Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality.

The coupling relationship between land-use carbon emissions (LCE) and ecological environmental quality (EEQ) is critical for regional sustainable development. Rapid urbanization promotes a notable increase in LCE, which imparts significant stress on EEQ. This study used land use and cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data from the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) to evaluate LCE, applied a remote sensing ecological index (RSEI) model to calculate EEQ, and combined gravity and centroid movement trajectory models to analyze the spatiotemporal evolution characteristics of LCE and EEQ. Four-quadrant and coupling degree (CD) models were used to analyze the synergistic relationship and interaction intensity between LCE and EEQ based on three different scales of pixels, counties, and cities. The results show that: (1) LCE and EEQ exhibit clear spatial inequality distribution, and the total amount of LCE increased from 40.16 Mt. in 2000 to 131.99 Mt. in 2020; however, LCE has not yet reached peak carbon emissions. (2) From 2000 to 2020, cities with a strong correlation between LCE and EEQ showed an increasing trend, and the centroid of LCE moved sharply to Jiangxi during 2000-2005 and 2005-2010. (3) High-CD areas were primarily located in quadrant II, and low-CD areas in quadrant IV. The relationship between LCE and EEQ has improved over the past 21 years, and CD has been increasing. (4) The stability of the coupling results between LCE and EEQ was affected by different research scales; the larger the research scale is, the greater the change in the results. This study provides a scientific basis and practical scheme for LCE reduction, ecological environmental management, and regional sustainable development in the UAMRYR.

Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective.

Domestic attempts to advance the Sustainable Development Goals (SDGs) in a country can have synergistic and/or trade-off effects on the advancement of SDGs in other countries. Transboundary SDG interactions can be delivered through various transmission channels (e.g., trade, river flow, ocean currents, and air flow). This study quantified the transboundary interactions through these channels between 768 pairs of SDG indicators. The results showed that although high income countries only comprised 14.18% of the global population, they contributed considerably to total SDG interactions worldwide (60.60%). Transboundary synergistic effects via international trade were 14.94% more pronounced with trade partners outside their immediate geographic vicinity than with neighbouring ones. Conversely, nature-caused flows (including river flow, ocean currents, and air flow) resulted in 39.29% stronger transboundary synergistic effects among neighboring countries compared to non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is essential to enhance collaboration among countries and leverage transboundary synergies.

Custom machine learning algorithm for large-scale disease screening - taking heart disease data as an example.

Heart disease accounts for millions of deaths worldwide annually, representing a major public health concern. Large-scale heart disease screening can yield significant benefits both in terms of lives saved and economic costs. In this study, we introduce a novel algorithm that trains a patient-specific machine learning model, aligning with the real-world demands of extensive disease screening. Customization is achieved by concentrating on three key aspects: data processing, neural network architecture, and loss function formulation. Our approach integrates individual patient data to bolster model accuracy, ensuring dependable disease detection. We assessed our models using two prominent heart disease datasets: the Cleveland dataset and the UC Irvine (UCI) combination dataset. Our models showcased notable results, achieving accuracy and recall rates beyond 95 % for the Cleveland dataset and surpassing 97 % accuracy for the UCI dataset. Moreover, in terms of medical ethics and operability, our approach outperformed traditional, general-purpose machine learning algorithms. Our algorithm provides a powerful tool for large-scale disease screening and has the potential to save lives and reduce the economic burden of heart disease.

Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools.

Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)1 and Fang et al. (2022).2.

An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery.

Accurate characterization of molecular representations plays an important role in the property prediction based on deep learning (DL) for drug discovery. However, most previous researches considered only one type of molecular representations, resulting in that it difficult to capture the full molecular feature information. In this study, a novel DL framework called multi-modal molecular representation learning fusion network (MMRLFN) is developed, which could simultaneously learn and integrate drug molecular features from molecular graphs and SMILES sequences. The developed MMRLFN method is composed of three complementary deep neural networks to learn various features from different molecular representations, such as molecular topology, local chemical background information, and substructures at varying scales. Eight public datasets involving various molecular properties used in drug discovery were employed to train and evaluate the developed MMRLFN. The obtained models showed better performances than the existing models based on mono-modal molecular representations. Additionally, a thorough analysis of the noise resistance and interpretability of the MMRLFN has been carried out. The generalization ability and effectiveness of the MMRLFN has been verified by case studies as well. Overall, the MMRLFN can accurately predict molecular properties and provide potentially valuable information from large datasets, thereby maximizing the possibility of successful drug discovery.

A systematic review on tannery sludge to energy route: Current practices, impacts, strategies, and future directions.

