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The harmful effects of various substances on the marine environment were reviewed by collecting and studying the relevant literature. Various sources for the pollution of marine environment were identified and the causes for the same are understood. Many of the pollutants that are let into the sea are directly or indirectly by human activities. Some of these substances are biodegradable, while some are not. Several laws and policies have been taken in preventing marine pollution at the national and international levels. Simulation of oil spills has been done by developing models in some parts of the world. The pollution off the shore is increasing at an alarming rate and to address this problem of pollution in the oceans is a difficult task, and a variety of approaches are urgently required. In this paper, the definition of coastal pollution, causes of coastal pollution, its impacts and preventive measures are discussed.

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Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach.