Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO<span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mrow is="true" /><mrow is="true"><mn is="true">2</mn></mrow></msub></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="1.509ex" role="img" style="vertical-align: -0.582ex;" viewbox="0 -399.4 453.9 649.8" width="1.054ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"></g><g is="true" transform="translate(0,-150)"><g is="true"><use transform="scale(0.707)" xlink:href="#MJMAIN-32"></use></g></g></g></g></svg><span role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></span></span><script type="math/mml"><math><msub is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></script></span> plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO<span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mrow is="true" /><mrow is="true"><mn is="true">2</mn></mrow></msub></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="1.509ex" role="img" style="vertical-align: -0.582ex;" viewbox="0 -399.4 453.9 649.8" width="1.054ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"></g><g is="true" transform="translate(0,-150)"><g is="true"><use transform="scale(0.707)" xlink:href="#MJMAIN-32"></use></g></g></g></g></svg><span role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></span></span><script type="math/mml"><math><msub is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></script></span> plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework f
{"title":"Spatio-temporal neural networks for monitoring and prediction of CO2 plume migration from measurable field data","authors":"Yingxiang Liu, Zhen Qin, Fangning Zheng, Behnam Jafarpour","doi":"10.1016/j.jclepro.2024.144080","DOIUrl":"https://doi.org/10.1016/j.jclepro.2024.144080","url":null,"abstract":"Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mrow is=\"true\" /><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.509ex\" role=\"img\" style=\"vertical-align: -0.582ex;\" viewbox=\"0 -399.4 453.9 649.8\" width=\"1.054ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"></g><g is=\"true\" transform=\"translate(0,-150)\"><g is=\"true\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMAIN-32\"></use></g></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math></span></span><script type=\"math/mml\"><math><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math></script></span> plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mrow is=\"true\" /><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.509ex\" role=\"img\" style=\"vertical-align: -0.582ex;\" viewbox=\"0 -399.4 453.9 649.8\" width=\"1.054ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"></g><g is=\"true\" transform=\"translate(0,-150)\"><g is=\"true\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMAIN-32\"></use></g></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math></span></span><script type=\"math/mml\"><math><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msub></math></script></span> plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework f","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542051","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 : 2024-10-31DOI: 10.1016/j.jclepro.2024.144124
Xiwen Cui, Dongxiao Niu
With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recursive unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models’ results. Then the error correction was performed to obtain the final prediction results. This study conducted an experiment on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the two datasets were reduced by 89.69% and 87.43% lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.
{"title":"Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment","authors":"Xiwen Cui, Dongxiao Niu","doi":"10.1016/j.jclepro.2024.144124","DOIUrl":"https://doi.org/10.1016/j.jclepro.2024.144124","url":null,"abstract":"With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recursive unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models’ results. Then the error correction was performed to obtain the final prediction results. This study conducted an experiment on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the two datasets were reduced by 89.69% and 87.43% lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556076","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 : 2024-10-31DOI: 10.1016/j.jclepro.2024.144117
Integrated Pest Management (IPM) is an important strategy in global agriculture, aimed at maintaining the growth trend of food production without compromising environmental integrity or human health. Despite various measures taken by the government to encourage farmers to adopt IPM technologies, the adoption rate of IPM in China remains insufficient. This study finds that the number of farmers who adopt IPM to a high degree is significantly lower than those who view IPM as a win-win solution for profits and the environment. To address the gap between attitudes and behaviors, this research utilizes data from a survey of 480 households in Dengkou County, Inner Mongolia, China, employing structural equation modeling to analyze the impact of farmers' perceived factors on IPM adoption rates. Additionally, a generalized linear mixed model is used to assess farmers' attitudes toward financial subsidies, while an Ordered Logit model conducts an empirical analysis of the factors influencing barriers to IPM adoption. The findings indicate that: (1) Farmers' overly optimistic expectations regarding the initial costs of IPM hinder their adoption; (2) Farmers who are skeptical about the economic benefits of IPM demonstrate greater interest in cash subsidies; (3) Land characteristics and farmer attributes influence farmers' evaluations of barriers to IPM adoption. To develop reasonable and effective policies aimed at narrowing the adoption gap for IPM, it is essential to thoroughly understand the characteristics of farmers and their perceptions of IPM, and to design incentive measures that account for group differences. Findings of the study provide information regarding the IPM adoption gap among farmers in China, thereby offering practical insights and empirical evidence to assist policymakers in developing appropriate policies. It also provides a framework for countries in similar situations globally to examine and address the adoption gap.
