Shapes of Hyperspectral Imaged Microplastics

Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers

Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, filmed, and fibrous morphologies, respectively. However, none of the existing models demonstrates universal applicability across all three morphologies. Scientists now have to rely on the predominate microplastic morphology extracted from filed samples to determine the appropriate settling model used for transport modeling. Given the spatiotemporal variability in morphologies and the coexistence of diverse morphologies of microplastics in natural aquatic environments, the extracted morphological information poses significant challenges in reliably determining the appropriate model. Evidently, to reliably model the transport of microplastics in aquatic environments, a universal settling model for microplastics with diverse shapes is warranted. To develop such a universal model, a unique shape factor, which can explicitly distinguish between the distinct morphologies of microplastics, was first proposed in this study by using a specifically-modified machine learning method. Using this newly-proposed shape factor, a universal model for predicting the settling velocity of microplastics with distinct morphologies was developed by using a physics-informed machine learning algorithm and then systematically evaluated against independent data sets. The newly-developed model enables reasonable predictions of the settling velocity of microplastic fragments, films, and fibers. In contrast to purely data-driven models, the newly-developed model is characterized by its transparent formulaic structure and physical interpretability, which is conducive to further expansion and improvement. This study can serve as a paradigm for future studies, inspiring the adoption of machine learning techniques in the development of physically-based models to investigate the transport of microplastics in aquatic environments.

Colloid and Interface Science for Understanding Microplastics and Developing Remediation Strategies

Microplastics (MPs) originate from industrial production of <1 mm polymeric particles and from the progressive breakdown of larger plastic debris. Their environmental behavior is governed by their interfacial properties, which dominate due to their small size. This Perspective highlights the complex surface chemistry of MPs under environmental stressors and discusses how physical attributes like shape and roughness could influence their fate. We further identify wastewater treatment plants (WWTPs) as critical hotspots for MP accumulation, where the MPs are inadvertently transferred to sewage sludge and reintroduced into the environment. We emphasize the potential of colloid and interfacial science not only to improve our fundamental understanding of MPs but also to advance mitigation strategies in hotspots such as WWTPs.

Mass concentration of plastic particles from two-dimensional images

Imaging techniques play a crucial role in characterizing environmental plastics. However, most reported findings rely on two-dimensional projections of particles resting on flat surfaces. This limitation makes it challenging to accurately determine mass concentration, which is essential for deriving toxicologically relevant exposure data. The primary issue arises from the loss of information regarding particle height or thickness. This study aims to evaluate the assumptions necessary to compensate this loss of information. To achieve this, we used a set of environmental plastic particles, mesoplastics and microplastics, from marine campaigns, and precisely measured their three spatial dimensions and mass. Our study demonstrated the feasibility of estimating the mass of plastic particles through two-dimensional images. However, for enhanced accuracy, additional information derived from the dataset of particles under examination is necessary. Specifically, estimating the mass of platelike particles requires information about their height. Similarly, calculating the volume for elongated shapes as cylinders, should be limited to particles with the same width and height and for which their length can be precisely determined, even if the image depicts twisted forms. In conclusion, while obtaining mass concentration from single two-dimensional images enables reasonable estimations, achieving the precision needed for exposure data requires acquiring additional information from the sample and carefully considering the shape of each individual particle.

Distribution characteristics and integrated ecological risks evaluation modelling of microplastics and heavy metals in geological high background soil

The microplastics (MPs), a novel pollutant, and heavy metals (HMs) significantly affect soil ecology. The study investigated HMs and MPs in Qianxi's high geological background soil, established a model for risk evaluation with MPs types and shapes, and proposed a two-dimensional comprehensive index model for MPs-HMs combined pollution and risk evaluation criterion. The results revealed a high soil Cd concentration, with a mean value of 0.38 mg·kg-1. Additionally, soils from soybean-wheat intercropping-potato-corn rotation (SWI-PCR) exhibited significantly higher concentrations of Hg, As, and Pb compared with those from soybean-wheat intercropping-corn rotation (SWI-CR). Moreover, the soil exhibited a high abundance of MPs (8667.66 ± 3864.26 items·kg-1), mainly characterized by PS and fiber. The mean of adjusted ecological risk index (ARI) for MPs in soil was 525.27, indicating a grade 3 risk. The two-dimensional combined index (TPI) was used to assess the ecological risk of MPs-HMs combined pollution, exhibiting an exceedance rate of 56 % with a mean of 445.07. The risk level of the combined pollution was graded as 6, indicating high risk. The microplastic risk evaluation model and the comprehensive evaluation method of combined pollution established in this study provide a reference for the future risk evaluation of multi-pollutant combined pollution.

Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis

Microplastics are an emerging anthropogenic pollutant risk with a significant body of research dedicated to understanding the implications further. To generate the databases required to characterize the impact of microplastics on our environment, and improve recovery and recycling of current plastic materials, we need rapid, in-line characterization that can distinguish individual polymer types. Here, autofluorescence spectroscopy was investigated as an alternative characterization method to the current leading techniques based on vibrational spectroscopy. It was confirmed that the autofluorescence of seven common textile polymers (acrylic, polyester, nylon, polyethylene, polypropylene, cellulose/cotton, wool) arose due to the cluster-triggered emission phenomenon. Both simulated polymer aging via photooxidation and dyeing of the polymers were found to affect the resultant autofluorescence spectra. A total of 1485 spectra from 39 unique sample groups (polymer type, colour, and degree of photooxidation) were analysed via machine learning (k-means cluster analysis). Correct identification of the polymer type was achieved in 71% of the cases from only eight input values (normalized intensity values at three autofluorescence emission wavelengths, the total autofluorescence emission intensity, the sample RGB colour values, and the sample shape). This represents a significant step towards automated polymer identification at the sub-second time scales required for the in-line characterization of microplastics.

Characteristics and release potential of microplastics in municipal solid waste incineration bottom ash

Incineration is an effective method for reducing and safely treating municipal solid waste. However, microplastics (MPs) inevitably remain in the bottom ash, potentially introducing new pollution risks during subsequent treatment processes. This study conducted an analysis of the accumulation and release potential of MPs in bottom ash samples collected from 4 municipal solid waste incineration plants in Zhejiang, China. The results showed that the abundance of MPs ranged from 20 to 118 items g-1. Remarkably, MPs were found to accumulate predominantly in smaller bottom ash particles below 4.75 mm accounted for up to 70% of the total MPs. Most MPs in the bottom ash were under 100 μm in size, with a majority exceeding 50% being less than 50 μm, typically manifesting as shafts and fibers. In scenarios of secondary crushing, the abundance of MPs increased gradually with the degree of bottom ash crushing. When bottom ash was crushed to a particle size of less than 0.6 mm, the abundance of MPs reached up to 87-901 items g-1, which is 5-10 times higher than the original bottom ash. It is estimated that the annual release of MPs may reach up to 4.05 × 1016 particles. Re-incinerating thoroughly crushed bottom ash at 600 °C successfully decomposed the MPs. Mechanical stress can significantly increase the risk of MPs releasing in bottom ash. This risk can be eliminated by using secondary incineration to achieve complete MPs decomposition.

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Treating wastewater for microplastics to a level on par with nearby marine waters

Retention of microplastics (MPs) at the third largest wastewater treatment plant (WWTP) in Sweden was investigated. The plant is one of the most modern and advanced of its kind, with rapid sand filter for tertiary treatment in combination with mechanical, biological, and chemical treatment. It achieved a significantly high treatment efficiency, which brought the MP concentration in its discharge on par with concentrations measured in marine waters of the same region. This novel data shows that properly designed modern WWTPs can reduce the MP content of sewage down to background levels measured in the receiving aquatic environment. Opposite to current understanding of the retention of MP by WWTPs, a modern and well-designed WWTP does not have to be a significant point source for MP. MPs were quantified at all major treatment steps, including digester inlet and outlet sludge. MPs sized 10-500 µm were analyzed by a focal plane array based micro-Fourier transform infrared (FPA-µFTIR) microscopy, a hyperspectral imaging technique, while MPs above 500 µm were analyzed by Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Mass was estimated from the hyperspectral images for MPs <500 µm and from microscope images >500 µm. The overall treatment efficiency was in terms of MP counts 99.98 %, with a daily input of 6.42 × 1010 and output of 1.04 × 107 particles. The mass removal efficiency was 99.99 %. The mechanical part of the treatment, the pre-treatment, and primary stages, reduced both the MP counts and mass by approximately 71 %. The combined biological treatment, secondary settling, and final polishing with rapid sand filtration removed nearly all the remaining 29 %. MPs became successively smaller as they passed the different treatment steps. The digester inlet received 1.04 × 1011 MPs daily, while it discharged 9.96 × 1010 MPs, causing a small but not significant decrease in MP counts, with a corresponding MP mass reduction of 9.56 %.

Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.