We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Predicting microplastic impacts on carbon and nitrogen cycling in agroecosystems using Gradient Boost Regression (GBR) modeling
AI summary Read the abstract
Researchers applied Gradient Boost Regression (GBR) machine learning to predict how microplastics affect carbon and nitrogen biogeochemical cycling in agroecosystems, training the model on lab-based measurements to develop mechanistic understanding of MP impacts on soil nutrient cycles and crop quality.
Microplastics can seriously disrupt soil carbon (C) and nitrogen (N) biogeochemical cycling in agroecosystems, strongly influencing crop growth and quality. However, our mechanistic understanding of how microplastics affect these cycles remains poorly understood. Specifically, direct in situ measurements in greenhouse and field settings are often impractical. However, lab-based and ex-situ measurements can train machine learning models to develop mechanistic understanding of microplastic behaviour. Thus, we aim to train and test the gradient boost regression (GBR) model to estimate the effects of key microplastic properties on C and N cycling and subsequently on plant biomass. The role of soil type in controlling the microplastics effects was also estimated. During prediction, datasets from published experiments (n = 52) were divided into a ratio of 80:20 for training and testing the model. GBR prediction showed R2 values ranged between 57% to 99% and MSE values ranged between 0 to 0.09 for the contents of dissolved organic carbon (DOC), soil organic carbon (SOC), soil organic matter (SOM), ammonium (NH4+), and nitrate (NO3-), emissions of CO2 and N2O. Overall, there was distinct effects of microplastic properties on soil C pools. Microplastic size contributed 34% in altering DOC while the maximum CO2 emissions (39%) were altered by microplastics incubation period in soil. However, microplastic shape contributed 47 to 60% to SOC and SOM. Microplastic size strongly altered NH4+ and NO3- by 36 to 51% in microplastic polluted soils resulting in the highest N2O emission. Plant biomass was strongly (76%) affected by microplastic types. Our results conclude that GBR model appeared a powerful machine learning tool for predicting the impacts on C and N cycling as well as plant performance following microplastic pollution. As microplastic pollution is increasing in soils globally, there is an urgent need to implement and strengthen such tools in modern research to tailor sustainable solutions.
More Papers Like This
Simulating carbon and nitrogen cycling in microplastic contaminated agroecosystems using gradient boost regression model
AI summary Read the abstract
Researchers trained a gradient boosting regression model on data from 55 published studies to predict how microplastic contamination disrupts carbon and nitrogen cycling and plant biomass in soils, finding that MP size most strongly affects dissolved organic carbon and CO₂ emissions, while MP type governs plant biomass responses.
Global Meta-AnalysisIntegrated with Machine LearningAssesses Context-Dependent Microplastic Effects on Soil MicrobialBiomass Carbon and Nitrogen
AI summary Read the abstract
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.
A generative physics-informed machine learning model for soil microplastic accumulation dynamics
AI summary Read the abstract
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.
Global Meta-Analysis Integrated with Machine Learning Assesses Context-Dependent Microplastic Effects on Soil Microbial Biomass Carbon and Nitrogen
AI summary Read the abstract
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.
Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.