Literature DB >> 33746556

Characteristics of HONO and its impact on O3 formation in the Seoul Metropolitan Area during the Korea-US Air Quality Study.

Junsu Gil1, Jeonghwan Kim2, Meehye Lee1, Gangwoong Lee2, Joonyeong An3, Dongsoo Lee4, Jinsang Jung5, Seogju Cho6, Andrew Whitehill7, James Szykman7, Jeonghoon Lee8.   

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.

Entities:  

Year:  2021        PMID: 33746556      PMCID: PMC7970509          DOI: 10.1016/j.atmosenv.2020.118182

Source DB:  PubMed          Journal:  Atmos Environ (1994)        ISSN: 1352-2310            Impact factor:   4.798


  39 in total

1.  Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing.

Authors:  Renyi Zhang; Alexei F Khalizov; Joakim Pagels; Dan Zhang; Huaxin Xue; Peter H McMurry
Journal:  Proc Natl Acad Sci U S A       Date:  2008-07-21       Impact factor: 11.205

2.  Observation of nitrous acid (HONO) in Beijing, China: Seasonal variation, nocturnal formation and daytime budget.

Authors:  Jiaqi Wang; Xiaoshan Zhang; Jia Guo; Zhangwei Wang; Meigen Zhang
Journal:  Sci Total Environ       Date:  2017-02-23       Impact factor: 7.963

3.  Impacts of potential HONO sources on the concentrations of oxidants and secondary organic aerosols in the Beijing-Tianjin-Hebei region of China.

Authors:  Jingwei Zhang; Junling An; Yu Qu; Xingang Liu; Yong Chen
Journal:  Sci Total Environ       Date:  2018-08-05       Impact factor: 7.963

4.  An observational study of nitrous acid (HONO) in Shanghai, China: The aerosol impact on HONO formation during the haze episodes.

Authors:  Lulu Cui; Rui Li; Yunchen Zhang; Ya Meng; Hongbo Fu; Jianmin Chen
Journal:  Sci Total Environ       Date:  2018-03-07       Impact factor: 7.963

5.  Atmospheric hydroxyl radical production from electronically excited NO2 and H2O.

Authors:  Shuping Li; Jamie Matthews; Amitabha Sinha
Journal:  Science       Date:  2008-03-21       Impact factor: 47.728

6.  Development of ion drift-chemical ionization mass spectrometry.

Authors:  Edward C Fortner; Jun Zhao; Renyi Zhang
Journal:  Anal Chem       Date:  2004-09-15       Impact factor: 6.986

7.  Enhanced photochemical conversion of NO2 to HONO on humic acids in the presence of benzophenone.

Authors:  Chong Han; Wangjin Yang; He Yang; Xiangxin Xue
Journal:  Environ Pollut       Date:  2017-09-25       Impact factor: 8.071

8.  Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5.

Authors:  Bun Theang Ong; Komei Sugiura; Koji Zettsu
Journal:  Neural Comput Appl       Date:  2015-06-26       Impact factor: 5.606

9.  Investigation of factors controlling PM2.5 variability across the South Korean Peninsula during KORUS-AQ.

Authors:  Carolyn E Jordan; James H Crawford; Andreas J Beyersdorf; Thomas F Eck; Hannah S Halliday; Benjamin A Nault; Lim-Seok Chang; JinSoo Park; Rokjin Park; Gangwoong Lee; Hwajin Kim; Jun-Young Ahn; Seogju Cho; Hye Jung Shin; Jae Hong Lee; Jinsang Jung; Deug-Soo Kim; Meehye Lee; Taehyoung Lee; Andrew Whitehill; James Szykman; Melinda K Schueneman; Pedro Campuzano-Jost; Jose L Jimenez; Joshua P DiGangi; Glenn S Diskin; Bruce E Anderson; Richard H Moore; Luke D Ziemba; Marta A Fenn; Johnathan W Hair; Ralph E Kuehn; Robert E Holz; Gao Chen; Katherine Travis; Michael Shook; David A Peterson; Kara D Lamb; Joshua P Schwarz
Journal:  Elementa (Wash D C)       Date:  2020       Impact factor: 6.053

10.  Hydroxylamine released by nitrifying microorganisms is a precursor for HONO emission from drying soils.

Authors:  M Ermel; T Behrendt; R Oswald; B Derstroff; D Wu; S Hohlmann; C Stönner; A Pommerening-Röser; M Könneke; J Williams; F X Meixner; M O Andreae; I Trebs; M Sörgel
Journal:  Sci Rep       Date:  2018-01-30       Impact factor: 4.379

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