Search

Microplastics (MPs) are emerging contaminants in aquatic ecosystems and have caused substantial concern worldwide.

Globally, fluxes of microplastics to marine environments are thought to be dominated by stormflow from urban environments, which may be moderated by storage in estuaries. Fluvial transport of microplastics is primarily a supply-limited phenomenon, but flow field and particle characteristics can result in a wide range of transport modes, from surface load to bedload, with potential ramifications for estuarine transport and fate. Here we report preliminary findings from microplastic monitoring campaigns conducted in a number of streams draining urban watersheds in Southern California, and estuarine wetland and benthic sediment deposits. These studies will serve as the basis for microplastic flux, accretion, and composition evaluation, and inform the optimization of microplastic monitoring in urban systems.

FTIR spectral identification is today's gold standard analytical procedure for plastic pollution material characterization. High-throughput FTIR techniques have been advanced for small microplastics (10 um - 500 um) but less so for large microplastics (500 um - 5 mm) and macroplastics (> 5 mm). These larger plastics are typically analyzed using ATR, which is highly manual and can sometimes destroy particles of interest. Furthermore, spectral libraries are often inadequate due to the limited variety of reference materials and spectral collection modes. We advance a new high-throughput technique to remedy these problems. FTIR plate readers are high throughput devices for measuring large particles (> 500 um). We created a new reference database of over 6000 spectra for transmission, ATR, and reflection spectral collection modes with over 600 plastic, organic, and mineral reference materials relevant to plastic pollution research. We also streamline analysis in plate readers by creating a new particle holder for transmission measurements using off-the-shelf parts and fabricating a non-plastic 96-well plate for storing particles. We validated the new database using Open Specy and demonstrated that transmission and reflection spectra reference data are needed in spectral libraries.

Fourier transform infrared (FTIR) and Raman microspectroscopy are methods applied in microplastics research to determine the chemical identity of microplastics. These techniques enable quantification of microplastic particles across various matrices. Previous work has highlighted the benefits and limitations of each method and found these to be complimentary. Within this work, metadata collected within an interlaboratory method validation study was used to determine which variables most influenced successful chemical identification of un-weathered microplastics in simulated drinking water samples using FTIR and Raman microspectroscopy. No variables tested had a strong correlation with the accuracy of chemical identification (r = ≤0.63). The variables most correlated with accuracy differed between the two methods, and include both physical characteristics of particles (color, morphology, size, polymer type), and instrumental parameters (spectral collection mode, spectral range). Based on these results, we provide technical recommendations to improve capabilities of both methods for measuring microplastics in drinking water and highlight priorities for further research. For FTIR microspectroscopy, recommendations include considering the type of particle in question to inform sample presentation and spectral collection mode for sample analysis. Instrumental parameters should be adjusted for certain particle types when using Raman microspectroscopy. For both instruments, the study highlighted the need for harmonization of spectral reference libraries among research groups, including the use of libraries containing reference materials of both weathered plastic and natural materials that are commonly found in environmental samples.

Stormwater treatment systems remove and accumulate microplastics from surface runoff, but some of them can be moved downward to groundwater by natural freeze-thaw cycles. Yet, it is unclear whether or how microplastic properties such as density could affect the extent to which freeze-thaw cycles would move microplastics in the subsurface. To examine the transport and redistribution of microplastics in the subsurface by freeze-thaw cycles, three types of microplastics, with density smaller than (polypropylene or PP), similar to (polystyrene or PS), or greater than (polyethylene terephthalate or PET) water, were first deposited on the top of packed sand-the most common filter media used in infiltration-based stormwater treatment systems. Then the columns were subjected to either 23 h of drying at 22 ⁰C (control) or freeze-thaw treatment (freezing at -20 ⁰C for 6 h and thawing at 22 ⁰C for 17 h) followed by a wetting event. The cycle was repeated 36 times, and the effluents were analyzed for microplastics. Microplastics were observed in effluents from the columns that were contaminated with PET and subjected to freeze-thaw cycles. Comparison of the distribution of microplastics in sand columns at the end of 36 cycles confirmed that freeze-thaw cycles could disproportionally accelerate the downward mobility of denser microplastics. Using a force balance model, we show that smaller microplastics (50 µm) can be pushed at higher velocity by the ice-water interface, irrespective of the density of microplastics. However, plastic density becomes critical when the size of microplastics is larger than 50 µm. The coupled experimental studies and theoretical framework improved the understanding of why denser microplastics such as PET and PVC may move deeper into the subsurface in the stormwater treatment systems and consequently elevate groundwater pollution risk.

