Waste Management Bulletin最新文献

{"title":"The evolving landscape of AI-driven risk management in the biogas production: A systematic and bibliometric review","authors":"Mohamed Abourida ,&nbsp;Michael Short ,&nbsp;Oleksiy V. Klymenko ,&nbsp;Noor M. Khamis ,&nbsp;Charf Mahammedi ,&nbsp;M.K.S. Al-Mhdawi ,&nbsp;Abdel-Hamed Sakr","doi":"10.1016/j.wmb.2025.100271","DOIUrl":"10.1016/j.wmb.2025.100271","url":null,"abstract":"<div><div>This review presents the first combined systematic and bibliometric review synthesising artificial intelligence (AI)-driven approaches to risk management in biogas production within wastewater treatment plants (WWTPs), with emphasis on decision-optimisation and operational safety. Seven academic databases: Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, Taylor &amp; Francis, and Google Scholar were systematically searched from 2015 to March 2025, and screening followed PRISMA 2020 guidelines. Of 3,716 retrieved records, 109 studies met the inclusion criteria. Bibliometric mapping (VOSviewer) and qualitative synthesis identified five thematic clusters: (i) biogas process safety, (ii) IoT integration and renewable-energy, (iii) optimisation and supply-chain resilience, (iv) AI-driven decision-support frameworks, and (v) advanced machine-learning techniques. The analysis reveals a marked increase in publications since 2020, reflecting a shift from conceptual modelling toward applied digital risk solutions. Europe and China remain leading contributors, although collaboration networks are fragmented and methodological heterogeneity persists. Full-scale validation of AI models in operational WWTP-based biogas plants remains limited, with most studies relying on laboratory experiments, simulations, or pilot-scale data. Constraints include publication bias, database coverage, English-language restrictions, inconsistent performance metrics, and limited access to long-term Supervisory Control and Data Acquisition (SCADA) Systems datasets. The review demonstrates that AI-driven methods have significant potential to improve safety, operational efficiency, and regulatory assurance in biogas facilities. However, achieving practical and scalable implementation will require rigorous multi-site validation, standardised evaluation indicators, integration of explainable AI, and alignment with plant-level risk-governance frameworks.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884129","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":"Detecting volume changes in municipal solid waste landfill using airborne laser scanning","authors":"O. Brovkina ,&nbsp;M. Pikl ,&nbsp;F. Zemek ,&nbsp;J. Michálek","doi":"10.1016/j.wmb.2025.100272","DOIUrl":"10.1016/j.wmb.2025.100272","url":null,"abstract":"<div><div>Accurate and operative monitoring of municipal solid waste (MSW) landfills is critical for operational safety, spatial planning, and regulatory compliance. Traditional point-based surveying methods are precise, but they are limited in spatial density coverage and hence, efficiency. The objective of the study is to evaluate the application of airborne laser scanning (ALS) for detecting volume changes at operational MSW landfill in the Czech Republic. Specifically, the study determines optimal spatial resolution of digital terrain model (DTM) from ALS for estimation of landfill volume, estimates uncertainties related to slope steepness and different vegetation cover affecting the accuracy of ALS-derived DTM, and formalizes and applies the method for detecting landfill volume changes using ALS. Two ALS datasets (10 points/m²) were collected in a five-month interval and processed at multiple spatial resolutions (0.3 m to 1.5 m). GPS reference points were measured for ALS data co-registration and to assess the accuracy of ALS-derived elevations. Positional errors and their propagation into elevation errors were quantified, and vegetation-induced uncertainties were considered. Results indicate that DTM resolutions of 0.3–0.8 m provide the most reliable estimates of volume change, especially in heterogeneous areas such as vegetated slopes. Differences in the standard deviation (SD) of elevation changes for selected areas at the landfill between the DTM resolution of 0.3 m and coarser resolutions were minimal for stable surfaces such as roads and compacted waste (0.01–0.02 m), but higher for vegetated areas, where the SD increased by up to 0.10 m due to surface roughness and variable laser penetration through the canopy. Comparison with independent GPS reference points showed that finer DTMs (0.3 and 0.5 m) reduced both bias and variability (mean differences ≤ 0.23 m, SD ≤ 0.24 m), whereas coarser DTMs (0.8 and 1.5 m) increased systematic errors due to surface smoothing and vegetation-induced misclassification. The findings recommend acquiring ALS data in early spring or late autumn, when vegetation cover is minimal and the influence of canopy on DTM accuracy is reduced. The study presents a novel workflow integrating ALS data with error modeling to improve landfill monitoring protocols. While the workflow was demonstrated on a specific site, it has potential for adaptation and application in other MSW landfills.