Micro- and nanoplastic contamination in livestock production: Entry pathways, potential effects and analytical challenges

Ramachandraiah, Karna; Ameer, Kashif; Jiang, Guihun; Hong, Geun-Pyo

doi:10.1016/j.scitotenv.2022.157234

Cited by 25 publications

(8 citation statements)

References 90 publications

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“…Overall, the presence of MNPs in ecosystems can lead to complex and multi-faceted effects on ecosystem structure and functioning. animal feed and water (Enyoh et al, 2020;Ramachandraiah et al, 2022). Carnivores, such as lions and tigers, may get exposed through the food-chain process or trophic transfer by devouring or feeding on the tissues and bio-system of herbivorous animals (Mateos-Cárdenas et al, 2019).…”

Section: Implications For Ecosystem Structure and Functioningmentioning

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: Implications For Ecosystem Structure and Functioningmentioning

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

View full text Add to dashboard Cite

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“…The cycle from the production of plastics to their entry into the environment includes different stages, as reported by Woods et al (2021) [ 9 ]: production for textile manufacturing and use, tires use, or packaging production, among others [ 10 ]. Additionally, there are different pollution sources by MPs; due to this, they can be differentiated between primary and secondary sources [ 11 ]. Regarding the primary MPs, they are commonly generated during the manufacturing of different products or the fabrication of microbeads or microfibers; while the secondary ones are formed from larger plastic litters due to degradation processes by external factors (chemical, physical or biological) [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, there are different pollution sources by MPs; due to this, they can be differentiated between primary and secondary sources [ 11 ]. Regarding the primary MPs, they are commonly generated during the manufacturing of different products or the fabrication of microbeads or microfibers; while the secondary ones are formed from larger plastic litters due to degradation processes by external factors (chemical, physical or biological) [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity of Microplastics

et al. 2023

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Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

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“…The cycle from the production of plastics to their entry into the environment includes different stages, as reported by Woods et al (2021) [7]: production for textile manufacturing and use, tires use or packaging production, among others [8]. Besides, there are different sources of pollution by MPs; due to this, they can be differentiated between primary and secondary sources [9]. The primary MPs are commonly generated during the manufacturing of different products, or during the fabrication of microbeads or microfibers; the secondary ones are formed from larger plastic litters due to degradation processes by external factors (chemical, physical or biological) [9].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, there are different sources of pollution by MPs; due to this, they can be differentiated between primary and secondary sources [9]. The primary MPs are commonly generated during the manufacturing of different products, or during the fabrication of microbeads or microfibers; the secondary ones are formed from larger plastic litters due to degradation processes by external factors (chemical, physical or biological) [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity for Microplastics

Astray¹,

Soria²,

Barreiro³

et al. 2023

Preprint

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Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem with micro and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.

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Micro- and nanoplastic contamination in livestock production: Entry pathways, potential effects and analytical challenges

Cited by 25 publications

References 90 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

Machine Learning to Predict the Adsorption Capacity of Microplastics

Machine Learning to Predict the Adsorption Capacity for Microplastics

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