Bisphenol F (BPF) is widely used in the plastic manufacturing industry as a replacement for bisphenol A (BPA) because BPF and BPA have similar structures and comparable properties. However, BPF is ubiquitously present in the environment and has higher toxicity to humans. This study is the first to report BPF degradation using the white-rot fungus Phanerochaete sordida YK-624 under ligninolytic conditions (pH=4.5, 30 °C). P. sordida YK-624 almost completely degraded BPF within 4 days. Moreover, functional genes involved in BPF degradation were detected by RNA-Seq. Metabolic processes and peroxidases were enriched by GO analysis, and the metabolic pathway was enriched according to the KEGG pathway analysis. These results suggested that P. sordida YK-624 could secrete higher levels of ligninolytic enzymes lignin peroxidase (LiP) and manganese peroxidase (MnP) for BPF degradation. The results indicated that LiPs and MnPs are important for BPF degradation and cytochrome P450s play a small role. Furthermore, reliability of the RNA-Seq results was validated by qRT-PCR.

As the economy enters the new normal in China, more and more intellectuals are concerned about the energy consumption in the economic green transformation and development process; this article computes the energy consumption embodied in various final demand types by the utilization of the environmentally extended input-output (EEIO) model during 2000–2014. Then, the structural decomposition analysis is adopted for exploring energy consumption changes’ driving factors considering various final demand categories. Furthermore, the decoupling between economic growth and energy consumption embodied in various final demand categories is revealed by the application of the Tapio model in China. The results indicate as below: (1) the energy consumption embodied in various final demand categories increased significantly during the study period; (2) the gross fixed capital formation (GFCF) was most conducive to the growth of energy consumption, while the construction department emerged as the greatest energy user in GFCF; (3) coal and oil were two main energy sources consumed regardless of any final demand categories; (4) in the three drivers, the scale effect had a primary contribution to increasing energy consumption. The intensity effect made a crucial contribution to mitigating energy consumption. The structural effect had a mild contribution to the energy consumption changes; (5) the five final demand categories were roughly characterized by the weak decoupling over the period 2000–2014. GFCF determined the decoupling index between economy growth and energy consumption largely. Finally, this article put forward implied policies concerning the reduction of energy utilization in China.

Shipping is a significant contributor to global greenhouse gas (GHG) and air pollutant emissions. These emissions mainly come from using diesel fuel for power generation. In this paper, the natural gas is proposed as an alternative marine fuel to be used instead of conventional marine diesel oil. Numerical analysis of environmental and economic benefits of the natural gas-diesel dual-fuel engine is carried out. As a case study, a container ship of class A7 owned by Hapag-Lloyd has been investigated. The results show that the proposed dual-fuel engine achieves environmental benefits for reducing carbon dioxide (CO₂), nitrogen oxides (NOx), sulfur oxides (SOx), particulate matter (PM), and carbon monoxide (CO) emissions by 20.1%, 85.5%, 98%, 99%, and 55.7% with cost effectiveness of 109, 840, 9864, 27761, and 4307 US$/ton, respectively. The results show that the conversion process to the dual-fuel engine will comply with the current and future IMO regulations regarding air pollutant emissions. On the other hand, using the proposed dual-fuel engine on the container ship will improve the ship energy efficiency index by 29.6 % with annual fuel cost saving of 4.77 million US dollars.

Selection of appropriate treatment processes for wastewater treatment (WWT) plants at the design stage involves a careful examination of different economic, environmental, and social parameters. Designers and decision-makers seek a compromise among such conflicting elements, which can be facilitated by decision support tools that are adapted for the ambiguity of individual opinions and decision parameters. This study aims to improve the qualification and efficiency of decision-making in WWT processes. A multi-stage framework is proposed to help select investments, technology, appropriate technology-specific system, and companies that apply such systems. The framework combines the Analytic Hierarchy Process (AHP), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), cash flow analysis, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within fuzzy logic. The main contribution is the description and formation of an integrated framework to guide businesses and researchers for the evaluation of several WWT decision processes. To the best of the authors’ knowledge, no study in the literature fuses multiple stages of this WWT process with the proposed approaches.

The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.

In industrial areas, increased human activities generate high emissions of metals that contaminate the environment, and eventually affect human health. Therefore, this study aims to investigate the sources of toxic elements in indoor dust samples, and to evaluate possible health risks due to expose of human body through dermal contact, inhalation, and ingestion. A total 36 indoor dust samples were collected from the windows of twelve (12) industrial buildings, which are facing the main roads in the biggest export process zone (DEPZ) in Dhaka City, Bangladesh. The indoor dust samples were analyzed using X-ray fluorescence (XRF) technique for the determination of Cu, Zn, As, Pb, Fe, Cr, Co, Ni, K, Ca, Ti, Rb, and Sr. This investigation revealed that Cu, Zn, Pb, Fe, and Sr concentrations were slightly higher in various degree than that of the background value in soil recommended by the Chinese Environmental Protection Administration (CEPA). Subsequently, ANOVA (α = 0.05, p < 0.001) test and %RSD values (33% to 168%) showed that the concentrations of these metals were present non-homogeneously in different sampling points. However, statistical analysis and several geochemical indices have been proposed that these heavy metals might be come in indoor dust samples because of anthropogenic events. This investigation also showed that the most-extreme permissible hazard index (HIs) levels known as non-cancer risk for As, Mn, and Zn because of dust exposure in the study area were below than that of the safe limit (HI = 1) but with an exception of Pb (HI = 1.9E+00) for children. On the other hand, the degree of cancer-causing risk linked with exposure to arsenic in indoor dust (i.e., 0.502E-06 to 0.121E-05) falls within the range of threshold values (10⁻⁶ to 10⁻⁴). Therefore, following USEPA health risk models, it has been suggested that these metals present in indoor dust samples might not have significant impact on causing non-cancer and cancer risk to children and to adult at this moment in the DEPZ area, Bangladesh. However, a continuous monitoring should be needed to assess more accurately the non-cancer and cancer risk to children and adult.

The production of cement contributes to 10% of global carbon dioxide (CO₂) pollution and 74 to 81% towards the total CO₂ pollution by concrete. In addition to that, its low strength-to-weight ratio, high density and thermal conductivity are among the few limitations of heavy weight concrete. Therefore, this study was carried out to provide a solution to these limitations by developing innovative eco-friendly lightweight foamed concrete (LFC) of 1800 kg/m³ density incorporating 20–25% palm oil fuel ash (POFA) and 5–15% eggshell powder (ESP) by weight of total binder as supplementary cementitious material (SCM). The influence of combined utilization of POFA and ESP on the fresh state properties of eco-friendly LFC was determined using the J-ring test. To determine the mechanical properties, a total of 48 cubes and 24 cylinders were prepared for compressive strength, splitting tensile strength and modulus of elasticity each. A total of 24 panels were prepared to determine the thermal properties in terms of surface temperature and thermal conductivity. Furthermore, to assess the environmental impact and eco-friendliness of the developed LFC, the embodied carbon and eco-strength efficiency was calculated. It was determined that the utilization of POFA and ESP reduced the workability slightly but enhanced the mechanical properties of LFC (17.05 to 22.60 MPa compressive strength and 1.43 to 2.61 MPa tensile strength), thus satisfies the ACI213R requirements for structural lightweight concrete and that it can be used for structural applications. Additionally, the thermal conductivity reduced ranging from 0.55 to 0.63 W/mK compared to 0.82 W/mK achieved by control sample. Furthermore, the developed LFC showed a 16.96 to 33.55% reduction in embodied carbon and exhibited higher eco-strength efficiency between 47.82 and 76.97%. Overall, the combined utilization of POFA and ESP as SCMs not only enhanced the thermo-mechanical performance, makes the sustainable LFC as structural lightweight concrete, but also has reduced the environmental impacts caused by the disposal of POFA and ESP in landfills as well as reducing the total CO₂ emissions during the production of eco-friendly LFC.

The aim of the “One Belt and One Road” (OBOR) project proposed by China is to encourage extension of global value chains, facilitate regional integration and increase efficient resource allocation. In recent times, scientific literature has examined the investment decisions of the OBOR investment and how it will affect the governmental policies, environmental initiatives, and bilateral flow of economic resources. Current study, based on Web of Science database, uses bibliometric methodology to map the research trends in OBOR publications. We contribute in the economic literature in the associated fields of OBOR publications in the following ways: (1) identify the most influential researchers, articles, and academic institutions, (2) mapping the interdisciplinary character of OBOR investments and its bibliometric similarity to adjacent fields, (3) visualize nature and trends of the research field, and (4) synthesizing future research areas. Although OBOR initiative has received considerable traction, but to this date, there is no bibliometric study on this topic. The findings of current study will help policymakers and academics to navigate the OBOR literature, provide a systematic basis for developing the field, and suggest promising future research avenues.

Metallic glasses (MGs) with unique disordered atomic stacking structures exhibit excellent catalytic performance in wastewater treatment. The catalytic degradation of Orange II (AO II) aqueous solutions by four CuZr-based MG ribbons under such processing parameters as pH values, the dosage of ribbons, and temperature was investigated in this paper. The catalytic performance of the MG ribbons was characterized by using the degradation efficiency of the dye wastewater. The phase constituent, surface morphology, and chemical valence state of elements on the surface of MG ribbons before and after use were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS), respectively. The results indicate that the Cu₄₆Zr₄₂Al₇Y₅ MG ribbon has the best catalytic performance among the Cu₄₆Zr₄₇₋ₓAl₇Yₓ (x = 0, 1, 3, 5) MGs in the degradation process, and the dye in the wastewater can almost be completely decolorized within 60 min under the conditions of pH = 2, the dosage of ribbons being 1.8 g/L and water bath temperature of 313 K, with the degradation efficiency and chemical oxygen demand removal being 96.05% and 51.73%, respectively. Furthermore, the Cu₄₆Zr₄₂Al₇Y₅ MG ribbon still shows superior structural stability and degradation performance after repeated use, and the corrosion pits on the MG surface promote the physicochemical reaction between the wastewater and the ribbons.

To improve the filtration efficiency of submicron dust by dual-layer granular bed filters, filtration experiments for micro-silica powder were conducted for removing particles smaller than 1 μm that account for more than 96% (by volume) using a dual-layer granular bed filter with an external electric field. Electrostatic enhancement methods, including dust pre-charging, application of an electric field to the lower filter layer, and that to both the upper and lower filter layers, were examined. Results showed that the average filtration efficiency of a dual-layer granular bed filter for micro-silica powder without electric field was 76.52%, the average outlet dust concentration was 263.53 mg/m³, and the filtration cycle time was 73 min. With pre-charged dust, the average outlet dust concentration dropped to 82.51 mg/m³. A decrease in the thickness of the lower filter layer from 45 to 25 mm with electric field reduced the pressure drop from 2570 to 1770 Pa. Meanwhile, the application of an electric field to the lower/upper filter layer reduced the average outlet dust concentration to 25.98 mg/m³. Increasing the initial face velocity from 0.25 to 0.45 m/s increased the average outlet dust concentration from 25.98 to 30.27 mg/m³ and increased the pressure drop from 2570 to 3500 Pa.