F. Canonaco

Ubiquity of organic nitrates from nighttime chemistry in the European submicron aerosol

In the atmosphere night time removal of volatile organic compounds (VOC) 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 (CTM) 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.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.

A user-friendly tool for comprehensive evaluation of the geographical origins of atmospheric pollution

Various receptor methodologies have been developed in the last decades to investigate the geographical origins of atmospheric pollution, based either on wind data or on backtrajectory analyses. To date, only few software packages exist to make use of one or the other approach. We present here ZeFir, an Igor-based package specifically designed to achieve a comprehensive geographical origin analysis using a single statistical tool. ZeFir puts the emphasis on a user-friendly experience in order to facilitate and speed up working time. Key parameters can be easily controlled, and unique innovative features bring geographical origins work to another level.

Size-Resolved Identification, Characterization, and Quantification of Primary Biological Organic Aerosol at a European Rural Site

Primary biological organic aerosols (PBOA) represent a major component of the coarse organic matter (OMCOARSE, aerodynamic diameter > 2.5 μm). Although this fraction affects human health and the climate, its quantification and chemical characterization currently remain elusive. We present the first quantification of the entire PBOACOARSE mass and its main sources by analyzing size-segregated filter samples collected during the summer and winter at the rural site of Payerne (Switzerland), representing a continental Europe background environment. The size-segregated water-soluble OM was analyzed by a newly developed offline aerosol mass spectrometric technique (AMS). Collected spectra were analyzed by three-dimensional positive matrix factorization (3D-PMF), showing that PBOA represented the main OMCOARSE source during summer and its contribution to PM10 was comparable to that of secondary organic aerosol. We found substantial cellulose contributions to OMCOARSE, which in combination with gas chromatography mass spectrometry molecular markers quantification, underlined the predominance of plant debris. Quantitative polymerase chain reaction (qPCR) analysis instead revealed that the sum of bacterial and fungal spores mass represented only a minor OMCOARSE fraction (<0.1%). X-ray photoelectron spectroscopic (XPS) analysis of C and N binding energies throughout the size fractions revealed an organic N increase in the PM10 compared to PM1 consistent with AMS observations.

Limited formation of isoprene epoxydiols‐derived secondary organic aerosol under NOx‐rich environments in Eastern China

Secondary organic aerosol (SOA) derived from isoprene epoxydiols (IEPOX) has potential impacts on regional air quality and climate yet is poorly characterized under NOx-rich ambient environments. We report the first real-time characterization of IEPOX-derived SOA (IEPOX-SOA) in Eastern China in summer 2013 using comprehensive ambient measurements, along with model analysis. The ratio of IEPOX-SOA to isoprene high-NOx SOA precursors, e.g., methyl vinyl ketone and methacrolein, and the reactive uptake potential of IEPOX was lower than those generally observed in regions with prevailing biogenic emissions, low NOx levels, and high particle acidity, elucidating the suppression of IEPOX-SOA formation under NOx-rich environments. IEPOX-SOA showed high potential source regions to the south with large biogenic emissions, illustrating that the interactions between biogenic and anthropogenic emissions might have played an important role in affecting the formation of IEPOX-SOA in polluted environments in Eastern China.

Characterization of Primary Organic Aerosol from Domestic Wood, Peat, and Coal Burning in Ireland

An aerosol chemical speciation monitor (ACSM) was deployed to study the primary nonrefractory submicron particulate matter emissions from the burning of commercially available solid fuels (peat, coal, and wood) typically used in European domestic fuel stoves. Organic mass spectra (MS) from burning wood, peat, and coal were characterized and intercompared for factor analysis against ambient data. The reference profiles characterized in this study were used to estimate the contribution of solid fuel sources, along with oil combustion, to ambient pollution in Galway, Ireland using the multilinear engine (ME-2). During periods influenced by marine air masses, local source contribution had dominant impact and nonsea-spray primary organic emissions comprised 88% of total organic aerosol mass, with peat burning found to be the greatest contributor (39%), followed by oil (21%), coal (17%), and wood (11%). In contrast, the resolved oxygenated organic aerosol (OOA) dominated the aerosol composition in continental air masses, with contributions of 50%, compared to 12% in marine air masses. The source apportionment results suggest that the use of domestic solid fuels (peat, wood, and coal) for home heating is the major source of evening and night-time particulate pollution events despite their small use.

