Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning DOI
Xinyu Wang,

Yongyi Zhao,

Ke Hu

и другие.

ACS ES&T Air, Год журнала: 2024, Номер 1(10), С. 1239 - 1251

Опубликована: Авг. 27, 2024

We conducted thermal desorption measurements and machine learning analysis to investigate the volatility precursors of ambient oxygenated organic aerosols (OOA), with a focus on link between them, in variety urban marine locations. found that OOA species measured by an iodide-based Chemical Ionization Mass Spectrometer equipped Filter Inlet for Gases AEROsol (FIGAERO-CIMS) accounted 16 ± 13% OA those locations represented mostly secondary moderate-volatility portion OA. On average, 25.1% number 26.8% mass detected FIGAERO-CIMS winter campaign at site Wuhan, megacity central China, might be attributed decomposition fragments. Our results show precursor differed systematically according location, season, pollution level. The ocean atmosphere was characterized high fractions extremely low compounds (ELVOC) aliphatic species, while inland aromatic fell primarily into (LVOCs) semivolatile (SVOCs) range. volatilities summer, days, daytime were lower than winter, clean nighttime. When PM episode developed from particle growth then period, shifted Low-Mw Median-Mw highly nonvolatile species. study this work also provides important data future closure studies SOA formation its precursors.

Язык: Английский

Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution DOI Creative Commons

Lei Lü,

Wei Xu, Chunshui Lin

и другие.

ACS ES&T Air, Год журнала: 2025, Номер 2(5), С. 891 - 902

Опубликована: Апрель 15, 2025

Accurate source apportionment of particulate matter (PM), especially organic aerosol (OA), is crucial for targeted mitigation efforts. Positive Matrix Factorization (PMF) powerful in attribution primary OA (POA); however, it often struggles to differentiate sources oxygenated (OOA) due their similar chemical profiles. In this study, a support vector regression machine learning (ML) model was developed enhance the OOA Dublin from 2016 2023. Rolling PMF analysis identified four POA factors and differentiated into less- more-oxidized (LO-OOA MO-OOA), highlighting significant role (47-74% total OA). The ML further distinguished locally produced (LO-OOAlocal MO-OOAlocal) transboundary transport exhibited robust performance across different pollution scenarios. relative importance revealed that LO-OOAlocal more impacted by fossil fuel emissions like hydrocarbon-like (20%) coal (14%), whereas MO-OOAlocal most influenced LO-OOA (17%), providing insights formation mechanisms. During mixed episode, results show despite contribution transport, local heating were critical OA, with accounting 68% reaching 78% during hours. These findings highlight ongoing need reduce achieve cleaner air Dublin. model's ability quantitatively separate offers invaluable future quality regulations.

Язык: Английский

Процитировано

0

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling DOI
Dejia Yin, Bin Zhao, Shuxiao Wang

и другие.

Environmental Science & Technology, Год журнала: 2024, Номер 58(19), С. 8380 - 8392

Опубликована: Май 1, 2024

A comprehensive understanding of the full volatility spectrum organic oxidation products from benzene series precursors is important to quantify air quality and climate effects secondary aerosol (SOA) new particle formation (NPF). However, current models fail capture due absence reaction pathways. Here, we develop a novel unified model framework, integrated two-dimensional basis set (I2D-VBS), simulate by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The successfully reproduces O/C distributions oxygenated molecules (OOMs) as well concentrations SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation are two main pathways for OOMs with similar contributions, but contributes more low-volatility products. NOx can reduce about two-thirds SOA, most extremely compared clean conditions, suppressing dimerization autoxidation. I2D-VBS facilitates holistic product which helps fill large gap in predictions NPF, growth, formation.

Язык: Английский

Процитировано

3

Resolving Atmospheric Oxygenated Organic Molecules in Urban Beijing Using Online Ultrahigh-Resolution Chemical Ionization Mass Spectrometry DOI
Yi Yuan, Xin Chen, Runlong Cai

и другие.

Environmental Science & Technology, Год журнала: 2024, Номер unknown

Опубликована: Сен. 27, 2024

Gaseous oxygenated organic molecules (OOMs) are crucial precursors of atmospheric aerosols. OOMs in urban atmospheres have complex compositions, posing challenges to understanding their formation, evolution, and influences. In this study, we identify 2403 gaseous Beijing using online nitrate-based chemical ionization Orbitrap mass spectrometry based on one-year measurements. We find that can be identified with higher accuracy wider coverage, compared previously used spectrometry. With optimized OOM resolving capabilities, previous knowledge expanded. First, clear homologous oxygen-addition characteristics the revealed. Second, lower concentrations or masses characterized high confidence, e.g., above 350 Da. particular, dimers (e.g., C

Язык: Английский

Процитировано

1

Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere DOI
Zeqi Li, Bin Zhao, Dejia Yin

и другие.

Environmental Science & Technology, Год журнала: 2023, Номер 58(2), С. 1223 - 1235

Опубликована: Дек. 20, 2023

Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude mechanism of organics' contribution to nanoparticle in polluted environments remain unclear because current observations models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes spectrum vapors contributions coupling advanced oxidation modeling kinetic gas-particle partitioning. The applied Nanjing, typical city, effectively captures distribution (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing rates (GRs), with 4.91% normalized mean bias. Simulations indicate as particles grow from 4 40 nm, relative fractions GRs attributable increase 59 86%, remaining H2SO4 its clusters. Aromatics contribute much condensable (∼37%), especially (∼61%), contributing most (32–46%) 4–40 nm grow. Alkanes also 19–35% GRs, while biogenic volatile compounds minimally (<13%). Our helps assess impacts predict future changes.

Язык: Английский

Процитировано

3

Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning DOI
Xinyu Wang,

Yongyi Zhao,

Ke Hu

и другие.

ACS ES&T Air, Год журнала: 2024, Номер 1(10), С. 1239 - 1251

Опубликована: Авг. 27, 2024

We conducted thermal desorption measurements and machine learning analysis to investigate the volatility precursors of ambient oxygenated organic aerosols (OOA), with a focus on link between them, in variety urban marine locations. found that OOA species measured by an iodide-based Chemical Ionization Mass Spectrometer equipped Filter Inlet for Gases AEROsol (FIGAERO-CIMS) accounted 16 ± 13% OA those locations represented mostly secondary moderate-volatility portion OA. On average, 25.1% number 26.8% mass detected FIGAERO-CIMS winter campaign at site Wuhan, megacity central China, might be attributed decomposition fragments. Our results show precursor differed systematically according location, season, pollution level. The ocean atmosphere was characterized high fractions extremely low compounds (ELVOC) aliphatic species, while inland aromatic fell primarily into (LVOCs) semivolatile (SVOCs) range. volatilities summer, days, daytime were lower than winter, clean nighttime. When PM episode developed from particle growth then period, shifted Low-Mw Median-Mw highly nonvolatile species. study this work also provides important data future closure studies SOA formation its precursors.

Язык: Английский

Процитировано

0