Uncertainty in Environmental Micropollutant Modeling.
Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is...
| Publicado en: | Environmental Management Vol. 74; no. 2; pp. 380 - 399 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178294877&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178294877 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0364152X O5H jtl: Environmental Management issn: 0364152X maglogo: N pubinfo: dt: Aug2024 vid: 74 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178294877 177551307 10.1007/s00267-024-01989-z 178294877 ppf: 380 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Uncertainty in Environmental Micropollutant Modeling. aug: au: Ahkola, Heidi Kotamäki, Niina Siivola, Eero Tiira, Jussi Imoscopi, Stefano Riva, Matteo Tezel, Ulas Juntunen, Janne affil: https://ror.org/013nat269 Finnish Environment Institute (Syke), Latokartanonkaari 11, 00790, Helsinki, Finland sug: ab: Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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