Environmental Quality Management最新文献

{"title":"Strategic Roadmap for Addressing Microplastic Pollution in the Global South: Bridging Monitoring Gaps, Harmonizing Methods, and Building Analytical Capacity","authors":"Guilherme Malafaia,&nbsp;Thiarlen Marinho da Luz","doi":"10.1002/tqem.70346","DOIUrl":"https://doi.org/10.1002/tqem.70346","url":null,"abstract":"<p>Microplastic (MP) pollution represents a growing environmental challenge, especially in tropical and subtropical coastal regions of the Global South, where methodological fragmentation, funding discontinuity, and dependence on external analytical infrastructure limit the production of comparable data and the formulation of evidence-based public policies. In this critical review we propose, we propose a strategic roadmap structured around five complementary pillars: (1) implementation of continuous and standardized monitoring programs; (2) methodological harmonization adapted to regional ecological and institutional realities; (3) priority assessment of ecological impacts on sensitive coastal ecosystems; (4) improved understanding of dispersal sources and processes through integrated source-sink approaches; and (5) strengthening scientific and institutional capacity, reducing dependence on international laboratories. The framework articulates scientific production, regulatory instruments, territorialized funding strategies, and reconfiguration of international cooperation, focusing on technical symmetry and capacity transfer. By integrating technical, institutional, and financial dimensions into a scalable and adaptable model, we offer an operational pathway to territorialize global commitments, including the Global Plastics Treaty, and consolidate analytical autonomy and sustainable governance of plastics in the Global South.</p>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tqem.70346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Alternative 2D Shape Descriptor Index for Rapid Prediction of Microplastics Morphology Using Deep Feature Embeddings and Machine Learning","authors":"Aan Priyanto,&nbsp;Kamilah Nada Maisa,&nbsp;Eka Sentia Ayu Listari,&nbsp;Dian Ahmad Hapidin,&nbsp;Khairurrijal Khairurrijal","doi":"10.1002/tqem.70342","DOIUrl":"https://doi.org/10.1002/tqem.70342","url":null,"abstract":"<div>\u0000 \u0000 <p>Microplastic morphology influences particle behavior, environmental fate, and ecological risk, yet commonly used two-dimensional (2D) shape descriptors often struggle to represent complex and irregular geometries. This study introduces the Shape Descriptor Index (SDI), a composite metric integrating area, length, and circularity, designed as an alternative and machine-compatible proxy for microplastic morphology. Using deep feature embeddings extracted from scanning electron microscopy (SEM) images with Inception V3, we evaluated the predictability of SDI relative to classical descriptors across multiple machine learning models. SDI demonstrated the strongest performance, particularly with the AdaBoost model, achieving an R² of 0.919 along with reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) compared to the other descriptors. These findings indicate that SDI aligns well with deep visual representations and offers a robust, scalable metric for rapid morphology assessment. The approach supports high-throughput and objective analysis, making SDI particularly suitable for large-scale environmental monitoring and automated microplastic characterization.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Mushrooms as Bioindicators to Monitor Heavy Metal Pollution","authors":"Germina Neenu,&nbsp;Stanislaus Antony Ceasar,&nbsp;Neenthamadathil Mohandas Krishnakumar","doi":"10.1002/tqem.70348","DOIUrl":"https://doi.org/10.1002/tqem.70348","url":null,"abstract":"<div>\u0000 \u0000 <p>Mushrooms are spore-bearing eukaryotic organisms that are capable of accumulating heavy metals. They are considered bioexcluders or hyperaccumulators of heavy metals since they can grow in the metal-contaminated areas. Industrialization, urbanization, and other anthropogenic activities cause the release of heavy metals into the environment. The heavy metal contamination in the environment causes various toxic effects on the organisms. Traditional methods of finding heavy metal contamination are costly and cause various disruptions in the environment. Therefore, detection of heavy metal contamination using bioindicators such as mushrooms offers a quick and sustainable solution. Since mushrooms can be simply grown in natural soils contaminated by the heavy metals, further research on optimizing mushrooms can greatly help to monitor the prevalence of heavy metals at the polluted site. Analytical methods such as inductively coupled plasma-mass spectrometry (ICP-MS), atomic absorption spectrophotometry (AAS), and inductively coupled plasma-optical emission spectrometry (ICP-OES) are used to determine heavy metal concentrations in the mushroom samples. Various omics tools can be used for understanding gene-level expression of mushrooms during the metal stress condition. Advances in molecular biology, synthetic biology, and gene-editing technologies have significantly enhanced the utility of mushrooms for monitoring and remediating heavy metal pollution. CRISPR/Cas technology can also be used in mushrooms to engineer strains with enhanced heavy metal uptake and tolerance for improved monitoring.