{"title":"Narrowing the gaps between perception and adoption behavior of integrated pest management by farmers: Incentive and challenge","authors":"","doi":"10.1016/j.jclepro.2024.144117","DOIUrl":"10.1016/j.jclepro.2024.144117","url":null,"abstract":"<div><div>Integrated Pest Management (IPM) is an important strategy in global agriculture, aimed at maintaining the growth trend of food production without compromising environmental integrity or human health. Despite various measures taken by the government to encourage farmers to adopt IPM technologies, the adoption rate of IPM in China remains insufficient. This study finds that the number of farmers who adopt IPM to a high degree is significantly lower than those who view IPM as a win-win solution for profits and the environment. To address the gap between attitudes and behaviors, this research utilizes data from a survey of 480 households in Dengkou County, Inner Mongolia, China, employing structural equation modeling to analyze the impact of farmers' perceived factors on IPM adoption rates. Additionally, a generalized linear mixed model is used to assess farmers' attitudes toward financial subsidies, while an Ordered Logit model conducts an empirical analysis of the factors influencing barriers to IPM adoption. The findings indicate that: (1) Farmers' overly optimistic expectations regarding the initial costs of IPM hinder their adoption; (2) Farmers who are skeptical about the economic benefits of IPM demonstrate greater interest in cash subsidies; (3) Land characteristics and farmer attributes influence farmers' evaluations of barriers to IPM adoption. To develop reasonable and effective policies aimed at narrowing the adoption gap for IPM, it is essential to thoroughly understand the characteristics of farmers and their perceptions of IPM, and to design incentive measures that account for group differences. Findings of the study provide information regarding the IPM adoption gap among farmers in China, thereby offering practical insights and empirical evidence to assist policymakers in developing appropriate policies. It also provides a framework for countries in similar situations globally to examine and address the adoption gap.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542032","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 : 2024-10-31DOI: 10.1016/j.jclepro.2024.144128
The accumulation of over 11 million tons of spent lithium-ion batteries (LIBs) by 2030 highlights a critical environmental challenge posed by their large-scale retirement. The efficient recycling valuable metals from spent LIBs can both reduces environmental impact and mitigates the pressing issue of metal resource scarcity. In this context, deep eutectic solvents (DESs) have become a promising option as an eco-friendly solvent, exhibiting great potential for recycling spent LIBs. Therefore, this work proposed an innovative green DES system consisting of choline chloride (ChCl), DL-malic acid (MAL), and glycerol for the efficient leaching of valuable metals from spent LIBs. Comprehensive experiments were conducted to determine the optimum leaching conditions (130 °C, 60 g/L, MChCl:MAL:Glycerol of 1:1:3, 3 h), achieving high leaching efficiencies of 94.6% for Ni, 96.8% for Co, 93.8% for Mn, and 96.4% for Li. Through characterization techniques, kinetics studies, and density functional theory (DFT) calculations, the leaching process was predominantly governed by surface chemical reaction (1-(1-x)1/3 = kt) within the shrinking core model, exhibited activation energies of 49.89 kJ/mol, 47.66 kJ/mol, 50.51 kJ/mol, and 22.24 kJ/mol for Ni, Co, Mn, and Li, respectively. The propensity for DES to leach metal ions followed the order: Li > Co > Ni > Mn, determined by binding energy and energy gaps. Cl− and −COOH within the DES were capable of forming stable complexes with reduced transition metal ions, revealing an efficient coordination leaching mechanism. Additionally, a life cycle assessment (LCA) was conducted on the environmental impacts of the DES leaching process, confirming it as an effective and environmentally friendly method for recycling spent LIBs. This work avoided the employment of corrosive acids and alleviated the generally harsh conditions associated with DESs leaching, providing a viable solution for recovering spent LIBs.
