LEMAIRE Vincent

A methodology rested on model-based machine learning using simple linear regressions and the parameterizations of the main physics and chemistry processes has been developed to perform highly-resolved air quality simulations. The training of the methodology is (i) completed over a 6-month period using the outputs of the chemical transport model CHIMERE, and (ii) then applied over the subsequent 6 months. Despite rough assumptions, this new methodology performs as well as the raw CHIMERE simulation for daily mean concentrations of the main criteria air pollutants (NO2, Ozone, PM10 and PM2.5) with correlations ranging from 0.75 to 0.83 for the particulate matter and up to 0.86 for the maximum ozone concentrations. Some improvements are investigated to expand this methodology to several other uses, but at this stage the method can be used for air quality forecasting, analysis of pollution episodes and mapping. This study also confirms that including a minimum set of selected physical parameterizations brings a high added value on machine learning processes.

The organic fraction of atmospheric aerosols has proven to be a critical element of air quality and climate issues. However, its composition and the aging processes it undergoes remain insufficiently understood. This work builds on laboratory knowledge to simulate the formation of oligomers from biogenic secondary organic aerosol (BSOA) in the troposphere at the continental scale. We compare the results of two different modeling approaches, a first-order kinetic process and a pH-dependent parameterization, both implemented in the CHIMERE air quality model (AQM) (www.lmd.

Particulate pollution covers many health and climate issues in Europe and worldwide. Organic aerosols, which represent a significant fraction of particulate pollution, are a complex material, emitted directly in condensed form or resulting from the oxidation of a very large number of volatile organic compounds in the atmosphere. Because of the great variety of its emission sources, whether biogenic or anthropogenic, the composition and the processes of formation and chemical ageing of organic aerosols remain today associated with many questions. Since field observation of the formation of organic matter and its evolution within air masses is difficult to implement, modeling remains an indispensable complementary tool to simulate organic aerosol and follow its transport over long distances. This is why this work was first focused on identifying the major processes governing both the formation and photochemical aging of the organic fraction of particles. Based on an innovative methodology, an operational chemical scheme dedicated to the emission and oxidation of semi-volatile compounds has been developed for air quality models. Its implementation and its evaluation within the CHIMERE model was carried out by confrontation with the data of the summer and winter campaigns of the MEGAPOLI campaign. Finally, this work also aimed at implementing in CHIMERE and discussing the mechanisms of oligomerization of organic compounds in condensed phase, a phenomenon which is nowadays recognized as one of the major process of aging of organic aerosol.

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