Pollution Monitoring Research Articles

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

SummaryThe underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate.

Cobalt-Modified Black Phosphorus Nanosheets-Enabled Ferrate (VI) Activation for Efficient Chemiluminescence Detection of Thiabendazole.

The development of chemiluminescence-based innovation sensing systems and the construction of a sensing mechanism to improve the analytical performance of compounds remain a great challenge. Herein, we fabricated an advanced oxidation processes pretreated chemiluminescence (AOP-CL) sensing system via the introduction of cobalt-modified black phosphorus nanosheets (Co@BPNs) to achieve higher efficient thiabendazole (TBZ) detection. Co@BPNs, enriched with lattice oxygen, exhibited a superior catalytic performance for accelerating the decomposition of ferrate (VI). This Co@BPNs-based ferrate (VI) AOP system demonstrated a unique ability to selectively decompose TBZ, resulting in a strong CL emission. On this basis, a highly selective and sensitive CL sensing platform for TBZ was established, which exhibited strong resistance to common ions and pesticides interference. This was successfully applied to detecting TBZ in environmental samples such as tea and kiwi fruits. Besides, the TBZ detection mechanism was explored, Co@BPNs-based ferrate (VI) AOP system produced a high yield of ROS (mainly 1O2), which oxidized the thiazole-based structure of TBZ, generating chemical energy that was transferred to Co@BPNs via a chemical electron exchange luminescence (CIEEL) mechanism, leading to intense CL emission. Notably, this study not only proposed an innovative approach to enhance the chemical activity and CL properties of nanomaterials but also offered a new pathway for designing efficient CL probes for pollutant monitoring in complex samples.

Technological progression associated with monitoring and management of indoor air pollution and associated health risks: A comprehensive review

AbstractIndoor air pollution (IAP) is a prevalent issue, and in the absence of any concrete and stringent guidelines, particularly in developing countries the concern becomes graver. Technological strategies like the use of the Internet of Things (IoT) and cloud computing have been explored for real‐time monitoring and these interventions may be investigated to improve indoor air quality (IAQ) and human health. This review article explores the prospects of IoT and smart environments for the improvement of indoor living conditions through automation. Some specific interference like sensors, air pollution simulations and modeling, the concept of smart ventilation, and fuzzy logic controllers (FLC) have been elaborated with pieces of evidence taken from previously published studies in leading indexing databases. Furthermore, some automated health risk assessment tools like the Human exposure model (HEM), Integrated Fuzzy‐stochastic modeling (IFSM), and proximity and interpolation models have also been described. The findings suggested that IoT‐based gadgets require ambient intelligence capabilities for ambient assisted living (AAL). The studies showed that innovations in technology like sensors and modeling techniques may yield crucial information on pollution exposure enabling long‐term and sustainable predictions. However, efficient AAL systems may also face challenges in designing interfaces, usability, and accessibility. Although IoT can aid in mimicking real‐world scenarios, nevertheless its‐micro spatial scale application requires thorough investigation for reliable information extraction.

Rapid sequential detection of Al3+ and glyphosate using an “Off-On-Off” fluorescent probe based on salicylate modified layered double hydroxides

A fluorescent probe based on salicylate modified layered double hydroxide (LDH-SA) is presented, enabling the swift sequential detection of Al3+, fosetyl-Al and glyphosate in aqueous environment. The probe was synthesized using a simple co-precipitation procedure, and its properties and synthesis conditions were thoroughly characterized and optimized. A unique “off-on-off” fluorescent response was observed when the probe sequentially interacted with Al3+ and glyphosate, and the detection method based on this phenomenon was established. The limits of detection for Al3+ and glyphosate were determined as 0.03 μmol/L and 0.03 mg/L, respectively, with rapid detection periods of one minute and four minutes. The LDH-SA/Al3+ complex requires Al3+ to generate a chelation-gathered fluorescence effect, which is the mechanism by which it quenches LDH-SA. This is possible due to the inhibition of excited-state intramolecular proton transfer and photoinduced electron transfer processes within LDH-SA after incorporating Al3+. Upon interaction with glyphosate, competitive complexation between glyphosate and Al3+ is initiated, which leads to a recovery of the fluorescence spectrum of LDH-SA and demonstrating the “off-on-off” behavior. An “INHIBIT” logic gate system was devised utilizing the response, indicating potential applications in fluorescence-based devices. Such a rapid, sequential detection capacity is impressive. It attests to the utility of LDH-SA as a probe for Al3+ or glyphosate, and suggests promise for applications in pollutant analysis or environmental monitoring applications.

Analysis of Binding Modes between Three Perfluorosulfonates and GPER Based on Computational Simulation and Multiple Spectral Methods.

Perfluorinated compounds (PFCs) belong to a significant category of global environmental pollutants. Investigating the toxicological effects of PFCs within biological systems is of critical significance in various disciplines such as life sciences, environmental science, chemistry, and ecotoxicology. In this study, under simulated human physiological conditions (pH = 7.4), a combination of multiple spectroscopic techniques and computational simulations was employed to investigate the impact of perfluorinated compounds (PFCs) on the G protein-coupled estrogen receptor (GPER). Additionally, the research focused on exploring the binding modes and toxicological mechanisms between PFCs and GPER at the molecular level. All three perfluorinated sulfonic acids (PFSAs) can induce quenching of GPER fluorescence through static quenching and non-radiative energy transfer. Steady-state fluorescence calculations at different temperatures revealed apparent binding constants in the order of 106, confirming a strong binding affinity between the three PFSAs and GPER. Molecular docking studies indicated that the binding sites of PFSAs are located within the largest hydrophobic cavity in the head region of GPER, where they can engage in hydrogen bonding and hydrophobic interactions with amino acid residues within the cavity. Fourier transform infrared spectroscopy, three-dimensional fluorescence, and molecular dynamics simulations collectively indicate that proteins become more stable upon binding with small molecules. There is an overall increase in hydrophobicity, and alterations in the secondary structure of the protein are observed. This study deepens the comprehension of the effects of PFCs on the endocrine system, aiding in evaluating their potential impact on human health. It provides a basis for policy-making and environmental management while also offering insights for developing new pollution monitoring methods and drug therapies.

Leachate derived humic-like substances drive the variation in microbial communities in landfill-affected groundwater

Landfills are commonly used for waste disposal in many countries, and pose a significant threat of groundwater contamination. Dissolved organic matter (DOM) plays a crucial role as a carbon and energy source, supporting the growth and activity of microorganisms. However, the changes in the DOM signature and microbial community composition in landfill-affected groundwater and their bidirectional relationships remain inadequately explored. Herein, we showed that DOM originating from more recent landfills mainly comprises microbially produced substances resembling tryptophan and tyrosine. Conversely, DOM originating from older landfills predominantly comprises fulvic-like and humic-like compounds. Leachate leakage increases microbial diversity and richness and facilitates the transfer of foreign bacteria from landfills to groundwater, thereby increasing the vulnerability of the microbial ecosystem in groundwater. Deterministic processes dominated the assembly of the groundwater microbial community, while stochastic processes accounted for an increased proportion of the microbial community in the old landfills. The dominant phyla observed in groundwater were Proteobacteria, Bacteroidota, and Actinobacteriota, and humic-like substances play a crucial role in driving the variation in microbial communities in landfill-affected groundwater. Predictions using PICRUSt2 suggested significant associations between various metabolic pathways and microbial communities, with the Kyoto Encyclopedia of Genes and Genomes pathway “Metabolism” being the most predominant. The findings contribute to advancing our understanding of the transformation of DOM and its interplay with microbial communities and can serve as a scientific reference for decision-making regarding groundwater pollution monitoring and remediation.

Application of isotope as a nuclear technique in groundwater pollution assessment due to human activities

Clean and sustainable groundwater is an important asset for human life and ecosystems. However, the threat of groundwater pollution is increasing as a result of human activities such as industrial, agricultural, and municipal. The objective of this review article is to examine how isotope techniques, as one of the nuclear techniques serve as an effective tool in studying, detecting, and understanding the issue of groundwater pollution stemming from anthropogenic activity on the surface. This article covers the description and discussion of the types of isotopes used in the detection of groundwater pollution and factors that influence the variation in the composition of isotopes in groundwater based on the findings of previous studies. In addition, this article also comments on the study of groundwater pollution using isotopes in the context of groundwater pollution assessment in Malaysia. This article serves as a guide or reference to researchers, environmental professionals, and water resources managers who want to understand and implement nuclear techniques in the monitoring and assessment of groundwater pollution. This article also outlines a holistic view on the resolution of groundwater pollution issues through the application of nuclear techniques and isotopes as a significant tool in ensuring sustainable environmental management.

