Machine learning approaches for predicting microplastic pollution in peatland areas

Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this problem, we proposed a deep learning (DL) method to automatically detect and quantify microparticles free from biomass and entrapped in biomass from microscope images. Firstly, we introduced a "TU Delft-Interaction between Particles and Biomass" dataset containing labeled microscope images. Then, we built DL models using this dataset with seven state-of-the-art model architectures for a instance segmentation task, such as Mask R-CNN, Cascade Mask R-CNN, Yolact and YOLOv8. The results show that the Cascade Mask R-CNN with ResNet50 backbone achieves promising detection accuracy, with a mAP50box and mAP50mask of 90.6 % on the test set. Then, we benchmarked our results against the conventional ImageJ processing method. The results show that the DL method significantly outperforms the ImageJ processing method in terms of detection accuracy and processing cost. The DL method shows a 13.8 % improvement in micro-average precision, and a 21.7 % improvement in micro-average recall, compared to the ImageJ method. Moreover, the DL method can process 70 images within 1 min, while the ImageJ method costs at least 6 h. The promising performance of our method allows it to offer a potential alternative to examine the interaction between microparticles and biomass in biological wastewater treatment process in an affordable manner. This approach offers more useful insights into the treatment process, enabling further reveal the microparticles transfer in biological treatment systems.

Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices

The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and characterize, MP pollution and its allied threats to human health in the indoor urban environment, where 80 household dust samples were collected from the whole study area. The presence of MPs in household dust of the urban indoor environment was 25.8 ± 6.43 particles/g with a significant variety, whereas the fiber shape (73%), 0.5-1.00 mm ranged MPs size (58%), blue color (21%), and polystyrene polymer (34%) was the most ubiquitous MPs category. The pollution load index (1.61-2.96) indicated significant pollution due to the high abundance of MPs. Besides, other risks evaluating indices including contamination factor (1.00-3.51), and Nemerow pollution index (1.60-3.51) represent moderate to high MP-induced pollution. The polymer hazard index (119.54 ± 70.34) indicated significant risks for the selected polymers to the indoor environment living inhabitants. Machine learning approaches, especially random forest and support random vector machine were effective in predicting the number of MPs, where EC, salinity, pH, OC, and texture classes acted as controlling factors. Children and adults might be ingesting 4.12 ± 1.01 and 2.27 ± 0.57 particles/day through the ingestion exposure route, which has significant health effects. Polymer-associated lifetime cancer risk assessment results show that there are moderate risks for both adults and children, but children tend to be more susceptible to MP risks. The overall study found that Dhaka was the most severely MPs induced risky division among the others. This study reveals that high quantities of MPs in indoor environments could pose a serious health hazard' to different exposure groups.

Review on personal protective equipment: Emerging concerns in micro(nano)plastic pollution and strategies for addressing environmental challenges

The COVID-19 pandemic was caused by the SARS-CoV-2 virus, marking one of the most catastrophic global health crises of the 21st century. Throughout this period, widespread use and improper disposal of personal protective equipment (PPE) emerged as a pressing environmental issue, significantly impacting various life forms. During the COVID-19 pandemic, there was a high rate of PEP disposal. An alarming 1.6 × 106 tons of plastic waste each day has been generated since the onset of the outbreak, predominantly from the inadequate disposal of PPE. The mismanagement and subsequent degradation of discarded PPE significantly contribute to increased non-biodegradable micro(nano)plastic (MNP) waste. This pollution has had profound adverse effects on terrestrial, marine, and aquatic ecosystems, which have been extensively of concern recently. Accumulated MNPs within aquatic organisms could serve as a potential route for human exposure when consuming seafood. This review presents a novel aspect concerning the pollution caused by MNPs, particularly remarking on their role during the pandemic and their detrimental effects on human health. These microplastic particles, through the process of fragmentation, transform into nanoparticles, persisting in the environment and posing potential hazards. The prevalence of MNP from PPE, notably masks, raises concerns about their plausible health risks, warranting global attention and comprehensive exploration. Conducting a comprehensive evaluation of the long-term effects of these processes and implementing effective management strategies is essential.

Microplastics monitoring in freshwater systems: A review of global efforts, knowledge gaps, and research priorities

The escalating production of synthetic plastics and inadequate waste management have led to pervasive microplastic (MP) contamination in aquatic ecosystems. MPs, typically defined as particles smaller than 5 mm, have become an emerging pollutant in freshwater environments. While significant concern about MPs has risen since 2014, research has predominantly concentrated on marine settings, there is an urgent need for a more in-depth critical review to systematically summarize the current global efforts, knowledge gaps, and research priorities for MP monitoring in freshwater systems. This review evaluates the current understanding of MP monitoring in freshwater environments by examining the distribution, characteristics, and sources of MPs, alongside the progression of analytical methods with quantitative evidence. Our findings suggest that MPs are widely distributed in global freshwater systems, with higher abundances found in areas with intense human economic activities, such as the United States, Europe, and China. MP abundance distributions vary across different water bodies (e.g., rivers, lakes, estuaries, and wetlands), with sampling methods and size range selections significantly influencing reported MP abundances. Despite great global efforts, there is still a lack of harmonized analyzing framework and understanding of MP pollution in specific regions and facilities. Future research should prioritize the development of standardized analysis protocols and open-source MP datasets to facilitate data comparison. Additionally, exploring the potential of state-of-the-art artificial intelligence for rapid, accurate, and large-scale modeling and characterization of MPs is crucial to inform effective strategies for managing MP pollution in freshwater ecosystems.

Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis

Microplastic analysis is a crucial step for locating the environmental contamination sources and controlling plastic contamination. A popular tool like Fourier transform infrared (FTIR) spectroscopy is capable of identifying plastic types and can be carried out through a variety of containers. Unfortunately, sample collection from water sources like rivers usually involves filtration so the measurements inevitably include the membrane filter that also has its own FTIR characteristic bands. Furthermore, when plastic particles are small, the membrane filter's spectrum may overwhelm the desired plastics' spectrum. In this study, we proposed a novel preprocessing method based on the dictionary learning technique for decomposing the variations within the acquired FTIR spectra and capturing the membrane filter's characteristic bands for the effective removal of these unwanted signals. We break down the plastic analysis task into two subtasks - membrane filter removal and plastic classification - to increase the explainability of the method. In the experiments, our method demonstrates a 1.5-fold improvement compared with baseline, and yields comparable results compared to other state-of-the-art methods such as UNet when applied to noisy spectra with low signal-to-noise ratio (SNR), but offers explainability, a crucial quality that is missing in other state-of-the-art methods. The limitations of the method are studied by testing against generated spectra with different levels of noise, with SNR ranging from 0 to - 30dB, as well as samples collected from the lab. The components/atoms learned from the dictionary learning technique are also scrutinized to describe the explainability and demonstrate the effectiveness of our proposed method in practical applications.

Artificial intelligence in microplastic detection and pollution control

The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection. Focusing on image processing, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and hyperspectral imaging (HSI), the review highlights how AI enhances the efficiency and accuracy of these techniques. AI-driven image processing automates the identification and quantification of MPs, significantly reducing the need for manual analysis. FTIR and Raman spectroscopy accurately distinguish MP types by analyzing their unique spectral features, while HSI captures extensive spatial and spectral data, facilitating detection in complex environmental matrices. Furthermore, AI algorithms integrate data from these methods, enabling real-time monitoring, traceability prediction, and pollution hotspot identification. The synergy between AI and spectral imaging technologies represents a transformative approach to environmental monitoring and emphasizes the need to adopt innovative tools for protecting ecosystem health.

Landfill-mined soil-like fraction (LMSF) use in biopolymer composting: Material pre-treatment, bioaugmentation and agricultural prospects

Polylactic Acid (PLA) based compostable bioplastic films degrade under thermophilic composting conditions. The purpose of our study was to understand whether sample pre-treatment along with bioaugmentation of the degradation matrix could reduce the biodegradation time under a simulated composting environment. Sepcifically, we also explored whether the commercial composts could be replaced by landfill-mined soil-like fraction (LMSF) for the said application. The effect of pre-treatment on the material was analysed by tests like tensile strength analysis, hydrophobicity analysis, morphological analysis, thermal degradation profiling, etc. Subsequently, the degradation experiment was performed in a simulated composting environment following the ASTM D5338 standard, along with bioaugmentation in selected experimental setups. When the novel approach of material pre-treatment and bioaugmentation were applied in combination, the time necessary for 90% degradation was reduced by 27% using compost and by 23% using LMSF. Beyond the improvement in degradation rate, the water holding capacity increased significantly for the degradation matrices. With pH, C: N ratio and microbial diversity tested to be favourable through 16s metabarcoding studies, material pre-treatment and bioaugmentation allow LMSF to not only replace commercial compost in polymer degradation but also find immense application in the agricultural sector of drought-affected areas (for better water retention) after it has been used for PLA degradation.

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Uncertainty in Environmental Micropollutant Modeling

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in the outdoor urban environment using machine learning and multivariate approaches. The occurrences of MPs in the urban road dust were 52.76 ± 20.24 particles/g with high diversity, where fiber shape (77%), 0.1-0.5 mm size MPs (75%), blue color (26%), and low-density polyethylene (24%) polymer was the dominating MPs category. Pollution load index value (1.28-4.42), showed severe pollution by MPs. Additionally, the contamination factor (1.00-5.02), and Nemerow pollution index (1.38-5.02), indicate moderate to severe MP pollution. The identified polymers based on calculated potential ecological risk (2248.52 ± 1792.79) and polymer hazard index (814.04 ± 346.15) showed very high and high risks, respectively. The occurrences of MPs could effectively be predicted by random forest, and support random vector machine, where EC, salinity, pH, OC, and texture classes were the influencing parameters. Considering the human health aspect, children and adults could be acutely exposed to 19259.68 and 5777.90 MP particles/ year via oral ingestion. Monte-Carlo-based polymers associated cancer risk assessment results indicate moderate risk and high risk for adults and children, respectively, where children were more vulnerable than adults for MP pollution risks. Overall assessment mentioned that Dhaka was the most polluted division among the other divisions.

Artificial intelligence-empowered collection and characterization of microplastics: A review

Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

Connecting the Dots: Livestock Animals as Missing Links in the Chain of Microplastic Contamination and Human Health

Plastic pollution is a global diffuse threat, especially considering its fragmentation into microplastics (MPs) and nanoplastics (NPs). Since the contamination of the aquatic environment is already well studied, most studies have now focused on the soil. Moreover, the number of studies on the exposure routes and toxic effects of MNPs in humans is continuously increasing. Although MNPs can cause inflammation, cytotoxicity, genotoxicity and immune toxicity in livestock animals, which can accumulate ingested/inhaled plastic particles and transfer them to humans through the food chain, research on this topic is still lacking. In considering farm animals as the missing link between soil/plant contamination and human health effects, this paper aims to describe their importance as carriers and vectors of MNP contamination. As research on this topic is in its early stages, there is no standard method to quantify the amount and the characteristics of MNPs in different matrices. Therefore, the creation of a common database where researchers can report data on MNP characteristics and quantification methods could be helpful for both method standardization and the future training of an AI tool for predicting the most abundant/dangerous polymer(s), thus supporting policy decisions to reduce plastic pollution and perfectly fitting with One Health principles.