Classification and Quantification of Microplastics (<100 μm) Using a Focal Plane Array–Fourier Transform Infrared Imaging System and Machine Learning

Data driven AI (artificial intelligence) detection furnish economic pathways for microplastics

Microplastics pollution is killing human life, contaminating our oceans, and lasting for longer in the environment than it is used. Microplastics have contaminated the geochemistry and turned the water system into trash barrel. Its detection in water is easy in comparison to soil and air so the attention of researchers is focused on it for now. Being very small in size, microplastics can easily cross the water filtration system and end up in the ocean or lakes and become the prospective challenge to aquatic life. This review piece provides the hot research theme and current advances in the field of microplastics and their eradication through the virtual world of artificial intelligence (AI) because Microplastics have confrontation with clean water tactics.

Smartphone microscopic method for imaging and quantification of microplastics in drinking water

Analysis of microplastics in drinking water is often challenging due to smaller particle size and low particle count. In this study, we used a low cost and an easy to assemble smartphone microscopic system for imaging and quantitating microplastic particles as small as 20 μm. The system consisted of a spherical sapphire ball lens of 4 mm diameter attached to a smartphone camera as a major imaging component. It also involved pre-concentration of the sample using ZnCl2 solution. The spike recovery and limit of detection of the method in filtered distilled and deionized water samples (n = 9) were 55.6% ± 9.7% and 34 particles/L, respectively. Imaging performance of the microscopic system was similar to a commercial bright field microscopic system. The method was further implemented to examine microplastic particles in commercial bottled and jar water samples (n = 20). The particles count in bottled and jar water samples ranged from 0-91 particles/L to 0-130 particles/L, respectively. In both sample types, particles of diverse shape and size were observed. The particles collected from water samples were further confirmed by FTIR spectra (n = 36), which found 97% of the particles tested were made of plastic material. These findings suggested that the smartphone microscopic system can be implemented as a low-cost alternative for preliminary screening of microplastic in drinking water samples. RESEARCH HIGHLIGHTS: Ball lens based smartphone microscopic method was used for microplastic analysis. Particles of diverse shape and size were found in bottle and jar water samples.

Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor

Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.

Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review

The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.

Underappreciated microplastic galaxy biases the filter-based quantification

Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.

Microplastics as vectors of other contaminants: Analytical determination techniques and remediation methods

The ubiquitous and persistent presence of microplastics (MPs) in aquatic and terrestrial ecosystems has raised global concerns due to their detrimental effects on human health and the natural environment. These minuscule plastic fragments not only threaten biodiversity but also serve as vectors for contaminants, absorbing organic and inorganic pollutants, thereby causing a range of health and environmental issues. This review provides an overview of microplastics and their effects. This work highlights available analytical techniques for detecting and characterizing microplastics in different environmental matrices, assessing their advantages and limitations. Additionally, this review explores innovative remediation approaches, such as microbial degradation and other advanced methods, offering promising prospects for combatting microplastic accumulation in contaminated environments. The focus on environmentally-friendly technologies, such as the use of microorganisms and enzymes for microplastic degradation, underscores the importance of sustainable solutions in plastic pollution management. In conclusion, this article not only deepens our understanding of the microplastic issue and its impact but also advocates for the urgent need to develop and implement effective strategies to mitigate this critical environmental challenge. In this context, the crucial role of advanced technologies, like quantitative Nuclear Magnetic Resonance spectroscopy (qNMR), as promising tools for rapid and efficient microplastic detection, is emphasized. Furthermore, the potential of the enzyme PETase (polyethylene terephthalate esterase) in microplastic degradation is examined, aiming to address the growing plastic pollution, particularly in saline environments like oceanic ecosystems. These innovations offer hope for effectively addressing microplastic accumulation in contaminated environments and minimizing its adverse impacts.

The fate of microplastics from municipal wastewater in a surface flow treatment wetland

Microplastics (MPs) are an anthropogenic pollutant of emerging concern prominent in both raw and treated municipal wastewater as well as urban and agricultural run-off. There is a critical need for the mitigation of both point- and diffuse sources, with treatment wetlands a possible sustainable nature-based solution. In this study, the possible retention of MPs in treatment wetlands of the widely used surface flow (SF) type was investigated. In- and outflow water, as well as atmospheric deposition, at a full-scale reed-based SF wetland (operating as a polishing phase of municipal wastewater treatment) was analyzed for MPs in a size range of 25-1000 μm. FPA-based μFT-IR spectroscopic imaging was used in combination with automated data analysis software, allowing for an unbiased assessment of MP numbers, polymer types and size distribution. Inflow water samples (secondary treated wastewater) contained 104 MPs m-3 and 56 MPs m-3 in sampling campaigns 1 and 2, respectively. Passage through the SF wetland increased the MP concentration in the water by 92 % during a rain intense period (campaign 1) and by 43 % during a low precipitation period (campaign 2). The MP particle numbers, size and polymer type distribution varied between the two sampling campaigns, making conclusions around the fate of specific types of MPs in SF wetlands difficult. Atmospheric deposition was measured to be 590 MPs m-2 week-1 during the rain-intense period. Our findings point towards atmospheric deposited MPs as an important factor in the fate of MPs in SF wetlands, causing an increase of MP concentrations, and potentially explaining the variations observed in MP concentrations in wetland effluent and removal efficiency. Furthermore, atmospheric deposition might also be a reason for the considerable inter-study variation regarding MPs removal efficiency in SF wetlands found in the available literature.