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147687023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an Enhanced Microbial Consortium Immobilized on Coconut Coir for Efficient Greywater Treatment Optimized via RSM and ANN","authors":"Nikita Rajpal,&nbsp;J. K. Ratan,&nbsp;Neetu Divya,&nbsp;Venkata Ratnam Myneni","doi":"10.1002/tqem.70347","DOIUrl":"https://doi.org/10.1002/tqem.70347","url":null,"abstract":"<div>\u0000 \u0000 <p>This study reports the development of an augmented microbial consortium for the efficient bioremediation of laundry and kitchen greywater. An indigenous consortium isolated from kitchen sludge was enhanced with <i>Micrococcus luteus</i>, <i>Rhodococcus equi</i>, and <i>Aspergillus niger</i>, resulting in significantly improved pollutant removal. Process optimization using Response Surface Methodology (RSM) identified optimal conditions at 33.2°C, pH 8.0, an inoculum size of 198 µL, and a C/N ratio of 1.9. Under these conditions, maximum removal efficiency of 83.5% (COD), 81.5% (oil and grease), and 87.8% (sulphate) were achieved within 96 hrs. The Artificial Neural Network (ANN) model demonstrated high predictive performance across training (R² = 0.992), validation (R² = 0.893), and testing (R² = 0.816) phases, with an overall R² of 0.964. The RSM model provided robust individual response predictions (R² for COD = 0.966, oil and grease = 0.997, and sulphate = 0.984). These results indicate that ANN captured the nonlinear relationships among operating variables with acceptable predictive capability under the limited dataset conditions, while RSM effectively described individual parameter interactions. Growth kinetic analysis indicated substrate inhibition at higher concentrations, with the Haldane model providing the best fit (R² = 0.977). The use of coconut coir as a support matrix provides a promising foundation for future pilot-scale investigations into decentralized treatment systems.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Criteria Decision Approach for Composting of Food Waste in the Hospitality and Tourism Industry","authors":"Şennur Merve Yakut,&nbsp;Burcu Şimşek Yağlı","doi":"10.1002/tqem.70344","DOIUrl":"https://doi.org/10.1002/tqem.70344","url":null,"abstract":"<div>\u0000 \u0000 <p>Considering the importance of Responsible Consumption and Production (SDG-12) and circular economy principles, this study proposes an integrated experimental and decision support framework for evaluating the composting suitability of selected food waste generated in the hospitality and tourism (H&amp;T) industry. In the first stage, a controlled small-scale composting experiment was conducted using pineapple peel, carrot peel, banana peel, coffee waste, and tea waste, and key operational parameters namely temperature, pH, and moisture content. These were systematically monitored over a 20-day period. The temperature dropped to room temperature from day 5 and remained constant thereafter. Moisture dropped below 20% in the 2nd, 5th, 6th, 7th, 16th, 17th, 18th, 19th, and 20th samples and even to zero in the 7th sample. In the second stage, the Comprehensive Normalization Technique with Mixed Collection (MACONT) method, one of the Multi-Criteria Decision-Making (MCDM) methods, was applied to rank food waste based on their composting performance. To validate the robustness of the result, a comprehensive sensitivity analysis was performed. The findings indicate that pineapple peel and tea waste exhibit superior composting performance under the specific experimental conditions, while coffee waste demonstrates limited suitability and may require alternative valorization pathways. The proposed framework offers a practical decision support tool for environmental managers and practitioners in the H&amp;T industry, enabling evidence-based selection of food streams for composting applications and contributing to sustainable waste management strategies.