Bottom-Up Evaluation of the Uncertainty of the Quantification of Microplastics Contamination in Sediment Samples

Distribution characteristics and microbial synergistic degradation potential of polyethylene and polypropylene in freshwater estuarine sediments

The microbial degradation of polyethylene (PE) and polypropylene (PP) resins in rivers and lakes has emerged as a crucial issue in the management of microplastics. This study revealed that as the flow rate decreased longitudinally, ammonia nitrogen (NH4+-N), heavy fraction of organic carbon (HFOC), and small-size microplastics (< 1 mm) gradually accumulated in the deep and downstream estuarine sediments. Based on their surface morphology and carbonyl index, these sediments were identified as the potential hot zone for PE/PP degradation. Within the identified hot zone, concentrations of PE/PP-degrading genes, enzymes, and bacteria were significantly elevated compared to other zones, exhibiting strong intercorrelations. Analysis of niche differences revealed that the accumulation of NH4+-N and HFOC in the hot zone facilitated the synergistic coexistence of key bacteria responsible for PE/PP degradation within biofilms. The findings of this study offer a novel insight and comprehensive understanding of the distribution characteristics and synergistic degradation potential of PE/PP in natural freshwater environments.

Framework to Quantify Uncertainty in Microplastic Concentrations in Wastewaters and Sludges Incorporating Analytical Recovery Information into Data Analysis

Wastewater treatment plants (WWTPs) serve a pivotal role in transferring microplastics (MPs) from wastewater to sludge streams, thereby exerting a significant influence on their release into the environment and establishing wastewater and biosolids as vectors for MP transport and delivery. Hence, an accurate understanding of the fate and transport of MPs in WWTPs is vital. Enumeration is commonly used to estimate concentrations of MPs in performance evaluations of treatment processes, and risk assessment also typically involves MP enumeration. However, achieving high accuracy in concentration estimates is challenging due to inherent uncertainty in the analytical workflow to collect and process samples and count MPs. Here, sources of random error in MP enumeration in wastewater and other matrices were investigated using a modeling approach that addresses the sources of error associated with each step of the analysis. In particular, losses are reflected in data analysis rather than merely being measured as a validation step for MP extraction methods. A model for addressing uncertainty in the enumeration of microorganisms in water was adapted to include key assumptions relevant to the enumeration of MPs in wastewater. Critically, analytical recovery, the capacity to successfully enumerate particles considering losses and counting error, may be variable among MPs due to differences in size, shape, and type (differential analytical recovery) in addition to random variability between samples (nonconstant analytical recovery). Accordingly, differential analytical recovery among the categories of MPs was added to the existing model. This model was illustratively applied to estimate MP concentrations from simulated data and quantify uncertainty in the resulting estimates. Increasing the number of replicates, counting categories of MPs separately, and accounting for both differential and nonconstant analytical recovery improved the accuracy of MP enumeration. This work contributes to developing guidelines for analytical procedures quantifying MPs in diverse types of samples and provides a framework for enhanced interpretation of enumeration data, thereby facilitating the collection of more accurate and reliable MP data in environmental studies.

How many microplastics do you need to (sub)sample?

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.

Characteristics and quantification of small microplastics (<100 µm) in seasonal svalbard snow on glaciers and lands

Small microplastics (SMPs < 100 µm) can easily be transported over long distances far from their sources through the atmospheric pathways and reach even remote regions, including the Arctic. However, these sizes of MPs are mostly overlooked due to different analytical challenges; besides, their pathways through atmospheric depositions, such as snow depositions, are mostly unknown. The spatial variability in bulk snow samples was investigated for the first time in distinct sites (e.g., glaciers) near Ny Ålesund, the world-known northernmost permanent research settlement in the Svalbard Islands, to better comprehend the presence of SMP pollution in snow. Seasonal snow deposited over the tundra and the summits of different glaciers were also sampled. A sampling procedure was designed to obtain representative samples while minimizing plastic contamination, thanks to rigorous quality assurance and quality control protocol. SMPs' weight (µg SMP L-1) and deposition load (mg SMPs m-2) result from being lower in the remote glaciers, where they may be subject to long-range transport. The SMPs' minimum length was 20 µm, with the majority less than 100 µm. Regarding their size distribution, there was an increase in the size length deriving from the local input of the human presence near the scientific settlement. The presence of some polymers might be site-specific in relation to the pathways that affect their distribution at the sites studied. Also, from the snow surface layer collected at the same sites to evaluate the variability of SMPs during specific atmospheric deposition events, the results confirmed their higher weight and load in surface snow near the scientific settlement compared to the glaciers. The results will enhance the limited knowledge of the SMPs in polar atmospheric compartments and deposition processes.

Retention efficiency for microplastic in a landscape estimated from empirically validated dynamic model predictions

Soils are recipients of microplastic that can be subsequently transferred to the sea. Land sources dominate inputs to the ocean, but knowledge gaps about microplastic retention by land hinder assessments of input rates. Here we present the first empirical evaluation of a dynamic microplastic fate model operating at landscape level. This mechanistic model accounts for hydrology, soil and sediment erosion, particle characteristics and behavior. We predict microplastic concentrations in water and sediments of the Henares river (Spain) within the measurement uncertainty boundaries (error factors below 2 and 10, respectively). Microplastic export from land and discharge by river fluctuates in a non-linear manner with precipitation and runoff variability. This indicates the need of accurate dynamic descriptions of soil and stream hydrology even when modeling microplastic fate and transport in generic scenarios and at low spatio-temporal resolution. A time-averaged landscape retention efficiency was calculated showing 20-50% of the microplastics added to the catchment over a multiannual period were retained. While the analysis reveals persistent uncertainties and knowledge gaps on microplastic sources to the catchment, these results contribute to the quantitative understanding of the role of terrestrial environments in accumulating microplastics, delaying their transport to the sea.

