Pub Date : 2026-01-01DOI: 10.1016/j.ese.2025.100651
Pengzhao Lv , Yu Jiang , Jialin Wang , Yige Shi , Zhengda Lin , Duo Wei , Wei Zuo , Jun Zhang
The rise of antimicrobial resistance and the ecological harm inflicted by broad-spectrum disinfectants underscore the urgent need for species-specific strategies that eradicate pathogenic bacteria without disrupting beneficial microbial communities. Staphylococcus aureus thrives in diverse aquatic environments across wide temperature ranges, posing persistent risks to human health and exacerbating resistance challenges, yet existing agents lack the precision to target this pathogen selectively. Here we show that triethylenetetramine-functionalized carbon dots, derived from corn straw biomass via one-step hydrothermal synthesis, exhibit intrinsic oxidase-like activity that selectively eliminates S. aureus. These nanomaterials achieve complete bactericidal efficacy (100 %) against S. aureus at 50 μg mL−1 within 1 h at 37 °C, retaining robust activity (80 %) even at 4 °C, through synergistic preferential binding to cell-wall polysaccharides—facilitated by retained biomass cellulose moieties—combined with membrane disruption and generation of superoxide radicals (·O2−) and singlet oxygen (1O2). This selectivity spares Bacillus subtilis and Gram-negative species such as Escherichia coli and Pseudomonas aeruginosa, owing to differences in cell-wall architecture and reduced affinity. Amine chain length tunes the oxidase-mimicking potency, enabling oxygen-dependent reactive oxygen species production without external stimuli. By upcycling abundant agricultural waste into rapidly photodegradable (within 11 days under visible light) precision disinfectants, this approach provides a sustainable way for ecologically compatible pathogen control, advancing rational design principles for next-generation nano-antimicrobials.
{"title":"Selective eradication of pathogenic bacteria using amine-modified corn-straw carbon dots","authors":"Pengzhao Lv , Yu Jiang , Jialin Wang , Yige Shi , Zhengda Lin , Duo Wei , Wei Zuo , Jun Zhang","doi":"10.1016/j.ese.2025.100651","DOIUrl":"10.1016/j.ese.2025.100651","url":null,"abstract":"<div><div>The rise of antimicrobial resistance and the ecological harm inflicted by broad-spectrum disinfectants underscore the urgent need for species-specific strategies that eradicate pathogenic bacteria without disrupting beneficial microbial communities. <em>Staphylococcus aureus</em> thrives in diverse aquatic environments across wide temperature ranges, posing persistent risks to human health and exacerbating resistance challenges, yet existing agents lack the precision to target this pathogen selectively. Here we show that triethylenetetramine-functionalized carbon dots, derived from corn straw biomass via one-step hydrothermal synthesis, exhibit intrinsic oxidase-like activity that selectively eliminates <em>S. aureus</em>. These nanomaterials achieve complete bactericidal efficacy (100 %) against <em>S. aureus</em> at 50 μg mL<sup>−1</sup> within 1 h at 37 °C, retaining robust activity (80 %) even at 4 °C, through synergistic preferential binding to cell-wall polysaccharides—facilitated by retained biomass cellulose moieties—combined with membrane disruption and generation of superoxide radicals (·O<sub>2</sub><sup>−</sup>) and singlet oxygen (<sup>1</sup>O<sub>2</sub>). This selectivity spares <em>Bacillus subtilis</em> and Gram-negative species such as <em>Escherichia coli</em> and <em>Pseudomonas aeruginosa</em>, owing to differences in cell-wall architecture and reduced affinity. Amine chain length tunes the oxidase-mimicking potency, enabling oxygen-dependent reactive oxygen species production without external stimuli. By upcycling abundant agricultural waste into rapidly photodegradable (within 11 days under visible light) precision disinfectants, this approach provides a sustainable way for ecologically compatible pathogen control, advancing rational design principles for next-generation nano-antimicrobials.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100651"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.ese.2026.100658
Shubiao Wu, Rebekka R.E. Artz, Alexandra Barthelmes, Shihao Cui, Diana Vigah Adetsu, Vera Eory, Mark S. Reed, Florian Humpenöder, Tom S. Heuts, Christian Fritz, Agata Klimkowska, Annalea Lohila
