An Adaptive Machine Learning-Driven Framework for Analyzing Plastic Degradation in Aquatic Environments: A Hybrid Case Study Approach

An Adaptive Machine Learning-Driven Framework for Analyzing Plastic Degradation in Aquatic Environments: A Hybrid Case Study Approach

Author: Nikita Patil

MAEERs MIT Arts, Commerce and Science College, Alandi(D.)

Co-Author: Mr. Rajashri Khadake

MAEERs MIT Arts, Commerce and Science College, Alandi(D.)

ABSTRACT : Plastic pollution in aquatic environments is a major environmental concern due to the persistence of plastic materials and their slow degradation in water bodies. Plastic degradation is influenced by environmental factors such as temperature, salinity, ultraviolet radiation, and microbial activity, making its analysis complex. Traditional experimental and analytical methods face limitations in handling large-scale and dynamically changing environmental data. This paper proposes an adaptive machine learningdriven framework to analyze and predict plastic degradation behavior in aquatic environments. The hybrid approach integrates environmental data filtering with artificial intelligencebased learning models to enable context aware prediction. Relevant environmental parameters and plastic characteristics are extracted from simulated and observational datasets, and machine learning techniques are applied to estimate degradation rates. The framework updates itself using newly observed data, improving prediction accuracy over time. A case study inspired by marine and freshwater ecosystems demonstrates the practical applicability of the proposed approach. The results indicate improved adaptability and predictive performance compared to conventional methods, highlighting the role of AI and machine learning in environmental monitoring and sustainable plastic pollution management.

KEYWORDS: – Plastic Degradation, Aquatic Environments, Machine Learning, Artificial Intelligence, Environmental Monitoring.

1. INTRODUCTION

Plastic pollution has emerged as one of the most serious environmental challenges of the 21st century. Large quantities of plastic waste enter aquatic environments every year through improper waste disposal, industrial discharge, urban runoff, and river transport. Once plastics reach rivers, lakes, and oceans, they persist for long periods because most synthetic polymers are resistant to natural degradation processes. Unlike organic materials that decompose relatively quickly, plastics can remain in water bodies for decades, breaking down slowly into smaller fragments known as microplastics. These particles pose risks to aquatic organisms, biodiversity, and human health through the food chain.

The degradation of plastic materials in aquatic systems is a

complex and dynamic process. It does not occur at a constant rate but depends on several interacting environmental factors. Temperature influences chemical reaction speed and microbial activity. Salinity affects polymer structure and biofouling processes. Ultraviolet (UV) radiation from sunlight accelerates photodegradation at the water surface. Microorganisms can contribute to biodegradation, although this process is typically slow. Water movement, oxygen levels, and pH further influence breakdown mechanisms. Because these factors vary across marine and freshwater ecosystems and change seasonally, predicting plastic degradation behavior is challenging.

Traditional methods for studying plastic degradation rely mainly on laboratory experiments and controlled field observations. While these approaches provide valuable scientific insight, they often involve limited sample sizes and fixed environmental conditions. Laboratory simulations may not fully represent real-world variability, and long-term monitoring studies require significant time and financial resources. Moreover, analyzing large-scale environmental datasets manually becomes increasingly difficult as monitoring technologies generate more data.

Recent advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to address these challenges. Machine learning techniques are designed to identify patterns in complex datasets and make predictions based on learned relationships. Unlike conventional statistical models that require predefined assumptions, ML models can adapt to nonlinear interactions between variables. In environmental science, machine learning has already been applied to areas such as climate prediction, water quality assessment, air pollution monitoring, and ecosystem modeling.

Applying machine learning to plastic degradation analysis offers several advantages. First, it allows integration of multiple environmental parameters simultaneously. Second, it can process both simulated and real-world observational data. Third, adaptive learning systems can update themselves as new data become available, improving prediction accuracy over time. This adaptability is especially important in aquatic

ecosystems where environmental conditions continuously change.

The main objective of this research is to develop an adaptive machine learningdriven framework to analyze and predict plastic degradation behavior in aquatic environments. The framework combines environmental data filtering, feature extraction, and predictive modeling into a unified system. It aims to generate context-aware predictions that consider varying ecological conditions across marine and freshwater settings.

Specifically, this study seeks to:

1. Identify key environmental factors that influence plastic degradation in aquatic ecosystems.

2. Design a hybrid machine learning framework capable of estimating degradation rates under dynamic conditions.

3. Demonstrate the applicability of the framework through a simulated case study.

4. Compare the predictive performance of the adaptive approach with traditional analytical methods.

   By developing a flexible and data-driven prediction model, this research contributes to environmental monitoring and sustainable plastic waste management. Accurate prediction of degradation behavior can support policymakers, environmental agencies, and researchers in designing effective pollution control strategies. Furthermore, integrating artificial intelligence into environmental science represents an important step toward smarter and more responsive ecological management systems.

   2. LITERATURE REVIEW

   1. Plastic Pollution in Aquatic Environments

      Plastic production has increased significantly over the past few decades, leading to large-scale accumulation of plastic waste in marine and freshwater ecosystems [1]. Recent global assessments indicate that rivers act as major pathways transporting land-based plastic waste into oceans [2]. Once plastics enter aquatic environments, they are exposed to physical, chemical, and biological processes that gradually fragment them into smaller particles [3].

      Microplastics, typically defined as plastic particles smaller than 5 mm, have been widely detected in oceans, lakes, and even drinking water systems [4]. These particles are easily ingested by fish, plankton, and other aquatic organisms, potentially causing physiological stress and toxic effects [5]. Because plastics do not completely mineralize under natural conditions, their persistence creates long-term ecological risks.

      Studies between 2020 and 2024 emphasize that environmental variability significantly affects degradation rates [6]. For example, marine plastics exposed to strong sunlight degrade faster than submerged plastics in deeper waters [7]. Similarly, freshwater ecosystems show different degradation patterns due to variations in microbial populations and salinity levels

      [8]. These findings highlight the need for context-aware predictive models.

   2. Environmental Factors Influencing Plastic Degradation

      Plastic degradation in aquatic environments occurs through several mechanisms: photodegradation, thermo-oxidative degradation, hydrolysis, and biodegradation [9]. These processes are influenced by environmental parameters.

      Temperature: Higher temperatures accelerate chemical reactions and microbial activity, leading to faster degradation [10]. Climate changeinduced warming may therefore alter plastic breakdown dynamics.

      Ultraviolet Radiation: UV radiation breaks polymer chains and weakens material structure, particularly at the water surface [11]. Photodegradation is often considered the initial step in fragmentation.

      Salinity and pH: Salinity can affect surface properties and biofilm formation on plastics [12]. Changes in pH influence hydrolytic reactions, especially for biodegradable polymers.

      Microbial Activity: Certain microorganisms have shown the ability to degrade specific polymer types, although degradation efficiency remains limited [13]. Biofilm formation on plastic surfaces can either enhance or inhibit degradation depending on environmental conditions.

      Because these factors interact in complex and nonlinear ways, predicting degradation behavior using simple regression models becomes challenging.

   3. Limitations of Traditional Analytical Approaches

      Conventional methods for studying plastic degradation primarily involve controlled laboratory experiments and statistical modeling [14]. While laboratory experiments provide insight into chemical mechanisms, they often use constant environmental conditions that do not reflect real- world variability. Field experiments, on the other hand, are influenced by unpredictable natural factors and may produce inconsistent results.

      Statistical models such as linear regression are commonly used to estimate degradation rates. However, these models require predefined assumptions about relationships between variables [15]. In complex ecosystems where variables interact nonlinearly, such assumptions may reduce predictive accuracy.

      Moreover, environmental monitoring systems now generate large volumes of data through sensors, satellite imaging, and automated sampling devices [16]. Traditional analytical techniques may struggle to process and interpret such high- dimensional datasets efficiently.

   4. Machine Learning in Environmental Monitoring

      Machine learning (ML) has emerged as a powerful tool for environmental data analysis. ML algorithms can detect hidden patterns, model nonlinear relationships, and handle large

      datasets without strict prior assumptions [17]. In recent years, ML has been applied successfully in areas such as water quality forecasting [18], marine pollution detection [19], climate modeling [20], and ecosystem risk assessment [21].

      Supervised learning techniques such as Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) are commonly used for predictive modeling tasks. These models can learn relationships between environmental inputs and output variables through training on historical data [22]. Ensemble methods, in particular, have shown improved accuracy and robustness in environmental prediction tasks [23].

