Machine learning prediction and interpretation of the impact of microplastics on soil properties

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Do Added Microplastics, Native Soil Properties, and Prevailing Climatic Conditions Have Consequences for Carbon and Nitrogen Contents in Soil? A Global Data Synthesis of Pot and Greenhouse Studies

Microplastics threaten soil ecosystems, strongly influencing carbon (C) and nitrogen (N) contents. Interactions between microplastic properties and climatic and edaphic factors are poorly understood. We conducted a meta-analysis to assess the interactive effects of microplastic properties (type, shape, size, and content), native soil properties (texture, pH, and dissolved organic carbon (DOC)) and climatic factors (precipitation and temperature) on C and N contents in soil. We found that low-density polyethylene reduced total nitrogen (TN) content, whereas biodegradable polylactic acid led to a decrease in soil organic carbon (SOC). Microplastic fragments especially depleted TN, reducing aggregate stability, increasing N-mineralization and leaching, and consequently increasing the soil C/N ratio. Microplastic size affected outcomes; those <200 μm reduced both TN and SOC contents. Mineralization-induced nutrient losses were greatest at microplastic contents between 1 and 2.5% of soil weight. Sandy soils suffered the highest microplastic contamination-induced nutrient depletion. Alkaline soils showed the greatest SOC depletion, suggesting high SOC degradability. In low-DOC soils, microplastic contamination caused 2-fold greater TN depletion than in soils with high DOC. Sites with high precipitation and temperature had greatest decrease in TN and SOC contents. In conclusion, there are complex interactions determining microplastic impacts on soil health. Microplastic contamination always risks soil C and N depletion, but the severity depends on microplastic characteristics, native soil properties, and climatic conditions, with potential exacerbation by greenhouse emission-induced climate change.

Earth at risk: An urgent call to end the age of destruction and forge a just and sustainable future

Human development has ushered in an era of converging crises: climate change, ecological destruction, disease, pollution, and socioeconomic inequality. This review synthesizes the breadth of these interwoven emergencies and underscores the urgent need for comprehensive, integrated action. Propelled by imperialism, extractive capitalism, and a surging population, we are speeding past Earth's material limits, destroying critical ecosystems, and triggering irreversible changes in biophysical systems that underpin the Holocene climatic stability which fostered human civilization. The consequences of these actions are disproportionately borne by vulnerable populations, further entrenching global inequities. Marine and terrestrial biomes face critical tipping points, while escalating challenges to food and water access foreshadow a bleak outlook for global security. Against this backdrop of Earth at risk, we call for a global response centered on urgent decarbonization, fostering reciprocity with nature, and implementing regenerative practices in natural resource management. We call for the elimination of detrimental subsidies, promotion of equitable human development, and transformative financial support for lower income nations. A critical paradigm shift must occur that replaces exploitative, wealth-oriented capitalism with an economic model that prioritizes sustainability, resilience, and justice. We advocate a global cultural shift that elevates kinship with nature and communal well-being, underpinned by the recognition of Earth's finite resources and the interconnectedness of its inhabitants. The imperative is clear: to navigate away from this precipice, we must collectively harness political will, economic resources, and societal values to steer toward a future where human progress does not come at the cost of ecological integrity and social equity.

Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks

With the rise in global plastic production and agricultural demand, the released microplastics (MPs) have increasingly influenced the elemental cycles of soils, leading to notable effects on greenhouse gas emissions. Despite initial research, there remains a gap in establishing a detailed modeling approach that comprehensively explores the impacts of MPs on GHG emissions. Herein, we utilized literature mining to assemble a comprehensive dataset examining the interplays between MPs and emissions of CO2, CH4, and N2O. Five automated machine learning frameworks were employed for predictive modeling. The GAMA framework was particularly effective in predicting CO2 (Q2 = 0.946) and CH4 (Q2 = 0.991) emissions. The Autogluon framework provided the most accurate prediction for N2O emission, though it exhibited signs of overfitting. Interpretability analysis indicated that the type of MPs significantly influenced CO2 emission. Degradable MPs (i.e., polyamide) inherently led to elevated CO2 emission, and the environmental aging further exacerbated this effect. Although both linear and nonlinear correlations between MPs and CH₄ emission were not identified, the incorporation of specific MPs that elevate soil pH, augment soil water retention, and cultivate anaerobic conditions may potentially elevate soil CH₄ emission. This research underscores the profound influence of MPs on soil GHG emissions, providing vital insights for shaping agricultural policies and soil management practices in the context of escalating plastic use.