Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to some parameterizations of emissions in air quality models

This study examines the uncertainties on air quality modeling associated with the integration of wildfire emissions in chemistry-transport models (CTMs). To do so, aerosol concentrations during the summer of 2007, which was marked by severe fire episodes in the Euro-Mediterranean region especially in the Balkans (20–31 July, 24–30 August 2007) and Greece (24–30 August 2007), are analyzed. Through comparisons to observations from surface networks and satellite remote sensing, we evaluate the abilities of two CTMs, Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the regional particulate matter (PM) concentrations and optical properties. During the two main fire events, fire emissions may contribute up to 90&thinsp;% of surface PM2.5 concentrations in the fire regions (Balkans and Greece), with a significant regional impact associated with long-range transport. Good general performances of the models and a clear improvement of PM2.5 and aerosol optical depth (AOD) are shown when fires are taken into account in the models with high correlation coefficients. Two sources of uncertainties are specifically analyzed in terms of surface PM2.5 concentrations and AOD using sensitivity simulations: secondary organic aerosol (SOA) formation from intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions' injection heights. The analysis highlights that surface PM2.5 concentrations are highly sensitive to injection heights (with a sensitivity that can be as high as 50&thinsp;% compared to the sensitivity to I/S-VOC emissions which is lower than 30&thinsp;%). However, AOD which is vertically integrated is less sensitive to the injection heights (mostly below 20&thinsp;%) but highly sensitive to I/S-VOC emissions (with sensitivity that can be as high as 40&thinsp;%). The maximum statistical dispersion, which quantifies uncertainties related to fire emission modeling, is up to 75&thinsp;% for PM2.5 in the Balkans and Greece, and varies between 36&thinsp;% and 45&thinsp;% for AOD above fire regions. The simulated number of daily exceedance of World Health Organization (WHO) recommendations for PM2.5 over the considered region reaches 30 days in regions affected by fires and ∼10 days in fire plumes, which is slightly underestimated compared to available observations. The maximum statistical dispersion (σ) on this indicator is also large (with σ reaching 15 days), showing the need for better understanding of the transport and evolution of fire plumes in addition to fire emissions.</p

Long term modelling of the dynamical atmospheric flows over SIRTA site

International audienceThe atmospheric flow knowledge is important for its role in pollutant dispersion and wind energy. In this work, the hourly atmospheric flow output (8760 states) from Weather Research and Forcasting (WRF) model for the year 2011 over SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) are analyzed and clustered into a finite number of representative atmospheric states using two clustering methods: non-controlled clustering and controlled clustering. The resulting representative situations of those clusters are used to specify boundary conditions for flow downscaling over the heterogeneous SIRTA. For flow downscaling, the CFD code Code_Saturne is used to simulate each representative atmospheric state. To assess the efficiency of WRF clustering and Code_Saturne downscaling, the measurements in SIRTA over the same year are used as reference. The Mean Absolute Error (MAE) and the Kullback-Leibler divergence (KL) metrics were computed for the distributions of the atmospheric flow features in order to: (i) compare the difference between the performance of the two clustering procedures, and (ii) compare the distribution of flow properties between WRF mesoscale model and Code_Saturne. It is clearly demonstrated that the two clustering methods are comparable in benefit, and that Code_Saturne improves considerably the flow features modeling in comparison to measurements