The Application of Metal-Organic Frameworks in Water Treatment and Their Large-Scale Preparation: A Review.

Over the last few decades, there has been a growing discourse surrounding environmental and health issues stemming from drinking water and the discharge of effluents into the environment. The rapid advancement of various sewage treatment methodologies has prompted a thorough exploration of promising materials to capitalize on their benefits. Metal-organic frameworks (MOFs), as porous materials, have garnered considerable attention from researchers in recent years. These materials boast exceptional properties: unparalleled porosity, expansive specific surface areas, unique electronic characteristics including semi-conductivity, and a versatile affinity for organic molecules. These attributes have fueled a spike in research activity. This paper reviews the current MOF-based wastewater removal technologies, including separation, catalysis, and related pollutant monitoring methods, and briefly introduces the basic mechanism of some methods. The scale production problems faced by MOF in water treatment applications are evaluated, and two pioneering methods for MOF mass production are highlighted. In closing, we propose targeted recommendations and future perspectives to navigate the challenges of MOF implementation in water purification, enhancing the efficiency of material synthesis for environmental stewardship.

Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.

PM<sub>2.5</sub> air pollution trends and patterns in Kandy, Sri Lanka

Air pollution is the leading environmental hazard that has devastating health effects on human health. Identification of trends and patterns of air pollution is crucial in a specific geographic area to inform and implement effective control and mitigation measurements. Standard air pollution monitoring networks are costly for most low-and middle-income countries. The aim of this study was to investigate the variations in the mean concentrations of air pollutants that occur at various times of the day, days of the week, months of the year, and seasons during the period of January 2019 to December 2019 in Kandy, Sri Lanka using low-cost sensors. A regression analysis with dummy variables was used to model the relationship between the concentrations of air pollutants and categorical independent variables, the time of day and day of the week. Considering temporal variations, the study found that pollution is highest in the early morning and evening, and lowest in the afternoon. The worst days for the air quality were Wednesdays and Thursdays, while Sundays had the lowest PM2.5 pollution. The analysis of air quality variations by month showed that December had the lowest air pollution of the examined months. The variation in PM2.5 was greatly influenced by wind speed, wind direction, and rainfall patterns. The study emphasized the importance of long-term consistently management and monitoring of air pollution levels.

Emerging Technologies for Automation in Environmental Sensing: Review

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

Gas Detecting Evaluation by Graphitic-based Silicon Carbide Nanosurface doped with Manganese: A Promising Device for Air Pollution Monitoring

Silicon carbide (SiC) as a direct broad bandgap semiconducting material has the potential to bring a considerable developement into optoelectronic and electronic devices. This article aims to investigate Physico-chemical properties of manganese (Mn)–doped graphene-like silicon carbide (SiC) monolayer sheet by the first-principles methods based on the density functional theory (DFT) for scavenging of CO, CO2, NO, NO2 gas molecules. The results recommend that the adsorption of these gas molecules on Mn-embedded SiC sheet monolayer is more energetically desired than that on the pristine ones. Gas molecules of CO, CO2, NO, NO2 have been adsorbed on the Mn site of doped SiC monolayer through the formation of covalent bonds. The assumption of chemical adsorptions has been approved by the projected density of states (PDOS) and charge density difference plots. Charge density difference calculations also indicate that the electronic densities were mainly accumulated on the adsorbate of CO, CO2, NO, NO2 gas molecules. The ability of SiC nanosheet for monitoring of CO, CO2, NO and NO2 is fluctuated by their selectivity and sensitivity through NMR, NQR, IR and HOMO/LUMO approaches which can represent the efficiency of Mn–doped SiC surface as the promising sensors toward air pollution detecting.

