ES&T in the 21st Century: A Data-Driven Analysis of Research Topics, Interconnections, And Trends in the Past 20 Years

Predicting sorption of diverse organic compounds in soil-water systems: Meta-analysis, machine learning modeling, and global soil mapping

In recent decades, the environmental detection of various organic compounds (OCs) has highlighted the limitations of conventional soil-water sorption models, which simplify complex experimental conditions and often overlook OCs with polyfunctional and ionizable structures. To address these shortcomings, we compiled a comprehensive soil-water sorption dataset encompassing 20,945 data points for 419 OCs with various functional groups and 1037 different soils. Meta-analysis of the dataset revealed the trends of soil sorption associated with OC substructures, soil properties, and solution conditions. Machine learning models employing the XGBoost algorithm, in conjunction with MACCS fingerprints and experimental conditions, were developed to cover the entire spectrum of speciation for cationic, neutral, and anionic species. Among these, the individual models tailored to each speciation achieved an overall root-mean-square-error value of 0.32 for log Kd. Model interpretation revealed that the models correctly understood the contributions of various substructures, such as multiple aromatic rings and nitrogen or oxygen atoms, to sorption. The models were also found to accurately capture isotherm nonlinearity and the pH effect on the sorption of ionizable OCs. Finally, utilizing soil properties from the Harmonized World Soil Database, the models predicted the sorption of diverse OCs based on global soil properties under simulated environmental scenarios.

Utilizing large language models for identifying future research opportunities in environmental science

Facing pressing global challenges such as climate change, biodiversity loss, resource scarcity, and environmental pollution, the field of environmental science urgently needs innovative research methods. However, identifying meaningful and cutting-edge research topics is a significant challenge, as it requires a thorough understanding of existing literature and the ability to discern knowledge gaps. Traditional bibliometrics often fall short of capturing nascent interdisciplinary fields. Recent advancements in artificial intelligence (AI) offer potential solutions to this challenge. This study explores the capabilities of large language models (LLMs) in identifying and analyzing emerging research opportunities in environmental science. We employ a text retrieval method based on word embeddings, utilizing the emergent reasoning abilities of LLMs combined with embedded search techniques to dynamically integrate the latest literature. By comparing the GPT-3.5 API with supplementary literature, ChatGPT, and GPT-4, we find that the GPT-3.5 API provides a more comprehensive, detailed, and current analysis of cutting-edge environmental science, emphasizing the importance of understanding the dynamics and timeliness of the field. Our findings underscore the critical role of interdisciplinary research, AI, and big data in addressing urgent environmental challenges. LLMs can serve as valuable tools for researchers, offering guidance and inspiration for future directions in environmental science research.

Microplastic contamination in Chinese topsoil from 1980 to 2050

China's soil is experiencing significant microplastic contamination. We developed a machine-learning model to assess microplastic pollution from 1980 to 2050. Our results showed that the average abundance of microplastics in topsoil increased from 45 items per kilogram of soil in 1980 to 1156 items by 2018, primarily due to industrial growth (39 %), agricultural film usage (30 %), tire wear (17 %), and domestic waste (14 %). During the same period, microplastic levels in cropland rose from 98 to 2401 items per kilogram of soil, and exposure levels for the Chinese population increased from 808 to 3168 items per kilogram. By 2050, a reduction in the use of agricultural films is expected to decrease cropland contamination by half. However, overall levels are anticipated to remain steady due to other persistent sources, indicating a continued spread of microplastics into subterranean environments, water bodies, and human systems. This study highlights China's microplastic challenges and suggests potential global trends, emphasizing the need for increased awareness and intervention worldwide.

Advances and future directions of environmental risk research: A bibliometric review

Environmental risk is one of the world's most significant threats, projected to be the leading risk over the next decade. It has garnered global attention due to increasingly severe environmental issues, such as climate change and ecosystem degradation. Research and technology on environmental risks are gradually developing, and the scope of environmental risk study is also expanding. Here, we developed a tailored bibliometric method, incorporating co-occurrence network analysis, cluster analysis, trend factor analysis, patent primary path analysis, and patent map methods, to explore the status, hotspots, and trends of environment risk research over the past three decades. According to the bibliometric results, the publications and patents related to environmental risk have reached explosive growth since 2018. The primary topics in environmental risk research mainly involve (a) ecotoxicology risk of emerging contaminants (ECs), (b) environmental risk induced by climate change, (c) air pollution and health risk assessment, (d) soil contamination and risk prevention, and (e) environmental risk of heavy metal. Recently, the hotspots of this field have shifted into artificial intelligence (AI) based techniques and environmental risk of climate change and ECs. More research is needed to assess ecological and health risk of ECs, to formulize mitigation and adaptation strategies for climate change risks, and to develop AI-based environmental risk assessment and control technology. This study provides the first comprehensive overview of recent advances in environmental risk research, suggesting future research directions based on current understanding and limitations.

