1 INTRODUCTION

Microplastics are tiny plastic flecks with a length of less than 5 millimetres. In addition to the air, soil, lakes, rivers, and oceans, they can also be present in our food and drinking water. Primary microplastics and secondary microplastics are the two main categories of microplastics. Primary microplastics are those that are made specifically to be small, like plastic pellets used in manufacturing or microbeads used in cosmetics and personal care goods. When bigger plastic products, such as bottles and bags, degrade over time, secondary microplastics are produced. The worldwide problem of marine plastic pollution has harmful local effects. Due to their possible effects on the populations of marine organisms, microplastics in marine habitats are of concern (Tuuri & Leterme, 2023). Microplastic pollution may affect the aquaculture sector, particularly when plastic goods are widely used. Plastic contamination of marine ecosystems is a local and worldwide problem that has an impact on human health, economic activity, and wildlife (Narisu, 2023; Richon et al., 2023; Tarafdar et al., 2023). Debris made of fragmented plastic, such as microplastics and nanoplastics (Gaur et al., 2023), is making its way up the food webs and may have undetermined consequences on marine and lake organismal systems. High quantities of microplastics in the air cause cancer in workers in plastic-related businesses. Marine litter creates environmental, chemical, and psychological harm, endangering marine life and the world. It is 80% land-based and 20% water and ocean-based. Overall, understanding the long-term consequences and evolution of microplastic effects in the environment requires effective monitoring.

One of the primary impacts of microplastics on marine ecosystems is their ingestion by a wide range of marine organisms, from tiny plankton to large predators. Mistaken for food, these particles can accumulate in the digestive tracts of animals, leading to blockages, malnutrition, and even death. Furthermore, microplastics have been found to adsorb and concentrate toxic chemicals from the surrounding water, which can then be transferred to organisms upon ingestion, causing further harm (Tarafdar et al., 2023). Microplastics also disrupt marine food webs by altering the availability and quality of prey. They can interfere with feeding behaviours, reduce the nutritional value of consumed prey, and even transfer toxic chemicals up the food webs, impacting higher trophic levels including commercially important fish species. Moreover, microplastics can indirectly affect marine ecosystems by serving as vectors for the transportation of invasive species (Gaur et al., 2023). These particles provide a substrate for organisms to attach to and hitchhike across oceans, potentially introducing non-native species to new environments and disrupting local biodiversity (Tarafdar et al., 2023).

Furthermore, the global issue of marine plastic pollution has negative effects on human health, the economy, and wildlife (Alpizar et al., 2020; Gupta et al., 2024). Production, consumption, trash management, and the marine environment are the four primary phases in the flow of plastic garbage identified by the framework. The framework can be used to create efficient and effective policies by identifying interconnections between them. The framework can assist policymakers in designing effective and efficient policies by identifying the crucial policy entry points and the potential interconnections between policies. The high rate of recycling in India is undoubtedly a good thing, but because the recycling industry is largely unregulated and operates outside of legal channels, there are worries about leaks during collection and transportation. A deposit reimbursement programme might boost the value of plastic containers and encourage currently informal businesses to transition to the formal economy. By adopting adjustments in our own lives, such as lowering our reliance on single-use plastics and selecting sustainable alternatives, we can all contribute to reducing marine plastic pollution. A comprehensive understanding of microplastics in maritime ecosystems can be obtained by using numerical models to examine the transit of microplastics by looking at various characteristics (Horton & Barnes, 2020; Mghili et al., 2024). The availability of data on the physical and chemical characteristics of microplastic is limited. Tools for machine learning (ML) and computer vision can be used to gather environmental data, create precise models, and streamline data collecting. To fill the gap between numerical modelling and ML for microplastic analysis, image-based ML has the ability to reveal microplastic transport behaviours.

This paper is organised as follows: Section 2 gives a brief theoretical background and methodology of models implemented. Section 3 describes the evaluation metrics and dataset used in existing models. Section 4 gives the result discussion and research challenges. Lastly, Section 5 outlines the conclusion.

2 LITERATURE REVIEW

Averaging 3.4 microplastics per millilitre of sediment, (Ling et al., 2017) indicated that microplastics are common in marine sediments at 42 sites in southeast Australia. Microplastic filaments, wave exposure, and particles with finer sediments all showed favourable connections according to their study. Using a Van Veen sediment grab, samples were taken from depths of 5 to 13 metres. Using a size-graded method, microplastics were recovered while excluding biological material (Yap & Yasid, 2023). The samples were filtered into a Büchner vacuum device, where the supernatant was collected and used again. Microplastics were sized and categorised as filaments or particles. The associations between microplastic concentrations and other parameters were investigated using statistical analysis. South-eastern Australia's coastal marine sediments include microplastics in addition to other contaminants like heavy metals and sewage. This emphasises the need to manage marine habitats' exposure to microplastics, as suggested in Australia via a plastic microbead ban.

O'Connor et al. (2016) looked into how organic compounds partition (distribute) across different forms of plastic litter and water. The researchers gathered information on plastic–water partition coefficients for several chemicals and plastic kinds (low-density polyethylene, high-density polyethylene, polystyrene, polypropylene, and polyvinyl chloride) from prior investigations. They discovered that hydrophobicity (a chemical's water-fearingness) is typically the driving force for partitioning. They also evaluated several approaches for calculating this partitioning and discovered that Kow (octanol–water partition coefficient) is an adequate descriptor in most circumstances. The article then investigates how environmental conditions like as temperature and weathering may impact this partitioning in real-world circumstances. Finally, the research addresses how these data might be used to estimate the dangers caused by chemicals absorbed by oceanic plastic trash.

The growing usage of plastics has resulted in a substantial amount of plastic garbage entering freshwater (lakes, rivers) and marine (oceans) environments. This plastic trash does not disappear; it degrades over time into microplastics (less than 5 millimetres) and even smaller nanoplastics. Aquatic creatures such as fish, plankton, and invertebrates commonly mistake microplastics for food. They can be consumed directly or indirectly via the food web. Cássio et al. (2022) underline the urgent need for more study on microplastic pollution, which includes developing improved tools to detect and quantify microplastics and contaminants in aquatic habitats, assessing the possible health concerns associated with seafood eating, and so on.

Jahnke et al. (2017) explained how plastic in the oceans fits three criteria: exposure on a planetary scale, non-reversible exposure, and an unidentified disruptive influence on Earth system processes. This study analyses the possible planetary boundary threat of plastic weathering in the marine environment. They analysed prior research on the subject and noted knowledge gaps. They then used this knowledge to create a theoretical framework for comprehending how plastic weathers in the marine environment and what effects it might have. Their objective is to include improving our knowledge of plastic fragmentation, biofilm development, and sedimentation in marine environments, examining geographical distribution and sinking behaviour, determining the risk of plastic pollution end points, and conducting discovery-oriented research on the effects of weathering plastic.

Microplastic pollution causes gastrointestinal blockages, internal damage, stunted growth, and toxic chemical build-up, endangering both human health and marine ecosystems. Consuming infected seafood can also dangerous. Sharma and Chatterjee (2017) suggested cutting less on single-use plastics, enhancing waste management techniques with composting and recycling initiatives, and creating new technologies to eliminate microplastics from the environment. Additionally, they stress the need for greater study to determine how microplastics affect human health and marine ecosystems, allowing for more effective and focused solutions to the microplastic contamination issue (Rakesh Kumar et al., 2022). In order to reduce plastic usage and recognise its detrimental impacts, there must be increased public awareness of microplastic pollution. Numerous initiatives should be undertaken, with global campaigns organised by international organisations like the IMO (International Maritime Organization) and UNEP (United Nations Environment Programme) to reduce the pollution caused by microplastics. The industries that produce plastics ought to be accountable for their end-of-life products as well.

According to Jambeck et al. (2018), Africa produces 12–20 million metric tonnes of plastic garbage annually, of which 2–5 million end up in the ocean as a result of population increase, urbanisation, and poor waste management. Lack of awareness, poor waste management, lax enforcement of environmental laws, and economic problems like poverty are some of the difficulties. Campaigns to raise public awareness, investments in waste management, the development of recycling technology, and the promotion of the principles of the circular economy are some solutions. In order to estimate how much plastic waste enters the ocean each year, they reviewed the literature on plastic waste generation, improper management, and marine debris in Africa. In order to understand the difficulties and prospects for tackling the issue, they also spoke with researchers, policymakers, and practitioners.

Smith et al. (2018) outlined the present level of knowledge on the presence of microplastics in seafood and its possible effects on human health. It goes into the sources of microplastics in the marine environment, how they get into the food webs, and any possible negative health impacts they may have if consumed. In order to address the problem of microplastics in seafood, they also point out research gaps and suggest options for mitigation and adaptation. Size, chemical composition, and dose all affect how harmful microplastics are. The knowledge they currently have is limited and is largely based on their research. The impact of microplastics on ecological systems and food security must be evaluated. It is crucial to standardise data collection procedures, evaluate the main seafood production regions and countries, and gather information on the presence of degraded plastic in food. It is also necessary to develop techniques for analysing the physical and chemical alterations of micro- and nanoplastics as well as data on the toxicity of exposure to additive/monomer mixtures.

