Preserving stone-built cultural heritage from environmental degradation poses significant challenges, as moisture ingress and extreme weather accelerate weathering, leading to structural damage and escalating maintenance costs worldwide. While hydrophobic coatings show promise for protection, achieving long-term durability under harsh conditions remains elusive. The present research demonstrates a robust hydrophobic nanocomposite coating based on silica nanoparticles (SiNPs) functionalized with 1 H,1 H,2 H,2H-perfluorodecyltriethoxysilane (PFDTS), synthesized via alkaline hydrolysis of tetraethylorthosilicate (TEOS) and applied by spray coating to diverse heritage stones including sandstone, granite, and marble. The coatings achieve water contact angles of 130°–137° and sliding angles of 9°–10°, conferring exceptional self-cleaning properties that endure after saline exposure, wet-dry cycles, and marine simulations. Additionally, various water absorption tests, including the Karsten tube, ASTM D6489 surface uptake, ASTM C642 immersion tests, and droplet impact tests, showed a significant decrease in water absorption compared to uncoated stones. The overall results suggest that the water penetration at the coated surface was reduced by a factor of about 80–100 for the stone samples. This research study offers a scalable, cost-effective approach to enhance the longevity of cultural monuments, minimising preservation expenses and safeguarding irreplaceable historical assets for future generations.

Rooftop Rainwater Harvesting (RRWH) offers a viable solution to the pressing issue of saline groundwater in regions like Ainavolu, a village in Andhra Pradesh, India. This study examines the potential of RRWH systems to provide a sustainable alternative water source in rural settings faced with water scarcity due to saline groundwater. Firstly, in view of the limitation in terms of spatial resolution associated with satellite imagery, a UAV-based survey is conducted to create a high-resolution orthomosaic of the study region, enabling precise delineation and classification of rooftop materials to estimate harvestable rainwater. Findings of this study suggest that RRWH could significantly alleviate water shortages by potentially collecting approximately 20.16 million litres of rainwater annually. However, despite this substantial capacity, the adoption of RRWH remains limited due to financial, technical, behavioural, and institutional factors. Through comprehensive fieldwork, including focus group discussions and one-on-one interactions, we identified 17 critical factors hindering RRWH adoption. Based on these insights, we propose a tailored roadmap to promote RRWH implementation, incorporating strategies such as partnerships with local vendors, specialized training programs, subsidies, and targeted awareness campaigns. This study not only underscores the practicality of RRWH in offsetting the challenges posed by unsuitable groundwater but also provides a scalable model for enhancing water security through community-based initiatives and technological integration. Since the scenario of water scarcity and responses of residents change with the cultural and economic characteristics, it is suggested to update the factors while adopting the proposed framework.

Emerging trends in IoT and Drone technology are revolutionizing environmental monitoring through effective data collection and analysis. This research proposes a novel geospatial data sensing platform mounted on a Unmanned Aerial Vehicles to collect selected environmental parameters including moisture, temperature, and PM2.5. The designed platform is built using Arduino Mega micro controller, PM2.5 sensor, GPS sensor, and a DHT sensor enabling to collect geospatial data. The collected data is further stored on a SD card embedded within the designed platform. The stored data can be further processed and visualized using an open source GIS environment. For demonstration, the data is collected within a University campus located in Andhra Pradesh, India. The recorded data analysis shows that the mean temperature is 39.4°C with a variance of 9.2°C, mean humidity is 29.2% with a variance of 82.0%, and mean dust concentration is 143.6 mg/m3 with a variance of 5.3 mg/m3. The applications of the developed tool can be extended to various other potential applications such as precision agriculture, climate monitoring, and disaster management.

Climate change poses a risk to the human societies and environment, encouraging a shift towards clean energy sources. Among these sources, offshore wind energy emerges as a favorable solution, due to its steady and strong wind resources, coupled with mature technology. Establishing offshore wind farms requires substantial financial investment. However, uncertainties induced by climate change may not only impact the cost-effectiveness of offshore wind farms but also influence the suitability of regions for their development. Therefore, the present study presents a novel framework for identifying optimal regions for off-shore wind farms by considering future projections under the various Shared Socioeconomic Pathway (SSP) scenarios. A weighted multi-model ensemble (MME) of ten CMIP6 climate models was considered. Offshore wind energy resource are classified based on resource richness, stability, risk, and economic viability. Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to assign weights to each factor, offering insights into their influence on wind resources. The findings reveal that projections for the SSP2-4.5 and SSP5-8.5 scenarios show that the western and northeastern offshore regions within the study areas have emerged as the top-ranking regions due to their abundant wind energy resources and favorable stability, risk and economic factors. By employing a novel methodology, this study produces suitability maps that identify promising wind regions for future development, providing important information for long-term planning in India’s offshore wind sector.

Renewable energy sources, free of environmental risks, are vital for achieving net-zero CO2 emissions and addressing climate change to meet Sustainable Development Goals. This study explores the evolution of solar energy research using bibliographic coupling and keyword co-occurrence analysis of 6,460 articles from 1988 to 2024. The findings reveal a significant increase in solar power-related publications, with China leading in research output, followed by the United States and India. Top journals include Renewable Energy and Energies, with a growing focus on Energy and Engineering. This analysis serves as a vital reference for solar energy researchers and professionals.

Solar energy is one of the widely accessible renewable energy resources, offering a wide range of applications from thermal uses to electricity production. The technology available to harness solar energy is popular; it has turned into a plausible alternative renewable source. This chapter provides insight into how solar energy can be harnessed for both residential and commercial purposes, highlighting its traditional roles in drying and passive temperature regulation alongside contemporary advancements in solar thermal and photovoltaic (PV) technologies. These modern applications not only produce electricity but also generate thermal energy for processes like desalination, water treatment, and cooking. The adoption of solar technology is promoted by both policy incentives and technological breakthroughs, paving the way for its widespread use across various sectors. India, setting a notable example, aims to achieve a renewable energy capacity of 175 GW, with solar energy contributing 100 GW. The rise of both grid-connected and off-grid solar PV microgrids reflects rapid development, with ongoing research into grid-tied inverters addressing reliability and power quality challenges. Moreover, rooftop solar PV systems are increasingly favored for rural electrification due to their simplicity and cost-effectiveness. This chapter aims to offer a comprehensive overview of solar energy applications, thoroughly examining the technical, economic, and environmental ramifications of these technologies.

Aerial ropeway transit (ART) systems are emerging alternatives to augment existing transit systems in congested cities in the Global South, especially in urban areas with limited transit coverage because of road width constraints or topography. Integration of aerial cable car stations to an existing transit network can improve the overall accessibility of various population segments with significant positive benefits in relation to reducing transport-related social exclusion. This study evaluated the impact of introducing ART in the city of Varanasi (India) and assessed the spatial accessibility improvements to critical facility locations such as heritage sites, educational institutions, hospitals, and employment centers. Several multimodal transit expansion scenarios were considered in this study and the potential benefits of each case were quantified using the two-step floating catchment area (2SFCA) method. A multi-criteria decision-making (MCDM) approach was subsequently employed for identifying the optimal locations of ART stops. Microlevel analysis findings suggest that the mean accessibility values could increase up to 10.92% in the first phase of the ART implementation, which could subsequently increase to 24.7% and 49.8% for the subsequent transit expansion scenarios. The study also investigated the Varanasi ART DPR prepared by Varanasi Development Authority (VDA) and showed that a significant increase of 16% in accessibility levels could be achieved if optimal stop locations identified in this study were implemented. The proposed two-step (2SFCA+MCDM) method for identifying the optimal locations of ART stations in a multimodal transit network is expected to be an effective tool for transit system redesign using place-based accessibility measures.

Elevated fluoride (F⁻) levels in groundwater, primarily due to geogenic processes, pose significant health risks, including dental and skeletal fluorosis and neurological disorders. This study aimed to quantify source-dependent F⁻ exposure at the community level in selected tropical dry regions of Andhra Pradesh, India. These locations include Chintal Cheruvu, Rompicharala, Shantamangalur, Thimmapur, and Nadendla. Community surveys and drinking water sample analyses were conducted in these regions. Dental Fluorosis Index (DFI) was used to estimate exposure levels across age and sex groups. Findings of surveys indicate that groundwater consumption with high F⁻ (4.3 mg/L) results in the highest exposure dose (0.62 mg/kg/day), with Chintal Cheruvu identified as the most affected. A strong positive correlation was observed between exposure dose, water F⁻ content, and the Community Fluorosis Index (CFI), with R² values of 0.98 and 0.97, respectively. Dental fluorosis prevalence exceeded 80% across all age groups, and household surveys revealed 100% unawareness of F⁻ exposure risks. Though there exist many ways to determine the impact of fluoride, the hierarchy of regions may change with the type of parameter chosen. To address this, we developed the Fluoride Impact Index (FII), a multi-criteria index computed considering various parameters indicating the impact of fluoride in a region. The magnitude of FII for Chintal Cheruvu is 0.563 which is highest among the considered regions indicating that it is most impacted region that needs remedial measures first in the hierarchy. Rompicharala with FII as 0.252, Nadendla (0.223), Shantamangalur (0.214), and Thimmapur (0.188) follows the hierarchy. These findings highlight the urgent need to raise awareness about F⁻ exposure risks and to identify sustainable alternative water sources. Immediate interventions, including human health risk assessments using the USEPA approach and the provision of safe drinking water, are critical to achieving SDG-6 of safe drinking water for all by 2030.

