Articles | Volume 16, issue 24
Atmos. Chem. Phys., 16, 15653–15664, 2016
https://doi.org/10.5194/acp-16-15653-2016
Atmos. Chem. Phys., 16, 15653–15664, 2016
https://doi.org/10.5194/acp-16-15653-2016

Research article 20 Dec 2016

Research article | 20 Dec 2016

Exploiting stagnant conditions to derive robust emission ratio estimates for CO2, CO and volatile organic compounds in Paris

Lamia Ammoura1, Irène Xueref-Remy1, Felix Vogel1, Valérie Gros1, Alexia Baudic1, Bernard Bonsang1, Marc Delmotte1, Yao Té2, and Frédéric Chevallier1 Lamia Ammoura et al.
  • 1LSCE, Unité mixte CEA-CNRS-UVSQ, UMR 8212, 91191 Gif-sur-Yvette, France
  • 2LERMA, Unité mixte CNRS-ENS-OP-UCP-UPMC, UMR 8112, 75005 Paris, France

Abstract. We propose an approach to estimate urban emission ratios that takes advantage of the enhanced local urban signal in the atmosphere at low wind speed. We apply it to estimate monthly ratios between CO2, CO and some VOCs from several atmospheric concentration measurement datasets acquired in the centre of Paris between 2010 and 2014. We find that this approach is not very sensitive to the regional background level definition and that, in the case of Paris, it samples all days (weekdays and weekends) and all hours of the day evenly. A large seasonal variability of the ΔCO ∕ ΔCO2 ratio in Paris is shown, with a difference of around 60 % between the extreme values and a strong anti-correlation (r2 = 0.75) with atmospheric temperature. The comparison of the ratios obtained for two short measurement campaigns conducted in two different districts and two different periods (autumn and winter) shows differences ranging from −120 to +63 %. A comparison with a highly resolved regional emission inventory suggests some spatial variations of the ratio within the city.

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Short summary
We propose a new approach to estimate urban emission ratios that takes advantage of the enhanced local urban signal in the atmosphere at low wind speed. We apply it to estimate monthly ratios between CO2, CO and some VOCs from atmospheric measurement datasets acquired in the centre of Paris between 2010 and 2014. We find that this approach is little sensitive to the regional background level definition. With this new method, we may reveal spatial and seasonal variability in the ratios in Paris.
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