Nanobubbles in water and wastewater treatment systems: Small bubbles making big difference.

Since the discovery of nanobubbles (NBs) in 1994, NBs have been attracting growing attention for their fascinating properties and have been studied for application in various environmental fields, including water and wastewater treatment. However, despite the intensive research efforts on NBs' fundamental properties, especially in the past five years, controversies and disagreements in the published literature have hindered their practical implementation. So far, reviews of NB research have mainly focused on NBs' role in specific treatment processes or general applications, highlighting proof-of-concept and success stories primarily at the laboratory scale. As such, there lacks a rigorous review that authenticates NBs' potential beyond the bench scale. This review aims to provide a comprehensive and up-to-date analysis of the recent progress in NB research in the field of water and wastewater treatment at different scales, along with identifying and discussing the challenges and prospects of the technology. Herein, we systematically analyze (1) the fundamental properties of NBs and their relevancy to water treatment processes, (2) recent advances in NB applications for various treatment processes beyond the lab scale, including over 20 pilot and full-scale case studies, (3) a preliminary economic consideration of NB-integrated treatment processes (the case of NB-flotation), and (4) existing controversies in NBs research and the outlook for future research. This review is organized with the aim to provide readers with a step-by-step understanding of the subject matter while highlighting key insights as well as knowledge gaps requiring research to advance the use of NBs in the wastewater treatment industry.

Quantitative impact assessment of temperature on the economics of a rooftop solar photovoltaic system.

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

Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities.

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

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.

A comprehensive review of dye-sensitized solar cell optimal fabrication conditions, natural dye selection, and application-based future perspectives.

Dye-sensitized solar cells (DSSC) constructed using natural dyes possess irreplaceable advantages in energy applications. The main reasons are its performance, environmentally benign dyes, impressible performance in low light, ecologically friendly energy production, and versatile solar product integration. Though DSSCs using natural dyes as sensitizers have many advantages, they suffer from poor efficiency compared to conventional silicon solar cells. Moreover, the difficulty in converting them to practical devices for the day-to-day energy needs has to be addressed. This review will outline the optimization of conditions to be followed for better efficiency in DSSCs using natural dyes as sensitizers. This review has taken into account the importance of the first step towards the fabrication of DSSC, i.e. the selection process. The selection of plant parts has a noticeable impact on the overall efficiency of the device. Accordingly, a proper study has been done to analyse the plant's parts that have shown better results in terms of device efficiency. In addition to this, a wide range of techniques and factors such as extraction methods, the solvent used, coating techniques, immersing time, and co-sensitization have been taken into consideration from the studies done over the period of 10 years to examine their influence on the overall performance of the DSSC device. These results have been addressed to stipulate the best suitable condition that will help supplement the efficiency of the device even further. Also, the future perspectives, such as the DSSCs use in wearable devices, incorporating various approaches to enhance the power conversion efficiency of DSSCs using natural dyes, and thermochromism ability for DSSCs have been discussed.

Linking Economic Growth, Urbanization, and Environmental Degradation in China: What Is the Role of Hydroelectricity Consumption?

Achieving environmental sustainability has become a global initiative whilst addressing climate change and its effects. Thus, this research re-assessed the EKC hypothesis in China and considered the effect of hydroelectricity use and urbanization, utilizing data from 1985 to 2019. The autoregressive distributed lag (ARDL) bounds testing method was utilized to assess long-run cointegration, which is reinforced by a structural break. The outcome of the ARDL bounds test confirmed cointegration among the series. Furthermore, the ARDL revealed that both economic growth and urbanization trigger environmental degradation while hydroelectricity improves the quality of the environment. The outcome of the ARDL also validated the EKC hypothesis for China. In addition, the study employed the novel gradual shift causality test to capture causal linkage among the series. The advantage of the gradual shift causality test is that it can capture gradual or smooth shifts and does not necessitate previous information of the number, form of structural break(s), or dates. The outcomes of the causality test revealed causal connections among the series of interest.

Dominance of Fossil Fuels in Japan's National Energy Mix and Implications for Environmental Sustainability.

Despite the drive for increased environmental protection and the achievement of the Sustainable Development Goals (SDGs), coal, oil, and natural gas use continues to dominate Japan's energy mix. In light of this issue, this research assessed the position of natural gas, oil, and coal energy use in Japan's environmental mitigation efforts from the perspective of sustainable development with respect to economic growth between 1965 and 2019. In this regard, the study employs Bayer and Hanck cointegration, fully modified Ordinary Least Square (FMOLS), and dynamic ordinary least square (DOLS) to investigate these interconnections. The empirical findings from this study revealed that the utilization of natural gas, oil, and coal energy reduces the sustainability of the environment with oil consumption having the most significant impact. Furthermore, the study validates the environmental Kuznets curve (EKC) hypothesis in Japan. The outcomes of the Gradual shift causality showed that CO2 emissions can predict economic growth, while oil, coal, and energy consumption can predict CO2 emissions in Japan. Given Japan's ongoing energy crisis, this innovative analysis provides valuable policy insights to stakeholders and authorities in the nation's energy sector.

Data related to crystalline photovoltaic plant performance in the semi-arid climate of India.

This article presents performance data concerning a 1MW crystalline photovoltaic (PV) plant installed in the semi-arid climate of India. Data includes the daily average samples from January 2012 to February 2016, related to solar irradiance on the plane of the array, electrical energy injected into the grid, reference yield, final yield, and the performance ratio. Furthermore, the decomposition time series for the performance ratio by applying the classical seasonal decomposition (CSD), Holt-Winters seasonal model (HW), and Seasonal and Trend decomposition using Loess (STL) is also provided for quantifying of the degradation rate of the PV system. The data are provided in the supplementary file included in this article. The dataset is related to the paper entitled "Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India." [1].