We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand
AI summary Read the abstract
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.
Microplastics (MPs) have emerged as a pervasive environmental pollutant due to their persistence and global distribution. However, MPs relationships with covariables remain largely unexplored. This study investigates factors influencing MPs occurrence and distribution in the Bang Pakong Watershed, using 40 soil samples across various land-use types and assess machine learning for their spatial distribution. Samples were sorted into three sizes: 1.2 μm-500 μm, 500 μm-1 mm, and 1-2 mm and analyzed using zinc chloride (ZnCl) density separation, hydrogen peroxide (HO) digestion, and Fourier transform infrared spectroscopy (FTIR) for polymer identification. Results reveal a significant MPs presence, averaging 1121 ± 2465.6 items/kg dry soil, with particles <0.5 mm (49 %), fragments (74.2 %), transparent (49 %), and polypropylene (PP) (52 %) predominating. Urban soils contained highest concentrations (67.6 %) at 2331 ± 4114 items/kg, followed by irrigation (555 ± 571), agricultural (552 ± 432), and forest soils (417 ± 365). Predictive modeling incorporated 14 variables, including soil properties and environmental factors. The Random Forest model (RF), optimized for complex non-linear relationships and high data variability, shows higher predictive accuracy (R = 0.82), with silt content and distance-to-river as key variables. Spatial distribution analysis, developed on model predictions and inverse distance weighting (IDW), demonstrates a concentration gradient increasing southwestward toward the Bang Pakong River. Flood susceptibility and drainage density analysis correlate with interpolation results, suggesting that these factors influence MPs transport and deposition processes. These results refine MPs management, emphasizing urbanization and hydrological factors as drivers for distribution, necessitating targeted mitigation in high-risk areas.
More Papers Like This
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
AI summary Read the abstract
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.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
AI summary Read the abstract
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.
Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study
AI summary Read the abstract
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.
Land use shapes the fate of soil microplastics in China: Insights from machine learning
AI summary Read the abstract
A massive study analyzing nearly 1,500 soil samples across China found that farmland, especially orchards and fields covered with plastic sheeting (mulch), had far more tiny plastic particles than forests or natural areas, with mulched fields showing about 6 times more contamination. Since these are the soils growing our food, this matters because it suggests the crops we eat may be absorbing more microplastics from heavily farmed, plastic-mulched land, highlighting a need for smarter farming practices to reduce this exposure.
Machine learning prediction and interpretation of the impact of microplastics on soil properties
AI summary Read the abstract
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.
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.