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Big Data Analytics in Mapping the Global Microplastic Distribution
Summary
Researchers reviewed how big data tools—including machine learning, remote sensing, citizen science, and geospatial analytics—can be combined to map microplastic distribution globally, while noting that data standardization and interoperability remain key obstacles to translating these computational approaches into effective pollution policy.
On account of their potential environmental impacts to health, the presence of microplastics in terrestrial, lacustrine, and marine environments has raised a great deal of concerns among researchers. The identification of the sources, transport, and the ecological impact of microplastics requires mapping them at a global scale. Big data analytics, such as advanced computational methods, machine learning, and geospatial technologies, is a potential approach to address this challenge. This chapter focuses on the use of big data approaches for mapping the global distribution of microplastics, from data collection and analysis to visualisation. It deals with large scale datasets including remote sensing, citizen sensing and IoT. It runs them through deep learning and statistical techniques to spot hotspots and trends. It also addresses the problems related to data standardisation, storage, and interoperability, and the vicissitudes to overcome these hindrances. Case studies of applied applications and future outlooks on the value of big data e-government in the aspect of global policies to reduce the impact. The influence of microplastic pollution is also elaborated. This is where your big data analysis comes into play for all researchers, policy makers and green lobbies to seize the opportunity for combating the microplastic disaster.