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Papers
61,005 resultsShowing papers similar to Machine learning prediction and interpretation of the impact of microplastics on soil properties
ClearA generative physics-informed machine learning model for soil microplastic accumulation dynamics
Researchers developed a physics-informed machine learning model to simulate and predict microplastic accumulation dynamics in soils, combining experimental data with mechanistic equations to overcome the limitations of heterogeneous field conditions. The integrated model outperformed purely data-driven approaches in predicting MP transport and retention in soil.
Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study
Using machine learning on data from 621 sites across China, researchers identified nine key factors driving microplastic distribution in soil, including population density, plastic production, and agricultural practices. The study estimated that Chinese topsoil contains a substantial stock of microplastics, with concentrations varying widely by region. This large-scale analysis helps predict where microplastic contamination is worst, which is important for understanding human exposure through food grown in contaminated soil.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand
Researchers used machine learning techniques to analyze the distribution and influencing factors of soil microplastic contamination in the Bang Pakong Watershed in Thailand. The study identified key environmental and land-use variables that predict microplastic occurrence, providing a data-driven approach for understanding how microplastics distribute across agricultural and urban landscapes.
Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect
Using machine learning, researchers discovered that microplastics that have been weathered by sunlight and environmental exposure move through soil significantly faster than fresh ones. The aging process changes the plastic surface chemistry, making particles more mobile and more likely to reach deeper soil layers and groundwater. This means microplastics in agricultural soil and landfills may contaminate underground water supplies more quickly than previously thought.
An introduction to machine learning tools for the analysis of microplastics in complex matrices
This paper introduces machine learning tools that can speed up the identification and counting of microplastics in complex samples like soil and water. While focused on analytical methods rather than health effects, faster and more accurate detection of microplastics is essential for understanding how much exposure humans actually face through food, water, and the environment.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
Researchers applied machine learning models — including random forest and support vector regression — to predict the spatial distribution of soil microplastic pollution in China's Taihu Lake region, finding that soil texture, population density, and proximity to known plastic sources were the dominant drivers, with nearly half of urban soils showing serious contamination.
Global Meta-Analysis Integrated with Machine Learning Assesses Context-Dependent Microplastic Effects on Soil Microbial Biomass Carbon and Nitrogen
This global meta-analysis pooled data from 90 studies to examine how microplastics affect soil microbes. In lab settings, microplastics increased microbial biomass by about 10%, with biodegradable plastics having the strongest effects. Temperature was the most important factor influencing these changes. The results suggest microplastics are altering soil ecosystems in ways that could affect agriculture and carbon cycling.
Global Meta-Analysis Integrated with Machine Learning Assesses Context-Dependent Microplastic Effects on Soil Microbial Biomass Carbon and Nitrogen
This meta-analysis pooled data from 90 studies to assess how microplastics in soil affect microbial biomass, which is critical for healthy soil function. The research found that in controlled lab settings, microplastics increased microbial biomass carbon by about 10%, but the effect varied greatly depending on plastic type, size, and soil conditions. These soil-level changes matter because altered microbial activity can affect nutrient cycling in agricultural soils that produce the food people eat.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Source tracking, pollution load, and risk assessment of microplastics pollution in agricultural soils of Bangladesh using machine learning and multi-matrix approaches
One of the first comprehensive assessments of microplastic contamination in agricultural soils of Bangladesh found widespread MP occurrence across 64 samples from eight areas, with ecological risk assessment indicating potential harm to soil organisms. The study linked MP sources to irrigation water, plastic mulch, and sewage sludge application.
Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks
Researchers used machine learning frameworks to model how microplastics in soil affect greenhouse gas emissions, including carbon dioxide, methane, and nitrous oxide. They found that the type of microplastic significantly influenced CO2 emissions, with biodegradable plastics like polyamide leading to higher levels that worsened with environmental aging. The study suggests that microplastic contamination in agricultural soils could have meaningful implications for climate-related greenhouse gas output.
Integrating metagenomics analysis and machine learning to identify drivers of antibiotic resistance genes abundance in microplastic-contaminated soil
Researchers integrated global soil metagenomic datasets with machine learning to identify which microplastic properties, climatic variables, and soil characteristics best predict antibiotic resistance gene (ARG) abundance in microplastic-contaminated soils. Microplastic type and surface area were stronger drivers of ARG enrichment than climate or soil chemistry, pointing to plastic material properties as key targets for antibiotic resistance management.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Drivers of soil microplastic contamination and machine learning-based abundance standardization: A global meta-analysis
This global meta-analysis of 1,247 monitoring datasets found that methodological factors account for over half (51.75%) of the variation in reported soil microplastic abundance, while land use type drives much of the remaining variation. Machine learning-based standardization revealed that agricultural soils had the highest contamination, underscoring the pathway from plastic-polluted soil to food crops.
Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning
Researchers used interpretable machine learning algorithms to predict global marine microplastic distribution patterns based on calibrated field data. The study found that biogeochemical and human activity factors had the greatest influence on microplastic concentrations, which ranged from about 0.2 to 27 particles per cubic meter across the world's oceans, providing a framework for pollution management and decision-making.
Global Meta-AnalysisIntegrated with Machine LearningAssesses Context-Dependent Microplastic Effects on Soil MicrobialBiomass Carbon and Nitrogen
This global meta-analysis of 90 studies found that microplastics in soil can increase microbial activity and affect carbon and nitrogen cycles, particularly biodegradable plastics which had the strongest effects. While focused on soil health rather than direct human impact, these changes could affect the quality of crops grown in contaminated soil and the broader food system.
Mapping the Plastic Legacy: Geospatial Predictions of a Microplastic Inventory in a Complex Estuarine System Using Machine Learning
Researchers applied machine learning geospatial modelling to predict microplastic distribution across a complex estuarine system, using sediment samples as a training dataset to generate spatial inventory maps of microplastic accumulation. The model leveraged the estuary's role as a land-sea interface and plastic accumulation bottleneck to produce high-resolution predictions of microplastic hotspots for monitoring and management purposes.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.