Influence of long-term natural degradation processes on near-infrared spectra and sorting of post-consumer plastics

Marine plastic pollution: A systematic review of management strategies through a macroscope approach

Alternatives to address the ocean plastic crisis have been a hot topic in scientific literature, although a systemic approach to assess their effectiveness and identify bottlenecks is still lacking. To contribute to discussions on this topic, this study aims to conduct a literature review on current scientific information regarding management strategies for marine plastic pollution. The PRISMA method was used to select the most relevant articles from the Scopus® database, resulting in a sample of 176 articles after applying exclusion criteria for full-text evaluation. Unlike other literature review studies, Odum's Macroscope is used here to develop a model that provides a systemic view of the plastic crisis on a large scale, encompassing various compartments and their interactions. Specifically, eight compartments are identified: industry, consumers, waste collection & management, freshwater systems, fisheries, aquaculture and shipping, marine ecosystems, marine plastic collection and recycling, and life cycle. Each piece of literature reviewed is categorized into one of these compartments and discussed accordingly. The highlights of the results indicate that: (i) waste collection & management and freshwater systems, which are primary pathways for plastic litter reaching the ocean, have been relatively under-investigated compared to other compartments. (ii) Most studies originate from developed countries, raising doubts about the effectiveness of management proposals in underdeveloped countries. (ii) Existing strategies for collecting and recycling marine litter are unlikely to be implemented at a large scale due to operational obstacles, thus offering insufficient mitigation for the plastic crisis. (iv) The development of new biomaterials has proven mostly ineffective and harmful. (v) Alternatives management for microplastic pollution are still in their infancy, resulting in scarce information across all compartments. (vi) No studies focus on the origin of the plastic issue, which lies in the petrochemical industry. From a general perspective, the literature indicates that there is no one-size-fits-all management strategy to the plastic crisis, and the available options are often scattered and disconnected, making a systemic approach essential for studying such a transboundary issue. While efforts exist, stakeholders must act to effectively address the problem, or at least make meaningful progress. The marine plastic crisis operates systemically, analogous to the climate crisis, both stemming from human dependence on fossil fuels. Similar to achieving carbon neutrality, designing a globally sustainable economy should prioritize achieving plastic neutrality as a core component.

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging

The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.

NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations

Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are primarily determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. This data article aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm-1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes n = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for n = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review

Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.