Improvement of Synthesis and Dielectric Properties of Polyurethane/Mt-QASs<Superscript>+</Superscript> (Novel Synthesis)

Polyurethane-based nanocomposites were prepared and their dielectric properties were characterized. Polyurethane (PU) composites were prepared with different organoclay content (1, 3, 5, and 10 wt% for all cases). The composites included quaternary ammonium salts such as 1-methyl-di-octyl-1 phenyl ammonium iodide (QAS-1), 1-methyl-di-nonyl-1 phenyl ammonium iodide (QAS-2), and 1-methyl-di-dodecyl-1 phenyl ammonium iodide (QAS-3) which were newly synthesized for modification of Na⁺-montmorillonite. Addition of aluminum silicate enhanced the dielectric properties at a constant concentration. Dielectric constants of nanocomposites compounded with 3 %- and 5 %-organoclay were close in value. The characterization of PU/organoclay composites was carried out using Fourier transform infrared and X-ray diffraction.     

Analysis of hazardous material releases due to natural hazards in the United States

Natural hazards were the cause of approximately 16,600 hazardous material (hazmat) releases reported to the National Response Center (NRC) between 1990 and 2008-three per cent of all reported hazmat releases. Rain-induced releases were most numerous (26 per cent of the total), followed by those associated with hurricanes (20 per cent), many of which resulted from major episodes in 2005 and 2008. Winds, storms or other weather-related phenomena were responsible for another 25 per cent of hazmat releases. Large releases were most frequently due to major natural disasters. For instance, hurricane-induced releases of petroleum from storage tanks account for a large fraction of the total volume of petroleum released during 'natechs' (understood here as a natural hazard and the hazardous materials release that results). Among the most commonly released chemicals were nitrogen oxides, benzene, and polychlorinated biphenyls. Three deaths, 52 injuries, and the evacuation of at least 5,000 persons were recorded as a consequence of natech events. Overall, results suggest that the number of natechs increased over the study period (1990-2008) with potential for serious human and environmental impacts.     

Detecting micronuclei frequency in some aquatic organisms for monitoring pollution of Izmir Bay (Western Turkey)

Micronuclei tests is a system of mutagenicity testing used for determining the pollution and chemicals causing changes in DNA fragments such as micronuclei in the cytoplasm of interphase cells. Damage caused on the DNA by genotoxic pollutants is the first consequence occurring in the aquatic organisms. Thus, it was attempted to determine whether pollution affected the erythrocytes and gills of fish Gobius niger and haemolymph and gills of mussels Mytilus galloprovincialis living in Izmir Bay at the level of DNA by the means of micronuclei (MN) test. Organisms used in the MN test were collected from seven locations (Alsancak, Alaybey Shipyard, Karsiyaka, Bostanli, Göztepe, Konak and Pasaport) which are known as the most polluted part of inner Bay of Izmir (Western Coast of Turkey). According to the results of the present study, frequency of MN was found at high level in Alaybey Shipyard and Pasaport where wastes from existing dockyard contributed to high level of pollution. In conclusion, this study indicates that the micronuclei test gives sensitive results in monitoring the pollution, especially the pollution of harbor, and thus it might be used as standard method in regular monitoring of pollution of coastal ecosystem.     

A Study of the Photocatalytic Oxidation of Humic Acid on Anatase and Mixed-phase Anatase–Rutile TiO2 Nanoparticles

In this study, slurry photocatalytic oxidation process was investigated for natural organic matter removal from aqueous humic acid solutions by using different titanium dioxide (TiO₂) under UV-A irradiation. Bench scale experimental studies were conducted at different humic acid concentration at the range of 10–50 mg/L and different pH. Anatase and mixed-phase anatase–rutile TiO₂ nano particles used in the photocatalytic reactor. The results were evaluated in terms of the parameters that are specific to organic matter content such as dissolved organic carbon concentration, ultraviolet absorbance at 254 nm (UV₂₅₄), specific ultaviolet absorbance at 254 nm, and color (VIS₄₀₀). It was observed that increasing humic acid concentration decreases photocatalytic degradation efficiency. The reactivity of the mixed-phase anatase–rutile (Degussa P-25) TiO₂ was greater than individual anatase particles and the highest efficiency was observed at pH 3 for anatase TiO₂.     

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.     

Insights into the Viscoelastic Peculiarities of Cyanobacterial Extracellular Polymeric Substance (EPS)

Journal of Polymers and the Environment - Extracellular polymeric substances (EPSs) can be defined as renewable, high molecular weight polymeric materials produced by bacteria and microorganisms....     

16S ribosomal RNA tools identify an unexpected predominance of Paenibacillus-like bacteria in an industrial activated sludge system suffering from poor biosolids separation.

Molecular biology tools targeting 16S ribosomal RNA (16S rRNA) were used to identify a predominant bacterial population in a full-scale dairy wastewater activated sludge system suffering from poor biosolids separation. Gram and acridine orange staining indicated that viable, Gram-positive microorganisms were present in samples removed from the influent waste stream and represented approximately 50% of total cell counts in samples removed from the mixed liquor. Subsequently, the "full-cycle 16S rRNA approach" showed that phylogenetic relatives of Paenibacillus spp., a low guanine-plus-cytosine percent DNA-content, Gram-positive microorganism, represented up to 30% of total 4,6-diamidino-2-phenylindole (DAPI)-stained cell counts in samples of mixed liquor. Although fluorescent in situ hybridizations with 16S rRNA-targeted oligonucleotide hybridization probes identified Paenibacillus-like spp. in samples removed from the influent waste stream, their abundance was less than 10% of total stained cell counts. Results of this study suggest that Paenibacillus-like spp. were present in low abundance in the influent waste stream, increased in relative abundance within the treatment system, and should be examined further as a candidate bacterial population responsible for poor biosolids separation. This study demonstrates that the full-cycle 16S rRNA approach can be used to identify candidate bacterial populations that may be responsible for operational upsets in full-scale activated sludge systems without prior information from cultivation or microscopic analyses.     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.