Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100521
Juan C. Sanchez-Hernandez , Mallavarapu Megharaj
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 (Tenebrio molitor 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 (Hermetia illucens) 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.
{"title":"Insect farming: A bioeconomy-based opportunity to revalorize plastic wastes","authors":"Juan C. Sanchez-Hernandez , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100514
Minhyuk Jeung , Min-Chul Jang , Kyoungsoon Shin , Seung Won Jung , Sang-Soo Baek
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.
{"title":"Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics","authors":"Minhyuk Jeung , Min-Chul Jang , Kyoungsoon Shin , Seung Won Jung , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100518
Ander Castro-Fernandez , Sofía Estévez , Juan M. Lema , Antón Taboada-Santos , Gumersindo Feijoo , María Teresa Moreira
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.
{"title":"Large-scale commercial-grade volatile fatty acids production from sewage sludge and food waste: A holistic environmental assessment","authors":"Ander Castro-Fernandez , Sofía Estévez , Juan M. Lema , Antón Taboada-Santos , Gumersindo Feijoo , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100522
Shengyue Chen , Jinliang Huang , Jiacong Huang , Peng Wang , Changyang Sun , Zhenyu Zhang , Shijie Jiang
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.
{"title":"Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms","authors":"Shengyue Chen , Jinliang Huang , Jiacong Huang , Peng Wang , Changyang Sun , Zhenyu Zhang , 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}
Microbial electrochemical technologies (MET) can remove a variety of organic and inorganic pollutants from contaminated groundwater. However, despite significant laboratory-scale successes over the past decade, field-scale applications remain limited. We hypothesize that enhancing the electrochemical conductivity of the soil surrounding electrodes could be a groundbreaking and cost-effective alternative to deploying numerous high-surface-area electrodes in short distances. This could be achieved by injecting environmentally safe iron- or carbon-based conductive (nano)particles into the aquifer. Upon transport and deposition onto soil grains, these particles create an electrically conductive zone that can be exploited to control and fine-tune the delivery of electron donors or acceptors over large distances, thereby driving the process more efficiently. Beyond extending the radius of influence of electrodes, these diffuse electro-conductive zones (DECZ) could also promote the development of syntrophic anaerobic communities that degrade contaminants via direct interspecies electron transfer (DIET). In this review, we present the state-of-the-art in applying conductive materials for MET and DIET-based applications. We also provide a comprehensive overview of the physicochemical properties of candidate electrochemically conductive materials and related injection strategies suitable for field-scale implementation. Finally, we illustrate and critically discuss current and prospective electrochemical and geophysical methods for measuring soil electronic conductivity—both in the laboratory and in the field—before and after injection practices, which are crucial for determining the extent of DECZ. This review article provides critical information for a robust design and in situ implementation of groundwater electro-bioremediation processes.
{"title":"Groundwater electro-bioremediation via diffuse electro-conductive zones: A critical review","authors":"Federico Aulenta , Matteo Tucci , Carolina Cruz Viggi , Stefano Milia , Seyedmehdi Hosseini , Gianluigi Farru , Rajandrea Sethi , Carlo Bianco , Tiziana Tosco , Marios Ioannidis , Giulio Zanaroli , Riccardo Ruffo , Carlo Santoro , Ugo Marzocchi , Giorgio Cassiani , Luca Peruzzo","doi":"10.1016/j.ese.2024.100516","DOIUrl":"10.1016/j.ese.2024.100516","url":null,"abstract":"<div><div>Microbial electrochemical technologies (MET) can remove a variety of organic and inorganic pollutants from contaminated groundwater. However, despite significant laboratory-scale successes over the past decade, field-scale applications remain limited. We hypothesize that enhancing the electrochemical conductivity of the soil surrounding electrodes could be a groundbreaking and cost-effective alternative to deploying numerous high-surface-area electrodes in short distances. This could be achieved by injecting environmentally safe iron- or carbon-based conductive (nano)particles into the aquifer. Upon transport and deposition onto soil grains, these particles create an electrically conductive zone that can be exploited to control and fine-tune the delivery of electron donors or acceptors over large distances, thereby driving the process more efficiently. Beyond extending the radius of influence of electrodes, these diffuse electro-conductive zones (DECZ) could also promote the development of syntrophic anaerobic communities that degrade contaminants via direct interspecies electron transfer (DIET). In this review, we present the state-of-the-art in applying conductive materials for MET and DIET-based applications. We also provide a comprehensive overview of the physicochemical properties of candidate electrochemically conductive materials and related injection strategies suitable for field-scale implementation. Finally, we illustrate and critically discuss current and prospective electrochemical and geophysical methods for measuring soil electronic conductivity—both in the laboratory and in the field—before and after injection practices, which are crucial for determining the extent of DECZ. This review article provides critical information for a robust design and <em>in situ</em> implementation of groundwater electro-bioremediation processes.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100516"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865797","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100513
