Here are a few use cases for this project:

1. Ocean cleanup efforts: Utilize the "Microplastic Dataset" computer vision model to identify and locate microplastic pollution in ocean water samples, allowing for targeted cleanup efforts and better understanding of microplastic distribution in marine environments.

2. Recycling facility improvements: Integrate the model into recycling facilities to identify and sort microplastic residues in materials, ensuring proper disposal or treatment to prevent their release into the environment.

3. Microplastic research: Aid researchers in studying the impact of microplastics on ecosystems and human health by automating the detection and analysis of microplastics in various samples, such as water, soil, or air.

4. Supply chain monitoring: Help industries monitor and evaluate their supply chain processes to identify and reduce microplastic contamination in their products or packaging materials, promoting greener manufacturing practices.

5. Consumer education and awareness: Develop a mobile app that uses the "Microplastic Dataset" model to enable users to identify potential microplastic contamination in consumer products such as cosmetics or food packaging, encouraging more informed purchasing decisions and raising public awareness on the issue of microplastic pollution.

This FTIR hyperspectral image shows an environmental plankton sample which has been spiked with microplastics in the size range between 10 and 200 µm. The added particles are one of the following polymer types:

polyethylene

polypropylene

polystyrene

poly(methyl methacrylate)

polyacrylonitrile

This hyperspectral image has been used as a source for training data for the creation of random decision forest classifiers. For a closer description of the dataset and the sample preparation see Hufnagl et al. (2019).

If you reuse this dataset please cite

Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers, Analytical Methods, 2019, DOI:10.1039/C9AY00252A

this dataset consists of a quantitative imaging dataset of zooscan-imaged mesoplankton and microplastics, collected with the manta net (333 µm mesh size) aboard the schooner tara during the tara pacific expedition (2016-2018). samples were collected at the surface across the pacific oceans from open-ocean stations (32 samples, open-ocean label: [i00_oa###]), with 28 samples located on the great pacific garbage patch, and from stations in coastal waters of pacific islands (44 samples; island label: [i##_oa###]). the full description and discussion of this dataset can be found in the associated data paper mériguet et al. (in rev). this dataset consists of 137 497 plankton individuals, plankton parts, non-living particles, microplastics and imaging artefacts, ranging from 200 µm to a few mm, individually imaged and measured with the zooscan (gorsky et al., 2010). the objects were classified into 173 taxonomic and morphological groups. all images and their taxonomic annotations are available in the open-access ecotaxa (picheral et al., 2017) project at these links: for plankton images https://ecotaxa.obs-vlfr.fr/prj/1344, and for plastics images https://ecotaxa.obs-vlfr.fr/prj/1345. the 'ecotaxa zooscan tara pacific manta 333 microns export plankton/plastic' dataset contains the ecotaxa tsv exports which associate each object with these metadata (station name, sampling coordinates, sampling date and time, etc., the main metadata are found in the classic metadata zooscan tara pacific manta 333 microns.csv table) and describe it by numerous morphological features extracted from each individual object by zooprocess. the csv table named 'export ecotaxa zooscan read me.csv' defines the 160 variables found in the ecotaxa tsv export. the 'descriptors zooscan tara pacific manta 333 microns.csv' table combined the data from which we calculated quantitative descriptors of the planktonic communities: abundance (ind.m-3), biovolume (mm3.m-3; as a proxy of biomass) calculated from the area, riddled area and ellipsoidal measurement of each object (see vandromme et al., 2012 for the 3 calculations of biovolume), and shannon diversity index. this was done for all taxonomic annotations and for several levels of grouping; living or non-living, plankton groups and trophic association. the individual biovolumes of organisms were arranged in normalised biomass size spectra (nbss) as described by platt (1978), with size expressed as equivalent spherical diameter (esd, µm). nbss calculations were made for all taxonomic annotations and for the different levels of grouping. there are available for each station in the nbss zooscan tara pacific manta 333microns csv tables.

