Regional Air Quality Modeling System (RAQMS) predictions of the tropospheric ozone budget over east Asia

2003 ◽  
Vol 108 (D21) ◽  
Author(s):  
R. B. Pierce ◽  
J. A. Al-Saadi ◽  
T. Schaack ◽  
A. Lenzen ◽  
T. Zapotocny ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Tropospheric Ozone ◽  
Air Quality Modeling ◽  
Quality Modeling ◽  
Regional Air Quality

Updating Chinese SO2 emissions with surface observations for regional air-quality modeling over East Asia

2020 ◽  
Vol 228 ◽  
pp. 117416 ◽  
Author(s):  
Changhan Bae ◽  
Hyun Cheol Kim ◽  
Byeong-Uk Kim ◽  
Younha Kim ◽  
Jung-Hun Woo ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Air Quality Modeling ◽  
So2 Emissions ◽  
Quality Modeling ◽  
Regional Air Quality ◽  
Surface Observations

Isoprene Emission Factors for Subtropical Street Trees for Regional Air Quality Modeling

2016 ◽  
Vol 45 (1) ◽  
pp. 234-243 ◽  
Author(s):  
Kristina A. Dunn-Johnston ◽  
Jürgen Kreuzwieser ◽  
Satoshi Hirabayashi ◽  
Lyndal Plant ◽  
Heinz Rennenberg ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Quality Modeling ◽  
Emission Factors ◽  
Street Trees ◽  
Isoprene Emission ◽  
Quality Modeling ◽  
Regional Air Quality

scholarly journals Surface And Aloft NO2 Pollution Over The Greater Tokyo Area Observed By Ground-Based And MAX-DOAS Measurements Bridged By Regional Air Quality Modeling

2021 ◽  
Author(s):  
Syuichi Itahashi ◽  
Hitoshi Irie
Keyword(s):  
Air Quality ◽  
Early Morning ◽  
Air Quality Modeling ◽  
Optical Absorption Spectroscopy ◽  
Visible Range ◽  
Observation System ◽  
Near Surface ◽  
North East ◽  
Quality Modeling ◽  
Regional Air Quality

Abstract To advance our understanding of surface and aloft nitrogen dioxide (NO2) pollution, this study extensively evaluated NO2 concentrations simulated by the regional air quality modeling system with a horizontal grid resolution of 1.3 km by using the Atmospheric Environmental Regional Observation System (AEROS) ground-based observation network and aloft measurement by multi-axis differential optical absorption spectroscopy (MAX-DOAS) over the greater Tokyo area. Observations are usually limited to the surface level, and gaps remain in our understanding of the behavior of air pollutants above the near-surface layer, particularly within the planetary boundary layer (PBL). Therefore, MAX-DOAS measurement was used, which observes scattered sunlight in the ultraviolet/visible range at several elevation angles between the horizon and zenith to determine the aloft NO2 pollution averaged over 0-1 km. In total, four MAX-DOAS measurement systems at Chiba University (35.63°N, 140.10°E) systematically covered the north, east, west, and south directions to capture the aloft NO2 pollution over the greater Tokyo area. The target period was Chiba-Campaign 2015 conducted from 9 to 23 November 2015. The evaluations showed that the air quality modeling system can generally capture the observed behavior of both surface and aloft NO2 pollution in terms of spatial and temporal coverage. The diurnal variation, which typically showed an increase from evening to early morning without daylight and a decrease during the daytime, was also captured by the model. During Chiba-Campaign 2015, two cases of episodic higher NO2 concentration were identified: one during the nighttime and the other during the daytime as different diurnal patterns. These were related to a stagnant wind field, with the latter also connected to a lower PBL height in cloudy conditions. Comparison of the modeled surface and aloft NO2 concentrations showed that aloft NO2 concentration exhibited a strong linear correlation with surface NO2 concentration, with the aloft value scaled to 0.4-0.5-fold the surface value, irrespective of whether the day was clean or polluted. This scaling value was lower during the nighttime and higher during the daytime. Based on this synergetic analysis of surface and aloft observation bridged by modeling simulation, this study contributes to fostering understanding of aloft NO2 pollution.

Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling

2021 ◽  
Vol 755 ◽  
pp. 142621
Author(s):  
Chien-Yuan Chen ◽  
Ho Wen Chen ◽  
Chu-Ting Sun ◽  
Yen Hsun Chuang ◽  
Kieu Lan Phuong Nguyen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Impact Assessment ◽  
Air Quality Modeling ◽  
Quality Modeling ◽  
Regional Air Quality
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