Browsing by Author "Arghya Chattopadhyay"

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Chemical fractionations and mobility of heavy metals in soils of eastern Uttar Pradesh
(Indian Council of Agricultural Research, 2021) Abhik Patra; Satish Kumar Singh; Arghya Chattopadhyay; Vinod Kumar Sharma; Ravindra Kumar Rekwar
In the current investigation, an attempt has been made to assess the various chemical forms and mobility factor (MF) for lead (Pb), cadmium (Cd), chromium (Cr) and nickel (Ni) in four soils of eastern Uttar Pradesh.For this purpose, two different surface soil samples were collected from each of Entisol, Inceptisol, Vertisol and Alfisol soil orders during 2019-20.The modified Tessier sequential extraction procedure was applied to determine the chemical pools of Pb, Cd, Cr and Ni in each soil.Results indicated that total metal content follows the order of Pb>Cr>Ni>Cd across the orders, whereas mobility factor of micronutrients could be arranged as: Cd>Pb>Ni>Cr.The bioavailable metal fraction of all micronutrients, i.e.exchangeable pool (Fl) was maximum in Inceptisol.The highest carbonate bound (F2), Fe-Mn oxides bound (F3), organically bound (F4) and residual (F5) fraction were recorded in Vertisol, Alfisol, Inceptisol and Alfisol, respectively for all heavy metals.The mean percentage value of Pb and Ni fractions were in the order of F5>F3>F4>F2>F 1, whereas Cd whose chemical fractions follows the order of F2>F3>F4>F5>F 1.The Pb was highly mobile in Vertisol, Ni in Inceptisol, while Cd and Cr in Entisol.Therefore, availability and mobility of heavy metals in the soil environment depends mainly on their association with various chemical fractions of the soil which relies on the mineralogical origin of the metals. © 2021 Indian Council of Agricultural Research. All rights reserved.
Classification and Hazards of Arsenic in Varanasi Region Using Machine Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Siddharth Kumar; Arghya Chattopadhyay; Jayadeep Pati
Groundwater plays a significant role in sustaining life in terrestrial and marine ecosystems. Arsenic contamination in aquifers poses a serious threat to the ecosystem due to its carcinogenic effect. Arsenic contamination in aquifers of the Varanasi region was noticed after water sampled from random sites of the Varanasi region of Uttar Pradesh, India. Under the Capacity Building of Urban Development (CBUD) scheme, Varanasi was chosen by the Ministry of Housing and Urban Poverty Alleviation (MoHUPA) and the Ministry of Urban Development (MoUD). In this study, various machine learning classifiers have been developed to classify water samples collected from the Varanasi region as safe or unsafe for consumption. The water with less than 10 µg/L As concentration is termed safe per World Health Organisation (WHO). Firstly the water samples parameters were ranked then the samples were trained and tested. Various parameters obtained from confusion matrices such as accuracy, precision, and recall are used to analyze the performance of different machine learning classifiers like Simple Logistic, MLP Classifier, and Random Forest. Among these models, Simple Logistic outperforms other classifier models. The Simple Logistic algorithm was considered the best model among the different classifiers. It has the highest accuracy of 79.03%, the highest precision of 77.00%, the highest recall of 79.00%, and a high ROC area of 69.40%. Thus, this model can be used for classification, and policymakers may devise plans to tackle the As poisoning in the Varanasi region. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Classification of arsenic in groundwater samples using extreme learning machine and crow search algorithm for smart cities
(IGI Global, 2024) Siddharth Kumar; Harshdeep Singh Dhillon; Arghya Chattopadhyay; Jayadeep Pati
This research aims to classify the arsenic contamination in the groundwater along the banks of river Ganga of Varanasi, India. The groundwater is vital for various purposes, including agriculture and drinking. Groundwater contamination with high levels of arsenic pose a significant health risk. To tackle this problem, the authors build a model for classifying arsenic levels in groundwater samples that incorporates the extreme learning machine (ELM) algorithm and crowd search optimisation (CSO) technique. In the hybrid approach, they initialize the ELM components and randomly assign weights while employing CSO to guide the search for optimal solutions. By classifying new groundwater samples as having high or low arsenic concentrations, the developed model can be used to evaluate the new groundwater samples. The proposed hybrid approach offers a promising solution for monitoring and managing groundwater quality, ensuring a healthier environment for the city's population. © 2024, IGI Global. All rights reserved.
