Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 10 km daily-level ultraviolet-radiation-predicting dataset based on machine learning models in China from 2005 to 2020
Yichen Jiang
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Su Shi
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Xinyue Li
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Chang Xu
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Haidong Kan
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Shanghai Key Laboratory of Meteorology and Health, IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, WMO/IGAC MAP-AQ Asian Office Shanghai, Fudan University, Shanghai, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
Shanghai Key Laboratory of Meteorology and Health, IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, WMO/IGAC MAP-AQ Asian Office Shanghai, Fudan University, Shanghai, China
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Aerosol vertical distribution that plays a crucial role in aerosol–photolysis interaction (API) remains underrepresented in chemical models. We integrated lidar and radiosonde observations to constrain the simulated aerosol profiles over North China and quantified the photochemical responses. The increased photolysis rates in the lower layers led to increased ozone and accounted for a 36 %–56 % reduction in API effects, resulting in enhanced atmospheric oxidizing capacity and aerosol formation.
Qingyang Xiao, Guannan Geng, Shigan Liu, Jiajun Liu, Xia Meng, and Qiang Zhang
Atmos. Chem. Phys., 22, 13229–13242, https://doi.org/10.5194/acp-22-13229-2022, https://doi.org/10.5194/acp-22-13229-2022, 2022
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We provided complete coverage PM2.5 concentrations at a 1-km resolution from 2000 to the present, carefully considering the significant changes in land use characteristics in China. This high-resolution PM2.5 data successfully revealed the local-scale PM2.5 variations. We noticed changes in PM2.5 spatial patterns in association with the clean air policies, with the pollution hotspots having transferred from urban centers to rural regions with limited air quality monitoring.
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Atmos. Chem. Phys., 21, 12227–12241, https://doi.org/10.5194/acp-21-12227-2021, https://doi.org/10.5194/acp-21-12227-2021, 2021
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This study firstly investigates the composition of sugars in the fine fraction of aerosol over three sites in southwest China. The result suggested no significant reduction in biomass burning emissions in southwest Yunnan Province to some extent. The result shown sheds light on the contributions of biomass burning and the characteristics of biogenic saccharides in these regions, which could be further applied to regional source apportionment models and global climate models.
Jinhui Gao, Ying Li, Bin Zhu, Bo Hu, Lili Wang, and Fangwen Bao
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Light extinction of aerosols can decease surface ozone mainly via reducing photochemical production of ozone. However, it also leads to high levels of ozone aloft being entrained down to the surface which partly counteracts the reduction in surface ozone. The impact of aerosols is more sensitive to local ozone, which suggests that while controlling the levels of aerosols, controlling the local ozone precursors is an effective way to suppress the increase of ozone over China at present.
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Short summary
Limited ultraviolet (UV) measurements hindered further investigation of its health effects. This study used a machine learning algorithm to predict UV radiation with a daily and 10 km resolution of high accuracy in mainland China in 2005–2020. Then, uneven spatial distribution and population exposure risks as well as increased temporal trend of UV radiation were found in China. The long-term and high-quality UV dataset could further facilitate health-related research in the future.
Limited ultraviolet (UV) measurements hindered further investigation of its health effects. This...
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