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Environmental cost of food wastage: Integrated response through a mix of environmental policy instruments
Food, when wasted, reaches landfills and emits greenhouse gases. The impact of greenhouse gases (GHGs), in turn, is felt by even those who do not waste food in the place. Externalities thus created are known to distort market efficiency and the most widely discussed externality is climate change. This study takes the case of United States of America (USA) to ascertain the GHGs resulting due to food wastage. The difference between cost per capita due to emissions from animal-based products and emissions from plant-based products comes out to be $122. In the year 1997 total GHG emission for the entire population of the USA due to food wastage was 401.98 billion kgCO2eq, costing the country 45.42 billion US dollars. Two decades later, in 2017, the food waste costs went up by 6 billion US dollars amounting to 51.14 billion US dollars and 452.64 billion kgCO2eq of GHG emissions The novelty of this research lies in highlighting the carbon footprints of food wastage in terms of GHG's and monetizing these emissions. The study proposes an integrated response through a mix of environmental policy instruments of economic incentives, command and control and moral suasion. 2023 ERP Environment and John Wiley & Sons Ltd. -
Environmental degradation in geopolitical risk and uncertainty contexts for India: A comparison of ecological footprint, CO2 emissions, and load capacity factor
This study assesses the role of geopolitical risk and uncertainty in the degradation of the environment by forming the functions for ecological footprint, CO2 emissions, and load capacity factor for the period 19902019 in India. Besides, the specified function endogenizes economic growth, renewable energy consumption, and natural resource rent as the additional covariates. The use of the autoregressive distributed lag model (ARDL) confirms the long-run relationship between study variables. Further, the dynamic simulations of the autoregressive distributed lag model (DYNARDL) outcomes show that geopolitical risk improves the quality of the environment by reducing the ecological footprint and CO2 emissions. However, it degrades the environment by reducing the load capacity factor. Furthermore, the uncertainty improves the environmental quality by reducing the CO2 emissions and ecological footprint, but the reduced load capacity factor due to uncertainty implies the degradation of environmental quality in India. Given these findings, the study proposes different environmental conservation policies. 2023 Elsevier Ltd -
Environmental hazards and disasters - A response towards mitigating disaster management
In today's world, our entire planet is under tremendous strain from various natural catastrophic events such as earthquakes, tsunamis, drastic weather changes, hurricanes, global warming, diminishing of glaciers, occurrences of landslides, etc. Such a difficult situation is encountered due to the overhasty extraction of non-renewable natural resources of our planet and the growing rate of presence of humans in the world. It affects the environment to a great extent. Such ventures lead the entire globe towards disastrous events which are irreversible and prepare us to face the worst situation. Hence, the policymakers should develop sophisticated policies focused on advanced disaster management technologies and adopt new methodologies. Several integrated research on disaster prevention programs is already being conducted, even though continuous exploration in this field aligned with all possible consequences is very important. The conclusions and suggestions from research papers will be carefully analyzed to drive the most effective approach to handling such atrocious situations. 2023 Author(s). -
ENVIRONMENTAL JURISPRUDENCE IN INDIA: A JOURNEY TOWARDS ATTAINING ECO-CENTRIC IDEALS
Environmental Law has had a long, arduous journey in India, but has been able to keep up with the many changes that have taken place, around the globe, and has helped shape India's environmental legal regime. By tracing the growth of environmental law, through different ages, and by highlighting some of those factors, which have contributed immensely to its growth, the idea is to identify certain false grounds and figure out ways to make environmental law more effective. By looking at it through a sociocultural lens, the aim is to examine as to whether culture, tradition and rituals can be imbibed into law or given a legal recognition, and thereby giving more power to law. The development of Earth Jurisprudence principles and the way in which it is sought to be imbibed in India and the challenges that it faces too are discussed. 2022 Universitat Rovira i Virgili. All right reserved. -
Environmental Management: Pragmatic Suitability of Low Cost Activated Carbon in Lead (II)Ion Removal by Continuous Mode of Adsorption
Heavy metals such as chromium, lead, and arsenic are usually present in trace amounts in natural waters but many of them are toxic even at very low concentrations. An increasing quantity of heavy metals in our resources is currently an area of greater concern, especially since a large number of industries are discharging their metal containing effluents into freshwater without any adequate treatment. Activated carbons show a significant ability in removing heavy metal ions from an aqueous solution by adsorption, which has been examined by many researchers. Activated carbon derived from Manilkarazapota tree-wood (MZTWAC), which was found to be a suitable adsorbent for the removal of lead ions through continuous adsorption mode, was examined in this paper. A breakthrough curve has been plotted to find the effect of initial concentration and adsorbent bed height in the adsorption of lead (II)ion through MZTWAC. The breakthrough time and the saturation time increased as the initial concentration increased from 40 mg.L-1 to 60 mg.L-1. The saturation time was in the incremental mode when the bed height was increased from 5 cm to 7 cm bed thickness for 40 mg.L-1 concentration. Adams-Boharts model perfectly fits with this fixed-bed column in the removal of lead(II) from an aqueous solution using MZTWAC. Activated carbon derived from MZTWAC is better suited for the purpose of detoxifying metal-contaminated wastewater. 2021 Technoscience Publications. All rights reserved. -