Plastic-aggregates are made up from unused or waste plastic and natural aggregates which have recently been emerged as a significant addition to the existing emerging contaminants list mainly in the coastal environment. The transformation from plastics/microplastics to Plastic-aggregates signifies a crucial shift in our understanding and use of plastics and prompting us to reconsider their fundamental characteristics along with possible environmental threats. When plastic waste is incinerated for the purpose of disposal, it combines with organic and inorganic substances present in the surrounding environment, leading to a new type of material. Besides, some natural factors (physical, chemical, biological or in combination) also act upon discarded plastics to combine with rocks and other earthen materials to form plastic-aggregates. Our research aims to build fundamental knowledge and critically review the possible formation process, classification, and possible degradation of all such polymer-rock compounds along with their impact on the ecosystem. The knowledge gap related to the degradation and release of secondary pollutants from these agglomerates is to be addressed urgently in future research. Development and standardization of proper sampling and reporting procedures for plastic-aggregates can enhance our understanding related to their impacts on human health as well as to the entire environment as these aggregates contain different toxic chemicals.

The equitable deployment of Electric Vehicle Charging Infrastructure (EVCI) is essential to address range anxiety and ensure widespread adoption of electric vehicles. This paper aims to identify the unserved areas of Delhi in terms of public Electric Vehicle Charging Infrastructure (EVCI) using a novel accessibility analysis approach. This study addresses accessibility gaps to address the Delhi EV policy's ambitious target of providing 3000-m access to public EV charging stations. Enhanced Two-Step Floating Catchment Area (E2SFCA) method is employed to quantify the accessibility levels to EVCI's at 100 m grid level. Global Moran I and Local Moran I analysis is conducted to identify areas where intervention is required. The location-allocation models indicate that installing at least 105 additional EV charging stations in the urban core and 150 in the peri-urban fringes would allow 93 % of the population to achieve the accessibility targets and an additional service coverage of 176.6 km2. The proposed methodology aims to achieve equitable accessibility to ECVIs which would lead to a better match of the supply-demand gap hence leading to the successful implementation of these infrastructures. The optimized yet balanced growth methodology and case-study for EV charging network expansion presented in this study is expected to aid policymakers in ensuring equity and spatial distributive justice in transportation electrification efforts.

Droughts are one of the most severe natural hazards, and its occurrences are increasingly exacerbated due to climate change. While numerous studies have analyzed drought occurrences using multi-model ensembles (MME) developed considering uniform weights to general circulation models (GCMs), biases inherent in these models impeded the attainment of reliable predictions. Also, studies conducted were region specific and were limited to considering a specific socio-economic pathway (SSP). The inconsistency in findings drawn across different SSPs limits the applicability of these results to implement best management practices to combat drought effectively. In this study, Drought Prone Index (DPI) built on the mathematical framework of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. This index represents the frequency and severity of the possible drought events considering near future (2024–2060) and far future (2061–2100). Further, to overcome the limitation of bias, a multi-criteria decision-making (MCDM) framework integrating CRiteria Importance Through Intercriteria Correlation (CRITIC) and analytical hierarchy process (AHP) methods has been proposed to create differential weighted multi-model ensemble. The proposed framework is demonstrated considering India as study area. Findings of our study indicate a significant increase in rainfall and temperature ranging between 100–440 mm, and 0.75–3.5 °C across different SSP scenarios. Alongside a decline in rainfall in certain regions of Northeast India and the Western Ghats is observed from the derived spatial maps created using the data of developed MME. Spatial variation of DPI computed at a district level indicates that though the frequency of drought occurrences in the near and far future periods does not substantially increase, the severity of droughts is found to be intense. Findings highlight that it is imperative to consider the influence of climate change while assessing the droughts. These findings can assist policymakers and stakeholders in prioritizing resource allocation and implementing targeted mitigation strategies.

This study proposes a framework that improves the precision of offshore wind resource assessment. Built on the theory of statistical downscaling and multi-criteria techniques, this framework allows to downscale available Global Climate Model (GCM) data using various statistical-downscaling techniques that help improve the granularity of assessments. Secondly, as per the proposed algorithm, the study area is split into four quadrants and weights for each considered GCM in all the quadrants are evaluated following which weighted ensembles and mosaics are created. Subsequently, best mosaic ensemble is identified using the proposed framework and is further used to estimate harnessable wind power. The proposed framework is demonstrated considering the data of 13 GCMs of the CMIP6 archive with an extent of the Indian offshore region. Spatial findings providing actionable insights into harnessable offshore wind energy in India suggest that the southeast (SE) quadrant with a high median WPD (247.39 W/m2) is a plausible region for installations.

Groundwater salinization of coastal aquifers as a result of climate change and anthropogenic activities is a widely acknowledged phenomenon. Sundarbans, in India is one such area where this phenomenon is noticed at an unprecedented rate making drinking water unpotable for consumption. Studies identifying the prime drivers causing this detrimental phenomenon are limited as the existing studies explicitly lack analyzing the holistic view. Building on this gap, this study aims to conduct a systematic literature review and identify the list of drivers that are promoting groundwater salinization. The influence of wide range of parameters depicting the climate change i.e., varying rainfall pattern, sea level rise (SLR), El Nino-Southern Oscillation (ENSO) and tropical cyclones (TC) on qualitative and quantitative variations in the groundwater at various temporal scales is studied with the help attributes collected from literature. The study reveals a significant drop in groundwater levels (GWL) between 1996 and 2017. This depletion is noted to be primarily attributed to variations in the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), affecting rainfall patterns and recharge rates. During tropical cyclones, GWL rapidly raised, while it is noted that the groundwater quality is sensitive to ENSO. Sea-level rise, changing rainfall patterns, and increasing population density exacerbate groundwater salinization. Existing sources of water, i.e., shallow aquifers exhibit high salinity, and deep aquifers exceed permissible limits. The study evidences the needs to address drinking water scarcity and potential migration resulting from these complex interactions between climate, population, and groundwater management.

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

Increasing the footprint of installed renewable energy capacity helps to mitigate CO2 emissions. Numerous countries have been devising strategies for harnessing various renewable sources to meet the rising demands. Solar, wind, hydro, geothermal, and biomass are a few of the renewable sources that are being used to generate electricity across the globe. Based on the nature of the source's availability, biomass is considered one of the plausible resources to generate electricity consistently. In light of this, extensive research works is being conducted to explore different approaches to convert biomass to energy. Microwave pyrolysis is one of the new approaches to convert biomass into energy. This study aims to understand the global trends in adopting micro-wave-assisted pyrolysis with the help of bibliometric analysis performed based on keyword occurrences. A total of 510 scientific contributions have been made between 1979 and 2023, addressing various aspects of microwave-assisted pyrolysis to convert biomass into energy. To gain insights into the adaptability of microwave-assisted pyrolysis, temporal growth in the total number of publications and citations has been studied. Prominent publications, top journal sources, highly contributing countries, and researchers are also identified to facilitate future research in this area. Findings suggest that attention to microwave pyrolysis is increasing by 7.59%, and China is designated as the top nation and the most frequent partner in microwave-assisted pyrolysis of biomass, followed by the United States and Malaysia. Bioresource Technology, Journal of Analytical and Applied Pyrolysis, Fuel, and Energy are popular journals focusing on microwave-assisted pyrolysis. Based on the bibliometric study of prior existing work, this study presents a road map for collaborations to conduct research on microwave-assisted pyrolysis of biomass to generate energy.

Together with a human-centered approach to designing and operating production and logistics in an industrial context, digital technologies can lead to a sustainable, resilient, and human-centric Industry 5.0 (I5.0). This article is one of the first interdisciplinary studies integrating digital technologies and circular economy (CE) concepts in I5.0. Using expert-based surveys of industry leaders and analytical hierarchical process techniques, the article advances CE and technology management by empirically investigating the influence of I5.0 on CE aspects in manufacturing. The novel results presented here can enable policymakers and industry leaders to design effective CE strategies.

Workplace energy consumption exceeds household usage, due to which, even small changes in workplace energy behaviour can minimise emissions associated with energy consumption. Despite global workplace energy conservation efforts, measuring progress is impeded due to the involved complexity. Building on this gap, this study developed Workplace energy conservation index (WECI), that can assist a company in measuring the attainment of energy conservation with respective to the benchmarking company. The proposed index is built by considering individual and organizational enablers. A total of 20 enablers identified through extensive literature review complemented with the outcomes of focus group discussions are the components of developed index. For demonstration of the proposed WECI, a target company and a benchmarking company from the automobile sector have been selected and the involved computations are expounded. Findings suggest that the attainment of target company is 46 % indicating the scope of improvement. Detailed evaluation of WECI guides the stakeholders to identify the thrust area that can improve the attainment of energy conservation at the workplace. The proposed framework can be extended to companies in other sectors where the relevant enablers can be added in the phase of focus group discussions.

Hydrogen is the potential future resource to cater the energy and chemical requirements. Microwave-assisted pyrolysis (MAP) could be the potential technology to obtain green hydrogen from lignocellulosic biomass waste. The proximate and elemental composition varies with the type of lignocellulosic biomass, which influences the yield of hydrogen. In MAP, the operating parameters including microwave power, heating rate, temperature, and susceptor play an important role in hydrogen production. Cellulose, hemicellulose, and lignin present in the lignocellulosic biomass undergo decomposition when they are subjected to MAP. Most importantly, the susceptor material added to the feedstock induces the plasma, which would help the cleavage of the bonds to form hydrogen gas. When the microwave power intensity is high, then the generation of hydrogen would be high. During the MAP, the formed char from the biomass would act as susceptor cum catalyst, hence it further speeds up the hydrogen generation pathways. The energy and time required for the MAP are very less compared to conventional pyrolysis. The present review manuscript would help the research community to understand the possible applications of MAP for hydrogen production.

Population of India is growing exponentially thereby the necessity to enhance the power generation capacity is increasing. Considering the detrimental impacts of conventional approaches to generate electricity on the environment, it is imperative to minimize the dependency on fossil fuels and make a transition towards the use of renewable sources. Harnessing energy using floating solar photovoltaic modules is one of the promising renewable alternatives that can curtail carbon-dioxide emissions while meeting the required energy demand. In this study, governing parameters obtained from ECMWF ERA5 datasets are used to evaluate techno-economic feasibility of the floatovoltaic solar system at selected locations in Gujarat and Tamil Nadu. The suitability of these regions for installing floatovoltaic systems is assessed by analyzing crucial parameters such as panel temperature, solar power output, Capacity Factor (CF) and Levelized Cost of Energy (LCOE). Findings depict that a total of 991 and 880 TWh of electricity can be generated with a capacity factor of 26.9% and 23.8% at Gujarat and Tamil Nadu locations, respectively, with an installed capacity of 420 MW floatovoltaic system. Implementation of this alternative renewable source can curtail carbon emissions by more than 700 billion metric tons at each location, minimizing the detrimental impact on the environment. Economic analysis reveals LCOE value at the Gujarat and Tamil Nadu locations is 0.072 and 0.08 USD/kWh, respectively. Promoting the adoption and installation of floatovoltaics can help India to meet its goal of net-zero emissions by 2050 and be self-sufficient in terms of energy.

Biomass pyrolysis is the most effective process to convert abundant organic matter into value-added products that could be an alternative to depleting fossil fuels. A comprehensive understanding of the biomass pyrolysis is essential in designing the experiments. However, pyrolysis is a complex process dependent on multiple feedstock characteristics, such as biomass consisting of volatile matter, moisture content, fixed carbon, and ash content, all of which can influence yield formation. On top of that, product composition can also be affected by the particle size, shape, susceptors used, and pre-treatment conditions of the feedstock. Compared to conventional pyrolysis, microwave-assisted pyrolysis (MAP) is a novel thermochemical process that improves internal heat transfer. MAP experiments complicate the operation due to additional governing factors (i.e. operating parameters) such as heating rate, temperature, and microwave power. In most instances, a single parameter or the interaction of parameters, i.e. the influence of other parameter integration, plays a crucial role in pyrolysis. Although various studies on a few operating parameters or feedstock characteristics have been discussed in the literature, a comprehensive review still needs to be provided. Consequently, this review paper deconstructed biomass and its sources, including microwave-assisted pyrolysis, and discussed the impact of operating parameters and biomass properties on pyrolysis products. This paper addresses the challenge of handling multivariate problems in MAP and delivers solutions by application of the machine learning technique to minimise experimental effort. Techno-economic analysis of the biomass pyrolysis process and suggestions for future research are also discussed.

Renewable energy sources are gaining prominence as eco-friendly and sustainable alternatives to fossil fuels due to their availability and minimal greenhouse gas emissions. Nonetheless, the critical challenge is the availability of renewable resources, which fluctuates with changes in climatic conditions. This limitation poses a consistent challenge to generating base load power if it relies solely on a single type of renewable resource. Addressing this, integrating multiple renewable sources into hybrid systems has emerged as a viable solution. This study presents a framework, integrating Geographic Information Systems (GIS) and Hybrid Multi-Criteria Decision Making (MCDM) techniques to identify plausible locations for the deployment of Hybrid Offshore Solar and Wind Power Plants (HOSWPP) and the developed framework is demonstrated considering Indian Exclusive Economic Zone (EEZ) as a study area. Using the proposed approach, Indian EEZ region is classified into five suitability classes. The effectiveness of regions within each class is further assessed in terms of complementarity measured using Kendall's coefficient. Findings suggested that Kendall's coefficient for highly suitable class is −0.41 indicating the regions identified in this study are the prime locations for installing HOSWPP. A total of twenty optimal sites for HOSWPP deployment, predominantly in the offshore regions of Tamil Nadu and Gujarat. Eighteen sites are located along Kanyakumari to Thisayanvilai in Tamil Nadu, including areas in the Gulf of Mannar and near Valinokkam are found plausible. The rest of the two sites are in the offshore regions of Gujarat. This study provides a strategic roadmap to increase the renewable footprint, contributing to the global transition towards cleaner energy sources.

Artificial intelligence (AI) has emerged as a prominent tool in the modern day. The utilization of AI and advanced language models such as chat generative pre-trained transformer (ChatGPT) and Bard is not only innovative but also crucial for handling challenges related to water research. ChatGPT is an AI chatbot that uses natural language processing to create humanlike conversations. ChatGPT has recently gained considerable public interest, owing to its unique ability to simplify tasks from various backgrounds. Similarly, Google introduced Bard, an AI-powered chatbot to simulate human conversations. Herein, we investigated how ChatGPT and Bard (AI powdered chatbots) tools can impact water research through interactive sessions. Typically, ChatGPT and Bard offer significant benefits to various fields, including research, education, scientific publications, and outreach. ChatGPT and Bard simplify complex and challenging tasks. For instance, 50 important questions about water treatment/desalination techniques and 50 questions about water harvesting techniques were provided to both chatbots. Time analytics was performed by ChatGPT 3.5, and Bard was used to generate full responses. In particular, the effectiveness of this emerging tool for research purposes in the field of conventional water treatment techniques, advanced water treatment techniques, membrane technology and seawater desalination has been thoroughly demonstrated. Moreover, potential pitfalls and challenges were also highlighted. Thus, sharing these experiences may encourage the effective and responsible use of Bard and ChatGPT in research purposes. Finally, the responses were compared from the perspective of an expert. Although ChatGPT and Bard possess huge benefits, there are several issues, which are discussed in this study. Based on this study, we can compare the abilities of artificial intelligence and human intelligence in water sector research.

The escalation in energy demand due to the rising population highlights the need for the transition toward sustainable power generation alternatives. In this context, floating solar photovoltaic (FPV) systems emerge as an innovative and environmentally friendly alternative, offering the dual benefits of energy generation and conservation of terrestrial resources. Based on ERA5 datasets, an in-depth analysis of the potential and efficiency of FPV systems, specifically within the Indian Exclusive Economic Zone (EEZ), is conducted in this study. Findings of this study evidence the substantial capacity of the Indian EEZ that could yield energy that is equivalent to 43 times of annual consumption by utilizing 10% of the EEZ region. A full-scale utilization of the EEZ for FPV systems could revolutionize the energy landscape, potentially generating 433 times the country's present annual energy requirements. A complete transition to such renewable energy sources within the EEZ is projected to result in an annual reduction of 595 billion metric tons in carbon emissions.

