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Laccases are extensively used in the textile industry due to their ability to decolorize dyes, modify fabric surfaces, and bleach textiles. Identifying a laccase with both high thermal stability and alkali tolerance suitable for textile applications presents a significant challenge. A novel alkaline laccase, LacCT, was discovered from Caldalkalibacillus thermarum and successfully expressed it in Escherichia coli. LacCT displayed optimal activity at 65°C and maintained high stability across a pH range of 6.0-10.0, with an optimal pH of 7.5. Through rational design, the thermal stability of the best variant, G190P/Q254Y/G336M/D510F (LacCT-11), was significantly enhanced, resulting in a half-life of 63.2 min at 60°C - 1.8 times longer than that of the wild type. This research introduces a promising new laccase with considerable potential for decolorizing textile wastewater and improving the ramie degumming process.

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The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

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A novel modification technique employing a layer-by-layer (LbL) self-assembly method, integrated with a pressure-assisted filtration system, was developed for enhancing a commercial polyethersulfone (PES) microfiltration (MF) membrane. This modification involved the incorporation of tannic acid (TA) in conjunction with graphene oxide (GO) nanosheets. The effectiveness of the LbL method was confirmed through comprehensive characterization analyses, including ATR-FTIR, SEM, water contact angle (WCA), and mean pore size measurements, comparing the modified membrane with the original commercial one. Sixteen variations of PES MF membranes were superficially modified using a three-factorial design, with the deposited amount of TA and GO as key factors. The influence of these factors on the morphology and performance of the membranes was systematically investigated, focusing on parameters such as pure water permeability (PWP), blue corazol (BC) dye removal efficiency, and flux recovery rate (FRR). The membranes produced with the maximum amount of GO (0.1 mg, 0.55 wt%) and TA as the inner and outer layers demonstrated remarkable FRR and significant BC removal, exceeding 80%. Notably, there was no significant difference observed when using either 0.2 (1.11 wt%) or 0.4 mg (2.22 wt%) in the first layer, as indicated by the Tukey mean test. Furthermore, the modified membrane designated as MF/TA0.4GO0.1TA0.4 was evaluated in the filtration of a simulated dye bath wastewater, exhibiting a BC removal efficiency of 49.20% and a salt removal efficiency of 27.74%. In conclusion, the novel PES MF membrane modification proposed in this study effectively enhances the key properties of pressure-driven separation processes.

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Highly colored azo dye-contaminated wastewater poses significant environmental threats and requires effective treatment before discharge. The anaerobic azo dye treatment method is a cost-effective and environmentally friendly solution, while its time-consuming and inefficient processes present substantial challenges for industrial scaling. Thus, the use of iron materials presents a promising alternative. Laboratory studies have demonstrated that systems coupled with iron materials enhance the decolorization efficiency and reduce the processing time. To fully realize the potential of iron materials for anaerobic azo dye treatment, a comprehensive synthesis and evaluation based on individual-related research studies, which have not been conducted to date, are necessary. This review provides, for the first time, an extensive and detailed overview of the utilization of iron materials for azo dye treatment, with a focus on decolorization. It assesses the treatment potential, analyzes the influencing factors and their impacts, and proposes metabolic pathways to enhance anaerobic dye treatment using iron materials. The physicochemical characteristics of iron materials are also discussed to elucidate the mechanisms behind the enhanced bioreduction of azo dyes. This study further addresses the current obstacles and outlines future prospects for industrial-scale application of iron-coupled treatment systems.

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The development of the textile industry has negative effects on the natural environment. Cotton cultivation, dyeing fabrics, washing, and finishing require a lot of water and energy and use many chemicals. One of the most dangerous pollutants generated by the textile industry is dyes. Most of them are characterized by a complex chemical structure and an unfavorable impact on the environment. Especially azo dyes, whose decomposition by bacteria may lead to the formation of carcinogenic aromatic amines and raise a lot of concern. Using the metabolic potential of microorganisms that biodegrade dyes seems to be a promising solution for their elimination from contaminated environments. The development of omics sciences such as genomics, transcriptomics, proteomics, and metabolomics has allowed for a comprehensive approach to the processes occurring in cells. Especially multi-omics, which combines data from different biomolecular levels, providing an integrative understanding of the whole biodegradation process. Thanks to this, it is possible to elucidate the molecular basis of the mechanisms of dye biodegradation and to develop effective methods of bioremediation of dye-contaminated environments.

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The textile industry is known for its significant environmental impact, and as such, there is a growing need to assess the industry's production practices. Life cycle assessment (LCA) is an effective tool for measuring the environmental impact of textile products from raw material production to end-of-life disposal. Surat city, known as textile hub of India, has multiple industrial clusters that play host to different elements of the textile value chain. This research aims to evaluate the environmental performance of a Surat-based textile company. The study involves the collection of data from textile manufacturing facilities, including gray cloth production and wet processes (gate-to-gate approach). The data collected has been analyzed using the GABI (9.2.1 version) software. The study provides potential environmental impact data on present textile technology and production situation. Significant hotspots have been determined throughout supply chain and the associated drivers identified. However, the industry's environmental impact can be reduced by practicing ecoefficiency, immaterialization, and recycling textile waste. This research highlights the importance of LCA in identifying the environmental impact of the textile industry and provides a basis for developing sustainable practices to minimize the industry's environmental impact.

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The textile industry is widely considered as one of the most pollutant-releasing sectors globally. As the world is moving toward sustainability, it is essential to thoroughly assess how a textile product affects the environment. The aim of this study is to investigate the environmental impact of textile manufacturing in Bangladesh using life cycle assessment (LCA) method. A cradle-to-gate LCA was conducted to produce 1000 units of cotton polo shirts following ISO 14040 standards. LCA was also conducted for each final product at every manufacturing stage of cotton polo shirt, including the production of 1000 kg of cotton fibers, 1000 kg of yarn, 1000 kg of grey fabrics, 1000 kg of dyed-finished fabrics, and finally, assembling of 1000 pieces of polo shirts. Inventory analysis revealed that for producing 1000 pieces of polo shirts, 0.12 hector of land, 363.89 kg of cotton fiber, 324.84 kg of yarn, 320.45 kg of knitted fabric, and 299.5 kg of dyed-finished fabrics were required. The study also found that 1550.9 kWh of electricity, 15.47 L of diesel, and 72.54 m3 of natural gas were needed to produce 1000 pieces of polo shirts. The potential environmental impacts were categorized into 12 different types based on the CML 2001 method and calculated using openLCA 2.0 software. The outcomes of LCA revealed considerable environmental impacts in different categories during manufacturing of cotton polo shirts in Bangladesh. For example, the global warming potential (GWP) associated with the manufacturing of 1000 pieces of polo shirts was 1345.97 kg CO2-eq. Among several production stages of polo shirts, the highest contributor of GWP was dyeing section (38.36 %), followed by cotton fiber production (29.32 %) and yarn manufacturing (18.92 %). The obtained data also revealed that for cultivating cotton fibers, manufacturing of yarn and grey fabrics, and finally dyeing-finishing of same quantity fabric (1000 kg), the GWP were 1084.41 kg CO2-eq, 783.67 kg CO2-eq, 145.88 kg CO2-eq, 1723.88 kg CO2-eq, 314.94 kg CO2-eq, respectively. The outcome of the impact assessment will be crucial for decision-making when it comes to taking remedial actions to lessen negative environmental consequences for the sustainable development of textile industry in Bangladesh.

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This study explored the valorisation of silkworm by-product, a major by-product of the silk industry (sericulture), which amounts to 16 million tonnes annually. The focus was on transforming waste into energy resources through pyrolysis under CO2 conditions. In one-stage pyrolysis, the evolution of syngas under N2 was found to be comparable to that under CO2. A notable allocation of carbon to biocrude rather than syngas was observed. The two-stage pyrolysis resulted in increased syngas production. However, achieving a homogeneous reaction between CO2 and the volatiles liberated from silkworm byproduct proved challenging. Indeed, the reaction kinetics governing CO2 reactivity was not fast although the temperature windows of the reaction were aligned in the two-stage pyrolysis. To address this issue, pyrolysis was performed using a Ni-based catalyst to expedite the reaction kinetics. Consequently, syngas formation, particularly CO formation, was significantly enhanced under CO2 conditions compared to that under N2 conditions. The syngas yield under CO2 was 36.42 wt% which was 2-fold higher than that of N2. This suggested the potential of CO2 altering the carbon distribution from biocrude to syngas. This strategy would contribute to the establishment of sustainable production of silk by converting sericulture by-product into energy/chemical resources.

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The clothing industry is considered one of the most polluting industries on the planet due to the high consumption of water, energy, chemicals/dyes, and high generation of solid waste and effluents. Faced with environmental concerns, the textile ennoblement sector is the most critical of the textile production chain, especially the traditional dyeing processes. As an alternative to current problems, dyeing with supercritical CO2 (scCO2) has been presented as a clean and efficient process for a sustainable textile future. Supercritical fluid dyeing (SFD) has shown a growing interest due to its significant impact on environmental preservation and social, economic, and financial gains. The main SFD benefits include economy and reuse of non-adsorbed dyes; reduction of process time and energy expenditure; capture of atmospheric CO2 (greenhouse gas); use and recycling of CO2 in SFD; generation of carbon credits; water-free process; effluent-free process; reduction of CO2 emission and auxiliary chemicals. Despite being still a non-scalable and evolving technology, SFD is the future of dyeing. This review presented a comprehensive overview of the environmental impacts caused by traditional processes and confronted the advantages of SFD. The SFD technique was introduced, along with its latest advances and future perspectives. Financial and environmental gains were also discussed.

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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.

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We piloted the development and implementation of a multifaceted intervention package for improving respiratory health among textile workers using a pre-post design at six mills in Karachi. The intervention, implemented following a baseline survey (n = 498), included health and safety training of workers and managers, promotion of cotton dust control measures, and the provision of facemasks. Follow-up surveys were conducted at 1, 6, and 12 months post-intervention. Knowledge, attitude, and practice (KAP) scores and respiratory symptoms were assessed through a questionnaire and spirometry was conducted. The intervention was provided to 230 workers and led to an improvement in KAP scores that was more likely among workers with a higher educational status, spinners, smokers, those with a permanent employment status, working in morning shifts, and with ⩾5 years of textile experience. We found the intervention acceptable and feasible in these textile mills henceforth, trials are required to determine its effectiveness.

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Based on the framework theory of industrial digitization, digital industrialization, digital governance and digital value-added in a sustainable environment, this paper systematically studies the relevant elements of governance and value distribution in the sustainable environment of global trade, and its impact on the development path of human beings. This paper explores the way to embed digital technology into the global value chain to realize digital empowerment, measures the competitiveness index of Keqiao's textile industry in the global value chain, analyzes the technical and environmental challenges faced by China's textile industry in the digital age, and proposes the corresponding countermeasures to deal with the impact of global value chain participation and to improve the international competitiveness of China's textile industry.

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The contradiction between the rapid textile expansion and intensive energy consumption, highly environmental pollution calls for the adoption of cleaner production (CP). However, current evaluation system mainly targeted on CP at production stage, guidance and support on the life cycle assessment is still in its infancy. Meanwhile few studies brought the combination of water conservation and carbon reduction into considerations. This study compared the existing CP evaluation systems including guidelines for the whole industry, standards for textile industry and indicators for the dyeing and finishing sector by quantifying the differences of indicator score compositions. Comparisons analysis from six aspects suggested that all the evaluation systems had relevant indicators regarding "pollutant emissions". "Management", "process equipment and techniques" and "resource and energy consumption" have also been well concerned while "product characteristic" seemed to be overlooked at current stage. From the perspective of whole life cycle, the key of textile processing is the "printing and dyeing" (44.23 %) followed by "fabric manufacturing"(28.85 %) and setting (15.38 %). With regards to the environmental impacts, resources depletion gained the highest attention since their indicator scores reached up to 25.71 %, 18.47 % and 20.62 % for EMAS, ERG 2018 and HJ-1852006. Cleaner production awareness and social impact also played significant roles in ISO 14031:2021 and WMG. Subsequently, a set of new comprehensive CP evaluation indicator system was established, including 3 scopes and 7 goals. The newly-built indicator system incorporated with life cycle perspectives gave a powerful tool to measure the CP level in textile industry and of CP will benefit from water reuse and energy utilization with high efficiency.

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BACKGROUND: Exposure to dust in textile mills adversely affects workers' health. We collected epidemiological data on textile workers suffering from respiratory diseases and assessed work absence associated with illnesses in Faisalabad, Pakistan. METHODS: We recruited 206 workers using multistage sampling from 11 spinning mills in Faisalabad, Pakistan. The data were collected using 2-week health diaries and face-to-face interviews. The data pertains to socio-demographics, occupational exposures, the state of the workers' health, and other attributes. A theoretical framework of the health production function was used to estimate the relationship between cotton dust exposure and respiratory illnesses. We also estimated functional limitations (e.g., work absence) associated with dust exposure. STATA 12 was used to calculate descriptive statistics, an ordered probit for byssinosis, a probit model for chronic cough, and three complementary log-log models for blood phlegm, bronchitis, and asthma to measure dose-response functions. A Tobit model was used to measure the sickness absence function. RESULTS: We found that cotton dust exposure causes a significant health burden to workers, such as cough (35%), bronchitis (17%), and different grades of byssinosis symptoms (22%). The regression analysis showed that smoking cigarettes and working in dusty sections were the main determinants of respiratory diseases. Dusty work sections also cause illness-related work absences. However, the probability of work absence decreases with the increased use of face masks. CONCLUSION: The study's findings imply the significance of promoting occupational safety and health culture through training and awareness among workers or implementing the use of safety gadgets. Promulgating appropriate dust standards in textile mills is also a need of the hour.

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Surging environmental issues confiscate stakeholders' attention due to growing competition. Sustainable performance in this regard is viewed as a suitable tool because it not only gauges firm's financial performance but also covers social and environmental areas. However, sustainable performance cannot be achieved without creating sustainable business advantage. Green supply chain practices are known to be implemented in terms of sustainability as they are cross-looped as well as cross-organizational. The practices help in the reduction of environmental impact of business activities without compromising on quality, performance, cost, and energy usage efficiency. The reason encourages the authors to explore the role of selected green supply chain factors, customer preference, and leadership factors in shaping sustainable business advantage. The study has examined the association by adopting the quantitative research method. The researcher has gathered data from 530 employees in the purchasing department, logistics, import/export officers, and packing staff. The data was analyzed statistically by employing SPSS for preliminary analysis and AMOS for testing the structural model and the hypotheses of the study. Using confirmatory factors analysis and structural equational modeling, the researcher has analyzed the study's measurement model and the hypotheses of the study. The findings of the research have depicted that there is a significant positive impact of lean management and green product design with sustainable business advantage. Moreover, there exists a mediation of customer preferences and leadership factors between green product design and sustainable business advantage. The study has a few limitations as well and uses a quantitative method; therefore, the study has recommended that future researchers utilize qualitative or mixed methods. The study has several theoretical and practical implications.

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This study compares biochar (BCW) systems' pollutant removal effectiveness to conventional subsurface flow (CCW) in constructed wetland systems to treat textile wastewater. The two systems were identical in construction, but the biochar was 0.1 m thick over gravel and sand (maximum flow rate of 0.021 m3 h-1) as the primary medium over CCW (flow rate of 0.02 m3 h-1). The results revealed that the BCW approach was more efficient than the CCW system (pebble over sand and gravels) in removing and lowering heavy metals below thresh hold limits such as Cr, Cd, Cu, Pb, Ni, and Zn. The alkaline nature of textile water achieves neutrality in both CCW and BCW. However, BCW is more efficient due to a larger active surface area and the ability to filter out more metal and organic ions. TDS reduction efficiency in BCW was 53.07%, compared to 40.04% in CCW. Heavy metal removal was 100% in BCW at 3 to 12 h, whereas it takes 6 to 24 h in CCW (82% for Cr to 93% for Cu). The quick removal of Na from textile wastewater by BCW was reversed and achieved equilibrium in 24 h in contrast to the CCW system (> 24 h). The findings obtained at the lab scale level demonstrated that the BCW system was more effective in reducing TDS, neutralizing the alkalinity of textile wastewater, and removing heavy metals. This study strongly supports the potential application of biochar-constructed wetlands for textile wastewater treatment.

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Microplastic (MP) is a contaminant presently causing a significant environmental risk. The present study aims to extract, measure, and classify MP in sediment samples from two seasons (monsoon and summer) in Noyyal River, South India. Microplastic was separated from sediments using the Sediment Microplastic Isolation technique. Microplastics were detected in four forms: foams, films, fragments, and fibres. Dominant polymer types during monsoon are Polystyrene (29%), Polycarbonate (13%), Nylon (13%), and Ethylene Vinyl Acetate (13%). Throughout summer, Polystyrene (17%) was the prevalent polymer type, followed by Nylon (14%), Polycarbonate (11%), and Polyvinyl Chloride (9%). Scanning Electron Microscope (SEM) demonstrated that MPs exhibit diverse surface morphologies, including foamy, fibrous, and granular nature. It also shows tearing and fracturing of MPs and aging, indicating substantial summer degradation. Using Polymer Hazard Index (PHI) and Pollution Load Index (PLI), MP vulnerability in sediments indicates that despite lesser PLI, the MPs pose an extreme danger threat to the environment during the summer season compared to the monsoon. The study thus provides insight into the seasonal variation of MPs and their threat in Noyyal River sediments, which will aid in formulating guidelines for the minimization of MPs in river systems.

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Silica aerogels are highly porous materials with exceptional thermal insulation performance. They become even more attractive if combined thermal and acoustic insulation is achieved. Silica aerogel composites reinforced with fibres are an ingenious way to surpass the fragility stemmed from the aerogel's intrinsic porosity, and textile fibres are good sound absorption materials. Reclaimed fibres are a relatively low-cost feedstock and were obtained in this work exclusively through mechanical processes from textile wastes, thus promoting the concept of circular economy, namely for cotton, polyester and wool fibres. These reclaimed fibres were used as reinforcement matrices for silica aerogel composites obtained from sol-gel transformation of tetraethyl orthosilicate and isobutyltriethoxysilane/or vinyltrimethoxysilane precursors and dried at ambient pressure after silylation. Silica aerogel composites reinforced with reclaimed cotton fibres had the best sound absorption coefficient (a peak value of 0.89), while the polyester-reinforced composite exhibited the lowest thermal conductivity (k = ~24 mW m-1 K-1, Hot Disk). The better combined results on thermal and acoustic insulation were achieved by the wool-reinforced composites. The thermal conductivity values were less than 27 mW m-1 K-1, and the sound absorption coefficient achieved a peak value of 0.85. Therefore, the aerogel composites developed here can be selected for thermal or/and acoustic barriers by choosing a suitable type of fibre. Their design and preparation protocol followed environmental-friendly and cost-effective approaches.

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Background: Sleep is a physiological process that is essential to human physical, emotional, and mental health. Sleep difficulties affect an estimated 150 million individuals globally, with roughly 17% of the population in developing countries. As a result, the purpose of this study was to determine the prevalence and associated factors of poor sleep quality among textile and garment manufacturing workers in Addis Ababa, Ethiopia, in 2022. Methods: A facility-based cross-sectional study was done from July 15th to August 15th, 2022, using a simple random sample technique. The degree of self-reported poor sleep quality was quantified using a validated, interviewer-administered, standardized Pittsburgh Sleep Quality Index. To determine the relationship between independent variables and poor sleep quality, multivariable logistic regression analysis was carried out with a p-value of less than 0.05 and a 95% confidence interval (CI). Results: The prevalence of poor sleep quality was 75.4% (95% CI: 70.8, 80). Working more than 8 h per day (AOR = 2.83, 95% CI: 1.01, 7.94), work dissatisfaction (AOR = 3.27, 95% CI: 1.52-7.05), and using electronic materials before sleeping (AOR = 2.08, 95% CI: 1.01-4.30) were all associated with poor sleep quality. Conclusion: Poor sleep quality was common among garment and textile industrial workers. Work dissatisfaction, working hours, and the utilization of electronic materials before bedtime were all substantially related to poor sleep quality, which should be taken into account and addressed early to reduce poor sleep quality.

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Increasing utilization of data, enabled by digitalization, constitutes a major driver toward circular economy but is not without potential paradoxical tensions. A two-round disaggregative Delphi study and analysis of the qualitative material generated in it explored these tensions. They were found to cohere around three themes: consumer concurrence, business transparency, and technology relevance. The first theme is connected with consumers' behavior and their perceptions as to data's value, the transparency one involves alignment of business interests and practices with data-driven developments, and the third pertains to the actual environmental impact of digital technologies used to initiate data-driven circular economy. Business decision-making should address both the positive and the negative effects, in both the short and long term. Insight as to these tensions supports discovering how businesses can successfully utilize data in their efforts promoting circular economy within the complex reality of dynamically changing business environments.

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