Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species Article Swipe
Junlei Zhan
,
Yongchun Liu
,
Wei Ma
,
Xin Zhang
,
Xuezhong Wang
,
Fang Bi
,
Yujie Zhang
,
Zhenhai Wu
,
Hong Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/amt-15-1511-2022
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/amt-15-1511-2022
The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing, China, from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using concentrations of measured/initial VOC species (R2=0.82/0.81) was better than that using total VOC (TVOC) concentrations (R2=0.77). Meanwhile, the RIs of initial VOC species correlated well with their O3 formation potentials (OFPs), which indicate that the model results can be partially explained by the maximum incremental reactivity (MIR) method. O3 formation presented a negative response to nitrogen oxides (NOx) and relative humidity (RH), and a positive response to temperature (T), solar radiation (SR), and VOCs. The O3 isopleth calculated by the RF model was generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis.
Related Topics
Concepts
Ozone
Volatile organic compound
NOx
Relative humidity
Chemistry
Reactivity (psychology)
Ground Level Ozone
Environmental chemistry
Environmental science
Sensitivity (control systems)
Beijing
Atmospheric chemistry
Atmospheric sciences
Meteorology
Photochemistry
Organic chemistry
Engineering
Combustion
China
Electronic engineering
Law
Alternative medicine
Political science
Pathology
Geology
Medicine
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/amt-15-1511-2022
- OA Status
- gold
- Cited By
- 32
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220685349
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220685349Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/amt-15-1511-2022Digital Object Identifier
- Title
-
Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound speciesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-16Full publication date if available
- Authors
-
Junlei Zhan, Yongchun Liu, Wei Ma, Xin Zhang, Xuezhong Wang, Fang Bi, Yujie Zhang, Zhenhai Wu, Hong LiList of authors in order
- Landing page
-
https://doi.org/10.5194/amt-15-1511-2022Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/amt-15-1511-2022Direct OA link when available
- Concepts
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Ozone, Volatile organic compound, NOx, Relative humidity, Chemistry, Reactivity (psychology), Ground Level Ozone, Environmental chemistry, Environmental science, Sensitivity (control systems), Beijing, Atmospheric chemistry, Atmospheric sciences, Meteorology, Photochemistry, Organic chemistry, Engineering, Combustion, China, Electronic engineering, Law, Alternative medicine, Political science, Pathology, Geology, Medicine, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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32Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 3, 2023: 7, 2022: 6Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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