Search

Väitöstilaisuuden Lectio PraecursoriaLapin yliopisto 30.9.2022 Väitöskirjan tiedot:Äijälä, M. (2022). Tourist Landscapes as Multispecies Trails: Storying a Mushing Landscape Through Mobile Video Ethnography. Acta electronica Universitatis Lapponiensis 343. .

Particulate organic nitrate (pON) can be a major part of secondary organic aerosol (SOA) and is commonly quantified by indirect means from aerosol mass spectrometer (AMS) data. However, pON quantification remains challenging. Here, we set out to quantify and characterize pON in the boreal forest, through direct field observations at Station for Measuring Ecosystem Atmosphere Relationships (SMEAR) II in Hyytia''la'', Finland, and targeted single precursor laboratory studies. We utilized a long time-of-flight AMS (LToF-AMS) for aerosol chemical characterization, with a particular focus to identify CxHyOzN+ ("CHON+") fragments. We estimate that during springtime at SMEAR II, pON (including both the organic and nitrate part) accounts for similar to 10% of the particle mass concentration (calculated by the NO+/NO2+ method) and originates mainly from the NO3 radical oxidation of biogenic volatile organic compounds. The majority of the background nitrate aerosol measured is organic. The CHON+ fragment analysis was largely unsuccessful at SMEAR II, mainly due to low concentrations of the few detected fragments. However, our findings may be useful at other sites as we identified 80 unique CHON+ fragments from the laboratory measurements of SOA formed from NO3 radical oxidation of three pON precursors (beta-pinene, limonene, and guaiacol). Finally, we noted a significant effect on ion identification during the LToF-AMS high-resolution data processing, resulting in too many ions being fit, depending on whether tungsten ions (W+) were used in the peak width determination. Although this phenomenon may be instrument-specific, we encourage all (LTOF-) AMS users to investigate this effect on their instrument to reduce the possibility of incorrect identifications.

The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments., Atmospheric Chemistry and Physics, 21 (13), ISSN:1680-7375, ISSN:1680-7367

Climate change and the sustainability crisis require urgent actions in all fields of the society. Recently in Finland, the nationwide Climate University project (blogs.helsinki.fi/climateuniversity) was established by the funding form the Ministry of Education and Culture to advance teaching of climate and sustainability topics in Finnish higher education. The Climate University network consists of 11 universities as well as universities of applied sciences all around Finland and includes collaborators from schools and the working life.In the beginning of the project, a study of needs was conducted. We asked our collaborators about the needs of higher education in the time of climate crisis. The answers emphasized on multidisciplinarity, holistic understanding, data and statistics, science communication, including the private sector and markets, consumer perspective, as well as values and ethics.Based on the study of needs, we will produce the following open access learning materials by the end of 2020: Systemschange.now - Systems thinking in global challenges; Sustainable.now - Introduction to sustainability in climate change; Solutions.now - Project course in private sector collaboration; Climate.now for schools - High school level course on basics of climate change; Climate data and statistics - Statistical tools for analysing climate data; and Science communication in climate change. Open learning principles were already piloted in the Climate.now project in 2016 (www.climatenow.fi) and the Climate.now courses (2-5 ECTS) are currently run in eight universities in Finland. We have also produced open online courses on the multidisciplinary topics of sustainability leadership (www.leadforsust.fi) and circular economy (www.circularnow.fi). Collaboration and co-creation with multidisciplinary experts from all across the society has been highly fruitful, and we foresee Climate University has the potential to thoroughly reshape and re-create the Finnish climate education field.

With the recent developments in mass spectrometry, combined with the strengths of factor analysis techniques, our understanding of atmospheric oxidation chemistry has improved significantly. The typical approach for using techniques like positive matrix factorization (PMF) is to input all measured data for the factorization in order to separate contributions from different sources and/or processes to the total measured signal. However, while this is a valid approach for assigning the total signal to factors, we have identified several cases where useful information can be lost if solely using this approach. For example, gaseous molecules emitted from the same source can show different temporal behaviors due to differing loss terms, like condensation at different rates due to different molecular masses. This conflicts with one of PMF's basic assumptions of constant factor profiles. In addition, some ranges of a mass spectrum may contain useful information, despite contributing only minimal fraction to the total signal, in which case they are unlikely to have a significant impact on the factorization result. Finally, certain mass ranges may contain molecules formed via pathways not available to molecules in other mass ranges, e.g. dimeric species versus monomeric species. In this study, we attempted to address these challenges by dividing mass spectra into sub-ranges and applying the newly developed binPMF method to these ranges separately. We utilized a dataset from a chemical ionization atmospheric pressure interface time-of-flight (CI-APi-TOF) mass spectrometer as an example. We compare the results from these three different ranges, each corresponding to molecules of different volatilities, with binPMF results from the combined range. Separate analysis showed clear benefits in dividing factors for molecules of different volatilities more accurately, in resolving different chemical processes from different ranges, and in giving a chance for high-molecular-weight molecules with low signal intensities to be used to distinguish dimeric species with different formation pathways. In addition, daytime dimer formation (diurnal peak around noon) was identified, which may contribute to NPF in Hyytiälä. Also, dimers from NO3 oxidation were separated by the sub-range binPMF, which would not be identified otherwise. We recommend PMF users to try running their analyses on selected sub-ranges in order to further explore their datasets.

