Microplastic discharge from a wastewater treatment plant: long term monitoring to compare two analytical techniques, LDIR and optical microscopy while also assessing the removal efficiency of a bubble curtain

Bäuerlein, Patrick S.; Pieke, Eelco Nicolaas; Oesterholt, Frank; Laak, Thomas L. ter; Kools, S.A.E.

doi:10.2166/wst.2022.419

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…While this method provides valuable information and cost effective, it is time-consuming, and limited to larger MPs. This method is not suitable for smaller particles (<50 μm) (Bäuerlein et al, 2023). Several modified spectroscopic techniques are employed for its detection and quantification.…”

Section: Human Health Concerns and Implicationsmentioning

confidence: 99%

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Sharma,

Barman,

Mishra

et al. 2024

Land Degrad Dev

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In recent decades, the ubiquity of micro and nanoplastics (MNPs) pollution has emerged as a global concern due to its potential risks to both ecological and human health. The detrimental effects of MNPs have resulted in significant changes in the physicochemical and biological properties of terrestrial soil and benthic sediment. These alterations have led to a disrupted nutrient cycle, potential future climate hazards, and further impacts on ecosystem services. This review aims to discuss the possible origins, composition, abundance, life cycle, and transport processes of MNPs in aquatic and terrestrial ecosystems. It seeks to understand the potential ecological and human health risks associated with MNPs pollution and explores their implications for biodiversity conservation and ecosystem services. Moreover, this review highlights the advanced analytical techniques used to detect and quantify MNPs. It further suggests policy frameworks to combat this pollution in natural environments. The findings of this review are intended to assist environmentalists, microbiologists, hydrologists, and policymakers in identifying scientific gaps and pragmatic solutions to reduce MNPs pollution. Future research should investigate the intake mechanism and impact of MNPs on plants in different ecosystems, as well as their effects on food chains and human health. This will ensure that the ecological and human health risks associated with MNPs pollution can be timely mitigated.

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Section: Human Health Concerns and Implicationsmentioning

confidence: 99%

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Sharma,

Barman,

Mishra

et al. 2024

Land Degrad Dev

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“…Microplastic particle counts were estimated initially using a fluorescent staining and automated imaging approach. Confocal microscopy was performed by scanning filters (or portions thereof) using excitation: 487 nm and emission: 525 nm for Nile Red and (only for the HSE samples -see below) excitation: 409 and emission: 450 nm for DAPI using a Nikon A1R HD according to standard methods described by [10,49,54,56,58] . Depending on the number of particles, either whole, half, or one-quarter of each filter was scanned and the images stitched together to create a composite.…”

Section: Water Column Mp (Gbe)mentioning

confidence: 99%

A baseline for microplastic occurrence in three New England estuaries

Brown,

Moore,

Mogensen

et al. 2023

Water Emerg Contam Nanoplastics

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Although microplastics (MP) have been documented in estuarine habitats, limited published data exist for New Hampshire and northern Massachusetts hampering meaningful, regional comparison with other geographies. Here we synthesize previously unpublished data from several independent baseline studies spanning three estuarine systems including Great Bay Estuary (GBE), Hampton-Seabrook Estuary (HSE), and Great Marsh Estuary (GME) to compare geographic data for MP to other published regional studies. Data include water column in GBE (n = 179 from 7 sites), surface waters and salt marsh sediment cores from HSE (n = 72 water samples from 12 sites and n = 77 sediment cores from 8 sites), and surface waters from GME (n = 42 water samples at 10 sites). Samples were analyzed for MP characteristics initially via either automated confocal microscopy or light microscopy, allowing initial estimation of the number and size distribution of putative MP. Particles from representative samples were analyzed using laser direct infrared spectrometry (LDIR) to determine elemental analysis. MP were found in > 98% of samples collected including surface waters, water column, and marine sediments. Counts ranged from 1 to 144,000 MP particles m-3 and mean MP differed significantly among regions, sites within regions, and across years. In the GBE water column, MP tended to peak during June-August in 4 of the 5 years studied. Most MP were roughly circular and ~50 μm in diameter. LDIR confirmed that many types of plastics are in these estuarine waters and also revealed that despite the digestion processes, biogenic materials often remained, predominantly chitin, rubber, wood, and coal. These data allow us to address the realistic levels of risk that estuarine MP pose in NH and northern MA estuaries and can be used to populate existing hydrodynamic models that will predict the tributary sources, movements, and fate of MP within these aquatic habitats.

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“…Therefore, reliable methods for identifying polymers and microplastics are needed. Methods describing how to detect nano- and microplastics in the environment include Fourier transform infrared (FTIR) spectroscopy, , Raman spectroscopy, , laser-directed infrared (LDIR) spectroscopy, ,, and thermogravimetric analysis (TGA)/pyrolysis. , Comprehensive reviews can be found in refs and . With spectroscopic methods, polymers are identified mostly through comparison with reference spectra from existing libraries.…”

Section: Introductionmentioning

confidence: 99%

Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models

Tian

Béen

Sun

et al. 2023

Environ. Sci. Technol. Lett.

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Identifying environmental polymers and microplastics is crucial for the scientific world, environmental agencies, and water authorities to estimate their environmental impact and increase efforts to decrease emissions. On the basis of different spectroscopy techniques, e.g., laser-directed infrared imaging and Raman spectroscopy, polymers can be observed and represented as spectroscopic signals. The latter can be further analyzed and classified by data science, in particular, machine learning (ML). Past studies applied a variety of ML models to identify polymers from small or large data sets. However, a comprehensive comparison of multiple models across different data set sizes is still needed, which is presented in this study. Furthermore, we also provide a practical data augmentation technique to generate synthetic samples when only a limited number of samples are available. Our results show that the ensemble ML model, compared to neural network models, takes the least training time to achieve the best performance, i.e., a classification accuracy of 99.5%. This study provides a generic framework for selecting ML models and boosting model performance to accurately identify polymers.

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Microplastic discharge from a wastewater treatment plant: long term monitoring to compare two analytical techniques, LDIR and optical microscopy while also assessing the removal efficiency of a bubble curtain

Cited by 9 publications

References 34 publications

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

A baseline for microplastic occurrence in three New England estuaries

Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models

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