Methanogenic Potential of Sewer Microbiomes and Its Implications for Methane Emission

The sewer system, despite being a significant source of methane emissions, has often been overlooked in current greenhouse gas inventories due to the limited availability of quantitative data. Direct monitoring in sewers can be expensive or biased due to access limitations and internal heterogeneity of sewer networks. Fortunately, since methane is almost exclusively biogenic in sewers, we demonstrate in this study that the methanogenic potential can be estimated using known sewer microbiome data. By combining data mining techniques and bioinformatics databases, we developed the first data-driven method to analyze methanogenic potentials using a data set containing 633 observations of 53 variables obtained from literature mining. The methanogenic potential in the sewer sediment was around 250-870% higher than that in the wet biofilm on the pipe and sewage water. Additionally, k-means clustering and principal component analysis linked higher methane emission rates (9.72 ± 51.3 kgCO2 eq m-3 d-1) with smaller pipe size, higher water level, and higher potentials of sulfate reduction in the wetted pipe biofilm. These findings exhibit the possibility of connecting microbiome data with biogenic greenhouse gases, further offering insights into new approaches for understanding greenhouse gas emissions from understudied sources.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.

A data-driven analysis to discover research hotspots and trends of technologies for PFAS removal

The frequent detection of persistent per- and polyfluoroalkyl substances (PFAS) in organisms and environment coupled with surging evidence for potential detrimental impacts, have attracted widespread attention throughout the world. In order to reveal research hotspots and trends of technologies for PFAS removal, herein, we performed a data-driven analysis of 3975 papers and 436 patents from Web of Science Core Collection and Derwent Innovation Index databases up to 2023. The results showed that China and the USA led the way in the research of PFAS removal with outstanding contributions to publications. The progression generally transitioned from accidental discovery of decomposition, to experimentation with removal effects and mechanisms of existing methods, and finally to enhanced defluorination and mechanism-driven design approaches. The keywords co-occurrence network and technology classification together revealed the main knowledge framework, which was constructed and correlated through contaminants, substrates, materials, processes and properties. Moreover, adsorption was demonstrated to be the dominant removal process among the current studies. Subsequently, we concluded the principles, advances and drawbacks of enrichment and separation, biological methods, advanced oxidation and reduction processes. Further exploration indicated the hotspots such as alternatives and precursors for PFAS ("genx": 1.258, "f-53b": 0.337), degradable mineralization technologies ("photocatalytic degrad": 0.529, "hydrated electron": 0.374), environment-friendly remediation technologies ("phytoremedi": 0.939, "constructed wetland": 0.462) and combination with novel materials ("metal-organic framework": 1.115, "layered double hydroxid": 0.559) as well as computer science ("molecular dynamics simul": 0.559, "machine learn"). Furthermore, the future direction of technological innovation might lie in high-performance processes that minimize secondary pollution, the development of recyclable and renewable treatment agents, and collaborative control strategies for multiple pollutants. Overall, this study offers comprehensive and objective review for researchers and industry professionals in this field, enabling rapid access to knowledge guidance and insights into research frontiers.

Impacts of regional socioeconomic statuses and global events on solid waste research reflected in six waste-focused journals

The research pertaining to solid waste is undergoing extensive advancement, thereby necessitating a consolidation and analysis of its research trajectories. The existing biblio-studies on solid waste research (SWR) lack thorough analyses of the factors influencing its trends. This article presents an innovative categorization framework that categorizes publications from six SWR journals utilizing Source Latent Dirichlet Allocation. First analyse changes in publication numbers across main categories, subcategories, journals, and regions, providing a macro-level study of SWR. Temporal analysis of keywords supplements a micro-level study of SWR, which highlights that emerging technologies with low Technology Readiness Level receive significant attention, while studies on widespread technologies are diminishing. Additionally, this study demonstrates the substantial influence of socioeconomic factors and previous SWR publications on current and future SWR trends. Finally, the article confirms the impact of global events on SWR trends by examining the structural breakpoints of SWR and their correlation with global events.

Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

Driving Role of Zinc Oxide Nanoparticles with Different Sizes and Hydrophobicity in Metabolic Response and Eco-Corona Formation in Sprouts (Vigna radiata)

Zinc oxide nanoparticles (ZnO NPs) cause biotoxicity and pose a potential ecological threat; however, their effects on plant metabolism and eco-corona evolution between NPs and organisms remain unclear. This study clarified the molecular mechanisms underlying physiological and metabolic responses induced by three different ZnO NPs with different sizes and hydrophobicity in sprouts (Vigna radiata) and explored the critical regulation of eco-corona formation in root-nano systems. Results indicated that smaller-sized ZnO inhibited root elongation by up to 37.14% and triggered oxidative burst and apoptosis. Metabolomics confirmed that physiological maintenance after n-ZnO exposure was mainly attributed to the effective stabilization of nitrogen fixation and defense systems by biotransformation of the flavonoid pathway. Larger-sized or hydrophobic group-modified ZnO exhibited low toxicity in sprouts, with 0.89-fold upregulation of citrate in central carbon metabolism. This contributed to providing energy for resistance to NP stress through amino acid and carbon/nitrogen metabolism, accompanied by changes in membrane properties. Notably, smaller-sized and hydrophobic NPs intensely stimulated the release of root metabolites, forming corona complexes with exudates. The hydrogen-bonded wrapping mechanism in protein secondary structure and hydrophobic interactions of heterogeneous functional groups drove eco-corona formation, along with the corona evolution intensity of n-ZnO > s-ZnO > b-ZnO based on higher (α-helix + 3-turn helix)/β-sheet ratios. This study provides crucial insight into metabolic and eco-corona evolution in bionano fates.

Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS

Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.

Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning

Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead data from China were sourced from 1180 surveys published between 1980 and 2022. Additionally, regional statistical figures for 15 natural and socioeconomic variables were obtained or estimated as predictors. A machine learning model, using the random forest algorithm and 2973 generated samples, was created to predict county-specific blood lead levels in China from 1980 to 2040. Geometric mean blood lead levels in children (i.e., age 14 and under) decreased significantly from 104.4 μg/L in 1993 to an anticipated 40.3 μg/L by 2040. The number exceeding 100 μg/L declined dramatically, yet South Central China remains a hotspot. Lead exposure is similar among different groups, but overall adults and adolescents (i.e., age over 14), females, and rural residents exhibit slightly lower exposure compared to that of children, males, and urban residents, respectively. Our predictions indicated that despite the general reduction, one-fourth of Chinese counties rebounded during 2015-2020. This slower decline might be due to emerging lead sources like smelting and coal combustion; however, the primary factor driving the decline should be the reduction of a persistent source, legacy gasoline-derived lead. Our approach innovatively maps lead exposure without comprehensive surveys.

Bibliometric insights into the evolution of uranium contamination reduction research topics: Focus on microbial reduction of uranium

Confronting the threat of environment uranium pollution, decades of research have yielded advanced and significant findings in uranium bioremediation, resulting in the accumulation of tremendous amount of high-quality literature. In this study, we analyzed over 10,000 uranium reduction-related papers published from 1990 to the present in the Web of Science based on bibliometrics, and revealed some critical information on knowledge structure, thematic evolution and additional attention. Methods including contribution comparison, co-occurrence and temporal evolution analysis are applied. The results of the distribution and impact analysis of authors, sources, and journals indicated that the United States is a leader in this field of research and China is on the rise. The top keywords remained stable, primarily focused on chemicals (uranium, iron, plutonium, nitrat, carbon), characters (divers, surfac, speciat), and microbiology (microbial commun, cytochrome, extracellular polymeric subst). Keywords related to new strains, reduction mechanisms and product characteristics demonstrated the strongest uptrend, while some keywords related to mechanism and performance were clearly emerging in the past 5 years. Furthermore, the evolution of the thematic progression can be categorized into three stages, commencing with the discovery of the enzymatic reduction of hexavalent uranium to tetravalent uranium, developing in the groundwater remediation process at uranium-contaminated sites, and delving into the research on microbial reduction mechanisms of uranium. For future research, enhancing the understanding of mechanisms, improving uranium removal performance, and exploring practical applications can be considered. This study provides unique insights into microbial uranium reduction research, providing valuable references for related studies in this field.

Scientometric analysis of electrocatalysis in wastewater treatment: today and tomorrow

Electrocatalytic methods are valuable tools for addressing water pollution and scarcity, offering effective pollutant removal and resource recovery. To investigate the current status and future trends of electrocatalysis in wastewater treatment, a detailed analysis of 9417 papers and 4061 patents was conducted using scientometric methods. China emerged as the leading contributor to publications, and collaborations between China and the USA have emerged as the most frequent partnerships. Primary article co-citation clusters focused on oxygen evolution reaction and electrochemical oxidation, transitioning towards advanced oxidation processes ("persulfate activation"), and electrocatalytic reduction processes ("nitrate reduction"). Bifunctional catalysts, theoretical calculations, electrocatalytic combination technologies, and emerging contaminants were identified as current research hotspots. Patent analysis revealed seven types of electrochemical technologies, which were compared using SWOT analysis, highlighting electrochemical oxidation as prominent. The technological evolution presented the pathway of electro-Fenton to combined electrocatalytic technologies with biochemical processes, and finally to coupling with electrocoagulation. Standardized evaluation systems, waste resource utilization, and energy conservation were important directions of innovation in electrocatalytic technologies. Overall, this study provided a reference for researchers to understand the framework of electrocatalysis in wastewater treatment and also shed light on potential avenues for further innovation in the field.

A bibliometric analysis of emerging contaminants (ECs) (2001-2021): Evolution of hotspots and research trends

Emerging contaminants (ECs) have attracted increasing attention in the past two decades because of their ubiquitous existence and high environmental risk. Understanding the progress of research and the evolution of hot topics is critical. This study provides a bibliometric review, along with a quantitative trend analysis of approximately 8000 publication records dated from 2001 to 2021. Wider distribution in various subjects was discovered in terms of publication numbers, indicating a strong tendency for EC research to become an interdisciplinary topic. Visualization of term co-occurrence analysis revealed that the ECs study went through three stages over time: identification and detection, traceability and risk, and process and control. Quantitative trend analysis revealed that antibiotics, microplastics, endocrine disrupting chemicals (EDCs), per/poly-fluoroalkyl substances (PFAS), pesticides, heavy metals, and nanoparticles are attracting increasing attention, whereas conventional pharmaceuticals, persistent organic pollutants, and materials such as benzotriazole, diclofenac, bisphenol A, carbamazepine, triclosan, and titanium dioxide exhibit a downward trend. PFAS and EDCs are considered potential future core hotspots for the hysteretic rise in research attention compared with conventional ECs. Furthermore, analysis of research linkage and the developing stages of ECs could be possible approach to determine the evolution of hotspots in ECs study. This study provides objective and comprehensive insights into the research landscape of ECs, which may shed light on future developmental directions for researchers interested in this field.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning

As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization.