      Adaptive or incremental learning models further enhance predictive systems by updating model parameters when new data become available [24]. This is especially useful in dynamic environments where conditions change over time.

   5. Applications of AI in Plastic Pollution Research

      Recent research has begun exploring AI applications in plastic waste monitoring. Machine learning models have been used for detecting marine litter from satellite imagery [25], classifying plastic types using spectroscopic data [26], and predicting microplastic concentration in water systems [27].

      However, there is still limited research focusing specifically on predictive modeling of plastic degradation rates under varying environmental conditions. Most existing studies examine either chemical degradation mechanisms or spatial distribution of plastic waste, rather than dynamic degradation behavior [28].

      An adaptive, hybrid framework that integrates environmental filtering, feature extraction, and predictive learning remains underexplored. This gap highlights the need for a comprehensive system capable of continuously updating predictions as new ecological data become available.

   6. Research Gap

Based on the reviewed literature, several gaps can be identified:

1. Limited integration of multiple environmental variables in degradation prediction models.

2. Insufficient application of adaptive machine learning systems for real-time environmental monitoring.

3. Lack of unified frameworks combining environmental preprocessing with AI-based prediction.

   This study addresses these gaps by proposing an adaptive machine learning framework designed specifically for predicting plastic degradation in aquatic environments under dynamic conditions.

   3. CONCEPTUAL FRAMEWORK

   To address the complexity of plastic degradation in aquatic environments, this study proposes an adaptive machine learningdriven conceptual framework. The framework

   integrates environmental data processing, feature engineering, predictive modeling, and continuous model updating. It is designed to function in both marine and freshwater ecosystems where environmental conditions vary over time.

   The conceptual model is structured into five major components:

   1. Environmental Data Collection

   2. Data Preprocessing and Filtering

   3. Feature Extraction and Selection

   4. Machine Learning Prediction Model

   5. Adaptive Feedback and Model Updating

      1. Environmental Data Collection

         The first stage involves gathering environmental and plastic- specific data from monitoring systems. These inputs include:

         • Temperature

         • Salinity

         • Ultraviolet (UV) radiation intensity

         • pH level

         • Dissolved oxygen

         • Microbial activity indicators

         • Water flow velocity

         • Plastic type and polymer composition

           Data may be obtained from sensor networks, laboratory simulations, satellite observations, or historical environmental datasets. Both simulated and real-world observational datasets are incorporated to improve generalizability.

      2. Data Preprocessing and Filtering

         Raw environmental data often contain noise, missing values, or inconsistencies. Therefore, preprocessing is required before applying machine learning algorithms. This stage includes:

         • Data cleaning

         • Handling missing values

         • Normalization or scaling

         • Outlier detection

         • Environmental filtering to remove irrelevant variables

         This ensures that only relevant and reliable features are used for prediction.

      3. Feature Extraction and Selection

         In this stage, significant variables influencing plastic degradation are identified. Feature selection techniques help reduce dimensionality and improve model efficiency. Important derived features may include:

         • Combined UVtemperature interaction index

         • Seasonal variation indicators

         • Polymer susceptibility score

         • Microbial exposure intensity

         Feature importance ranking methods (such as tree-based importance metrics) help determine which variables most strongly influence degradation rates.

      4. Machine Learning Prediction Model

         The processed data are then fed into supervised machine learning algorithms. The framework allows use of models such as:

         • Random Forest

         • Support Vector Machine (SVM)

         • Artificial Neural Network (ANN)

         • Gradient Boosting Models

         The output variable is the estimated plastic degradation rate under specific environmental conditions.

         The model learns patterns between environmental parameters and degradation behavior through training and validation phases.

      5. Adaptive Feedback Mechanism

      A key innovation of the proposed framework is its adaptive learning capability. When new environmental data become available, the system:

      • Re-evaluates prediction errors

      • Updates model parameters

      • Retrains incrementally if required

      This continuous feedback loop improves predictive accuracy over time and ensures responsiveness to environmental changes.

      Conceptual Diagram Description

      Figure 1: Proposed Adaptive Machine Learning Framework

      Analytical Chart Description

      Figure 2: Influence of Environmental Factors on Degradation Rate

      Interaction Model Diagram

      Figure 3: Environmental Interaction Network

      For example:

      • Temperature influences microbial activity

      • UV radiation interacts with polymer structure

      • Salinity affects biofilm formation

        This diagram highlights that degradation is not caused by a single variable but by multiple interacting parameters.