Dry Sliding Wear Behavior of Chemically Treated Sisal Fiber Reinforced Epoxy Composites
(Taylor and Francis Ltd., 2022) Sudhakar Behera; Rakesh Kumar Gautam; Sunil Mohan; Arghya Chattopadhyay
The effect of fiber surface treatment on the structural, thermal, and tribological properties of sisal fibers and their epoxy composites were investigated in this research work. Sisal fibers were modified with alkali (NaOH), glutamic acid, and a combination of both alkali and glutamic acid. To analyze the effect of chemical modification on the properties of sisal fibers, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), and thermogravimetric analysis (TGA) were performed. It is observed from the experimental results that there is an improvement in the surface roughness, crystallinity, and thermal stability of chemically treated fibers in comparison to untreated fibers. Microhardness properties of chemically treated sisal fiber reinforced epoxy composites (SFREC) also showed minor improvement. The dry sliding wear experiments were carried out according to Taguchi design of experiment (DOE) methods. The results of the wear test showed an increase in the wear resistance of chemically modified SFREC relative to untreated SFREC. The best wear properties were demonstrated by alkali treated SFREC. It is also observed from the findings of ANOVA that the applied load and sliding distance have the most defining effect on wear volume loss of SFREC. © 2021 Taylor & Francis.
Effect of phosphorus application on arsenic species accumulation and co-deposition of polyphenols in rice grain: Phyto and food safety evaluation
(MDPI AG, 2021) Arghya Chattopadhyay; Anand Prakash Singh; Deepak Kasote; Indrajit Sen; Ahmed Regina
The present study was aimed at exploring the effect of soil application of different concentrations of orthophosphate (P) (0, 10, 20, 30, and 40 mg kg−1 ) on rice agronomic and yield parameters, arsenic (As) species accumulation, and polyphenol levels in the grain of rice grown under As spiked soil (10 mg kg−1 ). The contents of As species (As(V), As (III), MMA and DMA) and polyphenols in rice grain samples were estimated using LC-ICP-MS and LC-MS/MS, respectively. P treatments significantly reduced the toxic effects of As on agronomic parameters such as root weight and length, shoot and spike length, straw, and grain yield. Among the treatments studied, only the treatment of 30 mg kg−1 P helps to decrease the elevated levels of As (V), As (III), and DMA in rice grains due to As application. The study revealed that 30 mg kg−1 was the optimal P application amount to minimize AS accumulation in rice grains and As-linked toxicity on agronomic parameters and chlorophyll biosynthesis. Furthermore, the levels of trans-ferulic acid, chlorogenic acid, caffeic acid, and apigenin-7-glucoside increased in response to accumulation of As in the rice grain. In conclusion, the precise use of phosphorus may help to mitigate arsenic linked phytotoxicity and enhance the food safety aspect of rice grain. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Hemp fiber surface modification: Its effect on mechanical and tribological properties of hemp fiber reinforced epoxy composites
(John Wiley and Sons Inc, 2021) Sudhakar Behera; Rakesh Kumar Gautam; Sunil Mohan; Arghya Chattopadhyay
In this research work, the effects of sodium carbonate and hydrogen peroxide treatment of hemp fiber on the water absorption, mechanical, and tribological properties of hemp fiber reinforced epoxy composites (HFREC) were investigated. The change in surface roughness and fiber size after chemical treatment was confirmed by the scanning electron microscopy (SEM) images. Fourier transform infrared analysis confirmed the removal of hemicellulose and lignin content of the fiber after both the chemical treatment. X-ray diffraction analysis showed an increase in the crystallinity index of the chemically treated fiber. The experimental results also revealed that both sodium carbonate and peroxide modification have resulted in enhancement of water resistance and mechanical properties such as tensile strength and tensile modulus and reduction in impact properties of treated HFREC. Tribological test results revealed that the treated HFREC have improved wear and frictional properties in comparison with untreated HFREC. The best tribological and mechanical properties were exhibited by peroxide treated HFREC, which was also confirmed through the SEM images of worn and fractured surfaces of the composites. © 2021 Society of Plastics Engineers.
Iron in the soil-plant-human continuum
(Elsevier, 2021) Abhik Patra; Vinod Kumar Sharma; Hanuman Singh Jatav; Asik Dutta; Ravindra Kumar Rekwar; Arghya Chattopadhyay; Ankita Trivedi; Kiran Kumar Mohapatra; Ajin S. Anil
Iron (Fe) is essential for plants and animals and it is the fourth most common element and second most common metal in the Earth’s crust. In cultivated soils, Fe is mostly present in the Fe3+ and Fe2+ forms under oxic and anoxic environments, respectively. Iron should be present in the range >10-7.7 M in soil solution with a redox potential of soil-root environments under <12 to avoid its deficiency. The accessibility of Fe to plants is influenced by soil reaction, soil organics, aeration of the soil, presence or absence of other macro- and micronutrients, etc. Iron is required for the biogenesis and functioning of chlorophyll, energy transmission, metabolism of cells, fixation of nitrogen (N), and respiration of plants. Deficiency symptoms of Fe are first seen as the yellowish color between leaf veins, especially in young leaves, which could result in the necrosis at a later stage. Available soil test methods are not very effective in assessing available Fe in soils, whereas Fe2+ content in soil is a reasonably good predictor of plant Fe status. The supply of iron sulfate to the soil proved to be successful to eradicate Fe deficiency only when used along with compost and manure. As a comparison to soil application, foliar application of Fe had a major advantage to rectify its deficiency. To increase Fe level in edible parts of crops, agricultural techniques (e.g., agronomic biofortification and genetic biofortification) seem to be economic and efficient. The path to genetic biofortification is a long-term method that needs significant energy and money, but agronomic biofortification provides a simple solution to the overwhelming Fe deficiency problem. © 2021 Elsevier Inc. All rights reserved.
Low input sustainable agriculture: A viable climate-smart option for boosting food production in a warming world
(Elsevier B.V., 2020) Deepranjan Sarkar; Saswat Kumar Kar; Arghya Chattopadhyay; Shikha; Amitava Rakshit; Vinod Kumar Tripathi; Pradeep Kumar Dubey; Purushothaman Chirakkuzhyil Abhilash
Maximizing food production for feeding a rapidly growing human population while minimizing critical resource use and soil quality degradation is a major challenge for global sustainability. Sustainable agricultural practices based on low-external input is of paramount importance for reducing environmental trade-offs and planet healthy food production. Therefore, a critical assessment was made on viable low-input technologies aimed to reduce the negative effects of agricultural production as well as the use of various crop simulation models for forecasting the agricultural production under changing climatic scenario. While crop simulation models are helpful for predicting the growth and yield of individual crops under current as well as futuristic scenarios, it is difficult to model the response of multiple cropping systems under changing climatic conditions. As a matter of fact, the developing countries, majorly dependent on agriculture are most vulnerable to climate change. The increasing price of agrochemicals is another setback for subsistence farmers in resource-poor nations. In this backdrop, the current review aimed to assess the impact of climate change on agriculture, and the role of low input sustainable agriculture (LISA) for ensuring the food security while safeguarding the critical natural resources for human-wellbeing and also for attaining UN-Sustainable Development Goals. In addition, evidence-based impacts of LISA in emerging economies from Africa and South Asia are highlighted and suitable ecological indicators for measuring the sustainability of such LISA are addressed in brief. We conclude that the large-scale implementation of LISA will facilitate agricultural sustainability, and therefore, suitable policy frameworks are imperative for its worldwide adoption. © 2020 Elsevier Ltd
Mechanical and tribological properties of chemically modified jute/epoxy composites
