Electronic waste (e-waste) contains valuable metals whose recovery is essential for sustainability and the circular economy. Biological technologies, including bioleaching, biosorption, phytoremediation, bioaccumulation, biomineralization and bioelectrochemical systems (BES) offer environmentally friendly approaches for metal recovery. This study employed the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate these technologies across 16 criteria: Environmental Impact, Safety, Scalability, Resource Availability, Compatibility, Waste Management, Adaptability, Social Acceptance, Recovery Efficiency, Reaction Rate, Yield, Purity, Operational Stability, Energy Consumption, Cost, and Chemical Consumption. Criteria weights were determined based on literature. The decision matrix was normalized, weighted, and analysed to determine the ideal and negative-ideal solutions. Biosorption ranked highest (closeness coefficient C = 0.852), while Phytoremediation ranked lowest. The study offers a structured decision framework for selecting sustainable metal recovery technologies.

Heavy metal contamination in aquatic ecosystem poses a serious threat to human health, ecological integrity, and sustainable water resources, necessitating the development of efficient and environmentally responsible remediation technologies to address this issue. In this context, biochar has gained increasing attention as a promising eco-friendly adsorbent for heavy metal removal owing to its unique physico-chemical characteristics, including its high surface area, porous structure, mineral content, and abundant surface functional groups. This study provides a rigorous evaluation of the fundamental adsorption mechanisms governing the interactions between heavy metal and bamboo biochar, as well as the key parameters influencing adsorption efficiency. Critical factors affecting biochar performance, such as feedstock selection, production conditions, and physico-chemical properties, have been thoroughly examined. The influence of various bamboo biochar modification strategies, including physical and chemical methods, is reviewed to assess their effectiveness in enhancing adsorption capacity, selectivity, and stability. Special emphasis is placed on advanced modification techniques involving the incorporation of metal oxides and nanomaterials, which significantly improve heavy metal removal through synergistic adsorption, precipitation, and complexation. Additionally, surface functionalization approaches were analysed for their role in strengthening metal–biochar interactions. Beyond performance optimization, this study evaluates the environmental sustainability and feasibility of bamboo biochar-based remediation technologies for metal removal, addressing challenges related to scalability, regeneration, long-term stability, reuse and potential secondary pollution. Limitations associated with real-world applications, including variability in biochar properties and complex water matrices, are also discussed in this review. By integrating recent advancements and representative case studies published over the past few years, this study offers an up-to-date and holistic understanding of the role of bamboo biochar in heavy metal remediation. Overall, this study aligns with SDG 6 (Clean Water and Sanitation) and aims to provide valuable guidance for researchers, policymakers, and practitioners to develop sustainable and effective water treatment strategies.

This study examines the impact of different inoculum-to-substrate ratios on biohydrogen production using various waste materials. The research aims to optimize biohydrogen yield by analyzing different combinations of agricultural, industrial, and animal wastes as substrates in anaerobic fermentation. A lab-scale study was conducted using 250 mL serum bottles, with the experiment running for 30 days to assess biohydrogen production. The substrates used in the study included rice husk, sugarcane waste, cow dung, and anaerobic sludge. The effect of different inoculum-to-substrate ratios on biohydrogen yield was also examined by varying inoculum concentrations (1, 1.5, 2, 2.5, and 3

This study presents a sustainability evaluation of leaching processes to recover critical metals from waste lithium-ion batteries (LiBs) by integrating life cycle assessment (LCA), life cycle costing (LCC), multi-criteria decision-making (MCDA), and PESTLE approaches. Four leaching methods using different leaching agents, i.e., glycine, acetic acid, nitric acid, and microorganisms (bioleaching), were assessed for their environmental and economic performance. Based on the LCA results, bioleaching exhibits the lowest environmental impact, making it the most sustainable option, while nitric acid leaching has the highest environmental impact as compared to other leaching methods. From an economic perspective, glycine leaching is the most expensive method as compared to other leaching processes, with the cost of 5537 USD/t due to the high cost of glycine. The combined LCA-LCC data were further analyzed using MCDA to establish a ranking among the alternatives and Pareto front analysis to identify non-dominated solutions to balance environmental and economic trade-offs. In addition, a PESTLE assessment was performed to evaluate regulatory, economic, social, technological, legal, and ecological drivers influencing process adoption. This integrative approach revealed that the most sustainable leaching method was bioleaching, followed by nitric acid, acetic acid, and glycine. The combination of LCA, LCC, Pareto analysis, MCDA and PESTLE analysis provides a robust framework for process-level decision-making for sustainable recycling of waste into useful secondary resources.

