best prediction performance and excellent generalization ability. ● pH and protein were the top two input features for the modeling. ● The maximal VFA production was predicted to be 650 mg COD/g VSS.
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. ● A PSO-SVR model for predicting CODeff in wastewater was proposed. ● The CODeff prediction performances of the three models in the paper were compared. ● The CODeff prediction effects of different models in other studies were discussed.
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.
● Small molecular chains formed on photo-aged polylactic acid microplastics (MPs). ● Oxygen-containing functional groups generated on photo-aged polyamide MPs. ● Photo-aging has the opposite influence on the imidacloprid adsorption on two MPs. ● Electrostatic interactions and hydrogen bonds were the main mechanisms. ● High pH value and low ionic strength increase the adsorption capacity.
The photo-aging behavior of microplastics (MPs) in natural environment has become a global concern. The ultraviolet radiation has enough energy to change the polymer structure and physical-chemical properties of MPs. Less attention has focused on the interactions of the photo-aged polar and biodegradable MPs with organic pollutants. This work investigated the structural properties of aged polar polyamide (PA) MPs and biodegradable polylactic acid (PLA) MPs exposed to ultraviolet irradiation and their adsorption behavior and mechanism for neonicotinoid insecticide imidacloprid (IMI). The results showed that the MPs had extensive changes in surface morphology and chemical properties after photo-aging. The C–N bond of PA MPs was disrupted to form more carbonyl groups. The oxygen-containing functional groups on the surface of aged PLA MPs were broken and generated relatively smaller molecules. The adsorption capacity of IMI on PA MPs decreased by 19.2 %, while the adsorption capacity of IMI on PLA MPs increased by 41.2 % after photo-aging. This depended on the natural structure of the MPs and their ability to absorb ultraviolet light. The electrostatic interactions, hydrogen bonds, van der Waals interactions, and polar-polar interactions were the main adsorption mechanisms of IMI on MPs. High initial solution pH and low ionic strength favored the adsorption of IMI by altering charge distribution on the MPs surface. The formation of the humic acid-IMI complexes decreased the concentration of IMI in the water phase and further decreased the adsorption on MPs. These results are enlightening for a scientific comprehension of the environmental behavior of the polar MPs.
● A controlled-release fertilizer was developed based on chitosan biopolymer scaffold. ● Chitosan-MMT scaffold achieved a well-controlled nutrient release. ● Highly water-absorbing chitosan-MMT hydrogels enhanced the soil water retention. ● Physically crosslinked chitosan-MMT hydrogels exhibited excellent degradability.
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.
● The main direct seal up carbon options and challenges are reviewed. ● Ocean-based CO2 replacement for CH4/oil exploitation is presented. ● Scale-advantage of offshore CCS hub is discussed.
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.
● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters.
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.
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 HHAalk). The hydrochar, prepared under high temperature (200 °C) and strong acidic (pH 0) conditions, achieved high HHAalk yields (i.e., 67.9 wt% and 68.8 wt% calculated based on weight of hydrochar). The sources of HHAalk 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.
● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases.
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.
● Online learning models accurately predict influent flow rate at wastewater plants. ● Models adapt to changing input-output relationships and are friendly to large data. ● Online learning models outperform conventional batch learning models. ● An optimal prediction strategy is identified through uncertainty analysis. ● The proposed models provide support for coping with emergencies like COVID-19.
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.