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Atmospheric New Particle Formation (NPF) is a common phenomenon all over the world. In this study we present the longest time series of NPF records in the eastern Mediterranean region by analyzing seven years of aerosol number size distribution data obtained with a mobility particle sizer. The measurements were performed at the Finokalia environmental research station on Crete, Greece during the period June 2008–June 2015. We found that NPF took place 29 % of the available days, undefined days were 26 % and non-event days 45 %. NPF is more frequent in April and May probably due to the biogenic activity and is less frequent in August and November. The NPF frequency increased during the measurement period, while particle growth rates showed a decreasing trend, indicating possible changes in the ambient sulfur dioxide concentrations in the area. Throughout the period under study, we frequently observed production of particles in the nucleation mode during night-time, a feature rarely observed in the ambient atmosphere. Nucleation mode particles had the highest concentration in winter, mainly because of the minimum sinks, and their average contribution to the total particle number concentration was 9 %. Nucleation mode particle concentrations were low outside periods of active NPF and growth, so there are hardly any other local sources of sub-25 nm particles. Additional atmospheric ion size distribution data simultaneously collected for more than two years period were also analyzed. Classification of NPF events based on ion measurements differed from the corresponding classification based on mobility spectrometer measurements, possibly indicating a different representation of local and regional NPF events between these two measurement data sets. We used MALTE-box model for a simulation case study of NPF in the eastern Mediterranean region. Monoterpenes contributing to NPF can explain a large fraction of the observed NPF events according to our model simulations. However the parametrization that resulted after sensitivity tests was significantly different from the one applied for the boreal environment.

Background The evidence on the association between ultrafine (UFP) particles and mortality is still inconsistent. Moreover, health effects of specific UFP sources have not been explored. We assessed the impact of UFP sources on daily mortality in Barcelona, Helsinki, London, and Zurich. Methods UFP sources were previously identified and quantified for the four cities: daily contributions of photonucleation, two traffic sources (fresh traffic and urban, with size mode around 30 nm and 70 nm, respectively), and secondary aerosols were obtained from data from an urban background station. Different periods were investigated in each city: Barcelona 2013–2016, Helsinki 2009–2016, London 2010–2016, and Zurich 2011–2014. The associations between total particle number concentrations (PNC) and UFP sources and daily (natural, cardiovascular [CVD], and respiratory) mortality were investigated using city-specific generalized linear models (GLM) with quasi-Poisson regression. Results We found inconsistent results across cities, sources, and lags for associations with natural, CVD, and respiratory mortality. Increased risk was observed for total PNC and natural mortality in Helsinki (lag 2; 1.3% [0.07%, 2.5%]), CVD mortality in Barcelona (lag 1; 3.7% [0.17%, 7.4%]) and Zurich (lag 0; 3.8% [0.31%, 7.4%]), and respiratory mortality in London (lag 3; 2.6% [0.84%, 4.45%]) and Zurich (lag 1; 9.4% [1.0%, 17.9%]). A similar pattern of associations between health outcomes and total PNC was followed by the fresh traffic source, for which we also found the same associations and lags as for total PNC. The urban source (mostly aged traffic) was associated with respiratory mortality in Zurich (lag 1; 12.5% [1.7%, 24.2%]) and London (lag 3; 2.4% [0.90%, 4.0%]) while the secondary source was associated with respiratory mortality in Zurich (lag 1: 12.0% [0.63%, 24.5%]) and Helsinki (4.7% [0.11%, 9.5%]). Reduced risk for the photonucleation source was observed for respiratory mortality in Barcelona (lag 2, −8.6% [−14.5%, −2.4%]) and for CVD mortality in Helsinki, as this source is present only in clean atmospheres (lag 1, −1.48 [−2.75, −0.21]). Conclusions We found inconsistent results across cities, sources and lags for associations with natural, CVD, and respiratory mortality., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 747882. While writing the manuscript, Dr. Rivas was funded by the postdoctoral fellowship programme Beatriu de Pinós (2018 BP 00114), funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 programme of research and innovation of the European Union under the Marie Sklodowska-Curie grant agreement No 801370. Currently, Dr. Rivas is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 886121. This work was supported by FEDER funds; projects HOUSE (CGL2016-78594-R) and CAIAC (PID2019-108990RB-I00), the Government of Catalonia (AGAUR 2017 SGR41). The authors also acknowledge the Project PI16/00118 funded by the Instituto de Salud Carlos III and co-funded by the European Regional Development Fund (ERDF) “A way to make Europe”. HW’s post was partially funded by the UK National Institute for Health Research Health Protection Research Unit on Environmental Exposures and Health at Imperial College London in partnership with Public Health England, King’s College London and the MTC Toxicology Unit, Cambridge. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health & Social Care or Public Health England. This work was produced using statistical data from ONS. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.

