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Bacteriophages are key viruses that can kill thousands of harmful microbes generally present at polluted sites. Such bacteriophages are abundantly present in the river Ganga, where millions of people in India and abroad drink its water and take baths every day for spiritual reasons. Besides bacteriophages, several pathogenic and zoonotic microbes are present in the river Ganga. It is interesting to study the diversity and abundance of bacteria and their respective phages present in polluted or non-polluted sites. Thus, the metagenomics study was carried out at the most polluted sites of river Ganga near Kanpur and non-polluted sites at Farakka, which harbors several harmful bacteria and their phages. The results revealed a significantly higher percentage of Microviridae phage family, ssDNA viruses, and Mimiviridae virus family near Kanpur than Farakka. In addition, compared to Kanpur, Farakka has a more significant percentage of Myoviridae, an unidentified phage family, and Retroviridae viral families. Despite heavy drainage of untreated and contaminated effluents from the leather industry, pesticide industry, paper mills, metropolitan cities, and other sources, the vast number of said phages kills several harmful pathogenic microbes in polluted sites to maintain the Ganga water's healing power or natural sterility. In a polluted aquatic environment, the varieties of bacteriophages were identified in the Ganga and their interaction with the microbial host. The taxonomic diversity of several bacteriophages found in pathogenic host systems was investigated to get exceptional knowledge of these small viruses in the aquatic environment. [ABSTRACT FROM AUTHOR]

In order to assess the potential of wetland plants to remediate metals from a paper mill effluent contaminated wetland site in Northeast India, 25 abundant plant species belonging to 15 different families, soil, and water samples from the sites were tested for Pb, Zn, Mg, and Fe by atomic absorption spectrophotometer. The results showed that metal accumulation by wetland plants differed among species and tissues. Plants thrived in high Pb, Zn, Mg, and Fe which indicated their tolerance. According to the criteria used for selecting plants for phytoremediation such as high metal tolerance, short life cycle, wide distribution, large shoot biomass and translocation factor (TF) >1; five species each were Mg and Fe accumulators, nine species were Pb accumulators and, eight species were Zn accumulators and the rest were excluders. Alternanthera sessilis was the only plant species that had TF > 1 for all the four metals. The study indicated great promise for phytoremediation, as these accumulators could be used in future for practical phytoremediation approaches and reduction of the risk from harmful metals to human health. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on "blasting vibration" is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around the rock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations. [ABSTRACT FROM AUTHOR]

Pines play a significant role in forest biodiversity globally and generate huge forest litter. Dry pine needles due to low ignition temperature and high frictional force with the ground catch fire quickly. Annual forest fires in the northern states of India greatly impact the Indian economy besides causing huge loss to biodiversity, livelihood, and environment. Pine needles are also considered unfit for fodder consumption due to presence of tannins. Although the presence of softwood lignin in pine needles makes it difficult to degrade easily, the presence of holocellulose (68.5%) containing 45–51% cellulose makes this biomass a potential substrate to be used in pulp-making industries for low-grade paper sheets. The good fiber length of pine needles (1.3–1.4 mm) with a diameter of 30–32 μm, maybe considered important property for paper making. The use of pine needles in the pharmaceutical and food industries are due to the presence of secondary metabolites (α-pinene, β-pinene, caryophyllene etc.). The various other potential applications of pine needles are for producing bio-ethanol (yield, 3.98%; purity, 94%), biogas (yield, 23.1 L kg−1), smokeless briquettes (calorific value, 18.77 MJ kg−1), biochar (calorific value, 25.6 MJ kg−1), bio-composites (tensile strength, 21–60 MPa), and bio-pesticides. This paper comprehensively reviews the current applications of pine needles along with its future prospective applications that can have the dual advantage of providing employment opportunities to the people along with environmental protection. [ABSTRACT FROM AUTHOR]

The paper examines the impact of macroeconomic variables on CO2 emissions, very few research studies are available to estimate the asymmetric impact and causality. Because of the significance of asymmetries, this paper examines the asymmetric impact of economic growth, crude oil use, and FDI inflows on CO2 emissions in the India wherein COP (Crude oil price) is comprised as the extra variable. The implicate aggressive growth of selected variables over the period 1990–2020 is also assessed. This study uses the methodology ARDL and NARDL model to determine the macroeconomics variable's effects on CO2 emission over the period 1990-2020. Additionally, also applied the EKC (Environmental Kuznets Curve) hypothesis with an application of ARDL and NARDL model. With help of the ARDL and NARDL model, the study shows the results that a rise in economic growth would reduce CO2 (carbon dioxide) emissions while a decrease in economic growth would raise CO2 emissions which indicates an inverted U-shaped Curved relationship between economic growth and CO2 emissions. The positive and negative shockwaves in COP (crude oil prices) have a satisfactory and substantial impact on CO2 emissions as well. Besides, the crude oil consumption with positive shockwave confirmations has a positive and substantial impact on CO2 emission. In addition, the results of FDI inflows support the pollution heaven hypothesis. In light of these outcomes, this paper also recommended policy implications and future research, the policy implications are where the descending flow of FDI allows limited space to India in FDI selection; however, the existence of emission merging and implementation of carbon pricing may facilitate India in achieving its environmental targets. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers. [ABSTRACT FROM AUTHOR]

During the last 20 years, the association between polycyclic aromatic hydrocarbons (PAHs) and health risk has become one of the hotspots in the fields of public health and the environment. A bibliometric study of 1392 research articles retrieved from the Web of Science Core Collection (WoSCC) published between 2002 and 2021 was performed to give an in-depth statistical evaluation of research progress and future trends on PAHs and health risk (PHR). According to the findings, the annual output of significant scientific papers increased exponentially. China ranked first among the 86 nations in terms of the number of publications (NP), followed by the USA and India. Logistic regression analysis showed that there was a positive relationship between the second tertile of 180-day usage count (AOR = 1.62; 95% CI: 1.16–2.26) and increased odds of open access publishing after adjustment for the confounders, indicating that open access papers on PHR were more preferred over the preceding 6 months than non-open access articles. The most popular terms were "PAHs," "risk assessment," and "source identification." According to the bibliometric study, the research hotspots that require more exploration include identifying PAH sources in media such as soil, water, dust, and food and evaluating their linkages to health hazards using appropriate risk models. Understanding the environmental behavior, bioavailability, and health concerns of PAHs and their derivatives in various media is critical for environmental and public health protection. This paper provides an overview of current research status and future perspectives for PHR research. [ABSTRACT FROM AUTHOR]