Technological advancements can accelerate the attainment of Sustainable Development Goals (SDGs). However, technology adoption is associated with complex, interrelated factors, even more so in the context of rural areas in emerging economies. We examine the adoption of one technology that can be crucial for resolving water scarcity issues facing countries around the world–the Rainwater Harvesting (RWH) technology and the critical success factors (CSFs) that promote its adoption in rural India. Building on an extensive literature review, focus group discussions, and field visits, this paper identifies a list of factors that promote its adoption. To derive the CSFs, the relevance of each factor is analysed using Fuzzy-Delphi, and the significance is determined using D-DEMATEL technique. The novel results presented here suggest that awareness about RWH technologies, their perceived usefulness, ease of use, and tax incentives for companies are some crucial factors that can increase RWH technology adoption. Furthermore, community-based workshops explaining the architecture and operational aspects of the RWH System as well as simplifying the RWH system architecture can accelerate its usage in rural areas. Based on these results, the paper presents a new roadmap for leveraging technology to attain SDGs in rural areas of developing countries.

Pavement monitoring involves periodic damage detection and condition assessment of pavements for efficient pavement management. Unmanned aerial vehicle (UAV)-based pavement monitoring requires multidisciplinary knowledge of pavement distress, drone type, payload, flight parameters, drone deployment, and image processing. Owing to the availability of various UAVs, data sensing devices, operating ecosystems, and post-processing tools, selecting an appropriate combination of these systems is crucial. Therefore, the primary objective of this study is to provide essential knowledge on the prevalent challenges of existing monitoring techniques and discuss the potential advantages of UAVs over conventional pavement monitoring practice. A state-of-the-art review emphasizing UAV technicalities in the context of image-based pavement monitoring is presented. A detailed workflow and checklist for drone deployment is drafted for novice users to ensure safe and high-quality data acquisition. Finally, the present challenges and future scope of UAV-based pavement monitoring is discussed. Overall, this study aims to provide inclusive and comprehensive information on UAV-based pavement monitoring to beginner researchers.

The learning factory is an emerging ‘hands-on’ approach to teaching advanced manufacturing technologies. This study aims to identify the key challenges for implementing learning factory in I4.0 arena. Since no past research works addressed the challenges associated with learning factory, participatory surveys were conducted to identify the key challenges. Industry leaders, policymakers, trainers, and academicians were selected as participants of the survey to collect a broad perspective from individuals at various levels. The experts’ feedback was synthesised in a mind map depicting challenges in implementing learning factories. Then, the interrelationship between the identified challenges is evaluated using decision-making trial and evaluation laboratory technique. Consequently, the significance and nature of each challenge is determined. The challenges identified in this work, and the findings of empirical analysis will help the industry and academia in creating and implementing Industry 4.0 learning factories.

Streetscape design can encourage social interaction and community building, creating a sense of place and improving the overall well-being of the resident community. Detailed investigation of streetscape quantitatively can identify the opportunities to reduce energy use, improve air quality, and enhance the natural environment. Data derived from street view services are typically used to analyze the streetscape. However, the availability of street view services is limited to selected regions, because of which conducting a study for an area deprived of street view services is a challenge. Building on this gap, this study proposes a new system introduced as Urban scan to overcome the limitation. • The proposed system can capture the streetscape in 360°. • Helps to analyze the streetscape composition with the least computational effort. • The accuracy of the classification is tested with different datasets and is noted to be above 96.02%.

Purpose: This study aims to propose a conceptual model indicating the impact of Industry 4.0 (I4.0) technologies on lean tools. Additionally, it prioritizes I4.0 technologies for the digital transformation of lean plants. Design/methodology/approach: The authors conducted a questionnaire-based survey to capture the perception of 115 experts of manufacturing industries from Germany, India, Taiwan and China. The impact of I4.0 on lean tools, using analysis of variance (ANOVA). Further, the authors drew a prioritization map of I4.0 on the employment of lean tools in manufacturing, using the Best–Worst Method (BWM). Findings: The findings indicate that cloud manufacturing, simulation, industrial internet of things, horizontal and vertical integration impact 100% of the lean tools, while both cyber-security, big data analytics impact 93% of the lean tools and advanced robotics impact 74% of the lean tools. On the other hand, it is observed that augmented reality and additive manufacturing will impact 21% and 14% of the lean tools, respectively. Practical implications: The results of this study would help practitioners draw up a strategic plan and roadmap for implementing lean 4.0. The amalgamation of lean with I4.0 technologies in the right combination would enhance speed productivity and facilitate autonomous operations. Originality/value: Studies exploring the influence of I4.0 on lean manufacturing lack comprehensiveness, testing and validation. Importantly, no studies in the recent past have explored mapping and prioritizing I4.0 technologies in the “lean” context. This study thereby attempts to establish a conceptual model, indicating the influence of I4.0 technologies on lean tools and presents the hierarchy of all digital technologies.

Today, the health care and medical sector is adopting digital technologies aggressively. However, this adoption also has significant challenges, especially during COVID-19. This research aims to identify and categorize the significant challenges related with application of Industry 4.0 (I4.0) technologies in the medical device industry. An expert-based survey is carried to capture the perception of medical device industry leaders about the challenges associated with the implementation of digital technologies. Further, interpretive structural modeling (ISM) method was used for an empirical investigation of the hierarchy and interdependencies of identified challenges. The authors have proposed a mind map and conceptual model of hierarchy and interdependencies of challenges associated with the digital transformation of the medical device industry towards I4.0. Industry leaders and policymakers worldwide are defying challenges while the digital transformation of the organizations post COVID-19. The I4.0 implementation challenges identified and ategorized in this research may aid as a guide for medical device manufacturing organizations while designing a strategy for I4.0 transformation and to make sure that they start on the right-footing. Most of the existing work is focused on the advantages of I4.0 for managing the organization's post-COVID-19, lacks thoroughness and testing. Owing to the identified gap, this study intends to empirically identify the critical challenges associated with applying I4.0 technologies in the medical device manufacturing sector. This study is a pioneer in identifying and categorizing the vital challenges needed to deal with this critical situation. A potential area of future research can be the validation of the identified challenges with a larger sample size.

The amalgamation of Unmanned Aerial Vehicle (UAV) based systems with models built on Artificial Intelligence (AI) and Computer Vision approaches have enabled several applications in urban planning and smart cities, such as remote health monitoring of roads and infrastructure. However, most of such existing models are trained and evaluated for clear lighting conditions, and they do not perform well under low visibility. This work proposes a fast and lightweight approach for deployment on UAV-based systems that can (i) detect the low-visibility condition in a road image captured by a UAV, and (ii) alleviate it and enhance the quality of the road image. The proposed approach achieves state-of-the-art results and thus establishes itself as an essential precursor to downstream Computer Vision tasks related to remote monitoring of roads, such as identification of different distress conditions.

Integrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy.

The smart city mission was launched in the year 2015 with the objective to retrofit the existing cities by improving the core infrastructure. It is expected that this mission drives economic growth and enhances the quality of life. Being a new initiative with no standard definition of a smart city, it is challenging to plan developmental activities. In this regard, the Ministry of Urban Development has prepared a general architecture of ICT standards containing two dimensions, i.e., performance indicators, and leading indicators. Performance indicators contain three first-level indicators and thirteen second-level indicators, while the leading indicators contain four and seven, respectively. Few of these indicators are interdependent, which infers that improving one indicator will significantly impact others. Studying this interdependency would help in the transition of an existing city into a smart city. Therefore, this study is built on the theory of the Fuzzy-DEMATEL technique, which is used to determine the significance of each first-level indicator and to assess their nature, i.e., cause and effect. Findings demonstrate that improving causal variables such as citizen beneficial services, efficient governance, intelligent facility, and cybersecurity consequently improves liveable environment, information resources, and innovation which are the effect variables. The outcomes of this study may be helpful to propose the thrust areas for research in building smart cities.

Biomass is a viable alternative to fossil fuels due to the abundant availability of solid waste and the associated greenhouse gas emissions. Various conversion methods, including physical, thermal, biochemical-microbial, and chemical processes, have been utilized to convert biomass to energy. Microwave-assisted pyrolysis (MAP) is one of the prominent techniques to convert biomass into energy. Various parameters affect the yield and quality of the product in MAP. Studies addressing comprehensive insight into all influencing parameters are limited. Moreover, the relative hierarchy of the parameters is not evaluated in any of the past research works. Considering this limitation, this study proposed a two-step approach based on a multi-criteria technique that aid stakeholders to analyze the significance of each parameter. The proposed approach is built on the theory of Fuzzy Delphi and the Analytical Hierarchy Process. A total of 27 different parameters affecting MAP are identified through extant literature. Analysis based on the proposed approach suggests that microwave power is the most significant parameter influencing MAP. The impact of co-processing feedstock is very minimal among all the identified parameters. The relative hierarchy of all the parameters drawn in this study help stakeholders performs MAP with the least resources.

