In this study, the concentration and sources of aliphatic and petroleum markers were investigated in 105 samples of Anzali, Rezvanshahr and Astara cores from the southwest of Caspian Sea. Petroleum importation was diagnosed as a main source in most depths of cores by the results of unresolved complex mixture, carbon preference index and hopanes and steranes. From the chemical diagnostic parameters, petroleum inputs in sediment of cores were determined to be different during years and the sources of hydrocarbons in some sections differed than Anzali and Turkmenistan and Azerbaijan oils. Diagenic ratios in most sediments of upper and middle sections in Astara core were determined to be highly similar to those of Azerbaijan oil, while the presence of Turkmenistan and Anzali oils were detected in a few sections of Anzali and Rezvanshahr cores and only five layers of downer section in Anzali core, respectively.

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

This paper proposes adaptive microwave technologies for assessing the state of water systems. The expected results will allow the rapid assessment of temperature, salinity, disturbance of the water surface, the concentration of inorganic and organic substances, etc., by providing predictions of the distribution of anomalies of these parameters. To achieve this goal, experimental measurements from the monitoring of the aquatic environment in real time and the relevant analyses will be presented. The accuracy of the method used will be calculated from measurements at Kara-Bogaz-Gol Bay located in western Turkmenistan and elsewhere.

Nitrogen dioxide (NO₂) is one of the main air pollutants, formed due to both natural and anthropogenic processes, which has a significant negative impact on human health. The COVID-19 pandemic has prompted countries to take various measures, including social distancing or stay-at-home orders. This study analyzes the impact of COVID-19 lockdown measures on nitrogen dioxide (NO₂) changes in Central Asian countries. Data from TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, as well as meteorological data, make it possible to assess changes in NO₂ concentration in countries and major cities in the region. In particular, the obtained satellite data show a decreased tropospheric column of NO₂. Its decrease during the lockdown (March 19–April 14) ranged from − 5.1% (Tajikistan) to − 11.6% (Turkmenistan). Based on the obtained results, it can be concluded that limitations in anthropogenic activities have led to improvements in air quality. The possible influence of meteorology is not assessed in this study, and the implied uncertainties cannot be quantified. In this way, the level of air pollution is expected to decrease as long as partial or complete lockdown continues.

The occurrence of n-alkanes and biomarkers (hopane and sterane) in surface sediments from Southwestern coasts of Caspian Sea and 28 rivers arriving to this lake, determined with a gas chromatography–mass spectrometry method, was used to assess the impacts of anthropogenic activities in the studied area. The concentrations of total n-alkanes (Σ21 n-alkane) in costal and riverine sediments varied from 249.2 to 3899.5 and 56 to 1622.4 μg g⁻¹, respectively. An evaluation of the source diagnostic indices indicated that petroleum related sources (petrogenic) were mainly contributed to n-alkanes in costal and most riverine sediments. Only the hydrocarbons in sediment of 3 rivers were found to be mainly of biogenic origin. Principal component analysis using hopane diagnostic ratios in costal and riverine sediments, and Anzali, Turkmenistan, and Azerbaijan oils were used to identify the sources of hydrocarbons in sediments. It was indicated that the anthropogenic contributions in most of the costal sediment samples are dominated with inputs of oil spills from Turkmenistan and Azerbaijan countries.

Based on the environmental Kuznets curve (EKC) hypothesis, the aim of this paper is to examine the relationships among per capita CO₂ emissions, per capita real GDP, per capita renewable energy consumption, and urbanization in a panel of five Central Asian countries (Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan) from 1992 to 2013. For robustness checking, three estimator techniques reveal no evidence of inverted U-shape EKC consistently. Moreover, renewable energy consumption plays negative impact on emissions, while urbanization plays positive, significantly. The findings of heterogeneous panel causality suggest that there are bidirectional causalities; each other expect no causality from emissions to renewable energy. Finally, some implications, such as developing a small renewable energy project and sustainable urbanization and strengthening in-regional and out-regional cooperation, are given in this region.

This article investigates the long-run and causal linkages between economic growth, CO₂ emissions, renewable and non-renewable (fossil fuels) energy consumption, the Composite Trade Intensity (CTI) as a proxy for trade openness, and the Chinn-Ito index as a proxy for financial openness for a panel of the Commonwealth of Independent States (CIS) region including Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan over the period of 1992–2015. It is the first time that CTI and the Chinn-Ito indexes are used in an economic-pollution model. Employing three panel unit root tests, panel cointegration estimation methods (DOLS and FMOLS), and two panel causality tests, the main empirical results provided evidence for the bidirectional long-run relationship between all the variables in all 12 sampled countries except for economic growth-renewable energy use linkage. The findings of causality tests indicated that there is a unidirectional short-run panel causality running from economic growth, financial openness, and trade openness to CO₂ emissions and from fossil fuel energy consumption to renewable energy use.