Recent advances on the methods developed for the identification and detection of emerging contaminant microplastics: a review

The widespread use of plastics, popular for their versatility and cost-efficiency in mass production, has led to their essential role in modern society. Their remarkable attributes, such as flexibility, mechanical strength, lightweight, and affordability, have further strengthened their importance. However, the emergence of microplastics (MPs), minute plastic particles, has raised environmental concerns. Over the last decade, numerous studies have uncovered MPs of varying sizes in diverse environments. They primarily originate from textile fibres and cosmetic products, with large plastic items undergoing degradation and contributing as secondary sources. The bioaccumulation of MPs, with potential ingestion by humans through the food chain, underscores their significance as environmental contaminants. Therefore, continuous monitoring of environmental and food samples is imperative. A range of spectroscopic techniques, including vibrational spectroscopy, Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, hyperspectral imaging, and nuclear magnetic resonance (NMR) spectroscopy, facilitates the detection of MPs. This review offers a comprehensive overview of the analytical methods employed for sample collection, characterization, and analysis of MPs. It also emphasizes the crucial criteria for selecting practical and standardized techniques for the detection of MPs. Despite advancements, challenges persist in this field, and this review suggests potential strategies to address these limitations. The development of effective protocols for the accurate identification and quantification of MPs in real-world samples is of paramount importance. This review further highlights the accumulation of microplastics in various edible species, such as crabs, pelagic fish, finfish, shellfish, American oysters, and mussels, shedding light on the extreme implications of MPs on our food chain.

When microplastics meet electroanalysis: future analytical trends for an emerging threat

Microplastics are a major modern challenge that must be addressed to protect the environment, particularly the marine environment. Microplastics, defined as particles ≤5 mm, are ubiquitous in the environment. Their small size for a relatively large surface area, high persistence and easy distribution in water, soil and air require the development of new analytical methods to monitor their presence. At present, the availability of analytical techniques that are easy to use, automated, inexpensive and based on new approaches to improve detection remains an open challenge. This review aims to outline the evolution and novelties of classical and advanced methods, in particular the recently reported electroanalytical detectors, methods and devices. Among all the studies reviewed here, we highlight the great advantages of electroanalytical tools over spectroscopic and thermal analysis, especially for the rapid and accurate detection of microplastics in the sub-micron range. Finally, the challenges faced in the development of automated analytical methods are discussed, highlighting recent trends in artificial intelligence (AI) in microplastics analysis.

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging

The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.

Microplastics and biobased polymers to combat plastics waste

Microplastics (MPs) have become the major global concern due to their adverse effects on the environment, human health, and hygiene. These complex molecules have numerous toxic impacts on human well-being. This review focuses on the methods for chemically quantifying and identifying MPs in real-time samples, as well as the detrimental effects resulting from exposure to them. Biopolymers offer promising solutions for reducing the environmental impact caused by persistent plastic pollution. The review also examines the significant progress achieved in the preparation and modification of various biobased polymers, including polylactic acid (PLA), poly(ε-caprolactone) (PCL), lignin-based polymers, poly-3-hydroxybutyrate (PHB), and poly(hydroxyalkanoates) (PHA), which hold promise for addressing the challenges associated with unplanned plastic waste disposal.

Improved separation and quantification method for microplastic analysis in sediment: A fine-grained matrix from Arctic Greenland

Microplastic analysis requires effective separation and purification methods, which greatly depend on the matrix and target particle size. Microplastics-sediment extraction usually involves intermediate steps, increasing processing time and particle loss, particularly for particles <100 μm. Here, we propose an improved separation and quantification method for fine-grained sediment that minimizes microplastic loss by reducing intermediate steps. First, the sample is treated with CH3COOH, KOH and NaClO, and only transferred for the density separation (ZnCl2). The extraction efficiency, visually evaluated on spiked samples, was higher than 90% for particles >100 μm and 83% for 63-75 μm particles. This indicates that a sequential extraction method reduces the risk of particle loss, particularly of the small size fraction. Comparatively, the extraction of ABS particles (20-100 μm) was low (30%) but the recovery, assessed via μFTIR, was higher (55%). Additionally, the proposed method can be adapted to other sediment types and environmental matrices.

A review on constructive classification framework of research trends in analytical instrumentation for secondary micro(nano)plastics: What is new and what needs next?

Secondary micro(nano)plastics generated from the degradation of plastics pose a major threat to environmental and human health. Amid the growing research on microplastics to date, the detection of secondary micro(nano)plastics is hampered by inadequate analytical instrumentation in terms of accuracy, validation, and repeatability. Given that, the current review provides a critical evaluation of the research trends in instrumental methods developed so far for the qualitative and quantitative determination of micro(nano)plastics with an emphasis on the evolution, new trends, missing links, and future directions. We conducted a meta-analysis of the growing literature surveying over 800 journal articles published from 2004 to 2022 based on the Web of Science database. The significance of this review is associated with the proposed novel classification framework to identify three main research trends, viz. (i) preliminary investigations, (ii) current progression, and (iii) novel advances in sampling, characterization, and quantification targeting both micro- and nano-sized plastics. Field Flow Fractionation (FFF) and Hydrodynamic Chromatography (HDC) were found to be the latest techniques for sampling and extraction of microplastics. Fluorescent Molecular Rotor (FMR) and Thermal Desorption-Proton Transfer Reaction-Mass Spectrometry (TD-PTR-MS) were recognized as the modern developments in the identification and quantification of polymer units in micro(nano)plastics. Powerful imaging techniques, viz. Digital Holographic Imaging (DHI) and Fluorescence Lifetime Imaging Microscopy (FLIM) offered nanoscale analysis of the surface topography of nanoplastics. Machine learning provided fast and less labor-intensive analytical protocols for accurate classification of plastic types in environmental samples. Although the existing analytical methods are justifiable merely for microplastics, they are not fully standardized for nanoplastics. Future research needs to be more inclined towards secondary nanoplastics for their effective and selective analysis targeting a broad range of environmental and biological matrices.

Impact of per- and polyfluorinated alkyl substances (PFAS) on the marine environment: Raising awareness, challenges, legislation, and mitigation approaches under the One Health concept

Per- and polyfluorinated alkyl substances (PFAS) have long been known for their detrimental effects on the ecosystems and living organisms; however the long-term impact on the marine environment is still insufficiently recognized. Based on PFAS persistence and bioaccumulation in the complex marine food network, adverse effects will be exacerbated by global processes such as climate change and synergies with other pollutants, like microplastics. The range of fluorochemicals currently included in the PFAS umbrella has significantly expanded due to the updated OECD definition, raising new concerns about their poorly understood dynamics and negative effects on the ocean wildlife and human health. Mitigation challenges and approaches, including biodegradation and currently studied materials for PFAS environmental removal are proposed here, highlighting the importance of ongoing monitoring and bridging research gaps. The PFAS EU regulations, good practices and legal frameworks are discussed, with emphasis on recommendations for improving marine ecosystem management.

Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning

Vibrational Circular Dichroism (VCD) spectra often differ strongly from one conformer to another, even within the same absolute configuration of a molecule. Simulated molecular VCD spectra typically require expensive quantum chemical calculations for all conformers to generate a Boltzmann averaged total spectrum. This paper reports whether machine learning (ML) can partly replace these quantum chemical calculations by capturing the intricate connection between a conformer geometry and its VCD spectrum. Three hypotheses concerning the added value of ML are tested. First, it is shown that for a single stereoisomer, ML can predict the VCD spectrum of a conformer from solely the conformer geometry. Second, it is found that the ML approach results in important time savings. Third, the ML model produced is unfortunately hardly transferable from one stereoisomer to another.

Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning

We investigated the adsorption mechanism of 66 coexisting pharmaceuticals and personal care products (PPCPs) on microplastics treated with potassium persulfate, potassium hydroxide, and Fenton reagent for 54, 110, and 500 days. The total adsorption capacity (q_e) of 66 PPCPs on 15 original microplastics was 171.8 - 1043.7 μg/g, far below that of 177 long-term aged microplastics (7114.0 - 13,114.4 μg/g). Around 69.8% of q_e was primarily influenced by the total energy, energy of the highest occupied molecular orbital, and energy gap of PPCPs, calculated using the B3LYP/6-31 G* level. Furthermore, 111 aged microplastics exhibited similar total q_e values. Additionally, we developed predictive models based on attenuated total reflectance Fourier transform infrared spectroscopy to predict the individual and total q_e on 192 microplastics. These models, including the maximal information coefficient and gradient boosting decision tree regression, exhibited high accuracy with R_training² values of 0.9772 and 0.9661, respectively, and p-values below 0.001. Spectroscopic analysis and machine learning models highlighted surface functional group alterations and the importance of the 1528-1700 cm^-1 spectral region and carbon skeleton in the adsorption process. In summary, our findings contribute to understanding the adsorption of PPCPs on microplastics, particularly in the context of long-term aging effects.

Framework for data-driven polymer characterization from infrared spectra

Automating infrared spectra interpretation in microplastic identification is of interest since most current methodologies are conducted manually or semi-automatically, which requires substantial processing time and presents a higher accuracy limited to single-polymer materials. Furthermore, when it comes to multicomponent or weathered polymeric materials commonly found in aquatic environments, identification usually becomes considerably depreciated as peaks shift and new signals are frequently observed, representing a significant deviation from reference spectral signatures. Therefore, this study aimed to develop a reference modeling framework for polymer identification through infrared spectra processing, addressing the limitations above. The case study selected for model development was polypropylene (PP) identification, as it is the second most abundant material in microplastics. Therefore, the database comprises 579 spectra with 52.3% containing PP to some degree. Different pretreatment and model parameters were evaluated for a more robust investigation, totaling 308 models, including multilayer perceptron and long-short-term memory architectures. The best model presented a test accuracy of 94.8% within the cross-validation standard deviation interval. Overall, the results achieved in this study indicate an opportunity to investigate the identification of other polymers following the same framework.

Source or sink role of an urban lake for microplastics from Guangdong-Hong Kong-Macao greater bay area, China

Plastic production and consumption in China are larger than others in the world, and the challenge of microplastic pollution is widespread. With the development of urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area, China, the environmental pollution of microplastics is becoming an increasingly prominent issue. Here, the spatial and temporal distribution characteristics, sources, and ecological risks of microplastics were analyzed in water from an urban lake, Xinghu Lake, as well as the contribution of rivers. Importantly, the roles of urban lakes for microplastics were demonstrated through the investigations of contributions and fluxes for microplastic in rivers. The results showed that the average abundances of microplastics in water of Xinghu Lake were 4.8 ± 2.2 and 10.1 ± 7.6 particles/m³ in wet and dry seasons, and the average contribution degree of the inflow rivers was 75%. The size of microplastics in water from Xinghu Lake and its tributaries was concentrated in the range of 200-1000 μm. In general, the average comprehensive potential ecological risk indexes of microplastics in water were 247 ± 120.6 and 273.1 ± 353.7 in wet and dry seasons, which the high ecological risks of them were found through the adjusted evaluation method. There were also mutual effects among microplastic abundance, the concentrations of total nitrogen and organic carbon. Finally, Xinghu Lake has been a sink for microplastics both in wet and dry seasons, and it would be a source of microplastics under the influence of extreme weather and anthropogenic factors.

Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning

Microplastics (MPs) are currently recognized as emerging pollutants; their identification and classification are therefore essential during their monitoring and management. In contrast to most studies based on small datasets and library searches, this study developed and compared four machine learning-based classifiers and two large-scale blended plastic datasets, where a 1D convolutional neural network (CNN), decision tree, and random forest (RF) were fed with raw spectral data from Fourier transform infrared spectroscopy, while a 2D CNN used the corresponding spectral images as the input. With an overall accuracy of 96.43% on a small dataset and 97.44% on a large dataset, the 1D CNN outperformed other models. The 1D CNN was the best at predicting environment samples, while the RF was the most robust with less spectral data. Overall, RF and 2D CNNs might be evaluated for plastic identification with fewer spectral data; however, 1D CNNs were thought to be the most effective with sufficient spectral data. Accordingly, an open-source MP spectroscopic analysis tool was developed to facilitate a quick and accurate analysis of existing MP samples.

Comparison of two rapid automated analysis tools for large FTIR microplastic datasets

One of the biggest issues in microplastic (MP, plastic items  <5 mm) research is the lack of standardisation and harmonisation in all fields, reaching from sampling methodology to sample purification, analytical methods and data analysis. This hampers comparability as well as reproducibility among studies. Concerning chemical analysis of MPs, Fourier-transform infrared (FTIR) spectroscocopy is one of the most powerful tools. Here, focal plane array (FPA) based micro-FTIR (µFTIR) imaging allows for rapid measurement and identification without manual preselection of putative MP and therefore enables large sample throughputs with high spatial resolution. The resulting huge datasets necessitate automated algorithms for data analysis in a reasonable time frame. Although solutions are available, little is known about the comparability or the level of reliability of their output. For the first time, within our study, we compare two well-established and frequently applied data analysis algorithms in regard to results in abundance, polymer composition and size distributions of MP (11-500 µm) derived from selected environmental water samples: (a) the siMPle analysis tool (systematic identification of MicroPlastics in the environment) in combination with MPAPP (MicroPlastic Automated Particle/fibre analysis Pipeline) and (b) the BPF (Bayreuth Particle Finder). The results of our comparison show an overall good accordance but also indicate discrepancies concerning certain polymer types/clusters as well as the smallest MP size classes. Our study further demonstrates that a detailed comparison of MP algorithms is an essential prerequisite for a better comparability of MP data.

Unaccounted Microplastics in the Outlet of Wastewater Treatment Plants—Challenges and Opportunities

Since the 1950s, plastic production has skyrocketed. Various environmental and human activities are leading to the formation and accumulation of microplastics (MPs) in aquatic and terrestrial ecosystems, causing detrimental effects on water, soil, plants, and living creatures. Wastewater treatment plants (WWTPs) are one of the primary MP management centers meant to check their entry into the natural systems. However, there are considerable limitations in effectively capturing, detecting, and characterizing these MPs in the inlet and outlet of WWTPs leading to “unaccounted MPs” that are eventually discharged into our ecosystems. In order to assess the holistic picture of the MPs’ distribution in the ecosystems, prevent the release of these omitted MPs into the environment, and formulate regulatory policies, it is vital to develop protocols that can be standardized across the globe to accurately detect and account for MPs in different sample types. This review will cover the details of current WWTP adoption procedures for MP management. Specifically, the following aspects are discussed: (i) several processes involved in the workflow of estimating MPs in the outlet of WWTPs; (ii) key limitations or challenges in each process that would increase the uncertainty in accurately estimating MPs; (iii) favorable recommendations that would lead to the standardization of protocols in the workflow and facilitate more accurate analysis of MPs; (iv) research opportunities to tackle the problem of ‘missing MPs’; and (v) future research directions for the efficient management of MPs. Considering the burgeoning research interest in the area of MPs, this work would help early scientists in understanding the current status in the field of MP analysis in the outlet of WWTPs.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.

Abundance, characteristics, and spatial-temporal distribution of microplastics in sea salts along the Cox's Bazar coastal area, Bangladesh

Microplastics (MPs), together with microfibers, have emerged as a contaminant of concern all around the globe. MPs have been detected in freshwater, seawater, sediment, and aquatic species among others. As suggested by several recent investigations, sea salts, a daily intake item by humans, are also contaminated by MPs. The current article describes MPs' occurrence, distribution, type, and timeline variation in raw sea salts from Cox's Bazar, Bangladesh. MPs have been detected in every collected salt sample, and quantity varied from 28.53 ± 2.43 to 93.53 ± 4.21 particles per kg, which was about 52.48 ± 1.72 to 67.46 ± 3.81 µg/kg of raw salt. Microfibers were MPs' dominant shape category, and the plastic types were mainly polyester or nylon. Other types of MPs were polyethylene (PE), polypropylene (PP), polycarbonate (PC), polyurethane (PU), and polystyrene (PS) in decreasing amounts. The majority of the MPs in the sea salts were in the size range of ˂ 3-1 mm. The total amount of MPs and plastic-type variation due to sampling location (p ˃ 0.05) and because of the time period (p ˃ 0.05) was found insignificant. Acetaldehyde, a volatile toxic substance produced by the degradation of polyester polymer chains, was detected in MPs in the range of 0.37 to 1.72 µg/g by headspace GC-MS analysis. Hence, the sea salts contaminated with MPs pose a public health hazard. Microplastics extraction from sea salts and their characterization.