Mercury monitoring in the coastal areas of the Republic of Korea using the black-tailed gull egg as an indicator

In the southeast and east coasts of the Republic of Korea, it is essential to monitor mercury accumulation in coastal organisms in view of the higher mercury distribution in sediments and human samples. However, mercury pollution monitoring in organisms, especially higher trophic-level organisms that can exhibit high mercury accumulation, is limited. Here, we examined the applicability of the eggs of the black-tailed gull (Larus crassirostris), which belongs to a high trophic level, for mercury monitoring in coastal areas. Breeding sites were selected in West, Southeast, and East Seas with different mercury concentrations in other matrices (sediment and biological samples of residents). The 5-year mean total mercury concentration in eggs collected during the breeding seasons from 2016 to 2020 was lower in Baengnyeongdo (705 ± 81 ng/g dry weight (dry), West Sea) than in Hongdo (1,207 ± 214 ng/g dry, Southeast Sea) and Ulleungdo (1,095 ± 95 ng/g dry, East Sea). The different patterns of mercury concentration in gull eggs among the breeding sites was consistent with those in the other matrices among the coastal areas. These results support the applicability of the black-tailed gull egg as an indicator for establishing a monitoring framework in the coastal areas of the Republic of Korea.

Mariculture in the Black Sea and its Impact on the Environment According to Satellite Observations

Satellite monitoring of film pollution on the surface of the Black Sea was carried out. A new source of them has been discovered – fish cages in the Adler-Kudepsta marine area, which produce biogenic films caused by oil and fat additives to fish feed used in industrial mariculture. It was concluded that despite their biogenic origin, they have a negative impact on local marine ecosystems.

Life starts with plastic: High occurrence of plastic pieces in fledglings of northern fulmars

Plastic pollution threatens many organisms around the world. In particular, the northern fulmar, Fulmarus glacialis, is known to ingest high quantities of plastics. Since data are sparse in the Eurasian Arctic, we investigated plastic burdens in the stomachs of fulmar fledglings from Kongsfjorden, Svalbard. Fifteen birds were collected and only particles larger than 1 mm were extracted, characterised and analysed with Fourier Transform InfraRed spectroscopy. All birds ingested plastic. In total, 683 plastic particles were found, with an average of 46 ± 40 SD items per bird. The most common shape, colour and polymer were hard fragment, white, and polyethylene, respectively. Microplastics (< 5 mm) were slightly more represented than mesoplastics (> 5 mm). This study confirms high numbers of ingested plastics in fulmar fledglings from Svalbard and suggests that fulmar fledglings may be suitable for temporal monitoring of plastic pollution, avoiding potential biases caused by age composition or breeding state.

Machine learning for public wellness: optimizing hygiene practices and pollution monitoring in smart cities

Introduction. Public health in urban areas is of paramount importance, particularly in the context of smart cities where technology plays a vital role. The integration of sophisticated infrastructure and data-driven systems in smart cities has the potential to significantly enhance public health outcomes. This improvement hinges on optimizing various factors, especially in the realms of hygiene standards and pollution monitoring. The ability to adhere to stringent hygiene procedures and closely monitor pollutants is essential for mitigating health risks in densely populated urban environments. As metropolitan areas become increasingly complex, there is a pressing need to prioritize the optimization of these processes.&#x0D; Materials and Methods. To address the challenges associated with public health optimization in smart cities, this study introduces Optimized Public Wellness using Machine Learning (OPWML). OPWML employs advanced machine learning techniques to augment hygiene protocols and pollution surveillance in smart urban areas. The proposed approach incorporates real-time validation, enhanced data-collecting efficiency, intelligent intervention impact, and increased throughput. The methodology aims to streamline processes and overcome the limitations of current approaches, providing more precise and prompt outcomes.&#x0D; Results. Simulation findings demonstrate the superior performance of OPWML compared to other methods. The average estimate accuracy achieved by OPWML is 86.76%, showcasing its efficacy in delivering accurate results. Real-time validation latency is notably low at 12.99 ms, indicating the system’s responsiveness. With a data collection efficiency of 22.96 GB/hour, OPWML demonstrates its ability to efficiently gather relevant data. The smart intervention impact of 33.20% underscores the system’s effectiveness in implementing intelligent interventions. Additionally, the throughput of 314.67 kbps signifies the high processing capacity of OPWML.&#x0D; Limitations. While OPWML exhibits promising results, it is essential to acknowledge certain limitations in this study. The simulation-based nature of the findings may not fully capture real-world complexities. Additionally, the generalizability of the results to diverse urban contexts requires further investigation. Limitations such as data privacy concerns and potential technological barriers should also be considered when implementing OPWML in practical settings.&#x0D; Conclusion. In conclusion, Optimized Public Wellness using Machine Learning (OPWML) emerges as a powerful tool for transforming public health processes in smart cities. The study highlights OPWML’s capacity to significantly enhance hygiene protocols and pollution surveillance, ensuring a healthier and environmentally sustainable urban setting. While acknowledging certain study limitations, the overall outcomes emphasize the potential of OPWML in revolutionizing public health practices and contributing to the well-being of urban populations in the era of smart cities.

