Characterising microplastics in shower wastewater with Raman imaging

Toy building bricks as a potential source of microplastics and nanoplastics

Microplastics and nanoplastics have become noteworthy contaminants, affecting not only outdoor ecosystems but also making a notable impact within indoor environments. The release of microplastics and nanoplastics from commonly used plastic items remains a concern, and the characterisation of these contaminants is still challenging. This study focused on evaluating the microplastics and nanoplastics produced from plastic building bricks. Using Raman spectroscopy and correlation analysis, the plastic material used to manufacture building blocks was determined to be either acrylonitrile butadiene styrene (correlation value of 0.77) or polycarbonate (correlation value of 0.96). A principal component analysis (PCA) algorithm was optimised for improved detection of the debris particles released. Some challenges in microplastic analysis, such as the interference from the colourants in the building block materials, was explored and discussed. Combining Raman results with scanning electron microscopy - energy-dispersive X-ray spectroscopy, we found the scratches on the building blocks to be a significant source of contamination, estimated several thousand microplastics and several hundred thousand nanoplastics were generated per mm2 following simulated play activities. The potential exposure to microplastics and nanoplastics during play poses risks associated with the ingestion and inhalation of these minute plastic particles.

Characterising microplastics in indoor air: Insights from Raman imaging analysis of air filter samples

We are directly exposed to microplastic contamination via indoor air that we breathe daily, for which the characterisation of microplastics is still a challenge. Herein, two typical air filter samples were collected, one from an air-conditioner and another from a personal computer, both of which have been working for around half a year to collect and accumulate microplastics in the indoor air, like microplastic banks. After the sample preparation to remove the mineral dusts, Raman imaging was employed to directly and simultaneously identify and visualise microplastics of polyethylene terephthalate (PET) fibres, distinguish them from other fibres such as cellulose and cross-check them with a scanning electron microscope (SEM). To count the microplastics and to avoid the quantification bias, several areas were randomly scanned and imaged to statistically estimate the percentage of microplastic fibres in the analysed samples. The microplastics amount, which has been estimated at 73-88,000 fibers per filter per half a year, varies and depends on the indoor environment so that the air filter can work as a good indicator to monitor the quality of the indoor air from the microplastic perspective. Overall, human are directly exposed to this emerging contamination every day, raising environmental concerns. Raman imaging characterisation and its corresponding statistical information can help pursue further research on microplastics.

From celebration to contamination: Analysing microplastics released by burst balloons

Air balloons are a ubiquitous presence in our daily lives, and their rupture may release a substantial quantity of debris, as investigated herein. We employ Raman imaging to capture the fragments resulting from balloon explosions, enabling the identification and direct visualisation of minute microplastic particles / fragments with an improved signal-to-noise ratio for precise quantification. To circumvent the generation of misleading confocal Raman images, we recommend employing terrain mapping to scan the three-dimensional surface of the sample. It is important to acknowledge that the analysis of microplastics at the micro-scale inherently poses limitations in terms of throughput, as it necessitates a trade-off between low and high magnifications. We conduct explosive experiments on ten-to-hundred balloons, collecting debris from various angles and positions. Our investigation involves the random testing of multiple samples / sample positions at the micro-scale, with subsequent extrapolation to estimate the total amount of microplastics. The amalgamation of these results through statistical analysis indicates that each balloon explosion can potentially release tens-to-thousands of microplastics, highlighting a concern that has hitherto received insufficient attention. The characterisation approach, particularly the random Raman scanning method in combination with SEM and the statistical analysis on accumulated samples employed in this report, has the potential to serve as a useful tool in future research on microplastics and even nanoplastics.

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.