PMCAMx-UF, a three-dimensional chemical transport model focusing on the simulation of the ultrafine particle size distribution and composition has been extended with the addition of the volatility basis set (VBS) approach for the simulation of organic aerosol (OA). The model was applied in Europe to quantify the effect of secondary semi-volatile organic vapors on particle number concentrations. The model predictions were evaluated against field observations collected during the PEGASOS 2012 campaign. The measurements included both ground and airborne measurements, from stations across Europe and a zeppelin measuring above Po Valley. The ground level concentrations of particles with a diameter larger than 100 nm (N100) were reproduced with a daily normalized mean error of 40 % and a daily normalized mean bias of −20 %. PMCAMx-UF tended to overestimate the concentration of particles with a diameter larger than 10 nm (N10) with a daily normalized mean bias of 75 %. The model was able to reproduce, within a factor of 2, 85 % of the N10 and 75 % of the N100 zeppelin measurements above ground. The condensation of organics led to an increase (50 %–120 %) in the N100 concentration mainly in central and northern Europe, while the N10 concentration decreased by 10 %–30 %. Including the VBS in PMCAMx-UF improved its ability to simulate aerosol number concentration compared to simulations neglecting organic condensation on ultrafine particles.

New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles. Occurrence of NPF events is typically analyzed by researchers manually from particle size distribution data day by day, which is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time that a deep learning method, i.e., transfer learning of a convolutional neural network (CNN), has successfully been used to automatically classify NPF events into different classes directly from particle size distribution images, similarly to how the researchers carry out the manual classification. The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images was used in the training stage of the CNN and the rest of the images for testing the success of the training. The method was utilized for a 15-year-long dataset measured at San Pietro Capofiume (SPC) in Italy. We studied the performance of the training with different training and testing of image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., classes 1 and 2) and nonevent days can be identified with an accuracy of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, and thus overlapping or confusion between different classes occurs. Hence, we cross-validated the learning results of CNN with the expert-made classification. The results show that the overlapping occurs, typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into classes 1 and 2 by CNN, indicating that the manual and CNN classifications are very consistent for most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of “quantifiable” (i.e., clear events, classes 1 and 2) and “nonquantifiable” (i.e., weak events, Class 3). This would better describe the difference of those classes: both formation and growth rates can be determined for quantifiable days but not both for nonquantifiable days. Furthermore, we investigated more deeply the days that are classified as clear events by experts and recognized as nonevents by the CNN and vice versa. Clear misclassifications seem to occur more commonly in manual analysis than in the CNN categorization, which is mostly due to the inconsistency in the human-made classification or errors in the booking of the event class. In general, the automatic CNN classifier has a better reliability and repeatability in NPF event classification than human-made classification and, thus, the transfer-learned pretrained CNNs are powerful tools to analyze long-term datasets. The developed NPF event classifier can be easily utilized to analyze any long-term datasets more accurately and consistently, which helps us to understand in detail aerosol–climate interactions and the long-term effects of climate change on NPF in the atmosphere. We encourage researchers to use the model in other sites. However, we suggest that the CNN should be transfer learned again for new site data with a minimum of ca. 150 figures per class to obtain good enough classification results, especially if the size distribution evolution differs from training data. In the future, we will utilize the method for data from other sites, develop it to analyze more parameters and evaluate how successfully CNN could be trained with synthetic NPF event data.

This paper revisits the atmospheric new particle formation (NPF) process in the polluted Central European troposphere, focusing on the connection with gas-phase precursors and meteorological parameters. Observations were made at the research station Melpitz (former East Germany) between 2008 and 2011 involving a neutral cluster and air ion spectrometer (NAIS). Particle formation events were classified by a new automated method based on the convolution integral of particle number concentration in the diameter interval 2–20 nm. To study the relevance of gaseous sulfuric acid as a precursor for nucleation, a proxy was derived on the basis of direct measurements during a 1-month campaign in May 2008. As a major result, the number concentration of freshly produced particles correlated significantly with the concentration of sulfur dioxide as the main precursor of sulfuric acid. The condensation sink, a factor potentially inhibiting NPF events, played a subordinate role only. The same held for experimentally determined ammonia concentrations. The analysis of meteorological parameters confirmed the absolute need for solar radiation to induce NPF events and demonstrated the presence of significant turbulence during those events. Due to its tight correlation with solar radiation, however, an independent effect of turbulence for NPF could not be established. Based on the diurnal evolution of aerosol, gas-phase, and meteorological parameters near the ground, we further conclude that the particle formation process is likely to start in elevated parts of the boundary layer rather than near ground level.

The vertical profile of new particle formation (NPF) events was studied by comparing the aerosol size number distributions measured aloft and at surface level in a suburban environment in Madrid, Spain, using airborne instruments. The horizontal distribution and regional impact of the NPF events was investigated with data from three urban, urban background, and suburban stations in the Madrid metropolitan area. Intensive regional NPF episodes followed by particle growth were simultaneously recorded at three stations in and around Madrid during a field campaign in July 2016. The urban stations presented larger formation rates compared to the suburban station. Condensation and coagulation sinks followed a similar evolution at all stations, with higher values at urban stations. However, the total number concentration of particles larger than 2.5 nm was lower at the urban station and peaked around noon, when black carbon (BC) levels are at a minimum. The vertical soundings demonstrated that ultrafine particles (UFPs) are formed exclusively inside the mixed layer. As convection becomes more effective and the mixed layer grows, UFPs are detected at higher levels. The morning soundings revealed the presence of a residual layer in the upper levels in which aged particles (nucleated and grown on previous days) prevail. The particles in this layer also grow in size, with growth rates significantly smaller than those inside the mixed layer. Under conditions with strong enough convection, the soundings revealed homogeneous number size distributions and growth rates at all altitudes, which follow the same evolution at the other stations considered in this study. This indicates that UFPs are detected quasi-homogenously in an area spanning at least 17 km horizontally. The NPF events extend over the full vertical extension of the mixed layer, which can reach as high as 3000 m in the area, according to previous studies. On some days a marked decline in particle size (shrinkage) was observed in the afternoon, associated with a change in air masses. Additionally, a few nocturnal nucleation-mode bursts were observed at the urban stations, for which further research is needed to elucidate their origin. © Author(s) 2018., This work was supported by the Spanish Ministry of Agriculture, Fishing, Food and Environment; the Ministry of Economy, Industry and Competitiveness; the Madrid City Council and Regional Government; FEDER funds under the project HOUSE (CGL2016-78594-R); the CUD of Zaragoza (project CUD 2016-05); the Government of Catalonia (AGAUR 2017 SGR44); and the Korean Ministry of Environment through “The Eco-Innovation project”. The funding received by ERA-PLANET (http://www.era-planet.eu, last access: 16 November 2018), the trans-national project SMURBS (http://www.smurbs.eu, last access: 16 November 2018) (Grant agreement No. 689443), and the support of the Academy of Finland via the Center of Excellence in Atmospheric Sciences are acknowledged. These results are part of a project (ATM-GTP/ERC) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 742206). The authors also acknowledge the Doctoral program of Atmospheric Sciences at the University of Helsinki (ATM-DP). Markku Kulmala acknowledges the support of the Academy of Finland via his Academy Professorship (no. 302958). We also thank the City Council of Majadahonda for logistic assistance, and the Instituto de Ciencias Agrarias, Instituto de Salud Carlos III, Alava Ingenieros, TSI, Solma Environmental Solutions, and Airmodus for their support.

Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiälä, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.

New particle formation and growth were observed at a coastal site (Preila station, Lithuania) during 1997 and 2000-2002. The total amountof data analysed covers 291 one-day periods, 45 (15%) of which were long-term, new particle formation days. Short-term nucleationevents (from a few minutes to one hour) and long-term events (from one to eight hours) were identified. The mean particlegrowth rate, condensation sink and condensable vapour source rate during nucleation events were 3.9 nm h-1, 1.45 × 10-3 cm-3 s-1 and 7.5 × 104 cm-3 s-1 respectively.The average formation rate J10 was 0.4 cm-3 s-1. The nucleation events were accompaniedmainly by air masses transported from the north (43%) and north-west (19%). Meteorological parameters and trace gas (O3, SO2,NO2) concentrations were also analysed. It was found that nucleation events are related to high levels of solar radiation.