{"title":"Beyond carbon sequestration: The critical oversight of emission avoidance in restoration of wetland ecosystems","authors":"Shubiao Wu, Rebekka R.E. Artz, Alexandra Barthelmes, Shihao Cui, Diana Vigah Adetsu, Vera Eory, Mark S. Reed, Florian Humpenöder, Tom S. Heuts, Christian Fritz, Agata Klimkowska, Annalea Lohila","doi":"10.1016/j.ese.2026.100658","DOIUrl":"10.1016/j.ese.2026.100658","url":null,"abstract":"","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100658"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.ese.2026.100662
Sofia Tisocco , Sören Weinrich , Henrik Bjarne Møller , Alastair James Ward , Liam Kilmartin , Xinmin Zhan , Paul Crosson
Anaerobic digestion harnesses microbial processes to convert organic wastes into renewable biogas, offering a sustainable pathway for energy production. In agricultural settings, biogas plants often co-digest livestock manure with crop residues, yet seasonal variations in feedstock quality introduce fluctuations that challenge process stability and yield optimization. Mechanistic models such as the Anaerobic Digestion Model No. 1 (ADM1) provide detailed biochemical simulations but require extensive substrate characterization, limiting their practicality for full-scale operations. Here we show that a simplified ADM1, alongside machine learning approaches—random forest and long short-term memory (LSTM) networks—achieves comparable accuracy in predicting daily biogas and methane production from a full-scale plant over 2023–2024. All models yielded Nash-Sutcliffe efficiencies above 0.78, with random forest excelling when incorporating feedstock quantities and maize silage volatile solids. While LSTM proved effective even with minimal inputs, it incurred a training time 141 times greater than ADM1, highlighting critical trade-offs in computational efficiency. These findings advance hybrid modelling strategies for real-time monitoring, enabling operators to balance predictive precision with data requirements to enhance renewable energy integration and agricultural sustainability.
{"title":"Machine learning vs. ADM1: Reliable biogas prediction with minimal data requirements in full-scale plants","authors":"Sofia Tisocco , Sören Weinrich , Henrik Bjarne Møller , Alastair James Ward , Liam Kilmartin , Xinmin Zhan , Paul Crosson","doi":"10.1016/j.ese.2026.100662","DOIUrl":"10.1016/j.ese.2026.100662","url":null,"abstract":"<div><div>Anaerobic digestion harnesses microbial processes to convert organic wastes into renewable biogas, offering a sustainable pathway for energy production. In agricultural settings, biogas plants often co-digest livestock manure with crop residues, yet seasonal variations in feedstock quality introduce fluctuations that challenge process stability and yield optimization. Mechanistic models such as the Anaerobic Digestion Model No. 1 (ADM1) provide detailed biochemical simulations but require extensive substrate characterization, limiting their practicality for full-scale operations. Here we show that a simplified ADM1, alongside machine learning approaches—random forest and long short-term memory (LSTM) networks—achieves comparable accuracy in predicting daily biogas and methane production from a full-scale plant over 2023–2024. All models yielded Nash-Sutcliffe efficiencies above 0.78, with random forest excelling when incorporating feedstock quantities and maize silage volatile solids. While LSTM proved effective even with minimal inputs, it incurred a training time 141 times greater than ADM1, highlighting critical trade-offs in computational efficiency. These findings advance hybrid modelling strategies for real-time monitoring, enabling operators to balance predictive precision with data requirements to enhance renewable energy integration and agricultural sustainability.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100662"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100630
Xu Li , Youzhao Wang , Feng Ma , Chaoyue Zhao , Yanping Zhang , Yaonan Zhu , Yang Zhang , Shujie Hou , Bingzhen Li , Fuxin Yang , Liying Hao , Tong Zhu
Hyperthermophilic composting (HC) represents a promising approach for converting organic solid waste into valuable resources by leveraging extreme temperatures to enhance microbial degradation and detoxification processes. In this high-temperature environment, microbial communities undergo dynamic succession, where thermophilic bacteria dominate and drive efficient organic matter transformation through adapted metabolic pathways and stress responses. These adaptations include the stabilization of cellular structures and enzymes, often mediated by heat shock proteins (HSPs) that prevent protein misfolding under thermal stress. However, the integrated mechanisms linking community-level functional shifts to molecular-level protein remodeling in thermophiles during HC remain poorly understood. Here we show a coordinated interaction of functional succession and molecular adaptations within thermophilic bacteria in HC, which collectively achieve heat resistance. This interaction encompasses enhanced metabolic and genetic modules, accounting for 97 % of the variance observed in thermophile abundance. Metagenomic analyses revealed upregulation of translation, transcription, amino acid metabolism, and cell wall biosynthesis, coupled with suppression of mobilome functions to maintain genomic stability, as confirmed by partial least squares path modeling and Boruta analyses. Molecular dynamics simulations of key enzymes from the thermophile Truepera further demonstrated intrinsic structural rigidity, reduced hydrophobic exposure, and hierarchical chaperone activity involving DNAJ, DNAK, and GroEL for protein repair. These findings enhance our comprehension of microbial thermotolerance and establish a foundation for optimizing composting efficiency and advancing heat-resistant microbial applications in biotechnology and waste management. Additionally, they offer insights into the evolution of thermophiles, protein engineering, and stress adaptation across various biological and industrial systems, thereby promoting the integration of environmental engineering and systems biology.
{"title":"Chaperone-mediated thermotolerance in hyperthermophilic composting: Molecular-Level protein remodeling of microbial communities","authors":"Xu Li , Youzhao Wang , Feng Ma , Chaoyue Zhao , Yanping Zhang , Yaonan Zhu , Yang Zhang , Shujie Hou , Bingzhen Li , Fuxin Yang , Liying Hao , Tong Zhu","doi":"10.1016/j.ese.2025.100630","DOIUrl":"10.1016/j.ese.2025.100630","url":null,"abstract":"<div><div>Hyperthermophilic composting (HC) represents a promising approach for converting organic solid waste into valuable resources by leveraging extreme temperatures to enhance microbial degradation and detoxification processes. In this high-temperature environment, microbial communities undergo dynamic succession, where thermophilic bacteria dominate and drive efficient organic matter transformation through adapted metabolic pathways and stress responses. These adaptations include the stabilization of cellular structures and enzymes, often mediated by heat shock proteins (HSPs) that prevent protein misfolding under thermal stress. However, the integrated mechanisms linking community-level functional shifts to molecular-level protein remodeling in thermophiles during HC remain poorly understood. Here we show a coordinated interaction of functional succession and molecular adaptations within thermophilic bacteria in HC, which collectively achieve heat resistance. This interaction encompasses enhanced metabolic and genetic modules, accounting for 97 % of the variance observed in thermophile abundance. Metagenomic analyses revealed upregulation of translation, transcription, amino acid metabolism, and cell wall biosynthesis, coupled with suppression of mobilome functions to maintain genomic stability, as confirmed by partial least squares path modeling and Boruta analyses. Molecular dynamics simulations of key enzymes from the thermophile <em>Truepera</em> further demonstrated intrinsic structural rigidity, reduced hydrophobic exposure, and hierarchical chaperone activity involving DNAJ, DNAK, and GroEL for protein repair. These findings enhance our comprehension of microbial thermotolerance and establish a foundation for optimizing composting efficiency and advancing heat-resistant microbial applications in biotechnology and waste management. Additionally, they offer insights into the evolution of thermophiles, protein engineering, and stress adaptation across various biological and industrial systems, thereby promoting the integration of environmental engineering and systems biology.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100630"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100632
Jun Sun , Xuesong Gao , Zhiyong Deng , Yudong Zhao , Qi Wang , Xiyi Zhao , Xu Liu
Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and R2. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.
