Abstract

Abstract. Organic aerosol (OA) represents a large fraction of submicron aerosols in the megacity of Beijing, yet long-term characterization of its sources and variations is very limited. Here we present an analysis of in situ measurements of OA in submicrometer particles with an aerosol chemical speciation monitor (ACSM) for 2 years from July 2011 to May 2013. The sources of OA are analyzed with a multilinear engine (ME-2) by constraining three primary OA factors including fossil-fuel-related OA (FFOA), cooking OA (COA), and biomass burning OA (BBOA). Two secondary OAs (SOA), representing a less oxidized oxygenated OA (LO-OOA) and a more oxidized (MO-OOA), are identified during all seasons. The monthly average concentration OA varied from 13.6 to 46.7 µg m−3 with a strong seasonal pattern that is usually highest in winter and lowest in summer. FFOA and BBOA show similarly pronounced seasonal variations with much higher concentrations and contributions in winter due to enhanced coal combustion and biomass burning emissions. The contribution of COA to OA, however, is relatively stable (10–15 %) across different seasons, yet presents significantly higher values at low relative humidity levels (RH < 30 %), highlighting the important role of COA during clean periods. The two SOA factors present very different seasonal variations. The pronounced enhancement of LO-OOA concentrations in winter indicates that emissions from combustion-related primary emissions could be a considerable source of SOA under low-temperature (T) conditions. Comparatively, MO-OOA shows high concentrations consistently at high RH levels across different T levels, and the contribution of MO-OOA to OA is different seasonally with lower values occurring more in winter (30–34 %) than other seasons (47–64 %). Overall, SOA (= LO-OOA + MO-OOA) dominates OA composition during all seasons by contributing 52–64 % of the total OA mass in the heating season and 65–75 % in non-heating seasons. The variations in OA composition as a function of OA mass loading further illustrate the dominant role of SOA in OA across different mass loading scenarios during all seasons. However, we also observed a large increase in FFOA associated with a corresponding decrease in MO-OOA during periods with high OA mass loadings in the heating season, illustrating an enhanced role of coal combustion emissions during highly polluted episodes. Potential source contribution function analysis further shows that the transport from the regions located to the south and southwest of Beijing within ∼ 250 km can contribute substantially to high FFOA and BBOA concentrations in the heating season.

Highlights

  • Organic aerosol (OA) is ubiquitous in the atmosphere and constitutes a large fraction of submicron aerosol worldwide (Zhang et al, 2007; Jimenez et al, 2009)

  • Organics present a pronounced seasonal variation, with the average concentration in winter being twice higher than the average in summer mainly due to coal combustion emissions in the heating season

  • fossil-fuel-related OA (FFOA) and biomass burning OA (BBOA) present similar seasonal variations with high concentrations in the heating season, which are mainly caused by enhanced coal combustion and biomass burning emissions in winter

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Summary

Introduction

Organic aerosol (OA) is ubiquitous in the atmosphere and constitutes a large fraction of submicron aerosol worldwide (Zhang et al, 2007; Jimenez et al, 2009). Model simulations of SOA have been improved during the last decade contributing to significant improvements in understanding the formation mechanisms and volatility of OA, the discrepancy between model simulations and ambient observations can still be substantial (Shrivastava et al, 2011; Fu et al, 2012; Fast et al, 2014). While a better understanding of SOA formation and evolutionary mechanisms is essential to improve model performances (Shrivastava et al, 2017), constraining the models with observations, long-term measurements would be one of the most effective ways to reduce the radiative forcing uncertainties

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