Roles and regulation of quorum sensing in anaerobic granular sludge: Research status, challenges, and perspectives

Anaerobic granular sludge (AnGS) has a complex and important internal microbial communication system due to its unique microbial layered structure. As a concentration-dependent communication system between bacterial cells through signal molecules, QS (quorum sensing) is widespread in AnGS and exhibits great potential to regulate microbial behaviors. Therefore, the universal functions of QS in AnGS have been systematically summarized in this paper, including the influence on the metabolic activity, physicochemical properties, and microbial community of AnGS. Subsequently, the common QS-based AnGS regulation approaches are reviewed and analyzed comprehensively. The regulation mechanism of QS in AnGS is analyzed from two systems of single bacterium and mixed bacteria. This review can provide a comprehensive understanding of QS functions in AnGS systems, and promote the practical application of QS-based strategies in optimization of AnGS treatment process.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

Deep mining of reported emerging contaminants in China's surface water in the past decade: Exposure, ecological effects and risk assessment

The identification and management of high-risk contaminants have raised great concern from governments. Facing the growing amount of data on the occurrence of emerging contaminants (ECs) in surface water, a deep mining and quality control strategy was developed to integrate data on all reported ECs in Chinese surface water over the past decade, and an exposure and effect database was further built. In addition, multilevel risk characterization was carried out to prioritize high-risk areas, contaminants and endpoints. A total of 1038 ECs, mainly pharmaceutical and personal care products (PPCPs) and industrial chemicals, were curated, with concentrations ranging from 0.02 pg/L to 533 µg/L. For individual risk, all the provinces had acceptable risks except for Henan, which was characterized with a medium chronic risk. Nine ECs, including 4-nonylphenol and estrone, dominated individual risks. Conversely, for multisubstance risk, 76.20% and 73.87% of aquatic organisms were affected acutely and chronically at the national level, with acute and chronic risks exceeding the safety threshold of 5% in 11 and 19 provinces, respectively. Nineteen ECs, including sitosterol and chyfluthrin, dominated the multisubstance risk. In addition, 9 MoAs mainly inducing electron transfer inhibition, neurotoxicity and narcosis toxicity are high-risk endpoints. The study revealed the ecological risk status and key risk entities of Chinese surface waters, which provided the latest data to support the control of ECs in China.

Highly Selective Electrochemical Nitrate to Ammonia Conversion by Dispersed Ru in a Multielement Alloy Catalyst

Electrochemical reduction of nitrate to ammonia (NH3) converts an environmental pollutant to a critical nutrient. However, current electrochemical nitrate reduction operations based on monometallic and bimetallic catalysts are limited in NH3 selectivity and catalyst stability, especially in acidic environments. Meanwhile, catalysts with dispersed active sites generally exhibit a higher atomic utilization and distinct activity. Herein, we report a multielement alloy nanoparticle catalyst with dispersed Ru (Ru-MEA) with other synergistic components (Cu, Pd, Pt). Density functional theory elucidated the synergy effect of Ru-MEA than Ru, where a better reactivity (NH3 partial current density of -50.8 mA cm-2) and high NH3 faradaic efficiency (93.5%) is achieved in industrially relevant acidic wastewater. In addition, the Ru-MEA catalyst showed good stability (e.g., 19.0% decay in FENH3 in three hours). This work provides a potential systematic and efficient catalyst discovery process that integrates a data-guided catalyst design and novel catalyst synthesis for a range of applications.

Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends

Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful tool for exploring and managing wastewater treatment systems. This study provides a summary of the current status and trends in AI research as applied to wastewater treatment, based on published papers and patents. Our results indicate that, at present, AI is primarily used to evaluate removal of pollutants (conventional, typical, and emerging contaminants), optimize models and process parameters, and control membrane fouling. Future research will likely continue to focus on removal of phosphorus, organic pollutants, and emerging contaminants. Moreover, analyzing microbial community dynamics and achieving multi-objective optimization are promising directions of research. The knowledge map shows that there may be future technological innovation related to predicting water quality under specific conditions, integrating AI with other information technologies and utilizing image-based AI and other algorithms in wastewater treatment. In addition, we briefly review development of artificial neural networks (ANNs) and explore the evolutionary path of AI in wastewater treatment. Our findings provide valuable insights into potential opportunities and challenges for researchers applying AI to wastewater treatment.

A comprehensive review on traditional and modern research of the genus Bupleurum (Bupleurum L., Apiaceae) in recent 10 years

ETHNOPHARMACOLOGICAL RELEVANCE

The genus Bupleurum (family Apiaceae), comprising approximately 248 accepted species, is widely distributed and used in China, Japan, India, Central Asia, North Africa and some European countries as traditional herbal medicines. Certain species have been reported to have significant therapeutic effects in fever, inflammatory disorders, cancer, gastric ulcer, virus infection and other diseases.

AIM OF THE REVIEW

we performed a comprehensive review of the ten-year research progress in phytochemistry, pharmacology, toxicity, along with bibliometrics research of the genus Bupleurum, aiming to identify knowledge gaps for future research.

MATERIALS AND METHODS

All the literatures are retrieved from library and electronic sources including Web of Science, PubMed, Elsevier, Google Scholar, CNKI and Baidu Scholar. These papers cover studies of the traditional use, phytochemistry, pharmacology, and toxicology of the genus Bupleurum.

RESULTS

There is a long history of using the genus Bupleurum in traditional herbal medicine that dated back to over 2000 years ago. Twenty-five species and 8 varieties with 3 variants within this genus have been reported to be effective to treat fever, pain, liver disease, inflammation, thoracolumbar pain, irregular menstruation and rectal prolapse. The main phytochemicals found in these plants are triterpene saponins, volatile oil, flavonoid, lignans, and polysaccharides. Many of these compounds have also been shown to have anti-inflammatory, anti-tumor, antimicrobial, immunoregulation, neuroregulation, hepatoprotective and antidiabetic activities. Meanwhile, improper usage of Bupleurum may induce cytotoxic effects, and polyacetylenes may be the main poisonous compounds.

CONCLUSIONS

This article summarized recent findings about Bupleurum research from many different aspects. While a small number of Bupleurum species have been investigated through modern pharmacology methods, there are still major knowledge gaps due to inadequate studies and ambiguous findings. Future research could focus on more specific phytochemistry studies combined with mechanistic analysis to provide better guidance to utilize Bupleurum as medicinal resources.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO₂ during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Oxidation and Nanoparticle Formation during Ce(III) Sorption onto Minerals

The sorption of Ce(III) on three abundant environmental minerals (goethite, anatase, and birnessite) was investigated. Batch sorption experiments using a radioactive ¹³⁹Ce tracer were performed to investigate the key features of the sorption process. Differences in sorption kinetics and changes in oxidation states were found in the case of the sorption of Ce(III) on birnessite compared to that on other minerals. Speciation of cerium onto all of the studied minerals was investigated using spectral and microscopic methods: high-resolution transmission electron microscopy (HRTEM), electron energy loss spectroscopy (EELS), and X-ray absorption spectroscopy (XAS) in conjunction with theoretical calculations. It was found that during the sorption process onto birnessite, Ce(III) was oxidized to Ce(IV), while the Ce(III) on goethite and anatase surfaces remained unchanged. Oxidation of Ce(III) by sorption on birnessite was also accompanied by the formation of CeO₂ nanoparticles on the mineral surface, which depended on the initial cerium concentration and pH value.

Hotspots and trends of biological water treatment based on bibliometric review and patents analysis

In order to reveal the hotspots and trends of biological water treatment from the perspectives of scientific and technological innovation, both of the bibliometric review and patents analysis were performed in this study. The Web of Science Core Collection database and Derwent Innovation Index database recorded 30023 SCI papers and 50326 patents, respectively were analyzed via information visualization technology. The results showed that China ranked the first in both papers and patents, while the United States and Japan had advantages in papers and patents, respectively. It was concluded through literature metrology analysis that microbial population characteristics, biodegradation mechanism, toxicity analysis, nitrogen and phosphorus removal and biological treatment of micro-polluted wastewater were the research hotspots of SCI papers. Activated sludge process and anaerobic-aerobic combined process were the two mainstream technologies on the basis of patent technology classification analysis. Technology evolution path of biological water treatment was also elucidated in three stages based on the citation network analysis. Furthermore, the future directions including research on the law of interaction and regulation of biological phases and pollutants and the technology innovations towards the targeted biotransformation or selective biodegradation of pollutants and resource reuse of wastewater were prospected.

Methane Emissions from Municipal Wastewater Collection and Treatment Systems

Municipal wastewater collection and treatment systems are critical infrastructures, and they are also identified as major sources of anthropogenic CH₄ emissions that contribute to climate change. The actual CH₄ emissions at the plant- or regional level vary greatly due to site-specific conditions as well as high seasonal and diurnal variations. Here, we conducted the first quantitative analysis of CH₄ emissions from different types of sewers and water resource recovery facilities (WRRFs). We examined variations in CH₄ emissions associated with methods applied in different monitoring campaigns, and identified main CH₄ sources and sinks to facilitate carbon emission reduction efforts in the wastewater sector. We found plant-wide CH₄ emissions vary by orders of magnitude, from 0.01 to 110 g CH₄/m³ with high emissions associated with plants equipped with anaerobic digestion or stabilization ponds. Rising mains show higher dissolved CH₄ concentrations than gravity sewers when transporting similar raw sewage under similar environmental conditions, but the latter dominates most collection systems around the world. Using the updated data sets, we estimated annual CH₄ emission from the U.S. centralized, municipal wastewater treatment to be approximately 10.9 ± 7.0 MMT CO₂-eq/year, which is about twice as the IPCC (2019) Tier 2 estimates (4.3-6.1 MMT CO₂-eq/year). Given CH₄ emission control will play a crucial role in achieving net zero carbon goals by the midcentury, more studies are needed to profile and mitigate CH₄ emissions from the wastewater sector.

Mapping the research on desulfurization wastewater: Insights from a bibliometric review (1991-2021)

Desulfurization wastewater in coal-fired power plants (CFPPs) is a great environmental challenge. This study aimed at the current status and future research trends of desulfurization wastewater by bibliometric analysis. The desulfurization wastewater featured with high sulfate (8000 mg/L), chlorite (8505 mg/L), magnesium (2882 mg/L) and calcium (969 mg/L) but low sodium (801.82 mg/L), and the concentrations of the main contaminants were critically summarized. There was an increasing trend in the annual publications of desulfurization wastewater in the period from 1991 to 2021, with an average growth rate of 15%. Water Science and Technology, Desalination and Water Treatment, Energy & Fuels, Chemosphere, and Journal of Hazardous Materials are the top 5 journals in this field. China was the most productive country (58.3% of global output) and the core country in the international cooperation network. Wordcloud analysis and keyword topic trend demonstrated that removal/treatment of pollutants dominated the global research in the field of desulfurization wastewater. The primary technologies for desulfurization wastewater treatment were systematically evaluated. The physicochemical treatment technologies occupied half of the total treatment methods, while membrane-based integrated processes showed potential applications for beneficial reuse. The challenges and outlook on desulfurization wastewater treatment for achieving zero liquid discharge are summarized.

Carbon dots promoted soybean photosynthesis and amino acid biosynthesis under drought stress: Reactive oxygen species scavenging and nitrogen metabolism

With global warming and water scarcity, improving the drought tolerance and quality of crops is critical for food security and human health. Here, foliar application of carbon dots (CDs, 5 mg·L^-1) could scavenge reactive oxygen species accumulation in soybean leaves under drought stress, thereby enhancing photosynthesis and carbohydrate transport. Moreover, CDs stimulated root secretion (e.g., amino acids, organic acids, and auxins) and recruited beneficial microorganisms (e.g., Actinobacteria, Ascomycota, Acidobacteria and Glomeromycota), which facilitate nitrogen (N) activation in the soil. Meanwhile, the expression of GmNRT, GmAMT, and GmAQP genes were up-regulated, indicating enhanced N and water uptake. The results demonstrated that CDs could promote nitrogen metabolism and enhance amino acid biosynthesis. Particularly, the N content in soybean shoots and roots increased significantly by 13.2 % and 30.5 %, respectively. The amino acids content in soybean shoots and roots increased by 257.5 % and 57.5 %, respectively. Consequently, soybean yields increased significantly by 21.5 %, and the protein content in soybean kernels improved by 3.7 %. Therefore, foliar application of CDs can support sustainable nano-enabled agriculture to combat climate change.

Biological filtration for wastewater treatment in the 21st century: A data-driven analysis of hotspots, challenges and prospects

Biological filtration has been widely used in wastewater treatment around the world, yet achieving satisfactory removal of pollutants remains a challenge due to the complexity of water pollution. In order to reveal the hotspots and trends of biological filtration from the perspective of research innovation, 5454 SCI papers and 14,287 patents collected from the Web of Science Core Collection and Derwent Innovation Index database were analyzed by visualization techniques. The results showed that China ranked first in the number of both papers and patents, while the USA and Japan contributed significantly in papers and patents, respectively. Co-occurrence analysis obtained the mapping knowledge domains and demonstrated distinct associations between contaminants ("nitrogen", "pharmaceuticals", "personal care products"), chemicals ("carbon", "activated carbon", "media"), process ("biodegradation", "adsorption" or "ozonation") and characteristics ("kinetics", "performance", "diversity"). Moreover, this review summarized the recent advances of biological filtration media, microorganism and combined process being applied. It was concluded that environmentally friendly biological filtration ("phytoremedi", "microalga", "recirculating aquaculture system"), bio-enhanced biological filtration ("bioaugment", "fungi", "low augment") and emerging pollutants ("emerging contamin", "antibiotic resistance gen", "organic micropollut", "trace organic chem") were the hotspots through data-driven analyses. Technology evolution path of biological filtration generally indicated the transition from conventional biological filtration for nitrogen and phosphorus removal to Fenton-biofiltration combined technology and finally to ozone-biological filtration. Furthermore, the technical innovation direction of the collaborative control of multi-media pollution, the low-carbon biological filtration and short-process technology was prospected. This work can serve as a quick reference for early-career researchers and industries working in the area of biological filtration.

Data-driven analysis on immobilized microalgae system: New upgrading trends for microalgal wastewater treatment

Microalgal immobilization is receiving increasing attention as one of the most viable alternatives for upgrading conventional wastewater treatment. However, an in-depth discussion of the state-of-the-art and limitations of available technologies is currently lacking. More importantly, the reason for the hesitant development of immobilized microalgae for wastewater treatment remains unclear, which hinders its practical application. Thus, comprehensively understanding and evaluating details on immobilized microalgae is urgently needed, especially for the current advances of immobilization of microalgae in wastewater treatment over the last few decades. In this review, scientometric approach is used to explore research hotspots and visualize emerging trends. Data-driven analysis is used to scientifically and methodically determine hotspots in the current research on immobilized microalgal wastewater treatment, along with that the implicit inner connection underlying the frequent co-occurring terms was explored in depth. Four hotspots focusing on immobilized microalgae for wastewater treatment were identified, mainly demonstrating: (1) main factors including light, temperature and immobilization methods would majorly affect the treatment performance of immobilized microalgae; (2) immobilized microalgae membrane bioreactor, immobilized microalgae-based microbial fuel cell and immobilized microalgae-based bed reactor are three dominant treatment systems; (3) immobilized microalgae have a higher robustness and tolerance for treating various types of wastewater; and (4) a complete sustainable circle from wastewater treatment to resource conversion via the immobilized microalgae can be achieved. Finally, several new directions and new perspectives that expose the necessity for fulfilling further research and fundamental gaps are pointed out. Taken together, this review provides helpful information to facilitate the development of innovative and feasible immobilized microalgal technologies thus increasing their viability and sustainability.

