We investigated how water vapor affects the earliest steps of atmospheric aerosol formation, a key process influencing clouds and climate. By benchmarking quantum-chemical methods, we identified reliable approaches for modeling hydrated molecular clusters of common atmospheric acids and bases. We show that humidity moderately stabilizes certain clusters but only modestly alters particle formation rates. These findings sharpen our understanding of clusters and their role in aerosol formation.

We developed a new automated method for calculating aerosol particle growth rates. We found that the method produces results comparable to conventional approaches, while offering improved speed and ease of use.

This study analysed the average carbon oxidation state (OSc) during secondary organic aerosol formation from mixed volatile organic compounds (VOCs) using three mass spectrometry techniques. Notable discrepancies in OSc were observed across the techniques, with FIGAERO-CIMS reporting higher values. The results also show that OSc in mixed-VOC systems is influenced not only by products from individual precursors but also by accretion products formed through interactions between VOC products.

We trained machine learning models to estimate the number of aerosol particles large enough to form clouds and generated daily estimates for the entire globe. The models performed well in many continental regions but struggled in remote and marine areas. Still, this approach offers a way to quantify these particles in areas that lack direct measurements, helping us understand their influence on clouds and climate on a global scale.

Organic aerosols consist of complex chemical mixtures that are challenging to characterize using chemical ionization mass spectrometry alone. This study presents a method for coupling liquid chromatography and chemical ionization mass spectrometry for the offline analysis of organic aerosols. The evaluation of the method using standards and laboratory-generated and field-collected organic aerosols showed that it can provide detailed characterization of environmentally relevant mixtures.

Vehicle emissions significantly impact air quality, but distinguishing between gasoline and diesel aerosol sources in the air is still difficult. This study used advanced chemical analysis and statistical methods to discover unique aerosol molecular markers from each vehicle type. By simulating real-world driving and atmospheric ageing, researchers found specific markers for both primary and secondary particle emissions, offering a promising new approach to improve air pollution source tracking.

This study presents atmospheric ice-nucleating particle (INP) data from the Gruvebadet observatory in Ny-Ålesund. A moderate summertime increase in INP levels is observed at -15 °C but not at other temperatures (-18 and -22 °C). Conversely, a marked seasonal evolution was observed for the contribution of super-micrometer INPs, which is at its maximum throughout summer up to early autumn. We show that marine biogenic INPs may be relevant in the Arctic during seasons of reduced sea ice coverage.

This study presents a new method to measure ultrafine airborne particle concentrations down to 3 nm at high concentrations. By growing the particles into droplets, subsequently electrically charging them, and detecting the electrical signal, the system enables accurate and compact measurements of the particle number concentration. This approach is useful for onboard monitoring of vehicle emissions where space is limited and high concentrations are measured.

Three different particulate matter (PM) measurement techniques were compared in Finland, showing good correlation (R about 0.8). Although the measured PM concentrations were low, decreasing trends were observed in all seasons. The decrease followed the trends observed in concentrations of anthropogenic pollutants, resulting from the EU legislation to improve air quality. The highest PM concentrations were measured in summer, indicating the dominance of organic compounds to the PM mass.

Back carbon is an atmospheric pollutant from combustion and contributes to the Arctic warming. However, its properties change as it travels through the atmosphere, affecting its impact. We recreated Arctic transport conditions in a laboratory to study how black carbon evolves over time. Our findings show that temperature and altitude strongly influence its transformation, providing key insights for improving climate models and understanding Arctic pollution.

This study investigates the decay of particle lung-deposited surface area (LDSA) concentrations following indoor particle emissions, with a focus on cooking activities. Two decay functions were derived and validated using measurement data. Applying the functions, it is shown that from 66.5 to 82.9 % of the exposure to cooking-generated particles occurred during the decay phase following the active cooking event. This highlights both the applicability and importance of the derived decay functions.