Microplastic Project

## Overview

Microplastic Project is a dataset for object detection tasks - it contains Objects annotations for 754 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Microplastic Detection

## Overview

Microplastic Detection is a dataset for object detection tasks - it contains Microplastic annotations for 9,556 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

This dataset supplements the research article "Know What You Don’t Know: Assessment of Overlooked Microplastic Particles in FTIR Images", published 04 07 2022 in microplastics (DOI 10.3390/microplastics1030027). It comprises:

This dataset contains 2 csv files. One file contains data on microplastic abundance expressed in number of particles from 12 transects in Port Vila Bay and Mele Bay in Efate Island (Vanuatu) in 2018 with indication of volume and surface surveyed and transect info (duration, coordinates, comments). The file also contains information on blank used and contamination checks. The other file contains spectrum analysis of particles carried out with a ATR-FTIR. A READ-ME text file contains the description of columns content for the two csv files and a brief description of particles that have been analysed with the FTIR and photographed . A .zip file includes two folders with pictures of microplastics (particles with size < 5 mm, Arthur et al., 2008) and macrolitter in diameter (particles with size > 5 mm). The Commonwealth Litter Project (CLiP) supported Solomon Islands to take action on plastics entering the oceans. The assessment of microplastics in seawater was part of the action plan to define scientific baselines for future monitoring purposes and comparison. Samples were acquired towing a manta trawl from a 7.5 m vessel for 30 mins at less than 3knots. The trawl had a mouth of 60x18cm and a net mesh size of 335micron. The amount of water filtered was measured by a General Oceanics mechanical flowmeter (one-way clutch). The sample was then rinsed in a jar through a 315 micron sieve and frozen at -18 degrees centigrade. High concentrations of organic matter were found in Mele Bay (coral spawning event). In the lab, visible plastic particles >5mm were removed and analysed with a ATR-FT-IR to identify particle composition comparing their spectrum to a polymers library. The rest of the sample was chemically digested with 30% KOH:NaClO solution with an incubation of 1 day at 40 degrees centigrade. Samples were then filtered, stained with Red Nile dye and a digital image was acquired through a microscope. Number of particles was counted and corrected for blank samples. A subsample of particles (between 1 and 10%) was processed through a ATR-FT-IR. In case of samples with high organic content, a sieving stage through a 5mm mesh wad added at the beginning of the process. ATR-FT-IR is the attenuated total reflection Fourier Transform infrared spectroscopy. A Thermo Fisher Scientific Nicolet iS5 ATR-FTIR with an OMNIC software (version 9.9.473) was used and polymers were identified based on the percentage match of IR spectra to a polymer library. Only spectra matched greater than 70 percent were accepted Spectra were collected in the range 4000 – 650 1/cm at a resolution of 4 1/cm.

Image: https://nanopartikel.info/en/basics/cross-cutting/nanoplastic-in-the-environment/

"This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

Source: Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning

https://catalog.data.gov/dataset/microplastic-and-nanoplastic-chemical-characterization-by-thermal-desorption-and-pyrolysis/resource/e3aca1a5-6fc6-4e31-8d87-ae4c065f8068

Microplastic Detection On Salt

## Overview

Microplastic Detection On Salt is a dataset for object detection tasks - it contains Microplastics annotations for 1,046 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Microplastic Detection Market Outlook

The global microplastic detection market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% during the forecast period. This robust growth is primarily driven by heightened environmental awareness and stringent regulations on plastic pollution globally. As public and governmental concern over microplastic pollution increases, there is a surge in demand for effective detection technologies and strategies that can be applied across various industries, including water treatment, food and beverage testing, and environmental monitoring.

One of the key growth factors for this market is the increasing global emphasis on environmental conservation and sustainability. Governments around the world have implemented strict regulations to curb plastic waste, which in turn is driving the demand for advanced microplastic detection technologies. Consumers are also becoming more aware of the impact of plastic pollution on marine and terrestrial ecosystems, leading to increased pressure on industries to adopt sustainable practices. The integration of microplastic detection solutions in industrial processes is becoming a necessity to comply with these regulations and to maintain a positive brand image.

Technological advancements in detection methodologies also significantly contribute to market growth. Innovations in spectroscopy, microscopy, and chromatography have enhanced the sensitivity and accuracy of microplastic detection. These advanced technologies allow for the identification of even the smallest particles, providing comprehensive data that can be used to address pollution sources effectively. The development of portable and on-site detection devices is another trend that is facilitating market expansion by enabling real-time analysis and monitoring of microplastic content in various environments.

The increasing incidences of microplastic contamination in food and water sources have raised serious health concerns, thereby driving market growth. Studies revealing the presence of microplastics in drinking water and seafood have led to a surge in demand for testing and monitoring solutions. The food and beverage industry, in particular, is focusing on implementing rigorous testing procedures to ensure product safety and maintain consumer trust. This trend is positively impacting the microplastic detection market, as it underscores the need for reliable and efficient detection methods.

