Abstract

Abstract. Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.

Highlights

  • Particulate matter of an aerodynamic diameter smaller than 10 μm (PM10) has been extensively explored at many sites around the globe due to its various adverse effects on human health and climate

  • The offline aerosol mass spectrometer (AMS) technique was recently developed by Daellenbach et al (2016), where aqueous filter extracts are measured after nebulization with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS; Canagaratna et al, 2007), and the resulting organic mass spectra are analyzed with positive matrix factorization (PMF; Paatero, 1997)

  • The offline AMS technique was applied to a set of 150 filter samples covering a yearly cycle at three sites in Estonia

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Summary

Introduction

Particulate matter of an aerodynamic diameter smaller than 10 μm (PM10) has been extensively explored at many sites around the globe due to its various adverse effects on human health and climate. The offline aerosol mass spectrometer (AMS) technique was recently developed by Daellenbach et al (2016), where aqueous filter extracts are measured after nebulization with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS; Canagaratna et al, 2007), and the resulting organic mass spectra are analyzed with positive matrix factorization (PMF; Paatero, 1997) This technique has significantly increased our capability to investigate and identify the seasonal behavior of OA sources at several sites around the globe (Huang et al, 2014; Daellenbach et al, 2017; Bozzetti et al, 2017a). We applied the technique to an unprecedented dataset of 150 PM10 filter samples from three sites in Estonia covering a full year (September 2013– September 2014), where anthropogenic and natural emissions of primary and secondary organic aerosols could be extracted

Sampling sites
Offline AMS technique
PMF input and uncertainties
Method
Interpretation of PMF factors
PMF uncertainty analysis: factor sorting and solution selection
Estimation of traffic contribution to EC and OC
Scaling to organic carbon
Exploration of the dust factor
Seasonal variation of organic aerosol sources
Findings
Conclusions
Full Text
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