{"title":"A hierarchical transformer and graph neural network model for high-accuracy watershed nitrate prediction","authors":"Jun Sun , Xuesong Gao , Zhiyong Deng , Yudong Zhao , Qi Wang , Xiyi Zhao , Xu Liu","doi":"10.1016/j.ese.2025.100632","DOIUrl":"10.1016/j.ese.2025.100632","url":null,"abstract":"<div><div>Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and <em>R</em><sup>2</sup>. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100632"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100635
Myrsini Sakarika , Joost Brancart , Shreyash Anil Gujar , Steven De Meester , Luis Diaz Allegue , Leen Bastiaens , Peter Ragaert , Siegfried E. Vlaeminck , Heleen De Wever , Korneel Rabaey
Microbial protein (MP)—the protein-rich biomass derived from recovered or virgin resources—is attracting interest as a source of food and feed. However, its potential as a feedstock for protein-based bioplastics remains underexplored. Proteins offer desirable properties, including superior oxygen-barrier capabilities and complete biodegradability, making them ideal for applications from food packaging to agricultural mulches. Currently, most protein-based bioplastics derive from crops such as wheat, restricting applications and competing with food production. MP can overcome these limitations by supplying diverse proteins from various inputs, including CO2, biomass, and liquid side-streams. In this review, we evaluate bioprocessing pathways for producing MP from renewable and waste-derived substrates from an interdisciplinary viewpoint. We also examine the technical, regulatory, market, and environmental factors to address, delineating the pathway from substrate to MP-based plastics and highlighting key challenges throughout the production chain. Novel strategies—such as efficient co-recovery of proteins with other cellular products like polyhydroxyalkanoates or direct use of microbial biomass without extraction—are essential to maximize environmental and economic sustainability. Carefully chosen processing methods for recovered proteins, including wet and dry blending or extrusion with other biopolymers, can yield diverse products. Concurrently, policy and market developments are vital for adopting MP-based bioplastics. Addressing these challenges will enable MP-based bioplastics to propel the shift toward a circular economy, diminishing dependence on fossil-derived plastics and alleviating plastic pollution.
{"title":"Microbial protein-derived bioplastics from renewable substrates: pathways, challenges, and applications in a circular economy","authors":"Myrsini Sakarika , Joost Brancart , Shreyash Anil Gujar , Steven De Meester , Luis Diaz Allegue , Leen Bastiaens , Peter Ragaert , Siegfried E. Vlaeminck , Heleen De Wever , Korneel Rabaey","doi":"10.1016/j.ese.2025.100635","DOIUrl":"10.1016/j.ese.2025.100635","url":null,"abstract":"<div><div>Microbial protein (MP)—the protein-rich biomass derived from recovered or virgin resources—is attracting interest as a source of food and feed. However, its potential as a feedstock for protein-based bioplastics remains underexplored. Proteins offer desirable properties, including superior oxygen-barrier capabilities and complete biodegradability, making them ideal for applications from food packaging to agricultural mulches. Currently, most protein-based bioplastics derive from crops such as wheat, restricting applications and competing with food production. MP can overcome these limitations by supplying diverse proteins from various inputs, including CO<sub>2</sub>, biomass, and liquid side-streams. In this review, we evaluate bioprocessing pathways for producing MP from renewable and waste-derived substrates from an interdisciplinary viewpoint. We also examine the technical, regulatory, market, and environmental factors to address, delineating the pathway from substrate to MP-based plastics and highlighting key challenges throughout the production chain. Novel strategies—such as efficient co-recovery of proteins with other cellular products like polyhydroxyalkanoates or direct use of microbial biomass without extraction—are essential to maximize environmental and economic sustainability. Carefully chosen processing methods for recovered proteins, including wet and dry blending or extrusion with other biopolymers, can yield diverse products. Concurrently, policy and market developments are vital for adopting MP-based bioplastics. Addressing these challenges will enable MP-based bioplastics to propel the shift toward a circular economy, diminishing dependence on fossil-derived plastics and alleviating plastic pollution.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100635"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100638
