An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters.

Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets.

Temporal distribution of trace metals pollution load index in the Nile Delta coastal surface sediments.

The Mediterranean coastal area of the Nile Delta is socio-economically vital, however, it is under significant environmental stress due to pollution from land-based activities. The study investigates the temporal variations of trace metals to assess the development of the anthropogenic pollution status in the coastal sediments. The average concentrations, the enrichment factor, and the geoaccumulation index revealed that Cr, V, Ni, and Co are pollutants of concern.