(Taylor and Francis Ltd., 2023) Sudhakar Behera; Rakesh Kumar Gautam; Sunil Mohan; Arghya Chattopadhyay
The purpose of the present work is to assess the effectiveness of low-cost and environmental friendly chemical modification of jute fibres based on the usage of sodium hydroxide (AT), sodium carbonate (ST) and sodium hydrogen carbonate (SHT) on the morphological, water absorption, mechanical and tribological characteristics of jute fibre-reinforced epoxy composites (JFREC). Mechanical properties like tensile strength, tensile modulus and impact strength showed appreciable improvement for the AT JFREC (38.08%, 30.56% and 31.66%), ST JFREC (70.03%, 33.06% and 41.30%) and SHT JFREC (24.69%, 8.88% and 22.61%) when compared to untreated JFREC. The experimental results also confirmed that the improved fibre-matrix adhesion, attained by chemical modification, increased the water absorption resistance and the tribological properties of chemically modified JFREC. Improved mechanical and tribological properties attained by the chemically modified JFREC can be found as a potential application in automotive and packaging industries. © 2023 Institute of Materials, Minerals and Mining Published by Taylor & Francis on behalf of the Institute.
Monitoring Phytoremediation of Metal-Contaminated Soil Using Remote Sensing
(Springer International Publishing, 2022) Bhabani Prasad Mondal; Rabi Narayan Sahoo; Bappa Das; Priya Paul; Arghya Chattopadhyay; Sonia Devi
Phytoremediation is an effective tool which can be employed to revive the degraded or metal-contaminated soils. However, assessment of contamination caused by heavy metal in soil and its monitoring on long-term basis is essential to assess the efficacy of phytoremediation processes. Conventional techniques for monitoring the contaminated sites are noticeably expensive, time intensive, and destructive in nature. Remote sensing (RS) may assist as an efficient alternative technique for detecting metal contamination and monitoring phytoremediation on a long-term basis. The RS data from various sources at various scales such as proximal sensing data (laboratory and field-based spectroradiometric data), airborne data (dronecollected data), and space-borne data (satellite data) are crucial for monitoring the extent of contamination and to detect changes in land use pattern and surface cover of the polluted site over a time period. Most of the RS based techniques use vegetation reflectivity within the red-edge position of the electromagnetic radiation for indirect estimation of contamination level that is associated with heavy metal and organic carbon (hydrocarbon) concentration in soil. In proximal sensing, laboratory- and field-based spectroscopic data are employed to predict the level of contamination through correlating the characteristic reflectance spectra of the spectrally active soil constituents with metals. To determine the efficiency of phytoremediation, monitoring of revegetation or biorecultivation is also necessary using RS data. One of the most promising techniques to monitor revegetation is to calculate various indices related to soil, vegetation, and moisture through interpreting the remote sensing-based data product. The most frequently used vegetation index such as normalized difference vegetation index (NDVI) helps to measure the phytoproductivity of the polluted area. RS based indices are useful to detect metal-induced vegetation stress. However, a few key limitations are there in obtaining satisfactory results using RS based methods such as complexity of spectra, non-availability of unique spectral feature for particular metal, and noisy spectra due to variation in atmospheric conditions. In spite of so many challenges, RS based techniques are considered as non-destructive, time-saving, and cost-effective alternative techniques especially for large phytoremediation areas. Recently both airborne and space-borne hyperspectral RS data are used for continuous and detailed monitoring of the contaminated areas. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022.
