Industrialization,servicification, and environmental Kuznets curve: non-linear panel regression analysis

Environmental Science and Pollution Research - This paper investigates whether the impact of income on CO2 emissions is invariant to endogenously estimated threshold levels for the economic...     

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

Environmental Science and Pollution Research - The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular,...     

Insights into Effective Adsorption of Lead ions from Aqueous Solutions by Using Chitosan-Bentonite Composite Beads

Journal of Polymers and the Environment - The chitosan (Ch)—bentonite (B) composite was synthesized and its adsorption properties were investigated for lead ions. The characterization of the...     

Predictive modelling and seasonal analysis of water quality indicators: three different basins of Şanlıurfa,Turkey

Environment, Development and Sustainability - The objective of this paper is twofold. First; we demonstrate the application of data mining techniques to predict quality indicators (TDS, Hardness,...     

Comparative investigation of toxicity induced by UV-A and UV-C radiation using Allium test

Organisms are increasingly exposed to ultraviolet (UV) rays of sunlight, due to the thinning of the ozone layer and its widespread use in sterilization processes, especially against the SARS-CoV-2 virus. The present study was conducted with the purpose of evaluating the damages of UV-A and UV-C radiations in Allium cepa L. roots. The effects of two different types of UV on some physiological, biochemical, cytogenotoxic, and anatomical parameters were investigated in a multifaceted study. Three groups were formed from Allium bulbs, one of which was the control group. One of the other groups was exposed to 254 nm (UV-C) and the other to 365 nm (UV-A) UV. Growth retardation effect of UV was investigated with respect to germination percentage, total weight gain, and root elongation, while cytogenotoxicity arisen from UV exposure was analyzed using mitotic index (MI) and chromosomal aberration (CA) and micronucleus (MN) frequency. Oxidative stress due to UV application was investigated based on the accumulation of malondialdehyde (MDA) and the total activities of superoxide dismutase (SOD) and catalase (CAT) enzymes. Also, anatomical changes induced by UV-A and UV-C were analyzed in root meristematic cells. UV treatments caused significant reductions in growth-related parameters. Both UV treatments caused a significant increase in MDA levels and induction of SOD and CAT enzymes in root meristematic cells. A decrease in MI and an increase in the frequency of MN and CAs were observed in root tip cells, indicating the cytogenotoxic effect of UV application. Anatomical damages such as epidermis cell damage, cortex cell damage, necrotic zones, giant cell nucleus, and indistinct transmission tissue occurred in cells exposed to UV. All of the physiological, biochemical, cytogenetic, and anatomical damages observed in this study were more severe in cells treated with UV-C compared to UV-A. This study suggested that UV exposure triggered growth inhibition, cytogenotoxicity, oxidative stress, and meristematic cell damages in A. cepa roots depending on the wavelength.

     

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.

     

Solid waste composition and COVID-19-induced changes in an inland water ecosystem in Turkey

Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste...