Leaching characteristics of the fine fraction from an excavated landfill: physico-chemical characterization

Journal of Material Cycles and Waste Management - Leaching of fine fraction (<10 mm) obtained from landfill mining activities in an Estonian landfill was done. On-site excavation...     

Ionic liquid-based dispersive liquid–liquid microextraction for the determination of formaldehyde in wastewaters and detergents

Spectrophotometry in combination with ionic liquid-based dispersive liquid–liquid microextraction (DLLME) was applied for the extraction and determination of formaldehyde in real samples. The method is based on the reaction of formaldehyde with methyl acetoacetate in the presence of ammonia. The variation in the absorbance of the reaction product was measured at 375?nm. An appropriate mixture of ethanol (disperser solvent) and ionic liquid, 1-hexyl-3-methylimidazoliumhexafluoro-phosphate [C₆MIM][PF₆] (extraction solvent) was rapidly injected into a water sample containing formaldehyde. After extraction, sedimented phase was analyzed by spectrophotometry. Under the optimum conditions, the calibration graph was linear in the range of 0.1–20?ng?mL^?1 with the detection limit of 0.02?ng?mL^?1 and limit of quantification of 0.08?ng?mL^?1 for formaldehyde. The relative standard deviation (RSD%, n?=?5) for the extraction and determination of 0.8?ng?mL^?1 of formaldehyde in the aqueous samples was 2.5%. The results showed that DLLME is a very simple, rapid, sensitive, and efficient analytical method for the determination of trace amounts of formaldehyde in wastewaters and detergents, and suitable results were obtained.     

Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements.     

Approaches for prevention and environmental management of novel COVID-19

Environmental Science and Pollution Research - The World Health Organization (WHO) recognized a novel coronavirus as the causative agent of a new form of pneumonia. It was subsequently named...     

Interaction between meteorological parameters and COVID-19: an ecological study on 406 authorities of the UK

Environmental Science and Pollution Research - Understanding the factors affecting COVID-19 transmission is critical in assessing and mitigating the spread of the pandemic. This study investigated...     

A critical review of state-of-the-art electrocoagulation technique applied to COD-rich industrial wastewaters

Environmental Science and Pollution Research - Electrocoagulation (EC) is one of the emerging technologies in groundwater and wastewater treatment as it combines the benefits of coagulation,...     

Effect of condensing cavity on the performance of a passive solar desalination system: an experimental study

Environmental Science and Pollution Research - In this study, the effects of adding a condensing cavity in a passive single-basin solar still are investigated experimentally under the climate...     

Physiopathology and effectiveness of therapeutic vaccines against human papillomavirus

Environmental Science and Pollution Research - Human papillomavirus (HPV) is a well-known sexually transmitted disorder globally. Human papillomavirus (HPV) is the 3rd most common cancer that...