We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
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.
Microplastic pollution is one of the greatest environmental concerns for contemporary times and the future. In the last years, the number of publications about microplastic contamination has increased rapidly and the list is daily updated. However, the lack of standard analytical approaches might generate data inconsistencies, reducing the comparability among different studies. The present study investigates the potential of two image processing tools (namely the shapeR package for R and ImageJ 1.52v) in providing an accurate characterization of the shape of microplastics using a restricted set of shape descriptors. To ascertain that the selected tools can measure small shape differences, we perform an experiment to verify the detection of pre-post variations in the shape of different microplastic types (i.e., nylon [NY], polyethylene [PE], polyethylene terephthalate [PET], polypropylene [PP], polystyrene [PS], and polyvinylchloride [PVC]) treated with mildly corrosive chemicals (i.e., 10% KOH at 60 °C, 30% H2O2 at 50 °C, and 15% H2O2 + 5% HNO3 at 40 °C; incubation time ≈ 12 h). Analysis of surface area variations returns results about the vulnerability of plastic polymers to digestive solutions that are aligned with most of the acquired knowledge. The largest decrease in surface area occurs for KOH-treated PET particles, while NY results in the most susceptible polymer to the 30% H2O2 treatment, followed by PVC and PS. PE and PP are the most resistant polymers to all the used treatments. The adopted methods to characterize microplastics seem reliable tools for detecting small differences in the shape and size of these particles. Then, the analytic perspectives that can be developed using such widely accessible and low-cost equipment are discussed.
More Papers Like This
2D imaging tools for harmonisation in plastic pollution data
AI summary Read the abstract
Researchers assessed four methodologies for counting and categorising visible meso- and microplastics — hand counts, ImageJ, the Saturna Imaging System, and an image segmentation model from The Ocean Cleanup — applying them to plastics collected from coastlines, marine surface waters, and riverbanks. They found that 2D imaging tools can improve data harmonization across studies but vary in performance depending on particle type and sampling environment.
2D imaging tools for harmonisation in plastic pollution data
AI summary Read the abstract
Researchers evaluated four 2D imaging and image segmentation methodologies for measuring the morphological characteristics -- size, shape, and colour -- of mesoplastics and microplastics, aiming to harmonize physical characterization data across studies conducted in different marine environments.
Methods for sampling, processing, identification,and quantification of microplastics in the marine environment
AI summary Read the abstract
This paper reviews and compares the various methods used to collect, process, identify, and quantify microplastics across different environmental samples. It highlights the lack of standardized protocols as a major obstacle to comparing results across studies and calls for methodological harmonization.
Leveraging AI tools for microplastic data quality assessment
AI summary Read the abstract
Researchers explored how AI tools can improve data quality assessment in microplastic studies, which vary widely in methodological rigor. The approach aims to standardize quality evaluation so that human health risk assessments based on microplastic research are more reliable.
A Review of Analytical Methods Used in Microplastics Quantification
AI summary Read the abstract
This review evaluates the various analytical methods used to detect and quantify microplastics in the environment, highlighting inconsistencies in sampling and analysis across studies. Standardizing methods is a critical priority for the field, as inconsistent approaches make it difficult to compare results and track pollution trends over time.
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.