Qi Zhang , Yanan Li , Carolien Kroeze , Milou G.M. van de Schans , Jantiene Baartman , Jing Yang , Shiyang Li , Wen Xu , Mengru Wang , Lin Ma , Fusuo Zhang , Maryna Strokal
Antibiotics are extensively used in livestock production to prevent and treat diseases, but their environmental impact through contamination of rivers and groundwater is a growing concern. The specific antibiotics involved, their sources, and their geographic distribution remain inadequately documented, hindering effective mitigation strategies for river and groundwater pollution control caused by livestock production. Here we develope the spatially explicit MARINA-Antibiotics (China-1.0) model to estimate the flows of 24 antibiotics from seven livestock species into rivers and leaching into groundwater across 395 sub-basins in China, and examine changes between 2010 and 2020. We find that 8364 tonnes and 3436 tonnes of antibiotics entered rivers and groundwater nationwide in 2010 and 2020, respectively. Approximately 50–90% of these amounts originated from about 40% of the basin areas. Antibiotic inputs to rivers decreased by 59% from 2010 to 2020, largely due to reduced manure point sources. Conversely, antibiotic leaching into groundwater increased by 15%, primarily because of enhanced manure recycling practices. Pollution varied by antibiotic groups and livestock species: fluoroquinolones contributed approximately 55% to river pollution, mainly from pig, cattle, and chicken manure; sulfonamides accounted for over 90% of antibiotics in groundwater, predominantly from pig and sheep manure. While our findings support existing policies promoting manure recycling to mitigate river pollution in China, they highlight the need for greater attention to groundwater pollution. This aspect is essential to consider in developing and designing future reduction strategies for antibiotic pollution from livestock production.
{"title":"More inputs of antibiotics into groundwater but less into rivers as a result of manure management in China","authors":"Qi Zhang , Yanan Li , Carolien Kroeze , Milou G.M. van de Schans , Jantiene Baartman , Jing Yang , Shiyang Li , Wen Xu , Mengru Wang , Lin Ma , Fusuo Zhang , Maryna Strokal","doi":"10.1016/j.ese.2024.100513","DOIUrl":"10.1016/j.ese.2024.100513","url":null,"abstract":"<div><div>Antibiotics are extensively used in livestock production to prevent and treat diseases, but their environmental impact through contamination of rivers and groundwater is a growing concern. The specific antibiotics involved, their sources, and their geographic distribution remain inadequately documented, hindering effective mitigation strategies for river and groundwater pollution control caused by livestock production. Here we develope the spatially explicit MARINA-Antibiotics (China-1.0) model to estimate the flows of 24 antibiotics from seven livestock species into rivers and leaching into groundwater across 395 sub-basins in China, and examine changes between 2010 and 2020. We find that 8364 tonnes and 3436 tonnes of antibiotics entered rivers and groundwater nationwide in 2010 and 2020, respectively. Approximately 50–90% of these amounts originated from about 40% of the basin areas. Antibiotic inputs to rivers decreased by 59% from 2010 to 2020, largely due to reduced manure point sources. Conversely, antibiotic leaching into groundwater increased by 15%, primarily because of enhanced manure recycling practices. Pollution varied by antibiotic groups and livestock species: fluoroquinolones contributed approximately 55% to river pollution, mainly from pig, cattle, and chicken manure; sulfonamides accounted for over 90% of antibiotics in groundwater, predominantly from pig and sheep manure. While our findings support existing policies promoting manure recycling to mitigate river pollution in China, they highlight the need for greater attention to groundwater pollution. This aspect is essential to consider in developing and designing future reduction strategies for antibiotic pollution from livestock production.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100513"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932702","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ese.2024.100517
Jicheng Gong , Zhicong Yin , Yu Lei , Xi Lu , Qiang Zhang , Cilan Cai , Qimin Chai , Huopo Chen , Renjie Chen , Wenhui Chen , Jing Cheng , Xiyuan Chi , Hancheng Dai , Zhanfeng Dong , Guannan Geng , Jianlin Hu , Shan Hu , Cunrui Huang , Tiantian Li , Wei Li , Kebin He