Regionally, North America and Europe are expected to dominate the microplastic detection market, given their stringent environmental regulations and advanced technological infrastructure. However, the Asia Pacific region is anticipated to witness the fastest growth due to increasing industrialization, urbanization, and efforts to address severe plastic pollution issues. Countries like China and India are ramping up initiatives to combat plastic waste, driving the demand for microplastic detection technologies across various sectors.

Technology Analysis

In the microplastic detection market, technology plays a pivotal role in determining the effectiveness and efficiency of detection processes. Spectroscopy is one of the central technologies used in this domain. It leverages the interaction of light with microplastic particles to identify and quantify their presence in samples. Technologies such as Raman and Fourier-transform infrared (FTIR) spectroscopy offer high accuracy and are widely used in environmental and industrial applications. These methods are particularly valued for their ability to identify the chemical composition of microplastic particles, providing crucial data for environmental assessment and policy-making.

Microscopy is another essential technology in the microplastic detection market, offering detailed visual representation of microplastic particles. Techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) enable high-resolution imaging and surface analysis of microplastics, which is critical for understanding their morphology and potential environmental impact. This technology is predominantly used in research laboratories and industrial sectors where detailed analysis of microplastic characteristics is required. The increasing demand for precise analytical methods is driving advancements in microscopy technologies, enhancing their application scope.

Chromatography, particularly gas chromatography-mass spectrometry (GC-MS), is widely employed in the detect

The goal of this research was to test the effectiveness of a new science curriculum focused on increasing awareness and knowledge of microplastic pollution in middle school students. The curriculum utilized three teaching methods including lectures, object-mediated learning, and peer collaboration to enhance student engagement and learning. The 84 participants were 8th-grade students between the ages of 13 and 15. Lesson effectiveness was evaluated using pre-and post-assessment questionnaires to compare student knowledge and awareness of microplastic pollution before and after the lesson. In the pretest, 90% of student definitions of microplastics were descriptive referring to microplastics as “small” or “tiny” pieces of plastic. The remaining 10% stated they had no previous knowledge of microplastics. In the post-assessment, 100% of students provided a definition, with 23% of them identifying microplastics with the scientifically defined size (< 5 mm). The number of student definitions that also contained one or more elements of microplastic pollution, i.e. impacts and/or sources of origin increased between the pre- and post-assessment. Of the student posters, 81% demonstrated plastic pollution in an aquatic environment. A total of 9 posters (n = 16) contained explicit text and/or images on microplastic pollution with 5 highlighting harmful ingestion of microplastics by marine life. Student ratings of their enjoyment and learning experience from taking part in the lesson were high, with most students rating 4 or 5 on a 1 (no enjoyment or knowledge learned) to 5 (enjoyable/learned something new) scale. The results indicate that this curriculum was successful in increasing student knowledge and awareness of microplastic pollution.

Behavioural dataset from the paper " Pasquini et al. Microplastics reach the brain and interfere with honey bee cognition were the effects of polystyrene (PS), plexiglass (PMMA) MPs, and a combination of the two, at three evaluated concentrations (0.5, 5 and 50 mg/L-1) on sucrose responsiveness and appetitive olfactory learning and memory.

This dataset contains two CSV files. One CSV file reports locations and technical details for the cores, along with their slices, subsamples and the number of microplastic particles and fibres recorded in each 5g sample processed. Details of how the cores were sampled and processed can be found in the publication and its supporting information.

The second CSV file contains x-y data for the micro-ATR-IR analysis of representative particles picked from some of the filters, to provide some numerical data on polymer types and fractions of Nile Red positive particles that could be positively identified as plastics (positives), those that were biological in origin (false positives) and those that could not be confidently identified. Details of the procedure for this appear below. Images of the particles along with their IR spectra can be found in the ESI accompanying the publication.

