Environmental Health Implications Research Articles

COVID-19 pandemic microplastics environmental impacts predicted by deep random forest (DRF) predictive model

BackgroundMicroplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.MethodsThe research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.ResultsThe findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.ConclusionsThe study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.

Observing super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate at an agro-forest site in Thailand.

Coarse aerosol particles containing chloride in tropical forests are significant for understanding biogeochemical cycles and atmospheric processes, with implications for environmental health and climate change mitigation. This study explored the sources of super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate. Aerosol samples were collected from an agro-forest site in Thailand during the dry season and analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy. By examining the morphology and elemental compositions of individual aerosol particles, along with employing Positive Matrix Factorization (PMF) and backward trajectory analysis, potential sources were identified. The findings revealed two primary sources for the super-coarse aerosol particles: a mixture of biomass burning smoke and inorganic salts (likely from saline soil and sea salt), as well as halophilic fungal spores. FTIR analysis indicated the presence of compounds linked to biomass burning and clay minerals, influenced by prevailing northeast and southeast winds. Recommendations for future research include continued monitoring, correlation with meteorological parameters, and the application of transmission electron microscopy (TEM) for more detailed visualization and confirmation of aerosol sources.

Predicting removal of arsenic from groundwater by iron based filters using deep neural network models

Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R2) 0.990–0.999 for As, 0.774–0.976 for Iron (Fe), 0.934–0.954 for Phosphorus (P), and 0.878–0.998 for predicting manganese (Mn) in the effluent. Sobol sensitivity analysis revealed that As (total order index (ST) = 0.563), P (ST = 0.441), Eh (ST = 0.712), and Temp (ST = 0.371) are the most sensitive parameters for the removal of As, Fe, P, and Mn. The comprehensive approach, from data expansion through DLNN model development, provides a valuable tool for estimating optimal As removal conditions from groundwater.

Rainfall-induced microplastic fate and transport in unsaturated dutch soils

Microplastic pollution has become a growing concern in terrestrial ecosystems, with significant implications for environmental and human health. Understanding the fate and transport of microplastics in soil environment is crucial for effective mitigation strategies. This study investigates the dynamics of microplastic (Low-density polyethylene (LDPE), polybutylene adipate terephthalate (PBAT), and starch-based biodegradable plastic) transport in unsaturated soils under varying rainfall intensities and soil types, aiming to elucidate the factors influencing their behavior. Effluent samples were analyzed to measure microplastic transport, with microplastic balance analysis ensuring experimental accuracy. The setup replicated real-world flow conditions, providing insights into microplastic transport in unsaturated porous media. Microplastic balance analysis revealed high recovery factors (between 64 % and 104 %), indicating the reliability of the experimental approach. Microplastic transport varied significantly between sandy loam and loamy sand soils, with loamy sand soils exhibiting higher wash-off rates due to their unique properties. LDPE microplastics showed a higher tendency to detach from soil columns compared to PBAT and starch-based particles. Higher rainfall intensity in loamy sand soil columns resulted in an increased washout of LDPE, PBAT, and starch-based particles by 92 %, 144 %, and 85 %, respectively, compared to low rainfall intensity. In sandy loam soil, increased rainfall intensity resulted in a significantly higher washout of LDPE, PBAT, and starch-based particles with percentages of 93 %, 69 %, and 45 %, respectively. This underscores the important role of water flow in mobilizing microplastics within the soil matrix.

Systematic Review and Spatiotemporal Assessment of Mercury Concentration in Fish from the Tapajós River Basin: Implications for Environmental and Human Health

Systematic Review and Spatiotemporal Assessment of Mercury Concentration in Fish from the Tapajós River Basin: Implications for Environmental and Human Health

Evaluating the impact of gadolinium contamination on the marine bivalve Donax trunculus: Implications for environmental health

