Classification of household microplastics using a multi-model approach based on Raman spectroscopy

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.

Quantification and polymeric characterization of microplastics in composts and their accumulation in lettuce

Organic fertilizers have become a vector for the transport of microplastics (MPs), which pose human health concerns through the food chain. This study aimed to quantify and characterize MPs in eight different compost samples of various raw materials and their subsequent translocation to lettuce (Lacuta sativa) grown on contaminated composts. The results revealed that the MP abundance ranged from 3810 to 16530 MP/kg. Municipal solid waste compost (MSWC) had highest abundance (16082 ± 632 MP/kg), followed by leaf compost (LC) and organic compost (OC) (6299 ± 1011 and 3680 ± 419 MP/kg, respectively). MPs of <100 μm in size were most dominant in MSWC and LC. Fragments and fibers were the prevalent shape types, with white/transparent colored MPs being more abundant. Polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) were the dominant polymers. MPs accumulation in the lettuce leaves was greatest in the lettuce plants grown on MSWC, followed by those grown on LC and OC, indicating that MSWC grown lettuce is not suitable for human consumption. The decrease in the growth (leaf length, number of leaves, leaf fresh and weights) and physiological (membrane stability index, relative water contents) parameters of lettuce was in line with the trend of MP accumulations. Hence, it is highly important to regulate the plastic contents in compost because it is a threat to ecosystems and human health.

Characterization of HDPE microparticles in sludge aerobic digestion and their influence on the process

The occurrence of microplastics (MPs) in wastewater has been studied in the last years. The high efficiency of their removal from wastewater is linked to their transfer to the sludge. In this work, the effect of high-density polyethylene (HDPE) on aerobic digestion was evaluated and these MPs were monitored, characterizing them by three different techniques. Two parallel batch digesters were monitored. AD-Control (meaning Aerobic Digester) operated as a reference, with no external HDPE particles, whereas these polymeric fragments were introduced to the second aerobic digester (AD-HDPE) using ring pulls as microplastic support. FTIR, Raman spectroscopies and fluorescence analysis of these microparticles showed some relevant results that should be highlighted. Higher fluorescence appeared after 7 days in the digester. It coincided with an increase of active volatile suspended solids (AVSS) in the AD-HDPE, which means that an increase of the microbial activity took place. Despite the presence of HDPE particles in the sludge, the digester performance was not compromised. Besides, the HDPE particles did not affect the microbial diversity (Shannon index) of the bacterial community at the end of the experiment compared to the bacterial community of the aerobic digester control tank. Based on the analysis of the relative abundances of microbial taxa, it was concluded that HDPE had selective effects on sludge microbial community, increasing the relative abundance of Bacteroridota phylum.

Microplastics in water: Occurrence, fate and removal

A global study on tap water samples has found that up to 83% of these contained microplastic fibres. These findings raise concerns about their potential health risks. Ingested microplastic particles have already been associated with harmful effects in animals, which raise concerns about similar outcomes in humans. Microplastics are ubiquitous in the environment, commonly found disposed in landfills and waste sites. Within indoor environments, the common sources are synthetic textiles, plastic bottles, and packaging. From the various point sources, they are globally distributed through air and water and can enter humans through various pathways. The finding of microplastics in fresh snow in the Antarctic highlights just how widely they are dispersed. The behaviour and health risks from microplastic particles are strongly influenced by their physicochemical properties, which is why their surfaces are important. Surface interactions are also important in pollutant transport via adsorption onto the microplastic particles. Our review covers the latest findings in microplastics research including the latest statistics in their abundance, their occurrence and fate in the environment, the methods of reducing microplastics exposure and their removal. We conclude by proposing future research directions into more effective remediation methods including new technologies and sustainable green remediation methods that need to be explored to achieve success in microplastics removal from waters at large scale.

Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

Evaluation of phthalate migration potential in vacuum-packed

In recent years, the presence and migration of PAEs in packaging materials and consumer products has become a serious concern. Based on this concern, the aim of our study is to determine the possible migration potential and speed of PAEs in benthic fish stored in vacuum packaging, as well as to monitor the storage time and type as well as polyethylene (PE) polymer detection.As a result of the analysis performed by µ-Raman spectroscopy, 1 microplastic (MP) of 6 µm in size was determined on the 30th day of storage in whiting fish muscle and the polymer type was found to be Polyethylene (PE) (low density polyethylene: LDPE). Depending on the storage time of the packaging used in the vacuum packaging process, it has been determined that its chemical composition is affected by temperature and different types of polymers are formed. 10 types of PAEs were identified in the packaging material and stored flesh fish: DIBP, DBP, DPENP, DHEXP, BBP, DEHP, DCHP, DNOP, DINP and DDP. While the most dominant PAEs in the packaging material were determined as DEHP, the most dominant PAEs in fish meat were recorded as BBP and the lowest as DMP. The findings provide a motivating model for monitoring the presence and migration of PAEs in foods, while filling an important gap in maintaining a safe food chain.

Co-exposure with cadmium elevates the toxicity of microplastics: Trojan horse effect from the perspective of intestinal barrier

Microplastics (MPs) have been shown to adsorb heavy metals and serve as vehicles for their environmental transport. To date, insufficient studies have focused on enterohepatic injury in mice co-exposed to both MPs and cadmium (Cd). Here, we report that Cd adsorption increased the surface roughness and decreased the monodispersity of PS-MPs. Furthermore, exposure to both PS-MPs and Cd resulted in a more severe toxic effect compared to single exposure, with decreased body weight gain, shortened colon length, and increased colonic and hepatic inflammatory response observed. This can be attributed to an elevated accumulation of Cd resulting from increased gut permeability, coupled with the superimposed effects of oxidative stress. In addition, using 16 S sequencing and fecal microbiota transplantation, it was demonstrated that gut microbiota dysbiosis plays an essential role in the synergistic toxicity induced by PS-MPs and Cd in mice. This study showed that combined exposure to MPs and Cd induced more severe intestinal and liver damage in mice compared to individual exposure, and provided a new perspective for a more systematic risk assessment process related to MPs exposure.

Visualization and characterisation of microplastics in aquatic environment using a home-built micro-Raman spectroscopic set up

Microplastics (MP) which are tiny plastic particles of sizes range from 1 μm (μm) to 5 mm (mm), have become a growing cause of concern due to their widespread presence in the environment and their potential impacts on ecosystems and human health. Marine organisms have the potential to consume microplastics, which could lead to physical injuries, blockages, or the transfer of harmful substances up the food chain. Humans may indirectly consume microplastics through contaminated seafood and water, although the complete scope of health risks is currently under investigation. An essential step in gaining a comprehensive understanding of microplastic pollution in waterbodies is the identification of microplastics, which is also crucial for further development of effective environmental regulations to address its adverse impacts. Majority of the researchers are accomplishing it globally using commercial platforms based on Raman spectroscopy. However, the development of indigenous Raman systems, which can enable microplastic identification, particularly in developing nations, is the need of the hour due to the outrageous cost of commercial platforms. In the current study, a custom-designed micro-Raman spectroscopy system was developed to detect and characterize microplastics from waterbodies. The developed system enabled visualization, size measurement and characterization of microplastics. Experimental parameters were fine-tuned, and a standardized Raman database was established for each type of plastic. This system exhibited high resolution which was capable of analysing microparticles of size up to 5 μm. Principal component analysis was carried out on the experimental Raman data, demonstrating good classification amongst different kinds of plastics. The performance of the developed system in analysing real samples was evaluated through experiments conducted on water samples obtained from the shore of Malpe Beach in Udupi district. The results revealed the presence of polyethylene and polyethylene terephthalate in the samples, along with the detection of pigments like copper phthalocyanine and indigo blue.