The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms, targets, strategies, and governance systems. This report, based on a monitoring indicator system for coordinated governance of air pollution and climate change, employs an interdisciplinary approach combining natural and social sciences. It establishes 20 indicators across five key areas: air pollution and climate change, governance systems and practices, structural transformation and technologies, atmospheric components and emission reduction pathways, and health impacts and co-benefits. This report tries to provide actionable insights into the interconnectedness of air pollution and climate governance. It highlights key policy gaps, presents updated indicators, and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air quality. Compared to previous editions, this year's report has updated four key indicators: meteorological impacts on air quality, climate change and its effects, governance policies, and low-carbon building energy systems. The aim is to further refine the monitoring framework, track progress, and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean air. The report comprises six chapters. The executive summary chapter is followed by analyzing air pollution and climate change interactions. Governance systems and practices are discussed in the third chapter, focusing on policy implementation and local experiences. The fourth chapter addresses structural transformations and emission reduction technologies, including energy and industrial shifts, transportation, low-carbon buildings, carbon capture and storage, and power systems. The fifth chapter outlines atmospheric component dynamics and emission pathways, presenting insights into emission drivers and future strategies. The sixth chapter assesses health impacts and the benefits of coordinated actions. Since 2019, China Clean Air Policy Partnership has produced annual reports on China's progress in climate and air pollution governance, receiving positive feedback. In 2023, the report was co-developed with Tsinghua University's Carbon Neutrality Research Institute, involving over 100 experts and multiple academic forums. The collaboration aims to continuously improve the indicator system and establish the report as a key resource supporting China's efforts in pollution reduction, carbon mitigation, greening, and sustainable growth.
{"title":"The 2023 report of the synergetic roadmap on carbon neutrality and clean air for China: Carbon reduction, pollution mitigation, greening, and growth","authors":"Jicheng Gong , Zhicong Yin , Yu Lei , Xi Lu , Qiang Zhang , Cilan Cai , Qimin Chai , Huopo Chen , Renjie Chen , Wenhui Chen , Jing Cheng , Xiyuan Chi , Hancheng Dai , Zhanfeng Dong , Guannan Geng , Jianlin Hu , Shan Hu , Cunrui Huang , Tiantian Li , Wei Li , Kebin He","doi":"10.1016/j.ese.2024.100517","DOIUrl":"10.1016/j.ese.2024.100517","url":null,"abstract":"<div><div>The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms, targets, strategies, and governance systems. This report, based on a monitoring indicator system for coordinated governance of air pollution and climate change, employs an interdisciplinary approach combining natural and social sciences. It establishes 20 indicators across five key areas: air pollution and climate change, governance systems and practices, structural transformation and technologies, atmospheric components and emission reduction pathways, and health impacts and co-benefits. This report tries to provide actionable insights into the interconnectedness of air pollution and climate governance. It highlights key policy gaps, presents updated indicators, and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air quality. Compared to previous editions, this year's report has updated four key indicators: meteorological impacts on air quality, climate change and its effects, governance policies, and low-carbon building energy systems. The aim is to further refine the monitoring framework, track progress, and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean air. The report comprises six chapters. The executive summary chapter is followed by analyzing air pollution and climate change interactions. Governance systems and practices are discussed in the third chapter, focusing on policy implementation and local experiences. The fourth chapter addresses structural transformations and emission reduction technologies, including energy and industrial shifts, transportation, low-carbon buildings, carbon capture and storage, and power systems. The fifth chapter outlines atmospheric component dynamics and emission pathways, presenting insights into emission drivers and future strategies. The sixth chapter assesses health impacts and the benefits of coordinated actions. Since 2019, China Clean Air Policy Partnership has produced annual reports on China's progress in climate and air pollution governance, receiving positive feedback. In 2023, the report was co-developed with Tsinghua University's Carbon Neutrality Research Institute, involving over 100 experts and multiple academic forums. The collaboration aims to continuously improve the indicator system and establish the report as a key resource supporting China's efforts in pollution reduction, carbon mitigation, greening, and sustainable growth.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100517"},"PeriodicalIF":14.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883115","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}