Gadolinium (Gd), commonly used in contrast agents for medical imaging, has been detected in hospital wastewater and aquatic environments, raising environmental concerns. This study examined the accumulation and cellular impacts of Gd in the clam species Donax trunculus, commonly used as bioindicator of contamination. Gadolinium accumulation in clams increased with exposure and over time. Biological responses varied with Gd levels: low concentrations (10 and 50 µg/L) led to low metabolic activity and glycogen content, but high antioxidant activities and lipid peroxidation levels (LPO); high concentrations (250 and 500 µg/L) resulted in increased metabolic activity, while antioxidant enzyme activity was inhibited and LPO levels were the lowest. Metabolic activity decreased after two weeks, suggesting limited long-term metabolic resilience. The study underscores D. trunculus as an effective early warning species for Gd pollution and highlights the ecological risks of rising Gd levels, emphasizing the need for environmental monitoring and regulation.

The Effect of Polyethylene Microplastics on Growth and Antioxydant Response of Oscillatoria Princeps and Chlorella Pyrenoidosa.

This study investigated the impacts of polyethylene microplastics (PE-MPs) with varying particle sizes (13μm and 6.5μm) on the growth and antioxidant responses of two freshwater algae species, Oscillatoria princeps (O. princeps) and Chlorella pyrenoidosa (C. pyrenoidosa). The results revealed a significant reduction in chlorophyll a content in both algal species upon exposure to PE-MPs, indicating a disruption of photosynthesis. Furthermore, Superoxide Dismutase (SOD) activity decreased in O. princeps, while Catalase (CAT) activity increased in both species, indicating complex physiological responses to microplastic stress. Notably, phycotoxin levels in O. princeps decreased with PE-MP exposure, while those in C. pyrenoidosa increased, particularly with 6.5μm PE-MPs. These findings underscore the potential toxic effects of PE-MPs on freshwater algal growth and metabolism, as well as their influence on toxin production. This study contributes valuable insights into the ecotoxicological impacts of microplastics in freshwater environments, highlighting the need for further research on their biological effects and environmental health implications.

Integrated environmental and social assessment of alum-contaminated water in An Giang province

ABSTRACT This study investigates alum-contaminated water in An Giang province within the Mekong Delta, emphasizing methodological approaches to assess its distribution and impacts. The research conducted from March to May 2021 involved surveys of residents, public sector employees, and provincial managers, complemented by expert interviews and extensive fieldwork. Surface water samples were collected during both rainy and dry seasons across three districts, with parameters such as turbidity, pH, and temperature measured onsite, and aluminum (Al), iron (Fe), and copper (Cu) concentrations analyzed in the laboratory using atomic absorption spectrometry. The data were further processed using QGIS software to create comprehensive maps illustrating seasonal variations in water quality. The findings revealed significant spatial and temporal variations in water quality, with pH levels fluctuating markedly between the dry and rainy seasons. Household surveys indicated a lack of awareness regarding effective water treatment methods, with most respondents relying on local authorities for solutions. The study highlights the need for community education and participation in addressing alum contamination, offering insights into the environmental and public health implications of water quality in the region. This research contributes valuable knowledge to the ongoing efforts in environmental conservation and water quality management in the Mekong Delta.

Dynamic Spatio-Temporal Modeling for Enhanced Air Quality Prediction: Implications for Information Management and Public Health Decision Support Systems

Air quality forecasting has significant implications for environmental monitoring, public health, and information management systems. This study proposes three advanced deep learning models: Graph Neural Networks with Dynamic Spatio-Temporal Attention (GNN-DSTA), Multi-Resolution Convolutional Recurrent Neural Networks (MRC-RNN), and Variational Autoencoders with Spatio-Temporal Latent Embeddings (VAE-STLE). These models address the challenges of capturing complex spatio-temporal dependencies in environments with sparse data samples. The GNN-DSTA model introduces a temporal attention mechanism that dynamically captures evolving spatial-temporal dependencies. MRC-RNN combines CNN's spatial pattern recognition with RNN's temporal modeling across multiple spatial resolutions. VAE-STLE provides a probabilistic framework for robust and interpretable forecasting. Experimental results demonstrate significant improvements in prediction accuracy: GNN-DSTA reduces RMSE by 15-20%, MRC-RNN improves accuracy by 12-15%, and VAE-STLE shows a 10-12% improvement with enhanced uncertainty estimation. These models advance AQI predictions through dynamic attention mechanisms, multi-resolution analysis, and probabilistic forecasting. Furthermore, this study explores the implications of these advanced predictions for information management and public health decision support systems. We discuss the integration of real-time data, scalability considerations for large-scale deployments, user interface design for effective communication of predictions, and ethical considerations in using AI-driven models for public health decision-making. The proposed approach not only enhances the accuracy and reliability of AQI predictions but also provides a framework for developing more effective and responsive public health interventions and environmental policies.

