The particle phase state during the biomass burning events

Liu, Yuechen; Meng, Xiangzhi; Wu, Zhijun; Huang, Dandan; Wang, Hongli; Chen, Jie; Chen, Jingchuan; Zong, Taomou; Fang, Xin; Tan, Tianyi; Zhao, Gang; Chen, Shiyi; Zeng, Liangwei; Guo, Song; Huang, Xian; He, Lingyan; Zeng, Limin; Hu, Min

doi:10.1016/j.scitotenv.2021.148035

Cited by 12 publications

(18 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DeRieux et al (2018) predicted the viscosity of biomass-burning particles using their chemical composition and found that their viscosity varied between 10 −2 and 10 9 Pa s, depending on the RH. Liu et al (2021) found that ambient and lab-generated biomass-burning particles are in a nonsolid state at RHs of between 20 % and 50 %. Li et al (2020) predicted the viscosity of ambient biomassburning organic aerosols from the volatility distribution and found that it varies from 10 −2 Pa s (liquid state) to above 10 12 Pa s (solid state) in Athens (Greece) and from 10 −2 Pa s (liquid state) to ∼ 10 9 Pa s in Mexico City (Mexico).…”

Section: Phase States Of Particles At the Omp Sitementioning

confidence: 99%

Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources

et al. 2022

View full text Add to dashboard Cite

Abstract. Free tropospheric aerosol particles have important but poorly constrained climate effects due to transformations of their physicochemical properties during long-range transport. In this study, we investigate the chemical composition and provide an overview of the phase states of individual particles that have undergone long-range transport over the North Atlantic Ocean in June and July 2014, 2015, and 2017 to the Observatory of Mount Pico (OMP) in the Azores. The OMP is an ideal site for studying long-range-transported free tropospheric particles because local emissions have a negligible influence and contributions from the boundary layer are rare. We used the FLEXible PARTicle Lagrangian particle dispersion model (FLEXPART) to determine the origins and transport trajectories of sampled air masses and found that most of them originated from North America and recirculated over the North Atlantic Ocean. The FLEXPART analysis showed that the sampled air masses were highly aged (average plume age >10 d). Size-resolved chemical compositions of individual particles were probed using computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX) and scanning transmission X-ray microscopy with near-edge X-ray absorption fine structure spectroscopy (STXM-NEXAFS). CCSEM-EDX results showed that the most abundant particle types were carbonaceous (∼ 29.9 % to 82.0 %), sea salt (∼ 0.3 % to 31.6 %), and sea salt with sulfate (∼ 2.4 % to 31.5 %). We used a tilted stage interfaced within an environmental scanning electron microscope (ESEM) to determine the phase states of individual submicron particles. We found that most particles (∼ 47 % to 99 %) were in the liquid state at the time of collection due to inorganic inclusions. Moreover, we also observed substantial fractions of solid and semisolid particles (∼ 0 % to 30 % and ∼ 1 % to 42 %, respectively) during different transport patterns and events, reflecting the particles' phase-state variability for different atmospheric transport events and sources. Combining phase state measurements with FLEXPART CO tracer analysis, we found that wildfire-influenced plumes can result in particles with a wide range of viscosities after long-range transport in the free troposphere. We also used temperature and RH values extracted from the Global Forecast System (GFS) along the FLEXPART-simulated path to predict the phase state of the particles during transport and found that neglecting internal mixing with inorganics would lead to an overestimation of the viscosity of free tropospheric particles. Our findings warrant future investigation aiming at the quantitative assessment of the influence of internal mixing on the phase states of the individual particles. This study also provides insights into the chemical composition and phase state of free tropospheric particles, which can help models to reduce uncertainties about the effects of ambient aerosol particles on climate.

show abstract

Section: Phase States Of Particles At the Omp Sitementioning

confidence: 99%

Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This observation (i.e., dominance of liquid-like particles in biomass burning) was similarly observed by Liu et al (not strictly related to wildre burning events), where they also establish that numerous chamber-related studies have underestimated the liquid character of true, ambient biomass burning particles. 27…”