Environmental Pollutants as Emerging Concerns for Cardiac Diseases: A Review on Their Impacts on Cardiac Health
Comorbidities related to cardiovascular disease (CVD) and environmental pollution have emerged as serious concerns. The exposome concept underscores the cumulative impact of environmental factors, including climate change, air pollution, chemicals like PFAS, and heavy metals, on cardiovascular health. Chronic exposure to these pollutants contributes to inflammation, oxidative stress, and endothelial dysfunction, further exacerbating the global burden of CVDs. Specifically, carbon monoxide (CO), ozone, particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), heavy metals, pesticides, and micro- and nanoplastics have been implicated in cardiovascular morbidity and mortality through various mechanisms. PM2.5 exposure leads to inflammation and metabolic disruptions. Ozone and CO exposure induce oxidative stress and vascular dysfunction. NO2 exposure contributes to cardiac remodeling and acute cardiovascular events, and sulfur dioxide and heavy metals exacerbate oxidative stress and cellular damage. Pesticides and microplastics pose emerging risks linked to inflammation and cardiovascular tissue damage. Monitoring and risk assessment play a crucial role in identifying vulnerable populations and assessing pollutant impacts, considering factors like age, gender, socioeconomic status, and lifestyle disorders. This review explores the impact of cardiovascular disease, discussing risk-assessment methods, intervention strategies, and the challenges clinicians face in addressing pollutant-induced cardiovascular diseases. It calls for stronger regulatory policies, public health interventions, and green urban planning. 2025 by the authors. -
Environmental sensors: Safeguarding the ecosystem by monitoring sanitary pad disposal
This chapter focuses on the applications of environmental sensors in general and their role in identifying and addressing the issues related to the improper disposal of sanitary pads, which is a growing concern. It also gives an overview of the pollutants associated with it, and the role that environmental sensors can play in mitigating this problem. By harnessing the power of advanced sensing technologies, we can gain a better understanding of the environmental impact of sanitary pad disposal and work towards sustainable solutions. This chapter aims to provide valuable insights and guidance for researchers and practitioners working to create a cleaner and healthier environment and generate self-awareness for individuals in safeguarding ecosystem. 2024, IGI Global. All rights reserved. -
Environmental value development among preadolescents: a content analysis of reflective responses
Addressing the environmentally detrimental values prevalent in society in the context of rapid climate change is the need of the hour. Combining empathy with cognitive skills such as reflective thinking effectively creates new values among people. The present study attempts to reveal the pattern of environmental value development among 33 preadolescents by reflecting upon the empathy-generating story experiences and the related contents. The study is part of a more extensive quasi-experimental study, and it specifically performs a content analysis on the participants responses in their workbooks. Biospheric nature-related values are the most highly developed, and social justice is the least developed value, implying the need to focus more on the value of social justice. Stories are aids, and reflective thinking and empathetic elicitation are effective techniques for passing environmental values. Empathy generation instead of negative emotions from self-concern and emotion regulation through reflective thinking may be helpful to promote well-being in the context of climate change. Reflective thinking helps environmental value development by enhancing comprehension, emotion regulation, and self-awareness of values, implying a shift from telling the moral of a story to exploring the same through reflective thinking. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents
Pyrano[2,3-d]pyrimidine and its analogues have gained considerable courtesy because of their diverse biological functions and wide-ranging applications, from pharmaceutical agents to essential natural pigments. However, synthesising pyrano[2,3-d]pyrimidine with multiple reactants is challenging and requires advanced green chemistry solutions. This study investigates the generation of thirteen new pyrano[2,3-d]pyrimidine analogues through a single-step, open-flask, multicomponent reaction (MCR) strategy involving aldehydes, phenylhydrazine, ethyl acetoacetate, and barbituric acid via deep eutectic solvents (DES). These DESs serve as environmentally friendly alternatives to traditional solvents. A ternary deep eutectic solvent (TDES) was evaluated for its catalytic solvent activity among ten different formulations. TDES-7 (5 mL) demonstrated the best performance, achieving 95 % product formation within 30 min at room temperature. Its remarkable catalytic activity and ability to produce high yields across multiple reaction cycles make it a standout choice for this application. The collaboration between MCR and TDES underscores an important blend of two significant green aspects, demonstrating their potential to achieve a green and productive sustainable synthesis method with an noble E-factor of 0.1236. 2024 Elsevier B.V. -
Environmentally responsible behaviour among the teachers: role of gratitude and perceived social responsibility
