Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park
Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023073334 |
| _version_ | 1797669722807861248 |
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| author | Qiaoli Wang Dongping Sheng Chengzhi Wu Jingkai Zhao Feili Li Shengdong Yao Xiaojie Ou Wei Li Jianmeng Chen |
| author_facet | Qiaoli Wang Dongping Sheng Chengzhi Wu Jingkai Zhao Feili Li Shengdong Yao Xiaojie Ou Wei Li Jianmeng Chen |
| author_sort | Qiaoli Wang |
| collection | DOAJ |
| description | Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O3 formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O3 tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO2) leads to a larger decrease capacity in O3 concentration. More importantly, there is a local reachable maximum incremental reactivity (MIRL) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O3 concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O3 and the precursors control. |
| first_indexed | 2024-03-11T20:48:40Z |
| format | Article |
| id | doaj.art-ab1ded00f3134b3b86424bbfb0c57db0 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T20:48:40Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ab1ded00f3134b3b86424bbfb0c57db02023-10-01T06:02:38ZengElsevierHeliyon2405-84402023-09-0199e20125Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial parkQiaoli Wang0Dongping Sheng1Chengzhi Wu2Jingkai Zhao3Feili Li4Shengdong Yao5Xiaojie Ou6Wei Li7Jianmeng Chen8College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China; Corresponding author.College of Environment, Zhejiang University of Technology, Hangzhou, 310032, ChinaTrinity Consultants, Inc. (China Office), Hangzhou, 310012, ChinaCollege of Environment, Zhejiang University of Technology, Hangzhou, 310032, ChinaCollege of Environment, Zhejiang University of Technology, Hangzhou, 310032, ChinaCollege of Environment, Zhejiang University of Technology, Hangzhou, 310032, ChinaCollege of Environment, Zhejiang University of Technology, Hangzhou, 310032, ChinaKey Laboratory of Biomass Chemical Engineering of the Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University (Zijingang Campus), Hangzhou, 310030, ChinaCollege of Environment, Zhejiang University of Technology, Hangzhou, 310032, China; Zhejiang University of Science & Technology, Hangzhou, 310023, China; Corresponding author. College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China.Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O3 formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O3 tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO2) leads to a larger decrease capacity in O3 concentration. More importantly, there is a local reachable maximum incremental reactivity (MIRL) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O3 concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O3 and the precursors control.http://www.sciencedirect.com/science/article/pii/S2405844023073334Ozone formation rulesSensitivity and response analysisMaximum incremental reactivityArtificial neural network |
| spellingShingle | Qiaoli Wang Dongping Sheng Chengzhi Wu Jingkai Zhao Feili Li Shengdong Yao Xiaojie Ou Wei Li Jianmeng Chen Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park Heliyon Ozone formation rules Sensitivity and response analysis Maximum incremental reactivity Artificial neural network |
| title | Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| title_full | Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| title_fullStr | Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| title_full_unstemmed | Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| title_short | Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| title_sort | exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park |
| topic | Ozone formation rules Sensitivity and response analysis Maximum incremental reactivity Artificial neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023073334 |
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