The Station for Measuring Ecosystem Atmosphere Relations (SMEAR) II is well known among atmospheric scientists due to the immense amount of observational data it provides of the earth–atmosphere interface. Moreover, SMEAR II plays an important role in large European research infrastructures, enabling the large scientific community to tackle climate and air pollution related questions, utilising the high-quality long-term data sets recorded at the site. So far, the well-documented site was missing the description of the seasonal variation of aerosol chemical composition that is crucial for understanding the complex biogeochemical and -physical processes governing the forest ecosystem. Here, we report the sub-micron aerosol chemical composition and its variability utilising data measured between 2012 and 2018 using an Aerosol Chemical Speciation Monitor (ACSM). We observed a bimodal seasonal trend in the sub–micron aerosol concentration culminating in February (2.7, 1.6, 5.1 µg m−3 for median, 25th, 75th percentiles, respectively) and July (4.2, 2.2, and 5.7 µg m−3 for median, 25th, 75th percentiles, respectively). The wintertime maximum was linked to an enhanced presence of inorganic aerosol species (ca. 50 %) whereas the summertime maximum (ca. 80 % organics) to biogenic secondary organic aerosol (SOA) formation. During the exceptionally hot Julys of 2014 and 2018, the organic aerosol concentrations were up to 70 % higher than the 7–year July mean. The projected increase of heat wave frequency over Finland will most likely influence the loading and chemical composition of aerosol particles in the future. Our findings suggest strong influence of meteorological conditions such as radiation, ambient temperature, wind speed and direction on aerosol chemical composition. To our understanding, this is the longest time series reported describing the aerosol chemical composition measured online in the boreal region, but the continuous monitoring will be maintained also in the future.

As part of the European ACTRIS project, the first large Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM) intercomparison study was conducted in the region of Paris for 3 weeks during the late-fall - early-winter period (November-December 2013). The first week was dedicated to the tuning and calibration of each instrument, whereas the second and third were dedicated to side-by-side comparison in ambient conditions with co-located instruments providing independent information on submicron aerosol optical, physical, and chemical properties. Near real-time measurements of the major chemical species (organic matter, sulfate, nitrate, ammonium, and chloride) in the non-refractory submicron aerosols (NR-PM1) were obtained here from 13 Q-ACSM. The results show that these instruments can produce highly comparable and robust measurements of the NR-PM1 total mass and its major components. Taking the median of the 13 Q-ACSM as a reference for this study, strong correlations (r2 > 0.9) were observed systematically for each individual Q-ACSM across all chemical families except for chloride for which three Q-ACSMs showing weak correlations partly due to the very low concentrations during the study. Reproducibility expanded uncertainties of Q-ACSM concentration measurements were determined using appropriate methodologies defined by the International Standard Organization (ISO 17025, 1999) and were found to be 9, 15, 19, 28, and 36 % for NR-PM1, nitrate, organic matter, sulfate, and ammonium, respectively. However, discrepancies were observed in the relative concentrations of the constituent mass fragments for each chemical component. In particular, significant differences were observed for the organic fragment at mass-to-charge ratio 44, which is a key parameter describing the oxidation state of organic aerosol. Following this first major intercomparison exercise of a large number of Q-ACSMs, detailed intercomparison results are presented, along with a discussion of some recommendations about best calibration practices, standardized data processing, and data treatment.

The interactions between organic and inorganic aerosol chemical components are integral to understanding and modelling climate and health-relevant aerosol physicochemical properties, such as volatility, hygroscopicity, light scattering and toxicity. This study presents a synthesis analysis for eight data sets, of non-refractory aerosol composition, measured at a boreal forest site. The measurements, performed with an aerosol mass spectrometer, cover in total around 9 months over the course of 3 years. In our statistical analysis, we use the complete organic and inorganic unit-resolution mass spectra, as opposed to the more common approach of only including the organic fraction. The analysis is based on iterative, combined use of (1) data reduction, (2) classification and (3) scaling tools, producing a data-driven chemical mass balance type of model capable of describing site-specific aerosol composition. The receptor model we constructed was able to explain 83±8 % of variation in data, which increased to 96±3 % when signals from low signal-to-noise variables were not considered. The resulting interpretation of an extensive set of aerosol mass spectrometric data infers seven distinct aerosol chemical components for a rural boreal forest site: ammonium sulfate (35±7 % of mass), low and semi-volatile oxidised organic aerosols (27±8 % and 12±7 %), biomass burning organic aerosol (11±7 %), a nitrate-containing organic aerosol type (7±2 %), ammonium nitrate (5±2 %), and hydrocarbon-like organic aerosol (3±1 %). Some of the additionally observed, rare outlier aerosol types likely emerge due to surface ionisation effects and likely represent amine compounds from an unknown source and alkaline metals from emissions of a nearby district heating plant. Compared to traditional, ion-balance-based inorganics apportionment schemes for aerosol mass spectrometer data, our statistics-based method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron atmospheric aerosols physical and chemical properties. The results also shed light on the division between organic and inorganic aerosol types and dynamics of salt formation in aerosol. Equally importantly, the combined methodology exemplifies an iterative analysis, using consequent analysis steps by a combination of statistical methods. Such an approach offers new ways to home in on physicochemically sensible solutions with minimal need for a priori information or analyst interference. We therefore suggest that similar statistics-based approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosol mass spectrometric data.

Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.

Using chemical ionization mass spectrometry to detect particle-phase acids (acid-CIMS) and aerosol mass spectrometry (AMS) measurements from Colorado, USA, and two studies in Hyytiala, Finland, we quantify the fraction of organic aerosol (OA) mass that is composed of molecules with acid functional groups (facid). Molecules containing one or more carboxylic acid functionality contributed approximately 29% (45-51%) of the OA mass in Colorado (Finland). Organic acid mass concentration correlates well with AMS m/z 44 (primarily CO2+), a commonly used marker for highly oxidized aerosol. Using the average empirical relationship between AMS m/z 44 and organic acids in these three studies, together with m/z 44 data from 29 continental northern hemispheric (NH) AMS datasets, we estimate that molecules containing carboxylic acid functionality constitute on average 28% (range 10-50%) of NH continental OA mass with typically higher values at rural/remote sites and during summer and lower values at urban sites and during winter.

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Numerous observations of CCN number concentration exist, and many closure studies have been performed to predict CCN number concentrations based on particle number size distributions, chemical composition, and the κ-Köhler theory. Most of these studies provide details for short time periods or focus on special environmental conditions. These observations, however, cannot address questions of large-scale temporal and spatial CCN variability. Here we analyze long-term observations of CCN number concentrations, particle number size distributions and chemical composition from twelve sites on three continents. Eight of these stations are part of the European Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS). We group the observatories into categories according to their official classification: coastal background (Barrow, Alaska; Mace Head, Ireland; Finokalia, Crete; Noto Peninsula, Japan), rural background (Melpitz, Germany; Cabauw, the Netherlands; Vavihill, Sweden), alpine sites (Puy de Dôme, France; Jungfraujoch, Switzerland), remote forest sites (ATTO, Brazil; SMEAR, Finland) and the urban environment (Seoul, South Korea). Expectedly, CCN characteristics are highly variable across regions. However, they also vary within categories, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behavior, most continental stations exhibit very similar relative activation ratios across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the activation ratios spread more widely across the SS spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g., at Barrow (Arctic Haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season), or Finokalia (forest fire influence in fall). The rural background and urban sites exhibit relatively little variability throughout the year while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles, was highest at the coastal site of Mace Head (0.6) and the lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies to predict CCN number concentrations from the particle number size distribution and chemical composition measurements. The prediction accuracy for the average concentrations is high. The ratio between predicted and measured CCN concentrations is between 0.87 and 1.4. The temporal variability is also well represented, as reflected by Pearson correlation coefficients > 0.87. We also conducted a series of sensitivity studies for the ratio of predicted versus measured CCN concentration, where we varied the hygroscopicity parameter κ, and made simple assumptions for aerosol particle number concentrations and size distributions. Uncertain particle number concentrations and their size distributions significantly impair the accuracy in predicting temporal variability and hence of absolute concentrations, while the effect of uncertain κ values is limited to the predicted CCN number concentration. Information on CCN number concentrations at many locations is important to better characterize ACI and their radiative forcing. Long-term comprehensive aerosol particle characterizations are labor intensive and costly. For observatories where such efforts are out of scope to obtain nevertheless long-term information of CCN number concentrations, we recommend conducting collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can be calculated based on continued particle number size distribution information only. This approach is a good alternative to deriving kappa from time-resolved chemical composition measurements which are costly and may still not cover the appropriate size range. Additionally, given the variability in observations at sites of the same category, a certain density in spatial coverage of observations is needed, especially along coastlines. We recommend operating "migrating-CCNCs" at priority locations, identified by model evaluation, around the globe where long-term particle number size distribution data are already available.

International audience; We evaluated a modified VBS (volatility basis set) scheme to treat biomass-burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was param-eterized with novel wood combustion smog chamber experiments using a hybrid VBS framework which accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February-March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from −61 to −29 %. We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass-burning-like aerosol (BBPOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBPOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBPOA fractions were found to represent 12 to 64 % of the total residential-heating-related OA, with increasing contributions at stations located in the northern part of the domain. Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and road transportation sector contributed about 30-40 % to SOA formation (with increasing contributions at urban and near industrialized sites), whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60-70 %. Contributions to OA from residential combustion precursors in different volatility ranges were Published by Copernicus Publications on behalf of the European Geosciences Union. 7654 G. Ciarelli et al.: Modelling winter organic aerosol at the European scale with CAMx also assessed: our results indicate that residential combustion gas-phase precursors in the semivolatile range (SVOC) contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain. On the other hand, the oxidation products of higher-volatility precursors (the sum of intermediate-volatility compounds (IVOCs) and volatile organic compounds (VOCs)) contribute from 15 to 38 % with no specific gradient among the stations. Although the new parameterization leads to a better agreement between model results and observations, it still under-predicts the SOA fraction, suggesting that uncertainties in the new scheme and other sources and/or formation mechanisms remain to be elucidated. Moreover, a more detailed characterization of the semivolatile components of the emissions is needed.

Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.