A beaker method for determination of microplastic concentration by micro-Raman spectroscopy

Fourier-transform infrared (FT-IR) spectroscopy method for measuring small microplastic (SMP) concentration in marine environment is time-consuming and labor-intensive due to sample pre-treatment. In contrast, Raman spectroscopy is less influenced by water and can directly measure SMP samples in water, making it a more efficient method to measure SMP concentration. Therefore, a method that can directly estimate the concentration of SMPs in water was developed, and the relationship between SMP concentration and experimental Raman spectra were established by testing with standard polyethylene (PE) samples. It was found that average spectra acquired in water solution could reflect characteristic peaks of the plastic after baseline correction. Further investigation found that there is a significant functional relationship between correlation coefficient of sample spectra and the concentration of PE particles, and such relationship can be modelled by Langmuir model. The empirical functional relationships can be used to estimate SMP concentrations by measuring average Raman spectra. The developed methodology is helpful for developing rapid SMP identification and monitoring methods in a more complex manner.•A method of directly measuring MP concentration in water is proposed.•Experimental procedures are provided.•Data analysis methods are outlined.

Microplastics' Shape and Morphology Analysis in the Presence of Natural Organic Matter Using Flow Imaging Microscopy

Ubiquitous microplastics in urban waters have raised substantial public concern due to their high chemical persistence, accumulative effects, and potential adverse effects on human health. Reliable and standardized methods are urgently needed for the identification and quantification of these emerging environmental pollutants in wastewater treatment plants (WWTPs). In this study, we introduce an innovative rapid approach that employs flow imaging microscopy (FlowCam) to simultaneously identify and quantify microplastics by capturing high-resolution digital images. Real-time image acquisition is followed by semi-automated classification using customized libraries for distinct polyethylene (PE) and polystyrene (PS) microplastics. Subsequently, these images are subjected to further analysis to extract precise morphological details of microplastics, providing insights into their behavior during transport and retention within WWTPs. Of particular significance, a systematic investigation was conducted to explore how the presence of natural organic matter (NOM) in WWTPs affects the accuracy of the FlowCam's measurement outputs for microplastics. It was observed that varying concentrations of NOM induced a more curled shape in microplastics, indicating the necessity of employing pre-treatment procedures to ensure accurate microplastic identification when utilizing the FlowCam. These observations offer valuable new perspectives and potential solutions for designing appropriate treatment technologies for removing microplastics within WWTPs.

Super-resolution imaging of micro- and nanoplastics using confocal Raman with Gaussian surface fitting and deconvolution

Confocal Raman imaging can directly identify and visualise microplastics and even nanoplastics. However, due to diffraction, the excitation laser spot has a size, which defines the image resolution. Consequently, it is difficult to image nanoplastic that is smaller than the diffraction limit. Within the laser spot, fortunately, the excitation energy density behaves an axially transcended distribution, or a 2D Gaussian distribution. By mapping the emission intensity of Raman signal, the imaged nanoplastic pattern is axially transcended as well and can be fitted as a 2D Gaussian surface via deconvolution, to re-construct the Raman image. The image re-construction can intentionally and selectively pick up the weak signal of nanoplastics, average the background noise/the variation of the Raman intensity, smoothen the image surface and re-focus the mapped pattern towards signal enhancement. Using this approach, along with nanoplastics models with known size for validation, real samples are also tested to image microplastics and nanoplastics released from the bushfire-burned face masks and water tanks. Even the bushfire-deviated surface group can be visualised as well, to monitor the different degrees of burning by visualising micro- and nanoplastics. Overall, this approach can effectively image regular shape of micro- and nanoplastics, capture nanoplastics smaller than the diffraction limit, and realise super-resolution imaging via confocal Raman.

Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging

In this study, an automated identification workflow for Raman micro-imaging (RMI) was developed, and the performance was evaluated by artificial samples of microplastic (MP) microsphere with different sizes and types. Theoretical detection rate and estimated particle size were derived and compared with experimental data. Results show that the proposed workflow can identify plastic types and estimate the size of the MP microspheres under different conditions for most cases. However, size of laser spot and discrepancy between sample surface and focal plane can influence RMI results in two ways. Firstly, small particles are more likely to be detected. Secondly, estimated sizes of particles are more likely to be overestimated. The derived uncertainties can serve as a reference for future experimental design and further investigation of more complex situations. The workflow is accessible online, and interested researchers can adjust the parameter values as necessary to suit their specific circumstances.