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694292","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":"Heavy metals in the soil, water, plants, and river sediments of Bangladesh: A synthesis","authors":"Sharmin Aktar Hasi ,&nbsp;Samiul Ahsan Jyoti ,&nbsp;Jagadish Chandra Joardar ,&nbsp;Milton Halder","doi":"10.1016/j.wmb.2025.100266","DOIUrl":"10.1016/j.wmb.2025.100266","url":null,"abstract":"<div><div>Heavy metals (HMs) contamination is an emerging environmental and public health issues in south Asian country like Bangladesh. HMs from various anthropogenic sources accumulate in the soil, are taken up by plants and entering our food chain, which causes health disorder. However, this study synthesizes updated data on heavy metals contamination in agricultural soils, water, sediments, and food crops across the major industrial areas of Bangladesh from 2010 to 2024. We found that the agricultural soils surrounding of the Dhaka city is highly contaminated with Mn, Zn, Cu, Pb, Cr, Ni, As, Cd. Heavy metals content in water from Buriganaga and Sytalokka river of Dhaka city was greater in compared to countryside river. Similarly, the plants growing near Dhaka city were highly contaminated with HMs (Zn, Mn, Cu, Pb, Ni, Cr), which were <em>Oryza sativa</em> and <em>Amaranthus dubius</em>. Furthermore, in case of sediments, Sytalokka river sediment followed by Buriganga were contained higher content of heavy metals like Cd, Pb, Ni, Zn, Cr and Cu in compared to the other rivers across the Bangladesh. However, different remediation strategies like application of organic amendments, phytoremediation, bioremediation are currently using across the Bangladesh to limit the plant uptake and food chain contamination. This synthesis will provide baseline information and first nationwide comparison of heavy metals data among the different regions of Bangladesh that can be used by policymakers for risk prioritization over the country. Furthermore, this study underscores the urgent need for policy interventions to mitigate heavy metal pollution and ensure sustainable food safety.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683866","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":"Artificial intelligence in waste management systems: Applications, challenges, and prospects","authors":"Imane Belyamani","doi":"10.1016/j.wmb.2025.100269","DOIUrl":"10.1016/j.wmb.2025.100269","url":null,"abstract":"<div><div>Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based material classification. Beyond summarizing existing approaches, it proposes a multilayer framework that connects data sensing, planning, sorting, and treatment within an adaptive lifecycle perspective. The review further examines existing gaps related to policy support, infrastructure readiness, data standardization, and scalability. While numerous studies demonstrate the potential of AI to improve operational efficiency and material recovery, real-world implementation remains limited by economic, regulatory, and technological barriers. To address these limitations, a strategic roadmap is presented, integrating technical innovation with governance and investment pathways to enhance implementation potential. By consolidating these insights, the study offers a comprehensive synthesis that clarifies how AI can strengthen circularity across the waste lifecycle and guide the sector toward scalable, evidence-based sustainability transitions.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617494","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":"Managing process-safety risks in wastewater-based biogas: human–organisational drivers and PSMS implications for waste management operations in the UK","authors":"Mohamed Abourida ,&nbsp;Abdel-Hamed Sakr ,&nbsp;Noor M. Khamis ,&nbsp;Michael Short ,&nbsp;Oleksiy V. Klymenko","doi":"10.1016/j.wmb.2025.100267","DOIUrl":"10.1016/j.wmb.2025.100267","url":null,"abstract":"<div><div>Biogas production within wastewater treatment plants (WWTPs) provides renewable energy recovery but also introduces complex process-safety challenges arising from the interplay of human, organisational, and technical factors. This study systematically evaluates how these dimensions interact to influence process-safety outcomes in UK wastewater-based biogas facilities, addressing a gap where prior safety research has largely prioritised the examination of technical and operational failures in the petrochemical and chemical sectors. A mixed-methods design was adopted, integrating a PRISMA-guided literature review, a structured national survey of industry professionals (n = 90), and analysis of historical incident reports (n = 63). Triangulated quantitative and qualitative data were examined using correlation and regression analyses to identify interdependencies among risk drivers. Findings reveal that gas-leak risk shows the strongest correlation with general site risks (r = 0.857, p &lt; 0.001), followed by human and organisational factors (HOFs) (r = 0.768, p &lt; 0.001) and process factors (r = 0.733, p &lt; 0.001). The regression model (R² = 0.754) confirms that site-level governance (e.g., maintenance discipline, leadership visibility, and resource sufficiency) exerts the greatest influence on incident probability. These findings demonstrate that technical safeguards alone are insufficient without robust organisational and behavioural integration within Process Safety Management Systems (PSMS). The study demonstrates that HOFs directly shape safety outcomes in WWTPs, presenting a novel empirical contribution to literature, emphasising predictive maintenance, competence-based training, proactive reporting, and AI-enabled monitoring within PSMS as enablers of safer and more sustainable biogas recovery in wastewater operations.