The chemical composition of submicron aerosol during the comprehensive field campaign HUMPPA-COPEC 2010 at Hyytiälä, Finland, is presented. The focus lies on online measurements of organic acids, which were achieved by using atmospheric pressure chemical ionization (APCI) ion trap mass spectrometry (IT-MS). These measurements were accompanied by aerosol mass spectrometry (AMS) measurements and Fourier transform infrared spectroscopy (FTIR) of filter samples, all showing a high degree of correlation. The soft ionization mass spectrometer alternated between gas-phase measurements solely and measuring the sum of gas and particle phase. The AMS measurements of C, H and O elemental composition show that the aerosol during the campaign was highly oxidized, which appears reasonable due to high and prolonged radiation during the boreal summer measurement period as well as the long transport times of some of the aerosol. In order to contrast ambient and laboratory aerosol, an average organic acid pattern, measured by APCI-IT-MS during the campaign, was compared to terpene ozonolysis products in a laboratory reaction chamber. Identification of single organic acid species remains a major challenge due to the complexity of the boreal forest aerosol. Unambiguous online species identification was attempted by the combinatorial approach of identifying unique fragments in the MS2 mode of standards, and then comparing these results with MS2 field spectra. During the campaign, unique fragments of limonene-derived organic acids (limonic acid and ketolimononic acid) and of the biomass burning tracer vanillic acid were detected. Other specific fragments (neutral loss of 28 Da) in the MS2 suggest the occurrence of semialdehydes. Furthermore, an approach to determine the average molecular weight of the aerosol is presented. The campaign average organic molecular weight was determined to be 300 g mol−1. However, a plume of aged biomass burning aerosol, arriving at Hyytiälä from Russia, contained organic compounds up to 800 Da (MWom≈450 g mol−1), showing that the average molecular weight can vary significantly. The high measurement frequency of both AMS and APCI-IT-MS enabled the partitioning of selected organic acids between gas and particle phase as a function of the total particulate mass to be quantified. Surprisingly high fractions of the higher molecular weight organic acids were observed to reside in the gas phase. These observations might be a consequence of large equilibration timescales for semi-solid boreal forest aerosol, as has been recently hypothesized by Shiraiwa and Seinfeld (2012).

Aerosol Formation Most atmospheric aerosol particles result from a growth process that begins with atmospheric molecules and clusters, progressing to larger and larger sizes as they acquire other molecules, clusters, and particles. The initial steps of this process involve very small entities—with diameters of less than 2 nanometers—which have been difficult to observe. Kulmala et al. (p. 943 ; see the Perspective by Andreae ) developed a sensitive observational protocol that allows these tiny seeds to be detected and counted, and they mapped out the process of aerosol formation in detail.

The field application of an aerosol concentrator in conjunction with an atmospheric pressure chemical ionization ion trap mass spectrometer (APCI-IT-MS) at the boreal forest station SMEAR II at Hyytiälä, Finland, is demonstrated in this study. APCI is a soft-ionization technique allowing online measurements of organic acids in the gas and particle phase. The detection limit for the acid species in the particle phase was improved by a factor of 7.5 to 11 (e.g. ∼40 ng m3 for pinonic acid) by using the miniature versatile aerosol concentration enrichment system (mVACES) upstream of the mass spectrometer. The APCI-IT-MS was calibrated in the negative ion mode with two biogenic organic acid standards – pinic acid and pinonic acid. Pinic acid was used as a surrogate for the quantification of the total amount of organic acids in the ambient aerosol based on the total signal intensities in the negative ion mode. The results were compared with the total organic signal of a C-ToF-AMS during the HUMPPA-COPEC 2010 field campaign. The campaign average contribution of organic acids measured by APCI-IT-MS to the total submicron organic aerosol mass was estimated to be about 60%, based on the response of pinic acid. Very good correlation between APCI-IT-MS and C-ToF-AMS (Pearson's R = 0.94) demonstrates soft-ionization mass spectrometry as a complimentary technique to AMS with electron impact ionization. MS2 studies of specific m/z ratios recorded during the HUMPPA-COPEC 2010 field campaign were compared to MS2 studies of selected monoterpene oxidation products formed in simulation chamber experiments. The comparison of the resulting fragments shows that oxidation products of the main VOCs emitted at Hyytiälä (α-pinene and Δ3-carene) cannot account for all of the measured fragments. Possible explanations for those unaccounted fragments are the presence of unidentified or underestimated biogenic SOA precursors, or that different products are formed by a different oxidant mixture of the ambient air compared to the chamber ozonolysis.

We evaluated a modified VBS (Volatility Basis Set) scheme to treat biomass burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was parameterized with novel wood combustion smog chamber experiments using a hybrid VBS framework that accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February-March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from −61 % to −29 %. We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass burning-like aerosol (BBOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBOA fractions was found to represent 12 to 64 % of the total residential heating related OA, with increasing contributions at stations located in the northern part of the domain. Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and transportation precursors contributed about 30–40 % to SOA formation (with increasing contributions at urban and near industrialized sites) whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60–70 %. Contributions to OA from residential combustion precursors in different volatility ranges were also assessed: our results indicate that residential combustion gas-phase precursors in the semi-volatile range contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain. On the other hand, higher volatility residential combustion precursors contributed from 15 to 38 % with no specific gradient among the stations. The new retrieved parameterization, although leading to a better agreement between model and observations, still under-predicts the SOA fraction suggesting remaining uncertainties in the new scheme or that other sources and/or formation mechanisms need to be elucidated.