Mercury pollution, with India ranked as the world's second-largest emitter, poses a critical environmental and public health challenge and underscores the need for rigorous research and effective mitigation strategies. Nanocellulose is derived from cellulose, the most abundant natural polymer on earth, and stands out as an excellent choice for mercury ion remediation due to its remarkable adsorption capacity, which is attributed to its high specific surface area and abundant functional groups, enabling efficient Hg(II) ion removal from contaminated water sources. This review paper investigates the compelling potential of nanocellulose as a scavenging tool for Hg(II) ion contamination. The comprehensive examination encompasses the fundamental attributes of nanocellulose, its diverse fabrication techniques, and the innovative development methods of nanocellulose-based nanocomposites. The paper further delves into the mechanisms that underlie Hg removal using nanocellulose, as well as the integration of nanocellulose in Hg detection methodologies, and also acknowledges the substantial challenges that lie ahead. This review aims to pave the way for sustainable solutions in mitigating Hg contamination using nanocellulose-based nanocomposites to address the global context of this environmental concern. [ABSTRACT FROM AUTHOR]

Recycling solid plastic waste is helpful for resource utilisation in a sustainable manner, and this can lead to reverse logistics to recover waste in supply chain management. In developing economies, it is very essential to recover waste and recycle it, as mismanagement of it could hasten the development of negative socioeconomic and environmental issues. This paper investigates plastic waste recycling by means of reverse logistics management. We feature Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi-criteria decision-making approach to selecting a suitable warehouse that will assist the decision-maker and supply the product to the re-manufacturing cell by inspecting the waste. The paper also focuses on the carbon emissions that are emitted during re-manufacturing through a mathematical model that minimises the total cost and total carbon emissions. A case study of the plastic re-manufacturing industry in Tripura, India, is shown here to present the steps of the decision support system. The model is programmed with real-life data from the case study and yields a total cost of Rs. 2894.37 and total emissions of 414.15 gm/lt, when the disposal cost is considered; without it, the total cost is Rs. 2668.69 and the total emissions are 413.54 gm/lt. The results obtained are promising for reducing carbon emissions from the recycling process of plastic waste at a lower cost. This research will also help the environment cope with plastic waste, which has become a threat to nature, as well as carbon emissions that come from the industry. Finally, some managerial insights and suitable analyses with pictorial representation are proposed. [ABSTRACT FROM AUTHOR]

Waste foundry sand (WFS) is the by-product of the foundry industry, which is produced about 0.6 tons per 1 ton of foundry industry production. While it cannot be recycled or reused, it will be disposed of in landfills. Today, with increasing attention to environmental issues, the reuse and recycle of materials because of limited resources have been considered. Due to the desirable properties of WFS, many studies have been done on the properties and performance of its use in various industries, especially in concrete technology. The purpose of this paper is to conduct a bibliometric analysis of foundry sand research during the years 1971-2020. Various aspects, such as document types, languages, major journals, key countries, authors, and keywords, have been examined. The collaborations among authors and countries were constructed, visualized, and evaluated through the application of the social network analysis method based on co-authorship relations. Also, keyword cluster analysis has been performed using co-occurrence relations to discover the most prominent issues related to WFS. The results showed that the number of publications (TP) has improved significantly in recent years, especially in 2018-2020. It has increased from 1 in 1971 to 38 in 2020. The engineering subdivision with 33% has had the highest number of papers. Also, India, China, and the USA have the highest number of publications, respectively. Statistical data from the author keyword study showed that in general, papers can be classified into three categories in terms of subject: first, characteristics of WFS and its importance; second, the use of WFS in concrete and the study of mechanical and physical properties as one of its applications; and the third, investigation on environmental effects and damage caused by disposal and landfill of WFS and efforts to find solutions for recycling and reuse. The keywords "Waste Foundry Sand," "Foundry Sand," and "Compressive Strength" with a growth rate of 2700%, 1900%, and 1100%, respectively, are important topics in the field of WFS research. [ABSTRACT FROM AUTHOR]

Solar still is the easiest method to purifying the saline water for domestic usage, but this method needs much improvement for better performance since it has lower productivity. In this paper, an experimental investigation is completed to analyse the semi cylindrical solar productivity still. The semi-cylindrical still is incorporated with direct atomizer and scrap used as an energy storage medium. Experiments are carried out during March 2020 in Kovilpatti, the southern part of India, with a depth of 2 and 4 cm saline water. Comparative performance analysis is made for conventional semi-cylindrical still, semi-cylindrical still with an atomizer, and semi-cylindrical still with atomizer and storage materials. The experimental study shows that the productivity of still increases while decreasing the water depth. Compared with 2-cm and 4-cm depth, solar still with 2-cm depth gives promising distillate productivity. The solar productivity still having 4-cm depth of water without atomizer gives only 2670 ml; on the other side, 2-cm water depth without atomizer gives 3100-ml productivity. The maximum productivity of solar still is found on a semi-cylindrical solar still combined with atomizer and energy storage medium having 2-cm water depth. Results revealed a higher freshwater production rate of 3610 ml found while incorporating atomizer and energy storage with the solar still. The system efficiency improved up to 35.20% compared to conventional semi-cylindrical still with 4 cm of saline water depth. [ABSTRACT FROM AUTHOR]

The paper focused on occurrence, characterization, and analytical methods of microplastic (MP) pollution in the lentic environment mainly for the Indian scenario. To understand the flow of MP from plastic waste, a material flow diagram was developed using STAN, assigning the transfer coefficients based on existing scientific literature and primary survey from local recycling facilities and industries. The quantity, morphology, and polymers of MP in the water and sediments of the lentic environment were compared for various states from 2011 to 2022. The reasons for the geographical heterogeneity in microplastics may be the migratory routes of MPs in the ecosystems like commercial uses and wastewater characteristics which possibly discharged in lentic system. Factors like particle density, water surface area, water surface depth, wind speed and direction, and water flow size mainly affect MP concentrations in the lentic water body, and mainly PHI and PLI are keys to MP risk analysis. The surface characteristics of MPs reveal that it absorbs many toxic contaminants including heavy metals. The impacts of MP on ecosystem and human health were also discussed. The impacts of socioeconomic conditions on MP concentrations for different states in India were also added. Proposed methods for plastic waste generation control also included which will help for developing policy in future to prevent MP pollution in lentic environments and also motivate future researchers to establish new standardized methods of MP analysis. [ABSTRACT FROM AUTHOR]

