A lagrangian particle tracking approach of evapotranspiration partitioning to explore evaporation and transpiration travel time distributions in the root zone.

Evapotranspiration is a key component of the hydrological cycle, influencing water and biogeochemical cycles in the critical zone. Particularly, the travel time of evapotranspired water is critical for describing the origin and young water contribution to evapotranspiration, but is yet poorly understood. In this study, we revisited the Lagrangian particle-tracking model, EcoSLIM, to separate evaporation and transpiration particles using a mass balance approach.

Variable-complexity machine learning models for large-scale oil spill detection: The case of Persian Gulf.

Oil spill is the main cause of marine pollution in the waterbodies with rich oil resources. In this study, we developed and compared the performance of variable-complexity machine-learning models to detect oil spill origin, extent, and movement over large scales. To this end, we trained Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) models by using the statistical, geometrical, and textural features of Sentinel-1 SAR data.

Power of a remote hydrogen bond donor: anion recognition and structural consequences revealed by IR spectroscopy.

Natural and synthetic anion receptors are extensively employed, but the structures of their bound complexes are difficult to determine in the liquid phase. Infrared spectroscopy is used in this work to characterize the solution structures of bound anion receptors for the first time, and surprisingly only two of three hydroxyl groups of the neutral aliphatic triols are found to directly interact with Cl(–). The binding constants of these triols with zero to three CF3 groups were measured in a polar environment, and KCD3CN(Cl(–)) = 1.