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617495","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":"An analytical framework for medical waste forecasting using machine learning: Paving the path toward zero waste in healthcare","authors":"S M Shahinur Rahman,&nbsp;Md Ruhul Amin,&nbsp;A M Almas Shahriyar Azad,&nbsp;Md. Ariful Haque,&nbsp;Md. Limonur Rahman Lingkon","doi":"10.1016/j.wmb.2025.100270","DOIUrl":"10.1016/j.wmb.2025.100270","url":null,"abstract":"<div><div>This study presents a robust framework for accurately forecasting medical waste (MW) generation in healthcare settings, addressing escalating environmental and public health concerns. Using machine learning (ML) models, including Linear Regression (LR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Random Forest (RF), the research integrates critical patient-related variables, such as indoor and outdoor patient counts, surgeries, and deaths. Based on eight years of comprehensive data (2016–2023) from the medical colleges in Rajshahi, Bangladesh, the LR model showed the most reliable outcomes, achieving <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> values between 0.91 and 0.95 for all waste categories and outperforming others with mean absolute errors (<em>MAE</em>) as low as 1.39 kg for recyclable waste. Projections reveal a significant 50 % surge in general waste by 2030, reaching approximately 987.75 tons, while infectious waste will grow from 66.54 to 735.23 tons. Monte Carlo simulations quantified variability across waste categories, demonstrating fluctuations within <span><math><mrow><mo>±</mo><mn>5</mn><mo>%</mo></mrow></math></span> of mean values, ensuring robustness. Finally, the research represents a novel policy recommendation for zero waste generation at the study location, which advocates for category-specific MW management strategies, aligning with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 12 (Responsible Consumption and Production), and 13 (Climate Action). This work introduces a novel precedent for incorporating artificial intelligence into healthcare operations to attain zero waste generation while tackling public health and environmental issues. Thus, this work offers an innovative methodology that integrates machine learning to link patient data with waste generation, providing practical insights for resource optimization and operational planning.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100270"},"PeriodicalIF":0.0,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694291","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":"Valorization of contaminated Eichhornia crassipes in phytoremediation of tannery waters: Bioethanol production","authors":"Gianella Paredes,&nbsp;Stefani Gonzales,&nbsp;Alejandra Lázaro,&nbsp;Rosario Benegas","doi":"10.1016/j.wmb.2025.100268","DOIUrl":"10.1016/j.wmb.2025.100268","url":null,"abstract":"<div><div>In this research, a sustainable system to obtain bioethanol from Eichhornia crassipes biomass contaminated with total chromium was developed A phytoremediation system was applied in order to remove chromium from tannery wastewater, using the resulting biomass as raw material. The dry biomass (DS) was subjected to an optimized pretreatment using a Box–Behnken experimental design in which 45 treatments were evaluated. The structural changes in the biomass were characterized by SEM and FTIR. After this, hydrolysis with 3 % H₂SO₄ using a steam explosion was performed. Chromium retention was higher in roots (99.29 %), leaves and stems (94.14 %). Treatment using C₀ = 1.56 % had the highest removal efficiency (R = 95.51 %), followed by 3.13 % (93.47 %). Regarding valorization, the bioethanol yield reached 71.35 % in relation to reducing sugars and 0.12 mL/g with respect to the initial biomass. These results confirm the feasibility of integrating phytoremediation with bioethanol production as a sustainable alternative for treating tannery effluents.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"4 1","pages":"Article 100268"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652052","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":"Engineering solutions for reducing open dumping and illegal waste disposal in Ghana","authors":"Ilham Ku-nwa Hamid ,&nbsp;Edna Korlekie Dapaah ,&nbsp;Hamdala Hunsulu Hamid","doi":"10.1016/j.wmb.2025.100265","DOIUrl":"10.1016/j.wmb.2025.100265","url":null,"abstract":"<div><div>Rapid urbanization in Ghana has intensified the challenges associated with municipal solid waste management, particularly in relation to open dumping and illegal disposal practices. This study investigates the drivers, patterns, and spatial distribution of informal waste disposal across six municipalities, across Accra and Kumasi, using a mixed-methods approach that integrates household surveys (n = 600), key informant interviews, and machine learning-based route optimization. Results reveal that over 60 % of households resort to informal dumping due to limited-service coverage, high collection costs, and socio-cultural perceptions of waste. The waste stream is predominantly organic, with compostable materials accounting for over 70 % of total waste generated. Engineering-oriented interventions such as community-scale composting, AI-enhanced collection routing, and participatory monitoring using geolocation tools are proposed to address these systemic inefficiencies. The findings underscore the need for decentralized, context-sensitive, and technology-supported waste governance models. Policy recommendations are offered to align local practices with circular economy principles and improve environmental health outcomes.