Revisiting the environmental Kuznets curve: evidence from Turkey

Environment, Development and Sustainability - This paper uncovers the link between economic development and environmental degradation in Turkey by employing two distinct methods. We test the...     

Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Environmental Science and Pollution Research - Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local...     

Correction to: WEEE closed-loop supply chain network management considering the damage levels of returned products

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-020-11851-4     

Land degradation assessment by geo-spatially modeling different soil erodibility equations in a semi-arid catchment

Land degradation by soil erosion is one of the most serious problems and environmental issues in many ecosystems of arid and semi-arid regions. Especially, the disturbed areas have greater soil detachability and transportability capacity. Evaluation of land degradation in terms of soil erodibility, by using geostatistical modeling, is vital to protect and reclaim susceptible areas. Soil erodibility, described as the ability of soils to resist erosion, can be measured either directly under natural or simulated rainfall conditions, or indirectly estimated by empirical regression models. This study compares three empirical equations used to determine the soil erodibility factor of revised universal soil loss equation prediction technology based on their geospatial performances in the semi-arid catchment of the Saraykoy II Irrigation Dam located in Cankiri, Turkey. A total of 311 geo-referenced soil samples were collected with irregular intervals from the top soil layer (0-10?cm). Geostatistical analysis was performed with the point values of each equation to determine its spatial pattern. Results showed that equations that used soil organic matter in combination with the soil particle size better agreed with the variations in land use and topography of the catchment than the one using only the particle size distribution. It is recommended that the equations which dynamically integrate soil intrinsic properties with land use, topography, and its influences on the local microclimates, could be successfully used to geospatially determine sites highly susceptible to water erosion, and therefore, to select the agricultural and bio-engineering control measures needed.     

Testing the environmental Kuznets curve for G7 countries: evidence from a bootstrap panel causality test in rolling windows

Environmental Science and Pollution Research - The Environmental Kuznets Curve hypothesis is a theoretical proposition explicating the link between a locality’s income level and environmental...     

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.     

Treatment of textile dyeing wastewater by biomass of Lactobacillus: Lactobacillus 12 and Lactobacillus rhamnosus

The main purpose of this study was to investigate the effectiveness of Lactobacillus 12 and Lactobacillus rhamnosus as both cells and biomasses for the removal of dye from real textile dyeing wastewater. The removal experiments were conducted according to the Box–Behnken experimental design, and the regression equations for the removal of dye were determined by the Minitab 14 program. The optimum variables were found to be 10 g/?L biomass concentration for biomasses, 3 for initial pH of the solution, and 20 °C for temperature with an observed dye removal efficiency of about 60 and 80 % with L. 12 and L. rhamnosus biomasses, respectively. Scanning electron microscopy and Fourier transform infrared spectroscopy images also showed that the biomass characteristics studied were favored by the sorption of the dye from the textile industry wastewater. Consequently, these biomasses may be considered as good biosorbents due to their effective yields and the lower cost of the removal of dyes from the effluents of the textile dyeing house.     

Are low maternal estriol levels a predictor for pro-opiomelanocortin (POMC) deficiency caused by POMC mutation during pregnancy?

Isolated adrenocorticotropic hormone deficiency is a rare cause of adrenocortical insufficiency, especially in children, and may be an underestimated cause of neonatal death. Low estriol levels are usually correlated with compromised uteroplacental perfusion and associated with fetal death. A 30-years old woman applied for pregnancy follow-up. Ultrasonographic evaluation and karyotype of the fetus are normal. Low estriol level 0.34 MoM (% 0.24) was detected in maternal triple screening test. Amniocentesis was performed, and chromosomal disorders, steroid sulfatase deficiency, and Smith–Lemli–Opitz syndrome (SLOS) were excluded with karyotype, fluorescence in situ hybridization (FISH), and molecular analysis of SLOS, respectively. As their first child had pro-opiomelanocortin (POMC) deficiency, POMC gene analysis was performed from both amniotic fluid and ethylene diamine tetra aceticacid (EDTA) blood sample of affected previous child, and homozygote mutation was detected. Fetus is diagnosed as POMC deficiency. We are presenting this case to discuss possible relationship of low maternal E3 levels and fetal POMC deficiency. © 2013 John Wiley & Sons, Ltd.     

Investigation of the effects of battery types and power management algorithms on drive cycle simulation for a range-extended electric vehicle powertrain

Fuel cell (FC) hybrid vehicle power trains are an attractive technology especially for automotive applications because of their higher efficiency and lower emissions compared to conventional vehicles. This study focuses on the design of an FC hybrid power train system and evaluation of its simulations for a given speed profile through two alternative power management algorithms (PMAs). Parameters suitable for a small vehicle were taken into consideration in the mathematical model of the vehicle. The proposed hybrid power train consists of an energy storage system, composed of a 4-kg battery pack (either lithium-ion (Li-ion) battery, nickel metal hydride, or nickel–cadmium) and a direct methanol fuel cell (DMFC) as the range extender. The PMAs basically aim to fulfill the power requirements of the vehicle and decide how to command the power split between the battery and the FC. The model comprising a DMFC, a battery, and PMAs was developed in MATLAB/Simulink environment. The polarization curve of the FC was obtained using a one-dimensional DMFC model. Vehicle power requirements for a drive cycle were calculated using the equations of longitudinal dynamics of vehicle, and the results were integrated into MATLAB/Simulink model. As a result of the simulations, methanol consumption, state of charge of the battery, and power output of the FC were compared for the PMAs. This comparison shows the effect of PMAs on the hybrid vehicle performance for three battery types. The results indicate that the vehicle range could be increased when proper strategy is used as PMA.