A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques

Releasing characteristics of toxic chemicals from polystyrene microplastics in the aqueous environment during photoaging process

Microplastics (MPs) are pervasive in the environment and inevitably undergo photoaging due to UV irradiation. This study delved into the dynamic releasing and transformation process of toxic chemicals from polystyrene microplastics (PS MPs) during photoaging, a subject that remains underexplored. It was revealed that photoaging led to substantial alterations in the physicochemical properties of PS MPs, initiating polymer chain scission and facilitating the release of a large number of toxic chemicals, including numerous organic compounds and several inorganic compounds. The kinetic analysis revealed a dynamic release pattern for PS MPs, where under varying UV intensities (2, 5, and 10 mW/cm2), the release rate (kDOC) initially increased and then decreased, peaking at a total irradiation energy of approximately 7 kW·h/m2. Furthermore, chemicals in leachate were transformed into compounds with smaller molecular weight, higher oxidized and greater unsaturated state over the prolonged photoaging. This transformation was primarily attributed to two reasons. Firstly, the aged PS MPs released chemicals with higher oxidized state compared to the pristine MPs. Secondly, the chemicals previously released underwent further reactions. Besides, among the complex leachate generated by aged PS MPs, the organic chemicals characterized by small molecular weight and high oxidized state exhibited notable acute toxicity, whereas heavy metal ions showed lesser toxicity, and anions were non-toxic. This study shed more light on the photoaging process of PS MPs, releasing characteristics of organic chemicals, and the potential environmental risks associated with plastic wastes.

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.

Microplastic contamination of packaged spirulina products

Microplastic (MP) contamination in commercially sold spirulina products has not been previously investigated. In this study, 29 spirulina samples in various packaging types were purchased from different brands and origins to assess the presence of MPs. Microplastic analysis was conducted using microscopic and μ-Raman techniques. To ascertain whether the content is indeed spirulina and make a comparison with the MP level, C-Phycocyanin levels were also analyzed. A total of 251 MP-like particles were observed. Out of the 29 examined packaged spirulina brands, 26 showed potential MPs upon visual inspection, with 35 particles confirmed as MPs (73% of the analyzed particles). The mean abundance of MPs was estimated at 13.77 ± 2.45 MPs/100 g dw. Powdered spirulina had a higher but not statistically significant MP abundance (17.34 ± 4.22 MPs/100 g dw) compared to capsule/tablet forms (10.43 ± 2.45 MPs/100 g dw). Fragments accounted for 38.3% while fibers constituted 61.7% of the identified MPs, with sizes ranging from 0.07 to 2.15 mm for fragments and 0.19 to 5.691 mm for fibers. The color distribution of MPs in spirulina samples was predominantly blue (52.8%), followed by black (25.4%), white (10.9%), and others (10.9%). Ten synthetic polymers and cellulose were identified through μ-Raman analysis, with polypropylene (31.6%) and polystyrene (8.3%) being the most prevalent. The correlation between C-Phycocyanin and MPs concentrations, was not found statistically significant. The abundance and composition of MPs were found to be influenced by packaging and processing stages. Identifying potential sources of MPs in spirulina products and evaluating their risks to human health is crucial.

Microplastics in the Lung Tissues Associated with Blood Test Index

Microplastics (MPs) have received a lot of attention and have been detected in multiple environmental matrices as a new environmental hazard, but studies on human internal exposure to MPs are limited. Here, we collected lung tissue samples from 12 nonsmoking patients to evaluate the characteristics of MPs in human lung tissues using an Agilent 8700 laser infrared imaging spectrometer and scanning electron microscopy. We detected 108 MPs covering 12 types in the lung tissue samples, with a median concentration of 2.19 particles/g. Most of the MPs (88.89%) were sized between 20 to 100 μm. Polypropylene accounts for 34.26% of the MPs in the lung tissues, followed by polyethylene terephthalate (21.30%) and polystyrene (8.33%). Compared with males and those living far from a major road (≥300 m), females and those living near the main road (<300 m) had higher levels of MPs in lung tissues, which positively correlated with platelet (PLT), thrombocytocrit, fibrinogen (FIB), and negatively related with direct bilirubin (DB). These findings help confirm the presence in the respiratory system and suggest the potential sources and health effects of inhaled MPs.

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.

Raman spectra characterization of size-dependent aggregation and dispersion of polystyrene particles in aquatic environments

Aqueous environments are generally thought to be a source of pooling and re-distribution for both micro-plastics (MPs) and nano-plastics (NPs); however, significantly less data on NPs than MPs have been reported. The occurrence of salts, proteins, and other organic matter may promote or inhibit the aggregation of NPs to form agglomeration particles, making their detection more difficult. In this study, 80 and 500 nm polystyrene nano-plastics (PS-NPs) modified by four different functional groups (PS-Bare, PS-COOH, PS-NH₂, and PS-CHO-500 nm) were selected to mimic the flocculation and/or sedimentation of NPs in salts (NaCl, CaCl₂, and Na₂SO₄) and protein solutions. The results showed that the 80 nm PS-NPs are only colloidal in pure water. All four strong electrolyte solutions that were tested significantly promoted the aggregation of PS-NPs, including those that were protein-coated. In addition, 500 nm PS-CHO did not flocculate but gradually settled into sedimentation. Therefore, Raman spectrometry can be used to analyze assembled PS-NPs, but is not suitable for analyzing normal PS-NPs. By combining fractal morphology, this study provides insight into the comprehensive analysis of PS-NPs in water solutions, including the digestion of biological samples.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

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.

Microplastic emission characteristics of stormwater runoff in an urban area: Intra-event variability and influencing factors

Stormwater runoff is considered a major pathway for land-based microplastic transportation to aquatic environments. By applying time-weighted stormwater sampling at stormwater outlets from industrial and residential catchments, we investigated the emission characteristics and loads (number- and mass-based) of microplastics to aquatic environments through urban stormwater runoff during rainfall events. Microplastics were detected in stormwater runoff from industrial and residential areas in the concentration range of 68-568 n/L and 54-639 n/L, respectively. Polypropylene and polyethylene were found as major polymers accounting for around 60 % of total microplastics. The fragment was the dominant shape of microplastics, and the most common size class was 20-100 μm or 100-200 μm. The microplastic load emitted from industrial and residential areas were estimated to be 1.54-46.1 × 10⁸ and 0.63-28.5 × 10⁸ particles, respectively. The discharge characteristics of microplastics inter- and intra-event were affected by the land-use pattern and rainfall characteristics. The concentration of microplastics did not significantly differ between industrial and residential catchments, but the composition of polymer types reflected the land-use pattern. The microplastics in stormwater were more concentrated when the number of antecedent dry days (ADDs) was higher; the concentration of microplastics was generally peaked in the early stage of runoff and varied according to rainfall intensity during a rainfall event. The contamination level and load of microplastics were heavily affected by the total rainfall depth. Most microplastics were transported in the early stage of runoff (19-37 % of total runoff time), but the proportion of larger and heavier particles increased in the later period of runoff. The microplastic emission via stormwater runoff was significantly higher than that through the discharge of wastewater treatment plant effluent in the same area, implying that stormwater runoff is the dominant pathway for transporting microplastics to aquatic environments.

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.

Evidence of Microplastics in Bronchoalveolar Lavage Fluid among Never-Smokers: A Prospective Case Series

Microplastics (MPs) are abundant in air, but evidence of their deposition in the respiratory tract is limited. We conducted a prospective case series to investigate the deposition of microplastics in bronchoalveolar lavage fluid (BALF) and determine the internal dose of MPs via inhalation. Eighteen never-smokers aged 32-74 years who underwent fiberoptic bronchoscopy with BALF were recruited from Zhuhai, China. Control samples were obtained by performing the same procedure using isotonic saline instead of BALF. Laser direct infrared spectroscopy combined with scanning electron microscopy detected the presence and characteristics of MPs and quantitatively analyzed the microplastic in BALF and control samples. Concentrations of total and specific MPs in BALF and control samples were compared using the Wilcox test. Thirteen types of MPs were observed in 18 BALF samples. Polyethylene (PE, 86.1%) was the most abundant in BALF, followed by poly(ethylene terephthalate) (PET, 7.5%) and polypropylene (PP, 1.9%). Compared with the control samples, BALF had significantly higher concentrations of PE (median [IQR] of BALF: 0.38 [8.05] N/g), PET (0.26 [0.54] N/g), polyurethane (0.16 [0.24] N/g), PP (0.16 [0.11] N/g), and total MPs (0.91 [6.58] N/g). The presence of MPs in BALF provides novel evidence that MPs penetrate deep into the respiratory tract.