Wang et al. (2020) discussed the impact of plastic nanoparticles and microplastics on the food webs and human health. The possible harm to the digestive system, decreased ability to reproduce, elevated risk of cancer, and immune system disruption are then covered in relation to plastic nanomaterials and microplastics. They have also used epidemiological research, which examines how persons who have been exposed to nano- and microplastics fare which also have an impact on health, and this method can be used to discover potential health hazards linked to exposure to plastic nanoparticles and microplastics. They also faced in determining the extent of an organism's exposure to plastic nanomaterials and microplastics, the difficulty in separating the effects of plastic nanomaterials and microplastics from those of other environmental factors, and the lengthy timeframes over which those effects may manifest.

Schmaltz et al. (2020) looked at 52 new methods to stop and collect marine plastic pollution, with 59% of them concentrating on removing trash from waterways. It highlights the necessity for an all-encompassing strategy that incorporates technology with activism and policymaking. The two primary kinds of technology covered are those that prevent plastic from entering rivers and those that collect waste after it has entered, including drones, booms, nets, beach clean-ups, and vessels-based collection. They conducted a comprehensive review of marine plastic pollution technologies, identifying and organising them based on their specific design, current use, and potential for scale. They created a database of each technology, including details about its developer, description, development status, and potential applications. It takes constant cooperation to develop innovative solutions to stop plastic from leaking into seas because of the interconnectedness of technological developments, legislation, and individual and industrial activities.

Owens and Kamil (2020) discussed on how it is challenging to monitor and mitigate due to the absence of data on this issue. In two case studies from India and Indonesia, they present a way for modifying coastal collection techniques for use in river assessment. For the purpose of collecting trash from river shorelines, a standard approach is created. The procedure includes information on the location and amount of the litter, as well as a list of elements that need to be noted regarding the litter. To determine the types and amounts of litter in the two rivers, the data gathered are analysed. The study's findings are used to advocate for the methodology's broad application in order to enhance our knowledge of plastic contamination in freshwater systems. It has been demonstrated that this practise works well for gathering information on plastic contamination in waterways. They also urge wider use of this approach to increase our knowledge of plastic pollution in freshwater systems in their concluding statement.

Vázquez-Rowe et al. (2021) said that the growing concern over microplastics in the marine environment—which can enter the environment from a variety of sources and be consumed by a variety of organisms—is covered in the study. It draws attention to the detrimental effects of microplastics on marine ecosystems, such as physical harm, obstruction of digestive tracts, and disruption of eating and reproduction. Microplastics research in fisheries and aquaculture involves difficulties with sampling and detection, comprehension of the consequences on marine creatures and ecosystems, minimising the impact, and informing the public and policymakers of the risks. More research is required to understand the cumulative effects of exposure to various stressors as well as the long-term effects of smaller microplastics and nanoplastics, which are difficult to detect.

Microplastics can cause intestinal obstructions, decreased nutrition, and starvation in aquatic invertebrates and fish. Additionally, dangerous compounds like pesticides and heavy metals can be leached from the environment by them. Microplastics have the potential to harm living things physically, including abrasions to the gills and intestines and entanglement in fishing gear. They may also weaken the immune system, which leaves people more vulnerable to illness. Microplastics can also hinder reproduction by lowering fecundity and hatching success. Various elements, including the type of plastic, the size and form of the particles, the environmental concentration, and the species of organism, affect how microplastics affect aquatic organisms. Key conclusions were gathered after reviewing relevant studies. Introduction, categories of microplastics, impacts on aquatic creatures, and research implications comprised the sections of the review. Hodkovicova et al. (2022) took a methodical approach, meticulously assessing the research's quality and separating their own interpretations from the conclusions of the original investigations.

Binelli et al. (2022) discussed that plastic pollution was a serious environmental problem, with increased plastic manufacture resulting in its widespread prevalence in aquatic habitats. Plastic samples were gathered from nine strategically chosen places throughout Milan's waterways, which included both natural rivers and man-made canals. Plastic debris was analysed using an optical microscope and a μFT-IR spectrometer. This approach enables examination of smaller plastic particles (10–20 μm) compared to existing methods. Surprisingly, the two natural rivers, the Olona River and the Lambro River, have higher plastic contents than the man-made canals. This shows that plastic contamination might originate somewhere other than urban wastewater. The recovered plastic trash was of numerous sorts, including shards, fibres, and films. It underlines the need of implementing ways to reduce plastic consumption and prevent it from entering aquatic areas in order to promote a healthy ecosystem.

Bhatt et al. (2021) looked at the rising worry over microplastic pollution in our environment. Microplastics, tiny particles of plastic trash, are collected in alarming numbers. These pollutants come from a variety of sources, including the breakdown of bigger plastic goods, poor waste disposal procedures, and even products containing microplastics themselves. Microplastics have far-reaching negative consequences on wildlife health, soil quality, and, potentially, human health. Fortunately, academics are already looking for potential answers to this growing problem. Biodegradation technologies, enhanced filtering techniques, and new oxidation methods are among the promising solutions for removing microplastics from the environment. One especially fascinating approach includes using microorganisms, nature's smallest cleaning force, to break down these hazardous plastic bits.

