Atmospheric CO, O3, and SO2 Measurements at the Summit of Mt. Fuji during the Summer of 2013

Kato, S.; Shiobara, Yasuhiro; Uchiyama, Katsumi; Miura, Kazuhiko; Okochi, Hiroshi; Kobayashi, Hiroshi; Hatakeyama, Shiro

doi:10.4209/aaqr.2015.11.0632

Cited by 18 publications

(12 citation statements)

References 34 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common indicatory air pollutant in summer was O 3 , but, in winter, it was PM 2.5 . Previous studies have shown that atmospheric relative humidity is negatively correlated with O 3 concentration and that a lower relative humidity is conducive to the formation of O 3 (Kato et al, 2016;Li et al, 2017b;Gong et al, 2018). In summer, low relative humidity and high temperatures and wind speeds are conducive to the dispersion, diffusion, and dilution of air pollutants.…”

Section: The Top Five Days With the Highest Aqis From 2017 To 2019mentioning

confidence: 98%

Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

Zhang

Cui

Wang

et al. 2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

This study investigated the AQI (air quality index) and atmospheric pollutants including PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 in Chongqing, Luzhou and Chengdu from 2017 to 2019. In addition, the impacts of the COVID-19 event on the air quality in the three cities in 2020 were compared and discussed. For the combined AQIs for the three cities, in spring, the daily AQIs ranged between 25 and 182 and averaged 72.1. In summer, the daily AQIs ranged between 24 and 206 and averaged 77.5. In autumn, the daily AQIs ranged between 22 and 170 and averaged 61.1, and in winter, the daily AQIs ranged between 28 and 375 and averaged 99.6. The distributions of the six AQI classes in spring were 3%, 94%, 3%, 0%, 0%, and 0%; in summer, they were 11%, 74%, 15%, 0%, 0% and 0%; in autumn, they were 29%, 70%, 1%, 0%, 0%, and 0%, and in winter, they were 1%, 52%, 44%, 3%, 0%, and 0%, respectively. The average AQIs, in order, were Chengdu (85.4) > Chongqing (73.8) > Luzhou (73.2). Both the highest AQIs and PM 2.5 (as the major indicatory air pollutant) occurred mainly in the low temperature season (January, December, and February), while O 3 was the main air pollutant in June and August when the weather was hot. In February 2020, during the epidemic prevention and control actions taken in response to COVID-19 for the three cities, the combined AQIs for the top five days with the highest

show abstract

Section: The Top Five Days With the Highest Aqis From 2017 To 2019mentioning

confidence: 98%

Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

Zhang

Cui

Wang

et al. 2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…The trip intensity and daily concentration of PM 10 from January 12 to March 27, 2019 and those in 2020 during the same period in Shenzhen, Guangzhou, and Foshan, respectively. world's largest coal producer and consumer (Kurokawa et al, 2013;Kato et al, 2016). It can be seen that SO 2 concentrations in Guangzhou and Foshan from January 12 to March 26 in 2020 were reduced, indicating that factory shutdowns caused by the Chinese New Year holiday and epidemic prevention actions led to significant reductions in SO 2 emissions.…”

Section: So2 Concentrationmentioning

confidence: 99%

Impact of the COVID-19 Event on Trip Intensity and Air Quality in Southern China

Wan

Cui

Wang

et al. 2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

The COVID-19 epidemic discovered and reported at the end of December 2019 and began spreading rapidly around the world. The impact of the COVID-19 event on the trip intensity, AQI (air quality index), and air pollutants, including PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 in Shenzhen, Guangzhou, and Foshan (the so-called 'three cities') from January 12 to March 27, in 2019 and 2020, are compared and discussed. In 2020, the combined trip intensity in the three cities ranged between 0.73 and 5.54 and averaged 2.57, which was 28.4% lower than that in 2019. In terms of the combined AQIs for the three cities, from January 12 to March 26, 2020, the daily AQIs ranged between 21.0 and 121.3 and averaged 56.4, which was 16.0% lower than that in 2019. The average AQIs in order were Guangzhou (57.5) > Foshan (54.1) > Shenzhen (44.1). In 2019, the distribution proportions of the six AQI classes were 45.2%, 50.4%, 4.40%, 0%, 0%, and 0%, respectively, while those in 2020 were 62.7%, 37.3%, 0%, 0%, 0% and 0%, respectively. For the combined data for the three cities, on the top five days with the highest AQIs during the epidemic period, the average concentrations of

show abstract

“…It is critical to ensure more accurate representation of BVOC emissions within air quality simulations, as this could alter regimes of ambient ozone formation and the relative importance of controls on anthropogenic NO X and VOC emissions [36,37]. However, better representation of BVOC emissions in Japan is not a single solution for eliminating overestimation of its ambient ozone concentrations, as these are greatly influenced by ozone transport from the continent, particularly during summer 2013 [38]. Therefore, it is necessary to find other ways to reduce simulated ambient ozone concentrations at the larger regional scale.…”

Section: Discussionmentioning

confidence: 99%

Effects of a Detailed Vegetation Database on Simulated Meteorological Fields, Biogenic VOC Emissions, and Ambient Pollutant Concentrations over Japan

et al. 2018

View full text Add to dashboard Cite

Regional air quality simulations provide powerful tools for clarifying mechanisms of heavy air pollution and for considering effective strategies for better air quality. This study introduces a new vegetation database for Japan, which could provide inputs for regional meteorological modeling, and estimating emissions of biogenic volatile organic compounds (BVOCs), both of which are essential components of simulations. It includes newly developed emission factors (EFs) of BVOCs for major vegetation types in Japan, based on existing literature. The new database contributes to improved modeling of meteorological fields due to its updated representation of larger urban areas. Using the new vegetation and EF database, lower isoprene and monoterpene, and higher sesquiterpene emissions are estimated for Japan than those derived from previously available default datasets. These slightly reduce the overestimation of ozone concentrations obtained by a regional chemical transport model, whereas their effects on underestimated secondary organic aerosol (SOA) concentrations are marginal. Further work is necessary, not only on BVOC emissions but also the other simulation components, to further improve the modeling of ozone and SOA concentrations in Japan.

show abstract

Atmospheric CO, O3, and SO2 Measurements at the Summit of Mt. Fuji during the Summer of 2013

Cited by 18 publications

References 34 publications

Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

Impact of the COVID-19 Event on Trip Intensity and Air Quality in Southern China

Effects of a Detailed Vegetation Database on Simulated Meteorological Fields, Biogenic VOC Emissions, and Ambient Pollutant Concentrations over Japan

Contact Info

Product

Resources

About