Section: Papermentioning

confidence: 99%

“…1 Therefore, given the importance of aerosol particle physical properties, many research efforts have targeted a further understanding of the interplay between aerosol size, phase state, and chemical composition [14][15][16][17][18][19][20][21] with some specically focusing on the chemical composition of size-resolved biomass burning (including wildres). [22][23][24][25][26][27] As examples, size-dependent aerosol mixing states have been observed from biomass burning events in China, 25,26 and brown carbon was found to have a size-dependence in wildre specic plumes in Northeast Canada. 23 Size-resolved secondary organic aerosol (SOA) composition and evolution in wildre aerosols remains as a signicant knowledge gap though, particularly with respect to how molecular level information inuences particle physical properties.…”

Section: Introductionmentioning

confidence: 99%

Case study evaluation of size-resolved molecular composition and phase state of carbonaceous particles in wildfire influenced smoke from the Pacific Northwest

Vandergrift,

Lata,

Mathai

et al. 2023

Environ. Sci.: Atmos.

View full text Add to dashboard Cite

show abstract

“…14,25,26 The Aerosol Inorganic−Organic Mixtures Functional groups Activity Coefficients Viscosity (AIOMFAC-VISC) model was used to calculate the viscosity of particles by which the particle phase state can be further determined. 27 As a unique online method, particle rebound measurements 28 are conducted to identify the phase state of atmospheric particles in forests, 10 rural areas, 29 and polluted megacities. 12 Although these aforementioned methods were proposed, currently, the global particle phase state has not been widely discussed.…”

Section: Introductionmentioning

confidence: 99%

“…The dynamic viscosity of laboratory-generated particles was quantified using a series of offline measurement techniques, such as bead-mobility technique, poke-and-flow technique, and measurement using optical tweezers. , These methods were used to detect the viscosity of field particles. , Based on the laboratory measurements, several semiempirical methods have also been developed to estimate the glass-transition temperature ( T g ), and the particle phase state was determined by comparing T g with ambient temperature. ,, The Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients Viscosity (AIOMFAC-VISC) model was used to calculate the viscosity of particles by which the particle phase state can be further determined . As a unique online method, particle rebound measurements are conducted to identify the phase state of atmospheric particles in forests, rural areas, and polluted megacities . Although these aforementioned methods were proposed, currently, the global particle phase state has not been widely discussed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements

Qiu,

Liu,

et al. 2023

Environ. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

The particle phase state plays a vital role in the gas− particle partitioning, multiphase reactions, ice nucleation activity, and particle growth in the atmosphere. However, the characterization of the atmospheric phase state remains challenging. Herein, based on measured aerosol chemical composition and ambient relative humidity (RH), a machine learning (ML) model with high accuracy (R 2 = 0.952) and robustness (RMSE = 0.078) was developed to predict the particle rebound fraction, f, which is an indicator of the particle phase state. Using this ML model, the f of particles in the urban atmosphere was predicted based on seasonal average aerosol chemical composition and RH. Regardless of seasons, aerosols remain in the liquid state of mid-high latitude cities in the northern hemisphere and in the semisolid state over semiarid regions. In the East Asian megacities, the particles remain in the liquid state in spring and summer and in the semisolid state in other seasons. The effects of nitrate, which is becoming dominant in fine particles in several urban areas, on the particle phase state were evaluated. More nitrate led the particles to remain in the liquid state at an even lower RH. This study proposed a new approach to predict the particle phase state in the atmosphere based on RH and aerosol chemical composition.

show abstract

The particle phase state during the biomass burning events

Cited by 12 publications

References 48 publications

Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources

Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources

Case study evaluation of size-resolved molecular composition and phase state of carbonaceous particles in wildfire influenced smoke from the Pacific Northwest

Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements

Contact Info

Product

Resources

About