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The carbon cycle is affected by the marine mucilage. The release of dissolved organic carbon (DOC) from mucilage contributes to the organic carbon reserve in the marine ecosystem. This infusion of organic carbon stimulates the growth and metabolism of microbial communities in and around the mucilage. As these microbes consume DOC, they respire and convert organic carbon into carbon dioxide (CO2) through microbial respiration. This cycle contributes to the exchange of CO2 between the ocean and the atmosphere, potentially affecting atmospheric CO2 levels and global carbon budgets.[7]

Mucilage events affect the efficiency of the biological pump, a vital mechanism in the ocean carbon cycle. The biological pump explains how carbon moves from the ocean surface to its depths through the sinking of organic particles such as marine snow and phytoplankton. By trapping organic matter and microorganisms, mucilage can accelerate the sinking rate of organic particles and facilitate their transfer to deeper ocean layers.

Marine mucilage is a natural occurrence in marine environments, but its presence in excessive amounts can indicate environmental stress and poor water quality. Biogeochemistry plays a crucial role in the formation and dynamics of marine mucilage. Factors such as nutrient availability, temperature, salinity, and microbial activity influence the production and degradation of organic matter that contributes to mucilage formation. Excessive nutrients, often from Anthropogenic sources such as agricultural runoff and wastewater discharge, can accelerate phytoplankton growth and mucilage formation, leading to eutrophication.

Understanding how mucilage interacts with biogeochemistry is vital for monitoring and managing coastal ecosystems. Recent studies have utilized advanced remote sensing techniques, such as Sentinel-2 satellite imagery, to map mucilage distribution and assess environmental conditions. These images, combined with advanced processing techniques, allowed them to notice subtle changes in water quality and identify areas affected by mucilage accumulations. Through the use of spectral indices such as Normalized Difference Turbidity Index (NDTI), Normalized Difference Water Index (NDWI), and Automated Mucilage Extraction Index (AMEI). By employing spectral indices and deep learning methods like Convolutional Neural Networks (CNNs), researchers can improve mucilage detection over large areas. [6]By integrating remote sensing data with biogeochemical models and field observations, researchers can gain insight into the underlying mechanisms that drive mucilage formation and develop strategies to mitigate its effects on coastal environments.