Articles | Volume 16, issue 21
https://doi.org/10.5194/gmd-16-6049-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6049-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0
Jiangyong Li
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou, 511443, China
Chunlin Zhang
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou, 511443, China
Wenlong Zhao
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Shijie Han
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou, 511443, China
Yu Wang
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Air Quality Studies, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Hao Wang
CORRESPONDING AUTHOR
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou, 511443, China
Boguang Wang
CORRESPONDING AUTHOR
Australia–China Centre for Air Quality Science and Management (Guangdong), Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China
Guangdong Provincial Observation and Research Station for Atmospheric Environment and Carbon Neutrality in Nanling Forests, Guangzhou, 511443, China
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Jonas Enarsson, Vilhelm Malmborg, Branka Miljevic, Axel Eriksson, Hao Wang, Zoran Ristovski, and Aneta Wierzbicka
EGUsphere, https://doi.org/10.5194/egusphere-2026-3807, https://doi.org/10.5194/egusphere-2026-3807, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Reactive oxygen species and oxidative potential are strongly linked to aerosol-induced adverse health effects and have emerged as widely used metrics for assessing particle toxicity. We found that soot-like particles can interfere with methods used to measure reactive oxygen species, causing misleading results. We have investigated this artefact and propose an explanation for the interference and a correction method that improves the accuracy of reactive oxygen species measurements.
Sihang Wang, Bin Yuan, Xianjun He, Ru Cui, Xin Song, Yubin Chen, Caihong Wu, Chaomin Wang, Yibo Huangfu, Xiao-Bing Li, Boguang Wang, and Min Shao
Atmos. Chem. Phys., 24, 7101–7121, https://doi.org/10.5194/acp-24-7101-2024, https://doi.org/10.5194/acp-24-7101-2024, 2024
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Emissions of reactive organic gases from industrial volatile chemical product sources are measured. There are large differences among these industrial sources. We show that oxygenated species account for significant contributions to reactive organic gas emissions, especially for industrial sources utilizing water-borne chemicals.
Tingting Hu, Yu Lin, Run Liu, Yuepeng Xu, Shanshan Ouyang, Boguang Wang, Yuanhang Zhang, and Shaw Chen Liu
Atmos. Chem. Phys., 24, 1607–1626, https://doi.org/10.5194/acp-24-1607-2024, https://doi.org/10.5194/acp-24-1607-2024, 2024
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We hypothesize that the cause of the worsening O3 trends in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and Pearl River Delta from 2015 to 2020 is attributable to the increased occurrence of meteorological conditions of high solar radiation and a positive temperature anomaly under the influence of West Pacific subtropical high, tropical cyclones, and mid–high-latitude wave activities.
Tingting Hu, Yu Lin, Run Liu, Yuepeng Xu, Boguang Wang, Yuanhang Zhang, and Shaw Chen Liu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-781, https://doi.org/10.5194/acp-2022-781, 2023
Revised manuscript not accepted
Short summary
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We hypothesize that the cause of the worsening O3 trends in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta from 2015 to 2020 is attributable to the increased occurrence of meteorological conditions of high solar radiation and positive temperature anomaly under the influence of West Pacific Subtropical High, tropical cyclones as well as mid-high latitude wave activities.
Kaixiang Song, Run Liu, Yu Wang, Tao Liu, Liyan Wei, Yanxing Wu, Junyu Zheng, Boguang Wang, and Shaw Chen Liu
Atmos. Chem. Phys., 22, 8403–8416, https://doi.org/10.5194/acp-22-8403-2022, https://doi.org/10.5194/acp-22-8403-2022, 2022
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We developed an observation-based method to investigate the sensitivity of ozone formation to precursors during two elevated ozone episodes observed at 77 stations in Guangdong, China. We found approximately 67 % of the station days exhibit ozone formation sensitivity to NOx, 20 % of the station days are in the transitional regime sensitive to both NOx and volatile organic compounds (VOCs), and only 13 % of the station days are sensitive to VOCs.
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Short summary
Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due to slow computational efficiency with large chemical equations. The model introduced in this study, ROMAC, boosts efficiency by up to 96 % using an advanced atmospheric solver and an adaptive optimization algorithm. Moreover, ROMAC exceeds traditional box models in evaluating the impact of physical processes on pollutant concentrations.
Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due...