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A Model Study on the Effects of Emission Reductions on European Air Quality Between 1990 and 2020

  • Sebnem AksoyogluEmail author
  • André S. H. Prévôt
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

We simulated the ozone and particulate matter (PM) concentrations in Europe with the regional air quality model CAMx for 1990 (reference year for the Gothenburg Protocol), 2005 (reference year for the revised Gothenburg protocol), 2006 (for model validation) and 2020 (target year for the revised Gothenburg protocol) to investigate the effects of emission reductions on air quality. The three emission scenarios used for 2020 were Baseline, Mid and MTFR, prepared by IIASA using the GAINS model. Comparisons between 1990 and 2005 suggested a large decrease (up to 50 %) in PM2.5 concentrations as shown also by observations. PM2.5 levels were predicted to decrease further by about 35 % (Baseline) and 45 % (MTFR) until 2020. Although ozone damage indicators AOT40 and SOMO35 were modeled reasonably well for the reference case 2005, the relative change between 1990 and 2005 did not match some of the observations. The significant decrease in indicators predicted for 2020 should be further investigated, because background ozone concentrations might have a strong influence on correct prediction of these indicators with thresholds. Our results suggested that the deposition of nitrogen compounds decreased by 20–50 % between 1990 and 2005 and the main reduction was due to the oxidized nitrogen species. On the other hand, the modeled deposition of the reduced nitrogen species in 2005 was higher than in 1990. The deposition of the oxidized nitrogen species was predicted to decrease further by about 40 % until 2020 while deposition of reduced nitrogen species would be higher in the future.

Keywords

Emission Reduction Ozone Concentration Emission Scenario Gain Model Main Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was financially supported by the Swiss Federal Office of Environment, FOEN. We are grateful to ECMWF, TNO, IIASA, INFRAS, Meteotest, NABEL/EMPA, ENVIRON, and M. Tinguely for providing us with various data or help.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Laboratory of Atmospheric ChemistryPaul Scherrer InstituteVilligenSwitzerland

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