This thesis presents the Atmospherically Relevant Chemistry and Aerosol Box Model (ARCA box), which is used for simulating atmospheric chemistry and the time evolution of aerosol particles and the formation of stable molecular clusters. The model can be used for example in solving of the concentrations of atmospheric trace gases formed from some predefined precursors, simulation and design of smog chamber experiments or indoor air quality estimation. The backbone of ARCAs chemical library comes from Master Chemical Mechanism (MCM), extended with Peroxy Radical Autoxidation Mechanism (PRAM), and is further extendable with any new reactions. Molecular clustering is simulated with the Atmospheric Cluster Dynamics Code (ACDC). The particle size distribution is represented with two alternative methods whose size and grid density are fully configurable. The evolution of the particle size distribution due to the condensation of low volatile organic vapours and the Brownian coagulation is simulated using established kinetic and thermodynamic theories. The user interface of ARCA differs considerably from the previous comparable models. The model has a graphical user interface which improves its usability and repeatability of the simulations. The user interface increases the potential of ARCA being used also outside the modelling community, for example in the experimental atmospheric sciences or by authorities.

Atmospheric aerosols affect the Earth's radiative balance, visibility and human health. Therefore the formation processes and growth of these particles are important and should be studied to understand how human and natural processes affects these processes. One poorly understood and relatively little studied part of aerosols is particulate organic nitrates (pONs). These pONs are mostly formed during nighttime when NOx, mainly emitted from fossil fuel combustion and industrial processes, and volatile organic compounds (VOCs), from both natural and anthropogenic sources, reacts in the atmosphere. The quantification of these pONs is still hard due to instrumental restrictions, although much improvement has happened during recent years. One main reason for these challenges is the difficulty to separate inorganic nitrates from organic nitrates with real-time instruments. During this work, we generated pure pON in well controlled laboratory conditions and sampled it with an Aerosol Mass Spectrometer (AMS), an instrument widely used for measuring the chemical composition of atmospheric aerosols. We used four different pON precursors to generate pON. I investigated the fragmentation patterns of pON detected by the AMS, utilizing the high resolution of the newest model of the AMS. As older versions of the AMS has difficulties to separate nitrate-containing organic fragments due to lower resolution than the AMS I used, I was able to study pON mass spectrum with better resolution than anyone before me. I found mass spectral differences for the different pON precursors, and was able to find unique fragments for some of the pON precursors that possibly can be used as marker fragments.

A better understanding of the limiting step in a first order phase transition, the nucleation process, is of major importance to a variety of scientific fields ranging from atmospheric sciences to nanotechnology and even to cosmology. This is due to the fact that in most phase transitions the new phase is separated from the mother phase by a free energy barrier. This barrier is crossed in a process called nucleation. Nowadays it is considered that a significant fraction of all atmospheric particles is produced by vapor-to liquid nucleation. In atmospheric sciences, as well as in other scientific fields, the theoretical treatment of nucleation is mostly based on a theory known as the Classical Nucleation Theory. However, the Classical Nucleation Theory is known to have only a limited success in predicting the rate at which vapor-to-liquid nucleation takes place at given conditions. This thesis studies the unary homogeneous vapor-to-liquid nucleation from a statistical mechanics viewpoint. We apply Monte Carlo simulations of molecular clusters to calculate the free energy barrier separating the vapor and liquid phases and compare our results against the laboratory measurements and Classical Nucleation Theory predictions. According to our results, the work of adding a monomer to a cluster in equilibrium vapour is accurately described by the liquid drop model applied by the Classical Nucleation Theory, once the clusters are larger than some threshold size. The threshold cluster sizes contain only a few or some tens of molecules depending on the interaction potential and temperature. However, the error made in modeling the smallest of clusters as liquid drops results in an erroneous absolute value for the cluster work of formation throughout the size range, as predicted by the McGraw-Laaksonen scaling law. By calculating correction factors to Classical Nucleation Theory predictions for the nucleation barriers of argon and water, we show that the corrected predictions produce nucleation rates that are in good comparison with experiments. For the smallest clusters, the deviation between the simulation results and the liquid drop values are accurately modelled by the low order virial coefficients at modest temperatures and vapour densities, or in other words, in the validity range of the non-interacting cluster theory by Frenkel, Band and Bilj. Our results do not indicate a need for a size dependent replacement free energy correction. The results also indicate that Classical Nucleation Theory predicts the size of the critical cluster correctly. We also presents a new method for the calculation of the equilibrium vapour density, surface tension size dependence and planar surface tension directly from cluster simulations. We also show how the size dependence of the cluster surface tension in equimolar surface is a function of virial coefficients, a result confirmed by our cluster simulations.