The focus of sustainable development goals (SDGs) is to promote the use of renewable energy so that countries can achieve better environmental quality. However, the progression is plodding, and still, 80% of energy comes mainly from conventional sources in developing countries. The implementation of procedures depends on the political attitudes, political stability, and quality of institutions. India has a diverse political structure ranging from central government to state government to local governments. In the late '80 s, India witnessed a stiff rise in regional and national political parties, which leads more political competition. This paper tries to explain the possible relationship between political competition and CO2 emission in India. With the application of the time series non-linear ARDL (NARDL) model, this study tries to find the asymmetric relationship between political competition and CO2 emission. In our empirical model, we also include other important elements of environmental quality like innovation and fossil fuel consumption. Empirical results show that political competition is asymmetrically related to CO2 emissions in the long run. Fossil fuel consumption and innovation also have a significant relationship with emissions. Based on the results, a few policy recommendations have been discussed. [ABSTRACT FROM AUTHOR]

This paper describes the process of extracting ethanol from Jatropha curcas and its various blending effects on spark-ignited engine performance for environmental sustainability. Alternatives to conventional fuel sources have to be found because of the depletion of fossil fuels and stringent regulations. Every day, the growing population and improved transportation increase the energy demand. Bioethanol is an effective substitute for gasoline and SI engine diesel. Worldwide, passenger cars typically blend 10% bioethanol with gasoline. Some nations, like India, have stated plans to blend 20% bioethanol with gasoline starting shortly. From leftover jatropha deoiled cake (JDC), bioethanol was produced utilizing the fermentation and vacuum distillation methods. Four different blends were prepared on a volumetric basis at different engine speeds at a constant compression ratio of 10:1 and the wide-open throttle was tested for various performances and emissions. Bioethanol enrichments reduce CO and CO2 emissions but increase nitrogen oxide emissions. JDCE 15 was found to have the best engine performance out of all the fuel blends tested. This study suggests that, if NOx emission reduction measures are carried out, JDC can be used as a source for the manufacturing of second-generation bioethanol. [ABSTRACT FROM AUTHOR]

Parboiling rice mills produce a large amount of wastewater. The effluent from the rice mill contains high concentration of organic and inorganic compounds. Continuous discharge of this wastewater from rice mills could be a major cause of eutrophication of the water bodies, leading to a robust and undesirable growth of vegetation and algal bloom. Eutrophication leads to deoxygenation of water bodies, resulting in the mortality of aquatic flora and fauna. It is therefore very important to treat wastewater and ensure safe disposal. There are various types of processes already in existence, but the most important thing is to select a cost-effective technique. Phytoremediation technique has shown promising results for low-income countries like India. It is useful for the small-scale industrial and domestic wastewater treatment. It is a slow process, requiring a large area of land. It is an eco-friendly approach and ideal for rural areas where there is ample land availability. This paper addresses the techniques used by various researchers for rice mill wastewater treatment and also focuses on reusing and recycling of waste from rice mills. The performance of every technique is discussed in detail with its specific advantages and disadvantages. This paper also focuses on the advanced techniques of water treatment with valuable by-products such as silica, activated adsorbent, electricity and methane gas. [ABSTRACT FROM AUTHOR]

In this paper, the BASIC (Brazil, South Africa, India, and China) countries, which play an increasingly significant role in combating climate change, are selected as the research objects. First, we investigate the driving factors of the evolution of carbon emissions in the BASIC group from 2000 to 2016. Second, we use the Monte Carlo simulation to analyze the potential evolution trend of carbon emissions in the benchmark scenario, optimization scenario, and enhanced scenario in the BASIC group from 2017 to 2030 to judge whether the countries can achieve their own autonomous contribution goals. Third, we evaluate whether the BASIC INDC can provide practical support for making the global temperature control goals described in the Paris Agreement. The results show that first, the contribution rate of the energy consumption structure and population size to the carbon emissions of the BASIC are low, and the contributions of R&D efficiency, R&D intensity, and investment intensity to the carbon emission change in the BASIC group are more prominent. Second, under the benchmark scenario, Brazil, South Africa, India, and China from the difference between the complete INDC targets respectively is 265.50 Mt, 207.86 Mt, 1034.70 Mt, and 4660.82 Mt. Under the optimization scenario, China could meet the INDC goals. The gap between Brazil, South Africa, and India is 60.06 Mt, 9.50 Mt, and 413.74 Mt, respectively. Under the enhanced scenario, China and South Africa could meet the INDC target, while Brazil and India are 15.69 Mt and 228.28 Mt away from the INDC target, respectively. Third, although the INDC goals of Brazil and India can achieve the carbon reduction allocated under the 2 °C targets, the INDC targets submitted by the BASIC are not enough to reach the carbon reduction allocated under the global 1.5 °C destination; Brazil, South Africa, India, and China's gap is 64.14 Mt, 246.15 Mt, 236.84 Mt, 3399.64 Mt, respectively. Fourth, Brazil, South Africa, and China should increase its R&D on energy-saving and emission reduction technologies, and India should develop reasonable population policies and green economic development policies. [ABSTRACT FROM AUTHOR]

Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly important for policy-makers to formulate scientific and reasonable low-carbon environmental protection policies. To accurately predict the CO2 emissions of the world's major economies, this paper proposes a new fractional grey Bernoulli model (FGBM(1,1, t α )). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1, t α ) model. The new model can be transformed into other grey prediction models through parameter adjustment, so the new model exhibits high adaptability. Second, this paper employs four carbon emission datasets to establish a grey prediction model, calculates model parameters with three optimization algorithms, adopts two evaluation criteria to evaluate the accuracy of the model results, and selects the optimization algorithm and model results that yield the highest model accuracy, which verifies that the FGBM(1,1, t α ) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1, t α ) model to predict the CO2 emissions of the USA, India, Asia Pacific, and the world over the next 5 years. The forecast results reveal that from 2020 to 2024, the CO2 emissions of India, the Asia Pacific region, and the world will gradually rise, but that in USA will slowly decline over the next 5 years. [ABSTRACT FROM AUTHOR]

The nexus of remittances and CO2 emission is very important and gathers a significant place in empirical research. This paper tries to find out the asymmetric relationship between carbon emissions, remittances, and financial development in India for the period 1980–2014. Based on the theoretical linkages, we develop a nonlinear ARDL model with the use of time series data in this study. The results of the NARDL bound test suggest that there is long-run cointegration among the variables. The findings show that positive shock in remittances causes an increase in CO2 emissions, where negative shock reduces it. The coefficient for financial development is positive but becomes statistically insignificant. Empirical results also support the existence of asymmetric long-run relationship among the variables. Based on the findings, the paper recommends the proper channelization of remittances and financial development towards environment-friendly energy sources and projects without compromising economic growth. [ABSTRACT FROM AUTHOR]