Offshore wind energy assessments help in identifying suitable locations for offshore wind farms. Its importance is further amplified in the context of climate change as wind power potential is susceptible to it. The present study aimed to assess the offshore wind potential of India and its sensitivity to climate change with the help of two different ensemble variants developed using nine CMIP6 Global Climate Models (GCMs). First ensemble is created with equal emphasis on all GCMs, while differential weights derived using Shannon entropy technique is used to develop the other ensemble. Created ensembles are further compared with ERA5 data. Comparative results suggest that differential weighted ensemble is superior to uniform weights in terms of bias. Owing to this, weighted ensemble is further used to study the impact of climate change on wind power density (WPD) for the near (2021–2045) and far-future (2075–2099) periods under two shared socioeconomic pathways (SSP) scenarios, i.e., SSP2-4.5 and SSP5-8.5, the most widely used and probable scenarios. Findings suggest that WPD variation in the study area ranges between ＋10% and −20%. These variations are examined to study further the impact of climate change on geographical variations of WPD distributions. With the regions in Arabian Sea as an exception, WPD appears to increase in future scenarios. WPD varies more in far-future scenarios compared to near-future scenarios. The future variations of the WPD across study areas are prominent in the case of SSP5 - 8.5 compared to the variations noted in the case of SSP2-4.5. Findings of this study help stakeholders to understand the impact of climate change on offshore wind potential. Moreover, plots showing the variation of WPD for near and far-future scenarios complemented with additional studies help in choosing an appropriate location to tap the offshore wind potential in India.

Harnessing offshore wind energy helps to achieve carbon neutrality. However, the availability of wind resources is sensitive to climate change and also depends on the available foundation technologies of wind turbines. Investigating annual energy production (AEP) and CO2 equivalent emission avoidance using offshore wind farms helps to make appropriate energy strategies. This study uses an ensemble developed using CORDEX-South Asia regional climate models by assigning weights derived from the CRITIC multi-criteria technique to estimate AEP under two representative concentration pathways (RCP), i.e., RCP4.5 and RCP8.5 scenarios in the North Indian Ocean. To account for the impact of climate change, inter and intra-annual variations in the wind power density (WPD), capacity factor (CF), and AEP are estimated. Estimates based on the feasibility of foundation technology show that the cumulative AEP obtained from the 240 MW wind farm in historic, near- and far-future scenarios are 357.91 TWh, 808.6 TWh, and 4888.78 TWh, respectively. In the near future, harnessing offshore wind energy can reduce CO2 emissions by 4500 million tons annually. The findings of this study suggest that harnessing offshore wind energy by installing farms within the study area could help in the massive reduction of CO2 emissions leading to carbon neutrality.

The Earth-Air-Heat-Exchanger (EAHE) is an effective solution for reducing energy demand. GIS based tools are commonly used to assess the suitability of EAHE sites, relying on geospatial data for geological and climatic parameters. However, lack of comparable data for different regions limits their applicability. In this regard, a framework that utilizes ERA5 reanalysis data to derive necessary geological and climatic parameters is proposed and demonstrated by considering India. Findings indicate that 25% of country's area falls under excellent category, benefiting 21% of the population. Additionally, 47% and 32% of the area are classified as moderate and good, respectively, providing thermal comfort to 51% and 28% of the population. Technical suitability of installing EAHE in an excellent category region is assessed through design and simulation study. Field studies are performed to collect climatic and geological parameters required for design. A computer model is developed using these design variables to determine the outlet temperature from EAHE. The simulation studies align with site suitability maps generated using GIS-MCDM framework, highlighting its reliability. Carbon footprint analysis reveals that EAHE adoption can reduce CO2 by 66.2% compared to conventional air conditioning units. The proposed GIS-MCDM framework can be extended to other regions lacking field data.

Technological advances can significantly transform agrarian rural areas by increasing productivity and efficiency while reducing labour intensive processes. For instance, the usage of Unmanned Aerial Vehicles (UAVs) can offer flexibility collecting real-time information of the crops enabling farmers to take timely decisions. However, little is known about the barriers to the adoption of such technologies by rural farmers in emerging economies like India. Building on an extensive literature review, focussed group discussions, and field visits, the barriers impacting the adoption are identified and classified into technical, social, behavioural, operational, economic, and implementation categories. The relevance of each barrier and its importance is evaluated using a hybrid multi-criteria framework built on the theory of Fuzzy Delphi and Fuzzy Analytical Hierarchy Process to identify the most crucial barriers to the adoption of UAVs to implement precision agriculture in rural India. The paper suggests new avenues for accelerating technology adoption in rural areas of emerging economies.

Rooftop rainwater harvesting systems (RRWHS) effectively provide water access by storing precipitated water. The amount of water harvestable using these systems is proportional to the availability of rooftop areas in the region. The use of satellite imagery has gained traction in recent times considering the challenges in conducting a manual survey to determine the rooftop area. However, the limitations on spatial resolution impaired stakeholders from conducting similar assessments in areas with small residential units. In this regard, the use of unmanned aerial vehicles (UAVs) providing high-resolution spatial imagery for the delineation of rooftops of all scales has become popular. The present study is an attempt to utilize UAV-generated orthomosaics to estimate the harvestable quantity of rainwater for setting up an RRWHS. A study area in the Gurgaon district, India, is selected, and the steps involved in estimating the quantity of water harvestable using UAVs are demonstrated. In addition to these computations, a suitable site for constructing the storage unit is identified with the aid of a weighted overlay technique implemented using a Geographic Information System. The results from the study show that nearly 11,229 m3 of water can be harvested per year in the study site using the RRWHS.

The developmental works to set up a geothermal power plant by Oil Natural Gas Corporation (ONGC) in Ladakh are in niche stages. Existing studies addressing the pre-drilling power estimates of the geothermal field in Ladakh using coupled simulations explicitly correspond to specific operating conditions. Though simulating the reservoir response under unexplored operating conditions would help to analyze the optimal scenarios and devise strategies, the involved computational effort is a major barrier. In these circumstances, adopting neural network models to predict the response for unstimulated operating conditions is a compelling solution. However, studies focused on analyzing the feasibility of using neural network models are limited. Building on this research gap, this study investigates if Convolutional Neural Networks (CNN), Recurring Neural Networks (RNN), and Deep Neural Networks (DNN) can be used to estimate extractable temperature from a geothermal reservoir. Accuracy metrics reveal that the developed network models can estimate extractable temperature for a chosen operating condition under a doublet extraction scheme without compromising accuracy and with just one-tenth of computational effort involved in conducting a simulation studies. The maximum deviation between estimated and simulated temperature fields is 1.3 K, 0.8 K, and 1.1 K for CNN, RNN, and DNN models, respectively. Results suggest that RNN architecture is preferred over CNN and DNN. The developed model serves as a benchmark and helps planners to estimate the extractable power from Puga geothermal field under various operating conditions with the least computation effort while ensuring the physics captured.

Leadership helps to build strong organizations with resilient cultures. It is established that leadership needs a transition powered by digital technologies to tackle the shift from workplace culture to remote work, which is being practiced even after the pandemic to reduce operational costs and improve flexibility. The transition from leadership to e-leadership requires a profound understanding of the critical success factors (CSFs). The primary objective of this study is to identify the critical success factors of e-leadership using a systematic literature review and questionnaire survey technique. The identified CSFs are grouped under (i) Technology Management, (ii) E-Motivation and well-being, and (iii) E-change management categories. The Fuzzy Delphi technique is used to find the relevant CSFs and the relative dominance of each CSF category; the CSFs are then analyzed using the fuzzy analytical hierarchy process. The results suggest that employee engagement using digital technologies is the most critical success factor, while role clarity has relatively the least significance for the transition to take place. The findings of this study facilitate the smooth transition from leadership to e-leadership.

Understanding the nexus between land use land cover (LULC) and land surface temperature (LST) of a rapidly growing city may help planners mitigate the effects of uncontrolled urbanization on the micro- and macro-environment. The primary focus of the study is to monitor the transient LULC of Surat, one of the rapidly growing cities in India. To comprehend the urban dynamics, the study analyses the tri-decadal LULC of Surat using temporal Landsat imagery corresponding to 1990, 2001, 2009, and 2020. Besides classification of satellite data to derive LULC using the maximum likelihood algorithm, emphasis has been given to evaluate the normalized difference vegetation index and normalized difference built-up index, which help in differentiating vegetation and built-up from other land-use types. In addition, the LST of Surat is computed, and zonal analysis is performed to examine its association with LULC. Results show that the built-up area of Surat increased by 3.22 times during the considered time, while the aerial extent of vegetation decreased by 1.58 times. Future land-use dynamics are predicted using the Markov model. Findings revealed that the built-up area is expected to increase by 20% between 2020 and 2030, while the vegetation area is likely to decrease by 13%. The developed model attained an accuracy of 52.08%, which is in agreement with the past studies. The findings of this study help urban planners and stakeholders to devise effective policies that can mitigate the detrimental effects of rapid urbanization on environment.

