Abhijit Debnath

This review focuses on research outputs of water purification, wastewater treatment, metallic remediation, and sorption-based experimental studies. It aims to identify the leading nations contributing to these areas and identify the journals that have published the highest number of papers from 2010 to 2025, and centers on yearly publication trends. A thorough quantitative analysis was carried out to examine key characteristics of adsorbents derived from various materials, as well as symmetry and asymmetry of wastewater treatment for the removal of metallic pollutants. Key adsorption mechanisms—including ion exchange, surface complexation, electrostatic attraction, and pore filling—are discussed alongside the structural roles of symmetric (ordered) and asymmetric (heterogeneous) adsorbent architectures. Data was collected from the Scopus database, focusing on specific keywords like “metal,” “water,” “removal,” “adsorption,” “purification,” “drinking water,” “nano adsorbent,” etc. Among approximately 29,598 publications encompassing research papers, reviews, short communications, conference papers, and book chapters, China emerged as the leading publisher with 11,957 papers, trailed by India (4324 papers), the USA (1825 papers), Iran (1739 papers), Saudi Arabia (1484 papers), Egypt (1318 papers), and Republic of Korea (1194 papers). The bibliometric mapping of conventional adsorbents and nanomaterials used in sorption-based technologies was analyzed using VOSviewer, revealing major research clusters, research hotspots, networks, and evolutionary patterns in wastewater treatment and sorption-based water purification. This study indicates that several journals from Elsevier Ltd. and Springer Nature are leading the field with a large number of publications per year. The analysis reveals a consistent upward trend in the number of research publications in recent years. In sum, the bibliometric data provided highlights the growing relevance of these areas among academicians and acts as a catalyst for further research, motivating researchers to investigate new adsorbents or modifications that could improve adsorption performance while maintaining economic viability and efficiency.

The present study characterizes the heavy metal contamination in the bottom sediments of the Ganga River and the toxicity status of the associated aquatic environment. Cd was identified as a heavy metal of substantial concern, with levels surpassing baseline values, showing the effect of anthropogenic factors such as untreated urban-industrial wastewater and runoff from agricultural regions, among other local causes. The grain size distribution reveals that sediments contain dominant sand fractions (up to 60%), followed by silt and a small percentage of clay fractions. FTIR and XRD characterization of grains indicates the presence of silicate (mainly quartz), feldspar, clay, and carbonate group of minerals, while SEM–EDS characterization reveals the morphological variation of grain sizes, viz., platy, irregular, spongy, triangular or rectangular, crystal, and hexagonal appearances and EDS peaks confirmed the presence of metallic constituents, viz., Cr, Fe, Ni, Cu, Zn, Cd, Pb, and others. Risk indices, including EF, show mild to severe enrichment as a result of higher Cd levels, while mPEL_Q shows a 21% likelihood of toxicity with a medium–low degree of risk at specific sites as a result of synergistic action of heavy metals.

Environmental noise is an unpleasant by product of urbanisation and industrial development. This undesirable sound causes serious negative effects on individuals, including hypertension, hearing loss, sleep difficulties, and has a detrimental effect on the environment. The current study on traffic noise was conducted from September to November 2016 at 15 locations using a sound level metre at various times of day, including morning (6 a.m. to 9 a.m.), morning (9 a.m. to 12 p.m.), afternoon (2 p.m. to 5 p.m.), evening (5 p.m. to 8 p.m.), and night (8 p.m. to 11 p.m. In addition, a road map was created using GIS to visualise the monitoring locations. The monitoring data showed that the highest and lowest average noise levels (LAeq) were 86.3 dB(A) [Govindpur] in the morning and 64.2 dB(A) [Vinod Vihari Chowk] in the night. A student's t-test was also conducted to see if there was a statistically significant difference between the means of the LAeq levels for the morning, afternoon, evening, and night. To determine the degree of significance, analysis of variance was also calculated for the examined squares in the morning, afternoon, evening, and night. The observed value of F critical was less than the F calculated values and was not significant at 5% levels of significance. In light of this, the noise levels in various squares did not considerably vary at various times. According to the analysis, the measured value surpasses the noise guidelines recommended by the CPCB and BIS, which is caused by excessive traffic, narrow roads, intense construction, and inadequate traffic management in the areas. Some hospitals, nursing homes, and educational institutions are also plagued by a very loud atmosphere.

The rise in environmental pollutant levels in recent years is mostly attributable to anthropogenic activities such as industrial, agricultural and other activities. Additionally, these activities may produce excessive levels of dangerous toxicants such as heavy metals, organic pollutants including pesticide and herbicide chemicals, and sewage discharges from residential and commercial sources. With a focus on environmentally friendly, sustainable technology, new technologies such as combined process of nanotechnology and bioremediation are urgently needed to accelerate the cost-effective remediation process to alleviate toxic contaminants than the conventional remediation methods. Numerous studies have shown that nanoparticles possess special qualities including improved catalysis and adsorption as well as increased reactivity. Currently, microorganisms and their extracts are being used as promising, environmentally friendly catalysts for engineered nanomaterial. In the long term, this combination of both technologies called nano-bioremediation may significantly alter the field of environmental remediation since it is more intelligent, safe, environmentally friendly, economical and green. This review provides an overview of soil and water remediation techniques as well as the use of nano-bioremediation, which is made from various living organisms. Additionally, current developments related to the mechanism, model and kinetic studies for remediation of agricultural contaminants have been discussed.

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L₁₀, L₉₀, L_dn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas L_Aeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50–55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7–7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R² linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method. Graphical abstract