The related literature reveal that the papers on environmental pollution do not sufficiently analyse the cement production which is an important determinant of air pollution and health problems by using econometric methods. To fill this gap, this paper aims to examine the relationship between cement production, air pollution, mortality rate, and economic growth by employing MS Bayesian Vector Autoregressive (MScBVAR) and Markov Switching Bayesian Granger causality (MScBGC) approaches from 1960 to 2017 for China, Brazil, India, Turkey and the USA. MSIA(2)-BVAR(1) model for China, MSIAH(2)-BVAR(3) models for India, MSIAH(3)-BVAR(2) for Brazil, and MSIAH(3)-BVAR(1) for Turkey, and MSIAH(2)-BVAR(2) for the USA were selected. The MScBGC results revealed that the cement production is granger cause of mortality rate, air pollution, and economic growth in all regimes for China, India, Brazil, Turkey, and the USA. [ABSTRACT FROM AUTHOR]

India is one of the highly developing countries in the world and it has the second-largest agricultural source of income, which covers 61% of the entire income of the country. The most valuable income group, by giving the appropriate training in this technology, will make their entire country to become one of the most highly developing counties in the world. In recent years, many developing, developed, and underdeveloped countries face shortages of fish, fruits, and vegetables due to natural disasters like earthquakes, tsunami, and any other unexpected events. Now the main issue of this paper is to preserve the food products from post-harvest to consumer-level, which cover 60% of losses due to the unavailability of preservative methods. This paper mainly focused on the conventional methods to advanced solar drying technologies for perseverating fish, fruits, and vegetables and also it discusses the technology used for drying the yield range of fish like Atheriniformes, Catfish, Chilwa, etc. Fruits like banana, mango, and papaya, and vegetables like bitter gourd, cabbage, and cocoa beans have been reviewed and also discussed some problems along with their solutions in concern with food products drying, thereby the selection of dryer for drying products will be made easy by this review article. On the whole, this investigation would help researchers in fish drying to choose the better drying methods for acquiring better results for particular fish, fruits, and vegetables that enable any entrepreneur to select the appropriate method reducing the cost and time. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the dynamic nexus between economic complexity index (ECI), technological development (TIN), human capital (HC) and environmental quality in India for transition towards a sustainable environment. This study is based on secondary data covering the period from 1985 to 2018. For empirical analysis, this study applied "Stochastic Impacts by Regression on Population, Affluence, and Technology" (STIRPAT) model framework under the estimation of autoregressive distributed lag (ARDL) model and vector error correction model (VECM) model. The empirical findings of model 1 show ECI, TIN, HC and urbanization (URB) as the helping hands to mitigate the problem of environmental degradation by shrinking the level of EF, whereas for model 2, ECI and TIN failed to influence the CO2 emissions, but HC served as a stimulant for environmental quality enhancement by declining the level of CO2 emissions. In contrast, GDP growth and URB strengthen the CO2 emissions levels. Moreover, in VECM framework, estimated findings reveal that the covariables Granger-cause EF and CO2 emissions, inferring that causality flows asynchronously from its covariables to EF and CO2. Impulse response function (IRF) revealed that the responses in EF and CO2 emissions ascribed to changes in its covariables. The outcome of the study has some implications for environmental policy strategists to prepare sustainable environment policies and other responsible authorities for sustainable development goal (SDGs), academician and scholars. All the stakeholders involved in environmental economics and policymakers can evaluate this study to design proper policy framework with respect to the environment. There are few studies that explore the dynamic nexus between ECI, TIN and HC with environmental quality in the control environment of URB and GDP growth using the STIRPAT model for India. [ABSTRACT FROM AUTHOR]

Over the past decades, air pollution has turned out to be a major cause of environmental degradation and health effects, particularly in developing countries like India. Various measures are taken by scholars and governments to control or mitigate air pollution. The air quality prediction model triggers an alarm when the quality of air changes to hazardous or when the pollutant concentration surpasses the defined limit. Accurate air quality assessment becomes an indispensable step in many urban and industrial areas to monitor and preserve the quality of air. To accomplish this goal, this paper proposes a novel Attention Convolutional Bidirectional Gated Recurrent Unit based Dynamic Arithmetic Optimization (ACBiGRU-DAO) approach. The Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) model is determined in which the fine-tuning parameters are used to enhance the proposed method by Dynamic Arithmetic Optimization (DAO) algorithm. The air quality data of India was acquired from the Kaggle website. From the dataset, the most-influencing features such as Air Quality Index (AQI), particulate matter namely PM2.5 and PM10, carbon monoxide (CO) concentration, nitrogen dioxide (NO2) concentration, sulfur dioxide (SO2) concentration, and ozone (O3) concentration are taken as input data. Initially, they are preprocessed through two different pipelines namely imputation of missing values and data transformation. Finally, the proposed ACBiGRU-DAO approach predicts air quality and classifies based on their severities into six AQI stages. The efficiency of the proposed ACBiGRU-DAO approach is examined using diverse evaluation indicators namely Accuracy, Maximum Prediction Error (MPE), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The simulation result inherits that the proposed ACBiGRU-DAO approach achieves a greater percentage of accuracy of about 95.34% than other compared methods. [ABSTRACT FROM AUTHOR]

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution. [ABSTRACT FROM AUTHOR]

Cadmium pollution turns out to be a global environmental problem. This study conducted a quantitative and qualitative bibliometric analysis based on 9188 research items from the Web of Science Core Collection published in the last 20 years (2000–2020), presenting an in-depth statistical investigation of global freshwater cadmium research progress and developing trend. Our results demonstrated that the researchers from China, the USA, and India contribute the most to this field. The primary sources of cadmium are mining, industry, wastewater, sedimentation, and agricultural activities. In developing countries, cadmium exposure occurs mainly through the air, freshwater, and food. Fish and vegetables are the main food sources of cadmium for humans because of their high accumulation capability. Source evaluation, detection, and remediation represent the main technologies used to clean up cadmium-contaminated sites. To mitigate the risk of cadmium contamination in freshwater, biomarker-based cadmium monitoring methods and integrated policies/strategies to reduce cadmium exposure merit further concern. [ABSTRACT FROM AUTHOR]

