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With the rapid expansion of microplastic research and reliance on semantic descriptors, there is an increasing need for plastic pollution data harmonization. Data standards have been developed but are seldom implemented across research sectors, geographic regions, environmental media, or size classes of plastic pollution. Harmonization of existing data is currently hindered by increasingly large datasets using thousands of different categorical variable descriptors, as well as various metrics used to describe particle abundance and differing size ranges studied across groups. For this study, we used manually developed relational databases to build an algorithm utilizing artificial intelligence capable of automatically curating harmonized, more usable datasets describing micro to macro plastic pollution in the environment. The study algorithm MaTCH (microplastics and trash cleaning and harmonization) can harmonize datasets with different formats, nomenclature, methods, and measured particle characteristics with an accuracy of 71-94% when matching semantically. All other non-semantic corrections are reported within a 95% confidence interval and with model uncertainty. All steps of the algorithm are integrated in an open-source software tool for the benefit of the scientific community and ease of integration for all plastic pollution data.

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Microplastic (MP) pollution is a major global issue that poses serious threats to aquatic organisms. Although research on MP pollution has been extensive, the relationship between MPs and water quality parameters in estuarine water systems is unclear. This work studied the spatiotemporal distribution and characteristics of MPs in the Karnaphuli River estuary, Bangladesh. MP abundance was calculated by towing with a plankton net (300 μm mesh size) at three river gradients (up-, mid- and downstream) and the association between physicochemical parameters of water (temperature, pH, salinity, electrical conductivity, total dissolved solids, and dissolved oxygen) and MP distribution patterns was also investigated. Mean MP abundance in water was higher during the wet season (April) (4.33 ± 2.45 items per m 3 ) compared to the dry season (September) (3.65 ± 2.54 items per m 3 ). In descending order, the highest MP abundance was observed downstream (6.60 items per m 3 ) > midstream (3.15 items per m 3 ) > upstream (2.22 items per m 3 ). pH during the wet season (April) and temperature during the dry season (September) were key physicochemical parameters that correlated with river MP abundance ( r = -0.74 and 0.74 respectively). Indicating that if the Karnaphuli River water has low pH or high temperature, there is likely to be high MPs present in the water. Most MP particles were film-shaped, white in color, and 1-5 mm in size. Of the six polymers detected, polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and cellulose were predominant, comprising roughly 17-19% each. These results can be used to model MP transport in the freshwater ecosystem of the Karnaphuli River estuary in Bangladesh to help develop future mitigation strategies.

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. To assess if the children's playgrounds pose a higher exposure risk than other places inside the park, we evaluate 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 -1 , and >50 % of identified plastics were either polyethylene or polypropylene. Microplastic concentrations inside the playgrounds were on average >5 times greater than concentrations outside the playgrounds in the park, indicating that children playing within the playground may be exposed to more microplastics than children playing outside the playground in the same park. By comparing the microplastic composition found inside and outside the playgrounds with the plastic composition of the plastic structures in the playground, we show that plastic structures and other products used inside the playgrounds could contribute to elevated microplastic concentration. The population density was slightly correlated with a microplastic concentration in the park soil but did not correlate with microplastic concentration inside the playgrounds. Therefore, playgrounds in urban parks may have microplastic exposure risks via inhalation or ingestion via hand-to-mouth transfer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Microscopy is often the first step in microplastic analysis and is generally followed by spectroscopy to confirm material type. The value of microscopy lies in its ability to provide count, size, color, and morphological information to inform toxicity and source apportionment. To assess the accuracy and precision of microscopy, we conducted a method evaluation study. Twenty-two laboratories from six countries were provided three blind spiked clean water samples and asked to follow a standard operating procedure. The samples contained a known number of microplastics with different morphologies (fiber, fragment, sphere), colors (clear, white, green, blue, red, and orange), polymer types (PE, PS, PVC, and PET), and sizes (ranging from roughly 3-2000 μm), and natural materials (natural hair, fibers, and shells; 100-7000 μm) that could be mistaken for microplastics (i.e., false positives). Particle recovery was poor for the smallest size fraction (3-20 μm). Average recovery (±StDev) for all reported particles >50 μm was 94.5 ± 56.3%. After quality checks, recovery for >50 μm spiked particles was 51.3 ± 21.7%. Recovery varied based on morphology and color, with poorest recovery for fibers and the largest deviations for clear and white particles. Experience mattered; less experienced laboratories tended to report higher concentration and had a higher variance among replicates. Participants identified opportunity for increased accuracy and precision through training, improved color and morphology keys, and method alterations relevant to size fractionation. The resulting data informs future work, constraining and highlighting the value of microscopy for microplastics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2022. Published by Elsevier Ltd.)

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).

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.

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. This 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 reference the reporting guidelines quickly. We intend 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.