Casabianca et al. (2021) investigated the possible harmful impacts of microplastics and nanoplastics on phytoplankton, which are microscopic marine plants that are essential to ocean ecosystems. These microscopic plastic pollutants are accumulating in the world's seas, raising worries about their influence on the foundation of the marine food web, which suggests that microplastics and nanoplastics can affect phytoplankton in a variety of ways. These include reduced cell development and lower chlorophyll concentration (chlorophyll is required for photosynthesis). The report underlines the importance of additional research employing natural plankton assemblages and mesocosm experiments (controlled aquatic ecosystems) to provide a more realistic picture of the ecological impact.

Skirtun et al. (2022) examined the region's marine aquaculture's present methods and sources of plastic contamination. It identifies the primary causes of plastic pollution using the DPSIR framework. Rough weather, poor plastic trash disposal, limited access to recycling facilities, low prices of consumable plastics, and high recycling expenses are the main pathways. Due to challenges in identifying the source of the plastic pollution and a lack of precise classification in official monitoring systems, assessments of beach litter frequently understate plastic pollution from aquaculture. In order to find previous studies on plastic contamination from aquaculture, a literature study was also conducted and aquaculture farmers, business leaders, and government representatives are consulted as stakeholders to learn about current procedures and difficulties. Assessments of beach litter to calculate the level of plastic pollution from aquaculture. The primary causes of plastic pollution from aquaculture were also determined by them using the DPSIR framework (Drivers, Pressures, State, Impacts, and Responses).

Lin et al. (2022) examined the advancements achieved in the previous years in the use of ML algorithms for the identification and quantification of microplastics. They propose to improve the precision and effectiveness of microplastics categorisation and detection by combining ML approaches with conventional identification techniques. Microplastics can be detected using artificial neural networks (ANNs) in a variety of environmental samples. FTIR, Raman, and NIR data are examples of high-dimensional, low-sample data that are appropriate for support vector machines (SVMs). For locating MPs in hyperspectral pictures, tree-based algorithms like decision trees (DTs) and random forests (RFs) are used. Key features are extracted from datasets using the dimensionality reduction approach principal component analysis. The continuous development of more sophisticated algorithms, the creation of a library of MP structural features, and the integration of ML approaches with original identification methods should be the main goals of future research on ML to support MP identification in the environment.

Microplastics can be discovered in the environment, food, and the human body. Because that are not biodegradable, that can build up in the food webs and release harmful compounds that are dangerous to human health. When contaminated food is ingested, the gastrointestinal tract becomes the main route for human exposure to microplastics. Microplastics can also be absorbed through the skin or breathed. The toxicity of these particles varies with their size, shape, and composition, with smaller particles being more dangerous. Yuan et al. (2022) investigated microplastics health effects on humans using a valid method. They found 212 studies, summarised their findings, and discussed implications. The rigorous methodology ensures that relevant studies are considered and conclusions are based on sound evidence. A precise definition of microplastics type and size range, standardisation of microplastics samples, emission prevention techniques, and trustworthy methodologies for determining pollution levels are necessary for the risk assessment of microplastics in marine ecosystems. In order to stop the discharge of plastic into aquatic habitats, new technologies are required, such as environmentally friendly polymers and green additive chemicals.

Yu and Hu (2022) said that big data can be used via ML to assess the ecological threats posed by microplastics, but pressing problems including a dearth of databases and literature need to be fixed. For microplastics risk investigations, standardised procedures as well as enhanced causality and interpretability are required. Due to inconsistency and unequal distribution, the existing microplastics data availability is insufficient for extensive ML study. For a global microplastics database, standardisation of data collecting and testing procedures is essential. By comprehending the physical, chemical, and environmental characteristics of microplastics, ML can forecast their global distribution and movement patterns. Additionally, it can forecast the biological impacts of nanoplastics, which differ from larger microplastics in terms of their special features and possible effects. Using interpretable models, the relationship between microplastics and climate change is also investigated. Xiang et al. (2022) examined the sampling, extraction, and qualitative and quantitative analysis of microplastics in aquaculture. It talks about the risks associated with microplastics, their prevalence, and the techniques used to gather them. The authors also go through the technologies' drawbacks, namely the challenge of locating tiny microplastics and the possibility of false positives. In order to guarantee accurate and trustworthy results, they also emphasise the necessity for standardised methodologies for detecting microplastics in aquaculture. They searched for articles that fit their inclusion and exclusion criteria, extracted information on sampling, extraction, identification, and quantification techniques, and then analysed the information to identify the most widely used techniques for finding microplastics in aquaculture.