The impacts of dust aerosols on human health and climate change are increasing as the particulate matter (PM) mass concentrations and frequency of Sand and Dust Storm (SDS) episodes have shown an increasing trend in recent studies, especially for the Middle East and North Africa (MENA). In this thesis, particulate matter (PM10 and PM2.5) concentrations were measured during May 2018–March 2019 in the urban atmosphere of Amman, Jordan. The PM sampling was 24-hours every 6 days. The overall mean PM10 mass concentration was 64±39 μg/m3 with the median (+interquartile range) value of 49.2+53.5 μg/m3, the PM2.5 mass concentration varied between 15 μg/m3 and 190 μg/m3 with an annual average 47±32 μg/m3 and with the median (+interquartile range) value of 35.8+26.3 μg/m3. The PM2.5 / PM10 ratio was 0.8±0.2. According to the Jordanian Air Quality standards, the annual mean PM10 needs to be below a limit value of 120 μg/m3, which was true in this work. However, the PM2.5 mass concentration was three times higher the corresponding limit value (65 μg/m3). However, both exceeded the World Health Organization (WHO) air quality annual guideline of 20 μg/m3 for PM10 and 10 μg/m3 for PM2.5. The results show that the observed PM10 mass concentrations in Jordan were lower than what was reported in other cities in the Middle East but were higher when compared to other Mediterranean cities. During the measurement period, Jordan was affected by Sand and Dust Storms (SDS), which were observed on 14 sampling days. The source origins of these SDS were traced back to North Africa, the Arabian Peninsula, and the Levant. The 24-hour PM10 concentrations during these SDS episodes ranged between 108.1 μg/m3 and 187.3 μg/m3. In the future, measurements with a higher time resolution (one sample per day) are recommended for a more precise seasonal trend interpretation.

Cloud condensation nuclei (CCN) participate in controlling the climate, and a better understading of their number concentrations is needed to constrain the current uncertainties in Earth’s energy budget. However, estimating the global CCN concentrations is difficult using only localised in-situ measurements. To overcome this, different proxies and parametrisations for CCN have been developed. In this thesis, accumulation mode particles were used as a substitute for CCN, and continental proxy for number concentration of N100 was developed with CO and temperature as tracers for anthropogenic and biogenic emissions. The data utilised in the analysis contained N100 measurements from 22 sites from 5 different continents as well as CO and temperature from CAMS reanalysis dataset. The thesis aimed to construct a global continental proxy. In addition to this, individual proxies for each site (the site proxy) and proxies trained with other sites’ data (the site excluded proxy) were developed. The performance of these proxies was evaluated using a modified version of K-fold cross-validation, which allowed estimating the effect of dataset selection on the results. Additionally, time series, seasonal variation, and parameter distributions for developed proxies were analysed and findings compared against known characteristics of the sites. Global proxy was developed, but no single set of parameters, that would achieve the best performance at all sites, was found. Therefore, two versions of global proxy were selected and their results analysed. For most of the sites, the site proxy performed better than the global proxies. Additionally, based on the analysis from the site excluded proxy, extrapolating the global proxy to new locations produced results with varying accuracy. Best results came from sites with low concentrations and occasional anthropogenic transport episodes. Additionally, some European rural sites performed well, whereas in mountainous sites the proxy struggled. Comparing the proxy to literature, it performed generally less well or similarly as proxies from other studies. Longer datasets and additional measurement sites could improve the proxy performance.

Atmospheric aerosol particles affect Earth’s radiation balance, human health and visibility. Secondary organic aerosol (SOA) contributes a significant fraction to the total atmospheric organic aerosol, and thus plays an important role in climate change. SOA is formed through oxidation of volatile organic compounds (VOCs) and it consists of many individual organic compounds with varying properties. The oxidation products of VOCs include highly oxygenated organic molecules (HOM) that are estimated to explain a large fraction of SOA formation. To estimate the climate impacts of SOA it is essential to understand its properties in the atmosphere. In this thesis, a method to investigate thermally induced evaporation of organic aerosol was developed. SOA particles were generated in a flow tube from alpha-pinene ozonolysis and then directed into a heated tube to initiate particle evaporation. The size distribution of the particles was measured with parallel identification of the evaporated HOM. This method was capable of providing information of SOA evaporation behaviour and the particle-phase composition at different temperatures. Mass spectra of the evaporated HOM and particle size distribution data were analyzed. The obtained results suggest that SOA contains compounds with a wide range of volatilities, including HOM monomers, dimers and trimers. The volatility behaviour of the particulate HOM and their contribution to SOA particle mass was studied. Furthermore, indications of particle-phase reactions occurring in SOA were found.