Children spend many hours in urban parks and playgrounds, where the tree canopy could filter microplastics released from the surrounding urban hotspots. However, the majority of children's playgrounds also contain plastic structures that could potentially release microplastics in situ. Yet, no study to date has quantified microplastic concentrations in different parts of the park to assess if the children's playgrounds pose a higher exposure risk than other places inside the park. We evaluated the extent of microplastic contamination in the sand, soil, and leaf samples from 19 playgrounds inside urban parks in Los Angeles, CA, USA. The average microplastic concentration in sand samples collected inside the playground was 72 p g

Microplastics are of major concerns for society and is currently in the focus of legislators and administrations. A small number of measures to reduce or remove primary sources of microplastics to the environment are currently coming into effect. At the moment, they have not yet tackled important topics such as food safety. However, recent developments such as the 2018 bill in California are requesting the analysis of microplastics in drinking water by standardized operational protocols. Administrations and analytical labs are facing an emerging field of methods for sampling, extraction, and analysis of microplastics, which complicate the establishment of standardized operational protocols. In this review, the state of the currently applied identification and quantification tools for microplastics are evaluated providing a harmonized guideline for future standardized operational protocols to cover these types of bills. The main focus is on the naked eye detection, general optical microscopy, the application of dye staining, flow cytometry, Fourier transform infrared spectroscopy (FT-Ir) and microscopy, Raman spectroscopy and microscopy, thermal degradation by pyrolysis–gas chromatography–mass spectrometry (py-GC-MS) as well as thermo-extraction and desorption gas chromatography–mass spectrometry (TED-GC-MS). Additional techniques are highlighted as well as the combined application of the analytical techniques suggested. An outlook is given on the emerging aspect of nanoplastic analysis. In all cases, the methods were screened for limitations, field work abilities and, if possible, estimated costs and summarized into a recommendation for a workflow covering the demands of society, legislation, and administration in cost efficient but still detailed manner.

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

Earth science reviews 228, 104021 (2022). doi:10.1016/j.earscirev.2022.104021, Published by Elsevier, Amsterdam [u.a.]

This report summarizes the group discussions and priorities on microplastics data management that emerged from a virtual workshop hosted by the University of Waterloo���s Water Institute, the Federated Research Data Repository (FRDR), and the Gordon Foundation., The workshop is funded by Compute Ontario. Additional support was provided by the NSERC/ECCC Alliance Grants - Plastics science for a cleaner future program, the Global Water Futures Program, University of Waterloo Library, Federated Research Data Repository and the Gordon Foundation

Microplastic pollution research has suffered from inadequate data and tools for spectral (Raman and infrared) classification. Spectral matching tools often are not accurate for microplastics identification and are cost-prohibitive. Lack of accuracy stems from the diversity of microplastic pollutants, which are not represented in spectral libraries. Here, we propose a viable software solution: Open Specy. Open Specy is on the web (www.openspecy.org) and in an R package. Open Specy is free and allows users to view, process, identify, and share their spectra to a community library. Users can upload and process their spectra using smoothing (Savitzky-Golay filter) and polynomial baseline correction techniques (IModPolyFit). The processed spectrum can be downloaded to be used in other applications or identified using an onboard reference library and correlation-based matching criteria. Open Specy's data sharing and session log features ensure reproducible results. Open Specy houses a growing library of reference spectra, which increasingly represents the diversity of microplastics as a contaminant suite. We compared the functionality and accuracy of Open Specy for microplastic identification to commonly used spectral analysis software. We found that Open Specy was the only open source software and the only software with a community library, and Open Specy had comparable accuracy to popular software (OMNIC Picta and KnowItAll). Future developments will enhance spectral identification accuracy as the reference library and functionality grows through community-contributed spectra and community-developed code. Open Specy can also be used for applications beyond microplastic analysis. Open Specy's source code is open source (CC-BY-4.0, attribution only) (https://github.com/wincowgerDEV/OpenSpecy).

Volunteer cleanup operations collect large datasets on anthropogenic litter that are seldom analyzed. Here we assess the influence of land use in both near-stream and watershed scale source domains on anthropogenic litter concentration (standing stock, kg km−1) in riparian zones of Iowa, USA. We utilized riparian litter concentration data on four classes of anthropogenic litter (metal, recyclable, garbage, and tires) from volunteer cleanup operations. Anthropogenic litter data were tested for correlation with near-stream and watershed scale land uses (developed, road density, agricultural, and open lands). Road density (road length/area) and developed land use (% area) were significantly correlated to anthropogenic litter, but agricultural (% area) and open lands (% area) were not. Metal objects correlated to near-stream road density (r = 0.79, p = 0.02), while garbage and recyclable materials correlated to watershed scale road density (r = 0.69, p = 0.06 and r = 0.71, p = 0.05 respectively). These differences in the important spatial scales of land use may be related to differences in transport characteristics of anthropogenic litter. Larger, denser metal objects may be transported more slowly through the watershed/channelized system and thus, dependent on more proximal sources, whereas smaller, less dense garbage and recyclable material are likely transported more rapidly, resulting in concentrations that depend more on watershed scale supply. We developed a linear regression model that used near-stream road density and the total amount of observed litter to predict an average anthropogenic litter density of 188 kg km−1 and a standing stock of 946 t in all Iowa streams (>4th Strahler order). The techniques employed in this study can be applied to other professional and volunteer litter datasets to develop prevention and cleanup efforts, inform investigations of process, and assess management actions.

River flow is a major conveyance of microplastic (1-5000 μm) pollution from land to marine systems. However, the current approaches to monitoring and modeling fluvial transport of microplastic pollution have primarily relied on sampling the surface of flow and assumptions about microplastic concentration depth profiles to estimate the depth-averaged concentration. The Rouse profile was adapted to show that fluvial transport of microplastic pollution includes all traditional domains of transport (bed load, settling suspended load, and wash load), as well as additional domains specific to low-density materials with rising velocities in water (rising suspended load and surface load). The modified Rouse profile was applied to describe the positively buoyant particle concentration depth profiles and compared to field observations to showcase the utility of this approach. A procedure was developed for assessing the uncertainty and bias from using a surface sample to estimate the depth-averaged concentration while assuming either surface load or wash load concentration depth profiles. Both assumptions may introduce a large amount of uncertainty due to the range of suspended microplastic concentration depth profiles. Monitoring microplastic pollution and estimating the depth-averaged concentration of microplastics in fluvial systems would further benefit from broader adoption of depth-integrated sampling, characterization of particle concentration depth profiles, and estimation of uncertainties in depth-averaged concentration based on the sampling approach.

Re-use restricted to non-commercial and no derivative uses. The ubiquitous pollution of the environment with microplastics - a diverse suite of contaminants - is of growing concern for science and currently receives considerable public, political, and academic attention. The potential impact of microplastics in the environment has prompted a great deal of research in recent years. Many diverse methods have been developed to answer different questions about microplastic pollution, from sources, transport, and fate in the environment, and about effects on humans and wildlife. These methods are often insufficiently described, making studies neither comparable nor reproducible. The proliferation of new microplastic investigations and cross-study syntheses to answer larger scale questions are hampered. We - a diverse group of 23 researchers - think these issues can begin to be overcome through the adoption of a set of reporting guidelines. This collaboration was created using an open science framework that we detail for future use. Here, we suggest harmonized reporting guidelines for microplastic studies in environmental and laboratory settings through all steps of a typical study, including best practices for reporting materials, quality assurance / quality control, data, field sampling, sample preparation, microplastic identification, microplastic categorization, microplastic quantification, and considerations for toxicology studies. We developed three easy to use documents - a detailed document, a checklist, and a mind map - that can be used to quickly reference the reporting guidelines. It is our intention that these reporting guidelines support the annotation, dissemination, interpretation, reviewing, and synthesis of microplastic research. Through open access licensing (CC BY 4.0), these documents aim to increase the validity, reproducibility, and comparability of studies in this field for the benefit of the global community.

Microplastic is exposed to numerous weathering processes in the environment, which change particle properties and thus influence transport behaviors, including settling and rising velocities in aquatic environments. However, the extent to which particles in the environment differ from virgin particles in their transport behaviors has not yet been investigated. The settling and rising velocities of weathered fluvial microplastic and macroplastic particles collected from environmental samples are determined in this study and the transferability of theoretical approaches to predicting their transport behaviors is examined. The settling velocities of the environmental particles were between 0.16 and 3.52 cm/s and the rising velocities between 0.18 and 19.85 cm/s. Formulas were applied that were developed using experiments with virgin microplastic, but do not account for the effects of environmental impacts such as degradation, fragmentation and biofouling on the velocities. Accordingly, the transferability of the formulas to environmental particles must be verified. The formulas proved to be suitable for describing the settling and rising velocities of environmental microplastic particles in the shapes of pellets, fragments and foams, and were less suitable for describing the velocities of films and macroplastic particles. Further experiments will be necessary in the future to integrate effects from biofilm and particle deformation on the transport behaviors to adequately model the behavior of the highly diverse micro- and macroplastic particles in the aquatic environment.

Urban areas are the primary source of human-made litter globally, and roadsides are a primary accumulation location. This study aimed to investigate how litter arrives at roadsides and determine the accumulation rate and composition of roadside litter. We monitored select roadsides in the Inland Empire, California, for litter abundance (count) and composition (material, item, and brand type). Receipt litter with sale time and location information was used to investigate whether wind, runoff, or human travel were dominant transport agents. Only 9% of the receipts could have experienced runoff, and wind direction was not correlated with receipt transport direction. However, human travel and receipt transport distances were similar in magnitude and distribution, suggesting that the displacement of litter from the place of purchase was predominantly affected by human travel. The median distance receipts traveled from the sale location to the litter observation location was 1.6 km, suggesting that most sources were nearby to where the litter was found. Litter accumulation rates were surprisingly stable (mean 40 349 (33 255–47 865) # km−1yr−1or 1170 (917–1447) kg km−1yr−1) despite repeated cleanups and the COVID-19 stay-at-home orders. A new approach was employed to hierarchically bootstrap litter composition proportions and estimate uncertainties. The most abundant materials were plastic and paper. Food-related items and tobacco products were the most common item types. The identified branded objects were from the primary manufacturers (Philip Morris (4, 2%–7%), Mars Incorporated (2, 1%–3%), RJ Reynolds (2, 1%–3%), and Jack in The Box (1, 1%–3%)), but unbranded objects were prevalent. Therefore, identifiable persistent labeling on all products would benefit future litter-related corporate social responsibility efforts. High-resolution monitoring on roadsides can inform urban litter prevention strategies by elucidating litter source, transport, and accumulation dynamics.

In the recent years, human litter, such as food waste, diapers, construction materials, used motor oil, hypodermic needles, etc, is causing growing problems for the environment and quality of life in modern cities. Data about this waste has a significant importance in the field of environmental sciences due to its important use cases that span saving marine life, reducing the risk from natural hazards, community cleaning efforts, etc. In addition, such litter spreads several diseases in urban areas with high populations such as undeveloped neighborhoods in large modern cities. In this paper, we introduce a data-driven approach that enables environmental scientists and organizations to track, manage, and model human litter data at a large scale through smart technologies. We make a major on-going effort to collect and maintain this data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of collected datasets, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, we provide a scalable data management and visualization framework that digests datasets from different sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form. On top of this backend, frontend applications are built to visualize litter data at multiple spatial levels, from continents and oceans to street level, to enable new opportunities for both environmental scientists and organizations to track, model, and clean up litter data. The framework is currently managing thirty real datasets and provide different interfaces for different kinds of users.