Volatility distribution of secondary organic aerosols, i.e. the particle mass fractions of semi-volatile, low-volatility and extremely low-volatility organic compounds was characterized in a boreal forest environment of Hyytiälä, Southern Finland. This was done by interpreting field measurements using a Volatility Tandem Differential Mobility Analyzer (VTDMA) with a kinetic evaporation model. The field measurements were performed during April and May of 2014. On average, 40 % of organics in particles was semi-volatile; 34 % low-volatility organics and 26 % extremely low-volatility organics. The model was, however, very sensitive towards the vaporization enthalpies assumed for the organics (ΔHVAP). The best agreement between the observed and modeled temperature-dependence of the evaporation was obtained when effective vaporization enthalpy values of 80 kJ/mol were assumed. The low effective enthalpy value might result from several potential reasons, including molecular decomposition or dissociation that might occur in the particle phase upon heating, mixture effects and compound-dependent uncertainties in the mass accommodation coefficient. In addition to the VTDMA-based analysis, semi-volatile and low-volatile organic mass fractions were independently determined by applying Positive Matrix Factorization (PMF) to High-Resolution Aerosol Mass Spectrometer (HR-AMS) data. The factor separation was based on the oxygenation levels of organics, specifically the relative abundance of mass ions at m/z 43 (f43) and m/z 44 (f44). The mass fractions of these two organic groups were compared against the VTDMA-based results. In general, the agreement between the VTDMA results and the PMF-derived mass fractions of organics was reasonable with a linear correlation coefficient of around 0.4 with ΔHVAP = 80 kJ/mol set for all organic groups. The prospect of determining of extremely low volatile organic aerosols (ELVOA) from AMS data using the PMF analysis should be assessed in future studies.

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesising this “raw” data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorisation), with exploratory classification (clustering), and show the results can not only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorisation to extract spectral characteristics of the organic component of air pollution plumes together with an unsupervised clustering algorithm, k-means++, for classification, reproduces classical organic aerosol speciation schemes. In addition to the typical oxidation level and aerosol source driven aerosol classification we were also able to classify and characterise outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimise weighting for mass spectral variables. This both improves algorithm-based classification and provides important clues for a human analyst on the relative importance of variables and data structures.

International audience; In the atmosphere nighttime removal of volatile organic compounds is initiated to a large extent by reaction with the nitrate radical (NO3) forming organic nitrates which partition between gas and particulate phase. Here we show based on particle phase measurements performed at a suburban site in the Netherlands that organic nitrates contribute substantially to particulate nitrate and organic mass. Comparisons with a chemistry transport model indicate that most of the measured particulate organic nitrates are formed by NO3 oxidation. Using aerosol composition data from three intensive observation periods at numerous measurement sites across Europe, we conclude that organic nitrates are a considerable fraction of fine particulate matter (PM1) at the continental scale. Organic nitrates represent 34% to 44% of measured submicron aerosol nitrate and are found at all urban and rural sites, implying a substantial potential of PM reduction by NOx emission control.

Atmospheric aerosol particles impact human health in urban environments, while on regional and global scales they can affect climate patterns, the hydrological cycle, and the intensity of radiation that reaches the Earth’s surface. In spite of recent advances in the understanding of aerosol formation processes and the links between aerosol dynamics and biosphere-atmosphere-climate interactions, great challenges remain in the analysis of related processes on a global scale. Boreal forests, situated in a circumpolar belt in the Northern latitudes throughout the United States, Canada, Russia, and Scandinavia, are, of all biomes, among the most active areas of atmospheric aerosol formation. The formation of aerosol particles and their growth to cloud condensation nuclei sizes in these areas are associated with biogenic volatile organic emissions (BVOC) from vegetation and soil.

Four periods of EMEP (European Monitoring and Evaluation Programme) intensive measurement campaigns (June 2006, January 2007, September–October 2008 and February–March 2009) were modelled using the regional air quality model CAMx with VBS (Volatility Basis Set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February–March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosols (OA). Model performance for selected gas phase species and PM2.5 was evaluated using the European air quality database Airbase. Sulfur dioxide (SO2) and ozone (O3) were found to be overestimated for all the four periods with O3 having the largest mean bias during June 2006 and January–February 2007 periods (8.93 and 12.30 ppb mean biases, respectively). In contrast, nitrogen dioxide (NO2) and carbon monoxide (CO) were found to be underestimated for all the four periods. CAMx reproduced both total concentrations and monthly variations of PM2.5 very well for all the four periods with average biases ranging from −2.13 to 1.04 μg m-3. Comparisons with AMS (Aerosol Mass Spectrometer) measurements at different sites in Europe during February–March 2009, showed that in general the model over-predicts the inorganic aerosol fraction and under-predicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of volatility basis set scheme (VBS) on OA was investigated as well. Two sensitivity tests with volatility distributions based on previous chamber and ambient measurements data were performed. For February–March 2009 the chamber-case reduced the total OA concentrations by about 43 % on average. On the other hand, a test based on ambient measurement data increased OA concentrations by about 47 % for the same period bringing model and observations into better agreement. Comparison with the AMS data at the rural Swiss site Payerne in June 2006 shows no significant improvement in modelled OA concentration. Further sensitivity tests with increased biogenic and anthropogenic emissions suggest that OA in Payerne was largely dominated by residential heating emissions during the February–March 2009 period and by biogenic precursors in June 2006.

