Shafferlambertsen9811

g to the evaluation of the predictions made, the performance of the AERMOD model was acceptable in the prediction of pollutant concentrations in the study area.A better socioeconomic development is necessary for environmental sustainability. The current study scrutinizes the asymmetric socioeconomic factors of CO2 emissions in China by using the nonlinear ARDL approach. This study is based on annual data covering the period from 1980 to 2019. Results show that positive change in economic growth is the leading driver of CO2 growth, while a negative change in economic growth is offsetting CO2 emissions in China. Concurrently, positive and negative changes in energy consumption have adverse impacts on CO2 emissions in the long term, while negative shock has a small influence on CO2 emissions compared to the positive shock of energy. Positive years of schooling, shocks are found to be beneficial for fighting against CO2 emissions in China in the long run. The CO2 emissions are asymmetrically affected by the social and economic structure of China. Based on these empirical findings, thereby China should improve its socioeconomic development and standards of CO2 emissions.A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Talabostat molecular weight Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.Development of efficient sorbents for selective removing and recovery of uranium from radioactive wastewaters is highly important in nuclear fuel industries from the standpoint of resource sustainability and environmental safety issues. In this study, carbon powder waste was modified by various chemical activating agents under atmosphere of nitrogen gas at 725 °C to prepare an efficient sorbent for removal and recovery of uranium ions from radioactive wastewaters of nuclear fuel conversion facility. Activation of the carbon powder with KOH, among different activators, provided maximum porosity and surface area. The activated samples were modified by reacting with ammonium persulfate in sulfuric acid solution to generate surface functional groups. The synthetized sorbents were characterized with FT-IR, XRD, BET, and SEM-EDS techniques. The effects of solution pH, contact time, initial uranium concentration, and temperature on the sorption capacity of the sorbent with respect to U(VI) from wastewater were investigated by batch method, followed by optimizing the effect of influential parameters by experimental design using central composite design. The sorption of UO22+ ions on the sorbents follows the Langmuir isotherm and pseudo-second-order kinetic models. Maximum sorption capacity for U(VI) was 192.31 mg g-1 of the modified sorbent at 35 °C. Thermodynamic data showed that sorption of U(VI) on the sorbent was through endothermic and spontaneous processes. The sorption studies on radioactive effluents of the nuclear industry demonstrated that the modified sorbent had a favorable selectivity for uranium removal in the presence of several other metal ions.This paper made the first attempt to summarize the rules from a regional perspective and use panel data to explore the carbon Kuznets curve (CKC) between e-commerce and carbon dioxide emissions. The impact of online shopping on carbon emission has mixed conclusions. No CKC tests set mainly focuses on the e-commerce sector, which can help this research determine the relationship between e-commerce and carbon emissions. From a macro point of view, we examine both developed and developing regions by testing the CKC hypothesis. We try to explain it by exploring the econometric relationship between e-commerce and CO2 emissions. At first, we attempt to accurately measure the CO2 emissions by carefully distinguishing the carbon emission increments caused by the primary energy resulting from the secondary energy. Then, we use panel data collected from different Chinese cities during 2001-2017. The analyzed variables are stationary at their first differences with the LLC test, IPS test, Fisher-ADF test, Fisher-PP test, CADF, and CIPS unit root tests. The analyzed variables are cointegrated by employing the Pedroni panel cointegration test, the Kao panel cointegration test, and the Westerlund panel cointegration test. Using the DOLS, we also find that increases in trade openness decrease carbon emissions while increases in foreign direct investment (FDI) and market size contribute to the level of emissions. The quadratic-shape CKC hypothesis is supported for China, Eastern China, and Western China, and it is an inverted "U" shape. The cubic-form CKC is supported for Central China, and it is an "N" shape. Our study provides important insights for enacting regional and country-level e-commerce regulations and reducing carbon dioxide emissions.