The radiative properties of clouds depend partially on the cloud droplet number concentration, which is determined by the concentration of cloud condensation nuclei (CCN) when the clouds are formed. In turn, CCN concentrations are determined by the atmospheric particle size distribution and their chemical composition. We use a novel Lagrangian modelling framework to examine the origins and history of gas and aerosol components observed at the boreal forest measurement site SMEAR II, and their potential to act as CCN. This framework combines: (a) global emission datasets, (b) backward trajectories from the FLEXible PARTicle dispersion model (FLEXPART) airmass dispersion model, (c) a detailed description of atmospheric chemistry and aerosol dynamics from the Model to Simulate the Concentration of Organic Vapours, Sulphuric Acid and Aerosol Particles (SOSAA). We apply this SOSAA-FP (FP standing for FLEXPART) framework to simulate a period from March to October 2018 with 1 h time resolution, focusing on the concentrations of CCN between 0.1 % and 1.2 % maximum supersaturation as calculated by the κ-Köhler theory (with respective dry particle diameter of activation ca. 175–35 nm). We find that the model PM1 fraction of primary particles, sulfates and secondary organic aerosol correlate well with the observed organic aerosol and sulfate trends and explain most of the observed organic aerosol and sulfate PM1 mass. Our results show that primary particle emissions play a considerable role in CCN concentrations even at a rural site such as SMEAR II. Changes in atmospheric cluster formation rates had a relatively weak impact on the CCN concentrations in the sensitivity runs. Enhanced cluster formation increased (decreased) the CCN concentrations for the highest (lowest) maximum supersaturation. Without any cluster formation our modelled median CCN concentrations changed by −48 % and +23 % for supersaturations of 1.2 % and 0.1 %, respectively, whereas omitting primary particle emissions had a decreasing effect in all calculated CCN supersaturation classes (−82 % and −33 % decrease in median CCN of 1.2 % and 0.1 % supersaturation, respectively). While the enhancing effect of cluster formation to high supersaturation (i.e., small diameter) CCN concentrations is unsurprising, the weak sensitivity to cluster formation rates and the decreasing effect to lowest supersaturation CCN was unexpected, as was the strong influence of anthropogenic primary emissions. The Lagrangian model framework showed its power, as it was possible to trace down the causes behind the unexpected outcomes by comparing how the particle population evolved along the trajectories in different sensitivity tests.

Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to ensure continuous time representation. To learn fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN to explore feature-based interpolation, such that we can fill the spatial gaps given coarse observation stations. To demonstrate the efficiency of our model for spatiotemporal reanalysis, we use data from 2013-2017 collected in northern China for PM2.5 analysis. Extensive experiments show that AQ-Net excels in air quality reanalysis, highlighting the potential of hybrid spatio-temporal models to better capture environmental dynamics, especially in urban areas where both spatial and temporal variability are critical.

We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

The Pan-Eurasian Experiment Modelling Platform (PEEX-MP) is one of the key blocks of the PEEX Research Programme. The PEEX MP has more than 30 models and is directed towards seamless environmental prediction. The main focus area is the Arctic-boreal regions and China. The models used in PEEX-MP cover several main components of the Earth's system, such as the atmosphere, hydrosphere, pedosphere and biosphere, and resolve the physical-chemical-biological processes at different spatial and temporal scales and resolutions. This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system, online integrated, forward/inverse, and socioeconomical modelling, and other approaches with a particular focus on applications in the PEEX geographical domain. The employed high-performance computing facilities, capabilities, and PEEX dataflow for modelling results are described. Several virtual research platforms (PEEX-View, Virtual Research Environment, Web-based Atlas) for handling PEEX modelling and observational results are introduced. The overall approach allows us to understand better physical-chemical-biological processes, Earth's system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks, impact, consequences, etc. for population, environment and climate in the PEEX domain. This work was also one of the last projects of Prof. Sergej Zilitinkevich, who passed away on 15 February 2021. Since the finalization took time, the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

In the last few decades, atmospheric formation of secondary organic aerosols (SOA) has gained increasing attention due to their impact on air quality and climate. However, methods to predict their abundance are mainly empirical and may fail under real atmospheric conditions. In this work, a close-to-mechanistic approach allowing SOA quantification is presented, with a focus on a chain-like chemical reaction called "autoxidation". A novel framework is employed to (a) describe the gas-phase chemistry, (b) predict the products' molecular structures and (c) explore the contribution of autoxidation chemistry on SOA formation under various conditions. As a proof of concept, the method is applied to benzene, an important anthropogenic SOA precursor. Our results suggest autoxidation to explain up to 100% of the benzene-SOA formed under low-NO x laboratory conditions. Under atmospheric-like day-time conditions, the calculated benzene-aerosol mass continuously forms, as expected based on prior work. Additionally, a prompt increase, driven by the NO3 radical, is predicted by the model at dawn. This increase has not yet been explored experimentally and stresses the potential for atmospheric SOA formation via secondary oxidation of benzene by O3 and NO3.

Abstract. In the last decades, atmospheric formation of secondary organic aerosol (SOA) gained increasing attention due to its impact on air quality and climate. However, methods to predict its abundance are mainly empirical and may fail at real atmospheric conditions. In this work, a close-to mechanistic approach allowing SOA quantification is presented, with focus on a chain-like chemical reaction called “autoxidation”. A novel framework is employed to a) describe the gas-phase chemistry, b) predict the products’ molecular structures and c) explore the contribution of autoxidation chemistry on SOA formation under various conditions. As a proof of concept, the method is applied to benzene, an important anthropogenic SOA precursor. Our results suggest autoxidation to explain up to 100 % of the benzene-SOA formed under low-NOx laboratory conditions. While under atmospheric-like day-time conditions, the calculated aerosol mass continuously increases, as expected based on prior work. Additionally, a prompt increase, driven by the NO3 radical, is predicted at dawn. This increase has not yet been observed experimentally and questions the applicability of the widely accepted concept of OH-based SOA mass yield in the atmosphere.

The Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) is seamless/ online integrated numerical weather prediction and atmospheric chemical transport modelling system. It is capable of simultaneous simulation of meteorology – atmospheric composition and downscaling/nesting for regional–subregional–urban scales. The research and development are focused on: multi-scale modelling up to fine resolution; improving parameterizations describing urban processes, boundary/surface layer structures; implementation of emissions, aerosol/chemistry mechanisms, aerosol feedback and interactions. The Enviro-components includes: gas-phase chemistry; aerosol microphysics and deposition processes; urban sublayer physics parameterisations; direct/indirect/combined aerosol feedbacks due to radiation; locally mass-conserving semi-Lagrangian numerical advection scheme; natural and anthropogenic emission inventories. The model has modules for pre-processing of the ECMWF’s initial/ boundary conditions for meteorology-chemistry-aerosols, observations for data assimilation, and selected emission inventories. The model has been developed through HPC projects such as Enviro-HIRLAM at CSC and Enviro-PEEX(Plus) at ECMWF, as well as other research projects.The research and development of Enviro-HILRAM and its application will be demonstrated on examples, where this model is used as a research tool for studies in domain of the Pan-Eurasian Experiment (PEEX; https://www.atm.helsinki.fi/peex) programme. The examples include: Integrated modelling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires; Integrated modelling and analysis of influence of land cover changes on regional weather conditions/ patterns; High-resolution integrated urban environmental modelling with integration of the urban large-eddy simulation (PALM model) and meteorological simulations into a seamless modelling chain; Effects of spring air pollution and weather on Covid-19 infection/situation in Finland; Meteorology integration between seamless and trajectory (FLEXPART model) models; and others. The Enviro-HIRLAM model generated output provides valuable input (3D meteorology and atmospheric composition) to assessment studies, and it as can be integrated into GIS environment for further risk/ vulnerability/ consequences/ etc. estimation, and other studies.

The files contain the raw data from simulations of the plots for main manuscript figures 2 & 3. Any data not archived here (mainly ADCHEM simulations) can be provided upon request (contact: lukas.pichelstorfer@helsinki.fi) On the data naming, content and structure: file names are structured as __.dat "..._time.dat": contains all data output times during the simulation [s] "...T.dat": gas phase temperature during the simulation [K] for all output times (columns) "..._DIAMETER.dat" contains the diameters [nm] of all (100) size bins (lines) for each output time (columns); "...SPC_NAMES.dat": contains the chemical species names "...N_bins.dat": particle number concentration [1/m3] for each size bin (lines) and each output time (columns) "...conc.dat": contains the gas-phase concentrations [1/ccm] of chemical species for all output times (number of species * number of times) "mass_spec.dat": contains the particle phase mass ([µg/m3] for all chemical species -> lines) sum over 100 size bins and for all output times (columns) "FlowTube_autoAPRAM_composition.dat": composition C/H/O/N for each autoAPRAM-fw species "FlowTube_autoAPRAM_names.dat": autoAPRAM-fw species

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.

These are the datasets that have been used for the article "Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air 1 Quality in Beijing, China," which is published in the journal Atmospheric Chemistry and Physics, by Foreback et al. (2022).

Intense and frequent new particle formation (NPF) events have been observed in polluted urban environments, yet the dominant mechanisms are still under debate. To understand the key species and governing processes of NPF in polluted urban environments, we conducted comprehensive measurements in downtown Beijing during January–March, 2018. We performed detailed analyses on sulfuric acid cluster composition and budget, as well as the chemical and physical properties of oxidized organic molecules (OOMs). Our results demonstrate that the fast clustering of sulfuric acid (H2SO4) and base molecules triggered the NPF events, and OOMs further helped grow the newly formed particles toward climate‐ and health‐relevant sizes. This synergistic role of H2SO4, base species, and OOMs in NPF is likely representative of polluted urban environments where abundant H2SO4 and base species usually co‐exist, and OOMs are with moderately low volatility when produced under high NOx concentrations.

Major atmospheric oxidants (OH, O3 and NO3) dominate the atmospheric oxidation capacity, while H2SO4 is considered as a main driver for new particle formation. Although numerous studies have investigated the long-term trend of ozone in Europe, the trends of OH, NO3 and H2SO4 at specific sites are to a large extent unknown. The one-dimensional model SOSAA has been applied in several studies at the SMEAR II station and has been validated by measurements in several projects. Here, we applied the SOSAA model for the years 2007-2018 to simulate the atmospheric chemical components, especially the atmospheric oxidants OH and NO3, as well as H2SO4 at SMEAR II. The simulations were evaluated with observations from several shorter and longer campaigns at SMEAR II. Our results show that daily OH increased by 2.39% per year and NO3 decreased by 3.41% per year, with different trends of these oxidants during day and night. On the contrary, daytime sulfuric acid concentrations decreased by 2.78% per year, which correlated with the observed decreasing concentration of newly formed particles in the size range of 3-25 nm with 1.4% per year at SMEAR II during the years 1997-2012. Additionally, we compared our simulated OH, NO3 and H2SO4 concentrations with proxies, which are commonly applied in case a limited number of parameters are measured and no detailed model simulations are available.