Tuantuan Fan , Zhenfei Yan , Chenglian Feng , Fengchang Wu
{"title":"Beyond animal testing: An integrated framework for modern chemical hazard identification and risk assessment","authors":"Tuantuan Fan , Zhenfei Yan , Chenglian Feng , Fengchang Wu","doi":"10.1016/j.ese.2025.100638","DOIUrl":"10.1016/j.ese.2025.100638","url":null,"abstract":"","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100638"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100628
Simon Elias Bibri, Jeffrey Huang
<div><div>Buildings are among the largest contributors to global energy consumption and carbon emissions, making their transformation essential for advancing environmental sustainability goals. Innovative technologies such as artificial intelligence (AI) and digital twins (DTs) offer powerful tools for optimizing performance in smart, green, and zero-energy buildings. However, existing research remains fragmented—AI and AI-driven DT applications are often confined to isolated functions or specific building types—resulting in a limited, non-cohesive understanding of their collective potential in the built environment. This fragmentation, in turn, has hindered the development of integrated strategies that link building-level efficiencies with the broader environmental objectives of smart cities. To address these interrelated gaps, this study conducts a comprehensive systematic review of leading-edge AI and AI-powered DT solutions applied across smart, green, and zero-energy buildings. It aims to provide a holistic understanding of how these solutions enhance environmental performance through the analysis of key building-related indicators. By synthesizing, comparing, and evaluating recent research, it examines how AI and AI-powered DT technologies facilitate integrated, system-level strategies that promote environmentally sustainable smart practices across the built environment. The study reveals that AI enhances smart buildings by enabling dynamic energy optimization, occupant-centered environmental control, improved thermal comfort, renewable energy integration, and predictive system management. In green buildings, <span>AI</span> contributes to greater resource efficiency, minimizes construction and operational waste, promotes the use of sustainable materials, strengthens cost estimation and risk assessment processes, and supports adaptive design strategies. For zero-energy buildings, <span>AI</span> facilitates multi-objective optimization, advances explainable and transparent AI-driven control systems, supports performance benchmarking against net and nearly zero-energy standards, and enables renewable energy integration tailored to diverse climatic and regulatory contexts. Furthermore, AI-powered DTs enable real-time environmental monitoring, predictive analytics, anomaly detection, and adaptive operational strategies, thereby enhancing building performance, energy optimization, and resilience. At broader spatial scales, these technologies foster interconnected urban ecosystems, advancing environmental sustainability, sustainable development, and smart city initiatives. Building on these insights, this study introduces a novel integrated framework that positions AI and AI-driven DTs as systemic enablers of environmentally sustainable smart built and urban environments, emphasizing their cross-scale convergence in promoting carbon neutrality, circular economy principles, climate resilience, and regenerative urban strategies. The findings offer
{"title":"AI and AI-powered digital twins for smart, green, and zero-energy buildings: A systematic review of leading-edge solutions for advancing environmental sustainability goals","authors":"Simon Elias Bibri, Jeffrey Huang","doi":"10.1016/j.ese.2025.100628","DOIUrl":"10.1016/j.ese.2025.100628","url":null,"abstract":"<div><div>Buildings are among the largest contributors to global energy consumption and carbon emissions, making their transformation essential for advancing environmental sustainability goals. Innovative technologies such as artificial intelligence (AI) and digital twins (DTs) offer powerful tools for optimizing performance in smart, green, and zero-energy buildings. However, existing research remains fragmented—AI and AI-driven DT applications are often confined to isolated