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Over Boreal regions, monoterpenes emitted from the forest are the main precursors for secondary organic aerosol (SOA) formation and the primary driver of the growth of new aerosol particles to climatically important cloud condensation nuclei (CCN). Autoxidation of monoterpenes leads to rapid formation of Highly Oxygenated organic Molecules (HOM). We have developed the first model with near-explicit representation of atmospheric new particle formation (NPF) and HOM formation. The model can reproduce the observed NPF, HOM gas-phase composition and SOA formation over the Boreal forest. During the spring, HOM SOA formation increases the CCN concentration by ~10 % and causes a direct aerosol radiative forcing of −0.10 W/m 2 . In contrast, NPF reduces the number of CCN at updraft velocities < 0.2 m/s, and causes a direct aerosol radiative forcing of +0.15 W/m 2 . Hence, while HOM SOA contributes to climate cooling, NPF can result in climate warming over the Boreal forest. Forests emit compounds into the atmosphere that are oxidized into highly oxygenated molecules that serve as precursors for cloud condensation nuclei–a process that impacts the climate, but is poorly represented in models. Here the authors create a new model that accurately depicts highly oxygenated molecule and climate dynamics over Boreal forests.

The oxidation of aromatics contributes significantly to the formation of atmospheric aerosol. Using toluene as an example, we demonstrate the existence of a molecular rearrangement channel in the oxidation mechanism. Based on both flow reactor experiments and quantum chemical calculations, we show that the bicyclic peroxy radicals (BPRs) formed in OH-initiated aromatic oxidation are much less stable than previously thought, and in the case of the toluene derived ipso-BPRs, lead to aerosol-forming low-volatility products with up to 9 oxygen atoms on sub-second timescales. Similar results are predicted for ipso-BPRs formed from many other aromatic compounds. This reaction class is likely a key route for atmospheric aerosol formation, and including the molecular rearrangement of BPRs may be vital for accurate chemical modeling of the atmosphere. The oxidation of aromatics contributes significantly to the formation of atmospheric aerosol. Using toluene as an example the authors demonstrate a molecular rearrangement channel in the oxidation mechanism and show that the bicyclic peroxy radicals are much less stable than previously thought and can lead to aerosol-forming low-volatility products with up to 9 oxygen atoms on sub-second timescales

A multitude of biogeochemical feedback mechanisms govern the climate sensitivity of Earth in response to radiation balance perturbations. One feedback mechanism, which remained missing from most current Earth System Models applied to predict future climate change in IPCC AR6, is the impact of higher temperatures on the emissions of biogenic volatile organic compounds (BVOCs), and their subsequent effects on the hydroxyl radical (OH) concentrations. OH, in turn, is the main sink term for many gaseous compounds including methane, which is the second most important human-influenced greenhouse gas in terms of climate forcing. In this study, we investigate the impact of this feedback mechanism by applying two models, a one-dimensional chemistry-transport model, and a global chemistry-transport model. The results indicate that in a 6 K temperature increase scenario, the BVOC-OH-CH 4 feedback increases the lifetime of methane by 11.4% locally over the boreal region when the temperature rise only affects chemical reaction rates, and not both, chemistry and BVOC emissions. This would lead to a local increase in radiative forcing through methane (ΔRF CH4 ) of approximately 0.013 Wm −2 per year, which is 2.1% of the current ΔRF CH4 . In the whole Northern hemisphere, we predict an increase in the concentration of methane by 0.024% per year comparing simulations with temperature increase only in the chemistry or temperature increase in chemistry and BVOC emissions. This equals approximately 7% of the annual growth rate of methane during the years 2008–2017 (6.6 ± 0.3 ppb yr−1) and leads to an ΔRF CH4 of 1.9 mWm −2 per year.

In this study we modeled secondary organic aerosol (SOA) mass loadings from the oxidation (by O3, OH and NO3) of five representative biogenic volatile organic compounds (BVOCs): isoprene, endocyclic bond-containing monoterpenes (α-pinene and limonene), exocyclic double-bond compound (β-pinene) and a sesquiterpene (β-caryophyllene). The simulations were designed to replicate an idealized smog chamber and oxidative flow reactors (OFRs). The Master Chemical Mechanism (MCM) together with the peroxy radical autoxidation mechanism (PRAM) were used to simulate the gas-phase chemistry. The aim of this study was to compare the potency of MCM and MCM + PRAM in predicting SOA formation. SOA yields were in good agreement with experimental values for chamber simulations when MCM + PRAM was applied, while a stand-alone MCM underpredicted the SOA yields. Compared to experimental yields, the OFR simulations using MCM + PRAM yields were in good agreement for BVOCs oxidized by both O3 and OH. On the other hand, a stand-alone MCM underpredicted the SOA mass yields. SOA yields increased with decreasing temperatures and NO concentrations and vice versa. This highlights the limitations posed when using fixed SOA yields in a majority of global and regional models. Few compounds that play a crucial role (>95 % of mass load) in contributing to SOA mass increase (using MCM + PRAM) are identified. The results further emphasized that incorporating PRAM in conjunction with MCM does improve SOA mass yield estimation.

