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61,005 resultsShowing papers similar to Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
ClearMicroplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
Scientists developed a new workflow combining 3D laser scanning confocal microscopy with machine learning to detect and quantify microplastics in agricultural soils more efficiently and without hazardous chemicals. The method was validated on soil samples spiked with transparent microplastics, which are particularly difficult to identify. Faster, safer detection tools are essential for understanding how widely microplastics from plastic mulch films and organic fertilizers contaminate farmland soils.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
Scientists developed a faster, safer way to find tiny plastic particles (microplastics) in farm soil without using dangerous chemicals. This new method could help us better track plastic pollution in the soil where our food grows. Better detection of microplastics in agricultural soil is important because these particles can potentially move from contaminated soil into our food supply.
Automated identification and quantification of invisible microplastics in agricultural soils
Researchers developed an automated method combining laser direct infrared and FTIR spectroscopy to identify microplastics in agricultural soils, revealing that particles smaller than 500 micrometers account for over 96% of soil microplastics that are invisible to traditional visual inspection.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.
Application of hyperspectral and deep learning in farmland soil microplastic detection
Hyperspectral imaging combined with deep learning was applied to detect and classify microplastics in farmland soil, offering a non-destructive, rapid alternative to time-consuming chemical extraction methods. The model achieved high classification accuracy across polymer types, demonstrating the potential for field-deployable microplastic monitoring in agricultural settings.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
Researchers combined hyperspectral imaging with machine learning algorithms to detect polyethylene and polyamide microplastics in soil samples. This rapid detection approach could support large-scale soil monitoring for microplastic contamination, which is important given that agricultural soils may accumulate plastics from mulch films, irrigation water, and sewage sludge.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
A Low-Cost Approach for Batch Separation, Identification and Quantification of Microplastics in Agriculture Soil
This study developed a low-cost method to efficiently separate and identify microplastics from agricultural soil, particularly film-type fragments that come from mulching plastics. Having reliable, affordable analytical methods is essential for generating the large-scale data needed to understand how widespread agricultural microplastic contamination is and how it changes over time.
Study on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy
Researchers developed a rapid method for detecting and quantifying microplastics in soil using terahertz time-domain spectroscopy combined with machine learning algorithms. The classification models achieved high accuracy in identifying different types of microplastics including polyethylene, polystyrene, and polypropylene. The study suggests that terahertz spectroscopy could provide a faster and more efficient alternative to current methods for monitoring microplastic contamination in agricultural soils.
Innovative approach for determining polypropylene microplastics pollution in calcareous soils: Vis-NIR spectroscopy
Researchers demonstrated that visible and near-infrared (Vis-NIR) spectroscopy combined with statistical modeling can accurately detect and quantify polypropylene microplastics in agricultural calcareous soils, with a model accuracy of R² = 0.91. This is promising because it could enable rapid, low-cost field screening of soil microplastic contamination without expensive laboratory analysis.
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.
Microplastics in agricultural soils: Extraction and characterization after different periods of polythene film mulching in an arid region
Researchers developed a new method to extract microplastics from agricultural soil and found that fields mulched with plastic film for 30 years had the highest microplastic concentrations, at 40 mg per kilogram of soil, with particle size decreasing as the years of mulching increased. The study highlights the long-term accumulation of microplastics in soils under continuous plastic film agriculture.
Microplastic Pollution In Agricultural Lands And Its Environmental Impact Assessed Through Remote Sensing
Researchers combined field sampling and remote sensing to assess microplastic pollution in agricultural soils across three Indian locations, finding microplastics in both surface and subsurface layers and correlating pollution levels with land use patterns detectable by satellite imagery.
Influences of land use and depth profile on the characteristics of microplastics in agricultural soils
Researchers examined how land use and soil depth profile influence microplastic characteristics in agricultural soils, finding that wastewater and sludge application, plastic mulching, and atmospheric deposition are key sources, and that MP type and abundance vary with soil management practice and depth, highlighting the importance of vertical distribution in soil MP studies.
Research Progress on Source Analysis, Ecological Effects, and Separation Technology of Soil Microplastics
This review synthesizes recent progress on soil microplastic sources (primarily agricultural plastic mulch and wastewater irrigation), ecological impacts on soil structure and microbial communities, and available separation and detection technologies for assessing contamination.
Microplastic accumulation in agricultural soils: Source apportionment and impact on soil microbial community structure
Researchers investigated microplastic accumulation patterns in intensively farmed agricultural soils at multiple depth intervals, using polymer fingerprinting to apportion contamination sources among plastic mulch, treated wastewater irrigation, and organic amendment application. The study assessed impacts on soil microbial community structure using FTIR-confirmed microplastics extracted by zinc chloride density flotation.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Abundance and characteristics of microplastics in soils with different agricultural practices: Importance of sources with internal origin and environmental fate
Microplastic abundance and characteristics were examined in soils representing four agricultural practice types in Chinese farmland to evaluate the influence of land use on plastic particle accumulation. Microplastic concentrations and polymer types varied by agricultural practice, with plastic mulch film use and irrigation water source as key drivers of farmland soil contamination.
Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil
Researchers developed a rapid, non-destructive method using visible and near-infrared spectroscopy to quantify microplastics in farm soil. The study suggests this approach could overcome the limitations of current extraction-based methods, which are time-consuming and prone to errors and biases.
Quantification of small (1–10 µm) microplastic particles in soil matrices using automated scanning electron microscopy: possibilities and limitations
Researchers developed an automated SEM-EDX method for quantifying small (1-10 µm) microplastic particles in soil matrices, applying a gold coating to polycarbonate membranes to improve elemental contrast and using Monte Carlo simulations to optimise an acceleration voltage of 3 kV for particle detection. They achieved largely concentration-independent recoveries of ~70% for polyethylene and ~50% for PVC from soil suspensions, demonstrating both the promise and current limitations of this approach for small microplastic analysis.
Automated Quantification of Microplastics – Challenges and Opportunities
Researchers developed an image analysis algorithm to automate microplastic quantification in soil and organic fertilizers by analyzing heated samples where plastics visibly melt, finding preliminary results broadly consistent with FPA-microFTIR validation but with a tendency to overcount elongated or irregularly shaped particles.