Recent studies have shown that low volatility gas-phase species can be lost onto the smog chamber wall surfaces. Although this loss of organic vapors to walls could be substantial during experiments, its effect on secondary organic aerosol (SOA) formation has not been well characterized and quantified yet. Here the potential impact of chamber walls on the loss of gaseous organic species and SOA formation has been explored using the Generator for Explicit Chemistry and Kinetics of the Organics in the Atmosphere (GECKO-A) modeling tool, which explicitly represents SOA formation and gas–wall partitioning. The model was compared with 41 smog chamber experiments of SOA formation under OH oxidation of alkane and alkene series (linear, cyclic and C₁₂-branched alkanes and terminal, internal and 2-methyl alkenes with 7 to 17 carbon atoms) under high NO_x conditions. Simulated trends match observed trends within and between homologous series. The loss of organic vapors to the chamber walls is found to affect SOA yields as well as the composition of the gas and the particle phases. Simulated distributions of the species in various phases suggest that nitrates, hydroxynitrates and carbonylesters could substantially be lost onto walls. The extent of this process depends on the rate of gas–wall mass transfer, the vapor pressure of the species and the duration of the experiments. This work suggests that SOA yields inferred from chamber experiments could be underestimated up a factor of 2 due to the loss of organic vapors to chamber walls.

Atmospheric chambers have been widely used to study secondary organic aerosol (SOA) properties and formation from various precursors under different controlled environmental conditions and to develop parameterization to represent SOA formation in chemical transport models (CTMs). Chamber experiments are however limited in number, performed under conditions that differ from the atmosphere and can be subject to potential artefacts from chamber walls. Here, the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) modelling tool has been used in a box model under various environmental conditions to (i) explore the sensitivity of SOA formation and properties to changes on physical and chemical conditions and (ii) develop a volatility basis set (VBS)-type parameterization. The set of parent hydrocarbons includes n-alkanes and 1-alkenes with 10, 14, 18, 22 and 26 carbon atoms, α-pinene, β-pinene and limonene, benzene, toluene, o-xylene, m-xylene and p-xylene. Simulated SOA yields and their dependences on the precursor structure, organic aerosol load, temperature and NO_x levels are consistent with the literature. GECKO-A was used to explore the distribution of molar mass, vaporization enthalpy, OH reaction rate and Henry's law coefficient of the millions of secondary organic compounds formed during the oxidation of the different precursors and under various conditions. From these explicit simulations, a VBS-GECKO parameterization designed to be implemented in 3-D air quality models has been tuned to represent SOA formation from the 18 precursors using GECKO-A as a reference. In evaluating the ability of VBS-GECKO to capture the temporal evolution of SOA mass, the mean relative error is less than 20 % compared to GECKO-A. The optimization procedure has been automated to facilitate the update of the VBS-GECKO on the basis of the future GECKO-A versions, its extension to other precursors and/or its modification to carry additional information.

Toluene represents a large fraction of anthropogenic emissions and significantly contributes to tropospheric ozone and secondary organic aerosol (SOA) formation. Despite the fact that toluene is one of the most studied aromatic compounds, detailed chemical mechanisms still fail to correctly reproduce the speciation of toluene gaseous and condensed oxidation products. This study aims to elucidate the role of initial experimental conditions in toluene SOA mass loadings and to investigate gas–particle partitioning of its reaction products at different relevant temperatures. Gaseous and particulate reaction products were identified and quantified using a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHemical Analysis of aeRosol ONline (CHARON) inlet. The chemical system exhibited a volatility distribution mostly in the semi-volatile regime. Temperature decrease caused a shift of saturation concentration towards lower values. The CHARON–PTR-ToF-MS instrument identified and quantified approximately 60 %–80 % of the total organic mass measured by an aerosol mass spectrometer. A detailed mechanism for toluene gaseous oxidation was developed based on the Master Chemical Mechanism (MCM) and Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) deterministic mechanisms, modified following the literature. The new mechanism showed improvements in modeling oxidation product speciation with more observed species represented and more representative concentrations compared to the MCM–GECKO-A reference. Tests on partitioning processes, nonideality, and wall losses highlighted the high dependency of SOA formation on the considered processes. Our results underline the fact that volatility is not sufficient to explain the gas–particle partitioning: the organic and the aqueous phases need to be considered as well as the interactions between compounds in the particle phase.