The growing amount of tannery sludge (TS) generated from leather processing often undergoes uncontrolled landfilling, or open dumping, releasing a significant volume of harmful pollutants, including carcinogenic chromium (Cr) into the air, water, and soil. Therefore, the sustainable TS management through advanced valorization technologies becomes vital to align with the Sustainable Development Goals (SDGs) and mitigate the adverse environmental, health, and social impacts. Moreover, TS, as biomass, can be considered a renewable energy source for bioenergy generation, which could be a viable solution for meeting contemporary environmental standards and expediting transition towards a circular economy. However, TS valorization is sensitive and critical due to the potential risk of transforming Cr(III) to Cr(VI) during the valorization process. Therefore, there is an urgency to consider efficient and holistic TS valorization technologies in the design, implementation, and operations phases to avoid any environmental and health hazards. In pursuit of this goal, this systematic literature review (SLR) comprehensively and critically analyzes the existing TS valorization processes to develop sustainable energy recovery solutions from TS. This SLR contributes uniquely to the existing literature in different ways. Firstly, it provides a critical evaluation of the current TS valorization technologies identifying the available waste-to-energy recovery options. Secondly, the review encompasses extensive research from three reputed databases such as Scopus, Web of Science, and ScienceDirect, without temporal restrictions to offer a comprehensive understanding of current TS management practices and available valorization techniques. Moreover, the review categorized 124 published papers into distinct groups, revealing promising avenues for future research in this field. The findings indicated that most of the work concentrating on Chrome (Cr) recovery, pyrolysis, anaerobic co-digestion, and solidification while gasification and biodiesel or biofuel production from TS remained largely unexplored. Additionally, vital aspects such as process optimization, life cycle assessment of different valorization techniques, environmental, economy, energy, emergy, and exergy (5E) analysis, life cycle energy balance, and techno-economic analysis including exergoeconomic and exergoenvironmental are completely absent in the literature. Future studies need to concentrate on process optimization, exergy and energy analysis, and techno-economic assessment including exergoeconomic and exergoenvironmental analysis to understand the feasibility and environmental benefits of various TS valorization technologies and to develop industry-scale valorization plants for TS management in an economically and ecologically sustainable manner. Moreover, the review will serve as a comprehensive guide for scholars, authorities, and stakeholders to advance research in this field and formulate policies for the eco-friendly management of TS, paving the way towards clean energy solutions.

Promoting sustainable management of hazardous waste-to-wealth practices: An innovative integrated DPSIR and decision-making framework.

Sustainable valorization of tannery sludge (TS) is vital for achieving several sustainable development goals (SDGs) in the tannery industry. TS is considered a hazardous waste by-product posing a significant environmental challenge. However, TS can be utilized for energy or resource recovery by considering it as biomass and implementing the circular economy (CE) concept. Therefore, this study aims to develop an innovative DPSIR (Driver, Pressure, State, Impact, and Response) framework for promoting sustainable valorization of TS. Further, the study extends to quantify the importance of subjective DPSIR factors by offering interval-valued intuitionistic fuzzy number-based best worst method (IVIFN-BWM), which is relatively new in the literature and able to deal with the uncertainty, inconsistency, imprecise, and vagueness in the decision-making process. The study also investigates the most appropriate TS valorization technologies concerning identified DPSIR factors using a novel IVIFN-combined compromise solution (CoCoSo) approach. This research contributes to the literature by developing a comprehensive solution approach that combines the DPSIR framework, IVIFN-BWM, and IVIFN-CoCoSo method in addressing sustainability and resource recovery challenges for the tannery industry. The research findings highlight the potential of sustainable valorization of TS in reducing the waste amount and promoting sustainability and CE practices in the tannery industry. The findings indicated that response factors 'creation of national-level policies and awareness campaign' and 'facilitating financial support to adopt waste valorization technologies' received the highest priority among other DPSIR factors for managing and fostering sustainable valorization of TS. The IVIFN-CoCoSo analysis confirmed that the most promising TS valorization technology is 'gasification', which is followed by pyrolysis, anaerobic digestion, and incineration. The study's implications extend to policymakers, industrial practitioners, and researchers, who can leverage the research findings to develop more sustainable TS management practices in the tannery industry.

Conversion of medical waste into value-added products using a novel integrated system with tail gas treatment: Process design, optimization, and thermodynamic analysis.