The last mile connectivity through public transport is a challenging task in India. However, according to the Society of Indian Automotive Manufacturers (SIAM) statistics, three-wheelers sales increased by 10.27% in the year financial year of 2018–2019 compared to the previous financial year. In this growth, it is recorded that the passenger carrier three-wheeler sales increased by 10.62% and goods carrier three-wheeler grew by 8.75% in the year 2019 compared to the previous year. The existing design consideration in the three-wheeler development shows poor performance in the real-world scenario because the three-wheeler's open drive compartment creates more aerodynamic drag to the vehicle. This increases the amount of energy consumption to achieve the same amount of range (km/L). Three-wheeler's extra energy consumption will directly increase the amount of exhaust emission in internal combustion engines and electrical energy consumption in electric vehicles. The present paper attempts in designing a solar-powered electrical auto-rickshaw for rural transportation. The paper aims to obtain an optimal solar module placement angle and analyzes the solar-powered electrical auto-rickshaw performance by incorporating the National Advisory Committee for Aeronautics (NACA) aerodynamic design principles. The optimal solar module placement angle is identified by analyzing the various configurations like front alone tilt at 16°-degree, rear alone tilt at the 5°-degree and combined front at 16°-degree, and rear at 5°-degree to reduce the aerodynamic drag effect. The paper also aims to identify the effect of the optimal angle on vehicle speed, and solar power generation to enhance the performance and energy efficiency for achieving environmentally sustainable transportation. [ABSTRACT FROM AUTHOR]

With ever-improving social and medical awareness about menstrual hygiene in India, the demand for sanitary napkins has increased significantly. The utilization of high-quality and environment-friendly raw materials to produce these pads is further supporting the growth of the market. However, with improving demand and usage, the need for proper disposal techniques becomes more relevant, since all of these pads get contaminated with human blood which makes them a biohazard and can cause significant damage to human health and the environment. One sanitary pad takes around 800 years to degrade naturally and the plastic and super absorbent polymers (SAPs) in sanitary pads are non-biodegradable and can take multiple decades to degrade. Waste management technologies such as pyrolysis, gasification, and resource recovery can be adopted to manage tons of sanitary waste. Currently, sanitary waste treatment mainly focuses on landfilling, incineration, and composting, where biohazard wastes are mixed with tons of solid waste. Disposable sanitary pads have a high carbon footprint of about 5.3 kg CO2 equivalent every year. Innovative solutions for sanitary pad disposal are discussed in the manuscript which includes repurposing of derived waste cellulose and plastic fraction into value-added products. Future aspects of disinfection strategies and value addition to waste cellulose recovered from napkins were systematically discussed to promote a circular economy. [ABSTRACT FROM AUTHOR]

Municipal solid waste management (MSWM) is perceived as a global issue regardless of the place of waste generation. The amount of unmanaged waste is increasing rapidly, along with its impact on the environment and human health. In hilly areas, specifically the North Eastern Region (NER) states of India, due to the unique topography coupled with socio-economic factors, there are inadequate waste management practices marked by insufficient infrastructure, minimal research studies, and limited data availability. This paper comprehensively reviews the existing status of MSWM practices and waste treatment technologies, identifies the challenges, and discusses the prospective approaches for MSWM in NER states of India. NER, is characterized by its hilly terrain and has the most diverse demographic profile in the country. The study highlights the notable increase in waste generation in the urban population in NER. The total amount of waste generated in NER is about 2907 tons per day, with a collection rate of 86.96%, treatment at 31.09%, and landfilling at 33.67%. The biodegradable fraction makes up the majority of waste composition (more than 50%) in NER, followed by recyclables and inert. The existing MSWM consists of waste collection, transportation, and disposal with limited source segregation and treatment. All the states of NER practice open dumping and burning as the primary waste treatment and disposal system. The study discusses the challenges and prospects to ensure effective MSWM in NER. This review is a region-specific study that considers cultural diversity, topography, and socio-economic dynamics. The outcome of this review will be helpful to the researchers and policymakers in making appropriate waste management plans and improve the MSWM system in NER. [ABSTRACT FROM AUTHOR]

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000–2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient (r ), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of r , MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models. [ABSTRACT FROM AUTHOR]

This paper analyzes the economics of a grid-interactive rooftop solar photovoltaic (PV) system and the impact of the temperature on it. The analysis related to energy metrics, lifecycle costing, and environmental economics was performed considering the PV system's life as 30 years. The system economics is also compared at different conditions like theoretical, temperature-corrected, and real electricity generation data. The parameters like energy payback time (EPBT), energy return on energy invested (EROI), and lifecycle conversion efficiency are determined as 5.95 years, 5.04, and 0.078, respectively, based on actual generation. The unit electricity cost of the rooftop PV system was estimated as INR 5.37 at the 5% interest rate. The electricity cost varies with the interest rate variation and operation system life. The results show a reduction in overall economic performance on the increase in module temperature. The effect of temperature on the economics of the system is presented in terms of the per degree rise of module temperature. One degree increase of module temperature 8.5 days in EPBT of the PV system increases, and INR 0.021 increases in the unit cost of electricity considering a reference temperature 25 °C. A PV system has environmental benefits by reducing greenhouse gas emissions, which are also affected by the rise of module temperature. The system lost INR 355.93 in carbon credits at an increase of one-degree module temperature. [ABSTRACT FROM AUTHOR]

Globally, E-waste is experiencing an unprecedented growth in the recent years. This growth will be fueled further by the COVID-19 pandemic owing to the new work culture where people are becoming more dependent on their electronic products than ever before. However, governance of E-waste, particularly in the Global South, has been a complex phenomenon. Considering this, the current study attempted to assess the design, adoption, and implementation of E-waste policies in India—a major electronics manufacturing hub with a massive consumer electronics market. Taking hints from theoretical concepts such as policy transfer, policy convergence, and policy effectiveness, the study addressed the primary research question: why India adopts E-waste policy approaches that seem inadequate and ineffective in its local contexts and attempts to identify alternative approaches. Through expert interviews and policy document analysis, it was observed that E-waste policy approaches in India are largely influenced by the European Union's Waste Electrical and Electronic Equipment (WEEE) Directive. All the experts interviewed recognized absence of the informal sector in India's policy efforts of both 2011 and 2016 as a significant lacuna in the country's E-waste policy responses. In this paper, the author argues that there should be policy change towards a healthy collaboration between the informal and formal sector where best-of-the-two-worlds could be wisely used for sustainable E-waste governance in India. [ABSTRACT FROM AUTHOR]

Use of biodiesel in diesel engine helps to reduce HC, CO, and smoke emissions due to their enormous oxygen content, whereas NOx emissions formed by Zeldovich mechanism shoot up. Implementation of Bharat Stage (BS) VI by April 2020 in India has created extreme pressure on automobile manufacturers to include after treatment technology in their systems. Selective catalytic reduction (SCR), a NOx control technology, is operated using aqueous urea solution as the reductant. There are several parameters that need to be monitored to enhance the NOx conversion efficiency of SCR retrofit. The uniformity index of ammonia, which determines the conversion efficiency, is greatly influenced by parameters like exhaust gas temperature, injection angle, injector position, mass flow rate, and SCR geometry. This paper considers two types of SCR design, namely SCR with and without mixer design and their impact on NOx reduction. The effect of mass flow rate on urea conversion in SCR design without mixer is 27%, but the impact is reduced greatly in SCR design with mixer with less than 2% variation. The UI resulting from different cases ranges from 0.59 to 0.83. Using Taguchi technique and CFD tool, the impact of parameters on both the SCR designs has been investigated and the optimum SCR design is reported. [ABSTRACT FROM AUTHOR]

