Role of the boundary layer dynamics effects on an extreme air pollution event in Paris

The physical and chemical aerosol properties are explored here based on ground-based observations in the Paris region to better understand the role of clouds, radiative fluxes and dynamics on aerosol loading during a heavy regional air pollution that occurred in March 2014 over North-Western Europe. This event is primarily characterized by a fine particle mass (PM2.5) increase from 10 to more than 120 mu g m(-3) and a simultaneous decrease of the horizontal visibility from 40 to 1 km, mainly due to significant formation of ammonium nitrate particles. The aerosol optical depth (AOD) at 550 nm increased steadily from about 0.06 on March 6 to more than 0.9 five days later. The scattering of the solar radiation by polluted particles induced, at the peak of the heavy pollution event, an instantaneous shortwave flux decrease of about 300 W m(-2) for direct irradiance and an increase of about 150 W m(-2) for diffuse irradiance (only scattering). The mean surface aerosol effect efficiency (effect per unit optical depth) is of about -80 W m(-2) with a mean aerosol direct radiative effect of -23 W m(-2). The dynamical and radiative processes that can be responsible for the diurnal cycle of PM2.5 in terms of amplitude and timing are investigated. A comparative analysis is performed for 4 consecutive days (between March 11 and 14), showing that the PM2.5 diurnal cycle can be modulated in time and amplitude by local processes such as the boundary layer depth development (ranging from 100 m to 1350 m), surface relative humidity (100%-35%), thermal structure (10 degrees C-16 degrees C for day/night amplitude), dynamics (wind speed ranging from 4 m s(-1) to 1.5 m s(-1)) and turbulence (turbulent kinetic energy reaching 2 m(2) S-2) near the surface and wind shear along the vertical. Finally, modeled and measured surface PM2.5 loadings are also compared here, notably illustrating the need of accurate boundary layer depth data for efficient air quality forecasts. 2016 Elsevier Ltd. All rights reserved.