Drivers of soil microplastic contamination and machine learning-based abundance standardization: A global...

Area of Science:

• Environmental Science
• Ecotoxicology
• Data Science

Background:

• Soil microplastics (MPs) are a growing environmental concern, but global data is inconsistent.
• Understanding the drivers of soil MP contamination is hindered by fragmented monitoring and varied methodologies.

Purpose of the Study:

• To conduct a global meta-analysis of soil MP contamination.
• To standardize MP abundance data using machine learning.
• To identify key drivers influencing soil MP abundance and characteristics.

Main Methods:

• Global meta-analysis of 1247 soil MP monitoring data points from 153 studies.
• Machine learning (random forest) model for abundance standardization.
• Analysis of factors influencing MP abundance, color, polymer composition, size, and shape.

Main Results:

• Methodological factors (51.75%) were the dominant driver of MP abundance variation, exceeding environmental (35.18%) and socioeconomic (13.07%) influences.
• Land-use type dictated MP color and polymer composition.
• Detection size limit and identification method influenced small-sized MPs (SMPs) proportion and MP shape.
• Harmonized abundance estimates yielded a mean of 1503.20 items/kg.

Conclusions:

• Methodological standardization is crucial for accurate global soil MP assessments.
• Machine learning provides a robust approach to harmonize disparate MP data.
• Future research should prioritize comparability of MP characteristics beyond abundance for holistic environmental risk assessment.