Methane ( _4 ) is a powerful short-lived climate pollutant, and landfills are a major anthropogenic source. Yet emissions inventories often rely on first-order decay (FOD) models and operator-reported recovery data that may miss real-world fugitive fluxes. This study compares methane generation, capture, and reporting in Brazilian and USA landfills by integrating high-resolution satellite plume quantification with official inventories from the clean development mechanism (CDM) program from the United Nations Framework Convention on Climate Change (UNFCCC) records. Thirteen Brazilian sanitary landfills, receiving about 18 Mt/a of municipal solid waste (MSW), were analyzed using Greenhouse Gas Satellite (GHGSat) (25–50 m) and National Aeronautics and Space Administration Carbon Mapper (CM-NASA) (30 m) short-wave infrared (SWIR) retrievals; fluxes were annualized and normalized by MSW throughput (t/t of _4 /MSW), adopting a conservative ±(20 ∼ 40 _4 /MSW before tropical decay effects, while an independent monitoring program from the Brazilian Ministry of Science, Technology and Innovation (MCTI) suggests much lower methane recovery ( ∼ 0.009 t/t of _4 /MSW) than implied by CDM reports. Overall, results show that landfill methane estimates are highly sensitive to parameter choices and data granularity, and that satellite-integrated measurement, reporting and verification (MRV) can provide scalable third-party verification and improve the credibility of methane mitigation and reporting in Brazil. These findings underscore the need for enhanced methane monitoring and recovery through the adoption of a structured national methane capture program analogous to the USA Environmental Protection Agency (EPA) Landfill Methane Outreach Program (LMOP), alongside increased investment in alternative waste management strategies, including composting of source-separated organics and waste-to-energy technologies.
High-volume fly ash (HVFA) cementitious materials offer a promising low-carbon alternative to ordinary Portland cement (OPC), yet their application is hindered by the low hydraulic reactivity of fly ash (FA). This study develops high-performance HVFA cementitious materials with 60
Anaerobic digestion (AD) of textile waste is limited by the complex structure of fibers that resist microbial degradation. This study investigated the mono- and co-digestion of common textile wastes (cotton, polycotton, and wool) with food waste (FW) under mesophilic conditions. Digestion performance was evaluated at substrate-to-inoculum (S/I) ratios of 0.5 and 1 and various feedstock mixing ratios. Methane yields increased with decreasing S/I ratios. Among the textiles, cotton showed the highest methane yield (103 mL/g volatile solids (VS)) and biodegradability (BD) of 25.87
The remediation of cadmium-contaminated wastewater using biochar is frequently hindered by inefficient, trial-and-error experimental designs that lack predictive foresight. This study aims to overcome this limitation by developing a highly optimized gradient boosting decision tree (GBDT) architecture capable of accurately estimating aqueous Cd(II) adsorption capacities directly from complex physicochemical and operational parameters. To systematically construct this predictive framework, four hyperparameter tuning algorithms, Bayesian probability improvement (BPI), evolution strategies (ES), Gaussian process optimization (GPO), and batch Bayesian optimization (BBO), were rigorously evaluated, followed by an integrated Shapley additive explanation (SHAP) analysis to extract mechanistic transparency. Quantitative results established GPO as the most computationally efficient and robust strategy, completing structural tuning in 57 s while decisively preventing the severe mathematical overfitting observed in parallel batch frameworks. The superior model achieved exceptional generalization on completely unseen data, registering a testing coefficient of determination (R2) of 0.970 and a mean squared error (MSE) of 553. Furthermore, the algorithmic feature attribution successfully internalized fundamental thermodynamic behaviors, quantifying the initial concentration, carbon content, and adsorption time as the primary driving forces while mathematically confirming that excessive biochar carbonization suppresses heavy metal chemisorption through the destruction of oxygenated functional groups. Ultimately, this optimized machine learning (ML) approach delivers a reliable, virtual screening tool that dramatically accelerates the targeted design and industrial deployment of sustainable carbonaceous adsorbents.
This study developed a solid-based soil amendment (SSA) synthesized from coal gangue (CG) and municipal sewage sludge (SS) for ecological restoration applications. A hierarchical porous structure was successfully constructed through aluminum powder foaming in alkaline cement paste. The optimal formula was determined through system parameter optimization: 0.4
