Incineration of municipal solid waste is increasingly being adopted in developing countries from the past couple of decades as a waste management strategy. This is driven by rapid urbanization, population growth and scarcity of land for landfills. MSW incineration reduces the volume of waste by 70–90% but still leaves behind substantial quantities of residual ash for disposal. Managing and disposing ash safely adds significant costs over just burning the waste, undermining the economics of incineration. The present study explores the potential applications of municipal solid waste incinerated (MSWI) ash as a landfill liner. This study provides a comprehensive characterization of both fresh and aged incinerated MSW ash (fly ash and bottom ash) collected from waste to energy plant (WTE). Analyzing the physicochemical properties of incinerated ash incorporating its mineralogy, morphology, and chemical composition is essential for its effective application in geotechnical engineering. This approach offers a sustainable alternative to traditional liner materials

Incineration of municipal solid waste (MSW) along with energy recovery has been proven to reduce the volume of waste destined for landfills by as much as 90%. According to Indian solid waste management regulations, all municipal solid waste must be treated in composting facilities, waste-to-energy facilities, or other processing plants before being disposed of in landfills. This requirement not only lessens the spatial footprint of landfills but also contributes positively toward environmental sustainability. However, one challenge that remains is how to effectively reuse or dispose of the residues left behind after the incineration process. Even with comprehensive resource recovery, current estimates indicate that 25–35% of total MSW generated remains as residue that accumulates in landfills if not further used. Therefore, this research focuses on exploring the potential application of incinerated MSW ash as landfill liners. The study undertakes a meticulous analysis of both bottom ash and fly ash through extensive geotechnical characterizations. In order to gauge their suitability as landfill liners, the study conducts detailed geotechnical analysis on various aspects such as hydraulic conductivity and compressibility. Ultimately, this research promotes the massive utility of incinerated MSW ash, thereby encouraging a sustainable approach toward landfill management.

Municipal solid waste (MSW) is typically composed of organic and inorganic constituents that can decompose and release substantial amounts of phosphate into the environment, while impoundments contain the same phosphate-contaminated leachate. Stormwater retention ponds, on the other hand, have high concentrations of phosphate resulting from surface runoff. Infiltration of these waste liquids into the subsurface can contaminate the groundwater which necessitates the use of engineered liners to prevent such conditions. Compacted clay and geosynthetic clay liners are commonly used liners in these waste containment systems, but availability of these materials in remote areas of developing countries is challenging. This study proposes using bentonite amended fly ash as a potential sustainable alternative liner. Fly ash is a locally available by-product of coal combustion at power plants, and use of this will prevent its disposal and utilize it as a useful resource material. Preliminary studies showed 80% fly ash and 20% bentonite mix proportion is optimal to provide required hydraulic conductivity. The present study reports laboratory testing to investigate phosphate retention at this optimal mix conditions. Batch tests are conducted using fly ash and bentonite to determine their removal potential under different phosphate concentrations. In addition, column experiments were conducted on optimal bentonite-amended fly ash to assess the hydraulic conductivity and phosphate retention potential. Overall, the test results showed that the optimized bentonite-amended fly ash will serve as an effective low permeable liner with efficient phosphate retention.

This study investigates the effectiveness of bentonite-amended flyash (BAF) liners as barriers against infiltration in municipal solid waste (MSW) landfills. Laboratory experiments were conducted to evaluate the compaction properties and hydraulic conductivity (HC) of the BAF liners with varying mix proportions. Flyash, a byproduct of coal-fired power plants, amended with bentonite was utilized as a cost-effective alternative to conventional clay liners. Bentonite, a highly expandable clay mineral, was incorporated to enhance hydraulic performance. Standard Proctor compaction tests were performed to determine optimal compaction properties of different BAF mixes. Further, BAF specimens with different mix proportions were prepared by compacting at maximum dry density (MDD), and optimum moisture content (OMC). HC tests were performed on the compacted samples using a flexible-wall (triaxial) permeameter apparatus following ASTM standards. Compaction test results revealed that a mix ratio of 80:20 (Flyash: Bentonite) resulted in highest MDD and lowest OMC. Results also revealed a significant reduction in HC upon the addition of bentonite to the BAF liner. As the bentonite content increased, the HC decreased, indicating improved barrier properties. The optimal performance was achieved with a bentonite content of 20-30%, resulting in good compaction properties (high MDD and low OMC), and a HC below the regulatory limit of 10-7 cm/s, rendering the mix proportion optimal for barrier application. Overall, this study highlights the potential of BAF liners as efficient and cost-effective barrier systems for MSW landfills. The significant reduction in HC because of bentonite amendment, and excellent heavy metal and nutrient adsorption capabilities of flyash renders BAF effective for application as a barrier material in landfills

