Use of a convolutional neural network for the classification of microbeads in urban wastewater

Applications of mathematical modelling for assessing microplastic transport and fate in water environments: a comparative review

Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.

Evidence of internalized microplastics in mussel tissues detected by volumetric Raman imaging

Microplastics are a global ecological concern due to their potential risk to wildlife and human health. Animals ingest microplastics, which can enter the trophic chain and ultimately impact human well-being. The ingestion of microplastics can cause physical and chemical damage to the animals' digestive systems, affecting their health. To estimate the risk to ecosystems and human health, it is crucial to understand the accumulation and localization of ingested microplastics within the cells and tissues of living organisms. However, analyzing this issue is challenging due to the risk of sample contamination, given the ubiquity of microplastics. Here, an analytical approach is employed to confirm the internalization of microplastics in cryogenic cross-sections of mussel tissue. Using 3D Raman confocal microscopy in combination with chemometrics, microplastics measuring 1 μm in size were detected. The results were further validated using optical and fluorescence microscopy. The findings revealed evidence of microplastics being internalized in the digestive epithelial tissues of exposed mussels (Mytilus galloprovincialis), specifically within the digestive cells forming digestive alveoli. This study highlights the need to investigate the internalization of microplastics in organisms like mussels, as it helps us understand the potential risks they pose to aquatic biota and ultimately to human health. By employing advanced imaging techniques, challenges associated with sample contamination can be overcome and valuable insights into the impact of microplastics on marine ecosystems and human consumers are provided.

Reductions of Plastic Microbeads from Personal Care Products in Wastewater Effluents and Lake Waters Following Regulatory Actions

Plastic microbeads were widely used as exfoliants in personal care products (PCPs; e.g., hand/body washes) in North America, but restrictions were imposed on their use in PCPs in the U.S. (2017) and Canada (2018). We provide the first assessment of whether restrictions are effectively reducing microbeads entering surface waters. We examined their abundance, character, and trends in wastewater treatment plant (WWTP) effluents in Toronto, Canada, from 2016 to 2019, and in adjacent Lake Ontario surface waters (2015 and 2018), encompassing the period before and after the bans. Microbeads isolated from PCPs purchased in 2015 provided a visual morphological key with "irregular" and "spherical" microbead categories. Median concentrations of irregular microbeads, composed of polyethylene plastic, declined by up to 86% in WWTP effluents from 8.4 to 14.3 particles/m3 before to 2.0-2.2 particles/m3 after the bans, while those of spherical microbeads, predominantly synthetic/polyethylene wax, ranged within 0.5-2.3 particles/m3 and did not differ before and after the bans since, as nonplastic, they were not regulated. Similarly, amounts of irregular microbeads declined relative to spherical microbeads in Lake Ontario, indicating that product changes may be influencing observations in lake waters. The results suggest that the Canadian and U.S. restrictions effectively and rapidly reduced plastic microbeads entering waters via WWTPs.

Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence

In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.

Identification and detection of microplastic particles in marine environment by using improved faster R-CNN model

Microplastics refer to plastic particles measuring less than 5 mm, which has led to serious environmental problem and the detection of these tiny particles is crucial for understanding the corresponding distribution and impact on the marine environment. In this paper, an improved faster region-based convolutional neural network (R-CNN) model was developed for the identification and detection of microplastic particles. In the proposed model, the residual network-50 (ResNet-50) is employed as the backbone with the replacement of the traditional one to enhance the feature extraction capability and the feature pyramid networks (FPN) module is introduced together for solving the multi-scale target detection. By using the improved Faster R-CNN model, the network model performance is enhanced where the average confidence of detecting unique microplastic particles in the marine environment reaches as high as 99%. Moreover, the microparticles mixture was bounded precisely via the predicted bounding boxes without missing detection and wrong detection. In this way, the successful identification of polystyrene microplastic particles from the particles suspension with similar shapes but various conditions of backgrounds, brightness, distributions and object sizes, was achieved by employing the proposed improved Faster R-CNN model, enabling the accurate detection of microplastic particles in marine environment.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