Microplastics are a rising problem in our seas, as they have the potential to harm microscopic species such as copepods, which comprise the base of the marine food web. Traboni et al. (2023) looked into how microplastics can alter the foraging behaviour of a particular copepod species, Centropages typicus, found in the Mediterranean. Researchers monitored adult female copepods in a lab environment, exposing them to water with and without microplastics, as well as supplementary nutrients. While the copepods' overall food intake (daily ration) did not vary considerably, the study implies they may have adjusted their eating habits. Copepods appear to avoid eating microplastics and may compensate by choosing other natural food sources to satisfy their nutritional requirements. This implies that they may adjust their feeding methods in the presence of microplastics. The study focused on a single copepod species, Centropages typicus. Microplastic effects might vary across different zooplankton species.

A key issue for the future of plastic waste pollution and microplastics is the sharp rise in the demand for plastics on a global scale in recent years. Understanding the long-term impacts and evolution of microplastic pollution requires effective microplastic monitoring. Although Phan and Luscombe (2023) rely on empirical information about the behaviours and properties of microplastics, numerical models are useful for investigating the transport of microplastics. Microplastics can be used to extract data that can be used to increase the precision of numerical models using ML and deep learning models. The use of numerical models since they allow for the systematic examination of the impacts of many parameters (such as size, shape, density, and plastic type). Therefore, numerical models contribute to a more complete understanding of the fate and transport processes of microplastics in the ocean environment (either locally or worldwide). Some challenges faced are like there are no common shape descriptors for microplastics, which makes it more difficult to incorporate their physical characteristics into model. For a better understanding of the mechanisms influencing the transfer of microplastics, more field data are required. The precision of model predictions can be increased by incorporating a greater diversity of microplastic forms.

2.1 Algorithm based

Jang et al. (2017) said that microplastics and marine debris made of expanded polystyrene (EPS) from South Korea and the Asia-Pacific coast include the brominated flame retardant hexabromocyclododecane (HBCD). Between 2013 and 2015, they gathered 121 EPS buoys, 14 refurbished buoys, and EPS waste from 12 different nations along with the Korean coast. The amounts of HBCD in raw materials and EPS marine debris were examined by the researchers. Cleaning, air-drying, and grinding of EPS buoys and microplastics were done during sample preparation. With the aid of gas chromatography–mass spectrometry (GC–MS), HBCD was extracted and examined. Accuracy was ensured via quality control procedures. They discovered HBCD with average quantities of 14.4 mg/kg and 1.9 mg/kg in 99% of EPS buoys and 82% of EPS microplastics, respectively. Their research comes to the conclusion that an important source of HBCD in the marine environment is EPS debris.

Small plastic flecks having a diameter of under 5 mm are known as microplastics. Commercial aquatic species like fish, bivalves, and prawns can acquire microplastics. Depending on the study, the concentrations of microplastics in commercial aquatic species change. The potential for microplastic contamination to harm commercial aquatic species must be further investigated, as must the potential dangers to human health. For determining the plastic type, Wu et al. (2020) used a method called Fourier transform infrared spectroscopy (FTIR) for measuring microplastic size and ANOVA was also used for comparison of the abundance of microplastics and the abundance ratio among the commercial species.

Michel et al. (2020) used four spectroscopic methods examined by the authors—attenuated total reflectance-Fourier transform infrared (ATR-FTIR), near-infrared (NIR) reflectance spectroscopy, laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF)—which can be used to distinguish between various kinds of plastics. The methods were used with ML classifiers to increase identification accuracy. With success rates of 99%, 91%, and 97%, respectively, they have discovered that ATR-FTIR, NIR reflectance spectroscopy, and LIBS had the highest success rates for classifying consumer plastics. With success rates of 99%, 81%, 76%, and 66% for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively, the success rates for identifying marine plastic debris (MPD) were lower but yet encouraging. The distinct mixture of elements that make up a plastic is known as its “elemental signature.” This signature can be used to determine the type of plastic, but the environment has the ability to alter it. A plastic that has been buried in dirt could have a different elemental signature than one that has been exposed to sunshine, for instance.

In order to forecast plastic trash creation in the EU-27 by 2030, Fan et al. (2022) used an artificial neural network (ANN) model. Despite a 55% recycling rate, it shows that the predicted generation is 17 million tonnes. In order to control plastic waste, they compare four scenarios: the business as usual, greater recycling, increased energy recovery, and waste reduction. The least detrimental effects on acidification, eutrophication, aquatic toxicity, plastic marine pollution, and abiotic depletion are caused by Scenario 4, which reduces waste. The potential for global warming is higher than it was in 2018, and Scenario 3 performs better in this regard. They gathered information from the EU-27 countries between 2000 and 2018 on the production of plastic trash, energy use, rate of use of circular materials, economic complexity index, population, and real GDP. To predict the amount of plastic garbage generated in 2030, they created an ANN model that was trained on data from 2000 to 2018 and evaluated using Shapley additive explanations which is shown in Figure 1. For the management of plastic waste in 2030, they examined four scenarios: business as usual, greater recycling, increased energy recovery, and waste reduction.

