Nanomaterials as a sustainable choice for treating wastewater.

Wastewater containing toxic substances is a major threat to the health of both aquatic and terrestrial ecosystems. In order to treat wastewater, nanomaterials are currently being studied intensively due to their unprecedented properties. The unique features of nanoparticles are prompting an increasing number of studies into their use in wastewater treatment. Although several studies have been undertaken in recent years, most of them did not focus on some of the nanomaterials that are now often utilized for wastewater treatment. It is essential to investigate the most recent advances in all the types of nanomaterials that are now frequently employed for wastewater treatment. The recent advancements in common nanomaterials used for sustainable wastewater treatment is comprehensively reviewed in this paper. This paper also thoroughly assesses unique features, proper utilization, future prospects, and current limitations of green nanotechnology in wastewater treatment. Zero-valent metal and metal oxide nanoparticles, especially iron oxides were shown to be more effective than traditional carbon nanotubes (CNTs) for recovering heavy metals in wastewater. Iron oxide achieved 75.9% COD (chemical oxygen demand) removal efficiency while titanium oxide (TiO2) achieved 75.5% COD. Iron nanoparticles attained 72.1% methyl blue removal efficiency. However, since only a few types of nanomaterials have been commercialized, it is important to also focus on the economic feasibility of each nanomaterial. This study found that the large surface area, high reactivity, and strong mechanical properties of nanoparticles means they can be considered as a promising option for successful wastewater treatment.

Impacts of COVID-19 on Sustainable Development Goals and effective approaches to maneuver them in the post-pandemic environment.

In the pursuit of constructing a sustainable world for all through the instrumental seventeen Sustainable Development Goals, the COVID-19 pandemic emerged and affected the efforts concentrated on these goals. Therefore, there is a pressing need to analyze the extent of the impact that unfolded from the pandemic on each Sustainable Development Goal and further to direct the post-pandemic situation to accelerate the progress in every goal. Besides, there exists a knowledge gap in understanding the Sustainable Development Goals and its interaction with each goal through synergic and trade-off effects. To address the aforementioned imperative problems, this study is formulated to perform an impact assessment as well as to provide direction in the post-pandemic environment to effectively progress towards the Sustainable Development Goals by using a hybrid qualitative and quantitative framework. A detailed investigation is carried out to examine the pandemic impacts in every goal, and a quantified impact analysis is performed in terms of the targets of the Sustainable Development Goals with the aid of ranking methodology. The results indicate that SDG 1 and SDG 8 are the most impacted goal. To provide deeper perspectives into the Sustainable Development Goals, a critical analysis of the targets and indicators is performed to characterize the goals from their elemental point of view, such as nature of goals, depending factors, locus of the goal, and Sustainable Development Goal interactions. Further, a novel parameter, the degree of randomness, is proposed whose application in environmental research is immense. The impact on each goal and impact interaction between all the SDGs are also mapped, through which the dynamics of Sustainable Development Goal interactions is elaborated. In context with the post-pandemic scenario, the strategies to achieve the Sustainable Development Goals with environmental focus are presented with prioritization factor that supports quick recovery. The introduced prioritization factor is formulated by employing a multi-criteria analysis methodology. In addition, the fundamental elements of SDGs are built upon one another to frame an optimized and effective approach to achieving the SDGs in the post-pandemic environment. Despite the strategies, a conceptual framework to align the business practices with the SDGs is propounded. This study deep down would provide a unique perspective to the research community and would impart deeper knowledge in connection with sustainability, while the solutions framed would steer the policy and decision-makers.

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).

A hover view over effectual approaches on pandemic management for sustainable cities - The endowment of prospective technologies with revitalization strategies.

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable.

COVID-19: Impact analysis and recommendations for power sector operation.

The demand of electricity has been reduced significantly due to the recent COVID-19 pandemic. Governments around the world were compelled to reduce the business activity in response to minimize the threat of coronavirus. This on-going situation due to COVID-19 has changed the lifestyle globally as people are mostly staying home and working from home if possible. Hence, there is a significant increase in residential load demand while there is a substantial decrease in commercial and industrial loads. This devastating situation creates new challenges in the technical and financial activities of the power sector and hence most of the utilities around the world initiated a disaster management plan to tackle this ongoing challenges/threats. Therefore, this study aims to investigate the global scenarios of power systems during COVID-19 along with the socio-economic and technical issues faced by the utilities. Then, this study further scrutinized the Indian power system as a case study and explored scenarios, issues and challenges currently being faced to manage the consumer load demand, including the actions taken by the utilities/power sector for the smooth operation of the power system. Finally, a set of recommendations are presented to support the government/policymakers/utilities around the world not only to overcome the current crisis but also to overcome future unforeseeable pandemic alike scenario.

Effect of Transition Metal Cations on Stability Enhancement for Molybdate-Based Hybrid Supercapacitor.

The race for better electrochemical energy storage systems has prompted examination of the stability in the molybdate framework (MMoO4; M = Mn, Co, or Ni) based on a range of transition metal cations from both computational and experimental approaches. Molybdate materials synthesized with controlled nanoscale morphologies (such as nanorods, agglomerated nanostructures, and nanoneedles for Mn, Co, and Ni elements, respectively) have been used as a cathode in hybrid energy storage systems. The computational and experimental data confirms that the MnMoO4 crystallized in ß-form with α-MnMoO4 type whereas Co and Ni cations crystallized in α-form with α-CoMoO4 type structure. Among the various transition metal cations studied, hybrid device comprising NiMoO4 vs activated carbon exhibited excellent electrochemical performance having the specific capacitance 82 F g-1 at a current density of 0.1 A g-1 but the cycling stability needed to be significantly improved. The specific capacitance of the NiMoO4 electrode material is shown to be directly related to the surface area of the electrode/electrolyte interface, but the CoMoO4 and MnMoO4 favored a bulk formation that could be suitable for structural stability. The useful insights from the electronic structure analysis and effective mass have been provided to demonstrate the role of cations in the molybdate structure and its influence in electrochemical energy storage. With improved cycling stability, NiMoO4 can be suitable for renewable energy storage. Overall, this study will enable the development of next generation molybdate materials with multiple cation substitution resulting in better cycling stability and higher specific capacitance.