Municipal solid waste landfills require liner systems to prevent leachate migration into the environment. Liner selection typically focuses on engineering performance, cost, and ease of construction, with limited emphasis on sustainability. This study assessed the sustainability of four composite liner systems using the triple bottom line approach, considering environmental, economic, and social impacts, along with technical equivalence based on leachate infiltration rates. The four systems analyzed were: (1) geomembrane (GM) over compacted clay liner (CCL) (GM/CCL), (2) GM over geosynthetic clay liner (GCL) (GM/GCL), (3) GM over soil mixed with lime and cement (SA) (GM/SA), and (4) GM over fly ash mixed with bentonite (FAB) (GM/FAB). Life cycle stages–material extraction, construction, monitoring, and disposal–were evaluated. The study focused on DeKalb County Landfill in DeKalb, Illinois, USA, with environmental impacts quantified using the Eco-Indicator 99 and TRACI methods in SimaPro 8.0.1. Results showed that GM/FAB was the most sustainable liner in terms of environmental impact and second in economic and social considerations. However, GM/GCL was the most preferred based on economic and social impacts

This study employs machine learning (ML) techniques and artificial neural networks (ANN) to predict the interface friction angle between the landfill liner and the soil. The interface behavior is majorly affected by the thickness of landfill liner (t), mass of landfill liner (m), tensile strength of landfill liner (T), cohesion of soil (cu), angle of shearing resistance of soil (?), shear strength (?), and normal stress (?). As the stability of landfill liner varies significantly from that of the soil due to the non-homogeneity and anisotropic character of the soil, it is critical to comprehend the interface behavior between the landfill liner and the soil. However, no prior research employing machine learning techniques to analyze the interface behavior between landfill liners and soil has been reported; a study using machine learning algorithms and artificial neural networks is carried out on 66 datasets to probe the interface behavior with the help of an ANACONDA navigator. Further, to understand the impact of input variables on the output variable, Pearson’s correlation coefficients were determined. Mean absolute error (MEA) is considered as a loss function, and the best model was chosen based on the r-value. Random forest regressor (RFR) model is determined to be the best model among the available models with an r-score of 0.99 and a minimum mean absolute error of 0.46.

Coal mine waste rock is generated during coal extraction and is usually disposed of in non-engineered dumps. The dumps are extended vertically to 100–120 m height to reduce the spatial footprint. The waste mass generally consists of loose, cohesionless material associated with high heterogeneity, so the dumps are prone to slope failures. A typical dump configuration in Jharkhand, India (total height, H = 125 m; slope angle, ? = 3V:1H) is considered for evaluation in this study. A 2D limit equilibrium numerical analysis is performed to estimate the slope stability. A parametric study is conducted to understand the effect of bench height (H), bench width (W), and slope angle (?) on the factor of safety. The heterogeneity of the material is analyzed using the probabilistic descriptors (mean, standard deviation, and coefficient of variation (CoV)). The influence of CoV on the shear strength parameters is studied at various intervals ranging from 10 to 80% and compared using Monte Carlo simulation and alternative point estimate methods. Further, the modified slope geometry and the benches are recommended as remediation methods to achieve the desired safety factor. The results provide valuable insights into understanding the influence of slope geometry and material heterogeneity during the stability analysis of coal mine dumps.

Soil and groundwater contamination is caused by improper waste disposal practices and accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many systems and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.

