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Observations and modeling of air quality trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe

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      Trends in air quality across the Northern Hemisphere over a 21-year period (1990–2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the observed trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe. <br><br> The model successfully reproduced the observed decreasing trends in SO<sub>2</sub>, NO<sub>2</sub>, 8 h O<sub>3</sub> maxima, SO<sub>4</sub><sup>2&minus;</sup> and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO<sub>3</sub><sup>&minus;</sup> in the US, potentially pointing to uncertainties of NH<sub>3</sub> emissions. The model failed to capture the 6-year trends of SO<sub>2</sub> and NO<sub>2</sub> in CN-API (China – Air Pollution Index) from 2005 to 2010, but reproduced the observed pattern of O<sub>3</sub> trends shown in three World Data Centre for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resolution employed in these calculations, predicted SO<sub>2</sub> and NO<sub>2</sub> concentrations are underestimated relative to all urban networks, i.e., US-AQS (US – Air Quality System; normalized mean bias (NMB) = −38% and −48%), EU-AIRBASE (European Air quality data Base; NMB = −18 and −54%) and CN-API (NMB = −36 and −68%). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Programme), SO<sub>2</sub> is overestimated (NMB from 4 to 150%) while NO<sub>2</sub> is simulated well (NMB within ±15%) in all seasons. Correlations between simulated and observed O<sub>3</sub> wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and observed SO<sub>4</sub><sup>2&minus;</sup> was found compared to that for SO<sub>2</sub>. Underestimation of summer SO<sub>4</sub><sup>2&minus;</sup> in the US may be associated with the uncertainty in precipitation and associated wet scavenging representation in the model. The model exhibits worse performance for NO<sub>3</sub><sup>&minus;</sup> predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO<sub>3</sub><sup>&minus;</sup> as well as seasonal variations of NH<sub>3</sub> emissions. There are high correlations (<i>R</i> > 0.5) between observed and simulated EC, although the model underestimates the EC concentration by 65% due to the coarse grid resolution as well as uncertainties in the PM speciation profile associated with EC emissions. <br><br> The almost linear response seen in the trajectory of modeled O<sub>3</sub> changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NO<sub>x</sub> and volatile organic compound (VOC) emissions with a ratio of 0.46 may provide the most effective means for O<sub>3</sub> reductions for the region devoid of nonlinear response potentially associated with NO<sub>x</sub> or VOC limitation resulting from alternate strategies. The response of O<sub>3</sub> is more sensitive to changes in NO<sub>x</sub> emissions in the eastern US because the relative abundance of biogenic VOC emissions tends to reduce the effectiveness of VOC controls. Increasing NH<sub>3</sub> levels offset the relative effectiveness of NO<sub>x</sub> controls in reducing the relative fraction of aerosol NO<sub>3</sub><sup>&minus;</sup> formed from declining NO<sub>x</sub> emissions in the eastern US, while the control effectiveness was assured by the simultaneous control of NH<sub>3</sub> emission in Europe.

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      Atmospheric Chemistry and Physics
      Atmos. Chem. Phys.
      Copernicus GmbH
      March 10 2015
      : 15
      : 5
      : 2723-2747
      © 2015



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