Multi-method analysis of microplastic distribution by flood frequency and local topography in Rhine floodplains

Cross-platform detection of microplastics in human biological tissues: Comparing spectroscopic and chromatographic approaches

Microplastic (MP) contamination in ecosystems underscores concerns about human bioaccumulation, yet analytical challenges persist due to complex biological matrices and polymer diversity. To systematically evaluate the efficacy of complementary analytical platforms, we conducted this study to systematically evaluate Raman microscopy and pyrolysis gas chromatography-mass spectrometry (py-GC/MS) for complementary MP detection in human biological samples. Building upon prior research frameworks, 48 paired endometrial and urine samples from parturient women were analyzed under rigorously controlled protocols to minimize exogenous contamination. Raman microscopy identified six polymer types, with polytetrafluoroethylene (PTFE) and polystyrene (PS) constituting primary components across both sample types. Particle size distributions spanned 1.23-6.98 μm, exhibiting comparable mean diameters in urine (2.85 ± 1.26 μm) and endometrial samples (2.89 ± 1.40 μm). Subsequent py-GC/MS analysis revealed previously undetected polymer co-occurrences (PS, PC, PE, and PVC) in samples initially classified as single-polymer PTFE or PS via Raman spectroscopy, thereby exposing inherent disparities in method-specific sensitivity and resolution. The follow-up multi-method comparison demonstrates that Raman microscopy excels in particle-specific morphological characterization, while py-GC/MS provides polymer quantification and composite identification. Our findings underscore the necessity of integrating orthogonal analytical approaches to overcome methodological limitations and achieve comprehensive MP profiling in complex biological systems.

Pulmonary hazards of nanoplastic particles: a study using polystyrene in in vitro models of the alveolar and bronchial epithelium

BACKGROUND

Nanoplastics (NPs) are released into the environment through the degradation of plastic objects, leading to human exposure. Due to their small size, concerns have been raised about the potential hazards to the respiratory tract, as ultrafine and nanoparticles are known to penetrate till the alveolar regions of the lungs, potentially impairing their functions. Thus, in the present study, we used model polystyrene nanoparticles doped with the fluorescent metal europium (PS-Eu) to enhance the understanding of NPs hazard and investigate adverse outcomes associated with exposure in human lungs using alveolar (A549) and bronchial (Calu-3) cell models grown in 2D and 3D submerged conditions or quasi air-liquid interface (ALI) conditions (3D).

RESULTS

Briefly, after in-dept physicochemical characterization of the particles, we assessed their impact on ROS production, cell viability (AlamarBlue and lactate dehydrogenase assays) and barrier integrity (lucifer yellow assay and TEER measurement), finding no negative effects in either model. However, in alveolar cells, particles increased acidic organelle activity. Transmission electron microscopy and Raman microscopy showed, in both models, a dose- and cell-dependent particle uptake with PS-Eu accumulating in numerous and large endo-lysosomes, which, in transwells-grown A549 cells, often contained also lamellar bodies (LBs), organelles involved in surfactants storage and secretion. After extensively quantifying surfactant proteins (SP) in the pellet and supernatant fractions of treated A549 cells, we observed a significant reduction in several members of this family, including surfactant protein B, which is crucial for lamellar body formation and surface tension regulation in the lungs. In quasi-ALI Calu-3 cultures instead, PS-Eu significantly upregulated interleukin 6 (IL-6) and increased transforming growth factor beta β (TGF-β), zonula occludens 1 (ZO-1), and mucin (MUC) 5B mRNA expressions causing a moderate proinflammatory response.

CONCLUSION

Our results show that PS-Eu exposure does not induce acute cytotoxicity in these models, but affects cell-specific functions like surfactant, mucin, and cytokine production. This underscores the limitations of relying solely on standard cytotoxicity tests for particle hazard assessment and highlights the importance of investigating cell function-specific signaling pathways. To support researchers in hazard assessment, we propose specific classes of biomarkers to test in in vitro lung models.

Sequential combination of micro-FTIR imaging spectroscopy and pyrolysis-GC/MS for microplastic quantification. Application to river sediments

Several studies have focused on quantifying microplastics (MP) in the environment using μ-FTIR and Py-GC/MS, the most common analytical methods. However, their application to complex matrices like sediments is affected by interferences specific to each method. In this study, we developed a protocol combining μ-FTIR and Py-GC/MS for sequential analysis of MP (10-500 μm) in 16 river sediment samples, targeting PE, PP, and PS polymers. Mass concentrations were estimated from the particle volume in μ-FTIR and measured directly by Py-GC/MS using internal calibration. Results show consistency between the two methods across different sites, with variability of two orders of magnitude in concentration ranges from 0.3 to 50 items g-1 and 0.2 to 17 μg g-1 for μ-FTIR, and 0.8 to 21 μg g-1 for Py-GC/MS. Replicate analyses (2 to 6 per site) revealed that intra-site variability was mainly influenced by sample preparation and, to a lesser extent, by the measurement technique. While estimated and measured concentrations were similar, discrepancies were observed in polymer proportions: PP predominated in μ-FTIR, while PS was more prevalent in Py-GC/MS. These differences are explained by the specific limitations of each method, especially the limited detection of synthetic fibres and tyre or road abrasion particles by μ-FTIR, which are detected as MP by Py-GC/MS. This comparative study provides recommendations for evaluating compatibility between studies using either technique and offers guidelines for selecting the most appropriate method based on research interests.

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.