Evolution of Research on Global Soil Water Content in the Past 30 Years Based on ITGinsight Bibliometric Analysis

Research on soil water content (SWC) has involved a wide range of disciplines and attracted constant attention. Current literature reviews primarily focus on a specific type of research on SWC and few systematic studies have been performed to fully evaluate the development and changes in hotspots of SWC research. In this study, a bibliometric analysis and visualization are used to understand the development of SWC research in countries of Europe, Asia, Oceania, and North America. The research data came from the Web of Science database and the time span was 1987-2021. Since 1987, the numbers of international SWC research papers have increased rapidly. The United States and China have the closest exchanges and most publications in the field of SWC. Keyword network maps indicated that early research on SWC was mostly in small-scale farmlands and woodlands, with diverse research hotspots including those focused on SWC stress, soil physical modeling, soil hydrothermal processes, and SWC measurement. Due to climate change, remote sensing technology development, and policies, research on SWC gradually focused on watershed, regional, and global scales, with research hotspots including those focused on evapotranspiration, land-air energy exchange, and remote sensing satellite inversion of SWC products. In addition, in recent years, the research of SWC and SMAP has attracted considerable attention worldwide. The United States has more influence in the SWC sector than China. Although the number of articles that have been published by European countries was small, the influence of those papers should not be underestimated.

Patent analysis of chemical treatment technology for wastewater: Status and future trends

In order to reveal the status and trends of chemical treatment for wastewater, the patents analysis from both structured and unstructured data was performed in this study. 35,838 patents recorded in the Derwent Innovation Index database were adopted. The results showed that China was the country with the largest number of patents in the field, and the United States was the main exporter of international technology flows. Chemical processes combined with biological and physical processes was the mainstream, and ozonation and electrochemical treatment were the major single technologies. Technology evolution path generally showed the transition from biological process-combined chemical treatment to electrochemical treatment and finally to physical process-combined chemical treatment. Furthermore, future trends were revealed from both patents and papers. It demonstrated that efficient removal of ammonia nitrogen, green water treatment agents and resourcezation of wastewater were the key innovation directions, and technologies with regard to efficient use of energy (including photocatalytic technology and microbial fuel cell) were the main research hotspots. Overall, this study provided a comprehensive understanding for the research and application of chemical treatment for wastewater technologies.

A comprehensive analysis of evolution and underlying connections of water research themes in the 21st century

This work aimed to reflect the advancements in water-related science, technology, and policy and shed light on future research opportunities related to water through a systematic overview of Water Research articles published in the first 21.5 years of the 21st century. Specific bibliometric analyses were performed to i) reveal the temporal and spatial trends of water-related research themes and ii) identify the underlying connections between research topics. The results showed that while top topics including wastewater (treatment), drinking water, adsorption, model, biofilm, and bioremediation remained constantly researched, there were clear shifts in topics over the years, leading to the identification of trending-up and emerging research topics. Compared to the first decade of the 21st century, the second decade not only experienced significant uptrends of disinfection by-products, anaerobic digestion, membrane bioreactor, advanced oxidation processes, and pharmaceuticals but also witnessed the emerging popularity of PFAS, anammox, micropollutants, emerging contaminants, desalination, waste activated sludge, microbial community, forward osmosis, antibiotic resistance genes, resource recovery, and transformation products. On top of the temporal evolution, distinct spatial evolution existed in water-related research topics. Microplastics and Covid-19 causing global concerns were hot topics detected, while metagenomics and machine learning were two technical approaches emerging in recent years. These consistently popular, trending-up and emerging research topics would most likely attract continuous/increasing research input and therefore constitute a major part of the prospective water-related research publications.

Application of the IoT in the Food Supply Chain─From the Perspective of Carbon Mitigation

With the rising demands on supply chain transparency and food security, the rapid outspread of the Internet of Things (IoT) to improve logistical efficiency, and the rising penetration of sensor technology into daily life, the extensive integration of the IoT in the food sector is well anticipated. A perspective on potential life cycle trade-offs in regard to the type of integration is necessary. We conduct life cycle assessment (LCA) integrated with shelf life-food loss (SL-FL) models, showing an overall 5-fold leverage on carbon reduction, which is diet dependent and a function of income. Meat presents the highest leverage, 35 ± 11-times, owing to its high carbon footprint. Two-thirds (65%) of global sensors (1 billion) engaged in monitoring fruits and vegetables can mitigate less than 7% of the total reduced carbon emissions. Despite the expected carbon emission reductions, widespread adoption of the IoT faces multiple challenges such as high costs, difficulties in social acceptance, and regional variability in technological development. Furthermore, changes in the distribution of transportation resources and dealer service models, requirements regarding the accuracy of sensor data analysis, efficient and persistent operation of devices, development of agricultural infrastructure, and farmer education and training have all increased uncertainty. Nonetheless, the research trend in smart sensors toward smaller chips and the potential integration of machine learning or blockchain as further steps make it possible to leverage these advantages to facilitate market penetration. These insights facilitate the future optimization of the application of IoT sensors for sustainability.

