Detailed knowledge on the formation of new aerosol particles in the atmosphere from precursor gases, and their subsequent growth, commonly known as new particle formation (NPF) events, is one of the largest challenges in atmospheric aerosol science. High pre-existing particle loadings are expected to suppress the formation of new atmospheric aerosol particles due to high coagulation and condensation (CS) sinks. However, NPF events are regularly observed in conditions with high concentrations of pre-existing particles and even during intense desert dust intrusions that imply discrepancies between the observations and theory. In this study, we present a multi-site analysis of the occurrence of NPF events under the presence of desert dust particles in dust-influenced areas. Characterization of NPF events at five different locations highly influenced by desert dust outbreaks was done under dusty and non-dusty conditions using continuous measurements of aerosol size distribution in both fine and coarse size fractions. Contrary to common thought, our results show that the occurrence of NPF events is highly frequent during desert dust outbreaks, showing that NPF event frequencies during dusty conditions are similar to those observed during non-dusty conditions. Furthermore, our results show that NPF events also occur during intense desert dust outbreaks at all the studied sites, even at remote sites where the amounts of precursor vapours are expected to be low. Our results show that the condensation sink associated with coarse particles (CS_C) represents up to the 60 % of the total CS during dusty conditions, which highlights the importance of considering coarse-fraction particles for NPF studies in desert-dust-influenced areas. However, we did not find a clear pattern of the effect of desert dust outbreaks on the strength of NPF events, with differences from site to site. The particle growth rate (GR) did not present a clear dependence on the CS during dusty and non-dusty conditions. This result, together with the fact that desert dust has different effects on the growth and formation rates at each site, suggests different formation and growth mechanisms at each site between dusty and non-dusty conditions, probably due to differences in precursor vapours' origins and concentrations as well as changes in the oxidative capacity of pre-existing particles and their effectiveness acting as CS. Further investigation based on multiplatform measurement campaigns and chamber experiments with state-of-the-art gaseous and particulate physical and chemical properties measurements is needed to better understand the role of catalyst components present in desert dust particles in NPF. Finally, our results reveal a significant impact of NPF events on the cloud condensation nuclei (CCN) budget during desert dust outbreaks at the studied sites. Therefore, since desert dust contributes to a major fraction of the global aerosol mass load, and since there is a foreseeable increase in the frequency, duration and intensity of desert dust episodes due to climate change, it is imperative to improve our understanding of the effect of desert dust outbreaks on NPF and the CCN budget for better climate change prediction.

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (N_tot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high N_tot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large N_tot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

High-altitude remote sites are unique places to study aerosol–cloud interactions, since they are located at the altitude where clouds may form. At these remote sites, organic aerosols (OAs) are the main constituents of the overall aerosol population, playing a crucial role in defining aerosol hygroscopicity (κ). To estimate the cloud condensation nuclei (CCN) budget at OA-dominated sites, it is crucial to accurately characterize OA hygroscopicity (κ_OA) and how its temporal variability affects the CCN activity of the aerosol population, since κ_OA is not well established due to the complex nature of ambient OA. In this study, we performed CCN closures at a high-altitude remote site during summer to investigate the role of κ_OA in predicting CCN concentrations under different atmospheric conditions. In addition, we performed an OA source apportionment using positive matrix factorization (PMF). Three OA factors were identified from the PMF analysis: hydrocarbon-like OA (HOA), less-oxidized oxygenated OA (LO-OOA), and more-oxidized oxygenated OA (MO-OOA), with average contributions of 5 %, 36 %, and 59 % of the total OA, respectively. This result highlights the predominance of secondary organic aerosol (SOA) with a high degree of oxidation at this high-altitude site. To understand the impact of each OA factor on the overall OA hygroscopicity, we defined three κ_OA schemes that assume different hygroscopicity values for each OA factor. Our results show that the different κ_OA schemes lead to similar CCN closure results between observations and predictions (slope and correlation ranging between 1.08–1.40 and 0.89–0.94, respectively). However, the predictions were not equally accurate across the day. During the night, CCN predictions underestimated observations by 6 %–16 %, while, during morning and midday hours, when the aerosol was influenced by vertical transport of particles and/or new particle formation events, CCN concentrations were overestimated by 0 %–20 %. To further evaluate the role of κ_OA in CCN predictions, we established a new OA scheme that uses the OA oxidation level (parameterized by the f₄₄ factor) to calculate κ_OA and predict CCN. This method also shows a large bias, especially during midday hours (up to 40 %), indicating that diurnal information about the oxygenation degree does not improve CCN predictions. Finally, we used a neural network model with four inputs to predict CCN: N₈₀ (number concentration of particles with diameter > 80 nm), OA fraction, f₄₄, and solar global irradiance. This model matched the observations better than the previous approaches, with a bias within ± 10 % and with no daily variation, reproducing the CCN variability throughout the day. Therefore, neural network models seem to be an appropriate tool to estimate CCN concentrations using ancillary parameters accordingly.

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number ($AC{I}_{Nd}$) and effective radius ($AC{I}_{reff}$) of the droplets with altitude. Furthermore, $AC{I}_{Nd}$ and $AC{I}_{reff}$ for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum $AC{I}_{Nd}$ values are obtained when the activation of new droplets stops, while $AC{I}_{reff}$ reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of $30\%$ at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of $18\%$ cause an ACI variability of $30\%$ while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining $AC{I}_{reff}$ uncertainty below $16\%$ for the uncertainty of all LARSS input parameters of $10\%$. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index ($AC{I}_{Rs}$), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the $AC{I}_{reff}$, which depends on the Angstrom exponent. The use of $AC{I}_{Rs}$ to derive $AC{I}_{reff}$ presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.