functions or specific building types—resulting in a limited, non-cohesive understanding of their collective potential in the built environment. This fragmentation, in turn, has hindered the development of integrated strategies that link building-level efficiencies with the broader environmental objectives of smart cities. To address these interrelated gaps, this study conducts a comprehensive systematic review of leading-edge AI and AI-powered DT solutions applied across smart, green, and zero-energy buildings. It aims to provide a holistic understanding of how these solutions enhance environmental performance through the analysis of key building-related indicators. By synthesizing, comparing, and evaluating recent research, it examines how AI and AI-powered DT technologies facilitate integrated, system-level strategies that promote environmentally sustainable smart practices across the built environment. The study reveals that AI enhances smart buildings by enabling dynamic energy optimization, occupant-centered environmental control, improved thermal comfort, renewable energy integration, and predictive system management. In green buildings, <span>AI</span> contributes to greater resource efficiency, minimizes construction and operational waste, promotes the use of sustainable materials, strengthens cost estimation and risk assessment processes, and supports adaptive design strategies. For zero-energy buildings, <span>AI</span> facilitates multi-objective optimization, advances explainable and transparent AI-driven control systems, supports performance benchmarking against net and nearly zero-energy standards, and enables renewable energy integration tailored to diverse climatic and regulatory contexts. Furthermore, AI-powered DTs enable real-time environmental monitoring, predictive analytics, anomaly detection, and adaptive operational strategies, thereby enhancing building performance, energy optimization, and resilience. At broader spatial scales, these technologies foster interconnected urban ecosystems, advancing environmental sustainability, sustainable development, and smart city initiatives. Building on these insights, this study introduces a novel integrated framework that positions AI and AI-driven DTs as systemic enablers of environmentally sustainable smart built and urban environments, emphasizing their cross-scale convergence in promoting carbon neutrality, circular economy principles, climate resilience, and regenerative urban strategies. The findings offer","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100628"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100636
Zhicong Yin , Yu Lei , Xi Lu , Qiang Zhang , Jicheng Gong , Xin Liu , Wei Li , Cilan Cai , Qimin Chai , Renjie Chen , Wenhui Chen , Hancheng Dai , Zhanfeng Dong , Jingli Fan , Guannan Geng , Cunrui Huang , Jianlin Hu , Shan Hu , Moyu Li , Tiantian Li , Kebin He
Addressing climate change and air pollution exhibits strong synergy, and the Chinese government is actively promoting the integrated management of these two issues. Since 2019, the China Clean Air Policy Partnership has released annual reports on China's progress in climate and air pollution governance. These reports track and analyze the challenges and propose solutions for China's pursuit of carbon neutrality and clean air by developing and monitoring key indicators across five areas. This report is the fourth annual report. Building on previous research, it further refines the collaborative governance monitoring indicator system, including the addition of climate change and extreme weather, atmospheric greenhouse gases, and enhanced efficiency of pollution removal technologies. The report includes the following components: (1) an analysis of the interactions between air pollution and climate change; (2) a discussion of governance systems and practices, with an emphasis on policy implementation and local experiences; (3) coverage of structural changes and emission reduction technologies, including energy and industrial transitions, transportation, low-carbon buildings, carbon capture and storage, and power systems; (4) an overview of atmospheric dynamics and emission pathways, examining emission drivers and offering insights for future coordinated governance; and (5) an evaluation of the health impacts and benefits of joint actions. These efforts underscore China's commitment to integrated control, resulting in slowed carbon emission growth, improved air quality, and enhanced health benefits.