As part of the European ACTRIS project, the first large Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM) intercomparison study was conducted in the region of Paris for three weeks during the late fall–early winter period (November–December 2013). The first week was dedicated to tuning and calibration of each instrument whereas the second and third were dedicated to side-by-side comparison in ambient conditions with co-located instruments providing independent information on submicron aerosol optical, physical and chemical properties. Near real-time measurements of the major chemical species (organic matter, sulfate, nitrate, ammonium and chloride) in the non-refractory submicron aerosols (NR-PM1) were obtained here from 13 Q-ACSM. The results show that these instruments can produce highly comparable and robust measurements of the NR-PM1 total mass and its major components. Taking the median of the 13 Q-ACSM as a reference for this study, strong correlations (r2 > 0.9) were observed systematically for each individual ACSM across all chemical families except for chloride for which three ACSMs showing weak correlations partly due to the very low concentrations during the study. Reproducibility expanded uncertainties of Q-ACSM concentration measurements were determined using appropriate methodologies defined by the International Standard Organization (ISO 17025) and were found to be of 9, 15, 19, 28 and 36 % for NR-PM1, nitrate, organic matter, sulfate and ammonium respectively. However, discrepancies were observed in the relative concentrations of the constituent mass fragments for each chemical component. In particular, significant differences were observed for the organic fragment at mass-to-charge ratio 44, which is a key parameter describing the oxidation state of organic aerosol. Following this first major intercomparison exercise of a large number of ACSMs, detailed intercomparison results are presented as well as a discussion of some recommendations about best calibration practices, standardized data processing and data treatment.

Article, Measurements of the hygroscopicity of 15–145 nm particles in a boreal forest environment were conducted using two Hygroscopicity Tandem Differential Mobility Analyzer (HTDMA) systems during the Pan-European Gas-Aerosols-climate interaction Study (PEGASOS) campaign in spring 2013. Measurements of the chemical composition of non-size segregated particles were also performed using a high-resolution aerosol mass spectrometer (HR-AMS) in parallel with hygroscopicity measurements. On average, the hygroscopic growth factor (HGF) of particles was observed to increase from the morning until afternoon. In case of accumulation mode particles, the main reasons for this behavior were increases in the ratio of sulfate to organic matter and oxidation level (O : C ratio) of the organic matter in the particle phase. Using an O : C dependent hygroscopic growth factor of organic matter (HGForg), fitted using the inverse Zdanovskii–Stokes–Robinson (ZSR) mixing rule, clearly improved the agreement between measured HGF and that predicted based on HR-AMS composition data. Besides organic oxidation level, the influence of inorganic species was tested when using the ZSR mixing rule to estimate the hygroscopic growth factor of organics in the aerosols. While accumulation and Aitken mode particles were predicted fairly well by the bulk aerosol composition data, the hygroscopicity of nucleation mode particles showed little correlation. However, we observed them to be more sensitive to the gas phase concentration of condensable vapors: the more sulfuric acid in the gas phase, the more hygroscopic the nucleation mode particles were. No clear dependence was found between the extremely low-volatility organics concentration (ELVOC) and the HGF of particles of any size., published version, http://purl.org/eprint/status/PeerReviewed

Ambient aerosol particles can take up water and thus change their optical properties depending on the hygroscopicity and the relative humidity (RH) of the surrounding air. Knowledge of the hygroscopicity effect is of crucial importance for radiative forcing calculations and is also needed for the comparison or validation of remote sensing or model results with in situ measurements. Specifically, particle light scattering depends on RH and can be described by the scattering enhancement factor f(RH), which is defined as the particle light scattering coefficient at defined RH divided by its dry value (RH Here, we present results of an intensive field campaign carried out in summer 2013 at the SMEAR II station at Hyytiälä, Finland. Ground-based and airborne measurements of aerosol optical, chemical and microphysical properties were conducted. The f(RH) measured at ground level by a humidified nephelometer is found to be generally lower (e.g. 1.63±0.22 at RH = 85 % and λ = 525 nm) than observed at other European sites. One reason is the high organic mass fraction of the aerosol encountered at Hyytiälä to which f(RH) is clearly anti-correlated (R2≈0.8). A simplified parametrization of f(RH) based on the measured chemical mass fraction can therefore be derived for this aerosol type. A trajectory analysis revealed that elevated values of f(RH) and the corresponding elevated inorganic mass fraction are partially caused by transported hygroscopic sea spray particles. An optical closure study shows the consistency of the ground-based in situ measurements. Our measurements allow to determine the ambient particle light extinction coefficient using the measured f(RH). By combining the ground-based measurements with intensive aircraft measurements of the particle number size distribution and ambient RH, columnar values of the particle extinction coefficient are determined and compared to columnar measurements of a co-located AERONET sun photometer. The water uptake is found to be of minor importance for the column-averaged properties due to the low particle hygroscopicity and the low RH during the daytime of the summer months. The in situ derived aerosol optical depths (AOD) clearly correlate with directly measured values of the sun photometer but are substantially lower compared to the directly measured values (factor of ~ 2–3). The comparison degrades for longer wavelengths. The disagreement between in situ derived and directly measured AOD is hypothesized to originate from losses of coarse and fine mode particles through dry deposition within the canopy and losses in the in situ sampling lines. In addition, elevated aerosol layers (above 3 km) from long-range transport were observed using an aerosol lidar at Kuopio, Finland, about 200 km east-north-east of Hyytiälä. These elevated layers further explain parts of the disagreement.

Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM1) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May–June and September–October) and 2009 (February–March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.