到 2030 年，废旧锂离子电池（LIB）的累积量将超过 1100 万吨，这凸显了大规模淘汰锂离子电池所带来的严峻环境挑战。从废旧锂离子电池中有效回收有价值的金属，既能减少对环境的影响，又能缓解金属资源稀缺的紧迫问题。在此背景下，深共晶溶剂（DES）作为一种生态友好型溶剂已成为一种前景广阔的选择，在回收废锂电池方面展现出巨大的潜力。因此，本研究提出了一种由氯化胆碱（ChCl）、DL-苹果酸（MAL）和甘油组成的创新型绿色 DES 系统，用于从废锂电池中高效沥滤有价金属。通过综合实验确定了最佳浸出条件（130 °C, 60 g/L, MChCl:MAL:Glycerol of 1:1:3, 3 h），镍的浸出效率高达 94.6%，钴的浸出效率高达 96.8%，锰的浸出效率高达 93.8%，锂的浸出效率高达 96.4%。通过表征技术、动力学研究和密度泛函理论（DFT）计算，镍、钴、锰和锂的浸出过程主要受收缩核心模型中的表面化学反应（1-(1-x)1/3=kt）控制，活化能分别为 49.89 kJ/mol、47.66 kJ/mol、50.51 kJ/mol 和 22.24 kJ/mol。DES 浸出金属离子的倾向性依次为根据结合能和能隙确定，DES 对金属离子的浸出倾向依次为：Li > Co > Ni > Mn。DES 中的 Cl- 和 -COOH 能够与还原过渡金属离子形成稳定的络合物，揭示了一种高效的配位浸出机制。此外，还对 DES 沥滤过程的环境影响进行了生命周期评估 (LCA)，证实它是回收废 LIB 的一种有效且环保的方法。这项工作避免了腐蚀性酸的使用，缓解了 DESs 沥滤过程中普遍存在的恶劣条件，为回收废 LIB 提供了一个可行的解决方案。
{"title":"Efficient leaching of valuable metals from spent lithium-ion batteries using green deep eutectic solvents: Process optimization, mechanistic analysis, and environmental impact assessment","authors":"","doi":"10.1016/j.jclepro.2024.144128","DOIUrl":"10.1016/j.jclepro.2024.144128","url":null,"abstract":"<div><div>The accumulation of over 11 million tons of spent lithium-ion batteries (LIBs) by 2030 highlights a critical environmental challenge posed by their large-scale retirement. The efficient recycling valuable metals from spent LIBs can both reduces environmental impact and mitigates the pressing issue of metal resource scarcity. In this context, deep eutectic solvents (DESs) have become a promising option as an eco-friendly solvent, exhibiting great potential for recycling spent LIBs. Therefore, this work proposed an innovative green DES system consisting of choline chloride (ChCl), DL-malic acid (MAL), and glycerol for the efficient leaching of valuable metals from spent LIBs. Comprehensive experiments were conducted to determine the optimum leaching conditions (130 °C, 60 g/L, M<sub>ChCl:MAL:Glycerol</sub> of 1:1:3, 3 h), achieving high leaching efficiencies of 94.6% for Ni, 96.8% for Co, 93.8% for Mn, and 96.4% for Li. Through characterization techniques, kinetics studies, and density functional theory (DFT) calculations, the leaching process was predominantly governed by surface chemical reaction (1-(1-x)<sup>1/3</sup> = kt) within the shrinking core model, exhibited activation energies of 49.89 kJ/mol, 47.66 kJ/mol, 50.51 kJ/mol, and 22.24 kJ/mol for Ni, Co, Mn, and Li, respectively. The propensity for DES to leach metal ions followed the order: Li > Co > Ni > Mn, determined by binding energy and energy gaps. Cl<sup>−</sup> and −COOH within the DES were capable of forming stable complexes with reduced transition metal ions, revealing an efficient coordination leaching mechanism. Additionally, a life cycle assessment (LCA) was conducted on the environmental impacts of the DES leaching process, confirming it as an effective and environmentally friendly method for recycling spent LIBs. This work avoided the employment of corrosive acids and alleviated the generally harsh conditions associated with DESs leaching, providing a viable solution for recovering spent LIBs.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542053","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 : 2024-10-31DOI: 10.1016/j.jclepro.2024.144123
Improving soil nitrogen (N) supply capacity is recognized as a viable solution for sustaining cereal production for food security, since more than half of N absorbed by crops comes from the soil through the gross N mineralization (GNM) process. However, significant uncertainties exist regarding GNM patterns driven by commonly used fertilization practices in croplands. Based on soils collected from 13 long-term fertilization trials spanning over 30 years across China's uplands by using the 15N dilution technique, we found that manure amendment led to the highest increase in GNM (1.9–9.7 folds), followed by straw return (0.8–4.7 folds) and chemical fertilizer application (0.07–3.9 folds), compared to the unfertilized treatment. Fertilization-induced GNM changes were primarily influenced by the initial soil pH in the chemical fertilizer and straw treatments, and by soil clay content in the manure treatment. Application of chemical fertilizer and straw in higher pH soils and manure in higher clayey soils had a greater promotion on GNM, mainly due to the enhanced soil properties (e.g., total dissolved N) and associated microbial attributes (e.g., N-acquiring enzyme activity, bacterial and fungal biomass). Manure amendment also facilitated GNM in low pH soils by promoting microbial attributes. These findings underscore the importance of differentiated fertilization managements at the district level to maximize soil N supply across China's uplands, with prioritizing application of chemical fertilizer and straw in neutral and alkaline soils and manure in acidic and heavier texture soils. This knowledge is crucial for developing policies aimed at buttress food security and reduce soil N loss in China.
