Environmental Science and Ecotechnology最新文献

{"title":"Green AI – A multidisciplinary approach to sustainability","authors":"Jerry Huang , Suchi Gopal","doi":"10.1016/j.ese.2025.100536","DOIUrl":"10.1016/j.ese.2025.100536","url":null,"abstract":"","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"24 ","pages":"Article 100536"},"PeriodicalIF":14.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin","authors":"Jeffrey Huang,&nbsp;Simon Elias Bibri,&nbsp;Paul Keel","doi":"10.1016/j.ese.2025.100526","DOIUrl":"10.1016/j.ese.2025.100526","url":null,"abstract":"<div><div>Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the critical need for innovative urban development solutions. In response, sustainable smart cities are increasingly turning to cutting-edge technologies—such as Generative Artificial Intelligence (GenAI), Foundation Models (FMs), and Urban Digital Twin (UDT) frameworks—to transform urban planning and design practices. These transformative tools provide advanced capabilities to analyze complex urban systems, optimize resource management, and enable evidence-based decision-making. Despite recent progress, research on integrating GenAI and FMs into UDT frameworks remains scant, leaving gaps in our ability to capture complex urban flows and multimodal dynamics essential to achieving environmental sustainability goals. Moreover, the lack of a robust theoretical foundation and real-world operationalization of these tools hampers comprehensive modeling and practical adoption. This study introduces a pioneering Large Flow Model (LFM), grounded in a robust foundational framework and designed with GenAI capabilities. It is specifically tailored for integration into UDT systems to enhance predictive analytics, adaptive learning, and complex data management functionalities. To validate its applicability and relevance, the Blue City Project in Lausanne City is examined as a case study, showcasing the ability of the LFM to effectively model and analyze urban flows—namely mobility, goods, energy, waste, materials, and biodiversity—critical to advancing environmental sustainability. This study highlights how the LFM addresses the spatial challenges inherent in current UDT frameworks. The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data, estimating flow data in new locations, predicting the evolution of flow data, and offering a holistic understanding of urban dynamics and their interconnections. The model enhances decision-making processes, supports evidence-based planning and design, fosters integrated development strategies, and enables the development of more efficient, resilient, and sustainable urban environments. This research advances both the theoretical and practical dimensions of AI-driven, environmentally sustainable urban development by operationalizing GenAI and FMs within UDT frameworks. It provides sophisticated tools and valuable insights for urban planners, designers, policymakers, and researchers to address the complexities of modern cities and accelerate the transition towards sustainable urban futures.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"24 ","pages":"Article 100526"},"PeriodicalIF":14.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143306924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell protein production from CO2 and electricity with a recirculating anaerobic-aerobic bioprocess","authors":"Zeyan Pan ,&nbsp;Yuhan Guo ,&nbsp;Weihe Rong ,&nbsp;Sheng Wang ,&nbsp;Kai Cui ,&nbsp;Wenfang Cai ,&nbsp;Zhihui Shi ,&nbsp;Xiaona Hu ,&nbsp;Guokun Wang ,&nbsp;Kun Guo","doi":"10.1016/j.ese.2025.100525","DOIUrl":"10.1016/j.ese.2025.100525","url":null,"abstract":"<div><div>Microbial electrosynthesis (MES) represents a promising approach for converting CO₂ into organic chemicals. However, its industrial application is hindered by low-value products, such as acetate and methane, and insufficient productivity. To address these limitations, coupling acetate production via MES with microbial upgrading to higher-value compounds offers a viable solution. Here we show an integrated reactor that recirculates a cell-free medium between an MES reactor hosting anaerobic homoacetogens (<em>Acetobacterium</em>) and a continuously stirred tank bioreactor hosting aerobic acetate-utilizing bacteria (<em>Alcaligenes</em>) for efficient single-cell protein (SCP) production from CO₂ and electricity. The reactor achieved a maximum cell dry weight (CDW) of 17.4 g L⁻¹, with an average production rate of 1.5 g L⁻¹ d⁻¹. The protein content of the biomass reached 74% of the dry weight. Moreover, the integrated design significantly reduced wastewater generation, mitigated product inhibition, and enhanced SCP production. These results demonstrate the potential of this integrated reactor for the efficient and sustainable production of high-value bioproducts from CO₂ and electricity using acetate as a key intermediate.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"24 ","pages":"Article 100525"},"PeriodicalIF":14.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound","authors":"Lin Wang ,&nbsp;Baihua Chen ,&nbsp;Jingyi Ouyang ,&nbsp;Yanshu Mu ,&nbsp;Ling Zhen ,&nbsp;Lin Yang ,&nbsp;Wei Xu ,&nbsp;Lina Tang","doi":"10.1016/j.ese.2025.100524","DOIUrl":"10.1016/j.ese.2025.100524","url":null,"abstract":"<div><div>Ground-level ozone concentrations rebounded significantly across China in 2022, challenging air quality management and public health. Identifying the drivers of this rebound is crucial for designing effective mitigation strategies. Commonly used methods, such as chemical transport models and machine learning, provide valuable insights but face limitations—chemical transport models are computationally intensive, while machine learning often fails to address confounding factors or establish causality. Here we show that elevated temperatures and increased solar radiation, as primary meteorological drivers, collectively account for 57 % of the total ozone increase, based on an integrated analysis of ground-based monitoring data, satellite observations, and meteorological reanalysis information using explainable machine learning and causal inference techniques. Compared to the year 2021, 90 % of the stations reported an increase in the Formaldehyde to Nitrogen ratio, implying a growing sensitivity of ozone formation to nitrogen oxide levels. These findings highlight the significant causal role of meteorological changes in the ozone rebound, urging the adoption of targeted ozone mitigation strategies under climate warming, particularly through varied regional strategies that consider existing anthropogenic emission levels and the prospective increase in biogenic volatile organic compounds. This identification of causal relationships in air pollution dynamics can support data-driven and accurate decision-making.