Mapping, Distribution, Function, and High-Throughput Methodological Strategies for Soil Microbial Communities in the Agroecosystem in the Last Decades

The intricate interplay between SMCs and agroecosystems has garnered substantial attention in recent decades due to its profound implications for agricultural productivity, ecosystem sustainability, and environmental health. Understanding the distribution of SMCs is complemented by investigations into their functional roles within agroecosystems. Soil microbes play pivotal roles in nutrient cycling, organic matter decomposition, disease suppression, and plant‒microbe interactions, profoundly influencing soil fertility, crop productivity, and ecosystem resilience. Elucidating the functional diversity and metabolic potential of SMCs is crucial for designing sustainable agricultural practices that harness the beneficial functions of soil microbes while minimizing detrimental impacts on ecosystem services. Various molecular techniques, such as next-generation sequencing and high-throughput sequencing, have facilitated the elucidation of microbial community structures and dynamics at different spatial scales. These efforts have revealed the influence of factors such as soil type, land management practices, climate, and land use change on microbial community composition and diversity. Advances in high-throughput methodological strategies have revolutionized our ability to characterize SMCs comprehensively and efficiently. These include amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics, which provide insights into microbial taxonomic composition, functional potential, gene expression, and protein profiles. The integration of multiomics approaches allows for a more holistic understanding of the complex interactions within SMCs and their responses to environmental perturbations. In conclusion, this review highlights the significant progress made in mapping, understanding the distribution, elucidating the functions, and employing high-throughput methodological strategies to study SMCs in agroecosystems.

Origin, distribution, fate, and risks of potentially toxic elements in the aquatic environment of Bengaluru Metropolis, India

This study probes the water quality, including pH, total dissolved solids (TDS), and potentially toxic elements (PTEs) concentrations, and the associated environmental and human health implications, in forty one surface water bodies in Bengaluru metropolis, southern India. The pH in 54%, TDS in 63%, Ni in 12%, and U, Mo, Pb, Cr, Co, and Cu in 5% of the forty one water bodies exceed the WHO’s permissible limits for drinking purpose. Total dissolved solids display a significant positive correlation with Pb, Cr, Co, Cu, and Ni, suggesting the association of these elements with particulate matter. Sources of metal pollution include industries in the city's west (high levels of Pb, Cr, Co, Ni, and Cu), traffic in the city Centre (Mo), and geogenic (U) sources in the city's north, east, and south. The degree of contamination is high in 25%, moderate in 10%, and low in 65% of the forty one water bodies, with the highest degree of contamination in Narasappanehalli Lake in the industrial zone, Deepanjali Nagara Lake, and Govindraj Nagar drainage systems. Uranium, Pb, and Mo display a low to medium degree of contamination, whereas Cr, Co, Ni, and Cu display a medium to high degree of contamination. The non-carcinogenic risk through ingestion of contaminated water is medium to high for adults and children and the carcinogenic risk is high in all water bodies. Channels transport contaminated water from Bengaluru water bodies to the Pinakani and Cauvery Rivers and then to the northern Indian Ocean. The states of Karnataka and Tamil Nadu rely on water from these rivers for domestic and agricultural use exposing large populations to contaminated waters. Additionally, contaminated waters can negatively impact flora and fauna of Peninsular India as well as the marine biota of the northern Indian Ocean.