The COVID-19 pandemic has generated substantial medical waste (MW), posing risks to society. Based on widespread MW incineration, this study proposes an integrated system with tail gas treatment to convert MW into value-added products with nearly zero emissions. Herein, steam generators and supercritical CO2 cycles were used to recover energy from MW to produce high-temperature/pressure steam and electricity. A simple power generation cycle achieved a net electricity efficiency of 22.4% through optimization. Thermodynamic analysis revealed that the most energy and exergy loss occurred in incineration. Furthermore, a pressurized reactive distillation column purified the resultant tail gas. The effects of inlet temperature, pressure, liquid/gas ratio, and recycle ratio on the removal and conversion efficiencies of NO2 and SO2 were evaluated. Nearly 100% of the SO2 and 75% of the NO2 generated by the incineration of MW have been converted into their acid forms. Based on the proposed tail gas treatment unit, high-purity CO2 (∼98% purity) was finally obtained.

Consumption-based emissions at city level in China and the spatial heterogeneity analysis of the influential factors.

It is of great significance to identify the critical influential factors of pollutant emissions for emission mitigation. However, city disparity implies different priorities for regional mitigation. This study aims to estimate the consumption-based emissions of 309 prefecture-level cities in China based on the multi-region input-output table and the sectoral NOx emission inventory and investigate the emission transfer phenomenon among cities and sectors. In addition, a geographically weighted regression method is used to analyze the spatial heterogeneity in the driving factors of regional consumption-based emissions. The results reveal that the top 10 cities in consumption-based emissions account for 25.2% of emissions and contribute 22.6% to GDP. The consumption-based emissions are mainly driven by local demand (72.79%) at the regional level and by construction activities (94.43%) at the sectoral level. Besides, the results also show the spatial variances in contributions of driving forces to consumption-based emissions. Economic growth has been identified as the most important factor which promotes consumption-based emissions. However, disposable personal income, per capita road area, urbanization, and percentage of tertiary industry GDP are conducive to reduce consumption-based emissions in some cities of China. It could be concluded that policies without consideration of the emissions from a consumption perspective are difficult to achieve effective emission reduction.

Navigating Chinese cities to achieve sustainable development goals by 2030.

Achieving the 17 United Nations sustainable development goals (SDGs) in China largely depends on the transition of cities toward sustainable development. However, significant knowledge gaps exist in evaluating the SDG index at the city scale and in understanding how to simulate pathways to achieve the 17 SDGs for Chinese cities by 2030. This study aimed to quantify the SDG index of 285 Chinese cities and developed a forecasting model to simulate the performance of each SDG in each city until 2030 using varied scenarios. The results indicated that although the SDG index in Chinese cities increased by 33.97% during 2005-2016, Chinese cities, which continued their past paths, achieved an average of only five SDGs by 2030. To promote the joint achievement of all SDGs, we designed different paths for all SDGs of each of the 285 cities and simulated their SDG index until 2030. Under the scenarios, 216 Chinese cities (75.79%) could achieve 9-13 more SDGs in 2030 and the overall SDG index can improve from 74.57 in 2030 to 97.49 (target score 100) by adopting more intensive path adjustment. We lastly determined a cost-effective path for each SDG of each city to promote joint achievement of all SDGs by 2030. The proposed simulation model and cost-effective path serve as a foundation for other countries to simulate SDG progress and develop pathways for achieving SDGs in the future.

Integrated crop-livestock-bioenergy system brings co-benefits and trade-offs in mitigating the environmental impacts of Chinese agriculture.

Agricultural bioenergy utilization relies on crop and livestock production, favouring an integrated crop-livestock-bioenergy production model. Yet the integrated system's exact contribution to mitigating various environmental burdens from the crop production system and livestock production system remains unclear. Here we inventory the environmental impacts of each process in three subsystems at both national and regional scales in China, ultimately identifying key processes and impact categories. The co-benefits and trade-offs in nine impact categories are investigated by comparing the life cycle impacts in the background scenario (crop production system + livestock production system) and foreground scenario (integrated system). Freshwater eutrophication is the most serious impact category in both scenarios. Except terrestrial acidification, the mitigation effects on the other eight impact categories vary from 1.8% to 94.8%, attributed to fossil energy and chemical fertilizer offsets. Environmental trade-offs should be deliberated when expanding bioenergy utilization in the identified critical regions.

Synergistic CO2 reduction effects in Chinese urban agglomerations: Perspectives from social network analysis.