Purpose: Based upon the broaden-and-build theory of positive emotions, this study aims to assess the role of perceived social responsibility (PSR) in mediating the relationship between gratitude and environmentally responsible behaviour (ERB) among teachers. Design/methodology/approach: Data were collected, following a correlational design, from a total of 292 school teachers in Kerala state, India. In total, 256 data were taken for final analysis. Out of the total participants, 63.3% were female and the remaining 36.7% were male. Confirmatory factor analysis was carried out to verify the factor structure and discriminant as well as convergent validity of the study variables. The relationship between gratitude and ERB with mediating role of PSR was tested. Findings: The mediation analysis output revealed that PSR fully mediates the effect of gratitude on ERB, and it is concluded from the findings of the study that ERB can be enhanced by humanizing the citizens to integrate social responsibility in their acts and promoting the significance of having positive emotions like gratitude to widen their thoughtaction repertoires. Research limitations/implications: In line with the broaden-and-build theory, a positive state of mental faculty can be a prime facilitator to increase concern for green environments as an outcome of an expanded thoughtaction repertoire. The findings imply the importance of inculcating enduring personal resources like the sense of gratefulness as it weighs the effect of producing altruistic acts like ERB along with many other benefits associated with having a positive emotion which is obviously considered to be a fair contribution to serve social resources in the community. Social implications: The study findings can be an inspiration for the formation of policies to encourage pro-environmental behaviour and to further expansion of policies like national education policy of India. As teachers being the facilitators of knowledge and wisdom, they are potential sources to inspire students to practice healthy behaviours, they can be better models by practicing ERB. Originality/value: The authors have verified the application of broaden-and-build theory of positive emotion in the context of ERB along with identifying its relationship with gratitude and PSR. 2023, Emerald Publishing Limited. -
Environmentally sustainable zinc oxide nanoparticles for improved hazardous textile dye removal from water bodies
A sustainable, affordable, and cost-effective method was developed to synthesize zinc oxide nanoparticles (SB-ZnO-NPs) using leaf extracts of Strobilanthes barbatus. The synthesized SB-ZnO-NPs displayed an absorbance maximum at 359 nm with a band gap of 3.24 eV. The average diameter of the SB-ZnO-NPs, as determined by FESEM analysis, was 84.23 nm. The particles had nearly spherical morphologies. By using FTIR analysis, it was established that functional groups played a part in the formation of SB-ZnO-NPs. Reactive Yellow 86 (RY-86) and Reactive Yellow 145 (RY-145) textile dyes were degraded by SB-ZnO-NPs under the impact of UV irradiation, and the degradation rates were 87.50 and 91.11%, respectively, in 320 min. When dye solutions treated with SB-ZnO-NPs were tested for phytotoxicity, the results showed a sharp decline in the effectiveness of the inhibition compared to dye effluents. The synthesised SB-ZnO-NPs can, therefore, be employed as a substitute potential catalyst for the breakdown of textile colours both before and after release into water bodies. 2023 The Author. -
Envisaging an Intelligent Blockchain Network by Intelligence Sharing
Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. 2022 IEEE. -
Envisioning the potential of Natural Language Processing (NLP) in Health Care Management
Natural Language Processing (NLP) continues to play a strategic role in disease detection, intensive care, drug discovery and control of mushrooming infections during the current pandemic. It energizes chat programs to reduce outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, and Deep learning language models. It supports more languages around the world. NLP powered AI such as Health map and Copweb platforms track patient requests and perform incident detections. This study looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier electronic health records (EHRs) maintenance in the health care industry. This research work explores the strategic approach and potential of NLP which maximizes the value of the EHR and healthcare data, making data a critical and trusted component in improving health outcomes 2021 IEEE. -
Enzyme based bioelectrocatalysis over laccase immobilized poly-thiophene supported carbon fiber paper for the oxidation of D-ribofuranose to D-ribonolactone
A modified electrode based on laccase immobilized poly-thiophene-3-carboxylic acid supported on carbon fiber paper was developed for the electrocatalytic oxidation of D-ribofuranose to otherwise difficult-to-access D-ribonolactone, a precursor for C-nucleoside based drug like Remdesivir. The electrochemical oxidation of D-ribofuranose was achieved by the TEMPO-mediated electrochemical process. The experimental parameters were optimized and validated using Design of Experiment (DoE) statistical tool indicating the concentration of TEMPO and stirring as important parameters in bulk electrolysis. The mechanism for the electrochemical oxidation of D-ribofuronose followed single electron anodic oxidation of TEMPO mediated by laccase to the corresponding oxoammonium nitrosonium species which was vital for the mediated electrochemical oxidation. The mechanism for the electrochemical oxidation was established using cyclic voltammetry and computational studies. The plausible interactions of laccase enzyme with TEMPO mediator were studied using molecular docking experiments. This facile method was successfully applied for the oxidation of D-ribofuranose to D-ribonolactone. 2022 -
Enzyme based bioelectrocatalysis over laccase immobilized poly-thiophene supported carbon fiber paper for the oxidation of D-ribofuranose to D-ribonolactone /
Molecular Catalysis, Vol.524, ISSN No: 2468-8231.
A modified electrode based on laccase immobilized poly-thiophene-3-carboxylic acid supported on carbon fiber paper was developed for the electrocatalytic oxidation of D-ribofuranose to otherwise difficult-to-access D-ribonolactone, a precursor for C-nucleoside based drug like Remdesivir. The electrochemical oxidation of D-ribofuranose was achieved by the TEMPO-mediated electrochemical process. The experimental parameters were optimized and validated using Design of Experiment (DoE) statistical tool indicating the concentration of TEMPO and stirring as important parameters in bulk electrolysis. -
Enzyme immobilization on biomass-derived carbon materials as a sustainable approach towards environmental applications
Enzymes with their environment-friendly nature and versatility have become highly important green tools with a wide range of applications. Enzyme immobilization has further increased the utility and efficiency of these enzymes by improving their stability, reusability, and recyclability. Biomass-derived matrices when used for enzyme immobilization offer a sustainable solution to environmental pollution and fuel depletion at low costs. Biochar and other biomass-derived carbon materials obtained are suitable for the immobilization of enzymes through different immobilization strategies. Environmental pollution has become an utmost topic of research interest due to an ever-increasing trend being observed in anthropogenic activities. This has widely contributed to the release of various toxic effluents into the environment in their native or metabolized forms. Therefore, more focus is being directed toward the utilization of immobilized enzymes in the bioremediation of water and soil, biofuel production, and other environmental applications. In this review, up-to-date literature concerning the immobilization and potential uses of enzymes immobilized on biomass-derived carbon materials has been presented. 2022 Elsevier Ltd -
Enzyme immobilized conducting polymer-based biosensor for the electrochemical determination of the eco-toxic pollutant p-nonylphenol
The unbridled release of harmful endocrine disruptors (EDs) into the environment is deteriorating human and animal health. A facile and efficacious biosensor was developed by immobilizing laccase over electropolymerized poly anthranilic acid on a carbon fiber paper (CFP) electrode, Lac/PAA/CFP for the detection of p-nonylphenol (PNP). PNP is a persistent phenolic endocrine disruptor and a harmful eco-toxic pollutant. Physico-chemical and electrochemical characterization of the fabricated electrode was carried out to study the modification of the Lac/PAA/CFP electrode. Cyclic voltammetric studies divulged that the prepared sensor has catalytic activity approximately twice greater than that of the bare CFP electrode. The influence of pH and scan rate was scrutinized for the modified electrode. Under optimized conditions differential pulse voltammetric studies were used for the quantification and the results revealed that the biosensor has a low limit of detection (LOD) and limit of quantification (LOQ) of 1.74 nM and 5 Nm, respectively with a broad linear dynamic range of 5250 nM. In the presence of interferants, the developed biosensor exhibited good selectivity toward the electrochemical detection of PNP. Molecular docking studies carried out revealed the hydrogen bonding interaction of the Asn264 residue of laccase Trametes versicolor. Further, the fabricated biosensor was accessed for its practicality in real samples collected from tap water and lake water. 2023 Elsevier Ltd -
EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Epidemiological Transition in India and Determinants that Are Shifting Disease Burden: A Systematic Review
Indias disease burden patterns are shifting towards increased morbidity and mortality from Non-communicable disease and chronic diseases. This is one of the first studies conducted using the PRISMA guidelines and checklist to understand the role played by various determinants of health in this epidemiological transition happening in India. The search on 9 reputed bibliographic databases yielded 459 articles and finally 58 articles were selected based on carefully curated selection criteria. The results confirm the relation between India's demographic transition and the increasing disease burden from Non-communicable diseases (NCDs). 21 studies significantly associated urban residential status, increasing income, better living conditions and education with increasing NCDs' prevalence. 12 studies found that NCDs were more prevalent among women than men. Increased physical activity, a healthy diet and a lower hipto-waist ratio were observed to protect against NCDs. While 9 studies found smoking tobacco and alcohol consumption were not significantly associated with the prevalence of NCDs. It is of foremost importance that Indias public health policy focus must shift towards inclusivity as there is an affluent gradient to the increased morbidity and mortality from NCDs. Copyright2024 by authors, all rights reserved.