Sustainable biosurfactant production from secondary feedstock-recent advances, process optimization and perspectives.

Biosurfactants have garnered increased attention lately due to their superiority of their properties over fossil-derived counterparts. While the cost of production remains a significant hurdle to surpass synthetic surfactants, biosurfactants have been anticipated to gain a larger market share in the coming decades. Among these, glycolipids, a type of low-molecular-weight biosurfactant, stand out for their efficacy in reducing surface and interfacial tension, which made them highly sought-after for various surfactant-related applications. Glycolipids are composed of hydrophilic carbohydrate moieties linked to hydrophobic fatty acid chains through ester bonds that mainly include rhamnolipids, trehalose lipids, sophorolipids, and mannosylerythritol lipids. This review highlights the current landscape of glycolipids and covers specific glycolipid productivity and the diverse range of products found in the global market. Applications such as bioremediation, food processing, petroleum refining, biomedical uses, and increasing agriculture output have been discussed. Additionally, the latest advancements in production cost reduction for glycolipid and the challenges of utilizing second-generation feedstocks for sustainable production are also thoroughly examined. Overall, this review proposes a balance between environmental advantages, economic viability, and societal benefits through the optimized integration of secondary feedstocks in biosurfactant production.

Navigating Sustainability through Greenhouse Gas Emission Inventory: ESG Practices and Energy Shift in Bangladesh's Textile and Readymade Garment Industries.

As the world's demand for textiles and clothing rapidly increases, this industry's greenhouse gas (GHG) emissions are becoming a major environmental concern. Bangladesh, a key player in the global textile supply chain and one of the top producers, contributes significantly to these emissions. However, accessible data on activity and GHG emissions, crucial for researchers, the private sector, and policymakers in decision-making, is scarce. To address this gap, this study combines a detailed field survey with expert interviews to establish a comprehensive emission inventory. This inventory aims to identify hotspots and facilitate the adoption of effective mitigation strategies. Focusing on a prominent industrial zone's textile and readymade garments (RMG) industries, the research employs a mix of top-down and bottom-up approaches and follows the IPCC guidelines to develop a GHG emission inventory for 2022. The study evaluates various emission sources, including scope 1 (onsite fuel combustions), scope 2 (grid electricity usage), and scope 3 (waste and wastewater treatment). In the total emissions (6043.5 Gg CO2eq.), textile and RMG industries contribute 67.8% and 32.2%, respectively, with scope 1 emissions dominating at 85%. Notably, scope 2 emissions exhibit significant uncertainty (-10.4% to +11.9%), largely due to variations in national grid emission factors. This study forecasts GHG emissions until 2030, considering current trends (26 thousand Gg CO2 eq.). It also explores various energy mix scenarios, factoring in the depletion of existing natural gas reserves (ranging from 8 thousand to 33 thousand Gg CO2 eq.). This study delves into the impact of the Environmental, Social, and Governance (ESG) system on industries' GHG emissions. Besides improving worldwide emission databases and identifying hotspots, this research aims to promote a sustainable transition in both Bangladesh and other developing textile manufacturing nations across the globe.

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.

Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility.

The growing interconnectedness of urban infrastructure networks presents challenges to their ability to handle unforeseen disruptions, particularly in the context of extreme weather events resulting from climate change. Understanding the resilience of interconnected infrastructure systems is imperative to effectively manage such disruptions. This study investigates the role of interconnectedness in enhancing the resilience of public transportation systems in Hong Kong, a city heavily reliant on public transit. Our results demonstrate that interconnected transportation systems improve resilience by reducing topological vulnerabilities, increasing attack tolerance, and enhancing post-disruption interoperability. Findings also identify the potential to integrate vulnerable systems for greater robustness and highlight the marginal benefits of enhancing intermodal transfer. Strengthening interconnectedness among modes of urban public transit fosters a safe-to-fail system, presenting a distinct resilience-by-design approach. This complements conventional resilience-by-intervention approaches that focus on improving individual systems or introducing entirely new systems.