This repository provides additional files for in the publication "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)" by Stefan Dittmar, Aki Sebastian Ruhl and Martin Jekel (DOI: 10.1021/acsestwater.3c00457)

It contains:

- image processing routine for particle tracking written in Python (1_particle_tracking_algorithm.zip)
- single particle raw data from settling experiments (2_settling_data.zip)
- single particle data from appyling empirical model for interactions between settling particles (3_model_results_data.zip)
- additional video & animated graph referenced in publication or SI (4_videos.zip)

This dataset provides number counts and measurements of horizontal dimensions of polyethylene, polypropylene, polystyrene particles detected in filtered seawater samples collected at three discrete depths in the Atlantic Ocean along a north-south passage of cruise AMT26/JR16001 (September-November 2016). The number data are presented as total number counts of polymer-specific plastic particles detected in each pre-selected area of a filtered seawater sample (IR image marker). The measured horizontal dimensions of the polymer-specific plastic particles include i) maximum Feret diameter defined as the longest distance between any two points along the particle boundary and ii) particle length measured as length of major axis of the best fitting ellipse. Plastic particles were detected and identified using Fourier-Transform infrared imaging technique with their particle count and horizontal dimensions measured subsequently FIJI Image J software. The data on polymer-specific plastic counts can be used to assess the spatial/vertical abundance and distribution of plastic litter in the study area. The measured horizontal dimensions (in essence, particle size) can be used to investigate particle size distribution of plastic contaminants in the study area. Sample collection and analytical work was performed by Katsiaryna Pabortsava (NOC Southampton, UK). Note that both datasets represent the analysed subset of samples collected for microplastics analysis during AMT26/JR16001. The remaining samples are stored at NOC Southampton awaiting resources for analysis.

The ubiquitous presence of microplastics and their marine ecotoxicity are major public concerns. Microplastics are ingested accidentally by the marine fauna or are taken up indirectly through the food chain. These particles can accumulate in cells and tissues and affect the normal biological functions of organisms, including their defense mechanisms. There is limited information available about the response of immune cells to microplastics; the degree of uptake by the cells, the response of different organs or the impact of environmental concentrations of microplastic are matters that remain unclear. Moreover, very little is known about the toxicity of different polymer types. This study aimed to shed light on the physical impact of small microplastics (1–5 μm) on cells from Atlantic salmon. Immune cells from intestine, blood, and head kidney were exposed to green fluorescent polyethylene microplastic (PE-MP), yellow fluorescent polystyrene microplastic (PS-MP) and both. High (50 mg/L), medium (5 mg/L), and low (0.05 mg/L) concentrations were tested for 1, 24, 48, and 72 h to study cell mortality and microplastic uptake. Quantitative data of microplastic uptake by fish immune cells were obtained for the first time by imaging flow cytometry. Salmon immune cells showed a relatively low ability to phagocytose microplastics. Less than 6% of the cells ingested the particles after 48 h of exposure to high concentrations. Cells also phagocytosed microplastics at low concentrations although at low rates (

Understanding the biological impacts of plastic pollution requires an effective methodology to detect unlabeled microplastics in environmental samples. Detecting unlabeled microplastics in an organism generally requires a digestion protocol, which results in the loss of spatial information on the distribution of microplastic within the organism and could lead to the disappearance of the smaller plastics. Fluorescence microscopy allows visualization of ingested microplastics but many labeling strategies are nonspecific and label biomass, thus limiting our ability to distinguish internalized plastics. While prelabeled plastics can be used to avoid nonspecific labeling, this approach precludes the detection of environmental microplastics in organisms. Also, using prelabeled microplastics can affect the viability of the organism and impact plastic uptake. Thus, a method was developed that employs nonspecific labeling with a tissue-clearing technique. Briefly, unlabeled microplastics are stained with a fluorescent dye after ingestion by the organism. The tissue-clearing technique then removes tissue-bound dye while rendering the structurally intact organism transparent. The internalized plastics remain stained and can be visualized in the cleared tissue with fluorescence microscopy. The technique is demonstrated using polystyrene beads in living aquatic organismsTigriopus californicusandDaphnia magnaand by spiking a model vertebrate (Cephalochordata) with different microplastics.

Background

The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141) published in Science of the Total Environment.

Description of the dataset

The zipped folder

• data.zip contains subfolders imagelab_data (contains microplastics data by depth and the blanks), additional_data (weights of aliquots and polymer densities) and soil_data (data on soil properties and images of soil profiles).
• images.zip contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd.
• results.zip contains the results of reading and wrangling of microplastics data.

Description of the code

The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd).

Disclaimer

The data and code are provided as is without any warranty.

Funding

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.