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"24 ","pages":"Article 100524"},"PeriodicalIF":14.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular dynamics of photosynthetic electron flow in a biophotovoltaic system","authors":"Jianqi Yuan ,&nbsp;Jens Appel ,&nbsp;Kirstin Gutekunst ,&nbsp;Bin Lai ,&nbsp;Jens Olaf Krömer","doi":"10.1016/j.ese.2024.100519","DOIUrl":"10.1016/j.ese.2024.100519","url":null,"abstract":"<div><div>Biophotovoltaics (BPV) represents an innovative biohybrid technology that couples electrochemistry with oxygenic photosynthetic microbes to harness solar energy and convert it into electricity. Central to BPV systems is the ability of microbes to perform extracellular electron transfer (EET), utilizing an anode as an external electron sink. This process simultaneously serves as an electron sink and enhances the efficiency of water photolysis compared to conventional electrochemical water splitting. However, optimizing BPV systems has been hindered by a limited understanding of EET pathways and their impacts on cellular physiology. Here we show photosynthetic electron flows in <em>Synechocystis</em> sp. PCC 6803 cultivated in a ferricyanide-mediated BPV system. By monitoring carbon fixation rates and photosynthetic oxygen exchange, we reveal that EET does not significantly affect cell growth, respiration, carbon fixation, or photosystem II efficiency. However, EET competes for electrons with the flavodiiron protein flv1/3, influencing Mehler-like reactions. Our findings suggest that the ferricyanide mediator facilitates photosynthetic electron extraction from ferredoxins downstream of photosystem I. Additionally, the mediator induces a more reduced plastoquinone pool, an effect independent of EET. At very high ferricyanide concentrations, the electron transport chain exhibits responses resembling the impact of trace cyanide. These insights provide a molecular-level understanding of EET pathways in <em>Synechocystis</em> within BPV systems, offering a foundation for the future refinement of BPV technologies.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100519"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insect farming: A bioeconomy-based opportunity to revalorize plastic wastes","authors":"Juan C. Sanchez-Hernandez ,&nbsp;Mallavarapu Megharaj","doi":"10.1016/j.ese.2024.100521","DOIUrl":"10.1016/j.ese.2024.100521","url":null,"abstract":"<div><div>Managing plastic waste is one of the greatest challenges humanity faces in the coming years. Current strategies—landfilling, incineration, and recycling—remain insufficient or pose significant environmental concerns, failing to address the growing volume of plastic residues discharged into the environment. Recently, increasing attention has focused on the potential of certain insect larvae species to chew, consume, and partially biodegrade synthetic polymers such as polystyrene and polyethylene, offering novel biotechnological opportunities for plastic waste management. However, insect-assisted plastic depolymerization is incomplete, leaving significant amounts of microplastics in the frass (or manure), limiting its use as a soil amendment. In this perspective, we propose a novel two-step bioconversion system to overcome these limitations, using insects to sustainably manage plastic waste while revalorizing its by-products (frass). The first step involves pyrolyzing microplastic-containing frass from mealworms (<em>Tenebrio molitor</em> larvae) fed on plastic-rich diets to produce biochar with enhanced adsorptive properties. The second stage integrates this biochar into the entomocomposting of organic residues, such as food waste, using black soldier fly (<em>Hermetia illucens</em>) larvae to produce nutrient-rich substrates enriched with carbon and nitrogen. This integrated system offers a potential framework for large-scale industrial applications, contributing to the bioeconomy by addressing both plastic waste and organic residue management. We critically examine the advantages and limitations of the proposed system based on current literature on biochar technology and entomocomposting. Key challenges and research opportunities are identified, particularly concerning the physiological and toxicological processes involved, to guide future efforts aimed at ensuring the scalability and sustainability of this innovative approach.