Articles | Volume 18, issue 11
Atmos. Chem. Phys., 18, 7799–7814, 2018
https://doi.org/10.5194/acp-18-7799-2018
Atmos. Chem. Phys., 18, 7799–7814, 2018
https://doi.org/10.5194/acp-18-7799-2018
Research article
04 Jun 2018
Research article | 04 Jun 2018

Quantification of the enhanced effectiveness of NOx control from simultaneous reductions of VOC and NH3 for reducing air pollution in the Beijing–Tianjin–Hebei region, China

Jia Xing et al.

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Cited articles

Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013. 
Carter, W. P.: The SAPRC-99 chemical mechanism and updated VOC reactivity scales, California Air Resources Board, 2003. 
Cohan, D. S., Hakami, A., Hu, Y., and Russell, A. G.: Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis, Environ. Sci. Technol., 39, 6739–6748, 2005. 
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., and Feigin, V.: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015, The Lancet, 389, 1907–1918, 2017. 
Chou, C. C.-K., Tsai, C.-Y., Shiu, C.-J., Liu, S. C., and Zhu, T.: Measurement of NOy during Campaign of Air Quality Research in Beijing 2006 (CAREBeijing-2006): Implications for the ozone production efficiency of NOx, J. Geophys. Res., 114, D00G01, doi:10.1029/2008JD010446, 2009. 
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Short summary
NOx is the common precursor for both PM2.5 and O3 pollution, while the effectiveness of NOx controls for reducing PM2.5 and O3 are largely influenced by the ambient levels of NH3 and VOCs. This study developed a new method to quantify the nonlinear effectiveness of emission controls for reducing PM2.5 and O3. The new method not only substantially reduces the computational burden but also provides a series of quantitative indicators to quantify the nonlinear control effectiveness.
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