Enhancing global microplastic pollution analysis using machine learning: a longitudinal study of seasonal trends and anomaly detection

Microplastic pollution is a pressing environmental issue, necessitating a comprehensive understanding of its spatial and temporal patterns. This research introduces a pioneering analysis that merges machine learning methodologies with conventional techniques to delve deeper into the dynamics of global microplastic pollution. By leveraging the L-BFGS-B algorithm, we efficiently solved an unconstrained optimization problem with five variables. The algorithm efficacy in minimizing the objective function and suitability for the problem. Employing machine learning algorithms, we bolster spatial interpolation to forecast microplastic concentrations in regions with sparse data, generating a high-resolution global heatmap of pollution. Through clustering algorithms, we unveil areas with similar pollution characteristics, shedding light on potential pollution hotspots that can guide targeted management strategies. Conducting a longitudinal study utilizing time series analysis techniques, such as ARIMA and STL, enables us to model and predict microplastic concentrations over time, thereby illuminating pollution trends and variations in pollution sources. Additionally, anomaly detection algorithms aid in identifying exceptional pollution spikes or drops, allowing timely detection of specific events or shifts in pollution sources, which is crucial for informed interventions and policy formulation. This integration of machine learning and traditional methods advances our comprehension of global microplastic pollution, empowering decision-makers to devise effective strategies for environmental preservation and fostering a sustainable marine ecosystem.