We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Image analysis techniques: a tool to study the residence time of microplastics on sandy beaches?
AI summary Read the abstract
Researchers examined whether image analysis techniques can be used to study the residence time of nurdles (pre-production plastic pellets) on sandy beaches, evaluating this approach as a tool for understanding microplastic persistence and dynamics in coastal environments.
Nurdles, or pre-production thermoplastic resins, are an important source of microplastic pollution in marine and coastal ecosystems due to losses mainly during their production and transport. As a consequence of their behavior in the aquatic environment, microplastics received great attention because of their harmful effects on marine organisms. As they enter the marine environment, pellets begin to undergo photooxidation and erosion, turning yellow. The yellowing degree can be used to infer their residence time in the environment, contributing to studies on toxicity, sources, and the role of microplastics as a threat to human health and biota. In this study, we have tried to use the RGB color to evaluate the yellowing degree of 225 photographs of pellets found on sandy beaches from Santa Catarina island. We created two proposals for the intervals of the yellowing degree, the first by using only values from the blue (B) band and the second with values from the blue and the red (R) band. We compared both proposals with the visual classification from the images, the results showed a low efficiency of the created intervals (51.77% for band B and 50.44% for bands B and R). The results also showed that both proposals can well classify the low yellowing degree classes as null yellowness (70.31%) and low yellowness (72.37%), but can't classify the moderate yellowness class (0%). The high yellowing degree classes classified with the first proposal showed overlap in the results, high yellowness (44.74%) and very high yellowness (0%), then we tried the second proposal and the results were 31.58% for high yellowness and 38.46% for very high yellowness. Based on this work, we discussed the limitation of the methodology and future perspectives to classify the yellowing degree. Also see: https://micro2022.sciencesconf.org/427377/document
More Papers Like This
A Simplified Experimental Method to Estimate the Transport of Non-Buoyant Plastic Particles Due to Waves by 2D Image Processing
AI summary Read the abstract
Not a microplastics paper in the strict sense — this study develops and validates an image-processing method to track the movement of non-buoyant plastic debris particles under wave action in a laboratory wave tank, advancing the physical modeling tools used to predict where plastic pollution accumulates in coastal environments.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
AI summary Read the abstract
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.
The Method of Studying Microplastics Pollution in the Beach and Bottom Sediments of Different Granulometric Composition
AI summary Read the abstract
This paper presents a method for studying microplastic pollution on beaches, describing a field sampling and laboratory analysis protocol for characterizing plastic particles in beach sediments. Standardized beach monitoring methods are essential for generating comparable data across different coastal locations.
Microplastic deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
AI summary Read the abstract
Researchers integrated remote sensing, GNSS altimetric surveys, micro-Raman spectroscopy, and machine learning models to predict microplastic deposition patterns on urban sandy beaches along the central Sao Paulo coastline, finding MP concentrations ranging from 6 to 35 MPs/m2.
Image processing tools in the study of environmental contamination by microplastics: reliability and perspectives
AI summary Read the abstract
Researchers assessed the reliability of image processing tools for studying microplastic contamination, finding that while these tools offer efficiency gains, inconsistent methodologies limit comparability between studies and call for standardization.
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.