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Machine learning prediction and interpretation of the impact of microplastics on soil properties
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Researchers applied machine learning models to predict how microplastics affect soil properties, finding that microplastic release into soils is 4 to 23 times higher than into oceans. The models identified key factors influencing soil changes, including microplastic type, size, and concentration, as well as soil texture. The study suggests that machine learning can help address the complexity of soil-microplastic interactions and improve predictions of environmental impacts.
The annual microplastic (MP) release into soils is 4-23 times higher than that into oceans, significantly impacting soil quality. However, the mechanisms underlying how MPs impact soil properties remain largely unknown. Soil-MP interactions are complex because of soil heterogeneity and varying MP properties. This lack of understanding was exacerbated by the diverse experimental conditions and soil types used in this study. Predicting changes in soil properties in the presence of MPs is challenging, laborious, and time-consuming. To address these issues, machine learning was applied to fit datasets from peer-reviewed publications to predict and interpret how MPs influence soil properties, including pH, dissolved organic carbon (DOC), total P, NO-N, NH-N, and acid phosphatase enzyme activity (acid P). Among the developed models, the gradient boost regression (GBR) model showed the highest R (0.86-0.99) compared to the decision tree and random forest models. The GBR model interpretation showed that MP properties contributed more than 50% to altering the acid P and NO-N concentrations in soils, whereas they had a negligible impact on total P and 10-20% impact on soil pH, DOC, and NH-N. Specifically, the size of MPs was the dominant factor influencing acid P (89.3%), pH (71.6%), and DOC (44.5%) in soils. NO-N was mainly affected by the MP type (52.0%). The NH-N was mainly affected by the MP dose (46.8%). The quantitative insights into the impact of MPs on soil properties of this study could aid in understanding the roles of MPs in soil systems.
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