{"title":"Response of soil gross nitrogen mineralization to fertilization practices in China's uplands","authors":"","doi":"10.1016/j.jclepro.2024.144123","DOIUrl":"10.1016/j.jclepro.2024.144123","url":null,"abstract":"<div><div>Improving soil nitrogen (N) supply capacity is recognized as a viable solution for sustaining cereal production for food security, since more than half of N absorbed by crops comes from the soil through the gross N mineralization (GNM) process. However, significant uncertainties exist regarding GNM patterns driven by commonly used fertilization practices in croplands. Based on soils collected from 13 long-term fertilization trials spanning over 30 years across China's uplands by using the <sup>15</sup>N dilution technique, we found that manure amendment led to the highest increase in GNM (1.9–9.7 folds), followed by straw return (0.8–4.7 folds) and chemical fertilizer application (0.07–3.9 folds), compared to the unfertilized treatment. Fertilization-induced GNM changes were primarily influenced by the initial soil pH in the chemical fertilizer and straw treatments, and by soil clay content in the manure treatment. Application of chemical fertilizer and straw in higher pH soils and manure in higher clayey soils had a greater promotion on GNM, mainly due to the enhanced soil properties (e.g., total dissolved N) and associated microbial attributes (e.g., N-acquiring enzyme activity, bacterial and fungal biomass). Manure amendment also facilitated GNM in low pH soils by promoting microbial attributes. These findings underscore the importance of differentiated fertilization managements at the district level to maximize soil N supply across China's uplands, with prioritizing application of chemical fertilizer and straw in neutral and alkaline soils and manure in acidic and heavier texture soils. This knowledge is crucial for developing policies aimed at buttress food security and reduce soil N loss in China.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542055","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 : 2024-10-30DOI: 10.1016/j.jclepro.2024.144121
This paper presents a novel approach to enhancing 3D printed concrete (3DPC) by incorporating ultra-fine glass powder (UFGP), focusing on its mechanical properties and high-temperature resistance. Investigation like fresh properties, basic physical properties, residual compressive strength after exposure to 400 °C and 800 °C, hygric properties such as water vapor diffusion resistance, liquid water transport, and moisture buffering capacity were performed the observe the effect of UFGP replacement ratio on 3DPC, which demonstrates significant improvements, highlighting the potential of UFGP to elevate 3DPCs’ performance. Results showed significant improvements, particularly with a 20% UFGP mix, which showed the lowest compressive strength loss (9.0% at 400 °C and 53.7% at 800 °C). Additionally, the water vapor diffusion resistance factor for the 20% UFGP mix was measured at 65.03. These results suggest that incorporating UFGP in 3DPC enhances thermal resilience and mechanical properties, offering a solution for high-temperature construction. This study contributes to sustainable construction by emphasizing the importance of mechanical resilience for structural integrity under extreme temperatures.
