Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans.

This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH-were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy.

A First Record on Microplastic Ingestion by Tropical Estuarine Copepods of Bangladesh.

Microplastics (MPs) pollution is a profound problem around the world yet it's study on the effect on zooplankton including copepods are very limited. The study was conducted between January 2021 and January 2022 in the Lower Meghna Estuary to investigate MPs ingestion in two different family of copepod: Calanoid and Cyclopoid. A method of acid digestion along with Scanning Electron Microscope (SEM) was used to identify MPs ingested by copepods from the conducted area.