The assessment of particle selection and blank correction to enhance the analysis of microplastics with Raman microspectroscopy

Although microplastics research has received enormous attention in the last decade, both the research practices and the quality of produced data should still be improved. In this study, the identification process of microplastics with Raman imaging microscope was improved by decreasing the time needed for the analysis. To do that, new features, including terrain mosaic and automatic particle selection, were utilized and various ways of handling the produced microplastics data were implemented and discussed. Furthermore, blank correction of microplastic concentrations was demonstrated and its effects on the recovery of spiked microplastics was assessed with aqueous and solid samples. Six types of microplastics, including fragments and fibers, were spiked in triplicates of ultra-pure water and reference sediment samples. The spiked samples were pretreated by a modified method of the universal enzymatic purification protocol. Microplastics were analyzed with Raman imaging microscope, using terrain mosaic combined with automatic particle selection. The microplastics data were subjected to different identification steps to estimate the potential overestimation and underestimation of microplastics counts. With the complete correction of Raman-based data, the average recovery rates of fragments (77-80%) were higher than fibers (20-33%). The decrease in recovery rates of spiked microplastics (49-57%) were observed when blank correction was applied (28-47%). The impact of the blank correction depended on the polymer, causing exclusion of PE, PET, and PP from sediment samples. For the completely corrected Raman-based data, the average recovery rates of microplastics were higher for water than sediment samples both with and without blank correction. The results demonstrated the impact of blank correction on the microplastics recovery rates. To our knowledge, this is the first study to explore the use of automatic particle selection of Raman imaging microscope for microplastics analysis. Hence, potential drawbacks and advantages of the new features of Raman imaging microscope were explicitly discussed.

Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates

Micro(nano)plastic (MNP) pollutants have not only impacted human health directly, but are also associated with numerous chemical contaminants that increase toxicity in the natural environment. Most recent research about increasing plastic pollutants in natural environments have focused on the toxic effects of MNPs in water, the atmosphere, and soil. The methodologies of MNP identification have been extensively developed for actual applications, but they still require further study, including on-site detection. This review article provides a comprehensive update on the facile detection of MNPs by Raman spectroscopy, which aims at early diagnosis of potential risks and human health impacts. In particular, Raman imaging and nanostructure-enhanced Raman scattering have emerged as effective analytical technologies for identifying MNPs in an environment. Here, the authors give an update on the latest advances in plasmonic nanostructured materials-assisted SERS substrates utilized for the detection of MNP particles present in environmental samples. Moreover, this work describes different plasmonic materials-including pure noble metal nanostructured materials and hybrid nanomaterials-that have been used to fabricate and develop SERS platforms to obtain the identifying MNP particles at low concentrations. Plasmonic nanostructure-enhanced materials consisting of pure noble metals and hybrid nanomaterials can significantly enhance the surface-enhanced Raman scattering (SERS) spectra signals of pollutant analytes due to their localized hot spots. This concise topical review also provides updates on recent developments and trends in MNP detection by means of SERS using a variety of unique materials, along with three-dimensional (3D) SERS substrates, nanopipettes, and microfluidic chips. A novel material-assisted spectral Raman technique and its effective application are also introduced for selective monitoring and trace detection of MNPs in indoor and outdoor environments.

Pollution characteristics and source analysis of microplastics in the Qiantang River in southeastern China

Microplastic pollution resulting from industrialization and urbanization is increasingly serious. Hangzhou is a city with high industrial/urban growth in Southeast China. Focusing on the microplastic pollution in the Hangzhou section Qiantang River, six samples were collected and analyzed during different hydrological periods (normal, wet, and dry periods) and the relationship between microplastic pollution and economic development was investigated. Results showed that more microplastics were found during the dry period than that of the wet period (49.8 vs. 13.2%). Microplastic abundance was 1.5-9.4 items L^-1, showing significant spatial differences in sampling sites. Among the collecting microplastics, debris and fibers accounted for 36.4 and 30.9%. Polyethylene terephthalate and polyvinyl chloride were the main polymers, accounting for 48.3 and 31.8%, respectively. Microplastics with size <1 mm accounted for 60% of the microplastics in surface water samples. Spatially, microplastic abundance was the highest in the middle of the river. Redundant analysis revealed that the per capita GDP (p = 0.002), high-end equipment industry (p = 0.028) and fashion manufacturing (p = 0.006) influenced microplastic abundance. Urbanization coupled with rapid economic development led to increase in local microplastic pollution. Our results provide insight into microplastic distribution patterns in urban river systems in China.