Search Results (25)

The Yamuna River, among India’s most polluted waterways, is burdened by industrial, agricultural, and domestic discharges containing complex organic and inorganic contaminants. This study introduces a novel, integrated approach combining comprehensive pollutant profiling by liquid chromatography–mass spectrometry (LC-MS) with bioremediation using laboratory-validated native algal consortia. Water samples from a severely polluted Delhi stretch revealed alarming levels of heavy metals (e.g., lead: 47.33 mg/L) and over 550 organic pollutants, including polychlorinated biphenyls, dioxins, carcinogens, and neurotoxins. Two consortia, each assembled from indigenous algal strains, were evaluated under controlled conditions against both pollutant-rich water and non-polluted controls. Consortium 1 (Scenedesmus, Chlorococcum, Oocystis) outperformed Consortium 2 (Chlorella, Klebsormidium, Monoraphidium), achieving up to 87.07% reduction in lead and >95% removal of nitrate and phosphate, alongside substantial decreases in chemical and biological oxygen demand. By integrating high-resolution chemical analytics with native microbial remediation, this work provides the first demonstration of simultaneous removal of diverse pollutant classes in Yamuna water. The findings establish native algal consortia as cost-effective, sustainable bioremediation tools and underscore LC-MS as a critical method for holistic aquatic pollution assessment. Full article

The present study aimed to investigate the occurrence of eight potentially toxic elements (PTEs) in selected varieties of watermelon (Citrullus lanatus var. Arka Shyama and Crimson Sweet) and muskmelon (Cucumis melo var. Cantaloupe and Kajri) grown near riverbanks in the Yamuna and Ganga River basins of Northern India. For this purpose, samples of melon fruits were collected from ten sampling sites from May to June 2024 and analyzed using ICP-OES. The results showed that the levels of PTEs varied significantly across the sampling sites, with muskmelons exhibiting slightly higher concentrations compared to watermelons. Specifically, the concentration (mg/kg dry weight) ranges for the watermelon varieties were Cd (0.05–0.20), Cr (0.40–1.10), Cu (1.50–4.90), Pb (0.01–0.11), As (0.01–0.08), Fe (80.00–120.00), Mn (9.00–15.80), and Zn (5.00–18.00). For muskmelons, the ranges were Cd (0.05–0.23), Cr (0.40–1.00), Cu (2.40–4.80), Pb (0.01–0.08), As (0.02–0.08), Fe (80.00–120.00 g), Mn (9.00–15.00), and Zn (8.00–18.00). In particular, the variability coefficients (CV%) indicated differential contamination in Crimson Sweet. On the other hand, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) tools facilitated the identification of sites of significant contamination and their respective interactions. The health risk studies using the health risk index (HRI), dietary intake modeling (DIM), and the target hazard quotient (THQ) also revealed no significant health risk of eight PTEs in melon fruits. Therefore, this study provides valuable insights into the biomonitoring of PTE contamination in widely consumed summer fruits of Northern India and the subsequent health risk assessment. Full article

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, and urbanization is responsible for these developments. Monitoring and mapping of changes in land cover and land use is essential to the sustainable development and management of the area. The study attempts to track changes in LULC pattern for the years 2002, 2013, and 2023 in the Hindon River Basin, a major tributary of the Yamuna River, using remote sensing and geographic information system techniques. Images obtained from Landsat data were employed to extract historical land use and land cover maps. Additionally, the CA-Markov model was implemented to forecast future land use and land cover patterns. This study examines the historical and predicted LULC in the area. Field observations and site-specific interviews were used to confirm and determine the ground realities. High-resolution images were used to evaluate the accuracy of the classified map. According to the results, the agricultural land decreased from 60.98% in 2002 to 54.70% in 2050, while built-up areas increased from 12.95% to 21.25% during the same period. By 2050, vegetation is predicted to increase to 2.58%, whereas surface water, fallow land, barren areas, and dry water bodies are predicted to decrease to 0.58%, 18.87%, 1.20%, and 0.83%, respectively. The rapid pace of urbanization is facilitating economic growth within the country; however, this development is occurring at the expense of the natural landscape, which subsequently diminishes the overall quality of human life. In order to maintain sustainable development in the Hindon Basin, proper urban planning is essential. Important policy implications for the sustainable management of land use and conservation in the Hindon River basin are highlighted by the study’s research and findings. Full article