Meyers et al. (2022) used the approach for detecting and identifying microplastics in the paper is semi-automatic, economical, and quick. The technique was evaluated using environmental samples that had been contaminated with microplastic particles as well as a dataset of known microplastic and non-microplastic particles. Under a fluorescence microscope, they utilised Nile red staining to pinpoint the presence of microplastic particles in samples. To categorise photos of microplastic particles as plastic or non-plastic and to determine their polymer composition, they developed a ML model. The model was evaluated on datasets of known microplastic and non-microplastic particles as well as on environmental samples that had been injected with microplastic particles, and it performed extremely well in both scenarios.

High quantities of microplastics can be found near polar oceans and in subtropical gyres, which pose a threat to the ecosystem globally. Proteobacteria are the most prevalent hosts of microplastic degradation genes (MDGs), with Gammaproteobacteria and Alphaproteobacteria being the two most significant subgroups. The biggest potential for microplastic biodegradation is in the northern Atlantic Ocean and Mediterranean Sea. In order to anticipate the global distribution of microplastics, the authors utilised a ML model to collect data on microplastic abundance from 9445 maritime samples. Chen et al. (2023) found 6049 metagenome-assembled genomes (MAGs) from 712 maritime samples that contained microplastic degradation genes (MDGs). Using the prevalence of MDGs, it evaluated the possibility of microplastic biodegradation in various maritime settings. They also identified microorganisms and their related genes using an RF model, MAGs, and MDGs.

Seyyedi et al. (2023) discussed the causes of plastic pollution that how it enters the food webs and seas, whether it poses a hazard to people or aquatic life, the difficulties associated with oceanic plastic trash, the rules and regulations that are already in place, and solutions to the issue. They used the IBK, M5P, and DS ML algorithms to create a soft sensor for the prediction of accumulated micro- and macro-plastic in Earth's oceans. Three statistical measures—correlation coefficient, mean absolute error (MAE), and root mean square error (RMSE)—were used to assess the soft sensor's performance. IBK, M5P, and DS algorithms were used in a separate evaluation for the prediction of accumulated micro- and macro-plastic trash. Information was gathered on the quantity of plastic garbage produced and handled in various nations. Various nations were grouped according to how prosperous they were. The WARM model was developed to assess how waste management systems stack up from an environmental standpoint. Plastic is a fantastic substance that we can use in practically every aspect of our lives, every day, in every activity. Bioaccumulation and biomagnification, which gauge the speed at which toxins spread throughout a food web, are key concepts in ecological risk assessment.

Table 1 illustrates the various articles studied for effect of marine plastic pollution, algorithms used, input and output parameters. Table 2 illustrates the challenges solved and drawbacks of various articles.

TABLE 1. List of articles illustrating the input and output parameters.

Author	Algorithm	Data span	Input parameters	Output parameters
(Jang et al., 2017)	Gas chromatography–mass spectrometry (GC–MS)	2013–2015	1. Ionisation energy in a mass spectrometer	1. Retention time: The length of time it takes for a chemical to elute from a Gas Chromatography column
			2. Range of the mass spectrometer	2. Mass spectrum: A visualisation of the relative abundance of the ions (m/z) generated by the mass spectrometer
			3. Temperature of the Gas Chromatography column
			4. Column Gas Chromatography
(Wu et al., 2020)	Fourier transformed infrared spectroscopy (FTIR)	October 2017–January 2018	1. Sediment samples	1. Abundance of microplastics in sediment
	ANOVA		2. Commercial species samples	2. Abundance of microplastics in commercial species
			3. Hydrogen peroxide
			4. Saturated sodium chloride
(Michel et al., 2020)	1. Support vector machine (SVM)	June 2017–July 2019	Spectra of the plastic samples collected using Fourier transform infrared spectroscopy (ATR-FTIR)	1. Recognising consumer plastics
			Near-infrared reflectance spectroscopy (NIR)
	2. K-nearest neighbours (KNN)		Laser-induced breakdown spectroscopy (LIBS)	2. Recognising marine plastic waste
			X-ray fluorescence (XRF)
	3. Linear discriminant analysis (LDA)
(Fan et al., 2022)	Artificial neural network (ANN) model	2010–2018	1. Energy consumption	1. It might be applied to accurately predict the production of plastic garbage in 2030
			2. Circular material use rate
			3. Economic complexity index
			4. Population
			5. Real gross domestic product
(Meyers et al., 2022)	Decision tree classifier	–	1. Values for pixel intensity	1. Class label that can be either plastic or non-plastic
			2. Shape and size of the particles
(Chen et al., 2023)	Random forest model	1986–2019	1. Microplastic abundance (9445 samples)	1. Ranks of microplastic abundance
			2. Anthropogenic drivers	2. Geographic coordinates
	CD-HIT		(17 drivers)	3. Importance of each factor
			3. Geographic coordinates	4. Receiver operator characteristic (ROC) curve
	10-fold cross-validation
(Seyyedi et al., 2023)	Conceptual model	1986–2015	1. Volume of people	1. Accumulated trash from the oceans
			2. Economic growth in a certain location	2. Rate of generation of plastic waste production in a particular area
	Soft sensor model		3. Localised manufacture of plastic	3. Recycling of plastic waste in a particular area
			4. Plastic disposal at a specific location
	USEPA-WARM model

TABLE 2. List of articles illustrating the challenges solved and their drawbacks.

