Debasis Sau

Plastic waste is a major source of microplastic (MP) pollution, posing adverse environmental and public health risks. This study assessed MP abundance in 24 urban ponds and lakes within the Kolkata Municipal Corporation, India, during the post- and pre-monsoon seasons of 2022–2023. Results showed that MP concentrations were significantly higher in the post-monsoon season (20 ± 3.46 items L⁻¹; ANOVA, p < 0.05), with red, white, and black particles being the most prevalent. MPs prevalently ranged between 500 and 1000 µm with fibers constituting 58–59% of the total MPs. Fourier-transform infrared (FTIR) analysis identified polyethylene (PE) as the dominant polymer. MP abundance showed negative correlations with dissolved oxygen and turbidity, and positive associations with pH, TDS, BOD₅, and total coliform counts. A Support Vector Machine (SVM) model was developed for morphology-based MP classification, achieving an accuracy of 89%. Additionally, remote sensing and GIS techniques were used to develop index-based models for water-body identification and MP quantification (items L⁻¹) using spectral data from Sentinel-2 imagery. These models demonstrated high validation accuracy of 98.01% and 92.60%, respectively. Metal analysis of sediment and MPs of water detected chromium, suggesting possible MP–metal interactions within sediments. Although the Pollution Load Index (PLI) indicated relatively low contamination levels, the Polymer Hazard Index (PHI) exceeded 1000, indicating substantial ecological risk. Future studies should focus on long-term monitoring, socio-economic and health impact assessments and development of effective mitigation strategies particularly addressing plastic-waste derived-MPS. Graphical Abstract

Cupola furnace slag (CFS) poses significant challenges for casting industrialists, who must navigate the complexities of its disposal to maintain operational efficiency and environmental responsibility. This study aims to investigate and implement the reuse of CFS in the construction industry which in turn creates a new method for solid waste management. This study clarifies the mechanical and durability properties of concrete that contains CFS‐coarse‐aggregates (CCA) and cupola‐fine‐aggregates (CFA), which are natural fine and coarse aggregate substitutes. To accomplish this, compressive strength, split tensile strength, water penetration, impact resistance, surface abrasion loss, and rapid chloride penetration tests were conducted. In M30 grade concrete, the experimental procedures involved varying degrees of weight replacement (0% to 40%) of natural fine‐aggregate (FA) and natural coarse‐aggregates (CA) with CFA and CCA, respectively. The experimental findings revealed that compressive strength increased with up to 20% replacement of FA, but decreased afterward. Conversely, compressive strength decreased with the replacement of CA, though up to 30%, replacement still met M30 grade concrete requirements. A leachability test was conducted to detect toxic and heavy materials in CFS. SEM, EDX and XRD techniques were also employed. Replacing FA and CA with CFA and CCA, respectively, proved economically beneficial compared to standard concrete.

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.