Paris emission inventory diagnostics from ESQUIF airborne measurements and a chemistry transport model

Abstract

[1] During the Atmospheric Pollution Over the Paris Area (ESQUIF) experiment a series of airborne measurements were collected in the vicinity of the city of Paris during smog episodes. They are used in combination with an air quality photochemical model in order to diagnose uncertainties in the current emission inventory. Diagnostics are made by comparing simulated with observed concentrations for nitrogen oxides, carbon monoxide, and primary hydrocarbons, taking into account the chemistry and transport processes of these compounds. An emphasis is put on the uncertainty of the results, taking into account the finiteness of the measurement samples, possible errors in the model transport, and chemistry and measurement errors. We examine, in particular, possible sources of bias in the model. For instance, we show that boundary layer depth is underestimated by at most 30% on average. However, sensitivity experiments showed that these model biases, taken individually, cannot alter the qualitative aspects of our results. Only a conspiracy of these biases could possibly shift all our diagnostics toward significantly different results. There is reasonable consistency between simulated and measured concentrations. NO y simulations agree with measured concentrations to within 35%; CO concentrations agree to within a factor of 2. There are significant underestimations and overestimations in some individual primary hydrocarbons. However, the total mass and reactivity of the measured hydrocarbon mixture, which accounts for only about half of the total emitted mass, agree with modeled values to within an estimated uncertainty of 40%. The analysis of results provides clues for improving emission inventories. It is found, for instance, that temperature dependence, which is not considered here, can be a key factor and that hydrocarbon emissions from solvent use may suffer from inaccurate totals or speciation. Another source of uncertainties may be the temporal or spatial distributions of solvent activities.

Air quality assessment for Portugal

“…proposed assessment task, this modelling system is being also applied in real-time air quality forecasting over Portugal (Monteiro et al, 2005a). The model version used here is primarily described in Schmidt et al (2001), and further updates, especially for the smallerscale version, can be found in Vautard et al (2003). The meteorological input variables are taken, as in Schmidt et al (2001), from the European Centre for Mediumrange Weather Forecast (ECMWF): 3D fields of horizontal wind, temperature, specific humidity, cloud liquid water content, and 2D fields of surface pressure, heat fluxes, 2 m temperature and cloud cover, with different resolutions according to the domain of simulation (36 × 36 km for the European domain and 9 × 9 km resolution for the Portugal domain).…”

“…CHIMERE has been used for several research applications, including sensitivity to anthropogenic or biogenic emissions (e.g., Beekmann and Derognat, 2003;Menut, 2003;Derognat et al, 2003), emission diagnostics (Vautard et al, 2003), or photo-oxidant forecasting over several regions of Europe (namely Italy and Belgium), being the official model for air quality forecasting over France and Paris region (Vautard et al, 2000). Results showed a reasonable skill for daily maximum forecasts of ozone with an averaged root mean square (RMS) error of about 10 ppb and 0.8 of correlation, which is in agreement with the ozone forecast model intercomparison experiment described in Tilmes et al (2002).…”

SCIAMACHY formaldehyde observations: constraint for isoprene emission estimates over Europe?

“…The model is also used for forecasts of pollutant levels as part of the French national air pollution forecasting system, Prev'Air (www.prevair.org). CHIMERE has been extensively validated with surface and airborne measurements for the different scales (Schmidt et al, 2001;Blond and Vautard, 2004;Menut et al, 2000;Vautard et al, 2003;Blond et al, 2007;Honoré et al, 2008). As an example, the averaged bias for daily ozone maxima over Europe is smaller than 10% (Honoré et al, 2008).…”

Inverse modeling of emissions for local photooxidant pollution: Testing a new methodology with kriging constraints

“…The emission inventory used in our study has been elaborated by AIRPARIF for the ESQUIF field campaign of the summer of 1998 as described in Vautard et al (2003). It includes the hourly 6×6 km fluxes of sixteen anthropogenic emitted species: carbon monoxide, sulfur dioxide, methane, nitrogen oxides speciated in 10% of NO 2 and 90% of NO, HONO and non-methanic VOCs speciated into ten sub-families according to various studies (Theloke et al, 2000(Theloke et al, , 2001Schmitz et al, 2000;Rudd and Marlowe, 1998;Hassel et al, 2000).…”