Secondary aerosol formation alters CCN activity in the North China Plain

Abstract

Abstract. Secondary aerosols (SAs, including secondary organic and inorganic aerosols, SOAs and SIAs) are predominant components of aerosol particles in the North China Plain (NCP), and their formation has significant impacts on the evolution of particle size distribution (PNSD) and hygroscopicity. Previous studies have shown that distinct SA formation mechanisms can dominate under different relative humidity (RH). This would lead to different influences of SA formation on the aerosol hygroscopicity and PNSD under different RH conditions. Based on the measurements of size-resolved particle activation ratio (SPAR), hygroscopicity distribution (GF-PDF), PM2.5 chemical composition, PNSD, meteorology and gaseous pollutants in a recent field campaign, McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain), conducted during the autumn–winter transition period in 2018 at a polluted rural site in the NCP, the influences of SA formation on cloud condensation nuclei (CCN) activity and CCN number concentration (NCCN) calculation under different RH conditions were studied. Results suggest that during daytime, SA formation could lead to a significant increase in NCCN and a strong diurnal variation in SPAR at supersaturations lower than 0.07 %. During periods with daytime minimum RH exceeding 50 % (high RH conditions), SA formation significantly contributed to the particle mass and size changes in a broad size range of 150 to 1000 nm, leading to NCCN (0.05 %) increases within the size range of 200 to 500 nm and mass concentration growth mainly for particles larger than 300 nm. During periods with daytime minimum RH below 30 % (low RH conditions), SA formation mainly contributed to the particle mass and size and NCCN changes for particles smaller than 300 nm. As a result, under the same amount of mass increase induced by SA formation, the increase of NCCN (0.05 %) was stronger under low RH conditions and weaker under high RH conditions. Moreover, the diurnal variations of the SPAR parameter (inferred from CCN measurements) due to SA formation varied with RH conditions, which was one of the largest uncertainties within NCCN predictions. After considering the SPAR parameter (estimated through the number fraction of hygroscopic particles or mass fraction of SA), the relative deviation of NCCN (0.05 %) predictions was reduced to within 30 %. This study highlights the impact of SA formation on CCN activity and NCCN calculation and provides guidance for future improvements of CCN predictions in chemical-transport models and climate models.

Markedly different impacts of primary emissions and secondary aerosol formations on aerosol mixing states revealed by simultaneous measurements of CCNC, V/HTDMA and SP2

“…The SPAR deviations due to multiple-charge particles were corrected using a modified algorithm based on Hagen and Alofs (1983) and Deng et al (2011). More details about the system can be found in Ma et al (2016) and Tao et al (2021).…”

“…For instance, variations in the mixing state of black carbon (BC) particles can significantly alter their absorption and radiative effects (Bond et al, 2013;Lack et al, 2012;Moffet et al, 2016;Matsui et al, 2018;Peng et al, 2016). Using simple internally mixing state assumptions for aerosol chemical compositions in estimating CCN number concentrations can lead to substantial overestimations (Deng et al, 2013;Farmer et al, 2015;Ren et al, 2018;Ching et al, 2017Ching et al, , 2019Tao et al, 2021). The aerosol mixing state varies widely due to complex emissions and atmospheric transformations, leading to significant uncertainties in estimating the effects of aerosol based on simplified mixing state assumptions (Ervens, 2015;Wang et al, 2022;Fu et al, 2022).…”

“…The mixing state of primary aerosols can vary greatly depending on their type and emission conditions (Cheng et al 2012;Wang et al, 2022;Ting et al, 2018;Liu et al, 2021), and can be significantly altered during aging processes or secondary formations (Wehner et al 2009;Cheng et al 2012;Wang et al, 2022;Tomlin et al, 2021;Lata et al, 2021). Primary aerosol emissions such as biomass burning, fossil fuel combustion and cooking tend to contribute to weakly hygroscopic (Herich et al, 2008(Herich et al, , 2009Wang et al, 2020;Kim et al, 2020) and low-volatility aerosols (Hong et al, 2017;Saha et al, 2018), while formation of secondary aerosol including aging of BC-containing aerosols and primary organic aerosols mainly contribute to aerosols with strong CCN activity (Mei et al, 2013;Ma et al, 2016;Tao et al, 2021) and high hygroscopicity (Chen et al, 2018;Kim et al, 2020;Wang et al, 2020). To enhance our understanding of the mixing state of aerosols from different emission sources and to improve its characterization in models, it is important to study the impact of The North China Plain (NCP) is among the most polluted regions in China with various primary emission sources and strong secondary aerosol formations playing critical roles in air pollution (Xu et al, 2011;Tao et al, 2012;Liu et al, 2015).…”

“…The complex mixing state of aerosols in the NCP contributes to uncertainties in evaluating their climate and environmental effects (Zhuang et al, 2013;Nordmann et al, 2014;Zhang et al, 2016;Tao et al, 2020;Shi et al, 2022), particularly with regards to BC particles (Wu et al, 2017;Liu et al, 2019;Zhao et al, 2019;Wang et al, 2011;Zheng et al, 2019). Meteorological conditions can greatly impact the secondary aerosol formation in the NCP, which can be significantly exacerbated during severe pollution events (Kuang et al, 2020), and consequently affect the aerosol mixing states (Tao et al, 2021). This study obtained aerosol mixing state through concurrent measurements of CCN activity, hygroscopicity, volatility and BC particle at a regional site in the NCP, using CCNC, HTDMA, VTDMA and SP2 instruments.…”

Evaluation of the contribution of new particle formation to cloud droplet number concentration in the urban atmosphere

“…where κ i and ε i are the hygroscopic parameter and volume fraction for each individual (dry) component in the mixture, respectively. The κ value and density (ρ) of each species used in the calculation are given in Table 1, which are from Petters and Kreidenweis (2007) and Topping (2005). In Eq.…”