For millennium, mining sector is a source not only of mineral extraction for industrialization, economic expansion, and urban sprawling, but also of socio-environmental concern. It, therefore, has been the central attention of the business and public policy sustainable development scheme for several years. Thus, gradually, mining industries are getting involved with the concerns such as carbon emissions mitigation and carbon accounting to govern a rhetorical shift towards "sustainable mining". However, there is scarce knowledge about how the emergence of a "green and self-sustaining" forestry reclamation strategy coupled with potential carbon sequestration capacity in degraded mining areas will be an impeccable option for achieving sustainable development goal-13 (SDG-13: climate action) and ecosystem services during United Nation decade of ecosystem restoration. This paper reviews the extent to which reforestation and sustainable land management practices that employed to enhance ecosystem carbon pool and atmospheric CO2 sequestration capacity to offset CO2 emission and SOC (soil organic carbon) losses, as consequences of coal mining, to partially mitigate global climate crisis. Moreover, future research is required on mining innovation concepts and its challenges for designing an SDG impact framework, so that it not only synergies amongst SDGs, but also trade-offs between each individual "politically legitimized post-2015 development agenda" (i.e. UNSDGs) could be depicted in a systematic way. In a developing country like India, it is also an utmost need to assess the environmental impact and economic performance of such technological innovation and its possible synergistic effect. [ABSTRACT FROM AUTHOR]

Currently, the most crucial economic and ecological issues are related to environmental degradation and sustainability. On this backdrop, this paper examines the impact of financial globalization and natural resource rent on load capacity factor, using the novel dual adjustment approach and time–frequency domain causality approaches, in the case of India. This study contributes to the extant body of knowledge in the area of environmental economics. First, it is the first attempt to analyze the factors responsible for load capacity factor, specifically for India. As such, studies on environmental concerns on both the supply and demand sides are put into consideration. Empirical results show that only renewable energy consumption lessens the load capacity factor, while economic growth and financial globalization are positively correlated with the load capacity factor, and natural resource rent is insignificant in the short run. In the long run, only economic growth is negatively correlated with load capacity factor, while the other series positively influence load capacity factor. To reap greater ecological merits, policymakers should focus on transitioning from conventional non-renewable energy sources that contribute to rising carbon emissions to more cost-effective and dependable renewable sources of energy that support sustainable growth and a healthy environment. [ABSTRACT FROM AUTHOR]

Rural areas largely lack access to improved drinking water, sanitation, and hygiene (WaSH) facilities in India. This requires documentation of WaSH practices at the local level for better understanding and sustainable development. In this paper, a global positioning system (GPS)-based household survey was carried out in 67 villages of Phagi tehsil using individual questionnaires to evaluate the existing WaSH conditions spatially at the panchayat level. Three sub-indices were used for WaSH risk areas mapping and prediction with the integration of machine learning algorithms. Survey results indicate the improvement in the availability of toilet facilities; however, a gap was found between toilet ownership and its usage by villagers. Data show that only six panchayats have almost zero open defecation practices among the 32 panchayats of Phagi tehsil. The findings highlight that presence of toilets in house, water supply in toilets, and high literacy rate lead to an increase in toilet usage by the population. WaSH index scores indicate that panchayats like Mandawari, Mendwas, Chandma Kalan, and Rotwara have worst conditions and fall in the high-risk category. Moreover, support vector machine regression (SVMR) results reveal that WaSH scores are mainly affected by open defecation (r = 0.94), water supply in toilets (r = 0.92), and female members' participation in sanitation facilities decision-making (r = 0.53), followed by literacy rate (r = 0.33). Findings demonstrate the association between gender inequalities and WaSH conditions, and the potential of the WaSH index as a monitoring tool by local policymakers to shrink the WaSH gaps. [ABSTRACT FROM AUTHOR]

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future. [ABSTRACT FROM AUTHOR]

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R2) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021–2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data. [ABSTRACT FROM AUTHOR]

In developing nations, solid residential fuels are the major sources of primary energy for various domestic activities. To date, the emission inventory of inorganic trace gases over National Capital Territory (NCT) was prepared using either default or country-specific emission factors. In this paper, we report (for the first time) the spatial variation of emission factors (EFs) of inorganic trace gases (SO2, NO, NO2, CO, CO2, and CH4) from the residential fuels used in slums and rural areas of NCT determined using dilution chamber in the laboratory. 147 residential fuel samples, including fuelwood, dung cake, crop residues, coal, etc., were collected at 149 NCT locations out of 675 slum clusters and 146 rural villages. The range of EF(s) of SO2 (0.02 ± 0.01 to 0.04 ± 0.01 g kg−1), CH4 (0.10 to 0.34 g kg−1), NO2 (0.01 to 0.02 g kg−1) is lower than the CO (3.55 ± 1.72 to 6.07 ± 1.53 g kg−1) and CO2 (0 to 129.45 ± 46.94 g kg−1). The north and north west districts of NCT are emission hotspots for CH4, NO, and NO2 emissions, whereas, the southern and northern areas of NCT are for CO2. These citywide emission inventories (0.05° × 0.05°) of inorganic trace gases are prepared using laboratory-determined EFs and available consumption data determined by recent survey information. Among solid residential fuels, fuel wood, and dung cake are two major contributors to inorganic trace gases in NCT. [ABSTRACT FROM AUTHOR]

By 2040, India hopes to have completed its energy supply to fulfill the country's rising energy demands. Renewable and conventional sources must be used in an environmentally acceptable manner to achieve sustainable growth. India must enhance its use of renewable and clean energy sources, including geothermal, wind, and solar, to satisfy its growing demand. While solar and wind energy output has increased significantly, geothermal energy has yet to be fully harnessed. Among the many forms of geothermal energy found on the surface are volcanoes, fumaroles, erupting geysers, steaming fields, and hot springs. A total of about 340 geothermal springs may be found in India, spread throughout both orogenic (in the Himalayas) and non-orogenic (in the Peninsula). There were 31 places extensively evaluated, and deep drilling was performed in sixteen of them. Average temperatures range from 35.0°C to the boiling point of water in these springs. Medium (100.0–200.0°C) and low (100°C) enthalpy geothermal energy resources/systems are found in India, with the latter being the most abundant. The essential component of a geothermal system is understanding the heat source and harnessing it. Studies so far have indicated that some geothermal areas have sufficient geothermal potential for direct heat usage and power generation. If the Puga (J&K) field is explored to a depth of at least 500 m, reservoir simulation studies have shown that it might produce more than 3 MW of power. India's diverse geothermal sites and the current status of exploration for future research are discussed in the paper. [ABSTRACT FROM AUTHOR]

