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Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Environmental Modeling & Assessment volume 18, pages 249–257 (2013)Cite this article

Abstract

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.

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Acknowledgments

The authors acknowledge the Portuguese Environmental Protection Agency for the observational dataset support. Thanks are extended to the Portuguese ‘Ministério da Ciência, da Tecnologia e do Ensino Superior’ for the financing of ENSEMBLAIR (POCI/AMB/66707/2006) project, for the postdoc grants of H. Martins (SFRH/BPD/66874/2009) and J. Ferreira (SFRH/BPD/40620/2007) and also for the PhD grants of I. Ribeiro (SFRH/BD/60370/2009) and E. Sá (SFRH/BD/60474/2009).

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Authors and Affiliations

  1. CESAM & Department of Environment and Planning, University of Aveiro, Aveiro, Portugal

    A. Monteiro, I. Ribeiro, O. Tchepel, A. Carvalho, H. Martins, E. Sá, J. Ferreira, V. Martins, A. I. Miranda & C. Borrego

  2. IES/REM, Joint Research Center, European Commission, TP 441 21020, Ispra, Italy

    S. Galmarini

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  1. A. Monteiro
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Monteiro, A., Ribeiro, I., Tchepel, O. et al. Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM. Environ Model Assess 18, 249–257 (2013). https://doi.org/10.1007/s10666-012-9344-0

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  • DOI: https://doi.org/10.1007/s10666-012-9344-0

Keywords

  • Air quality modelling
  • Ensemble techniques
  • Static and dynamic approaches
  • Minimum least square
  • BMA methodology