Microplastic burden in marine benthic invertebrates depends on species traits and feeding ecology within biogeographical provinces

The microplastic body burden of marine animals is often assumed to reflect levels of environmental contamination, yet variations in feeding ecology and regional trait expression could also affect a species' risk of contaminant uptake. Here, we explore the global inventory of individual microplastic body burden for invertebrate species inhabiting marine sediments across 16 biogeographic provinces. We show that individual microplastic body burden in benthic invertebrates cannot be fully explained by absolute levels of microplastic contamination in the environment, because interspecific differences in behaviour and feeding ecology strongly determine microplastic uptake. Our analyses also indicate a degree of species-specific particle selectivity; likely associated with feeding biology. Highest microplastic burden occurs in the Yellow and Mediterranean Seas and, contrary to expectation, amongst omnivores, predators, and deposit feeders rather than suspension feeding species. Our findings highlight the inadequacy of microplastic uptake risk assessments based on inventories of environmental contamination alone, and the need to understand how species behaviour and trait expression covary with microplastic contamination.

How to establish detection limits for environmental microplastics analysis

Establishing analytical detection limits is crucial. Common methods to do so are suitable only for variables with continuous distributions. Because count data for microplastic particles is a discrete variable following the Poisson distribution, currently-used approaches for estimating the detection limit in microplastics analysis are inadequate. Here we evaluate detection limits with techniques for low-level discrete observations to develop proper approaches for estimating the minimum detectable amount (MDA) in microplastic particle analysis, using blank sample data from an interlaboratory calibration exercise for clean water (representing drinking water), dirty water (ambient water), sediment (porous media) and fish tissue (biotic tissues). Two MDAs are applicable: MDA_A to evaluate analytical methods, estimated with replicate blank data; and MDA_B for individual sample batches, calculated with a single blank count. For illustrative purposes, this dataset's overall MDA_A values were 164 counts (clean water), 88 (dirty water), 192 (sediment), and 379 (tissue). MDA values should be reported on a laboratory-specific basis and for individual size fractions, as this provides more useful information about capabilities of individual laboratories. This is due to wide variation in blank levels, as noted by MDA_B values (i.e., among different laboratories) from 14 to 158 (clean water), 9 to 86 (dirty water, 9 to 186 (sediment), and 9 to 247 (tissue). MDA values for fibers were considerably greater than for non-fibers, suggesting that separate MDA values should be reported. This study provides a guideline for estimation and application of microplastics MDA for more robust data to support research activities and environmental management decisions.

Cascading Polymer Macro-Debris Upcycling and Microparticle Removal as an Effective Life Cycle Plastic Pollution Mitigation Strategy

Plastic pollution caused by material losses and their subsequent chemical emissions is pervasive in the natural environment and varies with age. Cascading the life cycles of plastic losses with solid waste reclamation via re-manufacturing virgin polymers or producing fuels and energy may extend resource availability while minimizing waste generation and environmental exposure. Here, we systematically investigate this cascaded plastic waste processing over other waste end-of-life management pathways by analyzing the environmental consequences of plastic losses across the entire life cycle. Plastic losses can form volatile organic chemicals via photo-degradation and pose non-negligible global warming, ecotoxicity, and air pollution effects that worsen by at least 189% in the long run. These environmental burdens increase by above 9.96% under high ultraviolet radiation levels and participation rates, which facilitate plastic particulate compartment transport and degradation. Cascaded plastic waste processing aided by fast pyrolysis upcycling technologies can effectively cut environmental losses and outperform landfills and incineration in reducing 23.35% ozone formation and 19.91% air pollution by offsetting the external monomer manufacturing and fuels and energy production while saving at least 25.75% fossil fuels.

Determination of microplastic contamination levels and trends in vast oceanic sediment areas with uncertainty

Small plastic particles, designated as microplastics, are known vehicles of several contaminants desorbed from their surface after being ingested by marine organisms. The monitoring of the levels and trends of microplastics in oceanic areas is essential to identify relevant threats and respective sources whose management should be improved to protect the environmental resources. However, the assessment of contamination trends in large oceanic areas is affected by contamination heterogeneity, sampling representativeness, and the uncertainty of collected sample analyses. Only contamination variations not justifiable by system heterogeneity and their characterisation uncertainty are meaningful and should be taken seriously by the authorities. This work describes a novel methodology for the objective identification of meaningful variation of microplastic contamination in vast oceanic areas by the Monte Carlo simulation of all uncertainty components. This tool was successfully applied to the monitoring of the levels and trends of microplastic contamination in sediments from a 700 km² oceanic area from 3 km to 20 km offshore Sesimbra and Sines (Portugal). This work allowed concluding that contamination has not varied between 2018 and 2019 (difference of mean total microplastic contamination between -40 kg^-1 and 34 kg^-1) but that microparticles made of PET are the major type of studied microplastics (in 2019, mean contamination is between 36 kg^-1 and 85 kg^-1). All assessments were performed for a 99 % confidence level.