India's sustainable development goals consist of higher economic growth through large investments on the one hand and ambitious carbon emission reduction plans through increased renewables on the other. It needs to be seen if the two policies related to capital formation and energy transitioning to renewables complement each other or if they have been divergent in the case of India. This paper studies the dynamic association between carbon dioxide emissions, economic growth, renewable energy (RE) consumption, and gross capital formation and tests for the existence of Environmental Kuznets Curve (EKC) hypothesis for India over the time period 1970–2018. It also tries to see if there is any possible conflict between the economic and energy goals of the country by augmenting the interaction term between renewable energy consumption and gross capital formation in the EKC framework. The empirical results not only confirm long-run relationship among the underlying variables but also indicate an "N"-shaped EKC in the long run for India which is a departure from the traditional inverted U-shaped EKC hypothesis. Renewable energy consumption is found to reduce emissions, whereas gross capital formation and the interaction term between renewable energy consumption and gross capital formation are found to raise emissions in the long run. The study concludes that India needs to align its economic policy of "Make in India" with its energy policy so that investments under the former facilitate extensive penetration, adaptation, and usage of renewable energy. A policy dichotomy between the two goals may defeat India's Intended Nationally Determined Contribution (INDC) objective of drastic reduction in carbon dioxide emissions through increased renewables by 2030. [ABSTRACT FROM AUTHOR]

This study deals with the pollution impact of biomedical waste (BMW) generation due to the COVID-19 pandemic at both the global and national levels. This discussion is important in light of clear scientific evidence that, apart from the airborne transmission of the disease, the virus also survives on different surfaces and poses the risk of infection. Moreover, an investigation is conducted on BMW generation in tons/day in India during the COVID-19 period, with implications for future projection. Additionally, a pioneering study was conducted to estimate the usage of facemasks during the COVID-19 pandemic in India. This paper also provides a feasible solution, by adopting a modern perspective, towards managing BMW generated in the context of SARS-CoV-2 at isolation wards and crematoriums. Strategical approaches have been suggested for segregating and safely disposing BMW. The latest availability of disposal facilities is discussed based on source data provided by the Central Pollution Control Board (CPCB), India. Among the many disposal methods, incineration technologies are examined in depth. The impact of existing incineration technology on the environment and human health has been extensively studied. This study suggests strategies for controlling BMW generation during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The present work deals with a detailed study of India's Damodar river basin's pollution profile (groundwater, surface water, industrial water, and mine water). The present paper aims to create a large data bank comprising the latest (2019; through water sampling and analysis) and historical (1980–2018; through literature survey) data on heavy metal loads (HML) and other hydrochemical parameters in water bodies of the Damodar basin (at 99 sampling locations). Bivariate and multivariate statistical analyses were applied to determine the possible sources of the HML in the groundwater and surface water. Anthropogenic inputs from industrial effluents and mining activities were identified as the sources of the HML. The degree of HML exposure (Fe, Cu, Zn, Mn, As, Co, Cd, Hg, Cr, Ni, and Cu) was considered for computing the heavy metal pollution indices (HMPI). Associated potential health risk to the local population was also studied. Most HMPIs (vary within 50–9000) appeared to cross the critical value (~ 100). According to the results of noncarcinogenic risk, most hazard indices (varied within 0.01–116.34) surpassed the allowable limit (~ 1), demonstrating detrimental health effects on adults and children. Ni, Cd, As, and Cr showed very high cancer indices (varied within 9.5 × 10−5–1.76 × 10−1) that could be considered as high risk (≫ 1 × 10−4, allowable limit) for cancer via ingestion and dermal pathways. A carcinogenic risk assessment map of the basin was also prepared for the first time. Durgapur and Burnpur-Asansol cities have been identified as the most vulnerable areas. The authors also compared the water quality parameters of the Damodar river with other highly polluted and major rivers of India. The authors recommended (i) strict regulation and efficient management of HML monitoring, (ii) initiating public awareness programme about Damodar's pollution, and (iii) a detailed medical survey to understand the impact of water pollution on the population. [ABSTRACT FROM AUTHOR]

In the absence of effective drying techniques, a lot of food gets wasted as there is a lack of post-harvest processes. In India, most of the agricultural produces like paddy, maize, wheat, corn, oil seeds, pulses, chillies, etc. require a temperature range of 50–80 °C for effective drying. Hence, in these conditions, solar drying techniques seem to be the most economical; also, it is safe and eco-friendly. Various types of solar dryers are used across India and worldwide; these are direct solar dryer, green house dryer and indirect solar dryer. Nowadays, indirect type solar dryers are most commonly used because of their several advantages over direct solar dryers. In case of indirect type solar dryers, the products to be dried are kept inside a separate compartment known as drying chamber. Hot air is obtained from the solar collectors either by direct heating method or by using a secondary heating medium and then supplied to the drying chamber for heating of the products. This paper presents a detailed review of various innovative designs of indirect type solar dryers and compares the performance of different types of dryer configuration in terms of collector efficiency, dryer efficiency, drying time and maximum air temperature. Also, the effects of various operating parameters on the thermal performance of such dryers have been discussed. [ABSTRACT FROM AUTHOR]

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO2) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO2 emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO2 emissions; correspondingly, this study builds a model for CO2 emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO2 emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO2 emissions. Finally, this study forecasted a CO2 emission trend, with a slowdown predicted in China by 2022; however, CO2 emission's reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO2 emissions without harming economic development. [ABSTRACT FROM AUTHOR]