The Volatility-Hygroscopicity Tandem Differential Mobility Analyzer (VH-TDMA) was applied to study the hygroscopicity and volatility properties of submicron atmospheric aerosol in a boreal forest environment in Hyytiälä, Finland during the summer of 2010. Aitken and accumulation mode particles (50 nm, 75 nm and 110 nm) were investigated. The results suggest that the particles were internally mixed at all sizes. Hygroscopicity was found to increase with size. The relative mass fraction of organics and SO42− is probably the major contributor to the fluctuation of the hygroscopicity for all particle sizes. The Cloud Condensation Nuclei counter (CCNc)-derived hygroscopicity parameter κ was slightly higher than κ calculated from VH-TDMA data under sub-saturated conditions, which can be explained by the fact that particulate organics have a different degree of dissolution in sub- and supersaturated conditions. Also, the size-resolved volatility properties of particles were investigated. Upon heating, small particles evaporated more compared to large particles. There was a significant amount of aerosol volume (non-volatile material) left even at heating temperatures above 280 °C. Using size resolved volatility-hygroscopicity analysis, we concluded that there was always hygroscopic material remaining in the particles of different sizes at all different heating temperatures, even above 280 °C. This indicates that the observed non-volatile aerosol material was not consisting solely of black carbon.

Submicron aerosol particles were collected during July and August 2010 in Hyytiälä, Finland, to determine the composition and sources of aerosol at that boreal forest site. Submicron particles were collected on Teflon filters and analyzed by Fourier transform infrared (FTIR) spectroscopy for organic functional groups (OFGs). Positive matrix factorization (PMF) was applied to aerosol mass spectrometry (AMS) measurements and FTIR spectra to identify summertime sources of submicron aerosol mass at the sampling site. The two largest sources of organic mass (OM) in particles identified at Hyytiälä were (1) biogenic aerosol from surrounding local forest and (2) biomass burning aerosol, transported 4–5 days from large wildfires burning near Moscow, Russia, and northern Ukraine. The robustness of this apportionment is supported by the agreement of two independent analytical methods for organic measurements with three statistical techniques. FTIR factor analysis was more sensitive to the chemical differences between biogenic and biomass burning organic components, while AMS factor analysis had a higher time resolution that more clearly linked the temporal behavior of separate OM factors to that of different source tracers even though their fragment mass spectrum were similar. The greater chemical sensitivity of the FTIR is attributed to the nondestructive preparation and the functional group specificity of spectroscopy. The FTIR spectra show strong similarities among biogenic and biomass burning factors from different regions as well as with reference OM (namely olive tree burning organic aerosol and α-pinene chamber secondary organic aerosol (SOA)). The biogenic factor correlated strongly with temperature and oxidation products of biogenic volatile organic compounds (BVOCs), included more than half of the oxygenated OFGs (carbonyl groups at 29% and carboxylic acid groups at 22%), and represented 35% of the submicron OM. Compared to previous studies at Hyytiälä, the summertime biogenic OM is 1.5 to 3 times larger than springtime biogenic OM (0.64 μg m−3 and 0.4 μg m−3, measured in 2005 and 2007, respectively), even though it contributed only 35% of OM. The biomass burning factor contributed 25% of OM on average and up to 62% of OM during three periods of transported biomass burning emissions: 26–28 July, 29–30 July, and 8–9 August, with OFG consisting mostly of carbonyl (41%) and alcohol (25%) groups. The high summertime terrestrial biogenic OM (1.7 μg m−3) and the high biomass burning contributions (1.2 μg m−3) were likely due to the abnormally high temperatures that resulted in both stressed boreal forest conditions with high regional BVOC emissions and numerous wildfires in upwind regions.

Ambient aerosol CCN and hygroscopic properties were measured with a size-segregated CCNc in a boreal environment of Southern Finland at the SMEAR II station since February 2009. The overall median critical diameter Dc for CCN activation is reported at 75 nm, exhibiting a clear maximum in February and a minimum in July. The overall median aerosol hygroscopicity parameter κ is reported at 0.22, indicating that ambient aerosol in Hyytiala is less hygroscopic than the global continental and European continental averages. It is, however, more hygroscopic than ambient aerosol in an Amazon rainforest, the European high alpine site or the mountainous forest. The low hygroscopicity in the boreal forest is attributed to a large organic fraction present in the aerosol mass comparative to other locations within Europe. Aerosol mass spectrometer (AMS) data were used to demonstrate a positive correlation between κ and sulphate and ammonia, and a negative correlation between κ and the organic mass fraction. No distinguishable effect of atmospheric new particle formation (NPF) on Dc and κ was observed. Ambient aerosol was found to be internally mixed in the summer, and externally mixed during the rest of the year.

Atmospheric aerosol particles influence the climate system directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. Apart from black carbon aerosol, aerosols cause a negative radiative forcing at the top of the atmosphere and substantially mitigate the warming caused by greenhouse gases. In the future, tightening of controls on anthropogenic aerosol and precursor vapour emissions to achieve higher air quality may weaken this beneficial effect. Natural aerosols, too, might affect future warming. Here we analyse long-term observations of concentrations and compositions of aerosol particles and their biogenic precursor vapours in continental mid- and high-latitude environments. We use measurements of particle number size distribution together with boundary layer heights derived from reanalysis data to show that the boundary layer burden of cloud condensation nuclei increases exponentially with temperature. Our results confirm a negative feedback mechanism between the continental biosphere, aerosols and climate: aerosol cooling effects are strengthened by rising biogenic organic vapour emissions in response to warming, which in turn enhance condensation on particles and their growth to the size of cloud condensation nuclei. This natural growth mechanism produces roughly 50% of particles at the size of cloud condensation nuclei across Europe. We conclude that biosphere-atmosphere interactions are crucial for aerosol climate effects and can significantly influence the effects of anthropogenic aerosol emission controls, both on climate and air quality. © 2013 Macmillan Publishers Limited. All rights reserved.