Health Risk Assessment from Exposure to Ambient Volatile Organic Compounds (VOCs) at a Truck Tire Factory in the Yangtze River Delta, China

Occupational health risk assessments of exposure to VOCs still need to be extensively studied to improve the safety standards in the industry. Based on the monitoring of organic pollutants at various workstations in a truck tire factory in Jiangsu Province, both semi-quantitative and quantitative health risk assessment methods were employed to assess health risk levels. The findings indicated that VOCs were categorized into five classes, which included alkanes, aromatics, halocarbons, carbon disulfide, and oxygenated volatile organic compounds (OVOCs). The highest concentration of total volatile organic compounds (TVOCs) was found in shaping workshop; alkanes were the most abundant class (74.2%), followed by aromatics (24.02%) and OVOCs (1.96%). Although the results of the semi-quantitative risk assessment showed that most of the organic compounds had low R values, various kinds of VOCs were detected; particularly, many harmful organic compounds (such as toluene, ethyl benzene) were detected in all the sampling sites. The quantitative risk in the calendering and vulcanizing workshop exceeded the acceptable level; both the carcinogenic risk of ethylbenzene in the tire-strip storage room, and trichloroethylene and perchloroethylene in the calendering workshop were unacceptable. Thus, the calendering and vulcanizing processes in rubber tire manufacturing should be priority-controlled processes.

Benzene: A critical review on measurement methodology, certified reference material, exposure limits with its impact on human health and mitigation strategies.

Benzene is a carcinogenic pollutant with significant emission sources present in the atmosphere. The need for accurate and precise measurement of benzene in the atmosphere has become increasingly evident due to its toxicity and the adverse health effects associated with exposure to different concentrations. Certified reference material (CRM) is essential to establish the traceability of measurement results. The present review compiles the available national and international measurement methods, certified reference materials (CRMs) for benzene and the limit of benzene in fuel composition (v/v) worldwide. Overall, the review indicates the benzene level in the atmosphere and the resulting impacts on the environment and human health, which frequently exceed the exposure limits of different environment regulatory agencies. An extensive literature review was conducted to gather information on monitoring and analysis methods for benzene, revealing that the most preferred method, i.e. Gas Chromatography- Flame Ionization Detector and Mass Spectrometry, is neither cost-effective nor suitable for real-time continuous monitoring. By analysing existing literature and studies, this review will shed light on the understanding of the importance of benzene pollution monitoring in ambient air and its implications for public health. Additionally, it will reflect the mitigation strategies applied by regulators & need for future revisions of air quality guidelines.

Self-powered system based on triboelectric nanogenerator in agricultural groundwater pollution monitoring and control

Self-powered system based on triboelectric nanogenerator in agricultural groundwater pollution monitoring and control

Monitoring plastic pellet pollution in coastal environments through handheld Raman spectroscopy: Data from the Mediterranean coasts (Southern Italy)

This paper examines the distribution and chemical properties of beached plastic pellets along the Ionian and Tyrrhenian coasts of Southern Italy. Three locations have been sampled: Agnone Bagni (SR) and Paradiso (ME) on the Ionian coast of Sicily, Baia del Tono in Milazzo (ME) on the Sicilian Tyrrhenian coast, and Pizzo Calabro (VV) in Calabria on the Tyrrhenian coast. Variations in shape, size, compactness, color, and other physical features, correlated with residence times and transport, has been highlighted. Raman spectroscopy, used in a portable configuration, enabled rapid identification of polymer types, demonstrating its utility for on-site plastic pollutant monitoring. Polyethylene and polypropylene were the predominant polymers. Principal component analysis of the spectra determined the optimal chemometric classification of pellets by composition, avoiding interference or distortion. In conclusion, the study provided preliminary insights into pellet abundance, composition, weathering extent, and distribution across these shorelines, underscoring the importance of regular beach monitoring.