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100265"},"PeriodicalIF":0.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571072","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":"Assessment of spent mushroom substrate recycling via soil biosolarization","authors":"Carolina R. Conte ,&nbsp;Abigail Nagano ,&nbsp;Maya C. Gentry ,&nbsp;Christopher W. Simmons","doi":"10.1016/j.wmb.2025.100264","DOIUrl":"10.1016/j.wmb.2025.100264","url":null,"abstract":"<div><div>Biosolarization is an alternative to pesticides for soil pest management that can utilize food industry organic matter byproducts as soil amendments to create conditions conducive to pest suppression. Spent mushroom substrate (SMS) is the main byproduct of mushroom cultivation. New uses are needed for this waste stream as its production continues to increase. This study evaluated two types of SMS as sole amendments and as co-amendments with cover crop biomass for biosolarization. SMS consisted of rice bran with hardwood sawdust (BS SMS) and soybean hulls with oak sawdust (SO SMS). Cover crop biomass was a mix of <em>Secale cereale</em> and <em>Trifolium incarnatum</em>. Gas evolution, pH, biocidal organic acid (BOA) production, phytotoxicity, and soil nitrogen were tracked during and/or after simulated biosolarization. SMS and cover crop treatments were compared to rice bran-amended soil and unamended, solarized soil. Results indicate that SO SMS treatments can produce BOA concentrations and phytotoxic conditions comparable to rice bran-amended soil, with BOA content correlating to radish seed germination indices with a coefficient of determination of 0.683. The pH of treatments including cover crop biomass, whether as a sole or co-amendment, significantly decreased during biosolarization, but were higher than pH of rice bran controls. SO SMS and cover crop biomass as sole amendments significantly increased total nitrogen content compared to solarized soil (P &lt; 0.05), likely due to the addition of nitrogen-containing organic matter, but not mineral nitrogen. These findings suggest that certain SMS compositions may be valuable amendments for agricultural pest control via biosolarization.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570999","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":"Soil microbial decomposition of agricultural wastes shapes soil aggregation enhancing organic carbon content in the soils under different land-uses in southwest Bangladesh","authors":"Monmon Akter ,&nbsp;Masum Billah ,&nbsp;Saria Afrin ,&nbsp;Walid Hossain ,&nbsp;Sonia Nasrin ,&nbsp;Mahbub Ul Islam ,&nbsp;Faridul Islam ,&nbsp;Milton Halder","doi":"10.1016/j.wmb.2025.100263","DOIUrl":"10.1016/j.wmb.2025.100263","url":null,"abstract":"<div><div>Soil aggregates are important for aeration, microbial activity, root growth, and infiltration. Soil aggregation can vary depending on microbial activity, soil organic carbon (SOC), and soil management practices. However, the underlying drivers and mechanism of aggregation under different land-uses in southwest Bangladesh remain largely unknown. The objective of current study was to explore the underlying mechanism of aggregation in the soils of southwestern Bangladesh. In current study, soil samples were collected from three widely practiced rice-based land-uses (fallow–rice–fallow, fallow–rice–watermelon, and shrimp–rice–fallow) across the Khulna district of southwest Bangladesh. Results exhibited that the aggregate stability indicated by mean weight diameter (MWD) in fallow-rice-fallow and fallow-rice-watermelon were significantly greater than shrimp-rice-fallow land-use (P &lt; 0.05). Microbial activity in fallow-rice-fallow land-use was 3 times higher than shrimp-rice-fallow land-use (P &lt; 0.05). SOC was also higher in the fallow-rice-fallow and fallow-rice-watermelon land-uses than shrimp-rice-fallow land-use. Scanning electron microscopy and Fourier transform infrared spectroscopy results indicated aggregated morphological features and greater polysaccharides intensity in fallow–rice–fallow and fallow–rice–watermelon land-uses, respectively. A positive relationship was found between microbial biomass carbon (MBC) and MWD (r = 0.56; P &lt; 0.01), and between SOC and MWD (r = 0.38; P &lt; 0.05), indicating the dominant role of soil microbes in aggregation and the enhancement of SOC. Principal component analysis results also support the dominant role of MBC and SOC in aggregation and identified two distinct land-uses based on the soil properties. The study results demonstrate that microbial activity plays a key role in aggregation, enhancing the decomposition of agricultural waste and increasing SOC under investigated land-uses of southwest Bangladesh.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 4","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464927","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}