{"title":"Enhancing thermo-mechanical and moisture properties of 3D-Printed concrete through recycled ultra-fine waste glass powder","authors":"","doi":"10.1016/j.jclepro.2024.144121","DOIUrl":"10.1016/j.jclepro.2024.144121","url":null,"abstract":"<div><div>This paper presents a novel approach to enhancing 3D printed concrete (3DPC) by incorporating ultra-fine glass powder (UFGP), focusing on its mechanical properties and high-temperature resistance. Investigation like fresh properties, basic physical properties, residual compressive strength after exposure to 400 °C and 800 °C, hygric properties such as water vapor diffusion resistance, liquid water transport, and moisture buffering capacity were performed the observe the effect of UFGP replacement ratio on 3DPC, which demonstrates significant improvements, highlighting the potential of UFGP to elevate 3DPCs’ performance. Results showed significant improvements, particularly with a 20% UFGP mix, which showed the lowest compressive strength loss (9.0% at 400 °C and 53.7% at 800 °C). Additionally, the water vapor diffusion resistance factor for the 20% UFGP mix was measured at 65.03. These results suggest that incorporating UFGP in 3DPC enhances thermal resilience and mechanical properties, offering a solution for high-temperature construction. This study contributes to sustainable construction by emphasizing the importance of mechanical resilience for structural integrity under extreme temperatures.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542067","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 : 2024-10-30DOI: 10.1016/j.jclepro.2024.144116
The phenomenon of acidic failure frequently occurs in the anaerobic digestion (AD) of sole corn stover (CS), which also lacks a systematic understanding of domestication and recovery strategies. Therefore, this study focused on methane production efficiency and dominant microbial responses throughout the domestication, start-up, acidification, and recovery stages. During the 230 days of domestication, AD of sole CS achieved stable methane production rates (74.60–92.74 mL/g TS) and methane percentages (41.18–48.44%). The recovery of methane production could be achieved by diluting and adding NaHCO3. CS with a smaller particle size (2 mm) exhibited slower recovery but had a better recovery effect compared to 5 and 8 mm. Before and after the acidification stage, the dominant bacterial genera changed from Pseudomonas, Caproiciproducens and Proteiniphilum to Pseudomonas; the dominant archaeal genera changed from Methanosaeta and Methanobacterium to Bathyarchaeia. The above results provide feasible strategies for the recovery of AD applications of CS.
{"title":"Process exploration of domestication, start-up and rapid recovery strategies for anaerobic digestion of sole corn stover: Methane production efficiency and dominant microbial responses","authors":"","doi":"10.1016/j.jclepro.2024.144116","DOIUrl":"10.1016/j.jclepro.2024.144116","url":null,"abstract":"<div><div>The phenomenon of acidic failure frequently occurs in the anaerobic digestion (AD) of sole corn stover (CS), which also lacks a systematic understanding of domestication and recovery strategies. Therefore, this study focused on methane production efficiency and dominant microbial responses throughout the domestication, start-up, acidification, and recovery stages. During the 230 days of domestication, AD of sole CS achieved stable methane production rates (74.60–92.74 mL/g TS) and methane percentages (41.18–48.44%). The recovery of methane production could be achieved by diluting and adding NaHCO<sub>3</sub>. CS with a smaller particle size (2 mm) exhibited slower recovery but had a better recovery effect compared to 5 and 8 mm. Before and after the acidification stage, the dominant bacterial genera changed from <em>Pseudomonas</em>, <em>Caproiciproducens</em> and <em>Proteiniphilum</em> to <em>Pseudomonas</em>; the dominant archaeal genera changed from <em>Methanosaeta</em> and <em>Methanobacterium</em> to <em>Bathyarchaeia</em>. The above results provide feasible strategies for the recovery of AD applications of CS.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541768","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 : 2024-10-30DOI: 10.1016/j.jclepro.2024.144110
Global warming and rapid population growth are intensifying land competition for energy and food production, threatening urban food and energy security due to high emissions associated with food transportation. The Rooftop Agrivoltaics (RAV) model integrates rooftop agriculture with photovoltaic energy generation, aiming to produce clean energy and culti-vate vegetables with 'zero food miles.' This study employs Geographic Information Systems (GIS), life cycle assessments, biogeochemical models, and solar energy simulations to rigor-ously assess RAV's potential for carbon reduction. Analyzing lettuce as a model crop in Fu-zhou, China, we found approximately 69.7 km2 of viable space for RAV, with a potential to produce 8.875 × 105 tons of lettuce annually—about 20% of the local vegetable production. This model could reduce carbon emissions from food transportation by 2.798 × 105 t CO2-eq annually and generate about 1850 GWh of solar power yearly, accounting for 0.74% of Fu-zhou's total electricity demand. Annually, RAV could reduce Fuzhou's carbon emissions by approximately 1.614 × 106 t CO2-eq, which translates to a reduction of about 2.68% of the city's total carbon emissions. RAV will cause 6.2 × 106 t CO2-eq emissions over a 30-year lifespan. However, its net carbon reduction over the lifespan will reach 3.242 × 107 t CO2-eq. Furthermore, in scenarios of electric grid transformation and urban expansion, RAV's potential electricity generation by 2030 is expected to increase to between 1916 GWh and 1987 GWh. In conclusion, RAV has significant carbon reduction potential and can play a role in future urban planning to contribute to addressing global climate change.
