Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM₁) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May–June and September–October) and 2009 (February–March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.

In the atmosphere nighttime removal of volatile organic compounds is initiated to a large extent by reaction with the nitrate radical (NO₃) forming organic nitrates which partition between gas and particulate phase. Here we show based on particle phase measurements performed at a suburban site in the Netherlands that organic nitrates contribute substantially to particulate nitrate and organic mass. Comparisons with a chemistry transport model indicate that most of the measured particulate organic nitrates are formed by NO₃ oxidation. Using aerosol composition data from three intensive observation periods at numerous measurement sites across Europe, we conclude that organic nitrates are a considerable fraction of fine particulate matter (PM₁) at the continental scale. Organic nitrates represent 34% to 44% of measured submicron aerosol nitrate and are found at all urban and rural sites, implying a substantial potential of PM reduction by NO_x emission control.

Sea spray aerosol (SSA) generated by bubble bursting at the ocean surface is an important component of aerosol‐cloud interactions over remote oceans, providing the atmosphere with ice‐nucleating particles (INPs) or particles required for heterogeneous ice nucleation. Studies have shown that organic INPs are emitted during phytoplankton blooms, but changes in INP number concentrations (n_INPs) due to ocean biological activity have not been directly demonstrated in natural SSA. In this study, a clean sector sampler was used to differentiate ice nucleation and composition of pristine SSA from terrestrial aerosol at the Mace Head Research Station in August 2015. Average n_INPs active at −15 °C (n_{INPs,−15 °C}) were 0.0011 L⁻¹, and large variability (up to a factor of 200) was observed for INPs active warmer than −22 °C. Highest n_INPs in the clean sector occurred during a period of elevated marine organic aerosol from offshore biological activity (M1, n_{INPs,−15 °C} = 0.0077 L⁻¹). A peak in n_INPs was also observed in terrestrial organic aerosol (T1, n_{INPs,−15 °C} = 0.0076 L⁻¹). The impacts of heating and hydrogen peroxide digestion on n_INPs indicate that INPs at Mace Head Research Station were largely organic and that INPs observed during M1 and T1 were biological (i.e., protein containing). Complexities of predicting increases in n_INPs due to offshore biological activity are explored. A parameterization for pristine SSA INPs over the North Atlantic Ocean was developed, illustrating that SSA is associated with a factor of 1,000 fewer ice‐nucleating sites per surface area of aerosol compared to mineral dust.

Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles > 20 nm) across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies based on κ–Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of κ. The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.

A new sea spray source function (SSSF), termed Oceanflux Sea Spray Aerosol or OSSA, was derived based on in-situ sea spray aerosol measurements along with meteorological/physical parameters. Submicron sea spray aerosol fluxes derived from particle number concentration measurements at the Mace Head coastal station, on the west coast of Ireland, were used together with open-ocean eddy correlation flux measurements from the Eastern Atlantic Sea Spray, Gas Flux, and Whitecap (SEASAW) project cruise. In the overlapping size range, the data for Mace Head and SEASAW were found to be in a good agreement, which allowed deriving the new SSSF from the combined dataset spanning the dry diameter range from 15 nm to 6 μm. The OSSA source function has been parameterized in terms of five lognormal modes and the Reynolds number instead of the more commonly used wind speed, thereby encapsulating important influences of wave height, wind history, friction velocity, and viscosity. This formulation accounts for the different flux relationships associated with rising and waning wind speeds since these are included in the Reynolds number. Furthermore, the Reynolds number incorporates the kinematic viscosity of water, thus the SSSF inherently includes dependences on sea surface temperature and salinity. The temperature dependence of the resulting SSSF is similar to that of other in-situ derived source functions and results in lower production fluxes for cold waters and enhanced fluxes from warm waters as compared with SSSF formulations that do not include temperature effects.