Recent advancements in atmospheric mass spectrometry provide huge amounts of new information but at the same time present considerable challenges for the data analysts. High-resolution (HR) peak identification and separation can be effort- and time-consuming yet still tricky and inaccurate due to the complexity of overlapping peaks, especially at larger mass-to-charge ratios. This study presents a simple and novel method, mass spectral binning combined with positive matrix factorization (binPMF), to address these problems. Different from unit mass resolution (UMR) analysis or HR peak fitting, which represent the routine data analysis approaches for mass spectrometry datasets, binPMF divides the mass spectra into small bins and takes advantage of the positive matrix factorization's (PMF) strength in separating different sources or processes based on different temporal patterns. In this study, we applied the novel approach to both ambient and synthetic datasets to evaluate its performance. It not only succeeded in separating overlapping ions but was found to be sensitive to subtle variations as well. Being fast and reliable, binPMF has no requirement for a priori peak information and can save much time and effort from conventional HR peak fitting, while still utilizing nearly the full potential of HR mass spectra. In addition, we identify several future improvements and applications for binPMF and believe it will become a powerful approach in the data analysis of mass spectra.

To derive the comprehensive aerosol in situ characteristics at a rural background area in Saudi Arabia, an aerosol measurements station was established to Hada Al Sham, 60 km east from the Red Sea and the city of Jeddah. The present sturdy describes the observational data from February 2013 to February 2015 of scattering and absorption coefficients, Angstrom exponents and single scattering albedo over the measurement period. The average scattering and absorption coefficients at wavelength 525 nm were 109 +/- 71 Min(-1) (mean +/- SD, at STP conditions) and 15 +/- 17 Mm(-1) (at STP conditions), respectively. As expected, the scattering coefficient was dominated by large desert dust particles with low Angstrom scattering exponent, 0.49 +/- 0.62. Especially from February to June the Angstrom scattering exponent was clearly lower (0.23) and scattering coefficients higher (124 Mm(-1)) than total averages because of the dust outbreak season. Aerosol optical properties had clear diurnal cycle. The lowest scattering and absorption coefficients and aerosol optical depths were observed around noon. The observed diurnal variation is caused by wind direction and speed, during night time very calm easterly winds are dominating whereas during daytime the stronger westerly winds are dominating (sea breeze). Positive Matrix Factorization mathematical tool was applied to the scattering and absorption coefficients and PM2.5 and coarse mode (PM10-PM2.5) mass concentrations to identify source characteristics. Three different factors with clearly different properties were found; anthropogenic, BC source and desert dust. Mass absorption efficiencies for BC source and desert dust factors were, 6.0 m(2) g(-1) and 0.4 m(2) g(-1), respectively, and mass scattering efficiencies for anthropogenic (sulphate) and desert dust, 2.5 m(2) g(-1) and 0.8 m(2) g(-1), respectively.

There are limited measurements of the chemical composition, abundance and sources of atmospheric particles in the High Arctic To address this, we report 93 d of soot particle aerosol mass spectrometer (SP-AMS) data collected from 20 February to 23 May 2015 at Villum Research Station (VRS) in northern Greenland (81 degrees 36' N). During this period, we observed the Arctic haze phenomenon with elevated PM1 concentrations ranging from an average of 2.3, 2.3 and 3.3 mu g m(-3) in February, March and April, respectively, to 1.2 mu g m(-3) in May. Particulate sulfate (SO42-) accounted for 66 % of the non-refractory PM1 with the highest concentration until the end of April and decreasing in May. The second most abundant species was organic aerosol (OA) (24 %). Both OA and PM1, estimated from the sum of all collected species, showed a marked decrease throughout May in accordance with the polar front moving north, together with changes in aerosol removal processes. The highest refractory black carbon (rBC) concentrations were found in the first month of the campaign, averaging 0.2 mu g m(-3). In March and April, rBC averaged 0.1 mu g m(-3) while decreasing to 0.02 mu g m(-3) in May. Positive matrix factorization (PMF) of the OA mass spectra yielded three factors: (1) a hydrocarbon-like organic aerosol (HOA) factor, which was dominated by primary aerosols and accounted for 12 % of OA mass, (2) an Arctic haze organic aerosol (AOA) factor and (3) a more oxygenated marine organic aerosol (MOA) factor. AOA dominated until mid-April (64 %-81 % of OA), while being nearly absent from the end of May and correlated significantly with SO42-, suggesting the main part of that factor is secondary OA. The MOA emerged late at the end of March, where it increased with solar radiation and reduced sea ice extent and dominated OA for the rest of the campaign until the end of May (24 %-74 % of OA), while AOA was nearly absent. The highest O/C ratio (0.95) and S/C ratio (0.011) was found for MOA. Our data support the current understanding that Arctic aerosols are highly influenced by secondary aerosol formation and receives an important contribution from marine emissions during Arctic spring in remote High Arctic areas. In view of a changing Arctic climate with changing sea-ice extent, biogenic processes and corresponding source strengths, highly time-resolved data are needed in order to elucidate the components dominating aerosol concentrations and enhance the understanding of the processes taking place.