Industrial processing of chickpeas (Cicer arietinum) for protein production

AbstractThe increasing global interest in plant‐based proteins stems from concerns about the environmental impact, sustainability, animal welfare, and health implications associated with consuming animal‐based proteins. In the frame of alternative protein sources, chickpea (Cicer arietinum) emerged as a rich source of dietary proteins besides containing good amount of carbohydrate, fat, and fiber. As a protein ingredient, chickpea is available in three forms, namely, flour, concentrate, and isolate. This chickpea protein can be extracted using both wet and dry fractionation methods where the former one includes wet extraction followed by isoelectric precipitation, while the later one indicates dry milling followed by air classification. However, different nonthermal emerging technologies have been seen to assist in extracting protein as well as modifying their functionalities. This review gives an outline of the recently available literature on composition, industrial processing and associated technological challenges, functionality, and application of chickpea protein ingredients. Furthermore, discussion on the modification/improvement of chickpea protein functionality with the assistance of emerging technologies and the potentiality of by‐products produced during chickpea protein processing are also included. Based on the available findings and discussion, it is seen that apart from being a comparable source of alternative animal‐based protein to extract, chickpea derived by‐products can also be a potential source of valued ingredients that might contribute to the circular economy.

Lambda Cyhalothrin (LCT)-Induced Long-term Pesticide Effects of Albino Rats

Lambda cyhalothrin (LCT), a prominent pyrethroid insecticide, has been linked to a number of negative side effects in non-target animals, including mammals. This study investigated the long-term consequences of lambda- cyhalothrin (LCT) exposure on albino rats, including physiological, biochemical, and behavioural alterations. Adult albino rats were given sub-lethal lambda-cyhalothrin (LCT) doses over an extended period to simulate chronic exposure. Body weight, organ histology, serum biochemical markers, and behavioural reactions were all examined to determine the toxicological impact. The biochemical markers of liver and kidney function showed substantial variation, indicating organ damage. Histopathological evaluation of the liver and kidney tissues revealed structural damage consistent with the biochemical abnormalities discovered. The neurotoxic potential of lambda-cyhalothrin (LCT) was further demonstrated by the exposed rats' notable behavioural changes, which included decreased locomotor activity and increased anxiety-like behaviour. These findings emphasise the potential long-term risks associated with repeated exposure to lambda cyhalothrin (LCT), emphasizing the need for additional research and strong regulations to understand the implications for environmental safety and public health.

Aminocarb Exposure Induces Cytotoxicity and Endoplasmic Reticulum Stress-Mediated Apoptosis in Mouse Sustentacular Sertoli Cells: Implications for Male Infertility and Environmental Health.

Exposure to pesticides, poses a significant threat to male fertility by compromising crucial cells involved in spermatogenesis. Aminocarb, is a widely used carbamate insecticide, although its detrimental effects on the male reproductive system, especially on sustentacular Sertoli cells, pivotal for spermatogenesis, remains poorly understood. In this study, we investigated the effects of escalating concentrations of aminocarb on a mouse Sertoli cell line, TM4. Assessments included cytotoxic analysis, mitochondrial biogenesis and membrane potential, expression of apoptotic proteins, caspase-3 activity, and oxidative stress evaluation. Our findings revealed a dose-dependent reduction in the proliferation and viability of TM4 cells following exposure to increasing concentrations of aminocarb. Notably, exposure to 5 μM of aminocarb induced depolarization of mitochondria membrane potential, and a significant decrease in the ratio of phosphorylated eIF2α to total eIF2α, suggesting heightened endoplasmic reticulum stress via the activation of the eIF2α pathway. Moreover, the same aminocarb concentration was demonstrated to increase both caspase-3 protein levels and activity, indicating an apoptotic induction. Collectively, our results demonstrate that aminocarb serves as an apoptotic inducer for mouse sustentacular Sertoli cells in vitro, suggesting its potential to modulate independent pathways of the apoptotic cascade. These findings underscore the deleterious impact of aminocarb on spermatogenic performance and male fertility, highlighting the urgent need for further investigation into its mechanisms of action and mitigation strategies to safeguard male fertility.