4. METHODOLOGY

This study follows a structured methodology to design, implement, and evaluate the proposed adaptive machine learning framework for predicting plastic degradation in aquatic environments. The methodology combines environmental data analysis with supervised learning techniques and adaptive updating mechanisms.

The overall methodological process consists of five stages:

1. Data Collection

2. Data Preprocessing

3. Model Development

4. Model Evaluation

5. Adaptive Updating Mechanism

1. Data Collection

   The dataset used in this study includes both simulated and observational environmental data inspired by marine and freshwater ecosystems. Since long-term degradation datasets are limited, synthetic data were generated based on realistic environmental ranges reported in recent environmental studies.

   The input parameters considered in this research include:

   • Water temperature (°C)

   • Salinity (ppt)

   • Ultraviolet (UV) radiation intensity (W/m²)

   • pH level

   • Dissolved oxygen (mg/L)

   • Microbial activity index

   • Water flow velocity (m/s)

   • Plastic type indicator (e.g., polyethylene, polypropylene, PET)

     The output variable is:

   • Estimated plastic degradation rate (percentage mass loss per unit time)

     The dataset is divided into training (70%), validation (15%), and testing (15%) subsets to ensure reliable performance evaluation.

2. Data Preprocessing

   Before training the machine learning models, preprocessing steps are applied to improve data quality and reliability:

   1. Data Cleaning

      Missing values are handled using interpolation or mean imputation methods. Extreme outliers are removed to reduce noise.

   2. Normalization

      Since environmental variables are measured in different units, minmax scaling is applied to normalize feature values between 0 and 1.

   3. Feature Engineering

      Additional interaction features are created, such as:

      • Temperature × UV interaction term

      • Salinitymicrobial interaction index

      • Seasonal variation indicator

        These derived variables help capture nonlinear environmental relationships.

   4. Feature Selection

   Feature importance ranking is performed using tree-based methods to identify the most influential variables affecting degradation rate. Less significant features are excluded to improve computational efficiency.

3. Model Development

   Three supervised machine learning algorithms are selected for comparative analysis:

   1. Random Forest Regression

   2. Support Vector Regression (SVR)

   3. Artificial Neural Network (ANN)

      1. Random Forest

         Random Forest is chosen due to its robustness against overfitting and ability to handle nonlinear relationships. It works by combining multiple decision trees to improve predictive accuracy.

      2. Support Vector Regression

         SVR is effective for high-dimensional datasets and models nonlinear relationships using kernel functions.

      3. Artificial Neural Network The ANN model consists of:

   • Input layer (environmental parameters)

     • Hidden layers (nonlinear transformation)

     • Output layer (predicted degradation rate)

       The neural network is trained using backpropagation to minimize prediction error.

4. Model Evaluation

   Model performance is evaluated using standard regression metrics:

   • Mean Absolute Error (MAE)

   • Mean Squared Error (MSE)

   • Root Mean Squared Error (RMSE)

   • Coefficient of Determination (R²)

   Cross-validation is performed to ensure model stability and prevent overfitting.

   Figure 4: Model Performance Comparison Chart

5. Adaptive Updating Mechanism

A major component of the methodology is the adaptive feedback system. After initial model training:

1. New environmental observations are introduced.

2. Prediction errors are recalculated.

3. The model parameters are updated incrementally.

This process ensures that the framework continuously learns from new data, improving long-term prediction reliability. Incremental learning techniques are applied to update the model without full retraining, reducing computational cost.

Methodology Flow Diagram Description

Figure 5: Methodology Workflow

This diagram visually represents the structured research process from data acquisition to adaptive prediction.

5. RESULTS / SYNTHESIS

This section presents the findings obtained from applying the proposed adaptive machine learning framework to the simulated aquatic environmental dataset. The results focus on prediction accuracy, environmental factor influence, and the impact of adaptive updating.

1. Comparative Model Performance

   Three supervised learning modelsRandom Forest (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN)were trained and tested on the dataset. Their predictive performance was evaluated using MAE, RMSE, and R² metrics.

   The results indicate that:

   • The Random Forest model produced the lowest prediction error among the three approaches.

   • The Artificial Neural Network showed strong nonlinear modeling capability but required careful tuning to avoid slight overfitting.

   • The SVR model performed adequately but was less adaptable to complex feature interactions compared to RF.

   Overall, Random Forest demonstrated greater stability under variable environmental inputs.

2. Influence of Environmental Variables

   Feature importance analysis revealed that certain environmental parameters contributed more significantly to degradation rate prediction.

   Key observations:

   • Ultraviolet radiation showed the highest influence on degradation rate.

   • Temperature was the second most important factor.

   • Microbial activity index also demonstrated noticeable contribution.

   • <>Salinity and pH had moderate but context-dependent effects.

     This confirms that surface-level plastics exposed to sunlight degrade faster compared to deeper submerged plastics.

3. Environmental Scenario Simulation

   The framework was tested under two simulated ecosystem scenarios:

   1. Marine Ecosystem Scenario

      • Higher salinity

      • Strong UV exposure

      • Moderate microbial activity

        The model predicted relatively faster surface degradation rates due to increased UV exposure.

   2. Freshwater Ecosystem Scenario

   • Lower salinity

   • Reduced UV penetration

   • Variable microbial activity

   The predicted degradation rate was slightly lower compared to the marine surface scenario but showed higher biological influence.

   These simulations demonstrate that the framework can adapt to different environmental contexts and produce condition- specific predictions.

4. Impact of Adaptive Updating

   To evaluate the adaptive capability, new environmental data points were introduced after initial model training. The model was incrementally updated using the adaptive feedback mechanism.

   Results showed:

   • A reduction in prediction error after incorporating new data.

   • Improved stability in seasonal variation scenarios.

   • Better alignment between predicted and simulated degradation rates over time.

   This confirms that adaptive learning enhances long-term predictive performance compared to static models that remain

   unchanged after initial training.

   Figure 8: Adaptive Learning Performance Trend

5. Synthesis of Findings

The results collectively suggest that:

1. Machine learning models can effectively capture nonlinear environmental interactions influencing plastic degradation.

2. Random Forest provides strong robustness and interpretability for environmental prediction tasks.

3. UV radiation and temperature are dominant drivers of degradation in aquatic environments.

4. Adaptive updating significantly enhances model reliability under changing ecological conditions.

The synthesis of findings confirms that integrating environmental filtering with adaptive machine learning provides a flexible and scalable solution for monitoring plastic degradation dynamics.

6. DISCUSSION

The findings of this study demonstrate that an adaptive machine learning framework can effectively model and predict plastic degradation behavior in aquatic environments. This section interprets the results in relation to existing environmental research, discusses the strengths of the proposed approach, and highlights practical implications.

1. Interpretation of Key Findings

   One of the most significant outcomes of this research is the identification of ultraviolet (UV) radiation and temperature as dominant drivers of plastic degradation. This aligns with established scientific understanding that photodegradation and thermo-oxidative reactions play a major role in polymer breakdown, particularly in surface waters exposed to sunlight. The high importance of UV radiation observed in the model confirms the sensitivity of plastic materials to solar exposure.

   Temperature also showed strong influence, which may be linked to accelerated chemical reaction rates and increased microbial activity in warmer waters. This suggests that climate change, which is associated with rising global water temperatures, could alter plastic degradation patterns in future aquatic ecosystems.

   Microbial activity contributed moderately to degradation predictions. While biodegradation is generally slower than photodegradation, microbial biofilms can influence surface properties and long-term fragmentation. The models ability to capture this interaction highlights the advantage of machine learning in identifying complex variable relationships.

2. Advantages of the Adaptive Machine Learning Framework

   Compared to traditional analytical models, the proposed framework offers several advantages:

   1. Ability to Handle Nonlinear Relationships

      Environmental factors do not interact in simple linear ways. Machine learning algorithms, particularly Random Forest and ANN models, effectively captured nonlinear interactions among UV radiation, temperature, salinity, and microbial activity.

   2. Integration of Multiple Environmental Variables

      The framework allows simultaneous inclusion of diverse environmental inputs. This holistic approach improves prediction reliability compared to single-variable experimental models.

   3. Adaptive Updating Capability

      A major strength of this system is its feedback-based learning mechanism. The results showed that incorporating new data reduced prediction error over time. This adaptability is critical for real-world environmental monitoring, where conditions change seasonally or due to human influence.

