Observations and regional modeling of aerosol optical properties, speciation and size distribution over Northern Africa and western Europe

Abstract

Abstract. The aerosol speciation and size distribution is modeled during the summer 2013 and over a large area encompassing Africa, Mediterranean and western Europe. The modeled aerosol is compared to available measurements such as the AERONET aerosol optical depth (AOD) and aerosol size distribution (ASD) and the EMEP network for surface concentrations of particulate matter PM 2.5 , PM 10 and inorganic species (nitrate, sulfate and ammonium). The main goal of this study is to quantify the model ability to realistically model the speciation and size distribution of the aerosol. Results first showed that the long-range transport pathways are well reproduced and mainly constituted by mineral dust: spatial correlation is ≈ 0.9 for AOD and Ångström exponent, when temporal correlations show that the day-to-day variability is more difficult to reproduce. Over Europe, PM 2.5 and PM 10 have a mean temporal correlation of ≈ 0.4 but the lowest spatial correlation (≈ 0.25 and 0.62, respectively), showing that the fine particles are not well localized or transported. Being short-lived species, the uncertainties on meteorology and emissions induce these lowest scores. However, time series of PM 2.5 with the speciation show a good agreement between model and measurements and are useful for discriminating the aerosol composition. Using a classification from the south (Africa) to the north (northern Europe), it is shown that mineral dust relative mass contribution decreases from 50 to 10 % when nitrate increases from 0 to 20 % and all other species, sulfate, sea salt, ammonium, elemental carbon, primary organic matter, are constant. The secondary organic aerosol contribution is between 10 and 20 % with a maximum at the latitude of the Mediterranean Sea (Spanish stations). For inorganic species, it is shown that nitrate, sulfate and ammonium have a mean temporal correlation of 0.25, 0.37 and 0.17, respectively. The spatial correlation is better (0.25, 0.5 and 0.87), showing that the mean values may be biased but the spatial localization of sulfate and ammonium is well reproduced. The size distribution is compared to the AERONET product and it is shown that the model fairly reproduces the main values for the fine and coarse mode. In particular, for the fine mode, the model overestimates the aerosol mass in Africa and underestimates it in Europe.

Impact of biomass burning on pollutant surface concentrations in megacities of the Gulf of Guinea

“…The AOD due to mineral dust is mostly underestimated and many factors may explain this. As already discussed in Menut et al (2016), the modelled size distribution may be inaccurate, while it is very sensitive for the AOD estimation. It was also shown that a bias in AOD calculation may exist but is not necessarily related to erroneous modelled surface concentrations of particulate matter (PM).…”

“…The modelled AOD is calculated by FastJX for several wavelengths over the whole atmospheric column, as detailed in Menut et al (2016). At the boundaries of the domain, climatologies from global model simulations are used.…”

Interactions of atmospheric gases and aerosols with the monsoon dynamics over the Sudano-Guinean region during AMMA

“…Their life cycle is fully represented with emission, transport, chemistry and deposition (wet and dry). Menut et al (2016) have detailed and analyzed aerosol speciation and size distribution in the CHIMERE model during the summer 2013 over Europe and Africa using the AERONET network for aerosol optical depth (AOD) and EMEP network for PM concentrations. For the AOD calculation, the aerosol optical scheme in the CHIMERE model considers mixed aerosols following the "core-shell" hypothesis detailed in Péré et al (2009) and evaluated in Péré et al (2010).…”

An alternative way to evaluate chemistry-transport model variability

“…The temporal correlation is much better for 2013 than for other years. This is probably due to a size distribution that is not necessarily well simulated from one day to another (shown by AOD and explained in Menut et al, 2016) but the relative contributions of fine and coarse aerosol atmospheric load are fairly reproduced. This feature highlights the high sensitivity of the AOD calculation to the modeled aerosol size distribution, although the overall mass emitted and transported is realistic.…”