the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The evolution of aerosols mixing state derived from a field campaign in Beijing: implications to the particles aging time scale in urban atmosphere
Abstract. The mixing states and aging time scale of aerosol particles play a vital role in evaluating their climate effects. Here, we identified four different real-time mixing patterns of size-resolved particles using the field measurement by a humidity tandem differential mobility analyzer (H-TDMA) in the urban Beijing. We show that the particles with external, transitional and internal mixing state during the campaign account for 20–48 %, 17–24 % and 27–56 % respectively and the fraction highly depends on particles size. The diurnal variation of the mixing states of particles in all sizes investigated (40, 80, 110, 150 and 200 nm) present an apparent aging process from external to internal mixing state, typically spanning a duration of approximately 5–8 hours from 8:00–10:00 to 15:00–17:00. Additionally, the results illustrate that high ambient temperature during daytime or more humid atmosphere accelerates the aging process of aerosol particles, leading to the particles from external to internal mixing on both clear and cloudy days. Also, with the evolution of particulate pollution, the aerosol particles become more internally-mixed. Our result implies that those fine aerosol particles experience aging through both the photochemical process and aqueous growth in the polluted atmosphere of urban Beijing. Furthermore, through a comprehensive review of the aging timescale of particles adopted in current models and derived from observations, we show the great discrepancy between observations and models, highlighting the importance to parameterize their aging time scale based on more field campaigns.
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Status: open (until 17 Dec 2024)
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RC1: 'Comment on egusphere-2024-2999', Anonymous Referee #1, 13 Nov 2024
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This study uses H-TDMA to identify in real-time the four mixing states of aerosols across different particle sizes, proposing changes in the transition from external to internal mixing under various conditions. Additionally, the study explains the possible causes and diurnal variations of the aging process associated with the transition of aerosol mixing states. The study finds significant differences between observations and models regarding aerosol aging timescales, suggesting that the models need improvements. This research offers innovative insights, providing valuable observational evidence for understanding the evolution of aerosols in the atmosphere. The study also reviews a large amount of observational and simulation results, which are highly useful for further research in this field.
When considering the mixing states of different particle sizes, how can we distinguish the impact of aging from smaller aerosols on the internal mixing fraction of larger particles, as well as the influence of primary emissions? Clarifying the relationship between these two factors will help better explain the causes of mixing state changes across different particle sizes.
The paper presents many interesting results; however, most of the results only present phenomena or conclusions without providing reasonable explanations or sufficient interpretation of the data, which may lead readers to question whether the results are justified.
L137: You explain that σ cannot represent the mixing state of Beijing aerosols due to the influence of significant anthropogenic emissions. It would be helpful to provide a more detailed explanation of the relationship between anthropogenic emissions and mixing state, and the specific effects on σ and κ. You claim that σ alone is insufficient to characterize the mixing state of Beijing aerosols, but the relationship between the particle size distribution of the σ value and the vegetation-covered areas is not fully explained. Shouldn't you check whether the differences are due to methodological variations or regional characteristics?
L148: The sources of 150 nm and 200 nm aerosols mentioned here—are they primary emissions from traffic and biomass burning, or secondary aerosols? If they are from primary emissions, they should be similar to the 40 nm aerosols. Moreover, 80 and 110 nm aerosols should continue aging and form 150 and 200 nm aerosols. The aging of this group of aerosols should be more advanced—why is the influence of this aged aerosol group on the internal mixing fraction not reflected?
L176: While the conclusion that the standard deviation of κ-PDF alone is insufficient to characterize the mixing state in polluted regions may be correct, could you further explain why the relationship between κ and σ exhibits contrasting characteristics for different particle sizes?
L185: Earlier, you mentioned that larger particles may be related to traffic and biomass emissions, but here you claim that the peak for 40 nm particles is related to traffic emissions, which seems inconsistent with previous statements.
L190: If the large amount of external mixing in the early morning is due to fresh emissions, what is the source of the external mixing aerosols at night?
L214: Is the proportion of internal mixing state reduced with increasing humidity? Does this suggest that aerosols undergo less aging in high humidity conditions? This appears to contradict the conclusion in line 217.
L245: There is insufficient reliable evidence to support that this is a new particle formation process. I recommend not using this as a basis to explain the size characteristics of the mixing state.
L256: Clearly, the aging times of aerosols in different cities vary greatly. Given such large differences, how representative is your study?
L280: You point out that the observed aging time is similar to the aging times used in current models. Does this contradict the general conclusion that the aging timescale in models is inaccurately represented? Furthermore, do simulation results show variations in aging timescales under different environmental and pollution conditions? Can these results be directly compared to observations? You may also consider including a range for the simulated aging times, as this comparison would be more scientifically rigorous.
Citation: https://doi.org/10.5194/egusphere-2024-2999-RC1
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