Sustainable agricultural management is essential for ensuring food security and economic development. Efficient agricultural land use based on crop rotation practices can deliver greater soil fertility and higher economic potential. We proposed a decision support tool (DST) for preserving land fertility, maximizing agricultural profit, minimizing agricultural pollution, and water usage. The proposed DST links geoinformatics, stochastic pairwise comparison (SPC), and constraint optimization to suggest the suitable crops for growing. To demonstrate the proposed DST, suitability of seven major crops in Muzaffarnagar district in Uttar Pradesh (India), where the footprint of sugarcane cultivable region is nearly 90% is analyzed and the findings are presented. The crops cultivated in the study region and the criteria suitable for their cultivation are identified using the hybrid system approach. The DST primarily encompasses qualitative and quantitative analysis coupled with geospatial analysis. Qualitative analysis guides the decision-maker in finalizing the crucial criteria to be assessed for cultivation, while quantitative analysis uses beta distribution for pairwise comparison to understand the significance of finalized criteria. We collected the data concerning parameters related to the finalized criteria by considering 2700 soil samples. Data required at the ungauged locations are estimated using the kriging interpolation technique. The findings of this study suggest that sugarcane can be allocated up to 20% of the land area. In addition to the principal crops (i.e., sugarcane, wheat, and rice), potato, mustard, maize, and sorghum also have good cultivation potential in Muzaffarnagar and can be grown on up to 20%, 22%, 18%, 21% of the land area respectively while just 1.5%, 1.8%, 0.1%, and 0% of land area, is used for their cultivation. With the prime focus on knowledge transfer from scientific studies to farmers, we used an open-source geospatial repository to develop an interactive dashboard that can fetch farmers' locations and present each crop's suitability based on optimized crop rotation practices.

There is a strong opinion about the impact of Industry 4.0 (I4.0) technologies on the operations performance of manufacturing firms. However, there are several challenges while evaluating such situations. Determining the significance of I4.0 technologies in the context of the pandemic situation must consider several criteria for exhaustive understanding. This study aims to determine the significance and impact of I4.0 technologies on medical devices manufacturing firms’ operations during the COVID-19 outbreak. The set of technologies of I4.0 is evaluated over productivity, quality, cost, delivery, health, and safety parameters using a fuzzy analytical hierarchy process. The study reveals that the significance of big data analytics, autonomous robotics, and industrial internet of things (IIoT) in the business continuity of medical device manufacturing operations during the COVID-19 outbreak is high. Cloud technologies, digital simulations and augmented reality follow the order.

This research aims to identify the critical challenges associated with restarting manufacturing organizations post-coronavirus disease 2019 (COVID-19). The authors conducted an expert-based survey among various industry leaders of manufacturing organizations to capture a holistic view of business continuity plans and the associated challenges. The selected individuals are responsible for making business continuity policies and plans at their respective organizations. They were asked to reflect on their experience of the present-day challenges in managing business continuity in their organizations. Expert interviews were reflective and provided candid inputs. Consequently, the keywords of the experts' feedback were synthesized by using the mind map qualitative approach, which helps in the visualization of the critical challenges at an abstract level. Further, the interrelation between them and the significance of each critical challenge is evaluated using fuzzy theory with the decision-making trial and evaluation laboratory (DEMATEL) technique. The findings of these evaluations will help to assess the existing policies/ practices and to strengthen business continuity plans post-COVID-19. This study is a pioneering work that will help organizations to prepare action plans for kick-starting their broken-down economic engines.

Manufacturing organisations worldwide are embracing Industry 4.0 (I4.0) and its associated technologies, such as the Internet of Things (IoT), Advanced Robotics, Big Data, and Cybersecurity. However, its implementation poses considerable risks for SMEs in emerging economies. Based on a survey of industry experts and business leaders associated with implementing I4.0 in the dynamically evolving economy of India, this paper identifies and prioritises the critical risks linked with implementing I4.0 in SMEs. Empirical results using the Fuzzy-Analytical Hierarchy Process suggest a hierarchy of risks associated with SMEs' transition to I4.0, with financial and technological risks posing the most significant barriers to I4.0 adoption. The novel results presented here can enable strategy development to effectively manage the risks of implementing new technologies in emerging economy contexts.

Increasing the share of renewable energy power generation is imperative for the attainment of Sustainable Development Goal 7 (SDG-7). Though photovoltaic technology is a reliable renewable alternative in many countries, the land required to expand the installed capacity remained as a prime barrier. In this context, Floating Solar Panels (FSP), also popular as floatovoltaics, are identified as a viable alternative. Installation of floatovoltaics is in the nascent stages in many countries, especially in India, where photovoltaics are popular for harnessing solar energy. Research works addressing the FSP potential of hydropower reservoirs in India and determining their hierarchy to plan developmental works in phase wise manner are limited. This study analyses the FSP potential of all major hydropower reservoirs in India. National inventory data to facilitate floatovoltaic power estimates is created, and an active dashboard is hosted for better transparency and monitoring. Using the created geospatial database, the hierarchy of hydropower reservoirs is evaluated with the help of the proposed hybrid multi-criteria framework developed by integrating Shannon entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques. A wide range of governing parameters, such as (i) available area for installation, (ii) harnessable power, (iii) capacity factor, (iv) elevation, and (v) wind speed, are considered to evaluate the hydropower reservoirs. Overall dominance of each hydropower reservoir evaluated using the proposed multi-criteria approach helps to understand the hierarchy. The findings of this study help stakeholders prioritize the reservoirs for setting up FSP systems.

Quantifying street-level greenery has been the subject of interest for researchers as it has several implications for community residents. Green View Index (GVI) is a widely used parameter to compute the greenery along the streets. However, it does not account for the health of the greenery. The new Enhanced Green View Index (EGVI) that we propose computes the amount of greenery along the streets along with the health of the greenery. • The new indicator computes street-level greenery; • Considers the health of vegetation while calculating greenery; and • Helps to study the impact of street-level greenery on community residents precisely.

Harnessing offshore wind energy is a potential solution to meet rising energy demand. Concurrently, exploitable wind energy is susceptible to climate change. In this study, an ensemble of six RCMs derived from Coordinated Regional Climate Downscaling Experiment — South Asia (CORDEX-SA) regional climate models is created to study future variation of wind speed and wind power density. The variation with respective to the historic time periods is examined for near-future (2020–2046), mid-future (2047–73), and far-future time periods (2074–2099). Two Representative Concentration Pathways (RCP) scenarios, such as RCP4.5 and RCP8.5, are considered for the analysis. The credibility of RCM data is assessed by comparing it with European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis dataset which is validated against buoy datasets. Findings reveal that annual mean wind speed in the considered period ranges between 2% and −8% in the case of RCP4.5 while it is 7% and −16% in the RCP8.5 scenario. Seasonal percentage variation in WPD is between 80% and −56% in the RCP4.5 scenario, and it is relatively higher (105% and −75%) in the RCP8.5 scenario. By 2099, it is expected that WPD may increase by 10% and 30% in the southern coast, whereas 20% and 40% decrement is expected near the coast of Gujarat for RCP4.5 and RCP8.5 scenarios, respectively. The findings of this study enable stakeholders to harness wind energy in the Indian offshore region.

High resolution Light Detection and Ranging (LiDAR)-derived Digital Elevation Models (DEMs) improve hydrologic modeling and aid in identifying the targeted locations of best conservation practices (CPs) in agricultural watersheds. However, the inability of LiDAR data to represent the conveyance of water under or through the surfaces (i.e., bridges or culverts) impedes the simulated flow, resulting in false upstream pooling. Improper flow simulation affects the accuracy of pollutant load estimations and targeted locations delineated by watershed models or models built upon hydro-conditioned DEMs (hDEM). We propose a novel approach of Hydro-conditioning to modify LiDAR imagery through breach lines, which is essential to accurately replicate the landscape hydrologic connectivity. We compared variations in outcomes of Agricultural Conservation Planning Framework (ACPF), based on manual and automated hDEMs for Plum Creek watershed, Minnesota. The derived flow network, catchment boundaries, drainage areas, locations/number of practices depend on the chosen hDEM. Locations, size and shape of bioreactors, drainage management, farm ponds, nutrient removal wetlands, riparian buffers are severely affected by hydro-conditioning. Shuttle Radar Topography Mission (SRTM) validation of hDEMs showed that Mean Average Percentage Deviation (MAPE) for automated and manual hDEMs is 1.34 and 0.998 respectively. Also, proximity analysis with a buffer of 200 m showed that CPs’ locations delineated by manual hDEM match better with the existing ones as compared to automated hDEM. Results indicate that coupled approach of using automated and manual ‘hDEM’ is best suited for guiding stakeholders towards the field-scale planning in a cost-saving manner.