We introduce the Atmospherically Relevant Chemistry and Aerosol box model ARCA box (v.1.2.2). It is a zero-dimensional process model with a focus on atmospheric chemistry and submicron aerosol processes, including cluster formation. A novel feature in the model is its comprehensive graphical user interface, allowing for detailed configuration and documentation of the simulation settings, flexible model input, and output visualization. Additionally, the graphical interface contains tools for module customization and input data acquisition. These properties – customizability, ease of implementation and repeatability – make ARCA an invaluable tool for any atmospheric scientist who needs a view on the complex atmospheric aerosol processes. ARCA is based on previous models (MALTE-BOX, ADiC and ADCHEM), but the code has been fully rewritten and reviewed. The gas-phase chemistry module incorporates the Master Chemical Mechanism (MCMv3.3.1) and Peroxy Radical Autoxidation Mechanism (PRAM) but can use any compatible chemistry scheme. ARCA's aerosol module couples the ACDC (Atmospheric Cluster Dynamics Code) in its particle formation module, and the discrete particle size representation includes the fully stationary and fixed-grid moving average methods. ARCA calculates the gas-particle partitioning of low-volatility organic vapours for any number of compounds included in the chemistry, as well as the Brownian coagulation of the particles. The model has parametrizations for vapour and particle wall losses but accepts user-supplied time- and size-resolved input. ARCA is written in Fortran and Python (user interface and supplementary tools), can be installed on any of the three major operating systems and is licensed under GPLv3.

Comprehensive representation of nanoparticle dynamics is necessary for understanding nucleation and growth phenomena. This is critical in atmospheric physics, as airborne particles formed from vapors have significant but highly uncertain effects on climate. While the vapor–particle mass exchange driving particle growth can be described by a macroscopic, continuous substance for large enough particles, the growth dynamics of the smallest nanoparticles involve stochastic fluctuations in particle size due to discrete molecular collision and decay processes. To date, there have been no generalizable methods for quantifying the particle size regime where the discrete effects become negligible and condensation models can be applied. By discrete simulations of sub-10 nm particle populations, we demonstrate the importance of stochastic effects in the nanometer size range. We derive a novel, theory-based, simple and robust metric for identifying the exact sizes where these effects cannot be omitted for arbitrary molecular systems. The presented metric, based on examining the second- and first-order derivatives of the particle size distribution function, is directly applicable to experimental size distribution data. This tool enables quantifying the onset of condensational growth without prior information on the properties of the vapors and particles, thus allowing robust experimental resolving of nanoparticle formation physics.

Atmospheric new particle formation occurs frequently in the global atmosphere and may play a crucial role in climate by affecting cloud properties. The relevance of newly formed nanoparticles depends largely on the dynamics governing their initial formation and growth to sizes where they become important for cloud microphysics. One key to the proper understanding of nanoparticle effects on climate is therefore hidden in the growth mechanisms. In this study we have developed and successfully tested two independent methods based on the aerosol general dynamics equation, allowing detailed retrieval of time- and size-dependent nanoparticle growth rates. Both methods were used to analyze particle formation from two different biogenic precursor vapors in controlled chamber experiments. Our results suggest that growth rates below 10 nm show much more variation than is currently thought and pin down the decisive size range of growth at around 5 nm where in-depth studies of physical and chemical particle properties are needed.

Atmospheric new particle formation occurs frequently in the global atmosphere and may play a crucial role in climate by affecting cloud properties. The relevance of newly formed nanoparticles depends largely on the dynamics governing their initial formation and growth to sizes where they become important for cloud microphysics. One key to the proper understanding of nanoparticle effects on climate is therefore hidden in the growth mechanisms.In this study we have developed and successfully tested two independent methods based on the aerosol general dynamics equation, allowing detailed retrieval of time- and size-dependent nanoparticle growth rates. Here both methods were used to analyze particle formation from two different biogenic precursor vapors in controlled chamber experiments. Our results suggest that growth rates below 10 nm show much more variation than is currently thought and pin down the decisive size range of growth at around 5 nm where in-depth studie sof physical and chemical particle properties are needed.