Quantification of Conceptual Model Uncertainty in the Modeling of Wet Deposited Atmospheric Pollutants
| Autor: | Mouhamadou Moustapha Sy, Martin Steiner, Philipp Hartmann, M.-A. Gonze, Laura Urso |
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| Přispěvatelé: | Bundesamt für Strahlenschutz (BfS), Bundesinstitut für Risikobewertung - Federal Institute for Risk Assessment (BfR), PSE-ENV/SEREN/LEREN, Institut de Radioprotection et de Sûreté Nucléaire (IRSN) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Pollutant
010504 meteorology & atmospheric sciences Qualitative evidence media_common.quotation_subject Uncertainty budget 010501 environmental sciences Atmospheric sciences Bayesian inference 01 natural sciences 13. Climate action Physiology (medical) Atmospheric pollutants [SDE]Environmental Sciences Conceptual model Environmental science Natural variability Interception Safety Risk Reliability and Quality 0105 earth and related environmental sciences media_common |
| Zdroj: | Risk Analysis Risk Analysis, Wiley, 2021, ⟨10.1111/risa.13807⟩ |
| ISSN: | 0272-4332 1539-6924 |
| DOI: | 10.1111/risa.13807⟩ |
| Popis: | International audience; Conceptual model uncertainty and parameter uncertainty are dominant contributors to the total uncertainty of a radioecological model output. In the present study the focus is on conceptual model uncertainty, which is often not acknowledged. Conceptual model uncertainty is assessed by subtracting from the total uncertainty of the model output the propagated parameter uncertainty, obtained by means of Bayesian inference analysis. The conceptual model uncertainty is quantified for two process-based models, which describe the interception of wet deposited pollutants under equilibrium and kinetic conditions, respectively. The natural variability due the chemical valence of the elements considered is accounted for in both models. Quantitative evidence has been obtained that the conceptual model uncertainty can contribute to the total uncertainty budget of the models for interception of wet deposited pollutants at least as much as, if not more than, parameter uncertainty. |
| Databáze: | OpenAIRE |
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