Efficiency of the installed wind farms is location-specific. Various research works used the concepts of Geographical Information Systems (GIS) and Multi-criteria-techniques (MCDM) to identify suitable locations. However, research works addressing the micro-level site selection are limited. This study proposes a two-stage GIS-MCDM based algorithm that can identify the plausible regions for installing wind farms at the microscopic level. The developed tool, built on the philosophy of fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), differentiates the regions based on technological, economic, social, and environmental aspects. For demonstration purposes, India is chosen as a study region, implying national-level analysis as stage-1 and state-wise analysis as stage-2. Results suggest that the suitable area for wind farm development in India is approximately 1805131 km2, out of which about 650 km2 is considered as highly suitable, and the following best has 330321 km2. The most suitable locations are in the western and southern parts of India, mainly in Gujarat and Tamil Nadu states. These findings of stage-2 present the hierarchy of plausible regions within each state. The developed tool is the first of its kind, help the decision-maker to extend it for siting solar farms and other energy sources.

The Indian Government envisaged generating 10 GW using geothermal power by 2030. Reaching this milestone is linked with numerous challenges, as geothermal exploitation in India is in the nascent stages. In this work, possible barrier categories and barriers to harness geothermal energy in India are identified with the help of literature review and questionnaire-based surveys. Fuzzy Delphi method is used to find the significant barriers among the listed. Subsequently, Fuzzy Analytical Hierarchy Process (Fuzzy AHP) is used to determine the relative dominance of each barrier category and the barriers within each category. Outcomes of this research show that the resource barrier category obtained highest priority. This category includes various barriers such as (i) conceptualization of geothermal reservoir, (ii) estimation of theoretical heat energy, (iii) determination of extractable power, and (iv) selection of suitable extraction schemes. Results suggest that a comprehensive conceptual model presenting the subsurface variation of thermo-hydro-geological parameters with depth at a geothermal field can support the accurate depiction of the available and extractable thermal potential. Stability of the obtained hierarchy is examined by sensitivity analysis. Findings of this study help to identify the barriers that can be reasonably encountered and to propose developmental activities to harness geothermal energy.

The curriculum of an engineering program can be of great interest for the prospective students and their parents. The better the curriculum, the more will be the success rates and satisfaction levels for the students. Studies addressing the framework to evaluate the existing curriculum are limited. Owing to this research gap, the present study aims at finding the important components of industry ready undergraduate engineering program at universities in India and abroad. For the analysis the curriculum of an Indian university is considered, and the survey is conducted through an expert panel. The importance of different components of curriculum viz. foundation courses, core courses, skill and perspective courses, industry exposure, undergraduate research, entrepreneurship, co-curricular activities, advanced courses etc. are discussed and the collected expert opinion on these components are converted into fuzzy scales. Analytical hierarchy process is followed to find the weights of each component of the curriculum, and the results are presented in the form of tables and graph, which are discussed in detail. It is found that apart from the foundation and core courses, skill and perspective courses are also very important for an effective industry ready curriculum.

Energy is an essential commodity that helps in the economic growth of a country. Unfortunately, the availability of conventional sources used for the generation of electricity is reducing, which is one of the significant reasons for energy insecurity, especially in context to India. In this regard, the Indian Ministry has emphasized on the use of renewable energy to meet the increasing energy demand. Solar is one such renewable alternatives, which is being promoted in India. It is planned to extend the current installed capacity in the near future. In this regard, mapping the seasonal variation of solar radiation over the entire country would help in planning appropriate developmental activities. However, since the meteorological stations are confined to selected localities, the solar radiation received over the entire country remains unmapped. In this study, considering the solar irradiation fields of 72 discrete locations, irradiation over the entire nation is mapped using geospatial interpolation techniques. Additionally, the hierarchy of states that are relatively insensible to seasonal variations and receiving maximum radiation is determined using the TOPSIS multi-criteria decision-making technique. The findings of this study ratify that the IDW interpolation technique is most suitable for the estimation of solar radiation data. Further, the relative hierarchy of states drawn helps to plan the developmental activities optimally and to expand the solar installation capacity in the country.

Global reporting initiative (GRI) is the global standard of sustainability. It epitomizes the global best practice of triple bottom line, i.e., economic, environmental, and social impacts. This research is an expert-based analysis of 132 industry leaders and policymakers from 36 industries to evaluate the significance of Industry 4.0 (I4.0) technologies on GRI adoption. In the first phase, the influence of I4.0 on GRI standards is analyzed using basic descriptive statistics and analysis of variance. In the second phase, the significance of the GRI standards in the context of I4.0 is evaluated using the Fuzzy Analytical Hierarchy Process (AHP). The findings indicate that 85% of environmental, 65% economic, and 50% societal GRI standards are influenced by I4.0. It is also found that the influence on economic performance, indirect economic impacts, energy, and emissions are significantly high. Findings ratify that the social aspect, which is often overlooked, needs more focus in manufacturing. Most of the contemporary research on evaluating the impact of I4.0 on sustainability is conceptual, lacks comprehensiveness, and rigor by thorough testing and validation. This study is one of the pioneering works offering a conceptual framework that aids in integrating I4.0 with GRI.

The thermal data sets of Landsat for the years 2014 and 2019 are used to assess the transients of land surface temperature (LST) in Visakhapatnam, India. The variation in estimated temperature fields is compared with the land use pattern to validate temperature with reference to land use land cover (LULC). During the considered period, the built-up area in the study region increased by 63%. The aerial extent of water bodies has come down by 12.5%, and there is a significant drop in vegetation cover. The LST of the regions with the densely built-up area is high compared to the other types of land use. A mean rise of 4.8 °C in the LST has been noticed over the study area during this period. Few monitoring points representing rural areas within the proximity of the study region have been established, and the LST is monitored explicitly. As a result, it has been observed that the temperature in rural areas is relatively lower than the city region, which confirms the urban heat island effect. A micro-level study has been conducted by dividing the study area into four zones as per administrative boundaries. Statistical analysis using the zonal attributes affirms a positive correlation of 0.55 between LST and the built-up area. In contrast, a negative correlation of 0.52 between LST and vegetation cover is observed. The LULC results are validated using Google Earth Images captured at a finer resolution. Being selected as one of the cities under the smart city mission by the Urban Development Ministry of Govt. of India, it is expected that the land use pattern in Visakhapatnam will change drastically in the coming years. The findings of this study foster the relationship between LST and LULC, and the conclusions thus drawn would help planners for the sustainable development of Visakhapatnam.

The growth of COVID-19 cases in India is scaling high over the past weeks despite stringent lockdown policies. This study introduces a GPS-based tool, i.e., lockdown breaching index (LBI), which helps to determine the extent of breaching activities during the lockdown period. It is evaluated using the community mobility reports. This index ranges between 0 and 100, which implies the extent of following the lockdown policies. A score of 0 indicates that civilians strictly adhered to the guidelines while a score of 100 points to complete violation. Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is modified to compute the LBI. We considered fifteen states of India, where the spread of coronavirus is relatively dominant. A significant breaching activity is observed during the first phase of lockdown, and the intensity increased in the third and fourth phases of lockdown. Overall breaching activities are dominant in Bihar with LBI of 75.28. At the same time, it is observed that the majority of the people in Delhi adhered to the lockdown policies strictly, as reflected with an LBI score of 47.05, which is the lowest. Though an average rise of 3% breaching activities during the second phase of lockdown (L2.0) with reference to the first phase of lockdown (L1.0) is noticed in all the states, a decreasing trend is noticed in Delhi and Tamil Nadu. Since the beginning of third phase of lockdown L3.0, a significant rise in breaching activities is observed in every state considered for the analysis. The average LBI rise of 16.9% and 27.6% relative to L1.0 is observed at the end of L3.0 and L4.0, respectively. A positive spearman rank correlation of 0.88 is noticed between LBI and the cumulative confirmed cases. This correlation serves as evidence and enlightens the fact that the breaching activities could be one of the possible reasons that contributed to the rise in COVID-19 cases throughout lockdown.

There is a new public health catastrophe forbidding the world. With the advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from the experiences of various countries and the World Health Organization (WHO) guidelines, social distancing, use of sanitizers, thermal screening, quarantining, and provision of lockdown in the cities being the effective measure that can contain the spread of the pandemic. Though complete lockdown helps in containing the spread, it generates complexity by breaking the economic activity chain. Besides, laborers, farmers, and workers may lose their daily earnings. Owing to these detrimental effects, the government has to open the lockdown strategically. Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy. An attempt is made in this paper to predict the time after which the number of new cases stops rising, considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree, Support Vector Machine, and Gaussian Process Regression algorithm are used to project the number of cases. Thus, the projections are used in identifying inflection points, which would help in planning the easing of lockdown in a few of the areas strategically. The criticality in a region is evaluated using the criticality index (CI), which is proposed by authors in one of the past of research works. This research work is made available in a dashboard to enable the decision-makers to combat the pandemic.

