Various agricultural wastes such as peels of pea (Pisum sativum) pod, tea (Camellia sinensis), and ginger (Zingiber officinale) and banana (Musa lacatan) waste were used to adsorb Cr(VI) from the aqueous solutions. A comparative adsorption efficiency study for all these adsorbents was done in laboratory for various pH, adsorbent doses, initial chromium concentrations, contact time, adsorbent sizes, temperature, and mixing speeds up to the optimization. The equilibrium sorption data were fitted into Langmuir, Freundlich, and Temkin isotherms and also the various thermodynamic parameters were determined. The value of R ² was determined for Freundlich, Langmuir, and Temkin as 0.964, 0.963, and 0.858 (pea pod peels waste (PPP)), 0.969, 0.986, and 0.841(tea & ginger waste (T&G)), 0.985, 0.982, and 0.886 (banana peel waste (BW)). The maximum monolayer coverage (Q ₀) from Langmuir isotherm model for pea pod, tea & ginger and banana peels waste were found to be 4.33 mg/g, 7.29 mg/g, and 10 mg/g, respectively, with separation factors (R _L) 0.0331, 0.0343, and 0.0756 which are well within favorable sorption. From Freundlich isotherm model, the sorption intensity (n) for the same adsorbents was also less than unity showing normal sorption. The heat of sorption (B) was also determined from Temkin isotherm model as 0.215, 0.271, and 0.271, respectively, vividly proving a favorable physical sorption. The Gibbs free energy was found maximum for BW as 6.0679 joule/mole. Out of the above said combination, BW was found the best low-cost adsorbent with high potential for the removal of hexavalent chromium from aqueous solutions.

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR), and support vector machines (SVM)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers.

In the current investigation is presented the prospective substitution of cement and fine aggregates with fine slag material (Alccofine 1203) and coal bottom ash, respectively. The investigation was carried out in two steps, viz. Phase I and Phase II. In Phase I, a control mix was designed with basic ingredients of concrete, and then fine aggregates were partially replaced with five percentages (10%, 20%, 30%, 40% and 50%) of coal bottom ash (CBA). To improve the characteristics of coal bottom ash concrete mixtures, ultra-fine slag material, i.e., Alccofine 1203 (an innovative ultra-fine slag material, low calcium silicate, which offers reduced water demand depending upon the concrete performance) was used as a partial replacement of cement. In Phase II, the inspected effect of replacing 5%, 10%, 15% and 20% cement with Alccofine, a concrete mix containing 40% coal bottom ash, on concrete properties such as workability, compressive strength, split tensile strength, flexural strength, pulse velocity, rapid chloride penetration along with a microstructural analysis using SEM was studied. It was concluded from cost analysis that the 15% replacement of cement with ultra-fine material Alccofine in 40% coal bottom ash concrete achieved the properties of high-strength concrete, with an 8.14% increase in cost compared to the control increase. The significance of this work lies in the fact that we achieved a high-strength concrete by using 40% industrial waste, i.e., coal bottom ash, as a partial replacement of fine aggregates in combination with the 15% Alccofine inclusion as a partial replacement of cement. About 58% improvement in compressive strength was recorded for 40% coal bottom ash and 15% Alccofine mix.

In recent years, the surface water quality has been observed deteriorating due to industrial activities and pollution in the Punjab state. Evaluation of surface water quality is an important issue to assure from its safe and stable use. However, describing quality conditions is generally difficult considering spatial variability of pollutants and a wide range of indicators like biological, physical and chemical substances which can be measured. This paper includes the study of surface water quality parameters in the northern Punjab region. Samples from different sources across northern Punjab were analysed for pH, TDS, TSS, turbidity, DO, chloride content, iron content and hardness of surface water. The pH, TDS, TSS, turbidity, DO, chloride content, iron content and hardness were found in the range of 6.7-8.7, 1358.00-1430.60 mg/l, 135.10-141.21 mg/l, 0.001-0.62 NTU, 0.96-7.80 mg/l, 0.80-15.56 mg/l, 0.01-0.02 mg/l and 1.2-13.9 mg/l respectively. The obtained results were compared with the acceptance limits as given by Indian standard code IS: 10500-2012. The high values of water quality parameters obtained as a result of this study indicates the level of pollution of the different sources of surface water of northern Punjab. At the end the obtained results were critically examined and suggestions were made for better management of surface water resources.