China has released its ambitious target for carbon neutrality by 2060. With decades of top-down energy conservation and pollutant mitigation policies, the techno-mitigation space has gradually shrunk, while more mitigation space is required for a systematic approach. To help to uncover CO2 mitigation effects, location and better pathways from a systematic perspective, this paper combines disparity analysis and social network analysis to investigate the synergistic emissions reduction effect of urban agglomerations in three representative Chinese urban agglomerations, namely the Yangtze River Delta urban agglomeration (YRD), Chengdu-Chongqing urban agglomeration (CY) and Guangdong-Hong Kong-Macao urban agglomeration (GHM). Based on understanding of the carbon emission disparity characteristics of the three urban agglomerations using disparity analysis, this study uses social network analysis to study the synergistic CO2 reductions in each urban agglomeration from three perspectives: overall, individual, and connection. The findings emphasize that CY presented the greatest synergistic development capacity but with weak driving ability, indicating that overall synergistic emission reduction was difficult to achieve in a short period. GHM presented obvious fragmentation between the core and peripheral cities, resulting in a weak synergistic mitigation effect. YRD highlighted a solid synergistic development capacity with strong driving ability by its developed cities, thus generating the greatest potential to reduce CO2 emissions in the short and middle terms. Different cities assume different roles in synergistic CO2 reduction. Our results can be expected to enlighten more regionally oriented CO2 mitigation policy implications from an urban agglomeration perspective.

Transformation and Cytotoxicity of Surface-Modified Silver Nanoparticles Undergoing Long-Term Aging.

Silver nanoparticles (AgNPs) are constituents of many consumer products, but the future of their production depends on ensuring safety. The stability of AgNPs in various physiological solutions and aging in storage may affect the accuracy of predicted nanoparticle toxicity. The goal of this study was to simulate the transformation of AgNPs in different media representatives to the life cycle in the environment and to identify their toxicity to Hepa1c1c7 cells in a long-term aging process. AgNPs coated with citrate, polyethylene glycol (PEG), polyvinylpyrrolidone (PVP), and branched polyethyleneimine (BPEI) were studied. Our results show that the exposure media had a significant impact on the transformation of AgNPs. Citrate-coated AgNPs showed significant aggregation in phosphate-buffered saline. The aging of AgNPs in optimal storage showed that the charge-stabilized particles (citrate) were more unstable, with significant aggregation and shape changes, than sterically stabilized particles (PEG AgNPs, PVP AgNPs). The BPEI AgNPs showed the highest dissolution of AgNPs, which induced significantly increased toxicity to Hepa1c1c7 cells. Overall, our findings showed that storage and media of AgNPs influenced the transformation of AgNPs and that the resulting changes in the AgNPs' physicochemical properties influenced their toxicity. Our study contributes to the understanding of AgNPs' transformations under realistic exposure scenarios and increasing the predictability of risk assessments.

Holistic suitability for regional biomass power generation development in China: An application of matter-element extension model.

In the context of tremendously promoting bioenergy utilization, regional suitability for industrial development of biomass power generation is a critical factor when deploying region-specific strategies. An integrated framework is developed incorporating resource potential, development demands and development conditions to evaluate the suitability for regional industrial development of power generation utilizing agricultural bioresources. Twelve indicators reflecting local resource, environmental and socioeconomic features are used to measure the suitability of 31 provincial regions in China. An improved matter-element extension model combined with the entropy weight method is adopted to attain holistic and hierarchical suitability ranks. The results reveal that the distribution of holistic suitability ranks among regions is imbalanced with the eastern regions presenting more advantages compared with the western regions. Three regions belonging to Rank I (optimum) are Henan, Shandong and Xinjiang. Hainan, Tibet, Qinghai are classified into Rank V (unsuited). Moreover, there are great differences in the limiting factors of the suitability among regions. Resource potential is a limiting factor for Beijing, Shanghai, Fujian, Hainan and Guizhou; Development demands refrain Fujian, Guangxi and Yunnan; Tianjin and Ningxia are limited by development conditions. Tibet and Qinghai have the worst performance on each criterion. The results and region-targeted policy recommendations can provide insights for bioenergy utilization development in accordance with local conditions closely.

Multi-criteria decision making for sustainability assessment of boxboard production: A life cycle perspective considering water consumption, energy consumption, GHG emissions, and internal costs.

Papermaking is a capital-intensive industry that requires a high consumption of plant fibers, energy, and water. Previous sustainability assessments of papermaking industry primarily focused on separate evaluations for multiple criteria without the integration for criteria and could not compare the overall priority of the production alternatives. The life cycle sustainability for the most representative boxboard production is analyzed as a case study in this work. Life cycle water consumption, energy consumption, greenhouse gas emissions, and internal costs are selected as the assessment criteria. The two multi-criteria decision-making methods are applied to integrate the above criteria to obtain the sustainability sequence under different production pathways. When the papermaking enterprises are regarded as decision-makers, the alternative using waste paper as raw material to manufacture boxboard is the most sustainable, following by mixed fiber. The sustainability sequence of the alternatives using wood and straw as raw materials is controversial due to the different calculation models. Changing the proportion of raw materials and the criteria weights might adjust sustainability sequence of the alternatives.

