RESUMEN
Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson's correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.
RESUMEN
This study focuses on the short-term contamination and associated risks arising from the release of Polycyclic Aromatic Hydrocarbons (PAHs) due to the 2020 Baghjan oil blowout in upper Assam, India. Shortly after the Baghjan oil blowout, samples were collected from water, sediment, and fish species and examined for PAHs contents. The results of the analysis revealed ΣPAHs concentrations ranged between 0.21-691.31 µg L-1 (water); 37.6-395.8 µg Kg-1 (sediment); 104.3-7829.6 µg Kg-1 (fish). The prevalence of 3-4 ring low molecular weight PAHs compounds in water (87.17%), sediment (100%), and fish samples (93.17%) validate the petrogenic source of origin (oil spill). The geographic vicinity of the oil blowout is rich in wildlife; thus, leading to a significant mass mortality of several eco-sensitive species like fish, plants, microbes, reptiles, amphibians, birds and mammals including the Gangetic River dolphin. The initial ecological risk assessment suggested moderate to high-risk values (RQ >1) of majority PAHs concerning fish, daphnia, and algae species. This study highlights the need for recognizing the potential for short-term exposure to local species. To safeguard local ecosystems from potential future environmental disasters, it is imperative for the government to adopt a precautionary strategy.
