Abstract
Abstract. Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their concentrations. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is now adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface station measurements. The potential of PYVAR-CHIMERE is illustrated with inversions of both carbon monoxide (CO) and nitrogen oxides (NOx) emissions in Europe, using the MOPITT and OMI satellite observations, respectively. In these cases, local increments on CO emissions can reach more than +50 %, with increases located mainly over central and eastern Europe, except in the south of Poland, and decreases located over Spain and Portugal. The illustrative cases for NOx emissions also lead to large local increments (> 50 %), for example over industrial areas (e.g., over the Po Valley) and over the Netherlands. The good behavior of the inversion is shown through statistics on the concentrations: the mean bias, RMSE, standard deviation, and correlation between the simulated and observed concentrations. For CO, the mean bias is reduced by about 27 % when using the posterior emissions, the RMSE and the standard deviation are reduced by about 50 %, and the correlation is strongly improved (0.74 when using the posterior emissions against 0.02); for NOx, the mean bias is reduced by about 24 % and the RMSE and the standard deviation are reduced by about 7 %, but the correlation is not improved. We reported strong non-linear relationships between NOx emissions and satellite NO2 columns, now requiring a fully comprehensive scientific study.
Highlights
The degradation of air quality is a worldwide environmental problem: 91 % of the world’s population have breathed polluted air in 2016 according to the World Health Organization (WHO), resulting in 4.2 million premature deaths every year (WHO, 2016)
This paper presents the Bayesian variational inverse system PYVAR-CHIMERE, which has been adapted to the inversion of reactive species such as carbon monoxide (CO) and nitrogen oxides (NOx), taking advantage of the previous developments for long-lived species such as CO2 (Broquet et al, 2011) and CH4 (Pison et al, 2018)
We show the potential of PYVAR-CHIMERE, with inversions for CO and NOx illustrated over Europe
Summary
The degradation of air quality is a worldwide environmental problem: 91 % of the world’s population have breathed polluted air in 2016 according to the World Health Organization (WHO), resulting in 4.2 million premature deaths every year (WHO, 2016). The recent study of Lelieveld et al (2019) even suggests that the health impacts attributable to outdoor air pollution are substantially higher than previously assumed (with 790 000 premature deaths in the 28 countries of the European Union against the previously estimated 500 000; EEA, 2018). The main regulated primary (i.e., directly emitted in the atmosphere) anthropogenic air pollutants are carbon monoxide (CO), nitrogen oxides (NOx = NO + NO2), sulfur dioxide (SO2), ammonia (NH3), volatile organic compounds (VOCs) and primary particles These primary air pollutants are precursors of secondary (i.e., produced in the atmosphere through chemical reactions) pollutants such as ozone (O3) and particulate matter (PM), which are threatening to both human health and ecosys-
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