Electrospun nanofibrous membranes for membrane distillation application-A dynamic life cycle assessment (dLCA) approach.

Membrane distillation (MD) for water desalination and purification has been gaining prominence to address the issues relating to water security and the destruction of aquatic ecosystems globally. Recent advances in electrospun membranes for MD application have improved antifouling and anti-wetting performance. However, the environmental impacts associated with producing novel electrospun membranes still need to be clarified. It is imperative to quantify and analyze the tradeoffs between membrane performance and impacts at the early stages of research on these novel membranes. Life Cycle Assessment (LCA) is an appropriate tool to systematically account for environmental performance, all the way from raw material extraction to the disposal of any product, process, or technology. The inherent lack of detailed datasets for emerging technologies contributes to significant uncertainties, making the adoption of traditional LCA challenging. A dynamic LCA (dLCA) is performed to guide the sustainable design and selection of emerging electrospun poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) electrospun membrane (E-PH) and hybridizing polydimethylsiloxane (PDMS) on E-PH membrane (E-PDMS) for dyeing wastewater treatment technologies. The associated environmental impacts are related to the high energy demands required for fabricating electrospun nanofibrous membranes. After LCA analysis, the E-PDMS membrane emerges as a promising membrane, due to the relatively low impact/benefit ratio and the high performance achieved in treating dyeing wastewater.

Comparative Economic and Life Cycle Analysis of Future Water Supply Mix Scenarios for Hong Kong - A Water Scarce City.

Increasing urbanization and changes in climate have placed increasing stress on urban water supply systems. Policy makers have increasingly adopted alternative water supply sources, such as desalination and water reclamation to meet this challenge, however these technologies may increase the negative environmental impacts of the water supply system. These alternative sources are energy intensive, and more expensive to produce, which raises questions about their sustainability. In this study, a Life Cycle Assessment (LCA) and a economic portfolio choice model were used to determine the impacts of Hong Kong's long term water policy. The results of our study show that the current water policy will increase the carbon emissions of producing 1 m3 of freshwater by 11% to 1.65 kg CO2-Eq due to the addition of desalination. However, a fit-for-purpose water policy approach only increases emission by 4%, to 1.54 kg CO2-Eq, by instead relying on water reclamation to offset freshwater consumption. Impacts from increased energy consumption were mitigated by improved wastewater treatment, which reduced CH4 emissions. Although, ozone layer impacts increased due to higher NOx and N2O emissions, highlighting the need to consider emissions from wastewater treatment processes when evaluating water reclamation processes. Impacts to water prices were also minimized when reclaimed water was chosen over desalination, due to its lower unit production cost. By considering both cost and environmental impacts of such system level changes, decision makers can more accurately evaluate different water supply approaches for data-driven policymaking.

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.

Masks for COVID-19.

Sustainable solutions on fabricating and using a face mask to block the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread during this coronavirus pandemic of 2019 (COVID-19) are required as society is directed by the World Health Organization (WHO) toward wearing it, resulting in an increasingly huge demand with over 4 000 000 000 masks used per day globally. Herein, various new mask technologies and advanced materials are reviewed to deal with critical shortages, cross-infection, and secondary transmission risk of masks. A number of countries have used cloth masks and 3D-printed masks as substitutes, whose filtration efficiencies can be improved by using nanofibers or mixing other polymers into them. Since 2020, researchers continue to improve the performance of masks by adding various functionalities, for example using metal nanoparticles and herbal extracts to inactivate pathogens, using graphene to make masks photothermal and superhydrophobic, and using triboelectric nanogenerator (TENG) to prolong mask lifetime. The recent advances in material technology have led to the development of antimicrobial coatings, which are introduced in this review. When incorporated into masks, these advanced materials and technologies can aid in the prevention of secondary transmission of the virus.

