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}
Pub Date : 2025-11-01DOI: 10.1016/j.ese.2025.100633
Rundong Feng , Bin Chen , Shenghe Liu , Fuyuan Wang , Kaiyong Wang , Bojie Fu
Urban greenspaces enhance human well-being and promote sustainable development in rapidly urbanizing regions by delivering vital ecosystem services, including cooling, air purification, and recreation. In China, where cities accommodate a large share of the population amid persistent environmental pressures, disparities in greenspace exposure pose a major obstacle to equitable access; these disparities arise from geographic, climatic, socioeconomic, and landscape factors. Although awareness of such inequalities is growing, their long-term trajectories, demographic and city-scale patterns, and viable spatial optimization approaches remain largely unexplored. Here we show that greenspace exposure inequality across 246 Chinese cities increased by 25 % from 2000 to 2020 and is projected to rise further by 12.2–15.7 % by 2050 under middle-of-the-road and fossil-fueled development scenarios, disproportionately affecting older, less-educated women and megacity residents. Geodetector and random forest analyses reveal that this rise results from interactions among greenspace coverage, population density, and patch connectivity, which explain 83.9 % of the inequality. A network-based optimization approach that improves patch connectivity—without expanding total greenspace—can reduce disparities by 10.3–20.8 %, with greater efficacy in high-inequality cities and among vulnerable populations. Our results highlight how precise landscape interventions can advance social equity in greenspace access, supporting Sustainable Development Goal 11 for inclusive, resilient urban environments.
{"title":"Utilizing network optimization to mitigate rising greenspace exposure inequalities in Chinese cities from 2000 to 2050","authors":"Rundong Feng , Bin Chen , Shenghe Liu , Fuyuan Wang , Kaiyong Wang , Bojie Fu","doi":"10.1016/j.ese.2025.100633","DOIUrl":"10.1016/j.ese.2025.100633","url":null,"abstract":"<div><div>Urban greenspaces enhance human well-being and promote sustainable development in rapidly urbanizing regions by delivering vital ecosystem services, including cooling, air purification, and recreation. In China, where cities accommodate a large share of the population amid persistent environmental pressures, disparities in greenspace exposure pose a major obstacle to equitable access; these disparities arise from geographic, climatic, socioeconomic, and landscape factors. Although awareness of such inequalities is growing, their long-term trajectories, demographic and city-scale patterns, and viable spatial optimization approaches remain largely unexplored. Here we show that greenspace exposure inequality across 246 Chinese cities increased by 25 % from 2000 to 2020 and is projected to rise further by 12.2–15.7 % by 2050 under middle-of-the-road and fossil-fueled development scenarios, disproportionately affecting older, less-educated women and megacity residents. Geodetector and random forest analyses reveal that this rise results from interactions among greenspace coverage, population density, and patch connectivity, which explain 83.9 % of the inequality. A network-based optimization approach that improves patch connectivity—without expanding total greenspace—can reduce disparities by 10.3–20.8 %, with greater efficacy in high-inequality cities and among vulnerable populations. Our results highlight how precise landscape interventions can advance social equity in greenspace access, supporting Sustainable Development Goal 11 for inclusive, resilient urban environments.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100633"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520383","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.2025.100634
Dong Xu, Yi-Chen Wang
{"title":"GIEHP: A global, AI-powered platform for near real-time ecological intelligence","authors":"Dong Xu, Yi-Chen Wang","doi":"10.1016/j.ese.2025.100634","DOIUrl":"10.1016/j.ese.2025.100634","url":null,"abstract":"","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100634"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568818","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.2025.100637
Gabriele Ghiotto, Luca Francescato, Maria Agustina Biancalani, Laura Treu, Stefano Campanaro
Microbial communities drive essential bioprocesses, including the conversion of synthesis gas into biomethane, a sustainable energy source that supports circular carbon economies. In anaerobic environments, specialized consortia of bacteria and archaea facilitate syngas methanation through syntrophic interactions, where hydrogenotrophic methanogens play a central role in reducing carbon dioxide and monoxide with hydrogen. However, imbalances in gas ratios, particularly excess hydrogen, can disrupt these interactions and impair overall efficiency. Yet, the molecular mechanisms underlying microbial responses to such imbalances remain poorly understood. Here we show that hydrogen excess triggers profound metabolic and viral remodeling in a thermophilic anaerobic microbiome, leading to reduced methane yields and ecological instability. This reprogramming involves transcriptional downregulation of methanogenesis genes in the dominant archaeon Methanothermobacter thermautotrophicus, coupled with upregulation of CRISPR-Cas and restriction-modification systems that correlate with diminished activity of an associated phage, indicating activated host defenses against viral threats. Concurrently, bacterial species such as those from Tepidanaerobacteraceae enhance carbon fixation via the Wood–Ljungdahl pathway, serving as electron sinks to mitigate redox imbalance. These adaptive responses highlight the microbiome's resilience mechanisms under stress, revealing viruses as both stressors and selective forces in syntrophic systems. Such insights advance our understanding of microbiome dynamics in bioconversion processes and guide the engineering of more stable microbial consortia for optimized syngas-to-methane conversion amid variable feedstocks.
{"title":"Hydrogen excess drives metabolic reprogramming and viral dynamics in syngas-converting microbiomes","authors":"Gabriele Ghiotto, Luca Francescato, Maria Agustina Biancalani, Laura Treu, Stefano Campanaro","doi":"10.1016/j.ese.2025.100637","DOIUrl":"10.1016/j.ese.2025.100637","url":null,"abstract":"<div><div>Microbial communities drive essential bioprocesses, including the conversion of synthesis gas into biomethane, a sustainable energy source that supports circular carbon economies. In anaerobic environments, specialized consortia of bacteria and archaea facilitate syngas methanation through syntrophic interactions, where hydrogenotrophic methanogens play a central role in reducing carbon dioxide and monoxide with hydrogen. However, imbalances in gas ratios, particularly excess hydrogen, can disrupt these interactions and impair overall efficiency. Yet, the molecular mechanisms underlying microbial responses to such imbalances remain poorly understood. Here we show that hydrogen excess triggers profound metabolic and viral remodeling in a thermophilic anaerobic microbiome, leading to reduced methane yields and ecological instability. This reprogramming involves transcriptional downregulation of methanogenesis genes in the dominant archaeon <em>Methanothermobacter thermautotrophicus</em>, coupled with upregulation of CRISPR-Cas and restriction-modification systems that correlate with diminished activity of an associated phage, indicating activated host defenses against viral threats. Concurrently, bacterial species such as those from Tepidanaerobacteraceae enhance carbon fixation via the Wood–Ljungdahl pathway, serving as electron sinks to mitigate redox imbalance. These adaptive responses highlight the microbiome's resilience mechanisms under stress, revealing viruses as both stressors and selective forces in syntrophic systems. Such insights advance our understanding of microbiome dynamics in bioconversion processes and guide the engineering of more stable microbial consortia for optimized syngas-to-methane conversion amid variable feedstocks.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"28 ","pages":"Article 100637"},"PeriodicalIF":14.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736104","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号