   4. Scalability for Large Datasets

   Environmental monitoring systems generate large volumes of data. Machine learning models are well-suited for handling such high-dimensional datasets efficiently.

3. Practical Implications

   The results have important implications for environmental management and sustainable policy planning.

   Environmental Monitoring

   Government agencies and environmental organizations can integrate adaptive machine learning tools into monitoring systems to predict degradation trends in specific water bodies. This may improve decision-making for pollution control.

   Risk Assessment

   By identifying regions with slower degradation rates, policymakers can prioritize cleanup and intervention efforts in

   high-risk areas.

   Sustainable Material Design

   Predictive models can support the development of biodegradable plastics by simulating how new materials behave under various environmental conditions.

   Climate Change Considerations

   As temperature and UV exposure patterns shift due to climate change, adaptive prediction systems will be increasingly valuable for forecasting long-term pollution impacts.

4. Limitations of the Study

   Although the proposed framework demonstrates promising results, several limitations must be acknowledged:

   1. The dataset includes simulated components due to limited availability of long-term degradation observations.

   2. Environmental variability in real ecosystems may be more complex than modeled.

   3. Certain degradation mechanisms, such as chemical additives leaching, were not included in the prediction model.

   4. Real-time deployment would require continuous sensor calibration and infrastructure support.

   Future research should incorporate larger real-world datasets and expand the range of polymer types studied.

5. Comparison with Conventional Methods

Traditional regression-based models often assume fixed relationships between variables. In contrast, the adaptive machine learning approach adjusts dynamically and captures complex interactions. While laboratory experiments remain essential for understanding chemical mechanisms, AI-based predictive systems provide complementary large-scale analysis capabilities.

The discussion confirms that combining environmental science with artificial intelligence offers a forward-looking strategy for managing plastic pollution.

7. CONCLUSION AND FUTURE SCOPE Conclusion

Plastic pollution in aquatic environments remains a persistent global challenge due to the slow and complex degradation behavior of synthetic polymers. Environmental conditions such as temperature, ultraviolet radiation, salinity, pH, microbial activity,and water dynamics significantly influence the breakdown process. Because these factors vary across ecosystems and over time, predicting degradation behavior using traditional experimental approaches alone is difficult.

This research proposed an adaptive machine learningbased framework to analyze and predict plastic degradation rates in aquatic environments. The system integrates environmental

data preprocessing, feature extraction, supervised learning algorithms, and an adaptive feedback mechanism. By combining multiple environmental parameters into a unified predictive model, the framework provides context-aware and dynamic estimation of degradation rates.

The results demonstrated that machine learning models, particularly Random Forest, effectively capture nonlinear interactions among environmental variables. Ultraviolet radiation and temperature were identified as major influencing factors in degradation prediction. The adaptive updating mechanism improved model performance over time by incorporating new data and reducing prediction errors.

Overall, the proposed framework offers a flexible, scalable, and data-driven solution for environmental monitoring and sustainable plastic waste management. It bridges the gap between laboratory-based degradation studies and real-world environmental variability by enabling dynamic prediction under changing ecological conditions.

Future Work

Although the framework shows promising results, further improvements and expansions can enhance its practical applicability.

1. Integration of Real-Time Sensor Data

   Future research should integrate live environmental monitoring data from IoT-based water sensors to enable real- time degradation prediction.

2. Inclusion of Additional Polymer Types

   Expanding the model to include biodegradable plastics and emerging polymer materials would improve its industrial relevance.

3. Deep Learning Enhancements

   Advanced deep learning architectures such as recurrent neural networks (RNN) or long short-term memory (LSTM) networks could be explored to better capture seasonal and temporal variations.

4. Geospatial Modeling

   Incorporating satellite-based spatial datasets can help develop location-specific degradation maps for coastal and freshwater regions.

5. Policy-Oriented Decision Support Systems

   The framework can be integrated into environmental decision- support platforms to guide pollution mitigation strategies and resource allocation.

6. Climate Impact Modeling

Future studies should assess how climate changedriven shifts in temperature and UV radiation patterns influence long-term degradation dynamics.

By advancing these directions, adaptive AI-driven systems

can play a vital role in strengthening global efforts to monitor and manage plastic pollution sustainably.

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