An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)

Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol

Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact. This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000-2000 μm, 500-1000 μm, 250-500 μm). Furthermore, MP particles with diameters ranging from 30 to 250 μm were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 μm/pixel, and two spectral ranges, 1000-1700 nm (NIR) and 1000-2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 μm, the optimal setup is a spatial resolution of 150 μm/pixel and a spectral range of 1000-1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 μm, a higher spatial resolution of 30 μm/pixel with a spectral range of 1000-2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 μm for the 150 μm/pixel resolution and ranges from 100 to 200 μm for the 30 μm/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.

Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil

In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology's robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI's application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.

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.

In-situ detection of microplastics in the aquatic environment: A systematic literature review

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Microplastic pollution in the North-east Atlantic Ocean surface water: How the sampling approach influences the extent of the issue

A lack of standardization in monitoring protocols has hindered the accurate evaluation of microplastic (MP) pollution in the open sea and its potential impacts. As sampling techniques significantly influence the amounts of MPs contained in the sample, the aim of this study was to compare two sampling methods: Manta trawl (size selective approach) and grab sampling (volume selective approach). Both approaches were applied in the open sea surface waters of the North-east Atlantic Ocean. Onshore sample processing was carried out using the innovative tape lifting technique, which affords a series of advantages, including prevention of airborne contamination during analysis, without compromising integrity of the results. The results obtained indicated an MP concentration over four orders of magnitude higher using grab sampling compared to the Manta net approach (mean values equal to 0.24 and 4050 items/m3, respectively). Consequently, the sole quantification of MPs using results obtained with the Manta trawl resulted in a marked underestimation of abundance. Nevertheless, the grab sampling technique is intricately linked to a risk of collecting non-representative water volumes, consequently leading to an overestimation of MPs abundance and a significant inter-sample variability. Moreover, the latter method is unsuitable for use in sampling larger MPs or in areas with low concentrations of MP pollution. The optimal sampling method therefore is dependent on the specific objectives of the study, often resulting in a combination of size and volume selective methods. The results of this study have the potential to contribute to the standardization of monitoring protocols for microplastics, both during the sampling phase and sample processing.

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species

Introduction

This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade.

Methods

We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance.

Results

With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power.

Discussion

These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

A Novel High-Throughput Detection Method for Plastic Debris in Organic-Rich Matrices Based on Image Fusion

Plastic pollution pervades natural environments and wildlife. Consequently, high-throughput detection methods for plastic debris are urgently needed. A novel method was developed to detect plastic debris larger than 0.5 mm, which integrated an extraction method with low organic loss and plastic damage alongside a classification method for fused images. This extraction method broadened the size range of the remaining plastic debris, while the fusion solved the low spatial resolution of hyperspectral images and the absence of spectral information in red-green-blue (RGB) images. This method was validated for plastic debris in digestate, compost, and sludge, with extraction demonstrating 100% recovery rates for all samples. After fusion, the spatial resolution of hyperspectral images was improved about five times. Classification recall for the fused hyperspectral images achieved 97 ± 8%, surpassing 83 ± 29% of the raw images. Application of this method to solid digestate detected 1030 ± 212 items/kg of plastic debris, comparable with the conventional Fourier transform infrared spectroscopic result of 1100 ± 436 items/kg. This developed method can investigate plastic debris in complex matrices, simultaneously addressing a wide range of sizes and types. This capability helps acquire reliable data to predict secondary microplastic generation and conduct a risk assessment.

Underappreciated microplastic galaxy biases the filter-based quantification

Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.

Recognition and detection technology for microplastic, its source and health effects

Microplastic (MP) is ubiquitous in the environment which appeared as an immense intimidation to human and animal health. The plastic fragments significantly polluted the ocean, fresh water, food chain, and other food items. Inadequate maintenance, less knowledge of adverse influence along with inappropriate usage in addition throwing away of plastics items revolves present planet in to plastics planet. The present study aims to focus on the recognition and advance detection technologies for MPs and the adverse effects of micro- and nanoplastics on human health. MPs have rigorous adverse effect on human health that leads to condensed growth rates, lessened reproductive capability, ulcer, scrape, and oxidative nervous anxiety, in addition, also disturb circulatory and respiratory mechanism. The detection of MP particles has also placed emphasis on identification technologies such as scanning electron microscopy, Raman spectroscopy, optical detection, Fourier transform infrared spectroscopy, thermo-analytical techniques, flow cytometry, holography, and hyperspectral imaging. It suggests that further research should be explored to understand the source, distribution, and health impacts and evaluate numerous detection methodologies for the MPs along with purification techniques.

Worker studies suggest unique liver carcinogenicity potential of polyvinyl chloride microplastics

BACKGROUND

Plastic debris pervades our environment. Some breaks down into microplastics (MPs) that can enter and distribute in living organisms causing effects in multiple target organs. MPs have been demonstrated to harm animals through environmental exposure. Laboratory animal studies are still insufficient to evaluate human impact. And while MPs have been found in human tissues, the health effects at environmental exposure levels are unclear.

AIM

We reviewed and summarized existing evidence on health effects from occupational exposure to MPs. Additionally, the diverse effects documented for workers were organized by MP type and associated co-contaminants. Evidence of the unique effects of polyvinyl chloride (PVC) on liver was then highlighted.

METHODS

We conducted two stepwise online literature reviews of publications focused on the health risks associated with occupational MP exposures. This information was supplemented with findings from animal studies.

RESULTS

Our analysis focused on 34 published studies on occupational health effects from MP exposure with half involving exposure to PVC and the other half a variety of other MPs to compare. Liver effects following PVC exposure were reported for workers. While PVC exposure causes liver toxicity and increases the risk of liver cancers, including angiosarcomas and hepatocellular carcinomas, the carcinogenic effects of work-related exposure to other MPs, such as polystyrene and polyethylene, are not well understood.

CONCLUSION

The data supporting liver toxicity are strongest for PVC exposure. Overall, the evidence of liver toxicity from occupational exposure to MPs other than PVC is lacking. The PVC worker data summarized here can be useful in assisting clinicians evaluating exposure histories from PVC exposure and designing future cell, animal, and population exposure-effect research studies.

Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review

Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations.

Identification of Guiboutia species by NIR-HSI spectroscopy

Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.