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Impact of Wastewater Flow Rates on SARS-CoV-2 RNA Detection: Insights for Environmental Surveillance and Policymaking","authors":"André Felipe Bendix,&nbsp;Alex Batista Trentin,&nbsp;Deborah Catharine De Assis Leite,&nbsp;Betty Cristiane Kuhn,&nbsp;Felipe Beijamini,&nbsp;Simone Wendt,&nbsp;Flavia Regina Oliveira de Barros,&nbsp;Naiana Cristine Gabiatti,&nbsp;Juliana Morini Küpper Cardoso,&nbsp;Nédia de Castilhos Ghisi","doi":"10.1002/tqem.70341","DOIUrl":"https://doi.org/10.1002/tqem.70341","url":null,"abstract":"<p>Wastewater-based epidemiology (WBE) has been widely applied for SARS-CoV-2 surveillance. Yet, operational parameters such as influent flow rate and sewer system characteristics may reflect underlying hydraulic variability relevant to detection performance, raising questions about the representativeness of results from small and medium-sized plants. We conducted a systematic meta-analysis of 10 published studies assessing SARS-CoV-2 RNA detection across wastewater treatment plants of different sizes and flow ranges. Data were extracted into 2 × 2 tables and analyzed in R using the meta and metafor packages. Odds ratios (ORs) were pooled with Mantel–Haenszel random-effects models and DerSimonian–Laird τ² estimation. Subgroup analyses compared studies from developed versus emerging economies, meta-regression tested population size as a moderator, and publication bias was evaluated with funnel plots and Egger's test. The overall pooled OR was 2.08 (95% CI: 0.99–4.37, <i>p</i> = 0.053; <i>I</i>² = 82.3%), indicating a non-significant trend toward higher detection at higher flow rates. Subgroup analyses showed similar pooled effects across developed and emerging economies, though heterogeneity was lower in emerging settings. Meta-regression revealed no significant association between population size and detection (slope = 0.29, <i>p</i> = 0.52). Funnel plots were relatively symmetric, and Egger's test did not indicate significant publication bias (<i>p</i> = 0.169). Overall, these findings suggest that influent flow rate is not significantly associated with the probability of SARS-CoV-2 detection in WBE studies, supporting the inclusion of small and medium-sized treatment plants in surveillance networks. At the same time, the concentration of studies in high-income countries highlights inequities in the global WBE literature and in site deployment, with vulnerable populations often underrepresented. Expanding WBE infrastructure in underserved regions is necessary to ensure equitable and representative surveillance capacity.</p>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tqem.70341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Cow Bone Biochar in Pb(II) Adsorption: Comparative Analysis of Batch and Fixed-Bed Column Techniques","authors":"Quoc-Hoang Do,&nbsp;Van-Truc Nguyen,&nbsp;Vu-Anh Le,&nbsp;Nguyen Duy Dat,&nbsp;Van-Trong Nguyen,&nbsp;Ngoc-Kim-Anh La,&nbsp;Thi-Giang-Huong Duong,&nbsp;Thi-Dieu-Hien Vo","doi":"10.1002/tqem.70345","DOIUrl":"https://doi.org/10.1002/tqem.70345","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates cow bone biochar (CBBC), a food byproduct, as an adsorbent for Pb(II) removal from water. Physicochemical characterization revealed that CBBC possesses a mesoporous structure with a BET surface area of 78.9 m²/g and abundant oxygen-containing functional groups that provide active sites for metal binding. Batch experiments analyzed using the Langmuir model (<i>R</i>² = 0.99) confirmed the monolayer adsorption capability of CBBC. The effects of flow rate, starting lead concentration, and bed height on adsorption performance were evaluated in fixed-bed column experiments. The findings indicated that at an influent concentration of 100 mg/L, the maximum adsorption capacity was 151.35 mg/g, highlighting the effectiveness of CBBC. The adsorption mechanism was analyzed using the Thomas, Adams-Bohart, and Yoon-Nelson models, which agreed with the findings of earlier studies. CBBC effectively removes Pb(II) from water, making it a promising material for practical water treatment applications. This study advances knowledge on utilizing agricultural byproducts, such as cow bone, for environmental remediation, offering a sustainable approach to waste utilization and pollution reduction.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Artificial Intelligence Integration in Electrocoagulation for Wastewater Treatment","authors":"Niju Subramaniapillai,&nbsp;Gayathri Saravanakumar,&nbsp;Shrinidhi Arunachalam,&nbsp;Thamani Ganapathy,&nbsp;Priyadharshini Karuppusamy,&nbsp;Asaithambi Perumal","doi":"10.1002/tqem.70340","DOIUrl":"https://doi.org/10.1002/tqem.70340","url":null,"abstract":"<div>\u0000 \u0000 <p>Electrocoagulation (EC) is a reliable technology for the treatment of industrial wastewaters with complex characteristics. It is, however, sensitive to many different factors including operating conditions, the dynamics of electrodes and the characteristics of the wastewater being treated. In addition, these factors are correlated; therefore, there is an increasing need for the development of AI-based tools that can model non-linear (NL) systems and provide a more accurate optimization process. In real life applications, sensitivity has been reported not only in the efficiency of EC treatment, but also in the energy required per unit of wastewater treated, as well as difficulties when scaling up EC systems from laboratory to industrial applications. This comprehensive study of the basic principles; key process parameters; and the key processes of reactors, electrodes; etc., and