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The Hindon River is the main tributary of river Yamuna and it is a significant source of surface water, which flows through the major cities of western Uttar Pradesh, India. The indiscriminate development of industries and urbanization along river basin coupled with rapid population growths contribute various amounts of pollutant in the river. Therefore, the present study was conducted to assess the spatial–temporal variability of river water quality (seventeen physicochemical parameters and eight heavy metals) during pre- and post-monsoon seasons for 5 years data at 19 sampling sites along the river stretch. Indices associated with water quality and heavy metals were computed to scale the accurate state of risk associated to its use for drinking and irrigation. During the pre- and post-monsoon seasons, only four sites were found having safe water quality index (WQI) values. The mean heavy metal concentrations are found in order of Zn > Fe > Pb > Cu > Cr > Cd > Ni > Mn. Considering the spatial and temporal distribution, the study benchmarked the water quality of Hindon River for priority attention. [ABSTRACT FROM AUTHOR]

Potentially toxic metals (PTMs) contamination in the soil poses a serious danger to people's health by direct or indirect exposure, and generally it occurs by consuming food grown in these soils. The present study assessed the pollution levels and risk to human health upon sustained exposure to PTM concentrations in the area's centuries-old glass industry clusters of the city of Firozabad, Uttar Pradesh, India. Soil sampling (0–15 cm) was done in farmers' fields within a 1 km radius of six industrial clusters. Various environmental (geo-accumulation index, contamination factor, pollution load index, enrichment factor, and ecological risk index) and health risk indices (hazard quotient, carcinogenic risk) were computed to assess the extent of damage caused to the environment and the threat to human health. Results show that the mean concentrations of Cu (33 mg kg−1), Zn (82.5 mg kg−1), and Cr (15.3 mg kg−1) were at safe levels, whereas the levels of Pb, Ni, and Cd exceeded their respective threshold limits. A majority of samples (88%) showed considerable ecological risk due to the co-contamination of these six PTMs. Health risk assessment indicated tolerable cancer and non-cancer risk in both adults and children for all PTMs, except Ni, where adults were exposed to potential threat of cancer. Pearson's correlation study revealed a significant positive correlation between all six metal pairs and conducting principal component analysis (PCA) confirmed the common source of metal pollution. The PC score ranked different sites from highest to lowest according to PTM loads that help to establish the location of the source. Hierarchical cluster analysis grouped different sites into the same cluster based on similarity in PTMs load, i.e., low, medium, and high. [ABSTRACT FROM AUTHOR]

Groundwater and its upcoming crisis are the present-day concern of the scientist. This research mainly focuses on responses of groundwater dynamicity to some important drivers, viz. agricultural yield, groundwater irrigated area, groundwater draft, landuse/landcover, and stage of development. The result of this study has been done under three sections. In the first section, the spatiality of groundwater has been discussed where it has been noticed that the western side of the district groundwater level is near the surface due to low drafting and low agricultural yield. Moreover, hard rock geology in the western part disappoints the drilling process. On the eastern part, rich alluvial soil influences high agricultural yield hence groundwater level lowering down rapidly. In the second section, the nature of groundwater levels has been analyzed through the boxplot, and cluster diagram, where boxplots have been drawn over different geological facies, which depicts groundwater is highly fluctuating in hard clay geology. For example, high agricultural intensity and high groundwater draft is the characteristic feature of hard clay geology. The dendrogram in cluster analysis represents a homogeneous groundwater level fluctuating station in three different time series. Last section deals with the future of groundwater level where an artificial neural network (ANN) model has been applied to extract the predicted groundwater level for 2030. This type of environmental analysis, such as groundwater fluctuations in relation to different sensitive parameters and the use of a machine learning model, would aid potential researchers and communities in making wise groundwater use decisions. [ABSTRACT FROM AUTHOR]

A field experiment was undertaken to identify profitable land use based on productivity, net present value, biomass and carbon stocks in litchi based land uses (LBLUs) during 1995–2015 established on degraded riverbed lands of western Himalaya, India. Results of LBLUs (20 years) revealed that LCT + LH + LPG land use produced 6.10–25.27%, 5.82–14.92% and 3.45–13.62% more fruit yield, 14.61–25.22%, 10.18–16.38% and 9.10–16.62% more total biomass, 2.95–9.51%, 0.03–4.54% and 1.60–8.26% higher carbon in soil and 6.93–14.76%, 4.60–10.03% and 6.29–13.14% more total carbon stocks as compared to sole litchi land use of 1st, 2nd and 3rd phase, respectively. Similarly, 80.39% and 78.56% more NPV, 11.74% and 19.77% higher BCR with shortest payback period (5 years) were realized in LCT + LH + LPG land use for 20 and 50 years, respectively. Sensitivity analysis also revealed that changes in discount rate will not affect its performance ranking. [ABSTRACT FROM AUTHOR]

Land degradation neutrality (LDN) adopted in 2015 as target 15.3 of sustainable development goals (SDGs), is a challenge as well as opportunity in the present world to restore the degraded lands. Soil and water conservation (SWC) techniques in the form of bio-engineering measures have vast potential to attain LDN in sustainable manner. India has already announced a LDN target of 26 mha and is fully determined to meet the target by 2030. Therefore, this article proposes and recommends incorporation of SWC measures in effective ways at policy level as key to the success of LDN. [ABSTRACT FROM AUTHOR]

Agroforestry offers a huge potential for carbon sequestration, contributing to climate change mitigation and carbon trading. This study focuses on Bauhinia variegata and Celtis australis, two important agroforestry tree species in the Western Himalayas aiming to develop allometric models and biomass prediction ratios using empirical data collected through selective sampling and minimally destructive methods. Biomass components were categorized and weighed, and allometric equations were developed using diameter at breast height and height as independent variables. Model stability was validated using cross-validation techniques, and their predictive accuracy was assessed. Models, particularly based on diameter, has significant predictive ability for predicting the biomass components for both the species. B. variegata demonstrated a higher capacity for CO 2 absorption and carbon credit generation. The biomass expansion factor and root-to-shoot ratio for C. australis and B. variegata was estimated to be 1.39 and 1.40; and 0.24 and 0.17, respectively. The annual biomass of B. variegata and C. australis was 5.97 and 4.67 Mg ha -1  yr -1 , respectively. The total carbon stock for both species varied from 23.80 to 30.47 Mg C ha -1 . In B. variegata and C. australis, carbon sequestration was 105.93 and 82.11 Mg ha -1 , respectively, and net oxygen release ranged from 59.72 to 77.04 Mg ha -1 . The carbon sequestration by B. variegata translates into generating US$ 2119 in total carbon credits, with an annual credit of US$ 193, while C. australis yields US$ 1642 in total credits and US$ 149 annually. These findings highlight the utility of B. variegata and C. australis for carbon sequestration and provide valuable allometric equations for carbon credit estimation. The study emphasizes the importance of agroforestry in meeting India's Nationally Determined Contributions and addressing climate policy goals., Competing Interests: Declarations: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Ethics approval and consent to participate: This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication: Not applicable. Conflict of interest: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

A total of six vegetables (S. tuberosum, D. carota, S. lycopersicum, A. esculentus, S. oleracea and B. juncea) were analysed for five heavy metals (As, Cd, Cr, Hg, and Pb) to evaluate the contamination load in vegetables collected from five cultivated and two market sites (n = 504) at Delhi, India. The irrigation water samples and soil samples (n = 180) were only collected from cultivated sites. The results showed that the concentration of heavy metals in soil and water samples were well below the permissible level except for Cd 0.001-0.013 µg g -1 . Similarly, the concentration of Cd (>0.20 µg g -1 ) was detected higher in all investigated vegetables except for tomato. The evaluation index value was highest for spinach and lowest for tomato. The transfer factor values and metal pollution index was maximum in spinach and okra. Principal component analysis (PCA), Tukey's HSD (Honestly Significant Difference) test, and one-way and two-way ANOVA (Analysis of Variance) were also applied to statistically analyse the results., (Copyright © 2022 Elsevier B.V. All rights reserved.)

India produces around 19.0 million tonnes of tomatoes annually, which is insufficient to meet the ever-increasing demand. A big gap of tomato productivity (72.14 t ha -1 ) between India (24.66 t ha -1 ) and the USA (96.8 t ha -1 ) exist, which can be bridged by integrating trellis system of shoot training, shoot pruning, liquid fertilizers, farmyard manure, and mulching technologies. Therefore, the present experiment was conducted on tomato (cv. Himsona) during 2019-2020 at farmers' fields to improve tomato productivity and quality. There were five treatments laid in a randomized block design (RBD) with three replications; T 1 [Farmer practice on the flatbed with RDF @ N 120 :P 60 :K 60  + FYM @6.0 t ha -1 without mulch], T 2 [T 1  + Polythene mulch (50 microns)], T 3 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 8.0 t ha -1  + Single shoot trellis system + Side shoot pruning + Liquid Fertilizer (LF 1 -N 19 :P 19 :K 19 ) @ 2.0 g l -1 for vegetative growth + Liquid Fertilizer (LF 2 -N 0 : P 52 : K 34 ) @ 1.5 g l -1 for improving fruit quality], T 4 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 8.0 t ha -1  + Single shoot trellis system + Side shoot pruning + LF 1 @ 4.0 g l -1  + LF 2 @ 3.0 g l -1 ], and T 5 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 10.0 t ha -1  + Single shoot trellis system + Side shoot pruning + LF 1 @ 6.0 g l -1  + LF 2 @ 4.5 g l -1 ]. The results revealed that tomato plant grown on the raised beds with polythene mulch, shoot pruning, trellising, liquid fertilizers, and farmyard manure (i.e., T 5 ) recorded higher shoot length, dry matter content, and tomato productivity by 20.75-141.21, 18.79-169.4, and 18.89-160.87% as compared to T 4 -T 1 treatments, respectively. The T 5 treatment also recorded the highest water productivity (28.39 kg m -3 ), improved fruit qualities, net return (10,751 USD ha -1 ), benefit-cost ratio (3.08), microbial population, and enzymatic activities as compared to other treatments. The ranking and hierarchical clustering of treatments confirmed the superiority of the T 5 treatment over all other treatments., (© 2021. The Author(s).)