Single-particle volatility and implications for brown carbon absorption in Beijing, China

Sun, Jiaxing; Li, Yan; Xu, Weiqi; Zhou, Wei; Du, Aodong; Li, Lei; Du, Xubing; Huang, Feifei; Li, Zhijie; Zhang, Zhiqiang; Wang, Zifa; Sun, Yele

doi:10.1016/j.scitotenv.2022.158874

Cited by 6 publications

(4 citation statements)

References 58 publications

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“…The vigilance factor parameter, set at 0.75, determines the granularity of the clustering, with a higher value leading to finer, more distinct clusters. This setting was chosen based on prior successful applications in single-particle analysis [ 35 , 41 ] to ensure a balance between the specificity and generalizability of the identified particle types. The learning rate influences the speed at which the network learns, and 0.05 was selected to allow the algorithm to converge steadily and avoid overfitting, as suggested by Liu et al.…”

Section: Methodsmentioning

confidence: 99%

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

Chen,

Xiao,

Sun

et al. 2024

Environmental Science and Ecotechnology

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Section: Methodsmentioning

confidence: 99%

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

Chen,

Xiao,

Sun

et al. 2024

Environmental Science and Ecotechnology

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“…The Petri net is a directed bipartite graph that includes two types of elements, places and transitions, represented by circles and rectangles. Places can contain any number of tokens [28]. After the appearance of Petri nets, many extensions to Petri nets were derived, such as Stochastic Petri nets [29], CPN, and GSPN [30].…”

Section: The Definition Of Colored Petri Netmentioning

confidence: 99%

Reliability Evaluation Based on the Colored Petri Net Converted from AADL Models for the Wheel Brake System of Aircraft

Huang,

Cheng,

Zhou

et al. 2023

Aerospace

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Traditional reliability analysis methods such as Reliability Block Diagram, Fault Tree Analysis, and Markov Analysis are all subjective methods whose results significantly depend on the analysts’ skills and experiences. A model-based reliability method is proposed for the wheel brake system by using the architectural analysis and design language (AADL). The wheel brake system is modeled based on the AADL, and the AADL Error Model Annex is applied to describe the fault propagation of the system. An information extraction approach is proposed for the AADL-based model, and rules for transforming AADL-based models to colored Petri nets are given according to the information extracted. The reliability analysis of the wheel brake system is conducted in terms of the Colored Petri Nets. Through Monte Carlo simulation and linear regression, it is inferred that the lifetime of the wheel brake system follows a Weibull distribution with shape parameter 1.303 and scale parameter 9.992 × 103, and the accuracy of the method has been verified. In this study, the reliability analysis results are generated via the system model automatically; they do not depend on the analysts’ experiences and skills, and ambiguity among different analysts can be avoided.

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“…According to the mass spectral characteristics, temporal trends and size distributions (Dall'osto and Harrison, 2006;Phares et al, 2001), more than 99 % of the total particles were grouped into seven major classes including Total-EC, Total-ECOC, Total-OC, Total-IA, Biomass-K, HOM and Metals, and several subclasses (Table 1). The classification of particles is based on three principles: (1) particles (Sun et al, 2022a;Sun et al, 2022b). The detailed names of particle types and characteristics are given in Table S2, and the average mass spectra of each type of particle are depicted in Figs.…”

Section: Spamsmentioning

confidence: 99%

“…However, polluted events still occurred occasionally, particularly during wintertime with stagnant meteorological conditions and high anthropogenic emissions (Lei et al, 2021b;Xu et al, 2022;Feng et al, 2022;Zhou et al, 2023). Shortterm emission controls can improve air quality by reducing anthropogenic emissions temporarily, for example, the Beijing 2008 Summer Olympics (Wang et al, 2010;Okuda et al, 2011;Zhou et al, 2010), the 2014 Asia-Pacific Economic Cooperation (APEC) summit (Han et al, 2015;Gao et al, 2017;Ren et al, 2018), and the 2020 lockdown due to the corona virus disease (COVID-19) (Sun et al, 2020;Chang et al, 2022;Rajesh and Ramachandran, 2022;Zhang et al, 2022a).…”

mentioning

confidence: 99%

Mixing state and effective density of aerosol particles during the Beijing 2022 Olympic Winter Games

Sun

Liu

et al. 2023

Preprint

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Abstract. Mixing state and density are two key parameters of aerosol particles affecting their impacts on radiative forcing and human health. Here a single particle aerosol mass spectrometer in tandem with a differential mobility analyzer and an aerodynamic aerosol classifier was deployed during the Beijing 2022 Olympic Winter Games (OWG) to investigate the impacts of emission controls on particle mixing state and density. Nearly 760,000 particles were detected, which were classified into seven major classes. Our results showed the dominance of carbonaceous particles comprising mainly Total-EC (13.4 %), Total-OC (10.5 %) and Total-ECOC (47.1 %). Particularly, the particles containing organic carbon and sulfate were enhanced significantly during OWG although those from primary emissions decreased. The composition of carbonaceous particles also changed significantly which was characterized by the decreases in EC-NS, KEC-N, and amine-containing particles, and increase in ECOC-NS. This result indicates that emission controls during OWG reduced the mixing of elemental carbon (EC) with inorganic aerosol species and amines, yet increased the mixing of EC with organic aerosol. The average effective density (ρeff) of aerosol particles (150–300 nm) was 1.20 g cm−3 with higher values during OWG (1.26 g cm−3) due to the increase in secondary particle contribution. The two types of fresh particles, i.e., Total-EC and high molecular weight organic matter presented the lowest ρeff (0.97 g cm−3 and 0.87 g cm−3, respectively). In addition, the ρeff of most particles increased as the increases in pollution levels and relative humidity, yet varied differently for different types of particles, highlighting the impacts of aging and formation processes on the changes of particle density and mixing state.

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Single-particle volatility and implications for brown carbon absorption in Beijing, China

Cited by 6 publications

References 58 publications

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

Reliability Evaluation Based on the Colored Petri Net Converted from AADL Models for the Wheel Brake System of Aircraft

Mixing state and effective density of aerosol particles during the Beijing 2022 Olympic Winter Games

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