https://doi.org/10.1140/epjs/s11734-025-01808-4
Regular Article
Microplastic interactions in oceanic climate change: a multifractal analysis
1
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 632 014, Vellore, Tamil Nadu, India
2
P.B. Academic School, 63, M.G. Road, 700 041, Kolkata, West Bengal, India
3
CEMS.UL - Center for Mathematical Studies, Lisbon, Portugal
Received:
2
April
2025
Accepted:
12
July
2025
Published online:
24
July
2025
The Earth’s surface comprises approximately one-fourth land and three-fourths water, with both domains experiencing the profound impacts of climate change. Understanding the dynamics of ocean-based climate indicators is therefore essential. This study investigates the pivotal role of oceanic plastic debris, particularly microplastics, in influencing climate change. Five key ocean-related variables are examined: accumulated ocean microplastic debris (
cm), ocean heat content (top 2000 m), sea surface temperature, mass balance of the Antarctic ice sheet, and sea-level rise. Adopting a fractal perspective, the structural trends of these variables are modeled using the Fractal Regression Function (FRF). The multifractal properties of both the original and fractal regression function generated data are analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA), providing deeper insights into how plastic pollution affects oceanic climate dynamics. Despite the limited length of the datasets, multifractal detrended fluctuation analysis effectively captures their inherent irregularities, complexities, and multifractality across scales. The fractal regression function approach successfully represents both oscillatory behavior and long-term trends through fractal and directional coefficients, respectively, offering a nuanced interpretation of oceanic climate variability. Additionally, ARIMA modeling is applied for short-term forecasting, with comparative analysis between the predictions derived from original and fractal regression function estimated data.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