Characterizing aerosol chemical composition in response to meteorological changes and atmospheric chemistry is important to gain insights into new particle formation mechanisms. A BAECC (Biogenic Aerosols - Effects on Clouds and Climate) campaign was conducted during the spring 2014 at the SMEAR II station (Station for Measuring Forest Ecosystem-Aerosol Relations) in Finland. The particles were characterized by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). A PBL (planetary boundary layer) dilution model was developed to assist interpreting the measurement results. Right before nucleation events, the mass concentrations of organic and sulfate aerosol species were both decreased rapidly along with the growth of PBL heights. However, the mass fraction of sulfate aerosol of the total aerosol mass was increased, in contrast to a decrease for the organic mass fraction. Meanwhile, an increase in LVOOA (low-volatility oxygenated organic aerosol) mass fraction of the total organic mass was observed, in distinct comparison to a reduction of SVOOA (semi-volatile OOA) mass fraction. Our results demonstrate that, at the beginning of nucleation events, the observed sulfate aerosol mass was mainly driven by vertical turbulent mixing of sulfate-rich aerosols between the residual layer and the newly formed boundary layer, while the condensation of sulfuric acid (SA) played a minor role in interpreting the measured sulfate mass concentration. For the measured organic aerosols, their temporal profiles were mainly driven by dilution from PBL development, organic aerosol mixing in different boundary layers and/or partitioning of organic vapors, but accurate measurements of organic vapor concentrations and characterization on the spatial aerosol chemical composition are required. In general, the observed aerosol particles by AMS are subjected to joint effects of PBL dilution, atmospheric chemistry and aerosol mixing in different boundary layers. During aerosol growth periods in the nighttime, the mass concentrations of organic aerosols and organic nitrate aerosols were both increased. The increase in SVOOA mass correlated well with the calculated increase in condensed HOMs' (highly oxygenated organic molecules) mass. To our knowledge, our results are the first atmospheric observations showing a connection between increase in SVOOA and condensed HOMs during the nighttime.

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, ionbalance-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 statisticsbased approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosol mass spectrometric data.

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 mu gm(-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 ngm 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-yearlong 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.

The nearby cloud L1642 is one of only two known very high latitude (b| > 30 deg) clouds actively forming stars. It is a rare example of star formation in isolated conditions, and can reveal important details of star formation in general, e.g. of the effect of magnetic fields. We compareHerschel dust emission structures and magnetic field orientation revealed byPlanck polarization maps in L1642. The high-resolution (similar to 20 arcsec)Herschel data reveal a complex structure including a dense, compressed central clump, and low-density striations. ThePlanck polarization data (at 10 arcmin resolution) reveal an ordered magnetic field pervading the cloud and aligned with the surrounding striations. There is a complex interplay between the cloud structure and large-scale magnetic field. This suggests that the magnetic field is closely linked to the formation and evolution of the cloud. CO rotational emission confirms that the striations are connected with the main clumps and likely to contain material either falling into or flowing out of the clumps. There is a clear transition from aligned to perpendicular structures approximately at a column density ofN(H) = 1.6 x 10(21) cm(-2). Comparing theHerschel maps with thePlanck polarization maps shows the close connection between the magnetic field and cloud structure even in the finest details of the cloud.

The formation of new atmospheric particles involves an initial step forming stable clusters less than a nanometre in size (similar to 10 nm). Although at times, the same species can be responsible for both processes, it is thought that more generally each step comprises differing chemical contributors. Here, we present a novel analysis of measurements from a unique multi-station ground-based observing system which reveals new insights into continental-scale patterns associated with new particle formation. Statistical cluster analysis of this unique 2-year multi-station dataset comprising size distribution and chemical composition reveals that across Europe, there are different major seasonal trends depending on geographical location, concomitant with diversity in nucleating species while it seems that the growth phase is dominated by organic aerosol formation. The diversity and seasonality of these events requires an advanced observing system to elucidate the key processes and species driving particle formation, along with detecting continental scale changes in aerosol formation into the future.

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, statisticsbased 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 C C, 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 simi-larity 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.

New Delhi, India is the most polluted megacity in the world and routinely experiences high particulate matter (PM) concentrations. As part of the Delhi Aerosol Supersite Study, we have been measuring PM, concentration and composition in Delhi continuously since January 2017. This paper focuses on autumn, one of the most polluted seasons in Delhi when PM, concentrations steadily increase throughout the season and can exceed 1000 mu g m(-3) during episodic events. Positive matrix factorization on the organic aerosol (OA) spectrum suggests comparable seasonal average contributions from hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and oxidized OA (OOA), with BBOA dominating during episodic events. We demonstrate the influence of regional sources such as agricultural burning during this season through temporal trends of pollutants, PMF factors, meteorology, and nonparametric wind regression analysis. We use inorganic fragment ratios to show the influence of metals during the festival of Diwali. Furthermore, we demonstrate the influence of transitioning meteorology in governing PM, composition through the season. Overall, our analysis provides novel insights into the factors controlling PM, during one of the most polluted seasons in Delhi.