The volatility of atmospheric 20–500 nm aerosol particles was investigated at a boreal forest site in Hyytiälä, Finland. Measurements were performed continuously between January 2008 and May 2010. The ambient aerosol sample was heated step-wise to six temperatures ranging from 80 °C to 280 °C and the total mass concentration of aerosol particles was determined from the measured particle number size distributions before and after heating assuming particle density of 1.6 g cm−3. On average 19% of the total aerosol mass stayed in the condensed phase even after heating to 280 °C. The observed non-volatile residual at 280 °C had a seasonal pattern; during winter the aerosol mass fraction remaining after heating was the highest and during summer the lowest. Black carbon concentrations correlated positively with the non-volatile fraction of the aerosol, but could not explain the presence of the non-volatile material completely: most of the time a notable fraction of the non-volatile residual was something else than black carbon. Using additional information on ambient meteorological conditions and trajectories, and results from an Aerodyne aerosol mass spectrometer (AMS), the chemical composition of the non-volatile residual and its seasonal behavior was further examined. During winter and spring months the non-volatile mass fraction had a marked positive linear correlation with pollutant trace gases, such as CO, SO2 and NOx. This suggests an anthropogenic influence on the non-volatile fraction of the aerosol in winter and spring. The anthropogenic effect on the formation of the low-volatility material was furthermore supported by observed correlation between the non-volatile residual and the mass fractions of poly-aromatic hydrocarbons (PAHs) sampled simultaneously at the site. During the fall the aerosol particles had relatively more non-volatile material in them when the aerosol mass fractions of organic nitrate and organics in the AMS data were high, and when the measurement site was influenced by clean air masses passing over the forest. Thus, the existence of very low volatile organic nitrates in the aerosol phase can be speculated.

The online-coupled, regional chemistry transport model COSMO-ART is evaluated for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain. Measurements used in the comparison include long-term station observations, satellite and ground-based remote sensing products, and complex datasets of aerosol chemical composition and number size distribution from recent field campaigns. This is the first time these comprehensive measurements of aerosol characteristics in Europe are used to evaluate a regional chemistry transport model. We show a detailed analysis of the simulated size-resolved chemical composition under different meteorological conditions. The model is able to represent trace gas concentrations with good accuracy and reproduces bulk aerosol properties rather well though with a clear tendency to underestimate both total mass (PM10 and PM2.5) and aerosol optical depth. We find indications of an overestimation of shipping emissions. Time evolution of aerosol chemical composition is captured, although some biases are found in relative composition. Nitrate aerosol components are on average overestimated, and sulfates underestimated. The accuracy of simulated organics depends strongly on season and location. While strongly underestimated during summer, organic mass is comparable in spring and autumn. We see indications for an overestimated fractional contribution of primary organic matter in urban areas and an underestimation of SOA at many locations. Aerosol number concentrations can be simulated well, size distributions are comparable. Our work sets the basis for subsequent studies of aerosol characteristics and climate impacts with COSMO-ART, and highlights areas where improvements are necessary for current regional modeling systems in general.

The online-coupled, regional chemistry transport model COSMO-ART is evaluated for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain. Measurements used in the comparison include long-term station observations, satellite and ground-based remote sensing products, and complex datasets of aerosol chemical composition and number size distribution from recent field campaigns. This is the first time these comprehensive measurements of aerosol characteristics in Europe are used to evaluate a regional chemistry transport model. We show a detailed analysis of the simulated size-resolved chemical composition under different meteorological conditions. Mean, variability and spatial distribution of the concentrations of O3 and NOx are well reproduced. SO2 is found to be overestimated, simulated PM2.5 and PM10 levels are on average underestimated, as is AOD. We find indications of an overestimation of shipping emissions. Time evolution of aerosol chemical composition is captured, although some biases are found in relative composition. Nitrate aerosol components are on average overestimated, and sulfates underestimated. The accuracy of simulated organics depends strongly on season and location. While strongly underestimated during summer, organic mass is comparable in spring and autumn. We see indications for an overestimated fractional contribution of primary organic matter in urban areas and an underestimation of SOA at many locations. Aerosol number concentrations compare well with measurements for larger size ranges, but overestimations of particle number concentration with factors of 2–5 are found for particles smaller than 50 nm. Size distribution characteristics are often close to measurements, but show discrepancies at polluted sites. Suggestions for further improvement of the modeling system consist of the inclusion of a revised secondary organic aerosols scheme, aqueous-phase chemistry and improved aerosol boundary conditions. Our work sets the basis for subsequent studies of aerosol characteristics and climate impacts with COSMO-ART, and highlights areas where improvements are necessary for current regional modeling systems in general.

Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles >20 nm) across the range of 0.1 to 1.0% supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, , calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3).We performed closure studies based on –Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of . The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating “migrating-CCNCs” to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of longterm measurements can be achieved.