Pub Date : 2024-11-22DOI: 10.1016/j.ese.2024.100515
Yuqing Hao , Longzhu Xu , Meiyu Peng , Zhugen Yang , Weiqi Wang , Fanyu Meng
Depression is a leading mental health disorder worldwide, contributing substantially to the global disease burden. While emerging evidence suggests links between specific air pollutants and depression, the potential interactions among multiple pollutants remain underexplored. Here we show the influence of six common air pollutants on depressive symptoms among middle-aged and older Chinese adults. In single-pollutant models, a 10 μg m−3 increase in SO2, CO, PM10, and PM2.5 is associated with increased risks of depressive symptoms, with odds ratios (95% confidence intervals) of 1.276 (1.238–1.315), 1.007 (1.006–1.008), 1.066 (1.055–1.078), and 1.130 (1.108–1.153), respectively. In two-pollutant models, SO2 remains significantly associated with depressive symptoms after adjusting for other pollutants. Multi-pollutant models uncover synergistic effects, with SO2, CO, NO2, PM10, and PM2.5 exhibiting significant interactions, identifying SO2 as the primary driver of these associations. Mediation analyses further indicate that cognitive and physical impairments partially mediate the relationship between air pollution and depressive symptoms. These findings underscore the critical mental health impacts of air pollution and highlight the need for integrated air quality management strategies. Targeted mitigation of specific pollutants, particularly SO2, is expected to significantly enhance public mental health outcomes.
{"title":"Synergistic air pollution exposure elevates depression risk: A cohort study","authors":"Yuqing Hao , Longzhu Xu , Meiyu Peng , Zhugen Yang , Weiqi Wang , Fanyu Meng","doi":"10.1016/j.ese.2024.100515","DOIUrl":"10.1016/j.ese.2024.100515","url":null,"abstract":"<div><div>Depression is a leading mental health disorder worldwide, contributing substantially to the global disease burden. While emerging evidence suggests links between specific air pollutants and depression, the potential interactions among multiple pollutants remain underexplored. Here we show the influence of six common air pollutants on depressive symptoms among middle-aged and older Chinese adults. In single-pollutant models, a 10 μg m<sup>−3</sup> increase in SO<sub>2</sub>, CO, PM<sub>10</sub>, and PM<sub>2.5</sub> is associated with increased risks of depressive symptoms, with odds ratios (95% confidence intervals) of 1.276 (1.238–1.315), 1.007 (1.006–1.008), 1.066 (1.055–1.078), and 1.130 (1.108–1.153), respectively. In two-pollutant models, SO<sub>2</sub> remains significantly associated with depressive symptoms after adjusting for other pollutants. Multi-pollutant models uncover synergistic effects, with SO<sub>2</sub>, CO, NO<sub>2</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub> exhibiting significant interactions, identifying SO<sub>2</sub> as the primary driver of these associations. Mediation analyses further indicate that cognitive and physical impairments partially mediate the relationship between air pollution and depressive symptoms. These findings underscore the critical mental health impacts of air pollution and highlight the need for integrated air quality management strategies. Targeted mitigation of specific pollutants, particularly SO<sub>2</sub>, is expected to significantly enhance public mental health outcomes.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100515"},"PeriodicalIF":14.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746566","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ese.2024.100512
Jia-Qiang Lv , Wan-Xin Yin , Jia-Min Xu , Hao-Yi Cheng , Zhi-Ling Li , Ji-Xian Yang , Ai-Jie Wang , Hong-Cheng Wang
Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane—compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables. Here we present a mechanistically enhanced hybrid (ME-Hybrid) model that combines mechanistic modeling with data-driven approaches. This model harmonizes datasets with varying sampling frequencies and generates synthetic samples for ML training, thereby enhancing the monitoring of methane and sulfide in sewers. The optimal ME-Hybrid model integrates the backpropagation neural network with mechanistic frequency harmonization. We demonstrate that the ME-Hybrid model outperforms pure ML and linear interpolation in capturing fluctuating trends and extremes of sulfide concentrations, achieving a coefficient of determination (R2) of 0.94. Synthetic samples generated through mechanistic augmentation closely approximate real samples in modeling performance, statistical distribution, and data structure. This enables the model to maintain high predictive accuracy (R2 > 0.76) for sulfide even when trained on only 50 % of the dataset. Additionally, the ME-Hybrid model successfully assesses sewer methane concentrations with an R2 of 0.94, validating its applicability and generalization ability. Our results provide a reliable methodological framework for modeling and prediction under data scarcity. By facilitating better monitoring and management of sewer systems, the ME-Hybrid model aids in the development of strategies that minimize environmental impacts, enhance urban resilience, and ultimately lead to sustainable urban water systems.