Biodiesel emerged as a renewable and clean energy source with potential to decline earth warming. So, data-driven machine learning system for biodiesel prediction via regression is investigated in study. Support vector machines, Linear regression, regression trees, and Gaussian process regression with various functions are among the regression algorithms used. The developed models were evaluated using a variety of performance indicators (such as R2, residual analysis, MAE, and RMSE), as well as 5-fold cross validation. GPR model with Matérn class best results with R2 of 0.91 and 4.1134 RMSE. The built models demonstrate performance using various algorithms. As a result, proposed approach would certify fast calculation of biodiesel yield from algal oil, potentially reducing time-consuming, expensive, and labor-intensive laboratory testing.

Heavy metal persistence in aquatic systems continues to threaten ecological stability and public health, particularly where centralized treatment is limited. In this context, adsorption using waste derived carbon materials offers a technically viable and circular approach. This study evaluates coconut shell and bamboo derived biochar as low-cost adsorbents for copper (Cu²⁺) and lead (Pb²⁺) removal linking surface functionality with adsorption performance. Biochar was synthesized via slow pyrolysis and characterized for morphology, porosity, surface charge and functional groups that govern metal binding. Batch experiments assessed the effects of pH, dosage, contact time and initial concentration. Adsorption was strongly pH-dependent, with optimal uptake at pH 5-7. Rapid kinetics were observed, with equilibrium achieved at 60 min for Cu²⁺ and 45 min for Pb²⁺. Isotherm analysis indicated distinct mechanisms. Cu²⁺ adsorption onto coconut shell biochar followed the Langmuir model (qmax = 102.67 mg/g, R² = 0.99), achieving 99.32% removal and suggesting monolayer coverage. In contrast, Pb²⁺ adsorption onto bamboo biochar followed the Freundlich model (n = 3.29, R² = 0.981) with 98.92% removal, indicating heterogeneous multilayer adsorption. Kinetics for both metals conformed to the pseudo-second-order model, implying chemisorption-dominated interactions. FTIR and zeta potential analyses confirmed the role of oxygen containing functional groups and surface charge in metal uptake. Beyond performance, this biochar integrates waste valorisation with water treatment, supporting low-energy, decentralized applications. Their effectiveness, coupled with feedstock-driven surface variability, underscores the need for a systems-oriented approach that considers adsorption efficiency alongside material lifecycle and environmental sustainability.

Continuous accumulation of plastic litter in terrestrial ecosystems acts as a major pathway for the macroplastics (MaPs) and microplastics (MiPs) contamination into marine environment. This review synthesizes current knowledge on the sources, fate, and transport of plastic litter within soil-plant systems. It also presents a novel synthesis that connects plastic litter-induced modifications in soil properties and nutrient dynamics with physiological stress, root distortion, and reduced photosynthetic performance in plants. It was found that MaPs primarily affect soil structure by blocking pores and disrupting aggregation, whereas MiP impairs seed germination, nutrient uptake, photosynthesis, and redox imbalance via oxidative stress and leachates of toxic additives. Evidence indicates the uptake and vascular translocation of MiP in edible tissues causes potential risks to food chain. Finally, future research directions were proposed on soil remediation strategies, assessing long-term impact of MiPs and nanoplastics on plant genetic cycle.