Screening of polymer types and chemical weathering in macro- and meso-plastics found on lake and river beaches using a combined chemometric approach

In the environment, synthetic polymers, commonly known as "plastics", are well-known to undergo various chemical weathering processes, which modify their surface chemistry by introducing new functional groups. Such changes are important to monitor, as they can severely influence the toxicity caused by plastic debris. Therefore, in this study, two chemometric models are proposed to accelerate the chemical classification of macro- and meso-plastics found in the environment. For this purpose, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied on preprocessed infrared spectra of 83 plastic fragments found on public lake and river beaches. HCA associated all beach samples with a known plastic, whereas PCA enabled the association of only 39.8% (33 out of 83) of the beach samples with a known plastic. However, both techniques agreed on 93.9% of the samples identified. According to PCA and HCA results, polypropylene and polyethylene were the most frequently identified polymers in the samples. PCA turned out to be a very promising tool for fast screening of weathered plastics, since the distance of samples from the polypropylene cluster in the PCA plot was correlated with weathering. This was later confirmed by employing other characterization techniques such as micro-Raman, X-ray photoelectron spectroscopy and scanning electron microscopy. Finally, future experiments should focus on the applicability of the proposed combined chemometric approach for very small microplastics (<100 μm), as they have more important effects than larger plastics on aquatic ecosystems.

Recent advances in the application of machine learning methods to improve identification of the microplastics in environment

Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identification methods have demonstrated significant limitations such as low resolution, long imaging time, and limited particle size analysis. New and improved methods for MPs identification are topical research areas, with machine learning (ML) algorithms proven highly useful in recent years. Critical literature reviews on the latest developments in this area are limited. This study closes this gap and summarizes the progress made over the last 10 years in using ML algorithms for identifying and quantifying MPs. We identified diverse combinations of ML methods and fundamental techniques for MPs identification, such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and near-infrared spectroscopy. The most widely used ML model is the support vector machine, which effectively improves the conventional analysis method for spectral quality defects and improves detection accuracy. Artificial neural network models exhibit improved recognition effects, with shorter detection times and better MPs recognition efficiency. Our review demonstrates the potential of ML in improving the identification and quantification of MPs.

Addition of polyvinyl pyrrolidone during density separation with sodium iodide solution improves recovery rate of small microplastics (20-150 μm) from soils and sediments

The purpose of this study was to identify a method to accurately separate small microplastics (<100 μm) from soil and sediment. We initially conducted spike-and-recovery tests using polyethylene microbeads and density separation and found that the recovery rate of microplastic particles smaller than 100 μm was less than 60%. This result suggested that previous reports have underestimated the concentrations of microplastics smaller than 100 μm in soil. When polyvinyl pyrrolidone was added and dispersed in a heavy liquid, the recovery rate exceeded 90%, regardless of the microplastic particle size. This improved recovery rate was independent of the type of polymer (polyethylene, polypropylene, polystyrene, polyethylene terephthalate, or nylon 6) and the physicochemical properties of the soil (Andisols, Entisols, or Ultisols), and the method was also effective for marine and lake sediments. Using this method, we measured microplastic concentrations in paddy soil. The results showed that the most common particle size, 20-100 μm, accounted for 64% of all microplastics. Accurate separation from the soil of fractions smaller than 100 μm, which account for the majority of microplastics in soil, will enable an accurate assessment of the impact of microplastics on the soil ecosystem. The method identified in this study can serve as the basic technique for achieving that goal.

Low-toxic, fluorescent labeled and size-controlled graphene oxide quantum dots@polystyrene nanospheres as reference material for quantitative determination and in vivo tracing

Polystyrene (PS) is selected as a representative nanoplastic and persistent pollutant for its difficult degradation and wide application. The environmental risk assessment of PS is obstructed by the toxic dye-based fluorescent PS, which false positives could be induced by the leakage of dye. For high biocompatibility, low toxicity, hydrophilicity, good water dispersibility, strong fluorescent stability, graphene oxide quantum dots (o-CQDs) are selected and embedded into PS microspheres, i.e., o-CQDs@PS, by microemulsion polymerization and denoted as CPS. Meanwhile, the sizes of CPS, e.g., 100, 150, and 200 nm, could be controlled by optimizing the type and number of water-soluble initiators. The anti-interference, low toxicity, and in vivo fluorescent tracing of CPS are proven by the coexistence of metals (including Fe²⁺, Fe³⁺, K⁺, Ba²⁺, Al³⁺, Zn²⁺, Mg²⁺, Ca²⁺, and Na⁺) on the fluorescence intensity of CPS, the growth of Chlorella pyrenoidosa and Artemia cysts as aquatic phytoplankton and zooplankton cultured with CPS, and the transfer of CPS from water into brine shrimp. In the concentration range of 0.1-100 mg/L, CPS can be quantitatively determined, which is suitable for coastal water and wastewater treatment plants. Therefore, CPS with standard size is suitable as reference material of PS.