Author	Algorithm	Challenges solved	Drawbacks
(Jang et al., 2017)	Gas chromatography–mass spectrometry (GC–MS)	1. The GC–MS algorithm successfully separates and detects diverse chemical components in complex matrices including EPS debris and microplastics	1. The use of GC–MS analysis is restricted to specialised labs because it is expensive and time-consuming, especially for complicated materials
		2. Low concentrations: EPS debris and microplastics contain trace amounts of HBCD. At these low concentrations, the GC–MS method can identify and measure HBCD	2. Extensive sample preparation, such as extraction and clean-up, is frequently required for GC–MS analysis, which can be difficult for some substances
(Owens & Kamil, 2020)	Images of the riverside can be used to identify and categorise trash using Image processing methods	1. Utilising a strategy that is consistent and based on the NOAA Marine Debris Shoreline Field Guide	1. Gathering information from 10 quadrats at each sampling site, which makes the sampling procedure labour-intensive. This can take a lot of work, particularly if the riverbank is big or the debris is hard to spot
	Using ML techniques, it is possible to forecast the existence of debris in new locations and learn the patterns of debris along the riverbank	2. Using a sampling technique that is intended to be affordable and simple to use in remote places	2. The approach could not be sensitive enough to pick up on little variations in the quantity or kind of trash
		3. Utilising a classification system based on the dimensions, composition, and shape of the debris
(Fan et al., 2022)	Artificial neural network (ANN) model	1. The model can also be used to calculate how much a recycling programme will reduce the amount of plastic garbage produced	1. Since neural networks are “black-box” models, it is challenging to comprehend how they create predictions. Because of this, it may be challenging to pinpoint the primary causes of plastic trash production and to create sensible policies and actions
			2. The model will not be able to provide reliable estimates if the data are lacking or wrong
		2. The most efficient mix of waste minimisation, recycling, and energy recovery can be found using the model
(Skirtun et al., 2022)	DPSIR framework for statistical analysis	1. Developing standard techniques to measure plastic pollution from aquaculture	1. The findings may not apply to other regions because the study was only done in the North-East Atlantic region
		2. They offered suggestions for enhancing aquaculture producers' access to recycling facilities. Because many aquaculture producers do not have easy access to recycling facilities, this was an issue	2. The study did not take into account how the suggested fixes would affect society and the economy
(Meyers et al., 2022)	Decision tree classifier	1. Even in complex environmental data, the algorithm can accurately classify microplastic particles	1. The calibre of the training data affects how accurate the algorithm is. The algorithm may not generalise adequately to new data if the training data are not representative of the microplastic particles that are being categorised
		2. A larger group of researchers can use the technique because it is reasonably simple to implement
(Chen et al., 2023)	Random forest model	1. Increasing data availability	1. They used data from a limited number of marine environments
		2. Improving data quality	2. They did not account for the effects of climate change on the distribution of microplastics
	CD-HIT	3. Simplifying the model
	10-fold cross-validation
(Seyyedi et al., 2023)	Conceptual model	1. ML was utilised to forecast the total amount of accumulated ocean plastic garbage in order to address the data gap. This would make it possible for us to track the issue and create workable remedies	1. It is also difficult and expensive to invest in the infrastructure needed for the collection, classification, and recycling of plastic garbage. How much this will cost and how long it will take to implement are both unknown
		2. They advocated creating new technologies for the recycling of plastic trash to offset the high expense of recycling	2. Creating new technology for recycling plastic trash is a difficult and expensive endeavour. When and how successful these technologies will be used are both unknown
	Soft sensor model	3. Investing in infrastructure for the collection, classification, and recycling of plastic trash will help overcome the infrastructure gap. By doing this, it would be simpler to manage plastic debris and keep it out of the oceans
	USEPA-WARM model

Section 2 explains about the research examined and explores the detrimental impacts of microplastics on marine ecosystems and human health, strategies for detecting and reducing pollution, and the need for more study to address these concerns. They explored technologies for detecting microplastics and marine debris, such as FTIR, GC–MS, and ML classifiers, and created prediction models to estimate plastic trash creation and environmental implications.

