Characteristics of HONO and its impact on O3 formation in the Seoul Metropolitan Area during the Korea-US Air Quality study
DC Field | Value | Language |
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dc.contributor.author | Gil, J. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Lee, M. | - |
dc.contributor.author | Lee, G. | - |
dc.contributor.author | Ahn, J. | - |
dc.contributor.author | Lee, D.S. | - |
dc.contributor.author | Jung, J. | - |
dc.contributor.author | Cho, S. | - |
dc.contributor.author | Whitehill, A. | - |
dc.contributor.author | Szykman, J. | - |
dc.contributor.author | Lee, J. | - |
dc.date.accessioned | 2021-12-03T20:41:35Z | - |
dc.date.available | 2021-12-03T20:41:35Z | - |
dc.date.created | 2021-08-31 | - |
dc.date.issued | 2021-02-15 | - |
dc.identifier.issn | 1352-2310 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/129205 | - |
dc.description.abstract | Photolysis of nitrous acid (HONO) is recognized as an early-morning source of OH radicals in the urban air. During the Korea–US Air Quality (KORUS-AQ) campaign, HONO was measured using quantum cascade - tunable infrared laser differential absorption spectrometer (QC-TILDAS) at Olympic Park in Seoul from 17 May, 2016 to 14 June, 2016. The HONO concentration was in the range of 0.07–3.46 ppbv, with an average of 0.93 ppbv. Moreover, it remained high from 00:00–05:00 LST. During this time, the mean concentration was higher during the high-O3 episodes (1.82 ppbv) than the non-episodes (1.20 ppbv). In the morning, the OH radicals that were produced from HONO photolysis were 50% higher (0.95 pptv) during the high-O3 episodes than the non-episodes. Diurnal variations in HOx and O3 concentrations were simulated by the F0AM model, which revealed a difference of ~20 ppbv in the daily maximum O3 concentrations between the high-O3 episodes and non-episodes. Furthermore, the HONO concentration increased with an increase in relative humidity (RH) up to 80%; the highest HONO was associated with the top 10% NO2 in each RH group, confirming that NO2 is one of the main precursors of HONO. At night, the conversion ratio of NO2 to HONO was estimated to be 0.88×10−2 h−1; this ratio was found to increase with an increase in RH. The Aitken mode particles (30–120 nm), which act as catalyst surfaces, exhibited a similar tendency with a conversion ratio that increased along with RH, indicating the coupling of surfaces with HONO conversion. Using an artificial neural network (ANN) model, HONO concentrations were successfully simulated with measured variables (r2 = 0.66 as an average of five models). Among these variables, NOx, aerosol surface area, and RH were found to be the main factors affecting the ambient HONO concentrations. The results reveal that RH facilitates the conversion of NO2 to HONO by constraining the availability of aerosol surfaces. This study demonstrates the coupling of HONO with the HOx-O3 cycle in the Seoul Metropolitan Area (SMA) and provides practical evidence of the heterogeneous formation of HONO by employing the ANN model. © 2021 The Author(s) | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.subject | Aerosols | - |
dc.subject | Air quality | - |
dc.subject | Free radicals | - |
dc.subject | Metropolitan area networks | - |
dc.subject | Neural networks | - |
dc.subject | Nitrogen oxides | - |
dc.subject | Photolysis | - |
dc.subject | Aerosol surface area | - |
dc.subject | Artificial neural network models | - |
dc.subject | Differential absorption | - |
dc.subject | Diurnal variation | - |
dc.subject | Heterogeneous formation | - |
dc.subject | Mean concentrations | - |
dc.subject | Seoul metropolitan area | - |
dc.subject | Tunable infrared laser | - |
dc.subject | Inorganic acids | - |
dc.subject | hydroxyl radical | - |
dc.subject | nitric oxide | - |
dc.subject | nitrogen dioxide | - |
