Influences of Primary Emission and Secondary Coating Formation on the Particle Diversity and Mixing State of Black Carbon Particles

Lee, Alex K. Y.; Rivellini, Laura-Hélèna; Chen, Chia‐Li; Li, Jun; Price, Derek J.; Betha, Raghu; Russell, Lynn M.; Zhang, Xiaolu; Cappa, Christopher D.

doi:10.1021/acs.est.9b03064

Cited by 28 publications

(28 citation statements)

References 58 publications

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“…Summertime nitrate was low in daytime and high in the nighttime (Fig. 7c), which agrees with measurements at the roadside site in Hong Kong (Lee et al, 2015) and Shanghai (K. . Temperature-dependent gas-particle partitioning would be one of the possible reasons for the observed nitrate diurnal pattern (Appel et al, 1981;Xue et al, 2014;Griffith et al, 2015).…”

Section: The Influence Of Semivolatile Compounds On E Abs Diurnal Varsupporting

confidence: 87%

Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou

Sun

et al. 2020

Atmos. Chem. Phys.

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Abstract. Black carbon (BC) aerosols have been widely recognized as a vital climate forcer in the atmosphere. Amplification of light absorption can occur due to coatings on BC during atmospheric aging, an effect that remains uncertain in accessing the radiative forcing of BC. Existing studies on the absorption enhancement factor (Eabs) have poor coverage on both seasonal and diurnal scales. In this study, we applied a recently developed minimum R squared (MRS) method, which can cover both seasonal and diurnal scales, for Eabs quantification. Using field measurement data in Guangzhou, the aims of this study are to explore (1) the temporal dynamics of BC optical properties at seasonal (wet season, 31 July–10 September; dry season, 15 November 2017–15 January 2018) and diel scales (1 h time resolution) in the typical urban environment and (2) the influencing factors on Eabs temporal variability. Mass absorption efficiency at 520 nm by primary aerosols (MAEp520) determined by the MRS method exhibited a strong seasonality (8.6 m2 g−1 in the wet season and 16.8 m2 g−1 in the dry season). Eabs520 was higher in the wet season (1.51±0.50) and lower in the dry season (1.29±0.28). Absorption Ångström exponent (AAE470–660) in the dry season (1.46±0.12) was higher than that in the wet season (1.37±0.10). Collective evidence showed that the active biomass burning (BB) in the dry season effectively altered the optical properties of BC, leading to elevated MAE, MAEp and AAE in the dry season compared to those in the wet season. Diurnal Eabs520 was positively correlated with AAE470–660 (R2=0.71) and negatively correlated with the AE33 aerosol loading compensation parameter (k) (R2=0.74) in the wet season, but these correlations were significantly weaker in the dry season, which may be related to the impact of BB. This result suggests that during the wet season, the lensing effect was more likely dominating the AAE diurnal variability rather than the contribution from brown carbon (BrC). Secondary processing can affect Eabs diurnal dynamics. The Eabs520 exhibited a clear dependency on the ratio of secondary organic carbon to organic carbon (SOC∕OC), confirming the contribution of secondary organic aerosols to Eabs; Eabs520 correlated well with nitrate and showed a clear dependence on temperature. This new finding implies that gas–particle partitioning of semivolatile compounds may potentially play an important role in steering the diurnal fluctuation of Eabs520. In the dry season, the diurnal variability in Eabs520 was associated with photochemical aging as evidenced by the good correlation (R2=0.69) between oxidant concentrations (Ox=O3+NO2) and Eabs520.

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Section: The Influence Of Semivolatile Compounds On E Abs Diurnal Varsupporting

confidence: 87%

Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou

Sun

et al. 2020

Atmos. Chem. Phys.

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“…In contrast, Cappa et al (2012) reported in situ observations of soot absorption enhancement of only 6 % in ambient air. This discrepancy between simulation and observation could be attributed to the complex mixing structure and various morphologies of soot particles in the air (Adachi et al, 2016;Li et al, 2016a;Wu et al, 2018).…”

Section: Introductionmentioning

confidence: 91%

Tracing the evolution of morphology and mixing state of soot particles along with the movement of an Asian dust storm

Xu¹,

Fukushima²,

Sobanska³

et al. 2020

Atmos. Chem. Phys.

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Abstract. Tracing the aging progress of soot particles during transport is highly challenging. An Asian dust event could provide an ideal opportunity to trace the continuous aging progress of long-range-transported soot particles. Here, we collected individual aerosol particles at an inland urban site (T1) and a coastal urban site (T2) in China and a coastal site (T3) in southwestern Japan during an Asian dust event. Microscopic analysis showed that the number fraction of soot-bearing particles was 19 % and 16 % at T1 and T2 in China but surprisingly increased to 56 % at T3 in Japan. The dominant fresh soot (71 %) at T1 became partially embedded (68 %) at T2 and fully embedded (84 %) at T3. These results indicated that the tiny soot particles had lower deposition than other aerosol types and became more aged during the transport from T1 to T3. We quantified soot morphology using the fractal dimension and found an increasing trend of 1.65 at T1, 1.84 at T2, and 1.91 at T3. Furthermore, we found that the morphology compression of the soot aggregations was associated with secondary coating thickness and relative humidity. A unique mixing structure where multi-soot particles scattered in organic coatings instead of the sulfate core in individual core-shell particles was observed at T3 after the crossing of the East China Sea. The study provides understanding for important constraints of the soot morphological effects and provides a possible aging scale along with their transport pathway. These new findings will be helpful to improve optical calculations and regional climate modeling of soot particles during their transport in the atmosphere.

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“…As stated by Winkler (1973), “the same net composition of an aerosol can be caused by an infinite variety of different internal distributions of the various compounds.” An “internally mixed” aerosol refers to the state where the composition of all particles within the population is the same (and equal to the bulk composition of the aerosol), while an “externally mixed” aerosol has all particles of a population consisting of only a single species. In reality, aerosol mixing states are between internal and external mixtures as shown in many observational studies (e.g., Bondy et al., 2018; Healy et al., 2014; Lee et al., 2019; Ye et al., 2018; Yu et al., 2020). Aerosol mixing state greatly influences the particles' hygroscopicity (Fierce et al., 2017; Holmgren et al., 2014), their optical properties (Fierce et al., 2016; Lesins et al., 2002), their cloud condensation nuclei (CCN) activity (Ching et al., 2012; Wang et al., 2010), their ice nucleation potential (Knopf et al., 2018), their deposition in the human respiratory system (Ching & Kajino, 2018), and the aerosols' lifetime in the atmosphere (Koch et al., 2009).…”

Section: Introductionmentioning

confidence: 98%

“…In reality, aerosol mixing states are between internal and external mixtures as shown in many observational studies (e.g. Bondy et al, 2018;Healy et al, 2014;Ye et al, 2018; C. Yu et al, 2020;Lee et al, 2019). Aerosol mixing state greatly influences the particles' hygroscopicity (Fierce et al, 2017;Holmgren et al, 2014), their optical properties (Lesins et al, 2002;Fierce et al, 2016), their cloud condensation nuclei (CCN) activity (J.…”

Section: Introductionmentioning

confidence: 99%

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Zheng

Curtis

Yao

et al. 2021

Earth and Space Science

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This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χa, between 38% and 94% for χo, and between 20% and 87% for χh with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

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Influences of Primary Emission and Secondary Coating Formation on the Particle Diversity and Mixing State of Black Carbon Particles

Cited by 28 publications

References 58 publications

Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou

Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou

Tracing the evolution of morphology and mixing state of soot particles along with the movement of an Asian dust storm

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

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