Potential and Risk of Nanotechnology Application in Agriculture vis-à-vis Nanomicronutrient Fertilizers
(Springer Nature, 2021) S.K. Singh; Abhik Patra; Yukti Verma; Arghya Chattopadhyay; Amitava Rakshit; Suresh Kumar
Nanotechnology had a wide potential of its novel applications in the fields of plant nutrition to meet the future demands of the growing population because nanoparticles (NPs) have unique physicochemical properties, i.e., high surface area, high reactivity, tunable pore size, and particle morphology. Management of optimum nutrients for sustainable crop production is a priority area of research in agriculture. In this regard, nanonutrition concerns with the provision of nanosized nutrients for sustainable crop production. The application of nanomaterials for delivery of nutrients and growth-promoting compounds to plants has become more and more popular and their utilization at the proper place, at the proper time, in the proper amount and of the proper composition affects the use efficacy of fertilizers. Using this technology, we can increase the efficiency of micronutrients delivery to plants. In the literature, various NPs and nanomaterials (NMs) have been successfully used for better nutrition of crop plants compared to the conventional fertilizers. This review summarizes the synthesis of nanofertilizers, characterization of nanofertilizers, NPs, and NMs as micronutrient fertilizers and describing their role in improving growth and yield of crops, uptake, translocation, and fate of nanofertilizers in plants and environmental hazard of NPs and NMs application. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
Rhizodeposition: An Unseen Teaser of Nature and Its Prospects in Nutrients Dynamics
(Springer Nature, 2021) Abhik Patra; Vinod Kumar Sharma; Arghya Chattopadhyay; Kiran Kumar Mohapatra; Amitava Rakshit
Rhizodeposition is defined as all root-derived compounds and plant materials that are released from living roots during plant growth. A wide range of organic compounds are involved in this process, including inorganic ions, sloughed cells, mucilages, exudates and root hairs. Rhizodeposition has diverse functions in plant nutrition and soil ecology, such as improving nutrient availability, acting as allelochemicals, and serving as a carbon and energy source for rhizosphere soil microorganisms. It is mainly quantified through tracer techniques like carbon tracer technique, labeling plants with N15 and dwell labeling technique but, scientific review suggested that cotton wick method is the best technique for quantification. The rhizodeposition plays a crucial role for the mobilization of plant nutrients and serves a complex mixture to carry out ecological functions in the soil. It has been extensively reported that plants invest a large portion of their photosynthetic carbon in the development and maintenance of the rhizosphere through rhizodeposits), which improves the ability to optimally exploit water and nutrient distributions in the soil. Concentration of rhizodeposits has direct effect on C and N mineralization. Different organic acids and phenolic compounds present in rhizodeposition help in increasing different exoenzymes activity, which ultimately increase the mineralization of native, applied and fixed nutrients in soil. Plant root secretes phytosiderophores which improve the micronutrients uptake in plant. In nut shell, through understanding the relationship between rhizodeposits and its function, the insight information of change in microbial diversity and different nutrients transformation process can better understand. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
Role of physico-chemical properties of the soil in badlands forming processes around Chitrakoot, India
(John Wiley and Sons Ltd, 2022) Nikhilesh Singh; Medha Jha; Sanjay Tignath; Bhola Nath Singh; Arghya Chattopadhyay
Eco-restoration attempts in badlands are generally not fully successful because physical solutions such as reshaping and leveling of gullies and ravines to check erosion and soil losses prove to be temporary in nature. In this context, it leads to conceptualization that the cause of badland formation is not merely topographical but should be related to changes in the intrinsic properties of soils. There is a lack of understanding as to the formative role of physico-chemical characteristics of soils in the formation of badlands. The objective of our study is to determine such critical physico-chemical characteristics of soils that are responsible for the onset of a self-aggravating erosive network of badlands by undertaking a case-study of part of the Mandakini River watershed, Chitrakoot, India. Standard IS codes were followed in determining these properties. These soils have a silt-loam texture with high bulk density. Depletion of clays lowered Atterberg limits which consequently rendered the soils unstable even at low values of moisture content. The overlay analysis of these properties and drainage frequency shows perfect superimposition in categorizing badlands into zones of the severity of degradation. The chemistry of these soils has low sodium, high calcium with the presence of calcretes, low organic matter, and a low amount of illites and are mostly alkaline. The outcome of the study is helpful in understanding mutual interdependence between soil characteristics and processes of badlands that led to the intensification of rills-gullies-channels network. The findings would be useful in land management planning. © 2022 John Wiley & Sons, Ltd.