Resolving anthropogenic aerosol pollution types – deconvolution and exploratory classification of pollution events

Abstract. 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 chemistry and particle growth events at an urban downwind site in the North China Plain

The North China Plain (NCP) has experienced frequent severe haze pollution events in recent years. While extensive measurements have been made in megacities, aerosol sources, processes, and particle growth at urban downwind sites remain less understood. Here, an aerosol chemical speciation monitor and a scanning mobility particle sizer, along with a suite of collocated instruments, were deployed at the downwind site of Xingtai, a highly polluted city in the NCP, for real-time measurements of submicron aerosol (PM1) species and particle number size distributions during May and June 2016. The average mass concentration of PM1 was 30.5 (±19.4) μg m−3, which is significantly lower than that during wintertime. Organic aerosols (OAs) constituted the major fraction of PM1 (38 %), followed by sulfate (25 %) and nitrate (14 %). Positive matrix factorization with the multilinear engine version 2 showed that oxygenated OA (OOA) was the dominant species in OA throughout the study, on average accounting for 78 % of OA, while traffic and cooking emissions both accounted for 11 % of OA. Our results highlight that aerosol particles at the urban downwind site were highly aged and mainly from secondary formation. However, the diurnal cycle also illustrated the substantial influence of urban emissions on downwind sites, which are characterized by similar pronounced early morning peaks for most aerosol species. New particle formation and growth events were also frequently observed (58 % of the time) on both clean and polluted days. Particle growth rates varied from 1.2 to 4.9 nm h−1 and our results showed that sulfate and OOA played important roles in particle growth during clean periods, while OOA was more important than sulfate during polluted events. Further analyses showed that particle growth rates have no clear dependence on air mass trajectories. Published by Copernicus Publications on behalf of the European Geosciences Union. 14638 Y. Zhang et al.: Aerosol chemistry and particle growth events

Constructing a data-driven receptor model for organic and inorganic aerosol – a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

The interactions between organic and inorganic aerosol chemical components are integral to understanding and 10 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) 15 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, 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 sulphate (35% of mass), low and semi-volatile oxidised organic aerosols (27 and 12%), biomass burning 20 organic aerosol (11%), a nitrate containing organic aerosol type (7%), ammonium nitrate (5%), and hydrocarbon-like organic aerosol (3%). 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, simplistic inorganics apportionment methods for aerosol mass spectrometer data, our statisticsbased method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron 25 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/semi supervised machine30 learning applications in future analyses of aerosol mass spectrometric data. Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1069 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 19 October 2018 c

Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe

Biogenic volatile organic compound (BVOC) emissions are one of the essential inputs for chemical transport models (CTMs), but their estimates are associated with large uncertainties, leading to significant influence on air quality modelling. This study aims to investigate the effects of using different BVOC emission models on the performance of a CTM in simulating secondary pollutants, i.e. ozone, organic, and inorganic aerosols. European air quality was simulated for the year 2011 by the regional air quality model Comprehensive Air Quality Model with Extensions (CAMx) version 6.3, using BVOC emissions calculated by two emission models: the Paul Scherrer Institute (PSI) model and the Model of Emissions of Gases and Aerosol from Nature (MEGAN) version 2.1. Comparison of isoprene and monoterpene emissions from both models showed large differences in their general amounts, as well as their spatial distribution in both summer and winter. MEGAN produced more isoprene emissions by a factor of 3 while the PSI model generated 3 times the monoterpene emissions in summer, while there was negligible difference (∼ 4 %) in sesquiterpene emissions associated with the two models. Despite the large differences in isoprene emissions (i.e. 3-fold), the resulting impact in predicted summertime ozone proved to be minor (<10 %; MEGAN O3 was higher than PSI O3 by ∼ 7 ppb). Comparisons with measurements from the European air quality database (AirBase) indicated that PSI emissions might improve the model performance at low ozone concentrations but worsen performance at high ozone levels (>60 ppb). A much larger effect of the different BVOC emissions was found for the secondary organic aerosol (SOA) concentrations. The higher monoterpene emissions (a factor of ∼ 3) by the PSI model led to higher SOA by ∼ 110 % on average in summer, compared to MEGAN, and lead to better agreement between modelled and measured organic aerosol (OA): the mean bias between modelled and measured OA at nine measurement stations using Aerodyne aerosol chemical speciation monitors (ACSMs) or Aerodyne aerosol mass spectrometers (AMSs) was reduced by 21 %–83 % at rural or remote stations. Effects on inorganic aerosols (particulate nitrate, sulfate, and ammonia) were relatively small (<15 %).