Effective forecasting of the Water Quality Index (WQI) considerably impacts water resource management as well as public health safety. This study proposes a new approach for WQI forecasting using stacked regression ensemble modeling integrated with SHAP (Shapley Additive explanations), a form of Explainable Artificial Intelligence (XAI). The model was developed using a dataset of 1,987 water quality samples from Indian rivers (2005–2014), processed through six optimized machine learning algorithms: XGBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees, and AdaBoost, combined using Linear Regression as the meta-learner. The model was trained using seven normalized physicochemical parameters as predictors, and the computed WQI (via the weighted arithmetic method) served as the response variable. The stacked ensemble model outperformed all individual models, achieving the highest performance across all evaluation metrics, with R² reaching 0.9952, Adjusted R² at 0.9947, MAE recorded at 0.7637, and RMSE reduced to 1.0704. Among the individual models, CatBoost and Gradient Boosting demonstrated the strongest standalone performance. CatBoost achieved an R² of 0.9894, Adjusted R² at 0.9883 MAE of 0.8399, and RMSE of 1.5905, while Gradient Boosting attained an R² of 0.9907, Adjusted R² at 0.9898 MAE of 1.0759, and RMSE of 1.4898, respectively. SHAP analysis revealed that DO, BOD, conductivity, and pH were the most influential parameters contributing to the prediction of WQI. This integrated framework improves existing approaches by providing high predictive accuracy and model interpretability along with real-time environmental monitoring capabilities. It fosters anticipatory environmental surveillance, automated policy frameworks, and confidence among stakeholders regarding the sustainability of water resources.

This study presents an integrated decision-support framework that combines fuzzy logic with life cycle assessment (LCA) to evaluate sustainable municipal solid waste management (MSWM) strategies under data uncertainty, using Delhi as a representative metropolitan context. Seven MSWM scenarios,...

Urban waste management has emerged as a critical public health and environmental issue globally, driven by rapid urbanization, population growth, and increased tourism. This study focuses on the urban waste management (UWM) scenario in Guwahati, one of the fastest-growing smart cities in the...

Kryvyi Rih, Ukraine, a city marked with high mining, metallurgical, and automobile activities is such a case, and lacks capability with predictive soundness and real-time anomaly identification. This framework proposes an AI-based air quality monitoring system that combines traditional air...

Background: The growing waste amount generation and its negative impact on the environment have become a serious problem. Many regions of Ukraine are not ready for modern challenges in the field of municipal solid waste (MSW) management. Inadequate waste management strategies at the regional level, taking into account local technical and economic features, require the attention of researchers. In particular, the technological component of local and regional MSW management systems in rural communities of the Poltava region (Ukraine) needs to be analysed and modernized, as well as the development of a technology for the use of specialized community bins to ensure environmental safety and efficient waste management Objectives: This study aims to improve the efficiency of MSW management at the regional level by optimizing the waste collection system through the use of specialized community bins. A step-by-step approach was used to achieve the stated goal. Methods: The study was conducted using methods of system and factor analysis, as well as methods of structural-genetic synthesis. Results: It was revealed that the vast majority of rural settlements in the Poltava region were deprived of such an organizational structure as community waste systems; there was an irrational placement of special sites for community urns, which prevented the full collection of household waste from the population. The study found that the priority direction for optimizing the use of public household bins is the use of the entire range of bin capacities from 0.12 to 1.1 m3, which will reduce capital costs. Conclusion: The stated objective was successfully achieved, providing a scientifically based concept for the optimal planning of community bin systems as a component of waste management with an emphasis on local and regional characteristics of the region under study. The study filled a gap in organizational approaches to the development of solid waste management systems in rural areas and communities of the Poltava region, which increases the efficiency of waste collection from the population and reduces capital costs for organizing the process.

Anaerobic digestion (AD) is widely recognised as a sustainable technology for managing organic waste and generating renewable energy. Despite its potential, slow kinetics, instability under varying operational conditions, and inhibition from toxic intermediates often hinder AD processes. Nanobiotechnology has emerged as a mechanistically promising approach to enhance process stability and methane production by strengthening microbial performance, accelerating hydrolysis kinetics, and reinforcing syntrophic electron transfer pathways. The addition of zero-valent iron, carbon nanotubes, and metal oxides enhances hydrolysis rates, stimulates methanogenic pathways, and facilitates direct interspecies electron transfer (DIET). These mechanisms collectively improve methane yield while maintaining redox balance, buffering capacity, and long-term operational stability. Evidence from laboratory- and pilot-scale studies indicates that nanomaterial amendments can enhance methane production, typically by 10%–60% under optimised dosing conditions in most systems, with higher enhancements reported for selected conductive transition metal carbides under controlled experimental regimes. Reductions in lag phase duration of 15%–40% and improved tolerance to ammonia concentrations exceeding 1.5–3.0 g L ⁻¹ NH ₄ ⁺ –N have also been documented, depending on reactor configuration and substrate type. These enhancement ranges are derived from condition-resolved extraction of experimental studies meeting predefined inclusion criteria and were normalised against non-amended controls under identical operational settings rather than selectively cited maximum values. Additionally, integrating nanomaterials with pretreatment techniques, bioaugmentation, and bio-electrochemical systems offers synergistic pathways for optimising biogas production. However, the application of nanomaterials also raises important environmental and biosafety concerns, including their transformation during digestion, partitioning into digestates, potential impacts on soil and aquatic systems following land application, and challenges related to dose optimisation, recovery, and lifecycle risk assessment. This review applies a condition-resolved quantitative synthesis by extracting methane yield and production rate and stability indicators (e.g., lag phase, VFA, alkalinity, TAN/FAN tolerance) and normalising enhancements against non-amended controls within operational clusters (temperature regime, reactor configuration, ISR/SIR, substrate class, and nanomaterial dose).