{"title":"Green energy meets urban agriculture: Unveiling the carbon reduction potential of Rooftop Agrivoltaics","authors":"","doi":"10.1016/j.jclepro.2024.144110","DOIUrl":"10.1016/j.jclepro.2024.144110","url":null,"abstract":"<div><div>Global warming and rapid population growth are intensifying land competition for energy and food production, threatening urban food and energy security due to high emissions associated with food transportation. The Rooftop Agrivoltaics (RAV) model integrates rooftop agriculture with photovoltaic energy generation, aiming to produce clean energy and culti-vate vegetables with 'zero food miles.' This study employs Geographic Information Systems (GIS), life cycle assessments, biogeochemical models, and solar energy simulations to rigor-ously assess RAV's potential for carbon reduction. Analyzing lettuce as a model crop in Fu-zhou, China, we found approximately 69.7 km<sup>2</sup> of viable space for RAV, with a potential to produce 8.875 × 10<sup>5</sup> tons of lettuce annually—about 20% of the local vegetable production. This model could reduce carbon emissions from food transportation by 2.798 × 10<sup>5</sup> t CO<sub>2</sub><sub>-eq</sub> annually and generate about 1850 GWh of solar power yearly, accounting for 0.74% of Fu-zhou's total electricity demand. Annually, RAV could reduce Fuzhou's carbon emissions by approximately 1.614 × 10<sup>6</sup> t CO<sub>2</sub><sub>-eq</sub>, which translates to a reduction of about 2.68% of the city's total carbon emissions. RAV will cause 6.2 × 10<sup>6</sup> t CO<sub>2</sub><sub>-eq</sub> emissions over a 30-year lifespan. However, its net carbon reduction over the lifespan will reach 3.242 × 10<sup>7</sup> t CO<sub>2</sub><sub>-eq</sub>. Furthermore, in scenarios of electric grid transformation and urban expansion, RAV's potential electricity generation by 2030 is expected to increase to between 1916 GWh and 1987 GWh. In conclusion, RAV has significant carbon reduction potential and can play a role in future urban planning to contribute to addressing global climate change.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542064","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 : 2024-10-30DOI: 10.1016/j.jclepro.2024.144115
This critical review provides a detailed Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of carbon nanodots (CDs) synthesised using green chemistry principles, focusing on their potential for safe and sustainable innovation. CDs offer significant strengths, such as eco-friendly synthesis, biocompatibility, and tunable optical properties, making them promising candidates for applications in bioimaging, drug delivery, and environmental monitoring. However, several weaknesses, including low quantum yield, reproducibility issues, heterogeneity, and scalability challenges, limit their widespread commercial adoption. The analysis emphasises the crucial role that integrating green chemistry, and Safe and Sustainable by Design (SSbD) innovations can play in overcoming these limitations. These approaches provide pathways to improve the consistency and scalability of CDs production without compromising environmental and human health. The review also highlights key opportunities, such as advancing synthesis techniques, optimising purification processes, and integrating CDs with other nanomaterials to enhance their properties and broaden their applications. By addressing these challenges and capitalising on the strengths and opportunities identified, the development of green-synthesised CDs can move toward a more sustainable, scalable, and industrially viable future. This review highlights the importance of standardisation and continued innovation in synthesis methods to foster the widespread adoption of green chemistry in nanotechnology.