Bioconversion of Food Waste to produce Industrial-scale Sophorolipid Syrup and Crystals: dynamic Life Cycle Assessment (dLCA) of Emerging Biotechnologies.

Bioconversion of food waste into sophorolipid-based biosurfactants is a promising emerging technology. It is important to evaluate the environmental impacts associated with the latest advancements in sophorolipid production as it matures to maximize sustainability on scale-up. This study takes a dynamic Life Cycle Assessment (dLCA) approach to address the inherent uncertainties and evaluate the environmental performances. It demonstrates the dLCA framework by conducting the new traversal of food waste-derived industrial-scale sophorolipid production, with the combination of Techno-Economic Analysis (TEA). A systematic investigation of the environmental-economic implications of the two pathways to produce SL crystals and syrup. The global warming potential (GWP) for 1 kg of SL crystals and syrup was 7.9 kg CO2 eq. and 5.7 kg CO2 eq., respectively. The Ashby-like charts based on the LCA and TEA results at the pilot plant highlighted the trade-offs between systemic environmental costs and economic benefits for design decisions.

Guiding environmental sustainability of emerging bioconversion technology for waste-derived sophorolipid production by adopting a dynamic life cycle assessment (dLCA) approach.

Microbial biosurfactants are surface-active molecules that are naturally produced by a range of microorganisms. They have certain advantages over chemical surfactants, such as lower toxicity, higher biodegradability, anti-tumor, and anti-microbial properties. Sophorolipids (SLs) in particular are one of the most promising biosurfactants, as they hold the largest share of the biosurfactant market. Currently, researchers are developing novel approaches for SL production that utilize renewable feedstocks and advanced separation technologies. However, challenges still exist regarding consumption of materials, enzymes, and electricity, that are primarily fossil based. Researchers lack a clear understanding of the associated environmental impacts. It is imperative to quantify and optimize the environmental impacts associated with this emerging technology very early in its design phase to guide a sustainable scale-up. It is necessary to take a collaborative perspective, wherein life cycle assessment (LCA) experts work with experimentalists, to quantify environmental impacts and provide recommendations for improvements in the novel waste-derived SL production pathways. Studies that have analyzed the environmental sustainability of microbial biosurfactant production are very scarce in literature. Hence, in this work, we explore the possibility of applying LCA to evaluate the environmental sustainability of SL production. A dynamic LCA (dLCA) framework that quantifies the environmental impacts of a process in an iterative manner, is proposed and applied to evaluate SL production. The first traversal of the dLCA was associated with the selection of an optimal feedstock, and results identified food waste as a promising feedstock. The second traversal compared fermentation coupled with alternative separation techniques, and highlighted that the fed-batch fermentation of food waste integrated with the in-situ separation technique resulted in less environmental impacts. These results will guide experimentalists to further optimize those processes, and improve the environmental sustainability of SL production. Resultant datasets can be iteratively used in subsequent traversals to account for technological changes and mitigate the corresponding impacts before scaling up.

Dropout in Neural Networks Simulates the Paradoxical Effects of Deep Brain Stimulation on Memory.

Neuromodulation techniques such as deep brain stimulation (DBS) are a promising treatment for memory-related disorders including anxiety, addiction, and dementia. However, the outcomes of such treatments appear to be somewhat paradoxical, in that these techniques can both disrupt and enhance memory even when applied to the same brain target. In this article, we hypothesize that disruption and enhancement of memory through neuromodulation can be explained by the dropout of engram nodes. We used a convolutional neural network (CNN) to classify handwritten digits and letters and applied dropout at different stages to simulate DBS effects on engrams. We showed that dropout applied during training improved the accuracy of prediction, whereas dropout applied during testing dramatically decreased the accuracy of prediction, which mimics enhancement and disruption of memory, respectively. We further showed that transfer learning of neural networks with dropout had increased the accuracy and rate of learning. Dropout during training provided a more robust "skeleton" network and, together with transfer learning, mimicked the effects of chronic DBS on memory. Overall, we showed that the dropout of engram nodes is a possible mechanism by which neuromodulation techniques such as DBS can both disrupt and enhance memory, providing a unique perspective on this paradox.