Geomembrane (GM) liners anchored in the trenches of municipal solid waste (MSW) landfills undergo pull-out failure when the applied tensile stresses exceed the ultimate strength of the liner. The present study estimates the tensile strength of GM liner against pull-out failure from anchorage with the help of machine-learning (ML) techniques. Five ML models, namely multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector regression (SVR), random forest (RF) and locally weighted regression (LWR) were employed in this work. The effect of anchorage geometry, soil density and interface friction were studied with regards to the tensile strength of the GM. In this study, 1520 samples of soil–GM interface friction were used. The ML models were trained and tested with 90% and 10% of data, respectively. The performance of ML models was statistically examined using the coefficients of determination (R, R) and mean square errors (MSE, RMSE). In addition, an external validation model and K-fold cross-validation techniques were used to check the models’ performance and accuracy. Among the chosen ML models, MLP was found to be superior in accurately predicting the tensile strength of GM liner. The developed methodology is useful for tensile strength estimation and can be beneficially employed in landfill design.

The safety and stability of municipal solid waste (MSW) slopes are governed by unit weight (?), cohesion (c), and friction angle (?) of the MSW. Variability associated with the unit weight, cohesion, and friction angle of MSW is a major problem in the design of landfills because it negatively affects the performance of the slope. Variability associated with these properties may trigger catastrophic slope failures. The reported studies on the reliability-based design of MSW landfills adopted either Gaussian or lognormal distributions based on a reasonable approximation. The limitations are apparent when a coefficient of variation (COV) is higher. The accuracy of reliability-based designs depends on the selection of best-fit continuous and extreme value distributions (such as Gumbel and Weibull) that can model a high degree of variability precisely. The present study has undertaken to propose a suitable statistical model that gives a better representation of variability by optimizing the statistical parameters. A high degree of variability associated with the unit weight, cohesion, and friction angle of MSW is also investigated. A novel approach is proposed to determine reliable continuous probability density functions (PDFs) that can be fitted to the database consisting of 184 sample points collected from the most comprehensive experimental studies reported in the literature. The best-fit PDFs are recommended by optimizing the mean and standard deviation such that errors associated with quantiles (Q - -Q), percentiles (P-P), and cumulative distribution functions (CDFs) are as minimum as possible. This study signifies the selection of optimized PDFs for the representation of parameter variability, and it is proved that the probability of failure is either underestimated or overestimated considerably when other conventional PDFs are chosen. The recommended mean, COV, and PDF can be useful in the reliability-based design of engineered MSW landfills and for judging the performance of existing MSW slopes.

A novel statistical framework was developed to quantify the variability of the fitting parameters of the soil-water characteristic curve (SWCC). Reliable estimation of the mean, standard deviation, and associated probability density function (PDF) of the fitting parameters of SWCC is an important tool for addressing reliability-based designs in unsaturated soil mechanics. The assumption of either Gaussian or lognormal distributions may not be valid for representing a high degree of variability associated with fitting parameters. This study aimed to provide the most appropriate continuous PDFs by optimizing the mean and standard deviation such that errors associated with quantile, percentile, and cumulative distribution function (CDF) were as low as possible. A total of 261 and 111 sample points were collected from the most comprehensive experimental works on the fitting parameters of SWCC for clayey and silty soils, respectively. Optimum distributions suitable to the model fitting parameters of SWCC are highly dependent on the type of soil. The most appropriate PDFs for representing the fitting parameters af, nf, and mf of Fredlund and Xing's model were gamma, Weibull, and inverse Gaussian distributions, respectively. Similarly, fitting parameters af, nf, and mf for silty soils were represented by inverse Gaussian, Gumbel maximum, and Gumbel maximum distributions, respectively. This study found that the selection of inappropriate PDFs overestimated the probability of failure of unsaturated infinite slopes considerably, by 99.49% and 99.76%, respectively, for clayey and silty soils. Recommended mean, coefficient of variation (COV), and PDF are useful in the reliability-based design of unsaturated soil slopes and in judging the performance of existing infinite and finite slopes.