Assessment of sub-lethal effects of Celcron on Java barb through erythrocyte morphology and acetylcholinesterase activity: Implications for environmental health in aquatic ecosystems

Industrialization and the extensive use of chemicals have raised significant concerns about their environmental impacts, particularly on aquatic ecosystems. This study evaluated the sub-lethal effects of Celcron (Cec), an organophosphate insecticide, on the Java barb (Barbonymus gonionotus) through erythrocyte morphology and acetylcholinesterase (AChE) activity, aiming to refine biomarkers for environmental health assessments. We hypothesized that sub-lethal Cec exposure would induce significant erythrocyte abnormalities and decrease AChE activity in Java barb, with variable recovery rates between gill and kidney tissues. To test this, we exposed the juvenile Java barbs to two sub-lethal Cec concentrations - 0.01 ppm (10 % of the LC50) and 0.05 ppm (50 % of the LC50) -for 60 days. After the exposure period, the fish were placed in pesticide-free water to allow for recovery. Results indicated a significant decline in AChE activity in both liver and kidney tissues, with activity levels showing gradual recovery over time. Erythrocyte abnormalities, including nuclear and cellular changes, were significantly elevated in response to Cec exposure. The frequency of nuclear abnormalities such as micronuclei and binucleation increased in a concentration- and duration-dependent manner, with the gill blood exhibiting higher sensitivity and slower recovery compared to kidney blood. Cellular abnormalities such as twin, teardrop and spindle-shaped cells were also more prevalent in Cec-treated fish. Recovery from these abnormalities was observed but varied between gill and kidney blood, with gill blood showing higher sensitivity and slower recovery compared to kidney blood. This study underscores the utility of AChE activity and erythrocyte abnormalities as biomarkers for assessing pesticide impacts on aquatic organisms. The findings highlight the sensitivity of fish erythrocytes to environmental contaminants and emphasize the need for continued research to better understand the long-term effects of pesticide exposure on aquatic health and ecosystem stability.

Pollution of Soil by Pharmaceuticals: Implications for Metazoan and Environmental Health.

The use of pharmaceuticals has grown substantially and their consequential release via wastewaters poses a potential threat to aquatic and terrestrial environments. While transportation prediction models for aquatic environments are well established, they cannot be universally extrapolated to terrestrial systems. Pharmaceuticals and their metabolites are, for example, readily detected in the excreta of terrestrial organisms (including humans). Furthermore, the trophic transfer of pharmaceuticals to and from food webs is often overlooked, which in turn highlights a public health concern and emphasizes the pressing need to elucidate how today's potpourri of pharmaceuticals affect the terrestrial system, their biophysical behaviors, and their interactions with soil metazoans. This review explores the existing knowledge base of pharmaceutical exposure sources, mobility, persistence, (bio)availability, (bio)accumulation, (bio)magnification, and trophic transfer of pharmaceuticals through the soil and terrestrial food chains.

Assessing Heavy Metal Accumulation in Urban Plants: Implications for Environmental Health and Traffic-Related Pollution in Al-Diwaniyah City, Iraq

This study aimed to compare the ability of five plant species, including (Conocarpus erectus, Acacia sensu lato (s.l.), Melaleuca viminalis, Dodonaea viscosa and Lantana camara) to absorb and accumulate heavy elements in their tissues, which were grown in the central islands in the city of Diwaniyah. This included areas of street in front of the medical college, Umm Al Khail First Street, Umm Al-Khail Street, near Abbas Attiwi Bridge, Al-Adly Street in the Euphrates District, and Clock Field Street, respectively. Results showed that soil samples S1 and S3 were contaminated by Pb, and the rest of the sites were contaminated with nickel only. This indicates through the table findings a rise in these heavy metals’ concentrations with a rise in traffic momentum. Thus, the Pb concentrations in the growing plants’ shoot parts with respect to this research had surpassed the allowed critical limit of 5.00 mg.kg-1 dry matter, in which the highest value was recorded at the site with respect to S3 as well as S2. Meanwhile, the findings indicate that Cd concentrations in S3 and S1 had increased and exceeded the allowable limit of 0.20 mg.kg-1 dry matter. In the meantime, the nickel concentrations were within the permissible limits of 67.90 mg.kg-1 dry matter. The Zn concentration exceeded the permitted limits of 60.00 mg.kg-1 dry matter except for plants (Acacia s.l. and Lantana camara) in sites S5 and S2. The results confirmed that the values of Heavy Metals Bioaccumulation Coefficient (BAC) for most of the study elements had recorded the highest value in the Dodonaea plant for Zn, Cd, and Pb, except for Ni. It was more accumulated in the Melaleuca viminalis plant, which indicates the superiority of the Dodonaea plant in accumulating Pb, Cd, and Zn over the rest of the study plants, as they took the following order: Lantana camara < Acacia s.l. < Conocarpus erectus < Melaleuca viminalis < Dodonaea viscosa. The best plants accumulated nickel in the following order: Acacia s.l. < Lantana camara < Conocarpus erectus < Dodonaea viscosa < Melaleuca viminalis.

