Do renewable energy and natural gas consumption mitigate CO2 emissions in the USA? New insights from NARDL approach

Environmental Science and Pollution Research - This article attempts to explore the asymmetric impact of renewable energy and natural gas consumptions on CO2 emissions for the selected ten most...     

Effective antibacterial and antioxidant properties of methanolic extract of Laurus nobilis seed oil

This study was carried out to determine the in vitro antimicrobial and antioxidant activities of the essential oil, seed oil, and methanolic extract of seed oil obtained from Laurus nobilis L. (Lauraceae). The methanolic extract of seed oil exhibited more effective antibacterial activity comparing to essential oil and seed oil, GC-MS analyses of the essential oil resulted in the identification of 25 compounds. 1.8-Cineol (44.72%), a-Terpinyl acetate (12.95%), Sabinene (12.82%) were the main components. The fatty acid composition was characterized with the high content of linoleic acid (40.79%) and lauric acid (38.08%). The 50% (IC50) inhibition activity of the essential oil on the free radical DPPH was determined as 94.655 mg ml(-1), whereas IC50 value of methanolic extract of seed oil was found unstable. In the case of the linoleic acid system, oxidation of linoleic acid was inhibited by essential oil and methanolic extract of seed oil, which showed 64.28 and 88.76% inhibition, respectively. The inhibition value of the methanolic extract of seed oil was quite close to the synthetic antioxidant BHT, 92.46% inhibition.     

Pine needle and semi-permeable membrane device derived organochlorine compounds (OCPs) concentrations in air in Mersin Province to Taurus,Turkey

Abstract

Organochlorine pesticides (OCPs) were analyzed in three different ages (half-, 1.5-, 2.5-year-old) for needles and semi permeable membrane devices (SPMDs) at three deployment periods from sea level to 1881 meter above sea level. Individual HCHs concentrations ranged between 1.4 and 129?pg/g fw depending on the age and sampling season while 2.5-year-old needles showed higher HCHs levels compared to half and 1.5- year-old. Correlation between elevation and HCH concentration in SPMDs was found but not in needle samples. Concentrations of HCHs in SPMDs indicated clearly cold condensation effect on accumulation in winter period and increased with altitude. Concentrations of DDTs in half and 1.5-year-old needles were lower than 2.5-year-old needles. The highest total concentration of DDTs was detected in 1-year-period SPMD. Higher concentrations were found in 2.5-year-old needles for other OCPs. Seasonal and altitude-dependent changes were not observed for other OCPs in SMPDs. Total accumulation of OCPs in SPMDs were found higher than in needles. On the contrary, an increased accumulation rate was observed for HCHs in SPMD. In general, Total concentrations of DDTs and HCHs were similar to total of other OCPs in all altitudes when dominating endosulfan wasnot taken into account in the computation of total concentration of other OCPs.     

Precipitation Modeling by Polyhedral RCMARS and Comparison with MARS and CMARS

Climate change is becoming an ever important issue due to the possibility that it may result in extreme weather events such as floods or droughts. Consequently, precipitation forecasting has similarly gained in significance as it is a useful tool in meeting the increasing need for the efficient management of water resources as well as in preventing disasters before they happen. In the literature, there are various statistical and computational methods used for this purpose, including linear and nonlinear regression, kriging, time series models, neural networks, and multivariate adaptive regression splines (MARS). Among them, MARS stands out as the better performing precipitation modeling method. In this article, we used a recently developed method called robust conic mars (RCMARS), based on MARS (also on CMARS), to forecast precipitation owing to its ability to model complex uncertain data. In CMARS, which was developed as a powerful alternative to MARS, the model complexity is penalized in the form of Tikhonov regularization and studied as a conic quadratic programming. In RCMARS, on the other hand, CMARS is refined further by including the existence of uncertainty in the future scenarios and robustifying it with a robust optimization technique. To evaluate the performance of the RCMARS method, it was applied to build a precipitation model constructed as an early warning system for the continental Central Anatolia Region of Turkey, where drought has been a recurrent phenomenon for the last few decades. Furthermore, the performance of the RCMARS precipitation model was also compared to that of MARS and CMARS. The results indicated that RCMARS builds more accurate, precise, and stable precipitation models compared to those of MARS and CMARS. In addition to these advantageous features of the RCMARS precipitation model, it also provided a good fit to the data. As a result, we propose its use in precipitation forecasting for the region studied.