The need for electricity all over the world is enhancing significantly. A major portion of this demand is met by fossil fuels leading to substantial mining, environmental pollution, and increment in global warming. The escalating rate of extraction is declining their reserves at an alarming rate and enhancing their price as well. Thus, it has become essential to shift to alternative resources for power generation. Northeast India has a superfluous reserve of renewable energy which can competently fulfill the electricity requirement in the region. The surplus can significantly contribute to supplying the demand of other states in the country. In addition, the use of clean energy can conserve fossil fuels and expenditure and at the same time can slow down environmental pollution. This paper comprehensively analyzes the potential of perennial assets in this area; projects their economic and ecological benefits; and also makes a brief comparison of the conventional systems with the probable green alternatives. Renewable capacity in this regime is found 66,682 MW out of which 99.51% is yet to be explored and Rs. 5.66 × 1012 will be required to extract them. Utilization of these boons can save 187.39 Mt of combustibles and 648.61 Mt of greenhouse gasses from emission. Capital, energy, and carbon that will be invested during the installation and operation of the systems will be paid back in 3.6, 2.3, and 2.25 years, respectively. The average levelized cost of this unremitting energy will be Rs. 4.80/kWh. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 has revamped all aspects of human life including education sector, and it has completely changed the educational environment across the globe. Due to the pandemic, the methodical functions of educational institutions have stopped, and new phases have started like online class, online evaluation, and indoor activities. Students are the vital players in education sector, and their opinions play an indispensable role while formulating the policies by the government. In the pandemic, students' perspective on education environment finds new dimension. Therefore, this paper has made an honest attempt to know the coastal Karnataka students' stance on college educational environment. In this study, six major components of education environment are considered, namely (1) online class, (2) teaching and learning, (3) evaluation, (4) college administration, (5) extracurricular activities, and (6) teachers. The study is descriptive in nature, and data was collected from 347 college students of Coastal Karnataka. To support the main objective, a hypothesis has been developed with the help of review of literature and is tested by using ANOVA and independent t-test. The path analysis is used to analyze the casual relationships among components of college educational environment, overall perception, and student performance. The result of the study found that the college students have positive perception towards online class (M=3.14), teaching and learning (M=3.704), evaluation (M=3.38), college and administration (M=3.83), extracurricular activities (M=3.87), and teachers (M=3.63). The result of the hypothesis testing revealed that there is no significant difference in the student perception towards various components of college education system. Path analysis results show that there is relation and effect between components of college education, overall perception, and students' performance. The study concludes that students agree with policies and actions taken by colleges to carry out classes during COVID-19 pandemic irrespective of demographic and educational difference. [ABSTRACT FROM AUTHOR]

Characteristics of pulverized coals have significant influence on the spontaneous combustion and explosion processes. This paper presents an experimental and theoretical framework on physico-chemical characteristics of coal and their interrelations from spontaneous combustion and explosion perspectives. The chemical properties, morphology, bulk density, particle size, and specific surface area of pulverized coals from nine different coal subsidiaries of India are vividly investigated in five distinct sizes. Moreover, the effects of particle size on bulk density, specific surface area, and N2 adsorption capacity of pulverized coals are critically analyzed. With decrease in particle size, the bulk density of pulverized coals decreased, and the specific surface area and N2 adsorption capacity increased. The relationships of bulk density and specific surface area of pulverized coals with particle size are established. Moreover, the specific surface areas determined by both the particle sizing and BET methods are compared, and correlation factors between them are determined. This study generated insightful coal characteristic data, which can be useful for furthering research on spontaneous combustion and explosion involving pulverized coals. [ABSTRACT FROM AUTHOR]

This paper compares four prediction methods, namely random forest regressor (RFR), SARIMAX, Holt-Winters (H-W), and the support vector regression (SVR), to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models and suggest an effective model for the prediction of total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crops in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE) highlights the differences in accuracy among the four models. The mean absolute percentage eror (MAPE) and the mean square error (MSE) for the four methods are RFR (MAPE: 5.67; MSE: 549,900.02), SARIMAX (MAPE: 1.67; MSE:70,422.35), H-W (MAPE:0.75; MSE:16,648.58), and SVR (MAPE: 0.91; MSE: 17,832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the support vector regression (SVR) are relatively low as compared to SARIMAX and RFR. Based on these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crops. Holt-Winters model predicted 14,364.97 for the year 2025, and SVR predicted 13,696.67 for the year 2025. The decision-maker can use these predictions to build a suitable policy for the future. This approach can be contrasted with other forecasting methods, such as the neural network, and train the model to achieve better forecast accuracy. [ABSTRACT FROM AUTHOR]

Study was conducted utilizing a confirmed medication adherence scale to measure the socio-demographic profile, self-care, and medication adherence among Type-2 Diabetes Mellitus (T2DM) patients. The Fisher exact test was used to calculate the level of significance (P) using SPSS (Statistical Package for Social Sciences) Version 21.0. The research presented in this paper uses statistical evidence to assess the numerous aspects that may be linked to medication adherence. A prospective observational study was undertaken on participants visiting the outpatient department for 6 months at a North Indian tertiary care hospital to investigate the pattern and quality of life associated with T2DM. The study examined those who had T2DM for more than 2 years. At the time of the visit, subjects were interviewed using socio demographic information and a structured verified questionnaire. Fisher exact test was used to identify the parameters that were linked to medication adherence, with P0.05 being regarded statistically significant. A total of 350 T2DM outpatients were followed up on, with a male-to-female ratio of 1:0.95. A 13-item medication adherence scale was created and tested, revealing that approximately 32% of participants demonstrated high adherence to anti-diabetic drugs (score = 13). The P value was obtained using Fisher exact test for educational status, occupation, marital status, and the quantity and kind of anti-diabetic drugs was found to be 0.05. Only 32% of the respondents took their diabetes medication as prescribed, indicating the need to improve adherence among T2DM patients. The quantity and kind of anti-diabetic drugs, as well as the patients' educational status, occupation, and marital status, all demonstrated a statistically significant relationship with medication adherence. [ABSTRACT FROM AUTHOR]

Globally, vaccination plays a vital role in controlling the Covid-19 pandemic. However, the cold supply chain is essential for vaccine storage and logistics services. In a country like India, the last-mile logistics of vaccines is a challenging task. The cold chain is indispensable for the Covid-19 vaccine drive to the rural areas. The demand for cold storage increases rapidly due to the rapid Covid-19 vaccine drive. The conventional cold storage facility has a more significant threat to the grid power quality and environmental impacts. The energy demand and greenhouse gas emission of traditional cold storage lead to global warming. The micro cold storage facility has to be developed rapidly to accelerate the vaccine drive to the last mile of the county with reliable and affordable energy sources. In addition, climate change mitigation is ensured by the renewable energy utilization in the Covid-19 vaccine drive. The proposed novel micro cold storage aims to be silent, clean, mobile, without moving parts, and reliable for the last-mile vaccine logistics as a vaccine carrier to the remote rural areas. This paper deals with the novel design, development, and experimental investigation of solar photovoltaic powered thermoelectric-based micro cold storage as a Covid-19 vaccine carrier for rural areas. The design consideration of Covid-19 vaccine storage has been reported. The experimental results ensure the World Health Organization recommended vaccine storage (i.e., vaccine carrier) temperature range of +2 to +8 °C. Therefore, green energy and refrigeration system provide environmental sustainability by mitigating 700kg of annual carbon emission. [ABSTRACT FROM AUTHOR]