Monitoring of Contaminants in Aquatic Ecosystems Using Big Data

The aquatic ecosystem is the most complicated sphere among the hydrosphere's components. The complexity results from transboundary interactions and reservoir nature that accumulates all forms of terrestrial washouts, marine and atmospheric pollutants such as dissolved and suspended organic, inorganic, and emerging pollutants. The aquatic ecosystem remains a gigantic habitat, transportation route, economic source, and strong buffer for all-natural and anthropogenic environmental turnovers, yet UNEP studies indicate that about 68% of "the environment-related sustainable development goal indicators" have data shortages to evaluate their progress. Therefore, sustainable access to informed data, accurate predictions, and aquatic pollution decisions presents an environmental challenge requiring global intervention. Thus, the availability of continuous, dynamic, and verifiable information supplements diverse, massive, and complex data flow for monitoring the state of the aquatic ecosystem. Storage of these big data requires volumes greater than terabytes, through computation methods that enable high speed and accuracy in accessing varieties of analytical information in real time. Big data presents a new era that gathers all resources, time-based information, and meta- and deeper analysis. Therefore, it is essential to utilize big data's potential in identifying data gaps, likely vulnerabilities, and accurate predictions to maintain strategies for monitoring and mitigation of aquatic pollution for environmental sustainability. The aquatic ecosystem is the most complicated sphere among the hydrosphere's components. The complexity results from transboundary interactions and reservoir nature that accumulates all forms of terrestrial washouts, marine and atmospheric pollutants such as dissolved and suspended organic, inorganic, and emerging pollutants. Big data presents a new era that gathers all resources, time-based information, and meta- and deeper analysis. Therefore, it is essential to utilize big data's potential in identifying data gaps, likely vulnerabilities, and accurate predictions to maintain strategies for monitoring and mitigation of aquatic pollution for environmental sustainability. The majority of global nations, companies, and institutions experience an exponential growth of big data. Due to their complexity, seeking a data analytics expert will be necessary to harness the hidden information for the adoptive environment. The subsurface cycle involves