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.

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.

Pretreatment of sediment with hydrogen peroxide to remove organic constituents and aid deflocculation is a common component of particle size analyses of terrestrial and marine sediments. This study quantitatively determined the effect of a range of treatment levels on particle size distribution among four sediment types representing a range of mineral/organic particle size distributions, organic content and particle characterisation (charcoal or detrital plant material). The hypothesis was that complete removal of organic particles would lead to improved repeatability of results for a given sample and treatment level. Repeatability was assessed with a coefficient of variance calculation and a comparison of particle size distribution patterns within and across treatments. The effect of treatment levels on commonly used distribution descriptors (e.g. texture ratios and measures of central tendency) were then examined for each sample. Samples characterised primarily by detrital material responded most readily to treatment, whereas charcoal-dominated samples required higher levels of treatment to achieve increased repeatability and disappearance of large organic particles. Certain distribution descriptors, such as modal analysis, were found to be more resilient to organic particle presence, although amplitude of the organic distribution and the degree of overlap with the inorganic signal in some cases obscured even this metric. Thus, final treatment recommendations are based on sample characteristics and the types of distribution descriptors used in a study.

This is the publisher’s final pdf. The published article is copyrighted by the Geological Society of America and can be found at: http://gsabulletin.gsapubs.org/. To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work.The occurrence of two wildfires separated by 31 yr in the chaparral-dominated Arroyo Seco watershed (293 km²) of California provides a unique opportunity to evaluate the effects of wildfire on suspended-sediment yield. Here, we compile discharge and suspended-sediment sampling data from before and after the fires and show that the effects of the postfire responses differed markedly. The 1977 Marble Cone wildfire was followed by an exceptionally wet winter, which resulted in concentrations and fluxes of both fine and coarse suspended sediment that were ∼35 times greater than average (sediment yield during the 1978 water year was 11,000 t/km²/yr). We suggest that the combined 1977–1978 fire and flood had a recurrence interval of greater than 1000 yr. In contrast, the 2008 Basin Complex wildfire was followed by a drier than normal year, and although suspended-sediment fluxes and concentrations were significantly elevated compared to those expected for unburned conditions, the sediment yield during the 2009 water year was less than 1% of the post–Marble Cone wildfire yield. After the first postfire winters, sediment concentrations and yield decreased with time toward prefire relationships and continued to have significant rainfall dependence. We hypothesize that the differences in sediment yield were related to precipitation-enhanced hillslope erosion processes, such as rilling and mass movements. The millennial-scale effects of wildfire on sediment yield were explored further using Monte Carlo simulations, and these analyses suggest that infrequent wildfires followed by floods increase long-term suspended-sediment fluxes markedly. Thus, we suggest that the current approach of estimating sediment yield from sediment rating curves and discharge data—without including periodic perturbations from wildfires—may grossly underestimate actual sediment yields

Postfire debris flows are particularly complex to study because they do not form discrete initiation locations and commonly involve multiple simultaneously operating erosional processes. Although recent work has begun to elucidate a more mechanistic understanding of postfire debris flows, there is still a paucity of detailed sediment budgets characterizing these events. In this study, we seek to understand how postfire sediment sources and erosional processes change over multiple storm cycles. To do this, we performed repeat high‐resolution change detection in a headwater catchment burned by the 2018 Holy Fire in the Santa Ana Mountains, California, USA. This included terrestrial laser scanning in a zero‐order catchment (0.95 ha) and unmanned aerial vehicle structure from motion of a headwater channel network (up to 6.5 ha). During the initial storm events that produced runoff‐generated debris flows, we found that the evacuation of dry ravel and prefire colluvium accounted for half of the eroded material. These initial flows also acted to clear out much of the material stored within downstream headwater channel networks. In subsequent storm events of equal or greater rainfall intensity, total erosion from the study site was subdued, and the relative importance of shallow hillslope erosion from interrill and rill erosion was increased, as has been noted in similar studies in the region. Overall, this suggests that channel sediment supplies may be more rapidly depleted than hillslope sources, which may drive a trend of decreasing sediment fluxes over time from burned headwater catchments subject to repeated runoff events.