Eviction could hold a different meaning if a home’s immediate surroundings contribute to its residents’ livelihood, especially for informal laborers. This paper explores the notion of the fragility of a home within an expanded space—the space on which a home stands and its surroundings when turned into a contested area. It specifically looks at the slum of Yamuna Pushta in Delhi, which was demolished in 2004. The act uprooted thousands of low-income families who were blamed for polluting the river. The demolition was fueled by new urban visions and planning strategies, political and capitalist ambitions, projections of national pride, and an event-driven approach camouflaged under an environmentalist concern attempting to “clean” the river. Using the photographic works of artist, curator, and activist Ravi Agarwal as a case study, this paper argues the presence of a counternarrative in the works, challenging the projected environmentalist discourse around the river, the slum dwellers. This study further states the dual marginalization of the Pushta residents and the Yamuna by critiquing the economic format of majoritarianism through the growing normalcy and agreeability of the slum demolitions by the urban non-poor disguised as the “greater good”. Full article

The movement of emerging pollutants, particularly antibiotics, from surface water to crops through the process of plant uptake poses a significant public concern related to the agricultural utilization of untreated water and biosolids. It is essential to consider the potential risk of inadvertent human exposure and the accumulation of drug residues in fresh food crops, particularly in the case of various green vegetables. Nowadays, environmental research is mainly focusing on the bioaccumulation and threat posed by pharmaceutical residues, especially antibiotics and personal care products in the soil–plant ecosystem. In the present study, the concentration of pharmaceuticals in soil samples collected from agricultural fields irrigated with Yamuna water were evaluated using suitable extraction and sophisticated instrumentation techniques. Out of the five sampling locations, the highest concentration of ofloxacin was reported at Site-V (0.265 µg/mg) during the premonsoon period and the lowest concentration (0.014 µg/mg) during the postmonsoon at Site-II, in terms of the average concentration; this could be due to the combined effect of anthropogenic activities, surface water runoff, and the combined load of drains in the vicinity. For the quantification of the selected antibiotics in a green leafy vegetable (Spinach oleracea), the concentration of ofloxacin was found to be the highest at Okhla barrage Site-V (5.586 µg/mg) during the premonsoon period, with the lowest concentration observed at Site-I, from 1.382 µg/mg to 1.698 µg/mg, during the postmonsoon period. The higher concentration of ofloxacin in crops is because of its susceptibility to being absorbed at the soil’s surface. Plant absorption of antibiotics is influenced primarily by the biological characteristics of the plant, encompassing factors like the lipid and carbohydrate composition of the plant roots. Additionally, the physiochemical properties of the drugs, including molecular size, Kow, and pKa, play a significant role in this process. The antibiotics showed greater variation in their concentration during the premonsoon than in the postmonsoon period, which may be because of precipitation, dilution, and the leaching effect of the soil. For all three of the drugs studied, the concentrations followed the order of ofloxacin > amoxicillin > erythromycin. Thus, the effective management of contaminated soils and vegetables must consider continuous monitoring and risk assessment of high-priority antibiotics to prevent negative effects on the natural environment and human health. Full article

A semester-long internship was designed for undergraduate students at the University of Delhi, India. Teams comprising 30 students from all over the University were trained to carry out field exploration activities on the Yamuna River flowing in Delhi. The students were provided with portable kits to measure the physicochemical parameters at a selected bank of the Yamuna River. Students documented the invertebrate fauna in the riparian zone of the banks. At the same time, they noted the anthropogenic polluting activities on the bank of the Yamuna River. This internship educated students about the UN’s Sustainable Development Goals (SDGs). They all studied Ecology and Environmental Science in their undergraduate curriculum, but they all confessed that they were unaware of the 17 SDGs and the deteriorating health of the Yamuna River in the city. We educated students about the freshwater emergency recovery plan and the International Union for Conservation of Nature (IUCN) red list of ecosystems. This internship is a great example of an undergraduate-directed study or research experience that supported student constructivism and inquiry-based learning, and this research article elaborates on student reports and situational interest in freshwater biology to achieve SDG6. Full article

Identification of sources and transport pathways of heavy metals and major ions is crucial for effective water quality monitoring, particularly in large river systems. The Ganges river basin, the largest and the most populous river basin in India, remains poorly studied in this regard. We conducted a basin-level analysis of major ions, heavy metals, and stable isotopes of nitrate in the Ganges during the pre-monsoon season to constrain the sources and quantify the inorganic chemical composition of the river during its lean flow. Bedrock weathering, anthropogenic interferences, water contribution through tributaries, and surface water-groundwater interaction were identified as the major driver of metal and ion variability in the river. Heavy metals showed the highest concentrations in the upper section of the river, whereas ionic loads were the most variable in the middle. We find a significant impact of tributaries on the metal and ion concentrations of the Ganges in its lower reaches. Isotopic analysis of dissolved nitrate suggested synthetic fertilizers and industrial wastes as the main sources. We find that the otherwise clean waters of the Ganges can show high ionic/metallic concentrations at isolated stretches (As: up to 36 µg/L), suggesting frequent monitoring in the source region to maintain water quality. Except for water collected from the Yamuna and Kannauj in the middle stretch and the Alaknanda and Rishikesh in the upper stretch, the WQI showed acceptable water quality for the sampled stations. These findings provide an insight into the modifications of dissolved inorganic chemical loads and their sources in different sections of the basin, needed for mitigating site-specific pollution in the river, and a roadmap for evaluating chemical loads in other rivers of the world. Full article

