Environmental impacts of new Coronavirus outbreak in Iran with an emphasis on waste management sector

Journal of Material Cycles and Waste Management - The COVID-19 pandemic have brought several environmental problems worldwide, among which management of municipal solid wastes (MSW) is of great...     

Reviewing the strategies for climate change and sustainability after the US defiance of the Paris Agreement: an AHP–GMCR-based conflict resolution approach

Environment, Development and Sustainability - In the landmark Paris Agreement, the global economies agreed to put forward their best efforts in mitigation and adaptation of climate change. The...     

Single and joint oxidative stress–related toxicity of sediment-associated cadmium and lead on Bellamya aeruginosa

Environmental Science and Pollution Research - The biotoxicity of heavy metals in sediments toward benthic organisms has evoked great concern for the health of freshwater ecosystems. This study...     

Health risk assessment of occupational exposure to styrene in Neyshabur electronic industries

Environmental Science and Pollution Research - Styrene is one of the essential components in making thousands of everyday products. Occupational exposure to styrene causes pulmonary, neurological,...     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Evaluation of poly-aromatic hydrocarbons (PAHs) in the aquatic species of Suez Gulf water along El-Sokhna area to the Suez refineries

The Egyptian Red Sea environment especially along El-Sokhna area to the Suez refineries (Suez) is severely contaminated with organic compounds, as well as overfishing. This may be well contributory to recent serious declines in fish stocks. Fish embryos are also particularly vulnerable to oil exposure, even at extremely low concentrations of less than one part per billion. Consequently, even traces of oil pollution at levels often considered safe for wildlife can cause severe damage to fish. Sixteen polycyclic aromatic hydrocarbons (PAHs) were investigated in ten fish species of aquatic species by high performance liquid chromatography (HPLC). The compositions of PAHs determined in all samples were measured in order to use them as chemical markers for identifying different sources of PAH pollutants in the studied region. The total content of these16 PAHs ranged from 399.616 up to 67,631.779 ng/g wet weight. The data show that these values are considered to be alarmingly high enough to cause lethal toxicity effect by accumulation. All studied aquatic species samples are characterized by relatively high concentrations of the six-membered ring PAHs. The origin of PAHs in the collected samples is either petrogenic, biogenic, or mixed petrogenic and biogenic.     

Study of the accumulation of contaminants by Cyperus alternifolius,Lemna minor,Eichhornia crassipes,and Canna × generalis in some contaminated aquatic environments

Environmental Science and Pollution Research - Today, environmental pollution, especially heavy metal pollution, is known as a new and possibly more dangerous pollutant than other environmental...     

Aluminum interaction with human brain tau protein phosphorylation by various Kinases

Abstract

Phosphorylation is an indispensable process for energy and signal transduction in biological systems. AlCl₃ at 10 nM to 10 uM range activated in‐vitro [γ‐³²P)ATP phosphorylation of the brain (tau) T protein in both normal human or E.coli expressed T forms; in the presence of the kinases P34, PKP, and PKC. However, higher concentrations of ALCl₃ inhibited the T phosphorylation with P34, PKP, and PKC to a maximum at 1 mM level. AlCl₃ at 100 uM to 500 uM range induced non‐enzymatic phosphorylation of T with γ‐ATP, γ‐GTP, and α‐GTP. AlCl₃ activated histone phosphorylation by P34 in a similar pattern. The hyperphosphorylation of T by Al³⁺ was accompanied by molecular shift and mobility retardation in SDS‐PAGE. This may demonstrate the mechanism of the longterm neurological effect of Al³⁺ in human brain leading to the formation of the neurofibrillary tangles related to Alzeheimer's disease.     

Water quality assessment and meta model development in Melen watershed – Turkey

Istanbul, being one of the highly populated metropolitan areas of the world, has been facing water scarcity since the past decade. Water transfer from Melen Watershed was considered as the most feasible option to supply water to Istanbul due to its high water potential and relatively less degraded water quality. This study consists of two parts. In the first part, water quality data covering 26 parameters from 5 monitoring stations were analyzed and assessed due to the requirements of the “Quality Required of Surface Water Intended for the Abstraction of Drinking Water” regulation. In the second part, a one-dimensional stream water quality model with simple water quality kinetics was developed. It formed a basic design for more advanced water quality models for the watershed. The reason for assessing the water quality data and developing a model was to provide information for decision making on preliminary actions to prevent any further deterioration of existing water quality. According to the water quality assessment at the water abstraction point, Melen River has relatively poor water quality with regard to NH₄⁺, BOD₅, faecal streptococcus, manganese and phenol parameters, and is unsuitable for drinking water abstraction in terms of COD, PO₄^3?, total coliform, total suspended solids, mercury and total chromium parameters. The results derived from the model were found to be consistent with the water quality assessment. It also showed that relatively high inorganic nitrogen and phosphorus concentrations along the streams are related to diffuse nutrient loads that should be managed together with municipal and industrial wastewaters.