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100521"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A social-environmental impact perspective of generative artificial intelligence","authors":"Mohammad Hosseini,&nbsp;Peng Gao,&nbsp;Carolina Vivas-Valencia","doi":"10.1016/j.ese.2024.100520","DOIUrl":"10.1016/j.ese.2024.100520","url":null,"abstract":"","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100520"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics","authors":"Minhyuk Jeung ,&nbsp;Min-Chul Jang ,&nbsp;Kyoungsoon Shin ,&nbsp;Seung Won Jung ,&nbsp;Sang-Soo Baek","doi":"10.1016/j.ese.2024.100514","DOIUrl":"10.1016/j.ese.2024.100514","url":null,"abstract":"<div><div>Mesozooplankton are critical components of marine ecosystems, acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations. They play pivotal roles in the pelagic food web and export production, affecting the biogeochemical cycling of carbon and nutrients. Therefore, accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies. However, modeling these dynamics remains challenging due to the complex interplay among physical, chemical, and biological factors, and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models. Graph neural network (GNN) models offer a promising approach to forecast multivariate features and define correlations among input variables. The high interpretive power of GNNs provides deep insights into the structural relationships among variables, serving as a connection matrix in deep learning algorithms. However, there is insufficient understanding of how interactions between input variables affect model outputs during training. Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species. We find that forecasting accuracy is closely related to interactions within ecosystem dynamics. Notably, increasing the number of nodes does not always enhance model performance; closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing. Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100514"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale commercial-grade volatile fatty acids production from sewage sludge and food waste: A holistic environmental assessment","authors":"Ander Castro-Fernandez ,&nbsp;Sofía Estévez ,&nbsp;Juan M. Lema ,&nbsp;Antón Taboada-Santos ,&nbsp;Gumersindo Feijoo ,&nbsp;María Teresa Moreira","doi":"10.1016/j.ese.2024.100518","DOIUrl":"10.1016/j.ese.2024.100518","url":null,"abstract":"<div><div>The valorization of sewage sludge and food waste to produce energy and fertilizers is a well-stablished strategy within the circular economy. Despite the success of numerous laboratory-scale experiments in converting waste into high-value products such as volatile fatty acids (VFAs), large-scale implementation remains limited due to various technical and environmental challenges. Here, we evaluate the environmental performance of a hypothetical large-scale VFAs biorefinery located in Galicia, Spain, which integrates fermentation and purification processes to obtain commercial-grade VFAs based on primary data from pilot plant operations. We identify potential environmental hotspots, assess the influence of different feedstocks, and perform sensitivity analyses on critical factors like transportation distances and pH control methods, using life cycle assessment. Our findings reveal that, on a per-product basis, food waste provides superior environmental performance compared to sewage sludge, which, conversely, performs better when assessed per mass of waste valorized. This suggests that higher process productivity from more suitable wastes leads to lower environmental impacts but must be balanced against increased energy and chemical consumption, as food waste processing requires more electricity for pretreatment and solid-liquid separation. Further analysis reveals that the main operational impacts are chemical-related, primarily due to the use of NaOH for pH adjustment. Additionally, facility location is critical, potentially accounting for up to 99% of operational impacts due to transportation. Overall, our analysis demonstrates that the proposed VFAs biorefinery has a carbon footprint comparable to other bio-based technologies. However, enhancements in VFAs purification processes are necessary to fully replace petrochemical production. These findings highlight the potential of waste valorization into VFAs as a sustainable alternative, emphasizing the importance of process optimization and strategic facility placement.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100518"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms","authors":"Shengyue Chen ,&nbsp;Jinliang Huang ,&nbsp;Jiacong Huang ,&nbsp;Peng Wang ,&nbsp;Changyang Sun ,&nbsp;Zhenyu Zhang ,&nbsp;Shijie Jiang","doi":"10.1016/j.ese.2024.100522","DOIUrl":"10.1016/j.ese.2024.100522","url":null,"abstract":"<div><div>The escalating magnitude, frequency, and duration of harmful algal blooms (HABs) pose significant challenges to freshwater ecosystems worldwide. However, the mechanisms driving HABs remain poorly understood, in part due to the strong regional specificity of algal processes and the uneven data availability. These complexities make it difficult to generalize HAB dynamics and effectively predict their occurrence using traditional models. To address these challenges, we developed an explainable deep learning approach using long short-term memory (LSTM) models combined with explanation techniques that can capture complex patterns and provide explainable insights into key HAB drivers. We applied this approach for algal density modeling at 102 sites in China's lakes and reservoirs over three years. LSTMs effectively captured daily algal dynamics, achieving mean and maximum Nash-Sutcliffe efficiency coefficients of 0.48 and 0.95 during testing phase. Moreover, water temperature emerged as the primary driver of HABs both nationally and in over 30% of localities, with stronger water temperature sensitivity observed in mid-to low-latitudes. We also identified regional similarities that allow for the successful transferability in modeling algal dynamics. Specifically, using fine-tuned transfer learning, we improved the prediction accuracy in over 75% of poorly gauged areas. Overall, LSTM-based explainable deep learning approach effectively addresses key challenges in HAB modeling by tackling both regional specificity and data limitations. By accurately predicting algal dynamics and identifying critical drivers, this approach provides actionable insights into the mechanisms of HABs, ultimately aids in the implementation of effective mitigation measures for nationwide and regional freshwater ecosystems.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100522"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}