The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown microbes. The cluster annotation was achieved through the BLAST program of NCBI, which resulted in the grouping of assembled scaffolds into five classes, i.e., bacteria, archaea, eukaryota, viruses and others. The annotated cluster sequences were used to train machine learning algorithms (MLAs) to develop prediction models to classify unknown metagenomic sequences. In this study, we used metagenomic datasets of samples collected from the Ganga (Kanpur and Farakka) and the Yamuna (Delhi) rivers in India for clustering and training the MLA models. Further, the performance of MLAs was evaluated by 10-fold cross validation. The results revealed that the developed model based on the Random Forest had a superior performance compared to the other considered learning algorithms. The proposed method can be used for annotating the metagenomic scaffolds/contigs being complementary to existing methods of metagenomic data analysis. An offline predictor source code with the best prediction model is available at (https://github.com/Nalinikanta7/metagenomics). Full article

The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting data into three equal parts, where the models were evaluated using each part. The main aim of this study was to find an accurate prediction model for estimating the water quality of the Yamuna River. It is worth noting that the hybrid neuro-fuzzy and LSSVM methods have not been previously compared for this issue. Monthly water quality parameters, total kjeldahl nitrogen, free ammonia, total coliform, water temperature, potential of hydrogen, and fecal coliform were considered as inputs to model chemical oxygen demand (COD). The performance of hybrid neuro-fuzzy models in predicting COD was compared with classical neuro-fuzzy and least square support vector machine (LSSVM) methods. The results showed higher accuracy in COD prediction when free ammonia, total kjeldahl nitrogen, and water temperature were used as inputs. Hybrid neuro-fuzzy models improved the root mean square error of the classical neuro-fuzzy model and LSSVM by 12% and 4%, respectively. The neuro-fuzzy models optimized with harmony search provided the best accuracy with the lowest root mean square error (13.659) and mean absolute error (11.272), while the particle swarm optimization and teaching–learning-based optimization showed the highest computational speed (21 and 24 min) compared to the other models. Full article

The River Yamuna is one of the largest rivers in northern India. It serves as a major source of potable water to the National Capital Delhi. A study was carried out to monitor and quantify three common antibiotics in surface water, with the help of Ultra-Performance Liquid Chromatography (UPLC) coupled with the Mass spectrophotometer (MS/MS)-based method. All the major river water locations in the research area were sampled in triplicate to determine the significant presence of antibiotics from local areas, hospitals, drug manufacturing facilities, and residential areas. Using spiked, as well as actual samples, an optimized method for the detection and quantification of different concentrations of antibiotics was developed. Spatiotemporal variations in the physicochemical properties at the five sites, including reference site-6 (Jamia Millia Islamia), during the pre-monsoon and post-monsoon period, were also investigated. Maximum concentration was observed during the pre-monsoon season followed by the post-monsoon season. The highest detected antibiotic concentration from the river water samples was that of ofloxacin (145.3794 ng/mL), followed by amoxicillin (3.033 ng/mL) and erythromycin (2.171 ng/mL). An aquatic environment may be very vulnerable to dangers from these pharmaceutical residues, according to this risk assessment. Additionally, the levels and compositions of pharmaceutical residue in the aquatic ecosystem might be influenced by seasonal agricultural application, precipitation, and temperature. The findings revealed the existence of antibiotics because of their indiscriminate usage, which may have resulted in the development of resistant strains and ultimately contributed to the spread of antibiotic resistance among living organisms in the river. Thus, attention is required particularly to this section of river as it is the local lifeline source for urban consumers for the domestic water supply and farmers for cultivation. Full article

Water quality is highly affected by riverside vegetation in different regions. To comprehend this research, the study area was parted into wet and dry regions. The WASP8 was applied for the simulations of water quality profile over both Waterways selected from each region. It was found that the Ara Waterway, located in the wet regions, has a higher water quality variation in seasonal scale than that of the Yamuna Waterway, which is in the dry region. The interrelationship between river water quality variables and NDVI produce higher association for water quality variables with Pearson correlation coefficient values of about 0.66, 0.68 and −0.58, respectively, over the annual and seasonal scales in the energy limited regions. This approach will help in monitoring the seasonal variation and effect of the vegetation biomass on water quality for the sustainable water environment. Full article

Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area. Full article

Availability of water in the Ganges River basin has been recognized as a critical regional issue with a significant impact on drinking water supply, irrigation, as well as on industrial development, and ecosystem services in vast areas of South Asia. In addition, water availability is also strongly linked to energy security in the region. Hence, quantification of spatial availability of water resources is necessary to bolster reliable evaluation of the sustainability of future thermal power plants in the Ganges River basin. This study focuses on the risks facing existing and planned power plants regarding water availability, applying climate change scenarios at the sub-basin and district level up to 2050. For this purpose, this study develops an integrated assessment approach to quantify the water-energy nexus in four selected sub-basins of the Ganges, namely, Chambal, Damodar, Gandak, and Yamuna. The results of simulations using Soil and Water Assessment Tools (SWAT) showed that future water availability will increase significantly in the Chambal, Damodar, and Gandak sub-basins during the wet season, and will negligibly increase in the dry season, except for the Yamuna sub-basin, which is likely to experience a decrease in available water in both wet and dry seasons under the Representative Concentration Pathway (RCP) 8.5 scenario. Changes in the water supply-demand ratio, due to climate change, indicated that water-related risks for future power plants would reduce in the Chambal and Damodar sub-basins, as there would be sufficient water in the future. For 19 out of 23 districts in the Chambal sub-basin, climate change will have a moderate-positive to high-positive impact on reducing the water risk for power plants by 2050. In contrast, existing and future power plants in the Yamuna and Gandak sub-basins will face increasing water risks. The proposed new thermal power installations, particularly in the Gandak sub-basin, are likely to face serious water shortages, which will adversely affect the stability of their operations. These results will stimulate and guide future research work to optimize the water-energy nexus, and will inform development and planning organizations, energy planning organizations, as well as investors, concerning the spatial distribution of water risks for future power plants so that more accurate decisions can be made on the location of future power plants. Full article

Antibiotic resistance is one of the major current global health crises. Because of increasing contamination with antimicrobials, pesticides, and heavy metals, the aquatic environment has become a hotspot for emergence, maintenance, and dissemination of antibiotic and heavy metal resistance genes among bacteria. The aim of the present study was to determine the co-resistance to quinolones, ampicillin, and heavy metals among the bacterial isolates harboring extended-spectrum β-lactamases (ESBLs) genes. Among 73 bacterial strains isolated from a highly polluted stretch of the Yamuna River in Delhi, those carrying blaCTX-M, blaTEM, or blaSHV genes were analyzed to detect the genetic determinants of resistance to quinolones, ampicillin, mercury, and arsenic. The plasmid-mediated quinolone resistance (PMQR) gene qnrS was found in 22 isolates; however, the qnrA, B, C, and qnrD genes could not be detected in any of the bacteria. Two variants of CMY, blaCMY-2 and blaCMY-42, were identified among eight and seven strains, respectively. Furthermore, merB, merP, merT, and arsC genes were detected in 40, 40, 44, and 24 bacterial strains, respectively. Co-transfer of different resistance genes was also investigated in a transconjugation experiment. Successful transconjugants had antibiotic and heavy metal resistance genes with similar tolerance toward antibiotics and heavy metals as did their donors. This study indicates that the aquatic environment is a major reservoir of bacteria harboring resistance genes to antibiotics and heavy metals and emphasizes the need to study the genetic basis of resistant microorganisms and their public health implications. Full article

The Himalayas have become synonymous with the hydropower developments for larger electricity demands of India’s energy sector. In the Himachal Himalayas though, there are only three large storage dams with more than 1000 megawatts (hereafter MW) capacity that have very serious environmental issues. However, hundreds of small runoff-river hydropower plants across the Himachal Himalayas are a serious threat to the river regimes and Himalayan biota. There are 965 identified hydropower projects (hereafter HPPs) having a potential capacity of 27,436 MW in the Himachal Pradesh as of December 2019 as per the Directorate of Energy of the state. Out of the 965 identified, 216 are commissioned, including less than 5 MW plants, with an installed capacity of 10,596 MW, and were operational by December 2019. Only 58 projects are under construction among the identified with an installed capacity of 2351 MW, 640 projects are in various stages of clearance and investigation with an installed capacity 9260 MW, 30 projects are to be allotted with 1304 MW installed capacity, and merely four projects are disputed/cancelled with installed capacity of 50.50 MW. The large number of HPPs are sanctioned without proper consideration of negative environmental and geohazard impacts on the Himalayan terrestrial biota. In this work, our focus was on the hydropower and climate change impact on the Himalayan river regimes of the Chenab, the Ravi, the Beas, the Satluj, and the Yamuna river basins. We analyzed basin-wise rainfall, temperature, and soil moisture data from 1955 to 2019 to see the trend by applying the Mann–Kendall test, the linear regression model, and Sen’s slope test. A basin-wise hazard zonation map has been drawn to assess the disaster vulnerability, and 12 hydropower sites have been covered through the primary survey for first-hand information of local perceptions and responses owing to hydropower plants. Full article