Hybrid modelling of water resource recovery facilities: status and opportunities

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Biochar in the 21st century: A data-driven visualization of collaboration, frontier identification, and future trend

The massive amounts of publication data are highly valuable, because in addition to the advancement in science, technology, and policy, such data can provide critical information and guidance on what have been published, what topical changes have evolved, and what are the trending fields deserving more attention. In the 21st century, biochar has played an indispensable role in the long-term global development strategies in response to "Carbon neutralization", "Agricultural management", and "Environmental restoration", and accumulated many high-quality publications. Herein, this study provides a new data-driven bibliometric analysis strategy and framework for mining the core content of massive literature data, and aims at bringing unique insights for the research prospects as well as opportunities of biochar. The results show that biochar researches have made great progress from 1999 to 2020, but cross-disciplinary teamwork should be further emphasized. The research frontier identification reveals that sewage treatment, efficient removal, and functional composite materials will be the issues which must be paid continual attention at present and in the future. Furthermore, studies on global climate impact, biomass resource utilization, carbon sequestration, carbon cycle, and even the negative effects of biochar have gradually begun to be taken seriously.

Community Data Mining Approach for Surface Complexation Database Development

This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. The framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future.

How Aquatic Chemistry Took Root and Has Flourished in China: Classical Textbooks, a Tale of Two Manganese, and a Dynamic Community

In the history of environmental science and engineering, there are numerous great thinkers, mentors, and scholars, whose influence transcends geographical boundaries. Being assembled into a special tribute issue in ES&T celebrating its founding editor Jim Morgan, this Perspective tells a few stories related to China, a country that he never visited but one where his research and vision have a profound influence. The following stories are inspiring accounts of people who made indelible contributions to this discipline. Through providing an international angle, this perspective aims to reinforce the global aquatic chemistry community's appreciation of our discipline's roots and continuous growth in China, as well as the significant contributions from China. Given the universality of scientific knowledge, we believe that similar stories exist in many other countries, cultures, and fields, where their pioneers are celebrated and remembered.

What have we known so far about microplastics in drinking water treatment? A timely review

Microplastics (MPs) have been widely detected in drinking water sources and tap water, raising the concern of the effectiveness of drinking water treatment plants (DWTPs) in protecting the public from exposure to MPs through drinking water. We collected and analyzed the available research articles up to August 2021 on MPs in drinking water treatment (DWT), including laboratory- and full-scale studies. This article summarizes the major MP compositions (materials, sizes, shapes, and concentrations) in drinking water sources, and critically reviews the removal efficiency and impacts of MPs in various drinking water treatment processes. The discussed drinking water treatment processes include coagulation-flocculation (CF), membrane filtration, sand filtration, and granular activated carbon (GAC) filtration. Current DWT processes that are purposed for particle removal are generally effective in reducing MPs in water. Various influential factors to MP removal are discussed, such as coagulant type and dose, MP material, shape and size, and water quality. It is anticipated that better MP removal can be achieved by optimizing the treatment conditions. Moreover, the article framed the major challenges and future research directions on MPs and nanoplastics (NPs) in DWT.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Global Isotopic Hydrograph Separation Research History and Trends: A Text Mining and Bibliometric Analysis Study

Scientific research into isotope hydrograph separation (IHS) has rapidly increased in recent years. However, there is a lack of systematic and quantitative research to explore how this field has evolved over time. In this study, the methods of text mining and bibliometric analysis were combined to address this shortcoming. The results showed that there were clear periodical characteristics in IHS studies between 1986 and 2019. High-frequency words, e.g., catchment, stable isotope, runoff, groundwater, precipitation, runoff generation, and soil, were the basic topics in IHS studies. Forest and glacier/snow were the main landscapes in this research field. ‘Variation’, ‘spatial’, and ‘uncertainty’ are hot issues for future research. Today, studies involving the geographical source, flow path, and transit/residence time of streamflow components have enhanced our understanding of the hydrological processes by using hydrometeorological measurements, water chemistry, and stable isotope approaches. In the future, new methods, such as path analysis and ensemble hydrograph separation, should be verified and used in more regions, especially in remote and mountainous areas. Additionally, the understanding of the role of surface water in streamflow components remains limited and should be deeply studied in the future.

Microbial Roles in Dissolved Organic Matter Transformation in Full-Scale Wastewater Treatment Processes Revealed by Reactomics and Comparative Genomics

Understanding the degradation of dissolved organic matter (DOM) is vital for optimizing DOM control. However, the microbe-mediated DOM transformation during wastewater treatment remains poorly characterized. Here, microbes and DOM along full-scale biotreatment processes were simultaneously characterized using comparative genomics and high-resolution mass spectrometry-based reactomics. Biotreatments significantly increased DOM's aromaticity and unsaturation due to the overproduced lignin and polyphenol analogs. DOM was diversified by over five times in richness, with thousands of nitrogenous and sulfur-containing compounds generated through microbe-mediated oxidoreduction, functional group transfer, and C-N and C-S bond formation. Network analysis demonstrated microbial division of labor in DOM transformation. However, their roles were determined by their functional traits rather than taxa. Specifically, network and module hubs exhibited rapid growth potentials and broad substrate affinities but were deficient in xenobiotics-metabolism-associated genes. They were mainly correlated to liable DOM consumption and its transformation to recalcitrant compounds. In contrast, connectors and peripherals were potential degraders of recalcitrant DOM but slow in growth. They showed specialized associations with fewer DOM molecules and probably fed on metabolites of hub microbes. Thus, developing technologies (e.g., carriers) to selectively enrich peripheral degraders and consequently decouple the liable and recalcitrant DOM transformation processes may advance DOM removal.