Forecasting global plastic production and microplastic emission using advanced optimised discrete grey model
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature..
Plastic pollution has become a prominent and pressing environmental concern within the realm of pollution. In recent times, microplastics have entered our ecosystem, especially in freshwater. In the contemporary global landscape, there exists a mounting apprehension surrounding the manifold environmental and public health issues that have emerged as a result of the substantial accumulation of microplastics. The objective of the current study is to employ an enhanced grey prediction model in order to forecast global plastic production and microplastic emissions. This study compared the accuracy level of the four grey prediction models, namely, EGM (1,1, α, θ), DGM (1,1), EGM (1,1), and DGM (1,1, α) models, to evaluate the accuracy levels. As per the estimation of the study, DGM (1,1, α) was found to be more suitable with higher accuracy levels to predict microplastic emission. The EGM (1,1, α, θ) model has slightly better accuracy than the DGM (1,1, α) model in predicting global plastic production. Various accuracy measurement tools (MAPE and RMSE) were used to determine the model's efficiency. There has been a gradual growth in both plastic production and microplastic emission. The current study using the DGM (1,1, α) model predicted that microplastic emission would be 1,084,018 by 2030. The present study aims to provide valuable insights for policymakers in formulating effective strategies to address the complex issues arising from the release of microplastics into the environment and the continuous production of plastic materials.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:30 |
|---|---|
| Enthalten in: |
Environmental science and pollution research international - 30(2023), 59 vom: 28. Dez., Seite 123039-123054 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Balabantaray, Subhra Rajat [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Grey system theory |
|---|
| Anmerkungen: |
Date Completed 26.12.2023 Date Revised 26.12.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s11356-023-30799-9 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM364718528 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM364718528 | ||
| 003 | DE-627 | ||
| 005 | 20231229123925.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11356-023-30799-9 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1240.xml |
| 035 | |a (DE-627)NLM364718528 | ||
| 035 | |a (NLM)37980320 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Balabantaray, Subhra Rajat |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Forecasting global plastic production and microplastic emission using advanced optimised discrete grey model |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 26.12.2023 | ||
| 500 | |a Date Revised 26.12.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | ||
| 520 | |a Plastic pollution has become a prominent and pressing environmental concern within the realm of pollution. In recent times, microplastics have entered our ecosystem, especially in freshwater. In the contemporary global landscape, there exists a mounting apprehension surrounding the manifold environmental and public health issues that have emerged as a result of the substantial accumulation of microplastics. The objective of the current study is to employ an enhanced grey prediction model in order to forecast global plastic production and microplastic emissions. This study compared the accuracy level of the four grey prediction models, namely, EGM (1,1, α, θ), DGM (1,1), EGM (1,1), and DGM (1,1, α) models, to evaluate the accuracy levels. As per the estimation of the study, DGM (1,1, α) was found to be more suitable with higher accuracy levels to predict microplastic emission. The EGM (1,1, α, θ) model has slightly better accuracy than the DGM (1,1, α) model in predicting global plastic production. Various accuracy measurement tools (MAPE and RMSE) were used to determine the model's efficiency. There has been a gradual growth in both plastic production and microplastic emission. The current study using the DGM (1,1, α) model predicted that microplastic emission would be 1,084,018 by 2030. The present study aims to provide valuable insights for policymakers in formulating effective strategies to address the complex issues arising from the release of microplastics into the environment and the continuous production of plastic materials | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Grey system theory | |
| 650 | 4 | |a Microplastic | |
| 650 | 4 | |a Plastic | |
| 650 | 4 | |a Prediction | |
| 650 | 4 | |a Production | |
| 650 | 7 | |a Microplastics |2 NLM | |
| 650 | 7 | |a Plastics |2 NLM | |
| 650 | 7 | |a Water Pollutants, Chemical |2 NLM | |
| 700 | 1 | |a Singh, Pawan Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Pandey, Alok Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Chaturvedi, Bhartendu Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Sharma, Aditya Kumar |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Environmental science and pollution research international |d 1994 |g 30(2023), 59 vom: 28. Dez., Seite 123039-123054 |w (DE-627)NLM093849869 |x 1614-7499 |7 nnns |
| 773 | 1 | 8 | |g volume:30 |g year:2023 |g number:59 |g day:28 |g month:12 |g pages:123039-123054 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s11356-023-30799-9 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 30 |j 2023 |e 59 |b 28 |c 12 |h 123039-123054 | ||