Various extraction schemes are proposed in this study for the exploitation of the Puga geothermal reservoir by considering the field constraints and geological structural setting. Considering a 3D thermo-hydro coupled simulation model, the dynamic response of the reservoir under the proposed extraction schemes is studied in terms of extractable power. The transients of extractable power are further examined by evaluating minimum extractable power in the successive 10 years of reservoir lifetime. A mathematical model is proposed to estimate the probable drilling cost involved in installing the proposed extraction schemes. Considering the evaluated parameters, the dominance of each proposed scheme is studied using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In view of the diversified perception of decision makers, sensitivity analysis is performed to study the variation of hierarchy. The evaluated dominance helps to identify the favorable extraction scheme that can tradeoff between extractable power and drilling cost. The findings of this study, supported by the findings of sensitivity analysis, ratify that the doublet extraction scheme is the most favorable extraction scheme for the exploitation of the Puga geothermal reservoir.

Puga geothermal reservoir in India shows promising thermal manifestation zones. However, no systematic study is done to develop the 3D characterization of thermo-hydro-geological fields for this reservoir. A new methodology is developed to characterize porosity, thermal conductivity, density, specific heat, radioactive heat capacity and permeability as 3D block heterogeneity till a depth of 4 km from resistivity maps. The temperature field and stored heat energy in a geothermal reservoir are dependent on these parameters. Based on the developed characterization, 3D coupled flow and heat transport processes are simulated to estimate the extractable temperature and power to be generated from doublet extraction scheme with various operational conditions. The study finds energy recovery factor of 8.16% and 37.83% and minimum electrical power potential of 1.2 MWe and 50.4 MWe with 12% conversion efficiency from the depths of 250 m and 1875 m respectively over 50 years from Puga field. Sensitivity for fluid injection/extraction rate and well spacing is studied. The results show promising power potential from 1.4 to 2 km of depth. The block heterogeneity characterization is more reliable than layered and homogeneous characterization. The outcomes would certainly acquire a significant role in decision-making strategies for Puga geothermal exploitation.

The authors regret for the incorrect appearance of suffix b in the affiliation due to typo. The authors would like to apologise for any inconvenience caused.

Geothermal is a consistent and reliable source of renewable energy for various scale exploitation. However, harnessing geothermal energy is limited to small-scale direct heat applications in many countries, primarily due to various technical and economic reasons. One among the many reasons is a meager amount of field studies available for the reliable prediction of reservoir potential, especially in India. Assessment of temperature field depends on proper information of subsurface field properties. The assessed temperature field further determines the stored heat energy. The accurate assessment of reservoir potential depends on temperature field. Reasonable reservoir potential information would encourage policymakers to plan developmental works at various scales. Since the information on subsurface characteristics is limited in the absence of deep exploration data, assessment of reservoir potential is associated with uncertainties. In this regard, this study presents a methodology for preliminary assessment of reservoir potential in terms of temperature and thermo-hydro-geological features, which also predicts the stored heat energy. The study considers the geothermal status of India, where the developmental activities and exploration are still at nascent stages, and predicts the temperature and energy distribution of Puga geothermal reservoir based on the available resistivity data.

Evaluation of a potential geothermal reservoir depends on the conceptual model which governs fluid flow and heat transfer in the reservoir. Knowing the spatial variation of reservoir parameters is essential to develop a conceivable and explicable conceptual model. Lack of deep reservoir characteristics impaired the development of conceptual model for Puga geothermal field, India. This study proposes a methodology to develop a conceptual model of Puga geothermal field. The proposed methodology utilizes the resistivity model developed by National Geophysical Research Institute as preliminary data. The conceptual model developed in this study, maps the spatial variation of thermo-hydro-geological properties of Puga reservoir. The mapped properties of the reservoir are porosity, thermal conductivity, specific heat, radioactive heat capacity, density and permeability of reservoir. Furthermore, lateral extent of the possible heat source and spatial variation of steady state temperature of the reservoir are also estimated. The estimated reservoir temperature from the conceptual model of Puga geothermal field is in agreement with temperature interpretations of Na/K and Na-K-Ca geothermometer studies. The resulting conceptual model will further aid in the operational phase of reservoir development like volumetric assessment of reservoir potential and reservoir potential estimation under various extraction configuration.

This study aims to predict prospective significance levels of potential geothermal fields already identified by Geological Survey of India (GSI) and National Geophysical Research Institute (NGRI) by field investigations. Wide range of criteria's are considered in determining the relative significance level of each geothermal field in terms of cumulative score. These criteria's include useful resource base (URBfield), Areal extent (Afield), Minimum temperature as per geothermometry (Tmin), Maximum temperature as per geothermometry (Tmax), Utilization score (USfield), Cumulative discharge of thermal springs (Q), Minimum electrical resistivity (Rmin), Maximum electrical resistivity (Rmax) and Representative reservoir temperature as per Gas thermometry (Tgas). Fuzzy Analytical Hierarchy Process (Fuzzy AHP) is used to study the dominance of each geothermal field, evaluated over the aforementioned criteria to find cumulative score. URBfield depends on possible extraction temperature of reservoir estimated by COMSOL Multiphysics® modelling and simulation software. The geothermal fields are conceptualized as shallow homogenous reservoirs with injection and extraction wells. The attributes of remaining criteria are collected from published works of NGRI and GSI. The results of this study ratify that Puga geothermal field is the most significant site among the identified potential geothermal fields, to conduct further developmental works and for commercial extraction of geothermal energy.

The growing energy demand in progressing civilization governs the exploitation of various renewable sources over the conventional sources. Wind, Solar, Hydro, Biomass, and waste & Bagasse are the various available renewable sources in India. A reliable nonconventional geothermal source is also available in India but it is restricted to direct heat applications. This study archives the status of renewable alternatives in India. The techno economic factors and environmental aspects associated with each of these alternatives are discussed. This study focusses on prioritizing the renewable sources based on a parameter introduced as Energy Index. This index is evaluated using cumulative scores obtained for each of the alternatives. The cumulative score is obtained by evaluating each alternative over a range of eleven environmental and techno economic criteria following Fuzzy Analytical Hierarchy Process. The eleven criteria's considered in the study are Carbon dioxide emissions (CO2), Sulphur dioxide emissions (SO2), Nitrogen oxide emissions (NOx), Land requirement, Current energy cost, Potential future energy cost, Turnkey investment, Capacity factor, Energy efficiency, Design period and Water consumption. It is concluded from the study that the geothermal source is the most preferable alternative with highest Energy Index. Hydro, Wind, Biomass and Solar sources are subsequently preferred alternatives.

Among the distinguished zones of geothermal potential in India, the Puga Valley is identified as one of the potential sites for tapping geothermal energy at industrial scale. The hydrogeological properties and the temperature variations with depth have been examined under the Geological Society of India by drilling borewells at a few locations. The temperature distribution is one of the most essential parameters in quantifying the energy potential of a geothermal reservoir in its life time. Such temperature distribution has not been mapped for the Puga Valley. 2D Kriging technique is adopted in this study to assess temperature distribution for thermal manifestation zone at various depths and these are further used to estimate the thermal gradients at ungauged locations of the valley. From the results obtained, it is observed that the thermal gradient in the eastern zone of the valley is relatively higher. This indicates a possible bottom heat source in the eastern zone of the valley. The results of this study could be helpful in identifying the distinctive conceivable locations of injection and production wells for the extraction of entrapped heat within the rock strata. Also, a priority order is drawn in terms of thermal gradients at gauged and ungauged locations which may be helpful in deciding the zones of high and low heat sources in the reservoir.

Selection of site is the most fundamental and crucial decision, in the process of setting up a solar power plant. Since several factors influence the site selection process, multi criteria analysis is used to resolve this problem. In this study, seven districts of Rajasthan in India are considered as different alternatives for the installation of solar power plant. They are evaluated over few crucial criteria's such as solar radiation, land availability, water availability, cost of land, population benefitted, transmission losses and number of rainy days, which have a great impact on power generation. As the data corresponding to the criteria's considered is not available, thematic map is used as the raw data, with Arc GIS as interface, baseline data, corresponding to the criteria's for all study areas is extracted. A multi-criteria decision making technique, is used to choose the best suitable site for installation of solar power plant. Based on the literature, Fuzzy Analytical Hierarchy Process (Fuzzy AHP), which is advanced and a simple method, is used in this study for the location allocation of solar plant. Results dictate that Bikaner, which is one among the alternatives considered, is the optimal site for the installation for the solar plant in Rajasthan.