Articles | Volume 19, issue 5
https://doi.org/10.5194/acp-19-2881-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-2881-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2019
Research article |
| 07 Mar 2019
| 07 Mar 2019
Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
Ksenia Aleksankina
CORRESPONDING AUTHOR
School of Chemistry, University of Edinburgh, Edinburgh, UK
NERC Centre for Ecology & Hydrology, Penicuik, UK
Stefan Reis
NERC Centre for Ecology & Hydrology, Penicuik, UK
University of Exeter Medical School, European Centre for Environment
and Health, Knowledge Spa, Truro, UK
Massimo Vieno
NERC Centre for Ecology & Hydrology, Penicuik, UK
School of Chemistry, University of Edinburgh, Edinburgh, UK
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27 citations as recorded by crossref.
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
- Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019 R. Liu et al. 10.1021/acs.est.3c05424
- Atlantis Ecosystem Model Summit 2022: Report from a workshop H. Perryman et al. 10.1016/j.ecolmodel.2023.110442
- Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements Y. Ge et al. 10.5194/gmd-14-7021-2021
- Chemical Sensitivity Analysis and Uncertainty Analysis of Ozone Production in the Comprehensive Air Quality Model with Extensions Applied to Eastern Texas A. Dunker et al. 10.1021/acs.est.9b07543
- Integrated Assessment Modelling of Future Air Quality in the UK to 2050 and Synergies with Net-Zero Strategies H. ApSimon et al. 10.3390/atmos14030525
- Emulator-based global sensitivity analysis for flow-like landslide run-out models H. Zhao et al. 10.1007/s10346-021-01690-w
- Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
- The impact of urban land-surface on extreme air pollution over central Europe P. Huszar et al. 10.5194/acp-20-11655-2020
- Evaluation of atmospheric aerosols in the metropolitan area of São Paulo simulated by the regional EURAD-IM model on high-resolution E. De Souza Fernandes Duarte et al. 10.1016/j.apr.2020.12.006
- Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality M. Alifa et al. 10.1016/j.envres.2022.113587
- The Diversity of Planetary Atmospheric Chemistry F. Mills et al. 10.1007/s11214-021-00810-1
- Reduced-form and complex ACTM modelling for air quality policy development: A model inter-comparison T. Oxley et al. 10.1016/j.envint.2022.107676
- Life Course Air Pollution Exposure and Cognitive Decline: Modelled Historical Air Pollution Data and the Lothian Birth Cohort 1936 T. Russ et al. 10.3233/JAD-200910
- Uncertainty analysis of modeled ozone changes due to anthropogenic emission reductions in Eastern Texas A. Dunker et al. 10.1016/j.atmosenv.2021.118798
- Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf M. Pashayi et al. 10.1016/j.apr.2023.101948
- Assessment of climate change impact on maize yield and yield attributes under different climate change scenarios in eastern India R. Srivastava et al. 10.1016/j.ecolind.2020.106881
- Reduced-Form and Complex Actm Modelling for Air Quality Policy Development: A Model Inter-Comparison T. Oxley et al. 10.2139/ssrn.4158187
- Improving residential wood burning emission inventories with the integration of readily available data sources J. Zalzal et al. 10.1016/j.scitotenv.2024.174226
- Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity O. Wild et al. 10.5194/acp-20-4047-2020
- Regional aerosol forecasts based on deep learning and numerical weather prediction Y. Qiu et al. 10.1038/s41612-023-00397-0
- Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport P. Huszar et al. 10.5194/acp-20-1977-2020
- Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs Q. Ye et al. 10.1016/j.atmosenv.2022.119431
- Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation A. Yarce Botero et al. 10.3390/atmos12121633
- Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method S. Wang et al. 10.5194/gmd-17-3617-2024
- Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mapping Z. Lv et al. 10.5194/acp-22-15685-2022
- A hybrid model approach for estimating health burden from NO2 in megacities in China: a case study in Guangzhou B. He et al. 10.1088/1748-9326/ab4f96
Latest update: 16 Jul 2024
Short summary
Atmospheric chemistry transport models are widely used to underpin policies to mitigate the detrimental effects of air pollution on human health and ecosystems. Understanding the level of confidence in model predictions is thus vital. We present a comprehensive approach for uncertainty assessment and global variance-based sensitivity analysis to propagate uncertainty from model input data and identify the extent to which uncertainty in different emissions drives the model output uncertainty.
Atmospheric chemistry transport models are widely used to underpin policies to mitigate the...
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