English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Atmospheric chemistry transport models (ACTMs) are extensively used toprovide scientific support for the development of policies to mitigatethe detrimental effects of air pollution on human health andecosystems. Therefore, it is essential to quantitatively assess the level ofmodel uncertainty and to identify the model input parameters that contributethe most to the uncertainty. For complex process-based models, such as ACTMs,uncertainty and global sensitivity analyses are still challenging and areoften limited by computational constraints due to the requirement of a largenumber of model runs. In this work, we demonstrate an emulator-based approachto uncertainty quantification and variance-based sensitivity analysis for theEMEP4UK model (regional application of the European Monitoring and EvaluationProgramme Meteorological Synthesizing Centre-West). A separate Gaussianprocess emulator was used to estimate model predictions at unsampled pointsin the space of the uncertain model inputs for every modelled grid cell. Thetraining points for the emulator were chosen using an optimised Latinhypercube sampling design. The uncertainties in surface concentrations ofO3, NO2, and PM2.5 were propagated from the uncertainties inthe anthropogenic emissions of NOx, SO2, NH3, VOC, and primaryPM2.5 reported by the UK National Atmospheric Emissions Inventory. Theresults of the EMEP4UK uncertainty analysis for the annually averaged modelpredictions indicate that modelled surface concentrations of O3,NO2, and PM2.5 have the highest level of uncertainty in the gridcells comprising urban areas (up to ±7 %, ±9 %, and ±9 %, respectively).The uncertainty in the surface concentrations of O3 and NO2 were dominated by uncertainties in NOx emissions combinedfrom non-dominant sectors (i.e. all sectors excluding energy production androad transport) and shipping emissions. Additionally, uncertainty in O3was driven by uncertainty in VOC emissions combined from sectors excludingsolvent use. Uncertainties in the modelled PM2.5 concentrations weremainly driven by uncertainties in primary PM2.5 emissions and NH3emissions from the agricultural sector. Uncertainty and sensitivity analyseswere also performed for five selected grid cells for monthly averaged modelpredictions to illustrate the seasonal change in the magnitude of uncertaintyand change in the contribution of different model inputs to the overalluncertainty. Our study demonstrates the viability of a Gaussian processemulator-based approach for uncertainty and global sensitivity analyses,which can be applied to other ACTMs. Conducting these analyses helps toincrease the confidence in model predictions. Additionally, the emulatorscreated for these analyses can be used to predict the ACTM response for anyother combination of perturbed input emissions within the ranges set for theoriginal Latin hypercube sampling design without the need to rerun the ACTM,thus allowing for fast exploratory assessments at significantly reducedcomputational costs.

Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model