在输送过程中，下水道内的物理、化学和生物过程会显著改变污水成分。这导致硫化物和甲烷化合物的形成，从而造成下水道腐蚀和温室气体排放。这些化合物的可靠建模对于有效的下水道管理至关重要，但机器学习（ML）模型的开发却受到水质变量数据可获取性和采样频率差异的阻碍。在此，我们提出了一种机理增强型混合（ME-Hybrid）模型，该模型结合了机理建模和数据驱动方法。该模型可协调不同采样频率的数据集，并生成用于 ML 训练的合成样本，从而加强对下水道中甲烷和硫化物的监测。最佳 ME-Hybrid 模型集成了反向传播神经网络和机理频率协调。我们证明，ME-Hybrid 模型在捕捉硫化物浓度的波动趋势和极端值方面优于纯 ML 和线性插值，其判定系数 (R2) 达到 0.94。通过机理增强生成的合成样本在建模性能、统计分布和数据结构方面与真实样本非常接近。这使得该模型即使只在 50% 的数据集上进行训练，也能保持较高的硫化物预测精度（R2 > 0.76）。此外，ME-Hybrid 模型成功评估了下水道甲烷浓度，R2 为 0.94，验证了其适用性和概括能力。我们的研究结果为数据稀缺情况下的建模和预测提供了可靠的方法框架。通过促进更好地监测和管理下水道系统，ME-Hybrid 模型有助于制定战略，最大限度地减少对环境的影响，提高城市的适应能力，并最终实现可持续的城市水系统。
{"title":"Augmented machine learning for sewage quality assessment with limited data","authors":"Jia-Qiang Lv , Wan-Xin Yin , Jia-Min Xu , Hao-Yi Cheng , Zhi-Ling Li , Ji-Xian Yang , Ai-Jie Wang , Hong-Cheng Wang","doi":"10.1016/j.ese.2024.100512","DOIUrl":"10.1016/j.ese.2024.100512","url":null,"abstract":"<div><div>Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane—compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables. Here we present a mechanistically enhanced hybrid (ME-Hybrid) model that combines mechanistic modeling with data-driven approaches. This model harmonizes datasets with varying sampling frequencies and generates synthetic samples for ML training, thereby enhancing the monitoring of methane and sulfide in sewers. The optimal ME-Hybrid model integrates the backpropagation neural network with mechanistic frequency harmonization. We demonstrate that the ME-Hybrid model outperforms pure ML and linear interpolation in capturing fluctuating trends and extremes of sulfide concentrations, achieving a coefficient of determination (R<sup>2</sup>) of 0.94. Synthetic samples generated through mechanistic augmentation closely approximate real samples in modeling performance, statistical distribution, and data structure. This enables the model to maintain high predictive accuracy (R<sup>2</sup> > 0.76) for sulfide even when trained on only 50 % of the dataset. Additionally, the ME-Hybrid model successfully assesses sewer methane concentrations with an R<sup>2</sup> of 0.94, validating its applicability and generalization ability. Our results provide a reliable methodological framework for modeling and prediction under data scarcity. By facilitating better monitoring and management of sewer systems, the ME-Hybrid model aids in the development of strategies that minimize environmental impacts, enhance urban resilience, and ultimately lead to sustainable urban water systems.</div></div>","PeriodicalId":34434,"journal":{"name":"Environmental Science and Ecotechnology","volume":"23 ","pages":"Article 100512"},"PeriodicalIF":14.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705674","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}
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