The NOAA NCEI global Marine Microplastics product provides access to aggregated, comprehensive, standardized, quality controlled, global data on microplastics in marine settings, from 1972-Present. This archived database contains information on microplastics concentrations (reported in particles/m³), date of collection, latitude and longitude where data was collected, sampling instruments used for collection, and links to original publications of the data. The information in this database can be used to improve water quality and protect the ecosystem, especially coastal ecological habitats such as salt marshes and mangrove forests that help recycle nutrients, serve as breeding grounds for fingerlings, and permanent homes for oysters and other coastal marine wildlife.The metadata record for the Marine Microplastics data is accessible through these links: Metadata(html) ; Metadata (xml)

This dataset contains the version 1.0 CYGNSS level 3 ocean microplastic concentration data record, which provides 18 netCDF files, each containing one month of daily gridded maps of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatiotemporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed).

The CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan.CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.This dataset contains the version 3.2 CYGNSS Level 3 ocean microplastic concentration data record, which provides daily netCDF files, each file containing a gridded map of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatial temporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed). Version 3.2 uses CYGNSS MSS measurements that are derived from updated v3.2 Level 1 scattering cross section data and has updated the parameterizations in the data processing algorithm to use v3.2 data correctly.

Due to high spatiotemporal variability of aquatic systems, relationships between microplastic sources and sinks are highly complex and transportation pathways yet to be understood. Field data acquisitions are a necessary component for monitoring of microplastic contamination but alone cannot capture such complex relationships. Remote sensing is a key technology for environmental monitoring through which extrapolation of spatially limited field data to larger areas can be obtained. In this field study we tested whether microplastic distribution follows the same transport pattern as water constituents depictable from satellite images, namely chlorophyll-a, suspended particulate matter, and colored dissolved organic matter, and discuss their applicability as proxies. As rivers are a major source for marine microplastic contamination, we sampled three example river systems: the lower courses and river mouths of the Trave and Elbe estuary in Germany and the Po delta in Italy. For a full quantitative analysis of microplastics (>300 μm), ATR- and FPA-based μFT-IR spectroscopy and NIR imaging spectroscopy were utilized. Comparing water constituents with in-situ data using regression analysis, neither a relationship for the Elbe estuary nor for the Po delta was found. Only for the Trave river, a positive relationship between microplastics and water constituents was present. Differences in hydrodynamic conditions and spatiotemporal dynamics of water constituents and microplastic emissions among the river systems are possible explanations for the contrary results. Based on our results no conclusions on other river systems and likewise different seasons can be drawn. For remote sensing algorithms of water constituents to be used as microplastic proxy an adaption for each system as well as for different seasons would thus be necessary. The lower detection limit of 300 μm for microplastics could also have influenced relationships as microplastic abundance exponentially increases with decreasing size class. Further studies with improved sampling methods are necessary to assess our proposed method.

Microplastic pollution has emerged as an undeniable marine environmental issue. While a distribution map of microplastics in the upper ocean has been established, the patterns of microplastics within the water column remain unclear. In this study, a large-volume in situ filtration device with filtration efficiency of 30 m3/h was employed to investigate microplastics in the deep waters of the South China Sea. The abundance of microplastics ranged from 0.2 to 1.5 items per cubic meter (n/m3), with an average of 0.56 ± 0.40 n/m3. Microplastics are primarily fragments (72.58%) and fibers (20.97%), with the predominant polymer types being polypropylene (PP) and polyethylene terephthalate (PET). The average size of microplastics is 0.91 ± 0.97 mm, with no statistically significant differences observed across different water layers from 50 to 1000 meter (m). Non-metric Multidimensional Scaling (NMDS) analysis indicated that microplastics in the water column primarily originated from surface waters in the studied region. The occurrence of microplastics in the marine water column is a complex environmental process, influenced by a range of oceanographic mechanisms, including biological, chemical, and physical interactions. Our results provided reliable baseline data on microplastics in the water column of the South China Sea, contributing a better understanding to the vertical transport and fate of microplastics in this region.

This work was carried out for the Danish Environment Protection Agency (EPA) targeting microplastics (MP) in Danish marine sediments. It addresses the geographical distribution of MP in sediments from 23 stations, among which, 19 were analysed in 2022 (https://www.emodnet-ingestion.eu/submissions/submissions_details.php?menu=39&tpd=1185&step=001Denmark), and four were analysed in 2023. One of these four samples were collected from Skagerrak, and three were from the North Sea. It attempts to map the level and geographical distribution of MP in sediments from the North Sea and Skagerrak. The monitoring should furthermore create a baseline for future efforts to assess temporal changes of MP in Danish marine sediments.

The categorization of the source regions is from Lebreton et al’s supplementary material. The values for the ISA and PD scenarios are indicated in the left and right sub-columns of each column, respectively. See the Methods section for the details.