Role of soils for satisfying global demands for food, water, and bioenergy
(CRC Press, 2024) Bhabani Prasad Mondal; G. Bhupal Raj; Mahima Dixit; Tithli Sadhu; Vaibhav Pandit; Laxmi Prasanna; Arghya Chattopadhyay; Suman Dutta; Suchith Kumar
[No abstract available]
Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment
(Elsevier Ltd, 2020) Arghya Chattopadhyay; Anand Prakash Singh; Satish Kumar Singh; Arijit Barman; Abhik Patra; Bhabani Prasad Mondal; Koushik Banerjee
The Indo-Gangetic alluvium is prime region for intensive agricultural. In some areas of this region, groundwater is now becoming progressively polluted by contamination with poisonous substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell results in the formation of As(III) which is more toxic. Thus groundwater quality assessment of Gangetic basin has become essential for its safer use. Therefore we under took study on the spatial variability of arsenic by collecting georeferred groundwater samples on grid basis from various water sources like dug well, bore and hand pumps covering the river bank region of Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na, K, Ca, Mg, SAR, SSP, CO3, HCO3, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe As contamination in ground water of three sites of the study area. ARC GIS software is now able to process maps along with tabular data and compare them well, to provide the spatial visualization of information and using this tool, the Geographical Information System (GIS) of arsenic was developed. It was noticed from spatial maps that concentration of arsenic was more near the meandering points of Ganga. © 2019 Elsevier Ltd
The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water
(IWA Publishing, 2023) Arghya Chattopadhyay; Anand Prakash Singh; Siddharth Kumar; Jayadeep Pati; Amitava Rakshit
Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater. © 2023 The Authors.
VIS-NIR reflectance spectroscopy as an alternative method for rapid estimation of soil available potassium
(Indian journals, 2020) Bhabani Prasad Mondal; Bharpoor S. Sekhon; Priya Paul; Arijit Barman; Arghya Chattopadhyay; Nilimesh Mridha
Potassium (K) is an important macronutrient for crop plant and plays a crucial role in crop production. Therefore, accurate and rapid estimation of soil available K is necessary for judicious application of available K in an intensively cropped region. However, traditional soil chemical analysis for assessing soil available K is very much laborious, expensive and time consuming. The visible near-infrared (VIS-NIR) reflectance spectroscopy is considered as a promising alternative technique for rapid, non-destructive and ecofriendly estimation of available K and other soil properties. An experiment was carried out in an intensively cultivated region of Ludhiana district of Punjab to investigate the potential of VIS-NIR technique for accurate prediction of available K using multivariate model. A total of 170 georeferenced surface soil samples (0-15 cm) were collected from the study site for both chemical and spectral analysis of available K. A popular statistical technique namely, partial least square regression (PLSR) was employed to develop spectral model for K prediction. Important statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) were used to evaluate the efficacy of prediction model. The results showed that the R2 and RMSE and RPD values were 0.41, 0.09 and 1.44, respectively for independent validation dataset of PLSR model. The RPD value indicated acceptable prediction accuracy for soil available K with PLSR model. Comparatively lower performance of the studied prediction model could be ascribed to the less variation in the collected spectra of soil samples and the use of linear multivariate model. Therefore, the study suggested to explore advanced non-linear data mining techniques for achieving better prediction accuracy for soil available K. © 2020, Indian journals. All rights reserved.