Contrasting effects of secondary organic aerosol formations on organic aerosol hygroscopicity

Abstract

Abstract. Water uptake abilities of organic aerosol under sub-saturated conditions play critical roles in direct aerosol radiative effects and atmospheric chemistry; however, field characterizations of the organic aerosol hygroscopicity parameter κOA under sub-saturated conditions remain limited. In this study, a field campaign was conducted to characterize κOA at a relative humidity of 80 % with hourly time resolution for the first time in the Pearl River Delta region of China. Observation results show that, during this campaign, secondary organic aerosol (SOA) dominated total organic aerosol mass (mass fraction > 70 % on average), which provides a unique opportunity to investigate influences of SOA formation on κOA. Results demonstrate that the commonly used organic aerosol oxidation level parameter O/C was weakly correlated with κOA and failed to describe the variations in κOA. However, the variations in κOA were well reproduced by mass fractions of organic aerosol factor resolved based on aerosol mass spectrometer measurements. The more oxygenated organic aerosol (MOOA) factor, exhibiting the highest average O/C (∼ 1) among all organic aerosol factors, was the most important factor driving the increase in κOA and was commonly associated with regional air masses. The less oxygenated organic aerosol (LOOA; average O/C of 0.72) factor revealed strong daytime production, exerting negative effects on κOA. Surprisingly, the aged biomass burning organic aerosol (aBBOA) factor also formed quickly during daytime and shared a similar diurnal pattern with LOOA but had much lower O/C (0.39) and had positive effects on κOA. The correlation coefficient between κOA and mass fractions of aBBOA and MOOA in total organic aerosol mass reached above 0.8. The contrasting effects of LOOA and aBBOA formation on κOA demonstrate that volatile organic compound (VOC) precursors from diverse sources and different SOA formation processes may result in SOA with different chemical composition, functional properties and microphysical structure, consequently exerting distinct influences on κOA and rendering single oxidation level parameters (such as O/C) unable to capture those differences. Aside from that, distinct effects of aBBOA on κOA were observed during different episodes, suggesting that the hygroscopicity of SOA associated with similar sources might also differ much under different emission and atmospheric conditions. Overall, these results highlight that it is imperative to conduct more research on κOA characterization under different meteorological and source conditions and examine its relationship with VOC precursor profiles and formation pathways to formulate a better characterization and develop more appropriate parameterization approaches in chemical and climate models.

Strong light scattering of highly oxygenated organic aerosols impacts significantly on visibility degradation

“…The RH ranges of scanning-RH mode were 75 %-90 % from 11 December 2020 to 5 January 2021 and 60 %-90 % from 13 to 26 January 2021. The humidified nephelometer system provides direct measurements of the aerosol light scattering enhancement factor f (RH, λ) = σ sp (RH,λ) σ sp (dry λ) , where σ sp (RH, λ) is the aerosol scattering coefficient of light wavelength λ at condition of RH (Titos et al, 2016;, and f (RH, 525) is referred directly to as f (RH) was usually used to derive the optical hygroscopicity parameter κ sca through f (RH) = 1 + κ sca × RH 100−RH (Brock et al, 2016;Kuang et al, 2017Kuang et al, , 2018Kuang et al, , 2020cKuang et al, , 2021bXu et al, 2020). The nephelometer measurements are associated with truncation error and nonideality of the light source.…”

Measurement report: Distinct size dependence and diurnal variation in organic aerosol hygroscopicity, volatility, and cloud condensation nuclei activity at a rural site in the Pearl River Delta (PRD) region, China

“…5). As suggested by Kuang et al (2021), the daytime formation of aBBOA (Fig. 5) would lead to an increase in κ OA , which likely explained the noontime κ OA peak at 150 and 200 nm.…”

“…In order to minimize disturbance caused by mode switch, 15 min averaged data are used in the present study. More details on the quantification using ionization efficiency, composition-dependent collection efficiency, and external instrument as well as software for SP-AMS data analysis could be found in Kuang et al (2021).…”

“…Based on high-resolution data from the SP-AMS, source apportionment was performed for organic aerosols (OAs) in the bulk PM 1 with positive matrix factorization (PMF, Paatero, 1997;Paatero and Tapper, 1994) following the instruction in Ulbrich et al (2009). The input data, selection of solutions, mass spectral profile, and time series of each factor can be found in Kuang et al (2021). In brief, OA measured at the Heshan site could be divided into six components with identified sources and processes, including two from primary sources and four factors corresponding to secondary formation: a hydrocarbon-like OA (HOA) contributed mainly by vehicle exhausts mixed with cooking emissions, a biomass burning OA (BBOA) related to biomass burning combustion from the surrounding villages, an aged BBOA (aBBOA), a more oxygenated OA (MOOA) from regional transport, a less oxygenated OA (LOOA) provided by daytime photochemical formation, and a nighttime-formed OA (Night-OA) related to secondary formation during nighttime.…”

“…The diurnal characteristics of the size-resolved κ OA indicate that the OA in small particles (30-100 nm) was fresh and became aged in large particles. For the same campaign, Kuang et al (2021) reported the bulk κ OA of PM 1 based on aerosol optical hygroscopicity measurements, which could provide high-time-resolution data of κ OA . The relationship between κ OA and different OA factors was investigated, which showed a negative correlation (R = −0.25) between LOOA and κ OA and a positive correlation (R = 0.35) between aBBOA and κ OA .…”