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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.

Microplastic (MP) transport in the atmosphere, one of the least studied environmental compartments because of the relatively small size of air-borne MPs and the challenges in identifying them, may be inferred from their occurrence in snowfall. In this study, 11 sites across western coastal North America were sampled and analyzed for MP presence in fresh snowfall, months-old summer surface snow, and stratified deposits in snow pits. MPs were detected and characterized using a method integrating linear array µ-Fourier Transform Spectroscopy (µFTIR) and batch spectral analysis with open-source platform Open Specy. Recovery rate analysis from sample filtration to data analysis was conducted, and analysis of field or laboratory blanks suggested negligible contamination (≤ 1 polyamide fragment per blank). Concentrations of MPs in the fresh snowfall of remote sites and those proximal to sources were 5.1-150.8 p/L and 104.5-325 p/L of snowmelt water, respectively. Summer surface snow that was several months old had MP concentrations ranging from 57.5-539 p/L of meltwater, and snow sampled at different depths within a snowpack had concentrations ranging from 35-914 p/L. Our results demonstrate a streamlined method that may be used for measuring MPs in remote or pristine environments, contributing to a better understanding of long-range MP transport., 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 B.V. All rights reserved.)

Wastewater discharge from wastewater treatment plants continuously pumps microplastics into rivers, yet their transport distances within these waterways remain unknown. Herein, we developed a conceptual framework by synthesizing the microplastic data from the Yangtze River Basin to evaluate its transport distances, quantifying a significant spatial dependence between large-scale wastewater discharge and riverine microplastics ( p < 0.05). The presence of microplastics at a specific sampling site could be attributed to wastewater discharge within a large-scale range spanning >1000 km upstream, encompassing a substantial portion equivalent to one-third of the Yangtze River Basin. The dominance analysis indicated that the contribution of wastewater discharge in rivers with higher discharge (>100 m 3 /s) to riverine microplastic pollution exceeded 65% within the Yangtze River Basin. The spatial dependence framework of riverine microplastics on wastewater discharge advances our prior understanding of the prevention and control of riverine microplastics by demonstrating that such pollution is not limited to nearby environmental factors.

Brand names can be used to hold plastic companies accountable for their items found polluting the environment. We used data from a 5-year (2018-2022) worldwide (84 countries) program to identify brands found on plastic items in the environment through 1576 audit events. We found that 50% of items were unbranded, calling for mandated producer reporting. The top five brands globally were The Coca-Cola Company (11%), PepsiCo (5%), Nestlé (3%), Danone (3%), and Altria (2%), accounting for 24% of the total branded count, and 56 companies accounted for more than 50%. There was a clear and strong log-log linear relationship production (%) = pollution (%) between companies' annual production of plastic and their branded plastic pollution, with food and beverage companies being disproportionately large polluters. Phasing out single-use and short-lived plastic products by the largest polluters would greatly reduce global plastic pollution.

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.)

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 (10-500 µm) but less so for large microplastics (500-5 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, resulting from expensive spectral data collection. We advance a new high-throughput technique to remedy these problems using FTIR microplate readers for measuring large particles (> 500 µm). 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 future analysis in microplate readers by creating a new particle holder for transmission measurements using off-the-shelf parts and fabricating a nonplastic 96-well microplate for storing particles. We determined that particles should be presented to microplate readers as thin as possible due to thick particles causing poor-quality spectra and identifications. We validated the new database using Open Specy and demonstrated that additional transmission and reflection spectra reference data were needed in spectral libraries., (© 2024. The Author(s).)

Plastic pollution is distributed patchily around the world's oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species., (© 2023. The Author(s).)

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.

As global awareness, science, and policy interventions for plastic escalate, institutions around the world are seeking preventative strategies. Central to this is the need for precise global time series of plastic pollution with which we can assess whether implemented policies are effective, but at present we lack these data. To address this need, we used previously published and new data on floating ocean plastics (n = 11,777 stations) to create a global time-series that estimates the average counts and mass of small plastics in the ocean surface layer from 1979 to 2019. Today's global abundance is estimated at approximately 82-358 trillion plastic particles weighing 1.1-4.9 million tonnes. We observed no clear detectable trend until 1990, a fluctuating but stagnant trend from then until 2005, and a rapid increase until the present. This observed acceleration of plastic densities in the world's oceans, also reported for beaches around the globe, demands urgent international policy interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Eriksen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., 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.)

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.

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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.

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.

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)