dc.subject | nitrous acid | - |
dc.subject | ozone | - |
dc.subject | aerosol | - |
dc.subject | air quality | - |
dc.subject | Article | - |
dc.subject | artificial neural network | - |
dc.subject | atmosphere | - |
dc.subject | catalyst | - |
dc.subject | circadian rhythm | - |
dc.subject | controlled study | - |
dc.subject | data analysis software | - |
dc.subject | environmental impact | - |
dc.subject | humidity | - |
dc.subject | infrared radiation | - |
dc.subject | limit of detection | - |
dc.subject | maximum concentration | - |
dc.subject | meteorology | - |
dc.subject | particulate matter | - |
dc.subject | photolysis | - |
dc.subject | photooxidation | - |
dc.subject | priority journal | - |
dc.subject | South Korea | - |
dc.subject | surface area | - |
dc.subject | United States | - |
dc.subject | urban area | - |
dc.title | Characteristics of HONO and its impact on O3 formation in the Seoul Metropolitan Area during the Korea-US Air Quality study | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, M. | - |
dc.identifier.doi | 10.1016/j.atmosenv.2020.118182 | - |
dc.identifier.scopusid | 2-s2.0-85099257160 | - |
dc.identifier.wosid | 000632495000003 | - |
dc.identifier.bibliographicCitation | Atmospheric Environment, v.247 | - |
dc.relation.isPartOf | Atmospheric Environment | - |
dc.citation.title | Atmospheric Environment | - |
dc.citation.volume | 247 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Meteorology & Atmospheric Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Meteorology & Atmospheric Sciences | - |
dc.subject.keywordPlus | Aerosols | - |
dc.subject.keywordPlus | Air quality | - |
dc.subject.keywordPlus | Free radicals | - |
dc.subject.keywordPlus | Metropolitan area networks | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Nitrogen oxides | - |
dc.subject.keywordPlus | Photolysis | - |
dc.subject.keywordPlus | Aerosol surface area | - |
dc.subject.keywordPlus | Artificial neural network models | - |
dc.subject.keywordPlus | Differential absorption | - |
dc.subject.keywordPlus | Diurnal variation | - |
dc.subject.keywordPlus | Heterogeneous formation | - |
dc.subject.keywordPlus | Mean concentrations | - |
dc.subject.keywordPlus | Seoul metropolitan area | - |
dc.subject.keywordPlus | Tunable infrared laser | - |
dc.subject.keywordPlus | Inorganic acids | - |
dc.subject.keywordPlus | hydroxyl radical | - |
dc.subject.keywordPlus | nitric oxide | - |
dc.subject.keywordPlus | nitrogen dioxide | - |
dc.subject.keywordPlus | nitrous acid | - |
dc.subject.keywordPlus | ozone | - |
dc.subject.keywordPlus | aerosol | - |
dc.subject.keywordPlus | air quality | - |
dc.subject.keywordPlus | Article | - |
dc.subject.keywordPlus | artificial neural network | - |
dc.subject.keywordPlus | atmosphere | - |
dc.subject.keywordPlus | catalyst | - |
dc.subject.keywordPlus | circadian rhythm | - |
dc.subject.keywordPlus | controlled study | - |
dc.subject.keywordPlus | data analysis software | - |
dc.subject.keywordPlus | environmental impact | - |
dc.subject.keywordPlus | humidity | - |
dc.subject.keywordPlus | infrared radiation | - |
dc.subject.keywordPlus | limit of detection | - |
dc.subject.keywordPlus | maximum concentration | - |
dc.subject.keywordPlus | meteorology | - |
dc.subject.keywordPlus | particulate matter | - |
dc.subject.keywordPlus | photolysis | - |
dc.subject.keywordPlus | photooxidation | - |
dc.subject.keywordPlus | priority journal | - |
dc.subject.keywordPlus | South Korea | - |
dc.subject.keywordPlus | surface area | - |
dc.subject.keywordPlus | United States | - |
dc.subject.keywordPlus | urban area | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordAuthor | F0AM | - |
dc.subject.keywordAuthor | Formation mechansim | - |
dc.subject.keywordAuthor | HONO | - |
dc.subject.keywordAuthor | QC-TILDAS | - |
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