Aerosol modelling in Europe with a focus on Switzerland during summer and winter episodes

Abstract

This paper describes aerosol modelling in Europe with a focus on Switzerland during summer and winter periods. We modelled PM<sub>2.5</sub> (particles smaller than 2.5 μm in aerodynamic diameter) for one summer and two winter periods in years 2006 and 2007 using the CAMx air quality model. The meteorological fields were obtained from MM5 simulations. The modelled wind speeds during some low-wind periods, however, had to be calibrated with measurements to use realistic input for the air quality model. The detailed AMS (aerosol mass spectrometer) measurements at specific locations were used to evaluate the model results. In addition to the base case simulations, we carried out sensitivity tests with modified aerosol precursor emissions, air temperature and deposition. Aerosol concentrations in winter 2006 were twice as high as those in winter 2007, however, the chemical compositions were similar. CAMx could reproduce the relative composition of aerosols very well both in the winter and summer periods. Absolute concentrations of aerosol species were underestimated by about 20 %. Both measurements and model results suggest that organic aerosol (30–38 %) and particulate nitrate (30–36 %) are the main aerosol components in winter. In summer, organic aerosol dominates the aerosol composition (55–57 %) and is mainly of secondary origin. The contribution of biogenic volatile organic compound (BVOC) emissions to the formation of secondary organic aerosol (SOA) was predicted to be very large (>95 %) in Switzerland. The main contributors to the modelled SOA concentrations were oxidation products of monoterpenes and sesquiterpenes as well as oligomerization of oxidized compounds. The fraction of primary organic aerosol (POA) derived from measurements was lower than the model predictions indicating the importance of volatility of POA, which has not yet been taken into account in CAMx. Sensitivity tests with reduced NO<sub>x</sub> and NH<sub>3</sub> emissions suggest that aerosol formation is more sensitive to ammonia emissions in winter in a large part of Europe. In Switzerland however, aerosol formation is predicted to be NO<sub>x</sub>-sensitive. In summer, effects of NO<sub>x</sub> and NH<sub>3</sub> emission reductions on aerosol concentrations are predicted to be lower mostly due to lower ammonium nitrate concentrations. In general, the sensitivity to NH<sub>3</sub> emissions is weaker in summer due to higher NH<sub>3</sub> emissions

Modeling atmospheric ammonia and ammonium using a stochastic Lagrangian air quality model (STILT-Chem v0.7)

“…As indicated in Table 3, all MFBs and MFEs for p-NH + 4 meet the acceptable model performance criteria (MFE ≤ 75 % and −60 % < MFB < 60 %) suggested by Boylan and Russell (2006). All of the values are also comparable to values reported by other studies (Aksoyoglu et al, 2011;Appel et al, 2008;Tesche et al, 2006), indicating satisfactory performance of the model in simulating p-NH + 4 . Ratio-of-the-means (ROM) values presented in Table 3 indicate that the model predicted means of p-NH 4 measurements very well, with a 2 % to 8 % over-prediction.…”

Simulating secondary organic aerosol from missing diesel-related intermediate-volatility organic compound emissions during the Clean Air for London (ClearfLo) campaign

“…Several regional ACTM studies have also reported an underestimation of total OA (Simpson et al, 2007;Murphy and Pandis, 2009;Hodzic et al, 2010;Aksoyoglu et al, 2011;Jathar et al, 2011;Bergström et al, 2012;Koo et al, 2014) and SOA (Hodzic et al, 2010;Shrivastava et al, 2011;Zhang et al, 2013;Fountoukis et al, 2014), with normalized mean biases (NMBs) often in the range of −30 to −50 %. In some cases, this underestimation has been shown to be due to problems with the underlying emission inventories, particularly for domestic wood burning in wintertime (Simpson et al, 2007;Denier van der Gon et al, 2015).…”

Nanoparticle growth by particle-phase chemistry

“…More recent models have included reversibility (Roldin et al, 2014;Trump and Donahue, 2014), which is needed to reproduce perturbations of freshly formed SOA such as changes induced by isothermal dilution, thermal degradation, and/or aging. Regional air quality models show that oligomerization has the potential to significantly increase the SOA mass concentration (Aksoyoglu et al, 2011;Lemaire et al, 2016), and accurately representing this chemistry in these models is perhaps the greatest uncertainty for predicting SOA formation (Shrivastava et al, 2016).…”