{"title":"The 2024 report of the synergetic roadmap on carbon neutrality and clean air for China: Pollution and carbon reduction promote green economic development","authors":"Zhicong Yin , Yu Lei , Xi Lu , Qiang Zhang , Jicheng Gong , Xin Liu , Wei Li , Cilan Cai , Qimin Chai , Renjie Chen , Wenhui Chen , Hancheng Dai , Zhanfeng Dong , Jingli Fan , Guannan Geng , Cunrui Huang , Jianlin Hu , Shan Hu , Moyu Li , Tiantian Li , Kebin He","doi":"10.1016/j.ese.2025.100636","DOIUrl":"10.1016/j.ese.2025.100636","url":null,"abstract":"<div><div>Addressing climate change and air pollution exhibits strong synergy, and the Chinese government is actively promoting the integrated management of these two issues. Since 2019, the China Clean Air Policy Partnership has released annual reports on China's progress in climate and air pollution governance. These reports track and analyze the challenges and propose solutions for China's pursuit of carbon neutrality and clean air by developing and monitoring key indicators across five areas. This report is the fourth annual report. Building on previous research, it further refines the collaborative governance monitoring indicator system, including the addition of climate change and extreme weather, atmospheric greenhouse gases, and enhanced efficiency of pollution removal technologies. The report includes the following components: (1) an analysis of the interactions between air pollution and climate change; (2) a discussion of governance systems and practices, with an emphasis on policy implementation and local experiences; (3) coverage of structural changes and emission reduction technologies, including energy and industrial transitions, transportation, low-carbon buildings, carbon capture and storage, and power systems; (4) an overview of atmospheric dynamics and emission pathways, examining emission drivers and offering insights for future coordinated governance; and (5) an evaluation of the health impacts and benefits of joint actions. These efforts underscore China's commitment to integrated control, resulting in slowed carbon emission growth, improved air quality, and enhanced health benefits.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100636"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100631
Petr Znachor , Dušan Kosour , Luděk Rederer , Václav Koza , Vojtěch Kolář , Jiří Nedoma
Freshwater reservoirs are critical for water management but face increasing impacts from climate change, which alters their thermal regimes and affects ecosystem functions globally. In temperate regions, surface water temperatures have risen at rates often surpassing those of air temperature, driven by atmospheric warming, hydrological processes, and reservoir morphometry. However, long-term studies on reservoir-specific thermal responses, particularly short-term variability, remain scarce. An important question is how environmental drivers influence both long-term warming trends and daily thermal fluctuations in managed water bodies. Here we show that over 31 years (1991–2021), surface water temperatures in 35 Czech reservoirs increased by an average of 0.59 °C per decade, with air temperature, altitude, and retention time as primary predictors of mean temperatures. A novel corrected metric for day-to-day variability (DTDV) revealed that inflow rate, depth, and retention time strongly influence short-term fluctuations, and DTDV trends positively correlated with warming rates, indicating linked drivers of thermal reorganization. Seasonal patterns showed strongest warming in April, with an anomaly of minimal change in May, likely tied to regional climatic shifts. These findings elucidate climate-driven thermal dynamics in reservoirs, highlighting the interaction of climatic and local factors. By combining statistical modeling with process-based indicators, this study informs adaptive strategies to mitigate impacts on water quality, stratification, and biodiversity under changing climates.
{"title":"Tracking reservoir warming in a changing climate: A 31-year study from Czechia","authors":"Petr Znachor , Dušan Kosour , Luděk Rederer , Václav Koza , Vojtěch Kolář , Jiří Nedoma","doi":"10.1016/j.ese.2025.100631","DOIUrl":"10.1016/j.ese.2025.100631","url":null,"abstract":"<div><div>Freshwater reservoirs are critical for water management but face increasing impacts from climate change, which alters their thermal regimes and affects ecosystem functions globally. In temperate regions, surface water temperatures have risen at rates often surpassing those of air temperature, driven by atmospheric warming, hydrological processes, and reservoir morphometry. However, long-term studies on reservoir-specific thermal responses, particularly short-term variability, remain scarce. An important question is how environmental drivers influence both long-term warming trends and daily thermal fluctuations in managed water bodies. Here we show that over 31 years (1991–2021), surface water temperatures in 35 Czech reservoirs increased by an average of 0.59 °C per decade, with air temperature, altitude, and retention time as primary predictors of mean temperatures. A novel corrected metric for day-to-day variability (<em>DTDV</em>) revealed that inflow rate, depth, and retention time strongly influence short-term fluctuations, and <em>DTDV</em> trends positively correlated with warming rates, indicating linked drivers of thermal reorganization. Seasonal patterns showed strongest warming in April, with an anomaly of minimal change in May, likely tied to regional climatic shifts. These findings elucidate climate-driven thermal dynamics in reservoirs, highlighting the interaction of climatic and local factors. By combining statistical modeling with process-based indicators, this study informs adaptive strategies to mitigate impacts on water quality, stratification, and biodiversity under changing climates.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100631"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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