An urban-rural and sex differences in cancer incidence and mortality and the relationship with PM2.5 exposure: An ecological study in the southeastern side of Hu line.

This study investigates the urban-rural and sex differences in the increased risks of the ten most common cancers in China related to high PM2.5 concentration in the southeastern side of Hu line. Pearson correlation coefficient is estimated to reveal how the cancers closely associated with PM2.5 long-term exposure. Then linear regression is conducted to evaluate sex- and area-specific increased risks of those cancers from high level PM2.5 long-term exposure. The major finding is with the increase of every 10 µg/m3 of annual mean PM2.5 concentration, the increase of relative risks for lung cancer incidence and mortality are 15% and 23% for males, and 22% and 24% for females in rural area. For urban area, the increase of relative risk for ovarian cancer incidence is 9% for females, while that for prostatic cancer increases 17% for males. For leukemia, the increase of relative risks for incidence and mortality are 22% and 19% for females in rural area, while in urban area the increase of relative risk for mortality is 9% for males and for incidence is 6% for females. It is also found that with increased PM2.5 exposure, the risks for ovarian and prostatic cancer rise significantly in urban area, while risks for lung cancer and leukemia rise significantly in rural area. The results demonstrate the higher risks for lung cancer and leukemia with increased PM2.5 exposure are more significant for female. This study also suggests that the carcinogenic effects of PM2.5 have obvious sex and urban-rural differences.

Technology selection for ballast water treatment by multi-stakeholders: A multi-attribute decision analysis approach based on the combined weights and extension theory.

This objective of this study is to develop a generic multi-attribute decision analysis framework for ranking the technologies for ballast water treatment and determine their grades. An evaluation criteria system consisting of eight criteria in four categories was used to evaluate the technologies for ballast water treatment. The Best-Worst method, which is a subjective weighting method and Criteria importance through inter-criteria correlation method, which is an objective weighting method, were combined to determine the weights of the evaluation criteria. The extension theory was employed to prioritize the technologies for ballast water treatment and determine their grades. An illustrative case including four technologies for ballast water treatment, i.e. Alfa Laval (T1), Hyde (T2), Unitor (T3), and NaOH (T4), were studied by the proposed method, and the Hyde (T2) was recognized as the best technology. Sensitivity analysis was also carried to investigate the effects of the combined coefficients and the weights of the evaluation criteria on the final priority order of the four technologies for ballast water treatment. The sum weighted method and the TOPSIS was also employed to rank the four technologies, and the results determined by these two methods are consistent to that determined by the proposed method in this study.

GM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors.

Anaerobic digestion process has been recognized as a promising way for waste treatment and energy recovery in a sustainable way. Modelling of anaerobic digestion system is significantly important for effectively and accurately controlling, adjusting, and predicting the system for higher methane yield. The GM(1,N) approach which does not need the mechanism or a large number of samples was employed to model the anaerobic digestion system to predict methane yield. In order to illustrate the proposed model, an illustrative case about anaerobic digestion of municipal solid waste for methane yield was studied, and the results demonstrate that GM(1,N) model can effectively simulate anaerobic digestion system at the cases of poor information with less computational expense.

Dual-porosity Mn2O3 cubes for highly efficient dye adsorption.

Dual-porosity materials containing both macropores and mesopores are highly desired in many fields. In this work, we prepared dual-porosity Mn2O3 cube materials with large-pore mesopores, in which, macropores are made by using carbon spheres as the hard templates, while the mesopores are produced via a template-free route. The attained dual-porosity Mn2O3 materials have 24nm of large-pore mesopores and 700nm of macropores. Besides, the achieved materials own cubic morphologies with particle sizes as large as 6.0µm, making them separable in the solution by a facile natural sedimentation. Dye adsorption measurements reveal that the dual-porosity materials possess a very high maximum adsorption capacity of 125.6mg/g, much larger than many reported materials. Particularly, the adsorbents can be recycled and the dye removal efficiency can be well maintained at 98% after four cycles. Adsorption isotherm and kinetics show that the Langmuir model and the pseudo-second-order kinetics model can well describe the adsorption process of Congo Red on the dual-porosity Mn2O3 cube materials. In brief, the reported dual-porosity Mn2O3 demonstrates a good example for controlled preparation of dual-porosity materials with large-pore mesopores, and the macropore-mesopore dual-porosity distribution is good for mass transfer in dye adsorption application.