References

M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141

R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/

This dataset contains two csv files. The first one (South_Africa_Port_Durban_Microplastics.csv) reports the results of the study carried out in the Port of Durban in 2019. The file contains water measurement from CTD casts, microplastic abundance in water sampled by microplastic pump, microplastic abundance in sediment and particulate size analysis (PSA) results from Van Veen grab samples. GPS coordinates and time of deployment are reported for each measurement. CTD measured temperature, salinity and turbulence of the water. For microplastic pump casts, the amount of water filtered, the size of the four sieves used and the number of particles found on each filter are reported. Data from the grabs include PSA results and the number of particles found in the replicates (5g each) from each grab with lab blank values. A series of atmospheric blanks was also obtained leaving a jar open during sampling operation and microplastics abundances are reported. The csv second file (South_Africa_Port_Durban_Microplastics_FTIR) contains the profiles of the ATR-FT-IR spectrum analysis of plastic pieces found in the water samples. A .zip folder contains additional 18 FTIR profiles from water samples for which only a .tif image is available. A README text file contains the legend of the columns of the two csv files. The Commonwealth Litter Project (CLiP) supported South Africa to take action on plastics entering the oceans. The abundance of microplastics was investigated within The Port of Durban in the Durban Harbour (east coast of South Africa). A handheld CTD multi-channel logger (RBRconcerto3 C.T.D++, RBR Ltd., Canada), with attached optical backscatter turbidity (STM, Seapoint Sensors Inc, USA), was used to measure temperature, salinity and turbidity. Microplastics in water were sampled using a microplastic pump (KC Denmark Plankton Pump for Microplastics, Model 23.580) deployed through a crane from the quayside. The pump filtered 2000lt of water several sieves: 5mm, 500µm, 300µm, 200µm and 100µm . Sediment samples were collected at each microplastic pump site for sediment particle size analysis (PSA) and sediment microplastic analysis. Also, 15 additional sediment samples were taken from a boat on channels and areas of deposition. Five additional samples were taken in the harbour and surrounds where substrate was suitable. Each sediment and water sample was transferred to glass collecting pots. The sediment samples were dried at 50 degrees Celsius and, once dried, 5g triplicates were taken from a homogenised sample and underwent density separation before being chemically digested with a 30 percent KOH:NaClO solution. Each sample was then incubated for 72 hours before filtration Identification of the extracted microplastics was carried out using the fluorescence tagging of polymers using Nile Red coupled with digital imaging (Maes et al., 2017). For each sediment sample, a PSA was carried-out to relate abundance of microplastics to sediment type. Samples were freezed and then underwent PSA, based on a modified NMBAQC protocol from Mason (2011) for fast PSA screening based on wet splitting into silt/clay (< 63 µm), sand (63 µm – 4 mm) and gravel (> 4 mm) fractions. Once dry, samples were weighed, and the proportion of each fraction was calculated. Surface water samples were inspected for any suspected anthropogenic particles. Mesoplastics were manually removed, dried and characterised with ATR-FTIR. Samples with low to no organic content were filtered and stained with Nile Red before imaging and particle counting (Maes et al., 2017). For samples with high organic content, sieve rinse water was digested with a 30% KOH:NaClO solution with 24 hours incubation. Visible particles were analysed using ATR-FT-IR to identify polymer composition, comparing their spectrum to a polymers library. ATR-FTIR is the attenuated total reflection Fourier Transform infrared spectroscopy. A Thermo Fisher Scientific Nicolet iS5 ATR-FTIR with an OMNIC software (version 9.9.473) was used and polymers were identified based on the percentage match of IR spectra to a polymer library. Only spectra matched greater than 70 percent were accepted. Spectra were collected in the range 4000 – 650 1/cm at a resolution of 4 1/cm.

This repository provides additional files for the publication "Settling Velocities of Small Microplastic Fragments and Fibers" by Stefan Dittmar, Aki Sebastian Ruhl, Korinna Altmann and Martin Jekel (DOI: 10.1021/acs.est.3c09602).

It contains:

- single particle raw data from settling experiments (1_settling_data.zip)
- drag coefficients computed for each measured MP fragment and fiber (2_drag_coefficients.zip)
- guide (.pdf) for preparing microfibers following the protocol by M. Cole (2016, DOI: 10.1038/srep34519) and length measurements of produced fiber fractions (3_cryosectioning.zip)

Please consider the included readme files ('0_README_settling_data.txt', '0_README_drag_coefficients.txt', '0_README_length_measurements.txt') and revisit main publication and supplement information for further context on this data set.