Pub Date : 2026-01-01DOI: 10.1016/j.ese.2026.100656
Shihao Cui , Haonan Guo , Lorenzo Pugliese , Gitte Kragh , Sonia Mena , Shubiao Wu
Wetlands provide essential ecosystem services, from carbon sequestration and flood mitigation to biodiversity support, yet over 20 % have been lost in recent centuries, prompting global restoration efforts backed by policies like the UN Decade on Ecosystem Restoration. Despite rapid expansion of restoration projects, conventional monitoring remains short-term, expert-driven, and often disconnected from site-specific ecological dynamics, limiting adaptive management and long-term success. Citizen science has revolutionized ecological monitoring in other domains by enabling scalable, participatory data collection, but its application to wetland restoration has been largely overlooked. In this Perspective, we assess 120 restoration project sites worldwide and find that citizen science is currently integrated into fewer than 20 % of projects even in high-activity regions like Europe, leaving significant social and geographic potential untapped. We find that recent advances in affordable remote sensing, miniaturized sensors, and mobile platforms—supported by rigorous data-validation frameworks—are now overcoming historical constraints regarding data reliability and spatial continuity. These technological shifts, when coupled with emerging institutional recognition, allow citizen-generated data to serve as a scalable, cost-effective infrastructure for monitoring ecological change over meaningful timescales. Systematically integrating public participation into restoration practice is therefore essential for closing critical monitoring gaps and ensuring the long-term sustainability of global wetland ecosystems.
{"title":"Citizen science powers wetland restoration","authors":"Shihao Cui , Haonan Guo , Lorenzo Pugliese , Gitte Kragh , Sonia Mena , Shubiao Wu","doi":"10.1016/j.ese.2026.100656","DOIUrl":"10.1016/j.ese.2026.100656","url":null,"abstract":"<div><div>Wetlands provide essential ecosystem services, from carbon sequestration and flood mitigation to biodiversity support, yet over 20 % have been lost in recent centuries, prompting global restoration efforts backed by policies like the UN Decade on Ecosystem Restoration. Despite rapid expansion of restoration projects, conventional monitoring remains short-term, expert-driven, and often disconnected from site-specific ecological dynamics, limiting adaptive management and long-term success. Citizen science has revolutionized ecological monitoring in other domains by enabling scalable, participatory data collection, but its application to wetland restoration has been largely overlooked. In this Perspective, we assess 120 restoration project sites worldwide and find that citizen science is currently integrated into fewer than 20 % of projects even in high-activity regions like Europe, leaving significant social and geographic potential untapped. We find that recent advances in affordable remote sensing, miniaturized sensors, and mobile platforms—supported by rigorous data-validation frameworks—are now overcoming historical constraints regarding data reliability and spatial continuity. These technological shifts, when coupled with emerging institutional recognition, allow citizen-generated data to serve as a scalable, cost-effective infrastructure for monitoring ecological change over meaningful timescales. Systematically integrating public participation into restoration practice is therefore essential for closing critical monitoring gaps and ensuring the long-term sustainability of global wetland ecosystems.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100656"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927938","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.2025.100652
Peng Xiao , Congchao Zhang , Yu Tao , Tiefu Xu , Ying Chen , Lian Feng , Lingchao Kong , Zhidan Wen , Weibin Zheng , Hao Xu , Longxin Guo , Hangyu Guo , Zheng Pang , Zhiling Li , Chuan He , Shujie Xu , Kaishan Song , Jie Feng , Zhugen Yang , Shu-Chien Hsu , Nanqi Ren
The global intensification of harmful algal blooms severely compromises freshwater ecosystems, threatening biodiversity and critical ecosystem services through toxin exposure, hypoxia, and water quality degradation. Bloom formation involves a complex interplay of nutrient dynamics, hydrology, and microbial activity. Although subsurface processes—such as the release of sediment-bound nutrients and the germination of dormant cyanobacteria—are thought crucial to bloom initiation, these phenomena occur at fine spatiotemporal scales beyond the reach of conventional monitoring. As a result, the exact, rapidly evolving triggers of bloom emergence remain mostly unknown. Here we show meter-scale chlorophyll a (Chl-a) plumes rising from the sediment–water interface, triggered by heavy rainfall and directly seeding surface blooms. We captured these dynamics using a custom underwater drone that collected over 2.8 million data points at 5-m horizontal and 1-m vertical resolution. Algal blooms exhibit a clear vertical sequence: anomalous Chl-a levels first appear in deep benthic layers after rainfall-driven resuspension, then intensify simultaneously across near-bed depths, and finally reach the surface after a median lag of 0.8–1.5 days. These observations provide in situ evidence associating benthic algal seed stocks with surface bloom initiation, revealing that the origin and spatial heterogeneity of such events arise from rainfall-driven disturbances at the sediment–water interface. This robotic approach not only deciphers the subsurface origins of algal blooms but also empowers predictive modeling and adaptive management strategies, advancing global efforts to combat eutrophication amid escalating climate pressures and safeguard vital water resources.
{"title":"Ultrahigh-resolution 3D monitoring reveals sediment-derived plumes as algal bloom precursors","authors":"Peng Xiao , Congchao Zhang , Yu Tao , Tiefu Xu , Ying Chen , Lian Feng , Lingchao Kong , Zhidan Wen , Weibin Zheng , Hao Xu , Longxin Guo , Hangyu Guo , Zheng Pang , Zhiling Li , Chuan He , Shujie Xu , Kaishan Song , Jie Feng , Zhugen Yang , Shu-Chien Hsu , Nanqi Ren","doi":"10.1016/j.ese.2025.100652","DOIUrl":"10.1016/j.ese.2025.100652","url":null,"abstract":"<div><div>The global intensification of harmful algal blooms severely compromises freshwater ecosystems, threatening biodiversity and critical ecosystem services through toxin exposure, hypoxia, and water quality degradation. Bloom formation involves a complex interplay of nutrient dynamics, hydrology, and microbial activity. Although subsurface processes—such as the release of sediment-bound nutrients and the germination of dormant cyanobacteria—are thought crucial to bloom initiation, these phenomena occur at fine spatiotemporal scales beyond the reach of conventional monitoring. As a result, the exact, rapidly evolving triggers of bloom emergence remain mostly unknown. Here we show meter-scale chlorophyll <em>a</em> (Chl-<em>a</em>) plumes rising from the sediment–water interface, triggered by heavy rainfall and directly seeding surface blooms. We captured these dynamics using a custom underwater drone that collected over 2.8 million data points at 5-m horizontal and 1-m vertical resolution. Algal blooms exhibit a clear vertical sequence: anomalous Chl-<em>a</em> levels first appear in deep benthic layers after rainfall-driven resuspension, then intensify simultaneously across near-bed depths, and finally reach the surface after a median lag of 0.8–1.5 days. These observations provide <em>in situ</em> evidence associating benthic algal seed stocks with surface bloom initiation, revealing that the origin and spatial heterogeneity of such events arise from rainfall-driven disturbances at the sediment–water interface. This robotic approach not only deciphers the subsurface origins of algal blooms but also empowers predictive modeling and adaptive management strategies, advancing global efforts to combat eutrophication amid escalating climate pressures and safeguard vital water resources.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100652"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145871773","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.2026.100661
Yanghui Deng , Xingxing Yin , Changsheng Guo , Wenhui Qiu , Meng Zhang , Xu Tan , Jian Xu
Ephedrine is a prevalent sympathomimetic alkaloid and amphetamine-type stimulant precursor that has become a widespread contaminant in global aquatic ecosystems. While the neurotoxic effects of high-dose ephedrine exposure are documented in humans and other mammals, its impact on aquatic vertebrates at environmentally realistic concentrations remains poorly understood. Determining how these persistent residues affect neural development and physiological homeostasis is critical for evaluating ecological risks to aquatic life. Here we show that chronic, low-dose ephedrine exposure impairs neurodevelopment in adult zebrafish by simultaneously disrupting synaptogenesis architecture and neurotransmitter balance. Integrated transcriptomic and histopathological analyses reveal that ephedrine targets the synaptogenesis signaling pathway, resulting in reduced presynaptic vesicles and structural abnormalities in the postsynaptic density. Computational docking and biochemical assays further demonstrate that ephedrine engages the vesicular acetylcholine transporter and tyrosine hydroxylase with high affinity, triggering excitotoxic cascades and biphasic neurochemical dysregulation that manifest as anxiety-like phenotypes and cognitive impairments. These findings indicate that environmentally relevant concentrations of stimulant precursors pose a significant threat to the neural circuit integrity of aquatic species, necessitating urgent regulatory attention to pharmaceutical residues in surface waters.
{"title":"Ephedrine-disrupted synaptogenesis signaling and behavioral abnormalities in adult zebrafish","authors":"Yanghui Deng , Xingxing Yin , Changsheng Guo , Wenhui Qiu , Meng Zhang , Xu Tan , Jian Xu","doi":"10.1016/j.ese.2026.100661","DOIUrl":"10.1016/j.ese.2026.100661","url":null,"abstract":"<div><div>Ephedrine is a prevalent sympathomimetic alkaloid and amphetamine-type stimulant precursor that has become a widespread contaminant in global aquatic ecosystems. While the neurotoxic effects of high-dose ephedrine exposure are documented in humans and other mammals, its impact on aquatic vertebrates at environmentally realistic concentrations remains poorly understood. Determining how these persistent residues affect neural development and physiological homeostasis is critical for evaluating ecological risks to aquatic life. Here we show that chronic, low-dose ephedrine exposure impairs neurodevelopment in adult zebrafish by simultaneously disrupting synaptogenesis architecture and neurotransmitter balance. Integrated transcriptomic and histopathological analyses reveal that ephedrine targets the synaptogenesis signaling pathway, resulting in reduced presynaptic vesicles and structural abnormalities in the postsynaptic density. Computational docking and biochemical assays further demonstrate that ephedrine engages the vesicular acetylcholine transporter and tyrosine hydroxylase with high affinity, triggering excitotoxic cascades and biphasic neurochemical dysregulation that manifest as anxiety-like phenotypes and cognitive impairments. These findings indicate that environmentally relevant concentrations of stimulant precursors pose a significant threat to the neural circuit integrity of aquatic species, necessitating urgent regulatory attention to pharmaceutical residues in surface waters.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100661"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078756","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}
Construction activities generate substantial air pollutants and greenhouse gas emissions, contributing heavily to ambient PM2.5 exposure and associated mortality worldwide. In China, rapid urbanization has driven a massive expansion of the construction sector, with emissions arising from building material production, onsite operations, upstream supply chains, and operational energy use in buildings. Although end-of-pipe controls have markedly lowered pollutant emissions since 2013, further reductions are increasingly costly, and air quality and climate policies remain poorly integrated. The full lifecycle health burden imposed by construction-related air pollution, its temporal evolution, and the scope for health co-benefits from decarbonization—particularly across urban and rural divides—have been incompletely characterized. Here we integrate a detailed construction emission inventory, input–output analysis, inverse atmospheric modelling, and health impact assessment to quantify these impacts in China from 2000 to 2019. We show that construction-related emissions, including upstream power and industrial sources, caused 1.10 million (95% CI: 0.83–1.37 million) premature deaths in 2019, accounting for 50% (95% CI: 38–62%) of national ambient PM2.5-attributed mortality. Health burdens evolved through three phases: rapid increase with 130% CO2 growth during intense urbanization (2000–2008), decoupling via pollution controls that averted 0.36 million deaths despite rising CO2 (2008–2015), and synergistic declines from energy-mix optimization and technology upgrades (2015–2019). Urban mortality stems predominantly from upstream industrial emissions, whereas rural mortality is driven by residential heating; decarbonizing power and heavy industry offers the largest urban co-benefits, while rural clean-electricity heating requires concurrent power-sector greening to prevent CO2 penalties. These results position the construction sector as a pivotal target for integrated policies that jointly advance air quality, public health, and climate objectives.
{"title":"Construction activities drive half of China's ambient PM2.5 health burden","authors":"Zhanxiang Wang , Huizhong Shen , Ruixin Zhang , Ruibin Xu , Peng Guo , Zhiyu Zheng , Jinling He , Siqi Wu , Yilin Chen , Dong Xie , Jinjian Zhang , Lianming Zheng , Hang Su , Dabo Guan","doi":"10.1016/j.ese.2026.100666","DOIUrl":"10.1016/j.ese.2026.100666","url":null,"abstract":"<div><div>Construction activities generate substantial air pollutants and greenhouse gas emissions, contributing heavily to ambient PM<sub>2.5</sub> exposure and associated mortality worldwide. In China, rapid urbanization has driven a massive expansion of the construction sector, with emissions arising from building material production, onsite operations, upstream supply chains, and operational energy use in buildings. Although end-of-pipe controls have markedly lowered pollutant emissions since 2013, further reductions are increasingly costly, and air quality and climate policies remain poorly integrated. The full lifecycle health burden imposed by construction-related air pollution, its temporal evolution, and the scope for health co-benefits from decarbonization—particularly across urban and rural divides—have been incompletely characterized. Here we integrate a detailed construction emission inventory, input–output analysis, inverse atmospheric modelling, and health impact assessment to quantify these impacts in China from 2000 to 2019. We show that construction-related emissions, including upstream power and industrial sources, caused 1.10 million (95% CI: 0.83–1.37 million) premature deaths in 2019, accounting for 50% (95% CI: 38–62%) of national ambient PM<sub>2.5</sub>-attributed mortality. Health burdens evolved through three phases: rapid increase with 130% CO<sub>2</sub> growth during intense urbanization (2000–2008), decoupling via pollution controls that averted 0.36 million deaths despite rising CO<sub>2</sub> (2008–2015), and synergistic declines from energy-mix optimization and technology upgrades (2015–2019). Urban mortality stems predominantly from upstream industrial emissions, whereas rural mortality is driven by residential heating; decarbonizing power and heavy industry offers the largest urban co-benefits, while rural clean-electricity heating requires concurrent power-sector greening to prevent CO<sub>2</sub> penalties. These results position the construction sector as a pivotal target for integrated policies that jointly advance air quality, public health, and climate objectives.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"29 ","pages":"Article 100666"},"PeriodicalIF":14.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173901","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.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}