P18-09 Quantitative analysis of micro and nanoplastics in biological tissues: implications for environmental and human health

P18-09 Quantitative analysis of micro and nanoplastics in biological tissues: implications for environmental and human health

Assessing the physiological responses and uptake patterns of lanthanum and yttrium in rice and Phytolacca americana L.

The effects of rare earth elements (REEs) on plant physiology and bioaccumulation characteristics vary significantly. As a result, considerable attention has been directed towards the effects and toxicity of REEs on plants. REEs migrate and transform in the environment and are absorbed by plant roots. However, different plants exhibit varying capacities to absorb REEs, leading to diverse impacts on their growth. In our study, we subjected seedlings of rice and Phytolacca americana L. to a gradient of lanthanum and yttrium (La + Y) concentrations to evaluate the physicochemical effects and uptake patterns of these elements. We meticulously measured variables such as biomass, root length, and elemental concentrations of La and Y, as well as macronutrients (P, Na, K, Mn, Fe, Mg, Ca) and micronutrients (Cu, Zn) to decode the physiological responses to varying levels of La + Y exposure. Based on the known dualistic nature of REEs interaction with plants, our results reveal that the threshold of REEs to different plants is different, and this conclusion can serve well for the formulation of rare earth pollution remediation strategies. Importantly, the accumulation of La and Y in P. americana L. roots was significantly greater than in rice roots, by an order of magnitude, reflecting the unique uptake capacities of hyperaccumulators. Additionally, this research found that nutrient dynamics and the absorption patterns of La and Y within the plants diverged, suggesting that La uptake in P. americana L. may occur via Ca2+ ion channels. These insights enhance our understanding of REE-plant interactions and have broader implications for agriculture and environmental health.

Polystyrene nanoplastics enhance poxvirus preference for migrasome-mediated transmission

Since the emergence of a global outbreak of mpox in 2022, understanding the transmission pathways and mechanisms of Orthopoxviruses, including vaccinia virus (VACV), has become paramount. Nanoplastic pollution has become a significant global issue due to its widespread presence in the environment and potential adverse effects on human health. These emerging pollutants pose substantial risks to both living organisms and the environment, raising serious health concerns related to their proliferation. Despite this, the effects of nanoparticles on viral transmission dynamics remain unclear. This study explores how polystyrene nanoparticles (PS-NPs) influence the transmission of VACV through migrasomes. We demonstrate that PS-NPs accelerate the formation of migrasomes early in the infection process, facilitating VACV entry as soon as 15 h post-infection (hpi), compared to the usual onset at 36 hpi. Immunofluorescence and transmission electron microscopy (TEM) reveal significant co-localization of VACV with migrasomes induced by PS-NPs by 15 hpi. This interaction coincides with an increase in lipid droplet size, attributed to higher cholesterol levels influenced by PS-NPs. By 36 hpi, migrasomes exposed to both PS-NPs and VACV exhibit distinct features, such as retraction fibers and larger lipid droplets, emphasizing their critical role in cargo transport during viral infections. These results suggest that PS-NPs may act as modulators of viral transmission dynamics through migrasomes, with potential implications for antiviral strategies and environmental health.