Impact of commercial cooking on urban PM2.5 and O3 with online data-assisted emission inventory
Copyright © 2023 Elsevier B.V. All rights reserved..
Commercial cooking (CC) is an intensive near-field source contributing to ambient PM2.5 and O3 concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM2.5 and O3 were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM2.5 and O3 concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m3 of PM2.5, and accounted for 24±1 % of PM2.5 concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM2.5 was predicted in areas with elevated CC emissions, while the contribution to O3 was insignificant.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:873 |
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| Enthalten in: |
The Science of the total environment - 873(2023) vom: 15. Mai, Seite 162256 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yuan, Yingzhi [VerfasserIn] |
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| Links: |
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| Themen: |
Air quality modeling |
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| Anmerkungen: |
Date Completed 27.03.2023 Date Revised 27.03.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.scitotenv.2023.162256 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 27.03.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a Commercial cooking (CC) is an intensive near-field source contributing to ambient PM2.5 and O3 concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM2.5 and O3 were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM2.5 and O3 concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m3 of PM2.5, and accounted for 24±1 % of PM2.5 concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM2.5 was predicted in areas with elevated CC emissions, while the contribution to O3 was insignificant | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Air quality modeling | |
| 650 | 4 | |a Commercial cooking | |
| 650 | 4 | |a Emission inventory | |
| 650 | 4 | |a Impact assessment | |
| 650 | 4 | |a Online oil fumes monitoring data | |
| 700 | 1 | |a Zhu, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Che-Jen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shuxiao |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Yanghong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Haixian |e verfasserin |4 aut | |
| 700 | 1 | |a Xing, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Mengmeng |e verfasserin |4 aut | |
| 700 | 1 | |a You, Zhiqiang |e verfasserin |4 aut | |
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