Hazardous waste landfill leachate (HWLL) can contribute significantly to nitrate contamination in the environment, raising ecological concerns and potential human health risks, particularly through groundwater infiltration and drinking water exposure. This study investigates electrocoagulation (EC) process for effective nitrate (NO3-) removal, including an assessment of energy consumption. Kinetic models and adsorption isotherms were used to predict NO3- removal performance. Experiments were conducted using an initial NO3- concentration of 120 mg/L with varying electrolysis time (ET) and current density (CD). The influence of co-existing ions such as sulfate (SO42-), carbonate (CO32-), chloride (Cl-), calcium (Ca2+), and magnesium (Mg2+) were investigated. The EC process was optimized at pH of 7.53, an inter-electrode distance (IED) of 2 cm, CD of 15.90 mA/cm2, and an ET of 120 min, achieving maximum NO3- removal efficiency of 86%. A pseudo-second-order kinetic model with rate constant k (0.006 min-1) and R2 of 0.7986 best described the adsorption behavior. The experimental data for NO3- removal were assessed using the Langmuir, Freundlich, and Temkin adsorption isotherm models. Adsorption isotherm equilibrium data were best fitted to the Langmuir adsorption isotherm (R2: 0.9365), suggesting monolayer adsorption. At optimum operating condition, the energy and electrode consumption of 6.25 kWh/m3 and 0.87 kg/m3 were obtained, respectively. The findings provide the electrocoagulation-assisted adsorption mechanism for NO3- removal, particularly in treating HWLL. This study provides a pathway for scientific research in HWLL treatment, bridging the gap between batch-scale experiments and potential field applications.

Waste is one of the major challenges that humankind face today and by-products of different industries make it more challenging for their disposal as they can pose environmental risks. Distillery sludge from the alcohol production industry is one such waste product that presents a wide range of logistical as well as environmental challenges. This study aims at the scientific utilization of distillery sludge and its application as an agricultural amendment to improve the fertility of soil. The qualitative analysis of the distillery sludge revealed its richness in organic matter as well as essential nutrients making it a good alternative to fertilizers in agricultural fields. The presence of heavy metals was also observed; therefore, lysimeter studies were conducted with the application of sludge at different doses to soils from two different sources (agricultural and waste land soil). Sludge dose equivalent to 3 MT/acre (7.4 MT/ha) was observed to be the optimum dose for soil conditioning, whereas its repetitive application in consecutive years is not recommended. Field productivity of 26 and 24 MT/acre was observed upon application of the recommended sludge dose of 3 MT/acre and Farm Yard Manure (FYM) dose, respectively, indicating the suitability of sludge application. Furthermore, the study also confirmed that the use of distillery sludge as fertilizer did not have any adverse effects on the fruit juice quality, thereby, making it suitable for agricultural application upon regulated dosage.

Biohythane production from lignocellulosic biomass such as rice straw (RS) and potato peel waste (PPW) via anaerobic digestion (AD) stands as a viable biotechnological management fostering bioeconomic strategies benefiting the environment. It contributes to Sustainable Development Goals (SDG 7) concerning affordable, clean and renewable energy challenges. This study explores biomass conversion of two lignocellulosic wastes to Biohythane through "Optimization-Driven" AD. RS, a recalcitrant substrate having particle size (<0.425 mm), was co-digested with nutrient-rich co-substrate PPW in four different ratios based on weight/weight of (dry VSsubstrate), along with mixed inoculum anaerobic sludge (AS) and cow dung slurry (CDS) via AD at 11% TS. Batch experiments revealed that the [RSI-PP] ratio [(2:1):1/2] significantly improved yields of cumulative hydrogen (H-2) and methane (CH4), viz., 36.98 +/- 1.22 mL/g VS and 390.12 +/- 3.45 mL/g VS, respectively. The S Gompertz equation exhibited excellent fit (>0.90), underscoring a positive synergy between substrate and co-substrate. The result signifies that RS in combination with PPW with homogenous particle size, mixed inoculum at 11% TS, confirms process stability and positive synergistic effects by providing essential buffer capacity, micronutrients and hydration, thereby enhancing the AD and Biohythane production. The findings of the results support the process's synergistic potential for waste biomass conversion to biofuel.

The cement industry is responsible for approximately 8–10% of global carbon dioxide (CO₂) emissions, making it one of the largest contributors to climate change and environmental degradation. The transition toward green cement has emerged as a critical strategy for reducing the environmental footprint of the construction sector while supporting global net-zero and circular economy objectives. This review synthesizes recent advances in green cement technologies, emphasizing sustainable raw materials, supplementary cementitious materials, waste-derived alternatives, energy-efficient manufacturing processes, and emerging low-carbon innovations. The paper examines the environmental, economic, and engineering performance of green cement compared with conventional Ordinary Portland Cement (OPC), highlighting improvements in durability, resource efficiency, waste utilization, and greenhouse gas mitigation. Furthermore, the review discusses current industrial developments, policy initiatives, and technological innovations that are accelerating the adoption of sustainable cement production, with particular reference to India's rapidly growing cement industry. Despite significant progress, several challenges including high initial investment costs, limited standardization, raw material variability, technological barriers, and market acceptance—continue to constrain large-scale implementation. The review identifies future research priorities focusing on carbon capture and utilization, alternative binders, digital manufacturing technologies, artificial intelligence-driven process optimization, and life-cycle assessment for enhancing sustainability performance. The findings demonstrate that green cement technologies can substantially reduce carbon emissions while maintaining structural performance, thereby providing an effective pathway toward sustainable construction and climate-resilient infrastructure. This review offers valuable insights for researchers, industry practitioners, and policymakers seeking to accelerate the decarbonization of the global cement sector.

India's informal waste sector (IWS), embedded in the shadows of urban metabolism, remains vital yet systematically marginalised. Despite the sector's unprecedented contribution to the circular economy and resource recovery, it is highly vulnerable to socio-economic challenges and occupational marginalisation. This research presents a comprehensive vulnerability assessment across four strategically selected Indian cities - Nagpur, Mumbai, Ghaziabad and Muzaffarnagar - chosen to reflect geographical and infrastructural diversity. Primary data were collected through surveys and stakeholder interviews. The analysis focused on key parameters including age, wage levels, gender distribution, working hours and the use of personal protective equipment. Based on these indicators, a weighted vulnerability index (WVI) was developed through normalisation and expert-informed weight allocation, enabling the quantification of socio-economic and occupational risks in the study areas. To strengthen the robustness of the findings, the WVI results were also cross-validated using the technique for order of preference by similarity to ideal solution (TOPSIS) technique of multi-criteria decision analysis (MCDA). Both approaches converged in identifying Muzaffarnagar as the most vulnerable (WVI score = 0.71; TOPSIS closeness coefficient (CC) = 0.535) and Mumbai as the least vulnerable (WVI = 0.25; TOPSIS CC = 0.666), with only minor differences in the relative ordering of Nagpur and Ghaziabad. The study thus offers a transferable methodological framework that combines expert-driven weighting with MCDA validation, providing important insights into the precarious working conditions of IWS. The findings underscore the urgent need to integrate informal waste management into formal governance structures through inclusive, rights-based and spatially contextualised policy interventions.

Present research aims to examine the transformations of land use and land cover (LULC) within the Faridabad district, India, using high-resolution remotely-sensed images. LULC change analysis over the years 2007- 2022 revealed a significant decline in agricultural land from 65.4% of the total area in 2007 to 53.9% in 2022. Conversely, considerable increases have been observed in urban built-up areas (from 58.2% in 2007 to 93.3% in 2022), industrial areas (from 13.7% to 26.9%). Vegetation coverage decreased from 18.9% in 2007 to 12.7% in 2022 after primarily alleviating in 2017 due to green initiatives. Further, the LULC maps of 2007 and 2012 were used to predict the LULC of 2017 using Multi-Layer Perceptron Neural Network (MLPNN)-integrated Markov Chain Model (MCM). Subsequently, predicted LULC of 2017 were compared with observed LULC of 2017 to validate the model. Additionally, the integrated model has been applied to predict and validate LULC of 2022. Validation results produced R2 values and K statistics >0.8 for both 2017 and 2022 confirming the efficacy of the model. Finally, future LULC scenario has been predicted for 2027. Comparison of predicted LULC for 2027 with observed LULC of 2022 revealed that built-up would increase by 3.8% (built-up 149.3km2 in 2022 and 154.9 km2 in 2027). Vegetation would decrease by 3.1% (12.7km2 in 2022 and 12.3 km2 in 2027). From the present findings, it is recommended that a continuous monitoring is required to analyse the efficacy of implemented measures and adapt strategies as necessary.