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].

Life cycle assessment (LCA) of food waste treatment in Hong Kong: On-site fermentation methodology.

"Smart Food Waste Recycling Bin" (S-FRB) systems have recently been developed to facilitate the transformation of food waste into an end-product suitable for use as an energy resource following circular economy principles. This decentralized waste decomposition system utilizes fermentative microorganisms for the treatment of organic food waste and has emerged as a possible solution for coping with both landfill capacity and greenhouse gas emissions issues. This paper utilizes Life Cycle Assessment (LCA) to determine the environmental impacts associated with this S-FRB technology and identify environmental hotspots to reduce these impacts. In this paper, we have conducted an on-site pilot-scale study for 2 months at a canteen located at the City University of Hong Kong, which resulted in a 90% reduction in the mass of food waste treated in the S-FRB system. Based on this pilot-scale study hypothetical scenarios were developed to determine potential environmental impacts potential scaled-up deployments of the S-FRB instrument based on varied assumptions. Examination of the LCAs of these different scenarios demonstrated the potential for further reduction in CO2 equivalent emissions during food waste treatment. Cumulative Energy Demand (CED) and Energy Return on Investment (EROI) were also investigated to understand the energy balance energy of the S-FRB technology. Finally, using current waste treatment methods in Hong Kong as a benchmark, the environmental impacts of the S-FRB are compared with the conventional food waste treatment approaches such as landfilling and organic waste treatment facilities (OWTF).

A framework for sustainable nanomaterial selection and design based on performance, hazard, and economic considerations.

Engineered nanomaterials (ENMs) and ENM-enabled products have emerged as potentially high-performance replacements to conventional materials and chemicals. As such, there is an urgent need to incorporate environmental and human health objectives into ENM selection and design processes. Here, an adapted framework based on the Ashby material selection strategy is presented as an enhanced selection and design process, which includes functional performance as well as environmental and human health considerations. The utility of this framework is demonstrated through two case studies, the design and selection of antimicrobial substances and conductive polymers, including ENMs, ENM-enabled products and their alternatives. Further, these case studies consider both the comparative efficacy and impacts at two scales: (i) a broad scale, where chemical/material classes are readily compared for primary decision-making, and (ii) within a chemical/material class, where physicochemical properties are manipulated to tailor the desired performance and environmental impact profile. Development and implementation of this framework can inform decision-making for the implementation of ENMs to facilitate promising applications and prevent unintended consequences.

A network-based framework for assessing infrastructure resilience: a case study of the London metro system.

Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems.

Economic Dependence of U.S. Industrial Sectors on Animal-Mediated Pollination Service.

Declining animal pollinator health and diversity in the U.S. is a matter of growing concern and has particularly gained attention since the emergence of colony collapse disorder (CCD) in 2006. Failure to maintain adequate animal-mediated pollination service to support increasing demand for pollination-dependent crops poses risks for the U.S. economy. We integrate the Economic Input-Output (EIO) model and network analysis with data on pollinator dependence of crops to understand the economic dependence of U.S. industrial sectors on animal-mediated pollination service. The novelty of this work lies in its ability to identify industrial sectors and industrial communities (groups of closely linked sectors) that are most vulnerable to scarcity of pollination service provided by various animal species. While the economic dependence of agricultural sectors on pollination service is significant (US$14.2-23.8 billion), the higher-order economic dependence of the rest of the U.S. industrial sectors is substantially high as well (US$10.3-21.1 billion). The results are compelling as they highlight the critical importance of animal-induced pollination service for the U.S. economy, and the need to account for the role of ecosystem goods and services in product life cycles.

Understanding resilience in industrial symbiosis networks: insights from network analysis.

Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks.