Detecting the Great Pacific Garbage Patch floating plastic litter using WorldView-3 satellite imagery

Experimental detection of marine plastic litter in surface waters by 405 nm LD-based fluorescence lidar

Plastic pollution has become a global challenge, affecting water quality and health. Plastics including polystyrene (PS), polyvinyl chloride (PVC), polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE), are significant contributors to environmental pollution. With the growing need for investigation and detection of plastics found in natural waters, we propose the use of a portable laser diode (LD)-based fluorescence lidar system for in-situ detection of plastic litters in surface waters based on excitation-emission fluorescence spectroscopic data. The experiments were carried out in a controlled environment using a fluorescence lidar system with 405 nm excitation wavelength to determine the fluorescence signals of several plastics at 470 nm emission wavelength. Simultaneous detection of PET plastic and Chlorella vulgaris were also observed to determine the fluorescence influence of chlorophyll in surface waters. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was employed to study the chemical composition of the plastics used before and after being submerged in the water. Scanning electron microscopy (SEM) and high-resolution camera microscopy were used to analyze the morphology of the submerged PET samples. This study provides a basis for a new in-situ technique using a fluorescence lidar system for submerged or transparent plastics in surface waters.

An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea

Nowadays, microplastics (MPs) as emerging contaminants have posed great risks to marine ecosystems and human health. However, non-continuous field sampling data makes it difficult to meet the needs of scientific research and pollution control of marine MPs. Consequently, the development of rapid monitoring techniques for marine MPs to achieve efficient acquisition of data is increasingly essential. Remote sensing technology provides a convenient and effective tool for monitoring and mapping marine MPs pollution. Therefore, we established an inversion model based on multiple regression by combining the remote sensing data and the measured data to predict the MPs pollution status in the Bohai Sea. The feature variables of a model are crucial to the prediction, and we proposed three methods of variable selection, namely successive projections algorithm (SPA), band combination method, and remote sensing index method. By comparing accuracy evaluation metrics, an approach based on SPA was selected to analyze the abundance and spatio-temporal distribution of MPs in the Bohai Sea in 2022. The determination coefficient of the SPA model is 0.75, and the root mean square error is 0.38 items/m3. The error of the model is within an acceptable range. It was found that the MPs abundance on the sea surface of the Bohai Sea varied significantly in different seasons and regions. This study indicates that satellite remote sensing technology has great potential in monitoring marine MPs.

Marine plastic pollution detection and identification by using remote sensing-meta analysis

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML.

High-precision density mapping of marine debris and floating plastics via satellite imagery

The last couple of years has been ground-breaking for marine pollution monitoring purposes. It has been suggested that combining multi-spectral satellite information and machine learning approaches are effective to monitor plastic pollutants in the ocean environment. Recent research has made theoretical progress in identifying marine debris and suspected plastic (MD&SP) through machine learning whereas no study has fully explored the application of these methods for mapping and monitoring marine debris density. Therefore, this article consists of three main components: (1) the development and validation of a supervised machine learning marine debris detection model, (2) to map the MD&SP density into an automated tool called MAP-Mapper and finally (3) evaluation of the entire system for out-of-distribution (OOD) test locations. Developed MAP-Mapper architectures provide users with options to achieve high precision (abbv. -HP) or optimum precision-recall (abbv. -Opt) values in terms of training/test dataset. Our MAP-Mapper-HP model greatly increases the MD&SP detection precision to 95%, while the MAP-Mapper-Opt achieves 87-88% precision-recall pair. To efficiently measure density mapping findings at OOD test locations, we propose the Marine Debris Map (MDM) index, which combines the average probability of a pixel belonging to the MD&SP class and the number of detections in a given time frame. The high MDM findings of the proposed approach are found to be consistent with existing marine litter and plastic pollution areas, and these are presented with available evidence citing literature and field studies.

Sustainability in Numbers by Data Analytics

For a successful delivery of the United Nations Sustainable Development Goals (UNSDGs) and to track the progress of UNSDGs as well as identify the gaps and the areas requiring more attention, periodic analyses on the "research on sustainability" by various countries and their contribution to the topic are inevitable. This paper tracks the trends in sustainability research including the geographical distribution on sustainability research, their level of multi-disciplinarity and the cross-border collaboration, their distribution of funding with respect to the UNSDGs, and the lifecycle analyses. Cumulative publications and patents on sustainability could be fitted to an exponential function, thereby highlighting the importance of the research on sustainability in the recent past. Besides, this analytics quantifies cross-border collaborations and knowledge integration to solve critical issues as well as traditional and emerging sources to undertake sustainability research. As an important aspect of resource sustainability and circular economy, trends in publication and funding on lifecycle assessment have also been investigated. The analytics present here identify that major sustainability research volume is from the social sciences as well as business and economics sectors, whereas contributions from the engineering disciplines to develop technologies for sustainability practices are relatively lower. Similarly, funding distribution is also not evenly distributed under various SDGs; the larger share of funding has been on energy security and climate change research. Thus, this study identifies many gaps to be filled for the UNSDGs to be successful.

Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth

The exploitation of satellite remote sensing is expected to be a critical asset in monitoring floating and submerged plastic litter in all aquatic environments. However, robust retrieval algorithms still havel to be developed based on a full understanding of light interaction with plastic litter and the other optically active constituents of the atmosphere-water system. To this end, we performed laboratory-based hyperspectral reflectance measurements of submerged macroplastics under varying water clarity conditions (clear - 0 mg/L, moderate - 75 mg/L, very turbid - 321.3 mg/L) and submersion depths. This comprehensive optical dataset was used (i) to relate the plastic-related signal to submersion depth and turbidity parameters, and (ii) to investigate the top-of-atmosphere signal through full radiative transfer calculations. Simulated TOA radiation was used to explore the nominal pixel and spectral requirements based on WorldView-3, Sentinel-2, and Sentinel-3 missions with very high to moderate geo-spatial resolutions. Results showed that plastics remained detectable when submerged in the top ∼1 m of the water column regardless of water clarity conditions. At TOA, uncertainties attached to atmospheric correction were shown to be reasonable and acceptable for plastic detection purposes in the infrared part of the spectrum (> 700 nm). The impact of aerosols on the TOA signal was found to be complex as (i) over large plastic patches. The aerosols produced little impact on satellite observations mostly due to adjacency effects and (ii) optical signature from isolated/small extent plastic patches was critically altered suggesting the atmospheric transmittance should be accurately corrected for in plastic detection algorithms. The sensitivity analyses also revealed that the narrow band widths of Sentinel-3 did not improve detection performance compared to the WorldView-3 coarser band widths. It is proposed that high spatial resolution wavebands such as the pan-chromatic could be advantageously explored for submerged plastic monitoring applications.