Raman imaging to capture microplastics and nanoplastics carried by smartphones

The sources of microplastics and nanoplastics can be found almost everywhere, including being released from the activities of our daily lives. Unfortunately, the process for determining the sources of microplastics and nanoplastics is hampered by the limited techniques available for characterisation. Herewith, we advance Raman imaging by combining it with logic-based, algebra-based, PCA-based algorithms and their hybrid, which can significantly increase the signal-noise ratio and the imaging certainty, to enable the characterisation of microplastics. Consequently, we can capture and identify the microplastics carried by our smartphones. That is because, due to the friction and fragmentation etc., our clothes and the decoration trinkets that might be made of plastic fibres can release microplastics. The released microplastics stick on the phone surface, or are trapped in the charging port, speaker ports etc., towards accumulation. We estimate hundreds or thousands of microplastics can be captured and carried by a smartphone, depending on the clothing materials, pocketing styles, user habits etc. Due to the complexity of the samples (which shields the weak signals emitted from nanoplastics), further methodological improvements are required, such as optimisation of sample preparation (for better isolation of nano-sized plastics), refinement of data processing algorithms and combined use of Raman microscopy and scanning electron microscopy (SEM).

Quantification analysis of microplastics released from disposable polystyrene tableware with fluorescent polymer staining

Ingesting microplastics (MPs) from plastic tableware is an important source of health risk to human bodies. However, the comprehensive information of MPs released from disposable tableware has not been explored. Herein, a new visual quantification method for polystyrene MPs is proposed with carbon nitride fluorescent polymers staining, which can overcome the disadvantages of high signal background and photobleaching derived from organic dyes staining. Combining with fluorescence microscope and ImageJ software, the quantity, shape, and size distribution of MPs carried by the brand-new disposable polystyrene tableware (DPT) samples before usage and released from the clean DPT samples in different simulated usage scenes were studied. The brand-new DPT samples were found to carry a large number of MPs particles and the clean DPT samples could release MPs during usage. Fiber and fragment are the main morphology of the detected MPs and fiber accounts for 45-52 %. The particles with size <50 μm are the majority of the detected MPs and the distribution fraction of MPs particles is gradually decreased with the raising of particle size within 50 μm. The released MPs particles are increased with the raising of contact time and temperature, and greatly boosted for the DPT samples with cracks. The DPT samples are more like to release MPs in weak acidic condition (pH 4.0) than in weak alkaline (pH 8.3) and neutral (pH 7.0) conditions. The obtained results help to assess the food safety of tack-out food and the health risk of MPs exposure to human.

Accelerated transformation of plastic furniture into microplastics and nanoplastics by fire

Numerous plastic items are known to gradually degrade and release microplastics and nanoplastics under certain conditions, which can be significantly accelerated by fire combustion. Unfortunately there is a limited knowledge about this burning process because the characterisation on microplastics and nanoplastics is still a challenge. In this study, an outdoor plastic chair is subjected to a combustion process, the change in the surface functional groups (due to different degree of burning) and the release of microplastics and nanoplastics are investigated. During the combustion process, the plastic is molten, burned and deposited on solid surfaces including concrete, stone and glass. Scanning electron microscopy (SEM) results show that the peeling off the deposited plastic generates a large number of fragments. Through Raman imaging, these fragments are characterised as polypropylene (PP) microplastics and nanoplastics due to appearance of characteristic peaks. To further increase the sensitivity, several algorithms are tested and optimised, including logic-based, non-supervised principal component analysis (PCA)-based, algebra-based and their hybrids (to intentionally correct the non-supervised PCA) to enable the effective extraction of the key information towards plastics characterisation, particularly by distinguishing the signal from the background noise towards the visualisation of the different degrees of burning. Based on the findings from Raman imaging and SEM, it is estimated that tens of microplastics and nanoplastics are created per μm². Overall Raman imaging can be a suitable approach to characterise the microplastics and nanoplastics in a complex background, such as the fire-burned plastic items.

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.

Graphical abstract