The face behind the Covid-19 mask - A comprehensive review

The threat of epidemic outbreaks like SARS-CoV-2 is growing owing to the exponential growth of the global population and the continual increase in human mobility. Personal protection against viral infections was enforced using ambient air filters, face masks, and other respiratory protective equipment. Available facemasks feature considerable variation in efficacy, materials usage and characteristic properties. Despite their widespread use and importance, face masks pose major potential threats due to the uncontrolled manufacture and disposal techniques. Improper solid waste management enables viral propagation and increases the volume of associated biomedical waste at an alarming rate. Polymers used in single-use face masks include a spectrum of chemical constituents: plasticisers and flame retardants leading to health-related issues over time. Despite ample research in this field, the efficacy of personal protective equipment and its impact post-disposal is yet to be explored satisfactorily. The following review assimilates information on the different forms of personal protective equipment currently in use. Proper waste management techniques pertaining to such special wastes have also been discussed. The study features a holistic overview of innovations made in face masks and their corresponding impact on human health and environment. Strategies with SDG3 and SDG12, outlining safe and proper disposal of solid waste, have also been discussed. Furthermore, employing the CFD paradigm, a 3D model of a face mask was created based on fluid flow during breathing techniques. Lastly, the review concludes with possible future advancements and promising research avenues in personal protective equipment.

Microplastics in aquatic systems, a comprehensive review: origination, accumulation, impact, and removal technologies

Although the discovery of plastic in the last century has brought enormous benefits to daily activities, it must be said that its use produces countless environmental problems that are difficult to solve. The indiscriminate use and the increase in industrial production of cleaning, cosmetic, packaging, fertilizer, automotive, construction and pharmaceutical products have introduced tons of plastics and microplastics into the environment. The latter are of greatest concern due to their size and their omnipresence in the various environmental sectors. Today, they represent a contaminant of increasing ecotoxicological interest especially in aquatic environments due to their high stability and diffusion. In this regard, this critical review aims to describe the different sources of microplastics, emphasizing their effects in aquatic ecosystems and the danger to the health of living beings, while examining, at the same time, those few modelling studies conducted to estimate the future impact of plastic towards the marine ecosystem. Furthermore, this review summarizes the latest scientific advances related to removal techniques, evaluating their advantages and disadvantages. The final purpose is to highlight the great environmental problem that we are going to face in the coming decades, and the need to develop appropriate strategies to invert the current scenario as well as better performing removal techniques to minimize the environmental impacts of microplastics.

Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory

Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.

Identification of Trace Polystyrene Nanoplastics Down to 50 nm by the Hyphenated Method of Filtration and Surface-Enhanced Raman Spectroscopy Based on Silver Nanowire Membranes

Nanoplastics are emerging pollutants that pose potential threats to the environment and organisms. However, in-depth research on nanoplastics has been hindered by the absence of feasible and reliable analytical methods, particularly for trace nanoplastics. Herein, we propose a hyphenated method involving membrane filtration and surface-enhanced Raman spectroscopy (SERS) to analyze trace nanoplastics in water. In this method, a bifunctional Ag nanowire membrane was employed to enrich nanoplastics and enhance their Raman spectra in situ, which omitted sample transfer and avoided losing smaller nanoplastics. Good retention rates (86.7% for 50 nm and approximately 95.0% for 100-1000 nm) and high sensitivity (down to 10^-7 g/L for 50-1000 nm and up to 10⁵ SERS enhancement factor) of standard polystyrene (PS) nanoplastics were achieved using the proposed method. PS nanoplastics with concentrations from 10^-1 to 10^-7 g/L and sizes ranging from 50 to 1000 nm were successfully detected by Raman mapping. Moreover, PS micro- and nanoplastics in environmental water samples collected from the seafood market were also detected at the μg/L level. Consequently, the proposed method provides more possibilities for analyzing low-concentration nanoplastics in aquatic environments with high enrichment efficiency, minimal sample loss, and high sensitivity.

SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test

Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The purpose of this study was to develop a new high throughput screening method that could be used as a predictive expert system that automatically quantifies the size increase and malformation of the larvae and, thus, eases the application of the sea urchin embryo test in complex toxicant identification pipelines such as effect-directed analysis. For this task, a training set of 242 images was used to calibrate the size-increase and malformation level of the larvae. Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github.com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.

A review of microplastics in soil: Occurrence, analytical methods, combined contamination and risks

Microplastics (MPs) pollution is becoming a serious environmental issue of global concern. Currently, the effects of MPs on aquatic ecosystems have been studied in detail and in depth from species to communities. However, soils, the largest reservoir of MPs, have been less studied, and little is known about the occurrence, environmental fate and ecological impacts of MPs. Therefore, based on the existing knowledge, this paper firstly focused specifically on the main sources of soil MPs pollution and explored the main reasons for their strong heterogeneity in spatial distribution. Secondly, as a primary prerequisite for evaluating MPs contamination, we systematically summarized the analytical methods for soil MPs and critically compared the advantages and disadvantages of the different methods in the various operational steps. Furthermore, this review highlighted the combined contamination of MPs with complex chemical contaminants, the sorption mechanisms and the associated factors in the soil. Finally, the risks posed by MPs to soil, plants, the food chain and even humans were outlined, and future directions for soil MPs research were proposed, while the urgent need for a unified approach to MPs extraction and identification was emphasized. This study provides a theoretical reference for a comprehensive understanding of the separation of soil MPs and their ecological risk as carriers of pollution.

Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects

Microplastics (MPs, sizes <5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.

Microplastic detection and identification by Nile red staining: Towards a semi-automated, cost- and time-effective technique

Microplastic pollution is an issue of concern due to the accumulation rates in the marine environment combined with the limited knowledge about their abundance, distribution and associated environmental impacts. However, surveying and monitoring microplastics in the environment can be time consuming and costly. The development of cost- and time-effective methods is imperative to overcome some of the current critical bottlenecks in microplastic detection and identification, and to advance microplastics research. Here, an innovative approach for microplastic analysis is presented that combines the advantages of high-throughput screening with those of automation. The proposed approach used Red Green Blue (RGB) data extracted from photos of Nile red-fluorescently stained microplastics (50-1200 μm) to train and validate a 'Plastic Detection Model' (PDM) and a 'Polymer Identification Model' (PIM). These two supervised machine learning models predicted with high accuracy the plastic or natural origin of particles (95.8%), and the polymer types of the microplastics (88.1%). The applicability of the PDM and the PIM was demonstrated by successfully using the models to detect (92.7%) and identify (80%) plastic particles in spiked environmental samples that underwent laboratorial processing. The classification models represent a semi-automated, high-throughput and reproducible method to characterize microplastics in a straightforward, cost- and time-effective yet reliable way.

Multifeature superposition analysis of the effects of microplastics on microbial communities in realistic environments

Microplastic (MP) contamination has become an increasingly serious environmental problem. However, the risks of MP contamination in complex global climatic and geographic scenarios remain unclear. We established a multifeature superposition analysis boosting (MFAB) machine learning (ML) approach to address the above knowledge gap. MFAB-ML identified and predicted the importance, interaction networks and superposition effects of multiple features, including 34 characteristic variables (e.g., MP contamination and climatic and geographic variables), from 1354 samples distributed globally. MFAB-ML analysis achieved realistic and significant results, in some cases even opposite to those obtained using a single or a few features, revealing the importance of considering complicated scenarios. We found that the microbial diversity in East Asian seas will continually decrease due to the superposition effects of MPs with ocean warming; for example, the Chao1 index will decrease by 10.32% by 2065. The present work provides a powerful approach to identify and predict the multifeature superposition effects of pollutants on realistic environments in complicated climatic and geographic scenarios, overcoming the bias from general studies.

Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

The traditional manual analysis of microplastics has been criticized for its labor-intensive, inaccurate identification of small microplastics, and the lack of uniformity. There are already three automated analysis strategies for microplastics based on vibrational spectroscopy: laser direct infrared (LDIR)-based particle analysis, Raman-based particle analysis, and focal plane array-Fourier transform infrared (FPA-FTIR) imaging. We compared their performances in terms of quantification, detection limit, size measurement, and material identification accuracy and speed by analyzing the same standard and environmental samples. LDIR-based particle analysis provides the fastest analysis speed, but potentially questionable material identification and quantification results. The number of particles smaller than 60 μm recognized by LDIR-based particle analysis is much less than that recognized by Raman-based particle analysis. Misidentification could occur due to the narrow tuning range from 1800 to 975 cm^-1 and dispersive artifact distortion of infrared spectra collected in reflection mode. Raman-based particle analysis has a submicrometer detection limit but should be cautiously used in the automated analysis of microplastics in environmental samples because of the strong fluorescence interference. FPA-FTIR imaging provides relatively reliable quantification and material identification for microplastics in environmental samples greater than 20 μm but might provide an imprecise description of the particle shapes. Optical photothermal infrared (O-PTIR) spectroscopy can detect submicron-sized environmental microplastics (0.5-5 μm) intermingled with a substantial amount of biological matrix; the resulting spectra are searchable in infrared databases without the influence of fluorescence interference, but the process would need to be fully automated.

Characterizing photochemical ageing processes of microplastic materials using multivariate analysis of infrared spectra

Microplastics in the environment are an emerging concern due to impacts on human and environmental health. In addition to direct effects on biota, microplastics influence the fate and distribution of trace organic contaminants through sorption and transport. Environmental weathering may influence the rate and extent of chemical sorption. Changes in the surface characteristics of four common plastics including low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP), and polystyrene (PS) were followed under the influence of both artificial light (UV-B) and natural sunlight for up to six months. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra were collected at regular intervals. Principal component analysis (PCA) of the full dataset of UV-B weathered samples (n >500 spectra) simultaneously discriminated plastic type and extent of photochemical weathering. The magnitude of PCA scores correlated with exposure time and the loadings were consistent with surface chemistry changes including photooxidation. Projecting sunlight and UV-C exposed samples onto this PCA model demonstrated that similar chemical changes occurred, albeit at different rates. The results were compared to the carbonyl index (CI) with similar weathering trends indicating PP weathered at a faster initial rate than LDPE and HDPE. We propose that a multivariate approach is more widely applicable than CI as illustrated by PS, which lacked a stable reference peak. Kinetic analysis of the time series indicated that outdoor weathering occurred 5-12 times slower than the artificial exposure used here, depending on the plastic and the light source employed. The results provide unique insights into weathering processes and the photochemical age of naturally weathered plastics.

