Catalytic degradation of toxic organic dye using an aluminum-doped Cu0.4Zn0.6-xAlxFe2O4 (x = 0.0, 0.2, 0.4, 0.6) spinel ferrite in a photo-Fenton system

Catalytic activity of spinel ferrite in breaking down toxic dye materials are promising due to their uniqueness. In this study, aluminum-doped copper zinc ferrite, Cu_0.4Zn_0.6-xAl_xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6), a catalyst for toxic dye degradation is synthesized through chemical co-precipitation route. The formation of the spinel ferrite catalyst is initially confirmed by Fourier transform infrared spectra, which shows the frequency of metal-oxygen bond vibration at 539 and 427 cm⁻¹ attributed to the tetrahedral and octahedral sites respectively. Higher intensity sharp peak of X-ray diffraction for (311) plane is the evidence for the phase purity and the formation of spinel ferrite. The crystallite size is found to decrease with the increase of Al³⁺ ion. The surface structure of the obtained particles is investigated using a scanning electron microscope. Analyses of the material's magnetic characteristics using a vibrating sample magnetometer (VSM) revealed that it is, in fact, a soft magnet, as evidenced by the loop of its hysteresis, which is narrow. The catalytic degradation of methylene blue dye under the mechanism of the photo-Fenton process is studied with the obtained spinel ferrites and the result is found to be as high as 96.5%. The process follows pseudo-second order kinetics and the Langmuir isotherm.     

Water,sanitation, hygiene and waste disposal practices as COVID-19 response strategy: insights from Bangladesh

Environment, Development and Sustainability - The COVID-19 pandemic has caused a global emergence, and the absence of a proven vaccine or medicine has led to the implementation of measures to...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

The roles of nuclear energy,renewable energy,and economic growth in the abatement of carbon dioxide emissions in the G7 countries

Environmental Science and Pollution Research - In the current century, the G7 countries have attached more importance to energy security, and have prioritized low-carbon sources which have...     

Gasification characteristics of biomass for tar removal by secondary oxidant injection

Gasification experiments for sawdust were conducted using a fixed bed reactor at 900 °C by varying the secondary oxidant injection ratio to determine the optimal conditions for tar removal along with the enhancement of gasification efficiency. Secondary oxidant was injected as an oxidant at the top zone of the gasifier in varying ratios of 10–30% of the total amount of oxidant. This method was based on the primary method of tar removal and gasification efficiency improvement by thermal cracking of tar. Various gasification performance parameters were evaluated and tar content was estimated by measuring the fluctuation of weight of the activated carbon filter. The results showed that the concentration of tar in the producer gas decreased by injecting the secondary oxidant, even though syngas yield decreased. The recycling potential of the char produced in the gasification experiments was also assessed with the purpose of utilizing char as an adsorbent by determining its surface area and pore volume. The results demonstrated that the char produced from the gasification experiment had similar quality to that of the activated carbon used in this experiment.     

Risk assessment for hazardous lubricants in machining industry

Knowing that over two-thirds of lubricant disposals return to the environment with no purification process, adequate strategies are demanded to reduce their risks. For this reason, the main focus of the present study is to describe an environment-friendly approach. In the first part, two widely used lubricants (mineral-based and vegetable-based) were introduced, and the reasons for their hazards were investigated. The composition of mineral-based lubricant was characterized by x-ray fluorescence elemental analysis. The result showed the presence of phosphorus, chlorine, and zinc dialkyl dithiophosphate in its composition that many scholars considered them to be the leading risk factor in the chemical composition of mineral-based lubricants. It has been focused on the potential risks of vegetable-based lubricants, which many researchers have identified as a safe lubricant. The Pseudomonas microbe was cultivated in the vegetable-based sample, and the result showed that although vegetable-based lubricants are compatible with humans, bacteria colony can quickly grow there without making any apparent changes that lead to harm to operators in a mysterious way. In the present work, the hypothesis of the safety of unreinforced vegetable-based lubricants has been rejected, and a new window on the environmental issues of vegetable-based lubricants has been presented. In the final, to eliminate environment-human risks and to reduce consumption of lubricant and natural resources to green manufacturing, a comprehensive study on the possibility of completely removing lubricant was performed. The results showed that the machining without lubricant could be replaced with the traditional method.

     

Performance of Cow Dung Reinforced Biodegradable Poly(Lactic Acid) Biocomposites for Structural Applications

In this work, performance of cow dung (CD) reinforced poly(lactic acid) (PLA) biocomposites was investigated for the potential use in load bearing application. CD of average 4 mm size was blended with PLA at different CD ratios (0–50 wt%) and their effects on the biocomposite properties were studied. The results showed an improvement in the flexural properties, while the tensile and impact strength dropped by 20 and 28% with the addition of 50% CD. The decline in the tensile and impact strength was due to micro-cracking and voids formation at higher CD content. Also, the incorporation of CD slightly decreased the thermal stability of the biocomposite. However, dynamic mechanical properties of the biocomposites generally improved. SEM analysis of tensile and impact fractured surfaces indicated that the CD had a reasonable adhesion with matrix. Moreover, the SEM micrographs of soil burial studies showed an accelerated degradation of higher CD wt% biocomposites.     

Assessing groundwater quality and its sustainability in Joypurhat district of Bangladesh using GIS and multivariate statistical approaches

Sustainable groundwater quality is a key global concern and has become a major issue of disquiets in most parts of the world including Bangladesh. Hence, the assessment of groundwater quality is an important study to ensure its sustainability for various uses. In this study, a combination of multivariate statistics, geographical information system (GIS) and geochemical approaches was employed to evaluate the groundwater quality and its sustainability in Joypurhat district of Bangladesh. The results showed that the groundwater samples are mainly Ca–Mg–HCO₃ type. Principal component analysis (PCA) results revealed that geogenic sources (rock weathering and cation exchange) followed by anthropogenic activities (domestic sewage and agro-chemicals) were the major factors governing the groundwater quality of the study area. Furthermore, the results of PCA are validated using the cluster analysis and correlation matrix analysis. Based on the groundwater quality index (GWQI), it is found that all the groundwater samples belong to excellent to good water quality domains for human consumption, although iron, fluoride and iodide contaminated to the groundwater, which do not pose any significant health hazard according to World Health Organization’s and Bangladesh’s guideline values. The results of irrigation water quality index including sodium adsorption ratio (SAR), permeability index and sodium percentage (Na %) suggested that most of the groundwater samples are good quality water for agricultural uses. The spatial distribution of the measured values of GWQI, SAR, Fe (iron), EC (electrical conductivity) and TH (total hardness) were spatially mapped using the GIS tool in the study area.