Indigenous knowledge on use values of Karvan district plants,Iran

Traditional and indigenous knowledge on plants usage is a valuable source of information from cultural and natural perspectives, reflecting society’s complicated and close relationship with the environment. Communities have a valuable source of traditional knowledge on the utilization of natural resources, and it is worth to be documented and preserved for current and future applications. We conducted this research to collect and identify plant species of Karvan District and document the traditional knowledge on their use and consumption values. Regarding the results, 150 plant species are used by local communities, more than 30 % of which are directly consumed as food, 24 % (37 species) are used as medicinal plants, and 16.3 % are applied for decoration purposes; 58.6 % of the species are consumed in the raw form, and the remaining is processed before consumption. Leaves (35 %), seeds (21 %) and flowers (21 %) are the most frequent parts of the plants that are used. High number of young emigrants to industrialized areas in seek of job opportunities is threatening this precious source of indigenous knowledge. Attempts to preserve this empirical source of information by encouraging trans-generational knowledge transmission would help to maintain it for future applications.     

Effect of Acetamiprid on spatial memory and hippocampal glutamatergic system

Environmental Science and Pollution Research - Acetamiprid (ACE) is one of the widely used neonicotinoid insecticides. In mammals, in spite of the low-affinity nAChRs, neurotoxic effects following...     

A sustainable system for decontamination of cephalexin antibiotic using electrocoagulation technology and response surface methodology

The emergence of synthetic micropollutants in wastewater due to domestic and industrial use has presented new challenges for treatment processes. Among these pollutants, pharmaceutical and personal care products (PPCPs) are considered emerging contaminants due to their potential to enter drinking water sources. Antibiotics, in particular, are of significant concern due to their high consumption in veterinary and human applications. In this study, the electrocoagulation (EC) process is used as an efficient technique for the removal of cephalexin (CFX) from pharmaceutical wastewater. The study aims to explore the ability of the EC process to remove CFX and optimize its performance using the response surface method based on Central Composite Design (RSM-CCD). The effects of initial CFX concentration, electrolysis time, initial pH, and electrode type (non-insulated and insulated) were considered in the optimization process. This research is distinct as it examines the influence of key factors on the elimination of CFX. The results showed that electrolysis time had the most significant effect on CFX removal using the EC process. The analysis of variance (ANOVA) test was used to evaluate the importance of independent variables and their interaction. The optimal operating conditions for maximum removal efficiency (86.53%) were an initial CFX concentration, reaction time, and initial pH of 34 mg/L, 34.35 min, and 6.5, respectively, using an insulated electrode. Under these optimal conditions, predicted CFX removal was 93.54%. These findings demonstrate that RSM-CCD is a useful tool for optimizing electrochemical removal processes for micropollutants such as CFX from wastewater streams. The study highlights the importance of considering electrode type in optimizing EC processes for micropollutant removal from wastewater.     

Can tree species diversity be assessed with Landsat data in a temperate forest?

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.     

A new coordinating model for green supply chain and batch delivery scheduling with satisfaction customers

Environment, Development and Sustainability - Scheduling an integrated, detailed SC (supply chain) at the operational level is a crucial aspect of SCM (supply chain management). Simultaneous...