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning

The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy

Microplastics are contaminants of emerging concern - not only environmental, but also to human health. Characterizing them is of fundamental importance to evaluate their potential impacts and target specific actions aiming to reduce potential harming effects. This study extends the exploration of machine learning classification algorithms applied to FTIR spectra of microplastics collected at sea. A comparison of successful classification models was made in order to evaluate prediction performance for 13 classes of polymers. A rigorous methodology was applied using a pipeline scheme to avoid bias in the training and selection phases. The application of an oversampling technique also contributed by compensating unbalanceness in the dataset. The log-loss was used as the minimization function target and to assess performance. In our analysis, Support Vector Machine Classifier provides a good relationship between simplicity and performance, for a fast and useful automatic characterization of microplastics.

Recent advances on ecological effects of microplastics on soil environment

The mass production and wide application of plastics and their derivatives have led to the release of a large number of discarded plastic products into the natural environment, where they continue to accumulate due to their low recycling rate and long durability. These large pieces of plastic will gradually break into microplastics (<5 mm), which are highly persistent organic pollutants and attract worldwide attention due to their small particle size and potential threats to the ecosystem. Compared with the aquatic system, terrestrial systems such as soils, as sinks for microplastics, are more susceptible to plastic pollution. In this article, we comprehensively summarized the occurrence and sources of microplastics in terrestrial soil, and reviewed the eco-toxicological effects of microplastics in soil ecosystems, in terms of physical and chemical properties of soil, soil nutrient cycling, soil flora and fauna. The influence of microplastics on soil microbial community, and particularly the microbial community on the surface of microplastics, were examined in detail. The compound effects of microplastics and other pollutants, e.g., heavy metals and antibiotics, were addressed. Future challenges of research on microplastics include development of new techniques and standardization for the extraction and qualitative and quantitative analysis of microplastics in soils, toxic effects of microplastics at microbial or even molecular levels, the contribution of microplastics to antibiotic resistance genes migration, and unraveling microorganisms for the degradation of microplastics. This work provides as a better understanding of the occurrence, distribution and potential ecological risks of microplastics in terrestrial soil ecosystems.

Moving forward in microplastic research: A Norwegian perspective

Given the increasing attention on the occurrence of microplastics in the environment, and the potential environmental threats they pose, there is a need for researchers to move quickly from basic understanding to applied science that supports decision makers in finding feasible mitigation measures and solutions. At the same time, they must provide sufficient, accurate and clear information to the media, public and other relevant groups (e.g., NGOs). Key requirements include systematic and coordinated research efforts to enable evidence-based decision making and to develop efficient policy measures on all scales (national, regional and global). To achieve this, collaboration between key actors is essential and should include researchers from multiple disciplines, policymakers, authorities, civil and industry organizations, and the public. This further requires clear and informative communication processes, and open and continuous dialogues between all actors. Cross-discipline dialogues between researchers should focus on scientific quality and harmonization, defining and accurately communicating the state of knowledge, and prioritization of topics that are critical for both research and policy, with the common goal to establish and update action plans for holistic benefit. In Norway, cross-sectoral collaboration has been fundamental in supporting the national strategy to address plastic pollution. Researchers, stakeholders and the environmental authorities have come together to exchange knowledge, identify knowledge gaps, and set targeted and feasible measures to tackle one of the most challenging aspects of plastic pollution: microplastic. In this article, we present a Norwegian perspective on the state of knowledge on microplastic research efforts. Norway's involvement in international efforts to combat plastic pollution aims at serving as an example of how key actors can collaborate synergistically to share knowledge, address shortcomings, and outline ways forward to address environmental challenges.

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging

The Antibiotic mycelial residue (AMR) contains antibiotic residue, there are safety risks if it is used illegally in feed. This study investigated the feasibility of qualitative identification of AMR in protein feed and self-prepared feed based on attenuated total reflection mid-infrared spectrum (ATR-IR) and microscopic infrared imaging. Cottonseed meal (CM), soybean meal (SM), distillers dried grains with solubles (DDGS), nucleotide residue (NR), oxytetracycline residue (OR) and streptomycin sulfate residue (SR) and two self-prepared feed (broiler and pig) were used as research objects. The results showed that there were characteristic peaks at 1614 cm^-1, 1315 cm^-1, 779 cm^-1, 514 cm^-1 in the ATR-IR spectra of AMR, which were related to calcium oxalate hydrate. After detection, the content of total calcium and calcium oxalate in AMR were higher than those in protein feed. ATR-IR can quickly realize the qualitative discrimination of pure material samples. The combination of ATR-IR and partial least squares discriminant analysis (PLSDA) was effective in discriminating AMR from CM and SM with a single component (the classification errors were 0), but it cannot meet the discrimination of AMR from the fermented protein feed (such as DDGS and NR, the classification errors were 0.10 and 0.12) and self-prepared feed with complex components. Compared with ATR-IR, microscopic infrared imaging was less affected by the sample complexity. Multi-component samples belong to physical mixing and will not affect the infrared spectra of each component. Therefore, microscopic infrared imaging combined with effective information extraction algorithms such as cosine similarity can distinguish OR in the fermented protein feed and self-prepared feed. The above results showed that the advantages of ATR-IR and microscopic infrared imaging were complementary, which provided a new idea for the discrimination analysis of illegal feed additives.