The increase in the global population is directly responsible for the acceleration in the production as well as the consumption of textile products. The use of textiles and garment materials is one of the primary reasons for the microfibers generation and it is anticipated to grow increasingly. Textile microfibers have been found in marine sediments and organisms, posing a real threat to the environment as it is invisible pollution caused by the textile industry. To protect against the damaging effects that microplastics can have, the formulation of mitigation strategies is urgently required. Therefore, the primary focus of this review manuscript is on finding an environmentally friendly long-term solution to the problem of microfiber emissions caused by the domestic washing process, as well as gaining an understanding of the various properties of textiles and how they influence this problem. In addition, it discussed the effect that mechanical and chemical finishes have on microfiber emissions and identified research gaps in order to direct future research objectives in the area of chemical finishing processes. In addition to that, it included a variety of preventative and minimizing strategies for reduction. Last but not least, an emphasis was placed on the potential and foreseeable applications of machine vision (i.e., quantification, data storage, and data sharing) to reduce the amount of microfibers emitted by residential washing machines.

Water treatment and artificial intelligence techniques: a systematic literature review research

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.

Tracking microplastics contamination in drinking water in Zahedan, Iran: From source to consumption taps

Microplastics (MPs) that pollute drinking water are inherently toxic, act as an adsorbent of hazardous pollutants, and threaten human health. So, the fate of microplastics in drinking water from the source to consumption taps (CTs) was assessed in spring and winter in Zahedan city in Iran. Sampling was performed from 4 reservoirs (raw water), before and after two water treatment plants (WTPs), and 10 CTs. The reservoirs were sampled using a plankton net (pore size = 100 μm), and the remaining samples were taken using a sampling device (containing a stainless steel membrane as a filter with pore size = 5 μm). The combination of density separation techniques, digestion, observation, Micro-Raman and FTIR, and SEM analysis was performed to recognize MPs. The average number of MPs in raw water varied between 15.4 and 44.7 MP/m³ (winter) and 22-51.8 MP/m³ (spring). The results before and after the treatment plant showed that about 64 % and 75 % of particles were eliminated in WTP₁ and WTP₂, respectively. The average number of MPs in CTs was more than treatment water (CT_a = 85-390 MP/m³ and CT_b = 75-400 MP/m³), which is a probable confirmation of secondary contamination (abrasion from pipes, installations, and sealing materials). The dominant type of polymer detected in raw water, treated water, and consumption taps were PS. The estimated daily intake for children and adults was about 0.16-15 MP/kg/bw/year and 0.07-5.7 MP/kg/bw/year, respectively. The surface morphology of MPs showed that the particles were affected by continuous weathering, mechanical breakage, and oxidation. MPs threaten the environment and human health due to the adsorption and transport of hazardous pollution and their intrinsic toxicity, so a solution must be thought of to prevent the pollution of drinking water by MPs.

Microplastic emission characteristics of stormwater runoff in an urban area: Intra-event variability and influencing factors

Stormwater runoff is considered a major pathway for land-based microplastic transportation to aquatic environments. By applying time-weighted stormwater sampling at stormwater outlets from industrial and residential catchments, we investigated the emission characteristics and loads (number- and mass-based) of microplastics to aquatic environments through urban stormwater runoff during rainfall events. Microplastics were detected in stormwater runoff from industrial and residential areas in the concentration range of 68-568 n/L and 54-639 n/L, respectively. Polypropylene and polyethylene were found as major polymers accounting for around 60 % of total microplastics. The fragment was the dominant shape of microplastics, and the most common size class was 20-100 μm or 100-200 μm. The microplastic load emitted from industrial and residential areas were estimated to be 1.54-46.1 × 10⁸ and 0.63-28.5 × 10⁸ particles, respectively. The discharge characteristics of microplastics inter- and intra-event were affected by the land-use pattern and rainfall characteristics. The concentration of microplastics did not significantly differ between industrial and residential catchments, but the composition of polymer types reflected the land-use pattern. The microplastics in stormwater were more concentrated when the number of antecedent dry days (ADDs) was higher; the concentration of microplastics was generally peaked in the early stage of runoff and varied according to rainfall intensity during a rainfall event. The contamination level and load of microplastics were heavily affected by the total rainfall depth. Most microplastics were transported in the early stage of runoff (19-37 % of total runoff time), but the proportion of larger and heavier particles increased in the later period of runoff. The microplastic emission via stormwater runoff was significantly higher than that through the discharge of wastewater treatment plant effluent in the same area, implying that stormwater runoff is the dominant pathway for transporting microplastics to aquatic environments.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges

Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.

Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management

Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking. A smart waste material classification technique is required to simplify the procedures. Therefore, the study presents a new Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) method. The presented MDTLDC-IWM model facilitates the use of DL models for the identification and classification of waste materials in the IWM system. To accomplish this, the presented MDTLDC-IWM model follows two key phases, namely waste object recognition and waste object classification. At the initial stage, the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) algorithm is used. Next, in the second stage, the stacked sparse auto encoder (SSAE) model is applied for the waste object classification method. The SSAE model is effectively optimized using the Aquila Optimization Algorithm (AOA), which helps to accomplish maximum classification of waste objects. The MDTLDC-IWM model has achieved a precision of 96.84 percent and an F score of 96.71 percent. A benchmark dataset is used to test the experimental validity of the presented MDTLDC-IWM model. Extensive comparative analysis reported the enhanced performance of the MDTLDC-IWM model over recent state-of-the-art approaches.

Recent advances in the application of machine learning methods to improve identification of the microplastics in environment

Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identification methods have demonstrated significant limitations such as low resolution, long imaging time, and limited particle size analysis. New and improved methods for MPs identification are topical research areas, with machine learning (ML) algorithms proven highly useful in recent years. Critical literature reviews on the latest developments in this area are limited. This study closes this gap and summarizes the progress made over the last 10 years in using ML algorithms for identifying and quantifying MPs. We identified diverse combinations of ML methods and fundamental techniques for MPs identification, such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and near-infrared spectroscopy. The most widely used ML model is the support vector machine, which effectively improves the conventional analysis method for spectral quality defects and improves detection accuracy. Artificial neural network models exhibit improved recognition effects, with shorter detection times and better MPs recognition efficiency. Our review demonstrates the potential of ML in improving the identification and quantification of MPs.

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM_2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM_2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM_2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM_2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM_2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM_2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants

Carbon neutrality has been received extensive attention in the field of wastewater treatment. The optimal management of wastewater treatment plants (WWTPs) has great significance and urgency since the serious energy and materials waste. In this study, a full-view management method based on artificial neural networks (ANNs) for energy and material savings in WWTPs was established. More than 5 years of historical operating data from two typical plants (size 40,000 t/d and 10,000 t/d) located in Chongqing, China, were obtained, and public data in the service area of each plant were systematically collected from open channels. These abundant historical and public data were used to train two ANNs (GRA-CNN-LSTM model and PCA-BPNN model) to predict the inlets/outlets wastewater quality and quantity. The overall average prediction accuracy of inlets/outlets wastewater indicators are greater than 92.60% and 93.76%, respectively. By combining the two models, more appropriate process operation strategies can be obtained 2 weeks in advance, with more than 11.20% and 16.91% reduction of energy and material costs, respectively. This proposed method can provide full-view decision support for the optimal management of WWTPs and is also expected to support carbon emission control and carbon neutrality in the field of wastewater treatment.

Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil

Microplastics (MPs) are emerging environmental pollutants and their accumulation in the soil can adversely affect the soil biota. This study aims to employ hyperspectral imaging technology for the rapid screening and classification of MPs in farmland soil. In this study, a total of 600 hyperspectral data are collected from 180 sets of farmland soil samples with a hyperspectral imager in the wavelength range of 369- 988 nm. To begin, the hyperspectral data are preprocessed by the Savitzky-Golay (S-G) smoothing filter and mean normalization. Second, principal component analysis (PCA) is used to minimize the dimensions of the hyperspectral data and hence the amount of data, making the subsequent model easier to construct. The cumulative contribution rate of the first three principal components is reached 98.37%, including the main information of the original spectral data. Finally, three models including decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) are established, all of which can achieve well classification effects on three MP polymers including polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC) in farmland soil. By comparing the recognition accuracy of the three models, the classification accuracy of DT and SVM is 87.9% and 85.6%, respectively. The CNN model based on the S-G smoothing filter obtains the best prediction effect, the classification accuracy reaches 92.6%, exhibiting obvious advantages in classification effect. Altogether, these results show that the proposed hyperspectral imaging technique identifies the soil MPs rapidly and nondestructively, and provides an effective automated method for the detection of polymers, requiring only rapid and simple sample preparation.

Dynamics of airborne microplastics, appraisal and distributional behaviour in atmosphere; a review

The use of plastics is common across all aspects of human life owing to its durable and versatile nature. The generation and utilization of plastics are directly related to the anthropogenic activities. The extensive use of plastics and adoption of inappropriate waste-management frameworks has resulted in their release into the environment, where they may persist. Different environmental factors, such as, photochemical, thermo-oxidation, and biological degradation, can lead to the degradation of plastics into micro- (MPs) and nano-plastics (NPs). The behaviour and concentration of MPs in the terrestrial environment can depend on their size, density, and local atmospheric conditions. Microplastics and nanoplastics may enter the food web, carrying various organic pollutants, which bio-accumulate at different trophic levels, prompting organism health concerns. Microplastics being airborne identifies as new exposure route. Dietary and airborne exposure to MPs has led researchers to stress the importance of evaluating their toxicological potential. The primary goal of this paper is to explore the environmental fate of MPs from sources to sink in the terrestrial environment, as well as detail their potential impacts on human health. Additionally, this review article focuses on the presence of airborne microplastics, detailed sample pre-processing methods, and outlines analytical methods for their characterization.

Litter Detection with Deep Learning: A Comparative Study

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning

In this study, dark-field microscopy-based hyperspectral imaging augmented with deep learning data analysis was applied for effective visualisation, detection and identification of microplastics released from polypropylene medical masks.

Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study

The development of an automatic method of identifying microplastic particles within live cells and organisms is crucial for high-throughput analysis of their biodistribution in toxicity studies. State-of-the-art technique in the data analysis tasks is the application of deep learning algorithms. Here, we propose the approach of polystyrene microparticle classification differing only in pigmentation using enhanced dark-field microscopy and a residual neural network (ResNet). The dataset consisting of 11,528 particle images has been collected to train and evaluate the neural network model. Human skin fibroblasts treated with microplastics were used as a model to study the ability of ResNet for classifying particles in a realistic biological experiment. As a result, the accuracy of the obtained classification algorithm achieved up to 93% in cell samples, indicating that the technique proposed will be a potent alternative to time-consuming spectral-based methods in microplastic toxicity research.

Solid waste: An overlooked source of microplastics to the environment

Microplastics pollution is one of the most pressing environmental problems of the 21st century. While microplastics are pervasive throughout various environmental compartments, research to date has primarily focused on marine systems. Land-based microplastics sources (e.g., solid waste) have received comparatively little attention, although they account for the main flow of microplastics into aquatic environments. Solid waste microplastics sources primarily include landfill refuse, sludge, and food waste. Microplastics in these waste streams can be associated with various micropollutants that can have deleterious impacts on ecosystem health as they enter the food chain. Thus, understanding the occurrence, fate, and degradation pathways of solid waste microplastics is essential to develop comprehensive control and mitigation strategies. This study critically reviewed these key aspects of microplastics in municipal solid waste landfill refuse, sewage sludge, and food waste, and identified the interconnections of these components in the proliferation of microplastics to the environment. Additionally, microplastics related laws and regulations and their relevance to solid waste microplastics mitigation are discussed.

Abundance and characteristics of microplastics in municipal wastewater treatment plant effluent: a case study of Guangzhou, China

Wastewater treatment plants (WWTPs) have been proposed as significant sources of microplastics (MPs) in freshwater and estuarine environments. WWTPs, even those with high removal efficiencies, release millions of MPs per plant daily. China is the largest plastic producer worldwide, but only a few studies of MP pollution from WWTPs have been carried out in China. In this work, we present a comprehensive report concerning the MPs in effluent from six WWTPs in Guangzhou, which is the third largest city in China. The six WWTPs employ different treatment processes and serve different populations and types of factories. The average abundance of MPs in the effluents of all six WWTPs was 1.719 ± 1.035 MP/L. Fiber was the most common type of MP in the effluent, accounting for 39.48 ± 6.37% of all MPs. Fourier transform infrared spectroscopy confirmed that 35.7% of the particles were plastics, including polyethylene terephthalate (31.9%), polypropylene (26.6%), and polyethylene (9.7%). The results showed that advanced or tertiary treatment technologies could substantially remove MPs and that the size of the population served was positively associated with the abundance of MPs. The number of textile factories was a key factor contributing to the total release of MPs. In addition, the MP shapes and polymer compositions showed that the occurrence of MP types is regional, varies regionally, and is related to the types of factories in the vicinity. More studies on the effects of specific industries are suggested in order to improve the management of wastewater discharge and reduce MPs presence in the natural environment.

A comprehensive review of deep learning applications in hydrology and water resources

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

Characterization of microplastic pollution in tadpoles living in small water-bodies from Rize, the northeast of Turkey

Microplastic pollution is a growing problem for Turkey and other countries, but most studies focus on the pollution in oceans and seas. To understand the relationship between microplastics, fresh water, and terrestrial environment, we examined Pelophylax ridibundus and Rana macrocnemis tadpoles that can inhabit a wide range of both terrestrial and aquatic habitats, ecoregions and elevations, and that are members of Ranidae family. We characterized microplastics (MPs) in sediments, surface water, and tadpoles from the Rize province in northeastern of Turkey. The content of MPs in sediments, surface water, and tadpoles, ranged 64.17-472.1 items/kg, 1-13 items/L and 302.62-306.69 items/g, respectively. In sediment samples, polypropylene (PP) and polyethylene (PE) were the dominant pollutants; whereas, nylon and polyethylene terephthalate (PET) were found in surface water. In tadpoles, PET, nylon, and polyacrylic were the dominant MPs.

Garbage in garbage out: the contribution of our industrial advancement to wastewater degeneration

Natural water sources are habitually marred by insidious anthropogenic practices and municipal wastewater discharges that contain either of xenobiotic pollutants and their sometimes more toxic degradation products, or both. Although wastewater is considered as both a resource and a problem, as explained in this review, it is however daunting that, while the global village is still struggling to decipher the mode of proper handling, subsequent discharge and regulation of already established aromatic contaminants in wastewater, there emanates some more aggressive, stealth and sinister groups of compounds. It is quite ironic that majority of these compounds are the 'go through' consumables in our present society and have been suspected to pose several health risks to the aquatic ecosystem, eliciting unfavourable clinical manifestations in aquatic animals and humans, which has heightened the uncertainties conferred on freshwater use and consumption of some aquatic foods. This review therefore serves to give a brief account on the metamorphosis of approach in detection of aromatic pollutants and ultimately their implications along the trophic chains in the community.

Freshwater microplastics pollution: Detecting and visualizing emerging trends based on Citespace II

Microplastic particles with less than 5 mm in diameter has been detected in human feces and freshwater systems. Microplastics could cause serious physical and chemical harm to humans and organisms. Some previous studies on microplastics mainly concentrate on the marine environment, but few have focused on freshwater microplastics. Therefore, Citespace II is used to systematically analyze the related literature in order to comprehensively understand the research state of freshwater microplastics. The results show that there is still a large gap between research on freshwater and marine microplastics. Studies on freshwater microplastics have mainly been undertaken in developed countries such as the United States and Germany, while fewer studies have been conducted in the developing countries which face the most serious plastic pollution. Most studies focus on the rivers and lakes, but other freshwater sources with microplastic pollution, such as groundwater and reservoirs, have received less attention. This study also explored the possible opportunities and challenges that may be faced in freshwater research in order to introduce specific policies and measures to mitigate this emerging pollutant.

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.

Tiny, shiny, and colorful microplastics: Are regular glitters a significant source of microplastics?

Used in significant volumes in make-up, craft activities, and -more recently- in textile products, glitters are among single-use plastics, and are often made of polyethylene terephthalate. Even though a wealth of studies focus on the sources of microplastics in the environment and biota, glitters produced in various countries, and used extensively in entertainment events, shows and carnivals around the globe, not to mention by virtually anyone in daily life settings, have been relatively ignored as a major source of microplastics. That is why the present study focuses specifically on plastic glitters, and attempts to track them in the environment, in a manner comparable to their use in forensic science where glitters are often used as trace evidence associating a suspect with a specific murder case. Doing so led to various pieces of evidence of the presence of glitters -arguably a stealthy source of microplastics - in samples taken from the environment at a wide range of locations around the world.