本评论对利用绿色化学原理合成的碳纳米点（CD）进行了详细的优势、劣势、机会和威胁（SWOT）分析，重点关注其在安全和可持续创新方面的潜力。碳纳米管具有环保合成、生物相容性和可调光学特性等显著优势，因此在生物成像、药物输送和环境监测等领域具有广阔的应用前景。然而，包括量子产率低、可重复性问题、异质性和可扩展性挑战在内的一些弱点限制了它们的广泛商业应用。分析强调了整合绿色化学和安全与可持续设计（SSbD）创新在克服这些限制方面所能发挥的关键作用。这些方法为在不损害环境和人类健康的前提下提高清洁生产的一致性和可扩展性提供了途径。本综述还强调了一些重要机遇，如改进合成技术、优化纯化工艺以及将光盘与其他纳米材料整合以增强其性能并扩大其应用范围。通过应对这些挑战并利用已确定的优势和机遇，绿色合成 CD 的发展可以迈向更具可持续性、可扩展性和工业可行性的未来。本综述强调了合成方法标准化和持续创新对促进绿色化学在纳米技术中广泛应用的重要性。
{"title":"Green-synthesised carbon nanodots: A SWOT analysis for their safe and sustainable innovation","authors":"","doi":"10.1016/j.jclepro.2024.144115","DOIUrl":"10.1016/j.jclepro.2024.144115","url":null,"abstract":"<div><div>This critical review provides a detailed Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of carbon nanodots (CDs) synthesised using green chemistry principles, focusing on their potential for safe and sustainable innovation. CDs offer significant strengths, such as eco-friendly synthesis, biocompatibility, and tunable optical properties, making them promising candidates for applications in bioimaging, drug delivery, and environmental monitoring. However, several weaknesses, including low quantum yield, reproducibility issues, heterogeneity, and scalability challenges, limit their widespread commercial adoption. The analysis emphasises the crucial role that integrating green chemistry, and Safe and Sustainable by Design (SSbD) innovations can play in overcoming these limitations. These approaches provide pathways to improve the consistency and scalability of CDs production without compromising environmental and human health. The review also highlights key opportunities, such as advancing synthesis techniques, optimising purification processes, and integrating CDs with other nanomaterials to enhance their properties and broaden their applications. By addressing these challenges and capitalising on the strengths and opportunities identified, the development of green-synthesised CDs can move toward a more sustainable, scalable, and industrially viable future. This review highlights the importance of standardisation and continued innovation in synthesis methods to foster the widespread adoption of green chemistry in nanotechnology.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541767","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 : 2024-10-30DOI: 10.1016/j.jclepro.2024.143930
Recovering food of high nutritional quality has become an important strategy to address food and nutrition security and waste. Food donation policies have been crafted to address these issues; however, the effectiveness and possible unintended consequences of these policies for perishable surplus remain unclear. Using system dynamics and group model building, we analyzed two donation policy approaches, namely a ‘Farm to Food Bank’ tax credit for farmers and an organic waste ban in New York State. We simulated the effects of these policies on the recovery and redistribution of fresh fruit and vegetable surplus from the farm and retail sectors, reporting changes in quality, availability, and waste generation of these foods within the food rescue system. Simulation results showed how donation policies can unintendedly result in inadequate provision of poor-quality food to vulnerable populations, as well as considerable increases in waste at food rescue organizations. However, enhancing the capacity of these organizations allowed the tax credit to maximize food surplus distribution and minimize waste, and improving both the quality of retail donations and organizations' capacity ensured the success of the organics waste ban. Our findings underscore the need to address systematic issues when dealing with food loss and waste in future programs and policies.
{"title":"Weeding through surplus: Unintended policy consequences for perishable food recovery–Insights from a community-engaged simulation model","authors":"","doi":"10.1016/j.jclepro.2024.143930","DOIUrl":"10.1016/j.jclepro.2024.143930","url":null,"abstract":"<div><div>Recovering food of high nutritional quality has become an important strategy to address food and nutrition security and waste. Food donation policies have been crafted to address these issues; however, the effectiveness and possible unintended consequences of these policies for perishable surplus remain unclear. Using system dynamics and group model building, we analyzed two donation policy approaches, namely a ‘Farm to Food Bank’ tax credit for farmers and an organic waste ban in New York State. We simulated the effects of these policies on the recovery and redistribution of fresh fruit and vegetable surplus from the farm and retail sectors, reporting changes in quality, availability, and waste generation of these foods within the food rescue system. Simulation results showed how donation policies can unintendedly result in inadequate provision of poor-quality food to vulnerable populations, as well as considerable increases in waste at food rescue organizations. However, enhancing the capacity of these organizations allowed the tax credit to maximize food surplus distribution and minimize waste, and improving both the quality of retail donations and organizations' capacity ensured the success of the organics waste ban. Our findings underscore the need to address systematic issues when dealing with food loss and waste in future programs and policies.</div></div>","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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