Between December 2005 and 2013, the In-service Aircraft for a Global Observing System (IAGOS) program produced almost daily in situ measurements of CO and O₃ between Europe and southern Africa. IAGOS data combined with measurements from the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop-A satellite (2008–2013) are used to characterize meridional distributions and seasonality of CO and O₃ in the African upper troposphere (UT). The FLEXPART particle dispersion model and the SOFT-IO model which combines the FLEXPART model with CO emission inventories are used to explore the sources and origins of the observed transects of CO and O₃. We focus our analysis on two main seasons: December to March (DJFM) and June to October (JJASO). These seasons have been defined according to the position of Intertropical Convergence Zone (ITCZ), determined using in situ measurements from IAGOS. During both seasons, the UT CO meridional transects are characterized by maximum mixing ratios located 10^∘ from the position of the ITCZ above the dry regions inside the hemisphere of the strongest Hadley cell (132 to 165 ppb at 0–5^∘ N in DJFM and 128 to 149 ppb at 3–7^∘ S in JJASO) and decreasing values southward and northward. The O₃ meridional transects are characterized by mixing ratio minima of ∼42–54 ppb at the ITCZ (10–16^∘ S in DJFM and 5–8^∘ N in JJASO) framed by local maxima (∼53–71 ppb) coincident with the wind shear zones north and south of the ITCZ. O₃ gradients are strongest in the hemisphere of the strongest Hadley cell. IASI UT O₃ distributions in DJFM have revealed that the maxima are a part of a crescent-shaped O₃ plume above the Atlantic Ocean around the Gulf of Guinea. CO emitted at the surface is transported towards the ITCZ by the trade winds and then convectively uplifted. Once in the upper troposphere, CO-enriched air masses are transported away from the ITCZ by the upper branches of the Hadley cells and accumulate within the zonal wind shear zones where the maximum CO mixing ratios are found. Anthropogenic and fires both contribute, by the same order of magnitude, to the CO budget of the African upper troposphere. Local fires have the highest contribution and drive the location of the observed UT CO maxima. Anthropogenic CO contribution is mostly from Africa during the entire year, with a low seasonal variability. There is also a large contribution from Asia in JJASO related to the fast convective uplift of polluted air masses in the Asian monsoon region which are further westward transported by the tropical easterly jet (TEJ) and the Asian monsoon anticyclone (AMA). O₃ minima correspond to air masses that were recently uplifted from the surface where mixing ratios are low at the ITCZ. The O₃ maxima correspond to old high-altitude air masses uplifted from either local or long-distance area of high O₃ precursor emissions (Africa and South America during all the year, South Asia mainly in JJASO) and must be created during transport by photochemistry.

The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wide range of free and open products in relation to atmospheric composition at global and regional scales. The CAMS Regional Service produces daily forecasts, analyses, and reanalyses of air quality in Europe. This service relies on a distributed modelling production by 11 teams in 10 European countries: CHIMERE (France), DEHM (Denmark), EMEP (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (the Netherlands), MATCH (Sweden), MINNI (Italy), MOCAGE (France), MONARCH (Spain), and SILAM (Finland). The project management and coordination of the service is conducted by a Centralised Regional Production Unit. Every day, each model produces 24 h analyses for the previous day and 97 h forecasts for 19 chemical species over a spatial domain at 0.1 × 0.1° resolution (approximately 10 km × 10 km), with 420 points in latitude and 700 in longitude and 10 vertical levels. Six pollen species are also delivered for the surface forecasts. The 11 individual models are then combined into an ENSEMBLE median. In total, more than 82 billion data points are made available for public use on a daily basis. The design of the system follows clear technical requirements in terms of consistency in the model setup and forcing fields (meteorology, surface anthropogenic emission fluxes, and chemical boundary conditions). But it also benefits from a diversity in the description of atmospheric processes through the design of the 11 European chemistry-transport models (CTMs) involved. The present article aims to provide a comprehensive technical documentation, both for the setup and for the diversity of CTMs involved in the service. We also include an overview of the main output products, their public dissemination, and the related evaluation and quality control strategy.

The VBS-GECKO (volatility basis set – Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere) parameterization for secondary organic aerosol (SOA) formation was integrated into the chemistry-transport model CHIMERE. Concentrations of organic aerosol (OA) and SOA were simulated over Europe for the July–August 2013 period. Simulated concentrations with VBS-GECKO were compared to results obtained with the former H²O parameterization implemented in CHIMERE and to observations from EMEP, ACTRIS and other observations available in the EBAS database. The model configuration using the VBS-GECKO parameterization slightly improves the performances compared to the model configuration using the former H²O parameterization. The VBS-GECKO model configuration performs well for stations showing a large SOA concentration from biogenic sources, especially in northern Europe, but underestimates OA concentrations over stations close to urban areas. Simulated OA was found to be mainly secondary (∼85 %) and from terpene oxidation. Simulations show negligible contribution of the oxidation of mono-aromatic compounds to SOA production. Tests performed to examine the sensitivity of simulated OA concentrations to hydro-solubility, volatility, aging rates and NO_x regime have shown that the VBS-GECKO parameterization provides consistent results, with a weak sensitivity to changes in the parameters provided by the gas-phase mechanism included in CHIMERE (e.g., HO_x or NO_x concentrations). Different scenarios considering intermediate-volatility organic compound (IVOC) emissions were tested to examine the contribution of IVOC oxidation to SOA production. At the continental scale, these simulations show a weak sensitivity of OA concentrations to IVOC emission variations. At the local scale, accounting for IVOC emissions was found to lead to a substantial increase in OA concentrations in the plume from urban areas. This additional OA source remains too small to explain the gap between simulated and measured values at stations where anthropogenic sources are dominant.