REMO-HAM is a new regional aerosol-climate model. It is based on the REMO regional climate model and includes most of the major aerosol processes. The structure for aerosol is similar to the global aerosol-climate model ECHAM5-HAM, for example the aerosol module HAM is coupled with a two-moment stratiform cloud scheme. On the other hand, REMO-HAM does not include an online coupled aerosol-radiation nor a secondary organic aerosol module. In this work, we evaluate the model and compare the results against ECHAM5-HAM and measurements. Four different measurement sites were chosen for the comparison of total number concentrations, size distributions and gas phase sulfur dioxide concentrations: Hyytiälä in Finland, Melpitz in Germany, Mace Head in Ireland and Jungfraujoch in Switzerland. REMO-HAM is run with two different resolutions: 50 × 50 km2 and 10 × 10 km2. Based on our simulations, REMO-HAM is in reasonable agreement with the measured values. The differences in the total number concentrations between REMO-HAM and ECHAM5-HAM can be mainly explained by the difference in the nucleation mode. Since we did not use activation nor kinetic nucleation for the boundary layer, the total number concentrations are somewhat underestimated. From the meteorological point of view, REMO-HAM represents the precipitation fields and 2 m temperature profile very well compared to measurement. Overall, we show that REMO-HAM is a functional aerosol-climate model, which will be used in further studies.

Seasonal variations in cloud droplet number concentration (NCD) in low-level stratiform clouds over the boreal forest are estimated from MODIS observations of cloud optical and microphysical properties, using a sub-adiabatic cloud model to interpret vertical profiles of cloud properties. An uncertainty analysis of the cloud model is included to reveal the main sensitivities of the cloud model. We compared the seasonal cycle in NCD, obtained using 9 yr of satellite data, to surface concentrations of potential cloud activating aerosols, measured at the SMEAR II station at Hyytiälä in Finland. The results show that NCD and cloud condensation nuclei (CCN) concentrations have no clear correlation at seasonal time scale. The fraction of aerosols that actually activate as cloud droplet decreases sharply with increasing aerosol concentrations. Furthermore, information on the stability of the atmosphere shows that low NCD is linked to stable atmospheric conditions. Combining these findings leads to the conclusion that cloud droplet activation for the studied clouds over the boreal forest is limited by convection. Our results suggest that it is important to take the strength of convection into account when studying the influence of aerosols from the boreal forest on cloud formation, although they do not rule out the possibility that aerosols from the boreal forest affect other types of clouds with a closer coupling to the surface.

Atmospheric new particle formation events were analyzed based on particle size distributions measured with a Differential Mobility Particle Sizer (DMPS) and an Air Ion Spectrometer (AIS) during the BIOSOL (Formation mechanisms, marker compounds, and source apportionment for biogenic atmospheric aerosols) campaign on 22 May-29 June 2006 at the K-puszta measurement site in Hungary. The particle size distribution data were classified into different new particle event classes and growth and formation rates of the particles were calculated. New particle formation was observed on almost all days and the median diameter growth rates of nucleation mode particles increased with increasing particle size. The observed formation rate of 10 nm particles was typically somewhat larger than 1 cm(-3) s(-3) (median 1.2), and the growth rate for sub 3 nm particles was 1.7 nm h(-1) and for nucleation mode 6 nm h(-1). The ambient concentrations of gases or meteorological data were not able to explain the differences in the growth and formation rates or in the particle formation between the days. However, 0.3-1.8 nm cluster ion concentrations correlated negatively with wind speed.

Ultrafine particles (UFPs; diameter less than 100 nm) are ubiquitous in urban air, and an acknowledged risk to human health. Globally, the major source for urban outdoor UFP concentrations is motor traffic. Ongoing trends towards urbanisation and expansion of road traffic are anticipated to further increase population exposure to UFPs. Numerous experimental studies have characterised UFPs in individual cities, but an integrated evaluation of emissions and population exposure is still lacking. Our analysis suggests that the average exposure to outdoor UFPs in Asian cities is about four-times larger than that in European cities but impacts on human health are largely unknown. This article reviews some fundamental drivers of UFP emissions and dispersion, and highlights unresolved challenges, as well as recommendations to ensure sustainable urban development whilst minimising any possible adverse health impacts. Keywords: City environment, Particle exposure, Health impacts, Particle number concentration, Ultrafine particles

The ambient and laboratory molecular and ion clusters were investigated. Here we present data on the ambient concentrations of both charged and uncharged molecular clusters as well as the performance of a pulse height condensation particle counter (PH-CPC) and an expansion condensation particle counter (E-CPC). The ambient molecular cluster concentrations were measured using both instruments, and they were deployed in conjunction with ion spectrometers and other aerosol instruments in Hyytiälä, Finland at the SMEAR II station during 1 March to 30 June 2007. The observed cluster concentrations varied and were from ca. 1000 to 100 000 cm−3. Both instruments showed similar concentrations. The average size of detected clusters was approximately 1.8 nm. As the atmospheric measurements at sub 2-nm particles and molecular clusters are a challenging task, and we were most likely unable to detect the smallest clusters, the reported concentrations are our best estimates for minimum cluster concentrations in boreal forest environment.