3 EVALUATION

4 RESULT DISCUSSION AND RESEARCH CHALLENGES

The combination of research studies reveals a varied terrain concerning microplastics, and their impact on the food webs. Researchers utilise an array of techniques, from conventional examinations of microplastic data in marine organisms to state-of-the-art deep learning applications, demonstrating the dynamic methods employed in this domain. The studies highlight the value of customised approaches in various contexts by utilising a variety of datasets, such as national reports, challenge datasets, and customised datasets. This section digs into the many approaches and models used to address the complicated issue of microplastic contamination. Gas chromatography–mass spectrometry (GC–MS) stands out for its ability to detect chemical components inside EPS debris and microplastics, despite being limited to specialist labs due to its high cost and time requirements. However, issues remain in the significant sample preparation procedures, causing difficulty for specific chemicals. Innovative technologies, such as image processing tools and ML techniques, show promise for detecting and forecasting trash along riverbanks. However, the labour-intensive nature of sample processes and the possible insensitivity to tiny fluctuations in debris volumes remain barriers to overcome. ANN models provide useful predictive insights into waste reduction tactics, allowing for the discovery of the most efficient combination of waste minimisation, recycling, and energy recovery techniques. Table 5 shows the results of various AI models.

Michel et al. (2020) used SVM which showed the accuracy of 81% for marine plastic debris (MPS) and F1 score of 74% for marine plastic debris. Chen et al. (2023) utilised RF model and achieved an accuracy of 80% with CD-HIT preprocessing and 10-fold cross-validation. Meyers et al. (2022) utilised a DT classifier to classify plastic or non-plastic items with an accuracy of 95.8%, and for polymer-type classification, they achieved an accuracy of 88.1%. However, it is important to note that Wu et al. (2020) took a different approach by utilising Fourier Transform Infrared Spectroscopy (FTIR) but did not provide specific performance metrics. Despite the diversity of methodologies, all these studies highlighted the effectiveness of ML algorithms in addressing the pressing issue of marine plastic pollution. This collective research highlights the range of solutions being developed to combat marine plastic waste.

However, their “black-box” character makes it difficult to evaluate projections and identify the fundamental reasons for plastic waste creation. Furthermore, data dependability difficulties might develop if input data are incomplete or inaccurate. The DPSIR paradigm allows for organised statistical research, but it has limited generalisability beyond specific locations, and socioeconomic consequences are frequently neglected. The DT classifier and RF models are highly accurate in classifying microplastic particles, depending on the quality and representativeness of the training data. However, issues remain in assuring data availability and increasing data quality, as well as accounting for the impacts of climate change on microplastic dispersion. Conceptual and soft sensor models, as well as the USEPA-WARM model, use ML to anticipate total accumulated ocean plastic debris and recommend recycling technologies. However, establishing infrastructure for plastic trash disposal remains costly and difficult. In conclusion, while advances in methodologies and models provide valuable insights into microplastic pollution, addressing issues such as data availability, model interpretability, socioeconomic considerations, and climate change impacts is critical for developing effective mitigation strategies to combat this pressing environmental issue.

Performance evaluations provide concrete measures to assess how well the problem of plastic pollution in marine life is being addressed. These measures cover a wide range of topics, such as the decrease in plastic waste, the precision of prediction models, and the efficiency of systems for monitoring and detection. A commitment to applied solutions with immediate and direct impact is reflected in the emphasis on practical implementations, such as the identification of plastic hotspots and the real-time monitoring of marine environments.

A decrease in the harm caused by plastic to marine life, as indicated by metrics like lower aquatic species mortality rates and better overall ecosystem health, would be one tangible measure of success. To make sure that efforts are focused where they are most needed, predictive models' ability to accurately predict the flow and build-up of plastic waste in oceans is a critical benchmark. The evaluations' findings highlight how innovative and in-depth our understanding of the problem of plastic pollution in marine life is. They establish the foundation for forthcoming progress, exhibiting a dedication to pragmatic and implemented remedies that can yield a noteworthy and prompt influence on conserving our seas and aquatic environments.

5 SUMMARY AND CONCLUSION

Despite established procedures, human consumption of seafood results in low quantification of microplastics. The majority of microplastics are found in the digestive tracts of marine organisms, although these are usually eliminated before ingestion. Nanoplastics can be generated from industrial particles through the process of microplastic fragmentation. It is unknown, nevertheless, whether fisheries and aquaculture products, as well as aquatic environments, contain nanoplastics. Even though the risks to human health from microplastics in seafood seem minimal overall right now, it is important to keep in mind that the degradation of plastics already released into the environment and future inputs will inevitably lead to an increase in micro- and nanoplastics in the future.

CONFLICT OF INTEREST STATEMENT

The authors affirm that they have no known financial or interpersonal conflicts that would have appeared to have an impact on the research presented in this paper.