their limitations will provide an overview of how various types of artificial intelligence (AI) algorithms, including artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFISs), support vector machines (SVMs), and combinations thereof, can provide the necessary tools to predict, analyze and optimize the results of electrochemical processes. An assessment of how some of the main operational variables that can impact EC performance (e.g., pH, current density, electrode type and configuration, and length of treatment time) impacts the efficiency of pollutant removal, energy use, and electrode life, will also be included. These methods of analysis are able to accommodate complexity, multivariate nature and NL relationships of all the operational variables that have an impact on EC performance. Rather than a simple review of methods, this paper provides a holistic perspective that connects the mechanisms of electrochemical processes with current modelling trends being used to predict performance based on the use of data-centric methods in conjunction with physicochemical theories.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Separation of Oil and Grease From Wastewater: A Multidisciplinary Review of Treatment Technologies","authors":"Sukhendu Dey,&nbsp;Megha Santra,&nbsp;Apurba Ratan Ghosh","doi":"10.1002/tqem.70343","DOIUrl":"https://doi.org/10.1002/tqem.70343","url":null,"abstract":"<div>\u0000 \u0000 <p>Oil and grease (O&amp;G) wastewater generated from different sources possesses a varying complex chemical nature, physical states, and persistent in aquatic systems, resulting in significant environmental and operational challenges. This multidisciplinary review critically examines conventional, advanced, and emerging technologies for the separation and treatment of O&amp;G-laden wastewater. It explores the environmental implications and health risks associated with O&amp;G-contamination, including aquatic toxicosis, bioaccumulation, and infrastructure degradation. Uses of traditional approaches are acceptable due to their simplicity and cost-effectiveness; but for emulsified and dissolved fractions require an integration of advanced techniques. Membrane filtration (UF, NF, RO), electrocoagulation, adsorption using novel materials, advanced oxidation processes (AOPs), and magnetic separation offer enhanced removal performance and adaptability. Applications of biological methods like microbial degradation, enzymatic treatment, constructed wetlands, etc., represent eco-friendly alternatives. This review emphasizes hybrid and integrated systems such as membrane bioreactors (MBRs), physico-biological combinations, and electrochemical-adsorptive setups, which leverage synergistic mechanisms to improve removal efficiency and operational robustness. Here, some key designs are considered, such as pH, temperature, O&amp;G concentration, sludge management, energy demands, etc. It emphasizes the potentiality of O&amp;G recovery and reuse, notably in biodiesel production and energy recovery. Surfactants and natural organic matter mainly stabilized oil and water emulsion, reduce droplet coalescence, and intensify membrane fouling and coagulant demand, and difficult to operate. Elevated salinity and divalent cations modify interfacial charge, and alter electrocoagulation efficiency, adsorption equilibria, and microbial activity. High suspended solids and colloidal loads further exacerbate pore blocking, sludge production, and hydraulic instability, thereby limiting flux, increasing energy consumption, and complicating scale-up. Despite significant progress, it faces some challenges. Future research may establish predictive design models and standardized performance benchmarks. Present review is strongly connected to current research gaps and future directions towards low carbon &amp; energy and resource-recovery-oriented treatment systems to address monitoring of O&amp;G pollution in wastewater.</p>\u0000 </div>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Air Quality Dynamics in Higher Learning Environments: Evidence From Sokoine University of Agriculture, Morogoro, Tanzania”","authors":"","doi":"10.1002/tqem.70328","DOIUrl":"https://doi.org/10.1002/tqem.70328","url":null,"abstract":"<p>Mapunda, E., and E. Chambile. 2026. “Air Quality Dynamics in Higher Learning Environments: Evidence From Sokoine University of Agriculture, Morogoro, Tanzania.” <i>Environmental Quality Management</i> 35, no. 3: e70313. https://doi.org/10.1002/tqem.70313</p><p>In Appendix 1, label B1 requires correction, as the number 1 is missing.</p><p>In Appendix 2, labels A2, B1 and B2 require correction: in label A2, the number 2 is missing; label B2 is missing; and labels A2 and B1 require repositioning.</p><p>We apologize for this error.</p>","PeriodicalId":35327,"journal":{"name":"Environmental Quality Management","volume":"35 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tqem.70328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}