Evaluating BC and NO<sub><i>x</i></sub> emission inventories for the Paris region from MEGAPOLI aircraft measurements

Abstract

Abstract. High uncertainties affect black carbon (BC) emissions, and, despite its important impact on air pollution and climate, very few BC emissions evaluations are found in the literature. This paper presents a novel approach, based on airborne measurements across the Paris, France, plume, developed in order to evaluate BC and NO x emissions at the scale of a whole agglomeration. The methodology consists in integrating, for each transect, across the plume observed and simulated concentrations above background. This allows for several error sources (e.g., representativeness, chemistry, plume lateral dispersion) to be minimized in the model used.The procedure is applied with the CHIMERE chemistrytransport model to three inventories -the EMEP inventory and the so-called TNO and TNO-MP inventories -over the month of July 2009. Various systematic uncertainty sources both in the model (e.g., boundary layer height, vertical mixing, deposition) and in observations (e.g., BC nature) are discussed and quantified, notably through sensitivity tests.Large uncertainty values are determined in our results, which limits the usefulness of the method to rather strongly erroneous emission inventories. A statistically significant (but moderate) overestimation is obtained for the TNO BC emissions and the EMEP and TNO-MP NO x emissions, as well as for the BC / NO x emission ratio in TNO-MP. The benefit of the airborne approach is discussed through a comparison with the BC / NO x ratio at a ground site in Paris, which additionally suggests a spatially heterogeneous error in BC emissions over the agglomeration.

CHIMERE-2017: from urban to hemispheric chemistry-transport modeling

“…The performance of CTMs is often evaluated by comparing simulation results to data of measurements, either from routine networks (Solazzo et al, 2012a, b) or from dedicated field campaigns (e.g., Menut et al, 2015;Petetin et al, 2015). Simon et al (2012) presented an overview of performance evaluation studies for a large set of models and studied cases.…”

Estimation of the Paris NO<sub><i>x</i></sub> emissions from mobile MAX-DOAS observations and CHIMERE model simulations during the MEGAPOLI campaign using the closed integral method

“…Here, we apply a "local" top-down approach, the closed integral method (CIM), based on car multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in combination with wind information. For the quantification of emissions, car MAX-DOAS measurements are performed in large circles around large cities or other strong emissions sources (Rivera et al, 2009;Ibrahim et al, 2010;Wagner et al, 2010;Shaiganfar et al, 2011Shaiganfar et al, , 2015. In contrast to top-down approaches based on satellite observations (e.g.…”

Comparison of tropospheric NO<sub>2</sub> columns from MAX-DOAS retrievals and regional air quality model simulations

“…Multi-axis differential optical absorption spectroscopy (MAX-DOAS) (e.g., Hönninger et al, 2004;Wittrock et al, 2004) measurements have been used to investigate air pollution in many studies, including the FORMAT campaign in northern Italy (Heckel et al, 2005;Wagner et al, 2011), the Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI) in the Netherlands (Piters et al, 2012), campaigns in Canada Mendolia et al, 2013), China (e.g., Irie et al, 2011;Hendrick et al, 2014;Ma et al, 2013;Wang et al, 2014) and during shipborne measurements (Leser et al, 2003;Takashima et al, 2012;Peters et al, 2012).…”

“…A large number of studies applied MAX-DOAS data for satellite validation (e.g., Celarier et al, 2008;Valks et al, 2011;Irie et al, 2008Irie et al, , 2012Ma et al, 2013;Lin et al, 2014;Kanaya et al, 2014;Pinardi et al, 2014) but up to now, comparisons to regional air quality model simulations of tropospheric NO 2 have, to our knowledge, only been carried out by Vlemmix et al (2015) and Shaiganfar et al (2015). Several studies compared regional air quality model simulations to satellite data (e.g., Huijnen et al, 2010), although satellite data are usually only available at much coarser time steps compared to regional model data.…”