Metal nanoparticles (Au, Pt, Cu and Ni) were generated in the aerosol phase using spark ablation and analysed for size, shape and number concentration. Particles as small as 1 nm and up to > 60 nm show shapes from fully spherical to fractal-like as characterized by electron microscopy. Furthermore, the metal particles were mixed with TiO₂ nanoparticles, and the number and size of metal particles coating the TiO₂ were determined.

Quantitative real-time aerosol sampling on board aircraft platforms is challenging, especially at higher altitudes. Herein, we present comprehensive analyses of a new aircraft inlet system and tools for aerosol beam diagnostics for aerosol mass spectrometers (AMSs). The beam focusing of aerodynamic lenses and the thermal decomposition on the vaporizer were investigated. The new inlet system can be operated at higher altitudes while sampling aerosols over a broader size range than previous versions.

This study evaluates the performance of energy-dispersive X-ray fluorescence (ED-XRF) on particulate matter (PM₁₀) samples collected on quartz and Teflon filters through an inter-laboratory comparison. Detection limits were higher on quartz, but measurement repeatability was similar. Strong elemental correlations suggest that, with proper corrections, quartz filters can provide reliable results. These findings support their feasibility for ED-XRF analysis.

Quantification of autoxidation chemistry is a complex task essential to our understanding of atmospheric secondary aerosol formation and its impact on climate. In this work, we introduce the autoCONSTRAINT module, a semi-empirical method for deducing reaction rate coefficients for lumped autoxidation chemistry schemes based on experimental data. The theoretical approach is analytical and provides mathematically correct though non-unique solutions with low computational cost.

This study examines how sampling frequency influences the accuracy of low-cost particulate matter (PM) sensors. It finds that high sampling rates (15 s) are vital for detecting short pollution spikes like vehicle emissions, while lower rates (1 h) are enough for tracking long-term trends. Conducted in real-world outdoor conditions, the research offers practical guidance to balance energy use and data accuracy, supporting scalable, cost-effective air quality monitoring solutions.

New particle formation (NPF) is a process of forming new aerosol particles in the atmosphere. It was widely studied in boreal forest areas in Finland and Estonia, but its features and precursors in a Siberian boreal forest remained unclear. We did a comprehensive analysis using data from state-of-the-art instruments and implement both traditional and new methods of data analysis. Understanding these processes helps us grasp how Siberia’s atmosphere evolves and contributes to global climate dynamics.

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.

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.

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.

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.

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.

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.

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.

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.